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david-hoffman/pyOTF
notebooks/Microscope Imaging Models/Epi with Camera.ipynb
1
673248
{ "cells": [ { "cell_type": "code", "execution_count": 1, "source": [ "%pylab inline\n", "\n", "import scipy.ndimage as ndi\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "from pyotf.otf import SheppardPSF, HanserPSF\n", "import dphtools.display as dplt\n", "from dphtools.utils import bin_ndarray" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "metadata": { "jupyter": { "source_hidden": true } } }, { "cell_type": "code", "execution_count": 2, "source": [ "plt.set_cmap(\"inferno\");" ], "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {} } ], "metadata": { "jupyter": { "source_hidden": true } } }, { "cell_type": "markdown", "source": [ "## Is the PSF generated by `pyotf` what the camera sees?\n", "\n", "#### Short answer\n", "\n", "Not quite.\n", "\n", "#### Long answer\n", "\n", "What `pyotf` is modeling is the _wavefront_ at the camera due to a point source at the focus of the objective in a widefield [epifluorescence](https://en.wikipedia.org/wiki/Fluorescence_microscope#Epifluorescence_microscopy) (AKA, widefield or epi) microscope. But what the camera _records_ is more complex. First, each pixel acts a a square aperture (similar to the circular aperture in confocal microscopy) and then the intensity across the pixel is integrated and eventually converted into a single number. To model this we'll take the following approach:\n", "1. Use `pyotf` to model the _intensity_ point spread function (PSF) at the camera at a pixel size of $1/8^{\\text{th}}$ Nyquist, i.e. $\\lambda/4 \\text{NA}/8$\n", "2. Convolve this image with a square equal to the size of the camera pixel\n", "3. Integrate over the camera pixels" ], "metadata": {} }, { "cell_type": "code", "execution_count": 3, "source": [ "# We'll use a 1.27 NA water dipping objective imaging in water\n", "psf_params = dict(\n", " na=1.27,\n", " ni=1.33,\n", " wl=0.585,\n", " size=64,\n", " vec_corr=\"none\",\n", " zrange=[0]\n", ")\n", "\n", "# Set the Nyquist sampling rate\n", "nyquist_sampling = psf_params[\"wl\"] / psf_params[\"na\"] / 4\n", "\n", "# our oversampling factor\n", "oversample_factor = 8\n", "\n", "# we need to be just slightly less than nyquist for this to work\n", "psf_params[\"res\"] = nyquist_sampling * 0.99 / oversample_factor\n", "psf_params[\"size\"] *= oversample_factor" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 4, "source": [ "# calculate infocus part only\n", "psf = HanserPSF(**psf_params)" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 5, "source": [ "# for each camera pixel size we want to show 10 camera pixels worth of the intensity\n", "num_pixels = 10\n", "\n", "# gamma for display\n", "gam = 0.3\n", "\n", "# set up the figure\n", "fig, axs_total = plt.subplots(3, 3, dpi=150, figsize=(9,9), gridspec_kw=dict(hspace=0.1, wspace=0.1))\n", "\n", "# rows will be for different camera pixel sizes, the camera pixel size = subsample / 8 * Nyquist\n", "for axs, subsample in zip(axs_total, (4, 8, 16)):\n", "\n", " # for display zoom in\n", " offset = (len(psf.PSFi.squeeze()) - num_pixels * subsample) // 2\n", "\n", " # show the original data, shifted such that the max is at the center of the\n", " # camera ROI\n", " axs[0].matshow(psf.PSFi.squeeze()[offset-subsample//2:-offset-subsample//2, offset-subsample//2:-offset-subsample//2],\n", " norm=mpl.colors.PowerNorm(gam))\n", "\n", " # Use the convolution to shift the data so that the max is centered on camera ROI\n", " origin_shift = subsample // 2 - 1\n", " exact = ndi.uniform_filter(psf.PSFi[0], subsample, origin=origin_shift)\n", " \n", " # Show convolved data\n", " axs[1].matshow(exact[offset:-offset, offset:-offset], norm=mpl.colors.PowerNorm(gam))\n", " for ax in axs[:2]:\n", " ax.xaxis.set_major_locator(plt.FixedLocator(np.arange(0, offset, subsample) - 0.5))\n", " ax.yaxis.set_major_locator(plt.FixedLocator(np.arange(0, offset, subsample) - 0.5))\n", " \n", " # integrate across pixel\n", " exact_subsample = bin_ndarray(exact, bin_size=subsample, operation=\"sum\")\n", "\n", " # Display final camera pixels\n", " offset_sub = offset//subsample\n", " ax = axs[-1]\n", " ax.matshow(exact_subsample[offset_sub:-offset_sub, offset_sub:-offset_sub], norm=mpl.colors.PowerNorm(gam))\n", " ax.xaxis.set_major_locator(plt.FixedLocator(np.arange(0, offset_sub) - 0.5))\n", " ax.yaxis.set_major_locator(plt.FixedLocator(np.arange(0, offset_sub) - 0.5))\n", "\n", " # clean up plot\n", " for ax in axs:\n", " ax.xaxis.set_major_formatter(plt.NullFormatter())\n", " ax.yaxis.set_major_formatter(plt.NullFormatter())\n", " ax.tick_params(length=0)\n", " ax.grid(True)\n", " \n", "# label\n", "axs_total[0, 0].set_title(\"Intensity Incident on Camera\\n($\\\\frac{1}{8}$ Nyquist Simulation)\")\n", "axs_total[0, 1].set_title(\"Convolution with\\nCamera Pixel Function\")\n", "axs_total[0, 2].set_title(\"Integration to Final\\nCamera Pixel Intensity\")\n", "\n", "axs_total[0, 0].set_ylabel(r\"$\\frac{1}{2}\\times$ Nyquist Camera Pixel Size\")\n", "axs_total[1, 0].set_ylabel(r\"$1\\times$ Nyquist Camera Pixel Size\")\n", "axs_total[2, 0].set_ylabel(r\"$2\\times$ Nyquist Camera Pixel Size\");" ], "outputs": [ { "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ "<Figure size 1350x1350 with 9 Axes>" ] }, "metadata": { "needs_background": "light" } } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "The above figure shows each of the three steps (columns) for three different camera pixels sizes (rows). Gray lines indicate the final camera pixel grid. It's clear that the convolution has an effect on camera pixel sizes larger than Nyquist. Considering that we usually ask microscopists to image at Nyquist and therefore we usually model PSFs at Nyquist a natural question is: how different are the higher resolution calculations (such as in the figure above) from simulating directly with Nyquist sized camera pixels? Furthermore, when simulating PSFs for camera pixels that are larger than Nyquist, how important is the convolution operation (step 2)?\n", "\n", "It's safe to assume that the area with the highest resolution will be most effected and thus we can limit our investigations to the 2D infocus PSF." ], "metadata": {} }, { "cell_type": "code", "execution_count": 6, "source": [ "# keep our original parameters safe\n", "psf_params_wf = psf_params.copy()" ], "outputs": [], "metadata": { "jupyter": { "source_hidden": true } } }, { "cell_type": "code", "execution_count": 7, "source": [ "# for each camera pixel size we want to show 10 camera pixels worth of the intensity\n", "num_pixels = 64\n", "\n", "# set up the figure\n", "fig, axs_total = plt.subplots(3, 4, dpi=150, figsize=(9.25, 9),\n", " gridspec_kw=dict(hspace=0.1, wspace=0.1, width_ratios=(1, 1, 1, 1 / 12)))\n", "\n", "# rows will be for different camera pixel sizes, the camera pixel size = subsample / 8 * Nyquist\n", "for axs, subsample in zip(axs_total, (2, 4, 8)):\n", "\n", " # for display zoom in\n", " offset = (len(psf.PSFi.squeeze()) - num_pixels) // 2\n", "\n", " # show the original data, shifted such that the max is at the center of the\n", " # camera ROI\n", " # axs[0].matshow(psf.PSFi.squeeze()[offset-subsample//2:-offset-subsample//2, offset-subsample//2:-offset-subsample//2],\n", " # norm=mpl.colors.PowerNorm(gam))\n", "\n", " # Use the convolution to shift the data so that the max is centered on camera ROI\n", " origin_shift = subsample // 2 - 1\n", " exact = ndi.uniform_filter(psf.PSFi[0], subsample, origin=origin_shift)\n", " \n", " # Show convolved data\n", " # axs[1].matshow(exact[offset:-offset, offset:-offset], norm=mpl.colors.PowerNorm(gam))\n", " \n", " # integrate across pixel\n", " exact_subsample = bin_ndarray(exact, bin_size=subsample, operation=\"sum\")\n", " exact_subsample /= exact_subsample.max()\n", "\n", " # Display final camera pixels\n", " offset_sub = offset//subsample\n", " axs[0].matshow(exact_subsample[offset_sub:-offset_sub, offset_sub:-offset_sub], norm=mpl.colors.PowerNorm(gam))\n", "\n", " # Directly simulate at Nyquist\n", " psf_params_wf['res'] = psf_params['res'] * subsample\n", " psf_params_wf['size'] = psf_params['size'] // subsample\n", " low_res = HanserPSF(**psf_params_wf).PSFi.squeeze()\n", " low_res /= low_res.max()\n", " \n", " # display direct simulation\n", " axs[1].matshow(low_res[offset_sub:-offset_sub, offset_sub:-offset_sub], norm=mpl.colors.PowerNorm(gam))\n", " \n", " # Calculate percent of max difference and display\n", " difference = (exact_subsample - low_res)\n", " im = axs[2].matshow(difference[offset_sub:-offset_sub, offset_sub:-offset_sub] * 100, cmap=\"viridis\")\n", " plt.colorbar(im, ax=axs[2], cax=axs[3])\n", " \n", " # clean up plot\n", " for ax in axs[:3]:\n", " ax.xaxis.set_major_locator(plt.NullLocator())\n", " ax.yaxis.set_major_locator(plt.NullLocator())\n", " \n", "# label\n", "axs_total[0, 0].set_title(\"Integration to Final\\nCamera Pixel Intensity\")\n", "axs_total[0, 1].set_title(\"Intensity Incident on Camera\\n(Nyquist Simulation)\")\n", "axs_total[0, 2].set_title(\"Difference (%)\")\n", "\n", "axs_total[0, 0].set_ylabel(r\"$\\frac{1}{4}\\times$ Nyquist Camera Pixel Size\")\n", "axs_total[1, 0].set_ylabel(r\"$\\frac{1}{2}\\times$ Nyquist Camera Pixel Size\")\n", "axs_total[2, 0].set_ylabel(r\"$1\\times$ Nyquist Camera Pixel Size\");" ], "outputs": [ { "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ "<Figure size 1387.5x1350 with 12 Axes>" ] }, "metadata": { "needs_background": "light" } } ], "metadata": { "jupyter": { "source_hidden": true } } }, { "cell_type": "markdown", "source": [ "Presented in the figure above is a comparison of the \"exact\" simulation (first column) to the \"direct\" simulation (second column), the difference is shown in the third column. As expected smaller camera pixels result in smaller the differences between the \"exact\" and \"direct\" calculations. But even at it's worst (i.e. Nyquist sampling on the camera) the maximum deviation is about 7% of the peak PSF intensity. \n", "\n", "Of course, we know that single numbers are no way to evaluate resolution, or the loss thereof. Therefore we'll take a look in frequency space." ], "metadata": {} }, { "cell_type": "code", "execution_count": 8, "source": [ "from pyotf.utils import easy_fft\n", "from dphtools.utils import radial_profile" ], "outputs": [], "metadata": { "jupyter": { "source_hidden": true } } }, { "cell_type": "code", "execution_count": 9, "source": [ "fig, ax = plt.subplots(figsize=(4,4), dpi=150)\n", "\n", "k_pixel_size = 2 / psf_params_wf[\"res\"] / len(exact_subsample)\n", "abbe_limit = 1 / nyquist_sampling / k_pixel_size\n", "\n", "for l, d in zip((\"Exact\", \"Direct\"), (exact_subsample, low_res)):\n", " o = abs(easy_fft(d))\n", " ro = radial_profile(o)[0]\n", " ax.plot(np.arange(len(ro)) / abbe_limit * 2, ro, label=l)\n", "\n", "ax.legend()\n", "ax.set_xlabel(\"Spatial Frequency\")\n", "ax.set_ylabel(\"Intensity\")\n", "ax.set_xlim(0, 2.6)\n", "ax.set_ylim(0)\n", "ax.yaxis.set_major_locator(plt.NullLocator())\n", "ax.xaxis.set_major_locator(plt.MultipleLocator(1 / 2))\n", "\n", "def formatter(x, pos):\n", " if x == 0:\n", " return 0\n", " if x / 0.5 % 2:\n", " x = int(x) * 2 + 1\n", " if x == 1:\n", " x = \"\"\n", " return r\"$\\frac{{{}NA}}{{2\\lambda}}$\".format(x)\n", " elif int(x):\n", " x = int(x)\n", " if x == 1:\n", " x = \"\"\n", " return r\"$\\frac{{{}NA}}{{\\lambda}}$\".format(x)\n", " return r\"$\\frac{NA}{\\lambda}$\"\n", " \n", "ax.xaxis.set_major_formatter(plt.FuncFormatter(formatter))" ], "outputs": [ { "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ "<Figure size 600x600 with 1 Axes>" ] }, "metadata": { "needs_background": "light" } } ], "metadata": { "jupyter": { "source_hidden": true } } }, { "cell_type": "markdown", "source": [ "We see (figure above) that the exact simulation, which includes convolution and then integration, redistributes the OTF support slightly towards the DC component, which makes sense as both convolution and integration will blur high frequency information. Note that the OTF cutoff remains nearly the same in both cases: $2 NA / \\lambda$.\n", "\n", "What about PSFs for large camera pixels? We follow the exact same procedure as above." ], "metadata": {} }, { "cell_type": "code", "execution_count": 10, "source": [ "# for each camera pixel size we want to show 10 camera pixels worth of the intensity\n", "num_pixels = len(psf.PSFi.squeeze())\n", "\n", "# Directly simulate at Nyquist\n", "psf_params_wf['res'] = psf_params['res'] * oversample_factor\n", "psf_params_wf['size'] = psf_params['size'] // oversample_factor\n", "low_res = HanserPSF(**psf_params_wf).PSFi.squeeze()\n", "\n", "# set up the figure\n", "fig, axs_total = plt.subplots(3, 4, dpi=150, figsize=(9.25,9),\n", " gridspec_kw=dict(hspace=0.1, wspace=0.1, width_ratios=(1, 1, 1, 1 / 12)))\n", "\n", "# rows will be for different camera pixel sizes, the camera pixel size = subsample / 8 * Nyquist\n", "for axs, subsample in zip(axs_total[::-1], (8, 4, 2)):\n", " \n", " subsample2 = oversample_factor * subsample\n", " # for display zoom in\n", " offset = (len(psf.PSFi.squeeze()) - num_pixels) // 2\n", "\n", " # show the original data, shifted such that the max is at the center of the\n", " # camera ROI\n", " # axs[0].matshow(psf.PSFi.squeeze(), norm=mpl.colors.PowerNorm(gam))\n", "\n", " # Use the convolution to shift the data so that the max is centered on camera ROI\n", " origin_shift2 = subsample2 // 2 - 1\n", " exact = ndi.uniform_filter(psf.PSFi[0], subsample2, origin=origin_shift2)\n", " \n", " # Show convolved data\n", " # axs[1].matshow(exact, norm=mpl.colors.PowerNorm(gam))\n", " \n", " # integrate across pixel\n", " exact_subsample = bin_ndarray(exact, bin_size=subsample2, operation=\"sum\")\n", " exact_subsample /= exact_subsample.max()\n", "\n", " # Display final camera pixels\n", " offset_sub = offset//subsample2\n", " axs[0].matshow(exact_subsample, norm=mpl.colors.PowerNorm(gam))\n", "\n", " origin_shift = subsample // 2 - 1\n", " exact_low_res = ndi.uniform_filter(low_res, subsample, origin=origin_shift)\n", " exact_low_res_subsample = bin_ndarray(exact_low_res, bin_size=subsample, operation=\"sum\")\n", " exact_low_res_subsample /= exact_low_res_subsample.max()\n", " \n", " low_res_subsample = bin_ndarray(low_res, bin_size=subsample, operation=\"sum\")\n", " low_res_subsample /= low_res_subsample.max()\n", " \n", " # display direct simulation\n", " axs[1].matshow(exact_low_res_subsample, norm=mpl.colors.PowerNorm(gam))\n", " \n", " # Calculate percent of max difference and display\n", " difference = (exact_subsample - exact_low_res_subsample)\n", " im = axs[2].matshow(difference * 100, cmap=\"viridis\")\n", " plt.colorbar(im, ax=axs[2], cax=axs[3])\n", " \n", " # clean up plot\n", " for ax in axs[:3]:\n", " ax.xaxis.set_major_locator(plt.NullLocator())\n", " ax.yaxis.set_major_locator(plt.NullLocator())\n", " \n", "# label\n", "axs_total[0, 0].set_title(r\"$\\frac{1}{8}\\times$\" + \"Nyquist Simulation\\nwith Convolution\")\n", "axs_total[0, 1].set_title(r\"$1\\times$ \" + \"Nyquist Simulation\\nwith Convolution\")\n", "axs_total[0, 2].set_title(\"Difference (%)\")\n", "\n", "axs_total[0, 0].set_ylabel(r\"$2\\times$ Nyquist Camera Pixel Size\")\n", "axs_total[1, 0].set_ylabel(r\"$4\\times$ Nyquist Camera Pixel Size\")\n", "axs_total[2, 0].set_ylabel(r\"$8\\times$ Nyquist Camera Pixel Size\");" ], "outputs": [ { "output_type": "display_data", "data": { "image/png": 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0Yp7/JXcfxgiWmAEf0KirwbwwMmsytFFZJUZm7cRm/8isjMoKAMAcRGVVEzN7sNKXyRcp/VA/Sukf+edJuqxD/yGdXKr0w+BTkl7bocKnWGF1icr9MXGapKt6XOYkTfXz0a1jdSn1b3F6fv5GSX8xszuUKjP+Xalj3bYjMeVbIf4nv3yVpNvM7E6lFlsnKFX0fbPN4p2MK90O8FVJt1oaJehOpQqRTypVDl4k6X1Ny31W6cfVIyT9ycxusTQq0yozi3xxn1C6xp6X13WdpVGOblf6l/0XSn1sKD8/MN/GNDL6cUzc/WpJx+eXB0m60czuUrod8kv5sblD32YzOjbu/jGlflqk1Irq5nyu3q3UEnFcqcPfdrdgYcT54EZdjVipeT4yq4/AqKw5H4zMWmBzY2RWRmUFAGAOorJqY4cq/ZO9wt2PcfdPu/s7lCoVNtE0+j1w93uV+uB5TbcfSoUKq+d26M+nZ3m97+hxmT9K+lV+2a5j9WJ83d0PU8r/JUo/zsaUKmJeq+63dkmpxcAbc7prlc7RiyW9xN2P6iX/Be9Vqvz4ulKF3aTSsfyLUquEwyXt4+6rm7bnaqV+i85W6vx2S6URBXfM29WRu39f6XaONyrdVvk7pUqqzZVGO7paadSvQyU9uR99tZSk9GPi7m9XOk8uy+scl/RHpc6F99DUv+Htlp/RscnrOFqp494zlX6UbaL0o+d8pW3eP1+7mIVsQKOuRjgjs0oavVFZc54YmZWRWQEAwAgK/aibZxojm93c9H7j9bQqkPK/5NHYWi/puPtKBUayc/evq4cOqc1sG6XOaKX2Hau3Sud0TbWwal5nt2VrSi1bPtpm/gpNtUYKzXP3a5R+KP6/5nnduPslmsGoWbnCr+32BJZfofbbu09g+eWBmI4HZbrHpFPe8/y254mkC9TlXO12bLqln2POV6qc6klw36/U8EaYnNds41FXj1KqiHiZ0i3WL3b36bTSnIn3K7XUa4zM+tnO4WlkVjP7iVLH3Eco9QfY75FZ+z4qq1I/WydLkqUBOV6lNEJgY1TWo9st32enaKoPwVEambVfx6R5ZNZW3zsYmRUAAAwFLas21ugb4WNm9nQz2y73HfVxpf5BPje8rA3ca5UqNP+o9h2rA8DIsCGMuhrhs2dk1vk6Kqs0uiOz9uuYzHRk1pkeG0ZmBQAAbVFZ1cTdf6L0L/zOkn6s1FfCj5Q6Yd3TC8Ntz2V5iOg355cnDKmfFwAIG/KoqxGzYWTWOTUqa057to/M2pdjopmPzDrTY8PIrAAAoC0qq1q7Qal/nmOUbjN6s1JHzt9rjIY2V+UOqm9W+gdzE0m/VOoIFwBKY2ZbmNlWjUlTn0dLiu9H+3ca9qirEbNkZNa5NiqrNPtHZu3XMZnpyKwzOjaMzAoAADqhsqqJmR2k1NHrf7j7ce5+trufIOnZkh6r+L/hs9WOSn1U3KL0A+m57j4x1BwBmIt+qdQHTWNq3JL0r03vfyy4vlEYdTXiNI3wyKxzcFRWaZaPzNqvY1LCyKwzPjaMzAoAANqhsmpjb5R0T/5yeD93/5XSiFL7DCNTg+Lulqe/cfdXuvutJa9/RV7/ijLXC2B+G4VRVyNmycisc2ZU1ryOuTAyaz+OyYxGZi3j2OT1MDIrAADYiNEV0QOZ2e+VRkla1vyDx8yuk3Sfu+/ecmEAAEqWR2a9QakC4Nnufk4J6zxNqa+jz/LnAQAAAEYNLas29jtJSyUdWHzTzJ4habmmRq8BAGAQGJkVAAAA80q4mfY88gFJz5X0eTM7WdKVSv0xvF5p5JsPdFgWAIDSMDIrAAAA5iMqq5q4+yVm9kSlIb9fKOl1SqMmfUfSu93998PMHwBg7jOzVZIWKg14ITEyKwAAAOYRKqtacPcrJL1k2PkAAMxbO+bHWyR9T9LbGZkVAAAA8wUdrAMAAAAAAGBk0ME6AAAAAAAARgaVVQAAAAAAABgZVFYBAAAAAABgZFBZBQAAAAAAgJFBZRUAAAAAAABGBpVV84iZLTczz9Pyaa5jZV7+glIzhxkrHNt9hpT+ipz+qmGkD4wiyt25jXIXmBvMbJ/G9dwhZi8z+7aZ/dXMajn+G00xLzSz88zsTjOr55gT+78FiDCzBWZ2jZmtN7MdBpz2Sfl8OHyQ6QKzGZVVkHT/D6qVZrZy2HkpMrPnmtkpZnalmd1hZhNmdruZXWZmJ5rZ3w47j3PdqJ4bwGw3qtcW5e7wjeq5AYySQkV+caqb2T1mdqOZ/cTMPm5mLzazBTNM66mSzpP0PElbSrpD0q2S7izEvEjSWZKeKWlTSbflmHtmkjZKdZSkh0v6tLvf0DzTzPY3sx+Z2Wozu8/MLjCzfTut0Mxemc+9j3ZJ+zhJGyS918yWTnsLgHlkbNgZwEBNSPp94XnRcknvzs9XDig/bZnZzpLOkPSkwts1SXdL2kzSk/P0RjM7X9Ih7n7bwDM6PyxX7Ny4W+n8uqnP+QFmE8pdTMdyUe4Cvbi18HyxpG0lbSdpT0mvl3S7mb1T0knu3qr11BpNldWt/IvS76aLJb3Q3e9oEfOv+fFMSYe5+5reNgH9ZGYPknSspPWS3t9i/oGSvqrUmKPxeb23pHPM7CB3P7vFMltJOl6pDD6mU/ru/iczO1XSayS9WdK/T39rgPmBllXziLvf5O675Glkv9ia2ZMlXar0g2m10gfK4ySNu/uWkhZI2lXpQ+FWpX+wth9ObtHg7l/P59azhp0XYFRQ7qKfKHeBxN23KUybSRqX9FilSoHrlFpDfULS58zMWix/WaOsbpPE7vnxi20qqooxp1FRNZJeLWlzSd909xuLM/I58WGl38b/KWmppE3ye1VJH2l13kg6QenceoO73xvIw0n58f+a2cJpbQUwj1BZhZFiZltK+prSh8mfJf2tu7/D3X/T+CfM3evu/lt3P06pKe8pktr2MQAAaI9yF8Bc4+41d7/c3U+QtJukL+ZZL5P09mmsckl+vG+GMRiCXNF0ZH75uRYhO0vaUenPmGPcfcLdN0h6q6S/KLV2fWTTOp8l6eWSznL3byjA3X8l6QqlCq4X974lwPxCZdWIMrMr8v3Pb2gxb8/CvflfbTF/PN9n7cX7rNt19Js7Zj2/8Lr5/v/TOuTzWYXOJteZ2e/M7N1mtmiam/5WTf1b/1J3v7JTsLuvcffXSLq8Rd42M7N3mdkvcv8Fa83sajP7pJk9vMM23d9hrpktM7P/MLOr8vK3m9m3WvXZYmZH5+VuNbO2t9hacn2OPbbF/G3M7PjcX8x9+b75K83sg2b2kE77o016XTsNbd7uwnurFDw3LNDRr5ntlPf/1Xl/3pOPz7vMbNNI/s3sEWb2GTO7wVIHmTea2afMbLvgLgFaotyVRLlLudsi/5S7mM1yK6dXSPplfuvtlm4Ju1+7a7bw3vL81qlN1+PyFsudX4xpzk8uJ48xs0stdcS+Pl9bX7DUN9ZGmj9L8nV9ipldl5df1WKZbczsA2b2azO7O39eXGtmnzazx7RJp5Rr31JH5q8ys+/l8nm9md1sZj/NZc/D2izX874J2k/pj5a7JH23xfyt8uMqd6813nT3SUmr8ssHF/K5SKmV1L2SNvrO0MUX8uOre1wOmH/cnWkEJ0kfVfrX+mst5h2T57lS543WNP/v8rx1khYX3l9eWG554f2fKXUU2Zh3S9P0kULsyhxzgdK9+fU83ZkfG+s4T1K1x20eU/oQcUk/mOH+21XSDYX8rFXq4LLxep2kF7VZthHzUklXF5ZfXZi3QdKzm5Z7iKTJPP8fOuRt7xxTLx6Hwrw7C+msVvqHrvH6DklP75LvfZre36cxr8s+22j5Hs+NFTlmVZv1H5L3e2Nd9zS9/pOkR7dYbp9CzDOVvhg0lp8ozLtJ0nbDvnaZZu8kyl3KXcrdjfIvyl2mEZ0KZaMH419cOHcPb5rX8potXHe1PP/uputxh8LzYplxf0zT+v62KXZSDywn65L+rUXelxdiXla4Jhvl1aqm+OcXYhrlZ7FcW6/Ur1ZzOjO+9iU9TOnPjOI23ampstolndhiuWntm+Cx/6+8ju+1mb+LpsrbauH9MaXWVi5p58L778vvHTWNvDy9sH3Lhn0dMTGN8jT0DDC1OTDSgYUPvErTvB8UPjBd0uOb5h+b37+g6f3iB93ypnn3fzh1ydfKHHen0gf3cZK2yvM2lfSeQhqH97jNTy0s+88z2HfLJF2b13Oj0sgtlTzvcZJ+qqkfTo9rsXzxy8aV+cO6IsmUOhe+Ks9f1eLYfCfP+2KH/H06x1zY9P4OmvrBdKWkvyvM26uQ7u1q8QWhkO99pnlsZ7r8isZ+aTHvCUpflFzSRZIem9+vSHqB0q1HLumPkjZpl34+JmdJ2iXPW6D0Y6zxZeZ/Bn2tMs2dSZS7lLuUuxulL8pdphGd1Htl1SaaqjD5bNO8jtdcLntc0ooO6295PRfmLy+UN1/J1+hYnre1UofbjQqhA1os21j/vZIukfSkwvxiRcpTlCqjXKn1zy7KFTCSHirp43neRHEdzfthOte+0mfSHwrLHylpszxvXOl2u6MlvamsfRM89j/Ly/57m/mmVHnvkj6Q8zqu1Hl647PHcuyuSmXrZWr6PArmZXFhW54z7OuIiWmUp6FngKnNgZG20NS/OE8ovL9QacSS1YUC9OimZc/L77+76f3iB93ypnn3fzh1ydfKwjpWtok5M88/t8dtPqKw7qfNYN+9La9jg6TdWsxfptTZpkv6Vov5jTz8RdLWLebvXoj5u6Z5h+b31zY+nJvmL9JUK4YjmuZ9svDhvk2LZbfX1A/lj3XI9z7TPLb9/NH03TzvaklLWszfo/DB/ZZ26edze6MvBkpDEXu+Nsame+4wze+Jcpdyt8WylLuUu0wjOqnHyqq8TKMi5aKm9ztecyqnsuor6lLBK+lNOeZXTe8vL6x/lZoqmJtiL1OHipkc85Ec8412+2E6176k9+Z56yTt0cNxmfa+Cax7gaYqKVu27s1xL9ZUa+X1mqrwq0k6MMeY0oiQE2rxx0sPeboir/s9ZV0PTExzcaLPqhHl7ndK+nV+uW9h1lOVauQvlvS95vmWRpbYM788v49ZXC/pQ23mnZUfH9vjOrcsPG830krES/LjV939iuaZnkbr+GB++Vwz26zNek5x97+0WP5ypR9d0sbbeJbSP06L1LrjxBcqDQG/Tml4XEn3d/x4SH55krvf0iLdGzU1isihbfI8csxsc0nPzi+P9xYj5Lj7L5U6eJbSbUDtHOfu9RbvN865xWrqABOIotyl3G2RLuUu5S7mlkY596COUSXLfWQdlF9+oEPo/+THx3XoL+9j7t6yE3cze5xSa9QJpVvfuqWzn5lV28RM59o/PD9+OpcxXZW8b1rZWmlEP0n6a7sgd/+qUqvgi5UqtyYl/Vip9dPXc9hrJD1N0ofd/deW+qt8d+4LbH3uQ+w9ZjbeJU+35cdte9gOYN6hsmq0nZcfiz+a9i3M+4nSj5dn2FTHsk9T+sK+VmkY8n65st0HpdKtBVLvXwSKQ8J671lKHTpq6ofMDzqEnpsfK0pNjVvptP9abqO7r9XUj6GXt1iu8d5Z7n534f2HFdYVyfeW7TqnHEFP0NSxjWzbYzt8yLc7Jn8uPB/oF1DMOZS7va6AcncUUe4CrVn3kL7YU1O/u84zs1taTUq3Izfs2GZdF3dI5+n5sSLp9x3SafzxslQP/NOiqKdr38x21FTlyzc75LFZmfumlQcXnnf8U8bdv+fuT3f3pXl6hrufK6UO65Uq065TatUnSWfk55OSPp8f35Xf76SRjwd3jALmOSqrRlvjH/q9Cj+Knpkfz8tf0C9Rur3iSU3zf+Lu6/uYt3s7zJvMj21HZmrjtsLzdh+c3TxIU/+e3NQh7sbC863bxES2sdWX+8Y/P8/IH9ySJDN7sKTnNMW0ysNM8z1qet22MbX54ZNbZ7R6f7Lwstu/WUAnlLu9o9wdPZS7QGtb5MfbB5xusQXNQ7pMDUvarGuj1qct0ql2SWOrwjIt05nGtb9N4fn1HfLYLs/qkN/ovmmlOFLuTD6jP6LUSvf17r7GzPaXdLDSLX17uPsrlf4ouErSwXl+O2tb5A1AEyqrRpIBMgkAACAASURBVNuPlL6cbyLpKWa2RGmkjLsl/TzHNLcC2Lfp/dmk+I/JHiWsr1MrAW/zvAw/UvqQNkn/VHj/UE2NKnJOMG+dlJ3vUTKXtw2jjXJ3Zih3Z6+5vG2AzGwTSQ/PL68ZcPKNCv217m7B6YI266oF0rmqh3RWlbWRBb2UJ2Xum1aKFZNbtI3qwMyep3Tb+BfcvdEq7cD8eLK7r5bur+Br3Dp+QIdVNv4cGHSlKTCrUFk1wnKB1/hxtK9S094Fkn7k7o0PqkYrgH3NbKnSCCDF92eT/1X6QShNfQD06g5NfYjv0CGuOK/t/evT4e4u6XP5ZfGWlMbzLzT9KyU98F+yTvnevvA8mu/70zKzlv/gdOg/pgzFbdu+bdTUvEmlEWGAgaPcnRbK3Y1R7gKj5zmaqhi5YMBpN/rEW2xmjxhAOg/Pn0+DdHPh+fIeluv3vimW2z3fspz34yeUysg3FWa1q/i8uml+K418lPpZCMw1VFaNvvt/FKlwK0ph/iVKo3E8TdKzlJrj3qc0RGsv7u9AMXc6O3D5h8Qp+eWzzOwZ0WXNrJLXsUHSbxrr6LDIfvmxLukXPWY1onG7yaPM7Mlm9iilDi+L84qu09T965F83+7u13WIKyr+AGn3g+xvOyw/03PjF4V1RLbt1+4+MY10gLJQ7gZQ7nZEuQuMkNy33jvyy7slfWPAWfiJplob9XOwhkZ/Vgs0/T8gpsXd/6SpW4tf0MOifd03efCU+yvxprGK9yj1kfVWd7+1xfzFbV53al3W6P/wd9PIDzBvUFk1+ho/kPaU9Nym95S/XF6sVDA2PoQvavEPcjf3FJ5vPo18luWDmuq48QtmtmunYDNbbGafUBrWvOGL+fHFZrZbi2U2kfTW/PI7TR3ulsLd/6CpjikP09S/+1e0Gh0ltwr4Un75mtyJ4wOY2bZKo5BI0hd6yM4fNHVv/ItarLci6d86LD+jc8Pd75L0/fzyX/NtVc15eFwhb71sG9APlLsdUO6GUO4CI8LMFks6TVO3Or8/XyMDk0c5bYyi969mtnOn+DxC3nT8r6RGefe+3G9fP9Jp5zP58VVmFrq1fED75kf58SkdozZO6/GS/kVpVMD/bprd+POieZ1PbZrfvM6Haapj9Qt7yQ8w31BZNfoulrRBqQO+xyl1hnt5U0zjR1TjX9rp3Iryh5yOlD5ghvUv/21KX57vUepw8VIzO87MdmvkyZJdzOytSk1vX6cHju7ySaUPiHFJ3zWz5zZaAJjZ7kpf4B+mtL3H9nFzTs+Ph2qqD5XT28RK0nGS7lJqGvwDM3taY4aZ/Z3SiE6bK7UE6DS07wPkH9Zn5pfvMLND8j+Myi0Pvq50brVTxrlxjNIwyo+Q9P18HGRmldwPwHeU+pW5RtLJ01g/UCbKXcpdyl1gFsvn+W5mdrRS33wvzbNOV6qgH4Y3K/VRtKmki8zs8OLtwGa2lZkdZGZf0zQrkHMl/GuVOhJ/qFJ5/uJihbWZbWdm/2Rm50r6zxlsTysfUroNbqGkH5rZkWa2aU533Mx2NrN3mdlbmpbr9765ID92atH6APkz7FNKt7m/Ju/bokYF22vNbK+8zF6a+oPjLLXWyMOt7n5VND/AvOTuTCM+Kf0b4Hn6cov5f1uY75Ke3GY9ywsxy1vM/3Rh/mqlzmpXSfpQIWZlnn9Bh/zu01jPDLb50Ur9xhS3a0Lpg2yi6f3vSdqyafndlJoiN2LWKjX7brxeJ+nFbdJuxOzTIX8X5JiVHWK2VPqy0FhfTdJ2XbZ7b6UfTo1l7stT4/WdkvbqNd9K/ZLcVIjZUNgf9+R0Oy0fOTdW5Pmr2uTvJU374+58XBqv/yTp0dM9nyLHjYkpOolyl3KXcpdyl2mkp0LZ6Eq3eTWmO/O1Xyyz/qpU4dBuXR3P+XztuaQVHdYRKcf2UKrYb8TWlSrD723K77lNyy0vzFse2Df7K/3R0lhmMr9e3ZTOp3rZD5FtVbrV7spCTC1v42ThvRPL2jfBc2XrQln4yOAyb8zx7+kQc1YhX2sKz8/ssMznc8yHh30NMTGN+kTLqtnh/MLzVqNN/a+mbhm4R9PvC+SflT74r8ivH6p0j/ZW7RboF3f/nbs/UdLzlZrdXqX042FTpW38maQPS3qiuz/H3W9vWv4KSbsqbc+vlD4gFyr9g3ySpF3d/at93obblf65bvihu3caQlzufqGkXST9l9J97BWl1gu/U/q36tHu/uNp5OVGpR/Xn9bUMOb3KfXj8oScbiczPjfc/UtKx+RkpeOwUOm4/ErSuyXt5u7cu49RQblLuUu5C8weD8nT1kotBm9R6l/wk5JerFRpPfQWhJ5uSX6MpDcotdy8TdIypXLnaqWKjEMlHTTDdM5ValX5b5IuUqqo3lypAui3SmX8CyUdNZN02qR9rVLF0+uV/mS4U2mE3Vsl/VTSO5U+S5qX69u+8XSrYaOfsv/TLd7Mtpf0H0qtXI/rEHpIjluldN5dn1+/tFVw7qz9H/PLU1rFAJhi7j7sPAAAAAAA0Bd5AJELlSruH+lD+BFsZodJ+qyk891930GnD8w2tKwCAAAAAMxZ7v4jSedI2knSwYNOP/eB1Rho5JhBpw/MRlRWAQAAAADmurco3Qr5rsYgIAN0sNJt2V9x958OOG1gVhobdgYAAAAAAOgnd7/czI5Q6rD+bzTVn+AgjEt6j6RTB5gmMKvRZxUAAAAAAABGBrcBAgAAAAAAYGRQWQUAAAAAAICRQWUVAAAAAAAARgaVVQAAAAAAABgZVFYBAAAAAABgZFBZBQAAAAAAgJFBZRUAAAAAAABGBpVVAAAAAAAAGBlUVgEAAAAAAGBkUFkFAAAAAACAkUFlFQAAAAAAAEYGlVUAAAAAAAAYGVRWAQAAAAAAYGRQWQUAAAAAAICRQWUVAAAAAAAARgaVVQAAAAAAABgZVFYBAAAAAABgZFBZBQAAAAAAgJFBZRUAAAAAAABGBpVVAAAAAAAAGBlUVgEAAAAAAGBkjA07A9NlZrdIWiLphmHnBQAGaAdJa9x9m0EnTLkLYB6izAWAwRpouWtmZ0vaaRBpZde4+wsHmN6sNWsrqyQtkWxZxRY8plugqx5bo3ssLLY2mVmp6cpiDeHca7H1KZa/SjDdqOj+c58IxZmqsbjAdkTzVg1eOjVNhuKCZ4rcY+eyK3YOmI3H4kJRcfXgdkSPiFnsHFA03eC168FrN7z/AulGr4s+WSLZsqot7Fru1qPlbvSqC5eT5Z6tFmyAXPfotR7LX/icDovtv/B2BPMX2X8ezFs1WF7Vwp8dwes8eC5HP3srFv3qVe65HM1f9HhEtyP8HSyq9LKgc1wuc5cFV1a2JabKsiXapGuZCwBzxRrdV/5nR2c7LVhgj3nE8tj3jJn446oJbdgQ/cWJ2VxZdUPFFjxm2aKduwZO1NeEVlirbwjF1YNfhKuVhbH11WM/DsarS0Nx6yZuC8VVKgtCcYvGNg/FRUUrKtasvz4UNz62VShu4Vj375rRvG06Fqvov2fyllBctEJw/eS9obiJydg5sGTBjqG4siss103eFYqrB6/JReOxc2CitjoUV6kEKyPr62PrC/7IrgauybUb/iT3iWH9y35D1RY+ZvPFu3UNXFe7J7TCaHkarUSJ7MNe1rewumkobvWGW0Nx0XNh8fiDQnHxCohY2bZ6Q6zMWjC2WSgusv8mg9fRFuM7hOLuDF4eY8HP6PXBc3nD5N2huKULYp8f0YrSaMXh2ok7QnHRa3LpgoeE4qL7L1r5Ff2uFl1ft2tyzYZVofX0yQ1LtMlj9rS/L2+NZf+ROqx0S/5jovTtjRrGdgzrWHBsZ2ZYxyOqxHR/6udote4Z6HfdRywf1+UXxn4XzcTue1+v3/4h9jmG2V1ZBQAAAAAAMAPew10BM0sHcX2rrDKzMUnPl/RkSVtJutTdP5PnbZvf+6178O9tAAAAAACAErmkWribkpmlg7i+VFaZ2d6STpe0nVJnAC5pXNJncsizJJ0m6RBJZ/YjDwAAAAAAAJh9yu2IRpKZ7S7pO5K2lvQRSQdr494rz5S0RtKLyk4fAAAAAAAgqi7v+4Te9KNl1bskLZT09+5+nrTxqHjuvsbMfidpjz6kDwAAAAAA0JWrl5GsZ5YO4vpRWbW3pEsaFVUd/EnS/n1IHwAAAAAAIMBVG8jIkVRX9aIflVWbSropELdQUmy8ZQAAAAAAgD7gNr3RU3qfVZJulvToQNxukq7vQ/oAAAAAAACYpfrRsuocSa8yswPd/eutAsxshaQdJX1oJgm56pqor+kaV6tviK0vOFylBRuEWcl1gXWfiKVrsXTHKotDcbVgupO1daG4SiV22lUqS2Pp1leH4qzWfb8sGts8tK71fl8obqyyMBS3bvKuUFx0W6P7rubrQ3ETtclQ3Fh1USwueO5NeCzd6LURFb12o2VBtGwJlVUDaaLcXl11ravd0zVusr42tL5wuRss16Lrq9dj59YGxa71qGplQSgu8tkm9aHctVj+JmuxsihyPBZWl4XWdV/ttlBc1WLbGi13a8FzObrvJoLri56j0XI3eu7Va7HydEMtdm1Et8Mq5X5nin73q1uX/A1gKPNSWPNYRiO2PtIdjDK3g3NqtMyn4zGEr7ouqTaAhGm71Zt+VFYdJ+lQSV8ws+MlnZXfX2Jmu0k6QNI7JN0u6YQ+pA8AAAAAABDCbYCjp/TKKne/3sz+QdJXJB2jVDHlkg7Ok0n6q6QD3f2WstMHAAAAAACIcGkgHaxTHdabfrSskrtfZGY7SzpC0n6Slit1pn6jpB9IOtndY+3vAQAAAAAA+mSW3PQ9r/SlskqS3P1eSSfmCQAAAAAAAOiqb5VVAAAAAAAAo4wO1kdTuUOvSDKzupltMLOju8SdatZtCBYAAAAAAID+qXn/J/Sm9MqqbEzS8blCarxD3BwZSxQAAAAAAMxG9QFM6E2/bgP8nqQtJL1C0s5mdqC7/6VPaQEAAAAAAPQs3QbY/3Y0NK7qTb9aVt0iaR9JZ0jaU9JlZvb4PqUFAAAAAACAOaKfowGul/RyM7tC0vskXWRmK9z9qyUmolp9QyCs3EZ3ZrE6vrrHuuSq+0QorqoFobjx6rJQ3FhlYShuor42FDdZXx2KW1TdMhQ3Phbbjlp9fSguctyqHe9anTJZXxeKG6ssCsVFz6lqcH3V4LGtWKwI2FC/O7a+Smx945XFobiyRa8189jxiB63aBlUdlnVHx7aj/Ov3F0aiiu73K0F48arW4XiVI2F1evldTk5XlkSittQvy8Ut6CySShufe3eUFzFguVpsPyrBtc3UQ/mj3K3pTlV7hq9ZgBAX7lUH0SzJ5pW9aRfLavu5+7/KekApds0v2RmK/udJgAAAAAAQDeN2wD7PVFX1Zu+tawqcvdvmdlTJX1T0jvNbHfRxxgAAAAAABiyQfRZhd4MpLJKktz9t2b2JElnSjpQNIIDAAAAAABD5JLqTgfro6YftwH+SdJtrWa4+52S9pd0kkTVJQAAAAAAAB6o9JZV7r68y/yapNeb2XEKd+UKAAAAAABQPm4DHD0Duw2wmbvfOKy0AQAAAAAAXKZa/8eek1Mh1pOhVVYBAAAAAAAM2yD6rEJvZlxZZWbXKvUVtp+7X5dfR7m77zTTPAAAAAAAAPTKNZjbAOlgvTdltKxanh/Hm14DAAAAAAAAPZlxZZW7Vzq97qc0xORE1zgL9uNuFsv6WGVxKG6yvjYUV60sDKYbi6t7vdQ4D8ZVK4tCcdHjUauvDsVFVa37/pusrwutayy4rdH1RfImSTWtj8XVY3HVaizd6LGNnivRc69i492DJFWC127dJ0NxUWWXBdH9N1Tuof0YLU+jcWVfI2PV2LEb/XI3th1R0bIjWo5HrpEN9ftC6xqvLAnFRdcXvX4narHPovLL3Vj+Rr3cdZVbrkXLgmjXI7Ol3O3KuH0FAGaiNrhqDATRZxUAAAAAAJinTPUBdLAuOljvCZVVAAAAAABgXprNfVaZ2YMkXSXpwZJ+7+679CGZoZhx9aGZjZnZ1ma2WZv5W5rZyWZ2o5mtM7Nrzex4M1s207QBAAAAAABmouaVvk99coKkrfq18mEqY4+tkHSzpDc2z8gVWD+R9CpJ20paoNQB+9GSfmBmtOwCAAAAAADogZk9S9IrJH1q2HnphzIqq/ZRatHWage9Q9IjJa2RdJSk3SUdKOk6SU+SdEQJ6QMAAAAAAPTMJdVlfZ/KvA3QzBZLOknSbyV9qMRVj4wyWjbtIelyd7+5xbxXKB37le7+8fzelWb2W0m/k3SwpJNLyAMAAAAAAECPTLXZ18H6uyXtpNR4aKLMFY+KMiqrHiLpB81vmtljJG0tqSbptOI8d7/azC5TamkFAAAAAAAwcC71s0+pB6RTBjN7rKQ3SzrV3X9kZstLWvVIKaOyapmkaov398yPV7j77S3m/0nSE0tIHwAAAAAAYFrqA2lZNXNmVlHqgukuSW8dcnb6qozKqjsk7dzi/b2UKg8vbbPcuKR7SkgfAAAAAABg1O1kZle2muHuuwaWP0rSUyS9sk2joDmjjMqqSyW9wMye7e7flyQz20rSAXn+uW2We7SkP88kYTNTtbKwe1ywlrTuk6G4yfraUNyGydtCcQvHtw7F1b0eioty1UJxY5XFobiJWix/dY/dUlux8VCcl7hfXLF1Rc+p6PqirGUjxhZxFj3nY8cimm70XImee9F0o9dGdL+sn/hLKE5j5Y4SW60s6BozYVZeG+LpMAvlM3pdRsvdmtaH4jZM3BmKWzi+ZSguem5Fz+moeLm7OhQXvdYjn6mSVK/HjluZouXusIT3XfBYRJVd7kbVg+VQ9Litn4h9314wvkUoLloGVboNTG2l9i8CABhB7qaa97+89xmmYWY7SPoPSRe6+2ll5GmUlVFZ9XFJL5T0DTP7sqS/SnqRpE0l3STp7OYF8j2Vj5J0RgnpAwAAAAAATMtgOliXJF0TbEHVyickLZD0uhLzM7JmXFnl7uea2XslvVPSy5X+8zdJ65SaprX6C/F1Oeb7M00fAAAAAABguuoD6GC9BM9X6qvqk/bAlr+L8uNDzeyCRqy73zfAvJWujJZVcvd3m9nZkg6U9GBJN0o6w92vbbPIBkkfkfTdMtIHAAAAAADolcsG0rLKVcqthptL2rvNvMWFeaXU9QxTaRvg7j+X9PNg7DvLShcAAAAAAGAu8zadXuVulq6T9Ht332WQeeqnWV/bBgAAAAAAMF2D6GAdvaGyCgAAAAAAzEsuqT6Q2wDRCyqrAAAAAADAvFWbHR2szytUVgEAAAAAgHnJZaqX0/l513T6sl73VdIANmDAqKwCAAAAAADzFi2rRs/srqxyV70+Wdrq6j4RiqtWFobiFo5vHYobrywOxdWC+YsyVUNxVYudJrVKLM5VD8VVgulO+tpQXMR4ZUkoLnosoutbX7svFOeqheKqtiC4vuCxCB7b8LkSvGE7ur1R0WtNwWvXPbb/avX1sfVZYH0+/Lvd69693I2WzdFyd6waO3YLx7cMxUXPhUmPHbuoaLlbsdgXpui1WXa5W1d5n71Vi32mRo9FdH31Ej87pB72XeD6keLHNnqu1Esud6Nx8XI3du1Gz+XSyt0RKHNlc+7PcgAAuprdlVUAAAAAAADT5JJqdLA+cqisAgAAAAAA85NLdR9AK1Zqq3oy48oqM7t2Bou7u+800zwAAAAAAAD0zgbSsmoO9oHeV2W0rFpewjoAAAAAAAAGyiXVB9DBOg2rejPjyip3us0HAAAAAABAOeizCgAAAAAAzFs1btEbOVRWAQAAAACAecllA7oNkAqxXvStssrMxiQ9X9KTJW0l6VJ3/0yet21+77fuPtmvPAAAAAAAAHRCy6rR05fKKjPbW9LpkrZT6vLeJY1L+kwOeZak0yQdIunMfuQBAAAAAACgm0G0rEJvSq+sMrPdJX1HUlXSRyRdLOkrTWFnSvqEpBdpJpVVVtF4dWnXsLpPhFZX1YJQ3FhlYSiu7vVQXC2YvzXrV4XiFi3YPhRXtWoozoJx45XFobjo9taDje4qNh6Kix63YYjmbbIe21ZX7NyrWKwIqAb3cfRcUfDYRs+BdRtuDMUtWbg8FFe12PGoVGIfapPB/RwxYTbUoURMFS2sbto1boPuC61vWOXupK8PxQ2r3I2Wa+PB71Vll7tmsYQjx60S3Ce14LGNri9e7q4NxcU/s8otd6PnSj14zg+r3I1uRyV47llJQ5BP2Aj80+6BQn8U8gkAQIn60bLqXZIWSvp7dz9PkqzpA9Td15jZ7yTt0Yf0AQAAAAAAunJJtYH0WYVe9KOyam9JlzQqqjr4k6T9+5A+AAAAAABAgKk+kD6raAXbi35UVm0q6aZA3EKlWwUBAAAAAAAGjpZVo6kflVU3S3p0IG43Sdf3IX0AAAAAAIDuXKr7AFo9UVvVk35UH54jaVczO7BdgJmtkLSjpG/3IX0AAAAAAADMUv1oWXWcpEMlfcHMjpd0Vn5/iZntJukASe+QdLukE/qQPgAAAAAAQFcuqdaXdjwbp4O40iur3P16M/sHSV+RdIxSxZRLOjhPJumvkg5091vKTh8AAAAAACDGBnMbIB2s96QfLavk7heZ2c6SjpC0n6TlSp2p3yjpB5JOdve7+pE2AAAAAABAVH0ALavQm75UVkmSu98r6cQ8AQAAAAAAjJQ0GmD/Wz1xG2Bv+lZZNQjuNa2buK1rnFmslnS8uiwUV/d6KK5sixZsH4qr2sJQnKsWi/NYnAVro8eC+aupGoqbqK8OxUXyV7FyL4no+urBfezBc2+ssjgUVy15e6PnSvTci57L0WujbGWXBRO1e7vGRM+Bfqn7pFZvuLW09Y1XlwbTjW139NyKGla5W/eJUFxUtNydDK5vsr5u+plpUnY5GV1fLbiPo9dctbIgFBc9Fq5YutFzZdTL3fi1UWqymqh1/g4x7DJXkmTcNgIAmH9Kb+tmZst7iH1+2ekDAAAAAABE1d36PqE3/bgx81dm9vJOAWa2yMw+qamRAgEAAAAAAAbMVPdK3yc6WO9NP24DXCDptDwi4GubO1I3sydIOkPSoyRd24f0AQAAAAAAunJJtQFUJNFnVW/60bLqCZJ+LekQSb8xs2c2ZpjZ2yX9RKmi6lRJj+tD+gAAAAAAACHcBjh6Sq+scverJD1F0vGStpV0rpmdaGYXSjpO0r2SDnL3I9w91jM2AAAAAAAA5oW+jAbo7pOS3mZm31Hql+qoPOuHkl7u7rf0I10AAAAAAIAod+U+pfqfDuL6UlklSWa2qaQjJW1aeHsXSbtKorIKAAAAAAAMXZ3Oz0dOX6oPzewZkn4j6WWSfqnUN9X7Jf2NpO+b2QlmtqAfaQMAAAAAAMSYat7/idEAe1N6ZZWZvV/pdr/tlfqteqq7X+7ux0h6pqQbJb1R0s/MbPey0wcAAAAAAIhwpdsA+z1xF2Bv+nEb4NuUKqQOc/cLijPc/ce5guqTSq2uLpW0ZPpJmSqV7g20xiqLQ2sbqywMxdW9Hopz1UJxpmoormqxuGi6k7V1oTirRvMXO53GKoticaEoqe4TwcjuqhoPxVWCx8KC9cGTWh+KqwbOd0laWN0kFBc1WY+dKzWfjK0veO6NVWPnStVi125U2ddutGyJqNXvlnssf/1gMlWs+3USPVfLLnej5lu5GzlmkrQgGFcPXuuRMrBS8nUUXV+0fK5UYvt4QaXccjf62Tbfyt2ossrdWv3OUtYDAAB604/Kqi9Leq2739VqprvfK+mfzOxbkj7Rh/QBAAAAAABC6s4teqOm9Moqdz80GPdFM7uo7PQBAAAAAACi6GB99PRtNMAId79xmOkDAAAAAID5K/VZ1f/KKvqs6s2MK6vM7Fql/b6fu1+XX0e5u+800zwAAAAAAAD0zlT30seea5kO4spoWbU8P443vQYAAAAAAAB6MuPKKvcHVkE2vwYAAAAAABhJPqAO1rkPsCdD7bMKAAAAAABgWFyD6WCduqreUFkFAAAAAADmrYG0rEJPSqusMrPnSTpA0g6S1kv6jaRT3f26stIAAAAAAAAoE5VVo6eUyiozO0PSoY2X+fEFkt5iZoe6+9llpNOsYhUtGtu8a1zNJ0Lrm6ivDcW510NxY5XFobiqxQ6DWTUU516Lra8aW180f1H1YP7GKgtDcQurm5a6vlCatkkoboOvCcXF8xbb1krwmE3W1wfTjQmfK9Wlpa6v7GujFmyjOxksM8xiXflFj9swmVW1ePxBXeMm6rFzf1jlbiV8TMa7B0mqBz9nhlXuRoXL3WBZVGa5u8iWheLKLnfL3tZRL3eHdW3UKXfb88DOMX5kAQDmlhl/QpvZEZJeKmlS0umSfilpmaTnS9pT0v+Y2Y7ufvdM0wIAAAAAACiLywbSssoH0C/WXFLG30mvkFSX9Fx3/2Hh/feb2amSDpN0kKRTS0gLAAAAAACgNNwGOHpibaQ7213SJU0VVQ3HKd0WuHsJ6QAAAAAAAJSqLuv7hN6U0bJqU0nXtJl3TSEGAAAAAABgZLgG07Iq2D0jsjJaVpmklr0W+1SPuGWkAwAAAAAAgDluFgyBAgAAAAAA0Ac+oD6raFrVk7JaPL3CzGqtJqVD0m7+ZEnpAwAAAAAA9Kzu1vcJvSmrZdV09zxHDAAAAAAADIVrMJVJTvVHT2ZcWeXu9EcFAAAAAABmJafl08iZF31WTdbWxeLqq0Nx1cqiUNxErd49SFKtEjsM45XFoTgL3t1ZtXIP/1hwv1SC6VY1Hopzi+3ncXXP35gtDK0rmrcFtiQUN+nrQ3HRbY3mL3ojcN2robjJeuxaK/vcq/tEKG6ivja2vnrsDmVvPbbERmrBMmissjSQ6PBvdnfvvt3RcrcWPCbVYPk3UYuV45VwuRsKCxtWuRv9XKgodq1XLVbGRMqicYttQzX4lSVa7k54fG35wQAAIABJREFU7BytB3ssKHvfVWw45a4r9jkzrHI3ajJYFnQtW0agzAUA9F+dVk8jh1ZRAAAAAAAAGBnzomUVAAAAAABAM9dgRgMsq62umR0t6emSdpe0taRFkm6RdIGkD7r7lSUlNVRUVgEAAAAAgHlrlvVZ9Q5JSyX9RtLl+b1dJR0m6VAzO8DdvzuszJWFyioAAAAAADA/+WBaVpXWtEr6R0k/d39gB5xm9jpJn5D0aTN7qEc6mh1h9FkFAAAAAADmKZN7/yeV1Im7u1/cXFGV3/+kpD9K2lbSo0pJbIiorAIAAAAAAJj9Gq2pNgw1FyXgNkAAAAAAADAvzbYO1tsxs8OUWlT9QdK1fU6u76isAgAAAAAA85b3uyZpyk5m1nK0PnfftZcVmdm/KnWsvlTSo/PzP0t6mbvXZ5rRYetLZZWZmaT/o9Tx1yMlLVPrGzTd3XfqRx4AAAAAAAC6qZfUn9SAPVvSswqvb5D0cnf/+ZDyU6rSK6vMbIGkb0vaV+17EPMO88JSc73uFYaVSmwzF1W3DMWZqqG4uk+E4lyxSs9acH1jtjAWV1kUiqsHBxGoWGw/VzUeiltgS2LpBo9HZL8s8WWhdcXF9vGaksvG6DmwwdfEVhjMX8Vix7ZisWM2Wd+o38CWotdGVLWyIBQX3V6vLg3F1X2ye5BZ/9sQd+ShMita7o5Xt5pphh5g1Mvd6DkTZcGuJ81icdFyN5ruuHUvA6Plbj14zBZqcSiu7HI3sq2StE73xlYYvM7LPqei19Ckry813bLL3fFguVvrth02K3+8ADMX/NzQ7G+8AUhS7gB9IK7ptQVVO+6+nySZ2eaSdpf0LkkXmNmx7v6+MtIYpn50sP5mpdq9bym1qjpd6SvXQqWmaSslrZZ0vLvTwTsAAAAAAMA0uPtd7v5jSc+T9HNJ7zWzJw85WzPWj9sAXyLpDqX7JFebWV2S3H1C0u8l/buZnS/pfDP7vbt/pg95AAAAAAAA6GiudLDu7hNm9iVJT5T0Akk/63OSfdWPlk2PkHSZu6/Or+uSZDZ130+u9btY0uv7kD4AAAAAAEB3njpY7/c0oK48bsuPDx5Ian3Uj5ZVNUn3FF43Kq0eLOmWwvs3KdX2AQAAAAAADMUA+6zqt73z4zVDzUUJ+tGy6iZJDy28/mN+fGpT3GMl3deH9AEAAAAAAAJM7v2fShhjTma2l5m9xOyBo5uZ2biZHSXp5ZLWSvrSjBMbsn60rLpE0ovMbLG7r5X0HUkflvQRM1sv6UZJr1bqbP2bfUgfAAAAAABgrtlJ0qmSbjOzn0u6XdJWSqMB/o2kdZJWuPsNw8tiOfpRWXWmpOdK2l/S2e7+RzM7UdKblEYIlFKV4mpJb+tD+gAAAAAAACGD6GC9JBdKOk7pdr/HKlVUbZC0StJXJX3U3f/YdulZpPTKKnf/tlKNXvG9N5vZzyQdIGkLSX9Q2olXl50+AAAAAABAhCt3gD6AdGa8DvfrJB1TwqpGXj9aVrXk7l+U9MVBpQcAAAAAANDNHOpgfc4ovbLKzE6QdKe7v7fsdTdzn9Ca9dd3jatUlobWNz62LBRXq6/uHiSpYuPBuNhhqPtkKK6maiguevDHKgtDcVXFtneBLQnFLfbYcatrcSiu4t3HE1jssbyNe2zvTVjsmLnqobgyt1VSuI+/Db4mFhhMNnwuB+Oi67NgBqPrm6yvDcVVg9fQxOS9XWPcY+dKv9R9Uqs33NI1rmILYiuMFVeq1dfHVhfc12WXu7EoaUHwcyFa7laCOzBa7i7x2OfgZLAsivyFuNAXhVa10GP7ZL3FzpXJ4FFbENzWseDnQrjcVazcNYuVa5PBa2jSY3HRa6Psa21D7Z7uQYqXBZO1zuX4sMtcSZLxAwpBwfJgpNMchWsO848PqLJqAK235pJ+lGhvULp3EgAAAAAAAOhJP24DvFH/n717D5P1qgr8/11VfTlJTgKRcDVIMIoSQAcFRVEuiqASLgHR8TZGUec3o8iMqMM4gCC/UdAZlFFxeEYioz8HHQjKHRTRETQgoEQJCAxIAiGJIUCSc+tLvev3R/XB0PQ5ter0fquqT38/z1NPn65atfd+b7vqrN57v/0kwSRJkiRJkppy0NPi6SNZ9YfAD0TE2Zk5eU6LJEmSJEnSnLhm1eLpYwTUs4FrgNdHxP17KF+SJEmSJKmNnMFDU+ljZNWrgDXgwcC7IuI6xsmrYzvEZmZ+cw9tkCRJkiRJmsiRVYunj2TVw27z7wDutvXYiflFSZIkSZIkfVYfyap79lCmJEmSJElSUwnkDIbROFJnOs2TVZl5desyJUmSJEmS2osZTQN0quE0+hhZJUmSJEmStPgSmEWyyqFVU+ktWRURS8DFwAOB84B3ZOZlW6/dbeu592Xm5inXwZDlpfMmxm12h0vljbq1U23KjjK7UtxmHi3FDWK5FLdR3N4uN0pxq8NzSnEZte0dMCzFdZxRijuYtfat5uT9dzBWS2UNo9aZjYo90mpXO7ZrUTtmh+KWUtxm1s75atyoeE6tjWrtG3XrpbiI2o1NR8XtiOI5WlXtW5LKNTTfT7mIIStLt5sYtzkq9kNd7SOgekyq5XUU6y2eW5vdTvcQ2aHe4kfeKrV+bVj8XIjizX83i/3uWXmwFLeak/vUg1T73ep1Xiuv0jaAtahdv4fjUCluI2vnyrz63fI12fjaqGrdF1Dqd6U5K15vMSh8Py2W1Vzx/0XZFdtXLE+qmsU0QE2nl2RVRDwU+F3gCxmPdUtgGbhsK+SbgZcC3wlc3kcbJEmSJEmStPc0T61HxP2A1wN3Al4IPInPn5x5OXAEeGLr+iVJkiRJkspyBg9NpY+RVc8CVoFHZuZbAGLblKnMPBIR7wfu30P9kiRJkiRJJbNZYF3T6GPS8kOBtx9PVJ3ENcDdeqhfkiRJkiSpxpFVC6ePkVXnANcW4lah8SrGkiRJkiRJU3Bk1eLpY2TVdcC9C3H3Ba7uoX5JkiRJkiTtUX0kq/4YuE9EXHKigIi4FLgH8Loe6pckSZIkSZpsFlMAnQo4tT6SVb8AHAJeFhHPjYgHbD1/ZkTcNyKeAbwIuAl4QQ/1S5IkSZIkFcUMHppG82RVZl4NPBr4NPCfgHcwziE+CbgS+HngVuBxmXl96/olSZIkSZLKHFW1cPpYYJ3MfFtE3At4MvAI4ALGi6l/HHgz8OLM/Mxu64kYsLp09uS4US0nF1GLG8ZqKa61pUGt3micg6zWu8yBWnnF/TfI2nas5nIp7pyY3L67HaiVdbuVUhg3r9fiPnGslmm/pdjJHSmey9VjUbURx4qR59TCirdgSLpS3Ga3ViuwsVHW6h3E5PNvvTtU3t4+BANWh5OPX2bbNi4NzmhaXr3ettdItX+u1juk1mctF/o/oPxFajVr7Tu78Llw59Vah3qH1Vo/edNabSNuWCv+hbO4Tw7HoVJc+VgUDaL2Va567uWw7bU7r353sztaihvGyfuW0WjXX1d3LwsnYfgX+z2p+H0tBsXjO5z8xWlwRq0PylGtL4hhbRu6o7XviMGoFJdd8f87jb+P6DRmMmnh9JKsAsjMW4Ff3XpIkiRJkiRJE/WWrJIkSZIkSVp46QjVRdNbsioiLgAeAtwVONF8gczM5/bVBkmSJEmSpJOpzLjWbDVPVkXEAeB/AN9z/KmThCdgskqSJEmSJM2HyaqF08fIqucD3wv8E/B7wIeBwz3UI0mSJEmSdOoyZjMN0KmGU+kjWfVdwCeBf5GZ1/dQviRJkiRJkk5TfSSrDgJvMlElSZIkSZIWXTgNcOH0kay6CrhzD+VKkiRJkiS1ZbJq4Qx6KPO/AF8TEV/fQ9mSJEmSJEntHF+3qs+HptJ8ZFVmvjwizgdeFRG/BrwZuJYT5Coz85pTrgvospsYd2Dp9qXyhrFcitvsjpXiksltA1genFmKqxpE7bAOqW1v1VKsluLOzLNLcWdkbb8cLNZ7twOTt/eb7nprqaxHPuCdpbg/ftcDS3Fvua62T7pjtZT/Wh4sxUUxX32k2Ld2OSrFDQe1c2/ERrHezVq9w1q9G92RUlx1/60Oa8djlJO3d2Pzn8ji9vYhSTa7tYlxq8PaOV3t/9a7Q6W4qmGx3xjEsBhX63cH1MqrWo4Dpbhqv7uatfIOUtt/d15dmRjzoPNq1/nF972yFPfa935lKe7tn5zcNoBca9vvLhW/elX73Sx8DwJYKV5rHbV+vNrvxqDWT45ycr8yjYPLtUH+k/r7jc1/atGc3Qn/g7PvRe06GpwxuQ+/+t/cp1TWVU95USnuPr/2b0tx9/jNq0px3eGjpTiKfZVU5siqhdPHNECAvwM+Dfzc1uNEssc2SJIkSZIkaY9pniiKiIuBV26VfSNwNXC4dT2SJEmSJEm75siqhdPHqKbnAAH8IPA7melhlyRJkiRJiyeZTbLKzMhU+khW3Rv4i8z8nz2ULUmSJEmS1I4LoC+cPpJVnwTaroQrSZIkSZLUg3DU08Kp3VpiOq8AHhQR5/RQtiRJkiRJUjs5g4em0key6hnAR4BXRsQ9eyhfkiRJkiRJp6k+pgG+FhgBDwc+EBEfBa5l51xiZuY399AGSZIkSZIk7UF9JKsetq38L9l67GRXg+GGLHHO0l0mxq1lbQmtze5YKW5pcKAUF8WBa6PcKMW1NohhKW41DpbihizvpjmfZzlrp+cwaovh3W5lcswjH/DOUll3/O2nl+Ie+YPPK8W9+03fVIq77lhtW6v77mhxHcHqsV2JM0tx1Wuyy81i3KgUV7UyqJ3zSVeKa9m3BPNd/HEYy5y7fPeJcYdGnyyVt97VzoXlQe3cqva7m7lWihtl7RhXz8GlwWop7kCcXYobFj/Gu+K5upq19g2jtp/vsDr5fL34vleWyrrglT9Wirv4Cb9RivvQFQ8sxd24XtvW6r47GkdKcdVjW+13j+WtpbjNrnZtVPu/qtb9brVvGUbtuM1V5cbaxe9CmpFiH9lajiZfH1c95UWlsh71hfcvxV11ba28b/v1byzFNVc9FsXPe52+XLNq8fSRrHLqnyRJkiRJ2hu8G+DCaZ6sysyrW5cpSZIkSZLU3KwWQHf01lTmM0ZVkiRJkiRJ2kEf0wAlSZIkSZL2Bkc9LZxeklUREcD3Ao8DvhQ4G3ZcFTgz88I+2iBJkiRJkjSJC6wvnubJqohYAV4HfBM7J6hgnLd0BTNJkiRJkjRfJqsWTh9rVj0N+GbgtYxHVf0u40O/CtwbeDZwGPjlzHTNLEmSJEmSND85g4em0sc0wO8CPgV8T2YejogOIDM3gA8APx8Rfwb8WUR8IDMv66ENkiRJkiRJ2oP6GNn0JcBfZ+bhrd87gIgYHg/IzLcCfwn82x7qlyRJkiRJKons/6Hp9DGyagTccpvfjyet7ghcf5vnrwUes7uKNrll8/qJcUuD1VJ5S4MDpbjN7lgpLsd5uomWB2eW4qoGUTusUcxVrueRUtxKVLejtp83YrMUNyqOqbx5fXLMH7/rgaWyHvmDzyvFVcurtA3q21rdd1UjNkpx1XOleu5Vz+XW1rtDpbjqdlT7llFO3s855zHEo9zg0xsfmxg3LB67lcHBUlz1mFQNo/a5MPjnv7NMiKtt74Baea373VXOKMWtxVopbpS1/XfT2uTz9bXv/cpSWRc/4TdKcdXyKm0DGGXts7y67wbFfmNErR+vnivVc6/6nanLWvu6HJXiWl/j1b5lo6vtv7kKl3ndc4r9BtF23EAMJ5d3n1+rjRW46toXleKq5d1jeFUprvm3nOqx0P6WQM6grzVhNZU+RlZdC3zRbX7/v1s/H7Qt7iuAtt9MJEmSJEmSpuGaVQunj2TV24H7RMTxP+O+fuvnCyPi2yLifhHxa4wXW39HD/VLkiRJkiRNFMxmGmCLsVsRcWZEPD4iXhIRfxcRt0TE4Yi4MiKeFRG1ocV7QB/JqsuBI8C3AGTm/wV+Fbg74zsEvgf4sa2Y/9BD/ZIkSZIkSaeb7wH+EPghxvmcNwJvBe4JPAd4Z0TcaX7Na6f5gjCZ+Trgrtuee1pEvBN4PHAu8EHgv2Xmh1rXL0mSJEmSVLZ3pumtA78J/Mpt8ykRcVfgdcD9GQ8W+p75NK+dma1enJm/D/z+rOqTJEmSJEmaZK/crS8zfwf4nR2evy4ifgz4K+AJEbGSmcXbeC2mJsmqiPgm4HzgXZn5vgmxFwEPAD6WmX/Won5JkiRJkqRTskeSVRNcufVzFbgDcN0c27Jru05WRcTdGQ83+xjw1YW3fIzxHMvzI+JLM/MTu22DJEmSJEnS1GZ1t77+6/jirZ8bwKd6r61nLRZY/2FgBfiZzLx1UvBWzE8DZwBPblC/JEmSJEnSfvbUrZ9vzMy1ubakgRbTAL8FuDEz/6j6hsx8dUTcAHwb8NwGbZAkSZIkSZraDNesujAirtrphcy8z6kWGhHfzngw0AbwzFMtZ5G0SFZ9OfCXp/C+dwFfv5uKAxjE5MFhxzY/UyuvUBbAMFZLcVVro0OluKVBrd4uR6W4TWrJ1mq9m8Xk7ZEohZF0pbjVbrkU94ljkyt+y3Vnl8p695u+qRR3c3FJu08c2yjFHaru40HtnDoah0tx1WM7orYdm13bRH/1XGldL8V61zdvKsVlTi4vi9d3X4Io9QnVfndtNHFALgBLgzNKcVVdHi3WW+v/Rlk796M4oLla70YeK8VV+91NNktxq1lr3w1rkyt++ydXSmV96IoHluJuWqt927xhrdZBHyp+Vh6OWr+7HrVzr3psO4qf+cX+r9qfVrXvd2sObdzQpJx597lbjZgcE8WLXHtT4fsBQHd0cr9xj9/c8f/In+fbfv0bS3H3GNbKq7QNKG+rpH8WEfcG/j/GKZKfzswrJ7xlT2iRrDoLuPkU3nczcLBB/ZIkSZIkSadmdiOrPrybEVTbRcT5wBuBc4EXZOYLW5U9by2SVZ8G7nwK77vz1nslSZIkSZLmYobTAJuJiPOAPwG+CPht4Kfm26K2Wiyw/j7gQRFRnqMREWcCX7f1XkmSJEmSJBVExNnAGxgvy/RK4EcyK/PG944WyarXMJ4K+Iwp3vMMxncDfE2D+iVJkiRJkk5NzuDRSESsAq8CHgC8CfjuXIhFFttqkax6MXA98PSIeEacZJXyiBhExDOBpwM3bL1XkiRJkiRpPvZIsioihsDLgIcDbwWekJnF23rtLbtesyozj0TEE4A/BZ4D/EhEvBz4G+DGrbA7Al8FPAk4HzgGPDEzj+y2fkmSJEmSpFOSM1qzqk0dPw5csvXvTwIvip3vCPtTmfnJJjXOSYsF1snMt0fE1zG+XeJ9gX+/Q9jxPXgV8H2ny+0UJUmSJEnSHrZ3Vns69zb/vuSEUfBsxsmsPatJsgogM/8O+IqIeBTwaOD+wB0YJ6k+CbwHeF1mvrFVnZIkSZIkSftBZj6bcSLqtNcsWXVcZr6J8SJfkiRJkiRJC20m0wA1lebJqlnK7FjbvHVi3GZ3uFTecHCgFDdirRQXDEtxSW3h/s1uuVZedqW44WClFAfn1OqNWr1VHWeU4tZioxR3S6ED6o7Veqnrju04L/jzjIrjSQ9l7ZyqbmtH7VhsFutdLy4vN8pa+9ZGt9TK62prBZ7kvg6foyu2r/W1WzXqjhXqbHudTSvpSsdv1B0tlTeI1VLcxqjWj7e2WdyOar87GNQ+dleL/W4Xm6W4qpVyv1vrOypdYK7V+skb12vX+ah4LA4VP8vL21q0kZOv83Fc7dxr3e92Xe2cqva71WtjXroJn4O5CPNCdl6LRKeD4vWRXe16i8L3ku5wrW+pKl8h5W0tlrjgfYv2oAXo7vW59nSySpIkSZIkaVdMVi0ck1WSJEmSJGlfCmYzDdBxstOpjSmVJEmSJEmSZsCRVZIkSZIkaX9KZjMN0KmGUzFZJUmSJEmS9i8TSQvHZJUkSZIkSdq3ZrFmlabTfM2qiLgsIn6oEHdpRFzWun5JkiRJkqSynMFDU+ljgfVLgW8oxD0Y+IEe6pckSZIkSdIeNc9pgCvAaI71S5IkSZKkfc5pgItnLsmqiAjgq4Abd1NOMmJj85MT4waDs0rlDQerpbhRt1aKi6gNXBvGSiku6UpxS4MzSnGrw4OluEHUTpMhy6W4pajt50HW9t+huKUUd6RwPNaytk+Ws7ZPNmKzFHdkcKgU1xXPgUFx0GT1WHTFvHL1nB/EuaW4tVFtv4yydk0uRe3a6LJ23DJr+6Xat3TdeiEqSmX1JXPE+ubNE+MGxX5tMKhdS9V+t7qvq/1a9VwYDmrbuzKo9TFL1e1gWIpbjgO1eot92+GoXZuVuGq/u5q1fbIWtXOlug1VS8WvVNVjUf3Mr57LUfxcWC/ul9bXRrW81n1BN9ooxUlzlbX+ILvKdb7gYwWK2yo1Z7Jq4TRJVkXEW7Y99a07PHfbOi8E7gL8bov6JUmSJEmSpjarNaVMiE2l1ciqh93m38k4EXWXk8RvAK8FfqpR/ZIkSZIkSVOb79wF7aRVsuqeWz8D+AjwCuCnTxC7DnwyMx13LUmSJEmSpM/RJFmVmVcf/3dEPAd4z22fkyRJkiRJWkhO0Vs4zRdYz8zntC5TkiRJkiSpD94NcPHUbg0zhYi4c0Q8JCLuvO35e0bEyyLivRHxuoj4mtZ1S5IkSZIkTSVn8NBUmo+sAp4O/ARwEXADQEQcBN7GeNH12HrtIRHxlZn5kR7aIEmSJEmSNJnJpIXTfGQV4zsDvj8zP3Cb5y4F7gq8DPgy4N8DZ+HdACVJkiRJ0hxF9v/QdPpIVn0h4zsC3tbFwCbw1Mz8UGa+EHgP8PAe6pckSZIkSdIe1cc0wLOBW4//EhEBfC3w7sy86TZxH2CcxDplEcucuXKPiXGjXCuVN4ja7hgOV0txXW6U4pKuFFduXzGuarOr7b9q6nM9j9QCoxa2WTy+SzH5uEVxI44W21Z1NA6X4lpuK9SPxYjauVw9VwYxLMVVz+VkVIrrcrMUV73WlgZnlOKq7VteOntiTLdxK5m18vowiCXOWrnLxLiN7mipvGHxXG3d7zY/F4rbUVW9loaxXIo79s8fyydX7Ns28lgpbjkOTIxZKn4VORq1/mpQ7MfXo3aOttxWgGNZOxaZte8Go+I5X1U9l2tXUP1aq37+rgzPKdZcNOHjaNT18XfdKWXhz/HR+IuJ9qZiv1ESxXO/ZZ3SvMxqTSlHV02lj2TVtcA9b/P7A4DbAX++Q93rPdQvSZIkSZI0UTCbaXr+WWE6ffy56ArgayLicRFxDvAMxjnE12yLuzfjxJYkSZIkSdJ8eDfAhdPHyKr/DDwBeOXW7wH8WWb+1fGAiLiA8R0BX9JD/ZIkSZIkSSUugL54mierMvMfIuIbgKcCdwTeDfzytrBHAVcCf9S6fkmSJEmSJO1dfYysIjP/Frj0JK+/GHhxH3VLkiRJkiSVObJq4fSSrJIkSZIkSdoTTFYtHJNVkiRJkiRpf8oZrVllQmwqu05WRcRHGO/2R2TmP279XpWZeeFu2yBJkiRJknRKTCQtnBYjqy7Y+rm87XdJkiRJkiRpKrtOVmXm4GS/9ymAQUyubmO0WSpvvbu5FDccHCjFBcNS3GBQOwzDWJ4cNIXN7ljT8rqsbS9RC1vPI6W4zVyrFVhwpNi2IbVjMWKjFFfdhuo+6RiV4qrt67J2DXVZLa8WV1W9NqrbMerWS3GbHC2WV7vWlgZnTQ7Kef/ZJwgm97tdV9vXG92tpbjh4IxSXFXrfjfpSnGtz/1BFPvd4mmzziL3u7VjNqJ47mXtutzI2nVePQcyi3FzOqeq9c6r3x1RjOtqx21i3zL3Pleak2JfJZ0uwv5+4bhmlSRJkiRJ2r/MVS0ck1WSJEmSJGnfmskC65pK82RVRDxkmvjM/IvWbZAkSZIkSSoxWbVw+hhZ9edMd6iLC25IkiRJkiTpdNdHsup32DlZNQDuDnwVcA7wauDTPdQvSZIkSZI0Wc5oGqCjt6bSPFmVmZee7PWI+ALgt4CLgK9rXb8kSZIkSVKZiaSFM/n+441l5qeA7wNuB/zirOuXJEmSJEkCCMYjq3p/zHtD95iZJ6sAMvMI8NfAY+dRvyRJkiRJEjAeWdX3Q1OZS7Jqy0Hg3DnWL0mSJEmSpAXTxwLrE0XEY4CHAO+bRX1LwwOluMGgtjsyu1q9gzNKccOo1RtRu3Fi5qgUN8rNUly1fZvdsVLcIJZLcdVU6ig3SnEbMbl9XXHfrcSZpbj1PFKKG1Hbhuq2RtR23ma3VorrivVWtT73qufUyqC2X0ZRa99md7QUN1xaLcUNCtu7MYq5/2Wm0hcter87KF4j1XOreo0ser9b7TuqfVHlnK4e29b9bkf1s7Ldtk5T3rz63dbXxnLxs7yL2nlQ7XeXl84uxU06bhsjJ21I0n4wkwXWNZXmyaqIuOwkLx8E7gXcj/GUzf/aun5JkiRJkqSyNFu1aPoYWXVpIeYa4NmZ+Ts91C9JkiRJkjRZzmhklfmwqfSRrHr4SV5bB67LzI/2UK8kSZIkSdJ0TCQtnObJqsz8P63LlCRJkiRJ0v4wlwXWJUmSJEmSFkHxPh+aoT4WWH/Ibt6fmX/Rqi2SJEmSJEkn5TTAhdPHyKo/Z3eHevI90SVJkiRJkhqYyQLrmkofyarfAb4AuJhx0upvGd/9D+CLgH8BDIDXAJ/qoX5JkiRJkqTJEsgZZKtMiE2lj2TVfwTeDvwJ8JTM/OBtX4yIewEvZJy0elBmXtdDGyRJkiRJkrQH9ZGseh7jqXyPy8xj21/MzA9GxBOADwHPB/7VqVbUZcexzc9MjFsanFEqb7kY12Vt9bVkVIobVTOsudm03s3R5x2enQ3PqoVF7XQaRG2mZ1fc3rXRLaU4OGdixHCwXKszD5XigkEpbrNbq9Vb3NZBnFuMqx6LjVLcqHjMNkaHS3E5PFCKi2w7e7h6DVX7lkG0PQ/mKXPE0Y01pXR/AAAgAElEQVTJg2KHg5VSefPqd7tiv9tl7Zgser9b1bovqvSBK4MzS2Udy1tLcYPiagLz2NY+tO53l4r9bvWarLLfPYmIebdAkk57TgNcPH0kqx4F/NlOiarjMvNoRLx1K1aSJEmSJGk+TFYtnD6SVecA5xXizgPO7qF+SZIkSZKkiYLZjKxqNU42Ir4a+Bbga4CvBe4GrGVmbXj0HtFHsuq9wEMj4hsz8607BUTENwAPY7z4uiRJkiRJ0nzMYoH1dp4JPG7ejehbH8mq5wMvB94UEf9z69/XMB5Ydw/gSYzXqRpsxUqSJEmSJGmyK4ArgXduPa6fb3P60TxZlZmXR8RPAL8M/GvgR7eFBLAO/GRmXt66fkmSJEmSpKq9tMB6Zn7OoJ84TW/E0cfIKjLz1yPiNcCTgQcznkMZwCeAtwG/nZn/2EfdkiRJkiRJJclsFljfQwmxRdBLsgogM68GntVX+ZIkSZIkSbu1l0ZW7RfNk1UR8bOMR05d17psSZIkSZKkdhK6mQ2tujAirtrx1cz7zKARe8aghzL/X+DqiPjDiPj2OF0nUEqSJEmSJKm5PqYBPge4lPGtFB8LfCIiXgJclpnXtK0q6br1iVEbudm01kEsl+KCYSkuGZXiuuJ2DGO1FLc0PFAsr+1pstkdK8WNits7KpwDAJXDMWKjVFT1WAwa77vqtq6NDpXiWh/banlZPPeq19Ao10px1eNRrbdqs6u1b2N068SYzG63zdmVJOly8nXSjWrXUlW1361a9H53ELW/JSW186FyzAA2i9dS19X2Sw4nt68rHovqdbQ0qB2L8r4rbut61PrdpeK5Um1f9Vypnnvz6ndbq/e7h0/6+rz7XEnSjMxuGuCHHUFV03xkVWY+B/hi4NuBPwTuxHjtqo9ExOsj4pKIaPs/QUmSJEmSpFMQ2f9D0+ljGiA59sbM/A7gfODpwIeBbwVeAVwbEb8YEV/SR/2SJEmSJEkTJZA5g8e8N3Rv6SVZdVuZeWNm/lJmfhnwMOB/Mx5t9TPAByLiTyPikr7bIUmSJEmStJ0jqxZP78mq4yLinsAjgW88/hRwHfBw4BUR8Y6IOH9W7ZEkSZIkSdLi6TVZFRHLEfGdEfEnwIeAnwXOAF4IXJSZ5wPfALwBeCDwa322R5IkSZIk6XPkDB6aSi+3aImIewM/DHw/cAfGo6jeDvx34H9n5mdvB5eZfwVcHBFvZzzKSpIkSZIkaSYi9042KSIeDTxz29MrWzmV456bma+bYbOaa56sioi3Al/POEF1C+ME1X/PzL+f8NarGI+ukiRJkiRJmo1u3g2Yyh2Br932XGx77o6za04/+hhZ9WDg3cCLgf+VmUeK7/st4C96aI8kSZIkSdLnGS+A3v/IqlaLrGfmS4GXtiltcfWRrPqazHzXtG/KzCuAK3pojyRJkiRJkvaI5smqU0lUnaqIIQeWz5sY1+VG03oHUVuXvsu2YwmPrX+8FHdgpXZTxWGsluIihqW46n4eFeO63CzFRfF4ZGFsZ7XOLkeluKpK26C+raNcK9Zb245hLJfiBsW4yNo5Vd2O6rVx5uoFpbiqel9QK295ePbEmFF3M9n4/JvGIJY4a+XOE+PWR4ca19t2X1fP/Xn1u9Vrqdrvbhavpdb9bss6q/1ktbyq6rZW6622rnW/W/1OMq9+N6h9LrTvd8866euj7tO1gvpU+Wt/RP/tkKTT2d5Zsmrf6GWBdUmSJEmSpD1hDy2wvl/sOlkVET+zm/dn5i/ttg2SJEmSJEmnotV6Umqnxciq51EfNHd8jPJt401WSZIkSZKk+XBk1cJpkaz6eaab4Xln4AeAM6Z8nyRJkiRJUjsJ0Xa56RPWo7pdJ6sy89mVuIi4A/AfgO9nnKg6DPzGbuuXJEmSJEnS6aP3BdYj4guAnwZ+DDgLOAr8F+CXMvOTfdcvSZIkSZK0s5zRNECHVk2jt2RVRJwL/BTw48DZjJNUvwI8PzNv7KteSZIkSZKkMvNIC6d5sioibg88DXgK4yTVGvCrjJNUN7SuT5IkSZIk6VSFC6wvnGbJqoi4HfCTwFP55yTVrwHPy8zrW9UjSZIkSZLUjMmqhbPrZFVEnAP8e+DfAbdjnKT6DeAXM/O63ZZ/UtmxMTrcrLguN4pxm6W4iEEpbnlwRinuzNULSnGtZY5KcRvd0ab1BrX9N8q1UtxmNzluOFwuldVapW1QP0eXonZOVc/latzKoHbMqgZR66Kq18Ywase3ei5vdrXbhoyKx3dQad+cP0iTjrXRLRPjuq56btXO6aS2r6v9xqL3u9X90rrfrV5zm92xYtzkcz8a9xtd8TOr2u9m1s694WClFNe6311uu/ua97tLsVqKq57Lo+Lx2BzVypvY7y7Cf14i5t0CSZJmrsXIqo8yTlKtM05S/ULvSSpJkiRJkqTdSij+XXT39aisRbLq9ox3+xLwZODJUf8LUGbmWQ3aIEmSJEmSNDXXrFo8rdasCmC49ZAkSZIkSdobTFYtnF0nqzKz8WoJkiRJkiRJM2KyauGYaJIkSZIkSdLCaDUNUJIkSZIkae+ZxQLrmorJKkmSJEmStD9lzmaBdacaTsVklSRJkiRJ2r9MJC0ck1WSJEmSJGn/Mlm1cPZ2siqCwWDyJkRxHflofGPDtY1/qgUu36kUNozVXbTm8yWjUtyoeN123WYpbjhYqZWXtfKCYSmuYqM7UopbGRwsxa13h3bTnM9T3dbqvhtErQsYdeu1uKjVWz33Wh5bgI3uaCmueu2uLJ1XihsOatdu5XhsRMCcP0sr7YzBvPrdm2qBy3cohQ1iuRRXPaercd2C97stjXKtFDevfreq/plVO+er/W4XtUU2qudeVbV/rve7tWt3ZfncUlyrfncjolROryr/gVqEdkqS1NDeTlZJkiRJkiTthiOrFo7JKkmSJEmStH95N8CFY7JKkiRJkiTtS5HM5G6A4eCtqZiskiRJkiRJ+5fTABdO25VtJUmSJEmSpF1wZJUkSZIkSdqnsn4r5t3WozKTVZIkSZIkaf9yGuDCMVklSZIkSZL2L5NVC6f5mlUR8X2FmAsj4sGt65YkSZIkSSpLxsmq3h/z3tC9penIqoh4JvDsiPgm4MmZn5+ejIgvA94CnBMRX56Z155qfZnJqFub3C6GpfIiarm7pcEZpTiWziuFZXaluMGg1r6uWF51v2x2R0txyagUN4jlpvW2FMX8bVLdx9V8cK286j7OrMVVz+VNaseieszK11DRoHjtbna1/bxSvHbL+6+4X0bd+uSgef/VJ7PWzqJqvzuM1VLcyvK5pbjqNVw9t1ovc9C6/6v2u+ujW0px1c+PlqrHbF4q30cAVobn1Mqjdp0ter87Kn4nqV671b5gRO14dLl58oB597mSJO1TracBvhD4NuAHgJWI+Fd5m0xMRFwE/ClwJ+Apu0lUSZIkSZIk7dpMFljXNJomqzLzloj4FuANwHcDqxHxLzNzFBFfAfwJcB7wo5n5kpZ1S5IkSZIkTSehOBJ41/WorPmaVZl5GHgU8OfAE4HLI+LrgT8D7gBcaqJKkiRJkiQthFmsWaWp9HI3wMw8GhHfDrwKeMzWYwR8X2b+fh91SpIkSZIkTSWZzTRA81VTaT6y6rjMXAN+BYitx3sZJ68kSZIkSZKkHfWWrIqIRwCXAxvA24B/AbwhIs7qq05JkiRJkqSpOA1w4fSSrIqIbwVeAwyBxwMPB/4AeAjwpog4u496JUmSJEmSpmKyauE0X7MqIh7LODE1Ah6fmW/eev57gXXg+4E3R8SjMvMzreuXJEmSJEkqM5m0cJomqyLiicDLgGPAozPzrcdfy8wuIi4F1oAfBv40Ih6RmZ9u2QZJkiRJkqSahK6bTT0qaz2y6jrgE8B3ZeY7tr+YmQn8aESsA18MHNlNZQEMYnliXGbtxKvGbXZHS3FVo26tVm+0PVxLg9VSXERttuhodKwUl8PasmXDYvuq+2+Uk+NWhwdLZW12tW1dGhwoxa1v3lSKq6ruu2RUihsVt3e4VKt3UDynNovHtnrzjuq5Ut1/1b6g2rfsCREMCn3RqFsvFVfeN8VJ69XyqudCNJ4t37rf3RwdLsUtN+53u26zFFe5Rg4u37lU1np3qBS3Mqj144c2bijFVVX3XdWo2L8sL9VWVphXv7s5qm1H+TOfYvuK5+ieEDHvFkiSNHNNsx+Z+VcR8SWZedJvCJn54xExzMza/5QlSZIkSZJaS2YzDdCBVVNpvmbVpETVbeJMVEmSJEmSpPlyzaqF0zxZJUmSJEmStGdU57drZkxWSZIkSZKkfSnJmawxm84DnErblWMlSZIkSZKkXXBklSRJkiRJ2p+S2UwDdGDVVExWSZIkSZKk/csF1heOySpJkiRJkrR/df2vWaXpNE1WRcQScAfgU5m5MSH2C4CDmXlNyzZIkiRJkiSVObJq4TRJVkXEecCvAk8AVoGNiHgD8KzM/PsTvO2/At+/qzZEMBysTAwbdeul4qp3AKjGVdoGkNE2i7sxurVpeYOoHaKlwVmluC43S3Ebm7XtSGr7bxDLE2NGJ8+xftbS4EAprlpe9ZwadcdKcV3xnF9eOrsUVz221XNls1srxVXP5eVhbTsq58A4rrYd1b6lKmIv3PMiSvuxi9p13rrfrR679v3u4ablVbdjODijFDfK2jW3OTpaiqPY7w5jcvs2uiPFslZLcdXyqrrivutGtf6eYTGseGzb97u1c3l5WP1caNvvVr9DVO2JfrfyH6iI/tshSdIM7foTOiLOAv4C+G7gABDACvA44J0R8eMne/tu65ckSZIkSTolmWTX9f5oOXorIg5ExHMi4oMRcSwiPhERl0XE+c0qmbMWf076SeDLgfcAXw+cBdwPeAmwDLwwIn6pQT2SJEmSJEltZfb/aCQiDgB/CjwLOAi8CvgY8IPA30TEhc0qm6MWyaonArcA356Zb8/Mo5l5VWb+CPAY4GbgaRHxPyIcoyxJkiRJkhZIl/0/2vlZxgOFrgDulZnflZlfCzwNuCNwWcvK5qVFsupLgL/KzBu2v5CZrwceDHwc+CHgD7YWYZckSZIkSZq/7Pp/NBARy8BTtn79scw89NlNyHwB8HfAQyLiq5tUOEctklUjxiOrdpSZ72ecsPog41FYr9oatiZJkiRJkjQ/mWTX/6PRVMBvAG4PfDgz/3aH11+x9fMxLSqbpxbJqo8CF50sIDM/znin/g3wrcAbgXMa1C1JkiRJkrQffOXWz785wet/sy1uz2oxJe9dwKUR8cWZ+ZETBWXmTRHxcODVwMOAppM2JUmSJEmSptZomt4MfNHWz4+f4PWPb4vbs1qMrHoNEIzvCnhSW/MpvxX4o633SJIkSZIkzc1MpgGOXRgRV+30KDb14NbPIyd4/fC2uD2rxciqNwE/AqxXgjNzPSK+A/hx4Nxd1Hv3zA2Orl9TqXQX1Zy6jerND4vtq5aXxazwqLu5FFdW3c+Nt6M6SG+9OzQxZmPzn0plRTHXmsW2ZY6K5VX3Sa193catteKq5+iobQ66+bnc+FqbR9+SbALcfeYVj909c4Mj6x+dHDmvv04tfL/76VJc2dz63ZrR6DMTY6r9bmv1frftdT7qin8nXPh+t3gu7/F+d9znzu3vq3c/wiGuyD+eHOl8BUmniSMcghl/1z3MLVzRvXEm9TRw/EPpRD3/aTMoaNfJqsw8Crxkyvd0wH/bZdVHxmVtfGyX5fSn9ReHxuVVv6g3N6cvVJVET+bmDFqyOJqfA/M6tqfJdhTdnRP/JaVvRyDJXLffPdXi9l2/W4ix391lgW2LK1d7mmxHwd0h59bnJh2HuWVx+1xJam/W33U/DM0SSaX6MvOxu3j/8dEGZ53g9TO3fk4eqbHgWoysmovMvMu82yBJ+4n9riTNjn2uJPVvl4mjeTg+tez8E7x+/ra4PavFmlWSJEmSJEnq15VbP7/qBK8ff/7vZtCWXpmskiRJkiRJWnx/CdzMeKH2++/w+nds/Xzt7JrUD5NVkiRJkiRJCy4z14Ff3/r11yPis2tXRcRPAl8BvC0z3zmP9rUUOac75UmSJEmSJKkuIg4Afw58LXAd8FbgHlu/3wQ8KDP/79wa2IjJKkmSJEmSpD0iIs4A/iPwPYzvoPhp4I3AMzPztLiLrMkqSZIkSZIkLQzXrJIkSZIkSdLCMFklSZIkSZKkhWGySpIkSZIkSQvDZJUkSZIkSZIWhskqSZIkSZIkLQyTVZIkSZIkSVoYJqskSZIkSZK0MExWSZIkSZIkaWGYrJIkSZIkSdLCMFklSZIkSZKkhWGySpIkSZIkSQvDZJUkSZIkSZIWhskqSZIkSZIkLQyTVZIkSZIkSVoYJqskSZIkSZK0MExWSZIkSZIkaWGYrJIkSZIkSdLCMFklSZIkSZKkhWGySpIkSZIkSQvDZJUkSZIkSZIWhskqSZIkSZIkLQyTVZIkSZIkSVoYJqskSZIkSZK0MJbm3YBTFRHXA2cCH5t3WyRphu4OHMnMu8y6YvtdSfuQfa4kzdZM+92IeDVw4Szq2vLhzHzsDOvbs/Zssgo4E+LsQaxc1KrAzFGrovoRMe8WnFQ0HqjX5VrT8gax2rCstpdOl5uNy1vcfQeQdE3Lay5z3i04oaTtuTKlMyHOHsaBZv1ulxutihpr3k+2Lq/tuWW/e+rsd3drsa+NVv34uM+Ns5sUNr0zg8HZZ3KwWZ8rSYvuCIdm/X+FC4GLLrrXSu8Vve+D673XcTrZy8mqjw1i5aKzD9yrWYFHNz7VrCyAiLb/iWj9Rb215cEZTcu75dgHmpZ3cLVdwvycpbs2Kwvgls3r2pa3wPsOYKM72rS81lr/Jzaz3QfuxuYNJJvz+iv7x4Zx4KJzz7xfswJvWbu2WVkAg0HbfnLYOGHQOjm3OjynaXmfOfrepuW1/Iw+Z9j2D6y3jK5vWt7NR9/XtLxzDnx50/LWRrc0LW8Qy03LGzVO9nVdm358Y/OGJuWcoo+dycGLvi4e2a7EwbBdWftRw89zABr/X6GprvEf8Rf93NtPx3bBXTF6A4e5ZabfdS+61wp//3/u0Xs993vo1SasptBb9iMiloCLgQcC5wHvyMzLtl6729Zz78ts/L9CSZIkSZKkkqSbyWiuxZ29sYh6SVZFxEOB3wW+kPE48QSWgcu2Qr4ZeCnwncDlfbRBkiRJkiTpZBIYtR5dd4J6VNd8fGJE3A94PXAn4IXAk/j8hQ0uB44AT2xdvyRJkiRJUlVH9v7QdPoYWfUsYBV4ZGa+BSC2LXibmUci4v3A/XuoX5IkSZIkaaKEmUwDNF01nT5Wfnso8PbjiaqTuAa4Ww/1S5IkSZIkaY/qY2TVOUDl9k6rwILfFkKSJEmSJJ2+klHOYtyTY6um0Uey6jrg3oW4+wJX91C/JEmSJElSiWtKLZ4+pgH+MXCfiLjkRAERcSlwD+B1PdQvSZIkSZI0UQIjsveH6bDp9DGy6heAfwm8LCJ+GXjV1vNnRsR9gccDPwvcBLygh/olSZIkSZJKHFm1eJqPrMrMq4FHA58G/hPwDsbJyicBVwI/D9wKPC4zr29dvyRJkiRJkvauPkZWkZlvi4h7AU8GHgFcwHgx9Y8DbwZenJmf6aNuSZIkSZKkioSZLLDu2K3p9JKsAsjMW4Ff3XpIkiRJkiQtnG7eDdDn6S1ZJUmSJEmStMiOL7A+i3pU13zNqojoImI9In5yQtxvR8Rm6/olSZIkSZKqRtn/Q9NpnqzasgT88lZCavkkcdFT/ZIkSZIkSRN1M3i0EhFfHRFPj4hXRsS1EZERcewUy/ro1vtP9PjyE7xvEBH/LiL+PiKORsSNEfHyiLhod1v3z/qaBvhG4FzgB4B7RcQlmflPPdUlSZIkSZK0HzwTeFzjMv/nCZ6/efsTERHAHwDfAXwGeB1wHvBE4NER8fDMfMduG9RXsup64BLgt4DvBf46Ih6fme/pqT5JkiRJkqSpjNes6n/SV8OZgFcAVwLv3Hpcv9sCM/PSKcJ/kHGi6kPAN2bmDQAR8UTgFcDvRcSXZ+auln3q826Aa8D3R8R7gf8MvC0iLs3MV7SrY8TRjU+1Km7hRbSdtbkUq03LWxvd2rS81eW7NS2vpVFuzLsJJ9V63zU/tsOzm5a3mWtNy3P1wxPrcoNb1q5tWF7jY9f4Vi4xaNzvDtr2u4c3bmha3srSXZqW1zXsK9fzSLOyoG3boP2+a31sDyzdvml5m13ba7fr2i5j2qpvyUX4QBgMmxUVA1fg2J12x2LhDfbbfbj20bFddKM51JnQzaK7b1RHZj7/tr+PBzrN1NO2fv7M8UQVQGZeHhGvBh7LeOTX5buppK81qz5ra0c+nvF/If4gIp7dd52SJEmSJEmTHB9Z1fdjAf78sWsRcU/gIuAo4+l/2x0fnPSY3dY1k5R5Zr42Ih4EvAZ4ZkTcj+Z//5YkSZIkSZrOLKYBLrKI+GngQmANuAr4w8y8cYfQr9z6+d7MHYep/822uFM2s/Gdmfm+iHgA46Fgl+BEG0mSJEmStH9cGBFX7fRCZt5n1o25jV/a9vuvRMRPZOZLtj3/RVs/P36Ccj6+Le6U9TEN8Brgkzu9kJmfBr4F+O+wz1OXkiRJkiRprhLoMnp/LOhonVcDTwDuAZwJ3Bd4AbAK/FZEPH5b/MGtnydaTPTwtrhT1nxkVWZeMOH1EfBvI+IXcCU7SZIkSZI0RzOcBvjhOY+g+hyZ+RPbnroKeFpEfAB4MfB84I9u8/rxHdV77m1ut3nIzBMNG5MkSZIkSepdEoz6v/ccubcml/0W8FzgXhFxz8z8x63nj98m/qwTvO/484d224D9dk9SSZIkSZKkz+pyTyWSepeZXUR8GLgTcFfgeLLqmq2f55/gredviztlu05WRcRHGA8Be0Rm/uPW71WZmRfutg2SJEmSJElq5tytn7cdJXXl1s/7RsTyDncE/Kqtn3+328pbjKy6YOvn8rbfJUmSJEmSFlYymzWrFnSB9R1FxH2AL2O8kPo/HH9+a4DS+4F7A4/mc9ezAviOrZ+v3W0bdj0xMzMHW48Pbvu99Nht/ZIkSZIkSadqlIPeH/MSET8eEf8QEb+47flHRcRX7xD/FcDLGS+m/luZub4t5AVbP38pIu50m/c9AXgs4ymD25NYU3PNKkmSJEmStE8F3QwWWKfR6K2IeDTwzG1Pr0TE22/z+3Mz83Vb/z6P8Sipu257z9cBPxcRVwMfBm4E7sl4Kt8S8H+A/7hDEy4Dvh24BPiHiPjTrToeChwDvm+H6YFTM1klSZIkSZL2pT04DfCOwNduey62PXfHQjlvAu4OPBD4SuB2wC3A24DfA347M0fb37S1+PqTgKcCPwRcDBwG/hB4VmZeNdXWnECLBdaXgC8A1jLz5h1evwPwC4znM54HfAK4HPj5zLx1e7wkSZIkSZI+X2a+FHjpFPHPBp69w/NXAFecYhtGjKcDvmBS7KlqMdbtUuA6xlm1zxERtwP+Cvhh4G7ACuMF2H8SePNWokuSJEmSJGkuTuc1q/aqFnvsYYxHtP2PHV77WeBLGa8g/xTgfoznNf4j8ADgyQ3qlyRJkiRJmloCHdH7Yy/dDXARtBjZdH/g7zPzuh1e+wHGx/7ZmfkbW89dFRHvA94PPAl4cYM2SJIkSZIkTSkY7aEF1veLFsmqOwNv3v5kRFwE3AkYsW0+ZWZ+KCL+mvFIK0mSJEmSpJlLmMk0PUdWTafFETkbGO7w/Ndt/XxvZt60w+vXALdvUL8kSZIkSZJOEy1GVn0KuNcOz38j4+ThO07wvmXGt0WUJEmSJEmai24m0wA1jRbJqncAj4mIR2XmmwAi4jzg8Vuv/8kJ3ndv4BO7rTxicU+q1m1bHpzZtLzWhoOVpuV1udm0vIidBgAuhtZta33uDRrfuLP19i5H22uj9bl3WolgMGh4PnTtiurD8uCMeTfhpJYat695v7vAX/xat611vzuMtp+prbe39bUx6tabljeI1SblhOuLSNJpLzMYZf/9fc6gjtNJi/9x/AbwWOCPIuJ/AzcCTwTOAa4FXr39DRFxAfBlwO81qF+SJEmSJOmUzGaBdU1j18mqzPyTiHgu8Ezg+xlP/QvgGPCDmbmxw9v+zVbMm3ZbvyRJkiRJ0qnqZrDAuqbTZC5HZv5cRLwauAS4I/Bx4Pcy8yMneMs68ELgDS3qlyRJkiRJ0umh2cIjmflu4N3F2Ge2qleSJEmSJOlUJDGTaYDpOohTabtqsiRJkiRJ0h4yiwXWNR2TVZIkSZIkaV9KoJvJyCpNw2SVJEmSJEnat0YusL5wPCKSJEmSJElaGI6skiRJkiRJ+1ISdDNY/NwF1qdjskqSJEmSJO1bTgNcPCarJEmSJEnSvpTAyAXWF47JKkmSJEmStD8ldDmDKXpmq6ay62RVRHxkF2/PzLxwt22QJEmSJEnS6aHFyKoLGpQhSZIkSZI0YzGTaYC4wPpUdp2synQlMkmSJEmStPck0M0greEswOm4ZpUkSZIkSdq3Ro56WjgmqyRJkiRJ0r6UxIxGVpkQm0ZvyaqIWAIuBh4InAe8IzMv23rtblvPvS8zN/tqgyRJkiRJkvaWXpJVEfFQ4HeBL2S8ilgCy8BlWyHfDLwU+E7g8j7aIEmSJEmSNInTABdP82RVRNwPeD0wBF4I/CXw8m1hlwMvAp7IbpJVEQyi3SZEtB36tzw4s2l5G92RpuWtDA42Le/A8HZNy1sbHWpa3vLgjGZlReO7RbRsG8Bmt9a0vNVh23Oly1HT8ta7tufK6vCcpuU1vXYj5rw6YzCM1XalDRb7WtrojjYtr/W5dcbSgablHRvd3LS8lYZ9x5DlZmVB27YBbGbbfrf1Z2rrfndtdEvT8g4std3eVtfuRvifF0naD2YxDVDT6WNk1bOAVeCRmfkWgNj2QZ+ZRyLi/cD9e6hfkiRJkiRpogRG3uVccZ8AACAASURBVA1w4fSRrHoo8PbjiaqTuAb4lh7qlyRJkiRJKgi6mUwDdLTuNPpIH54DXFuIW2U8VVCSJEmSJEkC+hlZdR1w70LcfYGre6hfkiRJkiRpIqcBLqY+jsgfA/eJiEtOFBARlwL3AF7XQ/2SJEmSJEmTJXQZvT/MVk2nj2TVLwCHgJdFxHMj4gFbz58ZEfeNiGcwvhPgTcALeqhfkiRJkiRpogRGDHp/tMpVRcRXR8TTI+KVEXFtRGREHDuFcm4fEd8TEf8rIt4XEYcj4taIeEdEPDUidrwVc0S8dKvOEz3+n91vZQ/TADPz6oh4NPBy4D8BP8v4+D9p6xHAjcAlmXl96/olSZIkSZJqtkY+zaCeRp4JPK5BOT/FOGfTAX8LvAa4I/Bg4GuA74iIR2XmkRO8/03ATjmdDzRoWy9rVpGZb4uIewFPBh4BXMB4MfWPA28GXpyZn+mjbkmSJEmSpNPUFcCVwDu3Hqc6COgQ45lxL8rMz94kLyK+lHHe5huAZzAegLST52Xmn59i3RP1kqwCyMxbgV/dekiSJEmSJC2crpcVkvqRmc+/7e8RpzZiKzOfd4LnPxQRTwf+F/DdnDhZ1aveklWSJEmSJEmLbHw3wP6nAe6x9dWv3Pp5t3k1oHmyKiIuyMyPFmMvzszXtm6DJEmSJElSxWzWrNpTvnjr58mmGD4hIp7IeMmnfwRek5n/0KoBfYysek9EPCUzf/dEARFxAPgV4EcZb5gkSZIkSdKMBV3OYhpgAFwYEVft9Gpm3mcGjah66tbPV50k5inbfn9+RPwm8NTM3NxtA/o4IivASyPi9yPi9ttfjIivYrzS/L/m/2fvzuMkK+tD/3++vczGsIVFwUHBUVE24xIENSDRaBAQAZdEjYJec70qmkA0xqiBmMSoNyhXYq4/E9R4k7iAIFvCVSIqskQBQfGyOCK7Cggzw6zdXd/fH6faNEVXd1XXc6qr6c/79TqvM3XOc77PU9vpnm8/S5V9kyRJkiRJ6rsEJojat4UyDDAi3kK1UN6DwHTzWl0LvAV4CrCCqhfW25rl3wp8tEQ76khWPZNqfOOrgOsj4rDJE81Jui4H9gY+Azy9hvolSZIkSZIGzZrM3He6bb4bBhARhwKnU+Xw3piZd7eWyczTM/NTmXlLZm7KzFsz85PAIcBW4MSI2KPXthRPVjXHKB5IlU3bHfhaRHw8Ir5JtSzieuDYzHxTZm4oXb8kSZIkSVKnGhm1b4MuIg4AzqUaLffOzDynm+sz84fAeVRTPb2o1/bUshpgc3zin0TERVRjHCfHMl4C/H5mzjRJlyRJkiRJUu0y6cucVTnA4wAjYjVwMbADcEpmfmKOoW5p7nfrtU21JKsAImI74M3AdlMOPxXYl5lnlJckSZIkSeqLBoPf86kuEbE78DXgscDpmXlqD+F2bO4f6rVdtaQPI+IQ4HrgNVSTbz0d+BBVdu3iiDgtIpbUUbckSZIkSVJngomsf2MAE2IRsSNVj6q9qOYV/6MeYi0Fjmg+vLrXthVPVkXEh6iG+62imrfqoMz8QWb+GXAYcCfVMojfjYj9S9cvSZIkSZIkiIi3R8SNzVzN1OMrgIuA/YAvAW/OnHmwYkTsHRFHR8Rwy/FdgC8Ae1AtuHd5r+2uYxjgn1AlpF6fmZdOPZGZ324mqP6eqtfVVVRLHQ6EkVg6302Y0ZKhlUXjNXKsaLwlQ2XfytGR5UXjDTNaLNaKh41uHTzDI+WeK0DSKBpvc2Nt0Xilvxuln2/Je0swNM/L3mbRe8fI0GDfd5cOl/2uT+SWovFGhsq2b+XIrkXjjVDu/V1G2e95aUMjw7MX6kKDiaLxxhobi8Yr/d0o/TtJqXtLDOBfwSVJZSV9mrOqUJyIOAJ4f8vhJRFx5ZTHH8zMC5v/3hnYm0fOI/VXwEHABDAO/GPEI3/uZebxUx7uRjUJ+/0RcSNwF7Ar8CxgW6pc0KtmS3p1oo5k1ZeAt2Tmg9OdzMz1wOsi4gLgkzXUL0mSJEmS1JGFsFrfFLsAz2k5Fi3HdukgzuT8UsNUnYnaOX7Kv28GPk6V5FoNHAhsaR4/n2rOqwc6qHtWxZNVmfm7HZb7QkRcVrp+SZIkSZKkTi2kCdYz87PAZ7sofwpwyjTHj+fhiahOYt1ND/NadaO21QA7kZl3zmf9kiRJkiRp8aqGAdafrJrfqTwWnp6TVRHxE6rX/UWZeWvzcacyM1f32gZJkiRJkiQ9OpToWbVncz/a8liSJEmSJGmARV8mWGcBDTUcBD0nqzIf/q62PpYkSZIkSRpI2acJ1h0H2JV5nbNKkiRJkiRpviT9mWDdXFV3TFZJkiRJkqRFqy89q9SVYsmqiHgp8HJgD2ALcD3wmcy8tVQdkiRJkiRJenQrkqyKiH8GfnfyYXN/FPDHEfG7mXleiXokSZIkSZJKsmfV4Ok5WRURbwJ+DxgHPg9cC2wLHAkcDPxTRDwhM9f2WpckSZIkSVIpSfQlWZWuBtiVEj2r3gA0gMMz85Ipxz8UEZ8BXg8cC3ymQF2SJEmSJEnF2LNq8AwViLE/cGVLomrSX1MNC9y/QD2SJEmSJElFNYjaN3WnRLJqO2BNm3NrppSRJEmSJEmSZlRiGGAAE9OdyMxGRECZpJgkSZIkSVIxSX+GAWbtNTy6FFkNUJIkSZIkacHJPs1ZZbaqK6V6PL0hIiam26jeknbnxwvVL0mSJEmS1LVGRu2bulOqZ9VcX3nfMUmSJEmSNC+S/iST0vRHV3pOVmWm81FJkiRJkiSpiAU9Z1UwxOjQ8mLxtkysLxYLYHhoSdF4y4a3LxpvydCKovFGWFo03jJWFo03Srn3Y5vGNsViAQwPlf0qjrG1aLzNPFQ0XunP8nhuKRpv80TZ5zvRKPd+ZE67nkXfBEMsHS63wOuGsZ8XiwUwUvBnAsDykWVF440MlV0cdwll7+NLomy8pVnu9St9391Q+Gf0lthcNN7W3Fg0HsNlwzWy7EwOWybWFY033thUJE4jG0Xi9KRoGwp/ENSb8G/+A2MQvuuaV+kwvYGzoJNVkiRJkiRJvWg4RG/gmKySJEmSJEmLUtKf1QBdDLA7JqskSZIkSdKi5TDAweNAaUmSJEmSJA0Me1ZJkiRJkqTFKfszDNBxgN0xWSVJkiRJkhap6NMwQIcadsNklSRJkiRJWpScYH0wmaySJEmSJEmLVppJGji1JKsiIoDXAkcDTwa2Zfo+b5mZq+togyRJkiRJkhae4smqiFgCXAj8Fu0HZeYM5yRJkiRJkvqiYXpi4AzVEPNk4IXABVS9qj5PlZxaCjwNOAXYAHw0M+uoX5IkSZIkqSOZUfum7tQxDPDVwC+B12TmhohoAGTmGHAT8BcR8Q3gGxFxU2aeWUMbJEmSJEmSZuQE64Opjp5NTwL+MzM3NB83ACJieLJAZn4b+A7w1hrqlyRJkiRJml1WE6zXvZmt6k4dyaoJYN2Ux5NJq11ayt0F7F1D/ZIkSZIkSY86EfGsiHhPRHwlIu6KiIyIzT3E2yEiPh4Rt0XElub+9IjYYYZrhiLiDyPiBxGxKSLujYgvR8Q+c21HqzqGAd4FPH7K4x839wcB5045fgDwUA31S5IkSZIkdWSBzSn1fuDoEoEiYifgCqr5xn9ClbPZF3gH8NKIOCgz72+5JoAvAq8AHqRaYG9n4DjgiIg4LDOv6rVtdfSsuhLYNyKWNx9f1NyfHhGHR8T+EfEJqsnWe34CkiRJkiRJc1P/5OpVMqxYQuwK4C+Ao4DH9hjrY1SJqq8Ae2fmqzNzP+ATVFM8nTbNNSdQJapuAZ6ama/IzBcArwSWA/8cET13jKojWXU2sBH4bYDM/DHwcWAPqhUCvw+8rVnmT2qoX5IkSZIkqSONjNq3UjLzw5n555l5QWb+fK5xIuKxwGuBMeCtmTk+5fS7gHuB10bEY1ouPbm5f/fU+jPzbOA8YDUFen4VT1Zl5oWZuVtmnjfl2MnAa4AvA18H/g54ZmbeVLp+SZIkSZKkTiT9mWB9AOdXP5wqJ/St1qRXZm4BzgeGm+UAiIi9gH2ATVTD/1qd1dwf1Wvj6pizalqZ+QXgC/2qT5IkSZIkSdN6enN/TZvz1wBvnFJu6jU/zMyxNtdMLTdnxZNVEXEa8EBmfrB07FaN3MK6zeU6Zy0d3b1YLIDGw3rR9W7LRNn56EdHls9eqAvLWFk03k6NnYvGW8nSYrG2HxktFgtg7fiKovEeYkvRePcX7oO5genua3NX+rtR+rv7aNLILTy46YfF4i0Z6XWY/cOVfu82T6wtGm/lyK5F4y2JsveOnSdaF+7tTdn77pJisQDWjpf9GVj6vnvf8L1F441n2fZtnLh/9kJdKP3dDYYLxRkAUcesHZqTAX8vYrTgf+2i8Kc/y/YpybHCv6uVfm+zUTaearfAJlgvZXJhvDvbnL+zpdxcr5mTOnpWvR34ag1xJUmSJEmSysk+JauqnO3qiLhh2tOZ+9bfiIeZ7G2ysc35DS3l5nrNnNSRrLqTeiZulyRJkiRJKmoA55Pqh8kMXbunP10Gb7ZriqkjWXUO8IaI2DYz19cQX5IkSZIkqYg+DgNcMw89qNqZzNds0+b85HwTU+dcme2ayeM9z9NSRw+oU4DbgYsi4hk1xJckSZIkSSoj+7ANntub+1Vtzq9qKTfXa+akjp5VXwW2AM8DvhcR91A1dPM0ZTMzX1hDGyRJkiRJkjS965r7Z7Y5P3n8+mmu2S8iRqdZEXC6a+akjmTVC6b8O4Ddm9t0BjO/KEmSJEmSFoVFuhrgvwMN4DcjYtfM/MXkiYhYChzVPP9vk8cz89aI+H/A04AjgHNbYr6iub+g18bVMQxwry62J9ZQvyRJkiRJ0qwSyOzDNk/PLyLeHhE3RsSHHva8M+8B/hVYAnwyIqZ2ZvoIsAvwL5n5s5aQp02WiYhdp9RzLPAy4FYemcTqWvGeVZl5W+mYkiRJkiRJ5UWfelaVqSMijgDe33J4SURcOeXxBzPzwua/dwb2BnabJtwfAgcBxwE3RsT3gH2B/YA1wB9Nc82ZwEuBY5rXXNKs41Cq6Z9eN83wwK7V0bNKkiRJkiRp8CWQ0YetWIt3AZ4zZYMqEzb12C4dPfXM+4DfAD5B1cPqGGB74AzgwOb51msawCuBk4G7gSOB/YFzgGdn5uVzfWJT1TFnFQDNLmRHUj3xnYGrMvPM5rndm8d+lJnjdbVBkiRJkiTp0SIzPwt8tovypwCnzHD+AeAdza3TmBNUwwFPm63sXNWSrIqIQ4HPA4+jyvAlMErVXQzghVQv7quAs+togyRJkiRJ0mzSpd8GTvFhgBGxP3ARsCtwOlX3sNbBmWcDG6nGRUqSJEmSJM2P7MOmrtTRs+oDwFLgxZn5HwARD89VZebG5nKHz6ihfkmSJEmSpI70Z4J1daOOCdYPBa6cTFTN4HZg9xrqlyRJkiRJ6ow9qwZOHcmq7YC7Oii3FBiuoX5JkiRJkiQtUHUMA7wHeFoH5fYDbquhfkmSJEmSpI44DHDw1NGz6v8C+0bEMe0KRMTxwBOAC2uoX5IkSZIkaXb9GALoUMCu1ZGs+mvgIeBfI+KDEfHs5vEVEbFfRLwP+CRwP3BaDfVLkiRJkiR1KPqwqRvFk1WZeRtwBPAA8GfAVVQ5xFcC1wF/AawHjs7Mn5WuX5IkSZIkqWP2qho4dcxZRWZeFhFPAd4EvAjYk2oy9TuBrwOfyswHe61nKJaycunqXsPUJqLs/PGjQ8uLxhtmtGi8UZYUjbeSpUXjPWencu07aq+fFosFcP6texaNd9X9RcOxrvB7W/qzt3xkh6LxxhqbisbLnCgWa2z8F2SOF4vXraFYyrbLnlIsXiPHisUCiMJ/g1kyvLJovJHC97WluaxovNL33WftWC7eS/e4u1gsgIvuKLsg8dUPFA3H+sLv7dbC7+2KkZ2Kxts68VDReEmjSJzxiXuLxJGmE6Nl/yu26bB9i8W69NOfLhYL4AVvfnPReMu/cUPReDk2f79bSZpeLckqgMxcD3y8uUmSJEmSJA0eez4NnNqSVZIkSZIkSQPP1QAHTm3JqojYEzgE2A3a9j3PzPxgXW2QJEmSJEmaSdqzauAUT1ZFxDLg08BrJg/NUDwBk1WSJEmSJGl+mKwaOHX0rPow8FrgF8A/A2uADTXUI0mSJEmSpEeZOpJVrwbuA349M39WQ3xJkiRJkqTeZfRnzirnxepKHcmqlcDFJqokSZIkSdKgC4cBDpw6klU3AI+pIa4kSZIkSVJZJqsGzlANMf8ncGBEPLeG2JIkSZIkSeVMDgWsc1NXivesyswvR8Qq4KsR8Qng68BdtMlVZubtpdsgSZIkSZKkhamOYYAA1wMPAH/e3NrJGtsgSZIkSZI0M4cBDpziiaKIOBL4SjP2vcBtwIbS9UiSJEmSJPXMZNXAqaNX06lAACcA/5SZvu2SJEmSJGnwJP1JVpkZ6UodyaqnAd/KzM/VEFuSJEmSJKkcJ0AfOHWsBngf8FANcSVJkiRJkvQoV0ey6izgoIjYrobYkiRJkiRJxUTWv6k7dSSr3gf8BPhKROxVQ3xJkiRJkqQysg+bulLHnFUXABPAYcBNEfFT4C6mf3syM19YQxskSZIkSZK0ANWRrHpBS/wnNbfp9JRfHIoRthvZrZcQDzORY8Vi1SEKd4RbQdmRmts0tikab/uR0aLxjtrrp8ViPeebry4WC4BDv1g03I1rn1A03trxsu/txNB40XiljQ4tm+8mtLWRn9KYx/qHYoTthh9bLN7W3FgsVh2GKXsfWsbKovHK33eXFI330j3uLhbr0CuOKRYLgIPPKRrux+vL/T4CsK7wfXdsaGvReKUND5f9rpWykdvmuwnQmCgXa6iOX/01Z1F2QudLP/3pYrFesupZxWIBXHpnubYBHP7k5xWNJzlMb/DU8RPLoX+SJEmSJEmak+LJqswcgD9BSZIkSZIkdSDL9nRU7+wLLEmSJEmSFqd+TYDuUMOu1LEaoCRJkiRJ0sKwwFYDjIhlEXFqRNwcEZsj4u6IODMiVnUR4/iIyA6217dc99lZyr+lxHOspWdVRATwWuBo4MnAtsB0/eoyM1fX0QZJkiRJkqTZLKQJ1iNiGXAJ8FzgHuCrwJ7ACcCREXFwZq7pINSPgc+1Obc98PLmvy9rU+Zi4GfTHL+pg7pnVTxZFRFLgAuB32L6BBVUeUUHhUqSJEmSJHXuvVSJqiuAF2fmQwARcRLwt8CZwKGzBcnMy2iTiIqI/0GVrPpOZv6kTYi/ycxLu259h+oYBngy8ELgAqpeVZ+nSk4tBZ4GnAJsAD6amQ5DlCRJkiRJ82eBDAOMiFHgxObDt00mqgAy8zTgeuCQiHhWj1W9rrn/fI9x5qyOZNGrgV8Cr2l2PWsAZOZYZt6UmX8BHAGcHBFvrKF+SZIkSZKkziyQZBXwfGAHYE1mXjvN+bOa+6PmWkFE7EXVc2sr8KW5xulVHXNWPQn4VmZuaD5uAETEcGZOAGTmtyPiO8BbqbqoSZIkSZIk9d0CmrPq6c39NW3OX9NSbi4me1VdmJkPzFDu2Ig4DhgGbgXOz8wbe6j3YepIVk0A66Y8nkxa7cLDJ9+6ix6yfZIkSZIkST1JIPswpXaVEFsdETdMezpz3w6iPL65v7PN+Ttbys3Fa5v72YYAntjy+MMR8ffAOzNzvIf6gXqGAd7Fw1+YHzf3B7WUOwB4CEmSJEmSJM1mZXO/sc35DS3luhIRBwJ7Aw9QLZw3nWuBtwBPAVYATwTeBjxINXruo3Opu1UdPauuBI6LiOWZuQm4CPgYcHpEbKHK9P0B1WTr59dQvyRJkiRJUmf6NwxwTYc9qNqZ7ALWrsW9dhGbHAL4xczcOl2BzDy95dCtwCcj4lvA1cCJEXFaZt7RS0Pq6Fl1NlWW77cBMvPHwMeBPahWCPw+VdZtI/AnNdQvSZIkSZI0q6Cas6r2rUxz1zf327Q5v6K573oUW0SMUC2YB3NYBTAzfwicRzWH1Yu6vb5V8Z5VmXkhsFvLsZMj4rvAy4EdgZuB/5WZt5SuX5IkSZIkqWMLZ4L125v7VW3Or2op140XA7sCP8nMy+dwPcBkjme3GUt1oI5hgNPKzC8AX+hXfZIkSZIkSbNZQKsBXtfcP7PN+cnj188h9uQQwP8zh2sn7djc9zw/eZFhgBHxWxHx+ojYp4Oy+zTLHlaibkmSJEmSpEXgO8BaqlUFnzHN+Vc09xd0EzQiVgJHNx/OKVkVEUuBI5oPr55LjKl6TlZFxB5Us8S/D+hkAq07gD8DLoiI3XutX5IkSZIkac6yD1uJZlaTnp/RfHhGRPxq7qqIOAk4ALgsM7875fjbI+LGiPjQDKGPpZrv6sqZpmuKiL0j4uiIGG45vgvVSLo9qHp/zXUY4a+UGAb434AlwLszc/1shTNzfUS8CzgXeBPwwQJtkCRJkiRJ6k7BZNKs9ZTxl1QTmD8XuCUivg08AXgOcD9wQkv5nYG9mXkeqckhgLNNrL4bVS7n/oi4EbiLap6rZwHbAncCr8rMnp9tiWGAvw3cm5nndnpBZp4H/Bw4vED9kiRJkiRJc9KP1QBLyczNwGFUHX82Ui1ktyfwOeAZmfnjrp57xG7AbwFjwBdnKX4z8HGqidRXA8cAz24+PhU4IDNv7qb+dkr0rHoq1bjJbn2PKhM4Z40cZ934Pb2EqFVLz7iejQ4tLxqvtOGhsvP1rx1fMXuhLpx/657lgh0623e4O0XbBqwd31o03oahDUXjbWRd0Xhb2Vg03lhjU9F4mRPFYjUoF2tO9ec46yZ+VjDeWLFYAFFmKsZfWTK8smi80jYMLSkab+142Z8zF91RcLT/weeUi0XhtgFrx7cUjVf6vru593lOH6b0fXfrRNn2JY1Cceb3ngvAUNnfJzVAeu948DAvePObi8W69M5PF4sFZdsGsDxvKBpPWmgycxPwgeY2W9lTgFNmOH8PHeaGMvNu4I86amSPSmQXtqGa4Ktba4HB/l+AJEmSJEl6dFs4qwEuGiX+BP0A8Jg5XPeY5rWSJEmSJEkSUKZn1Y+AgyJiebMr2qwiYgVwMPCfBeqXJEmSJEmak5JzSqmMEj2rzqcaCvi+Lq55H7C8ea0kSZIkSdL8yD5s6kqJZNWngJ8B74mI90VE25gRMRQR7wfeQ7Ua4KcK1C9JkiRJkjQ3JqsGTs/DADNzY0QcC1xCtVThmyPiy8A1wL3NYrsAzwReCawCNgPHZWbZpWQkSZIkSZI6lX0aBmjCqisl5qwiM6+MiIOB/wPsx/RLGUZzfwPwusy8rkTdkiRJkiRJevQokqwCyMzrgQMi4iXAEcAzgJ2oklT3Ad8HLszMfy9VpyRJkiRJUk/s9TRwiiWrJmXmxcDFpeNKkiRJkiSV5mqAg6d4skqSJEmSJGnBMFk1cExWSZIkSZKkxctk1cAZmu8GSJIkSZIkSZPsWSVJkiRJkhaloD9zVkX9VTyqmKySJEmSJEmLU9KfYYAONeyKySpJkiRJkrR4mUgaOCarJEmSJEnSotWPYYDqTvEJ1iPizIh4Ywfljo+IM0vXL0mSJEmSpIWrjtUAjwee30G55wFvqKF+SZIkSZKkzmQfNnVlPocBLgEm5rF+SZIkSZK0yDkMcPDMS7IqIgJ4JnBvL3EauYV1m28q0yhg6ejuxWIBRJTtuDbe2FI03vDIaNF4Y2wtGu8hyj7fq+4vF+vGtU8oFwxYOz7Yr13p93aCsaLxNo0/WDTeRJZ9/TIbBWPNb46/kVtYu+lHxeItGXlssVhQw3238GdhaGS4aLwtsblovNL3jqsfKBfrx+t3KxcMWDte9rmWfu1Kv7elP8sbxwv+UAUmGmV/zpS675a8f0utcmy8aLzl37ihWKzDn/y8YrEAlme5tkH5106y59PgKZKsioj/aDn0O9Mcm1rnauCxwOdL1C9JkiRJktS1fg3TMyHWlVI9q14w5d9JlYia6c/lY8AFwB8Xql+SJEmSJEmPAqWSVXs19wH8BDgLeFebsluB+zKz7DggSZIkSZKkLsV8N0CPUCRZlZm3Tf47Ik4Fvj/1mCRJkiRJ0kByiN7AKT7BemaeWjqmJEmSJElSHVwNcPCUXTYJiIjHRMQhEfGYluN7RcS/RsQPI+LCiDiwdN2SJEmSJEldyT5s6krxZBXwHuAbwA6TByJiJXAZ8CpgH+Bw4JKIeGIN9UuSJEmSJGmBqiNZ9QLg/2XmTVOOHQ/sBvwrsDfwR8A2uBqgJEmSJEmaT/asGjh1JKseR7Ui4FRHAuPAOzPzlsw8Hfg+cFgN9UuSJEmSJHUksv5N3akjWbUtsH7yQUQE8Bzg6sy8f0q5m4BVNdQvSZIkSZI0u370qrJ3VdeKrwYI3AXsNeXxs4HtgUunqXtrDfVLkiRJkiTNKuhPz6eov4pHlTp6Vl0BHBgRR0fEdsD7qHKI57eUexpVYkuSJEmSJEkdiIhlEXFqRNwcEZsj4u6IODMiuhq9FhE/jYicYXtqm+uGIuIPI+IHEbEpIu6NiC9HxD5lnmE9Pav+CjgW+ErzcQDfyMzLJwtExJ5UqwL+Yw31S5IkSZIkdWYBDdGLiGXAJcBzgXuArwJ7AicAR0bEwZm5psuwn2tzfO009QfwReAVwIPAhcDOwHHAERFxWGZe1WX9j1A8WZWZN0bE84F3ArsAVwMfbSn2EuA64NzS9UuSJEmSJHVqgU2A/l6qRNUVwIsz8yGAiDgJ+FvgTODQbgJm5vFdFD+BKlF1C/CbmfnzZv3HAWcB/xwRT83M8W7a0KqOnlVk5rXA8TOc/xTwqTrqliRJkiRJ6tgCSVZFxChwYvPh2yYTVQCZeVpEvAE4JCKelZlX19SMk5v7d08mqpr1nx0R5wEvA44Gzu6lkjrmrJIkSZIkSVoYFs5KgM8HdgDWNDsJtTqruT+qWI1TRMReVFM6baIaWKUvlQAAIABJREFU/ldb/bX0rJIkSZIkSVJRT2/ur2lz/pqWch2JiHcBq4EtwA3AOZl57wz1/zAzx0rVP52ek1UR8ROqPOGLMvPW5uNOZWau7rUNkiRJkiRJXcs+zVlVpo7HN/d3tjl/Z0u5Tn2k5fHHIuIdmdm6KF5d9T9CiZ5Vezb3oy2PJUmSJEmSBlv/5qxaHRE3TNuEzH07uH5lc7+xzfkNLeVmcx7wDaqF8e4Fngi8kWrBvH+IiPszc+rCeKXrb6vnZFVmDs30uE5DsZSVS8t1zNoysb5YLIChKDvKculwz+/3wySNovE289Dshbpwf+FP0jqWFIu1dnybYrEANgxtmL1QF8bYWjRe6fe29Gev9Hdj88RE0XgTWfb9mE9DsZTtlj21WLwNYz+fvVAXhqPc9xxg2fD2ReM1KPvZ2prtfk+Ym/uGp+vtPXfrc1mxWOsG/L67JTYXjVf6vS392Sv93diUDxSNN56bisQZiPl2s+TPzOGCsRahou8FEGV/2c2xnhbeWtxKv7dacCIH4o7fiWju2zU42hyfVma+o+XQDcDJEXET1aJ4HwamJqtmq78Y56ySJEmSJEmLV/9yVWs67EHVzmQPm3Z/xVvR3Pfa2+AfgA8CT4mIvTLz1g7rnzzec28HVwOUJEmSJEkafLc396vanF/VUm5OMrMBrGk+3K3f9UMNPasi4pBuymfmt0q3QZIkSZIkqRN9mWC9jOua+2e2OT95/PoCde3Y3E/tJTVZ/34RMTrNioDF6q9jGOCldNeJzsHzkiRJkiRpfiycZNV3gLVUE7U/IzOvbTn/iub+gl4qiYh9gb2pJlK/cfJ4Zt4aEf8PeBpwBA+fz6pY/VBPsuqfmP6tHgL2oMq0bUc163zZ2TQlSZIkSZI6lX3qWVWgjszcGhFnAH8GnBERL87MDQARcRJwAHBZZn538pqIeDvwduCczPzTKcdfAtyXmVdPrSMiDgC+QDWZ+j9kPmK1qNOATwMfiYjLM/MXzeuOBV4G3Mojk1hdK56syszjZzofEb9GNVnXPsDBpeuXJEmSJEnq2MLpWQXwl8CLgOcCt0TEt4EnAM8B7gdOaCm/M1Uvqd1ajh8M/HlE3EY1P9W9wF5UHYxGgG8Cf8ojnQm8FDgGuDEiLmnWcSiwGXjdNMMDu9b3CdYz85fA64DtgQ/1u35JkiRJkqSFKDM3A4dRrda3EXg5sCfwOeAZmfnjDkNdTJV4Wgc8HTgOeBJwGfBm4IWZuXGa+hvAK4GTgbuBI4H9gXOAZ2fm5XN9blPVMQxwVpm5MSL+k6qL2Fvmow2SJEmSJGlxC/ozDDAKxsrMTcAHmttsZU8BTpnm+BXAFXOsf4JqOOBpc7m+E/OSrGpayX/NLi9JkiRJktR/C2sY4KIwL8mqiDgKOAT40XzUL0mSJEmSBH2aYF1dKZ6siogzZzi9EngK1XjGAP62dP2SJEmSJEkdS7NVg6aOnlXHd1DmduCUzPynGuqXJEmSJEnSAlVHsuqwGc5tBe7JzJ/WUK8kSZIkSVLnsk/DAO281ZXiyarM/GbpmJIkSZIkSbUwkTRw5nM1QEmSJEmSpHkVjflugVrVMcH6Ib1cn5nfKtUWSZIkSZKkGdmzauDU0bPqUnp7q4cLtUOSJEmSJGlGfZmzSl2pI1n1T8CvAUdSJa2upVr9D+DxwK8DQ8D5wC9rqF+SJEmSJEkLVB3Jqj8FrgS+BpyYmTdPPRkRTwFOp0paHZSZ99TQBkmSJEmSpJklkH3oWmXvra7Ukaz6G6qhfEdn5ubWk5l5c0QcC9wCfBh4/VwrShqMNTbNuaGtlg5vWywWQETZEY2NnCgab3NjbdF4y4a3LxpvA2NF4w0zWizWxNB4sVgAG1lXNN5E4dcuKTvj4OaJsp+9oSj33gKMDq0oGm8klhaLNTb+CzLLfv66kTTYMlHu87psZIdisQCCoaLxSt93xxobi8YrPXB+PLcUjbeVgp/9oa3FYgFs5qGi8Uq/dg3KfvZKfm8Bhgve1wBGh5YXjTcyVKZ9ExMDMAggyt7XNEDSGZ2lQeEwwMFTR7LqJcA3pktUTcrMTRHx7WZZSZIkSZKk+WGyauDUkazaDti5g3I7A2W7MkmSJEmSJHUo6E/Pqqi/ikeVOvoV/xA4NCJ+s12BiHg+8IJmWUmSJEmSJAmop2fVh4EvAxdHxOea/76dqmPdE4BXUs1TNdQsK0mSJEmSND/6McG6ulI8WZWZZ0fEO4CPAv8d+IOWIgFsBU7KzLNL1y9JkiRJktQpJ1gfPHX0rCIzz4iI84E3Ac8DdqdKUt0NXAZ8JjNvraNuSZIkSZKkjiT9mWDdhFhXaklWAWTmbcAH6oovSZIkSZLUK3tWDZ7iE6xHxHsjYrfScSVJkiRJkvToV8dqgH8J3BYR50TESyPCFRolSZIkSdIASmj0YXMcYFfqSFadCtwFHA2cD9weEadExONrqEuSJEmSJGnusg+bulI8WZWZpwJPBF4KnAPsSjV31U8i4qKIOCYihkvXK0mSJEmS1K3I+jd1p46eVWTl3zPzFcAq4D3AGuB3gLOAuyLiQxHxpDrqlyRJkiRJmlUCmX3Y5vuJLiy1JKumysx7M/Mjmbk38ALgS1S9rd4N3BQRl0TEMXW3Q5IkSZIkSYOv9mTVpIjYC3gx8JuTh4B7gMOAsyLiqohY1a/2SJIkSZIkOQxw8NSarIqI0Yh4VUR8DbgFeC+wHDgd2CczVwHPB/4N+A3gE3W2R5IkSZIk6WGcYH3gjNQRNCKeBvw34PeBnah6UV0J/G/gS5m5ebJsZl4OHBkRV1L1spIkSZIkSeqLSLNJg6Z4sioivg08lypBtY4qQfW/M/MHs1x6A1XvKkmSJEmSpP5ozHcD1KqOnlXPA64GPgX8S2Zu7PC6fwC+VUN7JEmSJEmStEDUkaw6MDO/1+1FmXkFcEUN7ZEkSZIkSXqEagL0+ocBOsl6d4onq+aSqBoU47mlaLzRWFE03tbGQ0XjLRlaWTRe6ddvy0TZ57t8ZIei8UraSqcdEDuzafzBovGWDpf9rAzFaNF4pb8bo0Nlv7slvxs5732Uo+j7N94ofN8dWl403paJdUXjLR3ermi8Ro4Xjbdx4v6i8VaM7FQ0Xkml77sbx8u+dsuGty8abziWFo1X+rtR+rtb6t6SzogrSYuDt/uBU+tqgJIkSZIkSQMts/6toIhYFhGnRsTNEbE5Iu6OiDMjYlUXMXaIiNdExL9ExI8iYkNErI+IqyLinRHT/4U6Ij4bETnD9pYSz7HnnlUR8e5ers/Mj/TaBkmSJEmSpLlYSEP0ImIZcAnVwnb3AF8F9gROAI6MiIMzc00Hof4Y+DOq6eWvBc4HdqGah/xA4BUR8ZIZ5iG/GPjZNMdv6vzZtFdiGODf0HmnuWjup5Y3WSVJkiRJkjS791Ilqq4AXpyZDwFExEnA3wJnAod2EOch4K+BT2bmXZMHI+LJwNeB5wPva9Y3nb/JzEvn+BxmVSJZ9Rd0N8LzMcAbgOVdXidJkiRJklRWHyZYL6E5NO/E5sO3TSaqADLztIh4A3BIRDwrM6+eKVZm/k2b47dExHuAfwF+j/bJqlr1nKzKzFM6KRcROwF/Avw+VaJqA/B3vdYvSZIkSZI0JwnRjzWMyuTDng/sAKzJzGunOX8WcABwFDBjsmoW1zX3u/cQoyfFVwNsFRG/BrwLeBuwDbAJ+J/ARzLzvrrrlyRJkiRJml75CdDb1tO7pzf317Q5f01Lubl6YnM/3ZxUk46NiOOAYeBW4PzMvLHHen+ltmRVROxINWHX24FtqZJUHwM+nJn31lWvJEmSJElSx/o3CnB1RNwwbRMy9+3g+sc393e2OX9nS7m5emdz/9UZypzY8vjDEfH3wDszc7zH+ssnqyJiB+BkqoZvC2wBPk6VpPp56fokSZIkSZIWgZXNfbsV+ja0lOtaRLwFeBHwINWCeq2upZrc/T+okmOPBQ4H/hJ4K7AV+KO51j+pWLIqIrYHTqLKwE0mqT5BNUP8TF3HJEmSJEmS5kX0b4L1NR32oGonmvt2DY42xzsLHnEocHoz/hsz8+7WMpl5esuhW4FPRsS3qObJOjEiTsvMO3ppy1AvFwNExHYR8efAT4H3A0upJk5fnZl/aKJKkiRJkiQNrMz6tzLWN/fbtDm/orl/qM35tiLiAOBcYAnVUL5zurk+M38InEc1h9WLuq2/VYmeVT8Ftqfq6vV3wF9n5j0F4kqSJEmSJNUngYWzGuDtzf2qNudXtZTrSESsBi6mWmnwlMz8xNyaxy3N/W5zvP5XSiSrdqB62UeANwFviui451lmZruMoCRJkiRJUq36OAywV9c1989sc37y+PWdBoyI3YGvUc09dXpmnjr35rFjc991z65WpeasCqquXsOF4kmSJEmSJOm/fAdYS7Wq4DMy89qW869o7i/oJFhE7EjVo2ov4DP0MDF6RCwFjmg+vHqucSb1PGdVZg71svVavyRJkiRJ0pwtkDmrMnMrcEbz4RkR8auRahFxEnAAcFlmfnfK8bdHxI0R8aGpsSJiBXARsB/wJeDNmTM3NCL2joijI2K45fguwBeAPah6f10+1+c4qdhqgJIkSZIkSQvOwhkGCPCXVBOYPxe4JSK+DTwBeA5wP3BCS/mdgb155DxSfwUcBEwA48A/TjelU2YeP+XhblSTsN8fETcCdwG7As8CtgXuBF41W9KrEyarJEmSJEnS4tWPCdYLyczNEXEY8KfAa4CXAw8AnwPen5l3dBhqcn6p4Wacdo6f8u+bgY9TJblWAwcCW5rHz6ea8+qBDuufkckqSZIkSZK0OGX2Z4L1gnVk5ibgA81ttrKnAKdMc/x4Hp6I6qTeu+lhXqtuOGeUJEmSJEmSBoY9qyRJkiRJ0uK1sOasWhQWdrIqk0aOF4xXLhRQtm3A0uHtisbLwgNzN088VDRe6ddvrLGpWKzRoWXFYkHZtgFM5Jai8TZPTBSNNzq0YqDjbZlYVzReZsHv2rz/IM2in69Go+z3fKKxtWi8ZSPbF43XyLGi8Up/Vkvfd7cW/LkwPDxaLBaUbRuU/+xtKjPdw6+MDi0f6Hibx9cWjVfMvN9zJUl94f1+4CzsZJUkSZIkSVIvTFYNHJNVkiRJkiRp8VpAqwEuFk6wLkmSJEmSpIFhzypJkiRJkrQoRUL0YRhgONKwKyarJEmSJEnS4uWcVQPHZJUkSZIkSVqkEhr9SFaZEOuGySpJkiRJkrR42bNq4JiskiRJkiRJi5fJqoFTfDXAiHhdB2VWR8TzStctSZIkSZKkha1osioi3g98LiLOjIhoU2Zv4FvAv0fE40rWL0mSJEmS1LGk6llV+zbfT3RhKT0M8HTgcOANwJKIeH1mNiZPRsQ+wCXArsCJmXlX4folSZIkSZI615cJ1tWNosmqzFwXEb8N/Bvwe8DSiPjdzJyIiAOArwE7A3+Qmf9Ysm5JkiRJkqTuJPxXH5t661HHis9ZlZkbgJcAlwLHAWdHxHOBbwA7AcebqJIkSZIkSQOhH8MA1ZXiySqAzNwEvBT4v8BRwLeBbYHXZebn66hTkiRJkiRJC1/pOat+JTO3RMTHgBc3D/0Q+Gpd9UmSJEmSJHUl6c+cVXau6kotPasAIuJFwNnAGHAZ8OvAv0XENnXVKUmSJEmS1BWHAQ6cWpJVEfE7wPnAMPBy4DDgi8AhwMURsW0d9UqSJEmSJHXFZNXAKT4MMCJeRpWYmgBenplfbx5/LbAV+H3g6xHxksx8sHT9kiRJkiRJHTOZNHCK9qyKiOOAs6iG/h0+magCyMwGcDzwD8BvAJdExI4l65ckSZIkSdLCVrpn1T3A3cCrM/Oq1pOZmcAfRMRW4InAxl4rrHJgi8NYo+eX62FGYmnReBONrUXjlZY5Md9NaKt020p/Lyay7Htb+rM3nluKxiv9+j2q7lOZNBrjxcI1Cr93Q4U/W2ONTUXjjQwV/uwXbl8wXDReMrif/dJtK/09H89B/+yV/e6WVureks6GK0mLQEKjH7+z+DOlG0WTVZl5eUQ8KTNn/J9MZr49IoZzkLMHkiRJkiTp0S3pzzBAc1VdKT5n1WyJqinlTFRJkiRJkqT55ZxVA6d4skqSJEmSJGnBaJisGjRFJ1iXJEmSJEmSemHPKkmSJEmStCgl2ZcFkVy0ozsmqyRJkiRJ0uKU9GcYoLmqrpiskiRJkiRJi5cTrA+cviWrImIFMJKZ6/pVpyRJkiRJ0owa9Q8DVHf6OcH6hcAv+1ifJEmSJEnSo0pELIuIUyPi5ojYHBF3R8SZEbFqDrF2iIiPR8RtEbGluT89InaY4ZqhiPjDiPhBRGyKiHsj4ssRsU9vz+y/9Hs1wOhzfZIkSZIkSe1l1r8VEhHLgEuADwArga8CdwAnANdExOouYu0E/CfwTmAcOBdYD7wD+G7zfOs1AXwR+Biwiqpj0g3AccD3IuI5c35yU/Q8DDAiftRh0cdPUz4zc99e2yBJkiRJktS1TLIfwwDLJazeCzwXuAJ4cWY+BBARJwF/C5wJHNphrI8BTwa+Arw6M8ebsf4XcCJwGvCGlmtOAF4B3AL8Zmb+vHnNccBZwD9HxFMnY81ViTmrnko1r32nvaaeOuXfzmImSZIkSZLmzwKZYD0iRqmSSABvm0xUAWTmaRHxBuCQiHhWZl49S6zHAq8FxoC3tiSX3gX8LvDaiHj3ZEKq6eTm/mHHM/PsiDgPeBlwNHD23J5lpcQwwPuokk6fosrI7dVmu6pZbuqxJxaoX5IkSZIkaW4aWf9WxvOBHYA1mXntNOfPau6P6iDW4VQ5oW+1JKPIzC3A+cBwsxwAEbEXsA+wiWr4Xy/1z6hEz6q9qbqO/XfgecD/yMzvtBaKiM0AmXlbgTolSZIkSZIWk6c399e0OX9NS7leY72xJdbkv3+YmWM91j+jnntWZeYDmXk88NvAcuCbEfH/RcSOvcaWJEmSJEmqVTbq38p4fHN/Z5vzd7aUKx2rZP0zKtGzCoDMvCQi9gdOAU4Cjo6IP87Mz5eqQ5IkSZIkqZhMstwwvRnrAVZHxA3Tn+5o8bmVzf3GNuc3tJQrHatk/TMqMWfVr2Tm5sx8D/Bs4KfAZyPikojYu2Q9kiRJkiRJRSycnlWTC9u1y651uvDdXGPNdk0xxXpWTZWZ10fEQcA7gA8C36d95k2SJEmSJGle9KVnVWVNhz2o2lnf3G/T5vyK5v6hNud7jTXbNZPHO6l/RrUkqwAyM4HTI+IrwCeBIyibfdsjGWds/Oezl3y0iG6SpB2EK9uxjsyJovFKGxv/RbFYG/lpsVgADcq+dovpvQBIiv2lohlwcJeuTcYB9pin6ovfd7PwH2Wiqz8mzW6s+H23bLxGub/SAd39Ka4T4xP3Fou1kbLrs2Tx+27Z96L0XWhi4pdF45X+7pa+75Zr37z+PN1jIw9xxcS/lYs42L8eSBIbqxxHX3/X3cA6rmj8e1/qKeD25n5Vm/OrWsqVjlWy/hnVlqyalJl3UGDZwmlsBEjG76gh9mAq/Xth2XADL3O8WKzCqZFFp+R7sQjtwfz1VB34+27x+9oiu++Wbt8A530Xnf79wfhRZ17vuUmDDawb2HuuJNWg3/fdNVAskdRxfT24rrl/Zpvzk8evrynW5DX7RcToNCsCdlP/jCL9TVKSJEmSJGmgRcQS4BfA9sAzM/PalvPXAQcAB2bmd2eJtRvV6n3jwB6Z+Ysp55YCdwA7AY/LzJ9NOfcj4GnAMZl5bkvMrwIvA16VmV+e8xOl8ATrkiRJkiRJKi8ztwJnNB+eERG/mjsqIk6iSlRdNjVRFRFvj4gbI+JDLbHuAf4VWAJ8MiKmjrz7CLAL8C9TE1VNp02WiYhdp9RzLFWi6lbgXHpkzypJkiRJkqQFICKWAZcCzwHuAb4NPKH5+H7goMz88ZTypwB/DnwuM49vibUzcCWwmmqI4veAfYH9mo8Pysz7Wq4ZAs4CjgEeAC4BdgYOBbYAL8zMy3t9nvaskiRJkiRJWgAyczNwGPBBqvm9Xg7sCXwOeMbURFUHse4DfgP4BFUPq2OohhieQTWU8L5prmkArwROBu4GjgT2B84Bnl0iUQX2rJIkSZIkSdIAsWeVJEmSJEmSBobJKkmSJEmSJA0Mk1WSJEmSJEkaGCarJEmSJEmSNDBMVkmSJEmSJGlgmKySJEmSJEnSwDBZJUmSJEmSpIFhskqSJEmSJEkDw2SVJEmSJEmSBobJKkmSJEmSJA0Mk1WSJEmSJEkaGCarJEmSJEmSNDBMVkmSJEmSJGlgmKySJEmSJEnSwDBZJUmSJEmSpIFhskqSJEmSJEkDw2SVJEmSJEmSBobJKkmSJEmSJA0Mk1WSJEmSJEkaGCarJEmSJEmSNDBMVkmSJEmSJGlgmKySJEmSJEnSwDBZJUmSJEmSpIFhskqSJEmSJEkDw2SVJEmSJEmSBsbIfDdgriLiZ8AK4I75bosk9dEewMbMfGy/K/a+K2kR8p4rSf3V1/tuRJwHrO5HXU1rMvNlfaxvwVqwySqqH97bRozuM98NaSdzbL6bMKOhWDLfTZhRkvPdhLZGWDrfTZjROFvmuwkzCmK+mzCjRm6d7yYMqhUQ2w7H0oG9707k5vluwoyGYrDvHUljvpvQ1ijL5rsJMxpjsD97MeCd6Rs50D+3tp2nelcEQ9uuYOXA3nMZGuyf5wP8q+TgywF/8WLAP3uDboBfvo2N9f3+fWQ1sM8+T6n//8Y/utn/Y3RjISer7ogY3Wfp6O7z3Y62Nm+9c76bMKNtlj5xvpswo7HGpvluQlu7j+4/302Y0d1jP5jvJsxodGj5fDdhRus33zLfTZjBBMzfX9nvGI6l+2y/fHD/3/TLjdfNdxNmtHJpP/9w171Bvu8+bsDvu3d53+3Jus03zXcT2piYz8rvWMHKfQ6OF89nG2Y0tGKb+W7CzAY94TLAGpsHOoHM0LLB/uPPwBvgZN93Np7Lhsbavv6uu89TlvCDbz6h9nr2P/Q2E1ZdWMjJKkmSJEmSpB4kjb705jKB3o3aklURMQIcCfwGsDNwVWae2Ty3e/PYjzJzvK42SJIkSZIktZPARNafrDJV1Z1aklURcSjweeBxVCNiExgFzmwWeSHwWeBVwNl1tEGSJEmSJGk2DVNJA6f4bJsRsT9wEbArcDrwSh45hdvZwEbguP+fvTuPs6uqEj3+WxVCQoCACioYIRiFhwjIoAwigwJOoOAsaAMPe3gi2o3thIqhtUHbJy0PtB9PO86traCiYIOA2IICIkFQlMEgQwLYgAgZCIHUen+cU3ZZ1L11b91z7rmV+n0/n/PZ3Hv23XvVkNi9svY6Ve8vSZIkSZKkqauOyqqTgFnAwZn5Q4AY08AtM1dFxG+AXWrYX5IkSZIkaUIJfelZZe1Wd+pIVu0HXDmSqGrjDuCgGvaXJEmSJEnqQLK2L08PNV3VjTqSVXOBZR3MmwXMqGF/SZIkSZKkjtizavDUkay6G9i+g3nPAW6vYX9JkiRJkqQJJbC2D8kq02HdqbzBOvADYIeIOLzVhIg4GtgaOL+G/SVJkiRJkjRF1VFZdQrwRuBrEfEJ4Nzy/TkR8RzgMOBE4H7gtBr2lyRJkiRJ6ojHAAdP5cmqzLw9Il4BfBP4AEViKoHXlVcA9wKHZ+Y9Ve8vSZIkSZLUiYS+NFg3HdadOiqryMzLI2Jb4FjgQGA+RTP1pcDFwFmZ+cc69pYkSZIkSerUcNMB6HFqSVYBZOZy4FPlJUmSJEmSNFBssD6Y6miwLkmSJEmSJE1K5cmqiBiOiDURccIE8z4fEY9Vvb8kSZIkSVKn1mb9l7pTV2XVesAnyoTUzDbzoqb9JUmSJEmSJjTch0vdqatn1QXAE4CjgG0j4vDM/K+a9pIkSZIkSepa0bOq/joai6u6U1dl1T3A/sBXgb2An0XEc2vaS5IkSZIkqXsJw324zFZ1p7YG65n5SGa+BXg/MA+4PCJeW9d+kiRJkiRJmvpqfxpgZn4cOIzimOa/R8TCuveUJEmSJEmayMgxwLovC6u6U3uyCiAzzwP2BG4DPhQR5wAb9WNvSZIkSZKkVvqRrKpSRMyOiJMj4uaIWB0Rd0XEooiYN8n1nhkRn42I28r17o2In0bEuysNvAt1NVh/nMz8dUTsDpwDHI4nNiVJkiRJUoMSGM6p02A9ImYDlwB7A3cD5wLzgWOAQyJir8xc0sV6hwP/BswCrgWuAJ4E7Aj8NfCJikLvSh3JqjuA+8a7kZkPRMRBwBnA32DCSpIkSZIkNagfTwOs0IkUiaorgIMzcwVARJwAfBJYBOzXyUIRsTPwdWA5cFBmXj7q3hCwa7Whd67yY4CZOT8z39Pm/trMfBuwFfCMqveXJEmSJEla10TETOD48uVxI4kqgMw8Dbge2DcidutwyTOA9YGjRyeqyvWGM/PnFYQ9KX07BjhWZi5tam9JkiRJkqQkWNuHdt5ZTfXWPsCmwJLMvHac+2cDOwGHAte0WygitgdeCNxc9hkfKI0lqyRJkiRJkprWj55VFdm5HBe3uL94zLx2XlyOF5V9sN4A7E7Rrul64BuZ+dBkA+1Vz8mqiLiV4os5MDN/V77uVGbmgl5jkCRJkiRJ6lbSn55VZcPuBRFxw7j3M3foYJmtyrHVSbWlY+a1M7Lfw8AvgO3G3D81Il6TmT/uYK3KVVFZNb8cZ455LUmSJEmSNNDWZv3HACuyUTmuanF/5Zh57TyhHP8WeAB4NfBD4CnAh4EjgO9ExA6Zeffkwp28npNVmX/+Ux37WpIkSZIkSSzpsIKqlZESsJzgfidmlON6wJsz8wfl6weBIyPiWcDzgOOAD3YbaK/sWSVJkiRJkqapYLgPDda7yyO1tLwcN2xxf045rmhxf7y1lo1KVI32eYpk1f4dR1chk1WSJEmSJGla6nPPql7dUY7zWtyfN2ZeO7eV4+3uUPWYAAAgAElEQVQT3H9yB2tVruf0YUSsFxFPjohNWtx/UkScFRFLI2J1RNwaEZ+IiI173VuSJEmSJKkXa3Oo9qsi15Xjri3uj7x/fQdrXVuOT2xx/0nl2EmVVuWq+I4dDdwNvHPsjTKB9VPgrcCWwPoUDdhPAC6OCCu7JEmSJElSIxIYJmq/Kqqs+glFT6kFEbHLOPdfW47ndbDWJRQN2RdExNPHub9/OS7uNsgqVJGs2p/i5/vZce6dCDyLolP98cCOwOHA74DdgWMr2F+SJEmSJGmdlplrgDPLl2dGxJ96V0XECcBOwOWZefWo998eETdGxKlj1loFnAHMBP5lzFovBY6iyPX8v7q+nnaqqGzaBfhli0cZjnxxCzPz0+V7N0TEr4HfAK8DzqogBkmSJEmSpC4Fa6dOg3WAjwIHAnsDt0TEZcDWwB7A/cAxY+ZvBmwHbDHOWicDLwReUa51FUWPqj0pips+kJk/qyrwblTxE3kKcNPYNyPi2RRf5DDwhdH3MvMW4GcUlVaSJEmSJEl9l/SnZ1VFxwDJzNXAAcBHKE6xHUbRbumLwC6Z+dsu13oR8AHgj8DLgB2AS4FDMvOUisLuWhWVVRsDM8Z5f69y/FVm3j/O/TuA3SrYX5IkSZIkaVKG+1JZVZ3MfBg4qbwmmrsQWNjm/hrglPIaGFUkq/4AbDvO+y+kSFJe1eJzM4GHKthfkiRJkiSpa5nB2qzsiF7bfdS5KpJVVwGHRsRLMvNCgIjYjKIUDeCiFp/bHrirl40zH2X1mqW9LFGrDWctaDqEtmbNmNt0CG3NiFlNhzBlzZ6xadMhtLXe0GD/bIcH+M/uqjW3UfzjRzPW5mr+sOq6iSc2ZPb685oOoa2ZQ3OaDmHKGma46RDamjm0QdMhtDXov3uD+mf3kUd7+j9VezcUDM3ZcOJ5DYkZ4x2u0LpgxtyZTYfQ3nBVB6o0eEzoqFBFsurTwCuB70TEN4B7gdcAc4FlwHfHfiAi5lM0+PpqBftLkiRJkiRNSn8arKsbPSerMvOiiPgI8CHgLRRH/wJYDRyTmY+O87H/Vc65sNf9JUmSJEmSJms4TVYNmioqq8jMD0fEd4HDgc2BpcBXM/PWFh9ZA5wO/EcV+0uSJEmSJHUrib5UVqVHHLtSSbIKIDOvAa7pcO6HqtpXkiRJkiRpsvrRYF3dqSxZJUmSJEmSNJUkMNyXyip1w4OZkiRJkiRJGhhWVkmSJEmSpGlrrQ3WB47JKkmSJEmSNC0lwXAfmp/bYL07JqskSZIkSdK0ZWXV4DFZJUmSJEmSpqUE1tpgfeCYPpQkSZIkSdLAsLJKkiRJkiRNTwnD2Yd+UpZWdaXnZFVE3NrDxzMzF/QagyRJkiRJUveiL8cAscF6V6qorJpfwRqSJEmSJEl9lcBwHxqsW1jVnZ6TVZm2zZckSZIkSVPTWqueBo6JJkmSJEmSJA0MG6xLkiRJkqRpKYk+HQO0eqsbtSWrImI94BDgecBmwFWZuai8t2X53q8z87G6YpAkSZIkSWrHY4CDp5ZkVUTsB3wZeBpFy/sEZgKLyikvBr4AvB44p44YJEmSJEmSJtKPyip1p/KfSETsCHwfeDJwOvA6Hv+MxnOAVcBrqt5fkiRJkiSpEwmszaHaL58G2J06KqtOAmYBB2fmDwEi/jxXlZmrIuI3wC417C9JkiRJkqQpqo5k1X7AlSOJqjbuAA6qYX9JkiRJkqQOBMN96VllX6xu1JGsmgss62DeLGBGDftLkiRJkiRNaOQYYD/2UefqSFbdDWzfwbznALfXsL8kSZIkSdLEEoazD1VPZqu6Ukf68AfADhFxeKsJEXE0sDVwfg37S5IkSZIkTSiBtQzVfpmr6k4dyapTgBXA1yLiIxGxe/n+nIh4TkR8EPgMcD9wWg37S5IkSZIkaYqq/BhgZt4eEa8Avgl8ADiRIln5uvIK4F7g8My8p+r9JUmSJEmSOhP9OQZog/Wu1NGzisy8PCK2BY4FDgTmUzRTXwpcDJyVmX+sY29JkiRJkqRODddy6Kw+ETEbeD/wJmAr4A/ABcBJmbm0i3Vuo2jR1Mr2mXljD6FOWi3JKoDMXA58qrwkSZIkSZIGSvE0wPqrnqrqWVUmqi4B9qZ4wN25FAVCxwCHRMRembmky2W/2OL9BycbZ69qS1ZJkiRJkiQNuv4cA6zMiRSJqiuAgzNzBUBEnAB8ElgE7NfNgpl5dMUx9qzyWreImN/F3EOq3l+SJEmSJGldExEzgePLl8eNJKoAMvM04Hpg34jYrYn4qlTHwcxfRMRb2k2IiNkR8S8U5WqSJEmSJEkNCIZzqParogbr+wCbAksy89px7p9djodWsVmT6jgGuD7whfKJgH8ztpF6ROwKfBXYDri1hv0lSZIkSZImlMDaPjypr6KeVTuX4+IW9xePmdeRiHg3sAB4BLgB+HZm3jupCCtSR7JqV+DfgNcDe0fEUZl5KUBEvA9YSJHQ+jzwjhr2lyRJkiRJ6kgfe1YtiIgbxruRmTt08PmtyrHVE/+WjpnXqX8a8/qfI+IdmfmvXa5TmcqPAZaPNXw+8AlgS+CiiPhURPwncAqwHHh1Zh6bmSur3l+SJEmSJKkTmfTlGGBWU1q1UTmuanF/5Zh5E/ku8Gpga2AO8BzgNGAW8LmIOGyScfaslqcBZuZjwHsj4vsUfalGGoBdArwlM++pY19JkiRJkqQBtaTDCqpWRkrAWqW+uioRy8yxp91uAN4VETcBZwEfB77TVYQVqaPBOgARMRf4S2AuxTcsgP8B9PKDkSRJkiRJqswwUftVkeXluGGL+3PKcUWL+536HPBfwLYRsU2Pa01KLcmqiNiX4pGJRwDXUjT3OhXYArgwIk6LiPXr2FuSJEmSJKkzwdqs/6roaYB3lOO8FvfnjZk3KZk5DCwpX27Ry1qTVXmyKiJOpTjuN4+ib9WemfnLzPwAcABFw693AldHxI5V7y9JkiRJktSJpE89q6oJ97py3LXF/ZH3r69gryeUY69VWpNSR8+q91IkpP4iM380+kZmXlYmqP6FourqKv67TK1rQ7E+G856Rg+h1mvWjLlNh9DW7Ni46RDa2iTmNx1CS3OHO+1X14wNYtJ/rPriwbi/6RDaq6WbXzVWr5nB2gb3H4pZbDRrQYMRtDdzaLB/9zcY2qTpENp6csxvOoSW5g4P9v9mzZoxu+kQ2loeDzQdQnsD+vfuo4/+vtkAEqrqyDstDfXt6V6TE7V1ZOndwH/vBvzPRQ43HUF7wwP+/WtAH58G2KufAA9SPFVwl8y8dsz915bjeb1sEhE7ANtRNHK/sZe1JquOvyG/Aew0NlE1IjOXZ+abKZJVq2vYX5IkSZIkaZ2SmWuAM8uXZ0bEn3pXRcQJwE7A5Zl59aj33x4RN5an4Bj1/ksiYrexe0TETsA3Kc4tfq7cs+8q/3eszHxjh/O+HhGXV72/JEmSJElSpypsgN4PHwUOBPYGbomIy4CtgT2A+4FjxszfjKJKamzvqb2AD0fE7RT9qe4FtqE4Srge8J/A+2v6GibUaNF1Zi5tcn9JkiRJkjR9FT2r6k9WVXX4MjNXR8QBFImkI4DDgAeALwIfysw7O1zqQuDpwPMoHoq3CfAQcDnwVeDzmdlYB5Kek1URcSvF9/3AzPxd+bpTmZmD2/xEkiRJkiStw4Lh7EcPueoSYpn5MHBSeU00dyGwcJz3rwCuqCyoilVRWTW/HGeOeS1JkiRJkjS4sk8N1u1r35Wek1WZf56CHPtakiRJkiRJ6tSAPihYkiRJkiSpXkl/GqxbWNUdk1WSJEmSJGna6ssxQHWlsmRVRLycogv904FHgOspusf/rqo9JEmSJEmSqmSyavBUkqyKiK8Cbxx5WY6HAn8fEW/MzO9WsY8kSZIkSVJVkuhLsir7cNRwXdJzsioijgXeBDwGfBm4FtgYOATYC/hSRGydmQ/2upckSZIkSZLWbVVUVh0FDAMvy8xLRr1/akR8HvgL4NXA5yvYS5IkSZIkqTIeAxw8QxWssSNw5ZhE1YhTKI4F7ljBPpIkSZIkSZUaJmq/1J0qKqvmAkta3Fsyao4kSZIkSdLASPpTWZW177BuqSJZFcDa8W5k5nBEQDUVXJIkSZIkSdXJPh0DNFvVlUqeBihJkiRJkjQV2bNq8FRV8XRURKwd76LIH7a6/1hF+0uSJEmSJGkdUFVl1WTTkKYvJUmSJElSI5LoU88q0x/d6DlZlZn2o5IkSZIkSVNSegxw4NizSpIkSZIkTVvDVj0NHJNVkiRJkiRpWkr602DdhwF2xyN8kiRJkiRJGhhWVkmSJEmSpGnLnlWDx2SVJEmSJEmanrI/xwA9B9gdk1WSJEmSJGmaij5VVlm91Q2TVZIkSZIkaVqywfpgssG6JEmSJEmSBoaVVZIkSZIkadpKy54GTi3JqogI4EjgVcCzgI0Z/4BmZuaCOmKQJEmSJEmayLD9pAZO5cmqiFgfOB94Ea07iGWbe5IkSZIkSX3Rnwbr6kYdPaveBbwYOI+iqurLFMmpWcD2wEJgJfCJzLRnliRJkiRJasRIg/W6L08adqeOY4BvAP4AHJGZKyNiGCAzHwVuAv4hIi4FLo2ImzJzUQ0xSJIkSZIkaQqqo7LpmcDPMnNl+XoYICJmjEzIzMuAnwBvq2F/SZIkSZKkiWXRYL3uy9Kq7tSRrFoLPDTq9UjSavMx85YB29WwvyRJkiRJUkcyo/arShExOyJOjoibI2J1RNwVEYsiYl6P6z4rIh6OiIyIC6qKdzLqSFYtA7Ya9fq35bjnmHk7AStq2F+SJEmSJKkD9SeqimRVNQmriJgNXAKcBGwEnAvcCRwDLI6IBT0sfxZFv/HG1ZGsuhLYISI2KF9/vxxPj4iXRcSOEXEGRbP1q2rYX5IkSZIkqSP9aLBeoROBvYErgG0z8w2ZuQfFw+42BybVFzwijgUOAD5bVaC9qCNZdQ6wCjgIIDN/C3wKeDrFEwJ/ARxXznlvDftLkiRJkiStUyJiJnB8+fK4zPzTabXMPA24Htg3Inbrct0nA58ALga+VlG4Pan8aYCZeT6wxZj33hURVwOHAU8Abgb+T2beUvX+kiRJkiRJnUjKBuh92KcC+wCbAksy89px7p9N0XLpUOCaLtb9P8AGwP8Ceup7VZXKk1WtZObXga/3az9JkiRJkqSJVN0AvUY7l+PiFvcXj5k3oYh4OfAG4KTM/G2vTdqrUnmyKiJOAx7IzI9UvfZYSfLo8MN1bzNpM2Ig+pK1tEnMbzqEtnaf/dSmQ2jpJVs+2HQIbV141+B+7wB+vrrpCNpbkfc1HUJLyXDj+w/y37uD7skxv+kQ2tp1/S2bDqGlg7dc3nQIbf3grsH93gEsXtN0BO09NHx30yGMK33O+NQWdXQ8qc7wgqc1HUJLF3z3K02H0NZLX/nmpkNoa2jJsqZDmMDapgMYLNmnZFU1/5My8jC7pS3uLx0zr62I2BD4DHAT8PHeQqtWHZVVb6foRi9JkiRJkjTQ+vhPEwsi4oZxY8jcoYPPb1SOq1rcXzlm3kQ+CmwNvCgzB+qftur454alNa0rSZIkSZI0XY2UgLXKr3VcIhYRu1M0a/9SZl7aa2BVq6Oy6tvAURGxcWYOds2+JEmSJEma1vrYs2pJhxVUrYzkWDZscX9OOa5ocR+AiFgP+CzwIPD3PcRTmzqSVQuBA4DvR8Q7WnSolyRJkiRJat7UaVF4Rzm2aoI+b8y8VuYBzwXuAb4Z8WfJuk3L8fkR8SNgRWYe0n2ovakjWXUu8AjwAuDnEXE3xTdqvJbKmZkvriEGSZIkSZKkCU2hpwFeV467trg/8v71Ha731PIazxOA/Siqr/qujmTV/qP+O4Aty2s8Uyd/KUmSJEmS1ikJZB8yExVt8ROK5NGCiNhlnJNsry3H89rGknkbLfpbRcT+wKXAhZn50p6i7UEdyaptalhTkiRJkiRp2srMNRFxJvAB4MyIODgzVwJExAnATsDlmXn1yGci4u3A24FvZ+b7m4h7MipPVmXm7VWvKUmSJEmSVL3o0zHAyvb4KHAgsDdwS0RcBmwN7AHcDxwzZv5mwHbAFlUF0A9DTQcgSZIkSZLUiAQy+nBVFG7maoqH2n0EWAUcBswHvgjskpm/rWanZtVxDBD406MQDwGeR5HJuyozF5X3tizf+3VmPlZXDJIkSZIkSe30o2dVlTLzYeCk8ppo7kJgYRdr/4gKy8Amq5ZkVUTsB3wZeBrFF5nATGBROeXFwBeA1wPn1BGDJEmSJEnShKZYsmo6qPwYYETsCHwfeDJwOvA6Hp+VO4eiXO01Ve8vSZIkSZKkqauOyqqTgFnAwZn5Q4CIP89VZeaqiPgNsEsN+0uSJEmSJHWkPw3W1Y06GqzvB1w5kqhq4w5gyxr2lyRJkiRJ6kz24VJX6qismgss62DeLGBGDftLkiRJkiR1xMqqwVNHsupuYPsO5j0HuL2G/SVJkiRJkibWr8onq6u6UscxwB8AO0TE4a0mRMTRwNbA+TXsL0mSJEmSpCmqjmTVKcAK4GsR8ZGI2L18f05EPCciPgh8BrgfOK2G/SVJkiRJkjoUfbjUjcqTVZl5O/AK4AHgA8BVFAVvrwOuA/4BWA68KjPvqXp/SZIkSZKkjtlgfeDU0bOKzLw8IrYFjgUOBOZTNFNfClwMnJWZf6xjb0mSJEmSpI6ZTBo4tSSrADJzOfCp8pIkSZIkSRo8Pg1w4NTRs0qSJEmSJEmalNoqqyJiPrAvsAUwq8W0zMyP1BWDJEmSJElSO+kxwIFTebIqImYDnwWOGHmrzfQETFZJkiRJkqRmmKwaOHVUVn0cOBL4L+CrwBJgZQ37SJIkSZIkTV5Gf3pW2RerK3Ukq94A3Ac8NzPvqWF9SZIkSZKkSoSVVQOnjgbrGwGXmaiSJEmSJElSt+qorLoBeEoN60qSJEmSJFXLyqqBU0dl1f8Gnh8Re9ewtiRJkiRJUnVG+lbVeakrlVdWZeY3I2IecG5EnAFcDCyjRa4yM++oOgZJkiRJkqSOWFk1cOo4BghwPfAA8OHyaiVrjEGSJEmSJKk9k1UDp/JEUUQcAnyrXPte4HZgZdX7SJIkSZIk9STpT7LKhFhX6qhqOhkI4BjgS5npj0SSJEmSJEkdqSNZtT3w48z8Yg1rS5IkSZIkVccG6AOnjmTVfcCKGtaVJEmSJEmqVHgebOAM1bDm2cCeETG3hrUlSZIkSZKqk3241JU6klUfBG4FvhUR29SwviRJkiRJktZRdRwDPA9YCxwA3BQRtwHLGD+XmJn54hpikCRJkiRJ0hRUR7Jq/zHrP7O8xtNTMdx6zGLLmTv2ssS0Nnd4o6ZDaOslWz7YdAgtvf76g5oOob2dLmo6grZuXjLYv3sPDz296RBaWskShnmksf1nMpunDfDfu8MMNx1CW3OHN246hLYO3nJ50yG0dOQvB/zftna8pOkI2vrtrYP9u/fIjGc0HcK4HuaOZgPIZHh1c3/nT2TG3JlNh9De0GA3TL7gu19pOoSWXrbdC5sOoa0Lbhrc7x3Ay3d8UdMhtGcz8cexZ9XgqSNZ5dE/SZIkSZI0NUyxBF5EzAbeD7wJ2Ar4A3ABcFJmLu1wjfUo2jg9D9ge2ByYCdwJ/AD4eGY29q82lSerMvP2qteUJEmSJEmqXL8aoFe0R5mougTYG7gbOBeYDxwDHBIRe2Xmkg6Wmg18GFgBXA9cA6wPPBd4G3BkRLwoMxdXE3l36qiskiRJkiRJmhqm1jHAEykSVVcAB2fmCoCIOAH4JLAI2K+DdVYD+wBXZeZjI29GxAzgIxSVW58B9qw0+g7V8TRASZIkSZIkVSgiZgLHly+PG0lUAWTmaRQVUvtGxG4TrZWZj2XmT0Ynqsr31wInUSSz9oiIDSv7ArpQS2VVRARwJPAq4FnAxsB4h0AzMxfUEYMkSZIkSdJEplCD9X2ATYElmXntOPfPBnYCDqU41jdZCQyX12MTzK1F5cmqiFgfOB94EeMnqKD4wqdWBzNJkiRJkrTumTrJqp3LsVUfqcVj5nWtLD56HzAHuDgzG3ksbR3HAN8FvBg4j6Kq6ssUP/pZFB3mFwIrgU9kpscQJUmSJElSc7IPVzW2KsdWT/xbOmZeRyLi4xHxhYj4FnAL8FHgRuCvJhVlBeo4BvgGiscmHpGZKyNiGCAzHwVuAv4hIi4FLo2ImzJzUQ0xSJIkSZIkTaiPxwAXRMQN493IzB06+PxG5biqxf2VY+Z16jXA6BZNvwKOzMzfdblOZeqobHom8LPMHPkmDcOfOsoDkJmXAT+heByiJEmSJEmS2htpp9QqvTapdkuZ+czMDGBz4KXAI8A1EXHUZNarQh2VVWuBh0a9HklabQ7cM+r9ZRRNvyRJkiRJkvovgexDS+0ivbSkwwqqVpaXY6sn9M0pxxUt7reVmfcBF0bElRRPFvyXiPhhZt45mfV6UUdl1TL+/Hzkb8txzzHzdmKS30BJkiRJkqRKTJ2eVXeU47wW9+eNmTcpmfkgRR/yDYCDellrsupIVl0J7BARG5Svv1+Op0fEyyJix4g4g6LZ+lU17C9JkiRJkjShoOhZVftVTbjXleOuLe6PvH99BXvdV46bV7BW1+pIVp1D0ezrIIDM/C3wKeDpFJm5XwDHlXPeW8P+kiRJkiRJnZk6lVU/AR6kaNS+yzj3X1uO51Ww137luKSCtbpWebIqM8/PzC0y87uj3nsXcATwTeBi4NPArpl5U9X7S5IkSZIkrWsycw1wZvnyzIj4U++qiDiBot3S5Zl59aj33x4RN0bEqaPXiohXlqffYsz7cyLiHymSVfcAF9T05bRVR4P1cWXm14Gv92s/SZIkSZKkiUR1lU/98FHgQGBv4JaIuAzYGtgDuB84Zsz8zYDtgC3GvL8r8GHgroi4lqJi66nAc4Enlq9fn5mN9BqvJFkVES+iaOT188z89QRznw3sDtyZmZdWsb8kSZIkSdKkTKFkVWaujogDgPdTnGA7DHgA+CLwoS6e3PctYGPghcDzKBJUD1M8JO8s4IzMvLvi8DvWc7IqIp4OnA/cCezWwUfuBL4NzIuIZ2XmXb3GIEmSJEmS1LVqe0q136eqpTIfBk4qr4nmLgQWjvP+9cC7qouqWlX0rHorsD7wnsxcPtHkcs67KR6BeGwF+0uSJEmSJE1KP54GqO5Ukaw6CLg3M7/T6QfK5uu/B15Wwf6SJEmSJElaR1SRrPofwNUTznq8n1M0+ZIkSZIkSZKAahqsb0jRJb5bDwIbVbC/JEmSJEnS5HhMb+BUkax6AHjKJD73lPKzkiRJkiRJjbCn1OCp4hjgr4E9I2KDTj8QEXOAvcrPSpIkSZIkNSP7cKkrVSSrvkdxFPCDXXzmgxRPA/xeBftLkiRJkiRpHVFFsuos4B7gfRHxwYhouWZEDEXEh4D3UTwN8KwK9pckSZIkSZocK6sGTs89qzJzVUS8GrgEOBn4y4j4JrAYuLectjmwK/A6YB6wGnhNZq7qdX9JkiRJkqRJyT71rDJh1ZUqGqyTmVdGxF7AV4DnAH83zrQoxxuAN2fmdVXsLUmSJEmSNGkmkgZOJckqgMy8HtgpIl4CvALYBXgSRZLqPuAXwPmZeUFVe0qSJEmSJPXCpwEOnsqSVSMy80LgwqrXlSRJkiRJ0rqv8mSVJEmSJEnSlGFl1cAxWSVJkiRJkqYvk1UDx2SVJEmSJEmaloL+9KyKiadoFJNVkiRJkiRpekr6U1ll9VZXhpoOQJIkSZIkSRphZZUkSZIkSZq+rHoaOCarJEmSJEnStNWPnlXqTuXHACNiUUT8zw7mHR0Ri6reX5IkSZIkqWPZh0tdqaNn1dHAPh3MewFwVA37S5IkSZIkdSSy/kvdabLB+vrA2gb3lyRJkiRJ0oBppGdVRASwK3BvL+s8xiPc9egvqwmqBrNnbNp0CG1tEHOaDqGtC+96atMhtLbTRU1H0NaFd23SdAhtPRT3NB1CW38YvrPpEFpay6ON7v8oq1k2wH/vzhzaoOkQ2po1Y3bTIbT1g7u2bDqE1na8pOkI2vrBXRs3HUJbDw3d1XQIbd239tamQxhX03/nEsHQ7FnNxtDO8ICXCgx4KcNLX/nmpkNo6YKbvtJ0CG0N8vcOYGh4WdMhtDfof3ab4Ldk4FSSrIqIH45566XjvDd6zwXAU4EvV7G/JEmSJElS1/rVU8qEWFeqqqzaf9R/J0Uiql1ZzKPAecDfV7S/JEmSJElS16LpAPQ4VSWrtinHAG4Fzgbe3WLuGuC+zGy4rlqSJEmSJE17Vj0NnEqSVZl5+8h/R8TJwC9GvydJkiRJkjSIBrzF3bRUeYP1zDy56jUlSZIkSZI0PQxVvWBEPCUi9o2Ip4x5f5uI+FpE/Coizo+I51e9tyRJkiRJUleyD5e6UnllFfA+4B3As4HfA0TERsDlFE3Xo7y3b0TsnJmD+axiSZIkSZK07jOZNHAqr6yieDLgbzLzplHvHQ1sAXwN2A74O2BDfBqgJEmSJElqUGT9V6XxRsyOiJMj4uaIWB0Rd0XEooiY18Uam0bEERHxbxHx64hYGRHLI+KqiHhnRMysNuru1JGsehrFEwFHOwR4DHhnZt6SmacDvwAOqGF/SZIkSZKkifXjCGCFRwEjYjZwCXASsBFwLnAncAywOCIWdLjU3wNfBd4ArAK+B/wM2Bn4FPDDiJhTTdTdqyNZtTGwfORFRASwB3BNZt4/at5NQMdZP0mSJEmSpGnuRGBv4Apg28x8Q2buAbwL2BxY1OE6K4BTgK0yc/fMfGNmvhjYEbgD2Af4YOXRd6iOZNUyYJtRr3cHNgF+NGbeesCaGvaXJEmSJEmaUNCfY4BRRazF0bzjy5fHZeaKkXuZeRpwPUV/8N0mWiszP5aZH8jMZWPev4WiFznAmyoIe1LqSFZdATw/Il4VEXMpMnFJUVvQc2kAABTTSURBVFI22vYUiS1JkiRJkqRmTJEjgBTVTpsCSzLz2nHun12Oh/a4z3XluGWP60xaHU8D/Efg1cC3ytcBXJqZPx2ZEBHzKZ4I+K817C9JkiRJktSRqhug12jnclzc4v7iMfMm6xnleE+P60xa5cmqzLwxIvYB3klxXvIa4BNjpr2EIlP3nar3lyRJkiRJ6lj/klULIuKGcUPI3KGDz29Vjktb3F86Zt5kvbMcz+1xnUmro7KKshzt6Db3zwLOqmNvSZIkSZKkddBG5biqxf2VY+Z1LSL+BjgQ+CPwscmu06taklWSJEmSJElTQv8qq5Z0WEHVykif9lYR99THPSL2A04v1/+fmXlXL+v1wmSVJEmSJEmanrJPPauq2WN5OW7Y4v6cclzR4n5LEbETRaum9YF3ZOa3uw+vOj0nqyLiVopv+4GZ+bvydacyMxf0GoMkSZIkSdKkTJ0G63eU47wW9+eNmdeRiFgAXEjxpMGFmXnG5MKrThWVVfPLceaY15IkSZIkSQMtcspkq64rx11b3B95//pOF4yILYGLgKcCp2fmyZMPrzo9J6syc6jda0mSJEmSJPXsJ8CDFE8V3KV8uN1ory3H8zpZLCKeQFFRtQ3weeDvqgq0VyaWJEmSJEnS9JV9uKoIM3MNcGb58syI+FPvqog4AdgJuDwzrx71/tsj4saIOHX0WhExB/g+8BzgG8BfZg5OiZkN1iVJkiRJ0rTVlwbr1fkocCCwN3BLRFwGbA3sAdwPHDNm/mbAdsAWY97/R2BPYC3wGPCvEY9/mGBmHl1h7B2rPFkVEft2Mz8zf1x1DJIkSZIkSR2ZQsmqzFwdEQcA7weOAA4DHgC+CHwoM+/scKknlOOMcp1Wjp5kqD2po7LqR3T3o55RQwySJEmSJEntZZ8qqyrcIzMfBk4qr4nmLgQWjvP+0TSUiOpEHcmqLzH+j2EIeDpFd/q5wHcpsn+SJEmSJEkSUEOyaqLzjBHxROBzwLOBvareX5IkSZIkqWNT6BjgdNH3pwFm5h+ANwObAKdOMF2SJEmSJKkWQXEMsPar6S90iul7sgogM1cBPwNe2cT+kiRJkiRJQFFZVfelrtTRs6pTG/Hf3eclSZIkSZL6ri8N1tWVRiqrIuJQYF/g5ib2lyRJkiRJ0mCqvLIqIha1ub0RsC2wI8WRzU9Wvb8kSZIkSVLH0tKqQVPHMcCjO5hzB7AwM79Uw/6SJEmSJEkTyz4dAzQf1pU6klUHtLm3Brg7M2+rYV9JkiRJkqTumEgaOJUnqzLzP6teU5IkSZIkqQ4x3HQEGquRBuuSJEmSJEnSeOposL5vL5/PzB9XFYskSZIkSVJbHgMcOHX0rPoRvf2oZ1QUhyRJkiRJUlt9abCurtSRrPoS8ETgEIqk1bUUT/8D2Ap4LsXxw+8Bf6hhf0mSJEmSpIklkH3IVpkQ60odyar3A1cCFwHHZ+bNo29GxLbA6RRJqz0z8+4aYpAkSZIkSZqQlVWDp45k1ccojvK9KjNXj72ZmTdHxKuBW4CPA38x2Y2CYObQBpMOtG7rDc1qOoS2Hoz7mw6hrZ8/7rdncNy8ZKOmQ2jrobin6RDaenBosH/31svB/bMbDT8XIxga6L93Zw7NaTqEtpbHA02H0NbiNU1H0Npvb9246RDaemjorqZDaGvQf/cG9c9uEE2HoF7kYD/ea2jJsqZDaOnlO76o6RDaGhoe3O8dMPC/e9JUUEey6iXApeMlqkZk5sMRcVk5V5IkSZIkqRlWVg2cOpJVc4HNOpi3GTDY/0wqSZIkSZLWWUF/jgFaq9udOs6T/ArYLyJe2GpCROwD7F/OlSRJkiRJakZm/Ze6Ukdl1ceBbwIXRsQXy/++g6KwbmvgdRR9qobKuZIkSZIkSY2wwfrgqTxZlZnnRMQ7gE8Afw381ZgpAawBTsjMc6reX5IkSZIkSVNXHZVVZOaZEfE94FjgBcCWFEmqu4DLgc9n5u/q2FuSJEmSJKkjSX8arFu91ZVaklUAmXk7cFJd60uSJEmSJPXKY4CDp/IG6xFxYkRsUfW6kiRJkiRJ1UoY7sNlaVVX6nga4EeB2yPi2xHx8ojwCY2SJEmSJGkwZR8udaWOZNXJwDLgVcD3gDsiYmFEbFXDXpIkSZIkSVqHVJ6sysyTgWcALwe+DTyZonfVrRHx/Yg4PCJmVL2vJEmSJElStyLrv9SdOiqryMIFmflaYB7wPmAJ8FLgbGBZRJwaEc+sY39JkiRJkqQJJZDZh6vpL3RqqSVZNVpm3puZ/5SZ2wH7A9+gqLZ6D3BTRFwSEYfXHYckSZIkSdJYVlYNntqTVSMiYhvgYOCFI28BdwMHAGdHxFURMa9f8UiSJEmSJE21BusRMTsiTo6ImyNidUTcFRGLus2pRMR+EfHhiDg/Iu6NiIyIG6uNdnJqTVZFxMyIeH1EXATcApwIbACcDjw7M+cB+wD/ATwPOKPOeCRJkiRJkqaqiJgNXELRG3wj4FzgTuAYYHFELOhiudOBhRQ9xzerNtLerFfHohGxPfBW4C3AkyiqqK4E/i/wjcxcPTI3M38KHBIRV1JUWUmSJEmSJPVF5JQ6p3cisDdwBXBwZq4AiIgTgE8Ci4D9OlzrBxStmq4G7gMWVx7tJFWerIqIyyi+cQE8RJGg+r+Z+csJPnoDRXWVJEmSJElSfww3HUBnImImcHz58riRRBVAZp4WEUcB+0bEbpl5zUTrZeZ7Rq09v+Jwe1JHZdULgGuAs4B/y8xVHX7uc8CPa4hHkiRJkiTpcYoG6PVXVlXUZH0fYFNgSWZeO879s4GdgEMp8jJTVh3Jqudn5s+7/VBmXkFRxiZJkiRJktQfU+cU4M7l2Oq43uIx86asypNVk0lUSZIkSZIkNaJ/PasWRMQN44eQO3Tw+a3KcWmL+0vHzJuyan0aoCRJkiRJkiqxUTm2are0csy8KavnyqqIeM/Es1rLzH/qNQZJkiRJkqTJqKifVCeWdFhB1UqUY6uIo8X7U04VxwA/RucnPMf7xpqskiRJkiRJzejfMcBeLS/HDVvcn1OOK1rcnzKqSFb9A921I3sKcBSwQZefkyRJkiRJqk5CDPdnnwrcUY7zWtyfN2belNVzsiozF3YyLyKeBLwXeAtFomol8Ole95ckSZIkSZqc7FNlVSV7XFeOu7a4P/L+9VVs1qTaG6xHxBMj4lTgd8C7KI4C/m9gm8x8X937S5IkSZIkrQN+AjxI8VTBXca5/9pyPK9/IdWjtmRVRDwhIv6RIkn1XmAG8M8USar3ZOZ9de0tSZIkSZLUkezDVUWYmWuAM8uXZ0bEn3pXRcQJwE7A5Zl59aj33x4RN5ZFRFNGFT2r/kxEbEpRQXU8sDHwCPAp4OOZ+fuq95MkSZIkSZqsmDoN1gE+ChwI7A3cEhGXAVsDewD3A8eMmb8ZsB2wxdiFIuKtwFvLl7PKceuIuHLUtLdl5uLqwu9MZcmqiNgEOAF4J/+dpDoD+Fhm3lPVPpIkSZIkSZWZQsmqzFwdEQcA7weOAA4DHgC+CHwoM+/sYrl5FEmu0WaPeW9uD+FOWs/JqoiYC/wd8LfAJhRJqk8Dp2bm3b2uL0mSJEmSVIsEps7TAIulMh8GTiqvieYuBBZ2e69pVVRW3UaRpFpDkaQ6xSSVJEmSJEmSJqOKZNWmFDnC9YBjgWMjotPPZmZuOPE0SZIkSZKk6k2xnlXTQlU9q4LiaX8zKlpPkiRJkiSpfiarBk7PyarMHKoiEEmSJEmSpL4zWTVwKnsaoCRJkiRJ0pTTjwbr6opVUZIkSZIkSRoYVlZJkiRJkqTpKbM/DdY9atgVk1WSJEmSJGn6MpE0cExWSZIkSZKk6ctk1cCZ0smq4VzD8tW3NB1GS8OzFjQdQnsD/tNfkfc1HUJLDw89vekQ2vrD8J1Nh9DWejmr6RDaevixPzYdQkvDrG12/3yEh1bf1GgM7cxef17TIbQ34H/vPjR8d9MhtPTIjGc0HUJb9629tekQ2po5NKfpENpa+di9TYcwrqb/ziWAiGZjmMqGB/3/+Wz496udHPDfu4H/2WrKMVk1cGywLkmSJEmSpIEx4P/GK0mSJEmSVKPhpgPQWCarJEmSJEnStBRJX54GGJ407IrJKkmSJEmSNH3Zs2rgmKySJEmSJEnTVPapab8JsW7YYF2SJEmSJEkDw8oqSZIkSZI0fXkMcOCYrJIkSZIkSdOXyaqBU/kxwIh4cwdzFkTEC6reW5IkSZIkqWNJkayq/Wr6C51aKk1WRcSHgC9GxKKIiBZztgN+DFwQEU+rcn9JkiRJkqSuDGf9l7pSdWXV6cBVwFHAlyPiz9aPiGcDPwKeCrw3M5dVvL8kSZIkSZKmsEp7VmXmQxFxEPAfwJuAWRHxxsxcGxE7ARcBmwF/lZn/WuXekiRJkiRJ3UnI4f7so45V3rMqM1cCL6GooHoNcE5E7A1cCjwJONpElSRJkiRJGgj96FmlrtTyNMDMfDgiXg6cCxxaXmuBN2fm1+vYU5IkSZIkqStJf3pKma/qSuWVVSMy8xHgn4Eor19RJK8kSZIkSZIGg5VVA6e2ZFVEHAicAzwKXA48F/iPiNiwrj0lSZIkSZI0tdWSrIqIlwLfA2YAhwEHAP8O7AtcGBEb17GvJEmSJElSV6ysGjiV96yKiFdSJKbWAodl5sXl+0cCa4C3ABdHxEsy849V7y9JkiRJktQxk0kDp9JkVUS8BvgasBp4RWZeNnIvM4cj4mjgEeCtwCURcWBmPlBlDJIkSZIkSZ1JGB7uzz7qWNXHAO8G7gIOGp2oGpGFvwI+A/weWFXx/pIkSZIkSZ1J+nMMsMJcVUTMjoiTI+LmiFgdEXdFxKKImDeJtTaNiE9FxO0R8Ug5nh4Rm1YXcfcqrazKzJ9GxDMz87EJ5r09ImZk5toq95ckSZIkSVpXRcRs4BJgb4qCoXOB+cAxwCERsVdmLulwrScBVwDPAm4FvgPsALwDeHlE7JmZ91f+RXSg8gbrEyWqRs0zUSVJkiRJkpo1tRqsn0iRqLoC2DYz35CZewDvAjYHFnWx1j9TJKq+BWxXrvUc4AzgmcBpVQbejVqeBihJkiRJkjQlDGf9VwUiYiZwfPnyuMxcMXIvM08Drgf2jYjdOljrqcCRwKPA28YUHr0buBc4MiKeUknwXTJZJUmSJEmSpqUkyRyu/6qmadU+wKbAksy8dpz7Z5fjoR2s9TKKnNCPM/P3o29k5iPA94AZ5by+M1klSZIkSZKmp6Q/lVXVFFftXI6LW9xfPGZev9aqnMkqSZIkSZKkwbdVOS5tcX/pmHn9WqtylT4NUJIkSZIkaUqptgF6Owsi4obxQ8gdOvj8RuW4qsX9lWPm9WutypmskiRJkiRJ09fwcNMRdCrKsVV2LVq8X/dalassWRUR2wC7AX+gaND1WPn+DOAYYH/gicBtwNmZ+cOq9pYkSZIkSZqU/lVWLemwgqqV5eW4YYv7c8pxRYv7da1VuUqSVRFxCvAe/jvzdmtEHAzcA1wE7MWfZ+X+OiLOysy3VbG/JEmSJElS1zLJflRWVZMQu6Mc57W4P2/MvH6tVbmek1UR8RrgfcBq4MLy7ZcAXwEuBvameOThN4CHgOcDf0uRsPrPzPz3XmOQJEmSJEmalP5VVvXqunLc9f+3d3+vmlVlHMC/zxkyECdvNBAShYJyDGHAtC4UBC+LJjLQOxEFhYjqDwhBQVDvtMi7ulEEb4YM9Xo0lQJBMAf80UWgIoo65ZQN8z5d7PfoMJw5Op593ncd/XxgszlrvXutdQ7nLDbfs/baZ6jfLH9xxW3Nbo6VVXckOZnkuu5+Pkmq6gdJjiS5PMlD3X3HKZ//U1X9OcnTSW5NIqwCAAAA2N4zST7ItFH7we5+4bT6G5bnxz9DW08mWSS5pqq+3t1vb1ZU1VeT/GhZ/8TOh332NmZo42CSZzaDqiTp7mczhVH7k9x/+gXd/VySZ5fXAgAAAKzHonf/mEF3/y/Jg8svH6yqj/ebqqpfJ7kiydPd/ddTyn9eVUer6p7T2nozySNJzknyu6o6dTHTvUkuTPJwd781y+DP0hwrq/YneWOL8jeX563qNsuvmqF/AAAAgM+n98zbAJPk7iTXZ9py6ZWqOpLkkiRXJ3k30wvuTnVBkm8nuWiLtn6Z5PtJfprkaFX9LdMTct9N8lqSX+3GN/BZzLGy6t0k39yifLPswBmuO5Bp+RoAAADA6nWnF7t/zLUvVnf/N8l1Se5KcjzJoSSXJvljkoPd/epZtPVOku8leSDTCqufJDk/0+qtq5b1azHHyqq/JDlUVTd092NJUlU/S3Jlkn8kubeqftjd/9m8oKpuz5TWPbVVgwAAAAArsbdWVmWZr/xmeXzaZ+9Mcuc29e8l+cXyGMYcYdV9SX6c5NGqOros+06S9zOlcs8nebmqnsr0NsArk1ybpJP8fob+AQAAAPiC2HFY1d3PVdVNSX6b5LJl8etJbu7uF6vqliR/SHJbpoCqlp+5r7sP77R/AAAAgM+rZ9oAnflUz/TcZFXty7Si6qMkr3d/so6uqr6V5MYk30jyVpLDW7xi8Wz7O5Zpc/dhVZ2z7iFsayP71j2EbXXGXYq5L19Z9xC2dTIn1j2EbdUs2+XtnkVOrnsIZ9R9Ikn/q7u/tuq+98a8O/bf5vjz7rg3aubdnamP/1c4plHn3WnOrXQvVv4DrKpjlY39526MPO2O/XsF7D3HF8fSWazsXreqXqpsHDg35+16X8fz73QWf+/uy3e9sy+AOR4DTJJ098kkL52h7tVMO9bP6fjy/M+Z253N9FbJcY15W7g3LPLRuofAl9fF+WT+W7U9MO+OHRiMHmiMzLzLmlyc9Nrm3M4iHy4+GHbOBdgFq77Xfa2zyIc5trL+VtXRXjfbyioAAAAA2Kmxn8UBAAAA4EtFWAUAAADAMIRVAAAAAAxDWAUAAADAMIRVAAAAAAxDWAUAAADAMIRVAAAAAAxDWAUAAADAMIRVAAAAAAxDWAUAAADAMIRVAAAAAAxDWAUAAADAMIRVAAAAAAxDWAUAAADAMIRVAAAAAAxDWAUAAADAMIRVAAAAAAxDWAUAAADAMIRVAAAAAAxDWAUAAADAMP4Pwzj99fJcZbYAAAAASUVORK5CYII=", "text/plain": [ "<Figure size 1387.5x1350 with 12 Axes>" ] }, "metadata": { "needs_background": "light" } } ], "metadata": { "jupyter": { "source_hidden": true } } }, { "cell_type": "markdown", "source": [ "As expected, the larger the final camera pixel size the smaller the relative difference in simulation pixel size and thus the smaller the difference in the simulations. Now for the question of whether the convolution step is even necessary when looking at camera pixels larger than Nyquist.\n", "\n", "First note that without convolution to redistribute the intensity before integration (a kind of interpolation) we won't have a symmetric PSF using an even shaped camera pixel (relative to the simulation pixels). So instead of looking at 2x, 4x, and 8x camera pixel sizes like we've been doing above we'll use odd sizes of 3x, 5x and 9x. As a sanity check let's look at the difference between the two methods with no convolution step for either. The result is a measure of the integration error between a finer and coarser integration grid." ], "metadata": {} }, { "cell_type": "code", "execution_count": 11, "source": [ "# set up the figure\n", "fig, axs_total = plt.subplots(3, 4, dpi=150, figsize=(9.25,9),\n", " gridspec_kw=dict(hspace=0.1, wspace=0.1, width_ratios=(1, 1, 1, 1 / 12)))\n", "\n", "# rows will be for different camera pixel sizes, the camera pixel size = subsample / 8 * Nyquist\n", "for axs, subsample in zip(axs_total[::-1], (9, 5, 3)):\n", " # Directly simulate at Nyquist\n", " psf_params_wf['res'] = psf_params['res'] * oversample_factor\n", " c = np.log2(subsample) % 2\n", " if c < 1:\n", " c = 1\n", " else:\n", " c = -1\n", " psf_params_wf['size'] = psf_params['size'] // oversample_factor + c\n", " low_res = HanserPSF(**psf_params_wf).PSFi.squeeze()\n", " \n", " subsample2 = oversample_factor * subsample\n", "\n", " # Use the convolution to shift the data so that the max is centered on camera ROI\n", " shift = len(psf.PSFi[0])%subsample + 1\n", " shifted = psf.PSFi[0, shift:, shift:]\n", " exact = ndi.uniform_filter(shifted, subsample2)\n", " \n", " # integrate across pixel\n", " exact_subsample = bin_ndarray(shifted, bin_size=subsample2, operation=\"sum\")\n", " exact_subsample /= exact_subsample.max()\n", "\n", " # Display final camera pixels\n", " offset_sub = offset//subsample2\n", " axs[0].matshow(exact_subsample, norm=mpl.colors.PowerNorm(gam))\n", "\n", " exact_low_res = ndi.uniform_filter(low_res, subsample)\n", " exact_low_res_subsample = bin_ndarray(exact_low_res, bin_size=subsample, operation=\"sum\")\n", " exact_low_res_subsample /= exact_low_res_subsample.max()\n", " \n", " low_res_subsample = bin_ndarray(low_res, bin_size=subsample)\n", " low_res_subsample /= low_res_subsample.max()\n", " \n", " # display direct simulation\n", " axs[1].matshow(low_res_subsample, norm=mpl.colors.PowerNorm(gam))\n", " \n", " # Calculate percent of max difference and display\n", " lexact = len(exact_subsample)\n", " llow = len(low_res_subsample)\n", " if lexact <= llow:\n", " difference = (exact_subsample - low_res_subsample[:lexact, :lexact])\n", " else:\n", " difference = (exact_subsample - low_res_subsample[:llow, :llow])\n", " im = axs[2].matshow(difference * 100, cmap=\"viridis\")\n", " plt.colorbar(im, ax=axs[2], cax=axs[3])\n", " \n", " # clean up plot\n", " for ax in axs[:3]:\n", " ax.xaxis.set_major_locator(plt.NullLocator())\n", " ax.yaxis.set_major_locator(plt.NullLocator())\n", " \n", "# label\n", "axs_total[0, 0].set_title(r\"$\\frac{1}{8}\\times$\" + \"Nyquist Simulation\\nwithout Convolution\")\n", "axs_total[0, 1].set_title(r\"$1\\times$ \" + \"Nyquist Simulation\\nwithout Convolution\")\n", "axs_total[0, 2].set_title(\"Difference (%)\")\n", "\n", "axs_total[0, 0].set_ylabel(r\"$3\\times$ Nyquist Camera Pixel Size\")\n", "axs_total[1, 0].set_ylabel(r\"$5\\times$ Nyquist Camera Pixel Size\")\n", "axs_total[2, 0].set_ylabel(r\"$9\\times$ Nyquist Camera Pixel Size\");" ], "outputs": [ { "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ "<Figure size 1387.5x1350 with 12 Axes>" ] }, "metadata": { "needs_background": "light" } } ], "metadata": { "jupyter": { "source_hidden": true } } }, { "cell_type": "markdown", "source": [ "As expected the integration error decreases with increasing camera pixel size. Now to test the effect of convolution on the process." ], "metadata": {} }, { "cell_type": "code", "execution_count": 12, "source": [ "# set up the figure\n", "fig, axs_total = plt.subplots(3, 4, dpi=150, figsize=(9.25,9),\n", " gridspec_kw=dict(hspace=0.1, wspace=0.1, width_ratios=(1, 1, 1, 1 / 12)))\n", "\n", "# rows will be for different camera pixel sizes, the camera pixel size = subsample / 8 * Nyquist\n", "for axs, subsample in zip(axs_total[::-1], (9, 5, 3)):\n", " # Directly simulate at Nyquist\n", " psf_params_wf['res'] = psf_params['res'] * oversample_factor\n", " c = np.log2(subsample) % 2\n", " if c < 1:\n", " c = 1\n", " else:\n", " c = -1\n", " psf_params_wf['size'] = psf_params['size'] // oversample_factor + c\n", " low_res = HanserPSF(**psf_params_wf).PSFi.squeeze()\n", " \n", " subsample2 = oversample_factor * subsample\n", "\n", " # Use the convolution to shift the data so that the max is centered on camera ROI\n", " shift = len(psf.PSFi[0])%subsample + 1\n", " shifted = psf.PSFi[0, shift:, shift:]\n", " exact = ndi.uniform_filter(shifted, subsample2)\n", " \n", " # integrate across pixel\n", " exact_subsample = bin_ndarray(exact, bin_size=subsample2, operation=\"sum\")\n", " exact_subsample /= exact_subsample.max()\n", "\n", " # Display final camera pixels\n", " offset_sub = offset//subsample2\n", " axs[0].matshow(exact_subsample, norm=mpl.colors.PowerNorm(gam))\n", "\n", " exact_low_res = ndi.uniform_filter(low_res, subsample)\n", " exact_low_res_subsample = bin_ndarray(exact_low_res, bin_size=subsample, operation=\"sum\")\n", " exact_low_res_subsample /= exact_low_res_subsample.max()\n", " \n", " low_res_subsample = bin_ndarray(low_res, bin_size=subsample)\n", " low_res_subsample /= low_res_subsample.max()\n", " \n", " # display direct simulation\n", " axs[1].matshow(low_res_subsample, norm=mpl.colors.PowerNorm(gam))\n", " \n", " # Calculate percent of max difference and display\n", " lexact = len(exact_subsample)\n", " llow = len(low_res_subsample)\n", " if lexact <= llow:\n", " difference = (exact_subsample - low_res_subsample[:lexact, :lexact])\n", " else:\n", " difference = (exact_subsample - low_res_subsample[:llow, :llow])\n", " im = axs[2].matshow(difference * 100, cmap=\"viridis\")\n", " plt.colorbar(im, ax=axs[2], cax=axs[3])\n", " \n", " # clean up plot\n", " for ax in axs[:3]:\n", " ax.xaxis.set_major_locator(plt.NullLocator())\n", " ax.yaxis.set_major_locator(plt.NullLocator())\n", " \n", "# label\n", "axs_total[0, 0].set_title(r\"$\\frac{1}{8}\\times$\" + \"Nyquist Simulation\\nwith Convolution\")\n", "axs_total[0, 1].set_title(r\"$1\\times$ \" + \"Nyquist Simulation\\nwithout Convolution\")\n", "axs_total[0, 2].set_title(\"Difference (%)\")\n", "\n", "axs_total[0, 0].set_ylabel(r\"$3\\times$ Nyquist Camera Pixel Size\")\n", "axs_total[1, 0].set_ylabel(r\"$5\\times$ Nyquist Camera Pixel Size\")\n", "axs_total[2, 0].set_ylabel(r\"$9\\times$ Nyquist Camera Pixel Size\");" ], "outputs": [ { "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ "<Figure size 1387.5x1350 with 12 Axes>" ] }, "metadata": { "needs_background": "light" } } ], "metadata": { "jupyter": { "source_hidden": true } } }, { "cell_type": "markdown", "source": [ "Clearly there's quite a bit of error, up to ~20% of the max value in the worst case. Again we see a decrease in error with increasing camera pixel size. Now turning to the more informative frequency space representation for the 3X Nyquist camera pixels." ], "metadata": {} }, { "cell_type": "code", "execution_count": 13, "source": [ "fig, ax = plt.subplots(figsize=(4,4), dpi=150)\n", "\n", "k_pixel_size = 2 / psf_params_wf[\"res\"] / len(exact_subsample) / subsample\n", "abbe_limit = 1 / nyquist_sampling / k_pixel_size\n", "\n", "for l, d in zip((\"Exact\", \"Direct with Convolution\", \"Direct\"), (exact_subsample, exact_low_res_subsample, low_res_subsample)):\n", " o = abs(easy_fft(d))\n", " ro = radial_profile(o)[0]\n", " ax.plot(np.arange(len(ro)) / abbe_limit * 2, ro, label=l)\n", "\n", "ax.legend()\n", "ax.set_xlabel(\"Spatial Frequency\")\n", "ax.set_ylabel(\"Intensity\")\n", "ax.set_xlim(0, 2.6)\n", "ax.set_ylim(0)\n", "\n", "ax.yaxis.set_major_locator(plt.NullLocator())\n", "ax.xaxis.set_major_locator(plt.MultipleLocator(1 / 2))\n", "ax.xaxis.set_major_formatter(plt.FuncFormatter(formatter))" ], "outputs": [ { "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ "<Figure size 600x600 with 1 Axes>" ] }, "metadata": { "needs_background": "light" } } ], "metadata": { "jupyter": { "source_hidden": true } } }, { "cell_type": "markdown", "source": [ "This is more concerning: if we use convolution our \"direct\" simulation is reasonably accurate, if we don't the roll-off in transmittence of spatial frequencies is unphysical. If you were to use the \"direct\" simulation without convolution you might be lead to believe that you could resolve higher frequency information than you could in reality." ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [], "outputs": [], "metadata": {} } ], "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": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
timothydmorton/starutils
notebooks/.ipynb_checkpoints/tests-checkpoint.ipynb
1
181
{ "metadata": { "name": "", "signature": "sha256:f5d1d9f85988e5c9dd1babce423f084149cbc7a4448950560051f720414c09ba" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [] }
mit
leewujung/ooi_sonar
tensor/2018-11-01_4D-parafac-rank3.ipynb
1
3671936
null
apache-2.0
mprego/NBA
Regression/.ipynb_checkpoints/Team Spiking-checkpoint.ipynb
1
290059
{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from nba_py import team\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "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>LEAGUE_ID</th>\n", " <th>TEAM_ID</th>\n", " <th>MIN_YEAR</th>\n", " <th>MAX_YEAR</th>\n", " <th>ABBREVIATION</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>00</td>\n", " <td>1610612737</td>\n", " <td>1949</td>\n", " <td>2015</td>\n", " <td>ATL</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>00</td>\n", " <td>1610612738</td>\n", " <td>1946</td>\n", " <td>2015</td>\n", " <td>BOS</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>00</td>\n", " <td>1610612739</td>\n", " <td>1970</td>\n", " <td>2015</td>\n", " <td>CLE</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>00</td>\n", " <td>1610612740</td>\n", " <td>2002</td>\n", " <td>2015</td>\n", " <td>NOP</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>00</td>\n", " <td>1610612741</td>\n", " <td>1966</td>\n", " <td>2015</td>\n", " <td>CHI</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>00</td>\n", " <td>1610612742</td>\n", " <td>1980</td>\n", " <td>2015</td>\n", " <td>DAL</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>00</td>\n", " <td>1610612743</td>\n", " <td>1976</td>\n", " <td>2015</td>\n", " <td>DEN</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>00</td>\n", " <td>1610612744</td>\n", " <td>1946</td>\n", " <td>2015</td>\n", " <td>GSW</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>00</td>\n", " <td>1610612745</td>\n", " <td>1967</td>\n", " <td>2015</td>\n", " <td>HOU</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>00</td>\n", " <td>1610612746</td>\n", " <td>1970</td>\n", " <td>2015</td>\n", " <td>LAC</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>00</td>\n", " <td>1610612747</td>\n", " <td>1948</td>\n", " <td>2015</td>\n", " <td>LAL</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>00</td>\n", " <td>1610612748</td>\n", " <td>1988</td>\n", " <td>2015</td>\n", " <td>MIA</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>00</td>\n", " <td>1610612749</td>\n", " <td>1968</td>\n", " <td>2015</td>\n", " <td>MIL</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>00</td>\n", " <td>1610612750</td>\n", " <td>1989</td>\n", " <td>2015</td>\n", " <td>MIN</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>00</td>\n", " <td>1610612751</td>\n", " <td>1976</td>\n", " <td>2015</td>\n", " <td>BKN</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>00</td>\n", " <td>1610612752</td>\n", " <td>1946</td>\n", " <td>2015</td>\n", " <td>NYK</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>00</td>\n", " <td>1610612753</td>\n", " <td>1989</td>\n", " <td>2015</td>\n", " <td>ORL</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>00</td>\n", " <td>1610612754</td>\n", " <td>1976</td>\n", " <td>2015</td>\n", " <td>IND</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>00</td>\n", " <td>1610612755</td>\n", " <td>1949</td>\n", " <td>2015</td>\n", " <td>PHI</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>00</td>\n", " <td>1610612756</td>\n", " <td>1968</td>\n", " <td>2015</td>\n", " <td>PHX</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>00</td>\n", " <td>1610612757</td>\n", " <td>1970</td>\n", " <td>2015</td>\n", " <td>POR</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>00</td>\n", " <td>1610612758</td>\n", " <td>1948</td>\n", " <td>2015</td>\n", " <td>SAC</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>00</td>\n", " <td>1610612759</td>\n", " <td>1976</td>\n", " <td>2015</td>\n", " <td>SAS</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>00</td>\n", " <td>1610612760</td>\n", " <td>1967</td>\n", " <td>2015</td>\n", " <td>OKC</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>00</td>\n", " <td>1610612761</td>\n", " <td>1995</td>\n", " <td>2015</td>\n", " <td>TOR</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>00</td>\n", " <td>1610612763</td>\n", " <td>1995</td>\n", " <td>2015</td>\n", " <td>MEM</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>00</td>\n", " <td>1610612764</td>\n", " <td>1961</td>\n", " <td>2015</td>\n", " <td>WAS</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>00</td>\n", " <td>1610612765</td>\n", " <td>1948</td>\n", " <td>2015</td>\n", " <td>DET</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>00</td>\n", " <td>1610612766</td>\n", " <td>1988</td>\n", " <td>2015</td>\n", " <td>CHA</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>00</td>\n", " <td>1610612762</td>\n", " <td>1974</td>\n", " <td>2015</td>\n", " <td>UTA</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>00</td>\n", " <td>1610610029</td>\n", " <td>1948</td>\n", " <td>1948</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>00</td>\n", " <td>1610610025</td>\n", " <td>1946</td>\n", " <td>1949</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>00</td>\n", " <td>1610610034</td>\n", " <td>1946</td>\n", " <td>1949</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>00</td>\n", " <td>1610610036</td>\n", " <td>1946</td>\n", " <td>1950</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>00</td>\n", " <td>1610610024</td>\n", " <td>1947</td>\n", " <td>1954</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>00</td>\n", " <td>1610610027</td>\n", " <td>1949</td>\n", " <td>1949</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>00</td>\n", " <td>1610610030</td>\n", " <td>1949</td>\n", " <td>1952</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>37</th>\n", " <td>00</td>\n", " <td>1610610033</td>\n", " <td>1949</td>\n", " <td>1949</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <td>00</td>\n", " <td>1610610037</td>\n", " <td>1949</td>\n", " <td>1949</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>00</td>\n", " <td>1610610031</td>\n", " <td>1946</td>\n", " <td>1946</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>00</td>\n", " <td>1610610023</td>\n", " <td>1949</td>\n", " <td>1949</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>41</th>\n", " <td>00</td>\n", " <td>1610610028</td>\n", " <td>1946</td>\n", " <td>1946</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>00</td>\n", " <td>1610610026</td>\n", " <td>1946</td>\n", " <td>1946</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>00</td>\n", " <td>1610610032</td>\n", " <td>1946</td>\n", " <td>1948</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>00</td>\n", " <td>1610610035</td>\n", " <td>1946</td>\n", " <td>1946</td>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " LEAGUE_ID TEAM_ID MIN_YEAR MAX_YEAR ABBREVIATION\n", "0 00 1610612737 1949 2015 ATL\n", "1 00 1610612738 1946 2015 BOS\n", "2 00 1610612739 1970 2015 CLE\n", "3 00 1610612740 2002 2015 NOP\n", "4 00 1610612741 1966 2015 CHI\n", "5 00 1610612742 1980 2015 DAL\n", "6 00 1610612743 1976 2015 DEN\n", "7 00 1610612744 1946 2015 GSW\n", "8 00 1610612745 1967 2015 HOU\n", "9 00 1610612746 1970 2015 LAC\n", "10 00 1610612747 1948 2015 LAL\n", "11 00 1610612748 1988 2015 MIA\n", "12 00 1610612749 1968 2015 MIL\n", "13 00 1610612750 1989 2015 MIN\n", "14 00 1610612751 1976 2015 BKN\n", "15 00 1610612752 1946 2015 NYK\n", "16 00 1610612753 1989 2015 ORL\n", "17 00 1610612754 1976 2015 IND\n", "18 00 1610612755 1949 2015 PHI\n", "19 00 1610612756 1968 2015 PHX\n", "20 00 1610612757 1970 2015 POR\n", "21 00 1610612758 1948 2015 SAC\n", "22 00 1610612759 1976 2015 SAS\n", "23 00 1610612760 1967 2015 OKC\n", "24 00 1610612761 1995 2015 TOR\n", "25 00 1610612763 1995 2015 MEM\n", "26 00 1610612764 1961 2015 WAS\n", "27 00 1610612765 1948 2015 DET\n", "28 00 1610612766 1988 2015 CHA\n", "29 00 1610612762 1974 2015 UTA\n", "30 00 1610610029 1948 1948 None\n", "31 00 1610610025 1946 1949 None\n", "32 00 1610610034 1946 1949 None\n", "33 00 1610610036 1946 1950 None\n", "34 00 1610610024 1947 1954 None\n", "35 00 1610610027 1949 1949 None\n", "36 00 1610610030 1949 1952 None\n", "37 00 1610610033 1949 1949 None\n", "38 00 1610610037 1949 1949 None\n", "39 00 1610610031 1946 1946 None\n", "40 00 1610610023 1949 1949 None\n", "41 00 1610610028 1946 1946 None\n", "42 00 1610610026 1946 1946 None\n", "43 00 1610610032 1946 1948 None\n", "44 00 1610610035 1946 1946 None" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#get list of teams\n", "team_list = team.TeamList()\n", "team_list.info()" ] }, { "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>LEAGUE_ID</th>\n", " <th>SEASON_ID</th>\n", " <th>TEAM_ID</th>\n", " <th>PTS_RANK</th>\n", " <th>PTS_PG</th>\n", " <th>REB_RANK</th>\n", " <th>REB_PG</th>\n", " <th>AST_RANK</th>\n", " <th>AST_PG</th>\n", " <th>OPP_PTS_RANK</th>\n", " <th>OPP_PTS_PG</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>00</td>\n", " <td>22015</td>\n", " <td>1610612739</td>\n", " <td>8</td>\n", " <td>104.3</td>\n", " <td>9</td>\n", " <td>44.5</td>\n", " <td>13</td>\n", " <td>22.7</td>\n", " <td>4</td>\n", " <td>98.3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " LEAGUE_ID SEASON_ID TEAM_ID PTS_RANK PTS_PG REB_RANK REB_PG \\\n", "0 00 22015 1610612739 8 104.3 9 44.5 \n", "\n", " AST_RANK AST_PG OPP_PTS_RANK OPP_PTS_PG \n", "0 13 22.7 4 98.3 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cle_id = '1610612739'\n", "cle_summary = team.TeamSummary(team_id=cle_id, season = '2015-16')\n", "#cle_summary.info()\n", "cle_summary.season_ranks()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "ename": "HTTPError", "evalue": "404 Client Error: Not Found for url: http://stats.nba.com/stats/?PlusMinus=N&TeamID=1610612739&Location=&Month=0&SeasonType=Regular+Season&Season=2015-16&PaceAdjust=N&DateFrom=&VsConference=&OpponentTeamID=0&DateTo=&GameSegment=&LastNGames=0&VsDivision=&LeagueID=00&Outcome=&MeasureType=Base&PORound=0&PerMode=PerGame&SeasonSegment=&Period=0&Rank=N&ShotClockRange=", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mHTTPError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-8-c2cc7cbcf5bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#General dashboard\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcle_db\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mteam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_TeamDashboard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mteam_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcle_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/nba_py/team.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, team_id, measure_type, per_mode, plus_minus, pace_adjust, rank, league_id, season, season_type, po_round, outcome, location, month, season_segment, date_from, date_to, opponent_team_id, vs_conference, vs_division, game_segment, period, shot_clock_range, last_n_games)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0;34m'Period'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mperiod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;34m'ShotClockRange'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mshot_clock_range\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m 'LastNGames': last_n_games})\n\u001b[0m\u001b[1;32m 106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0moverall\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//anaconda/lib/python2.7/site-packages/nba_py/__init__.pyc\u001b[0m in \u001b[0;36m_get_json\u001b[0;34m(endpoint, params)\u001b[0m\n\u001b[1;32m 72\u001b[0m headers=HEADERS)\n\u001b[1;32m 73\u001b[0m \u001b[0;31m# print _get.url\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m \u001b[0m_get\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\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 75\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_get\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/requests/models.pyc\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 838\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 839\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 840\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 842\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\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;31mHTTPError\u001b[0m: 404 Client Error: Not Found for url: http://stats.nba.com/stats/?PlusMinus=N&TeamID=1610612739&Location=&Month=0&SeasonType=Regular+Season&Season=2015-16&PaceAdjust=N&DateFrom=&VsConference=&OpponentTeamID=0&DateTo=&GameSegment=&LastNGames=0&VsDivision=&LeagueID=00&Outcome=&MeasureType=Base&PORound=0&PerMode=PerGame&SeasonSegment=&Period=0&Rank=N&ShotClockRange=" ] } ], "source": [ "#General dashboard\n", "cle_db = team._TeamDashboard(team_id = cle_id, )" ] }, { "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>GROUP_SET</th>\n", " <th>GROUP_VALUE</th>\n", " <th>TEAM_DAYS_REST_RANGE</th>\n", " <th>GP</th>\n", " <th>W</th>\n", " <th>L</th>\n", " <th>W_PCT</th>\n", " <th>MIN</th>\n", " <th>FGM</th>\n", " <th>FGA</th>\n", " <th>...</th>\n", " <th>TOV</th>\n", " <th>STL</th>\n", " <th>BLK</th>\n", " <th>BLKA</th>\n", " <th>PF</th>\n", " <th>PFD</th>\n", " <th>PTS</th>\n", " <th>PLUS_MINUS</th>\n", " <th>CFID</th>\n", " <th>CFPARAMS</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Days Rest</td>\n", " <td>0 Days Rest</td>\n", " <td>0 Days Rest</td>\n", " <td>19</td>\n", " <td>11</td>\n", " <td>8</td>\n", " <td>0.579</td>\n", " <td>48.8</td>\n", " <td>37.6</td>\n", " <td>84.5</td>\n", " <td>...</td>\n", " <td>14.3</td>\n", " <td>6.6</td>\n", " <td>4.0</td>\n", " <td>4.7</td>\n", " <td>21.1</td>\n", " <td>19.5</td>\n", " <td>100.2</td>\n", " <td>1.1</td>\n", " <td>152</td>\n", " <td>0 Days Rest</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Days Rest</td>\n", " <td>1 Days Rest</td>\n", " <td>1 Days Rest</td>\n", " <td>44</td>\n", " <td>34</td>\n", " <td>10</td>\n", " <td>0.773</td>\n", " <td>48.3</td>\n", " <td>39.7</td>\n", " <td>84.3</td>\n", " <td>...</td>\n", " <td>13.6</td>\n", " <td>6.8</td>\n", " <td>3.7</td>\n", " <td>4.3</td>\n", " <td>20.0</td>\n", " <td>20.6</td>\n", " <td>107.2</td>\n", " <td>7.7</td>\n", " <td>152</td>\n", " <td>1 Days Rest</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Days Rest</td>\n", " <td>2 Days Rest</td>\n", " <td>2 Days Rest</td>\n", " <td>14</td>\n", " <td>8</td>\n", " <td>6</td>\n", " <td>0.571</td>\n", " <td>48.4</td>\n", " <td>37.1</td>\n", " <td>81.4</td>\n", " <td>...</td>\n", " <td>13.6</td>\n", " <td>6.7</td>\n", " <td>3.5</td>\n", " <td>3.9</td>\n", " <td>20.9</td>\n", " <td>22.3</td>\n", " <td>102.4</td>\n", " <td>4.5</td>\n", " <td>152</td>\n", " <td>2 Days Rest</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Days Rest</td>\n", " <td>3 Days Rest</td>\n", " <td>3 Days Rest</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>1.000</td>\n", " <td>48.0</td>\n", " <td>37.3</td>\n", " <td>83.3</td>\n", " <td>...</td>\n", " <td>10.7</td>\n", " <td>6.7</td>\n", " <td>4.3</td>\n", " <td>4.3</td>\n", " <td>17.7</td>\n", " <td>19.7</td>\n", " <td>100.3</td>\n", " <td>20.7</td>\n", " <td>152</td>\n", " <td>3 Days Rest</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Days Rest</td>\n", " <td>6+ Days Rest</td>\n", " <td>6+ Days Rest</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.500</td>\n", " <td>48.0</td>\n", " <td>40.0</td>\n", " <td>92.5</td>\n", " <td>...</td>\n", " <td>11.0</td>\n", " <td>6.5</td>\n", " <td>7.5</td>\n", " <td>7.0</td>\n", " <td>21.5</td>\n", " <td>20.5</td>\n", " <td>100.5</td>\n", " <td>4.5</td>\n", " <td>152</td>\n", " <td>6+ Days Rest</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 31 columns</p>\n", "</div>" ], "text/plain": [ " GROUP_SET GROUP_VALUE TEAM_DAYS_REST_RANGE GP W L W_PCT MIN \\\n", "0 Days Rest 0 Days Rest 0 Days Rest 19 11 8 0.579 48.8 \n", "1 Days Rest 1 Days Rest 1 Days Rest 44 34 10 0.773 48.3 \n", "2 Days Rest 2 Days Rest 2 Days Rest 14 8 6 0.571 48.4 \n", "3 Days Rest 3 Days Rest 3 Days Rest 3 3 0 1.000 48.0 \n", "4 Days Rest 6+ Days Rest 6+ Days Rest 2 1 1 0.500 48.0 \n", "\n", " FGM FGA ... TOV STL BLK BLKA PF PFD PTS \\\n", "0 37.6 84.5 ... 14.3 6.6 4.0 4.7 21.1 19.5 100.2 \n", "1 39.7 84.3 ... 13.6 6.8 3.7 4.3 20.0 20.6 107.2 \n", "2 37.1 81.4 ... 13.6 6.7 3.5 3.9 20.9 22.3 102.4 \n", "3 37.3 83.3 ... 10.7 6.7 4.3 4.3 17.7 19.7 100.3 \n", "4 40.0 92.5 ... 11.0 6.5 7.5 7.0 21.5 20.5 100.5 \n", "\n", " PLUS_MINUS CFID CFPARAMS \n", "0 1.1 152 0 Days Rest \n", "1 7.7 152 1 Days Rest \n", "2 4.5 152 2 Days Rest \n", "3 20.7 152 3 Days Rest \n", "4 4.5 152 6+ Days Rest \n", "\n", "[5 rows x 31 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Splits\n", "cle_splits = team.TeamGeneralSplits(team_id=cle_id)\n", "cle_splits.days_rest()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>GROUP_SET</th>\n", " <th>GROUP_VALUE</th>\n", " <th>GP</th>\n", " <th>W</th>\n", " <th>L</th>\n", " <th>W_PCT</th>\n", " <th>MIN</th>\n", " <th>FGM</th>\n", " <th>FGA</th>\n", " <th>FG_PCT</th>\n", " <th>...</th>\n", " <th>TOV</th>\n", " <th>STL</th>\n", " <th>BLK</th>\n", " <th>BLKA</th>\n", " <th>PF</th>\n", " <th>PFD</th>\n", " <th>PTS</th>\n", " <th>PLUS_MINUS</th>\n", " <th>CFID</th>\n", " <th>CFPARAMS</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Last 5 Games</td>\n", " <td>Last 5 Games</td>\n", " <td>5</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>0.4</td>\n", " <td>49.0</td>\n", " <td>39.6</td>\n", " <td>82.4</td>\n", " <td>0.481</td>\n", " <td>...</td>\n", " <td>13.4</td>\n", " <td>5.8</td>\n", " <td>5.4</td>\n", " <td>5.0</td>\n", " <td>17.6</td>\n", " <td>18.6</td>\n", " <td>107.8</td>\n", " <td>5.0</td>\n", " <td>162</td>\n", " <td>Last 5 Games</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1 rows × 30 columns</p>\n", "</div>" ], "text/plain": [ " GROUP_SET GROUP_VALUE GP W L W_PCT MIN FGM FGA FG_PCT \\\n", "0 Last 5 Games Last 5 Games 5 2 3 0.4 49.0 39.6 82.4 0.481 \n", "\n", " ... TOV STL BLK BLKA PF PFD PTS PLUS_MINUS CFID \\\n", "0 ... 13.4 5.8 5.4 5.0 17.6 18.6 107.8 5.0 162 \n", "\n", " CFPARAMS \n", "0 Last 5 Games \n", "\n", "[1 rows x 30 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#last game splits\n", "cle_g_splits = team.TeamLastNGamesSplits(team_id = cle_id)\n", "cle_g_splits.last5()" ] }, { "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>Team_ID</th>\n", " <th>Game_ID</th>\n", " <th>GAME_DATE</th>\n", " <th>MATCHUP</th>\n", " <th>WL</th>\n", " <th>MIN</th>\n", " <th>FGM</th>\n", " <th>FGA</th>\n", " <th>FG_PCT</th>\n", " <th>FG3M</th>\n", " <th>...</th>\n", " <th>FT_PCT</th>\n", " <th>OREB</th>\n", " <th>DREB</th>\n", " <th>REB</th>\n", " <th>AST</th>\n", " <th>STL</th>\n", " <th>BLK</th>\n", " <th>TOV</th>\n", " <th>PF</th>\n", " <th>PTS</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1610612739</td>\n", " <td>0021501220</td>\n", " <td>APR 13, 2016</td>\n", " <td>CLE vs. DET</td>\n", " <td>L</td>\n", " <td>265</td>\n", " <td>46</td>\n", " <td>97</td>\n", " <td>0.474</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0.733</td>\n", " <td>8</td>\n", " <td>35</td>\n", " <td>43</td>\n", " <td>21</td>\n", " <td>4</td>\n", " <td>7</td>\n", " <td>10</td>\n", " <td>23</td>\n", " <td>110</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1610612739</td>\n", " <td>0021501203</td>\n", " <td>APR 11, 2016</td>\n", " <td>CLE vs. ATL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>83</td>\n", " <td>0.482</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>0.900</td>\n", " <td>9</td>\n", " <td>38</td>\n", " <td>47</td>\n", " <td>17</td>\n", " <td>9</td>\n", " <td>4</td>\n", " <td>15</td>\n", " <td>14</td>\n", " <td>109</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1610612739</td>\n", " <td>0021501191</td>\n", " <td>APR 09, 2016</td>\n", " <td>CLE @ CHI</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>83</td>\n", " <td>0.434</td>\n", " <td>19</td>\n", " <td>...</td>\n", " <td>0.611</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>42</td>\n", " <td>24</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>15</td>\n", " <td>18</td>\n", " <td>102</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1610612739</td>\n", " <td>0021501165</td>\n", " <td>APR 06, 2016</td>\n", " <td>CLE @ IND</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>74</td>\n", " <td>0.473</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.912</td>\n", " <td>7</td>\n", " <td>26</td>\n", " <td>33</td>\n", " <td>15</td>\n", " <td>7</td>\n", " <td>3</td>\n", " <td>10</td>\n", " <td>19</td>\n", " <td>109</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1610612739</td>\n", " <td>0021501159</td>\n", " <td>APR 05, 2016</td>\n", " <td>CLE @ MIL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>75</td>\n", " <td>0.547</td>\n", " <td>18</td>\n", " <td>...</td>\n", " <td>0.900</td>\n", " <td>2</td>\n", " <td>39</td>\n", " <td>41</td>\n", " <td>30</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>16</td>\n", " <td>14</td>\n", " <td>109</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1610612739</td>\n", " <td>0021501144</td>\n", " <td>APR 03, 2016</td>\n", " <td>CLE vs. CHA</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>45</td>\n", " <td>83</td>\n", " <td>0.542</td>\n", " <td>16</td>\n", " <td>...</td>\n", " <td>0.462</td>\n", " <td>15</td>\n", " <td>31</td>\n", " <td>46</td>\n", " <td>34</td>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>17</td>\n", " <td>19</td>\n", " <td>112</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1610612739</td>\n", " <td>0021501131</td>\n", " <td>APR 01, 2016</td>\n", " <td>CLE @ ATL</td>\n", " <td>W</td>\n", " <td>265</td>\n", " <td>39</td>\n", " <td>98</td>\n", " <td>0.398</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>0.645</td>\n", " <td>11</td>\n", " <td>46</td>\n", " <td>57</td>\n", " <td>27</td>\n", " <td>9</td>\n", " <td>6</td>\n", " <td>12</td>\n", " <td>23</td>\n", " <td>110</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1610612739</td>\n", " <td>0021501122</td>\n", " <td>MAR 31, 2016</td>\n", " <td>CLE vs. BKN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>87</td>\n", " <td>0.437</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>0.792</td>\n", " <td>9</td>\n", " <td>41</td>\n", " <td>50</td>\n", " <td>29</td>\n", " <td>10</td>\n", " <td>7</td>\n", " <td>17</td>\n", " <td>17</td>\n", " <td>107</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1610612739</td>\n", " <td>0021501111</td>\n", " <td>MAR 29, 2016</td>\n", " <td>CLE vs. HOU</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>31</td>\n", " <td>86</td>\n", " <td>0.360</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>0.857</td>\n", " <td>10</td>\n", " <td>28</td>\n", " <td>38</td>\n", " <td>21</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>31</td>\n", " <td>100</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1610612739</td>\n", " <td>0021501086</td>\n", " <td>MAR 26, 2016</td>\n", " <td>CLE @ NYK</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>83</td>\n", " <td>0.446</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>0.760</td>\n", " <td>15</td>\n", " <td>38</td>\n", " <td>53</td>\n", " <td>21</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>9</td>\n", " <td>23</td>\n", " <td>107</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>1610612739</td>\n", " <td>0021501069</td>\n", " <td>MAR 24, 2016</td>\n", " <td>CLE @ BKN</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>89</td>\n", " <td>0.438</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.778</td>\n", " <td>11</td>\n", " <td>34</td>\n", " <td>45</td>\n", " <td>22</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>14</td>\n", " <td>20</td>\n", " <td>95</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>1610612739</td>\n", " <td>0021501059</td>\n", " <td>MAR 23, 2016</td>\n", " <td>CLE vs. MIL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>83</td>\n", " <td>0.482</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.852</td>\n", " <td>17</td>\n", " <td>25</td>\n", " <td>42</td>\n", " <td>29</td>\n", " <td>6</td>\n", " <td>8</td>\n", " <td>12</td>\n", " <td>17</td>\n", " <td>113</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>1610612739</td>\n", " <td>0021501044</td>\n", " <td>MAR 21, 2016</td>\n", " <td>CLE vs. DEN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>48</td>\n", " <td>86</td>\n", " <td>0.558</td>\n", " <td>15</td>\n", " <td>...</td>\n", " <td>0.765</td>\n", " <td>8</td>\n", " <td>35</td>\n", " <td>43</td>\n", " <td>38</td>\n", " <td>10</td>\n", " <td>4</td>\n", " <td>12</td>\n", " <td>19</td>\n", " <td>124</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1610612739</td>\n", " <td>0021501033</td>\n", " <td>MAR 19, 2016</td>\n", " <td>CLE @ MIA</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>76</td>\n", " <td>0.526</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.846</td>\n", " <td>3</td>\n", " <td>23</td>\n", " <td>26</td>\n", " <td>19</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>14</td>\n", " <td>22</td>\n", " <td>101</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>1610612739</td>\n", " <td>0021501020</td>\n", " <td>MAR 18, 2016</td>\n", " <td>CLE @ ORL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>76</td>\n", " <td>0.500</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>0.741</td>\n", " <td>9</td>\n", " <td>32</td>\n", " <td>41</td>\n", " <td>23</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>19</td>\n", " <td>21</td>\n", " <td>109</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1610612739</td>\n", " <td>0021501005</td>\n", " <td>MAR 16, 2016</td>\n", " <td>CLE vs. DAL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>88</td>\n", " <td>0.443</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.846</td>\n", " <td>13</td>\n", " <td>35</td>\n", " <td>48</td>\n", " <td>21</td>\n", " <td>9</td>\n", " <td>4</td>\n", " <td>16</td>\n", " <td>19</td>\n", " <td>99</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>1610612739</td>\n", " <td>0021500994</td>\n", " <td>MAR 14, 2016</td>\n", " <td>CLE @ UTA</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>88</td>\n", " <td>0.398</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.625</td>\n", " <td>13</td>\n", " <td>26</td>\n", " <td>39</td>\n", " <td>18</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>8</td>\n", " <td>16</td>\n", " <td>85</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1610612739</td>\n", " <td>0021500983</td>\n", " <td>MAR 13, 2016</td>\n", " <td>CLE @ LAC</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>84</td>\n", " <td>0.488</td>\n", " <td>18</td>\n", " <td>...</td>\n", " <td>0.700</td>\n", " <td>9</td>\n", " <td>40</td>\n", " <td>49</td>\n", " <td>23</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>19</td>\n", " <td>114</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>1610612739</td>\n", " <td>0021500962</td>\n", " <td>MAR 10, 2016</td>\n", " <td>CLE @ LAL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>45</td>\n", " <td>85</td>\n", " <td>0.529</td>\n", " <td>16</td>\n", " <td>...</td>\n", " <td>0.700</td>\n", " <td>9</td>\n", " <td>29</td>\n", " <td>38</td>\n", " <td>22</td>\n", " <td>4</td>\n", " <td>6</td>\n", " <td>8</td>\n", " <td>20</td>\n", " <td>120</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1610612739</td>\n", " <td>0021500957</td>\n", " <td>MAR 09, 2016</td>\n", " <td>CLE @ SAC</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>90</td>\n", " <td>0.433</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>0.829</td>\n", " <td>15</td>\n", " <td>36</td>\n", " <td>51</td>\n", " <td>17</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>13</td>\n", " <td>21</td>\n", " <td>120</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>1610612739</td>\n", " <td>0021500938</td>\n", " <td>MAR 07, 2016</td>\n", " <td>CLE vs. MEM</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>80</td>\n", " <td>0.450</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0.828</td>\n", " <td>13</td>\n", " <td>36</td>\n", " <td>49</td>\n", " <td>23</td>\n", " <td>9</td>\n", " <td>7</td>\n", " <td>25</td>\n", " <td>22</td>\n", " <td>103</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>1610612739</td>\n", " <td>0021500922</td>\n", " <td>MAR 05, 2016</td>\n", " <td>CLE vs. BOS</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>42</td>\n", " <td>82</td>\n", " <td>0.512</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.765</td>\n", " <td>15</td>\n", " <td>32</td>\n", " <td>47</td>\n", " <td>27</td>\n", " <td>8</td>\n", " <td>4</td>\n", " <td>15</td>\n", " <td>21</td>\n", " <td>120</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>1610612739</td>\n", " <td>0021500917</td>\n", " <td>MAR 04, 2016</td>\n", " <td>CLE vs. WAS</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>42</td>\n", " <td>90</td>\n", " <td>0.467</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>0.750</td>\n", " <td>8</td>\n", " <td>39</td>\n", " <td>47</td>\n", " <td>24</td>\n", " <td>9</td>\n", " <td>5</td>\n", " <td>9</td>\n", " <td>19</td>\n", " <td>108</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>1610612739</td>\n", " <td>0021500884</td>\n", " <td>FEB 29, 2016</td>\n", " <td>CLE vs. IND</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>83</td>\n", " <td>0.422</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>0.840</td>\n", " <td>9</td>\n", " <td>31</td>\n", " <td>40</td>\n", " <td>22</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>11</td>\n", " <td>15</td>\n", " <td>100</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>1610612739</td>\n", " <td>0021500877</td>\n", " <td>FEB 28, 2016</td>\n", " <td>CLE @ WAS</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>32</td>\n", " <td>80</td>\n", " <td>0.400</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>0.867</td>\n", " <td>6</td>\n", " <td>33</td>\n", " <td>39</td>\n", " <td>20</td>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>14</td>\n", " <td>26</td>\n", " <td>99</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>1610612739</td>\n", " <td>0021500864</td>\n", " <td>FEB 26, 2016</td>\n", " <td>CLE @ TOR</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>74</td>\n", " <td>0.473</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>0.714</td>\n", " <td>10</td>\n", " <td>29</td>\n", " <td>39</td>\n", " <td>21</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>14</td>\n", " <td>26</td>\n", " <td>97</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>1610612739</td>\n", " <td>0021500845</td>\n", " <td>FEB 24, 2016</td>\n", " <td>CLE vs. CHA</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>45</td>\n", " <td>91</td>\n", " <td>0.495</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>0.647</td>\n", " <td>12</td>\n", " <td>35</td>\n", " <td>47</td>\n", " <td>26</td>\n", " <td>11</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>20</td>\n", " <td>114</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>1610612739</td>\n", " <td>0021500833</td>\n", " <td>FEB 22, 2016</td>\n", " <td>CLE vs. DET</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>34</td>\n", " <td>79</td>\n", " <td>0.430</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.923</td>\n", " <td>5</td>\n", " <td>35</td>\n", " <td>40</td>\n", " <td>19</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>17</td>\n", " <td>21</td>\n", " <td>88</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>1610612739</td>\n", " <td>0021500824</td>\n", " <td>FEB 21, 2016</td>\n", " <td>CLE @ OKC</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>80</td>\n", " <td>0.513</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.852</td>\n", " <td>13</td>\n", " <td>36</td>\n", " <td>49</td>\n", " <td>25</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>12</td>\n", " <td>21</td>\n", " <td>115</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>1610612739</td>\n", " <td>0021500803</td>\n", " <td>FEB 18, 2016</td>\n", " <td>CLE vs. CHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>42</td>\n", " <td>91</td>\n", " <td>0.462</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0.882</td>\n", " <td>11</td>\n", " <td>41</td>\n", " <td>52</td>\n", " <td>21</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>10</td>\n", " <td>22</td>\n", " <td>106</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>52</th>\n", " <td>1610612739</td>\n", " <td>0021500473</td>\n", " <td>DEC 29, 2015</td>\n", " <td>CLE @ DEN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>86</td>\n", " <td>0.442</td>\n", " <td>4</td>\n", " <td>...</td>\n", " <td>0.591</td>\n", " <td>9</td>\n", " <td>35</td>\n", " <td>44</td>\n", " <td>17</td>\n", " <td>12</td>\n", " <td>3</td>\n", " <td>12</td>\n", " <td>16</td>\n", " <td>93</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>1610612739</td>\n", " <td>0021500466</td>\n", " <td>DEC 28, 2015</td>\n", " <td>CLE @ PHX</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>71</td>\n", " <td>0.465</td>\n", " <td>17</td>\n", " <td>...</td>\n", " <td>0.720</td>\n", " <td>10</td>\n", " <td>27</td>\n", " <td>37</td>\n", " <td>21</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>18</td>\n", " <td>101</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>1610612739</td>\n", " <td>0021500453</td>\n", " <td>DEC 26, 2015</td>\n", " <td>CLE @ POR</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>28</td>\n", " <td>77</td>\n", " <td>0.364</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>0.600</td>\n", " <td>9</td>\n", " <td>33</td>\n", " <td>42</td>\n", " <td>21</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>17</td>\n", " <td>20</td>\n", " <td>76</td>\n", " </tr>\n", " <tr>\n", " <th>55</th>\n", " <td>1610612739</td>\n", " <td>0021500438</td>\n", " <td>DEC 25, 2015</td>\n", " <td>CLE @ GSW</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>30</td>\n", " <td>95</td>\n", " <td>0.316</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>0.720</td>\n", " <td>17</td>\n", " <td>38</td>\n", " <td>55</td>\n", " <td>12</td>\n", " <td>6</td>\n", " <td>4</td>\n", " <td>11</td>\n", " <td>22</td>\n", " <td>83</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>1610612739</td>\n", " <td>0021500424</td>\n", " <td>DEC 23, 2015</td>\n", " <td>CLE vs. NYK</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>32</td>\n", " <td>83</td>\n", " <td>0.386</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>0.880</td>\n", " <td>14</td>\n", " <td>34</td>\n", " <td>48</td>\n", " <td>23</td>\n", " <td>6</td>\n", " <td>4</td>\n", " <td>7</td>\n", " <td>15</td>\n", " <td>91</td>\n", " </tr>\n", " <tr>\n", " <th>57</th>\n", " <td>1610612739</td>\n", " <td>0021500405</td>\n", " <td>DEC 20, 2015</td>\n", " <td>CLE vs. PHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>85</td>\n", " <td>0.471</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>0.810</td>\n", " <td>6</td>\n", " <td>34</td>\n", " <td>40</td>\n", " <td>25</td>\n", " <td>11</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>19</td>\n", " <td>108</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>1610612739</td>\n", " <td>0021500384</td>\n", " <td>DEC 17, 2015</td>\n", " <td>CLE vs. OKC</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>88</td>\n", " <td>0.466</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>0.769</td>\n", " <td>16</td>\n", " <td>26</td>\n", " <td>42</td>\n", " <td>29</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>16</td>\n", " <td>22</td>\n", " <td>104</td>\n", " </tr>\n", " <tr>\n", " <th>59</th>\n", " <td>1610612739</td>\n", " <td>0021500367</td>\n", " <td>DEC 15, 2015</td>\n", " <td>CLE @ BOS</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>78</td>\n", " <td>0.462</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.900</td>\n", " <td>6</td>\n", " <td>40</td>\n", " <td>46</td>\n", " <td>17</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " <td>17</td>\n", " <td>89</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>1610612739</td>\n", " <td>0021500334</td>\n", " <td>DEC 11, 2015</td>\n", " <td>CLE @ ORL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>72</td>\n", " <td>0.569</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>0.667</td>\n", " <td>9</td>\n", " <td>36</td>\n", " <td>45</td>\n", " <td>28</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>18</td>\n", " <td>19</td>\n", " <td>111</td>\n", " </tr>\n", " <tr>\n", " <th>61</th>\n", " <td>1610612739</td>\n", " <td>0021500313</td>\n", " <td>DEC 08, 2015</td>\n", " <td>CLE vs. POR</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>79</td>\n", " <td>0.506</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>0.800</td>\n", " <td>6</td>\n", " <td>24</td>\n", " <td>30</td>\n", " <td>18</td>\n", " <td>10</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>18</td>\n", " <td>105</td>\n", " </tr>\n", " <tr>\n", " <th>62</th>\n", " <td>1610612739</td>\n", " <td>0021500291</td>\n", " <td>DEC 05, 2015</td>\n", " <td>CLE @ MIA</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>31</td>\n", " <td>85</td>\n", " <td>0.365</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>0.667</td>\n", " <td>11</td>\n", " <td>25</td>\n", " <td>36</td>\n", " <td>14</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>14</td>\n", " <td>26</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>63</th>\n", " <td>1610612739</td>\n", " <td>0021500288</td>\n", " <td>DEC 04, 2015</td>\n", " <td>CLE @ NOP</td>\n", " <td>L</td>\n", " <td>265</td>\n", " <td>39</td>\n", " <td>89</td>\n", " <td>0.438</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.833</td>\n", " <td>12</td>\n", " <td>31</td>\n", " <td>43</td>\n", " <td>18</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>14</td>\n", " <td>26</td>\n", " <td>108</td>\n", " </tr>\n", " <tr>\n", " <th>64</th>\n", " <td>1610612739</td>\n", " <td>0021500262</td>\n", " <td>DEC 01, 2015</td>\n", " <td>CLE vs. WAS</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>28</td>\n", " <td>83</td>\n", " <td>0.337</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>0.870</td>\n", " <td>10</td>\n", " <td>37</td>\n", " <td>47</td>\n", " <td>15</td>\n", " <td>8</td>\n", " <td>3</td>\n", " <td>18</td>\n", " <td>18</td>\n", " <td>85</td>\n", " </tr>\n", " <tr>\n", " <th>65</th>\n", " <td>1610612739</td>\n", " <td>0021500240</td>\n", " <td>NOV 28, 2015</td>\n", " <td>CLE vs. BKN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>87</td>\n", " <td>0.402</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>0.647</td>\n", " <td>12</td>\n", " <td>37</td>\n", " <td>49</td>\n", " <td>20</td>\n", " <td>9</td>\n", " <td>11</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>90</td>\n", " </tr>\n", " <tr>\n", " <th>66</th>\n", " <td>1610612739</td>\n", " <td>0021500227</td>\n", " <td>NOV 27, 2015</td>\n", " <td>CLE @ CHA</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>34</td>\n", " <td>81</td>\n", " <td>0.420</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.760</td>\n", " <td>12</td>\n", " <td>38</td>\n", " <td>50</td>\n", " <td>17</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>16</td>\n", " <td>22</td>\n", " <td>95</td>\n", " </tr>\n", " <tr>\n", " <th>67</th>\n", " <td>1610612739</td>\n", " <td>0021500219</td>\n", " <td>NOV 25, 2015</td>\n", " <td>CLE @ TOR</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>82</td>\n", " <td>0.439</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>0.813</td>\n", " <td>9</td>\n", " <td>33</td>\n", " <td>42</td>\n", " <td>22</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>10</td>\n", " <td>20</td>\n", " <td>99</td>\n", " </tr>\n", " <tr>\n", " <th>68</th>\n", " <td>1610612739</td>\n", " <td>0021500203</td>\n", " <td>NOV 23, 2015</td>\n", " <td>CLE vs. ORL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>43</td>\n", " <td>81</td>\n", " <td>0.531</td>\n", " <td>18</td>\n", " <td>...</td>\n", " <td>0.591</td>\n", " <td>8</td>\n", " <td>33</td>\n", " <td>41</td>\n", " <td>34</td>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>10</td>\n", " <td>15</td>\n", " <td>117</td>\n", " </tr>\n", " <tr>\n", " <th>69</th>\n", " <td>1610612739</td>\n", " <td>0021500191</td>\n", " <td>NOV 21, 2015</td>\n", " <td>CLE vs. ATL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>85</td>\n", " <td>0.482</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>0.842</td>\n", " <td>11</td>\n", " <td>40</td>\n", " <td>51</td>\n", " <td>27</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>14</td>\n", " <td>22</td>\n", " <td>109</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td>1610612739</td>\n", " <td>0021500176</td>\n", " <td>NOV 19, 2015</td>\n", " <td>CLE vs. MIL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>71</td>\n", " <td>0.563</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>0.800</td>\n", " <td>13</td>\n", " <td>30</td>\n", " <td>43</td>\n", " <td>29</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>17</td>\n", " <td>24</td>\n", " <td>115</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td>1610612739</td>\n", " <td>0021500160</td>\n", " <td>NOV 17, 2015</td>\n", " <td>CLE @ DET</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>80</td>\n", " <td>0.475</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>0.600</td>\n", " <td>3</td>\n", " <td>37</td>\n", " <td>40</td>\n", " <td>21</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>14</td>\n", " <td>25</td>\n", " <td>99</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td>1610612739</td>\n", " <td>0021500141</td>\n", " <td>NOV 14, 2015</td>\n", " <td>CLE @ MIL</td>\n", " <td>L</td>\n", " <td>290</td>\n", " <td>37</td>\n", " <td>91</td>\n", " <td>0.407</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>0.630</td>\n", " <td>14</td>\n", " <td>40</td>\n", " <td>54</td>\n", " <td>21</td>\n", " <td>6</td>\n", " <td>8</td>\n", " <td>20</td>\n", " <td>24</td>\n", " <td>105</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td>1610612739</td>\n", " <td>0021500130</td>\n", " <td>NOV 13, 2015</td>\n", " <td>CLE @ NYK</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>78</td>\n", " <td>0.423</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>0.581</td>\n", " <td>12</td>\n", " <td>37</td>\n", " <td>49</td>\n", " <td>12</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>12</td>\n", " <td>19</td>\n", " <td>90</td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td>1610612739</td>\n", " <td>0021500106</td>\n", " <td>NOV 10, 2015</td>\n", " <td>CLE vs. UTA</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>74</td>\n", " <td>0.500</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>0.767</td>\n", " <td>11</td>\n", " <td>24</td>\n", " <td>35</td>\n", " <td>24</td>\n", " <td>9</td>\n", " <td>6</td>\n", " <td>16</td>\n", " <td>21</td>\n", " <td>118</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>1610612739</td>\n", " <td>0021500094</td>\n", " <td>NOV 08, 2015</td>\n", " <td>CLE vs. IND</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>83</td>\n", " <td>0.458</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.680</td>\n", " <td>11</td>\n", " <td>37</td>\n", " <td>48</td>\n", " <td>25</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>10</td>\n", " <td>20</td>\n", " <td>101</td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td>1610612739</td>\n", " <td>0021500078</td>\n", " <td>NOV 06, 2015</td>\n", " <td>CLE vs. PHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>45</td>\n", " <td>88</td>\n", " <td>0.511</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.667</td>\n", " <td>11</td>\n", " <td>34</td>\n", " <td>45</td>\n", " <td>29</td>\n", " <td>9</td>\n", " <td>0</td>\n", " <td>17</td>\n", " <td>20</td>\n", " <td>108</td>\n", " </tr>\n", " <tr>\n", " <th>77</th>\n", " <td>1610612739</td>\n", " <td>0021500063</td>\n", " <td>NOV 04, 2015</td>\n", " <td>CLE vs. NYK</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>83</td>\n", " <td>0.398</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0.719</td>\n", " <td>9</td>\n", " <td>39</td>\n", " <td>48</td>\n", " <td>21</td>\n", " <td>5</td>\n", " <td>2</td>\n", " <td>11</td>\n", " <td>12</td>\n", " <td>96</td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td>1610612739</td>\n", " <td>0021500046</td>\n", " <td>NOV 02, 2015</td>\n", " <td>CLE @ PHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>42</td>\n", " <td>81</td>\n", " <td>0.519</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.750</td>\n", " <td>7</td>\n", " <td>36</td>\n", " <td>43</td>\n", " <td>28</td>\n", " <td>5</td>\n", " <td>9</td>\n", " <td>16</td>\n", " <td>19</td>\n", " <td>107</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td>1610612739</td>\n", " <td>0021500021</td>\n", " <td>OCT 30, 2015</td>\n", " <td>CLE vs. MIA</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>86</td>\n", " <td>0.453</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>0.857</td>\n", " <td>14</td>\n", " <td>35</td>\n", " <td>49</td>\n", " <td>25</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>11</td>\n", " <td>19</td>\n", " <td>102</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td>1610612739</td>\n", " <td>0021500011</td>\n", " <td>OCT 28, 2015</td>\n", " <td>CLE @ MEM</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>84</td>\n", " <td>0.488</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>0.647</td>\n", " <td>12</td>\n", " <td>42</td>\n", " <td>54</td>\n", " <td>29</td>\n", " <td>7</td>\n", " <td>2</td>\n", " <td>19</td>\n", " <td>25</td>\n", " <td>106</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td>1610612739</td>\n", " <td>0021500002</td>\n", " <td>OCT 27, 2015</td>\n", " <td>CLE @ CHI</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>94</td>\n", " <td>0.404</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>0.588</td>\n", " <td>11</td>\n", " <td>39</td>\n", " <td>50</td>\n", " <td>26</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>10</td>\n", " <td>21</td>\n", " <td>95</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>82 rows × 24 columns</p>\n", "</div>" ], "text/plain": [ " Team_ID Game_ID GAME_DATE MATCHUP WL MIN FGM FGA \\\n", "0 1610612739 0021501220 APR 13, 2016 CLE vs. DET L 265 46 97 \n", "1 1610612739 0021501203 APR 11, 2016 CLE vs. ATL W 240 40 83 \n", "2 1610612739 0021501191 APR 09, 2016 CLE @ CHI L 240 36 83 \n", "3 1610612739 0021501165 APR 06, 2016 CLE @ IND L 240 35 74 \n", "4 1610612739 0021501159 APR 05, 2016 CLE @ MIL W 240 41 75 \n", "5 1610612739 0021501144 APR 03, 2016 CLE vs. CHA W 240 45 83 \n", "6 1610612739 0021501131 APR 01, 2016 CLE @ ATL W 265 39 98 \n", "7 1610612739 0021501122 MAR 31, 2016 CLE vs. BKN W 240 38 87 \n", "8 1610612739 0021501111 MAR 29, 2016 CLE vs. HOU L 240 31 86 \n", "9 1610612739 0021501086 MAR 26, 2016 CLE @ NYK W 240 37 83 \n", "10 1610612739 0021501069 MAR 24, 2016 CLE @ BKN L 240 39 89 \n", "11 1610612739 0021501059 MAR 23, 2016 CLE vs. MIL W 240 40 83 \n", "12 1610612739 0021501044 MAR 21, 2016 CLE vs. DEN W 240 48 86 \n", "13 1610612739 0021501033 MAR 19, 2016 CLE @ MIA L 240 40 76 \n", "14 1610612739 0021501020 MAR 18, 2016 CLE @ ORL W 240 38 76 \n", "15 1610612739 0021501005 MAR 16, 2016 CLE vs. DAL W 240 39 88 \n", "16 1610612739 0021500994 MAR 14, 2016 CLE @ UTA L 240 35 88 \n", "17 1610612739 0021500983 MAR 13, 2016 CLE @ LAC W 240 41 84 \n", "18 1610612739 0021500962 MAR 10, 2016 CLE @ LAL W 240 45 85 \n", "19 1610612739 0021500957 MAR 09, 2016 CLE @ SAC W 240 39 90 \n", "20 1610612739 0021500938 MAR 07, 2016 CLE vs. MEM L 240 36 80 \n", "21 1610612739 0021500922 MAR 05, 2016 CLE vs. BOS W 240 42 82 \n", "22 1610612739 0021500917 MAR 04, 2016 CLE vs. WAS W 240 42 90 \n", "23 1610612739 0021500884 FEB 29, 2016 CLE vs. IND W 240 35 83 \n", "24 1610612739 0021500877 FEB 28, 2016 CLE @ WAS L 240 32 80 \n", "25 1610612739 0021500864 FEB 26, 2016 CLE @ TOR L 240 35 74 \n", "26 1610612739 0021500845 FEB 24, 2016 CLE vs. CHA W 240 45 91 \n", "27 1610612739 0021500833 FEB 22, 2016 CLE vs. DET L 240 34 79 \n", "28 1610612739 0021500824 FEB 21, 2016 CLE @ OKC W 240 41 80 \n", "29 1610612739 0021500803 FEB 18, 2016 CLE vs. CHI W 240 42 91 \n", ".. ... ... ... ... .. ... ... ... \n", "52 1610612739 0021500473 DEC 29, 2015 CLE @ DEN W 240 38 86 \n", "53 1610612739 0021500466 DEC 28, 2015 CLE @ PHX W 240 33 71 \n", "54 1610612739 0021500453 DEC 26, 2015 CLE @ POR L 240 28 77 \n", "55 1610612739 0021500438 DEC 25, 2015 CLE @ GSW L 240 30 95 \n", "56 1610612739 0021500424 DEC 23, 2015 CLE vs. NYK W 240 32 83 \n", "57 1610612739 0021500405 DEC 20, 2015 CLE vs. PHI W 240 40 85 \n", "58 1610612739 0021500384 DEC 17, 2015 CLE vs. OKC W 240 41 88 \n", "59 1610612739 0021500367 DEC 15, 2015 CLE @ BOS W 240 36 78 \n", "60 1610612739 0021500334 DEC 11, 2015 CLE @ ORL W 240 41 72 \n", "61 1610612739 0021500313 DEC 08, 2015 CLE vs. POR W 240 40 79 \n", "62 1610612739 0021500291 DEC 05, 2015 CLE @ MIA L 240 31 85 \n", "63 1610612739 0021500288 DEC 04, 2015 CLE @ NOP L 265 39 89 \n", "64 1610612739 0021500262 DEC 01, 2015 CLE vs. WAS L 240 28 83 \n", "65 1610612739 0021500240 NOV 28, 2015 CLE vs. BKN W 240 35 87 \n", "66 1610612739 0021500227 NOV 27, 2015 CLE @ CHA W 240 34 81 \n", "67 1610612739 0021500219 NOV 25, 2015 CLE @ TOR L 240 36 82 \n", "68 1610612739 0021500203 NOV 23, 2015 CLE vs. ORL W 240 43 81 \n", "69 1610612739 0021500191 NOV 21, 2015 CLE vs. ATL W 240 41 85 \n", "70 1610612739 0021500176 NOV 19, 2015 CLE vs. MIL W 240 40 71 \n", "71 1610612739 0021500160 NOV 17, 2015 CLE @ DET L 240 38 80 \n", "72 1610612739 0021500141 NOV 14, 2015 CLE @ MIL L 290 37 91 \n", "73 1610612739 0021500130 NOV 13, 2015 CLE @ NYK W 240 33 78 \n", "74 1610612739 0021500106 NOV 10, 2015 CLE vs. UTA W 240 37 74 \n", "75 1610612739 0021500094 NOV 08, 2015 CLE vs. IND W 240 38 83 \n", "76 1610612739 0021500078 NOV 06, 2015 CLE vs. PHI W 240 45 88 \n", "77 1610612739 0021500063 NOV 04, 2015 CLE vs. NYK W 240 33 83 \n", "78 1610612739 0021500046 NOV 02, 2015 CLE @ PHI W 240 42 81 \n", "79 1610612739 0021500021 OCT 30, 2015 CLE vs. MIA W 240 39 86 \n", "80 1610612739 0021500011 OCT 28, 2015 CLE @ MEM W 240 41 84 \n", "81 1610612739 0021500002 OCT 27, 2015 CLE @ CHI L 240 38 94 \n", "\n", " FG_PCT FG3M ... FT_PCT OREB DREB REB AST STL BLK TOV PF PTS \n", "0 0.474 7 ... 0.733 8 35 43 21 4 7 10 23 110 \n", "1 0.482 11 ... 0.900 9 38 47 17 9 4 15 14 109 \n", "2 0.434 19 ... 0.611 12 30 42 24 5 5 15 18 102 \n", "3 0.473 8 ... 0.912 7 26 33 15 7 3 10 19 109 \n", "4 0.547 18 ... 0.900 2 39 41 30 4 8 16 14 109 \n", "5 0.542 16 ... 0.462 15 31 46 34 7 1 17 19 112 \n", "6 0.398 12 ... 0.645 11 46 57 27 9 6 12 23 110 \n", "7 0.437 12 ... 0.792 9 41 50 29 10 7 17 17 107 \n", "8 0.360 14 ... 0.857 10 28 38 21 5 5 11 31 100 \n", "9 0.446 14 ... 0.760 15 38 53 21 6 5 9 23 107 \n", "10 0.438 10 ... 0.778 11 34 45 22 3 2 14 20 95 \n", "11 0.482 10 ... 0.852 17 25 42 29 6 8 12 17 113 \n", "12 0.558 15 ... 0.765 8 35 43 38 10 4 12 19 124 \n", "13 0.526 10 ... 0.846 3 23 26 19 6 3 14 22 101 \n", "14 0.500 13 ... 0.741 9 32 41 23 7 4 19 21 109 \n", "15 0.443 10 ... 0.846 13 35 48 21 9 4 16 19 99 \n", "16 0.398 10 ... 0.625 13 26 39 18 6 3 8 16 85 \n", "17 0.488 18 ... 0.700 9 40 49 23 3 4 8 19 114 \n", "18 0.529 16 ... 0.700 9 29 38 22 4 6 8 20 120 \n", "19 0.433 13 ... 0.829 15 36 51 17 5 1 13 21 120 \n", "20 0.450 7 ... 0.828 13 36 49 23 9 7 25 22 103 \n", "21 0.512 10 ... 0.765 15 32 47 27 8 4 15 21 120 \n", "22 0.467 12 ... 0.750 8 39 47 24 9 5 9 19 108 \n", "23 0.422 9 ... 0.840 9 31 40 22 4 2 11 15 100 \n", "24 0.400 9 ... 0.867 6 33 39 20 6 2 14 26 99 \n", "25 0.473 12 ... 0.714 10 29 39 21 4 0 14 26 97 \n", "26 0.495 13 ... 0.647 12 35 47 26 11 1 9 20 114 \n", "27 0.430 8 ... 0.923 5 35 40 19 8 2 17 21 88 \n", "28 0.513 10 ... 0.852 13 36 49 25 4 1 12 21 115 \n", "29 0.462 7 ... 0.882 11 41 52 21 8 8 10 22 106 \n", ".. ... ... ... ... ... ... ... ... ... ... ... .. ... \n", "52 0.442 4 ... 0.591 9 35 44 17 12 3 12 16 93 \n", "53 0.465 17 ... 0.720 10 27 37 21 8 8 17 18 101 \n", "54 0.364 11 ... 0.600 9 33 42 21 4 2 17 20 76 \n", "55 0.316 5 ... 0.720 17 38 55 12 6 4 11 22 83 \n", "56 0.386 5 ... 0.880 14 34 48 23 6 4 7 15 91 \n", "57 0.471 11 ... 0.810 6 34 40 25 11 6 10 19 108 \n", "58 0.466 12 ... 0.769 16 26 42 29 6 1 16 22 104 \n", "59 0.462 8 ... 0.900 6 40 46 17 5 7 14 17 89 \n", "60 0.569 11 ... 0.667 9 36 45 28 12 0 18 19 111 \n", "61 0.506 9 ... 0.800 6 24 30 18 10 4 8 18 105 \n", "62 0.365 6 ... 0.667 11 25 36 14 8 1 14 26 84 \n", "63 0.438 10 ... 0.833 12 31 43 18 8 1 14 26 108 \n", "64 0.337 9 ... 0.870 10 37 47 15 8 3 18 18 85 \n", "65 0.402 9 ... 0.647 12 37 49 20 9 11 19 19 90 \n", "66 0.420 8 ... 0.760 12 38 50 17 3 2 16 22 95 \n", "67 0.439 14 ... 0.813 9 33 42 22 4 2 10 20 99 \n", "68 0.531 18 ... 0.591 8 33 41 34 5 3 10 15 117 \n", "69 0.482 11 ... 0.842 11 40 51 27 4 8 14 22 109 \n", "70 0.563 11 ... 0.800 13 30 43 29 3 2 17 24 115 \n", "71 0.475 11 ... 0.600 3 37 40 21 8 2 14 25 99 \n", "72 0.407 14 ... 0.630 14 40 54 21 6 8 20 24 105 \n", "73 0.423 6 ... 0.581 12 37 49 12 6 1 12 19 90 \n", "74 0.500 11 ... 0.767 11 24 35 24 9 6 16 21 118 \n", "75 0.458 8 ... 0.680 11 37 48 25 5 4 10 20 101 \n", "76 0.511 10 ... 0.667 11 34 45 29 9 0 17 20 108 \n", "77 0.398 7 ... 0.719 9 39 48 21 5 2 11 12 96 \n", "78 0.519 8 ... 0.750 7 36 43 28 5 9 16 19 107 \n", "79 0.453 6 ... 0.857 14 35 49 25 4 4 11 19 102 \n", "80 0.488 13 ... 0.647 12 42 54 29 7 2 19 25 106 \n", "81 0.404 9 ... 0.588 11 39 50 26 5 7 10 21 95 \n", "\n", "[82 rows x 24 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#game logs\n", "cle_logs = team.TeamGameLogs(team_id = cle_id)\n", "cle_logs.info()" ] }, { "cell_type": "code", "execution_count": 2, "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>index</th>\n", " <th>LEAGUE_ID</th>\n", " <th>TEAM_ID</th>\n", " <th>MIN_YEAR</th>\n", " <th>MAX_YEAR</th>\n", " <th>ABBREVIATION</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>00</td>\n", " <td>1610612737</td>\n", " <td>1949</td>\n", " <td>2015</td>\n", " <td>ATL</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>00</td>\n", " <td>1610612738</td>\n", " <td>1946</td>\n", " <td>2015</td>\n", " <td>BOS</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>00</td>\n", " <td>1610612739</td>\n", " <td>1970</td>\n", " <td>2015</td>\n", " <td>CLE</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>00</td>\n", " <td>1610612740</td>\n", " <td>2002</td>\n", " <td>2015</td>\n", " <td>NOP</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>00</td>\n", " <td>1610612741</td>\n", " <td>1966</td>\n", " <td>2015</td>\n", " <td>CHI</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5</td>\n", " <td>00</td>\n", " <td>1610612742</td>\n", " <td>1980</td>\n", " <td>2015</td>\n", " <td>DAL</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>6</td>\n", " <td>00</td>\n", " <td>1610612743</td>\n", " <td>1976</td>\n", " <td>2015</td>\n", " <td>DEN</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>7</td>\n", " <td>00</td>\n", " <td>1610612744</td>\n", " <td>1946</td>\n", " <td>2015</td>\n", " <td>GSW</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>8</td>\n", " <td>00</td>\n", " <td>1610612745</td>\n", " <td>1967</td>\n", " <td>2015</td>\n", " <td>HOU</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>9</td>\n", " <td>00</td>\n", " <td>1610612746</td>\n", " <td>1970</td>\n", " <td>2015</td>\n", " <td>LAC</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>10</td>\n", " <td>00</td>\n", " <td>1610612747</td>\n", " <td>1948</td>\n", " <td>2015</td>\n", " <td>LAL</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>11</td>\n", " <td>00</td>\n", " <td>1610612748</td>\n", " <td>1988</td>\n", " <td>2015</td>\n", " <td>MIA</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>12</td>\n", " <td>00</td>\n", " <td>1610612749</td>\n", " <td>1968</td>\n", " <td>2015</td>\n", " <td>MIL</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>13</td>\n", " <td>00</td>\n", " <td>1610612750</td>\n", " <td>1989</td>\n", " <td>2015</td>\n", " <td>MIN</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>14</td>\n", " <td>00</td>\n", " <td>1610612751</td>\n", " <td>1976</td>\n", " <td>2015</td>\n", " <td>BKN</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>15</td>\n", " <td>00</td>\n", " <td>1610612752</td>\n", " <td>1946</td>\n", " <td>2015</td>\n", " <td>NYK</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>16</td>\n", " <td>00</td>\n", " <td>1610612753</td>\n", " <td>1989</td>\n", " <td>2015</td>\n", " <td>ORL</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>17</td>\n", " <td>00</td>\n", " <td>1610612754</td>\n", " <td>1976</td>\n", " <td>2015</td>\n", " <td>IND</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>18</td>\n", " <td>00</td>\n", " <td>1610612755</td>\n", " <td>1949</td>\n", " <td>2015</td>\n", " <td>PHI</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>19</td>\n", " <td>00</td>\n", " <td>1610612756</td>\n", " <td>1968</td>\n", " <td>2015</td>\n", " <td>PHX</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>20</td>\n", " <td>00</td>\n", " <td>1610612757</td>\n", " <td>1970</td>\n", " <td>2015</td>\n", " <td>POR</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>21</td>\n", " <td>00</td>\n", " <td>1610612758</td>\n", " <td>1948</td>\n", " <td>2015</td>\n", " <td>SAC</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>22</td>\n", " <td>00</td>\n", " <td>1610612759</td>\n", " <td>1976</td>\n", " <td>2015</td>\n", " <td>SAS</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>23</td>\n", " <td>00</td>\n", " <td>1610612760</td>\n", " <td>1967</td>\n", " <td>2015</td>\n", " <td>OKC</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>24</td>\n", " <td>00</td>\n", " <td>1610612761</td>\n", " <td>1995</td>\n", " <td>2015</td>\n", " <td>TOR</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>25</td>\n", " <td>00</td>\n", " <td>1610612763</td>\n", " <td>1995</td>\n", " <td>2015</td>\n", " <td>MEM</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>26</td>\n", " <td>00</td>\n", " <td>1610612764</td>\n", " <td>1961</td>\n", " <td>2015</td>\n", " <td>WAS</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>27</td>\n", " <td>00</td>\n", " <td>1610612765</td>\n", " <td>1948</td>\n", " <td>2015</td>\n", " <td>DET</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>28</td>\n", " <td>00</td>\n", " <td>1610612766</td>\n", " <td>1988</td>\n", " <td>2015</td>\n", " <td>CHA</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>29</td>\n", " <td>00</td>\n", " <td>1610612762</td>\n", " <td>1974</td>\n", " <td>2015</td>\n", " <td>UTA</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " index LEAGUE_ID TEAM_ID MIN_YEAR MAX_YEAR ABBREVIATION\n", "0 0 00 1610612737 1949 2015 ATL\n", "1 1 00 1610612738 1946 2015 BOS\n", "2 2 00 1610612739 1970 2015 CLE\n", "3 3 00 1610612740 2002 2015 NOP\n", "4 4 00 1610612741 1966 2015 CHI\n", "5 5 00 1610612742 1980 2015 DAL\n", "6 6 00 1610612743 1976 2015 DEN\n", "7 7 00 1610612744 1946 2015 GSW\n", "8 8 00 1610612745 1967 2015 HOU\n", "9 9 00 1610612746 1970 2015 LAC\n", "10 10 00 1610612747 1948 2015 LAL\n", "11 11 00 1610612748 1988 2015 MIA\n", "12 12 00 1610612749 1968 2015 MIL\n", "13 13 00 1610612750 1989 2015 MIN\n", "14 14 00 1610612751 1976 2015 BKN\n", "15 15 00 1610612752 1946 2015 NYK\n", "16 16 00 1610612753 1989 2015 ORL\n", "17 17 00 1610612754 1976 2015 IND\n", "18 18 00 1610612755 1949 2015 PHI\n", "19 19 00 1610612756 1968 2015 PHX\n", "20 20 00 1610612757 1970 2015 POR\n", "21 21 00 1610612758 1948 2015 SAC\n", "22 22 00 1610612759 1976 2015 SAS\n", "23 23 00 1610612760 1967 2015 OKC\n", "24 24 00 1610612761 1995 2015 TOR\n", "25 25 00 1610612763 1995 2015 MEM\n", "26 26 00 1610612764 1961 2015 WAS\n", "27 27 00 1610612765 1948 2015 DET\n", "28 28 00 1610612766 1988 2015 CHA\n", "29 29 00 1610612762 1974 2015 UTA" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Start of code that gets all all gamelogs for all teams within a season\n", "team_list = team.TeamList().info()\n", "team_list = team_list[team_list['MAX_YEAR']=='2015'].reset_index()\n", "team_list" ] }, { "cell_type": "code", "execution_count": 8, "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>Team_ID</th>\n", " <th>Game_ID</th>\n", " <th>GAME_DATE</th>\n", " <th>MATCHUP</th>\n", " <th>WL</th>\n", " <th>MIN</th>\n", " <th>FGM</th>\n", " <th>FGA</th>\n", " <th>FG_PCT</th>\n", " <th>FG3M</th>\n", " <th>...</th>\n", " <th>FT_PCT</th>\n", " <th>OREB</th>\n", " <th>DREB</th>\n", " <th>REB</th>\n", " <th>AST</th>\n", " <th>STL</th>\n", " <th>BLK</th>\n", " <th>TOV</th>\n", " <th>PF</th>\n", " <th>PTS</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1610612737</td>\n", " <td>0021501221</td>\n", " <td>APR 13, 2016</td>\n", " <td>ATL @ WAS</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>32</td>\n", " <td>81</td>\n", " <td>0.395</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>0.742</td>\n", " <td>9</td>\n", " <td>38</td>\n", " <td>47</td>\n", " <td>22</td>\n", " <td>13</td>\n", " <td>5</td>\n", " <td>22</td>\n", " <td>21</td>\n", " <td>98</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1610612737</td>\n", " <td>0021501203</td>\n", " <td>APR 11, 2016</td>\n", " <td>ATL @ CLE</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>87</td>\n", " <td>0.448</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.533</td>\n", " <td>10</td>\n", " <td>32</td>\n", " <td>42</td>\n", " <td>23</td>\n", " <td>8</td>\n", " <td>6</td>\n", " <td>15</td>\n", " <td>18</td>\n", " <td>94</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1610612737</td>\n", " <td>0021501188</td>\n", " <td>APR 09, 2016</td>\n", " <td>ATL vs. BOS</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>46</td>\n", " <td>88</td>\n", " <td>0.523</td>\n", " <td>17</td>\n", " <td>...</td>\n", " <td>0.818</td>\n", " <td>5</td>\n", " <td>39</td>\n", " <td>44</td>\n", " <td>31</td>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>17</td>\n", " <td>22</td>\n", " <td>118</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1610612737</td>\n", " <td>0021501173</td>\n", " <td>APR 07, 2016</td>\n", " <td>ATL vs. TOR</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>76</td>\n", " <td>0.434</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>0.810</td>\n", " <td>5</td>\n", " <td>36</td>\n", " <td>41</td>\n", " <td>23</td>\n", " <td>4</td>\n", " <td>12</td>\n", " <td>13</td>\n", " <td>19</td>\n", " <td>95</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1610612737</td>\n", " <td>0021501157</td>\n", " <td>APR 05, 2016</td>\n", " <td>ATL vs. PHX</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>95</td>\n", " <td>0.411</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>0.737</td>\n", " <td>13</td>\n", " <td>37</td>\n", " <td>50</td>\n", " <td>26</td>\n", " <td>16</td>\n", " <td>3</td>\n", " <td>16</td>\n", " <td>21</td>\n", " <td>103</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1610612737</td>\n", " <td>0021501131</td>\n", " <td>APR 01, 2016</td>\n", " <td>ATL vs. CLE</td>\n", " <td>L</td>\n", " <td>265</td>\n", " <td>38</td>\n", " <td>95</td>\n", " <td>0.400</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>0.885</td>\n", " <td>5</td>\n", " <td>43</td>\n", " <td>48</td>\n", " <td>25</td>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>15</td>\n", " <td>27</td>\n", " <td>108</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1610612737</td>\n", " <td>0021501113</td>\n", " <td>MAR 30, 2016</td>\n", " <td>ATL @ TOR</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>83</td>\n", " <td>0.446</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>0.692</td>\n", " <td>11</td>\n", " <td>33</td>\n", " <td>44</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>7</td>\n", " <td>18</td>\n", " <td>19</td>\n", " <td>97</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1610612737</td>\n", " <td>0021501099</td>\n", " <td>MAR 28, 2016</td>\n", " <td>ATL @ CHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>85</td>\n", " <td>0.424</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>0.893</td>\n", " <td>7</td>\n", " <td>41</td>\n", " <td>48</td>\n", " <td>22</td>\n", " <td>4</td>\n", " <td>13</td>\n", " <td>8</td>\n", " <td>13</td>\n", " <td>102</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1610612737</td>\n", " <td>0021501085</td>\n", " <td>MAR 26, 2016</td>\n", " <td>ATL @ DET</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>43</td>\n", " <td>95</td>\n", " <td>0.453</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>0.929</td>\n", " <td>5</td>\n", " <td>34</td>\n", " <td>39</td>\n", " <td>34</td>\n", " <td>6</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>21</td>\n", " <td>112</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1610612737</td>\n", " <td>0021501076</td>\n", " <td>MAR 25, 2016</td>\n", " <td>ATL vs. MIL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>97</td>\n", " <td>0.423</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>0.824</td>\n", " <td>17</td>\n", " <td>28</td>\n", " <td>45</td>\n", " <td>26</td>\n", " <td>10</td>\n", " <td>6</td>\n", " <td>11</td>\n", " <td>21</td>\n", " <td>101</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>1610612737</td>\n", " <td>0021501056</td>\n", " <td>MAR 23, 2016</td>\n", " <td>ATL @ WAS</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>45</td>\n", " <td>84</td>\n", " <td>0.536</td>\n", " <td>17</td>\n", " <td>...</td>\n", " <td>0.682</td>\n", " <td>7</td>\n", " <td>34</td>\n", " <td>41</td>\n", " <td>32</td>\n", " <td>10</td>\n", " <td>5</td>\n", " <td>16</td>\n", " <td>17</td>\n", " <td>122</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>1610612737</td>\n", " <td>0021501048</td>\n", " <td>MAR 21, 2016</td>\n", " <td>ATL vs. WAS</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>78</td>\n", " <td>0.487</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>0.765</td>\n", " <td>2</td>\n", " <td>31</td>\n", " <td>33</td>\n", " <td>23</td>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>14</td>\n", " <td>15</td>\n", " <td>102</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>1610612737</td>\n", " <td>0021501029</td>\n", " <td>MAR 19, 2016</td>\n", " <td>ATL vs. HOU</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>44</td>\n", " <td>88</td>\n", " <td>0.500</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>0.583</td>\n", " <td>9</td>\n", " <td>31</td>\n", " <td>40</td>\n", " <td>32</td>\n", " <td>12</td>\n", " <td>6</td>\n", " <td>16</td>\n", " <td>20</td>\n", " <td>109</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1610612737</td>\n", " <td>0021501015</td>\n", " <td>MAR 17, 2016</td>\n", " <td>ATL vs. DEN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>80</td>\n", " <td>0.500</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>0.857</td>\n", " <td>7</td>\n", " <td>33</td>\n", " <td>40</td>\n", " <td>32</td>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>12</td>\n", " <td>16</td>\n", " <td>116</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>1610612737</td>\n", " <td>0021501007</td>\n", " <td>MAR 16, 2016</td>\n", " <td>ATL @ DET</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>89</td>\n", " <td>0.438</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>0.824</td>\n", " <td>8</td>\n", " <td>38</td>\n", " <td>46</td>\n", " <td>25</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>12</td>\n", " <td>26</td>\n", " <td>118</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1610612737</td>\n", " <td>0021500984</td>\n", " <td>MAR 13, 2016</td>\n", " <td>ATL vs. IND</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>85</td>\n", " <td>0.471</td>\n", " <td>15</td>\n", " <td>...</td>\n", " <td>0.900</td>\n", " <td>9</td>\n", " <td>41</td>\n", " <td>50</td>\n", " <td>27</td>\n", " <td>11</td>\n", " <td>6</td>\n", " <td>15</td>\n", " <td>13</td>\n", " <td>104</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>1610612737</td>\n", " <td>0021500974</td>\n", " <td>MAR 12, 2016</td>\n", " <td>ATL vs. MEM</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>84</td>\n", " <td>0.429</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>0.706</td>\n", " <td>8</td>\n", " <td>39</td>\n", " <td>47</td>\n", " <td>27</td>\n", " <td>9</td>\n", " <td>12</td>\n", " <td>11</td>\n", " <td>14</td>\n", " <td>95</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1610612737</td>\n", " <td>0021500959</td>\n", " <td>MAR 10, 2016</td>\n", " <td>ATL @ TOR</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>82</td>\n", " <td>0.427</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0.704</td>\n", " <td>6</td>\n", " <td>27</td>\n", " <td>33</td>\n", " <td>17</td>\n", " <td>8</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>24</td>\n", " <td>96</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>1610612737</td>\n", " <td>0021500947</td>\n", " <td>MAR 08, 2016</td>\n", " <td>ATL @ UTA</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>80</td>\n", " <td>0.475</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.500</td>\n", " <td>4</td>\n", " <td>37</td>\n", " <td>41</td>\n", " <td>15</td>\n", " <td>9</td>\n", " <td>4</td>\n", " <td>16</td>\n", " <td>19</td>\n", " <td>91</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1610612737</td>\n", " <td>0021500929</td>\n", " <td>MAR 05, 2016</td>\n", " <td>ATL @ LAC</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>89</td>\n", " <td>0.449</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.850</td>\n", " <td>10</td>\n", " <td>43</td>\n", " <td>53</td>\n", " <td>26</td>\n", " <td>9</td>\n", " <td>3</td>\n", " <td>18</td>\n", " <td>26</td>\n", " <td>107</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>1610612737</td>\n", " <td>0021500921</td>\n", " <td>MAR 04, 2016</td>\n", " <td>ATL @ LAL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>68</td>\n", " <td>0.544</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>0.731</td>\n", " <td>3</td>\n", " <td>36</td>\n", " <td>39</td>\n", " <td>31</td>\n", " <td>6</td>\n", " <td>8</td>\n", " <td>10</td>\n", " <td>18</td>\n", " <td>106</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>1610612737</td>\n", " <td>0021500895</td>\n", " <td>MAR 01, 2016</td>\n", " <td>ATL @ GSW</td>\n", " <td>L</td>\n", " <td>265</td>\n", " <td>37</td>\n", " <td>80</td>\n", " <td>0.463</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>0.792</td>\n", " <td>7</td>\n", " <td>35</td>\n", " <td>42</td>\n", " <td>25</td>\n", " <td>9</td>\n", " <td>5</td>\n", " <td>17</td>\n", " <td>17</td>\n", " <td>105</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>1610612737</td>\n", " <td>0021500878</td>\n", " <td>FEB 28, 2016</td>\n", " <td>ATL vs. CHA</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>77</td>\n", " <td>0.494</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.600</td>\n", " <td>6</td>\n", " <td>42</td>\n", " <td>48</td>\n", " <td>29</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>15</td>\n", " <td>19</td>\n", " <td>87</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>1610612737</td>\n", " <td>0021500865</td>\n", " <td>FEB 26, 2016</td>\n", " <td>ATL vs. CHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>89</td>\n", " <td>0.416</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0.917</td>\n", " <td>13</td>\n", " <td>35</td>\n", " <td>48</td>\n", " <td>28</td>\n", " <td>14</td>\n", " <td>11</td>\n", " <td>11</td>\n", " <td>21</td>\n", " <td>103</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>1610612737</td>\n", " <td>0021500836</td>\n", " <td>FEB 22, 2016</td>\n", " <td>ATL vs. GSW</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>86</td>\n", " <td>0.419</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.625</td>\n", " <td>8</td>\n", " <td>39</td>\n", " <td>47</td>\n", " <td>23</td>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>17</td>\n", " <td>17</td>\n", " <td>92</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>1610612737</td>\n", " <td>0021500819</td>\n", " <td>FEB 20, 2016</td>\n", " <td>ATL vs. MIL</td>\n", " <td>L</td>\n", " <td>290</td>\n", " <td>44</td>\n", " <td>106</td>\n", " <td>0.415</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>0.667</td>\n", " <td>10</td>\n", " <td>39</td>\n", " <td>49</td>\n", " <td>31</td>\n", " <td>11</td>\n", " <td>8</td>\n", " <td>16</td>\n", " <td>27</td>\n", " <td>109</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>1610612737</td>\n", " <td>0021500808</td>\n", " <td>FEB 19, 2016</td>\n", " <td>ATL vs. MIA</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>87</td>\n", " <td>0.471</td>\n", " <td>16</td>\n", " <td>...</td>\n", " <td>1.000</td>\n", " <td>8</td>\n", " <td>34</td>\n", " <td>42</td>\n", " <td>27</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>21</td>\n", " <td>23</td>\n", " <td>111</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>1610612737</td>\n", " <td>0021500796</td>\n", " <td>FEB 10, 2016</td>\n", " <td>ATL @ CHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>43</td>\n", " <td>90</td>\n", " <td>0.478</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>0.933</td>\n", " <td>11</td>\n", " <td>35</td>\n", " <td>46</td>\n", " <td>25</td>\n", " <td>16</td>\n", " <td>7</td>\n", " <td>12</td>\n", " <td>14</td>\n", " <td>113</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>1610612737</td>\n", " <td>0021500780</td>\n", " <td>FEB 08, 2016</td>\n", " <td>ATL vs. ORL</td>\n", " <td>L</td>\n", " <td>265</td>\n", " <td>43</td>\n", " <td>92</td>\n", " <td>0.467</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>0.600</td>\n", " <td>8</td>\n", " <td>39</td>\n", " <td>47</td>\n", " <td>31</td>\n", " <td>9</td>\n", " <td>4</td>\n", " <td>14</td>\n", " <td>20</td>\n", " <td>110</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>1610612737</td>\n", " <td>0021500771</td>\n", " <td>FEB 07, 2016</td>\n", " <td>ATL @ ORL</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>91</td>\n", " <td>0.385</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>0.733</td>\n", " <td>18</td>\n", " <td>32</td>\n", " <td>50</td>\n", " <td>22</td>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>16</td>\n", " <td>13</td>\n", " <td>94</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>2430</th>\n", " <td>1610612762</td>\n", " <td>0021500479</td>\n", " <td>DEC 30, 2015</td>\n", " <td>UTA @ MIN</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>28</td>\n", " <td>80</td>\n", " <td>0.350</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.700</td>\n", " <td>15</td>\n", " <td>29</td>\n", " <td>44</td>\n", " <td>15</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>19</td>\n", " <td>24</td>\n", " <td>80</td>\n", " </tr>\n", " <tr>\n", " <th>2431</th>\n", " <td>1610612762</td>\n", " <td>0021500467</td>\n", " <td>DEC 28, 2015</td>\n", " <td>UTA vs. PHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>28</td>\n", " <td>84</td>\n", " <td>0.333</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0.889</td>\n", " <td>15</td>\n", " <td>38</td>\n", " <td>53</td>\n", " <td>13</td>\n", " <td>11</td>\n", " <td>9</td>\n", " <td>15</td>\n", " <td>18</td>\n", " <td>95</td>\n", " </tr>\n", " <tr>\n", " <th>2432</th>\n", " <td>1610612762</td>\n", " <td>0021500452</td>\n", " <td>DEC 26, 2015</td>\n", " <td>UTA vs. LAC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>74</td>\n", " <td>0.486</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.815</td>\n", " <td>8</td>\n", " <td>28</td>\n", " <td>36</td>\n", " <td>18</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>16</td>\n", " <td>22</td>\n", " <td>104</td>\n", " </tr>\n", " <tr>\n", " <th>2433</th>\n", " <td>1610612762</td>\n", " <td>0021500434</td>\n", " <td>DEC 23, 2015</td>\n", " <td>UTA @ GSW</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>80</td>\n", " <td>0.413</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0.857</td>\n", " <td>12</td>\n", " <td>28</td>\n", " <td>40</td>\n", " <td>14</td>\n", " <td>9</td>\n", " <td>3</td>\n", " <td>19</td>\n", " <td>18</td>\n", " <td>85</td>\n", " </tr>\n", " <tr>\n", " <th>2434</th>\n", " <td>1610612762</td>\n", " <td>0021500417</td>\n", " <td>DEC 21, 2015</td>\n", " <td>UTA vs. PHX</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>79</td>\n", " <td>0.456</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>0.784</td>\n", " <td>11</td>\n", " <td>36</td>\n", " <td>47</td>\n", " <td>16</td>\n", " <td>8</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>16</td>\n", " <td>110</td>\n", " </tr>\n", " <tr>\n", " <th>2435</th>\n", " <td>1610612762</td>\n", " <td>0021500395</td>\n", " <td>DEC 18, 2015</td>\n", " <td>UTA vs. DEN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>34</td>\n", " <td>73</td>\n", " <td>0.466</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.760</td>\n", " <td>5</td>\n", " <td>34</td>\n", " <td>39</td>\n", " <td>15</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>14</td>\n", " <td>19</td>\n", " <td>97</td>\n", " </tr>\n", " <tr>\n", " <th>2436</th>\n", " <td>1610612762</td>\n", " <td>0021500380</td>\n", " <td>DEC 16, 2015</td>\n", " <td>UTA vs. NOP</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>32</td>\n", " <td>67</td>\n", " <td>0.478</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0.793</td>\n", " <td>5</td>\n", " <td>29</td>\n", " <td>34</td>\n", " <td>17</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>12</td>\n", " <td>20</td>\n", " <td>94</td>\n", " </tr>\n", " <tr>\n", " <th>2437</th>\n", " <td>1610612762</td>\n", " <td>0021500364</td>\n", " <td>DEC 14, 2015</td>\n", " <td>UTA @ SAS</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>32</td>\n", " <td>79</td>\n", " <td>0.405</td>\n", " <td>3</td>\n", " <td>...</td>\n", " <td>0.824</td>\n", " <td>7</td>\n", " <td>25</td>\n", " <td>32</td>\n", " <td>18</td>\n", " <td>8</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>23</td>\n", " <td>81</td>\n", " </tr>\n", " <tr>\n", " <th>2438</th>\n", " <td>1610612762</td>\n", " <td>0021500356</td>\n", " <td>DEC 13, 2015</td>\n", " <td>UTA @ OKC</td>\n", " <td>L</td>\n", " <td>265</td>\n", " <td>39</td>\n", " <td>94</td>\n", " <td>0.415</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.600</td>\n", " <td>16</td>\n", " <td>28</td>\n", " <td>44</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>11</td>\n", " <td>24</td>\n", " <td>98</td>\n", " </tr>\n", " <tr>\n", " <th>2439</th>\n", " <td>1610612762</td>\n", " <td>0021500341</td>\n", " <td>DEC 11, 2015</td>\n", " <td>UTA vs. OKC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>78</td>\n", " <td>0.423</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.667</td>\n", " <td>11</td>\n", " <td>28</td>\n", " <td>39</td>\n", " <td>17</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>11</td>\n", " <td>16</td>\n", " <td>90</td>\n", " </tr>\n", " <tr>\n", " <th>2440</th>\n", " <td>1610612762</td>\n", " <td>0021500327</td>\n", " <td>DEC 09, 2015</td>\n", " <td>UTA vs. NYK</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>80</td>\n", " <td>0.488</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>0.826</td>\n", " <td>9</td>\n", " <td>42</td>\n", " <td>51</td>\n", " <td>26</td>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>14</td>\n", " <td>26</td>\n", " <td>106</td>\n", " </tr>\n", " <tr>\n", " <th>2441</th>\n", " <td>1610612762</td>\n", " <td>0021500318</td>\n", " <td>DEC 08, 2015</td>\n", " <td>UTA @ SAC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>92</td>\n", " <td>0.413</td>\n", " <td>15</td>\n", " <td>...</td>\n", " <td>0.682</td>\n", " <td>19</td>\n", " <td>25</td>\n", " <td>44</td>\n", " <td>23</td>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>22</td>\n", " <td>106</td>\n", " </tr>\n", " <tr>\n", " <th>2442</th>\n", " <td>1610612762</td>\n", " <td>0021500297</td>\n", " <td>DEC 05, 2015</td>\n", " <td>UTA vs. IND</td>\n", " <td>W</td>\n", " <td>265</td>\n", " <td>43</td>\n", " <td>92</td>\n", " <td>0.467</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.757</td>\n", " <td>19</td>\n", " <td>35</td>\n", " <td>54</td>\n", " <td>21</td>\n", " <td>7</td>\n", " <td>3</td>\n", " <td>15</td>\n", " <td>30</td>\n", " <td>122</td>\n", " </tr>\n", " <tr>\n", " <th>2443</th>\n", " <td>1610612762</td>\n", " <td>0021500279</td>\n", " <td>DEC 03, 2015</td>\n", " <td>UTA vs. ORL</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>31</td>\n", " <td>72</td>\n", " <td>0.431</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>0.750</td>\n", " <td>5</td>\n", " <td>34</td>\n", " <td>39</td>\n", " <td>18</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>19</td>\n", " <td>16</td>\n", " <td>94</td>\n", " </tr>\n", " <tr>\n", " <th>2444</th>\n", " <td>1610612762</td>\n", " <td>0021500259</td>\n", " <td>NOV 30, 2015</td>\n", " <td>UTA vs. GSW</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>89</td>\n", " <td>0.449</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>0.773</td>\n", " <td>10</td>\n", " <td>25</td>\n", " <td>35</td>\n", " <td>18</td>\n", " <td>9</td>\n", " <td>2</td>\n", " <td>8</td>\n", " <td>16</td>\n", " <td>103</td>\n", " </tr>\n", " <tr>\n", " <th>2445</th>\n", " <td>1610612762</td>\n", " <td>0021500243</td>\n", " <td>NOV 28, 2015</td>\n", " <td>UTA vs. NOP</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>82</td>\n", " <td>0.463</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>0.762</td>\n", " <td>12</td>\n", " <td>37</td>\n", " <td>49</td>\n", " <td>18</td>\n", " <td>11</td>\n", " <td>9</td>\n", " <td>15</td>\n", " <td>25</td>\n", " <td>101</td>\n", " </tr>\n", " <tr>\n", " <th>2446</th>\n", " <td>1610612762</td>\n", " <td>0021500226</td>\n", " <td>NOV 25, 2015</td>\n", " <td>UTA @ LAC</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>77</td>\n", " <td>0.506</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>0.720</td>\n", " <td>11</td>\n", " <td>28</td>\n", " <td>39</td>\n", " <td>19</td>\n", " <td>12</td>\n", " <td>4</td>\n", " <td>16</td>\n", " <td>20</td>\n", " <td>102</td>\n", " </tr>\n", " <tr>\n", " <th>2447</th>\n", " <td>1610612762</td>\n", " <td>0021500208</td>\n", " <td>NOV 23, 2015</td>\n", " <td>UTA vs. OKC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>28</td>\n", " <td>73</td>\n", " <td>0.384</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>0.700</td>\n", " <td>13</td>\n", " <td>28</td>\n", " <td>41</td>\n", " <td>15</td>\n", " <td>11</td>\n", " <td>3</td>\n", " <td>21</td>\n", " <td>18</td>\n", " <td>89</td>\n", " </tr>\n", " <tr>\n", " <th>2448</th>\n", " <td>1610612762</td>\n", " <td>0021500184</td>\n", " <td>NOV 20, 2015</td>\n", " <td>UTA @ DAL</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>80</td>\n", " <td>0.438</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>0.810</td>\n", " <td>18</td>\n", " <td>30</td>\n", " <td>48</td>\n", " <td>14</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>17</td>\n", " <td>24</td>\n", " <td>93</td>\n", " </tr>\n", " <tr>\n", " <th>2449</th>\n", " <td>1610612762</td>\n", " <td>0021500173</td>\n", " <td>NOV 18, 2015</td>\n", " <td>UTA vs. TOR</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>72</td>\n", " <td>0.486</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0.727</td>\n", " <td>7</td>\n", " <td>34</td>\n", " <td>41</td>\n", " <td>15</td>\n", " <td>9</td>\n", " <td>5</td>\n", " <td>17</td>\n", " <td>20</td>\n", " <td>93</td>\n", " </tr>\n", " <tr>\n", " <th>2450</th>\n", " <td>1610612762</td>\n", " <td>0021500148</td>\n", " <td>NOV 15, 2015</td>\n", " <td>UTA @ ATL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>76</td>\n", " <td>0.513</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0.706</td>\n", " <td>10</td>\n", " <td>30</td>\n", " <td>40</td>\n", " <td>21</td>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>17</td>\n", " <td>15</td>\n", " <td>97</td>\n", " </tr>\n", " <tr>\n", " <th>2451</th>\n", " <td>1610612762</td>\n", " <td>0021500128</td>\n", " <td>NOV 13, 2015</td>\n", " <td>UTA @ ORL</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>34</td>\n", " <td>87</td>\n", " <td>0.391</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>0.739</td>\n", " <td>12</td>\n", " <td>29</td>\n", " <td>41</td>\n", " <td>24</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>17</td>\n", " <td>24</td>\n", " <td>93</td>\n", " </tr>\n", " <tr>\n", " <th>2452</th>\n", " <td>1610612762</td>\n", " <td>0021500124</td>\n", " <td>NOV 12, 2015</td>\n", " <td>UTA @ MIA</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>34</td>\n", " <td>89</td>\n", " <td>0.382</td>\n", " <td>4</td>\n", " <td>...</td>\n", " <td>0.760</td>\n", " <td>11</td>\n", " <td>31</td>\n", " <td>42</td>\n", " <td>13</td>\n", " <td>10</td>\n", " <td>9</td>\n", " <td>10</td>\n", " <td>23</td>\n", " <td>91</td>\n", " </tr>\n", " <tr>\n", " <th>2453</th>\n", " <td>1610612762</td>\n", " <td>0021500106</td>\n", " <td>NOV 10, 2015</td>\n", " <td>UTA @ CLE</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>82</td>\n", " <td>0.488</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>0.857</td>\n", " <td>13</td>\n", " <td>24</td>\n", " <td>37</td>\n", " <td>30</td>\n", " <td>11</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>35</td>\n", " <td>114</td>\n", " </tr>\n", " <tr>\n", " <th>2454</th>\n", " <td>1610612762</td>\n", " <td>0021500091</td>\n", " <td>NOV 07, 2015</td>\n", " <td>UTA vs. MEM</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>31</td>\n", " <td>74</td>\n", " <td>0.419</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>0.833</td>\n", " <td>9</td>\n", " <td>40</td>\n", " <td>49</td>\n", " <td>18</td>\n", " <td>7</td>\n", " <td>10</td>\n", " <td>21</td>\n", " <td>20</td>\n", " <td>89</td>\n", " </tr>\n", " <tr>\n", " <th>2455</th>\n", " <td>1610612762</td>\n", " <td>0021500073</td>\n", " <td>NOV 05, 2015</td>\n", " <td>UTA @ DEN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>85</td>\n", " <td>0.435</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>0.647</td>\n", " <td>12</td>\n", " <td>31</td>\n", " <td>43</td>\n", " <td>23</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>26</td>\n", " <td>96</td>\n", " </tr>\n", " <tr>\n", " <th>2456</th>\n", " <td>1610612762</td>\n", " <td>0021500068</td>\n", " <td>NOV 04, 2015</td>\n", " <td>UTA vs. POR</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>88</td>\n", " <td>0.375</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>0.636</td>\n", " <td>16</td>\n", " <td>24</td>\n", " <td>40</td>\n", " <td>9</td>\n", " <td>12</td>\n", " <td>8</td>\n", " <td>9</td>\n", " <td>22</td>\n", " <td>92</td>\n", " </tr>\n", " <tr>\n", " <th>2457</th>\n", " <td>1610612762</td>\n", " <td>0021500033</td>\n", " <td>OCT 31, 2015</td>\n", " <td>UTA @ IND</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>88</td>\n", " <td>0.443</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>0.722</td>\n", " <td>15</td>\n", " <td>32</td>\n", " <td>47</td>\n", " <td>15</td>\n", " <td>13</td>\n", " <td>4</td>\n", " <td>17</td>\n", " <td>24</td>\n", " <td>97</td>\n", " </tr>\n", " <tr>\n", " <th>2458</th>\n", " <td>1610612762</td>\n", " <td>0021500023</td>\n", " <td>OCT 30, 2015</td>\n", " <td>UTA @ PHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>88</td>\n", " <td>0.409</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0.800</td>\n", " <td>13</td>\n", " <td>38</td>\n", " <td>51</td>\n", " <td>19</td>\n", " <td>10</td>\n", " <td>9</td>\n", " <td>7</td>\n", " <td>22</td>\n", " <td>99</td>\n", " </tr>\n", " <tr>\n", " <th>2459</th>\n", " <td>1610612762</td>\n", " <td>0021500007</td>\n", " <td>OCT 28, 2015</td>\n", " <td>UTA @ DET</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>75</td>\n", " <td>0.467</td>\n", " <td>2</td>\n", " <td>...</td>\n", " <td>0.714</td>\n", " <td>4</td>\n", " <td>34</td>\n", " <td>38</td>\n", " <td>15</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>25</td>\n", " <td>87</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2460 rows × 24 columns</p>\n", "</div>" ], "text/plain": [ " Team_ID Game_ID GAME_DATE MATCHUP WL MIN FGM FGA \\\n", "0 1610612737 0021501221 APR 13, 2016 ATL @ WAS L 240 32 81 \n", "1 1610612737 0021501203 APR 11, 2016 ATL @ CLE L 240 39 87 \n", "2 1610612737 0021501188 APR 09, 2016 ATL vs. BOS W 240 46 88 \n", "3 1610612737 0021501173 APR 07, 2016 ATL vs. TOR W 240 33 76 \n", "4 1610612737 0021501157 APR 05, 2016 ATL vs. PHX W 240 39 95 \n", "5 1610612737 0021501131 APR 01, 2016 ATL vs. CLE L 265 38 95 \n", "6 1610612737 0021501113 MAR 30, 2016 ATL @ TOR L 240 37 83 \n", "7 1610612737 0021501099 MAR 28, 2016 ATL @ CHI W 240 36 85 \n", "8 1610612737 0021501085 MAR 26, 2016 ATL @ DET W 240 43 95 \n", "9 1610612737 0021501076 MAR 25, 2016 ATL vs. MIL W 240 41 97 \n", "10 1610612737 0021501056 MAR 23, 2016 ATL @ WAS W 240 45 84 \n", "11 1610612737 0021501048 MAR 21, 2016 ATL vs. WAS L 240 38 78 \n", "12 1610612737 0021501029 MAR 19, 2016 ATL vs. HOU W 240 44 88 \n", "13 1610612737 0021501015 MAR 17, 2016 ATL vs. DEN W 240 40 80 \n", "14 1610612737 0021501007 MAR 16, 2016 ATL @ DET W 240 39 89 \n", "15 1610612737 0021500984 MAR 13, 2016 ATL vs. IND W 240 40 85 \n", "16 1610612737 0021500974 MAR 12, 2016 ATL vs. MEM W 240 36 84 \n", "17 1610612737 0021500959 MAR 10, 2016 ATL @ TOR L 240 35 82 \n", "18 1610612737 0021500947 MAR 08, 2016 ATL @ UTA W 240 38 80 \n", "19 1610612737 0021500929 MAR 05, 2016 ATL @ LAC W 240 40 89 \n", "20 1610612737 0021500921 MAR 04, 2016 ATL @ LAL W 240 37 68 \n", "21 1610612737 0021500895 MAR 01, 2016 ATL @ GSW L 265 37 80 \n", "22 1610612737 0021500878 FEB 28, 2016 ATL vs. CHA W 240 38 77 \n", "23 1610612737 0021500865 FEB 26, 2016 ATL vs. CHI W 240 37 89 \n", "24 1610612737 0021500836 FEB 22, 2016 ATL vs. GSW L 240 36 86 \n", "25 1610612737 0021500819 FEB 20, 2016 ATL vs. MIL L 290 44 106 \n", "26 1610612737 0021500808 FEB 19, 2016 ATL vs. MIA L 240 41 87 \n", "27 1610612737 0021500796 FEB 10, 2016 ATL @ CHI W 240 43 90 \n", "28 1610612737 0021500780 FEB 08, 2016 ATL vs. ORL L 265 43 92 \n", "29 1610612737 0021500771 FEB 07, 2016 ATL @ ORL L 240 35 91 \n", "... ... ... ... ... .. ... ... ... \n", "2430 1610612762 0021500479 DEC 30, 2015 UTA @ MIN L 240 28 80 \n", "2431 1610612762 0021500467 DEC 28, 2015 UTA vs. PHI W 240 28 84 \n", "2432 1610612762 0021500452 DEC 26, 2015 UTA vs. LAC L 240 36 74 \n", "2433 1610612762 0021500434 DEC 23, 2015 UTA @ GSW L 240 33 80 \n", "2434 1610612762 0021500417 DEC 21, 2015 UTA vs. PHX W 240 36 79 \n", "2435 1610612762 0021500395 DEC 18, 2015 UTA vs. DEN W 240 34 73 \n", "2436 1610612762 0021500380 DEC 16, 2015 UTA vs. NOP L 240 32 67 \n", "2437 1610612762 0021500364 DEC 14, 2015 UTA @ SAS L 240 32 79 \n", "2438 1610612762 0021500356 DEC 13, 2015 UTA @ OKC L 265 39 94 \n", "2439 1610612762 0021500341 DEC 11, 2015 UTA vs. OKC L 240 33 78 \n", "2440 1610612762 0021500327 DEC 09, 2015 UTA vs. NYK W 240 39 80 \n", "2441 1610612762 0021500318 DEC 08, 2015 UTA @ SAC L 240 38 92 \n", "2442 1610612762 0021500297 DEC 05, 2015 UTA vs. IND W 265 43 92 \n", "2443 1610612762 0021500279 DEC 03, 2015 UTA vs. ORL L 240 31 72 \n", "2444 1610612762 0021500259 NOV 30, 2015 UTA vs. GSW L 240 40 89 \n", "2445 1610612762 0021500243 NOV 28, 2015 UTA vs. NOP W 240 38 82 \n", "2446 1610612762 0021500226 NOV 25, 2015 UTA @ LAC W 240 39 77 \n", "2447 1610612762 0021500208 NOV 23, 2015 UTA vs. OKC L 240 28 73 \n", "2448 1610612762 0021500184 NOV 20, 2015 UTA @ DAL L 240 35 80 \n", "2449 1610612762 0021500173 NOV 18, 2015 UTA vs. TOR W 240 35 72 \n", "2450 1610612762 0021500148 NOV 15, 2015 UTA @ ATL W 240 39 76 \n", "2451 1610612762 0021500128 NOV 13, 2015 UTA @ ORL L 240 34 87 \n", "2452 1610612762 0021500124 NOV 12, 2015 UTA @ MIA L 240 34 89 \n", "2453 1610612762 0021500106 NOV 10, 2015 UTA @ CLE L 240 40 82 \n", "2454 1610612762 0021500091 NOV 07, 2015 UTA vs. MEM W 240 31 74 \n", "2455 1610612762 0021500073 NOV 05, 2015 UTA @ DEN W 240 37 85 \n", "2456 1610612762 0021500068 NOV 04, 2015 UTA vs. POR L 240 33 88 \n", "2457 1610612762 0021500033 OCT 31, 2015 UTA @ IND W 240 39 88 \n", "2458 1610612762 0021500023 OCT 30, 2015 UTA @ PHI W 240 36 88 \n", "2459 1610612762 0021500007 OCT 28, 2015 UTA @ DET L 240 35 75 \n", "\n", " FG_PCT FG3M ... FT_PCT OREB DREB REB AST STL BLK TOV PF PTS \n", "0 0.395 11 ... 0.742 9 38 47 22 13 5 22 21 98 \n", "1 0.448 8 ... 0.533 10 32 42 23 8 6 15 18 94 \n", "2 0.523 17 ... 0.818 5 39 44 31 10 10 17 22 118 \n", "3 0.434 12 ... 0.810 5 36 41 23 4 12 13 19 95 \n", "4 0.411 11 ... 0.737 13 37 50 26 16 3 16 21 103 \n", "5 0.400 9 ... 0.885 5 43 48 25 5 8 15 27 108 \n", "6 0.446 14 ... 0.692 11 33 44 24 3 7 18 19 97 \n", "7 0.424 5 ... 0.893 7 41 48 22 4 13 8 13 102 \n", "8 0.453 13 ... 0.929 5 34 39 34 6 7 4 21 112 \n", "9 0.423 5 ... 0.824 17 28 45 26 10 6 11 21 101 \n", "10 0.536 17 ... 0.682 7 34 41 32 10 5 16 17 122 \n", "11 0.487 13 ... 0.765 2 31 33 23 5 6 14 15 102 \n", "12 0.500 14 ... 0.583 9 31 40 32 12 6 16 20 109 \n", "13 0.500 12 ... 0.857 7 33 40 32 5 8 12 16 116 \n", "14 0.438 12 ... 0.824 8 38 46 25 10 3 12 26 118 \n", "15 0.471 15 ... 0.900 9 41 50 27 11 6 15 13 104 \n", "16 0.429 11 ... 0.706 8 39 47 27 9 12 11 14 95 \n", "17 0.427 7 ... 0.704 6 27 33 17 8 5 11 24 96 \n", "18 0.475 8 ... 0.500 4 37 41 15 9 4 16 19 91 \n", "19 0.449 10 ... 0.850 10 43 53 26 9 3 18 26 107 \n", "20 0.544 13 ... 0.731 3 36 39 31 6 8 10 18 106 \n", "21 0.463 12 ... 0.792 7 35 42 25 9 5 17 17 105 \n", "22 0.494 8 ... 0.600 6 42 48 29 4 5 15 19 87 \n", "23 0.416 7 ... 0.917 13 35 48 28 14 11 11 21 103 \n", "24 0.419 10 ... 0.625 8 39 47 23 7 7 17 17 92 \n", "25 0.415 9 ... 0.667 10 39 49 31 11 8 16 27 109 \n", "26 0.471 16 ... 1.000 8 34 42 27 7 4 21 23 111 \n", "27 0.478 13 ... 0.933 11 35 46 25 16 7 12 14 113 \n", "28 0.467 12 ... 0.600 8 39 47 31 9 4 14 20 110 \n", "29 0.385 13 ... 0.733 18 32 50 22 7 7 16 13 94 \n", "... ... ... ... ... ... ... ... ... ... ... ... .. ... \n", "2430 0.350 10 ... 0.700 15 29 44 15 8 8 19 24 80 \n", "2431 0.333 7 ... 0.889 15 38 53 13 11 9 15 18 95 \n", "2432 0.486 10 ... 0.815 8 28 36 18 7 4 16 22 104 \n", "2433 0.413 7 ... 0.857 12 28 40 14 9 3 19 18 85 \n", "2434 0.456 9 ... 0.784 11 36 47 16 8 6 10 16 110 \n", "2435 0.466 10 ... 0.760 5 34 39 15 8 1 14 19 97 \n", "2436 0.478 7 ... 0.793 5 29 34 17 4 4 12 20 94 \n", "2437 0.405 3 ... 0.824 7 25 32 18 8 3 11 23 81 \n", "2438 0.415 8 ... 0.600 16 28 44 14 6 2 11 24 98 \n", "2439 0.423 8 ... 0.667 11 28 39 17 7 4 11 16 90 \n", "2440 0.488 9 ... 0.826 9 42 51 26 6 2 14 26 106 \n", "2441 0.413 15 ... 0.682 19 25 44 23 7 0 12 22 106 \n", "2442 0.467 8 ... 0.757 19 35 54 21 7 3 15 30 122 \n", "2443 0.431 14 ... 0.750 5 34 39 18 4 8 19 16 94 \n", "2444 0.449 6 ... 0.773 10 25 35 18 9 2 8 16 103 \n", "2445 0.463 9 ... 0.762 12 37 49 18 11 9 15 25 101 \n", "2446 0.506 6 ... 0.720 11 28 39 19 12 4 16 20 102 \n", "2447 0.384 5 ... 0.700 13 28 41 15 11 3 21 18 89 \n", "2448 0.438 6 ... 0.810 18 30 48 14 4 2 17 24 93 \n", "2449 0.486 7 ... 0.727 7 34 41 15 9 5 17 20 93 \n", "2450 0.513 7 ... 0.706 10 30 40 21 7 7 17 15 97 \n", "2451 0.391 8 ... 0.739 12 29 41 24 14 6 17 24 93 \n", "2452 0.382 4 ... 0.760 11 31 42 13 10 9 10 23 91 \n", "2453 0.488 10 ... 0.857 13 24 37 30 11 0 16 35 114 \n", "2454 0.419 12 ... 0.833 9 40 49 18 7 10 21 20 89 \n", "2455 0.435 11 ... 0.647 12 31 43 23 6 6 10 26 96 \n", "2456 0.375 5 ... 0.636 16 24 40 9 12 8 9 22 92 \n", "2457 0.443 6 ... 0.722 15 32 47 15 13 4 17 24 97 \n", "2458 0.409 7 ... 0.800 13 38 51 19 10 9 7 22 99 \n", "2459 0.467 2 ... 0.714 4 34 38 15 4 5 12 25 87 \n", "\n", "[2460 rows x 24 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame()\n", "for index, tm in team_list.iterrows():\n", " log = team.TeamGameLogs(team_id=tm['TEAM_ID'])\n", " log_df = log.info().set_index('MATCHUP')\n", " df=df.append(log.info())\n", "df = df.reset_index(drop=True)\n", "df" ] }, { "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>Team_ID</th>\n", " <th>Game_ID</th>\n", " <th>GAME_DATE</th>\n", " <th>MATCHUP</th>\n", " <th>WL</th>\n", " <th>MIN</th>\n", " <th>FGM</th>\n", " <th>FGA</th>\n", " <th>FG_PCT</th>\n", " <th>FG3M</th>\n", " <th>...</th>\n", " <th>REB</th>\n", " <th>AST</th>\n", " <th>STL</th>\n", " <th>BLK</th>\n", " <th>TOV</th>\n", " <th>PF</th>\n", " <th>PTS</th>\n", " <th>Away Team</th>\n", " <th>Home Team</th>\n", " <th>Home Stats</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1610612737</td>\n", " <td>0021501221</td>\n", " <td>APR 13, 2016</td>\n", " <td>ATL @ WAS</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>32</td>\n", " <td>81</td>\n", " <td>0.395</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>47</td>\n", " <td>22</td>\n", " <td>13</td>\n", " <td>5</td>\n", " <td>22</td>\n", " <td>21</td>\n", " <td>98</td>\n", " <td>ATL</td>\n", " <td>WAS</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1610612737</td>\n", " <td>0021501203</td>\n", " <td>APR 11, 2016</td>\n", " <td>ATL @ CLE</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>87</td>\n", " <td>0.448</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>42</td>\n", " <td>23</td>\n", " <td>8</td>\n", " <td>6</td>\n", " <td>15</td>\n", " <td>18</td>\n", " <td>94</td>\n", " <td>ATL</td>\n", " <td>CLE</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1610612737</td>\n", " <td>0021501188</td>\n", " <td>APR 09, 2016</td>\n", " <td>ATL vs. BOS</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>46</td>\n", " <td>88</td>\n", " <td>0.523</td>\n", " <td>17</td>\n", " <td>...</td>\n", " <td>44</td>\n", " <td>31</td>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>17</td>\n", " <td>22</td>\n", " <td>118</td>\n", " <td>BOS</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1610612737</td>\n", " <td>0021501173</td>\n", " <td>APR 07, 2016</td>\n", " <td>ATL vs. TOR</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>76</td>\n", " <td>0.434</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>41</td>\n", " <td>23</td>\n", " <td>4</td>\n", " <td>12</td>\n", " <td>13</td>\n", " <td>19</td>\n", " <td>95</td>\n", " <td>TOR</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1610612737</td>\n", " <td>0021501157</td>\n", " <td>APR 05, 2016</td>\n", " <td>ATL vs. PHX</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>95</td>\n", " <td>0.411</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>50</td>\n", " <td>26</td>\n", " <td>16</td>\n", " <td>3</td>\n", " <td>16</td>\n", " <td>21</td>\n", " <td>103</td>\n", " <td>PHX</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1610612737</td>\n", " <td>0021501131</td>\n", " <td>APR 01, 2016</td>\n", " <td>ATL vs. CLE</td>\n", " <td>L</td>\n", " <td>265</td>\n", " <td>38</td>\n", " <td>95</td>\n", " <td>0.400</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>48</td>\n", " <td>25</td>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>15</td>\n", " <td>27</td>\n", " <td>108</td>\n", " <td>CLE</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1610612737</td>\n", " <td>0021501113</td>\n", " <td>MAR 30, 2016</td>\n", " <td>ATL @ TOR</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>83</td>\n", " <td>0.446</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>44</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>7</td>\n", " <td>18</td>\n", " <td>19</td>\n", " <td>97</td>\n", " <td>ATL</td>\n", " <td>TOR</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1610612737</td>\n", " <td>0021501099</td>\n", " <td>MAR 28, 2016</td>\n", " <td>ATL @ CHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>85</td>\n", " <td>0.424</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>48</td>\n", " <td>22</td>\n", " <td>4</td>\n", " <td>13</td>\n", " <td>8</td>\n", " <td>13</td>\n", " <td>102</td>\n", " <td>ATL</td>\n", " <td>CHI</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1610612737</td>\n", " <td>0021501085</td>\n", " <td>MAR 26, 2016</td>\n", " <td>ATL @ DET</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>43</td>\n", " <td>95</td>\n", " <td>0.453</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>39</td>\n", " <td>34</td>\n", " <td>6</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>21</td>\n", " <td>112</td>\n", " <td>ATL</td>\n", " <td>DET</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1610612737</td>\n", " <td>0021501076</td>\n", " <td>MAR 25, 2016</td>\n", " <td>ATL vs. MIL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>97</td>\n", " <td>0.423</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>45</td>\n", " <td>26</td>\n", " <td>10</td>\n", " <td>6</td>\n", " <td>11</td>\n", " <td>21</td>\n", " <td>101</td>\n", " <td>MIL</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>1610612737</td>\n", " <td>0021501056</td>\n", " <td>MAR 23, 2016</td>\n", " <td>ATL @ WAS</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>45</td>\n", " <td>84</td>\n", " <td>0.536</td>\n", " <td>17</td>\n", " <td>...</td>\n", " <td>41</td>\n", " <td>32</td>\n", " <td>10</td>\n", " <td>5</td>\n", " <td>16</td>\n", " <td>17</td>\n", " <td>122</td>\n", " <td>ATL</td>\n", " <td>WAS</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>1610612737</td>\n", " <td>0021501048</td>\n", " <td>MAR 21, 2016</td>\n", " <td>ATL vs. WAS</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>78</td>\n", " <td>0.487</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>33</td>\n", " <td>23</td>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>14</td>\n", " <td>15</td>\n", " <td>102</td>\n", " <td>WAS</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>1610612737</td>\n", " <td>0021501029</td>\n", " <td>MAR 19, 2016</td>\n", " <td>ATL vs. HOU</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>44</td>\n", " <td>88</td>\n", " <td>0.500</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>40</td>\n", " <td>32</td>\n", " <td>12</td>\n", " <td>6</td>\n", " <td>16</td>\n", " <td>20</td>\n", " <td>109</td>\n", " <td>HOU</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1610612737</td>\n", " <td>0021501015</td>\n", " <td>MAR 17, 2016</td>\n", " <td>ATL vs. DEN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>80</td>\n", " <td>0.500</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>40</td>\n", " <td>32</td>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>12</td>\n", " <td>16</td>\n", " <td>116</td>\n", " <td>DEN</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>1610612737</td>\n", " <td>0021501007</td>\n", " <td>MAR 16, 2016</td>\n", " <td>ATL @ DET</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>89</td>\n", " <td>0.438</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>46</td>\n", " <td>25</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>12</td>\n", " <td>26</td>\n", " <td>118</td>\n", " <td>ATL</td>\n", " <td>DET</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1610612737</td>\n", " <td>0021500984</td>\n", " <td>MAR 13, 2016</td>\n", " <td>ATL vs. IND</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>85</td>\n", " <td>0.471</td>\n", " <td>15</td>\n", " <td>...</td>\n", " <td>50</td>\n", " <td>27</td>\n", " <td>11</td>\n", " <td>6</td>\n", " <td>15</td>\n", " <td>13</td>\n", " <td>104</td>\n", " <td>IND</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>1610612737</td>\n", " <td>0021500974</td>\n", " <td>MAR 12, 2016</td>\n", " <td>ATL vs. MEM</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>84</td>\n", " <td>0.429</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>47</td>\n", " <td>27</td>\n", " <td>9</td>\n", " <td>12</td>\n", " <td>11</td>\n", " <td>14</td>\n", " <td>95</td>\n", " <td>MEM</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1610612737</td>\n", " <td>0021500959</td>\n", " <td>MAR 10, 2016</td>\n", " <td>ATL @ TOR</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>82</td>\n", " <td>0.427</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>33</td>\n", " <td>17</td>\n", " <td>8</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>24</td>\n", " <td>96</td>\n", " <td>ATL</td>\n", " <td>TOR</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>1610612737</td>\n", " <td>0021500947</td>\n", " <td>MAR 08, 2016</td>\n", " <td>ATL @ UTA</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>80</td>\n", " <td>0.475</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>41</td>\n", " <td>15</td>\n", " <td>9</td>\n", " <td>4</td>\n", " <td>16</td>\n", " <td>19</td>\n", " <td>91</td>\n", " <td>ATL</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1610612737</td>\n", " <td>0021500929</td>\n", " <td>MAR 05, 2016</td>\n", " <td>ATL @ LAC</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>89</td>\n", " <td>0.449</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>53</td>\n", " <td>26</td>\n", " <td>9</td>\n", " <td>3</td>\n", " <td>18</td>\n", " <td>26</td>\n", " <td>107</td>\n", " <td>ATL</td>\n", " <td>LAC</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>1610612737</td>\n", " <td>0021500921</td>\n", " <td>MAR 04, 2016</td>\n", " <td>ATL @ LAL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>68</td>\n", " <td>0.544</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>39</td>\n", " <td>31</td>\n", " <td>6</td>\n", " <td>8</td>\n", " <td>10</td>\n", " <td>18</td>\n", " <td>106</td>\n", " <td>ATL</td>\n", " <td>LAL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>1610612737</td>\n", " <td>0021500895</td>\n", " <td>MAR 01, 2016</td>\n", " <td>ATL @ GSW</td>\n", " <td>L</td>\n", " <td>265</td>\n", " <td>37</td>\n", " <td>80</td>\n", " <td>0.463</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>42</td>\n", " <td>25</td>\n", " <td>9</td>\n", " <td>5</td>\n", " <td>17</td>\n", " <td>17</td>\n", " <td>105</td>\n", " <td>ATL</td>\n", " <td>GSW</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>1610612737</td>\n", " <td>0021500878</td>\n", " <td>FEB 28, 2016</td>\n", " <td>ATL vs. CHA</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>77</td>\n", " <td>0.494</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>48</td>\n", " <td>29</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>15</td>\n", " <td>19</td>\n", " <td>87</td>\n", " <td>CHA</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>1610612737</td>\n", " <td>0021500865</td>\n", " <td>FEB 26, 2016</td>\n", " <td>ATL vs. CHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>89</td>\n", " <td>0.416</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>48</td>\n", " <td>28</td>\n", " <td>14</td>\n", " <td>11</td>\n", " <td>11</td>\n", " <td>21</td>\n", " <td>103</td>\n", " <td>CHI</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>1610612737</td>\n", " <td>0021500836</td>\n", " <td>FEB 22, 2016</td>\n", " <td>ATL vs. GSW</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>86</td>\n", " <td>0.419</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>47</td>\n", " <td>23</td>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>17</td>\n", " <td>17</td>\n", " <td>92</td>\n", " <td>GSW</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>1610612737</td>\n", " <td>0021500819</td>\n", " <td>FEB 20, 2016</td>\n", " <td>ATL vs. MIL</td>\n", " <td>L</td>\n", " <td>290</td>\n", " <td>44</td>\n", " <td>106</td>\n", " <td>0.415</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>49</td>\n", " <td>31</td>\n", " <td>11</td>\n", " <td>8</td>\n", " <td>16</td>\n", " <td>27</td>\n", " <td>109</td>\n", " <td>MIL</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>1610612737</td>\n", " <td>0021500808</td>\n", " <td>FEB 19, 2016</td>\n", " <td>ATL vs. MIA</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>87</td>\n", " <td>0.471</td>\n", " <td>16</td>\n", " <td>...</td>\n", " <td>42</td>\n", " <td>27</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>21</td>\n", " <td>23</td>\n", " <td>111</td>\n", " <td>MIA</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>1610612737</td>\n", " <td>0021500796</td>\n", " <td>FEB 10, 2016</td>\n", " <td>ATL @ CHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>43</td>\n", " <td>90</td>\n", " <td>0.478</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>46</td>\n", " <td>25</td>\n", " <td>16</td>\n", " <td>7</td>\n", " <td>12</td>\n", " <td>14</td>\n", " <td>113</td>\n", " <td>ATL</td>\n", " <td>CHI</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>1610612737</td>\n", " <td>0021500780</td>\n", " <td>FEB 08, 2016</td>\n", " <td>ATL vs. ORL</td>\n", " <td>L</td>\n", " <td>265</td>\n", " <td>43</td>\n", " <td>92</td>\n", " <td>0.467</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>47</td>\n", " <td>31</td>\n", " <td>9</td>\n", " <td>4</td>\n", " <td>14</td>\n", " <td>20</td>\n", " <td>110</td>\n", " <td>ORL</td>\n", " <td>ATL</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>1610612737</td>\n", " <td>0021500771</td>\n", " <td>FEB 07, 2016</td>\n", " <td>ATL @ ORL</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>91</td>\n", " <td>0.385</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>50</td>\n", " <td>22</td>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>16</td>\n", " <td>13</td>\n", " <td>94</td>\n", " <td>ATL</td>\n", " <td>ORL</td>\n", " <td>0.0</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>2430</th>\n", " <td>1610612762</td>\n", " <td>0021500479</td>\n", " <td>DEC 30, 2015</td>\n", " <td>UTA @ MIN</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>28</td>\n", " <td>80</td>\n", " <td>0.350</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>44</td>\n", " <td>15</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>19</td>\n", " <td>24</td>\n", " <td>80</td>\n", " <td>UTA</td>\n", " <td>MIN</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2431</th>\n", " <td>1610612762</td>\n", " <td>0021500467</td>\n", " <td>DEC 28, 2015</td>\n", " <td>UTA vs. PHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>28</td>\n", " <td>84</td>\n", " <td>0.333</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>53</td>\n", " <td>13</td>\n", " <td>11</td>\n", " <td>9</td>\n", " <td>15</td>\n", " <td>18</td>\n", " <td>95</td>\n", " <td>PHI</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2432</th>\n", " <td>1610612762</td>\n", " <td>0021500452</td>\n", " <td>DEC 26, 2015</td>\n", " <td>UTA vs. LAC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>74</td>\n", " <td>0.486</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>36</td>\n", " <td>18</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>16</td>\n", " <td>22</td>\n", " <td>104</td>\n", " <td>LAC</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2433</th>\n", " <td>1610612762</td>\n", " <td>0021500434</td>\n", " <td>DEC 23, 2015</td>\n", " <td>UTA @ GSW</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>80</td>\n", " <td>0.413</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>40</td>\n", " <td>14</td>\n", " <td>9</td>\n", " <td>3</td>\n", " <td>19</td>\n", " <td>18</td>\n", " <td>85</td>\n", " <td>UTA</td>\n", " <td>GSW</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2434</th>\n", " <td>1610612762</td>\n", " <td>0021500417</td>\n", " <td>DEC 21, 2015</td>\n", " <td>UTA vs. PHX</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>79</td>\n", " <td>0.456</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>47</td>\n", " <td>16</td>\n", " <td>8</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>16</td>\n", " <td>110</td>\n", " <td>PHX</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2435</th>\n", " <td>1610612762</td>\n", " <td>0021500395</td>\n", " <td>DEC 18, 2015</td>\n", " <td>UTA vs. DEN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>34</td>\n", " <td>73</td>\n", " <td>0.466</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>39</td>\n", " <td>15</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>14</td>\n", " <td>19</td>\n", " <td>97</td>\n", " <td>DEN</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2436</th>\n", " <td>1610612762</td>\n", " <td>0021500380</td>\n", " <td>DEC 16, 2015</td>\n", " <td>UTA vs. NOP</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>32</td>\n", " <td>67</td>\n", " <td>0.478</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>34</td>\n", " <td>17</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>12</td>\n", " <td>20</td>\n", " <td>94</td>\n", " <td>NOP</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2437</th>\n", " <td>1610612762</td>\n", " <td>0021500364</td>\n", " <td>DEC 14, 2015</td>\n", " <td>UTA @ SAS</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>32</td>\n", " <td>79</td>\n", " <td>0.405</td>\n", " <td>3</td>\n", " <td>...</td>\n", " <td>32</td>\n", " <td>18</td>\n", " <td>8</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>23</td>\n", " <td>81</td>\n", " <td>UTA</td>\n", " <td>SAS</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2438</th>\n", " <td>1610612762</td>\n", " <td>0021500356</td>\n", " <td>DEC 13, 2015</td>\n", " <td>UTA @ OKC</td>\n", " <td>L</td>\n", " <td>265</td>\n", " <td>39</td>\n", " <td>94</td>\n", " <td>0.415</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>44</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>11</td>\n", " <td>24</td>\n", " <td>98</td>\n", " <td>UTA</td>\n", " <td>OKC</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2439</th>\n", " <td>1610612762</td>\n", " <td>0021500341</td>\n", " <td>DEC 11, 2015</td>\n", " <td>UTA vs. OKC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>78</td>\n", " <td>0.423</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>39</td>\n", " <td>17</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>11</td>\n", " <td>16</td>\n", " <td>90</td>\n", " <td>OKC</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2440</th>\n", " <td>1610612762</td>\n", " <td>0021500327</td>\n", " <td>DEC 09, 2015</td>\n", " <td>UTA vs. NYK</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>80</td>\n", " <td>0.488</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>51</td>\n", " <td>26</td>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>14</td>\n", " <td>26</td>\n", " <td>106</td>\n", " <td>NYK</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2441</th>\n", " <td>1610612762</td>\n", " <td>0021500318</td>\n", " <td>DEC 08, 2015</td>\n", " <td>UTA @ SAC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>92</td>\n", " <td>0.413</td>\n", " <td>15</td>\n", " <td>...</td>\n", " <td>44</td>\n", " <td>23</td>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>22</td>\n", " <td>106</td>\n", " <td>UTA</td>\n", " <td>SAC</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2442</th>\n", " <td>1610612762</td>\n", " <td>0021500297</td>\n", " <td>DEC 05, 2015</td>\n", " <td>UTA vs. IND</td>\n", " <td>W</td>\n", " <td>265</td>\n", " <td>43</td>\n", " <td>92</td>\n", " <td>0.467</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>54</td>\n", " <td>21</td>\n", " <td>7</td>\n", " <td>3</td>\n", " <td>15</td>\n", " <td>30</td>\n", " <td>122</td>\n", " <td>IND</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2443</th>\n", " <td>1610612762</td>\n", " <td>0021500279</td>\n", " <td>DEC 03, 2015</td>\n", " <td>UTA vs. ORL</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>31</td>\n", " <td>72</td>\n", " <td>0.431</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>39</td>\n", " <td>18</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>19</td>\n", " <td>16</td>\n", " <td>94</td>\n", " <td>ORL</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2444</th>\n", " <td>1610612762</td>\n", " <td>0021500259</td>\n", " <td>NOV 30, 2015</td>\n", " <td>UTA vs. GSW</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>89</td>\n", " <td>0.449</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>35</td>\n", " <td>18</td>\n", " <td>9</td>\n", " <td>2</td>\n", " <td>8</td>\n", " <td>16</td>\n", " <td>103</td>\n", " <td>GSW</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2445</th>\n", " <td>1610612762</td>\n", " <td>0021500243</td>\n", " <td>NOV 28, 2015</td>\n", " <td>UTA vs. NOP</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>82</td>\n", " <td>0.463</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>49</td>\n", " <td>18</td>\n", " <td>11</td>\n", " <td>9</td>\n", " <td>15</td>\n", " <td>25</td>\n", " <td>101</td>\n", " <td>NOP</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2446</th>\n", " <td>1610612762</td>\n", " <td>0021500226</td>\n", " <td>NOV 25, 2015</td>\n", " <td>UTA @ LAC</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>77</td>\n", " <td>0.506</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>39</td>\n", " <td>19</td>\n", " <td>12</td>\n", " <td>4</td>\n", " <td>16</td>\n", " <td>20</td>\n", " <td>102</td>\n", " <td>UTA</td>\n", " <td>LAC</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2447</th>\n", " <td>1610612762</td>\n", " <td>0021500208</td>\n", " <td>NOV 23, 2015</td>\n", " <td>UTA vs. OKC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>28</td>\n", " <td>73</td>\n", " <td>0.384</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>41</td>\n", " <td>15</td>\n", " <td>11</td>\n", " <td>3</td>\n", " <td>21</td>\n", " <td>18</td>\n", " <td>89</td>\n", " <td>OKC</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2448</th>\n", " <td>1610612762</td>\n", " <td>0021500184</td>\n", " <td>NOV 20, 2015</td>\n", " <td>UTA @ DAL</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>80</td>\n", " <td>0.438</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>48</td>\n", " <td>14</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>17</td>\n", " <td>24</td>\n", " <td>93</td>\n", " <td>UTA</td>\n", " <td>DAL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2449</th>\n", " <td>1610612762</td>\n", " <td>0021500173</td>\n", " <td>NOV 18, 2015</td>\n", " <td>UTA vs. TOR</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>72</td>\n", " <td>0.486</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>41</td>\n", " <td>15</td>\n", " <td>9</td>\n", " <td>5</td>\n", " <td>17</td>\n", " <td>20</td>\n", " <td>93</td>\n", " <td>TOR</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2450</th>\n", " <td>1610612762</td>\n", " <td>0021500148</td>\n", " <td>NOV 15, 2015</td>\n", " <td>UTA @ ATL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>76</td>\n", " <td>0.513</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>40</td>\n", " <td>21</td>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>17</td>\n", " <td>15</td>\n", " <td>97</td>\n", " <td>UTA</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2451</th>\n", " <td>1610612762</td>\n", " <td>0021500128</td>\n", " <td>NOV 13, 2015</td>\n", " <td>UTA @ ORL</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>34</td>\n", " <td>87</td>\n", " <td>0.391</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>41</td>\n", " <td>24</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>17</td>\n", " <td>24</td>\n", " <td>93</td>\n", " <td>UTA</td>\n", " <td>ORL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2452</th>\n", " <td>1610612762</td>\n", " <td>0021500124</td>\n", " <td>NOV 12, 2015</td>\n", " <td>UTA @ MIA</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>34</td>\n", " <td>89</td>\n", " <td>0.382</td>\n", " <td>4</td>\n", " <td>...</td>\n", " <td>42</td>\n", " <td>13</td>\n", " <td>10</td>\n", " <td>9</td>\n", " <td>10</td>\n", " <td>23</td>\n", " <td>91</td>\n", " <td>UTA</td>\n", " <td>MIA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2453</th>\n", " <td>1610612762</td>\n", " <td>0021500106</td>\n", " <td>NOV 10, 2015</td>\n", " <td>UTA @ CLE</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>82</td>\n", " <td>0.488</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>37</td>\n", " <td>30</td>\n", " <td>11</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>35</td>\n", " <td>114</td>\n", " <td>UTA</td>\n", " <td>CLE</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2454</th>\n", " <td>1610612762</td>\n", " <td>0021500091</td>\n", " <td>NOV 07, 2015</td>\n", " <td>UTA vs. MEM</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>31</td>\n", " <td>74</td>\n", " <td>0.419</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>49</td>\n", " <td>18</td>\n", " <td>7</td>\n", " <td>10</td>\n", " <td>21</td>\n", " <td>20</td>\n", " <td>89</td>\n", " <td>MEM</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2455</th>\n", " <td>1610612762</td>\n", " <td>0021500073</td>\n", " <td>NOV 05, 2015</td>\n", " <td>UTA @ DEN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>85</td>\n", " <td>0.435</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>43</td>\n", " <td>23</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>26</td>\n", " <td>96</td>\n", " <td>UTA</td>\n", " <td>DEN</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2456</th>\n", " <td>1610612762</td>\n", " <td>0021500068</td>\n", " <td>NOV 04, 2015</td>\n", " <td>UTA vs. POR</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>88</td>\n", " <td>0.375</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>40</td>\n", " <td>9</td>\n", " <td>12</td>\n", " <td>8</td>\n", " <td>9</td>\n", " <td>22</td>\n", " <td>92</td>\n", " <td>POR</td>\n", " <td>UTA</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2457</th>\n", " <td>1610612762</td>\n", " <td>0021500033</td>\n", " <td>OCT 31, 2015</td>\n", " <td>UTA @ IND</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>88</td>\n", " <td>0.443</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>47</td>\n", " <td>15</td>\n", " <td>13</td>\n", " <td>4</td>\n", " <td>17</td>\n", " <td>24</td>\n", " <td>97</td>\n", " <td>UTA</td>\n", " <td>IND</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2458</th>\n", " <td>1610612762</td>\n", " <td>0021500023</td>\n", " <td>OCT 30, 2015</td>\n", " <td>UTA @ PHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>88</td>\n", " <td>0.409</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>51</td>\n", " <td>19</td>\n", " <td>10</td>\n", " <td>9</td>\n", " <td>7</td>\n", " <td>22</td>\n", " <td>99</td>\n", " <td>UTA</td>\n", " <td>PHI</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2459</th>\n", " <td>1610612762</td>\n", " <td>0021500007</td>\n", " <td>OCT 28, 2015</td>\n", " <td>UTA @ DET</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>75</td>\n", " <td>0.467</td>\n", " <td>2</td>\n", " <td>...</td>\n", " <td>38</td>\n", " <td>15</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>25</td>\n", " <td>87</td>\n", " <td>UTA</td>\n", " <td>DET</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2460 rows × 27 columns</p>\n", "</div>" ], "text/plain": [ " Team_ID Game_ID GAME_DATE MATCHUP WL MIN FGM FGA \\\n", "0 1610612737 0021501221 APR 13, 2016 ATL @ WAS L 240 32 81 \n", "1 1610612737 0021501203 APR 11, 2016 ATL @ CLE L 240 39 87 \n", "2 1610612737 0021501188 APR 09, 2016 ATL vs. BOS W 240 46 88 \n", "3 1610612737 0021501173 APR 07, 2016 ATL vs. TOR W 240 33 76 \n", "4 1610612737 0021501157 APR 05, 2016 ATL vs. PHX W 240 39 95 \n", "5 1610612737 0021501131 APR 01, 2016 ATL vs. CLE L 265 38 95 \n", "6 1610612737 0021501113 MAR 30, 2016 ATL @ TOR L 240 37 83 \n", "7 1610612737 0021501099 MAR 28, 2016 ATL @ CHI W 240 36 85 \n", "8 1610612737 0021501085 MAR 26, 2016 ATL @ DET W 240 43 95 \n", "9 1610612737 0021501076 MAR 25, 2016 ATL vs. MIL W 240 41 97 \n", "10 1610612737 0021501056 MAR 23, 2016 ATL @ WAS W 240 45 84 \n", "11 1610612737 0021501048 MAR 21, 2016 ATL vs. WAS L 240 38 78 \n", "12 1610612737 0021501029 MAR 19, 2016 ATL vs. HOU W 240 44 88 \n", "13 1610612737 0021501015 MAR 17, 2016 ATL vs. DEN W 240 40 80 \n", "14 1610612737 0021501007 MAR 16, 2016 ATL @ DET W 240 39 89 \n", "15 1610612737 0021500984 MAR 13, 2016 ATL vs. IND W 240 40 85 \n", "16 1610612737 0021500974 MAR 12, 2016 ATL vs. MEM W 240 36 84 \n", "17 1610612737 0021500959 MAR 10, 2016 ATL @ TOR L 240 35 82 \n", "18 1610612737 0021500947 MAR 08, 2016 ATL @ UTA W 240 38 80 \n", "19 1610612737 0021500929 MAR 05, 2016 ATL @ LAC W 240 40 89 \n", "20 1610612737 0021500921 MAR 04, 2016 ATL @ LAL W 240 37 68 \n", "21 1610612737 0021500895 MAR 01, 2016 ATL @ GSW L 265 37 80 \n", "22 1610612737 0021500878 FEB 28, 2016 ATL vs. CHA W 240 38 77 \n", "23 1610612737 0021500865 FEB 26, 2016 ATL vs. CHI W 240 37 89 \n", "24 1610612737 0021500836 FEB 22, 2016 ATL vs. GSW L 240 36 86 \n", "25 1610612737 0021500819 FEB 20, 2016 ATL vs. MIL L 290 44 106 \n", "26 1610612737 0021500808 FEB 19, 2016 ATL vs. MIA L 240 41 87 \n", "27 1610612737 0021500796 FEB 10, 2016 ATL @ CHI W 240 43 90 \n", "28 1610612737 0021500780 FEB 08, 2016 ATL vs. ORL L 265 43 92 \n", "29 1610612737 0021500771 FEB 07, 2016 ATL @ ORL L 240 35 91 \n", "... ... ... ... ... .. ... ... ... \n", "2430 1610612762 0021500479 DEC 30, 2015 UTA @ MIN L 240 28 80 \n", "2431 1610612762 0021500467 DEC 28, 2015 UTA vs. PHI W 240 28 84 \n", "2432 1610612762 0021500452 DEC 26, 2015 UTA vs. LAC L 240 36 74 \n", "2433 1610612762 0021500434 DEC 23, 2015 UTA @ GSW L 240 33 80 \n", "2434 1610612762 0021500417 DEC 21, 2015 UTA vs. PHX W 240 36 79 \n", "2435 1610612762 0021500395 DEC 18, 2015 UTA vs. DEN W 240 34 73 \n", "2436 1610612762 0021500380 DEC 16, 2015 UTA vs. NOP L 240 32 67 \n", "2437 1610612762 0021500364 DEC 14, 2015 UTA @ SAS L 240 32 79 \n", "2438 1610612762 0021500356 DEC 13, 2015 UTA @ OKC L 265 39 94 \n", "2439 1610612762 0021500341 DEC 11, 2015 UTA vs. OKC L 240 33 78 \n", "2440 1610612762 0021500327 DEC 09, 2015 UTA vs. NYK W 240 39 80 \n", "2441 1610612762 0021500318 DEC 08, 2015 UTA @ SAC L 240 38 92 \n", "2442 1610612762 0021500297 DEC 05, 2015 UTA vs. IND W 265 43 92 \n", "2443 1610612762 0021500279 DEC 03, 2015 UTA vs. ORL L 240 31 72 \n", "2444 1610612762 0021500259 NOV 30, 2015 UTA vs. GSW L 240 40 89 \n", "2445 1610612762 0021500243 NOV 28, 2015 UTA vs. NOP W 240 38 82 \n", "2446 1610612762 0021500226 NOV 25, 2015 UTA @ LAC W 240 39 77 \n", "2447 1610612762 0021500208 NOV 23, 2015 UTA vs. OKC L 240 28 73 \n", "2448 1610612762 0021500184 NOV 20, 2015 UTA @ DAL L 240 35 80 \n", "2449 1610612762 0021500173 NOV 18, 2015 UTA vs. TOR W 240 35 72 \n", "2450 1610612762 0021500148 NOV 15, 2015 UTA @ ATL W 240 39 76 \n", "2451 1610612762 0021500128 NOV 13, 2015 UTA @ ORL L 240 34 87 \n", "2452 1610612762 0021500124 NOV 12, 2015 UTA @ MIA L 240 34 89 \n", "2453 1610612762 0021500106 NOV 10, 2015 UTA @ CLE L 240 40 82 \n", "2454 1610612762 0021500091 NOV 07, 2015 UTA vs. MEM W 240 31 74 \n", "2455 1610612762 0021500073 NOV 05, 2015 UTA @ DEN W 240 37 85 \n", "2456 1610612762 0021500068 NOV 04, 2015 UTA vs. POR L 240 33 88 \n", "2457 1610612762 0021500033 OCT 31, 2015 UTA @ IND W 240 39 88 \n", "2458 1610612762 0021500023 OCT 30, 2015 UTA @ PHI W 240 36 88 \n", "2459 1610612762 0021500007 OCT 28, 2015 UTA @ DET L 240 35 75 \n", "\n", " FG_PCT FG3M ... REB AST STL BLK TOV PF PTS Away Team \\\n", "0 0.395 11 ... 47 22 13 5 22 21 98 ATL \n", "1 0.448 8 ... 42 23 8 6 15 18 94 ATL \n", "2 0.523 17 ... 44 31 10 10 17 22 118 BOS \n", "3 0.434 12 ... 41 23 4 12 13 19 95 TOR \n", "4 0.411 11 ... 50 26 16 3 16 21 103 PHX \n", "5 0.400 9 ... 48 25 5 8 15 27 108 CLE \n", "6 0.446 14 ... 44 24 3 7 18 19 97 ATL \n", "7 0.424 5 ... 48 22 4 13 8 13 102 ATL \n", "8 0.453 13 ... 39 34 6 7 4 21 112 ATL \n", "9 0.423 5 ... 45 26 10 6 11 21 101 MIL \n", "10 0.536 17 ... 41 32 10 5 16 17 122 ATL \n", "11 0.487 13 ... 33 23 5 6 14 15 102 WAS \n", "12 0.500 14 ... 40 32 12 6 16 20 109 HOU \n", "13 0.500 12 ... 40 32 5 8 12 16 116 DEN \n", "14 0.438 12 ... 46 25 10 3 12 26 118 ATL \n", "15 0.471 15 ... 50 27 11 6 15 13 104 IND \n", "16 0.429 11 ... 47 27 9 12 11 14 95 MEM \n", "17 0.427 7 ... 33 17 8 5 11 24 96 ATL \n", "18 0.475 8 ... 41 15 9 4 16 19 91 ATL \n", "19 0.449 10 ... 53 26 9 3 18 26 107 ATL \n", "20 0.544 13 ... 39 31 6 8 10 18 106 ATL \n", "21 0.463 12 ... 42 25 9 5 17 17 105 ATL \n", "22 0.494 8 ... 48 29 4 5 15 19 87 CHA \n", "23 0.416 7 ... 48 28 14 11 11 21 103 CHI \n", "24 0.419 10 ... 47 23 7 7 17 17 92 GSW \n", "25 0.415 9 ... 49 31 11 8 16 27 109 MIL \n", "26 0.471 16 ... 42 27 7 4 21 23 111 MIA \n", "27 0.478 13 ... 46 25 16 7 12 14 113 ATL \n", "28 0.467 12 ... 47 31 9 4 14 20 110 ORL \n", "29 0.385 13 ... 50 22 7 7 16 13 94 ATL \n", "... ... ... ... ... ... ... ... ... .. ... ... \n", "2430 0.350 10 ... 44 15 8 8 19 24 80 UTA \n", "2431 0.333 7 ... 53 13 11 9 15 18 95 PHI \n", "2432 0.486 10 ... 36 18 7 4 16 22 104 LAC \n", "2433 0.413 7 ... 40 14 9 3 19 18 85 UTA \n", "2434 0.456 9 ... 47 16 8 6 10 16 110 PHX \n", "2435 0.466 10 ... 39 15 8 1 14 19 97 DEN \n", "2436 0.478 7 ... 34 17 4 4 12 20 94 NOP \n", "2437 0.405 3 ... 32 18 8 3 11 23 81 UTA \n", "2438 0.415 8 ... 44 14 6 2 11 24 98 UTA \n", "2439 0.423 8 ... 39 17 7 4 11 16 90 OKC \n", "2440 0.488 9 ... 51 26 6 2 14 26 106 NYK \n", "2441 0.413 15 ... 44 23 7 0 12 22 106 UTA \n", "2442 0.467 8 ... 54 21 7 3 15 30 122 IND \n", "2443 0.431 14 ... 39 18 4 8 19 16 94 ORL \n", "2444 0.449 6 ... 35 18 9 2 8 16 103 GSW \n", "2445 0.463 9 ... 49 18 11 9 15 25 101 NOP \n", "2446 0.506 6 ... 39 19 12 4 16 20 102 UTA \n", "2447 0.384 5 ... 41 15 11 3 21 18 89 OKC \n", "2448 0.438 6 ... 48 14 4 2 17 24 93 UTA \n", "2449 0.486 7 ... 41 15 9 5 17 20 93 TOR \n", "2450 0.513 7 ... 40 21 7 7 17 15 97 UTA \n", "2451 0.391 8 ... 41 24 14 6 17 24 93 UTA \n", "2452 0.382 4 ... 42 13 10 9 10 23 91 UTA \n", "2453 0.488 10 ... 37 30 11 0 16 35 114 UTA \n", "2454 0.419 12 ... 49 18 7 10 21 20 89 MEM \n", "2455 0.435 11 ... 43 23 6 6 10 26 96 UTA \n", "2456 0.375 5 ... 40 9 12 8 9 22 92 POR \n", "2457 0.443 6 ... 47 15 13 4 17 24 97 UTA \n", "2458 0.409 7 ... 51 19 10 9 7 22 99 UTA \n", "2459 0.467 2 ... 38 15 4 5 12 25 87 UTA \n", "\n", " Home Team Home Stats \n", "0 WAS 0.0 \n", "1 CLE 0.0 \n", "2 ATL 1.0 \n", "3 ATL 1.0 \n", "4 ATL 1.0 \n", "5 ATL 1.0 \n", "6 TOR 0.0 \n", "7 CHI 0.0 \n", "8 DET 0.0 \n", "9 ATL 1.0 \n", "10 WAS 0.0 \n", "11 ATL 1.0 \n", "12 ATL 1.0 \n", "13 ATL 1.0 \n", "14 DET 0.0 \n", "15 ATL 1.0 \n", "16 ATL 1.0 \n", "17 TOR 0.0 \n", "18 UTA 0.0 \n", "19 LAC 0.0 \n", "20 LAL 0.0 \n", "21 GSW 0.0 \n", "22 ATL 1.0 \n", "23 ATL 1.0 \n", "24 ATL 1.0 \n", "25 ATL 1.0 \n", "26 ATL 1.0 \n", "27 CHI 0.0 \n", "28 ATL 1.0 \n", "29 ORL 0.0 \n", "... ... ... \n", "2430 MIN 0.0 \n", "2431 UTA 1.0 \n", "2432 UTA 1.0 \n", "2433 GSW 0.0 \n", "2434 UTA 1.0 \n", "2435 UTA 1.0 \n", "2436 UTA 1.0 \n", "2437 SAS 0.0 \n", "2438 OKC 0.0 \n", "2439 UTA 1.0 \n", "2440 UTA 1.0 \n", "2441 SAC 0.0 \n", "2442 UTA 1.0 \n", "2443 UTA 1.0 \n", "2444 UTA 1.0 \n", "2445 UTA 1.0 \n", "2446 LAC 0.0 \n", "2447 UTA 1.0 \n", "2448 DAL 0.0 \n", "2449 UTA 1.0 \n", "2450 ATL 0.0 \n", "2451 ORL 0.0 \n", "2452 MIA 0.0 \n", "2453 CLE 0.0 \n", "2454 UTA 1.0 \n", "2455 DEN 0.0 \n", "2456 UTA 1.0 \n", "2457 IND 0.0 \n", "2458 PHI 0.0 \n", "2459 DET 0.0 \n", "\n", "[2460 rows x 27 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for index, game in df.iterrows():\n", " splits = game['MATCHUP'].split(' ')\n", " if splits[1] == '@':\n", " df.set_value(index, 'Away Team', splits[0])\n", " df.set_value(index, 'Home Team', splits[2])\n", " else:\n", " df.set_value(index, 'Home Team', splits[0])\n", " df.set_value(index, 'Away Team', splits[2])\n", " if splits[0] == df.ix[index, 'Home Team']:\n", " df.set_value(index, 'Home Stats', 1)\n", " else:\n", " df.set_value(index, 'Home Stats', 0)\n", "df" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1230" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h_df = df[df['Home Stats'] == 1]\n", "len(h_df)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1230" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a_df = df[df['Home Stats'] == 0]\n", "len(a_df)" ] }, { "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>Team_ID_home</th>\n", " <th>Game_ID</th>\n", " <th>GAME_DATE_home</th>\n", " <th>MATCHUP_home</th>\n", " <th>WL_home</th>\n", " <th>MIN_home</th>\n", " <th>FGM_home</th>\n", " <th>FGA_home</th>\n", " <th>FG_PCT_home</th>\n", " <th>FG3M_home</th>\n", " <th>...</th>\n", " <th>REB_away</th>\n", " <th>AST_away</th>\n", " <th>STL_away</th>\n", " <th>BLK_away</th>\n", " <th>TOV_away</th>\n", " <th>PF_away</th>\n", " <th>PTS_away</th>\n", " <th>Away Team_away</th>\n", " <th>Home Team_away</th>\n", " <th>Home Stats_away</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1610612737</td>\n", " <td>0021501188</td>\n", " <td>APR 09, 2016</td>\n", " <td>ATL vs. BOS</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>46</td>\n", " <td>88</td>\n", " <td>0.523</td>\n", " <td>17</td>\n", " <td>...</td>\n", " <td>40</td>\n", " <td>26</td>\n", " <td>10</td>\n", " <td>6</td>\n", " <td>15</td>\n", " <td>21</td>\n", " <td>107</td>\n", " <td>BOS</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1610612737</td>\n", " <td>0021501173</td>\n", " <td>APR 07, 2016</td>\n", " <td>ATL vs. TOR</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>76</td>\n", " <td>0.434</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>46</td>\n", " <td>16</td>\n", " <td>10</td>\n", " <td>4</td>\n", " <td>11</td>\n", " <td>16</td>\n", " <td>87</td>\n", " <td>TOR</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1610612737</td>\n", " <td>0021501157</td>\n", " <td>APR 05, 2016</td>\n", " <td>ATL vs. PHX</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>95</td>\n", " <td>0.411</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>51</td>\n", " <td>19</td>\n", " <td>13</td>\n", " <td>4</td>\n", " <td>24</td>\n", " <td>19</td>\n", " <td>90</td>\n", " <td>PHX</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1610612737</td>\n", " <td>0021501131</td>\n", " <td>APR 01, 2016</td>\n", " <td>ATL vs. CLE</td>\n", " <td>L</td>\n", " <td>265</td>\n", " <td>38</td>\n", " <td>95</td>\n", " <td>0.400</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>57</td>\n", " <td>27</td>\n", " <td>9</td>\n", " <td>6</td>\n", " <td>12</td>\n", " <td>23</td>\n", " <td>110</td>\n", " <td>CLE</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1610612737</td>\n", " <td>0021501076</td>\n", " <td>MAR 25, 2016</td>\n", " <td>ATL vs. MIL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>97</td>\n", " <td>0.423</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>49</td>\n", " <td>16</td>\n", " <td>4</td>\n", " <td>9</td>\n", " <td>15</td>\n", " <td>16</td>\n", " <td>90</td>\n", " <td>MIL</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1610612737</td>\n", " <td>0021501048</td>\n", " <td>MAR 21, 2016</td>\n", " <td>ATL vs. WAS</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>78</td>\n", " <td>0.487</td>\n", " <td>13</td>\n", " <td>...</td>\n", " <td>44</td>\n", " <td>27</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>9</td>\n", " <td>17</td>\n", " <td>117</td>\n", " <td>WAS</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1610612737</td>\n", " <td>0021501029</td>\n", " <td>MAR 19, 2016</td>\n", " <td>ATL vs. HOU</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>44</td>\n", " <td>88</td>\n", " <td>0.500</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>51</td>\n", " <td>15</td>\n", " <td>11</td>\n", " <td>3</td>\n", " <td>17</td>\n", " <td>17</td>\n", " <td>97</td>\n", " <td>HOU</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1610612737</td>\n", " <td>0021501015</td>\n", " <td>MAR 17, 2016</td>\n", " <td>ATL vs. DEN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>80</td>\n", " <td>0.500</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>39</td>\n", " <td>22</td>\n", " <td>6</td>\n", " <td>4</td>\n", " <td>14</td>\n", " <td>22</td>\n", " <td>98</td>\n", " <td>DEN</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1610612737</td>\n", " <td>0021500984</td>\n", " <td>MAR 13, 2016</td>\n", " <td>ATL vs. IND</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>85</td>\n", " <td>0.471</td>\n", " <td>15</td>\n", " <td>...</td>\n", " <td>38</td>\n", " <td>24</td>\n", " <td>9</td>\n", " <td>5</td>\n", " <td>16</td>\n", " <td>14</td>\n", " <td>75</td>\n", " <td>IND</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1610612737</td>\n", " <td>0021500974</td>\n", " <td>MAR 12, 2016</td>\n", " <td>ATL vs. MEM</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>84</td>\n", " <td>0.429</td>\n", " <td>11</td>\n", " <td>...</td>\n", " <td>58</td>\n", " <td>15</td>\n", " <td>10</td>\n", " <td>5</td>\n", " <td>16</td>\n", " <td>14</td>\n", " <td>83</td>\n", " <td>MEM</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>1610612737</td>\n", " <td>0021500878</td>\n", " <td>FEB 28, 2016</td>\n", " <td>ATL vs. CHA</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>77</td>\n", " <td>0.494</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>41</td>\n", " <td>16</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>6</td>\n", " <td>12</td>\n", " <td>76</td>\n", " <td>CHA</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>1610612737</td>\n", " <td>0021500865</td>\n", " <td>FEB 26, 2016</td>\n", " <td>ATL vs. CHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>89</td>\n", " <td>0.416</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>49</td>\n", " <td>19</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>20</td>\n", " <td>20</td>\n", " <td>88</td>\n", " <td>CHI</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>1610612737</td>\n", " <td>0021500836</td>\n", " <td>FEB 22, 2016</td>\n", " <td>ATL vs. GSW</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>86</td>\n", " <td>0.419</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>43</td>\n", " <td>30</td>\n", " <td>14</td>\n", " <td>8</td>\n", " <td>13</td>\n", " <td>17</td>\n", " <td>102</td>\n", " <td>GSW</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1610612737</td>\n", " <td>0021500819</td>\n", " <td>FEB 20, 2016</td>\n", " <td>ATL vs. MIL</td>\n", " <td>L</td>\n", " <td>290</td>\n", " <td>44</td>\n", " <td>106</td>\n", " <td>0.415</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>65</td>\n", " <td>23</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>17</td>\n", " <td>21</td>\n", " <td>117</td>\n", " <td>MIL</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>1610612737</td>\n", " <td>0021500808</td>\n", " <td>FEB 19, 2016</td>\n", " <td>ATL vs. MIA</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>87</td>\n", " <td>0.471</td>\n", " <td>16</td>\n", " <td>...</td>\n", " <td>46</td>\n", " <td>27</td>\n", " <td>13</td>\n", " <td>1</td>\n", " <td>14</td>\n", " <td>12</td>\n", " <td>115</td>\n", " <td>MIA</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1610612737</td>\n", " <td>0021500780</td>\n", " <td>FEB 08, 2016</td>\n", " <td>ATL vs. ORL</td>\n", " <td>L</td>\n", " <td>265</td>\n", " <td>43</td>\n", " <td>92</td>\n", " <td>0.467</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>47</td>\n", " <td>37</td>\n", " <td>11</td>\n", " <td>8</td>\n", " <td>13</td>\n", " <td>22</td>\n", " <td>117</td>\n", " <td>ORL</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>1610612737</td>\n", " <td>0021500750</td>\n", " <td>FEB 05, 2016</td>\n", " <td>ATL vs. IND</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>76</td>\n", " <td>0.513</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>53</td>\n", " <td>23</td>\n", " <td>6</td>\n", " <td>4</td>\n", " <td>19</td>\n", " <td>18</td>\n", " <td>96</td>\n", " <td>IND</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1610612737</td>\n", " <td>0021500723</td>\n", " <td>FEB 01, 2016</td>\n", " <td>ATL vs. DAL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>42</td>\n", " <td>80</td>\n", " <td>0.525</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>37</td>\n", " <td>14</td>\n", " <td>5</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>18</td>\n", " <td>97</td>\n", " <td>DAL</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>1610612737</td>\n", " <td>0021500687</td>\n", " <td>JAN 27, 2016</td>\n", " <td>ATL vs. LAC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>79</td>\n", " <td>0.418</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>46</td>\n", " <td>18</td>\n", " <td>16</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>14</td>\n", " <td>85</td>\n", " <td>LAC</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1610612737</td>\n", " <td>0021500620</td>\n", " <td>JAN 18, 2016</td>\n", " <td>ATL vs. ORL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>80</td>\n", " <td>0.513</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>42</td>\n", " <td>21</td>\n", " <td>11</td>\n", " <td>5</td>\n", " <td>13</td>\n", " <td>13</td>\n", " <td>81</td>\n", " <td>ORL</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>1610612737</td>\n", " <td>0021500602</td>\n", " <td>JAN 16, 2016</td>\n", " <td>ATL vs. BKN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>44</td>\n", " <td>79</td>\n", " <td>0.557</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>36</td>\n", " <td>22</td>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>17</td>\n", " <td>17</td>\n", " <td>86</td>\n", " <td>BKN</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>1610612737</td>\n", " <td>0021500551</td>\n", " <td>JAN 09, 2016</td>\n", " <td>ATL vs. CHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>49</td>\n", " <td>94</td>\n", " <td>0.521</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>46</td>\n", " <td>16</td>\n", " <td>7</td>\n", " <td>6</td>\n", " <td>21</td>\n", " <td>13</td>\n", " <td>105</td>\n", " <td>CHI</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>1610612737</td>\n", " <td>0021500521</td>\n", " <td>JAN 05, 2016</td>\n", " <td>ATL vs. NYK</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>87</td>\n", " <td>0.425</td>\n", " <td>15</td>\n", " <td>...</td>\n", " <td>46</td>\n", " <td>20</td>\n", " <td>7</td>\n", " <td>6</td>\n", " <td>9</td>\n", " <td>20</td>\n", " <td>107</td>\n", " <td>NYK</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>1610612737</td>\n", " <td>0021500442</td>\n", " <td>DEC 26, 2015</td>\n", " <td>ATL vs. NYK</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>47</td>\n", " <td>88</td>\n", " <td>0.534</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>42</td>\n", " <td>25</td>\n", " <td>6</td>\n", " <td>8</td>\n", " <td>22</td>\n", " <td>15</td>\n", " <td>98</td>\n", " <td>NYK</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>1610612737</td>\n", " <td>0021500429</td>\n", " <td>DEC 23, 2015</td>\n", " <td>ATL vs. DET</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>43</td>\n", " <td>89</td>\n", " <td>0.483</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>46</td>\n", " <td>18</td>\n", " <td>7</td>\n", " <td>9</td>\n", " <td>14</td>\n", " <td>20</td>\n", " <td>100</td>\n", " <td>DET</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>1610612737</td>\n", " <td>0021500413</td>\n", " <td>DEC 21, 2015</td>\n", " <td>ATL vs. POR</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>77</td>\n", " <td>0.481</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>50</td>\n", " <td>24</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>18</td>\n", " <td>20</td>\n", " <td>97</td>\n", " <td>POR</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>1610612737</td>\n", " <td>0021500376</td>\n", " <td>DEC 16, 2015</td>\n", " <td>ATL vs. PHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>48</td>\n", " <td>78</td>\n", " <td>0.615</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>32</td>\n", " <td>20</td>\n", " <td>5</td>\n", " <td>2</td>\n", " <td>22</td>\n", " <td>21</td>\n", " <td>106</td>\n", " <td>PHI</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>1610612737</td>\n", " <td>0021500360</td>\n", " <td>DEC 14, 2015</td>\n", " <td>ATL vs. MIA</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>84</td>\n", " <td>0.393</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>54</td>\n", " <td>26</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>15</td>\n", " <td>16</td>\n", " <td>100</td>\n", " <td>MIA</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>1610612737</td>\n", " <td>0021500347</td>\n", " <td>DEC 12, 2015</td>\n", " <td>ATL vs. SAS</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>30</td>\n", " <td>80</td>\n", " <td>0.375</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>49</td>\n", " <td>26</td>\n", " <td>11</td>\n", " <td>2</td>\n", " <td>23</td>\n", " <td>16</td>\n", " <td>103</td>\n", " <td>SAS</td>\n", " <td>ATL</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>1610612737</td>\n", " <td>0021500286</td>\n", " <td>DEC 04, 2015</td>\n", " <td>ATL vs. LAL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>74</td>\n", " <td>0.500</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>42</td>\n", " <td>19</td>\n", " <td>10</td>\n", " <td>7</td>\n", " <td>16</td>\n", " <td>14</td>\n", " <td>87</td>\n", " <td>LAL</td>\n", " <td>ATL</td>\n", " <td>0.0</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>1200</th>\n", " <td>1610612762</td>\n", " <td>0021500860</td>\n", " <td>FEB 25, 2016</td>\n", " <td>UTA vs. SAS</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>77</td>\n", " <td>0.429</td>\n", " <td>3</td>\n", " <td>...</td>\n", " <td>43</td>\n", " <td>17</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>16</td>\n", " <td>96</td>\n", " <td>SAS</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1201</th>\n", " <td>1610612762</td>\n", " <td>0021500843</td>\n", " <td>FEB 23, 2016</td>\n", " <td>UTA vs. HOU</td>\n", " <td>W</td>\n", " <td>265</td>\n", " <td>38</td>\n", " <td>74</td>\n", " <td>0.514</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>35</td>\n", " <td>17</td>\n", " <td>15</td>\n", " <td>4</td>\n", " <td>14</td>\n", " <td>28</td>\n", " <td>114</td>\n", " <td>HOU</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1202</th>\n", " <td>1610612762</td>\n", " <td>0021500817</td>\n", " <td>FEB 19, 2016</td>\n", " <td>UTA vs. BOS</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>37</td>\n", " <td>68</td>\n", " <td>0.544</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>34</td>\n", " <td>18</td>\n", " <td>8</td>\n", " <td>3</td>\n", " <td>7</td>\n", " <td>31</td>\n", " <td>93</td>\n", " <td>BOS</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1203</th>\n", " <td>1610612762</td>\n", " <td>0021500758</td>\n", " <td>FEB 05, 2016</td>\n", " <td>UTA vs. MIL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>30</td>\n", " <td>77</td>\n", " <td>0.390</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>41</td>\n", " <td>9</td>\n", " <td>12</td>\n", " <td>7</td>\n", " <td>20</td>\n", " <td>17</td>\n", " <td>81</td>\n", " <td>MIL</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1204</th>\n", " <td>1610612762</td>\n", " <td>0021500743</td>\n", " <td>FEB 03, 2016</td>\n", " <td>UTA vs. DEN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>74</td>\n", " <td>0.446</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>41</td>\n", " <td>11</td>\n", " <td>12</td>\n", " <td>2</td>\n", " <td>14</td>\n", " <td>19</td>\n", " <td>81</td>\n", " <td>DEN</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1205</th>\n", " <td>1610612762</td>\n", " <td>0021500728</td>\n", " <td>FEB 01, 2016</td>\n", " <td>UTA vs. CHI</td>\n", " <td>W</td>\n", " <td>265</td>\n", " <td>38</td>\n", " <td>85</td>\n", " <td>0.447</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>42</td>\n", " <td>19</td>\n", " <td>4</td>\n", " <td>7</td>\n", " <td>14</td>\n", " <td>25</td>\n", " <td>96</td>\n", " <td>CHI</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1206</th>\n", " <td>1610612762</td>\n", " <td>0021500704</td>\n", " <td>JAN 29, 2016</td>\n", " <td>UTA vs. MIN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>76</td>\n", " <td>0.526</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>38</td>\n", " <td>17</td>\n", " <td>9</td>\n", " <td>2</td>\n", " <td>14</td>\n", " <td>13</td>\n", " <td>90</td>\n", " <td>MIN</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1207</th>\n", " <td>1610612762</td>\n", " <td>0021500690</td>\n", " <td>JAN 27, 2016</td>\n", " <td>UTA vs. CHA</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>81</td>\n", " <td>0.494</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>35</td>\n", " <td>10</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>18</td>\n", " <td>15</td>\n", " <td>73</td>\n", " <td>CHA</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1208</th>\n", " <td>1610612762</td>\n", " <td>0021500673</td>\n", " <td>JAN 25, 2016</td>\n", " <td>UTA vs. DET</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>34</td>\n", " <td>85</td>\n", " <td>0.400</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>42</td>\n", " <td>19</td>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>18</td>\n", " <td>95</td>\n", " <td>DET</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1209</th>\n", " <td>1610612762</td>\n", " <td>0021500608</td>\n", " <td>JAN 16, 2016</td>\n", " <td>UTA vs. LAL</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>41</td>\n", " <td>83</td>\n", " <td>0.494</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>44</td>\n", " <td>16</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>14</td>\n", " <td>14</td>\n", " <td>82</td>\n", " <td>LAL</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1210</th>\n", " <td>1610612762</td>\n", " <td>0021500591</td>\n", " <td>JAN 14, 2016</td>\n", " <td>UTA vs. SAC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>83</td>\n", " <td>0.422</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>54</td>\n", " <td>19</td>\n", " <td>12</td>\n", " <td>2</td>\n", " <td>15</td>\n", " <td>29</td>\n", " <td>103</td>\n", " <td>SAC</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1211</th>\n", " <td>1610612762</td>\n", " <td>0021500555</td>\n", " <td>JAN 09, 2016</td>\n", " <td>UTA vs. MIA</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>71</td>\n", " <td>0.549</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>39</td>\n", " <td>14</td>\n", " <td>7</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>16</td>\n", " <td>83</td>\n", " <td>MIA</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1212</th>\n", " <td>1610612762</td>\n", " <td>0021500518</td>\n", " <td>JAN 04, 2016</td>\n", " <td>UTA vs. HOU</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>30</td>\n", " <td>75</td>\n", " <td>0.400</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>34</td>\n", " <td>17</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>16</td>\n", " <td>16</td>\n", " <td>93</td>\n", " <td>HOU</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1213</th>\n", " <td>1610612762</td>\n", " <td>0021500503</td>\n", " <td>JAN 02, 2016</td>\n", " <td>UTA vs. MEM</td>\n", " <td>W</td>\n", " <td>265</td>\n", " <td>31</td>\n", " <td>75</td>\n", " <td>0.413</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>40</td>\n", " <td>17</td>\n", " <td>8</td>\n", " <td>4</td>\n", " <td>11</td>\n", " <td>24</td>\n", " <td>87</td>\n", " <td>MEM</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1214</th>\n", " <td>1610612762</td>\n", " <td>0021500489</td>\n", " <td>DEC 31, 2015</td>\n", " <td>UTA vs. POR</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>43</td>\n", " <td>86</td>\n", " <td>0.500</td>\n", " <td>15</td>\n", " <td>...</td>\n", " <td>39</td>\n", " <td>19</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>10</td>\n", " <td>16</td>\n", " <td>96</td>\n", " <td>POR</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1215</th>\n", " <td>1610612762</td>\n", " <td>0021500467</td>\n", " <td>DEC 28, 2015</td>\n", " <td>UTA vs. PHI</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>28</td>\n", " <td>84</td>\n", " <td>0.333</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>37</td>\n", " <td>20</td>\n", " <td>8</td>\n", " <td>9</td>\n", " <td>18</td>\n", " <td>27</td>\n", " <td>91</td>\n", " <td>PHI</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1216</th>\n", " <td>1610612762</td>\n", " <td>0021500452</td>\n", " <td>DEC 26, 2015</td>\n", " <td>UTA vs. LAC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>74</td>\n", " <td>0.486</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>34</td>\n", " <td>24</td>\n", " <td>9</td>\n", " <td>6</td>\n", " <td>12</td>\n", " <td>23</td>\n", " <td>109</td>\n", " <td>LAC</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1217</th>\n", " <td>1610612762</td>\n", " <td>0021500417</td>\n", " <td>DEC 21, 2015</td>\n", " <td>UTA vs. PHX</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>36</td>\n", " <td>79</td>\n", " <td>0.456</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>40</td>\n", " <td>12</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>13</td>\n", " <td>26</td>\n", " <td>89</td>\n", " <td>PHX</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1218</th>\n", " <td>1610612762</td>\n", " <td>0021500395</td>\n", " <td>DEC 18, 2015</td>\n", " <td>UTA vs. DEN</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>34</td>\n", " <td>73</td>\n", " <td>0.466</td>\n", " <td>10</td>\n", " <td>...</td>\n", " <td>44</td>\n", " <td>15</td>\n", " <td>10</td>\n", " <td>4</td>\n", " <td>14</td>\n", " <td>20</td>\n", " <td>88</td>\n", " <td>DEN</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1219</th>\n", " <td>1610612762</td>\n", " <td>0021500380</td>\n", " <td>DEC 16, 2015</td>\n", " <td>UTA vs. NOP</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>32</td>\n", " <td>67</td>\n", " <td>0.478</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>36</td>\n", " <td>14</td>\n", " <td>9</td>\n", " <td>2</td>\n", " <td>10</td>\n", " <td>24</td>\n", " <td>104</td>\n", " <td>NOP</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1220</th>\n", " <td>1610612762</td>\n", " <td>0021500341</td>\n", " <td>DEC 11, 2015</td>\n", " <td>UTA vs. OKC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>78</td>\n", " <td>0.423</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>45</td>\n", " <td>14</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>13</td>\n", " <td>19</td>\n", " <td>94</td>\n", " <td>OKC</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1221</th>\n", " <td>1610612762</td>\n", " <td>0021500327</td>\n", " <td>DEC 09, 2015</td>\n", " <td>UTA vs. NYK</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>39</td>\n", " <td>80</td>\n", " <td>0.488</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>36</td>\n", " <td>20</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>10</td>\n", " <td>24</td>\n", " <td>85</td>\n", " <td>NYK</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1222</th>\n", " <td>1610612762</td>\n", " <td>0021500297</td>\n", " <td>DEC 05, 2015</td>\n", " <td>UTA vs. IND</td>\n", " <td>W</td>\n", " <td>265</td>\n", " <td>43</td>\n", " <td>92</td>\n", " <td>0.467</td>\n", " <td>8</td>\n", " <td>...</td>\n", " <td>44</td>\n", " <td>17</td>\n", " <td>10</td>\n", " <td>2</td>\n", " <td>15</td>\n", " <td>34</td>\n", " <td>119</td>\n", " <td>IND</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1223</th>\n", " <td>1610612762</td>\n", " <td>0021500279</td>\n", " <td>DEC 03, 2015</td>\n", " <td>UTA vs. ORL</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>31</td>\n", " <td>72</td>\n", " <td>0.431</td>\n", " <td>14</td>\n", " <td>...</td>\n", " <td>40</td>\n", " <td>19</td>\n", " <td>13</td>\n", " <td>4</td>\n", " <td>10</td>\n", " <td>20</td>\n", " <td>103</td>\n", " <td>ORL</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1224</th>\n", " <td>1610612762</td>\n", " <td>0021500259</td>\n", " <td>NOV 30, 2015</td>\n", " <td>UTA vs. GSW</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>40</td>\n", " <td>89</td>\n", " <td>0.449</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>45</td>\n", " <td>21</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>15</td>\n", " <td>19</td>\n", " <td>106</td>\n", " <td>GSW</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1225</th>\n", " <td>1610612762</td>\n", " <td>0021500243</td>\n", " <td>NOV 28, 2015</td>\n", " <td>UTA vs. NOP</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>38</td>\n", " <td>82</td>\n", " <td>0.463</td>\n", " <td>9</td>\n", " <td>...</td>\n", " <td>35</td>\n", " <td>19</td>\n", " <td>8</td>\n", " <td>4</td>\n", " <td>16</td>\n", " <td>19</td>\n", " <td>87</td>\n", " <td>NOP</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1226</th>\n", " <td>1610612762</td>\n", " <td>0021500208</td>\n", " <td>NOV 23, 2015</td>\n", " <td>UTA vs. OKC</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>28</td>\n", " <td>73</td>\n", " <td>0.384</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>36</td>\n", " <td>22</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>16</td>\n", " <td>31</td>\n", " <td>111</td>\n", " <td>OKC</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1227</th>\n", " <td>1610612762</td>\n", " <td>0021500173</td>\n", " <td>NOV 18, 2015</td>\n", " <td>UTA vs. TOR</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>35</td>\n", " <td>72</td>\n", " <td>0.486</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>37</td>\n", " <td>14</td>\n", " <td>10</td>\n", " <td>4</td>\n", " <td>14</td>\n", " <td>17</td>\n", " <td>89</td>\n", " <td>TOR</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1228</th>\n", " <td>1610612762</td>\n", " <td>0021500091</td>\n", " <td>NOV 07, 2015</td>\n", " <td>UTA vs. MEM</td>\n", " <td>W</td>\n", " <td>240</td>\n", " <td>31</td>\n", " <td>74</td>\n", " <td>0.419</td>\n", " <td>12</td>\n", " <td>...</td>\n", " <td>44</td>\n", " <td>16</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>14</td>\n", " <td>21</td>\n", " <td>79</td>\n", " <td>MEM</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1229</th>\n", " <td>1610612762</td>\n", " <td>0021500068</td>\n", " <td>NOV 04, 2015</td>\n", " <td>UTA vs. POR</td>\n", " <td>L</td>\n", " <td>240</td>\n", " <td>33</td>\n", " <td>88</td>\n", " <td>0.375</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>48</td>\n", " <td>11</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>19</td>\n", " <td>25</td>\n", " <td>108</td>\n", " <td>POR</td>\n", " <td>UTA</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1230 rows × 53 columns</p>\n", "</div>" ], "text/plain": [ " Team_ID_home Game_ID GAME_DATE_home MATCHUP_home WL_home MIN_home \\\n", "0 1610612737 0021501188 APR 09, 2016 ATL vs. BOS W 240 \n", "1 1610612737 0021501173 APR 07, 2016 ATL vs. TOR W 240 \n", "2 1610612737 0021501157 APR 05, 2016 ATL vs. PHX W 240 \n", "3 1610612737 0021501131 APR 01, 2016 ATL vs. CLE L 265 \n", "4 1610612737 0021501076 MAR 25, 2016 ATL vs. MIL W 240 \n", "5 1610612737 0021501048 MAR 21, 2016 ATL vs. WAS L 240 \n", "6 1610612737 0021501029 MAR 19, 2016 ATL vs. HOU W 240 \n", "7 1610612737 0021501015 MAR 17, 2016 ATL vs. DEN W 240 \n", "8 1610612737 0021500984 MAR 13, 2016 ATL vs. IND W 240 \n", "9 1610612737 0021500974 MAR 12, 2016 ATL vs. MEM W 240 \n", "10 1610612737 0021500878 FEB 28, 2016 ATL vs. CHA W 240 \n", "11 1610612737 0021500865 FEB 26, 2016 ATL vs. CHI W 240 \n", "12 1610612737 0021500836 FEB 22, 2016 ATL vs. GSW L 240 \n", "13 1610612737 0021500819 FEB 20, 2016 ATL vs. MIL L 290 \n", "14 1610612737 0021500808 FEB 19, 2016 ATL vs. MIA L 240 \n", "15 1610612737 0021500780 FEB 08, 2016 ATL vs. ORL L 265 \n", "16 1610612737 0021500750 FEB 05, 2016 ATL vs. IND W 240 \n", "17 1610612737 0021500723 FEB 01, 2016 ATL vs. DAL W 240 \n", "18 1610612737 0021500687 JAN 27, 2016 ATL vs. LAC L 240 \n", "19 1610612737 0021500620 JAN 18, 2016 ATL vs. ORL W 240 \n", "20 1610612737 0021500602 JAN 16, 2016 ATL vs. BKN W 240 \n", "21 1610612737 0021500551 JAN 09, 2016 ATL vs. CHI W 240 \n", "22 1610612737 0021500521 JAN 05, 2016 ATL vs. NYK L 240 \n", "23 1610612737 0021500442 DEC 26, 2015 ATL vs. NYK W 240 \n", "24 1610612737 0021500429 DEC 23, 2015 ATL vs. DET W 240 \n", "25 1610612737 0021500413 DEC 21, 2015 ATL vs. POR W 240 \n", "26 1610612737 0021500376 DEC 16, 2015 ATL vs. PHI W 240 \n", "27 1610612737 0021500360 DEC 14, 2015 ATL vs. MIA L 240 \n", "28 1610612737 0021500347 DEC 12, 2015 ATL vs. SAS L 240 \n", "29 1610612737 0021500286 DEC 04, 2015 ATL vs. LAL W 240 \n", "... ... ... ... ... ... ... \n", "1200 1610612762 0021500860 FEB 25, 2016 UTA vs. SAS L 240 \n", "1201 1610612762 0021500843 FEB 23, 2016 UTA vs. HOU W 265 \n", "1202 1610612762 0021500817 FEB 19, 2016 UTA vs. BOS W 240 \n", "1203 1610612762 0021500758 FEB 05, 2016 UTA vs. MIL W 240 \n", "1204 1610612762 0021500743 FEB 03, 2016 UTA vs. DEN W 240 \n", "1205 1610612762 0021500728 FEB 01, 2016 UTA vs. CHI W 265 \n", "1206 1610612762 0021500704 JAN 29, 2016 UTA vs. MIN W 240 \n", "1207 1610612762 0021500690 JAN 27, 2016 UTA vs. CHA W 240 \n", "1208 1610612762 0021500673 JAN 25, 2016 UTA vs. DET L 240 \n", "1209 1610612762 0021500608 JAN 16, 2016 UTA vs. LAL W 240 \n", "1210 1610612762 0021500591 JAN 14, 2016 UTA vs. SAC L 240 \n", "1211 1610612762 0021500555 JAN 09, 2016 UTA vs. MIA W 240 \n", "1212 1610612762 0021500518 JAN 04, 2016 UTA vs. HOU L 240 \n", "1213 1610612762 0021500503 JAN 02, 2016 UTA vs. MEM W 265 \n", "1214 1610612762 0021500489 DEC 31, 2015 UTA vs. POR W 240 \n", "1215 1610612762 0021500467 DEC 28, 2015 UTA vs. PHI W 240 \n", "1216 1610612762 0021500452 DEC 26, 2015 UTA vs. LAC L 240 \n", "1217 1610612762 0021500417 DEC 21, 2015 UTA vs. PHX W 240 \n", "1218 1610612762 0021500395 DEC 18, 2015 UTA vs. DEN W 240 \n", "1219 1610612762 0021500380 DEC 16, 2015 UTA vs. NOP L 240 \n", "1220 1610612762 0021500341 DEC 11, 2015 UTA vs. OKC L 240 \n", "1221 1610612762 0021500327 DEC 09, 2015 UTA vs. NYK W 240 \n", "1222 1610612762 0021500297 DEC 05, 2015 UTA vs. IND W 265 \n", "1223 1610612762 0021500279 DEC 03, 2015 UTA vs. ORL L 240 \n", "1224 1610612762 0021500259 NOV 30, 2015 UTA vs. GSW L 240 \n", "1225 1610612762 0021500243 NOV 28, 2015 UTA vs. NOP W 240 \n", "1226 1610612762 0021500208 NOV 23, 2015 UTA vs. OKC L 240 \n", "1227 1610612762 0021500173 NOV 18, 2015 UTA vs. TOR W 240 \n", "1228 1610612762 0021500091 NOV 07, 2015 UTA vs. MEM W 240 \n", "1229 1610612762 0021500068 NOV 04, 2015 UTA vs. POR L 240 \n", "\n", " FGM_home FGA_home FG_PCT_home FG3M_home ... REB_away \\\n", "0 46 88 0.523 17 ... 40 \n", "1 33 76 0.434 12 ... 46 \n", "2 39 95 0.411 11 ... 51 \n", "3 38 95 0.400 9 ... 57 \n", "4 41 97 0.423 5 ... 49 \n", "5 38 78 0.487 13 ... 44 \n", "6 44 88 0.500 14 ... 51 \n", "7 40 80 0.500 12 ... 39 \n", "8 40 85 0.471 15 ... 38 \n", "9 36 84 0.429 11 ... 58 \n", "10 38 77 0.494 8 ... 41 \n", "11 37 89 0.416 7 ... 49 \n", "12 36 86 0.419 10 ... 43 \n", "13 44 106 0.415 9 ... 65 \n", "14 41 87 0.471 16 ... 46 \n", "15 43 92 0.467 12 ... 47 \n", "16 39 76 0.513 10 ... 53 \n", "17 42 80 0.525 14 ... 37 \n", "18 33 79 0.418 10 ... 46 \n", "19 41 80 0.513 9 ... 42 \n", "20 44 79 0.557 8 ... 36 \n", "21 49 94 0.521 10 ... 46 \n", "22 37 87 0.425 15 ... 46 \n", "23 47 88 0.534 8 ... 42 \n", "24 43 89 0.483 6 ... 46 \n", "25 37 77 0.481 9 ... 50 \n", "26 48 78 0.615 10 ... 32 \n", "27 33 84 0.393 8 ... 54 \n", "28 30 80 0.375 5 ... 49 \n", "29 37 74 0.500 10 ... 42 \n", "... ... ... ... ... ... ... \n", "1200 33 77 0.429 3 ... 43 \n", "1201 38 74 0.514 10 ... 35 \n", "1202 37 68 0.544 10 ... 34 \n", "1203 30 77 0.390 8 ... 41 \n", "1204 33 74 0.446 5 ... 41 \n", "1205 38 85 0.447 7 ... 42 \n", "1206 40 76 0.526 10 ... 38 \n", "1207 40 81 0.494 12 ... 35 \n", "1208 34 85 0.400 9 ... 42 \n", "1209 41 83 0.494 12 ... 44 \n", "1210 35 83 0.422 6 ... 54 \n", "1211 39 71 0.549 9 ... 39 \n", "1212 30 75 0.400 12 ... 34 \n", "1213 31 75 0.413 9 ... 40 \n", "1214 43 86 0.500 15 ... 39 \n", "1215 28 84 0.333 7 ... 37 \n", "1216 36 74 0.486 10 ... 34 \n", "1217 36 79 0.456 9 ... 40 \n", "1218 34 73 0.466 10 ... 44 \n", "1219 32 67 0.478 7 ... 36 \n", "1220 33 78 0.423 8 ... 45 \n", "1221 39 80 0.488 9 ... 36 \n", "1222 43 92 0.467 8 ... 44 \n", "1223 31 72 0.431 14 ... 40 \n", "1224 40 89 0.449 6 ... 45 \n", "1225 38 82 0.463 9 ... 35 \n", "1226 28 73 0.384 5 ... 36 \n", "1227 35 72 0.486 7 ... 37 \n", "1228 31 74 0.419 12 ... 44 \n", "1229 33 88 0.375 5 ... 48 \n", "\n", " AST_away STL_away BLK_away TOV_away PF_away PTS_away \\\n", "0 26 10 6 15 21 107 \n", "1 16 10 4 11 16 87 \n", "2 19 13 4 24 19 90 \n", "3 27 9 6 12 23 110 \n", "4 16 4 9 15 16 90 \n", "5 27 10 3 9 17 117 \n", "6 15 11 3 17 17 97 \n", "7 22 6 4 14 22 98 \n", "8 24 9 5 16 14 75 \n", "9 15 10 5 16 14 83 \n", "10 16 7 4 6 12 76 \n", "11 19 3 2 20 20 88 \n", "12 30 14 8 13 17 102 \n", "13 23 12 10 17 21 117 \n", "14 27 13 1 14 12 115 \n", "15 37 11 8 13 22 117 \n", "16 23 6 4 19 18 96 \n", "17 14 5 2 6 18 97 \n", "18 18 16 5 11 14 85 \n", "19 21 11 5 13 13 81 \n", "20 22 5 3 17 17 86 \n", "21 16 7 6 21 13 105 \n", "22 20 7 6 9 20 107 \n", "23 25 6 8 22 15 98 \n", "24 18 7 9 14 20 100 \n", "25 24 5 4 18 20 97 \n", "26 20 5 2 22 21 106 \n", "27 26 6 5 15 16 100 \n", "28 26 11 2 23 16 103 \n", "29 19 10 7 16 14 87 \n", "... ... ... ... ... ... ... \n", "1200 17 5 5 11 16 96 \n", "1201 17 15 4 14 28 114 \n", "1202 18 8 3 7 31 93 \n", "1203 9 12 7 20 17 81 \n", "1204 11 12 2 14 19 81 \n", "1205 19 4 7 14 25 96 \n", "1206 17 9 2 14 13 90 \n", "1207 10 6 0 18 15 73 \n", "1208 19 7 1 10 18 95 \n", "1209 16 4 4 14 14 82 \n", "1210 19 12 2 15 29 103 \n", "1211 14 7 8 17 16 83 \n", "1212 17 8 2 16 16 93 \n", "1213 17 8 4 11 24 87 \n", "1214 19 1 0 10 16 96 \n", "1215 20 8 9 18 27 91 \n", "1216 24 9 6 12 23 109 \n", "1217 12 3 0 13 26 89 \n", "1218 15 10 4 14 20 88 \n", "1219 14 9 2 10 24 104 \n", "1220 14 4 2 13 19 94 \n", "1221 20 10 3 10 24 85 \n", "1222 17 10 2 15 34 119 \n", "1223 19 13 4 10 20 103 \n", "1224 21 2 5 15 19 106 \n", "1225 19 8 4 16 19 87 \n", "1226 22 12 10 16 31 111 \n", "1227 14 10 4 14 17 89 \n", "1228 16 8 1 14 21 79 \n", "1229 11 5 7 19 25 108 \n", "\n", " Away Team_away Home Team_away Home Stats_away \n", "0 BOS ATL 0.0 \n", "1 TOR ATL 0.0 \n", "2 PHX ATL 0.0 \n", "3 CLE ATL 0.0 \n", "4 MIL ATL 0.0 \n", "5 WAS ATL 0.0 \n", "6 HOU ATL 0.0 \n", "7 DEN ATL 0.0 \n", "8 IND ATL 0.0 \n", "9 MEM ATL 0.0 \n", "10 CHA ATL 0.0 \n", "11 CHI ATL 0.0 \n", "12 GSW ATL 0.0 \n", "13 MIL ATL 0.0 \n", "14 MIA ATL 0.0 \n", "15 ORL ATL 0.0 \n", "16 IND ATL 0.0 \n", "17 DAL ATL 0.0 \n", "18 LAC ATL 0.0 \n", "19 ORL ATL 0.0 \n", "20 BKN ATL 0.0 \n", "21 CHI ATL 0.0 \n", "22 NYK ATL 0.0 \n", "23 NYK ATL 0.0 \n", "24 DET ATL 0.0 \n", "25 POR ATL 0.0 \n", "26 PHI ATL 0.0 \n", "27 MIA ATL 0.0 \n", "28 SAS ATL 0.0 \n", "29 LAL ATL 0.0 \n", "... ... ... ... \n", "1200 SAS UTA 0.0 \n", "1201 HOU UTA 0.0 \n", "1202 BOS UTA 0.0 \n", "1203 MIL UTA 0.0 \n", "1204 DEN UTA 0.0 \n", "1205 CHI UTA 0.0 \n", "1206 MIN UTA 0.0 \n", "1207 CHA UTA 0.0 \n", "1208 DET UTA 0.0 \n", "1209 LAL UTA 0.0 \n", "1210 SAC UTA 0.0 \n", "1211 MIA UTA 0.0 \n", "1212 HOU UTA 0.0 \n", "1213 MEM UTA 0.0 \n", "1214 POR UTA 0.0 \n", "1215 PHI UTA 0.0 \n", "1216 LAC UTA 0.0 \n", "1217 PHX UTA 0.0 \n", "1218 DEN UTA 0.0 \n", "1219 NOP UTA 0.0 \n", "1220 OKC UTA 0.0 \n", "1221 NYK UTA 0.0 \n", "1222 IND UTA 0.0 \n", "1223 ORL UTA 0.0 \n", "1224 GSW UTA 0.0 \n", "1225 NOP UTA 0.0 \n", "1226 OKC UTA 0.0 \n", "1227 TOR UTA 0.0 \n", "1228 MEM UTA 0.0 \n", "1229 POR UTA 0.0 \n", "\n", "[1230 rows x 53 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "full_sched = pd.merge(h_df, a_df, on = 'Game_ID', suffixes = ['_home', '_away']).reset_index(drop=True)\n", "full_sched" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([ u'Team_ID_home', u'Game_ID', u'GAME_DATE_home',\n", " u'MATCHUP_home', u'WL_home', u'MIN_home',\n", " u'FGM_home', u'FGA_home', u'FG_PCT_home',\n", " u'FG3M_home', u'FG3A_home', u'FG3_PCT_home',\n", " u'FTM_home', u'FTA_home', u'FT_PCT_home',\n", " u'OREB_home', u'DREB_home', u'REB_home',\n", " u'AST_home', u'STL_home', u'BLK_home',\n", " u'TOV_home', u'PF_home', u'PTS_home',\n", " u'Away Team_home', u'Home Team_home', u'Home Stats_home',\n", " u'Team_ID_away', u'GAME_DATE_away', u'MATCHUP_away',\n", " u'WL_away', u'MIN_away', u'FGM_away',\n", " u'FGA_away', u'FG_PCT_away', u'FG3M_away',\n", " u'FG3A_away', u'FG3_PCT_away', u'FTM_away',\n", " u'FTA_away', u'FT_PCT_away', u'OREB_away',\n", " u'DREB_away', u'REB_away', u'AST_away',\n", " u'STL_away', u'BLK_away', u'TOV_away',\n", " u'PF_away', u'PTS_away', u'Away Team_away',\n", " u'Home Team_away', u'Home Stats_away'],\n", " dtype='object')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "full_sched.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cle_id = '1610612739'\n", "cle_dunks = team.TeamShootingSplits(team_id=cle_id, season = '2015-16')\n", "#cle_summary.info()\n", "cle_summary.season_ranks()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
surprisoh/crowdfunding_prediction
3. Distribution Test.ipynb
1
319714
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Distribution Test\n", "## - 각 feature들에 대해 성공/실패 샘플에 대한 분포 검정(평균, 분포)\n", "\n", "## 가정\n", "- 각 카테고리별 분포 차이는 무의미 (샘플수가 너무 작음)\n", "- 지역별 차이는 무의미 (서울, 경기권에 몰려있음)\n", "- target(목표펀딩금액)이 결과에 가장 큰 영향을 미칠 것\n", "- 월별 분포 차이\n", "\n", "## [grammar_level 가정]\n", "\n", "\n", "- grammar_level은 개설자의 수준을 대변\n", "- 맞춤법과 신뢰도는 상관관계 (http://www.copydesk.org/forms/nrj-article-recall.pdf)\n", "- grammar_level이 결과에 영향" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "wadiz_df_original = pd.read_csv('wadiz_df_0329_1.csv', index_col=0)\n", "user_comment = pd.read_csv('user_data_all_0329.csv', index_col=0)\n", "provider_comment = pd.read_csv('provider_data_all_0329.csv', index_col=0)\n", "wadiz_df = pd.read_csv('wadiz_provider_analysis_0329.csv', index_col=0)\n", "provider_comment_grammar = pd.read_csv('comment_analysis.csv', index_col=0)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original DataFrame : 823\n", "User comment : 720\n", "Provider comment : 614\n", "Provider comment grammar check: 599\n", "Revised DataFrame : 599\n" ] } ], "source": [ "# 각 DataFrame별 샘플 수 비교\n", "print('Original DataFrame :', len(wadiz_df_original))\n", "print('User comment :', len(user_comment['project_id'].value_counts()))\n", "print('Provider comment :', len(provider_comment['project_id'].value_counts()))\n", "print('Provider comment grammar check:', len(provider_comment_grammar['project_id'].value_counts()))\n", "print('Revised DataFrame :', len(wadiz_df))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 최종 분석 샘플 : 599개 (0값 제거, grammar_level 측정 가능 샘플만 포함)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# grammar null값 제거\n", "wadiz_df = wadiz_df[wadiz_df['provider_grammar_level'].notnull()]\n", "# duration 처리\n", "wadiz_df['date_duration'] = wadiz_df['date_duration'].apply(lambda x: int(x[:-24]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Kolmogorov-Smirnov test : 두 분포의 차이 검정\n", "* 2 sample T-test : 두 샘플의 평균 차이 검정\n", "* Shapiro test : 단일 분포의 정규성 검정\n", "* Mann-Whitney U test : 비모수 검정법, 두 샘플의 평균 차이 검정" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Category Distribution" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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/r7mu3nDag5M/EJ7wEaupusKrcCSu3iCNgwEA/QhzyCvpKoCQ+qdAmKO30iWe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\nc9E9oPKgM1gJIR2DsQ/0LJXI0c2r7R7/lrH7OM6245PjONs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"text/plain": [ "<matplotlib.figure.Figure at 0xbef3828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = plt.figure(figsize=(10,8));\n", "sns.kdeplot(wadiz_df['funding_rate']);\n", "plt.xlim(-3, 10);\n", "plt.xticks(fontsize=15);\n", "plt.yticks(fontsize=15);\n", "plt.legend(fontsize = 15);\n", "plt.xlabel('funding_rate', fontsize=15);\n", "plt.ylabel('distribution', fontsize = 15);" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[all_sample vs share/public]\n", " K-S statistic : 0.0805\n", " p-value : 0.3296\n", "[all_sample vs book/movie]\n", " K-S statistic : 0.1714\n", " p-value : 0.1709\n", "[all_sample vs education]\n", " K-S statistic : 0.0727\n", " p-value : 0.9751\n", "[all_sample vs life/fashion]\n", " K-S statistic : 0.0761\n", " p-value : 0.6917\n", "[all_sample vs sports]\n", " K-S statistic : 0.3637\n", " p-value : 0.0183\n", "[all_sample vs tech/design]\n", " K-S statistic : 0.1013\n", " p-value : 0.404\n", "[all_sample vs game/comics]\n", " K-S statistic : 0.3634\n", " p-value : 0.2547\n", "[all_sample vs music/concert]\n", " K-S statistic : 0.108\n", " p-value : 0.8133\n", "[all_sample vs environment]\n", " K-S statistic : 0.1409\n", " p-value : 0.6719\n", "[all_sample vs figure/webtoon]\n", " K-S statistic : 0.5962\n", " p-value : 0.1489\n", "[all_sample vs travel]\n", " K-S statistic : 0.2445\n", " p-value : 0.2394\n", "[all_sample vs art/photo/exhibit]\n", " K-S statistic : 0.2148\n", " p-value : 0.1144\n" ] } ], "source": [ "# 전체 분산과 각 category 분산과의 분포 차이 검정\n", "# K-S : Kolmogorov Smirnov test\n", "for i in wadiz_df['category'].unique()[:-1]:\n", " all_data = wadiz_df['funding_rate']\n", " category_data = wadiz_df.loc[wadiz_df['category'] == i]['funding_rate']\n", " print('[all_sample vs {category_i}]'.format(category_i = i)), \n", " print(' K-S statistic :', round(sp.stats.ks_2samp(all_data, category_data)[0], 4))\n", " print(' p-value :', round(sp.stats.ks_2samp(all_data, category_data)[1], 4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 모든 test-statistics의 p-value들이 0.05이상이므로 귀무가설(null hypothesis : 분포가 같다) 기각(reject) 불가" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Area Distribution" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "seoul 387\n", "kyungki 109\n", "busan 19\n", "incheon 15\n", "kyungbuk 12\n", "chungnam 8\n", "jeonbuk 8\n", "kangwon 7\n", "chungbuk 7\n", "deagu 7\n", "deajeon 6\n", "kyungnam 5\n", "gwangju 3\n", "sejong 1\n", "ulsan 1\n", "jeju 1\n", "Name: area, dtype: int64\n" ] } ], "source": [ "print(wadiz_df['area'].value_counts())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 지역별 샘플개수가 작아서 분포의 차이 검정 불가" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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H5cuXY/369bj33nsBAKeffjoWL16cyuERZZVIw/P+y5oAMKMyD0DPoYClCytTPi4iomyX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\nJgRKReB3zeoPP0NdERvr356fTM76IH9+KOxuB9Yd+QSXD7gU6Rdw8b/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"text/plain": [ "<matplotlib.figure.Figure at 0xab13940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 가장 많은 부분을 차지하는 서울/경기만 분석\n", "plt.figure(figsize=(10,8));\n", "sns.kdeplot(wadiz_df.loc[wadiz_df['area'] == 'seoul']['funding_rate'], label = 'seoul');\n", "sns.kdeplot(wadiz_df.loc[wadiz_df['area'] == 'kyungki']['funding_rate'], label = 'kyungki', linestyle = '--');\n", "plt.xlim(-2, 6);\n", "plt.legend(fontsize = 15);\n", "plt.xlabel('funding_rate', fontsize=15);\n", "plt.ylabel('distribution', fontsize = 15);" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "seoul vs kyungki :\n", "Ks_2sampResult(statistic=0.08932508356446911, pvalue=0.48753561349755414)\n", "Ttest_indResult(statistic=0.77921371196874878, pvalue=0.43622695124347299)\n" ] } ], "source": [ "# Ks_2sampResult : Kolmogorov-Smirnov test\n", "# Ttest_indResult : 2 sample T-test\n", "\n", "seoul_dist = wadiz_df.loc[wadiz_df['area'] == 'seoul']['funding_rate']\n", "kyungki_dist = wadiz_df.loc[wadiz_df['area'] == 'kyungki']['funding_rate']\n", "print('seoul vs kyungki :'), \n", "print(sp.stats.ks_2samp(seoul_dist, kyungki_dist))\n", "print(sp.stats.ttest_ind(seoul_dist, kyungki_dist))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 지역간 평균 차이없음 (t-test p-value > 0.05), 분포 차이 없음 (K-S test p-value > 0.05)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Date_duration Distribution" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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gayLIAUAnHBqRa/qMnHlvoSQp0LdvRK+X5rTLMCR3DW26ABDkAKBTQkHuiGfk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\nXMgq+Z5yMZ+4kxdLr2ly0xHR2IMmjT0I8TcS4ONGIx93jK7a6yrwzP1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"text/plain": [ "<matplotlib.figure.Figure at 0xbd29a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = plt.figure(figsize=(10,8))\n", "sns.kdeplot(wadiz_df.loc[wadiz_df['success'] == 1]['date_duration'], label = 'success')\n", "sns.kdeplot(wadiz_df.loc[wadiz_df['success'] == 0]['date_duration'], label = 'fail', c='r', linestyle='--')\n", "plt.xlim(-50, 100);\n", "plt.xlabel('date_distribution', fontsize=15);\n", "plt.ylabel('distribution', fontsize = 15);\n", "plt.legend(fontsize = 15);" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[success_duration vs fail_duration]\n", "Ks_2sampResult(statistic=0.10977604783663342, pvalue=0.051436259213459096)\n", "Ttest_indResult(statistic=-1.616353326840209, pvalue=0.10654888083226143)\n" ] } ], "source": [ "# Ks_2sampResult : Kolmogorov-Smirnov test\n", "# Ttest_indResult : 2 sample T-test\n", "success_duration = wadiz_df.loc[wadiz_df['success'] == 1]['date_duration']\n", "fail_duration = wadiz_df.loc[wadiz_df['success'] == 0]['date_duration']\n", "print('[success_duration vs fail_duration]'), \n", "print(sp.stats.ks_2samp(success_duration, fail_duration)),\n", "print(sp.stats.ttest_ind(success_duration, fail_duration))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 성공/실패 Project 간 duration 차이 없음 (평균, 분포)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Target(목표펀딩금액) Distribution" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TjtuzOXVDY0Mkz5ATbt/CZGcit1JH+xUAgOAQ6tCppN2++rWQzYdNw5RlV8uL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\nevVqhg0bRlhYGFFRURQUFNyyf3x8PJGRkYSGhjJmzBi2b99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x44mMjGTp0qVMmDCB3Nxc\n07Il19086hYVFUVQUBDPPPMMkyZNYt++faxcuRLAtODwzJkzOXjwII8++iiKopgdY9iwYaxcuZId\nO3bw8MMPs3r1ambOnMnChQtvec5btQkhxK9RKfInoRBCCCFEiycjdUIIIYQQrYAUdUIIIYQQrYAU\ndUIIIYQQrYAUdUIIIYQQrYAUdUIIIYQQrYAUdUIIIYQQrYAUdUIIIYQQrYAUdUIIIYQQrcD/A9p3\nAdotGbXBAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xac99518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = plt.figure(figsize=(10,8))\n", "sns.kdeplot(wadiz_df.loc[wadiz_df['success'] == 1]['target'], label = 'success');\n", "sns.kdeplot(wadiz_df.loc[wadiz_df['success'] == 0]['target'], label = 'fail');\n", "#plt.xlim(-3, 10)\n", "plt.xticks(fontsize=15);\n", "plt.yticks(fontsize=15);\n", "plt.legend(fontsize = 15);\n", "plt.xlabel('duration', fontsize=15);\n", "plt.ylabel('distribution', fontsize = 15);" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "success_target = wadiz_df.loc[wadiz_df['success'] == 1]['target']\n", "fail_target = wadiz_df.loc[wadiz_df['success'] == 0]['target']" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shapiro test statistics : 0.7198625802993774\n", "Shapiro test p-value : 1.6610208519768035e-22\n" ] } ], "source": [ "#정규성 test (성공 샘플의 목표펀딩금액)\n", "print('Shapiro test statistics :', sp.stats.shapiro(success_target)[0]),\n", "print('Shapiro test p-value :', sp.stats.shapiro(success_target)[1])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shapiro test statistics : 0.4647589325904846\n", "Shapiro test p-value : 2.2738075406057426e-28\n" ] } ], "source": [ "#정규성 test (실패 샘플의 목표펀딩금액)\n", "print('Shapiro test statistics :', sp.stats.shapiro(fail_target)[0]),\n", "print('Shapiro test p-value :', sp.stats.shapiro(fail_target)[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "두 샘플 모두 정규분포를 이루지않아 (p-value < 0.05, 귀무가설 : 정규분포를 이룬다) t-test보다 mann-whiteney u test로 평균 차이 검정" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ks_2sampResult(statistic=0.099283578721723942, pvalue=0.10013782598635514)\n", "MannwhitneyuResult(statistic=39907.0, pvalue=0.034416467682326438)\n" ] } ], "source": [ "# 분포, 평균 검정\n", "# Ks_2sampResult : Kolmogorov-Smirnov test\n", "# MannwhitneyuResult : Mann-Whiteney U test\n", "print(sp.stats.ks_2samp(success_target, fail_target)),\n", "print(sp.stats.mannwhitneyu(success_target, fail_target))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "검정 결과 두 분포는 동일한 분포(p-value >0.05)이고 평균차이는 존재함 (p-value < 0.05)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 평균은 차이가 있음 (p-value <= 0.05) 분포는 같은 분포를 나타냄(p-vlaue >=0.05)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "성공 Project들의 target 평균 : 4107743.6559485532 Std : 4201818.2984275585\n", "실패 Project들의 target 평균 : 5486219.315789473 Std : 8552866.386800518\n" ] } ], "source": [ "print('성공 Project들의 target 평균 :', np.mean(success_target), \n", " 'Std :', np.std(success_target))\n", "print('실패 Project들의 target 평균 :', np.mean(fail_target),\n", " 'Std :', np.std(fail_target))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Month Distribution" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<월별 프로젝트 수>\n", "11.0 72\n", "1.0 71\n", "12.0 65\n", "10.0 62\n", "8.0 60\n", "9.0 45\n", "7.0 42\n", "6.0 42\n", "2.0 42\n", "5.0 36\n", "4.0 32\n", "3.0 27\n", "Name: month, dtype: int64\n" ] } ], "source": [ "print('<월별 프로젝트 수>')\n", "print(wadiz_df['month'].value_counts())" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<월별 성공/실패 분포>\n" ] }, { "data": { "image/png": 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SREdHN5cNGjQIMpkMsbGxmDJlivXeDBFRDySKInKq81BUX4KyhnKUN1RAq9AgwMkfvZz8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\n", "text/plain": [ "<matplotlib.figure.Figure at 0xba69fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "sns.kdeplot(wadiz_df.loc[wadiz_df['success'] ==1]['month'], label = 'success')\n", "sns.kdeplot(wadiz_df.loc[wadiz_df['success'] ==0]['month'], label = 'fail')\n", "plt.xticks(range(1, 12), fontsize=15)\n", "plt.yticks(fontsize=15)\n", "plt.xlabel('month', fontsize=15)\n", "plt.ylabel('distribution', fontsize = 15)\n", "plt.legend(fontsize = 15)\n", "print('<월별 성공/실패 분포>')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[success_month vs fail_month]\n", "\n", "Ks_2sampResult(statistic=0.038179048908444702, pvalue=0.97975316216512265)\n", "Ttest_indResult(statistic=0.14688205512885788, pvalue=0.8832749535256682)\n" ] } ], "source": [ "# Ks_2sampResult : Kolmogorov-Smirnov test\n", "# Ttest_indResult : 2 sample T-test\n", "success_month = wadiz_df.loc[wadiz_df['success'] ==1]['month']\n", "fail_month = wadiz_df.loc[wadiz_df['success'] ==0]['month']\n", "print('[success_month vs fail_month]'), \n", "print('')\n", "print(sp.stats.ks_2samp(success_month, fail_month))\n", "print(sp.stats.ttest_ind(success_month, fail_month))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* K-S test, t-test 결과 두 분포의 차이는 없음" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. 개설자 Grammar level Distribution" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0xbcbbd68>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PW4rtXbuwvu4dzC2ZjamuSaMah82sRzwhItwXh8Uk+489ERHRiPGAdcop6Uio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\nJGlCCCGEEG5IkjQhhBBCCDf0/wFaXgyH0TZtiwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xbc2b9b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "sns.kdeplot(wadiz_df.loc[wadiz_df['success'] == 1]['provider_grammar_level'], label = 'success')\n", "sns.kdeplot(wadiz_df.loc[wadiz_df['success'] == 0]['provider_grammar_level'], label = 'fail')\n", "plt.xticks(fontsize=15)\n", "plt.yticks(fontsize=15)\n", "plt.xlabel('grammar_level', fontsize=15)\n", "plt.ylabel('distribution', fontsize = 15)\n", "plt.legend(fontsize = 15)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "wadiz_df['log_grammar_level'] = wadiz_df['provider_grammar_level'].apply(lambda x: np.log(x))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0xbd34240>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7qp9RkVOOaydfCafHhT/uexFub8+o3s+mBSI6HYY3Iko4FnsgEIW70/RfDZ9g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"text/plain": [ "<matplotlib.figure.Figure at 0xbcc6a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# log scailing\n", "plt.figure(figsize=(10,8))\n", "sns.kdeplot(wadiz_df.loc[wadiz_df['success'] == 1]['log_grammar_level'], label = 'success')\n", "sns.kdeplot(wadiz_df.loc[wadiz_df['success'] == 0]['log_grammar_level'], label = 'fail')\n", "plt.xticks(fontsize=15)\n", "plt.yticks(fontsize=15)\n", "plt.xlabel('grammar_level', fontsize=15)\n", "plt.ylabel('distribution', fontsize = 15)\n", "plt.legend(fontsize = 15)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "success_grammar = wadiz_df.loc[wadiz_df['success'] == 1]['provider_grammar_level']\n", "fail_grammar = wadiz_df.loc[wadiz_df['success'] == 0]['provider_grammar_level']" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "success_log_grammar = wadiz_df.loc[wadiz_df['success'] == 1]['log_grammar_level']\n", "fail_log_grammar = wadiz_df.loc[wadiz_df['success'] == 0]['log_grammar_level']" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shapiro test statistics : 0.7470260858535767\n", "Shapiro test p-value : 1.6897630811000653e-21\n" ] } ], "source": [ "#정규성 test (성공 샘플의 grammar)\n", "print('Shapiro test statistics :', sp.stats.shapiro(success_grammar)[0]),\n", "print('Shapiro test p-value :', sp.stats.shapiro(success_grammar)[1])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shapiro test statistics : 0.8043199777603149\n", "Shapiro test p-value : 3.117458026664843e-18\n" ] } ], "source": [ "#정규성 test (실패 샘플의 grammar)\n", "print('Shapiro test statistics :', sp.stats.shapiro(fail_grammar)[0]),\n", "print('Shapiro test p-value :', sp.stats.shapiro(fail_grammar)[1])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ks_2sampResult(statistic=0.16116658204998024, pvalue=0.00074889546400997862)\n", "MannwhitneyuResult(statistic=49327.5, pvalue=0.017029217876484169)\n" ] } ], "source": [ "# Ks_2sampResult : Kolmogorov-Smirnov test\n", "# MannwhitneyuResult : Mann-Whiteney U test\n", "print(sp.stats.ks_2samp(success_grammar, fail_grammar)),\n", "print(sp.stats.mannwhitneyu(success_grammar, fail_grammar))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "grammar_level을 변형시키지않고 분석시 정규분포가 성립하지 않음.\n", "Mann-whiteney u test 실시하면 평균차이가 있다는 결과가 나오고 성공/실패 샘플 두 분포는 다른 분포임" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shapiro test statistics : nan\n", "Shapiro test p-value : 1.0\n" ] } ], "source": [ "#정규성 test (실패 샘플의 log_grammar)\n", "print('Shapiro test statistics :', sp.stats.shapiro(success_log_grammar)[0]),\n", "print('Shapiro test p-value :', sp.stats.shapiro(success_log_grammar)[1])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shapiro test statistics : nan\n", "Shapiro test p-value : 1.0\n" ] } ], "source": [ "#정규성 test (성공 샘플의 log_grammar)\n", "print('Shapiro test statistics :', sp.stats.shapiro(fail_log_grammar)[0]),\n", "print('Shapiro test p-value :', sp.stats.shapiro(fail_log_grammar)[1])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ks_2sampResult(statistic=0.16116658204998024, pvalue=0.00074889546400997862)\n", "MannwhitneyuResult(statistic=49327.5, pvalue=0.017029217876484169)\n" ] } ], "source": [ "# Ks_2sampResult : Kolmogorov-Smirnov test\n", "# MannwhitneyuResult : Mann-Whiteney U test\n", "print(sp.stats.ks_2samp(success_log_grammar, fail_log_grammar)),\n", "print(sp.stats.mannwhitneyu(success_log_grammar, fail_log_grammar))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# # Result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 각 Category는 전체 샘플과 동일한 분포를 이룬다.\n", "- 각 Area는 샘플 수가 작아 검정이 불가하다. 하지만 상대적으로 수가 많은 서울/경기는 검정이 가능하나 분포와 평균의 차이는 없다.\n", "- 성공/실패 샘플 간 펀딩기간의 평균차이는 존재하지만 분포의 차이는 존재하지 않는다.\n", "- 성공/실패 샘플 간 월(month)에 따른 분포차이는 없다.\n", "- 성공/실패 샘플 간 목표펀딩금액(target)은 분포는 동일하지만 평균 차이는 존재한다.\n", "- 성공/실패 샘플 간 개설자의 grammar_level 분포의 차이 존재하고 평균의 차이도 존재한다." ] }, { "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
ChangLab/FAST-iCLIP
bin/oldscripts/Fastclip_mm9.ipynb
2
626410
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "import cmath\n", "import math\n", "import sys\n", "import numpy as np\n", "import glob \n", "import subprocess\n", "import re\n", "from matplotlib_venn import venn2\n", "import pandas as pd\n", "from collections import defaultdict\n", "from operator import itemgetter\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import shutil\n", "from optparse import OptionParser\n", "mpl.rcParams['savefig.dpi'] = 2 * mpl.rcParams['savefig.dpi']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "*" }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Hell\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "*" }, { "cell_type": "code", "collapsed": false, "input": [ "global sampleName\n", "global outfilepath\n", "global logFile\n", "global logOpen\n", "\n", "### File name ###\n", "sampleName='MMhur'\n", "infilepath=os.getcwd() + '/' + 'rawdata/'\n", "outfilepath=os.getcwd() + '/results/%s/'%sampleName" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "*" }, { "cell_type": "code", "collapsed": false, "input": [ "# Create log and start pipeline\n", "logFile=outfilepath + \"runLog\"\n", "logOpen=open(logFile, 'w')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "*" }, { "cell_type": "code", "collapsed": false, "input": [ "### Parameters ###\n", "iCLIP3pBarcode='AGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCGATCTCGTATGCCGTCTTCTGCTTG' # Barcode sequence to trim from reads.\n", "q=25 # Minimum quality score to keep during filtering.\n", "p=80 # Percentage of bases that must have quality > q during filtering. \n", "iCLIP5pBasesToTrim=13 # Number of reads to trim from 5' end of clip reads.\n", "k='1' # k=N distinct, valid alignments for each read in bt2 mapping.\n", "threshold=7 # Sum of RT stops (for both replicates) required to keep file. \n", "expand=15 # Bases to expand around RT position after RT stops are merged.\n", "repeat_index=os.getcwd() + '/docs/mm9/repeat/rep' # bt2 index for repeat RNA.\n", "repeatGenomeBuild=os.getcwd()+'/docs/mm9/repeat/Mm9_repeatRNA.fa' # Sequence of repeat index.\n", "repeatAnnotation=os.getcwd()+'/docs/mm9/repeat/Mm_repeatIndex_positions.txt' # Repeat annotation file.\n", "start18s=4007\n", "end18s=5876\n", "start5s=6877\n", "end5s=7033\n", "start28s=8123\n", "end28s=12836\n", "rRNAend=13401\n", "threshold_rep=0 # RT stop threshold for repeat index.\n", "index=os.getcwd() + '/docs/mm9/mm9/mm9' # bt2 index for mapping.\n", "index_tag='mm9' # Name of bt2 index.\n", "genomeFile=os.getcwd()+'/docs/mm9/mm9.sizes' # Genome file for bedGraph, etc.\n", "genomeForCLIPper='-smm9' # Parameter for CLIPper.\n", "blacklistregions=os.getcwd()+'/docs/mm9/mm9-blacklist.bed' # Blacklist masker.\n", "repeatregions=os.getcwd()+'/docs/Mm_mm9_repeatMasker_formatted.bed' # Repeat masker.\n", "geneAnnot=glob.glob(os.getcwd()+'/docs/mm9/genes_types/*') # List of genes by type. \n", "snoRNAmasker=os.getcwd()+'/docs/mm9/snoRNA_reference/mm9_snoRNAmasker_formatted_5pExtend.bed' # snoRNA masker file.\n", "miRNAmasker=os.getcwd()+'/docs/mm9/mm9_miRNA.bed' # miRNA masker file.\n", "fivePUTRBed=os.getcwd()+'/docs/mm9/mm9_5pUTR.bed' # UTR annotation file.\n", "threePUTRBed=os.getcwd()+'/docs/mm9/mm9_3pUTR.bed' # UTR annotation file. \n", "cdsBed=os.getcwd()+'/docs/mm9/mm9_exons.bed' # UTR annotation file. \n", "utrFile=os.getcwd()+'/docs/mm9/mm9_ensembl_UTR_annotation.txt' # UTR annotation file. \n", "genesFile=os.getcwd()+'/docs/mm9/mm9_ensembl_genes.txt' # Gene annotation file. \n", "sizesFile=os.getcwd()+'/docs/mm9/mm9.sizes' # Genome sizes file. \n", "snoRNAindex=os.getcwd()+'/docs/mm9/snoRNA_reference/mm9_sno_coordinates_formatted.bed' # snoRNA coordinate file. " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "*" }, { "cell_type": "code", "collapsed": false, "input": [ "import datetime\n", "now=datetime.datetime.now()\n", "logOpen.write(\"Timestamp:%s\\n\"%str(now))\n", "logOpen.write(\"\\n###Parameters used###\\n\")\n", "logOpen.write(\"3' barcode:%s\\n'\"%iCLIP3pBarcode)\n", "logOpen.write(\"Minimum quality score (q):%s\\n\"%q)\n", "logOpen.write(\"Percentage of bases with > q:%s\\n\"%p)\n", "logOpen.write(\"5' bases to trim:%s\\n'\"%iCLIP5pBasesToTrim)\n", "logOpen.write(\"k distinct, valid alignments for each read in bt2 mapping:%s\\n\"%k)\n", "logOpen.write(\"Threshold for minimum number of RT stops:%s\\n\"%threshold)\n", "logOpen.write(\"Bases for expansion around conserved RT stops:%s\\n\"%expand)\n", "logOpen.write(\"\\n\\n\\n\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "*" }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Processing sample %s\" %(sampleName)\n", "logOpen.write(\"Processing sample: \"+sampleName+'\\n')\n", "read1=infilepath+sampleName+'_R1.fastq'\n", "read2=infilepath+sampleName+'_R2.fastq'\n", "unzippedreads=[read1,read2]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "*" }, { "cell_type": "code", "collapsed": false, "input": [ "def trimReads3p(unzippedreads,adapter3p):\n", " # Usage: Trims a specified adapter sequence from the 3p end of the reads.\n", " # Input: List of fastq files.\n", " # Output: List of 3p trimmed files.\n", " trimparam='-a'+adapter3p # Adapter string\n", " trimmedReads=[]\n", " try:\n", " for inread in unzippedreads:\n", " outread=inread.replace(\"rawdata/\", \"results/%s/\"%sampleName)\n", " outread=outread.replace(\".fastq\", \"_3ptrimmed.fastq\")\n", " process=subprocess.Popen(['fastx_clipper',trimparam,'-n','-l33','-Q33','-i',inread,'-o',outread],stderr=subprocess.STDOUT,stdout=subprocess.PIPE)\n", " stdout, stderr = process.communicate()\n", " logOpen.write(\"Trim 3p end of reads.\\n\")\n", " logOpen.write(\"Stdout: %s.\\n\"%stdout)\n", " logOpen.write(\"Stderr: %s.\\n\"%stderr)\n", " trimmedReads=trimmedReads+[outread]\n", " return trimmedReads\n", " except:\n", " logOpen.write(\"Problem with 3p trimming.\\n\")\n", " print \"Problem with 3p trimming.\"\n", "\n", "print \"Trim 3p adapter from reads.\"\n", "trimmedReads3p=trimReads3p(unzippedreads,iCLIP3pBarcode)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "*" }, { "cell_type": "code", "collapsed": false, "input": [ "def qualityFilter(trim3pReads,q,p):\n", " # Usage: Filters reads based upon quality score.\n", " # Input: List of fastq file names as well as the quality paramters p and q.\n", " # Output: List of modified fastq file names.\n", " qualityparam='-q'+str(q)\n", " percentrageparam='-p'+str(p)\n", " filteredReads=[]\n", " try:\n", " for inread in trim3pReads:\n", " outread=inread.replace(\".fastq\", \"_filter.fastq\")\n", " process=subprocess.Popen(['fastq_quality_filter',qualityparam,percentrageparam,'-Q33','-i',inread,'-o',outread],stderr=subprocess.STDOUT,stdout=subprocess.PIPE)\n", " stdout, stderr=process.communicate()\n", " logOpen.write(\"Perform quality filtering.\\n\")\n", " logOpen.write(\"Stdout: %s.\\n\"%stdout)\n", " logOpen.write(\"Stderr: %s.\\n\"%stderr)\n", " filteredReads=filteredReads+[outread]\n", " return filteredReads\n", " except:\n", " logOpen.write(\"Problem with quality filter.\\n\")\n", " print \"Problem with quality filter.\"\n", "\n", "print \"Perform quality filtering.\"\n", "filteredReads=qualityFilter(trimmedReads3p,q,p)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Perform quality filtering.\n" ] } ], "prompt_number": 80 }, { "cell_type": "code", "collapsed": false, "input": [ "def dupRemoval(filteredReads):\n", " # Usage: Removes duplicate reads.\n", " # Input: List of fastq file names.\n", " # Output: List of reads in FASTA format.\n", " program=os.getcwd() + '/bin/fasta_to_fastq.pl'\n", " noDupes=[]\n", " try:\n", " for inread in filteredReads:\n", " outread=inread.replace(\".fastq\",\"_nodupe.fasta\")\n", " process=subprocess.Popen(['fastx_collapser','-Q33','-i',inread,'-o',outread],stderr=subprocess.STDOUT,stdout=subprocess.PIPE)\n", " stdout, stderr=process.communicate()\n", " logOpen.write(\"Perform duplicate removal.\\n\")\n", " logOpen.write(\"Stdout: %s.\\n\"%stdout)\n", " logOpen.write(\"Stderr: %s.\\n\"%stderr)\n", " fastqOut=outread.replace('.fasta', '.fastq') # fastx_collapser returns fasta files, which are then converted to fastq.\n", " outfh=open(fastqOut, 'w')\n", " process=subprocess.Popen(['perl',program,outread],stdout=outfh)\n", " process.communicate() # Wait for the process to complete.\n", " os.remove(outread) # Remove the remaining .fasta file.\n", " noDupes=noDupes+[fastqOut]\n", " return noDupes\n", " except:\n", " logOpen.write(\"Problem with duplicate removal.\\n\")\n", " print \"Problem with duplicate removal.\"\n", " \n", "print \"Perform duplicate removal.\"\n", "nodupReads=dupRemoval(filteredReads)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Perform duplicate removal.\n" ] } ], "prompt_number": 81 }, { "cell_type": "code", "collapsed": false, "input": [ "def trimReads5p(nodupes,n):\n", " # Usage: Trims a specified number of bases from the 5' end of each read.\n", " # Input: List of fastq files.\n", " # Output: List of 5p trimmed files.\n", " trimparam='-f'+str(n)\n", " trimmedReads=[]\n", " try:\n", " for inread in nodupes:\n", " outread=inread.replace(\".fastq\", \"_5ptrimmed.fastq\")\n", " process=subprocess.Popen(['fastx_trimmer', trimparam, '-Q33', '-i', inread,'-o',outread],stderr=subprocess.STDOUT,stdout=subprocess.PIPE)\n", " stdout, stderr=process.communicate()\n", " logOpen.write(\"Perform 5' barcode trimming.\\n\")\n", " logOpen.write(\"Stdout: %s.\\n\"%stdout)\n", " logOpen.write(\"Stderr: %s.\\n\"%stderr)\n", " trimmedReads=trimmedReads+[outread]\n", " return trimmedReads\n", " except:\n", " logOpen.write(\"Problem with 5' barcode trimming.\\n\")\n", " print \"Problem with 5' barcode trimming.\"\n", "\n", "print \"Perform 5' barcode trimming.\"\n", "trimmedReads5p=trimReads5p(nodupReads,iCLIP5pBasesToTrim)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Perform 5' barcode trimming.\n" ] } ], "prompt_number": 82 }, { "cell_type": "code", "collapsed": false, "input": [ "def runBowtie(fastqFiles,index,index_tag):\n", " # Usage: Read mapping to reference.\n", " # Input: Fastq files of replicate trimmed read files.\n", " # Output: Path to samfile for each read.\n", " program='bowtie2'\n", " mappedReads=[]\n", " unMappedReads=[]\n", " try:\n", " for infastq in fastqFiles:\n", " outfile=infastq.replace(\".fastq\",\"_mappedTo%s.sam\"%index_tag)\n", " unmapped=infastq.replace(\".fastq\",\"_notMappedTo%s.fastq\"%index_tag)\n", " process=subprocess.Popen([program,'-x',index,'-k',k,'-U',infastq,'--un',unmapped,'-S',outfile],stderr=subprocess.STDOUT,stdout=subprocess.PIPE)\n", " stdout,stderr=process.communicate()\n", " logOpen.write(\"Perform mapping to %s index.\\n\"%index_tag)\n", " logOpen.write(\"Stdout: %s.\\n\"%stdout)\n", " logOpen.write(\"Stderr: %s.\\n\"%stderr) \n", " mappedReads = mappedReads + [outfile]\n", " unMappedReads = unMappedReads + [unmapped]\n", " return (mappedReads,unMappedReads)\n", " except:\n", " logOpen.write(\"Problem with mapping.\\n\")\n", " print \"Problem with mapping.\"\n", "\n", "print \"Run mapping to repeat index.\" \n", "mappedReads_rep,unmappedReads_rep=runBowtie(trimmedReads5p,repeat_index,'repeat')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Run mapping to repeat index.\n" ] } ], "prompt_number": 83 }, { "cell_type": "code", "collapsed": false, "input": [ "def runSamtools(samfiles):\n", " # Usage: Samfile processing.\n", " # Input: Sam files from Bowtie mapping.\n", " # Output: Sorted bedFiles.\n", " program = 'samtools'\n", " program2 = 'bamToBed'\n", " outBedFiles=[]\n", " try:\n", " for samfile in samfiles:\n", " bamfile = samfile.replace('.sam','.bam') \n", " proc = subprocess.Popen( [program,'view','-bS','-o', bamfile, samfile]) \n", " proc.communicate()\n", " bamfile_sort = bamfile.replace('.bam','_sorted') \n", " proc2 = subprocess.Popen([program,'sort',bamfile, bamfile_sort])\n", " proc2.communicate()\n", " bedFile = bamfile_sort.replace('_sorted', '_withDupes.bed') \n", " outfh = open(bedFile,'w')\n", " proc3 = subprocess.Popen( [program2,'-i', bamfile_sort+'.bam'],stdout=outfh)\n", " proc3.communicate()\n", " outBedFiles=outBedFiles+[bedFile] \n", " return outBedFiles\n", " except:\n", " logOpen.write(\"Problem with samtools.\\n\")\n", " print \"Problem with samtools.\"\n", "\n", "print \"Run samtools.\"\n", "logOpen.write(\"Run samtools.\\n\")\n", "mappedBedFiles_rep=runSamtools(mappedReads_rep)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Run samtools.\n" ] } ], "prompt_number": 84 }, { "cell_type": "code", "collapsed": false, "input": [ "def seperateStrands(mappedReads):\n", "\t# Usage: Seperate positive and negative strands.\n", "\t# Input: Paths to two bed files from Samtools.\n", "\t# Output: Paths to bed files isolated by strand.\n", " negativeStrand=[]\n", " positiveStrand=[]\n", " for mapFile in mappedReads:\n", " with open(mapFile, 'r') as infile:\n", " neg_strand=mapFile.replace('.bed','_neg.bed')\n", " pos_strand=mapFile.replace('.bed','_pos.bed')\t\n", " neg = open(neg_strand, 'w')\n", " pos = open(pos_strand, 'w')\n", " negativeStrand=negativeStrand+[neg_strand]\n", " positiveStrand=positiveStrand+[pos_strand]\n", " for line in infile:\t\n", " if str(line.strip().split('\\t')[5]) == '-':\n", " neg.write(line)\n", " elif str(line.strip().split('\\t')[5]) == '+':\n", " pos.write(line)\n", " return (negativeStrand,positiveStrand)\n", "\n", "def modifyNegativeStrand(negativeStrandReads):\n", " # Usage: For negative stranded reads, ensure 5' position (RT stop) is listed first.\n", " # Input: Bed file paths to all negative stranded.\n", " # Output: Paths to modified bed files.\n", " negativeStrandEdit=[]\n", " for negativeRead in negativeStrandReads:\n", " neg_strand_edited=negativeRead.replace('_neg.bed','_negEdit.bed')\n", " negativeStrandEdit=negativeStrandEdit+[neg_strand_edited]\n", " neg_edit = open(neg_strand_edited, 'w')\n", " with open(negativeRead, 'r') as infile:\n", " for line in infile:\t\n", " chrom,start,end,name,quality,strand=line.strip().split('\\t')\n", " neg_edit.write('\\t'.join((chrom,end,str(int(end)+30),name,quality,strand))+'\\n')\n", " return negativeStrandEdit\n", "\n", "def isolate5prime(strandedReads):\n", "\t# Usage: Isolate only the Chr, 5' position (RT stop), and strand.\n", "\t# Input: Bed file paths to strand seperated reads.\n", "\t# Output: Paths RT stop files.\n", " RTstops=[]\n", " for reads in strandedReads:\n", " RTstop=reads.replace('.bed','_RTstop.bed')\n", " f = open(RTstop, 'w')\n", " with open(reads, 'r') as infile:\n", " RTstops=RTstops+[RTstop]\n", " for line in infile:\t\n", " chrom,start,end,name,quality,strand=line.strip().split('\\t')\n", " f.write('\\t'.join((chrom,start,strand))+'\\n')\n", " return RTstops\n", "\n", "print \"RT stop isolation (repeat).\"\n", "logOpen.write(\"RT stop isolation (repeat).\\n\")\n", "readsByStrand_rep=seperateStrands(mappedBedFiles_rep)\n", "negativeRTstop_rep=isolate5prime(modifyNegativeStrand(readsByStrand_rep[0])) \n", "positiveRTstop_rep=isolate5prime(readsByStrand_rep[1]) " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "RT stop isolation (repeat).\n" ] } ], "prompt_number": 85 }, { "cell_type": "code", "collapsed": false, "input": [ "def fileCat(destinationFile,fileList):\n", " f = open(destinationFile, \"w\")\n", " for tempfile in fileList:\n", " readfile = open(tempfile, \"r\")\n", " f.write(readfile.read())\n", " readfile.close()\n", " f.close()\n", "\n", "def RTcounts(RTfile):\n", " posRT_R1=pd.DataFrame(pd.read_table(RTfile,index_col=None,header=None,sep='\\t'))\n", " posRT_R1.columns=['Chr','Start','Strand']\n", " cts=posRT_R1.groupby(['Chr','Start']).size()\n", " return cts\n", "\n", "def mergeRT(RTstopFiles,outfilename,threshold,expand,strand):\n", " # Usage: Merge RT stops between replicates and keep only those positions that exceed threshold.\n", " # Input: Files with RT stops for each replicate, outfile, threshold, strand, and bases to expand around RT stop.\n", " # Output: None. Writes merged RT stop file.\n", " cts_R1=RTcounts(RTstopFiles[0])\n", " cts_R2=RTcounts(RTstopFiles[1])\n", " m=pd.concat([cts_R1,cts_R2],axis=1,join='inner')\n", " m.columns=['Rep_1','Rep_2']\n", " m['Sum']=m['Rep_1']+m['Rep_2']\n", " m_filter=m[m['Sum']>threshold]\n", " f = open(outfilename, 'w')\n", " for i in m_filter.index:\n", " chrom=i[0]\n", " RT=i[1]\n", " count=m_filter.loc[i,'Sum']\n", " if RT > expand:\n", " read='\\t'.join((chrom,str(int(RT)-expand),str(int(RT)+expand),'CLIPread','255',strand))+'\\n'\n", " else:\n", " read='\\t'.join((chrom,str(int(RT)),str(int(RT)+expand),'CLIPread','255',strand))+'\\n'\n", " f.write(read*(count))\n", "\n", "print \"Merge RT stops.\"\n", "logOpen.write(\"Merge RT stops.\\n\")\n", "posMerged=outfilepath+sampleName+'_repeat_positivereads.mergedRT'\n", "strand='+'\n", "mergeRT(positiveRTstop_rep,posMerged,threshold_rep,expand,strand)\n", "negMerged=outfilepath+sampleName+'_repeat_negativereads.mergedRT'\n", "strand='-'\n", "mergeRT(negativeRTstop_rep,negMerged,threshold_rep,expand,strand)\n", "negAndPosMerged=outfilepath+sampleName+'_threshold=%s'%threshold_rep+'_repeat_allreads.mergedRT.bed'\n", "fileCat(negAndPosMerged,[posMerged,negMerged])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Merge RT stops.\n" ] } ], "prompt_number": 86 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Run mapping to %s.\"%index_tag\n", "mappedReads,unmappedReads=runBowtie(unmappedReads_rep,index,index_tag)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Run mapping to hg19.\n" ] } ], "prompt_number": 87 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Run samtools.\"\n", "logOpen.write(\"Run samtools.\\n\")\n", "mappedBedFiles=runSamtools(mappedReads)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Run samtools.\n" ] } ], "prompt_number": 88 }, { "cell_type": "code", "collapsed": false, "input": [ "def runRepeatMask(mappedReads,repeatregions):\n", "\t# Usage: Remove repeat regions from bedfile following mapping.\n", "\t# Input: .bed file after mapping (duplicates removed by samtools) and blastlist regions removed.\n", "\t# Output: Bedfile with repeat regions removed.\n", "\tprogram='intersectBed'\n", "\tmasked=[]\n", "\ttry:\n", "\t\tfor bedIn in mappedReads:\n", "\t\t\tnoRepeat=bedIn.replace('.bed','_noRepeat.bed')\n", "\t\t\toutfh=open(noRepeat, 'w')\n", "\t\t\tproc=subprocess.Popen([program,'-a',bedIn,'-b',repeatregions,'-v','-s'],stdout=outfh)\n", "\t\t\tproc.communicate()\n", "\t\t\toutfh.close()\n", "\t\t\tmasked=masked+[noRepeat]\n", "\t\treturn (masked)\n", "\texcept:\n", "\t\tprint \"Problem with repeat masking.\"\n", " logOpen.write(\"Problem with repeat masking.\\n\")\n", " \n", "def runBlacklistRegions(mappedReads,blacklistregions):\n", "\t# Usage: Remove blacklisted regions from bedfile following mapping.\n", "\t# Input: .bed file after mapping (duplicates removed by samtools).\n", "\t# Output: Bedfile with blacklisted regions removed.\n", "\tprogram='intersectBed'\n", "\tblackListed=[]\n", "\ttry:\n", "\t\tfor bedIn in mappedReads:\n", "\t\t\tnoBlacklist=bedIn.replace('.bed','_noBlacklist.bed')\n", "\t\t\toutfh=open(noBlacklist, 'w')\n", "\t\t\tproc=subprocess.Popen([program,'-a',bedIn,'-b',blacklistregions,'-v'],stdout=outfh)\n", "\t\t\tproc.communicate()\n", "\t\t\toutfh.close()\n", "\t\t\tblackListed=blackListed+[noBlacklist]\n", "\t\treturn (blackListed)\n", "\texcept:\n", "\t\tprint \"Problem with blacklist.\"\n", " logOpen.write(\"Problem with blacklist.\\n\")\n", " \n", "print \"Run repeat and blacklist region masker.\"\n", "logOpen.write(\"Run repeat and blacklist masker.\\n\")\n", "blacklistedBedFiles=runBlacklistRegions(mappedBedFiles,blacklistregions)\n", "maskedBedFiles=runRepeatMask(blacklistedBedFiles,repeatregions)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Run repeat and blacklist region masker.\n" ] } ], "prompt_number": 89 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"RT stop isolation.\"\n", "logOpen.write(\"RT stop isolation.\\n\")\n", "readsByStrand=seperateStrands(maskedBedFiles)\n", "negativeRTstop=isolate5prime(modifyNegativeStrand(readsByStrand[0])) \n", "positiveRTstop=isolate5prime(readsByStrand[1]) \n", "\n", "print \"Merge RT stops.\"\n", "logOpen.write(\"Merge RT stops.\\n\")\n", "posMerged=outfilepath+sampleName+'_%s_positivereads.mergedRT'%index_tag\n", "strand='+'\n", "mergeRT(positiveRTstop,posMerged,threshold,expand,strand)\n", "negMerged=outfilepath+sampleName+'_%s_negativereads.mergedRT'%index_tag\n", "strand='-'\n", "mergeRT(negativeRTstop,negMerged,threshold,expand,strand)\n", "negAndPosMerged=outfilepath+sampleName+'_threshold=%s'%threshold+'_%s_allreads.mergedRT.bed'%index_tag\n", "fileCat(negAndPosMerged,[posMerged,negMerged])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "RT stop isolation.\n", "Merge RT stops." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 90 }, { "cell_type": "code", "collapsed": false, "input": [ "def runCLIPPER(RTclusterfile,genome,genomeFile):\n", " # Useage: Process the mergedRT file and pass through CLIPper FDR script.\n", " # Input: Merged RT file.\n", " # Output: CLIPper input (.bed) file and output file.\n", " program='bedToBam'\n", " program2='samtools'\n", " program3='bamToBed'\n", " program4='clipper'\n", " \n", " bamfile=RTclusterfile.replace('.bed','.bam') \n", " outfh=open(bamfile, 'w')\n", " proc=subprocess.Popen([program,'-i',RTclusterfile,'-g',genomeFile],stdout=outfh)\n", " proc.communicate()\n", " \n", " bamfile_sort=bamfile.replace('.bam','.srt')\n", " proc2=subprocess.Popen([program2,'sort',bamfile,bamfile_sort])\n", " proc2.communicate()\n", " \n", " bamfile_sorted=bamfile_sort+'.bam'\n", " mapStats=bamfile_sorted.replace('.srt.bam','.mapStats.txt') \n", " outfh=open(mapStats, 'w')\n", " proc3=subprocess.Popen([program2,'flagstat',bamfile_sorted],stdout=outfh)\n", " proc3.communicate()\n", " \n", " proc4=subprocess.Popen([program2,'index',bamfile_sorted])\n", " proc4.communicate()\n", " \n", " CLIPPERin=bamfile_sorted.replace('.srt.bam','_CLIPPERin.bed') \n", " outfh=open(CLIPPERin, 'w')\n", " proc5=subprocess.Popen([program3,'-i',bamfile_sorted],stdout=outfh)\n", " proc5.communicate()\n", " \n", " CLIPPERout=CLIPPERin.replace('_CLIPPERin.bed','_CLIP_clusters') \n", " proc6=subprocess.Popen([program4,'--bam',bamfile_sorted,genome,'--outfile=%s'%CLIPPERout],)\n", " proc6.communicate()\n", " outfh.close()\n", " \n", " return (CLIPPERin,CLIPPERout)\n", "\n", "def modCLIPPERout(CLIPPERin,CLIPPERout):\n", " # Usage: Process the CLIPper output and isolate lowFDR reads based upon CLIPper windows.\n", " # Input: .bed file passed into CLIPper and the CLIPper windows file.\n", " # Output: Low FDR reads recovered using the CLIPer windows file, genes per cluster, gene list of CLIPper clusters, and CLIPper windows as .bed.\n", " program='intersectBed'\n", " CLIPperOutBed=CLIPPERout+'.bed'\n", " CLIPpeReadsPerCluster=CLIPPERout+'.readsPerCluster'\n", " CLIPpeGeneList=CLIPPERout+'.geneNames'\n", " f = open(CLIPperOutBed,'w')\n", " g = open(CLIPpeReadsPerCluster,'w')\n", " h = open(CLIPpeGeneList,'w')\n", " with open(CLIPPERout,'r') as infile:\n", " for line in infile:\t\n", " try:\n", " # *** Old CLIPper includes a header that cannot be parsed. Handle this. ***\n", " # *** Old CLIPper: Ensembl genes are parsed with <name>_<cluster>_<count>. ***\n", " chrom,start,end,name,stats,strand,start_2,end_2 = line.strip().split('\\t')\n", " readPerCluster=name.strip().split('_')[2]\n", " geneName=name.strip().split('_')[0]\n", " f.write('\\t'.join((chrom,start,end,name,stats,strand))+'\\n')\n", " g.write((readPerCluster+'\\n'))\n", " h.write((geneName+'\\n'))\n", " except:\n", " print \"\"\n", " f.close()\n", " g.close()\n", " h.close()\n", " CLIPPERlowFDR=CLIPperOutBed.replace('.bed','_lowFDRreads.bed')\n", " outfh=open(CLIPPERlowFDR,'w')\n", " # Intersect input reads with the CLIPper windows and report full result for both.\n", " proc=subprocess.Popen([program,'-a',CLIPPERin,'-b',CLIPperOutBed,'-wa','-wb','-s'],stdout=outfh)\n", " proc.communicate()\n", " outfh.close()\n", " return (CLIPPERlowFDR,CLIPpeReadsPerCluster,CLIPpeGeneList,CLIPperOutBed)\n", "\n", "print \"Run CLIPper.\"\n", "logOpen.write(\"Run CLIPper.\\n\")\n", "CLIPPERio=runCLIPPER(negAndPosMerged,genomeForCLIPper,genomeFile)\n", "CLIPPERin=CLIPPERio[0]\n", "CLIPPERout=CLIPPERio[1]\n", "clipperStats=modCLIPPERout(CLIPPERin,CLIPPERout)\n", "CLIPPERlowFDR=clipperStats[0] # Low FDR reads returned filtred through CLIPper windows\n", "CLIPpeReadsPerCluster=clipperStats[1] # Number of reads per CLIPper cluster\n", "CLIPpeGeneList=clipperStats[2] # Gene names returned from the CLIPper file\n", "CLIPperOutBed=clipperStats[3] # CLIPper windows as a bed file" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Run CLIPper.\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 91 }, { "cell_type": "code", "collapsed": false, "input": [ "def getBedCenterPoints(inBed):\n", " # Usage: Obtain ceter coordiantes of bedFile.\n", " # Input: BedFile.\n", " # Output: Center coodinates returned.\n", " outBed=inBed.replace('.bed','_centerCoord.bed')\t\n", " f=open(outBed, 'w')\n", " with open(inBed, 'r') as infile:\n", " for line in infile:\t\n", " elementList=line.strip().split('\\t')\n", " f.write('\\t'.join((elementList[0],str(int(elementList[1])+expand),str(int(elementList[1])+expand+1),elementList[9],elementList[4],elementList[5],'\\n')))\n", " f.close()\n", " return outBed\n", "\n", "def cleanBedFile(inBed):\n", " # Usage: Sort and recover only first 6 fields from a bed file.\n", " # Input: BedFile.\n", " # Output: Sorted bedFile with correct number of fields.\n", " program='sortBed'\n", " CLIPperOutBed=inBed.replace('.bed','_cleaned.bed')\t\n", " sortedBed=CLIPperOutBed.replace('_cleaned.bed','_cleaned_sorted.bed')\n", " f=open(CLIPperOutBed, 'w')\n", " with open(inBed, 'r') as infile:\n", " for line in infile:\t\n", " elementList=line.strip().split('\\t')\n", " f.write('\\t'.join((elementList[0],elementList[1],elementList[2],elementList[3],elementList[4],elementList[5],'\\n')))\n", " f.close()\n", " outfh=open(sortedBed, 'w')\n", " proc=subprocess.Popen([program, '-i', CLIPperOutBed],stdout=outfh)\n", " proc.communicate()\n", " outfh.close()\n", " return sortedBed\n", "\n", "def makeBedGraph(lowFDRreads,sizesFile):\n", " # Usage: From a bedFile, generate a bedGraph and bigWig.\n", " # Input: BedFile.\n", " # Output: BedGraph file.\n", " program='genomeCoverageBed'\n", " program2=os.getcwd() + '/bin/bedGraphToBigWig'\n", " cleanBed=cleanBedFile(lowFDRreads)\n", " outname=cleanBed.replace('.bed','.bedgraph')\n", " outname2=cleanBed.replace('.bed','.bw')\n", " outfh=open(outname,'w')\n", " proc=subprocess.Popen([program,'-bg','-split','-i',cleanBed,'-g',sizesFile],stdout=outfh)\n", " proc.communicate()\n", " outfh2=open(outname2,'w')\n", " proc2=subprocess.Popen([program2,outname,sizesFile,outname2],stdout=subprocess.PIPE)\n", " proc2.communicate()\n", " return outname\n", "\n", "print \"Make bedGraph\"\n", "logOpen.write(\"Make bedGraph.\\n\")\n", "bedGraphCLIPout=makeBedGraph(CLIPPERlowFDR,genomeFile)\n", "CLIPPERlowFDRcenters=getBedCenterPoints(CLIPPERlowFDR)\n", "allLowFDRCentersBedGraph=makeBedGraph(CLIPPERlowFDRcenters,genomeFile)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Make bedGraph\n" ] } ], "prompt_number": 92 }, { "cell_type": "code", "collapsed": false, "input": [ "def filterSnoRNAs(proteinCodingReads,snoRNAmasker,miRNAmasker):\n", " # Usage: Filter snoRNA and miRNAs from protein coding reads.\n", " # Input: .bed file with protein coding reads.\n", " # Output: snoRNA and miR filtered .bed file.\n", " program='intersectBed'\n", " proteinWithoutsnoRNAs=proteinCodingReads.replace('.bed','_snoRNAremoved.bed')\n", " proteinWithoutmiRNAs=proteinWithoutsnoRNAs.replace('.bed','_miRNAremoved.bed')\n", " outfh=open(proteinWithoutsnoRNAs, 'w')\n", " proc=subprocess.Popen([program,'-a',proteinCodingReads,'-b',snoRNAmasker,'-v','-s'],stdout=outfh)\n", " proc.communicate()\n", " outfh.close()\n", " outfh=open(proteinWithoutmiRNAs, 'w')\n", " proc=subprocess.Popen([program,'-a',proteinWithoutsnoRNAs,'-b',miRNAmasker,'-v','-s'],stdout=outfh)\n", " proc.communicate()\n", " outfh.close()\n", " return (proteinWithoutmiRNAs)\n", "\n", "def getLowFDRReadTypes(CLIPPERlowFDR,pathToGeneLists):\n", " # Usage: Given a list of genes, return all reads for the associated genes.\n", " # Input: Gene list and the path to lowFDR read file.\n", " # Output: List of reads assocaited with the given genes.\n", " lowFDRgenelist=[]\n", " for path in pathToGeneLists:\n", " outfile=path+'_LowFDRreads.bed'\n", " proc=subprocess.Popen('grep -F -f %s %s > %s'%(path,CLIPPERlowFDR,outfile),shell=True)\n", " proc.communicate()\n", " return_code=proc.wait() # *** Remove later. ***\n", " lowFDRgenelist=lowFDRgenelist+[outfile]\n", " return lowFDRgenelist\n", "\n", "def compareLists(list1,list2,outname):\n", " # Usage: Compare gene lists and output matches to the file. \n", " # Input: Two gene lists.\n", " # Output: Path file containing the matching genes.\n", " f=open(list1,'r')\n", " g=open(list2,'r')\n", " commonGenes=set(f.readlines()) & set(g.readlines())\n", " geneCategory=outname.split('.')[1]\n", " outputName=outfilepath+'clipGenes_'+geneCategory\n", " outfh=open(outputName,'w')\n", " for gene in commonGenes:\n", " outfh.write(gene)\n", " outfh.close()\n", " return outputName\n", "\n", "def getLowFDRGeneTypes(CLIPpeGeneList,geneAnnot):\n", " # Usage: Get all genes listed under each type, compare to CLIPper targets.\n", " # Input: .bed file passed into CLIPper and the CLIPper windows file.\n", " # Output: Path to file containing all CLIPper genes of each type.\n", " geneTypes=[]\n", " for genepath in geneAnnot:\n", " lowFDRgenes=compareLists(CLIPpeGeneList,genepath,os.path.split(genepath)[1])\n", " geneTypes=geneTypes+[lowFDRgenes]\n", " return geneTypes\n", "\n", "print \"Partition reads by type.\"\n", "logOpen.write(\"Partition reads by type.\\n\")\n", "\n", "pathToGeneLists=getLowFDRGeneTypes(CLIPpeGeneList,geneAnnot)\n", "pathToReadLists=getLowFDRReadTypes(CLIPPERlowFDR,pathToGeneLists)\n", "\n", "proteinCodingReads=outfilepath+'clipGenes_proteinCoding_LowFDRreads.bed'\n", "proteinBedGraph=makeBedGraph(proteinCodingReads,genomeFile)\n", "filteredProteinCodingCenters=filterSnoRNAs(getBedCenterPoints(proteinCodingReads),snoRNAmasker,miRNAmasker)\n", "filteredProteinCentersBedGraph=makeBedGraph(filteredProteinCodingCenters,genomeFile)\n", "\n", "lincRNAReads=outfilepath+'clipGenes_lincRNA_LowFDRreads.bed'\n", "filteredLincRNACenters=filterSnoRNAs(getBedCenterPoints(lincRNAReads),snoRNAmasker,miRNAmasker)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Partition reads by type.\n" ] } ], "prompt_number": 93 }, { "cell_type": "code", "collapsed": false, "input": [ "def sortFilteredBed(bedFile):\n", " bf=pd.DataFrame(pd.read_table(bedFile,header=None))\n", " bf.columns=['Chr','Start','Stop','CLIPper_name','Q','Strand']\n", " geneCounts=countHitsPerGene(bf)\n", " return geneCounts\n", "\n", "def countHitsPerGene(bf):\n", " # *** THIS MAY DEPEND UPON THE VERSION OF CLIPPER USED ***\n", " bf['geneName']=bf['CLIPper_name'].apply(lambda x: x.split('_')[0])\n", " geneCounts=bf.groupby('geneName').size()\n", " geneCounts.sort(ascending=False)\n", " return geneCounts\n", "\n", "def getSnoRNAreads(CLIPPERlowFDRcenters,snoRNAindex):\n", " program='intersectBed'\t\t\n", " bedFile=outfilepath+'clipGenes_snoRNA_LowFDRreads.bed'\n", " outfh=open(bedFile, 'w')\n", " proc=subprocess.Popen([program,'-a',CLIPPERlowFDRcenters,'-b',snoRNAindex,'-s','-wa','-wb'],stdout=outfh)\n", " proc.communicate()\n", " outfh.close()\t\n", " return bedFile\n", "\n", "def countSnoRNAs(bedFile_sno):\n", " bf=pd.DataFrame(pd.read_table(bedFile_sno,header=None))\n", " bf.columns=['Chr','Start','End','CLIPper_name','Q','Strand','Chr_snoRNA','Start_snoRNA','Stop_snoRNA','name_snoRNA','Type','strand_snoRNA']\n", " geneCounts=bf.groupby('name_snoRNA').size()\n", " geneCounts.sort(ascending=False)\n", " return geneCounts\n", "\n", "def countRemainingGeneTypes(remaining):\n", " for bedFile in remaining:\n", " try:\n", " bf=pd.DataFrame(pd.read_table(bedFile,header=None))\n", " bf.columns=['Chr','Start','End','ReadName','Q','Strand','CLIPper_winChr','CLIPper_winStart','CLIPper_winEmd','CLIPper_winaName','CLIPper_winP','CLIPper_winStrand']\n", " # *** THIS MAY DEPEND UPON THE VERSION OF CLIPPER USED ***\n", " bf['geneName']=bf['CLIPper_winaName'].apply(lambda x: x.split('_')[0])\n", " geneCounts=bf.groupby('geneName').size()\n", " geneCounts.sort(ascending=False) \n", " \n", " head,fname=os.path.split(bedFile)\n", " geneType=fname.split(\"_\")[1]\n", " outfilepathToSave=outfilepath+'/PlotData_ReadsPerGene_%s'%geneType\n", " geneCounts.to_csv(outfilepathToSave)\n", " \n", " except ValueError:\n", " print \"No reads in %s\"%bedFile\n", "\n", "print \"Generate sorted gene lists by gene type.\"\n", "logOpen.write(\"Generate sorted gene lists by gene type.\\n\")\n", "\n", "bedFile_pc=outfilepath+\"clipGenes_proteinCoding_LowFDRreads_centerCoord_snoRNAremoved_miRNAremoved.bed\"\n", "geneCounts_pc=sortFilteredBed(bedFile_pc) \n", "outfilepathToSave=outfilepath + '/PlotData_ReadsPerGene_proteinCoding'\n", "geneCounts_pc.to_csv(outfilepathToSave)\n", "\n", "bedFile_linc=outfilepath+\"clipGenes_lincRNA_LowFDRreads_centerCoord_snoRNAremoved_miRNAremoved.bed\"\n", "geneCounts_linc=sortFilteredBed(bedFile_linc)\n", "outfilepathToSave=outfilepath + '/PlotData_ReadsPerGene_lincRNA'\n", "geneCounts_linc.to_csv(outfilepathToSave)\n", "\n", "CLIPPERlowFDRcenters=getBedCenterPoints(CLIPPERlowFDR)\n", "allLowFDRCentersBedGraph=makeBedGraph(CLIPPERlowFDRcenters,genomeFile)\n", "bedFile_sno=getSnoRNAreads(CLIPPERlowFDRcenters,snoRNAindex)\n", "geneCounts_sno=countSnoRNAs(bedFile_sno) \n", "outfilepathToSave=outfilepath + '/PlotData_ReadsPerGene_snoRNA'\n", "geneCounts_sno.to_csv(outfilepathToSave)\n", " \n", "remaining=[f for f in glob.glob(outfilepath+\"*_LowFDRreads.bed\") if 'lincRNA' not in f and 'proteinCoding' not in f and 'snoRNA' not in f]\n", "countRemainingGeneTypes(remaining)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Generate sorted gene lists by gene type.\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_tRNA_LowFDRreads.bed" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_senseOverlapping_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_pseudogenes_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_TR_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_LRG_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_rRNA_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_3pncRNA_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_senseIntronic_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_IG_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_miR_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_snRNA_LowFDRreads.bed" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 409 }, { "cell_type": "code", "collapsed": false, "input": [ "def makeClusterCenter(windowsFile):\n", " # Usage: Generate a file of cluster centers.\n", " # Input: Raw CLIPper output file.\n", " # Output: File with coordinates for the center of each CLIPper cluster.\n", " cleanBed = cleanBedFile(windowsFile)\n", " centers=cleanBed.replace('.bed','.clusterCenter')\n", " f = open(centers, 'w')\n", " with open(cleanBed, 'r') as infile:\n", " for line in infile:\n", " elementList = line.strip().split('\\t')\n", " diff=abs(int((int(elementList[1])-int(elementList[2]))/2))\n", " f.write(elementList[0]+'\\t'+str(int(elementList[1])+diff)+'\\t'+str(int(elementList[1])+diff+1)+'\\n')\n", " f.close()\n", " return centers\n", "\n", "def getClusterIntensity(bedGraph,centerCoordinates):\n", " # Usage: Generate a matrix of read itensity values around CLIPper cluster center.\n", " # Input: BedGraph and cluster center file.\n", " # Output: Generates a matrix, which is passed into R.\n", " program=os.getcwd() + '/bin/grep_chip-seq_intensity.pl'\n", " program2='wait'\n", " proc=subprocess.Popen(['perl',program, centerCoordinates, bedGraph],)\n", " proc.communicate()\n", " logOpen.write(\"Waiting for Cluster Intensity file completion...\\n\")\n", " proc2=subprocess.Popen(program2,shell=True)\n", " proc2.communicate()\n", " \n", "print \"Get binding intensity around cluster centers.\"\n", "logOpen.write(\"Get binding intensity around cluster centers.\\n\")\n", "bedGraphCLIPin=makeBedGraph(CLIPPERin,genomeFile)\n", "centerCoordinates=makeClusterCenter(CLIPperOutBed) \n", "getClusterIntensity(bedGraphCLIPin,centerCoordinates)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Get binding intensity around cluster centers.\n" ] } ], "prompt_number": 94 }, { "cell_type": "code", "collapsed": false, "input": [ "def partitionReadsByUTR(infile,UTRmask,utrReads,notutrReads):\n", " program = 'intersectBed'\n", " outfh = open(utrReads,'w')\n", " proc = subprocess.Popen([program,'-a',infile,'-b',UTRmask,'-u','-s'],stdout=outfh)\n", " proc.communicate()\n", " outfh.close()\n", " outfh = open(notutrReads,'w')\n", " proc = subprocess.Popen([program,'-a',infile,'-b',UTRmask,'-v','-s'],stdout=outfh)\n", " proc.communicate()\n", " outfh.close()\n", "\n", "def extractUTRs(bedIn,fivePUTRBed,threePUTRBed,cdsBed):\n", " # Usage: Extract all UTR specific reads from the input file.\n", " # Input: .bed file\n", " # Output: Mutually exclusive partitions of the input file.\n", " fivePreads = bedIn.replace('.bed', '_5p.bed')\n", " notFivePreads = bedIn.replace('.bed', '_NOT5p.bed')\n", " partitionReadsByUTR(bedIn,fivePUTRBed,fivePreads,notFivePreads)\n", " threePreads = bedIn.replace('.bed', '_3p.bed')\n", " notThreePreads = bedIn.replace('.bed', '_NOT3p.bed')\n", " partitionReadsByUTR(notFivePreads,threePUTRBed,threePreads,notThreePreads)\n", " CDSreads = bedIn.replace('.bed', '_cds.bed')\n", " notCDSreads = bedIn.replace('.bed', '_NOTcds.bed')\n", " partitionReadsByUTR(notThreePreads,cdsBed,CDSreads,notCDSreads)\n", " return (fivePreads,notFivePreads,CDSreads,notCDSreads,threePreads,notThreePreads)\n", "\n", "print \"Intron and UTR analysis.\"\n", "logOpen.write(\"Intron and UTR analysis.\\n\")\n", "fivePreads,notFivePreads,CDSreads,notCDSreads,threePreads,notThreePreads=extractUTRs(filteredProteinCodingCenters,fivePUTRBed,threePUTRBed,cdsBed)\n", "geneCounts_5p=sortFilteredBed(fivePreads) \n", "geneCounts_3p=sortFilteredBed(threePreads) \n", "geneCounts_cds=sortFilteredBed(CDSreads) \n", "\n", "outfilepathToSave=outfilepath+'/PlotData_ReadsPerGene_5pUTR'\n", "geneCounts_5p.to_csv(outfilepathToSave)\n", "outfilepathToSave=outfilepath+'/PlotData_ReadsPerGene_3pUTR'\n", "geneCounts_3p.to_csv(outfilepathToSave)\n", "outfilepathToSave=outfilepath+'/PlotData_ReadsPerGene_CDS'\n", "geneCounts_cds.to_csv(outfilepathToSave) " ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "No columns to parse from file", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-96-839789b78d00>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[0mlogOpen\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Intron and UTR analysis.\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[0mfivePreads\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnotFivePreads\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mCDSreads\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnotCDSreads\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mthreePreads\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnotThreePreads\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mextractUTRs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilteredProteinCodingCenters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfivePUTRBed\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mthreePUTRBed\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcdsBed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m \u001b[0mgeneCounts_5p\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msortFilteredBed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfivePreads\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 37\u001b[0m \u001b[0mgeneCounts_3p\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msortFilteredBed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthreePreads\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 38\u001b[0m \u001b[0mgeneCounts_cds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msortFilteredBed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCDSreads\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-96-839789b78d00>\u001b[0m in \u001b[0;36msortFilteredBed\u001b[1;34m(bedFile)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msortFilteredBed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbedFile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mbf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbedFile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mheader\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mbf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Chr'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Start'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Stop'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'CLIPper_name'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Q'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Strand'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mgeneCounts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcountHitsPerGene\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mgeneCounts\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/site-packages/pandas-0.13.1_783_g2266194-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format)\u001b[0m\n\u001b[0;32m 441\u001b[0m infer_datetime_format=infer_datetime_format)\n\u001b[0;32m 442\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 443\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 444\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 445\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/site-packages/pandas-0.13.1_783_g2266194-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 227\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 228\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 229\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 230\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnrows\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/site-packages/pandas-0.13.1_783_g2266194-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 531\u001b[0m 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"\u001b[1;32m/usr/local/lib/python2.7/site-packages/pandas-0.13.1_783_g2266194-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 668\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 669\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 670\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 671\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 672\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/site-packages/pandas-0.13.1_783_g2266194-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1030\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'allow_leading_cols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1032\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1033\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1034\u001b[0m \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/site-packages/pandas-0.13.1_783_g2266194-py2.7-linux-x86_64.egg/pandas/parser.so\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:4477)\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: No columns to parse from file" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Intron and UTR analysis.\n" ] } ], "prompt_number": 96 }, { "cell_type": "code", "collapsed": false, "input": [ "def makeTab(bedGraph,genesFile,sizesFile):\n", " program = os.getcwd() + '/bin/bedGraph2tab.pl'\n", " program2 = 'wait'\n", " outfile=bedGraph.replace('.bedgraph','.tab')\n", " proc = subprocess.Popen(['perl',program,genesFile,sizesFile,bedGraph,outfile],)\n", " proc.communicate()\n", " proc2 = subprocess.Popen(program2,shell=True)\n", " proc2.communicate()\n", " return outfile\n", "\n", "def makeAvgGraph(bedGraph,utrFile,genesFile,sizesFile):\n", " # Usage: Generate a matrix of read itensity values across gene body.\n", " # Input: BedGraph.\n", " # Output: Generates two matricies.\n", " program= os.getcwd() + '/bin/averageGraph_scaled_tab.pl'\n", " program2 = 'wait'\n", " tabFile=makeTab(bedGraph,genesFile,sizesFile)\n", " outhandle=tabFile.replace('.tab','_UTRs')\n", " proc = subprocess.Popen(['perl',program,utrFile,tabFile,tabFile,outhandle],)\n", " proc.communicate()\n", " proc2 = subprocess.Popen(program2,shell=True)\n", " proc2.communicate()\n", "\n", "print \"Gene body analysis.\"\n", "logOpen.write(\"Gene body analysis.\\n\")\n", "bedGraphProtein=makeBedGraph(bedFile_pc,genomeFile)\n", "makeAvgGraph(bedGraphProtein,utrFile,genesFile,sizesFile)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Gene body analysis.\n" ] } ], "prompt_number": 554 }, { "cell_type": "code", "collapsed": false, "input": [ "def getGeneStartStop(bedFile,geneRef):\n", " try:\n", " bf=pd.DataFrame(pd.read_table(bedFile,header=None))\n", " bf.columns=['Chr','Start','End','ReadName','Q','Strand','CLIPper_winChr','CLIPper_winStart','CLIPper_winEmd','CLIPper_winaName','CLIPper_winP','CLIPper_winStrand']\n", " bf['geneName']=bf['CLIPper_winaName'].apply(lambda x: x.split('_')[0])\n", " merge=pd.merge(geneRef,bf,left_on='Ensembl Gene ID',right_on='geneName')\n", " ncRNA_startStop=merge[['Ensembl Gene ID','Gene Start (bp)','Gene End (bp)','Start','End','Strand']]\n", " outfilepathToSave=bedFile.replace(\".bed\",\".geneStartStop\")\n", " ncRNA_startStop.to_csv(outfilepathToSave)\n", " except ValueError:\n", " print \"No reads in %s\"%bedFile\n", "\n", "print \"ncRNA gene body anaysis.\"\n", "geneStartStopRepo=os.getcwd()+'/docs/all_genes.txt'\n", "geneRef=pd.DataFrame(pd.read_table(geneStartStopRepo))\n", "remaining=[f for f in glob.glob(outfilepath+\"*_LowFDRreads.bed\") if 'lincRNA' not in f and 'proteinCoding' not in f and 'snoRNA' not in f]\n", "for bedFile in remaining:\n", " st_stop=getGeneStartStop(bedFile,geneRef)\n", "\n", "# lincRNA file processing\n", "bedFile_linc=outfilepath+\"clipGenes_lincRNA_LowFDRreads_centerCoord_snoRNAremoved_miRNAremoved.bed\"\n", "bf=pd.DataFrame(pd.read_table(bedFile_linc,header=None))\n", "bf.columns=['Chr','Start','Stop','CLIPper_name','Q','Strand']\n", "bf['geneName']=bf['CLIPper_name'].apply(lambda x: x.split('_')[0])\n", "merge=pd.merge(geneRef,bf,left_on='Ensembl Gene ID',right_on='geneName')\n", "ncRNA_startStop=merge[['Ensembl Gene ID','Gene Start (bp)','Gene End (bp)','Start','Stop','Strand']]\n", "outfilepathToSave=bedFile_linc.replace(\".bed\",\".geneStartStop\")\n", "ncRNA_startStop.to_csv(outfilepathToSave)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "ncRNA gene body anaysis.\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_tRNA_LowFDRreads.bed" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_senseOverlapping_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_pseudogenes_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_TR_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_LRG_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_rRNA_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_3pncRNA_LowFDRreads.bed" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_senseIntronic_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_IG_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_miR_LowFDRreads.bed\n", "No reads in /arrayAhome/lmartin/CLIP/results/TEST/clipGenes_snRNA_LowFDRreads.bed" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "def makeRepeatAnnotation(repeatGenomeBuild,repeatAnnotation):\n", " repeat_genome=np.genfromtxt(repeatGenomeBuild,dtype='string')\n", " repeat_genome_bases=repeat_genome[1]\n", " repeat_genome_size=len(repeat_genome[1])\n", " repeatAnnotDF=pd.DataFrame(pd.read_table(repeatAnnotation,header=None))\n", " repeatAnnotDF.columns=['Name','Length','IndexStart','IndexEnd']\n", " repeatAnnotDF['End_for_extraction']=repeatAnnotDF['IndexEnd']+1 # Python list extraction is not end index inclusive; to extract sequence, use end + 1. \n", " return (repeat_genome_bases,repeatAnnotDF)\n", "\n", "def readBed(path):\n", " bedFile = pd.read_table(path,dtype=str,header=None)\n", " bedFile.columns=['Index','Start','Stop','Name','QS','Strand']\n", " bedFile['Start']=bedFile['Start'].astype(int)\n", " return bedFile\n", "\n", "print \"Record repeat RNA.\"\n", "repeat_genome_bases,repeatAnnotDF=makeRepeatAnnotation(repeatGenomeBuild,repeatAnnotation)\n", "repeatAnnotDF.set_index('Name',inplace=True,drop=False)\n", "# Get merged data for repeat index.\n", "repeatMerged=glob.glob(outfilepath+\"*repeat_allreads.mergedRT.bed\")\n", "rep=pd.read_table(repeatMerged[0],dtype=str,header=None)\n", "rep.columns=['Rep_index','Start','Stop','Read_name','Q','Strand']\n", "rep['RT_stop']=rep['Start'].astype(int)+expand\n", "for ix in repeatAnnotDF.index:\n", " end=repeatAnnotDF.loc[ix,'IndexEnd']\n", " repName=repeatAnnotDF.loc[ix,'Name']\n", " gene_hits=rep[(rep['RT_stop']<int(repeatAnnotDF.loc[ix,'IndexEnd']))&(rep['RT_stop']>int(repeatAnnotDF.loc[ix,'IndexStart']))]\n", " gene_hits['Repeat_End']=repeatAnnotDF.loc[ix,'IndexEnd']\n", " gene_hits['Repeat_Start']=repeatAnnotDF.loc[ix,'IndexStart']\n", " outfilepathToSave=outfilepath + '/PlotData_RepeatRNAreads_%s'%repName\n", " gene_hits.to_csv(outfilepathToSave)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Record repeat RNA.\n" ] } ], "prompt_number": 695 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import matplotlib\n", "matplotlib.rcParams['savefig.dpi'] = 2 * matplotlib.rcParams['savefig.dpi']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 144 }, { "cell_type": "code", "collapsed": false, "input": [ "def lineCount(filename):\n", "\ti=0\n", "\twith open(filename) as f:\n", "\t\tfor i,l in enumerate(f):\n", "\t\t\tpass\n", "\treturn i+1\n", "\n", "def plot_ReadAccounting(outfilepath,sampleName):\n", " rawRead1=infilepath+sampleName+'_R1.fastq'\n", " rawRead2=infilepath+sampleName+'_R2.fastq'\n", " reads3pTrim=[outfilepath+sampleName+'_R1_3ptrimmed.fastq',outfilepath+sampleName+'_R2_3ptrimmed.fastq']\n", " readsFilter=[outfilepath+sampleName+'_R1_3ptrimmed_filter.fastq',outfilepath+sampleName+'_R2_3ptrimmed_filter.fastq']\n", " readsNoDupes=[outfilepath+sampleName+'_R1_3ptrimmed_filter_nodupe.fastq',outfilepath+sampleName+'_R2_3ptrimmed_filter_nodupe.fastq']\n", " readsMappedReapeat=[outfilepath+sampleName+'_R1_3ptrimmed_filter_nodupe_5ptrimmed_mappedTorepeat_withDupes.bed',outfilepath+sampleName+'_R2_3ptrimmed_filter_nodupe_5ptrimmed_mappedTorepeat_withDupes.bed']\n", " readsMappedHg19=[outfilepath+sampleName+'_R1_3ptrimmed_filter_nodupe_5ptrimmed_notMappedTorepeat_mappedTo%s_withDupes.bed'%index_tag,outfilepath+sampleName+'_R2_3ptrimmed_filter_nodupe_5ptrimmed_notMappedTorepeat_mappedTo%s_withDupes.bed'%index_tag]\n", " readsMappedBlacklist=[outfilepath+sampleName+'_R1_3ptrimmed_filter_nodupe_5ptrimmed_notMappedTorepeat_mappedTo%s_withDupes.bed'%index_tag,outfilepath+sampleName+'_R2_3ptrimmed_filter_nodupe_5ptrimmed_notMappedTorepeat_mappedTo%s_withDupes.bed'%index_tag]\n", " readsMappedRepeatMask=[outfilepath+sampleName+'_R1_3ptrimmed_filter_nodupe_5ptrimmed_notMappedTorepeat_mappedTo%s_withDupes_noBlacklist_noRepeat.bed'%index_tag,outfilepath+sampleName+'_R2_3ptrimmed_filter_nodupe_5ptrimmed_notMappedTorepeat_mappedTo%s_withDupes_noBlacklist_noRepeat.bed'%index_tag]\n", " clipperIN=outfilepath+sampleName+'_threshold=%s_%s_allreads.mergedRT_CLIPPERin.bed'%(threshold,index_tag)\n", " clipperOUT=outfilepath+sampleName+'_threshold=%s_%s_allreads.mergedRT_CLIP_clusters_lowFDRreads.bed'%(threshold,index_tag)\n", " fileNames=['Raw (R1)','Raw (R2)','3p Trim (R1)','3p Trim (R2)','Filter (R1)','Filter (R2)','No dupes (R1)','No dupes (R2)','RepeatMapped(R1)','RepeatMaped(R2)','Hg19Mapped (R1)','Hg19Mapped(R2)','Blacklist (R1)','Blacklist (R2)','RepeatMask(R1)','RepeatMask(R2)','ClipperIn','ClipperOut']\n", " filesToCount=[rawRead1,rawRead2,reads3pTrim[0],reads3pTrim[1],readsFilter[0],readsFilter[1],readsNoDupes[0],readsNoDupes[1],readsMappedReapeat[0],readsMappedReapeat[1],readsMappedHg19[0],readsMappedHg19[1],readsMappedBlacklist[0],readsMappedBlacklist[1],readsMappedRepeatMask[0],readsMappedRepeatMask[1],clipperIN,clipperOUT]\n", " \n", " counts=[]\n", " counter=0\n", " for fileString in filesToCount:\n", " temp=lineCount(fileString)\n", " if counter < 8:\n", " temp=temp/4 # Fastq files\n", " counts=counts+[temp]\n", " counter += 1\n", "\n", " ind = np.arange(len(counts)) + 0.5\n", " plt.barh(ind,list(reversed(np.log10(np.array(counts)))),align='center',color='blue')\n", " plt.xlabel('log10(Counts per file)',fontsize=5)\n", " locs,pltlabels = plt.xticks(fontsize=5)\n", " plt.setp(pltlabels, rotation=90, fontsize=5)\n", " plt.yticks(ind,list(reversed(fileNames)),fontsize=5)\n", " plt.tick_params(axis='yticks',labelsize=5) \n", " ax=plt.gca()\n", " for line in ax.get_yticklines():\n", " line.set_markersize(0)\n", " plt.title('Read counts',fontsize=5)\n", " \n", " readDF=pd.DataFrame()\n", " readDF['File_name']=fileNames\n", " readDF['Reads_per_file']=counts\n", " outfilepathToSave=outfilepath + '/PlotData_ReadsPerPipeFile'\n", " readDF.to_csv(outfilepathToSave)\n", "\n", "plt.subplot(2,3,1) \n", "plot_ReadAccounting(outfilepath,sampleName)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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ens4YY+zy5cts+PDhjDHZU+lFixYxxhhLTU1ljo6OjDHGPv30U7Zp0ybGGGMF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\nxdzwnTt3YteuXTh58iSioqI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"text": [ "<matplotlib.figure.Figure at 0x2e75ab90>" ] } ], "prompt_number": 101 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_BoundGeneTypes(outfilepath,sampleName):\n", " record=pd.DataFrame() \n", " # Exclude specific files (e.g., UTR-specific reads).\n", " geneListToPlot=[f for f in glob.glob(outfilepath+'PlotData_ReadsPerGene_*') if '5pUTR' not in f and '3pUTR' not in f and 'CDS' not in f]\n", " for boundGenes in geneListToPlot:\n", " glist=pd.read_csv(boundGenes,header=None)\n", " glist.columns=['GeneName','Count']\n", " gName=boundGenes.split('_')[-1]\n", " record.loc[gName,'genesBound']=glist.shape[0]\n", " record.loc[gName,'totalReads']=glist['Count'].sum()\n", " record.sort('genesBound',inplace=True)\n", " outfilepathToSave=outfilepath + '/PlotData_ReadAndGeneCountsPerGenetype'\n", " record.to_csv(outfilepathToSave)\n", " ind = np.arange(record.shape[0]) + 0.5\n", " plt.bar(ind,record['genesBound'],align='center',color='blue')\n", " locs,pltlabels = plt.yticks(fontsize=5)\n", " locs,pltlabels = plt.xticks(ind,record.index,fontsize=5)\n", " plt.setp(pltlabels, rotation=90, fontsize=5)\n", " plt.tick_params(axis='xticks',labelsize=5) \n", " ax=plt.gca()\n", " for line in ax.get_xticklines():\n", " line.set_markersize(0)\n", " plt.ylabel('Number of genes bound',fontsize=5)\n", " plt.tick_params(axis='yticks',labelsize=5)\n", " plt.title('Bound genes by class',fontsize=5)\n", " \n", "plt.subplot(2,3,6)\n", "plot_BoundGeneTypes(outfilepath,sampleName)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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T4bmZmRnOnz9fYTm5Z7VUpYMHD8pqv9zy1aUNSnyGJeuQXp/c8tWlDfqqkcMf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\nxlZKPrkaQIkjEaqLt956S+8jO2uKvn3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"text": [ "<matplotlib.figure.Figure at 0x2aaaac436610>" ] } ], "prompt_number": 104 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_ReadsPerCluster(outfilepath,sampleName):\n", " readPerCluster=outfilepath+sampleName+'_threshold=%s_%s_allreads.mergedRT_CLIP_clusters.readsPerCluster'%(threshold,index_tag)\n", " clust=pd.DataFrame(pd.read_table(readPerCluster,header=None))\n", " clust.columns=['ReadsPerCluster']\n", " clust=clust['ReadsPerCluster']\n", " interval=10\n", " bins=range(min(clust)-10,max(clust)+10,interval)\n", " hist,bins=np.histogram(clust,bins=bins)\n", " width=0.7*(bins[1]-bins[0]) \n", " center=(bins[:-1] + bins[1:])/2 \n", " plt.bar(center, hist,align='center',width=width)\n", " locs,pltlabels = plt.yticks(fontsize=5)\n", " locs,pltlabels = plt.xticks(center,center,fontsize=5)\n", " plt.setp(pltlabels, rotation=90, fontsize=3.5)\n", " plt.tick_params(axis='yticks',labelsize=5) \n", " plt.xlabel('Reads per cluster (bin=%s)'%interval,fontsize=5)\n", " plt.ylabel('Frequency (RT stop count)',fontsize=5)\n", " plt.title('Reads per cluster',fontsize=5)\n", " plt.xlim(0,100) # Make the histogram easy to view.\n", " # plt.xlim(-interval,np.max(center)+interval)\n", " \n", "plt.subplot(2,3,2)\n", "plot_ReadsPerCluster(outfilepath,sampleName)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x2aaaac238850>" ] } ], "prompt_number": 108 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_ClusterSizes(outfilepath,sampleName):\n", " clipClusters=outfilepath+sampleName+'_threshold=%s_%s_allreads.mergedRT_CLIP_clusters'%(threshold,index_tag)\n", " clust=pd.DataFrame(pd.read_table(clipClusters,header=None,skiprows=1))\n", " clust.columns=['chr','start','end','name','score','strand','m1','m2']\n", " clust['clusterSize']=clust['start']-clust['end']\n", " clust['clusterSize']=clust['clusterSize'].apply(lambda x: math.fabs(x))\n", " plt.boxplot(clust['clusterSize'])\n", " plt.tick_params(axis='x',labelbottom='off') \n", " ax=plt.gca()\n", " for line in ax.get_xticklines():\n", " line.set_markersize(0)\n", " plt.ylabel('Cluster length (bases)',fontsize=5)\n", " locs,pltlabels = plt.yticks(fontsize=5)\n", " plt.title('Cluster size',fontsize=5)\n", "\n", "plt.subplot(2,3,3)\n", "plot_ClusterSizes(outfilepath,sampleName)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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PETFF4SEipig8RMQUhYeImKLwEBFTFB4iYorCQ0RM+V+US/NHZV6qcQAAAABJ\nRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x2aaaac423150>" ] } ], "prompt_number": 109 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_clusterBindingIntensity(outfilepath,sampleName):\n", " clusterCenterHeatmap=outfilepath+sampleName+'_threshold=%s_%s_allreads.mergedRT_CLIP_clusters_cleaned_sorted.clusterCenter_heatmap.txt'%(threshold,index_tag)\n", " hmap=pd.DataFrame(pd.read_table(clusterCenterHeatmap,header=None,skiprows=1))\n", " hmap_vals=hmap.ix[:,1:]\n", " sums=hmap_vals.sum(axis=1)\n", " hmap_vals=hmap_vals.loc[np.argsort(sums),:]\n", " plt.ylim(0,hmap_vals.shape[0])\n", " p=plt.pcolormesh(np.array(hmap_vals),cmap='Blues')\n", " plt.tick_params(axis='x',labelbottom='off') \n", " plt.xlabel('Cluster position',fontsize=5)\n", " locs,pltlabels = plt.yticks(fontsize=5)\n", " plt.ylabel('Cluster number',fontsize=5)\n", " plt.title('Read distribution',fontsize=5)\n", "\n", "plt.subplot(2,3,4)\n", "plot_clusterBindingIntensity(outfilepath,sampleName)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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mp3LEc86/0/z1ZENMB6uIhwl1C1P+saG+4lQtRxZqX/77vk3uszlYTCCKiTkzJy5ZGDtz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\nfCqby9uSKn2iU6FnY7CGCvD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"text": [ "<matplotlib.figure.Figure at 0x2aaac1517310>" ] } ], "prompt_number": 110 }, { "cell_type": "code", "collapsed": false, "input": [ "def readUTRfile(path):\n", " geneCounts=pd.read_csv(path,header=None)\n", " geneCounts.columns=['Gene_name','Count']\n", " return geneCounts\n", "\n", "def plot_readsBymRNAregion(outfilepath,sampleName): \n", " pc_5pReads=readUTRfile(outfilepath+'/PlotData_ReadsPerGene_5pUTR')['Count'].sum()\n", " pc_3pReads=readUTRfile(outfilepath+'/PlotData_ReadsPerGene_3pUTR')['Count'].sum()\n", " pc_CDSReads=readUTRfile(outfilepath+'/PlotData_ReadsPerGene_CDS')['Count'].sum()\n", " non_intronic=pc_5pReads+pc_3pReads+pc_CDSReads\n", " allProteinCoding=outfilepath +'clipGenes_proteinCoding_LowFDRreads_centerCoord_snoRNAremoved_miRNAremoved.bed'\n", " all_pc=pd.DataFrame(pd.read_table(allProteinCoding,header=None))\n", " pc_allReads=all_pc.shape[0]\n", " v=[float(pc_allReads-non_intronic)/pc_allReads,float(pc_5pReads)/pc_allReads,float(pc_CDSReads)/pc_allReads,float(pc_3pReads)/pc_allReads]\n", " pie_wedges=ax.pie(v,labels=[\"Intronic\",\"5p UTR\",\"CDS\",\"3pUTR\"],labeldistance=1.1,autopct='%1.1f%%')\n", " plt.rcParams['font.size']=5\n", " for wedge in pie_wedges[0]:\n", " wedge.set_edgecolor('black')\n", " wedge.set_lw(1)\n", "\n", "ax=plt.subplot(2,3,5)\n", "plot_readsBymRNAregion(outfilepath,sampleName)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x2aaabde01150>" ] } ], "prompt_number": 111 }, { "cell_type": "code", "collapsed": false, "input": [ "fig1=plt.figure(1)\n", "\n", "plt.subplot(2,3,1) \n", "plot_ReadAccounting(outfilepath,sampleName)\n", "plt.subplot(2,3,2)\n", "plot_ReadsPerCluster(outfilepath,sampleName)\n", "plt.subplot(2,3,3)\n", "plot_ClusterSizes(outfilepath,sampleName)\n", "plt.subplot(2,3,4)\n", "plot_clusterBindingIntensity(outfilepath,sampleName)\n", "ax=plt.subplot(2,3,5)\n", "plot_readsBymRNAregion(outfilepath,sampleName)\n", "plt.subplot(2,3,6)\n", "plot_BoundGeneTypes(outfilepath,sampleName)\n", "fig1.tight_layout()\n", "\n", "fig1.savefig(outfilepath+'Figure1.png',format='png',bbox_inches='tight',dpi=150,pad_inches=0.5)\n", "fig1.savefig(outfilepath+'Figure1.pdf',format='pdf',bbox_inches='tight',dpi=150,pad_inches=0.5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "*" }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_mRNAgeneBodyDist(outfilepath,sampleName):\n", " averageGraph=outfilepath+'clipGenes_proteinCoding_LowFDRreads_centerCoord_snoRNAremoved_miRNAremoved_cleaned_sorted_UTRs_scaled_cds200_abt0_averageGraph.txt'\n", " hmap=pd.DataFrame(pd.read_table(averageGraph,header=None,skiprows=1))\n", " hmap=hmap.set_index(0)\n", " avgTrace=hmap.loc['treat',:]\n", " plt.plot(avgTrace,color='blue',linewidth='2')\n", " plt.vlines(200,0,np.max(avgTrace),linestyles='dashed')\n", " plt.vlines(400,0,np.max(avgTrace),linestyles='dashed')\n", " plt.ylim(0,np.max(avgTrace))\n", " plt.tick_params(axis='x',labelbottom='off') \n", " plt.xlabel('mRNA gene body (5pUTR, CDS, 3pUTR)')\n", " plt.ylabel('Read density')\n", " plt.tick_params(axis='y',labelsize=5) \n", " plt.title('CLIP signal across average mRNA transcript.',fontsize=5)\n", "\n", "plt.subplot2grid((2,3),(0,0),colspan=3)\n", "plot_mRNAgeneBodyDist(outfilepath,sampleName)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, 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8ERGRazwK8fn5+cjPz8eGDRsQExODIHU5zQVpaWlo0KCB5XJ4eDjS0tI0bxMc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\ntSrxzrgyO42WnBz7WYFkIcX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y8KDjc6VSJdEj/uyz9tdrtdS0bGl8oSuzm28WB3S2Zs60\nr5gbpW6J6NfP/jURFQU0amR/G3dCvGoIEi5dAh5+WPxuX3jB2OJo3lScId5Z2Dx71vE6Z60qRlpZ\nrl6VHxwZHSzs7OxIcYX4zEx58UI2zStRSZeYmKiZJz0RMCE+PDwcZ23e9dLS0tCwYUOH25gr6fn5\n+cjMzEQdmxFb6gMB9TbPnj2L8PBwX+w+ueDKFeDNN8UbeFGRWAhH71Twrl0i8NetCyxb5vh9dStN\nWBhgcwIGgPhA9UaIBxwr9Vordbpi9Wr59eqBhTfdJKrq6nnTL10SoWLWLBGgvv3W/vv9+sm3rw7x\n//mP+HCVDXJ1dhLLHz3x6kqwUfv2aX/vp58cA1hGhphLX72wklaIr18faNLEtX26+WYxmPbll4Fm\nzYAJE0S/tTshXq1JE9ESZTKJg4vp0x3P6Dh73gPiYKtuXWDiRDFmwtkiYcUdyLSmygTEQm2JiZ7P\nwmTmbHyMLMQ7C8hGAnR2trzKbzTEqyvx6vUOfBXit2wBnnnGumaCVgtkIM4yRBSoAibER0dH49ix\nY0hNTUVeXh7WrFmDQarG1piYGCz9u2y7atUq9O7dG0E2JTz1EU2jRo0QGhqKPXv2QFEUrFixAjEx\nMb5/MKTryBH7yvHFi8CBA9q3f+opMXvGxYviA0fdOqP+MKhdG1Afq505ox/id+0SU68Bzgd+Xbwo\nPkRfekkcjLg6SC8oyLpip5lW+JBV4gHH2UbOnAE6dhQHRDExjmc3tEJ8586OVfAaNeSDLp1VOdUh\n3uj0koCxSrxsdhqjlfgxY4zvy4YN9i1TdeuKSryMVoivV8+1+wREiDeZRF/wiROiLaV8eaBmTde2\no1aunAjuU6eK5+65c0BsrONzyNksRHXqiLMx58+LdqspU7RXiy0OPXqIMxW2tBZ8Sk4WrUrTp4uf\n0xrzoJaSIsaO/OMf9tVvRZFX4m2nRnWnEm80QMvmj3c3xHfqZH/ZFwNbd+4UB6jvvSfeiw4cYIgn\n8oaACfEVK1bEggULMHjwYLRr1w4jRoxAhw4dMG3aNKz/u/lwypQpuHz5MiIiIvDRRx/hww8/tPx8\neHg4nn76aaxcuRINGzbE3r+H7i9evBiTJ09GmzZtcOuttyIuLs4vj4+sZBW/LVvkty0qsv9gzsx0\nXIlSFuJVJ3GchngAGDxYVOuMVOIHDhQB/qWXXJ/B48EHgdGj7a/TOhCQVeIBa5g3GzNGexvlymn3\naFer5nzmEjNXQ7w3K/FVq7rXTvP88+KgTN0Go0fdmiTrJzfTq8Q/95xoqzFK66yCp5X4GjWsv9/a\nta0LLTmbdlKtShVxUGHLyNz9vnLXXY4DiLWm7Jw+3dq6oSj6ix2ZFRYCd98NLFwIfPCBaKcyO3dO\nPg7HNujLquXZ2fozsBgN0LKDfqPTNKpDvPqMjLcr8efPi7UHzBRFTNvKEE/kuYBasXXQoEEO1ffp\nNtOEhISE4EuNVSq0Bqx27NjRYapK8i/1jCuACPGy1Rhl1a4jR+wHZMpCvDqgrF9v/wFZrZpYdfOn\nn+xvN2eO8xB/6JCxKRcBUUW9ft2+sv30047tPmlp4sNNHWbVId4c3gcMALZutV5//Lj2PjRtat8P\nrTZpEvDoo9rfN/NliNerxAcFaYd4Z+00zz0nxhDIVjU1St07bksrxNetK/6WU6car8j7MsTLqFuy\nnJH1+AcFiX+urA7rLTVqiCpyuXLWdrzjx0UIVL/+1VMwfv218+0fOGD/ulq2zHqGS7KMCQDxXmS+\nb9mg/aIiUa3XOvgxGqA//dTxOnd74n0Z4rdtEwdCaklJQKtW8p9hiCcyLmAq8VQ65eaKdpjeva0f\ngLKQ/MMP8g8P9VzngGPrjfrDsnZtx37kr76yv9yrl+N864D4cJedBrclO5WtpXNn0Tts9uKLojpb\ntap9u0lenuOHl6I4VvPMlfjHHjPeD24exKrlkUfE2QFn9ObZB3xXiTdXkmUhvk4d7QOASpWsrSi1\na7tfNdYL8bK+99q1rQdNrVsb/7niDvHuVOJlvFGNHzpUPA9dac+pUQOoXFlMu2rLdv50V+3eLQL6\nr786TtEKiOf4d9/Zv6Zt2RYUtGbe0qu2Gw3Q6hWNAWMhPjfXvqBQvrzj88ObIX7WLPkZgmbNPK/E\n5+WJgpDt77OoSKy9MXt28U5vSuQvDPElSFKSmGf8k09KzqIYU6aIVQV37BAVyePH5SFeNt0YIA/U\n6r5XWSXe2aDC4cPFLBrq9gBn068BzsOsLUURle5z50TbzRtvWL+n7ttXn0y6ft3+AzAkxBqMQ0Od\nDyo0cxbiTSYxw44z/qrEm0OGLMTXrCmCnEyDBtaDA5PJsQXJKL12mv79Ha+zfdwtWjh+f/x4+ToE\nWiFe9nuMiXF87mrR6ql3NcRrBWxPQ3zbtmJhqTlzgOhox+/feqv858yPS91SYxviz50TrW9G7Nwp\n+u/sy+YAACAASURBVOZfeEEc4MtmL0pNBT77THsbRkK8Xkj2pB/dSIhXv79Vreo43sObPfFaA8gv\nX/YsxF+7JgokrVqJ9zdzsScxERg5Enj8caBv35LzOUnkLob4EuLoUdGzvWSJmBlCazaTQFFUJE6l\nfvKJ9TpFEStpalWyZeFeVok/csT+g0b9YVCrlmgh0RISIlYv7dRJDFzTC5AtWwL//rf9da6EeHNQ\nr1/fcZ/UffvqEC+rwttWrPv0MbYPzkI8IAKus750WYhfskTMNz5kiP1c+4D3KvHmsCYL8TVqyAe8\nAo4HSe5OR6l3QNimjeN1tmMkZAcYFy7IWwm0QrwsrLdrJwYyG+HLdhrA8xBv+zuaNs3x+5995riQ\nEmB9XOozarbP0xkztMfbqM2ZYw19eXnA55873ubUKfs2NrXiqsTLGOmJl4V49XPUmxVsraLIhQue\nhfjVq61Top45A7zzjvja9nmyb5/9rGPkP4cOiUkX5s/3T+tdacYQX0KoezATEvyzH0asWydC4Z13\nOn5v+3bt6ohsVhhZJb6gQFTtc3PFQYF6fuvatfWrp336WFtZmjUD5s3Tvm3duo6rLLoS4mW/AzPZ\nDDq2tPrhzdq1sw5S1ONshUaz4cP1v68O8efPi5k7jhwRbUjqMyTersTLwnr16tphQL2aq3ocghHl\nyjkuwGTLZHIMkc5Waw0JkQ94Va/Eq6dFC/vBgnq0QnzNmo6/Iz2+CvG2Ff7evcVzybzP/fqJiqvs\nMZivUx/82L5u5szRvl/1APnly53v69at+q9/f4Z4I5V4dUCvVk08D0JCrG/K165pL4TmKq3nl6ch\nXj3V8L/+Jf9cUS+WR8UvM1O8J86aJc5K670myXUM8SWE+gMnUN+csrKAceO034j1BoTKQrysEg+I\n7bdvL1bQVPfI164tThFrtU+oZ2qR9cabhYU5tiPoLXrUvr0IIaNHAx9+KE7tanHWTqPVD29WrpyY\nLs8ZI5V4QCwCZevzz+2Dc1aWfWVt2TL9+f2LqxLv7OfM9HrbtfTo4XgQpzZ7tv3l+Hj7y2++aX/5\nkUfEwdeQIdbr7r9fPwzHxlq/rlJF/KzREK/VThMUpD0wV6Y4KvGAeKx//CHeDzZtEvspewzmv736\ndW5+b3TWStGli/2qq0bOTNhMiCb17LPWg4iS0k4TFASEh9v/slwpVGgpKtI+GLh8WXtFWyMhXtba\nJWttk40doOK1aJH9c37KFP/tS2nEEB+gLl0SM4aMHCkqza5UzPxpzRr9Ko7e4i9GK/EA8MUX8llu\nAGsw0WqpUc+LrDcNYFiYawMLq1UTIeTzz0Vfpl6F2Z12GjVnQa5KFceDBS333Sd69nv1EvM5jx7t\n2IJie/DorGro7Uq87DWgtyrqqFH2l/XOzmi55x7nt7n9dnGauFcvMXBZvYrp5MniOVG7tnhNm8/O\nLF0qqlMffSSfbcTWO++I+7ntNvGhWKOGOKMkO7BR03v+utJS46sQLwvbNWuKliNzK5HsMZiDvTrE\nm0O0bFVTNdsVZd1ZB1D9GgbEa6Z1a+37l439MfN1JV4W4gGgUSP7F6A3Qvz16/qtE1rv39nZzh+L\n7PuyNicjzwFfuXpVTE/qrcXFSiq9BdjIcwzxAWrKFHHaaflyESRkH3SB2Fu2aJH7P/v7746LDGlV\n4jdv1t6OsxCv7iXWC7ruhHij1PepbldRn/qWVSPvvVf/PpwdSNgymUQI3bHDOmhWHeJt/x5avehm\nrixSZKQSLzsDEhQkn8Lu4YftK92AeyHe6KDIiRPF7+2NNxyn86xeXZyduXRJjK8wH4xUriza4qZM\ncR7GW7QA/vc/Me7gvvvEdZUqGavG6z1/tQa3yp7HvgrxRoKWbLEt83Xm6TzNLl0S7yNGwoPt1JNG\np2i01bev/Hq9aWr/7//kK08DnoX4pCT9M2OAXoi3v97ZIHYjrlzR/75s+mAzZ9V4rc8FNX+F+IIC\nccb3oYfEwbd6Zuy8POCXXwLzM9zbtCYeIO9giA9AigKsWGG9fPq0vEodaPPpZmSIwazuKihwXDhJ\nqxKv105kDvGyAYn168sHEGpV42vXdi2MuhLi1R+cqan2l7U+cG1ptcq0bSsGnRqZdUaP+ndl+3vX\nC94hIa4d/BipxGu1GjzxhDXQDR0qPhjnzXPcplaIr1BBTIOqFhJivBXJX4xMDar3/NWacUZ9NgHw\nXYh3FvYA+VkY8/MvONj+jIJ5NVVXK4DuBD719JZGPf64/Hpn7TSqZVTsHDjgeOCqpi4M+DPE6/FW\niNdqafK1HTvsB/qPHWv9+s8/xWdTZKR4/nh7ca1AwxDvWwzxAUhrwI+aN95ovUnr9KgrbB9TTo7x\n34VZaKi1QiybOaRLF/nPaYV4X1bi1aEyLc2+h9NIiAccpyucPFnMBjB2rPMZZ5xRh3jbD0+96Tjr\n13ftvo1U4rUCTr9+IrD9/LNo59LallZP/MGD8vDTooXxaRz9JS5OVPr06IV4WU/8vHnygfO+mmLS\nSHju1Mn+/tUzM8laaoyE+GbNxP+K4l6I79DB9Z8BxPua7P6cBbqYGP0B0Bs3Og70t6V+zZrfrwIt\nxOsVaXJyjIdzf1Xi1eO0cnOtRZpp06yP7/Bh/QHVP/0kJrUwurhgIJItNMixCt7DEB+A9FbftOXP\nEH/tmvig79/fupCSXr+7UbaPSV2ZNsK2R3XIEPvQXqWKGHgmo7V6YFiYeBMyWk1wJcRXrmy/WmJh\nof2ZB/UHrlYl9KmnrPtfubKx1VeNUrfTvP22aLn5/Xf9qehcnZPdk0o8IIJNZKT+wYCs/zsuTvzu\nZN+THQQGmvLlRauOXhubXuhTH9Q++6xoRZKNZ9B6/umNS7CVkAAsWOB4fWSk858NDRVjB2rWFMFb\nPVhY/TxNT3c8qyeTlSWC0oYNzltR1EJCxMxF7lbjZXOoOwvxVauK3m+99qsdO8RUmOpWjX37HFeu\n1arEe6Mn3pMZbvTGDbgyqYO/QrzsIGPDBhFe1a8B9UKEZgcOAN26iXULOnVyvpJ4oJK1QfrrDElp\nxBAfgEpCiP/gA/Gh+t//ir7srl3lH9CTJjku621Lry/clZVRARHgbEN6aKhYCfbIEdFPfOoU0L27\n/Gf1KvGA8Wq8KyEecKzG2z7m69ftv6dVia9aVcxe9N//igOfiAjX9kGPuhJ/4QLw1ltiUR69U9qu\nhngjlXhZRcfT+zD3QZfUEA+I2XPi44G77pJ/X+/1N2aMmNYREK0a5nnaZc9jrUq8s3EZZuHhwIQJ\n4nlq65VXjP38+PHiw//ECceDD9kMNUYq8efPi95lvQHMAwbYzw5kZl5MbOdO5/cjIwvxztppQkPF\n83LnTu0xKU88IcYDRURYq+GzZonX7I4d9rcN1HYa9ZoTtlwJ8f4Ki7LPrg0bxBTLalrvfTNmWA8s\nCwvFQlYlkezA1NUz7KSNIT4AGa1oe/JGu3OnWHxBXZkxSt37vnevCMq25s0D5s4V7Q5a1FU428dk\ntKe1SxcxSOjkScceYZNJzBRx++360+l16iSvBpt/xlchXt23b/vmb7SdBhDV0L59XV/AxxmtBZIy\nM8Uqm67+nBYjlfhnnrG/XrbqqavMB0qyaST1Zi0KRFoVd72VWatXB777TlTLkpKsQV3WIqMVNh5/\nXMzMY0tvMas77xSzN40bJ8b+OGsHMkIW4n/5xf66tWv1p4fV8vjj4mBHzbzuQLVqYtpQV7lTiTf/\nfaKjnS9i9euvYtYkQMw2JWN+T1HPsnPmjOtnJtTUIV6vNU19ZkGvNdNoPzzgv0q87LPr4EH5e6bW\nQYn6turBsSUFQ7xvMcQHIFcq8dnZIsxMnixaHIxISRE9pbNmiYGAGza4vo+2S5trueUW8b/WSpSA\n46lodyrxLVqIqpM7s4+YVasm7281h2Kjg1s9DfG2LUSuhHhf0fvb6VUNvVmJN4f4yEjgn/8Uf+d7\n73VvvmH1tJOTJ4v/ZaHV6CJZgUJWca9Z0/kZDJPJ2FkOrZk0KlQQxYDx48VryDzdppo59JpMYvrS\nxYvFTC3eoH6+ffutfdirXFnMNFShgrFpORs0EGcbd+wQsx/JxlPYLh7m6vMdEG08as5CvO1ButaZ\nEVsffCDa3rTaY8zvV6Gh9u9xBQWeT5ygDvF6g8TVs/zoVeJdCfGBVIlPTxdjldQCbWybt8lWEmaI\n9x6G+ABkNMRfvAg895w4zTZ3rgjmRgaMrFxp/4HszgepkQF/t94q/tcLwHoh3mglPjra2O2cUS8C\nBVg/5PzRThPoIV6PNyvx5uePySRaL06dEgNYnS3CJDN1qjUIRUTYt4KMGGH9um1b77YlFQdZiNdr\npXGVXkW/Rg0xJ/b+/WK6Tdn9uvtcMkL9fNu1y/5y377W8G4k/HbsKM5Umg9GnIV4V1bbNTtzxjHg\n6B0Y16gBREVZLxt5HEVF2oE4KMjaSgU4rmisNTOYLfPifs2aiVZG2+q9KyG+Rw/7A8mLF7UDuLod\ny1wskvFHJT47W3shK/WijYAI97K/u9GpgQOd7MCUPfHeU0qeJsUvM9M7pxxljIb4S5fsV4s8e9ax\n51FGfZvsbCA52fj+qVfulAkOtva76y36ow7xZ85YDzCMVOIrVxZVPW+QVeLNFeLiqsTbziwRCCG+\nZk1jlUs1X1TivaFVKxFqfvpJ/LMdsDx/PvDSS6J9YsOGkvchKgvO7oRLM9v2paZNXWt7kQV+X4b4\nFi30vz94sPVrI+FXfYAoez7bPi/dqcQDjlVYvUp8v372xRMjjwOQh/iRI0U7ju2YJHdC/GefiYXK\nTp4E3n1XtGaZqQe26oX4xo0dz3zJ9vvbb+1X2QWAYcO0t+uPEK/3uZWXJ79edqZENubB2SrEgYjt\nNL5Vwj6m/C87W1TvatQQ1ZmICNcG2jiTlSXCuRGyF8K//gUMHy7CvdYbhnpBJUDMKW6UkcfbtKl1\nbme9ENaokX3Iz80VPezvv2+sEj9mjGvzuOsZNsw+KMfEWL82GiRdXVlXHeIPHBC9yUBghHiTydiq\npWremp3GdspQb6lVS4yBUB+cVK0q5tX/8EPt6SgDmbcr8W+8IVruXnxRVLZdOaiR3dboLDbuiIzU\nf33Yzq9uZD/U7ymyx2NbwHE3xKtn4NIL8bZzjQPGQvyFC4699889J1YLVo9VcifEm9vRzJ5/3vq1\nK5X48HDHMSiyGWo++8z+crduYrC0lpyc4p+H3dUJGQDH54GiyM+qezJY2F/YTuNbDPEuWrLEfkqo\n334Ts7Q4k5kJfPwxsHq1/dF0YaFYHfOWW8RpaFemVZSteLdhgxgA8/jjoj1EdhvZ1GtG++kBx57E\nW25xHFBpW7XTqsRXqCAW6VEHppQU4OmnjfVk6g2adVWNGqJPt2VLcXrXdjCY0RBvtDpm1qKFY0vN\n44+L54h6dhpfhiA9n3wiWrbi4oz/jKvtNFqVeFlvNcl5O8QHB4tpId94wzHgOVPcB5zlymnPPFWx\nov17jJHXqKww0K2b/WXbg3zZGY9vvxVB+IMPtO9H/X6v1U7Tp4/9/QHGp739+GP7y7IB2zk5OTh/\nfr/ddUZCvJptm4Q6cIaHa5/VCw93/ByQfXapA/LUqaKVJ5BaatyZGln9M5mZ8mKbN6b+LG6sxPsW\nQ7yLZIOR3nxT9JVrvekVFooZGR57TCyZ/tpr1u9t3Cje5P/4Q4SlGTO8t68//ywq87Zyc8WiQmqu\nvKjUlfi2bUUFecIEMctBz57Aq69av68V4mvWtM4e44zWgjKuhgtn4uLErA67dtl/2Bnpv27cWPTS\nuqJ8eesMEmZ//CFm1rCdSSMoSL70fHGoWlVMPfjpp8Z/xtXwqFXlnTTJte2UZb7uiXdFbKx9YNOr\nlnpLjx7y69UFBiMhXta2MH269TU4aJA4m2MmC/ENG4oWIr37sw1vOTn21degIFGU+ewzMQZEfZbP\naJubOgzKWo+ysrKwcaP9G9HrrwPffy++Xr9erAmSkOBYXNCiDvE1amg/H2++2XH2sDVrHD9r1AUk\n8xnfNWvEmdRx4xwLLsXdfy37fHVGXUTT6qlniCc1hngXpafLr1+50jrHstr27fYruC1caP167lz7\n2y5b5tHuOTh82P5yaqr8A8qVNzr1G+vNN4sPrAULRI/nzp32bSJaVWxzteull/SnfwTEWQoZ9Tzz\nvqI3U8nUqWKKua1b3eujHjBAHOTZUs8YVLWq56uveko9g4WWQYNcn9Nd9thuusm+l5n0yfrQ9Qaj\n+lLNmqJlo3NnUbj45z99f59aPfvq9xYjZ7RkM/H07y/C1v794vVp+5yVVcXNBQa9FW1TU0WRZ9Qo\nx4P0SpXE83/MGPl7qOw1M326mHFMj/b4AccqVEyMaMcZMUIMKP3oI/1Ck+0+qUN89eryg526dUXL\nnPrvtGePaMHZ//cJAkVx/Owxn/GLjARWrRJnUtXTm7oT4r/9VlT4mzUTX7vCnaC9dq3957LsLIR6\n2wUFInPExYnPnkDFdhrfYoh3kV4/+MqV8us3bbK/nJpq/ZDw9fRS69eL05RNm4oXutYqhs7e6BRF\nHIikpjpWQ5y1TmhV4m2nDjx82H52EFuRkWLpaRlX2zbcpbUqY+3a4szKnDn6p3Sd6d/f/rJ68JY/\n+uFl1PNJ24qMFGebPvnE9e3KDn6aNjU2CxIJssqsP39/994rpqJdubJ4XqdaB9ruVOK1Bks2aCAG\nwMuer2+8Yf36scesBwt6PdmpqcB//iMv3hiptA8caP26RQtRUNBqKwLE61e7YOIY4jMzxYGEbRDT\nei8G7IOoOqjVri2vxJvbaGRnO69fty5ydPWq/X5UrCj/bFHfh1bhTYuiiKLRyZPi36RJrg0odacS\nf+yYCOTm+9GqxNvmhRkzxMHx2rXizJfRsXS+8PXX4uDp9tsdzypwdhrfCrgQv3nzZkRERKBVq1aY\nKXm3yM3NxfDhwxEREYHu3bsj1eZ85JtvvolWrVohIiIC33zzjeX6ChUqICoqClFRURimN5TdAL0Q\nf/Wq44s9OVm+0IZ5O3ozt3jLmTOil/CR/2/vzOOiKvc//hkQAdcrXo1NzQURZRVwKRVNrmtq5ZYL\nXPduhpb+Wq5bYl5L86ZlWD8zcqkES80l0fzlBRPRSREFE7NcULgopoaCCyrn98fjmTnrzJlFmMHv\n+/XipWd7znPOnDnzeb7Pd/mHuoi/fl09DzTArEGRkSxtpDQGwNwPtDlLPMACw1JSlH+I335b2XrW\noIFpK5c98fdXtkLby09dmOoNYFYoIY4u4lu1Yu5bSUnWZSFRsio6yjU7M4/TPVRz1dBqie/enb2r\nZs9mLoKWMmsWs1ofOCB+R5pyPykoUHcZ0+I+l5wMxMWx2Y5Nm9jgIjxcfX+lyrNGlBOwK1mFv/rK\ntLC9f19uiW/USPkz4lP7qg0u+FlJpRlgpfeGdHbWUlFdVCR2czp3Tp5pxxTWurwsXMgCyQF1S/x/\n/sM0RV6euNjd7dvmi389Km7fBiZOZPGBWVkssYbw2VAS8c4YoOuoOJSd6+7du5g8eTIOHDgAHx8f\nREVFoU+fPogQJMdNSkqCl5cXTpw4gdTUVEyfPh3btm1DdnY2UlNTkZeXh6KiInTr1g1nz56Fm5sb\n/Pz8kGNJDkUVHjxQ/3LxXLlifFEVFCinLeS3+flZVrjCVn7/XdmnH2ACvrRUWajm5bGpcYC9nKVR\n8+ZEW7167GUrfembS9vGo1ZO3tJ0jrag07EfR2mlWnuJpMhIZiEsL1fe7ihiTE3E2/pZKFk2q/Lz\nrSmMHcsEFsBcPKTBkDWZ2rWZ1V1qkdRiiQ8JYW6AtqIUE2PKBc2U4NMi4n195bEqvXsbRb0U05mm\ntFd3iotjFUil8O94aTBpo0ZsVkhJxPMzB6ZcKisr1V1ppChVn7WE/Hz5uuJibckNOE4+aOjeXV63\nAGDPSrY4lhirVrH6BGqWeL1eXVPYM0sez6lTLHVorVqs6NnAgfKB02+/ib9zOTmsn126sGUldxoS\n8fbDoSzxer0eAQEBaN68Odzc3PDCCy9g586don3S0tIw+mHZxaFDhyI9PR2VlZXYuXMnhg0bhlq1\naqFFixZo06YNflaqrGADV66YtlYD4hfGxo3q+/EuNdZkALAFaaEMIWpTXEqlooWYCy51cVEWZEr+\n7NKc7127qlvOqlrkKbnU8AWtbMXNzXTpdkcR8Wr+tLZ+FmSJtw/vvst+aMPDmbiripk+R0LJMCAV\nh0oi3t4B8kJGjrQuKN2a+gwAe99++63cT7p+ffmMnxgOgPZ8jMIaJTx82k3pQIr/DJQGNLyIN5U8\n4MIF7W6ctlriT56Ur9NqbLt2TWx5rltXbDEXkpAgD8Y+dYq5IZkzFiphb2F8/z4bECYns8HFoEHA\nP/8p30/JJVjoUqlkib95U1thSsI8DiXiCwsL4Sd4m/r7+6NQ8g0U7uPm5oaGDRuipKQERUVF8BWY\nhIXHXr58GZGRkYiKisLmzZs190en04n+fHx0APi/RMVjhCL+wAH1tkePZlH1SmmkHiWmXmhqwSZq\nLjgAs6xIA4mUULJiqIl4YYXORYvU26xqgaIk4qdPt1/7CxfKA1x5qiu9pBS1z/pRWOJJxFtOs2bM\n/SAnx3QRnJqKkrCTWuKVvkuPUsTXr88E9ciRwJw5pt1dhNj6fuvVSzyL+eab5msueHis0dy+MHsW\nz40b7F/pbwn/GSgVR+STIJiyxJ88qexOo4SlIn7vXmZlbtmSZVFSmmHQauWWnqtZMzZwUpqh8fVV\nnv05eFDdEm8KS33/r1xh6bL54o8PHrABR9++bOb95En54CUpSf4ZKon4jRuZtqmsVE+baomLUk0g\nMTFRpin5P1twKBFv68WoUVBQgOzsbKSkpCAhIQGnlapIKMBxnOgvLY0Ds1ZwABLxzDPyrCn8A81x\nxvRczsK1a2xmoH9/ZnHlUx/q9erHjBunzS9d6QdJyTXjr39lU4zLl7NBUK9e6m1WtSV+8GDxORcu\nNN0/S/H0BFJTlS1wjiJoH5WIV/rqkzsNYSnWWuIfZTVZgM0opqayYMQOHbQdo3U/NXQ65lKzYQPz\nl541y/wxnp5LsGgRC8y1JvPXzZtM5KlZ4qWukaNGGb/7deqoDzJOnpS7pNjDEr9nD6s1snMnixtL\nTmYZbqRotcRLXXf8/dn1ffGFfPAYGMi2vfKKeH1WlnWW+O+/ZzVnvv7afCDukiXsmX/+eTbI+Ne/\nWHa5xER2T+LixNV3eW7dYmmrhcJcyV2prIy57qoJeODxc6lJTEyUaUr+zxYcSsT7+/ujSOBfUlhY\niGYSpSe0sN+7dw+lpaVo0qSJ7NiioiL4P/w2N3mYZy0gIADdu3dHttQRTSPSka6Pj7r/ndRPzBqq\nWrhdu8a+zLt3s2j5l15iI+pTp9SPmThRW9taLfEAs4i89pq8uIqUqspMw+PlxQLXEhNZXuK5c+1/\njiZN2AtUiqOI+GbN1H2KbYEs8YQ9cERLvBStWaxs/U4B7FpHjWJpbLVUk9bpyjB7NrO4njnDhL+W\nGhlCSkrULfEdOzLDD8De88J6IjqdujX+jTdYBhQhau9/6WdZXGx03dDrWQG52FgW6yVN8ayGLZZ4\ngGXu+u47Y0zA//yPscifNJtQZqbcEq+lgvTly8z4NXasMYZNieJiFrwtdGf56CPg9deNy5WV6imz\nX3+dGfrMZdj7z39MZ2ayRcRzHJttrM6MPI6CQ4n46OhonD59GgUFBaioqMCWLVvQX1gvG8CAAQOw\n4eETumnTJvTs2ROurq4YMGAANm/ejHv37uH8+fM4ffo0OnXqhLKyMlRUVABgbjWHDh1Cey3VhRSQ\nfpG9vdVFvNSVxtIXIaAsci3Nv20Jr78uf6mppX0E2PWbKqUtRMkSb6mlR1jSG2Av9qomIIC93J5/\n/tGdQ2lg9PARrnZ0OmXBYzpgTlu7UsgST1iKFhFfHZZ4IVpFfGjoo+2HOWrXZjEWJSVMnN65Y/r3\ngMfXl7mECOHFuU4HrFnDLPa//iqvHmvO3Ud6HiU8PMT1ER48YAK3spK5se7fz1xoxoxhVmctaLXE\nnzkjXhb+xsXGsli4P/9kwaI80voGBw6wJBRCpG6bLi7s/qmxYoX6tvx8eWzfH3/IkyrwrlFKZGQY\nZ0aqWsRzHDB8OBsQ+vhYnse/puFQIt7DwwOrV6/GwIEDERYWhhdffBEdO3bE/PnzseNh4uyEhARc\nu3YNwcHB+Pjjj7Hi4dMaGRmJkSNHIjQ0FM8++yySk5Ph5uaGs2fPonPnzggLC0P37t3xxhtvIEwt\n6beE1q2ZNTA6mj34StHxUhF/8iSzaEtdaUaNsvx+tGolX6dlRG4tlka3K5XvVkMpIFhLtL+QmTOZ\nBcDfn1XJtXIs5vDwKdeEaC2xXhUoCW4tcRGmIEs8YQ8cMbBVSlVa4u2Bqyu7P+7u2t+5ycniZaXZ\nECU3TK0xYjqdeu0OQNmlpqREHN+Vl6eeDUyK1t9GaXFF6XtRKbd9ixbiAZv0t9LVlbntCrXG1Kmm\njWCmcnpY46qjBJ9zRE3EZ2WZzgdvrYg/cgTgQxvv3wfmzbOuHVNUVJgexDgSDpViEgD69+8vs74v\nEIR3u7u745tvvlE8dvbs2Zg9e7ZoXWhoqNXpJfkv/JEjwJAhcquzkiU+L095SnDwYFbAx5LiTi1b\nyte1bs1epNKpxerAEuF286Z8naUhEE2bAmlplh3jjOh0zFIjnN50pMqlU6aw6WCemTNtryZLIp6w\nB9a601SlJV7JOKOEpUYOW3B3d8fkyZNR10wlLFPF3kxhriI3j1KwrBLJyep1AQAmcIU/+xcu2FZT\nRKslPi9PvKx1IPb3v4vfqUKaNmWzkmlpwCefsGf1tdeYYadBA8vFpjVBs0r83/+xWQ61DHt374or\n1UuRpiHVilT76PXM4m9NBiglDh9meu3SJWDGDGDZMvu0+6hwKEu8I/Pbb8aRJ0/r1kxom3vZSh3U\niAAAHT1JREFU6nQsZ6qSEGvaVD2Xs5KId3Njo9DVq41R/QAT9u++y7K5nDgBxMSw9aamJ211zbFV\nxBPqvPYas748+SQT89KKrtXJxIlGf05fX+W0Y5ZC7jSEPVBy25PqUqVnzZQgtDdKswXSWU1bZ7Ys\npV69evjss8+wfPlyk/s9ahGvxW0wPh4YP970PtLfzpwc22qyFBebDxa9fl0saN3ctLub8tnqlOCf\nzeBgJuLnzjUORJWeJR6l/OyA/Szxx44xka6UcYjnyBH1bdZa4pVSgJoaLFjKwoXG+Mfly5XP50iQ\niLeBwED2RZ050/R+ISFMkDz7rHzb11+zwcHYsfJtLVvKBdJbb7HCC5MmseDTvXuBXbuYcJ81iwWs\ndOjA8sFnZjK/OZWJC5t9Li35oTH1siHkuLqy7EDnzgFLlypbqquLhg3ZoFavZ7NVQv9TayFLPGEP\nAgPFlvbQULloVzJsVOX3S2kQMW+ecTZAp2PfeUfEWndO6WyIGlqs5Vp+t6QuiYcO2Sbib91iGWaG\nD2e+7YsWyfOcZ2SIlwMDtRvKvL3Va3A88YTp49S4cIENPNavB955x3j99hLxANMvplArLgnIRfy3\n3zKD5jvvqNfj4Tjl1N3SCue28NBz24C0mJrDwREyYMgjyan+NW1q3P/WLY5r0UJ935dfNu4n3VZc\nzLa98YZ8W04OxxUWclxkJFueOJHjKistv54bNziudm15+5MmqfdZy19hofY+7NsnPnb1asuvg6i5\nbNokf75+/bW6e0U4I598wt53DRty3M6d8u3373Ocr6/xOXvpparv44IFxvP7+XHcnTscd+UKx61Z\nw3GHD1d9f7Si9Bum5S8vT1v769aZb2vPHvPt/P67+Ji6dTlu7lzbfu+kf8nJxvP99JN8+6hRlt3b\nYcOUzxMXp37MiBHq/du9m+NmzzYuN2/OcRUVHDd4sHXXW6uWfJ3we6T05+Kivu2VV4zX8csvHOfq\natz20UfK13vmjHJbw4ZZdq/VuHNH3vaYMer7nzvHcT/+yN4ptsBrTmtwIPue47FvnziKXIhw1Ozp\nacw3qwRfJc/Tk03V8IwZYxxJK422W7ZkAUVHjrDH6fPPrfM9rl+fVVuTMnWqbb6glhzbvTuweDEQ\nFcX8zJTSKBKPL2SJJ+zFyy8zP+ErV5RdFV1dWeaMCRPYTGd1WL3nzGFVMOfMYZZFd3dmrR43jr0j\nHRVPT+uCgLVa4ocOZekwTaHFEt+qlXiGsLzcdBaTgABt/RMizGzz7rvy7cHBlrWnFjRsytXLVC2B\nPXvE/bpwgWUNstYSf+YM8MEH4nXS2Y34ePGyqQr3Qkv8V1+J3XJefVX5GLWAXVO1bCyhoEC+Thrn\nwLN3L6vYHhvLKu9ynH36YDG2jR9qJhCMiu7e5bjWreWjs8mTlY/96ivxfn36cNy9e8btDx5wXGYm\nsxAJR29Ll8rPYU+KizluwACOa9yY4wICOO7DD9n606eZhb9ZM8tG5cuW2bd/xOPNd9/Jn7GbN6u7\nVwRBSBHOImj50+nY76hWKivZb2ZhITtW2FZQkPZ2Bg3S1j93d4774gvLLdMdOrDzVFQwS790+88/\nW3ZfU1OVz/P+++rHFBcbZ+q1/H35Jce1bGmdJb68nOO2bjW9zw8/mLa+C/8GDjReR8+e8u1nzrBt\nt29z3Jw57PMMCVFv788/LbvfSuzapdz2a6+J9VplJcd5eYn3OXDA+vMKNaelkCXeDLVrAwkJ8vVq\nAStjxrDR4vr1LJhm927mw87j4sLywg4YIA5kedSBTN7ezPf+jz+YLz0/0g0IYBZ+LVlzGjYEjh9n\ngR4zZjza/hKPF0oWGzOJMgiCqAZmz7YsW9aLL1qWREGnY7+Zfn5sVoWnQwflaqpqSPOvK50nMJD5\nuo8ZY3lcxOnTLCXmunXyVJWrV7PU1JagpgFMWeK9vdlMfXk5+x03x4ULckv811+zWflJk9i9UKJO\nHfZnKj2qTscKNGrNviTMTqNU9XXbNvbvihUsBmHHDnWrOAD88ot4+d49dm1jx7LMP9JsONeusaKN\nwmw90jz/PB9+KPbKyMmRp888fVq9b48U68cONRdIRkVXrshHZtu32/ecFRViv/rly+3bvhZmzjSe\nPzSU45KSxNf8z39WfZ+IxwMlX1iCIByTigqOW7GC43r14rguXTguIYHjFi5ksU/p6Rz3229sVvrL\nL9m+tnDyJPNxtzQe7OJFZT9u/q+gQLz/mDGWW6fj4+Xrhg+37jpv31a2YqelaTv+xx/N93fsWPGy\nm5v4vkpjCfi/Fi3Y9vJy9bYDA9k+cXHa7l379mz/69eVt/fpw7Zr/Sw++8x4HQ8ecFxMjHj76NFG\na/3lyxz3l7+w9XXrMt/2igqOmzZNvf3atTkuP58dn5Ag3x4by9q1BqnmtOhY605Zs1G6oRMmGD+s\nhg3Zg2dvLl9mbjXffmtdAKut3LvHAk4XL+a4q1fZFCg/zdWqFccVFVV9n4jHg5UrScQTBGFfTAlK\nqYvPmTMc5+Fh3N6li+WiHuC4jz+2vr/BwXLhePWqtmOvXjXvyhIUJF728xO3ce2a8nGRkcZ9fHyU\n9xk3jm3X65W3S92SfXzY/unpyvs/8QQT41rv+/Tpxj5mZKjvt2QJx/3jH/L1deqYP8fkyUybeXsr\nb//LXzjuwgXlz+f+fY67dElZ29ki4smdRiMrVgCvvMLcYDZvfjSFOJo2ZTnBhw2zvXiONfCpK996\nC/DyYlOg6elsquv06aotiEI8XpSVVXcPCIKoacyapVwEqEkTuYtPq1YsHXOvXiwPvbTqrFb4Gi3W\nkJhoTIHati3w/ffst1gLXl5A586m98nPFy9LE2o0bKjsViR06blzR7ntWbPYv506sXsoZORI5goj\nbLu4mAW3qtXivHyZBeJqRehOI3WtEfLWW8D//q98vVJe/W7dxMvffMPSWfJ55KX8+ac8+Pf+fWDJ\nEqafvL3ZvdFaKVgLJOI1UrcukJTE/Mp7967u3lQt/v7qhSgIwh6QiCcIwt4EBbHfbGlGHTX/80GD\nWOaiL76wLk5t5EjtVVqVGDoUOH+e+WafOmV5kT9JsXuzSGt8uLgAjRrJ9xMWlhwyRL791i1xnOBH\nHxmL9XXuzGqeeHrKM/AcOcJq3Kjx5Zfq26TZf/LymN97djarj2MPvvlGfI9KS5kx1xQffSQW+cuW\nsSxYfCzCvn32zYhFIp4giGpHqToxQRCErfTqxQRefDwzRjVsyAoKmUNpNnzkSPX9X3gBSEmxvp88\n3t5sVsCa2fh+/Szbv29f+Toly7/w/fzCC8b/u7oC+/fLZztCQlj12mPH2HZe0EuLcGVkmLaar1ql\nvu255wAPD+NySQnre1QU85ywFT8/wMeHBWYLUZs5kB6bkcGcbJRmdBYsMAbu2gqJeIIgqp0XXwQa\nNTImClaa7iQI4tFw584dfPbZZ1i3bl11d+WR0KgRyyJz9SrL0KbV5UWYr7xuXeD999VnpV97rXrc\nYIVERgI9ehiXTRlHoqOBKVPk6xs3lq8TZpx59lmWfW/iRGD7drnLCU+9ekBYmLgKr1TEL1pkfY73\n6GjxtdqbiAj27/Dhlh9bWcms7/n56llrnnuOZcexFd1Dp3pCgO7hN5FuDUFUHevX/4S///1HhIS4\n4/jxOdX+g0gQjwtXr17FX//6V3h5eeHq1avV3R2HobSUpdQ8e5alVe7ThwnT3FzxfrVqsX3r1Kme\nfgq5cYO5gTRqxAR3796soJiQvn1Z+kUlwT5wIJCWJl6XnQ107Gh7344eZQMNe3DhAhtEKKUAtwdv\nv80s5rdusZkEYTEqrUyezNKNqvH000Bmpm2akyzxBEE4BN7edwAshLf3PhLwBEFUOw0bAitXArt2\nGSvJKonZp55yDAEPMME5aRLzr3d3N1aM50lNZfVrlAQ8oOwTby93x5AQ5UBjnjZttLfl788GKeao\nXx+4eROoqGDXXa+etvZ5S3ydOqYr45rClIAH2ODK1kENiXiCIAiCIAgN8OJOyMSJVd8Prbz6KrOu\n+/szy/KIEab3v3tXvk5J2FuDmxswbZr69mee0S7kdTqgRQvmA2+K1q2ZcHdzYzMQ588bg5b79mUu\nVr//Lj9OOFgzdw6t/b10SX59R4/a1i6JeIIgCIIgCA0oWU6HDav6fmilSROWqvLiReYiYm6W88aN\nR9ufxYuBPXuUtwUHa/NzF1bjXbmS+ezzwbNSpFVmGzdmwbQlJcwy7+XFjhfONrRpAzRrZlxWEvGW\nphl/+mmW0nPqVMuOMweJeKLGkZiYWN1dIGzgjFrta8Lhoe+ec3P79u3q7oLD06UL84vnWbzYcVxp\n7PH98/e3vR+m0OlY6sz//hcIDTWur1OHzRJ07y4/Ji5OvLxokfH/nTqxlJylpXLXIUAs+IV9EKaO\n1OmAtWtZf0JDgTVrxIMdaUAuwAZElsBn9XnxRebiYy8osFUBCmx1bnQ6HX12TsiePXvQ92HOM/r8\nnBP67jknfGArQN89LVy7BmzYwATvkCHVn5WGxx7fv+PHgfBw4/LKlfa3HvM8eMDiDU6dYiK3VSsW\nRCy1npeUsIxlGRlsP7Vc7bNmsUEVT0AAc1fR6gevBsexHPw//MA+68REYN48eWGsvn1ZjYG2beUF\nnc6eNVr7f/xRWgPAes1JIl4BEvHODQkJ54REvPND3z3nhER8zcBe37/Vq9kgpXNnJliF+dgfNRzH\nLOo//cSWBw5k7kBaOHeOBc+WlzM3mb17xQMSW3jwgOW89/EBAgPZuqVLgTffNO5z4AALct66laXv\nvHKFrZ8yRZ7zft484F//4pdIxNsVEvHODQkJ54REvPND3z3nhER8zaCmfP8KC4GPP2aW7ldfZQWw\ntFJcDGRlMbcnaaVee3PjBpulOHSIufwIYw7u3zcWhoqKks/WbN4sjKWoQSkmd+/ejeDgYAQFBWHJ\nkiWy7Xfv3sXIkSMRHByMp556CgUFBYZt7733HoKCghAcHIw9gsgJc20SBEEQBEEQ1Y+/P7BkCfDe\ne5YJeIBZyocOffQCHmDBtF99xbLbzJ8vFuq1arGCVNHRyu5W9pohqGWfZuzD3bt3MXnyZBw4cAA+\nPj6IiopCnz59ECHI6ZSUlAQvLy+cOHECqampmD59OrZt24bs7GykpqYiLy8PRUVF6NatG86ePYvK\nykqzbRIEQRAEQRBEVdCypTGHvS04lCVer9cjICAAzZs3h5ubG1544QXs3LlTtE9aWhpGjx4NABg6\ndCjS09NRWVmJnTt3YtiwYahVqxZatGiBNm3aQK/Xa2qTIAiCIAiCIKoCFxdxliNrcShLfGFhIfwE\ncyD+/v44fPiw6j5ubm5o2LAhSkpKUFRUhE6CPED+/v4oLCwEx3Fm21RD5ygh54TF0Gfn3NDn57zQ\nZ+fc0Ofn3NDn93jhUJZ4evgIgiAIgiAIwjwOZYn39/dHUVGRYbmwsBDNhGWzYLSwt2rVCvfu3UNp\naSmaNGkiO7aoqAjNmjVDZWWl2Tal1IToboIgCIIgCKLm4lCW+OjoaJw+fRoFBQWoqKjAli1b0L9/\nf9E+AwYMwIYNGwAAmzZtQs+ePeHq6ooBAwZg8+bNuHfvHs6fP4/Tp0+jU6dOmtokCIIgCIIgCGfC\noSzxHh4eWL16NQYOHIgHDx4gPj4eHTt2xPz58xEVFYVBgwYhISEBcXFxCA4ORoMGDQyCPjIyEiNH\njkRoaChcXV2RnJwMNzc3uLm5KbZJEARBEARBEM4KFXsiCIIgCIIgCCfDodxpCIIgCIIgCIIwD4l4\ngiAIgiAIgnAySMQTBEEQBEEQhJNBIp4gCIIgCIIgnAwS8QRBEARBEAThZJCIJwjisWLt2rXw8vJC\nREQEAgIC8MEHHxi2JSYmwt3dHYWFhYZ1Li7i1+TWrVvh4uKCX3/9tcr6bIq1a9diwYIFVh+fmJiI\ndevWWXTMhAkTcPz4cQBAz5490aFDB0RERCAiIgLXrl0z29/x48eL1vXs2RP79u3DwIEDDZ9L/fr1\nDW1mZ2ejZ8+eCAgIQGhoKDp27Gg4vzn+/e9/IzQ0FOHh4QgPD0dycrLhnAEBAYi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"text": [ "<matplotlib.figure.Figure at 0x2ddb60d0>" ] } ], "prompt_number": 115 }, { "cell_type": "code", "collapsed": false, "input": [ "def convertENBLids(enst_name):\n", " ensg_name=ensemblGeneAnnot.loc[enst_name,'name2']\n", " return ensg_name\n", "\n", "def getUTRbindingProfile(utr,hmap_m):\n", " if utr=='5p':\n", " ix=(hmap_m[range(201,601)].sum(axis=1)==0)&(hmap_m[range(1,201)].sum(axis=1)>0)\n", " screen=readUTRfile(outfilepath+'/PlotData_ReadsPerGene_5pUTR')\n", " elif utr=='3p':\n", " ix=(hmap_m[range(1,401)].sum(axis=1)==0)&(hmap_m[range(401,601)].sum(axis=1)>0)\n", " screen=readUTRfile(outfilepath+'/PlotData_ReadsPerGene_3pUTR')\n", " else:\n", " ix=(hmap_m[range(1,201)].sum(axis=1)==0)&(hmap_m[range(401,601)].sum(axis=1)==0)&(hmap_m[range(201,401)].sum(axis=1)>0)\n", " screen=readUTRfile(outfilepath+'/PlotData_ReadsPerGene_CDS')\n", " \n", " # Ensure all genes are also identified in pre-allocated gene lists.\n", " hmap_m_utrSpec=hmap_m.ix[ix,:]\n", " hmap_m_utrSpec_filter=pd.merge(hmap_m_utrSpec,screen,left_on='ENSG_ID',right_on='Gene_name',how='inner')\n", " sums=hmap_m_utrSpec_filter[range(1,601)].sum(axis=1)\n", " hmap_m_utrSpec_filter=hmap_m_utrSpec_filter.loc[np.argsort(sums),:]\n", " return hmap_m_utrSpec_filter\n", "\n", "def plot_geneBodyPartition(outfilepath,sampleName):\n", " treatMatrix=outfilepath+'clipGenes_proteinCoding_LowFDRreads_centerCoord_snoRNAremoved_miRNAremoved_cleaned_sorted_UTRs_scaled_cds200_abt0_treatmatrix.txt'\n", " hmap=pd.DataFrame(pd.read_table(treatMatrix,header=None,skiprows=1))\n", " \n", " # Ensure genes recoverd from this analysis are indepdently indentified using partitioning of CLIPper cluster data.\n", " hmap['ENSG_ID']=hmap.ix[:,0].apply(convertENBLids)\n", " bound_pc = outfilepath+'clipGenes_proteinCoding'\n", " pc_genes=pd.DataFrame(pd.read_table(bound_pc,header=None,))\n", " pc_genes.columns=['ENSG_ID']\n", " hmap_m=pd.merge(hmap,pc_genes,left_on='ENSG_ID',right_on='ENSG_ID',how='inner') \n", " \n", " # Isolate intronic bound genes.\n", " tosave=outfilepath+'PlotData_ExclusiveBound_Intronic' \n", " intronicBoundGenes=list(set(pc_genes['ENSG_ID'])-set(hmap_m['ENSG_ID']))\n", " np.savetxt(tosave,np.array(intronicBoundGenes),fmt=\"%s\")\n", " \n", " # UTR specific genes.\n", " geneTypes=['5p','cds','3p'] \n", " depth=50\n", " for i in range(0,3): \n", " utrMatrix=getUTRbindingProfile(geneTypes[i],hmap_m)\n", " tosave=outfilepath+'PlotData_ExclusiveBound_%s'%geneTypes[i] \n", " np.savetxt(tosave,utrMatrix['ENSG_ID'],fmt=\"%s\")\n", " plt.subplot2grid((2,3),(1,i),colspan=1)\n", " dataToPlot=utrMatrix[range(1,601)]\n", " p=plt.pcolormesh(np.array(dataToPlot)[-depth:-1,:],cmap='Blues')\n", " plt.title(geneTypes[i],fontsize=5)\n", " plt.vlines(200,0,depth,linestyles='dashed')\n", " plt.vlines(400,0,depth,linestyles='dashed')\n", " plt.tick_params(axis='x',labelbottom='off') \n", " plt.tick_params(axis='y',labelleft='off') \n", " plt.ylim(0,depth)\n", " plt.ylabel('Ranked genes (highest on bottom)',fontsize=5)\n", " plt.xticks(visible=False)\n", " plt.yticks(visible=False)\n", " plt.title('%s specific genes: %s'%(geneTypes[i],np.unique(utrMatrix['ENSG_ID']).shape[0]),fontsize=5)\n", " \n", "ensemblGeneAnnot=pd.DataFrame(pd.read_table(genesFile))\n", "ensemblGeneAnnot=ensemblGeneAnnot.set_index('name') # Make ENST the index\n", "plot_geneBodyPartition(outfilepath,sampleName)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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cur/5mzPJmNrHtkbrdGhnhYs3M3H8ehoG2JfteXG2bgpn66Z4d9clfDyip/Rf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\nH4ZGo9FuWKPRwNzcHI6OjrCystK9CJl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"text": [ "<matplotlib.figure.Figure at 0x2aab0addd350>" ] } ], "prompt_number": 120 }, { "cell_type": "code", "collapsed": false, "input": [ "fig2=plt.figure(2)\n", "plt.subplot2grid((2,3),(0,0),colspan=3)\n", "plot_mRNAgeneBodyDist(outfilepath,sampleName)\n", "plot_geneBodyPartition(outfilepath,sampleName)\n", "fig2.tight_layout()\n", "fig2.savefig(outfilepath+'Figure2.png',format='png',bbox_inches='tight',dpi=150,pad_inches=0.5)\n", "fig2.savefig(outfilepath+'Figure2.pdf',format='pdf',bbox_inches='tight',dpi=150,pad_inches=0.5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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OHYuSkhLUrl0bCxYs8HWTqpW33noLq1atwqVLl9CrVy8kJib6uklEROSnWEEjIiIiIiLy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yd12xkXF4cFCxYYlzMyMlQ/y/r165Gbm4uCggIkJycjOjoa7du3R0pKCvLy8vDw4UP8/PPPiImJ\nQfv27ZGcnIyioiLcv3/f2D4AaNSoEby9vfHJJ59g+PDhqm1q0KCBRPQIcXSPHz9GSkoK/P39je0S\nBOi1a9dw8OBBBAQEoE2bNti9e7fREnjs2DFcvXoVAQEButdCOCYATJw4EevWrTNaGocNG4Z9+/ZJ\ntjUYDBg/fjzGjBljFDA5OTn4/vvvFccrLCzEjBkzUKtWLQQEBODs2bO4cOGCcbuDBw+ibt26AIC3\n334ba9asUbRNvP2uXbvg5uZmtJqlpqYaxdry5csRFRUFAIiJiTFeo/z8fKxevRodOnQwew207l8A\nuHr1Kho2bKh7DIIgiNIICTSCIIhSiNxyIF4WXgsZAwX+/e9/48svv8SDBw8k79WsWROdOnUybpeU\nlIRnn31Wcvy+ffsiKSlJsq5du3Zo3bo1mjdvjvj4eAQHB8PLywseHh746aefMH78eAQHByMkJASp\nqam67Z42bRoePnwIPz8/BAYG4o033lD9zBEREejatSv8/f3RsWNHREZGok2bNhg6dCiCg4PRsmVL\njBo1ChEREWjdujViYmLQvHlzdO7cWRFPlpiYCH9/f6MgkRMdHY309HTjcrdu3RAaGgo/Pz/UqVMH\nL7/8srFdVatWRatWrRATE4N58+bB09MTPj4++OSTT9CpUyeEhoZi2LBhSEpKMmZXVPuO5NfFw8MD\nY8eOxcyZMwEAR44cMSblEDNhwgS0adMGrVq1QkhICGJiYiTWyVGjRiEsLAwtW7bE1atXjanx79+/\nj8TERAQGBsLPzw+HDh3CtGnTAPA4R7GLp8CSJUsQEBCAgIAAjB8/HsnJycbPIXwXfn5+RksjACxY\nsADLly83puePiopS3GNq10B+/4qtaPv370d0dLTuMQiCIEojBmZviiWCIAjiH0tubi68vb3x4MED\nREdHIykpSZLIwZl54YUX0KlTJ6OIkHPp0iUMHToUW7du1T3OiBEjMGLEiGIXCzk5ORg9erTDEm+Y\no0uXLvjll18s3n7r1q1YunQpFi9eXIyt4ty7dw/t27e3uEYcQRBEaYIsaARBEITNDBo0CKGhoQgO\nDkZiYmKpEWeBgYG4cOECEhISNLepV68eGjdujEOHDj3BlmlToUKFJybOAFglzgBta2BxsHjxYrz2\n2mtP5FwEQRBPGrKgEQRBEARBEARBOAlkQSMIgiAIgiAIgnASSKARBEEQBEEQBEE4CSTQCIIgCIIg\nCIIgnAQSaARBEARBEARBEE4CCTSCIAiCIAiCIAgngQQaQRAEQRAEQRCEk0ACjSAIgiAIgiAIwkkg\ngUYQBEEQBEEQBOEkkEAjzOLh4YHQ0FCEhoaib9++Jd0cIwsXLsT69esBAOfPn0erVq0QFhaGq1ev\nIj4+voRbZzsnT55EmzZt4OXlhalTp0reu3PnDvr06YOgoCD4+/sjIyMDADB8+HD4+voav6djx46V\nRNMJwioaNmxY0k3A1atXMXr0aOPyyJEj0bJlSyxcuBCTJ09Genp6CbbOdq5cuWLsD0JDQ1GtWjW8\n9tprAIC1a9ciODgYwcHB8Pf3x9q1a437LV26FP7+/vD398f3339fUs0nCCNxcXEIDQ1F06ZNMWDA\nADx48KCkmwQAWL9+PRYuXAgA+OuvvxATE4Pw8HCkp6eX6jFIamoqwsLCEBQUhBYtWmDNmjXG98R9\nio+PD/r06QMAuHjxImJiYtCiRQs0bdoU8+bNK6nm/20wMMZYSTeCcG4aNWqECxculHQzdJk1axYq\nVaqEF198saSbYjc3b95EVlYW1qxZA3d3d0yePNn4Xr9+/RAXF4dRo0ahsLAQubm5KFeuHEaMGIER\nI0YgOjq6BFtOENbhbH3L9evX0bt3b+zevbukm+JwgoODsWDBArRt2xYPHz5EmTJlAABHjhxBbGws\nbt26hatXr6J169Y4cuQIGGMICgrC3r17UbNmzRJuPfFPRny/JiQkICYmBmPHji3hVklZsWIFDh06\nhJkzZ5Z0U+zm2LFjqF69OmrUqIFTp06hdevWuH79Ojw8PCTb9erVCwMGDEBiYiL+7//+D2XKlMHM\nmTPx559/4qmnnsKlS5dQsWLFEvoUpR+yoBE207BhQ7z99tto2bIlwsPDcfbsWcU2y5cvR2BgIEJC\nQtCqVSsAwJIlSzBgwAC0b98eTZs2lQiQr7/+GgEBAQgMDMSYMWMk6/38/BAaGoqEhAQAwJQpU7B0\n6VKsX78ec+fOxYwZM9C9e3dj2wTef/99NG/eHKGhoaqd+smTJxEaGorw8HBMnjwZjRo1AgAUFBTg\n5ZdfNrbnu+++M7Y/MTERHTp0QJMmTfD6668bj7V27VqEhIQgKCgIAwYMQF5eHnJychAfH4/g4GAE\nBgaanZWuXr06wsPD4e7uLll/69Yt7Ny5E6NGjQIAuLq6oly5csb3aa6FcCbEv1nB8n769GmEhYUh\nLCwM7777rnHb33//3TgrGxwcjNu3b0uOlZ2djejoaISGhiIgIAC//fYbAKB+/foYOXIkQkJCEBMT\ngz///BMA/01HR0cjKCgIUVFROHfunHF9+/btERgYiNDQUBw/fhyZmZno0KEDAKBDhw44evQoQkND\nkZ6ejuHDh2Pbtm0AgJ07dyI8PBwhISEIDw83nkugsLAQI0eOhJ+fH7p164b4+Hjjvmr9AgA0aNAA\nL730kvGY165dAwBcu3YNXbt2RVBQECIiIrBv3z4AwKeffooWLVogJCQEvXv3tvi7OHbsGHJyctC2\nbVsAMA52AeD+/fuoU6cOAODXX39Fp06dUKFCBVSsWBFPP/00fv31V4vPQxDFgXC/5ufn4/Hjx8b7\nNTY2Fq+//jrCw8MREBCAPXv2KPZV61u2bt2Krl27olOnTvDz88MLL7xgfH5q/VbXrl1r7DfatGkD\ngI8Fpk6divT0dEyYMAFLlixBy5YtAUjHIGp9oZjr16+jffv2CA0NxUsvvSTZd+rUqWjRogUCAgIw\nffp0AMDWrVsRHx+Pbt26oVmzZhg4cKCx/bt27UJkZCSCgoLQpUsX3Lp1C4WFhRg6dCgCAwMRFBRk\nVkS2aNECNWrUAAA0a9YMbm5uyMnJkWxz584dbN++3dgPNWnSxLhNTk4OatSogbJly+qehzADIwgz\neHt7s5YtW7KwsDCWnJxsXN+wYUO2cOFCxhhjGzZsYHFxcYp9/f392Y0bNxhjjN2/f58xxtjixYtZ\nkyZN2MOHD1lBQQFr27Yt2717Nzt06BB7+umnWX5+PmOMsVdeeYWtXr2apaens3r16rFbt24xxhi7\ne/cuY4yxKVOmsKVLlypeC21jjLHVq1ezli1bstzcXMYYY3/99ZeijZ06dWK//vorY4yxBQsWsEaN\nGjHGGPvss8/Yu+++yxhjLC8vj0VGRrLr16+zxYsXs8DAQPbo0SOWn5/PAgMDWWZmJrt27RoLDQ01\nfs45c+awTz75hK1cuZL961//Mp7v3r17jDHGJk2axNatW6d53adMmcKmTJliXN69ezcLCQlhAwcO\nZP7+/mzIkCHGYw0fPpy1aNGC+fn5sXHjxrFHjx5pHpcgihut32znzp3Zpk2bGGOMffnll8bfabdu\n3djevXsZY/y3JvQBArNnz2azZs0yLgu/MYPBIDneiy++yBhjLDIykh0/fpwxxtjevXtZz549GWOM\nBQUFsaSkJMYYY4WFhez+/fvswoULLDY2ljHGWGZmpvE1Y/x3tW3bNvbo0SNWq1YttmvXLsYYY48e\nPWJ5eXmSNv73v/9lQ4YMYYwxdu3aNVaxYkW2bds2Rb8we/Zs9sknnxjbv2PHDsYYYzNnzmSTJ09m\njDH27LPPss2bNzPGGLt06RKLjIxkjDHm4+PDHj9+LLkG+/fvZ6NGjdL5Nhh755132DvvvCNZt27d\nOta8eXNWoUIF4+eaMWMGmzhxonGbiRMnspkzZ+oemyCeBF27dmWVK1dmffv2Na6LjY1lb7/9NmOM\nsYyMDObv76/YT61vSU1NZZUqVTKOTfr378+WL1+u+Vu9cuUKq1q1Kjt37hxjzNSfLVmyxPiMXrJk\nCZs6darxvELfJu8L1cYgo0aNYt988w1jjLGNGzcyg8HAGGNs7dq1bOjQoYwx3l/16NGDHThwgKWm\nprJatWoZj9mlSxeWmprK8vLyWIsWLdi1a9cYY4ytXLmSjRs3ju3du5d17drVeD7h83311Vfsq6++\n0r3uK1euZFFRUYr1X3/9NUtMTDQuFxYWstjYWFa7dm1Wrlw5tnHjRt3jEuZxK2mBSDg/WVlZqF69\nOs6cOWOclfb19QUAJCYmAgC6deum6l4YHR2NwYMHo0+fPujTpw/Kli0Lg8GAXr16wdvbGwAwYMAA\n7NixAwaDAcePH0dERAQA4NGjR/D19cW5c+fQt29fVKlSBQBQoUIF4/GZyGrEVCxIW7ZswbBhw+Dl\n5QUAqub2EydOoFOnTsbPM2vWLADA5s2bcerUKWzYsAEAnxXKzMyEwWBAfHw8PD09AQAhISG4ePEi\nrl+/jqysLLRv3x4A8PjxY3Tt2hU9evTAm2++iQkTJqBLly54+umnAUARX2aOoqIiHD58GPPmzUNU\nVBReeuklTJs2DbNmzcKsWbNQo0YN5OXl4bnnnsMHH3yAadOmWXV8gnAUv/32m+pv9vjx4+jcuTMA\nYMiQIZgzZw4A3k+MHTsWAwcORO/evRWxaa1bt8bzzz+PBw8eID4+HpGRkQCAypUrS47Xtm1b3Lp1\nCwcPHjT2TQDg7e2NP//8ExcvXsSAAQMAAC4uLihbtixu3rxp3E6tD2GMISMjAzVr1jTOnAu/fTG7\ndu0yHrtmzZp4+umnwRjD9u3bVfsFYbt27doBACIjI/HDDz8A4H2P2PXzzp07ALibYmJiIrp3745n\nn30WABAWFoZvvvlG/Yv4H0lJScZ4XYEePXqgR48e+O233zBs2DCcPn1a9xgEUZKkpKTg0aNH6N27\nN5YuXYrnnnsOgGkMEhgYCE9PT9y6dQtVq1Y17qfVtzz99NOoXr06AGDw4MHYvHkz3N3dVX+rO3fu\nRPv27fHUU08BkI5BBBhjqv2HvC9UG4Ps2rULn376KQCgS5cuqFy5MgDeD2zbtg2hoaEAgAcPHuDC\nhQuoWrUqYmNjjceMiIjApUuXkJGRgaysLHTp0gUAt+r7+fnB19cXZ86cwZgxYxAXF2eMjzMXEnLy\n5Em8/fbb2LRpk+K9ZcuW4Y033jAuz5gxA82aNUNqaipOnTqFZ555BseOHUP58uV1z0FoQwKNMIvQ\nifn6+iIqKgrp6elGgabWIYlZsGAB9u3bh02bNqF169bYv3+/Yj/hNWMMgwYNwieffCI5xscff2yz\n+56Li4vZfQ0Gg+Z7n3zyCXr06CFZd+LECaPgA7irYVFREQAgJiYGq1evVhznwIED+OWXXzB79mxs\n27bNanEGAPXq1UOVKlUQFRUFgPt/z507FwCM7gienp4YOnQoBegSJYolvzvx+2+++SZ69OiBjRs3\nomPHjtiwYQOaN29ufD8qKgrbt29HSkoKxo0bhxdffBEjRoxQPR5jDOXLl8fBgwcl78tdEq39PJYg\n/kzifkWrXxD3Iy4uLsZ+BAD++OMPxeDm559/xo4dO5CSkoJZs2bhyJEjcHV11W3Tnj17UK5cOfj7\n+6u+37FjR+Tk5ODmzZuoW7cuUlNTje9lZ2cbJ68IoqTx8vJCr169sGfPHqNAM9fPqPUtcsR9h9pv\nddWqVWbbpjWOsKQvFLdBzuuvv45x48ZJ1m3btk1zDOLr64sDBw4ojnP48GFs3rwZy5YtQ3JyMpYu\nXarbnmvXrqFPnz74/vvv0bhxY8l7V65cwfHjxxEXF2dct2PHDowfPx4Ad4v08fHBiRMnjJNphPVQ\nDBqhy/379/H48WMA3E96z5498PPzM76flJQEANi4cSMCAwMV+2dlZSEiIgITJ05Es2bNcOHCBTDG\nsH79euTm5qKgoADJycmIjo7GM888g59++sk4o3337l1cvHgRnTp1wurVq3Hr1rnqrXAAACAASURB\nVC3jekt55pln8J///Ae5ubma+zZv3hy///47AB4zJxAXF4evv/4aBQUFAIALFy5oZo8yGAyIiorC\nrl27cObMGQDcAnj27Flcv34d3t7eGDhwIKZMmaLaeaoh77Dr1auHevXqGTM3bt261fhdCNemqKgI\n69at0xyMEcSTQOs326JFC2NM07Jly4zbZ2Vlwd/fH6+//jo6d+6M48ePS453+fJlVKtWDSNGjMCr\nr75qFF937tyRHC86OhrVqlVDw4YNjX0TYwzHjh1DtWrV0KhRI+P6goICPHz40OxnMRgMCAwMxM2b\nN43JQ3Jzc5Gfny/Zrm3btvjxxx8BADdu3MDvv/+u2y/oERcXhy+//NK4LPzmL126hOjoaMyYMQMG\ng8GivnDZsmUYMmSIZJ3YOrdr1y64ubmhevXq6NixI7Zs2YK7d+/ir7/+wm+//UYCjShRcnJyjP1I\nfn4+UlJSJGMN4fd89OhRPH78WGI9A7T7ltTUVONYY/ny5YiOjtb8rUZFRWHnzp04f/48AJ6xUY6W\nwOrYsaOkL1TbV9x3bNq0yWgxj4uLw/fff28cd1y/fl1i8ZcTFBSEGzduYNeuXQB4H3fy5EncuXMH\nhYWF6N27Nz799FOzY5CcnBx069YN06ZNM8atiklKSkJCQoJkcqhx48bGyZ3Lly/j7NmzTpGltzRD\nFjRClwsXLmDYsGEoKipCbm4u3njjDQQHBxvfv3jxIsLCwmAwGIwdpZgJEybgxIkTcHFxQbt27RAW\nFoajR48iPDwcXbp0wdWrV5GYmGicZZkyZQpiY2Ph7u4Og8GA+fPno02bNpg0aRLatWsHb29v+Pr6\nYuXKlQCks1Zqr3v27ImMjAwEBwejbNmyiImJMVqdBObNm4fExES4ubmhQ4cOxpmpV155BZmZmQgI\nCICnpycqVqxoTEetNltWs2ZNLF68GAkJCTAYDCgsLMS0adNQsWJFvPrqq3B1dYWHhwc+++wzAMDk\nyZMRHh6usNBlZ2ejTZs2xoDbRYsWYc+ePfDx8cGSJUswcuRIPHz4EA0bNjS6RI0aNQpZWVnIyclB\naGgoZs+ebfa7JYjiIjg4WPU3+/nnn2PgwIF488030alTJ+Pv6LPPPsOvv/4Kd3d3+Pr6KlJU//HH\nH5gxYwY8PDxQrlw5LFq0CACftPjxxx8xYcIEVKxYEcnJyQB4RrXRo0dj+vTpKCoqQt++ffH+++8j\nKSkJI0eOxIwZM+Dm5oYffvgB3t7emv2IgIeHB1atWoWXX34ZRUVF8PDwwC+//GJ0MQKAQYMGYcuW\nLfDz80ODBg0QFhYGLy8vzX6hSZMmivMKy1999RVGjRqFFi1awGAwoFWrVli0aBGGDBli7BcGDRqE\nKlWqYP/+/Vi4cKGqm2NhYSFWrVqFtLQ0yfolS5YYrQJly5ZFcnIyDAYDfHx8MHXqVLRu3RoGgwHT\npk2jDI5EiSKUlikoKEBubi66du2Kl156yfh+fn4+IiIikJuba+wXxMybN8/ovij0LXv27EGbNm0w\ndOhQZGVlISoqCv379wcA1d9q79698d1336Fnz55wd3dHmTJlsHPnTgCm/kL8+xWvDwkJ0Ry/CEyb\nNg0JCQn4/PPPERERgfr16wPgbsjHjx9HeHg4PD094enpif/+97+S44vP5+HhgZ9++gljxozBo0eP\nUFhYiFdeeQWxsbEYPHgwGGNwcXExJgkRSgTIXR0///xznDp1Ch988AE++OADANzFtHbt2gC4QBNc\nMgUmTZqEIUOGwM/PD4WFhcawC8J2KM0+YTO2psheunQpMjMzJdkbS5Lc3FxjPNyyZcuQkpJi7AQJ\ngnBenC1Nv9CX3Lx5E23atMGhQ4ckmVYJgnAcHTp0wNKlS42CxlK2bt2KpUuXYvHixcXUMuvIy8sz\nxrXu3LkTb731FrZv317CrSJKGrKgETajF7tVnPs6mj/++ANvvfUW8vLyUK1aNaNViiAI58aZ+hGA\nJyTIz89Hbm4u3n//fRJnBOGEyK1dJc3p06cxbNgw5Ofnw9PT01jSh/hnQxY0giAIgiAIgiAIJ4GS\nhBAEQRAEQRAEQTgJJNAIgiAIgiAIgiCcBBJoBEEQBEEQBEEQTgIJNIIgCIIgCOL/27vz+KgKe///\n7yEMyRDCnrCEJSwqBEM2EFzprfe6QQitS1WqpSEpPmJs61J7rwsXKbVIv9Z7f9VqQbartVpFiSB8\nleK3SilqCClCAQuEsAQhYZEsZBuY3x/cRGL2nJOcZV7Px4OHmZkz53xyYt6Zz5w5nwPAJmjQAAAA\nAMAmaNAAAAAAwCa4Dloj7HR9DADGdPaVRMgPwF3IEABGtCdDOIIGAAAAADbBEbRm2Pka3h6Px7b1\nPfXUU5o3b54k9mF7Pf7443r66ae1YMECPf7441aX0yQ770Or34W2636pZeef3TXXXKPNmzdr06ZN\nuuaaa6wup0l23oeTJk3SZ599pk8++USTJk2yupwm2XkfkiFNs/PPrbKyUj6fTxL7sL1KSkrUq1cv\nSexDI4xkCEfQAAAAAMAmaNAAAAAAwCb4iGMzKv1WV9C8jqpv094T+v0nh5pd5tV7kpp87PL4JKWl\nZ2jZy0uCdh8aNT5xgiRp3PhE29ZYy+71WcUJ+8WuNd4ybbo2b96sXWdC1P1giX6fc1gzxw/SFSP7\nSpKe3rhXj11/Sd3yd67YqtdnTeiwenYdKVHskJ7KWrVTz996eb3HPss/o/HDenXYtltjd2GpunSR\nLhsUUXdf4jU36rPPPlPPPpG2/TnXsnt9VrH7frFrfTWBEF6DGHTO421yH+YXlWtkVLj2HivT//y9\nUL+46TLtOHRGcQZy8OLnbz94Rl08anR9d6/M1Ws/SK53n9n70Oj3YhaOoMF0U6el6IUXF1tdhqOl\nzviOJOmmm2+xuBKg8z30yKOSpMHDR1pciXPdc9+DkqQRI9mHCC5er5fXIAb5fD72ocVo0AAAAADA\nJmjQAAAAAMAmaNAc6vEn/9PqElpk9xqpzzgn1IjG2f1nZ/f6JPvXaPf6JGfUiIac8HOze412r0+y\nf412r88IT8DOFxCwSO11CypqgnPXGB0SAtiBz3vh97izIy7Y88NMf95zXIMjfLYeEvL5IXsOCck9\ncFrJI/pYWJXzkSFAQ3YbEmI2M4eEGMkQGrRGdEY4zv7j37X0roS628s+K1DaFTGSpLX/+FLTxg2q\nt/zeY2W6ZGCPevfV/pJ808Y9Rbp+TFS9+97afkQJA3tr9IAeOnKqQkP6+vT3g1/py/JK3Rw7UAXF\nZ7Wr6IxuuWi7B4rKtWzbEf3ipsuMfrtAp+PFlfN9ln9KURFhionsrk/2n9LkUX1VeLpC0X18TT7n\ntbxDujtxmCTp6OlKDe4TJkn68qtKnSipUtywXvp4b7GuuySywXOLSqp06ORZTejAxuZv+05Kkq4a\n3U+StPiTA/rR5BGmrHt3Yalqzp3X+GG9dOjEWQ3r392U9QYrMgRwn9oG7OO9xerfPVSx0T3rPb79\n4Bmd9ft15ah+dfelLv5U2T+a1Ox6/3GkROOG1F+XkQzhI44AAAAAYBM0aAAAAABgEzRoAAAAAGAT\nNGgAAAAAYBNdrS7AbbJW7Wxw3zenfkmqNyBEUt2AEEkNBoRIajAgRFKjA0IkNRgQIkm3xQ+p+3pI\n3wsn2CcM763aKmIiuysmsv4J5SOiwhkQAgAAgCYt+n/79Oi/jK53367CElVUn5OvW0iDQRyStPTT\nAk2O7qtxQ3p26tTZ2gmNjQ2KkqT44Q0nOLY0IERSgwEhRtGgwXTbcnOVty1XCUlJSk7uuNHXbrZ1\na4625+UpKXmCEpO4pAGCy5p3s1V0/LgGj79WURHDrS7HkTase1eqKlFK6gxFRTV80w5wK7/fr5XL\nlykkJESz0mZbXY4jVVdX65WVK+T1enXvrB9aXU5Q4iOOMN36dWuVlTlH69ausboUx1qTvVpZmXP0\nwfvrrS4F6HTPPftrZWXOUeGhA1aX4liLn39WWZlzVFDAPkRw8fv9ysqco5/++H6rS3GsyspKZWXO\n0SMP/cTqUoIWDRoAAAAA2AQNGgAAAADYBOegmeybA0H2HC2VJJVW+hUR1vTuLig+q34R3VRQXK7e\n3btpaD+fSipq1NPnbXZ7pRV+Rfi6qqzSrx7NrH/P0VKNGRxRd7vwVIUqa85r1IBw7Sos0fD+3RUe\n+vXzPz90RhFhXTXiG4NISiv86tJFOnPWr56+C8s3t10AAAC41zcHhEhqdDDIxWZPiqn7urMGhLTH\njb/dLEl66F9H6caxAzttuxxBAwAAAACboEEDAAAAAJugQQMAAAAAm6BBAwAAAACboEHrYLWDOZob\nECJJMZHdFRHWVXFDe2loP58k1RsQsvdYWYPn/P3gV4r430Edv9tSUHf/sx/ta7KONTuP6pKfZCu6\nr0+jBlwYABIb3bPegBBJGj+sV4MBIZIU4euq8NCuGtwnTD3CujIgBECHOVZeqcfX79auwhJNHtVX\nkhTdx9fsc+5OHFb39eA+YXVfD+odprhhvfTbv+bruksiG31uVM9QTRjRR4dPVtS7/92dRxssu+PQ\nGe08XKLCUxUNHmvOVaP76arR/epu/2jyiDY9vzljoyM0flgvSVJoV/68A+h8uwpLJEl/3nO8Xc//\nzUf72/ycLftPtmtbrfH+A1fr/Qeu7tQBIRINGgAAAADYBg0aAAAAANgEn0+D6RISk5SWnqHEpGSr\nS3GspOQJSkvPUHxCotWlAJ1uWsp0jY2NVc++/aWvrK7GmVJSZyguPl6R/Rv/OCfgViEhIUpLz1DX\nEF7itpfX61VaeobCQsNaXhgdwhMIBAJWF2E3Ho9HklRR0/SuyS8q1/6TZfq3sQMMbesfR0p0rKxS\n3b0hunJUv2aXfTB7l55LjTW0PSBY+LwXfo87O+Jakx9onT/9/bCyPy/S49df0uJFT1vrt3/N1wPX\njGx2mcMnK+rOBZYunIM2/fLB9ZbZceiMPB6P+oR7Fd23+fPi4ExkCOBOT2/cq8euv6TRx17ackD3\nXWnOucFGMoSPOAIAAACATdCgAQAAAIBN0KABAAAAgE3QoAEAAACATdCgtWDjnqJ6tzfsvnDhvZFR\n4YYHhEjSuCE9df2YKPUM9SrlpU+avdhe7YCQx9btMbxdALC7OxKG6g/3JivvePtGOS79tKDBfS0N\nCJGk8ip/vdvfHBAiSXHDeunyoT0bHRCSvaNQi/7fPmXvKNT6XcdaX/D/+uLL0jY/BwDgHjRoAAAA\nAGATNGgAAAAAYBM0aAAAAABgEzRoAAAAAGATrmnQtmzZoszMTI0bN07h4eHq0aOHxo0bp8zMTG3Z\nsqXd671+TFS922YMBmnMuCE9tea+ybpyVL8Wl336ljEdUkNb5OSfVtaqnXX/ACfrqPyAOWYmDWv1\nsk9v3Fv39exJMeozMavN2xszOEJSwyFRrZUaF61H/2W0JGnH8bI2Dwu5bFBE3dcf/bO4XTWgc5Eh\nAMzkigbtpptu0vPPP68pU6ZozZo1OnnypIqLi7VmzRpNmTJFzz//vG6++Warywwa23JztXTJYuXm\nbrW6FMfaujVHS5csVt62bVaX4nrkh/2seTdbS5cs1rFjbZ+AiAuyV7+jpUsWq6iofU0mWo8MsRe/\n36+lSxZrxbKlVpfiWNXV1Vq6ZLH+Z8Vyq0sJWl2tLsAMK1eu1IABDY9sjRw5UiNHjtT3vvc9/kh1\novXr1mrB/Hl67Im5Sk6eYHU5jrQme7UWLXxa8+YvUGJSktXluBr5YT/PPftrbfnbZo2NjdXAgQOt\nLseRnl20UDk5nykuPl5RUVEtPwHtRobYi9/vV1bmHIWGhmpW2myry3GkyspKZWXOUUREhO6d9UOr\nywlKrjiC1lgwfhN/oAA0hvwAYAQZAsBsrjiCVuvVV1/VU089pZqaGnk8HkmSx+NRfn6+xZUBsDvy\nA4ARZAgAs7iqQXvyySf16aef8k4VgDYjP+ztk/2nNHlU3xaXe+z6S+rdPp3zfLu3Obx3d0nSF1+W\n6rJBEdpztFRjBkco/fXtevnO+BafnxoX3eZtzv/gn5p7w6V1t6dcGlnv8Yfe3aXfTI9t83rR8cgQ\nwP6On6nSY9dfosLTFVJAiu7rq/f4fVeOqFtuQK/QBs8vLqlSZM+G9ze2HSNc8RHHWiNGjFDfvi3/\nAQeAbyI/ABhBhgAwi6uOoD3zzDOaMmWKvvWtb6lbt26SLny8YO7cuRZXBsDuyA8ARpAhAMziqgbt\nwQcf1IQJEzR69Gh16eKqg4MAOhj5AcAIMgSAWVzVoJ09e1b//d//bXUZAByI/ABgBBkCwCyueosn\nNTVVb731lmpqakxZn8/rafLf7B//rMXnv5J7sN3b/seREr205YD2HS9rdrkVOQXt3oYRE0f20fO3\nXl73D7DCgvnzmvwdbavOzI8F8+eZso1g0poBIaUVfq3fdUx7jpbqzNka7T9ern3Hy3SyrFoFxWfr\nLVt7u/B0hcqr/JKkkooaHSgul3ThRPDRA3tIkrp3u/Be5pjBEZKk/5PScUM6Lh4Q0hgGhJiLDAGc\n5eO9xSo8VaH9x8v1zy+bf4187Exl3dellRdyvnbwR3QfX4MBIRdrbECIpAYDQprKkJj+Ya36fpri\nqiNoy5cvl8fj0c9+9nXzZGTEbUVNoMnHNu7hopOA1Z6YO09PzJ3X6GNtfYHVmfkBwB7IEABGmJkh\nF3NVg1ZQUGB1CQAcivwAYAQZAsAsrmrQqqqq9F//9V/atGmTPB6PrrvuOv3kJz+pm6aEzpGQmKS0\n9AwlJiVbXYpjJSVPUFp6huITEq0uJWiQH/YxLWW6xsbGKipqgNWlOFZK6gzFxccrsn9kywvDFGSI\nPYSEhCgtPUNdQ1z1ErdTeb1epaVnKCzU2Mf00H6eQCDgmmPoM2fOVCAQ0F133aVAIKA33nhDkvSH\nP/yhTevxeC4ckmzpI47Xj2n+YpSv5B7UPcnD27TtWv84UqJNh0/qX0dGavSAHk0utyKnQLMmxrRr\nG4Cb1X60oLUR15n5gY5RWuHXXw+c0Ije4RrUO0wnSqsVUEB9wruptMKvmMjudcsWFJ9VTGR3FZ6u\nUO/uXoWHdlVJRY1OllVrRGR4vYuRHj5ZoaH9vj5X4avyGvUO93b694fORYYA9vPx3mKN6tdDlTXn\nde58QJcOavo18rEzlRrY60KTWVrpV0RY5zbtbc2Qi7nq7YW8vDzt2rWr7vb06dMVG9sxJ1S31JxJ\nandzJknjhvTUuCE9W1zO6ubso38W64W/FkiS7r8mRlMu5d1aOFNn5gcA9yFDEEwufhOrM113Sete\nZ54ordY/i0rrGrTGmrPjZ6oUUKBumY/3FmvsgJ4tfl//OFLSqtfoRrhqimOXLl20Z8+eutt79+7l\nWiQAWoX8AGAEGQLALK46grZo0SJde+21GjZsmCTp8OHDWrFihbVFAXAE8gOAEWQIALO4qkG75ZZb\ndPDgQe3cuVOSFBcXJ5+v6WscAEAt8gOAEWQIALO46th7UlKSunfvriuuuEJXXHGFfD6fkpKSrC4L\ngAOQHwCMIEMAmMUVR9C++uornTlzRlVVVTp06JACgYA8Ho9KS0tVVtb8Vcbb6kRptfYWlerKUf2a\nXW5FToGuGdZfowf0qDdFxgm++LJUlw2K0PwP/qkfXTFMA3s3XfuUSyMZDAJH68z8gDlKK/yK8DX8\n8xXh66qbYwfW3e7V3avC0xXq4pF6h3tVUHxWJ8uqlDyij8qr/Co8VaHovheOcJwur74w7bHSry+/\nqtSg/829zw+d0agB4XXr3H+8vN5tSTpbdU7dQ0M64luFA5AhgP30j+imS89FSJK2Hzyj6L4+9Y+o\nf8mLAb2+Hgby+aEzdQNI/l7wlaL7+pocFtLRA0IklzRob775pl577TUdOnRIP/jBD+ru9/l8euqp\npyysDIDdkR8AjCBDAJjNFQ1aRkaGMjIy9Morr+iee+6p99iXX35pUVUAnID8AGAEGQLAbK46B+03\nv/lNg/umTp1qQSUAnIb8AGAEGQLALK44gnbgwAEdOnRIZWVl+vjjj+t9/ru8vNzq8gDYGPkBwAgy\nBIDZXNGg5ebmau3atTp16pSWL19ed7/P59PixYtN3Vb/iG7qH9H8gBBJmjUxpu5rqwaEXLfoL5Kk\njx/9Vpued9mgCydVzr3h0nZtd1turvK25SohKUnJyRPatY5gt3Vrjrbn5SkpeYISmQLWoTozP9A6\na97NVtHx45qaMl0DBw5s8PjFA0Le/vyIvjt+SJPr2nrklFLjovW3fSd11eh+ionsLqnhSd59wi+c\nPB7dp/5Y9PHDetW7vSG/SKMGjKi7/VreId2dOKyV31nnyV79jk4UFysldYaioqKsLsfVyBB78fv9\nWrl8mUJCQjQrbbbV5ThSdXW1Xlm5Ql6vV/fO+mGjyzQ1QKM9sncUKjUuutll3t99TDeObfj3oDm1\nQ+7ih/dqYcn6WZ8Q07tN2+kIrmjQbrvtNt12223aunWrJkygIbDa+nVrtWD+PD32xFwatHZak71a\nixY+rXnzF9CgdTDyw36ee/bX2vK3zRobG9tog4aWPbtooXJyPlNcfDwNWgcjQ+zF7/crK3OOQkND\nadDaqbKyUlmZcxQREdFkg4aO5apz0Pr376+bb75ZPXr0UI8ePTR16lQVFBRYXRYAByA/ABhBhgAw\ni6satJkzZ2rGjBk6evSojh49qtTUVH3/+9+3uiwADkB+ADCCDAFgFlc1aKWlpZozZ4569uypnj17\n6kc/+pFKSkqsLguAA5AfAIwgQwCYxVUNWnh4uJYsWaKysjKVlZVp+fLl6tGjhynrzlq1U1mrdion\n/7Qp6+sMHz/6rTYPCAGCVUfmB4zL3lHY6P3fHBDy0pYDemnLAW3cU6SNe4rqTjy/anTLw51a474r\nR9S7bccBIbAGGQK0T0sDQiS1aUDI0k8Lmnysqb8l3/T+7mOt3l5HcFWD9uqrr+rNN9/UgAEDNGDA\nAL3xxht65ZVXrC4LgAOQHwCMIEMAmMUVUxxrjRo1Sh988IHVZQBwIPIDgBFkCACzuOoI2r59+3TT\nTTdp6NChGjp0qG655Rbt27fP6rIAOAD5AcAIMgSAWVzVoN1555368Y9/rMOHD+vw4cN64IEHdOed\nd1pdFgAHID8AGEGGADCLqxq0QCCgW265pe72zTffrEAgYMq6n7/1cj1/6+WaOLKPKet74++HW7Xc\n63mHTNkegOZ1ZH7AuOZOIt+4p6ju6/uuHKH7rhyh68dE6foxzV+g+bW8Q8or+EqStPVA6wdAvdPK\nk8w7y9/2nbS6BIgMQfAqPF0hSVqz86jFlVwwe1JMk4+1ZiCJ1PqhJIWnKvTFl6X1bpvBVQ1aSkqK\nFi9erLKyMpWWlmrJkiWaOnWq1WUBcADyA4ARZAgAs7hiSEhMTIw8Hk/d7V/96lf1Hp8/f35nlxTU\nEhKTlJaeocSkZKtLcayk5AlKS89QfEKi1aW4HvlhP9NSpmtsbKyiogZYXYpjpaTOUFx8vCL7R1pd\niuuRIfYSEhKitPQMdQ1xxUtcS3i9XqWlZygsNMzqUoKWK/7vLSgosLoEXGTqtBRNnZZidRmOljrj\nO0qd8R2rywgK5If9PPTIo1aX4Hg/+/l/WF1C0CBD7MXr9eqFFxdbXYaj+Xw+9qHFXPURR6tlrdrZ\n6mW/lzC0Vcvd2caLoF5cQ1vqAQAAANrqzNkaSVLK5YNbXHbP0dIWl2nvczfvO9HudbdXdF+fLhsU\nUe+2GWjQAAAAAMAmaNAAAAAAwCZccQ7axQoKCnT48GGdP39ekuTxeHTddddZXBUAJyA/ABhBhgAw\ng6satIcfflgbNmxQQkKCQkJC6u4nHAG0hPwAYAQZAsAsrmrQsrOztWfPHnXtas239fytl1uy3aZq\n6Mx6vv/KNknSq/ckddo2ATNZnR8AnI0MQTDKLyrXl6WV+uSzU0q7IqbF5ccMjmhxmfY+9+rR/du9\nbrtx1TloQ4cO1blz56wuA4ADkR8AjCBDAJjFVW/zDBo0SBMnTtTUqVMVFnbh4noej0dz5861uDIA\ndkd+ADCCDAFgFlc1aDfccINuuOEGeTweSVIgEKj7GgCaQ34AMIIMAWAWVzVos2bNUmVlpXbu3CmP\nx6PLL79coaGhVpcFwAHIDwBGkCEAzOIJBAIBq4swy/vvv6977rlHQ4cOlSQdPnxYr776qm644YY2\nraf2Ha+KGtfsmjp9v7dMyVddqg0/uabDtrEtN1d523KVkJSk5OQJHbYdN9u6NUfb8/KUlDxBiUkM\nXmkPn/frd7Fbg/ywjzXvZqvo+HFNTZmugQMHWl2OIS9sztf9V4/UjkNnFDesl274/zbrgx9f3eHb\nzV79jk4UFysldYaioqI6fHtuRIY4k9/v18rlyxQSEqJZabOtLseRqqur9crKFfJ6vbp31g9b/bxV\n24/o1vghHViZs7Q1Qy7mqiNoDz/8sP7yl78oNjZWkrR7927dfvvt2rlzp8WVBZf169Zqwfx5euyJ\nuTRo7bQme7UWLXxa8+YvoEHrJOSHfTz37K+15W+bNTY21vENmlWeXbRQOTmfKS4+ngatk5Ah9uD3\n+5WVOUehoaE0aO1UWVmprMw5ioiIaFODBvO4aorj+fPn64JRksaOHduurhVA8CE/ABhBhgAwi6uO\noMXHx+v73/++7rzzTgUCAa1atUrjx4+3uiwADkB+ADCCDAFgFlcdQVu2bJliY2P14osv6qWXXtKY\nMWO0fPlyq8sC4ADkBwAjyBAAZnHVETSfz6fHHnvMkm2v2XlUJyuqNWtijCXbb61Tb6RZXQJgS1bm\nB5yjqZPga4eAbNxTJF/XEF01up8k6f6rR0qS4ob1kqROGRACa5AhCHaNZWPugdNKHtGnLiM7wj+O\nlGjckJ4dsm6ruOoIGgAAAAA4GQ0aAAAAANiEqxq0tWvXtuo+APgm8gOA9u2MlAAAIABJREFUEWQI\nALO4qkF78sknG9z3xBNPWFAJAKchPwAYQYYAMIsrhoRs3LhRf/3rX3Xs2DHNnz+/7rojZWVl6tKl\n/T1o7RXAG/P4k/+pJ+bOq7udcvngdm+nJR/9s1hTLo1s8b7mvJJ7UL1DvR1aJ9DZFsyfp1/+4ilD\n67BDfqBx5VX+Vi/7VXmNeod79fbnRzSwu69uSEdTSiv8ivB9/SfwzNka9erurbfMoZNnNaxf93r3\nNXYSvPT1EJDrx3x9Ueivztao9zfWKUknSqvUPyK0wf27C0s1qHeY5JHOnQ+oX49uzX4PTdWN1iND\nAGPyi8q15chJVfnPK+2KmAaPJ4/oI0kdNiBEkqUDQszIkMa4okHr06ePhg8fLq/Xq+HDh9fd7/P5\n9Oijj7Z7vRU1XGASsLMn5s5r8kVKcy9uLkZ+AMGLDAFghBkZ0hhXNGhJSUlKSkrSrFmz6u47deqU\nDhw4oMjI1h9lgjkSEpOUlp6hxKRkq0txrKTkCUpLz1B8QqLVpbge+WE/01Kma2xsrCKjolpeGI1K\nSZ2huPh4Rfbn/+GORobYS0hIiNLSM9Q1xBUvcS3h9XqVlp6hsNAwq0sJWq76v3fSpEn68MMPVV5e\nrsmTJ+uyyy7TyJEj9cILL1hdWlCZOi1FU6elWF2Go6XO+I5SZ3zH6jKCCvlhHw89cuGoQ0lFjcWV\nONfPfv4fVpcQdMgQe/B6vXrhxcVWl+FoPp+PfWgxVw0JqaqqUnh4uN5++22lpaVp/fr12rx5s9Vl\nAXAA8gOAEWQIALO4qkE7d+6ciouL9fbbb+vGG2+UJJ0/f97iqtpu/GP/t97ti4eB7DpS0uC+1rgn\nebhSLh+sP/39sCTpiy9LDVYJuItb8gOANcgQBKORUeGamTSsbkDIZ/mn6h7L3lHY6HN2FZZ0RmmO\n5qoG7bHHHtO3vvUtjRo1ShMnTtTBgwc1duxYq8sC4ADkBwAjyBAAZnHVOWh33XWX7rjjDhUVFUmS\nhg8frjfeeMPiqgA4AfkBwAgyBIBZXHUEbd26dUpISNCVV14pSfr888/rPmYAAM0hPwAYQYYAMIur\nGrTHHntMmzdvVp8+Fy6KN378eB09etTiqgA4AfkBwAgyBIBZXNWgnT9/Xj171r+auN/vt6ia9vv8\n6ZuafCzW4NXS70gYKkm6bFCEofU0Jf317Up/fXuHrBvoSG7JDzfp6fO2etne4ReW/e74IbpqdL8W\nl4/w1f+Ef6/uDbc1rF/3erdrT2zfd6ysdTU1sk5J+qq8/uUDth44LUkaGx2h3uFe9e7uVb8e3Vpc\n/xdfltbVXXiqolU1oeOQIQhG+UXl2rinSO/vPiZJumJk37rHUuOiG31ObLSx17LBwFXnoI0dO1ZL\nlixRdXW1duzYoRdffFGTJk2yuiwADkB+ADCCDAFgFlcdQVu6dKl2794tj8eju+66Sz6fT7/73e+s\nLguAA5AfAIwgQwCYxVVH0Hr06KHf/OY3VpcBwIHIDwBGkCEAzOKqBm3Xrl1atGiRDh8+XHdxSI/H\now8//NDiyoLLyQO7tHTJp0pISlJy8gSry3GkrVtztD0vT0nJE5SYlGR1OUGB/LCPNe9mq+j4cU1N\nma6BAwdaXY4jZa9+RyeKi5WSOkNRUVFWlxMUyBB78Pv9Wrl8mUJCQjQrbbbV5ThSdXW1Xlm5Ql6v\nV/fO+qHV5QQlVzVot912m+bOnauJEycqJCTE6nI6zEf/LNYLfy3Qn9ImWl1KAy/fGa9f/mK1sjLn\n6bEn5tKgtdOa7NVatPBpzZu/gAatkwRLfjjBc8/+Wlv+tlljY2Np0Nrp2UULlZPzmeLi42nQOgkZ\nYg9+v19ZmXMUGhpKg9ZOlZWVysqco4iIiBYbtJFR4RoZFa7CUxW68beb9f4DV3dSle7mqgate/fu\nuvPOO60uA4ADkR8AjCBDAJjFVUNCvvvd7+oPf/iDqqurrS4FgMOQHwCMIEMAmMUVR9BiYmLk8Xjq\nbj/xxBN1X3s8HuXn51tRFgAHID8AGEGGADCbKxq0goICq0sA4FDkBwAjyBAAZnNFg1Zr5cqV9d7F\nkqSwsDAlJibqkksusagqAE5AfgAwggxBMIvu62NAiKTffLRfD00ZpRc2Gzty7qoG7YMPPtCnn36q\n6dOnS5LWrFmj+Ph4PfPMM7r77rv18MMPW1whALsiPwAYQYYAMIurGrSjR49q+/btCg8PlyT94he/\n0NSpU/Xxxx8rPj6ecATQJPIDgBFkCACzuGqKY2FhoXw+X91tn8+no0ePKjw8XBERERZWBsDuyA8A\nRpAhAMziqiNot99+u/7lX/5Ft99+uwKBgN5++23deuutqqio0PDhw60uD4CNkR8AjCBDAJjFVQ3a\nL3/5S23evFmbN2+Wx+PRggULdPXVF05YXL16tcXVmWfKpZGacmmk1WU0cMeyHN1/TYzVZQDtEiz5\nAaBjkCGA+XLyT2viyD6mrvOt7Ud0W/yQN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"text": [ "<matplotlib.figure.Figure at 0x2aab0bc70590>" ] } ], "prompt_number": 121 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_repeatRNA(outfilepath,sampleName):\n", " repeat_genome=np.genfromtxt(repeatGenomeBuild,dtype='string')\n", " repeat_genome_bases=repeat_genome[1]\n", " \n", " repFiles=glob.glob(outfilepath + '/PlotData_RepeatRNAreads_*')\n", " repFiles=[repFile for repFile in repFiles if 'rDNA' not in repFile]\n", " \n", " plotDim=math.ceil(math.sqrt(len(repFiles)))\n", " i=0\n", " for path in repFiles:\n", " name=path.split('RepeatRNAreads_')[-1]\n", " try:\n", " # Read in each RT stop file\n", " hits_per_rep=pd.read_csv(path)\n", " RTpositions=hits_per_rep['RT_stop']\n", " start=hits_per_rep.loc[0,'Repeat_Start']\n", " end=hits_per_rep.loc[0,'Repeat_End']\n", " # Histogram of RT stops across gene body\n", " bins=range(start,end+2,1)\n", " hist,bins=np.histogram(RTpositions,bins=bins)\n", " width=0.7*(bins[1]-bins[0])\n", " center=(bins[:-1] + bins[1:])/2\n", " # Normalize\n", " histPlot=np.array(hist,dtype=float)\n", " histPlot=np.array(histPlot/float(len(RTpositions)),dtype=float)\n", " # Subplot\n", " plt.subplot(plotDim,plotDim,i+1)\n", " plt.bar(center,histPlot,align='center',width=width,color='blue',alpha=0.45)\n", " plt.tick_params(axis='x',labelsize=2.5) \n", " plt.tick_params(axis='y',labelsize=2.5) \n", " plt.title('RT stops for %s: %s'%(name,len(RTpositions)),fontsize=5)\n", " plt.xlim(start,end) \n", " # Record data\n", " storageDF=pd.DataFrame()\n", " sequence=repeat_genome_bases[start:end+1]\n", " storageDF['Sequence']=pd.Series(list(sequence))\n", " readsPerBase=np.array(list(hist))\n", " readsPerBaseNorm=np.array(list(histPlot))\n", " storageDF['RT_stops']=readsPerBase\n", " storageDF['RT_stops_norm']=readsPerBaseNorm \n", " outfilepathToSave=outfilepath +'/PlotData_RepeatRNAHist_%s'%name\n", " storageDF.to_csv(outfilepathToSave)\n", " i+=1\n", " except:\n", " print \"No reads for repeatRNA %s\"%name \n", " plt.tight_layout()\n", "\n", "fig3=plt.figure(3)\n", "plot_repeatRNA(outfilepath,sampleName)\n", "fig3.tight_layout()\n", "fig3.savefig(outfilepath+'Figure3.png',format='png',bbox_inches='tight',dpi=150,pad_inches=0.5)\n", "fig3.savefig(outfilepath+'Figure3.pdf',format='pdf',bbox_inches='tight',dpi=150,pad_inches=0.5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Ky8uNjqfrTElUVJTVz5QJMwXl5PDN+izBkmN2GWOMMcYYsycm95xpnymTy+V4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KmLxwuCUNN8vfKet5dNJiIiknBbnealYR+sUn9nhUiVWDnzAa68R0REzRW31fEf05BVraJpch8s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\nVatWYf369eorLPosxaaKtXLlSqSkpMDW1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"text": [ "<matplotlib.figure.Figure at 0x2aab1205a750>" ] } ], "prompt_number": 122 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_rDNA(outfilepath,sampleName):\n", " plt.subplot2grid((3,3),(0,0),colspan=3)\n", " name='rDNA'\n", " rDNA=glob.glob(outfilepath + 'PlotData_RepeatRNAreads_rDNA')\n", " hits_per_rep=pd.read_csv(rDNA[0])\n", " RTpositions=hits_per_rep['RT_stop']\n", " start=hits_per_rep.loc[0,'Repeat_Start']\n", " end=hits_per_rep.loc[0,'Repeat_End']\n", " bins=range(start,end+2,1)\n", " hist,bins=np.histogram(RTpositions,bins=bins)\n", " width=0.7*(bins[1]-bins[0])\n", " center=(bins[:-1]+bins[1:])/2\n", " histPlot=np.array(hist,dtype=float)\n", " histPlot=np.array(histPlot/float(len(RTpositions)),dtype=float)\n", " plt.bar(center,histPlot,align='center',width=width,color='blue',alpha=0.45)\n", " plt.tick_params(axis='x',labelsize=2.5) \n", " plt.tick_params(axis='y',labelsize=2.5) \n", " plt.title('RT stops for %s: %s'%(name,len(RTpositions)),fontsize=5)\n", " plt.xlim(start,end) \n", " \n", " # Features of rDNA with respect to start of the bowtie index (index=0)\n", " rRNAstart=start\n", " plt.axvspan(start18s+rRNAstart,end18s+rRNAstart,facecolor='g',alpha=0.5)\n", " plt.axvspan(start5s+rRNAstart,end5s+rRNAstart,facecolor='r',alpha=0.5)\n", " plt.axvspan(start28s+rRNAstart,end28s+rRNAstart,facecolor='b',alpha=0.5)\n", " \n", " # Generate histogram for transcribed region\n", " plt.subplot2grid((3,3),(1,0),colspan=3)\n", " datarDNAOnly=RTpositions-start\n", " bins=range((start-start),(end-start+2),1)\n", " hist,bins=np.histogram(datarDNAOnly,bins=bins)\n", " width=0.7*(bins[1]-bins[0])\n", " center=(bins[:-1] + bins[1:])/2\n", " histPlot=np.array(hist,dtype=float)\n", " histPlot=np.array(histPlot/float(len(RTpositions)),dtype=float)\n", " plt.bar(center,histPlot,align='center',width=width,color='blue',alpha=0.45)\n", " plt.tick_params(axis='x',labelsize=2.5) \n", " plt.tick_params(axis='y',labelsize=2.5) \n", " plt.xlabel('rRNA locus position (bin=1 base)',fontsize=5)\n", " plt.ylabel('Normalized RT stop / bin',fontsize=2.5)\n", " plt.axvspan(start18s,end18s,facecolor='g',alpha=0.5)\n", " plt.axvspan(start5s,end5s,facecolor='r',alpha=0.5)\n", " plt.axvspan(start28s,end28s,facecolor='b',alpha=0.5)\n", " plt.xlim(0,rRNAend)\n", " \n", " # Individual regions \n", " plt.subplot2grid((3,3),(2,0),colspan=1)\n", " plt.bar(center,histPlot,align='center',width=width,color='green',alpha=0.75)\n", " plt.xlim(start18s,end18s)\n", " plt.xlabel('rRNA locus position (bin=1 base)',fontsize=5)\n", " plt.ylabel('Normalized RT stop / bin',fontsize=2.5)\n", " plt.tick_params(axis='x',labelsize=5) \n", " plt.tick_params(axis='y',labelsize=5) \n", " plt.title('18s Region',fontsize=5)\n", " plt.subplot2grid((3,3),(2,1),colspan=1)\n", " plt.bar(center,histPlot,align='center',width=width,color='red',alpha=0.75)\n", " plt.xlim(start5s,end5s)\n", " plt.xlabel('rRNA locus position (bin=1 base)',fontsize=5)\n", " plt.tick_params(axis='x',labelsize=5) \n", " plt.tick_params(axis='y',labelsize=5) \n", " plt.title('5.8s Region',fontsize=5)\n", " plt.subplot2grid((3,3),(2,2),colspan=1)\n", " plt.bar(center,histPlot,align='center',width=width,color='blue',alpha=0.75)\n", " plt.xlim(start28s,end28s)\n", " plt.xlabel('rRNA locus position (bin=1 base)',fontsize=5)\n", " plt.tick_params(axis='x',labelsize=5) \n", " plt.tick_params(axis='y',labelsize=5) \n", " plt.title('28s Region',fontsize=5)\n", " plt.tight_layout()\n", "\n", "fig4=plt.figure(4)\n", "plot_rDNA(outfilepath,sampleName)\n", "fig4.tight_layout()\n", "fig4.savefig(outfilepath+'Figure4.png',format='png',bbox_inches='tight',dpi=150,pad_inches=0.5)\n", "fig4.savefig(outfilepath+'Figure4.pdf',format='pdf',bbox_inches='tight',dpi=150,pad_inches=0.5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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f0n4dxyn6RSgDyhOLdSgW6/C7GQAAABUVCoWKZoaRqKkxZQsWLFAwGNSSJUu0efNmTZ06\nVcFgUOFwWJZlZbddddVVkqR9+/bpmGOO8bnVAAAAAFBcTYWypqYmRaPR7O3Zs2dr+fLl2du52yTp\nrrvu8qxtAAAAADAcNdd9EcDYwxg1AABQzwhlAAAAAOCjmuq+iNrC+mQAAADA0KiUAQAAAICPCGUA\nAAAA4CNCGQAAAAD4iFAGoKIikY0yjLjfzQAAAKgZhDIAAAAA8BGhDAAAAAB8RCgDAAAAAB8RygAA\nAADAR4QyAAAAAPDRBL8bAKD+JJMpSczACAAAUAoqZQAAAADgI0IZAAAAAPiIUAYAAAAAPiKUAQAA\nAICPmOgDvjEMQ4ZhSJKCwaDPrQEAAAD8QaUMAAAAAHxEKAMAAAAAH9VUKGtpaVFjY6NWrVolSVq5\ncqUaGxu1YcOGvG0vvPCC3ve+92np0qU+txgAAAAABldToWz9+vWKRqOKxzOL0iYSCUWjUTU3N+dt\nO+ecczRlyhRNmjTJ5xYDAAAAwOBqKpRJkm3bA24HAgEFAoG8bY888ogmT55c8n7dfRT6CoVCkqRw\nOKxwOFyR11HLIpGIIpGI380AAAAAPBUKhYpmhpGoqVC2YMECBYNBNTQ0aPPmzZo6daqCwaAWLlyo\n+fPnZ7f95je/0cKFC3Xw4MGS9+04TtEvN5S1traqtbV1lF4dCHsAAACoZqFQqGhmGImamhK/qalJ\n0Wg0e3v27Nlavnx59nbutksvvdTTtgEAAADAcNRUpQy1zTAMJZNJv5uBKhOJbFQkstHvZgAAAPiG\nUAZftLW1KZFI+N0MAAAAwHeEMgAAAADwEaEMAAAAAHxEKAMAAAAAHxHKMCyxWIzp6wEAAIAKIJTB\nM8lkUqZp+t0MAAAAoKoQygBUVCzWIdNM+90MAACAmkEoA1A1WLMMAACMRRP8bgCQKxaLydpuSdP9\nbgkAAADgDSplQI2jugQAAFDbCGUAAAAA4CNCGQAAAAD4iDFlACqCLpQAAADDQ6UMAAAAAHxEKMOQ\nIpGIIpGI383AIGKxDipVAAAANYrui8iKRCLabm33uxmoYbFYhwwj7nczAAAAagqhDAO4VbFgMOhz\nS1DPqOwBAABk0H0RAAAAAHxEKEPZYrGYDMPwuxljFotFAwAA1Be6L8JzsVhMyWSy4DbDMGTvsz1u\nESrNsiwlkym/mwEAAFATaqpS1tLSosbGRq1atUqStHLlSjU2NmrDhg1525599llddtlluvnmm31u\ncf2JRCJUyQAAAIAKqqlQtn79ekWjUcXjmdndEomEotGompub87ZddNFFeuqppzRlypSS9x0IBIp+\nhUKhUXpFtSmZTBatdBUTi8VkmmbBfcVisQq1DKUa7S6Qtu3INNPDfj5dNAEAQDUKhUJFM8NI1FQo\nkyTbtgfcdt+I3G333nuvPvrRj5a8X8dxin4RykZm9+7dmW6JNt0SMVAs1qFYrMPvZgAAAAwpFAoV\nzQwjUVOhbMGCBQoGg2poaNDmzZs1depUBYNBLVy4UPPnz89ue+SRR/T9739fS5cu9bvJY06hhaZT\nqZSSyUnZk9UwDJmmKcuyClbPMLYYRpy1zQAAwJhWUxN9NDU1KRqNZm/Pnj1by5cvz97O3XbVVVd5\n2jaUJjMBRHldHwEAAIB6VlOVsmq1aNEiLVq0yO9mjFgsFtPOnTsZ44VhM4w4sy4CAACUiVCGURGJ\nRAh3AAAAQAlqqvsiqkP/cWDuGLJgMFjyPizLkm3bGj9+fEXbBgAAANQaQhk8YZoHZNu2xo2jODtc\nuVPEu7MVBoMX+tUcAAAAVAhXyPCEZR2S4zgFp8U3DGPAjI0YW5LJlCKRjUyPDwAAxiRCGYCqYhhx\nghkAABhT6L6IkriTdsRiMVmWlf3/UOPIYrGYDMMY7eYBAAAANYtKGUaVYRh565I5jsOC0XXONNMj\nXtUeAABgLKFSVob29na/m+ALt9o1ffr0sp+bTCYPhzDyPwAAAFAIV8oAKiIW61AymTrcvbW0Spn7\nHAAAgLGMStkoCIfDkqRly5b53JLqYJqmHGfioI9hoWkUkrsMAAAAQL2iUgZfWZalRCKhzs5OTyYE\neWP79rqbfj8W65BhxP1uxoiYZrrkGRcjkY3avv2NUW4RAACAdwhlFRYOh9Xa2up3M0bEMAy1tbXV\nXXipZ8lkqmLTyEciG6lQAQAAeIjui/CcO/uiu5j0+PHjfW5RbSoUwtwwFQxe6HVzAAAAMEyEMpTN\ntm05jlNSd8PMeLLqmB79lS1blNi3T7t37/a7KWUppWplmmkZRlzTp7/dgxb5gwWlAQBAvSKUoSKG\nnqjD8qIZgzIMQ6mUlEox2x8AAACqB6GsAsbq+mWVUKiKlkqlGM9WgkpO7kG3RwAAAP8Qyiqgq6vL\n7yZUjGmaSiaTOuqoo7L3GYahZDI54LHJZLJi4ck9rvt/1CbTTMuy7JIfbxhxmWZ6FFsEAABQ/Zh9\ncRjC4XB2LbJ6Y9t2xUKRYRiHFxLO5zhOXoWs/2Ns22bdsjHCXWzasqyan9YfAABguKiUYQDLsgpW\nxkYLMzDCth0lkylJcSUSCUkqOGkJk30AAIB6VFOVspaWFjU2NmrVqlWSpJUrV6qxsVEbNmzI29bR\n0aHFixero4MLuEpIJpMFq2d0M/RWLNYxaCgZaq2y/uuPxWIdvq9H5nZ3LGeGTsOIa9++xCi2CgAA\nwFs1FcrWr1+vaDSqeDzTzSmRSCgajaq5uTlv29vf/nZdeeWVnrWru7tb3d3dnh3PC6Zp5nUhtCxr\nhF0KB7/otiyrLkOeFwsxG0a8YDdRvw2nTW53xmI6OzuzlTQAAIB6UVOhTMp0det/OxAIKBAIDNhW\nDncfhb5CoVD2cV1dXWptbc17bjqdVjrdN1lBe3v7gMfkqtYxaYZhyLZt2bZ9eIyPMeyJPGKxWNld\nIC3L0qFDh2Tbtg4ePDis4xYSiUT0xvbtFdufH2Kxjhoac5UJ4LZdzvp0DhN+AACAqhcKhYpmhpGo\nqVC2YMECBYNBNTQ0aPPmzZo6daqCwaAWLlyo+fPnZ7cdOHBAzc3NevDBB0vetzv5RKGv3FA2XNUa\nxApxHGdEATdXufvJvOcT6rJqNlqSyVSZAWh09K/YOY6TrRJ6UTEEAAAYbaFQqGhmGImamuijqalJ\n0Wg0e3v27Nlavnx59nbutocffrjix+/q6qq7borFOI6jZDKpWCyWF5AMwyh60rW1tUmS5s6dW/ax\nbNvWuHHjDoe4mvqswHemmR7xL4JKyZ8O31Es1lHC2mfV0XYAAAC/cPU7DEN1T8TY5VVFyDDiSiZT\nVTuerNJqq/smAABAeWqqUlat3PFk4XC4psNaMpmUc/TwqhbuAtO5i06Xo1oqPZWWGyaGrhiVxzTT\nGs7KBZHIxuwsjf1na3S3FZqOfrRZlqWJEz0/LAAAgO+olGFQnZ2dSqfTFRhjVk7o8nadtFqXGU/m\nf6jNrDM2/HZYlp2d7KPY9P6ZKfTrvzIIAADGFiplQ3j00Uf12muv6dRTTy37ue7EHsuWLat0syou\nHA6XPbnGUBfHpmlWVQWso9/4OK+4AaPSlTJXpd7j0ViYubwuhw6BCwAAjElUyobw2muvqbU18293\nd3fe1PeFtLe3q6ura8D9ra2tVd+1sf8FcU9PT7Zi5TiODMPI227bdt7aZclkUvF4XLFYTIZhlH2B\nbdvlLSI8lrhjyFxDredV7XK7UOaybacqZpIEAADwEqGsglavXq1t27bJMAy1t7f73ZxRVyxAuePL\nht/l0RlxRcvrJQi2b39j0Ak+DCNe0QlATDOdE14y//b0pEquTBlGvOITZxRaZyw3SJbCnVLWNNNM\n7AEAAMYMQtkI1craY8NhWdaA4JVIJAY8LhaLKZFI5D22WrouhsNhtbW1KbFvnzo7O2WaqQEVv5Fa\ns2aNdu7cVdF9lio3CNm2M2gIisU6inZRjMU61NnZ6UlQG9zQ4+Ns21EqVV7YAwAAqGaEslFgWVbB\nLoyuWujKWK7+XelY/Nl7pYbgWKxDiURCiURi1KtRlmVpzZo1eWGwWNfFwfTvvgkAAFBPmOijRK2t\nrUOOJ3PZtj3oItObNm2qVLMqprW1dcgxSslkclTHMVVDZW2k3LAxGpN65Hbpc6uY+d8PRz09fcFl\ny5ZXZBiGpk2bpne964ySjzNaU/gXMtj55E6QMn3625VMpoZRdQMAAKgNVMrKYFmWTNMctAqWTqcH\nhIuPf/zj2rRpk3bu3DnaTfRc6V0BRy9wFRo/1r8amUqllDZNSXZdTLfvToM/2KQYhmEoFivte2QY\n8VEJPbbtFK3GGUa8aPszP2t9IdQ00zLNXplmb12EdwAAgFxUyspQzuyAqVRKra2tampqUjweV3f3\nkVUbBsLhcEkTk/T09AyobLivyb1/5OuZla7c8XyWZcmRXdGuleFweNDQ09nZWbHvu2VZSiZTmjZt\n8McMNf2+YcRlWZbGjx8/4LmJREITJx4x6DGKMc3ekh/rdkcs9vOUmYWxN6/LYuaxBDIAAFB/qJRV\n0LZt24bs4hgOhwft2uiXwap/g+kfcBzHUTKZHHE1w7Ksikyi0traqkQioZ5Dh2RXuOtlOBzO7v/Q\noZ7s/bkzLZrmwMrpcGSmwLezMyy6+3ScgSE4tzJlGIUn97BtR+m0WXDiFm8Vfm8ch+URAADA2EEo\nG6b29vZBQ0P/ilE6vT8bfNLptLq7u0uett3r6d2LKVQFq6W1sg7HmJpqcySyUW1tbXldCw8dOqTB\nKkbxeFzf+95/Drrf3KpTLNaRV5E6dKhHnZ2d2rLllbLaWihEOY6tRCKhzs7O7H3u8XLHvxXZY/Z/\nme8ZIQ0AANQnQlkZ3IvOYt3Vcqfp7n+BapoHtHfv3pqedbHQRbdtDxyjNfLues6Iu0G2t7dnu2Ra\nliWnQt0qcwNya2ur2tvbZVmWLMvSs89ulGHElUgkstWpSoUJdz+WZQ1ZabXtI7V37161tbUN2GYY\nce3d25VtU25AtSxbptl7uCJ3QsWXDshtQ/46a8W5YbT/Y5ndEwAA1BNC2TC4U96vXr160JDV3t6e\nvXB3nF5ZlpW9iJfqY2r8zEK/lb9ArkTXta6uLrW3t8u2bdk5+7Ntu6TKYygUGtHxI5GN2e+1aabL\nngY+V24oGarS5753yWQqLyD3jePqC6jufjPhxzm8eLMty9pZMNQNbvBJO3Kl02bBrpfl7hvDN9Lz\nG6hmnN+oV5zb9YtQNgh3zFAx7kW/q//F8rZt2wY8x72vt7dX7e3tdTEjY/+qVjWNBdq2bduA9hRr\nX/9uoitWrCjpGO7rd4OHZVkDZhwcSZfJ/kFqaIcKTrqRTKbU3X0w7z73vUin3WDt9Pt35CzLUjpt\nqq2tTZHIxsNdJUvbvztxSaaZgYq1CaWf30At4vxGveLcrl+EskGsXr264Jpitm3LMAzt3r1br776\natHnp9Npbdq0KW8SjXQ6Lcuy1NPTo61bjZK7+rW2tlbFuLJCBgaOypxWQ73mSlUa3TBWyv76P6ar\nq2tAyLMsS3v3dunZZzdm7yulq14hsVhHCWOv+rPkOJlupT09PdmJRwpPee9OWd+/fb0VXqy5b//l\nTL1vWXZOwM3dh6mvfe1restb3lKpBgIAAPiGKfELcINAV1eXDMNQe3u7Zszo2+44TnaWxcHGPpmm\nOWD8T6kLULttWLZsmSRlK3Lu7UpqbW3V3r17h/38gZWnyozfcgNxsdfc3t6urVu3atOmTbr11lvz\ntr366qtlVadWr16trq4unX/++QW3lxKIc4/nOHZemHIcW7t2lV4VjUQ2KhbrGHQtr6FkuiF2K5nM\nhORiU9YXnoGxVz09lV3eIB6Pq7m5ucxnOUUqa6PTbRYAAMAPVMoGYRiG0um0urr2FqyYSYN31XMc\nRwcPHiw6MYNpxrV169YhL/jvuecebd26tfSGl2nTpk1VPSPhokWL9Ja3vEVnnnlm3nvV1dV1uKug\nodWrV+c9Z6jXk1vtam1t1auvvqo9e/YMuV5be3u7Nm3alF3bLfd7664jVrgNE2VZmZATiWzMTpk/\nlEQiUWbXxcIyXQAL76fYBwXlhMFSZmocbrjs3+XSVcqkJwAAALWASlkB7gW7e7FqGH+UZVWyktR3\nv2ma+vz4nRoQAAAgAElEQVTnP6/Vq1fruuuuk5SpDLkThGSOnwmH/QODG1BOPfVUvfbaazr11FO1\nePHigo/JrTYtWrRIkrRu3TpJ0u7du4f92kbT7t279dRTT+mII45QOp3Wcccdl/3euK/HnfnQnXI9\nHA7rnnvuGXLfTzzxRPZ9eOqpp/ICVKGQ7Ia+rq4udXd3q7W1VVu3bh0QaIoFiP6VJ3fSD3eR529/\n+35J0rRpDers7NRRR01WPB6vQFhOqrtbA8a4Dc0pa2zg+vXrh95jBcJlLkIZAACoFwGnmmZl8EEg\nkJk8wHGc7EX6448/3u9REyQV7vpVSUcccYSmTZumW2+9VbfccoskaebMmXr55Zezj5k5c6ZmzJih\npqamvOAmNUlqVWdnpyzL0rHHHqu5c+dmK3y53fI2bdqk4447Tn/60580ZcoUHTyYEySmH/7yU+zw\nVwHue7Rnz54BoeGEE04oGjCbJM2T1CLJrZHNnDmzYLAaP358Ngy5x5g0aZJM0xwkqLhHGNxdd4V0\n9913y3FsHX/88frUp26UJK1YESrahkqYMGGCenvLP4dz2yhlKmLr16/XxIkT9clPLs3enxl47OWv\nkha538krrrgie05L0owZM7IfOKC4QCBQVZPyAJXE+Y16UOiDdc7t6pabK8p+bi2FspaWFn3uc5/T\nkiVLdPPNN2vlypX6xS9+oXA4rN7e3uy2j3/847riiit0yimn6KGHHhp0n+6bBwAAAAAjNZx4VVNj\nytavX69oNKp4PNMVK5FIKBqNqrm5OW9bS0uL7rvvPp177rmjtgAuAAAAAFRCzY0p6z/boW3b2WpX\n7rZSE2oNFQoBAAAA1KGaqpQtWLBAwWBQDQ0N2rx5s6ZOnapgMKiFCxdq/vz52W3z5s3Tpz/9aT3/\n/POaNm2a380GAAAAgKJqakwZAAAAANSbmqqUAQAAAEC9IZQBAAAAgI8IZQAAAADgI0IZAAAAAPiI\nUAYAAAAAPiKUYcxqaWnR4sWLFY/HddFFF+kjH/mIJOkf/uEf9J73vEf79u3TypUr1djYqA0bNqil\npUWNjY1atWqVzy0HhvbAAw9o3rx5+vGPf6z/9//+H+c36sqPf/xjXXrppbr//vt1wQUX6JZbbpFl\nWVq4cKEuu+wy2batz3zmM7rgggu0detWPfzww2psbNRPf/pTv5sOlOQrX/mKvvvd73J+jyGEMoxZ\n8+bN05w5c9TS0qL77rtP5557ruLxuBobG/Wtb31L69evVyKRUDQaVXNzs9avX69oNKp4PO5304Eh\nXX/99XriiSe0a9cuzm/Uncsuu0z79u1TIpHQk08+qSOPPFKbN2/Wpz71Kd1www3avHmzjj76aD35\n5JNat26dXn75ZUWjUb300kt+Nx0YUktLi84++2x1d3dzfo8hhDKMae4yff2X63McR4FAQJJk27YC\ngYACgYBs2/a8jcBwJBIJrVq1Sm9729sGnLec36h1J5xwgn73u98pFArl/f52HCfvdu75DdSK559/\nXtFoVLfddhvn9xhCKMOY5X6y1Nvbq09/+tN6/vnn1dDQoGg0qptuukmXXHKJpk6dqmAwqIULF2r+\n/PkKBoNqaGjwu+nAkG6//XY98cQTeu655zi/UXfC4bDmz5+vcDis9773vTp06JBmz56t73znO/ru\nd7+r2bNnK5lM6v3vf78WLVqkmTNnqrGxUbNmzfK76cCQli1bpuuuu07f+ta3OL/HkIDTv0QAAAAA\nAPAMlTIAAAAA8BGhDAAAAAB8RCgDAAAAAB8RygAAAADAR4QyAAAAAPARoQwAAAAAfEQoAwAAAAAf\nEcoAAAAAwEeEMgAAAADwEaEMAAAAAHxEKAMAAAAAHxHKAAAAAMBHhDIAAAAA8BGhDAAAAAB8RCgD\nAAAAAB8RygAAAADAR4QyAAAAAPARoQwAAAAAfEQoAwAAAAAfEcoAAAAAwEeEMgAAAADwEaEMAAAA\nAHxEKAMAAAAAHxHKAAAAAMBHhDIAAAAA8BGhDAAAAAB8RCgDAAAAAB8RygAAAADAR4QyAAAAAPAR\noQwAAAAAfEQoAwAAAAAfEcoAAAAAwEeEMgAAAADwEaEMAAAAAHxEKAMAAAAAHxHKAAAAAMBHhDIA\nAAAA8BGhDAAAAAB8RCgDAAAAAB8RygAAAADAR4QyAAAAAPARoQwAAAAAfEQoAwAAAAAfEcoAAAAA\nwEeEMgAAAADw0QS/G1COlpYWfe5zn9OSJUt08803a+XKlfrFL36hcDis3t7e7LZzzjlHn/nMZ3Tp\npZfqa1/72qD7DAQCHrUeAAAAQL1zHKfs59RUpWz9+vWKRqOKx+OSpEQioWg0qubm5rxt48aN0+TJ\nk/WmN73J5xYDAAAAwOBqqlImSbZtD7jtVrvcbRdffLEikYhuvfXWkvc7nESL6hYIBPi+VoFQKKSW\nWIvmfXxexfa54pIVuuu3d1Vsfyis5Yctmjd9nkKhkGfHrLaf21AoJLW0KDRvXvHHtLRI87x9n6pd\nKBRS5m0JSZJWrAjorruq5/uKyuF7W7/q/Xvb0hLSvHmq6O/ukfTAq6lK2YIFCxQMBtXQ0KDNmzdr\n6tSpCgaDWrhwoebPn5/d1tLSooULF6qnp8fvJgMAAADAoGqqUtbU1KRoNJq9PXv2bC1fvjx7O3fb\nvEE+1QQAAACAalFTlTIAAAAAqDeEMgAAAADwEaEMAKpQJBJRJBLxuxkAAMADhDIAAAAA8BGhDHXr\nrruYMr1eNV3b5HcTPBGLxcZctYyf2/rU1MT3tV7xva1ffG+9RShD3WLNoPpVyTXPUF34ua1P7npl\nqD98b+sX31tvEcoAAAAAwEeEMgAAAADwEaEMAAAAAHxEKAOAOsJU+gAA1B5CGQBUOYIWAAD1jVAG\nAAAAAD4ilAEAAACAjwhlAAAAAOAjQhkA1DDGmwEAUPsIZQAAAADgI0IZAAAAAPiIUAYAAAAAPiKU\nAQAAAICPCGUAAAAA4CNCGQD4gFkTAQCAq6ZCWUtLixobG7Vq1SpJ0sqVK9XY2KgNGzYM2PbEE0/o\ns5/9rJ/NBQAAAIAh1VQoW79+vaLRqOLxuCQpkUgoGo2qubk5b9uuXbuUSqV07LHHlrzvQCBQ9CsU\nCo3SKwIAAABGLhLZqEhko9/NqHuhUKhoZhiJmgplkmTb9oDb7hvhbnvmmWf0xhtv6LnnntP+/ftL\n2q/jOEW/CGUAAACoVpHIRsViHX43Y0wIhUJFM8NITKhQ+zyxYMECBYNBLVmyRJs3b9bUqVMVDAYV\nDodlWVZ221VXXSVJ2rdvn4455hifWw0AAAAAxdVUKGtqalI0Gs3enj17tpYvX569nbtNku666y7P\n2gYAAAAAw1Fz3RcBAAAAoL9aHldHKAMADzAFPgAAKIZQBgAAAAA+IpQBAAAAgI8IZQAAAADgI89n\nX/zud7+rzs5OjR8/ntkRAQAAAIx5noeyX/ziF2pubvb6sAAAAEDNc2cXDAYv9LklqCTPuy9Onz5d\nt99+u+6++26vDw0AAAAAVcfTStnmzZv14Q9/2MtDAgAAAEBVY6IPAAAAAPCRp5Wy2bNna+nSpTr+\n+OO1Z88eNTU1eXl4AAAAAKg6nlfKLMvSbbfdJtu2vT40AAAAAFQdT0PZihUrdPLJJ2vVqlU6+eST\nvTw0AAAAULcMI65YrMPvZmCYPO2+yLpkAAAAAJDP00pZc3OzLMvy8pAAAAAAUNU8rZTNmDFDq1ev\nVjqd1oknnqjFixd7eXgAAAAAqDqeh7IZM2ZIknbt2uXloQEAAACgKnkaynK95S1v8evQAFB3IpGI\nJCkWi0mSgsGgj60BAADl8DyU3XnnnbIsS+PGjdO//du/eX14AAAAAKgqnoeyl156SY8++ijjyQAA\nAABAPiwefdppp+nWW2/VO9/5Tq1YsaKs57a0tKixsVGrVq2SJK1cuVKNjY3asGFD3rYXXnhB73vf\n+7R06dLReAkAAAAAUDGeh7JLLrlERxxxhC655JKy1y1bv369otGo4vG4JCmRSCgajaq5uTlv2znn\nnKMpU6Zo0qRJJe87EAgU/QqFQmW1EwAAAED9CYVCRTPDSHgeyv7jP/5DX/rSl/TlL395WM+3bXvA\nbfeNyN32yCOPaPLkySXv13Gcol9ehrJwOKxFixYpHA57dkygnkUikewkGAAAACMRCoWKZoaR8DyU\nzZ8/X1/4whe0YMGCsp+7YMECBYNBNTQ0aPPmzZo6daqCwaAWLlyo+fPnZ7f95je/0cKFC3Xw4MFR\neAUAUD8IrQAA+M/ziT4SiYS+/vWv61e/+lXZz21qalI0Gs3enj17tpYvX569nbvt0ksvHVlDAQAA\nAMADnoay1atX67HHHtOxxx6rCRMm6AMf+ICXhweAmmIYhiRp+vTpgz7OXZtsqMcBADDaIpGNkqRg\n8EKfW1JbPA1l1113nd71rndpwoQJ+t3vfufloQGgbpUa3gAAQHXyfEzZnXfeqV27dum///u/vT40\nAAAAAFQdz0PZ0qVLddppp+nGG2/0+tAAAAAAUHU8n+jjqquukiSdccYZXh8aAAAAAKqOp5WylStX\nenk4AMjD9O8AAKAaeVopW7NmjS644ILs4mpNTU1eHh4AakIkEsnOqAgAAOqf52PKRrraNQAAADCW\nxWId2annUR88DWXXXnutJk+erAsvvFDbt2/38tAAgBLQxRMA4IUtW15RJLJRW7a84ndTqoKnoeyx\nxx7Tzp07df755+vd7363l4cGAAAAUCUMw1As1rfW5ljnaSjbtWuX9u3bp1QqpWg06uWhAQAAxrxI\nZCPd3oAq5OlEH08++aQCgYCampoUCAS8PDQAAAAAVCVPQ9n06dO9PBwAAAAAVD1PQ1lra2v2/4FA\nQBdffLGXhwcAAABqitvdNBi80OeWYDR5OqbMcRytWrVK+/fv16pVq7w8NIA6wMyAAFAZjC0Dqoun\noWzevHlKJBKSpH379nl5aAAAAACoSp52X5Skxx9/XH/84x+1bt06rw8NwGOPPvqoIpGI9k7cO2Cb\nW/EKBoNeNwsAgJpDZbO+eR7KrrnmGu3du1cnnXSSHnnkEa8PD8BDr732mmIxyTqpx++mAACACmKs\nW2V52n1Rko444gilUinZtu31oQEAAACg6nheKVu2bJkmTpwo0zS9PjSAMSwSiSgWi7E0B4C6QaUC\nqB+eV8quu+46tbe364477vD60ABQF0ZzFspYLKZYLDYq+wYAjJ5kMiXDiPvdDAyT56Fs7ty5OuWU\nU/TnP/+57Oe2tLSosbExO53+ypUr1djYqA0bNuRte/bZZ3XZZZfp5ptvrnTzAdQQtzoGAABqx1hc\nssHTULZ582Zdf/316unp0cMPP1z289evX69oNKp4PPMpQCKRUDQaVXNzc962iy66SE899ZSmTJlS\n8r4DgUDRr1AoVHZbAfhv9+7dMgxDu3fv9rspQ6p09Ys13QDUorF4MT5SkchGxWIdfjdjzAiFQkUz\nw0h4Xikbqf4ThNi2nX0jcrfde++9+uhHP1ryfh3HKfpFKANqUyqVUjI5SalUyu+mDKrcih5dDAEA\n1SoW66jrkBgKhYpmhpHwNJQ99thjamlpyX6Va8GCBQoGg2poaNDmzZs1depUBYNBLVy4UPPnz89u\ne+SRR/T9739fS5curfyLAABVXyUqmUwS1AAAvjOMOGPbhsHT2RfvuusuxeNx/elPfxrWlPhNTU2K\nRqPZ27Nnz9by5cuzt3O3XXXVVSNrLABUCTdssdA2AAD1yfMp8T/4wQ8qHo/rzDPPVFNTk9eHH1PC\n4bBaW1vV1NSkZcuW+d0cjEGtra0yjLiOOSlz260sES4AAChfPXcLHOs8D2WzZs3Sjh07tG3bNq8P\nDWCMMgzDs2MRPAEAQLk8D2W33367/vznP+vAgQNeHxoAylILASsWiymZTPrdDAAAMAKez774t3/7\nt+rt7dWb3vQmrw8NAFXLMIxRm6ij2iYlAVCacqaHZyp5oLZ5Xim7/PLL9fTTT+vII49kTBkAqK/a\nNW3atLz73QpY//sBAKgV7ocFweCFeffHYh0yjGMk7S+4fazxPJQ988wzam5u1rhxNbdEGgCMGBUr\nAADQn+fJ6B3veIe+8IUvaMWKFV4fGkAd2rJliyKRiLZs2ZJ3fyQSGTDBx2h2ESwVCz8DAID+PK2U\nOY6j7u5uPfDAA14eFkAdywQtSfJuhkWveTl7JACg+rmLM4/1Ln/1xNNKWSAQ0KRJk3TbbbdRKSui\ntbVV7e3tfjcDqGq1PHGFYRhDhqzdu3fLMAz19vZ61CoAQC2KRDaydlmd8HxM2Y033qinn35af/M3\nf+P1oVGCcDgsSSw2DXigWDfGVCqlZHKSHMfxtkEAgKrjhq7p09/uc0uKKzaZR60dw0+ejym76aab\ndP311+tf//VfvT40AFQlwzBkmuao7b//OLZarjQCAFCPPK+UzZw5U1/96ld11llneX1oAFWsFhZq\nrjX9K3EEsdHxxvbt2s57ixrgdnWr5ooL6pNb7fOzylXtlTZPQ1lra6uuvvpqLw+JEtBlEX5xQwNB\nbHDuemWDMU1ThmGwphmAkgx2kVztF68jUc+vDbXN80qZZVn6+c9/rmQyyeLRAFCCQl0bDcOg8gXU\nkUqGBYIHUHs8DWVbt27VX/7yFy1btkwzZszw8tAAxjC30lRKxWm0VUMbUBkbCcWoYe6U6kC9qrUP\nJzwNZW+88YYCgYAeeughSdJdd93l5eEBoGxedLFMJpOyLGvA/ZZlEeIAABgDPA1loVDIy8NByq55\nxngxVINKBRzGohXmBrhKjCuLRCLavn27NH3EuwLgg2qYWMFLtVYVGS4qnPXL8zFl8FZXV5ffTQCq\nUi3P9mgYxqAVtKEWp5aKr5HmPnf69OnDaRoAwAPJZEpSvOpm0oxENiqZTOmooyb73ZSaQyirEcyQ\niHpQSlioN7U6GUcsFtO+ffv8bgYAoAbs3r1HqVRKkyf3hTG3eonSeL54NABImYv+sRjSvHDo0CEZ\nhqGDBw/63RQAwBiwY0enOjsnKJVKFdxuGPFsl9pcyeSuvC6ZkcjGUQlzkchGtbW1FWxDtaipUNbS\n0qLGxkatWrVKkrRy5Uo1NjZqw4YNeds6Ojq0ePFidXRU7xsPoE8kEinana6/3DBXzvOkzNTy1TJx\nRqmh1DTNvElAIpHIkK8hM0HIpIJT6QOoHbFYh2cXkaN1MTzSY3jRLlSWaaYPd6/0XyzWUTPnT02F\nsvXr1ysajSoezyTqRCKhaDSq5ubmvG1vf/vbdeWVV5a170AgUPSrVicoaW1tVXd3t9/NAFDAUOPC\nRkssFhsyyCaTSaqYqCpcmFdGLNbBRBF1rJrC0FAikY2enouxWEfF3ptQKFQ0M4xEzY0ps217wG33\nTei/rRyO44yoXaMpHA6rtbV12Ittd3V1KRwOlzwerbW1VdLwx68NNf6N8XFjR3t7u5JJ6ZhBHtM/\nIGzZskWGYWjatGl617veNartG02VHEs21O820zTLrooxoUdx/I6C3wigGEomgOXfZ1mWbLtHkcjG\nw39Dz/CncQW4E5PUg1AoVLRgM5JgVlOVsgULFigYDKqhoUGbN2/W1KlTFQwGtXDhQs2fPz+77cCB\nA2pubtaDDz7od5PL1t7ezoyJqHuRSKRoJcYwDMVi1T0pSLndJl2xWCwvPJmmOaz9DEfmffXmWACq\nz2OPPabOzk4dOnTI76agTIYRL7mq5DiO2to61Nb24qCPo/pcfWqqUtbU1KRoNJq9PXv2bC1fvjx7\nO3fbww8/7GnbAAzfUFUbt3q2e/fukvY33NDk974ryTQPKJnsLft5yWSSsWhAjYlENioW6xh0evRU\nKiXTnCCp/N8LqA65XfAsy9LEiT43qIjMOOi0382oOTVVKRvL2tvbs90Ky9Xd3V3wueFwONtNB6hm\nbvWs2KxOY4UbCN3uirt375ZhGOrt7R2yCzbjxFBNRvopPZ/yF+cGNKm2JjkYLfV+rtTCOMFYrEOm\n6U1I83JynEqrqUoZypdOp2VZltrb2/1uCsY4d9bAo446SpKyk1xMmzbNz2aVrBrWG+s/Y2MqlVIy\nObLxtECtcC+0gsELfW4JKs0NTcW+t7t376nKcVJeKGUx5mofr2UY8ewswqUEZPdnvRILY1d7YM1F\npQyAp5LJZFnd/yKRiNra2vJ+sdZb1cc0zYLByrbtvOnwy5mx0bIOjagbYjKZHPOVSaCe5F4Y15pU\nKlXyWON6rowZRjxbcbJtx7PqE7xBpcxjtT6rV623H6Ov0Dmyb9++io/DKjfcSfLlgsRtYyVnORzN\n1+GuizaxWgcrVIGNVVA1Heuomg3kfnA1WHXBttMyzeqdbRqlcxyn6N+CZLI6qkOFuhFWsgpWbwhl\nVcgdAzbawaf/OLNKHW+kU+pj7Olf/TGMmCQpGAyqra1NiURCU6eO/Dj9q1FuYAoGgyU9v62tLdsF\ns1i3y0IhrNyqnmEYMs1x2TY7jlNSF8V0Oq14PK4jjzxStt0raeDFVz1VGIF6M9yxMO4EEDXSGxwj\nUGgSjULT44+mSlQi+yYtOXLkDaoTdF+sc5k1K+zsWmXFtLe3Z9dDY/wZ/JZMGoOGh8HW5fJzdsSh\npvofSSByHEeJRMKzmRGHs/ZZLWCCI2950ZWsnrurVVIymRowIVAymcq+d9U0QUI5E5SMhe9/uYtC\nm2aq6haRtm2naJuSydSAc88w4qNyPh48eECRyEZt2fJKxfc9UlTK6hAXHKhGmeqPWbRbnLtmV6W6\n+fWNv5pU9nNzJ/Xo7OyUYRiaO3duSc91q36DvQ7TNEsaGzbYjIq2bVP1GgXuh1NNTU0Kh8OKRCLK\nraNuPDzGsYGSxACLFi2SJK1bt87nlsDldmkMBi8ccvyRVxMijHbX07HQtTVTLcv8fXDHCub+abWs\n6hhrNtg5ZRhx9fSkNH780Z5O7Z+51pCk6vv7SShDHi8Xr2Z82tjWfxHl3L7xpplUImFWxYyH5coN\nXLFYTJ2dnZKUnXVyKO4slbY9vF/PzMQI1Ad3qvNp0xoquFdLpulku45Vy0QRuUHKfd3Tp789b3p/\nDM22M0FtXIF+cCOtJg41Q2YhuZWxoZZtqYRIZOMo/Mx4h1Dms9EMJu3t7WVfoHV1dam7u7vibcnl\ndpGcMWOGmpqaRvVY8E84HD4c8I+TlKkgDTaBhGVZ6uzsHLTCNFhlaLhdFssdVzYSpS7MbJqment7\nhnUMx3HU01P8uYlEQpKy34dKTkAClGr37j1KpVJjcorzSirU5atUpplZMsfttjjUtOvFDOdivZLK\nnTii3ipp8XjfrJqFgo+XXTtNc68Mw865nQn9EyceIclRT8/wulSW8xrc5QFqMdATyqpQOp3Ojuty\nu9KMpmLVsVIDI90lMRy5lbGhZhO0LOvwBBvlHSNTgdun8eOHNzbKXah5pIp1V7QsS4ZhyLIsOY4j\nwzA0ffr0ou+H25Zx48Zlx5hNnDhwBhTbPlKO0xfM3K6jbjAeP378iF+THyr5IdZw9rUxElFHv/Af\nNwzt6u5WrEbfU7+kUikZxjF0wR2EYcTLmrzDnexh2rShw1nu9Ph+r+NU69WN0RaLdQzonlhNhgo+\nhSYm6c8000XPw8cee0w7dnTqxBP/Wiec8Oai+xgshPl9jpeKUFaFLMuqWBfC4ZSL0+l0dkwFUCr3\nItf9EKH/+eOuxWWapjo7O2XbtsYV6mMhybJMSX3BxLZt9fTskZQatBtg7uyIQ3EXYh5q8Wo3EOYq\nZdxYf729vdmfR3cqY9u2D3dVzK9oZ24HCu6n/0yMpjn42LRYLDag/ZZlFQ1+brv8DG7V3LU5bhhK\nuVe+kDTyD+a2bHkl+7M4WNXMMOLZi6taucjyUqGL39xFhU0zMyOr+4GOZdkKBAIDHp97cVuL1aTc\nc2PLllcUi3Vo8uTyK4DVJneGRbebovuvF0o5JywrqWRy5G3K/UAilUopmTxyWOtmuvs56qjJOd11\nq/dcIJTVsdxg19XVVXCa/fb2dm3dulWmaWr16tWj2h73D7dbmZsxY8aoHg/VwTS75Azyy9QwjAKh\nxJI0sOutaZqHLyaGV+0pNLW8+0l9se6LbpAsFG6KdX00TVNtbW159/X29h6uYPWfytgc8OFJsQ9T\n+t+feR9K//jUDcbjxo3Lvq5yVHNYqpTW1lZt2rRJUt8HC29s355dm8w9/8a6QudCbg+PzHjQ0roE\nG4ZR8sD7QrO01ZPctcZyx3yV2kUwU33P7KOUi1DHcQZ0WxzJWLZKdWVMJBJqa2vTtGkN6uzs1FFH\nTda0aeWva5WZ9VaSqmsmwkrK/7vQF9bGjx/4wd5orhGW+d04QdK4w2Fx4N9px3G0a9dOHTqU/3fQ\nsiwlEons+TPc8Y59i2tP7refydnuutWMUFajyrs4Gjfoieh2nZL6ujkNV+54sXI9+uijeu2113Tq\nqadq8eLFw25DJYyFi0+vuedZbt/3/gHJPfccx1ahNbbc5xYbK9l/yvjc837gWmhGyVW1QopVy9x1\nxQ4dOiTJHR+WqZI5zuA/W/v376/IdP6FQp1bARuqeu4+xrKsQrm4blRiPcVs1ayOlPO7r3/X+twP\nAmOxmCzrbRo/fuhP1zMh4BhNnz62qo/9A0zfuk2lVwHdADZtWoMsK/N7MzdkZSr9wwsksdjr+va3\n79enPnXjsJ5fjv6v2213sYJ0KeGvWHc2t3oSiWysqUpgbiU08zfSNfB3uuNYMs3CY7FKWWS8kELV\n6Vis4/AMxfHDldgJh4/vyLIGtsu2HR04kJJ0jCZOPJB3f27vGKnvA4ZKdGvNhLPeEe9ntBHKqkxX\nV5csy1J3d7fC4fCoVK/6V6xy5Qa0Qs8brFujG8i2bduWty5aqRc9r732mjJ/418r7YWgJlhWt6xD\nh4oGqWQyWfSccwNKJsA5clOCGxr6gtUxkoy8qlNfVUjZ2y53hkP3+Ln3DyV3P8lkckC1LPMzNE2W\ndXmgmncAACAASURBVOjw7XE5ry+/S2a+Cert7S0wxiag/n90h+6WXDj8uc8bbAIgL2bIqiVtbW2a\n0N0tHf6gybIsOcxwWbJi3QwzF8YTJJUXxAYbe1Jvcn839u86lltRkPJ707rd3EwzLdt2shWH3J/t\n/Nlu+x4/lDVr1kiSrr766uG+LEkDZ1t0J4Bwx5flyh0rV+4xMvvKv6ivplkny2Hbjmw7P1jkfk8P\nHRre5FClKhbue3oy72e5fzsyHxQ6cotplmVnq6M9PSlZlp0N66Y5TlJpH6CW+kFEZqzeG5JOKqvd\no4nFo6tcV1fXgODUf/KPYouh5s6i6DhO3qLQra2tA/a7bdu2EZd2t27dqoMHDxacwbG1tXXISUva\n24d+TKlCoVBF9oORcZzevLBvWdawqrF5v/BjmX8SiYR6enqUSPxfdlZBV19lzs47puM42r17t0zT\n1KFDhwZti2EYBf/Q9F9YORaLZQNd5vEHlEwmD0+sYR6ukpUbrJx+/w5k27bS6UKVmt6857kBNffY\njuMM+trdyprXvP65zf39GQ6H1d7eru7u7uzvS3fGzD27d6utrU324fOqf5XMMAwmPRpES0uo7Ods\n2fJKdpFXt4rkXnC5X1J1LXpcCclkSp2dnXm/Izo7O7PLa7jcMNMXVPt+x/b0pJROm3Icu2CFILNv\nR8V+v7jvdTG5Cza3t/+gpNeVW7lyFwaORDaqs7NTlmWrp6eva6ptZ2bqKyWAF3pv+jOMuNasWaM1\na9Zku7G1tbVV/aLThUJqMZlKqZTpuufIcey8bvRtbW3q7OxUMln4fS20CHf/n9tkMqlvf/v+Ij9z\nmfMpE3gHdrXPPsrpleOksuPhLCuZ7R2TSCQGfb2l/KznfhBhmmml0+awZ330GqFslBULTCPhVqRy\nb5caZNzqW+7zC9m0aZNWr1497IDkThayevXqoq+/vb09b/+ZoLh3WMcrZMWKFRXbF4Yv88u28Pgv\nx3HKWILB6Qs3sb57M8Hk4CDrnvVNruHasWOHTLOv4lZsIedkv4vu3GNkxmIks4tL929r3zFzg01+\nyMkPRpnJN+Lx0qsAxYNd/v2Fxq2Vuv8tW7aU/byRqLafW7f7aeeOHUol+y5jE4mEUj09Sg2y/MBI\njcbfj0or9PentbVVnZ2d2rcvkXNf8e9rpmo28GfQHWuW+/OVe8FVqwpd/Ep9i+n2vb7ioamtrU0d\nHR0FP+TKfBDV1wXccezDFa6+/eV+4JLZRzo7AVBnZ2f29t69Q0861t5eXo+ezs7O7Idozz678fDf\ngExviM7OTsXjcTmOna2UmGZvSd/ztra2AWN5M+E9mQ267qyTXk6QUQmWZWeDS6ExZFJul8ZD2fvd\n97mtrS37/56eVN6HmMXORyn/57Z/Ncww4ocn7XKKBHjncHWv/3vtSOr7m2Tbfd/bviEKuQFv+NxF\ntkv9fvv9O5fui6MsN0AtW7as5HEMbqhxL1iPO+647LZNmzblXci2t7dr//79WrRokQ4cOKApU6ao\noaFhyJlquru7lU6ns7+c0+l03rbcY+a+Frf74lAnrtuVsdAEI93d3eru7lZXV5cWLVrk2UyP5Xap\nZGzZyHR3dw+Y2EIqHiZM09Szzz6r3D80xQKTlNvd1lLuLvMvUnoLVnwcp2+MVe66Xm53xNyxYu4f\niv7hxg1mAytOud2AB/9j0P9x5Veri+3flmUNvrab4zhDrNN2bN7z3e7Uo/3z0P/35mjtf6ixr62t\nrdlzLB6Pa/z4N2W39R4+F8aNH190yYNSFfo9M1R38dFSibF2kvuzktLRRw/+uFisQ3/5y15JlmKx\n3rz7C3U9k/rG1mTWPqpt/bsl9o2t6fu9uWvXzgEXp8lkKvs3252so0/53cgy72lmYojcCREcx9Ga\nNWs0ffrbFQxeOGD2y9zxcG5XxNz7Bztm36QMAz84K4dppmWaverpSenIIydnx9O5++8/lbxtO9n3\nzG13Nct8b3Pfo/HqP/4q38BuqpleIb2HJ5wq730u9gGCe865PVIyenP+7wbFgPr+BOcHysy3fGC3\nfjdguu23rGTe+meDcT9s/eMf/5hti9uOQCCgeDyueLxNDQ3SMcf0PS8cDuuee+7Rcccd59s1H6Gs\nAga7cN+0aZMMwygYTApxP73o7e3745ROp7Vt27a8x7mhzd2naZrKXMPs0e9//4qOPjr3k7DMD4Eb\nkLq6urKBq3+3Mldvb6+6urryJkHoH7L6T33+zDPPZP9IpNNpdXV15QU9N0weffivdO42V3f3DrW3\nJwbc75Vi07oTzgaXewGZO1ax3IXIi3UVzBhYkSjWvW6oroKZC+i+INc/CLlBpP8izMUuGtLptFpa\nWjR1qrtmWGkVLC8kEolBL3YGXxagR7FYrKxPDiv1s5I7Y2zu+NrrrrtuRPuVpJ07d2rr1q167bXX\n9NRTT+mII47QsmXL9NJLL6mrq0s9PT3Zc9iyrEx9wbZl5V48uMsb2LYs9f8gYHgWLVokSVq3bt2A\nD8EqrRLfJzc4Fwq3mb8t5b0nuRMQ9E1lnT8uLVNByfwsTpx4RLb7WymLHvu1yPFQXeTcIJP5WbVl\n2+PUd6E6ruhi8PmTPYzUZLkzFOZ/+OTIMOJ5k0K4Y7KGE4rdamDhYOAc/t3cd2w3jGYqeK9IOmrA\n9880ew+fFwEd+v/s3Xt809X9P/BXSFuK8gUKdgiUAtLiCmnpnYtcGp0i7UAFtII31B+IzrFN5qXe\nKHPKcMqcsglfBMUpVAXmxCLiV1q0QNsNG6BD12lpoeVaSlMDpUmT8/sjfD4kadImadJP0r6ej0ce\n0ORzOZ/b+Zz3OedzPs0X7V5YbPt+L6kyWFq+1D0y0Af88Ly1yD4oKy8vv1RB2vY5s2eP9TyVWtWG\nDx/R7pqs3QJtj6XZyQAfrbvv26fVvmu9bW8Ri8UaPEv/XrzY5MbIy9Yg0Zon2z6TLS4FbADQgPp6\nCywWPQLpmTIGZX7W3miGjjXCUiHTYrHIQYwU9X///fcYM2aMvMyCggLMnDkTZ8+evdSydXm50nxW\nly+G0tJS1NXV4fz582hqamrVhUFisVhw/ny4Xe2vtJ6CggK7Qpq0DdaWucs1OBcvXoTJZMLnn38u\nFzaMRqMcjNl3MbNnW2CwLfB3VlAkHRO+q809u3fvRkFBgRyQScGYNzWfjl0G22o5cm/5red3FSyW\nlZVdahFRIyIiQr4+zGazPJqiqzQYjcZWz7UFi3PnzrkY5MR+4BF33p8o5QfOrllvAwGpMun8+fP4\n4x//aPebp8tav349vv/+exiNJkh5o8lkwquvvor6+nqcO2eUB1uyPU8sQgBOzkWLNIropYKCN63x\n0r4qKCiA0WhUvMviiRMnMHPmTPTv3x/vvPNOq99tn2uWzompU6fK+6ugoABmsxk9egB1dWfla8S2\nxcVasDddOr/su+lVVVXL11J9/RFYLC0IDbUtrqguvdLBiLKyMly82ORWUNZR7QV17gZ9p06dRlNT\nEwyGH+3KB9Zz0upya4O4FHSIVu8Us9Vewb29Z64uV3qFtgqmHVuUpOd0bPPm2tpaly+Btg1KpQK2\n9K40x+fdhFDBtpB+OUBUy+uRBhsBrPv6cqChkstLtkGfyfTjpfc5Oj7nZAn41rI9e/banRdWngXj\nJ06cvFQBaR+s7dmzF0OGDHE6CqPtqxVsvwME6urqIATQo4cKzm/Bnt6X7ae3PXZCWC4NeKPChQvi\nUvBtxr59e52+1/DyfVq0c79qcTo6pKcVyb7GoMxHXHX5kFqiCgoKAFwe8VC66UotaVItsGMB1Fkr\nlpRJGQwGfPbZZ3Kr2tGjB3HllRdhNJ6H0WholUaDwQCDwfp9U1PbD/FaA6Z6HD16Vr4BWy9E63ql\nd/mcO3cOYWFhNkGg9JjiNdDrraMoGo1GlJaW4vTp060K0WazGaWlpejfvz9KS0vR1CQA2Pd3kQp5\nBQUF+OMf/4iTJ086TbOrLkBLlizB/PnzUV9fjxMnTmDQoEHy8frkk09arcu6L4+iqakJ69evxwMP\nPNChLj3tFdJsa8dd8eR1Ab5qqXAnXZIvvvgCFy9eRGWl9ZhbW27bfm7RFdtzxDpQhnSeevvsjvs3\nMOlFzmazGZWVlXZpaWxsbHf+QHwHirMWaVt6vR6hoaEoLy9HXZ3jM53WZ9yk87+urg5qtRozZ85E\naWkp0tPTW50fjt2rbc9Dx2eP2jpHpcE2Xn31VZSWluLMmTMQQrSqJXV2vkvXy6effor/+Z//wf33\n34+3335bzn+t++RyAVcIgfXr1+Po0aN2+Wtrzs7BEAjRAqPRiNzcXFx55ZVIT093um2O15TUXUaq\n7Dp//jxCQ0Pt3pXmjC+u8ba6oh47dgzHjoUiPd15+qXKqqNHj8JgMKCurg67d++Wu8xLlRnWgMN+\nsArAGnSdP38eKpXJpiLDgPp663yXu+ZJx0g4nMeXK0Lq6+thsVjkdxy5U7juSIvZnj17UVZWhqSk\nJHkZ0vKk55lcLdc6SMERGI0mqNXXIDy8Ub5W7Luo2Q93fvml89ZvNm3ahPPn7c/P9q5zx+lbk45T\n60FBpIqyffv2XgqyL1dWSQGSNJBCe0P6WwvY0vnR4qTlxjYPVdmVgZqa6mCxXMT33/9XDgr++te/\n2G2Dbdc5qeLFbG6E8/uA6FC3Y3/bt2+vQ8uhxLPKTletY+fPG1BVdUQOyqQWTOkY2VYyWkcqvNyl\nFXD14mrb71qPHOx8OseeLY7PXlu7Htrej/fsqcV1112eZtOmTTh3rh4//iid59K70tpev+19fffu\n3TAYDG69pNpfvadUopuPfyzVPA0cONBpAWPMmDE4e/Ys0i/dnRxrfocOHYqamhr576ioKBw7dky+\neW3bts3pekePHo2RI0fa/a5WqwOsUGe9oAYOHIi6ujoP09bWxegoDGFh9jcVtVoNlUqFkJAQXLx4\nEeHh4XIXjhkzZtgFhEajEREREfLN7ccfre++UKlUGDhwIADg1KlTsA6nesFu+VI3zptvvhn19fX4\n7LPP0KNHD7u09OjRA4MHD0afPn0wcuRIOdg4evQorrzySgwYMAAjR450GbxcffXVACAHk1dffTXO\nnTuHadOm4ZNPPpF/f/zxx+V51q9fL9fySMsvLf0B588fRXR0NB544AH5PLStVV+/fj3+85//oFev\nXvJ+cPxd6mr0ww8/yF3BbFs10tLScOHCBVRWViIkJEReji3p/P7qq68cWodC4OymjuGXPh5SqVSt\nAnkAQCGADM+X15Y+ffq4FXh1K1XWT+/evR1a3y975ZVX5IqTw4cP2/2mUllbNKRzUApAAODKK6/E\nyZMnMWbMGHz//ffyC61trz0pX87Pz79U0G+dr/Tu3RvR0dHyeT116tRLLbelMBhOARiDgQPrLuUB\nnri8rqlo+3QrBGAbbqrVamRmZgKwr9S4+uqrcf78efTr1w8nTpxAaGioy65pkvDwcPTt2xePP/44\nlixZgpkzZ+Lzzz+HyWRCaGgopk2bJgdJ7hQQpEBQ2h8DBw6U8xgAcnAotczY9qaQ7pehoaFudte0\n3XPLACx1Yx5P7h321Go1brjhBqdB0b59e/F///d/sFgs6NkzHCEh1jrp666b6HZw9te//gVnzpyx\nplLVQ95HPXqo7O6PS5fm2q33yy+/lPOyy9P1QLC9CNC6zc7S3PrY2u6DP/1pZYDnrSqEhYVi+PAR\nHR7q/09/WomLFy8iLm40br31Vo/mlUanHD58GE6dOo1vvz3cbrDtCz169ECvXle4CNyXQaVa5uOu\nsr7QA2FhIcjJeRqbNm1CRUUF3A8IbRUiLGwfYmJi7O5ftvc628dapMpIySeffCL/LpXbpLzVm/Aq\nqIKywsJCPPnkk8jOzsZjjz2Gl19+GVu2bMGrr76KlpYW+bf58+djxowZGDFiBN577702l9lWdwAi\nIiIiIiJPeBNeBdWQ+Lt27UJJSYn8EKBer0dJSQl27txp91thYSFWrVqFlJSUNkceIyIiIiIiUlrQ\nPVPmONqaxWKRW7scX47qjiBqKCQiIiIioi4oqFrKrH3FJ6B///44cOAA+vbtiwkTJuDGG2/E9ddf\nL/+WkZGBxYsXY//+/W0M9UxERERERKS8oHqmjIiIiIiIqKsJqpYyIiIiIiKiroZBGRERERERkYIY\nlBERERERESmIQRkREREREZGCGJQREREREREpqFsHZYWFhRg3bhxWrlypdFKoA1avXo2MjAy8++67\nmDRpEu6++24AwJ133onJkyejoaEBL7/8MsaNG4eioiKFU0ue+sMf/oA1a9Zg/Pjx+O1vfwuz2Ywb\nb7wR06ZNg8Viwa9//WuMHz8e33//vdJJJQ+Ul5dj0qRJeOmll3jddiF5eXm4/vrrsXDhQh7XLqKw\nsBC33XYb6uvrcd1117V7TFm2Ch7Ssd2zZw+mTZuGxx57DACPrVK6dVC2a9culJSUoL6+XumkUAcs\nWrQIn332GU6ePIlVq1YhJSUF9fX1GDduHN544w3s2rULer0eJSUl+OKLL5ROLnmgsLAQCQkJOH/+\nPHbs2IHw8HAcOHAAjzzyCB5++GEcOHAAV155JXbs2IFt27YpnVzywKZNm9C/f3+0tLTgjTfe4HXb\nRQwZMgQNDQ2Iiorice0iMjIykJiYiMLCwnbvsTt37mTZKohIx/a6667D559/jj59+vDYKqhbB2UA\nYLFYlE4CdZBer8fKlSsRFRXV6ngKIaBSqQDwWAej/fv3o6SkBE899RRsX6kohLD7m8c2+DQ1NeGj\njz7Ce++91+o3XrfB6/PPP8e//vUv5OXltfqNxzV4Sfmt46ttHY+pSqWCSqXi8Q1Cf/7zn+VWUIDH\nVgndOii74YYbMGHCBPTv31/ppFAH5OTk4LPPPkNxcTEWL16M/fv3o3///igpKcGvfvUraLVa9O3b\nFxMmTMCNN96odHLJA0uWLMEDDzyAN954AzfffDOam5sxduxYvPnmm1izZg3Gjh2LCxcuIDMzEzNn\nzlQ6ueSBWbNm4Wc/+xmmT5+OX/7yl7xuu4ixY8diypQpmDx5Mo9rF3H48GGUlJSgpaXFrXvs9ddf\nz7JVkDh8+DCKi4vx4Ycf4q233sLChQt5bBWkEo7VHkRERERERNRpunVLGRERERERkdIYlBERERER\nESmIQRkREREREZGCGJQREREREREpiEEZERERERGRghiUERERERERKYhBGRERERERkYIYlBERERER\nESmIQRkREREREZGCGJQREXVDPXr0QFJSEuLi4pCVlQW9Xg8AqKqqQo8ePbBixQp52vnz52PDhg3y\n3y0tLYiMjEROTo7L5c+fPx+7d+/23wb4yf79+7F06VIAwJdffolvvvlG/m3NmjXYtm2bT9bT0NCA\n66+/HgBQWFiI+++/3+l0WVlZHV7Xd999hwkTJiA8PBzLli1zOd3w4cM7vC5XPvnkE+Tm5vpt+URE\nwY5BGRFRNyKEgBACAFBWVoZvv/0WkZGR+Otf/ypPExERgbfeegtGoxEAoFKpoFKp5N+/+OILpKSk\nYMuWLS7XYzt9MElJSZEDl6+//hqHDh2Sf3vooYcwY8YMn6znr3/9K+666652p8vPz+/wugYMGIA3\n3ngDv/3tb9uczp/HbMaMGdiyZQsuXrzot3UQEQUzBmVERF1cVVUV0tLScMsttyA+Ph5NTU12v0+Y\nMAHV1dXy33369EFWVhbefvtt+TspkAOAvLw8PPzww7jmmmuwb9++dte/fft2aDQajB49GnfddRea\nm5sBAHv27EFqaioSExORlpaGuro6vPPOO3atOVKLW2NjI7KysjB27FjEx8fj3XffbbWejIwMLFmy\nBKmpqdBoNCguLgYA1NXVYdq0aYiPj0dycrLc+rVp0ybEx8cjMTER48aNA3C51erEiRNYs2YNnn/+\neSQnJ2P//v3Izc2VWwxLS0uRmJiI+Ph4TJ8+HfX19XIannrqKaSnpyMmJsZla+F7772HW2+9FYA1\nGDpz5gwyMzMRFxeHhQsXyvtbar2qqqrCuHHjcMcdd0Cj0eCGG27AhQsX2t33ABAZGYnU1FSEhoa2\nOZ0QAg8//DCSkpIwdepU1NXVAQBee+01pKenQ6PRYMaMGTAYDACAP/3pTxgzZgwSExPlbTEYDMjO\nzkZCQgISExOxfft2eRsnTZqETz75xK00ExF1NwzKiIi6gYMHD2LlypUoLy/HFVdcIX9vsVjwxRdf\nICEhwW76JUuW4M9//jMsFovd9xcvXsSuXbswffp03HHHHdi0aZPLdapUKly4cAH33XcfNm/ejMOH\nD0MIgddeew3Nzc2YPXs23njjDeh0OhQVFaFPnz6tWmukvz///HMMGzYMBw4cwKFDhzBr1iyn6+vZ\nsyf+9a9/YdOmTXjwwQcBAE8//TRSU1Nx6NAhLF++HHfffTcA4Pe//z127doFnU6HXbt22a1v0KBB\nWLRoEV544QV88803SElJsWsxvPvuu/HHP/4Rhw4dQmJiIp555hl5/v/5n/9BaWkp3nvvPbkrpK1T\np06hqakJAwYMAGANhvbs2YMNGzbg22+/hV6vxwcffGCXHgBy+svLyxEbG4sPP/wQALB06VIkJSW1\n+njaXfDo0aO47bbbUFZWhuzsbDz77LMAgPvvvx+lpaUoLy+HVqvFmjVrAACvvPIKdDoddDod3n//\nfQDA888/j5/97Gc4ePAgvvrqKzz55JPyOZSenh6UXVqJiDoDgzIiom5g7NixGDlypN13SUlJGDRo\nEI4ePYpFixbZ/TZ06FCkp6fjo48+svv+008/RUZGBsLCwnDrrbfi448/tmtFsyWEQHl5OaKjo/HT\nn/4UADB37ly5W+DVV1+NCRMmAAB69uyJsLAwp8tRqVRISkrCjh078MQTT2DXrl3o3bu302nnzZsH\nAIiPj0fPnj1RV1eHPXv2YO7cuQCAadOm4dSpU6irq8OUKVNw1113YfXq1Th//rycZsdtcPz79OnT\nOHv2LG688UZ5nV9//bU8ze233w4ASEtLw7Fjx1qlsbq6GoMGDbL77vrrr0dkZCQA4K677kJRUVGr\n+WyPYXp6urzsZcuWoaysrNXH06AsIiICN910EwBr0CmlobS0FBMnTsTYsWPx5ptv4j//+Y+cnnnz\n5mHDhg0wm80AgJ07d+K1116TW9t+/PFHnD59GoA10K2qqvIoTURE3QWDMiKibuDKK69s9V1ZWRmO\nHj2Kfv36Oe1W9uSTT9oN+AFYu/x98cUXGDFiBFJSUlBfX48vv/zS5XodW76kIMfV80s9evSQC/gA\n5GeQYmJi8M033yA5ORkvv/yy0xYo2+W3971KpcKbb76JF198EXV1dRg/frzcBbEtjs/XOVt2eHg4\nAECtVrdqaXSHEMLp/pGW67js559/3mlLmat95G4aJA899BDef/99HDhwAH/6059gMpkAWAP0X/7y\nl/j2228xfvx4+bjl5eXJgWFVVRWuvvrqNreLiIgYlBERdWs9e/bEq6++iueee65VcBEXF4fo6Gh8\n+eWXUKlUaGxsRFFREY4dO4YjR47gyJEjWLVqlcsujCqVCvHx8Th27Bj++9//AgA++OADTJ06FfHx\n8Thz5oz8TFpTUxNMJhOio6Oxf/9+ANZnwQoLCwFYu/z16tULd955J3Jzc+1GRbSVl5cHACgvL4fJ\nZMJVV12FyZMnY/PmzQCsLTlXX301BgwYgOrqaqSlpeHZZ5/Ftddei6qqKrugoVevXvLzUxIhBCIj\nIxEZGYmCggJ5nVOnTnV7nw8bNgwnTpyw+66goABnzpwBYA18J0+e7Pbyfve73zltKXMcadFVwCo5\nd+4cvvjiCwDAxo0bMWXKFACA0WjEVVddBYvFgvXr18vTHzt2DFOmTMFLL70ElUqFhoYGTJs2DW++\n+aY8zcGDB+X/nzhxAsOGDXN7u4iIupMQpRNARET+5+pZLcDa1e+aa67BRx99hHHjxtn9lpOTg4kT\nJwIAPv74Y9xwww12A0bMnDkTTz75JEwmk9OBJMLDw/HOO+/gtttug8ViQVJSEn71q18hLCwMW7Zs\nwSOPPAKLxYLQ0FB8/vnnmDJlClavXo1rr70WI0aMQGpqKgDg8OHD+PWvfw21Wo2wsDD8+c9/drqd\nJpMJaWlpaGpqwrp16wAAL774IubNm4f4+HiEhYXhb3/7GwDgiSeewLfffosePXrguuuuQ3JyMnbv\n3i1v/4wZMzBnzhysW7cOa9eutdtvf/vb3/DQQw/BZDIhKioKGzdudGu/A8DAgQMRHh6Os2fPYsCA\nAVCpVJg4cSLuueceVFdXY/Lkybjjjjtazd/WMWxLTU0NJkyYgMbGRgDAunXrUFxcjMGDB9tNFx0d\njc2bN+OJJ55A37595UA2JycHCQkJGDBgACZNmiQv5+6775b/P3fuXAwYMAAvvPACHnnkEcTFxSEk\nJASDBw/G559/DgD45z//Ca1W61aaiYi6G5Vor+qMiIgoCGi1WmzYsAHR0dFKJ6Vdy5cvx09+8hN5\nMJKuzmKxYOzYsfjnP/9p1w2TiIis2H2RiIiokz3yyCPyiIXdwaeffoo5c+YwICMicoEtZURERERE\nRApiSxkREREREZGCGJQREREREREpiEEZERERERGRghiUERERERERKYhBGRERERERkYIYlBERERER\nESmIQRkREREREZGCGJQREREREREpiEEZERERERGRghiUkV898MADGDhwIEaMGGH3/Zdffon4+Hho\nNBokJCSgsLDQreVVVVUhLCwMSUlJuPbaa3HPPfegpaXFq7StWbMG27Zt82peIlKelBckJSVh9uzZ\nTqc5cOAAUlNTodFoEBcXhw8//NDt5ffo0QNJSUmIi4tDVlYW9Hq9V+nctm0b1qxZ49W8RKSskydP\nIiMjA/Hx8Rg1ahRyc3Pl31iWIZ8SRH701VdfiW+++UYMHz7c7vuEhASxY8cOIYQQ27dvF4mJSQrn\n9QAAIABJREFUiW4t78iRI/KyzGazuP7668XGjRt9m2giCgqO+YozM2fOFKtXrxZCCHH48GHRr18/\nt5evUqnk/993333ipZde8jyRRBTUzpw5Iw4ePCiEEOL8+fPi2muvFUVFRUIIlmXIt9hSRn41efJk\nREREtPo+JiZGrnVuaGhAbGwsAGDTpk2Ij49HYmIi0tPT21x2jx49kJ6ejurqagDA3r17kZ6ejoSE\nBNx88804e/YsAGDr1q0YNWoUxo8fj9/85je4//77AQC5ubnYsGEDAKC0tBSJiYmIj4/H9OnTUV9f\nDwDIyMjAU089hfT0dMTExGD37t0+2CtE1Flc5TW7du2SW9nGjh0rX/OuTJgwQc5rvvvuO0yZMgUJ\nCQmYPHkyfvjhBwDAnj17MGbMGKSnp+PJJ5+EVqsFALzzzjtYtmwZAOCHH37AhAkTkJCQgOuuuw5V\nVVUAgPnz5+M3v/kNxo8fjxEjRuCDDz7w+b4gIs9dddVViI+PBwBcccUV0Gg0OHHiBACWZcjHlI4K\nqeuzrRGSVFdXi6FDh4qhQ4eKIUOGiGPHjgkhhBg9erQ4ffq0EEIIg8HQ5rKamprEhAkTRH5+vmhu\nbhZjxowRJ0+eFEII8eGHH4rFixeLCxcuiOHDh4szZ84IIYS48847xf333y+EECI3N1ds2LBBCCFE\nbGys2LlzpxBCiKeeekosWrRICCFERkaG+P3vfy+EEGLfvn1i6tSpPtsvRNQxvXr1EsnJySIlJUVs\n3rzZ6TR6vV6MGTNGREVFiYiICFFWViaEECIzM1OUlpYKIYRobm4WJpOp1bxSS5nZbBazZ88Wf/nL\nX4QQQqSnp4vDhw8LIYQoLS0VM2fOFEII8dOf/lR8++23QgghnnzySaHVaoUQQrzzzjsiNzdXCCHE\njTfeKP73f/9XCCHE6tWrxc033yyEEGL+/PliwYIFQgghjh07JmJiYjqya4jID44cOSKGDBki6uvr\nhRAsy5BvsaWMFPHAAw/gD3/4A44ePYrly5fLNT5TpkzBXXfdhdWrV+P8+fNO5z1+/DiSkpIwaNAg\nDBo0CJmZmTh48CCqq6tx8803IykpCS+88AJOnjyJ8vJyJCcn46qrrgIAzJ07F0IIeVlCCJw+fRpn\nz57FjTfeCACYN28evv76a3ma22+/HQCQlpaGY8eO+WV/EJHnqqursX//fmzatAmPPvooKioqWk3z\n2GOPYd68eTh27Bg+/vhjOQ+YMmUKfvnLX+K1117D8ePHERIS4nQdUl5z9OhRLFq0CHV1ddDpdJg3\nbx6SkpKwcOFCnDlzBmfOnMEVV1yBn/70pwCs+YiU19jmOXv27MHcuXMBWPMj27xmzpw5AICoqCiY\nTCbf7CQi8okLFy7gjjvuwOuvvy73AGJZhnyJQRkpoqioSH4wf86cOSgqKgIAvPnmm3jxxRdRV1eH\n8ePHO+1SNHjwYJSVlaGqqgqVlZXYv38/ACA2NhZlZWUoKyvDwYMH3er+o1KpoFKp7L6zzegAIDw8\nHACgVqthsVg831gi8ovIyEgA1mt/8uTJcl5gyzavmTJlCs6dO4czZ87gySefxPr162E2m3HDDTfg\nu+++c7qOsrIyHD16FP369cMnn3wCAOjdu7ec15SVlWHv3r2t8g1HjvmMMz179vRoeiLqHGazGdnZ\n2bjzzjsxa9Ys+XuWZciXGJSRIkaOHImCggIA1tGLYmJiAFhrvtPS0vDss8/i2muvlZ+3cKZv3754\n4YUX8OyzzyIhIQGnT5/G3r17AQAtLS347rvvoNFo8M0336Curg4AkJeXZ5dxCSEQGRmJyMhIOT15\neXmYOnWqPzabiHzEYDDAaDQCAE6dOoXi4mKMHj261XS2ec2BAwdgNpsxYMAAVFdXY/To0ViyZAlu\nuukmHD582OW6evbsiVdffRXPPfccBgwYgOHDhyMvLw+ANQ/597//jZ/85Ce4cOEC/vOf/wCwPlNi\nSyogTZ48GR999BEA5jVEwWLBggUYMWIEHnvsMbvvWZYhX2JQRn51++23Y+LEiaitrcXQoUPx2muv\nAQDWrl2Lp556CqNHj8Zzzz2HdevWAQCeeOIJJCQkIDExEddccw2Sk5NbLdM2I8rKysLJkyeh0+nw\n97//Hb/5zW8wduxYJCYmoqCgAL169cIrr7yCiRMnYty4cejbt69cW2S7rL/97W9YsmQJ4uPjUVZW\nht///vdOt4e110SB4ciRIxg3bhzGjh2LyZMn4/HHH8fYsWMBAEuXLpWHiF65ciU2bNiAMWPGYO7c\nuXj77behVqvx+uuvIz4+HsnJyaivr0dWVlarddhe7/Hx8bjmmmvw0Ucf4YMPPsCaNWvkobClmuy1\na9fitttuQ1paGpqamuS8xrYW+y9/+QvWrl2LhIQEvPvuu/jLX/7idH3Ma4gCw549e/DOO+/gq6++\nkgcH+vTTTwGwLEO+pRLt9bkgCnJNTU3o1asXhBB4+OGHkZqaiv/3//6f0skioi5GymsA4KWXXsKF\nCxdcFoqIiDzBskzXx5Yy6vJWrlyJ5ORkjBo1ChcuXMD8+fOVThIRdUF5eXnyy2D37t2Lxx9/XOkk\nEVEXwbJM18eWMiIiIiIiIgWxpYyIiIiIiEhBDMqIiIiIiIgUxKCMiIiIiIhIQYoGZTt27IBGo0Fc\nXBxWrFjR6vfm5mZkZ2dDo9Fg4sSJqK6uln9bvnw54uLioNFosHPnTvn7sLAwechS6YV+RNQ9MY8h\nIn9jPkNEPiEUcvHiRREVFSWqq6uF0WgUCQkJ4ptvvrGb5pVXXhGLFi0SQgixadMmMXPmTCGEEP/6\n179EQkKCMJlMoqqqSkRFRQmj0SiEEGL48OGduyFEFJCYxxCRvzGfISJfCVEqGCwpKUFsbCyio6MB\nALNmzUJ+fj6SkpLkabZv347c3FwAwOzZs7Fw4UJYLBbk5+djzpw5CAkJwbBhwxATE4PS0lJcd911\nHqeDL9AjCizCRwPCMo8hImd8lccAzGeIyDlv8hnFui/W1NRgyJAh8t9RUVGoqalxOU1oaCj69u2L\n06dPo7a2FoMHD3Y676lTp5CSkoLU1FRs2bKlE7aEiAIR8xgi8jfmM0TkK4q1lPmrVqe6uhqRkZH4\n73//iylTpiA+Ph6jRo1qdz5f1pz5g0qlCvg0+hq3uXuQttnXeUJ3y2OUOHe4Tq4zmNbrjzwhEPOZ\nYL2PBGO6mebOEUxp7kieoFhLWVRUFGpra+W/a2pqMHTo0FbTSLVGJpMJer0ekZGRreatra1FVFQU\nACAyMhIAEBsbi8mTJ2P//v3+3hQiCkDMY4jI35jPEJGvKBaUpaWloaKiAtXV1TAajdi6dSumT59u\nN01mZiY2btwIANi8eTMyMjKgVquRmZmJLVu2wGQyoaqqChUVFUhPT4fBYIDRaARgbfovLi7G6NGj\nO33biEh5zGOIyN+YzxCRryjWfTE8PBxr165FVlYWzGYz7r33XiQnJ2Pp0qVITU3FjBkz8Oijj+Ke\ne+6BRqNBnz595EwtJSUF2dnZSEhIgFqtxrp16xAaGopvv/0W9913HywWC5qamvD4449j7NixSm0i\nESmIeQwR+RvzGSLyFZUIlk6afiL1/Qz03RBM/Wl9hdvcPTg+U9bVtr+ztqu7PIPEdXatdXbmertq\nHgPYb1uw3keCMd1Mc+cIpjR3JJ9R9OXRRARotVpotVqlk0FERERECmFQRkREREREpCAGZUFi6dKl\nSieh03WnbdbpdNBqtd1qmyXdcZv9QYn9yHVyncG83q4qWPdnMKabae4cwZhmb/CZsi7cx5yCg1ar\nhU6nQ2JiIgoKCpROjmK66rXYVbeLKNh05WuxK28bUTDhM2VERERERERBikEZERERERGRghiUERER\nERERKYhBGVE3wyH4iYiIiAILgzKiAMXgiYiIiKh7YFBGRERERESkIAZlRERERERECmJQRkRERERE\npCAGZURdCJ9DIyIiIgo+DMqIiIiIiIgUxKCMiIiIiIhIQQzKiIiIiIiIFKRYULZjxw5oNBrExcVh\nxYoVrX5vbm5GdnY2NBoNJk6ciOrqavm35cuXIy4uDhqNBjt37rSbz2KxIC0tjc/VEBHzGSLyK+Yx\nROQrigRlzc3NWLBgAbZv346DBw9i48aNKCsrs5tm1apV6N+/P8rLy7F48WIsXrwYALB//37k5eXh\n0KFDyM/Px4MPPgij0Wg3X2xsLFQqVaduE3VPHFgjcDGfISJ/Yh5DRL6kSFBWUlKC2NhYREdHIzQ0\nFLNmzUJ+fr7dNNu3b8e8efMAALNnz0ZBQQEsFgvy8/MxZ84chISEYNiwYYiJiUFpaSkA4Pjx49i2\nbRsWLFgAIUSnbxcRBQ7mM0TkT8xjiMiXFAnKampqMGTIEPnvqKgo1NTUuJwmNDQUffv2xenTp1Fb\nW4vBgwfbzVtbWwsAWLJkCf7whz+gRw8+KkfU3TGfISJ/Yh5DRL6kyBXv6+Z4IQR27NiBvn37IiUl\nxauaJZVK5fKTm5vr0/QSdVe5ubkurzNfC7R8hnkMkf915zxGShPzGQpEXelxD3/lM4oEZbY1QoC1\nJmno0KGtppFqnEwmE/R6PSIjI1vNW1tbi6ioKOzduxf5+fkYMWIE5s6di+LiYtx6661up0kI4fLD\njIzIN3Jzc11eZ74WaPkM8xgi/+vOeQzAfIYCk1arhU6nUzoZPuOvfEaRoCwtLQ0VFRWorq6G0WjE\n1q1bMX36dLtpMjMzsXHjRgDA5s2bkZGRAbVajczMTGzZsgUmkwlVVVWoqKjAuHHj8Lvf/Q7Hjh3D\nkSNHkJeXh/Hjx+Pjjz9WYvOIKAAwnyEif2IeQ0S+FKLESsPDw7F27VpkZWXBbDbj3nvvRXJyMpYu\nXYrU1FTMmDEDjz76KO655x5oNBr06dNHztRSUlKQnZ2NhIQEqNVqrFu3DqGhoXbLF0JwxCKibo75\nDBH5E/MYIvIllejmQ/tIGV433w3kJal/dEFBQYeWodPpkJiYaLccb5btzjy+SLM/dNVrsatuF1Gw\n6crXYlfeNgp+rso5wcad8lNHrkUO7UPkQld6KJWIiIiIAheDMiIiIiIiIgUxKCMiIiIiIlIQgzIi\nIiIiIiIFeT364po1a1BbWwu1Wo2lS5f6Mk1ERERERETdhtdB2ZYtW7Bz505fpoWIiIiIiIJYoI7y\nHOi87r44fPhw5OTk4He/+50v00NERERERNSteNVSduDAAdx1112+TgsREREREVG3w4E+iIiIiIiI\nFORVS9nYsWOxcOFCREZG4vTp05g6daqv00VERERERNQteN1SZjab8dRTT8FisfgyPURERERERN2K\nV0HZsmXLEB0djZUrVyI6OtrXaSIiIiIioi7CYDBAp9MpnYyA5lX3Rb6XjIiIiIiIyDe8ainbuXMn\nzGazr9NCRERERETU7XjVUjZy5EisX78eRqMRgwcPxm233ebrdBEREREREXULXgdlI0eOBACcPHnS\npwkiIiIiIiLqTjr8nrKrr77aF+kgIj/RarXQarV23+l0ulbfEREREZEyvA7Knn/+eTzzzDN47rnn\nvF75jh07oNFoEBcXhxUrVrT6vbm5GdnZ2dBoNJg4cSKqq6vl35YvX464uDhoNBrs3LlT/n7atGlI\nSkrCqFGjkJ2djfPnz3udPiIKbsxjiMjfmM8QkS94HZQdOnQIL774IsrLy72av7m5GQsWLMD27dtx\n8OBBbNy4EWVlZXbTrFq1Cv3790d5eTkWL16MxYsXAwD279+PvLw8HDp0CPn5+XjwwQdhMpkAAH//\n+99RVlaGiooKmM1mrF+/3ttNJKIgxjyGiPyN+QwR+YrXQVlsbCwef/xxjBo1CsuWLfN4/pKSEsTG\nxiI6OhqhoaGYNWsW8vPz7abZvn075s2bBwCYPXs2CgoKYLFYkJ+fjzlz5iAkJATDhg1DTEwMSkpK\nAABXXHEFAMBkMsFoNGLIkCHebiIRBTHmMUTkb8xniMhXvA7KtFotwsLCoNVqvXpvWU1NjV0mExUV\nhZqaGpfThIaGom/fvjh9+jRqa2sxePBgl/NmZmZi4MCBCAsLw6xZs9xKj0qlcvnJzc31ePuIqLXc\n3FyX15mvMY8h6n46M48BmM8QdUf+yme8DsqWL1+OF198ES+99JJX8/s6g7Rd3vbt23H8+HEYDAZs\n2LDBrfmFEC4/XT0jczYQBJE/5ObmurzOfI15DFH305l5DMB8hqg78lc+43VQdv311+OZZ57BDTfc\n4NX8UVFRqK2tlf+uqanB0KFDW00j1RqZTCbo9XpERka2mre2thZRUVF284aHh+OWW25BcXGxV+kj\nIu8FQqDPPIaI/I35DBH5itdBmV6vx4svvoiUlBSv5k9LS0NFRQWqq6thNBqxdetWTJ8+3W6azMxM\nbNy4EQCwefNmZGRkQK1WIzMzE1u2bIHJZEJVVRUqKiqQnp6OxsZGnD17FoA149u+fTvi4+O93UQi\nCmLMY4jI35jPEJGvePXy6PXr1+Pjjz9Gv379EBISgp///OceLyM8PBxr165FVlYWzGYz7r33XiQn\nJ2Pp0qVITU3FjBkz8Oijj+Kee+6BRqNBnz595EwtJSUF2dnZSEhIgFqtxrp16xAaGorjx49j1qxZ\naGlpQVNTE6ZPn45FixZ5s4lEncpgMECn0/lseREREQCAc+fO+WyZwYZ5DBH5G/MZosAi9dIpKChQ\nOCWeUwkvO0Du3bsXISEh+Oc//4lf/OIXvk5Xp5H6b/urv3kwCOYT2J/c2S++2HdarRZFRUXo3bu3\nXRDlzbKleaQA79y5c62Wo9VqodPpkJiY6Ldj7k3au+q12FW3iyjYdOVrsStvGwUfZ+UOZ+Wczlh3\nZy+7I9dih14effLkSWzdutXbRRAREREREXV7XgdlCxcuRGxsbFC3khERERERESnNq2fKAOCOO+4A\nAMTFxfksMURERERERN2NVy1lL7/8sq/TQQEoEIY1JyIiIiLq6rxqKdu0aRPGjx8vP8Q2depUnyaK\nqCvjwCpEREREZMvrZ8o4wg8RERERETnS6XQB2dsqkHuBeRWU3XfffejVqxcmTpwov6WeiMhdgZwp\nEhEREXU2r4Kyjz/+GCdOnEB6ejpSU1N9nSYiIiIiIqJuw6ug7OTJk2hoaEBTUxNKSkp8nSYiIiIi\nIr9irw0KJF4N9LFjxw6oVCpMnTpVfnM1ERERERERec6roGz48OE+TgYREREREVH35FVQtnv3bvn/\nKpUKU6ZM8VmCiIiIiIgoePB1Px3n1TNlQgisXLkSjY2NWLlypa/TRJewrzMRERGRf7G8RYHAq6As\nIyMDer0eANDQ0ODTBBEREREREXUnXr88etu2bbjqqqvwySef+DI9QYE1KkRERERE9rRaLXQ6ndLJ\nCEpePVMGAPPmzUNdXR2GDh2KDz/80JdpIiIiIiIicqmrPcfmdUtZWFgYmpqaYLFYvF75jh07oNFo\nEBcXhxUrVrT6vbm5GdnZ2dBoNJg4cSKqq6vl35YvX464uDhoNBrs3LkTgPX9aRkZGYiPj8eoUaOQ\nm5vrddqIKPgxjyEif2M+Q0S+4HVL2ZIlSxAaGgqTyeTV/M3NzViwYAH27NmDQYMGITU1FTfddBOS\nkpLkaVatWoX+/fujvLwceXl5WLx4Mf7xj39g//79yMvLw6FDh1BbW4tJkybhhx9+QEhICN544w3E\nx8fjwoULSE5Oxs9+9jNMmjTJ283stqTm58TERKWTQl1QZ3RtYB5DRP7GfCYwdbUWFOoevG4pe+CB\nB/DDDz/g2Wef9Wr+kpISxMbGIjo6GqGhoZg1axby8/Ptptm+fTvmzZsHAJg9ezYKCgpgsViQn5+P\nOXPmICQkBMOGDUNMTAxKS0tx1VVXIT4+HgBwxRVXQKPR4OTJk95uIlFA47ONbWMeQ0T+xnyGyD1m\nsxkGg0HpZAQ0r4OypKQkjBgxAhUVFV7NX1NTgyFDhsh/R0VFoaamxuU0oaGh6Nu3L06fPo3a2loM\nHjy4zXmrqqpQXFyMG264wav0dQcs1FNXxjyGiPyN+Qx5i2WwwBBIx8GroOzAgQNYtGgRLl68iPff\nf9+rFatUKq/mc2d5Fy5cwB133IHXX38dERERbs/v6tPV+3PrdDqOlOMHgXSht6Uz05mbmwuVSoWG\nhgY0NDTYXWe+xjyGqPuR8hhnH39gPkPUtq44GqO/8hmvnynrqKioKNTW1sp/19TUYOjQoa2mqamp\nwTXXXAOTyQS9Xo/IyMhW89bW1iIqKgqAtXk0Ozsbd955J2bNmuV2eoQQHdwioss645k8dzO5QMoM\nc3NzkZubKxcwzp07J//m68IN8xii7kfKY5zxR2DGfIYosHRGmcdf+YxXLWUff/wxCgsL5Y830tLS\nUFFRgerqahiNRmzduhXTp0+3myYzMxMbN24EAGzevBkZGRlQq9XIzMzEli1bYDKZUFVVhYqKCqSn\npwMAFixYgBEjRuCxxx7zKl0UPIKlJYqUwTyGiPyN+QxRYDEYDEH77JpXLWVLly5FfX09/v3vf3s9\nJH54eDjWrl2LrKwsmM1m3HvvvUhOTsbSpUuRmpqKGTNm4NFHH8U999wDjUaDPn36yJlaSkoKsrOz\nkZCQALVajXXr1iE0NBR79uzBO++8g4SEBHnkoxdeeAE///nPPU4fR+4hJXH0y44L9DyGiIIf8xki\n8hWvuy/OmTMH9fX1GDNmDKZOnerVMqZPn96qRmnZsmXy/3v27OnyxdRPP/00nn76abvvrrvuug69\nN42IuhbmMUTkb8xniC7T6XQwGAzo3bu30kkJOl4HZfHx8Th+/DiOHj3qy/RQAOjshzI7s1WSLaBE\nREREFGi8DspycnJQUVGBH3/80ZfpIaIOCKRBPYiIiJTGylgKFl6/p+yWW25BS0sLmyeJXDAYDEEf\nJHEwFSIiClS8R1FX4nVQlpWVhS+++MLr0ReJujKdTgez2ez29HzTPREREZHvOAvadTodioqKAjKY\n97r74ldffYWdO3eiRw+v4zryoZycHBQXF2P8+PFYvny50snpktgFgoiIiIj8weugLCYmBs888wzC\nw8OxdOlSX6aJvFBcXAzdSR1Q3P607tYOdIXud77EfUFERERE/uBVUCaEwPnz57F69Wpfp4fawJYa\nZXVW98LOHv2SiIiIyJekin2+b9V9XgVlKpUKPXv2xFNPPYVevXqxpYzIC4HYn5mIiIjIF9jjyjNe\nPxD2i1/8Av3798ctt9ziy/QQBR2dTtctAyyOekVERESAtSykVADmy/KIkmUbr58p+9WvfoVPP/0U\nWVlZ+Prrr32ZJqKgZzAYIIRQOhkdxhouIiIiIv/zOigbPXo0VqxYAY1G48v0BD0+90XBrK3zl+c2\nERF1JV2h4pH3Ztc66/j66hh4FZTt3r0bc+fO7dCKg53UZY0XAfmDXq/32bL4/jMiIuruPC04M9ih\nzub1M2VmsxkffPAB3n77bV+mhwIQnx2yMpvNHr0QurPw+BCRr+Xk5ECr1SInJ0fppBDJ/HW/MxgM\nrMAkxXnVUvb999/jzJkzWLJkCUaOHOnrNJHCdDodMyciom6suLgY0OncefUlEVG3kpOTg+LiYowf\nPx7Lly/32XK9CsqOHTsGlUqF9957DwA4JD75RFfsKuBucCuECMhWOCIiIiK6rLi4GNbH1XxbbeVV\nUJabm+vTRASqQHxuLBDTRMEhkLs4sosUERH5U7AO6hFsFdYGgwFmsxlqtVrppAQdr58po+6LzzAp\nT6vV+qSLqVar7dCNqrKy0qvnThz77xcXs5MUERFRV6HEc/harTaoex0pGpTt2LEDGo0GcXFxWLFi\nRavfm5ubkZ2dDY1Gg4kTJ6K6ulr+bfny5YiLi4NGo8HOnTvl7x944AEMHDgQI0aM6HD6nL2JvKOF\nWApMDDS909jYCN1JXYeDKn9dU4GexxBR8GM+Q6Q825dXm81mufLasQJbaskLRIoFZc3NzViwYAG2\nb9+OgwcPYuPGjSgrK7ObZtWqVejfvz/Ky8uxePFiLF68GACwf/9+5OXl4dChQ8jPz8eDDz4Ik8kE\nALj//vuxY8eOTt+erqqpqQk6nQ6VlZVKJ4X8oCsP6sI8hoj8jfkMkfK8Lct0tKFFeqTINiDsCMWC\nspKSEsTGxiI6OhqhoaGYNWsW8vPz7abZvn075s2bBwCYPXs2CgoKYLFYkJ+fjzlz5iAkJATDhg1D\nTEwMSkpKAACTJ09GREREp2+PxFcHxlZbrTj+buExm80wGA1obGz02zqCma+DmrYyiEAZojpYWhW7\nah5DRIGD+YyyOrvM5Y9lBcs9NVgIISCE8Pt6DAbfV2orFpTV1NRgyJAh8t9RUVGoqalxOU1oaCj6\n9u2L06dPo7a2FoMHD25zXk+pVCq7T2FhIVpaWtDQ0OCTgU140XVNndkMXlxc7FVXQalftz9bO6Xa\novbk5uZCpVKhpaUFLS0t8vXW0NDg8zQFeh5j++kugycR+ZuUxzj7+ENXyGe6a/nEV89mk3uEEGhp\naenUdTo+1+arZ86qqqrQ0tKAlpYWFBYWoqGhAQ0NDR3OZxQLynydQXZ0eVJkLX0yMjIQEhKCfv36\ntVlg6q6ZWVcRiMfPYDB0uJbHsR+1GCgCorUzNzcXQgiEhIRApVIhIyMDQgj069fP5+sK9DzG9sOg\njMg3pDzG2ccfmM8oQ+o50tTUpHRSyAVX112glbm8MXz4cISE9JPjBOnfjuYzigVlUVFRqK2tlf+u\nqanB0KFDW00j1RqZTCbo9XpERka2mre2thZRUVF+TzMH+VCeEkFUe+sUQnTp88JgMPh0n3fW6EjB\nmMcQUXBhPqMM6T1RHbmXFBUVdXrLTVdmWxncVnDiqrwUiJXknU2xoCwtLQ0VFRWorq6G0WjE1q1b\nMX36dLtpMjMzsXHjRgDA5s2bkZGRAbVajczMTGzZsgUmkwlVVVWoqKhAenq6EpvRirN1b1D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eHh8jyOXRIlzgYPaSvQcZUGqStjUVERzOrAKQQGKtF4uendm26WLS0t2LdvHwD7YNu2tqnF7HCs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\nc2Fra4uAg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"text": [ "<matplotlib.figure.Figure at 0x2aaabde206d0>" ] } ], "prompt_number": 125 }, { "cell_type": "code", "collapsed": false, "input": [ "def getBindingFrac(type_specific):\n", " # 5' position on the negative strand is snoRNA stop coordinate.\n", " neg_data=type_specific[type_specific['strand_snoRNA']=='-']\n", " neg_data['diff']=np.abs(neg_data['Stop_snoRNA']-neg_data['Start']) \n", " neg_data['frac']=neg_data['diff']/(neg_data['Stop_snoRNA']-neg_data['Start_snoRNA'])\n", " # 5' position on the positive strand is snoRNA start coordinate.\n", " pos_data=type_specific[type_specific['strand_snoRNA']=='+']\n", " pos_data['diff']=np.abs(pos_data['Start_snoRNA']-pos_data['Start'])\n", " pos_data['frac']=pos_data['diff']/(pos_data['Stop_snoRNA']-pos_data['Start_snoRNA'])\n", " DF_snoProfile=pd.concat([neg_data,pos_data])\n", " return DF_snoProfile\n", "\n", "print \"snoRNA gene body anaysis.\"\n", "# logOpen.write(\"Gene body analysis.\\n\")\n", "bf_sno=pd.read_table(outfilepath+\"clipGenes_snoRNA_LowFDRreads.bed\",header=None)\n", "bf_sno.columns=['Chr','Start','End','CLIPper_name','Q','Strand','Chr_snoRNA','Start_snoRNA','Stop_snoRNA','name_snoRNA','Type','strand_snoRNA']\n", "snoTypes=pd.DataFrame(bf_sno.groupby('Type').size())\n", "snoTypes.columns=['Reads']\n", "snoTypes['Fraction']=snoTypes['Reads']/snoTypes['Reads'].sum(axis=1)\n", "outfilepathToSave=outfilepath+'/PlotData_readsPerSnoRNAType'\n", "snoTypes.to_csv(outfilepathToSave)\n", "\n", "fig5=plt.figure(5)\n", "ax=plt.subplot(2,2,1)\n", "pie_wedges=ax.pie(snoTypes['Fraction'],labels=snoTypes.index,labeldistance=1.1,autopct='%1.1f%%')\n", "plt.rcParams['font.size']=5\n", "for wedge in pie_wedges[0]:\n", " wedge.set_edgecolor('black')\n", " wedge.set_lw(1)\n", "\n", "i=2\n", "for sType in set(bf_sno['Type']):\n", " type_specific=bf_sno[bf_sno['Type']==sType]\n", " sno_profile=getBindingFrac(type_specific)\n", " \n", " if sType=='C':\n", " title=\"C/D_box\"\n", " elif sType=='H':\n", " title=\"H/ACA_box\"\n", " else:\n", " title=\"scaRNA\"\n", " \n", " outfilepathToSave=outfilepath+'/PlotData_snoRNAReadDist_%s'%sType\n", " sno_profile.to_csv(outfilepathToSave)\n", " \n", " plt.subplot(2,2,i)\n", " bins=np.arange(0,1,0.01)\n", " hist,bins=np.histogram(sno_profile['frac'],bins=bins)\n", " hist=np.array(hist/float(sno_profile['frac'].shape[0]),dtype=float)\n", " width=0.7*(bins[1]-bins[0])\n", " center=(bins[:-1] + bins[1:])/2\n", " plt.bar(center,hist,align='center',width=width,color='blue',alpha=0.75)\n", " plt.tick_params(axis='x',labelsize=5) \n", " plt.tick_params(axis='y',labelsize=5) \n", " plt.xlabel('Fraction of gene body (5p - 3p)',fontsize=5)\n", " plt.title('Binding profile for %s'%title,fontsize=5)\n", " i+=1\n", "\n", "fig5.tight_layout()\n", "fig5.savefig(outfilepath+'Figure5.png',format='png',bbox_inches='tight',dpi=150,pad_inches=0.5)\n", "fig5.savefig(outfilepath+'Figure5.pdf',format='pdf',bbox_inches='tight',dpi=150,pad_inches=0.5)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "snoRNA gene body anaysis.\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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ZGWnZOYqI1FQ2w5nj6XJBaWlphIWF8fPPP2NOimO2VxMRgF+BW4BNtGnThg0b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\nuDz88MNy4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"text": [ "<matplotlib.figure.Figure at 0x2aaca7da9290>" ] } ], "prompt_number": "*" }, { "cell_type": "code", "collapsed": false, "input": [ "def getncRNABindingFrac(type_specific):\n", " # 5' position on the negative strand is snoRNA stop coordinate.\n", " neg_data=type_specific[type_specific['Strand']=='-']\n", " neg_data['diff']=np.abs(neg_data['Gene End (bp)']-neg_data['RT_stop']) \n", " neg_data['frac']=neg_data['diff']/(neg_data['Gene End (bp)']-neg_data['Gene Start (bp)'])\n", " # 5' position on the positive strand is snoRNA start coordinate.\n", " pos_data=type_specific[type_specific['Strand']=='+']\n", " pos_data['diff']=np.abs(pos_data['Gene Start (bp)']-pos_data['RT_stop'])\n", " pos_data['frac']=pos_data['diff']/(pos_data['Gene End (bp)']-pos_data['Gene Start (bp)'])\n", " DF_ncRNAProfile=pd.concat([neg_data,pos_data])\n", " return DF_ncRNAProfile\n", "\n", "print \"ncRNA gene body anaysis.\"\n", "st_stopFiles=glob.glob(outfilepath+\"*.geneStartStop\")\n", "st_stopFiles=[f for f in st_stopFiles if 'rRNA' not in f]\n", "fig6=plt.figure(6)\n", "plotDim=math.ceil(math.sqrt(len(st_stopFiles)))\n", "i=1\n", "for st_file in st_stopFiles:\n", " name=st_file.split('clipGenes_')[1].split('_LowFDRreads')[0]\n", " tmp=pd.read_csv(st_file)\n", " tmp['RT_stop']=tmp['Start']+expand\n", " tmp_profile=getncRNABindingFrac(tmp)\n", " plt.subplot(plotDim,plotDim,i)\n", " bins=np.arange(0,1,0.01)\n", " hist,bins=np.histogram(tmp_profile['frac'],bins=bins)\n", " hist=np.array(hist/float(tmp_profile['frac'].shape[0]),dtype=float)\n", " width=0.7*(bins[1]-bins[0])\n", " center=(bins[:-1] + bins[1:])/2\n", " plt.bar(center,hist,align='center',width=width,color='blue',alpha=0.75)\n", " plt.tick_params(axis='x',labelsize=5) \n", " plt.tick_params(axis='y',labelsize=5) \n", " plt.xlabel('Fraction of gene body (5p - 3p)',fontsize=5)\n", " plt.title('Binding profile for %s'%name,fontsize=5)\n", " i+=1\n", "fig6.tight_layout()\n", "fig6.savefig(outfilepath+'Figure6.png',format='png',bbox_inches='tight',dpi=150,pad_inches=0.5)\n", "fig6.savefig(outfilepath+'Figure6.pdf',format='pdf',bbox_inches='tight',dpi=150,pad_inches=0.5)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "ncRNA gene body anaysis.\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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VFZKSktDW1gYHBwf87W9/67GcnHvzEBH1hbmIiIyNeYasDXvOiIiIiIiILAAXBCEiIiIi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\n/YaM28d4wRrTbVH3QFvd/Nvcb9+SRqal5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"text": [ "<matplotlib.figure.Figure at 0x345e57d0>" ] } ], "prompt_number": 141 }, { "cell_type": "code", "collapsed": false, "input": [ "logOpen.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 142 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-2.0
dipanjank/ml
tensorflow/ANN_iris_using_tensorflow.ipynb
1
157733
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Get the Data" ] }, { "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", "pylab.style.use('ggplot')\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sepal_length</th>\n", " <th>sepal_width</th>\n", " <th>petal_length</th>\n", " <th>petal_width</th>\n", " <th>species</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')\n", "data_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Make the 'species' Column Categorical" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_df = data_df.assign(species=data_df.species.astype('category'))" ] }, { "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>sepal_length</th>\n", " <th>sepal_width</th>\n", " <th>petal_length</th>\n", " <th>petal_width</th>\n", " <th>species</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_df.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x148ad270940>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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v3xfJs2aPhcu2tiG1sADqihtgBJzfDSllc9zq4Hkl8QyfK4sPH/98vP8vm4Ph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\n586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"text/plain": [ "<matplotlib.figure.Figure at 0x148aec7b5f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "sns.pairplot(data_df, hue='species')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Shuffle the Columns and Split Train and Test Data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.shuffle(data_df.values)\n", "test_df = data_df.groupby(by='species').head(10)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "virginica 40\n", "versicolor 40\n", "setosa 40\n", "Name: species, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df = data_df.iloc[data_df.index.difference(test_df.index)]\n", "train_df.species.value_counts()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Helper function to make feed dict\n", "\n", "def make_feed_dict(X, y, df):\n", " features = df.drop('species', axis=1).values\n", " labels = df.species.cat.codes\n", " labels_2d = np.atleast_2d(labels).T\n", " \n", " return {X: features, y: labels_2d}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Set up TensorFlow Layers and Train" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "code_folding": [], "collapsed": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "69835ff673e64d40bd2f78c810efb7dd" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tf.reset_default_graph()\n", "\n", "# Shape of X: n_training_rows * 4 cols\n", "# Shape of y:n_training_rows * 1 col \n", "with tf.variable_scope('input'):\n", " X = tf.placeholder(name='X', shape=(None, 4), dtype=np.float64)\n", " y = tf.placeholder(name='y', shape=(None, 1), dtype=np.int64)\n", "\n", "# Shape of w: 4 rows (n_cols from last layer) * 3 cols (n_classes) \n", "with tf.variable_scope('hidden'):\n", " w = tf.get_variable(name='w', \n", " shape=(4, 3), \n", " initializer=tf.truncated_normal_initializer(),\n", " dtype=np.float64)\n", " \n", " b = tf.get_variable(name='b', \n", " shape=1, \n", " initializer=tf.constant_initializer(1.0),\n", " dtype=np.float64)\n", " \n", " hidden = tf.add(tf.matmul(X, w), b, name='hidden')\n", " \n", "# The softmax layer calculates cross-entropy error \n", "# between the softmaxed output of the hidden layer \n", "# and the one-hot encoded train labels\n", "with tf.variable_scope('cross_entropy'):\n", " one_hot = tf.one_hot(indices=y, depth=3, name='one_hot')\n", " \n", " x_ent = tf.nn.softmax_cross_entropy_with_logits(\n", " labels=one_hot, \n", " name='cross_entropy_error', \n", " logits=hidden)\n", " \n", " loss_function = tf.reduce_mean(x_ent, name='loss')\n", "\n", "with tf.variable_scope('train'):\n", " optimizer = tf.train.AdamOptimizer(learning_rate=0.02)\n", " train_op = optimizer.minimize(loss_function)\n", " \n", "n_iter = 1000\n", "init_op = tf.global_variables_initializer()\n", "loss_values = np.zeros(n_iter)\n", "\n", "from IPython.display import display\n", "from ipywidgets import FloatProgress\n", "\n", "progress = FloatProgress(min=0, max=n_iter, description='Running training loop..')\n", "display(progress)\n", "\n", "with tf.Session() as sess:\n", " sess.run(init_op)\n", "\n", " train_dict = make_feed_dict(X, y, train_df)\n", " for i in range(1, n_iter+1): \n", " _, current_loss = sess.run([train_op, loss_function], feed_dict=train_dict) \n", " loss_values[i-1] = current_loss\n", " \n", " progress.value += 1\n", " \n", " progress.bar_style = 'Success'\n", " progress.description = 'Training complete.'\n", " \n", " # Evaluate with test data\n", " test_dict = make_feed_dict(X, y, test_df)\n", " test_preds = tf.argmax(tf.nn.softmax(hidden), axis=1, name='test_preds')\n", " test_results = sess.run(test_preds, feed_dict=test_dict)\n", " " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.text.Text at 0x148b2e92a20>,\n", " <matplotlib.text.Text at 0x148b3423208>]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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iIqo1r4GflJSEjz76CH369IFarfYsD+aT+BpHGxEXZcTRc3koKXXAoOO0PhER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\nklI7LKUOFFvtKCm90TG45XO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2xMcffwwAOHv2LIYNG4bS0tI77sNkMiE+Ph67du0C4H4U\ndW5ubrUfcapSqeB0OtGiRQsUFBTg559/BgB8/vnn+POf/3zL9r/++iuaNm2KCRMmoFOnTvj22289\nnQ6VSuUJ8wo9e/bEv//9b5SVlcHhcGDTpk2eGY/btVNKSgpmzJiBxMREzJkzB0aj0WdXERDVRxzh\nE/lB5WP0JpMJCxYswP/+7/8CAFq2bInExEQMHToUcXFxVZ5rXfnn7nScXxAEXLx4EcOHD4fJZMLS\npUsBuC9nmzt3LoYNGwYAWL58eZUz7G/nzTffxNy5c/HOO+9Aq9Vi9erVd5xVuFlSUhL++Mc/4u9/\n/zveeecdLFy4EDabrUpNlX+H3r1749NPP8WQIUOg1WrRqVMnnDlzBgDQp08fzJs3r8pMSFJSEk6c\nOIGRI0fC6XSiT58+GDt2LDIzM2/bNomJidi5c6dn/4888gifz06yxsfjEhERyQCn9ImIiGSAgU9E\nRCQDDHwiIiIZYOATERHJAAOfiIhIBhj4REREMsDAJyIikoH/D4VG9h2b5ZosAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x148b3231a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "loss_values = pd.Series(loss_values, name='cross_entropy_loss')\n", "ax = loss_values.rolling(window=20).mean().plot()\n", "ax.set(xlabel='Number of iterations', ylabel='Smoothed cross entropy loss')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2], dtype=int64)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Label codes for the IRIS species\n", "test_results" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "predicted_labels = np.apply_along_axis(lambda idx: data_df.species.cat.categories[idx], 0, test_results)\n", "actual_labels = test_df.species\n", "\n", "labels = pd.unique(data_df.species)\n", "\n", "from sklearn.metrics import confusion_matrix\n", "cm = confusion_matrix(predicted_labels, actual_labels.values, labels=labels)\n", "cm = pd.DataFrame(cm, index=labels, columns=labels)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x148b35ce2e8>" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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C5/bPx22xe/HFFzVhwgRJ0uLFi/Xiiy/6PFQwcVY7f/dxh6OOn5PAV35vN/eqquoAJIG3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\nih0AwPYodgAA2/t/wfWRyltBy8sAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x148b34c1ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.heatmap(cm, annot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Appendix: Cross Entopy Error" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's say at any given time, we have a batch of 5 training examples. Each example contains 4 features for 3 classes (same as in the IRIS problem).\n", "\n", "The output layer of 3 nodes will then produce a $5*3$ matrix." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.91804477, 0.93496251, 0.23093528],\n", " [ 0.6539292 , 0.77752673, 0.86195378],\n", " [ 0.89837069, 0.98221402, 0.40337227],\n", " [ 0.4002789 , 0.58142488, 0.24842138],\n", " [ 0.19059925, 0.78464687, 0.7392134 ]])" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "out = np.random.rand(5, 3)\n", "out" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each row is then softmaxed into a probability distribution:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def naive_softmax(row):\n", " return np.exp(row) / np.sum(np.exp(row))" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "q = np.apply_along_axis(naive_softmax, 1, out)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.39681128, 0.40358154, 0.19960718],\n", " [ 0.2973709 , 0.33649313, 0.36613597],\n", " [ 0.37077817, 0.40320588, 0.22601595],\n", " [ 0.32704312, 0.39199065, 0.28096623],\n", " [ 0.22015968, 0.39877635, 0.38106397]])" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the model predicted distribution." ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1., 1., 1., 1., 1.])" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(q, axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we also have 5 1-hot encoded training labels, corresponding to 3 classes. This is true distribution." ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p = np.array([\n", " [1, 0, 0],\n", " [1, 0, 0],\n", " [0, 0, 1],\n", " [0, 1, 0],\n", " [0, 0, 1],\n", "])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cross entropy error for example 1 is \n", "\n", "$- \\sum_i p_i \\space ln \\space (q_i)$" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.92429447200507864" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-np.sum(p[0, :] * np.log(q[0, :]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our goal is to build a model that's as close to the true distribution as possible. In that case, the value of q (for the 1st example) would be something like:" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ideal_q1 = np.array([1 - 2E-5, 1E-5, 1E-5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the corresponding cross-entropy error is:" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.0000200002686709e-05" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-np.sum(p[0, :] * np.log(ideal_q1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In other words, minimizing the cross-entropy error leads to a model that closely approximates the true distribution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we have a number of training examples, we calculate the cross entropy error of each sample and choose to minimize the mean, as in the case of MSE for regression problems." ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.1051049173025631" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xent_sum = -np.sum(p * np.apply_along_axis(np.log, 1, q), axis=1)\n", "np.mean(xent_sum)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
DaveBackus/Data_Bootcamp
Projects/Summer_15/Employment-Population-Ratio_DavidCai_Jul_15.ipynb
2
162314
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#The Rise and Fall of the US Employment-Population Ratio\n", "\n", "A research project at NYU's Stern School of Business. \n", "Written by David Cai ([email protected]) under the direction of David Backus, July 2015. \n", "\n", "## Abstract\n", "\n", "After the Great Recession, while the unemployment rate has almost returned to pre-2007 levels, the employment-population ratio has not made a similar recovery. I explore the roles of aging and other effects on employment by examining the labor force participation rate, a similar indicator that is less sensitive to cyclical variation. I also decompose the employment-population ratio into specific demographic groups to explore their contributions to the overall change.\n", "\n", "## The Employment-Population Ratio and the Unemployment Rate\n", "\n", "Historically, for more over two decades from 1989 to 2010, the employment-population ratio has generally moved in line with the unemployment rate, albeit in an inverse direction (Figure 1). However, from 2011 onwards, these two indicators have begun to diverge. Despite the unemployment rate improving to almost pre-recession levels, the employment-population ratio has failed to increase by the same amount. This finding indicates that past 2011, some component of the employment-population ratio other than the unemployment rate must have changed. \n", "\n", "Mathematically, the employment-population ratio can be decomposed into the product of the labor force participation rate and the employment rate of the labor force. Alternatively, the employment-population ratio can be represented as the labor force participation rate multiplied by one minus the unemployment rate. Since the unemployment rate before the crisis has been roughly equal to its level today, the change in the labor force participation rate represents the largest contribution to the decline in the employment-population ratio." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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XU8ZTwY8E7lel3DugygzMVLODyyskJC55VVVdNrRu3VSN+ptAlJrav5A0kDPq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"text/plain": [ "<matplotlib.figure.Figure at 0x4378ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"\"\"\n", "Creates a figure using FRED data\n", "Uses pandas Remote Data Access API\n", "Documentation can be found at http://pandas.pydata.org/pandas-docs/stable/remote_data.html\n", "\"\"\"\n", "\n", "%matplotlib inline\n", "import pandas as pd\n", "import pandas.io.data as web\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import datetime as dt\n", "from dateutil.relativedelta import relativedelta\n", "\n", "start, end = dt.datetime(1989, 1, 1), dt.datetime(2015, 6, 1) # Set the date range of the data\n", "data = web.DataReader(['EMRATIO', 'UNRATE', 'USREC'],'fred', start, end) # Choose data series you wish to download\n", "data.columns = ['Empl Pop Ratio', 'Unemployment Rate', 'Recession'] \n", "plt.figure(figsize=plt.figaspect(0.5))\n", "\n", "data['Empl Pop Ratio'].plot()\n", "plt.xlabel('')\n", "plt.text(dt.datetime(1990, 1, 1), 64.25, 'Employment-', fontsize=11, weight='bold')\n", "plt.text(dt.datetime(1990, 1, 1), 63.75, 'Population Ratio', fontsize=11, weight='bold')\n", "\n", "data['Unemployment Rate'].plot(secondary_y=True, color = 'r')\n", "plt.text(dt.datetime(1990, 1, 1), 4, 'Unemployment Rate', fontsize=11, weight='bold')\n", "\n", "def get_recession_months():\n", " rec_dates = data['Recession']\n", " one_vals = np.where(rec_dates == 1) \n", " rec_startind = rec_dates.index[one_vals]\n", " return rec_startind\n", "\n", "def shade_recession(dates):\n", " for date in dates:\n", " plt.axvspan(date, date+relativedelta(months=+1), color='gray', alpha=0.1, lw=0)\n", " \n", "shade_recession(get_recession_months())\n", "\n", "plt.suptitle('Figure 1. Employment-Population Ratio and Unemployment, 1989-2015', fontsize=12, weight='bold')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Source: Figure created using data from the Bureau of Labor Statistics (BLS) accessed through the Federal Reserve Economic Data (FRED). This graph is updated from Moffitt (2012)’s Figure 2. Recession data is from NBER accessed through FRED." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Labor Force Participation\n", "\n", "Since 1976, the labor force participation rate has trended upwards until hitting a peak around 2000 (Figure 2). Aaronson et al. (2006) note that this trend can be extended back to the early 1960s, with labor force participation rising from less than 60 percent its peak of 67.3 percent in 2000. After 2000, a reversal of the previous trend emerged, with a new trend of labor force decline until today. Aaronson et al. point out that a prolonged decline in labor force participation is unprecedented in the postwar era, thus leading observers to wonder if long-term structural changes in the labor market have occurred.\n", "\n", "After the publication of the 2006 paper, the labor force participation rate has continued to fall. Revisiting this issue, Aaronson et al. (2014) examine the decline in labor force participation from 2007 onwards. They attempt to break down the factors contributing to this decline into structural and cyclical components. The authors find that 1.3 percent, or nearly one half, of the 2.8 percent decline in the aggregate participation rate can be attributable population aging. Moreover, they note the contributions of declines in specific age/sex categories, such as among youth and adult men. Finally, they discover the existence of a cyclical component; however, its magnitude is much more uncertain. Of these three components, population aging represents the largest contributor to the labor force participation decline." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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+2td7/zjCYJv8jDoLCPOANyIa6f2AZcBBeZsLtUCfovwWbjFyfuXMtnD7QMEW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\nFuDI+HtBneVxHMdxnLpQb4O7PXAvsEdcHgX8Fdi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"text/plain": [ "<matplotlib.figure.Figure at 0x7a69a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start, end = dt.datetime(1976, 1, 1), dt.datetime(2015, 3, 1)\n", "data = web.DataReader(['CIVPART', 'USREC'], 'fred', start, end)\n", "data.columns = ['LFPR', 'Recession']\n", "plt.figure(figsize=plt.figaspect(0.5))\n", "data['LFPR'].plot(color = 'k')\n", "plt.xlabel('')\n", "shade_recession(get_recession_months())\n", "plt.suptitle('Figure 2. Labor Force Participation Rate, 1976-2015', fontsize=12, fontweight='bold')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Source: Figure created using data from the Bureau of Labor Statistics (BLS) accessed through the Federal Reserve Economic Data (FRED). This graph is adapted from Aaronson et al. (2014)’s Figure 9. Recession data is from NBER accessed through FRED." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "###Changes in the Age Distribution\n", "\n", "As population aging is the largest contributor to the labor force participation decline, further analysis is necessary to understand its nature. Aaronson et al. (2014) observe that the proportion of the working age population reported as retired in the Current Population Survey (CPS) has increased by more than one percent in 2014 compared to 2007, accounting for the majority of the 1.3 percent effect of aging. The authors argue that this change is the result of a shift of the age distribution of the population, as the leading edge of the baby boom generation reaches age 62. However, on the contrary, within-age participation rates have increased since 2007, making a positive contribution to total labor force participation (Figure 3). Aaronson et al. (2014) make a similar finding, observing that within-age retirement rates have decreased, likely due to changes in social security and pensions, increased education levels, and longer life spans. These same factors can also explain the increase in the within-age participation rates among older cohorts. That said, the most important implication of Figure 3 is that labor force participation rates decrease with age. As the population age distribution shifts towards older ages, overall labor force participation can be expected to decrease." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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yBmDy5Mm7S0tLHwUGrF+//sxdu3bts2XLlg+uXr36hwMHDtxjQJiZM2feCWwD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\nGIZhxBBz0IZhGIYRQ8xBG4ZhGEYM+T/94/jU1WnxOQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x83c6630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#file = '/Users/davidcai/lfpr.csv'\n", "file = 'https://raw.githubusercontent.com/DaveBackus/Data_Bootcamp/master/Code/Projects/lfpr.csv'\n", "df = pd.read_csv(file, index_col=0)\n", "\n", "start, end = dt.datetime(1980, 1, 1), dt.datetime(2010, 1, 1)\n", "data = web.DataReader('USREC', 'fred', start, end)\n", "data.columns=['Recession']\n", "\n", "# Take a simple averages of ratios for men and women\n", "df[\"Age 62\"] = df[[\"M62-64\", \"W62-64\"]].mean(axis=1)\n", "df[\"Age 65\"] = df[[\"M65-69\", \"W65-69\"]].mean(axis=1)\n", "df[\"Age 70\"] = df[[\"M70-74\", \"W70-74\"]].mean(axis=1)\n", "df[\"Age 75\"] = df[[\"M75-79\", \"W75-79\"]].mean(axis=1)\n", "\n", "# Convert years into datetime series\n", "df.index = df.index.astype(str) + \"-1-1\"\n", "df.index = pd.to_datetime(df.index)\n", "plt.figure(figsize=(plt.figaspect(0.5)))\n", "\n", "df[\"Age 62\"].plot()\n", "df[\"Age 65\"].plot()\n", "df[\"Age 70\"].plot()\n", "df[\"Age 75\"].plot()\n", "\n", "plt.text(dt.datetime(2007, 1, 1), 42, 'Age 62', fontsize=11, weight='bold')\n", "plt.text(dt.datetime(2007, 1, 1), 25, 'Age 65', fontsize=11, weight='bold')\n", "plt.text(dt.datetime(2007, 1, 1), 15, 'Age 70', fontsize=11, weight='bold')\n", "plt.text(dt.datetime(2007, 1, 1), 6, 'Age 75', fontsize=11, weight='bold')\n", "\n", "shade_recession(get_recession_months())\n", "\n", "plt.suptitle('Figure 3. Labor Force Participation Rates, By Age, 1980-2010', fontsize=12, fontweight='bold')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Source: Figure created using author's calculations, working on calculations from Leonesio et al. (2012), available at http://www.ssa.gov/policy/docs/ssb/v72n1/v72n1p59-text.html#chart1. Data is originally from Current Population Survey (CPS) monthly files. Recession data is from NBER accessed through FRED.\n", "\n", "Notes: I employ a oversimplification by taking a simple average of male and female participation rates to determine overall participation rates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Demographic Specific Employment Trends\n", "\n", "In addition to examining the contribution of the labor force participation rate in order to explain the decline in the employment-population ratio, an alternative approach is possible. Moffitt (2012) decomposes the aggregate employment-population ratio into contributions from specific demographic groups. After breaking down the overall employment population ratio into ratios for men and women, Moffitt observes different employment trends between the sexes (Figure 4). For men, he notes, on average, the ratio declined from 1970 to 1983, remained constant from 1983 to 2000, and continued to fall from 2000 onwards. For women, the ratio increased from 1970 to 2000 but began to decrease from 2000 onwards. Moffitt observes that men's wages declined from 1999-2007 while women's wages increased over the same period, which may account for differences in their employment trends. Moffitt concludes that while that \"about half of the decline [in participation rate] among men can be explained by declines in wage rates and by changes in nonlabor income and family structure,” the factors contributing to the employment decline among women are less clear. Moreover, after considering other proposed factors as taxes and government transfers, Moffitt finds their contributions insignificant and unlikely to explain the employment decline." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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T6kSoKsJBIvQEHsYU+wYAVTa5HngJYZRsC1Llix13/G3A6tU1zi5zpNNApu1Z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\nF4nKBODkWtTquz/7v3IURy3MI/4glK9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GpssilMZ9GcvOW8KSjvuy736taFWcjmtKgRyL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\nmUAJkI83wEtV+ySd0w6YoKq7l/N9jcfj21V2jVgstsjPPKdK27Ztj6/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"text/plain": [ "<matplotlib.figure.Figure at 0x8cf8be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start, end = dt.datetime(1970, 1, 1), dt.datetime(2015, 3, 1)\n", "data = web.DataReader(['LNS12300001', 'EMRATIO','LNS12300002', 'USREC'], 'fred', start, end)\n", "data.columns=['Men', 'Overall', 'Women', 'Recession']\n", "plt.figure(figsize=plt.figaspect(0.5))\n", "\n", "data[\"Men\"].plot()\n", "data[\"Overall\"].plot()\n", "data[\"Women\"].plot()\n", "plt.xlabel('')\n", "\n", "plt.text(dt.datetime(1971, 1, 1), 71, 'Men', fontsize=11, weight='bold')\n", "plt.text(dt.datetime(1971, 1, 1), 52, 'Overall', fontsize=11, weight='bold')\n", "plt.text(dt.datetime(1971, 1, 1), 37, 'Women', fontsize=11, weight='bold')\n", "\n", "shade_recession(get_recession_months())\n", "\n", "plt.suptitle('Figure 4. Employment Population Ratios, Overall and by Sex, 1970-2015', fontsize=12, fontweight='bold')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Source: Figure created using data from the Bureau of Labor Statistics (BLS) accessed through the Federal Reserve Economic Data (FRED). This graph is updated from Moffitt (2012)’s Figure 1. Recession data is from NBER accessed through FRED." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "Despite ongoing research, the decline in the employment-population ratio is still not well understood. Since the employment-population ratio can be broken down into the product of the labor participation rate and one minus the unemployment rate, a change in the employment-population ratio can be attributed to contributions from either or both of the two components. Six years after the end of the Great Recession, the unemployment rate has almost recovered fully while the employment-population ratio has failed to keep pace. This finding indicates that the majority of the decline in the employment-population ratio since 2007 can be attributed to contributions from changes in the labor force participation rate, which has led researchers to concentrate on this area.\n", "\n", "Studying recent trends in the labor force participation rate, Aaronson et al. (2014) argue that nearly one half of the decline can be attributed to population aging. Of the rest, economists have separated the remaining component into cyclical and residual factors. Mofitt (2012) took an alternate approach in explaining the change in the employment-population ratio by studying employment trends in different age-sex groups. He found that a disproportionate amount of the decline could be attributed to less educated and younger groups for both sexes, but that employment trends and reasons for the decline were different between the sexes. While Moffitt chose not to focus on the labor force participation rate, he provides important contributions to the area of residual effects, where much more research is needed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##References\n", "\n", "Aaronson, S., Cajner, T., Fallick, B., Galbis-Reig, F., Smith, C., & Wascher, W. (2014). Labor Force Participation: Recent Developments and Future Prospects. *Brookings Papers on Economic Activity*, 2014(2), 197-275.\n", "\n", "Aaronson, S., Fallick, B., Figura, A., Pingle, J. F., & Wascher, W. L. (2006). The recent decline in the labor force participation rate and its implications for potential labor supply. *Brookings Papers on Economic Activity*, 2006(1), 69-154.\n", "\n", "Donovan, S. A. (2015). An Overview of the Employment-Population Ratio.\n", "\n", "Leonesio, M. V., Bridges, B., Gesumaria, R., & Del Bene, L. (2012). The increasing labor force participation of older workers and its effect on the income of the aged. *Social security bulletin*, 72(1), 59-77.\n", "Chicago\t\n", "\n", "Moffitt, R. A., DAVIS, S. J., & MAS, A. (2012). The Reversal of the Employment-Population Ratio in the 2000s: Facts and Explanations [with Comments and Disscussion]. *Brookings Papers on Economic Activity*, 201-264." ] }, { "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": "3.4.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
rohinkumar/galsurveystudy
DR72/DR72_VAGC_correl_V06_LCDM.ipynb
1
120573
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlation function of DR72 SDSS VAGC Catalog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First import all the modules such as healpy and astropy needed for analyzing the structure" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import healpix_util as hu\n", "import astropy as ap\n", "import numpy as np\n", "from astropy.io import fits\n", "from astropy.table import Table\n", "import astropy.io.ascii as ascii\n", "from astropy.io import fits\n", "from astropy.constants import c\n", "import matplotlib.pyplot as plt\n", "import math as m\n", "from math import pi\n", "import scipy.special as sp\n", "from astroML.decorators import pickle_results\n", "from scipy import integrate\n", "import warnings\n", "from sklearn.neighbors import BallTree\n", "import pickle\n", "import multiprocessing as mp\n", "import time\n", "from cython_metric import *\n", "from progressbar import *\n", "from tqdm import *\n", "from functools import partial\n", "#from astroML.datasets import fetch_sdss_specgals\n", "#from astroML.correlation import bootstrap_two_point_angular\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read the data file (taken from http://cosmo.nyu.edu/~eak306/SDSS-LRG.html ) converted to ascii with comoving distance etc. in V01" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data=ascii.read(\"./output/DR7srarf.dat\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.remove_column('z')\n", "data.remove_column('ra')\n", "data.remove_column('dec')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s=np.array(data['s'])\n", "rar=np.array(data['rar'])\n", "decr=np.array(data['decr'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dat=np.array([s,rar,decr])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dat.reshape(3,len(data['s']))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dat=dat.transpose()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dat" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "LCDMmetric(dat[0],dat[1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Saving the objects:\n", "with open('datsLCDM.pkl', 'w') as f: # Python 3: open(..., 'wb')\n", " pickle.dump(dat, f)\n", "\n", "# Getting back the objects:\n", "with open('datsLCDM.pkl') as f: # Python 3: open(..., 'rb')\n", " dat = pickle.load(f)\n", "dat" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.402352, 0.980185, -0.003863],\n", " [ 0.335419, 1.016617, 0.003776],\n", " [ 0.373033, 0.950251, 0.010821],\n", " ..., \n", " [ 0.310267, 2.829918, 0.173514],\n", " [ 0.336202, 2.830242, 0.172112],\n", " [ 0.209897, 2.831786, 0.173661]])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Getting back the objects:\n", "with open('datsLCDM.pkl') as f: # Python 3: open(..., 'rb')\n", " dat = pickle.load(f)\n", "dat" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins=np.arange(0.,0.08,0.005)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045\n", " 0.05 0.055 0.06 0.065 0.07 0.075]\n" ] } ], "source": [ "print bins" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Nbins=len(bins)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "16" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Nbins" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "BT_D = BallTree(dat,metric='pyfunc',func=LCDMmetric) \n", "\n", "with open('BTDdatsLCDM.pkl', 'w') as f:\n", " pickle.dump(BT_D,f)\n", "\n", "with open('BTDdatsLCDM.pkl') as f:\n", " BTD = pickle.load(f)\n", " \n", "BTD" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "start_time=time.time()\n", "#arg=[(dat,bins)]\n", "#pool=mp.Pool(8)\n", "#%timeit\n", "#@pickle_results(\"DR72DD2ptc.pkl\")\n", "#def mf_wrap(args):\n", "# return BTD.two_point_correlation(*args)\n", "\n", "counts_DD=BTD.two_point_correlation(dat,bins,dualtree=True)\n", "print counts_DD\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime\n", "\n", "with open('BTDcDDLCDM.pkl', 'w') as f:\n", " pickle.dump(counts_DD,f)\n", "\n", "with open('BTDcDDLCDM.pkl') as f:\n", " counts_DD = pickle.load(f)\n", " \n", "counts_DD" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<sklearn.neighbors.ball_tree.BinaryTree at 0x7febfbe359c0>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "BT_D5 = BallTree(dat,metric='pyfunc',func=LCDMmetric,leaf_size=5) \n", "\n", "with open('BTD5datsLCDM.pkl', 'w') as f:\n", " pickle.dump(BT_D5,f)\n", "\n", "with open('BTD5datsLCDM.pkl') as f:\n", " BTD = pickle.load(f)\n", " \n", "BTD" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 80630 409324 1438869 3730151 7878013 14432055 23929184\n", " 36918484 53826861 75051823 100992244 131948382 168266959 210128923\n", " 257828439 311526856]\n", "Total run time:\n", "2902.14705515\n" ] }, { "data": { "text/plain": [ "array([ 80630, 409324, 1438869, 3730151, 7878013, 14432055,\n", " 23929184, 36918484, 53826861, 75051823, 100992244, 131948382,\n", " 168266959, 210128923, 257828439, 311526856])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "start_time=time.time()\n", "#arg=[(dat,bins)]\n", "#pool=mp.Pool(8)\n", "\n", "#@pickle_results(\"DR72DD2ptc.pkl\")\n", "#def mf_wrap(args):\n", "# return BTD.two_point_correlation(*args)\n", "counts_DD=BTD.two_point_correlation(dat,bins)\n", "print counts_DD\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime\n", "\n", "with open('BTD5cDDLCDM.pkl', 'w') as f:\n", " pickle.dump(counts_DD,f)\n", "\n", "with open('BTD5cDDLCDM.pkl') as f:\n", " counts_DD = pickle.load(f)\n", " \n", "counts_DD" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 80630, 409324, 1438869, 3730151, 7878013, 14432055,\n", " 23929184, 36918484, 53826861, 75051823, 100992244, 131948382,\n", " 168266959, 210128923, 257828439, 311526856])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts_DD" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DD5=np.diff(counts_DD)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 328694, 1029545, 2291282, 4147862, 6554042, 9497129,\n", " 12989300, 16908377, 21224962, 25940421, 30956138, 36318577,\n", " 41861964, 47699516, 53698417])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DD5" ] }, { "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": {}, "outputs": [], "source": [ "plt.plot(bins,counts_DD,'ro')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "BallTree.two_point_correlation works almost 10 times faster! with leaf_size=5 Going with it to the random catalog" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=ascii.read(\"./output/randDR7srarf.dat\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "&lt;Table length=1664947&gt;\n", "<table id=\"table4413851856\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>z</th><th>ra</th><th>dec</th><th>s</th><th>rar</th><th>decr</th></tr></thead>\n", "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>0.434325</td><td>37.837993</td><td>-0.620067</td><td>0.389815</td><td>0.660398</td><td>-0.010822</td></tr>\n", "<tr><td>0.293265</td><td>209.089259</td><td>17.276521</td><td>0.273097</td><td>3.649296</td><td>0.301532</td></tr>\n", "<tr><td>0.222746</td><td>258.464939</td><td>28.215748</td><td>0.211182</td><td>4.511064</td><td>0.492458</td></tr>\n", "<tr><td>0.279301</td><td>183.927141</td><td>37.379032</td><td>0.261027</td><td>3.210134</td><td>0.652387</td></tr>\n", "<tr><td>0.324714</td><td>170.682777</td><td>1.206543</td><td>0.299939</td><td>2.978976</td><td>0.021058</td></tr>\n", "<tr><td>0.3653</td><td>172.780433</td><td>32.981495</td><td>0.333879</td><td>3.015587</td><td>0.575636</td></tr>\n", "<tr><td>0.431968</td><td>154.275288</td><td>32.160072</td><td>0.387941</td><td>2.692612</td><td>0.561299</td></tr>\n", "<tr><td>0.276107</td><td>187.272243</td><td>16.128839</td><td>0.258254</td><td>3.268517</td><td>0.281501</td></tr>\n", "<tr><td>0.241847</td><td>130.655784</td><td>38.45825</td><td>0.22819</td><td>2.280374</td><td>0.671223</td></tr>\n", "<tr><td>0.270847</td><td>146.681736</td><td>53.845964</td><td>0.253675</td><td>2.560079</td><td>0.939789</td></tr>\n", "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n", "<tr><td>0.380025</td><td>22.479891</td><td>-10.801148</td><td>0.345999</td><td>0.392348</td><td>-0.188516</td></tr>\n", "<tr><td>0.376247</td><td>241.167565</td><td>43.744758</td><td>0.3429</td><td>4.209168</td><td>0.76349</td></tr>\n", "<tr><td>0.43682</td><td>202.263266</td><td>39.420732</td><td>0.391795</td><td>3.53016</td><td>0.688022</td></tr>\n", "<tr><td>0.341468</td><td>165.964837</td><td>38.414029</td><td>0.314044</td><td>2.896633</td><td>0.670451</td></tr>\n", "<tr><td>0.275896</td><td>136.73796</td><td>6.622873</td><td>0.25807</td><td>2.386528</td><td>0.115591</td></tr>\n", "<tr><td>0.320891</td><td>158.155643</td><td>25.049336</td><td>0.296701</td><td>2.760337</td><td>0.437193</td></tr>\n", "<tr><td>0.394642</td><td>148.921972</td><td>8.516993</td><td>0.35793</td><td>2.599179</td><td>0.14865</td></tr>\n", "<tr><td>0.232127</td><td>203.894397</td><td>24.951558</td><td>0.219557</td><td>3.558629</td><td>0.435487</td></tr>\n", "<tr><td>0.292155</td><td>229.837284</td><td>32.709686</td><td>0.272141</td><td>4.011417</td><td>0.570892</td></tr>\n", "<tr><td>0.186594</td><td>132.839805</td><td>35.648184</td><td>0.178512</td><td>2.318492</td><td>0.622178</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=1664947>\n", " z ra dec s rar decr \n", "float64 float64 float64 float64 float64 float64 \n", "-------- ---------- ---------- -------- -------- ---------\n", "0.434325 37.837993 -0.620067 0.389815 0.660398 -0.010822\n", "0.293265 209.089259 17.276521 0.273097 3.649296 0.301532\n", "0.222746 258.464939 28.215748 0.211182 4.511064 0.492458\n", "0.279301 183.927141 37.379032 0.261027 3.210134 0.652387\n", "0.324714 170.682777 1.206543 0.299939 2.978976 0.021058\n", " 0.3653 172.780433 32.981495 0.333879 3.015587 0.575636\n", "0.431968 154.275288 32.160072 0.387941 2.692612 0.561299\n", "0.276107 187.272243 16.128839 0.258254 3.268517 0.281501\n", "0.241847 130.655784 38.45825 0.22819 2.280374 0.671223\n", "0.270847 146.681736 53.845964 0.253675 2.560079 0.939789\n", " ... ... ... ... ... ...\n", "0.380025 22.479891 -10.801148 0.345999 0.392348 -0.188516\n", "0.376247 241.167565 43.744758 0.3429 4.209168 0.76349\n", " 0.43682 202.263266 39.420732 0.391795 3.53016 0.688022\n", "0.341468 165.964837 38.414029 0.314044 2.896633 0.670451\n", "0.275896 136.73796 6.622873 0.25807 2.386528 0.115591\n", "0.320891 158.155643 25.049336 0.296701 2.760337 0.437193\n", "0.394642 148.921972 8.516993 0.35793 2.599179 0.14865\n", "0.232127 203.894397 24.951558 0.219557 3.558629 0.435487\n", "0.292155 229.837284 32.709686 0.272141 4.011417 0.570892\n", "0.186594 132.839805 35.648184 0.178512 2.318492 0.622178" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataR" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1664947" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dataR)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR.remove_column('z')\n", "dataR.remove_column('ra')\n", "dataR.remove_column('dec')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "&lt;Table length=1664947&gt;\n", "<table id=\"table4413851856\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>s</th><th>rar</th><th>decr</th></tr></thead>\n", "<thead><tr><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>0.389815</td><td>0.660398</td><td>-0.010822</td></tr>\n", "<tr><td>0.273097</td><td>3.649296</td><td>0.301532</td></tr>\n", "<tr><td>0.211182</td><td>4.511064</td><td>0.492458</td></tr>\n", "<tr><td>0.261027</td><td>3.210134</td><td>0.652387</td></tr>\n", "<tr><td>0.299939</td><td>2.978976</td><td>0.021058</td></tr>\n", "<tr><td>0.333879</td><td>3.015587</td><td>0.575636</td></tr>\n", "<tr><td>0.387941</td><td>2.692612</td><td>0.561299</td></tr>\n", "<tr><td>0.258254</td><td>3.268517</td><td>0.281501</td></tr>\n", "<tr><td>0.22819</td><td>2.280374</td><td>0.671223</td></tr>\n", "<tr><td>0.253675</td><td>2.560079</td><td>0.939789</td></tr>\n", "<tr><td>...</td><td>...</td><td>...</td></tr>\n", "<tr><td>0.345999</td><td>0.392348</td><td>-0.188516</td></tr>\n", "<tr><td>0.3429</td><td>4.209168</td><td>0.76349</td></tr>\n", "<tr><td>0.391795</td><td>3.53016</td><td>0.688022</td></tr>\n", "<tr><td>0.314044</td><td>2.896633</td><td>0.670451</td></tr>\n", "<tr><td>0.25807</td><td>2.386528</td><td>0.115591</td></tr>\n", "<tr><td>0.296701</td><td>2.760337</td><td>0.437193</td></tr>\n", "<tr><td>0.35793</td><td>2.599179</td><td>0.14865</td></tr>\n", "<tr><td>0.219557</td><td>3.558629</td><td>0.435487</td></tr>\n", "<tr><td>0.272141</td><td>4.011417</td><td>0.570892</td></tr>\n", "<tr><td>0.178512</td><td>2.318492</td><td>0.622178</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=1664947>\n", " s rar decr \n", "float64 float64 float64 \n", "-------- -------- ---------\n", "0.389815 0.660398 -0.010822\n", "0.273097 3.649296 0.301532\n", "0.211182 4.511064 0.492458\n", "0.261027 3.210134 0.652387\n", "0.299939 2.978976 0.021058\n", "0.333879 3.015587 0.575636\n", "0.387941 2.692612 0.561299\n", "0.258254 3.268517 0.281501\n", " 0.22819 2.280374 0.671223\n", "0.253675 2.560079 0.939789\n", " ... ... ...\n", "0.345999 0.392348 -0.188516\n", " 0.3429 4.209168 0.76349\n", "0.391795 3.53016 0.688022\n", "0.314044 2.896633 0.670451\n", " 0.25807 2.386528 0.115591\n", "0.296701 2.760337 0.437193\n", " 0.35793 2.599179 0.14865\n", "0.219557 3.558629 0.435487\n", "0.272141 4.011417 0.570892\n", "0.178512 2.318492 0.622178" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataR" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rs=np.array(dataR['s'])\n", "rrar=np.array(dataR['rar'])\n", "rdecr=np.array(dataR['decr'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datR=np.array([rs,rrar,rdecr])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.389815, 0.273097, 0.211182, ..., 0.219557, 0.272141,\n", " 0.178512],\n", " [ 0.660398, 3.649296, 4.511064, ..., 3.558629, 4.011417,\n", " 2.318492],\n", " [-0.010822, 0.301532, 0.492458, ..., 0.435487, 0.570892,\n", " 0.622178]])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datR" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.389815, 0.273097, 0.211182, ..., 0.219557, 0.272141,\n", " 0.178512],\n", " [ 0.660398, 3.649296, 4.511064, ..., 3.558629, 4.011417,\n", " 2.318492],\n", " [-0.010822, 0.301532, 0.492458, ..., 0.435487, 0.570892,\n", " 0.622178]])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datR.reshape(3,len(dataR))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datR=datR.transpose()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.389815, 0.660398, -0.010822],\n", " [ 0.273097, 3.649296, 0.301532],\n", " [ 0.211182, 4.511064, 0.492458],\n", " ..., \n", " [ 0.219557, 3.558629, 0.435487],\n", " [ 0.272141, 4.011417, 0.570892],\n", " [ 0.178512, 2.318492, 0.622178]])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datR" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Saving the objects:\n", "with open('datRsLCDM.pkl', 'w') as f: # Python 3: open(..., 'wb')\n", " pickle.dump(datR, f)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.389815, 0.660398, -0.010822],\n", " [ 0.273097, 3.649296, 0.301532],\n", " [ 0.211182, 4.511064, 0.492458],\n", " ..., \n", " [ 0.219557, 3.558629, 0.435487],\n", " [ 0.272141, 4.011417, 0.570892],\n", " [ 0.178512, 2.318492, 0.622178]])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Getting back the objects:\n", "with open('datRsLCDM.pkl') as f: # Python 3: open(..., 'rb')\n", " datR = pickle.load(f)\n", "datR" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<sklearn.neighbors.ball_tree.BinaryTree at 0x7febfba91c30>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "BT_R2 = BallTree(datR,metric='pyfunc',func=LCDMmetric,leaf_size=2) \n", "\n", "with open('BTR2datsLCDM.pkl', 'w') as f:\n", " pickle.dump(BT_R2,f)\n", "\n", "with open('BTR2datsLCDM.pkl') as f:\n", " BTR = pickle.load(f)\n", " \n", "BTR" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "start_time=time.time()\n", "counts_RR=BTR.two_point_correlation(datR,bins)\n", "print counts_RR\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime\n", "\n", "with open('BTR2cDDLCDM.pkl', 'w') as f:\n", " pickle.dump(counts_RR,f)\n", "\n", "with open('BTR2cDDLCDM.pkl') as f:\n", " counts_RR = pickle.load(f)\n", " \n", "counts_RR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "BT_R = BallTree(datR,metric='pyfunc',func=LCDMmetric,leaf_size=2) \n", "tempR = pickle.dumps(BT_R) \n", "BTR = pickle.loads(tempR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "start_time=time.time()\n", "#arg=[(dat,bins)]\n", "#pool=mp.Pool(8)\n", "#%timeit\n", "#@pickle_results(\"DR72DD2ptc.pkl\")\n", "#def mf_wrap(args):\n", "# return BTD.two_point_correlation(*args)\n", "\n", "counts_RR=BTR.two_point_correlation(datR,bins)\n", "print counts_RR\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "counts_RR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RR=np.diff(counts_RR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RR" ] }, { "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": [ "DD" ] }, { "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": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "start_time=time.time()\n", "arg=[(dat,bins)]\n", "pool=mp.Pool(8)\n", "#%timeit\n", "#@pickle_results(\"DR72DD2ptc.pkl\")\n", "def mf_wrap(args):\n", " return BTD.two_point_correlation(*args)\n", "\n", "%timeit counts_DD=pool.map(mf_wrap,arg)\n", "\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime" ] }, { "cell_type": "raw", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from functools import partial\n", "\n", "def harvester(text, case):\n", " X = case[0]\n", " return text + str(X)\n", "\n", "\n", "partial_harvester = partial(harvester, case=RAW_DATASET)\n", "\n", "partial_qr=partial(BTD.query_radius,count_only=True)\n", "\n", "if __name__ == '__main__':\n", " pool = multiprocessing.Pool(processes=6)\n", " case_data = RAW_DATASET\n", " pool.map(partial_harvester, case_data, 1)\n", " pool.close()\n", " pool.join()\n", "\n", "mapfunc = partial(BTD.query_radius, count_only=True)\n", "map(mapfunc, volume_ids)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#ascii.write(\"DR72DDbinned.dat\",(bins[1:len(bins)],DDresult))\n", "start_time=time.time()\n", "@pickle_results(\"DR72DDmp1.pkl\")\n", "def ddcal(BTD,dat,bins,Nbins):\n", " counts_DD=np.zeros(Nbins)\n", " for i in tqdm(range(Nbins)):\n", " counts_DD[i]=np.sum(BTD.query_radius(dat, bins[i],count_only=True))\n", " DD = np.diff(counts_DD)\n", " print counts_DD\n", " print DD\n", " return DD\n", "\n", "def mf_wrap(args):\n", " return ddcal(*args)\n", "\n", "pool=mp.Pool(8)\n", "\n", "arg=[(BTD,dat,bins,Nbins)]\n", "%timeit DDresult=pool.map(mf_wrap,arg) \n", "#DDresult = ddcal(BTD,dat,bins,Nbins)\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%timeit dat" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DDresult[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DDresult[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:len(bins)],DDresult[0],'ro')" ] }, { "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": [ "def myfun(a,b):\n", " print a + b\n", " return a+b\n", "\n", "def mf_wrap(args):\n", " return myfun(*args)\n", "\n", "p = mp.Pool(4)\n", "\n", "fl = [(a,b) for a in range(3) for b in range(2)]\n", "\n", "p.map(mf_wrap, fl)" ] }, { "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": [ "counts_DD=np.zeros(Nbins)\n", "\n", "for i in range(Nbins):\n", " counts_DD[i]=np.sum(BTD.query_radius(dat, bins[i],count_only=True))\n", "DD = np.diff(counts_DD)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print counts_DD\n", "print DD" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:len(bins)],DD,'ro')" ] }, { "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": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=fits.open(\"/Users/rohin/Downloads/random-DR7-Full.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=dataR[1].data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(dataR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata=np.array(data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "type(tdata[4])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata=np.atleast_d(tdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata.reshape(len(tdata),3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata=np.asarray(data)\n", "tdata=tdata.transpose()" ] }, { "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": [ "tdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(tdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "BTD.two_point_correlationpoint_correlationpoint_correlationpoint_correlationtime\n", "stime=time.time()\n", "tpcf=BTD.two_point_correlation(dat,bins)\n", "print time.time()-stime\n", "print tpcf\n", "plt.plot(bins,tpcf)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stime=time.time()\n", "tpcfd=BTD.two_point_correlation(dat,bins,dualtree=True)\n", "print time.time()-stime\n", "print tpcfd\n", "plt.plot(bins,tpcfd)" ] }, { "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": [ "X" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(0)\n", "X = np.random.random((30,3))\n", "r = np.linspace(0, 1, 10)\n", "tree = BallTree(X,metric='pyfunc',func=LCDMmetric) \n", "s = pickle.dumps(tree) \n", "treedump = pickle.loads(s) \n", "treedump.two_point_correlation(X,r)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "BT_D = BallTree(data)\n", " BT_R = BallTree(data_R)\n", "\n", " counts_DD = np.zeros(Nbins + 1)\n", " counts_RR = np.zeros(Nbins + 1)\n", "\n", " for i in range(Nbins + 1):\n", " counts_DD[i] = np.sum(BT_D.query_radius(data, bins[i],\n", " count_only=True))\n", " counts_RR[i] = np.sum(BT_R.query_radius(data_R, bins[i],\n", " count_only=True))\n", "\n", " DD = np.diff(counts_DD)\n", " RR = np.diff(counts_RR)\n", "\n", " # check for zero in the denominator\n", " RR_zero = (RR == 0)\n", " RR[RR_zero] = 1\n", "\n", " if method == 'standard':\n", " corr = factor ** 2 * DD / RR - 1\n", " elif method == 'landy-szalay':\n", " if sklearn_has_two_point:\n", " counts_DR = KDT_R.two_point_correlation(data, bins)\n", " else:\n", " counts_DR = np.zeros(Nbins + 1)\n", " for i in range(Nbins + 1):\n", " counts_DR[i] = np.sum(BT_R.query_radius(data, bins[i],\n", " count_only=True))\n", " DR = np.diff(counts_DR)\n", "\n", " corr = (factor ** 2 * DD - 2 * factor * DR + RR) / RR\n", "\n", " corr[RR_zero] = np.nan\n", "\n", " return corr" ] }, { "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": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dr7fdat=np.array([data['s'][0:300] data['rar'][0:300] data['decr'][0:300]])\n", "dr7fdat" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dr7fdat[2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def LCDMmetric(p1,p2):\n", " costheta=m.sin(dec1)*m.sin(dec2)+m.cos(dec1)*m.cos(dec2)*m.cos(ra1-ra2)\n", " s1=DC_LCDM(z1)\n", " s2=DC_LCDM(z2)\n", " return np.sqrt(s1**2+s2**2-2.0*s1*s2*costheta)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#fdata=fits.open(\"/Users/rohin/Downloads/DR7-Full.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#fdata.writeto(\"./output/DR7fulltrim.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdata=fits.open(\"./output/DR7fulltrim.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cols=fdata[1].columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cols.del_col('ZTYPE')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cols.del_col('SECTOR')\n", "cols.del_col('FGOTMAIN')\n", "cols.del_col('QUALITY')\n", "cols.del_col('ISBAD')\n", "cols.del_col('M')\n", "cols.del_col('MMAX')\n", "cols.del_col('ILSS')\n", "cols.del_col('ICOMB')\n", "cols.del_col('VAGC_SELECT')\n", "cols.del_col('LSS_INDEX')\n", "cols.del_col('FIBERWEIGHT')\n", "cols.del_col('PRIMTARGET')\n", "cols.del_col('MG')\n", "cols.del_col('SECTOR_COMPLETENESS')\n", "cols.del_col('COMOV_DENSITY')\n", "cols.del_col('RADIAL_WEIGHT')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdata[1].columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdata.writeto(\"./output/DR7fullzradec.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat=fits.open(\"./output/DR7fullzradec.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['Z']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['RA']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "comovlcdm=DC_LCDM(fdat[1].data['Z'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['Z']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "comovlcdm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "comovlcdm.dtype" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#cols=fdat[1].columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nc=fits.Column(name='COMOV',format='D',array=comovlcdm)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nc1=fits.Column(name='COMOV',format='D')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdata[1].data['Z']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdata[1].data['RA']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nc" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nc.dtype" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#cols.add_col(nc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].columns.info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].columns.add_col(nc1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['COMOV']=comovlcdm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "comovlcdm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['Z']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['COMOV']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['RA']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['RA']=fdat[1].data['RA']*pi/180.0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "comovlcdm=DC_LCDM(fdat[1].data['Z'])\n", "comovlcdm" ] }, { "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": [ "Random catalog created based on the survey limitations also taken from http://cosmo.nyu.edu/~eak306/SDSS-LRG.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=fits.open(\"/Users/rohin/Downloads/random-DR7-Full.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=dataR[1].data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(dataR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=512\n", "dr72hpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"./output/pixdatadr72VAGCfullrand.dat\",'w')\n", "pixdata.write(\"z\\t pix \\n\")\n", "\n", "for i in range(0,len(data)-1):\n", " pixdata.write(\"%f\\t\" %data['z'][i])\n", " pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(dataR['ra'][i],dataR['dec'][i]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"./output/pixdatadr72VAGCfullrand.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "Tools for computing two-point correlation functions.\n", "\"\"\"\n", "\n", "#from .utils import check_random_state\n", "# From scikit-learn utilities:\n", "def check_random_state(seed):\n", " \"\"\"Turn seed into a np.random.RandomState instance\n", "\n", " If seed is None, return the RandomState singleton used by np.random.\n", " If seed is an int, return a new RandomState instance seeded with seed.\n", " If seed is already a RandomState instance, return it.\n", " Otherwise raise ValueError.\n", " \"\"\"\n", " if seed is None or seed is np.random:\n", " return np.random.mtrand._rand\n", " if isinstance(seed, (int, np.integer)):\n", " return np.random.RandomState(seed)\n", " if isinstance(seed, np.random.RandomState):\n", " return seed\n", " raise ValueError('%r cannot be used to seed a numpy.random.RandomState'\n", " ' instance' % seed)\n", "\n", "# Check if scikit-learn's two-point functionality is available.\n", "# This was added in scikit-learn version 0.14\n", "try:\n", " from sklearn.neighbors import KDTree\n", " sklearn_has_two_point = True\n", "except ImportError:\n", " import warnings\n", " sklearn_has_two_point = False\n", "\n", "\n", "def uniform_sphere(RAlim, DEClim, size=1):\n", " \"\"\"Draw a uniform sample on a sphere\n", "\n", " Parameters\n", " ----------\n", " RAlim : tuple\n", " select Right Ascension between RAlim[0] and RAlim[1]\n", " units are degrees\n", " DEClim : tuple\n", " select Declination between DEClim[0] and DEClim[1]\n", " size : int (optional)\n", " the size of the random arrays to return (default = 1)\n", "\n", " Returns\n", " -------\n", " RA, DEC : ndarray\n", " the random sample on the sphere within the given limits.\n", " arrays have shape equal to size.\n", " \"\"\"\n", " zlim = np.sin(np.pi * np.asarray(DEClim) / 180.)\n", "\n", " z = zlim[0] + (zlim[1] - zlim[0]) * np.random.random(size)\n", " DEC = (180. / np.pi) * np.arcsin(z)\n", " RA = RAlim[0] + (RAlim[1] - RAlim[0]) * np.random.random(size)\n", "\n", " return RA, DEC\n", "\n", "\n", "def ra_dec_to_xyz(ra, dec):\n", " \"\"\"Convert ra & dec to Euclidean points\n", "\n", " Parameters\n", " ----------\n", " ra, dec : ndarrays\n", "\n", " Returns\n", " x, y, z : ndarrays\n", " \"\"\"\n", " sin_ra = np.sin(ra * np.pi / 180.)\n", " cos_ra = np.cos(ra * np.pi / 180.)\n", "\n", " sin_dec = np.sin(np.pi / 2. - dec * np.pi / 180.)\n", " cos_dec = np.cos(np.pi / 2. - dec * np.pi / 180.)\n", "\n", " return (cos_ra * sin_dec,\n", " sin_ra * sin_dec,\n", " cos_dec)\n", "\n", "\n", "def angular_dist_to_euclidean_dist(D, r=1):\n", " \"\"\"convert angular distances to euclidean distances\"\"\"\n", " return 2 * r * np.sin(0.5 * D * np.pi / 180.)\n", "\n", "\n", "def two_point(data, bins, method='standard',\n", " data_R=None, random_state=None):\n", " \"\"\"Two-point correlation function\n", "\n", " Parameters\n", " ----------\n", " data : array_like\n", " input data, shape = [n_samples, n_features]\n", " bins : array_like\n", " bins within which to compute the 2-point correlation.\n", " shape = Nbins + 1\n", " method : string\n", " \"standard\" or \"landy-szalay\".\n", " data_R : array_like (optional)\n", " if specified, use this as the random comparison sample\n", " random_state : integer, np.random.RandomState, or None\n", " specify the random state to use for generating background\n", "\n", " Returns\n", " -------\n", " corr : ndarray\n", " the estimate of the correlation function within each bin\n", " shape = Nbins\n", " \"\"\"\n", " data = np.asarray(data)\n", " bins = np.asarray(bins)\n", " rng = check_random_state(random_state)\n", "\n", " if method not in ['standard', 'landy-szalay']:\n", " raise ValueError(\"method must be 'standard' or 'landy-szalay'\")\n", "\n", " if bins.ndim != 1:\n", " raise ValueError(\"bins must be a 1D array\")\n", "\n", " if data.ndim == 1:\n", " data = data[:, np.newaxis]\n", " elif data.ndim != 2:\n", " raise ValueError(\"data should be 1D or 2D\")\n", "\n", " n_samples, n_features = data.shape\n", " Nbins = len(bins) - 1\n", "\n", " # shuffle all but one axis to get background distribution\n", " if data_R is None:\n", " data_R = data.copy()\n", " for i in range(n_features - 1):\n", " rng.shuffle(data_R[:, i])\n", " else:\n", " data_R = np.asarray(data_R)\n", " if (data_R.ndim != 2) or (data_R.shape[-1] != n_features):\n", " raise ValueError('data_R must have same n_features as data')\n", "\n", " factor = len(data_R) * 1. / len(data)\n", "\n", " if sklearn_has_two_point:\n", " # Fast two-point correlation functions added in scikit-learn v. 0.14\n", " KDT_D = KDTree(data)\n", " KDT_R = KDTree(data_R)\n", "\n", " counts_DD = KDT_D.two_point_correlation(data, bins)\n", " counts_RR = KDT_R.two_point_correlation(data_R, bins)\n", "\n", " else:\n", " warnings.warn(\"Version 0.3 of astroML will require scikit-learn \"\n", " \"version 0.14 or higher for correlation function \"\n", " \"calculations. Upgrade to sklearn 0.14+ now for much \"\n", " \"faster correlation function calculations.\")\n", "\n", " BT_D = BallTree(data)\n", " BT_R = BallTree(data_R)\n", "\n", " counts_DD = np.zeros(Nbins + 1)\n", " counts_RR = np.zeros(Nbins + 1)\n", "\n", " for i in range(Nbins + 1):\n", " counts_DD[i] = np.sum(BT_D.query_radius(data, bins[i],\n", " count_only=True))\n", " counts_RR[i] = np.sum(BT_R.query_radius(data_R, bins[i],\n", " count_only=True))\n", "\n", " DD = np.diff(counts_DD)\n", " RR = np.diff(counts_RR)\n", "\n", " # check for zero in the denominator\n", " RR_zero = (RR == 0)\n", " RR[RR_zero] = 1\n", "\n", " if method == 'standard':\n", " corr = factor ** 2 * DD / RR - 1\n", " elif method == 'landy-szalay':\n", " if sklearn_has_two_point:\n", " counts_DR = KDT_R.two_point_correlation(data, bins)\n", " else:\n", " counts_DR = np.zeros(Nbins + 1)\n", " for i in range(Nbins + 1):\n", " counts_DR[i] = np.sum(BT_R.query_radius(data, bins[i],\n", " count_only=True))\n", " DR = np.diff(counts_DR)\n", "\n", " corr = (factor ** 2 * DD - 2 * factor * DR + RR) / RR\n", "\n", " corr[RR_zero] = np.nan\n", "\n", " return corr\n", "\n", "\n", "def bootstrap_two_point(data, bins, Nbootstrap=10,\n", " method='standard', return_bootstraps=False,\n", " random_state=None):\n", " \"\"\"Bootstrapped two-point correlation function\n", "\n", " Parameters\n", " ----------\n", " data : array_like\n", " input data, shape = [n_samples, n_features]\n", " bins : array_like\n", " bins within which to compute the 2-point correlation.\n", " shape = Nbins + 1\n", " Nbootstrap : integer\n", " number of bootstrap resamples to perform (default = 10)\n", " method : string\n", " \"standard\" or \"landy-szalay\".\n", " return_bootstraps: bool\n", " if True, return full bootstrapped samples\n", " random_state : integer, np.random.RandomState, or None\n", " specify the random state to use for generating background\n", "\n", " Returns\n", " -------\n", " corr, corr_err : ndarrays\n", " the estimate of the correlation function and the bootstrap\n", " error within each bin. shape = Nbins\n", " \"\"\"\n", " data = np.asarray(data)\n", " bins = np.asarray(bins)\n", " rng = check_random_state(random_state)\n", "\n", " if method not in ['standard', 'landy-szalay']:\n", " raise ValueError(\"method must be 'standard' or 'landy-szalay'\")\n", "\n", " if bins.ndim != 1:\n", " raise ValueError(\"bins must be a 1D array\")\n", "\n", " if data.ndim == 1:\n", " data = data[:, np.newaxis]\n", " elif data.ndim != 2:\n", " raise ValueError(\"data should be 1D or 2D\")\n", "\n", " if Nbootstrap < 2:\n", " raise ValueError(\"Nbootstrap must be greater than 1\")\n", "\n", " n_samples, n_features = data.shape\n", "\n", " # get the baseline estimate\n", " corr = two_point(data, bins, method=method, random_state=rng)\n", "\n", " bootstraps = np.zeros((Nbootstrap, len(corr)))\n", "\n", " for i in range(Nbootstrap):\n", " indices = rng.randint(0, n_samples, n_samples)\n", " bootstraps[i] = two_point(data[indices, :], bins, method=method,\n", " random_state=rng)\n", "\n", " # use masked std dev in case of NaNs\n", " corr_err = np.asarray(np.ma.masked_invalid(bootstraps).std(0, ddof=1))\n", "\n", " if return_bootstraps:\n", " return corr, corr_err, bootstraps\n", " else:\n", " return corr, corr_err\n", "\n", "\n", "def two_point_angular(ra, dec, bins, method='standard', random_state=None):\n", " \"\"\"Angular two-point correlation function\n", "\n", " A separate function is needed because angular distances are not\n", " euclidean, and random sampling needs to take into account the\n", " spherical volume element.\n", "\n", " Parameters\n", " ----------\n", " ra : array_like\n", " input right ascention, shape = (n_samples,)\n", " dec : array_like\n", " input declination\n", " bins : array_like\n", " bins within which to compute the 2-point correlation.\n", " shape = Nbins + 1\n", " method : string\n", " \"standard\" or \"landy-szalay\".\n", " random_state : integer, np.random.RandomState, or None\n", " specify the random state to use for generating background\n", "\n", " Returns\n", " -------\n", " corr : ndarray\n", " the estimate of the correlation function within each bin\n", " shape = Nbins\n", " \"\"\"\n", " ra = np.asarray(ra)\n", " dec = np.asarray(dec)\n", " rng = check_random_state(random_state)\n", "\n", " if method not in ['standard', 'landy-szalay']:\n", " raise ValueError(\"method must be 'standard' or 'landy-szalay'\")\n", "\n", " if bins.ndim != 1:\n", " raise ValueError(\"bins must be a 1D array\")\n", "\n", " if (ra.ndim != 1) or (dec.ndim != 1) or (ra.shape != dec.shape):\n", " raise ValueError('ra and dec must be 1-dimensional '\n", " 'arrays of the same length')\n", "\n", " n_features = len(ra)\n", " Nbins = len(bins) - 1\n", "\n", " # draw a random sample with N points\n", " ra_R, dec_R = uniform_sphere((min(ra), max(ra)),\n", " (min(dec), max(dec)),\n", " 2 * len(ra))\n", "\n", " data = np.asarray(ra_dec_to_xyz(ra, dec), order='F').T\n", " data_R = np.asarray(ra_dec_to_xyz(ra_R, dec_R), order='F').T\n", "\n", " # convert spherical bins to cartesian bins\n", " bins_transform = angular_dist_to_euclidean_dist(bins)\n", "\n", " return two_point(data, bins_transform, method=method,\n", " data_R=data_R, random_state=rng)\n", "\n", "\n", "def bootstrap_two_point_angular(ra, dec, bins, method='standard',\n", " Nbootstraps=10, random_state=None):\n", " # type: (object, object, object, object, object, object) -> object\n", " \"\"\"Angular two-point correlation function\n", "\n", " A separate function is needed because angular distances are not\n", " euclidean, and random sampling needs to take into account the\n", " spherical volume element.\n", "\n", " Parameters\n", " ----------\n", " ra : array_like\n", " input right ascention, shape = (n_samples,)\n", " dec : array_like\n", " input declination\n", " bins : array_like\n", " bins within which to compute the 2-point correlation.\n", " shape = Nbins + 1\n", " method : string\n", " \"standard\" or \"landy-szalay\".\n", " Nbootstraps : int\n", " number of bootstrap resamples\n", " random_state : integer, np.random.RandomState, or None\n", " specify the random state to use for generating background\n", "\n", " Returns\n", " -------\n", " corr : ndarray\n", " the estimate of the correlation function within each bin\n", " shape = Nbins\n", " dcorr : ndarray\n", " error estimate on dcorr (sample standard deviation of\n", " bootstrap resamples)\n", " bootstraps : ndarray\n", " The full sample of bootstraps used to compute corr and dcorr\n", " \"\"\"\n", " ra = np.asarray(ra)\n", " dec = np.asarray(dec)\n", " rng = check_random_state(random_state)\n", "\n", " if method not in ['standard', 'landy-szalay']:\n", " raise ValueError(\"method must be 'standard' or 'landy-szalay'\")\n", "\n", " if bins.ndim != 1:\n", " raise ValueError(\"bins must be a 1D array\")\n", "\n", " if (ra.ndim != 1) or (dec.ndim != 1) or (ra.shape != dec.shape):\n", " raise ValueError('ra and dec must be 1-dimensional '\n", " 'arrays of the same length')\n", "\n", " n_features = len(ra)\n", " Nbins = len(bins) - 1\n", " data = np.asarray(ra_dec_to_xyz(ra, dec), order='F').T\n", "\n", " # convert spherical bins to cartesian bins\n", " bins_transform = angular_dist_to_euclidean_dist(bins)\n", "\n", " bootstraps = []\n", "\n", " for i in range(Nbootstraps):\n", " # draw a random sample with N points\n", " ra_R, dec_R = uniform_sphere((min(ra), max(ra)),\n", " (min(dec), max(dec)),\n", " 2 * len(ra))\n", "\n", " data_R = np.asarray(ra_dec_to_xyz(ra_R, dec_R), order='F').T\n", "\n", " if i > 0:\n", " # random sample of the data\n", " ind = np.random.randint(0, data.shape[0], data.shape[0])\n", " data_b = data[ind]\n", " else:\n", " data_b = data\n", "\n", " bootstraps.append(two_point(data_b, bins_transform, method=method,\n", " data_R=data_R, random_state=rng))\n", "\n", " bootstraps = np.asarray(bootstraps)\n", " corr = np.mean(bootstraps, 0)\n", " corr_err = np.std(bootstraps, 0, ddof=1)\n", "\n", " return corr, corr_err, bootstraps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sklearn_has_two_point" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(KDTree)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyz=ra_dec_to_xyz(data['ra'],data['dec'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyz=np.asarray(dataxyz)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyz=dataxyz.transpose()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyz" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR=ra_dec_to_xyz(dataR['ra'],dataR['dec'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR=np.asarray(dataxyzR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR=dataxyzR.transpose()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins=np.arange(0.0,1.05,0.05)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#@pickle_results(\"tpcf_std.pkl\")\n", "tpcf=two_point(dataxyz,bins,method='standard',data_R=dataxyzR, random_state=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpcf " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:],tpcf,'ro')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpcfam=two_point(dataxyz,bins,method='standard',data_R=None, random_state=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpcfam" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:],tpcfam,'bo')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins2=np.arange(0.2,0.6,0.02)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpcfamb2=two_point(dataxyz,bins2,method='standard',data_R=None, random_state=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins2[1:],tpcfamb2,'go')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above doesn't show any BAO feature... It used inbuilt astroML method to generate random catalog... by shuffling the original data's content... That way all of the random points fall in the same survey area and will adhere to all the filtering criteria... the factor or ratio of data pts vs. random pts will be 1... instead of large no. in case if we take existing random catalog or create one" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rng = check_random_state(None)\n", "\n", "n_samples, n_features = dataxyz.shape\n", "Nbins = len(bins) - 1\n", "\n", "# shuffle all but one axis to get background distribution\n", "data_Rxyz = dataxyz.copy()\n", "print data_Rxyz\n", "for i in range(n_features - 1):\n", " rng.shuffle(data_Rxyz[:, i])\n", "print data_Rxyz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets see how it looks with a healpix map" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=512\n", "dr72hpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math as m\n", "\n", "def cart2sph(x,y,z):\n", " XsqPlusYsq = x**2 + y**2\n", " r = m.sqrt(XsqPlusYsq + z**2) # r\n", " elev = m.atan2(z,m.sqrt(XsqPlusYsq)) # theta\n", " az = m.atan2(y,x) # phi\n", " return r, elev, az\n", "\n", "def cart2sphA(pts):\n", " return np.array([cart2sph(x,y,z) for x,y,z in pts])\n", "\n", "def appendSpherical(xyz):\n", " np.hstack((xyz, cart2sphA(xyz)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang=cart2sphA(data_Rxyz)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#ang.resize((105831, 2))\n", "np.squeeze(ang, axis=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(ang.squeeze)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang2=ang[:,1:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang2.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang2[2,0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"./output/pixdatadr72VAGCfullrandam.dat\",'w')\n", "pixdata.write(\"pix \\n\")\n", "for i in range(0,len(ang2)-1):\n", " #pixdata.write(\"%f\\t\" %data['z'][i])\n", " pixdata.write(\"%d\\n\" %dr72hpix.ang2pix(ang2[i,0],ang2[i,1]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"./output/pixdatadr72VAGCfullrandam.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This method doesnt seem to produce right random catalogs...doing it with ra and dec as follows" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['z'],data['ra'],data['dec']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datzradec=np.array([data['z'], data['ra'], data['dec']])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datzradec" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rng = check_random_state(None)\n", "\n", "n_features, n_samples = datzradec.shape\n", "\n", "# shuffle all but one axis to get background distribution\n", "data_Rzradec = datzradec.copy()\n", "print data_Rzradec\n", "for i in range(1,n_features):\n", " rng.shuffle(data_Rzradec[:, i])\n", "print data_Rzradec" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "min(data_Rzradec[:, 1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "max(data_Rzradec[:, 1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "min(data_Rzradec[:, 2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "max(data_Rzradec[:, 2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "min(datzradec[:, 1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "max(datzradec[:, 1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "min(datzradec[:, 2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "max(datzradec[:, 2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "range(1,3)" ] }, { "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": [ "help(rng.shuffle)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_samples" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_features" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_Rzradec" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_Rzradec[0][2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(data_Rzradec[0][:])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_Rzradec[0][:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"./output/pixdatadr72VAGCfullrandamrd.dat\",'w')\n", "pixdata.write(\"z\\t pix \\n\")\n", "for i in range(0,len(data_Rzradec[0][:])-1):\n", " pixdata.write(\"%f\\t\" %data_Rzradec[0][i])\n", " pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(data_Rzradec[1][i],data_Rzradec[2][i]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"./output/pixdatadr72VAGCfullrandamrd.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyz" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR1=ra_dec_to_xyz(data_Rzradec[1][:],data_Rzradec[2][:])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_Rzradec[1][:]" ] }, { "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": [ "dataxyzR1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR1=np.asarray(dataxyzR1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR1=dataxyzR1.transpose()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins=np.arange(0.025,1.025,0.025)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#@pickle_results(\"tpcf_std.pkl\")\n", "tpcf=two_point(dataxyz,bins,method='standard',data_R=dataxyzR1, random_state=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpcf " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:],tpcf,'ro')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins=np.arange(0.0,1.05,0.05)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#@pickle_results(\"tpcf_std.pkl\")\n", "tpcf=two_point(dataxyz,bins,method='standard',data_R=dataxyzR1, random_state=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpcf " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:],tpcf,'ro')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "btpcf=bootstrap_two_point(dataxyz, bins, Nbootstrap=10,\n", " method='standard', return_bootstraps=False,\n", " random_state=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "btpcf" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.errorbar(bins[1:],btpcf[0],yerr=btpcf[1],fmt='ro-')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(plt.errorbar)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@pickle_results(\"tpcf_ls.pkl\")\n", "tpcfls=two_point(dataxyz,bins,method='landy-szalay',\n", " data_R=dataxyzR, random_state=None)" ] }, { "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": "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": [ "#------------------------------------------------------------\n", "# Set up correlation function computation\n", "# This calculation takes a long time with the bootstrap resampling,\n", "# so we'll save the results.\n", "@pickle_results(\"correlation_functionsdr72.pkl\")\n", "def compute_results(Nbins=16, Nbootstraps=10, method='landy-szalay', rseed=0):\n", " np.random.seed(rseed)\n", " bins = 10 ** np.linspace(np.log10(1. / 60.), np.log10(6), 16)\n", "\n", " results = [bins]\n", " for D in [data]:\n", " results += bootstrap_two_point_angular(D['ra'],\n", " D['dec'],\n", " bins=bins,\n", " method=method,\n", " Nbootstraps=Nbootstraps)\n", "\n", " return results\n", "\n", "(bins, r_corr, r_corr_err, r_bootstraps) = compute_results()\n", "\n", "bin_centers = 0.5 * (bins[1:] + bins[:-1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr_err" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_bootstraps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Plot the results\n", "\n", "label = '$0.15<z<0.25$\\n$N=33813$' \n", "\n", "fig = plt.figure(figsize=(6, 6))\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.errorbar(bin_centers, r_corr, r_corr_err,fmt='.k', ecolor='gray', lw=1)\n", "fig.text(0.8, 0.8, label, ha='right', va='top')\n", "plt.xlabel(r'$\\theta\\ (deg)$')\n", "plt.ylabel(r'$w(\\theta)$')\n", "plt.show()\n", "\n", "plt.show()\n", "fig.savefig(\"wth_dr72015025.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data=ascii.read('./input/sdssdr72_sorted_z.dat')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#m_max = 19\n", "\n", "# redshift and magnitude cuts\n", "data = data[data['z'] > 0.05]\n", "data = data[data['z'] < 0.15]\n", "#data = data[data['petroMag_r'] < m_max]\n", "\n", "# RA/DEC cuts\n", "RAmin, RAmax = 140, 220\n", "DECmin, DECmax = 5, 45\n", "data = data[data['ra'] < RAmax]\n", "data = data[data['ra'] > RAmin]\n", "data = data[data['dec'] < DECmax]\n", "data = data[data['dec'] > DECmin]\n", "\n", "#ur = data['modelMag_u'] - data['modelMag_r']\n", "#flag_red = (ur > 2.22)\n", "#flag_blue = ~flag_red\n", "\n", "#datag \n", "\n", "print \"data size:\"\n", "print \" total gals: \", len(data)\n", "#print \" blue gals:\", len(data_blue)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=512\n", "dr72hpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"./output/pixdatadr72005015.dat\",'w')\n", "pixdata.write(\"z\\t pix \\n\")\n", "\n", "for i in range(0,len(data)-1):\n", " pixdata.write(\"%f\\t\" %data['z'][i])\n", " pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(data['ra'][i],data['dec'][i]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"./output/pixdatadr72005015.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Set up correlation function computation\n", "# This calculation takes a long time with the bootstrap resampling,\n", "# so we'll save the results.\n", "@pickle_results(\"correlation_functionsdr720515.pkl\")\n", "def compute_results(Nbins=16, Nbootstraps=10, method='landy-szalay', rseed=0):\n", " np.random.seed(rseed)\n", " bins = 10 ** np.linspace(np.log10(1. / 60.), np.log10(6), 16)\n", "\n", " results = [bins]\n", " for D in [data]:\n", " results += bootstrap_two_point_angular(D['ra'],\n", " D['dec'],\n", " bins=bins,\n", " method=method,\n", " Nbootstraps=Nbootstraps)\n", "\n", " return results\n", "\n", "(bins, r_corr, r_corr_err, r_bootstraps) = compute_results()\n", "\n", "bin_centers = 0.5 * (bins[1:] + bins[:-1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr_err" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_bootstraps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Plot the results\n", "label = '$0.05<z<0.15$\\n$N=138051$'\n", "\n", "fig = plt.figure(figsize=(6, 6))\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.errorbar(bin_centers, r_corr, r_corr_err,fmt='.k', ecolor='gray', lw=1)\n", "fig.text(0.8, 0.8, label, ha='right', va='top')\n", "plt.xlabel(r'$\\theta\\ (deg)$')\n", "plt.ylabel(r'$w(\\theta)$')\n", "plt.show()\n", "fig.savefig(\"wth_dr72005015.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.errorbar(bins[0:len(bins)-1],r_corr,r_corr_err)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data=ascii.read('./input/sdssdr72_sorted_z.dat')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['z']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#m_max = 19\n", "\n", "# redshift and magnitude cuts\n", "data = data[data['z'] > 0.05]\n", "data = data[data['z'] <= 0.10]\n", "#data = data[data['petroMag_r'] < m_max]\n", "\n", "# RA/DEC cuts\n", "RAmin, RAmax = 140, 220\n", "DECmin, DECmax = 5, 45\n", "data = data[data['ra'] < RAmax]\n", "data = data[data['ra'] > RAmin]\n", "data = data[data['dec'] < DECmax]\n", "data = data[data['dec'] > DECmin]\n", "\n", "#ur = data['modelMag_u'] - data['modelMag_r']\n", "#flag_red = (ur > 2.22)\n", "#flag_blue = ~flag_red\n", "\n", "#datag \n", "\n", "print \"data size:\"\n", "print \" total gals: \", len(data)\n", "#print \" blue gals:\", len(data_blue)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=512\n", "dr72hpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"./output/pixdatadr7200501.dat\",'w')\n", "pixdata.write(\"z\\t pix \\n\")\n", "\n", "for i in range(0,len(data)-1):\n", " pixdata.write(\"%f\\t\" %data['z'][i])\n", " pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(data['ra'][i],data['dec'][i]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"./output/pixdatadr7200501.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Set up correlation function computation\n", "# This calculation takes a long time with the bootstrap resampling,\n", "# so we'll save the results.\n", "@pickle_results(\"correlation_functionsdr720501.pkl\")\n", "def compute_results(Nbins=16, Nbootstraps=10, method='landy-szalay', rseed=0):\n", " np.random.seed(rseed)\n", " bins = 10 ** np.linspace(np.log10(1. / 60.), np.log10(6), 16)\n", "\n", " results = [bins]\n", " for D in [data]:\n", " results += bootstrap_two_point_angular(D['ra'],\n", " D['dec'],\n", " bins=bins,\n", " method=method,\n", " Nbootstraps=Nbootstraps)\n", "\n", " return results\n", "\n", "(bins, r_corr, r_corr_err, r_bootstraps) = compute_results()\n", "\n", "bin_centers = 0.5 * (bins[1:] + bins[:-1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr_err" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_bootstraps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Plot the results\n", "\n", "label = '$0.05<z<0.10$\\n$N=78939$'\n", "\n", "fig = plt.figure(figsize=(6, 6))\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.errorbar(bin_centers, r_corr, r_corr_err,fmt='.k', ecolor='gray', lw=1)\n", "fig.text(0.8, 0.8, label, ha='right', va='top')\n", "plt.xlabel(r'$\\theta\\ (deg)$')\n", "plt.ylabel(r'$w(\\theta)$')\n", "plt.show()\n", "fig.savefig(\"wth_dr720501.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.errorbar(bins[0:len(bins)-1],r_corr,r_corr_err)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data=ascii.read('./input/sdssdr72_sorted_z.dat')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['z']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#m_max = 19\n", "\n", "# redshift and magnitude cuts\n", "data = data[data['z'] > 0.10]\n", "data = data[data['z'] <= 0.15]\n", "#data = data[data['petroMag_r'] < m_max]\n", "\n", "# RA/DEC cuts\n", "RAmin, RAmax = 140, 220\n", "DECmin, DECmax = 5, 45\n", "data = data[data['ra'] < RAmax]\n", "data = data[data['ra'] > RAmin]\n", "data = data[data['dec'] < DECmax]\n", "data = data[data['dec'] > DECmin]\n", "\n", "#ur = data['modelMag_u'] - data['modelMag_r']\n", "#flag_red = (ur > 2.22)\n", "#flag_blue = ~flag_red\n", "\n", "#datag \n", "\n", "print \"data size:\"\n", "print \" total gals: \", len(data)\n", "#print \" blue gals:\", len(data_blue)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=512\n", "dr72hpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"./output/pixdatadr72001015.dat\",'w')\n", "pixdata.write(\"z\\t pix \\n\")\n", "\n", "for i in range(0,len(data)-1):\n", " pixdata.write(\"%f\\t\" %data['z'][i])\n", " pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(data['ra'][i],data['dec'][i]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"./output/pixdatadr72001015.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Set up correlation function computation\n", "# This calculation takes a long time with the bootstrap resampling,\n", "# so we'll save the results.\n", "@pickle_results(\"correlation_functionsdr72001015.pkl\")\n", "def compute_results(Nbins=16, Nbootstraps=10, method='landy-szalay', rseed=0):\n", " np.random.seed(rseed)\n", " bins = 10 ** np.linspace(np.log10(1. / 60.), np.log10(6), 16)\n", "\n", " results = [bins]\n", " for D in [data]:\n", " results += bootstrap_two_point_angular(D['ra'],\n", " D['dec'],\n", " bins=bins,\n", " method=method,\n", " Nbootstraps=Nbootstraps)\n", "\n", " return results\n", "\n", "(bins, r_corr, r_corr_err, r_bootstraps) = compute_results()\n", "\n", "bin_centers = 0.5 * (bins[1:] + bins[:-1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr_err" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_bootstraps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Plot the results\n", "\n", "label = '$0.10<z<0.15$\\n$N=59112$'\n", "fig = plt.figure(figsize=(6, 6))\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.errorbar(bin_centers, r_corr, r_corr_err,fmt='.k', ecolor='gray', lw=1)\n", "fig.text(0.8, 0.8, label, ha='right', va='top')\n", "plt.xlabel(r'$\\theta\\ (deg)$')\n", "plt.ylabel(r'$w(\\theta)$')\n", "plt.show()\n", "fig.savefig(\"wth_dr7201015.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.errorbar(bins[0:len(bins)-1],r_corr,r_corr_err)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdatab,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdatab)" ] }, { "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": [ "help(hu.mollview)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "from astroML.datasets import fetch_sdss_specgals\n", "from astroML.correlation import bootstrap_two_point_angular" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(bootstrap_two_point_angular)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(astroML.correlation)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import astroML.correlation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sklearn.neighbors" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(sklearn.neighbors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sorted and reduced column set data can now be 'read' to reduce RAM requirements of the table reading. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sdssdr72=ascii.read('./input/dssdr72_sorted_z.dat')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a healpix map with NSIDE=64 (no. of pixels = 49152 as $NPIX=12\\times NSIDE^2$) because the no. of galaxies in the survey are less. For higher resolution (later for dr12) we will consider NSIDE=512 or even 1024. For now, we will create a 64 NSIDE map." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=64\n", "dt72hpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have data of galaxies with redshifts between 0 and 0.5 ($0<z<0.5$). To look at a time slice/at a certain epoch we need to choose the list of galaxies within a redshift window. As, measurement of redshift has $\\pm 0.05$ error. We can bin all the data into redshifts with range limited to 0.05 variation each. So, we have 10 databins with almost identical redshifts. We save each databin in a different file. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "j=0\n", "for i in range(1,17):\n", " pixdata = open(\"/home/rohin/Desktop/healpix/binned1/pixdata%d_%d.dat\"%(NSIDE,i),'w')\n", " pixdata.write(\"ra\\t dec\\t z\\t pix \\n\")\n", " #for j in range(len(sdssdr72)):\n", " try:\n", " while sdssdr72[j]['z']<0.03*i:\n", " pixdata.write(\"%f\\t\" %sdssdr72[j]['ra'])\n", " pixdata.write(\"%f\\t\" %sdssdr72[j]['dec'])\n", " pixdata.write(\"%f\\t\" %sdssdr72[j]['z'])\n", " pixdata.write(\"%d\\n\" %dt72hpix.eq2pix(sdssdr72[j]['ra'],sdssdr72[j]['dec']))\n", " #print dt72hpix.eq2pix(sdssdr72[j]['ra'],sdssdr72[j]['dec'])\n", " j=j+1\n", " except:\n", " pass\n", " pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(1,17):\n", " pixdata = ascii.read(\"/home/rohin/Desktop/healpix/binned1/pixdata%d_%d.dat\"%(NSIDE,i))\n", " mpixdata = open(\"/home/rohin/Desktop/healpix/binned1/masked/pixdata%d_%d.dat\"%(NSIDE,i),'w')\n", " mpixdata.write(\"ra\\t dec\\t z\\t pix \\n\")\n", " for j in range((len(pixdata)-1)):\n", " if 100<pixdata[j]['ra']<250:\n", " mpixdata.write(\"%f\\t\" %pixdata[j]['ra'])\n", " mpixdata.write(\"%f\\t\" %pixdata[j]['dec'])\n", " mpixdata.write(\"%f\\t\" %pixdata[j]['z'])\n", " mpixdata.write(\"%d\\n\" %pixdata[j]['pix'])\n", " #pixdata.write(\"/home/rohin/Desktop/healpix/binned1/masked/pixdata_%d.dat\"%i,format='ascii')\n", " \n", " \n", " #print dt72hpix.eq2pix(sdssdr72[j]['ra'],sdssdr72[j]['dec'])\n", " mpixdata.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now, take each databin and assign the total no. of galaxies as the value of each pixel. The following routine will calculate the no. of galaxies by couting the occurence of pixel numbers in the file." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"/home/rohin/Desktop/healpix/binned1/masked/pixdata%d_2.dat\"%NSIDE)\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixcl=hu.anafast(hpixdata,lmax=300)\n", "ell = np.arange(len(pixcl))\n", "plt.figure()\n", "plt.plot(ell,np.log(pixcl))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixcl=hu.anafast(hpixdata,lmax=300)\n", "ell = np.arange(len(pixcl))\n", "plt.figure()\n", "plt.plot(ell,np.sqrt(ell*(ell+1)*pixcl/(4*math.pi)))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "theta=np.arange(0,np.pi,0.001)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "correldat = np.polynomial.legendre.legval(np.cos(theta),(2*ell+1)*np.absolute(pixcl))/(4*math.pi)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.figure()\n", "plt.plot(theta[0:600]*180/math.pi,correldat[0:600])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.figure()\n", "plt.plot(theta*180/math.pi,correldat)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randra,randdec=hu.randsphere(2200000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randhp=hu.HealPix(\"RING\",NSIDE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randhppix=randhp.eq2pix(randra,randdec)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randpixdat=np.array(np.zeros(hu.nside2npix(NSIDE)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for j in range(len(randhppix)):\n", " randpixdat[randhppix[j]]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randmaphp=hu.mollview(randpixdat)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randcl=hu.anafast(randpixdat,lmax=300)\n", "ell = np.arange(len(randcl))\n", "plt.figure()\n", "plt.plot(ell,np.sqrt(ell*(ell+1)*randcl/(4*math.pi)))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "correlrand = np.polynomial.legendre.legval(np.cos(theta),(2*ell+1)*np.absolute(randcl))/(4*math.pi)\n", "plt.figure()\n", "plt.plot(theta[0:600]*180/math.pi,correlrand[0:600])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "finalcorrel=correldat-correlrand\n", "plt.figure()\n", "plt.plot(theta[0:600]*180/math.pi,finalcorrel[0:600])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "finalpix=hpixdata-randpixdat" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(finalpix,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cl=hu.anafast(finalpix,lmax=300)\n", "ell = np.arange(len(cl))\n", "plt.figure()\n", "plt.plot(ell,np.sqrt(ell*(ell+1)*cl/(4*math.pi)))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "correlrand = np.polynomial.legendre.legval(np.cos(theta),(2*ell+1)*np.absolute(cl))/(4*math.pi)\n", "plt.figure()\n", "plt.plot(theta[0:600]*180/math.pi,correlrand[0:600])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "finalcl=pixcl-randcl\n", "correlrand = np.polynomial.legendre.legval(np.cos(theta),(2*ell+1)*np.absolute(finalcl))/(4*math.pi)\n", "plt.figure()\n", "plt.plot(theta[0:600]*180/math.pi,correlrand[0:600])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(fits)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data[1].data['z']" ] }, { "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": 1 }
mit
jasonding1354/pyDataScienceToolkits_Base
NumPy/.ipynb_checkpoints/(8)random_module-checkpoint.ipynb
10
116480
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##内容索引\n", "1. 随机数 --- 二项分布binomial\n", "2. 超几何分布 --- hypergeometric\n", "3. 连续分布 --- normal、lognormal函数" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "from matplotlib.pyplot import show, plot\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##1. 随机数\n", "随机数在蒙特卡罗方法(Monto Carlo method)、随机积分等很多方面都有应用。真随机数的产生很困难,因此在实际应用中我们通常使用伪随机数。\n", "\n", "有关随机数的函数可以在NumPy的random模块中找到。随机数发生器的核心算法是基于马特赛特旋转演算法(Mersenne Twister algorithm)。随机数可以从离散分布或连续分布中产生。\n", "\n", "**分布函数有一个可选的参数size,用于指定需要产生的随机数的数量。该参数允许设置为一个整数或元组,生成的随机数将填满指定形状的数组。**支持的离散分布包括几何分布、超几何分布和二项分布等。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###硬币赌博游戏\n", "二项分布是n个独立重复的是非试验中成功次数的离散概率分布,这些概率是固定不变的,与试验结果无关。\n", "\n", "- 现在对一个硬币赌博游戏下8份赌注\n", "- 在硬币赌博游戏中,每一轮抛9枚硬币,如果少于5枚硬币正面朝上,你将损失8份赌注中的1份;否则,你将赢得1份赌注\n", "- 初始资本为1000份赌注" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 初始化一个全0的数组来存放剩余资本\n", "# 以参数10000调用binomial函数,进行10000轮硬币赌博游戏\n", "cash = np.zeros(10000)\n", "cash[0] = 1000\n", "outcome = np.random.binomial(9, 0.5, size=len(cash))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 9\n" ] } ], "source": [ "# 模拟每一轮抛硬币的结果,更新cash数组\n", "# 打印出outcome的最大最小值,检查输出中是否有异常\n", "for i in xrange(1, len(cash)):\n", " if outcome[i] < 5:\n", " cash[i] = cash[i-1] - 1\n", " elif outcome[i] < 10:\n", " cash[i] = cash[i-1] + 1\n", " else:\n", " raise AssertionError(\"Unexpected outcome\" + outcome)\n", "\n", "print outcome.min(), outcome.max()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<function matplotlib.pyplot.show>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ulTgwOd6G8J3cLF+uV/gX1hkXRLuHv1Fr7Vxv5tDUD43E7SWGkgmH+3P5nyjR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\ndZxeUGXbJk2WFU4uHBzH6Zj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"text/plain": [ "<matplotlib.figure.Figure at 0x2b8db90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(np.arange(len(cash)), cash)\n", "show" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##2. 超几何分布\n", "超几何分布(hypergeometric distribution)是一种离散概率分布,它描述的是一个罐子有两种物件,无放回地从中抽取指定数量的物件后,抽出指定种类物件的数量。\n", "\n", "NumPy的random模块中的hypergeometric函数可以模拟这种分布。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###模拟游戏秀节目\n", "游戏规则:\n", "- 每当参赛者回答对一个问题,他们可以从一个罐子里摸出3个球并放回\n", "- 罐子里有一个“倒霉球”,一旦摸到这个球,参赛者会被扣去6分\n", "- 如果摸出3个球全部来自其余的25个普通球,可以得1分\n", "- 100道问题被正确回答,其得分情况如何?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "points = np.zeros(100)\n", "# 第一个参数是罐中普通球的个数\n", "# 第二个参数是倒霉球的个数\n", "# 第三个参数是每次摸球的个数(采样数)\n", "outcomes = np.random.hypergeometric(25, 1, 3, size=len(points))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in xrange(len(points)):\n", " if outcomes[i] == 3:\n", " points[i] = points[i-1] + 1\n", " elif outcomes[i] == 2:\n", " points[i] = points[i-1] - 6\n", " else:\n", " print outcomes[i]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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PaLsvNj1GKuySrgReCDxG0o+As4HVko4AArgdeHNjvTSbXw5MbWzeK8aso/LA9DLgyZ5b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\ntd/qU2/JazZBszh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"text/plain": [ "<matplotlib.figure.Figure at 0x533e7d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(points)\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##3. 连续分布\n", "连续分布可以用概率密度函数(Probability Density Function, PDF)来描述。**随机变量落在某一区间内的概率等于概率密度函数在该区间的曲线下方的面积。**\n", "\n", "NumPy的random模块中有一系列连续分布的函数——beta、chisquare、exponential、f、gamma、gumbel、laplace、lognormal、logistic、multivariate_normal、nonchetral_chisquare、noncentral_f、normal等。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###3.1 绘制正态分布" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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bwPclXamuix1QR5LeL2kNoVfFXZLuiR1Tlpk1E0Znzyb0VJiVxkF6\nkn5DmCDpCElrJF0cO6YunAScD5ySvB8XJJWQtNkf+HPy+Z5LaINP+/2rvPnSBzo551yNSmMN3jnn\nXAl4gnfOuRrlCd4552qUJ3jnnKtRnuCdc65GeYJ3zrka5QneOedqlCd455yrUf8fHScTBAP7ZskA\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x5f27d50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 产生指定数量的随机数\n", "N = 10000\n", "normal_values = np.random.normal(size=N)\n", "\n", "# 绘制分布直方图\n", "dummy, bins, dummy = plt.hist(normal_values, np.sqrt(N), normed=True, lw=1)\n", "sigma = 1\n", "mu = 0\n", "plot(bins, 1/(sigma*np.sqrt(2*np.pi)) * np.exp(-(bins-mu)**2 / (2*sigma**2)), lw=2)\n", "show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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5M1Gyp5T2Tuu+SD91c/fy/sCALfDcibAxRqYa1npT2E7s+TQRkYjC/MfLTily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"text/plain": [ "<matplotlib.figure.Figure at 0x73baa30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.gridspec as gridspec\n", "gs = gridspec.GridSpec(2,2)\n", "fig = plt.figure(figsize=(10,8))\n", "ax = []\n", "\n", "N = 10000\n", "sigma = 1\n", "mu = 0\n", "\n", "for a in xrange(2):\n", " for b in xrange(2):\n", " ax.append(fig.add_subplot(gs[a,b]))\n", " normal_values = np.random.normal(size=N)\n", " dummy, bins, dummy = plt.hist(normal_values, np.sqrt(N), normed=True, lw=1)\n", " ax[-1].plot(bins, 1/(sigma*np.sqrt(2*np.pi)) * np.exp(-(bins-mu)**2 / (2*sigma**2)), lw=2)\n", "\n", "# 使得子图适应figure的间距\n", "fig.tight_layout()\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###3.2 对数正态分布\n", "对数正态分布(lognormal distribution)是自然对数服从正态分布的任意随机变量的概率分布。" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x5fb9550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "N = 10000\n", "lognormal_values = np.random.lognormal(size=N)\n", "\n", "dummy, bins, dummy = plt.hist(lognormal_values, np.sqrt(N), normed=True, lw=1)\n", "sigma = 1\n", "mu = 0\n", "\n", "x = np.linspace(min(bins), max(bins), len(bins))\n", "pdf = np.exp(-(np.log(x)-mu)**2 / (2*sigma**2)) / (x*sigma*np.sqrt(2*np.pi))\n", "plot(x, pdf, lw=3)\n", "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": "2.7.5" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
carefree0910/MachineLearning
_Dist/NeuralNetworks/e_AdvancedNN/AdvancedNN.ipynb
1
95331
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "root_path = os.path.abspath(\"../../../\")\n", "if root_path not in sys.path:\n", " sys.path.append(root_path)\n", "\n", "from Util.Util import DataUtil\n", "\n", "x_cv = y_cv = None\n", "(x, y), (x_test, y_test) = DataUtil.gen_noisy_linear(one_hot=False)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "plt.rcParams[\"figure.figsize\"] = (18, 8)\n", "\n", "def draw_acc(*models):\n", " plt.figure()\n", " for nn in models:\n", " name = str(nn)\n", " el, tl = nn.log[\"train_acc\"], nn.log[\"test_acc\"]\n", " ee_base = np.arange(len(el))\n", " cse_base = np.linspace(0, len(el) - 1, len(tl))\n", " plt.plot(ee_base, el, label=\"Train acc ({})\".format(name))\n", " plt.plot(cse_base, tl, label=\"Test acc ({})\".format(name))\n", " plt.ylim(0.6, 1.05)\n", " plt.legend()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 Iter 0 Snapshot 0 (acc) - Train : 0.531000 Test : 0.514000\n", "Epoch -1 Iter -1 Snapshot -1 (acc) - Train : 1.000000 Test : 0.865333 - Time Cost: 6.677941560745239\n" ] } ], "source": [ "from _Dist.NeuralNetworks.c_BasicNN.NN import Basic\n", "\n", "base_params = {\n", " \"model_param_settings\": {\"n_epoch\": 60, \"max_epoch\": 60, \"metric\": \"acc\"},\n", "}\n", "basic = Basic(**base_params).fit(x, y, x_test, y_test, snapshot_ratio=0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [], "source": [ "from _Dist.NeuralNetworks.e_AdvancedNN.NN import Advanced\n", "\n", "numerical_idx = [True] * 100 + [False]\n", "categorical_columns = []\n", "advanced_params = {\n", " \"data_info\": {\"numerical_idx\": numerical_idx, \"categorical_columns\": categorical_columns},\n", " \"model_structure_settings\": {\"use_dndf\": False, \"use_pruner\": False}\n", "}\n", "advanced_params.update(base_params)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "====================================================================================================\n", "This is a 2-classes problem\n", "----------------------------------------------------------------------------------------------------\n", "Data : 10000 training samples, 1500 test samples\n", "Features : 0 categorical, 100 numerical\n", "====================================================================================================\n", "Deep model: DNN\n", "Deep model input: Continuous features only\n", "----------------------------------------------------------------------------------------------------\n", "----------------------------------------------------------------------------------------------------\n", "Using dropout with keep_prob = 0.5\n", "Training without batch norm\n", "Hidden units: [512, 512]\n", "====================================================================================================\n", "Wide model: logistic regression\n", "Wide model input: Continuous features only\n", "----------------------------------------------------------------------------------------------------\n", "====================================================================================================\n", "Hyper parameters\n", "----------------------------------------------------------------------------------------------------\n", "This is a Wide & Deep model\n", "----------------------------------------------------------------------------------------------------\n", "Activation : ['relu', 'relu']\n", "Batch size : 128\n", "Epoch num : 60\n", "Optimizer : Adam\n", "Metric : acc\n", "Loss : cross_entropy\n", "lr : 0.001\n", "----------------------------------------------------------------------------------------------------\n", "Pruner : None\n", "----------------------------------------------------------------------------------------------------\n", "\n", "Epoch 0 Iter 0 Snapshot 0 (acc) - Train : 0.554000 Test : 0.570000\n", "Epoch -1 Iter -1 Snapshot -1 (acc) - Train : 1.000000 Test : 0.896667 - Time Cost: 8.59854769706726\n" ] } ], "source": [ "wnd = Advanced(**advanced_params).fit(x, y, x_test, y_test, snapshot_ratio=0)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\users\\carefree0910\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\ops\\gradients_impl.py:96: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "====================================================================================================\n", "This is a 2-classes problem\n", "----------------------------------------------------------------------------------------------------\n", "Data : 10000 training samples, 1500 test samples\n", "Features : 0 categorical, 100 numerical\n", "====================================================================================================\n", "Deep model: DNN\n", "Deep model input: Continuous features only\n", "----------------------------------------------------------------------------------------------------\n", "----------------------------------------------------------------------------------------------------\n", "Using dropout with keep_prob = 0.5\n", "Training without batch norm\n", "Hidden units: [512, 512]\n", "====================================================================================================\n", "Wide model: DNDF\n", "Wide model input: Continuous features only\n", "----------------------------------------------------------------------------------------------------\n", "Using DNDF with n_tree = 10, tree_depth = 4\n", "====================================================================================================\n", "Hyper parameters\n", "----------------------------------------------------------------------------------------------------\n", "This is a hybrid model\n", "----------------------------------------------------------------------------------------------------\n", "Activation : ['relu', 'relu']\n", "Batch size : 128\n", "Epoch num : 60\n", "Optimizer : Adam\n", "Metric : acc\n", "Loss : cross_entropy\n", "lr : 0.001\n", "----------------------------------------------------------------------------------------------------\n", "Pruner : None\n", "----------------------------------------------------------------------------------------------------\n", "\n", "Epoch 0 Iter 0 Snapshot 0 (acc) - Train : 0.514000 Test : 0.507333\n", "Epoch -1 Iter -1 Snapshot -1 (acc) - Train : 1.000000 Test : 0.896667 - Time Cost: 31.714452028274536\n" ] } ], "source": [ "advanced_params[\"model_structure_settings\"][\"use_dndf\"] = True\n", "wnd_dndf = Advanced(**advanced_params).fit(x, y, x_test, y_test, snapshot_ratio=0)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\users\\carefree0910\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\ops\\gradients_impl.py:96: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "====================================================================================================\n", "This is a 2-classes problem\n", "----------------------------------------------------------------------------------------------------\n", "Data : 10000 training samples, 1500 test samples\n", "Features : 0 categorical, 100 numerical\n", "====================================================================================================\n", "Deep model: DNN\n", "Deep model input: Continuous features only\n", "----------------------------------------------------------------------------------------------------\n", "----------------------------------------------------------------------------------------------------\n", "Using dropout with keep_prob = 0.5\n", "Training without batch norm\n", "Hidden units: [512, 512]\n", "====================================================================================================\n", "Wide model: DNDF\n", "Wide model input: Continuous features only\n", "----------------------------------------------------------------------------------------------------\n", "Using DNDF with n_tree = 10, tree_depth = 4\n", "====================================================================================================\n", "Hyper parameters\n", "----------------------------------------------------------------------------------------------------\n", "This is a hybrid model\n", "----------------------------------------------------------------------------------------------------\n", "Activation : ['relu', 'relu']\n", "Batch size : 128\n", "Epoch num : 60\n", "Optimizer : Adam\n", "Metric : acc\n", "Loss : cross_entropy\n", "lr : 0.001\n", "----------------------------------------------------------------------------------------------------\n", "Pruner : \n", "-> alpha : 0.01\n", "-> beta : 1\n", "-> eps : 1e-12\n", "-> gamma : 1\n", "-> max_ratio : 1.0\n", "-> method : soft_prune\n", "----------------------------------------------------------------------------------------------------\n", "\n", "Epoch 0 Iter 0 Snapshot 0 (acc) - Train : 0.513000 Test : 0.509333\n", "Epoch -1 Iter -1 Snapshot -1 (acc) - Train : 0.993000 Test : 0.916667 - Time Cost: 38.59485220909119\n" ] } ], "source": [ "advanced_params[\"model_structure_settings\"][\"use_pruner\"] = True\n", "wnd_dndf_pruned = Advanced(**advanced_params).fit(x, y, x_test, y_test, snapshot_ratio=0)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BasicNN acc - Train : 1.00000000 CV : None Test : 0.86533333\n", "WnD acc - Train : 0.99910000 CV : None Test : 0.89666667\n", "WnD & DNDF acc - Train : 1.00000000 CV : None Test : 0.89666667\n", "WnD & DNDF & Pruner acc - Train : 0.99540000 CV : None Test : 0.91666667\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x226f2b7e9b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"BasicNN \", end=\"\")\n", "basic.evaluate(x, y, x_cv, y_cv, x_test, y_test)\n", "print(\"WnD \", end=\"\")\n", "wnd.evaluate(x, y, x_cv, y_cv, x_test, y_test)\n", "print(\"WnD & DNDF \", end=\"\")\n", "wnd_dndf.evaluate(x, y, x_cv, y_cv, x_test, y_test)\n", "print(\"WnD & DNDF & Pruner \", end=\"\")\n", "wnd_dndf_pruned.evaluate(x, y, x_cv, y_cv, x_test, y_test)\n", "draw_acc(basic, wnd_dndf, wnd_dndf_pruned)" ] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
googleinterns/bizview-semi-supervised-learning
Utilities/read_TFRecord.ipynb
1
3241
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# read_TFRecord\n", "This file reads in tfrecord file and parsed the tfexample inside it.\n", "And present the contents of the tfexamples." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import tensorflow.compat.v1 as tf\n", "import matplotlib.pyplot as plt\n", "import IPython.display as display\n", "tf.enable_eager_execution()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "DATASET_FILE_PATH = '../Mixmatch/ML_DATA/streetview_v4_64-test.tfrecord'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Parse the label and image data from the tfrecord file.\n", "# Input(file_dir: String), Output(tf.data.TFRecordDataset)\n", "def parse_tf_records(file_dir):\n", " raw_image_dataset = tf.data.TFRecordDataset(file_dir)\n", "\n", " # Create a dictionary describing the features.\n", " image_feature_description = {\n", " 'label': tf.io.FixedLenFeature([], tf.int64),\n", " 'image': tf.io.FixedLenFeature([], tf.string),\n", " 'texts': tf.io.FixedLenFeature([], tf.string),\n", " 'embeddings': tf.io.VarLenFeature(dtype=tf.float32)\n", " }\n", "\n", " def _parse_image_function(example_proto):\n", " # Parse the input tf.Example proto using the dictionary above.\n", " return tf.io.parse_single_example(example_proto, image_feature_description)\n", "\n", " parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n", " \n", " return parsed_image_dataset\n", "\n", "parsed_image_dataset = parse_tf_records(DATASET_FILE_PATH)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cnt = 0\n", "distribution = {0:0, 1:0}\n", "\n", "for image_features in parsed_image_dataset:\n", " # Calculate the distribution of the dataset\n", " cnt += 1\n", " distribution[int(image_features['label'])] += 1\n", "\n", " # Print out info of the tfexample, and display the image.\n", " print(int(image_features['label']))\n", " print(image_features['texts'])\n", " image_raw = image_features['image'].numpy()\n", " display.display(display.Image(data=image_raw))\n", " \n", "print(cnt)\n", "print(distribution)" ] } ], "metadata": { "environment": { "name": "tf-gpu.1-15.m49", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/tf-gpu.1-15:m49" }, "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
uber/pyro
tutorial/source/RSA-hyperbole.ipynb
1
72113
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Understanding Hyperbole using RSA\n", "\n", " \"My new kettle cost a million dollars.\"\n", "\n", "Hyperbole -- using an exagerated utterance to convey strong opinions -- is a common non-literal use of language. Yet non-literal uses of langauge are impossible under the simplest RSA model. Kao, et al, suggested that two ingredients could be added to ennable RSA to capture hyperbole. First, the state conveyed by the speaker and reasoned about by the listener should include affective dimensions. Second, the speaker only intends to convey information relevant to a particular topic, such as \"how expensive was it?\" or \"how am I feeling about the price?\"; pragmatic listeners hence jointly reason about this topic and the state." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#first some imports\n", "import torch\n", "torch.set_default_dtype(torch.float64) # double precision for numerical stability\n", "\n", "import collections\n", "import argparse\n", "import matplotlib.pyplot as plt\n", "\n", "import pyro\n", "import pyro.distributions as dist\n", "import pyro.poutine as poutine\n", "\n", "from search_inference import HashingMarginal, memoize, Search" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As in the simple RSA example, the inferece helper `Marginal` takes an un-normalized stochastic function, constructs the distribution over execution traces by using `Search`, and constructs the marginal distribution on return values (via `HashingMarginal`)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def Marginal(fn):\n", " return memoize(lambda *args: HashingMarginal(Search(fn).run(*args)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The domain for this example will be states consisting of price (e.g. of a tea kettle) and the speaker's emotional arousal (whether the speaker thinks this price is irritatingly expensive). Priors here are adapted from experimental data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "State = collections.namedtuple(\"State\", [\"price\", \"arousal\"])\n", "\n", "def price_prior():\n", " values = [50, 51, 500, 501, 1000, 1001, 5000, 5001, 10000, 10001]\n", " probs = torch.tensor([0.4205, 0.3865, 0.0533, 0.0538, 0.0223, 0.0211, 0.0112, 0.0111, 0.0083, 0.0120])\n", " ix = pyro.sample(\"price\", dist.Categorical(probs=probs))\n", " return values[ix]\n", "\n", "def arousal_prior(price):\n", " probs = {\n", " 50: 0.3173,\n", " 51: 0.3173,\n", " 500: 0.7920,\n", " 501: 0.7920,\n", " 1000: 0.8933,\n", " 1001: 0.8933,\n", " 5000: 0.9524,\n", " 5001: 0.9524,\n", " 10000: 0.9864,\n", " 10001: 0.9864\n", " }\n", " return pyro.sample(\"arousal\", dist.Bernoulli(probs=probs[price])).item() == 1\n", "\n", "def state_prior():\n", " price = price_prior()\n", " state = State(price=price, arousal=arousal_prior(price))\n", " return state" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we define a version of the RSA speaker that only produces *relevant* information for the literal listener. We define relevance with respect to a Question Under Discussion (QUD) -- this can be thought of as defining the speaker's current attention or topic.\n", "\n", "The speaker is defined mathematically by:\n", "\n", "$$P_S(u|s,q) \\propto \\left[ \\sum_{w'} \\delta_{q(w')=q(w)} P_\\text{Lit}(w'|u) p(u) \\right]^\\alpha $$\n", "\n", "To implement this as a probabilistic program, we start with a helper function `project`, which takes a distribution over some (discrete) domain and a function `qud` on this domain. It creates the push-forward distribution, using `Marginal` (as a Python decorator). The speaker's relevant information is then simply information about the state in this projection." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "@Marginal\n", "def project(dist,qud):\n", " v = pyro.sample(\"proj\",dist)\n", " return qud_fns[qud](v)\n", "\n", "@Marginal\n", "def literal_listener(utterance):\n", " state=state_prior()\n", " pyro.factor(\"literal_meaning\", 0. if meaning(utterance, state.price) else -999999.)\n", " return state\n", "\n", "@Marginal\n", "def speaker(state, qud):\n", " alpha = 1.\n", " qudValue = qud_fns[qud](state)\n", " with poutine.scale(scale=torch.tensor(alpha)):\n", " utterance = utterance_prior()\n", " literal_marginal = literal_listener(utterance)\n", " projected_literal = project(literal_marginal, qud)\n", " pyro.sample(\"listener\", projected_literal, obs=qudValue)\n", " return utterance\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The possible QUDs capture that the speaker may be attending to the price, her affect, or some combination of these. We assume a uniform QUD prior." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#The QUD functions we consider:\n", "qud_fns = {\n", " \"price\": lambda state: State(price=state.price, arousal=None),\n", " \"arousal\": lambda state: State(price=None, arousal=state.arousal),\n", " \"priceArousal\": lambda state: State(price=state.price, arousal=state.arousal),\n", "}\n", "\n", "def qud_prior():\n", " values = list(qud_fns.keys())\n", " ix = pyro.sample(\"qud\", dist.Categorical(probs=torch.ones(len(values)) / len(values)))\n", " return values[ix]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we specify the utterance meanings (standard number word denotations: \"N\" means exactly $N$) and a uniform utterance prior. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def utterance_prior():\n", " utterances = [50, 51, 500, 501, 1000, 1001, 5000, 5001, 10000, 10001]\n", " ix = pyro.sample(\"utterance\", dist.Categorical(probs=torch.ones(len(utterances)) / len(utterances)))\n", " return utterances[ix]\n", "\n", "def meaning(utterance, price):\n", " return utterance == price" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK, let's see what number term this speaker will say to express different states and QUDs." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#silly plotting helper:\n", "def plot_dist(d):\n", " support = d.enumerate_support()\n", " data = [d.log_prob(s).exp().item() for s in d.enumerate_support()]\n", " names = support\n", "\n", " ax = plt.subplot(111)\n", " width=0.3\n", " bins = list(map(lambda x: x-width/2,range(1,len(data)+1)))\n", " ax.bar(bins,data,width=width)\n", " ax.set_xticks(list(map(lambda x: x, range(1,len(data)+1))))\n", " ax.set_xticklabels(names,rotation=45, rotation_mode=\"anchor\", ha=\"right\")\n", "\n", "\n", "# plot_dist( speaker(State(price=50, arousal=False), \"arousal\") )\n", "# plot_dist( speaker(State(price=50, arousal=True), \"price\") )\n", "plot_dist( speaker(State(price=50, arousal=True), \"arousal\") )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try different values above! When will the speaker favor non-literal utterances?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, the pragmatic listener doesn't know what the QUD is and so jointly reasons abut this and the state." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "@Marginal\n", "def pragmatic_listener(utterance):\n", " state = state_prior()\n", " qud = qud_prior()\n", " speaker_marginal = speaker(state, qud)\n", " pyro.sample(\"speaker\", speaker_marginal, obs=utterance)\n", " return state" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How does this listener interpret the uttered price \"10,000\"? On the one hand this is a very unlikely price *a priori*, on the other if it were true it would come with strong arousal. Altogether this becomes a plausible *hyperbolic* utterence:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_dist( pragmatic_listener(10000) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pragmatic Halo\n", "\n", "\"It cost fifty dollars\" is often interpretted as costing *around* 50 -- plausibly 51; yet \"it cost fiftyone dollars\" is interpretted as 51 and definitely not 50. This assymetric imprecision is often called the pragmatic halo or pragmatic slack.\n", "\n", "We can extend the hyperole model to capture this additional non-literal use of numbers by including QUD functions that collapse nearby numbers and assuming that round numbers are slightly more likely (because they are less difficult to utter)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#A helper to round a number to the nearest ten:\n", "def approx(x, b=None):\n", " if b is None:\n", " b = 10.\n", " div = float(x)/b\n", " rounded = int(div) + 1 if div - float(int(div)) >= 0.5 else int(div)\n", " return int(b) * rounded\n", "\n", "#The QUD functions we consider:\n", "qud_fns = {\n", " \"price\": lambda state: State(price=state.price, arousal=None),\n", " \"arousal\": lambda state: State(price=None, arousal=state.arousal),\n", " \"priceArousal\": lambda state: State(price=state.price, arousal=state.arousal),\n", " \"approxPrice\": lambda state: State(price=approx(state.price), arousal=None),\n", " \"approxPriceArousal\": lambda state: State(price=approx(state.price), arousal=state.arousal),\n", "}\n", "\n", "def qud_prior():\n", " values = list(qud_fns.keys())\n", " ix = pyro.sample(\"qud\", dist.Categorical(probs=torch.ones(len(values)) / len(values)))\n", " return values[ix]\n", "\n", "def utterance_cost(numberUtt):\n", " preciseNumberCost = 10.\n", " return 0. if approx(numberUtt) == numberUtt else preciseNumberCost\n", "\n", "def utterance_prior():\n", " utterances = [50, 51, 500, 501, 1000, 1001, 5000, 5001, 10000, 10001]\n", " utteranceLogits = -torch.tensor(list(map(utterance_cost, utterances)),\n", " dtype=torch.float64)\n", " ix = pyro.sample(\"utterance\", dist.Categorical(logits=utteranceLogits))\n", " return utterances[ix]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The RSA speaker and listener definitions are unchanged:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "@Marginal\n", "def literal_listener(utterance):\n", " state=state_prior()\n", " pyro.factor(\"literal_meaning\", 0. if meaning(utterance, state.price) else -999999.)\n", " return state\n", "\n", "@Marginal\n", "def speaker(state, qud):\n", " alpha = 1.\n", " qudValue = qud_fns[qud](state)\n", " with poutine.scale(scale=torch.tensor(alpha)):\n", " utterance = utterance_prior()\n", " literal_marginal = literal_listener(utterance)\n", " projected_literal = project(literal_marginal, qud)\n", " pyro.sample(\"listener\", projected_literal, obs=qudValue)\n", " return utterance\n", "\n", "@Marginal\n", "def pragmatic_listener(utterance):\n", " state = state_prior()\n", " qud = qud_prior()\n", " speaker_marginal = speaker(state, qud)\n", " pyro.sample(\"speaker\", speaker_marginal, obs=utterance)\n", " return state" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK, let's see if we get the desired assymetric slack (we're only interested in the interpretted price here, so we marginalize out the arousal)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "@Marginal\n", "def pragmatic_listener_price_marginal(utterance):\n", " return pyro.sample(\"pm\", pragmatic_listener(utterance)).price\n", "\n", "plot_dist(pragmatic_listener_price_marginal(50))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_dist(pragmatic_listener_price_marginal(51))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Irony and More Complex Affect\n", "\n", "In the above hyperbole model we assumed a very simple model of affect: a single dimension with two values (high and low arousal). Actual affect is best represented as a two-dimensional space corresponding to valence and arousal. Kao and Goodman (2015) showed that extending the affect space to these two dimensions immediately introduces a new usage of numbers: verbal irony in which an utterance corresponding to a high-arousal positive valence state is used to convey a high-arousal but negative valence (or vice versa). " ] } ], "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": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
ecell/ecell4-notebooks
en/examples/example07.ipynb
5
3959388
null
gpl-2.0
davidmarcus/chutes-and-ladders
chutes_and_ladders.ipynb
1
8877187
null
gpl-2.0
harmsm/pythonic-science
reference/jupyter_reference.ipynb
1
7065
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Jupyter Reference\n", "\n", "Jupyter notebooks are a way to program interactively, incorporating text, links, math, and graphics straight into the notebook. The gold standard for a notebook is that anyone could download your notebook off the internet and run it to reproduce an analysis you report in a publication. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Jupyter notebooks are built from \"cells.\" There are two basic types of cells in jupyter. **Markdown** cells (like this one) that can hold text and **code** cells (like the next one) that hold code. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "print(\"This is a code cell\")\n", "print(5*5)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Interacting with Cells\n", " * To edit a **code** cell, just click on it. \n", " * To run a **code** cell, click on it and type `Shift+Enter`\n", " * To edit a **markdown** cell, double click on the cell. \n", " * To make the **markdown** cell look pretty, click on it and type `Shift+Enter`.\n", " * You can change the cell type with the dropdown menu above that selects `Code` or `Markdown.`\n", " * You can create a new cell by clicking `Insert->Cell` on the menu. \n", "\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Code cells\n", "Each code cell contains python code. It can be run indepedently of others. To run it, click on the cell and hit `Shift+Enter`. To run all of the cells in order, go to the menu and select `Cell->Run All`. \n", "\n", "When you run a cell, all other cells \"know\" you ran the code. Try running the three code cells that follow this one.\n", "\n", "+ run the `x = 5` cell.\n", "+ run the `print(x)` cell (it should spit out `5`).\n", "+ run the `x = 7` cell.\n", "+ re-run the `print(x)` cell (it should now spit out `7`). \n", "\n", "What matters is what cell ran **last**, not where the cell is up and down on the page. Because `x = 7` was the last code ran, `print(x)` will now return `7` wherever that command is in the notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "x = 5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "print(x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "x = 7" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Sometimes bad things happen\n", "+ If your code is frozen, you can go to `Kernel->Interrupt`. \n", "+ To restart the whole session (clearing all previous runs) go to the menu and select `Kernel->Restart`. You'll have to rerun all cells at this point.\n", "\n", "### Graphics\n", "\n", "Lots of code needs to dump out a graph. This can be achieved by putting the \"magic\" directive `%matplotlib inline` in a code cell (this usually goes at the very top of the notebook). After that is called, all graphs will be captured by the notebook. Run the following to cells to see how it works." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "plt.plot([1,2,3],[4,5,6])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Markdown cells\n", "\n", "## <font color=\"red\">MARKDOWN CELLS ARE YOUR LAB NOTEBOOK</font>\n", "\n", "This is the place you should take notes describing what you're doing and why. Markdown let you annotate your science with pretty formatting. You write text normally, but then add a few simple [markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet) flags to make the cell pretty.\n", "\n", "### Formatting is not hard\n", " + **This is bold text.** \n", " + *This is italic text.*\n", " + This is math $x = 2$ using LaTeX. \n", " + This is code: `x = 2`.\n", " + [This is a link](https://google.com). \n", " \n", "### Lists aren't hard:\n", " + this\n", " + is\n", " + a\n", " + list\n", " \n", "### Neither are numbered lists:\n", " 1. this\n", " 2. is \n", " 3. a \n", " 4. numbered\n", " 5. list\n", " \n", "### You can put in a **big** equation:\n", "\n", "$$x = \\frac{sin(y)}{\\sqrt{e^{-z}}}$$\n", "\n", "### Or multiple lines of code:\n", "\n", "```python\n", "# This is a multi-line code block. It won't run, but it will look nice\n", "\n", "x = 2\n", "print(x*x)\n", "\n", "# See?\n", "```\n", "\n", "### You can make tables\n", "\n", "| Tables | Are | Cool |\n", "| ------------- | ------------- | ----- |\n", "| *a* | $x=7$ | 1600 |\n", "| **b** | `print(x)` | 12 |\n", "\n", "### There are several levels of headers\n", "\n", "# Biggest\n", "## Big\n", "### Still big\n", "#### Bold, not big\n", "##### Italic, not big\n", "\n", "\n", "### You can even use html to make things really interesting. \n", "\n", "Like <font color=\"purple\">adding color</font>.\n", "\n", "\n", "\n", " \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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 1 }
unlicense
PyladiesMx/Pyladies_ifc
5. Diccionarios/PythonDictionaries.ipynb
1
18738
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Hashing, hash tables (tabla hash) y diccionarios en Python\n", "\n", "En esta reunión veremos brevemente una nueva estructura de datos en Python llamada diccionarios y para entender mejor su funcionalidad hablaremos un poco de unos conceptos que en ciencias de la computación se llaman *hash tables*\n", "\n", "Empezaremos con hashing y hash tables.\n", "\n", "Imagina que estás en mi cuarto y que yo soy una persona en extremo desordenada.Mi ropa está tirada en el piso, sobre las sillas, en el baño; hay platos con comida sobre la cama, mesa, fregadero; y los zapatos están debajo de la mesa, en el clóset, bajo la cama... En fin, todo una zona de desastre!! Ahora imagina que te pido que me ayudes a encontrar un anillo que quiero mostrarte. __¿Cómo lo harías? ¿Cuánto tiempo tardarías?__ \n", "\n", "Pues lo que la mayoría de la gente haría es empezar a hacer una búsqueda de uno pasando por diferentes lugares. En el mejor de los casos, asumamos que tuviste suerte y lo encontraste en el primer intento. En este caso no tuviste que gastar mucha energía ni tiempo en encontrarlo. Ahora veamos la contraparte, en el peor de los casos, lo vas a encontrar después de haber puesto de cabeza mi cuarto y haber buscado en cada rincón, debajo de cada objeto. Esta vez tuviste que invertir mucho tiempo y energía en encontrar el anillo que yo quería mostrarte (y al final ni siquiera te gustó u.u)\n", "\n", "Pensemos un momento en qué estrategia usaríamos para resolver este problema..." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Tiempo para la reflexión\n", "\n", "## 3...\n", "\n", "\n", "## 2...\n", "\n", "\n", "## 1...\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Creo que todos estaríamos de acuerdo con que la solución a mi problema sería ordenar mi cuarto, pero reflexionemos acerca de lo que hacemos cuando limpiamos nuestro cuarto.\n", "\n", "Básicamente lo que haga cuando ordeno mi cuarto es algo así como crear contenedores que tengan una categoría especial de objetos, por ejemplo mi clóset es un contenedor para ropa, zapatos, calcetas, etc., mi librero es un contenedor para libros, libretas usadas, documentos, películas, videojuegos, etc., y mi tocador es un contenedor para espejo, crema, anillos, relojes, etc. A su vez, estos contenedores se pueden subdividir en nuevos contenedores, en el clóset tengo cajones para calcetas, pijamas, un espacio para zapatos y otro para colgar vestidos y chamarras.\n", "\n", "Cuando tengo todo arreglado dentro de los contenedores es más fácil para mi buscar un objeto en específico que si tuviera que buscarlo en un mar de desorden. Así si quiero mi anillo yo sé de antemano que tengo que buscar primero en el contenedor \"tocador\", luego dentro de eso hay un cajón para \"accesorios\" y dentro de ese cajón debería de ser capaz de encontrar mi anillo.\n", "\n", "Toda esta historia no es para presumir mi naturaleza desordenada, sino para explicar el concepto de hash tables.\n", "\n", "Las [hash tables (o tablas hash)](https://es.wikipedia.org/wiki/Tabla_hash) es una forma de arreglar datos de manera tal que tengamos pares de __*llaves-valores*__. Para hacerlo más claro las llaves en el ejemplo anterior serían los cajones y los valores serían las calcetas.\n", "\n", "La función principal de las tablas hash, como probablemente lo habrás inferido, es acceder de forma más eficiente a los valores. Así de sencillo...\n", "\n", "Para poder construir las tablas necesitamos de una [función hash (hashing)](https://es.wikipedia.org/wiki/Funci%C3%B3n_hash). Lo que esta función debe de ser capaz de hacer el tomar un par de elementos (llave, valor) y asignarles un espacio definido en la memoria...\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## ¿Qué tiene que ver todo esto con Python?\n", "\n", "Hay una implementación de tablas hash en Python y es una estructura que se llama diccionario. Justamente es lo que discutiremos hoy.\n", "\n", "Los [diccionarios en python](https://docs.python.org/3/tutorial/datastructures.html#dictionaries) son una forma de estructurar datos en los cuales vamos a encontrar elementos(valores) de acuerdo a un índice(llaves). Las llaves deben ser objetos inmutables que sean únicos, es decir que no existan dos llaves con el mismo nombre y los valores pueden ser de cualquier tipo.\n", "\n", "### Estructura de los diccionarios en Python\n", "\n", "Para que a python le quede claro que una serie de elementos son un diccionario, lo primero que tenemos que hacer es inicializarlos con las llaves __{}__. Una vez hecho esto lo que tenemos que hacer es ir escribiendo los pares llave-valor con dos puntos así `'llave':'valor'`, si queremos que nuestro diccionario tenga más de una entrada, lo que tenemos que hacer es separar con una coma cada para de llave-valor. Ejemplo de un diccionario de pares correspondientes:\n", "\n", "```python\n", "\n", "diccionario = {'Steve Jobs': 'Apple', 'Bill Gates': 'Microsoft', 'Mark Zuckerberg': 'Facebook'}\n", "\n", "```\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Ejercicio 1\n", "\n", "Crea un diccionario con los nombres de las personas en el grupo y la edad de cada uno de ellos" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Ejercicio 2\n", "\n", "¿Qué pasa si hay dos personas con el mismo nombre?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Ejercicio 3\n", "\n", "¿Cómo te imaginas que se declara un diccionario vacío?\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "deletable": true, "editable": true }, "source": [ "## Manipulación de diccionarios\n", "\n", "Como mencionamos anteriormente las llaves en los diccionarios van a ser los índices sobre los cuales vamos a poder acceder a sus valores, por ejemplo:\n", "\n", "`diccionario[llave]`\n", "\n", "Va a darte como resultado el valor de esa llave. Así como podemos acceder a los valores, de la misma forma podemos crear nuevas entradas en los diccionarios. Por ejemplo:\n", "\n", "`diccionario[llave_nueva] = valor_nuevo`\n", "\n", "va a agregar un nuevo par de llave-valor.\n", "\n", "Las iteraciones sobre los diccionarios son iguales a las iteraciones sobre otros objetos. \n", "\n", "\n", "Hay algunas funciones y métodos ya implementados en diccionarios que te pueden servir para operar sobre estos. A continuación te presento una lista de cosas que puedes hacer:\n", "\n", "1. `len(diccionario)`: Esto te permite saber cuántas entradas tiene tu diccionario.\n", "\n", "2. `del diccionario[llave]`: Con esto puedes borrar un elemento específico de un diccionario.\n", "\n", "3. `diccionario.pop[llave]`:Con esto puedes hacer algo parecido pero además de quitar ese elemento del diccionario, puedes guardar el valor de esa llave en una variable.\n", "\n", "4. `diccionario.popitem()`: Con este método puedes sacar un par de llave-valor de un diccionario y esto lo hace en forma de tuple(otro tipo de estructura de datos que veremos más adelante). El par llave-valor que se obtiene es arbitrario.\n", "\n", "5. `llave in diccionario`: Esto te permite saber si una llave en específico existe en un diccionario. Es exactamente como lo hacíamos en listas y strings... Como resultado obtienes una expresión booleana que te dice True si el elemento existe y False en caso de que no exista. Funciona igual con `not in`.\n", "\n", "6. `diccionario.clear()`: Este método te permite vaciar el contenido de un diccionario.\n", "\n", "7. `diccionario.update(diccionario2)`: Te permite unir dos diccionarios, y si tienen llaves iguales, lo que hacen es tomar los valores como un set(otro tipo de estructura de datos).\n", "\n", "8. `diccionario.copy()`: Esto te resulta en una copia superficial del diccionario.\n", "\n", "9. `diccionario.keys()`: Retorna una lista de las llaves del diccionario.\n", "\n", "10. `diccionario.values()`: Retorna una lista de los valores del diccionario.\n", "\n", "Recuerda que si quieres saber que otros métodos y funciones hay simpre puedes usar el __\"?\"__ o presionar *tab* después de escribir el nombre del diccionario seguido de un punto." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "deletable": true, "editable": true }, "source": [ "### Ejercicio 4\n", "\n", "Crea una función que te de como resultado un diccionario con las cuentas de una palabra. Por ejemplo:\n", "\n", "`contador('parangaricutirimicuaro')`\n", "\n", "debe darte como resultado\n", "\n", "`{'a': 4,\n", " 'c': 2,\n", " 'g': 1,\n", " 'i': 4,\n", " 'm': 1,\n", " 'n': 1,\n", " 'o': 1,\n", " 'p': 1,\n", " 'r': 4,\n", " 't': 1,\n", " 'u': 2}`\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "### Ejercicio 5\n", "\n", "Ahora haremos una función que en lugar de contar letras en palabras, cuente palabras en textos. Para esto vamos a usar un fragmento del poema de Sor Juana Inés de la Cruz llamado \"Hombres necios\"\n", "\n", ">Hombres necios que acusáis\n", "a la mujer sin razón,\n", "sin ver que sois la ocasión\n", "de lo mismo que culpáis:\n", "\n", ">si con ansia sin igual\n", "solicitáis su desdén,\n", "¿por qué queréis que obren bien\n", "si las incitáis al mal?\n", "\n", ">Combatís su resistencia\n", "y luego, con gravedad,\n", "decís que fue liviandad\n", "lo que hizo la diligencia.\n", "\n", "Como pista les dejo la función `string.split()`\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "#Código que ya preparado para tener en un string el fragmento de sor Juana\n", "\n", "sor_juana = open('sor_juana.txt', 'r').read()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "sor_juana" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "split = sor_juana.split()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "word_counter(split)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Bonus \n", "\n", "Las que quieran pueden hacer su código de tal modo que las mayúsculas o los signos no hagan que las palabras sean diferentes y se cuenten en dos categorías. Ejemplo\n", "\n", "*Que* es igual a *qué* por lo tanto deberían contarse como 2 y no como uno cada uno en su categoría." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "### Solución propuesta por Erika Peláez\n", "\n", "Aquí hago una aclaración porque creo que es didáctico mostrar que hay muchas formas de atacar los problemas en Python. La que muestro a continuación es la más clara en cuanto a sintaxis **en el nivel** que estamos viendo. Sin embargo hay que notar que hay soluciones que ocupan menos tiempo de cómputo y por lo tanto serían las más óptimas. En [esta página](https://docs.python.org/3/library/stdtypes.html#str.translate) se encuentran las descripciones de algunas de las funciones que utilicé. [En esta otra](http://stackoverflow.com/questions/1276764/stripping-everything-but-alphanumeric-chars-from-a-string-in-python) hay unos ejemplos con otros códigos que hacen algo parecido pero vienen los tiempos que toman cada una de las estrategias.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "### Súper Bonus\n", "\n", "Escribe una función que te permita encriptar textos. La encriptación sería recorrer el alfabeto en un número determinado de posiciones. Por ejemplo. Digamos que tengo un string que tiene el siguiente texto.\n", "\n", "\"La fiesta es a las 5\"\n", "\n", "Lo que haré es decir que voy a mover el abecedario en 2 letras. El abecedario quedaría así:\n", "\n", "a = c,\n", "b = d,\n", "c = e,\n", "d = f,\n", "e = g,\n", "f = h,\n", "g = i,\n", "h = j,\n", "i = k,\n", "j = l,\n", "k = m,\n", "l = n,\n", "m = o,\n", "n = p,\n", "o = q,\n", "p = r,\n", "q = s,\n", "r = t,\n", "s = u,\n", "t = v,\n", "u = w,\n", "v = x,\n", "w = y,\n", "x = z,\n", "y = a,\n", "z = b.\n", "\n", "Por lo tanto el texto quedaría encriptado como:\n", "\n", "\"Nc hkguvc gu c ncu 5\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": 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
Jbat1Jumper/me
notebooks/WIP.ipynb
1
1719
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "esto esta literalmente en wip\n" ] } ], "source": [ "print('esto esta literalmente en wip')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxError", "evalue": "'return' outside function (<ipython-input-2-64672a10de46>, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-2-64672a10de46>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m return\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m 'return' outside function\n" ] } ], "source": [ "return" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_ digo [return](/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ":D" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
JackWalpole/splitwavepy
devel/Error_Surf_Stack.ipynb
1
66209
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import sys\n", "sys.path.append(\"..\")\n", "import splitwavepy as sw\n", "\n", "import scipy\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Method:\n", "\n", "1) Split wave using set parameters, lots of noise, and variable polarisation.\n", "\n", "2) Try to recover the splitting parameters by error surface stacking." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# 1. Generate Synthetic Data\n", "\n", "noise_level = 0.1\n", "fast = 0.\n", "lag = 2.\n", "delta = 0.1\n", "\n", "listM = [ sw.EigenM(pol=np.random.randint(360),\n", " noise=noise_level,\n", " split = (fast, lag),\n", " delta = delta,\n", " lags=(4,)) for _ in range(40) ]\n", "\n", "# 2. Collect in Stack\n", "\n", "S = sw.measure.Stack(listM)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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u0zl5h7xp6VvgUD0KBzeJQzWR50lw3NT7KrKtiuuSqy5rZ9eRNYQXNviJsqGb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\ndJD/DynufiIHOKyPAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11550d320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 3. Plot\n", "\n", "cax = plt.contourf(S.lags,S.degs,S.stack(),26,cmap='magma')\n", "plt.colorbar(cax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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31u0HDB0Q1Eg0AJmMAIrPIlpVVVHVawlA/vUEoBoDaHptgSjN6CigjgigC4vM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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11555b940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cax = plt.contourf(S.lags,S.degs,S.stackpdf(),26,cmap='magma')\n", "plt.colorbar(cax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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sMc6xhoy+Z7f7kpALkccUUVuJu8Go2+ZmJYyuf1nGqPW+E+HwmvPOh8O8E7gN\neIzsq8QjPtsso+xNO2qN5LYTLMIc2v4z4yvuwFF3U+/jYSNNYHipNBhe6xKGF2kYd7znvFX1VuBW\n3+20lrp5b4voG5qLwGPC+o+YJ3FXxTbqLiOlTNrB2M2wDIXXvKXl1/WmI/AmiVncVd4zVcRdFnX7\nSpmMiroT7ml8tEkbcX7T0mTUSYrAa+HkD1bD4q6Crbh9Rd2xpkymJrS1i2oleQ9gx0Z1Mr248pBB\naEzg0N0Fq5x9wzD5ptPCiLtM3KZR9yhxJ8zp5FXd+vw+3meIRTNk0FLgMFtybRe505SQZ2lDOHGX\n4UvcZZhG3amafEflXcYLW6aD1bH0Hn2DE4EX9Msvdpl7yd+b3lPwkCaJPeIuI9Z0SRcYS3lXoUrq\npGr0HUzg4HytjZhk7vVGq81N4IakDc2L28fQwDJS1J2R5B2IIAIHbxIvKBOoqdwbGQFjO8nGQ4oE\n3KVJYhV3GSldUo2xlXeV1InL6BsMBA72EoegK99FPwwxUmFDuGgb/Oa4bSLuJO7qtFbeW7bPG1nV\nI8RNywJvAgd7iYP3aDxqXExhN5hk04S0odloG5K4Q9JaebvAVfQNngUObiVe0EWZu1pvxHBWZKzS\nhiTurjHW8nZNXYGDQdFZFxIvaKvMfS0IFUDYMF7ShiRuX3Ra3lVSJy6jb6g//tsoCge3Ei8YJcXQ\nYg+xYp/FmiO+hA3hpA3NirtsOGAIcU9O7GltUeVOy9slvgUOBlE4zBaQz8IOXSg6YLlAVNPChm5I\nG+zFvWXHnpH7x4FWy7vspmVVqk7a8SlwsJQ4hBN5G3Cwkp/JeiN1V/xL0u57fpJ2ZVot7ypUHXXi\nS+BAeInDYHl1TeiOl1qF9gkbwkgbkrhjo/Py9kHdhatM10HpFYmVyAuGyS5mqXsQdIHNSn5dEDYk\nabcZK3mLyF8D/w3YCXwPeFte9h4RuRA4H9gNvEtVb7PsqzGuo28wEzjUj8ILnIu8l7qCNJW9RxGX\nYbvkqkkBhDqLRSVpDzlHS8QtIocBVwMHAgqsUtVL+445GbgZeDLfdKOqXpTvewp4nsyXu1T1+LI2\nbSPv24ELVXWXiHwCuBD4ExE5iqxe5auAg4GvisiRVSoi16Vq3tuXwAEjiYMbkYMHmZfRoITLcLUu\ntm9ZQzuFDfbShk5G27uA96nqAyKyL3C/iNyuqo/2HfdvqnrGkHO8VlU3Vm3QSt6q+i89v94NnJU/\nPhO4RlV3AE/mpe5PAP6jzvk3bZ/byELpdVcdNF3/2zYaL2hc5oFxXaXItKyYL1lD9aII4yjtLTtf\nLD0mNKq6HlifP35eRB4DDgFRo9LiAAAWNklEQVT65e0Mlznv3weuzR8fQibzgqfzbXshIiuBlQDL\npvdz2J29qTNl3kTgUC8KL3ARjfdSJreY5e61fBx29R9N1szuurBhvKU9CBE5HDgOuGfA7l8RkW8C\nPwTe31OQXYF/EREF/lZVV5W1U/rqichXgYMG7Pqwqt6cH/Nhsq8Nny87Xz95J1cBrJi/3GhUfp0h\ngz4FDnYSB/ciH4SNIAeJ37dwTXBRpDcWWYM7YUM8UTY0L+3JiT111kBaKiJren5f1S9ZEVkA3AC8\nR1Wf63v+A8DPqOo2ETkd+BKwIt/3q6q6TkQOAG4XkW+r6p2jOlP6Kqrq60btF5HzgDOAU1W1eLXW\nAYf1HHZovq11FB/G0BKHwQJqunpPTKJ2WUXdtBJN3WK+LqNrCCts6I60Ddk46kaiiMwhE/fnVfXG\n/v29MlfVW0XksyKyVFU3quq6fPsGEbmJLM1sJ+9RiMhpwAeA16jqCz27VgNfEJFLyG5YrgDutWmr\nDF/Rd4Fp9Z1eKbioixmj0H3hUs4FNuXCTKquu46uob3ChtZKuxQREeAK4DFVvWTIMQcBP1ZVFZET\ngAlgk4jMBybyXPl84A3ARWVt2ua8PwPMJQvzAe5W1ber6iMich1Zsn4XcIGPkSY2mAoc6kfhBa5F\nXlBFcjEJ3oeUB2Fb1zEWWUN4YUPYKDtrr1zam/Zsq3SuBjgJOAd4SEQezLd9CHg5gKpeTjag4x0i\nsgv4KXB2LvIDgZtyh04BX1DVr5Q1aDva5JUj9l0MXGxz/rrUnS5ffNBCSxz2FotLmQ8ilDBDYyvo\nAhNRQztkDe0WNkQtbQBU9S5g5MVT1c+QBbz9258AjqnbZvQzLOsOFzRZ78S0cEPvB962oHFombcJ\nV4IGc0lDPVGDH1mDW2FXLfTbRFokdmE3TfTyDoVpFF7gUuRQLqy2y92lkAdhI2moL2qoX2U95uga\nmstjJ2lXo5Pytllt0EX5NNciH4SJ/HwI37eER2Er6AITUUPzkTU0I2zXKRGoJ+yNk+srH9tlOilv\nsBc4mEfhvfQLxpfMq9CkaE1xJWgwlzT4i6oLuhBdZ+0mYYeis/IG+/W+ez/srooZD5NRk1JvCpdi\n7sVG0lBf1NB8ZA1J2ONGK+Rts8aJq4INLqPxQVQRWeyC9yXjQdgKGswkDf6iamhW1jB+wp6c0ug/\nV8NohbxtKT6kLiVe4EvmgwgpxxhwIeiCUKKG5mUN7qPrrH0/o0TqCnvr7h/WOr6rjIW8C1xKvGCQ\nYEIKva24FHMvppIG/6KGbsoa/EbXSdaDaY28XS4P6yqVMoxhYuq61H0JuR8bQReEEDUkWRckYbun\nNfJ2Ta8AfIq8lzpya0r0oQRchgtBg5mkC3xG1eBH1uAnbw1J2LHRKnn7Ks7QL4pQMh9FLBL1hSs5\nF9hIGuKKqsGPrLN+xBNdg5mwt+1Io1CgZfKGMNV1YpR5W3At5X6akDT4japhfGQNSdiuaJ28IXx5\ntGFC6rrUfYt4ELZy7iWUqGE8ZA3houuCJO3htFLe0Fx9y15M5eZT+k0Ity4uBQ3mkob4RA3tlzUk\nYYegtfKGOARuQhsEa4prMRfYCHrmHAaihiTrKtjebGxK2DLV3kXenMhbRN4HfBJYpqob86oSlwKn\nAy8A56nqAy7a6qeQRRsl3hZ8CbkfF4KeOVcgUcN4yhraK+yuYP1pEZHDyMr2fL9n82+QlT5bAfwy\n8Df5v97oF0yS+XBCyXgQLgUN5pIGM1GDX1lDnDcZwc1QviRsd7j4JH2KrI7lzT3bzgSuzgsS3y0i\ni0RkuaoGe+UGCaoLQm9SvFVwLeeZ81pIGsKJGvzKGuIeFTKIJGw/2BYgPhNYp6rfzOuvFRwC/KDn\n96fzbY2+ijbi6xd/7BJ1jS8pz2rDUtAFpqKGMLKGeNMhbRH29p0/9nr+NlD6iRSRrwIHDdj1YbIC\nm2+w6YCIrARWAiyb3s/mVF7pqqxDSHlWe44EDXaSBjNRg39ZQxL2MGKVdp4+vho4EFBglape2nfM\nzwF/B7wa+LCqfrJn32lk9wkngc+p6sfL2iz95Krq64Z09ueBI4Ai6j4UeCAvab8OOKzn8EPzbYPO\nvwpYBbBi/vL2VQuIkNBCntW2QzkX2EoazEUNYWQN7RQ2hEuLxCrunF3A+1T1ARHZF7hfRG5X1Ud7\njtkMvAv4zd4nisgkcBnwerIsxX0isrrvuXth/ClX1YeAA3o68BRwfD7aZDXwThG5huxG5daQ+e4u\n0qSQe/Eh5wIXkgY7UYOZrCF+YUO7ouxeIhc3ud/W54+fF5HHyFLFj/YcswHYICJv7Hv6CcDavIo8\nuTfP7H3uIHwZ4VayYYJryYYKvs1TO60nFimDXzEXuBJ0QVOiBjNZQ3uFDenmYxVE5HDgOOCeik8Z\ndI+wdHSeM3Oo6uE9jxW4wNW520hMUoYwYi5wLWiwl3RBE7IG84rosUgbkriBpSKypuf3VXnadwYR\nWQDcALxHVZ/z2Zm4DNMikpz94ErSYCdqsJM1mAsb4pJ2DMybPtBL6kSmJphcWrlC1UZVPX7ouUTm\nkIn786p6Y41uVL5H2EtcBoqImOQcUszgV87gVtBgL+mZ81jKGuyEDfY1HLso7oJ50wfOPI4tB57P\nKr8CeExVL6n59PuAFSJyBJm0zwbeUvakeAzVMDHIuivRM7gXdEFMogZ7WUOqkm7CvOkDeWH7E013\no5eTgHOAh0TkwXzbh4CXA6jq5SJyELAG2A/YIyLvAY5S1edE5J3AbWRDBa9U1UfKGmzeWA3QtaF0\nw2iroMGdpGfOF5GsC9om7QVzl888Tvnv2ajqXcDID5yq/ogsJTJo361kAz0qMzbybkLYbRy90U+b\nBD1zXkeiBreyLmibtAeRRN48nZZ3SGH7FnVbbhIOog2SLvAhawgn7IWTBwfPe/eKvJckdb90Ut6+\npd2GiSqDaGMUPXN+D6IGf7KG5iLsJgQ+iGFS7yUJ3pxOyduntH0JO+Yx0f20VdAFPkVdEEtKZOHk\nwUD8o0+qCH4Ykd2wDE5n5O1D3K6F3RZRt13SEEbUBbEIexCFxHuJXeiJarRe3jFLO7ap4IPwKequ\nSbogZllXYZDQC8ZO7FMTTCyb33QvjGi1vGMom9VPLIsr9ZMkbUbbRV2XUWIfxtgJPxJaK29X4nYh\n7aaXLO2nTaM7+mlK0gXjJmsXmAjfBRsaaTUeWitvW2KQdgyLLQ08X4clneSc6AqtlLdt1N1E0Vpw\nI+sk6nKSoBPjQOvkbSPuNlQa36tdh7LuwlC8fpKoE+OKtbxF5I/I1u7eDfyzqn4g334hcH6+/V2q\nepttWzaYirsJace2+FI/KeWR6GXpbvOx2jZ8t5FW48G2evxrycr1HKOqO0TkgHz7UWTLGr4KOBj4\nqogcqaq7bdozjbpNxB1a2k2vPT2MJOpu0JRgE/6wjbzfAXxcVXfATI02yIR+Tb79SRFZS1an7T8s\n26tNKHGbSLupqi6DSCmP9pL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"text/plain": [ "<matplotlib.figure.Figure at 0x115771cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cax = plt.contourf(S.lags,S.degs,S.wolfe_silver(),26,cmap='magma_r')\n", "plt.colorbar(cax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'Pair' object has no attribute 'snrRH'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-242ab16a22ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontourf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlags\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdegs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestivo_helffrich\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m26\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'magma_r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/glyjw/Applications/splitwavepy/splitwavepy/measure/stack.py\u001b[0m in \u001b[0;36mrestivo_helffrich\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0;31m# weight by signal to noise ratio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msnrRH\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mM\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistM\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 82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;31m# should apply sigmoid (?) function to weights with min to max ranging from 1 to 21 to be consistent with original paper. Note: sheba does not bother with this.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/glyjw/Applications/splitwavepy/splitwavepy/measure/stack.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0;31m# weight by signal to noise ratio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msnrRH\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mM\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistM\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 82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;31m# should apply sigmoid (?) function to weights with min to max ranging from 1 to 21 to be consistent with original paper. Note: sheba does not bother with this.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'Pair' object has no attribute 'snrRH'" ] } ], "source": [ "cax = plt.contourf(S.lags,S.degs,S.restivo_helffrich(),26,cmap='magma_r')\n", "plt.colorbar(cax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 1. Generate Synthetic Data with variable noise\n", "\n", "# noise_level = 0.1\n", "fast = 0.\n", "lag = 2.\n", "delta = 0.1\n", "\n", "listM = [ sw.EigenM(pol=np.random.randint(360),\n", " noise=0.2*np.random.rand(1),\n", " split = (fast,lag),\n", " delta = delta,\n", " lags=(4,)) for _ in range(40) ]\n", "\n", "# 2. Collect in Stack\n", "\n", "S = sw.eigval.Stack(listM)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 3. Plot\n", "\n", "cax = plt.contourf(S.lags,S.degs,S.stack(),26,cmap='magma')\n", "plt.colorbar(cax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cax = plt.contourf(S.lags,S.degs,S.stackpdf(),26,cmap='magma')\n", "plt.colorbar(cax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cax = plt.contourf(S.lags,S.degs,S.wolfe_silver(),26,cmap='magma_r')\n", "plt.colorbar(cax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cax=plt.contourf(S.lags,S.degs,S.restivo_helffrich(),26,cmap='magma_r')\n", "plt.colorbar(cax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sigmoid (x): return 1/(1 + np.exp(-x))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = np.linspace(0,22,300)\n", "plt.plot(x,sigmoid(2*x-10))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = sw.Pair()\n", "b = a.copy()\n", "b.rotateto(b.pol())\n", "b.chop()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a.snrRH()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a.rotateto(a.pol())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dat" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
dataDogma/Computer-Science
Courses/DAT-208x/DAT208x - About.ipynb
2
2457
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DAT208x: Introduction to Python for Data Science \n", "-- --\n", "\n", "**Course Prerequisites**\n", "\n", "None, but previous experience in basic mathematics is helpful. \n", " \n", "**Course Schedule**\n", "\n", "This course is available in self-paced format. The deadlines associated with the graded quizzes and labs are set to the end date of the course, which is displayed on the course Home page. You can listen to the lecture, attempt the quizzes, and work on the labs exercises at any time prior to the deadline. You should complete the quizzes/labs on your own to fully learn the material before you start the next module.\n", "\n", "**Course Topics**\n", "\n", "_Module 1: Python Basics_\n", "\n", "Take your first steps in the world of Python. Discover the different data types and create your first variable.\n", "\n", "_Module 2: Python Lists_\n", "\n", "Get the know the first way to store many different data points under a single name. Create, subset and manipulate Lists in all sorts of ways.\n", "\n", "_Module 3: Functions and Packages_\n", "\n", "Learn how to get the most out of other people's efforts by importing Python packages and calling functions.\n", "\n", "_Module 4: Numpy_\n", "\n", "Write superfast code with Numerical Python, a package to efficiently store and do calculations with huge amounts of data.\n", "\n", "_Module 5: Matplotlib_\n", "Create different types of visualizations depending on the message you want to convey. Learn how to build complex and customized plots based on real data.\n", "\n", "_Module 6: Control flow and Pandas_\n", "\n", "Write conditional constructs to tweak the execution of your scripts and get to know the Pandas DataFrame: the key data structure for Data Science in Python.\n", "-- --" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
hglanz/phys202-2015-work
days/day05/Numpy.ipynb
1
196587
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# NumPy: Numerical Arrays for Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Learning Objectives:** Learn how to create, transform and visualize multidimensional data of a single type using Numpy. \n", "\n", "NumPy is the foundation for scientific computing and data science in Python. Its more data object is a multidimensional array with the following characteristics:\n", "\n", "* Any number of dimensions\n", "* All elements of an array have the same data type\n", "* Array elements are usually native data dtype\n", "* The memory for an array is a contiguous block that can be easily passed to other numerical libraries (BLAS, LAPACK, etc.).\n", "* Most of NumPy is implemented in C, so it is fast." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While this notebook doesn't focus on plotting, Matplotlib will be used to make a few basic plots." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `vizarray` package will be used to visualize NumPy arrays:" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "import antipackage\n", "from github.ellisonbg.misc import vizarray as va" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multidimensional array type" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the canonical way you should import Numpy:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = [0,2,4,6]\n", "a = np.array(data)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(a)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 2, 4, 6])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `vz.vizarray` function can be used to visualize a 1d or 2d NumPy array using a colormap:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'va' 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-9-5fc38d85ddca>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mva\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvizarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'va' is not defined" ] } ], "source": [ "va.vizarray(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The shape of the array:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(4,)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The number of array dimensions:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.ndim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The number of array elements:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The number of bytes the array takes up:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "32" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.nbytes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `dtype` attribute describes the \"data type\" of the elements:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dtype('int64')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.dtype" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating arrays" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Arrays can be created with nested lists or tuples:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = [[0.0,2.0,4.0,6.0],[1.0,3.0,5.0,7.0]]\n", "b = np.array(data)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 2., 4., 6.],\n", " [ 1., 3., 5., 7.]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocks24ce5a56-e2ab-412a-9af6-b025a23fc9be td {border: 1px solid white;}</style><table id=\"blocks24ce5a56-e2ab-412a-9af6-b025a23fc9be\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0]&#10;Color: (247, 251, 255)\" style=\"width: 30px; height: 30px;background-color: rgb(247, 251, 255);\"></td><td title=\"Index: [0, 1]&#10;Color: (186, 214, 235)\" style=\"width: 30px; height: 30px;background-color: rgb(186, 214, 235);\"></td><td title=\"Index: [0, 2]&#10;Color: (83, 158, 205)\" style=\"width: 30px; height: 30px;background-color: rgb(83, 158, 205);\"></td><td title=\"Index: [0, 3]&#10;Color: (11, 85, 159)\" style=\"width: 30px; height: 30px;background-color: rgb(11, 85, 159);\"></td></tr><tr><td title=\"Index: [1, 0]&#10;Color: (219, 233, 246)\" style=\"width: 30px; height: 30px;background-color: rgb(219, 233, 246);\"></td><td title=\"Index: [1, 1]&#10;Color: (137, 190, 220)\" style=\"width: 30px; height: 30px;background-color: rgb(137, 190, 220);\"></td><td title=\"Index: [1, 2]&#10;Color: (43, 123, 186)\" style=\"width: 30px; height: 30px;background-color: rgb(43, 123, 186);\"></td><td title=\"Index: [1, 3]&#10;Color: (8, 48, 107)\" style=\"width: 30px; height: 30px;background-color: rgb(8, 48, 107);\"></td></tr></tbody></table>" ], "text/plain": [ "<ipythonblocks.ipythonblocks.BlockGrid at 0x7f2a403b4810>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "va.vizarray(b)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((2, 4), 2, 8, 64)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.shape, b.ndim, b.size, b.nbytes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `arange` function is similar to Python's builtin `range` function, but creates an array:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = np.arange(0.0, 10.0, 1.0) # Step size of 1.0\n", "c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `linspace` function is similar, but allows you to specify the number of points:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "e = np.linspace(0.0, 5.0, 11) # 11 points\n", "e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are also `empty`, `zeros` and `ones` functions:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 6.91270953e-310, 1.69744513e-316, 6.91270974e-310,\n", " 6.91270974e-310],\n", " [ 6.91267078e-310, 5.04011780e-317, 0.00000000e+000,\n", " 0.00000000e+000],\n", " [ 6.91267078e-310, 6.91267078e-310, 0.00000000e+000,\n", " 0.00000000e+000],\n", " [ 6.91270974e-310, 6.91270974e-310, 0.00000000e+000,\n", " 0.00000000e+000]])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.empty((4,4))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0.],\n", " [ 0., 0., 0.],\n", " [ 0., 0., 0.]])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.zeros((3,3))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 1., 1.],\n", " [ 1., 1., 1.],\n", " [ 1., 1., 1.]])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ones((3,3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See also:\n", "\n", "* `empty_like`, `ones_like`, `zeros_like`\n", "* `eye`, `identity`, `diag`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## dtype" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Arrays have a `dtype` attribute that encapsulates the \"data type\" of each element. It can be set:\n", "\n", "* Implicitely by the element type\n", "* By passing the `dtype` argument to an array creation function\n", "\n", "Here is an integer valued array:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = np.array([0,1,2,3])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([0, 1, 2, 3]), dtype('int64'))" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a, a.dtype" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All array creation functions accept an optional `dtype` argument:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.+0.j, 0.+0.j],\n", " [ 0.+0.j, 0.+0.j]], dtype=complex64)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b = np.zeros((2,2), dtype=np.complex64)\n", "b" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 2., 4., 6., 8.])" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = np.arange(0, 10, 2, dtype=np.float)\n", "c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use the `astype` method to create a copy of the array with a given `dtype`:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 2, 4, 6, 8])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = c.astype(dtype=np.int)\n", "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPython's tab completion is useful for exploring the various available `dtypes`:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.float*?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The NumPy documentation on [dtypes](http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html) describes the many other ways of specifying dtypes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Array operations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Basic mathematical operations are **elementwise** for:\n", "\n", "* Scalars and arrays\n", "* Arrays and arrays\n", "\n", "Fill an array with a value:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.1, 0.1, 0.1],\n", " [ 0.1, 0.1, 0.1],\n", " [ 0.1, 0.1, 0.1]])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.empty((3,3))\n", "a.fill(0.1)\n", "a" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 1., 1.],\n", " [ 1., 1., 1.],\n", " [ 1., 1., 1.]])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b = np.ones((3,3))\n", "b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Addition is elementwise:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1.1, 1.1, 1.1],\n", " [ 1.1, 1.1, 1.1],\n", " [ 1.1, 1.1, 1.1]])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a+b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Division is elementwise:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 10., 10., 10.],\n", " [ 10., 10., 10.],\n", " [ 10., 10., 10.]])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b/a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As are powers:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.01, 0.01, 0.01],\n", " [ 0.01, 0.01, 0.01],\n", " [ 0.01, 0.01, 0.01]])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a**2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scalar multiplication is also elementwise:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 3.14159265, 3.14159265, 3.14159265],\n", " [ 3.14159265, 3.14159265, 3.14159265],\n", " [ 3.14159265, 3.14159265, 3.14159265]])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.pi*b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Indexing and slicing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indexing and slicing provide an efficient way of getting the values in an array and modifying them." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = np.random.rand(10,10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `enable` function is part of `vizarray` and enables a nice display of arrays:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "va.enable()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocksefd54a7e-ddf4-434b-bf6e-de26bdaff55b td {border: 1px solid white;}</style><table id=\"blocksefd54a7e-ddf4-434b-bf6e-de26bdaff55b\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0]&#10;Color: (25, 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style=\"width: 30px; height: 30px;background-color: rgb(32, 112, 180);\"></td><td title=\"Index: [9, 8]&#10;Color: (54, 134, 192)\" style=\"width: 30px; height: 30px;background-color: rgb(54, 134, 192);\"></td><td title=\"Index: [9, 9]&#10;Color: (8, 76, 149)\" style=\"width: 30px; height: 30px;background-color: rgb(8, 76, 149);\"></td></tr></tbody></table>" ], "text/plain": [ "array([[ 7.86088844e-01, 6.30221150e-01, 8.06024406e-01,\n", " 4.37956832e-02, 2.98683600e-01, 4.35470657e-01,\n", " 4.26669922e-01, 3.73541566e-02, 4.89105137e-02,\n", " 5.04741620e-01],\n", " [ 5.43732720e-01, 3.15827972e-01, 5.07269211e-01,\n", " 2.04134009e-01, 8.67723485e-01, 9.95193757e-02,\n", " 4.57010480e-01, 6.59265347e-01, 3.50341390e-01,\n", " 8.13851439e-01],\n", " [ 1.67910113e-01, 6.53718255e-01, 2.92972856e-01,\n", " 9.01651020e-01, 4.95505256e-01, 3.99009959e-01,\n", " 5.92869172e-01, 2.78978402e-01, 1.49213434e-02,\n", " 9.72136195e-01],\n", " [ 7.98143578e-01, 3.69845701e-01, 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3.76294388e-01,\n", " 6.81333907e-01, 9.39623137e-01, 3.66875349e-01,\n", " 9.60929816e-01],\n", " [ 5.75321419e-01, 6.43753454e-02, 4.49096608e-01,\n", " 8.88019696e-01, 2.84610264e-01, 9.04281357e-01,\n", " 2.75203683e-01, 7.49348598e-01, 6.67226003e-01,\n", " 8.88304014e-01]])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "List Python lists and tuples, NumPy arrays have zero-based indexing and use the `[]` syntax for getting and setting values:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.78608884399348022" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[0,0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An index of `-1` refers to the last element along that axis:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[-1,-1] == a[9,9]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract the 0th column using the `:` syntax, which denotes all elements along that axis." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blockscf54442a-66a8-4547-bb34-634e49454b57 td {border: 1px solid white;}</style><table id=\"blockscf54442a-66a8-4547-bb34-634e49454b57\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0]&#10;Color: (25, 103, 173)\" style=\"width: 30px; height: 30px;background-color: rgb(25, 103, 173);\"></td><td title=\"Index: [0, 1]&#10;Color: (92, 164, 208)\" style=\"width: 30px; height: 30px;background-color: 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130);\"></td></tr><tr><td title=\"Index: [8, 0]&#10;Color: (209, 226, 243)\" style=\"width: 30px; height: 30px;background-color: rgb(209, 226, 243);\"></td><td title=\"Index: [8, 1]&#10;Color: (122, 182, 217)\" style=\"width: 30px; height: 30px;background-color: rgb(122, 182, 217);\"></td><td title=\"Index: [8, 2]&#10;Color: (21, 98, 169)\" style=\"width: 30px; height: 30px;background-color: rgb(21, 98, 169);\"></td><td title=\"Index: [8, 3]&#10;Color: (8, 62, 129)\" style=\"width: 30px; height: 30px;background-color: rgb(8, 62, 129);\"></td><td title=\"Index: [8, 4]&#10;Color: (157, 202, 225)\" style=\"width: 30px; height: 30px;background-color: rgb(157, 202, 225);\"></td><td title=\"Index: [8, 5]&#10;Color: (157, 202, 225)\" style=\"width: 30px; height: 30px;background-color: rgb(157, 202, 225);\"></td><td title=\"Index: [8, 6]&#10;Color: (51, 131, 190)\" style=\"width: 30px; height: 30px;background-color: rgb(51, 131, 190);\"></td><td title=\"Index: [8, 7]&#10;Color: (8, 64, 130)\" style=\"width: 30px; height: 30px;background-color: rgb(8, 64, 130);\"></td><td title=\"Index: [8, 8]&#10;Color: (161, 203, 226)\" style=\"width: 30px; height: 30px;background-color: rgb(161, 203, 226);\"></td><td title=\"Index: [8, 9]&#10;Color: (8, 58, 122)\" style=\"width: 30px; height: 30px;background-color: rgb(8, 58, 122);\"></td></tr><tr><td title=\"Index: [9, 0]&#10;Color: (82, 157, 204)\" style=\"width: 30px; height: 30px;background-color: rgb(82, 157, 204);\"></td><td title=\"Index: [9, 1]&#10;Color: (234, 243, 251)\" style=\"width: 30px; height: 30px;background-color: rgb(234, 243, 251);\"></td><td title=\"Index: [9, 2]&#10;Color: (129, 186, 219)\" style=\"width: 30px; height: 30px;background-color: rgb(129, 186, 219);\"></td><td title=\"Index: [9, 3]&#10;Color: (8, 77, 150)\" style=\"width: 30px; height: 30px;background-color: rgb(8, 77, 150);\"></td><td title=\"Index: [9, 4]&#10;Color: (188, 215, 235)\" style=\"width: 30px; height: 30px;background-color: rgb(188, 215, 235);\"></td><td title=\"Index: [9, 5]&#10;Color: (8, 73, 144)\" style=\"width: 30px; height: 30px;background-color: rgb(8, 73, 144);\"></td><td title=\"Index: [9, 6]&#10;Color: (190, 216, 236)\" style=\"width: 30px; height: 30px;background-color: rgb(190, 216, 236);\"></td><td title=\"Index: [9, 7]&#10;Color: (33, 113, 181)\" style=\"width: 30px; height: 30px;background-color: rgb(33, 113, 181);\"></td><td title=\"Index: [9, 8]&#10;Color: (55, 135, 192)\" style=\"width: 30px; height: 30px;background-color: rgb(55, 135, 192);\"></td><td title=\"Index: [9, 9]&#10;Color: (8, 77, 150)\" style=\"width: 30px; height: 30px;background-color: rgb(8, 77, 150);\"></td></tr></tbody></table>" ], "text/plain": [ "array([[ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n", " 1.00000000e+00, 1.00000000e+00, 4.35470657e-01,\n", " 4.26669922e-01, 3.73541566e-02, 4.89105137e-02,\n", " 5.04741620e-01],\n", " [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n", " 1.00000000e+00, 1.00000000e+00, 9.95193757e-02,\n", " 4.57010480e-01, 6.59265347e-01, 3.50341390e-01,\n", " 8.13851439e-01],\n", " [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n", " 1.00000000e+00, 1.00000000e+00, 3.99009959e-01,\n", " 5.92869172e-01, 2.78978402e-01, 1.49213434e-02,\n", " 9.72136195e-01],\n", " [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n", " 1.00000000e+00, 1.00000000e+00, 7.54703719e-01,\n", " 2.95515517e-01, 4.66380007e-01, 8.76999455e-01,\n", " 4.42334318e-01],\n", " [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n", " 1.00000000e+00, 1.00000000e+00, 5.32967664e-01,\n", " 6.54909564e-03, 3.68745483e-01, 5.29064189e-01,\n", " 5.90511540e-01],\n", " [ 4.54264565e-01, 2.50049079e-01, 5.55925843e-02,\n", " 9.81617320e-01, 3.67646600e-01, 7.18046979e-01,\n", " 8.61974106e-01, 3.07957587e-01, 5.41643518e-01,\n", " 9.39267201e-01],\n", " [ 4.94103931e-01, 6.68557104e-01, 4.24978224e-01,\n", " 7.02038374e-01, 4.32877380e-01, 2.54139656e-01,\n", " 2.89041655e-01, 8.34200039e-01, 4.77011356e-01,\n", " 8.54627665e-01],\n", " [ 6.71257841e-01, 8.77932216e-01, 9.92802639e-01,\n", " 2.13533932e-01, 9.42605018e-04, 8.71018354e-01,\n", " 8.12625609e-01, 3.55368886e-01, 9.67785632e-01,\n", " 9.38963565e-01],\n", " [ 1.94400752e-01, 4.64026785e-01, 8.08708792e-01,\n", " 9.41873126e-01, 3.78951430e-01, 3.76294388e-01,\n", " 6.81333907e-01, 9.39623137e-01, 3.66875349e-01,\n", " 9.60929816e-01],\n", " [ 5.75321419e-01, 6.43753454e-02, 4.49096608e-01,\n", " 8.88019696e-01, 2.84610264e-01, 9.04281357e-01,\n", " 2.75203683e-01, 7.49348598e-01, 6.67226003e-01,\n", " 8.88304014e-01]])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note how even though we assigned the value to the slice, the original array was changed. This clarifies that slices are **views** of the same data, not a copy." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "va.disable()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Boolean indexing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Arrays can be indexed using other arrays that have boolean values." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ages = np.array([23,56,67,89,23,56,27,12,8,72])\n", "genders = np.array(['m','m','f','f','m','f','m','m','m','f'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Boolean expressions involving arrays create new arrays with a `bool` dtype and the elementwise result of the expression:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([False, True, True, True, False, True, False, False, False, True], dtype=bool)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages > 30" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ True, True, False, False, True, False, True, True, True, False], dtype=bool)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "genders == 'm'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Boolean expressions provide an extremely fast and flexible way of querying arrays:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ True, False, False, False, True, False, True, True, False, False], dtype=bool)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(ages > 10) & (ages < 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use a boolean array to index into the original or another array. This selects the ages of all females in the `genders` array:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([67, 89, 56, 72])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mask = (genders == 'f')\n", "ages[mask]" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([56, 67, 89, 56, 72])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages[ages>30]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reshaping, transposing" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "va.enable()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = np.random.rand(3,4)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blockse52f6f9e-4df8-4fb0-b97f-2c71f137a537 td {border: 1px solid white;}</style><table id=\"blockse52f6f9e-4df8-4fb0-b97f-2c71f137a537\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0]&#10;Color: (137, 190, 220)\" style=\"width: 30px; 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height: 30px;background-color: rgb(8, 59, 124);\"></td><td title=\"Index: [4, 1]&#10;Color: (45, 125, 187)\" style=\"width: 30px; height: 30px;background-color: rgb(45, 125, 187);\"></td></tr><tr><td title=\"Index: [5, 0]&#10;Color: (234, 242, 251)\" style=\"width: 30px; height: 30px;background-color: rgb(234, 242, 251);\"></td><td title=\"Index: [5, 1]&#10;Color: (8, 48, 107)\" style=\"width: 30px; height: 30px;background-color: rgb(8, 48, 107);\"></td></tr></tbody></table>" ], "text/plain": [ "array([[ 0.4347951 , 0.53906896],\n", " [ 0.04079732, 0.4572184 ],\n", " [ 0.30715318, 0.52362602],\n", " [ 0.15585384, 0.73605737],\n", " [ 0.91774346, 0.68806563],\n", " [ 0.10190149, 0.9588381 ]])" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.reshape(6,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `ravel` method strings the array out in one dimension:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocksd42560dd-7fd2-491a-a101-b37067000af4 td {border: 1px solid white;}</style><table id=\"blocksd42560dd-7fd2-491a-a101-b37067000af4\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0]&#10;Color: (137, 190, 220)\" style=\"width: 30px; height: 30px;background-color: rgb(137, 190, 220);\"></td><td title=\"Index: [0, 1]&#10;Color: (93, 165, 209)\" style=\"width: 30px; height: 30px;background-color: rgb(93, 165, 209);\"></td><td title=\"Index: [0, 2]&#10;Color: (247, 251, 255)\" style=\"width: 30px; height: 30px;background-color: rgb(247, 251, 255);\"></td><td title=\"Index: [0, 3]&#10;Color: (125, 184, 218)\" style=\"width: 30px; height: 30px;background-color: rgb(125, 184, 218);\"></td><td title=\"Index: [0, 4]&#10;Color: (185, 214, 234)\" style=\"width: 30px; height: 30px;background-color: rgb(185, 214, 234);\"></td><td title=\"Index: [0, 5]&#10;Color: (99, 168, 211)\" style=\"width: 30px; height: 30px;background-color: rgb(99, 168, 211);\"></td><td title=\"Index: [0, 6]&#10;Color: (222, 235, 247)\" style=\"width: 30px; height: 30px;background-color: rgb(222, 235, 247);\"></td><td title=\"Index: [0, 7]&#10;Color: (32, 111, 180)\" style=\"width: 30px; height: 30px;background-color: rgb(32, 111, 180);\"></td><td title=\"Index: [0, 8]&#10;Color: (8, 59, 124)\" style=\"width: 30px; height: 30px;background-color: rgb(8, 59, 124);\"></td><td title=\"Index: [0, 9]&#10;Color: (45, 125, 187)\" style=\"width: 30px; height: 30px;background-color: rgb(45, 125, 187);\"></td><td title=\"Index: [0, 10]&#10;Color: (234, 242, 251)\" style=\"width: 30px; height: 30px;background-color: rgb(234, 242, 251);\"></td><td title=\"Index: [0, 11]&#10;Color: (8, 48, 107)\" style=\"width: 30px; height: 30px;background-color: rgb(8, 48, 107);\"></td></tr></tbody></table>" ], "text/plain": [ "array([ 0.4347951 , 0.53906896, 0.04079732, 0.4572184 , 0.30715318,\n", " 0.52362602, 0.15585384, 0.73605737, 0.91774346, 0.68806563,\n", " 0.10190149, 0.9588381 ])" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.ravel()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [], "source": [ "va.disable()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Universal functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Universal function, or \"ufuncs,\" are functions that take and return arrays or scalars. They have the following characteristics:\n", "\n", "* Vectorized C implementations, much faster than hand written loops in Python\n", "* Allow for concise Pythonic code\n", "* Here is a complete list of the [available NumPy ufuncs](http://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs) lists the available ufuncs." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [], "source": [ "va.set_block_size(5)\n", "va.enable()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a linear sequence of values\"" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocksbcd05230-a6a3-4223-9cd0-5e41c15887cd td {border: 1px solid white;}</style><table id=\"blocksbcd05230-a6a3-4223-9cd0-5e41c15887cd\" 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242, 250)\" style=\"width: 5px; height: 5px;background-color: rgb(233, 242, 250);\"></td><td title=\"Index: [0, 8]&#10;Color: (231, 241, 250)\" style=\"width: 5px; height: 5px;background-color: rgb(231, 241, 250);\"></td><td title=\"Index: [0, 9]&#10;Color: (229, 239, 249)\" style=\"width: 5px; height: 5px;background-color: rgb(229, 239, 249);\"></td><td title=\"Index: [0, 10]&#10;Color: (227, 238, 249)\" style=\"width: 5px; height: 5px;background-color: rgb(227, 238, 249);\"></td><td title=\"Index: [0, 11]&#10;Color: (225, 237, 248)\" style=\"width: 5px; height: 5px;background-color: rgb(225, 237, 248);\"></td><td title=\"Index: [0, 12]&#10;Color: (223, 235, 247)\" style=\"width: 5px; height: 5px;background-color: rgb(223, 235, 247);\"></td><td title=\"Index: [0, 13]&#10;Color: (221, 234, 247)\" style=\"width: 5px; height: 5px;background-color: rgb(221, 234, 247);\"></td><td title=\"Index: [0, 14]&#10;Color: (219, 233, 246)\" style=\"width: 5px; height: 5px;background-color: rgb(219, 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214, 234);\"></td><td title=\"Index: [0, 30]&#10;Color: (181, 212, 233)\" style=\"width: 5px; height: 5px;background-color: rgb(181, 212, 233);\"></td><td title=\"Index: [0, 31]&#10;Color: (178, 210, 232)\" style=\"width: 5px; height: 5px;background-color: rgb(178, 210, 232);\"></td><td title=\"Index: [0, 32]&#10;Color: (175, 209, 231)\" style=\"width: 5px; height: 5px;background-color: rgb(175, 209, 231);\"></td><td title=\"Index: [0, 33]&#10;Color: (171, 208, 230)\" style=\"width: 5px; height: 5px;background-color: rgb(171, 208, 230);\"></td><td title=\"Index: [0, 34]&#10;Color: (169, 207, 229)\" style=\"width: 5px; height: 5px;background-color: rgb(169, 207, 229);\"></td><td title=\"Index: [0, 35]&#10;Color: (165, 205, 227)\" style=\"width: 5px; height: 5px;background-color: rgb(165, 205, 227);\"></td><td title=\"Index: [0, 36]&#10;Color: (161, 203, 226)\" style=\"width: 5px; height: 5px;background-color: rgb(161, 203, 226);\"></td><td title=\"Index: [0, 37]&#10;Color: (159, 202, 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187, 219);\"></td><td title=\"Index: [0, 45]&#10;Color: (125, 184, 218)\" style=\"width: 5px; height: 5px;background-color: rgb(125, 184, 218);\"></td><td title=\"Index: [0, 46]&#10;Color: (122, 182, 217)\" style=\"width: 5px; height: 5px;background-color: rgb(122, 182, 217);\"></td><td title=\"Index: [0, 47]&#10;Color: (117, 180, 216)\" style=\"width: 5px; height: 5px;background-color: rgb(117, 180, 216);\"></td><td title=\"Index: [0, 48]&#10;Color: (113, 177, 215)\" style=\"width: 5px; height: 5px;background-color: rgb(113, 177, 215);\"></td><td title=\"Index: [0, 49]&#10;Color: (109, 175, 215)\" style=\"width: 5px; height: 5px;background-color: rgb(109, 175, 215);\"></td><td title=\"Index: [0, 50]&#10;Color: (105, 173, 213)\" style=\"width: 5px; height: 5px;background-color: rgb(105, 173, 213);\"></td><td title=\"Index: [0, 51]&#10;Color: (102, 171, 212)\" style=\"width: 5px; height: 5px;background-color: rgb(102, 171, 212);\"></td><td title=\"Index: [0, 52]&#10;Color: (99, 168, 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5px; height: 5px;background-color: rgb(52, 132, 191);\"></td><td title=\"Index: [0, 68]&#10;Color: (50, 130, 190)\" style=\"width: 5px; height: 5px;background-color: rgb(50, 130, 190);\"></td><td title=\"Index: [0, 69]&#10;Color: (47, 127, 188)\" style=\"width: 5px; height: 5px;background-color: rgb(47, 127, 188);\"></td><td title=\"Index: [0, 70]&#10;Color: (44, 124, 186)\" style=\"width: 5px; height: 5px;background-color: rgb(44, 124, 186);\"></td><td title=\"Index: [0, 71]&#10;Color: (42, 122, 185)\" style=\"width: 5px; height: 5px;background-color: rgb(42, 122, 185);\"></td><td title=\"Index: [0, 72]&#10;Color: (38, 118, 184)\" style=\"width: 5px; height: 5px;background-color: rgb(38, 118, 184);\"></td><td title=\"Index: [0, 73]&#10;Color: (36, 116, 183)\" style=\"width: 5px; height: 5px;background-color: rgb(36, 116, 183);\"></td><td title=\"Index: [0, 74]&#10;Color: (33, 113, 181)\" style=\"width: 5px; height: 5px;background-color: rgb(33, 113, 181);\"></td><td title=\"Index: [0, 75]&#10;Color: (32, 111, 180)\" style=\"width: 5px; height: 5px;background-color: rgb(32, 111, 180);\"></td><td title=\"Index: [0, 76]&#10;Color: (29, 108, 177)\" style=\"width: 5px; height: 5px;background-color: rgb(29, 108, 177);\"></td><td title=\"Index: [0, 77]&#10;Color: (27, 105, 175)\" style=\"width: 5px; height: 5px;background-color: rgb(27, 105, 175);\"></td><td title=\"Index: [0, 78]&#10;Color: (25, 103, 173)\" style=\"width: 5px; height: 5px;background-color: rgb(25, 103, 173);\"></td><td title=\"Index: [0, 79]&#10;Color: (23, 100, 171)\" style=\"width: 5px; height: 5px;background-color: rgb(23, 100, 171);\"></td><td title=\"Index: [0, 80]&#10;Color: (21, 98, 169)\" style=\"width: 5px; height: 5px;background-color: rgb(21, 98, 169);\"></td><td title=\"Index: [0, 81]&#10;Color: (19, 95, 167)\" style=\"width: 5px; height: 5px;background-color: rgb(19, 95, 167);\"></td><td title=\"Index: [0, 82]&#10;Color: (17, 92, 165)\" style=\"width: 5px; height: 5px;background-color: rgb(17, 92, 165);\"></td><td title=\"Index: [0, 83]&#10;Color: (15, 90, 163)\" style=\"width: 5px; height: 5px;background-color: rgb(15, 90, 163);\"></td><td title=\"Index: [0, 84]&#10;Color: (13, 87, 161)\" style=\"width: 5px; height: 5px;background-color: rgb(13, 87, 161);\"></td><td title=\"Index: [0, 85]&#10;Color: (11, 85, 159)\" style=\"width: 5px; height: 5px;background-color: rgb(11, 85, 159);\"></td><td title=\"Index: [0, 86]&#10;Color: (9, 82, 157)\" style=\"width: 5px; height: 5px;background-color: rgb(9, 82, 157);\"></td><td title=\"Index: [0, 87]&#10;Color: (8, 80, 155)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 80, 155);\"></td><td title=\"Index: [0, 88]&#10;Color: (8, 77, 150)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 77, 150);\"></td><td title=\"Index: [0, 89]&#10;Color: (8, 74, 145)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 74, 145);\"></td><td title=\"Index: [0, 90]&#10;Color: (8, 72, 142)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 72, 142);\"></td><td title=\"Index: [0, 91]&#10;Color: (8, 69, 138)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 69, 138);\"></td><td title=\"Index: [0, 92]&#10;Color: (8, 67, 135)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 67, 135);\"></td><td title=\"Index: [0, 93]&#10;Color: (8, 64, 130)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 64, 130);\"></td><td title=\"Index: [0, 94]&#10;Color: (8, 60, 125)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 60, 125);\"></td><td title=\"Index: [0, 95]&#10;Color: (8, 58, 122)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 58, 122);\"></td><td title=\"Index: [0, 96]&#10;Color: (8, 55, 118)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 55, 118);\"></td><td title=\"Index: [0, 97]&#10;Color: (8, 53, 115)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 53, 115);\"></td><td title=\"Index: [0, 98]&#10;Color: (8, 50, 110)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 50, 110);\"></td><td title=\"Index: [0, 99]&#10;Color: (8, 48, 107)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 48, 107);\"></td></tr></tbody></table>" ], "text/plain": [ "array([ 0. , 0.12693304, 0.25386607, 0.38079911,\n", " 0.50773215, 0.63466518, 0.76159822, 0.88853126,\n", " 1.01546429, 1.14239733, 1.26933037, 1.3962634 ,\n", " 1.52319644, 1.65012947, 1.77706251, 1.90399555,\n", " 2.03092858, 2.15786162, 2.28479466, 2.41172769,\n", " 2.53866073, 2.66559377, 2.7925268 , 2.91945984,\n", " 3.04639288, 3.17332591, 3.30025895, 3.42719199,\n", " 3.55412502, 3.68105806, 3.8079911 , 3.93492413,\n", " 4.06185717, 4.1887902 , 4.31572324, 4.44265628,\n", " 4.56958931, 4.69652235, 4.82345539, 4.95038842,\n", " 5.07732146, 5.2042545 , 5.33118753, 5.45812057,\n", " 5.58505361, 5.71198664, 5.83891968, 5.96585272,\n", " 6.09278575, 6.21971879, 6.34665183, 6.47358486,\n", " 6.6005179 , 6.72745093, 6.85438397, 6.98131701,\n", " 7.10825004, 7.23518308, 7.36211612, 7.48904915,\n", " 7.61598219, 7.74291523, 7.86984826, 7.9967813 ,\n", " 8.12371434, 8.25064737, 8.37758041, 8.50451345,\n", " 8.63144648, 8.75837952, 8.88531256, 9.01224559,\n", " 9.13917863, 9.26611167, 9.3930447 , 9.51997774,\n", " 9.64691077, 9.77384381, 9.90077685, 10.02770988,\n", " 10.15464292, 10.28157596, 10.40850899, 10.53544203,\n", " 10.66237507, 10.7893081 , 10.91624114, 11.04317418,\n", " 11.17010721, 11.29704025, 11.42397329, 11.55090632,\n", " 11.67783936, 11.8047724 , 11.93170543, 12.05863847,\n", " 12.1855715 , 12.31250454, 12.43943758, 12.56637061])" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t = np.linspace(0.0, 4*np.pi, 100)\n", "t" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take the $sin$ of each element of the array:" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocks30af4177-ac61-4d11-a0ec-237ef06f349e td {border: 1px solid white;}</style><table id=\"blocks30af4177-ac61-4d11-a0ec-237ef06f349e\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0]&#10;Color: (106, 174, 214)\" style=\"width: 5px; height: 5px;background-color: rgb(106, 174, 214);\"></td><td title=\"Index: [0, 1]&#10;Color: (86, 160, 206)\" style=\"width: 5px; height: 5px;background-color: rgb(86, 160, 206);\"></td><td title=\"Index: [0, 2]&#10;Color: (65, 145, 198)\" style=\"width: 5px; height: 5px;background-color: rgb(65, 145, 198);\"></td><td title=\"Index: [0, 3]&#10;Color: (50, 130, 190)\" style=\"width: 5px; height: 5px;background-color: rgb(50, 130, 190);\"></td><td title=\"Index: [0, 4]&#10;Color: (34, 114, 182)\" style=\"width: 5px; height: 5px;background-color: rgb(34, 114, 182);\"></td><td title=\"Index: [0, 5]&#10;Color: (24, 101, 172)\" style=\"width: 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-5.13677392e-01,\n", " -6.18158986e-01, -7.12694171e-01, -7.95761841e-01,\n", " -8.66025404e-01, -9.22354294e-01, -9.63842159e-01,\n", " -9.89821442e-01, -9.99874128e-01, -9.93838464e-01,\n", " -9.71811568e-01, -9.34147860e-01, -8.81453363e-01,\n", " -8.14575952e-01, -7.34591709e-01, -6.42787610e-01,\n", " -5.40640817e-01, -4.29794912e-01, -3.12033446e-01,\n", " -1.89251244e-01, -6.34239197e-02, 6.34239197e-02,\n", " 1.89251244e-01, 3.12033446e-01, 4.29794912e-01,\n", " 5.40640817e-01, 6.42787610e-01, 7.34591709e-01,\n", " 8.14575952e-01, 8.81453363e-01, 9.34147860e-01,\n", " 9.71811568e-01, 9.93838464e-01, 9.99874128e-01,\n", " 9.89821442e-01, 9.63842159e-01, 9.22354294e-01,\n", " 8.66025404e-01, 7.95761841e-01, 7.12694171e-01,\n", " 6.18158986e-01, 5.13677392e-01, 4.00930535e-01,\n", " 2.81732557e-01, 1.58001396e-01, 3.17279335e-02,\n", " -9.50560433e-02, -2.20310533e-01, -3.42020143e-01,\n", " -4.58226522e-01, -5.67059864e-01, -6.66769001e-01,\n", " -7.55749574e-01, -8.32569855e-01, -8.95993774e-01,\n", " -9.45000819e-01, -9.78802446e-01, -9.96854776e-01,\n", " -9.98867339e-01, -9.84807753e-01, -9.54902241e-01,\n", " -9.09631995e-01, -8.49725430e-01, -7.76146464e-01,\n", " -6.90079011e-01, -5.92907929e-01, -4.86196736e-01,\n", " -3.71662456e-01, -2.51147987e-01, -1.26592454e-01,\n", " -4.89858720e-16])" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sin(t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As the next two examples show, multiple ufuncs can be used to create complex mathematical expressions that can be computed efficiently:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blockse79f3a05-6a41-4a97-9060-5edd6cf96814 td {border: 1px solid white;}</style><table 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title=\"Index: [0, 82]&#10;Color: (41, 121, 185)\" style=\"width: 5px; height: 5px;background-color: rgb(41, 121, 185);\"></td><td title=\"Index: [0, 83]&#10;Color: (37, 117, 183)\" style=\"width: 5px; height: 5px;background-color: rgb(37, 117, 183);\"></td><td title=\"Index: [0, 84]&#10;Color: (33, 113, 181)\" style=\"width: 5px; height: 5px;background-color: rgb(33, 113, 181);\"></td><td title=\"Index: [0, 85]&#10;Color: (30, 109, 178)\" style=\"width: 5px; height: 5px;background-color: rgb(30, 109, 178);\"></td><td title=\"Index: [0, 86]&#10;Color: (27, 105, 175)\" style=\"width: 5px; height: 5px;background-color: rgb(27, 105, 175);\"></td><td title=\"Index: [0, 87]&#10;Color: (24, 101, 172)\" style=\"width: 5px; height: 5px;background-color: rgb(24, 101, 172);\"></td><td title=\"Index: [0, 88]&#10;Color: (21, 97, 169)\" style=\"width: 5px; height: 5px;background-color: rgb(21, 97, 169);\"></td><td title=\"Index: [0, 89]&#10;Color: (18, 93, 166)\" style=\"width: 5px; height: 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style=\"width: 5px; height: 5px;background-color: rgb(8, 57, 121);\"></td><td title=\"Index: [0, 98]&#10;Color: (8, 52, 113)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 52, 113);\"></td><td title=\"Index: [0, 99]&#10;Color: (8, 48, 107)\" style=\"width: 5px; height: 5px;background-color: rgb(8, 48, 107);\"></td></tr></tbody></table>" ], "text/plain": [ "array([ 1. , 1.42800252, 1.65508314, 1.85352498,\n", " 2.0391912 , 2.21811727, 2.39335452, 2.56669572,\n", " 2.73930019, 2.91197017, 3.0852899 , 3.25970239,\n", " 3.43555487, 3.61312719, 3.79265037, 3.97431915,\n", " 4.15830075, 4.34474111, 4.53376955, 4.72550211,\n", " 4.92004424, 5.11749275, 5.31793737, 5.52146205,\n", " 5.72814588, 5.93806395, 6.15128796, 6.36788679,\n", " 6.58792691, 6.81147279, 7.03858718, 7.26933139,\n", " 7.50376552, 7.74194862, 7.9839389 , 8.22979383,\n", " 8.47957029, 8.73332467, 8.99111297, 9.25299087,\n", " 9.5190138 , 9.78923703, 10.0637157 , 10.3425049 ,\n", " 10.62565969, 10.91323513, 11.20528637, 11.50186863,\n", " 11.80303726, 12.10884775, 12.41935577, 12.73461719,\n", " 13.05468809, 13.37962479, 13.70948387, 14.04432217,\n", " 14.38419685, 14.72916532, 15.07928535, 15.43461502,\n", " 15.79521274, 16.16113729, 16.53244781, 16.90920379,\n", " 17.29146511, 17.67929205, 18.07274529, 18.47188589,\n", " 18.87677534, 19.28747555, 19.70404886, 20.12655804,\n", " 20.5550663 , 20.9896373 , 21.43033517, 21.87722446,\n", " 22.33037022, 22.78983796, 23.25569367, 23.72800383,\n", " 24.20683537, 24.69225577, 25.18433296, 25.68313539,\n", " 26.18873203, 26.70119236, 27.22058635, 27.74698452,\n", " 28.28045791, 28.82107809, 29.36891717, 29.9240478 ,\n", " 30.48654317, 31.05647702, 31.63392365, 32.21895792,\n", " 32.81165525, 33.41209162, 34.0203436 , 34.63648831])" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.exp(np.sqrt(t))" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [], "source": [ "va.disable()\n", "va.set_block_size(30)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f2a40339150>]" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ba7805UfHLvt61rPEapt7SU7wkJOuCWdzqbwgjViPm9Najx1xapp6qWvpZe2S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\neoJO5RIRRyiwJahiPTFsXVVA78AoB6v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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2a402e3110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(t, np.exp(-0.1*t)*np.sin(t))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general, you should always try to use ufuncs rather than do computations using for loops. These types of array based computations are referred to as *vectorized*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic data processing" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ages = np.array([23,56,67,89,23,56,27,12,8,72])\n", "genders = np.array(['m','m','f','f','m','f','m','m','m','f'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Numpy has a basic set of methods and function for computing basic quantities about data." ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(8, 89)" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages.min(), ages.max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the mean:" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "43.299999999999997" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the variance and standard deviation:" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(711.21000000000004, 26.668520768876554)" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages.var(), ages.std()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `bincount` function counts how many times each value occurs in the array:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 2, 0, 0, 0, 1, 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, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n", " 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.bincount(ages)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `cumsum` and `cumprod` methods compute cumulative sums and products:" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 23, 79, 146, 235, 258, 314, 341, 353, 361, 433])" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages.cumsum()" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 23, 1288, 86296,\n", " 7680344, 176647912, 9892283072,\n", " 267091642944, 3205099715328, 25640797722624,\n", " 1846137436028928])" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages.cumprod()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most of the functions and methods above take an `axis` argument that will apply the action along a particular axis:" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[2, 6, 5, 2],\n", " [7, 4, 1, 8],\n", " [6, 1, 3, 0]])" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.random.randint(0,10,(3,4))\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With `axis=0` the action takes place along rows:" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([15, 11, 9, 10])" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.sum(axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With `axis=1` the action takes place along columns:" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([15, 20, 10])" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.sum(axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `unique` function is extremely useful in working with categorical data:" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['f', 'm'], \n", " dtype='|S1')" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.unique(genders)" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array(['f', 'm'], \n", " dtype='|S1'), array([4, 6]))" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.unique(genders, return_counts=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The where function allows you to apply conditional logic to arrays. Here is a rough sketch of how it works:\n", "\n", "```python\n", "def where(condition, if_false, if_true):\n", "```" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([1, 0, 0, 0, 1, 0, 1, 1, 1, 0])" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.where(ages>30, 0, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `if_false` and `if_true` values can be arrays themselves:" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 56, 67, 89, 0, 56, 0, 0, 0, 72])" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.where(ages<30, 0, ages)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## File IO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NumPy has a a number of different function to reading and writing arrays to and from disk." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Single array, binary format" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.11893738, 0.34881727, 0.04730572, 0.09967683, 0.17042978,\n", " 0.09753468, 0.42821737, 0.43256054, 0.25493353, 0.78965296])" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.random.rand(10)\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save the array to a binary file named `array1.npy`:" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.save('array1', a)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "array1.npy CSV.ipynb DataScienceProcess.ipynb Numpy.ipynb\r\n", "\u001b[0m\u001b[01;32mChinook_Sqlite.sqlite\u001b[0m* DataIntro.ipynb JSON.ipynb SQL.ipynb\r\n" ] } ], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using `%pycat` to look at the file shows that it is binary:" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%pycat array1.npy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the array back into memory:" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a_copy = np.load('array1.npy')" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.11893738, 0.34881727, 0.04730572, 0.09967683, 0.17042978,\n", " 0.09753468, 0.42821737, 0.43256054, 0.25493353, 0.78965296])" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a_copy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Single array, text format" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[9, 2, 4],\n", " [0, 0, 8],\n", " [9, 9, 6],\n", " [4, 8, 7],\n", " [2, 6, 3]])" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b = np.random.randint(0,10,(5,3))\n", "b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `savetxt` function saves arrays in a simple, textual format that is less effecient, but easier for other languges to read:" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.savetxt('array2.txt', b)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "array1.npy CSV.ipynb JSON.ipynb\r\n", "array2.txt DataIntro.ipynb Numpy.ipynb\r\n", "\u001b[0m\u001b[01;32mChinook_Sqlite.sqlite\u001b[0m* DataScienceProcess.ipynb SQL.ipynb\r\n" ] } ], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using `%pycat` to look at the contents shows that the files is indeed a plain text file:" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%pycat array2.txt" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 9., 2., 4.],\n", " [ 0., 0., 8.],\n", " [ 9., 9., 6.],\n", " [ 4., 8., 7.],\n", " [ 2., 6., 3.]])" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.loadtxt('array2.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiple arrays, binary format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `savez` function provides an efficient way of saving multiple arrays to a single file:" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.savez('arrays.npz', a=a, b=b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `load` function returns a dictionary like object that provides access to the individual arrays:" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a_and_b = np.load('arrays.npz')" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.11893738, 0.34881727, 0.04730572, 0.09967683, 0.17042978,\n", " 0.09753468, 0.42821737, 0.43256054, 0.25493353, 0.78965296])" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a_and_b['a']" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[9, 2, 4],\n", " [0, 0, 8],\n", " [9, 9, 6],\n", " [4, 8, 7],\n", " [2, 6, 3]])" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a_and_b['b']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear algebra" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NumPy has excellent linear algebra capabilities." ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = np.random.rand(5,5)\n", "b = np.random.rand(5,5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember that array operations are elementwise. Thus, this is **not** matrix multiplication:" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.78372413, 0.47273738, 0.31153567, 0.40965476, 0.08740972],\n", " [ 0.25862387, 0.29022236, 0.17732679, 0.06547613, 0.40101443],\n", " [ 0.12667065, 0.47814036, 0.06160667, 0.42398101, 0.03274227],\n", " [ 0.03461579, 0.10325288, 0.68198206, 0.04473719, 0.05574423],\n", " [ 0.46070914, 0.31268264, 0.18929165, 0.01685043, 0.02724934]])" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a*b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get matrix multiplication use `np.dot`:" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1.58001604, 1.3775459 , 1.52535348, 1.7796161 , 1.3377806 ],\n", " [ 2.1501139 , 1.86328779, 1.58472464, 2.37983886, 1.47982503],\n", " [ 0.62227038, 0.63640089, 0.93216562, 0.8310279 , 1.00756454],\n", " [ 1.45513071, 1.33584842, 1.21990405, 1.56385159, 0.98995202],\n", " [ 1.21957263, 1.03686995, 0.86930139, 1.2717817 , 0.63968366]])" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.dot(a, b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or, NumPy as a `matrix` subclass for which matrix operations are the default:" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [], "source": [ "m1 = np.matrix(a)\n", "m2 = np.matrix(b)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "matrix([[ 1.58001604, 1.3775459 , 1.52535348, 1.7796161 , 1.3377806 ],\n", " [ 2.1501139 , 1.86328779, 1.58472464, 2.37983886, 1.47982503],\n", " [ 0.62227038, 0.63640089, 0.93216562, 0.8310279 , 1.00756454],\n", " [ 1.45513071, 1.33584842, 1.21990405, 1.56385159, 0.98995202],\n", " [ 1.21957263, 1.03686995, 0.86930139, 1.2717817 , 0.63968366]])" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m1*m2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `np.linalg` package has a wide range of fast linear algebra operations.\n", "\n", "Here is determinant:" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.012941957503533077" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linalg.det(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Matrix inverse:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.linalg.inv(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Eigenvalues:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.linalg.eigvals(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NumPy can be built against fast BLAS/LAPACK implementation for these linear algebra operations." ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [], "source": [ "c = np.random.rand(2000,2000)" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 1: 23.7 s per loop\n" ] } ], "source": [ "%timeit -n1 -r1 evs = np.linalg.eigvals(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random numbers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NumPy has functions for creating arrays of random numbers from different distributions in `np.random`, as well as handling things like permutation, shuffling, and choosing.\n", "\n", "Here is the [numpy.random documentation](http://docs.scipy.org/doc/numpy/reference/routines.random.html)." ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f785a7dd990>" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f785afe7150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(np.random.random(250))\n", "plt.title('Uniform Random Distribution $[0,1]$')\n", "plt.xlabel('value')\n", "plt.ylabel('count')" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f785a646e10>" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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xG/A/gVdn5mHAp4FPIGna2POWZocrgZuA91GF+NXAauC8iNgFmA8MjnnNeDfc\n6AGeQ3V/76/X97LeGdjcmbIljcfwlmaBzHwgIu6KiBcAJ1HduegqquHuGyLiWKpgbzU2kHehulHJ\nI8CKzDy803VLGp/D5tLssQx4G7AgM39GdTesX0bEzlSBvkv9vNEe90PAwojYrX7OIVRD57cDe0bE\nswEi4pCIePs0vg9p1jO8pdnja8DrgP9dT38c+A5wLXApsCQizqDe113fe/lS4Cf1a39az18PvBG4\nJCJuAM6mOvhN0jTxlqCSJBXGnrckSYUxvCVJKozhLUlSYQxvSZIKY3hLklQYw1uSpMIY3pIkFcbw\nliSpMP8fTdOtlkGdntwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f785a752950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(np.random.randn(250))\n", "plt.title('Standard Normal Distribution')\n", "plt.xlabel('value')\n", "plt.ylabel('count')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `shuffle` function shuffles an array in place:" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([9, 2, 1, 6, 5, 0, 7, 8, 3, 4])" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.arange(0,10)\n", "np.random.shuffle(a)\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `permutation` function does the same thing but first makes a copy:" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 9 6 1 7 2 5 3 8 4]\n", "[0 1 2 3 4 5 6 7 8 9]\n" ] } ], "source": [ "a = np.arange(0,10)\n", "print(np.random.permutation(a))\n", "print(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `choice` function provides a powerful way of creating synthetic data sets of discrete data:" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['f', 'f', 'f', 'f', 'm', 'm', 'f', 'f', 'f', 'f', 'f', 'f', 'f',\n", " 'f', 'f', 'f', 'f', 'f', 'f', 'm'], \n", " dtype='|S1')" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.choice(['m','f'], 20, p=[0.25,0.75])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resources" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [NumPy Reference Documentation](http://docs.scipy.org/doc/numpy/reference/)\n", "* [Python Scientific Lecture Notes](http://scipy-lectures.github.io/index.html), Edited by Valentin Haenel,\n", "Emmanuelle Gouillart and Gaël Varoquaux.\n", "* [Lectures on Scientific Computing with Python](https://github.com/jrjohansson/scientific-python-lectures), J.R. Johansson.\n", "* [Introduction to Scientific Computing in Python](http://nbviewer.ipython.org/github/jakevdp/2014_fall_ASTR599/tree/master/), Jake Vanderplas." ] } ], "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
undercertainty/ou_nlp
tools_matplotlib.ipynb
4
1141232
null
apache-2.0
Nathx/think_stats
resolved/pmf_test.ipynb
1
1578
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import thinkstats2\n", "pmf = thinkstats2.Pmf([1,2,2,3,5])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Pmf({1: 0.2, 2: 0.6000000000000001, 3: 0.2, 5: 0.2})" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pmf" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Pmf({1: 0.16666666666666669, 2: 0.5000000000000001, 3: 0.16666666666666669, 5: 0.16666666666666669})" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pmf" ] }, { "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 }
gpl-3.0
joshnsolomon/phys202-2015-work
assignments/assignment06/ProjectEuler17.ipynb
1
47943
{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Project Euler: Problem 17" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "https://projecteuler.net/problem=17\n", "\n", "If the numbers 1 to 5 are written out in words: one, two, three, four, five, then there are 3 + 3 + 5 + 4 + 4 = 19 letters used in total.\n", "\n", "If all the numbers from 1 to 1000 (one thousand) inclusive were written out in words, how many letters would be used?\n", "\n", "\n", "NOTE: Do not count spaces or hyphens. For example, 342 (three hundred and forty-two) contains 23 letters and 115 (one hundred and fifteen) contains 20 letters. The use of \"and\" when writing out numbers is in compliance with British usage." ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "First write a `number_to_words(n)` function that takes an integer `n` between 1 and 1000 inclusive and returns a list of words for the number as described above" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false, "nbgrader": { "checksum": "790b69a83b63d5a31b5fac1451dd3b54", "solution": true } }, "outputs": [], "source": [ "def number_to_words(n):\n", " \"\"\"Given a number n between 1-1000 inclusive return a list of words for the number.\"\"\"\n", " wrds = []\n", " ones = {1:'one',2:'two',3:'three',4:'four',5:'five',6:'six',7:'seven',8:'eight', \n", " 9:'nine',10:'ten', 11:'eleven',12:'twelve',13:'thirteen',14:'fourteen',\n", " 15:'fifteen',16:'sixteen',17:'seventeen',18:'eighteen',19:'nineteen'}\n", " tens = {2:'twenty',3:'thirty',4:'forty',5:'fifty',6:'sixty',7:'seventy',8:'eighty',\n", " 9:'ninety'}\n", " hundred = {1:'onehundred',2:'twohundred',3:'threehundred',4:'fourhundred',\n", " 5:'fivehundred',6:'sixhundred',7:'sevenhundred',8:'eighthundred',\n", " 9:'ninehundred'}\n", " \n", " if n<20:\n", " x=1\n", " while x<=n:\n", " wrds.append(ones[x])\n", " x+=1 \n", " elif n<100:\n", " x=1\n", " while x<20:\n", " wrds.append(ones[x])\n", " x+=1\n", " t=2\n", " while t*10 <= n: \n", " wrds.append(tens[t]) \n", " a=1\n", " while x<n-t+2 and a<10:\n", " wrds.append(tens[t] +ones[a])\n", " a+=1\n", " x+=1\n", " t+=1 \n", " elif n<1000:\n", " x=1\n", " while x<20:\n", " wrds.append(ones[x])\n", " x+=1\n", " t=2\n", " while t*10 <= 99: \n", " wrds.append(tens[t]) \n", " a=1\n", " while x<99-t+2 and a<10:\n", " wrds.append(tens[t] +ones[a])\n", " a+=1\n", " x+=1\n", " t+=1\n", " h=1 \n", " while h*100<=n:\n", " wrds.append(hundred[h])\n", " x=(h*100)+1\n", " while x<=n and x<(h*100)+20:\n", " b=x-(h*100)\n", " wrds.append(hundred[h]+'and'+ones[b])\n", " x+=1\n", " t=2\n", " while ((h*100)+(t*10)) <= n and t<10: \n", " wrds.append(hundred[h]+'and'+tens[t]) \n", " a=1\n", " while x<n-t+2 and a<10:\n", " wrds.append(hundred[h]+'and'+tens[t]+ones[a])\n", " a+=1\n", " x+=1\n", " t+=1\n", " h+=1\n", " elif n==1000:\n", " n=999\n", " x=1\n", " while x<20:\n", " wrds.append(ones[x])\n", " x+=1\n", " t=2\n", " while t*10 <= 99: \n", " wrds.append(tens[t]) \n", " a=1\n", " while x<99-t+2 and a<10:\n", " wrds.append(tens[t] +ones[a])\n", " a+=1\n", " x+=1\n", " t+=1\n", " h=1 \n", " while h*100<=n:\n", " wrds.append(hundred[h])\n", " x=(h*100)+1\n", " while x<=n and x<(h*100)+20:\n", " b=x-(h*100)\n", " wrds.append(hundred[h]+'and'+ones[b])\n", " x+=1\n", " t=2\n", " while ((h*100)+(t*10)) <= n and t<10: \n", " wrds.append(hundred[h]+'and'+tens[t]) \n", " a=1\n", " while x<n-t+2 and a<10:\n", " wrds.append(hundred[h]+'and'+tens[t]+ones[a])\n", " a+=1\n", " x+=1\n", " t+=1\n", " h+=1\n", " wrds.append('onethousand')\n", " \n", " \n", " return wrds" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['one',\n", " 'two',\n", " 'three',\n", " 'four',\n", " 'five',\n", " 'six',\n", " 'seven',\n", " 'eight',\n", " 'nine',\n", " 'ten',\n", " 'eleven',\n", " 'twelve',\n", " 'thirteen',\n", " 'fourteen',\n", " 'fifteen',\n", " 'sixteen',\n", " 'seventeen',\n", " 'eighteen',\n", " 'nineteen',\n", " 'twenty',\n", " 'twentyone',\n", " 'twentytwo',\n", " 'twentythree',\n", " 'twentyfour',\n", " 'twentyfive',\n", " 'twentysix',\n", " 'twentyseven',\n", " 'twentyeight',\n", " 'twentynine',\n", " 'thirty',\n", " 'thirtyone',\n", " 'thirtytwo',\n", " 'thirtythree',\n", " 'thirtyfour',\n", " 'thirtyfive',\n", " 'thirtysix',\n", " 'thirtyseven',\n", " 'thirtyeight',\n", " 'thirtynine',\n", " 'forty',\n", " 'fortyone',\n", " 'fortytwo',\n", " 'fortythree',\n", " 'fortyfour',\n", " 'fortyfive',\n", " 'fortysix',\n", " 'fortyseven',\n", " 'fortyeight',\n", " 'fortynine',\n", " 'fifty',\n", " 'fiftyone',\n", " 'fiftytwo',\n", " 'fiftythree',\n", " 'fiftyfour',\n", " 'fiftyfive',\n", " 'fiftysix',\n", " 'fiftyseven',\n", " 'fiftyeight',\n", " 'fiftynine',\n", " 'sixty',\n", " 'sixtyone',\n", " 'sixtytwo',\n", " 'sixtythree',\n", " 'sixtyfour',\n", " 'sixtyfive',\n", " 'sixtysix',\n", " 'sixtyseven',\n", " 'sixtyeight',\n", " 'sixtynine',\n", " 'seventy',\n", " 'seventyone',\n", " 'seventytwo',\n", " 'seventythree',\n", " 'seventyfour',\n", " 'seventyfive',\n", " 'seventysix',\n", " 'seventyseven',\n", " 'seventyeight',\n", " 'seventynine',\n", " 'eighty',\n", " 'eightyone',\n", " 'eightytwo',\n", " 'eightythree',\n", " 'eightyfour',\n", " 'eightyfive',\n", " 'eightysix',\n", " 'eightyseven',\n", " 'eightyeight',\n", " 'eightynine',\n", " 'ninety',\n", " 'ninetyone',\n", " 'ninetytwo',\n", " 'ninetythree',\n", " 'ninetyfour',\n", " 'ninetyfive',\n", " 'ninetysix',\n", " 'ninetyseven',\n", " 'ninetyeight',\n", " 'ninetynine',\n", " 'onehundred',\n", " 'onehundredandone',\n", " 'onehundredandtwo',\n", " 'onehundredandthree',\n", " 'onehundredandfour',\n", " 'onehundredandfive',\n", " 'onehundredandsix',\n", " 'onehundredandseven',\n", " 'onehundredandeight',\n", " 'onehundredandnine',\n", " 'onehundredandten',\n", " 'onehundredandeleven',\n", " 'onehundredandtwelve',\n", " 'onehundredandthirteen',\n", " 'onehundredandfourteen',\n", " 'onehundredandfifteen',\n", " 'onehundredandsixteen',\n", " 'onehundredandseventeen',\n", " 'onehundredandeighteen',\n", " 'onehundredandnineteen',\n", " 'onehundredandtwenty',\n", " 'onehundredandtwentyone',\n", " 'onehundredandtwentytwo',\n", " 'onehundredandtwentythree',\n", " 'onehundredandtwentyfour',\n", " 'onehundredandtwentyfive',\n", " 'onehundredandtwentysix',\n", " 'onehundredandtwentyseven',\n", " 'onehundredandtwentyeight',\n", " 'onehundredandtwentynine',\n", " 'onehundredandthirty',\n", " 'onehundredandthirtyone',\n", " 'onehundredandthirtytwo',\n", " 'onehundredandthirtythree',\n", " 'onehundredandthirtyfour',\n", " 'onehundredandthirtyfive',\n", " 'onehundredandthirtysix',\n", " 'onehundredandthirtyseven',\n", " 'onehundredandthirtyeight',\n", " 'onehundredandthirtynine',\n", " 'onehundredandforty',\n", " 'onehundredandfortyone',\n", " 'onehundredandfortytwo',\n", " 'onehundredandfortythree',\n", " 'onehundredandfortyfour',\n", " 'onehundredandfortyfive',\n", " 'onehundredandfortysix',\n", " 'onehundredandfortyseven',\n", " 'onehundredandfortyeight',\n", " 'onehundredandfortynine',\n", " 'onehundredandfifty',\n", " 'onehundredandfiftyone',\n", " 'onehundredandfiftytwo',\n", " 'onehundredandfiftythree',\n", " 'onehundredandfiftyfour',\n", " 'onehundredandfiftyfive',\n", " 'onehundredandfiftysix',\n", " 'onehundredandfiftyseven',\n", " 'onehundredandfiftyeight',\n", " 'onehundredandfiftynine',\n", " 'onehundredandsixty',\n", " 'onehundredandsixtyone',\n", " 'onehundredandsixtytwo',\n", " 'onehundredandsixtythree',\n", " 'onehundredandsixtyfour',\n", " 'onehundredandsixtyfive',\n", " 'onehundredandsixtysix',\n", " 'onehundredandsixtyseven',\n", " 'onehundredandsixtyeight',\n", " 'onehundredandsixtynine',\n", " 'onehundredandseventy',\n", " 'onehundredandseventyone',\n", " 'onehundredandseventytwo',\n", " 'onehundredandseventythree',\n", " 'onehundredandseventyfour',\n", " 'onehundredandseventyfive',\n", " 'onehundredandseventysix',\n", " 'onehundredandseventyseven',\n", " 'onehundredandseventyeight',\n", " 'onehundredandseventynine',\n", " 'onehundredandeighty',\n", " 'onehundredandeightyone',\n", " 'onehundredandeightytwo',\n", " 'onehundredandeightythree',\n", " 'onehundredandeightyfour',\n", " 'onehundredandeightyfive',\n", " 'onehundredandeightysix',\n", " 'onehundredandeightyseven',\n", " 'onehundredandeightyeight',\n", " 'onehundredandeightynine',\n", " 'onehundredandninety',\n", " 'onehundredandninetyone',\n", " 'onehundredandninetytwo',\n", " 'onehundredandninetythree',\n", " 'onehundredandninetyfour',\n", " 'onehundredandninetyfive',\n", " 'onehundredandninetysix',\n", " 'onehundredandninetyseven',\n", " 'onehundredandninetyeight',\n", " 'onehundredandninetynine',\n", " 'twohundred',\n", " 'twohundredandone',\n", " 'twohundredandtwo',\n", " 'twohundredandthree',\n", " 'twohundredandfour',\n", " 'twohundredandfive',\n", " 'twohundredandsix',\n", " 'twohundredandseven',\n", " 'twohundredandeight',\n", " 'twohundredandnine',\n", " 'twohundredandten',\n", " 'twohundredandeleven',\n", " 'twohundredandtwelve',\n", " 'twohundredandthirteen',\n", " 'twohundredandfourteen',\n", " 'twohundredandfifteen',\n", " 'twohundredandsixteen',\n", " 'twohundredandseventeen',\n", " 'twohundredandeighteen',\n", " 'twohundredandnineteen',\n", " 'twohundredandtwenty',\n", " 'twohundredandtwentyone',\n", " 'twohundredandtwentytwo',\n", " 'twohundredandtwentythree',\n", " 'twohundredandtwentyfour',\n", " 'twohundredandtwentyfive',\n", " 'twohundredandtwentysix',\n", " 'twohundredandtwentyseven',\n", " 'twohundredandtwentyeight',\n", " 'twohundredandtwentynine',\n", " 'twohundredandthirty',\n", " 'twohundredandthirtyone',\n", " 'twohundredandthirtytwo',\n", " 'twohundredandthirtythree',\n", " 'twohundredandthirtyfour',\n", " 'twohundredandthirtyfive',\n", " 'twohundredandthirtysix',\n", " 'twohundredandthirtyseven',\n", " 'twohundredandthirtyeight',\n", " 'twohundredandthirtynine',\n", " 'twohundredandforty',\n", " 'twohundredandfortyone',\n", " 'twohundredandfortytwo',\n", " 'twohundredandfortythree',\n", " 'twohundredandfortyfour',\n", " 'twohundredandfortyfive',\n", " 'twohundredandfortysix',\n", " 'twohundredandfortyseven',\n", " 'twohundredandfortyeight',\n", " 'twohundredandfortynine',\n", " 'twohundredandfifty',\n", " 'twohundredandfiftyone',\n", " 'twohundredandfiftytwo',\n", " 'twohundredandfiftythree',\n", " 'twohundredandfiftyfour',\n", " 'twohundredandfiftyfive',\n", " 'twohundredandfiftysix',\n", " 'twohundredandfiftyseven',\n", " 'twohundredandfiftyeight',\n", " 'twohundredandfiftynine',\n", " 'twohundredandsixty',\n", " 'twohundredandsixtyone',\n", " 'twohundredandsixtytwo',\n", " 'twohundredandsixtythree',\n", " 'twohundredandsixtyfour',\n", " 'twohundredandsixtyfive',\n", " 'twohundredandsixtysix',\n", " 'twohundredandsixtyseven',\n", " 'twohundredandsixtyeight',\n", " 'twohundredandsixtynine',\n", " 'twohundredandseventy',\n", " 'twohundredandseventyone',\n", " 'twohundredandseventytwo',\n", " 'twohundredandseventythree',\n", " 'twohundredandseventyfour',\n", " 'twohundredandseventyfive',\n", " 'twohundredandseventysix',\n", " 'twohundredandseventyseven',\n", " 'twohundredandseventyeight',\n", " 'twohundredandseventynine',\n", " 'twohundredandeighty',\n", " 'twohundredandeightyone',\n", " 'twohundredandeightytwo',\n", " 'twohundredandeightythree',\n", " 'twohundredandeightyfour',\n", " 'twohundredandeightyfive',\n", " 'twohundredandeightysix',\n", " 'twohundredandeightyseven',\n", " 'twohundredandeightyeight',\n", " 'twohundredandeightynine',\n", " 'twohundredandninety',\n", " 'twohundredandninetyone',\n", " 'twohundredandninetytwo',\n", " 'twohundredandninetythree',\n", " 'twohundredandninetyfour',\n", " 'twohundredandninetyfive',\n", " 'twohundredandninetysix',\n", " 'twohundredandninetyseven',\n", " 'twohundredandninetyeight',\n", " 'twohundredandninetynine',\n", " 'threehundred',\n", " 'threehundredandone',\n", " 'threehundredandtwo',\n", " 'threehundredandthree',\n", " 'threehundredandfour',\n", " 'threehundredandfive',\n", " 'threehundredandsix',\n", " 'threehundredandseven',\n", " 'threehundredandeight',\n", " 'threehundredandnine',\n", " 'threehundredandten',\n", " 'threehundredandeleven',\n", " 'threehundredandtwelve',\n", " 'threehundredandthirteen',\n", " 'threehundredandfourteen',\n", " 'threehundredandfifteen',\n", " 'threehundredandsixteen',\n", " 'threehundredandseventeen',\n", " 'threehundredandeighteen',\n", " 'threehundredandnineteen',\n", " 'threehundredandtwenty',\n", " 'threehundredandtwentyone',\n", " 'threehundredandtwentytwo',\n", " 'threehundredandtwentythree',\n", " 'threehundredandtwentyfour',\n", " 'threehundredandtwentyfive',\n", " 'threehundredandtwentysix',\n", " 'threehundredandtwentyseven',\n", " 'threehundredandtwentyeight',\n", " 'threehundredandtwentynine',\n", " 'threehundredandthirty',\n", " 'threehundredandthirtyone',\n", " 'threehundredandthirtytwo',\n", " 'threehundredandthirtythree',\n", " 'threehundredandthirtyfour',\n", " 'threehundredandthirtyfive',\n", " 'threehundredandthirtysix',\n", " 'threehundredandthirtyseven',\n", " 'threehundredandthirtyeight',\n", " 'threehundredandthirtynine',\n", " 'threehundredandforty',\n", " 'threehundredandfortyone',\n", " 'threehundredandfortytwo',\n", " 'threehundredandfortythree',\n", " 'threehundredandfortyfour',\n", " 'threehundredandfortyfive',\n", " 'threehundredandfortysix',\n", " 'threehundredandfortyseven',\n", " 'threehundredandfortyeight',\n", " 'threehundredandfortynine',\n", " 'threehundredandfifty',\n", " 'threehundredandfiftyone',\n", " 'threehundredandfiftytwo',\n", " 'threehundredandfiftythree',\n", " 'threehundredandfiftyfour',\n", " 'threehundredandfiftyfive',\n", " 'threehundredandfiftysix',\n", " 'threehundredandfiftyseven',\n", " 'threehundredandfiftyeight',\n", " 'threehundredandfiftynine',\n", " 'threehundredandsixty',\n", " 'threehundredandsixtyone',\n", " 'threehundredandsixtytwo',\n", " 'threehundredandsixtythree',\n", " 'threehundredandsixtyfour',\n", " 'threehundredandsixtyfive',\n", " 'threehundredandsixtysix',\n", " 'threehundredandsixtyseven',\n", " 'threehundredandsixtyeight',\n", " 'threehundredandsixtynine',\n", " 'threehundredandseventy',\n", " 'threehundredandseventyone',\n", " 'threehundredandseventytwo',\n", " 'threehundredandseventythree',\n", " 'threehundredandseventyfour',\n", " 'threehundredandseventyfive',\n", " 'threehundredandseventysix',\n", " 'threehundredandseventyseven',\n", " 'threehundredandseventyeight',\n", " 'threehundredandseventynine',\n", " 'threehundredandeighty',\n", " 'threehundredandeightyone',\n", " 'threehundredandeightytwo',\n", " 'threehundredandeightythree',\n", " 'threehundredandeightyfour',\n", " 'threehundredandeightyfive',\n", " 'threehundredandeightysix',\n", " 'threehundredandeightyseven',\n", " 'threehundredandeightyeight',\n", " 'threehundredandeightynine',\n", " 'threehundredandninety',\n", " 'threehundredandninetyone',\n", " 'threehundredandninetytwo',\n", " 'threehundredandninetythree',\n", " 'threehundredandninetyfour',\n", " 'threehundredandninetyfive',\n", " 'threehundredandninetysix',\n", " 'threehundredandninetyseven',\n", " 'threehundredandninetyeight',\n", " 'threehundredandninetynine',\n", " 'fourhundred',\n", " 'fourhundredandone',\n", " 'fourhundredandtwo',\n", " 'fourhundredandthree',\n", " 'fourhundredandfour',\n", " 'fourhundredandfive',\n", " 'fourhundredandsix',\n", " 'fourhundredandseven',\n", " 'fourhundredandeight',\n", " 'fourhundredandnine',\n", " 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'sixhundredandeightyfour',\n", " 'sixhundredandeightyfive',\n", " 'sixhundredandeightysix',\n", " 'sixhundredandeightyseven',\n", " 'sixhundredandeightyeight',\n", " 'sixhundredandeightynine',\n", " 'sixhundredandninety',\n", " 'sixhundredandninetyone',\n", " 'sixhundredandninetytwo',\n", " 'sixhundredandninetythree',\n", " 'sixhundredandninetyfour',\n", " 'sixhundredandninetyfive',\n", " 'sixhundredandninetysix',\n", " 'sixhundredandninetyseven',\n", " 'sixhundredandninetyeight',\n", " 'sixhundredandninetynine',\n", " 'sevenhundred',\n", " 'sevenhundredandone',\n", " 'sevenhundredandtwo',\n", " 'sevenhundredandthree',\n", " 'sevenhundredandfour',\n", " 'sevenhundredandfive',\n", " 'sevenhundredandsix',\n", " 'sevenhundredandseven',\n", " 'sevenhundredandeight',\n", " 'sevenhundredandnine',\n", " 'sevenhundredandten',\n", " 'sevenhundredandeleven',\n", " 'sevenhundredandtwelve',\n", " 'sevenhundredandthirteen',\n", " 'sevenhundredandfourteen',\n", " 'sevenhundredandfifteen',\n", " 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'ninehundredandtwentytwo',\n", " 'ninehundredandtwentythree',\n", " 'ninehundredandtwentyfour',\n", " 'ninehundredandtwentyfive',\n", " 'ninehundredandtwentysix',\n", " 'ninehundredandtwentyseven',\n", " 'ninehundredandtwentyeight',\n", " 'ninehundredandtwentynine',\n", " 'ninehundredandthirty',\n", " 'ninehundredandthirtyone',\n", " 'ninehundredandthirtytwo',\n", " 'ninehundredandthirtythree',\n", " 'ninehundredandthirtyfour',\n", " 'ninehundredandthirtyfive',\n", " 'ninehundredandthirtysix',\n", " 'ninehundredandthirtyseven',\n", " 'ninehundredandthirtyeight',\n", " 'ninehundredandthirtynine',\n", " 'ninehundredandforty',\n", " 'ninehundredandfortyone',\n", " 'ninehundredandfortytwo',\n", " 'ninehundredandfortythree',\n", " 'ninehundredandfortyfour',\n", " 'ninehundredandfortyfive',\n", " 'ninehundredandfortysix',\n", " 'ninehundredandfortyseven',\n", " 'ninehundredandfortyeight',\n", " 'ninehundredandfortynine',\n", " 'ninehundredandfifty',\n", " 'ninehundredandfiftyone',\n", " 'ninehundredandfiftytwo',\n", " 'ninehundredandfiftythree',\n", " 'ninehundredandfiftyfour',\n", " 'ninehundredandfiftyfive',\n", " 'ninehundredandfiftysix',\n", " 'ninehundredandfiftyseven',\n", " 'ninehundredandfiftyeight',\n", " 'ninehundredandfiftynine',\n", " 'ninehundredandsixty',\n", " 'ninehundredandsixtyone',\n", " 'ninehundredandsixtytwo',\n", " 'ninehundredandsixtythree',\n", " 'ninehundredandsixtyfour',\n", " 'ninehundredandsixtyfive',\n", " 'ninehundredandsixtysix',\n", " 'ninehundredandsixtyseven',\n", " 'ninehundredandsixtyeight',\n", " 'ninehundredandsixtynine',\n", " 'ninehundredandseventy',\n", " 'ninehundredandseventyone',\n", " 'ninehundredandseventytwo',\n", " 'ninehundredandseventythree',\n", " 'ninehundredandseventyfour',\n", " 'ninehundredandseventyfive',\n", " 'ninehundredandseventysix',\n", " 'ninehundredandseventyseven',\n", " 'ninehundredandseventyeight',\n", " 'ninehundredandseventynine',\n", " 'ninehundredandeighty',\n", " 'ninehundredandeightyone',\n", " 'ninehundredandeightytwo',\n", " 'ninehundredandeightythree',\n", " 'ninehundredandeightyfour',\n", " 'ninehundredandeightyfive',\n", " 'ninehundredandeightysix',\n", " 'ninehundredandeightyseven',\n", " 'ninehundredandeightyeight',\n", " 'ninehundredandeightynine',\n", " 'ninehundredandninety',\n", " 'ninehundredandninetyone',\n", " 'ninehundredandninetytwo',\n", " 'ninehundredandninetythree',\n", " 'ninehundredandninetyfour',\n", " 'ninehundredandninetyfive',\n", " 'ninehundredandninetysix',\n", " 'ninehundredandninetyseven',\n", " 'ninehundredandninetyeight',\n", " 'ninehundredandninetynine',\n", " 'onethousand']" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "number_to_words(1000)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Now write a set of `assert` tests for your `number_to_words` function that verifies that it is working as expected." ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [], "source": [ "assert len(number_to_words(582)) == 582\n", "assert len(number_to_words(1000)) == 1000\n", "assert number_to_words(5) == ['one', 'two', 'three', 'four', 'five']" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "2c71c0f9dbe2a57b1ddc17bf544d86ed", "grade": true, "grade_id": "projecteuler17a", "points": 4 } }, "outputs": [], "source": [ "assert True # use this for grading the number_to_words tests." ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Now define a `count_letters(n)` that returns the number of letters used to write out the words for all of the the numbers `1` to `n` inclusive." ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": true, "nbgrader": { "checksum": "be228a805b41eda2b30887a53523f76b", "solution": true } }, "outputs": [], "source": [ "def count_letters(n):\n", " \"\"\"Count the number of letters used to write out the words for 1-n inclusive.\"\"\"\n", " x=0\n", " nums = number_to_words(n)\n", " for number in nums:\n", " x+=len(number)\n", " return x\n", " " ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Now write a set of `assert` tests for your `count_letters` function that verifies that it is working as expected." ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [], "source": [ "# YOUR CODE HERE\n", "assert count_letters(5) == 19" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "3a06c9610681f0174008f2976e310e0a", "grade": true, "grade_id": "projecteuler17b", "points": 4 } }, "outputs": [], "source": [ "assert True # use this for grading the count_letters tests." ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Finally used your `count_letters` function to solve the original question." ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "21124\n" ] } ], "source": [ "answer = count_letters(1000)\n", "print(answer)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "a136658e13c4f1154caf307a2e852f99", "grade": true, "grade_id": "projecteuler17c", "points": 2 } }, "outputs": [], "source": [ "assert True # use this for gradig the answer to the original question." ] } ], "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
JanetMatsen/Machine_Learning_CSE_546
HW3/notebooks/dev_logistic_SGD--5--MNIST--eta0_for_60k.ipynb
1
6248
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.5.2 |Continuum Analytics, Inc.| (default, Jul 2 2016, 17:52:12) \n", "[GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)]\n" ] } ], "source": [ "import sys\n", "print(sys.version)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import time\n", "\n", "import pandas as pd\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys\n", "sys.path.append('../code/')\n", "\n", "from mnist_helpers import mnist_training, mnist_testing\n", "from hyperparameter_explorer import HyperparameterExplorer\n", "from least_squares_sgd import LeastSquaresSGD\n", "from kernel import Fourier" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_train_untransformed, y_train = mnist_training(shuffled=False) \n", "X_train = np.load('../notebooks/data/X_transformed_by_50_components.npy')\n", "\n", "X_test_untransformed, y_test = mnist_testing(shuffled=False)\n", "X_test = np.load('../notebooks/data/X_test_transformed_by_50_components.npy')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Can't do with HyperExplorer; not amenable to # of points changing." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No test data was provided.\n", "determine kernel bandwidth using 6000 points.\n", "median distance for 6000 samples from N: 2365.3743022656654\n", "eta0 search begins with eta0 = 100/0.0016666666666666668 = 60000\n", "Determining eta0 using 60000 points\n", "testing eta0 = 0.001666666666666667. (Try # 1)\n", ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, (done calculating hat{Y})\n", "average error: 0.1. (step = 0)\n", "Begin epoch 1\n", "............................................................ (epoch complete)\n", "Epoch iteration time: 4.0:31.\n", ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, (done calculating hat{Y})\n", "average error: 0.09281455757552602. (step = 12000)\n", "fit observation done: 3.0:17.\n", "0.220811049473\n", "Begin epoch 2\n", "............................................................ (epoch complete)\n", "Epoch iteration time: 4.0:16.\n", ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, (done calculating hat{Y})\n", "average error: 0.08352685635424476. (step = 24000)\n", "fit observation done: 2.0:33.\n", "0.182121471268\n", "Begin epoch 3\n", "............................................................ (epoch complete)\n", "Epoch iteration time: 4.0:14.\n", ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, (done calculating hat{Y})\n", "average error: 0.07559210732930001. (step = 36000)\n", "fit observation done: 2.0:26.\n", "0.15641874051\n", "Begin epoch 4\n", "............................................................ (epoch complete)\n", "Epoch iteration time: 4.0:6.\n", ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, (done calculating hat{Y})\n", "average error: 0.0714905081902154. (step = 48000)\n", "fit observation done: 2.0:30.\n", "0.144091278283\n", "\n", "!!! Max epochs (5) reached. !!!\n", "final normalized training (square loss): 0.14409127828349433\n", "testing eta0 = 0.008333333333333335. (Try # 2)\n", ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, (done calculating hat{Y})\n", "average error: 0.1. (step = 0)\n", "Begin epoch 1\n", "............................................................ (epoch complete)\n", "Epoch iteration time: 3.0:53.\n", ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, (done calculating hat{Y})\n", "average error: 1.2461792335712669e+157. (step = 12000)\n", "The sum of errors is concerningly big: 1.2461792335712669e+157\n", "fit observation done: 2.0:37.\n", "square loss/N/N grew to inf\n", "Model training raised an exception.\n", "Exploration for good eta0 started at 0.0016666666666666668; stopped passing when eta0 grew to 0.008333333333333335\n", "===== eta0 search landed on 0.001666666666666667, using 60000 points ====\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "../code/least_squares_sgd.py:306: RuntimeWarning: overflow encountered in multiply\n", " \"Compute Yhat before calling predict, but don't compute too often!\"\n" ] } ], "source": [ "ls60k = LeastSquaresSGD(X_train, \n", " y_train,\n", " max_epochs=2,\n", " eta0_search_start = 100,\n", " eta0_max_pts=X_train.shape[0],\n", " verbose=True,\n", " assess_test_data_during_fitting = False)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:mlpy3]", "language": "python", "name": "conda-env-mlpy3-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
bjodah/aqchem
examples/_regression.ipynb
2
2778
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from chempy.units import default_units as u\n", "from chempy.util.regression import least_squares, plot_fit, irls, plot_least_squares_fit, least_squares_units\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "help(least_squares)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = [0, 1, 2, 3, 4+1e-9]\n", "y = [3, 3.8, 5.2, 5.2, 10.8]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simple OLS with plotting:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res = least_squares(x, y)\n", "plot_least_squares_fit(x, y, res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Iterative least squares:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res_irls = irls(x, y, irls.gaussian, itermax=20)\n", "plot_least_squares_fit(x, y, res_irls)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y*u.m" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res_units = least_squares_units(x*u.s, y*u.m)\n", "#plot_least_squares(x*u.s, y*u.m, res_units, x_unit=u.s, y_unit=u.m)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res_units" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "err = [.1, .2, .2, .8, 3]*u.m\n", "res_weighted = least_squares_units(x*u.s, y*u.m, err**-2)\n", "plot_least_squares_fit(x*u.s, y*u.m, res_weighted, err, x_unit=u.s, y_unit=u.m)" ] }, { "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": 1 }
bsd-2-clause
cpatrickalves/simprev
notebooks/calc_probabilidades.ipynb
1
1033077
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cálculo das Probabilidades de Entrada em benefícios\n", "\n", "Os cálculos são baseados no novo modelo de projeção da Fazenda.\n", "\n", "Etapas:\n", " 1. Obter o nome de todas as tabelas dentro do arquivo \n", " 2. Salvar todas as tabelas da planilha\n", " * As tabelas serão salvas em uma estrutura de dados do tipo DataFrame\n", " * Os DataFrames serão salvos em dicionários (hashtables) para facilitar o acesso\n", " 3. Calcula as probabilidades ..." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# Ano de referência para o cálculo das probabilidades\n", "ano_prob = 2014\n", "\n", "# Arquivo com os dados da Fazenda\n", "arquivo = '../../datasets/FAZENDA/dados_fazenda.xlsx'\n", "\n", "# Função que retorna uma lista de benefícios de acordo como filtro\n", "def get_lista(filtros=[], info=''):\n", "\n", "# A tag info é usada no momento de leitura das tabelas no arquivo\n", "# Es = estoque\n", "# Co = conceções\n", "# Ce = cessações\n", "\n", "# Exemplos de filtros:\n", "# Apin = Aposentadoria por Invalidez\n", "# Auxd = Auxílio doença\n", "# SalMat = Salário Maternidade\n", " \n", " # Lista com todos os benefícios utilizando o mesmo padrão do simulador da Fazenda \n", " lista = [\"ApinUrbPisoH\", \"ApinUrbPisoM\", \"ApidUrbPisoH\", \"ApidUrbPisoM\", \n", " \"AtcnUrbPisoH\", \"AtcnUrbPisoM\", \"AtceUrbPisoH\", \"AtceUrbPisoM\",\n", " \"AtcpUrbPisoH\", \"AtcpUrbPisoM\", \"AtcdUrbPisoH\", \"AtcdUrbPisoM\",\n", " \"AinvUrbPisoH\", \"AinvUrbPisoM\", \n", "\n", " \"ApinRurH\", \"ApinRurM\", \"ApidRurH\", \"ApidRurM\",\n", " \"AtcnRurH\", \"AtcnRurM\", \"AtceRurH\", \"AtceRurM\",\n", " \"AtcpRurH\", \"AtcpRurM\", \"AtcdRurH\",\"AtcdRurM\",\n", " \"AinvRurH\", \"AinvRurM\", \n", "\n", " \"ApinUrbAcimH\", \"ApinUrbAcimM\", \"ApidUrbAcimH\", \"ApidUrbAcimM\", \n", " \"AtcnUrbAcimH\", \"AtcnUrbAcimM\", \"AtceUrbAcimH\", \"AtceUrbAcimM\", \n", " \"AtcpUrbAcimH\", \"AtcpUrbAcimM\", \"AtcdUrbAcimH\", \"AtcdUrbAcimM\",\n", " \"AinvUrbAcimH\", \"AinvUrbAcimM\", \n", " \n", " \"AuxdUrbPisoH\", \"AuxdUrbPisoM\", \"AuxdRurH\", \"AuxdRurM\", \n", " \"AuxdUrbAcimH\", \"AuxdUrbAcimM\", \"AuxaUrbPisoH\", \"AuxaUrbPisoM\",\n", " \"AuxrUrbPisoH\", \"AuxrUrbPisoM\", \"AuxaRurH\", \"AuxaRurM\", \n", " \"AuxrRurH\", \"AuxrRurM\", \"AuxaUrbAcimH\",\n", " \"AuxaUrbAcimM\", \"AuxrUrbAcimH\", \"AuxrUrbAcimM\",\n", " \n", " \"SalMatUrbPisoM\",\"SalMatRurM\", \"SalMatUrbAcimM\" , \n", " \"PensUrbPisoH\", \"PensUrbPisoM\", \"PensRurH\", \n", " \"PensRurM\", \"PensUrbAcimH\", \"PensUrbAcimM\" , \n", " \"LoasIdoH\", \"LoasIdoM\", \"LoasDefH\", \n", " \"LoasDefM\", \"RmvH\", \"RmvM\"]\n", " \n", " # lista que será retornada\n", " lista_final = []\n", " \n", " # Verifica se a lista está vazia, nesse caso retorna todos os benefícios\n", " if len(filtros) == 0: \n", " for l in lista:\n", " lista_final.append(info+l) \n", " \n", " # Aplica o filtro na lista\n", " else:\n", " for f in filtros: \n", " for l in lista:\n", " if f in l:\n", " lista_final.append(info+l) \n", " \n", " return lista_final\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Em seguida carregamos o arquivo Excel e obtemos cada uma das tabelas listadas nas variáveis" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total de tabelas de ESTOQUES: 66\n", "Total de tabelas de CONCESSÕES: 52\n", "Total de tabelas de CESSAÇÕES: 46\n", "Total de tabelas inexistentes: 35\n", "Total de tabelas incompletas e removidas: 26\n", "Total de tabelas carregadas: 164\n" ] } ], "source": [ "# lendo arquivo\n", "xls = pd.ExcelFile(arquivo)\n", "\n", "# Lista com as tabelas com dados ausentes\n", "tabsIncompletas = []\n", "tabsInexistentes = []\n", "\n", "# Dicionários que armazenarão os estoques, quantidades de benefícios concedidos e cessados\n", "# e probabilidades\n", "estoques = {}\n", "concessoes = {}\n", "cessacoes = {}\n", "prob = {}\n", "\n", "# Função que extrai as tabelas do arquivo\n", "# Recebe o arquivo (planilha) e a lista de tabelas a serem extraídas\n", "def carrega_dados(lista, xls): \n", " \n", " # Dicionário que salva as tabelas\n", " colecao_tabelas = {}\n", " \n", " # Lê cada uma das tabelas dentro do arquivo \n", " # Converte cada tabela em um DataFrame e salva no dicionário\n", " for i in lista:\n", " chave = i[2:] # Remove os 2 primeiro caracteres (ex: 'EsApidRurH' -> 'ApidRurH')\n", " try:\n", " colecao_tabelas[chave] = xls.parse(i, index_col=0) # Converte a tabela para um DataFrame\n", " colecao_tabelas[chave].drop('Total', inplace=True) # Elimina a linha 'Total'\n", " colecao_tabelas[chave].dropna(thresh=89, axis=1, inplace=True) # Elimina colunas com dados ausentes\n", " colecao_tabelas[chave].dropna(how='all', inplace=True) # Elimina linhas completamente vazias\n", "\n", " # Remove as tabelas que possuem dados ausentes\n", " if colecao_tabelas[chave].empty: \n", " tabsIncompletas.append(i) # Salva o nome da tabela incompleta \n", " colecao_tabelas.pop(chave) # Remove a tabela\n", "\n", " except:\n", " # Salva o nome das tabelas que não existem\n", " tabsInexistentes.append(i) \n", " \n", " return colecao_tabelas\n", "\n", "\n", "\n", "# Carrega os dados\n", "estoques = carrega_dados(get_lista([],'Es'), xls) # get_listas recebe um filtro vazio, retornando todas os benefícios\n", "concessoes = carrega_dados(get_lista([],'Co'), xls)\n", "cessacoes = carrega_dados(get_lista([],'Ce'), xls)\n", "\n", "# Exibe algumas informações sobre as tabelas\n", "print('Total de tabelas de ESTOQUES: %s' %len(estoques)) \n", "print('Total de tabelas de CONCESSÕES: %s' %len(concessoes)) \n", "print('Total de tabelas de CESSAÇÕES: %s' %len(cessacoes)) \n", "print('Total de tabelas inexistentes: %s' %len(tabsInexistentes))\n", "print('Total de tabelas incompletas e removidas: %s' %len(tabsIncompletas))\n", "print('Total de tabelas carregadas: %s' %(len(estoques)+len(concessoes)+len(cessacoes)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Após carregar todos os dados é possível ver o conjunto de dados de cada tabela." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estoques - Tabela: AuxrUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 341.230707 397.927501 449.374593 431.000632\n", "1 0.0 726.558921 863.576174 930.247404 1006.893071\n", "2 0.0 853.076768 993.243843 1152.834820 1143.910324\n", "3 0.0 852.551797 982.744436 1099.812817 1205.856822\n", "4 0.0 794.280092 914.498295 1040.491171 1085.638619\n", "5 0.0 751.232526 815.278905 951.246217 1009.517923\n", "6 0.0 634.689115 728.658802 841.527421 933.922197\n", "7 0.0 539.144517 664.087453 741.258090 804.779498\n", "8 0.0 479.822871 542.294339 667.762245 720.259277\n", "9 0.0 396.352590 478.247960 533.369844 636.264026\n", "10 0.0 358.029757 385.328214 501.346654 514.995882\n", "11 0.0 283.483972 349.630232 391.627858 483.497663\n", "12 0.0 248.310961 278.234269 354.354965 387.953065\n", "13 0.0 195.288958 250.410842 297.658171 334.406093\n", "14 0.0 148.566600 199.488721 239.911436 290.833556\n", "15 0.0 130.717609 157.491096 205.263395 244.111198\n", "16 0.0 98.169450 130.717609 150.141511 195.288958\n", "17 0.0 78.220577 98.169450 122.843055 139.117134\n", "18 0.0 61.946498 67.196201 85.570162 110.768737\n", "19 0.0 46.722358 51.447091 61.946498 71.920934\n", "20 0.0 25.198575 40.947685 43.572536 50.922121\n", "21 0.0 0.524970 0.000000 0.000000 1.049941\n", "22 0.0 1.049941 2.099881 1.049941 0.000000\n", "23 0.0 0.524970 1.574911 2.624852 2.099881\n", "24 0.0 0.524970 1.049941 1.049941 1.049941\n", "25 0.0 1.574911 1.049941 2.624852 2.099881\n", "26 0.0 3.149822 1.049941 2.099881 3.674792\n", "27 0.0 2.624852 2.624852 2.099881 2.624852\n", "28 0.0 1.049941 2.624852 5.774674 1.574911\n", "29 0.0 2.624852 1.049941 4.199763 6.299644\n", "... ... ... ... ... ...\n", "61 0.0 0.000000 1.049941 2.624852 0.000000\n", "62 0.0 0.524970 0.000000 1.049941 1.049941\n", "63 0.0 1.574911 0.524970 0.000000 2.099881\n", "64 0.0 1.049941 1.049941 0.524970 0.000000\n", "65 0.0 0.524970 0.524970 0.524970 0.524970\n", "66 0.0 0.000000 0.524970 1.049941 0.524970\n", "67 0.0 0.000000 0.000000 0.524970 1.049941\n", "68 0.0 0.524970 0.524970 0.524970 0.524970\n", "69 0.0 0.000000 0.524970 0.524970 0.524970\n", "70 0.0 0.000000 0.000000 0.524970 0.000000\n", "71 0.0 0.000000 0.000000 0.000000 0.524970\n", "72 0.0 0.000000 0.000000 0.524970 0.000000\n", "73 0.0 0.000000 0.000000 0.000000 0.524970\n", "74 0.0 0.000000 0.000000 0.000000 0.000000\n", "75 0.0 0.000000 0.000000 0.000000 0.000000\n", "76 0.0 0.000000 0.000000 0.000000 0.000000\n", "77 0.0 0.000000 0.000000 0.000000 0.000000\n", "78 0.0 0.524970 0.000000 0.000000 0.000000\n", "79 0.0 0.000000 0.524970 0.000000 0.000000\n", "80 0.0 0.000000 0.000000 0.000000 0.000000\n", "81 0.0 0.000000 0.000000 0.000000 0.000000\n", "82 0.0 0.000000 0.000000 0.000000 0.000000\n", "83 0.0 0.000000 0.000000 0.000000 0.000000\n", "84 0.0 0.000000 0.000000 0.000000 0.000000\n", "85 0.0 0.000000 0.000000 0.000000 0.000000\n", "86 0.0 0.000000 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AinvRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 4.000152 1.000000 0.000000 1.000000\n", "19 5.000253 8.000000 3.000000 3.000000\n", "20 8.000405 9.000000 14.000000 11.000000\n", "21 15.268630 19.000000 19.000000 21.000000\n", "22 34.792334 23.252747 30.000000 37.000000\n", "23 47.002379 52.305882 35.257353 47.000000\n", "24 71.301929 71.000000 72.328767 54.278351\n", "25 109.005518 94.000000 109.000000 106.354515\n", "26 148.007442 149.000000 124.000000 142.000000\n", "27 163.008252 185.000000 191.000000 155.000000\n", "28 200.911435 204.000000 236.000000 237.000000\n", "29 301.015137 248.965969 255.000000 309.000000\n", "... ... ... ... ...\n", "61 4698.842226 4425.505659 4913.313246 4838.789858\n", "62 4026.540958 4666.984651 4423.484029 4901.345590\n", "63 3815.120551 3994.820380 4615.811824 4417.204107\n", "64 3387.931555 3771.923878 3968.123764 4565.898995\n", "65 3088.782778 3368.157379 3730.889611 3914.854530\n", "66 2813.918505 3028.649242 3313.963840 3671.791129\n", "67 2707.097235 2748.551844 2978.006806 3247.333091\n", "68 2444.721808 2644.220472 2671.324783 2918.315123\n", "69 2448.411812 2372.374569 2582.356748 2591.513194\n", "70 1884.493231 2357.704132 2298.307349 2517.764332\n", "71 2300.159358 1841.844196 2258.701059 2228.731119\n", "72 1902.049331 2213.019926 1765.360357 2182.818905\n", "73 1680.732763 1816.904809 2113.516677 1701.763793\n", "74 1491.630693 1611.383378 1748.030864 2033.012407\n", "75 1502.460144 1414.549949 1540.713578 1693.709560\n", "76 1491.763643 1417.810951 1345.393749 1457.233382\n", "77 1448.637128 1428.105190 1335.768151 1269.576452\n", "78 1329.769627 1375.825062 1334.254226 1244.957610\n", "79 1623.595574 1244.502753 1290.712441 1251.631207\n", "80 1371.735763 1519.606635 1165.563933 1204.788650\n", "81 1862.454908 1271.458194 1421.086907 1071.036587\n", "82 1548.692150 1725.590904 1168.615151 1325.096619\n", "83 1622.420911 1419.778921 1583.850055 1083.612112\n", "84 1643.432091 1470.103306 1294.663394 1443.287450\n", "85 1555.768682 1494.663315 1335.108806 1179.282555\n", "86 1441.475832 1395.768150 1348.540710 1199.038860\n", "87 1303.373147 1281.817090 1243.497005 1197.662873\n", "88 1073.829430 1162.135252 1148.341721 1094.011003\n", "89 983.046091 969.474091 1032.112617 1012.449024\n", "90 2988.704260 3385.290226 3681.538575 3947.979190\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: ApinUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.000000 0.000000 0.000000 0.000000\n", "62 0.0 0.000000 0.000000 0.000000 0.000000\n", "63 0.0 0.000000 0.000000 0.000000 0.000000\n", "64 0.0 0.000000 0.000000 0.000000 0.000000\n", "65 0.0 25005.000086 27745.395074 31194.773678 31570.774695\n", "66 0.0 27898.555847 30275.560823 34049.334205 37887.571789\n", "67 0.0 27390.283451 28195.563680 30834.128131 34514.437336\n", "68 0.0 25502.575716 27203.889819 28150.166241 30776.514981\n", "69 0.0 23853.672680 24911.031296 26594.801701 27522.054930\n", "70 0.0 20918.905545 24159.577767 25243.699719 26928.519502\n", "71 0.0 22368.271261 20694.592811 23894.828024 24986.274721\n", "72 0.0 20776.667275 21972.024651 20278.902260 23469.033921\n", "73 0.0 19456.799460 20204.409744 21363.881463 19702.113172\n", "74 0.0 16617.436993 18915.533061 19660.356000 20799.652136\n", "75 0.0 16061.744917 16528.935320 18805.399889 19536.838936\n", "76 0.0 14144.721970 15826.444710 16205.167698 18435.659561\n", "77 0.0 12309.065575 13878.593581 15471.757344 15824.146493\n", "78 0.0 10944.554542 12084.318223 13634.066427 15141.355229\n", "79 0.0 12035.280406 11131.085958 12277.311374 13886.346550\n", "80 0.0 11096.757859 11775.807858 10843.989132 11990.871907\n", "81 0.0 11389.623552 10298.822780 10938.183972 10090.244681\n", "82 0.0 10103.728255 10470.695148 9458.924989 10056.173999\n", "83 0.0 9581.709664 9357.427435 9665.955870 8744.857251\n", "84 0.0 8614.740626 8504.707194 8261.299809 8537.535635\n", "85 0.0 7713.051122 8260.703340 8115.861266 7888.805568\n", "86 0.0 4471.308183 4809.059753 5124.284535 5038.450271\n", "87 0.0 3372.254268 3639.406733 3882.217480 4153.525029\n", "88 0.0 2964.757230 3332.392775 3575.193949 3824.805184\n", "89 0.0 2980.297897 3091.098144 3450.034713 3727.766460\n", "90 0.0 7805.561889 8818.568749 9610.963364 10614.755538\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: RmvH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 1.000174 0.000000 0.000000 0.000000\n", "19 3.000347 1.000094 0.000000 0.000000\n", "20 0.000000 3.000188 1.000000 0.000000\n", "21 0.000000 0.000000 3.000000 1.000000\n", "22 3.000521 0.000000 0.000000 2.000000\n", "23 1.000174 3.000283 0.000000 0.000000\n", "24 0.000000 0.000000 2.000000 0.000000\n", "25 3.500521 0.000000 0.000000 2.000000\n", "26 3.500521 3.500283 0.000000 0.000000\n", "27 2.000347 3.500283 3.000000 0.000000\n", "28 2.000347 2.000188 3.500000 3.000000\n", "29 5.000868 2.000188 2.000000 3.500000\n", "... ... ... ... ...\n", "61 1621.354721 1465.411753 1548.704212 1347.809152\n", "62 1522.095259 1556.235772 1395.090217 1492.793054\n", "63 1492.771127 1465.703372 1481.246441 1336.995856\n", "64 1526.294249 1422.634868 1399.976237 1412.885444\n", "65 1471.102508 1449.932642 1354.678469 1324.056263\n", "66 1388.084999 1378.433335 1371.152951 1285.470402\n", "67 1381.233626 1325.045141 1283.737498 1289.931746\n", "68 1380.357927 1305.171469 1264.197988 1216.478989\n", "69 1422.341378 1292.311149 1225.624093 1186.869536\n", "70 1142.575435 1349.998924 1202.440492 1148.760613\n", "71 1472.713128 1066.839173 1248.215676 1142.391024\n", "72 1371.712597 1356.415830 990.340854 1166.708839\n", "73 1363.440454 1269.298143 1256.541649 913.783276\n", "74 1249.039682 1252.368433 1169.180275 1152.162759\n", "75 1272.270086 1152.167944 1150.344680 1097.552698\n", "76 1282.234317 1151.332341 1045.947104 1047.873935\n", "77 1227.500855 1163.793665 1030.580201 941.516842\n", "78 1130.185916 1116.028214 1047.001189 913.489706\n", "79 1295.962183 1016.927541 985.688405 937.122198\n", "80 1103.596177 1160.400017 895.922559 873.994586\n", "81 1427.915278 960.971317 1015.783036 786.732836\n", "82 1391.482170 1259.212829 839.587958 902.317570\n", "83 1530.484614 1238.704112 1118.236592 726.750515\n", "84 1491.254928 1341.888256 1106.216039 958.650036\n", "85 1378.588909 1288.159706 1159.545113 952.895525\n", "86 1914.522601 1181.779789 1104.141684 981.756597\n", "87 1915.380569 1656.771394 999.478785 948.800900\n", "88 1572.446043 1644.285802 1399.500555 826.507651\n", "89 1698.486324 1342.326421 1414.123818 1171.754985\n", "90 8920.404299 8717.644325 8071.740437 7680.876405\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxdUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.729791 0.729788 0.000000 1.094683\n", "15 0.0 7.297918 5.108519 8.392578 8.392568\n", "16 0.0 44.517309 54.004341 62.032080 57.653282\n", "17 0.0 185.732027 186.825838 199.597347 204.705635\n", "18 0.0 494.798909 487.133772 499.540667 474.727324\n", "19 0.0 1060.387613 1043.962306 1150.512664 1095.777228\n", "20 0.0 1642.761371 1600.790938 1746.020757 1717.556947\n", "21 0.0 2160.183686 2075.518362 2187.908182 2222.570527\n", "22 0.0 2690.742157 2477.996732 2630.890282 2481.645396\n", "23 0.0 3113.656395 3001.255083 3006.731865 2873.176898\n", "24 0.0 3394.991137 3340.971643 3527.801385 3256.315835\n", "25 0.0 3698.219608 3611.358292 3887.222685 3775.560276\n", "26 0.0 3932.847593 3848.904436 4019.679422 4184.971632\n", "27 0.0 4157.258439 4150.671967 4382.384751 4300.278166\n", "28 0.0 4632.717628 4298.819047 4697.653705 4693.634120\n", "29 0.0 5195.752009 4800.183744 4838.138189 4980.805854\n", "... ... ... ... ... ...\n", "61 0.0 4063.477926 4149.943079 4956.365894 5172.011301\n", "62 0.0 3449.358443 3855.108484 4058.359894 4801.278753\n", "63 0.0 3027.174169 3228.220102 3777.026032 3975.158143\n", "64 0.0 2469.248737 2747.654284 3117.661179 3617.926667\n", "65 0.0 1419.808853 1551.530675 1816.446362 2075.883500\n", "66 0.0 793.283030 980.835974 1096.873787 1315.443861\n", "67 0.0 534.936962 608.643760 764.454638 891.436714\n", "68 0.0 344.461451 422.182750 472.173904 619.955378\n", "69 0.0 222.221416 273.670754 330.594710 398.099657\n", "70 0.0 144.133763 179.163119 227.694367 280.238809\n", "71 0.0 101.805875 112.752352 145.228142 187.920553\n", "72 0.0 72.614228 87.209746 81.006651 130.997048\n", "73 0.0 47.071530 59.842672 70.424698 74.073539\n", "74 0.0 37.219350 40.503271 51.085272 62.761815\n", "75 0.0 27.732066 32.110702 35.029904 37.949004\n", "76 0.0 17.879883 20.434083 24.447953 30.651120\n", "77 0.0 17.150091 14.595774 19.339426 22.258550\n", "78 0.0 8.027702 13.501091 12.406424 15.690454\n", "79 0.0 9.487286 5.473415 14.230899 10.946828\n", "80 0.0 5.108538 6.932993 4.743633 12.041512\n", "81 0.0 4.378747 2.554260 5.838318 4.378732\n", "82 0.0 4.378747 3.284049 3.284053 4.378732\n", "83 0.0 1.824478 2.919155 2.554264 4.013837\n", "84 0.0 1.094687 0.729789 2.919159 2.919154\n", "85 0.0 1.459582 0.729789 1.824474 1.459577\n", "86 0.0 1.094687 1.094683 0.364895 1.459577\n", "87 0.0 0.364896 1.094683 1.094685 0.364894\n", "88 0.0 0.364896 0.000000 0.729790 0.729789\n", "89 0.0 0.000000 0.000000 0.364895 0.364894\n", "90 0.0 1.459584 0.364894 0.729790 1.094683\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: ApinRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 149757.201109 137807.162085 151796.189161 146283.712429\n", "62 137216.854705 154441.107416 141803.219068 155658.645449\n", "63 144532.020249 140206.678044 157732.709028 144347.691286\n", "64 141373.236993 146909.779753 142222.751718 159621.978414\n", "65 132937.721458 142848.263751 148220.682404 143063.774888\n", "66 128924.954209 133623.244682 143457.086410 148338.964522\n", "67 134108.034057 128945.513824 133581.641921 143106.221457\n", "68 125122.201993 133779.276907 128480.478178 132893.354754\n", "69 125109.618742 124580.098591 133030.113073 127427.300950\n", "70 102437.757782 124252.198038 123601.802619 131562.759859\n", "71 123876.605858 101547.585410 123009.836887 122196.009332\n", "72 111581.071069 122552.844114 100353.388660 121362.919384\n", "73 109023.949710 110116.231980 120927.650982 98846.473007\n", "74 103384.409625 107080.160391 108490.935165 118753.384672\n", "75 108506.010648 101107.568071 104877.631436 106380.778865\n", "76 98172.949368 105931.885414 98698.703894 102322.598292\n", "77 82202.376587 95557.136657 103032.209382 95926.263539\n", "78 68063.108854 79766.485612 92772.309336 99692.441482\n", "79 75252.856116 65786.954972 77088.266185 89628.431424\n", "80 60222.524781 72426.600357 63242.035377 73997.846048\n", "81 63865.512241 57787.120906 69285.552247 60394.621170\n", "82 49107.095626 60924.755262 54927.767370 65953.327011\n", "83 44621.648506 46552.378380 57731.463812 51886.951369\n", "84 39155.065630 42059.206073 43800.033201 54268.430019\n", "85 35391.935667 36652.421509 39193.248667 40856.131739\n", "86 33107.982848 32827.623424 33888.954476 36221.545073\n", "87 28151.702250 30592.381602 30113.498641 31064.408535\n", "88 23224.629467 25745.351477 27875.948109 27376.840628\n", "89 20269.350309 21067.095443 23235.053183 25084.757515\n", "90 99135.746741 105461.256302 110899.340302 117321.747059\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: ApinUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 63202.063111 65189.130953 76055.945352 80491.025463\n", "62 0.0 65335.877377 72028.935958 75100.609739 86365.934170\n", "63 0.0 67485.701293 71297.890559 79166.131330 81935.427500\n", "64 0.0 67564.409204 73682.413939 78129.795946 85971.742962\n", "65 0.0 64224.883267 71701.121564 78239.709160 82276.485888\n", "66 0.0 60522.317677 67429.729888 75305.476484 81712.264306\n", "67 0.0 60712.308827 62924.551472 70042.911450 77845.976549\n", "68 0.0 58072.435302 62036.566844 64202.842013 71136.795767\n", "69 0.0 54949.509045 58735.010466 62565.289515 64641.712696\n", "70 0.0 47702.339896 55945.267517 59649.911245 63330.407407\n", "71 0.0 51607.361312 48027.628639 56223.596835 59775.183250\n", "72 0.0 49506.404239 51978.507364 48366.573573 56504.511728\n", "73 0.0 47277.161718 49743.356122 52086.425263 48427.498825\n", "74 0.0 44759.680810 47675.798079 50079.901306 52405.960640\n", "75 0.0 46732.662815 44004.850200 46803.724293 49135.199417\n", "76 0.0 46103.725055 47090.234579 44224.864483 47074.880765\n", "77 0.0 44143.220732 46136.716638 47079.631221 44147.737556\n", "78 0.0 41267.349986 43861.606547 45771.381320 46554.767383\n", "79 0.0 43515.180607 40504.139740 43005.225738 44826.098620\n", "80 0.0 37868.888675 41105.504783 38184.181850 40582.759759\n", "81 0.0 42032.105863 40255.033413 43526.382956 40462.631896\n", "82 0.0 37266.168761 41621.609766 39821.178997 42996.939826\n", "83 0.0 31949.595109 34904.726353 38928.710812 37214.224804\n", "84 0.0 27080.690450 29070.475236 31667.939998 35250.474511\n", "85 0.0 22331.372016 24821.722810 26574.125086 28924.528886\n", "86 0.0 18972.201200 21093.963562 23376.194263 24878.859881\n", "87 0.0 15986.916521 17700.832974 19681.852024 21826.698051\n", "88 0.0 12540.360386 14414.113876 15894.780605 17656.592374\n", "89 0.0 10476.972636 11428.386886 13072.190201 14402.560330\n", "90 0.0 27208.386860 32302.227039 37219.148574 42794.515844\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxaUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.244573 0.244532 0.244479 0.244339\n", "17 0.0 1.222863 2.200787 1.955828 2.444505\n", "18 0.0 6.846395 6.112525 7.821633 5.865917\n", "19 0.0 14.916355 16.380932 19.066945 21.507391\n", "20 0.0 37.632098 33.493139 40.087060 43.994712\n", "21 0.0 62.345698 68.913239 71.861990 74.543078\n", "22 0.0 103.427388 105.604812 122.189186 115.115259\n", "23 0.0 148.391487 156.701629 172.313797 191.825284\n", "24 0.0 197.056950 219.504645 242.468324 246.845423\n", "25 0.0 237.402626 276.725443 310.881142 338.743028\n", "26 0.0 289.735738 324.643625 387.899238 418.397453\n", "27 0.0 362.838463 379.901371 431.655090 506.407320\n", "28 0.0 423.217924 463.512813 485.436899 555.291258\n", "29 0.0 550.636762 517.524810 586.389141 613.224133\n", "... ... ... ... ... ...\n", "61 0.0 2195.788475 2117.549372 2175.905139 2088.986791\n", "62 0.0 1992.676599 2142.016806 2055.857992 2109.745256\n", "63 0.0 1938.888312 1944.763350 2078.324234 1991.326834\n", "64 0.0 1716.958755 1871.203293 1883.038099 2014.025447\n", "65 0.0 1441.757632 1495.721765 1462.416762 1472.088002\n", "66 0.0 1291.567420 1339.628604 1335.777026 1283.576938\n", "67 0.0 1051.395666 1227.111649 1265.529665 1259.408600\n", "68 0.0 965.228342 1008.486501 1167.995572 1205.723310\n", "69 0.0 936.232240 927.292521 975.814453 1127.540702\n", "70 0.0 809.004918 894.898399 880.929928 937.100381\n", "71 0.0 913.160524 767.150598 859.979509 852.223454\n", "72 0.0 832.201883 873.019542 733.040969 825.700324\n", "73 0.0 744.668251 795.191493 826.234330 702.146422\n", "74 0.0 630.744936 698.903499 755.550871 785.304876\n", "75 0.0 598.926827 591.631120 650.112378 715.865801\n", "76 0.0 535.345923 559.765342 553.448571 615.581963\n", "77 0.0 476.287672 504.023115 518.669408 525.335731\n", "78 0.0 360.645737 442.287956 465.280765 484.861957\n", "79 0.0 367.658768 335.894896 408.951294 431.350781\n", "80 0.0 301.370517 340.267980 311.493488 378.627085\n", "81 0.0 281.277726 276.621151 313.053716 287.770097\n", "82 0.0 224.312018 257.749095 256.762707 283.460115\n", "83 0.0 188.792154 200.544042 229.418679 232.290465\n", "84 0.0 153.641472 168.452816 178.994513 206.196817\n", "85 0.0 133.913167 135.999079 149.332341 161.141750\n", "86 0.0 93.129196 118.269275 118.885843 129.497940\n", "87 0.0 76.244116 77.948007 104.123757 107.131771\n", "88 0.0 54.976653 65.992464 68.150121 93.357440\n", "89 0.0 39.807980 48.116159 57.422979 56.646716\n", "90 0.0 98.358321 112.268625 129.867009 153.100355\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxrUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 339.130826 369.054134 425.750928 397.927501\n", "1 0.0 694.535731 826.828252 943.896633 903.473918\n", "2 0.0 796.904944 898.749185 1088.263470 1146.535176\n", "3 0.0 831.552985 892.974512 1050.990578 1143.910324\n", "4 0.0 743.357971 879.850254 978.019703 1042.066082\n", "5 0.0 703.985197 766.981635 904.523859 938.121959\n", "6 0.0 597.941193 701.360346 758.057140 877.750373\n", "7 0.0 508.696239 599.516104 698.210524 722.359158\n", "8 0.0 481.922752 519.720615 605.290777 649.388284\n", "9 0.0 408.951878 482.972693 511.846061 574.317528\n", "10 0.0 317.607043 403.702175 480.347841 496.096951\n", "11 0.0 290.833556 327.581479 384.278273 458.299088\n", "12 0.0 234.661732 280.334150 330.731301 348.580292\n", "13 0.0 209.463157 231.511910 288.208705 317.082072\n", "14 0.0 160.640917 189.514285 231.511910 273.509536\n", "15 0.0 119.168262 164.315710 202.113573 221.537474\n", "16 0.0 107.618915 119.168262 161.165888 208.413216\n", "17 0.0 88.195014 124.417965 132.292520 168.515472\n", "18 0.0 78.745548 95.544598 116.018440 143.316897\n", "19 0.0 69.821052 100.269331 103.944123 117.593351\n", "20 0.0 89.244954 107.093945 111.293708 125.467906\n", "21 0.0 69.296082 87.670043 85.045192 86.620103\n", "22 0.0 99.219390 117.068381 120.218203 117.593351\n", "23 0.0 129.667669 132.292520 153.816303 143.316897\n", "24 0.0 142.791927 154.866244 154.341274 158.541036\n", "25 0.0 161.690858 164.315710 176.390027 163.790739\n", "26 0.0 179.539849 181.114760 192.139137 194.763988\n", "27 0.0 170.090383 210.513098 204.738424 208.938187\n", "28 0.0 202.638543 186.364463 213.137949 205.788365\n", "29 0.0 226.787178 212.612979 201.588602 220.487534\n", "... ... ... ... ... ...\n", "61 0.0 16.799050 9.974436 20.998813 16.274080\n", "62 0.0 11.024377 12.599288 12.599288 22.048753\n", "63 0.0 9.449466 8.924495 11.549347 8.924495\n", "64 0.0 5.774674 7.349584 6.824614 8.924495\n", "65 0.0 3.674792 6.299644 6.299644 4.199763\n", "66 0.0 5.249703 3.674792 5.774674 6.824614\n", "67 0.0 4.199763 5.249703 4.199763 4.199763\n", "68 0.0 3.674792 3.149822 4.199763 2.624852\n", "69 0.0 1.049941 3.149822 2.624852 2.624852\n", "70 0.0 1.574911 1.049941 3.674792 1.574911\n", "71 0.0 2.099881 1.574911 0.524970 3.149822\n", "72 0.0 3.674792 1.049941 1.574911 0.524970\n", "73 0.0 1.049941 3.149822 0.524970 1.049941\n", "74 0.0 1.049941 0.000000 3.149822 0.524970\n", "75 0.0 0.000000 1.574911 0.000000 3.149822\n", "76 0.0 2.624852 0.000000 1.049941 0.000000\n", "77 0.0 0.524970 1.574911 0.000000 0.000000\n", "78 0.0 0.000000 0.000000 1.049941 0.524970\n", "79 0.0 0.000000 0.000000 0.000000 0.000000\n", "80 0.0 0.000000 0.000000 0.000000 0.000000\n", "81 0.0 0.524970 0.000000 0.524970 0.000000\n", "82 0.0 0.000000 0.524970 0.000000 0.000000\n", "83 0.0 0.000000 0.000000 0.524970 0.000000\n", "84 0.0 0.000000 0.000000 0.000000 0.000000\n", "85 0.0 0.000000 0.000000 0.000000 0.524970\n", "86 0.0 0.524970 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: PensUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 173.587310 184.089561 190.275270 196.308205\n", "1 0.0 565.280849 680.539141 709.055380 708.109922\n", "2 0.0 1072.074202 1050.589762 1169.365168 1195.349885\n", "3 0.0 1347.974345 1477.439013 1487.554736 1617.586751\n", "4 0.0 1616.367358 1737.435677 1899.400425 1881.320076\n", "5 0.0 1913.107828 2074.297233 2228.373651 2379.255953\n", "6 0.0 2237.293443 2324.355943 2519.304502 2631.452283\n", "7 0.0 2416.697877 2607.959847 2684.424382 2862.888523\n", "8 0.0 2721.083250 2858.662742 3082.199015 3144.050505\n", "9 0.0 2875.161311 3050.862718 3190.663771 3352.002330\n", "10 0.0 3389.484595 3457.215405 3674.670556 3818.985473\n", "11 0.0 3777.118006 3811.586206 3889.302733 4068.102668\n", "12 0.0 4241.195784 4364.484804 4395.379708 4471.644418\n", "13 0.0 4259.163674 4703.953084 4896.241121 4833.869437\n", "14 0.0 4726.769965 5010.117560 5587.656489 5771.648893\n", "15 0.0 4802.420535 5157.456301 5477.905016 6027.948641\n", "16 0.0 5268.857397 5385.725100 5817.247742 6119.850861\n", "17 0.0 5393.942708 5764.952370 5917.387894 6338.144748\n", "18 0.0 5752.210612 6034.631899 6447.039478 6621.159631\n", "19 0.0 5443.938009 5911.618264 6229.089113 6594.222354\n", "20 0.0 5227.553930 5622.989118 6123.556289 6416.457723\n", "21 0.0 82.218147 72.547710 93.365874 91.006172\n", "22 0.0 160.492466 139.149969 133.068028 148.283907\n", "23 0.0 188.934598 192.949078 174.782416 163.371121\n", "24 0.0 217.809967 228.089680 231.061440 213.472049\n", "25 0.0 258.981929 252.008242 266.235115 269.867187\n", "26 0.0 323.328054 302.081324 304.948923 311.755127\n", "27 0.0 393.561862 407.747433 386.809272 395.616583\n", "28 0.0 488.842775 484.910049 497.949581 475.483869\n", "29 0.0 609.893055 538.643688 543.132466 557.813803\n", "... ... ... ... ... ...\n", "61 0.0 3329.974484 3544.633728 3929.181802 4157.629677\n", "62 0.0 3200.954022 3476.772075 3689.578555 4085.990672\n", "63 0.0 3190.112438 3325.462452 3629.255609 3812.122214\n", "64 0.0 3033.388313 3330.773979 3468.629460 3789.532288\n", "65 0.0 2813.310913 3155.087625 3457.610517 3572.756084\n", "66 0.0 2728.988170 2960.327548 3333.313670 3620.678246\n", "67 0.0 2683.087954 2823.090372 3063.026083 3434.650499\n", "68 0.0 2527.444955 2648.552289 2786.029529 3013.492795\n", "69 0.0 2527.527491 2659.313424 2791.609495 2926.795054\n", "70 0.0 2190.449547 2553.595303 2670.138077 2806.970106\n", "71 0.0 2419.901726 2257.664697 2656.123500 2750.121971\n", "72 0.0 2289.728176 2433.610927 2263.679866 2662.676232\n", "73 0.0 2258.211533 2363.129610 2501.965317 2341.059616\n", "74 0.0 1941.314984 2150.445884 2253.107460 2378.637541\n", "75 0.0 2015.652363 1955.190606 2167.449469 2256.258619\n", "76 0.0 1960.832643 2091.945628 2010.320737 2250.674009\n", "77 0.0 1820.553782 1987.752141 2122.742537 2030.027791\n", "78 0.0 1611.597381 1766.511298 1931.367432 2058.512818\n", "79 0.0 1670.797792 1606.647513 1764.915534 1921.738743\n", "80 0.0 1420.474254 1593.243278 1534.999312 1681.678148\n", "81 0.0 1454.054069 1400.755384 1561.362215 1508.668935\n", "82 0.0 1239.438226 1373.492532 1331.865119 1471.950001\n", "83 0.0 1211.899559 1291.055006 1429.358480 1382.931690\n", "84 0.0 1115.861184 1188.142123 1257.220405 1408.122892\n", "85 0.0 974.461562 1087.486708 1147.889742 1216.387080\n", "86 0.0 850.658805 899.483680 1011.353324 1070.436444\n", "87 0.0 701.505525 785.978223 832.777150 934.616604\n", "88 0.0 631.124778 701.814808 772.989539 837.948248\n", "89 0.0 522.841068 558.969777 623.118391 690.937906\n", "90 0.0 1682.320302 1932.650128 2132.283975 2401.562420\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: ApinUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 30685.557999 31650.309505 36926.312329 39079.610834\n", "62 0.0 29600.063308 32632.317036 34023.922108 39127.615970\n", "63 0.0 29739.937393 31419.912083 34887.327898 36107.715226\n", "64 0.0 27410.543373 29892.587339 31696.868008 34878.306746\n", "65 0.0 26154.668428 29199.259929 31862.006545 33505.926338\n", "66 0.0 24139.128826 26894.127637 30035.343455 32590.669863\n", "67 0.0 23540.537357 24398.310379 27158.377031 30183.930647\n", "68 0.0 22571.774113 24112.565045 24954.559591 27649.670223\n", "69 0.0 21758.658593 23257.624361 24774.320972 25596.533652\n", "70 0.0 18545.416792 21750.050540 23190.318715 24621.198949\n", "71 0.0 20020.639253 18631.912245 21811.468774 23189.276679\n", "72 0.0 18721.172534 19656.014598 18290.138068 21367.552932\n", "73 0.0 17443.501557 18353.434902 19217.939638 17867.932855\n", "74 0.0 15593.560059 16609.488881 17447.040163 18257.402195\n", "75 0.0 16655.702631 15683.499615 16681.029218 17511.976013\n", "76 0.0 15046.490876 15368.449818 14433.302714 15363.438921\n", "77 0.0 13235.645671 13833.363849 14116.081854 13236.999969\n", "78 0.0 11529.683438 12254.492686 12788.064592 13006.934796\n", "79 0.0 11684.852533 10876.317029 11547.917620 12036.864944\n", "80 0.0 11324.658143 12292.565368 11418.946292 12136.238924\n", "81 0.0 7500.791403 7183.665970 7767.450913 7220.712720\n", "82 0.0 5217.807517 5827.632824 5575.546240 6020.199105\n", "83 0.0 5071.063952 5540.104623 6178.794486 5906.669967\n", "84 0.0 5557.708167 5966.067148 6499.138900 7234.374265\n", "85 0.0 5233.251637 5816.853592 6227.520795 6778.326833\n", "86 0.0 4151.902228 4616.231580 5115.678040 5444.523421\n", "87 0.0 3238.391452 3585.571121 3986.856457 4421.327421\n", "88 0.0 2758.303325 3170.443034 3496.121710 3883.639382\n", "89 0.0 2221.122613 2422.822834 2771.309830 3053.348858\n", "90 0.0 6336.529794 7522.828351 8667.924529 9966.365373\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: PensRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 25.047290 27.049272 42.069839 36.046392\n", "1 130.244591 129.235410 124.206190 112.144332\n", "2 271.512628 272.493887 261.433997 239.307994\n", "3 459.868253 424.772925 429.708363 411.529646\n", "4 596.125513 629.146029 602.999357 564.722947\n", "5 772.457118 767.397864 831.380143 782.005167\n", "6 994.877058 969.764836 999.659497 991.275789\n", "7 1203.269855 1194.173611 1193.978759 1209.556720\n", "8 1436.710602 1389.528636 1419.352895 1393.789971\n", "9 1634.083910 1676.050554 1660.750309 1649.119872\n", "10 1857.505740 1888.438257 1980.280750 1906.448484\n", "11 2207.161299 2143.903604 2181.618303 2222.855706\n", "12 2246.234413 2503.552946 2418.010729 2439.136637\n", "13 2450.624919 2541.621465 2862.737383 2741.525817\n", "14 2669.033335 2756.017782 2894.788930 3182.083814\n", "15 2889.448831 2999.456266 3076.099878 3201.107010\n", "16 3244.119783 3274.956455 3412.650273 3489.485880\n", "17 3359.845790 3692.719915 3767.235586 3822.907277\n", "18 3700.984339 3800.430588 4126.837528 4150.327386\n", "19 4079.697392 4013.306361 4164.411880 4490.769401\n", "20 4124.309142 4397.002811 4342.200024 4465.259381\n", "21 257.905327 234.219162 207.344204 230.295107\n", "22 430.813396 358.059565 350.397102 318.409799\n", "23 628.369711 539.983614 471.208652 444.392311\n", "24 767.734029 776.639998 678.125731 602.244887\n", "25 1030.722160 960.077948 960.152575 843.083776\n", "26 1230.547909 1204.027317 1153.255003 1165.066439\n", "27 1344.854213 1455.889845 1405.362664 1354.238139\n", "28 1631.761819 1561.156741 1714.384876 1679.354319\n", "29 2092.147700 1860.069188 1839.411461 1993.099454\n", "... ... ... ... ...\n", "61 38182.107407 34549.227041 37652.973824 36105.155182\n", "62 36597.205452 40126.535940 36470.713981 39598.623766\n", "63 40017.949733 38344.068544 42123.155901 38218.683724\n", "64 41126.908622 41923.956857 40172.640411 44033.648469\n", "65 40612.166141 42904.483501 43852.647459 41901.761038\n", "66 41797.893501 42214.242699 44685.702664 45579.311748\n", "67 45812.371234 43293.757419 44010.351501 46420.887129\n", "68 45223.795593 47353.969235 44879.328754 45635.436172\n", "69 48463.294312 46612.968189 49059.021648 46345.144497\n", "70 42007.006593 49916.323789 48276.109564 50526.158769\n", "71 55253.157016 43255.035776 51423.118912 49656.611617\n", "72 51779.398602 56668.612544 44437.915575 52762.412668\n", "73 50643.078077 52728.101988 58047.640076 45427.741117\n", "74 49450.524240 51538.050350 53808.585129 59009.487549\n", "75 54436.332590 50129.698941 52367.889680 54688.046819\n", "76 52762.360147 55057.699169 50794.966148 52996.963902\n", "77 48005.098866 53159.182989 55525.553427 51061.951497\n", "78 43358.960626 48079.184556 53363.147302 55531.374684\n", "79 50115.335296 43082.919968 47928.816451 53054.932681\n", "80 43218.276863 49579.544313 42583.850492 47454.820148\n", "81 48887.435930 42499.479606 48683.157553 41718.730516\n", "82 40601.192963 47673.766626 41325.625934 47523.521828\n", "83 39752.461005 39173.487497 45992.314561 40000.029277\n", "84 36414.772460 38015.590796 37577.549008 43946.431340\n", "85 32868.973705 34555.686518 35878.975659 35613.035754\n", "86 29532.821370 30778.538397 32442.776816 33759.826553\n", "87 25399.226644 27604.161215 28577.976641 30171.356307\n", "88 20204.674983 23446.271044 25354.178729 26332.192610\n", "89 18126.119509 18413.400303 21302.113525 23003.877813\n", "90 63095.295577 70428.777198 76622.799363 84475.913800\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AinvUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 2.000121 4.000203 2.000086\n", "18 0.0 4.454851 2.545611 5.091171 7.000307\n", "19 0.0 5.867092 18.001127 9.500513 15.000673\n", "20 0.0 20.176809 17.808159 32.896553 25.001207\n", "21 0.0 55.720293 46.113016 49.475132 61.002868\n", "22 0.0 90.222095 101.491917 96.634803 101.004661\n", "23 0.0 137.252952 135.055569 141.051676 144.006798\n", "24 0.0 221.884199 224.146926 243.983781 235.011105\n", "25 0.0 302.888613 336.079410 341.608543 349.808249\n", "26 0.0 477.032683 447.566644 484.975692 477.022163\n", "27 0.0 598.302325 642.455762 619.471392 663.030404\n", "28 0.0 913.717173 819.344596 850.202212 830.038022\n", "29 0.0 1187.423092 1073.203185 995.002717 997.045914\n", "... ... ... ... ... ...\n", "61 0.0 17422.680275 17752.223952 19333.045713 19752.462346\n", "62 0.0 17421.377738 18840.056339 19198.636347 20897.626616\n", "63 0.0 18367.715873 18678.638128 20282.270172 20639.420937\n", "64 0.0 18663.997064 19810.647102 20179.443931 21898.768731\n", "65 0.0 18337.375244 19578.997730 20847.662743 21252.946451\n", "66 0.0 17661.618062 19009.377975 20288.611166 21603.852330\n", "67 0.0 17995.348594 18112.993801 19475.220665 20786.971881\n", "68 0.0 17807.876972 18265.543892 18421.846238 19746.622625\n", "69 0.0 18110.342257 18191.697209 18665.400756 18798.073143\n", "70 0.0 15525.834661 18010.581292 18083.055446 18554.359034\n", "71 0.0 17203.481376 15429.167799 17860.345910 17925.101846\n", "72 0.0 16289.347994 17216.186184 15465.876277 17887.683861\n", "73 0.0 15439.766664 15971.114591 16869.111900 15152.704988\n", "74 0.0 14852.409400 15565.041596 16077.135251 17006.576093\n", "75 0.0 15112.190786 14517.814424 15201.567099 15712.093328\n", "76 0.0 14809.803300 14863.292169 14208.318401 14861.510924\n", "77 0.0 13311.644655 14233.616988 14272.688428 13674.384565\n", "78 0.0 11695.729641 12677.985645 13526.247647 13615.950257\n", "79 0.0 12332.249383 11150.223975 12036.748857 12827.555301\n", "80 0.0 10396.664128 11487.202346 10398.502222 11213.359546\n", "81 0.0 10708.360294 9595.766680 10591.003282 9582.905743\n", "82 0.0 9308.607746 10087.552502 8949.552475 9902.606632\n", "83 0.0 8202.315790 8327.472126 8990.235798 7975.579643\n", "84 0.0 7319.773783 7465.631019 7570.368453 8205.362282\n", "85 0.0 6208.763806 6485.111573 6589.157393 6661.811034\n", "86 0.0 5192.291813 5428.542824 5693.418144 5784.221158\n", "87 0.0 4636.135871 4559.620158 4759.993998 4983.176616\n", "88 0.0 3666.363421 3977.467251 3922.614854 4068.630947\n", "89 0.0 3100.549925 3173.357071 3373.359572 3344.265586\n", "90 0.0 8791.057320 9675.648630 10371.652648 11098.478501\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxaRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.0 0.0\n", "1 0.000000 0.000000 0.0 0.0\n", "2 0.000000 0.000000 0.0 0.0\n", "3 0.000000 0.000000 0.0 0.0\n", "4 0.000000 0.000000 0.0 0.0\n", "5 0.000000 0.000000 0.0 0.0\n", "6 0.000000 0.000000 0.0 0.0\n", "7 0.000000 0.000000 0.0 0.0\n", "8 0.000000 0.000000 0.0 0.0\n", "9 0.000000 0.000000 0.0 0.0\n", "10 0.000000 0.000000 0.0 0.0\n", "11 0.000000 0.000000 0.0 0.0\n", "12 0.000000 0.000000 0.0 0.0\n", "13 0.000000 0.000000 0.0 0.0\n", "14 0.000000 0.000000 0.0 0.0\n", "15 0.000000 0.000000 0.0 0.0\n", "16 0.000000 0.000000 0.0 0.0\n", "17 1.000000 1.000000 7.0 2.0\n", "18 14.000000 11.000000 4.0 15.0\n", "19 19.000000 26.000000 23.0 15.0\n", "20 31.000000 32.000000 42.0 34.0\n", "21 48.000000 46.000000 50.0 61.0\n", "22 78.000000 63.000000 73.0 66.0\n", "23 82.000000 98.000000 88.0 91.0\n", "24 127.000000 109.000000 124.0 110.0\n", "25 137.000000 148.000000 136.0 144.0\n", "26 182.000000 166.000000 176.0 169.0\n", "27 176.000000 206.000000 188.0 194.0\n", "28 205.000000 213.000000 240.0 217.0\n", "29 260.000000 225.000000 240.0 260.0\n", "... ... ... ... ...\n", "61 99.000000 92.000000 104.0 116.0\n", "62 63.814815 92.000000 75.0 94.0\n", "63 55.000000 52.736842 81.0 73.0\n", "64 49.000000 48.000000 51.0 80.0\n", "65 33.000000 35.000000 34.0 38.0\n", "66 34.000000 25.000000 30.0 27.0\n", "67 18.000000 33.000000 20.0 30.0\n", "68 43.000000 14.000000 28.0 19.0\n", "69 28.000000 31.000000 12.0 22.0\n", "70 16.000000 24.000000 25.0 8.0\n", "71 14.000000 13.000000 19.0 24.0\n", "72 11.000000 15.000000 13.0 21.0\n", "73 12.000000 12.000000 15.0 14.0\n", "74 10.000000 9.000000 14.0 14.0\n", "75 28.000000 9.000000 9.0 14.0\n", "76 15.000000 28.000000 9.0 8.0\n", "77 12.000000 16.000000 23.0 9.0\n", "78 10.000000 11.000000 14.0 20.0\n", "79 9.000000 9.000000 11.0 13.0\n", "80 0.000000 8.000000 9.0 11.0\n", "81 0.000000 0.000000 8.0 9.0\n", "82 0.000000 0.000000 0.0 8.0\n", "83 3.000000 0.000000 0.0 0.0\n", "84 2.000000 4.000000 0.0 0.0\n", "85 1.000000 2.000000 3.0 0.0\n", "86 3.000000 0.000000 2.0 2.0\n", "87 0.000000 2.000000 0.0 2.0\n", "88 0.000000 0.000000 2.0 0.0\n", "89 0.000000 0.000000 0.0 2.0\n", "90 0.000000 0.000000 0.0 0.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxrUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 306.869174 333.945866 385.249072 360.072499\n", "1 0.0 628.464269 748.171748 854.103367 817.526082\n", "2 0.0 721.095056 813.250815 984.736530 1037.464824\n", "3 0.0 752.447015 808.025488 951.009422 1035.089676\n", "4 0.0 672.642029 796.149746 884.980297 942.933918\n", "5 0.0 637.014803 694.018365 818.476141 848.878041\n", "6 0.0 541.058807 634.639654 685.942860 794.249627\n", "7 0.0 460.303761 542.483896 631.789476 653.640842\n", "8 0.0 436.077248 470.279385 547.709223 587.611716\n", "9 0.0 370.048122 437.027307 463.153939 519.682472\n", "10 0.0 287.392957 365.297825 434.652159 448.903049\n", "11 0.0 263.166444 296.418521 347.721727 414.700912\n", "12 0.0 212.338268 253.665850 299.268699 315.419708\n", "13 0.0 189.536843 209.488090 260.791295 286.917928\n", "14 0.0 145.359083 171.485715 209.488090 247.490464\n", "15 0.0 107.831738 148.684290 182.886427 200.462526\n", "16 0.0 97.381085 107.831738 145.834112 188.586784\n", "17 0.0 79.804986 112.582035 119.707480 152.484528\n", "18 0.0 71.254452 86.455402 104.981560 129.683103\n", "19 0.0 63.178948 90.730669 94.055877 106.406649\n", "20 0.0 80.755046 96.906055 100.706292 113.532094\n", "21 0.0 62.703918 79.329957 76.954808 78.379897\n", "22 0.0 89.780610 105.931619 108.781797 106.406649\n", "23 0.0 117.332331 119.707480 139.183697 129.683103\n", "24 0.0 129.208073 140.133756 139.658726 143.458964\n", "25 0.0 146.309142 148.684290 159.609973 148.209261\n", "26 0.0 162.460151 163.885240 173.860863 176.236012\n", "27 0.0 153.909617 190.486902 185.261576 189.061813\n", "28 0.0 183.361457 168.635537 192.862051 186.211635\n", "29 0.0 205.212822 192.387021 182.411398 199.512466\n", "... ... ... ... ... ...\n", "61 0.0 15.200950 9.025564 19.001187 14.725920\n", "62 0.0 9.975623 11.400712 11.400712 19.951247\n", "63 0.0 8.550534 8.075505 10.450653 8.075505\n", "64 0.0 5.225326 6.650416 6.175386 8.075505\n", "65 0.0 3.325208 5.700356 5.700356 3.800237\n", "66 0.0 4.750297 3.325208 5.225326 6.175386\n", "67 0.0 3.800237 4.750297 3.800237 3.800237\n", "68 0.0 3.325208 2.850178 3.800237 2.375148\n", "69 0.0 0.950059 2.850178 2.375148 2.375148\n", "70 0.0 1.425089 0.950059 3.325208 1.425089\n", "71 0.0 1.900119 1.425089 0.475030 2.850178\n", "72 0.0 3.325208 0.950059 1.425089 0.475030\n", "73 0.0 0.950059 2.850178 0.475030 0.950059\n", "74 0.0 0.950059 0.000000 2.850178 0.475030\n", "75 0.0 0.000000 1.425089 0.000000 2.850178\n", "76 0.0 2.375148 0.000000 0.950059 0.000000\n", "77 0.0 0.475030 1.425089 0.000000 0.000000\n", "78 0.0 0.000000 0.000000 0.950059 0.475030\n", "79 0.0 0.000000 0.000000 0.000000 0.000000\n", "80 0.0 0.000000 0.000000 0.000000 0.000000\n", "81 0.0 0.475030 0.000000 0.475030 0.000000\n", "82 0.0 0.000000 0.475030 0.000000 0.000000\n", "83 0.0 0.000000 0.000000 0.475030 0.000000\n", "84 0.0 0.000000 0.000000 0.000000 0.000000\n", "85 0.0 0.000000 0.000000 0.000000 0.475030\n", "86 0.0 0.475030 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcdUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtceUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 62.465221 51.228070 40.000000 35.000000\n", "62 0.0 90.910334 72.110398 59.900000 47.000000\n", "63 0.0 135.198426 113.260429 90.212271 75.000000\n", "64 0.0 127.037094 118.286663 100.006537 79.000865\n", "65 0.0 151.474157 144.473590 134.487700 114.001177\n", "66 0.0 189.665139 177.038020 169.812005 157.003111\n", "67 0.0 253.920162 229.012621 213.752494 206.004453\n", "68 0.0 260.352372 252.108144 228.522719 213.006277\n", "69 0.0 328.616399 308.091863 298.031973 269.003064\n", "70 0.0 280.912374 312.988125 292.165076 283.510302\n", "71 0.0 330.510828 307.003023 340.918488 319.006252\n", "72 0.0 340.067118 344.959074 322.354245 358.010603\n", "73 0.0 361.092966 366.243840 373.785455 350.000000\n", "74 0.0 354.295927 386.125617 390.859487 398.200002\n", "75 0.0 327.207590 356.153529 391.243800 394.010659\n", "76 0.0 338.808299 352.910653 384.231077 422.006837\n", "77 0.0 277.318958 335.060352 348.733610 382.000000\n", "78 0.0 268.551099 298.938913 357.386659 372.008168\n", "79 0.0 267.420380 282.947519 311.313060 369.198327\n", "80 0.0 189.644957 247.626755 263.260237 290.000000\n", "81 0.0 157.855033 174.632084 228.582905 238.319861\n", "82 0.0 151.424961 159.127365 174.677150 227.008236\n", "83 0.0 111.593858 141.771480 148.968833 164.272688\n", "84 0.0 80.297571 92.117968 116.689270 126.125749\n", "85 0.0 61.889279 77.881469 89.768842 111.011655\n", "86 0.0 51.131172 59.350667 73.650022 84.137331\n", "87 0.0 47.516501 53.364753 61.952946 74.007458\n", "88 0.0 35.746036 45.090984 52.013120 60.151518\n", "89 0.0 23.522729 28.900003 38.594559 44.146153\n", "90 0.0 60.668043 73.509084 84.293327 106.687086\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxdRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 3.000000 5.000076 5.000107 4.000145\n", "17 26.000000 27.000380 31.000749 27.000904\n", "18 74.000000 66.000943 76.001926 55.001917\n", "19 117.000000 107.001596 107.002782 107.003725\n", "20 151.000000 145.002144 148.003798 146.005099\n", "21 201.000000 174.002615 198.005215 199.007015\n", "22 302.000000 239.003467 249.006579 260.009257\n", "23 317.000000 329.004911 318.008371 332.011608\n", "24 370.000000 397.005899 421.010992 393.014030\n", "25 475.000000 425.006295 483.012704 489.017502\n", "26 524.000000 519.007693 572.015058 593.020828\n", "27 583.000000 629.009305 640.016529 685.024228\n", "28 646.000000 706.010445 774.020086 798.028169\n", "29 742.000000 766.011251 850.022332 875.030845\n", "... ... ... ... ...\n", "61 317.000000 344.005109 455.011902 589.020756\n", "62 223.000000 287.004288 328.008559 430.015260\n", "63 194.000000 217.003208 269.007007 294.010306\n", "64 156.000000 180.002615 196.005108 237.008425\n", "65 90.000000 112.001688 146.003798 176.006256\n", "66 82.000000 72.001079 87.002247 117.004195\n", "67 50.000000 64.000958 70.001872 69.002423\n", "68 39.413043 45.000669 56.001498 57.002061\n", "69 25.000000 33.393365 38.000963 50.001808\n", "70 22.000000 24.000365 30.427258 34.001157\n", "71 14.000000 18.000274 23.000588 30.484973\n", "72 9.000000 12.000182 18.000481 25.000868\n", "73 9.000000 11.000167 13.000348 17.000615\n", "74 2.000000 9.000137 9.000241 14.000506\n", "75 3.000000 3.000046 9.000241 8.000289\n", "76 8.000000 2.000030 2.000053 8.000289\n", "77 5.000000 5.000076 5.000134 1.000036\n", "78 3.000000 3.000046 3.000080 4.000145\n", "79 3.000000 0.000000 3.000080 2.000072\n", "80 2.000000 3.000046 2.000053 5.000181\n", "81 1.000000 1.000015 3.000080 0.000000\n", "82 2.000000 1.000015 1.000027 3.000108\n", "83 3.000000 3.000046 1.000027 1.000036\n", "84 1.000000 5.000076 1.000027 2.000072\n", "85 1.000000 0.000000 1.000027 2.000072\n", "86 0.000000 0.000000 0.000000 1.000036\n", "87 1.000000 0.000000 0.000000 0.000000\n", "88 0.000000 1.000015 0.000000 0.000000\n", "89 0.000000 0.000000 2.000053 0.000000\n", "90 1.000000 1.000015 0.000000 1.000036\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcpUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 228.015844 240.988337 248.338962 277.330628\n", "62 0.0 182.759278 236.177283 265.162074 253.095231\n", "63 0.0 176.275189 182.766491 246.717170 269.098304\n", "64 0.0 166.631232 177.554910 180.239783 253.177721\n", "65 0.0 140.274575 172.360657 181.481713 185.231258\n", "66 0.0 126.974961 146.871574 177.147508 188.600023\n", "67 0.0 93.627976 124.217223 143.646850 176.162407\n", "68 0.0 87.878621 90.652224 121.146557 146.181391\n", "69 0.0 94.100589 87.577306 91.280328 120.208743\n", "70 0.0 63.403012 95.102933 86.689017 90.458231\n", "71 0.0 81.001752 63.104964 95.103716 82.899901\n", "72 0.0 76.419081 82.372441 64.491813 98.270158\n", "73 0.0 71.760905 63.008525 73.490888 56.893518\n", "74 0.0 66.573585 76.220660 68.484352 77.200163\n", "75 0.0 62.435466 65.272354 69.038355 66.230928\n", "76 0.0 36.528370 57.531224 62.081095 64.849111\n", "77 0.0 52.342199 38.066419 57.084825 60.917858\n", "78 0.0 43.280792 50.807039 37.625086 55.521955\n", "79 0.0 42.085886 44.042635 51.858988 37.198634\n", "80 0.0 32.140555 40.647672 39.692084 49.164676\n", "81 0.0 30.729891 30.729379 39.099979 37.254818\n", "82 0.0 26.353984 29.177138 29.169574 35.772329\n", "83 0.0 22.473674 22.473300 27.148197 28.096969\n", "84 0.0 12.334426 20.873296 21.816425 23.724168\n", "85 0.0 10.647662 11.615437 20.321745 20.330882\n", "86 0.0 6.813681 9.733668 10.704259 20.444591\n", "87 0.0 5.739274 5.739179 7.650254 8.610406\n", "88 0.0 4.710002 4.709923 5.650443 6.595147\n", "89 0.0 4.775849 3.820615 4.774531 4.776678\n", "90 0.0 6.257184 9.832554 11.617279 15.198657\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: ApidUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AinvUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 4.541949 3.125181 5.625258 5.000175\n", "20 0.0 3.529649 3.647233 4.706075 5.000144\n", "21 0.0 8.871563 7.097142 12.774703 11.000335\n", "22 0.0 21.736489 19.231815 22.116380 25.000808\n", "23 0.0 34.014067 38.828315 40.815809 45.001532\n", "24 0.0 61.068156 53.123450 58.181086 70.002332\n", "25 0.0 86.497033 82.276330 78.478189 78.987233\n", "26 0.0 136.126876 149.633245 136.642840 133.004499\n", "27 0.0 153.182631 156.471508 164.950725 173.005460\n", "28 0.0 239.118468 224.252416 220.963355 245.007907\n", "29 0.0 340.216988 300.484936 278.613208 273.008627\n", "... ... ... ... ... ...\n", "61 0.0 12927.386153 12837.072454 13885.998124 13915.168775\n", "62 0.0 12101.618473 12820.692980 12762.277375 13761.707836\n", "63 0.0 11826.913246 11813.528493 12582.284214 12495.649779\n", "64 0.0 10665.704176 11073.791143 11071.050859 11819.037515\n", "65 0.0 9809.295580 10324.148495 10711.503019 10720.301909\n", "66 0.0 8694.571207 9171.786979 9692.721151 10032.869831\n", "67 0.0 8100.013349 8082.309060 8497.936116 8982.878381\n", "68 0.0 7077.312231 7332.107857 7318.456248 7698.608118\n", "69 0.0 6546.370839 6447.608498 6668.715598 6663.176535\n", "70 0.0 5324.734368 5925.759869 5820.592775 6023.961288\n", "71 0.0 5199.119731 4643.001549 5156.088835 5065.607381\n", "72 0.0 4193.812525 4340.052367 3874.558675 4310.469697\n", "73 0.0 3778.595586 3875.998356 4013.313348 3585.715999\n", "74 0.0 2937.609138 3182.723334 3258.514972 3381.208958\n", "75 0.0 2700.095769 2530.940861 2749.914646 2808.414355\n", "76 0.0 2162.759297 2296.409220 2148.607133 2331.696328\n", "77 0.0 1767.673524 1910.456349 2016.915657 1893.005937\n", "78 0.0 1347.346614 1518.418190 1644.166357 1730.449276\n", "79 0.0 1157.759999 1081.014167 1215.585985 1315.425107\n", "80 0.0 931.883035 1067.245709 990.201411 1117.337168\n", "81 0.0 805.027961 755.349006 863.181208 797.800247\n", "82 0.0 651.160413 709.981758 664.819300 757.046729\n", "83 0.0 593.443014 636.267585 687.093452 642.705136\n", "84 0.0 458.323306 479.798934 512.168252 552.408362\n", "85 0.0 410.907157 448.986824 467.728128 498.437655\n", "86 0.0 325.622710 352.439970 383.334918 394.969240\n", "87 0.0 257.306359 274.260936 296.458768 324.445343\n", "88 0.0 238.175962 266.221003 282.831805 303.816825\n", "89 0.0 188.868416 202.237344 225.157987 239.200398\n", "90 0.0 553.524761 627.633140 698.916801 772.293332\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxaUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.755878 0.000000 0.755661 0.755726\n", "18 0.0 2.267907 0.000000 3.022834 1.511323\n", "19 0.0 4.535053 6.803134 6.801423 6.045551\n", "20 0.0 9.890929 12.092253 12.847707 15.869539\n", "21 0.0 20.405894 22.748222 24.182670 25.694058\n", "22 0.0 29.476867 31.742164 42.383950 44.585712\n", "23 0.0 47.670260 44.591540 55.166482 80.946117\n", "24 0.0 62.730306 77.910235 80.861697 88.415957\n", "25 0.0 70.290823 96.739904 117.968537 123.178978\n", "26 0.0 86.918031 101.277421 137.539408 164.818754\n", "27 0.0 110.347817 125.462020 153.410837 182.122647\n", "28 0.0 135.352132 154.938151 178.350443 197.237952\n", "29 0.0 201.043938 195.067129 228.227525 243.336102\n", "... ... ... ... ... ...\n", "61 0.0 821.131978 798.322956 876.532707 846.656070\n", "62 0.0 674.074860 809.631366 761.636871 841.417138\n", "63 0.0 554.568004 641.516692 788.944797 736.645657\n", "64 0.0 544.268624 543.831299 617.162442 770.779239\n", "65 0.0 496.037225 506.267145 505.435836 564.964625\n", "66 0.0 465.849794 473.233113 479.667257 477.309364\n", "67 0.0 412.906348 457.432557 459.522205 462.065799\n", "68 0.0 409.360586 386.354782 436.786474 444.403005\n", "69 0.0 375.764445 394.680580 369.513748 425.368988\n", "70 0.0 319.972628 358.752763 371.080692 357.324695\n", "71 0.0 320.841833 301.599133 337.592381 357.459590\n", "72 0.0 318.577657 310.747079 281.053954 320.800324\n", "73 0.0 269.366980 309.412219 294.550106 269.719933\n", "74 0.0 261.645167 254.802057 296.110214 283.971593\n", "75 0.0 257.925526 249.241515 247.190075 290.783355\n", "76 0.0 233.711579 243.411210 233.841913 237.302342\n", "77 0.0 183.408644 220.699477 235.526057 223.801602\n", "78 0.0 184.025983 175.666010 206.334811 226.479443\n", "79 0.0 171.422444 171.176643 166.554246 198.441749\n", "80 0.0 145.743592 163.705563 157.941656 157.359438\n", "81 0.0 130.737840 141.231352 152.362335 148.786232\n", "82 0.0 107.233225 122.454301 129.866166 141.702650\n", "83 0.0 90.773621 101.982881 114.616659 115.486200\n", "84 0.0 65.618298 85.524710 89.096408 105.452530\n", "85 0.0 55.536032 61.121400 81.022866 86.026259\n", "86 0.0 40.987274 50.162029 54.217415 75.778090\n", "87 0.0 34.966008 37.980147 43.167070 51.211674\n", "88 0.0 21.329017 31.090537 32.689715 35.602512\n", "89 0.0 20.589523 20.589014 25.790456 29.684437\n", "90 0.0 41.454468 54.347862 67.130320 81.407929\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: ApinUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.000000 0.000000 0.000000 0.000000\n", "62 0.0 0.000000 0.000000 0.000000 0.000000\n", "63 0.0 0.000000 0.000000 0.000000 0.000000\n", "64 0.0 0.000000 0.000000 0.000000 0.000000\n", "65 0.0 35030.295415 38869.401419 43701.744995 44228.496709\n", "66 0.0 42537.295219 46161.545986 51915.467920 57767.679272\n", "67 0.0 44257.998522 45559.193205 49822.660641 55769.408863\n", "68 0.0 42723.546994 45573.697261 47158.960085 51558.787581\n", "69 0.0 41738.216690 43588.341141 46534.536259 48157.007428\n", "70 0.0 35761.185309 41301.163469 43154.486337 46034.711229\n", "71 0.0 38276.339080 35412.358977 40888.566177 42756.237728\n", "72 0.0 35637.997929 37688.381810 34784.186856 40256.185999\n", "73 0.0 33700.143764 34995.041935 37003.304584 34125.039306\n", "74 0.0 28705.425603 32675.221050 33961.849034 35929.901055\n", "75 0.0 26538.773694 27310.711016 31072.106701 32280.661280\n", "76 0.0 22701.730845 25400.830699 26008.666423 29588.519462\n", "77 0.0 19172.360552 21617.026773 24098.507589 24647.382057\n", "78 0.0 16362.018890 18065.956204 20382.817004 22636.201347\n", "79 0.0 16033.626414 14829.041602 16356.064612 18499.651468\n", "80 0.0 13974.442662 14829.588409 13656.124273 15100.424292\n", "81 0.0 14665.104788 13260.606425 14083.838100 12992.044460\n", "82 0.0 13324.703266 13808.655806 12474.342689 13261.989153\n", "83 0.0 12661.439854 12365.069367 12772.764274 11555.608341\n", "84 0.0 12066.102652 11911.986035 11571.061262 11957.966681\n", "85 0.0 9891.596997 10593.933198 10408.180582 10116.993162\n", "86 0.0 10268.249390 11043.887572 11767.793539 11570.677260\n", "87 0.0 8831.912867 9531.583505 10167.503333 10878.056109\n", "88 0.0 6606.783181 7426.036881 7967.104696 8523.348320\n", "89 0.0 5068.818850 5257.265240 5867.735909 6340.095371\n", "90 0.0 14297.159846 16152.647151 17604.047146 19442.656251\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxaUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.244409 0.000000 0.244339 0.244360\n", "18 0.0 0.733314 0.000000 0.977415 0.488677\n", "19 0.0 1.466382 2.199752 2.199199 1.954792\n", "20 0.0 3.198172 3.909957 4.154228 5.131320\n", "21 0.0 6.598122 7.355500 7.819320 8.308018\n", "22 0.0 9.531167 10.263637 13.704594 14.416520\n", "23 0.0 15.413890 14.418405 17.837748 26.173438\n", "24 0.0 20.283465 25.191804 26.146141 28.588765\n", "25 0.0 22.728113 31.280263 38.144413 39.829178\n", "26 0.0 28.104420 32.747442 44.472536 53.293148\n", "27 0.0 35.680299 40.567386 49.604467 58.888257\n", "28 0.0 43.765294 50.098314 57.668538 63.775699\n", "29 0.0 65.006342 63.073777 73.795990 78.681257\n", "... ... ... ... ... ...\n", "61 0.0 265.508061 258.132901 283.421552 273.761122\n", "62 0.0 217.958031 261.789408 246.270678 272.067145\n", "63 0.0 179.316211 207.430544 255.100530 238.189920\n", "64 0.0 175.985969 175.844563 199.555744 249.226807\n", "65 0.0 160.390638 163.698421 163.429622 182.677896\n", "66 0.0 150.629715 153.017066 155.097508 154.335097\n", "67 0.0 133.510771 147.908052 148.583727 149.406183\n", "68 0.0 132.364271 124.925483 141.232266 143.695025\n", "69 0.0 121.501161 127.617579 119.480036 137.540491\n", "70 0.0 103.461215 116.000537 119.986698 115.538780\n", "71 0.0 103.742267 97.520256 109.158455 115.582397\n", "72 0.0 103.010159 100.478189 90.877097 103.728845\n", "73 0.0 87.098184 100.046571 95.240996 87.212310\n", "74 0.0 84.601383 82.388705 95.745447 91.820498\n", "75 0.0 83.398659 80.590737 79.927416 94.023040\n", "76 0.0 75.569226 78.705543 75.611369 76.730278\n", "77 0.0 59.304076 71.361842 76.155927 72.364895\n", "78 0.0 59.503689 56.800542 66.717114 73.230759\n", "79 0.0 55.428411 55.348933 53.854309 64.164940\n", "80 0.0 47.125309 52.933204 51.069480 50.881223\n", "81 0.0 42.273290 45.666304 49.265440 48.109129\n", "82 0.0 34.673215 39.594858 41.991440 45.818695\n", "83 0.0 29.351101 32.975548 37.060604 37.341765\n", "84 0.0 21.217280 27.653898 28.808785 34.097438\n", "85 0.0 17.957240 19.763235 26.198254 27.816071\n", "86 0.0 13.252987 16.219589 17.530873 24.502387\n", "87 0.0 11.306047 12.280651 13.957811 16.558985\n", "88 0.0 6.896609 10.052937 10.570022 11.511857\n", "89 0.0 6.657498 6.657333 8.339188 9.598283\n", "90 0.0 13.404052 17.573053 21.706183 26.322762\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: LoasIdoH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 0.000000 0.000000 0.000000 0.000000\n", "62 0.000000 0.000000 0.000000 0.000000\n", "63 0.000000 0.000000 0.000000 0.000000\n", "64 0.000000 0.000000 0.000000 0.000000\n", "65 34019.763442 32689.133943 33184.396592 29616.418598\n", "66 50062.839277 48305.372948 48884.734842 49099.930458\n", "67 56541.889170 53136.723924 52665.702599 53155.494079\n", "68 57019.040135 57008.160970 54045.818380 54013.263765\n", "69 58775.595888 56250.879873 56439.784827 53816.749026\n", "70 48565.365494 57511.347749 55301.325230 55721.483395\n", "71 58827.612345 47282.550902 55939.582895 53805.744783\n", "72 49355.043774 56681.039545 45608.412186 53583.659158\n", "73 42343.697717 47309.984822 54217.887844 43222.664481\n", "74 35865.649278 40457.304453 45060.608684 51100.701855\n", "75 36236.231013 33965.891430 38391.344645 41933.576551\n", "76 36472.453457 34251.584277 32079.216402 35688.589600\n", "77 30486.645606 34444.689019 32250.878352 29683.685547\n", "78 24559.002629 28486.573987 32292.929302 29776.322825\n", "79 24085.911529 22868.693428 26513.703623 29505.249396\n", "80 17390.984165 22301.549369 21150.276636 24242.346627\n", "81 18151.559358 15955.955298 20497.527895 19166.093650\n", "82 11844.904173 16806.630511 14642.913638 18519.369449\n", "83 10277.006278 10741.123223 15350.165070 13049.372793\n", "84 8041.243991 9273.184602 9629.999977 13752.459683\n", "85 5673.111968 7241.377833 8290.074771 8486.359720\n", "86 3783.196958 5066.880785 6437.274720 7258.509930\n", "87 2389.756090 3355.245440 4503.818198 5552.124426\n", "88 1655.523788 2098.779050 2955.193016 3937.518109\n", "89 1265.670627 1477.548454 1818.734226 2563.988545\n", "90 3266.033333 3970.473808 4695.895740 5639.439330\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxdUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.364894\n", "15 0.0 1.459581 1.094682 2.919150 2.554256\n", "16 0.0 12.041533 10.581934 15.325539 18.609583\n", "17 0.0 61.302358 48.166051 56.923429 54.369173\n", "18 0.0 135.740960 126.253457 166.756457 164.931988\n", "19 0.0 309.430972 319.647475 397.004431 383.868255\n", "20 0.0 512.677495 530.556460 641.118368 631.631130\n", "21 0.0 729.790112 750.222911 834.876964 879.029112\n", "22 0.0 1007.475294 900.924298 1071.693026 1040.312161\n", "23 0.0 1232.250639 1160.364234 1286.980355 1334.051653\n", "24 0.0 1413.968405 1406.667947 1559.556008 1534.743231\n", "25 0.0 1744.563389 1638.011015 1833.591235 1839.429536\n", "26 0.0 1917.888596 1891.612660 2059.095590 2174.037130\n", "27 0.0 2126.243629 2105.075918 2342.253162 2402.825529\n", "28 0.0 2539.304960 2364.515833 2594.029869 2642.560741\n", "29 0.0 2916.606624 2708.246425 2812.236348 2962.572585\n", "... ... ... ... ... ...\n", "61 0.0 2868.806681 2935.576239 3488.749413 3784.313373\n", "62 0.0 2275.851862 2696.205498 2837.414019 3375.267448\n", "63 0.0 1922.998171 2140.106264 2621.032008 2773.922501\n", "64 0.0 1575.982777 1776.671362 2028.444513 2471.790453\n", "65 0.0 1198.316148 1402.289591 1617.574118 1855.849756\n", "66 0.0 888.155153 1010.757792 1268.005879 1487.671933\n", "67 0.0 710.086250 788.901934 954.197230 1142.847313\n", "68 0.0 547.707838 619.590880 708.988611 862.243998\n", "69 0.0 453.564861 498.081009 545.881092 653.159863\n", "70 0.0 302.133312 385.328602 440.791684 506.107670\n", "71 0.0 268.562947 251.412322 343.365045 401.018262\n", "72 0.0 205.436055 223.680336 224.409674 302.496942\n", "73 0.0 151.431551 171.865317 215.652223 202.880941\n", "74 0.0 108.738804 136.470548 164.567094 190.839446\n", "75 0.0 85.385504 87.939579 120.779841 139.024529\n", "76 0.0 64.221575 76.627849 81.006419 103.629833\n", "77 0.0 42.692752 56.193756 68.964924 69.329818\n", "78 0.0 35.029950 35.394768 48.165979 58.383005\n", "79 0.0 32.475683 28.096878 34.664909 44.152147\n", "80 0.0 22.258614 28.461772 24.447883 24.447883\n", "81 0.0 19.339452 18.974515 24.082989 22.623414\n", "82 0.0 15.690499 14.230886 16.055326 19.339370\n", "83 0.0 10.217069 11.311729 16.420220 15.325539\n", "84 0.0 7.297906 10.946836 9.122344 13.501070\n", "85 0.0 5.838325 4.378734 8.027663 9.122344\n", "86 0.0 4.378744 4.743629 3.284044 6.568088\n", "87 0.0 2.554267 3.284051 5.108513 2.554256\n", "88 0.0 0.729791 1.824473 3.284044 1.824469\n", "89 0.0 1.094686 1.459578 1.094681 2.189363\n", "90 0.0 0.729791 2.919156 3.648938 2.919150\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: LoasDefM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 761.000000 734.000000 766.000000 730.000000\n", "1 3152.000000 3384.000000 3424.000000 3376.000000\n", "2 5221.000000 5132.000000 5489.000000 5397.000000\n", "3 6504.000000 6639.000000 6569.000000 6831.000000\n", "4 7577.000000 7635.000000 7908.000000 7744.000000\n", "5 8451.000000 8508.000000 8789.000000 8917.000000\n", "6 9408.000000 9368.000000 9418.000000 9747.000000\n", "7 10127.000000 10236.000000 10298.000000 10378.000000\n", "8 10533.000000 10952.000000 11081.000000 11194.000000\n", "9 11046.000000 11291.000000 11887.000000 11933.000000\n", "10 12267.000000 11826.000000 12135.000000 12752.000000\n", "11 13180.415576 13084.000000 12741.000000 12954.000000\n", "12 13452.000000 14027.413495 13948.000000 13529.000000\n", "13 13222.000000 14246.000000 14818.411623 14680.000000\n", "14 13715.000000 13890.000000 14977.000000 15455.816882\n", "15 13993.000000 14343.000000 14597.000000 15603.000000\n", "16 14173.000000 14581.000000 14987.000000 15156.000000\n", "17 13396.000000 14670.000000 15148.000000 15550.000000\n", "18 12804.000000 13859.000000 15220.000000 15737.000000\n", "19 12291.000000 13323.000000 14380.000000 15715.000000\n", "20 12147.408234 12770.000000 13878.000000 14950.000000\n", "21 11671.000000 12661.409172 13302.000000 14410.000000\n", "22 12178.000000 12133.000000 13233.410695 13803.000000\n", "23 12674.000000 12738.000000 12738.000000 13759.412267\n", "24 13586.427179 13207.000000 13342.000000 13253.000000\n", "25 13251.422976 14137.428303 13743.000000 13885.000000\n", "26 12723.419347 13847.425289 14821.430568 14246.421355\n", "27 12185.000000 13326.422297 14518.427856 15484.433786\n", "28 13041.000000 12791.000000 14050.426494 15208.431629\n", "29 13611.000000 13735.000000 13507.000000 14672.429961\n", "... ... ... ... ...\n", "61 13809.000000 13291.000000 15385.000000 16263.530119\n", "62 12685.000000 14755.000000 14446.000000 16413.000000\n", "63 13288.000000 13522.000000 15810.000000 15343.000000\n", "64 12561.000000 13834.000000 14274.000000 16432.000000\n", "65 10833.000000 12425.000000 13685.000000 14156.000000\n", "66 9355.000000 10368.000000 11940.000000 13130.000000\n", "67 8256.000000 8950.000000 9884.000000 11393.000000\n", "68 6834.000000 7861.000000 8535.000000 9330.000000\n", "69 6177.564987 6509.000000 7513.000000 8114.000000\n", "70 4562.566990 5870.570901 6170.000000 7065.000000\n", "71 5179.592495 4306.573216 5582.579286 5798.000000\n", "72 3625.000000 4918.599537 4063.577705 5220.586122\n", "73 3063.000000 3416.000000 4651.604576 3798.587562\n", "74 2777.000000 2862.000000 3192.000000 4383.613952\n", "75 2491.557395 2588.000000 2661.000000 2972.000000\n", "76 1993.000000 2338.000000 2443.000000 2486.000000\n", "77 1553.559640 1865.000000 2181.000000 2249.000000\n", "78 1213.000000 1441.566876 1728.000000 2023.000000\n", "79 1088.000000 1136.000000 1332.571184 1580.000000\n", "80 714.000000 1015.000000 1048.000000 1189.576625\n", "81 893.655172 663.000000 937.000000 945.000000\n", "82 572.000000 826.665056 594.000000 833.000000\n", "83 522.000000 523.000000 758.664912 545.000000\n", "84 353.000000 470.000000 473.000000 684.000000\n", "85 95.000000 307.000000 423.000000 414.000000\n", "86 47.000000 87.000000 278.000000 380.000000\n", "87 39.000000 40.000000 80.000000 257.000000\n", "88 28.000000 36.000000 34.000000 70.000000\n", "89 22.000000 22.000000 33.000000 27.000000\n", "90 63.000000 74.000000 81.000000 99.000000\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxaRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.0 0.0\n", "1 0.000000 0.000000 0.0 0.0\n", "2 0.000000 0.000000 0.0 0.0\n", "3 0.000000 0.000000 0.0 0.0\n", "4 0.000000 0.000000 0.0 0.0\n", "5 0.000000 0.000000 0.0 0.0\n", "6 0.000000 0.000000 0.0 0.0\n", "7 0.000000 0.000000 0.0 0.0\n", "8 0.000000 0.000000 0.0 0.0\n", "9 0.000000 0.000000 0.0 0.0\n", "10 0.000000 0.000000 0.0 0.0\n", "11 0.000000 0.000000 0.0 0.0\n", "12 0.000000 0.000000 0.0 0.0\n", "13 0.000000 0.000000 0.0 0.0\n", "14 0.000000 0.000000 0.0 0.0\n", "15 0.000000 0.000000 0.0 0.0\n", "16 0.000000 0.000000 0.0 0.0\n", "17 0.000000 0.000000 0.0 1.0\n", "18 2.000000 0.000000 2.0 0.0\n", "19 0.000000 2.000000 0.0 3.0\n", "20 6.000000 1.000000 6.0 3.0\n", "21 11.000000 7.000000 1.0 10.0\n", "22 11.000000 14.000000 11.0 3.0\n", "23 19.000000 15.000000 20.0 12.0\n", "24 12.000000 21.000000 19.0 26.0\n", "25 24.000000 19.000000 26.0 21.0\n", "26 17.000000 31.000000 24.0 31.0\n", "27 21.000000 20.000000 33.0 31.0\n", "28 33.000000 25.000000 28.0 38.0\n", "29 36.000000 39.000000 34.0 36.0\n", "... ... ... ... ...\n", "61 15.000000 10.000000 11.0 23.0\n", "62 12.185185 14.000000 8.0 12.0\n", "63 11.000000 11.263158 13.0 9.0\n", "64 14.000000 11.000000 11.0 12.0\n", "65 7.000000 12.000000 9.0 9.0\n", "66 5.000000 7.000000 9.0 6.0\n", "67 3.000000 5.000000 5.0 7.0\n", "68 5.000000 3.000000 4.0 5.0\n", "69 6.000000 5.000000 2.0 5.0\n", "70 6.000000 6.000000 5.0 2.0\n", "71 3.000000 6.000000 6.0 4.0\n", "72 2.000000 3.000000 6.0 6.0\n", "73 1.000000 2.000000 3.0 6.0\n", "74 1.000000 1.000000 2.0 3.0\n", "75 2.000000 1.000000 1.0 2.0\n", "76 1.000000 2.000000 1.0 1.0\n", "77 0.000000 0.000000 2.0 1.0\n", "78 0.000000 0.000000 0.0 2.0\n", "79 1.000000 1.000000 0.0 0.0\n", "80 0.000000 1.000000 0.0 0.0\n", "81 0.000000 0.000000 1.0 0.0\n", "82 0.000000 0.000000 0.0 1.0\n", "83 0.000000 0.000000 0.0 0.0\n", "84 0.000000 0.000000 0.0 0.0\n", "85 0.000000 0.000000 0.0 0.0\n", "86 0.000000 0.000000 0.0 0.0\n", "87 0.000000 0.000000 0.0 0.0\n", "88 0.000000 0.000000 0.0 0.0\n", "89 0.000000 0.000000 0.0 0.0\n", "90 0.000000 0.000000 0.0 0.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxrUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 308.769293 360.072499 406.625407 389.999368\n", "1 0.0 657.441079 781.423826 841.752596 911.106929\n", "2 0.0 771.923232 898.756157 1043.165180 1035.089676\n", "3 0.0 771.448203 889.255564 995.187183 1091.143178\n", "4 0.0 718.719908 827.501705 941.508829 982.361381\n", "5 0.0 679.767474 737.721095 860.753783 913.482077\n", "6 0.0 574.310885 659.341198 761.472579 845.077803\n", "7 0.0 487.855483 600.912547 670.741910 728.220502\n", "8 0.0 434.177129 490.705661 604.237755 651.740723\n", "9 0.0 358.647410 432.752040 482.630156 575.735974\n", "10 0.0 323.970243 348.671786 453.653346 466.004118\n", "11 0.0 256.516028 316.369768 354.372142 437.502337\n", "12 0.0 224.689039 251.765731 320.645035 351.046935\n", "13 0.0 176.711042 226.589158 269.341829 302.593907\n", "14 0.0 134.433400 180.511279 217.088564 263.166444\n", "15 0.0 118.282391 142.508904 185.736605 220.888802\n", "16 0.0 88.830550 118.282391 135.858489 176.711042\n", "17 0.0 70.779423 88.830550 111.156945 125.882866\n", "18 0.0 56.053502 60.803799 77.429838 100.231263\n", "19 0.0 42.277642 46.552909 56.053502 65.079066\n", "20 0.0 22.801425 37.052315 39.427464 46.077879\n", "21 0.0 0.475030 0.000000 0.000000 0.950059\n", "22 0.0 0.950059 1.900119 0.950059 0.000000\n", "23 0.0 0.475030 1.425089 2.375148 1.900119\n", "24 0.0 0.475030 0.950059 0.950059 0.950059\n", "25 0.0 1.425089 0.950059 2.375148 1.900119\n", "26 0.0 2.850178 0.950059 1.900119 3.325208\n", "27 0.0 2.375148 2.375148 1.900119 2.375148\n", "28 0.0 0.950059 2.375148 5.225326 1.425089\n", "29 0.0 2.375148 0.950059 3.800237 5.700356\n", "... ... ... ... ... ...\n", "61 0.0 0.000000 0.950059 2.375148 0.000000\n", "62 0.0 0.475030 0.000000 0.950059 0.950059\n", "63 0.0 1.425089 0.475030 0.000000 1.900119\n", "64 0.0 0.950059 0.950059 0.475030 0.000000\n", "65 0.0 0.475030 0.475030 0.475030 0.475030\n", "66 0.0 0.000000 0.475030 0.950059 0.475030\n", "67 0.0 0.000000 0.000000 0.475030 0.950059\n", "68 0.0 0.475030 0.475030 0.475030 0.475030\n", "69 0.0 0.000000 0.475030 0.475030 0.475030\n", "70 0.0 0.000000 0.000000 0.475030 0.000000\n", "71 0.0 0.000000 0.000000 0.000000 0.475030\n", "72 0.0 0.000000 0.000000 0.475030 0.000000\n", "73 0.0 0.000000 0.000000 0.000000 0.475030\n", "74 0.0 0.000000 0.000000 0.000000 0.000000\n", "75 0.0 0.000000 0.000000 0.000000 0.000000\n", "76 0.0 0.000000 0.000000 0.000000 0.000000\n", "77 0.0 0.000000 0.000000 0.000000 0.000000\n", "78 0.0 0.475030 0.000000 0.000000 0.000000\n", "79 0.0 0.000000 0.475030 0.000000 0.000000\n", "80 0.0 0.000000 0.000000 0.000000 0.000000\n", "81 0.0 0.000000 0.000000 0.000000 0.000000\n", "82 0.0 0.000000 0.000000 0.000000 0.000000\n", "83 0.0 0.000000 0.000000 0.000000 0.000000\n", "84 0.0 0.000000 0.000000 0.000000 0.000000\n", "85 0.0 0.000000 0.000000 0.000000 0.000000\n", "86 0.0 0.000000 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcdUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcdUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: PensUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 79.635625 86.946750 79.944405 84.102182\n", "1 0.0 479.080033 511.650345 562.656774 518.166823\n", "2 0.0 935.919864 971.348780 1046.711568 1068.749141\n", "3 0.0 1377.584183 1503.962899 1539.358880 1639.956456\n", "4 0.0 1790.286168 1872.927705 2044.846865 2051.905838\n", "5 0.0 2305.723953 2454.612225 2548.254436 2740.556063\n", "6 0.0 2710.970663 2781.638476 3019.867341 3086.962490\n", "7 0.0 3208.521673 3390.882569 3501.763644 3719.360417\n", "8 0.0 3628.854395 3751.487248 4015.619215 4065.488507\n", "9 0.0 4166.331640 4324.906117 4515.961514 4722.524732\n", "10 0.0 4660.130113 4772.017516 4978.940722 5130.082861\n", "11 0.0 5387.211398 5390.859905 5551.872836 5714.206360\n", "12 0.0 5905.676538 6046.999576 6119.756301 6204.652909\n", "13 0.0 5852.103751 6503.422936 6722.219224 6698.535984\n", "14 0.0 6120.345200 6366.527634 7114.551028 7276.613057\n", "15 0.0 6275.686487 6907.331818 7244.316362 7938.107811\n", "16 0.0 6963.839167 7194.544726 7855.238947 8174.888833\n", "17 0.0 7576.242958 7930.032341 8269.231853 8882.482357\n", "18 0.0 8626.791388 8936.234775 9368.907805 9746.391156\n", "19 0.0 8564.998867 9695.197633 10133.696400 10562.377236\n", "20 0.0 9533.154688 9623.544978 10872.230157 11267.119660\n", "21 0.0 328.825340 343.076757 343.480960 331.653384\n", "22 0.0 600.710979 516.017465 543.322703 530.942977\n", "23 0.0 784.474964 754.697158 695.227901 709.042049\n", "24 0.0 1045.201814 1052.125194 1037.717348 962.912019\n", "25 0.0 1388.024630 1372.598811 1368.317866 1343.282624\n", "26 0.0 1715.894857 1717.929818 1709.985167 1673.547299\n", "27 0.0 2146.167885 2119.725748 2159.142662 2142.291669\n", "28 0.0 2724.672392 2567.234206 2552.623829 2578.369817\n", "29 0.0 3169.921086 3030.094494 2869.545158 2888.150988\n", "... ... ... ... ... ...\n", "61 0.0 48999.104403 47648.117998 51174.562580 50546.010325\n", "62 0.0 49720.332121 52084.252540 50841.597440 54404.585775\n", "63 0.0 51868.069452 52085.861194 54715.366971 53293.327616\n", "64 0.0 53841.596360 55425.370654 55716.295295 58410.769144\n", "65 0.0 53356.994096 56102.293043 57813.721950 57963.697170\n", "66 0.0 52779.343220 55749.397008 58731.075590 60417.489535\n", "67 0.0 55436.833089 54849.072716 57992.213811 60972.644358\n", "68 0.0 56019.469255 57605.593318 57105.640850 60265.358213\n", "69 0.0 57920.328733 58307.789219 60000.246600 59394.846357\n", "70 0.0 53592.222567 59963.509615 60449.523115 62105.580698\n", "71 0.0 60977.009298 55092.273437 61727.260088 62235.696936\n", "72 0.0 60830.151096 62409.061193 56497.258218 63192.644086\n", "73 0.0 59477.120518 61192.760568 62701.806664 56820.796882\n", "74 0.0 58439.529186 61695.786771 63504.226329 64995.517279\n", "75 0.0 62018.858107 59108.956889 62336.459548 64064.272697\n", "76 0.0 60422.391361 61950.534759 59067.383355 62239.483999\n", "77 0.0 56784.503512 60910.807249 62528.220340 59513.857571\n", "78 0.0 52676.387050 56860.531280 60933.115460 62405.121375\n", "79 0.0 55742.696632 52386.822724 56604.239997 60457.274642\n", "80 0.0 49866.728910 54443.519707 51213.958161 55350.161458\n", "81 0.0 52630.782937 48686.811393 53067.316610 49914.754450\n", "82 0.0 47821.707708 51863.726597 48086.258697 52337.710103\n", "83 0.0 43321.643578 45627.254105 49348.506409 45839.578769\n", "84 0.0 40299.936900 41981.282720 44330.799799 47784.740052\n", "85 0.0 35031.772542 37562.117661 38998.650334 41250.110950\n", "86 0.0 30317.522267 33202.452129 35542.827045 36758.814887\n", "87 0.0 26298.876102 28527.425160 31253.122825 33365.833357\n", "88 0.0 21429.548347 23855.688943 25784.676466 28279.605289\n", "89 0.0 18161.238441 19553.414628 21659.759878 23503.164463\n", "90 0.0 63100.806773 69554.280307 75998.048376 83170.563512\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: ApidUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcnUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 19047.193477 19355.611747 20612.860139 20676.770016\n", "62 0.0 18973.165516 20382.672601 20781.133587 22127.328135\n", "63 0.0 19044.844805 19917.493245 21494.273059 21884.417732\n", "64 0.0 18500.870878 19919.298560 20864.829164 22483.018274\n", "65 0.0 17387.421176 18913.063634 20400.584453 21359.834034\n", "66 0.0 16212.403725 17870.874445 19439.618189 20980.710616\n", "67 0.0 15122.229169 16156.337060 17814.620254 19362.706431\n", "68 0.0 14287.708776 15166.591127 16181.099451 17876.107607\n", "69 0.0 13683.575195 14523.329381 15427.826100 16450.327615\n", "70 0.0 12433.295829 13983.538152 14829.612043 15750.631149\n", "71 0.0 13128.954707 12903.209489 14497.038872 15390.111548\n", "72 0.0 12438.253063 13105.919924 12886.675890 14489.532915\n", "73 0.0 12011.870841 12534.304219 13187.495158 12971.969729\n", "74 0.0 11186.798745 12324.325953 12853.422865 13518.299392\n", "75 0.0 11222.058802 11153.012758 12278.110433 12820.827347\n", "76 0.0 10618.765127 11125.287996 11043.762209 12174.641608\n", "77 0.0 9778.991006 10421.660292 10905.866987 10845.946377\n", "78 0.0 8683.022331 9802.730602 10469.773079 10936.714931\n", "79 0.0 8218.931369 8215.908823 9253.992879 9903.191314\n", "80 0.0 7125.106955 7698.838112 7668.517678 8638.530591\n", "81 0.0 6755.338794 6763.948483 7275.265304 7242.995217\n", "82 0.0 5692.445104 6273.970871 6275.734300 6741.180973\n", "83 0.0 4822.257357 5206.890706 5719.118792 5737.632626\n", "84 0.0 4066.012980 4357.698112 4674.783623 5167.852582\n", "85 0.0 3275.020293 3577.633133 3827.894497 4114.732463\n", "86 0.0 2594.398194 2793.935137 3023.115246 3249.790431\n", "87 0.0 2062.841114 2241.088249 2412.853771 2600.773508\n", "88 0.0 1662.653206 1830.413450 1976.377257 2143.484178\n", "89 0.0 1372.994825 1465.772127 1610.863642 1740.478924\n", "90 0.0 3900.727109 4431.244654 4881.105436 5337.620777\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcpUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 18.052053 19.079088 19.661038 21.956313\n", "62 0.0 12.294543 15.888067 17.837926 17.026168\n", "63 0.0 13.777252 14.284596 19.282830 21.032087\n", "64 0.0 16.419277 17.495659 17.760217 24.947274\n", "65 0.0 12.767653 15.688097 16.518287 16.859566\n", "66 0.0 7.062023 8.168622 9.852492 10.489452\n", "67 0.0 7.399901 9.817527 11.353150 13.923022\n", "68 0.0 7.147599 7.373190 9.853443 11.889649\n", "69 0.0 6.927287 6.447071 6.719672 8.849259\n", "70 0.0 4.615756 6.923518 6.310983 6.585383\n", "71 0.0 5.021984 3.912411 5.896284 5.139666\n", "72 0.0 0.602172 0.649083 0.508187 0.774355\n", "73 0.0 10.261727 9.010147 10.509112 8.135708\n", "74 0.0 2.445459 2.799827 2.515648 2.835807\n", "75 0.0 3.582750 3.745540 3.961645 3.800546\n", "76 0.0 3.482670 5.485114 5.918905 6.182812\n", "77 0.0 2.672981 1.943954 2.915175 3.110918\n", "78 0.0 2.731905 3.206965 2.374914 3.504573\n", "79 0.0 0.925982 0.969035 1.141012 0.818452\n", "80 0.0 1.868829 2.363479 2.307916 2.858705\n", "81 0.0 2.279217 2.279179 2.900021 2.763167\n", "82 0.0 1.653745 1.830901 1.830426 2.244757\n", "83 0.0 1.532950 1.532924 1.851803 1.916520\n", "84 0.0 0.669162 1.132409 1.183575 1.287073\n", "85 0.0 0.355374 0.387675 0.678255 0.678560\n", "86 0.0 0.188251 0.268925 0.295741 0.564851\n", "87 0.0 0.262382 0.262377 0.349746 0.393641\n", "88 0.0 0.291378 0.291373 0.349557 0.408000\n", "89 0.0 0.225531 0.180422 0.225469 0.225570\n", "90 0.0 0.744748 1.170298 1.382721 1.808987\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: LoasIdoM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 0.000000 0.000000 0.000000 0.000000\n", "62 0.000000 0.000000 0.000000 0.000000\n", "63 0.000000 0.000000 0.000000 0.000000\n", "64 0.000000 0.000000 0.000000 0.000000\n", "65 44594.886456 45154.290684 47463.255093 44118.145047\n", "66 62701.148654 60891.571537 65541.970241 67403.722011\n", "67 71210.561674 66700.495591 66658.005139 71230.295816\n", "68 70629.328548 72707.061699 69006.448718 69032.953113\n", "69 72805.201264 70994.329991 73501.366939 69693.663825\n", "70 61542.684008 72529.986049 71058.324363 73511.233928\n", "71 76175.553070 61010.064542 71976.716289 70355.936207\n", "72 65848.982776 74675.902822 60288.728404 70549.015617\n", "73 55325.889845 64343.064720 73193.235443 58765.169120\n", "74 46789.464854 53849.718492 62832.396275 71053.222989\n", "75 45828.614982 45423.121969 52369.502849 60575.504603\n", "76 43338.468509 44457.122477 43940.485767 50321.866654\n", "77 36265.545661 41810.958928 42872.437324 42074.063336\n", "78 30447.979849 34703.826067 40065.205355 40903.980539\n", "79 31885.497840 28995.182484 33179.159310 38039.771455\n", "80 24654.406709 30330.686574 27521.006439 31325.883806\n", "81 26964.998903 23295.919839 28578.943142 25820.915536\n", "82 18880.416112 25351.438542 21923.353589 26675.741968\n", "83 16957.046504 17620.391527 23646.122872 20388.043645\n", "84 14940.292527 15706.967815 16342.862037 21893.847141\n", "85 12220.584461 13752.185277 14483.452803 14918.047733\n", "86 8720.497684 11155.765934 12608.704822 13147.696011\n", "87 6037.421535 7922.384578 10130.323147 11378.696058\n", "88 4357.747839 5419.315224 7155.053441 8988.696899\n", "89 3419.101175 3871.654037 4821.057401 6332.604493\n", "90 9840.947082 11499.912959 13315.682509 15638.166667\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxdUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 1.270219 1.270213 0.000000 1.905321\n", "15 0.0 12.702198 8.891497 14.607481 14.607463\n", "16 0.0 77.483426 93.995828 107.968298 100.346896\n", "17 0.0 323.270971 325.174776 347.403886 356.294984\n", "18 0.0 861.209165 847.867812 869.462305 826.274098\n", "19 0.0 1845.629636 1817.041000 2002.494412 1907.226095\n", "20 0.0 2859.264888 2786.214360 3038.990284 2989.448351\n", "21 0.0 3759.850625 3612.488631 3808.105763 3868.436391\n", "22 0.0 4683.300148 4313.011721 4579.126549 4319.362307\n", "23 0.0 5419.392349 5223.755216 5233.287684 5000.832115\n", "24 0.0 5909.062097 5815.039896 6140.221466 5667.694466\n", "25 0.0 6436.838399 6285.654232 6765.802710 6571.451655\n", "26 0.0 6845.213938 6699.108895 6996.346783 7284.041770\n", "27 0.0 7235.806305 7224.342395 7627.643958 7484.735512\n", "28 0.0 8063.354230 7482.195880 8176.377003 8169.380822\n", "29 0.0 9043.328842 8354.832952 8420.893560 8669.209992\n", "... ... ... ... ... ...\n", "61 0.0 7072.579112 7223.073748 8626.671666 9002.007580\n", "62 0.0 6003.689676 6709.907187 7063.671057 8356.738842\n", "63 0.0 5268.867995 5618.793180 6574.002837 6918.856447\n", "64 0.0 4297.785630 4782.357047 5426.362770 6297.086643\n", "65 0.0 2471.210776 2700.475711 3161.567710 3613.124163\n", "66 0.0 1380.727812 1707.168133 1909.134682 2289.561046\n", "67 0.0 931.070391 1059.358811 1330.551317 1551.566612\n", "68 0.0 599.543276 734.819027 821.829810 1079.046948\n", "69 0.0 386.781612 476.330399 575.407886 692.901837\n", "70 0.0 250.868212 311.837633 396.307413 487.762252\n", "71 0.0 177.195525 196.248126 252.773005 327.080152\n", "72 0.0 126.386776 151.790619 140.993985 228.003450\n", "73 0.0 81.929107 104.157581 122.575846 128.926741\n", "74 0.0 64.781156 70.496898 88.915121 109.238419\n", "75 0.0 48.268314 55.889433 60.970374 66.051136\n", "76 0.0 31.120358 35.566003 42.552239 53.348996\n", "77 0.0 29.850137 25.404288 33.660727 38.741532\n", "78 0.0 13.972405 23.498967 21.593674 27.309605\n", "79 0.0 16.512844 9.526608 24.769216 19.053212\n", "80 0.0 8.891530 12.067037 8.256405 20.958535\n", "81 0.0 7.621312 4.445751 10.161730 7.621285\n", "82 0.0 7.621312 5.715965 5.715971 7.621285\n", "83 0.0 3.175547 5.080858 4.445757 6.986178\n", "84 0.0 1.905328 1.270214 5.080865 5.080857\n", "85 0.0 2.540437 1.270214 3.175540 2.540428\n", "86 0.0 1.905328 1.905322 0.635108 2.540428\n", "87 0.0 0.635109 1.905322 1.905324 0.635107\n", "88 0.0 0.635109 0.000000 1.270216 1.270214\n", "89 0.0 0.000000 0.000000 0.635108 0.635107\n", "90 0.0 2.540440 0.635107 1.270216 1.905321\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcnRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 957.000000 943.000000 1043.000000 993.000000\n", "62 924.000000 965.000000 962.000000 1055.000000\n", "63 858.000000 921.000000 970.000000 965.000000\n", "64 754.000000 847.000000 922.000000 970.000000\n", "65 752.918263 761.000000 845.000000 912.000000\n", "66 644.000000 744.917419 738.000000 834.000000\n", "67 557.000000 632.000000 727.915567 727.000000\n", "68 451.000000 545.000000 617.000000 712.916442\n", "69 432.000000 448.000000 538.000000 607.000000\n", "70 379.961929 424.000000 438.000000 523.000000\n", "71 358.000000 364.960422 414.000000 427.000000\n", "72 331.000000 349.000000 352.959128 401.000000\n", "73 288.953642 324.000000 337.000000 345.958333\n", "74 239.000000 278.952055 309.000000 326.000000\n", "75 200.000000 234.000000 278.952055 304.000000\n", "76 169.000000 192.000000 220.000000 275.951557\n", "77 132.000000 159.000000 179.000000 212.000000\n", "78 97.000000 123.000000 157.000000 168.000000\n", "79 65.000000 93.000000 120.000000 150.000000\n", "80 34.000000 61.000000 88.000000 112.000000\n", "81 36.000000 34.000000 59.000000 82.000000\n", "82 24.000000 34.000000 30.000000 57.000000\n", "83 7.000000 20.000000 33.000000 29.000000\n", "84 3.000000 5.000000 19.000000 30.000000\n", "85 2.000000 3.000000 5.000000 17.000000\n", "86 1.000000 1.000000 3.000000 5.000000\n", "87 0.000000 1.000000 1.000000 2.000000\n", "88 1.000000 0.000000 1.000000 1.000000\n", "89 0.000000 1.000000 0.000000 0.000000\n", "90 3.000000 3.000000 4.000000 3.000000\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcnUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 18637.719289 20044.340248 22550.643710 23829.860350\n", "62 0.0 16560.142326 18382.239496 19796.259507 22255.177056\n", "63 0.0 15319.989788 16620.651159 18437.740284 19861.449366\n", "64 0.0 14022.120025 15745.178153 17078.230140 18943.454795\n", "65 0.0 12415.776090 14014.869419 15746.337721 17072.734381\n", "66 0.0 11129.124209 12516.493123 14112.450153 15875.194990\n", "67 0.0 10424.029316 11249.985445 12665.645625 14286.997563\n", "68 0.0 9386.066264 10206.475377 11012.402748 12385.325709\n", "69 0.0 8610.100887 9572.759539 10401.860984 11223.479288\n", "70 0.0 7561.903646 8734.288116 9713.700957 10545.944342\n", "71 0.0 7645.759858 7609.204328 8774.586251 9765.137170\n", "72 0.0 7072.910207 7650.201272 7612.264000 8761.938772\n", "73 0.0 6358.200846 6914.750250 7462.470660 7443.356764\n", "74 0.0 5696.553704 6636.068545 7210.133461 7782.639996\n", "75 0.0 5197.200333 5516.650507 6418.473123 6973.206108\n", "76 0.0 4523.979736 4933.267476 5221.942699 6092.903044\n", "77 0.0 3925.366631 4353.087132 4746.014769 5000.167996\n", "78 0.0 3351.597184 3848.273535 4267.707626 4648.277929\n", "79 0.0 3159.126849 3218.256431 3726.886689 4089.176884\n", "80 0.0 2610.945311 2942.993761 3021.417281 3479.749468\n", "81 0.0 2570.532930 2424.061562 2730.361311 2798.954928\n", "82 0.0 2293.287866 2439.857341 2302.349801 2606.702970\n", "83 0.0 1952.546953 2115.761261 2231.783319 2103.627108\n", "84 0.0 1765.505829 1983.422880 2142.572009 2266.465317\n", "85 0.0 1398.961381 1575.914942 1758.180720 1902.666643\n", "86 0.0 1272.488536 1382.758772 1564.654695 1722.550526\n", "87 0.0 1045.541408 1222.301297 1336.676376 1511.460821\n", "88 0.0 837.966277 943.938041 1106.133475 1209.297103\n", "89 0.0 728.008461 815.407956 911.634335 1068.552747\n", "90 0.0 2184.614400 2502.671415 2819.056507 3141.895709\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: PensUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 109.825705 116.470299 120.383890 124.200824\n", "1 0.0 434.175649 522.702164 544.604651 543.878472\n", "2 0.0 985.924899 966.166897 1075.397796 1099.294444\n", "3 0.0 1428.071158 1565.228634 1575.945434 1713.703969\n", "4 0.0 1893.747897 2035.592430 2225.351522 2204.168452\n", "5 0.0 2244.951670 2434.100665 2614.902869 2791.956912\n", "6 0.0 2757.986127 2865.310970 3105.630550 3243.878854\n", "7 0.0 3207.498208 3461.345589 3562.831117 3799.692917\n", "8 0.0 3631.173830 3814.768012 4113.067986 4195.606259\n", "9 0.0 4172.108604 4427.066594 4629.930056 4864.046309\n", "10 0.0 4594.149875 4685.953063 4980.694498 5176.300743\n", "11 0.0 5300.110147 5348.476455 5457.529483 5708.424306\n", "12 0.0 5755.372113 5922.677331 5964.602222 6068.094681\n", "13 0.0 5949.713620 6571.049124 6839.660251 6752.531960\n", "14 0.0 6008.874982 6369.078733 7103.271268 7337.170391\n", "15 0.0 6357.843205 6827.869042 7252.105668 7980.299106\n", "16 0.0 6969.978134 7124.578131 7695.423601 8095.726165\n", "17 0.0 7426.740744 7937.571639 8147.455053 8726.781204\n", "18 0.0 7745.459406 8125.744963 8681.059498 8915.515548\n", "19 0.0 8007.664876 8695.590923 9162.569085 9699.655405\n", "20 0.0 8154.948255 8771.824434 9552.705785 10009.630012\n", "21 0.0 96.041629 84.745284 109.063643 106.307199\n", "22 0.0 154.967250 134.359503 128.486943 143.178991\n", "23 0.0 195.625818 199.782472 180.972429 169.156996\n", "24 0.0 231.845311 242.787434 245.950690 227.227865\n", "25 0.0 290.819291 282.988309 298.964131 303.042703\n", "26 0.0 373.687700 349.131706 352.445945 360.312243\n", "27 0.0 393.585240 407.771653 386.832249 395.640083\n", "28 0.0 459.539292 455.842312 468.100194 446.981180\n", "29 0.0 654.950189 578.437125 583.257521 599.023473\n", "... ... ... ... ... ...\n", "61 0.0 6116.792194 6511.097315 7217.469291 7637.102589\n", "62 0.0 6214.767413 6750.278088 7163.449525 7933.097914\n", "63 0.0 6561.097902 6839.472007 7464.282789 7840.384171\n", "64 0.0 6530.549116 7170.787523 7467.575106 8158.443358\n", "65 0.0 6363.061722 7136.081975 7820.319124 8080.751891\n", "66 0.0 6308.181607 6842.933213 7705.107781 8369.364209\n", "67 0.0 6489.278844 6827.886726 7408.191868 8306.997459\n", "68 0.0 6749.156755 7072.555444 7439.667473 8047.073476\n", "69 0.0 6826.103461 7182.018252 7539.310771 7904.406944\n", "70 0.0 6314.945279 7361.874472 7697.860864 8092.340060\n", "71 0.0 7131.016731 6652.933280 7827.119967 8104.116613\n", "72 0.0 7113.975747 7561.006278 7033.046034 8272.691200\n", "73 0.0 7139.483633 7471.189003 7910.127182 7401.413273\n", "74 0.0 6880.541018 7621.757024 7985.617185 8430.529461\n", "75 0.0 7409.082208 7186.838464 7967.054038 8293.496388\n", "76 0.0 7174.480156 7654.208760 7355.551880 8234.979192\n", "77 0.0 6780.981147 7403.741612 7906.537706 7561.204900\n", "78 0.0 6385.056061 6998.816081 7651.966597 8155.709298\n", "79 0.0 6772.506550 6512.476167 7154.008743 7789.684834\n", "80 0.0 6199.630447 6953.677269 6699.472686 7339.649427\n", "81 0.0 6493.527862 6255.506112 6972.745554 6737.427426\n", "82 0.0 5919.995144 6560.285901 6361.458664 7030.553583\n", "83 0.0 5486.862479 5845.237932 6471.405452 6261.208650\n", "84 0.0 5063.143417 5391.113208 5704.551163 6389.260828\n", "85 0.0 4434.420747 4948.757144 5223.629421 5535.335934\n", "86 0.0 4036.463148 4268.142177 4798.975098 5079.330549\n", "87 0.0 3447.540838 3862.680942 4092.673729 4593.162555\n", "88 0.0 2878.990477 3201.455905 3526.132388 3822.453352\n", "89 0.0 2481.537182 2653.013259 2957.478961 3279.367690\n", "90 0.0 7850.659488 9018.840261 9950.444873 11207.050632\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtceUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 137.869303 99.753449 81.733077 67.000142\n", "62 0.0 240.300559 189.301905 138.671292 115.000197\n", "63 0.0 291.374759 224.929365 178.920971 131.000145\n", "64 0.0 328.433355 276.090946 213.506079 170.999952\n", "65 0.0 384.480244 341.354302 287.427868 222.999856\n", "66 0.0 468.378733 430.733358 382.496391 321.999244\n", "67 0.0 611.071509 515.290020 473.523645 421.998780\n", "68 0.0 691.545835 642.993142 542.651665 498.998020\n", "69 0.0 782.932702 727.763953 677.236790 570.998786\n", "70 0.0 752.723940 790.077258 736.064361 683.428807\n", "71 0.0 975.436880 897.630109 942.884809 877.997156\n", "72 0.0 1053.921466 1078.789846 993.155567 1042.995455\n", "73 0.0 1046.626224 1082.564454 1107.543509 1016.997795\n", "74 0.0 1020.729611 1114.505613 1152.167004 1179.757469\n", "75 0.0 1105.210187 1116.356975 1216.258758 1260.994874\n", "76 0.0 1076.149208 1187.087932 1198.573339 1304.996209\n", "77 0.0 1035.531443 1097.325066 1209.447036 1224.997995\n", "78 0.0 1000.936010 1115.108112 1184.771501 1300.995673\n", "79 0.0 964.980374 967.965641 1079.622759 1144.767035\n", "80 0.0 858.245198 979.705985 978.231242 1092.998636\n", "81 0.0 849.257729 857.547852 972.018169 972.630424\n", "82 0.0 717.047589 829.506873 840.256585 947.996273\n", "83 0.0 601.391764 673.587448 772.055525 789.680237\n", "84 0.0 487.566694 560.002547 630.222718 724.860440\n", "85 0.0 376.590400 436.785304 499.978433 564.994979\n", "86 0.0 320.186768 357.971881 407.814314 476.854864\n", "87 0.0 269.553761 296.382391 333.655206 379.996908\n", "88 0.0 198.892464 241.502058 263.525263 304.840390\n", "89 0.0 149.901451 173.479065 210.313317 223.843209\n", "90 0.0 393.671450 458.611430 536.508186 631.294726\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: ApidUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: LoasDefH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 919.000000 847.000000 943.000000 920.000000\n", "1 3986.000000 4240.000000 4380.000000 4322.000000\n", "2 6335.000000 6529.000000 6909.000000 6962.000000\n", "3 8318.000000 8363.000000 8743.000000 9026.000000\n", "4 10247.000000 10156.000000 10442.000000 10772.000000\n", "5 11864.000000 12014.000000 12230.000000 12611.000000\n", "6 13087.000000 13574.000000 13986.000000 14218.000000\n", "7 13979.000000 14766.000000 15442.000000 15895.000000\n", "8 14698.000000 15466.000000 16718.000000 17308.000000\n", "9 15842.000000 16209.000000 17289.000000 18509.000000\n", "10 17001.000000 17274.000000 17839.000000 18903.000000\n", "11 18535.584424 18340.000000 18770.000000 19270.000000\n", "12 18571.000000 19896.586505 19657.000000 20089.000000\n", "13 18427.000000 19799.000000 21181.588377 20879.000000\n", "14 19433.000000 19604.000000 21056.000000 22385.183118\n", "15 19616.000000 20495.000000 20764.000000 22163.000000\n", "16 19678.000000 20674.000000 21547.000000 21745.000000\n", "17 18910.000000 20608.000000 21640.000000 22528.000000\n", "18 18672.000000 19738.000000 21630.000000 22599.000000\n", "19 17793.000000 19526.000000 20576.000000 22539.000000\n", "20 17608.591766 18508.000000 20319.000000 21345.000000\n", "21 16686.000000 18282.590828 19288.000000 21053.000000\n", "22 17463.000000 17326.000000 18988.589305 19910.000000\n", "23 17609.000000 18129.000000 17942.000000 19615.587733\n", "24 18218.572821 18252.000000 18843.000000 18536.000000\n", "25 18077.577024 18870.571697 18953.000000 19458.000000\n", "26 17617.580653 18712.574711 19601.569432 19564.578645\n", "27 16980.000000 18230.577703 19414.572144 20211.566214\n", "28 17591.000000 17626.000000 18893.573506 20026.568371\n", "29 18180.000000 18206.000000 18283.000000 19452.570039\n", "... ... ... ... ...\n", "61 12282.000000 12164.000000 14000.000000 14415.469881\n", "62 11347.000000 13120.000000 12942.000000 14693.000000\n", "63 11565.000000 11949.000000 13853.000000 13482.000000\n", "64 10939.000000 12097.000000 12490.000000 14229.000000\n", "65 9297.000000 10369.000000 11359.000000 11771.000000\n", "66 7843.000000 8554.000000 9524.000000 10266.000000\n", "67 6563.000000 7309.000000 7927.000000 8702.000000\n", "68 5406.000000 6114.000000 6719.000000 7218.000000\n", "69 4756.435013 5010.000000 5639.000000 6095.000000\n", "70 3484.433010 4412.429099 4616.000000 5080.000000\n", "71 3562.407505 3206.426784 4054.420714 4146.000000\n", "72 2576.000000 3285.400463 2970.422295 3686.413878\n", "73 2450.000000 2386.000000 3042.395424 2666.412438\n", "74 2213.000000 2243.000000 2190.000000 2756.386048\n", "75 1978.442605 2010.000000 2029.000000 1957.000000\n", "76 1674.000000 1799.000000 1815.000000 1824.000000\n", "77 1222.440360 1535.000000 1629.000000 1635.000000\n", "78 777.000000 1101.433124 1394.000000 1447.000000\n", "79 688.000000 689.000000 1000.428816 1237.000000\n", "80 404.000000 619.000000 626.000000 873.423375\n", "81 470.344828 359.000000 537.000000 545.000000\n", "82 282.000000 416.334944 326.000000 479.000000\n", "83 249.000000 243.000000 382.335088 290.000000\n", "84 164.000000 219.000000 219.000000 332.000000\n", "85 59.000000 148.000000 186.000000 194.000000\n", "86 25.000000 50.000000 130.000000 164.000000\n", "87 12.000000 24.000000 47.000000 109.000000\n", "88 10.000000 11.000000 24.000000 39.000000\n", "89 10.000000 6.000000 9.000000 24.000000\n", "90 33.000000 40.000000 38.000000 45.000000\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcnUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 37482.089828 40310.928074 45351.324384 47923.941357\n", "62 0.0 33941.446567 37675.992593 40574.149125 45613.913697\n", "63 0.0 31129.368336 33772.240000 37464.464177 40357.361955\n", "64 0.0 27196.771346 30538.749436 33124.286432 36742.005318\n", "65 0.0 23886.095813 26962.512155 30293.598143 32845.386889\n", "66 0.0 20954.057582 23566.213539 26571.101873 29890.020427\n", "67 0.0 19039.451992 20548.057886 23133.756106 26095.149579\n", "68 0.0 17472.474918 18999.693802 20499.954442 23055.696253\n", "69 0.0 15303.266620 17014.259582 18487.873031 19948.186230\n", "70 0.0 12896.373035 14895.804418 16566.134263 17985.475432\n", "71 0.0 12648.494982 12588.020621 14515.929380 16154.612594\n", "72 0.0 11444.895014 12379.027563 12317.640087 14177.964435\n", "73 0.0 10399.312278 11309.590388 12205.428018 12174.165814\n", "74 0.0 8421.329854 9810.233531 10658.885236 11505.233155\n", "75 0.0 7853.488086 8336.209162 9698.953085 10537.209955\n", "76 0.0 7168.656363 7817.209923 8274.642005 9654.757695\n", "77 0.0 6360.479219 7053.537375 7690.218812 8102.036732\n", "78 0.0 5362.676402 6157.376481 6828.486158 7437.412372\n", "79 0.0 5116.238238 5211.999200 6035.731104 6622.463779\n", "80 0.0 4437.057366 5001.342651 5134.616089 5913.508775\n", "81 0.0 4621.236618 4357.914237 4908.571885 5031.887688\n", "82 0.0 4140.243289 4404.856072 4156.603475 4706.074905\n", "83 0.0 3727.569081 4039.158315 4260.653750 4015.993242\n", "84 0.0 3004.494864 3375.340801 3646.176915 3857.015533\n", "85 0.0 2583.038226 2909.764767 3246.299769 3513.078157\n", "86 0.0 2120.638455 2304.406949 2607.541694 2870.679602\n", "87 0.0 1611.941645 1884.457515 2060.792905 2330.263176\n", "88 0.0 1304.678594 1469.672217 1722.203753 1882.825225\n", "89 0.0 1003.556081 1124.035855 1256.683445 1472.994704\n", "90 0.0 2868.631682 3286.274460 3701.721827 4125.644128\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: SalMatUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: PensUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 204.746716 223.543940 205.540602 216.230431\n", "1 0.0 585.351053 625.146213 687.467047 633.108196\n", "2 0.0 1007.693248 1045.839120 1126.981294 1150.708873\n", "3 0.0 1326.050748 1447.701819 1481.773687 1578.608053\n", "4 0.0 1677.375969 1754.805450 1915.881971 1922.495747\n", "5 0.0 1850.864417 1970.380894 2045.549926 2199.915429\n", "6 0.0 2174.597793 2231.283788 2422.378428 2476.198621\n", "7 0.0 2347.948785 2481.397796 2562.538930 2721.773035\n", "8 0.0 2698.652692 2789.850475 2986.276225 3023.362281\n", "9 0.0 2930.209383 3041.735891 3176.106451 3321.383767\n", "10 0.0 3302.575434 3381.868626 3528.512492 3635.625020\n", "11 0.0 3698.003319 3700.507804 3811.033698 3922.466100\n", "12 0.0 4112.792904 4211.212176 4261.880943 4321.004087\n", "13 0.0 4257.488201 4731.332114 4890.509507 4873.279615\n", "14 0.0 4750.269918 4941.342966 5521.916922 5647.700412\n", "15 0.0 4776.172306 5256.892135 5513.357503 6041.374241\n", "16 0.0 5246.577261 5420.391519 5918.160522 6158.985702\n", "17 0.0 5638.523781 5901.827091 6154.272072 6610.676067\n", "18 0.0 5848.670306 6058.462368 6351.799924 6607.720760\n", "19 0.0 5677.149146 6426.280239 6716.930937 7001.074003\n", "20 0.0 6066.818868 6124.342483 6918.995151 7170.299485\n", "21 0.0 577.393035 602.417472 603.127222 582.358870\n", "22 0.0 945.367663 812.081420 855.052982 835.570414\n", "23 0.0 1313.345473 1263.492324 1163.930602 1187.057851\n", "24 0.0 1620.381960 1631.115314 1608.778753 1492.807651\n", "25 0.0 2053.602504 2030.779781 2024.446062 1987.406060\n", "26 0.0 2502.776983 2505.745144 2494.157202 2441.009507\n", "27 0.0 2958.695404 2922.242418 2976.582362 2953.351674\n", "28 0.0 3679.938357 3467.302585 3447.569831 3482.342324\n", "29 0.0 4644.585213 4439.710549 4204.472809 4231.734170\n", "... ... ... ... ... ...\n", "61 0.0 50050.063340 48670.100258 52272.181904 51630.148125\n", "62 0.0 48705.998242 51021.692825 49804.388867 53294.689438\n", "63 0.0 49991.877273 50201.790966 52736.181228 51365.580439\n", "64 0.0 48962.619900 50402.876951 50667.438806 53117.746891\n", "65 0.0 47847.070823 50308.875792 51843.573567 51978.061558\n", "66 0.0 46008.473147 48597.509532 51196.679408 52666.749438\n", "67 0.0 47229.197385 46728.457189 49406.244193 51945.410570\n", "68 0.0 46290.998813 47601.672907 47188.543339 49799.536886\n", "69 0.0 46067.478819 46375.649165 47721.761079 47240.250295\n", "70 0.0 41131.129286 46020.984917 46393.992103 47664.988439\n", "71 0.0 45658.357794 41251.985973 46220.130491 46600.837774\n", "72 0.0 44469.420071 45623.670308 41301.891632 46196.502633\n", "73 0.0 44185.249496 45459.789739 46580.852387 42211.880214\n", "74 0.0 40592.615671 42854.441092 44110.599260 45146.463190\n", "75 0.0 42438.580846 40447.378785 42655.910782 43838.227585\n", "76 0.0 41635.821926 42688.833979 40702.113895 42887.942933\n", "77 0.0 37994.923731 40755.863531 41838.086377 39821.154355\n", "78 0.0 34639.005089 37390.419933 40068.474979 41036.438487\n", "79 0.0 36064.734879 33893.532011 36622.141229 39115.000049\n", "80 0.0 32639.197089 35634.837271 33520.997079 36228.260169\n", "81 0.0 34679.602471 32080.831226 34967.244292 32889.950428\n", "82 0.0 30155.129108 32703.921430 30321.948103 33002.803141\n", "83 0.0 28057.322658 29550.554519 31960.628744 29688.066883\n", "84 0.0 25129.029794 26177.433152 27642.474768 29796.179567\n", "85 0.0 22772.943645 24417.833487 25351.673691 26815.270363\n", "86 0.0 19244.111416 21075.326751 22560.884682 23332.735537\n", "87 0.0 16086.108858 17449.234894 19116.449462 20408.721096\n", "88 0.0 13508.424395 15037.777056 16253.742120 17826.456431\n", "89 0.0 11262.320387 12125.649961 13431.856866 14575.006498\n", "90 0.0 36187.593781 39888.587328 43584.014903 47697.370617\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: ApidUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AinvRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 1.000024 0.000000 0.000000 0.000000\n", "17 0.000000 1.000034 0.000000 2.000047\n", "18 4.000095 3.000102 1.000030 1.000023\n", "19 16.000355 8.000271 11.000333 8.000164\n", "20 30.000687 32.000981 23.000697 31.000703\n", "21 43.733131 50.001658 57.001637 40.000913\n", "22 84.211384 70.749579 77.002274 81.001803\n", "23 105.002368 120.698135 103.745731 103.002365\n", "24 172.705651 150.004840 149.675710 143.724945\n", "25 219.005043 212.007006 180.005244 195.650020\n", "26 323.007482 282.009273 284.008488 231.005176\n", "27 333.007719 374.012353 354.010489 331.007612\n", "28 473.109599 403.013402 447.013248 431.009907\n", "29 587.013473 531.051632 497.014794 545.012460\n", "... ... ... ... ...\n", "61 10423.632192 9787.813722 10756.001634 10648.450526\n", "62 9638.882121 10328.353528 9745.800866 10771.897932\n", "63 9733.294163 9513.491905 10214.487912 9649.013681\n", "64 9287.452226 9541.390704 9340.150816 10023.328142\n", "65 8921.577988 9039.140502 9317.385409 9133.352900\n", "66 8455.417355 8628.635694 8751.294402 9052.415137\n", "67 8218.227797 8139.716520 8324.239405 8486.860343\n", "68 7422.571662 7905.039989 7825.906199 8044.868588\n", "69 7349.880317 7121.860278 7604.867666 7487.657375\n", "70 5935.738007 7033.528170 6824.894154 7279.401633\n", "71 6902.115073 5639.341903 6674.496289 6506.417332\n", "72 6231.189521 6581.197396 5352.797801 6351.326109\n", "73 5876.487076 5892.289511 6260.668632 5067.351850\n", "74 5471.570664 5551.799956 5588.134003 5884.122103\n", "75 5374.739743 5145.620211 5217.440866 5295.411047\n", "76 5136.429778 5026.356068 4846.749850 4880.878196\n", "77 4525.541069 4799.053772 4703.371842 4537.527362\n", "78 3862.386810 4202.314768 4474.878439 4380.143041\n", "79 4337.586705 3578.615978 3866.402717 4122.463056\n", "80 3524.415499 4017.526808 3285.533681 3528.292607\n", "81 4437.742741 3240.649973 3673.022478 2973.031649\n", "82 3850.475846 4024.543793 2944.472850 3356.980529\n", "83 4154.758496 3496.337994 3629.258827 2643.448892\n", "84 3835.740638 3717.021271 3120.429994 3275.788557\n", "85 3603.394391 3397.450422 3292.990138 2768.781519\n", "86 3287.674245 3200.339444 2978.548712 2918.028870\n", "87 3000.763396 2871.279273 2798.587220 2623.398035\n", "88 2369.280570 2609.952744 2547.734786 2403.044926\n", "89 2126.053700 2036.594330 2229.954743 2183.601650\n", "90 5948.587101 6678.934963 7077.674982 7641.199019\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcpUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 2299.235184 2270.986385 2530.690039 2748.442732\n", "62 0.0 1990.464678 2266.727612 2239.854639 2491.488186\n", "63 0.0 2059.730493 2086.424541 2379.303499 2339.753474\n", "64 0.0 1809.221462 2037.950439 2069.471433 2355.499328\n", "65 0.0 1575.356106 1821.328550 2056.424978 2090.450039\n", "66 0.0 1309.311514 1531.728657 1765.117652 1997.647331\n", "67 0.0 1225.606652 1335.669832 1565.644503 1804.621507\n", "68 0.0 971.625234 1191.332972 1303.639966 1534.303814\n", "69 0.0 888.776410 940.168138 1152.385000 1265.229294\n", "70 0.0 677.871375 857.442248 907.124960 1105.476035\n", "71 0.0 662.715345 676.699055 852.765720 901.739534\n", "72 0.0 582.736254 643.297784 658.351745 832.436139\n", "73 0.0 534.701336 589.241744 645.130026 659.709221\n", "74 0.0 397.994854 520.517000 572.069858 628.410375\n", "75 0.0 338.314962 362.449856 476.836245 514.893657\n", "76 0.0 311.035808 329.304358 354.485284 465.417580\n", "77 0.0 241.927123 317.049503 338.750573 363.731174\n", "78 0.0 165.495613 219.402711 291.253737 309.790560\n", "79 0.0 154.918166 167.067123 221.442769 286.033866\n", "80 0.0 139.458850 156.962230 171.083401 223.581965\n", "81 0.0 128.769422 144.322828 159.155738 168.322149\n", "82 0.0 82.136706 114.211758 123.817789 138.549197\n", "83 0.0 69.932755 77.555266 108.688223 118.201734\n", "84 0.0 48.804015 66.943472 70.686718 101.946309\n", "85 0.0 40.414821 55.378619 76.321253 77.804226\n", "86 0.0 20.298448 35.331367 53.364918 69.136893\n", "87 0.0 15.352911 17.354006 30.031279 43.370976\n", "88 0.0 16.262642 17.972989 22.248937 35.078766\n", "89 0.0 4.014041 14.449333 16.855030 19.259544\n", "90 0.0 20.681647 19.852713 33.082897 41.346435\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcnUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 136033.095448 138235.786983 147214.925604 147671.363369\n", "62 0.0 130432.950914 140122.750347 142862.010810 152116.561787\n", "63 0.0 127138.312998 132963.881632 143490.043831 146094.545784\n", "64 0.0 119182.303088 128319.790670 134410.883130 144835.230515\n", "65 0.0 109226.152609 118810.095751 128154.562325 134180.478418\n", "66 0.0 97571.243428 107552.431477 116993.614929 126268.384229\n", "67 0.0 89954.695283 96106.093938 105970.404140 115179.206542\n", "68 0.0 83095.870738 88207.361700 94107.639612 103965.635798\n", "69 0.0 76335.770799 81020.458990 86066.322607 91770.492765\n", "70 0.0 65944.571368 74166.853453 78654.318469 83539.283081\n", "71 0.0 64748.909366 63635.587174 71495.977935 75900.401823\n", "72 0.0 59464.854577 62656.839224 61608.676391 69271.622255\n", "73 0.0 54924.288829 57313.115859 60299.831936 59314.341746\n", "74 0.0 47631.511000 52474.910881 54727.716705 57558.649335\n", "75 0.0 45863.526552 45581.341690 50179.512849 52397.547170\n", "76 0.0 41671.595253 43659.360850 43339.426321 47777.376313\n", "77 0.0 37122.941961 39562.638919 41400.781220 41173.310993\n", "78 0.0 30765.062988 34732.333160 37095.750303 38750.185236\n", "79 0.0 29125.984959 29115.273756 32794.002686 35094.611247\n", "80 0.0 25472.113032 27523.190297 27414.795360 30882.571873\n", "81 0.0 23831.898091 23862.271880 25666.126685 25552.282297\n", "82 0.0 20111.864826 22166.441975 22172.672312 23817.132714\n", "83 0.0 17260.473380 18637.204895 20470.640690 20536.907901\n", "84 0.0 14811.854592 15874.418283 17029.511617 18825.685351\n", "85 0.0 12396.067388 13541.467669 14488.715763 15574.407585\n", "86 0.0 10519.502240 11328.564364 12257.820589 13176.920101\n", "87 0.0 8676.002651 9425.683565 10148.103781 10938.466220\n", "88 0.0 6894.722066 7590.393451 8195.678950 8888.641123\n", "89 0.0 5578.195097 5955.130161 6544.607091 7071.207279\n", "90 0.0 14606.332625 16592.863728 18277.374344 19986.803056\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AinvUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 1.000045\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 3.000249 0.000000 1.000057 1.000045\n", "19 0.0 2.725232 1.875152 3.375232 3.000174\n", "20 0.0 8.471346 8.753554 11.294832 12.000611\n", "21 0.0 16.130480 12.904187 23.227258 20.001061\n", "22 0.0 23.475938 20.770829 23.886229 27.001482\n", "23 0.0 30.991292 35.377705 37.188575 41.002319\n", "24 0.0 61.941954 53.883570 59.013574 71.003965\n", "25 0.0 118.519758 112.736478 107.532198 108.229699\n", "26 0.0 125.894611 138.385746 126.371792 123.006935\n", "27 0.0 208.085171 212.552822 224.071095 235.012746\n", "28 0.0 287.924697 270.024351 266.063963 295.016223\n", "29 0.0 464.849137 410.561989 380.677962 373.020246\n", "... ... ... ... ... ...\n", "61 0.0 23342.063719 23178.990683 25072.961323 25125.632697\n", "62 0.0 23477.799901 24872.843666 24759.514206 26698.463814\n", "63 0.0 24880.848392 24852.690240 26469.958766 26287.701722\n", "64 0.0 25578.483208 26557.157046 26550.585298 28344.406297\n", "65 0.0 25290.249387 26617.639159 27616.313573 27638.998802\n", "66 0.0 24800.774170 26162.005253 27647.940609 28618.195541\n", "67 0.0 25591.247581 25535.312508 26848.447984 28380.578492\n", "68 0.0 25347.559959 26260.116478 26211.222920 27572.745769\n", "69 0.0 26077.110167 25683.695785 26564.463826 26542.399270\n", "70 0.0 23617.726500 26283.560122 25817.094099 26719.130066\n", "71 0.0 26704.910815 23848.449102 26483.885664 26019.134076\n", "72 0.0 25987.783363 26893.987283 24009.464153 26710.672452\n", "73 0.0 24897.200937 25538.988678 26443.758419 23626.315570\n", "74 0.0 22765.830640 24665.412243 25252.780920 26203.632579\n", "75 0.0 23791.766590 22301.266094 24230.743278 24746.210703\n", "76 0.0 21909.485171 23263.404213 21766.118953 23620.874583\n", "77 0.0 19653.364594 21240.853962 22424.490849 21046.836629\n", "78 0.0 16806.526630 18940.438547 20508.995526 21585.271044\n", "79 0.0 17305.732649 16158.566700 18170.092329 19662.447525\n", "80 0.0 14375.791809 16463.978369 15275.446385 17236.719527\n", "81 0.0 14582.015193 13682.146724 15635.384225 14451.094719\n", "82 0.0 12532.361422 13664.448604 12795.243034 14570.270289\n", "83 0.0 10918.975674 11706.920668 12642.084448 11825.367537\n", "84 0.0 9707.720788 10162.594890 10848.207648 11700.531203\n", "85 0.0 8121.777544 8874.440466 9244.871353 9851.859909\n", "86 0.0 6862.035075 7427.170642 8078.237701 8323.414464\n", "87 0.0 5767.368989 6147.395749 6644.946952 7272.249390\n", "88 0.0 4547.482158 5082.944772 5400.094030 5800.759994\n", "89 0.0 3745.003161 4010.090767 4464.575867 4743.017721\n", "90 0.0 10538.180017 11949.078830 13306.199782 14703.165466\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: PensRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 29.140828 31.151192 24.111583 32.108912\n", "1 135.674398 131.638907 138.641603 105.357367\n", "2 266.286879 264.298227 286.325050 241.820243\n", "3 514.523984 420.038651 450.104715 460.612057\n", "4 666.238444 713.493850 604.798878 610.094253\n", "5 786.821182 877.273291 936.376805 785.664939\n", "6 1046.074069 974.761911 1112.168604 1151.957068\n", "7 1187.796421 1270.180259 1221.697206 1323.514140\n", "8 1399.802251 1439.030691 1531.107362 1478.063204\n", "9 1682.166829 1620.897921 1670.797272 1769.025293\n", "10 1765.588707 1916.331804 1872.709952 1915.572056\n", "11 2156.534202 2024.889608 2207.258169 2141.362768\n", "12 2261.058059 2477.115398 2364.005289 2496.517755\n", "13 2483.074815 2567.569877 2847.307088 2640.029381\n", "14 2674.035118 2812.713297 2927.722689 3165.912518\n", "15 2870.019703 2972.519835 3148.680049 3258.250565\n", "16 3182.511147 3230.804334 3436.009749 3505.013048\n", "17 3249.306393 3579.481172 3704.316598 3764.869628\n", "18 3517.203516 3558.864158 3943.423131 4045.922311\n", "19 3690.968359 3778.508989 3846.432275 4183.388591\n", "20 3723.749732 3867.880915 3996.735030 3996.175932\n", "21 61.879999 61.506001 49.227816 58.197403\n", "22 102.495327 88.019036 79.552732 81.275683\n", "23 122.406917 112.546241 111.086006 97.510380\n", "24 166.518243 142.465328 128.595110 128.966374\n", "25 218.294273 192.603213 165.204208 163.554770\n", "26 222.846444 240.343729 225.698978 196.096886\n", "27 271.996775 258.006949 269.229690 271.421577\n", "28 306.799052 316.218462 298.829086 320.913971\n", "29 394.702603 349.009654 359.296524 354.657097\n", "... ... ... ... ...\n", "61 6419.020013 6047.045265 6923.471978 6773.287204\n", "62 6243.070375 7094.973755 6748.991726 7644.213698\n", "63 7199.417390 6998.707482 7963.546799 7412.634066\n", "64 7313.132575 8018.304943 7774.927569 8716.569685\n", "65 7419.349017 8154.261569 8827.902262 8503.958103\n", "66 7554.539376 8201.436571 9004.128321 9592.837242\n", "67 8260.008941 8280.272102 9018.372574 9797.124917\n", "68 8287.695156 9053.179711 9126.333591 9760.375724\n", "69 9352.441549 9137.307497 9967.445465 9916.110727\n", "70 8274.273047 10198.072359 9978.169086 10741.235215\n", "71 10629.537501 8929.077532 11046.267651 10724.663414\n", "72 10046.946203 11557.689092 9733.856669 11901.806028\n", "73 10146.603181 10867.680088 12440.230697 10428.080477\n", "74 9938.873985 10901.748241 11651.613265 13262.427362\n", "75 10653.960400 10538.752877 11714.239249 12380.320693\n", "76 10709.014909 11304.465954 11227.277135 12356.151612\n", "77 9933.586143 11353.728939 12035.305651 11758.639847\n", "78 9300.867130 10469.232455 12007.953278 12434.288983\n", "79 10800.456234 9763.950454 10888.151926 12542.860370\n", "80 9345.459192 11208.140044 10087.514250 11279.565244\n", "81 10608.133202 9693.955595 11603.305665 10290.883341\n", "82 8974.874419 10796.470953 9804.292898 11825.716721\n", "83 8579.041398 9137.225471 10902.474418 9877.916988\n", "84 7849.927995 8658.669317 9183.339179 10833.872844\n", "85 7181.785128 7902.621284 8683.747291 9071.594493\n", "86 6692.275643 7093.948754 7768.035802 8548.600074\n", "87 5753.524660 6551.916995 6917.462169 7576.189952\n", "88 4638.888009 5592.572188 6340.245440 6614.124456\n", "89 4047.661205 4432.651362 5315.836579 6006.095824\n", "90 13633.709405 16394.067553 18976.188759 22165.965769\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxdRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 1.000000 0.000000 0.000000\n", "15 0.000000 0.000000 1.000000 0.000000\n", "16 34.000377 16.000000 29.000000 25.000000\n", "17 143.001433 107.000000 120.000000 110.000000\n", "18 265.002967 234.000000 264.000000 249.000000\n", "19 335.003859 310.000000 323.000000 369.000000\n", "20 366.004148 381.000000 384.000000 382.000000\n", "21 484.005669 427.000000 464.000000 475.000000\n", "22 570.006625 488.000000 492.000000 479.000000\n", "23 591.006801 540.000000 563.000000 554.000000\n", "24 654.007718 639.000000 670.000000 627.000000\n", "25 700.008083 694.000000 768.000000 754.000000\n", "26 811.009541 788.000000 795.000000 851.000000\n", "27 813.009516 874.000000 870.000000 911.000000\n", "28 862.010157 855.000000 1021.000000 947.000000\n", "29 1009.011641 958.000000 979.000000 1130.000000\n", "... ... ... ... ...\n", "61 1138.013840 1307.000000 1694.000000 1874.000000\n", "62 795.009679 942.000000 1138.000000 1452.000000\n", "63 549.006700 701.000000 838.000000 1034.000000\n", "64 387.004676 496.000000 633.000000 791.000000\n", "65 217.002640 298.000000 399.000000 506.000000\n", "66 133.001622 157.000000 234.000000 306.000000\n", "67 81.000981 105.000000 135.000000 181.000000\n", "68 54.587643 72.000000 99.000000 111.000000\n", "69 44.000515 51.607143 60.000000 78.000000\n", "70 19.000201 31.000000 39.573529 54.000000\n", "71 17.000214 20.000000 27.000000 32.516129\n", "72 14.000176 17.000000 17.000000 27.000000\n", "73 8.000101 7.000000 12.000000 13.000000\n", "74 6.000075 7.000000 8.000000 13.000000\n", "75 5.000063 7.000000 6.000000 8.000000\n", "76 4.000050 3.000000 3.000000 4.000000\n", "77 3.000038 0.000000 1.000000 6.000000\n", "78 0.000000 5.000000 2.000000 1.000000\n", "79 0.000000 1.000000 2.000000 5.000000\n", "80 1.000013 0.000000 0.000000 3.000000\n", "81 1.000013 0.000000 0.000000 0.000000\n", "82 0.000000 0.000000 0.000000 1.000000\n", "83 0.000000 0.000000 0.000000 0.000000\n", "84 1.000013 0.000000 0.000000 0.000000\n", "85 0.000000 1.000000 0.000000 0.000000\n", "86 0.000000 1.000000 1.000000 0.000000\n", "87 1.000013 0.000000 0.000000 0.000000\n", "88 0.000000 0.000000 0.000000 0.000000\n", "89 0.000000 0.000000 0.000000 0.000000\n", "90 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcpUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 735.308559 726.274431 809.329145 878.967701\n", "62 0.0 711.800529 810.593593 800.983678 890.969144\n", "63 0.0 625.520451 633.627178 722.571717 710.560752\n", "64 0.0 579.781148 653.079388 663.180572 754.840761\n", "65 0.0 487.373001 563.470290 636.202830 646.729273\n", "66 0.0 457.169260 534.830136 616.322030 697.513878\n", "67 0.0 388.746599 423.657218 496.602213 572.402632\n", "68 0.0 333.468777 408.874055 447.418624 526.584118\n", "69 0.0 338.252162 357.810920 438.576804 481.523293\n", "70 0.0 288.939066 365.480195 386.657186 471.203276\n", "71 0.0 282.076639 288.028633 362.969244 383.814346\n", "72 0.0 263.973511 291.407259 298.226548 377.084983\n", "73 0.0 229.939633 253.393851 277.427700 283.697246\n", "74 0.0 180.490068 236.053677 259.432821 284.983161\n", "75 0.0 194.131368 207.980416 273.617436 295.455481\n", "76 0.0 182.377802 193.089681 207.854675 272.900524\n", "77 0.0 125.380778 164.313587 175.560350 188.506757\n", "78 0.0 101.728391 134.864510 179.030570 190.424958\n", "79 0.0 87.284863 94.129897 124.766529 161.158807\n", "80 0.0 67.714816 76.213653 83.070246 108.561139\n", "81 0.0 53.383269 59.831164 65.980366 69.780438\n", "82 0.0 45.970682 63.922607 69.298958 77.543907\n", "83 0.0 40.159531 44.536828 62.415216 67.878438\n", "84 0.0 29.261424 40.137299 42.381637 61.123950\n", "85 0.0 13.630484 18.677241 25.740448 26.240602\n", "86 0.0 6.724204 11.704112 17.678030 22.902763\n", "87 0.0 7.666385 8.665620 14.995942 21.657041\n", "88 0.0 2.753298 3.042863 3.766790 5.938906\n", "89 0.0 0.990154 3.564254 4.157673 4.750801\n", "90 0.0 4.339327 4.165404 6.941300 8.675117\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxrRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 30.0 21.0 24.0 26.0\n", "1 71.0 73.0 73.0 56.0\n", "2 73.0 90.0 101.0 98.0\n", "3 101.0 77.0 104.0 109.0\n", "4 106.0 99.0 96.0 92.0\n", "5 98.0 99.0 98.0 101.0\n", "6 108.0 100.0 107.0 97.0\n", "7 103.0 101.0 93.0 100.0\n", "8 73.0 90.0 93.0 86.0\n", "9 81.0 59.0 90.0 94.0\n", "10 66.0 68.0 67.0 95.0\n", "11 69.0 62.0 69.0 71.0\n", "12 53.0 66.0 55.0 70.0\n", "13 37.0 51.0 51.0 47.0\n", "14 34.0 39.0 49.0 52.0\n", "15 29.0 32.0 38.0 43.0\n", "16 24.0 29.0 34.0 40.0\n", "17 21.0 24.0 27.0 28.0\n", "18 12.0 24.0 25.0 21.0\n", "19 15.0 11.0 19.0 19.0\n", "20 5.0 11.0 6.0 16.0\n", "21 0.0 0.0 0.0 0.0\n", "22 1.0 0.0 1.0 0.0\n", "23 0.0 1.0 0.0 1.0\n", "24 0.0 0.0 1.0 0.0\n", "25 0.0 0.0 0.0 0.0\n", "26 0.0 1.0 0.0 0.0\n", "27 0.0 0.0 1.0 0.0\n", "28 0.0 0.0 0.0 0.0\n", "29 0.0 1.0 0.0 2.0\n", "... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0\n", "62 1.0 0.0 0.0 0.0\n", "63 0.0 1.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxdUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.635106\n", "15 0.0 2.540436 1.905320 5.080850 4.445744\n", "16 0.0 20.958572 18.418106 26.674461 32.390417\n", "17 0.0 106.698198 83.834146 99.076571 94.630827\n", "18 0.0 236.260341 219.747119 290.243543 287.068012\n", "19 0.0 538.571900 556.353965 690.995569 668.131745\n", "20 0.0 892.327264 923.446025 1115.881632 1099.368870\n", "21 0.0 1270.216892 1305.780660 1453.123036 1529.970888\n", "22 0.0 1753.534497 1568.079976 1865.306974 1810.687839\n", "23 0.0 2144.761283 2019.641300 2240.019645 2321.948347\n", "24 0.0 2461.045337 2448.338719 2714.443992 2671.256769\n", "25 0.0 3036.453701 2850.996782 3191.408765 3201.570464\n", "26 0.0 3338.130309 3292.396422 3583.904410 3783.962870\n", "27 0.0 3700.777155 3663.934255 4076.746838 4182.174471\n", "28 0.0 4419.720137 4115.495543 4514.970131 4599.439259\n", "29 0.0 5076.422578 4713.766740 4894.763652 5156.427415\n", "... ... ... ... ... ...\n", "61 0.0 4993.225650 5109.439639 6072.250587 6586.686627\n", "62 0.0 3961.173811 4692.809223 4938.585981 5874.732552\n", "63 0.0 3347.023644 3724.905398 4561.967992 4828.077499\n", "64 0.0 2743.035172 3092.338383 3530.555487 4302.209547\n", "65 0.0 2085.697501 2440.718087 2815.425882 3230.150244\n", "66 0.0 1545.854979 1759.247762 2206.994121 2589.328067\n", "67 0.0 1235.921857 1373.102411 1660.802770 1989.152687\n", "68 0.0 953.298403 1078.412530 1234.011389 1500.756002\n", "69 0.0 789.440332 866.921736 950.118908 1136.840137\n", "70 0.0 525.870150 670.673515 767.208316 880.892330\n", "71 0.0 467.440140 437.589073 597.634955 697.981738\n", "72 0.0 357.566295 389.320897 390.590326 526.503058\n", "73 0.0 263.570184 299.135635 375.347777 353.119059\n", "74 0.0 189.262451 237.530206 286.432906 332.160554\n", "75 0.0 148.615482 153.060910 210.220159 241.975471\n", "76 0.0 111.779166 133.372577 140.993581 180.370167\n", "77 0.0 74.307741 97.806557 120.035076 120.670182\n", "78 0.0 60.970454 61.605428 83.834021 101.616995\n", "79 0.0 56.524692 48.903278 60.335091 76.847853\n", "80 0.0 38.741643 49.538386 42.552117 42.552117\n", "81 0.0 33.660772 33.025591 41.917011 39.376586\n", "82 0.0 27.309683 24.769193 27.944674 33.660630\n", "83 0.0 17.783049 19.688332 28.579780 26.674461\n", "84 0.0 12.702178 19.053225 15.877656 23.498930\n", "85 0.0 10.161742 7.621290 13.972337 15.877656\n", "86 0.0 7.621307 8.256398 5.715956 11.431912\n", "87 0.0 4.445762 5.715968 8.891487 4.445744\n", "88 0.0 1.270218 3.175538 5.715956 3.175531\n", "89 0.0 1.905327 2.540430 1.905319 3.810637\n", "90 0.0 1.270218 5.080860 6.351062 5.080850\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: SalMatRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0\n", "16 8.0 3.0 5.0 13.0\n", "17 197.0 152.0 205.0 179.0\n", "18 360.0 289.0 392.0 326.0\n", "19 525.0 443.0 496.0 501.0\n", "20 619.0 509.0 556.0 512.0\n", "21 682.0 543.0 689.0 606.0\n", "22 732.0 604.0 699.0 592.0\n", "23 801.0 688.0 722.0 581.0\n", "24 803.0 659.0 813.0 686.0\n", "25 814.0 741.0 844.0 704.0\n", "26 828.0 719.0 772.0 663.0\n", "27 731.0 655.0 734.0 675.0\n", "28 706.0 650.0 734.0 686.0\n", "29 660.0 575.0 635.0 610.0\n", "... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtceUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 649.638298 532.771930 416.000000 364.000000\n", "62 0.0 818.193008 648.993582 539.100000 423.000000\n", "63 0.0 949.994274 795.843279 633.891558 527.000000\n", "64 0.0 1034.139055 962.906614 814.098171 643.102558\n", "65 0.0 1061.775072 1012.703810 942.706599 799.104019\n", "66 0.0 1098.334861 1025.212275 983.367032 909.191806\n", "67 0.0 1157.623665 1044.070025 974.499005 939.175635\n", "68 0.0 1179.798248 1142.439166 1035.560774 965.247330\n", "69 0.0 1193.610325 1119.060489 1082.520658 977.080983\n", "70 0.0 1069.159919 1191.241073 1111.987998 1079.047699\n", "71 0.0 1103.561641 1025.070076 1138.312377 1065.148350\n", "72 0.0 1045.080223 1060.113981 990.645755 1100.223401\n", "73 0.0 975.982703 989.904779 1010.288687 946.000000\n", "74 0.0 848.704073 924.951036 936.290864 953.874822\n", "75 0.0 745.864651 811.846471 891.834203 898.141216\n", "76 0.0 666.423928 694.162759 755.768923 830.072504\n", "77 0.0 530.681042 641.175701 667.341019 731.000000\n", "78 0.0 444.737011 495.061087 591.854122 616.068232\n", "79 0.0 389.720668 412.348888 453.686940 538.045076\n", "80 0.0 297.546398 388.517840 413.046234 455.000000\n", "81 0.0 266.264928 294.563932 385.566487 401.990480\n", "82 0.0 238.848196 250.997483 275.524735 358.068495\n", "83 0.0 180.655395 229.508891 241.160434 265.935310\n", "84 0.0 157.893953 181.137113 229.453146 248.008662\n", "85 0.0 115.476928 145.316167 167.496379 207.132562\n", "86 0.0 91.236863 105.903472 131.418793 150.132021\n", "87 0.0 73.877209 82.969893 96.322555 115.064542\n", "88 0.0 51.782438 65.319719 75.347266 87.136717\n", "89 0.0 39.477271 48.501736 64.771728 74.088751\n", "90 0.0 107.963648 130.815309 150.006735 189.858226\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: ApinRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 105010.119757 97171.624320 106263.259239 102648.912616\n", "62 105620.703650 116578.485491 108351.956778 117818.234239\n", "63 115686.137248 112039.084841 123600.443213 114344.934190\n", "64 114726.498266 119623.875445 115772.867095 127267.824490\n", "65 109699.376776 118117.829402 122938.705191 118495.527335\n", "66 107983.504937 111008.874110 119525.330669 123796.586094\n", "67 107682.730237 108076.731076 110959.479188 119262.221303\n", "68 101350.616507 106930.080868 107397.621447 110072.864587\n", "69 104866.925755 100240.365903 105598.949022 105947.871357\n", "70 84955.063801 103249.813438 98651.302032 103842.262645\n", "71 100595.968367 83394.120447 101351.742872 96730.211135\n", "72 89131.261799 98347.123879 81549.895059 99012.666177\n", "73 82855.904698 86853.713034 95905.750727 79508.346726\n", "74 75339.491324 80530.381166 84476.452730 93201.008525\n", "75 75664.320416 72810.105384 78025.283996 81883.887472\n", "76 71035.692567 72932.140293 70123.290257 75246.646428\n", "77 63405.556222 68160.278395 70027.057305 67264.187252\n", "78 56913.479872 60533.409122 65129.334907 66908.154320\n", "79 64898.892884 54094.767817 57492.869381 62101.739158\n", "80 52437.583031 61300.938785 51082.948714 54461.079327\n", "81 56797.755080 49290.147071 57534.067424 48000.384264\n", "82 43701.418505 52925.414550 45916.673678 53627.001238\n", "83 40478.881582 40463.453136 48968.747312 42580.604740\n", "84 34974.939272 37227.706706 37108.981709 45004.695250\n", "85 31249.032327 31905.115389 33954.855679 33711.213186\n", "86 28931.344171 28275.965912 28780.807386 30666.993786\n", "87 25601.585194 26041.632511 25318.349675 25760.959683\n", "88 21060.370040 22823.723130 23139.334581 22452.726539\n", "89 19160.046925 18571.855559 20013.322013 20280.290812\n", "90 91661.227123 94423.803281 95557.321838 98024.435944\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: RmvM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 1.000454 0.000000 0.000000 0.000000\n", "16 0.000000 1.000368 0.000000 0.000000\n", "17 0.000000 0.000000 1.000328 0.000000\n", "18 0.000000 0.000000 0.000000 1.000311\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 1.000454 0.000000 0.000000 0.000000\n", "24 1.000454 0.000000 0.000000 0.000000\n", "25 0.500290 1.000368 0.000000 0.000000\n", "26 2.501199 0.500269 1.000328 0.000000\n", "27 3.001363 2.501006 0.000000 1.000311\n", "28 3.001363 3.001105 2.500919 0.000000\n", "29 7.003180 3.001105 3.000983 2.500840\n", "... ... ... ... ...\n", "61 1580.603265 1338.259175 1356.872243 1170.621420\n", "62 1613.873399 1527.526034 1276.449397 1306.693926\n", "63 1689.226807 1547.065297 1463.372684 1222.458331\n", "64 1551.637587 1616.156175 1485.654037 1406.636937\n", "65 1605.853633 1487.804066 1540.971185 1434.476892\n", "66 1675.900981 1536.319776 1423.447105 1478.076122\n", "67 1737.782549 1601.740615 1472.880391 1374.576611\n", "68 1732.650666 1661.638275 1545.460192 1406.040568\n", "69 1971.790387 1656.489117 1570.043297 1473.679464\n", "70 1679.388977 1860.892562 1560.215676 1487.793706\n", "71 2136.507240 1576.919377 1759.521851 1461.149972\n", "72 2238.563710 2007.543543 1489.286464 1641.897243\n", "73 2249.835513 2106.714816 1883.253053 1405.737528\n", "74 2344.271007 2103.636287 1951.653035 1750.487236\n", "75 2693.233909 2181.863699 1966.486532 1837.135364\n", "76 2834.329973 2495.848909 2035.911734 1820.800977\n", "77 3013.142819 2619.431692 2295.392496 1880.180456\n", "78 2962.439902 2763.251763 2377.998704 2114.296533\n", "79 3717.064866 2697.331498 2555.378418 2174.683067\n", "80 3491.286804 3407.180529 2484.124379 2319.858014\n", "81 4742.656974 3175.484026 3067.500511 2248.099847\n", "82 4630.014663 4281.763319 2841.592947 2802.715964\n", "83 5019.229420 4167.206089 3859.378583 2543.180084\n", "84 5248.574836 4537.194593 3694.315678 3458.623967\n", "85 5264.224270 4731.000814 4044.133615 3278.305297\n", "86 7115.748600 4661.333984 4179.584982 3542.539339\n", "87 7431.224467 6301.691225 4057.187236 3624.525534\n", "88 7173.086462 6560.463613 5490.536461 3528.779547\n", "89 7425.400551 6200.349234 5704.209712 4761.982098\n", "90 62354.913856 57952.882567 52620.546049 47738.898935\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcdUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtceUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 6029.289179 4362.409733 3574.344289 2930.044771\n", "62 0.0 7647.931216 6024.821367 4413.425034 3660.056375\n", "63 0.0 9886.864945 7632.254266 6071.107457 4445.068430\n", "64 0.0 11694.901285 9831.085398 7602.554608 6088.990434\n", "65 0.0 13006.839433 11547.903091 9723.589676 7544.011341\n", "66 0.0 13894.231437 12777.499374 11346.572770 9551.953806\n", "67 0.0 16147.096605 13616.144123 12512.499652 11150.994560\n", "68 0.0 16992.054802 15799.060836 13333.558476 12260.939590\n", "69 0.0 17837.631635 16580.716670 15429.551414 13009.120674\n", "70 0.0 16572.939817 17395.358588 16206.141077 15047.248926\n", "71 0.0 17380.270462 15993.914519 16800.259788 15644.095836\n", "72 0.0 16363.671149 16749.789093 15420.191755 16194.028849\n", "73 0.0 15286.992012 15811.904760 16176.748102 14854.240046\n", "74 0.0 13443.884892 14678.995305 15175.028144 15538.418239\n", "75 0.0 12676.303055 12804.152100 13949.984170 14463.088886\n", "76 0.0 10905.127568 12029.321983 12145.709038 13224.142178\n", "77 0.0 9751.926843 10333.856921 11389.744946 11536.193240\n", "78 0.0 8189.166305 9123.266307 9693.217906 10644.106933\n", "79 0.0 7696.246535 7720.055675 8610.582295 9130.143538\n", "80 0.0 6292.867249 7183.447948 7172.634766 8014.137840\n", "81 0.0 5797.904703 5854.501593 6635.993432 6640.173318\n", "82 0.0 4607.905475 5330.593556 5399.673573 6092.032495\n", "83 0.0 3796.545821 4252.312323 4873.934682 4985.198306\n", "84 0.0 3005.390174 3451.889088 3884.730407 4468.082968\n", "85 0.0 2352.159319 2728.132801 3122.833007 3528.922154\n", "86 0.0 1858.537757 2077.863059 2367.175592 2767.924408\n", "87 0.0 1459.125969 1604.352473 1806.114572 2056.967611\n", "88 0.0 1038.631636 1261.142191 1376.149048 1591.899798\n", "89 0.0 782.138145 905.158642 1097.348067 1167.942744\n", "90 0.0 1814.790681 2114.158263 2473.255441 2910.213034\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AtcnRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 38.000000 48.000000 52.000000 83.000000\n", "62 32.000000 38.000000 46.000000 52.000000\n", "63 25.000000 34.000000 38.000000 45.000000\n", "64 30.000000 26.000000 34.000000 37.000000\n", "65 32.081737 28.000000 27.000000 33.000000\n", "66 18.000000 32.082581 28.000000 26.000000\n", "67 21.000000 18.000000 32.084433 26.000000\n", "68 23.000000 20.000000 19.000000 31.083558\n", "69 15.000000 23.000000 20.000000 18.000000\n", "70 15.038071 15.000000 24.000000 20.000000\n", "71 12.000000 15.039578 16.000000 23.000000\n", "72 8.000000 12.000000 15.040872 15.000000\n", "73 14.046358 8.000000 12.000000 15.041667\n", "74 7.000000 14.047945 8.000000 12.000000\n", "75 8.000000 7.000000 14.047945 8.000000\n", "76 2.000000 8.000000 7.000000 14.048443\n", "77 4.000000 2.000000 8.000000 8.000000\n", "78 1.000000 4.000000 2.000000 8.000000\n", "79 1.000000 2.000000 3.000000 2.000000\n", "80 3.000000 1.000000 2.000000 2.000000\n", "81 2.000000 3.000000 1.000000 2.000000\n", "82 0.000000 2.000000 3.000000 0.000000\n", "83 2.000000 0.000000 1.000000 2.000000\n", "84 0.000000 1.000000 0.000000 1.000000\n", "85 0.000000 0.000000 1.000000 0.000000\n", "86 0.000000 0.000000 0.000000 0.000000\n", "87 0.000000 0.000000 0.000000 0.000000\n", "88 0.000000 0.000000 0.000000 0.000000\n", "89 0.000000 0.000000 0.000000 0.000000\n", "90 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxaUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.756385 0.756259 0.756094 0.755661\n", "17 0.0 3.781927 6.806335 6.048754 7.560077\n", "18 0.0 21.173722 18.904094 24.189823 18.141415\n", "19 0.0 46.131541 50.661011 58.967997 66.515520\n", "20 0.0 116.384110 103.583625 123.976524 136.061650\n", "21 0.0 192.815414 213.126728 222.246275 230.538029\n", "22 0.0 319.868014 326.602092 377.892285 356.014879\n", "23 0.0 458.927668 484.628294 532.911765 593.254588\n", "24 0.0 609.434465 678.858046 749.877399 763.414375\n", "25 0.0 734.210807 855.823772 961.456487 1047.624436\n", "26 0.0 896.060477 1004.019467 1199.648960 1293.970235\n", "27 0.0 1122.143952 1174.914100 1334.971892 1566.156761\n", "28 0.0 1308.878419 1433.497693 1501.301918 1717.339230\n", "29 0.0 1702.944354 1600.539620 1813.515091 1896.507184\n", "... ... ... ... ... ...\n", "61 0.0 6790.875305 6548.906646 6729.382471 6460.571668\n", "62 0.0 6162.714879 6624.576637 6358.114831 6524.770999\n", "63 0.0 5996.364818 6014.534442 6427.595772 6158.540486\n", "64 0.0 5310.007290 5787.036584 5823.637872 6228.740077\n", "65 0.0 4458.897755 4625.791654 4522.789873 4552.699940\n", "66 0.0 3994.407203 4143.045157 4131.133450 3969.695182\n", "67 0.0 3251.632363 3795.066006 3913.880706 3894.950201\n", "68 0.0 2985.144237 3118.927962 3612.238782 3728.918676\n", "69 0.0 2895.468517 2867.820811 3017.883710 3487.124736\n", "70 0.0 2501.994880 2767.636093 2724.436055 2898.153401\n", "71 0.0 2824.115037 2372.552782 2659.643074 2635.656064\n", "72 0.0 2573.735713 2699.971749 2267.062548 2553.628458\n", "73 0.0 2303.022032 2459.274349 2555.279970 2171.515539\n", "74 0.0 1950.693455 2161.486211 2336.678514 2428.698188\n", "75 0.0 1852.290166 1829.726866 2010.590795 2213.945216\n", "76 0.0 1655.654654 1731.176150 1711.640388 1903.799203\n", "77 0.0 1473.006269 1558.783173 1604.079498 1624.696313\n", "78 0.0 1115.362549 1367.855963 1438.965408 1499.523805\n", "79 0.0 1137.051622 1038.816069 1264.756270 1334.030759\n", "80 0.0 932.043148 1052.340629 963.350277 1170.973139\n", "81 0.0 869.902537 855.501231 968.175565 889.981374\n", "82 0.0 693.725723 797.135963 794.085380 876.651971\n", "83 0.0 583.874081 620.218932 709.518998 718.400519\n", "84 0.0 475.164202 520.970978 553.573092 637.701166\n", "85 0.0 414.150830 420.601893 461.837431 498.360177\n", "86 0.0 288.018981 365.769248 367.676098 400.495938\n", "87 0.0 235.798798 241.068391 322.021660 331.324492\n", "88 0.0 170.025300 204.093700 210.766647 288.724867\n", "89 0.0 123.113421 148.807973 177.591008 175.190275\n", "90 0.0 304.191001 347.211146 401.637349 473.490698\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AinvUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 1.000058 0.000000 0.000000 1.000033\n", "15 0.0 0.000000 1.000051 0.000000 0.000000\n", "16 0.0 1.000058 1.000051 1.000041 0.000000\n", "17 0.0 0.000000 1.000032 2.000043 1.000014\n", "18 0.0 2.545555 1.454593 2.909157 4.000059\n", "19 0.0 5.866922 18.000604 9.500237 15.000237\n", "20 0.0 25.825571 22.793786 42.106375 32.000624\n", "21 0.0 60.285776 49.891320 53.528914 66.001183\n", "22 0.0 95.579039 107.518007 102.372502 107.001821\n", "23 0.0 187.764212 184.758157 192.960927 197.003580\n", "24 0.0 268.141630 270.876080 294.848435 284.005175\n", "25 0.0 354.146865 392.954585 399.419421 409.006775\n", "26 0.0 543.021420 509.479294 552.063199 543.009445\n", "27 0.0 651.525991 699.607221 674.578211 722.012137\n", "28 0.0 899.379803 806.488051 836.861471 817.013683\n", "29 0.0 1276.710707 1153.902099 1069.821390 1072.018223\n", "... ... ... ... ... ...\n", "61 0.0 34890.717226 35550.662485 38716.421378 39556.346518\n", "62 0.0 32905.896182 35585.528725 36262.822834 39471.914461\n", "63 0.0 32652.354302 33205.081908 36055.864336 36690.772531\n", "64 0.0 30550.514827 32427.430521 33031.102549 35845.411704\n", "65 0.0 27891.525262 29780.058626 31709.724228 32326.169091\n", "66 0.0 24718.144050 26604.388196 28394.726441 30235.459291\n", "67 0.0 23124.451654 23275.628548 25026.122521 26711.754084\n", "68 0.0 21024.079545 21564.403681 21748.935108 23312.973549\n", "69 0.0 19147.938879 19233.954904 19734.798368 19875.071960\n", "70 0.0 15309.808968 17759.982958 17831.448709 18296.194603\n", "71 0.0 15631.941979 14019.712088 16228.801885 16287.642361\n", "72 0.0 13202.181869 13953.365179 12534.774961 14497.600249\n", "73 0.0 11562.303105 11960.211047 12632.689933 11347.332622\n", "74 0.0 9405.621171 9856.911483 10181.206269 10769.795515\n", "75 0.0 8586.451230 8248.738207 8637.233103 8927.304121\n", "76 0.0 7338.171955 7364.675381 7040.139664 7363.792784\n", "77 0.0 5955.158284 6367.616047 6385.095225 6117.435270\n", "78 0.0 4745.813330 5144.386466 5488.588430 5524.987342\n", "79 0.0 4440.717509 4015.082188 4334.310776 4619.072130\n", "80 0.0 3477.700550 3842.487305 3478.315396 3750.886456\n", "81 0.0 3352.653486 3004.314361 3315.910475 3000.287763\n", "82 0.0 2437.616178 2641.596021 2343.591490 2593.164821\n", "83 0.0 2154.795748 2187.675040 2361.786886 2095.230852\n", "84 0.0 1686.526488 1720.133004 1744.265233 1890.572201\n", "85 0.0 1410.682728 1473.471237 1497.111312 1513.618823\n", "86 0.0 1158.000148 1210.689542 1269.762812 1290.013967\n", "87 0.0 943.999344 928.419390 969.219052 1014.662984\n", "88 0.0 734.621983 796.957242 785.966575 815.223531\n", "89 0.0 582.111599 595.780749 633.330145 627.867905\n", "90 0.0 1483.736241 1633.035709 1750.505809 1873.177955\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Estoques - Tabela: AuxrRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 22.0 23.0 25.0 22.0\n", "1 59.0 50.0 65.0 57.0\n", "2 109.0 77.0 90.0 78.0\n", "3 106.0 133.0 87.0 90.0\n", "4 114.0 91.0 116.0 97.0\n", "5 107.0 112.0 101.0 116.0\n", "6 108.0 91.0 108.0 91.0\n", "7 103.0 108.0 91.0 89.0\n", "8 81.0 102.0 96.0 90.0\n", "9 81.0 77.0 88.0 95.0\n", "10 70.0 82.0 76.0 86.0\n", "11 55.0 64.0 68.0 74.0\n", "12 59.0 60.0 65.0 69.0\n", "13 41.0 50.0 52.0 59.0\n", "14 37.0 42.0 45.0 46.0\n", "15 35.0 35.0 38.0 38.0\n", "16 28.0 30.0 32.0 34.0\n", "17 15.0 36.0 28.0 36.0\n", "18 16.0 14.0 32.0 31.0\n", "19 26.0 16.0 13.0 26.0\n", "20 17.0 27.0 20.0 23.0\n", "21 14.0 14.0 14.0 13.0\n", "22 19.0 15.0 20.0 19.0\n", "23 34.0 26.0 20.0 21.0\n", "24 51.0 39.0 33.0 35.0\n", "25 46.0 48.0 42.0 33.0\n", "26 43.0 55.0 50.0 52.0\n", "27 63.0 52.0 62.0 54.0\n", "28 67.0 73.0 63.0 65.0\n", "29 75.0 66.0 69.0 64.0\n", "... ... ... ... ...\n", "61 10.0 0.0 6.0 7.0\n", "62 3.0 8.0 1.0 3.0\n", "63 2.0 4.0 6.0 1.0\n", "64 2.0 3.0 2.0 5.0\n", "65 2.0 1.0 3.0 4.0\n", "66 1.0 2.0 1.0 0.0\n", "67 1.0 1.0 0.0 1.0\n", "68 1.0 1.0 2.0 0.0\n", "69 0.0 0.0 1.0 1.0\n", "70 0.0 0.0 0.0 1.0\n", "71 0.0 0.0 0.0 0.0\n", "72 1.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 1.0\n", "79 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AinvRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0\n", "16 1.0 1.0 2.0 1.0\n", "17 2.0 4.0 2.0 1.0\n", "18 1.0 4.0 5.0 5.0\n", "19 4.0 9.0 6.0 11.0\n", "20 13.0 15.0 16.0 16.0\n", "21 20.0 15.0 17.0 24.0\n", "22 32.0 24.0 16.0 27.0\n", "23 29.0 25.0 27.0 33.0\n", "24 34.0 36.0 31.0 32.0\n", "25 41.0 40.0 48.0 37.0\n", "26 54.0 45.0 44.0 46.0\n", "27 62.0 46.0 60.0 48.0\n", "28 49.0 60.0 54.0 74.0\n", "29 56.0 74.0 78.0 83.0\n", "... ... ... ... ...\n", "61 95.0 96.0 92.0 105.0\n", "62 90.0 83.0 74.0 84.0\n", "63 63.0 76.0 80.0 70.0\n", "64 63.0 52.0 62.0 62.0\n", "65 31.0 37.0 48.0 41.0\n", "66 37.0 27.0 39.0 34.0\n", "67 18.0 22.0 30.0 33.0\n", "68 16.0 20.0 20.0 20.0\n", "69 16.0 5.0 19.0 15.0\n", "70 7.0 9.0 7.0 10.0\n", "71 4.0 6.0 8.0 8.0\n", "72 4.0 4.0 7.0 8.0\n", "73 3.0 8.0 5.0 4.0\n", "74 3.0 4.0 5.0 6.0\n", "75 2.0 1.0 2.0 7.0\n", "76 0.0 3.0 2.0 3.0\n", "77 4.0 3.0 2.0 1.0\n", "78 5.0 1.0 2.0 5.0\n", "79 2.0 2.0 1.0 1.0\n", "80 0.0 4.0 1.0 1.0\n", "81 2.0 2.0 1.0 3.0\n", "82 0.0 0.0 2.0 0.0\n", "83 1.0 2.0 5.0 2.0\n", "84 0.0 1.0 3.0 3.0\n", "85 1.0 2.0 1.0 1.0\n", "86 1.0 1.0 0.0 0.0\n", "87 0.0 1.0 0.0 0.0\n", "88 1.0 1.0 0.0 1.0\n", "89 0.0 0.0 0.0 1.0\n", "90 3.0 0.0 0.0 1.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: ApinUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.000000 0.000000 0.000000 0.000000\n", "62 0.0 0.000000 0.000000 0.000000 0.000000\n", "63 0.0 0.000000 0.000000 0.000000 0.000000\n", "64 0.0 0.000000 0.000000 0.000000 0.000000\n", "65 0.0 29874.522256 33673.659848 37627.195128 38798.267614\n", "66 0.0 2141.099493 2411.608508 2824.691410 3061.962979\n", "67 0.0 1166.817166 1288.059117 1450.971569 1564.958623\n", "68 0.0 780.146366 822.148361 957.841370 968.247781\n", "69 0.0 589.212648 615.610603 673.651076 671.500745\n", "70 0.0 453.917896 472.715294 524.351105 556.501216\n", "71 0.0 337.036039 320.613845 348.233177 349.019549\n", "72 0.0 228.560069 253.769260 257.372336 271.012876\n", "73 0.0 183.328392 166.511060 209.340174 197.831357\n", "74 0.0 153.307367 148.098780 150.100507 142.342074\n", "75 0.0 138.096714 130.887300 126.084426 141.537882\n", "76 0.0 121.685221 134.089436 100.467273 100.524064\n", "77 0.0 96.467560 97.665141 103.669417 97.709390\n", "78 0.0 86.860832 69.246186 76.851460 92.080042\n", "79 0.0 67.247096 64.843250 53.235647 81.223443\n", "80 0.0 55.638966 49.633105 51.634574 69.160556\n", "81 0.0 41.228874 32.821892 39.626534 55.087187\n", "82 0.0 35.624949 33.222159 35.223586 36.188663\n", "83 0.0 22.015418 24.016018 25.617153 32.167700\n", "84 0.0 18.813176 18.812548 17.611793 25.734160\n", "85 0.0 11.608130 13.609077 14.009381 15.681754\n", "86 0.0 10.007008 11.607742 9.206164 9.248214\n", "87 0.0 4.002803 5.603738 7.204824 8.846118\n", "88 0.0 3.602523 4.002670 6.804556 5.629348\n", "89 0.0 4.403084 2.001335 2.801876 2.412578\n", "90 0.0 8.806167 4.002670 5.203484 7.639829\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AuxdUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 5.773711 8.561079 10.973778 11.572396\n", "15 0.0 32.892676 46.270576 56.265553 66.042496\n", "16 0.0 277.662882 358.137858 354.353369 370.515975\n", "17 0.0 660.827204 873.025086 844.382545 826.627852\n", "18 0.0 1567.998908 1988.001709 1979.670206 1917.424440\n", "19 0.0 2848.013109 3477.628233 3515.799864 3502.842171\n", "20 0.0 3729.291842 4595.052576 4363.574135 4432.025775\n", "21 0.0 4276.919741 5054.905063 4937.402941 4830.874696\n", "22 0.0 4796.379018 5541.663290 5233.096347 5118.389070\n", "23 0.0 5045.698532 6092.426185 5665.463253 5377.570788\n", "24 0.0 5189.341635 6338.659099 6238.294499 5735.916065\n", "25 0.0 5431.662677 6399.810114 6333.067970 6138.156676\n", "26 0.0 5378.299805 6604.663749 6395.518822 6318.925668\n", "27 0.0 5611.697651 6489.089793 6623.773339 6365.215259\n", "28 0.0 6004.485291 6818.283366 6545.959273 6554.164453\n", "29 0.0 6114.360540 7203.735810 6827.486548 6459.589884\n", "... ... ... ... ... ...\n", "61 0.0 1974.261303 2456.419287 2651.264511 2742.659491\n", "62 0.0 1708.145521 2150.054928 2344.996323 2418.632365\n", "63 0.0 1507.465111 1840.429176 2030.747269 2126.927994\n", "64 0.0 1098.580981 1398.718013 1497.421705 1593.001238\n", "65 0.0 351.671908 474.732517 526.741262 537.917270\n", "66 0.0 189.832858 249.290655 288.111509 298.089188\n", "67 0.0 125.972011 152.061190 170.592339 199.524236\n", "68 0.0 89.755068 111.497941 110.136804 125.301220\n", "69 0.0 63.510907 78.884291 84.996885 83.201614\n", "70 0.0 42.165646 48.308990 53.871262 60.455840\n", "71 0.0 33.942463 38.728732 37.510362 42.299149\n", "72 0.0 21.695185 25.887096 33.519898 28.931021\n", "73 0.0 18.195962 19.160531 23.543738 25.339581\n", "74 0.0 12.072321 13.045468 18.156611 15.961941\n", "75 0.0 9.972787 12.637791 13.368054 14.764797\n", "76 0.0 7.173410 12.433963 10.574730 8.978590\n", "77 0.0 4.548992 4.484380 9.976160 10.175738\n", "78 0.0 5.248835 5.707391 6.584265 5.786205\n", "79 0.0 4.024107 2.446026 3.990464 5.985727\n", "80 0.0 3.149301 2.038355 2.394278 3.790962\n", "81 0.0 0.874806 1.630684 2.194755 2.394292\n", "82 0.0 0.699845 0.203835 0.798093 0.997622\n", "83 0.0 0.874806 0.815342 0.798093 1.396670\n", "84 0.0 0.349922 0.611506 0.798093 0.199524\n", "85 0.0 0.524884 0.203835 0.199523 0.199524\n", "86 0.0 0.174961 0.203835 0.199523 0.000000\n", "87 0.0 0.349922 0.407671 0.000000 0.598573\n", "88 0.0 0.174961 0.407671 0.199523 0.000000\n", "89 0.0 0.174961 0.000000 0.399046 0.000000\n", "90 0.0 1.574648 1.019173 0.798093 0.798097\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: ApinRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 5422.822076 5040.292909 4835.016828 4142.496237\n", "62 4106.923977 3966.739561 3763.683631 3295.576996\n", "63 3336.813141 3025.377484 2857.556021 2669.897889\n", "64 2373.423512 2199.182044 2008.499432 1833.990601\n", "65 1984.863039 1837.658948 1724.145574 1446.570097\n", "66 1384.997771 1378.995298 1240.543767 1046.135468\n", "67 1292.864875 1172.696800 961.196139 770.836661\n", "68 1056.523969 1066.543204 910.132594 743.807323\n", "69 998.440186 866.253401 846.052851 638.693233\n", "70 933.346293 934.351934 777.968125 636.691060\n", "71 876.263955 837.211379 697.868447 586.636731\n", "72 711.025609 739.069375 707.880906 549.596528\n", "73 705.016942 630.912881 597.743849 505.548719\n", "74 622.898491 545.789715 545.679058 488.530247\n", "75 600.866712 567.821593 521.649155 431.468313\n", "76 534.771374 514.744795 469.584364 370.402032\n", "77 516.745372 449.650609 437.544492 352.382473\n", "78 497.717926 421.610037 421.524557 329.357482\n", "79 437.631255 396.573811 367.457274 284.308587\n", "80 376.543140 392.568015 328.408681 289.314019\n", "81 335.483914 320.463686 282.351366 235.255345\n", "82 286.413133 266.385439 266.331430 216.234700\n", "83 249.359685 236.341968 215.267885 180.195583\n", "84 201.290349 226.327478 215.267885 163.177111\n", "85 199.287459 200.289804 172.214308 134.145601\n", "86 156.225345 164.237639 168.209324 124.134735\n", "87 146.210900 113.163739 126.156993 92.099965\n", "88 96.138674 127.184025 93.115876 84.091272\n", "89 93.134340 88.127514 68.084727 64.069541\n", "90 266.384242 254.368051 259.322708 199.216228\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: ApinUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 7760.045532 9144.507670 10542.112990 10448.475626\n", "62 0.0 5606.940835 6705.643825 7480.614125 7500.366799\n", "63 0.0 4634.911549 5333.123046 6008.279498 5830.641446\n", "64 0.0 3881.078554 4467.379170 4868.838886 4591.744721\n", "65 0.0 3669.920853 4280.857116 4654.885861 4424.631162\n", "66 0.0 2452.948633 2790.792250 2940.446497 2888.878704\n", "67 0.0 1926.462097 2049.631029 2132.492313 2081.515727\n", "68 0.0 1613.244840 1719.522186 1663.766280 1527.996132\n", "69 0.0 1267.650068 1426.717753 1351.282258 1267.101545\n", "70 0.0 1095.204612 1093.793482 1131.698891 1097.167504\n", "71 0.0 879.119897 836.885747 826.252797 791.850326\n", "72 0.0 677.112363 679.221821 650.304587 597.942187\n", "73 0.0 555.344755 568.716302 565.849446 462.559051\n", "74 0.0 439.208019 415.275518 407.496056 424.482543\n", "75 0.0 358.968093 375.859536 337.116772 311.663263\n", "76 0.0 289.989910 321.662562 286.443687 239.740971\n", "77 0.0 216.084715 235.088174 245.623702 194.613259\n", "78 0.0 199.192098 198.487620 185.801310 181.921090\n", "79 0.0 173.149315 173.852632 140.054776 144.549703\n", "80 0.0 151.329686 151.329214 131.609262 98.716871\n", "81 0.0 130.917775 101.355381 95.715827 93.781027\n", "82 0.0 80.943786 85.870531 76.009627 71.922291\n", "83 0.0 79.536068 59.123972 64.045149 64.871086\n", "84 0.0 56.308720 60.531686 40.819985 57.114761\n", "85 0.0 45.046976 40.823695 38.004814 34.550905\n", "86 0.0 32.377514 35.896697 28.855507 28.909941\n", "87 0.0 21.819629 25.338845 25.336542 21.153615\n", "88 0.0 14.077180 14.780993 16.187235 19.038254\n", "89 0.0 16.188757 17.596420 11.260685 10.576808\n", "90 0.0 32.377514 27.450416 26.744128 31.025302\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: PensUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 405.613907 468.762255 455.808496 466.045906\n", "1 0.0 578.710287 599.682257 591.819801 580.119904\n", "2 0.0 473.819048 478.568996 441.183625 465.070915\n", "3 0.0 423.698603 454.052142 462.145940 457.270983\n", "4 0.0 434.549421 429.044951 451.421035 428.996232\n", "5 0.0 434.549421 414.825175 443.133607 426.071257\n", "6 0.0 416.981430 431.986973 433.383693 419.733813\n", "7 0.0 435.066127 417.276860 425.096266 414.858856\n", "8 0.0 421.115075 440.813041 425.096266 388.534087\n", "9 0.0 417.498136 407.960456 411.446386 423.633779\n", "10 0.0 437.649655 418.747872 421.683796 406.083933\n", "11 0.0 458.834585 443.755063 434.846180 410.471394\n", "12 0.0 439.716477 421.689894 474.820829 445.571086\n", "13 0.0 464.518347 417.767198 464.583419 457.758479\n", "14 0.0 436.099538 397.663377 447.521069 466.045906\n", "15 0.0 437.132949 418.257535 467.995889 471.895855\n", "16 0.0 454.700940 440.322704 475.795820 493.833162\n", "17 0.0 413.364491 435.419333 480.183282 458.733470\n", "18 0.0 369.444514 364.320455 408.521411 403.646454\n", "19 0.0 304.856312 323.132140 337.347037 370.496745\n", "20 0.0 203.065306 217.709666 212.060637 235.947927\n", "21 0.0 25.318575 23.045843 19.499829 21.449812\n", "22 0.0 35.135982 32.362248 28.274752 35.099692\n", "23 0.0 34.619276 36.284944 35.587187 31.199726\n", "24 0.0 48.570328 49.033709 45.824597 46.312093\n", "25 0.0 52.703973 53.446742 55.087016 49.724563\n", "26 0.0 68.205141 62.763147 60.449469 70.686879\n", "27 0.0 85.256426 74.531237 71.174375 73.611853\n", "28 0.0 102.824417 95.615732 90.674203 92.136691\n", "29 0.0 118.842291 90.712361 102.374101 107.736554\n", "... ... ... ... ... ...\n", "61 0.0 464.001641 460.916861 497.245632 490.420692\n", "62 0.0 469.168697 459.445850 509.433025 524.057897\n", "63 0.0 480.536221 453.071468 479.208291 518.207948\n", "64 0.0 468.135286 478.568996 476.283316 511.383008\n", "65 0.0 421.631781 428.554613 488.958205 487.495718\n", "66 0.0 423.698603 414.334838 463.120932 469.458376\n", "67 0.0 435.582832 403.547422 440.208633 479.695786\n", "68 0.0 428.348954 413.844501 435.821172 438.746146\n", "69 0.0 428.865659 413.844501 438.258650 437.283659\n", "70 0.0 415.948019 392.269669 435.333676 424.121274\n", "71 0.0 423.181898 404.528096 394.871531 434.846180\n", "72 0.0 446.433650 397.663377 380.246660 410.471394\n", "73 0.0 402.513673 399.624725 431.921206 413.396369\n", "74 0.0 395.796500 393.740680 423.146283 404.133950\n", "75 0.0 416.464725 380.501579 365.621788 429.483727\n", "76 0.0 400.446851 427.083602 385.121617 389.509078\n", "77 0.0 349.809700 360.888096 375.859198 372.934224\n", "78 0.0 357.560285 346.177983 378.784173 367.084275\n", "79 0.0 368.411103 324.603151 355.384378 365.621788\n", "80 0.0 325.007831 310.873713 311.997259 320.772182\n", "81 0.0 337.925471 309.402701 328.572114 314.922234\n", "82 0.0 306.406429 315.777084 332.472079 320.772182\n", "83 0.0 257.836101 298.124949 288.597465 302.734841\n", "84 0.0 249.568811 244.187869 262.272696 274.460089\n", "85 0.0 210.815890 204.960902 234.485440 249.110312\n", "86 0.0 195.314722 176.521351 211.085646 214.985612\n", "87 0.0 142.094044 163.772587 173.548476 195.973279\n", "88 0.0 130.209815 120.622923 120.411442 155.511134\n", "89 0.0 102.824417 100.028766 119.436451 113.586502\n", "90 0.0 299.172550 289.298881 331.984584 383.171634\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: ApinUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 3279.259380 3864.308841 4454.912380 4415.342869\n", "62 0.0 2369.395031 2833.687679 3161.176560 3169.523694\n", "63 0.0 1958.632473 2253.684428 2538.993724 2463.926993\n", "64 0.0 1640.075848 1887.836223 2057.486070 1940.390927\n", "65 0.0 1550.844300 1809.015268 1967.073268 1869.771663\n", "66 0.0 1036.573146 1179.339944 1242.581209 1220.789562\n", "67 0.0 814.089154 866.138188 901.153916 879.612103\n", "68 0.0 681.729025 726.639971 703.078501 645.704413\n", "69 0.0 535.686725 602.905944 571.028224 535.454929\n", "70 0.0 462.814294 462.217976 478.236138 463.643778\n", "71 0.0 371.500677 353.653264 349.159966 334.622084\n", "72 0.0 286.135830 287.027250 274.807333 252.679900\n", "73 0.0 234.678971 240.329552 239.118069 195.469356\n", "74 0.0 185.601619 175.488163 172.200699 179.378891\n", "75 0.0 151.693631 158.831660 142.459646 131.703438\n", "76 0.0 122.544659 135.928967 121.046087 101.310337\n", "77 0.0 91.313617 99.344147 103.796276 82.240156\n", "78 0.0 84.175093 83.877393 78.516381 76.876668\n", "79 0.0 73.169869 73.467078 59.184696 61.084174\n", "80 0.0 63.949276 63.949076 55.615770 41.716021\n", "81 0.0 55.323560 42.831009 40.447833 39.630220\n", "82 0.0 34.205427 36.287383 32.120338 30.393101\n", "83 0.0 33.610550 24.984755 27.064359 27.413385\n", "84 0.0 23.795079 25.579631 17.249811 24.135698\n", "85 0.0 19.036064 17.251379 16.060169 14.600607\n", "86 0.0 13.682171 15.169316 12.193832 12.216835\n", "87 0.0 9.220593 10.707752 10.706779 8.939147\n", "88 0.0 5.948770 6.246189 6.840442 8.045233\n", "89 0.0 6.841085 7.435939 4.758569 4.469574\n", "90 0.0 13.682171 11.600065 11.301600 13.110750\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: PensRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 230.0 209.0 251.0 227.0\n", "1 256.0 248.0 218.0 206.0\n", "2 218.0 223.0 210.0 196.0\n", "3 193.0 211.0 189.0 190.0\n", "4 194.0 188.0 198.0 183.0\n", "5 194.0 165.0 193.0 167.0\n", "6 203.0 219.0 188.0 168.0\n", "7 215.0 171.0 219.0 179.0\n", "8 217.0 219.0 219.0 169.0\n", "9 197.0 228.0 217.0 175.0\n", "10 224.0 242.0 215.0 218.0\n", "11 226.0 198.0 197.0 198.0\n", "12 200.0 215.0 235.0 197.0\n", "13 253.0 244.0 239.0 236.0\n", "14 207.0 232.0 219.0 216.0\n", "15 248.0 230.0 267.0 217.0\n", "16 260.0 269.0 272.0 269.0\n", "17 261.0 272.0 285.0 290.0\n", "18 246.0 253.0 239.0 252.0\n", "19 216.0 226.0 263.0 220.0\n", "20 223.0 198.0 199.0 223.0\n", "21 166.0 150.0 162.0 135.0\n", "22 172.0 174.0 176.0 183.0\n", "23 214.0 198.0 196.0 198.0\n", "24 226.0 246.0 227.0 207.0\n", "25 280.0 232.0 247.0 264.0\n", "26 263.0 294.0 261.0 309.0\n", "27 301.0 275.0 293.0 339.0\n", "28 364.0 309.0 329.0 346.0\n", "29 391.0 396.0 371.0 346.0\n", "... ... ... ... ...\n", "61 2146.0 2169.0 2181.0 2101.0\n", "62 2163.0 2091.0 2226.0 2239.0\n", "63 2203.0 2308.0 2282.0 2286.0\n", "64 2191.0 2271.0 2314.0 2243.0\n", "65 2154.0 2148.0 2325.0 2157.0\n", "66 2189.0 2233.0 2279.0 2333.0\n", "67 2336.0 2220.0 2257.0 2347.0\n", "68 2221.0 2299.0 2346.0 2292.0\n", "69 2277.0 2281.0 2519.0 2256.0\n", "70 2233.0 2277.0 2447.0 2306.0\n", "71 2433.0 2307.0 2255.0 2410.0\n", "72 2280.0 2510.0 2444.0 2183.0\n", "73 2243.0 2336.0 2513.0 2329.0\n", "74 2211.0 2189.0 2414.0 2441.0\n", "75 2354.0 2261.0 2285.0 2247.0\n", "76 2043.0 2325.0 2300.0 2047.0\n", "77 1797.0 1989.0 2153.0 2050.0\n", "78 1740.0 1743.0 1906.0 1998.0\n", "79 1689.0 1680.0 1620.0 1818.0\n", "80 1596.0 1599.0 1582.0 1555.0\n", "81 1412.0 1511.0 1499.0 1380.0\n", "82 1221.0 1257.0 1335.0 1330.0\n", "83 1082.0 1092.0 1168.0 1218.0\n", "84 1037.0 945.0 942.0 1006.0\n", "85 810.0 784.0 856.0 852.0\n", "86 694.0 687.0 718.0 702.0\n", "87 494.0 574.0 608.0 601.0\n", "88 424.0 429.0 473.0 527.0\n", "89 283.0 309.0 395.0 382.0\n", "90 782.0 852.0 954.0 1004.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AinvUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.248373 0.248368\n", "15 0.0 0.248365 0.000000 0.000000 0.248368\n", "16 0.0 0.248365 0.496736 0.745118 0.496733\n", "17 0.0 0.993461 2.732043 2.235346 2.235307\n", "18 0.0 2.235286 4.967350 5.712556 5.215715\n", "19 0.0 9.686241 10.679801 13.412086 14.902049\n", "20 0.0 16.640466 18.130824 19.869761 20.366144\n", "21 0.0 31.542375 26.078582 30.549733 28.562246\n", "22 0.0 36.509678 37.006756 42.968268 39.490419\n", "23 0.0 55.385430 57.124501 62.093009 51.163723\n", "24 0.0 59.359273 68.549428 70.040896 69.046195\n", "25 0.0 72.025896 93.137811 81.962814 78.980863\n", "26 0.0 85.437614 88.667205 104.067749 98.601915\n", "27 0.0 110.274130 106.549692 107.545079 104.811057\n", "28 0.0 129.149882 124.928860 128.408330 119.216411\n", "29 0.0 145.790347 143.804808 154.487317 125.177227\n", "... ... ... ... ... ...\n", "61 0.0 958.441141 979.810297 1080.916276 1060.032942\n", "62 0.0 846.428455 893.378397 1017.084516 998.189383\n", "63 0.0 782.846975 806.946489 864.583850 919.953711\n", "64 0.0 599.801854 631.598889 695.939261 698.161421\n", "65 0.0 327.842007 343.740843 395.657137 405.087603\n", "66 0.0 193.476457 193.975146 236.947207 225.269425\n", "67 0.0 129.646612 128.406082 142.317387 157.216724\n", "68 0.0 80.718676 91.399305 102.081083 98.850313\n", "69 0.0 58.117447 55.634367 70.041031 62.837025\n", "70 0.0 41.476981 43.215981 49.177758 56.379459\n", "71 0.0 30.548914 27.817177 35.517239 29.059018\n", "72 0.0 19.620847 18.875935 28.562849 19.621046\n", "73 0.0 16.392100 12.915124 19.373038 19.869416\n", "74 0.0 11.921528 12.915119 16.889341 17.634102\n", "75 0.0 9.934606 8.941237 12.667006 11.673279\n", "76 0.0 7.202590 7.451033 7.202800 8.196135\n", "77 0.0 4.222208 3.725517 7.451180 6.457561\n", "78 0.0 2.235286 4.967350 5.215826 6.209193\n", "79 0.0 2.732017 4.222249 3.477210 4.967355\n", "80 0.0 4.222208 1.490207 3.228845 3.973884\n", "81 0.0 1.490191 1.241839 1.986981 1.241839\n", "82 0.0 0.248365 0.993471 0.745118 1.490204\n", "83 0.0 0.496730 1.241839 0.745118 1.241839\n", "84 0.0 0.248365 0.496736 0.000000 0.248368\n", "85 0.0 0.745095 0.993471 0.496738 0.248368\n", "86 0.0 0.248365 0.496736 0.000000 0.248368\n", "87 0.0 0.000000 0.248368 0.248373 0.248368\n", "88 0.0 0.000000 0.496736 0.248373 0.248368\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.496730 0.000000 0.000000 0.248368\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcdUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtceUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.000000 0.018868 0.056604 0.094340\n", "62 0.0 0.018868 0.018868 0.000000 0.018868\n", "63 0.0 0.000000 0.000000 0.000000 0.018868\n", "64 0.0 0.000000 0.018868 0.000000 0.000000\n", "65 0.0 0.000000 0.000000 0.000000 0.000000\n", "66 0.0 0.000000 0.000000 0.000000 0.000000\n", "67 0.0 0.000000 0.018868 0.000000 0.000000\n", "68 0.0 0.000000 0.000000 0.000000 0.000000\n", "69 0.0 0.018868 0.000000 0.000000 0.018868\n", "70 0.0 0.000000 0.000000 0.000000 0.000000\n", "71 0.0 0.000000 0.000000 0.000000 0.018868\n", "72 0.0 0.000000 0.000000 0.000000 0.000000\n", "73 0.0 0.000000 0.000000 0.000000 0.000000\n", "74 0.0 0.000000 0.000000 0.000000 0.000000\n", "75 0.0 0.000000 0.000000 0.000000 0.000000\n", "76 0.0 0.000000 0.000000 0.000000 0.000000\n", "77 0.0 0.000000 0.000000 0.000000 0.000000\n", "78 0.0 0.000000 0.000000 0.000000 0.000000\n", "79 0.0 0.000000 0.000000 0.000000 0.000000\n", "80 0.0 0.000000 0.000000 0.000000 0.000000\n", "81 0.0 0.000000 0.000000 0.000000 0.000000\n", "82 0.0 0.000000 0.000000 0.000000 0.000000\n", "83 0.0 0.000000 0.000000 0.000000 0.000000\n", "84 0.0 0.000000 0.000000 0.000000 0.000000\n", "85 0.0 0.000000 0.000000 0.000000 0.000000\n", "86 0.0 0.000000 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AuxdRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000\n", "14 1.0 0.000000 0.000000 0.000000\n", "15 1.0 0.000000 0.000000 1.000019\n", "16 56.0 57.001018 52.000451 46.000843\n", "17 217.0 207.003668 213.002017 183.003294\n", "18 345.0 386.007009 324.003110 341.006225\n", "19 432.0 414.007662 410.003883 485.009021\n", "20 581.0 528.009678 505.004906 568.010534\n", "21 734.0 699.013039 593.005739 622.011568\n", "22 900.0 932.017282 687.006742 721.013292\n", "23 1107.0 1079.020201 913.008919 847.015628\n", "24 1329.0 1340.025040 1087.010474 1052.019612\n", "25 1619.0 1653.031070 1294.012622 1378.025569\n", "26 1582.0 1784.033374 1451.014207 1541.028614\n", "27 1820.0 1973.037272 1636.015942 1712.031736\n", "28 2031.0 2090.038943 1732.016916 1805.033555\n", "29 2112.0 2300.042879 1896.018571 1909.035547\n", "... ... ... ... ...\n", "61 210.0 194.003552 213.001946 201.003716\n", "62 170.0 160.003015 170.001676 169.003160\n", "63 114.0 130.002477 138.001274 105.001896\n", "64 94.0 84.001555 92.000913 89.001647\n", "65 51.0 50.000922 57.000562 46.000881\n", "66 45.0 31.000595 57.000562 38.000689\n", "67 21.0 26.000499 28.000281 19.000345\n", "68 21.0 12.000211 22.000221 18.000326\n", "69 13.0 13.000230 19.000181 13.000249\n", "70 12.0 7.000134 9.000080 10.000192\n", "71 8.0 6.000115 14.000130 9.000172\n", "72 4.0 8.000134 4.000040 12.000230\n", "73 4.0 6.000115 2.000020 7.000134\n", "74 1.0 3.000058 5.000050 7.000134\n", "75 6.0 1.000019 2.000010 2.000038\n", "76 3.0 0.000000 2.000020 4.000077\n", "77 5.0 4.000077 1.000010 3.000057\n", "78 3.0 1.000019 1.000010 1.000019\n", "79 1.0 2.000038 2.000020 0.000000\n", "80 0.0 1.000019 1.000010 1.000019\n", "81 2.0 0.000000 0.000000 1.000019\n", "82 1.0 0.000000 0.000000 0.000000\n", "83 2.0 3.000058 1.000010 0.000000\n", "84 0.0 2.000038 1.000010 1.000019\n", "85 0.0 0.000000 0.000000 1.000019\n", "86 2.0 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 1.000010 0.000000\n", "90 2.0 0.000000 0.000000 1.000019\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcpUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 10.419204 13.358304 17.994709 11.394129\n", "62 0.0 10.419204 3.816658 19.888889 11.394129\n", "63 0.0 6.630403 8.587481 8.523810 12.343640\n", "64 0.0 3.788802 4.770823 3.788360 6.646575\n", "65 0.0 1.894401 0.000000 1.894180 4.747554\n", "66 0.0 0.947200 1.908329 2.841270 1.899022\n", "67 0.0 0.947200 0.000000 0.000000 2.848532\n", "68 0.0 0.000000 0.000000 1.894180 2.848532\n", "69 0.0 0.947200 0.000000 0.000000 0.949511\n", "70 0.0 0.000000 0.000000 0.000000 0.000000\n", "71 0.0 0.000000 0.000000 0.000000 0.000000\n", "72 0.0 0.000000 0.000000 0.000000 0.000000\n", "73 0.0 0.000000 0.000000 0.947090 0.000000\n", "74 0.0 0.000000 0.000000 0.000000 0.000000\n", "75 0.0 0.000000 0.000000 0.000000 0.000000\n", "76 0.0 0.000000 0.000000 0.000000 0.000000\n", "77 0.0 0.000000 0.000000 0.000000 0.000000\n", "78 0.0 0.000000 0.000000 0.000000 0.000000\n", "79 0.0 0.000000 0.000000 0.000000 0.000000\n", "80 0.0 0.000000 0.000000 0.000000 0.000000\n", "81 0.0 0.000000 0.000000 0.000000 0.000000\n", "82 0.0 0.000000 0.000000 0.000000 0.000000\n", "83 0.0 0.000000 0.000000 0.000000 0.000000\n", "84 0.0 0.000000 0.000000 0.000000 0.000000\n", "85 0.0 0.000000 0.000000 0.000000 0.000000\n", "86 0.0 0.000000 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: ApidUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AinvUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.421554\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.421554\n", "17 0.0 0.843095 0.421541 1.264623 0.843108\n", "18 0.0 2.529280 2.950788 1.686164 2.529323\n", "19 0.0 2.950828 2.107705 9.695445 5.901740\n", "20 0.0 8.009401 6.744658 13.067774 7.587955\n", "21 0.0 9.274044 9.273904 17.704726 14.332803\n", "22 0.0 19.391190 18.969349 18.126267 20.656122\n", "23 0.0 24.871297 24.027843 21.077055 32.459577\n", "24 0.0 34.988430 34.987911 36.674076 37.939789\n", "25 0.0 44.262493 39.624863 46.791062 37.096719\n", "26 0.0 53.536537 61.545000 54.378802 64.076148\n", "27 0.0 79.250963 74.612775 66.603494 73.771846\n", "28 0.0 97.799030 92.739042 94.425206 90.212355\n", "29 0.0 116.768639 101.591405 112.129932 101.594421\n", "... ... ... ... ... ...\n", "61 0.0 866.280424 937.928945 1052.588124 979.268939\n", "62 0.0 766.373662 816.525109 926.547335 894.536656\n", "63 0.0 682.064119 729.687642 792.075725 824.980424\n", "64 0.0 597.333069 630.203943 673.622676 688.397053\n", "65 0.0 482.672089 503.741613 569.080484 568.254292\n", "66 0.0 372.226593 365.897674 419.854935 454.013233\n", "67 0.0 314.053024 323.322023 335.968256 358.320590\n", "68 0.0 239.439093 252.503118 253.346200 267.686619\n", "69 0.0 189.274918 212.456714 217.515207 222.158811\n", "70 0.0 166.511346 174.518015 180.841131 173.258561\n", "71 0.0 135.738347 120.982295 136.157775 164.405958\n", "72 0.0 102.014548 111.708391 110.443768 110.868629\n", "73 0.0 86.838816 76.720480 86.837466 82.202960\n", "74 0.0 52.693468 59.437295 77.142021 75.879666\n", "75 0.0 46.791800 48.477226 51.428014 48.478671\n", "76 0.0 33.723820 37.938699 34.987911 45.106253\n", "77 0.0 28.665240 23.606302 29.929418 24.028565\n", "78 0.0 20.234285 22.763219 21.920137 26.979441\n", "79 0.0 19.391196 21.077055 13.910856 20.656135\n", "80 0.0 13.489528 10.960069 18.126267 14.332828\n", "81 0.0 10.960241 8.430822 10.538527 10.117290\n", "82 0.0 10.538694 8.009281 7.587740 6.323307\n", "83 0.0 6.323216 7.587740 6.744658 10.960398\n", "84 0.0 4.215477 5.058493 4.636952 3.372430\n", "85 0.0 4.637025 1.686164 5.058493 3.372430\n", "86 0.0 2.529286 1.686164 1.264623 4.637091\n", "87 0.0 1.686191 2.107705 0.843082 2.107769\n", "88 0.0 0.421548 1.686164 0.421541 1.686215\n", "89 0.0 1.264643 1.264623 1.264623 0.843108\n", "90 0.0 2.107739 0.421541 0.421541 2.529323\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: ApinUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.000000 0.000000 0.000000 0.000000\n", "62 0.0 0.000000 0.000000 0.000000 0.000000\n", "63 0.0 0.000000 0.000000 0.000000 0.000000\n", "64 0.0 0.000000 0.000000 0.000000 0.000000\n", "65 0.0 44995.152900 50717.178356 56671.748036 58435.544796\n", "66 0.0 3224.791286 3632.215191 4254.375043 4611.738767\n", "67 0.0 1757.387661 1939.994769 2185.363402 2357.043635\n", "68 0.0 1175.008079 1238.268880 1442.641275 1458.314767\n", "69 0.0 887.435553 927.194517 1014.611478 1011.372783\n", "70 0.0 683.662987 711.974463 789.745119 838.167624\n", "71 0.0 507.622782 482.888692 524.487217 525.671602\n", "72 0.0 344.243003 382.211524 387.638253 408.182788\n", "73 0.0 276.117856 250.788634 315.295189 297.961323\n", "74 0.0 230.902049 223.057198 226.072076 214.386805\n", "75 0.0 207.992708 197.134335 189.900544 213.175581\n", "76 0.0 183.274734 201.957193 151.317576 151.403111\n", "77 0.0 145.293457 147.097180 156.140447 147.163824\n", "78 0.0 130.824399 104.294312 115.748903 138.685250\n", "79 0.0 101.283406 97.662881 80.180230 122.333714\n", "80 0.0 83.799961 74.754304 77.768794 104.165341\n", "81 0.0 62.096374 49.434298 59.683028 82.968905\n", "82 0.0 53.656090 50.037155 53.051581 54.505120\n", "83 0.0 33.158258 36.171438 38.582968 48.448996\n", "84 0.0 28.335238 28.334293 26.525790 38.759196\n", "85 0.0 17.483445 20.497148 21.100060 23.618885\n", "86 0.0 15.071935 17.482862 13.865754 13.929086\n", "87 0.0 6.028774 8.440002 10.851460 13.323474\n", "88 0.0 5.425897 6.028573 10.248601 8.478574\n", "89 0.0 6.631652 3.014286 4.220012 3.633675\n", "90 0.0 13.263303 6.028573 7.837165 11.506636\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: LoasIdoH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 0.000000 0.000000 0.000000 0.000000\n", "62 0.000000 0.000000 0.000000 0.000000\n", "63 0.000000 0.000000 0.000000 0.000000\n", "64 0.000000 0.000000 0.000000 0.000000\n", "65 47456.705767 46501.091472 48495.581237 44862.457960\n", "66 7795.528141 7471.367142 8543.242239 8188.510820\n", "67 4077.167456 3600.369033 4132.274643 4285.641309\n", "68 2499.620363 2212.142109 2482.171765 2465.972238\n", "69 1620.643996 1469.415647 1665.140267 1670.368030\n", "70 1613.628221 1257.924036 1532.811240 1648.323580\n", "71 1158.605108 861.001392 926.303195 954.925454\n", "72 664.494106 672.563369 699.739860 637.284983\n", "73 462.038888 466.083408 493.226377 481.971819\n", "74 434.978042 316.736251 393.979606 373.753614\n", "75 350.788744 297.691983 288.717879 300.606124\n", "76 256.576910 240.559178 276.687967 189.381858\n", "77 211.475500 172.400744 178.443689 190.383879\n", "78 164.369583 135.314538 130.324043 156.315185\n", "79 125.281694 126.293569 127.316565 108.218205\n", "80 122.274934 96.223671 92.229322 97.195980\n", "81 76.171270 89.207362 85.211874 71.143449\n", "82 50.112678 52.121155 54.134602 58.117184\n", "83 50.112678 47.109506 40.099705 46.092939\n", "84 42.094649 40.093196 27.067301 38.076776\n", "85 27.060846 26.060578 26.064809 30.060612\n", "86 37.083381 18.041938 14.034897 19.038388\n", "87 23.051832 20.046598 22.054838 17.034347\n", "88 10.022536 10.023299 11.027419 14.028286\n", "89 8.018028 10.023299 10.024926 13.026265\n", "90 42.094649 41.095526 36.089735 35.070714\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AuxdUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 2.270608 2.555060 3.352099 5.028157\n", "15 0.0 19.678610 19.588833 25.559747 33.102016\n", "16 0.0 120.720713 160.968986 199.449781 195.679173\n", "17 0.0 429.145176 544.654625 552.258053 615.949074\n", "18 0.0 996.797535 1301.805422 1510.957942 1540.291937\n", "19 0.0 2208.546284 2667.062070 3039.095603 3313.134660\n", "20 0.0 3254.919039 4009.323566 4386.220077 4675.763461\n", "21 0.0 4146.890362 4986.636307 5312.656226 5543.119663\n", "22 0.0 5057.026550 5748.897706 6187.972813 6482.545918\n", "23 0.0 5882.771720 6874.404412 7045.271942 7293.754239\n", "24 0.0 6300.942490 7664.771344 8096.992707 8225.638514\n", "25 0.0 7026.780805 8437.253153 8958.481947 9300.406393\n", "26 0.0 7532.748496 9269.353269 9652.785346 9977.111057\n", "27 0.0 8214.688355 9529.544151 10360.078024 10516.380501\n", "28 0.0 9088.116408 10362.921801 10708.277246 11318.370552\n", "29 0.0 9790.491897 11545.065687 11542.949729 11512.792078\n", "... ... ... ... ... ...\n", "61 0.0 2601.741122 3062.247792 3409.503239 3392.743485\n", "62 0.0 2081.771317 2521.851141 2751.653898 2935.600889\n", "63 0.0 1747.991627 2037.240145 2212.385069 2408.064168\n", "64 0.0 1323.009023 1602.452917 1751.052505 1907.344292\n", "65 0.0 934.734651 1097.827146 1293.491051 1340.001572\n", "66 0.0 687.616536 794.200011 907.999707 1007.305701\n", "67 0.0 524.511022 591.923886 685.504170 726.567391\n", "68 0.0 409.088323 487.166133 494.853557 541.363935\n", "69 0.0 319.020771 398.164635 381.301222 437.029881\n", "70 0.0 232.359141 270.837117 295.822709 333.952859\n", "71 0.0 191.866585 203.979531 214.534314 261.044683\n", "72 0.0 134.722893 153.304029 177.661229 177.242209\n", "73 0.0 99.528427 117.107249 131.150860 133.664929\n", "74 0.0 63.198660 87.298133 111.876290 106.848142\n", "75 0.0 54.494652 62.599149 85.059502 78.774317\n", "76 0.0 38.978815 51.953035 43.158271 62.013821\n", "77 0.0 28.004197 31.512497 45.672344 41.901230\n", "78 0.0 25.733586 25.124829 32.263950 28.073824\n", "79 0.0 20.435495 20.440538 23.045679 29.749878\n", "80 0.0 15.515839 11.497803 12.989382 15.084443\n", "81 0.0 12.109922 9.794425 15.503457 15.922468\n", "82 0.0 12.488358 12.349492 12.151358 7.123209\n", "83 0.0 7.190267 7.239355 7.123210 7.123209\n", "84 0.0 3.784351 6.813513 5.028148 7.961234\n", "85 0.0 1.892175 4.684290 5.447160 4.609135\n", "86 0.0 3.027481 1.277534 3.771111 4.190123\n", "87 0.0 2.270611 2.129223 2.095062 1.257037\n", "88 0.0 1.892175 2.555067 0.419012 2.095062\n", "89 0.0 1.135305 1.703378 1.257037 0.838025\n", "90 0.0 1.513740 1.277534 1.676049 2.933086\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: LoasDefM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 2579.0 2694.0 2823.0 2773.0\n", "1 2537.0 2697.0 2689.0 2651.0\n", "2 1767.0 1776.0 1876.0 1735.0\n", "3 1338.0 1386.0 1424.0 1380.0\n", "4 1188.0 1183.0 1333.0 1214.0\n", "5 1070.0 1072.0 1124.0 1160.0\n", "6 1051.0 994.0 1048.0 1085.0\n", "7 982.0 988.0 1023.0 1055.0\n", "8 963.0 978.0 1069.0 1034.0\n", "9 994.0 955.0 1018.0 1021.0\n", "10 1059.0 984.0 1029.0 988.0\n", "11 1025.0 997.0 1035.0 993.0\n", "12 1015.0 983.0 986.0 937.0\n", "13 934.0 865.0 883.0 839.0\n", "14 840.0 821.0 890.0 860.0\n", "15 802.0 797.0 836.0 727.0\n", "16 755.0 723.0 756.0 784.0\n", "17 739.0 655.0 751.0 760.0\n", "18 758.0 666.0 751.0 775.0\n", "19 710.0 725.0 737.0 803.0\n", "20 730.0 684.0 738.0 755.0\n", "21 798.0 709.0 806.0 700.0\n", "22 814.0 710.0 744.0 741.0\n", "23 883.0 795.0 794.0 724.0\n", "24 900.0 778.0 806.0 727.0\n", "25 845.0 794.0 841.0 764.0\n", "26 919.0 789.0 887.0 818.0\n", "27 958.0 848.0 970.0 903.0\n", "28 1031.0 886.0 896.0 888.0\n", "29 1022.0 989.0 991.0 967.0\n", "... ... ... ... ...\n", "61 1830.0 1661.0 1927.0 1887.0\n", "62 1690.0 1510.0 1718.0 1635.0\n", "63 1437.0 1256.0 1404.0 1424.0\n", "64 840.0 797.0 851.0 811.0\n", "65 124.0 184.0 229.0 244.0\n", "66 47.0 40.0 59.0 65.0\n", "67 29.0 35.0 35.0 37.0\n", "68 16.0 28.0 31.0 25.0\n", "69 22.0 20.0 35.0 27.0\n", "70 14.0 17.0 15.0 18.0\n", "71 11.0 16.0 16.0 22.0\n", "72 13.0 11.0 12.0 10.0\n", "73 6.0 3.0 13.0 13.0\n", "74 10.0 6.0 9.0 6.0\n", "75 4.0 5.0 5.0 6.0\n", "76 3.0 10.0 4.0 7.0\n", "77 2.0 9.0 5.0 6.0\n", "78 8.0 4.0 2.0 6.0\n", "79 1.0 3.0 4.0 2.0\n", "80 3.0 0.0 3.0 2.0\n", "81 1.0 1.0 0.0 0.0\n", "82 3.0 0.0 3.0 6.0\n", "83 1.0 1.0 2.0 1.0\n", "84 1.0 0.0 0.0 1.0\n", "85 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 1.0\n", "87 1.0 0.0 1.0 0.0\n", "88 0.0 1.0 0.0 1.0\n", "89 0.0 0.0 0.0 0.0\n", "90 0.0 1.0 0.0 0.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcdUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcdUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: PensUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 259.340623 296.461643 280.548054 283.725269\n", "1 0.0 342.559609 357.985404 388.573353 378.088544\n", "2 0.0 278.102722 302.199611 267.839196 270.380967\n", "3 0.0 261.458924 275.422430 282.772104 287.855648\n", "4 0.0 252.380490 267.134255 270.380967 279.594890\n", "5 0.0 259.038008 247.688921 257.672109 261.484766\n", "6 0.0 249.354345 259.802408 255.448058 263.708817\n", "7 0.0 242.999440 275.741206 274.193625 250.999958\n", "8 0.0 222.724269 256.933424 274.829068 249.411351\n", "9 0.0 231.197475 247.370146 273.240460 278.959447\n", "10 0.0 249.959574 268.728135 280.865776 271.969575\n", "11 0.0 269.024287 274.147326 267.203753 262.755652\n", "12 0.0 273.260890 289.448572 289.126534 274.829068\n", "13 0.0 256.617092 275.741206 290.397420 305.965771\n", "14 0.0 252.077875 278.291414 318.356909 303.424000\n", "15 0.0 287.483771 284.985709 296.751849 300.564507\n", "16 0.0 281.431481 312.400442 338.691082 333.289818\n", "17 0.0 305.338026 348.422125 341.232854 374.911330\n", "18 0.0 279.918409 297.736747 347.587283 336.784754\n", "19 0.0 285.365470 315.588201 352.670827 349.493612\n", "20 0.0 233.315776 248.964025 279.912611 275.146789\n", "21 0.0 117.717039 127.510384 132.807573 128.041751\n", "22 0.0 143.439271 137.073663 139.162002 136.620230\n", "23 0.0 166.437973 174.370451 178.241742 162.355669\n", "24 0.0 192.462819 194.134560 194.127816 194.763258\n", "25 0.0 215.158907 235.894211 217.639204 214.461989\n", "26 0.0 228.776559 250.239129 266.886031 253.541730\n", "27 0.0 281.128867 269.046911 266.568310 265.297424\n", "28 0.0 305.640641 302.199611 300.246785 312.002479\n", "29 0.0 326.823655 340.771502 312.637922 342.503740\n", "... ... ... ... ... ...\n", "61 0.0 1506.112346 1554.351585 1674.392124 1609.576945\n", "62 0.0 1474.943053 1594.198580 1669.944024 1655.328836\n", "63 0.0 1500.967899 1578.897333 1660.094658 1652.469343\n", "64 0.0 1467.680305 1567.102623 1603.222516 1654.057950\n", "65 0.0 1365.093991 1516.417245 1579.711127 1578.122520\n", "66 0.0 1326.056721 1386.037877 1504.093419 1513.625063\n", "67 0.0 1261.297219 1328.020652 1413.542801 1467.555450\n", "68 0.0 1278.848859 1338.540259 1357.623823 1370.014960\n", "69 0.0 1198.353403 1293.592849 1336.336485 1291.855480\n", "70 0.0 1188.367125 1180.427383 1273.109913 1354.446608\n", "71 0.0 1191.695884 1221.230706 1227.040301 1253.411182\n", "72 0.0 1159.013519 1210.711099 1193.044104 1183.194738\n", "73 0.0 1073.373617 1142.811819 1200.987140 1157.459300\n", "74 0.0 1066.110869 1094.357873 1143.479555 1147.927656\n", "75 0.0 1052.795831 1068.218244 1070.403618 1091.373235\n", "76 0.0 960.498410 1051.323118 1037.360585 1035.136535\n", "77 0.0 864.872229 955.052778 1006.541603 967.461863\n", "78 0.0 820.085284 857.188558 903.599848 929.653009\n", "79 0.0 765.009445 777.813344 836.242898 856.577072\n", "80 0.0 719.314657 751.354939 754.588481 761.260632\n", "81 0.0 666.962349 707.682633 713.602412 708.836590\n", "82 0.0 561.047275 624.800883 645.292297 623.687237\n", "83 0.0 485.091037 521.517472 567.450538 575.393574\n", "84 0.0 424.265523 428.753667 482.618906 473.722705\n", "85 0.0 338.928235 384.762585 401.917654 407.636640\n", "86 0.0 261.458924 292.636332 340.279690 339.326525\n", "87 0.0 202.146483 232.387675 258.625273 277.688561\n", "88 0.0 159.477839 161.300636 179.512628 215.415154\n", "89 0.0 107.428146 127.829160 132.489851 149.329089\n", "90 0.0 245.722971 251.514233 311.367036 320.898680\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: ApidUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcnUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 1004.378011 1022.886723 1157.922115 1190.452710\n", "62 0.0 743.573446 781.432173 888.983556 946.893657\n", "63 0.0 533.247184 561.571120 643.601660 668.560485\n", "64 0.0 278.612189 291.512200 339.468326 361.622796\n", "65 0.0 95.768558 104.462044 133.207315 162.793356\n", "66 0.0 34.213072 32.810897 42.906777 55.105761\n", "67 0.0 20.331539 20.051104 25.239281 39.541539\n", "68 0.0 16.405448 14.161968 18.088151 28.043645\n", "69 0.0 13.180446 10.936966 12.619640 22.154479\n", "70 0.0 10.235878 7.571745 14.582696 19.770769\n", "71 0.0 7.291310 4.907613 8.553312 13.320731\n", "72 0.0 5.188048 3.926090 6.169602 9.955494\n", "73 0.0 4.066308 2.103263 5.468511 10.095712\n", "74 0.0 2.383698 1.682610 5.047856 7.291348\n", "75 0.0 3.505438 2.243480 3.084801 6.590256\n", "76 0.0 1.682610 1.542393 1.822837 4.206547\n", "77 0.0 0.981523 0.981523 1.963055 3.926110\n", "78 0.0 1.402175 1.121740 2.383710 1.963055\n", "79 0.0 0.701088 1.121740 0.841309 1.542400\n", "80 0.0 0.981523 0.701088 0.560873 1.121746\n", "81 0.0 0.420653 0.560870 0.841309 0.981528\n", "82 0.0 0.560870 0.560870 0.701091 0.981528\n", "83 0.0 0.000000 0.000000 0.140218 0.420655\n", "84 0.0 0.140218 0.140218 0.140218 0.841309\n", "85 0.0 0.000000 0.000000 0.560873 0.000000\n", "86 0.0 0.140218 0.140218 0.140218 0.000000\n", "87 0.0 0.280435 0.140218 0.000000 0.140218\n", "88 0.0 0.140218 0.140218 0.000000 0.000000\n", "89 0.0 0.000000 0.280435 0.140218 0.000000\n", "90 0.0 0.280435 0.000000 0.280436 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcpUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.611182 0.783588 1.055556 0.668371\n", "62 0.0 0.611182 0.223882 1.166667 0.668371\n", "63 0.0 0.388934 0.503735 0.500000 0.724068\n", "64 0.0 0.222248 0.279853 0.222222 0.389883\n", "65 0.0 0.111124 0.000000 0.111111 0.278488\n", "66 0.0 0.055562 0.111941 0.166667 0.111395\n", "67 0.0 0.055562 0.000000 0.000000 0.167093\n", "68 0.0 0.000000 0.000000 0.111111 0.167093\n", "69 0.0 0.055562 0.000000 0.000000 0.055698\n", "70 0.0 0.000000 0.000000 0.000000 0.000000\n", "71 0.0 0.000000 0.000000 0.000000 0.000000\n", "72 0.0 0.000000 0.000000 0.000000 0.000000\n", "73 0.0 0.000000 0.000000 0.055556 0.000000\n", "74 0.0 0.000000 0.000000 0.000000 0.000000\n", "75 0.0 0.000000 0.000000 0.000000 0.000000\n", "76 0.0 0.000000 0.000000 0.000000 0.000000\n", "77 0.0 0.000000 0.000000 0.000000 0.000000\n", "78 0.0 0.000000 0.000000 0.000000 0.000000\n", "79 0.0 0.000000 0.000000 0.000000 0.000000\n", "80 0.0 0.000000 0.000000 0.000000 0.000000\n", "81 0.0 0.000000 0.000000 0.000000 0.000000\n", "82 0.0 0.000000 0.000000 0.000000 0.000000\n", "83 0.0 0.000000 0.000000 0.000000 0.000000\n", "84 0.0 0.000000 0.000000 0.000000 0.000000\n", "85 0.0 0.000000 0.000000 0.000000 0.000000\n", "86 0.0 0.000000 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: LoasIdoM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 0.000000 0.000000 0.000000 0.000000\n", "62 0.000000 0.000000 0.000000 0.000000\n", "63 0.000000 0.000000 0.000000 0.000000\n", "64 0.000000 0.000000 0.000000 0.000000\n", "65 59578.246970 61277.137028 67287.028242 64424.244255\n", "66 7749.534792 7642.339287 9565.858660 9287.696515\n", "67 4396.785367 4075.446190 4957.474150 4894.541858\n", "68 2830.229997 2755.414886 3237.104226 3056.456378\n", "69 1940.643176 1985.062272 2472.718288 2273.289570\n", "70 1996.806493 1719.250497 2066.450172 2062.706946\n", "71 1529.447465 1300.973101 1623.066203 1453.020109\n", "72 1047.044690 1090.329808 1225.826267 1012.802146\n", "73 807.347678 859.625249 1114.478709 858.374888\n", "74 679.977299 694.119804 884.761676 701.942082\n", "75 595.732324 562.718512 651.032117 598.656318\n", "76 483.405690 503.537777 589.841117 488.351134\n", "77 394.146133 412.259017 496.549920 418.156926\n", "78 366.064475 312.955750 378.180444 333.923876\n", "79 265.772838 290.888357 325.014493 284.787930\n", "80 250.729093 242.741319 271.848542 222.615917\n", "81 225.656183 222.680053 253.792181 180.499392\n", "82 176.513281 190.582027 195.610575 153.424484\n", "83 149.434539 141.431925 169.529165 112.310733\n", "84 112.326633 102.312457 152.475935 114.316282\n", "85 89.259557 103.315520 114.356951 84.233050\n", "86 59.172066 68.208304 80.250492 60.166464\n", "87 52.151651 52.159292 73.228574 42.116525\n", "88 36.104989 43.131722 34.106459 31.086006\n", "89 32.093324 25.076583 46.144033 16.044390\n", "90 128.373295 99.303267 87.272410 85.235824\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AuxdUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 27.226477 33.439282 44.026390 46.428025\n", "15 0.0 155.108494 180.731282 225.735317 264.960049\n", "16 0.0 1309.345340 1398.874192 1421.652608 1486.496384\n", "17 0.0 3116.192603 3410.006046 3387.631533 3316.400362\n", "18 0.0 7394.045775 7765.066501 7942.363626 7692.635915\n", "19 0.0 13430.072677 13583.496618 14105.258983 14053.273197\n", "20 0.0 17585.825118 17948.117784 17506.497996 17781.123443\n", "21 0.0 20168.215791 19744.285827 19808.677937 19381.290555\n", "22 0.0 22617.774687 21645.546770 20994.988940 20534.787588\n", "23 0.0 23793.464216 23796.807750 22729.628973 21574.615053\n", "24 0.0 24470.826727 24758.585067 25027.806738 23012.283045\n", "25 0.0 25613.514307 24997.438835 25408.034399 24626.057497\n", "26 0.0 25361.876684 25797.590109 25658.584905 25351.296003\n", "27 0.0 26462.486095 25346.162198 26574.333584 25537.008131\n", "28 0.0 28314.713017 26631.981007 26262.146430 26295.065306\n", "29 0.0 28832.840049 28137.545034 27391.623440 25915.635637\n", "... ... ... ... ... ...\n", "61 0.0 9309.814163 9594.689495 10636.775130 11003.448412\n", "62 0.0 8054.920262 8398.040816 9408.038491 9703.463567\n", "63 0.0 7108.592981 7188.653246 8147.282914 8533.156422\n", "64 0.0 5180.461553 5463.344593 6007.600483 6391.062032\n", "65 0.0 1658.341834 1854.288932 2113.266456 2158.104187\n", "66 0.0 895.174630 973.720749 1155.892717 1195.922792\n", "67 0.0 594.032820 593.945793 684.410154 800.483852\n", "68 0.0 423.248433 435.507135 441.864784 502.703860\n", "69 0.0 299.491635 308.119337 341.004360 333.801798\n", "70 0.0 198.836054 188.693259 216.129509 242.546594\n", "71 0.0 160.058866 151.273097 150.490186 169.702620\n", "72 0.0 102.305679 101.114107 134.480589 116.070184\n", "73 0.0 85.804764 74.840378 94.456608 101.661463\n", "74 0.0 56.928159 50.955149 72.843650 64.038716\n", "75 0.0 47.027612 49.362777 53.632137 59.235818\n", "76 0.0 33.826888 48.566634 42.425424 36.021772\n", "77 0.0 21.451197 17.515835 40.023984 40.824683\n", "78 0.0 24.751375 22.292873 26.415828 23.214040\n", "79 0.0 18.976053 9.554092 16.009593 24.014517\n", "80 0.0 14.850823 7.961743 9.605756 15.209198\n", "81 0.0 4.125230 6.369395 8.805278 9.605809\n", "82 0.0 3.300184 0.796174 3.201919 4.002421\n", "83 0.0 4.125230 3.184697 3.201919 5.603389\n", "84 0.0 1.650092 2.388523 3.201919 0.800484\n", "85 0.0 2.475138 0.796174 0.800480 0.800484\n", "86 0.0 0.825046 0.796174 0.800480 0.000000\n", "87 0.0 1.650092 1.592349 0.000000 2.401452\n", "88 0.0 0.825046 1.592349 0.800480 0.000000\n", "89 0.0 0.825046 0.000000 1.600959 0.000000\n", "90 0.0 7.425402 3.980856 3.201920 3.201936\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcnRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0\n", "... ... ... ... ...\n", "61 16.0 19.0 22.0 21.0\n", "62 18.0 16.0 24.0 20.0\n", "63 9.0 9.0 15.0 13.0\n", "64 7.0 10.0 6.0 3.0\n", "65 5.0 3.0 2.0 1.0\n", "66 1.0 1.0 1.0 0.0\n", "67 3.0 0.0 0.0 0.0\n", "68 0.0 1.0 0.0 2.0\n", "69 0.0 0.0 0.0 0.0\n", "70 1.0 0.0 1.0 0.0\n", "71 0.0 0.0 0.0 1.0\n", "72 0.0 1.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 1.0 0.0\n", "75 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 1.0 0.0\n", "82 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcnUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 90.730215 88.832093 122.239034 135.147681\n", "62 0.0 63.776887 60.739893 87.693220 100.601504\n", "63 0.0 44.795671 44.795671 51.249285 72.129381\n", "64 0.0 40.999428 36.064311 44.795671 62.638673\n", "65 0.0 22.018211 21.638587 26.953327 37.203575\n", "66 0.0 12.527603 17.842344 22.018211 28.472124\n", "67 0.0 14.425725 10.629481 18.981217 19.361044\n", "68 0.0 9.110984 14.046100 15.184973 13.286991\n", "69 0.0 8.731360 7.592487 9.870233 14.425876\n", "70 0.0 6.833238 3.796243 9.870233 13.286991\n", "71 0.0 2.657370 4.175868 4.935116 6.074053\n", "72 0.0 3.796243 1.898122 3.036995 5.694425\n", "73 0.0 2.277746 1.518497 1.898122 6.074053\n", "74 0.0 1.898122 1.138873 3.416619 2.657398\n", "75 0.0 1.138873 1.898122 1.518497 1.518513\n", "76 0.0 0.759249 1.898122 0.379624 1.138885\n", "77 0.0 0.759249 0.759249 1.518497 0.759257\n", "78 0.0 0.759249 0.379624 1.518497 0.759257\n", "79 0.0 0.379624 1.518497 1.138873 0.000000\n", "80 0.0 0.000000 0.379624 1.138873 1.518513\n", "81 0.0 0.759249 0.379624 0.379624 0.759257\n", "82 0.0 0.000000 0.000000 0.759249 0.379628\n", "83 0.0 0.379624 0.379624 0.759249 0.379628\n", "84 0.0 0.379624 0.000000 0.000000 0.379628\n", "85 0.0 1.138873 0.000000 0.000000 0.000000\n", "86 0.0 0.379624 0.000000 0.000000 0.759257\n", "87 0.0 0.759249 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.379624 0.379624 0.000000\n", "90 0.0 0.000000 0.379624 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: PensUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 379.386093 487.237745 479.191504 489.954094\n", "1 0.0 541.289713 623.317743 622.180199 609.880096\n", "2 0.0 443.180952 497.431004 463.816375 488.929085\n", "3 0.0 396.301397 471.947858 485.854060 480.729017\n", "4 0.0 406.450579 445.955049 474.578965 451.003768\n", "5 0.0 406.450579 431.174825 465.866393 447.928743\n", "6 0.0 390.018570 449.013027 455.616307 441.266187\n", "7 0.0 406.933873 433.723140 446.903734 436.141144\n", "8 0.0 393.884925 458.186959 446.903734 408.465913\n", "9 0.0 390.501864 424.039544 432.553614 445.366221\n", "10 0.0 409.350345 435.252128 443.316204 426.916067\n", "11 0.0 429.165415 461.244937 457.153820 431.528606\n", "12 0.0 411.283523 438.310106 499.179171 468.428914\n", "13 0.0 434.481653 434.232802 488.416581 481.241521\n", "14 0.0 407.900462 413.336623 470.478931 489.954094\n", "15 0.0 408.867051 434.742465 492.004111 496.104145\n", "16 0.0 425.299060 457.677296 500.204180 519.166838\n", "17 0.0 386.635509 452.580667 504.816718 482.266530\n", "18 0.0 345.555486 378.679545 429.478589 424.353546\n", "19 0.0 285.143688 335.867860 354.652963 389.503255\n", "20 0.0 189.934694 226.290334 222.939363 248.052073\n", "21 0.0 23.681425 23.954157 20.500171 22.550188\n", "22 0.0 32.864018 33.637752 29.725248 36.900308\n", "23 0.0 32.380724 37.715056 37.412813 32.800274\n", "24 0.0 45.429672 50.966291 48.175403 48.687907\n", "25 0.0 49.296027 55.553258 57.912984 52.275437\n", "26 0.0 63.794859 65.236853 63.550531 74.313121\n", "27 0.0 79.743574 77.468763 74.825625 77.388147\n", "28 0.0 96.175583 99.384268 95.325797 96.863309\n", "29 0.0 111.157709 94.287639 107.625899 113.263446\n", "... ... ... ... ... ...\n", "61 0.0 433.998359 479.083139 522.754368 515.579308\n", "62 0.0 438.831303 477.554150 535.566975 550.942103\n", "63 0.0 449.463779 470.928532 503.791709 544.792052\n", "64 0.0 437.864714 497.431004 500.716684 537.616992\n", "65 0.0 394.368219 445.445387 514.041795 512.504282\n", "66 0.0 396.301397 430.665162 486.879068 493.541624\n", "67 0.0 407.417168 419.452578 462.791367 504.304214\n", "68 0.0 400.651046 430.155499 458.178828 461.253854\n", "69 0.0 401.134341 430.155499 460.741350 459.716341\n", "70 0.0 389.051981 407.730331 457.666324 445.878726\n", "71 0.0 395.818102 420.471904 415.128469 457.153820\n", "72 0.0 417.566350 413.336623 399.753340 431.528606\n", "73 0.0 376.486327 415.375275 454.078794 434.603631\n", "74 0.0 370.203500 409.259320 444.853717 424.866050\n", "75 0.0 389.535275 395.498421 384.378212 451.516273\n", "76 0.0 374.553149 443.916398 404.878383 409.490922\n", "77 0.0 327.190300 375.111904 395.140802 392.065776\n", "78 0.0 334.439715 359.822017 398.215827 385.915725\n", "79 0.0 344.588897 337.396849 373.615622 384.378212\n", "80 0.0 303.992169 323.126287 328.002741 337.227818\n", "81 0.0 316.074529 321.597299 345.427886 331.077766\n", "82 0.0 286.593571 328.222916 349.527921 337.227818\n", "83 0.0 241.163899 309.875051 303.402535 318.265159\n", "84 0.0 233.431189 253.812131 275.727304 288.539911\n", "85 0.0 197.184110 213.039098 246.514560 261.889688\n", "86 0.0 182.685278 183.478649 221.914354 226.014388\n", "87 0.0 132.905956 170.227413 182.451524 206.026721\n", "88 0.0 121.790185 125.377077 126.588558 163.488866\n", "89 0.0 96.175583 103.971234 125.563549 119.413498\n", "90 0.0 279.827450 300.701119 349.015416 402.828366\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtceUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.281905 0.357080 0.516826 0.526223\n", "62 0.0 0.206730 0.253715 0.385270 0.281905\n", "63 0.0 0.122159 0.131556 0.159746 0.263111\n", "64 0.0 0.093968 0.084572 0.084572 0.093968\n", "65 0.0 0.000000 0.028191 0.075175 0.075175\n", "66 0.0 0.028191 0.000000 0.046984 0.028191\n", "67 0.0 0.028191 0.009397 0.009397 0.028191\n", "68 0.0 0.018794 0.028191 0.028191 0.000000\n", "69 0.0 0.000000 0.009397 0.009397 0.018794\n", "70 0.0 0.000000 0.000000 0.018794 0.037587\n", "71 0.0 0.000000 0.009397 0.009397 0.009397\n", "72 0.0 0.000000 0.000000 0.000000 0.000000\n", "73 0.0 0.009397 0.000000 0.009397 0.000000\n", "74 0.0 0.000000 0.000000 0.000000 0.000000\n", "75 0.0 0.009397 0.000000 0.009397 0.000000\n", "76 0.0 0.000000 0.000000 0.000000 0.018794\n", "77 0.0 0.000000 0.000000 0.009397 0.018794\n", "78 0.0 0.009397 0.000000 0.000000 0.000000\n", "79 0.0 0.000000 0.000000 0.000000 0.000000\n", "80 0.0 0.000000 0.000000 0.000000 0.000000\n", "81 0.0 0.000000 0.000000 0.000000 0.000000\n", "82 0.0 0.000000 0.000000 0.000000 0.000000\n", "83 0.0 0.000000 0.000000 0.000000 0.009397\n", "84 0.0 0.000000 0.000000 0.009397 0.000000\n", "85 0.0 0.000000 0.000000 0.000000 0.000000\n", "86 0.0 0.000000 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: ApidUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: LoasDefH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 3087.0 3247.0 3444.0 3588.0\n", "1 3268.0 3286.0 3623.0 3404.0\n", "2 2288.0 2461.0 2504.0 2399.0\n", "3 2151.0 2093.0 2323.0 2300.0\n", "4 2032.0 2013.0 2237.0 2357.0\n", "5 1875.0 1959.0 2240.0 2292.0\n", "6 1807.0 1927.0 2164.0 2106.0\n", "7 1756.0 1769.0 2139.0 2118.0\n", "8 1800.0 1772.0 2049.0 2045.0\n", "9 1681.0 1687.0 1900.0 1859.0\n", "10 1616.0 1609.0 1723.0 1731.0\n", "11 1580.0 1560.0 1633.0 1562.0\n", "12 1424.0 1410.0 1498.0 1416.0\n", "13 1399.0 1410.0 1458.0 1454.0\n", "14 1395.0 1349.0 1431.0 1414.0\n", "15 1295.0 1305.0 1292.0 1311.0\n", "16 1176.0 1195.0 1306.0 1277.0\n", "17 1188.0 1099.0 1234.0 1215.0\n", "18 1163.0 1192.0 1213.0 1274.0\n", "19 1080.0 1002.0 1086.0 1104.0\n", "20 1003.0 992.0 1052.0 1056.0\n", "21 1116.0 1003.0 1059.0 1033.0\n", "22 1128.0 953.0 916.0 1005.0\n", "23 1062.0 992.0 982.0 904.0\n", "24 1018.0 967.0 972.0 954.0\n", "25 1017.0 999.0 1042.0 980.0\n", "26 1076.0 944.0 1058.0 914.0\n", "27 1152.0 941.0 1007.0 942.0\n", "28 1148.0 993.0 967.0 1004.0\n", "29 1102.0 1003.0 999.0 1011.0\n", "... ... ... ... ...\n", "61 1835.0 1795.0 1720.0 1810.0\n", "62 1665.0 1514.0 1684.0 1678.0\n", "63 1543.0 1398.0 1505.0 1475.0\n", "64 979.0 920.0 916.0 932.0\n", "65 80.0 100.0 114.0 125.0\n", "66 22.0 34.0 34.0 27.0\n", "67 8.0 17.0 35.0 15.0\n", "68 5.0 10.0 13.0 10.0\n", "69 7.0 4.0 9.0 4.0\n", "70 6.0 9.0 7.0 6.0\n", "71 6.0 8.0 3.0 9.0\n", "72 1.0 3.0 3.0 2.0\n", "73 2.0 1.0 2.0 2.0\n", "74 2.0 5.0 6.0 3.0\n", "75 2.0 0.0 2.0 1.0\n", "76 1.0 1.0 2.0 0.0\n", "77 2.0 4.0 2.0 0.0\n", "78 1.0 0.0 0.0 1.0\n", "79 2.0 1.0 1.0 1.0\n", "80 2.0 1.0 1.0 1.0\n", "81 0.0 0.0 1.0 1.0\n", "82 0.0 2.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 1.0\n", "86 0.0 0.0 1.0 0.0\n", "87 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 1.0 0.0\n", "90 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcnUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 148.269785 145.167907 199.760966 220.856059\n", "62 0.0 104.223113 99.260107 143.306780 164.401280\n", "63 0.0 73.204329 73.204329 83.750715 117.872616\n", "64 0.0 67.000572 58.935689 73.204329 102.363061\n", "65 0.0 35.981789 35.361413 44.046673 60.797454\n", "66 0.0 20.472397 29.157656 35.981789 46.528664\n", "67 0.0 23.574275 17.370519 31.018783 31.639492\n", "68 0.0 14.889016 22.953900 24.815027 21.713377\n", "69 0.0 14.268640 12.407513 16.129767 23.574523\n", "70 0.0 11.166762 6.203757 16.129767 21.713377\n", "71 0.0 4.342630 6.824132 8.064884 9.926115\n", "72 0.0 6.203757 3.101878 4.963005 9.305733\n", "73 0.0 3.722254 2.481503 3.101878 9.926115\n", "74 0.0 3.101878 1.861127 5.583381 4.342675\n", "75 0.0 1.861127 3.101878 2.481503 2.481529\n", "76 0.0 1.240751 3.101878 0.620376 1.861147\n", "77 0.0 1.240751 1.240751 2.481503 1.240764\n", "78 0.0 1.240751 0.620376 2.481503 1.240764\n", "79 0.0 0.620376 2.481503 1.861127 0.000000\n", "80 0.0 0.000000 0.620376 1.861127 2.481529\n", "81 0.0 1.240751 0.620376 0.620376 1.240764\n", "82 0.0 0.000000 0.000000 1.240751 0.620382\n", "83 0.0 0.620376 0.620376 1.240751 0.620382\n", "84 0.0 0.620376 0.000000 0.000000 0.620382\n", "85 0.0 1.861127 0.000000 0.000000 0.000000\n", "86 0.0 0.620376 0.000000 0.000000 1.240764\n", "87 0.0 1.240751 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.620376 0.620376 0.000000\n", "90 0.0 0.000000 0.620376 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: SalMatUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: PensUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 597.659377 633.538357 602.451946 609.274731\n", "1 0.0 789.440391 765.014596 834.426647 811.911456\n", "2 0.0 640.897278 645.800389 575.160804 580.619033\n", "3 0.0 602.541076 588.577570 607.227896 618.144352\n", "4 0.0 581.619510 570.865745 580.619033 600.405110\n", "5 0.0 596.961992 529.311079 553.327891 561.515234\n", "6 0.0 574.645655 555.197592 548.551942 566.291183\n", "7 0.0 560.000560 589.258794 588.806375 539.000042\n", "8 0.0 513.275731 549.066576 590.170932 535.588649\n", "9 0.0 532.802525 528.629854 586.759540 599.040553\n", "10 0.0 576.040426 574.271865 603.134224 584.030425\n", "11 0.0 619.975713 585.852674 573.796247 564.244348\n", "12 0.0 629.739110 618.551428 620.873466 590.170932\n", "13 0.0 591.382908 589.258794 623.602580 657.034229\n", "14 0.0 580.922125 594.708586 683.643091 651.576000\n", "15 0.0 662.516229 609.014291 637.248151 645.435493\n", "16 0.0 648.568519 667.599558 727.308918 715.710182\n", "17 0.0 703.661974 744.577875 732.767146 805.088670\n", "18 0.0 645.081591 636.263253 746.412717 723.215246\n", "19 0.0 657.634530 674.411799 757.329173 750.506388\n", "20 0.0 537.684224 532.035975 601.087389 590.853211\n", "21 0.0 271.282961 272.489616 285.192427 274.958249\n", "22 0.0 330.560729 292.926337 298.837998 293.379770\n", "23 0.0 383.562027 372.629549 382.758258 348.644331\n", "24 0.0 443.537181 414.865440 416.872184 418.236742\n", "25 0.0 495.841093 504.105789 467.360796 460.538011\n", "26 0.0 527.223441 534.760871 573.113969 544.458270\n", "27 0.0 647.871133 574.953089 572.431690 569.702576\n", "28 0.0 704.359359 645.800389 644.753215 669.997521\n", "29 0.0 753.176345 728.228498 671.362078 735.496260\n", "... ... ... ... ... ...\n", "61 0.0 3470.887654 3321.648415 3595.607876 3456.423055\n", "62 0.0 3399.056947 3406.801420 3586.055976 3554.671164\n", "63 0.0 3459.032101 3374.102667 3564.905342 3548.530657\n", "64 0.0 3382.319695 3348.897377 3442.777484 3551.942050\n", "65 0.0 3145.906009 3240.582755 3392.288873 3388.877480\n", "66 0.0 3055.943279 2961.962123 3229.906581 3250.374937\n", "67 0.0 2906.702781 2837.979348 3035.457199 3151.444550\n", "68 0.0 2947.151141 2860.459741 2915.376177 2941.985040\n", "69 0.0 2761.646597 2764.407151 2869.663515 2774.144520\n", "70 0.0 2738.632875 2522.572617 2733.890087 2908.553392\n", "71 0.0 2746.304116 2609.769294 2634.959699 2691.588818\n", "72 0.0 2670.986481 2587.288901 2561.955896 2540.805262\n", "73 0.0 2473.626383 2442.188181 2579.012860 2485.540700\n", "74 0.0 2456.889131 2338.642127 2455.520445 2465.072344\n", "75 0.0 2426.204169 2282.781756 2298.596382 2343.626765\n", "76 0.0 2213.501590 2246.676882 2227.639415 2222.863465\n", "77 0.0 1993.127771 2040.947222 2161.458397 2077.538137\n", "78 0.0 1889.914716 1831.811442 1940.400152 1996.346991\n", "79 0.0 1762.990555 1662.186656 1795.757102 1839.422928\n", "80 0.0 1657.685343 1605.645061 1620.411519 1634.739368\n", "81 0.0 1537.037651 1512.317367 1532.397588 1522.163410\n", "82 0.0 1292.952725 1335.199117 1385.707703 1339.312763\n", "83 0.0 1117.908963 1114.482528 1218.549462 1235.606426\n", "84 0.0 977.734477 916.246333 1036.381094 1017.277295\n", "85 0.0 781.071765 822.237415 863.082346 875.363360\n", "86 0.0 602.541076 625.363668 730.720310 728.673475\n", "87 0.0 465.853517 496.612325 555.374727 596.311439\n", "88 0.0 367.522161 344.699364 385.487372 462.584846\n", "89 0.0 247.571854 273.170840 284.510149 320.670911\n", "90 0.0 566.277029 537.485767 668.632964 689.101320\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: ApidUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AinvRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.0 0.0 0.0\n", "1 0.000000 0.0 0.0 0.0\n", "2 0.000000 0.0 0.0 0.0\n", "3 0.000000 0.0 0.0 0.0\n", "4 0.000000 0.0 0.0 0.0\n", "5 0.000000 0.0 0.0 0.0\n", "6 0.000000 0.0 0.0 0.0\n", "7 0.000000 0.0 0.0 0.0\n", "8 0.000000 0.0 0.0 0.0\n", "9 0.000000 0.0 0.0 0.0\n", "10 0.000000 0.0 0.0 0.0\n", "11 0.000000 0.0 0.0 0.0\n", "12 0.000000 0.0 0.0 0.0\n", "13 0.000000 0.0 0.0 0.0\n", "14 0.000000 0.0 0.0 0.0\n", "15 0.000000 0.0 0.0 0.0\n", "16 0.000000 1.0 3.0 2.0\n", "17 6.000806 4.0 8.0 8.0\n", "18 9.001208 8.0 12.0 14.0\n", "19 16.002014 24.0 26.0 20.0\n", "20 28.003625 25.0 25.0 26.0\n", "21 28.003760 29.0 45.0 34.0\n", "22 42.005505 39.0 32.0 28.0\n", "23 53.006848 47.0 42.0 51.0\n", "24 62.008191 56.0 54.0 49.0\n", "25 74.009802 73.0 71.0 67.0\n", "26 85.010876 68.0 82.0 66.0\n", "27 71.009399 93.0 88.0 112.0\n", "28 104.013159 76.0 104.0 85.0\n", "29 99.013293 102.0 103.0 95.0\n", "... ... ... ... ...\n", "61 210.027526 231.0 258.0 300.0\n", "62 169.021618 202.0 217.0 216.0\n", "63 162.021081 163.0 159.0 156.0\n", "64 106.013696 116.0 116.0 112.0\n", "65 65.008593 80.0 69.0 61.0\n", "66 44.005505 29.0 39.0 47.0\n", "67 23.003088 25.0 27.0 25.0\n", "68 14.001880 19.0 13.0 20.0\n", "69 13.001746 16.0 15.0 15.0\n", "70 8.001074 10.0 9.0 10.0\n", "71 7.000806 5.0 4.0 4.0\n", "72 8.001074 7.0 5.0 3.0\n", "73 1.000134 2.0 2.0 4.0\n", "74 4.000537 1.0 2.0 3.0\n", "75 4.000537 2.0 3.0 6.0\n", "76 2.000269 2.0 1.0 4.0\n", "77 3.000269 0.0 2.0 1.0\n", "78 3.000403 3.0 4.0 2.0\n", "79 1.000134 0.0 2.0 2.0\n", "80 0.000000 2.0 2.0 1.0\n", "81 1.000134 1.0 0.0 0.0\n", "82 0.000000 3.0 1.0 2.0\n", "83 0.000000 0.0 0.0 1.0\n", "84 2.000269 0.0 2.0 0.0\n", "85 0.000000 0.0 0.0 1.0\n", "86 1.000134 0.0 1.0 1.0\n", "87 0.000000 0.0 0.0 0.0\n", "88 0.000000 0.0 0.0 0.0\n", "89 0.000000 0.0 0.0 0.0\n", "90 0.000000 1.0 0.0 0.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcpUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 33.087272 36.285592 49.893071 49.647642\n", "62 0.0 27.438225 24.996741 30.579624 21.620747\n", "63 0.0 20.175166 16.126930 21.727628 20.819979\n", "64 0.0 9.684080 11.288851 18.508720 27.226126\n", "65 0.0 14.526119 12.095197 15.289812 12.812295\n", "66 0.0 7.263060 5.644425 9.656723 11.210758\n", "67 0.0 7.263060 3.225386 7.242543 12.011526\n", "68 0.0 4.035033 4.031732 2.414181 3.203074\n", "69 0.0 4.035033 5.644425 3.218908 4.003842\n", "70 0.0 1.614013 0.806346 1.609454 7.206916\n", "71 0.0 2.421020 1.612693 1.609454 1.601537\n", "72 0.0 0.807007 0.806346 0.000000 2.402305\n", "73 0.0 0.807007 0.000000 0.000000 1.601537\n", "74 0.0 0.807007 0.000000 0.804727 0.800768\n", "75 0.0 0.807007 0.000000 0.804727 0.000000\n", "76 0.0 0.000000 0.000000 0.000000 0.000000\n", "77 0.0 0.000000 0.000000 0.000000 0.000000\n", "78 0.0 0.000000 0.806346 0.804727 0.000000\n", "79 0.0 0.000000 0.000000 0.000000 0.000000\n", "80 0.0 0.000000 0.000000 0.000000 0.000000\n", "81 0.0 0.000000 0.000000 0.000000 0.000000\n", "82 0.0 0.000000 0.000000 0.000000 0.000000\n", "83 0.0 0.000000 0.000000 0.000000 0.000000\n", "84 0.0 0.000000 0.000000 0.000000 0.000000\n", "85 0.0 0.000000 0.000000 0.000000 0.000000\n", "86 0.0 0.000000 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcnUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 6158.621989 6272.113277 7100.120189 7299.590543\n", "62 0.0 4559.426554 4791.567827 5451.048922 5806.140746\n", "63 0.0 3269.752816 3443.428880 3946.421853 4099.463805\n", "64 0.0 1708.387811 1787.487800 2081.544076 2217.390343\n", "65 0.0 587.231442 640.537956 816.797551 998.212558\n", "66 0.0 209.786928 201.189103 263.094790 337.896241\n", "67 0.0 124.668461 122.948896 154.761641 242.459898\n", "68 0.0 100.594552 86.838032 110.912510 171.957374\n", "69 0.0 80.819554 67.063034 77.380821 135.846326\n", "70 0.0 62.764122 46.428255 89.417837 121.229949\n", "71 0.0 44.708690 30.092387 52.447001 81.679753\n", "72 0.0 31.811952 24.073910 37.830623 61.044868\n", "73 0.0 24.933692 12.896737 33.531689 61.904655\n", "74 0.0 14.616302 10.317390 30.952328 44.708917\n", "75 0.0 21.494562 13.756520 18.915312 40.409983\n", "76 0.0 10.317390 9.457607 11.177230 25.793606\n", "77 0.0 6.018477 6.018477 12.037017 24.074032\n", "78 0.0 8.597825 6.878260 14.616377 12.037016\n", "79 0.0 4.298912 6.878260 5.158721 9.457656\n", "80 0.0 6.018477 4.298912 3.439148 6.878295\n", "81 0.0 2.579347 3.439130 5.158721 6.018508\n", "82 0.0 3.439130 3.439130 4.298934 6.018508\n", "83 0.0 0.000000 0.000000 0.859787 2.579361\n", "84 0.0 0.859782 0.859782 0.859787 5.158721\n", "85 0.0 0.000000 0.000000 3.439148 0.000000\n", "86 0.0 0.859782 0.859782 0.859787 0.000000\n", "87 0.0 1.719565 0.859782 0.000000 0.859787\n", "88 0.0 0.859782 0.859782 0.000000 0.000000\n", "89 0.0 0.000000 1.719565 0.859787 0.000000\n", "90 0.0 1.719565 0.000000 1.719574 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AinvUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.578476\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.578476\n", "17 0.0 1.156936 0.578459 1.735377 1.156953\n", "18 0.0 3.470799 4.049212 2.313836 3.470858\n", "19 0.0 4.049267 2.892295 13.304555 8.098651\n", "20 0.0 10.990883 9.255342 17.932226 10.412556\n", "21 0.0 12.726287 12.726096 24.295274 19.668159\n", "22 0.0 26.609520 26.030651 24.873733 28.345321\n", "23 0.0 34.129586 32.972157 28.922945 44.542587\n", "24 0.0 48.012800 48.012089 50.325924 52.062796\n", "25 0.0 60.739115 54.375137 64.208938 50.905896\n", "26 0.0 73.465402 84.455000 74.621198 87.928361\n", "27 0.0 108.751970 102.387225 91.396506 101.233264\n", "28 0.0 134.204517 127.260958 129.574794 123.793717\n", "29 0.0 160.235523 139.408595 153.870068 139.412733\n", "... ... ... ... ... ...\n", "61 0.0 1188.751520 1287.071055 1444.411876 1343.799776\n", "62 0.0 1051.654672 1120.474891 1271.452665 1227.526077\n", "63 0.0 935.961077 1001.312358 1086.924275 1132.077680\n", "64 0.0 819.689068 864.796057 924.377324 944.651432\n", "65 0.0 662.345776 691.258387 780.919516 779.785777\n", "66 0.0 510.787172 502.102326 576.145065 623.018720\n", "67 0.0 430.958612 443.677977 461.031744 491.704689\n", "68 0.0 328.569800 346.496882 347.653800 367.332409\n", "69 0.0 259.732114 291.543286 298.484793 304.856969\n", "70 0.0 228.494850 239.481985 248.158869 237.753703\n", "71 0.0 186.266667 166.017705 186.842225 225.605735\n", "72 0.0 139.989252 153.291609 151.556232 152.139247\n", "73 0.0 119.164385 105.279520 119.162534 112.802842\n", "74 0.0 72.308503 81.562705 105.857979 104.125715\n", "75 0.0 64.209950 66.522774 70.571986 66.524756\n", "76 0.0 46.277442 52.061301 48.012089 61.896963\n", "77 0.0 39.335816 32.393698 41.070582 32.973148\n", "78 0.0 27.766456 31.236781 30.079863 37.022482\n", "79 0.0 26.609529 28.922945 19.089144 28.345338\n", "80 0.0 18.510977 15.039931 24.873733 19.668194\n", "81 0.0 15.040169 11.569178 14.461473 13.883431\n", "82 0.0 14.461701 10.990719 10.412260 8.677144\n", "83 0.0 8.677020 10.412260 9.255342 15.040384\n", "84 0.0 5.784680 6.941507 6.363048 4.627810\n", "85 0.0 6.363148 2.313836 6.941507 4.627810\n", "86 0.0 3.470808 2.313836 1.735377 6.363239\n", "87 0.0 2.313872 2.892295 1.156918 2.892381\n", "88 0.0 0.578468 2.313836 0.578459 2.313905\n", "89 0.0 1.735404 1.735377 1.735377 1.156953\n", "90 0.0 2.892340 0.578459 0.578459 3.470858\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: PensRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 229.0 222.0 234.0 198.0\n", "1 272.0 244.0 253.0 232.0\n", "2 213.0 216.0 221.0 226.0\n", "3 224.0 248.0 225.0 210.0\n", "4 185.0 212.0 221.0 197.0\n", "5 205.0 199.0 222.0 186.0\n", "6 193.0 224.0 190.0 185.0\n", "7 205.0 204.0 187.0 184.0\n", "8 232.0 184.0 215.0 209.0\n", "9 209.0 202.0 220.0 185.0\n", "10 245.0 199.0 234.0 200.0\n", "11 228.0 239.0 225.0 188.0\n", "12 199.0 221.0 260.0 207.0\n", "13 233.0 223.0 251.0 237.0\n", "14 206.0 225.0 250.0 211.0\n", "15 247.0 215.0 254.0 233.0\n", "16 252.0 240.0 280.0 227.0\n", "17 221.0 218.0 261.0 204.0\n", "18 144.0 142.0 147.0 162.0\n", "19 125.0 134.0 139.0 133.0\n", "20 93.0 69.0 80.0 77.0\n", "21 23.0 30.0 29.0 25.0\n", "22 32.0 26.0 40.0 37.0\n", "23 38.0 39.0 34.0 35.0\n", "24 49.0 36.0 45.0 48.0\n", "25 55.0 42.0 52.0 69.0\n", "26 67.0 62.0 60.0 56.0\n", "27 67.0 67.0 71.0 79.0\n", "28 74.0 62.0 73.0 79.0\n", "29 88.0 98.0 90.0 95.0\n", "... ... ... ... ...\n", "61 775.0 746.0 825.0 804.0\n", "62 830.0 834.0 831.0 840.0\n", "63 934.0 867.0 894.0 804.0\n", "64 804.0 964.0 925.0 912.0\n", "65 822.0 902.0 955.0 890.0\n", "66 955.0 949.0 999.0 972.0\n", "67 954.0 968.0 1022.0 918.0\n", "68 1008.0 966.0 1022.0 969.0\n", "69 1042.0 1043.0 1065.0 1066.0\n", "70 1107.0 989.0 1102.0 1051.0\n", "71 1135.0 1100.0 1128.0 1130.0\n", "72 1022.0 1186.0 1149.0 1148.0\n", "73 1023.0 1151.0 1211.0 1130.0\n", "74 1072.0 1051.0 1131.0 1199.0\n", "75 1137.0 1091.0 1150.0 1168.0\n", "76 1068.0 1127.0 1197.0 1105.0\n", "77 1015.0 1141.0 1217.0 1087.0\n", "78 1071.0 999.0 1130.0 1190.0\n", "79 989.0 1048.0 1002.0 1108.0\n", "80 1033.0 976.0 971.0 980.0\n", "81 973.0 979.0 952.0 995.0\n", "82 891.0 901.0 925.0 952.0\n", "83 753.0 868.0 935.0 863.0\n", "84 725.0 784.0 837.0 866.0\n", "85 664.0 715.0 755.0 771.0\n", "86 559.0 650.0 608.0 720.0\n", "87 501.0 562.0 623.0 579.0\n", "88 414.0 452.0 505.0 520.0\n", "89 288.0 333.0 396.0 423.0\n", "90 974.0 1155.0 1306.0 1421.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AuxdRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 1.000018 0.000000 0.000000\n", "15 1.000009 0.000000 0.000000 1.000009\n", "16 293.002036 252.003441 270.002078 245.001791\n", "17 785.005979 801.012414 729.005570 711.005605\n", "18 1077.008564 1092.017477 1060.008578 1105.008729\n", "19 1146.009188 1101.017783 1059.008578 1014.008371\n", "20 1164.009215 1176.018936 1024.008405 1057.008586\n", "21 1250.010050 1119.017963 1041.008332 1029.008264\n", "22 1320.010435 1166.018828 1078.008833 962.007780\n", "23 1332.010637 1281.020576 1110.009006 1055.008568\n", "24 1402.011352 1328.021260 1227.009972 1097.008953\n", "25 1459.011682 1385.022161 1266.010301 1292.010404\n", "26 1604.012773 1514.024197 1365.011167 1322.010565\n", "27 1592.012792 1493.024594 1524.012242 1402.011317\n", "28 1750.013828 1676.027152 1516.012133 1556.012543\n", "29 1880.015075 1743.028287 1605.013099 1589.012722\n", "... ... ... ... ...\n", "61 571.004942 560.009495 542.004649 520.004441\n", "62 410.003567 396.006847 432.003774 423.003626\n", "63 333.002888 344.005910 360.003145 348.003026\n", "64 252.002118 260.004486 204.001778 224.001925\n", "65 74.000633 75.001261 97.000830 74.000654\n", "66 42.000376 52.000901 53.000465 50.000412\n", "67 30.000275 34.000595 27.000228 30.000260\n", "68 28.000229 30.000523 25.000219 20.000170\n", "69 16.000147 17.000306 17.000146 7.000063\n", "70 7.000046 14.000252 6.000055 8.000072\n", "71 7.000064 4.000036 4.000036 3.000027\n", "72 7.000064 6.000108 5.000046 4.000036\n", "73 2.000018 4.000072 6.000046 2.000018\n", "74 5.000046 6.000108 3.000027 1.000009\n", "75 4.000037 0.000000 4.000036 3.000027\n", "76 3.000028 4.000072 2.000018 3.000027\n", "77 3.000018 5.000090 2.000018 2.000018\n", "78 4.000037 1.000018 2.000018 1.000009\n", "79 2.000009 1.000000 0.000000 5.000045\n", "80 1.000009 0.000000 0.000000 1.000009\n", "81 0.000000 0.000000 1.000009 1.000009\n", "82 1.000009 1.000018 0.000000 2.000018\n", "83 0.000000 0.000000 0.000000 0.000000\n", "84 0.000000 0.000000 2.000018 0.000000\n", "85 0.000000 1.000018 0.000000 0.000000\n", "86 1.000009 0.000000 0.000000 0.000000\n", "87 1.000009 0.000000 0.000000 0.000000\n", "88 0.000000 0.000000 0.000000 0.000000\n", "89 0.000000 0.000000 0.000000 0.000000\n", "90 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcpUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 8.509113 9.331631 12.831090 12.767973\n", "62 0.0 7.056337 6.428457 7.864217 5.560246\n", "63 0.0 5.188483 4.147391 5.587733 5.354311\n", "64 0.0 2.490472 2.903174 4.759921 7.001792\n", "65 0.0 3.735708 3.110544 3.932108 3.294961\n", "66 0.0 1.867854 1.451587 2.483437 2.883091\n", "67 0.0 1.867854 0.829478 1.862578 3.089026\n", "68 0.0 1.037697 1.036848 0.620859 0.823740\n", "69 0.0 1.037697 1.451587 0.827812 1.029675\n", "70 0.0 0.415079 0.207370 0.413906 1.853415\n", "71 0.0 0.622618 0.414739 0.413906 0.411870\n", "72 0.0 0.207539 0.207370 0.000000 0.617805\n", "73 0.0 0.207539 0.000000 0.000000 0.411870\n", "74 0.0 0.207539 0.000000 0.206953 0.205935\n", "75 0.0 0.207539 0.000000 0.206953 0.000000\n", "76 0.0 0.000000 0.000000 0.000000 0.000000\n", "77 0.0 0.000000 0.000000 0.000000 0.000000\n", "78 0.0 0.000000 0.207370 0.206953 0.000000\n", "79 0.0 0.000000 0.000000 0.000000 0.000000\n", "80 0.0 0.000000 0.000000 0.000000 0.000000\n", "81 0.0 0.000000 0.000000 0.000000 0.000000\n", "82 0.0 0.000000 0.000000 0.000000 0.000000\n", "83 0.0 0.000000 0.000000 0.000000 0.000000\n", "84 0.0 0.000000 0.000000 0.000000 0.000000\n", "85 0.0 0.000000 0.000000 0.000000 0.000000\n", "86 0.0 0.000000 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AuxdUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 3.729399 3.444956 4.647917 6.971888\n", "15 0.0 32.321473 26.411379 35.440361 45.898241\n", "16 0.0 198.279821 217.032469 276.550952 271.322742\n", "17 0.0 704.856912 734.351012 765.744085 854.056102\n", "18 0.0 1637.207342 1755.207953 2095.047961 2135.721578\n", "19 0.0 3627.465020 3595.966401 4213.916791 4593.890946\n", "20 0.0 5346.098040 5405.720772 6081.798288 6483.270267\n", "21 0.0 6811.131757 6723.419306 7366.366250 7685.919785\n", "22 0.0 8306.000672 7751.166807 8580.053394 8988.499431\n", "23 0.0 9662.260101 9268.673410 9768.758084 10113.296019\n", "24 0.0 10349.091911 10334.315249 11227.041853 11405.418185\n", "25 0.0 11541.257597 11375.842801 12421.556422 12895.658376\n", "26 0.0 12372.292977 12497.752971 13384.256230 13833.956318\n", "27 0.0 13492.356887 12848.565080 14364.966573 14581.690796\n", "28 0.0 14926.933890 13972.197732 14847.768940 15693.705613\n", "29 0.0 16080.562653 15566.067536 16005.100216 15963.284540\n", "... ... ... ... ... ...\n", "61 0.0 4273.274680 4128.790362 4727.512664 4704.274103\n", "62 0.0 3419.241285 3400.180323 3815.358936 4070.414194\n", "63 0.0 2871.019063 2746.785383 3067.625311 3338.947950\n", "64 0.0 2172.999040 2160.567208 2427.955721 2644.665121\n", "65 0.0 1535.271085 1480.186598 1793.515036 1858.005098\n", "66 0.0 1129.387666 1070.809932 1259.004556 1396.699203\n", "67 0.0 861.492195 798.083565 950.499066 1007.436070\n", "68 0.0 671.914188 656.839998 686.148771 750.638636\n", "69 0.0 523.981180 536.840393 528.700585 605.972235\n", "70 0.0 381.642288 365.166294 410.178699 463.048796\n", "71 0.0 315.134589 275.023048 297.466703 361.956554\n", "72 0.0 221.277944 206.697904 246.339613 245.758613\n", "73 0.0 163.472185 157.894238 181.849762 185.335693\n", "74 0.0 103.801731 117.702980 155.124234 148.152358\n", "75 0.0 89.505684 84.401649 117.940898 109.226053\n", "76 0.0 64.021428 70.047627 59.841935 85.986463\n", "77 0.0 45.995977 42.487905 63.327872 58.098961\n", "78 0.0 42.266574 33.875492 44.736204 38.926304\n", "79 0.0 33.564632 27.559722 31.954431 41.250269\n", "80 0.0 25.484258 15.502344 18.010680 20.915626\n", "81 0.0 19.890151 13.205700 21.496617 22.077605\n", "82 0.0 20.511720 16.650665 16.848700 9.876823\n", "83 0.0 11.809778 9.760732 9.876824 9.876823\n", "84 0.0 6.215673 9.186574 6.971876 11.038803\n", "85 0.0 3.107836 6.315770 7.552864 6.390886\n", "86 0.0 4.972538 1.722483 5.228907 5.809896\n", "87 0.0 3.729404 2.870804 2.904948 1.742969\n", "88 0.0 3.107836 3.444965 0.580990 2.904948\n", "89 0.0 1.864702 2.296644 1.742969 1.161979\n", "90 0.0 2.486269 1.722483 2.323957 4.066927\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: SalMatRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 40.001775 37.001300 65.002576 48.003367\n", "15 134.005945 170.005973 208.008243 209.014659\n", "16 1698.075337 1686.059241 1912.075770 1860.130458\n", "17 10270.455662 9410.330641 10154.402391 9476.664633\n", "18 15499.687664 14013.492377 14201.562768 13725.962651\n", "19 17976.797565 16760.588899 16314.646504 15184.064913\n", "20 19046.845039 18066.634788 17553.695604 15710.101806\n", "21 20173.895042 18736.658330 18108.717598 16148.132527\n", "22 20442.906977 19198.674563 18144.719025 16169.134000\n", "23 19702.874144 19069.670030 17910.709752 15750.104612\n", "24 19034.844506 18346.644626 17685.700835 15911.115904\n", "25 18020.799517 17701.621963 16916.670361 15294.072628\n", "26 16422.728616 16792.590023 15856.628354 14411.010696\n", "27 14980.664637 14975.526179 14763.585040 13549.950307\n", "28 14166.628521 13786.484401 13158.521436 12681.889427\n", "29 12469.553228 12826.450669 12119.480261 11227.787445\n", "... ... ... ... ...\n", "61 0.000000 0.000000 0.000000 0.000000\n", "62 0.000000 0.000000 0.000000 0.000000\n", "63 0.000000 0.000000 0.000000 0.000000\n", "64 0.000000 0.000000 0.000000 0.000000\n", "65 0.000000 0.000000 0.000000 0.000000\n", "66 0.000000 0.000000 0.000000 0.000000\n", "67 0.000000 0.000000 0.000000 0.000000\n", "68 0.000000 0.000000 0.000000 0.000000\n", "69 0.000000 0.000000 0.000000 0.000000\n", "70 0.000000 0.000000 0.000000 0.000000\n", "71 0.000000 0.000000 0.000000 0.000000\n", "72 0.000000 0.000000 0.000000 0.000000\n", "73 0.000000 0.000000 0.000000 0.000000\n", "74 0.000000 0.000000 0.000000 0.000000\n", "75 0.000000 0.000000 0.000000 0.000000\n", "76 0.000000 0.000000 0.000000 0.000000\n", "77 0.000000 0.000000 0.000000 0.000000\n", "78 0.000000 0.000000 0.000000 0.000000\n", "79 0.000000 0.000000 0.000000 0.000000\n", "80 0.000000 0.000000 0.000000 0.000000\n", "81 0.000000 0.000000 0.000000 0.000000\n", "82 0.000000 0.000000 0.000000 0.000000\n", "83 0.000000 0.000000 0.000000 0.000000\n", "84 0.000000 0.000000 0.000000 0.000000\n", "85 0.000000 0.000000 0.000000 0.000000\n", "86 0.000000 0.000000 0.000000 0.000000\n", "87 0.000000 0.000000 0.000000 0.000000\n", "88 0.000000 0.000000 0.000000 0.000000\n", "89 0.000000 0.000000 0.000000 0.000000\n", "90 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtceUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.000000 0.981132 2.943396 4.905660\n", "62 0.0 0.981132 0.981132 0.000000 0.981132\n", "63 0.0 0.000000 0.000000 0.000000 0.981132\n", "64 0.0 0.000000 0.981132 0.000000 0.000000\n", "65 0.0 0.000000 0.000000 0.000000 0.000000\n", "66 0.0 0.000000 0.000000 0.000000 0.000000\n", "67 0.0 0.000000 0.981132 0.000000 0.000000\n", "68 0.0 0.000000 0.000000 0.000000 0.000000\n", "69 0.0 0.981132 0.000000 0.000000 0.981132\n", "70 0.0 0.000000 0.000000 0.000000 0.000000\n", "71 0.0 0.000000 0.000000 0.000000 0.981132\n", "72 0.0 0.000000 0.000000 0.000000 0.000000\n", "73 0.0 0.000000 0.000000 0.000000 0.000000\n", "74 0.0 0.000000 0.000000 0.000000 0.000000\n", "75 0.0 0.000000 0.000000 0.000000 0.000000\n", "76 0.0 0.000000 0.000000 0.000000 0.000000\n", "77 0.0 0.000000 0.000000 0.000000 0.000000\n", "78 0.0 0.000000 0.000000 0.000000 0.000000\n", "79 0.0 0.000000 0.000000 0.000000 0.000000\n", "80 0.0 0.000000 0.000000 0.000000 0.000000\n", "81 0.0 0.000000 0.000000 0.000000 0.000000\n", "82 0.0 0.000000 0.000000 0.000000 0.000000\n", "83 0.0 0.000000 0.000000 0.000000 0.000000\n", "84 0.0 0.000000 0.000000 0.000000 0.000000\n", "85 0.0 0.000000 0.000000 0.000000 0.000000\n", "86 0.0 0.000000 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: ApinRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 12024.506189 11898.985347 12103.852336 11181.710041\n", "62 7062.743465 6939.487452 6876.687757 6228.979845\n", "63 4868.541727 4558.487351 4484.578507 4194.393473\n", "64 3284.787161 3102.819079 2925.247969 2680.725324\n", "65 3584.497677 3636.163102 3996.536108 3771.648298\n", "66 1285.046427 1246.140263 1275.724791 1205.124277\n", "67 823.953325 837.109509 803.716640 791.394995\n", "68 614.456677 632.594132 612.307814 525.927054\n", "69 475.126370 463.167178 447.956780 429.757535\n", "70 459.088349 383.967595 390.834775 336.593314\n", "71 333.791311 347.876646 363.776983 295.520916\n", "72 327.777053 337.851383 338.723472 288.508555\n", "73 241.572690 278.702328 263.562938 234.413201\n", "74 236.560809 199.502745 205.438792 256.452049\n", "75 190.451498 182.459797 182.389562 190.335505\n", "76 169.401596 140.353690 157.336050 172.303720\n", "77 120.285157 126.318321 148.316786 151.266638\n", "78 91.216244 93.234951 100.214045 142.250746\n", "79 81.192481 91.229899 82.175517 99.174816\n", "80 69.163965 64.161687 83.177657 65.114778\n", "81 69.163965 45.113686 59.126286 48.084759\n", "82 33.078418 35.088423 48.102742 41.072398\n", "83 22.052279 29.073264 32.068494 39.068867\n", "84 16.038021 24.060633 37.079197 33.058272\n", "85 11.026139 15.037895 18.038528 20.035316\n", "86 9.021387 16.040422 17.036388 12.021190\n", "87 4.009505 7.017685 10.021404 9.015892\n", "88 3.007129 4.010105 5.010702 7.012361\n", "89 3.007129 5.012632 2.004281 4.007063\n", "90 16.038021 21.053054 23.049230 19.033551\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcdUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtceUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 29.718095 37.642920 54.483174 55.473777\n", "62 0.0 21.793270 26.746285 40.614730 29.718095\n", "63 0.0 12.877841 13.868444 16.840254 27.736889\n", "64 0.0 9.906032 8.915428 8.915428 9.906032\n", "65 0.0 0.000000 2.971809 7.924825 7.924825\n", "66 0.0 2.971809 0.000000 4.953016 2.971809\n", "67 0.0 2.971809 0.990603 0.990603 2.971809\n", "68 0.0 1.981206 2.971809 2.971809 0.000000\n", "69 0.0 0.000000 0.990603 0.990603 1.981206\n", "70 0.0 0.000000 0.000000 1.981206 3.962413\n", "71 0.0 0.000000 0.990603 0.990603 0.990603\n", "72 0.0 0.000000 0.000000 0.000000 0.000000\n", "73 0.0 0.990603 0.000000 0.990603 0.000000\n", "74 0.0 0.000000 0.000000 0.000000 0.000000\n", "75 0.0 0.990603 0.000000 0.990603 0.000000\n", "76 0.0 0.000000 0.000000 0.000000 1.981206\n", "77 0.0 0.000000 0.000000 0.990603 1.981206\n", "78 0.0 0.990603 0.000000 0.000000 0.000000\n", "79 0.0 0.000000 0.000000 0.000000 0.000000\n", "80 0.0 0.000000 0.000000 0.000000 0.000000\n", "81 0.0 0.000000 0.000000 0.000000 0.000000\n", "82 0.0 0.000000 0.000000 0.000000 0.000000\n", "83 0.0 0.000000 0.000000 0.000000 0.990603\n", "84 0.0 0.000000 0.000000 0.990603 0.000000\n", "85 0.0 0.000000 0.000000 0.000000 0.000000\n", "86 0.0 0.000000 0.000000 0.000000 0.000000\n", "87 0.0 0.000000 0.000000 0.000000 0.000000\n", "88 0.0 0.000000 0.000000 0.000000 0.000000\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 0.000000 0.000000 0.000000 0.000000\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AtcnRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0\n", "... ... ... ... ...\n", "61 0.0 2.0 1.0 0.0\n", "62 0.0 1.0 0.0 0.0\n", "63 0.0 0.0 1.0 0.0\n", "64 1.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 1.0\n", "67 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 1.0 0.0\n", "69 0.0 1.0 0.0 0.0\n", "70 0.0 0.0 1.0 0.0\n", "71 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0\n", "75 0.0 1.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 1.0\n", "78 0.0 0.0 1.0 0.0\n", "79 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0\n", "87 0.0 1.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Concessões - Tabela: AinvUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.751658 0.751643\n", "15 0.0 0.751635 0.000000 0.000000 0.751643\n", "16 0.0 0.751635 1.503285 2.254973 1.503277\n", "17 0.0 3.006539 8.268062 6.764895 6.764776\n", "18 0.0 6.764714 15.032839 17.288079 15.784472\n", "19 0.0 29.313759 32.320599 40.589395 45.098512\n", "20 0.0 50.359534 54.869849 60.132446 61.634666\n", "21 0.0 95.457625 78.922386 92.453562 86.438773\n", "22 0.0 110.490322 111.994643 130.036145 119.511026\n", "23 0.0 167.614570 172.877571 187.913914 154.838293\n", "24 0.0 179.640727 207.453169 211.966842 208.956549\n", "25 0.0 217.974104 281.865722 248.046496 239.022130\n", "26 0.0 258.562386 268.336192 314.943315 298.401900\n", "27 0.0 333.725870 322.454494 325.466862 317.192810\n", "28 0.0 390.850118 378.075915 388.605938 360.788162\n", "29 0.0 441.209653 435.200756 467.529551 378.827555\n", "... ... ... ... ... ...\n", "61 0.0 2900.558859 2965.228971 3271.208994 3208.009142\n", "62 0.0 2561.571545 2703.657548 3078.033045 3020.850144\n", "63 0.0 2369.153025 2442.086101 2616.515755 2784.083208\n", "64 0.0 1815.198146 1911.426456 2106.141632 2112.866621\n", "65 0.0 992.157993 1040.273112 1197.388932 1225.928632\n", "66 0.0 585.523543 587.032741 717.080362 681.739545\n", "67 0.0 392.353388 388.599134 430.699330 475.789637\n", "68 0.0 244.281324 276.604428 308.931009 299.153636\n", "69 0.0 175.882553 168.367936 211.967251 190.165553\n", "70 0.0 125.523019 130.785806 148.828107 170.622828\n", "71 0.0 92.451086 84.183948 107.486873 87.942167\n", "72 0.0 59.379153 57.124801 86.440597 59.379754\n", "73 0.0 49.607900 39.085423 58.629199 60.131405\n", "74 0.0 36.078472 39.085407 51.112714 53.366605\n", "75 0.0 30.065394 27.059131 38.334536 35.327189\n", "76 0.0 21.797410 22.549282 21.798047 24.804208\n", "77 0.0 12.777792 11.274641 22.549727 19.542709\n", "78 0.0 6.764714 15.032839 15.784809 18.791066\n", "79 0.0 8.267983 12.777919 10.523183 15.032853\n", "80 0.0 12.777792 4.509856 9.771548 12.026282\n", "81 0.0 4.509809 3.758214 6.013261 3.758213\n", "82 0.0 0.751635 3.006571 2.254973 4.509848\n", "83 0.0 1.503270 3.758214 2.254973 3.758213\n", "84 0.0 0.751635 1.503285 0.000000 0.751643\n", "85 0.0 2.254905 3.006571 1.503292 0.751643\n", "86 0.0 0.751635 1.503285 0.000000 0.751643\n", "87 0.0 0.000000 0.751643 0.751658 0.751643\n", "88 0.0 0.000000 1.503285 0.751658 0.751643\n", "89 0.0 0.000000 0.000000 0.000000 0.000000\n", "90 0.0 1.503270 0.000000 0.000000 0.751643\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AinvRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 1.000000 0.000000 0.000000 0.000000\n", "19 0.000000 1.000000 1.000000 0.000000\n", "20 0.000000 2.000000 0.000000 0.000000\n", "21 0.000000 1.000000 0.000000 0.000000\n", "22 1.000000 0.000000 1.000000 0.000000\n", "23 2.000000 0.000000 3.000000 1.000000\n", "24 3.000000 2.666667 1.000000 3.000000\n", "25 2.000000 0.000000 3.000000 0.000000\n", "26 2.000000 1.000000 4.000000 4.000000\n", "27 6.000000 4.000000 2.000000 1.000000\n", "28 4.000000 12.000000 5.000000 4.000000\n", "29 8.000000 9.000000 5.000000 5.000000\n", "... ... ... ... ...\n", "61 112.168421 104.211401 109.252315 108.252927\n", "62 110.018692 128.667396 118.035398 105.480549\n", "63 97.283784 112.355649 118.529412 123.775210\n", "64 98.230769 116.970711 111.688596 123.561290\n", "65 91.482353 89.680191 104.468182 105.751807\n", "66 88.921466 94.246050 97.885845 81.740741\n", "67 91.678241 94.438554 92.270588 99.056604\n", "68 83.150990 90.636782 97.600973 93.148148\n", "69 79.791980 89.724576 90.935065 87.843902\n", "70 88.997319 73.709763 105.600000 73.069307\n", "71 96.923445 61.873876 91.515663 79.157360\n", "72 81.763158 114.006897 102.777778 74.262248\n", "73 81.241558 77.451537 98.968750 74.965608\n", "74 86.700713 82.345404 91.322196 87.537313\n", "75 76.129032 88.048662 66.339623 79.857143\n", "76 70.459103 66.725191 92.098446 78.361582\n", "77 101.652062 85.600000 91.838710 84.412281\n", "78 93.514986 69.298153 90.336105 86.079602\n", "79 104.363184 119.880000 79.937330 81.593264\n", "80 111.032397 116.815085 90.157761 86.392857\n", "81 146.310680 92.313725 104.833005 82.331325\n", "82 140.745968 145.534615 108.620192 89.709141\n", "83 149.264706 167.720779 128.392197 118.301235\n", "84 170.229503 147.618151 135.231683 114.621338\n", "85 147.865320 168.209139 116.293388 117.529412\n", "86 161.269725 151.119266 129.588000 131.874126\n", "87 181.438737 162.482042 127.468619 152.888889\n", "88 162.454352 128.344111 135.307175 130.634703\n", "89 116.324074 124.205240 132.906040 122.902743\n", "90 461.301576 565.488030 571.330435 647.570964\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: PensUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000 0.000000\n", "15 0.088435 0.000000 0.000000 0.000000 0.000000\n", "16 0.109405 0.000000 0.000000 0.000000 0.000000\n", "17 0.147832 0.000000 0.000000 0.000000 0.000000\n", "18 0.211907 0.000000 0.000000 0.000000 0.000000\n", "19 0.279948 0.000000 0.000000 0.000000 0.000000\n", "20 0.365175 0.188330 0.020846 0.183469 0.000000\n", "21 0.494752 0.286497 0.070356 0.278903 0.000000\n", "22 0.562311 0.346005 0.164868 0.340475 0.304596\n", "23 0.507304 0.323735 0.281480 0.324764 0.256751\n", "24 0.413976 0.270137 0.434615 0.288743 0.241704\n", "25 0.269174 0.177281 0.606327 0.244069 0.199080\n", "26 0.249080 0.181638 0.798008 0.243365 0.192104\n", "27 0.375594 0.301668 1.091435 0.329679 0.361535\n", "28 0.625885 0.521522 1.372897 0.483771 0.326221\n", "29 0.928765 0.779671 1.517347 0.655909 0.728684\n", "... ... ... ... ... ...\n", "61 51.947012 53.806820 57.277682 56.646446 55.472051\n", "62 54.258803 56.430212 59.678358 59.199321 60.195649\n", "63 55.499653 58.480337 60.962146 61.797082 62.364351\n", "64 56.309609 60.572736 62.000360 65.066794 62.663868\n", "65 56.187025 62.126414 62.674974 68.383203 65.414191\n", "66 58.736486 65.695283 65.303359 73.320222 73.015009\n", "67 61.750939 68.904717 67.956408 77.342980 82.182369\n", "68 63.429725 69.862556 68.933216 78.383356 75.706690\n", "69 70.071060 75.614917 75.139122 84.408726 78.884092\n", "70 74.738741 78.648821 78.952168 87.035864 84.081265\n", "71 81.414198 83.886539 85.300042 92.149979 92.344945\n", "72 85.740209 86.776979 89.414684 94.739371 89.853794\n", "73 91.793965 91.481589 95.517907 99.435854 87.338342\n", "74 90.296729 88.778672 94.001421 96.368002 98.063124\n", "75 93.345176 90.667190 97.606478 98.856789 97.564487\n", "76 99.580241 96.466375 104.382009 104.994230 97.453990\n", "77 104.167680 101.448600 109.426007 109.915584 106.164984\n", "78 104.920153 103.400945 110.414047 111.302349 115.645235\n", "79 108.606539 108.886316 114.299600 116.089408 126.048955\n", "80 107.469204 110.078437 112.570875 115.502744 113.970620\n", "81 108.235669 112.601243 113.867259 118.089616 113.638201\n", "82 102.832344 108.195526 109.249521 114.531354 110.560659\n", "83 105.612825 112.002547 113.851062 120.676474 117.279618\n", "84 101.331900 107.985287 111.526190 119.570194 123.219682\n", "85 94.825878 101.223467 107.652270 116.814128 117.929069\n", "86 85.858436 91.758044 99.715673 109.601193 118.351737\n", "87 78.180515 83.583268 92.256773 102.786585 107.325355\n", "88 78.092137 83.424950 92.977519 105.058624 109.510074\n", "89 71.293228 75.988725 84.924919 97.357338 101.192152\n", "90 299.828832 316.946781 350.852772 420.638995 453.176627\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: RmvH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 1.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 1.000372 0.000000\n", "24 0.000000 1.001251 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.500000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 59.555052 55.269598 69.064608 51.200000\n", "62 60.077892 60.981035 58.307736 55.538462\n", "63 92.132154 67.609338 81.579859 61.372093\n", "64 81.650925 83.090753 76.232964 62.120000\n", "65 80.642779 81.578152 71.187795 63.584906\n", "66 85.035634 80.017025 82.035388 81.708333\n", "67 87.433832 78.315391 81.108685 76.538462\n", "68 100.694740 86.035963 73.549227 67.333333\n", "69 106.827700 87.348762 94.040592 70.142857\n", "70 94.454616 82.447668 88.573031 59.254545\n", "71 125.670266 103.804520 89.628809 65.323529\n", "72 120.213081 99.131015 70.510418 79.337838\n", "73 123.283811 113.841450 109.195229 88.928931\n", "74 139.382318 112.987105 99.816850 91.183908\n", "75 147.157606 112.341288 100.066691 68.283951\n", "76 105.792037 126.776883 118.882547 107.641686\n", "77 127.167342 116.653488 113.952687 113.898438\n", "78 150.865520 111.855779 127.076004 96.982759\n", "79 138.085025 131.831731 124.298817 88.298507\n", "80 171.959651 151.724060 133.467024 108.000000\n", "81 188.113654 158.972917 118.579335 95.438152\n", "82 203.243088 158.567857 144.163740 113.156553\n", "83 230.491070 179.972152 145.426575 135.763285\n", "84 237.261628 204.565764 165.107494 134.909953\n", "85 259.535032 172.053947 187.874051 149.528926\n", "86 338.629445 227.783705 186.532998 175.061303\n", "87 326.460774 292.865172 223.446525 151.567319\n", "88 314.749986 253.811837 245.861603 171.011785\n", "89 257.892652 251.808221 220.045601 249.462336\n", "90 2071.328848 1838.557064 1871.461343 1622.734101\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: ApinRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 1238.440221 1204.254837 1239.321889 1095.293893\n", "62 1262.448755 1402.296745 1374.356961 1213.325564\n", "63 1408.500672 1459.308810 1453.377485 1412.378975\n", "64 1486.992513 1572.810640 1470.381902 1502.403130\n", "65 1581.562189 1594.806444 1659.431004 1522.408498\n", "66 1702.605215 1640.288791 1770.459841 1705.457615\n", "67 1834.067346 1857.883257 1745.453346 1717.460835\n", "68 1802.831367 1929.883279 1887.821513 1787.943181\n", "69 1890.968450 1931.666310 2044.908427 1969.473701\n", "70 2046.565827 1968.274298 2175.985774 2171.019912\n", "71 2316.829550 2097.329088 2177.909923 2213.459746\n", "72 2425.586524 2521.712521 2259.896267 2150.284163\n", "73 2616.827957 2631.517198 2647.112898 2260.934497\n", "74 2957.236364 2923.774539 2741.610922 2755.000010\n", "75 3149.175290 3101.827913 2988.804092 2807.768431\n", "76 2874.871402 3361.840563 3232.562652 2898.784735\n", "77 2858.858699 3094.627760 3484.100622 3259.019182\n", "78 2901.042232 2926.997285 3270.896793 3489.780358\n", "79 3164.921953 3072.019923 3057.197523 3224.044694\n", "80 3107.729157 3242.904151 3155.060732 3074.481217\n", "81 3099.779817 3142.363259 3451.259034 3132.189537\n", "82 2863.695844 3185.001034 3369.667226 3224.757094\n", "83 2781.360568 2990.426145 3299.732711 3284.075001\n", "84 2721.817202 2885.004194 3089.096266 3167.101713\n", "85 2902.368801 2895.198145 2953.665468 2910.177802\n", "86 2726.758980 2834.275781 2872.259406 2807.657261\n", "87 2614.885938 2772.884995 2827.228827 2775.403181\n", "88 2393.853010 2503.070453 2819.132212 2684.950810\n", "89 1967.597448 2300.415580 2325.702788 2556.301317\n", "90 13630.653062 14097.561418 14781.107435 14483.750562\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: ApinUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 463.422610 474.177285 511.698304 506.982640\n", "62 0.0 500.635153 493.036712 527.263010 547.221800\n", "63 0.0 554.242357 519.490638 556.084695 592.876209\n", "64 0.0 591.528648 612.142476 622.578927 638.228999\n", "65 0.0 573.104797 635.646743 697.946436 694.389103\n", "66 0.0 615.418188 630.875436 723.582149 699.268417\n", "67 0.0 703.732886 689.279285 686.130274 794.234878\n", "68 0.0 722.709301 744.290264 711.592333 731.034856\n", "69 0.0 661.591276 737.535195 773.074090 775.219451\n", "70 0.0 729.146641 709.920212 833.257690 830.372591\n", "71 0.0 781.300640 748.844869 777.097123 851.047726\n", "72 0.0 835.049466 861.333105 859.112477 875.959242\n", "73 0.0 866.249484 890.505984 969.080085 845.311909\n", "74 0.0 980.495145 940.753844 1015.022153 1026.430804\n", "75 0.0 1016.137642 1058.462667 1071.278297 1051.343433\n", "76 0.0 1243.641273 1197.539942 1210.636743 1115.160890\n", "77 0.0 1292.885885 1301.283551 1377.695320 1330.299243\n", "78 0.0 1362.469813 1367.884872 1391.165166 1432.129950\n", "79 0.0 1527.175055 1478.570610 1451.649295 1489.052481\n", "80 0.0 1648.367558 1646.508661 1659.758793 1552.173070\n", "81 0.0 1831.966236 1793.645679 1905.378361 1787.502950\n", "82 0.0 1924.424663 2056.477997 2072.383351 1976.397935\n", "83 0.0 1852.761808 1863.914446 2047.924441 2163.104355\n", "84 0.0 1738.095459 1872.072241 1960.145228 2090.895668\n", "85 0.0 1635.065286 1666.600916 1815.861251 1897.455774\n", "86 0.0 1563.095898 1631.157331 1731.894184 1896.842723\n", "87 0.0 1401.197486 1526.383727 1641.783987 1785.659457\n", "88 0.0 1348.416919 1293.401557 1501.581435 1629.031861\n", "89 0.0 1113.717489 1274.646307 1387.210630 1474.326215\n", "90 0.0 4337.772174 4900.849854 5675.339839 6271.987885\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: ApinUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 224.998690 230.220247 248.437270 246.147743\n", "62 0.0 226.810029 223.367596 238.873634 247.915855\n", "63 0.0 244.246302 228.931740 245.058193 261.271662\n", "64 0.0 239.980218 248.343145 252.577161 258.926317\n", "65 0.0 233.388761 258.858078 284.228740 282.780066\n", "66 0.0 245.457535 251.622608 288.598378 278.900925\n", "67 0.0 272.864772 267.260545 266.039550 307.955935\n", "68 0.0 280.904891 289.293046 276.583913 284.140893\n", "69 0.0 261.973927 292.045858 306.118389 306.967899\n", "70 0.0 283.473062 275.998331 323.948703 322.827053\n", "71 0.0 303.098974 290.508032 301.468255 330.156766\n", "72 0.0 315.779451 325.718782 324.879037 331.249752\n", "73 0.0 319.613608 328.563348 357.554247 311.888427\n", "74 0.0 341.588895 327.743659 353.617555 357.592148\n", "75 0.0 362.155404 377.240208 381.807748 374.702885\n", "76 0.0 405.876901 390.831192 395.105486 363.945823\n", "77 0.0 387.651358 390.169265 413.080124 398.869161\n", "78 0.0 380.660392 382.173306 388.677586 400.122736\n", "79 0.0 410.082529 397.031089 389.802081 399.845718\n", "80 0.0 492.942881 492.386980 496.349420 464.176003\n", "81 0.0 326.921440 320.082989 340.022117 318.986796\n", "82 0.0 269.447539 287.936933 290.163915 276.724556\n", "83 0.0 294.071758 295.841914 325.048119 343.329563\n", "84 0.0 356.705356 384.201105 402.276122 429.109736\n", "85 0.0 383.169833 390.560059 425.538514 444.659806\n", "86 0.0 342.070025 356.964681 379.010071 415.107633\n", "87 0.0 283.833718 309.192083 332.568149 361.712299\n", "88 0.0 296.589791 284.488938 330.278950 358.312191\n", "89 0.0 236.108577 270.225554 294.089238 312.557779\n", "90 0.0 1010.218752 1141.353263 1321.723341 1460.676015\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: PensRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 1.003568 0.000000 0.000000 0.000000\n", "2 1.003568 2.005178 0.000000 0.000000\n", "3 3.010704 0.000000 1.002206 0.000000\n", "4 1.003568 4.010356 2.004412 3.004508\n", "5 0.000000 1.002589 2.004412 4.006011\n", "6 3.010704 5.012945 4.008824 3.004508\n", "7 0.000000 3.007767 7.015442 3.004508\n", "8 2.258028 1.002589 6.013236 2.003006\n", "9 1.003568 8.020712 4.008824 3.004508\n", "10 4.014272 3.007767 4.008824 6.009017\n", "11 8.028544 5.012945 4.008824 5.007514\n", "12 3.010704 9.023301 2.004412 7.010519\n", "13 6.021408 8.020712 6.013236 8.012022\n", "14 5.017840 11.028479 10.022059 10.015028\n", "15 8.028544 13.033656 15.033089 13.019536\n", "16 11.039248 17.044012 8.017647 19.028552\n", "17 13.046384 17.044012 15.033089 18.027050\n", "18 18.064223 20.051779 20.044119 29.043580\n", "19 10.035680 19.049190 30.066178 28.042077\n", "20 16.057088 26.067313 29.063972 15.022541\n", "21 4152.808407 4064.537566 4313.474490 4287.424288\n", "22 6.021408 5.012945 1.002206 2.003006\n", "23 4.683317 9.023301 3.006618 0.000000\n", "24 5.017840 4.583264 5.011030 2.003006\n", "25 8.028544 8.020712 2.004412 2.003006\n", "26 6.021408 10.025890 10.022059 3.004508\n", "27 10.035680 7.018123 5.011030 7.010519\n", "28 6.774084 7.797914 8.017647 6.009017\n", "29 10.035680 10.025890 5.011030 2.403607\n", "... ... ... ... ...\n", "61 395.405781 401.644655 382.167522 326.252812\n", "62 433.361677 441.945506 379.581586 402.907827\n", "63 507.768289 458.192470 493.860327 450.223454\n", "64 524.670230 561.071473 492.850627 521.781434\n", "65 632.701597 655.948485 620.273133 606.510494\n", "66 707.947516 648.202273 699.507715 640.475934\n", "67 753.278371 729.668068 688.506654 724.229381\n", "68 834.063327 872.044964 811.131295 753.700109\n", "69 917.220948 946.097169 909.962549 834.771662\n", "70 983.120688 928.180051 975.872384 940.759228\n", "71 1246.594437 1101.412276 1050.189558 994.996978\n", "72 1279.771878 1334.022520 1168.635689 1098.976829\n", "73 1384.408563 1437.281749 1506.144438 1179.019539\n", "74 1600.287509 1503.061746 1603.269836 1535.584569\n", "75 1763.503957 1693.821542 1713.455060 1596.443835\n", "76 1737.084112 1989.937471 1803.180594 1584.188441\n", "77 1921.580628 1917.119548 2005.982951 1888.675377\n", "78 2068.847833 1991.330927 2116.832399 2194.456424\n", "79 2331.125500 2202.904409 2080.408571 2131.610043\n", "80 2529.462662 2417.479986 2324.006078 2132.558491\n", "81 2739.842119 2566.714997 2609.492395 2326.747718\n", "82 2627.337348 2744.060507 2762.741400 2499.696043\n", "83 2632.218885 2797.062391 2850.251875 2754.939615\n", "84 2722.024956 2860.705427 2851.803273 2941.763132\n", "85 2817.202908 2879.234177 2964.636006 2751.516004\n", "86 2676.830850 2738.633050 2879.723360 2830.645728\n", "87 2511.435428 2644.655929 2802.417937 2737.859486\n", "88 2321.775214 2463.003289 2772.110220 2608.763067\n", "89 2021.221835 2226.361718 2355.008397 2594.687542\n", "90 10285.507158 11170.702939 11949.222383 12497.121281\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AinvUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.666697 0.000000\n", "18 0.0 0.636441 1.272806 0.000000 1.272890\n", "19 0.0 0.500063 1.500099 2.000101 1.500198\n", "20 0.0 3.070517 3.509009 3.070320 2.631930\n", "21 0.0 3.842950 3.842751 4.803378 5.284164\n", "22 0.0 6.798932 3.399264 7.769609 7.284617\n", "23 0.0 6.335119 5.067772 5.912335 2.956359\n", "24 0.0 5.434114 12.226209 7.697878 8.604176\n", "25 0.0 9.681716 13.830447 15.213242 7.837743\n", "26 0.0 14.498733 14.497980 15.433096 17.772728\n", "27 0.0 18.192904 21.542826 17.712766 13.405385\n", "28 0.0 23.688286 21.167123 21.166785 17.136263\n", "29 0.0 34.699120 25.541013 26.504458 30.361710\n", "... ... ... ... ... ...\n", "61 0.0 678.713841 659.460298 718.734659 692.374697\n", "62 0.0 703.022430 728.505686 755.934387 758.179367\n", "63 0.0 757.181128 783.102712 793.510797 816.487757\n", "64 0.0 818.744490 842.481169 844.970580 877.924889\n", "65 0.0 796.857762 841.241618 919.897298 847.767819\n", "66 0.0 780.181128 831.033701 863.388960 922.229861\n", "67 0.0 841.217450 787.776723 827.551869 897.246525\n", "68 0.0 858.249593 851.618545 859.725472 866.214970\n", "69 0.0 874.035516 912.999602 890.645561 876.309969\n", "70 0.0 843.210662 856.956433 908.787870 958.922511\n", "71 0.0 952.864551 877.951171 942.506250 931.466835\n", "72 0.0 957.680188 1015.802359 898.473628 931.695483\n", "73 0.0 956.404788 928.740573 1015.217055 919.333953\n", "74 0.0 1070.966771 1014.086656 1053.758342 1079.810310\n", "75 0.0 1049.190454 1007.957805 1030.003145 1033.907280\n", "76 0.0 1079.736077 1089.864663 1013.052825 1034.769266\n", "77 0.0 988.040444 1093.475984 1138.653867 992.331843\n", "78 0.0 1069.821893 1022.585285 1096.127367 1110.092859\n", "79 0.0 1044.355461 1025.268075 1033.092397 1035.789401\n", "80 0.0 1087.925623 1044.112640 978.836277 960.824342\n", "81 0.0 1101.729963 1040.189986 1053.101750 970.827046\n", "82 0.0 1002.014481 1098.546299 1017.285746 1043.999618\n", "83 0.0 944.680742 993.969301 1010.762301 935.959309\n", "84 0.0 944.391938 899.558989 917.381998 959.248426\n", "85 0.0 808.526815 848.439640 838.063219 853.583367\n", "86 0.0 783.178452 760.970868 788.255875 764.899056\n", "87 0.0 708.848770 756.806814 727.016474 687.589740\n", "88 0.0 591.615779 599.521844 626.079679 626.936964\n", "89 0.0 534.863512 522.863867 581.385612 567.561624\n", "90 0.0 2002.396908 2064.531439 2243.690348 2279.268857\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcdUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtceUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.614035 0.350877 0.438596 0.263158\n", "62 0.0 1.000000 0.600000 0.500000 0.900000\n", "63 0.0 1.495017 0.872093 0.373754 0.872093\n", "64 0.0 0.984634 0.875230 0.547019 0.984634\n", "65 0.0 0.998800 1.498200 1.248500 0.749100\n", "66 0.0 1.325300 2.650600 1.619811 1.767067\n", "67 0.0 1.259218 3.417877 1.439106 1.978771\n", "68 0.0 2.169376 3.073283 2.711720 3.073283\n", "69 0.0 2.374666 5.181090 3.238181 4.533454\n", "70 0.0 4.785658 3.329154 5.409875 3.745298\n", "71 0.0 6.002680 6.222693 6.466173 4.378932\n", "72 0.0 7.119782 7.365291 4.910194 3.928155\n", "73 0.0 6.211420 7.291667 7.831790 5.401235\n", "74 0.0 10.602372 7.951779 7.362758 9.144763\n", "75 0.0 7.318223 11.282261 9.147779 8.537927\n", "76 0.0 9.774299 9.437255 6.740896 9.774299\n", "77 0.0 10.639712 8.237197 9.953279 12.012579\n", "78 0.0 8.659440 5.647461 8.659440 12.047916\n", "79 0.0 5.290287 8.545849 11.394465 13.836136\n", "80 0.0 11.309810 7.785235 8.174497 10.120805\n", "81 0.0 7.071692 5.955109 7.816080 11.538023\n", "82 0.0 7.371950 8.147945 8.147945 7.775406\n", "83 0.0 7.636896 8.400586 8.400586 8.782430\n", "84 0.0 3.034021 4.382475 7.433947 8.764950\n", "85 0.0 1.400075 4.900549 5.931895 6.978700\n", "86 0.0 6.515166 2.162422 2.521386 7.542105\n", "87 0.0 3.131398 3.143082 4.697097 3.914247\n", "88 0.0 2.488448 2.858753 3.267146 3.280267\n", "89 0.0 1.866883 3.777953 3.399464 3.360390\n", "90 0.0 10.095221 10.472379 13.762270 13.331142\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcnUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 112.300810 108.670427 127.868293 143.814631\n", "62 0.0 89.561187 100.363866 121.383847 128.225492\n", "63 0.0 105.542831 107.921191 114.784583 137.877574\n", "64 0.0 100.695273 107.269075 138.515680 133.024833\n", "65 0.0 103.333740 114.942612 122.790526 132.371475\n", "66 0.0 116.899717 114.497085 124.237694 114.953298\n", "67 0.0 106.846059 112.885765 121.452795 134.454008\n", "68 0.0 107.634603 107.352710 130.407487 138.205263\n", "69 0.0 112.023946 117.043634 122.425526 133.591862\n", "70 0.0 109.456635 117.616569 126.514964 141.209543\n", "71 0.0 111.139787 107.441240 120.565508 129.611811\n", "72 0.0 118.405078 122.730062 123.424500 126.055316\n", "73 0.0 120.371746 118.453059 151.833601 132.150200\n", "74 0.0 114.726692 127.534186 138.912603 140.430175\n", "75 0.0 131.066818 114.318065 147.407362 144.571023\n", "76 0.0 115.730190 117.689901 129.281432 148.639768\n", "77 0.0 104.226518 115.659091 123.654744 149.664181\n", "78 0.0 117.014323 120.839005 112.738061 132.070305\n", "79 0.0 110.325967 111.114267 107.660048 134.481988\n", "80 0.0 111.594949 118.941574 112.342136 118.184628\n", "81 0.0 107.585540 106.213436 108.306768 110.856047\n", "82 0.0 112.688383 122.101505 124.767872 113.007436\n", "83 0.0 109.312896 108.057231 125.820360 113.447991\n", "84 0.0 112.201193 115.735378 136.273141 139.180069\n", "85 0.0 102.585827 111.515040 115.294400 125.545369\n", "86 0.0 95.603316 112.373173 113.656782 137.022701\n", "87 0.0 89.614746 107.024324 115.472936 112.651468\n", "88 0.0 92.887598 93.378110 100.376202 111.586648\n", "89 0.0 76.765124 97.050249 102.760828 112.686303\n", "90 0.0 355.503834 403.852625 467.217072 537.567072\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcpUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.939508 1.853276 1.853276 1.872992\n", "62 0.0 3.799928 0.936968 0.936968 4.734681\n", "63 0.0 0.000000 5.565049 2.782524 0.937375\n", "64 0.0 2.768835 0.910302 3.641208 3.679944\n", "65 0.0 0.929305 1.833149 2.749723 1.852650\n", "66 0.0 2.881410 0.947313 0.000000 1.914781\n", "67 0.0 0.939625 0.926754 2.780262 0.936613\n", "68 0.0 0.937627 2.774349 1.849566 4.673105\n", "69 0.0 1.888737 1.862864 0.931432 2.824022\n", "70 0.0 2.835259 0.000000 0.932140 3.768225\n", "71 0.0 0.954699 3.766484 1.883242 3.806553\n", "72 0.0 4.023848 2.976545 0.992182 0.000000\n", "73 0.0 2.661128 2.624675 1.749783 1.768398\n", "74 0.0 3.911860 2.893705 3.858273 0.974830\n", "75 0.0 0.958866 0.000000 0.945731 3.823167\n", "76 0.0 1.851274 3.651829 1.825915 3.690678\n", "77 0.0 1.929256 0.951414 0.951414 0.961535\n", "78 0.0 1.907383 1.881254 0.940627 1.901268\n", "79 0.0 1.984123 0.000000 2.935414 2.966642\n", "80 0.0 0.000000 0.945050 0.000000 0.955103\n", "81 0.0 0.943882 0.930952 1.861904 2.822567\n", "82 0.0 2.862068 2.822862 1.881908 3.803857\n", "83 0.0 0.000000 2.808434 1.872289 0.946104\n", "84 0.0 0.000000 2.845621 0.000000 2.875893\n", "85 0.0 0.000000 0.000000 0.967702 1.955994\n", "86 0.0 1.973260 1.946229 0.973114 0.000000\n", "87 0.0 0.969563 0.956282 1.912564 1.932910\n", "88 0.0 1.909640 0.941740 0.000000 0.951759\n", "89 0.0 0.968169 0.000000 0.000000 0.000000\n", "90 0.0 0.906048 1.787274 1.787274 0.903144\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: ApidUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AinvUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 1.000190 0.000000 1.000178 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 1.875317 0.625055\n", "20 0.0 0.882506 0.882416 0.882496 0.294141\n", "21 0.0 1.774504 1.064594 1.774483 1.419470\n", "22 0.0 0.000000 1.923223 1.923397 1.923239\n", "23 0.0 3.663347 4.709665 2.616717 3.139803\n", "24 0.0 3.475797 3.971891 5.958351 1.985984\n", "25 0.0 4.641804 3.375513 5.063727 3.797484\n", "26 0.0 5.196143 7.273951 10.911980 4.676180\n", "27 0.0 6.361417 5.088615 12.298521 7.208891\n", "28 0.0 15.428488 9.982193 9.983134 8.621092\n", "29 0.0 12.257581 10.565803 11.412098 10.143256\n", "... ... ... ... ... ...\n", "61 0.0 314.536632 307.266071 310.858299 319.496987\n", "62 0.0 300.155870 281.849127 328.730086 305.465430\n", "63 0.0 293.570813 283.191807 313.849274 297.505118\n", "64 0.0 275.582155 268.576383 267.541758 267.310262\n", "65 0.0 257.809264 267.006073 270.015884 270.273841\n", "66 0.0 233.174160 233.897876 260.227441 265.654130\n", "67 0.0 215.455568 210.865457 252.024622 233.949621\n", "68 0.0 209.728155 211.312108 205.719325 203.519115\n", "69 0.0 186.059331 201.842742 210.482689 207.782558\n", "70 0.0 174.164441 173.154276 194.337853 187.068934\n", "71 0.0 170.898026 162.874501 158.613374 157.982689\n", "72 0.0 154.866965 149.109112 137.224334 134.014863\n", "73 0.0 133.975935 145.728109 147.025623 131.174449\n", "74 0.0 127.570060 120.676476 124.937598 124.880345\n", "75 0.0 117.930341 111.452720 110.790415 112.514731\n", "76 0.0 94.662780 92.834803 101.365791 97.282177\n", "77 0.0 80.975517 90.484863 98.299973 90.854771\n", "78 0.0 74.100665 75.230128 82.555877 86.816326\n", "79 0.0 65.146317 64.316944 68.357779 71.703376\n", "80 0.0 64.317145 59.585278 61.300779 64.104698\n", "81 0.0 55.755065 54.060552 55.427244 56.214265\n", "82 0.0 49.486397 49.734619 50.801953 49.236592\n", "83 0.0 48.736122 45.725585 53.194011 52.444437\n", "84 0.0 41.130319 40.995079 43.436167 45.082024\n", "85 0.0 40.694121 41.169921 42.123326 46.841487\n", "86 0.0 34.085921 35.990078 38.241505 40.849768\n", "87 0.0 28.488126 31.784020 35.111952 34.020673\n", "88 0.0 31.455699 32.620678 35.665573 37.256859\n", "89 0.0 25.680389 27.224618 31.668095 30.391688\n", "90 0.0 104.614035 111.453267 122.971925 136.917727\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: ApinUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.000000 0.000000 0.000000 0.000000\n", "62 0.0 0.000000 0.000000 0.000000 0.000000\n", "63 0.0 0.000000 0.000000 0.000000 0.000000\n", "64 0.0 0.000000 0.000000 0.000000 0.000000\n", "65 0.0 885.296327 896.785097 917.741934 824.977797\n", "66 0.0 970.464390 913.062674 1035.661447 1094.353185\n", "67 0.0 981.104414 1020.453177 997.517232 1173.127318\n", "68 0.0 993.967323 1119.072143 1088.296710 1146.022824\n", "69 0.0 1027.873318 1044.209443 1142.823307 1150.548292\n", "70 0.0 1063.980299 1095.722309 1172.891839 1212.751117\n", "71 0.0 1175.705306 1056.075982 1144.662652 1167.718190\n", "72 0.0 1212.534147 1213.378023 1169.433313 1190.872133\n", "73 0.0 1231.455909 1253.876218 1274.079692 1240.170702\n", "74 0.0 1133.182078 1252.395830 1324.797210 1362.039825\n", "75 0.0 1051.520183 1109.614656 1284.728394 1334.708003\n", "76 0.0 1004.772279 1082.873458 1181.443627 1231.839662\n", "77 0.0 988.298424 1037.453837 1156.838497 1201.156563\n", "78 0.0 980.444245 977.233888 1031.451614 1171.578878\n", "79 0.0 958.106808 901.048895 930.376540 981.630302\n", "80 0.0 992.401992 925.995661 890.222014 935.309573\n", "81 0.0 1112.227042 1030.907948 988.340585 922.448881\n", "82 0.0 1108.504631 1109.087282 1043.553544 938.004663\n", "83 0.0 1129.400560 1057.954264 1108.564851 1053.141380\n", "84 0.0 1133.663768 1130.806804 1129.563444 1113.552068\n", "85 0.0 1004.321670 1075.509015 1120.410709 993.033728\n", "86 0.0 1197.808618 1205.221785 1316.295668 1317.771413\n", "87 0.0 1111.750473 1132.122586 1260.597054 1220.863614\n", "88 0.0 942.959809 955.191430 1105.313943 1071.921262\n", "89 0.0 725.414415 730.301176 815.311762 833.283269\n", "90 0.0 2890.067222 3088.106586 3513.739228 3604.282503\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: LoasIdoH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 0.000000 0.000000 0.000000 0.000000\n", "62 0.000000 0.000000 0.000000 0.000000\n", "63 0.000000 0.000000 0.000000 0.000000\n", "64 0.000000 0.000000 0.000000 0.000000\n", "65 1728.481591 1717.928640 1480.239911 1196.640802\n", "66 2167.111335 2123.383825 2134.788188 1879.006210\n", "67 2428.486593 2318.602989 2247.882921 2084.116046\n", "68 2424.480842 2522.832268 2394.005319 2138.144979\n", "69 2385.424768 2619.941288 2529.118496 2249.204451\n", "70 2530.633245 2576.892959 2566.149514 2519.349114\n", "71 2795.012816 2607.927801 2432.037176 2466.320717\n", "72 2413.465026 2804.148088 2566.149514 2389.279461\n", "73 2201.160219 2464.767081 2887.418623 2425.298750\n", "74 2160.101270 2298.580511 2469.068195 2747.471273\n", "75 2384.423331 2168.434402 2152.303325 2444.308930\n", "76 2467.542666 2419.234307 2175.335378 2140.146050\n", "77 2145.616242 2363.653565 2267.899688 1942.039964\n", "78 1969.828094 2059.311895 2148.799925 2321.243028\n", "79 1882.703008 1871.100599 1971.160439 2217.187306\n", "80 1615.319123 1802.023048 1905.596207 1838.984778\n", "81 1537.206977 1531.719591 1617.354764 1709.915661\n", "82 1191.710946 1361.969150 1501.180481 1554.832614\n", "83 1012.453586 1140.280140 1364.142663 1432.767248\n", "84 812.166032 986.107057 1069.896190 1217.503217\n", "85 657.944615 739.189328 960.804810 987.528823\n", "86 370.531975 578.649623 760.637142 822.440418\n", "87 309.444271 382.429336 478.400729 633.339154\n", "88 187.268863 278.312449 385.322763 459.245927\n", "89 153.494075 212.238270 225.188627 280.150021\n", "90 555.797962 587.659739 674.565044 721.386303\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcpUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 19.063829 15.270201 15.238482 20.475147\n", "62 0.0 5.930573 7.422518 13.332781 13.270041\n", "63 0.0 14.667607 12.367168 17.740877 16.889682\n", "64 0.0 13.719167 6.868191 20.561775 21.222982\n", "65 0.0 11.529433 11.543900 13.055911 16.816379\n", "66 0.0 11.935323 10.456513 14.161505 10.385690\n", "67 0.0 11.461037 14.535531 12.978461 14.437081\n", "68 0.0 10.489750 12.753538 5.240549 10.431777\n", "69 0.0 10.205775 10.218582 11.654122 12.324237\n", "70 0.0 10.584671 2.826121 10.575939 11.227917\n", "71 0.0 6.353506 6.361479 4.232177 8.424523\n", "72 0.0 7.619216 6.935252 7.612931 5.510623\n", "73 0.0 9.149075 5.637265 10.547917 13.297824\n", "74 0.0 5.539308 9.012671 6.918423 11.017388\n", "75 0.0 10.231645 4.481962 4.472652 10.811042\n", "76 0.0 5.075390 5.716978 6.339003 8.201927\n", "77 0.0 8.617430 5.309688 5.960992 7.910589\n", "78 0.0 4.986331 7.488883 8.718882 9.917547\n", "79 0.0 5.149830 5.156292 5.788780 12.163250\n", "80 0.0 6.097252 5.426581 3.384568 10.105925\n", "81 0.0 4.268827 2.849456 8.530611 6.367852\n", "82 0.0 1.935818 5.168659 3.868442 5.133651\n", "83 0.0 3.196494 1.920303 5.110171 5.721890\n", "84 0.0 2.516731 3.149862 3.771983 4.379939\n", "85 0.0 3.010390 5.274794 3.007907 8.232820\n", "86 0.0 1.511976 0.756937 3.021458 3.759050\n", "87 0.0 0.671242 0.672084 2.012063 2.002595\n", "88 0.0 0.000000 1.723567 0.000000 0.855946\n", "89 0.0 1.614573 0.808300 0.000000 2.408475\n", "90 0.0 1.663761 3.331698 0.831194 8.272831\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: LoasDefM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 120.0 98.000000 96.0 87.000000\n", "1 222.0 230.000000 207.0 190.000000\n", "2 175.0 188.000000 204.0 183.000000\n", "3 157.0 161.000000 178.0 147.000000\n", "4 158.0 134.000000 125.0 134.000000\n", "5 144.0 146.000000 133.0 136.000000\n", "6 142.0 128.000000 127.0 118.000000\n", "7 143.0 149.000000 141.0 117.000000\n", "8 142.0 134.000000 137.0 122.000000\n", "9 158.0 147.000000 147.0 148.000000\n", "10 137.0 138.000000 136.0 113.000000\n", "11 160.0 139.000000 141.0 136.000000\n", "12 161.0 158.000000 157.0 140.000000\n", "13 166.0 179.000000 182.0 134.000000\n", "14 166.0 150.000000 177.0 167.000000\n", "15 186.0 174.000000 173.0 179.000000\n", "16 159.0 177.000000 200.0 173.000000\n", "17 174.0 198.000000 202.0 175.000000\n", "18 172.0 196.000000 188.0 202.000000\n", "19 172.0 175.000000 159.0 187.000000\n", "20 160.0 175.000000 183.0 170.000000\n", "21 137.0 161.000000 167.0 169.000000\n", "22 186.0 164.000000 151.0 160.000000\n", "23 204.0 191.000000 155.0 175.000000\n", "24 169.0 187.000000 197.0 151.000000\n", "25 196.0 183.000000 189.0 178.000000\n", "26 175.0 199.000000 195.0 199.000000\n", "27 182.0 162.000000 181.0 180.000000\n", "28 205.0 194.000000 206.0 222.000000\n", "29 229.0 223.000000 191.0 204.000000\n", "... ... ... ... ...\n", "61 665.0 734.000000 750.0 758.000000\n", "62 657.0 719.000000 713.0 732.000000\n", "63 723.0 731.000000 735.0 744.000000\n", "64 681.0 758.000000 736.0 756.000000\n", "65 769.0 781.000000 838.0 820.000000\n", "66 549.0 620.000000 631.0 687.000000\n", "67 471.0 482.000000 534.0 643.000000\n", "68 393.0 424.000000 393.0 523.000000\n", "69 349.0 366.000000 430.0 452.000000\n", "70 320.0 326.000000 314.0 438.000000\n", "71 277.0 277.000000 308.0 346.000000\n", "72 243.0 285.000000 256.0 316.000000\n", "73 211.0 211.000000 260.0 264.000000\n", "74 208.0 215.000000 219.0 271.000000\n", "75 170.0 170.487106 181.0 214.000000\n", "76 155.0 148.000000 173.0 186.000000\n", "77 107.0 156.000000 167.0 173.000000\n", "78 98.0 106.000000 136.0 151.000000\n", "79 78.0 109.000000 106.0 146.000000\n", "80 72.0 69.000000 97.0 117.000000\n", "81 72.0 75.000000 75.0 92.000000\n", "82 65.0 56.000000 76.0 75.000000\n", "83 54.0 56.000000 77.0 54.000000\n", "84 29.0 49.000000 59.0 68.715789\n", "85 6.0 32.000000 22.0 50.000000\n", "86 5.0 14.000000 23.0 37.000000\n", "87 7.0 6.000000 9.0 18.000000\n", "88 2.0 3.000000 8.0 9.000000\n", "89 1.0 2.000000 1.0 4.000000\n", "90 12.0 15.000000 18.0 9.000000\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcdUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcdUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: PensUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000 0.000000\n", "15 0.981119 0.378845 0.007467 0.203615 0.000000\n", "16 1.024491 0.304400 0.119802 0.282403 0.000000\n", "17 1.108460 0.229499 0.346216 0.408670 0.000000\n", "18 1.274774 0.159042 0.711943 0.606851 0.000000\n", "19 1.451249 0.080245 1.192102 0.852962 0.000000\n", "20 1.684127 1.523486 1.813030 1.173864 0.000000\n", "21 1.193528 1.232313 1.385555 0.920296 0.000000\n", "22 1.559344 1.651763 1.762897 1.271681 1.137670\n", "23 1.842884 1.891509 1.915784 1.547855 1.223700\n", "24 2.365923 2.280979 2.186465 2.017045 1.688451\n", "25 2.956379 2.632854 2.378025 2.540410 2.072145\n", "26 3.497224 3.011134 2.698425 3.099158 2.446366\n", "27 4.135884 3.549209 3.275094 3.823972 4.193481\n", "28 4.700469 4.103403 3.983642 4.547802 3.066718\n", "29 4.962187 4.467571 4.606517 5.002494 5.557531\n", "... ... ... ... ... ...\n", "61 476.433030 495.968630 475.345458 481.487429 471.505223\n", "62 534.524015 552.092631 535.355996 538.353602 547.414127\n", "63 587.290497 603.522278 591.086508 593.962999 599.415310\n", "64 654.139227 670.405229 662.353648 668.072576 643.400559\n", "65 709.215689 726.402198 723.363760 734.076976 702.205357\n", "66 780.299670 795.673310 793.592995 809.129662 805.761470\n", "67 860.857493 871.558204 866.440113 885.965995 941.401332\n", "68 955.696788 958.929956 946.767699 969.623778 936.512684\n", "69 1065.243243 1058.087496 1034.813194 1061.112334 991.661495\n", "70 1184.476227 1163.878636 1125.463564 1156.782922 1117.513710\n", "71 1297.368615 1269.195196 1221.554104 1253.345718 1255.997482\n", "72 1406.999632 1377.188613 1326.468266 1356.573117 1286.616538\n", "73 1486.481432 1461.414973 1414.839374 1443.135574 1267.561579\n", "74 1611.658436 1596.370323 1558.994076 1589.980309 1617.948211\n", "75 1694.002976 1694.745628 1674.406407 1715.179589 1692.757954\n", "76 1780.239627 1800.543408 1792.093811 1837.827697 1705.842707\n", "77 1910.658094 1954.143346 1952.027760 2001.071186 1932.789533\n", "78 2049.009420 2118.644876 2117.276785 2165.890189 2250.400662\n", "79 2201.633877 2300.075048 2293.522336 2333.377230 2533.562416\n", "80 2342.482739 2470.625623 2452.808363 2466.230305 2433.516179\n", "81 2433.455297 2587.176709 2567.369040 2577.155348 2480.008899\n", "82 2519.667164 2697.867432 2684.754312 2705.645085 2611.842903\n", "83 2482.306518 2675.455827 2678.185962 2720.957868 2644.367114\n", "84 2452.362561 2660.373951 2687.685746 2766.476357 2850.913969\n", "85 2293.289183 2504.683961 2563.921748 2695.434323 2721.161096\n", "86 2208.976255 2422.310500 2508.879595 2675.174400 2888.759933\n", "87 2149.847581 2359.341267 2468.159933 2657.132686 2774.464282\n", "88 2053.431641 2247.083576 2369.217975 2566.000161 2674.724421\n", "89 2014.152313 2189.070355 2320.313647 2518.376517 2617.573020\n", "90 9539.099914 10078.838126 10767.674779 11739.276953 12647.343679\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: ApidUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcnUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 239.864892 240.511671 257.339653 239.997697\n", "62 0.0 243.691886 252.504215 268.977009 271.891862\n", "63 0.0 277.251943 266.195038 287.170604 305.904816\n", "64 0.0 283.138361 291.609110 306.518041 303.419413\n", "65 0.0 271.902957 297.233633 311.079510 315.719932\n", "66 0.0 290.970407 284.883010 332.861814 346.904988\n", "67 0.0 292.020530 304.726451 322.512922 344.253022\n", "68 0.0 290.072395 316.216689 327.192338 334.309172\n", "69 0.0 313.741047 323.230895 343.422925 346.278536\n", "70 0.0 319.328114 327.620309 366.125766 372.000733\n", "71 0.0 374.109497 355.083692 374.466498 385.558610\n", "72 0.0 377.305163 386.311054 400.486643 382.662595\n", "73 0.0 400.873687 402.385785 422.812339 416.829492\n", "74 0.0 439.124321 430.295416 459.510623 456.854387\n", "75 0.0 467.091781 456.747443 475.563288 480.654561\n", "76 0.0 467.074380 488.677025 497.966235 484.317801\n", "77 0.0 459.954514 505.505164 511.089321 508.721379\n", "78 0.0 488.633621 529.234697 546.361763 559.682455\n", "79 0.0 487.077343 491.995811 526.057208 554.130186\n", "80 0.0 483.483276 456.277983 488.312517 501.881823\n", "81 0.0 469.802208 489.096305 496.096277 491.629795\n", "82 0.0 455.774622 473.011443 502.808118 475.636097\n", "83 0.0 399.437628 427.832080 480.258831 466.245923\n", "84 0.0 396.121089 400.441099 422.385620 431.001888\n", "85 0.0 338.614976 359.963167 375.560909 390.130767\n", "86 0.0 309.501915 308.634153 340.907227 346.363866\n", "87 0.0 260.272926 274.613319 296.358893 307.717589\n", "88 0.0 234.747232 245.691881 262.607488 267.351009\n", "89 0.0 211.813285 211.512378 239.180172 243.620009\n", "90 0.0 835.576357 853.963499 990.871723 1074.127518\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcpUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.074381 0.146724 0.146724 0.148285\n", "62 0.0 0.255628 0.063032 0.063032 0.318510\n", "63 0.0 0.000000 0.434951 0.217476 0.073263\n", "64 0.0 0.272832 0.089698 0.358792 0.362609\n", "65 0.0 0.084584 0.166851 0.250277 0.168626\n", "66 0.0 0.160257 0.052687 0.000000 0.106495\n", "67 0.0 0.074263 0.073246 0.219738 0.074025\n", "68 0.0 0.076262 0.225651 0.150434 0.380087\n", "69 0.0 0.139041 0.137136 0.068568 0.207893\n", "70 0.0 0.206408 0.000000 0.067860 0.274328\n", "71 0.0 0.059190 0.233516 0.116758 0.236000\n", "72 0.0 0.031707 0.023455 0.007818 0.000000\n", "73 0.0 0.380538 0.375325 0.250217 0.252879\n", "74 0.0 0.143695 0.106295 0.141727 0.035809\n", "75 0.0 0.055023 0.000000 0.054269 0.219386\n", "76 0.0 0.176503 0.348171 0.174085 0.351875\n", "77 0.0 0.098522 0.048586 0.048586 0.049103\n", "78 0.0 0.120395 0.118746 0.059373 0.120009\n", "79 0.0 0.043655 0.000000 0.064586 0.065273\n", "80 0.0 0.000000 0.054950 0.000000 0.055535\n", "81 0.0 0.070007 0.069048 0.138096 0.209348\n", "82 0.0 0.179598 0.177138 0.118092 0.238697\n", "83 0.0 0.000000 0.191566 0.127711 0.064535\n", "84 0.0 0.000000 0.154379 0.000000 0.156022\n", "85 0.0 0.000000 0.000000 0.032298 0.065283\n", "86 0.0 0.054518 0.053771 0.026886 0.000000\n", "87 0.0 0.044325 0.043718 0.087436 0.088367\n", "88 0.0 0.118137 0.058260 0.000000 0.058879\n", "89 0.0 0.045720 0.000000 0.000000 0.000000\n", "90 0.0 0.107840 0.212726 0.212726 0.107495\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: LoasIdoM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 0.000000 0.000000 0.000000 0.000000\n", "62 0.000000 0.000000 0.000000 0.000000\n", "63 0.000000 0.000000 0.000000 0.000000\n", "64 0.000000 0.000000 0.000000 0.000000\n", "65 1600.752321 1493.755804 1354.992443 1192.841949\n", "66 1725.889307 1789.102963 1660.215999 1581.116007\n", "67 1981.168758 1871.199462 1866.366991 1692.194410\n", "68 2114.314510 2171.552505 1930.413901 1795.267162\n", "69 2156.360538 2158.537207 2160.582484 1924.358279\n", "70 2315.534783 2176.558389 2227.631594 2110.489657\n", "71 2663.916152 2370.786691 2186.601542 2333.647169\n", "72 2450.682728 2659.125613 2236.638190 2207.558171\n", "73 2137.339716 2440.869068 2626.924049 2299.623154\n", "74 1937.120539 2175.557213 2362.730545 2578.820221\n", "75 2102.301360 2056.417172 2148.072856 2415.705089\n", "76 2078.275059 2248.124166 2062.997784 2065.457872\n", "77 1858.498705 2071.434824 2181.597877 1963.385826\n", "78 1904.084374 1989.338326 1970.443220 2164.527799\n", "79 1780.949581 1911.246534 1898.881494 2007.416905\n", "80 1774.943005 1841.164157 1906.396310 1824.287646\n", "81 1786.956156 1804.120615 1875.373588 1807.275638\n", "82 1480.620815 1732.596414 1751.359726 1773.251623\n", "83 1467.606568 1470.728737 1665.219664 1698.198648\n", "84 1348.476158 1460.716969 1393.020296 1643.308662\n", "85 1132.239447 1317.188788 1340.982181 1366.964851\n", "86 850.931503 1069.256835 1273.933072 1293.913289\n", "87 666.729860 772.908499 970.710982 1199.846894\n", "88 567.621367 593.697850 770.564388 964.680905\n", "89 407.172022 517.608412 583.427322 651.459823\n", "90 1557.705198 1777.088841 2014.475471 2197.551108\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcnRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0\n", "... ... ... ... ...\n", "61 14.0 11.0 11.0 9.0\n", "62 16.0 15.0 15.0 14.0\n", "63 6.0 17.0 14.0 10.0\n", "64 14.0 12.0 10.0 11.0\n", "65 18.0 13.0 19.0 9.0\n", "66 12.0 11.0 21.0 15.0\n", "67 9.0 8.0 17.0 22.0\n", "68 17.0 8.0 12.0 13.0\n", "69 8.0 4.0 8.0 19.0\n", "70 9.0 10.0 11.0 12.0\n", "71 13.0 14.0 15.0 12.0\n", "72 3.0 8.0 11.0 10.0\n", "73 7.0 12.0 16.0 8.0\n", "74 9.0 5.0 6.0 10.0\n", "75 9.0 9.0 7.0 3.0\n", "76 12.0 13.0 14.0 8.0\n", "77 6.0 9.0 6.0 11.0\n", "78 5.0 4.0 3.0 9.0\n", "79 9.0 4.0 4.0 3.0\n", "80 0.0 1.0 2.0 10.0\n", "81 1.0 1.0 5.0 3.0\n", "82 1.0 5.0 1.0 0.0\n", "83 2.0 3.0 4.0 2.0\n", "84 1.0 0.0 1.0 0.0\n", "85 0.0 0.0 0.0 1.0\n", "86 0.0 2.0 0.0 0.0\n", "87 0.0 0.0 0.0 1.0\n", "88 0.0 0.0 0.0 1.0\n", "89 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 2.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: PensUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000 0.000000\n", "15 0.117077 0.000000 0.000000 0.000000 0.000000\n", "16 0.144728 0.000000 0.000000 0.000000 0.000000\n", "17 0.203545 0.000000 0.000000 0.000000 0.000000\n", "18 0.285337 0.000000 0.000000 0.000000 0.000000\n", "19 0.411785 0.000000 0.000000 0.000000 0.000000\n", "20 0.569671 0.293793 0.032519 0.286210 0.000000\n", "21 0.577936 0.334666 0.082185 0.325796 0.000000\n", "22 0.542952 0.334093 0.159192 0.328754 0.294109\n", "23 0.525270 0.335200 0.291449 0.336266 0.265844\n", "24 0.440652 0.287545 0.462621 0.307349 0.257279\n", "25 0.302264 0.199075 0.680865 0.274073 0.223554\n", "26 0.287875 0.209929 0.922301 0.281271 0.222025\n", "27 0.375617 0.301686 1.091499 0.329698 0.361557\n", "28 0.588366 0.490259 1.290600 0.454771 0.306666\n", "29 0.997380 0.837271 1.629444 0.704366 0.782517\n", "... ... ... ... ... ...\n", "61 95.420875 98.837135 105.212722 104.053211 101.895978\n", "62 105.345418 109.561287 115.867679 114.937612 116.872019\n", "63 114.146026 120.276392 125.380725 127.097936 128.264638\n", "64 121.228352 130.406393 133.479909 140.081602 134.908369\n", "65 127.082117 140.515649 141.756365 154.667065 147.951843\n", "66 135.772087 151.857666 150.951715 169.483063 168.777550\n", "67 149.349955 166.651980 164.358415 187.060645 198.765123\n", "68 169.379419 186.557314 184.075653 209.310813 202.163185\n", "69 189.241189 204.213505 202.928524 227.962979 213.042579\n", "70 215.467669 226.740215 227.614746 250.919598 242.401652\n", "71 239.913050 247.198598 251.363937 271.549475 272.124006\n", "72 266.386978 269.608128 277.803233 294.346549 279.167509\n", "73 290.212633 289.225033 301.986117 314.372964 276.125887\n", "74 320.035828 314.655426 333.166249 341.554046 347.562014\n", "75 343.115754 333.272083 358.779337 363.375199 358.624987\n", "76 364.353615 352.960308 381.922779 384.162832 356.573887\n", "77 387.991326 377.863621 407.576915 409.400431 395.430644\n", "78 415.687608 409.668594 437.454099 440.973499 458.179763\n", "79 440.231908 441.365968 463.308482 470.563393 510.933985\n", "80 469.047114 480.435058 491.313250 504.109332 497.422407\n", "81 483.359835 502.855652 508.509439 527.365679 507.486511\n", "82 491.163627 516.780083 521.814333 547.042235 528.076793\n", "83 478.160951 507.090353 515.459485 546.361462 530.982233\n", "84 459.786531 489.975815 506.042422 542.541536 559.100836\n", "85 431.518140 460.631246 489.886391 531.578683 536.652375\n", "86 407.407071 435.401317 473.161078 520.068887 561.591111\n", "87 384.217244 410.769011 453.394852 505.143491 527.449224\n", "88 356.231489 380.558085 424.133865 479.244022 499.550120\n", "89 338.375861 360.661887 403.075344 462.082779 480.283789\n", "90 1399.171168 1479.053219 1637.277777 1962.939824 2114.778847\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtceUrbPisoH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 1.833143 1.140125 1.207192 1.006078\n", "62 0.0 3.472802 2.833075 2.071496 2.345863\n", "63 0.0 4.122320 3.492521 3.034486 1.746409\n", "64 0.0 5.326684 4.343296 4.070133 2.950413\n", "65 0.0 7.263922 5.541253 5.053163 3.991189\n", "66 0.0 9.065851 7.794023 7.761412 5.544336\n", "67 0.0 12.762435 11.048622 8.897240 7.366372\n", "68 0.0 15.955501 14.078383 12.709651 10.403242\n", "69 0.0 18.752814 18.963048 15.851594 13.624275\n", "70 0.0 20.897336 19.029175 18.594719 17.119019\n", "71 0.0 29.118744 26.464116 24.920033 23.437076\n", "72 0.0 35.639813 34.127087 31.646218 31.467357\n", "73 0.0 37.741965 36.973028 36.396326 32.682569\n", "74 0.0 39.517720 38.318075 42.269847 40.999528\n", "75 0.0 47.876490 46.192392 48.117075 49.324181\n", "76 0.0 49.670041 53.083173 54.340642 52.099575\n", "77 0.0 50.012882 54.716589 59.036320 56.545266\n", "78 0.0 55.110771 60.556499 57.833635 68.295220\n", "79 0.0 57.823772 58.937911 63.060222 65.071184\n", "80 0.0 60.481331 58.087560 61.808045 68.534726\n", "81 0.0 61.964786 62.348073 67.458571 64.014409\n", "82 0.0 50.227438 60.057473 62.481317 65.039965\n", "83 0.0 56.065057 57.705986 67.278069 64.411901\n", "84 0.0 46.621611 50.250838 56.385382 55.838996\n", "85 0.0 44.022967 41.810433 48.578959 55.622100\n", "86 0.0 35.249889 41.292859 49.375770 47.619284\n", "87 0.0 32.121667 40.693186 42.880866 46.159316\n", "88 0.0 30.521441 29.572122 38.411615 38.891900\n", "89 0.0 20.425618 25.874860 26.520388 32.169052\n", "90 0.0 77.708801 90.178098 100.847693 113.548842\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: ApidUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: LoasDefH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 121.0 114.000000 103.0 108.000000\n", "1 213.0 209.000000 246.0 231.000000\n", "2 197.0 181.000000 209.0 193.000000\n", "3 171.0 181.000000 209.0 160.000000\n", "4 196.0 163.000000 176.0 151.000000\n", "5 161.0 159.000000 159.0 148.000000\n", "6 179.0 185.000000 172.0 167.000000\n", "7 157.0 190.000000 179.0 152.000000\n", "8 160.0 152.000000 158.0 155.000000\n", "9 180.0 179.000000 183.0 165.000000\n", "10 182.0 188.000000 200.0 154.000000\n", "11 189.0 176.000000 217.0 149.000000\n", "12 204.0 200.000000 182.0 192.000000\n", "13 204.0 211.000000 208.0 203.000000\n", "14 225.0 211.000000 235.0 249.000000\n", "15 246.0 238.000000 273.0 250.000000\n", "16 236.0 271.000000 300.0 277.000000\n", "17 269.0 258.000000 338.0 267.000000\n", "18 270.0 276.000000 293.0 256.000000\n", "19 256.0 279.000000 296.0 283.000000\n", "20 270.0 284.000000 289.0 319.000000\n", "21 270.0 299.000000 257.0 266.000000\n", "22 269.0 279.000000 277.0 297.000000\n", "23 291.0 279.000000 266.0 303.000000\n", "24 251.0 273.000000 313.0 276.000000\n", "25 270.0 298.000000 289.0 281.000000\n", "26 244.0 290.000000 286.0 261.000000\n", "27 282.0 283.000000 303.0 274.000000\n", "28 290.0 285.000000 289.0 304.000000\n", "29 281.0 307.000000 324.0 299.000000\n", "... ... ... ... ...\n", "61 849.0 873.000000 954.0 929.000000\n", "62 890.0 941.000000 979.0 1004.000000\n", "63 930.0 861.000000 1028.0 999.000000\n", "64 879.0 928.000000 980.0 1030.000000\n", "65 1125.0 1311.000000 1425.0 1406.000000\n", "66 675.0 707.000000 753.0 923.000000\n", "67 513.0 560.000000 633.0 768.000000\n", "68 459.0 437.000000 523.0 631.000000\n", "69 392.0 393.000000 455.0 519.000000\n", "70 301.0 321.000000 394.0 523.000000\n", "71 291.0 306.000000 304.0 403.000000\n", "72 258.0 250.000000 250.0 312.000000\n", "73 245.0 190.000000 248.0 276.000000\n", "74 241.0 222.000000 209.0 235.000000\n", "75 191.0 179.512894 203.0 201.000000\n", "76 150.0 179.000000 185.0 189.000000\n", "77 111.0 137.000000 176.0 149.000000\n", "78 84.0 112.000000 141.0 172.000000\n", "79 56.0 87.000000 84.0 128.000000\n", "80 59.0 60.000000 88.0 92.000000\n", "81 48.0 41.000000 51.0 71.000000\n", "82 50.0 50.000000 39.0 45.000000\n", "83 31.0 30.000000 37.0 47.000000\n", "84 10.0 29.000000 27.0 27.284211\n", "85 2.0 12.000000 33.0 29.000000\n", "86 4.0 3.000000 9.0 26.000000\n", "87 1.0 1.000000 5.0 11.000000\n", "88 2.0 0.000000 3.0 3.000000\n", "89 0.0 5.000000 0.0 1.000000\n", "90 7.0 4.000000 4.0 4.000000\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcnUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 225.846788 218.545769 257.154365 289.223849\n", "62 0.0 183.563413 205.704440 248.786711 262.809255\n", "63 0.0 214.457169 219.289866 233.235897 280.159572\n", "64 0.0 195.304727 208.055022 268.659751 258.009914\n", "65 0.0 198.798658 221.132390 236.230602 254.662916\n", "66 0.0 220.100283 215.576578 233.916320 216.435541\n", "67 0.0 195.153941 206.185443 221.833092 245.579761\n", "68 0.0 200.365397 199.840644 242.757880 257.273699\n", "69 0.0 199.107111 208.028913 217.594484 237.441107\n", "70 0.0 186.671725 200.588003 215.763682 240.824404\n", "71 0.0 183.860213 177.741652 199.453325 214.418759\n", "72 0.0 191.594922 198.593314 199.717004 203.974010\n", "73 0.0 196.876979 193.738823 248.335192 216.141521\n", "74 0.0 169.602775 188.536352 205.357294 207.600751\n", "75 0.0 198.055034 172.745999 222.747227 218.461236\n", "76 0.0 183.384987 186.490326 204.858159 235.533198\n", "77 0.0 168.883741 187.408545 200.364324 242.508790\n", "78 0.0 187.227138 193.346767 180.384965 211.317252\n", "79 0.0 178.674033 179.950691 174.356548 217.794954\n", "80 0.0 189.645179 202.130080 190.914953 200.843721\n", "81 0.0 193.414460 190.947727 194.711064 199.294091\n", "82 0.0 203.444726 220.438936 225.252726 204.020735\n", "83 0.0 208.687104 206.289940 240.201180 216.581335\n", "84 0.0 190.941261 196.955651 231.906316 236.853256\n", "85 0.0 189.414173 205.901045 212.879245 231.806604\n", "86 0.0 159.325655 187.273098 189.412271 228.352241\n", "87 0.0 138.161664 165.002517 178.027988 173.678049\n", "88 0.0 144.622121 145.385828 156.281566 173.735763\n", "89 0.0 105.820346 133.783291 141.655295 155.337514\n", "90 0.0 466.814446 530.301565 613.505841 705.882894\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: PensUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000 0.000000\n", "15 0.746690 0.288324 0.005683 0.154963 0.000000\n", "16 0.771854 0.229336 0.090259 0.212763 0.000000\n", "17 0.824957 0.170802 0.257667 0.304147 0.000000\n", "18 0.864253 0.107825 0.482673 0.411424 0.000000\n", "19 0.961933 0.053189 0.790162 0.565370 0.000000\n", "20 1.071764 0.969534 1.153797 0.747037 0.000000\n", "21 2.095747 2.163851 2.432932 1.615972 0.000000\n", "22 2.454015 2.599459 2.774355 2.001305 1.790406\n", "23 3.085303 3.166710 3.207350 2.591374 2.048684\n", "24 3.667903 3.536213 3.389689 3.127036 2.617614\n", "25 4.374006 3.895346 3.518323 3.758574 3.065768\n", "26 5.100995 4.391993 3.935881 4.520383 3.568231\n", "27 5.701708 4.892920 4.515027 5.271706 5.781110\n", "28 6.348446 5.542050 5.380301 6.142255 4.141905\n", "29 7.270623 6.545909 6.749493 7.329680 8.142923\n", "... ... ... ... ... ...\n", "61 486.651820 506.606430 485.540921 491.814628 481.618318\n", "62 523.619305 540.829507 524.434313 527.370765 536.246448\n", "63 566.046794 581.691433 569.705495 572.477936 577.733024\n", "64 594.862941 609.654963 602.332994 607.533688 585.097381\n", "65 635.978355 651.390094 648.665421 658.272335 629.691946\n", "66 680.197862 693.599273 691.785834 705.329359 702.393261\n", "67 733.404240 742.520670 738.160332 754.795332 802.023255\n", "68 789.728276 792.399964 782.349833 801.236673 773.875728\n", "69 847.251244 841.559852 823.048417 843.965686 788.727307\n", "70 909.065579 893.257274 863.774353 887.811433 857.672974\n", "71 971.443518 950.347828 914.675137 938.480058 940.465646\n", "72 1028.576397 1006.783278 969.704554 991.712476 940.571251\n", "73 1104.299474 1085.677730 1051.076955 1072.098058 941.665032\n", "74 1119.472255 1108.852995 1082.891122 1104.414435 1123.841188\n", "75 1159.181005 1159.689191 1145.771365 1173.671845 1158.329054\n", "76 1226.726358 1240.717274 1234.894831 1266.409108 1175.460977\n", "77 1278.435208 1307.531506 1306.115952 1338.931265 1293.243514\n", "78 1347.390201 1393.181172 1392.281541 1424.248804 1479.821308\n", "79 1424.425923 1488.115965 1483.876454 1509.662005 1639.178984\n", "80 1533.221799 1617.094973 1605.433068 1614.218112 1592.805783\n", "81 1603.458235 1704.748719 1691.697002 1698.145420 1634.133447\n", "82 1588.836792 1701.205261 1692.936467 1706.109647 1646.960423\n", "83 1607.669265 1732.762684 1734.530860 1762.232144 1712.628036\n", "84 1529.170927 1658.876450 1675.906723 1725.036617 1777.687698\n", "85 1490.787977 1628.208410 1666.716847 1752.208623 1768.932709\n", "86 1402.152353 1537.566716 1592.516632 1698.072611 1833.646480\n", "87 1314.987077 1443.127087 1509.687684 1625.275752 1697.043413\n", "88 1294.410205 1416.481491 1493.470669 1617.515153 1686.051056\n", "89 1249.035341 1357.507186 1438.895028 1561.719664 1623.234345\n", "90 5470.565122 5780.098838 6175.138810 6732.341588 7253.107509\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: ApidUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AinvRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 1.000000 0.000000 1.000000 0.000000\n", "19 1.000000 0.000000 1.000000 0.000000\n", "20 1.000000 0.000000 0.000000 3.000000\n", "21 1.000000 1.000000 1.000000 1.000000\n", "22 2.000000 1.000000 2.000000 2.000000\n", "23 4.000000 4.000000 1.000000 0.000000\n", "24 10.000000 1.333333 7.000000 1.000000\n", "25 7.000000 4.000000 3.000000 3.000000\n", "26 4.000000 7.000000 9.000000 8.000000\n", "27 4.000000 4.000000 7.000000 6.000000\n", "28 11.000000 11.000000 3.000000 5.000000\n", "29 14.000000 11.000000 10.000000 10.000000\n", "... ... ... ... ...\n", "61 373.831579 335.788599 331.747685 328.747073\n", "62 326.981308 343.332604 348.964602 337.519451\n", "63 357.716216 377.644351 358.470588 360.224790\n", "64 381.769231 376.029289 357.311404 358.438710\n", "65 348.517647 343.319809 344.531818 318.248193\n", "66 304.078534 359.753950 352.114155 359.259259\n", "67 361.321759 335.561446 345.729412 281.943396\n", "68 341.849010 359.363218 327.399027 351.851852\n", "69 329.208020 335.275424 306.064935 336.156098\n", "70 301.002681 330.290237 361.400000 343.930693\n", "71 342.076555 325.126124 344.484337 328.842640\n", "72 405.236842 342.993103 312.222222 285.737752\n", "73 327.758442 371.548463 338.031250 315.034392\n", "74 360.299287 303.654596 348.677804 334.462687\n", "75 383.870968 354.951338 336.660377 310.142857\n", "76 340.540897 359.274809 308.901554 289.638418\n", "77 310.347938 339.400000 328.161290 308.587719\n", "78 306.485014 329.701847 352.663895 336.920398\n", "79 322.636816 306.120000 306.062670 325.406736\n", "80 397.967603 311.184915 328.842239 334.607143\n", "81 407.689320 339.686275 326.166995 267.668675\n", "82 400.254032 420.465385 340.379808 293.290859\n", "83 435.735294 343.279221 393.607803 308.698765\n", "84 432.770497 481.381849 410.768317 324.378662\n", "85 496.134680 441.790861 421.706612 355.470588\n", "86 445.730275 431.880734 403.412000 320.125874\n", "87 497.561263 402.517958 382.531381 369.111111\n", "88 357.545648 341.655889 354.692825 341.365297\n", "89 355.675926 374.794760 350.093960 325.097257\n", "90 1181.698424 1277.511970 1425.669565 1367.429036\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcnUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 1713.090373 1717.709602 1837.893319 1714.038851\n", "62 0.0 1675.284587 1735.865839 1849.109732 1869.148183\n", "63 0.0 1850.860146 1777.047195 1917.074490 2042.139101\n", "64 0.0 1823.972622 1878.541049 1974.584129 1954.622821\n", "65 0.0 1708.068931 1867.193865 1954.172368 1983.323068\n", "66 0.0 1751.149607 1714.513774 2003.265007 2087.781158\n", "67 0.0 1737.086344 1812.667615 1918.470571 2047.791718\n", "68 0.0 1687.031741 1839.084313 1902.917580 1944.308368\n", "69 0.0 1750.249061 1803.189529 1915.833641 1931.764072\n", "70 0.0 1693.674460 1737.655178 1941.883072 1973.043128\n", "71 0.0 1845.019840 1751.189057 1846.780486 1901.484166\n", "72 0.0 1803.822172 1846.877574 1914.648293 1829.435006\n", "73 0.0 1832.995247 1839.909317 1933.309753 1905.953180\n", "74 0.0 1869.717638 1832.125642 1956.519091 1945.209282\n", "75 0.0 1908.961332 1866.684969 1943.583603 1964.391170\n", "76 0.0 1832.956495 1917.732520 1954.186496 1900.625462\n", "77 0.0 1746.076329 1918.995413 1940.193951 1931.204787\n", "78 0.0 1731.291659 1875.146482 1935.829878 1983.026801\n", "79 0.0 1726.089042 1743.518949 1864.224635 1963.708752\n", "80 0.0 1728.442917 1631.184546 1745.707355 1794.217349\n", "81 0.0 1657.397013 1725.463911 1750.158842 1734.401714\n", "82 0.0 1610.288270 1671.187341 1776.461382 1680.460456\n", "83 0.0 1429.720986 1531.354235 1719.007127 1668.850238\n", "84 0.0 1443.007684 1458.744811 1538.685295 1570.073024\n", "85 0.0 1281.669633 1362.473292 1421.511293 1476.658720\n", "86 0.0 1254.936923 1251.418410 1382.275994 1404.401018\n", "87 0.0 1094.669180 1154.982741 1246.441387 1294.214374\n", "88 0.0 973.454305 1018.839784 1088.985747 1108.656270\n", "89 0.0 860.553739 859.331214 971.739762 989.777903\n", "90 0.0 3128.828514 3197.679451 3710.334400 4022.087004\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AinvUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 1.000000 1.000088 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 1.125216 0.375041\n", "20 0.0 2.118062 2.117847 2.118038 0.705955\n", "21 0.0 3.226444 1.935669 3.226406 2.580914\n", "22 0.0 0.000000 2.077128 2.077315 2.077145\n", "23 0.0 3.337791 4.291124 2.384173 2.860774\n", "24 0.0 3.525531 4.028723 6.043607 2.014400\n", "25 0.0 6.360282 4.625188 6.938408 5.203380\n", "26 0.0 4.805564 6.727189 10.091758 4.324685\n", "27 0.0 8.641427 6.912437 16.706462 9.792646\n", "28 0.0 18.577581 12.019648 12.020782 10.380734\n", "29 0.0 16.747918 14.436388 15.592707 13.859049\n", "... ... ... ... ... ...\n", "61 0.0 567.936474 554.808538 561.294769 576.893034\n", "62 0.0 582.318759 546.802680 637.754298 592.619596\n", "63 0.0 617.599092 595.764276 660.259870 625.875880\n", "64 0.0 660.900903 644.099666 641.618429 641.063256\n", "65 0.0 664.681836 688.392975 696.152847 696.817910\n", "66 0.0 665.116145 667.180504 742.284104 757.763429\n", "67 0.0 680.712061 666.210029 796.248627 739.142322\n", "68 0.0 751.146312 756.819279 736.788594 728.908490\n", "69 0.0 741.157167 804.029521 838.446279 827.690453\n", "70 0.0 772.502033 768.021473 861.980699 829.739591\n", "71 0.0 877.805625 836.593358 814.706377 811.466912\n", "72 0.0 959.663580 923.983909 850.337552 830.449434\n", "73 0.0 882.768661 960.203844 968.753177 864.309646\n", "74 0.0 988.640164 935.216392 968.239160 967.795455\n", "75 0.0 1039.137642 982.060392 976.224518 991.418250\n", "76 0.0 958.966064 940.448037 1026.869843 985.501441\n", "77 0.0 900.302769 1006.029668 1092.919693 1010.142379\n", "78 0.0 924.316574 938.405264 1029.785161 1082.929127\n", "79 0.0 973.781044 961.383908 1021.784692 1071.793336\n", "80 0.0 992.195205 919.198569 945.662929 988.917877\n", "81 0.0 1009.929143 979.235292 1003.991101 1018.246945\n", "82 0.0 952.424927 957.202263 977.744389 947.617139\n", "83 0.0 896.713775 841.321806 978.736115 964.944433\n", "84 0.0 871.179030 868.314515 920.019077 954.879870\n", "85 0.0 804.338870 813.743279 832.587787 925.844508\n", "86 0.0 718.312257 758.439656 805.885282 860.850703\n", "87 0.0 638.544397 712.419901 787.013527 762.553151\n", "88 0.0 600.582144 622.825036 680.961069 711.343421\n", "89 0.0 509.207106 539.827044 627.935135 602.625729\n", "90 0.0 1991.675192 2121.882651 2341.178525 2606.683127\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: PensRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 3.013711 0.000000 0.000000 0.000000\n", "1 1.004570 0.000000 0.000000 0.000000\n", "2 0.000000 2.008114 1.004342 0.000000\n", "3 1.004570 2.008114 3.013027 1.003609\n", "4 3.013711 0.000000 3.013027 1.003609\n", "5 0.000000 3.012171 4.017369 3.010826\n", "6 1.004570 1.004057 6.026054 2.007217\n", "7 3.013711 2.008114 4.017369 3.010826\n", "8 6.780850 6.024342 5.021712 4.014434\n", "9 4.018281 6.024342 5.021712 1.003609\n", "10 2.009141 7.028400 7.030396 9.032477\n", "11 5.022852 7.028400 6.026054 4.014434\n", "12 5.022852 12.048685 6.026054 5.018043\n", "13 10.045703 3.012171 8.034739 5.018043\n", "14 5.022852 7.028400 7.030396 11.039694\n", "15 2.009141 23.093313 14.060793 16.057737\n", "16 18.082266 10.040571 10.043423 10.036085\n", "17 22.122292 22.089256 20.086847 18.064954\n", "18 31.141680 30.121712 31.134613 33.119082\n", "19 22.100547 38.154169 26.108559 29.104648\n", "20 21.095977 42.170397 45.195406 36.126299\n", "21 3662.989544 3702.234971 3876.735404 3987.297035\n", "22 2.009141 5.020285 3.013027 2.007217\n", "23 2.343997 5.020285 3.013027 1.003609\n", "24 2.009141 3.442481 2.008685 4.014434\n", "25 3.013711 7.028400 5.021712 0.000000\n", "26 0.000000 6.024342 4.017369 2.007217\n", "27 3.013711 2.008114 3.013027 3.010826\n", "28 11.301416 2.231238 5.021712 1.003609\n", "29 3.013711 1.004057 3.013027 3.612991\n", "... ... ... ... ...\n", "61 101.461602 101.817852 110.147689 106.617534\n", "62 94.609475 110.647455 125.793378 122.135885\n", "63 133.652358 121.490906 138.820295 130.921343\n", "64 132.806661 135.940597 148.875650 148.534064\n", "65 145.223196 151.361604 170.821894 147.925450\n", "66 170.359154 200.280835 180.809297 204.212765\n", "67 190.269048 212.082385 202.877154 196.561075\n", "68 246.032332 181.942067 226.633938 206.170619\n", "69 241.137093 227.288634 232.036976 263.426582\n", "70 240.468604 258.280066 333.709681 288.684365\n", "71 348.422720 307.679755 303.431701 320.641402\n", "72 367.457162 341.808077 352.451617 316.809799\n", "73 382.252471 401.094395 457.140583 392.158112\n", "74 402.235616 395.431322 423.081709 497.502380\n", "75 501.049003 486.527566 477.371537 497.734674\n", "76 489.325245 541.393340 518.023661 493.963214\n", "77 534.702107 538.038635 567.881048 596.301125\n", "78 550.024523 590.214690 626.533579 654.187034\n", "79 624.004907 603.230852 659.013262 623.826952\n", "80 720.828050 690.566912 663.980009 672.051823\n", "81 716.156975 714.805918 758.528657 756.455957\n", "82 703.210188 794.248656 817.871337 767.812476\n", "83 718.411500 753.242262 862.747505 903.441059\n", "84 691.807344 736.658305 788.875729 934.006270\n", "85 709.060587 783.370798 801.351895 874.757902\n", "86 682.792428 783.614484 835.224090 877.756192\n", "87 642.419812 799.403437 793.175356 828.225116\n", "88 594.182608 640.956055 728.135158 819.093491\n", "89 544.462879 598.806950 699.195987 706.746947\n", "90 2233.224531 2461.116450 3055.279722 3312.539632\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: ApinUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 0.000000 0.000000 0.000000 0.000000\n", "62 0.0 0.000000 0.000000 0.000000 0.000000\n", "63 0.0 0.000000 0.000000 0.000000 0.000000\n", "64 0.0 0.000000 0.000000 0.000000 0.000000\n", "65 0.0 631.934001 640.134808 655.094023 588.877988\n", "66 0.0 636.489811 598.842260 679.250022 717.743648\n", "67 0.0 607.183535 631.535602 617.341060 726.022207\n", "68 0.0 593.319813 667.997487 649.626990 684.084911\n", "69 0.0 587.436542 596.772748 653.131237 657.546118\n", "70 0.0 622.387183 640.955027 686.096207 709.412338\n", "71 0.0 687.069240 617.159180 668.928255 682.401658\n", "72 0.0 706.897694 707.389666 681.770252 694.268913\n", "73 0.0 710.981853 723.926233 735.590721 716.013345\n", "74 0.0 655.993819 725.006105 766.918926 788.478502\n", "75 0.0 636.398997 671.558821 777.540816 807.789377\n", "76 0.0 626.041451 674.703797 736.119715 767.519871\n", "77 0.0 634.508728 666.067555 742.715060 771.168206\n", "78 0.0 655.819162 653.671754 689.937991 783.669119\n", "79 0.0 719.181288 676.352051 698.366187 736.838669\n", "80 0.0 788.041776 735.310157 706.903193 742.706103\n", "81 0.0 863.808851 800.652543 767.592688 716.418032\n", "82 0.0 840.546265 840.988072 791.295778 711.261184\n", "83 0.0 854.688597 800.620681 838.920902 796.978468\n", "84 0.0 809.393024 807.353260 806.465549 795.034032\n", "85 0.0 783.127779 838.636675 873.649127 774.325917\n", "86 0.0 521.585645 524.813708 573.180819 573.823432\n", "87 0.0 424.495274 432.273878 481.328774 466.157513\n", "88 0.0 423.147973 428.636845 496.003488 481.018708\n", "89 0.0 426.519692 429.392946 479.376360 489.943012\n", "90 0.0 1577.837753 1685.957724 1918.332683 1967.764958\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcpUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 6.096722 4.883498 4.873354 6.548069\n", "62 0.0 2.120804 2.654331 4.767872 4.745436\n", "63 0.0 4.454412 3.755791 5.387735 5.129235\n", "64 0.0 4.396429 2.200973 6.589204 6.801094\n", "65 0.0 3.566898 3.571374 4.039149 5.202537\n", "66 0.0 4.167429 3.651076 4.944740 3.626347\n", "67 0.0 3.635293 4.610483 4.116600 4.579256\n", "68 0.0 3.600158 4.377106 1.798594 3.580261\n", "69 0.0 3.884133 3.889007 4.435347 4.690381\n", "70 0.0 4.511660 1.204619 4.507938 4.785840\n", "71 0.0 2.704292 2.707685 1.801374 3.585795\n", "72 0.0 3.451426 3.141597 3.448579 2.496255\n", "73 0.0 3.934411 2.424214 4.535960 5.718513\n", "74 0.0 2.512068 4.087233 3.137495 4.996369\n", "75 0.0 5.871107 2.571832 2.566490 6.203575\n", "76 0.0 2.975987 3.352186 3.716915 4.809251\n", "77 0.0 4.466056 2.751791 3.089334 4.099729\n", "78 0.0 3.065045 4.603336 5.359404 6.096211\n", "79 0.0 2.901546 2.905187 3.261547 6.853087\n", "80 0.0 2.960546 2.634899 1.643391 4.906973\n", "81 0.0 1.769705 1.181284 3.536491 2.639887\n", "82 0.0 1.083448 2.892821 2.165109 2.873228\n", "83 0.0 1.835616 1.102752 2.934563 3.285848\n", "84 0.0 1.508957 1.888563 2.261568 2.626080\n", "85 0.0 1.015298 1.779001 1.014460 2.776638\n", "86 0.0 0.500868 0.250748 1.000909 1.245249\n", "87 0.0 0.335180 0.335601 1.004712 0.999984\n", "88 0.0 0.000000 0.291803 0.000000 0.144913\n", "89 0.0 0.398271 0.199385 0.000000 0.594105\n", "90 0.0 0.349083 0.699041 0.174397 1.735767\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtceUrbAcimM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 6.385965 3.649123 4.561404 2.736842\n", "62 0.0 9.000000 5.400000 4.500000 8.100000\n", "63 0.0 10.504983 6.127907 2.626246 6.127907\n", "64 0.0 8.015366 7.124770 4.452981 8.015366\n", "65 0.0 7.001200 10.501800 8.751500 5.250900\n", "66 0.0 7.674700 15.349400 9.380189 10.232933\n", "67 0.0 5.740782 15.582123 6.560894 9.021229\n", "68 0.0 9.830624 13.926717 12.288280 13.926717\n", "69 0.0 8.625334 18.818910 11.761819 16.466546\n", "70 0.0 18.214342 12.670846 20.590125 14.254702\n", "71 0.0 20.042695 20.777307 21.590278 14.621068\n", "72 0.0 21.880218 22.634709 15.089806 12.071845\n", "73 0.0 16.788580 19.708333 21.168210 14.598765\n", "74 0.0 25.397628 19.048221 17.637242 21.905974\n", "75 0.0 16.681777 25.717739 20.852221 19.462073\n", "76 0.0 19.225701 18.562745 13.259104 19.225701\n", "77 0.0 20.360288 15.762803 19.046721 22.987421\n", "78 0.0 14.340560 9.352539 14.340560 19.952084\n", "79 0.0 7.709713 12.454151 16.605535 20.163864\n", "80 0.0 17.744702 12.214765 12.825503 15.879195\n", "81 0.0 11.928308 10.044891 13.183920 19.461977\n", "82 0.0 11.628050 12.852055 12.852055 12.264435\n", "83 0.0 12.363104 13.599414 13.599414 14.217570\n", "84 0.0 5.965979 8.617525 14.617818 17.235050\n", "85 0.0 2.612348 9.143755 11.068105 13.021300\n", "86 0.0 11.625459 3.858557 4.499082 13.457895\n", "87 0.0 4.868602 4.886769 7.302903 6.085753\n", "88 0.0 3.604816 4.141247 4.732854 4.751862\n", "89 0.0 3.133117 6.340390 5.705187 5.639610\n", "90 0.0 17.965255 18.636439 24.491063 23.723836\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: ApinRurH\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 0.000000\n", "29 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 1099.421661 1090.318535 1096.279579 979.270631\n", "62 1220.468086 1295.378443 1281.326771 1261.348586\n", "63 1590.610046 1498.437766 1613.411461 1475.407743\n", "64 1718.195039 1719.024399 1702.434164 1739.480723\n", "65 1704.653785 1806.058676 1991.507885 1847.510578\n", "66 1804.692153 1843.596920 2008.512222 2131.589085\n", "67 2053.372129 1933.074642 2001.510436 2012.556190\n", "68 2152.635075 2134.148599 2161.219976 2070.108661\n", "69 2226.557673 2219.390849 2409.237113 2197.662100\n", "70 2219.012712 2269.805336 2419.196351 2360.214893\n", "71 2698.028774 2352.802103 2467.284965 2531.833874\n", "72 2659.295604 2834.649246 2520.333445 2489.980940\n", "73 2726.147791 2673.820820 2924.320412 2595.389426\n", "74 2804.890587 2796.661212 2811.818241 2965.558783\n", "75 3034.107800 2953.691393 2955.725975 2786.755107\n", "76 3001.301600 3151.791640 3018.046619 2888.791423\n", "77 3093.343912 3118.938298 3221.625899 3075.705291\n", "78 3467.318805 3090.524999 3219.773880 3139.023532\n", "79 3503.546778 3309.597052 3166.404036 3036.659604\n", "80 3751.814462 3593.832025 3416.630059 2930.153516\n", "81 3642.719135 3655.369619 3668.572941 3349.576592\n", "82 3552.684658 3658.741851 3662.141917 3466.066066\n", "83 3300.894097 3433.209677 3585.038604 3608.803522\n", "84 3233.390408 3141.524103 3385.569471 3536.725504\n", "85 3270.917382 3119.325825 3160.907799 3160.476496\n", "86 3214.443120 3019.206047 3113.280571 2945.910231\n", "87 2941.171587 3029.586875 2919.249972 2830.123657\n", "88 2724.042671 2765.267099 2772.307009 2760.532526\n", "89 2476.051602 2442.784875 2570.556824 2499.075243\n", "90 16470.509078 15760.026086 16203.864056 15131.317459\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: RmvM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.000000 0.000000 0.000000 0.000000\n", "1 0.000000 0.000000 0.000000 0.000000\n", "2 0.000000 0.000000 0.000000 0.000000\n", "3 0.000000 0.000000 0.000000 0.000000\n", "4 0.000000 0.000000 0.000000 0.000000\n", "5 0.000000 0.000000 0.000000 0.000000\n", "6 0.000000 0.000000 0.000000 0.000000\n", "7 0.000000 0.000000 0.000000 0.000000\n", "8 0.000000 0.000000 0.000000 0.000000\n", "9 0.000000 0.000000 0.000000 0.000000\n", "10 0.000000 0.000000 0.000000 0.000000\n", "11 0.000000 0.000000 0.000000 0.000000\n", "12 0.000000 0.000000 0.000000 0.000000\n", "13 0.000000 0.000000 0.000000 0.000000\n", "14 0.000000 0.000000 0.000000 0.000000\n", "15 0.000000 0.000000 0.000000 0.000000\n", "16 0.000000 0.000000 0.000000 0.000000\n", "17 0.000000 0.000000 0.000000 0.000000\n", "18 0.000000 0.000000 0.000000 0.000000\n", "19 0.000000 0.000000 0.000000 0.000000\n", "20 0.000000 0.000000 0.000000 0.000000\n", "21 0.000000 0.000000 0.000000 0.000000\n", "22 0.000000 0.000000 0.000000 0.000000\n", "23 0.000000 0.000000 0.000000 0.000000\n", "24 0.000000 0.000000 0.000000 0.000000\n", "25 0.000000 0.000000 0.000000 0.000000\n", "26 0.000000 0.000000 0.000000 0.000000\n", "27 0.000000 0.000000 0.000000 0.000000\n", "28 0.000000 0.000000 0.000000 1.500851\n", "29 1.000453 0.000000 0.000000 0.000000\n", "... ... ... ... ...\n", "61 41.462098 59.805800 66.977491 47.828058\n", "62 53.941508 56.091677 61.727958 56.494944\n", "63 70.894635 57.472454 70.468153 49.655830\n", "64 82.377045 62.003629 54.809101 45.908862\n", "65 94.391410 61.519677 72.854881 61.453345\n", "66 80.995904 71.091255 59.010582 60.329587\n", "67 101.605241 85.790634 61.936389 52.492022\n", "68 92.346859 74.061516 81.497287 69.709475\n", "69 99.214296 90.764497 84.014402 72.900566\n", "70 112.586578 115.663517 101.482600 74.790012\n", "71 136.379007 103.325948 116.429578 97.733129\n", "72 145.841118 138.005159 104.539770 97.720031\n", "73 176.775776 149.312770 131.875562 98.128614\n", "74 165.673051 142.156998 160.255744 107.881134\n", "75 202.913118 198.828006 138.001801 120.788800\n", "76 242.286955 196.401584 188.203069 139.443318\n", "77 265.921556 245.537971 236.138013 169.203044\n", "78 307.239485 253.322731 227.020098 181.123016\n", "79 360.031603 268.369975 215.793390 209.826507\n", "80 412.176554 331.516148 257.634761 232.139590\n", "81 490.043864 390.304086 338.529921 217.692732\n", "82 503.926360 468.735403 369.961977 291.015303\n", "83 588.716718 484.360826 477.717323 330.436559\n", "84 649.960470 500.804621 482.047333 387.318921\n", "85 769.810315 578.313246 520.293916 423.723101\n", "86 1000.936704 738.706268 582.644453 493.232406\n", "87 1033.184622 907.768257 668.746010 524.740887\n", "88 1083.941149 929.842895 868.383044 590.295983\n", "89 1099.793823 1014.903836 878.225016 737.875334\n", "90 13030.636857 12032.922237 11216.380129 9707.626610\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcdUrbPisoM\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0 0.0\n", "... ... ... ... ... ...\n", "61 0.0 0.0 0.0 0.0 0.0\n", "62 0.0 0.0 0.0 0.0 0.0\n", "63 0.0 0.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 0.0 0.0\n", "66 0.0 0.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 0.0 0.0\n", "68 0.0 0.0 0.0 0.0 0.0\n", "69 0.0 0.0 0.0 0.0 0.0\n", "70 0.0 0.0 0.0 0.0 0.0\n", "71 0.0 0.0 0.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0 0.0\n", "73 0.0 0.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 0.0 0.0 0.0\n", "79 0.0 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 0.0 0.0\n", "81 0.0 0.0 0.0 0.0 0.0\n", "82 0.0 0.0 0.0 0.0 0.0\n", "83 0.0 0.0 0.0 0.0 0.0\n", "84 0.0 0.0 0.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 0.0 0.0\n", "87 0.0 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtceUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.000000 0.000000\n", "18 0.0 0.000000 0.000000 0.000000 0.000000\n", "19 0.0 0.000000 0.000000 0.000000 0.000000\n", "20 0.0 0.000000 0.000000 0.000000 0.000000\n", "21 0.0 0.000000 0.000000 0.000000 0.000000\n", "22 0.0 0.000000 0.000000 0.000000 0.000000\n", "23 0.0 0.000000 0.000000 0.000000 0.000000\n", "24 0.0 0.000000 0.000000 0.000000 0.000000\n", "25 0.0 0.000000 0.000000 0.000000 0.000000\n", "26 0.0 0.000000 0.000000 0.000000 0.000000\n", "27 0.0 0.000000 0.000000 0.000000 0.000000\n", "28 0.0 0.000000 0.000000 0.000000 0.000000\n", "29 0.0 0.000000 0.000000 0.000000 0.000000\n", "... ... ... ... ... ...\n", "61 0.0 80.166857 49.859875 52.792808 43.997735\n", "62 0.0 110.527198 90.166925 65.928504 74.660661\n", "63 0.0 139.877680 118.507479 102.965514 59.258760\n", "64 0.0 189.673316 154.656704 144.929867 105.058738\n", "65 0.0 245.736078 187.458747 170.946837 135.020589\n", "66 0.0 268.934149 231.205977 230.238588 164.470069\n", "67 0.0 337.237565 291.951378 235.102760 194.650744\n", "68 0.0 392.044499 345.921617 312.290349 255.619297\n", "69 0.0 427.247186 432.036952 361.148406 310.403178\n", "70 0.0 460.102664 418.970825 409.405281 376.914366\n", "71 0.0 518.835880 471.535884 444.023515 417.600291\n", "72 0.0 553.360187 529.872913 491.353782 488.576704\n", "73 0.0 551.258035 540.026972 531.603674 477.360645\n", "74 0.0 520.482280 504.681925 556.730153 539.998961\n", "75 0.0 549.123510 529.807608 551.882925 565.727929\n", "76 0.0 503.329959 537.916827 550.659358 527.949570\n", "77 0.0 470.987118 515.283411 555.963680 532.504641\n", "78 0.0 450.889229 495.443501 473.166365 558.757908\n", "79 0.0 461.176228 470.062089 502.939778 518.978300\n", "80 0.0 443.464158 425.912440 453.191955 502.513656\n", "81 0.0 423.035214 425.651927 460.541429 437.028042\n", "82 0.0 322.772562 385.942527 401.518683 417.961117\n", "83 0.0 353.934943 364.294014 424.721931 406.628008\n", "84 0.0 287.378389 309.749162 347.562854 344.194898\n", "85 0.0 274.964610 261.145263 303.421041 347.412047\n", "86 0.0 204.609486 239.686162 286.603762 276.408169\n", "87 0.0 173.878333 220.276964 232.119134 249.865765\n", "88 0.0 159.385295 154.427878 200.588385 203.096474\n", "89 0.0 106.574382 135.006797 138.374960 167.847894\n", "90 0.0 358.230724 415.713084 464.898974 523.450151\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AtcnRurM\n", " 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.0 0.0 0.0\n", "1 0.0 0.0 0.0 0.0\n", "2 0.0 0.0 0.0 0.0\n", "3 0.0 0.0 0.0 0.0\n", "4 0.0 0.0 0.0 0.0\n", "5 0.0 0.0 0.0 0.0\n", "6 0.0 0.0 0.0 0.0\n", "7 0.0 0.0 0.0 0.0\n", "8 0.0 0.0 0.0 0.0\n", "9 0.0 0.0 0.0 0.0\n", "10 0.0 0.0 0.0 0.0\n", "11 0.0 0.0 0.0 0.0\n", "12 0.0 0.0 0.0 0.0\n", "13 0.0 0.0 0.0 0.0\n", "14 0.0 0.0 0.0 0.0\n", "15 0.0 0.0 0.0 0.0\n", "16 0.0 0.0 0.0 0.0\n", "17 0.0 0.0 0.0 0.0\n", "18 0.0 0.0 0.0 0.0\n", "19 0.0 0.0 0.0 0.0\n", "20 0.0 0.0 0.0 0.0\n", "21 0.0 0.0 0.0 0.0\n", "22 0.0 0.0 0.0 0.0\n", "23 0.0 0.0 0.0 0.0\n", "24 0.0 0.0 0.0 0.0\n", "25 0.0 0.0 0.0 0.0\n", "26 0.0 0.0 0.0 0.0\n", "27 0.0 0.0 0.0 0.0\n", "28 0.0 0.0 0.0 0.0\n", "29 0.0 0.0 0.0 0.0\n", "... ... ... ... ...\n", "61 0.0 1.0 1.0 0.0\n", "62 0.0 0.0 1.0 0.0\n", "63 1.0 0.0 0.0 0.0\n", "64 0.0 0.0 0.0 0.0\n", "65 0.0 0.0 0.0 1.0\n", "66 1.0 0.0 0.0 0.0\n", "67 0.0 0.0 0.0 2.0\n", "68 0.0 1.0 0.0 1.0\n", "69 0.0 0.0 0.0 0.0\n", "70 1.0 1.0 0.0 1.0\n", "71 0.0 1.0 0.0 0.0\n", "72 0.0 0.0 0.0 0.0\n", "73 3.0 0.0 0.0 0.0\n", "74 0.0 0.0 0.0 0.0\n", "75 0.0 0.0 0.0 0.0\n", "76 0.0 0.0 0.0 0.0\n", "77 0.0 0.0 0.0 0.0\n", "78 0.0 0.0 1.0 0.0\n", "79 0.0 0.0 0.0 0.0\n", "80 0.0 0.0 0.0 1.0\n", "81 0.0 0.0 1.0 0.0\n", "82 1.0 0.0 0.0 1.0\n", "83 0.0 0.0 1.0 0.0\n", "84 0.0 1.0 0.0 0.0\n", "85 0.0 0.0 0.0 0.0\n", "86 0.0 0.0 0.0 1.0\n", "87 0.0 0.0 0.0 0.0\n", "88 0.0 0.0 0.0 0.0\n", "89 0.0 0.0 0.0 0.0\n", "90 0.0 0.0 0.0 0.0\n", "\n", "[91 rows x 4 columns]\n", "------------------\n", "\n", "Cessações - Tabela: AinvUrbAcimH\n", " 2010 2011 2012 2013 2014\n", "ÍNDICE \n", "0 0.0 0.000000 0.000000 0.000000 0.000000\n", "1 0.0 0.000000 0.000000 0.000000 0.000000\n", "2 0.0 0.000000 0.000000 0.000000 0.000000\n", "3 0.0 0.000000 0.000000 0.000000 0.000000\n", "4 0.0 0.000000 0.000000 0.000000 0.000000\n", "5 0.0 0.000000 0.000000 0.000000 0.000000\n", "6 0.0 0.000000 0.000000 0.000000 0.000000\n", "7 0.0 0.000000 0.000000 0.000000 0.000000\n", "8 0.0 0.000000 0.000000 0.000000 0.000000\n", "9 0.0 0.000000 0.000000 0.000000 0.000000\n", "10 0.0 0.000000 0.000000 0.000000 0.000000\n", "11 0.0 0.000000 0.000000 0.000000 0.000000\n", "12 0.0 0.000000 0.000000 0.000000 0.000000\n", "13 0.0 0.000000 0.000000 0.000000 0.000000\n", "14 0.0 0.000000 0.000000 0.000000 0.000000\n", "15 0.0 0.000000 0.000000 0.000000 0.000000\n", "16 0.0 0.000000 0.000000 0.000000 0.000000\n", "17 0.0 0.000000 0.000000 0.333339 0.000000\n", "18 0.0 0.363670 0.727297 0.000000 0.727345\n", "19 0.0 0.500048 1.500055 2.000043 1.500154\n", "20 0.0 3.930148 4.491402 3.929896 3.368774\n", "21 0.0 4.157825 4.157609 5.196946 5.717126\n", "22 0.0 7.202619 3.601096 8.230930 7.717142\n", "23 0.0 8.666543 6.932793 8.088168 4.044345\n", "24 0.0 6.566994 14.775075 9.302697 10.397936\n", "25 0.0 11.320166 16.170992 17.787800 9.164134\n", "26 0.0 16.504368 16.503510 17.567982 20.231260\n", "27 0.0 19.811305 23.459229 19.288455 14.597899\n", "28 0.0 23.316587 20.834984 20.834652 16.867373\n", "29 0.0 37.308301 27.461556 28.497446 32.644742\n", "... ... ... ... ... ...\n", "61 0.0 1359.194586 1320.637377 1439.340408 1386.551862\n", "62 0.0 1327.884823 1376.018178 1427.826137 1432.066506\n", "63 0.0 1346.043603 1392.124496 1410.626987 1451.473210\n", "64 0.0 1340.177327 1379.031157 1383.105995 1437.047875\n", "65 0.0 1212.037061 1279.545820 1399.182728 1289.472306\n", "66 0.0 1091.894834 1163.065053 1208.347538 1290.697742\n", "67 0.0 1080.984464 1012.311857 1063.423867 1152.983160\n", "68 0.0 1013.254288 1005.425636 1014.996720 1022.658258\n", "69 0.0 924.111671 965.308128 941.673355 926.516434\n", "70 0.0 831.478270 845.032784 896.143041 945.580110\n", "71 0.0 865.820298 797.750261 856.408228 846.377265\n", "72 0.0 776.180116 823.286941 728.194415 755.120047\n", "73 0.0 716.218210 695.501444 760.260667 688.457154\n", "74 0.0 678.213714 642.193105 667.316090 683.814083\n", "75 0.0 596.129495 572.701911 585.227641 587.445894\n", "76 0.0 535.002987 540.021643 501.961821 512.722192\n", "77 0.0 442.014296 489.182421 509.393406 443.934115\n", "78 0.0 434.105024 414.937676 444.779080 450.445902\n", "79 0.0 376.061774 369.188601 372.006059 372.977223\n", "80 0.0 363.912837 349.257325 327.422279 321.397258\n", "81 0.0 344.937852 325.670456 329.712968 303.953789\n", "82 0.0 262.394418 287.672905 266.393456 273.388935\n", "83 0.0 248.173089 261.121478 265.533096 245.881918\n", "84 0.0 217.594432 207.264611 211.371155 221.017470\n", "85 0.0 183.704011 192.772536 190.414927 193.941234\n", "86 0.0 174.666755 169.713955 175.799137 170.590031\n", "87 0.0 144.334159 154.099266 148.033426 140.005445\n", "88 0.0 118.540883 120.125005 125.446346 125.618119\n", "89 0.0 100.417752 98.164883 109.152027 106.556647\n", "90 0.0 337.960356 348.447292 378.685357 384.690223\n", "\n", "[91 rows x 5 columns]\n", "------------------\n", "\n" ] } ], "source": [ "# A execução desta célula é opcional\n", "\n", "# Função que obtém cada um das tabelas do dicionário e exibe parte do conteúdo\n", "def print_tabelas(colecao, titulo=''):\n", " for i in colecao: \n", " print('%s - Tabela: %s' % (titulo, i))\n", " print(colecao[i])\n", " print('------------------\\n')\n", "\n", "\n", "# Exibe o parte do conteúdo de cada tabela\n", "print_tabelas(estoques, 'Estoques')\n", "print_tabelas(concessoes, 'Concessões')\n", "\n", "\n", "print_tabelas(cessacoes, 'Cessações')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "est_acumulado = {}\n", "\n", "for clientela in ['UrbPiso', 'UrbAcim', 'Rur']:\n", " \n", " est_acumulado[clientela] = pd.DataFrame(index=range(0,91), columns=range(2011,2015))\n", " est_acumulado[clientela].fillna(0.0, inplace=True)\n", " \n", " for beneficio in get_lista([clientela]):\n", " if beneficio in estoques.keys(): \n", " est_acumulado[clientela] += estoques[beneficio]\n", "\n", " \n", "def get_clientela(beneficio):\n", " for clientela in ['UrbPiso', 'UrbAcim', 'Rur']:\n", " if clientela in beneficio:\n", " return clientela\n", " \n", " return 'Clientela não identificada'\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2014" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#### Calcula probabilidades de entrada em aposentadorias\n", "tag_apos= ['Apin', 'Atcn', 'Apid', 'Atcp', 'Ainv', 'Atce', 'Atcd']\n", "\n", "for beneficio in get_lista(tag_apos):\n", " # Verifica se o possui os dados de estoque e concessões do benefício\n", " if beneficio in estoques.keys() and beneficio in concessoes.keys():\n", " \n", " clientela = get_clientela(beneficio)\n", " # Calcula a probabilidade de entrada\n", " prob_entrada = concessoes[beneficio][ano_prob] / (est_acumulado[clientela][ano_prob-1] + (concessoes[beneficio][ano_prob]/2))\n", " \n", " prob[beneficio] = pd.DataFrame(prob_entrada)\n", " prob[beneficio].columns = [ano_prob] # nome da coluna no Dataframe\n", " prob[beneficio][ano_prob].fillna(0, inplace = True) # Substitui os NaN (not a number) por zeros\n", "\n", "ano_prob " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GdnY2hmEwaNAgr9pRSnkceOvXr8cwDNtXUFAQnTt35rHHHuPMmTNejaspfR44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\nizHmMCCDnRzWtFpbJvCiv5rLC1MDwxamvn+q6hqPMWYV9odeWXc38Ba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"text/plain": [ "<matplotlib.figure.Figure at 0x1720d727080>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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3qDj9GgzEAMOVUrNuZVxCiNYlIyMDg8HAww/XPiFVWlpKamoqn3/+eYvHYTAYePnll+us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"text/plain": [ "<matplotlib.figure.Figure at 0x1720d61f940>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Am2++GYB27drRr18/VqxYwciRI0PjnXOkpaVFnf+GG26osoZzRQEqIlUrKYHC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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1720f5f37b8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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WnJzMzTf/dMbNPn26ceNGMjIy6N69O506dWLixImcO3eOiooKFixYQExMDB07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\niEgQexzMncaYv4ced0y9xphaEVkG/EREPgnVMB07ENd3Va29MTRV13s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"text/plain": [ "<matplotlib.figure.Figure at 0x1720f693358>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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EVVWtXn5+fkRHR/PJJ580a59xcXF26zZt2oSqqpw6darZ+r8dkLNnJc2PeUef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\nLzuROU1JI0hMTCQmJgZXV1diY2M5d+4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r7+f41OOZlu3RKmzw4JbnZtSN3D7xdn6++WeG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\n6RcCu4iJUUFAir3DqSkpfFhVR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"text/plain": [ "<matplotlib.figure.Figure at 0x1720f7049b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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paSmzZ8/GGMOIESOatR+1BU1TB5E3K1asIDY2lvXr11NSUkKnBv4yVVZWRrt2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\nZreFw5y4aBFlNTX8c+xYfjB4MHlJfMyXKjQ07Dn+v6oKFi5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"text/plain": [ "<matplotlib.figure.Figure at 0x1720f786e10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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yH374IRMnTuT6669n6tSp/PGPf2TcuHGsXr2aoUOH1vochgwZQnx8PBMnTuSO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\nMBxIBZ6obwUz+xUwE7gO6A/8H7DazKL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"text/plain": [ "<matplotlib.figure.Figure at 0x1720f7f7ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plota as figuras para cada tipo de aposentadoria\n", "for t in tag_apos:\n", " temp = pd.DataFrame()\n", " for beneficio in get_lista([t]):\n", " if beneficio in prob.keys():\n", " temp[beneficio] = prob[beneficio][2014]\n", "\n", " temp.plot(figsize = (5,5)).legend(bbox_to_anchor=(1, 1))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Cáculo das probabilidades de morte" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "populacao = carrega_dados(['PopIbgeH', 'PopIbgeM'], xls)\n", "\n", "ano_inicio = 2014\n", "ano_fim = 2060\n", "\n", "periodo=list(range(ano_inicio,ano_fim))\n", "\n", "def calc_prob_morte_ufpa(pop, sexo):\n", "\n", " periodo = list(pop[sexo].columns)\n", " txMort = pd.DataFrame(index=range(0,91), columns=periodo) \n", " \n", " for ano in periodo[0:-1]:\n", " for idade in range(0,89):\n", " pop_atual = pop[sexo][ano][idade] \n", " pop_prox_ano = pop[sexo][ano+1][idade+1] \n", " txMort[ano][idade] = 1 - (pop_prox_ano/pop_atual)\n", " \n", " # Calculo para a idade de 89 anos\n", " txMort[ano][89] = txMort[ano][88]\n", "\n", " # Calculo para a idade de 90 anos\n", " txMort[ano][90] = 1 - (pop[sexo][ano+1][90] - pop[sexo][ano][89] * (1 - txMort[ano][89])) / pop[sexo][ano][90]\n", " \n", " # Repete a Prob do ultimo ano como valor do antepenultimo\n", " txMort[periodo[-1]] = txMort[periodo[-2]]\n", "\n", " return txMort\n", "\n", " \n", "for sexo in populacao.keys(): \n", " prob['txMort'+sexo[-1]] = calc_prob_morte_ufpa(populacao, sexo) \n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "ÍNDICE\n", "17 5.000047\n", "18 9.000023\n", "19 20.000164\n", "dtype: float64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Casos em que o número de Concessões é maior que o Estoque (O que gera erro no cálculo das probabilidades)\n", "\n", "erros = concessoes['AinvRurH'][ano_prob-1].shift(1).gt(estoques['AinvRurH'][ano_prob])\n", "\n", "temp = estoques['AinvRurH'][ano_prob][erros] + concessoes['AinvRurH'][ano_prob-1].shift(1)[erros]\n", "\n", "#estoques['AinvRurH'][ano_prob][erros] = temp\n", "#novo = concessoes['AinvRurH'][ano_prob-1][erros].shift(1) + estoques['AinvRurH'][ano_prob].shift(1)\n", "\n", "#concessoes['AinvRurH'][ano_prob-1][erros]\n", "#estoques['AinvRurH'][ano_prob][erros].shift(0)\n", "\n", "#estoques['AinvRurH'][ano_prob]\n", "\n", "erros\n", "temp\n" ] }, { "cell_type": "code", "execution_count": 11, "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>2011</th>\n", " <th>2012</th>\n", " 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gpl-3.0
ES-DOC/esdoc-jupyterhub
notebooks/ec-earth-consortium/cmip6/models/sandbox-2/ocnbgchem.ipynb
1
79398
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocnbgchem \n", "**MIP Era**: CMIP6 \n", "**Institute**: EC-EARTH-CONSORTIUM \n", "**Source ID**: SANDBOX-2 \n", "**Topic**: Ocnbgchem \n", "**Sub-Topics**: Tracers. \n", "**Properties**: 65 (37 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/ocnbgchem?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:53:59" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'sandbox-2', 'ocnbgchem')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --&gt; Time Stepping Framework --&gt; Passive Tracers Transport](#2.-Key-Properties---&gt;-Time-Stepping-Framework---&gt;-Passive-Tracers-Transport) \n", "[3. Key Properties --&gt; Time Stepping Framework --&gt; Biology Sources Sinks](#3.-Key-Properties---&gt;-Time-Stepping-Framework---&gt;-Biology-Sources-Sinks) \n", "[4. Key Properties --&gt; Transport Scheme](#4.-Key-Properties---&gt;-Transport-Scheme) \n", "[5. Key Properties --&gt; Boundary Forcing](#5.-Key-Properties---&gt;-Boundary-Forcing) \n", "[6. Key Properties --&gt; Gas Exchange](#6.-Key-Properties---&gt;-Gas-Exchange) \n", "[7. Key Properties --&gt; Carbon Chemistry](#7.-Key-Properties---&gt;-Carbon-Chemistry) \n", "[8. Tracers](#8.-Tracers) \n", "[9. Tracers --&gt; Ecosystem](#9.-Tracers---&gt;-Ecosystem) \n", "[10. Tracers --&gt; Ecosystem --&gt; Phytoplankton](#10.-Tracers---&gt;-Ecosystem---&gt;-Phytoplankton) \n", "[11. Tracers --&gt; Ecosystem --&gt; Zooplankton](#11.-Tracers---&gt;-Ecosystem---&gt;-Zooplankton) \n", "[12. Tracers --&gt; Disolved Organic Matter](#12.-Tracers---&gt;-Disolved-Organic-Matter) \n", "[13. Tracers --&gt; Particules](#13.-Tracers---&gt;-Particules) \n", "[14. Tracers --&gt; Dic Alkalinity](#14.-Tracers---&gt;-Dic-Alkalinity) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Ocean Biogeochemistry key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of ocean biogeochemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of ocean biogeochemistry model code (PISCES 2.0,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Model Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of ocean biogeochemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.model_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Geochemical\" \n", "# \"NPZD\" \n", "# \"PFT\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Elemental Stoichiometry\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe elemental stoichiometry (fixed, variable, mix of the two)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Fixed\" \n", "# \"Variable\" \n", "# \"Mix of both\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Elemental Stoichiometry Details\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe which elements have fixed/variable stoichiometry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of all prognostic tracer variables in the ocean biogeochemistry component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Diagnostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of all diagnotic tracer variables in the ocean biogeochemistry component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.diagnostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.8. Damping\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe any tracer damping used (such as artificial correction or relaxation to climatology,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.damping') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Time Stepping Framework --&gt; Passive Tracers Transport \n", "*Time stepping method for passive tracers transport in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time stepping framework for passive tracers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"use ocean model transport time step\" \n", "# \"use specific time step\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Timestep If Not From Ocean\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Time step for passive tracers (if different from ocean)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.timestep_if_not_from_ocean') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Time Stepping Framework --&gt; Biology Sources Sinks \n", "*Time stepping framework for biology sources and sinks in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time stepping framework for biology sources and sinks*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"use ocean model transport time step\" \n", "# \"use specific time step\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Timestep If Not From Ocean\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Time step for biology sources and sinks (if different from ocean)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.timestep_if_not_from_ocean') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Transport Scheme \n", "*Transport scheme in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of transport scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Offline\" \n", "# \"Online\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Transport scheme used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Use that of ocean model\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Use Different Scheme\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Decribe transport scheme if different than that of ocean model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.use_different_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --&gt; Boundary Forcing \n", "*Properties of biogeochemistry boundary forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Atmospheric Deposition\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how atmospheric deposition is modeled*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.atmospheric_deposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"from file (climatology)\" \n", "# \"from file (interannual variations)\" \n", "# \"from Atmospheric Chemistry model\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. River Input\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how river input is modeled*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.river_input') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"from file (climatology)\" \n", "# \"from file (interannual variations)\" \n", "# \"from Land Surface model\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Sediments From Boundary Conditions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List which sediments are speficied from boundary condition*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_boundary_conditions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Sediments From Explicit Model\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List which sediments are speficied from explicit sediment model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_explicit_model') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --&gt; Gas Exchange \n", "*Properties of gas exchange in ocean biogeochemistry *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. CO2 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is CO2 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_present') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. CO2 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe CO2 gas exchange*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"OMIP protocol\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. O2 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is O2 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_present') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.4. O2 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe O2 gas exchange*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"OMIP protocol\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.5. DMS Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is DMS gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_present') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.6. DMS Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify DMS gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.7. N2 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is N2 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_present') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.8. N2 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify N2 gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.9. N2O Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is N2O gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_present') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.10. N2O Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify N2O gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.11. CFC11 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is CFC11 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_present') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.12. CFC11 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify CFC11 gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.13. CFC12 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is CFC12 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_present') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.14. CFC12 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify CFC12 gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.15. SF6 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is SF6 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_present') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.16. SF6 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify SF6 gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.17. 13CO2 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is 13CO2 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_present') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.18. 13CO2 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify 13CO2 gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.19. 14CO2 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is 14CO2 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_present') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.20. 14CO2 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify 14CO2 gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.21. Other Gases\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any other gas exchange*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.other_gases') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --&gt; Carbon Chemistry \n", "*Properties of carbon chemistry biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how carbon chemistry is modeled*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"OMIP protocol\" \n", "# \"Other protocol\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. PH Scale\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If NOT OMIP protocol, describe pH scale.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.pH_scale') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sea water\" \n", "# \"Free\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Constants If Not OMIP\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If NOT OMIP protocol, list carbon chemistry constants.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.constants_if_not_OMIP') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Tracers \n", "*Ocean biogeochemistry tracers*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of tracers in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Sulfur Cycle Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is sulfur cycle modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.sulfur_cycle_present') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Nutrients Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List nutrient species present in ocean biogeochemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.nutrients_present') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Nitrogen (N)\" \n", "# \"Phosphorous (P)\" \n", "# \"Silicium (S)\" \n", "# \"Iron (Fe)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Nitrous Species If N\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If nitrogen present, list nitrous species.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_species_if_N') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Nitrates (NO3)\" \n", "# \"Amonium (NH4)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Nitrous Processes If N\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If nitrogen present, list nitrous processes.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_processes_if_N') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Dentrification\" \n", "# \"N fixation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Tracers --&gt; Ecosystem \n", "*Ecosystem properties in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Upper Trophic Levels Definition\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Definition of upper trophic level (e.g. based on size) ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_definition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Upper Trophic Levels Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Define how upper trophic level are treated*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Tracers --&gt; Ecosystem --&gt; Phytoplankton \n", "*Phytoplankton properties in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of phytoplankton*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Generic\" \n", "# \"PFT including size based (specify both below)\" \n", "# \"Size based only (specify below)\" \n", "# \"PFT only (specify below)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Pft\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Phytoplankton functional types (PFT) (if applicable)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.pft') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Diatoms\" \n", "# \"Nfixers\" \n", "# \"Calcifiers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Size Classes\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Phytoplankton size classes (if applicable)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.size_classes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Microphytoplankton\" \n", "# \"Nanophytoplankton\" \n", "# \"Picophytoplankton\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Tracers --&gt; Ecosystem --&gt; Zooplankton \n", "*Zooplankton properties in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of zooplankton*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Generic\" \n", "# \"Size based (specify below)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Size Classes\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Zooplankton size classes (if applicable)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.size_classes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Microzooplankton\" \n", "# \"Mesozooplankton\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Tracers --&gt; Disolved Organic Matter \n", "*Disolved organic matter properties in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Bacteria Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there bacteria representation ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.bacteria_present') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Lability\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe treatment of lability in dissolved organic matter*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.lability') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Labile\" \n", "# \"Semi-labile\" \n", "# \"Refractory\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Tracers --&gt; Particules \n", "*Particulate carbon properties in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is particulate carbon represented in ocean biogeochemistry?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.particules.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Diagnostic\" \n", "# \"Diagnostic (Martin profile)\" \n", "# \"Diagnostic (Balast)\" \n", "# \"Prognostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Types If Prognostic\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, type(s) of particulate matter taken into account*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.particules.types_if_prognostic') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"POC\" \n", "# \"PIC (calcite)\" \n", "# \"PIC (aragonite\" \n", "# \"BSi\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Size If Prognostic\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, describe if a particule size spectrum is used to represent distribution of particules in water volume*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_prognostic') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"No size spectrum used\" \n", "# \"Full size spectrum\" \n", "# \"Discrete size classes (specify which below)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Size If Discrete\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic and discrete size, describe which size classes are used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_discrete') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Sinking Speed If Prognostic\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, method for calculation of sinking speed of particules*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.particules.sinking_speed_if_prognostic') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Function of particule size\" \n", "# \"Function of particule type (balast)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Tracers --&gt; Dic Alkalinity \n", "*DIC and alkalinity properties in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Carbon Isotopes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which carbon isotopes are modelled (C13, C14)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.carbon_isotopes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"C13\" \n", "# \"C14)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Abiotic Carbon\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is abiotic carbon modelled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.abiotic_carbon') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Alkalinity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is alkalinity modelled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.alkalinity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Prognostic\" \n", "# \"Diagnostic)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
abhipr1/DATA_SCIENCE_INTENSIVE
Capstone/StockPredicition.ipynb
1
1109715
null
apache-2.0
zzsza/TIL
reinforcement_learning/textbook summary/02. 강화학습 기초(1) - MDP와 벨만 방정식.ipynb
1
14537
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 02. 강화학습 기초(1) - MDP와 벨만 방정식\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 강화학습이 결국 어떠한 방정식을 풀어내는 방법이라면 그 방정식이 무엇인지 아는 것이 중요할 것입니다\n", "- 이 방정식은 벨만 방정식을 가리킵니다\n", "\n", "## MDP\n", "- 구성 요소 : 상태 / 행동 / 보상 함수 / 상태 변환 확률 / 감가율\n", "- 강화학습에서는 사용자가 문제를 정의해야 합니다\n", "\n", "### 상태\n", "- 상태 S는 에이전트가 관찰 가능한 상태의 집합\n", "- 내가 정의하는 상태는 에이전트가 학습하기에 충분한 정보일까?란 생각을 곱씹어봐야함\n", "- S = {(x1,y1), (x2,y2), (x3,y3), (x4,y4),(x5,y5)} 이런 식!\n", "\n", "![image](http://thevoid.ghost.io/content/images/2016/06/maze1.png)\n", "(위 사진은 참고를 위해 가져왔을 뿐, 설명과 상관 없음)\n", "\n", "- 5 x 5 그리드월드에서 S = {(1,1),(1,2),(1,3)....(5,5)}\n", "- 에이전트는 25개의 상태집합 안에 있는 상태를 탐험하게 됩니다. 시간은 t일 때 상태를 S_t로 표현합니다! 예를 들면 아래와 같음" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{c}\n", "S_t = (1,3)\n", "\\end{array}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "t= 1일때 S_t = (1,3)일 수도 있고, (4,2)일 수도 있음. 이것들은 확률 변수 ( Random Variable )라고 하며, \n", "\n", "시간 t에서의 상태 S_t가 어떤 상태 s다를 수식으로 표현하면 아래와 같습니다" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{c}\n", "S_t = s\n", "\\end{array}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 행동\n", "- 에이전트가 상태 S에서 할 수 있는 가능한 행동의 집합은 A입니다. \n", "- 보통 에이전트가 할 수 있는 행동은 모든 상태에서 같습니다\n", "- 시간 t에 에이전트가 특정한 행동 a를 했다면 아래와 같이 표현할 수 있습니다" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{c}\n", "A_t = a\n", "\\end{array}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{c}\n", "A_t\n", "\\end{array} 는 어떤 t라는 시간에 집합 A에서 선택한 행동입니다. 이 친구도 확률변수\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A = {up, down, left, right}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 보상 함수\n", "- 에이전트가 학습할 수 있는 유일한 정보로 환경이 에이전트에게 주는 정보\n", "- 보상함수 : 시간 t일 때 상태가 s이고 그 상태에서 행동 a를 했을 경우 받을 보상에 대한 기댓값 E\n", "- 아래와 같은 수식으로 정의합니다\n", "- 조건부 확률, 기댓값" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{c}\n", "R_s^a = E[R_t+1|S_t=s, A_t=a]\n", "\\end{array}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 어떤 상태에서 행동한 것은 시간 t인데, 보상을 받는 것은 t+1\n", "- 보상을 에이전트가 알고 있는 것이 아니고 환경이 알려주기 때문에 위와같은 수식이 나온 것" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 상태 변환 확률\n", "- 에이전트가 앞으로 나아가는 행동을 하면 s보다 앞에 있는 s'라는 상태에 도달할 것입니다\n", "- 하지만 넘어진다거나 바람이 불면 상태가 변환할 수 있는데, 이를 수치적으로 표현한 것" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{c}\n", "P^a_{ss'} = P[S_t+1=s'|S_t=s, A_t=a]\n", "\\end{array}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 상태 변환 확률 : 상태 s에서 행동 a를 취했을 때 다른 상태 s'에 도달할 확률\n", "- 에이전트가 알지 못하는 값으로서 에이전트가 아닌 환경의 일부이며, 환경의 모델이라고 부릅니다" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 감가율\n", "- 에이전트는 항상 현재에 판단을 내리기 때문에 현재에 가까운 보상일수록 더 큰 가치를 가짐\n", "- 현재로부터 일정 시간이 흐른 후, 보상을 받으면 100이라고 하지 않고 감가시켜 현재의 가치로 따짐" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{c}\n", "\\gamma \\in [0, 1]\n", "\\end{array}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 현재의 시간 t로부터 시간 k가 지난 후에 보상을 R_t+k를 받을 것이라면 아래와 같은 수식으로 표현\n", "$$\n", "\\begin{array}{c}\n", "\\gamma^{k-1}R_{t+k}\n", "\\end{array}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 정책\n", "- 정책은 모든 상태에서 에이전트가 할 행동\n", "- 상태가 입력으로 들어오면 행동을 출력으로 내보내는 일종의 함수" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{c}\n", "\\pi(a|s) = P[A_t=a|S_t=s]\n", "\\end{array}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 시간 t에 S_t = s에 에이전트가 있을 때 가능한 행동 중에서 A_t=a를 할 확률\n", "- 최적 정책을 얻기 위해 현재의 정책보다 더 좋은 정책을 학습해나가는 것이 강화학습\n", "- 환경은 에이전트에게 실제 보상과 상태를 알려줍니다. 이 과정에서 예상한 보상에 대해 틀렸다는 것을 알게 됩니다\n", "- 이 때 받을 것이라 예상하는 보상을 가치함수라 합니다" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 가치함수\n", "- MDP -> 가치함수 -> 행동 선택\n", "- 현재 시간 t로부터 에이전트가 행동을 하면서 받을 보상들을 합한다면 아래와 같습니다\n", "$$\n", "\\begin{array}{c}\n", "R_{t+1}+R_{t+2}+R_{t+3}+R_{t+4}+...\n", "\\end{array}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 에이전트가 시간마다 보상을 받을 수도 있고 게임이 끝날 때 한 번에 받을 수도 있습니다\n", "- 감가율을 고려한 보상들의 합은(반환값)\n", "$$\n", "\\begin{array}{c}\n", "G_t = R_{t+1}+\\gamma R_{t+2}+\\gamma^2 R_{t+3}+\\gamma^3 R_{t+4}+...\n", "\\end{array}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 어떠한 상태에 있으면 앞으로 얼마의 보상을 받을 것인지에 대한 기댓값을 고려해볼 수 있는데, 이것이 바로 가치함수입니다\n", "$$\n", "\\begin{array}{c}\n", "v(s) = E[G_t|S_t=s]\n", "\\end{array}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 각 스텝마다 받는 보상이 모두 확률적이고 반환값이 그 보상들의 합이므로 반환값은 확률변수입니다\n", "- 하지만 가치함수는 확률변수가 아니라 특정 양을 나타내는 값으므로 소문자로 표현 \n", "- 상태의 가치를 고려하는 이유는 현재 에이전트가 갈 수 있는 상태들의 가치를 안다면 그 중에서 가장 가치가 높은 상태를 선택할 수 있기 때문\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{c}\n", "v(s) = E[R_{t+1}+\\gamma R_{t+2}+\\gamma^2 R_{t+3}+\\gamma^3 R_{t+4}+...|S_t=s]\n", "\\end{array}\n", "$$\n", "\n", "이를 감마로 묶어주고 반환값의 형태로 표현하면\n", "\n", "$$\n", "\\begin{array}{c}\n", "v(s) = E[R_{t+1}+\\gamma G_{t+1}|S_t=s]\n", "\\end{array}\n", "$$\n", "\n", "이 보상은 앞으로 받을 것이라 예상하는 보상이기에 가치함수로 표현해보면\n", "\n", "$$\n", "\\begin{array}{c}\n", "v_{\\pi}(s) = E_{\\pi}[R_{t+1}+\\gamma v_{\\pi}(S_{t_1})|S_t=s]\n", "\\end{array}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 큐함수 ( 행동 가치함수)\n", "- 상태가 입력으로 들어오면 그 상태에서 앞으로 받을 보상의 합을 출력하는 함수\n", "- 어떤 상태에서 어떤 행동이 얼마나 좋은지 알려주는 함수\n", "\n", "$$\n", "\\begin{array}{c}\n", "q_{\\pi}(s,a)\n", "\\end{array}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 가치함수와 큐함수 사이의 관계\n", " 1. 각 행동을 했을 때 앞으로 받을 보상인 큐함수를 \n", " $$\\begin{array}{c}\\pi(a|s)\\end{array}$$ 에 곱함\n", " 2. 모든 행동에 대해 큐함수와 큐하수를 곱한 값을 더하면 가치함수가 됩니다" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 가치함수와 큐함수 사이의 관계식\n", "\n", "$$\n", "\\begin{array}{c}\n", "v_{\\pi}(s) = \\Sigma\\pi(a|s) * q_\\pi(s,a)\n", "\\end{array}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "큐함수 또한 벨만 기대 방정식의 형태로 나타내면 아래와 같습니다\n", "\n", "$$\n", "\\begin{array}{c}\n", "q_\\pi(s,a) = E_{\\pi}[R_{t+1}+\\gamma q_{\\pi}(S_{t+1}, A_{t+1})|S_t = s, A_t = a]\n", "\\end{array}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 벨만 기대 방정식 ( Bellman Expectation Equation )\n", "- 정책을 고려한 가치함수의 표현 \n", "#### 현재 상태의 가치 함수와 다음 상태의 가치함수 사이의 관계를 말해주는 방정식\n", "\n", "$$\n", "\\begin{array}{c}\n", "v_\\pi(s) = E_\\pi[R_{t+1}+\\gamma v_\\pi(S_{t_1})|S_t=s]\n", "\\end{array}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## 벨만 기대 방정식 ( Bellman Expectation Equation )\n", "- 가치 함수는 어떤 상태의 가치에 대한 기대를 나타냅니다. 어떤 상태의 가치함수는 에이전트가 그 상태로 갈 경우에 앞으로 받을 보상의 합에 대한 기댓값" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{c}\n", "v_\\pi(s) = \\Sigma\\pi(a|s)(R_{t+1}+\\gamma\\Sigma P_{ss'}^a v_{\\pi}(s'))\n", "\\end{array}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "위 식을 이렇게 표현할 수 있습니다\n", "- 각 행동에 대해 그 행동을 할 확률을 고려하고\n", "- 각 행동을 했을 때 받을 보상과\n", "- 다음 상태의 가치 함수를 고려합니다" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 참 가치함수 : 어떤 정책을 따라서 움직였을 경우에 받게 되는 보상에 대한 참값\n", "- 최적의 가치 함수 : 수많은 정책 중에서 가장 높은 보상을 주는 가치함수\n", "\n", "- 더 좋은 정책이라는 것은 가치함수(수(정책을 따라갔을 때 받을 보상들의 합)를 통해 판단할 수 있습니다\n", "- 최적의 가치함수는 아래와 같이 표현합니다" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{c}\n", "v_*(s) = max[v_{\\pi}(s)]\n", "\\end{array}\n", "$$\n", "\n", "최적의 큐함수\n", "$$\n", "\\begin{array}{c}\n", "q_*(s,a) = max[q_{\\pi}(s,a)]\n", "\\end{array}\n", "$$\n", "\n", "선택 상황에서 판단 기준은 큐함수!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{c}\n", "\\pi_*(s,a) = > 1 if a = argmax q_{*}(s,a) /// 0 otherwise\n", "\\end{array}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "큐함수 중에서 max를 취하는 것이 최적의 가치함수.\n", "\n", "$$\n", "\\begin{array}{c}\n", "v_*(s) = max[q_*(s,a)|S_t=s, A_t=a]\n", "\\end{array}\n", "$$\n", "\n", "그렇다면 이제 큐함수를 가치함수로 고쳐서 표현하면!!!! 벨만 최적 방정식이 탄생!!!!!!!!!!\n", "\n", "$$\n", "\\begin{array}{c}\n", "v_*(s) = max E[R_{t+1}+\\gamma v_*(S_{t+1})|S_t = s, A_t = a]\n", "\\end{array}\n", "$$" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" }, "latex_envs": { "LaTeX_envs_menu_present": true, "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 } }, "nbformat": 4, "nbformat_minor": 2 }
mit
mirjalil/DataScience
notebooks/statistics-probability/ipynb/Review.ipynb
1
9977
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "### Bayes Rule (theorem)\n", "\n", "Question: find the **\"posterior\"** probabilities if various outcomes $A_j$ for $j=1..d$ given some data $B$ and given lieklihood of this data $B$ for each outcome condition on each outcome and given prior information on the outcomes.\n", "\n", " * outcomes are $A_j$s (events) \n", " * data is $B$ (event which actually occured) \n", " * Posterior probability: $\\mathbf{P}[A_j ~|~ B]$ for $j=1..d$ \n", " * Likelihoods: $\\mathbf{P}[B ~|~ A_j]$ for $j=1..d$ \n", " * Priors: $\\mathbf{P}[A_j]$ for $j=1..d$ \n", " \n", "**Answer: (Bayes theorem)** \n", "\n", "$$\\mathbf{P}[A_j ~|~ B] = \\frac{\\displaystyle \\mathbf{P}[B ~|~ A_j] ~ \\mathbf{P}[A_j]}{\\text{normalizing factor}}$$\n", "\n", "where the $\\text{normalizing factor} = \\displaystyle \\sum_{i=1}^d \\mathbf{P}[B~|~A_j] \\mathbf{P}[A_j]$\n", "\n", "Note: for this to work, the $A_j$ have to **span all possibilities** and are **disjoint**. For example, with $d=2$ we typically use the notation $A_1 = A$ and $A_2=A^c$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Other stuff\n", "\n", " * $\\mathbf{P}[A^c] = 1 - \\mathbf{P}[A]$\n", "\n", " * $\\mathbf{P}[A\\cup B] = \\mathbf{P}[A] + \\mathbf{P}[B] - \\mathbf{P}[A \\cap B]$ \n", " 0\n", " * $\\mathbf{P}[A\\cup B\\caup C] = \\mathbf{P}[A] + \\mathbf{P}[B] + \\mathbf{P}[C] - \\mathbf{P}[A \\cap B] - \\mathbf{P}[A\\cap C] - \\mathbf{P}[B\\cap C] + \\mathbf{P}[A\\cap B\\cap C]$\n", " \n", " * If $A\\&B$ are independent: $\\mathbf{P}[A \\cap B] = \\mathbf{P}[A]~ \\mathbf{P}[B]$ (this is in fact the definition of independence)\n", " \n", "##### Example: \"Chevalia de Mere\" problem\n", "\n", "\n", "$$ \\mathbf{P}[\\text{at least one \"six\" in 5 die tosses}] = 1 - \\mathbf{P}[\\text{no \"six\" in 4 dies tosses}] = 1 - \\left(\\frac{5}{6}\\right)^4 \\approx 0.5177$$\n", "\n", "$$ \\mathbf{P}[\\text{at least one \"six-six\" in 24 tosses of two dice}] = 1 - \\mathbf{P}[\\text{nop \"six-six\" in 24 tosses of two dice}] = 1 - \\left(\\frac{35}{36}\\right)^{24} \\approx 0.4914$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discrete vs. Continuous Random Variable\n", "\n", " * For discrete RV: we have probability mass function (**PMF**) $p_X(x) = \\mathbf{P}[X=x]$\n", " \n", " * For continuous case: we have density $f_X(x) = \\mathbf{P}[x-dx \\le x \\le x] \\frac{1}{dx}$\n", " \n", "In both cases, we have the **cumulative distribution function (CDF)** is $F_X(x) = \\mathbf{P}[X\\le x]$\n", " \n", " * The CDF always exists.\n", " \n", "For The continuous case, if density exists, then $\\displaystyle f_X(x) = \\frac{\\displaystyle d~F_X(x)}{\\displaystyle dx} = F'(x)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Expectations\n", "\n", " * **Discrete**: $\\mathbf{E}[X] = \\displaystyle \\sum_x x~p_X(x)$ \n", " sum over all values of $x$ such that $p_X(x)\\ne 0$\n", " \n", " * **Continuous**: $\\mathbf{E}[X] =\\displaystyle \\int_{-\\infty}^\\infty x ~f_X(x) ~ dx$ \n", " Note: integrate over the range of the random variable which is the same as the interval(s) where $f_X$ is non-zero" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Chebyshev Inequality\n", "\n", " * It works for every random variable\n", " \n", " * Assume that the 1st moment of random variable $X$ exists; i.e., $\\mathbf{E}[X] < \\infty$\n", " \n", "Then,\n", "\n", "$$\\mathbf{P}[|X| > u] \\le \\frac{\\mathbf{E}[|X|]}{u}$$\n", "\n", "**Remark: ** If $\\mathbf{E}[Y^2] < \\infty$, then \n", "\n", "$$\\displaystyle \\mathbf{P}[|Y|> a] = \\mathbf{P}[|Y|^2 > a^2] \\text{applied Chebyshev to }X=|Y|^2 \\text{&} u=a^2 \\Longrightarrow\\\\ \\le \\frac{\\mathbf{E}[|Y|^2]}{a^2}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Application: Weak Law of Large Numbers\n", "\n", "Assume $X_1,X_2,...X_n$ are $iid$ with $\\mathbf{E}[|X_i|^2]<\\infty$.\n", "\n", "Then, compute $\\displaystyle \\mathbf{P}[|\\frac{X_1 + X_2 + ... +X_n}{n} -\\mathbf{E}[X_1]| > \\epsilon]=?$\n", "\n", "**Answer:** We use Chebyshev's inequality: Let's use $Y = \\frac{X_1 + X_2 + ... +X_n}{n} -\\mathbf{E}[X_1]$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "$$\\displaystyle \\mathbf{P}[|Y| > \\epsilon] \\le \\frac{\\mathbf{E}[|Y|^2]}{\\epsilon^2} = \\frac{\\mathbf{Var}[Y] + 0}{\\epsilon^2} = \\frac{\\displaystyle \\frac{n\\mathbf{E}[X_1]}{n^2}}{\\epsilon^2} = \\frac{\\mathbf{Var}[X_1]}{n\\times \\epsilon^2}$$\n", "\n", "We used the fact that $\\mathbf{E}[Y] = 0$ and $\\mathbf{E}[|Y|^2] = \\mathbf{Var}[Y] + (\\mathbf{E}[Y])^2$\n", "\n", "We see that when $\\epsilon$ is fixed, and $n\\to \\infty$, we see that the empirical mean converges to the expectation of that random variable.\n", "\n", "This means (by definition) that $$\\frac{X_1 + X_2 + ... + X_n}{n} \\to \\mathbf{E}[X_i] \\ \\ \\text{ converges in probability}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Special Discrete Random Variables\n", "\n", "### Bernoulli\n", "\n", "p\n", "\n", "\n", "\n", "### Binomial:\n", "\n", " * Definition: sum of Bernoulli random variables\n", " \n", "$(n choose k)p^k (1-p)^{n-k}$\n", "\n", "When $n$ is large like ($n=10000$, we can a[pproximate the binomial probability using two methods:\n", " * Poisson approximation \n", " * Central limit theorem (normal approximation) \n", " \n", " **Poisson approximation** Let $X_n\\sim Binomial(n, \\frac{\\lambda}{n})$, then $X_n$ is approximately $\\sim Poisson(\\lambda)$ for large $n$. (This is convergence in distribution, i.e. theor CDF will converge):\n", " \n", " This approxim means that $X_n \\to X$ in distribution, this really means $F_{X_n} \\to F_X(x)$ at any value $x$\n", " \n", " There are two ways to prove this: \n", " * write the CDF of both and use compute their limits \n", " * proof via moment generating functions and the $\\lim_{n\\to\\infty} (1+\\frac{x}{n})^n = e^x$ \n", " \n", " **Normal approximation** Let $X_n\\sim Binomial(n,p)$. \n", " Standardize $X_n$, then with large $n$, the following convergence in distribution:\n", " $$\\frac{X_n -np}{\\sqrt{np(1-p)}} \\to \\mathcal{N}(0,1) \\ \\ \\text{in distribution}$$\n", " \n", " Proof by CLT\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Recall on Mixtures\n", "\n", "### Probability Generating Functions (PGF)\n", "\n", "$$P_X(z) = \\mathbf{E}[z^X] = \\mathbf{E}[e^{X~\\ln z}] = M_X\\left(\\ln z\\right)$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Modeling with Poisson\n", " \n", " \n", " * Poisson process $N(t) = \\text{# of successuve events in interval [0,t]}$ when $t$ changes\n", " \n", " Moreover, let $X_1,X_2, ...$ be the times between each arrival. Then, these are $iid$ Exponential $\\sim Exp(\\lambda)$.\n", " \n", " Note: $\\lambda = \\mathbf{E}[N(1)] = \\frac{1}{\\mathbf{E}[X_i]}$\n", " \n", " This means average frequency of arrivals is $\\frac{1}{\\text{average time of arrivals}}$\n", " \n", " Now let $Y_n = X_1 + X_2 + .. +X_n$ this is time of $n$th arrival. Then\n", " \n", " $$Y_n \\sim \\Gamma(\\alpha=n, \\theta=\\frac{!}{\\lambda})$$\n", " \n", " * $\\alpha = \\text{scale paramater}$ \n", " * $\\theta = \\text{shape parameter}$\n", " \n", " Indeed, each $X_i \\sim \\Gamma(n=1, \\theta=\\frac{1}{\\lambda})$ and $X_i \\sim Exp(\\lambda)$ \n", " \n", " That means $Exp(\\lambda) \\text{eauivalent} \\Gamma(\\alpha=1, \\theta=\\frac{1}{\\lambda})$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general: the density of $\\Gamma(\\alpha,\\theta)$ is $$f_X(x) = \\frac{1}{\\theta} \\frac{1}{\\Gamma(\\alpha)} \\left(\\frac{x}{\\theta}\\right)^{\\alpha-1} e^{-x/\\theta}$$\n", "\n", "when shape parameter $\\theta=1$: $\\displaystyle \\Gamma(\\alpha,1)$ is $f_X(x) = \\frac{1}{\\Gamma(\\alpha)} \\left(x\\right)^{\\alpha-1} e^{-x}$\n", "\n", " **A quick tip to remember the genral formula:** basically, use the formulae for shape parameter 1, and when shape parameter is $\\theta$, devide anywhere you see $x$ by $\\theta$\n", "\n", " * $\\mathbf{E}[X] = \\alpha \\theta$ \n", " * $\\mathbf{Var}[X] = \\alpha \\theta^2$ \n", " * Meditate on these formulaes (expectation, variance): you can see that $\\theta$ is a shape parameter, similar to $\\sigma^2) in normal dist.$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Geometric\n", "\n", "### Negative Binomial\n", "\n", " The sum of geomeric random variables" ] }, { "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": "3.5.0rc4" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
basp/notes
mathjax_ref.ipynb
1
2019
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### matrices\n", "$\n", "\\begin{matrix}\n", "A_11 & A_12 & \\cdots & A_{1m} \\\\\n", "A_21 & A_22 & \\cdots & A_{2m} \\\\\n", "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", "A_{n1} & A_{n2} & \\cdots & A_{nm}\n", "\\end{matrix}\n", "$\n", "\n", "The above undecorated matrix is generated with: \n", "\n", "```\n", "\\begin{matrix}\n", "A_11 && A_12 & \\cdots & A_{1m} \\\\\n", "A_21 && A_22 & \\cdots & A_{2m} \\\\\n", "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", "A_{n1} & A_{n2} & \\cdots & A_{nm}\n", "\\end{matrix}\n", "```\n", "\n", "We can add brackets either by using `\\left` or `\\right` or by replacing `matrix` with:\n", "\n", "`pmatrix` $\\begin{pmatrix}1 & 2 \\\\ 3 & 4\\end{pmatrix}$\n", "\n", "`bmatrix` $\\begin{bmatrix}1 & 2 \\\\ 3 & 4\\end{bmatrix}$\n", "\n", "`Bmatrix` $\\begin{Bmatrix}1 & 2 \\\\ 3 & 4\\end{Bmatrix}$\n", "\n", "`vmatrix` $\\begin{vmatrix}1 & 2 \\\\ 3 & 4\\end{vmatrix}$\n", "\n", "`Vmatrix` $\\begin{Vmatrix}1 & 2 \\\\ 3 & 4\\end{Vmatrix}$." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### braket\n", "\n", "$\\langle{x}\\rvert$\n", "\n", "$\\lvert{x}\\rangle$\n", "\n", "$\\langle{a}\\mid{b}\\rangle$" ] }, { "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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
RaspberryJamBe/ipython-notebooks
notebooks/en-gb/Output - RGB LED.ipynb
2
4782
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"Dotstar01.png\" height=\"300\" />" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time\n", "from dotstar import Adafruit_DotStar" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Dotstar LEDs are designed for use in a LED strip and the software library reflects that\n", "# so we'll make use of a strip of one LED\n", "numpixels = 1\n", "datapin = 23 # GPIO pin 23 is connected to the data channel of the LED\n", "clockpin = 24 # GPIO pin 24 is connected to the clock channel of the LED\n", "strip = Adafruit_DotStar(numpixels, datapin, clockpin)\n", "strip.begin() # always call this before starting to use the LED" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# and we're ready to start!\n", "# first we set the brightness of the LED(s) (max 255, so 127 is about 50%)\n", "strip.setBrightness(127)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixel_index = 0 # Index of our first (and only) pixel\n", "color_red = 0xFF0000 # Red\n", "color_green = 0x0000FF # Green\n", "color_blue = 0x00FF00 # Blue" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# configure color\n", "strip.setPixelColor(pixel_index, color_blue)\n", "# activate configuration\n", "strip.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "color = color_red # make a color variable and store it in memory\n", "\n", "while True: # repeat forever\n", " strip.setPixelColor(pixel_index, color)\n", " strip.show() # show color\n", " time.sleep(0.5) # wait half a second\n", " \n", " color >>= 8 # color cycle Red -> Green -> Blue -> Black\n", " if(color == 0): color = 0xFF0000 # At black, start again at Red\n", "\n", "# click the stop button (or choose Kernel > Interrupt from the menu) to stop the execution" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import colorsys \n", "\n", "sat = 1\n", "value = 0.5\n", "length = 500\n", "\n", "for teller in range(0, length + 1):\n", " hue = teller/float(length)\n", " color = list(colorsys.hsv_to_rgb(hue, sat, value))\n", " intcolor = int(color[0]*256)*256**2 + int(color[1]*256)*256 + int(color[2]*256)\n", " strip.setPixelColor(pixel_index, intcolor)\n", " strip.show()\n", " #time.sleep(1.0/length)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import math\n", "def makeColorGradient(frequency1, frequency2, frequency3, phase1, phase2, phase3, center=128, width=127, length=500):\n", " for i in range(length):\n", " rood = math.sin(frequency1*i + phase1) * width + center\n", " groen = math.sin(frequency2*i + phase2) * width + center\n", " blauw = math.sin(frequency3*i + phase3) * width + center\n", " intcolor = int(rood) * 255**2 + int(groen) * 255 + int(blauw)\n", " strip.setPixelColor(pixel_index, intcolor)\n", " strip.show()\n", " time.sleep(10.0/length)\n", "\n", "print(makeColorGradient(.01,.01,.01,2,0,4))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "strip.setBrightness(0)\n", "strip.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": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }
cc0-1.0
m2dsupsdlclass/lectures-labs
labs/02_backprop/Backpropagation_numpy.ipynb
1
24816
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Backpropagation in Multilayer Neural Networks\n", "\n", "### Goals: \n", "- implementING a real gradient descent in `Numpy`\n", "\n", "### Dataset:\n", "- Similar as first Lab - Digits: 10 class handwritten digits\n", "- [sklearn.datasets.load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.datasets import load_digits\n", "\n", "digits = load_digits()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sample_index = 45\n", "plt.figure(figsize=(3, 3))\n", "plt.imshow(digits.images[sample_index], cmap=plt.cm.gray_r,\n", " interpolation='nearest')\n", "plt.title(\"image label: %d\" % digits.target[sample_index]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preprocessing\n", "\n", "- Normalization\n", "- Train / test split" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn import preprocessing\n", "from sklearn.model_selection import train_test_split\n", "\n", "data = np.asarray(digits.data, dtype='float32')\n", "target = np.asarray(digits.target, dtype='int32')\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " data, target, test_size=0.15, random_state=37)\n", "\n", "# mean = 0 ; standard deviation = 1.0\n", "scaler = preprocessing.StandardScaler()\n", "X_train = scaler.fit_transform(X_train)\n", "X_test = scaler.transform(X_test)\n", "\n", "# print(scaler.mean_)\n", "# print(scaler.scale_)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train.dtype" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_test.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y_train.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y_train.dtype" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Numpy Implementation\n", "\n", "## a) Logistic Regression\n", "\n", "In this section we will implement a logistic regression model trainable with SGD using numpy. Here are the objectives:\n", "\n", "- Implement a simple forward model with no hidden layer (equivalent to a logistic regression):\n", "note: shape, transpose of W with regards to course\n", "$y = softmax(\\mathbf{W} \\dot x + b)$\n", "\n", "- Build a predict function which returns the most probable class given an input $x$\n", "\n", "- Build an accuracy function for a batch of inputs $X$ and the corresponding expected outputs $y_{true}$\n", "\n", "- Build a grad function which computes $\\frac{d}{dW} -\\log(softmax(W \\dot x + b))$ for an $x$ and its corresponding expected output $y_{true}$ ; check that the gradients are well defined\n", "\n", "- Build a train function which uses the grad function output to update $\\mathbf{W}$ and $b$\n", "\n", "\n", "### One-hot encoding for class label data\n", "\n", "First let's define a helper function to compute the one hot encoding of an integer array for a fixed number of classes (similar to keras' `to_categorical`):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def one_hot(n_classes, y):\n", " return np.eye(n_classes)[y]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "one_hot(n_classes=10, y=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "one_hot(n_classes=10, y=[0, 4, 9, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The softmax function\n", "\n", "Now let's implement the softmax vector function:\n", "\n", "$$\n", "softmax(\\mathbf{x}) = \\frac{1}{\\sum_{i=1}^{n}{e^{x_i}}}\n", "\\cdot\n", "\\begin{bmatrix}\n", " e^{x_1}\\\\\\\\\n", " e^{x_2}\\\\\\\\\n", " \\vdots\\\\\\\\\n", " e^{x_n}\n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def softmax(X):\n", " # TODO:\n", " return None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure that this works one vector at a time (and check that the components sum to one):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(softmax([10, 2, -3]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that a naive implementation of softmax might not be able process a batch of activations in a single call:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X = np.array([[10, 2, -3],\n", " [-1, 5, -20]])\n", "print(softmax(X))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a way to implement softmax that works both for an individual vector of activations and for a batch of activation vectors at once:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def softmax(X):\n", " exp = np.exp(X)\n", " return exp / np.sum(exp, axis=-1, keepdims=True)\n", "\n", "\n", "print(\"softmax of a single vector:\")\n", "print(softmax([10, 2, -3]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Probabilities should sum to 1:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(np.sum(softmax([10, 2, -3])))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"sotfmax of 2 vectors:\")\n", "X = np.array([[10, 2, -3],\n", " [-1, 5, -20]])\n", "print(softmax(X))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sum of probabilities for each input vector of logits should some to 1:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(np.sum(softmax(X), axis=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Implement a function that given the true one-hot encoded class `Y_true` and and some predicted probabilities `Y_pred` returns the negative log likelihood." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nll(Y_true, Y_pred):\n", " Y_true = np.asarray(Y_true)\n", " Y_pred = np.asarray(Y_pred)\n", " \n", " # TODO\n", " return 0.\n", "\n", "\n", "# Make sure that it works for a simple sample at a time\n", "print(nll([1, 0, 0], [.99, 0.01, 0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check that the nll of a very confident yet bad prediction is a much higher positive number:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(nll([1, 0, 0], [0.01, 0.01, .98]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure that your implementation can compute the average negative log likelihood of a group of predictions: `Y_pred` and `Y_true` can therefore be past as 2D arrays:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nll(Y_true, Y_pred):\n", " Y_true = np.atleast_2d(Y_true)\n", " Y_pred = np.atleast_2d(Y_pred)\n", "\n", " # TODO\n", " return 0." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Check that the average NLL of the following 3 almost perfect\n", "# predictions is close to 0\n", "Y_true = np.array([[0, 1, 0],\n", " [1, 0, 0],\n", " [0, 0, 1]])\n", "\n", "Y_pred = np.array([[0, 1, 0],\n", " [.99, 0.01, 0],\n", " [0, 0, 1]])\n", "\n", "print(nll(Y_true, Y_pred))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load solutions/numpy_nll.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us now study the following linear model trainable by SGD, **one sample at a time**." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class LogisticRegression():\n", "\n", " def __init__(self, input_size, output_size):\n", " self.W = np.random.uniform(size=(input_size, output_size),\n", " high=0.1, low=-0.1)\n", " self.b = np.random.uniform(size=output_size,\n", " high=0.1, low=-0.1)\n", " self.output_size = output_size\n", " \n", " def forward(self, X):\n", " Z = np.dot(X, self.W) + self.b\n", " return softmax(Z)\n", " \n", " def predict(self, X):\n", " if len(X.shape) == 1:\n", " return np.argmax(self.forward(X))\n", " else:\n", " return np.argmax(self.forward(X), axis=1)\n", " \n", " def grad_loss(self, x, y_true):\n", " y_pred = self.forward(x)\n", " dnll_output = y_pred - one_hot(self.output_size, y_true)\n", " grad_W = np.outer(x, dnll_output)\n", " grad_b = dnll_output\n", " grads = {\"W\": grad_W, \"b\": grad_b}\n", " return grads\n", " \n", " def train(self, x, y, learning_rate):\n", " # Traditional SGD update without momentum\n", " grads = self.grad_loss(x, y)\n", " self.W = self.W - learning_rate * grads[\"W\"]\n", " self.b = self.b - learning_rate * grads[\"b\"] \n", " \n", " def loss(self, X, y):\n", " return nll(one_hot(self.output_size, y), self.forward(X))\n", "\n", " def accuracy(self, X, y):\n", " y_preds = np.argmax(self.forward(X), axis=1)\n", " return np.mean(y_preds == y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Build a model and test its forward inference\n", "n_features = X_train.shape[1]\n", "n_classes = len(np.unique(y_train))\n", "lr = LogisticRegression(n_features, n_classes)\n", "\n", "print(\"Evaluation of the untrained model:\")\n", "train_loss = lr.loss(X_train, y_train)\n", "train_acc = lr.accuracy(X_train, y_train)\n", "test_acc = lr.accuracy(X_test, y_test)\n", "\n", "print(\"train loss: %0.4f, train acc: %0.3f, test acc: %0.3f\"\n", " % (train_loss, train_acc, test_acc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evaluate the randomly initialized model on the first example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plot_prediction(model, sample_idx=0, classes=range(10)):\n", " fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n", "\n", " ax0.imshow(scaler.inverse_transform(X_test[sample_idx:sample_idx+1]).reshape(8, 8),\n", " cmap=plt.cm.gray_r, interpolation='nearest')\n", " ax0.set_title(\"True image label: %d\" % y_test[sample_idx]);\n", "\n", "\n", " ax1.bar(classes, one_hot(len(classes), y_test[sample_idx]), label='true')\n", " ax1.bar(classes, model.forward(X_test[sample_idx]), label='prediction', color=\"red\")\n", " ax1.set_xticks(classes)\n", " prediction = model.predict(X_test[sample_idx])\n", " ax1.set_title('Output probabilities (prediction: %d)'\n", " % prediction)\n", " ax1.set_xlabel('Digit class')\n", " ax1.legend()\n", " \n", "plot_prediction(lr, sample_idx=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Training for one epoch\n", "learning_rate = 0.01\n", "\n", "for i, (x, y) in enumerate(zip(X_train, y_train)):\n", " lr.train(x, y, learning_rate)\n", " if i % 100 == 0:\n", " train_loss = lr.loss(X_train, y_train)\n", " train_acc = lr.accuracy(X_train, y_train)\n", " test_acc = lr.accuracy(X_test, y_test)\n", " print(\"Update #%d, train loss: %0.4f, train acc: %0.3f, test acc: %0.3f\"\n", " % (i, train_loss, train_acc, test_acc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evaluate the trained model on the first example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_prediction(lr, sample_idx=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## b) Feedforward Multilayer\n", "\n", "The objective of this section is to implement the backpropagation algorithm (SGD with the chain rule) on a single layer neural network using the sigmoid activation function.\n", "\n", "- Implement the `sigmoid` and its element-wise derivative `dsigmoid` functions:\n", "\n", "$$\n", "sigmoid(x) = \\frac{1}{1 + e^{-x}}\n", "$$\n", "\n", "$$\n", "dsigmoid(x) = sigmoid(x) \\cdot (1 - sigmoid(x))\n", "$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def sigmoid(X):\n", " # TODO\n", " return X\n", "\n", "\n", "def dsigmoid(X):\n", " # TODO\n", " return X\n", "\n", "\n", "x = np.linspace(-5, 5, 100)\n", "plt.plot(x, sigmoid(x), label='sigmoid')\n", "plt.plot(x, dsigmoid(x), label='dsigmoid')\n", "plt.legend(loc='best');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load solutions/sigmoid.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Implement `forward` and `forward_keep_all` functions for a model with a hidden layer with a sigmoid activation function:\n", " - $\\mathbf{h} = sigmoid(\\mathbf{W}^h \\mathbf{x} + \\mathbf{b^h})$\n", " - $\\mathbf{y} = softmax(\\mathbf{W}^o \\mathbf{h} + \\mathbf{b^o})$\n", "\n", "- Notes: \n", " - try to keep the code as similar as possible as the previous one;\n", " - `forward` now has a keep activations parameter to also return hidden activations and pre activations;\n", "\n", "- Update the grad function to compute all gradients; check that the gradients are well defined;\n", "\n", "- Implement the `train` and `loss` functions.\n", "\n", "**Bonus**: reimplementing all from scratch only using the lecture slides but without looking at the solution of the `LogisticRegression` is an excellent exercise." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "EPSILON = 1e-8\n", "\n", "\n", "class NeuralNet():\n", " \"\"\"MLP with 1 hidden layer with a sigmoid activation\"\"\"\n", " \n", " def __init__(self, input_size, hidden_size, output_size):\n", " # TODO\n", " self.W_h = None\n", " self.b_h = None\n", " self.W_o = None\n", " self.b_o = None\n", " self.output_size = output_size\n", " \n", " def forward_keep_activations(self, X):\n", " # TODO\n", " z_h = 0.\n", " h = 0.\n", " y = np.zeros(size=self.output_size)\n", " return y, h, z_h\n", "\n", " def forward(self, X):\n", " y, h, z_h = self.forward_keep_activations(X)\n", " return y\n", " \n", " def loss(self, X, y):\n", " # TODO\n", " return 42.\n", "\n", " def grad_loss(self, x, y_true):\n", " # TODO\n", " return {\"W_h\": 0., \"b_h\": 0., \"W_o\": 0., \"b_o\": 0.}\n", "\n", " def train(self, x, y, learning_rate):\n", " # TODO\n", " pass\n", "\n", " def predict(self, X):\n", " if len(X.shape) == 1:\n", " return np.argmax(self.forward(X))\n", " else:\n", " return np.argmax(self.forward(X), axis=1)\n", "\n", " def accuracy(self, X, y):\n", " y_preds = np.argmax(self.forward(X), axis=1)\n", " return np.mean(y_preds == y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load solutions/neural_net.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n_hidden = 10\n", "model = NeuralNet(n_features, n_hidden, n_classes)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.loss(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.accuracy(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_prediction(model, sample_idx=5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "losses, accuracies, accuracies_test = [], [], []\n", "losses.append(model.loss(X_train, y_train))\n", "accuracies.append(model.accuracy(X_train, y_train))\n", "accuracies_test.append(model.accuracy(X_test, y_test))\n", "\n", "print(\"Random init: train loss: %0.5f, train acc: %0.3f, test acc: %0.3f\"\n", " % (losses[-1], accuracies[-1], accuracies_test[-1]))\n", "\n", "for epoch in range(15):\n", " for i, (x, y) in enumerate(zip(X_train, y_train)):\n", " model.train(x, y, 0.1)\n", "\n", " losses.append(model.loss(X_train, y_train))\n", " accuracies.append(model.accuracy(X_train, y_train))\n", " accuracies_test.append(model.accuracy(X_test, y_test))\n", " print(\"Epoch #%d, train loss: %0.5f, train acc: %0.3f, test acc: %0.3f\"\n", " % (epoch + 1, losses[-1], accuracies[-1], accuracies_test[-1]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(losses)\n", "plt.title(\"Training loss\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(accuracies, label='train')\n", "plt.plot(accuracies_test, label='test')\n", "plt.ylim(0, 1.1)\n", "plt.ylabel(\"accuracy\")\n", "plt.legend(loc='best');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_prediction(model, sample_idx=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## c) Exercises\n", "\n", "### Look at worst prediction errors\n", "\n", "- Use numpy to find test samples for which the model made the worst predictions,\n", "- Use the `plot_prediction` to look at the model predictions on those,\n", "- Would you have done any better?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load solutions/worst_predictions.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hyper parameters settings\n", "\n", "- Experiment with different hyper parameters:\n", " - learning rate,\n", " - size of hidden layer,\n", " - initialization scheme: test with 0 initialization vs uniform,\n", " - implement other activation functions,\n", " - implement the support for a second hidden layer.\n", "\n", "\n", "### Mini-batches\n", "\n", "- The current implementations of `train` and `grad_loss` function currently only accept a single sample at a time:\n", " - implement the support for training with a mini-batch of 32 samples at a time instead of one,\n", " - experiment with different sizes of batches,\n", " - monitor the norm of the average gradients on the full training set at the end of each epoch.\n", "\n", "\n", "### Momentum\n", "\n", "- Bonus: Implement momentum\n", "\n", "\n", "### Back to Keras\n", "\n", "- Implement the same network architecture with Keras;\n", "\n", "- Check that the Keras model can approximately reproduce the behavior of the Numpy model when using similar hyperparameter values (size of the model, type of activations, learning rate value and use of momentum);\n", "\n", "- Compute the negative log likelihood of a sample 42 in the test set (can use `model.predict_proba`);\n", "\n", "- Compute the average negative log-likelihood on the full test set.\n", "\n", "- Compute the average negative log-likelihood on the full training set and check that you can get the value of the loss reported by Keras.\n", "\n", "- Is the model overfitting or underfitting? (ensure that the model has fully converged by increasing the number of epochs to 50 or more if necessary)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load solutions/keras_model.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load solutions/keras_model_test_loss.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Homework assignments\n", "\n", "- Watch the following video on [how to code a minimal deep learning framework](https://www.youtube.com/watch?v=o64FV-ez6Gw) that feels like a simplified version\n", "of Keras but using numpy instead of tensorflow:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo(\"o64FV-ez6Gw\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **Optional**: read the following blog post on Reverse-Mode Automatic Differentiation from start to section \"A simple implementation in Python\" included:\n", "\n", " https://rufflewind.com/2016-12-30/reverse-mode-automatic-differentiation" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.9" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
wachtlerlab/RWeiss
Freqchange + FFT.ipynb
1
21233
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "def freqchange(freq1, freq2, f_sample=40000, N=10):\n", " \"Erstellt einen Frequenz\u00fcbergang Freq1 auf Freq2 mit je N Perioden Rechteckfunktion und entsprechende FFT\"\n", " freq1, freq2 = float(freq1), float(freq2)\n", " print \"Samplingfrequenz:\", f_sample/1000., \"kHz\"\n", " f1 = tile(concatenate((ones(f_sample/(2*freq1)), zeros(f_sample/(2*freq1))), axis=1), N)\n", " f2 = tile(concatenate((ones(f_sample/(2*freq2)), zeros(f_sample/(2*freq2))), axis=1), N)\n", " f = concatenate((f1, f2), axis=1)-.5\n", " xaxis = arange(0, (N/freq1)+(N/freq2), ((N/freq1)+(N/freq2))/f.size)\n", " print f.size\n", " print xaxis.size\n", " #plot(xaxis, f)\n", " plt.xlabel('t in s')\n", " plt.ylim(-0.5,1.5)\n", " #figure()\n", " FT = fft.fft(f)/f.size\n", " faxis = fft.fftfreq(f.size, (1./f.size))\n", " plot(faxis[:50], np.abs(FT[:50]))\n", " #plot(faxis[:20], real(FT[:20]))\n", " #plot(faxis[:20], imag(FT[:20]))\n", " plt.xlabel('Freq in Hz')\n", " #print abs(FT[])\n", " return f;\n", " \n", "x = freqchange(200,400, N=1)\n", "figure()\n", "z = freqchange(400,800, N=1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Samplingfrequenz: 40.0 kHz\n", "300\n", "300\n", "Samplingfrequenz:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 40.0 kHz\n", "150\n", "150\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7fe401da3cd0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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1
4678
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# XDAWN Decoding From EEG data\n\n\nERP decoding with Xdawn ([1]_, [2]_). For each event type, a set of\nspatial Xdawn filters are trained and applied on the signal. Channels are\nconcatenated and rescaled to create features vectors that will be fed into\na logistic regression.\n\nReferences\n----------\n.. [1] Rivet, B., Souloumiac, A., Attina, V., & Gibert, G. (2009). xDAWN\n algorithm to enhance evoked potentials: application to brain-computer\n interface. Biomedical Engineering, IEEE Transactions on, 56(8),\n 2035-2043.\n.. [2] Rivet, B., Cecotti, H., Souloumiac, A., Maby, E., & Mattout, J. (2011,\n August). Theoretical analysis of xDAWN algorithm: application to an\n efficient sensor selection in a P300 BCI. In Signal Processing\n Conference, 2011 19th European (pp. 1382-1386). IEEE.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Alexandre Barachant <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn.preprocessing import MinMaxScaler\n\nfrom mne import io, pick_types, read_events, Epochs\nfrom mne.datasets import sample\nfrom mne.preprocessing import Xdawn\nfrom mne.decoding import Vectorizer\nfrom mne.viz import tight_layout\n\n\nprint(__doc__)\n\ndata_path = sample.data_path()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set parameters and read data\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\ntmin, tmax = -0.1, 0.3\nevent_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4)\n\n# Setup for reading the raw data\nraw = io.read_raw_fif(raw_fname, preload=True)\nraw.filter(1, 20, fir_design='firwin')\nevents = read_events(event_fname)\n\npicks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,\n exclude='bads')\n\nepochs = Epochs(raw, events, event_id, tmin, tmax, proj=False,\n picks=picks, baseline=None, preload=True,\n verbose=False)\n\n# Create classification pipeline\nclf = make_pipeline(Xdawn(n_components=3),\n Vectorizer(),\n MinMaxScaler(),\n LogisticRegression(penalty='l1'))\n\n# Get the labels\nlabels = epochs.events[:, -1]\n\n# Cross validator\ncv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n\n# Do cross-validation\npreds = np.empty(len(labels))\nfor train, test in cv.split(epochs, labels):\n clf.fit(epochs[train], labels[train])\n preds[test] = clf.predict(epochs[test])\n\n# Classification report\ntarget_names = ['aud_l', 'aud_r', 'vis_l', 'vis_r']\nreport = classification_report(labels, preds, target_names=target_names)\nprint(report)\n\n# Normalized confusion matrix\ncm = confusion_matrix(labels, preds)\ncm_normalized = cm.astype(float) / cm.sum(axis=1)[:, np.newaxis]\n\n# Plot confusion matrix\nplt.imshow(cm_normalized, interpolation='nearest', cmap=plt.cm.Blues)\nplt.title('Normalized Confusion matrix')\nplt.colorbar()\ntick_marks = np.arange(len(target_names))\nplt.xticks(tick_marks, target_names, rotation=45)\nplt.yticks(tick_marks, target_names)\ntight_layout()\nplt.ylabel('True label')\nplt.xlabel('Predicted label')\nplt.show()" ] } ], "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": 0 }
bsd-3-clause
cmgerber/PLOS_Cloud_Explorer
ipython_notebooks/time_slider_attempt.ipynb
1
47337
{ "metadata": { "name": "", "signature": "sha256:c60ae8e8900ecfbae17d6b50828ac51503b127ce43e73aef720edb7b4f7907c4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import unicode_literals\n", "import json\n", "import numpy as np\n", "import pandas as pd\n", "from pandas import DataFrame, Series\n", "import text_analysis as txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import some example time series data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with open('biotech500.json', 'rb') as fp:\n", " data = json.load(fp)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "articles_list = data['response']['docs']\n", "articles = DataFrame(articles_list)\n", "articles = articles[articles['abstract'].notnull()].ix[:,['abstract', 'publication_date']]\n", "articles.abstract = articles.abstract.apply(txt.wordify, 3)\n", "articles = articles[articles['abstract'].notnull()]\n", "articles.publication_date = pd.to_datetime(articles.publication_date)\n", "articles.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>publication_date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>7 </th>\n", " <td> [objective, paper, assess, attitude, malaysian...</td>\n", " <td>2014-01-29</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td> [atrazine, atz, metolachlor, met, two, herbici...</td>\n", " <td>2012-05-15</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td> [due, environmental, persistence, biotoxicity,...</td>\n", " <td>2013-08-05</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td> [intensive, use, chlorpyrifos, resulted, ubiqu...</td>\n", " <td>2012-10-08</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td> [background, complex, characteristics, unclear...</td>\n", " <td>2012-08-09</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 2 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " abstract publication_date\n", "7 [objective, paper, assess, attitude, malaysian... 2014-01-29\n", "16 [atrazine, atz, metolachlor, met, two, herbici... 2012-05-15\n", "17 [due, environmental, persistence, biotoxicity,... 2013-08-05\n", "34 [intensive, use, chlorpyrifos, resulted, ubiqu... 2012-10-08\n", "35 [background, complex, characteristics, unclear... 2012-08-09\n", "\n", "[5 rows x 2 columns]" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "print articles.publication_date.min(), articles.publication_date.max()\n", "print len(articles)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2008-04-30 00:00:00 2014-04-11 00:00:00\n", "57\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The time series spans ~9 years with 57 data points.\n", "\n", "# We need to resample!\n", "\n", "Something like a monthly aggregate `set` of words.\n", "\n", "There are probably many ways to do this..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "articles_timed = articles.set_index('publication_date')\n", "articles_timed" ], "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", " </tr>\n", " <tr>\n", " <th>publication_date</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2014-01-29</th>\n", " <td> [objective, paper, assess, attitude, malaysian...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-05-15</th>\n", " <td> [atrazine, atz, metolachlor, met, two, herbici...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-08-05</th>\n", " <td> [due, environmental, persistence, biotoxicity,...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-08</th>\n", " <td> [intensive, use, chlorpyrifos, resulted, ubiqu...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-08-09</th>\n", " <td> [background, complex, characteristics, unclear...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-04-15</th>\n", " <td> [reusing, filtering, facepiece, respirators, f...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-05-15</th>\n", " <td> [study, focus, impact, senior, executives, ind...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-05-17</th>\n", " <td> [hacek, organisms, haemophilus, species, aggre...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-12-28</th>\n", " <td> [objectives, atrioventricular, block, avb, inf...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-07-13</th>\n", " <td> [objective, assess, efficacy, video, assisted,...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-11-22</th>\n", " <td> [energy, mining, mineral, processing, industri...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-09-14</th>\n", " <td> [background, mesenchymal, stem, cells, msc, re...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-08-17</th>\n", " <td> [objective, estimate, effectiveness, anterior,...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-06-21</th>\n", " <td> [journal, policy, research, data, code, availa...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-02-28</th>\n", " <td> [polycyclic, aromatic, hydrocarbons, pahs, tox...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-04-23</th>\n", " <td> [article, uses, data, thomson, reuters, web, s...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-10-23</th>\n", " <td> [animal, models, become, popular, platform, in...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-11-17</th>\n", " <td> [sensory, analysis, studies, critical, develop...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-07-11</th>\n", " <td> [trinitrotoluene, tnt, released, nature, manuf...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-24</th>\n", " <td> [objectives, catheter, related, staphylococcus...</td>\n", " </tr>\n", " <tr>\n", " <th>2014-03-18</th>\n", " <td> [nanopods, extracellular, structures, arising,...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-08-15</th>\n", " <td> [since, launched, plos, one, published, fifty,...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-05-03</th>\n", " <td> [mark, van, ommeren, colleagues, describe, cho...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-07-19</th>\n", " <td> [background, medical, devices, increasingly, d...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-05</th>\n", " <td> [month, debate, examines, whether, current, pa...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-03-22</th>\n", " <td> [introduction, although, individuals, lower, l...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-06-24</th>\n", " <td> [background, suboptimal, left, ventricular, lv...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-07-31</th>\n", " <td> [sanket, dhruva, rita, redberg, comment, resea...</td>\n", " </tr>\n", " <tr>\n", " <th>2008-04-30</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2014-01-22</th>\n", " <td> [background, confirmation, diabetic, sensorimo...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-12-14</th>\n", " <td> [oxidative, damage, microbial, hosts, often, o...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-08-05</th>\n", " <td> [miniaturization, active, implantable, medical...</td>\n", " </tr>\n", " <tr>\n", " <th>2014-03-27</th>\n", " <td> [laboratory, soil, degradation, study, conduct...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-10-15</th>\n", " <td> [optimised, reduction, dissolved, nutrient, lo...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-08-05</th>\n", " <td> [background, field, synthetic, biology, promis...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-05-31</th>\n", " <td> [environmentally, degradable, parameter, edk, ...</td>\n", " </tr>\n", " <tr>\n", " <th>2014-04-11</th>\n", " <td> [several, applications, tissue, engineering, r...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-06-14</th>\n", " <td> [laccases, versatile, biocatalysts, bioremedia...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-04-25</th>\n", " <td> [background, cervical, disc, arthroplasty, use...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-02-20</th>\n", " <td> [embryonic, stem, es, cell, based, gene, manip...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-06-21</th>\n", " <td> [presence, uniformly, small, collagen, fibrils...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-05-09</th>\n", " <td> [biosorption, heavy, metals, using, dried, alg...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-03-21</th>\n", " <td> [characterizing, quasi, stiffness, work, lower...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-05-16</th>\n", " <td> [common, wheat, hexaploid, species, genes, pre...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-09-23</th>\n", " <td> [study, two, strains, aspergillus, sp, lysinib...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-04-27</th>\n", " <td> [escherichia, coli, chrr, enzyme, obligatory, ...</td>\n", " </tr>\n", " <tr>\n", " <th>2014-03-07</th>\n", " <td> [novosphingobium, pentaromativorans, halophili...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-12-12</th>\n", " <td> [depending, speciation, environmental, contami...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-08-09</th>\n", " <td> [background, several, materials, used, tissue,...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-12-11</th>\n", " <td> [efforts, increase, affinity, design, new, the...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-10-05</th>\n", " <td> [concerns, regarding, commercial, release, gen...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-09-25</th>\n", " <td> [cassava, brown, streak, disease, cbsd, cassav...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-04-03</th>\n", " <td> [phytate, major, storage, form, organic, phosp...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-04-01</th>\n", " <td> [aim, study, isolate, identify, marine, derive...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-11-13</th>\n", " <td> [introduction, composite, biomaterials, design...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-09-29</th>\n", " <td> [previous, works, demonstrated, ligninolytic, ...</td>\n", " </tr>\n", " <tr>\n", " <th>2014-04-02</th>\n", " <td> [background, aim, study, evaluate, impact, qua...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>57 rows \u00d7 1 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ " abstract\n", "publication_date \n", "2014-01-29 [objective, paper, assess, attitude, malaysian...\n", "2012-05-15 [atrazine, atz, metolachlor, met, two, herbici...\n", "2013-08-05 [due, environmental, persistence, biotoxicity,...\n", "2012-10-08 [intensive, use, chlorpyrifos, resulted, ubiqu...\n", "2012-08-09 [background, complex, characteristics, unclear...\n", "2011-04-15 [reusing, filtering, facepiece, respirators, f...\n", "2013-05-15 [study, focus, impact, senior, executives, ind...\n", "2013-05-17 [hacek, organisms, haemophilus, species, aggre...\n", "2012-12-28 [objectives, atrioventricular, block, avb, inf...\n", "2011-07-13 [objective, assess, efficacy, video, assisted,...\n", "2013-11-22 [energy, mining, mineral, processing, industri...\n", "2011-09-14 [background, mesenchymal, stem, cells, msc, re...\n", "2012-08-17 [objective, estimate, effectiveness, anterior,...\n", "2013-06-21 [journal, policy, research, data, code, availa...\n", "2013-02-28 [polycyclic, aromatic, hydrocarbons, pahs, tox...\n", "2012-04-23 [article, uses, data, thomson, reuters, web, s...\n", "2013-10-23 [animal, models, become, popular, platform, in...\n", "2010-11-17 [sensory, analysis, studies, critical, develop...\n", "2012-07-11 [trinitrotoluene, tnt, released, nature, manuf...\n", "2012-10-24 [objectives, catheter, related, staphylococcus...\n", "2014-03-18 [nanopods, extracellular, structures, arising,...\n", "2012-08-15 [since, launched, plos, one, published, fifty,...\n", "2011-05-03 [mark, van, ommeren, colleagues, describe, cho...\n", "2012-07-19 [background, medical, devices, increasingly, d...\n", "2010-01-05 [month, debate, examines, whether, current, pa...\n", "2013-03-22 [introduction, although, individuals, lower, l...\n", "2013-06-24 [background, suboptimal, left, ventricular, lv...\n", "2012-07-31 [sanket, dhruva, rita, redberg, comment, resea...\n", "2008-04-30 [according, world, health, organization, repor...\n", "2014-01-22 [background, confirmation, diabetic, sensorimo...\n", "2012-12-14 [oxidative, damage, microbial, hosts, often, o...\n", "2011-08-05 [miniaturization, active, implantable, medical...\n", "2014-03-27 [laboratory, soil, degradation, study, conduct...\n", "2013-10-15 [optimised, reduction, dissolved, nutrient, lo...\n", "2011-08-05 [background, field, synthetic, biology, promis...\n", "2012-05-31 [environmentally, degradable, parameter, edk, ...\n", "2014-04-11 [several, applications, tissue, engineering, r...\n", "2013-06-14 [laccases, versatile, biocatalysts, bioremedia...\n", "2012-04-25 [background, cervical, disc, arthroplasty, use...\n", "2013-02-20 [embryonic, stem, es, cell, based, gene, manip...\n", "2011-06-21 [presence, uniformly, small, collagen, fibrils...\n", "2012-05-09 [biosorption, heavy, metals, using, dried, alg...\n", "2013-03-21 [characterizing, quasi, stiffness, work, lower...\n", "2013-05-16 [common, wheat, hexaploid, species, genes, pre...\n", "2013-09-23 [study, two, strains, aspergillus, sp, lysinib...\n", "2012-04-27 [escherichia, coli, chrr, enzyme, obligatory, ...\n", "2014-03-07 [novosphingobium, pentaromativorans, halophili...\n", "2012-12-12 [depending, speciation, environmental, contami...\n", "2010-08-09 [background, several, materials, used, tissue,...\n", "2012-12-11 [efforts, increase, affinity, design, new, the...\n", "2011-10-05 [concerns, regarding, commercial, release, gen...\n", "2012-09-25 [cassava, brown, streak, disease, cbsd, cassav...\n", "2013-04-03 [phytate, major, storage, form, organic, phosp...\n", "2013-04-01 [aim, study, isolate, identify, marine, derive...\n", "2013-11-13 [introduction, composite, biomaterials, design...\n", "2011-09-29 [previous, works, demonstrated, ligninolytic, ...\n", "2014-04-02 [background, aim, study, evaluate, impact, qua...\n", "\n", "[57 rows x 1 columns]" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using pandas time series resampling functions\n", "\n", "These are geared towards numeric data. This is still pretty close to what we want, though." ] }, { "cell_type": "code", "collapsed": false, "input": [ "articles_monthly = articles_timed.resample('M', how='sum', fill_method='ffill', kind='period')\n", "articles_monthly.abstract = articles_monthly.abstract.apply(lambda x: np.nan if x == 0 else x)\n", "articles_monthly.fillna(method='ffill', inplace=True)\n", "articles_monthly" ], "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", " </tr>\n", " <tr>\n", " <th>publication_date</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2008-04</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2008-05</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2008-06</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2008-07</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2008-08</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2008-09</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2008-10</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2008-11</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2008-12</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2009-01</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2009-02</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2009-03</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2009-04</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2009-05</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2009-06</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2009-07</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2009-08</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2009-09</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2009-10</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2009-11</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2009-12</th>\n", " <td> [according, world, health, organization, repor...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01</th>\n", " <td> [month, debate, examines, whether, current, pa...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-02</th>\n", " <td> [month, debate, examines, whether, current, pa...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-03</th>\n", " <td> [month, debate, examines, whether, current, pa...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-04</th>\n", " <td> [month, debate, examines, whether, current, pa...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-05</th>\n", " <td> [month, debate, examines, whether, current, pa...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-06</th>\n", " <td> [month, debate, examines, whether, current, pa...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-07</th>\n", " <td> [month, debate, examines, whether, current, pa...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-08</th>\n", " <td> [background, several, materials, used, tissue,...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-09</th>\n", " <td> [background, several, materials, used, tissue,...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-10</th>\n", " <td> [background, several, materials, used, tissue,...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-11</th>\n", " <td> [sensory, analysis, studies, critical, develop...</td>\n", " </tr>\n", " <tr>\n", " <th>2010-12</th>\n", " <td> [sensory, analysis, studies, critical, develop...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-01</th>\n", " <td> [sensory, analysis, studies, critical, develop...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-02</th>\n", " <td> [sensory, analysis, studies, critical, develop...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-03</th>\n", " <td> [sensory, analysis, studies, critical, develop...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-04</th>\n", " <td> [reusing, filtering, facepiece, respirators, f...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-05</th>\n", " <td> [mark, van, ommeren, colleagues, describe, cho...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-06</th>\n", " <td> [presence, uniformly, small, collagen, fibrils...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-07</th>\n", " <td> [objective, assess, efficacy, video, assisted,...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-08</th>\n", " <td> [background, field, synthetic, biology, promis...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-09</th>\n", " <td> [background, mesenchymal, stem, cells, msc, re...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-10</th>\n", " <td> [concerns, regarding, commercial, release, gen...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-11</th>\n", " <td> [concerns, regarding, commercial, release, gen...</td>\n", " </tr>\n", " <tr>\n", " <th>2011-12</th>\n", " <td> [concerns, regarding, commercial, release, gen...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01</th>\n", " <td> [concerns, regarding, commercial, release, gen...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-02</th>\n", " <td> [concerns, regarding, commercial, release, gen...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-03</th>\n", " <td> [concerns, regarding, commercial, release, gen...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-04</th>\n", " <td> [article, uses, data, thomson, reuters, web, s...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-05</th>\n", " <td> [biosorption, heavy, metals, using, dried, alg...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-06</th>\n", " <td> [biosorption, heavy, metals, using, dried, alg...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-07</th>\n", " <td> [trinitrotoluene, tnt, released, nature, manuf...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-08</th>\n", " <td> [background, complex, characteristics, unclear...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-09</th>\n", " <td> [cassava, brown, streak, disease, cbsd, cassav...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10</th>\n", " <td> [intensive, use, chlorpyrifos, resulted, ubiqu...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-11</th>\n", " <td> [intensive, use, chlorpyrifos, resulted, ubiqu...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-12</th>\n", " <td> [efforts, increase, affinity, design, new, the...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-01</th>\n", " <td> [efforts, increase, affinity, design, new, the...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-02</th>\n", " <td> [embryonic, stem, es, cell, based, gene, manip...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-03</th>\n", " <td> [characterizing, quasi, stiffness, work, lower...</td>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <td>...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>73 rows \u00d7 1 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ " abstract\n", "publication_date \n", "2008-04 [according, world, health, organization, repor...\n", "2008-05 [according, world, health, organization, repor...\n", "2008-06 [according, world, health, organization, repor...\n", "2008-07 [according, world, health, organization, repor...\n", "2008-08 [according, world, health, organization, repor...\n", "2008-09 [according, world, health, organization, repor...\n", "2008-10 [according, world, health, organization, repor...\n", "2008-11 [according, world, health, organization, repor...\n", "2008-12 [according, world, health, organization, repor...\n", "2009-01 [according, world, health, organization, repor...\n", "2009-02 [according, world, health, organization, repor...\n", "2009-03 [according, world, health, organization, repor...\n", "2009-04 [according, world, health, organization, repor...\n", "2009-05 [according, world, health, organization, repor...\n", "2009-06 [according, world, health, organization, repor...\n", "2009-07 [according, world, health, organization, repor...\n", "2009-08 [according, world, health, organization, repor...\n", "2009-09 [according, world, health, organization, repor...\n", "2009-10 [according, world, health, organization, repor...\n", "2009-11 [according, world, health, organization, repor...\n", "2009-12 [according, world, health, organization, repor...\n", "2010-01 [month, debate, examines, whether, current, pa...\n", "2010-02 [month, debate, examines, whether, current, pa...\n", "2010-03 [month, debate, examines, whether, current, pa...\n", "2010-04 [month, debate, examines, whether, current, pa...\n", "2010-05 [month, debate, examines, whether, current, pa...\n", "2010-06 [month, debate, examines, whether, current, pa...\n", "2010-07 [month, debate, examines, whether, current, pa...\n", "2010-08 [background, several, materials, used, tissue,...\n", "2010-09 [background, several, materials, used, tissue,...\n", "2010-10 [background, several, materials, used, tissue,...\n", "2010-11 [sensory, analysis, studies, critical, develop...\n", "2010-12 [sensory, analysis, studies, critical, develop...\n", "2011-01 [sensory, analysis, studies, critical, develop...\n", "2011-02 [sensory, analysis, studies, critical, develop...\n", "2011-03 [sensory, analysis, studies, critical, develop...\n", "2011-04 [reusing, filtering, facepiece, respirators, f...\n", "2011-05 [mark, van, ommeren, colleagues, describe, cho...\n", "2011-06 [presence, uniformly, small, collagen, fibrils...\n", "2011-07 [objective, assess, efficacy, video, assisted,...\n", "2011-08 [background, field, synthetic, biology, promis...\n", "2011-09 [background, mesenchymal, stem, cells, msc, re...\n", "2011-10 [concerns, regarding, commercial, release, gen...\n", "2011-11 [concerns, regarding, commercial, release, gen...\n", "2011-12 [concerns, regarding, commercial, release, gen...\n", "2012-01 [concerns, regarding, commercial, release, gen...\n", "2012-02 [concerns, regarding, commercial, release, gen...\n", "2012-03 [concerns, regarding, commercial, release, gen...\n", "2012-04 [article, uses, data, thomson, reuters, web, s...\n", "2012-05 [biosorption, heavy, metals, using, dried, alg...\n", "2012-06 [biosorption, heavy, metals, using, dried, alg...\n", "2012-07 [trinitrotoluene, tnt, released, nature, manuf...\n", "2012-08 [background, complex, characteristics, unclear...\n", "2012-09 [cassava, brown, streak, disease, cbsd, cassav...\n", "2012-10 [intensive, use, chlorpyrifos, resulted, ubiqu...\n", "2012-11 [intensive, use, chlorpyrifos, resulted, ubiqu...\n", "2012-12 [efforts, increase, affinity, design, new, the...\n", "2013-01 [efforts, increase, affinity, design, new, the...\n", "2013-02 [embryonic, stem, es, cell, based, gene, manip...\n", "2013-03 [characterizing, quasi, stiffness, work, lower...\n", " ...\n", "\n", "[73 rows x 1 columns]" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using the `sum` aggregation method worked\n", "because all the values were lists. The three abstracts published in 2013-05 were concatenated together (see below)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "articles_timed['2013-05']" ], "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", " </tr>\n", " <tr>\n", " <th>publication_date</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2013-05-15</th>\n", " <td> [study, focus, impact, senior, executives, ind...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-05-17</th>\n", " <td> [hacek, organisms, haemophilus, species, aggre...</td>\n", " </tr>\n", " <tr>\n", " <th>2013-05-16</th>\n", " <td> [common, wheat, hexaploid, species, genes, pre...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3 rows \u00d7 1 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ " abstract\n", "publication_date \n", "2013-05-15 [study, focus, impact, senior, executives, ind...\n", "2013-05-17 [hacek, organisms, haemophilus, species, aggre...\n", "2013-05-16 [common, wheat, hexaploid, species, genes, pre...\n", "\n", "[3 rows x 1 columns]" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "articles_timed['2013-05'].applymap(len)" ], "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", " </tr>\n", " <tr>\n", " <th>publication_date</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2013-05-15</th>\n", " <td> 148</td>\n", " </tr>\n", " <tr>\n", " <th>2013-05-17</th>\n", " <td> 147</td>\n", " </tr>\n", " <tr>\n", " <th>2013-05-16</th>\n", " <td> 108</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3 rows \u00d7 1 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ " abstract\n", "publication_date \n", "2013-05-15 148\n", "2013-05-17 147\n", "2013-05-16 108\n", "\n", "[3 rows x 1 columns]" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "articles_timed['2013-05'].applymap(len).sum()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "abstract 403\n", "dtype: int64" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "articles_monthly['2013-05'].applymap(len)" ], "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", " </tr>\n", " <tr>\n", " <th>publication_date</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2013-05</th>\n", " <td> 403</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1 rows \u00d7 1 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ " abstract\n", "publication_date \n", "2013-05 403\n", "\n", "[1 rows x 1 columns]" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check what it would take to make a slider along the `PeriodIndex`" ] }, { "cell_type": "code", "collapsed": false, "input": [ "len(articles_monthly)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "73" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "' '.join(articles_monthly.ix[0]['abstract'])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "u'according world health organization reports three quarters world population access medical imaging addition developing countries medical equipment available used sophisticated disrepair health personnel trained use goal study introduce demonstrate feasibility new concept medical imaging centered cellular phone technology may provide solution medical imaging underserved areas new system replaces conventional stand alone medical imaging device new medical imaging system made two independent components connected cellular phone technology independent units data acquisition device dad remote patient site simple limited controls image display capability advanced image reconstruction hardware control multiserver unit central site cellular phone technology transmits unprocessed raw data patient site dad receives displays processed image central site different conventional telemedicine image reconstruction control patient site telecommunication used transmit processed images patient site primary goal study demonstrate cellular phone technology function proposed mode feasibility concept demonstrated using new frequency division multiplexing electrical impedance tomography system developed dynamic medical imaging medical imaging modality system used image cellular phone simulation breast cancer tumors medical imaging diagnostic mode image minimally invasive tissue ablation irreversible electroporation medical imaging interventional mode'" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Making the slider" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import display, Image, HTML, clear_output\n", "from IPython.html import widgets" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "from jinja2 import Template\n", "\n", "def textbarf(t):\n", " \n", " html_template = \"\"\"\n", " <style>\n", "\n", " #textbarf {\n", " display: block;\n", " width: 666px;\n", " padding: 23px;\n", " background-color: #ddeeff;\n", " }\n", "\n", " </style>\n", "\n", " <div id=\"textbarf\"> {{blargh}} </div>\"\"\"\n", " \n", " blob = ' '.join(articles_monthly.ix[t]['abstract'])\n", " \n", " html_src = Template(html_template).render(blargh=blob)\n", " display(HTML(html_src))\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "widgets.interact(textbarf,\n", " t=widgets.IntSliderWidget(min=0,max=72,step=1,value=0),\n", " )" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " <style>\n", "\n", " #textbarf {\n", " display: block;\n", " width: 666px;\n", " padding: 23px;\n", " background-color: #ddeeff;\n", " }\n", "\n", " </style>\n", "\n", " <div id=\"textbarf\"> according world health organization reports three quarters world population access medical imaging addition developing countries medical equipment available used sophisticated disrepair health personnel trained use goal study introduce demonstrate feasibility new concept medical imaging centered cellular phone technology may provide solution medical imaging underserved areas new system replaces conventional stand alone medical imaging device new medical imaging system made two independent components connected cellular phone technology independent units data acquisition device dad remote patient site simple limited controls image display capability advanced image reconstruction hardware control multiserver unit central site cellular phone technology transmits unprocessed raw data patient site dad receives displays processed image central site different conventional telemedicine image reconstruction control patient site telecommunication used transmit processed images patient site primary goal study demonstrate cellular phone technology function proposed mode feasibility concept demonstrated using new frequency division multiplexing electrical impedance tomography system developed dynamic medical imaging medical imaging modality system used image cellular phone simulation breast cancer tumors medical imaging diagnostic mode image minimally invasive tissue ablation irreversible electroporation medical imaging interventional mode </div>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x108b4cdd0>" ] } ], "prompt_number": 15 } ], "metadata": {} } ] }
agpl-3.0
pdh21/XID_plus
docs/notebooks/examples/bayesian_hierarchical_linear_regression.ipynb
6
631233
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bayesian Hierarchical Linear Regression\n", "Author: [Carlos Souza](mailto:[email protected])\n", "\n", "Probabilistic Machine Learning models can not only make predictions about future data, but also **model uncertainty**. In areas such as **personalized medicine**, there might be a large amount of data, but there is still a relatively **small amount of data for each patient**. To customize predictions for each person it becomes necessary to **build a model for each person** — with its inherent **uncertainties** — and to couple these models together in a **hierarchy** so that information can be borrowed from other **similar people** [1].\n", "\n", "The purpose of this tutorial is to demonstrate how to **implement a Bayesian Hierarchical Linear Regression model using NumPyro**. To motivate the tutorial, I will use [OSIC Pulmonary Fibrosis Progression](https://www.kaggle.com/c/osic-pulmonary-fibrosis-progression) competition, hosted at Kaggle.\n", "\n", "## 1. Understanding the task\n", "Pulmonary fibrosis is a disorder with no known cause and no known cure, created by scarring of the lungs. In this competition, we were asked to predict a patient’s severity of decline in lung function. Lung function is assessed based on output from a spirometer, which measures the forced vital capacity (FVC), i.e. the volume of air exhaled.\n", "\n", "In medical applications, it is useful to **evaluate a model's confidence in its decisions**. Accordingly, the metric used to rank the teams was designed to reflect **both the accuracy and certainty of each prediction**. It's a modified version of the Laplace Log Likelihood (more details on that later).\n", "\n", "Let's explore the data and see what's that all about:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Patient</th>\n", " <th>Weeks</th>\n", " <th>FVC</th>\n", " <th>Percent</th>\n", " <th>Age</th>\n", " <th>Sex</th>\n", " <th>SmokingStatus</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>ID00007637202177411956430</td>\n", " <td>-4</td>\n", " <td>2315</td>\n", " <td>58.253649</td>\n", " <td>79</td>\n", " <td>Male</td>\n", " <td>Ex-smoker</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ID00007637202177411956430</td>\n", " <td>5</td>\n", " <td>2214</td>\n", " <td>55.712129</td>\n", " <td>79</td>\n", " <td>Male</td>\n", " <td>Ex-smoker</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>ID00007637202177411956430</td>\n", " <td>7</td>\n", " <td>2061</td>\n", " <td>51.862104</td>\n", " <td>79</td>\n", " <td>Male</td>\n", " <td>Ex-smoker</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>ID00007637202177411956430</td>\n", " <td>9</td>\n", " <td>2144</td>\n", " <td>53.950679</td>\n", " <td>79</td>\n", " <td>Male</td>\n", " <td>Ex-smoker</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>ID00007637202177411956430</td>\n", " <td>11</td>\n", " <td>2069</td>\n", " <td>52.063412</td>\n", " <td>79</td>\n", " <td>Male</td>\n", " <td>Ex-smoker</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Patient Weeks FVC Percent Age Sex SmokingStatus\n", "0 ID00007637202177411956430 -4 2315 58.253649 79 Male Ex-smoker\n", "1 ID00007637202177411956430 5 2214 55.712129 79 Male Ex-smoker\n", "2 ID00007637202177411956430 7 2061 51.862104 79 Male Ex-smoker\n", "3 ID00007637202177411956430 9 2144 53.950679 79 Male Ex-smoker\n", "4 ID00007637202177411956430 11 2069 52.063412 79 Male Ex-smoker" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train = pd.read_csv('https://gist.githubusercontent.com/ucals/'\n", " '2cf9d101992cb1b78c2cdd6e3bac6a4b/raw/'\n", " '43034c39052dcf97d4b894d2ec1bc3f90f3623d9/'\n", " 'osic_pulmonary_fibrosis.csv')\n", "train.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the dataset, we were provided with a baseline chest CT scan and associated clinical information for a set of patients. A patient has an image acquired at time Week = 0 and has numerous follow up visits over the course of approximately 1-2 years, at which time their FVC is measured. For this tutorial, I will use only the Patient ID, the weeks and the FVC measurements, discarding all the rest. Using only these columns enabled our team to achieve a competitive score, which shows the power of Bayesian hierarchical linear regression models especially when gauging uncertainty is an important part of the problem.\n", "\n", "Since this is real medical data, the relative timing of FVC measurements varies widely, as shown in the 3 sample patients below:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/pdh21/anaconda3/envs/xidplus/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " FutureWarning\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x360 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def chart(patient_id, ax):\n", " data = train[train['Patient'] == patient_id]\n", " x = data['Weeks']\n", " y = data['FVC']\n", " ax.set_title(patient_id)\n", " ax = sns.regplot(x, y, ax=ax, ci=None, line_kws={'color':'red'})\n", " \n", "\n", "f, axes = plt.subplots(1, 3, figsize=(15, 5))\n", "chart('ID00007637202177411956430', axes[0])\n", "chart('ID00009637202177434476278', axes[1])\n", "chart('ID00010637202177584971671', axes[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On average, each of the 176 provided patients made 9 visits, when FVC was measured. The visits happened in specific weeks in the [-12, 133] interval. The decline in lung capacity is very clear. We see, though, they are very different from patient to patient.\n", "\n", "We were are asked to predict every patient's FVC measurement for every possible week in the [-12, 133] interval, and the confidence for each prediction. In other words: we were asked fill a matrix like the one below, and provide a confidence score for each prediction:\n", "\n", "<img src=\"https://i.ibb.co/0Z9kW8H/matrix-completion.jpg\" alt=\"drawing\" width=\"600\"/>\n", "\n", "The task was perfect to apply Bayesian inference. However, the vast majority of solutions shared by Kaggle community used discriminative machine learning models, disconsidering the fact that most discriminative methods are very poor at providing realistic uncertainty estimates. Because they are typically trained in a manner that optimizes the parameters to minimize some loss criterion (e.g. the predictive error), they do not, in general, encode any uncertainty in either their parameters or the subsequent predictions. Though many methods can produce uncertainty estimates either as a by-product or from a post-processing step, these are typically heuristic based, rather than stemming naturally from a statistically principled estimate of the target uncertainty distribution [2].\n", "\n", "## 2. Modelling: Bayesian Hierarchical Linear Regression with Partial Pooling\n", "The simplest possible linear regression, not hierarchical, would assume all FVC decline curves have the same $\\alpha$ and $\\beta$. That's the **pooled model**. In the other extreme, we could assume a model where each patient has a personalized FVC decline curve, and **these curves are completely unrelated**. That's the **unpooled model**, where each patient has completely separate regressions.\n", "\n", "Here, I'll use the middle ground: **Partial pooling**. Specifically, I'll assume that while $\\alpha$'s and $\\beta$'s are different for each patient as in the unpooled case, **the coefficients all share similarity**. We can model this by assuming that each individual coefficient comes from a common group distribution. The image below represents this model graphically:\n", "\n", "<img src=\"https://i.ibb.co/H7NgBfR/Artboard-2-2x-100.jpg\" alt=\"drawing\" width=\"600\"/>\n", "\n", "Mathematically, the model is described by the following equations:\n", "\n", "\\begin{align}\n", "\\mu_{\\alpha} &\\sim \\mathcal{N}(0, 100) \\\\\n", "\\sigma_{\\alpha} &\\sim |\\mathcal{N}(0, 100)| \\\\\n", "\\mu_{\\beta} &\\sim \\mathcal{N}(0, 100) \\\\\n", "\\sigma_{\\beta} &\\sim |\\mathcal{N}(0, 100)| \\\\\n", "\\alpha_i &\\sim \\mathcal{N}(\\mu_{\\alpha}, \\sigma_{\\alpha}) \\\\\n", "\\beta_i &\\sim \\mathcal{N}(\\mu_{\\beta}, \\sigma_{\\beta}) \\\\\n", "\\sigma &\\sim \\mathcal{N}(0, 100) \\\\\n", "FVC_{ij} &\\sim \\mathcal{N}(\\alpha_i + t \\beta_i, \\sigma)\n", "\\end{align}\n", "\n", "where *t* is the time in weeks. Those are very uninformative priors, but that's ok: our model will converge!\n", "\n", "Implementing this model in NumPyro is pretty straightforward:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import numpyro\n", "from numpyro.infer import MCMC, NUTS, Predictive\n", "import numpyro.distributions as dist\n", "from jax import random" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def model(PatientID, Weeks, FVC_obs=None):\n", " μ_α = numpyro.sample(\"μ_α\", dist.Normal(0., 100.))\n", " σ_α = numpyro.sample(\"σ_α\", dist.HalfNormal(100.))\n", " μ_β = numpyro.sample(\"μ_β\", dist.Normal(0., 100.))\n", " σ_β = numpyro.sample(\"σ_β\", dist.HalfNormal(100.))\n", " \n", " unique_patient_IDs = np.unique(PatientID)\n", " n_patients = len(unique_patient_IDs)\n", " \n", " with numpyro.plate(\"plate_i\", n_patients):\n", " α = numpyro.sample(\"α\", dist.Normal(μ_α, σ_α))\n", " β = numpyro.sample(\"β\", dist.Normal(μ_β, σ_β))\n", " \n", " σ = numpyro.sample(\"σ\", dist.HalfNormal(100.))\n", " FVC_est = α[PatientID] + β[PatientID] * Weeks\n", " \n", " with numpyro.plate(\"data\", len(PatientID)):\n", " numpyro.sample(\"obs\", dist.Normal(FVC_est, σ), obs=FVC_obs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's all for modelling!\n", "\n", "## 3. Fitting the model\n", "A great achievement of Probabilistic Programming Languages such as NumPyro is to decouple model specification and inference. After specifying my generative model, with priors, condition statements and data likelihood, I can leave the hard work to NumPyro's inference engine. \n", "\n", "Calling it requires just a few lines. Before we do it, let's add a numerical Patient ID for each patient code. That can be easily done with scikit-learn's LabelEncoder:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "\n", "le = LabelEncoder()\n", "train['PatientID'] = le.fit_transform(train['Patient'].values)\n", "\n", "FVC_obs = train['FVC'].values\n", "Weeks = train['Weeks'].values\n", "PatientID = train['PatientID'].values" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "numpyro.set_host_device_count(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, calling NumPyro's inference engine:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "sample: 100%|██████████| 4000/4000 [00:27<00:00, 144.62it/s, 63 steps of size 1.09e-01. acc. prob=0.88] \n" ] } ], "source": [ "nuts_kernel = NUTS(model)\n", "\n", "mcmc = MCMC(nuts_kernel, num_samples=2000, num_warmup=2000)\n", "rng_key = random.PRNGKey(0)\n", "mcmc.run(rng_key, PatientID, Weeks, FVC_obs=FVC_obs)\n", "\n", "posterior_samples = mcmc.get_samples()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Checking the model\n", "### 4.1. Inspecting the learned parameters\n", "First, let's inspect the parameters learned. To do that, I will use [ArviZ](https://arviz-devs.github.io/arviz/), which perfectly integrates with NumPyro:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/pdh21/anaconda3/envs/xidplus/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n", " data = yaml.load(f.read()) or {}\n" ] }, { "data": { "image/png": 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hQ23Ty8AR3o0paoEjh6mcPOVcyyc/h+LxUD1/AX16msjTX0DU69ilEuV33mUt/gMPgqJQPesMTHb8wNPNaZqRJz/nxHhNTWEsLKB1xprTxAIfewR3by/G0hJWLoddqWAsxXHFOrGKJcKf/hTgxFKV3ngDLLtNwHpGRtBnZpr9S9hi5VwfapsS6+7rdaZ7rtxPWkekOaUNnKmtxZe+u+68tEgYYRhtUw87fuBp6ten0KensCtOspnw576Pyrvv4nvgASrHjiFMJ77Jf/AAxsICnpERqmfPIkwL357dmJksgSOHKb/zTrMeru4u1FC4OU039IknKL32+up1XZkC2ah74GOPOOetKM3r792zB3dvb7PtQ088geLzUXjm2eZxIt//JHZdb96najCAe3AQLdrZvEcin/9+0DTsUqktfqzR5sb8XHPKZ2OqnntwAGNxicgXngLAymTQotFm7JZnbBRXdzdqMEjplVebx3P19ODqiqH4/Ov6ZyuBo0ewV2LcbhZrCc71bsTNBT72CJX3jhF+8nMUV6bhNq4NQuAaGGh+r6gKajiCZ3QEbNuZ6rhtCM/4OHaxiNbVBUJg5fMIw8CzfTtWqdQ8n8a0VzUQwK5UmmUDRJ7+AvWJCaePL7SP03l37kDxeBC6jqurC6tQwNXXjxrwg2WBpmEup3D19qAoCgDVc+fWTRfeCDnF8S7yO69f5/xCngeHOri4WODX//qhe12ldSwvL1MqlRgbG7vXVbmr/NvnL/O60FHdKg8UBL/8wwfafv+987/Htdw19nft52TiJL/2qRvOZLolcrkcX/nKV3jyySeZnJzkbyz+Syguwi/kYOVmBXjvL/6U9NwMnYPbKKaW+dxP/8wdKV8iudd8kFMcV8o7BPwO4AGuA38TZ7bIHwPDwCzwo0KIjOK8Mb+CkwCkAvxNIcTxleP8LeDnVw77y0KI37tZuQ8//LB49+WX0cJO/IGRTKIFg5jZHNXTp/GMjWKlUljFEt7du0AIfHv2OEHuxSJqKISiOpNarGKxeZz61at4duwA0wRVbfPmGAsLuHp7Udy3N9oudN2Jk+hxBqzsahVUFVGtokWjCF2n8J0X8B86hKu3h9rZswQebr+UpTffxLd7d/MY4GSIVPx+FFV1EpK88CK+fXtxDww4cRuNc1E3n8QjLIvKseMEHv0YrGSWbC1rLXa1Su3SZYzFRTp+YON4Y1hJAjI7S/XceXz79+EdH8fWdYSuI+p1FI8HLRx2xKjH8WzY9TpC19HCYQrPPYerrw9jYREtGiX0+Pduei5WPo/W0bG+zuUyRiKJd7z9PZz/1jOgQMfTTzc/q34fgUc/jhYKUnr1VaxiiY4feJriK6/g6u7B/+ADNyzfTKUQ9TqVU6cJHDmMGomgtcQ7N+onhADDaJ53o70URWkalxu1bet13woNkRz5wlPYlSpaKNj8zVhYwNXfj6Jp1K9do3b5SrNMIQRWKgWKgqu7G2Ga2LVa09hubmfboCjY5YpjwAcCbW3rHR/Dt39/+zlUKigez5a8po02aWBmMhgLC/gPHKB6+nRzICJw9IjjyXa52rYHp39XT5/Gf+AAiseDVShQO3+e4Pc4g7i2riMqFdRAoHk9yu++h2/PbrToqgdTn53F1d+P2nLN8t96huD3fk9zUKJRHkJgptO4urudgZuVBEKt+zb2b21PcBLaKC6N8tvvtH1v12ooqkp9ehr34BBaKLjh/nattjrAs9LHGvFdWiiEEILiCy8SOHoE98AAwrJQNM1JkGI58YytmJkMiqq2tcWtYOXzWPk8nuFh7GoV1e9vlilsGyyr7flqVyro09Pr+s2tIIRArDxLXB0dMgbtXvAvv3kRn1tl70CEPzo2y9d/6uP3ukrr+MpXvkIqleJnf/ZnCbQ8vP6y8XN/fpb/HHVGjT55vcYf/e/t1+IX3/5F+gJ9HOw5yK8e/1X++w/+93XHqJUMMvEygzu3/iCYmpri2Wef5Qtf+AIvfOu/89Pp/xcQ8DMnoHtnc7tnv/yr9IyO4w9HmHjnDX7k5/6f2zpPieR+44MWaPeKm70fWw25/LeeaXofJHcHYVmIer3NIL8RZjaLFomgaNotl4GqYmWzqH4/qt9/u9W9ad0UTUOLOAnGjEQCV2dn01DX5xewMmn8Bz+4mGVhWQhdvyvne8MybRtRq216PYWuUzl9muDHPrbpMY14HC0WWydK7iTVc+fRZ2ZuOlBwP2OmUqjhMKp3fcz+VjAWFjASSQJHDt/SfsIwbnvQ6cPGzd6PMgbtLrKYqzIQ9dMXvrUpjiV985TJd4JcLkcul2NwcJBrLWmK71cShRoXF9fHj22FedMijMKIx82V2vr0u3PFOYbDw4xFxpgtzGKL9euzvPlnV/nzXz1JIVVd99uNKJfLhEIhOjs78WevILp3wfZHIXmxbbv0/Bzd24YJRTup5HM3OJpEIvkw0jpqHjh86K7FhUkcFE3bkjgDHMFzi+KsUYaiKLhisbsmVlydnU1xBuDu62vzanm2DX2g4gxW2vYDFGcAiqpu6XoqHs+WxBmAe4236W7g27uH4GN3dmBer5mU8x9MyIyru/u2xRmAe2jolsUZ8JERZ5shBdoazr/yIlffe2vzDbfAYr7KtqifvoiPRH5rSUL++Mof89gfPMbvX/r9O1KHBlXdolRvD2COx+P09/fTMdLBb1/67bY1HIQQ/L0/OMV/fWdmS8e3bYvj3/pzqqVbW9TwVviZ3z/JF770Orp564tbLigW4y43D4T91JUS1TVtMVeYY3t4O33BPhQUlsrtC6sKWzB1JkUw6mX+SnbL5ZZKJUKhEJFIhKidxoqOQc8eWF6NdbBti8zCHF3bhwlEOyln79zCjhKJ5P7CPTS05SlgDYQQzF3OYNv354KqEolkPYrbjaur644eMz6ZZ2Hi5jZIvWqus1P0qoll3LuFwS3r3pX9YUUKtDU8/1tf5Nmv3Jn4o8VclcGon/4OH8W6Sbl+8wxPF0tVfnm6xN/Y97f4D2f+A4a9fo2PTCaDbd9aRxdC8PnffI0f/MobbSIsnU4Ti8W4pF7itdprTOVXgzfPLeT55plFfu07V7a0yvr06ZO8+l9/lzPfeXbTbW+HUt3k9FwOj0vl/GL+lvfPqYJRr4f9QS9/peMY+dd/q+33dC1NT6AHVVEZ7RhlOj/dvn+yghCw59E+0gtb93A2BJqqqnS7a+j+PogOQ341QLSYWkZRVcJdPQSjnZTzueYikBKJ5KOFXlv/njDqFtWijl65+TvE0C1S8zcfJCtmakwci39go/CtCCGoV7eW6dC2bCqFGy82vJZ6xaBe2doCw7dKpaDfdXFsGlbTgK6V1p9LpaCjV00Wr+W29E6+n6gU9DtybYQtNrw/GmUsXsuRWSyTWdraYsPvh5kLaepVs5nc5G5hGTaWZROfypNLVEDZfJ9SpkZlzayt6fMplibvzewcYQsmTyaplvQt992tbGfULSaOxZk4Fr9hv7gTWLfhFNiMcr7O4tWbXw8p0Fow6o6Xy6zX13WOS6UqpwuVLR+rZlikSjqDUR8+t0aH301ik1T7v3J9gYTnCLG+H8Wtubmcvtz2+9LSEl/60pc4efLklusBcGouR6FqMJeu8M0zqxloMpkMXV1dlFXnYXY1t7qo5ntTGZ56sB/DtJlJb37eS9euoLndLFze2oKoAPpiieJr8xv+VqstouurWdCuJUv0hn18ek8PFxZuXaAVNRj1edhlJLkaGIbrq5mI6ladulUn4nGmkoxGRpkuTLftn5or0bM9REdvgMLy1qc4NgQaQEwrU/F0Qccw5OZW65ZOEe7qRlEU/JEICO6qJ1IikdxdCulqm8EghNjy6PX0uRS5ZIVa2SC/XEXYq/vOXto4M6QQgpkLaQrL1Q2N01ZxUck7omezUfg7RTlfp1Z2jPNCqsbM+fULFIMjyFpJL5aZv7L12QQzF9LMXFhtn3Ku3mz3SkHH1K0b7pteKGHUb/z7/JUM104kblz2+TTZ+PsTBVNnUs36z15qP5dGHabPpyhla23CVa+ZFDO3t4yPdYsiuLVMcAzXG3lGbFs06zV/JcPcpSymbrE867zbamVj3TmuZfpcqu0+Si+WmD6XwrYFlUK7sV9IVSlla6QWipsOUmzGxLE4xUxtwz7TuBfrFYP4ZJ6rJxJt91diuoBprN9PCIFtOwMUWx2kAJg8nWTuYoZCqkpytoCyBYV2I0FhW+127dK1HKVsDSEEtbLBxLHVJX5M3XKeP0IwfS6FZdjoNZPl2WLzft6MetUkNV/CXrlOc5cyjshcOX6j/7WK3FrZYO5yhqvHN77fLMNubl9vGbDSqxvfv3rN3HRwRdiCXHJjO7eQrjJ5KrnhbxPH4hRSVZbnipve/+V8HWOlPxXSVQrLVUq5m9+3UqC1UEyncfv8KIpKrcVAtoTgh09d4/MnJsgZW7uxlvI1wj4XYZ8zl3azVPuGLXg5UyJWeYX3CgYHuw9yNnW2bZvZWcfrcuHC1kUQwFvXUnx8vAvDFvyHV683v8/n80SjUTJmBpftYmJ5NRXy6bkcD4/GOLgtyum5zUddlmem2Pf4p0kvOMIjUU7wr9/91xvG0xmJMqV3lii8MEP+2SnMNS8X0yzy5ltPcOr0Tza/u5YssbM3xLhWYGrp1g2LmkdlR9DLcO4S0/5thFKrKWGLehGX4sLvcubVj3aMtnkTAfKpKpEeP9FeP7nkrQm04ErWoShFSlonRLdDflWYlrIZQjFnTSFV1Qh0dFDOfTDG04eRTPZtLl782Q/dKLLko0P8ep7UXLFpzC1cyTJ52nnJZxbLpBfbn4u1soFti+YLPDlTYPZimsR0nqmzKeYurwqVQqrK9NkU8anVgSrbEtQrRtMAyyyWmx6yaknn2omEkznMFuRTq4bIRkZFYrpA/PrqseevZEnOFG7olShl6zc12BYmssxfzjpeuxWDxDLspqEGjlC4djKJaVhNQ2kjz4RpWM2yqkWd9EKpra6r7WGzcDVLrWQweTrJ/JUM188sb1i/5dki6cUShfT657pl2G2GYsPwrVcMhC2az6B61WB5bmNRIFquK0BqvkQ5X6dS0KmWdCzTJrNURgiBaVhtRnLzfNa0Rau4XryaY2kyd0MDE2j2BWGLZvvVygaTJ5NNEbzWA9Ha/nrNxDJtJk8mmTgWZ/pcivRCieunl5m/lHWuS8lo276wXGVpMteccWLbNtl4hWzC6Uf5ZNXps2vOTYjVdtVrJtPnUs2+1zDCr51IMH8lQ2axTHLGiUuvr/Eub2qUt7w/Zi+mqRbbhWpiqtDWZ6pFnYljcSZPJ5uDCfWqc87Flr6TX65w/fQyE8fibdc9G69w7USCmfOptkGKVbFhoK8INyFEm/htvTbVslPPhvdoo/Ns9AXLtDF0a3XwY422K2ZrFDN1MotlZi+mm/UxdIvrZ5ZJTOdZni2i1xyhNX0uRTZRbt67jefb9NnUhv125nyKzFKprW+Yuk1yxmnbRv+72jL4kZgurLsWjXsDHME6fT6NbdnrBnVaty9lnWfN9LkU104kmteiVjbaBLJeM4lfz5OcKTBxLN4csDB1C8u0KWdX7fb8cnWd+I1P5cnGy233fzlfbxu0MXWLhYksiSmnr8av56mWNhe5ch20FoqpZWLbO6nXChSWk/jDjkdlulrHFIJHO4K8mC7wP/c7xvTixCWe+/e/wQ/+45+ne3i07VhLK+tuNeiL+G7qQTtXrBBULXa7EpwpVvip2G6u5663bTMTn6F7vJu52TlM08S1xcUzT83miAU9eF0qU6nVl2ylUiEQCLBQWKS/vp25zKpX5/pymb/28HbmMhWuJjcfjSosJ3ngE5/l/CsvYNRqfP3y1/n9y7/PcGSYH9v3Y23b5r4xSX0yj+LTUINu9JkCrpiv+Xsy+RyRyCHK5auUy5PYwuBaEnZGwPUnv05x5BH4ka0Hnuq2jelV2RcOMHDuGInIj+PWc1BOQbCbgl4g7Ak3A/lHI6OcSjhryqTmS7z+RxN4gy56RyKEOn2Uci03bDJOam6WHUc3Dkxu9aCFrCxxItCxItCEAEWhlEkT6lydpx6IdlLOZelZ06ckDjMzv00m8zrbt/9vhMO3n+r2o0Dl2LskQZoAACAASURBVDFwuQgcvvVAbcmtYRk2iekCfaPOe6OUq1M5m2Ln0V4qLQZHasF5nnYNOs+Fcr7OwkSWSLd/wwREa0fj9ZqJXjfbRsMbxmbD+E0tFFEUhfHDPeQSzjFtW5Ccbn+W55ed0d++0Q4S03lGHuwmv+wYX93bQrg8GpVCncpKbqZCqooQMHawm0K6Smqu1Fa/gR1R0gslglEv2Xi5eY6NafmNZ2dDrKqaQnwqTyjqJCJIThecdsvr6867lK2xeM0ZLBx5oKtNtPaPr6auF0KQXPHSpObahXAhVcXjd+H2amgulVK21mwzhLNvYqpAIOIhOVPE63e1GfKmbqG51DbPz9Du1RTmxUyNUNSLbYsNR927BkNklkrQEuIc6fJTzm08eGvUHcNuo+lbes3E1O1mP0jOFNCrJppLJb1YIhDxEu314/a5WJjIsvNIL9l4Zd3gANA0rscf6lknZN0erU1oNGgcxzFwC1QKdVRVZefRXqbPpdBcatt2sNo/bctuDhRcO5Fg/KEeXB6NWtlg9mKaUNTX5l0oLFfJL1dRtXaF0RC2vSORplhqMHcxw8iDXc06enyOvVTO11FVhbnLGYIdXiLdfmplg9R8iZ7hcLOMRp/VqyaVgk5ydjVB2dyl9gFUo+6IIL3W3k7ZpTK9I5HmcVqZPpciEPGQS1bo7A+uGyxRVfWm4SzT5xyRl5gqUMxU2f1I/7pt1vbBWtkgl6gQ7QtQLTnPpGKmumaf5bZySysCpXUAw9QtcokKydkCow92o6+E79QrBoqq4PG52gZ0WgcUmvfbDTBbPNnx63n6xzvIxiuk5ovsergPAKNuko1XKKZX+4hlWo7YVVanuw7uXL03p84ss/uR/qYQbbRXox0bzF/J0NkXJJso4w24m1NzZy+kqVUMbCtMpMu/4aBUI1tv43yX55yBm2CH83yrFOrMrjw7VE2BTTTah06gLZQW+OV3fplfevyXiPlid/TYxfQyvY+eRvVnKKRT9I07adAvlmrsD/n5VCzMa9liU6Cdfv4ZskuLnPz2N3jyb//99nquxJ812EygvZsvM6Tl2Bf0cEw36OoZ5UTiRPN3IQTHrh/jkvsSn/B+gmQyyeDgIAAvf/0yiakCT/3tB+noac90JITg1FyOJ/f38eBQBydmsuQrBh0BN5VKBb/fT7wc56H0Z3ivFuV/uv4WX/zrDzGdLjPWHWRHb4g3r248JaWVUiZNbGgbvmCIQnqZ4/HjfHr7p/nu7HfbBJpdt6hPF3APhzFmiwS/tw99oUTg8GrK6WzuHXq6vw+XK8zk5K+xnHoes/QLdC17qCoq/qXLG1XhhsxWdTAF24IeovNv4tr/45wOPMijyUsw9gRFvUjYE25uP9oxylTB8aC9+43rzZfArqN9BDo8mHWr+cB/8Xf+PdNnTvITv/JlekbWryXXyOKIZeI3smTtEIQHwNIdgRjqoZTNEIytCrRgtJOK9KBtiBCCfP4UgcA4hcJZKdBugl2rMfPjPwGaxp5TJ+96xrKPOoZukV9uNxrWGlgN8QOOwbY0mW8allvNDtswfEzTYmkyj6KwofdHCCfuo0Hr3w0ao8GJaccL1WokbuRxagiFiWNxPD7XOhHViHHR4852G4mBVhpewIZwW/1/tQ0njsXp2R5uG6FeO/rcagC2erwa3oa15YEj8hqCr1FXy7Sd6Ucr7bl2/5kL6XWGcGvZm8X4bNQeG127BlNnN/b6wXrDEmjzojnCelX4mbq9aSzNRtd8I3HWihCiWY5t283+ebOy1iZJvn5muU0Irp36daOplA3xvJH3pl41MOoWRt1i/kqGcKeP3rFI2/Uq5+ttnuaG8d7K9AZTcteKwczSxnFvuWSFYNRLfrna9Og00Gtm837ayJO9Wa6Bxrk3BNbEsTijB7qbQvRGJGcLbWJzs3Ib93jrQEWlqDcHnbLx1T7XGLgId/ooZm9t2q1tC1RVaRt4KqSrdPYHmlNWW+/ttfdSYrpAYrr9vBavtdtRraJqebaIN7hxWzVEZGvcZG3l7+W54g295cmZImzguG2N920cR1U3n6r6oZvi+Mz1Z3h94XW+NfmtO37sfGoeNZBBUQXl4mTz+4ulKvtDfh7pCHKqJQ5tceISn/hf/xYz586sO9ZirsZgdNUr1B/xEb+JQDteKNNNkv5AjG1eD3i3MVdY9Wg9P/08ak1l5+BORFgQjzsPpNR8iWvHk/SNhHn5v11Zd9zpdAXdtKmZNmPdQdyawkzG6XzlcpmsUcbGpqoPMZvcS9jn4svfvYZpCQajfnZ0B7meuvlL1tR1qsUCoVg34VgX2VSCy5nL/J2H/g6nkqco6qudWZ/O4+r24xkMonhU3ANBzBXD5LWv/x4v/+evksudIBp9hI7IIfIL7+CaU9gvfhdlZhHNvQ+PUSG/fON4gLVMFWsodYswFZT0NQY8bt717IPkJYB1Am0sMsZyZZlsvsDcxQw/9A8PUS+beAMuXG4Nb8BFJa9jmSbzF88z+tARrrz9RnP/tG7yp/EMlmWtetBKcYTqIme4QHNBqBeKzjDqWg9acMWDJllPvb6EEAY9PU9SKt+aUP+oUT1zFvfIMO6hIaq3GLcquX3WGjitxmOrATF9PrXO0LtVipnqTQ38zVhrRN/KEiJ3Myh/LWsNosa0tga3k/Bko/inm00TvFHZHxamz6e2dH7vl63EfzU8qK3cTAjebgbAqbPLTaO8mK1tOEhxt1mYyK4TZ3eL6XMbTzW8m7ROmW5wq+IMHE/q1eMJxBqFs1mc4q3QKsCzifKG06PfD/nlyobtsRG1LSTN+dAJtHOpc+yN7W3zLt0pytWzaHYvojpAubIqdi6Wq+wP+tgf8nO9Wqdq2dQrFfLLSR789Oco5zIUM+2jLIu5KgMdrR40L8mbxKBdLFXZni5x5PI4Y34vNTXGUnkJ3dKxbIsvnvgiASPAgeEDFH3FpkBbuJJleH+M7/3RXRvGCZyZy3FgqIOlXJXxniCKojCbqWAYBoZh8OalKQJ6lGtmD8HwaX7y4yO8O5VhuCuApiqM94SYTlVYylX59rmlDeN+StkMbp8fbyBAqKuba8nLBN1B9sX2sTe2l7cWV5ctqF3P49sRRfG4QFFwxfyY6Sq1Uolj3/gzTr3wP6jV5giF9hAM7sbQCnR+TSPkT6DUTxPpG0DXepi+MrGuHjdiulxD021ciTPQtZPRkJ/TrnHqS+eBFoGWuAjP/BMCtkVPoIfzFybpHAjQOeDEkDVeboGIh0qhTmpuBk8gwMHPPcXU6dUFav/9XJKfuTTLt+POwyAQCEBuDsPfS7W28uAK9UHJEZnllhg0uAsCzajBVz8Ff/ZTd+6Y94hSeYJgYCfBwA6qla0tAfFRpXrqJIEjRwk9/jjlN97YfAeJRLIpH4TIkWwNTdvchH2/CUMkHxy3Elce7vRtvtGHnA+dQIuX4zw58iTnU+fv+LF15Sp+9wNo9FE3FprfN6Y4xtwuej1urpRrnDp7mZIrzC8+P0VsaDup2XZjcanQ7kHru4kHrVw3manq/C8nxxk7G2VY1UgYKgF3gEQlwankKbS6hqZqPDD4AHPMNQVaYrpA72gEj8/FyANdzJxvH224tFTggcEIC7kq+/sjGKbNXKZCpVLB5XIxE5/HZ8UoiihKx2mGXG7mMhWGY85Uyd6wFxT4x398hr/79ZP8wXtz6+pfyqQIr0zRC8W6mMlOMRweRlEUntj2BK/Nv9bc1pgr4hkJgxAI08bV7cPM1Jg7f4b+nbvZfnAYlQiuUpbI6efx1Uy6PvbD2F/rQVhxQruTaK4eZq9ufWHtuaqO1xQw8xYMP8qI38tSeBhzsUWguUPwP/4uHPuPcPK/MhYZY2Zimf7xDkqZGqpLacZvBDq8lPM6qdlpesd2MLRnP6mZKaoJ55q8minySCTIHy1lCAaDqKoK+TnM0AClUskZYQ8PQNHZvpzLEozeRYF24j+DXoHLz0Lyw+11KpevEgzuxOfbRrW2sPkOH2Hqk9fx7t5N8InHKb32+r2uzkeSSPftLejrcm+8cHJH99YWX76TuNwa2/bEmjFFN8MfXJ1GGxsI3vA81tK6kPdatu2JbRhf03rs1r/9oQ92Ku8HXV6DgR3Rdd+NP9Sz4bY7DvfSsz284W8bEYjc/uLEXv8Ht8jw6EFnwfet9LP+sQ5GH7w/Fojv7Auyfa/zzm/EJ60lHLvxs8PtXZ2a1/qMacR8NgjdQMRsRdi+H6K9gdt+Vg3v39q6ca1xp/cLW33ebZUPpUB7YtsTpGop8vU7655UAwtEOz6Ox9WPaTuu8IJpMV/T2Rd0OvqugJfr1Tp/8PJZPJ3d/MXpRfy9Q6Rmp9vrma/SH1m9cfo7bhyDdvKF6/TqgtcG3qQ+qLCtJpip6vQGeklWkpxZPsPh8GE6OzvZE9vDNfMayeRKRrClMl1DjodnaE/nunUVLi4V2N0fJp6vsac/jADms9VmgpBUMY7LGkNVVOzANLnZNJqq0BlwHrKqqjDY4ePYdIZf+9GH+NJLVzHWTDcoZtKEVgRaONbNfGmBodAQAJ/c9kneWHgDy3YCOPWFEp6hMHbNBEuAW0VRFdJXZ+kb30Xfnm4o+eB3vx/31TfYd6VI4EeeYuBCjlo2RDZyCp/aw9zUJFtlqaYTsIGp12Dsk2zzeShE+vFkrmDoeYzSaXYm6yznSvDUv4XJ7zLaMUp+sUbPcJjCco1Qp68ZXxCIeKjkdXKJONG+frTkMr5qnUs///NYQjBRrvFz4/28UarhXUkQQm6WeNXDwsICX/7yl6l7ok2BVisV8YVWH6zBaCeV/B0SaELAsd+Bz/4L2Ps0XPrmnTnuPaJcniQY3Infv41abV5mcrwJ+swMnpERgh//OPrsLMaCFLQfBJ4V4ynaGyA2EKR3OEJHT4BIl79NaGzbE2P0QDe7jvY140Z2Humlf6yDkQMbGymNBAamvYwQJjsO99I/3uEMAm1C70hknZG+FYNi7KFuAhEPOw734vG58Ic8dA21G4LRXscY274/RqjTx/D+Lrq3hRk/tCoYxg720L0tTEd3oJlYI9Ltp2swREeP867cdbSPUNRHpMvfNNT8Yedd5AuuGv4jDzp1Wj231fPatqezKV52tMQ2w6qo2XHE+X773lhbIoEGowe6NxQ0O470Mry/qy3We9ve9fu30tnnvJ8boic2sNp23UPtZdxIYHkD60VPOOZj55H281NUpS1pieZS6RvtQHOpdPYHm7+1xilFutYLgaFd7eKvf8wxhntHIm3XocGOI73EBn3sPNrN9v2b5wboHXYSZwQjXtwejR1Hetvq7Q97GNq1+nn73hh9o04dGu3o8bnQXCq7H+ln7CFHeHX0BG54L3j8Ljx+F9v2xPAF3Qzv72LbHqeuowe6b+qR6ewP4tvgGmzGjsO97H6knx1HeukdjtA7HGHXw330DIfxhz3sfqS/7bxbCXV6NxyYgNXMj4qi4PWvXsuuoVBbn490+Zr3ZoPe4ci6gaORFuGqaSo7j/Y1B00a/dXlan9WxAZC7Dzat67/dPQE6B2J0DcW2bDurTREaus1a00E43JphKK+Zl8YO7h6fygbxHA17qeNjtv43R92nhu7jvZtWKeBHVGCGwxQNJ4HvqCbod2duD3rn53jh3qaz5ZWGvdb4/mz1b70oUoSUjWr5Oo5hsPDDAQHmCnMcLDn4B05tmEU8HQU6B34BOVCkmLZScN+uVRlxO8huNI5R/xeruYrzM4t8cmdQ3ymv5elZCfBNQJtKV9joKPdg5Ys1JtZXhoIIbg4n2PvkSh/kX+Zv2o+xVDZ4kWXSa/fEWjXctcYVAaJxWJEvVHwQ61Wo1KpUMrUCK9kQOwaDHL6hdm2elyOF/mpx72oqkJfxIffrbKQrVIulwkGgxRL06jKDsZ7QsRtF9PzC/jdGp6W0dLOgIdsxeBHjgzx7165xutXl/nM3tXOXc5mCHU6N0Qo1kU8meTASvKGfbF9uBQX59Pn2c8uUEDr8mHl6ig+DStbR4v5KM+l6XlkB3YoDieLEB0mteeH6HvxnzHVWaOuubENneHIDJc8nZTmT7NVErpJVJgwfxz+2tfoLWrUAhEU2+DCub9Pf/4NCpnH+Lr1NP9g2yMor/8aw4d/kExWIdoXILNYprPPz8yFDEbdItjhoZyvk0/G6R0Zo/jSd+kMhEheu8J8YhlNgceiIbpVSMRWsg6lp5kvQjAYZMeOHUzOnWC/P46wbWqlEv7Q6os62NFJKXuHBNrMW6CXYffnoZaHs38En/ynd+bY94BqdZau2BN4vX0IYaPrKbzejY2ajzr6zAye0RFUv5/gxz9O8ZVXiP3Yj22+o+S2cHs0ugZDBKNeZi4k6N7uR1VdGwbtR7r8bQKjf7yDalFH1VSq9isE+Uwzg9jabG7D+7uIx99EsUbQXNuIdPmJdPmbWcZaCUS8VIs6Q7s78YfcKKqCoirNOKpQ2Ka4mGDw8D4UFBR1NeZj5IEujJqJUliEDmfAbfTAqiFn6gb1+iK1Qlebd21w53rPjhAGhjVLbGA1kVKr8WlZNrGBIIqqMNgiDtoFbaeTsS5bw+t30bUtRCDiIT6Vx+XREELHFmUUtZ9Qp5dte2LYoogtiqhKGAXHmFVQ0DSVgR1RfCE3iqI0swZu2xNrXhdPv2td7JumqWhBFV/Q3Uz4oigKiqIghGDHoV7HwFSgXEzi83fjcmv0DIepVwxmLtTp7As4mRyB2GCQSI+PxjiT6rLwRq9Tz407n1WVUKeX2GCwLSlIYy0sVVObde8fd4RYsMNL/1gH2USFkQfahX6ww8vgrii+kEG9HGRhIk+0N0AhXaVrMER6scTAjiiKqjgCWS3i8YZQVU/TqG+kHR/c7cUXDGMbTruY2mtkMmG6uj7VbBchBP1jHc3kLJEuP4V0lXC3j2hfYF3dwDFet++NIWxBMOKlXKjj8bvwhz24vRpun7bOOFYUBc2l0jUYRHOpZJZKjDzYTXLGSdk+8mB3U8gEIp42L02jjw3sjBJcTFIq2JSKNNu1ayhE12DIWXstX8ew0hiVUDM7KsCOQ73YQmDqFnOXMo6RH/U2E0FomrrufFsZf6gHZSVBRr1i4gu51g2e+ILu1SQXwjH2XR4VU28fMNdcKppLbcaX9gyHsUxBMVNlx6FeNLfaXIuuQWvCip6RMKqqMHqw20m5X7dgCXwhKOUa0xAtureFmtvHBoJMn08xfqhn00Efj8/l9BfTxhtwromqKYw91NtMFNJsl8POu90ybOrWBVTX4+w82tfMeqq5FAyjiqr4CEac+yQ2uOKw2N1JIOKhmK4hhJMIKdzlIxTzMnsh0ybwWrOXhmM+wjEfc5czVIs6iqJg23WifT6CUW+zfUcPdnP97BzBSAelTL35jG54J3u2h6lXTArpKkN7Opk6HUdT7WZ/mzgWx7RvnkvhQ+VBS5QThD1hAu4AI5ERZgp3LgYllThGveAh2jNOMDiK4nFeYBfKNR4IrY4QDPs8nJnLMegx6Ort4Ymd3Vyph0jNrdalVDcp1kz6WwRad8iLJQSZNVmhzFSVhBuGwj6ytSy+WJjuokm8btDr62X+lRrZqwZBI0hnZyeKorC9YzvekJdkPEW9YjYFWudAkEKq2lxcMVvWSZfqeNwqgx0+VFUh7HOTKNZWMjgGqChZLKuXh0djeC0fs4lFNFVBbRGRAa+LsM+Foig8ub+f715OYllVTNN5yVTyOQJRZ4Qj1BkjZWfZFt4GOA/Owz2P8Ztv/QXH355HHQhQNIpYmRpahxcrW8PV5cdcrtC9fQR3qEooW8Qa/QTF2atUPC7K198kFekGxYXhC6L02ajlLPXK1mIBUqbJXnMBYmMQ6qXP40Z3qSxEtpMpvMvV8qMMD13AVL3M6xEoLzOo+NFKPjp6/JQyNc5oJgsRhVzCycpUyevkkwk6evupnDhO967d1Ab7uHTiFCN+L6qicBSDiYjzIqgnr2GFBqnVajzyyCPMpOuIYhy9VkUIG29wdVQ1cCezOF76Juz/IScxyejjMPcemLe+MOn9gn/2AsEaKIqGzzdArbZ+yq0ErFwOu1jEvc25D4NPPE75zbc22UvyftDcKp0DPlRXBdtzgmTy284PtvM8tu3VhXWV0gJ6eQHbdgwuX9BNZ68f2zYQtkEm+0bTUOnZuUxkaHVKvy/oxhd0ExtwDD4hbGzbJDaSYts+x8jtHYmwczDBYF+Z8cM9BCIeFNUxmBV3Als4Blpk+bsMui7gNxP4Qm7HU2Mk6KydwO2zUcwzmFMvQWU1pb1lVjEu/gkdnct4OibZ9XAf0f5Ac4RZCAuxkqZPCItgzCbcl6JQOLeyxlW7QWnbBqoKrhWju1aPE49/A8PIIuzVxBGqXSfS429Oo3R7NCLdfgb3lvEGVHR7gprlxAIrikIg4iGdfpVAz3m274ux60gMj9/FrkecQTNbu0ipdBnTLNM3FmT8cBR/2O2UWXASOLVOBVvrrQ/H/IyteLz6xiL0DHrQVmaEKIpCqfIuprWalMLjc9E7HEFzq+w43Nv0dKiaicut4vZomGYBd8DxjgJ4u98k0p9D1QyEEIx1zaEgmh5MIWy6t68s4xJdHfWPdPsZeaCLej1JPP4NhLDJF5xkZrY6QSr1MuX6a/Tv8CBUJ269ayjE+EM9TXtC85SpnvoNMhd+i3z+JBQWQQhi9WOEOwxK1depVq+iVCZJn/5VwFnDNJH4Jtv3WPSNhRHCwBdyMby/i11H+4j0LDO4M0GxeBKRm4VyurmfsG1G94WIDSxTKl2hUDyN1vE6fTsqqJpgefk7KO5Emzir6yksy5mZtONwLy6P5qyNJWy8LoO+kQj9I0G8aq3R2Uid/zLV3CXWks+fxrP4J0Qqf0YwqjKwo4POviDhmNPflPQEysIfohdfplJ+rtnPEBbawlsI8ri9Ft6AIKwmUXH6eTL5POXyypJJjf5sNrJeGphmEZfHWfbB7dUIdXrbRE5sJQa+gVU9TrH+bTTfEv6Qh4r5Mn07bTT/FJbliMauwRDBiJdQpw9FUejo9qEJA82tYtsmincRzaUyuLvO6IFu3KVpdu5zg603B5TcHq35ty0KEHgLIUwq5otUzJex8tPYxSU0LDweR+i63BqWVcO2nfMb2BGlsy/YvOe7hkKMHnC8rGMHu1E1la7BED3DYef89fSK0A41Pc+GUUDRDAYilzGmX0St5wnHfNi2QddogqrpTN/3uatQzSHMGvbiKRRtHtMsEojaBDtNOgdnUM00Qsmy86hz7w3t6mT73hiaW6V7KNzmoRva3cmOQ70Mj4Gv5ziJxDN4fC5skadUukKlcp1g32n6xzro3h6ixz7hDIYLwdDuToKdOopWarbleHSCwNJLzePbokbdal/reC0fKg9avBKnP+ioz+Hw8B0VaOnkMYxCB6qmEerYiZarIoTFpVKVfcFVgbbd5+EPl8vsddUJdcZ4bEcXv/7nLnYvziNsG0VVieerxIIefC03maYq9IS8TM39GdrAbqLRhwEwFkpkYh66XBa2sAl2Rui6liXRYdI3u4/66SB7rE9jP5Igts3xUg1HhlECCvHZNP6Ip/li8wXdePwuStk60b4Ai/kqPWEvy0W9mbCkM+ghXdKpVCr41BAlb5ZKMczjO3t4Ne5jOZ/Cdne1TWPUFHCvvKg+vaeHf/InZ3iv/59Qry9y9PCfMn0+RP+481APdsbIaCX6ff189atfZXx8nFPX+ih6X+Dk7Ce57L/KF3//J3gm++/w7evCzNTROr1ouka4u5vE3BKDlkFO7ae6+AblQAAreZV8KAKqxjy9aF15Kp4IqbkZhvbs2/Ta5m2bB/UJGH6MtG7yDy/PklYFc91BupRuFpOD7Bx5j927+7g6NcP2jiG6Mha2ajnr4aQq/MeZBF5V4SeWSgQ6PJTP1ykk40R6+yhcu0b3J7+XZDLO/LUpRoZ3AfBAvcRveSKOUZSfwzv4aaxrVaLRKGUlhJm9Sq1Uwu3zo7lcUErC/DGC4TFq5RKmYeByv8/5/JMvwVO/4vzdOQLeMCxfhoE74Hm2bTj/Z7DzsxC4s0tebFjc9OvsPz2Nnfz/4Kefxu/bTrU6T0fHkbte9ocNfWYG98BAM7V+8LHHWP6NL67z4EvuHGYlTnLhz0FVGSgep9Z1hMTUf6Mrb+Pa9yMks98FwBVw0VFOUZxI4hr/LMHgblKpl+iYnaReT6KMHMJWVDr7ckQjJuXqJKjQsz2AYhwnvlQARaGSO4/fP0wq+QLu+ASKaaBvP8COI59HpC6jZq5ipy9hjz6GFhnBtnQyl34bI9SLWYzTOXQQT11QqlzDujSF3z9Mvj6FL5nB33WQ9JX/hDs1iwFEFgcR1WVwByj6FUifJGDr0B0mkfgmfq2bkLsTqz5AOv8uQpgI4RijWot9WSicolZbIhTai8fTg8sVbArZvr6nSSz9BVopA+Ee0snv4rt+HNdDP07I1Y9x+X9g1+fZNvQ5WO7Cig5Rnv8O9vx75NxeYtsOYVbc/P/svXm0Xdd93/c587nz+O6b8AbMA0GAk0hKpAbLsiXVkodISu0mnWK7UlfW6kqzsrzc1llt4jiry8rQRm0c2UnsqLak2JasiZJMUQNFkQBIEAAxPOABb57uu/N45ql/nIcHPIKkJEtuKlvff4B37rnn7LPP3vvu796/3/cbrb4AUUA7EU8S1YREJG5ivPQn6Ec/hFTYD0YTp3GZMJnD6F0D+c5upra9SEGaIkiXGPcvknjgw7i+yvq1P8DcPIw68gC9a79H0vMRJn6VbnOebPEx/IufIEy/l164TTYbj6/d7kuo2giF1ElqrW+QTR0nDA8gSiKh2wMnoNH+BpGiI8sZ8huraDRJHryfXuNbKKKGb2zh3vg3eN44ptPh0GP/gEbn2zS3RUKjRqK+xsR9v4rjrKGJOUx7A0FOYpqLhHYH1AS+P8QyVwkD0fWrAAAAIABJREFUB8eJQ+vD0ME2vonVd8hOpWk2M2S9ND1jBWX0QQbt82iBj9zdxhrZQF37NsrRn0enR7Lxu9j6Sez+8wS1RSTAGwOpVyOSVdzFf4M//TBZeQXzpRrO1Enk7jbSIN4FtA88gr51BicaIk88ykDooixeIJs+gG8sEMkqzvRpiEKs4BJRuE0Q2PR6FwmcHsnMIYLQodP4Nqo2gmxbCL1N9MN/A8/tI/depnv2HML0YzCs4W3ISCf/c7z+EnJnC6f7CdxTv4jTvIzgDInUJHJnC9uKF+Z1+ZMI1/ZRjkKadh994klS7SW8zjxaBzTA2hdg9o/gdxswMOjPv4w/sh+tBL1r53AqBymM/zRhYGFZq5gbXyc78DHGpsg22/i5UbqaCaJEpfJeHGsDWckxNBcJnS7F1ClCVceXn6VyaJxmtUVo2FSiM4Rk6PevYBrLYPdxu1+hYFXptiwK23Ui8wb6kXdhGBMkEtMk/A326y9RWxeRk2METpVxxcK9PI81MkuqP8R1mkzr4wjyL9E58zGC3BihlsIS0kTRUURJYPp+jaU5GW2wyeDyxxEljSj0SJYeRjr6PgRBotF4ercvVfJvRdUMfJ4nn3kPodrGMdoo27cQzQ5WUiM3aBMqGjVDRFu5SPbx/4VMyWQw9wd0Mu/AGa6iJkaRutvYwQZh8zpGJkVQnITAI5NXcYzzJJo3GdQMAj9euPdL+xDNPl7lAJGaQF86wwAI0iXM3BRa5WEG9hwEPsbqBqUjv0y9+sdEapJ0Yj+6NoZtriPc+jra1EGIInqN57GC1q5PhGh0GdZeIF84Tdf9Du6VDUxzhcyDf49G4zkiVWUsC501EBsvIYk6dk2nuN2gko4YJPaS71fjR4ugGduMJWOCNpWZ4nr73lWQvyh63cvIxKvN2eIs0RJY1hZzQ4s370uxaNocTOpMJ1TaA4eEb5AqFJkuJpEzRcIwYtBuki1XqPWdWFzjVThc6tOr/kMuN4s8+cTziKKK17BopiT2YVPQC8gZjULPw50A7/IYjbecRzs3jVIVKT6xQ9Ay07TUFo2tHpni3lyAVF5j2I0JWn3gUMnobPcsxncES8opjaXGEMMwkPwEfbnNw9cucOyDD6JGOqYyIHQCgrvc6b0w2nWrPz2Vp9qz6JoBR2d/hbPP/C6Dzs/gXo9XGNOFIobs4jVctre3qdXrNIJJMkdavG8qz8e7V3ggcYpACJFHEgQdGyErkRDSJLN5LHOFLA43OiF2Zx3vcAm5W8XSphAjj6SyDyPfxJFHaKwsfU8EzYhCjjiLNEoP8OtXznA8NU7NHbJelHmHW0LqRsgFjdLoAmfPXuYtmQRa22WodREEgYv1AYfycX1eWevyllNjGD2HYadNOpWmXd2mcvpBzj37Naq1OmPKTrzxoINZyLNqOUxaDdL7jiMtv4LjOGgjs0Td5+7knzkD+L13QqKA2lrk/tIMZq9DtnxvPPP3DGcIrQWYuMukeOx+2L7ywyFo81+Gz/4KPPLL8L5/8YNf77sg+tZvsTqTY3rjOgzraNoYjlP97l/8awh3bQ11Znr3b3Vmhsj38Ta3UPdN/ics2V9dCGGAtnYJYSfURV+JrQ2GAC/+FrfjKXTABsRAJZz7HEPfRQdua/yqm9cQfA+f5wGQ8+NEio6rfwlt/Qri5HFCPYO2cpHhykXuzpxR6ksMFv63vQV75RXCiVPQWkB2TOTOFpMqCM1t+jvDfAgMBnOIxGFoPrdQ7hIm7t+18nsbZucqFB9DtHqE9fMYfrwbeJvqOFP3gygh9WoE6RJyZ5PIOIMwdhjDbGMNWsi92m75nbCIvvhS/By1OznG/iv/Dz1R2t1N661+AUlO7k7EAATPIbV8Lv6ceKdIAkgVkIwOdnIO0evhXf1d/Nwocq/G3dIeYSKD4LsI3o4pr9gh7MbP4577JwCUAXdpCTbOI7ltImB4/p/H5xPb/oQ3P4FoGwz4M4LyYex6HSWv0et/AR1wOctg+jTaWlxGVS2iuXd2J4e3yz33e+QAGhASt6NJpUoENFc+jVq7I5IVAE77GbT1q9ytE317ac+Zup9W61vIjWUYnkcqzRBkSgi+i7pyMa7j/Dii1cN04jp1qxe4ewajVG/iOC2cyx/fPaat7k0z0G+d2f1/BEgr57i9tK2t7xV105fOczsj31t+Bh1YvNVF1Xrsm04j+C760kt7vwOEyRy+eYb+XccAbi8nG+f+d4QDj5PIL4AN0do5IjWB4Vpw7rfwRg/F9RKBU7+IWr3XmghiotklDj9TemApGsFgbw5vYuMKmnQdQfDodgVkQBq2EHwvfv5hC/vK76P7JmEyi2j2sQBxeS0eEwbX47Fg/8PUq19AW72EEPiEO222xzcIshXEdAlunaEEIEUIiEiegbJT33sm8Te/Rj/2aka69hSuJGMWJlBaG1imi2bn8Co+2voVPCFedFcaK9yOqbHtKtGl30HwPeRWHJ2ihBFC+zlUeRzn1hkmgWhn7yEM4hZnNF8isBcJsqOo3U2CzAhSb5v+ThkTgJlaJciUUbdvsRsc2r0z7mk73aC58seIVh/Z6OBVX0Zv7U3bcd0WSquFELjI3e24X02ewN3c6+Entzbi676qnUrDFgxbuJuX9oydg5d+Gy2Ma8LnLHebS+lL54lklch3Ee7qvwB+dZ4ucW6/aa7E17r4f6ADQapAaHQQiPuEHw6Rb36DPnH4YmLsMG+EH6kQx23jzg5aJRXnZ/2wYLvLZNLxZD+Vy+P0VXrdeeYMmz/e7vDEuRssmjbjmoJpeMj2gHShiCAIPH6oTJQdob0Vd+Cu6VFI3qvsdGrkKo70BKpaptF8BgC/ZdFUBPRoSFEvImVV6NuonsVyqs51/Txrueu4LYVCIQ4jHE+NM5SH9JvmbjjCbaTzGsaOB0Wj7zCa1WJPtp0dtPGcjhdEtPomniMyW/X4ey99CvWT/56EmGGo95F9sO4yIB3YHoYbh1zoisRswaPuv5XZ2Y/QXCkgSddwLZe5515BTaVwlZDGSp1HH32UVL7M6VSCoTek16uxJRv8dPp9dPQBSimB37HxFZ+MWkCUI3xjEzny2fjqJcJ0RDg6Q9Jo4Ckquunw8nUFKdUEqczK9yAU4ocRdgQHrSV+vfFt/rzVp+KcRQQCzSE3CBAtCd3NEoTPMRwW2PJbiJ0BptzD9EwuDwzeeqDEoWyCufrODlrHRk+lEbeqKOPjFGcPMOz3aOUKFDrxSGMOhxyVBS40G0iRS2r8MIlEAsuy0EozyFYLezBAT2fg6mchOwkf/jbCh36fN5dWMNrf3SD8DVGfg9w+SNyVEzJ2P9R+SAqo81+Oc9tuPAV/2WId9RuIm5doHDmBUD4K1VfQtFFs53v3w/vrBHd9HWXqDkETJAntwAHcpe9dXOfH+P4hfBdz2T3n+i7Ca4QbC/7ePDK5W0VpLKOtXwFA3byOvvjia15T6r/2b6K4dRnRuYvQwGsaqn6/0BfPoW7duKfMANr6FbTVS8jdKtrGVSQjDttWt2+hbswh9/b2XXvxqde9z92hjsAecvZGuH1P0bwjKPbq+zYbFs3l2i45A3ZDT18Lrvv6vkyifcfmxrz2Ct2FVbza1p5z7p7cuXeRs++G2/veSu1eBeNXE6C9n11Bv3UGubuN4HsotQX0hXNoO+QM4jZ2d/t4NaThD8+L6o3gOgHN+l4vvl7Hwbbi+cfd7/H1kFw6S8K+U17BvXO9u+vu9cjZa0HdmAPr3nsPmga2fadt3t0PpGF7t52K5ut75+nLL6MvnUcI4me83WYh7s/q1nXCMCIMwt3cw9fFq/q0EPgozTV8z2drfQjWcHcceXWo8e533L31L4gC2XIC0bwjQOe7AZ73qty3YRt16zqi2UepLe7pC7efS92+9cblJx4fbvdR5VXk7G7I3Tt+b+rm3J3yCneijjbXBizefGPz+NsIwzdO/bg9Vt/df6MIgnaBKHhtKnX3u3wtfLf6+JElaGPJMWrmD29yFoodSqP3AyDJCr6RYL1xnSiKONMd8hPFDF+odykrCpETEJm9XXPhtxws05JzdLZixt6zPHKvITU7kVqj7R1hYuJvUt36EyDOQWuIEVLYo6SXENMqXt8maws4x3w2h5tYmTaikyCfjyfalWSFHj2snk+mtJeg3d5BA6gPbEYyOts9ezcfrpzR0GSRet+iafV58zWVzeIk5pkXmMZmoHQpiSK9uwxUGwOHjunu7qrtzzfZNA4jihpO8wHs/g0Etnj+T77FwBsSAZ1Gm4mJCaT8OBPCkLHUGNtGlb6aIjsYY0OuIRXiHDRHskgpeSxrg6SfwJdzeCuLhKUkQvkIeaeLL0WkTBuXHILcQhHKbK+u3FPHQ3evqXbXDxAAzVnggtlHUCc5s/CvyYsBbcp0ex6Br5PTJ/G8GpOT46zLJzGaW3iqTX1Y52bg8a6TYxwppVjomqRyGq4dkhkZx1lYQD18CC2VQtV1OtPTZBbjH4HhcMjJpMqldouhkCZfLO8StPTofoQowOtukkin4foX4NTfBEGAQz+FLEb4az+g119jHkZetcM4et+uQfcPjLWz8NhHwLPinbq/TJz/9xiHHkHNzMa7f9tXdnbQfkzQXgvexibKq3bK5IlxvOr/tyamf50g7kwMJDm1a6yraiNIcgpRurMfoevjZDKnyWYeIpGY3vMZQLdjYxoegnhvgIus3FFGEyX9nu8CpNNH0RP7vqcy3z5PT0zu2FeMA5BMHUC4SwFNEHLY9r2LjsbQY311gKrMkkodIpk6gCjpu+V7PbRbNgg7OWuh8D2v74iSTi73ENls/FstvIZiXy73INncaURxb3l9P8S2fIIgRNUqJBJTqFqZXsdh0Hf31Mfte2UyJ0ilDyOMP/Bdyya5UwSdPMnkDKoazw0iWcVR71Wzi9tFgkzmPiCub1nZK66iyFPUqgay8irlTcbRoxOkUvHquyi/thy7ohRQlAqN7cLuffbUR+61VexeD7ncgySTM/cc/35MpF/vnoJ4Jx3EMn2MobcbtdNqOrS3477Qbds0GxbpzNE937lzode/t6ZVSKUO4zeLRKFAEIS7eYXZ3Ok95+r6xJ6/ZTnNymIP51Xm7M2GdQ+hjKK4Taczx0ml43ckKzlkuUA6Hd8nsyOidhuiqO45Jogy2exJBGE2vk89ZGUxJnm3y/1GCsaaNkYmc4J05tied+/74RsSlkzmPtLpI/c8P8Tj1u3xZ21lwPpKn0RiCkkss7Ei0+/du6ihJyb2tBnH9lleiImuqpbI5R5EVnKkd+pJEKU970LVRnb7UhhG6OoMUQSp1CFUdW9aRTZ7inT6CMsLJsNB3J/vJs+vRjI5w9KCsdt+FWWvmqYgvDE9SiZnIRQJLR2JPNnsQ/d8LsvpPeWUlSzJ1AHy+Yff8Nq7539PZ/3/BNvmNg+NxpVQSVaoGbUfSk6F63YRJI/RmTuVFro5Gr1b5OWf4LF8mrcXM3yu1uV/nBXQTZvIsUnt7Gg9Olvkz/w0rc03Jmg5ZYMbw8cZG/1ZFhc/imVt4TVtGlGSyG9R1IuYsk3oi+SGIiNHZjCuGUhpH9nP7EqGVpIVmmETc+iyMZij2y3vkrdUPvboAnZDLee2eruebMWUEhO0oYtsG7xrweCbP/fLTP7Hj/Jkt8+XEscYFWVaw50G7gXUBw6KKFAf2IznElSS22wbD+JYPkYnQeBu8tB7PsL5p15mcekKiVCh0+lSLpfpKz0Szg0q2ggd+pApoA8zLIt1+gkLv+NgBQ66kMDsr5KlRCjoJAdtwrSAVDhNPvxdeomAibrNTL6PKnVICiWM6sae9//pG5/mt879Fn/r+N/i1x/9dQA6no8ahiwqUC48xkg6wal9b+EbYYul8CBn3T6K4JDOzgAXmZmZpL16HKlnEOk+FxYaeELEY0fKLCx3ObPcRtElRCkiXdyHs7yMduAggiCQLVdo5QpkzsW+b4ZhcCyT5Ov1Lt0ow1gut0vQiuURLDFN0NlETyZjsnM7V4yIVlhEfvFfw8V/DEfeDT/3f33XdhwFIcLd/iZGHTKv+kEs7IfOCgDOSg8i0Pb/BfxEzDZ0V2HqUZh8EDZfhvIbb9f/hRGGcPVP6bz9Z0kkKlBUobOMdvz0jwna68Db2CD91if3HFNGx/BrPyZof2kQwBs9SCjcz+Klpzh48CjJmceJNuOFFkEQ41Xr4kE65xcxhgOmPvRfosgJosYc/kZsJr613sESAorTpwhzOfpSPDEbyT+B0Figt/VV8F1Sj/0aUgjceCpe/U0WYfQU4uoLyHIaXRslDFxMs4F6+J0oeplo+VlqvRrmWp3UfW8jLw3Q1QroeSKzh6Lk0LQxBEFEEhO0mpdRtQSDXoZBX+TwsVmCwEIKKoS2zMBfRy4W2Vytcfgnfxaql1CyBzBvDPDK+1GEp5EL+3GTaYTifozhAgl1DGduQDgzS1JI47z0NEH5PqxKj2Du02QLh8iUprCiLsFwE+XYh8AdoNfXd5UBOy2T4vRP4aUymAufRZbTCIKC53UQBBEBkaR+GNdvIBT3I3TX2No06LTWSO57J4cmR1Ds2wITCwSdEcz+fuQogYAIRhE9rRD4DuqDv0ROkultX0acepx6TaY0WaR7/SskTr6d0PeojD1B64VbhMM5FheqlGePkEqptFwVWzvBvgOjvPL8n1JWbTKpadREFreWRDqexNoeYItlssd/Dvfi/4lfniCf/QC3/vzzqLlJ0qmDmOYyrtsmnTmO2zlGWJhF39cjLTyBvHWFMDuOU5lG0yqIKPTq30L1EkS1FXzNQjj4brh0DQBn5gEiSQFJpnj8wwTGJkqnCsMalA7SX/kCkTaLvP9BvGufQlYyDPcdIddTWb9lMXPgFAgOtr2NoU3SnFvm6Ikp+sE23uhB5OYaim2SKb6J4XUTJakQjJXYuP4l9j/wCwRrV+mVAvRbZ/BT70QZP0ouIZCsfg57sEDoQXXFpXwoQd1MIGfvQwoKLCx/jSBZIO1IdNsexdIJrnaa5MM8k0URobWEmBmHR38Vrvwpy0vrFMsGUWiQzd6PWDkJI0fZev6j5AslupsmySmdouYgjp5Ctau4Th1FraDq46jaCNuSz0hyClHWUda/QxhKRGyTTtyHYc2hKHnSqVGy6Qk6Cx1awnWc8BEOJFLIxQBmn0Q3hyipIqtP/weCcJPZ8UcZLkNVPU0/anI03yRbehORaxJO/R3SrfOoajx/3AommDzxCwT9p9HdJdLlU9w4fwHHbeCNnuC+B34Kd+5TaLk8vltnMPoAI80uemJHpTI/Q33+IpI8iSqlGETHkadc0sfehFS9hCCIVLc2UVQTffookhnC9DuQUxX0QZUo8mmtrVAo5ZB8E6YeY7j0OYLqVRJOhDHU8VKnkDLXMdJTVPYn2ba3KWRO4jkhan0ee0uF/Cr2/odhbhldD8nnD8bRPb0N0qlDkNtHVkrCgZ/CRSO7JNLbXCWafDup2QfoXXua6ktPc6wyRSo9hnL8HShKguHafyRM5ilqxxA3LiOKCsrYw0jCJos3F1HUAlHoI8kpEol9eK5Hv3cLDj9Abt8HSWz+IQkdEsk0hhWQSo0xEPPkGCAICqIo4jh1LGsdWcmgnfo7tPov4vUccumD1AY2FaWEnH4bfudlkumTyKqEgIQw+RCqmoaV51CS+zBHxkk34qioteUqrpcif+xexdu78SNF0GpGbTcHbSQxghM49N0+Oe0HM6xr1a7imwqlydndY1JUwrLX6GoB//VkmaIi848WtgjDkKQxQEwkUbSY9EwVEwy0AtW1eDu2Z3nkXuVzEEUhOmus9EZR1RLl8rtYX/kPuOGTeFGE69QoJUp8s/otRoMMRVGilI9DlBQtQkBgfrHL6Hgq9kcL6wykdbwGfOITK3z4wx9G07Qd+d94RafV7vOubz7FJ4tv3fVkKyRVZEmkafhMb13H1JP4iR7f/MmfICGZiMrLFBG5taM2udExySUUiimVzeuLCM98ntF9C8ybj9PaGKAnQ+y2x8zpNC//+SQ35s6TDVKYtk2pVGLDWWfccyiHZbb1JqPpccJ6gJCRue7d4qArYg0MZEElrG+RDtMEQZKsvY2ZkpC9aWxBg3DIdx5+nPuVs6SkPoZQwHYdbjz/LKV90xSmp/idV36Hj77to/zm2d/kPbPv4YHKA3Q8n4TvcT2ZR0ud4HA6wXtG3sNzt2rUvVGuREkqUQ1S44iWTiFvsnZrhJy1jZTz+c6NLodEFVkWOTqZpRH4CIKArPpoyRG8Wy+TfPwxALIjFdqqTurqFQLfxzAM3pLL8PENGEoFVFUlkUhgmibj4+MMSBH1txjRHIhSUIyllbn6GQxHoKQZ8DP/DD7738E7/qddqevXgrs1pP6vLpJ73wEyT+6cN2xA6lU5bIVZ6K0TeS7Nf3eVyAuZ/CdPIHwPJrR7UL8ekz01BeMPwNYlOP2L3981vlfUrgAC3aRPPrEPCklYfhZNG91NeP8x9sLdWN9VcLwNZXwMZ3HpP1GJ/urDJY0vP0FzfZtAL9IOp6jf2CBlOUwdPk4gakTVOeTcJG2vhuEFTMpJ5l/4NgeOTKGqZVzXQ8iX8awUq900h468hREhIPBtuo0+SmI/HPwZBEFka/4W6UKZ9OH3YRtDMqVYAj84/vNEjZu4axdwhj025ZMcSu7HCgKkQ++n8cLz9DtZnAvLpJ88TXr9GagcZ2icZLu5TOVQAt1YRzv+0zSX+miRjVU4SK16E2G4n6NPvh3rS0/B6H0I2QputYdlJ6F8CEebQJQlGHSRowgz/0HK+2cR6ybegkHl9H8GUcTWzd8lkyhRW1kgkz1Od2uLsBUQJB6n3pQ5/cTPk4pc6G1C8Qjbt26AOY86cQzb1bi6+DIHpRJBCRTex+j0FLXNeSR7iWD2J/AaEPoeSrZNtG8/TrqOV/8Wuh7gyRnm5g2mc4fQlAzJcYl6c5vVNZlyaRyztY4UeowcfYJ262WKpRW2F25x8sn/lciPGN78BnJSp+2dRp+P8DZsSh8Q2VpbhXYNZTzDUJtEGy9hVC9jXJ8jDEK01GM0rS5mmCUQMiSKOeqCTm1jA0EJqNWewVVm8F60KJWvE/geIEBxP3oUsFUNSadUQGBz9Qazxx+N5yClo3E+CzBst0jm8uTG3knrlRWkjU1IJWh3DPxDj5FOnUb2M/grX8BYjLCfsFHSE1BQublusH7+HE986O+zfOllCm0d2X4b+fuOkS7up99YwwjBz46iH3wM8UqT7csvQHSKyIgY6AbD6gBJLjGhv5eeVKBm3WB0/CCD7SZB+e1sLjRobbqcmH0cJ3ye5soG3nbE+MmjkJtCVbK4K3XkRIlOWMRoN6A9RBrLohZ/GlEQEUdETCWgdN+7Uc58G0OGYP99KNHTbG+1cK5cwlzo4AtlKoc/RCqhwMpzMH6KhRfPEuZPYg+SRNgMvDpDwSdfPoZsj5MoR9y4dIWUkGf6/tP0nn8WLbcfQZQQJx+hG0UIkodx6zyVd/23KGxgRxGEXbTxJ+ndyKPoGp10hfWuxbHMGMuXnyVb9nDKpwhtj45RoGrWwEyiMQWnfoFgdJRhv83FL36WUbnF5INvIQhzuGtrbIRXEfJTiNkJbm5HSHKa5PSTbC0usX5mHdd9kOmph1FKdTpXVzC3ahw8nkY++DY6pkhLHRJ6Js3hKum1iPTRfQz8FNrIw+j1C3StFHJYZKb8k0QYkBqL5wOFGQSgcX0DuTRBbmrn+PibCW9tIusFVm9sIGTKaLNHiIIQ1zvKcHOIp2zjr54n9cDPE1YU0icfJZes0Gl+A9a+ii2cpG1lmLjvF+I2Lgg0/Qq1L57FL8JBdcigOoZjDDh8QMJlnG44i5U/jBC6pAWN1soaxmAae7FP+dEZjPEMW1cvICZyCHYPb2Q/euYEoprBHBll6blPI1WOIKTewcGxR/A8H0oHcWkgzryLhTPfhq1raPsn6BlVHKXA0UOjCLbJdneCA4/9NwRbAbr1EM3mDSbffhpnbhW7cABd1rDDCuvXnidd0Mg9+iFyhVGMW030qfchpiXk/hCjegF9n42hVgjEHNZfFZGQKIqoGlXGU3EIhiIpFPUiNbP2AxO05uYrRG4GUbqzZS6LY/jBeVKSxMOaRqNqYvgBi32bpGcgZu8wX0EQyI9P0FmIJX57lse+wt6wA8epIRCy2I7DFfbP/l3Ov/RBxLHHKKsyXafNZHqSl2ov8aTwTkqqjCPE4SuiJyAlQz78O2fZd7zAH/7yY0hIeEqf/+oXPsK3zz3D008/zfvf/370tIKz45UxdvF5Cs98nubPvpnxHW+XUjr+t22FPLF6g7MPPUpCdBm3etwwDzLMnuNYYNA2JKIoYrVlMlNKkUsoBL/zr+ice4773prij/ZHNNaGiGIXSRUQEvPIqsL6/BalcgldltE0jbWOy5FCCdEMqSoNZnOPIBke6bEcc905jmQfxq3X8VSBmzfPMT2skXMPkbLXGCRAaCVYlsfRXIcvvv09vEd8llDtsyRFuMkCX/7YPyOZy3P/b3yYtJLm3bPv5mbnJp+59ZmYoPkBum9zS9Pw5UlOpBI8Pvo4ifnfp+4VKUYSp5nD0Ypo4SipVJd612HCy5BPmjy94fJ4Jm5fRyZzWFFEx3AQRQdJzeFtbKDu+wAAmXKFjiBRGg7oXr+OKIocy2fYJMEgGYcM3N5By2Qy1EKdaFinIEUxybm9Ezz/ZfyRkySc87FE/ou/B6vPxyGQr4HOn3yG9h8+Tea9H8F4sXqHoBmNmJDdjVQZJA335k3EpIygSjhLPfTXMct8XTRvQvlI/P+JB+Dcx9/4/B8EWxdh35uw7A3G9Q9AIQOdVTRtDNdtEEXhdw1H+OuEyPPwt2v3EDR5dAzjhTOv860f4weFaxrcfOYF8loWEmXWLs2RoUC7Ok+yrbG0vEgqM8KR2QF92yFE4cIffAZZhhZ1AAAgAElEQVRF17h49StYkoOcLJEVHALLoW1W2bgxR2V6P+VonOrGFaRUngMHHmRp4QJ9r0XQtwm0iFSQIXuowrGfeCfzX/0yfkJDckv0LjlYlVtghyBFiIHEsNtECAUIQ5yNa2xvFzGurUNJJSLi2oU2bq3FQ5MGxvYqrizRai1AoFJ98QbRtk8QSuQaHWpBlcFKHdESqV9YYPnci6haknSyQLKSp3p9ga3F6wRbDtMH72fwYptWJ87V3jh3mSD0MHodrOpNEsUxepKH1A9Z+PNnKZ44iPPK56g89FOsvbKAIuUo9gQ6zTXCvsfGtasY2gDN0Gi3h0h5Fbs5yuZnnyWZyVMsTeDUDKzLZwkmRbrLIsn8SZqrK4idiGxhG7F0gq16D1FK4DkO3U4N35RJ5ApU1zdQDh5i6ctnMbUhTWmBrbUbtMM6w5s1XNtmUGyTI8WNz36dTq2KEEhkhgqs1hgOgTAe09ur62huAjESkFyNpYVVBFlkeDbOQUslfLyVFRL77wdFY9BtYg0MLHuDjjSOl8pTtwPcVagUU5jDKgtnzrJv+gTLN85z8j3vJnB9rn3xGSIvYGLyCO3mBoKoEgwCtsR5xoMtVhCwOz7+rTWCIGCt9kUSaY08Buu+CyJc+tpX0KMktfWb+B0LQ1jHFK/jrHqEYsjGZsC+XBuCkMD3ECWZC1cs3FyCTFgkKqXYNLZwjAW6rW22txfIV8YY+B3yssJgvkZ7tsniKzkixyQSl2jOLVIutxF8HbF8FIDW0hbiTtScZQwJuh6pbA4pm2MYKDSev4mz1sX3fa7ceBZN3CL0PUI2aK97JFWNzReXkOrzTH7gA2y/coHa4i3cukGnDrnyKL2LFn424uLa58kpRTLlCqhQn1+kvbiOVkozbHag4xFGHqKmEC5fZtCrMP/5b6BIKp7jMgyTSKmbDHsd6MW75YIhUbu5QHN1lVQ2R7u2jbs1oO6vkEhlUFMpxNBl4U/+gCh/CDQBwYCalKd27gaV0ARXxRNnEDSZyPHxXIdBK00+3MkXdGPRkMUXXqA0OkVkiniBzvxSwIwisnYl3r33PQ8ckaHTRqhLLNaaaGISqxMyiHxGR6e58eXnOHT8UfqdOdKnKgyaHazhAGezT60+z0brObTjU6xeu4QX2hiqDVGJnOPgtZoEhsvLL82Tv38Kv7EGdZn5576DEyQ4Lr2ddPE8dFcgVebs19Y5lFzn5a9+huK+t5J97CBXnnkaxZTwOgHp0nGiRIhvOaxfvMLylfNEoobt2ty88B0S9WuYtQ6CB+XiNL1r61y69gyJQpZ0kKXmT5AZOYDgBzi9ATXDYtjuIEoW+cw462fmcBwbUVO5uRzgr38Za9BH7BRI+g22xIhyVmVQLVP1hmzWTcJnzqAlUhj9Dg1jA1GRkJwk2/UbRIaBJ8gM7TGMtkj7lZdRdB2tprDvvpMsXztLVNLRLJXuuRbaVIH23Aph7a8IQeu7fUzP3M1BAxhNjVIzahwpHPmBrt1rzyOLe81udW0fkvw078qm+MxHL9BvWnzwgM5LE31yokWY2rs1OTY1jXehg2fb9C2P0ugmc3MfZ2bmvyeVOoBtbyKrY9R2Yt3T6aMkxaM0xi8zqr6X1qDF/eX7+bOFP+PB6CcZUWV6fogkSEiuRKCFHBQ0nl1qY7gB+/0DqE6BRFbh/e9/Px/72Md44oknds0M5+fneejmN1n9G38b1fMRvvEM3qOPUPAd/CCk54rMbm5z7dSbOaj2eFNkMR8KBN4YUriGH85SHzgxQSsmycoRyYsvUfpH/wDvX/5zakWH+lof19ogkdMZDC4zceidrC+oFEpFNCkORVlvm4yfHGez12dZbvFocQbN6VEaGeWF1jN8oPAWgq7DMAwQmz6+1cMXk/gZnUju0Wj6LAtjiLJEkRTNm/+UkdO/gpYyMB2J0vQMIgJfv/wlHh9/PPZqm/1pfvXpXyWMQlqeT8K1WJTBErJMJ1TSapqyaLAcFHAChxJtDKFDMnmAMNrA9WSGUY6itMRKP+LvHojjrrN5lWwocHNrAJGJII7gbm7uToS1cgVLlBgbq9C5eYtUKkVJU8kHJhuFozzGHYKWSqUYkkQ1m2SS4t7wwPWXYOpXSN748zi/a/pxWDvzmgTN3epR+81/SuQ7KL/0Loy1cQLDQ0opcYhj+lUmzoIAhRnCpZuo+44hFXTsG+2/AEG7BSM7/W78NFQvx6GIr5ET8gOjMQ8jR7Htp0gkpkDPQW8DVcoCAq7b+rFZ9V3wqlXERAIpv3eMkooF/B+WAfqPcS8CCAceTnue0AuR1Aqm2EAJBeavx4n5PWeVlU8/Rd+W4jCzCGSvj+gOIDUOrRpDd0gkaQRjZcQeNDvLdJ2LiJ6Bl6hwvdHC6a7j6yWEMEQyIwLzBp3tG9zcsOisvIRTGiUVFiAMUeoGrdYcoSojOi6oATgisi+wcn0LwckiyjKhYzGsbRCoKVSzzbU/+jihI+CqPrpdw9eLyG6f+lpEulRh9daXESIbWc4QOhnmv/xNfN3H6TcYOnX0bg6zv43Q6hC4o1ytbZDJlOgPush2iJWKvdii7jpi4OLUV1DVDELg0rrwbVoLy9AbUL/5JSwhiy2pmGId1/cRIx/b6CCEMp7n4HYHWJev4CbjBVyjvk5z7nmEwMVLjjHuHcCx+tiOgaCBam7T8FVS4ghiD1RjEy85huE1kHwXr9/FxiXVFrDtIaIVsXD+aTxBRUiI+EYPQZSI2mAMr4EogV5BsRrYaw28jRVsfRQREEUHuos4ho6cnYBUGdPoEUURom8jhg6u6SAAva0miXyWwB3gy1lUI8+lP/wsWTHAdzxqapra0rfRVZl2dYPt89dRjS2uGibmdhvHshADl/XqLdxEhUQ6g2UMoQ6DYRWo4iVHUYI4N8e2TVyzg2E1EVMTIAsYdhuvs0AilcDoJbBr11ALEVL6NMbWPNbKBbrnnuXAk79Ir9lAFUNcH9TeMoa2n2DdIJR0IlEBIiIEuhuLyHYbo2cg+ja3vvo8UARChDBAMbfpG4Dg4CY2UNw2kpJD8of4WgGr34/zy5oLDLcFgrBAy38B0fUIhDJBRkYdxqHAbm8N1WkTBFU2DRPdrNL55B8xHMa/TWLkEyFirV1BDT1kL40zrFKT+2yuzTM2c5jBWh3RNQhSOoInkdU0TNdGGc0RrNbB6yLrRSR7kyA9yRATLBPZ7RPIaaLQIzIslp85Rzjs0HfXsK0B4k4Km2UMsIwBkjtEcm0YXsUpTCJZLhAg2+1Y6U+SsB0lrqNECt/sIwUu/Y0VhJSOE5k4rZv4rsv2sIfruijmAEV1uXrr36LmRpBClyCQgHi8MW6uEspJbLuJrxagJBG6PqHvs3nzOo36EsLZJXzS8bMIEr7QwxkOSfc20VwPLQoJJRPZ6TNIAB2QPBMpiujcWEUZdBB9G8kXcfweg+oiN772LH5oEckuYigR6knsrocVvMzG6jnEIEsggtZpEgg3abTriKFHvV4gVBX0bpXN1tdQzS7e5QAhrSB7bbpWj6B7A73TwY4cos4GvqAjrw4QI48gkvDVPKqZwrdrOLZAu7pMqKQIlDRC6OE7CaShTyTKmL0eYgSD7StUa6tY6Rn0zS0aukMkSESCjBxYdJ77Cr46i2zWcKw6m90Boh9Hr1l9Hdnok8o/zIt/dh1NquM0ckhBhsD3GTZqRJJCp/nGVmE/MgStalQZSYygSHdCByuJCk3rB1S6A0xnkzAq78lnUpP7CISQtzeauJLA3/7Hb+bf/cYLXF5uUxRt7PTexN+xkTxBIkO7uknPcih5v41pjXD5ykd49E1fxLa3SOgTdEwXPwiRJZGM+yDV1A1G1Z+lbbfRZZ2t4RYDT2BUV7ju+SiiguiIdIWAR8pZljyBa5s9Ro0xZC+D41mUK5OcOHGCS5cucd/+R7CHHl/96ld5uN3h647HWEZh+zf+ZwB0PYH+jr+PE0ogyYgS7LeuoM6eYMLxSLgj/JE0igD8399cIIpgtpxiZOEKZiaP/JMnkH5b4LBZo7ZWxuovM3FsjP7gCjMn/wsu35oh7Q5QAp+O6WG6PjOT42xt3aKldzk2MoXrdylPzDJ3dQ65qOPP2fQabSrFMhVPY+A4BAfKRIHBC04LycuRlV2eXNviG4dCPuhleVfW4IvbDnJqhP0nTvLZ+if4lRP/AwBHCkfwfI+vnf8ancoDJNwh66JPGChMaHH7GREHzAkSmu2CbOO4dYrFJ+l2XyKffwv9XhLTCchJEWPF2BBWViRKCDx17QL7/D6RN07Y66FMxjtWfmkE3fLITU+xtbFOOh1bIMzY22wX4vDFZDJJp9NBFEU8JYdmd0mpIZTfGzck34X+Jvrjp3Cua+idlVgm/zv/8p52G3kBtX/xFYRkkrFf+4d0PvVJEo/9Gn7DRErlwGhC6jWIS2aMYHsd5dAjqNNZOn96k9z7D3x/uZzNeTgZ7xxS2B8bYbcW7pC2HyYa8wQn3o/X66DrkyDqIOsI/S00dQTHqf6YoN0Fb2MDZd++e96nXCgQ/Jig/aVBCD3U4SYegJNA8jxEaa+qYhRBvdNCAUI1jXiXqJFitxF2ZKuFwEHf3CSSVEIpiejFimiKVcd1Ooihj+gZyN7gjvkt0FqJpcm1Vg3fiSeskrYjYmABrg6KA0IEDthdBXwf8EC+gST5SG4sIhBaOyIbO3rosjuA0EceVLGHWzt6DAKiO0R0A3BBIUQIHIJwFKdxCzl0wAKJWMnQtuqogQy+QiBrSF4fiMBXQPZioroD3zWRAdv2kKNWXGbYI4+PLUEYMPBN5DBEHe6VQgdQzG2ai9txncsJQj8BYYhn23RXX0JWM7vn3YY7CEByGFzvooQWghOC6KMIEYGfj+tIlO7UfRigmncsP4IgQum3QN5RhpPj3x3RatBe+BYyEqGoIbl3qftFImq/i2cbiEIfUS9CKCEGPrbRQgJCSSMSZcL2KoqkEglx1M9gLlaWu3tCpxpbBAZIao7oLkEN5S5xNdnr3WmDUYQ6qIMXga/ieB0kPwQR3F4YK+mqNgjgmCbXvvEpZFEGw0UVAUkHp4oESNx5rkhSdhUK/U51p4wdwiiP6Pmg3iXsFUW79aj4cd9Rvb3qkoIZIbOjfhmoSKGBNLxzjbvfg+gZhGGIU69zryrAzjnukMgdgqchhyJNYx15p2/efn12X0IUAwKjAWHcAmU7LoPs9vb0Y0IfY6kKoYSXKqNYTXqrzV1Jd1/LI/kGgZpHICSIAiRBRGvXwFVAs+66VITaq4PkETkacnBHaVS2Yah0ED0zlnHvbKDs9BHPjc+zqi2kRETgewjJMUTfQnJ7hIqD4Dso/jaKCYP1uN8M1AyCbyOEIDKM60bWcHaMtYfDzq6yoDgUARU12mt1s9um7SSBN0SWXZpXz+2USQRfR6JHrRO/186ghwgoghvXie/Qqq/t3kcZ3pnje2YXAhnRH6AOfJACEEJ2MnLQam0IQ2TicSQEBAIUqw6EiL6FUV3ebRu3x1V5Z4zz5RKyYQICaC7NdhN21LTle7iGhuzGz15t1vcoLsodG1AxtuaRA4lA85CbTaD5fZGuH5m4oK3hFuPp8T3HyskyDavxOt/43hAEAX2/wUo94syZO+E/61IC00qirC5y7PFxUnmN8HCG6s0uBcHCSO4laJN5HStZpLO1gRotIdLjoQf/EElKUKt9AdveIp3chygIdHcUEvX+DKK8xKgm07JamJ7JTGI/phsyrkp0vABFUpBcmWXboRQ63DeR49pWD93UkEkyGMQN8fDhwywtLaGnZcyhizsYkB/0aOYq5HM6M5/6JIee/RbJt72NR9ZfwQ1Frh0s0IuSjFq3kKcPM27ZFJ0SFanGWE7n85c2uVUfcKiSZqK+yubYfhyvSnRfnrd0lxk0LQh7jB28j8HgChNH8qT9KVRbA8tkrW0ykU8wWhnBNwOGsslENkU+gsnRA7SdNk46JBoEtPU+ibBIzvbomzbS/hKhq/JNJcQ2U+TlgCc3rvOd5DhdSaWtdBkXM1gI7Lv/NKvUeGAkVtq6OX+TrJnlcy9+jrn1DWS/TU5I0PACJjSVILDJ0cERJfLWkHo6i+PUyGYfwDDmKRYLGILClpVgXPJIpOOhvW23SUUOX7h2Fs9u4w0C5EoFUYtDUe1skZRtos7up1+r7RK0SXuLViYmcbdz0AB8vYjk99GdGpQOxQ2ptw5ahlRlir6fgM4qVI7HOV+vUm2yrrYIO4ukHn+E7HvfjbuyAkENfycHkWH93hw0IEqPEvWqKJUk2myW0PIxz9do/Nv/l703C7Ituc7zvszc85mnOjXdqjt39+0RAAGSAMHJEiWZhGiHzUESzQfbIfvBYTv8YMshPzjCITv8YosRjDAf9CLhQYSDNoOULZkEYAASSEIAiG70cLvvPNStuc58zp4z0w+n7q17uxsgDZEPILEiKqrqnNx7Z+Zeuff6c631r7eI3/pTbng8HeIoxKkX7Y3vfsz3KsM75PUmrttCqfCJF3AZ5tj/AVHI+yQ/BWjvF9Vuo4fD78r+9QP5N5DTcDbs8rcsz8AGpQuFB+p0C127yJleAqbT9iLXmDQA78wIEzpHZZMnbQBEJiELcBYLSJdg54lkIZj3sduVLmgHcn/ZR+Oc9fPJsad/21OToDhlh1RPsaA9pp7PA9Afbu6KWIJ2UMUUkX73DR81zyDzlz+Fu+zbU+N8YhBl/vKaT6791PhOAZKTTHhC4Wflcg6KpxgurQCtkGXyxLB+LDKffXDOAKlznHy8pNcunSfz9hjAPg2MsWJ5vcf91+6yn9pZ9qtcnj9PLeXCRSwkalYs75c9Hbc59fCU2RIsJCMoPJx4fNpG4iZHeIsl2BU6X+7YPx7vd5rnfHI25tM5sRZsqZY6eCpevA+mhNJ7MtfCZKfzas/mPwshC5F5fjp3Ekq5bPdk/OqJLgldQBYsf4ozeC3zFKx+9j69X07H/aSNFWdjteIUtH9npj4nGy/7VTzWoVM9/zD9fUr35PvYoCm85b00xdk9tWIJFt7XVhbZk7E+ARdPrXMnGyN0gZMcI/MZaapJdXZWzE2rs76U7lL3tFpS+Ft5pi8I5GNKfK3O1ggsx3v6LNCn1P9ufIDKJ8tnT54+aVpqjTl9J8h8hjDFU7oLony6uh5L/cgep/As+2kKl2TxvudJ4S3/z/0nrIpPS148xfpoBSJ1cKZPzaV2lvf6/fp9uk4en3t5vDxtZ0779MFnj/XNM/d4ea5n142zeFwawD77XH1/28J/Mj9n9+ND5PGzyp62fUoP/jTyfQPQ9hf7rFeepf7shT2O4z8ZoA0GAz772c+ys7Pzge9u3LiB58/5yPM/ype//GXG4+XD96b0SOc+s9F9zl1b0mS2rjaZHyfUTcLkfcl9682QkdNiuPeIFe9NvOgTSOlybvNX2dv7HGm2SxBu0opcRqdw3z1aI+ABfVcyTIdM8ylX7ctEgaJjBKOixBEObh5BNMS++6/4W//477P42h8gjEC4kvnpztHW1hb7+/u4gaTMDOe8iEnUoLJ1GSefEb74Ik6rRfvnP8MnDt5jKjz2V9oMdEjPy3HWt2kNhzTyJj/ifYN+xcN3FO8dzLjUq9A8eMi9xjppto/6yBYvDx5gCovj5fQ2X0Dcz8g+9+tYYfDnLcxszMNhzFY7otPpkOcFicyoGosAplJxtXWVPecIT3s8jHbx0i6BmTGazHC3WuQLeMerQuxRdRNec2JKJMeyy0OxSz+8zGI85htfmPOL3/77qOPlYvjyl7/Ma5uv0bna4db+IVIP2PS3CKSg7Sry/JiqSDGOh8ByUO2R5yc0G69iTEazacmwPMxWWTc5YW25UH/v/u/RdixVLpEnA7J5+YwhHEdVwsUU78IFpuMxtVoNypzt5BGjU0/W4xBHABt18PQcNx9B7XTzYXQfWttU2m1GqYsd3YPm+aUxMHlWf+O3TsAcUnluBXnwDRo/93NkN79MeZIs28eDD3jQjo+P+aO37mFm+6h2iFCS6ifXGf32LVTNY/w7t7Hln0CbnMcw3nk2LHPt1SVRyJ+1WAuzA1IPAv+p9d/chvGDH1Dtf4gUj3Y/tBi1arWweY6N/3Q1pH4g34OEC1y3Ak65pIDPojOAZE6NKycnT073W6xYvrTzgEIXlKXGBD7PGBh58MQQ8dUKAg+QpGW2NBq0s6wcmwXgpaAdgnoVq7Ll+dMQCg8lo9O2z3ZZJDGuVHhhdAri1LKvfoqIFlCZQfAslTjmtO+Fi3XKZfSJdHGdOkpGVJwGGInjnRpQ0p4ZjU/LY4O79FAigjzAWh9DRL/dJaw1kFEB0jwBQErUlgb2KVBQpglZgMkdjLUIHeF7bYRRnOueWxqKp3NMmC37ZNQSFGbh8vPi1OhMIhxRQykHv9oljDowr4OAsNoAKzD2KUAQuKd9CpdAMo2W/ZQa/ARjLHni4DrVM4O1dJYG6WOvVlwlnwckiSaTJdJEy/mfN07n2DtThyLAyOgU2C0NV6HkKYBwcWwTtIPFgHYROoQgPjtGK9AuWSxJU7PUhcfAUp8CdFWe6sjpxoFnmXuPsBZc5YGXUfNrSDeiMKd0+FqdAWmlcfynjGftgD4D2VW/ttQV71QXjFz2zUuX9yUL8KMqJNGyP7kPRuLKJkpUCeuVJ+vC5qf34rFuPQZ0sPytndMxy+WYvASkQWgfsgBRm0I0Oz2OZ84V1qpEzRZKKVQAWIX0lud3rL/sV+lBpk7nbAlQdBxQlhaBszTMkyrt2hpedKrfafhkr1USoMoK2l088RBjHEgjwnoT5bhgFK5q4okOkuBMlwsP0gpRs4Xvd3C9CkHUwOQerhvhndqopbZI1zsbm1ZYEyH8ApyC1DmhLCVhvY4XhlRWXYJGtLyu6VFttRB2qSO9cP3MK/xYtIsp5ZO/AUQZoTwP5SyfVQBRtUrUqCOkwHGC5TPBqCdASLlL3SKIT9elj0jqOLqOMBK8jM3w6lIvH2+GPe5LFiDKZaST0hVIl/0VwiMVmqk8WLaN63j15WaZ59WRMoRoTr7WXl4XAU4JQp2tPX0K6AtvqUdJBKXCVXWUaeCpNp5qL3W28MEtlj9OAUZwfvMqQVTHdWtPdES5/oeWCHm/fN8AtNvj21xoXnjms27Y/VOFOH7xi1/k6OiIz3/+8x/47r333sP3Yja3XuXatWtPvGg3pUc5dXGjY1r95Y2/cLVJEZdUizkn4bO1SVbrAcdEzAYnrIa3qNU/vuxj968wnb1NHN8nCNZoRR7DRb7cxT4IMEh6HJOUCcfJMRv5JVpNj3pqGJcaayHUHi9cuMGFN7+Cyo556Z/9E1y3ggnSJwCtVqvhui7zZArCEsSWUaMHYRM9H5DnS0WOPvIRnh88YOaEzMNV5iKk0lzF7a9Q3T/ANx5X3HepSrjUqzCOCy52q4QP73Ij6pOl+3g/9CJrwwFaaYwuaXb7dH5dkr77JlN1Cz/tkQ+PeXgyZ7sT0Wg0wIIwEjMrmEg4mmX88OoP83r2JpGsobYFXtrEETHp0RRRsaQLxVHQQCVQcydU1vu8PBnyUF4llfdYD89TDk4Y7sZMoh3+4H+/wWAwYDAY8PGLH2cohsTKQdqEbuUi676HEIIsP6KhBFYGGE9yFK4hpYPrdqlEVwiDEUZl7OVdeoUlrC0fbl/f/zrrFY+sqGPtgnRhnqk1NXUDovkE220zz7IlQJsfsJaeMHCXOhRF0VMArUdAjCzmS/IOWFLXN7eJ6k3GmY8+vr3M6+o990z9Mmstxf1H9Nr/F82Tfwi/9R/SeWFK+tZXKYbxEpwJCeGzuWVf+cpXCDrncc0QXVk+5Bo/c56N/+FTtH7xKjJ0yO7+CcVAxw+XlN7BU+Q86x+FvW999+O+F0nHUGYkKsEPnvKgN7dg9ADP75Hlf3YF6/8iSPHoEe7GBz1o0veRUfSDPLQ/J5GOR2X7U3jnLiHaKyiqkHu4Tp3+2iqykhA5NZRZwQlqFLJJtXkRt7aC36iCrFI4EWHWIi8NsdcHLnKu+woXVy4SBC1QBmkjwubzEGyBVmxcvIarA86tXcFrbuH5FbI8QEdVXD+iDCNUpYMLWE8jpIspVnDdGhefW6eXTvGHO4hTkNbudfEqLr5YJay3CNU6udkkDXrYIiJqXqXTfY3Nzo8jaxYZCHAiTL7cJe545wi9Jko1UKbJ2uYWlVZBIRd4NcXqehNTEeD7WFvlYu9VnMDHq/jkWc5sHlGpPU8wFVzu9HjllY+yvt1l++VXaK9UUY6DsQ2ME+GGIdJ4eEFEbmPyyhZeqwdWcHntVYj71Lav0b36Iu3wedxyBblyldJZwdCgWb3C+kd+iqixjfUDKD1M7uIWOdXBEBOsY+oS6WqksDhhA7PeJ6tmlIVEyW38+hUqq9dQlS5lLjGxi6puIcINYsfByCYmifCCGk53A4lPf+Vj1Leu0Kv3MYXFGAeExRiw4xnEAllpI0zAxsU+V668gPRddK2B623SWH8Z0bmIFm38KKK3sopf66JcDzdQWARalUjbxrcbSNElaPp4jS612nmk8gmdGsZtErQvENQCpF+nVCtYApTv4dZabKxdQrk+Sjcowi6OH3LxwlV66z3w1yi9KjJsYWQD4yVEfZ+w/zJu6zI0V9DFBpINlG/RToL0VmirdVq9l0nKAm3MKZCzePRRuoPb3EaZdZAuxnYQUuE3uuSRQYdT0BI3qCK8Cl7UxjYqjJSP9BUm81Fll7DSAaOot5pUgz6diz+G6G1DXFt6Jd0QV1Uh70BcR9YuUQ82ENalF20wUCOGtsbGy/82l7ovsd0+hx808f0GqJJ+82OYUqK0wCQGKUJY1CD3kdZHqQAjC3qvXKayepHK2jZe/yJ+NaDQGWXcwZFddBKAp6CoE65sE7gB260u65UrMItQfgUvrCClAi8iDwJMr0G41QezBPvlRGEKEDLAlCGxk5I/pvwAACAASURBVIBQaEKMrZCkDbLMp6iHZEmE8iTB5kUC2UUVmzjyCjQ2Oal16f3wT3Gu8RI2DSn9Ft3GK6x2rtFo1sE4NF/7MTb664hOyLxSwzg5heiSCI1xE9yGxfX6UAQIKWhUOriyRqO/RmMlYjHQeKKHLOvgJWyHL7FReZmwu4EyPiUSX67w/Gs/iwo7OJ5HI+yio03yWQ23bCN1BEhiUcUNO6wEW1S9FWS3jaqvgFIoOqA8pqELwoMywk7WkKFAVq+An2J1g0y2cTs/gjNpUhQvIB2H+vmXaW5fpZSCWv0SwcoGCMPq5kU23Jdx1DqBatNtrqJqa4jaGpdf/BnObb2ATRWuqqLcLsq9hDVVgvo2Gxc/Tbd5jfbWa1SeewnPqVBbq9FsXvzu75Q/75fWn5XcHN38ABlIN/yTQxzzPOfmzZv8yq/8Cru7u08AzWN5+PAOjlfQ7j/PJz7xCd5880201tyQAWYEtZUxQi4N2asbdWbC4sVT9oPqM+fpVH0GxmM+GtGPdmk3lkUHXbdBvf4KcXyXINigVfEYxTlmUUBhmMhVqsU9QidkZ7ZDc7FCsx3QSDSjoqTMLArBx3pTKrM97v2nP07n5IDWzJKHiyfjEULQ7/c5Pj4GZRCTjKTdY5RZ+lWP27eXhYRVtUoeBAQ6Y+b0UK6LaG3h9HrYgwMWMkcLlyAfE/kOjhQEaMSDe7zh9UjSPcJLLxDX1tB6TpEmuO/dQHQrOP/dX2XSGuBmEaIsGH7pt9iwY6SUVEVAWEQk4zlzR3A0TfnprZ/m88MvUXEabF9bwS1jjK0w0QmHh7vsicsobRG5puWOcXtdXtrb4YF4DpeHhKqByBOKy/vMLr7N0cOS62/c5uLFi1xpX+He5B6mWscrBLXoPOvBcqcmz47p+gFGRTgq58hbwfP6CCGoVC7jqCHaydkr2nRyRVj1sNbyraNvcaHVYJxJYndKkgrc/lmdsYGBus7JPI8YQTWK0OMdmtmUk1O39tMhjqK2QkXEy2Rq/zRkdnQfWudRjkOsmpjj0wLQK9fg6PqTa5XHCQ3zG5g4J/+FL8Av/hOch/8MGQSk77y+ZHCsdJ8h7ZhOp7z33ntcfeGTCDHg4f6ZR04oiRCC4Lk26Y1nQ4A+IJMdaJx79rONj8L+t0GXH37M9yqzA6j0SPMjAv8pgNY69aB5K+TZv1mY8180yXc/PMQRll60H+Sh/fmIVB7FXk4+KMimDqJWp1MmXDp3mabfo9lo0tv4BM3ua3QbL4GzoNEtudD/IbY3f5L+xo/iRk2CygWkWsU1IWU2Jt55g+zwNqsXXqXdfon29mvUm1UajS5eb4vJRLHVeolU9smG+0BCQyR06iGN7Re5fO3TtGUDcXKEVhmFjIicPouyxnx4iJeGdGRAu/EKDed5mvF5zkUdQgST/QXZPEeYGuQ5EwXWm4OM0WaBNjX0yEPLkFrjPKGKmKk5RW+FRC3Q1Q5l9VWC4mXq9Tb16mUa6QWEbaNEwKXzn0LWVyjkgiwuUWqVqLuOatYR1FjsWeK9AlkGCN2m3XuF3FaACr3Vl0gqimbXQysJ/gbieAb7Q1aaHwd3BRmu4mZN/KKL6nfxay+ixwVhtYXbaNM6f4k4LnBti7Ks0vLqGL+kEm5Q82tc2HyF7bXnuLD2V9CLOdPJBDutEXW3qIgeTjonjnM2+y+SlSUyqFLqMeZ4RDBJwIQ4UYtEw4XtT5EvcjIZYEVBzXjI+mWM1EROE1lfYIhRoz3U7IR0OmSlmlALLlJkq+QxKAGqVLixRz54gIMlmxnUYA+Dy6VPf4ZL5z+DDJoU0gOTUto2brPF+fM/RbfRodl/ng3l0y0S1noXCYN1ovwF4pmDQ4jTXENWGmg1QU9dQtvAaoWxY4TrUExCFnKbbJ4iYoVNcs5VtskcxWRcMDtKsIVCyoAN1acpapSyglSSlY1zWEZURBdv/QX83nPg5mxcfAlbKjI5A6vZWrEEqz6d+iWKoEMlPIcgwMQVTJIT5y3czmVmM4k0TUJbocgyNvsX6V74OA29gkOL/rmfptNoIReGatnF1CvommHhCha+R643KOQWqijw/Dr9Sz+E33mOtcp51jpNioOM5O4xk4cTbKWLjh1sXifFRaxfITEeIujR6H0a2ewg601k2MHIGmuNEBUnrG5e497JgHihGRVALtGRZi4LtLCYYkKoApQJaF/+MRbhNTwREjJknil67cuYsI4NKqi8Q76fk01dHG2wdsoiXWALS1TvEeMiC2jJNRazBS3XJxg/wtu9w3xxhLEpmV3BmUQUus+iFAhTw324R3ZvwOD26ywkqGqfK5dexHMEZbKK2/0E117795je/AqOe4lyVsHLXLR/Dq0cvL1DgqOcCxf/HQLvEsYtwM/wmj2Mq+ivfYRyUVKKE7TN6bdXWHdeI2iHOCvbVIMXqPpdgtolKud+nOH4CK9Rwe2u4wTnKJICEa6RVQOE8sidBsKJWW++QGv1Y9R6fdrBFdJBSbwYQlhjXjQoJwEyUyi1xVbrAqmssThesNG+jA2q1Gs9WLgUWUmZxRi3R8N4pJOcIncQtoOxitKpYWwHvd6joWqcq0C9c5l5GlOMFpRjTTo1CG9OcXhI+fpN1MEeTrtBmXjMJxnlxEVkHo2gy2IhaSYVsu9QZP6xfF+QhBhruDW69QGA1gt7f6IH7d69e3Q6Hfr9Pv1+n0ePHvH8888DMJlMKIojdK4IKx2CyOL7Pm/fvc9BUCWfODiVs/Cpvu8yUQYRT9jzq2hrUaeJ+M3QJVERo8E9tsMBvfYLT45r1D/GdPptwmCDdnTMYJFTniQ4rYAj+mwW92kHbXZmO/zoNKKxFlBflCRNC4WLlhr19oTZ6otc3PxZdvtvsf7ggL01zWJx5iFptVqMRiOMLLDTjHJljYNJyk9ur3L79m2uXVuCxqRWZ3N2RBzUiBwLrW2cbheyjISCfbFKLTvipFxBG8vu9duoep0Tr8rxdMT29jrjc9dw4hNkpUr57bdwPn6FOL5DGua4iUBLST46ofjdX2f2U9do6IhG0WAxnJAFioNJyl+99jLD4gQpHK61LyL9R/xOpcM/+GWLJx/yivOT9IY5pU6pOSluK+Dy117nC899krQRMNt3QITsed/mYuc8/vV97n6j4OKnNjlfP8/ufBen7VHJPGS4xbq/9IRl+TErQY1yHuGpGfveRXx/CbTCcBvBDSaqS2QzwryCXxHszHZIy5SrvS7m0YRxoGkZF9HqPJn/47ygiSUuMtJKhagsSQ7vUM9i9vLy9PzLEEdrLaLSIxQZRKtnFPujB3B+WWC4DFZgcsrys/ICHL795FrlnZuEfJVbX2tz+deeA8dB6IzGT7zG/O2vwXzjA/ln7777LhcuXMB1ugg5Ynd3l+eee+6ZNt52jflX977rmloCtPcBgOY2uCEcvwerL3334///yHQPaquk2T6V6KndpuY2XP8dPP+vM55888/uen8BpNw/wF1f+9DvHueh/UUXIYQCvgnsWmt/TghxAfhNltRx3wL+A2ttLoTwgX8CfAwYAL9krb1/eo7/FviPWCa5/OfW2t/7btc0RhO6Y47vXcetPk+USdz4gMH+64g8JyclKQ9oLW6y3wzoehPmkylO3GBextj4gNXOOsnbf0zZMGz0txHjQ6yZU8xzvLtfJz+ck1xxaGQekbpAKTVqfgsqqzjpgK12yIPhkEC4KJGRyhmiUHDwbcRiiNAenX4HbQ4Ru4fkUQttRzjFAhavk918SLXbpphP8FY87HQAJ4/onG9y4mikzlFHx/i1BOFHmLu3McLDPX8Ftx+TfesdHDmn9BpUW02KHNLhCH39myh3iqzsod0QOzxGqAqz/mWCxQGpGOLvTmis/gSJG6APHpKkQ3LPx5mn5M4Uu5tRL46I8EhJccoNOknOPJwxSDOE8ekdTql4Ibr/gNjbIrMlTiWkzCboTJOXhiRPCHyHdD5l8N6/RooFwaKkmziEjsNo9ICs18Sf3SMeH1EupsS6pNNs4B+cYOb7qDBH339AojW91gqzlTYmjQncOkbmiNEditJgN5u05kfMZlOOv7K/ZPfr97H+Ajl9gOO36KxskmYuTrPKnAQrBjhS4p/8Ie7KR5ntv0tZNFBEpPMFrqiSFiOSYkZTTqg5IWkypFaTTH7vn1KEL1CO51i3IC+n5EcZzSRmuvN1ynqL0d5beEcDhNHohoZwD3n3m6xvvICr5tjUJVQz4nhEzAS3LdmMHEYHR7hOBz3ex8vuEfQ1TlKlnlYwZo9mZwVPCgZ37hMEn6ZS75Pf/yN0BQp1SHicka8KChGTjb6K6wRQGnI0RyfvkC/2KU1KswqDmSaUVXxu48ZDtCfIJyMcPSUjohYtqI/eJmGPZHKFlVYXpe9TPPp95NaPkb53k7I8ZtgbUUn6SOsSOAFG5uTjh/jDlMn6Ksqu0SajLhfM3TexO6vMWs/h7D1E7N8n2TYUiwXIGcX1u+RBA1yBngxR7QC9uEt1bqn0FEYvGMQ5vrOBSXOskaTDI4b33sARc+bH+4ijCa2oyzy0JHlMjotVI9phgjFz1M7X8EuH5NEuojGnJgboxQ0WucaprHKuucKdeER6khPu3cX3XLL1FZrdTSpiyHD3DaTjI8Mxq5N9qo0uiAWF1+D84AgnLhAZOHtvUWyt0W83GOZz/MERUbnAXesQFzP8u7tkN1xso82k8hKuZ5g1ZxSTY/JHA4rCIE2MlJtE9T6lnmAKD13MqaaPGFgPVyTks33qdcnhwXtw9zbBdMR03bIy6ZIUVWJrcYrPo+vbpMUjWiImOJijyzndfICsXCa+8XnavUskt77FuNEhJKW+8QkMmvnsDcpJD+59G0danGwENqe2+mOo2/eIxTE6cFltpRi9xkp9Fasli/kJzaiNKY9xygxPGOJ7X0fpiOlelfASuLGHO38T91xCOsuJGm+zSNbQQjG78zaFuUOrfZ7kocPkcIQtD1lpb2LnYxiNMUoQusfsTfZp2Tcxukme+sQPLV0lsOmUih1/1/fY9wVAezR7hECwUX02r6IXLXPQnmZffL/cvHmTK1eWuTKbm5vPALS9vT06LTCnsdFCCLa3t3nvwQMir0uRtRHqzFgVQmBYJg8soirDoqTnudiigDzHrTaIDwdMsgbhU7XZKpUrWFvi+6u0qxNGi5zyJEV1A3btCufzB7SDNrfHtylGksarIf79KZ4AjxaVYIrz9QOyq3+TDpe4sdbn4sO7jH/SMp+f5Rg1m02GwyHaaphliJe32JukvPTDG7z3tS8+aZeHAWvxEQv69GUCzS2E55FV6piyZOY2aee7XJ9epl3x+OM3bvPac1fp1AJu3ali4n3mrXXCR/eJtjaI33id8Fc/yVH8x0SDKlI0qFRq/Kv6Nf798yt883f/T1qmStM0ScZzTORwOMtQUrGZNYn1lHChyP1d/peO4O99ruTNf9fh97132TrOSfQcr5IjvRmbt95j92/8PIu1OrvvGISs8Wh+g5947T/m1vTz2PwzrPbXafgNIjdiIKAVe8Sq+4TBMc+O6Po1jHRwxYADr3cG0KJtLP+SES+wLQ6hvIZRO7wzeMCL3RepmoCWhKTagllJUTub/5OipOtIFqMhSSUijGPy7B5tYzjMS3JjiKIIrTVFUSCCNo6w2Er7TGHHD6D5dwDQlRVkvGRlY+UFePu3znTx3c+ReR9BtjXSO40v3/gY1TJg9PtvYxfHiMdhk6dy/fp1XnvtNcpBRiDGHBx8sMizt1Gj2JtjjX3iOf6AjHeWIYZPixBnYY5/lgBtdgC1NbJ0n3brU2efPyYJ+YEH7RmxxlAOBji9D2e1VK0m5fAvhQftvwDeBR6zOf3PwP9qrf1NIcRvsARe/9vp75G19rIQ4pdP2/2SEOIa8MvAi8A68AUhxFVr7XdkJFB6jn/yx6zMRnjVBrrIMTpG794EUeIAKhYkzgxXvY6YvYjVPrl6F6cYY44PUOIW6XwGdRfSADU/oZAzCu8O8b4CMyK6ZUm3NjCzA4J7C+y2Yp5MMdZDhhblZDg7e1iVEpgBBQF+tk9dlISDOXY8RH88oVnbxBRNZDChtGDvvIurcuK0wGqN3T2hq2JoHODs/AFyvcK6I/COMtIypOjl1ERMEDsEw68RN4ZIf4LKOjhHXyLcbOBhyL75ECNLhJbI8YQsV1QzjQpK3LtfgkqIEx/jJDOi/X9O8DDCRFDUOjjzHNWb4U8WFIWh2HeIKhFRfZXi1u+i1gX+3TH1WUm8doW6mCDzGWpSoVS3cZ0G6mRKdnIf2i7NvENsZsiqj3/7EY6voBVSlFMcHaB1hX6ZoB++hdADzM0v4PkJeToi9Ry8gxirCgrZRwZDavMGwh4xefBb9HqXCJ3bTEYCd77DxEiqDwaImqY+X+DSp1KUBFmBmKYY6yKyYypphuPNOYpiuqmHW/HR7g4VU0OnM2RN4RQpG36CRSPTR2iVsV3NEff20DZCejlmCMmxg2cGNPMDYpNiGzVW1QD7MMe4IXa6SyBKVFFBYJGD13HkA2IzRy00rl0nqx2T3XiIKjyaGzl57DPt3KDVvYLIbpLv7WFtSe38KoUe4BYXQAdE+RRrC9q9Y5yduzgXfhJdvgFTydqlNez9GcF8n8JReCbFvXMAxrCo1JGpoJxP8ccxupTYrRXKxXWU6VNfS1Cjr9N6MGXRatJq93HzHJG8yXZlgL5/QFa/hCrmmFlMfudLKC+hKQ323h2Kzt/EKeaUdgzeMbXRDGEEK4dHKJOg8xPmboI4vwr5An/4Hk5+gmPmyOQBFA3wZ8hFQTAVuA0ff3yL1r2SgY6RYUQ+uk8gIrrzHUSYYg92EN2LqCKndv9fYDbWKfIJq2WKzDSh22axOMaGJdX9E4rYw109QB9OIepAfoydTAkdQW4VlewQY2akzRL0LRzVpeEKXLekMfwWjN6l7HyM6mKBrS5YFO/iivvEKsNB4+kJRV5i5T6oW2SmTyUtkfvv0I/vkJgatUkVOS2xXo4wu+hyBS0tNvoc3tFl8qPBslbb4JjGzFJKiEbHuJd6jEQKLDh56x/heRtshUO0ihB336F0HORWn9Lcx1URjpmTZwcExYLD/l1aoy3s4VeIDubo9TVm49/DPbHQ2kDnU0y8T3B4D1XtIMvJaX7vddTMwwQZ+ta3l0yhwqNTWYBxEe/+NpFbUhzPELUGKqxg8ltEdShNjp4MEPERQkQI4RM1WxS+RiczgnJIeVfhlAEyG+EUK6xFFYrJglzcw892kXmK3ZviHo6I5g4qe0Q+eRsuX8DME4w8IFg0sO/MqJ6boY1GCYVMbqCcQ+S4SRovEGHwnV4nwPcJQHtn8A7XOteQ7ytG2wk65CZnmk8/tFi1tZabN2/yC7/wCwCsr6/z1ltvPfn++PiYapgi9Bkj4+bmJl99512CyyvotIOV158AwFIb3HwOboV24HGSl3Sx3PvlXya/c5crP/OfUCwWHCXPkpl4/pmh3I48houCUqfE7YAD20UWb9Hwu2R5TjIqaK5WyK8PcQ249Kg6E2rvHVP8Zz/C/t4RO2ubfOz1f85Auszn20/O3Wg0uHv3LsJtIuMMZ2OTk1sZr17Z5o/+xYjFYkGlUiF3c/rFiG+ai7xkH0LzVQAmUQMvKynCkHX9kMEi59JKlTce7PEjV69y2Z1w8mgLM98l9yP62TGNlTXy//tLtK/9l9w++ByNo3MIP8RZaLLpiI//nc/whV/7n7jY++vUdZ18kiBrVQ6nSxah6rFmaia4o5xvhXdYKT1e3lXUvZLfNTO6yXWMLRGRxZh97PEuRijyyOewBOv6TIdH7IoBRfYIESTkJz7iqmC1ssYDA8IOuZ9KPhKcedAa4XmU1gg1ZNfrL+OmgSg8j7EHzKykZycgc+L0OrdGe1xpXiHMXVoWdG2DIp5ShGeeikFectF3mQ0HJJ5HMJ2i4x0Cv0qkJPtZwXbo4zgOcRyjHIfMOvjeU/mMowdL8AHYSh9nNoUyW+agHd98UmvMOfoyM/HDeNtPMcWtvUowGWCme5jDB6jqCrYwWGNIdc7Ozg6/9Eu/RPHl+ygbc7z/6ANrRrWWid16nOG0v8PDY/JoWZz6/bLxUdj9Fnz0Vz/8uO9FZvunHrTrBMHTJCFbMD/Ak/Uf5KA9JfqU5Oj9NdAei9Nq/4UPcRRCbAI/C/wD4L8Sy927nwb+9mmTfwz89ywB2s+f/g3wW8Cvn7b/eeA3rbUZcE8IcRv4BPAdK30bPScfv4V0XZwjRe65pN4OStQR0sdqQx7/ISiLWsRkvIGjNYtwjodPEe6gZ1O8apV2MSH3v07praJKibUldvYAG/qQgXX+EHsQIESb/GRMme/hpHVyL6bR2aV0uqiyhi2Ocd2EMozxignWSzEEyLeqqPoxcr5AigUWS+EZHPbRoqRwNjDeBCtzpC4w4i7q0CKjEiPPw3SOzQ8QfkGpBUUmUDcV0hbk7gQnVtidC0zNHcrmHFlYgsKn8HK03KQmBJBiTr6JzV6k3/IwQU5Zubvka0ieh3GMJUfvDpBlBRvcoAheplQPMLPX8fM+8r7ASEEoUvTkq6S+g6cr2HsDdM9FmoTCL8kdixhKSA6QNgNR4lVT0ijBTdcRWoPI0ZUbMOsj9YQsyMlnv48ZbaNJcLxH4G4isNhHQwj38OI6sZ+Rzw2V2nXK9EcID9/G1EaIky5auZR+ipQpRV6li8TMdontkDLJyKsHqDJABzn1ssCZn8diMDpFORHZwT0MU4ogIxxfRHuasryDm5xHTA1SJBhVUDo5ZXqbQL9M7s2heh/SGkoekTkF1CBPBb7sgzrGyAs4xuKM3yZp3MWECpFcx8w6FAFAHccmZIMF+fw29sSjUBFiMsMVgHcMo9chdsmyDOs4FD2J+16BlDvkIsC889vkwQTHBsgH91H1LuW9KfqVI9i1FFg8s029JfAe7VGmOQYoi30YPETMx+TeYkl6cjglih4RZB3ckynlIMes3KEYl1AZ4SQuhb+D9BYIkWIdgQwEpQRx9w+QIqaUTWx4l1w6uHodkWpstkvhj3DcFCdvUfqH6GGKmyQUrdtwJDBeE4XAb0rcEiQVlL6PUT4tfwXBArN3DxvexVgHbzCn45SM1BeR1kNVU/zDkrb/JrpWQ6aXMaM51ZMTtDMBWuTOQ5z7hqK0MPkmxm2QVvdAa1wTEfgJUXJCMX9AJzmmyEMMIToXlI0j3Pwy5f43CBq7pFLgj9bA3cPcdyidBYIJUpagMqwEiUEezkkrU1R9iA1LymSIOd6nqPpEWmB1CzPeRyU76GlGHhzDwmIDS6M8T+HEWDelLN8FIXGsQU338ZqvI0MfeSSYVktAovavI6RDFEiqdkLi/z+Igxp27ZCF2SUYtyjcAfbkPsKbkCmBOS5R6hD0gtIM0fIIoS8AJfrBHmQGE56gxZxAXkQXQ3Ie4YkWMh9iaGHUQ1g0yU9mGLeKM+ti5QIoAQetbpH6JaX1cSOXSHax6CUZkMrRQYpbfo3kqEIliwmooL0KrjMmE02srZFFhjx+ExUNUDszjKliKjewqQJ7iUKcwNE+1s2xeY6+NsIZaZRzERkPvuu77PsDoJ28w4udFz/wuatcWn6Lk+TkQwHawcEBZVmyeZqP0ev1ODk5C4k8OTnBVTOUPPNgrK2tMfvKv8S9aCkXPRAlZTnFdRsczzOqeg6yRs91OM5LNr76FShK+n/vv+Fv/9pv8LXtGpP42fAvazKEcFgsbtOqhOyOJ5jcZdB2yNQqpvh/CdQm57iIGzhEnYA0KbC5xZMdaoO3mDZdqhc22L19nXmzS+Y3qD56yNx99QmAbDabjMdjwrCDKgxZb5XKgxG9xpJJ8ejoiAsXLmBsQq+YM7IhvWIPmp8B4NirUYsL0nqNLfMO87Tg1Y0G79wo8T55hfM37iG7c/7av/XX+M3P/h9oMycqNbYoqGy9yuC9CX7aR0UpauHQbKZceuF5viAEcX5CpahgZgVez+doNMVYQ3iUMw5HtMYOXw0O+YlZD7wpOpwQBz9BYb+BEB6l61PsfItJFLBRTpm7dTxrmYYJUebw2Xc/y99auUri7HHrX5/jxU9u0q+exwpJJO6xk9szD1p+TBS+QlDmpM4yH2zmLO9ZFG1jOSLFZdWRWHfGaPomt8cTPr35aYKFS0NbdLjOzJuQOWdhtydFyWoUMTpZeqac4xOs3oPqZTZ8j910CdAeE4UIYyhw8b0lgQjpFJLhE++UqnbQCw81O1jmfAkJk4fgVVHpTVLzN3C3nyIB6VxG7n8bWV+juHMddWGd43/0FsX+gtkvtlhZWSGKIoaxsyRImR2QpilBcAbEhBA4nZBykHx3gPbCz33w8/WPwpf/xw8/5nuV2QG2tkqafvFZgBY0IGjiJzl5PsBajRDfmWr6L4uUxyc47TZCffhc/CXJQfuHwH8NPN756ABja+3jBMlHwONwjA1gB8BaWwohJqftN4CvPXXOp495IkKIvwv8XYBrKz7YMdoRZEkDmQoc7xjEIaLcQOgK2j9BWonAwZELrGORi0NMvkBX50gycMZ4FuyOQPAQ7QiwFqseARW01HB7iiM8tBMhGSP8I0rXLFnjFgVaJTimh3GraO8hyJyCAGMliDGmmOMc5FjdQHuPsDJBsWSWLAVIlQMLpDFYJCKQ2MRHygTjX18mr1tnaexVS4z1lkRQWuKSgNDo+AQlNAoQwmK8eMmsJzT4oOUBxjug1Av8gxaqch9jDcL4OOoYbaqU7k2UCTB4gMUGb4P1EU5OLmd4+UWUnCD8IU7iIJoabUpMGCDjTSwGzV2ktOCBcRyqNsTNU2RQoI3CLXMEyzA8ihOMDFBuSO4d4aoFFCVKGDQLdHQDa+Vy/MKQ1ccoo/ERlHs14A+RUYrQgrA2YLbwcExCIRXGPSKVFYw7QGYNtDPG6hxhAkhK3DhDZR201Lj+EdghWvdgeIiLweYBNwYubQAAIABJREFUZbCLS0Zp5mgsrh+jinNgB3jEFPWvM5cVIhsTVVM8k5PjLRnzIijtFIkki65jREZsQ3ybIVSJQZOJA+wuFP4ROnAR+RqWAk8XyHQX608h38CwgNjikiNtii0kzp13sR5gJRoXE1rAIIRGWI/SHSPz84gbCdakOJ7GSIX3KEN6t2gES7r3Urh40xKBi5nMYT5CyylFaJBmBy3vY/oSiV0SL3o5hm/CsUVLwC6XgUVAISiq7yIsCDNEEGMVWDkAGyJNBhUwGOyjAWAovAOoLsAuKdutOsAgCYREK4nMfOJ6sSwRkCsWjsCTOZ5aIABTfRdLSbirl32RCpwKytFYJmhnjDoWFN4jIAFvjNRgnGOstOAOEEWIrycYBFqmGCsopYbJEY7S2DAhD1MMJdJaCvUAV18AHePZDJM3IZhjrAA5xMgpAouwLtgM6z3CuA7KlJRC4DoTnMqSFdSbCYxyMD4INcTVCcKpo8QYIQ2FFBTeeMnxaRLk+D61psDBglXEc4GYFwjsad02CwiUlRg5wsQaNxZk4YjgrkbYBDhAOqfs0RoMCuveJxcaVRGUgYOQJarMwAywuoNRElccUNYh5w2wS0Cj5RxTg1wMqDgLsHM8X4Mdgpxj5QykpsQisKhCYop8Sf6pGmhbgimQ7j7IKvEoxbgamWryWo61GXMWWBVjZQhmjhYuSkEhH2CtQIqSLLRokSL27yFUTKqGuGGKc4/TKg0DivCpOogfIt8XJCHvDP4/9t40xpIsPc97zjmxx92X3LOy9qpeh9PdHA6XIYekJIqkKVsiKQqmYZuAYRu2CEg2vAGyf9kwIBm2YUMwQEiyFhuQTAGmhjslDme4zUzP0j1T0z3VVdVVWZmV+83Mu8d2Fv+42Ru7e0QL+jGi5vuVGScib+SJiBvn/d7ve9/XeLb3wWVTveTDlRzfKm+UF0IJ3W6X0Wj0tqLhYDDAE0OC4J1enW63i5lOqI9KVNDH6ehtGe+jcUFfZghXo+97DCrN5Ld+i8ZP/AStn/kZbJwgpSCbL7/nPPJ8D99rMZ2+QSf1OZuVmEnJIFYIfwWpz/Gkx4a9QrMfo9IAM9eURYavGtQOzrl7SdHoxYxHY2I/4aR/navbE6qqevv/abVaTKdTomihUFjWmmy0Y4QQ9Pt9jo8XTIPQc7puzsSFdIsn0LqEtY4DL6Uz1wQmpEaJ0RUf8X22TchBo4nUGeMg5tq1azihGdbAOzrGX1vDj5qMDpYxUUpUlygds84UqRTdjSvM8iO8woOJJu3GHI1z7p3fozXyOZXn6LHl5WDOx8ebIBWH6TIu/hhHwRtoFaG9FnbvdSaNlCvVjHN6GAyn6Yh2VSP1U/bXDFF8wNH2hNFJRjPeRNqcdXY5cZK1CwatLE7IsoBQF+yGLdaLI87coiTM81rgIirPI/EDhDdlOnuTB8MHCwatFtCooPC6DKMR5+/SxDgpNav1lPPzM1KlMEeHyOwM1VxhPfJ5Uiyu01tCIcJoDAreMl8fPobayqKXC4jqDQrVXLBIUi5MoI/vwqPPUnITM54QbLxLrKN7HU4f4PUv4QaPMbSoDmZ4/ZiHr9zj0qUF8LOTEhf1WE4FZx/Qj+T1IvRp9r7tb8cHiYTAgkE7eh2q/P1j/6IxPcQmDawt3/OcAtDewp+eI4SkLL95Jupfl9CDE1S/96Hjqt3GDP/kAjQhxL8BHDvnvvzuzR+wq/vnjH2zY97Z4NwvOOdecs695JTAIakIqYTCSY3nNMJKtDdCB2doISmkJJOOoDQIB2VwSFkbYbFot/gusEgmosaR6lJdWOyWyUVCSS3MnUthKIIdNJryLeAjc8DhY7DeEUZWlMJR4lN6AZqIkgCD5E5tidPQQ8gMgUQ6mMmUkgAn5gtWDYV2PlXg02hYrPOpCLBYPCokkpKAQkAlLJlnKYSkEqCFReMjsJT4aORCuVpNsd451j+kQmHlDB3sUDiPHJ9cODK1j/bvYYWgUgUmfLQoCcRhhcYhccJDh2+i1QgvkMR1h3ECIzysLKnCQ7LorSoBSUVAJSVSFQgWc2+RaBSVN6H0RoBCBLtk8WMiN8Rah8CivRFSCCTgxAJ4zAmZBxKLj8anFAWlWNw6DoHwJK1mhXSLPbQcUga7lEKSe4ck5gzfFghGWDS5UBg0QixeKkZm4O8iqLDSUMQ7RHqGdh5OlCAKYl3h/B2sMGjnc+K12Y7WcYA1iwVvQInEoTA4oXGiAm9OKSROVgxlxEwEFAQ4aRHKIqRGWINDYa0ib4RU/gAnM+bRY6w0+KUjKC1V8JAyvE+Bj3bBO35egERggMpFVHhYaXEiA8+iCTBqjAn20FK+fVjgqouHraLyt3HqGCsX8+pdXDe5gI2UBFT4uPHiUdb45ISUhFSEaHxAUAifUuUIBAp3sUDOmNV9JJaKACNzkDN8OaXwLnzrCKgIKVAYYTEC8mBAKTwKueA37tcU2/UKy4VyvKkoEGgUmhAhLCZ4ROkUBkmlZhg5YeRFPAgvY12FEhnGOyIPz7DCkAU7F3OgcE4xkk2qQFG1JIZFgiCgvPifPWbKu5jHihKfLNwlFxYT3iUyp3jaUeFT4rBOYhEUQpCLAIGlEg4pDRJNZR2l9HD+IVpUVHjk/h5GWiqPi/tb4pCUwS6FU0hhsMJRACWaAot2Cc55SATFxdmWKASLhJMRGoUjoECj3p5r7SQSgxF2YaeWSIS0KCx58IRClVgncdEDtFCYi2ushaIUPpXzCVyFJ2aoQCIDBQ6UsxRqQikEJf7FfIRo52Nw4BxV+BCBwAiLUzNK74xFbkeT1R0lHlrkC2cAY1DVFCcUVr01pqgA5xb3YhbsYfCo/CHCGbTz0EJSCUEWnTD1v7mo2rc8QHPO8cbZG9zu3P7A8X7c/1Alx3v37nHz5jsMRxRF1Ot1BoMBzjkGgwFSDEnidzLzcRxjwojOYEjcW0VnPmW5+PuHo5w+U6RosmwlJ2XF/JVXST/+XQgp2f6+HyUsNbbovOc88nyfMFplOnuDThq+DdBOI0EQreG7GThHV6yQNAJk4kFlCWyOECmtvSmvrhYkHY9ZNqXmN9hZusJHth1BGCyUHK2l0WhgrSUODFlQJxOKzc6CnVlaWlooPAJ+npF4GgcLT5i4xcOjNzmL6nTH5/jWY2p6OODq72xzEDX53OffoJA+h6OY6WiCX7aZNjy8oxOCC4YyO+6gk4hmP6aKeizli0Vz1OoyLwYIBHpW0eynHI5zXtl+mUBLZkHGwXyGFpaN6VWcLtmNL9GgQzOPKTwwsoPMzxlL2Pj85zk2K0RWUsaaehHxE9d+gq/X91F6zvqNFk/unlGP1xF2wqYYMvd8emLxCijKYyYTSWxL9oMOa8Uxp8U7fYiu6pIJn0gF+GrKeL7Nk8kTrrauEtd9GlpxVngoM+bxcJEcKK1lpA1rrRbj8YRGGGIGA1Q5ImytsBkF7GYlXxrN+IOVy9ybzBFViRUSywXb8a7yRoAorZGLdCGUAdB/Ck7uYh+9TKlvoweH71Xr61yD4Q7e6iZMjylOAuJnuiQvLLHzZOdtgGamFSRLrNQkp6fvBzZeN0YPPgRkGb04nw8AaDboMnT/CZNff+Vfnhny9JgyDAmCDlL+EcK/tYUY7RIEvW+XOV6EGQwWgj8fEqrT/pPeg/a9wJ8TQmyzEAX5IRaMWksI8dYNtAG81Vz8BNgEuBhvAmfv3v4Bx3xgOAXzGwqLIe88Jvf2EQYCY3CiwooJ2koqB/pikWisD9ZhrcazFmUqSutjreIg6HLqdbBYCic5vx6iMcy6PrnyqboLxGisxaDJUAz9lFmgkM6ikVTeAU4YfJfjV0MqUaCVpBQKrSpOg4LSgrGK0vk8CDcZ06DEZ+inWLfwynKuAlcRmBxpKkokhXD4ulqUXTuLXxVIU6CtBueopTkC0NbHsyXaSXSocPYcIwdQdwtA2/HINyTOaiolwRoCU6CVh3UCg6BAoZGgHaVzVIC0hsoJCqGxwoC0OOs4VG3mXIAYBzmKAoVwC1bFYJgj0A58bSiDI/LVU3BQOQ9jwYoKqPB0Qe7v4bkB2hqMFWjno53PE3+DfW8Jax0WyYKHseROMVIhhQLQi0WjFy8WZghmNzK8LYtYF2gLOEtaZuAqMm/A3N+hEhU5Hk4oAluibMmCWnDEVYHDIYQjUwHZgi7CAEZI9m93iaYeCCgCn7zhoYWhtA5PG0Yq4Ex08G1BheNRtMKJ1yauSkrhMe0n+J5F2QwrtzEI3JJEO0XhJM4ZNAoLGCHIpKUELA6HoRASYS0lFmM1qqwwzHFCLxbYQjHYihl1AqzQDDuGor8AWkpbxFWP7Fr9IlEAxbUIZy2erZAGlHBMWz7GGkgqEJrSCYqrYN0i0VC1DO7ijKwFr3LgWDBRCFjWVM7Hsrg/jHOM6nvk6pDSLP6XEo9HwQaZChdrFyTWBydy4qok1CVVuE2pBDPfo5SAEfhW4xkHVuKA2bJH4STCWSoHhXdC4Z1xKhtMVUruFEGlCXSBs45MhFgpUbrCOIkBnoRLjNcDdDOntODpAm3BWcl2sMH9aIucgjvRLYay9laKALVc4pzFsyVnrZipVJR4lPhMe4uvw1BX1MoSnGW+ERIvT3kjXOFQtjGxQDlQLKyhNAsgfRSkaDVCOItwhsosQEdiC+LNHCiwYkTufMYiogoWz6+0lnwlWIDVZPEMaudhhUQrgWcr4qpEVYagMhTOp7CCr3dvLK6fWCRIqvARWlikq3AOVOXAOrDgXbQJR7rCWIlDLFhkDUGUg3MIUS28AtHkCpLlEuUsaZWR+wfk3gEYCHSJF1VUrQXwtxdzUOGjbIlyGoNAhxbrLNZBogv8yiIslD6Y+BGeyHHOYZ3D1S0CKIT/nmTGB8W3PEA7zU/JdMZG/YNlo3txj8H8/QzadDrl8PCQ69evv2f7W2WO4/EYYwzIMbXm1nuPTeo0h2M665vkI8jzBYO2ezanaycUcZv+3DI8PUMfHRFeiJBMPvbdJHlJNH1veVGe75Mk15hO7170oJWYccHAFzTDPtYJlJ3Qsl3iuo/wJUZCS1ZYF9LfL3iwFVAkM/JqTipr3OveYPPE0XAV07/7M/A/XUc9/DRKKSQ587DJcF6x2V4AtLcUHgGa04pZrU4kNLNolbI85Tf+8L9lniZ0c0OmMg5YRuNRXDtiqfY5nthT/v3so2zMt3j1917GL1NMEqAOT/A3F+uY8jygTCXNpRrTcIkoW/hpqbjOfH6KiiVFWdFfSZnkmvsPvoEIFGXN8sCc8nxRYN0WriE4ViuEE8fVyQpFWOCiNTxZUXqK51ZWmc3WqJSmihxxpvjxqz/Og3CP+eyc1rxi54tHhEEfYSbUgj6h0YwOD3HOUJannJ9b6q7g1G+yWh1zXLyz+DdFh7nz8aVPKGfsz09YSpZoBA3iekDT+BzMHc0i4/CiZPa00oRSsNLpMMsyGrUaenBKoCfE3U0Oior/feeIn/v6I47jOn9lqBleMGr2LWn6C4n9tyKq1ZjZZMGgwUUf2hvw5Mvo5GmqvT389XdVXSUdCOrEmw2EHpLveiQfXUJuJhwXZ+8AtEkJtSV6kflAgKY6Eeb8QwDa5GDB+L1LgGTv7uv8n3/1P+bz/8XfYZJtMXx5xPyr/5IA0/SIMlAEfvf9Y98WCnlf6MEAr/fBAiEA3p/wEkfn3H/jnNtwzl1mIfLxaefczwK/A/zUxW7/HvBPLn7+1MXvXIx/2i2yC58C/pIQIrxQgLwBvPzNPlsYwaweY65CM8ywvkELQeU5juopOE1EhTIWaQ35NUm4WSA3QfUdYQmBKnE1S2ArOrMpbT0ishVaSPbSZcowQLcUXDbITkFxbZH5zS95zJd8VEORrhZUUuBZjYs0SWuO5yzaCfytOboJKMd8JWCeeBgHoZ0hlaFXnOM7TWQqTq7ViazBw2CEILQl0jpKFRPbktDTSG0Q1vJa/yoTL8Z3FnxBHBfklwOqZy1JNCe0JYkscMuGKM+RNY9sSXC8EnPnyiZZw8O3mlqREVQGXfoEYYN6GBNXc5JWhteYI6xGbRScPx0QlyXpVOMZg6w0Q1WjiiKUFVQrKS6VeJXG6+XkUvF6epn719cYbnjMwmjhtY2l9GZMPIk1mshYvAxALMYVROIcrgFXDIaFKbfB0azmiEqhJZyFCn07gyuG/EZI6DRFEKCoSEtNMrMUxsNzJToUBKpgGEdYPISBWSfA3g559JEahfIpKwm5wVueYpRlthJQxpZchJSdhPB6RZUG7KllKukR9Srs8oJ18F2BNAaER3XJMVz3SJbmyHXJ/LZPKUNyFeFpy8w1mKcRrWIGTuM3MoarErFWwWZF4GbojRJbt+iWIAk1tXoOy5oHT1/ijWcuES9VVMIxua4INnPkumHnUoOYAmdACofwcqw0lLdBXXdIXzC65FM9pbFrPju1i94fDNLPSJmRXs6ZPxMyiwWHy32m7QSzqshv+gybDVKdUxc59TgnXq2IrEajOY7rzFUNJUoEJc5qGsuGZrJInjyJl/jt9e/kq90bpNWcSgXs32zxYOsGKoSoMqQ6J6wKam6Kbyvq44JQF5y0VkAqDI6pCvGdJtEZ3XzETPgQWkrP4yjtYZcE42cFYb0iWimJq5LIWhSOXHk82egx2QhxnUWixTYcsc5odPq4jWscXu9flGpa6pOMtj+iYQsaywXFukesK6K84ujZBkY4QpsTugxlDAWCcz/klAamElgH426Dw6CPNo7Mg0drS/hrHvQdBomnFgbeNlWkZkZYlrSbY7wsIwhy4mGJ7YOWBuMdUXkTpLQcXakxzuqkVY72LC4Q+JnlPKkx9hMiUyKuLsCXj6aTa2ZXPJodh7qsSVslntVIW+FSoCORpWA3XWLcbyL6hhozZiKhcgFDVccuR5glBVcFVU0g1h3JckakNQiJMGCVICoq3NIW2lMYKXArgqBpSauSuJYRUdBSBYExCAylklDNcaKgEorcC6hWBXlP4rYMwy2PyWqHyCiM5+E2K7zLM/SmovAlrl9hPImRjmhe4WcC2fGQqxnj9ZjTW20a7RkGQa3MiM03rzb6lu9Bezx+zEZ9A++PZs8v4sMYtPv377O5ufme/hpYlDAOBgOSZNGXJYM5ze6Nt8edc5xECf3BlNWPPMfens9kuA1r8PhsRrMcc1Zrszo2ZIf3Cba2kPGiJC1uVFSeT+9o5z2fmed7LK/8m+w8/gUub/mMZiVW+xwJy3IUMHU+nh1RNy3i9sJMOfMlDVewdDymiBR6tcupO8J5JYw0O6rBkzWf9UevM/2Bl+CFn4df/Dl893NYM6cMauwPM1686FFqtVqMRiPyPKM9sUxqdYRzTKIVdnb/DkP7IlVb0jy9S+7lnIg2vjznL6v/lcvpLU6KZR5u7fIX71/jztfvEdkm8zRhnJ/w313+Ct3f+6/pTQx6E3qbHeZhH396USZnfJyzxArGIiNtx9Qjj/3dU24HAbQ9HpQPeT63OLWEuNLkyPVJx4pL8y5343vgXyOgxLeOm15INV1iFD2hChTh3LIql+jmbe76O/zIcsz9LxyTf0Ih7JQs2qSLZWdnh/X1OkJI9l89obWxxXbYZ32Ss39cvX29qnmbiQ7wQ0ldTrirY67UFyxrECkaxnFaQrcSHI8WIh2DUtPzPWqdLnmpaTabVKcDIjvjTrrMHxxMuJVG/PqLN/nVX/kV/lYY8o9IeBGHqfLFg3hhUv1WRLU6Ux28i0G7Da/9EmJwF9P7z9BHf5fgXUbZ1dER070lgtsCNZmjdY3wWovHO49JXURceDjrsNMSGsu05hX3PwigNUP0qPjA543Rk4XE/oVqqtEVv/q//Q0+/oM/TedOk+TmL3H++lfY+cWSG0//Wby3FCb/RWN6QuFDEHwAK9Tagke/S7Cy9G0G7SL0yT+PQfuTLxLyIfFfAf9QCPHfA68Af/ti+98G/sGFCMgZC1CHc+41IcT/A7zOoqP8P/1mCo4A1vm4rMFDf42t+h7lXJArhwkkSbfkpFFHewHrpwNOZj2yVNEpRwxshxOxzvqVxywPTiiuCOZfqDFtBuBLplXK/soKrhIobUmDCZmsMZ9HNLQgWC6Z2QAyiWpb9t0ycivnXHewtZBN7xE2C1ClIvRnzPoeSWpohRmzpYDaqcaNJMPnJIdxh3nYoSmmjGJBtz0jHWekaxk7xSaiU/JYX+VZ9QqysJQ6YZi2Cbuag1mfq+wwszGn3Qa7/iYvFg84SRTVFUkgcyQSseUzryUcyTqt/pRkDA+i69y+dh9ZWN4IbzIKE17MDWlRkPc0olUhjGbelUyDhAO3hknGRN6MWjYhUiWli3m0kaDOJZ61TLtTapfOwSj0LKTY8lg5PSeLm9CVcCLJNmJwFt00mKnGnlcELUE0lZQi4PRKi93OOpHK+Mj868yKOkpUHHxXh0HZJX4wZ+pChte7xMbhByVZoHBrHiK1DA9SjvUqYi0iuXfGqFWHKuKul2EjH/GcT5qdIxUcxT3CSuNXjlyFtMyUYb3Jo+4VBqrJtdEjbEdxyhbfP/wi02ZMFkesq4q5fZpB1GfvqVN62ZiTZ0Js4DNSAbHJOOh1Gfsp3WJKFgcUhWQ3WKHVLqgtZ2SE7HiXqQUZ86rBrmeIQ0Nzo6BKPFrTIaYRMk5T9mvrXFUPGPhNwHEmI2Y00TXL2FQM4x6Jrnh1/TYyF2ywhwk9zH6NM5Vyw7xJ4EpW5yV+LvhaYwsbSDbcEbXIY1DcxtW3icspJ3RIzZDRckpRBTzyUzxX8Dhep3EpI5EFmR8RlAVTHdEOJ8zWapypmPogw5MFdz5+g3yW4NuA5pNjlLZ0qoxBt8/ne8+xOd4nVZoJju3eKg1/RJ6nKAznNxuEDwvcTDG2MQOvztJyzFF0xmQ7Zrk6QV/zmRYpqRhyRz6F9S1F7jMjpDGd8DDpYaTP6s0dJqpJNIyIGgPWwhMqz6dcU7yyfgulI1b1Lll1DTzDYQ1uXX/I3CScF21MHjBp+uRFwH5rlZXpIZ4W9PWQ4qpiWgTE2yWZiZF+SWAdh+0OciCwWjONIvpiRCUifF0SGpjGCTKbkQnF8c0OJ8Eal7Uhq/t4QcKgFyDuhISR43SthhIV3mbFuVznrOrRDPZ4GF7jafMGM+szv+JTyBpLa3NO+x3ciUMfeNTNkMLzyQgR1RJTN+RJpNGhY5oYdCypz3J2Oits2EeYJGa4nDIum9RtTFFG3F2/yuZrJ2gJ01AySNaYpSlBqCl9jzWzT+PAEkWG6TVDteMzTuooMaOb54hVx9datwiEYyvbJV4bU9wPsWnIeadDMYrpZwPGcYrXcsz6PlIKSieJy5wqCJmJJlf2ThH1ksdbfVb9E5yV7J9usLFySNEoGTZqNF8tMKEkb0Xs9xoshbBfXsYJTacsGK9fY3+qWTk9At740HfKvxIA7XLj8oeO95M+Xz356vu2379//215/XdHr9djZ2eHJEnotBr4SUWz985+Z5XhLEq5Up7TX21wdNBhcv5ocS6DGR+dnjHa7LI+LAl3HhDefsdHquYN2InqrJwdvOcz82KfZuMFivKEejAjrCwowYm1XAk8zrVAMiSqUqL6ot9gLBwNnbN8fMbRpTr1sMHRfAHQipOcYRAzWDdc3dtjevUvw9OfwIwPWf+Nz1GWHUywwe55xr/1HYsFfLPZZDgc8ut3vsJ1B7MkxSKYeD32Hv+/DPK/SlZ/QjKbUKqSc9kjXf1FNnZrXNU3+MraF3h6KWPj3nXcCdTjhFPl8X/8iOJ23Gd3skd3WuEahs5aCx20ETjmpwPcrCJt9dCV5dSbIKRguRExOi7xlE/cj3jj9Ak/dt5GqADv5gbHbomNCbTyhFF7jnbrBLYicYbVJzukG88wis+YhgZVGI7+3tfYmG5yb/V1Vn9ig/nnjjh7c4qsTdmLN1gLPI4OjijLPj4dzooJa8mAO1JyMy9pfVkyOHqN+ic2mI1aTHWIcpaWGLKvIy41FkqfRTYjdDlNJYhki2JmyIo55rN7/FipST7exHo+SaNOMRuisPxiEfNj/YAvj2dIIYjjmB8tR/z1qI2HwZYLoRKGu3D7ubfvmyitcVB4f4RBu4vDh7COjGNkcyGO44xh9z/8jzBHFer+L1N/yeKtthBKsPtkl7W0T/l4jIwX5TCitUw9334fgzZ7+ZB8e4Q+/zCA9l4PtO2vfgU/DNkMbiJeErRu/RDNyf/C3uOf5nN////mE//Bz33w3/njRDGFakbu6Q8GaJ2r8JW/Rxh8krL4NkCDBYMWP//8h46rVvtfCx80AOfcZ4DPXPz8kIUK4x/dJwd++kOO/x9YKEH+saIKYUzARKU8qt8kORhgwoDB7TZKWIZejCgSNqvf5fRKE4umDBRerhh6KTN1i0nQxqiMWitjdKnFsuuwo2qUnsIJn5dvP8vN8BHOeLgIJuVVAhky8p5w99JVNjnj6ck9njQbDOmgnOVY36KdWWr2Cb4oaXiKs0aHSnnEMmDMNc7aBaWXUogUVUlyz6dwAfevXme5GKCrmKqjSXKfdXHAfnuFQGtah1POWk124y1WN4ZEwZRv+E/hCUPmUibFUwS1insxdIszUplBKyC1OX2TcJj3OI3r1PwhD1rXmM8SOkUH1AkN/5hd7xqDAJ7mdc69OudejWU3pJZr7q9dwu8W9A6HpOcZTkvO4w4EksZccajWwZ/R8meYZoiTjmG6xHG9S88dcNhe4QojHps+yzxksFknb5XUVYnbEQinCctV+vMeJ+GIr4vnWKkNqAclQ3rkIkIKj0lZZyY1R0pRaZ+V8SFf6LzEJfOEak1SqAQlJH4Usd9coZHlzJsNPAfL1QkP6jeIi5JSgZU5pYzwVcXoVsrr6dNUwqPSAXm8wuOozsZsyDkpuYuZLseMJzGWgEr5eFYwDNoEskBLn4mXsqQP6XFKYA1nsoNcWoXHZxxdSpgbwcQPcStGZjw4AAAgAElEQVSCPdbpVmMMcBY3KMOAhsvpxIuEzqvxR9A0aLoxj9mk8AJSN8e1A8bdmJFsM6+vszaOmeknyFrCeTsBPErls6FaLI8NpRvxJI7oqRMy00dJw7DWZPf5Fc5nHoFO0dkz3E/POZCrbNgjLJJx1CByBQOxhOcKJksx06zGsWzRMXNOo5T51pjzaIm0LNF1AYXkzHbpVQm5Dcn9MamdYQPoFBNOVYewl4ENCWXFpGhx2FnF783xy5hakfNErLJUP+XsWpeJajE/z8nxeeO5ZXbNKqkukfGc++Y2B+oyG/ZNjsIlPFdh/YQTVSNqZszCBnOvAdIy9ZY595s0mTANggUQMD67/hX61lDomKFs8Dq30aHj2Otxs0oRsmJab1J6EWW3QZFYMpcw8lLOxJA5McK3CL8kuFagQ4/x9Tq+nqACjdzqEVQPuaeuMRcJs6TDJetRxCXKSKQ2PAqv4F+pOC7X0V7F4NYqcVSw6gbkqk4U9KmbFClTRmGJh2PWqTGRKc1wSikiXu9tMoxD0kszBq1VtspdwsunUEQUaoXdpMPUj+iJI3I/odMZ06oaTHyf1/wmqZsjtGQe1gjllEG9jStC4msOT0858VscBpt07SF5IChJ8CzMpc9BtEw/PGDvxhI6ACUMD3tPs+IGzLwEP1Tcv3WZVjVhSkIjHPKotoW8Ao/9TZ55vM3D3jq+LDC+YKk6oUXJuRczVgkvX2pTRDG94ARtA2JK2nHB12pXWZEDTr0uN73HzNOINze2uOG2CYxAGklpI6zuYIOI8yUPb1J903fKtzxA2x5ts9XY+tDxXvx+kRBjDG+++Saf/OQn37d/t9vllVdeIY5jaokGBHH8jqjHk6KEeoPA7NFajoniFebzBYNxdDwAo9FLXdx5SfPRm0QvvGtBLQ44Drusjd+RLzemoCwHpOkV4ngTVz1mzVOQ+ByVFcuBx9cqg1IjvCIiqS8YhxNrqeUZvbMBs/U2sRezP9lHUyBNg7ABl+WM2h2P02xRKje8/pME/D7leAfr3eDJ2fztHrR6vc5ercUXvvgF/stUUYUeXma4Nwho/eO/wpdTzS2/QTgZUYmKaTDDhYf88O4m+5uKIumwU76Bk5qubiESCdM5tbDDf17/89y71uD37V/ntPT5/Fd+n4gU3zqO/vAP8DJBd3WDYV5xLqeL65AqzDxF2IJ6L+bxZMhT+iZlOUNs9jiVfb574qgqQ+U7dkaGy0i6UiOOjllrZ5ync87DGULUOdjf5Up6jVd7r/Lo8HVq7ZBsOEXGU7ajOhtxxMnJCUVxjMzqTG3GVTdAijof3XueX9tQPL074fT/+gbSPEOgNNZUdOyAvQpeuDC5np2fIUTBsvAxSYuabfL4l79KvK34d8Yl5XNjRBQTpil5aCkI+J1Jyf94Y4lPHQ+xzpEkCctHR/TCAE9U2GIxJ0z2ofFOP2RUqzPKJYwvAFprC6zB+FfAjvA3Nt72/5t9/vPY2Yxrf+1HefQ3fpvhmwnquxbnvLOzw9bqBuXOhGCjjqz5iPoyyf4dTk9P31YBzd44Y/Sb2wSbddysojqa4S+n732Ahjvv6T97+OUvcvO7vpfszoDuv/s0NEPU6VfxL6WcvfyQ6V84pdb5gPLEP05MjyBsUtrRhwC0K3D2iCD4aYry2yWOcFHi+E1FQlqY8RinNcL7lv/6/1cqnJA8aG2QZ32eHc8xYkQzT/DnHnl6ysjVuDSPEeY623yCLfVP8XLDI3kFpyTNcIBQCeRX+cbTJZfdDn4+R0pJKmYMZI+k6vFmWuFlggkdVnTCbu151u2niHzLa/4NtPM4Dlrshkvs+0v87OGXGF1THLv2QvxBnNNxA4ZxSnMeMVXwyuYLbARTIpGh7AjPRLTOmuRhwqOkDj6sikPejDapZEjD36dXzji41QQFr9Wuc216j8fuebrZMke1U6gEI9khUBXCZORejYlo4aKYM9lhtaoxT08hLHjgPc0z5ct0K0NaNOnanMwDjCGMFHmxxSSJiIqCo/wFPt3Z4nF9nY8Pcg4v38WsKG7d3SHWJUOVcpS0cV5BYubkiaSVNLnj15h1fArZ4iCo89HJY7RJSV2dgbvCUa1F183ZJyJIj1keF8h4DVEJRkmLM9Vm/+Ya1r5ALZsT2Sm98gHCWupuTreKeVM1yWWKCyMOi01CMqBFqM4YPxfh2ZJtu8H6rE7PHTPyEoY0CZ1j7Aq2vSZ7z2/Rrc6QscGzhoiC1VlAgzrnfk6pfN5ML1HPx5gyYBg38XVA6SIet/q8NHrIqWgSeXOMazJVGSdmg/vpBt9dfJGmzQi3euzEDY6tR6s85sBboxQxT7yEp7jDpGphqoi+V5LYkK+3nuNr5Z/mxelnsE6yLy8RiBlaehzbDVbHbVzD0TIFZ23LcX6DSnpkAlb0MYfNGsyuMPKWKdWzRLzMhFs0w2NOvA5H3i060zqh/5i6tgxpMxQBpUv45cafoaE1Kwy4lN9n5iWcNRWXTrogfUZxg1HaZtPsUFqFswqjn+NLGykb+jGVrhHYNm0zYvtSk1kRcy9Z47nxEZdKwZebz3N7vo2qYL6UUHpdNqsDZrSQxsP1j5mRoJKSsUt5rfYUDfE6KXNiqwmSGS/HzyOKNmu5j9QTVp0Es8LqvOBJO2Qa1rldjQmrmPM455F/ica8w3aquTk9wPmamUrYS3u0TmeEVuMbyUnURzkQvs95tURztsxh18cvzhj7LdqzNmXqUBb+/tJP8f0rXyOIZ4z8OplqExQZfR5y0FpBOIGSjn/S/GF6eszlmUA4n4Hq0Qj3+NTSd/LJ2ZeYWcmJXMKXljOvw4OrV/j46QhrEu50Qm6XEyI7onB93vCvcRLF/PL3fD8/evYHTFwBus5BHCBVSVOckgZTDkWPJAjRtYTWJKamJsxVxI66RmhzdqMbjFp9Nou7fD1psKbPCVmUhVZejbouKVBIs0JD5JzJEk+UDItVjpOU69k+j8UtlqSHpxX75TUOkzktNaBmhsiszakXYLMu9cxyEHscqj6d+pg7nXWsCbmaPOCuuM5v3vxeKukjhKOfpVTiMTvxhLoseaK2oNbkpfI+QakofcVnou9nlUN25Hfw2VbMD47+GdvtS4xXe0ySkN9RV/mh2e+ReyGUTYpkTp1D7re+j94z7/eifXd8y7+hdye7fHz14x863o8XZtXvjv39fcIwpP8BZq3dbpfT01PCMGSzP8WWEeJd/mq7WUkQ10FkNPsJaX2Tovw9tLHMBsfUen3CpRT3tQnLjx8R/ts/9faxkdhmJ17h+ZN7uLJcmD8X+3heA8+rkyRXmc8fcjnso2OPnbzkvJhypqEjJojcI6r5OOc4qDS1vGTl9ITqqWV86Tg8P0Q4gXAJW/2M73Qj7pU1ikfbAJydn3OWXCM+e0IhfU5nJRvtRfmlkJIvXrrJz/+D/5lZvFCf9kzJfp7w4s/+Y37ht36SlUGCKnLQFdqf0z19iXls6PZ62Ow2D8OvsWsMf941cH6G0WNe2Okgnip5/Te+hA59VPuIxyd7+NwkDlvsf+lzNHiB5tWbjO58kXF9wRbFUUFeNamyfSb+lM3KI3Jb5PkI008Y0eXKdMrczAmM4v75I14SHm0fqr09uhsVex2fUXiO560w6mWsxqvcyFb4lYe/wvPrfxEnRvhmyhOvxkeaNc7OzphPD3CTBM/zWC8O+N7xd2G8GX/zZoe/8FlD9Ke2iH91Sjs5orQlTTfgPMtZCxbdnLPzc5Sn6RnHWZLSGTWJXi353Z+6in04ov3pHawf4EmwvYRM1Rlpww92FnN+XGriOGZ2fs4n9+4Suoq8WPTqvWXK/Pb9lNY4n7oFcIOFkmPaRWeruPz0PQIhk9/8LRo//uPIpZTexz7P4W/W6R7t4Zxjd3eXH/ihj1H+4QTzXImqBZAuEZRDqqpiOp1Sr9eZfOYJjR++RPLxFfb/2h9w8rfusPzzL6Aa7ypTHL3XpPro0QO+58/8JdxdS7BRB1EHL6R2xePK4Uf5xu9/hu/8cz/5oc/vN43ZCdSWFgmO5Or7x5uXQOfENmL6bQYNWKg4ftMSx2YThMCMx3idzofu9+34/x9zFfIoXqM7e5bQ/xRPrn8HjfIuQgYc1J5lmN8iT33uJ8+DmlOSci++yXa0xc3ZPoVfp/IN+/OPsuLuM0gUPf2Es1qbnWQTNfgkiRzhpOIr6Q38KuBZsTCV/1ztk5wFPrkKqeuAJnAla7AybvKm/D5uia+iRMSZ7DMSKV9c2qZSkHs3eP76MfXuhCflJYSROKuo2yHL8deZiTXeCNrcLC0nwQrRaMpO8izjIKYxP+Ket8WkM2OdbSbtkJP5i1R+wNPmt5kWMfvqCrE3oqqV/L78Aa6fKf5ZZFgettDtA+qRQxhFMHqG7fYM7cPTBZymq+TqKqKMOGl0mWQlhUq4MXmMR5fl2gGxNyIONijDlKOogXtmjbr0UfEhevAMB5Q8G/8GO+lV9rMXGEhD5AztrEYcTGlpxSho0rRnuOwyhX2eZvEK2+FNDq/EZJMjatExe/41vtSWFPiEheDGKCFUjl7tVRLPR01ijJczFjVc1OdxKkmkxzxQHLiKlfkabV7h5fYazoGomtTzJomecSf+Dp5EMWE5w9qv80b8NB0CPp/k9OQefhny3YM5r8c3Cd2cdf0yr9b6CLfMpXDMKl/gG7XLPDcaEZQRoehwED3LYVLRymOU9nlcq9HMY079LttqgzY9GsEA466zkR/hiS476nsZhAX9LOUbHY+b42+gTQoKjr1l5m6V1bzOry5/ksvVQ6LJLTbsl6iw3KnfpO33OQ1Lfnj8aY78PuvzE+6I76GwMZ47x7oVduPNhTUDNb7Y/hGeHiW4FF6pzXnpvMZ+2EEEZ+w3l7g8fcRh8DE2vDOeGXUBgVJtbPQNPKkxJPi2gacqdjs9BCV5Dp/1P4nnFN9xHvKw2eFg+jSXZ5pH4TJSlTxjf5sq0LxsnuexH5Kku8xkn1/rXcd3Je28ww8Mv8yw3uYk7DORt7jfPOZjk5d5PdriSXSDF3XC/SBgL4Wnxgrqjzjy21yfXiIThlhvU492OeB53qxp1oopJ9GAV6MXCGzF2nyPM/40E09A4XgtHFBzD+n6b1K463wx/RjtasCD9JRQl1zKxrwRvci4c4mNsY8ID2m7gEdKMqTPy3GMjUtCofmdjR9m3Tzi2nzIGd/Nl/oZf/bUY6MasZPeZBRYXg9u4oTFr0omqs71/Gucqme4qR2tSnImVtkLu8xSyVXX5015iVVvj0A3sL7Hm6GiLTPejL6TcThjNXzAHj6vrW7wzGTINPsIjejr1MoRpd/gd7vfTy96TPv0OonJ0d6Iptxnw8EvRS9Rz30mkcarIk6bAY86xzRGMf3ZQ44SgRp9hH+6eonUHfLjR4+ZUMeGJaNmijx+kSeBphJ9PjG6z53bL7Ckj+nLR1T+Ko/TOq8FmqfnbawaMrIv8DCaE8QvE8qI49WbfK67zFJ5Tl3UOJDL7ARtfAOd0qdNzqb4Cl/pNTkd/ymcjVGiYrc15dr4TSrR4BvNywRJQTxc5yPTQ16v3aJ87jZLhWTU/TLNw6d4w3b57HLIrWGXKv4yEtjz1xm6Z4Bf+9B3yre8SMjh7JDV2uqHjvfj/vsYtL29PTbexS68O5rNJtZaTk5OUPYU3mVSDbCblzRtilUlSEuzexXrRuwPczp2Qmd5hVY/Rpz+f+y9Wawt2X3e91tDzbXnfaZ77rlT39vNHsgm2aRatmRZjmU7UuDAQCL4QUgMJwEMBHAenNcA9mueDORFQB5iKwqkIAkSB7FjW5JDyZSHSGSz1XP3ne+Zz9nz3jXXWisPp9lkR+wmYEgOEfF7rKq9axf2Wljrq///+76C/eNDghe+1+Lou/uc+2OqwKe8fx+Aojz+JL8pie+Q5Y+47mkuPTipGv72Rx+QuYhUVLQ5RB2fi3XFDEtSOvYuLxC3DhBCMJ1PkVYgkPxC5wMeBF2e7cTI994FYDab4Q2vk6mIlVQMY48kuOLgvzVd8dobv01Ey+F+n8ZKfFOw8ASqe4dV3aKUj4k7+E1BZEIG65fI0i7XbmomF7d4WDse6BZDwKQ9pHJrhD/i4e9/i2w6wfkhczXnpa+/zkZavHiPyYOPGJo+O889R7OcU7gG5xyOOY0NMG3DhZnzUt1Q531cueQyumrzvF46ClsQFPBAPGNjffo9j+bsjM5asO4lOCCWXZZiyWg04me8L/OPZ9+gtxvh54qwzTlrKm7GMb7vszh/jLNdev0eg+yYf/eyz2T4ByxEgHt1zOpozQWOkapobIUMavpVzZgrrdlmPsULYFDXTMOYGy7gSf+MJ6ni7MU+9eGGiBhXFrhhzFp3+WIa4SvJfuhxWNaIDz9k9tZb/Lm3v0klPFS5ANPA5uJTBC1IUpa5wK1OrwKKAKdD6uoaZnWBt/+9alv+7W+T/OTruM4BcTohGFg23/gGs9kMay17L9+kucho5yWy40O6jcguGQ6HTKdT6pMNzcmG+LVtpJToYYR3LWX5Tx5/egItjz6poJm2YfLsCT07IrjVQ0hxpU0b3iEcXjKwYx5/543PnLs/FJtzSHeo6+kPrqApDb0DwqL9cQXtY5jLCepzCJqQEtXv/4lpc/y3Cdd4ZCd71EbwRvAcTVfwZHiHSSemISK3Y3AKpxt64VPeG95gJr7CzfU+K/9FahWQq4gmqKmTmncWd3g3/QJLcZN+OST0JpQyRrcBLywG3KxCOuEFg/A++9mY1+spX5v6iObeldGCf0IneEa/+wHvxV/i7egnOfOvU6gR+5sOSeWhpgPe0h2UaImjQ6L0GV50ybmMOQ6vUYWW8eYGwl+Ry4CZ/DK9qs9O4XMSPYdtv8T1VcyNTUitAowGhONY7XLhfYmF2uE82qYQEQfzmI517C76VGj8YEJrNVYKcIJadOg0Ax6ELxJt7jFY3mTjpSS1z0p3aYXkcfcOG7XPoNzhIIsIvCmdWnNjU+PMPZCGqJWc2obFesBheIu9XNFP3+Max/TaZxTKRzifx+ktnkbXuQxHnHv71Crgg/RLmLBi4AQX6S1WPY84OeJLs4RX5iGjM8VFeWWiblA8Gd7m6HqfpI04i/aolMdJ0eU466B8wZ6KWHtdLqI9AqPxrOB6oZh6eyyGlpE3YS/vcBxc51z8Wb42s9ypcjbzDrGpuS5nHId7dDsfkOuYoZjzkltz4F1gooqkCfENvNv7Eg/He3QbRaUCenUXJzzQLYMqpNYez3GK3+6T+SGn0XWCeMKT6A4Xw5Ct5G1eVB/RejW92ucRXyUi4Cg+4CTaxzUa35V8edrhrrFcjz9gkH+NoP4yfnMLi6JTd3gveY1ShjzrbpGLiItK8pvua+g2pBs8ZZS8TWwKvjqNqJ1PoUK+sEpRoqQbPeac27y12uNJfIt9saQSIb30nStjFutY6NtEdothrThMbvC4c4cbG4URPlbs89o04c/XH9ENH/O19pihrnjm36VUEY2WPEqf5zB+kRfNGZUPvtpww1zwxYXh1mbEPe8Dzkc+Z9EWlVK0nuFVt+B++xpuvk2v8igshPUWmUzwwxlxHtOvNLGag6r4MP4yx9F1Vj1JNiyYhENMcJ1G+sy9PmfhFpHYoCjx1AadntPxcnq1h1YFWMHS73KPEq+9y6n8M9ypCg7UhJk3AhyLsEsUJTwKx2Sz53jFZHw9X/Mz7iOC8mUu5OtU9pTxrOWx/+c5D29fuZgKzbXc595ij4keEwSPeK+4y9PhCKly7nfuMuUnGG7usKNqotbjleXVS34rNZGVIAQP1Q1qPMZtTadRvLDs8EJuQfTxkxM2eouz4FWW3l1uZyFbZUolfQ71XZ519vld85N8ixfYzkbsVpovZis8Flgz4XpTgh3yfu9lFvJFHsZf4Atzn1frkqNRn4s4YhHeolun+OGEP+XepVvtcjQcQpJx4t3C02sGMsM3A1TbYz6GB3yVQz3mzN/F+LssdJ9lqvnqZs5tuWFpQ8qloc49hkcJB7mmlz7gQfcuhhGD4H124zeQWCyS+9FdngWv8lOXPt22Q5K+R6RP2ak7dOvkynHSauLwEjOc8GeaY7r+MaWMKIXHq2vN2ks/d0350Sdo+Rk78c5nnh/HYzbNhqL9Xm7T8fEx+/t/KFMUACklg8GAPM+xzQVaDj51/rCs6RUS8JjP5/S374GX83SWcc2rSUdjdncS0ukppR9QfOyY5pzD1fcpVEQd+NSPrja2VXlCGF79lji+TZ4/Zlcp3vCu9Ob/0bbi3EaMtaRcN0Qdj6fTHBcrepuarfkM/85trLUslguoW4SEn8i+wT/WL/HoRoD3wYf8nQfH/O21Q4/GZF7IUhqu9aJPnuu/e/sD7j5+H+u3bNIhxnoM64x5pbms7nEwjHngW5qwh6inRG1E0AoaP+TG7il1OSCSGjU4JreWk+wBvpUsBgMu3nmb5/b3cF7EsahxoSKThioYUs5XpG3E+Lnb5Os5zlmm6yltdoLxA6JOl6ebY16uVlTLEFevOXaGDht6UtDanF4bcr9zzqYJSTtg84Io08x7PYJ6QEd3WBUzRqMRXx+9Rmx83jz7bZKJo7spmMuC7aOcwWDAYnnEkdehjRO2VjO+Pvd5vPURI5Mxu9dj9XDJsYKhc1hRUIWW55CY+qrNcDOfEcSKflVzoUKua8lvRP+Ki7qhl/h4z3W4bbap10tINUuvy/PJlVHNQeDx9v/4P1P+vb9Ps7NDtR8z1108W1y1DuoAou+NRz+KyG0ApobiY2MHY2jNPmZ6/km8gVksqJ88IfzSqxTnXRRTOrd8yvff5+T4mL29PVTio0cR9dEG1fEh2YLNJePxmMlkQvavT0le20F+TOhVzyd8YUj+1gSTfV+f9OIQ+lcEbXL4jDDtwHlLcOf7guIHt9HuMTL0KJ/OaerP0LP9MGwuIN2iric/mKDBFRnMsh9r0ABb15jlEv0DOge+H39Cwqr/rcMi8B004hlP6y2mdcR3Vvu8Wd3gPI8IWbCVvMEw/AhtWuLVgL7OWOuQ2WbDWdUhqxW+bbFOIa3lohlyfzXkw/UWlc6IxAlpK4mjBwyCRzTCw3Mt2+oR0rUE0RnD5A2MgVp6BGqBbCXd9AFteE6pjkjDd3kw2ePVixt0spqwrRDOoU1DJiP2ipBi43PZppznCUsXYoRC1yWFb/kgDzlru1gksTrj/fU2k2yMtobSHqPVEY/WI07aJYX3GOck3Sxiu8w4zS0VGilrpHM4NE/yPu9kMcOsYu0CCNY8LBLeyhMezbqclT1AMG9j3lpuY6yHsi2N85lUCdM84jDrkrQF/TJmXsegl0R6QrDoctL0mNqUi0pzUne4Ef0+0sKTZhuH5LG+TptOSbx36MT38YMZuTvmJFvwznyLEo/MnBM0LV3vlESfkXTv01gwraBo+0zln6aU8VVu2HyPfDOicRoPSyt9ChnSLRPGdQelcly0YC1jWlEjVAaqIdJrusEho6LhbnVCdT4maCKC8IRC+PTSD3in9yozOcQ3DVYpDpMDQsY4B43UZDpk1XrkRtEoj0Z6nJg+m8bn6OIaDR6lDMllSCU9XHTOpetRNF1qcfWcT/IhT8ohv2O+yNvzOzyajni46EL0iLNWcFalFNLH8zMeVobSCJYqwQBazrgoEyoR8qTZUOkLGqGpncew0AznB4zqDbdMxu11RGIdHfEhUfSANSGXdYp1indWe1ihsAiWbUCDJJMRnknwqz69qnuVkSc1rdRczwKkDRHhAgO0ooTWcZQFTJoJynuAkoYnmx73lztsLKRyzR8sdjksIo7bmlbNqJTP7uoA0WgeTTpU8pRZE1FJTYviufIpU3dO5ipe3ViO1ikfZDt02xHOuyQOT7DaccqQuE7I2zFZ0vDufIvLMiY0DRM1Bn3IMH6fTvCYo7zLsb/Le92XsMBchuhgSiVDtsNnoJ8wbzwcME7eJqsUYVPQKXosq5pe8IjTokNmNcdFB4kGIVg2hqSec+A/Yu112Flex5iUa6wIxZK4+xGZi6lUwPESnpUDchlRCx8b5QxqQSsVrZRcFjEndR8hYGJSnlUex43helOSrDo4CuZFwGme4lDUleCjZY+jYoiIzyiMJtDP2I/+NV3vmMZo1qVHX5zgJw+I4lM2cslxkdLNIprFNpM64sG6z0drn55/SK0dGxHxbPYi03nIcZ7S6iXKOvzwMTOb8CRTnLkpK9WnpkeviLlWOLRtmbLGbubc3hyRG8X7y20mwvG49pk1EZkJkDbgYFbjhUcE0Qc4K8hrH1kl4ARF6/E4UzzN+zycXyecX2O3vspxe7bp8N56myd5l1oqnF6xPt0nkzkn65R3F1sYO2eyCXlruUfQecblOvrcNeVHusWxMQ3TYspusvuZ10Q6IvVSJvmEg+7VxvHk5ITXXnvtMz+TJAl5ntM0F6SdT4ffHpY115ctQifM53P2xs+hjgxPL6eMdU3c2+J6JyBrTjnZu8G4ael5mrqe4FxFriIqAfXjK4L2/RW0OL5Dfvj3GArJPw2hpxV/utPyL1xKX10yrw1Rx+fp4wmy07J7uGDW7dEd7lGf1ZiNwRNXJg+jybf4X8V/zp+6/Tu89s6Uf3a5ZFAW/FqyzU8HIVlZsJf0AXhaVDRv/h7dg1sEbx1RbfexVnFndsr73ed48Htn3Bkf8J2LCYVI8Yun2K6j21a4AKS8z0H/NXa8LS7DDyncHebVU16st1k2LZ7vs17OsX5IKWv+4Pwdam/AO/Y5bqQPMNKS7oxRyoO24NnZM8zqGKG2SAZDHi6/zV+vGuqlgDbj2AhSCjwRgSm4FuzyG50T5ochN9KMdXcXKy2LeMR2MyRVHZ7OZwyHQ3xP8OpbQ34n/r94pf7P6C1KWjdn9OES1R/yhlxQuGt8ZCVe8NfZGgieeob9esLl9sski5LLSLFlIny/YBkKduzV/+icI5vPiF/3+XUAACAASURBVDo7dM8a3mgUXSV4w3+Aztf83KiLuxNz6/4W08UT+qFj4vV4PglxzjF6+w94Np3xV375l/mN/+FXkfWCUnWpmyXexQeIzu4n7ohwlcnmJV1cPEasjq9s9IsFVvZojt+i+ws/D0D+ne8QPP88Kk3YvKeJyQl2b+O+teTyW99m/2OnR/9Gl/Y8I7jdh3QM1ZLtYZfZ2YS9Nyu2/+ZXPrm36gXQWvyDlOqjOfFXtq+qeN8XUn3x+CHbt56jeryk8zPfF4MxuIVYPCF6/me5MX+Rs/sfcvDyZxtXfCZWJ9DZo64ffC5B89YLKj35REv3JxVmMkEEATJJPvc6PRjQ/pig/ZFDOoemIZqP8XXOpk5xvqBpHF4hibpP+XCzg6gdrRQ0QrMllrycd/h2oJjmEXMCrGmZM6BXS3TeI4mmlCLlcqXxXMOzagsjBals2U4yyrWPZzLO62163opj06dsJNgIrwnYB9TWU7JJiCClGTR41vLmOsYnw7Mt6zbhaJlSeB5bpU/SONazPrGbotRHoByPim2iNmPbrikbj5HfgDlnZ/McrVDk/pR6vc3SqxEIrK1ZbTzK2mfjdtDOYBF065IgPmJWhBQmwm0i7poZHy63UVYxZIOrezgtUSagyEqWlSY3Ib0i4b6xFE2HnJS4DVmFKzSWdbniBd2yXA8IkxUJFWJpmEUdjARlIG4CwtJjrhSbTcAyD+hVsHQ+dXhVyWuRSOHoiCnOCE6nEdIaKCpCRii95uI8gjbEioCwDVhJiac2RLoBp9BWUDYB6ryPMZb1sMt5K7iXzrB5ysqumTTbSNHSign4jsx4ZM0ucR2RBjMynfJk2qeRV6HAkS0pRIzXQk8UPI7gldDnVl6zEYprbZfjZMXJKsUJSS+q2KyHxE2HRTAltJLNSrMfrZg2EfXqBueuwgdMGxLtHlGXEiskoatpLfTKDtoEVLpiFVicO2Wz9pllMcPaEjiolw+ubOJ1xQkRrSfZZH0ilSGxKOMAxWTTZ9VqQLHvVzwxZwzMM56UHTqmJit9lLN0bU0p4OkkQRDQSoNsJ+RBw1AIisWAXHhsOmeMwgwjFE+nQ7bSHH9zxrGMKG1MYks8DEYWnGQddtUEqg5R02eT93nZK1mUMZtkgZQKZnDk9QibkOnmBjvigpWJScsOQTxDWMts1UEqTeGWuKxCmphQ1MwvamodkpMixRLZDimLDqWGTbomsDlV5bGqhlxUMbk27MannFYe1rQ8ybo4IXm5e47NDplaTVMmWEKUK0nsMZcuYcvPOM36zMKUzrTPGME6nlFnIQ+qLr6rsG5BKj0wHuM8ppu+T5mPOSs1HjVPcBykZ3w0GxK3jo6VbGVdZm7N6WrIyJvT5gYTaTzZIuwRi9qjlj7XvQWTZYRPi2+OYBNxWHeJBodclH0iU3OSXRFEXxnKVYC+HGBFBWpJUARsez1EVCBbn0wvWWZdtIro6AaH5vGyS1L5ZIsR+1XAJnwPv16yaiOe1H3GgGl8KgwnZUoY1Fxs+hihcF5LVAa8v36d7dCyKCD3K3RgkDhSW7O0Ac8WQxpPs1yBki3rjc9umzCqE6xesmk8LhYJp6RYIdAtNAiWMkS5lk3tMbCGZ+XVWluIFNF4DGvFZdqi9fvMliHSkyzWmtZqQiN5akfEpqYjMrK1Rzj77G4X+BEnaOf5OaEO6frdz71uHI25LC456B5QVRXT6ZTd3c8mdZ7n4fs+rZkSJS986txRWXN30aDjDrPZjHv37mJqydHqCT0qkl6fNPR5XB6x2LrJZVawf/iMzD8kCA4oVIxxlvzxQ+DKYj9NrsKy4/gORfGMyFmWoWY38JgWEzp+n0Ba/KTC8xVPpzk2nTOaTXm2u8++PyRrMuImJoxi3ALqg3+HzfEO7+44/pPNmr/4T36dtNPl17/+c1gpWemW58RVVfF/v1jwlafvcfC11wm/+Vu0QZdCSg5Wl0hhuPj2t9n/hdf5poYq6RDnFfkgJ3UppZNUq/fZ71Wk4g5TeYwF0rJlZ92w0jWF5zE/PaEKNV/odnhr/ia++Dn6NuW/v/Hz/HuyQkpJLxgzMxknlyfI5SnSjVGDlLNqw3WTkBcLZCg4c8mV3bBoEM6w09lFG8dDF/CCv6JMv8gmnFKoF2jtEF+l5IsTRqMR99/8PbY/qHn3Lxl6T+GaWFOtjujMFvzTn9nnL9sZvfYeP7GT0n1jyH9zL6RrfK6XZxy3Lc/1Q2b1hlfqPkFUcyE1A2MxZkPbrtjMpyT9W3RKw1HuWEnB3c6L/POiZD/0qbYlY9vlcLpgqGvO/D1eiENW//AfMnpwn/V/+Iv0bt7AWotuN8hkzLRdM/rg9/E71/h/I0wS2nCMvzq9ck9s1uAM9dOnn+TPFd95k+grX6ZeralPGlycIOM+2AXtN7/Jtf/ybwHg3+hQ3p8TfcmDIAUvZjsRXL5f4R9cw9uKP7mv6gWYZUV4d0D5cHFF0PIZtOUnRibnjx6wv/8F3EOLt/d9pfrhbfjg/yR8ccDuO3c4e3j/34ygLQ9x175CXU7x/M8wGhneRp18BzESVNXpJy9D/iSinU7RW1s/lKSqwQDz/++w6v9P4HD03BKPBFnHIEPKcE2/iElrQVaOQdWMSsNlZ4H2GtR8i8euZNSE5H5BE1QgWgarLpUGaR3DrI9FUYsEpzKQFq+NyKi5zCISKwBHI1smLiRuxvgYpAupFSxsQzwdsLeCSaw5W+4RNBDYFmtDSjnGP27YVzUrV/Os6uDRENcBiU0IicmKLgemoZGCRhbU85jLlSakwDlFaFtml7sMWkevUeQiQtsI5S6QKKKPs9xaodl1OSLv4dUzZN2hxQcscZ1gRYHKd9H2Kvx4GcK41TTWoWSOsn0aoehlHRIjqT3FTpuAsLSq5sM8xDeO8XKEC1ZoO2C39Vn64DCEdcyz85tUwZrU2CszgLaL59ZINNIZlFXUwSUaj7r1aKVH4jZoWxEgaNoGLxvQqKvqmMChXEW9blgREDtL2Eiys8FVcLHMMcsEX5dMii2iOmTLthQy5DxZsvZqbmQGZboIp4lcTqcKSGoJTYTQMIs2BI1CtzXjVpPJgK6/4Ol8yDrTRCiWAMuEMQ3CBiyaM1JrCEyLoUa2EUs7ZlHFV2S1EiQIOgaMqCmP9tkRlqPuhqgOqPDwbEtgDV6j2CyHxH5LUqZYNeWsidAqJK4cgW0wuk8tMnIsadXDOcPaq7BCcX/lcWDEJwHKh/XV+CtXAutLllmPSKzQRjHMO1QKrF6S4dGTDtkoBCXZepe5EFRCojaK0+WIRgp6ZZ8HQnLHbfANXEpJL4+w0qJ8wajssPAdIZKgbWml4H0To41lb53QSA9nfbwKTqVAijVt28eXkloYOnkHB2gkC0+QNopKL0kqn5N+wVau8MoBHQmt1Kx1gXAQtqBWCbFcsww96iKl2wYEynC0fJ5VekHmlSSFpFcNOGkHjIqYBStK1TLKOoStj/CXuGWPhdsmlTWFawldS0NOISzDvHelZ9cGz9UENKQuRjrH4ekLiFaQ+z5hdY2BCsmKgh1r8ExE6HJK6dMrIoJGc9ldkpiYzeIaubJMg5JxKWm15PHJXXYsZJ4krCUfIfBERbPYIw0ndIseS7/EKkPoKvr5iFpqpPORboN0CZe1IqJP3EhsEdKGOU5qgo0k5CrEubGCpAqphEKW2zxtu0QG4kDi0xDUirV1dKuEC53SFQ2i9TlXDcJ5CByTwiGMxmnLkWfw6qvMyIXxSJqKUmnSOsB4GarxcJVPQolrPRqdkloP1IZSWMJGo90WvpLsGcgI8FuNcRZP1Ii1YJAHNJ6l9efMXJfEeshmSceWBOUOK98yyK8q25FpaOYJofv8NeVHusXxLDtjN9n9oRuOrfh7OrTpdEqn0/lD+WffD/ddPY9ckXZufur4YVkTLhqCQZ/5fI6UClP5TJfHRG1O3OtzEPokq2fgd4l/8T/g2V/7a1z85f+C9Jdz0rZGSMX66VPgKqT6uy2Ovj9GyoCkaciVYNf3uCwu6fghGxPh+gsAns5yyviYZLngeHcfoXosqgWdJsWLUqQw5Nd/gbvDLk97L3Ay3ubPSjDf+Zf8pyFExZW2bOtjXc4/enZMdH7MjVdeZbBqMV5MLj36TQaeZOX1GZ++RT/xyHckO6uAjbcmUjVzl6BXU3aTKbp+GWdyKr1mUITElxcsZYdJFLG6OCOLLF8b3uThYsZCOm65gtQ2/KapcM7RUUOkKTm+OKa/WiOamsXAsicUyAGmmKISzbmI8QqBsgVWKGLts7vQPNKKQK1ZpDvM/XM8l1F6N9AyxZUV68MnvPON30QgeOnmCygLN6VPETY8aB/xf6uIrXZC1vjcOIwZ2EN+a0fxkdhlPzviuKxpEo+FMYytx8DzOHEe/aYm8Hcoy2Oy+ZxOPyIyinVrOfMkd8MXmLaCa4FHTsXGK/EXHpKKw3DIXV9y8Xf/Li/8zE9x4gRKKeI4JvJaOr0dTsMx03ffhs4ffqkQxCmNP4TVMUwf4oI9RHWMWS7xb90CYPOtf8Fh+n/wzW99hcvXfg38GBEE4Pt033+Pvb0rXZt/0MFuGmR6pfEj2WIcOnYuYpKf/DSxUT0fs6oJbveon6yuDi6fXWnk1NXnLx4/ZCs4ILjVRajvm6P9m7B4SnC3T9J0uLj/6HPn72dicYhJxzhX/+Cg6o/vJRaHJMldNpvPzhP5k4AfloH2XajhELP4MUH7o4YQDaqReLam9nMaL2NYeWgqamVIVE3QtDS2wMOiGk0lofAcuSzoFRW9pqbTGpy/wigD0uLMmrUs8V1F0HqElU9oKxQGYzWtKjH4jDKPpJRk2uBZRSscLS1KNjR1TBFnGLlBlxmNtZRArmrSRuEjmYsxTT1CSotF4dmWGoFWU7Sor9occfi2JrANkSkR5RaFbKl1hbYRAoNuGpRpsaIkrrr4rcbJFqEqPNaU3hqJQdcxRs8Rck7pSlRdENiQVoL1apyuUYTEbUSv7NPb9FE2A3/GxqvY+KBtS64VuWqprI/D0vqGxnesiclkjLQVGPCrgJAMIVs6G8WgVvQayKVBq4LAFmAMmYJuHuDKbYwdMspC6rLPRltq2dCqAkd7tWGXDeDYSElcjEgLTehKQpcTizVGljhZIOuScVHTrTS7skC7itLPGVVwsIK42LkiILrC6hWtNAjXYLyCTFr8xpHUMV0L1jmk19DNRyRNADikNXiy+pgAaVq9opcP6FaKXFpqJ/DJiWsYZjGe85G2IkRQeQIrK1rRkCtLUiQoq+g1CvvxBtJKrsJ/G8hViawkua5Yq5JWrrFCYoRB2h5pFZOJFYVXkBiPgpig3LkKOscg7AaMuxoDNiSxmp1SEpc+g9zHYpCmxTSKjvEIqoC11+LVAblqMapC6gzaGCs0gbE0rsRvWhYipG22CHCoxuE1Md0iIHQl3TYlaEOsdASuwacmaHOUaWlURuFnhHJDYta0ZoFVGyw1hhZpBC2WXFu6paIWDcJprF2QrnsgC7So0WKJbD06dRcnwWAJ2xrbxFDtoF1DIAo6rcHqlk7ZZ1wM6GcBmBxV+UhnGeYhnSbF6DWVyTGNI6wkjTYoQkIjKXWFEYLK3GDiCzzZ0K09gkbhTMDKMxgsjTRYUV1p3hqBEUuUWyKoqEWFlC2oOd22wghJ3YzIhMfaMygytgpFUof0qhhnNc5CaCyeq4lEjkdDrQxe66Ndjt84wtaCs5ReRUxGQEHgunjWYamJ65pWzRF6Qdc0jNsc3RpkVeJbD98aFBZ0hqSksgGl19BpJMI6jFoROIN1DmcrWgBhiStNUrcgClayYePXlNIyXo1IiwDfgZAtcRvRqTR+45OWMZ3SJyJHUjPVIa1sQFa8JKBjM6xoqHRBqyyxy0hrBTicN6XVG+ImwShLqRx1a9nJLLZN6G7Ath1aMlRrWEmBbX2WTpL7lkZ/brTmjzZBO81O2Us+2yDku/huBQ2uCNpo9Pm23nVdU1UV0i/oju5+cnzRGrLWEC4butsjZh8L6V0Tk5WXqDoj7vUZacXW7JAX/+B3OP8Lf4l7/+pfon7lF3Eq4b/6/V9BRCmb01Occ58iaEII4vgOncqywbEdaC7zyyv9mhuQJxMu64Zn04x19IBwNWO6tw+6w6JcEDchygvRFBSjn+CV3QOyzs+yGI/YTXrYtMvN3/0t/LJiKjWj/JLLuiF/dJ/RwU0GYYTXglOahfVJVUEoWt7uv4J76xsMIo/Lfs7OMmTlr9DacGr79M2ArfAZ880Bo3KXeTQhqAS9yQUn6XUmOzs4a6kix8+fT1msdtje9lAm4GfLM/6Bsdi8pauHqLbicnbJ3sYQNDnncc5dY6hkH1vMOWnmXMgO/lIjXXG1CGY54yLhKFV47Zr5tRgTzAnNKUV8HaFSVFvxO7/y3/IX/sbfxPMDdvSYdbii0wzIIsO3i+/wetOgvTXMuoQnEf7wV1Gi4XHyNa5XZxxnG2otWRpLP87ZEilnraTfVIThNcrymM18Rhpr8DqMheQoVvT1LYwT7PgeeZ6z6FR0qh5CNWxkQvTPfgvV73P3ta9wWF5FIsRRSBIKgu4OG5HwbL35lMX+dxGmKZXuXWWhzR5hvOu46gJ/fwfp++Srp5TvvMvWT/9VXrr8++T9DzncVUjtkFvX6CxXdJUCwNtNrtoUv7vqptvElyCMQ9/7dJVada8qaN61hHZaYKv2Sn/2cXujNYbLp4+Jy+TT+jO4qvQtj1AdH9HXtM82P3QO/0AsD2mSFK37SOn94Gs6e7A+JU1eYJN99G92nx+CxXnOb//ah/z+P3qMMfaP5R5/FGgnl59rsf9dqEH/xxq0PwZ4hCzdDnPfUUvQbc22zAldiRIZ13tTlF3hx5ZXdY5rYyZ+wM34nLnXY6MgCkdcazwMjoUIWWqDCDwMko3MKfxjvPCSjcyxUcV+8ghtaoZ6xheDJ3xRnzB0c1RkeSV5zCg447kg45pXcCue4tcbtK2RZNio5Hpnwu1wxa3+KcPhmmHH8GIwIfMLyrihdhVGaPK2YR4YpFdBA6GzPN9ZURBSKMlKKUpbI5KAIujgNSW2qcj0hhvhIZUsWBaWVRtgG0shayoTswkHzHSEEAXaFtwLLrkpWyZJyCoOcAhaz2MYJmwFE+Z2zZKEmd9ByhXzACZeSOlFXA9mHHoBa5ujuyWZFKxTCMOKtYr4au+YO92nvJZ8yOvhGV8OF6TK8EJnRpiGLGXEQsbgWnIBc7/EIpj4EUni8EVFRc1S7HE7XHEepAhn0KIk8A0y9lCmorCSsK0IK4H2SgZ9yVxLWtewHT4jjJ6R02LaFiuhVinTwDHVEZqcCzGgwqORkEkNdUWcp1gEochIe5brXcuXkhkvJ6dcHyy5nazZ80uCXsGZr9mWC3aDc5Zey0preqqPsR574SVf9y/5KiXKGWrbMNUdSlWzFaxYSo/UronqJYI5Sq3J/YomyJkHYJUiajfc8ma8GMzwRcGJVMTRjC19xBe6hwghSLwFVZ1T6BJhGzANUTBFOkumNYM4ozOCO3rCl6IJz4UFd3RDoT2MBoVBWENrFpyFPtdij9c6R9zWC14MVrwWHHNTXNJvNujWcDM95S9GZ/y0l3E7XSHrPmvf53ZwQhuULCOf1+Nz4mjNODa85k848J9RqzXXghOcaPFMCRheknO2vCWta6lazW1xzkoFrKVPrnw2vsbYEmMMG52SBT6ibbEUbFRFEF7QeDmFnyH0miJsuAy7mNqhIsfdZIHUc+Y+BB1onMOoglaWyNCnpeBG5eFLi7UGrUpua8Pt5BTYUJKBHqJTn5UfgG3Roqa1JY1Yc7t/jJMTfFuwkoahnnO39yH3okNEUHIQLKldwLUkp9U1x76iymLyRjLThrFuMCYgxTLwK15ODnG6pNUVVViy9H00LSUpA73hrjflTniMpWGNx77KCEKfRbPDSntE0vLFcMkras7znXPaMAUMmQxQXsMkGGCjkFv1hEz6LITDqTUWx1IkrPDJpSIVJZlqybVFWEdXGwbRBQfektpBGPgMRMmdqEJqwbZcshABZX6AcAG3egVhXPO8v0I7hUeLZYap11RBTumVrH1Dx83ptTNe5oiRX3JXbUALGglWGibK0MoVlWzI/R4Xckhnt+FgeMhPdB6SuJC5iLgRzTEAbUPhBELXaANeJbHejOfDI8bdk89dU36kCdp5fv65+rPv4lpyjZPN1YNOp1PGP+Qt8nq9plyv8ZKG7uB79t2HZc0dp5CNY2t/i/nHmxjhOjRmgc1XJL0+5uyMoC4QTvLmX/2lqwwp75TJ3/grDKoN0gpqHM3ZyR9qu6rUAd1aMXeWnY8raI1tUHabpL/gfzqbX1XQeEq8XrDaP6AlIiokYRvilw2BrikYcm3/gMa/g+7GZB99RHzwHP4/+N/wqpqFkwyyS353vuGrsxMOvvAy5vKUaReQLae2S+TnDF3JY4YI03CjOedRMmW0UhSqxGnHke0TlCmj4DFHq4DtfJeNP8eYiqhq+NC7Run7EAUgM7ZFSrN6levVryJdxFdEzTGC+7/9Jl2/h17l5IucqLIMRcmpXHOrqTC2D8UCsRsyYczBScPKZCgpEdMZo7rDsluj24pF1KenjpD1MXW4i5EJtDmd0ZjnX/8peju72LVm5Z9jzQ5GtNTzh/z7R8dIZbg1uU5w44xVvKHvLtiE90il4KgoKI1j6SxpmjM0XSrRZdg0eMEORXlKNp/S8x1e1OcagotIYuUYZZcoAVmWUWwLttQ+gWzwS1j82q8z/KVf4kYUcFTWOOeIgoA0MFh/m9yGTJXm7aMvUJftp8ZqkKSUsnNVQZs9onXXsOUKf29I08x55x/9x4g04ubX/hbuocfz+3+Hxzst1itpB1vUSUz5xneuxvHHLovttLz68nSH5oHgUXTJbPFpV7+rFscaGWrUMKQ5zT622L8iaNPjQ7wwxB5XBHf6n55gvetQraBYEL+4Rbcdkq+WP3QefwqmgfUpVRh8tv4MoLsH6zOS5B7ZH0MF7fzxiv/lv/4WUgqevD3ln//aj26Vrp18voPjd6EHA9oftzj+kUMIx3ZQ4vQKLVbcrTRh2aPyevjKMnea7YFHFDkuvS7DqGaPkG4guUHGSneR3gadNHSJ6a8DDsKCbd+x3fXwZIz2BJ4Zck80dO2KhemhZUlPNhybLo/1F+j5KanTnMl9+oFHv60ovYCz1TYTz+dYB6x0j47ySJyjG+acyJJls8Epx4Ooy1J26CY+O/0AVEDay7gWFCBiorZP6Lqct7vsK8nNYMq1qGaMQReOkb9mU/cYtkP2XI/zcIc0bCj0Fm0zorZDsnJIV0puRjnPRzl9P8KogFxJTsIpt2rBTUpuBGuGYUZeKWbaZ6iW3PQMt8OauRwS6gZnLNfDCuV7HHQa+j3JxHrMbcgWJYQ+N7wFj5q7nPv73Je7XPiWp0GfOJQY69DW0hrJC/GGfiDZTiTX1JpQLaipCHxDZTVLO6C2klkcM/KXDHsZVCkpObEs6PVjtgcVUa9FuQEd1aFtFDUBo0TgeR6XSIJegR/OUdEKp1saKdkJHduhJBQTpqbLvB1w0Ib0Q03jS/qexsQGa1qqRnMpNWubsKBm7cU0XoKQPvs9g4sGhM5hSp/rqiD1NnSTHCNKnqiWRa3p9xvGA8e9cMZtr8YPNc9HGyJhSQNQXsANf0WpQ4I44G6cc1MVDKOIMtxjwTYr1yFUDhcFTNMRc2/F7eEZgWrY1D6XImblCoQ/o1+GbMeWQPhIFaJMRe77TG2fUjcs1VX734mImARdOrFlL56yH+c0VvJkdZeaFBCs6ZL6irHXsG8qfCRNVZBJn0Il3PIztBTU3oBhN6afCt6NBohQUGc+b8qQuY6wSYdpMKZwgqibMogEp8kAEw3wvSvDjI23y410yb14w61kTRRn3E0LtO/T8RXCWPq2Q2J7DFLNyJ+wG1bsdgTDqGGUlNRtS6eQbImMjZTs1jURDQJBbgMGUcq1IOGOWrKFQzgY+BVGl2xEQOQ5Jm6M8RSl6THSF3gNVCJFNI661Uz1ABt1WekhL6kZ/w97bxprXZaX9/3WWnveZ77zve9bb701dE09mW7E0LQhdmKctkVkJxADwSaxCA6yLRLJX6wIJ/4UK18yKIqFMTYKMgqBKCI2oSF0Q2yGKrqb6qaGruEd7nzvmc+ep7VWPlxXkwJqQG6R/tDPl6Nzzl9nn6O9117n0f//PM9SDKm8iEhmnCqXE9cQDy2d3uYRX9DKHrHnUHc+WypmIGJ2+oKR2/K0n7AVSdw4JvW3mLs3naddaekHFdeiRx1J0p7AqBzH3riGT7qYtjokqhWPlg13g4Io9pj7EwIHNu6AnqywsaIRLh9yljzpFoQo8miL1B0wiizDvmAYlci2Q1nBM8GaOPYZBoZEOhyyZtz4UO1hqgPGJqTSc3pBy9KzxEGL50ieCjLu9tZEhUua9olky2ksiQcZMxujAkXPBvQiHzWQLOQI0xkKYs4Ch5d9lyTa4qCv2IsFA0ewljuMYsXaiXhEFDzlJdBqdNrjvj1ka+Bx4BdkZkHoGHw/5mAoeVqs2ZM1yJYtv0O7MXXw7lE3X9ME7Sq/Yj96HwSt9/sEbT6fv2sHrW1b1us1oduiPEPg//7nn1YNT1aCpK843LshaMYYjJjQc9c0aUo0GpM//wKt73Ny588w0zfdiKJ4SCJu8cvf8Cl66yV6b4f8wYuAwPvCz8Mv/h1Y3OOsugVY1sKy798QtLIrEd0uO4M1P3M8Y1O2VOWCXppSHTzCRlvuZGM8G6LSliBSlGnLm77Az75IsxVhTk75+OdfpJ/mmMbSIOgnC/7VYs3tq2OOnn6W09/+HJdbAQjN1A7p99ZstyXSUYTPfhN789/l9XjGoNAYAAGtcMnzPtv+Pc7XlqDuUbgJAoHuj5nXll5b04YRzqDPbz7+I4Cg/mgf6W+UowAAIABJREFUIQyBN+bjxZz/52d/jaHfQ+UNKtGIYcjY5lyJNXebii4fYMsNj/37386cHW7nLmtd4PgKNZ1xq7OseyVWCOrugFHSoJtLxp6hUBFYzdOf+A6EEAx391itKpJww6ob47sTHtw5ZPL6i3RVn8NuwmjnNRIHtlmg2gteGj7Dea1JNjWpsPS9hl4zwXN32dEa1JBsdYbRhoGsGfshhz2fjbSkJkDpJRf5BUVRwGGIr2JC0ydYldT37jH41KfY9120vclC85QioOaFzxRUNqQZuBy/Kvi5f/B58s3vux4GcUxho5uw6uUD2naPrixx93xe/OIPEZ9t0/vYt2HWDbbs2H78E8QVzEdziriP7fUpXngBANsZMJb2Kr95HuxRXYZk+5bFYvG2daKGHjptsMbiHfZoz7O3ddCu77/J7TsfxFYa9/APWMU6PvT2YXNK9PQ2h70nuL7/5nuu47dh+QD8PrWq8d5JfwYQ74Jp6atDsvyrS56KpOEX/+cv8S1/6XH+9F/5AH/hRz7Mm5+fMj9Lv6rH+WpBz+c4W+9zxPEPnO+v498c1lh8WbLu32enLZl1MY2x7LsZrlczLzZ8UeTcD89pqyV+d03cVFylO0RobntLXpdTfltl5F2B7SrIOsgd6jxg27V0VtI1HtN6m7zweVVcUKmMVTFiKgOKwueVumKjG7rEUDQxD90hdVngGMNtN+UoXFJH9wl1y9x4nBCzMD3cBGZzTTcN2Vr7qHnApa5YOyvmjeZhrWg7jegEtgzQmUvbOHi5i50OqNs+TeWwqkvGKqETG+ouQK9jpHK5rRpGomHsFdAYCpuTbzJqLDkt9JcUusZUlrpqWGYhr4WS0lS84tzngXeBEzpUa8FCJqj9eyxG5+w6CbY9ZdYKfJ2C6njFSTgVHcYKdB5Qa4dlatjMFOnC5yw/ItOWC65ZN2siPWfbm9OYSwZWc+I/5IH/GpiSvnPGRXeGVQl32obHW8u8g56bs+46kBtU0UPbGlM72FWP3+klYGHpXhJtaj7cWoou5Cx0SZWDtYZQK4LUY8AViJRjcU3aJfRlx56/4TG3RNaKNVNe9l8m6xpSY6gTj3IWUF/0OGsK2rYhFyWJXWB0yj15zovhCa+KPgcExIlP25WU1ZqXvJxST0jsgPMgo8wimlWfTbOLWPeZ6RVRmNHzYWwN8/wWe7LEaS3TIidjSbd2mJaKIu/zbGHYbQRN6+EuRzzMdjivR9TdgEMRs2pTEjJUk3PGgLJp6Rcu+apP13Qc+yEzUjbLgNPa0naSW86KQ29OXTv86vgS2RhUbmhrxSKTzNocYTTz9kZzdLJjWSiPM+eIk7JP0ZaItmTStGwyh+JCsK5XFG1Gp+2Nw2ltSUVBHHR0rUdcBJAVpKJmY1OuOg/HKO6WPnlr0aLBCEGel1RmzbX08Ean7LhL9pwzaqfEkQ2B0bzY7LNoQdY1Q2PJuoaYBUOnITOSdSWYmxFtqdArwWN+STUraa6veGAXzDzLyhFsbIPIBQMz5IQ7rMpd/MpDuhte7p2wrjoOkw7h5FSmoS8LvHhBkXp8Wt5imDk8tam4LyRJM8AUY9btJUY3bMQVWjf0mo7HgopKV5SmIKwyyrbFtorNps+0GvBC17Irr+mVHQ/1Fcq5IJanSPuQWZ3zkjziPB9Qdis6kVPZnC/LS06iM2pdM29SVvIeL/s1je1IxBxZw1O6YWb2aBYh87XhSrSU+1+iZIpvGjQ1TzoNh40gaQVJIUnKmjtVRyO3eaBbiq4BC27lQjbiau3TtYpN3pIkA6q1ImkN970pputwqpai0pw2MU+omrPgAcfRgiYPsVXOQK8RNuZWo2nLPqou+JfBq2RNyqbI0eWGZ3SDmB5xty0oa8nazriWa86t4tR9ja665qqo0Vj6bsiwjOkamKldEJZQdjjZmLWFq1K9657yNW0ScpVf8dzWc+9Zd6t3i/PsHLjpoH3kIx95x9rFYkEcx0ycHN24KPX7WrWzquFWDou+Yn84QCnFZrOhYptdeaMPC3t95p/+JdymYTH8KMuswVpNWR6zFPs8fG7Ihx98mmZnh+z8iwRDiXjtn8HRx+Anv5OrJ/8WnZ9TypBdz2VWzHCkg7TfwDA+Zr6p2Op7eFcaIxVeb8y0brmVjRAIVOkSbAcUacP/rXK81ZdYTjzcy0u84YDjDzzFRkaEQnMljnhw+gq3zk84fPJp7v/4T3C5N0FhiFVHbztlUhaYkaS581Gqh7+ECMeE1Zqg87HA0INF6tFTU3xXoYoB68GbGGlxhwdoYwmLKUU0IugF/OKrDsiKl7oTdp0ER4z5WPoq17e/lfFTh4hzB9UJhtKlKzfMzZzHqwpljzD1b5FMPOrE57HaJdEpxvNYuJq/7H2Bf+xMWPgOSg8Zns3wOss4qlnZm7Gz7eGNRf1wZ49FekYWhQzzGMfbpnvyNg9/7VWeWX6AQjV43UMSx7AtCx50OZ/pf5BLLbhaV7gB+FYTF/u4/S22jWEu+xTTS3pbW8jNkl1ni8NxyGVnoO0Y+S2vLV+jKAoODg6Y6zc56j5Af76g9x3fjgxv7FQfCTweljXJZUEkK775+7+Fz/zqfXSQ8omnLngx+Bi/8pMv8+/96J9CCIEf98iyAJILbJ3SFH+OLimZ/YUljnqS4eUW4Uc/RHOc4N3uIxzJ4XXDxe6GTRix47rkLzwPgM4acAT1w+Rm/LZ5EuXnRIdDZrO354jJyAUBJmtxD2Kaixz0KTzxZ4Eb/dmt8dN40R/Qn72F0W3YnOE/9iyh6HH1yj3ufvSdnVX/EKYvw+5zNO07ZKC9BeVAvEuPEXl+D2MapPTeuf6PgV//mdd45INbPPfJfx2TMfB47k8f8YVPn/Dn/vp735f+pNHN5vgfeOo965ydXbrZ13PjvtpoTc2L3TGdbMmtwKHhDbviIJWM0o6ZrSn7imVjMRWs4ykfEhFVMiD3Amp3jqcFXdcxUyn7TkhehVwKzaV4A78B5T2Odg1L/QCDopdMONcnXLoOUvdpvfu0wnJfrhCu5M7qAOPmHAcz9hixqjUPd1omzinN8T4tAe1gwX0zYY8Ba3WF1i7bskfTdVRrmNo5uh8i3C3m6pqnHElfKY6Dc5zFBr0TM7QeORmR7SPKPlNvyXYz4DXnFMU1j8xuE+WXtCpGxQVJ75Tddp9167BXKGaqZMdMua+GHDVDOlvROpLRIuDKztBtgzU9/GJCg2a5Kdh0OUG7x/3glGlbMEkCJAOEc8URY3INv+o84I4boJtdpFhgRcYdnTErWzodMt+ZE9UTkipg0MWcDTasugUVDVoJbhVDViphWQtuTXPaMON137JlWvK1g19PQGcIGTCvctasGZsRy+qMB47PqkwIugEL9xQjLbLK6JUjbBGj8iWrYcFkuUPtp7SNRhZ9nK7ieFSBVZTyClMK+kygqumlfRKT0c8L1kOP4DTFG93hje0pd+qA34gU4TJmiKFkRd0aloHErQ3htKabXHDBDmcqhbkgli13bQmFS4XkQdtyqmvu4pN2Ja5saXRB3UHZdUgEbZwhu4qR2mZPr2lNB+mI2ngcOx2baknWODxqYeCs8a2hoWWZz/DWEW2Q0bUdz++8wlpM2E49PFNSVTFf9s7whMRvGlJH41cubV5iSoeVsDfX2HqHVXZGGMfM3Zx1WxPWIWvHsraax+Y9ynpGPR5RdgmzcI8H4ZcRTsBds8NHzADdOgR5xMppedgkOMowSUfsZfGN5MQsoda8PJjxaLLH86M3uHu+S9wMqPsTlIg4TXK0kWxcSeueEBiXIPexMueyNgTliI6QL+1eM3YDSvcNtss+cdvnIogx7TWnqsFrIug2IHNmzeNsccVCgckKatewrlMOy0c49c6ppEFoiC9jNmzjqwWNe4JvJJt2zSKpiPFx64JLf8nCVlyF99kut3m83EFQct4KFupRIn3M4eYRRHGJ8YcoB5pE8uX9Y56rt0jUik0SUIoGp5lw4c+JzZx5OaaWDVvZHo4d4HSGE3eJH1ToqKY0BcpYSu2yKizDzuW3t+6zM3v0RiO2Krn2rnBln6aNeNk/wescvDoiWSqGyz6bcctL7oon0x5TtcRfh/Ss4v7onGF1h1wbznqWRi7ZamsC5dOvIalOMC30eRxtHDbOipVJaTvFy+oBsb+NKVsCZ0wlXLrNhKTpqJwFUVNxsBTMhz1aO2dYDXghPKE/3eflcMpT6VNs7JoTd0rsrtCO4Haxj98MyOvFzfncLZh2Dlo4NJVBmDc4Vc9Q1mc4ruIjzREtlgf+nGZ9Tlz+0XFgb+FrnqDtxe+cgfYWDnuHnGc3NujvpUGbz+dsb28T1ifo+u0ZBMdlw1aqediTbHku4/GY1WpFzjZDc4oYjNGbDfm//FfMvuUTFIMexbyiqs6R0mVe9BmNFOvBLnFbo2f/giAE/pNPgxeRti2ffPBLGH+Hzt2hLzTreo0SCqEPkIPP842dz5thx/jKoxgF7DaCy6ZltAnpJgbTxMQ7Y96Y5YRbEqeesZBbOHVN+5/+MPL/+hVSfAaq48Q+we3jz+H5Pv3tHeT5GesP7hDRsBsajILBRYLeElwHE2ZbDVscofSMcXnj6DdyO+a5YiAEtycewWZMLUtWw4qgeZqhqxFtgx5EhFHAP//dAs96XOTnZEGC0+1zqznlVEsSRxLsjEnrgt75imlfkNuUJ8qGebFPqxxOmoqxzdhHcmESKuXz+CPH2NYjbny+4E6QxsVfX7BrO06DPUL7CwgU6Yu/C9/6bQz39klfOyGLJd4iYM2Y3+opPlTnmNljzFTNC41Ppjp2nRZhDGsZI7BciI6twEWULk4X0OtGRMbQdZLNdMZo7y7tbM6Ws8PuJOalizW17tj3HV5fvU6v6BFFERdMud0+y9bVrxJ993d95Rp7JPT43ftLykVJPKyI9o8InD6ePufeRcYn/+4H+Jn/+nnufWHGEx/bJYh7pEsXsgvIXDoOSeIVm62cb3nun3Py4vcw+Y9+gPrhBu/RAbQVO9cpX77rsuxrDuua+vISvdmgE4Ea+Ji8pZuXNNkBQXzC7u43cPyvTW3egpAC1ffQSY172KN8eQHeKQwfAW46aI/tPfuH9WdvYXgL1qcIV9GNDc395D3X8dtw/QrsPfvuGWhvob+PV5Y4zoC8uE+/9/Qf71h/BM5fW3Hx+prv//vf/LbXP/jtR/zMf/U8Vd4SxO+gi/v/Cd18/r40aM7eLu3067lxX20IIwizLYTsY9YVsUlQtqZWOXl7zbgJibMFrvSYiAWm3MHbpOhQsOoF+HpFZm8zWZySxUNmUZ/99Iq1PWWrzaGKKYNjpv2AW9enzIf7GC+in8a0cQXaENqCJNoFLUidbU7dc3Zm5+wry3q0IpGH7C522FltkCpn7ZaY8wUTWTLbCVGlYbw4J4gmnHpzdG3Zqzq89TVXexPiTcVavoSkpWnG+LqgSQWZukBnp/jqCToRIIKMmhF3Vq8SutDlilI5pKJFXq+JI8EyLGlYcuJItJV0WtNrV1zLiKHZQRUbln6PrWxOnHV4piB3BXGtGYkFfetx8egQ0QwINw/p0gtmrHmkmTOKNozUlMGVIGwy3GaGcCy2aimlJlAdolWgdikaQVfMSIMS7zKjF1mCZg9rG97oW6yJ8POCxJzRlqd4zhN0jUR1U1p3jWszzuotnKZiu1tQmIxHUoXUZ8TRgFPnmiDN6bcpuvdBRm3DuniNRhmCRUXo+AQuOPWGtMxxjcabpiTDQ6AjKiq20jmrYEKlXJSpaJ0aVVVYbbkXj4hWOUk7Y7t9nMKLcJM5Q39JkOZsHI+i3WHfXvDozMdOlvjGpVMhpjhl3a15qu3zejgkSMGvYOG1+HJJ7vSpZR9tW1RliYoLulDd6MOiFNlNSTuL9jRpmHGwOGcRxVhi7vXXhOuUrCfZXRbEZUIrJ6RqTFSlbJ/tIEfbOOV97vsG36xvtEUbS+EEhF3FOLOcyDnamdKrK4ZliGhDOqaYNKKnEoIUvKbH2M3xA4XSj+MYzSUJPbkkynzudk8jpIMymt+TCbtlRVkadPWA2zajFh6uvqAaPUXPBow399k4LlsbTcExt+Z9mnpF01UMlgmukQy8BefDA7SVxEuXnrnHZnyLTB3RX8zYNPewvT4fePOStDckbjoaNjyYPEerarbKNbUKGV6/TuptI61LWKRoWqLqFbyywjWGItph2K1xqjeopKXGI5l4uHpKIdb0lw1e0+CVBZU7oB6fM1kUHN/6EI3oc3hVIxyX+/0SURm85oqoVozyCxzVx3Ydpb2kEw3Wa+g/aDlWGcPCYocDXG/MorpPuMlxRgbb1shoQNO+Si/RSH+bXtugnIqUiN4mQ0qLKwVWX7K2PQ5efxTJmrWfYbHERc1CCZquzyDv6GIfYZYcXjlIauxiwCFzru3LVHKHyeYSP7zDozOD052CDrB+n6DSiOaUrnOo2/XNOLezRavWVG7C0osImwa/0kSVRS1O0Y5P7WhMWNHTHVQBoVgRtiWJPIL2nI27psnO2d1YPDMlDh2W6g38PGfXhS4M8dMSgh36yZJEpXTC4/GXLPOxg5E5rRNQtCV7m89xMeojjE+TPKSTBwRdSlSMqN+DgX1tE7Ti6n1p0A56ByRNwuXyEq01o9HoHWtnsxk7Ozu005zuDxC0h2XNrU3LeqiI1U2g9XK5ZKW3GeoMObzL+md/FqEU1ff8h2S/qdDLiqKYE4V3Wc1advo+x8PbbM+/gHUfEBx9F3g3ZOfnn/ubfO9Lf5aL4MMQPE1VrAhUQGtadLaD2bvmrlV82V2zNYNme4ftouPlsuGxDDgwVGZA/2CX33p9xl/Zv8OnXxpy56UpZRSxtb3FeWvZhD5DWVOYfT507/cYbB+RrWpGyyVl9DRDW7EVG9beFoNkgS8F99uO1Y7lkUuHNB4zrkO0tERSc537jIzFDzYM6iFubZlOSvz8iIle0HUCqzw2rcv+MKAoSp7bfo6VN8U0d8nI+aDs+OKmJNrfwVzNkQcHJOqKXhcQa8s897DhkHubNdsdjESfL9ua3uaSP7X9Kv/s9CMc3Yl42QyInA3zgebAbHhDBuz3v8B0us3sizdaq+HuHvUrHmXfpVf06OQWP7Avub3Vx53v00QdP7T3V/mhzTG7nsSqMR+r7vG58ClWQ4e9gSRaD6l7Z0yKHrlyMVVNtkgY7e3TXZYIAePIY1a3lBjuRn1eX/0mTxdPE0URU3mNNv8Oexc/h//441+5xu6EPi/8zpRP7nhETQG9XQgnHC2nfHbT8HFX8fFP3eULnz7m8W/YIYh7TEsJ1QYB2HiPzTdn3F4NECtNe33FF3/lB+iLv8h4s48XfoCAkGHScX37BPEv5vhPPUXx+c/j7H0E1fdwd0Lq+xuaZEDs/Ab7+5/i+eef/0Nr5S2rfe9Wn/Yqx/YvEKPbGKOZHt/HDRz8u+9E0G7fuD4C/hNDvN/8Y5KZ6Svw+J+hae4RBrfevXZwCOkl/d7TZNmXvyoE7fn/8z4f/9Sjf4iEDbZCDp4Y8voL13z433qP7/UnjG7+/lwcnZ1dzGaDqSrku7jdfh1/TFgYpxuiuqCWEefKMKo1A7OgkD6lGqHdjIHuiNqWsCqZi5yq7ejN5xjPoc8CS0OvXOOaEzJtcY1Dh4vjSJwuoTdPSF0fr8ro1SXK1ow2NQtl8PGRdULtSPa6YxoZkYUejvHwk4Itd8ZAr6ikS1KfUOkY44CnNf3FA6wwFL5Hr1ogtWWn3nDpx7R2wGR1hjQSmpxibNhaXVO4I9xW0LZTtk1BI6ZUQhEXLZl9wJZxmWvDQGkuI4thzWF1zSizXJgOKUPCuiImpSLGCAHyAqNzNCG3r68oVU2tAqQYUWiBJ6BHzcbpcev8VUrl0m9TXCMovI5WCTojedSsabTHhYyRrk8jFaYniZoFrRpgnZvujGMVtTfEdDV9LaGsoWyY6A0XxkU0C6KupPVi0Clxk+AZzYSCqZZMmoQURSg7mq6Ho2usgsaRDNMlnoTG79HQZ7J5jVoNSJ0+kZmT+9vI6gJ/7SDdllYpjAgYdyUiT5h4LUWnqYIJnatwbU2tNAEGRIl1JzxxfYxvNBfCIy7OGSmJ9XyyxscjYbRWKE8QWUlsJNMmY7vQpG5IpDu2uoSyhcB2+KpFSpdJdUGnPETZYIKasNHspGfMlSJoNoDAS36PVQfKMZh8xqBw6USEV1cIU6FqDaJBbvrUqg+hIacDPcNxI3yRcGeeYW1K0lmE9BjUGQetwGI5k5ZKGDwnw6o1XrODRXPpLomFYCMdxq2PNSUbp8MzAlkZMvuAqpOEywDhxBx2Uzb1FCslnjZsWcNcKjIEyhEYxgTU7GnNSTFjr0mxImcjIxA3E1K95RJrfCrp05KTKwepfXrphjIa4nYzGhMglhuOTMrEaOYiJErPSGREnCV0nodVEePFQ0AihCCsMhAGVxjQlmG24KBbI23LTPkMsWx11yRdQ+0arPKBgDDXdDzAKA9PNyitkX6fg65htZGMMUSXr7BWPo1tUEITlEtaJvi6xACdmrDR1yh/TKsXaCfC4iGAqOzohMYrCqwX0wiPETl22eJalyDfYDGsXIddc01PRGSpj69zhLBEpsDXJaFw6GwHbUslBHELUrm0yuOgybhuLY0TMEpmpI6Pg6KqLXF5Ta5yQhUyadfkgaLprjCyZSUqtC4YLy9wdEC/W5M723ROSOl5yLrA2hVuHOGlIdJAZ6By+sRWE5qazhi6ysfRGtl1OFKwrStqdU1jQ5pEo40kd3xK0cM3GlUnFI6Dh+RWVnPqugTJPa6VRyMcrJY4IsBLlwywWFwaJ6T2YFTUBLZDthvWXolpDH1Tobp321C+hjVoVVexqTfvS4PmK5/dcJdXz19lMpkg5Tv/rLc6aK5XUJX+2947LhvksqEduwghmEwmrFYr5u0Ep6uJhiOW/8tPI0dDJt/4jSz6CrluKYqHhNGjLIuGw1HAa9EhJmpJupBg9OxXPv//yB2ud7+Pre6XsY4kya4Z+kMO4gOq1QjDgjrLOepdsL8wcHDEdqo5S1MIQgJZ0toIMfJINjXfvTfmr372HKMr1sMh0WqFqBpWwicyOVFpoROEr73JyfP36Rc1uEOwLcOoYtO7xfZmhi8F56biOlrSn9Vc97YIdUQjNJ5tuJR7BJWmau/jW49x0XE2KTCDiLEjqdOb7sjZJXzszphxWPOxwSNk/orSCtYm55G65NevNng7Y2gK1LPPsQkqtitN7UhMNqONenx5vmTSFvQkdE7LnWjFNBsxq2KejZ7gojug566YbrnsGYeJLbBjzdDtc3l6idWaaGtCR0QRxcRtxBPBLdbZJTu3XPx8AoHmh89/np8b/CB7fgBqi6PymAZJGkl2xyFR3acaPGRcuyRuiC5K8kXFcHcfnbjkSjMwkqu8Zi4MT/UnPNg8oCgKoihiEWukmLEXPoLJft/FcK8TnOmO3VGHxEC0jQwGbHUbXvEjdJry5Dfuki4rrh8k+HGPomzB62G8LYxcUj9jOLrKOfmnf4/6TsfoG7+TxXOfZfHU88z/mx+jTTXbC0Xv4AQRRQTPPEPx/AvorEH1XPzHRlT31jQLF9d8ib29PVarFVVVvW09vGW1Lwce0ld0RQTDW6wuLuh7E9Jkzi/81D/gJ//zv8Fnf+ofkcz/P2Nzo0dgcwbA+OOPMpH7fzyjkMsvwf6HaZp3yUB7C/19SC7p/WuC9m+K2WnK4jznmU/80Q6yz3zikFd/893dl/6kYa193wRN9WJkHNN9vYv2VYVjDT11j9BLUdHrhGZN364QTo7nThnbOQOt8WxLp0L6OkM6NYFMUGwwTcouX8APXkf6Z0TFA7zmHltNgmMslbPC+q/Tc+fgCBxnSV+neLQYf0VIh2dLBnbFWGcEtiNy32Cna3EE1HHOXpvT1xm+dx/tXrPTLZnokr6dM+kuGLVTBt0aQ8PQQBWvMOElE1PQ0ymxTYiUQlTbVMJjt9vQ65Z4pqOVIdZu2OkSHJOyY1asXUBA5m3odQlDLlj0NpROgDGSo3rBoU440JahXaHIEe4c104JzDkDPUe6M/o6BRpCM0WwQEgHT5QI94S+PSOkQzohcbdh2T9D2Ya1tqQ0hN59HOeUXrcCaxAqZqAbpDunVRmjbs64WWBti3Lgjk440ms82XErPWGrSYlMhxSaWDpMupqec40yBiMKMgd8kzPgjP2uYEiFlYYtU+B4Lr6jiLslE1NicVA659EuwRMBvtVUnsRVNZ4R9HVNH00Ph2G3ou3OULZESI3nnDFsF4y7gtCUjIXGCU6xwQV5eE7rzwhtzn6zZFTmHCWCSnmsejkT5xjtXlBFa9yspmc6InFFvyup8NioCscs2eoyttoNUDPqSg5NyvbsmqP1JUiDcDv22oKxrnB0zb7OCLqEgemIdY4gxTOWiSkYdxVptKanKybdmoCMse0YmzmGa1bRKV30Mq6FoNUM6oyeaajdnNzfMBQtZX9G7a8JnRljm7OlMwbOOa5acdRkSKuRSuBKiZWgAOOlWF8gpMKaBiNzPP+USF7TtxUNDZ5tGJsSzzvDM4pQCwolESIj8RNqJ8KEGZ4IsLKPxMNK8FkSskEFryNFTcwbjNtfZaQLQtvRU5fEpsA1EseCaxVDWiKpEEgGzZqJXtA3a/a7DWOdUyl1E69hS2IqUlWzVhLtlGyCJYVxyN2GzvHZ0ZrIpkxMzoAF+23BoVzgiRZpCirR0TlXlFiwObebhIFNCL1jeuKYUZsyNClLb8GwbTkyGSFXGAXStrimw6iCvqwRUUmPju3NGtlluCjGMmCgHJAGIy1KKZJwhpVLpKlRpIz1BtdUN3b0XclIXTHw3mSoW0LbMew29Ls1pbA4pBx1CVr22GlrRl1OpebM3JLOqxjYilgn7DQrfBbocEloNgy6NZ7RN5EXyjK0GQqLo67o+edIwMlzourilLssAAAgAElEQVSSYb0gcF9npBM8awFJv1sxrjN6uqVPyV7XsFIhOZpSvE7rZkgJE1PhBq8w7BJCCcKf49uGRnQM2xaHivXgms5NGBrLKNgQC4WVHlJoAp3R6zImpmDk3SN3gpv7MkuaaIHrnL/rnvI1S9CmxZSe2yNyo/cu5mbM8d783nta7L/VQZMqoSg8yvImzFlby2lRo1cNYnJD3N7qoF3kI2Sr8boOk6aMv/f7uBX6XESC3qYjyR8QRXdZ5Q23RiGIlsaRZBp8cfOHadF0fD4p8CbfT6Cv+OTid7hKrgidkFv9WxRrDylCFptz7sbH7K1a3Efvsr1quCgb1NaEuCtA1PyWydjuBOZ//IfceXPOL3zcJx8M4OISlWWsvCF9UePrlmUeMuwMm5/9WeZjDx+P3Dr0wzX5+EmO0muktizsG4z1CHdTcOH3QYXkqsTvCi7kLn7tUlYnVEJze224nHQ4w3NGRiK8G71anTo8vtNjK7LcfeME3IbMaObukP8eh1+sSv674w+w6WKunZaz7ZqjtqNxA3RyQRPHXBuHnm7xhUCKhid7C55f7iPkCLcbU3a79NSU2dhlp3I5kAmfcz/AUViSBAHVSy+RhZrWjXGDAZm75sPscrm5YHC7oqfGJNWMv/nwp1jKHTJ1hHaGuMUpLZK5B+PAJdOK5dZL3G33WXk76CKh2hgGuzsYPabtC/zKUGvDSlo+ONrjJDkhz3OiKKLogXB+j+7bnuYz+Y/zE699hleyEv+ipNj2CJslBSFGSNwwoq8z3nzyacoXv4jjKp75lgNe/Y0LgrhHlWcQjrHOhCz8HF4SY7Ip6y99ht6TH+fu0X/J3eO/x+rR1xn92HejDp4gPleEowXq1h7uwT7F7/zOV8iW/9iQ+s0NSImTfZHA9xmPx1xfX79trbxltS+EwN0WNO6HwYu5fvAmj+5/iF+//t/Ye/wJ/u2//p9R5zn/5L/4G3z2n/44dZF/ZcQRILw9RknF7MX3aRSSzyG9gP0P0jTvoUED6N900KLoLmV5/O617wO/92tnPPOJA7zgjx4wuPuRbdJFxez0a8csxOQFtizfl4sjgLO393WC9lWGlQ2ubNh4Ca2wRJGLGiSU2ytwaqpgigwuWPfOyGVHFyyhf0W38xA/cBjIOUbcxLEMbXLjIBjM6aIZkX/MRJ0RUWHdGb7/JqGzpgOyozNKLyEdzMijSxQdvinAOEhpsUfnhFHLtl0Bkqx3jhIV0kuowwsILyjjM8roCtyUBsMqukSZEkcaIltRRlOkdWi9DV0wJ27WBPKc2jq4bkbgX2DCC/B8rqIZWjqUgLESBwluQRvMKP01WJfOOMRdRm01mWwxgxzppAQyw1MFStUoK2mihm27IdYWT05pRUcjNcoKxOAhXdjiSAvGw9MpMr7EQ+IFZzT9ksZf0akS4+Y40RWR8xDHFOS9M0J7Rk/OMf4K1xj6OkHYDWf9KdnolKv4nPM7p9RujpANfdPiWQmmpZIFpb8itobQSvq2IpGaSnTguPSdKW0wB+8K17vCQZD0TrncOWURXTDtPcSKBNe7jw4eEKgVg937eMEFuMcUXoIOpkhVId0Kx30Dz24Aha+hk4aNvyJTNTNR0wqLrzI8/5IivqQvL8Bd4xtD35mSqSWllzJ3CrSXIZ2CoUxw+wM0iqp3BdE1Re8KAIWkFJp82CJ3TlmFF6TRjMi5YOXPKLbeRJKxcioEAitbJAIbnhJ5Z0hVYzyHXZ3QMzW+tgx0hwlfxQC+qThoU/pGUQ0TGKyIO4urHNbRFYW3wgjL0JTssSBE0WIpsAjZopycwk0wQOGtwVli3RzrZoTehp7RDMwGvX1Gub1CCPCdFB3OwYnQvZAyusYVMDYFngXjlITeCVk457p3zlAm9JwZw+AhIrrEtZJiMKOMLwhEyZAVTVDiI1DGAxSdk2P9kNRN8OTsJmA+mCOVR6CmiPCcJp4h/EtqKgwVQ62x8hJHV+jgDbzQwQZ9esbQehlNfEZfXRKbikIalJW4ssH3NnTeGZmX4ADK3aCjC7RbYRG4Tk6jbh5dqUF1BCbFk0vG8TXTR94kkS21M8VXS2RwD8e/x1DMMShiuSDyztkRZ7hByubgmvWgZr1TY4cbFJJIrmilBjclCN+gZ2q00GhVYK2ikobMKalli41OCMIHNG5KE11TjVY4XoC2DbGoEf4cEVxAdAneBuEV1OE1tQOtsDeE2J6j40uMU9ITllFX4SLxbIPvnBE4CYHs6LwELV22XAmTFUJ2lPE5dTSliy9wrINwPbrgCmUVhZdQBlOs7JDS0nkpyAYBeFjaYIl1FL6oMMEVeXiNsorG2zDqrhmKS4he5XInoY2m5Hsrmq0LfCCwEle0VNIiANcaOi+lJxsCN3/XPeVrlqBdF9fsRrvvu/6of8Tx+vhdCZox5is2/JYNUm1xfn7DYC/qlklhEEoQDm7GmiaTCcvlkuM0oisV8voa23WM/vJfYt93WfQk25khKe4TRXdZFg0Ho5C/GHyeVMd0Y4la3RgofHaZ8A2DCKfyeeXgO/m7xz/BZXqNKxxuyZAyrfG9Q4rynKH7gK11Rf34Y2wtG+YIvMGIQZNj3JxfLnPitGH10z/N89/zH3MdF6T9Hpe/8RJ+tmEdjRn5hjyoEbnDrX/3z+N96dd5cOChLFzaMZE8YRV+mEGV01Sa2rzEx7JniMa7pJGPtJaNmxDqggs74WQzYNJCqgx7/owtBJ955AVGyRIz2EELzTeFDuuyZTuC4vUvsW5uca7hfz38DwiUy1/76BE+BZ8efooXry+xoWUXddNab1c0YY+l18evLdpaZLrAdzzOwx/DG3w/DzdrvHqPoVlzOZbs5S6H3Tkvuk+wH55S9WKy//0fcfHpv00eR0zKlHW04XbScFleoeOUnjNmOX8TL+jxHfYX+Y1yj05GXHQ1B2XGHMNQKRJlKIbnaKeB4nshW1MnPnHfR3gHyIOQetNwMAzIjOHpYAcXl3W/43/48j/hhWef5h8++gxrvp2/7f4tfuZyynd9/lVemKcsQ4lTzMiJyLKM0FUIYLo3pvjdLwDw1Dfv8+YXZjh+QFKtmXU5iTFk8UsMysd4dXdI8Irk8Af/Ds1FRm/nSZ5+6u9zff8nYf8O6tFvQuUu+eQCfJ/q9dfpZktUz8M97GGbDnc/RtgOsin7+/tcXV29bb2ooYfe3OS2ucOSVt0YY0wfvEmebeiNJnzy+36QRz74Ef78j/wof+2//Z/YzKb85I/+MCfT5saWnxs9WxHlZK+8nQC+Iy5ehJ1nwA3ftwaN9JIwfISyPH1/x3gH6NZw7/NTnv3WP5xJ9xYcV/Hkx/d47beu3rHmTxp6MUcOBkjff+9iwNndpb1+n+fj63hfsMrBBBJlBdXQpRRjMi8mChVB16GQtLFAWEk9Sul6FaonUI5PsX2BdWuqvk83CqlNhPFy2m1B2wdv0NEFHsWwQzmWyAZgHZJHz3Fch3bkko4s2Y5A7OXo/g56sEW7Len+X/beNFbS7D7v+53t3Wq7VXX3vnd6ne7mDGfIISluQ1s7tDqWIUWxA1v64NgWAjhxYFifLMNxDASBgQBxYNhxEiWGJSeARcWJI8miYpEUxVUzGnE4ZLNnepme7r7dd6v93c+SD5eURICcIR0lZhI9n9+qt+rUe06d5zz///MIw3TwiBLB6foh003JfFMQbSr0OhggVQnlEJbbDfnWkmqjYXbuiOVYo1HE3rHaOabadFSbgmr3HssNyXJwSqka5sMUO0qZj0+oNhp8dwhpAp0RutenVRV+2NIOEmLn8Z0ObpBTJ6fUm8dMxjNm25bZuSnFGKbnaupRxnLkWaQ9qs4JziyY7ByRbt+k3n+MXJMwSHAjiR2u4fYOiTuG3lqLWRfo0RIpQJgObb+L25QUm4F8q2CzrVmPLW4QsGsBu/aQ1f6S6e6CxVZLM4TFGLzSNDsNxe4CFZ3QRkcstqYEFHJUYDYPmG9KTjY19brG9gaQ9HE9gRFgVMuqG1h1jtHeEFKD7XiaoSXED2mFoxk7usOKdV1QJRGFacnimmYk6LUFDA+x6xKkpkiXeBVQ/Qlx1KeTRAwzzWqcMRlv0K5J9HrAxQtW5pR29z5+2MMEgw4G5QOrrZriXMnhYMg0ceRbOcoDXY/rG2w8R3tNNPTIZEmbSoodR9Vx2Cyj3G2hp1jsLQmqRZiEcq3CDzaxo5h6S1DsFpzuPiTp1/ixpUojFqOS2ajL/NwRq7jBDiVhQ5ElBQwaVuuO5bqn11PItYhyb4ZJHXEEDA1io6Xegno9YqxKevKIfHiIQFH3a9RWTr7jUb0W7RWkgUHWEHQPK7rUo4xypPCdLn3vKbYD1UgiomPoBeguEFGM7fYwA03dM6BX5EmCzTRNdoxSGdIaVGMoR0v80KCDxEVL7M4RDDOWmwesNnKaYUo76FNvKZqdU0IG3bUGO1K0W4Jie0HTq/AdSaUrZHQf0cs4HZSopEBGR8i2IRaW/JzFxlPyZEqdTVj1Soq1gFMVwnvs4BaFsgihcENJ3esxG6fUnSE20UzWDcU4ga2HuHM5Ko0xSlMMT0G0JCyJkGgl6cUVIblFl0AVBAtafDrHJ5J2MKPoTMnXLOUoQakZbmCwHYHtQb5zynz/kGI352TnhHrnMdUop8wkbCp8iHGqRTSCauApdg7IdyqKwQnlAKbjHt4IsvVTqrFi2etQj2eEdILdKiEeAopy8yGzrRX5zpLlpqW4+BgpK1obUfc01Rjs9iNOxxNCWhCjiLzGDRWx0Cx3c8qNQ2JTYzstbVyAyqnSY3RX4Mcx+XCKkPcZiIAdWkKa4CJF04koxxHlWCHSEtFX+Dal1BGZADtUtGmN6sbM909Z9hdUezOqYUq11TLdLnBZjkRQqDdvMfi2JWhHxdG3RNB2O7sc5AdvStBmsxlKKfr9PpgVvf4FHjw4K8O6V9ZcryViHDOKzgja5uYmx8cnPJy3NKsIef8h8ZNPYra2UEKgxzGjlaMq/kBBG6uCn4g/QWXBjjzi9bPyto9Nlnz3qIdbNrxw8UfYrY+49ODjnD++xbkvfRTbQrSYIvxj9Owu2jpewLPuBU5IXNBsVC25brkVPDSB6K/9bb7vz/8ApZkw73VxD+4xyOfMVJdIB0La4PWIN65cJcsPeX23jwmegzAik/fIuQBKUlmPrG/ytuI6g8vXkF1Jd7lklkzohoKDdsAXK8+VqGUqJMvJB9g+/CD/enqV8/U9HocOjWrYqgqOlzWdxZy/p/5DHjTnSIPgP578Gv9JSPhzVyyHTYenljd4qf0OVnHLLlCHDOELfLLGsRqwPRHkPuBtyXLZp178DzSLf4jYP6VTj+kk93g0gK25YLO6y83sIuvRbXy7Iv/4x7i7foll1sVVklmnIXrtS0z8lBaLFJLl8eu03W2+X/wmL+YKCTwUERdmS4rGsiYkp1FF2mzw5csfI6nextq9y7QrhTn1+NUBnfND8lnN9loKlSOuPGHjr/BLb/9+vuCv0xQdsvYlzpWBT/z2R/nn7zjP3+Jv89FRYIKHekIuB8znc3rkVCR0/ZLbN89UpvG5Lr1RwukDy6fW71Hbkls8YjW+R5LusxIKRUz67DO0BznRboetrR9lIHeYtreZyTV60w7l2yqKT32a5OpVqhsvn4VHK4HsGGRqztwWp6+zvb3No0ePvma+fLUHDcAkx7T+AgCP77zGvaMv8ME//e8jxB84OK5tbfNjf+Nv8t0//Zf4X//7X+DmgYP2rGxS7MbwqP3mJvPBS7D7TkIINM0JcbTx5tf3d2FxQJruU5b3CSF8c/f5OnjjxoTuKGG023nT665/cIebn3uMs98ewdXfbHnjV2G2NrFHf+zk+EcJGyRF2SEuHfvtJUzjiWrDzPaoexop4LZ6mmbU5VIbGBNgKjk/jXBNzGLTUKeKVibMun1WA0m/LbCJ5rEZ4RxIF7Blh6rocNAbU/uISbFGU0k6RYfRQvNb0XUmyV1eW1/yBfcdnMyGFMsuOV0WUYJH0qkEsq3YWlp2XWCe9hG+w7HbYqPSXF0lUF3BCUXIYnQqKOKYhg7UHR7W1xnYDZIoRXXglBH32WRUeraPB0T1ksJUJPoNnG0IHUnPtVCNeRCtcxj2qDOojOTqYcL1g4x63mW22GSQB6J5oOYeurAU7FJGm2gTs9/ssXAZw6riZLmDX40IhUM2lteyDWbWI5YRnaLFnDpmax1k0CS5oZilxFOJybs02S63/Q65GnKv+ywHXKcIa3gRg4twrSG1HdI6A5/QklEkmkWacVjt0nYMplIsnaKs5xyEMbVNUfaEvImoqx107LBKU2UGkUa03YhuHujqkv4i5nHX0DcJHzyMKZo+n51+L5qEoQyUJiYtJMuRwjcJx/PzPJAjfK+i2XiMM5rExzStYdUmyFJxYLY4cuco/RiZalS3Ii1G5FUfM86QmSIThk5QhFqRtYoiXuKMo5NI0qhL5gTFKGLYGVDqA27o86zaHqdlHxsLhJcs2KasnqB1hpPNCj98lSKDSfchazn0CocqNXFTcTLfJ29rjnsrDuOUB4vzzPMN5r2USbHFsuoi6g5h0eVe3GdlVjitKOlTkrFsN6hcRrPoc6IktYqwwRBMSt7pohFkuoMSGpNL1uaedjVm2oDSS0IJjdDc6j3Fl+S7+aK+hm4cEzegU5SkueAgXaOI5ngiQiJBFQwWA/qLXW6pC0SqplYxR+mY46ZP3ouYjhNi5QDJcX+NxVaDpqFTO1oZ443iqJTkbkFtu8QLw+NmgFgZxsuK8cIyqAo6ruJQtVjOk7cDvBiQFXO8m3MUbXCSXKJKMspJRhtZXNZSdCRlJjlRHU4GHVYmZWkERV+zkimN77JMF8yk4Vhu0SGwtvQc53vE0jC1HXo2ISsarMxYL1PaOGavyFhvNjhljzy5woNol9plxDju+/M8mF/naLXDohribcROcxebQrcRHCUDZm7MaZ0xqCVp7ZCsYesBC3WBG8nTfKG5hOsE2m4HmXrik22MKymbgkUdQ22ICse51SZ1mdCZFcRli/Yr7ke7nFabPCyfQaWStInxAh67XTp2QrvqEpsKrwoe+E1qqYnaFpO36CIgVYMyllYp6jWL1TVp68nVVdqspkkleSJ5EJ9jGaVIBJETeKkQShLVLQ23OVR75OWzdIsGLe5SDSKWTcprmxc4XBvRKQz9tmE8E9i5Iq0MLlWEPEa2jtivCOGUqcxwmUKkb75X+bY1CflWCdq57jk+Un+E0egbB78dHx+fhViHgIor1jaf4u4bZwTt9bLhfAl2zTDUZ8PS6/XwJqUpPbbQmMmMwV/587//fv31lKwKNPmUJLnIvHzA9s1/yivJUyTrCShoPvYK/scCH5ss+Q/2NvCLe5x2N/nwE9/LX771S/yFC5f43nf9HR7ekrQ5bMZHJEeeZa9L/eu/TDX89+iUgtX87FRrkWVcufUycJ4nfuL70R1oVMmy3yMtjzlVCR5BESuiOidkm/xaXvMXTMTJcMBTvuSEPhsdw5friDzp0lGBUC2oowuMLyt47TbD2YxJckwiWnKfcNtUXDvZ4pZ0LLLn+eCj/4qP/AmNv7HNyWibvspZlDVH85Kj+32SjYKfSguKyYhR/zxtu0T+3q8Skgs8U73ALf8kTdJyuXI0rkfTLqk6XU7kmM3TgoGc44Kg7u7T1iVRA3eqe1yvNmh69yiigs7pPpv+LieDH0Kqq3hvWU4knzRDirSHmB9RdgX56Ro9n7EqIpaywHvHG+0aa7zGB4ZdXpiecqIV735U8LlKk9nAw3jCc/kmem3O7a3Pce3RBzDJ7+Fv1rT3P0fviR8m/8gJg/2EZJnzN+89REY9/vrBP+IH3vdz/PMv/rf8wMN/jen9MPZIMxp+gCuix08sXuJ/Hr8bz4oqGuPmc8bulJKUd6mCl1cl729bhDFcfd8Wd774gFe3ZmzNMlbesohrTvxnGf6GofvsZZDyTEF7/kzxGcfXeaP9GDMO2ZwG7jwDxS++yPAn/yzlyy8j+3/q7OF1AQSwdh5m99jZeY4vfelLXzNfVP+sBw0gUveY1+/CO8f07gNilXH+Q1/fNv/6899JZzjiX/xnP0vnxY+z9/4foP/2HdpbJwQXvr4t/x/G4Rfg/IdwLsf7EmPeogetfw4WD4njHbwvadspUfTmAZDfCLdfPOLyu9563dk83yPtRdx75ZRL73wLAvn/AOzx8bdE0PTmJvaPFbQ/UniXUBTvpQwV22FC6yPu2CHn7q9x2HtIW11FiJqLYUBSL2nK97AsM2Zo0sWKly706bealVTsTo8pzAbX4lssbc1B8wSXym08h7wuhmz4Gb4SXFskHDrDaf0MG1IQx4/ZnkUs3bs5OV/w1OubnAzvEK/GLNJT5CLFiqfo+COK3DK3hhVd7gy6VOqQvQPFqgmsREPOGHN8Fesky42PMzqNeDUYApqN6VOci1+gFPC466iLDCcDj/NnidoBs8EthvWIO6lgFJ1wq9pjXI+IllsIU+FkywNzCRUe0YYnCJUnFSWn4Yjb4iK+1TgcSlpM3aFNO2wdPU1IBIkSfFENWZtfZhEfMx98ka3oFaJpj1ftE+AkWdkQLS9R8JjUHXMvbNIgeV+Vc9e/h1PdoniVMhRsLGNmMiCbXahLEjRlWDHt3MXZEV4KhFlB3YU4x4mS7kqxXe1yP004FYFeaTiKesjlPo/7RyR+DVf0GJY7NNNjVHaOY9sy6x1AXrI2OwcY7hJI1V02fcPLWcGd/Enq6DGjepuObJi6R5jTd5BpQ9RWqPUZczTHfpe+1DyVzzhJjzmp3oVpj1g4yKtttlfnGG68wHRyAXmywUFfMNA3eLpsedkcYoUhtpasqXh7AbFN+fzpVQ7TAdfiF6maiunR86yFEW8MKoSIkO6YWUiIT9/OCkO1UWNWNzhRmtPQx/t3Ej2uOBx9kUJ2UM0eAkfiloxPBhx3WozznKqU3nSHpO3Srn+UlYqYNjvIKlCKbTrKM3Se/2OkSK1D4NlXC9KiZjV9D2XvNW6xzSpIdmpH2kY8cAmxXmJ9Qmm7DOWAR/4G5xuJXo7pqJLCJ7RxjzuFoOq8QZZvswoZznXIqoRGaw5typ4/Yb3yPLYJy77lRnUR5Q21aNmcXyBvHtIkC6ZKcyE85qB4AqPnTOQ6ExI2mh6qHWALz+udY8x8wLyWjBvDK13LbrFObHex9ogjfw4hHbVVHPULktOU99fwqrtCLBakQlIkW7wRLbgoa/zyEq46z3T0eY7SMe8ob/GwvE46WWNDLDmkh28PaNshRackKacsuuvkbo+kHHI7v0yxe4vb9jn2Fq/gxSW+ZCLUSnCMIi0PeBxPqIUhhJIOu7xuclQFuhEE+zSSOXLwOSYu4/X8Gb5TnrA8fpLS5bTDmzwI5+gc79CaiNOQUZVT+uIIqRbc92P06iJjG9CiQTYDToQkZs7DJKeyhmnxDB/wLxBCxaftO7GUtNmY4/jL7MyGfEGeZ9cKjsqGRnhe1JdYXz7JiiUn0S2K0uCahGnYxbcpvdU2SXofnT1CzxvyOGL3ZJ0HzQ6zrEtSXyWxJdL3qDZi5icF32k/wUeiAVHsoDG4uo9oNogNdKoNvjA6z1Mrxc30CXx7wN7RBR6Pb3A6+0GS9iHrKueG36KfNKR1xDyk5LIi06fMuo+ZSsm1HBbZmx+SflsTtK3srS32v4qdbIdZmL2pgvbV/rPV7ASdWS5cfi+f+NQv473nXlmzvvSUa4ahOQuPE0Jg1rYZtQp/oIlay9pP/ru//367vZg689TlVYo2IaMkefG/4ddH/yk79tcReUr1e5/ni6sST+CZTsKjZcvEWt7Y3GRyN+Z9RcFY7jHtF9y1z7KbHpLdiln2Bvyzn/yr/NinTtloWjAxM5sxT3r8O5/5HG3vCX7xb32abBDxTPR9lN2IIAT3+1v0guSwO+DafEaWjviwyvhp1+DjITvllEnos5b2mc0aTtMBiSxp3FXubvRYX9+izmJGB4+wLJASNtWcL/kN3nlyEQscN5/m2WidN4obWKsJ6T6NqFlKyb03HvNY7vJ9V/4Blw9+hhfjJZHZpCyW/C8vfYSd9ecp0oR3h9/hBQGXbEkbLmLzx5yeG1GSEvIILX4DLTz9d38vq4//C1InWNQlSdvjJDlivR4yaZ9iV/8GZdLlKDxPrF4gvO0aL5/epln7UVRekozWaA932a83qPyIFTWuN+BLx542xPyZrSEvTE/ITczOQ43oOI6s49C8zvvzEen6K9T7D3DHz/HsBztM209jypv0Nrrk84a000McOz6Zz/i58A+5efQ8k+mMSFVUpUBejggPzpwOy0c/yg+N/z4flu+hSaHtbLOcz7lYP+KElGeils9fepLqyzdJn3k7V79ji1/+6L8i2QddTtnSPQ4fQ9ub0J2OePk7Kv7cP/kQ/2zyn2N2zhQfmU/YvPRTHD3+eTbuwetiSHOhRChN+8YXUV1DaD0+b/GVhd3zML3H9sUf4ujoCGst+isHFF9V0EII6OYm3r2X6Z0HBBu4evF9SPONQxb3n3qG736y5Fd//n/kp555nvVnr3D/wwfkd07oPvkWhGZyB971UzTNCVr3UOotyvb6u1CcIp0jjncpq/v/RgTNtZ67nz/mx3/2PW95rRCC6x/Y5suffvRtQdDax4/RW9/8eqk3tyhfeun/xk/0/0N4ME0PR4+jyYDEWwZeMNUNYvYMxkeM2h5zkZLLglkzooxbdBAsm132DleUcWBrdcrQj8kbz+35OvvmkLa5Ri5zQrGHUjWHgxVdm9IuN5iHIS4kzHyHWXkBzCkrUXDlQUZsYzYn76WWDbO4ZNQoXFFShF0sCidyGhT905rtZkTwLVNSWlGhlcXUEocjHD/PfTVHiJaezej4mKrZZmlOUad7bJguTZPSdWvUwhMvrmKdRlZr1G0X1b+HXm3gVEvsIxLXYzti8KgAACAASURBVO14zEOTcIygGxJM3adXboJuGdoerVMsxAodYpJFhpZngvyJuIIHBIJuu87J8ioPkx3qaYdetmShV8jZZRK3xtKf8LC9RLfYZBhifjc+ou8KFr5iU0bUrmbaShQ9fF3iiTDtiEgaLtUjJhgO9RRYRwdYa9cxXtN4uGkzaCLGuqAjDN1iD+Elw5WhIzWyHiBZoujRrAwaz6bbPDNs0Q3eSpSPuGPW6Dc1g3qICoatfIzzCZiKITscyhLtLSvT4vKn0MqxHTJmZo5q+zzZCtp2izry+ChHtxmt6/B68SxZvYEDonpOUryN22LBadGjcAKlI5K6T2tPqGWBY53NSrHTDvh06EPbJ/Yx66eXmK3dI1s+hTAlxjdkTYfTxyuyeJ9TBvQO1ilYcig9o8k7Geia4/gQoaEWfcr6IvtVzFIUbGrF/VDS8R0Wp99FPbiND4GN2SUeDm8SpjENisw5ui4jqta433+ARrLTxkxCTDQ/RyIUR7Tc9w65UCT+HMELuumMjBgmb+M0Kuk2Q9ZUyejEELQjEZb7rss8csSLTUgL2uIpjuWSbTciUetk5iav+236hTsr81QVwscUwhIXu4RyTNNdMD38EGX3EbRr7Jd7aAJeeKZWYQjU4hEhQKe6QMdqmkaT1xZrBwi/yRoB6y0ztSCrLtL4wCvt23EEOmxioxUsO1wUgVZeZOnB6pKyGjCadDh272foRhxFx6zISHyMz8/jKOnXHSrhaGqJTioGraKgRjy8yL51nJp1El9j1RzhI9ZDD6kHuHaOEg3jxWVmckUWDN1yRBwMhS45FYEDd55etU7adLnvxpiQMTU58eIqmcuYyIKOlYxsSiNLKrFFkUDA0W02OFEL+m5IskwYCo9RGlt1ubU2ZyfEvFJ9CB/AqikL0ccVBUl5jp7PeBwEeT1ka7HDw9GXiBpJVjgIQ6LiGtoUlP1jRJA0bYr0mmG9x7LZ41g44jRH5NeQArbmmrmeYfw6LY7N0xUEx6fVO/DmmALF1mqdU73CKctGsUdL4NLkXTyQU3bvpswHCuE8aycX8U7RiF3uWE8qHZMG+u0IEDS6Zq28xqC8yBqeW6oitDnwa9/wL+XblqAdFoe8d/u93/T1vdCj0AWdzjcuTTo8PGR7e5v56S2Ck2xvn9mfn56ecq9qeG5hebgds2P+0LB0RgznHl9Kolih0j+w5j8XR9heRZ5fY1I0/MXko4idd7DI3sme/QgijLFHR3z8aMp3DntQWgiB00c3GI/m/NzmJf7e4Zd5XML9juLG3YZLT80RDx3LtQHv295gUr7Gs9MOyfwcj5tnuDYFZa7TUnH+GUH/iTFP/8r7iVWN7f8286xPpxeBVzTtnA19js5kSpul9NyQLXGDIsQkvsvhouY47hPEjFH5JK/tZ1z0MS7JGNQVe6eOWDes+wWz5RVks4vPpiwkfLL/PTxrLC7UrK1NKb1Ct5alb/i+DcnMHbHertGae1Q2YhVWvP8hVKsH3OtvM+Iexq0T1QsKvYnJb3F//QmGTMjqFCl/i9jtsv/e76b5xIcpR4Lzi/O4OOd2EriSa455O1eSXwTgd/X7GZqPc3r9WZb2s1QmZTCd8PQzl2hf1Oy5PqLZoVKOqN/l4HhKR/T40fUBfz1IkjihrruQNLwUeZy4Q7k9QZtXKfKnCdHnWZ98iOmzvwh/ecl5M0EIsFrSNI6fCf8YGZ6nEg23JjM2mwnCGdx+jPjCHq5x3HvxHFd+RHIttqRRQxjsM5/PyYoDShIuuQUfvv40xYsvkD7zdrrDhMMLr/L8YUTQXbo+x40N/RevUR7c4x/sNPwX1/9LHt05Yba4wTMbz1DP7/P3X59xvnqG97iP0i+/h/Lpj5O/8AJu+oDgVjSPArJrsCcVPHUeJrfp9Xqkacrx8TE7O2fuhaoXEVpPKC1y+QZm5Hnw6S9Qu4Lr3/En3nJOPn1tkzthwMf/6c/zAz/zHzEVR6Qv3XtzghYCTO7C6NJX+s++CfKT9CHuw/KANN2jLO4x6H/jsPpvhDduTOgM37q88au49r5tPvcv71IsGrL+H0049r8p7OERZuubrzjQm5u0R3+soP1RQopAHc+QaKQVNEGhiYiCQboeVjagW45tD+P7VBg0S7wIaFHQrSXd1hLcgFY1pCimQjOrr6Ljggx4wJK+7+JXl2mjitfceVrhkSLQyBolPWNpsG6Et5q5XhIHjROO0eoyzgd6foAjIKMCicDgiVoBTuGEwiiLkKBDxErmOOHoqkDXJqSui/aGRhXckxs4PwIFsQcZtQRb4qxGuQijGxQxK+XYmj8JCEQ0wzrDoo3weskGKUesMF5itUUHQ1L2CFGJl5ahHVKJmkblIASt16xpQ+2hDS21KhnaAfV8jUQ6+kWPbSS5k2S24ZAa6IAMCI5IbQcNDF2GJ2Z3IWicAQKBgAySRrQEFfDO49D0XJdKVgxDirGOCE2LYBUUHkckBCWW1ktMvCBGEDyUoiExLUhDy4K1ElbR2Z0Sn+BMS9WCCUOWPiJxBgkIU+MpkQScdKxLTbAGj6JuUjIhCbKlNpabYZuuHmPVlLHroNoUgqB0Gm81WoJ2ksil1LTUISXJL2C8PottwXMjbJ45NusSh+Uld40MsJEjuJLIGjanV4hNQ+tBq4DHo4NAtymbxYhGNUROI0Ug1g22TTjPLlXlSYOhVf4s5y5ktKJkXWjKaIFpU3LXwaoGi2Vtuc0bPgEBu6UFBJU7G+dWOCrZ0F/uEoLCBYVHkAEheLSpwCuEjylliWo7mMZQigrl5VeMzy2BwHZxjtb2kbaDnW/hvWboB9SiZaYkn/VXsUpgbEJaKSqzIpgFA2LikNGoFaro4FXDVj0kokeDBuFRQZMZS6i7DPNdeu0AnCHHEhU7ONWyUjWoGpwkQoNqSaWk11YsdUGsWxYEEqGo2ppYtwTpyKoBAUFncYWARyqH1Es2SZAImmAIHrrSEMlwZqrTDEh9oDFzlIS+S0B5xjpCewl0sCEi6BoLjOeXMfEJSgU6TZfCGlpliVWDxtNzXdZXGSYoUJ6ZdLjWkbkewQd8UKQBejogTEHkIkKISXxE3WgqVbHmBUF7VtaRSY9WGbXP2JxuUmExqkVLTy8Yai/phByhe+TRgt16C0l0liU3u4hBkSkorCVVEo9mNL1CY3K0i7C+A2GKCRkjPKbo0KiaEAQ+QOZTvFng2pTEZ3R0TaUCdnEJREsbYM1t0HiBkBZhciqv6PiEWDdExRY+aug4g1MVSIcmIBCsESOjkrqJ6bmUhhbpHSItME2Cqd78UPXbtgftuDj+lkocVaFoRcuy/cbOakdHR2xtbbGY3sbXKUop9vb2uH//Pm98xWL/tCd/X0EDaEyXji9ogkKe+9pN2G5iEN0pq/wJ5vM5P82/hD/5N9jqJ8hugxebEAL3P/ZbfNeojzs8RokpR5vPMRQNr3UaPtUfsvHKf02dn7CMn2SQLlg7kdSDmPd9fsYrsw2eetmifMYqjrg/nlON/wnrez3uvPgZ2njCx67+KqrpsdJDglTo7Yy1ZUTtCuqmz5+6+THund8i8R2y9pRMVSynMQ+mBY/iPpWZcHm1z2tZxONZjo9iIl1x5VBSFSN6suLKwx8CPB31iLka8Fm1y87RFsddjVEtSSYIvk9X1Ow+GPG40GRlhIxKSmvIZcH6qeBtTEn666zMgivJLl1bc1BGtDrhkVNshAlPqZconEFbSXb5SVKVcDJs2D7dwvRbbkWG621KcvIaOEHXW17rdol1yoE4gXAWLrF5esD3X7qAsQkbuk+cb+ISGGSCfOFp7Jih0ewlHbxaZ5EeoBPFjdTz7BN3aDoHYMccHj9Pt7zLYP52evlVsnKLz3/hL9IdB77sG2RpeZe/SyyfY8mSuw8fcWF5QOhsUvYh2JrHH7sNQbB77k/zVLjBTnvC4/UnWSwWiNUjrOmyU53w5fUt8s+cZZKFELjV/T2emyvqzkWMs5ijwEfLgnub8FeH53jGXkPtpPzdz/5dJtWEfHKLKhpw841nCULh3RHVOwP16weo0UWql1+kvb8keqKPzxt8tgfTM+fDnZ2drzEKEVoiu+aszHH+gPiJlLsv/Q47vcv0r359C/o/DLH2BN/zvh1e/cxv8+jWTezQU999C6v9/BhsBYMnvjmDkK+ivwvzh6TJPlX15ta13wiv/c4hT77nm19zOoOYvetDXv3cv32zEHt4iN78VhS0jT/uQfsjhkAgUFgZCEArQAYJXoHJQVuslCghcMJhTEXEnA5LYl3iJXgCyjR4CYGaIB0qydHUONmyIzukJjCgw0a1C4knNg2xthjj8VGNkx4jPcaUJKYh08dEypFKTyoVIiow8QqrWpSygEfKFh3VeBxWOjyBSlm0qolUi5WOKK7QsqFOaoz2yJDTaoVHoUKg6zytrCn0ChUVWHW2wdbSIXVOnM4IQKyWmHhFogJOWcYqIhNnY6Z1jU1mOFGAailkDlGFilpQLbGpcMLSDUuIJ0hdY3WJiSokAUyF1xVGTBiXRzx3r0s/DEhVS6obEumRumVdLolkhRAOJwSJaTCmRsYFMlqiZE6rLbFu6aDoh4SumiEINFgcYEyFiSu8dFgCWi1IxYJWQ2VqpKnIg+asfgakdgQ8IgiULEjD/GzjjccbR2RKIt3g8CSqwckI5c96VKQ+c6vMdI2QFi8lg+UeWoOVgoBDKIcXgVjOSXXDsNkCXWF1DbJAYhGioVUNQZeoeEVkKiq9ZJXkeOGwsgU8GSviUCBUiTQlAz1HBgUBPJ42nZJKiZOOEK2QqqFVNUQ5jXRgGrxsMcoTVIOQgcgsCMkMJws64qw6R9GyUY1Yr7YxpqEXYvpiRWamWGlxOBLTsrfaZ2d5Hm1KpG4QUUnXTPAIpPTYtKGOWlI5JWaB0CU+KpBRjtGWSFtS6XHKYqUF1WJcjJRnv6PTFZmqMFEBnI15N1RE2iIDrNeKddvFReB1gUIigkTpHC89pZAUeFphCXicDHjZsOXWiLVH6AavG7ypURJEVKF0iRAeawo6UkOArvaEEDCyIhINLS0maonVBIXFe5Dq7FlN4hlSNwThcdLTSocyDbGpEaaiVRYvHU44mrhCE9NTkoFcofTZPnkVW7xsaJKasxkYKHRGjETJFYlZkEZnc6dSAhUkUXR2IBTwWOlwukZGK4Zmgo4KMBWdqEHoKau4phU1QbRIGrrxCb1kRhTNCarBmIpWNSwlJHLBSC7pRif46GwNQngi3aJjjZMWqWqEO9uHG21JjKXjC3ANiViRURP7FBUk2qUEIemZBUHWeGnR0p291tTEpqGjZxCtsNIi4yVBF1hR4pBEwpFKwISzmAvpCcITZInQJdJUBBEQQeLxON0SVHvmasnZQYyTDiEWqCiHuESaGev5MXHISZIlafL/Ypv9b6XEcTFd0Jd9Hq6+/hd2znF8fMzW1hb56h7YPgD7+/vcv3+fh6sKu2g47EpG+g8UtDzEXH7wCkGAvf61guNuHBF1DyiWG2Sv/AKHeg/OP89mL0ZlBdVcI8djrv76r/Bdox7uU/8TMml5ZIaMdUwkSz5x+YP0H//vtCevMMuuk6gF66cW558j+907RPFtmmcbTrduMY0z9h/d4FNXLOv7m6ioz63P/DZr3XXubH+WebTPmg081oGLk7u4SFMUgvfd/j2+eG6ECyXTmaYfz/nyXc2ishyva7yZM84HzITg0eQYYxuWpuTKQ3V2euAdVloEknP5Q2bxiDaH4qZjOSzxdYf++AErp9in5nu/+wmmLahlYNE7Bd/BGY+cW86PZmw7zzIqeML1GIoV/1uxyeOd9zPfFuzZCU9Hv8JR8xz1WoqQkjREHPdKxstNsn7gdaO5otdIF5+nLVI2VMudjsToc+jjO5iwiWota4sZF9a3qJIFkRwzqDfR0TEjOUd1Je54CMD71roEvcMkndLPBKPsS7x7/JBL2c8i5JKiDKiwAwIqdZu9+Y+QxLusPfsL3NYBUVna6Q+SuJR5mHMwX7Jjp4jeNsvphFC+wfR3Dzn/9Jjt7R9jUH6Sc9Uhv9k5I/YsH+PjIb38hFJp7r52m9C2vDp9lUZWXLKew25GYwQ7n9ng3cc90ve+nR/Mc9qDFZevv43GNfyZX/phRrblLz33d6hNS97fQ9qXCR2B/JNX0VvXyD/5SZr7S+In+uhxSmu3fp+gfX0nxxg3WcHiAHNli4Ojm1zJ3km033vrSbm2T9ce8v4f/7P85s//I+ILA9RcvrmJx+TuWYaa0t8iQTsHiwOS5Bxl9eCbe80fQlNZ7n7+mKvvfevcxT+M6+/f4cuffvR/yZjkjwLt0SF6+5tfL83WFvbw8N/65/7/EoQIOCno+RojGgZ2Qi0VuRGoUBJQ+BCIVAHCUxmQXlOT44EQBBEBZEMtDVq2jPIKgkfSEElP167o1zVFZFilNVY3Z8qPr+mVJUK1GJvTr+c4E5AqJ0iHDIKSmq67i0tykB4pFWiLEQ0dn5P5FalxeA8ehZcalCQyAaSg05Z0RY4yC+poyiqWlFJio4qgc2rhKYxDxZaurVgpQ6lhKBak+iwbzGBBzNHSo4xDK4jCCh2XJBSs2TlSOULsztQB09BKiZWSIDxWeDw1JC3jZoFE0UpNbBt6ogRVAAVJHAhxS90xWDTSe7SXZxEuqqZVNU40OBxDptRJyyLKsRIcGhEEMgRaFVhlBWUamCdQKk2pBLn0eDOnUQ6P57SToxKH8YoQDEpKiGoq42iiBi9qpqmnlREeiUOga8n6sqRf1cSiZp6taE1F6gvypKSMlrQm4AgUUYNmQiZLElWAbtFJSSsVSoARjkoYGhZ4aVFRTixLRnWFjmoGVUXiLM7kRCqgjKORkkZ7tBQssxmtOlNQlbE0wZB4R4RHypqorciNxagVVgqEaaiMRTuHCOGMXNAS0IAgJJZIWboiJ1IFUtZUWlPLiEJLvDiz2m87FU51aGRMqSWokia25JGiURCkxkmNx+E5y0yttEJFK+pIMNQT2rimkJJGwiSxTKOYoCWpKfHGUxhFFQWizglOS6SxVMpQyRgnJZWRhNhRRWf9brpt0LoiSjy7qzlCzADHPF5QqIJSBxANtQIrFOP8lEjV9KsV63mBJTAxjlaV4CUyOFJVEIsa6SQWSQiKuA1IbVGAjkpikzNPWvrxnNYIpHHUEWgDUkq0rGmjA1o0lRI42RC3Hrz/CnmTCOFAnx1QC39G5KO0Jq2W1CLQaE8dnwVVz/WMlZkxi8AqQSMtQVbEZkotExSBMopxpkB5gfGWRksa5XDGsYgVtdY00uCSmmVSIH3A6BYR59gIWp0jTPmV3y1iqbo4GQERQkKrA41qSX2FIkLikVga7fHCE4WCjptRKEOLphEepxsEgX6TUwuH7Tlsp8aoklg2KG2x6myuahqMt1Qy4iQuMUGcHRqEQCXBqRKrJdp7CIqgHEujqJRECU/HrzDujLiO8yVSVhTGEDAoPK22GAoEnlZK8njFoCrQoaCR0FKgrMNpwCuCNIRYMVpVIBuCbt70P+XbkqD54DkqvzWTkJOTEzaiDQ5WXz9A9uTkhDiO6Xa7lMUBSpz1qOzv73PvjTcQ04a0azgW/msUtEkNT73xBYzzNPtf60K3Gxt6nTv4ecITN/47/tX4p0AINvvJWW7Ew4rlu9/Dc6/dYLOZ4G59Hrm7z2HdshEnCAQbu+/hUL6DZ9de5M5qiCwc/dzRjK7w492/htma0Vk8ZhF3/k/23jRWmuw+7/udU3v1vtzuu7/rzDsbh+SIm0RKoUxGkGIrihUZSowIghQghhAnARwENgwEQhIjyacgSBDEgGEhsgUrgRIkQSwvoimORYkSaVKc4Qw5nOVd79a37+299jpLPtzhcGhzmZGsWFD0ABd9u+rUqW6guquf8zz/5480kv3ZG3y1sSRse+zceh/zl7/MY70bLII5PhP20oTQdbgmzgGBdUp605yzXpOakqN0h36Us2bIbttnvZ8zylLO0LynGbFJLonDBee2w+OnmlLWtK0mdJc0ZMXepmDm9/iF0ifIK6b9BViIvUtOhKQpMr76uVP+7Gt/iVJZvubdw+iQjV7iKM2uTOkevUoSVrDKKKXD8+JJjrd/kMu+z61iSsu5x7k6YNly0UbjlXDRLmhXY0S1ZOI47LglFXPqrMMBG45iSeQ8iZsW7JshAnB1jTEeJkoxechWtQXlbzOwE+gEmLOr2qZPDtsYZ8jMr9iLcv4y/z2/73ySweEPYp2cIkvIzcdAgKkdgsMbPPnkf4vsfpnv818Aazl58CHq4mqlZ+IFbNk1Tm+fzeUUGaxxFoZr7xnSbD7OTZPgWcX/aXpcrtdgDTSG2PSCp5sxb9x5ivzFF3n+6Hl+aP+H2G7kTHpHFNJhcOt97L004fv//M/D6oT6NCHc6/C3fuRv8V89+wtYv8lkkdHqt3g1ihmdHuCogPypHNEckf7O71DcX+Ffa+FtN6izAayPQdffnqC1ffT5BMI2E3WBxGG8dQun+Q4sfZ19WD7iuR/7cao8Z11egraoWfGdj5nfg/5NAMrq4t0paOtjouiAIn/3BO3ely8Y7rfobEXfe/DbcOPZIcmi5PIo+d6D/whxZXF8NwraCKsUej7/I3xV//+CNIZBleBqgxUaXK6UMWeJqE/wy/vE2RmByTjvnGLUFOpzLiOH2lvh6DWN8gGFt6B054TmAkdqHH3VSwhh8GyNsXMsc0o/x6FC+IbATTHxBYMqx7hnVO5DPFvRqgsKv2agl/SrCZ6BTn5J026QniJ3faS5xJgTTv2UTXRB5a9pqCnaTRHREiMM0lFYBIYljeKM0NRIa+jkU3K/YOaeIqjI/QWjLEOiccyKUbKkUedIPUOWc+b+jHkAlVMSKQV6RRYUXDg5m6BACokja6wQaD9l1TzDd96gU6+phCHxXFbtCak3R3mWfn1O6la4MsMvZ/j5gji/YBVdkkQ5pbOhZElDTQiVQopLfLXAtwohllfqYL3EVymjYk0jO6VZPMKXKZetFcZfUwQbEi9j4RQsfc06ymiqU5rVjE52jicrXLsmcTYkvmbtlTgWWmXJoEoZVCHWkeBUdPNzrGPY+OdoUmonwwBaXGDROJ5m1Z7TKxIaWuPYS2b+mpqS0tfIeglqicMGlzWJzNm4ltqT+OKSMihYhCW12BCXhtrJMf6c0svQooYiZdG6z6IxpwgzGjLDlfe5vjgmd0+ohEshXUpP4BlDpDOkOAFnRpxdUvo5nn/C2i5YxDNapiBzlyh/gydmtMsLmukZvtJsHE0tSoxeElUTVLhmEeR4VcnK9dAeWJOxcS8JZYX1U+pAkUcrNl7KPJ6QyEdk8YYWGR2zAafAyoTSMXhyQUOdUkUrimABToEvfYQ5x6pzFl5O4q4ZqxW+W5Hj0FE5zbpGeRkruWAh1iSOYiU3zOQGVypCdYxQGkcKVGSp/HNmwZTOssS3OQs/ob16DVkfEeTn2CAirgtUmKDlFOsrXFGjw5KmeUikT1jFa8pY0ahSovIMT6XEosS3Ff1yjqKiFi61WrAOFKVTA4rILDBugasM2oHLtqYMNzSKI9q5JdAJFo1rKqJqivYE2i1p2Iqwvo8S0EonYM9pFMdoLyV3S1pmxlApAuHjy5KFVyBchWNrPJY4LHDMgtTPUH5GQElb5VwGF3hoHFGznS2J5ZJWeUa7OGEZzGhtppBPqZyM42jDgoxM+Cy9NRdegrJnjC9eprk6IlApuVOQOguMlyIdj1jneEBgKpoqwxNTtOsAimF2n3Z+j9Ke0dAJwilolxfkImXp5iThCuXmJNGKZeeUXnVMkD9i0sqpnRVdCuaBZCUK5sEKn5oylBhnjnHnTKKCuatJnDnr6Azk61T6lLbOaes5JgZrl7SrklK4JA40dc4yKtgEipWfYrwFtbvBVsck8YSQC0JVUKE5bSW4NgVZs5Ypc5mQufq731P+P7hvvWssigXWWvrhOy/0Pz8/Z6+59x0VtG/YG4UQVPU5nndF/vb29lgsFjyWVHTHMfNa03tbDdrpsuBwfkagNPU4+5Y5tz3LqPsyz2afZeNvcTb4CADjdkgczpk/yvjsRz9OWOQU//t/je5/gM2oh7KWThBTGcn19i2+Mn8vTzS/yqN5iTiKKBpt9v9Mk89Mr/MT6v8hTs+Yxj26Wc3ZdoojHExY4YZdtBDsFw6pu6a5eUiQT/nJRwZb5rgGlMwwC40JHdaRz6TsMWy60N5mHFnWW0uuXS65EIYfiGP6dk5ruGEdxGwtarRa44oKjyY4ExrVVyikhwxhUM5o7e+y8VekyW2O3AFNkdL4WErgGj6XKT56/OepjcNic8661eLubEDciFi1FOFmSR1crSK/0oh4JEKeyF9joz5JqlL8oMGn7/4GqUqpXUug+6zuvowETH3MKvZQeYvD/JzzSNJ3x0xVmz13hPJcimaTxcUFoVujVm1843HcnDK0C1TcQj26ipB/uhlh3C6XEva9L/Ml8yFebfw5wp0Bsm5wSAxk0K1w1YjgySdIRIdflv8+P8PfpBdXHM8gyzKacYNJZ0hLr/GG19nMLtFbIQ0EezdaaGsZVx1OgxHdIOT3OyOqxi6yOUJkC55tx9z74EdIPvc7fOrhp/j4wccZhHOyTo6+8EGVuIMB/rMfxiYL6mmOt9tgGA35odYNRPeQ8+mU2we3+axZ0dn41FHBevw6xgpMlqPOHuLvv0nQZj54MayO37I4GvPN6HinE6CnM+jf5MVP/zr7jTsEw3dWo0XnEFbHOK7HD//cX+Jrv/0ZpvkDigfL73zM2wjau1LQOvt/KAXt1c9PuPORd6eeATie5PEPjnnld8++9+A/IlhrryyO74KgCdfF296mPvmD2UH/FN8OFkmOEpeUNkGagmZZsr8BHzgRfSqrUXbB4bKkr5f4nuTackEWGmJ5iXaglSj2kgtqLSm9Ba18wbCShNUZZHJHPgAAIABJREFUR45H5fh00oxutqBXTWipNRUrsDm5nRJpgXUDouoEYzfE6QxjE0LpYFyBEQptFqy8BKVnGCHwRIhXxTh5SKdIkaKkn1/i5BvWYoW/UdzTTRYVSCGQakEtMqQoaFUJowxyNNfWl2h3ihCKfr2m8hNyWaMA32Zs5zXbmaJTnYOZ0CxmjLKccXYBdU2lLyjLYyoWaJuwtynpZBZp53SLSzo2Jahcwk2OMgk1BVvFDCvW4FdILJUnUUpQqJLcCwiM4b7bpnYNyBKpc5RdYJVHVVfMwg6OWoOssV6J9RXGbthZz+gmE67NphwUl9xINzTLFY1EIYTGMxpPprTqBbtZxSDZIPSGps4o65SZE3Pf9Mgqn1aV0sjnSG9Dp35IpCqsWJD6l1yEkrVtsZdeEBQTmlmJsClRdUZcJ/RNybiY0aiyNy1WNVG5plUtuJ4bOnlFIz2hVc25ynC2xEVGbc5RVtFI1Zvq44JQKLTxqHWNrJbEmyXBZoXnRgyTGe18grLgCMhZYYVF6yYrx8cJ17SznGZas1WWDPKcQqxwlANKgSixbkoS+tTZmka6JihzQtXASk1mC9oqQ6oNbrVBqCVlWbCzOUWamsjkTN0LKq3QNuWxZcm4gtF6A1pTySUeGaNiRTdbE25WKM+hU6R0dIWbh8SVoFtXNJVmThtb52izgmxGd2NppH1UMWW4WbBXlOwmUyJy9rM1B0WKX1fUrsYzM4J8SklCLw/ZykNiu0dnvWRLrSkbIRGCwAkQFpRdYMgwHqQmp5dlBKzQekEn6SCWA/KqxvU2eE5AaK8shqFZkdZv4FYLrJ3TTStEcUmSgclz1tJlpRMwEq9aE8ocNFjXo3LX1F6B8ZbUZoJwFIm7xiqLp5Z4rke/PKOSmrW/ReE16CRr4iJDyZqgythdr+iWM26v57SKSyIzpV1MiKoZlopBeoExKdYs0Cw43KS46Rz0HK0fEBUXKNdgJOwvAwrfIZQWtGJrU9PIfFxzwX4ZsVMaeklGEXWoGj6JUfhFScspmFOzthG1MFibYlSCqNc42lDagmG5oAzA8Xy2soK5hCPrIRzefL8Va93GSecop2aQ5IQ2wZGKYZrQLnM6ZUygInxTclAsiNSURj6lKkKCShHYDZ3qkrByaZuaZn1VS5dxjhYJxlbETk1Mjm9PkSoBvcDPDGGu2SoKrLpS1KUDO0lGo9bU/obO+gX26gV+cY/UOaF2CoKySZB9dwfLH0uCNs2mDKMhjvzOKXFvh7WW6XTKjf6N70jQzs/PGY2uSJmyM8LwqoYmCAKi/oAbyyXdUcxSqW9R0NwHdzHC4DkOwv/WlX+3ekQdZzwX/d/8Tucv0mteKTKDqCBwCjxnxKe9AKRk+X/9fczgI1y0HLZ8F+022BhwThX3s2cIWfEsR8i7fdKxg62WXDaf5ejJT+BUcxZRi7iwfP5wxnZjm8JPWF6m+NduYz7/ZXJ3gZeWNIsF40nKeubiqhqvPsMa8KzHyWDExHTYaUUUXoc2OVn4kMdnJalRvO9Tpyhd0Z1LgtEtFv0GvdURhdQI49Byzykqn54q+D/kDAfB7d1nuAgvePHR97PbO6LhlLz46tfg+ovUTknLaeEiyIqaeTPi1XyI5xs2cc1BkpG7Hh/MNF9uaSayy1PZPyPXP05eLbjTeoL/7Qu/zHJb0t34CNmj2ZE0jWFiS2pHIlcu/c09ktjBd7tklccm3CWoS2wzZn50xMC4eKstFt6Shzt9BmJB7mxRzRKy9YrdwEc7Acpz2QnO+TvOz3Fitq76X5ght/UuGS8i4hV+eog87PO3jy+o+Nd4KK6z20yYZCVpmtJTMeth++qGs3OL9eUFl80bLITiV774iGd++2U+uxlwEo6Q1Hzu9nv4G4c/j2xtIcsFz7YiXju8wZdf/jTTbMoPbz1H0S6hkOhTl/z1E5qf/AQi7FC7T+LE8ptq1uoYOvtMJhOeuv4UJ40eQjygkzyF9jLsHY0z3MeWbyBcibfToDrP3ora7/V6WGtZLr9JoJyOj14kZNE1Hr70IgftJ7jK538H6B7A+gSM4fqz72fnsTu8nrxA8tp3Caf45wja9+yB9g28rRdaUZxi7TvvT5YsSs7urrj9DuL1vx2e+IEdXvvCBF3/q+mJppdLrFLvKmYfwNvdpT799o6DP8UfAEZTsaFCslY+yzpnXTqkTherXRw0GQ0yEUHdwuSC2rpI6VMtfGaiT2busBF9tPVYiQ5T26aWbfzMx1QbcmVYGTg1bcoqpMxTTvOaad0itz51KdBVRVIbllUTYyMeMOahipiIJsvKY1UPIU8ZXdYUa4+LMmRRO5SVQRUSWUpmssNMdEkrF50IjqyDUGtyQo51h4uyC7XPRrRQ2QBZ7lCULmkZMK3G3NOWVLok0iOv98nLQ85Fl7mK0cahzAOmymdqG6xLwyaPKcsAWWZESYyX1LSyisK0uG+2ODMtHsqYi0zgZi3OGaDrq9V5axWe6uOJLnPZpcBnsFxTW7jUAXllkLViUaZU5QFpFTCpGky1z4US5LVlqprUOiEoa4zaQF5QFiEL7XNsx2S6hVd1cDW41iCMw8b2OVU9Flpyqtuc6i7VxiLTFINFmAJpr1wXynRZ0mFlAmZ2i6gKmPv7nNldpKnxa4OtC2SREG5K0qyJW7kY61KVLnmtKZMKVYNWHgkxJ3pIYnpkVY8j3WZtIlqbinhaQ5nhFAnnOqKqD7FGk9oBG9FmfFFw+3zG9amPW29x37+OthbXDVl6DWyhcJcBaW2p0g2bMqIiwGio8KiRCK0IVhV5fgdsg41yWCuY1x7TOuacBlPbYqbaLNUWMztGLnxsUjG3EWvlsSla1FWPlXkcsWmznnlcv0jYuqzoXjQwJkCXAjcZImuNsg5elrEpa450l4XtsslCgqQmXwU0lcQUMaooMXmKuy6o04i5gnMdU5gmlgWnYsSRbpOIGN91GE/Puagj7AZIE6zSWCPI64DaHrCgy1oMKUSDjXTo5RlShWSlwi1jNsQ8MENKQuZyQKAMl7WHuykwGjInJFAhIldYOqT1DpemSYaiNoZJeJMozWgmG6Z6jJP52MqwIaTagM4lGS1SBkTnAeF6QGpb2KrEGkuhGhjRZYODmVv8uokjWkgbYSwsnT5ZoVlXgrN6i5XtkBvNpfFZE0EVMFcSvwhR1oHa4CiNNAGrqs1m3cICa9HizHRA7SPW10jUkFPbJbMxSeaRWIkVO6S1T7Res1mPsalPmNfYPCTbDLBGsFQOOlekqkmqwZ/n+IklUJpKulyILl62R5C3qEtDkF+gNjkTvcW6FJQ6YFNZtDIcmybZpo3MI7zKYaNCWtMe7TKmFi650+W4aqNUA1tFNOsCrT0q45DlhmPVJqssdWUZL1bowhKaiMF6iMwTSn2Ttb6JqTLc2iHOW9Rmia4EeaXBgMJhrQybwsWsOqQyZumPkTpAIMmLGuPExKs1pQlQxkWiWSnLWfXdG1X/sUxxfLf1Z6vVCq01d4Z3+M2j3/z2c06n3Llz5+qJu6TZuvXNnVtj2vdmxI8/dZVK+GYN2rqoeebRS5TtFjIIkV6N1gXOm92/0/RVmvOYWpa8mH+A3fjqh3LsnDEregSjA9TFOeFBl80xuFXMWeSw7/kcl4bSWi7/6Rfxoo8z2fokf/buBvccyh1YPXrA7Q9+BO8TP0Hy8i+SuTHCSr66M+fp6BkSd0m69Nl97r2sPvsbeIVh4TWJRUnuH+N6H+WiustucUTW3KZRN3gwPCQYCn6wkjxQPlH5CjVL3r/a59es4LH3QOd3U6bHuzz5kadYvv4CveV9ZjsH+FYzcI6Ya8Fh2yG/PCVsbBPdbzANL1gUu/x7t3+XOrnGTE/oFS12986YntwkkJaVE3DUdsEIJswJtMf1OuG82uUJ3eYzfWjrNY2sS262UWrNteZjJOf/GD3y2Uq2AE33zg6+tRy5gthIupOc7d17qIaLlk2sgSM54tbmEUEzZPb6A261D1g8lCwb53xdrGh6GUb59A4OOX7lZR7/8EeJVEFSDXh/FiNqweuFS2UMgT9mUGxzzK9zkDeJ0uvMygm/dCz4K+ED/sfi53nO/z3ydkiySallj2cvvoZo7dAa7XKvUvytG/t8Mqx57jTnf/23H6f/6hkvBF3+4+GE/+xoxD9pP0uhJ/z1asP7WzH/pRfxSwf3+Qs3fo7ywd/nbBwSf8GnTA3Z/Sl7/9HHQQgq7314nbdJ5KtjdHOXk3snHB4esr/6UcJ7/x3j9JPUtaZ4zxnh57fR518FwNtpoCYp9slDxOIh8qZkPB4zmUze6ifotAOqdc3X8pj20Kfjb6GX5Tv7YDbeJDzJObR3+OhP/wx/76//FTb3zvmOESOL+/Ceq1YWVTl9ZymOcFWDtjp6a3xZTQmDd6aIvfbPJhw+1Sdseu/sXP8ctg5bNDoBD16+5Nb7/2Ak7w8DdX6OOxgg3Hf3de7t7f2pgvYvERUuk2rEwPRpmktW9Gi4bVQpmesRrrDUtNC6pLSC1LaQssAVBlFKHEfgaoXnOixkl7S2SCPJbJccQcEcV1vaZptKzqnriJW9yc5mgzYbZmEX4/Su+ipJTc8OyEsXR54Clrx2QMSEJUzETZzK0LA1kdhh5oKDYlTvkIk2rp6DlLh5i8Kv8ZQhsJaALlooClURuRCVWxhjqQVsKctgU2HbHWpmVz+ebUUgPXwLq9pB4rLWDVq6T+1MMNpH0cQKF6VLHnmHeHUAZUkiBB3RplmvUAQMhIcrmkjroMQRSzUglT4YF6G2kEJT+pcI2eRSNBDWQVhFWPtYkVCpAF80ya1E2BLshgiLZxSR9bDcYCI02kJorxKXnVriCI9SWXK9QyTXSOEAXTb1BuXUpMa8aQ1T5CKiMj6UhpFqEVuwwiLVVYDMSkSMhAf1TQp1QV/38aVDbeacm+sICQYwVlDj41mH3Cwp6IMnkMpBVh5WKCSgxVWmpLQuifDx7RDHSE6dCcbTSMCrAhZRF99a/GKEsWOmYknsNvCJaJZLZrpP23OJMAhbk8sOGzlCqj5ePcP6HmvbwsNFKx+nTFB4dKlxK0mmHXLRxqNAvLm+HdQO1jTwrIepQwQepcgZ2D65SDGmwpc1DXuVOu3bmksOkRiUkSRljLTQlw20Y6nEkjz3wRh21BjHalLOWNmYUDtoIpCGqd0HIYhrcJ2KCp+4hMrGVESMSkHmrNG2Iq2vk8karTUnXpst2ya3K6IyphAbgjJABwJHOSjtUtgeG19Tyx7rusQTPShg2zbwwg5WrLDUOFawoMeGbca6j280iQwpTBuvtlihWBtLux4ykAEn+FgqhA2o8HBtjalqXOGijcRKiadCrNE4wsPN2xj7Xs65wFU1DjFaOQS2xJgGSQCybrNyc1Dw7MkZk/YuIt4mrR6yckMkljpz0AxwsGxsk1ScUcseBgi1RNXgCckyCnGMwa8MiCtdpzQDnCKlsi7GdpC5whEutdOjrF2CssJVhpnoMdKWBilTbxcjNYI+HRtQFjkLuUPH9vGLBUGacdZo0jWWxBuwtAalHRzXYVh1SRxJoWpaBFcpoqbBWmVI3aaLj7Rdauugtcc5ETJQuMayVA06JkaLktyzVGqAtSBqjcADcRMlBIlYs6ddtBWcejfomAYNI5izR5MOrg6Zlh0EDoFxuKh7gGDk9JgxxUqoMhcJlOqQ3M249FKkljjkWOtiTAD26vs20t/djfTHUkE7S88YN945QfuGOnbQPviuCtp4PMZajRsldPtPvbVv3RsiiznOKKTlSDx5pRCcLHI+Nn0F5bmoMEbXkrL8ZvLZJnmFJ08v+fXwJ6kXNb3GFUGryyMW5YiL9hbvT6Z0hhN0AfXD+5z6cBj6vLKZs+VakuOvUFcuX+9/ApJbOIsUs2OYvvY1bn7fh8i+dMGlaCBtgHQVW+0RTa/JwptSbgxOZ5vfuvlv0Z67PBjscdIesVO8DG6DhOs4qxPOBjewwOPFmgf9HbZnCSeJJhFfZ0fe4lC2WEuXr4t/SKAqvvr+76c7vsnSDdi/WCKMSyQLGnJG5UCrFzCuLkh3DgguXFa6TSUEMjgg8FJM85KoiNnfSnnmuRGBAON1eTi0jDcFp15JTzXp2YTp5km2ll/jse0ANy1YZx9gYyzKVkR1gx+I38dRvGBcH4JYUWQv4Fs4cg0dP6J9JhjGryGAE99Bey2K0uHPLX6bli+YHx1z63APYQUzo/haPmHT3aHlrRkc3uDoq19htbjLSJwyy3u0i2M62YaW6/DCOqNhnwJhOZMlrL9CkB7y9yYrnmpGhDyPqts8vvskZf8+eZ5wQZcnly9D95DnZcTf/PhfYDBRfOLX/y7PXtS8txUjNyds/B7Hy5d4MjnnY9kJSdBH2Jy90KMwNZPtA/6d5R2Wr/8Ks55P/Po1Nnkfk9dE73vf1XUmHsdrvi21dHXEwjTo9/u0220+duvHOPN8mlmb5tlzLFpfwN35MOXdlzBFgdMNQApMuAfLbwaFnJ19067ndHxUKnjptSWh18CxDjqpUcvvUkf2DUj5FnECGN+4RXs45o2jL2LVd1Cb3qag5cUJYbj7vc8DV8fM7yMQhOHuu6pDe+3z59z58Lu3N34DVz3Rdvj67/6rSXNU5+e42+/+9f9JJGhCiAMhxGeEEK8IIb4qhPhP3tzeF0J8Sgjx+puPvTe3CyHE/yCEeEMI8RUhxHNvm+tn3xz/uhDiZ7/XuV2taa80jUzgOlvssEujqnGtYmtVs1cOiY2LYy0t7bJT+ETLHLmCgerTriOcyqG97qFqh1Zl2C238G1AZDTdbI9+khArwW49pl8NaNoOe5liO0nZz0p8JdlZFVxPWnQrD0+77NYdRuaAoHQZFENGasBIDJFRhPCbBMBIDelxg1aREJqrKOp2MqRlh3SsT4uCEEPHNuhWXepGj5noIYxLXJW4lASqpJlleJUiti12ixatwiGoFL6BASNi5RHmNXUQg+3RkyNiA50sZ5Bqnp1saGpNrD1i2aCVCRpJxm7Zp1eEaGmRUtGsFHcu5wSFoLnICEpwrEs/sXTSPv26SawkW1mHLkNCIp48P8MTiqIZEVU1YWWJ66sY+JgundKloRXD0qFhW/hel6Z7QN9s01tLwlrj5H1k6iA3gkgJurnk8QvD7UuDqzMI2/RLn/2NZJCkBFYQGUGzCumsJZFtYk2ElS6d0qFV+gS1BiPQkYsOffq2x1C3OFjUBFlAZBStfMOo6tPUPo5wCHSM68Q0qhXWF/SNi/VdrCfJg5BV2KGfrOlvUnyrUHWNP18TFhU4ltRpIP0OnoB+ntNb58RlA79ssMHHrw29oosjHGJnTG/dg8QSaR/PgGtCYiUJdEWcBWybIf2qpFXU9JMS48f0TIe+bRDgsJMkjG1A5HeJ8PGJGdohocqJ8xrX1Lhei35WE2iPjtimoVtsFwGuAK9y8HNoGMn7pg+JrMEBwsLi25AYS2hqah2yldd84OScoa6onQadyqdrQoQjcK1BYogyh245oFXFSBHSNilbeU5UpcSqJCxyhNvBSkt308SrWzQLy0A7eLWiUypGWYlTG4LaBSdACOjWLtr6ODpib5EQG8PNi1fZWxzz9PmcIHF54uRVdheG/sqnUYdEJmBsegyqBjtZwEiPaSsPV/r0TIc9vUNQSoTycMMdtBMSVYLAGDqmTeSMaakxkRzhOH0c42DzBkHms64jUIZGVdEzTTpZwhNn9/FliCsb+AY6yicWPXAj2GzTqpvcSBq4VUiXEU1jiTIXK/aI3A6RAkdb/Lqglxe0tUMoNJ5VNLIVQbWmkSf4ysOPrjFKSzrFhrDa0NTgW0NUCLqZoZ96dCtwdcrWRcp4tuF6uiZUFS7Qtj1Ct83hxvLU6Rv0Mks729CpAzq6TTNVRFKQuzG+dRHSo11ViNIlNi1MBltiTOgfUPs+Qmpi5xp93aWbaZ44f8Sdi4cElSasNDtZgyiHMNP05JBQWyJl2MtbBMrQSRI+8jDhBx8ucKSPDCKE6+FXJZFyKd0GQzNiu2rT0Ba/lpi6oLcu8MqMsPLYKbfwjUO3btCR330B+o8lQTtNTtlv7r/j8d8gX7vNXU6Sk38hmawoClarFVtbW2TZEcYIusPH3tp/1OihWFF33W+tP7t3zLX5MVVdkfgBKnNI10dv7a/Ov8R4M+Mfjz6Bu1b031TQ8vwhmd7mda/DY6cv03zfTRCC8o0XOXEsB6HPo80xQySdp2+DgBcWdwjFmvi8wOyXiLqmO7rOG//keRQu/bXABgV7zT086XEujtEG/pvfyrhx45C9S8VRd5vFcJvR9D53not4T95nvJ4yHe6Qeiv+zdnnmLttmnfvcbLMoTfjvZvrBM0RPVNwfPQ1vKrkt24d8MYLklUINycGv47xnQwrZ5Sew0rXjOtLptGYcseB5QcJ3SW/+/A9NNtzjF5zrdjDBKc89lQfV4LvXmfRMuxeLlm3KxplREek6HyPweRzRH3BZh7yKPkwS1OiTI2f+hyqIZdhxsC5hjFzjtcvE+HywHfYkgnR3CeM53RTzdcjMGGP8fKMT6x/j16Rs7qYIA9KQq9iIa96przYjeg6a3wSjl95gZe/+peJ5w4ISMUj+nXK2Pf43DIh2twmbRyRent4q5dwqg6/fubxs1s1J+Wc5trlycMfYFZ16Q9/nwetEaPqVR4GY37xeMFP/cav8m88XNOefQW9LFHzAjefUge73EsTPr55hecbN/gvnnqClk746d9/BYo3+KHgx8m/9E8p1q8iajDzAUXRJN6u3lJK6nofP3wbKVgecZJKbt26UoffP3o/r/kBi80XaZ19CJMbnP51ZByTffFLV43YdxpotmHxAPgXo/bdbsh0nZKXmmAT4IxDghttyte/Sx3Z29E9eIugAdz+8Pdzd/0CxdG3OT6bQ76E3jW0Lqjr2TsnaN1D0DVszoiig3dch3Z5nJAsCq695zs3uH8nePxDY45emZOu3qG6+C8R9fk57rvogfYNeLu71Cd/4iyOCvhPrbVPAh8B/kMhxFPAXwM+ba19DPj0m88Bfgx47M2//wD4n+GK0AG/CHwY+BDwi98gdd8JPpK+22eQnXFbLTCupGgMqd0Oovc41m3SrRrszAU75zMcGdFQXQ6lD14XY9qEyqf2mnSrFnv5mEgLHKHYXzzi1uyYZzcFkdJov0k/W3EtP0cY0E5IV0fcLDxuJiUNLTHCwQYuJhxRBhEjscdWXrO7PiGuFcM85KnpGvAIlEM3SxhkM24sprQdH+E28Aw07C4t08SjQ6BKpHTZywPGtY/xXMq4jWtjjBshXZ+tsuBg42HdJl5wQKO24EQczqfcTCw7NkZan67XoyEkgTtE96/z1LykV8ATy5xn05I94ZJHW7jtpynjHmXcISAGp0nb7zFWHtuZ4vF0RcNUNMuUw2WKg8T1txipAU3TwjUeW+WAlh2yt5xyWNWE8XX6aggyANHEipCiOeZ9aU1fxTwxW/Le2SUIF99zebxMMZGDCVxwOsTWoa/ajNSQQWm5s1xx58KjW4ZslR7X1jn9bI4UHkaGDMslz8wuOFytME6DuNT06z6BgKiWdBhxhwWPmZimqXliepdWscaRPo7o0Xf38LwO1xKH0IDju4yJseEY1zTYXWb0Crh+fko/X7CvU35wkvDMrED7LW7rLZ5ZlPjGp6ldnql9BrZGeT57WU3sjDiYTrh9MeX7Jiu2qRgqhS+uruuDxYRbm5TtqUXoNjvOkBY77M7PiHWNdWKemNXcSGNGYpvdKkC4LbT0cbVmlCbcvnhILHqoILhyOgRNhnKfXp6D0yBydgi8PlumQ2xdGn4f0xggpMft2YQn1i5PX6bUfgsXsH7M9QzuzBeEboTyWnhewGGaE3oxB4s14xL2NytatknLlewm5/hG4gUtbs7uMbZLOm6F44xouiOE2+LpsxlbWcZY+UivRR2NuHXxiG6x5vrlGaPpimdOjtkqC9paE9qKx6Zf57HJBFHFOIXgqdkxN+sNI9ukgSZSOcbxiIzCEZL9rOax1YKmrWjmGa7wcb1tiBrsbqY8O9kwKFw8Z4BvJdpp0KxKesoB6+CIgJ4p6OiIdh0S6ZInLl6lITo4ODw1eYVb89f42MkRo2RDGXeQwmcrmRCbJg49NBGt3HIwP+Xm9B5xXTDW8MHzYx6b3qdfNxn7BuM36VWCdlFzaCJU0KJba96fVTi2gYOkmRmGScZusuLm2jJWXZrta/SsguZj9NI1jyeX3KlWdKtdbs5OwHEZ1SVbtsHhm9eR8mJ2Mhivz6m8FtLrE4s+ruPhWMM4Kyi7N3BEi7Co2cqWPD5PGZc1sdEYp8l4PWW8PuNaablRbXHz/IQbyQwfh+0k5+mT15BOg1a7SSxc9grDnckbxNpCo0sle5TeNh0T0qMiqB32Lh/R32Q0HU3kuFRBi4bY41C7XMtCbizP2EsTDvQuYW1wrUcZdfBtk508ouNKHqPBoejjSslO3SdythDfw8T4x9LieJwcv6sm1ScnJ9y+fZtxPKbSFYty8S0BI9PplF6vRxAEXEy+SrXyidudt/afZ4Jb1mWSzRi8jaAVn/0tpns3KWVG3miiyoj18i5b2z8AQPPul7m/9xFOBx2+L1/Sa1xZpLL8IUrs8YqN+NDFBP8X/hryV/8q9cMvccRf5EeigE8nE57MHJzhFq3LkPXdnJH3PG5hkNdTbun38dlfe4Ot0YSkjrg+zynDgo9Vhi+7kn/42kv8iPwAf+MTj/Gjn7jN//RrHi+0txhKH/3yV3nvn3mS1774NdyqwbrTRDn3+bHZ8/zn1U9xNLlk6yBg7l/wM6ef4KUOjNNLzuf7XD+Z8NjMMl8YvIM+6cNHtNcJpiOYahcrBPfmlu/Pp3zZ6dMvzyg3z/De9st8ffFhfmIM+47PNX1ILj+FTSp8t8CrDqhdQeU6zLswqrq0nYILG3ISt5k5BTdThu8mAAAgAElEQVTkCc9XO/ywXuN5Pm7lcHT+AOe2g9R9XHXMI99jbAu+4LkcfvBHML/6DwDo5AWvRYLddp/eYsZt9ZD1cciX6iFqsGDoOrxuLc8qeCHMuG275It7xDdfxlTX8RZDRDBh6iR0XEspBZ9bJvzU2ZCs+5sU0yFxNWM6Ltlbulxf/RIvdT5B50gw9Hwu830OD18itf8u8TLlH9QtfuXZm3z+f1kx+j4Irx/idjTl3SVhNUM2D1h6A35a/F3+tvODTOIhAyz+6iuY4DaPrt3m4u4vEQQerdcDtMrwlaa5nUK+wIY96ryHJ3//mx+E1TF39eM886ErBcqVLmr4BOnpl+janyKe30H1L7CqJv2d36b5sY/i7TSo8y38zT8CrhS0z3zmM29N6XR97m2+xo0n30P4qE10a4DbCSheX9D44DtQbTqHsPwmQbv1gQ/z9X/0m7zymed57sZPfuvYxX3oHIAbUGb3cd02rvsO4vwBpHOlol2+ThjuvWMF7bXPT7j13AjXe2f1rt8JjU7AwRM9XvvCOe//1w//UHO9W6jzKd676IH2DXh7e9Snf7IUNGvtGXD25v8bIcQrwB7wE8DH3xz2y8DzwF99c/vfsVerer8nhOgKIXbeHPspa+0cQAjxKeBHgV/9Tud2sVzfPCR1AzqLGW2x4LLZh1pgfQ+3SClbA3aSBT6azCT03RDHlLzHeYiZJtwdPEZY5zy7PMFqTeJ3aK9WKOFA6CGJ2E1nHKYLUAUuisI6SK+F8CqGZokOXW6vjzHSpfIsR3uPs3P6Olr4ROkG43vktmB0uaBDQXt5F6cuWRGClHRExdr1SaqKpy9fIwscaiNJnQaDZMa0M2JUL6mFxAqHwFRIfdXrSwuH1vocYyV1GPDa3tN0FzM6xYKWyaiMJMo1gYxIpcut5WsIJC+On8U4Lh1TUgiFUIr2pqAtT8gJ0F6O8WF/chclffKogYoCnsgLZuEWh/N7SN9BuQ7d1UOsNByFh2zPHrJudmnrFBH5bKkUUXTJ3RXj1Tkm9JiMt5krjycmL+N6kr30mMDUaCfguckLpE6A4xient7nhZ2n6ZUTbi9PKKRHjkMsFUWvxXal2Do/oXR9Ct/BtZabs7sYoGFrZCC5ma/Znp8QFCuUlVdXTaXwTUU+GDDr+9xePcBxJYGr6ak51xYLAjTWWeHWOY2Zw8sHzxAaQWnh2vBVdpbn7FwW5Nbh6fmaPCsQjk9fVzx78hLCgSry6ImUzmLOur1HvLpkUBR4wnK9nBFITYLEWNhZnnDR3yUSNX61pCszhkaR/b/cvVmsbNl53/dbw55rrlNnnu49d+65yW5OskhrsCwpESLFCpTAkhMEMBL4JUEeAuTRBpK8WAESQIEc2IiDJIaciBrNSJAoiiJFNcnm1OPtO5xz75mHmqt21R7XysO5bJKy2GzZRETl/3JQ3/5q1646e+29/vv71v9f9FnojZBCgIDSs1hV0pid4QQBfl7QdaAmQmpkzCOX9b1dRFniaUGdnBEONy7eIshmTLUPWBbEmDR1aY/PqMqUFM1e6xr1WY+VZIoyAuYzSgRe4RH2D1nQI2LjI5TiqW4XpzigJhLGuBjp4+DxzLBPqXyqkxOC2EAh0NMDZp11qqKkMuviUyWYnwKW0kqUo6jYgtXuXR7UrlM8MXOvF1OsV6OZZVgP1ouCtL9H5YkvnT9OaI173F28iS9dVk2Ck46QSoOu4Imc5fEuuasolUA4ivXRAW6eMp/5TKyLYy8tAApHsz1IOG/M2Yr3OAlWuZruMZj2mBgFYcBCPmA9yUmFw1xqCqUJijlrvT18BZnnUS8NylhKqgTzcyJyrFvhoyePkcZghSQLPDCGYPIYqSWoAOP6dMoh3jjjQ/NTsFXm8z6esDSzSwuKip1z27icGwdlJYsplNoSljmRLRjagpX+Y1xXY5VA6jbtcUFDnjN1HFYGjwnEjBuTIw5th1IJXOUQFjmlga3+HkXgMa0t0DIZcRixlo6pnZVokxNmCXPHxxhw+wmha9nuFwxVgNaSSJYsDc+JpWR5eIiUJWGakAvN+uAxbRtjdI2JdJHCsjY9xo0PKaRi0Nnk9tmXmYmQYibItGGxvCB3WshQURpNNU9pF4pJkRJg2UmndOMuYdwFVzOMFlkdPeSsfYX67AKd5ajikDCOGYZN9uUKm/F7z1N+YCtoa5W195VrreXw8JD19XW01CxHyxxNvnPS8e0CIYPum5i0hpDy3ffnFymBanJydMSS9y2C5n/5CxRLq+TViMbiEkVRYzLaAyBLu3RO+hzd/ruMGg5eaQnNZWvkfL7PXK2ykg4Zpw7lxsfwn3uRcrDHYZKx6bvMJmMqaQObJfgVB39cIMU7qFaOUBB1tjl8Z8C0PmcYVlmMJeNOhw/dfZPPfW2LiTmg4sdsBSEAq6nDSdSm9C11K5jHDr5b0l34MLMKOMaSlA5CwOeuPMVWdcrA9NlKNvnTRpOl0QWTfpvadMrffCvj8Qt1Zo7HacOlNr7AqIRHZpVBZZVmeQHW8iAJeHju4NqcuPElEgPZzNIoKjRlROaNyHp9glqCQNPK7nDSatCvZLyUpVSdlLkz4g+2P8ShG/EjjT/mj6xlVPSpLy0jqw6PszNu1K6gxiF3otc5djVL3nMIYCL7kIwopxGu6dINNb1GC78/43c7n+Cme4wxJXFxwBI+WeLwzHTIPVKa5S2m7Rn1rZjp/Z/BLYBAciZSxpUG46Lka4MYjj3i9mvM8Qj8jPuNgJ+YPqbf/X2S8GUWrKRhBYNMcPf8I/zn8n+krVvcWurwwVqINTWqzRR35yrYC5L7fSI7pLJwja7eQTYfcmd0xie7E1IC/MNf5sdbAX9QCk5eShhWoTJ4ER1foMZ95FJIcbFHfjZDKFDJ/W8OBOzogIOxZXt7+91zeOH6j9E0RwiVUzt7nkn76wilmfzhpwFwliOySevdFsfFxUVmsxmTyWXrZDF4zH58l3Z9h6XaFbzNKt61BumDIda8Dw+t+vp3VNA6m1tUdJM3X/3Mv57b34PW5bFftje+v2vAt77sNejdJ/DfXwXNGMu9L53+W7U3fjtuffSvxhOtOP/LKTh+E876GtnR8f9vvdCEENvAC8AXgaUn5O2bJO6bJcc14ODb3nb4JPbd4t8VFjC+QyShkD5C+XTmMzpFTDvpUSVl6cm9KdMuK8mQ2vSM9uSMxrCLkprNwQGr0yNK7WM8j4rNMTpESo00AotAY3FNQqRTCh2gHY2rJH6Z0bQjOnKI56UU2kXisXG8i2Mc1uwF2tUIoWnMexilAIHOc4wOiJTFl5JCeaxcHHJ9tIunZrTlhCU5pG5nmDCkWkwxQqGxSKXIHZ9CK6yWlxWmIMBUAhztcufsLnVhqMkSv7A0igTjeixOT9gaH1CVM1JdcuP8HoHWpFEdoRwkHtV0xPLsmFvxG6yM92kNTkncCEFBFE8ptEvhekSUSM+7rOBJhSPBCMVWfEjgahp2glIKKSWZdmkOuyz1z8B1kQiWew/YGT5AOgonGKNcTelWMEKTqxAHhRIaJOxcvM1SMmTuV7CuxtcWtAJcUv+SGCAdHAGuVDha4UtF4bhkro+VAV4ek7sSrV1cpXB8B0KPMI5ZPjtg7vnkjqbUgmvxLot2BFZiKci1JpCSl47vsXl6l2fO7rJxvI/rJxjl40tL6jkIGsRRk2m1iXF8rICqzbg2u4dA0Bqc4mUJoRY4jsBD4GiDr6EqDEjFwuiUTu+Iajyg4iQEKgPPQyp1+UBMSBSS9dmIxTxGAkoqro33+fDZ57jZe4XV8z0cpajblEI7rHUfcrP3kIASRzk4WhM4sDg8Y3t4RFXkFMrBRXN9sM9iNsEIyLUk1xq0RDoK5Tp40hBqQGscFG2d4CiQMsJojZYGlIPAEokcWQgUUDeW5bMDjPYwQhMkCbnjUEqNwoDjEWAo8bg6fMT14SMicowHSmnQ4nIZlhSYyCV3NYUXMK63mdRbrCV9GiZAIqnFfYz2CbAs2Dk1dwquxNEGF8jcgGFtAYsDQuE4gkAZAmXxlWV1dobA5ersiMhkWEcRSlhIx4DAComWElAIK7nZf0BVlBjpAS658ggLRUUWOK7Aui6OABAYIUEIlLFILK4ROJnBaEFT9VlJjwnTCdLWQTi4EhCWqoIaFiNdtIWFdMhSMSMNApQVaCHwpOR29x2EVER5iqs0pfQwykOSIaSDp8pLr0UpCZQhVCWeBKtcNCWtdEpr2mP78B3cYkZdWAonJCozFILcryIcgasNVVmgn5y3NVGwxIBw3rsco66hUA6t8QBfQlWVNG385LMvjeZCDK4jEdLFMZKlswOMVHgqIwtqGK9OqevoIgNV4kjDyvCQ5vCIlbiHlGBkhYXpBb4q8YuMxe4RuePRGRzhZsWT67dD7gU4NmNjuI8S7y249gNJ0I6mR++boA2HQ7Ise5eAbVQ32J/sf0fONyX2Aabj+0j7rb7P86yg0y9YaC4yPD9j0b2sgpkkoXPvG4S+Q6YVS+sb5KZFPLkkaMmD30SXEnX9J5hj6UsLw0vTufn8Medyida0RxCFDM5OCD74w2AKzKNHLDuCRObU6h+GaU6WFNz1SsREUG9nZDOfw90un/iPbvJmLKgUBX7ps1ur8kuD/5JbpkcpR/gNGJ7PAVi4GHHu1RkXE6Ik43R3xNXmiIuF5ylkSTWPODTL1CPBN1Zu0O+9wna6SdUEJIv36TDANQZZ2SZMDf+iXXKkfKZhRH1agHE5LDrsVa/xkv86o7DDYJ6xN1/iI+EFx945q86Q14sLprM6IjEkRjE7HFIyACxryY9y/3pA7KQ8M/KIVIaxI+JOwFx6XAsesoHkYdmnvryCDSzDVs7HDt6iPl/md3RKN8hxzgVbmeEbo29gkwn+MKTjXHBeFey1WixP+vzOwidorc6oKY/52/cIoinRdIE7acleIajUAhavvo7zjRZn9w/I0oKiFvK2B491wEGS8eGpoQgkeWWPqc3oNzv8aejy1PCcrc2/z1npsqQ1zqxEC/j9yU+TCJ+F5i5uJBmezbA0wA7xru5Q9t8hf/AArGV15RrHhY+0Oc9Nv8xvnA0Z6YB1fH75zh2e5jUcLKWCLwY/izPuYzoLpJUVLl57g+zRCHdZICZPiMj0DGtK6uu3cN1veZTdfOoXWClHlG5McHGNaf3rOD/3MvnREfnZOc5KRNqtQ3wBWYzWmtXVVfb3L8fQw8/9P9RcHz3UVEwdd6uGsxyBFOTH78P7q7FxqS75BF4Y4VYCev0DRud/bs1W7yG0Ltszk/nBX56gta9D9wF+sEbyPgja0b0BUklWdurfM/f9YPuZBWajjIv9yfdO/j4iPz7BWfk3WIO2tIRNEsrh+2xX/WsEIUQF+HXgv7DWjt8r9S+I2feI//nP+ftCiFeFEK9KQCJxckNAhktCISHVFotFW4kuFDO/QqkCcm3pVyLiwCOcxwgEWoJWEoOikILEUWQOZI4gdyQFLggXrXOse7meyGhL5lhiX2KFxAiJlBmIS3Nn7JO4BHSBFRLXSAI3p3AskTujri9AlSAtbvlEXEJn+DqlVBahDVU1IfVdlNa4bkyh9aXRr4yYBBGZ9CmFR+q65NqjlAWOSNEqpU330shbKTLtUNVDQnVpZps4FXLPUjgKc/mjYhVU6idI99JixaiUTBkQgkDHjCKPXAcoYdDKIByQxYxcKgIZE4qczNGknsQqQeJeThCt0KSuInV8rIRUC5pmSERC6E1QfoobjTBSUmpLoUtSv0TlE9p0CSSXE/9giI66hHJGQ06xWNzcgLTkEpSVFMol1RLpJhilSFWF2PcppIsRitjTTH3JJIqwUlI4GkWBxUFKQSpCpAAt5uQaUhUxcxW5Dz5zBJpcK9LLGTe5K0h8B7cs8XILEkyYkvqWOAwxPiAlSlgyBxLHIfUujaW1jImY4RUG14lp6jOs0Ex8F5CXHqBSUmVCiz5Ii9EFTTkgf0L0C6FIHQeLxkiDthaVl7jhlHJriA0zHAxKWGJPkbqW0hXkWmOxZFphHA9pHXItKJUhU1A4JeU3CZEsEG6BceyTASlwc8PE90gdCaJEWYNEsyhHREzRMqcq+0S6R6lK5o7PKHKZhprCkxTKwTypuhnn0gNOUVBKRe4ajLYUrqVZ3yfTJXgloZuReA5SWozwGQcVYk9QSEkhBIkrSB3JJHCZ+i5SSlo8IQXCI1QxYeuETAsQJVZLPJ2jygKhQKIptYsQglxKXDJyqVmSfTwfRkFIKTzmvoPxSzrtx4TklFpTaIfchUJKjFS41oDWRGSUUpM7EZnrYKSDp1OqskehLIW6NKqWlBghUdJgBCAkvpzhlQUGQaFg5l9W7BydEhhBVU2pqyEVHeO4OcgcqaAuYnItmHmaTJcoNUdSEqiCOAgIowmFA205wpMGIxTogtC5tLQyUpO7CiNKfDKMsmASjAUl50QmJfMFcVShZqfEnkvpghSSSGTkykWIgqY6p9AFiBJP5NT0jER6gKAuUoJ6l7ru4tgCyeXYKZQm04pCg5WS3BEIp6BaucAXOa62eCpGqozU1WiVU9U9sBKMg9FQKMXccymkwWDIHEHshySuT6txcvl93gM/cAQtzmOG6ZCVynfVefsOHB4esrq6ilKXLUpbtS32x99J0E5OTlhZudxfku7ju99qQXo8T7kyNGxsrJF2L1h6QtDiV16hW2lT6x4xL3I2r10nyesk6eWaDfH6rzPeus6NaoXpLOdcW+bnc4piQpb10P1DDs0irY2r9I8OcbeeBWu59dZr3H/1t7ECtnb+Q8phk9HFnK+7OZX9jO5yFX+u6VwZc/W65DyL2I5Lwjncd0r+7ouCX01/hbZsIBZTekdTzHxO6+KMkQpI0jkySbh4+5hVeUqY7OHPF2nOWpzQZEk4jEWA691lb36V1yh4cfuz1LXBVyXj6COsXamzMLPce6YkUSG1pIItA4ZUeMvb4qZOKBp1FnXKQdnkFz96Ha+4hVt5izvOBymziEzOcGsBZb/k7PQ1QCNJOOqsUc1CTqY/So5iFBf8eOMBq9kpadrk7+ByVo4xo8ckF69yVh1zjTtoJ+fLV84YBRk8mHLDlrztHFC4PsEg5FY05LwqOVpoUY9HdMMVjGpxo9rHHmaU0TmlLDDyDt0Cptf/d4bd2wRxwOjsHc7HKeVCjdddj591ZwgEPzYWHK54SD9FCcs/X/l72Eaf+niR1dVf4CTJWXU102FGO5S83Vjm/57+x8w6Bftmn0ev9WivrzE8P8HduUr28Osoc8ikqHBnbY1+XtI5hA+1fwdHWH6//RwvyXXqjuZn1KeJpi71M8OvLbsUQnK+uYFa2GZ07y7pozHedhVGT6rFvQfEboftqzvfce6HzS0SUWWqHiBslaLdY/SxS4W26Z98Fne1QhErbLAAw8txs7m5+S5Be+PPvsTN1RVkD4QvUXUPIQXetQbJg/cxsW9uXxKvb4O7VmE1vMabn/n0d+b2H0L78vjj+AFRdO177//bsXADuu9cVtDeR4vjvVdOufGhJYR8n7YB3wNKS66/vMTdL/x/64mWHx3hrL//NbvfhHCcSy+0/f3vnfzXCEIIh0ty9n9Yaz/5JHz2pHWRJ3/Pn8QPgY1ve/s6cPwe8e+AtfafWGs/aK39oJQWJUusl+OHXQI9QSz0mC64PNx6jv2lHaaBwro51smoBjH1ckijesAk0MSeJnUsRlp0dUDmQb2+h/RSJpFHb2MFpaekqmDmOIDE7xzSqO/TdI+weMwdnyS0zANN6hdc3Ak4a7eYBZpRu4WnM+a+S+6VVOQE301IPH05mQsso7DCqK0ZvmQY7bikjgtCMdlqMlrfJNUOoTNFKUs9OqPSOEDIAmUl7eg+Sib4rSOMl5IELpGckDTqzB1FzRnS7Dxi+rTFSEHpglEW18Lp2jq5ksSRJPEc1MIJx1vLeLUBqjbjaOsKlWCCCMeUnkIqTeGVDP2QXGsS1yesT5iHLqI6whEZg+YyB6vXsfKyxSoNCzLPcraygYgmiCBluLiCFJZW1iOqHpF4MAs0vShk6ob0l9YIOl28+gCjMwonI3dKonCEvG45fb7O1HNJPIekWpJrcFQBlRmRvsBpKmzFwXQ8CjeBSoyUKYt2SI0pLdHDEXNyJck8h8LPacouFZnQqT0gq+RMw4A8VORa0KicU22cM/UDpn7E+eIis0qVfqdKvfUIGcyYuQGxF5J7grDaoy7HNBni2BzV6ON2jvEWz5C6xEhJu/WQ0pUgDYE3wpMZ/dU2TiPBqwwoqglzTxNHBt05IfMs7coe9cYRTvOc0slwm2P04iNEOGEYVZhVGvQXFnn49CYmzFGypB09osqMg6UqRkv2r+4ghSWonnN8dZXplYgHO3cYt2oUGxH7qzfIfJcFc4Fb7VO0JrhudlnV0C5GgZEK3JKmPgNlsEsj8kbCKHDJXYfR0gK62YVgghP08Wt9stWEabNNpXJBrbaP8GcMthcoNqqMggjhwDRyKHVB7jokQYWqjglNQjXqIaRk0G6CNhQuHK9usrdxk35zjdxxifwzVuRjymDGydo1Mq3JHYcsmmGkxVVTarUjhLU4zowmPZr1x/idLtOmJAsliScolUVa8GYxhVNiwhS/dkIoRkxqHZSFthihvTGZAzV/AlpCkGICg3TnlwQ+TMg1+CrGeCXzyFBuO+Q7AYUHqefh+iNKnTPoLGOqM2TznEm7QbjQ5bzdwXWm1BceUnFmjAKPIjR0t9aQTo4bDkgrHkLnTJtVtBPjOXOqcsQ0iJi5AUvBQxb9Q1w1x/fG3N9Zw3V6VIIBIkhIHAjcHn7rBDcckgY+cfWSbOW+RyjmBGJMuHBI0ZBkkYd/45yos89Cc5eF2j1KpwSlEUGJ0zhl7kiyMCVxHYyn8IM5qaPxvCHGKXHchIruUvdPMF7BvFYQBwpTjdH+lEJdPrwI3RmOmqMx6HqPiafQy/t8+dlb9LaWyUOHuRsRuH0yram39qhH+7Treyy17hHUYwYri5RhQauxi8AhbO0zCyT16nvfe3/gCNreaI/laJlAB+8r//DwkI2Nb91HN6ubPJ48fvd1WZacnp6yunopNlByTqX6rYnf43nKYr/g+p0riNGAjrqcrE3+6DO82tqh3N9DKs3OzVsMpz6l7UKREu5+hez2j7Pkatzc0PehezAlnu3ieCv8/PH/xb5YpbW+Qf/oAFsElLUGP/raq3zhld/FLzWd+hJx7waqagmjC6LDGXdb27Tnc9aennP64At4ScGV1iY2tXRbDj//kz/KK77HjThh0rmgezAlffCQ02aH1F4+iTXNBtO3H+L3HxNExwTzFaKkzQELVIY5epQxaJ3zs52X+Cf1t1itHeG3lpmWDlZVePRGj/9gK+Ct1svE9SqF00HkMHdCCqGI5gqnleOJGTU94GMf/3H8/DkGtdfoe1MUhotwjHROcHKXKSOsmWCyNylslZVBhCNTRsLFTFPCZkEj72KnHZ4pFZV8zMP4lK6Gw3BMq1tl5coi04pFG4k3E9zwM3YrKaN6G3fW5Flnj0nFoXAbyHzKzjgnLn6KG+EDnPw2F5yS1U54p7zOT9cLcmeKPf5JZtd/nJ4pSYoS0/Co2yo/Fe1TWsu184zP1TW2FLScjH+58WHWg9/CjVeZTfY4TjPWKx7TfkLuSbJC8NOPp9x4OKVi/oxHbz5m86mrDE6O8HZ2SHd30Y0zclapBj5LypLbm0gcfkjc45OdH+Za4pGkp9woXyULUqoP4B9/8QFCwP/w0Z9gtr5DebFH+miEe2MN5n3IZtB7wHlZY2tr618bI4W8SWzPMaNHtCsfYVh+A9VsMvr1TyK0xF2vYvzVd4VCvknQ+sdHHB/3ufn0FhVTw9uqI56U5P1rTdL7g+89QDu3LpUZi+zdUHNrg/XGTd767B99Z3td7yG0L8dmHN8niq7/+b29N5afgZPX8P010vQEY4rvmppnJQ+/dvF9a2/8Jm5/ZIV7Xz6jyMvvnfx9gDXmkqCt/SWrjU/gXr1Kurv3fT6qvzqIyxP0nwJvW2t/+ds2/TbwTSXGvwf81rfFf+mJmuOHgdGTFsjfB/6WEKL5RBzkbz2JfVcU2mX8N+bwUgLbXUwrv2y7uWG4tfpZGstHVFcv6FR20SYm2xA4W3OKNZ/4RUH6wQJxc4rXPKKtT+m0uqg1jdx2KV+AcGHIsnOf9sop8/UW82c1ck3SMKdUzTmV1pTxpkcY9tArIxYXdllsHLNw/ZDsSpOso5CbY9arb9K6sktUO6F7fZXkAyXxywJ3Z4zeiinuWNr+Mf7imMnSEuPrAc7aEcHOG0w2I6bXWpRPp5RrJTU7oqZH2EXDdLHJ7HlYsH3Wl3apP39K71YbVZkxXauimudkixoVZeQ3Z1zcWGD4Qk5r+3U6G3usuHdZDh+wsHyfmpqwou7SvbnE8Kkm7bVdhjcc/FtT5h8bYZ+eorePCTcPiar7tCv3aegjVsI3MRWL3zxjW7/J4sY92s8/ZOnmI5b8x7Q291npHLAW7OL7CY2FISzNoJWR3HaoV06oMiBqTrgavsZ6eA+zoAncCWpxzLb/Jp3VB9hlDzyHoNbDKTOa6SGVxiFrS1+h8dw7rFfeYiHcp77+kOlWhF1OqVeOCeoTvBf6zF8o8G+MyZ+x1NePiBonnL0Y4X/4kP6NDoWT4asYFnPCxjF6ZcrpzR32n19Gbgi0ukCEF3RqE0Zri+iOxbMpy/4B9s6ci402D7afo1zxGCyvkT1fYHZmDHaaJMsV7I6PvOZycXUdR6V4zTOsX2I1uM1jyqWMyUqT8oZEPj9l/lKBuTPArmbkiwXGy5nfrFFUXMJnu9gbCfW4z6r7kNHiFZLrOepKj53G1+nfjBCLmsGNNrp+xvrNN4hfmnGz9SV4eoq7kbG59BbNjbe4vfg5uDFjuf42t1f/lNazpzjLQxbcQ7zNGEdk4G586hMAACAASURBVCQsy0NmKw5h/Yhq9QiuZSTPa4ZXFwnWeyxXdzG1jHJ9RmRnzJsReejh6SnZFQ/v6oC6P8RzMsSzBY3GGdWNd8g+lOE/c0r92Qs2wrepLx7j17voYM7oyiJt2SXUI6iULFYfEqz0WWkfsLX4KosLZyxsPiJ4/ozZekgQDbmy+nUqqyd4jSPmHyoYvwyBmmCRVOyAxeYe6uqQeDtEXZ/QWDyAp/rEH0owVwY0wj3WnLvUkjH5bQ3CwmbO0tIDllqvU1zN8CoZrjE05SPS7Sph5YL4aYeweUrk9Ag6I1rBI6YbPk69z1p4F73WQ25eoHe6yGBEJ9yltXrIxsI9Wv4J8VqVya0Zsx2f9jN3MTf6CCC/M8d5akq5XRAun1O7dpda9T72hUNq0RHi6hi/0qPYLMGxLIUP0S/3CP0BvjtheGWR5AMT3OU+azt7RNmU4opAXZ2gq2PUsoArJe5GH/NCTqe9h//CKY3GAbrRwyz6lC+mNBcOSBo+pdAoleCbBHslYa36Ft5qTKkFtcoR8pkcF40M5+gbCZXnzvDDHur6IVe9P6Ue7pPuGOrJmHZ6gVoZEmyPyLYVkejheVOSWzlRbcSK3sMnp06MFQ4fX/hNxNoZvRdd3M0BUf0cnsmY3fQprgjiGw1GNxaxdzKurnyDyp0hnsyp3HyMeiojX/Yu/5/vgR84kZCHw4fsNHa+d+ITPH78mE984hPvvt6qbfF7j37v3dcXFxe4rkutVsOYFJwxjdZT727fP5/hpYb1ax0yLyCaDLF2kfFn/pi8/TT26TvU2hGNRgNrWwh3gr33+6Suwt38mwgh6FjJaSC4OJgyi48xqcuqP2U6FfgLK/QPdimCp5g89Tw7X3uFf/VMHb9SpeFrTFIlvdbnucevM2hq7qnn+Gj6VXrJfX79TQ9lSv6X45B/B5iEkthW+JXOCn+nd8orK18hHD/Fo6++yVe2buPYFFGTjJ2CMDnDPHqA8/Efwu4WUCyyxyoL4QzHFMT+jJ8/hKdu/QafevC3cTyHLiEfjXLaT28Stg/5x71nCdtfYGyW8GfHOM0aHy7vMTwc0PxwwfDRR1muvIGjXSaDq5Sr/4J7JuBlXqBbzllOr5CrCfPKMno0wmYzjmp77JzfYTHYY+IpZJKwGzap2x6zcYdZCavFiE8Xf4Mof0irrDAJf5r1mx12jtbpTU8pozrbgWEXQRK5NPp1muZNhLCExkUIh2tfsqRX/l1a9n8jHV3lYW2PBXHGSbzKD1UK9pwf4casyaNCcRre4bo35Y2izrJd51Hvq9wOP8DqxYRfv615PvG5aChirfhhYbF+yvDxMSfpHa7WGrz99gnngYMzzbjjwcIo4Ch3WGr8Ktc/9N/ytU/9KnplBcoSax6g5KWIx7aYs99+nvEo4IXwf+KTzf8OefB5jo9+jdR7EWVewbsXMB/8KVpKFvr7/Eo74h9UupTTHHe7A34dJiekJ29zVlR46S+opAj5FPW0QSq7LB6+yGn9t9G/8AHm//MfYo3Bu1KnfHsF9YSgbWxscHZ2xqu/+xvcWc4x7QBPhvibjXf36V9vMPjN+5i0QHrvcRmJFiBoXFbHFm8D0NnYIn7zhKw/4+T+O6zeuHWZ2/9Wi+O/UQWtcxPSCW6SIYQmy86/qwrk3jcuaC6HNJff24fkL4uFjQr1TsC9L51x52PvU4Hy3wLFxQUIge68T7+4Pwdv5yrZ7sPvnfjXBx8DfhF4XQjx9Sex/wb474F/KYT4T4F94OefbPsU8FPAA2AG/CcA1tq+EOIfAV9+kvcPvykY8t2gvQkbyTGFCZisBYQR1E2GY88wwYC1ZIazHGOiCtXjc1QgaCW7zGiSlg0ayZhMu8xu5wySBvV4hLGSpn9KfQ7WVZTPzgmKMRX/EUKUyPSyZSzSE9zFVxkuNZkPFvGSGB2WbM+/xoir+O2UyqyP8EvmK3Vq+QVykrHlf56JWsbRBVlHUBaGWiZwdMHi+C3yZuty4m4EE1HhxsI3KPAxTk7uOviHCd6zFzT0jHAyx3Uysg0fTIm0JfXKCb5OsVjm7QbKFmyau6gIGv4bpGlKthmyEd8jqk+Yuwu4y33IC5RVpOQQndHApeI6SAzg0GodYqKSpbMLCqeOjQpSUYVqRClKJtanygU7o4dIH0ZRhNqsY5TGc7vMtl2siGnaXbztA+JkmUo6xbZLynpKNTon0wUro7ucN5vgzkjyRWpJio5KMkfh6BkL6QS5UsUb76FrFgJFWRqKDUGZVgFL2ztACAMbI3KaLOoTxvMFlALjhohqyrAS8LL9I+wox115FdWpMp4t4OZzlmp9RpVVnk3/kMLmNGsZa9fPUXgcBtv4CNCX8vGz63UCOaW+8Rr+pIMuJnT8t0l1RLbsUqFH7tRxcsGkecJ1zhgvBRgyGrtTpl7IcCkidC5YCPYYmQ2CIiOXPp2kixEl6Q3LdLYKyjBbq+GnKeQzvFtz+u4a1+UfUI8PKcsQH4Wv2phqiF/kZFc9lmcDOkmCEQ6PliJWuz3KpIatzRDBgCvFI0zWwC9S5NyjWFLkNR9TapzrE/KFlFjXEVlG5SJH6hxbCxBlSdMkSHUMqc/kpQqRHdCqTxlVOoR2RqYivKJEKEu5mZG4Pr57SIhAmYK1mYP0R3ijkOnVCmGW4BhDsWqQjmW61cCVQ2rZnLzu4XmCUp+wrkZUhEQ5MZlN8Jq76GVDFp7iZC26nSWuj94iz3KcoCDTM3w7h40WRabx7QzHxsQbEWVNcHPwNo1ggt2yFK5mahLceI7diIj8AZXZkPHNFgZJtZJRFgpHZKxWXqPwXRacLnZDs7h7hOlIrBoxXa5TszFGC1r+mDTWuAsTaucj9LqDnx8hyxMuriwRlkNawZhK3WWeWWQrwz0wCEcSuAlaO3Sct6hMhpSrLkKvMLuhURbmVyNq5ojaUUYWuFRdF+3M8cSItern8WagG2NcnZBsNqjpPqmNcOQA0QJnGhHYjNX8hOxGjOuUxAs1KrMp1daMYJ6QLjWw4ZhstQJugZ+WyGhOvjihngxIwyV0r6ApxjgvCmLbxJuPCPMUb6OP1Ir5ikLLGLPcws4UYl5Qaw4vW3NVl9r2A7ruDlHlnOJmBznLsVrTfvCAauySjKvI2glNeUDutJg3HKLsmCQIKf2QQAxIjYcUFmktyi0ZX28hZUngJbTdPmnjPYWBf0AJWv39EbTpdMr5+fl3iCJs1jZ5PP5WBe34+JjV1VWEEEynjyhTxcozz7y7vb83YmUlQClJt1LHG/ZI3nqLIk15Kj7DfvRFqmmMEIKlzk2EKim/8k857kg2a88BsITknYpgtDtjMnib1ukjHr78j7jypYiJH9E7/GPy6oz7P/0zfPDPPgONOp3KIrN3LpdGPPD2eLr/kDeXSwo83OodZuO7vJJ/jE2R8F/9yG2OP7mLi+AzgwlUNigHEa8NvsrHlv8zHn3tEW93rlIrx6TVJkl7n1Zyl/ThQ8Y/95OMz/ZRky125TrL5ZTF8JgL7xrnu78Ft3I6ww8zkY8Z5S5fH+f81z+1zacf/58E5QfoNRo0xoss9L/IWfsaL8y/yNC41DvXSPdc/OY5w1nGaBJQzzY5ds9YKRxOxYjbg3+frHpAswLxbI41bbrRZ/lA9hOsVD7FzPPRCl6vPMXL6lPsx+ssFgWiGNP3l/is+3VuzDY4Hq1w50adK/dWsZMehVAE1Qark4K3F0asH69i1SkyLdG5peI0SJMxR/VnkOd/G6HOSZwKH6w/5uzkZV6fejyKeny8bPD5o7ucV7dZ7L1NJakSyKt8ZfA6v+R5nPiCwoN53uSLm1f4wPkhfp4gOiUX+xP8tkRUNUcnMYvrU+YTTa0yRTa2+OcXQ/7h6tsQfIOyKJiNR7hXrmAnr2Ptz1GOUjbzHo/r11lP2rwR/x5X3X0+X1njxeNfo9q9TmAN1n2e4ugtwg+/zLNvfpm1H/4l/Hv/K0eOZENLqK3D6JDk6A1scwfH+U6zZVsa0vkVKuUqn3c/xQ99dUzwU1t0X3xItSwZ/fbv4D/1w2SvLuD0dxFAGIYs1Gu89bnP8ItXH3AytdQFCP9blwtV93BXK8zf6BF94HsIVHRuwcXddwlae2OLR+NXuLL8PG/9yacvCdrsUvCF5jZ5PiDLu0Th+39Qc3lQDizeRpy+ju+vM58ffFeCdvfPTrnxfa6ewaUn2vM/tsmX/9Uetz+y8n1rn/xuyA8PcVZX3xU9+svCvbrD9E/+5Pt8VH91sNZ+nr94/RjAj/4F+Rb4B99lX/8M+Gfv97OlNYSTEbLIWZofMFDX8PMMR7rk4QlLSUqRadSSz9jx8POC0hoc5rQnE3pyEaUzAjtjmg0pRh4yyKgVKUJMmHcCdAKkMc0sZ1RvUC3OcBczirOM2RQado50+8xlQJsYZ5bi+qeEwRBnMsGQ0zInBNOCtGWRqkDPLD5dSp1RnY9RVFj0LtDJhJwpIxVhU4dOOUaPPBK/Rl31mckm0ytVfNdByxl1/QATh4xo4MtdapMVfBmSG4WQktCM0MpQjvvkjiJWEf6soDPrU7gSz58inJxGYiBNGJkA5lNq6RnWwklrFTFPac8KgrohGIyQsqRYnjLOPJReQIcTKuOMqV/BmJhWMYHcpRYbZk7IzGpKJ8ZLM9zRAEe6VMMppowxNibQhprn4MVdpE0xvqGV5ejMUDgnuHJCnCzgzbss84CJ08RtHzNYLFA2xUHTKhST3EeLCcZCMk/wygJfndGUx0RJTpZqKnJM34NgktP2H+ElGd4sxUYepj0lmuY4sy7am7I9+gpDtYXmiHYRYkSB0inVeZ9uFLJa7HP2TEhtMsWVAQvpgBMbEeRD2kXKiadZmme4cUrs9RgS0Zy7ZGENP71g1rT4TclQ12lm5zSnc/w0IZQBgZ5RmAKTZrgio9XNydyCi3qT5ZMCV8YwyzCVlKA4xBpDVWRQjBh3C/wox7pTKpkmkwY1n1Ci8TyJOzrHMRnB/JThOAAT40YZdXPEuNDUU5fc08RTyQoXOEyxI5e4OCU1daTu4Wrw4pgka+HbnJCcdMNQH0woKHBrPstmhLYpnkm4NetfmsIXAwobIWRG4QhMXKJnY3y3i5I+/WIVax0WxSPOUoVKEyLtE6hdBvk2YTHC5iFKaioVw8A4OGWCng5wjMbGirV8xMyMaJguUpZUTM501YPZGWVmiRjjyJTpfEq7GNOxDuvjY0SSXlaHrCVMBwx0gDWGRnaCK3OyTKDyJQpfURETIpFjUKz39zhWbVLTpJ72KFd9QmKcpQQ1GmAizYLTI49zsvEMEyr8qyWFNZDGRMoS5BPmhabar6A9lzQAMx0xv7JOkE2IbEklSQlUj0IWyNyyfPEY2ZTMJwkqU1TSU0abK0ivIMiOEEslbjyHIidJLpUlW0nMwLi45SkUy8y3BZ1hl0GaghKYbEYjn1CMx4y9BlkY0g73GWUeUWIYXUwQZUZbjtD5lHFRJUkvDeNb1jBe8GmWe8gwoBhJ5tkCDjk1OydPSzQKW4XMTKn45xhvifX0lLFoQp7i2zlCZizMxxTGMCtcVvJjQj2n8FLCNKHqeCgpYHaAM8rJhaLoO0xki2ZDUsbHFCVgGwipmZWayugc6xuUmzNYeO+50w9ci+OD4QOuNd7fk/Pd3V3W19fxff/d2HplnTiP6c17ABwcHLD+pKpwcfw1srFPrfOtH8XsxTSv1xjmBcdRnbzbZfrpT9O7+Rw7x/fI11apLlw+nb5+8yncgUY8+gK9rW1c91LKf9lKkkASdQL6jz7LoGxx48Wf5c5qnUNbpX98RDHL+PrOVUopuX13xFZti+NXLxjqnMdjy3q3x8FiBaSlcu3fY1b2mVmf61evUcug7mu2lebTvTG1ssaDtZfIbUkQnTI/jHkQLtFKhxi/jb1dIzh6A91scj4ec1Z5CMZjOlvj0EiCxjssqOt8trLO73ztF2k0hlTVHDXN+ILs82cnI77efYMb9gFfXnkOq6pcffQ2VZGymF+wtLnBH791hx19Rl82+aN7j5HeBWX8DANnxKppcS6HjL6c0E8uWHvxt4lahswJyXRGe76ALqdYp0IjTNgNlnjKeZOvzhs8tAOElDynzrnXzLk9fpb5tKTd9HlkHxMlHraIOQ/bfHwW88ZmAucjcvH/svfmwZZd13nf7+wzn3Pne9/8Xvfrfj2hgUYDIEAAJABJlCyKFhmSGqjRLrEUVcWqSuUPxZWKSynHSUWuVFyW7VRKsiuRPGiKBksWBVmTJVKEIIEACBDd6Hl883THc8989t7541GgIFKkOKiixPr+u/ess88dat1z1/7W+j4LYZRUhiCwm3j2gNcv9ylm30+oX2G23qA9f4NmOsfybckbB6/TOTaLK1y2/RaN0RaNcZ/KOclr6TYPHRZcmXFQsuKOe4w3wod4aGMfb8fDXmyQ75ssuzb/YHBAayqpfBMxrfCyPlZ7FbvskZp/h+s3/ke6q82jNseTJwirbcrOadKrfY7H69zzlzCaBr80iPlO6xo/d+ybMEQD9+A1uoOCjeZFqEpaH/4w7YVFnvZ7dKsdPtYticY5NI+MoM3BTbyVhz8vR6qDlMo+h9Y9Ls/tsv8nn2Bl+aNE6VWs8yc4/ImfwD3RpCxm0bufY1KC0QH1+Tm6dsT+neJo7jUp37Z28Ogsyev7f/6Sn4+Zs3Bw/a2HnaUV9iZ3OW4/wNUXPkGRpXB4A1rHwXIYj18nDM9imn+5Nue3YeFh2H3jyAst/cL93ZPDlJ2bo695e+OfYu2xGcpCcv/N/l/J+n8W5ebmVzR/9qdwT56guP3/Kwbt/zUYuaZYl0x2JdYeFIcFSWZgDy2WX8nIS8W4hJ3rNikZUREzrQRJoditwNTbxHcV5mFELR1iHGZUKDSHpNWU2ngbM5tQVqDLPub0DmQRMgdjRuGaoDNNMcqQecwgKegbNmZ5QP3mhLIvsasSNjSTsct0y8Lf8anSKclU4EYWxtRkulUyOhhyqEwGA590S+NUUzoHE/Qwx5Yj7GiAM97D2S7x+nu001vIGMgSavk2IrcwyyHJxiHRjibvl7jFPmG6hysleaqwsowgSzCLjEY1pbQU8UAz3jCItMBAo1TCNBekuUWwN4BpTrkO9t6UqoRMWpRDEz9NsLZ3YbyDWW6zeHeH5u2cRGqUShFFhD+5S3PzNt1rECQlCs10P2C0EWBnu5AVmHGGO96jtiXRY8Xemx5BP8JRU9QkIcWASJHvBoynAXYREfQtROQRZuAXmjQHURVouYendzBkRnk/xIw8RGSRZwpL71DfjFkZ3sGy95kmFWmiGGFQbBfMfyahntymUhO0VNTIqe8M6NwsIO6TlQmjXOIMByyOr0OZ0zhI0HGCP1nHuTdibfgiYXyArWLc3QQvG5EqRRKVuOMh6U0FOzFqHNHcnjBNK2pbLvWtlCKpKLUiUVMmakhRjEkyyO40yPcE3vaI9voGVXGId2eP9H5FsCHp3MioDnKiuGS/5EiKP+qjhhFpNoDbGdmWCbFCRDHeJKI4NCE3CO7lBFdNxOWAPLNwKsFB3ybOJWacoZKYiRLIIsdTGUF/jOgbyLhExmPkxg5ZtsM0q7AnBdUtkzBL2C8yonTMaMtHyQoz2aaIN5imCQx3GUUZ7mafciuhzIZYhUG+6dIptqHaYaoSgv4UR+Y0Bn0at3287REWY4xqgiEV9SsDJnseWQRlYVFmikRKosigymL03QnWmyUlEWLfx/EVsqlgGpFGY4xK06/q5LlDqyzxzIKxNBhtOIjIwC5LbF0Ry4rBTZdSGUSHDjpJEemAsoqItw3SHZN8JPAHB/jZAeSSqsgJr2fYch9jp08VDdH3FbK00UOXdGKQ72jSMmCqXKLCJk9sqkFF58aU5sEUNyup0hyzmOCmKUY1JhpIvKkDkY2cFjgHU1S0T5nvkMcmodzHuJPgHk7Is5SJFEzu1DC2fBpRjJmUaDHEKEzSaoQc50zumZgJJIMAUQ7wb06xkymdfIN2cgO5B800xh+k2CrHIMPZKCi0RTXNqW8qzKzCKAa0NocgbHSkmG56yDLF22wjDJORMilsgZFUBAfbyLQCYxusiECv4xcH5FlI825Mfk8Q3XJpFDeo9JCDBQ/ZguqqoLY9Ih1q1EATa4dSGsQxlHHG5GBEFWcUaYXOdxFVn8ZgnXKnhH6KqiSN/IsLrf21Y9CuD67zw4/88F8q9vbt25w69fZizjZtTrVOcXVwlWeWnuHu3bt86EMfAmCw9xmE6r01RwPQ2Ew5+Y2r3ElzdKfL/s5txh/7DQ6OX8RePU2SJjQ+W6Ctra0xeSFlq7NM0HvsrTW8SoNr4nQVabXD8ws/yD8Jfc4vNLi2N2HVEOSNnMs7O4zm5nj42g674lFGuzG36hab0RyzGyOSc6eY9zx+LX6aRfXjPBW9yhNP/1dsvBDRadg8Ztr8VpTy9MRiuqy4aJ9mevu3sDhLKgVzzpGsbLJWMP8rU4rVY8TDmMpMyfw9LuQzPG9XtOtXeE8g+a13fDcf+NRNSu0DivZ4wtkTDX7kF19nbm3I++o3+PnGu1F6l8Z0SqUMNvUSdnuRT++1eGfz97k/XuZnX32N43MJ9/eOE3QkNd3CkPeIjJKWmuX6q2vYC5foFys0si4Hnddw5QRTruDOSNwi45izQTud5QY7vLu3wCPje7x4agt2v4uVEw2ye33uB/s8Y56FLOGeCnla2fz2iiL6RJ9dtQS2JnEFedDGNQpqcwFFfxbTf4Oz6S22Vwfk4S53xBOo4jMczG1jbnU4lHB2bsLOXp+9tVN4U4Pa+pBPzTlUOuY/WV/PxfgKqpjg5qsEx1fwr+4zKEue7NawXMFgroN1+xAr2aVwF+lsacazPR7wv5386Y+xv36bhaUa1ljiP/4U6Zt9zhiv8ELvXTSS51mVJ/jBxz/KT77yJi8m87zj2E0m99a4oRVPAsHjj9O59hn6B1MWdYvdzh7/6NP3+CczZ2H7dYJ8n875r/+8HCm2pxiNGez8M3zH2ffBz/0k+fTYEaP8QYH3j++S3byOOHYWffCrR3myvcXo1lUWzvTQvdNwTSKO25Qbb1cn9C/0GD1/BxkVmHXn8679FnpnYeNP3npo2TZGz6Eu28wsr3L1k3/Axcbm0QwZMB6/SrP52F+02hfH/MNw5+PUlh4mjm9+wZCrL+5w4pEZvND+gse/WghTcPE9K7z+u+usXuj9lVzjT1Fsbn7F82cAztoaxcYGuigwnC/yHf4NvjQUiMLAVilD3cYTOYUhmJJR0IGJomoX+IlBpR2ksJBWhVHaR/qQUuOpEXJPY/YEuiywi4hUmsj9gKHWGGhyC5yuJLhrU80KRuMaKU2C+RHKMKh2PYzYQgkTaQl2VI2pqtMSU6K9Bq4F0jRRVUmRGOhdgeokTCaCXII2NHngInwoUhc5MpgcBGQ2TKsG3ugARYtcWxxkLu1ximn5TIVP3Uow5zO0MtASxNRAaZtkYrMzrGO7+miWRRmEskLmHhMMMDUT6SELB5wEIQRGs6IwJKIwkNttlJ/jCoUcwCRp4rqKMvNJUhvPHmFbEqfIyJSDGgum2sOaOhyaNl4zRk4tROUyxsKuXIyRiTIchFWjSGyMhZzUgljB3mEdfaiBkn7uoksLSgG2Qu9rPGNIntSZGpKBY2C6GYf3ekciBVpgFRKJZmpArBcQKLwD/4jbtVxKLdnLQV4VlKYmJcBwC6zZDHPbYE8bMB9gVHCAiS5M9FAzlC3cIEHveGhMdKYx/SPvMluVTO/PYpsGlVFRWh5lVTExDOxSs0eAUpKydLHMCs8pKfc9ct2kEKAdlzyFTNcx7YwkMDGSBDkvqTIDc+AQGzZmXzPUNuybGHbGfllHSZPtQYPALSi3QZogSoG0FNaxGFVBOfIoCgtwyFNNjsNB3KMVTohUDW1KtDQZ95uoHU3diXBEiepVVNik9+tgaFK/RLRzsoGPcCWup9CbAaYEo1aSDXyywkYLyNZbiIWCEgcmBuOiSc1PKSIByymGtnBGJdF6HRRUeOybAllZ6ImL2c3JpiGmX2FXEZOBSVQElKWFSjpwsiLarhHHCXVjHwyFPAyPOif8EkoTqX2qAjJtUdyvU5WCeuiih4I0NVHCwnQMqqYkWpdkCmyvhWNBleXs7HewogIZCyayTeAo9m4toS2bKBE49ZRkFOCro/dZxk3KPjhmQT6vyTddkkJh3lFIqRkctChwYNfCNHJMEaBrYLhj4tsNoiyk41dUGNzVGoME2hKbAaYyqA5NZL3COAjYlw5KAsJCmRpZy1BRyMgAcSDQsoJNH2U4yExjVCalpdG7PpPIo1I+YwWl0AirQuQWolAUpSKKG1gStJ2xv99jRo+gVpEmM7RFShE1EKpir2hidyLQEEUhVizJZlKqwwYyLQmlwSiq0UwnDLM9JvfrSGDcKSA40k1l10cISdLWyFRijH0MIZAaRK7w9Q5p5CBqgDQopI2ULltXPQQwEV3MSqNEhbAkVi5QDZcicyktgdaSQuc0h1BNHNKbPsZSQjnKvugt5a8Vg7Yb7zLKR5xtn/2SsVprbt++zdra57dBPdR7iMuHlxkOh0yn07cYtMnkBp73OQXH7e0IL1c8eLbD1WlGb34ReeUKKk2xt9apnnsPg+1N2otH57f1kKVxxKUgpPHZ9kaAaVRgeAaz448hg5yV498EwPnFBld2IlqtOcbmgPVKMf3Gd7N8AM6njtou5ZyDc6gwSwmmj/Rb/OQfbCILl8fFpzl27BgHGxG9GZ8nlcWokpSJx5Ahj538FnZlwrB1EkdWnDt1jMiOGHkCPJf70wmlKFnIXVJ/m7XS4rz3Gl1b88S+x35rhmPTe+ipiz2VzAnNOCv4tz/4DnarCeOrmuVRRmHGvLD4MP40oa8a/NJ4gXeU9xg1b5PEPT5zT/G9TzzAStfBxOLNcJ3GcETkGQRihuPRVvPzUgAAIABJREFUYwxHZ9hvHDIfLfPiyu/TsjapNjR3Z5eZjXYoqzoXizplecjCydNE5SGmSIijFssdl0/+0a9R2Iql5gWcXFHlEy7UZ5j4kgOzj57MUZgB/YbJKGxSZGMef/cCRmZwvTmFlUssHRiYvZv0s/M8ZLW4tfUP2Ysb9OyS7UdbLGzd5r7f5F2Zh79X8VLH5HvGv8A1HuQZXkGYgr46h9dJ6U16OHnFv3jgOIMliyN7FE0/n7Kx3+JM8yw3oxusrf19bNejH/8MlbHOXtqk/ugK+Z0RDx1e53Jp8onBJ1kaLLE/+QQfLv4Dv17+F1hKMWo+gbt9BcPzyK9do7O0THZ7QGWd4O/5h/xKlfD77adI7r3CgDZLJ858Xi6U2zEY4IRDTh8OiS6s8gs//aP4jXcyXrkPNYe9H/vHOBceQqRboCQv/Py/4eI3vY9Z9oicZebNEzSeWKHYiN4m6mHWHLy1FslnDr54ss6chf1rb3uqd/w4ZVjywPnneP23n0fvvH7EfgHj8Wu0vtICbekdsPkKYXCaaXzj8w5XpeTKC9s8+Mxf7XzY+WcW6W/HbP9lhFS+ChR37uKcWP2Kz7c6Hcx6neL+/S8d/Df4opDaQAFaCwptUWBTSQuhBEo7jNMGchCQag9ZuqjUg7FPMfUwiiPz03HaQg9dstt1DGwObp9A79v08wX62SyDuIOT57AToPAo+yEy1eQjC4nAuG9jpgoAVWlyZVIpnyT3URiAwUFcI68slDTQ8kjaXg09ZOVgqTFa5qidGtm9Djp10KVFpQyy0iAuNUpCqiwKLVBKkFUCmcM06qETh0Lb9O/PkW21yIw6ju7jqj1UZZFPQyaTOYypQKU2eVFDaZu9cplp2SNXIf1khnLQJF6fwbyXUd5psTWeY7g3jx44CK0YTebY7a8winxSFaCkSSkt0psdijsBh6XHsGqigGQaEB82GE87VIaJrARq4GCT4ek9tqctZG5TbdUYZXXkQcBYthnKDrFsoO6EDO7OU+26VFs+eWkipWacd6B00LHJ9pvHmCQdDpIVqARKm2gl0MoiycBhRFpyJMFdeKjUIZcuoEjTOtFkgWLaJLnTQ1UmSnsUWw3y3QbF/RrxeofCCInyDoMrxzEyGxmD0oJCCdTYJL/bwhZT+mlAUWnGkxoH/Q6kFrIU5MpFKo9p0WSQNNkb1RnFYGgTXQlIJT57aMNAlh4HOzPkiUd6bwYOHSQG4KK1IM8D0FAVAqVtEIKoDEm0TaTqpHkNjaCoHMrR0caPjDwKPDAEWdVASQOlDWxdYJoFWllEMqQwPJIqYJw3UNo+KpYyE1kKZOmQ7tscXO1hVAqVOmTbs5R4KGVRbdeQVY1S2QhVkuU+6e0W2f0ZilKRTwpkFJLkAcmoxuDOArs3jiGljdIuYCK1S1SFGFqgDgNILGRhk6oGUTqD0gLDUgyLWZLbs5BAmZtUChQWUnsoZRJNmsRZSFY5mKaN0gKr1KS5i95z0JmJKRRoA5nbjNZnqQqXVHocTpuMRx1GsoMSdVRiYYkKpU0K5RCXNRw1RQ5d9u+vMBnUjnJAuxhohnGbyaSOs1Vh5pqkaDCNfYZJDa0ckC6priOFR46FSFxGNxbojxpUlUeqLVIlmFZ19vaPU96pYQ4EDAVq6HJ4Y5lKWRgYR0WEFFTSRkZdDMNEKxtDVGBAlbuo1MHERJkmUnuMBz3GMiCrHJTWWBIoPaRwOEiaHBg1xFhRVsCggSFdtGkhk4Bh2iLWNmQGojAABSMffRigKgtZmBRbDTSKyWiGYeaTagcVK0Zpjcm4RZYFyN06cr2Fsd5kOg2RmU215yGjNtKwqJSg+uzvo1QGai+g2vSJipDJzS65dEkyi71Rl8lkBqUsqDzIfTAdiH3K0qNKTDLdgntNhpOQSlmozOTw2gmG21/c4uevVYF26fASD3QfwDa/9M727u4uUsq35PP/LB7sPcibh29y69Ytjh8/jmUdEYVFtU6n98hbca+9vMvGkkvDtXl1EvPIXJez2zvIJx5n9f4Vet/yXvqb6/SWP1vUvfDjrLdPkbqSRv1z7WSbw5T/Jn+eWvgGZRnwwaUjVu/8QoNb+1Pq3gx3t95gXGux/42n2Zt/F+m9AiewaHW2eGJ6hdsLBkZ4nmsjg+86VbGtFjnu38epSqJ+RvdEk9PDz3o0LD7K/eg+j849htHPkUZBYth887NPMrIT7h08gOEKbMvmwDngYn7UazB2pzwSL/IEivq1D/Dgeslvnn8aaU9Y2LxNuDzDnVSQ8wp1UfKL429h+SBjJ/B5cfE8S4e7bLpLTDLJ6fqQnXAHXXSQ0uTvPvoc870pXTXPS/VLiKpixxzyR8d/glnjCaKiYrtxn8VRgm2kuEZK2x7yRm+NpWQdPZnndCVoFodktTr3G4dcKB/GVwYvbh/w8e3f5ZR1nLwUTGoNsvGQpB7wuNniyrESb3COup5Qm0o2mh2KeI+21AxFwY57ksKB5YGPM3OTZn+Bk6fex6XAY7106OkRH8+vMjcsGXs+T45Os1Pvs9h2eLN1AlMr1oKXWVhcZE+M+MXrN7hRN/nh5JDQMrnZkZzeHzPvSzaxuHbD57mLT3Lp8BJC2Cx3/zuoXcEpX2Z0GGI2HJxmwqz/dVSqwnDmWaqa3L7zT/menatERsib6VMM1B5nbl8hfPe7iV98kfbCEvaeiZ55gAfCHb7tjYwfni6zOxwxClbf5n/2p8g3JqhJgXOqh9j7JA+8//v5+o0a/2b9CtIKiZ9RJJc+jXaOrJ9u/cfn2bx6mae+/bs5HU65P1jEt2vU3rmAlpqq//Ydn79Um+PCxaMWxj9D5688+DB7xX0WxQmKLOX2paswfxGlCsaTz3x1DFoxpV4FxNPPL9CuvbhDveuxeKb1BU7+2sHxLB795mN86mN/tQqJ+e3buKe+TLXLPwf39GmyG5//Wf0NvjwIFFpqDrM2JpCWFklpEhcum9Uipa6wLIdUW+zSIzEchmnAYVijYUZYpFSGx03xEFVVERUW0rAY5N0jRVKnpOiYJE7IKJ9lmjscRl125CrxfJPrW0f3JFtICt8jVS5S2biuxsWlUDalFOiqxC1ThomPpXOEyChDhY3FJGuSFw6Z59FwSlAVpTy6f/ajEJVppHaQbkDDjwmNCVXmMshauBwyLBx235ynmgrM6si/axjXEConTw12izmKKkcYGX3fY9toMRrPYE5MXJ3iiAxTxEQZVEmNwLBQSiGqmKxWkJU2CDjsLKCKhFIZyKpgP+txkLWJcpfDqImKXSghKUKEtBmmTYZOl928Q5oXrFtd0rpDXLkIcmLLpyot8nHI7t4i0wzSyiVJAxQmWtTJK4eq0hiWyyhrEJcOQoAjMjwrIS0bGLEizTy2qgZJYZBkNj4HpJUmSgO2hjV24xZ3nLPcs1bYfew8YOAbA6LMIikMKsMmcW2GZRPHiFGlQVaZDDOfoqYIrIS9pMUoq3Fg1BiOjpEkbUol2Vd10tTimjrGoW4jyKmJKQoTS2fYIoGaIlcW6BKhcgwkhiEwhcE+XcZugHJdDMPmoGwzVB5VZYBUJIWDMMAnZVDVUaWF1C7CLNFuQGA46NwhskNKIK0sDjfnGdyYI8oDssJikAYEZUloZVSWST9rYGuHtpWQJDXyBYvSr5GVLqFlodZDys0QpQWDrIZQYOkSgcJQJWPbZVDUwVYgFHFikJSQ+xZUinESIJMpWlWEVNRUjE3ONA0Z1zyGC7OUgQ1aYYgSi4Q081CFRVlVjJOQ7e0ZtnePk1Ww4c+xW7WBiDxN2TWb2HaF1gZZFYBWlJXBOKkxnQZYRsKBaNA2M+bMAVa1y8R1GWUh23KWStuERoInxsSVT1yGjGPBMLHIMhOhIgxTorQBlsS0U6o8pkw1aQ9GrS6WU1FpQcPO8cQuwtvhRvs8SqSkuUAqRWUI8o7JNHO53n6QawsXyF0HIStCGWMrhWEVDP0m6IxJFjDOPAy7JJQVxthAj20qLci1R+zUUaWBksbRZ2coLCqyQlEVFagKCWhXY5EitUmFS1lpRnlIP5slLkzQJVJWyBKUUmhdUNUEk3qNqRuwIxsUhUlRHXk8po5DJgSmKjmM6ui4TjEK8KcZu94siWljaIlG4JkpqtQQphxGNarUoihBq4xQlNhlxkY2T4mLMBVF4ZGXKyijSceWWFKTFg32wzloW0SyTv/GDFJCKW2Keh3fKT4rXVQCkgOny2Hok+QutoixjJLcklha40pFrmwOpi36bpvqS4xx/PUq0A4ucaF34UsHAtevX+fMmTOILzAc/+jMo3x6/9NcvnKZ8+fPA1AWBcIbsbT27FtxG68fUpytA/DyOOZxWTB/4wbbhsmt1jLzy13SaHLEoI034dIvkz72fbj1FCGP2oq01rSGl/jh3X/LL5+7QD48Ri8/Wn+m7tIOHZIiYCvaoWaZ3Nxd5+apb+P89Z9l5UwLkW3ycP8G15YNpmWN73z2QX6mVsOeOAzbLfLf+3FacwHBco2F7RRDa+70LjDMhswHy5y6OyCTl5hYTS7MO8SmYDppoaOU1mjC2J6wxgZT3WDqbdJMlnn4+rOcFHWOrxe8eP40UhxyavMed+dOcifo8cKrv85zZcl7w/uYfZNbTg3bHuDECYEQfK/xSZZO9FhqziHMnFZN4lgWXrDP6fQiL9Uv0aLGrjEimia83PsT2tEqe/W7zO7bfL3KGdomi851rtXPsWJsIAY1ZGnQLQY8f2PMYFZzbu/dtLwR/2qccK8+5vGVpyik5KA7TyNu8aY+4OI04aVzNvvJAsf1PVqDgqszS2TliNFnNslXXqGzconypTrGqE+/dZdevEJVaF6ttdk0LRbNQ87HF+h1nqI7OiB3nuWT/sssFpf4Y57jQ6++RNMYYHUCXq8f8A+NRS7NwIWdIx+wKy2TU/sV53o2l5hHNdf4hoffxb3JPQ7TQxbXnmTz9+foFlukQ001HOLzSTLxDdjlJk+f/H7OnLmC6z5CK1nlI8bP8NPB32H2D9e5s7RC8JGPEL/4Ip3FFdp5D+vsY4jDa3z3uXmeHMBABFTdz2eddSkpNyK00vjf+HW48g2CC4/Ru7LD9539R6T5AQfvmQMp2f1ff4zCWeHjv/LzPPd9H8Wv1ZlTO0xH58hnCoRj4izXKNbf7vnrne9S7aeU+8lfnKxBBzonYfu1t55affhRbqy/RHlzwlMf+BB/dBP0/MOMxq/iOjN43lc4V2VasPIkwf4WeXFAWX7Oqy1PK15+/h5PfOuJt7U5/1XhwtctM9iJ2bz+V8OiaSkp7tzBPfVliqn8OXjnz5NdufI1elX/+WIahEz9FqFOyHGo6QrDkiRrdbyFCKtmktVc7pxZQNcEhm1QNxPqTsW4NY9qNWg3MvbO9jgMm0ShR6gGWJakpSPs5YCap8lDF2vNxOpY6I7Amk0wTYN8SbLV6TE938E9ZjEI60hf0e4k1N0BKlDUSWiFCm26uA2LndYsWSPA0AZpN4CGIuqYbPW6WPOSJW9MjZzQL1myYmpmTjEzS/NUhdcBV1fUg5xax6WuFfHpefw5Rd6wKUob38spZk06dkYVBrTnIpoyQXkhnmdjPhKy9a4ubieiJWOcKkf4JmI+xDOmhMGU5pyi0SpIL87Tf6BHrSlx5ke4q4K2OaURFBhLFt4s9Fdq1E9aiHM+zXnJsLmIu5xiPuZSX0jwlypajQnt1pDMDPFkRTBjErd8RvNdEq8Bi5r4Yg27MWHWnmCULi21g920MaSF6Tn0rAi7W6DtghkzwW6ZrJi72Cs5NZ3j+yWq28KYD7ApMetQUzFtERF2cqyllLZzSNps4Dc9vEDjzUuCmmLoe+yszDB5uI2pDdLKRvc86gsmnZokv9hgtlnSmLPxj9dJF2vsnz9JtTDHcLlO9lwLcw26/ohWx0QXAa5pY+eCFSui7WQwL1CLLmXXwg9yFs1DZt0BjU5Gfa3COGPTMibU3CmWIxE1C1EXGH5JpVwGC3Pkz3VgpolRc/A7JlVLkoQ+xYxJbgkK4ZNLsE2YdAKULKnJnLpKUPMKs2VhzmhEw2XqhayfOMnGNy8Rn1+h3RngdTM2G01Uz8Nvl3S7U7o6InQMmqaE0CH2XZQAccqjcjWBmVHN1ck7dW4vncYpXVpmiuhJ5hjjtUomtsdgsY16okt/rcfktM/GxdPkdkiWhJSxTzuI0bbAwMaetbHOWjgPG+Tv7BH4imi1TtVw8OYDes0RumFR60mKQpIWBmYpmalN6TpTgq5mpjXCmTMp6gZF3eJgbgExH+K6mmmnTs+KCFVBICpqVcq8NWZODPDMlLhwyFyLqFcjCXy6YUnPjjADge1ArTYkbntEgcdWrcflBx5k/6EFhmsLpHYTP7CwOiZFJ8AKbNSCT3gmodO7T7czZiUccxA0cY0Cz9CU5yxqi4oZBoSdhIN3zDEtPIzCZsHPuHPhNP1HGvRnPayei5QWwq9odVM2ajPk9YAGFlHuse/U2Ty+TL/ewS80oZY4UlFrp4RiSF2UKL+BWznUZcWqs498oovVqjDqLuNTizhhjl+PmNR99i4uEXSmbFrH2Zvr4S4YjGcXmAQ+9+vL6FlwH/Dggk9vISfv1Kh6dSxP0Ao1LW9M14mwC5OiofF7JeaDBl7HwlcGDV3g9cY4VkZLDAnMgr21LrgmQadCr1o4C+AZRwXpxoPHyGdC/GaGLGz8uRARGOy1mxg9kyypU5/V4NlsNDpsNGdJez1YsQmsGLcefdF7yl+rGbRX9l7hBx78gb9U7PXr13n22We/4LG11hq+6fP67ut85Ds+AsD2rZcwTM3M4hGDNjlMKQ8yFh9Y4X6as5mVnP7NXyd64gmKS1e4fOodPHLvJr2V41i2DS/8ODz4bbRPnsS/OuWNT73Es3/72zjc3+ZfiH/K9af/ewzjBtlgjc1rQ849fcTsPTTjsbfbwKrZnDVtmn94HuTv0Ni5TG3yOuta8227m/zOhUXeUcu4rzSGqKimS0yXxoSf/D9ZWXgOez5E7yYszCest3tcbJzg3924zrdubfGHS/vUqWh+/EdRVZ357QFVF7zBkNZoESF22ajanPJv8unep2jd/TBpHeYiWD0cHPWLZyXXhjGzMuPap/f5prrmnp1hJSWfsgzCep1X4jU6nmQkOnRP2Byr1ngDiSEDAEprkxP9r+PG/AtkHRNPQblzjD1mmYQ71LIGtXSFh8o/ZmqYyL06fX+Rk/kt4m2XfpnRqCZ8Si/T6KU889oJ3vPdK/z6x25xby7jI+5ZPlnboVMeYm1bHF7UfPDwPv9yaY5aVLHKBhtFl+3eHKHdZvfGddJv+QWi3Qvs31/gmdWf4rjhkvUUg1s5d8UWkQVPePdZOAxZOnWWueEtbvjnuGT+GtvZA5hS89F//VO89sQxXjHWKR2XH7k24DeWOnz4jZCtJGez6fNd2xG9ZyT/cuNBPvTQWUI75OHew/zJzp/w/pPvZy73SUOL5Ds02cu/jV/8Rwbjv0V3dR1tPkqzcR2t/gmjg/+N547f4xPWd+G8LvmDv/0UwYrH2uYWnc2CoUzI509gvXaFBz+wxDt/9B6rnS1+pfZhzmr9tsIjX48wXBPvbAfRW6Vyj8ONP8a78BAXrpbcPPth/uDebzL3mEc5ucwrgxO4quT8u78BkgFiOmSuOkV05mgTxFltUtybED72OZEd4Zj4F3okr+3TfO/qX5ywy0/A5stw4ihnGzOzyBZIJGuWx8vC481XL+GtfJpu9+u+ugJq9d2I9U8RLK4SRVfodN4FwB/90k3mTzY5/lD3K1/7y4Dtmjz23uN86mN3WDrz2Ne8KCzW1zEcB2vuS6hofgl4D55n9Ku/+jV6Vf/5Qgiozs5i3O0zONvDinPq2xNUz8UwffyqYu/MIgstl+beEDtwsWoCIUAaNgcPLTN/r89aa5tsKcDREq+viOcalFHOdLVNbZozbXjoqmKmSDBLia0Vrq4YCZO41SBvNfHSCQudmNjrslNmyEWB1RFsqgWKXogACsNm+eoe8fIyacMmsATxXoaKc6ymSaUd4rAL123WT84RqgqxF+MLyU73JFY5JFyt2DjZoj4sEBMDc9EmmCjshkX8yBn6tqZ7bZ8DaxHXs0BWVMfrpI+uUpQVTlEyV/Ng4uPeiig7HvJ0h2Aromy4jFZnsAcas+3RrDnEfocozgnaiuLYInWtMJMC0XUYz9ZwHINDZWOhMU2b3uUNLMtHn+ySVRpyiXtH0ctSDh+cxSRjcqpHvVFjjMLSBjoraFuKWNYoahGZZxFMTaxmQJDnxK4mOrFINVtn5uoeSvv40kbmAtfV5ItNdDvAHiYIWbLz7Aq20ujLfWbHMRNPATnxyQ5OPiU6ERBGBtFKi2BvirYsWos1TEBfrVPO1DBP+BjrYyazIUbbYXAMtLDIZ0MspRG6xJYp7aUmMjCx44rpO5bJLdi3Bc60IA5cDqd9qsrACAKUBicpMHXB+nSR1iRHGBVNt8IUJRgS07MpGgZUAYZV4Fg2iWkh5hyo1xg8GjB/Y59B5VJDgyFwz/XIixznXkkx36CoBfTu9yntELOYUrk1slM+pdJQhvgbI6JOG391hpVCklkJ6UpIKcGt+5Q3CzDb5MJi9KiPCm3wLey9BH8QU3Nt+qtdAq+PVwnkXA+ZFHRqDlnSQipJ2zKJ6i2CKmfjzCraMPBdwbxQCKOgNAyspRqJbZLOhMxc3kIKE1FaGIFFudrEQGHVPHBNXGHgqhSnlKQn6rj4mOsbOEJiIIiX2ozWOtRyk2BrgGEIIKKcrzE9s4xvGbhZStn28McxwzM9jE3B4OQi3jjDHyTYWuNPC0q3xqDhkTccZptwubXIvD0EraljYBiS/myIMVC4tslCVzDEp80G1bEO+XhK1KshpGLmMEfJiqIlKCyb6/UzBKcd8iJn7fdvEpqCwxmDa8sX8I6V+J5LXSjymo+NxY2HzxOaNqbWMAqo4hC5APpsi03bxCxMIsOneauPlgJ33sPrBsiaj/nHdylnAwanV+ndPkAUBm5csvXwcRpXt5kutEnMObyZBmZLUuqK0NDI7gpaKXwhQBjU7TFdsUPUC5k0m8waBsWoTuZZkBz5rwW6pGoFRxsbexOGDy8wHqSEtwdIJIZpkaAYdzqoWR+ZSUZRjyArmcx4BGbF9nieO8+dwHQtsqKGE/cJHZ9GoVGpS65gJlRML84Qmy7um+vEKOp1yI632DfaGBIGpMhJia8VWc2lZnlYZQk3DxD/X/FBG2Ujrg6u8uTCk18ydjwes7+//wXnz+BI6vqcd454LiYMj3yOtu+9ALqDYZgA3Hh5j4Nlj8e7NX7ncMJ7bcXkZ36GpX/2z8j+yx9i95t+iI0rl1g5fwFGG/D6z8Hfe5Fk8ItUhc/W3U/wrPog1q/+EK87FzEf/rucuPQ9jL0PvFWgTYcD/Ouf5qY7z6ndA971hwO2wzt0H5igX9IYv/DPSZ76KM0kZf6EQ7yd8cuZ5r/d+nlujldZ0H/IvfCbebj4P7C634i0NA/HBca8TeWe4zMv3eSB1Tm2vS4LVsju1S3meYaV7bvsnzlHtTHh6as5r59ZYqoNhCGx402OO6/yqeQ5PNPi0fUt1tuLBIbmAb2BsH36/RO4vTpL4TLDey6ZMWDff4Cn1b8nSJYo0Vzvv8DAdtEyYJwceb9sTG/x7dkH2O3Pc2XmkIdu1lnvnKYzOsX62f+ZtYM1PGeVMyNFryp5eXcBDMGJxk02d04zsHawnDbH5KuI6BS+tqitNnhGXOanDXjlhqLozfHOFZuN371M/V0GlrA5m4dca67zgTgmbt7mcnMJI5hnR14ndGNk8T7watzZ/A98w/yEu48vce2VOt3Fd6Kciju1DRb7TzG39a957Ox7ed03SCyPe5zBME3E/AK6/hT16g5mtMCKW7LbMhBa8vE/uszKcB+nsFm8f5uEGa6NUp4Cnlt+jt+7/3u8/+T7OdeJuB2tYNkmd/b+d9zzD7F++T4/Ih/kpw5u8PXmdzB9/S7y+DbHdlL+/vC3kUpiPHqKf7/5x/xPH/xm4v90k769jztpszbdIxAj1i6WdDbG/LvWRYr1ff7r45/7s57fGYHUb8ngq7Pfibj2f9P81h9g8vxv8OD7/gfGB8/zs++w+Oj/lfG6WfDBcybZtSGB9SKx970M4yF3c81FwD3RZPRrtz4v34LHZhn+4g0af+v4Xywrv/w43Hi7z++ZJ9/N/t1NgtcF73n2FM//7E9z8fvHnHvoR77kb8AXxepz8NK/onnuQ4zGr9LpvIs3P7nF+pUBH/kHT3x1a3+ZeOi5JV77nXU2rw5ZOd/5mq6dXb6M98ADX3Xh550/T/a//Bj6zxX4f4MvD7ZyqS03iLWkXq+TdeqMZgJajkUyCkjP25yYa1LJCqNlM3FtepMYUVYoQyC0JHl0BTEcM3OyBVGByEqypQaFFZBGHVozmzTRjE0DPJtmuUBkG8B93LqJY1u0Q03khpieT9CtU2UJpq6YjTYYmQGebaKBpgn5o+/E5j4WR38GrMWApPLQlk1caibDkMk7S0LLpDQ9vNUe6eEcln1ANesgTwq6VY7driGlwo9zjIaLExeo0Dqa2SkyvHbBqOvT2h0hLqzQDV2mUYH0TWxdMWnZTJabZGtdGrMd/EKQLCkmszWsbkWVa4SjCKoK8cAcTUsThj6q0aM8qai1HYTSGAYYlaaqJK70CF0BApZ8m2maoQ3FwcIZ6qKgFabkFxcIHYtksELpj+k4MBW7CLPH3Lka8d4p/NYu/vUDXMtk85lVMts8agnUJXuih5VlGIZk58IC3X6MpQ3yWZ/uJCHXDmGjRugco3rkCuZti5q7yPzJebTaovBcDBfUQo06UBxr4SlNO7CwLIvJ4yvMpTnTuRpJPcRAYZtNzBNTlNL4liBTGq/WwD2IcBoOli2wFh5hkOxiBhFNzyMqCoTKGdVqOGQM5k+OAAAgAElEQVQ4ShH6ITQcLhcnWVK3qHITzncZKpskr+G+Z58yKfBmQoJLuxTaZ7A2QyEktiVoSouockFtgmNRUwLV85ChRSsUZE0fIUAPm/j+mLYDGw8+RFs7BHKCNxlQ6/ZIQgfL9fBrNqHjEEcRk6aPKcA2W3hiRBFosv+HvfcMs+Sq7r1/latOnTo5dTide3JPnlFAo5EQKAwKJkkm2YRrbGNsMLbB4IsNGBvja7BJBmMbRDRBIEAZSQySJmk0OfZ09/R07j6dTs6nqu6H1rQ0VrABvdd+Hr///nKqeteuXbvWqr3W3mv/V1cYVZFAgLrronYEsApVcG3KNBDiCcYdFRkbM6phV2ycqJdArohdq6OLEsXOGCp1aq6LKZg4gkatLBIIudRXBpEdHb9UpX5lL0JFoOGXMUTwzAUR5TFcpw4ahAUwNiRwMgVsj4pdk6kTRA3HULQpyltaCQO2C2rNQCzVKZQ9iKaEx+OiCC7CmiYUCYQBm0Wfn/oWE7fSoKKvw3ZTeKwqNamK7DNwnQayJFLUNDzlKrJrIwhgyTZFyUVtUonVRWRVQgvo1HIOQQnsRDel3gLVfA5dFFCzdTKWjluvo+MgSSA4DpahUPcFMJ0SEa8GjoNjGNj1ICEjhX1FE3m7GS1SxF8TqdYKVIwIhEqosoQT9OOiERFqlIpVhN44VRGUoIlcq4Dukrqmk4KxEkufpro+ijSh46Ry2IKNoMkUwhoBfwCnVCYSslhYrCMLAlVsJKEBkopHFKmrYYxqCo9HQTEVqnUXIaoT8+nkylXydgxFaFDtiOIWCuRCGoYkUPKqqD6VelsAazrLYsBDydSJqBJNySZq6QIVuYEa0jBzeQh78IsuVRroXg3F8aCIOvUtURqZEgW3jqy4JGQRqGFqULbBr8goPo2KayM5No4UxfXkkADJFkFy0BWRomoh1148uua/jYO2f3o/a0Jr8GsvvmkOYGBggM7OTjRNe8Ey3lkvw9bw8vFC6giBZCcAruNyes8Uj69T+G2fh989M8of3/dDzCuuoDEzw2y0hYYucP7QAV7+1t+GJz4Ffa+DUCfp4YNoSicNZZz6w3+JXZjnp52fpTY7w02c54v6Km49sEAhneGuj/9vrohdyxdzXrpEFX1ugqc2/YTfjL6TmmeUmjfHuw79iKFmgbh6BedbLZRyht5KiXyiC9Ps5nBjPa91/hnh9A9ZCAR4Tc7ksbrNlHQ17zr3Y6YCPvqDzWwuCZyJv48dc3fRNDnO4ZWvYtE6xm17xvh4z9vIqgXkmkWPspEWdYZzORFNcIjYKZ5q2sCZ7pVsmz5Nf9LiYG0z/aULKBUPedElKdcYWpnFN5hHyFfRVYmzmX7GSmvYlISj43DX8ePU7TqrGxonqht5OHYv64trcSMRfN0P8pS/wq8PtyPJcRL1InMoHGhdS/v8BFKkQXFeBH8/camM4T1G28ztuFqdfd//NjO+ftoyPTyShr95/WquWNnGl+66B4UKw4Ewq6pljgXPkyy3UvafZmDxStKJXoTU/URyXfT4JiioSZ7M38wbF76D98oSxx7u46DmovvGUAyRolsjuPNWXn10ljdvbgbeTc/4d0g13cGfv/33cMpV3iB/lr1GNz+OzbF53kcleYHi2QCrhVHqShQtNcotgSIPXIjyVuCW7lv44vEvMl+apZkLnKhsZPvsFfR3fZW7qiVet/HVbBhYpH9zF7GmjSx+77dxfk2m5ckqpccPsLu3j3xyM8ey08xe+00i9/pw2qaYGByiO9EHE0/hl4+y4MT51MBdvM1sIqjIvLl5aYWofHwOQZfRupf2WynXvRVO/B3uyr8i9Ym/QS16aUncxhb7CHOmwwZhipheIXsohRF5jFzhOhatEfr7l8h2PG0WjXQFO1dF8j2je1rnks5WL2TRu19gb1fPK+ChP4NaCdSlFdfVV13DD3/6YRLxN9N67W0k8/sY/NmjXH7VfzxJ86Jo2QyuTaweZax6kOnzWfb+YIjb3rsJj+//LVOhrEps3dXBvruHeP2qbYgvYV608tGjGJt/yb16z4La2YlbrVKfnET9FSj7/6dDlGWqtSa8PRbZhVXEIh7wX8A0a1xY0KgWbQKB9QydOYfU0oJdd8g2qbTnG5SiPmpaiUwjTtFbJyA0EKQStYhJLXsWXUgSborh0ddSK+cR7SPUehP4Ks3YkgNKDavWjCw42DUXr5yn4q3h8XpB7KCcWWDWMIjFLMqZNPV6AFUpUc9beIwVSKEKdqMHQRjDFDVEcZaKcw2pygxio0gsEEWSdQRyyF1N+P1+crkcTu1xXFaTyVbQqw1EaQBhzWvwm8MUBQ+z0xqzzQaBiEGsdZGs1odlVSgWprHrAZI9PeTzOmrwHCNtFYKeKIqYxd7YjmxvRsosUKkXlvImBUsIdo56pYtoNMn4uX5oaUXRZpDFDCEpjGitoZDK4JSqaPE49tY1hCJNpAtnEBoB8DbwNxSoVjECGUClnBlF0lSaw31Iqo6czSFpMl0rVpLSBpid96NutajvLRKJ+HE9LdiNNqDM4uRjGEIGXVaxujooeIMY1iyuv5t5bRGPrBAJdCAIEoJvFdVQjnA4TCgQYGpiFEs/DUAoFGJhcRHT9eO63cAitdoCxdZFGkUNXdIJNW9BlAbITQZxajrR9hiVyhAGfixflOzG9ajlp2gUbXzBZkJrr6DUKCPOnUUNX8AKNpEpTTNWK5CvR1kv9SJKFdp8ATy2B62WJVePE423U3BcSpkUZnAeuzKPanjAENE9OxA0h7qURy+bCE4eUbcpmxp5Q6ajT2V2IYQ7X8WKJmhr7+Dk/v1UOyqI3vXEPUGghlIdYVCoEfQlKWd1/MERVCuAZXkIRqJMjY4haZtwGzrK+tXofpt6IYfc8OLx5Cg0RqlVYiieMKYpYK28jlJ1As9iP05ewuuvknWz2IleKj0lokIfyvAgjYSDPq9hRbyoeKjaJfzNQRJNcexKmVLDoVScx5VlrBadWmUR172Ar6mXcmaCci5OoCVPcUGlWu2kIZYISF7i7SHKY09hxYNUWy9DN/y4LhQXZ8hGspQdkZiVQzRjxGUdSZwGx8EwTcTWNtILSXS9gTCfp1G26W+ep1VegR8Lxa/jy19AVheoFFoINWcpFZrxBJuRJkaRezxYjRTiZI7qxhvpXbuduUceQ/SfwR9ZS84Kk0/9kITmJzDTAD2JEVlHeT7L3FwRX6xBJBxkrukxhEIbstqGarRRydepV9IY/g0UK6M4YgnTJ6M1mikuiASbBGrxCm4tieV6ia/uwq77mRj+Nt7uZoRymcxkHL9ZpSZmkeLNGI6PyrxOBfCuSOO7Yjup0QNkuxxCLTHyTiuj2jwdsT6qjb3oRi+qHESQNar1YzQaImJPAuH0UbSYh7JQJ19IoJoCsaSGt1ikVGnBdfw0NbcQrl9gOD+EWQtSWZij2C2im604zQV8ZZPCmIma6CFHDSHQQPD240ei6mwEpYGux/GpLoIkUpFVUC0KKS+61yFgT+F6HaLBGNlMFrtWx4qUIN5Jom0jc/NVBNHCdRwaSpqmlizlcplSsUZ6IoS41cf8/ntedEz5b+Og/eT8T7ix88b/VNn+/n5WrVr1gv+fmZnBO+cl7UkzkB6g09NGzTlPrPVtAIydWaTqODQ6TequS/XESWIP3k/sR3cz/aE/48lVO9AmT1ItlWhPhuD+78K7DtBoFMnnT9HV80csLH6dfY/v5bGVf0GoOcyeuf1UpGZOWkFu1gp8/y+/QFPvCjbG1vOV/RcQaSLbegZXthHOtzDS9jIiqZ/SmZvhaMRlej7BgZVd/NWFf+S0dwdr1qzB1LYjhQYQdn4e9+7/xWH3bVxffyWq7VJT4rQNHeVUbD1pIc5mj87QoM2NWiepxn5mDZdTHQVeva+GkKoy3VFBKoUR1Sj78q9km/kt9rivRBBltp84y5duexuf+sJH+MnGDJHJHoZLYRoZk4CT5V3Nszx69BxVFaqaieJXmHHq+LKX8Vu3buP939vHV/Yd5iZpM6bg4UJlEy21p9jTFSVp2wx2p2ia8bCqEiKtVFFdh6nxy9i9vY8ttcOkyh7qVhArfx7TyDPZVOGaEx3cZS7wikN7eeq6RX5n+i38jQ0npkWu7bOYiiXpLU3Tt/pqdjx1Lz+oDnBKameTM0D/QpXT3d1sHKyR7e9mq/oonWuSnD5+O+nMEaL9/4AW+A2iuSQD/ge4tfNWRkdmqckttFW91GVom5yhUf45Nq9lONbEA9vWc/rJv6Ecc/hZope/6M/Tn2jlqhGo61nKmkGzOcIb1jRx64Ey8/kKMSvGtclruW/Px7nDdRhZlHCaytyVVnlHS43OlgnEw11cnVeYaoygXDtFr7KTqnMEpvNsafPy6VwJS/azu+zwira7aJ6weGI4x85XbqMxegBn9ClqkW0Mn7uSb7wmwFuGp8g2bN5RVWjMVwj/xprlVS0hEKcaeCXCk/+CeeWVZO7+Ee1v+gP2/Mtvcmatn51PpRB65yhNPUVhokjFqRG7vJOexTCHDx9m586dqEmLyvks5qbYsr4JooBnc4zS4dQLO2jBdgh3w/mfweqbAQi3JmlpctCFJ8idfwMrXxnkob/zM3r8FN1bfgUnTZRgxY0EUnOcVI5w4vsH2HH7euIdvl++zl8Ba3c0c/KxSfr3TbPmJWSPLB06TPR9f/gr1yNIEp7NmygdOID6ute9BC37nwlF14j2bENuCETjGrqu02i0oKoq+a4RynaZObVM14ZNTKQnWBFpR1JVRFFCA3Jjo5ieMmrJ4VxjlF5/L4Zt0NN9DTEtRrlcZn5+HtXwk4y/HtddYmX0zKfwhDYyUZ5CHc9hBIPk6g4Nu05KTRO1W/AlOmhqamJmZhqpuYArifgaAfxdfhAEZmdnUVSJ5uaXM1OZISRtwxZsjECCMDZzmSyCIJBI9CJJEoIgkM1mKUgbGa4PE+4Is8q7GcG+HFXVEMVVpBeGMFcFcdtcQk6FQGAzrUkTURSZnZqkZjt4/Um8fnCcDgbVk8Ss1QQElcXFNKoqU64UaBSytHZ2Mevk6WzqZHZ2kWA4Qa6lgiAImFoPBWeMrK6zPriGSrzM7Ozs08nbLSR/hIT1co6kjjBbXuTKtsvJptMgdKDrOq6zkpbuMLML84iCQNeaVcvEYpGOLqqqjGvOIwRdyuGNNBltzM8uEIt1Qp+OMnmBhCPjdl/FROkY/ugKsnqBglmhJAiYuSwhKYSmaVQsC/3pkORYogVRjGAYIRynQDSqUK/VMQyDxUUP0eQmpudtdF+ApG8j9Xod113AF1hLZqGIbgQIBFuRZRlRFAkGwbbbmZpK4Q/G8RoqoFLVNnFgYp4evZuEtZ6Z/Anikopa9DHTKDFTP8OuLTcyNTJKZzKJpmmUxsfxhpuBZpp7g+TG/pmyWmPl2l4kJCqVCrPTU5wr9dN+4y4aM3MopTyatoIpYTdteohQNErKniMU8hDvuR1V05c5A2qlVvYP53Bdiy1XbcF1VyMIHkDCcTLoVhq/vw1dN3FdF0VR6E/9nISj05zYzj1D9yBrEkE7iNbRjWn5MH1rCYfXMFeaZ7x8HnWqghVrQdY8eFpaoLmFFlWmqcuhWq2i60vtEQSBhdoCGSFPup5mY2wje+b24Jf8rEysQnH6WEgv4LduplGo4I03Ma/N47gOpikTCsQREanHx1G3bKaaTiMCsViMGVFkIVVlJpiltbGCc1KGRnWWyxN+bHstqnYOVVmH5fMxPT2NrIuMi6fQZYNAczPNYjOn7dN0t12OVlLI53KMeadYaF3g2qbVpMMJfJZFUD6C5FtHPdJFtVpny44rqde3IYoiCVFkrrYa2RND2jhIU+x6kCQmhAm294RRdY18ZR5FUQjFoowprURDTWi+CkVPg0AgiObYZPMp/P46sryB/kY/dQQu1CNsalpNItyEKEpIErT17qIhKMzWn2AkuMiCadAlrGZF00py9RzzIRlSRVrbtqLqBjUnjE9YS3ekl2MTx5AqafyBLjR9AkXpRJLiuK7LzIwGQoNirEi09SZKtoBPq9PW3YQkC9jOOUzzZUxMzC6lChFr+LyrWe9dTaFQoJobw2NtIhAKoaojSGIL4eRS0vdMJoNkyPj9lyMICkZLhGKpiN2w8Xg8pPNpCuxAURXWbItQqVRxxXEG84/hC16H11tmZv0A4fWbEZ7+PorpcWRZptFo0BxfhSQNYJoi2WIPZqtIySnhtj2X5PDZ+G9BEnJq/hTHZo9xc9fN/2HZXC7H6OjoMvnH8+HgwYNs3rCZGzpu4N7z9zJ4cD/eRI140w5c1+XwgyMsbPCzM+TjR2eG+OhXP0/sD/8Qp1ikcuYMdwfW0FmaJLRyLeLuj8GGN0CwnXR6L6bZQ7wRwx9IcVTfwn0TGqdMeL1xAiPwMnyOTS5/AkHp5frf/n1KwzluLauc7u6mNn4ce3otpZMOlZ02SsFBdBr0jQl4UyPEywtsE8YZmS+xfv16Gvkt+FpPIfReRy75Cm6q38XR25op2A5Xez0UZR8PJHYSS5zBNReIqSL2wDBHtnRRkkr4sm0cX9/GG4YfxQweQhQEym4NVapiCoewA2fR5lq47tQF8vEwj2zcxmWnGuxMn+DRWgtddZE7wjOMHNpPa2qOfNhClouYhQJXDG3i1ZVTvHxVnM2tLudnTFadMHGlaW4SiuyYvYYTzXvonh7m4cVRes8atHmSrPXcz1wtgbfsp79jAyv9o2QznSwGdKjX2G9VWZnfSk6E1bl99Df7KeoK6yu9vLUywDf3DPPuB0+T6uimPCyQ8Qq8rJqmZzbAwdIgcSHH23MZ9rR40cJ17Fmd3YUu7N591BsneGTxDThDP6fZX6KvGEHQh9DUEGYjhPNEigPtF5AEgUR2jvYpk83ZAzRVRollhonHbqSlc4lZ7XB0iE8b7aQp0N5ow/BJ+BuDdHatpkuQ+dIDSzm43rP5PXD2Hk7VAgxvlvk/hft53XdkotplXJj4DPoOk9/vr3Jf5gDlaoTY0QNkxjrxt5fwb1vLH7THkV2RH1Tewfy6J8ml78eZX2BBbKbS/wjdSoqma25DN0Ry3z/G3Rt7uHswxcQ3TiMENYw1l+63El75fpTU3YRedyMLX/saj/7TVzH0Llb9+jyDHQKpwSCl5F+Trt3Ik6mf0bl5G9u3b+fQoUPYto2xJkz5xHNp9c1tCcqn5mkslF9Yede+Go5965JTl69QmGs8Tvn0IuWxs7zsTTfw0Bc/Q2HxV0zyvHIX0uDPqWY6ab/sLKuvfPEP4f+XECWRHXf0su+HQ2TnXqR/fgHUJiaojY1hbt/+ktTnveYa8o88+pLU9T8VFbfCYH0Q0/IhaiIHMgcQ5aUhtu5rMGVMM1mZZFaeZdGzSFYsousGJ3MnOZE7QdFXxInYVCIlLMtiWppmTptjqDhEzaghaiKKolBUixwqHCVDFld2WQw0OFc/zwyzKH4vTSt6iCebSIdy6GEfTa1NtLYuGfKtrUlSYpoFd4HO3i5EVcKVXGzRZkQfAWC4NMxMY4ajuaO0WCaq10vGzuD3+6mpNYpKkYpdoWSVGGMMj7m0Ij5cGWHanUEQBepuHRIgC1Pk1H6kkISgCwyVhnBdl7aV3UhRmYPpgxzKHEIUPRiSzfnSKQRFpKmpCW/QS6W9QabDpd+cZNazSEGokGhO8FTmKfSIgRARGVBGKXn9FNwSQ8UhptxpREnCERwa4QZHs0cpUQIPiKJITWlwTj1P+7p25IDMhG+K49WTjOlj2IKNg7P8TiVZRhRVJkkwdnU3M84iR4vHCLYEEUyBirfORLtJvq+HTCNDa99Gcn4HS3YRnw4XTutpEs0JgqEgyWQS27XZt7iP49XjHC33U7EriKKXs+WzjDgjSJJEojPBTGUGV7RwNA85cmgejUkhxqnqOUaMEU7WTxKMBzmSO8J0dRrREBmtztLa0UWwPUjdqTNSGmG0OkrOdrlQG6VEDVlaiiKoU8fRHCyfxbHaCcaCKfJCnrJdphgsonpVjIhBxRXwmHHKboXDucMczB3kRO0EmWiByIo28lKBZFcn1772DuaEp8eGHi+W38doeZR6hx/Jpyw7Z9l6luSqLqKJJmLRGA23Qceq9bR39CAIEpIUxgxfAbrEkfwRjhaOMlmZRFZU5rUi+9P7UWQFw+/hZFOBY8owh7KHcF2XvJ0j609Tkxx8HpNFXxp/1I/rugzbExxIH2CmPkNeyuMIDg7OUsqmynlS1RQ1p8bBzEHC4TBlp8y5+jmOVo4yLA9zVhwh3N6BFbJIJpPEVsWYshY4VxrEavMzuF7jvH0e2SdTp854fRzF0OjevIVwLE5LXx/JniSeUATT2sH6jdvJC6s4V5phsjZJKB4i0Z7Em2wl3NSKpmpU9CXW5PPF8+T1IvOBpZC4YCiIaZoseBZQTAWP0Uch1srpymkc16GpvYmCW6BOnenKFJIg4Aoe1KabKLI0sZFMJvFZOmllBt3QKW9bzekNXtL+NKeqpxiyh7BWWKztW4vVkmDYl+VoSSDjZMhZOXK+HH5/ANkrczR3jHFhnOHiMAfzwxzJnaMa7GLV6lVopklkZRvztXlGGiOUg1Vmmoqcr49guzahcJhFcY7W7lZcr0swGARAVTfjCCGeyjzFeGWcEWWElJNi0bfIOX2CYDDIiD5PVipQFRyCsSupuwI2NsONYU7lT+G4DhW7gtfrZeW61bS2tyF4BI6UChwvXWBOTnG2cZaurV0IXQLjSg7J5+OMc4ZhZZhTjVMczB1kwBkgk8iwEF5A1TXQYahWpC53UrArlCWR5Obt5NwC55XzHC0dJRqPEo1FcQIOmq4hy90oah8j8jhnqmfQwzr1dS9O8CV95CMf+U8PQF/+8pc/8s53vvM/Xf4/A9uxec/u9/CGVW/g8ubL/8PyBw4cQFEUNr9AaE86nea+++7jta99LU2BJj59+NPEj44R7Jpg5cr/zUR/ljN7p/mXTTofmBwk/OEPYl11Fe3v/QOmP/RnVLa/jH0NhRXzR/H7KqzJ/xzhjm+CYjA6+iX8TojYTz5Fqq2FVF5hpBxhsNniTfbnWdv1J0x/5WtkVJtgZR2dPUEefWiMhV4v96/wc8XBPawt3IEm64x3HcAzIGHVFjh//U5e9vBubhD2sze+lvauHaxfv57jDxaRo98lFnsF954R6LLPMDv5OOu4mrvcCvFzQxwJreWGTQ5jswdpmYoTP/tvPLXjSk77+mld7OCn2mW87vhujq9OszJzKzVlkWrtLKekNlqNHGtzBxnyvpHb1gT52MpO3nTPKe7seRUVUaejWuOy6DiDo0PYskgj1sm8lMFazKOarbxDvhtzy5vIpY9w4ILOFtlDX7dFeDHGVMFgOniWMXmQsmGyfrGPl8mnSXoe4UD+Do6WKuzdsJG3a//EodO/xqxwiGhG5eGts9w6dhvDdpFk5hAPbShRkbdxS7mZ7qYYscM/4uvVFlrCzfgvnOBUq4iWFWlP1dkdTXHj0O8S1cf5SSRBd2YYX8qlGPaTlsPUnQyq04eTa3DBHaJeWYsQWmCqMc5N9SuQyw6/u6GZNV4fx7xhbt79JMH8aR5aczt33Hc7HmQ+rN1E34URngz1UlYF1kz0s7nWQdvVcZKTX+Ss7/cJa16+fHaSt+3oJGz46Xzi83w65HIqLPKGIx6u7GthJnQIf3ATWe1J9IEWZoV1rCOLOTXO4s/SNG1eZKBjK7dd9QruHJihoiroiVtpavk+yYLEUwMOG+w9eJ0c4o1/Taf9IAePRTAaJr85XCbTsPlBi0Km2cMKj7a8t0iKJagcOoO08CR78yaL6QXu+MTnsXxJStpupHtEQu0Co+vOULW72HjjrQQCAY4ePYrX6yWxIknmnvN4L29CUKRlvRMNGadQp3R0FmND9Pn3MkVWwAMfWHLUPCFwHPTdH+JIsQNamgie7aNr102UqnmOPPATVl15NZL8yy3yu/5WGj/9a2acG5G7HqSl5Y2I4n9dwIA/YjzNInmBns0xFE36jy96EaS/9S1Ej4H/tttekvYpra3M/u3fYqzf8JKHOX70ox+d/shHPvLll7TS/4b4wj994SO3v/N2JsoTjORGAJisTDJeHqehN2hpbsFjehA0YSlkOO4hLaUp1AqYfhMzaKJpGqIgUqvVkCUZM2hSrVZpGA2GFoZYHV1NIVIgn88jB2XOL56nYBeoOTUA0loJO+BSNsropo5hGBTUAqMLowyXhpmuT+M4Sw5IzVujqBWZd+aZb8zj4pKpZ6g5NepuHcNrUPPVyKt5qlqVsdIYSkShKBQZTg8jekUEBBKJBH6fn3w1T0Wo4Nou44xjeS3ypSyy62I1W1T0CvOFecaKYzR8DRbri1SqFeLNcXJajlxuiSF2tjFL3V8nTRqfz4fpNSmVSoiCSK6RY7wyjuM6eBIeikIRy7JQvAqFQoGiXaRoF0lLaTxJDwW7gM/yMZodpaW1hUgkQld7F4JHIE+e2cossVgMv9+PLMsocYWZ+gxDmSFyTo6hwhA5NYeuG7i4y3kg1ahKUS4ynZ/GVhzUFi+T+UlkV2akPILu14lEIhgeA5/lY3BxkMnqJG7E5WzqLIbHwPSYhMNhpqvTzBZnKdpFqk4VOSaz6C5SVaqYXhPZkEnX05Q9Zeaz8wDPrPw4C0iKhGAJzFRmmMvPQRgyjQxnZ88i+2Q0n0atVqO9rZ3RhWfyHRbUAq2drRQLRQL+AJFIhKpapayXUb0qI9kRFhoLpKopul0vbjiG0tVOMBBE1VS8AS/5fJ5sIwthSNfTjC6Oous6iZYE88xjmibzxQWEgMDp1GnGK+PUjToL9gK6oTOTm2GqMkVWyDJeGmekMMJUZYpUbWFpJTcaWtqLbQmEQ2EURcHv92MYxiUrYC2tLcimzHh1HNM0cXGZUwo4MshRmXnmcTV3+Z5yUKagFlgQFphnnuIcryEAACAASURBVGAoiM/nW5bBeCyO3+/H4/Fgekwsn4XP56OgFcgIGVa2rWSoOIQoieSrecpSGUESyJVyZNwMga4AruZiB2wm8hMEQ0EKehlZkvFaXvJKkfHSOFP5KepCHcmSyIgZRgujxGIxisUiBbtAup7G9JrUa3Vs3aZULWEYBnbDpkGDklwiV8sxI2SRAgqGYXBu8RwFucD5+fNkhAzzlSWZyTayOCGHRWeRufwcg8VBJioTWFGLql7FGwqSrSxNIPoD/qX+lhXGc+OMZEbw+X34A0Fqeg0BYakdtk1BKBBvioMCNbFGqbTE6ByPx5FlGZ/lo+SWqGpVPKaHaq1KtVLFxmaiMkEkGsHr9TJZmkQ2ZIrFIoIucDozQMPXwOP10FAbBENBGmoDRVGo1+s0pAZoIAdlBmYHyEpZzi+eJytn8fg9xGIxJquTjOZGsfwWR+ePMufOYftsioUitmuTb+RxcHAMB0dwUHSFolRE0zQkSaJcLhOOhKlWqsRisaWoCKPB6ZnTNFwbjxVCCAqk3TSu5pJVs6iqim7oDKWHqAfqyH6ZjJRhcH6EWbIkk0ki4QiZ6hLD9L133vuC4+N/uYN25+k76V/s52Mv+xiS8OJGS6FQ4O6772bXrl34/c+/V+2+++6jpaWFDRs2kDAT7B3YjeQ9ycbOHURCN3P/F05Q7Kmx6/ufJ3jPj3nkjW/l9g/+Eemvf53inj08cctbiR/8Njfe8WtcOf0FHtJuxr9qB7rmcPbMB+g9eAx112dxm1eTKzyCMqLzpr5zRDWJU9+bo+IK3H/T7WyuKww/OIZlynypx2WxBGtShwkERQLNLt+b6OGNx36CK7jcv+oKRtZ10vnEKMfjG7hpVx9eT5Kff3uQFVcIjE88zqFBk09ueivvm7uPy3K7+b6yiqFCkjZrmD8eXMs3Wr/PbfvmyG3p47wXxo0JUvkrmHUTKPoJbjgl4vpvwEYgZ2VBVcnMlnmt8hBNHRJH9kcR8zPcGd9BxZHZUagxZNRpOfkjtJ5ulECIrMdiT/MeSvIk7eUOfiZvQjzwGR4/OY9RdThprGVF1o9rN/h7JkkqTRxsOcJ1J2IEva0E1MOEtTn2FN7FF2+8gpvLT9FdXaRy4tfQi/u40FTDrARpW7gePfM9zq7bykLwKapDrybRqLEu1MGf3LCV1554BKescpd7JSdat9DUmOUG+zxl+1oeijzKRiXFvK/OWWU9HWcOo7S2kpqz6F2zgtLIPjLCzXxdidKunaApdSUDnse5feEy3r/BRK8WcQwvi9UKaysnESfrhDdfzUKkl/75Ev2+Jm7N7Cef93Iu1kJOKHJVziE0LmNrJvvHernxdSu5Z/8YdRe6lLNoh+/kb4NRVvra6U7lEK+eIPwVjeRVf8jEwjcpRfewYfB6FhoZag8No4g+zPUhMvkxQt7rWXEsww+aZM6URXoTt+A37sRFwSzW8Uk1xOwY8rm76ZT3kB5/OVLdxV914NVdfGR8hn+bXkQRBHo9Oooo4DRv4Wc/vJN5JciWE+eJ3XwLgdYr0EMRMid3I4w2yHVF8LZN0tzzGiRpycE7cuQImy/fSn0sj52vo3VdqoNqh5/84xPYi1W03sBznTTVA7kpGN4Nq2+BwYcRhh7G/6Z/5t5vfIpodxh5n0rvq65mZOgY/Xsfo3vr5Ussqr8gjv1smvyFITasNFiMCBRLg4TDV//C9byUaFkRZH4sz6EHRmnvC6N5fvHnAqinZpn+0w8S/+CforS0vCRtEz0e1GSSqfe/H+81O5HDLx3T5f8UB+1zX/zcR256w03ouo6iKJTLZVpbW/H7/ZjmUmifJElIkoTP51vWD4/HsxxSB6Cq6rKhaBgGlUqFUCiEz+8jq2YRRXHZocjlcvgDfvw+P8ViEWB5bJQkCQEBQRBQPSqWZWGaJqZp4jgOHs/SypeiKBgeg0AwgGEZ+P1+NI+GYRjLbdQ0DVmW0VQNURSxLAtd1/F6vWjqkjHjNb0YhkHezhMIPB3qLICAsHwvVVFRNXXJiNH1pTo1DQFhuc9EScTn86E8rfeyLKNrOgjgMT14PB50Q0fTNFRVRZZlJFGiVCohCALNzc2YXhNVUTEMY8mo9/mXwo5EkXQ1vbyao+v6Jc8oiiKapmFZFpIuoagKlXKFSDSCIAgILNV/Ebquoxs6qqKiaRoFCoQj4eWclJIkIctLRrlu6Ciygs/vw/SYz9xP15jPLBnRsiIvE5tdlBVZkjEMA0mSyOVyWD6LWnXJIQ8Gl1ZSJHGprRd/AxieJSdGEiX8/qXnv+jU1Gq1ZVlRVXX5XV90eIBlGbQsC3liGtf04jYtTb4psoIoiJimSaVawWf5qNpLYYOapqEoCrK0RHRyUdb9/iU5vcgdIAgC+Xz+Epk1DAPTa1Iul7Ftm3A4fIl+KIqCJEno+tLkgyzL2I6N1/RSdsrLdeu6TiAQWH5uWV5qy0UH72JY6MVzy3qiqXgtL7L0jD4KgoAoiJeMZ1OFqaX3Jcl4Te+y3no8nuX2SpKEKIp4vV4UWVm+/tl1XdRHVVnSCb/PT71ep1QqoapLOhvwB5ZkyPQuf0v8fj8YYHktdEvH4/EsP4/ft9SXXsuLaZqUyiUcxyEQCKDrOqqqUqNGvV4nFA6hKury8/v9fnL5HMFgcLnPK/UKhWLhmb57Wh51XV/6pnhNBIRlXc3lcoQj4WX9vQhRWNI5x3UolUrEE3Esn4UoiMv6KElLsorK8nu6qEMX5Vc3dARRQPEqeE3vM+d1fene4TCBQGBZTlzXRfAIeEwPltdClmR8fh8+nw+v5cV1XQzjufnIJElamvyRFXy+Z7ZH1J06kixRKVeIx+JL+qAoNMTGsu4JgoDP50OSlo5FUSSTzaDrOh7Ds/y99Pv9fPuz337B8fG/NMRx7+Revnziy3xyxydRxBc3VrLZLN/5zndYtWoVbW1tz1tmYGCA4eFhrrvuOgAcx+ayp1TWxUr8cHqWH/zjHoyBH3P5596H25rkA5/4HL/3zreS+e73WPjil2j5h7/nwv3fpqm9mbVn/gL5mj/Gf9kb+cpX/pW93307vnwd8zXfZ6TrJn5c3IljVVm1/jDGwo85c7eK68Kb3/9hwhN1ikM5gq7LmFAlFZXwzE5wqrOF2thxHimXecfpz1PWXL676w7edP+PeHvlcX5w7S30joyS//iHGdgzSiBqkC9uo1h6lNT2KNviLSz2fYiJiTE+e+KvyUoWf53bTr9bZtPUlZhzR9nn93PGd5oVtSIZx49sneC+q8p0LhTYKjyA7XVBkmj2mpjzE/x4dC3z5d08GThF71SMt6Q1dgk6W4s1xkSFfquJxdQMeX+UenMNW5FJhQJsOnQIRVTQnAa3yQN0erezq6DS6oj8vTPNtd1B9gTvo6UY4UzHNO8S/4lVwnnudN7IPetqGA2dl/MIE4fezJ72L2NlFY51zbFt7jU0qqdR1CZOhh/mVe71fECo8z3HwB3O89ZojI++/3f4cP4w19cepyj7sGa3Eq82eK0xwbCzwIHIOa7jQU429VHzhIiORTmql7hwPku1XOIeqUbe9vFO4S5kzwBXDryFb0SmGTcq7Lhwgg/fe4RN/Ue4t+fXqSRy9H71b/lesYVPd7yNV88/gJY8x/rJQbaNnuHJjlV8dH0c0/cjaryCzeU6jfE8r1sf4DOPn+GHP/g9julx/sL6XaLiEC0vn6E2eTXc2MXJmffiOxwhGN9OJvghmu7+OQ0xjO2W8Lzxr9jonKL4kwe5+oZu/mF1Gw3gUws631I/h90+h+4vUZK84E/ivvsoDf33aNZr2NUG04rEDsti/2VreG97nG9NL7B+7yned/AkX//K55hXerkjfoDETTuZ+uCHcDILqF+fwbfqN6mOKkQGzrHx9Fn2Pnw9pdIoGzZsIJPJcPbsWXyvaCf/xCSNxUuTVouaROQdfVT6F0l/bwCnZj9XSa/5Uzj3AIw9CQe+ANvfiR6p0nPzFIf6n+R09QALXzvNy699O4qm8+3//UdMD577hb4rp5+Y5MiDozS97t0o5+5ibdefMz19N/Pzu3+hel5qiKLAtW9eRef6CHd98jAzw9lfuA6nUmHi3e/G96pX4dn20jJS+m66ifBv/RZTf/J+nKcNuP8fvxwk+ZmB+vnwfOfXRdYtR5EoikJzSzOK+sy4KDz9Z6nW8rl4Io7ltdA0jb6ePlqfXv3cGNt4Sd0+3bds8CuKQjx6aWoGRVaWjSxgeZ/Zs9vr9Xqf+5zSpZOqoiheYsx4TS+RSGT5WNM0Lmt/Zn/pRWcElhxVTdOWDd5nQ9M0QsHQknFq+emJ9TynTDweJ5FIoCtLjtCvAlEU6Yv14TGWHEtREAn4A8RiMXqDzySG1zQNj+Gh2duMLC85V8/ux4uQRAld09kS30JQDy6f12V92aiLxqIk4gminijXd1z/vO2KxWL4rKX+TXgSlxjBAsKljr6isrN1JzuTOy9xQgOBAMlkkmQyCSw5RVsSW55zr0Qisby6CBAwnstCK8syTYlnwscVRVl2xv892nyX2m0XjfJ2T/tzykajUZpbmvEoz1/XRZimSTz2jCxHjMhzysTNOBtiG573ekO+1DA3dANNfWHyuWfDqz5XThVFeVHyun8PAYH10fWXnLvo3Hs8HixrSddN03yOTl6UmxeCJEqsi6675P1cRHe8mxUdK5bl+9lItiYv0Z+Lv29c+VyOiIuTFs8+fvY1/x5b4lvwmT6SyeTyxMrzIWJE6PR3Lh+vjawloAXYFNuEgIDP8rEmtuYSub44yXNx0mVncicAgUBgeVLhYvmLDqkkSsshlRexvek/3jbgNb20tLSwIrSCdv8z8iuLMr3B3uV8zs3eZyZzmluaCQaCz6nrxSBcXLL/z2Dr1q3uoUOHfqEbPB8c1+Gugbv4+8N/zyd2fIJrktc8b7larcbAwAAnT55keHiYjRs3csMNN1zyEbqIVCrFnXfeya233srq1atxnDqHH/sksws/JNq0hke+Oss1x6bINScZec+H+Ec9yE+6ouhf+Vdy991H8vOf5JFH72Ps5HF+a+Up1F2fwF21i9yRT3HszG4en9lJYPNqvhHawlytjpyr87/0B9kqfZXZ6Q5mj++kb+vrOXtknnKhTtAvcahD5cGkwsrxOdzqecIXetiUuoeOqTOsmpnlzz/wMWYjIT76g0+Sz3k519XNzpFx1Nkhjna+n0hilnOxGsXLDC4vfZbGZJDUgQSrJ0zubF9NxJzjN2b28Ji5HsmBVMIHjTB6XeBgo4sT8UMokd1sXVjNB6aepP6oypMvu4yaZzMTRpah4izHQ83UiytYbafx1iPszNdwlQA5w6E0d5rd4QA3FA4iRSz6mw9w3JdDcAT+/N8sxiPdzK1s58pSnon8TWz3wSNCjX81zyA1/YiOvMVfTpwnlcixrVzl0c4+/tn9XVLBOG8+8zjq9CpOeZ4klD9NVVaI1y7DcjZyvvVHjCj9ZI0atx77dULWJiwETENg2LGpx710r8nw1XKV952DcL3Bk/4f8prFR/iQcS1PxAe4rL6OC+6tyNVFrjuyBzHxGqasKUbSZU4JSX4r+i300FpWT17BXcIRulNbsJIjZLKjlGQVn+3n/1x7HR8b+yb7Z5/iaMcfozgORZ/F39nvI12OUh/uwBNI8/34jfzL2b/gX7o+ztapGC3zPo4oE3zNlhCZ5ZbacTa+vk42v5fFskRszsE4LaEf86DEV6LOTFArzpG5TqTRdTPG2DZyrQYb5h7G07iP3O/sxfB4OF6q8d5Tw0wLMpF6lj8Y+xp66ywb7GsJnt6O5Lq4uTTqqjYGPD5O75tm26s6WHd1C6XsAj//6QMM/PRehno3cOjqm/mbse9w1fnvkh5egR38DSQfnO+sknrom6zqnyF7i4rStpZc2yEWpY1onls488QEb3nzWzCP16gOpIm8bS2S/9IByS7WWfzuOexMFf+uTvQVwUvp9498Ax76IK7rkn3Nxzm18K+0tb2DRPTXGTq4H2/Dj7y3hmd7nPONE+z7/rdpW7eBdde+gpZVa9Gfx4CDpUTUT/54mKHDKV71exuWSEG+8yaoZCkkkhw1DrL98gfQtNjzXv//Emf3TfHE9wa54te6WXd1ywunJ3gW3Hqdife8F7dSIfnlf0L4JcM/X/Qets3om9+C2tVJ4kMfQnyWAf3LQhCEw67rbn0JmvffGr19ve5nfvwZdnXt4p6he5iZnqGlpYWbOm9iIj/ByfmThI0wC+UFNEmjale5qvUqxvPjSILEqtAS8dXeyb1kq0vO+xXNVyAhMVueRRZl/JqfoB5k99huyo0yK0Mr6Q50U7NrSIJEuprGq3iRRImHRx4GYHN8MwkzAYDrujxw4QGCepB1kXVYqoXt2EiiRLqSxqN4eHR0aS9is7eZTn8nxXqRY7PHltsX1IPMlmYp1ovMFGdo97cznhtfJvlqOA0eGXsEx3F4RfsrOJQ6RMSIMFWYIuqJsja8lp+N/YxKo8Kurl3cP3w/ABtiG2gyl4zJ2dIsQT3IaG6UDl8H+VqegfQAW+NbUaQl469QK3A4dZiVoZUICNiuTdQTXZ7sHUwPMpgeZEt8C6cWTlFtVLkmeQ1nFs5gKAZrQmvIVDPMlecwJIMWq4UD0wdYG167zCY9W5rFLbsczh4GQJM0rmu/jmK9iCIqPDn9JPlanpe3vZxUKYXrujR7m1FEhcnCJCfmThAyQpTqJVaHVtPkbcJxHR688CB9kT6SviUnyXGcZYflIhpOg9HcKOcWz9Eb7KXd144kSKRKKU6mTrIhuoGYFePBCw9iqRarw6up2lVGc6MEtSAXshd4ZccrUURluY//PTr9nawOrwZg39Q+klZyWVZkYWnVwnEdcieO4m3r4qfp/TR7myk1lkJOF8uLaJLG5vhmvKoXAYGfjvz0kntc3Xo1J+ZOcHnz5cwUZzg2ewwAj+JBFmS2RrYiKRJVu4pX9TKWGyOkh0hX0rRarcyV5xjNjdIX6UOXdYr1Io+NP0bEiCwb05OFSY7PHmdX1y4aTgPHdRjNjTKYHmRX1y6AZZ0qN8qM58f5v+y9d5hkR3mo/1an6cl5c5a0Sau0yiihQM5ZBJPhknwd4Gdsc7G5jwEDJtkGgzHigkjGiAxKCCSUV5t3Z3d2cg7d0zn3SfX745zu6Z60O5umd7fe55lnuqtPn676Kpzvq++rqu2t26n1To9vBRmtrFvJRGpiTnkV2v/l7ZfjFm7+OPxHADY0bmAwPgjYEy0ZI8Omxk1IJF6XlwcHHgRgfeN6huJDbG3ZyrHIMa5beR2t/lb2BfexvmE9GT1DR6iDO9feScbM0BfrI5QLFcOSV9WtYjw1jku4uHPdneTNPL2x3qKRF8wEafG3kDWy+Nw+qj3VPDX2FB29HbS1tfGSzS/BJVz4PX4AhhJDHAkdob2mnWpPNcOJYTY1bWI8NU7OsCdgL227lFZ/K3W+Ou7vv5/nr30+GSPDcxPPAfCiDS8ilA3RVt3GrsldbPRvxFXlYm/A7jPVnmqyRpbrVl5XNKALsr5h1Q08O/4sANtat1HnrWMkOcK21m14XV4eGXqES1svZVXdqqIxF8/HafDZXtmMnkG3dOq8deiWjmEZPD76OBc1XcSWli1IKcmZOaSUhLJ2GPeRkL1zalt1G41VjfTF+srq96WbXspAfIDOcCcAt6y5hSdGnwDgzvV3FsfH0vIUylTnq+PWNbeW5VMiiefjmNKkzluHKU16Y72srV/Ls+PP8rKLXjbv8/GsG2ife+5zPDL0CH6Pn0/f9OlZM33Hjh1jYmyMyMgI3aOjtLS3c9lll7Fjx46ymbkC8Xic+++/n6GhTq6/Zi0rG1Ziaj1M9f8YelI0Bjbi2htgsH0FB195KT9teIbrc1n+YtxgTc8k/tWNdMlGIgmdw7EVbL9Yw71JI+5xkfLrWMJLKL+J3aEdXBxNkXP7qK3rYn1jkjWrOxl55r3kApfh9scxqqIcWLOaPRtbyLvc5PVDXBuc5FV7IjQdG0Kv3sSkN8CNfbv53XXXMnHpZtZPDuFfvp54VtAc3UYqlqDaW091ZojNPT/CqxuE6vzk2tw03pTA3JFnb+9OHg3fwseee5ya/BCJy/y4V0FTRmO1OcZrq95KuvkAK/KNXBLfTr0pqBU6TYMBnr/vKQYaV3C0eRM9KzaiNa2jxkjRnElyrYBV/qMczuXI6Rvwim1IzzJGPRY6aWjspjon8RteTJHgDb//Bf952920tumsn0iTbqzi+9t/hV9afCiS4M6Ehd9lMOZu4MMXvYThla/hpvxzXH2gm0DATV7maUi5qMnk8da/Ft0a46FLf0ewMcByo5VrRy9mqmU1Hu8K2uKTrPN6aaqvo8kdoip6BW2JdsL+YR4fuJ+tzztGa1Wc7YNZ/quxhR/XeZC51Ui3nxcdEtSlfMRFA6M1a8lediWXeBp4+7Ck3x3jaPIJ9q3u4brBN6N7Y4TrB5ms78Fd08qTG99FnZUhi4u7f/ldctXVNLfl2N7YhasmDaEaspPt9NVcTMfGbURbQ3xksIvNkZdQq1v8q3eS35rr2dHSyWZtmNunOllx1MIXDeP1avhrDOrWuch7byYwfpjBd7Xhrx+lOnYRvtRqsp5GAskE1WTxmBaNdc14W7bzRCjCgy03MupvJOEVbEybbMpkqHEPccPwz2lL1ZLWaolENPKpBJZhsHrz1Vx93U5W16QZlHX0T0S5uvsJ2uQfwUqSCgk0j5++9S+n8apX4/rqp7AGQxgrPBir3BjNWfRWH8HL/fRP3cZ1qVtYG2rC3OjDU+fDcktWvmYHfo8faUnSfzpK8ukYUrrwrarDXS8R5HFlh6gd+l+kaqAuY2HVteHZ+jrEuhvAVwcbbsJIQOi7R/C0VsMlVRw78iQ9h54lGh6naflKmleuonH5CmrqGwmN5dH1ywgOJlizrYVb795MfYv9AEJLwzNfh9gwXZc0Ek3uY/XqtyAtnbVr31E8F3EpmOyP84fvdeLxudhy/QpaV9fhr/Xi9rhoXlGDcAmsdJrA5z5H1datpB9/AjMWY9137jkthtN8aKNjBL/wBVb982eVgbYILrnsEtlxoIMqdxUpLYXX7cWSVnGWPqWlqPPVkTNySOzn7swZ/AL399/PjrYds7wOBQpK/h3r7igqWzMJZoLkjXzRCCi9d6lyO5NwNkydr44q9/TEy2hylOW1y8siXSxpEcwEiwr9YgikA6T0FBc1XYRhGXaYkzj9AT3399/P7etux+uyPYTH8zrMR0bPFOVcmk/d1DGlOW8dnCoFg/r2dbfP21aiuSgNvoZZZQukAyyvtb1Lh6YOkTfz1Hpri4ZEwXBZDBk9U/RqZY0sjw4/ysXNF7O5eXPxmkLbrfXW0lo9O1TakhZ7A3vZ2rK1zBu8GO7vv7/MuARbDqXeSWBRZzsWjIaXbHwJDww8AMC1K65l9+RuXrD+BWiWRo2npux+pfI4Gj5Kq7+1KPNSeqO9rG9cTzgbZl9gHy/e+GLGkmOz+uZEaoL9wf2z6saSFqY08bq89EZ78Xv8rKk/sbXCuqnz8ODDbGvdxqamTbM+t6Q1q+9JKXl6/Glaq1uLEzMzSWgJnhx9ct52FM/HyZt5uiJdJLUkL1j/guLkSne0m7Se5qplVzGSGKGluqXMWD6T7A/uZyI1Ucy3Zmr43OXH71jSIpKLYFomy2uX88ToEyyvXV7WzmfyzPgzrKhdUeb5Ox73999fWQba3sBe3MLNZW2XzTlYPvPMM8T7+lj/6c/YGfT5ENXVuHw+hOPOl1KCZYFl2WcM6BoCC8tIgyFxaYJctZuR1lXsvvwajNvv4AN33UKDx013Kk1174/ROn/LpSuuxd16EZ//9uOMmQ3sa7ichLsOl8tEIMECpMAtAClpEDl2+Ca57bIHMDOtrB16Pa3pVXhdEn31E4TX/QGtKspErop/4hUkG17GBx6M489Ocdmx3xBoriJW52F07QpagkNUpeNkZTXXr3wBK1zLSRseQrqLUP4o+MYQjbBCC9P0WCfVWQ2XqbFnx3pabh6jfmMUaiS64cHrMYjn6/m3/e9nMLEGELiA6z3D1AmNmFXDhNGC5tKwdI3rA51sTo2SXtOItqIRd9LAlUnjD4wAkjqfwbrmDJua4vR469mnvRASl6JVCTI1degegfBV8YFDe5GHf8LgsjYOb99OrK6a5Z793BYbYjx7KYP6NXQ2bOCYp4pQTYRr1z5EPtHAZV2bQBsABG7DQFoGpgyR8vtJ1mQ5+rwhPr4qBYAlBdJyoRs+ciNvoGXq+Uz53BxqMGgIaoiUi2+KPF5M7FbgwofJ9z0G/7ntKQwtxSXBrYzqbQz7cgTWLCPWto6bhnKI3gRPomEJF4g81/q/RYP7Ymq1ZYw3dRBu6qZ/5Scw3etZE36GrcH7yMcauWssRc5wkXV50EUNHrfFsi0JVl02RnXeYM14Do8mSFp3oCdeTTbZxqPojKPzGmrYTB5/8JNkx0Lkwm7MvAtvnUHTVj++NVv507L1PLvOw0qRZodM49YTuGQW6dZpyDawavwVaPIijLZmHmmBr9XrjAoBQuCWJm8K38+NAw/i8ur46prxt7Xg5hpSx27hylgWIXIINCBPXEZ4svYitOZxtvkOUZUMs2/1jWx7wRt5YVsj2bFegk//mMS+x9GjAfRmjeQLTDJGI4/130pN0kuLy490S+L+FO+4+8Nc1OTsTPTEl5BP/Bt6rhXd2oDBCvA0IOqbqX7FNth4Kx78iOGnoef3ML4f9Cy85X+gfjlWRif51Dj5/jhSN3FVe3BvryfTmCIWmCAWmCCbTJAIp7n8rneyfEMDDW1zKzH2uGExMvo94vF9uFw+tm75LG73iYejnAkM3aR3b5ChjjDRiTT5jIFpWLz9M8/D43NjJhJEfvAD8p2deFetpu3PP4J7jlCzSuZCMdB2XLlDdhzoJbFcFgAAIABJREFUOC336op0saFxQ5mRNJOskZ1XaV+IeD6O3+Nf8N6KyiFv5k9bXaX1NHsDe7lx5Y1FZflUyJt5fC5fmdFyNg68NywDt3Cf1t95cuxJGn2NXNZ+GVJKJPK0TxrE83GeGntqXqNGSkkwE5zTyDtXGU+NE8qGZoVyLhWmZaJZ2kmNnWeChZ6PSxLieCJI08TKZLDSaWQ+j5XPIzXdXnTscoHLBULMei08Htzt7bh8Z+dAWmlamLE8osqNu678NzXLImNaJDUdI5vBq+VxC/vsGsNbQ4PfjZbNIKUsLgidGdMPthIHErfHHpCkptmGqcgST4XIZtx4vTU0ObsblaKnEtgiElg5DSubRebySAlIcDc04GppwTQMMvEY1Q0N+PzVtgGspTA9NaQNSYN//sFcWnLOEC1pSbu+ZgyixxvApTTJ6xmypoYFaLpGJpmh2l2N3+cvW3xpWJJoWiOnW1T73NRWudGsNNF8hLaqVqpzdmiAcLsxMxnS6TxaVQNmtQ8T8HlcVHlc+L1uqjzlC4GjuoFPCGo903ViWRZCCPJmHpdw4XP7sCwdMlGElIjaNnC5kYaFldFBghQg8ybCJXDV+XCdyA5+lgnSghN8iBqWxD2HrGfLVmLoFtKSeH3uEwqtO2X0LBh58NtnLikuLC4UA+1sPh8VCsW5yek0uBXnPuekgaZQKBSKcx9loCkUCoVCMZuFno8VcVC1QqFQKBQKhUKhUCiUgaZQKBQKhUKhUCgUFcOiQhyFEFPA0HEvPDO0AaEl+u1KRMljNkom5Sh5lKPkMZuzIZP1Usr2M/wbS44QIgks7tC+yuR86SeqHJXD+VAGUOWoNM6Hcsz7fFyUgbaUCCH2XAjrGE4UJY/ZKJmUo+RRjpLHbJRMTh/niyxVOSqL86Ec50MZQJWj0jhfyjEfKsRRoVAoFAqFQqFQKCoEZaApFAqFQqFQKBQKRYVwLhlo31rqDFQYSh6zUTIpR8mjHCWP2SiZnD7OF1mqclQW50M5zocygCpHpXG+lGNOzpk1aAqFQqFQKBQKhUJxvnMuedAUCoVCoVAoFAqF4rxGGWgKhUKhUCgUCoVCUSEsmYEmhPiOECIohOgoSWsRQvxeCNHj/G920oUQ4t+EEL1CiENCiJ0l33mHc32PEOIdS1GW04EQYq0Q4lEhRKcQ4ogQ4i+c9AtZJn4hxHNCiIOOTP6vk75RCLHLKd9PhBA+J73Ked/rfL6h5F5/56R3CSFetDQlOj0IIdxCiP1CiN867y90eQwKIQ4LIQ4IIfY4aRdyv2kSQtwnhDjmjCc3XsjyOBsIIV7s9KVeIcTfLnV+5mOB58ynhBBjTh86IIR4acl3KnKsOF39fikRQmwpkfkBIURCCPGX50J9iPNEh5unHP/ijJ+HhBC/EEI0OekbhBDZknr5Zsl3rnbaY69TVrHEZVh0G1rqcWyecvykpAyDQogDTnpF1sVpRUq5JH/ArcBOoKMk7QvA3zqv/xb4vPP6pcADgABuAHY56S1Av/O/2XndvFRlOkV5rAR2Oq/rgW5g+wUuEwHUOa+9wC6nrP8D3O2kfxP4oPP6Q8A3ndd3Az9xXm8HDgJVwEagD3AvdflOQS5/DfwI+K3z/kKXxyDQNiPtQu433wPe67z2AU0XsjzOgrzdTh/a5Mj7ILB9qfM1T17ne858CvjYHNdX7FhxOvp9Jf057WgSWH8u1AfniQ43TzleCHic158vKceG0utm3Oc54EanjA8AL1niMiyqDVXCODZXOWZ8/iXgHyq5Lk7n35J50KSUjwORGcmvwlYucP6/uiT9XmnzLNAkhFgJvAj4vZQyIqWMAr8HXnzmc3/6kVJOSCn3Oa+TQCewmgtbJlJKmXLeep0/CdwB3Oekz5RJQVb3AXc6MyevAv5bSpmXUg4AvcB1Z6EIpx0hxBrgZcC3nfeCC1geC3BB9hshRAP2Q+4eACmlJqWMcYHK4yxxHdArpeyXUmrAf2PLteJY4DkzH+faWLHYdl5J3An0SSmHFrimYurjfNHh5iqHlPJhKaXhvH0WWLPQPZyyNEgpn5G2hXAv02U/48xTF/MxXxta8nFsoXI4ussbgR8vdI+lrovTSaWtQVsupZwA+0ECLHPSVwMjJdeNOmnzpZ/TCDsU7Spsj9EFLRNhh/MdAILYg3cfECsZPEvLVyy783kcaOX8kslXgb8BLOd9Kxe2PMA22h8WQuwVQrzfSbtQ+80mYAr4f8IOg/22EKKWC1ceZ4NzUlYznjMAH3FCur5TCE2jsst2Ovp9JXE35crnuVYfcH6OM+/G9sIU2OiMrX8SQtzipK3GznuBSinHYtpQpdfFLUBAStlTknYu1cWiqTQDbT7mih+VC6Sfswgh6oCfAX8ppUwsdOkcaeedTKSUppTySuwZrOuAbXNd5vw/r2UihHg5EJRS7i1NnuPSC0IeJdwkpdwJvAT4sBDi1gWuPd9l4sEOEfmGlPIqII0dajQf57s8zgbnnKzmeM58A7gIuBKYwA4lgsou2+no9xWBsNcNvxL4qZN0LtbHQpyT44wQ4hOAAfzQSZoA1jlj618DP3KiFiqxHIttQ5VYhlLeTPkExrlUFydFpRlogULYgfM/6KSPAmtLrlsDjC+Qfk4ihPBiPzR/KKX8uZN8QcukgBOm9Rh2/HqTEMLjfFRavmLZnc8bsd3l54tMbgJeKYQYxA4/uAPbo3ahygMAKeW48z8I/ALbkL9Q+80oMCqlLHhF7sM22C5UeZwNzilZzfWckVIGnMkwC/gvpsPmKrZsp6nfVwovAfZJKQNwbtaHw3kzzgh7w5KXA291QuVwwgLDzuu92BE9m7HLURoGueTlOIk2VMl14QFeC/ykkHYu1cXJUmkG2q+Bwi4+7wB+VZL+dmFzAxB33OcPAS8UQjQ77tsXOmnnHE587T1Ap5TyyyUfXcgyaRfTuydVA3dhr5l4FHi9c9lMmRRk9Xrgj87A+mvgbmHvargRuAR7Eek5hZTy76SUa6SUG7DDYf4opXwrF6g8AIQQtUKI+sJr7PbewQXab6SUk8CIEGKLk3QncJQLVB5nid3AJcLeTdWH3Td/vcR5mpP5njMz1mO9BrsPQYWOFaex31cKZd6Bc60+SjgvxhkhxIuBjwOvlFJmStLbhRBu5/UmbPn3O2VJCiFucPrY25ku+5JwEm2oksexu4BjUspi6OK5VBcnjVyi3UmwB6MJQMe2eN+DvT7mD0CP87/FuVYAX8e2kA8D15Tc593Yixx7gXctVXlOgzxuxnbDHgIOOH8vvcBlcjmw35FJB9O792zCHlB6sUNCqpx0v/O+1/l8U8m9PuHIqotzdEefGbJ5PtO7OF6w8nDKftD5OwJ8wkm/kPvNlcAep9/8Ent3tAtWHmdJ5i/F3hGxr9AGK/FvgefM9536P4StlK0s+U7FjRWns98v9R9QA4SBxpK0iq8PzhMdbp5y9GKvxyr0kcJuyK9z2ttBYB/wipL7XIOtp/QBXwPEEpdh0W1oqcexucrhpH8X+MCMayuyLk7nn3AKo1AoFAqFQqFQKBSKJabSQhwVCoVCoVAoFAqF4oJFGWgKhUKhUCgUCoVCUSEoA02hUCgUCoVCoVAoKgRloCkUCoVCoVAoFApFhaAMNIVCoVAoFAqFQqGoEJSBplAoFAqFQqFQKBQVgjLQFAqFQqFQKBQKhaJCUAaaQqFQKBQKhUKhUFQIykBTKBQKhUKhUCgUigpBGWgKhUKhUCgUCoVCUSEoA02hOEmEEOuEEE8IIeJCiKeEEBctdZ4UCoVCoagE1DNSoTh5lIGmUJw8/wR0Aa3Az4DPLG12FAqFQqGoGNQzUqE4SZSBplCcPFcBP5BSGsDvgMuXOD8KhUKhUFQK6hmpUJwkykBTKE6eBiDlvF4JaEuYF4VCoVAoKgn1jFQoThJloCkUp4gQoh34P8CfljovCoVCoVBUEuoZqVAsHmWgKRSnRgsQAO4Adi1xXhQKhUKhqCTUM1KhOAk8S50BheIcJwJUAVcDPxJC5KSUP1/iPCkUCoVCUQmoZ6RCcRIoD5pCcYpIKXUp5bPAz4Hrlzo/CoVCoVBUCuoZqVAsHmWgKRSnxnuFEA1CiDbgLqBjqTOkUCgUCkWFoJ6RCsVJoEIcFYpTYxswjL071U+BHy1tdhQKhUKhqBjUM1KhOAmUgaZQnBoflVLuWepMKBQKhUJRgahnpEJxEqgQR4VCoVAoFAqFQqGoEJSBplAoFAqFQqFQKBQVgpBSLnUeFAqFQqFQKBQKhUKB8qApFAqF4gJFCLFWCPGoEKJTCHFECPEXTnqLEOL3Qoge53/zUudVoVAoFBcOi/KgtbW1yQ0bNpy53CgUCoXivGLv3r0hKWX7UudjLoQQK4GVUsp9Qoh6YC/wauCdQERK+TkhxN8CzVLKjy90L/V8VCgUCsViWOj5uKhdHDds2MCePWozHoVCoVCcGEKIoaXOw3xIKSeACed1UgjRCawGXgU837nse8BjwIIGmno+KhQKhWIxLPR8VCGOCoVCobjgEUJsAK4CdgHLHeOtYMQtW7qcKRQKheJCQxloCoVCobigEULUAT8D/lJKmVjE994vhNgjhNgzNTV15jKoUCgUigsKZaApKhvLgl3fgl99GMb3L3VuTpmxnMZoTlvqbCgUCgchhBfbOPuhlPLnTnLAWZ9WWKcWnOu7UspvSSmvkVJe095ekcvsFArFGUBaksn++FJnQ3Eeoww0RWXzwN/Annugpg2+9yqY6lrqHJ0094xOcfOuTm7ZdYyvDQWWOjsKxQWPEEIA9wCdUsovl3z0a+Adzut3AL8623lTKM5HRo9FiE6mlzobp4yumSTCWaSljqpSnBkWtUmIQnFWOforOPpL+MBTUL8chICH/w+89adLnbNFcyyd5bP9E/zu6s14heA1+3tZ5ffx2uVq926FYgm5Cfgz4LAQ4oCT9vfA54D/EUK8BxgG3rBE+VMozisySY1MUqN5Re1SZ+WUsOd2FIqTZ7w3tuDnykBTVCaGBg9/El76Rds4A7j5r+HL2yHYCcu2LW3+FskXByZ59+o2ttdVA/Af29fzv44McktzHe0+7xLnTqG4MJFSPgnMp2ndebp/z9BM3B4XwqWUO4XifEAy/wByuujePcmmK9vxeN1n+JcUM0nH8tQ2VZ2Re6eiuQU/VyGOispk/71Q3QzbXzWd5m+Ay14P++5dunydBFOaziPhBO9e01ZMu7WlnrvaGvh8/+RJ31fqOlY2ezqyqFAozgL9B6eYGkmelnuZunVa7nMuYOgmg4dD6HlzqbNy0mRMi7R5evKfTWn07FFh8pXAYs4SPhmCQ/aeRRdSf68UTNNirCeKoS/NuKMMNEXlYRrwxJfhjk/aYY2lXHE3HPkFnOFB8XTy04kI2+uqOZTMEtWNYvrHN67kvkCEdx7u52+6RoiUfHY8Eg8/TM+tt9G182omPvkPSOPEv6tQKJYOQ7Mf9lrOIBE6uQkWKSV9B4JLpjicbfoPTKHlDLLJpdlgyTItundPYp3CeqNHwgkeDp3wBqHzEhhIMHosOq9hkIzkyKX0k7q3tOSc99XzJt27T34ysZKQUp5SPRZxVJPg4OInXDIJjXQ8f0LXxoKZRd//fMeyZNFwPZ9RBpqi8ui6H3y1cPEcEUZrrgVTr7gdHTOmhTXHg02zLL42EmQgk+fLg5Nc8dQR3nSgjy8NTPKRo0NYEnozeUKawXs6Bk5oNi75x0eZ+MT/YdXn/pmL//QYuc5Ogl/+ypkolkKhOEOERlNMDhx/FzhpyVnKXGGYMHWLRDjL1PDilcRsUjvh2f8z4SWwLIlpzu8VME1r0Rsw5NI64fHUqWZtFgWFvjQ/uZSOrp24gWxKuajr5yMeyixYHxN9MSZm7C5oWZKhI+Hj3rtnb4Dw2OwNPAqTCottB4GBBEMd8/9uJqERnzq7USChkRS9e23vo2XJU57kOJlJg9GuCGPd0UV952zNSWu5U5vsNXQT0yjv13ONYaeCljEuCMNVGWiKymP3t+Ha9872ngG43LDlxbYRVwHEdNuw2vT4IW58tpN9ifKH2xcHJonqJr/eeQkPXbOFPTdu52XtjYzmNV67vJnHr9vCWE7n4xtXMJrTeTg8/6yQNE2mvv51Rv/8z/FtWI82Moq7ro7VX/0KsZ/8hFx395kurkKhOA5a1iCXPr4HozC6hVJ5Mtn5r09GcrOVOUdbG+2KMtkfJxpY/K54I8cipKLlSlPPngCRidn36tkTOK0KFsBEb4y+fXOeXgBA374gweHkokK7IhNpwmOn30Cbi+HOMOM9Cy/yLyUZyREaSZ6QAtyzJ3BC5Z7XYJqRbmgm+Yx+QgaWNqMtph5/HAw7zTIkqWj+hI2aeChDPquTSWhYpkUmUW7MTPbHCQye2Fb1M/uUYVroCxj4894nM32fwECC/gP2+YWGZi7OK+OIcrFG60zjpdIYPBwq1pO05KJDK/sPTDHSGSGb1Ipe10Q4u2iDVNfMBSdwzgpnwCgutOMTMeyVgaaoLGIjMLILLn/T/Ndc8kLo/cPZy9M8mFLyzsMDuIWg46YdfHDdMt52qJ9x55yzKU3nW6NTrKrycEmtH4BlVV7evrqNr2xdx9tXt7Ghxs8bVjRzz1iID69bxjdHZissuVyOA/v3c+yDHyJ8z3eoveEGmt7wBhIPPkD/q16NlcnQdPebCH3jG2e1/AqFYjZDHWGGj87vNZAS4lPTs79P9YZ48rFh8tnjK+6GaTESyRT1hpnKXjapkQhlCY2mGD4Bj8lMD5WUkmxibsVhsj8+a91TcChBLq0z0RsrU24j4+nierH5QsrymeOXV8+bC3pgZjGPQjV0JMxY17SCmM8aRUPJ0EwCAycXLiXN2Z41sGU1cixSllbYEOB4+nx4PIWUEj1vHtfzMNEXL25Z37c/SDY1j9InZ/yf65JCxmZMjJqJJGbCWQdlWoz3Rpnoixe9aqVoOWPO9NGuCNHJDKNdkVmfnQhazpjVpx7vmeIPnae2Dk/PT7fBVDRf5pWRlpxlFCZC2aLRUJCXaVgLGt2h0SST/THGY7an8ETafYFZvx/OMnBwinzWIBXNExqd23OeSWhISzJyLII2Y1wJDCYYObpwPRTGlfB4ir4D80+izPzNAlrOKBvPFuv903IGAwenmOw7uXPmLEsSHkvRvXtyXhkthmR44c08ZiKlxCoxLqWcjhYYPhomHc+fUJitMtAUlUXHz+CSF0B10/zXbLgFJg9B9sRnL88E3xqZIm6Y/Pu2dbT5PLxzdRuvaG/ik71jAPxwPMzqKh93tjYueJ8Prl3GTyej3Nxcx/5EhrGSg6yDwSBf//rXGf3ud8nv2U3O5WL5l79E8xvfyPrvf5+mN76Bkfe+j4aXv5zUnx5HGxw8k0VWKBTzUFDY5HGmXdPxPIHBBMmSHbxyulX2QC+l9EE+HMmwbzg6r6I93hNjciBOZCJV5iko5C8WOPmwINOwZnkLYsEM8WCWZDRXptyGxpLEpzKYusVIZ6QYUmboJqPHImX36d49SffuSXr3zlYE3W6BYZx4CNpMZTST0NAMi0xKI52YNnSGOkJFJTUVzRMPZUjH83TvniQ2lWEqkSszjArG5kyDuFA3PXsDJMJZ+/uBDIlwtjhDnsjZ9VAwe/KO0h0aTc5SwE3DKvNqpmL5BT0PqWiO6GSm+N2i52NG+5humzbjPbFZ4ZaRcdvQyyWP7/3NJjWGOsJoOaPsPoOHQwwfjZCM5GZ5y8LjKfKL0NRzKZ3RgpHrfM2yJLmU7QlM5gzyi/RGDRycOq7nIhbIYOgmmaTG8NFwmfE9ORAnGZqtrJd6Uk3DKrZlQzeJTKQZH0vzXN8iJhocytqztA1/XTMZORohNJqc09sNtkGcjObIJsvXuumaSXwqQzZdLgPTtOY07vWcXbexQIaJvviC3unRrgj5zNxtZ2a/hBnhwnMYwsBxNwWaz3sZnUgXQ51LZaTlDJKR4xtb0pJ0754s9vepkSTduycXDLnWDKs4HsSDWXr3BYv5i05kyqIFxrqjdHeG6Z5c2HhUBpqisjh8H1x2nCOHalpg+aUw+OTZyVMJWSPLfd338e9HfskXByf4t23rqHJNd6O/37SSp6Ip9sbTfH88jBBwc3P9gvfcWFPFXa0N/HQyyq3N9TwQsmeNstksP/jBD7jhiiu55Nld1La0Mnbbrdz/xz8C9jksbe97H/UvfhGBz3yG+he+kNh99525wisUinkpKDPF9yew3iiZNZBSks4bpCIz1pk5a7RKPVO7HhuxvTbz6AkLhQTpeZPgcKJcqTmJ/cGzSY1EeHrdUKlBKkvWlWlZk74DwaLyNXg4RCyQJZPUMA1rVpicZZ1ISN/07xS8Flpu2humlXhEtJzBaFeEh/aNcWzCVoT0vFn08CQzOr1jCdIxW+4FQ+jIoSkee3aMvo5Q8V4jnZFiGcrWTJWIMp+2fzs4PF1fOd3k0WNBxkPpoqgL6w4jE+ky5XEs5nhHHCV35FiEiT5b8TcN64RCNy3DzpBlzvSMTr8wNJNULFdUggv1ozntt2AQ63mzKNdgz1T5fbDb2sFdEzz22HDZbxm6aa+Dm3HGU8AyecLIzbq2lNL2m4zkyCQ1wmOpojdGWpLhzvCc6+ROhNI+mU1qZYZBoV8EhxNEJzPFc84Ka8sLn7vcYlbfLvWgaTkDy7InMwYPFYwySbY/iWXJWXVzopTKptTJWeqpskyrGJJYKqNc2g4znc9zFhlLF9t44f75rFGcRAoOJ0hGsmXrOwsTGqXIObqwtGRx8sY0LHsCI5SlZ2+A7t2TGJrJ8NEwwaFEiaHrFPB43uaxNAMdIY4dnqJ79yThsZTtPZtnHWpwKMlEX4zBw9N9e651iIU6nxneWTDuxntisya7HuiYYG9/hIGJBFmnD4dG7HzM5WENRLIkjxPurM5BU1QOU10QHbRDGI/Hxttg4HHY9vIznq0C4WyYdz/0bup8jeypeit1uYfxGzXAJcVrGr0e3rOmjX/qGydvSSbzeW5qqjvuvT+4tp23He7nrzes4NFwkveuaefBBx9kzZo1bB0aJL5iOVY6w22f/Af+41v/SV9fHxdddBEAyz76UQZe/Rpqdl5N7Gc/I/P+D3Agq3MgmeFAIkNXOsf66io+tnEFd7U2nCnxKBSKEgYOTrHxinbGuqLzhkB1B5JU19kTONFAmvZ105M54fFUmQJfUCalYc3ppZtv9lpakmggUwz1yUTSjB6LIhZz/qKEZM6gvtpTDN1raK2edVlwOFkM30zFypVxLWcQmbAVlsK6n1k/I20FtjBznZzjnKC+fUFqGqtIRmwDQyCQSJatmx7bEqFs0RDSTYu8o4ANHJrC47HPkhqOZMhMJdm5pjxaw5JgxDUiwkU2qc1apxcYjNPYbpfdMMwFw9se67JnzXc/PY5o985ybaWiOYJDCXx+D88eC7DR5aXO7ynKokA6lic8nqKhfbbMczmjxHO2sEYrmfYohMdSeLwuAoMJVl3cXJRngaGOMJZl0W5B8pnn8N90C8jWclkkcoRzOjnTQmjlyuzMyYL8jDZbGnIKEJ/K8vgzo9z+oo3UVXnKwuwMU5LRjKKHIjyexPRK3HOsU8+ldfy1023bNC1cLjHrYOmZIahlhpOclmUupVPbWDXtyRZ2325dVf5cl1IihCgaKZphcWQszraV9cWSFzzJBaKTaaZGkrSuqqN1dfn9gkMJ3J7pyd8yL1RJUYY6Qmy+doV9/31Baup9QHn45uixKJZl4XbP7ZOZK9xuqGSCwuVyzZpAKfXslhq3s+5d0iYToSxTI0nqm/3FNNOReyyYoXMiyR1ra4sGqJR2iK/H66KqpmS8cj7PJjV6RxOEknmu3tBMeDw1p3GWS+mYplW8r5Yz7D41lsLr95CMZFm9uZnaRvu8s4KnbHRGG50ciCNcglQsh5YzaFpeU8yHlJLxoxF68wYbWmtprfeRzxqYplU2obUYlIGmqByO/Q42vxC8sx9Cs9h0Gzz0iTOfJwcpJR9//ONc0X4FLas/RHAqzpuXT/Leh9/LvS+5l/UN64vXvmdNO/86FGBnQy0Z00ur7/jd7KqGGlZX+TAtydPRKPfu+SGPjDzEW154N5Mf/Br++iZa3/Nu6poaueOOO/j973/Ppk2bEELgqqpC+/D7GPzcl4lUVfPDe35E8rbbubKhhneubmNbXTX7Emn+/OgQn7xoFW9Z1Xrc/CgUilNn4ODchgjAwJStKOvhcgNgaDRBMJZlXW35ODif56b4vXnWnPU4SmFfME1jtYfcb45i5TXEVZczkNdYM2bStKKmqLylE/myg3HD4yn2DtmKytUbmmfdv/SogPmMxLkwpMQEqhyldiCcpmEgTmKB9R4FD13p7xSM1UxJ6NpCu2MWPEQSkMbCBs3IsQgpadFt6uz0zH1YbcEDN5c3MpczsHSLfN7AlHM/BwreBS2Yo9eV56L2Ouqry68tHGx+ZM8kqaxBY7WXoxMJtiyvp3PCVuQF094Uy7KPBVi+oQHTkLg99veTofJQ1MCgrVDP9HZNDSeLCvlY0IWIBGF8GNPYOCv/HT6LZZEEt1fXzlm++SgNOY0FMgSHE6TyBoF4Fn9TTZlSOxnPEUjkuNxRnIekye54hutq/GQSGhnnXk3Laxg+Gmbt1ha8VW6OHgjiF4K21fWExuYPJ0tGcoTHU1hSYlFusIz3xLjkmuVMDdnfL3hFZhoCgcEEtY1VRa/naG+cTH4O471krV/hTMRSw2LjFe1o2dm7FJaen2gaVtGAzekWY5E0TbV2+8xnDcIpjeYaLy6XQMpp73Sp0TzRG0PLmfj87jknQhYiE8yAaYHbZW8a43gUC5NIo5Es+XoPVZQbvgXjtXQeodQQzOQNBkJpVgsPpiVBM4uG4OrNzYx1R6mu9xUP7E7E8oSSc6/RzGgmiayO1+WCTnts3DcUY33ai75lAAAgAElEQVRrDa11PrIpjVxGLxruoZEERiCLUdtSPJi6YHhaFmQ0gzq/p2i8GZpFLq3z7K5xatwussEUNVVOv3XGAiFg8FBJ+eZZ3zsfykBTVA7dD8K17zuxa9fdCJF+SE5C/YrjXp5NafTsthdRb7y8jWXrF+dJ+nXfrxlPj/PhG77I6w4O84PLNnJj09vJGWk+8oeP8OOX/Zg6nz0D1uL1UO1yEczrvLh94fVnBYQQvGN1Gz8YPEj9+Gf5liHYvGot/7HvK0TfFOP1zyR409W38duvHSSbtohbSbq7utm85RL+cOjvsap/zrJaN6s2XM6nD+9i/V+9v+z+W2r9bKn184YDfVzVUMO2uhMwghUKxQljWfOfr5TTLfze8tnriLMOxIhNP7TjU1kO7w+QzBm0bWkvu750/YMx43dymonX48LtEmQ1E4/LhdcjymaAYxkNzTCpdc5MHLAMMtk81RFnncVoEpdr2sroPzDFqoub5w2tK6xtSWR0XC5R9PycKAdNjai0uMtbjW5JommNTGrhkJ/CDP2pbgV+IkzEctT6PESqICLnDnWCacV5rk2H85NZrKyJRJLRTHyOR2Sm96iAaUnC6fwsA61Q90NTaTKaSdjnxrIk0YzThnQLr0dMr3vL6tRVeYsGWIHgcALNsIr5AMhJSSCZY21dVbH+S3cFNUxRXAtTuslHTrfsNWAuCE/G6B8axrN82ZzlAohndEzv3P2j1PMyeTjC2udVMxhK01jto7nWW5RtQdnPSAsjrpGbMhkV03mKB22jruAde9LIcb2nCs9x1l5O9MVwTWXoxmS4ycNrZU3RMJBSks8aRYNxvh0sE6FscbJi72CUTe22PiAtsFJ2H8rpFjXhLEjItFXPGhNg4UmduegLpjgyFqf64nqWJy2aG6oYDKXxLa+nvtpjG00SppJ5PG4XzY53MR3X0HSTdAo8blvABQNrvDdGRjMZjWTY2FaH1zNjM6KuCK60jtVaTXg8xdNHg7hcgqvW2d7oQCJHYhK211aXladgJM/0rpfS3RvFtayOw8OxsgmhQn0U2vjAVBq/z1383LIkLpcojsHj0SxxZ0fSVserKKVkMJRmKJzhrpXlHsv0cIDUxAiuHVfinmF87x8umaAqtEXLIpvUmAjPblvJnE5rnQ/hEmXrVmdukmPlFjaM1Ro0RWWQDtlnm11y1wldbnlq+E3DW/iXXzzDkz2hBUM7ho+G+dGndjHUYW8f+6uvHmDPA4MnnDXd0vnGwW/wiks/zusODJMxLd54sI/bnuvisnVvY2PjRv7h6X8o5iGQ18mYFiM5jec1Hj+8scDNdRqjfZ9kZdMdNLk/yDde8Q2+secy/urwOh5+Xg3veORDyLUprrxzPY3mBn753w/yxJ5/YTy8C9elD9L+zvdC7yEye3ajjY7Ouv/Ohlo+vG4Zf9c9ekbONVoMRiyPNp5a9DlHx72vaTEQSpO7QA7wVVQOWc1k1+7xOT87MhYneZxdGvcPx+wtxx0FYDJU/uBPhLPU+NxICQ8cnsAomZk+Mp7gwHCMaFrn6HiCkaj93UyJl8Kvh1kdeYjCD5jY5wlFLJOxoQT7h8u9KIYp6eqYX1kc646SyZv0BFN0BWyFprAOzzAlpiU5PBpnNDp3eE8GSXaOtTyaMf8aOwArN/eMeWoeL4A536YXc42BlsRMTXsqeoMpkmmdwRlKWMFLUryVBfoc25FbWbt8k25JIJ4rzvaXeo/sGyycrcJGIAU0p+5dc1iF+4Zi9ARSBJKz5aEZdp0Ufi+R0XkklebhVIp41iCR1YkXvJO6hXuk1Os0nbGJWI4jY3G0QhhizwTa2Oy2/2w2S9zx2oxGM8UNU46HZUnCKY3+qRSWRbGdTPTFOWhqjFuzx/cDwzGe6wtzaDSObshiaN2oZfJgduH1e75kN+6xAwQnkkzEc7ZRZUl0x8Na8PJICw6PxumaSJbVmWHKotwK/bLw+xaSfP8gVibDkbE45C0mw2mOjMVJ5eZ/TkkLovMd1yFtDxFMt5dsb5LBcJpxp61KJO6RJKmJFHnDYjiSoX8qVbLhikX3ZJKDI9NtefhomKxmYlp2e03mDA6MRkmU9FN7I51sMR95p00X6qwgh1yovI2bTkb1Gf1bNyR7B6NF75o+lUMED7E89Ry5OdbxSgu0pEYkrTFeMrYUxq+j40mOjidnhbSW3UNKxpxw69INUwaydjuaj7xukSvxihYmZxJCloXwhlMambzJ0EiCYCJfNlYDSM1pK5ML70KqDDRFZdDzMKy5Dqpnh9DMJKebvOd7u/lK4jaS4XH+8icH+OcHjs157cixCA99q4Pnv3ULL/7AelZd+99c/trfcujRfg7+YWTW9dFclAcGHuBPI38iqdmd7zd9v0HzX8lnJxpp9rrpunkHA7dewQfWtfPujkFu2fp3dEW6uPfovQDsS6TZWuvHAmLmic30Sin57LP/yIb222iPbia6aj2JoQF+23+EkYDF64+8lEsbruAz6Y/yXO0jvPNjr8RTFyAd/x4dqY9xo38VK1/353hTfoytPmL3/WzW/aWUfGDtMvqyeZ6Inp2zguYi+fgogS/vIfTtw4S+04F1Gg5vBXi0K8hNn/8jr/j3J7nm04/ww11Dp+W+CsWJIIGpwky9lOWatpTEkjknrk7OqYVblkQzZNGQGwzPsxGClOQGUxjO9aU7gfVPpTBiMYJ9wyVpaULJPMuSz9najYCUAWPRDPloljyyuA7Ezof9fySSOe4uY50TtuejoAoZhsVUIs/BkRhdE0k0wyIQzzEayTKVyDMWzXJ0LEFOM7EsyWQ8Z4cyORwejbFvdyfPHR7CsuDIWIKcbpHTTOIZna6JJA+MDBB3tn1P5YyiEl3AtGzDr6DAGnFbGSqElEopZylMBYxwGH24fHJL9seoK9kaXR8dY2IyimZYRaWyJ5jkyX0TZd8r9abmFtiMxUqmsPTZz4mMZrJvKEZvIEXe8QQUy+QYPYWt2xPZaeNAFhXh6RC4vYNR9g5GKWjGqbxJKmcb14nIRLE99gRS9AYdb09On9cjXPjdUo5UecnM2HVv/+A4hwZHMcIRpGVRFerCnQ/N+u5MOvZObz4xEZ/2hOh5g6kZxpluSMJJrdiOdMMioxmY2Ipwf38fGc20jTYLAvE8ewejJPcfwozb7Sg+NUwincIlbW/0Y/vG2T8c49BorMxIsPucRSpvUCqayUSuKLdCXgtVrpsmBmCl7fY3FsuQc9brzdoYR0K6sHNiVrcNqjkYe+4AnaN2nc63a6zllEVoFsGSCQFLwmFT4/6JGNmSiUypaWSSGY6OJxgKZ0g5hsi4W/KEmUNKScSRvZlIYgWiaGOjpA8cKt5jMJSmt6SfFTBNi2+PBsiaFodGY4RS0/npDthjzL7haa9yZrwXvxnjyHi5B7hrIsnR4SiTPfMfE5A3TPKGWayH+ZiaMemS1S3CWYHLkoQTeZJZg0OjceLBaLGddIzF+cNT0+NDofp6vRZj6GBOy7NzIsFIIMn4VIrBkC2Twtie6jiCns3AHBMNpagQR0Vl0PWAfQD1CfDp3x0lnTf53dvWUv3zv+eDH9nHq//jabatrOc1V60pXpcIZ3n4v45w+59tY8Pl9ezZ8zpqajaxcv1VxG7+Ck//8s85+tQoWtZi5cVNxK/p5ktHPs+W5i3kzBwjyRE+evVH+UrHrxhs/t8ILD7QOko4U01j4ybesrKVtVU+3t0xwL/f8CU+9dg7uWbFNTwd9NMSCvHKmM53TTerDRdbl9UvuBbt0ZFH6Yv38YmrPsb9+37BE8vX88sv/BM1nnoeab2Oy6eeo2vv7dx+5yf572PfZF9gHzdcOspgdBvX/raf7/8aNo08xDpfLaYeJXLfj9He9m7ueWaYx7qmGItmkUja66rYuKqOfzFHuPXW7adcbYsleyxC8k8jLPvwlXha/IS+30n0vm5a3rx1wRmv4/HI0QB/9ZMDfOH1l/PiHSvYPxLj/ffuQUp42w3rj38DheI0kHce0O7RFFa9D+msZajNmshoinhdFf6pJ+21IK6ds74/FS9XGg6NxtnsD9Ke7yLecifZbAZr1EAgQEqiWb1sJ7AabRIR7yUq1xbTommNnG7SEoli+L14ayVjMRcimqctrnNwjaB90laY+oJpYhmNK9Y3MSBMmiydREanyoiS9zQzMJUmktbY1F5HfUlIoxCgDQ0TrWlkNG1rLaXKXyAx7c1xWZK+gSj6Sls2A8EU69rsSAPDlMi8hiWnw4qOjieQpok2PIJn5QqG8zCcynNZA3RNJmmu9bGpfXr900gkQziZx2tILr9ker1tJK2xsb2WR8J/oD65A6+wf79zPMG2VU7I+zwGibdEj9aDUwybNYw7RWqrryqrA82QxDMauRKPWuGuuikJxtIsa5rOb66nF1dLM9CAsCTCyUOno5weT9EE25j3uEVZmGUhDLZUGQYIui3MyThuBFiSTeE/MdJ+Aznd3qCmJmsgYnm6p1Ks1iwKwfBdEwnaV2u01vnK7uc27QmHI9KNayJBaGUVd/iqiY1Mgq6ja3m0eASzqomVso/4RI7adbeybzjKznXNiDncBMOlu4SWVIll2eUKxKfb02g0UwwXLhDP6rh8bqRuyy4QzzHmE4RTGtrgEN51a9AMi6pEAndjA6F0nlZZvmmO3wizPL2Xbs+LuXxNI72BFMvqp9charqJr68P15by50tBGS9ke1coQLy+ivZABlerhqHrWBZYecD0AF57Q5HxBJvaaukNpti2qoGJuGN8Z3QaSjbI2DsYpc2yd2x0udzFJluVN8lXuYueu75givZ4nlUtfoKON1VDMmLpBKTJmDRowUWHV+dqILX7aYLhBFx+C4ZlFb2jmgCXhIPhFMEGNzd7/UTGw7iAVm8NEttLmHBBkyVmTT6ZliQXiWBFYyRaWgDQSycv5oh2MfNz7+iayht4CiGDc5zbFy45KqDUQJQWJPPl/chtpMhrPjrGk+xc10zGuW9rTCMd0xhu89thxJPjmCkdd+P0sphM3mQ8li3rm6FEFEMX0NJezFNDXGPEI6mu8ZHOmXQHkly9oZk9tVVMeSyuGDg4ZzkLKANNcUaRUpKORvBVV+Orrpn7IiMPfX+EO/+xmJTPpOl84jEsy2Lr826hptGObf5DZ4AHOya5/3/fQnWtB6ml2fXcs+xc18Q//uoId25bToPfi6VrPPyVR9hWf4iLD/0z3ek6vHXN7Njxb4BgcNdWmjb9hvT49bzovTdw394H+dGe/+Tz1/8Ld+y4BYCfdf+M//vsZwgt/xReK8Mloptdg8/ynb1HuLTtUv7xxn/klpa1fGjdMr42keBdl76H/++3f0VH+7toTu3BMnYzJb7IB3fdS7bxeu4J/ZidI39kwFrOvU0f4kW33MRd25djSYt/3fev/MXOvyBx+CiRVWt5/rHd5LI6X93wFl6Tm2TnjmtoOPogDzz5Stat+TNi3McPc0e4euAlvOItmxFtW3j8100Y8WvZ8Mg/o9XH+cTffosVL7yTz19s0N7bhdXbQ7YrzOQuF4+4lvM9/V3c3JRj7NgRTF2nde16Nl55NfWtbWekLVh5g9gvemh65cV4l9sKSuubtxL4yl5yR8JU7zi5352M5/jYfQf56t1Xcue25QDsXNfM/3vnddz9rWe4dkMLW1YsfNSBQnGqSOA5l8EVzvtcWsfXUEU4nUfk8kjL1kSTMec8HGd5qrQk0rBw+dz0d/RwdE0LruoadmpudMPCTIwwmcvhjnWyS3pZ1dDOGquK/eNxJj2SdSWBMM25bjDHibocA23GznpYEksXeE2JMEwiuq1otDv3iGU0sEwOTSXp0/PcGeug39rMmvRugjVXEUnb6+J6xyK0t0yHb1uGwdRUhFGvjsdRwsp+FskBn8UVmouWhI7bkmQmdGSdm4muPlprt2HEYrhqZ2804TbS+MY70FgOzvq5iZTOingOUjGs6rbpChC2kuY1JE1J3fEazagnS2Ji4MVWtjOayXDvKMvJYyWTjOfBJ6Wd5yrJdYXv6RqW20tcx449suzdNEOxNJ5sCqPOfkZNxrNMJfO01c/eVMQ0cqTGDrBs+7Xgnx6TdMMCD7TFNKrSBnHnu17dwnA2+BgrCedyWTqWy94V0p+3yFW56A2mqClZk1P0TJboyyIxxLirGpf00ZLO2pqrYSGkVfSK1WZNdCdcVDdtAy1pwYhbkg6laQnlcJkSyy2QlslFCZ2GrM5kLRihEMO+ZqYa3WjhGLgFXj1lO46zWVw+F5OdI0RCE7jb/JhS4kGAhA5do9NrQWT2mh63lUNIi/3DMOIrUeilhVvmmBkMNpXMM5LKkak2iOaBWsoN5nT5bwx4JTWi3LDwmsli3YSSeeJZnURGoy2cJ1DjpnM0zpbIIKRcPJ1qYH0szd7Bkqw5BkLCNNEdhX1V6mlqU36C+TVY6TxNvjGouo2MUWtv9z7DOAcIJvP0h9IlnmZJvEQEluORXxkdItRUxRSry8oRKTFaOnwWRmqEwXQtCDdebYQrUkdh9ZsJhu2JEH1sjPyqFZjChRuBBBLpHKGsIFMFvwtGubogSrvqiLtgwGNxZdrC5Z02JnsCKRJZncbIMfC00T+VwmdPLwH2RkE6Eq+TYmWzuP0+XMLerAVgOJxhWYN/1nq99qhGxu8mXTNtxmQ1EyFNTOEi5JIsc8ZciaS4P4qUVJlRqib3EMlcAmIjhiWLLciYmsLT2jq9Y6g2e9ImkMiVGWeWaSLcEpnU0AeH8W60jXYTsJJJ0n43Y84PdE4kybgEJ7JdiDLQFAtyLHKM5yaeI2tkWd+4nueteh4NvhPbYCM2OcFv//ULhIYHkBIuvu5Gbn3LO2lctrz8wsEnoW45tF1sfy8wyc8+80nq29rxVlXxzH0/4pUf/XuWbd7OP/32KJ942TaWNfiJpDW6rO2EDz/E6i3vIKsHecc9z/Hz9+3k8Jc/h5HewvXv3kHOWMZY+PNcv6cZsWGIZx63GD7soq7l/2fvPePsusp7/+/a7fTpTaORNJJlSS5ykcE2LhDHGFNsiikXY8o1vUMglNwklAskYMgNCYEbwqWEbhNTbQwG28hFbpLViyVN0/Q5vey+11r3xRnJduwYcv/5fPi/0O/lOfvs2WfvPfus5/mV5+VUmg1u/fIjPLz5Z7w1+272/fAY+aseptRZ5IZHbkB2XIkwsvSlMnw0Xs/cosN1665jR2oH1972Dv70rC9gWgXmgoBvlKc4qznIvpFTScIBDPNK1pePUks0X3rgvRRsg+tXvY+PRHv5xMJfcNWPPsP8857B6pEpQhlyad+l/Mu+f+A1GxL2/3YXqrfAm8d+xYX9EVvCWyinz+e6+s/4Oi9lc2uQyZFjHB0ts0dKXnjeKK84cxW/+FIat2c1etUU797/Q3qL9yCrVdKXXUb6Ty7F7O1lpNGgcuOPKf/jh7itr58zn/McrFSKow9t465vfpWNF13Ks6+7/kRRPPNolW3/9iitss/IGnjWVcMU1m14alf806C1bQ6rP0vmrMcKMcPw6fyTHLVfjJHa0I3xuAXGH4pP/mI/L9q84kRxdhybRzp5+3NO4X/8ZC//9vZn/X9i6E7iJH4fNGCqGL82T8qTzLkmTtZkZmyGfNVHYZIsSLSUyEr1RIGW1CNUqEgNZWhZBno5ZVB5Htg2c+ES6dgla8OCOUusaoxwJq4BJUMzIhPwfOayFoPoEz/qemyBlqugkH6C1+t491qV6yjTYuXsQ2Qyp+CnhvFLRUwvIFrTQ9L0yHWGNJdlOJZ6bFGbzM3jy16SIMLq7iGamaVpAs4T/8cetRUZzYnFVt2AAaUwlU9SjVAiBRpKB8ZQfoSqPTl9sRAdIxuPUTMGSeQycyLAnX2YlQt7mBYv4eiuSQZ7JHtWjuLrhEHdPg47VsSPW9jtmKzSMhNyQDw/j9ndg+WkmJ0uEacga0JHbNBR9khEAivy7bpPJczfeyczq89jXpuEaZPVi4uIVpMuM8Rq1aiefiHKWy6ilEa7NSBDuCxCs6Umv8y0BTP7sEYvwLIMpIb9RR8d1qFjiDBWzCzL5eKZCeaGh9lCuxHVvrCaVc27mC48ByeyKHgJAhM/bZ2QQAL4OYP5WkC4PJZBIKhPP0R3x0YwV5MsLoEA4bC8DAedxCTFIgvGAGmngU7a/FklamAmkDJgZ6VJmLNxTJv42AzKyaJNSCTIlouR7We84mH5GvKCPm8HllaMqlE6Up3UjBrbs5JnottqX9Vm+R5uNAgqZaKOAcLiErl0FqNQoKQlK1oPoIvzTHVdiRgqILRAGYKucIze2Z0sdDwfs6MDFcdo38fs6CAG6qEkaNe+Tzg3dqmKmW7fI8dlmcffTWpVlFng8bGcwTK7IgAVSZJYYfYYaDQTSz6ddYVqedD12MiG4/VUKDWVGIYBrUO0p8CEVdEiS40AnW1iZNvN6+nK4yTSQqDQjIcRvRKSUhGjsxPDcnCT44t3zSnzP6BkbSKTJHQFPq3UMN2NmGpHu1Cq+xFYAgwDK/QZdB9iUZxKNbuGot2iNzpeTGq056PjhNL0LIe6UpzfMUhfME6hdpRax5UUmyEdjzsv00tNiiHo/vYxVxcX6RocwLTbLGvDj0FrpDpAhzwT6AcEjSCmK+tw81KVlvLYYuQwlCJZXGJNbh+yWYflRnGxGdLwE1b1ZB4/0A/leVj2Ez3+VlJnTe1udojzmHAyDGQzJ67FVNkl6ycgl+iX+9rXNfBx7BpRlOXxrSx/cZxWZJEf3kgUPc5zZij6lDjB2Np9KeJSiKpWMTOalCfIKxOjtZuGihlwJTPGWgq1gIbjEE1OoVYOE6v2ffH7cNKDdhJPiWpQ5T13voc3/uqN7CvtY8Fb4Dv7v8PlN13O5x76HG789IMig1aLmz71Pxg9+1ze868/4i3/9HXyXd18+8Pv4fCD9z1hW3XoNiqFS9l5+zEqczV+9oVPc+oFF/HKv/4ML/vIx7n8jW/nZ5//NP9y2066sg4vOXslWms+cNMuprsv5I0rJvmrq07nK9dtYfdMjX/40td5aPpCLnvnFZibX8Kx3AKDw9eQPeVqjvzTh7hp701sHb2R2zM3s+ENKZLE5lk730pzdhqjEHDH1rvZdtM2ns9LqBdeSMbpIZz9Cgcm9rB582Ye3fco1tQpzA18jJuP7aAaBcyHAXOZqzljajM6SWg5Ppft+g618RStzos51vo0Y42P8qzpfl478DqC4Qv58am38blfPcqXH/k6Lxk5l933v5y3p/6ZgYN3EhsWl2UP8I61P2W0/lsO/HY9a3cuUS4qXnX0CLc3L+OyA6/igHeIX+65A8/zsFMml7/pDH4ycA5GOUeu0QTLYv2dd7Dik5+g+9WvpuOKK4ifeR5zuYT5oXVc8NB+1q0/l0uvfQPX/MUnedM/fg2tNd/64DuZ3r2dqR99k9v+4X7OaPwdL+7/LOmlbdz4d4dY+OIbYOngH3w/qVDSumeWjueuRsR+W9J642vhhnVk7no+lreL5o+3/qfv032zde49UuLPn7fxKd9/y7PXMV/z+c2BpzfjnsRJ/FfgXL2N5vhdqMltUFwiURrVWpY8KcXhY2W0lHimoGpoPKHpmR9nqHo/hiOws48tE1bM3YIzvZel+BhlPce2lEVXXKHtaAFbw3D1CObUbpJyhTm/RdXUTGZMDnamaB4r0lwsU2rV2S6XF/wCjviKUspAC0ApuoIiOXeSxtw0e5fHEwknxsgoMvmYzGB7oTeQniAbLmEmCqdbUlsso+rLHjWlaUQRA9E+7N4UxrLsrakSKkiWaiVks8mkpXCSMl3+EVoSNtY03b6JUZVUUg7h8RRJrdqyxskpjETgyALdaoqyP4clAyYcAyueBQ31altuFSaCAyTMVMs4AyZ025giJjP6OKbP82jNLCIWp9CVJspz6a2HFKRBKYRjHmSUIOseps9/tP0ZQCjFRLNEmMyxlBaUTYWOYzpmj5A0qiSxTzI7yyMP7qTYDBGVaYKJn1E0NPuzGh006alWGKpHkCS4c4KFY1V86THtA8SYWUVSLKIf50vyLZMkCth6dCeDy+yhQBGhMbSkFsW4XoyQikrgIh9Hl1kdDnNVj5IXstNRCDci7babq1Ys6Y1MJpYT/bTXwpIeZty+T1QiMYPtHIt3EkroYp7BZsxUw6cjOEzB3YH2vPZf05pAwUoPsonAylsMVgMI2wVl3TapVz1Gym3fkJsJObNWZnXlPii7TGx9lMnF+gnG6VFbcTAFstliKQ4oyQRDh7RMgUGdVfNN1i55gMZUIc0gQTUaGBkTVasiK1WSWoNc89esDx5AaDATiRfHHKcTuy2oSpu7LLHso2wXKIZOIJGkay5oSIqldqNk+bQ6fW1mU9XqyHINBfRWF9jQfJSSrUkfOwRohNYniq1GqJAa6rbBtKmJl+9xR7VLLDmzjcM7DmDFEjNoS/j6qxFoTWPmIJNGTDR1DFp1rMbCie8gKxXSPQKlQvzj5aVos1NWktDhF5GNBv78ItGxtt/+jB2/gGNz6PpyI8QSaK24ZaHEjKlh+diSx1UGmaTN5s0aCbLRQD+uBzMr0yzkQDYaFCKFLRyseA6nMo5yXaLJKbprHumgA0NL4pljJEtL1Bs++/dP4soY1WxS2P0gWmss7aPjiFhC2i1gZNsN2zCR7Co2UfUAJ4gQrQbK9UBpbNnAcpZl1QtNMm4Xqu6i1WOF1XGva6buoxbqjItln6CGFa2HWBjbzszSY80hlYQkrSeOLYnjtlzRN0B5Pkm5gpEynzBe43jYZVPO4MljVKKAFfHEE12CUcRCALHnE5hP3zQ+yaCdxJNQC2pc/6vrOa33NG5/xe0n4uMBJuuT3PDwDVx767V87YqvMZgbfMp93PWv/8KKUzdx8X97HUII8j29XPbf38q6Ledzyz98jurcLBe87FU0yz7i4Z+zx/kQoWxw35YBpqUAACAASURBVI3fo6M3zaXXvuEE47Hp4udQq9R4yz2T/P1rL8AwBD/dOctkyeXq116L+OZzQcZccfoQp/UIxifX8+wzehk8tR8pfeYXbmbLuT9gtrebdzx6PVnzZwwuXMyKs+p8cuKjXND1MlbKUUasLlac/z0+cbROyhhEBFeTTgQrjJihlsvShiXOPfdc1mzcxMX37OYVfki2eT/ewre4XPVw98hH+MZzLiBb+y5blsrcIV/CVeEit4lR6qkZZvwuXlgtcNG2Cj/ufQMvK93PR1ZN8vfV3bwpu5eNO+pEvaeydaHAg5lz6d1tYa+X5Fb53C4uwGIOtE9gHuKCnMPBI1kub63nnjN2cOet93DVK6/kS3fsYrarn+w+F/90mK8s8YvPfRaRzbJm3Slc8Izz+NnffpyOri6C4iLjQ53o97yLlVvvwMznyff08oJ3fYBDt32fn3z249j5q3n+SxRrL/8aZLp4NjBw7zF+cWOWF37lvax86w0wfO7vvafcu/ZjZ6uk7n49TN0PPWvhrFfBCz6PKAzRdf8vWfp5Qu70e7DOuvQPvlf/128Oc/3Fo3TnnKd8P22bvPtPT+UrvxvjeWf8/nEMJ3ES/68wUgYIkJUqZpwmS4nxeicpMU1GjT5h23BhkWL/Aq2eATYmLWwVkB//LjuHL0PEAcq3mMkJerwqYaVJ3JXCbsJz6wmBv4TujcEUnFGtkkqKNBlktQduBoQWhKZgMQYpBH0ln6VODcpmyraYTFu4wmYgUqBhwO9HsoQ02v7dlG7SUZ3EyG1YPtplxq1eJhtO49oFpA1WziXVdInVKoRWpHWNqlWhGFY5oxrTzOXQUZW0MohMfcIPVIhmIXAxsoBSmLEBpiKrTHyrRXc0Ty0ZJRcm7UyTuRq2ztElZkjFDimzQKQ1MkrRGQ+x1JNiRZiQ15CNFMO+YL+KWWeZLPo+I16AteSTSRSlqMha2cs6eQCtcpTiFJ1ynkR2klImhoKqbSINSefy2qmhJRtq9+M0D6MqMVP2KE1jCJTGSnUS4aBZYjTZBhqqcQ9J0qTmmCwNmGStBDkxi60seost8v4krDiLo4uT/K5YZG0yhO1AzimScWCRNguTKgeEpiBXnyVdLbE4nJAePYdgosqsqalYCk+6SDNNb7PORCZki5hgJCwx0/En9My47J6dZWKggNPpwILCUCZCA36MpUEJA8PW6CRhRet+VLWIxyXIVotoWYG5EMFx0aqKFZaK6CkGWMl+bh0a4ZQ4JgwFGcOiKwVGHKKDMn3hOJZaB4BVa2AZXegwxDRihloTGC2DR1WFEIkIXGzHJKTNZFlSY0QeE65B/9IM6SDLWLaBY4ekGiGSmMGlh8glKSw7RUaVoPMUzD6DpAbxdJ3CuQ5m4DPqNilUPPLZTnzZ9uMJE1xRYq6m6bFslOehXJue8CB93SPkqxGBrKIkmH4FlwJ2ISC0LbKAjhNkM0KZISE2WkKRGoP1Jbqne7GsFJWVBZQbsLEpqUvNRMGhr9VmjTvVNIbWRK5L4PdQlTVW7DpK/4ouIrNBsz5CKtvBqNzFUXUKnikQneOMNlxmaP+faj+gt/ww846NDjV6cQkr65AJlyh4JbqDKsVwFMw2Y0cjpMubg3wHAoUOA3TkoVwXNz5AGo0wBLt60vQFijPqMdlVJnGg6W/04NgRYRihHiegSuGyyTvKeHI6vbFAxAED/n7Map1Jp/07LusljosBhd2WMVrpFlHNRIoIJ5HIUJNEIV1qEe35CEwMaZIaSMO2PbB6gGMqi3AEA4vzZCpV4vwAstFgmJ34rsOCdT7CD5Geh061ny2q2cQoFFhsBPTUQow4JL14jFZXRLHTYaCcwjRTZOYnMPU8rr3hxHezJCTlx8JISqUllNWNSuXBn+eYtDFVk40rspjL8lmFpFuPU3EMzDABA1I6AKnJxzFxYuADKakZLUW4madP+T7JoP3/AHFcY+rY/+HwkU9TLN7+R41Al0ryga0f4PTe0/nMJZ95QnEGMNo5ypcv/zIXDV/E237zNrz4yXrx0rFJjjx0P5df/7YnycrWnHUOr/n0F9h1+63ce+PNbP3iT0ibHs/5yBu4+JoVyGgHvn8hzeoTjc0Tg+fS5WiirTeRSMUXf3uYP79yI5mhje3kx+kHIYm4zruTdYnNN1ouYSJZXLqVbPYU0rn1vP2X7+Ass8n1B17FO1suz9s3idj7Jn4rNyAaHfzYned3k2neNtxglVzN/tXrUZikxm/hucG1VH5ns2Pffj4/XeKdS4J33hPyhv1X8K5DH+Fjcy/ge9umULrOUOo60rOv4w2qwLv9Mxis7+aOs4YYbN7Pe1QTYoeLi5pfN2e42/4yK01FdbuLm7j8xVw33qzNmlqVDekxfhxcinbggv5tyL4rUblXoXXCqQcO8MjgRsgNY4qE24/cyh2/+Tk3PTTNuw/9HDffiZtNMd6RIt77INGDdzF9311851N/TZRI+kbW0PjoF9jz4U/hmortH/qLx052eYxNe/6SFaPPw/d+w/7uBr9d2s54bRytNZsuWc2fvOEsbqt8mKWvfwgaT0wvewLCFvrmd9DcOkGh82448+Xw3kfgXQ/CpR+EjhUgBPZFLyJ7qqR+83aInp6dPY7Jksv9Y2XedMm6p93umi0rmSq7T0iJOomT+K+G8j10HOEuy9Z6nUnM5r1E9vyJDvxgaGIlKZwoZqBcY0MpQiHImB6mlSCUZJP7S1RzAYQma6foj1dSsTLYUftZ2hVLCuPHKOy6i+x8EZBUUhZZJRiqOXSHXazyYCZvgzWDIUrkVYVM0kN3UiBJtzvLiXjM0N/R7GY08FnjQleyyIpyiCMhPx8zXLoLoQTK82m4Cc0oYb+dwUvNMKCOkO9tMSrvBwRSwGmTB+gKxwjnDtEZSoZdiS2bbZ9GdTnCWmgMLR5LPdOKdNQkzpXwU4vIVIoOT9Ibm5jaAA2GsjFszUAsWO3H6LgtC+s0NGZOMx20u/3dyRLR0iJG2AKpKExVybeSJwR9RIaNl8oy3NqLrdvPG0ODxKWRamHpEE+AJQO055GbmEbLhHSrwGDosNoTNGwDQwtUHCMsAQhy0RDZ8f301PciDM2AP87pi79rS++UhiTGcdveMVdDVmepd+9kyDpAJdlGYJSpmWBKRaYeo2LJSjnFiriME1VJDhwknpkEoNKRsMHbhyVDho7dxVneNjLuIvZ8g7QZszA7i2kJpNdizfQDNP0FZKNJx9w8rWiBWAhWhgJTmNgpjUgiWJby6SikqyYRQvO7Qpq62WYy8vFja5Oc2cBIg1VbxG4WEZZmIW2z4I1TlNNk0i0MndAddOMYy2Etj1vbVAxJVUdIs8WGyi7OqtxPb7yA0JItjQrD7qNsblbZNDeBWChiKYshfztrwsMIU5PVE8StGqm4wLA7QW3/FIfN9mDmTmZBSdKVJc6ceZA1lQP0jj1Mn7+XtNVmdzuTIn3uBMnsHGKZtUxpn1O9Kn3qCNoPUJFgqHwf9akbSY4uUX9kFgWkqGFrH+IYHYagJYVam7Gy4gDcBkPz0+jaHP3eGKcG4zha0+t3ogFH+9imj1GPSZeqKOWTwm0XTonCMor0LjyEmQbbrXC4M4UhlgeHq6MYcUTJUcyUJojQdAYlzJZLWIsoeQsYC7MUVcRUzkIBOSlYMTOOThRJaHOKN4tqtOipjWGm2hpk1WiRlQYrfbARrGaMgeo4clkCOuwqBiPNuselphpakpZtVtEKAqxWGYo+AhB2+3nlhweIaP+PmTlNd76Ij4FGYOqY9S3IiRb91V+RUjVSSSe2yiC8OsKAVGkO7n4Ac8kHpYmTmGbkMWtNnmCbRatKslQk7c1CIsmoKjlvYTk9VCK0j1WvYwQeeVrkyBBa7XtaKINsvIBq1ShkNXLZV9ffslCN5onn0wDbOHvpVuazDVSiEMxxzqP3U9hxBLHszJjLKHwzwbZNalTalJoA5XpkFsboyk2D1pwSCKzIo6Ce3ol2skD7I6NafZD7H7iCavV+DOFw+MjfsGfPW5Hy6QcrPh1cKfnWbIm375/kvQenuKPc+IOLvm/s+wb1sM7HL/o4xlPFK9EeqvzhZ36YodwQNzx8w5Pef/CnP+LsK15wwsP079G9YiVXvf8T7L5TsDZ7H+bpz0NYDvfd9F02XXwpp120mW03Hz2xfSIV/7x1jD9/8RaO7dvF9266lZRl8sIz24t7Nr0IDt4Ce35ItrWBXRmFdgy+e/8Us7M3snL4Wv7XHX+PE43zPi/PkjqT+poWr6+/jeus2/nS5n+gNVSkb+kiblpYh6tsMgOLGGa7hZhz9/ON7OcoDo7xzZ/ch711hivHFpgbuZXxKEBJG7u+jrqs88MHcuRLCfsMwSuNFFP9N/Jef4K9Xb2M/umf8gVzmjHrH1lrvYNXdP+QwyS8ZtLnZWGZr+e34PuwkBrgBdPb8P9bk3pa8QBrubj/KBdfdgNrr/466VFwDINzmrvZaW3gmZXNPNy/neiBST5S/DeC83xmntmPecikKwo4tbEJu/dqooVZjFqRSt9KBrIVrvn597B+t5WFS55PduuduIcPQ+TBD65l/7q3872h7Ty8qcjub/6A7+3+Nq/95Wu5+qdX8+MjP2bdlj6e9crT+UXxg1Ru/NhTD+9JIvjBq/EXujD6hki9+W9hy+uhc+RJm7oPPID76D0EwSZqf/NnRNNPHoHw73HzIzM8/8whOrP2026Xtk2uu2AN37h34vfu8yRO4v8VtvKxSxEN2yCRVtuiGbv0hD3k05W2ZMmokiyns/WHCq0kWghMYuxEcHapRDrSbAh/R1qniM0sjpIIrTCOx6d7LYz6dlaHhyCKaNkgRAvDbseKRyIg0iW6mwukpEenLDEatKOhhYBNwcPEoko+qZJTyzH5OoGgiO3W6PUMzETQ54EqlsjN7CRdMbDjAnnZwGxW2DTfQEea0IGp6SmENgBNKjFwmi7m3ByCdsFkhx5mVAPPZ7jiUjYgWtYFCVMzV7BwGtP0HTvEgKxhWJqc0SRutrCUR040UVqTSXqxtESYRSzfRfs+kSHI6YiBcAzbDOj3p+hMypzSnKN/dgeiUUeVimjfA9GW4ClhULPb4iFlRRh2e8HuaA+n+35WqAmEAB1LesNp1kSHyKoObJXHkj6diSYrYS5rIQBDSUKOP4M0gzPb6XQn6Aw7WTn3uGZT3I7hbtkGUejj6Ihu0WS0adMZhgz4aZRQ6DDESnT7mJdlhLHSOKFHrtWiM3FJNxNOPzROLtCMNHbTGcySajUhkdgyT9eRg4TaZ128nWxzhnXlhFXhbLs4MCKS0GMua5FVDXq9PiCiFrvkoiFcKyDDEWzhQuhjxmALk3TsoZMEYap2v0HAqCewk4BsUkdYmtAQWGGI1hEpu72GcaSNMHJtaZxWHO9WCBmxprWHXFKHpqLby7LBO8ap9d10JUWM0GVNc4LBsC0961Pd5L0VCBuEqZBxiK7Vsd0WpG1MnTBcCnB0i4J5GNlokimVsPwIx/NJiSYdqoYIDXLREiLRqNDFET5px8BwQBnQGZQwbEVXNIVAo1SMQYITSVBQCeax5AEG9OETl1ZIyUofDKMOzNJtz9DtHWFN+ACO9LFUzAXVabR8zJsmBDihg5EkDOkxGpYJaGa1whcJZjONFadZ09zDWrdFTxCT8bsZcg+zeW4n5y0dYqhsEZSqoDVagS80VTRSCGSSIITCyiWMyN3kaYcTdao+uqJOTos1fUHb57Ve7GM0PoRjROQjhUW7QZ7y5tCBBJlAErEpmqa7MUU67WJlkxPeRTPRrE0OYiKRIoWhbCopC980yFnNE9dco1jdqCN9sLwmp5fuI58UcaIiRtANRjeWyuDIDOnaPD2P7EVrSQjkvBDZbFD3XFqmYk0yi44iRApMq31P2jQopUzWJId4TmOGrCrhTC8SVWt06CWcpEYeSSHKk/UdiAN0FKF8RafOYZAwmzURWmPj06MqpJRLT6TJigwZw2FN/REsBVlpYEcGfcERBjwYXSizcXE3mUod01UoJGrZ2eYJjWG7dCdzDFY97NBnRXGCLvnYOImnwskC7Y+Ien0nu/e8lQ0bPsY5Z3+d9es/zIUX/BKlY/bv/wBaq9+/k3+H24o1LnnwED9drHJuR5a1mRQfOHSMPzs0/YRo06fCRH2Cr+39Gp+79HOkzCcnUD0ehjD49CWf5vap29m1tOvE616jztGHH+DcK6868VorkSyG8Yki0W9G3H3jEqNnD9NVv4VG1zksTY5z+IF7ufhVr+X8F69j9kCF0pEqOlH8ct8Ctmlw9TPW8ezXv4Vjt36f9z1nNcZxv8JpV1Hd/RD3/uhR5r11TK0SHFnr8FdRnVfVPsi1exU3zX6XD4RFfiOvwlo/xUcXL+dsa4aBDWWePXOY3mdPc4qEzsXL+Zvtb2SnO4L09iGaW7HTJuuDUyh2HiPseYBXTzWYdMYYmnopkUyzELY4lBrjrHAt9/dV8UoH+ItMzJ3rZpkYbVHI3cGH5r7B+WNv5pL0JxjVU/y6uIofm+8HmeXFcY04NggOncaKxX60sji4yuPeu9dwxvp9dF5S5GHnQrr2pvGTDtZePI6Nx7O8cabDPi7ZM4ASmlusw3RffYD6RRYlM6DT81k9UmIVuxjesBukwEgUax49yD1TBtH0vzE4N8nmh7YiheDwu96NvuuzjBd6eEvrFlYNruCf/vwnnLb+PF5behZbX72VPzvvz/j2/m/z5tvfzOAzUpzzvFO4dc8VBA8/ce4aAPd8AWKflngV+UtWP2VIh44i5v/6Y8x+4IOkRkdIra4ThBcz9YoXU/7mt/7DxoJSmpt3zPDK855c7D0VXvesNfz24CKzTzG/5yRO4r8CWgs6I4euVh9aGwTSJNvqwBYWeV0kZbrk5CHmDYUSNoXWAqrpnehtyHiQnGxhKxuFpsvrQHhlbDNhpb8CR9GWUMYJLUPiLj//mrbBQDzL2uZujOWObH9SYrh8BDsO0SLBCgOMtAKhUTpiNLyTNeEkI5XDWLGPo1y00V4OpJtLmL6LEbUoJ1WY6EGFGqFNTG3SaVYYiSdZ4eYJ7T42VCs4Mk+nmEDgY7Ra0PLorUyzjkdZIadZ7+bYXA04beYQ+C20DrCdiC5RwrTA1BNk1AJDjSw6NhgMZ5gyICXnqKdMmna7Rd0fVxgJ5zGPnzPbYNSdQOiEASSdlRgMyEcGZr3JhvpeOuMZVJzQ1dpPp1D4tkN3Q5KxIRZ5VKtFR+ogneoYIl0jLVroKEGFERvLC2SpIJQiZ/aD1gxHx8jZDVKxSyEu05cskG6mMA0LlMYwHQyjn1SSonf6GGYAlltvZ8QrxVJa4SmXEW+aPr+M1opu1UEm7EJLQU88SzI9RSglkSUwtcCzUiStBrlokjR1ujyBpXwsAtLCwxDd9Mw2sGX7POVkkZ54kWxS4OxkimXbItKBbllHoFgRTzKYtIdLd1dadB0fG2k2iSyFUIqC1+KcZp203YdWRTZ4u1Geh5TqRKhCf32Gvtosa7xHWRmMc8XYOL1JiT43xrFdtGqzCpIWAheTtjdNoBBJRE8ys0xfJnRGHYw0AEuRmCZGEhMZIc10+xOxEGgJBC7D/gjCrSFbLlIoRvwxRt2jjLCfUEdky4J6I0CINoshDMgZvXQkBiKIcaplBppTbIqnWdF0MPVjHqTIEljpHqpZi57AYGj6MN3hUfqTOTLeYRw6MJBUlkOthJak4wY5I0ezOoOhqlQsk2w01A4PIiIftxUcOmlL4epGlWYui2WEDCSFNtPSqJENEoShcbMOqbiLntBgozdDR9CJFXoMlPKEtgGJIlMsQ7VOsjxfy4o1Xf4SGaMFUpKvV1k9ew+2zIKSdNlr2w8RA1LKw0hMAhWQSSpYKqBs+hSSeU5zx9vHulQjSh4hCUPWuPtIJx6pZokuNUOnmielWwQEbArHsFRIbzIPwiZHD6GIMbGxjAQMhWloXNkWO2bKk6yc2c7I3H761DQ6cMm1SoDGSELqxDRNi+7xbczqiL1DvXQJSadRIR2ZGFpjJJJ04pNakHS6XaxoHILEwdAhVqjpqJdY7c6jzSkEgtiQpEqTRCKipes4ZQlyEbMFSvSjadHZmuS02QfoTBbJqQYODU4NFmhamqybo6PVjVnzSAc+a73wBPN6duUwW2YPYam2LDtXS9EZdKGVwtAJBgsYhsRUEYXmTrQVok1F2nr6wL2THrQ/EsKoxJ6972DDqX/N0ODVJ143zSybz/wnHt7+cqZn/pXVq67/g/bnS8XHj85ya7HOFzaO8Py+zhML4tcN93HdnjH+8sgMn9sw8h+m2X1h+xd49aZXs757/R/0N/syfbztrLfx+Yc/z3df+F2EEBy4+05WnX4m+b5+blmq8c/TS+xoeJgCcqbJSwsFNvx8gbVrO7ns5UPozzf57k8fwOmd5fznv4LkgTrNPWNcmTbwv76PGQO+ZPm86YI1GIZgl7UGP9tL/uDvYMt/Z2G8zrab05SmPohv+YwPNDl65ioyR+q82X8QW3ZyX99PWSpcyps6z+O1947wi5VbaYWns7brVjpb+4mU5pp7b+AX2ffxEv8ZfKNDMbXzMhjIs1H1UgpHuCgap2J18I6la7ml/+fckd3JBWGDi42N9FlDjKV9KkaDFywO8nwGCPMzlMVD+JU5zl8cY22qxN+tez3PNu9gaW+GUvxcHirs56WNdUQsciTu5RX5u7inuZots0Um+4foHa3Re3qV/Q+9jH/xL+Gu1IcYuc8j8LtY/Yx55rYpzq0+xPf7OrnU8DlQmGLPjqvJewpdnyQ+PYTUIqnZaQr31HBkL6c0Yg702cikjx2j72TvaQXGe87k7O9Mc97WX3L4Y5Pc8LJeNqvL+fx7P046Y/PcN72Tb33wnZxz5VVcvvpyLll5CZ954DO86ddv4uvP+zqVo138+odHuXpzC+O4pnrpENz/ZaKX/Ib4R3Wy5w486f7RScLsn3+IZGmJdT//GVZfH1pqlj71M/qvexul73+f8NBBVnzqUwjniR6z+8fLCCG4cF3vk/b7VBjsSHPF6UPc9PA0f3bFht//gZM4if8kDGUiVEjHQgs60swnChmbCAcCpVFGBK6kIRR2bhgDTSqoYhgxSWDS7LLo9or4WYHQgu56laTu4q4VGInEcBTEgKkw4wBHZQCF4zcQ5LHjJm4zQnel6NLLMd2GQHoByvXw0hpTZCmEfdiGQ15JMmqW0O3FLaQp22n64iKRCDBi2NTaiS0lQtkYrQoi1YsnXDqkbjMKysNI7BMGeMfyiBMbL6rTqRWWbpJK0uSaFU6z5im6/WR0DS0ypIXPmuAgUjh4Vi+RlqDav0vZMIW0JL3FaUIrROKgDYM29yLIlV0ajotAYgkDkBhasqZL4NYtdKJJqwZCSQwFYTxD2ckyFLVIRIZ8tYmutkjSBVLaRiPJWFWk0bk8OLb9jQwT0trCSCRCa7RohwrgeQwE+8nrGQzVg9SQFlWspC25NI0CxXQ3RhDgNF16pg263DKZvKZXFVm0fIxEEtspbNEOIbHQIAVaCDpkmWHVYntmlHXyCIawqS//ZEe+hTY1GkU6MUG1ExgNJHlzBabXZjIztiDrtei3algxRLpCaAxRsttytaG4SNoygQS0wSmlOoZyKZsaU+YxkZjNiA4/ID04j0CQT5okdBM3PYJYt5MHfYUIJRhgJBH9dsKsBC0Ec2YM+hiWYZMJ6/jCAqOGoRNMAY62sYgQol2sndCJBR6mmsUIJDkXWhkTvXz1l4SPHWfpUFUM0yARJomSJLENJhhGiyjdi+3lkb5EJwUMkT4RbOGZgs54DM9MoSzoD3upp8GJG2BAnLTtWk3DQBpQ8LoJxCJZtQq0j07mqYuQMNGk0BBX0GYaiUVa++3wxbamDb8uibNZ0ODgggCPFCmvSiFdxks5oAoYWmGi2rLAbJV03IUkQJKmpUy6pUkiQ3Q9IUWIoR0SAVoqZJLQ76w5McIj46ZYYVgYVgPDtBmpH8aOMgR5gWsaHP8VdUzop4FCEcsQhUbbKzANSUHWMJYlrekkoICN0gpLJQgrIDdbxusuYKRDsl6NGI+08ElwEFojtMRzHM5tjBMnNtoKsUSKfuVzSjKBJouVhGTxce0h0rJBIiUZFeFZYMcN0r7CsxwqMiYyE/JBi3R1mlxfA0kaEdbJFcc5NFog1Yroyjl0a4uaKuNbmiGyxEmdhikgbmBJxX47z8VWjToa24swjBjXEIRZA8cDHYHR8jGTuP3sMBShkSepTtOVK2MZKZSTYnguJhL7UXaaJMxSsWxAkagEQ1v42kUl3WTJIWVCGoNeOY8tUwigadYYjvYhVUIsnn5Q9UkG7Y+EI0c+Q2/PsxkefsWT3rOsPGec/nnGx79IEMz93n0thTEv23mUCT/krmdu5AX9XU8owvoci++etY47Kw2+NVd+yn3cN3sf+0r7eOvmt/6nvse1m66l6Be5Z/YeAA5svYOBP3k+r9w1xv8cm+M1K3o5eMmZHHvO2Xwx08ODB4p88ZIMQy9dg5j4HWL4HJzeVYjZhJFDq5CNiJ5XbaTj/Vu41ZUcuHoNDQMu2lGh9NMj/O87x7j4dW9h1+2/4dYvP8wvvrSL7ZmtDK36KyzZwb0XruWXF5xGoawYGt1NrH6GnRzh3Yc9XrZ0jOktewhKZ/Mx40d8wrsTXJPv5wwKUpPvO0CnlLzY3kb6tD3oPDS9hOlGF/eGp3Np8xrctMuL51/AKa21PLTyHjqkw7/0/4iLm+th4Hf85PT/zdj5H2dRK/onns2GsXdxm/O33Bh+hR/0vIy9h1bR/cwFuq/5Joe69vNi70EW/HeyzmnxYHAqNaeHI30d9GZdNp7l8r1immj0Z1wX/Zb5PZ1ckn4Et3YRp/nXkDvV5ZneTg7Ya9jwSA9z2WO0RIa6bjLUcxbJ5izpRyFZqYlLJgOZHBPXXENuxB4eKgAAIABJREFU/Qac+n4aYZnTtqbJ3LGZ8FKT8d5eJjrSvPsHLd56zCa1nKDU0T/AOVe+iHt/8G0AUmaKT1z0Cc4dOJf33vVeLnnbpQSim0e+8TgWbetn4RnX0zpgkXvm4JPi87XWzP/VXxMvLLDq/3wNq68ttRCmoOelg7jzaxn65FeIpmeYed/7UdETtdo/2j7Ny7esfIxF/QPwyvNG+LcdMyfmm5zESfxXQkuJtdAEAVW7QSCSEwxw1Q0I/DGU1piJahdnROSSGYz6FI2mi4olTrFJpuUw7A8jpIuDQiPQSqFi2U6WM9rSulySwiSmu+iRaZUxpE8sQ4wkbP+yC4UhJJL252QUoPwqQmmyKg2mj2GEmEGMMtud4LxokVgRfsbEbEQEJYGvYwLloWOJMtvdf4wYWzRJLc+KssjQGfXTLS0MIYmFpIxBNlwiGy2Q9gIKokxshGQXq/jCxFEughCHWRLDbPvM3ICUCJFRQF+yhIib+GYLdEIYeqgECqU6G+cfxIg9ZpImraSENDwmlCSnKlhKoyUIwydn1HC1icjU0YYk0vM4SYTSChlE6ATspEXGqJGyW6gwQocRVqOGszSPjiVmmCCERCkfa26GZr1B1WuQmHladoRCImNFUBN0FKcRSqPRZJb9dkllhl5/kryqYyJJhCIyfUxg2aZDGk23sLATRYaIqBAy7OzEiBKMMMGKYzpmKwSuT+y10Nqg018AzyM2DOLlFDvbUkRKIgV0HTmEu3+KBAVCoswEw4jIm1DQLiQSHbkktTqh9gmRJJ5HPiySSCDROMqiYAEohBFjmBATExrgC5O+uC3RUiLCCH3wIjJYpISgqXI846gHUmIuziNlRJRIZBzjyASLEEcEZEotjOVEPq0UQkY8NrxNI4DEDzHdJlJJYulTkBWMVkQoHBrL51DIBFv5pFWNUHr4gUta9LT3IRJUAq5h0rDBtxKU3UR5TWh5ONpEJiliqQhdF8KYTLWJ1gkeCo3EM20sQ2GJkMTWSCuHDkLsxMMQ7VRJoSWGSpCGwEgCzLhBYARERoDWGqkkvgCSBA302JASYAqwlEvdtLEJyVXK5BaKKCmJmw38VpkgbCEQGEmIGfnYcYjwFVaSYPkRBgkIjaNCTEJEIoAGLUuTmIL4cYmBGshFNWzZYmU4RGCEGKSJAEspFCE+Li4+nbKDtJVg0Vaf2DoA0SKISmSXynQU2ymYhlYnEg1jIyStq1iqPWvP9mM2Vu7BEBGogPyyJ82SVls+iUZJiCNNqMGMPEgitBacI0I2hTvptKeJVRXTiEhLF0sq8kmLio4JaCcvSstCIZjPJG22TiTkRYte9wjD3n7EclxJttIiW6qRXizjxdGJ4y6lIDaamCpCJDERRZp+Fbs6jx80EK0GSauOazmEJHjKI0FguU10FJMsM2pKK1AG+cU6hkzQQkJwkEUzj0hiROyTUu1nx9PhZIH2R0Clch+Vyj2sX//R/3Cbjo6zGBx8EeMT//i0+xrzAl6w4zDndGT5wVmnMJB6aj9Ov2PztTPW8pmxOQ62nijzUlrxdzv+jvec+54nhYL8Pjimw/VnXs9X93yVytws+5TJW5M8p+XTbD1/E68Z7qXTNHn4FxPM/mCMb521lvecuoJrdo1xcGw7au2fkq3lOL/3BRTXFOl99SZS6zrpGMqx8tQu/unucd763FMZef8WigfLvCuxOX/NWtKdr2ducpxfPevLqA1HSDWfx+Fhm89uWcPmjhxnd7WY2dnHO+L7+NH8HG/M7eF907fwzEf3kjJiJgbX8m0+zN8ar+PnfQNMpQx2jTZZ6ruXyxbO44zkPJzhIzxv5F/5UTbHiNPBT5ZikoaNqzSvqV6BTZa/Gvki71x6EY0132f78E5Gs0uMPPIhHs1MM2y9i8bQr9hcW88Lk4jX7p1ge9dV/M+lDLORwRoCSmolWeNCHmy8n1rDwpIxPYFH4eoK5tGX0LfrPO4tObzg7q20GikmCwNcuqHFzSs0fVsCMoM+1yz9nN+6V7JuyWaJoyQd3Yz3trgnehbWrIm3QdMvXdY8o8LLX/EKyukCRD4jORM9coxD/Wnkjufwky3rGFiI2XXe+ylMPMjCpz51YoH5zBe/nKm9uyjPtr1hhjD4ywv/EoHgqwe+yuWv28Qj+/qoTc622bMjv0Ge8278PUXyzxp+0n1T/c538LZvZ9VX/xkz/8R7zj77Qnr7v0/9tlm6X/dJlBsw8573oMK2Lr4RxPx6/yIv/wPljcdx8fo+lNY8MPHUTYrfB6U0tf/L3ptH2XbV9b6fOVez2+rr9DltWtISkpAAhh7RACIIYoNXEWwu8uCqz5bxfCh41atXBREFrgoqCqIoihFpAxzSnTSHnOSck5y++qq9a3ern+39Yx+iQWniG8N73zXfMWpUjdp7rtq111xrz9/8fn/f73pO2i//lxr5PIH/PWE8HE4b5DalX2+QhHIsa2OcLxUVPaw14xBW4wnjEVYvsSxLUuWp+jmqgjIzeAzaCwwG4Ty2sLT1IqGxOOPH4dYOtBXU/ATBsM9yp0NeJucNNSxKFsQux345Oyz68msp8IAc75ujq4RCw0yxArqilJLSWHwhqETMotFkVcacHRD5cZB1VWVor6ksCGPpqIq4t44oU5rNPhkVWQBxtUoj2KRwDhkP8HhSLKsuROuYWKdcuv4gwhnA4jobNPoj2rYgNpv488xCgyFJ7NGOsYywchRlzqjKsZVBDUva3RMEZUmkFYFNwVkEEDjLhEswoiQME3aoDtYZvNGIsqJQAqssveYmAR7hLGa0TJCO8M5incdZjXcl3TiiChzOQVrGOA8ONw4ZThI6UlKYksTkOFkgUMSFoakjPALLeC2YhTXGWj1HE0tU5djCUkuGVBUQKFytSzAc4DspctPgXIXXBmcMQVRhcSRKYW2F8h5vCsKVdZbrJZVUGJVirKVIDLI0BDhCPNpbQqGBCl2dLzzzCmcleI9wFRaLzQWVgzIvcc4i8ESuwjDOttKBoOkGCKfwzhFai/aO0Em8B1NCnGnUqE/hAO9xVcBMd4JGp0MsCkqRIayl5jR1Z9BZjrcFWIPHYYQlAGLv8LlGjDJ0nuJMSnNYEDoBTmFcRaAHxLYgwmKkpWTsCOkdyDLDuIKKEeBo+D7h/DJhqw/O4v04IyyuhghTERcZ0SgnXtlgw1qGOELn8F4Q4DHCkBUpAQ7W+qjRAI3G4BB4UjxFlVFlOdYrnJGEawNk3if1Jd2pmH7UoFIwMIaiqIiSEoQlkhAXFY10SFAOCHsbSK1AKQrtHzVyaUiHQUBmcGmGiRyFrIhqI5bjFoUfshFCL/RYKQlcgcQQ+ZxGAI3eJs1hD6s90+m+sSupUyiZYclpDHrkwSa+2oolx4cF0mTjAlRlJK5CSAveo5wg8gopFc24IMSAh1SOEG783tXcNGGZYUyHthieZ13HRkHegxtk1Nc2SLwfy1+zFO8hdQrjt9Dq9pBYWqxT9+OCOBQW42DgFEWvQ5gV47MuNR6P1wozqKhEQmswxKBwfiwd9A6GVUGeDwFHRYUTFiEcXlbgPAqJc34sk8XjdEFcKJrDDFkpIhyVTcA6vDUIbxBaEZcpwni8KqHK0XqEH1qwnsnTC9TOrQD/1I/51fBEgfbvDGsrjj/8C1x04c8Qx7Nf87n797+RjY1bSbMT/+rjC0XFKw+f4vt2zvErF+8i/DpswrWTTd60dxuvP3qO6p9lrXzy3CepTMW3X/Ttj/8fAl520ctYSVd4xxc/xYe/9dW85eJdvO3iC2gEElUabv39I5w+3OEVP3M9+66c54d3b+Un927ju2vP41Mn2lzduJnWy/Zw8PN/xsKDDzx6XHdJm1ODnO+6YTe6EfImm3GxlvzNb9zHVd+8mw9e8BscmNzJLxcxR4sXMLX1ODevHeQPf+PXuKHzt/x88D5+f6bF6/e8ieWbb+VT8u28397CATXJey58MX9w4ZXcmAi2JTfyVzPzHMzPcOqKDierm6Be8KJujY8G56j2K17TrHN1LeRtTjMMevz4gd9D+YQ3rr+KU63DnN5/P3NBxo573ki0cAffn7yLX9z1RnTnFo6JP+CsvpVXbczww4OLeFJ2EQ8vtPiWUYa5ao00/gxHtKXmKp7VOc7eZ29S6Bj9mU1en30zb/kT+Ozlll03n2JhcoLZ+C/YdU6w8IlfZvLifWyd2GSrX+DSU/NstA5TH7UoSs++hdMs7bqAYrnJRN9Qzi7g04M87RnfRHTxFXTuvYOJeszM3Aatm+7mgWtO0mvH7NILHPjT95N94SC9P/wjABoTk1zxrOdx78f++tHzE8mIX33mr/LBhz9IZ3edy3ef5q4/+Tzc84dw9avIjxlqF04TztYfM1+KI0fovP0dXPDO3yGcmfmXE0oI6s96Llu3vxu1mBNe+DqIrmD5J34Oby23PrDKVRdMsXeu9bjmaSAFL3/KLv7ynqVveIyuLKcPd/j0Hx/jfT9zkA//yiH+9P+5kz97y108fNfaE4XaE3gUDo+xOanOaQ1H40VoZ4VW4M4vYs35cmNsPBCKPoFXCOewgUf7nM3A05GSoUsZyopcjos5Xxpml8/S2BwRRSlWlShjkVajSoXG09gydghzyuG0wTmHU4apUUlDa5wtkc5R76VMbHTY9AVLMsSakkCVuKKCYUonrNExFltmtFcWkGMBGkuDCqcsyJQ40YSbitp6ijKasLuJ2qwYqjrWeIz3CJUjlBov1hsGXzOAYYUaR00LLQzSjq27A2+ReEKvqIxFyQy0AmcwxlJ5BWYI3iKdI8QSJjmNJCccKopOlwpF4TXSWWqbfWR/zDQ1ZcFEmZDUxgu3VM9S+Gm8KgkG65h8QFoEaDMg7A5onj5LYDXOWYypKHNNpQzeeZwH2/TIiQovPFL48RpLWDyWyhiGogbaoERFXg1IzVa0nMU7xUBYpHaESuFHJaLKmOiss5lXmDyjKDJGowGbNsFYQyodmREY5anyAKvHu+7aa0pZ4IzFTh7DR5tgFUpVCG8pWxZrLFbbsXuk98hRAet9yk6CKBMyN2LDVFiv2AgEShl6XtAVBaZSKKtx1lBkCcrZsaFNb53AubGUrUgRbGDdeNNADUYo2ycUHazxSOuwtsSPKpx1aA9RaYiyBOcERarJe5bKGdJQkRfjDLLYjdCpR05n6KAaXy846sOUer8cx357R82VfDkCvZAlBTm13oBEK6Y6KbZQ7AgfPi8XdCAcgdQ4mQPFWM0qHc6VeFkRuh6tfEiYZ0BFFWREVYm3IJ1nVBr6NjwfPO6xKNAVGE0pNEZ6wsEmzg2YrY4yRQftDa2NAa2VAeuxY1iMUBQMQgU6oXQZFZBUGcNiiE8tA1kgnQYscbeLcI48zaiMZy0puEO36QcxSudkoaQXSaxQDCtNkGxilWPgPSox4B2hhDAE5wJKMUeQV1S9TUZpivGWamUJa8YbRxaL8x6bTUHiKWyTWlYRrIyolKFZdRFlSbzQY3IzxTuBdQJXljQpkEC6UVD2eshsSJykBCMgtwhbEpgcm0lqvRLnPA6JJkQIT19CYgpUUaKtG5upeIfxFmPHJiBNWxCWfbTUeBzWeYR22P4mWipwKU4rvLPkgadvDeFmhi7HCpxCCVZGC6iyxPux+VLsFENlUNrioyFCjE1RhBvPO9w4jsRphz3/c5QWNPMBzVpClBvi7pDcOCpKmp01gnwAlaKoLINU0exZfFpHKEVmBKQl9cpgzNf2mXiiQPt3xrmFdxPH8+zY8R1f97n12nZ27fpuzp591794rLCO1zx4hpdtm+G/7Nv+VfvKvhKv37OVdhDw22fHwb3WWd51+F386DU/Sij/bS2J9bDO5ft/nD+cupS31DTfuX1ceKb9ko/8+r0IAd/x09cxvbX56JgfCld4aecgb93xdKLn7mTrTZfw/Ne9nr9/x38j7W3ivecDZze4QUWU3ZI/v3uBLa0ah1PLFXXJifAgU7NbuOGeOp84VlLJkB+aOsmtf/5HZOkaPyz/mp+auI6Hpi/g8MJN3PHxc+gDi3RcjSurgmfcdw9r8xN88opZwmHI39Um+MluQaU3ObqrxuTKTl5z5l6ua4X8sn0vUwW8iYgQ+JmpR9hqJC8a7eLqbC+/s+cj1EWFPvTDbFGS2e1/TK6uYWfxHP5hax170f0cP3OW117R5x+2K968/KOcaiuepGKa2vDZbfewWJyginahdl9L/mTH2qHXcKy9k9X7/oDFPZdy5oWen9wzx8zSfs5kT+WawT1szN3HqSNtLrp+ia2iQ2ku4dxWz0UP30uzVOgiQe68kokTjuoKx/TBWR4+/Ys8/elPwU/OEM5vw509wa6TD/HXap3989tYnbmcyaOfYlDVueBdv0v3Xe+ifHgc2HrdLS/l2MHPUabpo+dx98RuXnPFa/i1Q7/Gta96DksrNdzhP8Nf9wNk96zRuuGx2WOuKFj5qZ9my0/8OPXLLvvqk+qqVxIlB9nyEs/8a66kef1zoHkLq299N3957xKveJzs2Zfxiut28w8PrpGU+qs+Jx8pHvz8Mh9755f4g//7Cxz6+zO0Z2q8+A3X8LrffCY/9PZncuO3HeDOvznFp993DKsfv5nPE/g/FR6FZmQcYV6Brlh2KwxlhccRKofHIU1GVGX4BOzY5Zq8sgylwEtDNUoYeViqR3gNQVFi0pIwLRBVjnHmvH20w3lHZSxhmYPTuMoR5hAZEKMKoTyRtpQioOpXiKIgamVopei6Gl1TES6dQTpLlBcUwyEKz2Yb8ijBSQveURaaKFOofBZfBLiRwimNdxUGxXIYkDpQI4MbKIQdf57ktjHuRfFg9RzSOMqiItcVelSQ5GPGD/6J8dB9i7IWnGVqvU8wGAEWkZRYD6bKoSiQRmGsZZSnpMMuRZWRZiOMdpRJgTzbw+eOuBJUOsQ6idCgiXHDBOUcUim8d4S+RBhNNCqpr4/Iqz5J0kWZ8ZJcWYH2EAh7vihjLCOtFI3uKhKFsIo8MngPwXBEY2MV5wMq6jhVoJxGa6DStPopgbZEuWZYDGGiQE0atFaU6Sa6uYLePv4sFM5jaYIusVmPcq3EK4l04zlnpSV1kk3HOJuuN0JaR1RWRHlFNlT00xFqkI7ldaWnm2pybQmT8TwaxoJcyPGi0adoBHiH8sX4b3hPmlvCsgIl8YWmQmO8xzlPkGQEYUYZjVmGOFfEzpJ6QSED+nGEqSbGbImucEZDYSmtQYmCkcvAlmgUUaVBOYaxJmgNKL1jrTYOYZbWYmyGNg5b+bE0Ektjckg5oYiHOYNyBGWOdx7jqvFC2zucFTgSrFxHG48HKuexMkPVCqROmVk4TdVNKMqxt8p4D87yYNXghJLYqgDjEMZiM0MiAqLKgnAQVBCOEMoSUmKKFApNiaeygvbiKnQVfj1lpvsgWdXHao+vcozTWGnwxpOXmsCNi1HnPMYY8nyTkcsRZclqOSCxDZJgkiIQ5F5S1g2mMuR9iyrGry+1UxjrEHoNWQ0xzqCdpyxKrDPI8/uLSkEl/XgDBE0VjVgXEKwYAuXAGtobXXAG58Y8cFApljx0ZIDWjqoKKFNDkJbEgxSxmdEcKIwTMCiZtglz2RDZ7+Or8vwb68ffhKMftkkCCJzBlA6hNdaWGFWgnSMLJKJ054tXgUMipCUuDJU2474842kVm+yojqKFxymNA4Jhhis1AT1avo8sNF5UNPodouLLRZ1HeEOhPbkOSLUh2siIBzlaK3AOYcc9qkMfcq/YBs4SdAaUWYFznkqMnTiFzxj4TaQdX9+pduSBIdgYIJXFlWMFhFVPMGj/2yDPz7Cw8D+47NJfQnwVC/uvxO7dr6HT+cS/6EX7+RNLzEcRP39gx+N6DYEQ/NaTdvPepQ4PJjkfP/txPJ5b9t/yuI7zZXjv+e2za3ym2Mt3fOyPuHLf+EO5SBUf/e3D7Lp0hm/9kauI648t/or7P8KrHyiZkgHv3Dsu3C57xrO45KZn8LG3/xqfPrrKuV7Oq67aydG71vi9z57ieYOAK561i9lbmlzzxR38xrN+i41TJ/ikfyW1fZK/O7jOifhifiD8C07VQm6fX6ORvIyX79vGYDXjE91zXBh0+VRb8ZTVq7hs4UMsTd3I8SfV2R3t4XjxSm7uhxy7cJmrzyoudR/jZc2nMbJrfHz2AVplwAsnhmyOLqdwFbf0vpv75+/k5fOGxTPPZL8+AMN3E8SWQ0ckL1nxrO+7h8U7dmKdZE/jU/ztltN8cPenmLOWJw+voJM36R6aYFe1wpbakwlvuZ+ph2aYyU8xJXZz9OqfY23PK7jx+Ou4+d5f5LbyO8iF56r2Qco9E9w1eQU73uM58Pwlpqt1rj15CYcuGvK0Ox/k+KWX8UitoGY82Y4aYikjXgxZWn4vz3/+8zE79pIsnmG+XKPeNbxi2w/S3flcGuWQz7z1b7Db9jL3Q69j7ZfGUsfp7TvYecllHPvibY85l99/xfezlq1xMFjm5l1/T6LnUNVeXGmoX/ZYhmzjN3+LaNcuZr7ne772xIqbcO33wd3vobZnkvkfuIrpl+1HZ5fQPzfgRVc9vnn/Zeyfb3HFzkluPfIvs9sG6zkff88R/vjNt3P6cIe9V87xvb94E69681O58SUH2Lp3EiEFQSC56LqtvOrNTyXplfzDu49g9NfWkj+B/wDwEusF9nyPo3UeXVQUxo57awBlt2GRMGFYdXUelbh4MM5RBgLhHaLUWO9RAsJHVghSzahcRjtJMHAEWrNRc+MFUzYg7PXxzhNoNbZaVyWhVFhjsAgqJNJYbKWRziGMQaQrOCfwusKUBU6A8RbrLIG2hMrjCoOuVxg9ZCN/gI3iYXCWpNxkU2o0Fmu7rEcj7tySUzlgI0VkOZ4YPAjnEesJMlV0YofT4+whf162ZrxEVBbnLLJIqeXjYjCRAVpKarnC18dMRVRoFnc7UuFohgN2RWcZZj3a6+s0HjjMZlRSKYOxnjhNKHurrPmQtGxBskZ7dYC1Du+hW2uy6eYYakdWWiJZUtiII9F2rPUMonVWq3PEcwv4iYTImvP855iREjiCwBAxJA4zhNOkgeX2rS3O2Ql0OYuxTbQFi0RmmqBQBM7jSo+2Gl85jJMIoRAmY4Ma2mn8+hnwFhGfD1Y+z+IEZUlVZMjNITrRCO/Q3jNwMUI4kiBGlYaNCO7Ys4F2DrSmiA1JPmDVljQSjXGCoDQIA15ZvAsoghYa2ObOEroxw+C8RxrDWhAxQrJeH7MScZZS5BvYzRIvGGf5RSVhbjGjgmFaoTPNWlTjgWiepWgC40MyHfAgswwiy2BqGa8Nq/WEk2zQGiVQFrQ6FbV6SaUEx6b6DMICK8aOj83VFZpJSdwzdKOIhVaNzDpCegxtwIZtwMBSuJzUnr8evAch8F4QjVJMpbkv3qQbGhLpSHLNiXIE2tMYjqVxnTBAUScPHFIZ8pHApQWN0RDqFqEd0hpWbIMvRVtwzlG0MyxgEESpAgRlMQKvcR4GLkbJGaydRdkhYWawpuBYOIVXDm1KChtQOYcSMPAhiau4L5qn7+ZQWuBsTpgPsE6jfINR3CSPAg6FWzkYbCP0FaYMqKoGOFimzTmalHaE6yZ0ZcXQKbQ2eC9BWzLn0NbivMV6i7XLBD5F+rFs2xtHFZ2luy2lX83hgeWapYo2CZwdZ/v1u6hhTqHnWFWeoc5ZL3PyZIguA0rroe8RytIpKzarksRbfFChK00gLFJKesT0suN4bUmQ57lTUD4kswm1NMdOa7wcywUbVYXNVqm8IAsChmaTGil6rkdrtYOwGhN4xOYqZBoZDDGTCYW06B2niVRKqzcgrCqkMzg7dsP01pLoVaqqBOEp7XmbGh/Tr28ymKgwwHCo8EXJ0OZ01Dq9ZgsfG+S5JczKKnkUEhhLuGOI273OwAmiQo1NhzxU7rHqoq/EEwXavxO89zz88Fu4YNerabcv/YbH1Wvb2bb1FhYX3/fo7z6zOeIT3RG/e/legm+QOfvnuKhZ58f3bedNxxd415few+uveT2BDL7+wK+A8543n1jmA6s9fi1fZu+E5n0n/gTvPP/43gfZvn+SAy+4gN/57Ele+75D/OD7DvHOz5xgY5AxuvNT1IMX8PvXXcxHOwM+3hlb3D7r+16HQ/LmPz3I/3XzHq566g4euH2FZ5qYiUbIDS/ax1vz30LvCpj6vKX/5EvYtSLxD/0V/e2X8d21z9A0Kb+yfx/7ooCHOpdz8dBzKK44tHkZk0HCnsumkDev8/wHX8CWzQ/zUOMWJrbsZEvTMuxdzHLbUDVz7shfyzWHV3he9nI+2/wwSd3y2R0Hqbce4QcWfoRVWRFc+XfMSM/0wy8hEiPaFx7m2PIu1ub2AQVPeeTT5LrBxpYaZ7M7mWztQ3MrN+R1RuYNnL3zmfSm5lBBm0suv5VoBBd8YYFvaf8lFy19ANt8P65/B7WZE2y77EN86Jpf5e7ZFfpM8EPBO/n+Y5/g8OyVDE5dycZlW7jkrOLw/gB8i0uPn6AfGuzsXvLFnYRLDpMnLJ77Q/bsrTM1v4Vozz7Kc2fYP9hGdd88Mzdcx7n5aXaufpRP/sFDzPyn70cvLZF+7nMAXPncb+bBz3zysXM0rPPGa9/IO+9/JwemjnP/4IWMDi7Qeso2RPBPt5jy+HGGH/kIO976S98Y43v9D8KDH4G8B8DE0y/k4W11frYoCU498nUGf3W88vqxWcg/x0NfWOYv/ushJucbfN9bn8a3vfHJXPXsC5iY/eo30Ho74sVvuAajLf/43oeeMB/5jw4vGMiUQnpaYh0j6hiaODPuX3Qeupsdhskmh80OjB9fG86fN0fw4y9vFaYoOGpnaI02GeqSJF3FeY/2fmxK4aFyBUWgkUVBoMcL+cAYEr3G5KnTVFYQjTq0w0nq7e0Io8n1iNxXLFRnUG4cylRGIV6VKAGpqJH5nEBbiCuMDPC5ph9f6TYbAAAgAElEQVQv42NDWUmK0rFuBVnoKE1FfbnHoXieJTGHAGr9lEgkDOwaidtEA4mIGSV9ROzInQQLcaao3LilRljPdrXO9HADY1J62pOGIWtu7HwmhMNVCqU1VsKqNnTGHWM455HCoGSdIFhjFJUMqfP59n4Ob0/pk1IEPVqDR6C7Sm5DFBJrIUMTDof0U4dygr6doLsj5rwXHyWGSkA4W+Daip6scS9zmEoilMMbg0WDdKAtOuA82xbQlzHdsE1mOhSso6sKb8esDYUay7lKy1kapPRo2w2s8aRqyHFfp69ifGj58q1SOE9U5JhqRK8WsFZsMGwbMg/9mmBj+hEqMwmFxLizVFGFjitUOaCxto52Fh1Y5IEFsrKBdlNYN4F0kOh1NAKnQkKnqBd9QlchK4UwY6OOnirRgcVKQTi5gdx+jk5HMbICHyqqCcWoqtHJTrFQdlgJKk7bDCUcfQkqH5IZh3aSlRlJL6wTd7psmg49WeAri8NQlwPSsmQp0dSSkEp1UVsGCOdIwhjvHMpoLGZcTHlDuZqiR5OUusWp6QEbNc2qaBG6cSeak4bKNTF5kzAvCFSI8ZBnA4QVpLpPtFYRpAoVhTgxXkD3R8dhuIxOcnKdoNaW0DlUVZciWYPeAJtpzqg+tX6JEdCThm7lSXPF0ZkuOZbMx5wLphACpHWYboYuNUGgMD5no52TeyiNYD2NWWg0KaylcJJShCRhDS8hReARZESkTmJERG4SukGNVRqU1rJWE1Bq6CZjqV66yrH+cVbMGk6WVOWASjcwxtNPFEZp1vsaU1TgTzN37hHCJAUkm3GdJFsg8RnGR+S6iZk2VNLRM2Pm32TruI0O5WaJtx5DHas7OO8x3jJUmmzvMkZUuM2UfrnEZlRjJTSsN8+SRj36pmKYbNJPDV9oXck5BCoZUFMaKoUbdkjKTWgUZEVEFRjWlKYoFJU+S00pQmMwvR5OKfqmScDYlMUYy0R6FmxJEQQ4Kei3oPIa6TRGBWw6S+4jun2P66c4pzGVxxZbMWIGUUCpNZUC0VwmqA3AQ5VKskBisRinCUrDSmuCotJEZZM8bhBYi3AOFQjKQI6DsD0My4y1MuNr4YkC7d8J6xsfIy/OsH//Gx732D17XsfyyofQekRhHT/3yBL/70U7mY//7SkJP7p7C4VOWKs9kxfsfcHjHu+856cfXuL2QcrHnnIxxV2f5xnP+XYOrR3iUx+7j+FQcec8PP83P8/JjZQXXL6Nb7liOw8sDXner3+avnk17eu3sHvXJP/90t381COL9LQhjCL8t/wIQRiSfehXUWxQZJoDXctzXv0kvrD6BTbyDa589bPpLXa5Y+oWhElx+7bz3Osv54LqBJ9stXhIG75tajvtOOb+0z0eqadsEymngojfe9ULeGDqIOsTZ9gxOMuW/hk+ry+npr+ZK4Jj+PAAD229jzPmKWwr7+B5o21sVbN88LLbaZnP8LTmHdxo9/LQ1N0I4Tn94LdxSa3Nw1O/g4+bHBzNcPH8TRyb+jydI3XawR4OXlVxoNnilzb/gsOxZ4doc5s7yE0TL2aqlxDs2EJ06f1M3fNKzvYvp1FXLNy8nSdPHKE1t8D6kWVa+dN47XzOe+ZC7mwZ9ix2ueTcCd6//3mkxza4diLlTGsv+5ZLvnjDK7nkxBnmNNx//TPYfXpIvj8gXA/wBg4dejHXX7/BNY2H6LQs0XCDlXNHeeGLL+bTe65n6vRR8n7Cg3d2mf+x19N5+zvw3nPxDU9j1Fln/cypx8yHF+57IfvKHD86RVk7QPlQj+b12x593HvP2tvextzrXku081+ahvyrmLsQ9n0T3P8nwJiR+G+q4pLmHCu/8N8fNQ15vHjR1Tt5cHnEme745njfJ85x99+d4dt/4lqe/vKLaE1/7QzAf46oFnDLf76apFc+Jlz9CfzHg3UGWuMeF+E9WTTDUDQYZBVGGzJticQAiozAVVSDEqH0mHWTECcDwlGI9gl9p8iLVZTULE4nKNWl8BblFEW1BWKw7Ry/a4PQaAwGHYTIPGXX4grKJRRVybrOaQcFzY0zTKwuUAWW1XAdN5+iAvDOUpiKkbQcnlyi326h3DRJ0MXPncFFKb7XRZUS07JEXuBUjfp0ha9VTDaW8PUhxtdRLiKsjTC7U+wlmyiZIXDkwTqZthiisQRTCDLnsamm7wOC0lHWDcvhgEA6smqTRjjBllbOvvARvHdI5zk+ucbJiS4Wy6by5M6T2oBBs8Z0PKJiAlfrcaIBD8hxf6oJDWk5oN1fJgw0eRXgBOiij0/XMdk6shzglKZUnqDMcMKxORVjCEl8wlo7pTYyrBRrPGwqMl0w1Dmmv4gNjiDXe2hh0DWBmjrvaugtRkpcKBFSkQQ5I9/DWEnlA7wP8BaUqigljHwNrQ2NrEdZVeiyxtrKJLLQ4Es8kAVbGM2CntCsNgecnVgnD1YYxhmiXuAnHbbuSCNJ1ksYGsmxeJKB6+JrFeXUkLQ2pG3WcA60b7Ak6uxrHeei8CypNShbJ8kNwzRHViV9FzKyIaXdRBOMw52FQM0khK2MB2ZjDu7pU84X+OkRSntaC4qJQUJiHJlrsDKzMWbAsgFZuYaSNRIf4xBoGWCNQ+mInhfEqsDLBGtKHJJ6rlluDzjSXmbQGmKdBZ1TFetEnbMkQ8URuZWeq5NkddzsAs3pRaZ7Gd4bjBdEcyew29cwNY3JCtYHNVZtk/VuTm4dJ/J1TrodeOdZCnYyMbnCXnuE07YNF9SJdq6NvSQDQzVRYo2jPUxoLa3Sn5kkDQq2sYalTTU9YrLeozR+3Ic4EZLMlDwUzCKEIxCO0GpyKVjXE/ish6zU2CV1RrE4hFNmnoWJhKPzA0ayjfdynHsmPUJIVFyDlmPUgNIZFCE2sATSEHaXCbefojl1jg0CAp8S6h5adnFzLeqrC0z1u3irGJqcbihIdUnfj1hUPZSyhGlJ3hsxsOO8u6mzRzFSYFxOX6+xamYZhTWkD5GAs5oBFTo/RT0ckAQJsekyLTY5Mb2VkdN4oVmSi3x0ZmyMY4Djrs3ZwRzH9TQGEEazs7eCdQFHG7s4OHERd9BgNapDaDi5pcLl57jNLzOyJZ3pORI78agDo/OOzPbZtB2EVhSiRmY0iS2pqopUGrq1GEWNIrTM9xaotxYwpWVDpjwcxSy6gPU0pB/UOXpgEROlxEE41qEP1lmyKxgRYT0UdnxPSxsttJR4IRkGkhEBy9MTBNZg8WRBwLKOWTYTY9UAEukd87U+gXuiB+1/ObQeceLEL3PpJW8hCJpff8BXoN2+lKmpa1lZ+SDvXFhnZz3ildv+FXOFxwEJzPffT7f5HB7J1dd9/j+H956ffWSJu4cZH37yhUyUGauPHOfKG5/NK3d9D/d8ssufT1QcWRny8f9yM+/47mv5rqfu4Ttv2M0vPm2C/5weRPgrOHrJ2Fr9W7dM803TbX7hxDK5Mrz9M6f45e99Bje99BW85Xc/hA09M1uazO5q8o7738Hrr3k99XaTD1y6yHWnPVGjg7SaG0/+OlbAg3HIFhzPf9JruFrGLM4KRgjmRMq3PavO0Y0l7llc5fb9n2IjXuJVnYyqfg237pzlge2XocOQC3VGVbb4YvDtXG7+nO9Zfyl/r/6KU7HgDf3v5G4KTk73UabGvoWbSWuCF5hT/E4oieOI+WgHa8UhRLvOlfPXktcWWQ2/hyef/GuWQ8GR2qvoLR7m0+Ys0nuuf+atTHwsYHLyQgbbX8ID7jJubn+RzY3r2f/clOZ0kwePf4mpznZeowX/dXYbncCSX15jR9yh35gletL3km2bYP9Ki42dh7n36tdy3b33cc8MCKNQwaXUj3ga947noMo/zdJlp7n9qj7tbh+tH2RmLsJ/0y3kYcRlc3/LXR89TfCsb8V2u+R33UUYx1zytG/i+Bc/95g5EciAnxTzfHJymksuvZDEaqK5fyp0kk98ErO2zuwP/uDjm6hP/WE49D/AWb54sksVCiafvotwx01svvvdj+9Y59GuhXzrldv58D2LPHJojfv/cYGX/vi1bN37tUMjvxriesiLf+xqTt6zzpHbvnEDkifwfxak0VQyQOgh3hmEtwhvaW16av0eYjCksAWtasQefRTrC3wxhDhFSsAIJrafJPUhPTtNpDTDqM+IgsHchRRRBEYR6S6y2cFgCSe6eCyjWJJMNCnDkLIRsbhlAnXBIubAiMLFbNQl3SCm0R3RC+bos49sqj5m9gSMhKFhc4zoE9UiRtFOHAYdB4g8I4gtF+kO7V4dT0p75wYVQzqpwdqK+dAybzPq9RI31eB2cRmJGMusEiRGOoLdI4KpNeLJHrFT9GTM+RYqMudYkztwHjZqMzRaA+rTy+PCxHriEx2GlWPRXo2SYM/nvDlriKgwcYiTgrAyZNbQiROyesRG2ER4x4MTy+ReUQlJFoFgg3AmQU9D0V5GiYDj+TwKhcXRaxm8FES0WAuaJFgKNBKLsQ38oMJUI5LKUcwp0t0rWBeipysIPUmlKBAsBzOs1GKUkAiVE5QFolJkromvLGp9EZ1aVGr5YusCzs3XEd5jnWTL6hq71teZaJ6jEwmqoEI1VvG1HE0fa3JS2wDTJvCSRjhFEGUgc5TJMaLNmS0TlDZEiwSvU/bIVZTs0tu8n47okoghVTikljbZt7pGNFhGW0sBTJSLPBTu5pgW+OlNnHRUJkYTYSXURUne9HjlECstunnCZnqEIrqKCM1SPM1ASkDhgxIdONIIiqqLUApXapJIsVEvGIiSgSxYVsscFQ2WhWXDabSIWQ9Dok3LoHWWXlCiow5yckBVQWFKrHNYbxlWTVaMwZYWnMR6g/YKbwwlU1T1kryWIw+coqCgEAYXNqgCg9GazGiqUJC1CtZrknUN90dPItAeKwNkM8EJQ56OCJ1BzZVU8xsIoxBCEIqcYEsf0SxJzvecRrllaf4Uqp6y0eoyCnKSqKSaiEipoZKKvm9T2Tr1IKNJh+nBBq2JBLYtUQkPUlP5DC3BCzAoDBLvNUquI2c9s2RcFxylcF2ct7jAsxZPMnDja9DYkCoMCZTBekfhNjF+g8pb6jJmYjhkTTZYFrOcDjVFlUKVoZ0jr9W4q3YVq34aYQ1m+wqFdCg0xhhkPEHlSvo7YHkuwfocIyWxMJwRs3QLg7aaRDfRLqfAU2XruFIxdPOMDOQiwAk/vk/4EBGVtC/o0osDzrXqJKFlFHr69YhObY6zQYz3cLbVY2GyIkGyGddYr8OJ5hDjK0xDUuqc1bDHeqPi0PQk66KObhSEjRzfNpxmjqMtT5imDCuLKIaIbX2yLTkb2x2L7SEjm2BsiVMZm/UJjtmdnGWOS9qHma+NaAcz5GKeVDlOmiY+UEQHOsS6gDxhZWKCs8xyUm1nYHLO1XaQxbBmCmLztTeZnyjQ/h1w+vRvMjV1LfPzz/03H2PP7tfy4OJHePdih7dd/NXDpr9R3LN+D0n2EG/cu403HV/APA551ttOr3LHIOWvrr2QLXHEyXvuZNeTrqQxMcnE0Zv441bOpbtD/vS1Nz7Gac97z2f/+L28dPYSsvl13vDXD7DUz8fHvPgCbusl/OwXTnDR1jbPvnQrFz3zmzk181RaypP2M25buI3SlLzkwpdwrrPEBxoH2N5RpM0BF63dhhmc5dMT03x6os136ZS6fDY7VxT35DneSY6LGn/26V1817uOkS78J4bFDMXweu47dYBrT53kjh0BR1vP49mDe7hOX0fUlCxsvohpfxdz9TXaTnNtvpM5s5sj7fv5wtrTOHvseVwRB3xp60EaVcQXfZMvXT9LwTEGpwM25SR/t/2LbOVp/MIDn+OT9Xn22Ab6zCJ7m1cw3DwEF+wiTpqwUGdz2/uY23kVCwcmuCAd0WMBV1tmdndCIKB7+y4u2pXxnJOX8pFmmy1PWuDKjTu4e/I6wve/h2eeeoBHZrYhu/dRNXeysv1GXnD37Zy54kbmRw1qJyT55UNa4kJmNyv+3re5ZjKkNvcCautn+MJtt3HLM67i9NYdqM98iouun+PuWxeZefWr6f3R+wC49GnP5OE7vvBYB0OVs/fM7fzj/C7yXLKsPL27b3v0vHff9S7mf+zHkLVvnJ0C4MLngQzhxCf48L1LvPzaC2jftBM59SR6f/JB9MrXzwn81/CqG3bzj3cu8bkPPMy3/MiVzO54fI6QX4n2TJ0X/dg13PE3p1g9Nfz/dKwn8P9TCI8vBVqGTE+eozZ3mkIKOrLBlxpbsVgqlzAdpuAFelaTNlKYX8Nv2cCEE/RFixU5i/cC6STxzgA3G7Ay1WUURzQXj5DHhiWZs2FjSh+yicFKhfOazFvS5iRGShprCXiLKw0oR7ItJlQVjVzRXq2QicQ5gRUNPAGZkLiiwNk6nSAABWVUxztJ0GpQ1RskMsbn61Qi5cIoBxw4zz6/wHX5aWIjqBp7KKIpAuHxqsGZ6R7HrzjFWRlxrJgk9ssI0eNhO8Fm2MVOjFgH7gt2MiiXsTYhbqyBtwRxAjWDJaJeN8zOLHOyaOKtgUphagXC2bHjWii4vyFYn1ob2+tHOY16gbJ9rFHkrqAC8jilOb+JmBtwdmqTIohJdias+bFJSdUQhFnBXJlStjMeMA0Otrok9Rqhc8ipNay1NMw4ZiALCkor8U5ybnMGV+RYX2Ot2aYXJ1RiE13G7Gh0ae8/jZEG5Tqcbjc5Wb+AS/feRRE0WKxJSl/DIbBIhBs7RK7VBF+KAo7qLlnh2IgUhfMIXacxrGiv9yhSgxIOt/Ukevsa2ggMIaYOsSrpZp64iIlCQd17drVGrDRWyGobPJS2ya2krisqCV1qJI0RvjWkCjWVEEgsTg85U0ZsqmicS+dDwCOdJ64UjY2cYX6AI819VBcJotmc0dQ6SoRU2zxaGrAVXjpMnlP2K+7ZvYYKYpQ0lAw5N5FRSY/3EukcEjkmSKyFyjApOtTiBqUM6bRrTBYlsU2htowUHfoipWtCRrQY1hTl2EWFRFhUW6GVIS5LmraD9RoRGYzsUys2WLMhUlSkTlDpOjqUeOnJogAdjfDWof0EymgCXXKicTmnJoYMp7qs1g2piLAuoCoE60VFpiHMKhJn6UeGnd01otkhaa1A+ADhLKo0qMAgrEP7gGZtyGVbH4Awoda1SLNElK9TUynaa7q+QU8p8qpHOniEGc7Rcn1iaRGmIqk3aZmUphjBVIme3iQD5pY1YWZAG3RpMGqdsm4JlGFKOOb0gMBC4WOKoM7qTElU5CR6xOLcFlwkiddLJtsrdMoGZ7cu0akr4oanUW9R1eqs+hYLXiDLAicCNuIWYxsQhxpY5LSjEiF52CKM25i4Tha36NVizsZTaBExE424bvIoVzbWuESsYMKACmj3/yd77x1n2VXd+X73PunmW7dyVafqrG4JxVYCCSSCkAAhgjHRGDzD2Ngew4w9zzNj+3neG7/x440D88E2YAMPbKIlwCIKRJAEEpJarUTn3F256uZw8tl7/ji3qrqFRB7s+Xy0Pp/qrnvu2evsvfbap9Zvr7XXCtiyELFoVohNqBp5DhTH6DmLdDMJgZRpQhthoLSJLyy6SRY7DOnZEY2x9Zj5kNlsg3nDREQKLzDxYxOhY4gjyvEyk5l5VCbG0z7LyQSBkJzJNWg6CY3cCPPlDF2dIxaSgnIpW/O4tiJJugSZPLbXSWutaQGbmsT5gNDtYOQX0Upgr/doOg7TmZi95RDP+eFHi54FaP+Lqd1+kvmFz7Nj+x/9THwGB6/jTv1ynpfvcmEh+zP36yP7P8Kbd72Z39k0CRr+Znrpx2r3qfkaty/U+dQlKTgDOPbQA2y/6rl86cGz/MXpZa7e3SUa/ASmcb56nXx0L6KZgD/FJS8Z4DWXr+f3P/skWmuGbJP/tHGMO0KXd790B0IIPvnQWV4g80xsNQn9Dh/am/bZlCZ/+vhjvODRk5DxuaQwzs3DxwmSkG8MTeIbBq9aVtz3yRM878pxIgQ+JrlykzdetY6xi/5fBnf+OYXBY9w6tZtZu8WRU0Mkpzo8lC1DexAvGOZ4pYqrTNzkuQzkPkqI4pb6azhonmDP9jvx4gzm7AvoCo8XdT7Bfc0xrprZzaOV/dwbfQNzLMFq17in/Ah/dPZirtff5VFHMOSXmJo+ypIzidIJu6/5DuMPvhHDg97Ow1TybazjV0EsuWbDPFF1gAeia+lu2kSzmOHkXRdw1Y79TH5jM6MqYvGyabZtHGPX/qNsvu1yWuYEI7MWQflrzG64jU1n5nh4okhcPQIDY9gHDdr+MR41FziZOLxwuE1hsMfy1ufwxB3/wHVbKnxz09UUTkRsuOgRTj6xjLr+5fT27iU4eYr1uy9ExTHzxw6vTe7Bf0IMb+dll/0e+RmJMdrg2H0HAOh++9uobpfyK17+kyuqlHDlO4ge+AB3H1zgdXvWY43ksMbzFG7+FZbe+96fnCewZ+MAL2hKis+psG7Hz+aNXqGRjUWue912vv6h/fjdZ84S+Sz970NCiJuFEEeEEMeFEM9cuBLAUQghmRFTzCRlXENwomgirBBTNunhYhNhmjFaW3TzJrnSMI5RQBZ9bKeNRpDPhcROhDJNjsYbMIUmSixmM0XauTK2WyNfi+gaHlHQZSGfhjE6RoSWgmamSJQbpBsVEVrRoIybybN3ssH+sQw6VCzVS2w6MwvEKCGJDYlOQrTrIpIY4YSgTTyZITBzlKMOC/YgcaHNZOY0x7wL6OQkCkFLOhhCYUcxOonZl5SwtY9ptAlijWEH5AKfbmCgHI2RC9htnaYd+0RGF2XO4VsFsoGmLYaoOYJHOiM8Ybc4k5njgD6N5/pM0mBL4wRW3ETKBE9GGIUAYadeQD8TYddNbJlB6Cwbc/PIrMZTGU7lBnFNE1dG6KRJTwe0tcS1Ek4PDNOSBjIBZWouUicYaddYCMosMoIVKoZYwEkmsUSBjlmlVQyYzo1QlQMEgyOAwZx/FD+2MF0fV7n4jiaREeOiyqRZQ5sGUV7gZMZQtsAzTWJDU1cFesVZ2nYdU8d0RAYdmZhEuHFEkigGrDmUGdBwImKpiK2EiDT7ZRaPULQ5nGkzF9ZAJtArMNwN2LRcI8oMYEcWKs6hyJB4BabGmnS1QWL0iPNgiBgj20IZEcgIJ9fidGYrQamLKPkcye6kE0hE3EF1W+zvVTjhThJLheqlZRXcrTZ+eQAhywSWhZtPwJbIfIe2HRDnJQqLMJslr7P0DMiaPkWnh5kdJpQx3mBaHsLSEq0DAqtIjE0oNHEvxtQQGh5SujjmAvm4QYYOUgi6A49RpEeUmCA1amiR/RMBrUTghy6dZJkj+To6tvBtg4HIYwGXBINR0WHGcFha38IIK+jIJLGatOQMR+2EQrZFRbeJhE0vUNQcxXIuQ1GkYfKeIViQJZIojybGMCIMs81SbhCpFKHwCYWLlh4nJuZYIq2RaMQmdn4RN5/lScch0hGxCFhQDepasqhyKN0mO9Akrz1U0GEXxwgzdc4OuYQIsnhgRLSckFZ2PRNRnZJoIpwAK/AQQYAnbVpWlUAYLDqj9KxhDolNVEUWI1KElk0kTQKVgKMxHZd5p0XVqbJQNnHCJvmez4w7woxbRAqFb8R0cy2csWliKRlZcsk1DpEVs5i+YiEWmKqJFpCrB2SWYpQvcLQgsSxcR+IMdcgVNGesQdKUQxFx5COsNKpL6hi7E5IkILXJSXeAYtila+XojJ+mU8kgHZswN8u4+TAFK0SFmmWjTtHqIZ0RsraBSiTD7SWyvQipBGary6xbIhQGhhMjsgIr65KYBjl8ct06VpDQNpvEicDNr+NoaRItLBSwmF0iihOqtYAZ8SgiPE072yDUHgEmWubxY8myHdDNVkncCBmGuJ0O+yfm+f6gQBdC/NKJZ/hjktKzAO1/ISkVcejwf2LL5neRyfyY526egeaDiLuT5/Gq+CM/c+2lk82TPLb0GL+885expOC9uzby3jOLzPo/PNTxcM/jD4/N8tGLNrM+YwPg97pMH/w+j1qb+Q9fOMBvbJ7gr97wag7UDvC9ue+d1/6xu77IlZteSt64C2PnNfzeTTs5W3f5p8dnATh7oMoggnuSAD9K+Oi9J9nYVNzw5ivxxo5xtHOY27bexrFOj6/qUS6rOSTFJi+68BToQbqY7Lc8ri8m7K+/hsBz+d1DZwCNbdV49w1X87XT30Sj2FTaxCu3vJKNp0ZYskJsBUbVo1uy8eeH+a/S5a5uBi9yuCt4OVsadd5UX8eF7jY+Nfo5dD5hq1PjsGXw7XX7KPsu+2sVXrRg8Yb5G+kt+KgNGU5s12wOd3FpeCdn7G3sy5qUzhTYv+tSqp19zE5soFYdpmRdTLY3Rqs6xsFNX6Ucv5Dq2e1s14vMHMuR7cHWC/by2ee/jl7TIOmanN41wKHGJLckVdZHH+buDXt4z/rX8LK5Y0xXTNr5o2ghefLCX+aaJ/cRWTbCjRj8jEH2W4IhX/JvT1fI197E6GWfwb/hJpJEce/HPsjQjbfQdhyqX/k7dj9vgicealJ++cto3n47UhrsuOY6jjzwnbXJ3fdRuOLtPLdzKacL88xe0eL42UGU36b6/g8w9I53IKynL6L+I+nSN8HMQ7xyXZf1lTQ8M/ecYezN19L5xjcJz5z5iVnuv2+OoYzFt4yf7hzbM9Gu504wsW2Aez995OfK91n6xZMQwgD+GrgF2A28UQix+5nu14ZESYkroN2DI/kmzcpZhgsnMQdmKBZjhq1aWgw14zITDPGkXkcSavYGmyHfZFwukbRnILNMyWhR1j1AE1sR0gqxMlATEb520KqL4UtELocshJBI/CFFrXKMQnGOxyrQCDNY9llOF08T1zLoXMiMM4ynsxiJIrGbyGwPayRkShh0zAStu9idDl63yElnI7OlzXSSLEEPYuGznGnjJ3liP6YpbHoRqCRB1CM6dZct8we5Ym4/AJIYFXYAACAASURBVEqmNd4q04LCvIQgwfMkCYrYyRJLg0Qo4qjDZGcR33awvZCm3UTXIqo9i25O8+iFLkYLRKQwohhbabJjHnXPpGorGlaRM3mDDJpttQW2ht8mtiK0MHCTHG1rhGZo08o3MUUNW8YkcYKT0UyrAY6qiCTyaWuBPevSLRo0ZQlX24w2PURkU821aRbmyHkaaXsEjknPlhxULq2wiG+GoDQijDCNOcpiP61Sla4ZISKoGibHnC5DlSeojib0CjW0jAlDSRIoyvWAMes468rLSMPCtdscd7MoUyKTCMOIUEKlCcYVKAnKTgvqdrRi/IDP6ImQ5eYgvpHBtEOEAR3HQmYgNAwCbbIvJ3kwB1JrpJ3QMwQ6FyOyIZ7h0rQi8mHCQWliGDU8c4F88wz5wTkS2cLXIWOyR1M7GJmQEWsZzwgII4Gf6SFVC7PTZTQ6zWi2ihBRGuaqHZq2IMq0aJptArtDseJRHOhg5Lo0BroUPAMhU13vlVweHewwG1Yo1iMQ6Zn7Ja+GagcoASWjx3prCdsXhG0z/UkSDCtiImhBVrFciwjbioVsD8eqE7mgpQ3CwFQhwgyxYoWUPvNa9DNsa2yzjRMmCN/GK/hkRIes4bGcyzG9YYhScYFCI8ExfbTUaK05645zqr4+TQQjHIo6Jp+EBMY8oR0xZyVEykAHEYlpIQr9elvCIJECL4pIIgOzWcA38+icwik1iax5hp06ZXWMSDaRuYgJt0XWDbCCkKnaEzR1kycL2+gpm4bMkut0uHzxMFpAJLPk4jncnEPXiFkclOhCJu03oEZhW34aYWmUEogQqo5DW2pqhSZ2FIKAKJYgFSIGKWNOJOtompql0R4ugowfgxDEhkADjg4JMh5tGXHS0nQyWaQdUzOzaCMtQ6+jEFSEjs72vawCrUNCItZHM1REHa0USilEHKERDGWPMGLO08KhaQ8wbY+RmAbCgth2sFxN7C7QLXgEYoo5uYWmU0+zmihBrWWgXZNYOBS8ENOMKbfTyJfjtqZqBpR6gkqnS1dkOC0cfKdJp3ya0DJQyuOoSJgxwQ0DQjPBUIrIOUFpvMq4aFCWPq62iQ2JE2nMdo1G1MGKIrQ0MQJoOM+m2f9nozNn/xYpbDZs+NWfmdd7zyzyytEKQ+ETtFr7fiZetx+9nZdtfhklOz1zs7uQ5dWjA/zZ6YVnbBMrzbsPTfNbG0e5vLwWDnZ838Ps23gT779/htf3bN7+2gso2AV+85Lf5M8f+fP0UC9Qm5mmdWKebDNLcfIIZEpkbYP/cuuFvOerR5hpuHz8e2f4892b+OD0Mu/fe4bLtM3k5jJD6wrMXD7DjoUr0d2I//q9B9hzYB94A1x94xTOgb+jnbdZ1tfRiCMuWHwJB72X8EldJ0gCNsk6UVLhtt1X0LK+hRt5nGmfoXLmBfyP5RwTAye5YsznxUYOU8P4ujIfLf83/spcpGX5fCZZx6Fkiptqb+JRZ4HO0Akc0+cVjQ3cK0JujL7MI63NZJXggkqBoTMxpY1dDid1Dm9a5mWnNpOVTxCqGTyRYWJaUcpqUD22bXEZ/O4eglP3EO15E7pjkdn0LSazESfybye2FOWoTFYvUBRtLp45w1CmxPwDo2y/4jj7Wht5oedy99BZHrhuO0eOtNmxcyvzuSGsEzbF9Q/TK1/OUL0fcy8SRGwQT2i6C8Os23yKib/4R8rfCNg2/1mO7LmRow/cx/Vll0MTU8h7XTZePsPJR5exbnolrc9/HhWG7Lz2eo48+N20JsriQVg6BBe9Bv/7NeyLKvxT8DVcPcDi5z5GND1N+TWv/qn1VWfKfN14Ae/M37N6LXvRMOGpHuXbXk3tQx/6ifi57ZC9XzrFjW/ayb3Hqyy1/Z+6b08lIQTPf/0OZo80OP1k9efG91n6Z6GrgONa65Na6xD4NHDbM92sgEa2QS9bJQYKnZD1UZXpSpNexuDYUJvlAtQDCxohhV4TmfHZpydYslo8Odqk08+mO9gJKXZdlIYYiywhw9YyWBopPWwipOUz16hgxZJYa+LAxnE8BhdiMktNTDPGktAsLKLyNawkYQAPMaCwMy2WpUM3HGRGDrLIMHHicyZnoEpdRJLQi9MS1QslQcswiQyTpZxPI+ix5cQJiDR5r0XkN1gyWiw7PvOJxAzT1Ng9lcHJe7hkUMJBoCk2e7hK0kpiJkqPg7WIHptEDowz4LRYKnVR0gABIRYagbA1iQEdKhhIxvCoOC4qMVhSNr7VZjbvoePU2DESjUVMZMZo36ZREJTcKK2DBYhQIbuK4dkehmziZ12yfoxrunQixZKR0NUZVLbKenOZIi65BQcReOTNFlao0HbEpswSoZE+M0tCfizCjgNE4mIUloitGsqAWAqEFdE2THrawmwvE3uSxPEgEyKUnRr0WrBIxLLjU8kuIrXG0yaG5aVl17SiICKEhnwUUQxDirKNUGDHQVowWEvC1gxLloXqZihGPYgDpNRUghnwY7QC3XbIhwHlTkBTRxy3Y2aTIp4K6ApNR5VZzER0jWUgwDd6NK0EPxuDDQOiQy2zxEb7BIaZcGq4ydJiTK59jKRyiKYWqDCkFPhM1HsYKA4Vwcu5tMpd5pwu0vRoUEJjIAnBTBBSQJKgUeTtNlPWPMUowgkMhAltRxIpgwCbQJhooRGxQLsaJSEJTFRgkLFqSK1RXYsIg06smCZLqE3iWDNAgMTCVhFFMyRyFN1yk2LtDCfyHVxTYotU7lLCMSFZcgIsHYEdE6guXVlDA5blgwlxZprIE4zSwUwPfgEC2XPY6DcZFF16RsLOuTqF2GXQriPKPlbQgySkEruUem06QULWjRloxBiWxpQBlk6InAAlNaFQoGAgDsgGIcUFl552KTY0heGHOW0oPO2wsbNIrAWR6ZGTPoaKaQ62ifI9YqUIMnU25WZoOYqWHmQu7xHZaUp7pCI2bBKdHkmQVkIgNCqOsFWLTSe7iJ4iDo8zWFtC5iJiCSQQYKGlREiNU+xhFxvYOqalU7uxV3AQOZ+8kUV4daJwlkJcI2N6dO2IXCPicKHJCcelorqMixqYKUDzTA8tBIHqIULYWquy+2ydwU6VbuIQGRZeXEAtZWlYHYaMOUrdZXYl6YapEopQS1w7JFaQoAgkRIlEoxFKYXVjAlNRwSUnuyznNBPFU4xljrBh3mXqzCw7T7dBS2qmRMqISCaU3YCRXBtLh2hfgRDkwg4lvUSPObQApMDMBZDTgIGUzwK0fxbq9U5w5swHuWDXn5JuxP70NOOH3L7Q4Hc3T7J+3Vs4O/2Rn5qXH/vceeJOXrfjdedd/73N49y51OS4+/TG6qcWavhK8W83rmXm86OEP/jGPGcy6/njHRu45sJRyiOph+O1O15LqEK+fOrLABy875tcueXl5EbPYmy7FACtFFfOfp/R1iLv/uNPsKd9hss++/e8ISP5q/kql4Ymu6+fpO7Xub97P5dWr+HTn/sO91Ditp6Jyrrs4X70xKUUvaN8aDjg+uYe2gdfweMbJVIvYxCjCtNMjdh8+tGDiMw0OTnGRl7O3zzeYcv2u7i5vp17dtt0hi2uqcV8doPFbHeELdZeLjdNrklcjoW3MaDG+As/y4vzeb4/v4kXygrPN77POm+JY/N5rj5wkk7ucpY6Jyld1OOBSovRzCi3Gl9lXm3k3uwEo+0RptdtYerAER6tXIxRvZaNR5+gd/qbFPIbGR2VlLs+h9d/kzi/hencAFfKw2SPTfMV8RY21hcZXZplQJVpHi8TbdQ0gzK/X2tQ3PYwcsblbifDZTNNPCehm7sPQY+TW27GzWURKkZrE1MWOWFlqGeGOf6bw+RPDXDDX30du+3iT27m7Oc/xH2bryU/HdCY/zjb9oxyaLaEOT5O5+67mdxxAUJKZg8fTL1nz3kdSWgRnGxyxQ3PpxO28Xce4MS+WcqvehXStn9qnX1susnH4pcwNf1PEHQAMIeyGIMZCi96Pa0vfZlo/gfrmj0TPXTnCTZdNMTui0e5YecIt+/7+Sb2yBQsrvvl7dz3maMk8bNFrP83pnXA9DmfZ/rXnpYiFNkwxBIRWlgYsUGmX1cn72mcSONbFkiFVIoNXpXx06dxnTraaAExEemZlIwMGBVdzP7mgUFCKCPmM3F6vgHoJNl+vWTJgWiAek9QiyIkmmlyFLSLJzTfH2yBJyn2InLdmEzcQ6IRUkPeR5oKFws/arOp3sTRARXVoZU4lOIWUbHKYkaxkInRpkD0DEq4hD0IpQKpGKy1QUBRNNDAnBUQOEt0E0noZoiURKEoJ21aZkTT8lGFah+IKfK5A/iFeXxHorTC0SGetunoHFpBqExcYYOQOCJkqQTJQgcz1iBAWjGIBKHB6KeFP5vrIQDbTpBOBGh0LqaVMyD9iBkmmCrBTDR2pDECg0RLsgQ4ZkDG9AD6mdcScnirNUxjaaJMHzNRgCST+KxrLTHs1cmKmEimqfqFFrQyPp6h8CObOdel0G1gkJDN9ggiTc0K0QiMnkUkNNmgiVISqTWhB1poQjNESwtkjqFezEgYgFYkwBIWsTLwcLD9mIneAhowdYipA5RQeEYMWtFuDtEJCzgKsiqgUkvwhcJJmqzzWyBAYYEZY6oVDRNpFk6hKBot8nNdJhoeo4cCDKUwnTbZKI3A8USL0AgAjdkTlOkxQBfD1JhZDzMTMGotYyUKGRtoIfC1ZM4ZhH6lOakTbBVjNQVbZ3oIAQEmbTMmUgYxa2fwpVD0kjS5ikVMUfgooRlZioh8C6UFGeExNuMy3mwzm/HIERB5NuUlTeRIQjMmMQOKykc0BIaOMIgRUmNKA5EIBBrskI3xPOO9FpavEWgMFLE2QLWZFAtIoYi0SUdnQGiCxMFKEhLhgE6Xr0NE3QpRgB00yXhVhus+hg/dMCHja7JRjG+18aVEATO2RqKwYo3sD79mh3Q0CA3SjNm43CI3F6A1hCrCkzFaKGIhiTyHUHkoMyKUId1ci5miR9ldolxrABKbDrFOk8BIOwZDog2DjhnTFF1iCwb9acxEI5Rgslmlu2yghUVgqXSjwYxp5zVWJkBGaSZX0Z+v8aqHa3cZljV2hTOMNfrnRZ06SJBGQscMMV2F1RJgJCg0OaOLYYQomRZVd4IYuyNZV2+jlEQbPU46Gk9kiFWWKMqAgDouhW4b0XGJkWltSm1gkFALTbq2R+C0SMwagRFiNDzyXowWUPJCyp0ACgG+DDETzWSrQzbpUNJ+evxWqvT9Y8cktkALyYOmzWyQJcGg3JtB9noINJK0Hl9WKCp0yBOieRag/cJJa8Whw/+ZDRveRrFwwc/M76/PLvGqsQE2Zh3WrX8ztdp9eN7Zn4rX105/janSFLuGdp13fcKxed1YhQ9OL/9AGzdR/NmpBf5o6yRW/81Q7Qa8/oMP0Gx3+fivXMbS3mUuffHG1TamNHnXpb/NJ574S7reHGceeZyKN0yBO2DqeqK5Oc689a0svef/4zcnQvblJnntZeuJa1Ve8e53kuQ0M2bClktGuOPoHVwzcQ2X7trKJ6TiuqPfoduYYNulZYyH/obF/CAPZQt8tzDDuuOvQu4weSiQnFQjXGacwVx/hN+54Tn87X0nsYRNq5vhwME9/MctWQYaCUPl7WhzjDPjFmaiWSeafHTs17CjPGbexQ5LbJI7ceRd/PvN72NDxmXHsbfS8uv8K+eLfDq8AWkJKpsnebR5nHDExumWyIQGly0cxjGbjMuT/FOmzNazISKbRUceta2TPJEvUm4c4ejm9Sy3H2Dk1GuotjKIzV9hnWXwUO9m1jPPUMZnx7Elyu0e604t4Re3UT80wKZdMxypjXFhM8+p3jGGsifQp45y3XKDkyOShUNZBrZ+j9rI1ZTaIU3bxi3nkA8PsqfcpSguI7d+hsNvvp7aKw3+9ec/zuUHjlIam2DXsKZRyNG8cy+7nl/k8IMLFF/zWpq334GQMvWifffb8MSnYc/b8Q7UcKbKnDUNxjf+B/522yR/ufPFHLjl5p8pLPcj3z3FVVdfj5i8DB7/5Or1zM5B4pqg+OIX0/jkp34sXstnOxzft8RzX7MNgDddvYlPPnSWKPn5Aqnte8bIFiwOfvenS2LyLP2LoKfLxHSeIgsh/o0Q4hEhxCNSaRzLp2K26RkJCMh4CZlFiVQJEdYqg7b2qUsP1+xSjBZZ325iN01mdAqwTBQNMyK26kBqeBo6BUMrCaImln2kH6O1RglF0/YwlSZB4osQV2dYNDQ6xYhINInQjIsFIqtGIGM80yXMzSHsBeyqoGK2KYUtiriMR1VGvQUGzTT0JxEwWgvQSHyREPezMyL7IMnQJComEoo4Mdm01GMmNjE8SRYf3/BRskOCplLLYLuaWBss1qtEjYA4sskQYpoRlbCTuiRXpa6o2ilYWrIimqbGNxSuiMjqEIFASkW+E5PvJsgEjH6CCLTCilOvgq0TYmeBqhniSY3ZNtjQ6jBZ9djSaFJJ0qRVDStGoHHaqWGVKJMht0GxHVOmRyQUbRExVEsYqoe4UjFvCjwzxiTBiRJkkoIbUxr0jJSf2YZumKMQzDOou2RFh2UzRMYraqWxQpWWbOhTHGksexrybTrCQiWQEQpTKI7bNkfttG5bXqSoM0uAQwQIOmZCz+qHcQuNRjKyFKQZL+0EU0sEAu2ZNK0Y0wwxzNRbUghCip5PgRCJJNdJKPs9lK4RRHGqyFogE5CJxowkCQKhNQV8RLS2fASwwa0xWnMZbvZACWJsrKaJrxzq2sHU6XyFMjX0e2TT2m8qBgRGnPJz6G9amBo/o7C9HtrqIHWakmJlhYq+1gO0rJBhr4sRCKSQJEpSbEep3vQTpBnnLG2tPIx8GxD4Kt1cNGLNQL1Jxo/J9RKyXcj1EnIqSOcZRdTtEocxIAm0pkwPtKDSiCi5AUZ/noUApQRhXWGiyMcBAlDCwPb740UDAeVll6ElcJoGAoEdKdJqeyLN6ijBIkYqMENIlGBZ+bhGgmckCJEm0Ilji4Gki0OIoQS2jolETNdIPVNOBMLw0AhMrRg0WkgnYli0KPodpBK07TZNM9VNz1DYSYyLINEGidSYOgWtNcOkbgbY1gIjVQ9F2lETiRPCohVRyFTJ4ZMJY0a7S2gtQEAsNFKnoG4u67NgexgiBpkgpMcQLUR/XpPQQmGiVmoFChhS3dXZ9wyFspp4ZoIZKQZrIQqRvmeET4YQtMCMARnQNZNVnTF8AQiGRIeQCKnA0gkWMSJZg05CJqmOiLR6otGxcLsa33dWljQgkMJAAFl8TK1wtc3I0rNZHH/hNDv7KaKoweap3/qZeS0FEZ9ZqK96rhx7mPGxWzlz9sM/Fb/PH/88r93+2qf97tc3jHLHQoNqGJ93/VPzNaayDi8cLAJwtubyS+9/gEnT523OQcK6TbZoM7Y5DZlUKuT06Q9gTf9n3lae5d6vvpgRbxRro8ZqfIfgyAFOveZVZLdvZsuXv0T8wpvI2QZHx7cy8Sd/wntu+wNuPNHkO5tcap/8GJ858hnevOvNiIsKnFg3wWt7NZSb5fnj+2DyctTJe/hCcRPbly9j0/AZ/mbWRSi4yFzgSnMf/+aql3PThcO0A5fEmyKY+yXeJjKIK2e4avlmTm6OedtJl7mcwbzWvP30fg5t2UBd38icfTeB1cYIBzlqVtk8dJZ9Z67lqmScu9Repo7O0P5+hrFulvtKo9SXH2XDFbMcmk2wDc27Gm1OWQF3qGtYzi5wQXsTW5eOsq98Gd76EptmDyLQNEcneaxwF3ZzGzVrClcHnBr/DgO1m7izkOPyiaMkLY/xxSUe2bEbr3uQgs7SPllmpmKy3q7z6naGiwY/gRHFHLvuJWxUkOvFxEMPYRQCDu14KYkBbq6HOjoPOUF4ahhpCAYq32CffQtPvnsSP5tj67fvJ3PsQR6d3E72EYfIuIuB8TzVdVfjPfEE4ZkzXHDt9Rz93r2owW0w/hzajy9x54jkln1HKRe2gX+IruHyO/WYtzx5ikYU/4DO/Siaabh849Aib712E1z9G/DQB6FfOySzo4J/tEHljW+k+dnPosIfXS7iwTtPcsmLNqzWOnv+9mEKjsmXn/zxPXA/DgkhuOa2rez9ymniMPm58n6WfmE0A2w45/N64DzErbX+W631Hq31HoliQPSQVoJrpLpebMfkOwlDSwFxaCKQJDL18KA1RXqUWiEitadRUcwFS1XsSKFECg6kSg2WlMTqf0JoGqZPXqVeZZmsGcT5IEEIAyHA6qbXTKERAnzfpRQGuHaEIZYoEDBe91JvFNDquUTNJk0R4tnJeShVyBR0uGZCy4yI+iE6KkkN/Ww3pm1Gaz2N1npd0W2UTo1XIymTaRvkOpCpuTgdPwVESlNSLuVGTKkVnQeRM0TMOL3Uawep2wAQsSCOTCSKjJeku9VKYPYEQhhsbTTYVq+R0wFaSwyhsWNNJARCCHJRSBEPiSA5d7D9osAAqp9NMOMnlDshZqyQQYhU6VpfsBWdfgiXRmCHgsFqwGDQJYhCpJCcm3hZaIORxZCOCPFjxVA9YrLRAQ3lRvoeEwKEkESdLrluTIleGnKHwtcWiZY4fkKozdTvpAU5QiwSMn0XoSA13ulvkC3JldnQDMoGFk99N6Vnh6p2zMZWi/XtDnmCNKzSU5SjiDwey+eGZgkgkETYfZ12sV0w1LkGpibnR1hRQpH0XKXR759qZdnoN9jSqiGQNOyYGScFYRKB2e9juZUqU6LXuPpS0TFTf5o0YxJM7N6aoEMyIOSq1xnSKsG5+VX1QZKebct5GpX6ltPsjyJECvoF5QWVRoxrJcQi1T8j0eR6Saqnfd5WnK5hBITG+dFIUoMVsgYuEpOmIciTAgWTBLfvkTH7C6fUiVfXgB2p1eUgUowGQJzCH7JeglaSJLLpBhohBBJJnrV+qERgExEbPTbVu8hQEsqYDAEDjQgnijGJUUqSiWKG6SARrK+1KdLD0cHag0UKYAKRao1NRB5/dZ0Uaz6lVvoejLAxEijhrY4hSV21jDQ8MslaUi0BOG7/GVqj0HTMdE2U8EBDpRGmWU77uhXKDkasyHsxQ80AJ1CAQERylecKEJexwpAJJgmGSFaHIll73RixxvEVMZKhesRQM2KoFiEERGY6/9meptiMQOi+hzBdV0U8jDCmt5rToe+F1h5Diz5mpFafKZ71oP1iyffnOXHyv7Prgv+GlD9hSvGnoQ9ML3PTUIktuTVeU1PvZGHhc3jeTxaatdBbYH91Py+ZevrC1FtyDjcMFvnY7NrZGaU1//9slV/fMIIQIgVnH3iAV166jldET3LBVddw8P45dj9vEiEEfrDAI4/8EsvVr3PJxX/H0O6P840nJ9hVvpTS3H9EacGZ//Jhhp6jGZMfQP7DrXziW4/x1mun+OgDZ/jSk/MsJxaXnc4wMzXG+w/fh1PrcpG5hfftf5JLTs4wU91BcQzyj70Pf8dLyfht7rU9np81uH9sHbHWbIh67DAWuE3ew61bXsF9s/dQGn2Y3vI1bBIGYiDDvkOHyYUljimfSuzgJDEvjpd43sktlDzNVzYMcXzgG5QszYmkx9mtS7glwQ1P7KJ9/59z6z2f5/j8BBW/TX6uwa5jZ3jBkdNc+tEGA8cD/vBIhm4yxLa4x/8YzFLqWYiKRnk+R0qbaemDjNVOUBuscMH3n4D8tTxqPMnVs7exrm3gb/siF4kBco+MslO2uGr/o1z82CNsnjmBlpq8U2Fm8WKqY9uo+WWev19xRHbojhr85XOex63fnuHEug77H5+isO4+usPXUOy4DCyElLuK3Dc3U9z6LSrFy8nlNGPjbaJujv/7197BsZ27may1CDOC3ILL9BOfZvfzJjj0WJvSzTfTvOMOxrZux9Y+0yMv466Ty/hn2uzfkOHBa3bxvt2b+dWHH6QQfIC/3vc9KpbBLfuOcsb7yZJyfPT+07z8OZOMljKw8xZQEZz4JgDOVAnVibA27MQcGaHz9bt/KK/5Ey0WT7e45BxPrxCCdzx/Cx+87+TPnHznqbR+V4XSUIYjDz3z2c5n6V807QW2CyE2CyFs4A3AF57p5vRPdB80KJBJeg7BjgA0diywojUds/segcFqSKGb7txaXnpvGtQkyLoJw9WYSivBDvV5z8oSIpWgaUVYKAwlyfcSDPRTDO/Uw5XTISDIBAlSp0aF9NPwtae6CmOhkapvWgiZJhLoP1n3R+rJZPV3/QMcUhNcJGsZUlVsIPxwFYBoIci6KdeoD1qTRK55NBJNsaMw43RXXghBsRkhEvphhoJiJ0EmBomeSsMPV56tRV+WkImS1PgSom9MrUlxbeTinH9Tez7tx0oQ0pqplPdSQJWOse+n8dN+r0gj46fzN9bqrRrwAoFxjqdeILAT2ff8pAZ42o5zDL7095yrVnUr20s9LBKDgfbaplTSB5ErvXD6Rr6BWsMn/V6v2IWZvvF+7tdOJBArGBiB7SsKXuolyhneOXefE2aoU++cIQQVL8TpGBTcfmibhtHFFOQ9taUTaEQEaSSpWAWxUvTT66+sJ0QKtM6RnvwJSgNJITCTPjetsd21tlJrzFiT76xtIAZGAkowQosMQQp2tMAgWe19JkjHJ1WqLzmvv1nR72YKjCHdT0jnJN9LVnr/tHJMpECQJpXQCEyVIPsATJyjr3aQTpCZQCzXQghlX0biHNEIqRFI7HP4GP33g+Mn52hamgDUJsGKJZVGjNHfJFjxRuYJzokhWJmhNPwzQ7QKPtNvNXagVteITbx6P0BVBWidAlTZ7/DKd8V2xPBSwMhyfN68CAH5TowZKZxQ9XXCYbQaMNCMKPTWZAESmch+qv+VHsFgPTpP/mYfQJ37Fiw1I6xoba06yUpQrUCtrEMvHd/wYkA2PXmXykpItBDnBQAAmKIBwEA9otiKGF0Mz5PX09GzAO3nSFprjhz5Pxkbu42BgT0/M79GFPP3GIPbcAAAIABJREFUc1XetWnsvOvZ7EbGx27j1On3/UT87jp1F8+dfO5qcpCno19bN8wn52uo/h+Ne+sdfKW4aahMrRvw1o88xBuu3MC7btzMqUcfZnLHlcwfa7LzmnG6vWM88sgvMTBwJXuuuJ1y+TKuknn+IBpGmE3EtiGqsxmcV76Iob/bC//HKRa3v4F9CxG/sfR/ccOWIn/61UO8ddMoo+UM79w8zh0vupVXzI6y701v5olNu/jl6n60N8jFm6Zh7EJmjnyJ78rtrE82sG7jo3zhbI4BGXAZB3kgmqJiJOSXj3LH0TsQRpvYW8eto5N83K3hPLmOdVcUub6W51sTBsrWbDc/RyEu8vojHh/fnmdvZphLGKIWSZKMQnxphLF7/gG/pNh82zx/fNUb+MTFL+VPr3sH904N8Q9vvJ7p55vkgw5TX2vifUVxz/7tbG0eYqxmIoIZTpYvYkvF51e/3maw1eTE1GbKqsdLgpt5oHSKbGsbIduwhMv86F7K9r/Cm88wNXqWY9u3cv+LXkRrx0W0t4xgJRKhfA4ULmBoIuCl07eSH/b4lc9+mkJk0a1EVJbrqIlHyBYiHr/wehLDRG7YQuv7ZSAmO7sZQ9ps3XqapcUtXOs+SO3aa1n/+rdgqhbNfI7gCy1Gd85Tne5ivOgVND/3eZh9ku3FJT58xOP++88STBV57+Vp+QX/8GFe9J0us4Uae5c93rdjkleNVnjt48eZ/hHZQleo40d8Zu80//r6zekFaaSFqx98PwDClDjbBgiONqi88Q00Pv3Dwxwf+sJJLnvJRpysed71V14ySaMX8u0jP16piR+XhBBc+uKNPPHNafRPYEw8S/8ySGsdA78NfA04BPyj1vrAD21DagYM1EMGqykgWqF8J8YJUx/IGixKDQOZ6NVPaXiT6LdJjWw7VJRba8bhCteslxpaGQIq9aD/Bz/1AqBTw8oOFGZ0rv6tGTDDiyHDi8F5Rg39Hgq9cm+a2c4Qa0aNQFBuK84FEueb+RLIIFURxz/HVNECM0rlVOio1TZKiqe0T43FrJtQbK8ZaE6gsCK1+lRDCYod2Zf9+UAx313zHK3JWq4ZmH3UKZ5i+J87knJb9Xfjnyq7lXlKv3NCGK7Gq3I0khWD83warJ5ffqPUjFaNbRAU29E57cTqmFbAWcZNyHVX0RMIQbEVM1RN36nGqgQktl6TW9tMAGM17PLczmnSUNyVvpYbCcXm8OpYSq1otV9GsgY4RF+PB+qpl1gABXwKnWSNsRDk3ASNBWqMPC6VxRwiyfd5xww012TyVKBvJufOjYU+x2Rd2+xYa6NXPwtWdFkAjq9WAV2mM4pWo6vjLbYiButPje5Yk7lYXQsp2F25LlfO5+mVOU/nxY4055rWQ9WVNQzp2lwBNSu9k08Z9cpTJRmtWNHuc8kO9SoQXxunPEffz3/C+XJKQy+FZnWD5Hzusr+n0wdg5vknpVY3ivRKyLZYBTyKCSC7IrXznp7V52zOCgj6uwCrbxyx4oM6Xw5mtAa6ct2EjJe+B4UWwAAwno5ene+REui+17X/jl3dwHh6WpkXAOMZgn3E6s+54xNYaHJ976F4SgtDy7SQOWvvh1SGPzjrT6VnAdrPkZaWv0qne5BtW3/v58LvQzPLPK9SYNfT1D2bmvotlpa+Qq/3w+sonEtfOfUVXrblZVS9KnccvYM/efBP+IPv/gF/tvfP+Nrpr+HHPs+rFAB4oJnGxXx8vsZbJ4eRwL/7xye4YtMg/+4lO5je/yS58gALpySbLx3Bix5n3743sGHD29i+/Q/TxCgn70V9+BZa4Wv46LrHME8cIko2cuYF9+P7c+AU+KpxA9ftGKM8vI7fmf1dFpsuw42EbXvGuC7fpiomqVz+Fr541XVcduowQ6UCZpzjgrn3wDXvZP2ph/j74YibJ0/z94d+HSkEvznZJadtrrYjkp230HrsYzy6cJjOzC3sqhzlH9sJN67vMtqdYkY0uGU25rGKzauHbLa5ZRaU4qaaR5z0uLn1LrJ2A4Fk4z0BQ99RNK9+F8a1DfZVKqjhL9LVWRrSYX/xAi7feph7hoqMXN3FemWW0hUeM1rx778Q8dt3+cQLJuYSvMAf5cbHH2T3qWMsFcvMX2zjWh5TapSF3AHGz76cypdj6lNfpDCxi7tHX8Cdk7cxu209ueXTXJ+7n7eLv+MFPIBzukM4dZpNYg6jfpiGWIezIcOHX/Farp1OaBRiHn1sN866u5kd34MdxnR9l0yvivO1AWr23dAoEsdVtmytMnmyRnPdJo4KyeRzLmd2IM/gwwHLy7ez7YpRTrZHiEdHeezL/51TUzspHt3PO1uSjXsmVnWt+Y+3M3HzK3nz7rfwnfFHqD/2PX5/8zivGq3wusePsxT86Dphn9k7zSUbBtg1cc6GwmVvgbMPwvJRYC3MsfSKWwkOHcY/evRpec0eaVCf63LxjRt+4DvblLzrxdt5z1ePkPycgdSWS4eJQ8XZQ/WfK99n6RdDWuuvaK13aK23aq3/nx927wrIssMUXGg2AOn5IIB8YvT9MakZIVbtfo0TKMqrO7upESd+IMPXU00XcJSx+vuagSHQjFBsjDC8GFBsRlTqK2FzK4aF/AF+55KhBdm+K8AOEoqJyZpBIVbHa+oVk+Rcb9MambGi1P7xQ5vXzOK1K4aCgfoaCFl53Iqxkwmeuled3iSRDNbWQPIK56Fzzn3IBIQ4H4CJviEnENihwopXZCpQrF+9M9tL0jFr0Tcaf5BWDSwNw4vpc0uxia2N1fnKeitAdQ0SPN140JBxzwdYEvoA+KleTI1EoslyrhrZQd9boGFoKSTXjTl/XvuAQad+BdOOEEIDxuporDi9d/i88zM2ig1pP84JQdRAvpuQGu12/0oBxyuSdVND2+onbMn66fk7LdKxCg0DjXAFJqAZRzN0nnxW5GWSkAkUxfaKEdyfwX5o8Mr8CCDrATirXNY8cWt8rVAxUA/Jd2IyvmJkqV+X6zztFKvzIvqeMiHS0M6VubSDtc2E89utriKeSufeK9TK+2JNNzSsgsIfaNXfVJGr0SA2axs2a3ebkab8P9l703DLqvLe9zea2ay+3X1bfUN1FBSCiCACighiBCRRQ0JMFNNoNCeJiV70nuhNzsEToz656hFDEg0gJCiNoiBCpJGuCiiqqL7vd9+tZjZjnA9z7V1VFCI5+iHnXt7nKfZizTHHGnPMMeZ8m//7f0eDk84/0aQTNlmb1aNNMiekDkhjKUxEgMIJ2rEUACiNhq3f0VjSpxhnc79hJcqcEEo9ac0fN/Q4YW7E3NoWpGeOr1dhj+8FSz/yZVPpy/CkMSROk7nS56eMzRGnwn1P/vuyoy977FiYm9PjZwp0ZKkeDciPR8fdJ/bn9XqyvG6g/YokiqbYtu3/ZsniG9E694pt4ihk9NABdj/3LNuffJztTz3OgS2bmBweOgVeNRPHfPPAMB/t73jFvny/i96e97N9+1+9JmjW3sm97JrYxT077+Ftd76NB/Y+QNErsriUFIW+eePNXHjHhdyy6R/4tY48tx0eZSqKeWhkknd3lPjmY7s5OFbjv155GkIItj/1BAvWncOWJw7Tt3YTzz//QZYsvpGB/g8mUJLnb4Pb38/+tj8BXWJq+im0GaL9s9+iq/c9vLDxI8Rxk/s2Huay1X1w2U08Wv413i1/xqHNIyw6s537d36HJWYv/3R0hgcvuYL3bbiL7btzZNwj1EKP/Xs2cdBWGCt2ko5TrB/t5c/evoSDY4d5KezkfUEfudW/id30XbxjV6NS+/n8FZcyWM1weHeK5wp7yRzU3N0riIF5zQdJHX4Tu8UR7kk9xcL9T7Mq6KCx9B4K0Tam9q8hc95fMpE7yup4E48PncFIZZTr9/4b86e380JhJaXiYSaF5YLaDBn3ILcNrGDTyrfw4d/XbDuzgCLLJYfW8/ZbP0WuPslkOoNrmzy08C2Epsjbx85lZvpWvKkBDheXMDpR4u70YxwTS7li5H7Oqf+Eo3qY61d8kKcPdlJUv8b0sMB6lqM2x2pRY6//E9zsbjrjGUqTvWyZP0L60Dj+wFMUTDs7+1aQOXaI4vg2/PU1xHRMdvg0fK+PxYsiRCTZMX2MAwcO8Iar3svethIyMkz8zfeZt67APx0a4boP/TGLj/6YdVf8Id35LuLD06SWVwAw9ToT99xD8Zqref+K93OwuJ1//9kTCCH4y/ldnF/K8esv7GTiVXLSotjwD4/t4Xdmo2ezkirB6vfCU19P9sGSEs1dE0jPJ3/F5Yzf/p1X7O/p7+9m7dsGcLxXZlS9+oxeLJY7n93/isf/d0UqyfLzunnpdbKQ/8+LtBbXjWmv1TiuDbWitUbii+NOiYyZjeJaXJG8uJ3QkpuIUCTkDVL+/NxFISRSSLLGoRA5CGlRwswZdRYPJzRzyrI1KlFEBS9TiGZHcbIvPh1rbJw4BvPjEdVjjZZCNNs+gxQGZX++CmHwW5/8l0W3XvZXWEquwHGT54EwJ+9RFYMbQqXpoKzEr8VUhpqteTj13WdJXPFu0zA7hbNKXnFmbgYBKI0GOKeAkRI1DgRFC0Jb1AnkASDwrMQzSRvRylGaVaLt3Od2IIuUhq6jEbnIQekYLS3Z0KXUyJFrvtz5evLcOE6S4wKSyrEAHXdhaceSQgiLg0GLltIqLFok4yxEDum4hKGajFGcrA5WhyMwAjdwOFkNPHE+VWLQS4OxXRg6XzZOgVBmrq2TZBVhrcQK0E6Ip6JWS3nCeZCe4gToWnIsOxnNReoKYxFdQ008efI7QiJB2LnIZbEFObQIchMxqcZsclYG0PTNxCgdtYyPlxsNx3eCJdkjs+KHMU5o8GsJxLYrVqdEaOdiP1ZQmQpxGyYpQj8bmoUT8tNAihiMQmLQxLhGImViwBmTOEB8GZPRjTkjYjZaaVsOkrnVISyeONkAAQFGAYLCWHLN0h0E0szCnC0CpQ2FsRAdgWcllSghQUkZ2YrwMxf1FmL2GpLfKUwf/+wGZm4OdJTU5zt5ZgGjyBiNsq35NxKvbslORnP5aSfB/OTs+bPPBnBDFwlMkT5+fyTIrEXIEyGMJ+691s8jcHRCrJSens3nmzND5z6rE4yz2bOtOdHRcKLjaNbNdsL/t56DTmghPnkXWTxAIk96zLwyLPzl8rqB9iuSnTu/QD6/mra2S076fnp0hCfv+g633fhnfOk3r+ZfPvUJHr31n1j/g7t59r7v8qOvfZlvfuz3+NoN1/G9mz7Hs/d9l2N7dnH7oREWZ/yTao69XAYHf5/p6a0MDz/4qmN7cfhFfv/Hv09sY1ZUVvDg1Q/ytYu/xh+c/gdcd9p1fOLMT/Cdy7/D37/17/nJvp/w8Iuf4t6hMe46OsaKbBpqEX/7wDb+7trTSbsaY2J2PPMzSl2nk+n/Acem/ppVq75KZ+cVSYj932+C+z8J77uTqb39hIOG33tmlCGRw+lbwIL5/wWl0jzx/N/wwoEJLlrewdYjU3zh0EreufIChkTMgc13cM+Ouznr4Yd4ccnppE2DdatTqLCLc91beXL0SvST/8K3y4or21y+sfG3qWRc1jJKMxY8xQzaGiamFvJsrZvR0X76eh9jzeIz+fCaHp4LXdzuImePe9w/IClrxcD+R3k+GmG3v5P5tUX0i/vpqUs2r6/Rvu9Fjg5cwm6Ro5x/EGnBPVhnxUiGya5pqtEEkzpLLRJcP7OH53WRA9rjyzPXM6b3MTiTZmYky7f6LufJpafTLPURK0FhZppRr8i/LX8Hmw/twBk5SLDasj7/E7Y4Z1E71sFA/xbe0jif6MWAFZWj9M8IisNfxdnsMZFtUMwMsvWZJRzuVFyinmHVngzrO7ZTak6wb3ApSkakzDTrty5j4PTt7Jp3EcJahrp6GM4tJ3tbk7GuHyK3VpmeeZJy9TADh7bRO28+B8cm8PsX82JfD94Lim/e+g2e7Hf58tTTqJGYRX43K+dfwJQ3jvQTpXPy/h/izpuHv2QJJb/E27Pncnu0AUg8+J9f3MuCtM91G3dT/znsifdvOkLKVVywuO3Ug2d9CJ6/Ferj6JKPKnk0d01QuvpqJu69F9M8Oc/tyO4Jhg9Ms/xNP79YvFaST75jGTf9aBtTjV8c3fuPyNKzu9jz4gj1qdcG7Xxd/s8VqwEE7gmccFIaEpoxiStj0kbitl7+WsRIAVId3weShNyjGPqJYfEyI0hiEoNLHPd7C2VwpUFwMnw38UlbAlPArQMW0s7x33KtPKHlcdEIvKiltEQKGbpURnqSVgIQbWjhJOfEqjVmm/wDIINURQAM7UDqFCV9LoYgDF31RBn0DHhmNqJ0PO9aiyTDyrNyjgnPCnE8ymgkKaMQrTwbpeJWjCT5jWLoEqCxAkoYXBnN3R8hTzQdk/O91v0pWYdCo4mUhrZ41ltfJRNrskaTjbIIlUQRPBWe5J23uAjSSGXIxRqBR0YGuCKi1oKBYQXNKD03Ti1buS4tA15IcJ0C+ThFPk6+c6REUCUTJRAzR9iWkZbEMDJGoU9aCYKuRqJHqBM899IIYtNF2nS2lEXdGndy7Z7Rc0o9ViOlC8JiTIpK5OI5YWKciuQs303hzDFnCvy6oDrSJDGWfE6SExRlkKSbHtamESTObRkbrHGYpeebVZBnTSgtEyMtZwSeiMi3jGWlLUJZDGUsFfJ1g5TJPpk13OF4KYbZ/84l6bX+VA3I1t6QscUVJ97XloIvLE6SYIoOHSwVhBSk5pwvHo6s4sq4Ve9K4JqYnNMg4zaoGk2q5bTxI82sOu4LRa8xaBmRZEhlW7NkKEZpQBHiJXlbJgFDGzoxcar1rJD4Zj6gSBmJjDSQI63Lc+NOyYDE0CjMXbO2ksIM+HNMORbtRkjyNGk5a7QhF7fWXZgwiXKKk8TF4gCCUjNNzthZ1CQgMEah6rOxSDsXM5vbj+KE+2HBM114VMip4+91RwE4hDI1t8tF5MwlAOadGtYKDMkarkbq+M096T4KlDKcGOMQJM8ijADj4hs9B0oHyJrk2iz+3BPNWklKhqSdJoWJEK2Od2hox1Jpscqq1tyAI6IkWvwq8rqB9iuQycmNHD5yF0sW3zj33cjB/dz9Pz7PzX/0uxzdvYPVF1/K737lZn7/5tv4wN/8He/9zF9z7Wf/G9f/7Vf5w1vu4N1/+n/Rv2IVh7a+xHc+92luWv8ia599hPU/uIcjO7cTBqeSK2idZeHCP2fb9r8ijk+tX7ZrYhcff/jj/N6Pfo8wDvnzdX/ODWtuoOSXTmkLsKZ9Df946T/y/oUXYhq7+PKWB3lXe4HP3rOZ967rZ0VP8iI6tG0Lyoeh2hcpLXyIM9beSrl0DsQR3Psx2PDP8DsPUGMRmSBHtraDStXww6JiuD6MlJoVp/0dP3hxmHX9EYWUwxcf3MYHzhmkNt5BZkDzFw/vYXAaLhpYQUopVk1u5lnzZjQeffIFsse2kDHH+EGphD8xxb7pHj512TKefOJJtqgSovoU20efZu+PX+Iz0fV0lh/gvWdcBsC+R3bR5Q+x6EiRzQXF7lSKQnSMPS+tYZg6l4dnsL/rSX5r5o1sF0+w9uGXOPbGKYI4y7PBDMvVixyJ+1jS53PZww2cEcveTOJd3LBlBQuCgJXRCJ+t34AYrDDuHmDNsEc928W0q0k3foge3UusPY5Uylz6s39n9dCLbM9u5an8DjbsPp8dQYPz4oW0Nyeo9m4gyh7hUOdljNSKnFM4wOo9PumxGrvW7GZxZg3hYYfxXoGXjXhb1vKCK3lD4Ud4Y1MUxBJ2d9aZ3OYji9/Hdxcyns3jT0+QKwqc3S7+UB4ZaTyni4F+H3/aMJ4v8sILL7D2PVezu73Ipz/4Ma6671/5xob1LNn+rzQy5zB+x51Uwk62Hn6SOEqUhPE77qB49VVz6+ojF/4JWwvb2Pr8YwAoIfjysn5cKfjw5j1ErwArvOWxPVx/7rxTPL8AtC+F3jOTKC3gLy1T3zKKv3w5Tk83Uw+c7LDY8MN9rLqgF9fXp/Z1grxlSTsrewr83YPbX7Xdf1SyJY/eJaXXyUL+fyCloRAbaVwVk1IZhIQO6+AogzACJQwlEyTsdE6IFgk+Sp1ooLnzifBomDZ8Z9aQ8FAIRCTpCT0c6+C1uAUlAqkMntskL9fSTo680mgDDZPCCB/PdIJNlKOORg5PWlxp8OJE5S3ZeC6JP2daCkTLI64ROEikdtHKopyYAilKAtzAxwiB1AKLRppMS2EskTWpxCCwEjsLbxOWdCuvyFUxjoyYHyk8GeEY0coJEziigqENpWNSqQAHF6zEN4pS4CZdCYvSsxEWgW/UnJ9bSIuvDRiJMQ45ZVEyTUQvNlqAxCJjH4ykYvScMp6LHQqRbhlJs8yLAiQteChIVaXsJMarFVVK0QK6hKTi1lG2jWyUQL0VFt8mmqnBIRBtiMBBCQsiURw1khiFNBpsAkWL0fixxm8Z8oY2fOmhQu8EP77AsS7CSiqxQgtDKDVaxUySxsQuGatxMHhuSNUE5CNNjzWJw0B04LauwRMGSwFDJzUyWFtAxQbH6pbi2qq3JyV1ChTlkmRKhMUKhbUax0mcA76wKCkQoozhtGSkTj9aOKSx9FuLIw0Kgatapo7IkZksENOGoErXhKAaJNDgJv7LlNjEaM+kGnSEDgpBVywpiQhlKkiZR2BxjSQTKaSRSGHxZIQnwzlnhy+T+msIS4zCtvKpAHwj8VvOEUTCSGjFrLJsyTdSWKOJ0HOkJtbmkSpBOylhKUUuTapgcpjIwaJQsYdE4BiHvkgTijaUSmMRpEyBGfqJhENkO3Clj4PBjasYU8GKFNBGU3biy0VkxPFokkIicclG+QRiqQWJfaDwIi8hBxHtYNpI2TYQvcm4JaRVQGwcSpGDDn3qUT6550Ym61+CazuIRAXXC8k3S7iyCysKCAuh0LhWIuxxqLVGI+nE0EZIst47W7WAhZX4RIkzRcBCFSIpYSnMOXBKaIrNdKs/gZYOwmqEsKSNg8Bi6CA1tQgrcygMhVhSDj0i4yClwXEMnldC6gUMYMmK45FYKQ3gk7ISR8RYsYSULZ5w3CJFwjIqjUBGGfqI5wzMkkgi25YqJVPEkxEuho5I0CUFOkwcVZ4XUhYC3YqSShVj6cLQjVAW/QuKVMPrBtovLdZaduz8Gwb6P4jvdxMFAQ//8818+5N/TKG9kw9+5Wau+PhfsOxNF5AtV15R4VRa0zF/Iae//XIu//gnmf+5r5CtVHlHbzv7Nz3P9276K7583dXc8omPcN+X/jsvPfowQT2p19LRcTm+38PevV+b6+/IzBFufPxGrr33WnqyPdz2zts4Vj/2c9kbTxQpJNeddh2/0TufI82An734d2zYP8LHLl4012b7hn9jwWUvMTMxzfJFd5DNLoHmNNz263D4BfidB6G6kGP3bWIo3sXMt26hsCjFdO/p3LPzHgA8r42N45exPHsnz+5Yz6Pbh/mtdf3s2zzKNdeu5YXmSt42CtWVFUJj2C6L7N2TYSC3Eecdf8m8gW18K1fggp7V3L71XeQcyZllw9D0GOuDImcumSRe4PDjUUNk88xUHuWaxVdzeMc44wcavGHtQc4OU9xdbNK08NbnHiYVlFkydRpk6yxc8gC5g+toe/AOpt4ak7eX44iIgdSTHJNFHorW8GbnaRS9jBSgv/RDTs9v5NraizyQzvCIWcMLZgXz1FaOVqbJ7s3whHcOZ8f7CVyP0XIFL4pw83l+eNZ5/JcvfYN6e47txZil1SEmisOIrmdYFFzO6OF+dvffS3/vxRzemGd16Qhv2inY1C84qhdSypfQQZ3px4oMlYrIwhn40wM8nWvyofZ/46xtTTYvHKZ75hD7pgW5zoNsnncJhckJjsRDHOy9gPQ3xxjvexj3UA+FwgSl6n4OHtnKlsjyu3WXerbE7915MwfPdkl/9yuI/XsRb/84E99/BCZjJr1R9r34PM2dO2lu3UrhHe+YWy/dlQFW1Aa55elvzX3nSck/rJjH0WbEn2zdfxJMd+OBCbYfm+bK039+xIu11yWOAGtJLS3T2DKKtZbiVVcxfuedc81GD8+w/6VRVr6l9+f3dYLcePlybn1qH9uOTr2m9q9Vlp3bxebHDv/KmSJfl/9MIvDCHOn4DKRxUELQ0SzhBjmUtLSFGhO6xJGPQZK2q8nHDh0WPFsmJxRKJ4rwtJujJrJIIYA0yG5KRtIRKLJRGzFJIVTX6cMXMcviDItn8nhBGisX4mkXiWSgkaUadpCzBTypSaleSjYkZXxSMsZIQR2PgoW0blJwayxwa2SUQQmFjtw5shFokYcIyGqFRw9SJE6PMduFUv1kTT+lMPG2O1YhrCAlXCyg7QCuzeJZjW80KQRpLyZWRXwj6DOSVAvChnXQjs+YKKIkOGIRjtvTSrZPkTbzcEXccncrhE6RlSEFDNbIJNMkdtBaoB2DFgJX54iEJrYOhbhAIXapOyk0Lo7sSZQu4ZNTIZ6wBMbHhjnieJA41szIJJJhUcwipR3lIy20hT4Z3YYvshjp0bQFNBbXKjoiDxO5SCvpabooBFJYxm2BKVEhIxWuNPhugCMM5cAnjgr0KIkrDbHUKGFIifgESKnFwaM9yOIJcCxk/AbG02R9yIo8UhVwSTFocjiOJSsjhACtFVqX50gRBQlkzJlVysmAUcQmi7Ua1CAFUUJaj0i7RDZxFihlceIeLN2k1fEosVagZAFI2OyE0LhWkDGAdnG0SyQrVCKPnkCgHYXxizhuFowkPy6xRrdqfDk0cUBALFwy0kc5EVHkYeIUzSCHMSrJxZPtIHroih084+IonRiQAhQegUpTt8k9zGmXWVaKpu3BqHn04s7Nh7ZgwyozIo0rI6SwFCKXcuSgkGgjUC1Hg29ky6kAaRlQdjVaLiITuHPEOpIEfphH0tFM4wmBVZCJe3FMP1J4IF1i4ZBI8ydgAAAgAElEQVSNBrG6H6kyyFgjLWiWgtX4MsKVLtJ6+E5ANayQiTKkY4XfMiGkSIzNUjOLRFGSOdJIrJSk7QD5qIq1CmsljptjmCphC4Zclx51clgkHoackeSjNAoHN5I4dCBVmaLoIRO7SeTLCgrNFB0oAtLkrSQXavyogI4yFInxrMRxQlxlqYQuKSPQrkcgV0JcJZAdaBGTijXpwCcvBdoNUU4EscLGiROlThIJNnEaFRoMGun6IATawlwhAtGCFkpFGmeusHeydQo4thOJixUKRyg869IWO0iSay06jcSBIQUBabImhd9yolkBFh9tJco6VB1FIfIwJj0X+SvEmrSIyLTWlEW19pto7ROLxZKLXhZZfpm8bqD9kjI6+igzM9vp7/8dhvft4dt/8ccc3bWd6276Cue//3oyxVeOVr2afO3AMDfM62bdpVfwrj/5FB/6f/+RG77+LS787Q/RNjCPDT+4h6/dcB2P3/Ft4jBkyeIb2bf/Zg6Pb+amp2/iiu9egUBw95V384kzP8HG4Y2sqKyg7Jdf8xgWFnqIUit59shm+pbejqOTgqj79t9CVP5nPHMl0zs+QedAN0wdhVsuA6Hgt+6FbBvxVIDcF5Pe++8U330pcnI3y06/nrt23IW1loNjNbYeM7xz7dns2PZRPvbWHsa2TdA+kGPL6E/I5B/le86n+eb+o3xg4gn2+j3oehuri08T96yh6h/mtrxixeSL7JiYzx+8ZRFPPPBTJmQ7Xukp3rP819m7MsM3bMDS8ibe2qiRHz/Iz+7exeben7JitJ02Kfmphs6JETrtJOfVV9KRd6it+T7i0NlMb7oP7TbZuXYZS2q9hAbabMyj47/B6NRVjDbbOJQFT1xKIVrERzu/hWdg0/T5fDl8H3GvR9eu77PgWBor0+zyKwwyxnnbPELHYc/AMpzJaSZ62gnSPoPbd7Jk1aPMqy3m3JGLeUbvJCeL5F5cTqr7aRpejZx/IXXTxqrcQZ5ctpT1hx5lxH8YrOW5Q8s40mlY5z2BGHkLt+faeckZ5M3FJ8hFKYZyHtueWUSm5wEmu87DAu7MFI1FS9FHoDS1kjisUa9vRKcjRgPN9047m6uiSc5627vZUy0Q7/TJX5ri8OMlDhzxcPrOQhUDFp5zDlsf/yljt99O/rLLkJmTobnXtr+Jn7CBID4O88toxbdXzeeZyRk+v+t4HbJbHt/Dtev6SLuvEvFaehlMHoTDz+EO5DH1mOhojcI735nUaduf5JJt+NFelp3bRSrrvqZ1P1DJ8DtvmseN39v0KzWmBldWqU8FHNvzqzX8Xpf/TCLxTRZjZteaJZOEeuhp5ihajSsModIEshMn8hDSJddMEzQ7wSxmaSMLVmClxIoYL2rDMVkaeMQyRwpLZDIYqYlsDiUcfGEhKiUKY6xJG0EQJ2iHpi5QcFLkjUaJhWiZYkp2ENvkuI5LKKsJyDPjnoGWFkdaHAke7RSMQ+y4hMrFlzFGeDhiMZ4MwQoaokhKFUH0g5tGSgeJINOC43lOCqUdXFkgJwy+SZSprEzT3czgmnUMq6WMsxRUJ65xiND4CEpS0Ra1MdMoI02erIWMtoTksCh6YwcHhdU+Wjqo0EOZNkCSaWbJGR8hnUQPl5AWiVEoZJm6bceQIUZhbIEcHRAspaIKGJGmLGI6mh6gELIEOEQmTcMtEMbt1HWJvKwxC8BLineniGMfG2uMSSOCLNbtxEPgqhQ+Fu3kmBY5AtoIhItrFTnlodwlDJocCxzLDL0YPMASAVJKtJdnRmTIxZIYiSMiSo5HVQuKbky/EqAVQZQhFXg09WICncN1fWzQneTiyMSgdWxCAmNaFpoVgqzJgRD4JHmFys0h3QGqpo+i8fBQ5I0mK1JomyJwK7Q3F+OKdqTKEZl5rcyrPtqCbmoZB4XBxA7WSFzpIGQ7aSXJCIe09cmYIp3CIaMDLA6ZuEjOurgIGqITIXNgBZHwQEBKpinaDL6Q1ONOApmnIQvkpcWqxWQVuCKmLJukMKR1CisT50KXq/DDPFYpqrZKJsofh9y1omc5aygFacpSkheKdusSK0XKiYgpAh4lFRBJjRdUiSljgfbYx7fgWghFO1rkSUlF2lqkckhrgxRp2oRHMVxGXa5ikn5CHLQp4kYeEo9C6OM3OvCsRjQ8svWFZI0hhYczC3nFkBZJSY1YeijVg6PLWCFwhcQxET1GUXU8fJ1vlZYoIfDIGUUkfFwsWsxHi34i0421BaZNhXHKpDCgkkiujrNoI3CFotMv0x06GBJIq7QJRNbxJUpB02bpEQFVlUNZyEiPHIaM20/d9hGaIv3NCgsDDyUSY1UISdgskBMVHC9N05tHSvqtUgEtUhoxD4tEoAjjZcTKBZEiFedbuW0ZrPISo0kmtDgZp0lk24miCtZoxusLyQpw5SzNv09gO0i5Ft/zyJvZ6KlAm06yogdDjpQ0pHVMEYNvXSJbYUZmCKWLkYtwyGCsxNF5mnGZKUo0VQrHCynELvOnSgSiAwEEukxIkkeonUQ3ioUmZV8d1fO6gfZLiLWWnbv+O/PmfZTdGzZy241/xrLz3sLVn/4chfbOX9zBK8j6yRm2zTS4pvNkwy6Vy9O/YjVnvesqfuNzX+CaG/+a/Zs38q1PfozJIcFTZgVX3vs+Dk4f5LZ33sZn3vgZOluwu0f2P8L5fef/h8axYbJGSSqi+I9ozzt85MEP8/TzH2bPrm+w/6HlTO+/ksXrOhHD2+Hmi6DnDHjvt8BNFt/U4wcZndiOc2gX1Xeugc6VnNv9Ji46tJ2Z/3kBP/jiDZxvnmTFbZ9k/vQh1tQ+wPaHnmXRqizffPzvubw3Ytwr8L3qBfzu5i9y3u4jxELQdcUHmL7/r7kr1U5H0Mlduy7CEzFXrW1n68GdPNAsQ/Fpzu29mFs2zdBhjrC3+DAX505j4qe3M3Rwgm09TzJvZ5UfdGtUSTIwcpg1wQKGQ4GjjrF3f8z8n61C7nmUyd+NWbrz3bwUWXrcDRwKVrDZFNlXOsQjRwfpTk2gw0l6sxGrDx/jC+Lt3N64mr2qgtOTxmYPc9qBDFu8dVx88DnGDkvah4fx65MM9Tk8es4biDNlfnjVNZz9/LNUDo+QPXYGCxqDbKulGJl/D4uDs5janWZ77w9xl7yT0S2ns7Z0CJMbZKsc4ciTmygZH21Dtk/2w9RBPlwoMCoEWuzg6/P6WTOk2TMwSXH4KF77etIyYGf3Ivr27WNc7WSkex2pbx5jpm0TbrOTXGcnDyx+A++ePkL5hWe49NILqTspVCOi6m9FXfg2ops+idNzFsH2h1l8znnseOpxxr77PUq/fu0p6+mSi36ddLPAnU/dftL3FVdz6+oFfOfIKN84MMTIdJP7Nh7i/WcPvPoC1R6sei9s+BZCSfwlJepbRlH5PLmLL2L8X/+VqdEGO549xpoT6p69FrnhgoXsG61x76+weLXSksXrOtj6s19tQezX5T+PGDwKzTxIRTb2yRqNEZYYF6M1sTYUhECicW0OgSB28xxjAQVVwMhuJukjiNtJxSkQoOQAjs2AUITkwUmUp4AqQrWB8UiZXibpYlpUMFYxY/PEOCAtgUwzIstYR80xv4Wyg0h0YHGZUOvINMqUlYOyRRrhKkZFF4HjM15aRJ+TZHJVVKJwV+NyAqUiyR7JO/2UzSA+BhN5+FLS7hYRwChFUtYhHbvEoq1FjaCRQjCu2zmmVyKFwqoMscnQCDtJ000nfRScPBkkeQr0xu14Jo21aRxnPpYUsdFErKRQ6wAhiaWAeAFZBmgLPLLGx9CJFQvQ+IzLBUAbAkWgSxjrEooCSxsuUqSRjkunhMhtx9WdaAq0xcBcwQOJMR2AJCLLjCwxIzOoxMRjymnHWoVBt6ChAmVcHEeQURZXVpAShvUSJsJB4tgHmUryp6Ii5chlXJzBsG5HGoUkgY66KodvNU0V4reiBw4SN3YIjQdaELcphOfSTQYZtJFulBBATBpX+4lnX0lMmCJu+ngR5F3LSPsZaJ3BRFlMrJFS49oUwuTwjIe0aWqqyLTIt2bAYkQPPlB0pzEqS2wVrgBhNCmjKYUdTMj5jHrtKJvAAC2WOM5gTJ6omSMbpMkZhwnZxmHfJ8YhshrPeqTDLCkpQCgadKAchXCSIthBK3dnsFkhH68kFDlimaZme5E2g7UaKwRNmTqe/wmMqQLG8WgPOpgftlPAoUGeWGimVAaDRign2WOigjYpIscj66bIUaEU+jRlN4Es0ojTRELTVHkc20ahVkQIj94oQ2jL1O18oIzBxYoMQggiuYwgehNNr5sJtwusIBBZYhQTToE6GSKRQVuFo7pAREl0RjhkvQw5VUBaj1SUOFUywmJNO+FUOwYXISQ1PJQy+I00kj5cKQhkG03rzM2HbxUpkYBEizZP2VQxYQaExrUOkUhhpA/CEgmFFhly4ULqlFCxy0zcw7joxghNRiX0/6HysCJPILqJTY6c6xAJHyMVsVchSnkMy/kcitcQMkhTtFNVLqHMYbEEOo2rM3TFLtqUSCExuBwR84nNQgKT6MGeUaSjDMrVNNxleEIRBhVM7NKIi2REGiWq9DbTLG24xLYMMsnba6peplSRDqPRwqCERRjNUNSXwHFRxKZC3GKfTEmXulhApNpwlaVTppmUZQQVqm6Ow2oVmThFppWXF0614Vpo4lNT5cRd0/SxsYPQHXQ6GYSQZEwGZSVSClIyInI0U6L9Vd8prxtov4SMjT1O0Bzm6PMe9//9/+Cyj/4pZ73rKqR8Zaa41yJf3T/Eb/ZUyahX76Nj3gKu/tTnmDq7nff86FqeGp7mhg7Jp1dfxfzC/Ll2oQl59OCjXNB7wWseQ2wtD41Moo7U6VjczufP+jDN6Y38tx0vMrXnSvoWXsbejSMs69sH37wEzvhtuOwLoFpJxmHM1OMH0Jvuo/PTn0IdehL8As5XzuSK2OXHlW7uLf8m6y66ivPM16ku/hoxPRw45JB56nJ2pY7wR+vewdrz+hBDDWqykwU7mmzq9wjL88iNPM7X2ixv7wx4bngVFx1bz9M/eAhry+RLezmnt49bnrufydHTuKh6kICQM4sfgk13M7ViDyu95fTFVZ72DzKVzzE+EeAOr2BnZHnkSJFo5zr0+m9z+Lx2htVCMrVeDswErG7/DlOmyvu8f2FjrDjaDFlamuFc9T1WFn7IMSkYEom3qxPJm7c8y2Q2IjXm81h+MecNPUtXo8newUF+esGFNCLBWzdtpM9ZwaPVQQ6fXyB1R4rN1mVfBtbV1rHF347ysqS2rqMw/xH2Usd4ZzI+nuaayYf47YcqHHUdFvSejWsint91Bkcrml+b/xL++Gpuy7Rx4Uw/6y/8LDu7DhFLycZnV9O2+DleWHwFsRQUtm9mx7I3Y3YM0eZeRC2yjDr7uGDkMYr7n0IIwZ49e/DfcClHqmkmGx6ZT1xEveNMTCNm5ol7yCPxtcP4vD78ZctOWVNOuYu31/r59kv/ckpkqs93uXX1Am7afYTPrd/DG+ZV6CunT+njFDn9/fDCHRDWE5hji8q++J6rmLjruzz3wF4Wrm0nV351GMHLJeUqPv3O5XzuvpeYab52ivBfJEvP6WLbM0eJw1cmRnld/s8WIzSH6aQpsrSLgJTVWCmoqwFG9XymZTeCFTRkN77j4QlLyCBTagBUDikDxnQXjchD234seYyU+LKIE/bg2iI5BWN+V8uTncYKQ00WqIsKR+QialIxozyMjBC6CLQRao/YVQQ6m3itjY8mRSQXE0kXbVPIMI2JKmSiHiYZYI8+i5qXJRYpUvE8HDT5MIW1i6lF7cyoDGOySF1oGjKLlDGjxQxIxZRbQaoCEo+c0aSMIhPmqZNhQrQxIotzuVZEKSKToolHhMOU14Ymy0Tcw0jYixWaUZ2Uxpi2CZmGERKkIZAOw3TTwjnSyHQilcAXMU0JNVElHeYIonWMya6kmXQwKEZkmqbVuFLhdhWQCpRWxEoz7AyyN+jFs5a600vauhTDVWjRTSXoxBUx9aiHKdlHWlmyREzQzXS0DAM0SZPSKSwaLcAVkpR0cVSFGImSGYRM3pUNb5wJlUlQnULStEtQWGLhUyNPVmryuISqSsZxSasMHgptHCLrMC4WMCJWE9pOlPDImgUEbpIDNyUC6kIyqdIckcuJUAgpGAwzuJ7DosilW2TJORWKOgdOmshmE8Ml7saigSxR7COtQ4if3DOtSKkIiSEyBUqxw6QsYWyKJlmmRAYddZOJPSLrM6zzRNbDkQJfJOQ5AZpIFAiFIpCKWEqaajphiXE6UUJikThoyp7AU4aMCpghx5Ttxdo0WmXwpM+MqjIkO3CtRoYpRsVyAGKTRUUZclEFG6Zw8AnlAHXamBF5hLeCvrQPXgexTKFEjlDmaYo2xtUgocgimccxvYrYeNRMB1b2YPUARqZJmSrp2MEC06JEiCYOPYKmS6RCsm4VhECaDAoHQ0RTOoTKoxHlcbwcDSmIpKThTKKlRWiJsBZLTCgTiHMkFIoMxhaZMWXGok5MpIj0AqT0mFE9IAUZYZjOlqnpLBgHIwQT5DFWEVtNiE9DOEyYNiKTwhcSB0FBRjg6BaqEI4oUZJppekELjPAw1qFhY2LHRyqF8RwCJ43Vfcj6fGzc13IgJe/sIM7QJEuoWmyHUlHAgIyZdHyOqDMI5BJKsZtAP4WlHhWwxsNQpCkHMaoEpKmTQcsUgevjWI8uZ4IIxQQlmtaD2CNQEiWyOG4V311MILMt5kTICKg5NWqNKkfDFQjbicAlLwMqskQ9XAaxJqvzjNuFTJPFWEnD1US0Y1BMqHkY6ZHxKni2g5gU06KMEpK0bmNal5j20kQ6h7EL6ZYSa9poqnassTgKHCFASvKqg1KUQuW6qXcl9+jV5HUD7ZeQffu+gZ04nae/912u/czfMG/NGb9cf/UmDwxPcH1P9Re2Ha4P80cPf5R7xON8ZNEHefN9HqtSV7Jt+3/FmOMsdBuObiDv5VlQXPCax7FhsoaNLbnDdbabJk89dwN/te4P6C2fyd9O3oGuLGNZ93Yy974P3v7XcN7H4YSFVtswRDh1lCjTJP/ms+C5b8OxrXDNP1P7jVv5/PQEW4YD7t08ztVvXsO8NRfh+1+h0DPKVxc5nKlylL77B/xo5BBXjD7HN5rvpDydY6xznIP/9gkeUguoZxaxZWIpsZVctncf6zc+yxO2iFd6ggsGruDvHxziQl1n0/wtLBtqZ+KlEmlziPV8ndN3LGanV8dGU7hhxJl7enl2BorOFPMv/VMGN96DqqQwl++lvPtd7B/fxXz1U1JylBk5RN0u4o1jTxKnsmwIejincoDzh4/xxWKBy/XD1KRhX6iZv+dx2vdnGE0PsGrsGN9ZdAkD8QE2rD2d0tBBVr24kd2r2jlrJGZXexe8bZjYaLbNPEFnzeFNU2vZcNTlxcnHGZQXk5sM2dfzCLJrLaPH3shZmT0sPTzBjKeY6Z6mM9/N0WHFLlNEb7uTVWE3D2d8Fs28xJdWn4lDOweqmpmd4LU9QlYNIiws3L6dmcoRposDZG+v8c3UlSy1L7CkvhsxHrBq1Sqefvpp3v0b76HpKp7b3c3ubf+T7Buvojm2Faetwvgdd9I1OcOxwVPrjM3KdUuWcsyM8cz+9accW55N8Y0Vg9we1Dhr1SuXljhFOldCeR68dA/e4hLBgSnimZD0WesI0wU2//Qgp1/yCyJxP0fedloHiztzfOmhXx1hSLUvS6bgsffFkV9Zn6/LfyYR1ITFIBmSqzBIZsgzgwdOlRE9j1GRA5FEwSJRImiUwcRgBaEImSpaEJKqngJZwhjNlKwQqSyOLGCUYcoropwsVigQhikK2FZ0bIaI0Gpi45M2ZZpuFseGuDbC2C7SUWeS5yI8qlEGZQyoEgEVjmrDsNJEjRz1KEc9zmJwwQpmRCcGhwhoCofJoJ1pOhjHwQpwjUfOpLBWMK1yTNGHMRFBKUugBFb7hHEWGXtMyjIm0AltSOiDVQTC5bBKsdupUlOJYjSTKRLZLEeUTzOXJ8RjKOwj1L1oJ40UhsDNEpChrgQhEQGJsn9ULiO2CUxKCD+JDlhF2KpkZZwSVvmMqT5iKZkmxwiD1EQaYxwmdC/TegAl0ljKGCKmtUMgXISyDGU7sHIR2AKRo7DCIQ4KhLqCVilKtg/tDCIR1GwfRmliBdrGCOGhgIJQSGUZkwlBgbWCWPsJhBHLiLOc0BZACJqBz5QaQAmHIMxgdQrbMvIC6VC3VUJToqldRmWTowQ0hSKSmnGng5ooEIg8dT3AuLuAyNd4VtIQgplWvqEFjKgSCQiUpomPF6bJO4pQeASksUiMiDlkFzCW6SAtJNa240YdSJtHGEtARNakiMoFJqqrkC0YVx2Po7IziSgLRV1UqbGIqXgxNk4TCA1SImS2RTQjUcYF8mDBFS3OPymZ0Zoxrxets9S0ZFhZasJB5CogPbRTwSeJIkrjIkyGhnKwWOrCw4ik+HoULwFhqZHisFzJpJxPJJJUECMsKRVB2meICrFuQ6oyUnr4TgXXehihMXitnCSHgIiYGCsMRjhMp3OELRZGI2Ka6SQKNuJUiXUOIyRSFjDCxVqIRVKqYMa3GJVEZCMkM9E00kBsHKQ1pK1HOkoTacWEdCikM0hdJtALqauAuszMsRRaAUYmEFwhoCFCmqQIKGD84/XgQqkxqko5rtAlF4LMciibOFtDVzLsLCHCZ4YqVggUDk6cBdWL1J0MpecRCsuI9DisIVaaWnsbM5USTZ1BCUPoJM6ZprXosIw2lim3xmFHMCVdGiqZe5lQtOAazZQsE7iLQXm4xqGR7QcERabIW4fYFtkrSgypDhraZ1IN0mE01SiJOsYyooFhON2FjueTNZKGnsEKFyMcGmSJhE+ER2RTTKgC416eBhmm7RoinWNcDGKkoJbqo6yaCNHEADrl06CHpmpDyAT10JAV9tsidZWjblLs9npxhSKtYiwRQ7aDrN9JV201gTi1xvGJ8rqB9r8pU1NbGRl+ki33j3HNjf8P1f7BX7rPmw8Mc0V7iQ7PedV2zw89zzX3XEM1VeWud93FBy78CFf+yad57OYXCGpNDh789lzbRw48wvm9578yG97PkR8NTcCxGu9b+DPmsZuh/q8yf+B6/rDyAbKxx5dHPsfa+DPwrq/A6pPhbNZaJn+8k+CFuyl/5LcRN18MYQ0+9O8weC7LysuQM+toy1saoeEjFywEYOezo1Tay/ys5rK6bYqvX3IjvTLgpiN/SU8tQqmI9yxO0TX0DF9qb3B1Z5H797yVQbmfySuuxK03uWL9fQzp7Ty9q0ncqPKX85azubaNebIdOxJyOHsxF9dnWD01n0fcTWxtX8TZ2wMqWvDs/2LvvYNtu64y398MK+28Tw436l5JV7rKsmxZtmVbTrKxsUEOCNxNQzUYaBdFdTfl99zNezSvXjfmdQNFQz9CmWBoyg8b44CNsWzZyjnrSjeHc+7JZ599dl5pzvn+WEey3ZaFAFV1F6VRdf45a+25wp5r7TnG943vq+XMXPIV8gfqNNe22XrrENO/gMnOQVZGEa+rfY5Pjf8gS7WM+3kH453znK/v4ayeJpeakRQ8EIZ8qebjR+eYFE+zfWCBg0tVvlV5NZfb+9mtc05cfAGvu+tuqsN1ap0Rw+AyTkWGyBmWg3lOfrDJG+76K6JhjxW5zcTyNMviEbTvMX7yVYxddBtPxAOiiVsYrQdwtaNExhfKD3G4fBV7Rws8u3I1JuszXvoa1nlsBl2O3XUbu+uvZWXmDH6S0B9sUy6tc3rXtRgh2PPkwzx5+CY+l4QcldeiTMDF4xWUMGx2z3DmzBnq/dO8bmqRE/EEC/dv0RilHGlcgksz2p/6FFNL6yysLZFnLyxTP33d27i+dZDfu/cPX3D7vJH4Z3r8aTagl39/D6jviqs/DE98GlX2CPbWiI+0EEKw/poPM2GWGZv7/jYVLxZCCH75PZfyp/ed4+R6/x80xguNefH1Mxx9heb4Tzb6YRXfSVpiinU5R7e0D0tILjSnp/bTU1V8XUe6wvZYOYNVElOq0mmUiUuFUp8NJJvlEJtGgEMKWNYB9wRXIMkReBg0qfDpBE0yPPId+WeEz5qoMnDjxGGN7fIlGCNByGJB6CxWFEmRxJF9ByWs5xe+XokIcAhi6oV4AD5D6XBYpM2wVpIKh3NZITJhA5Qb4ZzPMJ4i0UUPVV8ZYuEXZ2sLDcKajBhKn64o0RcRVjiOT1xO7hRGQO4K5cpBuVaIpAhYq81zdmo3RgiEVJyd3EtLjpHIGTr2UlKm0M4gjaPsNUmUYiTyHWcuyFxInEwzyEo4GWFUiBQeCZrz6SUsmyqZKJK3gXBYYCR9DB4jV2GbCp4AXEquPLJgjI2pCxiIGn2xowAnIC9P4KkyEosVGhNoElulTY3MJQgcikI0ReBYlVdxdmIKS0bq1UhG42QyoodEuJTMjWNHTYSVDMWQTBocjn5QpqWfQ+IFFkc82stQJGyGdXIEVnxbWnwgQrpigtwvPf+ZAXXapkIiPHCOstWF6EvY4Hx1jOPVKqHL8aoRgTDFIh/HWRkyEBFWSJzMMb56Xk30vAe+ilnXOQtyP6mcZVgrvNhS6dNV+8hoYKVHLgrnLGs02ijabpKEYEe1XRCaSZQ9hLK7kd5hZFomcBZLkWjlIgUs2iUINyKt+BgbkiclWmKGZT3BorqOqpyh5WZYVlWMSsl0Rs7zwvuEzhE4QVdUCYVHxRXoIAjKdciaZZCSkIxvGzJI2jLnuLqOFpOIopRBLSvhhCQTkgEhE0mTWl4iEP0C7a5OkuOTmAjrJJkATxfuRNo5NlSTkzTJRKHvmXo+VrCT9lmsFfjEBH6GrzU9WUYIRyazolAjJFkkEQ7sjlucFQrjSXIK+wUrDKns0Vd9YluoZUa6CbKOIyIVZRCwMnGIxUjNYyEAACAASURBVHqJkQjY0hW0i3HCsDxZJUcxUBW0Nfg2R4kdfdEdyf1UBXTGSrR0h5ZSZDKjKzzMjhDNUPhYp3DOMkKQWI0FUgzIHK0DJl2VqpQo5XPGD7HWo5VegZ+FeDZnSyisV1AyBzLCCM1AQUpQGJQ7yfbkDJuixpJfplvbh3AlyKOdAlnxu54rRSwinHMIZ4nTGl0jyLAkQrAd5iib04s0W2KE5yTscAAAuvVxelGNvteknVbpji5gozbBUBVWGsJKRsKwZq5AF2kxqfRZ0cHzSN/3i1cStH9gPHr3L7F9aoL3/7v/zNjcS1OIe7Ho5ob/vtLiZ3a/gO/Td8Sd5+/kI7d9hI9e/VF++YZfprzTcLjnsiv4oV/8Pzn51YiTJ36DPO89v/8bd730/jPnHJ9dXCJqrXDD7tPcuv86vt4vOOgn77uHf1t+F1PZGX7h0LUkF9/8PZ+Pn90k22hzylth1/H/C8YPwO7XQLXg2gohyLavYamd8Yn3X4GvJf12wtqZDvee/hQ6qPPh1/8lf96d4ibxV2zUD0C8h4eU46Z7/28+O/E2guxiBk9GxFnIVdkqZ7ePcMqfZ3+8xr98bJq/ul9ya9lw5MApjDPcdNmPs5zmnJNXcdNWidOskWRNtioV9o89wJsCnztJ2VSPseeLWySvejPbV7Qon3kPT402KVV6lL11TnWbfPPaeehOMB6NGIvXcbVxPN3nC2GZ64aGI16DwzXF5e1Nhrlk5AcoNYnSfer1Nq9+4AFcOmDP3BQn9mvO59s8wykuNWd4Jr0Of37Ao5ce5uzCl+iZAbWlmMW5IZsHPo/euoUgzdiYPUIuNSvuXVxTX4DKXrbCLtWgiS8cx9q7ONOo8VPmHFf2DvNH5X3wyB/xntYbWZ7ucaa8m1MP7qe+/1sszb6JrVqdC44eZXs+45PveT//++pxkCNmgmdIm46Flce47LLL2Pz6b9I4fA04QfnopZigQzcsof/9r+OShPH53VTHJzj7xPciZADMXM5H3BqPjO5jcWPpezZ/4fEl3lOtcrga8a+PLr40kY5L3wdn74b+BtEVkwyf3CBLDCc7U+x68jPkm5svee7/j3HBZIV/ccM+fvmLL59gyMWvnmHhmS1G/Vc80f6phROCzWYd6Sy+M3jOMhzL8VUCQrKhQrqiRLrz4y6Mw0poTU7hPI3UDosj05pePaUXhTjroaVjEAxpSclIFF0awllWoiYLtX3gHD1RIrMabYvkxiHYChSjcgWBJlYpaA0OUmI6qkPfNXDCslpp0pcKs1MT8bSkWx6nL+BMvUFfODatx7aLsDmFqIgYoW0xhzPSopFfQFtMECMxQpLKHG0sUvu0K/Mo3cSoWUZ+DZwlFt6OP5LDCIcQlmo2BJvs3E/Jmq6CsJwJUhYr7nnRh74MGImI2PlYIbEIhsR0xRiJmMCzKRuuwYnmBIPxcUpAD4OVFZZ9iXYJEkMgMlJbZiQyBrZPYmOkydEmIbMxIzISr8QIH9/k9OsTbNcbCKHpa8vm+B7yoFhkOSx9TxfnIyWxNmTxODkeqdRsyTJdGjgpSIVgICWBs6RSY4TFaUnsj2N1hPFCnBMMdmhjJovYElWOzFUZ6mmGfo2eqIOIQcJABqw5D4sDKYh1BSeKIgA42qrPVtAk9aawTnJC7mNbjLFcmiJ2EQMXkqDRegKCGkjoViNG1Tq5kOQStiRktkbqFK2siclKCOcYqRgQDG0JKyTtRoAxHqERNJJxjBJ0y0WSA9CnREKRGLRMHannsKUDdErjuB0PvKrXII3mWCn7bI4iTF4hNxk9OyITddIgZHOqxIoMWZd1EJZVJlm2ezFZSE/mtLRjqHJs5iPTwujByBxjPRIvpG4DhNPkeYTNFNYkRMYQU6JPDUuFkclITQha4nRAjsZzjghLoiQj5dj2ZovkxFiMpKD+EpEj6boamRrg9BBL4dM1okZgc4wJ6OQ+nu5SWJHnDHSFVAR0bJXceWQyQLod820nSYUlVgNQAoRggCDBI1YGnGNrvMpWVGNgYzwBSigyfCJT9AO2VQmFoRNWGOycI04g0DuKm5ae8ujJjAU9zoqvOKthW0YMREpfpuSmVwjJKB/jBQgB/SBglOSM0jE8NYYSHifVHCf8aTZUjpApq3aK05V9BVKKIxONHQ92RSnJscBQVIm1R05Iy5+jJytsNnZjpcZJjbGFUElLCoYqwTcGi9lRViz+cuNjsggjJIGARHoYPQcCMlnHqjEi6xE6RzOrEZuA3PjswLZIq3FIUitRaYMkcBjrsekUWy4jFjkJCrMjmPJcqibskESNCKxDOstmSTISGW1TYmACWq5GK58gFzk9Ukifs0P4/vFKgvYPiMe/8XlS9Rg33PxrNGdeRAr87xF/ttziVbUyl1S+P+R55/k7+didH+PXbvw1fvjCH/6e7bsuvYy3//iv0V2SPH7/r7DQXWBtuMarZl71Pfs65+j3j7Gx8XWWVz7LwuIfceLkr/Kle3+UZav4hcskV13xe/zgzAx3tntsjmLOP/RNXrX+x/xs/11QneLffOvfkJnvRkvan3mEeOluoosd8tDNUJmG/d9OEI+t9mj3Pbz6I+yaKPp7Tj26TmPa8fTsMh86fCtP5rOM9Dw/tnAnT3d24aVTXHUo4cvtizixeRH7h4d5bP1ySjLh4FhEanJMYLjnDa9lo/5abnYLvCZ1/OXmX7OrsouthRqn7QmaW5eQjm7geBYTiwM44bj84S3q6RbX6B7Rn4DZfz1buxfJBpNEWxez3VvlstL/xzdck6fDL9OqBAgzYCy8gXp7hSvFMQahzx/Uq/zAxjgqXGV5d5mLy49TP1vmqfLV3Lh1jmTmSi46eozJtRX+/C2G0skTjC5ssFA9RzN9loPeA5wYvJmj6iaOvHeesZP3w7lHiWTEYmOSc1MPYD2fvQ8foHbwNu5JLFHjVvp5xNTcNh/ZvIbb60+xqzZGf5Ty0NlZ9nWHfOiiG3mmPOIt3M8dx7pMqwbt5gZ6KyFoPogL51BOIaxl18lnuHJxkat/51OU3EES9Qwbcoq867Nvb5Op5a9Tv/6fsR5cwHT1SlbUA+y7WnDsmQyUInn6aQ4cvJjj9931whNYCC47fJhL44P89tc++T3z8QuPL/NDV83z6xfv5pHugL9a3/6+z8LzUZmEfa+HZz5PdNk4ydkOz96+yPiuKnNXzNP5whf+7jFeJD5600FObfS5/ej6P2qc56LcCJi/sMGJh16e8V6J/7UiCzVOSKyDZQkqM9iqx6DcIEu38LIhyhiMyxmJPhRLIxLhE+OxJMdZq1fYSMYJBzHSebTqs4RpSpil+CbDuASdlTlfniVTPn7usHZEO9RYkYEQLEzO0K/V0S4nw9FvlMiCiMxo+iJkoTLGMIjIsbSiBidVSK565EYjTbFc3FKKRysHWS7XSHCk2qeniup6JixOWISwLKo6HQmx8FHGopwjx5HJHOsUSUVhtMIqn5LyWNNNJCmZSXlmqs6wXGNJ7RQshEGIAhkSwrFdbbJdKRGYLSLbZZR7SOchpMPPEjxR7OecxAnHSBR0PIOmHVXZLNWY0jlSKqQ1IBx+PkK7mM2JCdrjNZy1pMpD76AUseoghaMjQzYmAlbKHhbBkbESTvtok1NJcvQwJ1bZjiCHYFXlJGLAkICVyhxHxsbACXrU6bqAzPmseB26csjGRJNsfBfblTGwgras0RN9lK+piCpSfbsH12HpB2XO12Y5p6ts65BlOcm5ib0kTUsuFEMZMSTkOcTMojB5iBkV8ulKWJwUoAIGYowYTWwiEi+kU42QztGRZQYShkFMFLTJtMdmOEFb1OgpyUjGhS9YnuygtQrncjpWkNoUg8M5GEXBTnIo2GbAjL8FUuKloNOckSdRboi2MdJa1qKIVWVIXMwg1yzaKut5mS2VkWYhOoEoGWeDSVo0abs6AsjJyaRFIJBOkHgZHfmc8XPhC5sLwwJl2m4M6xRbsoZxgvVGE2SOsDlr9XEEgj6GNgl9VyWVPhmC46N9nB/UWK8GbKscz+YI5+iSY4VEy4yO8EgwWCFJhA9YjCtkdKyARA3pUWJN7cbu+HoLZ0iyGjnFc69yi3WF+XzgcnqmySKKXskhlAHxHG1SsJJP7dx/GEYRwjjao4CWEIySBoNRFeOgqydQMqQvHKAZRj5tZVijyYnKPp6ZuQiEh6/KdIWPr4as1ANWZImh8smdpZz3yQJJp1KmX/LolBSJ88hGY88LqwgEGzKlI8sM3QhBRsNtE+eCajbAMxkaBxj6pYiRiLCuQMbz50V4DOW0St816VHCKg9siJcajPZ5zv+vraoMiMhEgsGxKoaY3EPnQxQ5xmUo5xhRZb0xhpHF81lYjjmEkAz0HBuMkeWCmITBjv+j9jRlFbBSK+PnEdXco+PXwBk6DOkYh3WS9qiKdW5HaZcdernDOUHXNzgE0ll61RArLJlQrNZ2Y0VILCRnxgxnVJ3WaAKnX0HQXtY4/dhDPP3Ab1EpX8rs3utfljEz6/jk+Q0+8iLo2bGtY3zszo/xiRs/wY27bvy+++269DIOHf73bPU/zze+9cdcP3s9vvpuifFO51EefPAHeOTRH+Xcwu+xsf639LpP4ZzhS8OPUBta/vnrfxAhBLOBz3W1Mn/8yOPcPHuUFXMF4Y2/yG/d9Fv00h4fu+tj5LaY4IMHj2J6ikfs01x0cBze8Z/g9B2w/9vn+9u3n0BLwY1XbPOV018B4PiDq7R7D3C2sc0PXfhD/MH5DX6imjJ35hxHsreR6x62/Qh11+PzBxf4wpVv5Iysc6t3N81skW1XJp1K+eLsPfxVdoirjx9l3S0y/9Q8rz17A0fPPMHg4DYuM9yWXEmls5+H9vUoZ9tcrK8lET4/sfSn2KRKcMm7sNfcS23xh/mKfww/muKi0pOcXb6YH7x3mrfdfRsxKxzL9nOw3uL14mHunSozYy1vMufZl17D5Lk76e7rMNYNOOtfATMtgjjk8qefYuT5XGavITjV4dmLAh4Ze5TZ+aM0Bn1OVcb5hngbr69/g8+++e3Ujt/BhcEBPrD2Fr7ai2jv/zrexjtpVM8zGO+z0j1Dm/dwY/Yg8196lDPzQw6WXsOe0SInWlNs1SOud7fj2RJfDvayJ/oWb9r7TuK5cxwtX8TyU+N4u+8jKR1ktdbgwqef5tK1BdJ2m/novSAsb6tuEadluk/8JtIv8cCyoPauW5ktH+TYqXOI8U9y9tkupfd9AJSi8tkvcPKh+0nj0QtP0Ivfyc/mW3xz9FUWTn47SXnyfIfuKOP1F05Q9zS/cWgPHz9+npXkJSBNl78fnvosquLj762zfsd5rrl5L43338L2Zz77j0K/Sr7mozcd5De/fuLlQ9Gun3lFzfGfYGRaY6TmiYk6AySdKCqoWJ7G2RAJ5FrSEQFbooIU+fO+PW2qdPIqQWJBCvpelaRc5dj0NC1pMVY9t8pgIBNikeGkIBaGmJQ2liEJCRl91UMIR1LUx0kUnBBjDNU6KZq1cJxREOzQtSyZglhBLiUjP+TbZshQjYcFbUkU9ekRGQPy5+x+kECvVObYxC42zFih7mhjtI5JpSzoUa6OQ9KvFXLgVhZUSwEMSiGP1UoYJchQpDJjWUnWJuZwCPpBnY6u47kc6Rx9crS1hKOYmIRcJEgKA+6lyTpWj8CGNF1K4gWkKB4PNEdqYxSpjqMfajLp0QsjjlcEORmpJ9meKHqFhHCsjE2yUa5zxN9P7nKMUMgd+pYDIgtg0aUFVggxCCKTMO620c6QqwBpc7AZA+HYloY89xhWwQpLEiqGfp2eGMNag0FiXM5AdPGUQyDJ8hCXanouoier9EoV+qKEEA7fJmAla3KcRT8kFRaNw1hXJNBWk5uA2BY9SMoZlmsl2qrHQCbkmMILq7hi+iLFpZK212HF1+R4lIN0J/EtDIO7JZ92tQpAKRuhXI7F0LdljMuJ1agQWEgKatei6JOJjA1d0PsKUqfECsHmVB0rU5YbUwyFeT7hH7gysU0ZTozTjTwGYYiRBUKKFFip0NYSmATx/PJVkOXeDhJrMErQ9lJsrhBYvLy4vxpJnvs4Aakd0JMxPZFQ0jFEgu1KvZgjQhO7kMfG5xj5YdGTFzr6VZ8hFbquUuihSfATSzkfkSBYLWksCuVscaVuxyDbJbRkgwzNSthjIAY8R071bcqp8m7S0CcjoR34WE8jHBhhebBcpZWX2arXiZt76TTmMAhy32MkQpyQhfGyg01Xf77vLBY+HS/DAV0ZsyYTNrwMa3zszhNuhUNriZYhHZmjyWioHokIEAp8ExPYlGGpgvECurUKq8E0qVCcmqlxslYkjA4wUuIcJFgy58idQ7olrIiLt4zRSGEZKMvS+AzlMMaJlJbIyHIF0uI5S5gZ0sxHm4S+H+AJQ5QnVPMBQuQ4BlS13ukkdQjnCLMEKRxrwuLIwOVYp+mrkBNe8b4p3nSOvrAYN6Ipe0QyIZUpFktCXlhyeJphIJGExCrH6SHnZYWeLHptlTRYV5ifL4/P0Y1mAYmxiizXJDuJYK41q3qCXl4hReCLAFWShCJhVBJsC43CkalXREJettheXeFv/ut/Ye/rPPZd8M9ftnH/emObqla8aaz6gts7SYefv/3n+bmrfu5Fk7Pn4uJrbqFafjXB2S/x6uzC79rWat3BY4//BHPzt/KG1z/Aq679DFde+QccPvzr7N77v/H1TsT75r/bUPv9M02+0lqjURLc3vqX7L9qkpJX4nfe8jus9Ff4pXt+iWzQZ/OTt2PSx+lGjvmf+kPYPAZJF3a/GgBjLF89ssqPvHoPt1zyLr546otsrw9pLfd5LLyD18+9ngF17tzq8yN3/yL/XX8INZhi/5UVfjr/fZ72FHpryDCLGGZl3l5b5bQZ45vpPK/d0yHdvBmvvspbmjeinvw035j5MleYw5SDFqubQ76knmJoNE/sPc75XVO8WjzChJyg1j/JrifOsPmqH+fRsSP0VEhl/Rr0dovrvT9j2R0mtm/h1Jsv54LF4zx+YAzEGOPhkKoY8vnQ5+puhS07xS1dweWLpxlt+izWa1wgtujpkJm1J7FCsbL7Zua2u2T7BXLyKibjMSZnjyLOlRgqx9sXBHr7EF3fEBgHrZwrs8s5PQo5592NrlxA88Q8+qKvc48L8O17qMmM7YM+t868mjCYIdeCnprilLiE6Mhf89rgBm4LZ/hRdTvffOIga40OK9E4g9MlotkHWdhzA0vzF6LzjAsXT3P8kusx/+1OpAuZLD9Ip15l9/pT5Jd/gIcfeYR3VifY6i6y1WnRXjhO2Zym89oPUHvnzdTCErXccuy+u194cu6/kdd1nmY2muR3PvenZEnBq/r840u8+4pZPFW8kt44VuV9082XRnU89G5YeRy2F+hWfWYV7Ll0jMqNN2IGfUaPfh/K5UuMD1y7m61ByjeefXlQr/1XTdJeG7K1PHhZxnsl/tcI6RwiLxYsx5sSLzIMpWAkEkZyFQds1JvE+Fip6OoqqSwW4wuyx4AyQxswNCVcrvCkwHhdKt6A7UqN1Pu2Z44AlDWsqxYryIKeR8QgDKm5Ib4yJMLgi5xcOFKlsUrS9lPOjQ+xUpFpS6ISIpXsSF4rcl8jBAQiw8tTFJZUj1htNthq1mhFmqE0OOEYlNyOMiLEOiCRmhxLLAecLxUkIKQk0kMcDisVCEcqM6wAI8HmPg6JNJaz0TStSLNSVQgZYWxETxq0MeSpD2mE2ElphTVEIma9GmHJkTj6IXTkkAEW6UYYFCZxeLZfoC1CkemUjXqftYkxnHRkwrIRrlINBjgt6dVLdOohsa/528PBTiJqMTIjIH/+7vdkC+WtMRg0cSiskBgUbSaIwwmE89Emo6e6pDJDCMtIhwzDIsELZIyQltTvMojaxJ6P1ZLtMGVjXDEarxCbPn2jONqscKYyTuY8EukhENjcFcp8LiC2GuuGdP0R58fGcQY0EDoYOcmqsFgyWrWMFZnRJiHLajuLa4GSkgElFA4rc4Yqxhd9SvFZyH2MsBydFvQjDytznJAItujLFkZmO8mXQ3qWXGo2xfi3PdZwGKvIjS7olwBSkEtFXIZ2GOCkxaoEhMOK4h4HO8WIxA/olkts6SFSsHMsgxRFX2ZuPIzRWCcLURihUMpgpSPMUoyQOJGR5JtkdBEWYq9MV0jMzvlICXnFZxDuWD0LUaik6nWMTkmEh5MD8hBONsqUpcFh8ITBaUlW8lkMZtgu18mkQuAITYLAIEVO2ZwhI8W3KUau0lcpA3L69PGsxSoNAlKR0Q7b9GQJpGUUBRgkK2OTJGGECeoMqopTcx5PhfvIpCqEZ2xOriXdarnwnpNyp5jg2PQgFyNSJ8mtoluqFuIbJqcx3OJkUHxPQ+nhEATk+HmOtiNAFhIlzpLmIXFSBidJTJklV6WnLM4JrPEYlUr0ZEaWa4wQZMbHKUUqU3w9ABy9qATKshoIrPBZp0FfBPRlIW8vACcKT8EkCNkOqrQmZnfmUUEtPD9ZxkiPXBRdnEXxCJzQDIKQPj7bBAyqEbnz2VY1lifG2WiMkYkB58ZChmTF96sE2hTvk01P0NGaWBQF4bXaiEzlWO3oixJGKrwgg0jhZEac+6R+wCjwsLLo0V0eG2OkNWuuyWa9iQw0ZZEgAI+csVEHqSy9rMYgLdPzqlj5SoL2skSWJnzx1/8jl7/zOgyrTE2982UZ1znH/7u4zk/vnvy+Qh6fePATXD55OR++5MMvedyLrvh3NC9p0/vibSwdexaAweA0Tx/5hSIZ2/XPkPK7TfI+ec8Z0qbPT188+13/f8PKg5wpT3HHgd9g9xXz+GHxuYpf4Xff9rucaJ/g439wK6J+EcftvVz29vcigjIc+wpc+HZQBRz/O986ibWOj918MW/e/WaWB8vcd+cRwvIWJw70+cChD/LfFtf5YLrI59YvYV1eQJA1eevB8yz5k3xrvEU+vJDgVAcvCllqfBChQy6VZ9n/yDeJ+we5ZRgSXnYNd751D295XNB74jYGp75JuBGh0zkG9fOEXgpGcM1jko38PMlDf8yZ9wVcW96F2nMPtdPv5k4xYGp4OZfWHuCvO4fwgoMc9y6hFI/YnJ1CqAYTUYn1muLpRJFtXMKfmWt4x9bt7J8cUFmo8lR0FfujVZ7Nppk6tYwTlq19V2F6ryO/aBfv3zrMG/0SHeu4S7WZ7G4ijWHx2AH2Lh7n0zffwtjZr9F1hn9x8g3U/yxmmN/L7tYN7Nn1MEHYYHT2IdbcO5mcW2Hq6w/zxMQ5VHMKk3k88EiV1BP8iF3lyfoa43Kb6dYyJTlNufI0K8EMYm2btfltdm0McAguOfIUxy+8gNHjTzBZfStJeYFZu8Wu4RYnK4YDFxzAf2qD+yqS3dtDFm6rU732YU48M6D5oQ+Rd7bZ3Rvx+F/8+QtPTC9CXPhWfqq5m8cmbueuvzhObixfemKF9149/127/tKBWU4PEz69uvXikz2swYVvxz31lzz67BY1wGwnCM+j8b73sf2Zz77Ep+aFw9eSf/Xmg/zmN46/LCia5ysOXjPFsQdeQdH+KYVvc6pkpFIxLFvKvuGMmmdFTJHUOlgpGRGw0pgECe2pCicm6ix5I3IBHaEZ4DMiomZjKl6Hkih0B7fqPRabS4xY3jmaoJ4NMNowtGukQtANQ0xY9JJYb4uKn7KmemyJBD+IkRpOTJYAR1WmJGWfeCakqkdQ9tishqx4ddZKMzTJ0CJF4NhuNNmoZnTTOstNr8gwCmyQQKSU/JwwTRmIKpvCcG7GMgo8zk/Xi74Wab5D5FfQ1gElkRArx/Rgg9DLMWg2oknWaxWSGjTNEF8bOn5ayHDbEkLELE9ItsemESh6MmKpBr1s7XnBByPAsYFgiFKOFE2SVBhJgVYCJ0zhppV7pAJ6YZtBdQk8i5SOpOQxLDtM2MXXJ1Cs0fVbGDViUqRAIaLSLy2DVyCJgTZIYbEI2qEgDSsYnZHrjPONlI2JiE61wnJjnL4rY5EYYTkXLpN4MU5YHqodph9VGHrF+KEsqFG+MCSeJA4gFgVSkueSbb/owwKH81uU7DKBNKyUU0ZBgNyRI8lE0U8jsMjKWdZmz/HkXo/I73C2vElV5WRC0yMCCrQMJ1gYO47xEtqix7ob0dUGJySjqE2nlLM23SCXiooY0o9KJMKQKkmrWQimJLpLL9xgrdIll3GB5IiUnsiwMscIj/5EyEIleX5egGA7qtAN61hhMaroY8zLAbKqCCNb0D1VzHmdkHg9UplgrMIKwcAVx17SMZkY7Vy33rFVyDlbK7HRKLFdnaAvApamLKfmLCMKhlGgEgKvR5V1tE5RwhKrovBQzHhIPc1A5SAKwZDcbpNXQlq1Bonw2ao3eLa5H4Sj7c6yFT2LJCcXPhHxThoisFJg3XO6qDAsT5EFVQbhGo4eDjhfHSMRAZkSGK8o6oxUTioly0pzar5G7kbgMlYaNfAVyIQzzNOTFUKTstSs0VGbhbqggIEW+DYnlhnWXyETmraoUMiiGAauj8jPUUoXmfD7eLKgLO9J1sEVaF3ufAZKApItkbBNilaOXrRF4vUZ+X16YUE9zfOAvh7S0h38cMBM2mKeIXEekRkf4STSAyMLFdu+ViyNzzIs12gpS+yGtMSIUOUMyUiV5oxMSLQj14rEK7QWliamaNUcfS+hIwWtckpOvkPH9jAC+mrI0BuBAyskG/UmuVAFUh9GLJYzeioj032ctKyP+ZybXAYEa80J+l7Ilq4yICbFI8t9hKXoRXPFNQxCj9PTVXIvZ763hhUK4aDphmRaMNIj+kFGKiV9v8xi/cVbpF5J0F5i3PGpTxJVa0xfOWR6+t0o9RJ8ml5C3N8ZsJJk3DLdfMHtdyzewT3L9/Dx13z876XE+HhnhZN2nCvfX+fzn/gPrJw8xjPP/iK7d/04kxNv+Z7913sx//XxRaYD5eeq3AAAIABJREFUj4Pl7/CNGm0z9pWf5YbOWT49nOCiV3+3BHo9qPOfFq/niNfiD+Z+n2e2HZe/433FxmN/Axd/O5H9w7vP8qZDU5R8TaACbt57MycfXOf04HZUGLB//DruPHqcxr33cFCtYjtjzByoIh/5f/hKdhXb0Tp7VEi0ntLfU+KB1dN8y85yw+Uh/9b+BLubX+Unc8vSwhEeDs7iJVOUVp+ByZuIuIJ6EPP5a9/Ms3tnaAy3GCQDBo98ktaBBqPoXSwHR6k1F5leugnXXuYS9bdkXonB5jpKGq5+/BHy6QZzpx5mq5kQcyGfmZ7kUJ5zVFxPdGKJZ7am2GykSCtx/hzr6RyXqEX2bK6w0GgyXl4kEeMc9z7EA0/dz1Wzi9zXC2iVThIOn+WL0xmlk+fYk3T5ize/law+Q//Il7jxeI6L4dQVd5NmNzJcqZMduJtnqIF5L3v1Gp27v8wzY6d4XfAq5kbL4AWs7Hodh1b+hmpe43PhYW5xX6PVuhIxeZL7Gtex+UyT8sRJFvbv59yug8h4xOTaOZbm9lD7VgjC8O7oXk6zh6X+nbxh/zVkcULpXdewa3NImlh00GJjsUMyfwjdaHDJ23+A1uYaaw8/9MKT88pbeceZB8lKQ+46dS9/89XTlAPF1bsb37VbWSl+/dBufvnkMsvx30F1vPz9pA99msRCdOk4w0fWAGjccgvdr30N0+u9+Of/jnj/tbtoD7KXDUU79NoZjj2whrUvD23ylfifH0JA6gliP8GVzyKsoy8VPV3lnCrem6nwif2QngSB5OxURlnFVHSXUTBEKh+hRowqmwzCHie9QqDJSUmgO8QiY0CJc+PzjOkUI3UhYCAdcdRH7fQJZXJEhiQWiu2ow0olwSmDloZYhGgEgcxRWqIr50nrPlJYYhHQrkzRFIaG32HkDYvjiwIBc2g262OAoFsrs9CcZ+CXyWVILn1yAvqqykh6ZNpiSLA4arLLKRnzwNg4PV2hN1Zj2PQJfMvutE2ORimL7/dIgxaBzRilQKOLrSxjnKQvKzg/INUCnCVx62TKkukeibe9Q+v08dyQpQtKbFcyUpnRitq45hZxmIGAQGb4zrLh9ekGfcghDfo4LJEyGOWRUoG8ROgkFocVkkxLUkL60iMJHMulovA4KGmQjoo/JBUZRho6lRWMSjnnlVjVGqMVo8DnTGTYLPfJlUQIh5FFgmI9xao3x2LVoVyKtpb1qQkGzQpTuoeRlqUwRnmWc2NzDIMSQio2A8GW70j9gEZV4smYc7Uam7PjDLRjKD3KeYwTkkjkKGXYLiUQLNHyRqyEHrkurruKYVDzCGVGKDO65UXO1TdYkpqVsIWRklxYNpqSdFyiI4irPt1oSOppzoZ7cLKgTUqvVyBe0jAgoqPKOAEZFqMsG2pErhyprqJ2kuuEgJXGLEtzY5yc2KZXqYKf41eXGY4Z0pJH4nm0x86xGO6mYxVW5EgkRmqMn2JlTKy3sCIu7B8Cx1AYLIrz83OsV0OOq5hA5py2ExjrMdARRilqMuHp6SrL+zzOTOaARywDhAT7HHVNalYbU2zsEmxVHcMdnVAHhfiLUpwqW+LdazT8LRYrMyzJGYIggF0wH62zWaqi7DpEgtbMNEM9ouuVWZpqEPktUIZY6EIYA8hkgUYveANG8rmef8lW2GM2bzMgpOOX8MnJd+T9h4TkpZBIW0Jt6Mg+liHKQqpCjLSEckA/2iA0KX6Y06eKk8X1eGJEEnSLhFKALwwCS+51if0VhLRoPSDWQ/oipSpTIr8HCDYmyiyPF+J1xngIEZBKSxA9Tl55gtR7FqUkwhV0y2HJJ5QZCWsF6q0lIDgfJHSqJ8FfRsvi91sLx1DBQqPJRr1EriSJ9OmLCIdFCEHq+6SyoMCmeIXptnX0ZcimKhH7AQMdsaknWBzzGIaafrk4XysEQ784VuJreqUW20GXcdHD7QiyDP0QpGDDSgauDFh0XqxPnJKkfozRA4wMdlpCFaF19GqKs7MRYqdgshSl5Ly4R+srCdpLiHNPPs6xe+/k7T/7UdbWvsjc3AdftrF/b3Gdn5yfIJDf+1V00y6/ct+v8PHXfJyxcOzvNe5d5+9CT7yPVD7Ba3/sHXz2P36c/maPfft+7gX3/89/e4yZi5q8+zsTRecwn/8o5zs+75q+ggemJTOHmt+x2dH6oz/G/dnn+NWVn+e+xgpHr1dUJyahfQ7WnoGDRTJ41/ENtkcZ/8cPXPr8528qvwvbcxy/5DQfOPRBfv/RI7zjyXv4gehx6nsOU8l2cdlFXbrJNg+zi3BwkB879CSZNZS8jCfn99MmolO7kp7w+Bk3zz1rp/iT7JMEqWDPxLvo7fsRKt05umNP0qzuIu/8B55t7mFKbPCBR7cYi3OO3LCHw+03ks3fT3b+VXzFQimf4+rmX3NbfBXNXfMMyqeYWT/Da4I3cdGJ+5mLHuWovYlvYHnHtuNSucb1p1Y5VQ7onK1ysjHFhQx5QNQ5tHwWgeNPDt9M89wRDmW/Tbc3x6B6MWEwZGP9ALPjV7MWrrHUaLKlUi7vTnNV/DCff+9PUj/7LfJj36A7O8/v+6sM/XVKz76GvQe+yVrzUp7JemyaV2FukNxl72K3N0cSeGyLgIee3IWUKT/tN7mjOsY75QNcqC7jRDMlos9Ql6n2znJufw1RmUUYw+GTJzly9XUM//yrBHoWW13kidLFLLX2kT54is3ZlInBWW6bv4a93Zhz93hU5h7kyL1nGPvwh0nvvpt9U/Pc/19+dUeV63+Ig2/D629w6643sXjVQ5z56iLvvWj6BQsQr2tW+eHpJr947PyLolfuwrcju4tc/9qEynUzDB5dx1mHv28f0aWX0v3yV/6uR+ZFw9eSn3nTAX73jlP/qHGei9kDDaQSLB1rvyzjvRL/88Mh6CkfITRCGAw5KlwmCYcMdKHC57Rkqdzn6K4KzinSaEiq+/SiFZJal06tTBJ1OabHOCOn6QQxi3KWVIasN6ZIpSS2JbxQ4oKUfuCoeIZx2WOjoYlrAe2xMr619BwshlWWvCnaXkji5ZR0h1ElYUuXcaU2SXlrR1Jc7dCFLMI6LJb1xkkW68usllcwWAaiRI4m9QLa0xXCKGe7Nkmqyni+w4iiT8hRCEZa6ch0wIqvaYXnGboAKxRlZ8i1h9Q5TmWMRIBSxSK9S5UNxmnLgFzFSM8woTcYUwP60icVOR1/lVYpYRBuE3sbdCoB2+USqY7J9QnK+ZCBLwgix4Y3ZMOr0Apzhuo8uZLEskCLrAzopnUyURgDOxzZ2ConKiNSpRFIcukx8gK2GiNyJTkjJYu6xBPeAbZ0iFQWpwypSkEpnPKwKsMKQyx9ekrgyRxPGDwkvSjmyXkPIyyJ9LD1Z0E4JI5RqEk9nzywOJ3RrUyQBP5zAn4YWsh8RKolBihbeKw0j/NTzuwfZ7UypKnX0dE6caSL3hvlMQo2cSwwkmFBpZN9NvQEW7LORq2E9QWBdEQuI65ukwdbSOdw0jHQJVphkVyN6sdIGscoi4QVuYe+LTGo9vB1HzvuM/RLbLhxHAJfFpjMdrREO5ggC0YgUyyObd/SEzGPBwvkSjCgUiBFfgUrNbmO6QZVtBAIp1hsnKNd2uRcdZbTF0wyvMBnFJRQWDxhGfoBsdQI7A79UiAddEsVnBBoaVjYewFOOaTzsMJh/TZDQjypEFaQUngTGiV5srnAehMSWd+h5uYEXp9hXOJUVGN1LCMLJGcnFI/smaYvBbHzGSrBsTHFIGigEGzOTbDpj7EmJhmGColmqAxJc0RDbZAJj7YcZ8Wr4KTEKoMVEUIWfm/rYZcz1TVGss20XmdG9VDSPo8WD0TIKPDI2GDgxaxVHUerARbB8tgcrdI4nSAj8Xxy0SMONlmp5Cw3Zsil5XxlBb+0wGhyksVgCiMkSMG46HPuknGGOmEjnaJlq4BjXI7wvS3AEQiFDUZ0SiNy18XomIV5wdJ4FSsERhStC6mXgpOEehWfjAodnHT8/+y9d7RlR33n+6mqnU++oW/o20HdrdytFhJKSCAQQRJgTBAYMBhjxmHWOL1ZnvGMn80b2+NZb5wYr3EAjD3YhofJGIQREhKSEIrdCt2SWp375nxy2Kmq3h/ngoch2AbW8ry39P2zz7nV595Ve+/6nW9yjEQISSsqgpvhi4zRoEW9Oiwvn1c9iqQ0nBIN1yWRPQSCVEqKoeF0rccgcFkaLbA6tg3fMRRUxtxEjZXaKKiEBB+/rym2cpQwdETEnOuxGTbohBEGRTtSnK2OkHsZA7c1/MtKH+k3sO6AUMWMuA2UMAgjGMiAjmvQUlBXZTI0fpRinCZWOqwWFlktrTIobbDkTaGkZOBH9B2BCpusex4CgZWCXAqKMvkuT5Mhnh/Q/hEk/T5f/sAf8bKf/BkS8xSeN0a5dNkPZe0z/YT7G11+Yvo7F1P/3mO/x8FtB7l5983/rHWttXxt8Wu8aOermdn+DtT4Q4xd0uTsl6fR6bcfmI8utPji0RX6NZdXjpb/4YVHP0h+7hGOFl7DzmWXkqO4o94GwKQpy7/+6zQ+/BeMv/hCJkYmec3T53FiZJP3P/V+OPoJuOBmCIbFjP/1jufYNRKxa+wfeqnEyTKJPcHxwjIXu1eRf/UOrncPcyB9kkMr0yjrU974b3zZHODU2BFumTrHQ0ffwYGx47x47Tken9zNrft9/urBFi/IH2T9iaMo/xzJ+YtMUqQ12EE7nKBRe4A9tsQd5U9xYXodWhb5P75wHD33IM6/eRE7ooxU9mH3g5xb3kHLfZyK+yBB0OPUbMY1u5ucyx5EeRcw19+k7o/wWv0R1jszdAcRm/Vr8DHceeEV2PObVBeLnPKvIi48TNH08ZeaJI7i8MilHO+m2P0utcIpvM4+midu5lJ1JbPtJ6ik25hYX+TZ/S9k7QW38jL/fj6/d3Qob1Aelzk3cloLTnafZpe4HKk0zR1H6C210Pat7AvmeeWRGKeyiFMbZUOXWD41y+LMNbym8RjHghOcETu4auMQ1oyyLTjE16rX0Zu3FPpVTu6qktciipuryKRDY2SEiaVx6qOg/EmStRcgViR7XncZTx1+jO7UDPsWN8jjFNeZ59jX5yj+yOtI5+e54pbXck7HrH7sY9++QR0P9r+J21pNnuoe5nC0wvSJPva7sEm/vmeKE/2YT6x892Fm6WzM2fw69jr34e+rQm5Iz7UAqL75Nhof//gPLE+87YoZTq93OTz7gw9VQj7fifb/N6TCYaM0lEsJK5FWItE0ow5GyGG8tHCw0tDyukhhIO1ytnqSdrRGpooYBacnIEbRcXv0REYqUgL0MJ1xdIznJvusl0+QFZp0ii0cYfCUpjm2RlZsU48GOFbTLAb0AtBOjufUabltcqnpBnWOjSxxKlhkw9sgFgm5MyBVOWmwRFf1SWRKW3WpFGapeZt0/CZnQlh2R9mUFcLIUAqHXo51X6MkZIXlYapd0AIsQrp05fB3SmzOZrGLgyJUGitz2n6LlfIZliOXrQpiwKJlQN/L6DgdjLB4JGgcBq4hVZbMrdMvrpBLBbLF3Mw03aohdwacmypRHws47eXIvE2gNug7fawcJjh2vFly6ZG4kgyXvvTJseQo8izkdHeCFI9+cVgC3nN7tAstlsZcms4mqTMYdkvlOcb2WfGGUi4NaNkiDWsYKek6Dn0xZAUjp4HwVyk6OcKXtFzNcxXLcqlFy3OZL2+yEiU0Czllcp4ZOU6rsIo2Q0mmcnNEsE5VpqzXVlmqHWPVT1iqnmPNz1mlT8MqFr2Y2O0jRDrsgRLgKUN3ZB5Ll77r4FhDCcumrKKjkDRwyaREWUvqWI4UI2JvA2kNwjpEBGhhh8MQGkdoViuSTqBYCAUtN2G5VCcL6sOONpnQknqLUZK0ZIUw13j5AKE0s2NTLEXZsIaBDCfXxMKnGZTp+cN+qdzpMhv2SN2Iqm+YC8Z5JjiPgXARVHG1S2tsFk9qVkcDNidCzo1PYoVhungcVxuUUVih0EqgHUUSRmgtkFpj5QBLjhGGTcbJcWl4TdqFs2yEm6yoEZZ9RU81yd0FBk6djULAOd/Q8jX4gr6rmCvWaY/WWSiv4ipnmAopLI4WHHd2kwoXkEiGCajzmaGu22RORqcUsRaOkihF4iikhIVolI2wSr4VvCG9GO055I7l+MhQUlmSCb7Q5KUm2pEsBNOcOO8g3fI6c7WMbrQIZCSex+niJKHQtAoeuXSJnC6twjLNqMZymJK60I1WaMgWqdunUeyTiz4xHsK6KCNZcvvEokk3WEJYjcRQkJaSNix6GSenm9y7az+DsAUOZI4CIBDZMH5fKeYKA1rOgLPRNrwsRSnB8ugkme8QFgyONDRUCSUt6+MZG0ELZYahIQLIh6ZTrNlkOSizUNMoOQxTKjmaUTfF83JCx9CTET1ZYqE4QDiSJCpQIaYpK0ROjBHw5L6LWdxegC3nWy4zzhQNJ3zNgA5zlT6najHd8hJSWCoyoa5KxNIfXudCIN0+kdJokWJdwSMHd9ApVak4XQbKZz2ERrjA+tQkOvI5Xt3JnLOdRG2dvx2BELAWqO/5THl+QPtH8PWP/w3bdu/h4he/jKXlTzE99eZ/ltTwe+GDC+vcNlFj1HO+7bUHFh/g3vl7+bVrfu2fve7RjaNoo9k/up9du36Wev1Bdl5nqWzbxZff/0ffclDNtOFXP32Et71sD41cc011a4CafxR7929zx+YVXPqqN3Lq0Brv3jbCBxfWyTc2mPvJd5OeOs6uly2TVN5BawaqpsD/eM2H+dSJT/GXxz4KB98GwFo75umlNv/2lRd88/+1xvLcQ4vMjx5hKp/mns/eQWfvTm4e7bBQu4Zso8rFV9WozX2dQ51bGPhNrpl4EQ81HW6o1NjZn0cPDPefKjDVm+Xq9Q1ePPMaLnn3T/CMmuCVT/8yQgiOXXIH/UqJ88UOvrb7NpiscvmpZ9j70BcIr/kZ5it3U1y/kW7tbuT6+ZS7N1Lp7GLf2Nd4wl5MfWQPf5IeZmotphi+gPnuMfaM9jFBQrs0x80bBxGHYaa+hDsdsxQrOqHkKtHnzORT7HIaFLt96rWAiwarfGDPj/BUFHKuOsvMDX/MxpE38PLBK8hkjZeefIQdy7Ms1KZ5IJpjx5OX8+Y7P0F3ZJpucTti/STnL1Y4tXKCSOzCWd7JyPn3sFm6gMPRLrr5JLc1ehQjeGV0I9sHS8TuCL3wpbhpmx9NFPfUruYnnLuZdq7i3MyA095ucu0yPXiQzPU4euA6rDRceGyWZ/eez9j9T2ER7HRLXNk6j5XJh+nOf4rJpSUuun4vp0Z3M9WJWTmyihVLHDt8jNo7fhzxpTuZ2r2Xxz/8IfLGdxhoLn87I09/lktLN3Bm78OYXs5T98x/x/1ccBTvu2gH/9epxe+a6vj4HbOIA29BPvsphIDoigl6h4dyxNKtt6IbDXpf+y7x//9EhJ7indfu4kNfO/MDrfMNXHjNJGee3CCN83/8zc/jf3toAVqooZHe+sQiIJEOmTTE8ixzI0+yFsRkTpPUayMKi7haUneHZbNaOFinSeK7Qz8VPeLwLH0nZaOwjNI+zWrE6khKOxiQeBasoO/UEbKFh2HZnaDrVlDGYhxNXrR4IidRq1gMmZAINSCXlnm3ylkvYtVfp11aYbW4QawylvxVjngZXTXCWukyPDQV0aVTbNEe30QCm+kkZ+N9bJQHKAZIM5R4JnKevtsAq9HSYdGZwNiA+coSgdOhojSRMqz4OR1ZxqgcYzo4uUYLTeb2QQhO+Q02vBZWCciGfU1SZDSiIlpIMmeAFRqjhoER68UWj44UaBaKrIxNYKwc9lWFDQYjFu35eGmGZ1LafoPNwGfJGcUaOCOnOedMsyKnWJXpN31CvpMTBwOyQCPNcPDOh1VyxCIjlwmanMVoiY4RLJgGayWBtYpcCBajBi0vYdb1aAqfngIlBQUnZqPYoOf1aDlFYseidE6UxGjZJpYRx6McZWMsMa1wjvXoLNptAYJcJXhC0qUNcp6NQZGGtgyET0OW0d4sq8UBUgkKNhnWCwBB1kdi8SxoESOchNiztCgwECCEIdXDBEGLIZWCTuQzHmyyTXbpBTWW1Rina1Vi1aDpu6zJKk0V8GS1w+p4DsE6uJsIa3GEQQiJtIKeH7AWCOIgpycCPKmpiD7CWmKl6CmPvvLJlcEVKdLCWjEndntgBMLmpMyTyICj3iXEziydaIn5WodWIcOxBmEhFw6OAYlg4EHDUUMPmKPJTY+40MQprCBIaHtd+qKIlpa6I1jyfWI3HgaYyHn6oSUQoP0Oj5cz1mQNX1jmSzXOVaZJrINUkkxtFRb7XSLbIjKap/OL0NLFzzNOXXAxaeQPvYd2KM89smNAK2qQixVCaXFReHYYub5YKlL3hmEjFugVx1jzmgzUgEU1iREOystZq41xYpsPXkLsGFar2+iWtuFGy2zWYtYKDrlr6RWGaZaNYkbiNFkeSWhGG3RtFWkV3eISSFitFukHHRwJy85OlkKBrh2ms+1ZVgtLrDsGZRVIjYulFzYZuCGpt4mTp0irMVuhOpkcptaeK9dJVIKW0MegvV1Yp0Ps9tiojZE7Fqs1c2MFmuPjWDUcmxKnS+Ykw5vKVniJFQOQDl1RBwvCChQWrRL6wQZWaASCrpMinC6pq3EIWByXGCdFiRwH8BxLv9rB+qv0C2tolWL9RbqRz0DVUTZGEqA0SDSuzlmODPXKImcqDeoFgyTFE5plf41D5eNIYZitgi81jtF0lIuRfZaDMm0vQKmEvhj6T4ef3dJTiq77/ID2fWNzcZ6n7/0KL3vXz5CmazQaDzI5+aM/nLXTnE+s1PnpmW+P1u+mXf7Tg/+J/3D1f2As/M7s2vfCnefu5BW7XoGSCscpoVSEVA6v/oVfGXrR7r/nm+99/72ncZWgdl6Zl46U8KSE7jp84l1sHvjXrPQC3PACXE/x7v3TzHb7fOZX/iP+zil2Xf0c+aW/inVKPPDQx7nuTW9lV2U3f37ZL/FXbs5H83UAfvMLz1IKHF53+T8YIpdONUnjmLmdG1y++AIe2Leff1X/G8gzHlU34PXHENlf8ZC+hHp1jst8ydnDL2fgQmFhQEsGXPbE08y2NaXKV7juFVMEdoSvn8t5/dF/x8AdcGHtb6ls1jiPMb40U+NVjz1NuhLwO3/2PtRlP062t4ujXWZ6+8gvvpfxs29jM4faxhKXyhMcrk8jR4tUN2+iPbabMcfHTVrcVDrJM2Y7x2tPEay9jOtWniO+eJKpibPEpyscK+3lZ51Pc3OnxLXrT7OvucDp8/fz80c/xiX2JB/beDXbpk+iauewpTmePpnwjtMvYvxElwPHT3N6ehftEFb6HrcefoLPvP499EvbyRtn+an7fE5ONFmpPsn59fPZPXKGTnWW5nqfvngLM9v7NL/wScajncR+gfnI5cTd97Gx/1r+dWeJ+2yPim1TXoROZYl9g5M8s+1SOtkGhdbFnN02Sr9YYGbxOBujNexITmDHMbXT7NBjPFlaYnHuA9xw1QtZWTzDoxddw55mjBcVKJZP8Phdp6i+9a30H36YK15xK2fHyiz//u9/+ybdfgWM7OWCOeiHD/Oid+3m0dvPUl/+zsmGN9RK/Oi26neUOq7PdVg522bna18PWQwLjxFduY3B0XVMopGex+h73sPGn/7ZD8yivfO63dx7fJ3ZzR88gbG6LWJ8psjpx9d/4LWex788rBCkuUsjXOX42Pkcr1RplkZZ9rvkJMOhRTTRTpcMl7lgDVAMvBIbld20izHNMEUABQuOyHBlRixctFBoEeJIgxSWwAoMhl40Ri8cpS01xnp03SJaeShrUIAnLYVglmLWx9c5pycyBmENledoFBkSayTGdujLEKssnjRsVlfBcVAYfCMpoMmijG4oaEUJCSF96yC0RWmDIEOLDO0s4aiEyOmCFbQjn57MQBiKTozGIcbQjMZpFCeQSEKV0gp7rAcpOmhjvWwrphI8OxyYrICOt87qWJ9jZYc8c5BaYvVQmpkricwBA896EQk+7TDAeGUKrkPubQVqkBA7A5SQBHTIwxF6tUmsI+kHayA0CIOrM6QWWKeBZoA0eiiCMwKt2iTeGXKnTTdcoVdeouMeRyuJkT2MTId7wWuihCANumhpWCqlaJHgGkkpMTg5OBgcmROKFGMSVCZwyQCBq2IG1Sdp+eu4NsZxOyih8KWh4CTkWzH5mbBkWPpSMitnGMgiubLUa2PElTKJDElw0dZBGUFPBGyECwzEOdqR5VRVsjYmGIy0kDm0KaAE1FWZgVxnPG0SkdEtbMM4k/hW0Pc1z065zKntSAVLlUlWK6ME0qCkJXNbBCKj6uRIY2kViiyNV+kXGlghUAg2ZQ3PMXRGc5RjaXsdJmQH6wgwln6wwZo/i7KWRK2QC4Ow68huRt0EGGdArnI2glNUVJ/tsg0IjBUIJEuR5rHty5yYrJDLhG61yNrICC4ecdCk4HeQEtbCPhtBmzVTwYjhz3vGI3VjXCxKCFTWwwkTHGnoOQGrdoS2M4ZnBYmnAIHwlhDhMqMqwyudIXG7BM5g6GFjiyMWlo6ToAxsROvUi+eI3ZAVZ3Qoc5QSG3msb1OUnBhXGXreHD18+iIaxupjhn2BSAaBw4Y/Rq4y+p7G8SVZWGWzlGAkLJbGaIQ+j+ydZNkfIRYBmSOIAktfFPGNR8HpkykXLRw2mKAlatRVlVhptAuuY1gQo3QZVnBsFBuEDCiYlHLeQsmEdlTGM4qWV6cZrJFID8/NmLJrWKGGpfOepukNOOJXSNw6nfAMi6NHaXot2kHMMe9CYscBP8YWDEhDP2iQFdZoyoiOU0YKQ6KWyYRBiC2pZ6FF2cYYJyZ1MmJ3gCSn6zQRogkIMtml789RdRsUREoqMjyZkHhdfKHxjCHzFYcu2k3qV5EaMBZjBUdg6DzPAAAgAElEQVRLPQjWCGWX0E9YPG8Xz1wo6HpteuE6mWqRudCTCie3QI40PRqFTTYLT9IqD0iwrIkibqqQ1pIgELkhl9/BAvI/4fkB7Xvg/o/8JZff/Boq2yZYXv4so6M34nmjP5S1P7SwzktrJc4vfLtJ8A8O/wGXjF7Cq8979T97XWstd83e9U1Z5ObmvUjpkecd+snj3Pxzv8RX/+qDdDY3OL7S4f33neZ3bzvI3fUOrxytgNHw6ffAvpv4+nHLwVe+mmMPrXLJDdPkd9/NWz/3cf7ix97FxM4HERffSrdxDb2pAQLLBdfdAMCeo5/hz6dv5v1HP8gnnvskdz67wtuv3vktzOORe86w6RziwOrlLF28F1HpcdA2aW8sceZslfFdRabOfp5n3Ws4NfY4l45O8NUFn5tGQzbTFdSxWTb7Hmba58ylL2d67ad5NHbZ+Lrk3LaHUEHGp8ohSsCL48v53EzETPh5/uOffID5K1+Pt+MqzlW+DmdfwVr0GE4/ZLUzQ9DVvFr+GUfMHh7Kd3DP9r+n2IagNs6x6HFK4TTnyzk+MxYzMf44zmCK5979b/mynqCzo8fIpk9XvIinzGW8vV3nshOHaQdFHpq+np31Bm+a+DzrZidzssTCwgHWJk9y5KKQ8FSLuYmMG555kG31NQ7tfjk67fH4Tbfy6YunSXZsZ/7Kn2HnwgpBWuevyg/hdW7gZO8S+hd+jVZiaXADjhTI8S7W6bErOIibKubqFm/HWwiJ2ace5njl1fyC9xBSljGFOR7lCvp9l0Iyy+ia5sHrb0SEmoOnT/KQvoLw4Qq1uZtY9tbptQ15xeC/Yop9+/Yx9oLzUM02Tr3BxumzxC3FydOHqNz2JoI772F07/kcefTr9J944tv2av3AT/HGja9wYGw/Dwy+woEbt/PQZ7+7x+s39k7zXG/AJ1e/lZF7/M5Z9r9kO37Bh/1vgiOfwB2PcKeKDI4Oh5/qm28jXVig/8ij/+xr6n/GeMnn9S+Y5i8fOPsDrfMNXHz9FE/fv/hDWet5/MvCSNCyQxL0UNLgsJX85jRJRYBGUh9PyL2QXLicUTvoOyWU2IaVPpmXkZRbFMnoB0WssGiRszYBiReRIpAGrEgxwufZoIBRAQYxrFi2goEYsiRaOCjyra61PrFVGB0wCGqkbhkHg2synCxhXY7QMDWkkfT9DnFpAdfaLU8P5ECmEkIvJ5SGQqlLN4rBtvGThFwOOD12nEZ5FQS0fZ+2N/S1Cb1IT2gyyghhkcZQVyEb5YROqYefQ5g7+LnL0MKWY9w+K6VVHG1IjGQgEzQSIS0OhhSNsBLfaLQM8Y2hlKeQ5/hWM/hGkTYWzxqMSPCNgxI5AoGyFk1MoFOsVPTwCLKMhTGXzfI43WCUjcLmMDFR9nF1TmI1dTmCazVCDHCkJjAZTU/SFmUyPCwKxxpORZpFr46DxliNtZal2hrtICXICnjao2RyPAyZV6KqDMVgFs+2aBdH6ITDM0ZPOaRS0hIFApvhC00cjJBHZWR0Dg+LFaClIbWGhmPQYUK9shshBakXYJTDpjdCRxTJhcNA1Vgb2QbSkLkuYWwxysFWMs5MbbLVVAZAR4ZYs0JucxyhkUbjGY2ylkw6gMAIiW9A++MMRxCLUZpesEy3uIy0EmEljs4RGLSbExo9TGlEMqr61IptGqNttLtA4oYslnaThRWszDBYMhUM0/KEAy5kdpiCOSx8FhhhSZ0u2htQFDlaOGjqDKI6vsw4eV6ZxmgHJTJy6bA42iYJOnjWoectM/DPIq1AGY2rsq2/QE5uh8yYMoZy3sb3jpGILj1nnbZaQiCIMo0yGTbIqXptpJQUvZTM6bLm1lnx59BAKnu0KXPGrRKIFNdapEiRNqFVDlkc9akHCevVvWglwNtEqSYe2ZBZ9krMFzdouesslJfpOw00OaHN2WEHlPICcqusXQC5k5LJJRIUKR4ajRbDcnFPNbCeR9HJmKteSqtQYUQllExKy5OsBA3Kgw6GOhkZVkoyJ0LIhGWxjRSPTW8ZjcYREsfps1ocp+OPMx8K5gOFY3Pq4TS9aAzHWr4+c4Cz4X4eKc7Q8MdpFjZoFtaRKka7dTp+h+eKZXIp6XnrtKJ5djotHM9FS8Pp2gxZURJHA6TNcaRA41MvzdF0XNadOXRwlFZljnK4AEA/miP3l1DWgpMjVI4jBwxUi56MkfSGwSfSQJairCUuTqDDEcgsw1gUiZKCAENiXCp5xoSx9EKNtH3W/BArAlAxgTPASj28Z0pBLi2JE4NYYUq1SVx364lgaZQ2iVRO4n3vhOrnB7TvgtkjT7J86gTXvP4tWGtZXhnKG38YaOeav1hc55d2T3zbaw8vP8xds3fxG9f+xvclpXx642liHXPFtisAmJ39ILt2/jR7zvtFTp3+PXZceoCLb3gZd3zwj/mVTz7Jz924l/HRkEOtHjeNluCr/wUGddpX/yrnnnqCvS98GfPPbjB59HOsvPe9/JvXvZxNlfKl7beSXvbrpEtdHnzik1x729uQUkH9LJz4Mhdc/+95/yvfz+8+9oeI4uFvkTemg5yTz86RjqREB7dx/7Yr+aXn3kerMs3DtTdSjHcy5z5MxcbEpcsxTo/Rc2/gGTdnev4Q/tlnOTJ2GW61S6jup1N5CbefbBPu0nzssv/CDrfMnbv+lunODFcmDg9sc7hi5QSv/HTMR299PQdGbkSLHtnkcXZsvAhz/h3Ic29hJYU987czsaPO/Z2LeGpiO295SGDaDW5J95OJjD0z88z6F/CUn3DLcoN6QXB6+x5eFB/h3JlhC/1B4/BH0SXsyJbZvtalvqPA8UGEKcOzhYu4tfIIHz/5embP7uZLE0/x8EQTk55iZ/I2ntwnuaLe5FTN5eDTx1l1c246e4pDB/Zz92SHUxfs51335cj5ZXJruWrV5+qZxzgx8giLicNGfiPRvg6PH9/g2sp+dsRLLFZ8bv/Mx1m/ZD8/32rxmcIFXB4/R601Q3tyA60F/ZkyQj1ArTFO16vy3O6L2XXyWZ5SF6Me2447GEftvI92Y5q+U+K54+/jJS95CUn9HPfOHOSAV8bxPCrjJ3j8rtOM/qv30LvnHq676WbObKsx+9u/hdX6W/bqhxsHmXJ6vHv8Sj567KMcfNUMy6ebLJ1sfse9XXQU77twJ+89ucjiVqpjc63PuSMbXHbTzPBNl70Znvks6IzClRP0Dg3THGUQMPrud7P+x//9B2bR3n39eXz68UW6yQ8uTTz/qgnaGwNWzrR+4LWex78slE0Zj49iSMEarMhQVhEXdg8HAwnW9RBBCRBEcU5fGXC2EoF1hmOHXqx+6NB3h3vXOE16URUt3WFJb6aRWtPwa+SOx5mpKU5P76Trl+j6MWuhx2q1Rau0gdSbqESDNbRFBSsVURZTTdu4oj+M31ZDOVqUZQQmYyANkchBSASaDhEdESKVwhUZY3EbqzaphpLYqVP31xj4lkRpHt9Xw1eKXkGCEHSCLp2wTUKEg6QV9un6fbTbRjgJq3IPrhn6vRAGYQXFPCPKDfVghXWnx0NBlQQf16YgchwlkUIhgG5pknq5BuRIEnxrAIMylp43T7MQYq2gpdY4uldwbG8Jd2uwUeEckcwZBuSnxF6OEQ69sEovKCAQNIrbMdKjVfU5tj2gF+aYQoQrcxQKSUBqi6xXpxj4VaSF2HXo1ybJalMYNFbktEoz9ApD6SoIPEKqjk8t0xRSSFSHTjgMKYiJ0HisjZZYd0bYUMO6g8gactcj8Uto1cEzBtc4eDqkU5qgV9mB9EpILKPdNk6m6akBzVLEo/u2E/sBnaiCkRatMhK/goNFuSeIscxVLkTo4T1NCcicCOO5LIcbKKuRRiC2YskNwwE4DooIK1EkGDVPvbLBqckavWCE2M3pbg2bw9/boslwlWKguuTKZ340w5Cj0q+hvbNIAYGGgTRoaZktTtAKpxHAmSmftZrHZmUELPg2JZcOCsHpkVVWCovkImN9PGR2W4HE7WNUgOtoHMcitMUVfXp+bfglhhtg6RNICAUUnJzMk2A1YFHYYZefFfi5Q45Gy97w0LxV3i2EQYqhT0zbHNfCXLXEZmUXQ6EoaNsndlaQWHIcznk7OVa7grIcYJUmj2aplwRNb5UoT6lHG9QLm6Sij5flxG6BOBzD+BnK6ZE6HppNErlC32mgGHpeDQYtFXNjbbQwqGydVrCJNC6eGZY7lwJNFsyTu1VipWgLn3PBOD23gxIJSeRzemeNFWlJMQgraftjNEvT9BwDuk/HNSxVQhAW1yjKMsdKSV349EZ30SzPkKthqqURAheDI4ZVAquF7XTDKkiBksPy98Sr0yos0yltslkKUHIDYQJa4RKb0SRWSM5OwaFLymyUUvJoD66KaVUrLAb7GMQJAwTaZIQmI3NzwCFVmsxpMq5TNstLIA1K5N98/j8dapa82lYfniF0NKEVbDWzI7C0gmkaxR1oP8I6EEhNJC25Cek5TYwNKGqDY0EKQ12O0LceEotMLe4AInGavLiE4x4BtgrMXQc15rMy4n/PZ8rzA9p3gDGa+/7mQ1z/lnfgRxGt1mHyvMvIyD9eEv1Pwf9Y2OCF5QIHS98a1d9Nu7z36+/lV6/6Vcajb5c+/lPwpXNf4hU7h/LGVutxev1TTE+/mamp29B6wOrq7bz4bT/BF1c8Br0eP/fSvfz9eourKwXGz94Nj30I3vI3PHnP3VxwzYs493iDF859hME9d7L7r/+ckdP/N7/Wf4jf3PZG1r6+TLI9R5Nz4RZ7xsN/Cgdug9IEl45eSr70U0RTt/OV+Tu++RkfuuspGtWjPDV2hNV9t/DqguZlvXN8uHmSp5b34kQ+2zfv4tzkJRxKHuGSMGDpzE6m0g7O8td4cOYqTge7uKC6zLufPcjkZsYj15c4PPM1ptNRPrvts1zTuo6zxbNcal7FFysdfvkDv8Xf3/hSjl57K5DzTPEuypvXMfBWsNEa1Y2rWNQp+83fk0qPxX5OO92Dv7mNZOe1HNbziMVZrvJO8kC8nVvWboD7RjlVjFGPLlHdfppwtsRJ/3IunFxhdKbAswvnIYEjV0f08Hj8hinceJKXHvgy+4uPc2/nAi5cmeFVD99OOjJBkG6iKz/LG87ApWdO8NEfewdX9sfYs3iCw2NloqX7UNM3kV58Mz/5hQZPdr9AtX85ZwcjRDPLnBADsD/OiF2AoEHJA1Oq0mUnC3OGxembcBzws9v52sgr+MnOAkl1lh2DWR5wr6Y50DTLu9kx6/LsxZfSrRa4eGmBc/uvoz3xZfrT91G2Bc5tXoRUizQaj7J71y6SKw8QPnuCfNCnMfcs7cWdzG48Qu0d70B+5u/YfeVVPCdyGh//+Df3wCDV/PVjS/QP/iQvPn4vUkge3nyQF966mwc/c+q7DlEvHinxlskRfvqZcyTG8ORdc1x47RSFytaNbvqKYS/amfsID46RLfXI1oZR4bW3v41sfoHuvfd+X9fWN3DBRIlLpsr83ZM/OPPluIpLXzzNke/iv3se/9+BISN26khACI3B4FoHZSOEgIIryIXESoFUEoukbC1GgLEaZxiCOEzaUxt0S+czzPwyIHIS36cocirkgCVzQoy0LE5s48zUDIlXRivNZrFF6GySOxntEDrR8JCc2wzL8PDZLIywVtoDgJUuA9dDSYO0oIzEAdYL20mFIpU+fUIkOQrNQLaYr1hOVAXNUoJ2UuJgjKS4Ey+HsayClQ6LYxmNqEkmG2gy6kGZRi2iYjawDA9LvkpphREmABW49IIimXTxrKCSDADolHcwCBOkcEmkRDolPKWQUjIUCUoSRxGJeMimqB5qq/B+4EdYO3TzbEQw8CRWZqyM1hiENYTj4MkcLbdS5xwfaSxSSOKgRqw8PEeBcskdyL0AIRW+HBAHDoOgQiQ0x3ZPceiiPUNWUg+PfMoF188RMkcrqPaaw/AHLPXKNI3qDsJMgHFQVgwlXaqNtBZHCKbddXKRgZWkGGKZI8UwqRAsXpqD9bBWknseSkAkc3JSovwoERk9KWiUJuiMTtGOtpG6IRZLp7gTgDIG3yjm/DYZAqGH3XGZcGgXJukFo6xWd5LpIZ+QucXh2GEkCgcdaZqlKlYOg/IHboJw2swX9vJs7UpSN6Trd+j6fWKvROrEeFbjJl2qaQNHZKxFPQLa2MI4G36CpY+WA3ITo63A1TlenhKECW23wQAXV2YsyBo94RF7NfLSNCtOlSfCIk/u28HZqb0kroPOcjLRwzMZfX/AwN0EIJPBFuMsEYDjeWjVp16eJPcLKCRWJGAEWmpy5ZMJB7N1jcZCYdE4SoFSLI/6tKmgrYO2Q5bEAirL6TuWWHVYjxZAQM+N6IuQoRlJ4gGxAmszsJDIHrnMhkOtzUiDErDlWwqKbBRn8C24JgehCTOHgtZYY7YKwjXS78OWfM6LM3zXYhxJ6vikToQVlo2wQCxy6t46a+WzbJaWcckZhA6pdwYhc4wMSJ0CygpOTPssbovRbshq1SF1guHNyhGsOwbyofzSKJd2tA0hwHhlzFZsvsSipcYVMVIJBlGVejiOlQLPupRtTJhvwtb9LWF4TWZuCaSDdgSu1igsvmyzVm0Pu+HcNWJhcNOEdmGG1fIuTtUuIbGWvtOlbo6grSGQA6JggVhaejIgl5KWF7FYzuhGBRwMBkUpNfRru2gXJ0mDUWJXMRcFDDyDEopSnlPua6zbpVneRt8vI7auyT4K4YCDxs8zPJOjDAycGM2wW88xllE7wIge0n7vEez5Ae074Jl778YYw4GbXgXA4tLHmJp847cVO38/6GnNBxbW+OVd386e/d6h3+OikYt47Z7Xfl9rZzrj9tO387p9rwNgdu7Pmdn+zqEHTbrs3fsrnD7zh8y2Yh4pHuSlS3eCzvjcaoPXlzR87ufgDe8nK05z9J47OXDNjZjf/RXKQcbuD38A7/5fgvJ23vDaf8d5rsf74g4PH/s7rn3jjw3Zs94mPPFRuO4XALj3uTWajWl+69o/5Hce/h2+dPZLLC4uct/hL5GRMHrRJXxhM+bfLfw/9LdfySP6QgqDXdxnN/kR9yFk7Zc5M3aIVy5dx+1mgwvbT5Dt38kzzqVc40dceuJyVi66i1vbD3PviOShwQmW/DVetfQGdD/lEr2dWdnjF/7ivzJ4geXPbnknbz8Vo6Sit/0+arO3kO6+C33uFhZS2LN4OxOXNHmstYOTe8/yko3HaEYBrzYX8lz9DCPjPqFs8dEdJ7jQrHLvi17ONn2E0U6Bs4Mupb5HSVzK+Xyeg/PzZMczpGt4XC3zmnMP8pnCq5AqIQy7rE7eSa9YpH92B6PLszi5y3WH/pp22GS+Lrnl0FN8cXqS0WcfZV8+yoEzjxEmGV85P2N0z4/ysZcFXPCVRwmOBtBUXLHzKJ1Kl5Ypsxjv5/KJv2Nw6FNcWbyakUGLKHP5/ftu58SeSX6m8TSi8gbeFj+B1S5UVziR7sMdzfDSr1Brdthe7/HgC17EvuMneMZdQX/xfow/4GAxZ2XxACjBiRP/neuvvx4dJqwEVa7eeQF5ElOdOM3jdz7HyE+9i/5jh7jqwAuZCx3O/OmfkG8OH5CfPDzP3vEiU6/4eeTcQ7x9+0v5yLGPcODGGXqthLNPbXzXff4be6fxhODXnpnj+CMrvOCVO/7hRSHgwFvg6CeRvkN42Ri9x1YAkGHI+C/+Amt/8AfY/Adjv3782p185OG5H0px9f6XzHD2qQ26je8dufs8/vdGLh0SFeC5Bjm0pTCQOUoIpHSQfg1PCqTtkQqfjZEdWBEjDXREhJUSpSxGuKRyGPvdLe3CFQakRQo9FNIpOTz+ieE3vXJrDwosiVshFy4b4RQWiTQJrqupGIXBEDt6+D6/iFCQuUV64STr1WmCQomKJ0mFJVFDdiIWPvXKbowdGry8PMZP+wggUykF0UALTepGFPKYTCoG0tAJJmkXqggBjsyAPs3QI/Yyyr1FlO5iJHgip14tokcKhDVJzx+hXp4gshsoPWTJS2aA9WFzZAyjBAOvQLdU5dSuSYwEHItFojDkwqKFpVndiXCHfUPfOOUUU4dAu+RCk7lrBEohRYwvOzSiZTxpiIQhNAZHw8CvYrZkTlo5lOhQljHKSYmEhwkr9D0PXwg8YZFKYWSO3FK+SAy+0tQrewhlinE0whG0ipWtTjnB3l6dfjQ8gH8jEEFgCU2OjDdpE2CFQDugPYWUhqJJ6UejrIxMo1WKEBmBSCjlKQqwVjKgx7mJ4zSrTRxrcHWCRSCFHu4UaQBJ4qRI6RJaTZ2AvLKN9cpejlQO4mU51uQ4WrPm1Ojjkct8a6+BtKCjLlUH3NyAzPFqgsAOB1yJQAhN7sacGy9xfMoQ+xGGmEa5iqlWKCV1YpuhPI2SHlZAaZAMvygADIbQaKRbASHIpaYiB0RqQEcGFNGgHCyWBVVmXU0hpUJELtoPKcY5YR7j5ymx1OTSIRCGRmlqOLiWJpBSsVGaJpNDZlVakBoS1WO1uEY/7NMubSctTGPU0HucI9AYOqUpGuVJOv4GqRsQux4DJ0UKyYZTo20LrFUFQaaHLKPNKAx6CIZfBrBVr+TRJLIag4fFIzIax+TDyoq0iwXCQRdlDUIYMpkTJglulrNQK6PdaMjaW4NGIZQlLoZYa5BJD+15DKrb2Zb3yYGcDKN8tJT4aUIlWSMXQ0m0RqOkZeBa2sVdCClwrCAThnLepJz00a5FR2OsF7eTBD6Z6mK0RRpwgXY4oFvsIRw9/OLADmsXrMwxMqUfFOiGFdphBSsMIxZ8cjI59OqmNsZgAY1RikhotPKw0sV1Y5wsBavxGDKwJnDw42RYtC7Az2MSVRh+vsijXtvFWm0vDhYjBYlwcP0Q3zoETkoc1jC5QKNQDJnijWgbfTm0IKWySUxMjoH8LCiHTnEcrBkyrgK0M6BZO4/pwhqhSIcsnIA9cYvx/lmKIsXzm2QlSUdBLBv/6AD2/ID2vyAd9Hngb/+al77zPUilSJJ11tbuYGbmHT+U9f9mcZOLCiFXV4vf8u/3L9zPV+e+ynuve+/3nRJ578K9jAQjXDZ2Gb3eGer1B77lc28bvwXXrfGRr/4RP/PSfZw/6vHlz/8dj7V6vPqeX4Qr3w0X3spzX7+PbaUq7X//f2JqE+z7o19DfeINMLIH3vQXCMfjN9YEn9vucLIcceH1W8ziYx+CPTfC+FDO+J+/eIxdIxGvu+h6/vjlf8xvPvib/PYn/jNReyeP7n2UbuUNvGU0YNfTH6HcbXHJ2o30M8Xu6NP0izU+e+QMjsx58mhOV7pMThue6F+Hr13OK5zjjqv/lOu3X8SN0UNErePMj93EaxbeiUpjzhZO8KOtG3Hv/28MZop8+K1vRAEvWco4YQ+x27kEo1P644fYufQjPOrlHFy8A6dgeGBQ5QZGuHb9KY6XL+YRHTPdmeNH9nX4vO9zy1iPvU/WuSu7gJKMUf4i2TP7OFOaYqbQZzkZp9ztUm4lODtyfupMkz3iGZ7t7iMYW2WjP8Fo44VMT9zPE8XLWIumcTbPcGqyih05TKFfxym9GGEM/y977x1u2VWeef5W3Omkm0NV3bqVSyoVpVBSKVgIIZsgEDnYgG0wHrCNobExbnDo6elnpsfdpjHGBLcZq00Gg0lCIgskhGJJlFJVSZVzvHXDuSftsFb/sU+VJOzGbsEz/XhG33/3nn3WDmevvdf3ve/3vh+4ZjOXdKcYTSWd6Yu4beCTzIsOm9LncOMLQ7r33sjl2wdwQoH+Pj8ckHSLNzDlthI/e5rVchmD+Tynk3GesyvhrxuvpK0V3UNf4FC8ic0tRXfZDEs7R9g+sJ6OOsTpwQ0EzZXgJY8tXcmSM/s4pZegMku08YeE7ZQd7c0klX0cOfIw46OjbF+zgWDXIaTWtE48xOldGzkycwcjb3877Q9/lIte+BJ2rV7Gyf/yfhZ7OR/83i5+7xfXQjwIl76Zl+1/kB0zO9jd3MWWG1Zy91f2/A9NnI0U/M2Gab5xcp49vzBIfeQnTOM3vhp2fh16iySXjdN+4AQ+L6uJ9Ze9DCEE81/5ytOaY2fjBReMc3KhywMH/2k65v9MVAYCVmwa5uHbDv/MYz0T/+tC9B2KCm2w/V6QUOVgPe2BIdJKjdxKJDnduIrQObJIEdIhcUirmM5mEKJAiJIO56RCKkVhNK7agzAgr8N85BnodAnznDDLCeiWSnZClYhcWX5Haom3OccHlyF5DC361f3+O8YHCVXfRHowcoCTjeX0ZMGihoAeUucl360SYWVB5FMO1OqclW2cbO9nptYgzDPiLCXutigEOCEppCFTAVWRMZwuInEsqBOk8iCxBukcjoKF8DAz8TF6xmCLlMJ5WlGFU4MrgHJXMi9Ap9gwI04zMjPA0dEphOwgdMZsOEm3tpTZZAwQhEWOEOrsD0MuJMpIQsqepUy2aNLFRD2M6tFKBssqvdXkKscWmp4M8Vox22iQo/BYjo60aCYtzlSmKITBAd2ohpIeKwvy2HN4oofwKQjHiEkJZEYhFKFoMxjOkdkIKT1eCo6PBCxUGjTjEboiRdGmG1RAehyWjooorGWutoa5xgSh66HxNPIegWijFQyzAHiQ4ET5nMtUhAw6ECySuAWSbJEwn2deVUvQQ6foKCC1TeajAawrUQstCnRR9n0l7TmivEckutRti9x4vPAgDGGv25c/h9DlhHmJdjjboDRuFgT957chRyqNdV26tsbx2iFctIjs901mFMwPriEJuoRFhhaCkcU29XYbhEcoQxEM4ciIVEYke9RsSscHpKQYLML1WMtOhuRpJB0iMYf2UBhBpbdI7lS/+0fi0EiRY8ixmaAdVQhdD2vmiegRFpJ2UOd0dRlOOlrhEONph1BJOtUxjGsT4tHSkfatBSDHGcNMXGdRdTFesmBLHza8J8xz4l4P7Reo6hmGfN0LYbYAACAASURBVJvZ+hRzA2uQwlHIWZyOUaKgWRulVZkgswm5DpAUSMBrjSw8cZ7Rq02jBEQ+o+M7zCQVRhZOEhbzeEq5f1MKj+JHGsi6wUuPFQ7py3ukiC2FEoS9Dl0Vc6wxRdA9ge+bYdtCIWWB94DsYUSOdh5hLMN6AZk7oswzH1dxypEJQSI89bxDKy6YqZfMla6JsaRUyakyj3KlpUMmc9oqZM420MIx2SutGkzRw2vPyVqJ8jopkVKQ2oBQZWjhyOQAuRAgSnTuZHUN949cTV0sUM1KJDLXAV4JeoMTJD7FScHJ+iqk0gTOkitDpdXG9gWAvBAUZHTcPMNpixiJlIbYdaj2ZrFxRmYjctFjPgjo2QQvIZQ5UZaifYbAI7RDes+xwVUIl1CohD2DG2gnIxwenmZ2sGCpmEeSn+X+/g/jmQTtJ+Ler/4DoytWMX3hJQAcOfIZhoaeQxhO/jPf/OdjMS/44MET/MH0+FP+f6J1gj/90Z/yJ5f/ydNSbTwbX9r1JV6x5hUIITh48GNMjL/yKaImQggeb7+BS4a+yluvnuC6N/0Wn9+9j8s7+xm0Bq79Y7z3bP/i37Ph7m0cr21g+TtehPzEC+GCV8ErPgZK4zo5A/ec5IU7tvGNa15GhoCsA/f+DVz5DgC2HZxl7+lFfve61QAMdYe44vgV3DdwH3sr99OrTXB/O+T3Tn0DJi/i7rkxqotruC1s8XrzHeYWXsHO4buZ2h+za/k6Lhl8nLB5OQ9ldd6wJeI7kzdSyJRnbb2Ar8zFVNp/Sze+gpaoQmeONX6C9La/4vDEFPMvzbhL3cAFc45IeI6Mf5X63hfTXfpNioOXMpMpRk98m/FL5tm2OMFdS0Nu+Oo+WqHgcO1ZfLN1iCyqMDp7F/dWIq4+k3Gf+VWeEx/mWBjRnrqDkVOOE+F57FkleMyvZMverSAECxsaXMs8t6+xDKkFtrWm2bnvEg4sbGJOb2XLmfv43vB1rD42y/GBhOu+fpRZ+bfMhU1e//3j3HrJ5eysdLlocQi05CVHb+Cm4R+wdOhy7liTcu9160hu2c/cPUsZX7uTbyzJifV6TqZLmKsexB+8g6WNjcxnMdl8jZkzI9wxmfDs7Dt01/02b209zrx9nAqL/KB7FYXvkclTRK1ZrjgzyM51a1hy6Ah7Nq4hulvQMbtYYy07Dl0FQrD/wN+wefMldFcOo3Y8yrU3vJLmzAlqI0fY+u1tNF77KigK1hWKWS3Zc8dtfPa/fZ3zJ+v8wpr+vX7520h238orll7Lp3Z8irVbxkEIHvspHmGVnueXf7DAV8bgyz8hGsLwahjfCA9/AbusiqpaOttL5E4oxci73sWpD/4VrtN52nMt0IpXb17Gp+858LTHeHJc9LzlPHLbEXrt7Ocy3jPx/35I4SlsQhoPl4VxUSIWRucQFgid4mRJa+qFBq9SvBWMyg5R7pg2s4Q2oJL3iHptOsYRyF4pPW1StFRkA5peXMdYQ5U2iciIXEHsuiRZEyU8QgikKBMgKSUxHeKiQysMGU5ncbp97pidsNgMBtqLFK5FrhdxieF0ZbpcQJTNGLSSEdBlf0ameiBLn6L24DBCq9K3yGVExoGSGOcReGxkMGHEqcpSUtUm6nVQqjy+wHdYMJ2yUq4cM+ZQuV4RBQvxCGf1zUSwSCrnGCpOMerOUJWL5bHjGTCLDIZniySebtDAkKOEZyLrYNMe2uc4rTjZWIPUgiA0DGdzZEaWwiTaE0pB3QPOYf0iS7OTDPXOMNBbROiyFB63MwbzLtLPUM8WceWKn9yVXmH9o6VrMkzeolK0YbBOqhQOiQsFZ+pLCIoM6OJwzJkYIz09W6FrSy8oaTxKplhfsFgdQyRjaGnwQhP4DFsUGCUJZYuKnMdVR9E+J1M5eT9BO9lYQUfHeCkIbMaI6zFVzOOtIDMOKR1aeQayDoicU7VhBBC5HsJDJZsjSHrIwEDN0q0tRRp5TolQUNI2nSxvC4mjmjeZcE1Guk3G8g61oke1SMltiFGUVE/hGU9PI8OyiKB9xlmoQePIhaOj5/s+bG3i1iw2y8i0wUuBFgU9McNSs0hERjMZxVnNQN4kUBWCeA4tCoxwGFfQbtRK6q5wFMJRGIkCCkp6Xi1vUum0sVkLkFSKRbQr6IY1ejamEILEFQzrJsu78xhyoqKHLhx5VCOTrjQaN2281WSiOAtsszQ9BY0hrOrRqdY4OrqCzERkRtDIU3I8BB102CW0Gb24QSBaKJ3TCWocr6yiVxulF1VReE42liCURzqJNxavc2LZYtEeRwhBpdchkF2QOY1sgSjL8QICm9FI53E+p+NzNCWapbWnruZpJaOcqq/Epj2a8RQ269EOBjgTr2bAzSGdo5a3GezMIVSZFOWuVJyMKBHVSPRo0CXppsRpjo9sP1mBIqiTO8fA4iKR6xB3UzrBIEjJBIIj9TU0K0NomTHYmsPLOrm059Qfu70uXsjSakNR+q2FCR5Jz0YsDC5F4Cn6nnM96ci8pxU2kEYghWcknWcwbaFNipcFOZ5jjVGM9KBBOM9ibZIk7ZIaybhpo3WGVzlCOrSKqMiQZjLEQlxn0YZ4UZ77Qq1CNe8S+4IB1wIhWZ6dpNpp0gvqdEyAUIrCQaFCTByC11iXUcv/adugc++Up/ku+v9kLJw+yQPf+BrXvOE3AHCux+Ejn2bZsjf+XMb/60On2FSNuXLgCfQscxnvvv3dvGD6BTxv+nlPe+x98/u4/8T9vGTVS+j1TnD8xE1MTf3GU7Y5dKbNn91aoVG7gOPH/o6hZcvZsfEy1j3wfXjVjaA0h39wK+t+eC/FVS+mfnHIxAPvgBv+Ep7zb/sUDFi85xhZveDCPXczUqvygf0nYNtnSoRt6nIA/q9bdmC15CWbJtm3bx+f+cxn+PXn/jov3Pkb3LnqNorK5bxxYoCx+z9KpxDcu/h8FguNXfYRVmUZ329uZE/9UTYsHeHR7iTrD23iVqNoVHvsSD7BdDzNu478Gn+85C/ZUzmG5TjLjhzlh2unmBlc4FU3H2b36BCt66dwQ02OFiGv3tPlUHGQ6eExRBbQXPkjhg++jv1FxnO2f41oOOW25ig3nK5xsFrFT64m0QPMy4TVaxSHpOPCJT38zgs4vaSNC+tsXvwOD4sdKCcQI8u54NheQBAd7+C1JZ57FrXRLsdW7mdicBc/PLqF7pkxcreU9QeWsYGDDDbn+PvVv8T04HqWnD7JHRdI7hj9EOcdPMWK4znvW6dp9k7x7Px8TObZGSxnqhhhqjXF5zYcx2x5C1d/ZQfTu06yZvEh9oQ9TqW/zMSxb1B77RVcbM5nRfcgj0+fx2/c9kVOjJ3HQSNp7/oBiV9J5HqY0XmqvUVmJxoY833adpTRsavYsHMfD6zegGsJwiIG71l+wQ4mT5xml9vM2Njj7N69g+rIEA9NrCbcdYjayBitE3dyaufFHDv+Hcb++I+Z//BHuPKlr+bRlUuZ/LsP8b9fv+6JG7MyApf8Or9y8gjf2v8tZntnuPylK7n3pn3k2VOFRc7Gtu8e5LJlA3xi00r+8LFDfO7YzFM32Pxm2Pq3CCC+dPwczRGgcs012Olpznz8E097vgG87rIpbnn4GHPtn/6Q/ZfEyFSViVV1Hv7BMyjav9aQAoq4gjEFqBzpCyLXpS56SJNT6A5B1sEZSyE8jhwRCCpWUCPDqB7oELQhDxW6gEg7GuEiSgkCVzCYL7Cie5iAFKW71GhR17MMyQUa2Rze5NTSHtZlJFmbMX2a0LRRFDSTMcoVThkOCQKcl3hZJjyBSsvtEHgr8UoS5x2qok3iF3HOo5zrc9wyhBNUhCCVPVzUY6zWpWEtI1mTiTNnaGRzRA2JjwxJr4fxDhdYjCgQwnG6MYaWoEMHIkP3URcnBVaWx6YkLESLIB2J6KKUI1RttOqRBAVIiXCOwkuccmglsKpFFgomxRxV0SLMM2q6i08GyMMKWE8vaJBGSdm3I0EISeFmcfkiXnQIRAvhfEkjxVFk8zSiY0QyRfbVIQGM9AjhseUVJQlzjPY0VBdIyIVCCkiMY7qakijHkGgjpSLzCi0dLu8iTQ66oC5n0KHAYahnc6S2CpS9i1ZkBKbABwE+dcwnyyDwTBYnEUZiiwyBK1UcoyqnqsvwEWBBiBypSrpcBY2WbWRwkpbo4I1EaJBSoiVUZIe6bVMS1iRFCJYUJT0Ix0Awx6A+DTZHxr1+L1WK9gUBjkhqqq6AAFrVIQrbw4UGLwqOjFyItxbvBYHJQQq8LI17jw7PEiQFI/kco36WYbtAprsldU329RtkwdFkDfWaYtg0qWuHVZ7CQxE1UWR4DNZnaJmD8nip8FYQxx6iFAKL9A4pPR2bEODASJwXKOeQxpW0VOVxUoKUJGQMdxeJ8i7G5xgKanRKFEeCRuK97yN1gkwGCCmQOLI4xiQ95hrLmTeWnFkQAqEc9aLFcG8WpyQV1yR2XbzSYKGhMvS5+SoI4w7SpgjZF+HQXYRpE6j+O0iCDyRCCaz3IEpkSFLSpE8ngwyLDnWRUykWCIouUhW4QqCzlFQGqMSQBnVEvz/K+gyvDQ1T9o6ZWgchHAQ5Jskw0mGkpJOMYV2B8J66a2MsFEainadIyl6/XBmckDgbEricSLWoi6ykdfuyF7MVLaE9UAfj8NpRLVLw/fOVCkRfbEkVeAUqcBhyikgjBQhXGqHrvvpoFhcMRKfRSQbSoUUPEXWomVlst4sMMmzYo9ARGZT1AmnwUuKVwsvS7LxplhKHTXpBlcneXMmFFWAMRL5H3c8xoOaoyC4nkw3IAgqzSIcWqYLCqRLwU4pch9SyFoX76UXiZxK0J8Udn/0E5199LcPLlgNw/PhXCYNxGvXNP/PYp9Oc/3roJH+0cuIp///A/R+gcAV/sPkPfqbxP/7ox3npqpfSCBscPPi3jI48jyiaOve5954/+vLD/PKly7how3s5ePBvuWPXnfS0Z2TfcQ7sPUp64ABzf/CH5JdvwYQPsin6MuKNN8P5T3i/ubRg8UdHePDorVz5itfygfOm+NjhUzz046/BVe8AIdh+dIEfH5zjDVuW8/iO7Xz2s5/lJS95Ce5kxMhsg8KMM3f84/zizNfwKuRbBy/CLExzbOIkbyx2sqO9gh3RfhIbcKb9aqZTy0xjjp0oXnXNLM0zc1y//VL+YuATCB9wyapLmeyOcN3BHRytDvC8++bpCcenX/NbZEOf5yv+9wkcXDNT8PjI55nY/woWl30TDq3CFgli9rsMXTjPQ61h0mHBNXds5bHhESbVeuba+1jQNdbre3gwrrJuoeBv7PX0TIfkSBe39hThEUMzWoOtjZPNLRIvNDk+OM7pZSO0H3+MGRXy+jOWdcu+RsW0uCddxgvPq7H+QEE7ULz33k9w3/hGTuzrYTa9lF+Yupi5qIPkmzx/2yL7B5ewe9U0lSRBR8vZfKrJo0GPK7oXckzPMrMkpbjhUkY+A1fNfoFPrqkScRFKGh48sZUjwSn2TJ8PcxlHz4Ss37uJm8drrGt/hu75v8WL2nM0h44wXpzmluKXmG/1mE+GOHBiLyt3PkTUbhN4wwPZJpK5gm71HiYDx48OPB9EQbP5Vc4/bz1z65cz96Uv86J3vJvFMycxZg/3f/dO4i2XEl9xBQP3beMoIW7IMvC9rz/1Br7y7Sx97DtcOfQs/v7xv2fFpmEqAwGP3PaPhTg6iymP3H6ES180zeWNCp/ftIr/sOcof77vWL9BGzj/JbBwFI7cT3LRKOn+BfIzXaBEkkff9fvM3HgjxdzTpyhODcVcOj3IPzzw85HJ33z9NNu+d+gZ4+p/pSEEWEoPL0NGSA8jHKFQjIQz1HwH69vkpkrFZQwWiwxFLbxqEpQrC7quiSDDqBTjLUjPYN5ES4cXOa1I0gkCCqXxQmDzHlpkhDoniTvEtoMMKhgJVqdgcrAJXgmqokeg8zIhsV2kzEFJnBPkuWCaTr9viLJqrBUoi1MaVF8AIDLn6JGl8VjZMyVEjvUZx+IVqGCOLD4NKkVIQdPWUVb3qfsFJ+ur0SonVh0qeRNcgVaWoXSRQHUR3uEkSKWoph0qdAiiAm/6tMw+IjImW9SKFmPFPEKXVCgQzCUTeDIyAQMmw8cRgbEo4QhkgZQFge8RCsWAW6CRLQCKWAkSmWLDDoVMGdAepXKUydBBjjCOdrcAXZA5jTMGLzyBihhOagzrmMA5IpNiVQ4KtLc4J4hFk6EwJQnriL465ljRQokyqZOmoBAWDwQ6JRtdg4grDGhHI2tSmBCpNU4pnHTUBmOUpETUAo0WkjDPGKj20BZMVTIdnySuLqI1eC3wRiIleCXxWjAoZnBpQRD0yGLJMk4RiybK5AjlCMjBOrz3hFXNeHaCyXwBQ4GSoJVHKUh9waQ6yUgtI5cZyBRpBHkkSbVHySYDbrH8/QU4J3A6QSSSrFIh0AblFdb0MEpggDyW+CEDQ8PYSFHEfdTU5Xhf0EpjtDRYC1Wt0MqiZL/3TxQgFaOiRc20SYWhkApjeyW11DpcoIh9ig1yhAaUZ7Y2hSxyKqrHEo4xqk8z1xhFhW3cQAimtDDPQ1sm6XhGFmcJRYGUAqGbVMIOWI9XgjP1lbgoIRAeKwV1kTEh5/DWIk2BE4qgL4QhvGdAtPBeoLzDAUIXGCtJZElZBBBSUDhPI5ilrruAI/FdQtMjGnAII9DKcZY7F6i8vO4ehuUMtaBLEcaIUKO86ycjEKOoSUnhHC0ziDI5kbQ0fN+g3YGzimKgQlAVCO1woQYjSOiglGFQO6JKQS2aJzElE8TjWaRDXS0yUGviC0dBhyLv4QuHczDgeoS+n0RLBVKidYrQBpQiqsziTI6Wsq+gqZGBZYzjaN1GSLAmw9oeQkAsMlQCge7S8DlFDq4qSp85X4qVVIouUkEzHkZph9QFQp59rpX9ikWa4gVolWODLrHvMqB6jMoOyhSIICdyLYZ8l0A1iUwbUbF0wipSaoRSpa/iwBi5hpgUj2Mh6iFGLMaC/md0LZ5J0Ppx9PGd7H3gPq589esAcC5l3/4PMb3ibU+7J+zJ8cEDJ7huqMbGJyk3fuHxL3Dz3pt53zXvwyjztMc+3TnNLftu4dfO/zXSdIYjRz/H8unffso2//DAEfbPtPj9562lWt3AcOMqPrrnQd4QtbjmV97Ejz7yAfa94Q0caYSsXvU4o9FR9O/cDhPPeso4rbuPkZqUmeIY66+6hlVxyLui07xz6W+SrnkBAO/71mN4HBcHx7jlllt43etex/nnn8+Pv72XUyt2Ypa8k19Y9Vbes+O/ct/CFexqraMVKQ5UPsxzz3TY1X0Z+1d9l2vn38S3j9TZEJ/m2yJmWX2G7Xtu5eWHruGvJj7D1OI0f3rdv+PWXd9j09GrGF8c5t2fuZHozAL/8TffxbXtW1gVwp1qmquOZzTdHFMjw7hUs7D8e+jdv81dumDjrq9Snehw1+wU8eNTnByN8UGVG+USrmzey/Vjuzmvc5xkpWP/zmsZETO0h0cYTu/mbl2w/GiVvUPncfHeLja2XPf9H7D8wEHOjFX51hXP5hvBtbwoPczWbs4143eyzw2Rb7sVHyase/Q4rYrnV7q7+Njaq+lOXcOvTv8uSa/gR5NHSI5+gqsfWeCj66bRi46XLywnDRX32Xt40ZnnEmcxnx++j1i/lG+88DzWf3GGFY99kiCIeKz9MtYc/iL/9oVXsaJ2EWtn93CqOkLvwa2MLbmexwPN0QOfYm13lLZ5kEZFoHJHdyQi5laO9brsm7yATfc/yBljOCrHMXsqmNSz9vzdnH/4GLuLTSybepQdO3bQWb6UTpZjDh1nyXkX0Dx+O8cf2sjx499i7A/fzfxXvoZfegkHRyoc++AHyY48KbGpTcLmN/GG2TN8fufnyVzGFS9fxf3fOEDaeWrCsu07h5g6b5ChJSUSfXE94aaL1/DN0/O8etseDnVT0AFc9Ktw90eQsSHaMERr6xMoWrRpE8mWyzj9sY897XkH8PotU3z23p+PWMj4yjrDS6s8evvRn3msZ+J/TQybDoN5i8SlnKnNo0eaTIqcwThBCYdAopVhabRIPeiCdPjCE4aWXFXoBRnCpyjh0SKCQoHXjBXzJCItaZTCglSlOloA9HutpHJYKTBhhSiyRDGYeBhkjDIOMVhBBxotDaEweOXRocVEPZSW9FTWT8gUXhYM+hZCgDEFgSp7zmzUASmwPmN5oBBIIp8SyxyrBJmuAZ6q9KigC0FAqwgobIhViySuTWwcoS5VV+v+FJFoARnG5kjjCE2GNCCVLPtolCROPKEthS1y6/Cq/0yQeUnNy3tYn5G4Li4sq94+ERQDEu81WgcUTiLz/jwVokTzumX/yUC8QGEzRm3GssAR2ggRaaR0eCewJsNEXUwiCYzHKoFTlsAU2MpJJpVio1T0oh4102Go0iZQkOcpUoHJU87aFZfhAcGA9Azni9REk4poExY98lwQ1Gv4wiOL0u/LZQVa5CA8hS6Q9YxAVdFIIltBSs1wWJAiia1npDpHoSxCK8IQwqhHHlsGXZdQUqJWAuqRJpYpKIHRHtlPBLwTBAKqYUolyFASiCVSwEDeQliJkwFeGIzokBpoDAxSGEstXqBjTxFVOiAjCKslOttuMSbn8UYSGodAoHWHUHWQolTkjMICkgBfDRFRQqwloemgfcF0ofFSMlzMMCY6WOkI8w7SOaq5KxNmJekZhbMhUijQFiEENZ0RaIWSOUO0qIgFpPFYC8oUuKTHgDiDTDMQBVo6ApVSNd2+r1VJCx50XRLVo6F6DNoc5XOMVaA9NsgYTmfJdEYadGkEKQOJp563MXgqxjCsHeOVBYoaNCqlKXzhFYWX5N2yh1V4kGFKKD01Mc8ACwQUhKZLhQwpweUpuVHnRIKkl9RDh1CCUDzB6BCinD9eSmLXQQlwWmFUFysdgSzXnUp4hpL5vk5NWejQNkVoBUKicQT1AhnlxF6B0gy4JpGSyMgQVitUA0MczRDpNnWTYqRCIIiCDjZ06KDFlM2YdCnT+TwNkaKFRhgNQjDSGEfVhkEZpPQQCUYqgrnKcoQosLoLUhMbjdZxeXJKoC2M56fQyqArMboWIKXDqAyFomIs3kEtSiikQQpBLcih8KAEA7JJ6HpU3QJCOsKgQxEsIIOCwXyx5PAKUFIT6i7YiLoQVMjRGvCeoVQjRJlkomskKsQbhbaCfKiBdBJpJUJnFL2cQpm+yNMTjIZ/Kp5J0ADvHN//+N9w+SteS1xvAHD02BcxpsHI8NOnHZ6NQ92UTx2b4Q9XPIGe3X74dt6/9f18+LoPM1GZ+Cnf/ufjo9s+ynVT17GstoxDh/4bg4NXU0nWnPv8VLPH/3nzdv7vlz+L2GpwDrG7y4+Ci3ntpos476LNnLf1EU4NxVz97COcmBtCvOlrJfXsSeF6Oc0fHOLHx77Nla9+HVIp8J63bPuPRMkAf3noNLtONLl711FeO3iY3Tse5c1vfjPT09McePQYncWCm1ceosUYf3Rfwi/Pt/j3jcMEnQnk9F385o6CTIfst0toHN1Cc99qqspx2ApaueTVo0fZMLOC/zL6ca44cwVvvey3+KvtH+Ty45dTy8dYd+SHPHf7/bz3d97DfmtJRr/Iw4+9mFQpfnVvhweHPs+Kgy/j5OqbyA4uZ8o3ONW+n/jSOR5ojTAmL+YXH9/Ktokp0mQjJj9Ew7W43NzEo1FE9VSF++UqjhQRbz36nzAbd7Gwv8K8bnB60xi6aBEu1Im6XSSSyX2HufjMVo6wmm+753P+vGHYLzKWFOyaD+k2VvCsE4e4fWPE9Xd8m/MrPf6cnJN/9iFem1/I1jXzUMyw4cG/w/Q8H5oMmav2CJJLcHnKnuA079z57/h+sp2skFyyMeXmF13Ey2/6Pluzx1G99QyYlOfu/h6/1pllWbSCWVfjyJyl+SPNx4cHec78PTTHNhOKHnJolld3vs131bXMZ4c4U1tBN3oWtU6Li+67DycF9+/dQiYziso9DAVt7t1/A54W9cajrFyxnAOrV7Hzxk9x3Zveinc5vYW7eODWb3NnU/GV9c/l1/bczfD0Sk5dtYWj7/0jvHNP3GBXv4tL9m9l1FS4ee/NTK4ZYGxljXtu2ntuk+aZLg//4DCXvnjFU+7NVXHIzRevZWMl4jn37uR9+47TufSt8Ng3YGZPSXPcegJfPJFIjbzzncx+9nNkx4/zdOO688aY72Tct3/2n9/4XxCbr5/mx9858AyK9q80BIJcarwI0FEHLIAjDoao24KaaWOlQcoCdEnfk1KXYgfe4oUmNoLYK4owQGuFRPSlpQVGWrQxgMCpAm1DjDJ4FeB0zDAtJoYE4cAQLqj0F1uAB6cERgbEypEIDVGENFVsxSBDRxG4cwqE1mXo0jAAJQ1ShQSqrPbLJKQmUoYrAgLFSG+GgayDCEsalcsd2jmkzFHCMJQsRcgu0kJieyjp8X1Ze5Sg6toMiVJNMIkUsS6ouhSCApkERDZCipJKWJgysVNGIkzr3HWXSjKuF1lqj2JkrwQQAgk+QLkeoZllnJNIW/5KSiisOtuLJ1HGYwONoFQg9I0EmRSEkQJfUphqqtNf8foSLfWeWBYoKZiWTQhOo22PKKgjpMRqwRR7GClmSeiBEmR5gVBNHJ5UOXwSEBpDjR511caqfg+qNORxAEYRFCk112VSebSxKGtwrs05hQEpUEFCEtUwylN4h5OGVqdDkWbIXqko51VC3QSMy1IhMPI9ZP83lsqS2wBjPIFSuLCCCAeoRyHVKECcdTKLNC6QFMbibYymILSSepIgVYyzEaZRwduAPJBEuk2kU4zuUsNQi9rYsEDJHl5basJSLRYZLuaITIANDLo2jgsDpJDEokwg6nmLBM9g0SQJ2yTWUZMOgSdnHtClUuo5YZjStkAAWguqScRwYMu/cSWl0WoQUdkzGRfIoEI+9C/FzwAAIABJREFUNEQ+niB1H9kIFC6y5KnDm1IEItA5a7RhWSAoKpauLIiDDsp4GmjWmpLSnIfl3A+DRRLd7D8fyt5MhMDbHl44EBIhSs8wEWSgC6piCK1DtImIRI7WGVbkhIVACoURGq0sSIP+CQDB+gwjTZn8INBSUBPdfi8eVKICG3mqWqN1VPYpKtBCo3SFQGQYWZTtAZUBpAmoRAtYZQiURArJRDFTioZIxaRcZFLkyKhaUh+BqhzHFClSylIoSZfWIoExmKSLVQUm9KADiqxgPIgZC+sM+GFQkkYUMmgkNeNQUqKEYqVqEaiMIZWhlACjGZU9xovTeG1ASpSqIKXCaItSijRVCBPQieuYgYhA5kgEg4HFKENYz/CBweiCEdvBBBngSWVwrqUHynMIlcBpB1IgqlWMVBidAgKjFL5/vQEwi1RrbcJKyoStg/dYk4LuEsk50rSDDAqU+uk9588kaMCOO35Ar7XIRS+8AYA8b7Jv31+yauW7fi7o2fv2HedVYwOsjMuXy/aZ7bzn9vfwZ1f/GRuGN/xMY++b38dNe2/i7Re9nV7vFIcOf5IV0297yjb//qZH+cXzxp4QZbjzL/mo2Mz18T46hz/Ckd/5XSrrl7EthEeKlzF3+Z8zsGTwH+2refsRelGPBT3Husv7vmcH70LN7uMDF17AXx86xZ9+7S5ebB5h7eQAb3nLWxgeLvf5o7+/n/nR3bRrr+V/250xYb/CNd0VXLH/teyYuJ3OA3ewZXo/j6SvpVk5xvqTl7Pd5iwLZ9nWafDG6DS7W7v40sj3uPzUFp4zdS2Ho8MMPTSIE22md7RY+vAjBFf+PnNJg1bYZKmHT0+9nNGOY2Wrw4qBYRZpUozdQXvPO9khHS966GMMDXTZdnoZl8RX0jtvNWlh+XS4jgvaDzA5nbKluYPuEs33jryKeaf5gPkA30wv46HhFuv2jXOgcSFvlm1yc4CRuVGODi/FTVeJTnXYOLKfV750FJl0GTv9AnYtVLmOH7Kjup7tzQH2DTVYP7MMFxR86D2vYZte5JYi55e6v8IFhwbIJiJMtJnn3v45vru6xvY5xfXHBdXhjWyTO1gSSW7Y/jZur9zJyLFruCM5n+Ov16z9zkcwssq9nRfzb3bfyI7rLuNCvY7V7cM8nqwiOr6bariJrXFIfPomNrUN1eAWNteOYArHogyoqK+RRUMcHxyD3LJh+3aOji0h+9EQ1cWCC9bdyeqjR9mTrmdy8h4OHDjAiTUrqf34buqVOpNrz8Olxzh41zCfue2TbPi930GcPMEFk9PsbM2Szs48tQ8sHkRc9XZ+Y6HFjY/cSOEKrvmVdey86zhHd5dUxLu+vIe1l42dQ8+eHKGS/B9rlnDTxWv40VyTq3fMcmDdK+GOvyBYWUdaRffxM+e2D1atovbCF3D6wx9+2vPPKMlrNi/ls/cefNpjPDmWrhtgaEmFB7/3jC/av7oQIEwXl3uE9Ajdf72KNt1ms0SjpMPqOQQOLzVCSLRQJIHH6HPDMEGV0aikF1kEoamWyZMAH1pUJUFogawE2KSFD0OKoHy/+O4J8rz4ieqsQKiQQCclpUxJhJJI2QQhUbL0+tJaMeDmGSQnwNMQRV8Nsa/IJyVGOgZJsEojpMS4rKwcA0p3+psKMOX5F8wihMVFGkYaoErFQC8kVGLyaowRCqRFBDGDJmXAtZFKoowmrAZEQ3WCaIBYSbSSJXXoSSGFQAcepRVeOoT0OFsFE1EJUirG42S5iDM6ZFBXqFnQwmFFQVjkRKYF4mwPXLnwUkIxmC3QyBfBGPqifIAnosNYOgsI3FnUQocYJ0EF+ECSyAwThFjpsF4ylLlS7h4HyoFSoMtr5wtXUhgDRac5w5SJzh1P3XeJVOm1pD20F1oEUYCJMqRW0KiD7KJU2YPUlREIx4RbJBAaJRROGqSJCcmY8HNowMiIs4meEQoZSGyc0QgcOghL5KzwFBkYZSkocDbHCM8VkeWSuI6KhlFKlZSwwp8rChTdLjZvoYsMJyWxLRjUCUo6Cm9RUjNq69Rlj5AcLaEReJxoUrFVAmXPTYjcKKLYUpEJStu+71S5HyEdIm6ghmpoEzIuZxgyPdxAmQhpnRHH0BAZG6wmsTHGF4wUc9igKH2rpEZLTxJGJDJGSslQdowhmTJkHYUJ8CbHmxwXBviaRAYKFwYUSpDqCCEUYmCASCbn5pwSLWQSYyZHUP6Je9aQIRQUPiMmp9GosKHSQmnAdplKz7DSpwjt0UaAKDCBI9ARQiqkliRBQmD6icRPrFOFhDDIscYRywztCpRySOGJ9CKhFSSmhlQBTmrGIhAotA5YYSvYIEAYRS0s53hgymfEmmIfNT+PtxFOS5aGJ/FKk6Y9sNW++bQk6/aYJGUo7xCJAoECIVBKYLUlSs4WZiVGGurSoBLIuilVZUlswFD/8SkljOsMpRUVsUgsC7RpgSiLVwhIIs1wrIEcKSQjEQzqjLNm4k5YvPdEeZvlLmJS95lsRuGlxeryWbAiOEFdtvAIhgNZ1njwaOmpavqW1h3odwaeZUUWYR2vDEZqjDJlz3AYMaojEhmUaqu+Q4P5J55ZOsdETypQ/xPx//sELe12uP0zf8c1v/qbKF1Wa/bu+yC12oUMDV3zM4+/s9Xh66fm+P2+cuPRxaO87Xtv452XvJNrlv1s43vvef/W9/Oata9hsjLJ3r3vZ3TkeVSr55/b5jvbT3DP3jP8yYvOK/+x73Z+/MBNfGvoSv5w3eUcO/JFiuAo02vuIa4MsvWYZfP1K/7RvvKZDos/PMzdh77Gla95PaLv38EdfwFbfoupMOKyhVkeHxQUExt54+teSxiWHhIn9p5g/mTIV9cdJ9RD/O6LhlGnvsvnZ15Dpdtgf+3b3P3s49RzxaPNLUQz65ldc5xDPmN3WuEXKfjx6D+wd/Q4K06vYl20nrWb17Htaw9woHKA6+VG1j72OYoX/Bs+d8FyVmazdKoVPn3q3cyFAa/Y3+HB0S+x/OCLObTu75jdv4Zn+Tqnmg8QXHWGe+cmWT06SPH4LTwWTXAovoAt7KKWdTiczFHBs//IFmaKmHeIT/Kfa29gYPlOHj4Z0Wh6hhvL2XbnbQydfgCBoVVZTubPcGDtFLdXns/M/g8zPL2b1/B1xns1onmYlAs8VF/BzZc8h82Pbuf/ubZLLlv8h2wHf73xBg7mhmftbnDn9AnO37yF9vrVrN/zIB/fEHJ3Oo/xy7lQnOBe/Qibeit4QB+hcWYjz558kE8lL+boy0LUD/+K0Zkmg3aRh7/wQSoXJFxQu4zp4gQzC4bqjkk+NNjg5YtNurrHvZWcfdH1PG9hN/fFFzOrDrFYHSc301S6i4wcOsD47CwPdJ9LczEmS/YxlRxj554bcG6O4ZEDTG68gKNjk9z/15/g0pe8EhsFtE9t5criEV6+ZTlj730P5tOfozowxPyrX87pD32I7s6dT9xol/8Ov3T6CD5tceuhW6kOhlz5ilXc+vEdHNp5hgOPzHDZDSt/6rw4vxLxpQtX8ycrJ3lL42X0HvoCZ07vJ7l0jNa9T0XLRt72NuZvvoV0//7/+QnYj1++dIpvPPLzEQsBuPIVq/nxdw7SXvj5jPdM/NMhhPhzIcROIcRDQogvCyEaT/rsvUKI3UKIx4QQz/+XjOf7C1Mpn0pb8eQURUnFW6sEDZtTc/8YcVX97+sgZUmFJxr/EUhpMGHQX0pLBgJL3bj+sXpEGICQVGzylDHr/YWIDAQ6SBFSolWI0QYrS5AJIZGBgDBgUmYkskCYLpP5MXRfkluUXTFsiGPOi49gQ4nps/KlVESJZ0IE/W0FQmoGlWepWuifgmRYVM/5PtV1FxMqhA7QUjOm66AMShtUEFLE4lxyUhGlipuSGq/ifi+TYKUZYFl/n6Omr2ApNaFL0ZTIBDpACImV+hytcjJsYYfqNAJBZHOEktho6OylRiBBZoz5GSQeKUTZaWcr+KACtoYKckTf6wghIRqEsA5CMtE7cm7RLLUhspopFMM9RcWOEEoIjKami3PX5lwIiTWWtewisDnLGQahEKJ7bpPQtJFCssY9wkR6+twi8cnhsgInNQiNkAalwv7wktjEBMoSKIuXEImciu/iRU4SRgijSIR9Ag0AnPN9hc2cUGsiXQqB1HUE/T6aPDuL+j/xPSFk6c8lFXGQM1yZpJZENKxDSI21w8QmJNQBDR0hAEX+lHwjUKU/mjh7D9gEdIIWhot6h7DK4uMcYRSDQmNDC3FJf6NPcyyPBRKp0VIhAOMLApMzVGsTmhp1EzKizbnDH3czBDgknql8Hz0LwiiMChFCMxUIXB9pS3x5TRtBg5HIMsw8ARk1W6Ue1hhUKcP/nb3zDrOrKvf/Z621++l1ep9MyZTUSYOEQAihFymiKAo2FGy3qFzLvepFf95rQQVFRUW9FFEUQWkihARDjyGV9EzKZCbTezll//7YZ85kgCtX9D99n2eS8+yz99p7tX3e8n2/rzEBPj/KNJC4mG6GbBYCMotCkPCFkVLD0iyiTpYi5dXHC2uDFFn9ROwpkniROKU0dGMSO2CB7SermYjce0fgogREwllKMgMoQAqFLTXCpsQet0iPihysVCBklog2iS8rCWdCaEJD0zzY4Wh2BGWQh75a7iTh7CBKGljuFEHXi2KbcpQMg+jSQApJRg4iyKLIYlkOmm5Sb8QJGVFU2IvKTYvPyMyQ/YhMfvzLjAgAroYHMwSUGPW+lwph+NCEhpMZR5caUilsUxJ0ZA49qBG3wa8bZKdgcsjL5S31pbw9LTSE8OZPl1kclfXacT1LzNQDCDOLqWcQQsM1gmRGvQibVBo+DUxdgjJIqZSXmysMDM2PcF1szcbRHRDg17PIoINiBsEjJIR9/4A4/ll59lc/J15WQfXCNgBGRnbT0fFz6uZ8+m/S/lcOdHJNSZxCU2dwcpAPPv5BLqy5kCvqr/ir236s/TF29u3kunnXMTy8g64TD1Fd88/57wfHU3zm/m18/sImwo4B/YfI/OJablzwJT5eWYT1jR8R2KIxcV4nvzyxminrnQiOs+f5jbPu47ouAw/sZ7RgjIwvS+1ij6mR41vh8LO8EjmDW2+9ldjWbXQ6EeafvSx/7VTHCM9/exuj4eN0F53Df7bUYjz/TX4xtZbYSAXRog0MxFzsbIpP+RsZEzrPtf2azuPV+MQIoaxkd+XNTMVcGjrmUDNRzfnnXcC9//NzesVh5vkcCu68na5TrsC1avhppc671U2smXycDWVeqYSLOoapzzaxL7IJJ3SYnr0fYUC51I7cgk+57OkvJvTsYVzfU3RMdrHVV0dl92YGli3htMwuDoYTHBhpIJMe4xPyerQjU+wtHaFxXzHtwXoae8L4VYqlm1/EnXyUnoI1yHaDsg9exQlTZ1xLcXz4KoLBYaq034Obps09TFppRNrHOBYs5plKh6/d/36aHvsNy6rG+NxIH8nCOnr8k4z49zB3/1yOVJRzLJrlpYBB1e5Jnix9G2QMhsP7mXvwXEbkc6wZreeZvvmcWK7TsagUtu1j14klXO68wFdEhJopjZhr4vosOHiYYLqax8I+Pn9igvJUmlFrECtUipGZZLzfJJy4m67C5RipcQ4nG1n+1AbSms72l1YSlG3U1jxMrLuTAyNzKSp6iuMdR2lvrEP8+h4qmlqRpkO/VcLAc9De/mP8a9Zg1dTQoDv86flNxG64nqMf/giZwZxnyfChTvsE1w6N8IOtP8B1XeaeWky8zM/Dt21j0dkVOEHjDfeGEIKLCyL8/LTVvFRxPk/f90k2VzlM7B0gMzhTEFovLiZ82aV0f/uWN7sNKYs6LKmK/c3IQhLlASpb4rz40KG/SXv/kP9Vfg80u67bCuwBbgQQQswFrgSagLOB7wjxBskCgBQSTWoYWhZdeYaN4fGC4bjjCASmEEghCGQGyGgGSzhKhQzh3df7ObZNgW5MYYkxpK7w6RYIKHCSoDseTEoKDN2HQFKWbqfZjuI3/DNt4K3xWE6xMpVJsziRf1afO4GhwFACdHtWP4SmKBQmtQzizxFzWEIQkwEcpWGZEAtJdC1DjTIo1RxqA4XEzRCO6UPTNIRQKKkoTnn5lLahCGg+zvYp6v0BqmQPpiEwtBCVdhIlJBqKpO7lloSljqs0pBqhLNt18ijjCm9MfUpHCYkrFWHd23tNE3+ibnw3ANXCI5aIKCtHfiLRNN2DMkrpGZJKoPu9CJPXvInSfAhpENBM/FbOCtVEjuhEInQDEfBjGhmccAxMC5Tu5b0iCLvDlOieoSytAG3s47TUYQKGF/W3ZQZdEyQzXSQy3QBkLYtSO0DQNJES2vwFGCELKXXCqV406UUK4o6FqRRS2QgmqR3Z/r+sRoFrBMH07qmkhj93f5WDDQqhCEtFQlNElYmhWRiOZwBLoDF7AL+0CEg9r1ZKoSPFTL58FcNUZ47iuBPEtFyfpYGQCr/hx1QmQmg4Mk1ZIIKpTDQJum4ihYYSUKsCrHaHKdQj1JgJKsyo11ZOedaExtzMCUzNQgkNzQgRlZ5hoElBNpMm7vdROnGIiNBzTKWpHHwT/LokrBxqzQQh5a31qKU8kpFsisLMceqz7TRkD3lpTbpOqSHIGB6KqDjVQaHpp8iKIk0/OUJJFIKAbiA0sJTOnNRuNKkIaAq/mEQTCksZFLgDxPsOEDfGQFMI6eWEpVIZr8aXZpHIjnh5XbZFxNTJaFDum0Dlnrdmchd1oof6qT7C2jiWbnuGiq7hN1Io04+uG0RTwwSzKRLKptqMoxkKqXIWj2YgpE7cjGFqFlruXWFpWWzNpMjVaXAGKNTT+FwPzpuVBoYaBlwc14MEm0LDkIqQ9FZFVJkEjTAuEwg7ghI5gxiICAeUhcjtDUuZJ9vvFJgBD4qqvP0XCEcoCIWoNk8qNyW88dWkzJNqmFJDEwpNaVRljqGmo6mCnANHgtIxTEmpPkPE4TcdNL/nh6s2ImjKwNK892td+kAeIiuEQJEhkelEVxoRNb13slS7HWTFOMHMMLWZYzjZ8VxE33NpCM2EQCD36ML7V+pIaWDpQdwcMyp4iIQ/J3/XBlrXgX1sefR3rLn2Og8DnJ1kx85/orLiQ7MYEN+svDQ4yqaBEW4oTzKeHucjT3yE+kg9H1340b+67d7xXr783Jf5zNLP4Gg6O3f+K1WV12OZMzXWbvrdThaWRzivtQgmR+Dut3PLoi8waYa55Nc/Y/TRXzM3XEZnTDIRWkbjihrO/fA/8cQPv8tQ98yP+fjL3Ux1jPDE5p+y6qpr87DPgSe+yT2+9/Dbx57ErlrAH8ZrWCcsbu7oZjSVYXjDUY58ZwuHx7PctfAo1f4wp0+9zPif7qZ9+CKc+AiD55Vyuq7z3WP9vOCb5N75N3OeWMvGbJpB18dg+feZ8o3yzuHLMYf9rF17FnfefSdu32GOBHdywQ93crD2fCoKV/Hlhgwtk9tI6EdoGHGY0ATLOibpTq7HP1RFuuE+nn9lLSuEn/7BJ2iq6mdDRx1V4SIiY/1siZbxir+et47uoy8UY3L4CRZMTrKh5zzO4kl0JukeiTCn5I9s6rGp6FRQnGRQ7Kft5W24ukOtswMjM8TGZe/lxweGmdO4kRMdtZiHt7FZns1lE0fpSe+gvmuSar2X/YXl/HDZJYy1X0vxg7uZWD2fr15+FqPZcV60ltISbebO0Xs409FZlTVIiT4eW+yjOztB9a4MRQUL6VB9RAPwtfIqwh2rOTP2JPe+choTV76CFq/F2rafkBxm1cvfZ+/CEpYF1xIcHWJSt3D+VMqtoSARBqgfL2NjaBdH7H5qRw9yIFDFzjHBVMJizC5nVMvS51gs37OHSWmx8fEEE/44y+NP0r9jKTBCOLyX5Nq1uBJevPPXbIu0Egm4uGnBhp89xOjoXgo+/W84v3sUXSm6qsuxW5rp+MQnZ/LRFr6LC6YEfUNHeKbjGYQQJMoDZDMuowOT/CUS1jVWvOXLnNv3DN/e+ns6qnyMvKq+Wvz972fkySeZ2L37zW1G4O1Lyv5mZCEAyy6qZtczxxnoGnvjk/8hb0pc133Mdd1pt/+zQGnu80XAPa7rTrquexDYByx5wwaFi+OOU6idwDXS2PoUbj7m5WJqJn7dR1BzKDEtSlLtFDlJNKGBAFvqFGk+Sswo+AsIkKLIFCTDSVwFxjTjlxAE3RHPGBNg6BEkahYc3xBpTMdHq+ylUPqQQmKLmbxGARQwTphJdDR8agZqE7L8JBwNA0mJ24MuoTjdhcoRCpwdqMTSY2TGvKidzzapkSlWpvZTaUQxbAPXpygxdaTIYkmdUtVPa2CCQjHKfK0LKQTl6UPMFf2s1YaY6/ZTqHkKcUX6CPWkCfhASZdWO5l/NiVNpNIJZTLogSQSQaV0OMcymTu+BU0FCOgGfneEFbYXNUrYMYSUFGR7vWgZadq0DDW6iTgJ4leoB71IoWaC6fPy0XLfZaXmqVq5MY5qARYFy/HZAXReVQpEgC2t/LwDmEqB8qIGuhPCpwQJBknSjxAC1zAIGBNYlkVCCZQQLBBe6ZBiMUmL3Y/hTnlRJOWnSfTk7xtyhylgnKjmEJK+PLQ1nIua5R9ren0IKE+3A+AEw9TKHgrdfjKZLEIIQoakLBhCkSWp+wkrEzeTr0o3q6NhpnDwjLOonovWSg0pdE85FVA5dRQpDXQxW90MGRJHA3SHOeECxHSkC0FEOaDpYIUI6jpN/hCaFAT0AEJIku4MVL3QddCkxJcepJAUQsCCVBcJMYZUFiEjRFybHVkWQiA0g7Nsh2i2DwuXBXYJhtAw3SlWmQYIRdPENqLZfqSUJJSfrDaeiyQKmsUAUkC57lLMOC1mGL+ciQyZytuvJW4fLqPU2UnKM8cxlInSfTRk2ilM91LqS3BmOI5uJFBSIqQgbsdR0ot+lksL051ESZ2kK1lAz6wIY6nIEBFT+DWboEwxR6UptTQkAsNnIHUdlIGmbKSyMTQDQ+n5uQwLB133geFHqUkEUCpOsNA4gQtYQkcCFemD+Xk3ckaYlAa1mkLK2SR3giyYAQwj7O2nV0V5Y5o/P0YRXwFLrDTVYhjTGiSpTeT3HXgGdrUdJqAs7JwzKWQIwqYXITVMf455VsNyc/pBLpqYESNUahNIMhRmjhPTtDxbY8hwiegeAqEidYRAdgQ0A6U5GMz0RwpJMODVZi1liLjp0jB5iCuiOnGfpCRzGAwBmiKY9SKcSdGP5qbzvVDKRkiNmDuMrds4mkW1GaXYzIM2Xlf+bg20TDrFo9+9mRWXvY1IUQkA+w98HU0FqKh431/dvuu6fHF/B9eXJ/Epl39e/89YmsVNp96UCy2/ecm6WW7ceCOrSldxevnp7D/wDZQWoLz8PflzNuzp5vc7u/jCRc2QzcCvP8DzieXcojfz37vWM3zH7ZRfvxrjHffRt7ua6NxnWH5JDZWtC2hZs477//uLTE2Mk+4dp//+/RwK7qGkuYny5lZSqRTrH/oV39lbQKC8mbe84z1886UJQrbOd85ooNLQ+a8HtjP6Yicv0cWxxGF6E2v4oPkcW7/3L9wx9kGcdJgzbljM/+z4Ge/bM0nn0DrqrDLG5AC/O3QUFxej6D6ydgdfiX2WrUf3UdRQyIbHnyJ9Yi8DlQP8x30axytWEm1dxVNRyaakwQeHv0pkbB332qcjgKiYYk736Wyo/hFqyiVz+CJsDcyi2zk8EaBdNZNY/zLaIpdDQwHMQAmjQ5t5af5izks9xw5ZR8XkETbqS3i48gwuHn+aIzWDVBxI0uUvozZtMPfAb3DGMhxYeRbJzQPU77oLU6/hZV8Te/USuo41Y4opDop6silFafIwu4pCvP/gA2wNVBHxj1DlWpyyDW6as4VN9/yE95d0sz5rEjmxjoPGMTbX7GToyG1Io5C4O8hzjS6FE1n2H83QWr6Co+Z+pmSCCW0X17oRDnQtZuuIzcGrh+kNVHPk6Rinx/fwi617cMb20xheTkWmi6KBE5w5BD+OB7mh/wT7rSM0Re9loKiKwOQQzvFRRqof4GDNRSzft5VfNq+hYOdO7Kk0Tv8YLz53BqlyF1+qi2MH2igs2siRI/s5NG8hw3fczk6nDmuin7oVa+ndBc8//SFkeYLIW95C3WSW53/zSwo//3lSHcfouSWXC6Z09PO+zjW9Pdy6+ZsMdo/x0iPtnP2+Jg5s6f7L87P8SfRV/8xPO37AV5PQs+kY2akZpUqLx4m88510f/Nbb3pP/q3JQoJxm5bTStjw8z1/M6PvH/Jn5Vrg4dznEuDkRXY0d+zPigBMzUDZPhBQoWwcN40mdWJiEl24CF/Cg3FJSZsTptAuptk6SkZLowtF1AzjaDNMv0jpERa8SsEpzXZRnvEcDeqk4F6hHqQg25s7LkloNqdOtVMzsSfnywXblOgSEoyRcAeonQWxgwK3n2W5tn1yCluHqJPxlGwjnoe3J3WJZnme7joGULFqTN2P8kXAMFmXPsgiLUyJHmKJ5WOuMcHJK9lxx1lhB7FEltB0VIwpAu4oje4oCSNAwAxgCm3W8ykpKXMdKhn2cueAuFOKrgdASOb6arhASxDJjeM0VC8hx2lIHyDuDlAlJzhdH8One4qii0upkkQy3eB6+VgAlu6Q1SymJ8Cfg3RFNIcW6cE3k+6rajACPiNIbDpyIRSOHWSe41IpRkA3qVY96FoYXQtTox2hJLOHCqaoynZS6nZhS4OQSBGO2oQSDkEzTV32MM2ZfZS4XYTlDADxLFNytqUo00zCZhipLKqndlGY6eUcA9YwhcqdXZ45Tq2ZmDEqhcQUKYqyvdRN7gFAkzMpTfJVCy+h5aCS013LfQgoL0JSofvxydkIB9P1jCb9VUFoNb3szACo2XTjjsgQVg6xbD+VZoRGX5CALij2abRZITQyLBjdCcAS82SnnaRSC9BshfFlPeeWePXmyYkI+JHV+ZjNAAAgAElEQVSOTblVQJWZwM6tpfLs8Ty7oWH4WaonmOeUksy1N72Gkx5/DAYuugDrJGizTZo57nGqsp0ePDYXzQ0ySjm9VGspihhjfvYYa8w+/GrGeWK4s4kjNKGj5QwiHxolU12zvq8zg5xlWyScQizTRLN8+cgbgFTT+VISIeRr5lQJyWL3MCQLSMgpXMehQQxSIrz1PddnYloGphHPn19gximwi0EIYpo+e4ylZG5mr1c7zpA5CK73vZ0b10DOWSEVXGqO4ldZsrlz6jJ9RMUkvpxDyVCvef3RIofya7BEDxJRJgF3lDliLN+/YGYIpIaPLDVTrxByBzlNH6IlNxeWm6Yic5TqqYMe0kEzQbOxbB+1ei+V2WOEzDCObhNUXmTf1IOENAdHgF8zvYirNYURsjE0i2r6aM7sI+EO0JA5gM+ZjU5oIkXp1GGa0nspzI7OMuhfT/5uDbSn7/kZmmmy8Dyvxldf3x/p6LiXuXO/yv8BzfKG8ljvEO0TU1xbEuPTT3+aoakhvrH6G38Vnf603L7tdrrHu7lx6Y10dv6G48fvo+mk5x4Ym+JT923lcxfMJeE34MGP0j46ynuK3s1nOp4l8NWbKfu3d2FcfQtP/+I39O25hmDJDiYz+wA49W1XE4jFefjmr9Jz1y5Eo8WLLz7IqquuYefOndxyyy0c3PEi1zalOfP8S/jIL7aTdeHrV8xHHBzi4+v7+FnEZd9qP109Dr9bmKF5ajftdzxJsRgnO7KUJRdXcOvOb3H90UUUpTvYlihiH8dZ13Uue4t+jRF/HBHcxlV2GY88/yzpcIqh/cNM9e3BDKU55/59mAuWMVBwNhEsbq4d5Yber+M4fp51r2NAF7R1jXDBMcmO+GZKS7fzw23vY7W0OKZ+yoLMGBva57KwZ5Bw0Qg/0c7glUATq4fv50RVPfMnf8YZY5O8PNpEp6+E7+rnUu9A5dzneLwzSN1hEyMaIbRlPaWdnRytOINY+GGyWY3jc32kVu3m0ueGuav/XzhmFLIrW4XRf5AX+iq4ZriX58rupXbbIS6onGJXKkab2s3tp1zO8HiIV57fwNvf815uWC154KBDaHIVt+g/5LDZzjUH0hgpP4/PKcRX3E2CNNu3ZhFWI/MPv8QLoxMkjq5mWfJPPLnlQ2RjO7HXLGFQGIxugbeKP3JbLMGciRC6TFIf7OFdnb3c44+g9B5W9Rbwsg8yxc/gZCc4Hiph1/ZiJlp30BNtwQnobC0qpe2FF3H8o1Rt2cmLL6+ismCYjo4q1JBOKLiB6BVXEh/o5hr7GKdc8Q66D27C9Ldy4LFiduz4KNHr3kf0pa1kxsY4sHMbpbfcQv/ddzPw6/u9RV55CpeXrKanfz/f+eVdNK4oorI1wfk3zOOF3x1k30sn/rft8fqy7IM4E/3cGnuB/Y7kt7/fN+vr2LXXMPbii4y//PKb2pO6klzZVsZPNh16U9e/nrSdV0X/8dG/vK//kLwIIR4XQmx/nb+LTjrn00AauHP60Os09bpWshDi/UKIF4UQL6rJMc7RJ0gySIXmebRjjHCJlaJGTlGfbc/n6wBU+Yq9fDWleflWLhjKzDvw8rCy3P8FTCuiAlPIPHxSF+n8I9vCS7hPZPspzOUtBaJxgieVd1EKotkh6unhEl+AwLRCI3pwNdDIEHK9a6UA25LoyiXoF5w8NEJAvRikabI9f6xV9NEm+6nLHEIAQcvIRUQ8xd7IKe+JV0U0kiZc7pxgruvliFqkKc09vwAKsj3Ecnl7umVSWmBTIsaoTPd446RbYEdJ2nFKQw6JyMw45yNHuolmB2ZN7hwriaEM6rNHWSB6qXIHKcz2sMD0cYpTQLERJiomMHxejlp19ghKSEoZR8djxQu5nsKnCQgaXuvLxQnK3JwiLSXEarwoBycp37nhtMUUjkjjV4oAswvXnmb1stRXymJfOWHlzWHEzTEC+sP4w0lsaaALL7sloemc5gshyVLi9lOiMh4rZa49Ey+vsVjp+OV0xMrFJ7T8ehJ4BitAuRilwT1O7eROWqf2UOwP0BCMU6heS9IEAk3IPNRxVj/owDgpWa4xc4Clmne/UjH6mvMFLnHNx2lOnAWiFyFgmehGExBiigVOGcFclMOIhHLRulwOqBBENIdGwMmMvWYz15oz7NQuUOmrIGEE8vtMI4Oj+4g7CbCCiFg1ttSZm+mhbOJIfn8qIbByzoOg9Bgi20SP1z+3HYc0LYbJ9J4J5vZAxB0m6g4hTT/JQBFG7sZluXGwmWS17KQ6l2+mSz9LjGLafBVIBBU4FDGDrFBAoxhkhezh4vRhtFflwGpq2lHhUpDtJZRbA9O11WJZr+bdQm2AhqTOKdbs+ZDCpTJ9KGeVa5TpQXShKNCDuXZn/vVLE3wzDIghw/tYqobBiWEJnVozgWFa+M20l8OVkxiTOG4KXUKr6GcxPTRkDjD9Bixwh/Lzt9DIRailpNQKYAsvehZinLrcO0Q4EdAMQtM15ICwEcDIMU0u4RgB4dU38wY+DLpFXI0SUlPYTBEQU3nYeIvbQYQRau0EiwJlABTrYeb4qglYhZSqCK+W6XdPIZPESKGExHSnwAqCL/6a818tf5cG2t7nN7F9/eOc/7FPIqVifPww23d8lIaGL2HbpW/cwBtIOuvyn/s7+GRlITe/+P/YP7CfW9fc6iUM/pWy4egG7th+B1877WsM9T7BK7s/R2vLd7Ftb8G4rsu//nIrbVVRLp5XDI/cSP+xbVzV+AUuP/o0y276BsX/8S/YV9xIf+dxdjydpqq5ivLyqzl44GbAS/o+98P/SuVQAz1d7fz+hdtpOuci7n/4ER555BHWLm3i3ZN3UHDWR/ns/Ts42D3Ku5aV07R/hN67d7Po7FreVZ7gk/u6eKnyAP2BeZQ/tI2zgrt4ZPTD+OKKVGMvqadNLhxbz/OTl/By+DhloxU82NfGZPdZaPEnOM8JktrbRp8+QHAkhD58BKb6aNt6BKu4lF2x82kNGnylXlBp7KItuI3S+O18M5BmXLp8/tBhAtlBOhse43B7LTV9NZTo47ToD/BsVzUDmXIKD2ynfVEZRl8Ppb4oHaMGTy6s5dqeA7zktjCehm8Pn8ZkU5CLtvycIyUj1OyNcNwpp7r9IG3tezlQWYnvwpcJrh+HNDR+/j8IhW6hoPmXvHX9JBvLllNdGWNo9yGy+z2q2tWj/fxyzXza2MVo1iRzaJRQoaLtSCtbavp5svv3vHfduVxR3knXwbUM9FbRUjCPkh3jHLECzO+c4uuN1azWDfZU6wSPRAlMVNBjCcatLVyfDdGeSfBk+7kMtd7NoxcuZqTboKnnIHP2/pZD6T2cmlhK55DDw+OnsLb9VD6bcPj42BaecPy8e2IH7eVTBEf7qR45yP1DRRxYuoTL1z/CHWe/i4l0itCecWKte1m6+xle7i0kIrPs37mMOcGXeXLb7/lj80qK7/8ZibmtuNks1QtqGe6comN7P/t7bib+3vdQOzTOc/ffi15eTul3bqXrppsY3bQJAGPdTVx2PMrD6m4Wnu+t71iJn3M+0MKTP9tFx96/oMi0ZsJFt5J46ovUrwiQ2NzDvcdnPN8qFCJ27TWcuPnmN703r15eyROvnOBgz2sVjjcjuqlY+dY6nv7F3tfUgvuH/N/Edd0zXddtfp2/3wAIId4FnA9c5c6EKo8CZSc1Uwq8bnE613W/77ruYtd1F1uGhiE8haaYfhalj7LIChBRDtMtmzKJFLMVWF0vQGl+rFfBhAq0AEktwCrZyXvtDM3Sy9NMKJtFvnJs0vjdsXxxbIA2cYIqlcJmkrVmDl6pKa8kCmBLAwRErMG8ElUd8aGkIOwOEDgJFlesHIqNEFVGzMt7OwlX1Sz6qWGYouwIicxQ/nhUTNFoBlnp8wKOWqI2/50SQSzNoiJUQZFWifGqyJ3+Kr9okT7Py6MRioQ7QFEuUmWQpk7zlCoVLCUQyJWpUTpJfymaJrFyfY9pPiyhYUndg829jthKxxApQOATDjEGaRX9BFQpq2UXiXQ3fuXgd0cISYOmcALbkphkiao4TfZMWoH+BhpVjEmaxdBrji8M1lLkK8wTxZwshlRoQtFgF+SPua4LloMIBvLHTDzFs1EM0GjEKTQ8+JS0gqAMmjMzTqlK3aTQCCC9OhBUaSF0JKcbcL4paMqxZFaLYcLuGIY7hdR9CMPB0hSa8OIU1nSh5Vy7a60MZ2k2idQJpIDlKc9IjVgRVogTLIx75X9sIWnRJQKJH+/d5j8JghsgxUrRRVRM5aOrlsiwUnRRKUYIa8vyFHo+UsRkhBariJr0CRqzXvQ3GCqiKONFsKxXsX5Oi8NJsF8B56peinL5g3VWkjlWctb5ocwQpjKIWlHPUPcVUmslQIicoZtmFR00GmGanVoCyspHenShmGsXIabhc0KQNF/Llq3lUNdKeAZVSXYATUg0YaBNBQhhcI5lUmFE0YRCFxJNFGCJDHFp5g1tgKA7QrE1SoHeS1G2i9WWRfSksUhmeqlN58ZLpFDCJShn4M7xbD/1umK533sdRkUhVs7JFJ52EOFB+fzumEcaw2wPV33mEGExRixXpiMipiiT49RO7aOBGcRJQKQ4399PqX8mn8wQUOS+9vfUF/Lydgv9QYTSWZHdR6EcZCAzljdAdaE4U57AJ7MU2iWsCtVjSI2wmGI5R1DK8XJHfUnawg15w7tID9HmqwCgQtc4Uw2yWPRgkvKcTokEWcfrZLkZodCIIITEsQzQQBO5NSM1FgYyNIoBzrUUl9rFBAyJrQkQHjX/G8nfnYHW3X6QR2/7Jude/08E40nS6VG2br2OkuIrKUie8ze5x13He9GF4PDRH7OpYxPfW/s9Qmbor253T/8ePrXhU9y04rOkTvyUV3Z/mnmt3yMcXpw/58d/PMTermFuurAR8cCHmdi/nnfN/zqtRzfx1q/cQsG/3Ujg0veSzWZ44Ou/wvQVcPrV8ykvfx/9A88yNLQV13UZf/w4BclqNg08QkdW8uz+dsrLy7nhhhtoPvwTxNL38o3nR3lo23HmRRzeeyzNxJ5+Cj48H6spxqLdv6TL5+P5uXXM37ubolCGwfQc0uPVnPmeFu792RO8d2wX49kQBwtCrFedbD1yPuMo9MAOymQ5T41OcEg7SjQVQes7xPGRvZwz6WN8bJDA9V+kdtTm4WKd7QUdfMS9lZbK2/h/7ZOotMtnd3cTmrTZWPn/0PUsP2q/jjXSIWt+nclxg2fH53LhjmeILZniyWMVHA4vJjaxjZHKet579LtUpNMc7ojxLf0yxhYl+PTP7yIx73nW7y+gusMkNR5gyfatbFi5ErPGh39wB3qPy+GqsxjuuZcCHfb795P2HeCKjeNs2OciHYOelmqO99fwT4MDPNT6J7pfaeXq7pd5NLmImO6A6RBjLV/d8C129u7kY9deyoLY00wcv4ynD4ySMrdz6p5xjhYZ7Ahp3Fsu+PDYEe5eaSFHktjDVfxhzCQ+0MClznYeP3QKh0ebOb3iEJ+60s/wfp02+tgxcoRS+RVWFpQy2DXBef2rOaEl6QhZnJEO8RsnzuLoywh9gq5ACS2du3ggnuZw9em0du/nvgWrKGlvZ+TAHMKho5wqniTlz9BLkr6XW7gscRelb11DuLef337jZpZe8Q72v/g7wiWrOfAHP91dmxg6bZyiYycY6eqk/eXNOAsWUPSlmzj6sY8zsWsXo5kQouOfiYhBHth3d36Nl9RHWH1VAw/dtpW+jr/AGKpYDvOupPzQf1Bk6Dy24RAb+obzX0ff+U4md+9h9Nln39T+TARMLl1Uwvc37H9T17+eVM2LkywP8NwDB9745H/IXyRCiLOBTwIXuq57crLfA8CVQghTCFEFzAGef6P2bKmjMjO5y4FoKVErgm2H8hGJU/QU89wZRj5bayEpG1huh2jVPMWsWnhrMqCsvNcdPKVHKQe/7v2WSAGV2Y48fA3IUVp7ogmJLopRwvPsLvFVENd8FGa7yaUHAaDrOrqh5/pgMh278wmNuYbNYtmPJJvvA0BcTHqeaDuUJxkxZNXJYwt4cKiASKGTQeIpvrooJiRLmGfFXjOGeUNyohIpLHQRwjRn51JppLFVI7ZcQompmKN5CtlCpxy/mg0ZOrskwSLRxwVGhnq7AIGZHw+ASjFCjRgmpnlsa2XBinwtOF2WI4A6vYyL9XEu9wVp8M1BCnBkE5Yw+cipF1AYnF0vdFoa7ULqrQKW+WfGRQqdGK99ZznKCzUUaAEKCjzl0JLNrzlvob+cpb5KCvUgES0w67sm+lkkeikQE9SGq4jlmClLTEmr7hmFC+0SNF3HZ1cQVwaCHNTMDrMoWE2NylCmlVGp3PxzB+OeAbzYSOIXsED0cr45whW1YUzxWppwRxZT4gsgBNgn5fLEi0twHE95jWhe306RUxQKL2o4j16kCGDIKuaKLCof+ZhZ39PHQEFBAcRjJAOns0qFiLq1OBkjP75CM0k6ktO1LpbSPesZJTYLjE4CYrbjK6778wQVfmXmo4FKOykii8Av3VexZ560B7V5FFqnk9AXvGZsPLIaC4QgUlSSh+ECuDkS2QJ3xvGYcPsJ58ZYl5XgTjM1giEUlUYUJQSaeHUtXe95SrInKNDGiWpD+EgRUBZKSBYU1WNKRdISNEYDGMbrr+FqMUy5miJsTEfLRN4RUCq9eYuKNG0co9Z1sYXu5UBqFs2ZfZxiD2OLKBLBXAZYLk4wT/TRoMYoAYrkSUW1ER6BjJxZ14ZQFLo2hiyftQ5CiQIqjRi20NH0EHHfpajc+6XSjFKWPc5yHSrNGKacyymh1RRX1QGgaTp+qxg5DQPNRbI1UQhIKnVvjJSIoQtFpeHPOw8sx08gGkdpGrooyz1jFUrTcfNpfYIWZx0IKCooZmF9FbqmY8oSbAVRK+cYY4hm8edTIv6uDLSBrk7u+9LnWHH5VVQtWEw2O8W2bR/Cssuorv743+QeI+kM/32wk7r0RjYcW8+P1v2IuP3Gocw3kp7xHm74w/V8rP409KNfZGKig6VLHiISmWFM3Li3m6//fg+3vKUW/6+uIntiJx9uvQm9Yxs3fOvHxD7wYcJvuxqA9T+9n5GhWs79UBu6odD1EOVl7+HAgW8w8nQH/duP86S9mePRAnzxJOHjB2ksK8HoeAEOPc23s5fx002HaHEl/zWsYxT5SH5wHlN2J08+/za2PNTHnAMv0e+PM9Zr8BF+zZbBd1G7Msidv3qQs/t6qTWe5zHtMr6ujzN44DomjR4M334KZTPn9TRSOFLE5sRm3KG97GcP67Q4Y6/sYuyLH2HwN/1M+RQ/qO7lc+4Xaar+LJvWB3k0qbiwc5Kzulx+H7qdpfFOftr5LtakHRqsrZS6m/ldVwtnbnuFovl9/MA+n8mMYI45SjaVwqncx1UDR9ndVcCXCq7FVxXklICGXb2DbVgs2OHgOlW8b9tDPHTqGszUGM5ZzxK408LVNOLvm6C762km+hN0tbeSnGdS6H+exhMNPNN6EelRwcTT4xj+AG8fGeIP1f+Dj0XEtEk2dabpjazgaCbKKUfO4Au//He292xnX+ETnBHezNjx8/hRaIAJAZ2apHl8hF+UOzgjCd6eeZm71tqEMwEYqmNL5ijvm2giknF59vlWUnY/55Ut5N51pTj7BjjLt43OCYvAWBVVvvls7nmI+c8E+VzA5sN9+/hDSHHW+DDtdUeIDR/FlxrFd2KUexclufDpzTy17jyeqa2i/MX9jCQNovs0misfpNwPLzKfwQM11B39BIfPvIiazdt44oXN1CxZjmUfIZUKMbbrQo52/w/mu06lpneEjXf/BDebJXjWWSQ//jEOX3Mtz3zzUaoXlfM5fxnf/tO36Bnvya/1OW0FLFxXwe++8/JfVtR5zecQvXtI1hzkU4cyfGDbQXaOeD800ucj/oH30/2Nm9903tcHVtVw/586ODYw/sYn/x9ECMHKK+t45dlOju3+2+S3/UPycgsQAH4vhNgihLgNwHXdHcC9wE7gEeB613Vf3wU/S14/18XSGqg0q6kxE0SCIWxhoQkvGlLR1IYuNVrFJJVihOXiBNXSYbXs5BTRxRLRPat1IT3660RZZf5+hvAILHSVRAARrQXL7yk5mihFMu3V9lQclVOwpqFeIpfXZaoAUa2QhU45emYmchASqf+lZ4Dhg/gcbH0lmijAUdO/R7nonRQsEr2ok/JQQEPXE2hacFZT9QuuJFJYlLtawzMUBUIpaqPl+KRJNDtIItuPEiHPay0yeWiYcRK0y5LNmLKO5sJKgiKVj8JoogBFDEPWoosiKsUIp9kzSrkh7Pz13vk2dWEDvwJTs5G52lxxAZaah6as/LvCL9IsYCY6GlI2BUYTpqzLH5PCR4EexHJmQwSNHFSu2AhxUesHiRQUAa9Nh3B02yNN0v3Mizbk2szByXw1BESKotp6bF8Ay8mxKgpYaEVY7KsgoASrVA9OKMKiIr9HNQ5oejGWf01u7GcIJAzTIhivxNBjaFaIlaqckEhhaRZWzqgvMcKUiXQ+Z0gIRUz4achKHL0q16ZASEVEKWrMOA2WVyrFFjZKeOvA0hQrRJolEiw1Ux825F8+e6xMy3MAaAqUorxlPlXzl6JcPyoTQLgOjlpGSNks9JUwJ1aKEB78cInoZrXspDl7GMfvxzjJ+NdFKbZcgmAmh04ThdhyEU4wBD4HNG+85OvMjcg5MKTwYZpleQr3k/dONneOYVivvpw0kmqzhSX+lfljy/xV6IFiCBRTUjsPzc3BFQWcHyhEKoWfFALpGQxCMddQ1KoUi0UPiiym349hJvJEHhEmsS2LuugSWpwiTMNHNLISycwzSaUR1/y02MX5Y0k9QIEKEpQuK4UHI1zhb8KSOrFoDCn8KOHiNyMI5UMISXlRMVKE0EQcJVxMkaVi7jIMx8ecgvn50gDTIwgQjs9E3VucOSzxz0NgUmMUU2YUUCeGvHIhQqJy+XVCGmRzJGOW1Am5o4TkHGy5hHC8GtCQQlBYVUuyqmZmLVkzJEESm/nZUZI5aLUSUSwxsw5dPAdSPH46JXOWokQ8N98h5ikP7l1rJmjzNxBQvtw8eSaW6fMBggwzDg1NeI6uPyd/NwZa77Ej3PuFG2lZs46F51yI62bZuesTZLITNDd982+SdwZw86FOrEwHHSce5EfrfkShr/CNL3oDmUhP8G9PXsf74lMUjT9FXd2/09ryPSxrZvO80jnE9Xdu5qtnhmh+8Dxcw8+nyq5l38AIn/nOT0i87Wri110HwPb1G9n5jMbCdUWU1M14McvK3s2Jzt08/Me7uSuznoP7dnHZBefx4Rs/w6kXvYX7/vNGJu55D98t+y9+9Fwnl05KvoJD9OJaQheUc+jId3jkiYt56scZMuPt7GlqQxtJ01pzlAdH/gXlczl4YD8Nx9pZ47uTX499jJuMGCMnFmMmHiQzVYTh9LB2bIzOYZPmgSZaD0d5uHkPpWODhJ7dxX3XzaXwpQoqhM0n5yv+1fwKVWVXM/zsAv6tHK45OMo17SO85PyMkvoDvHKwGm1ngJWpNFH9Zp7qq+a35loSJQN0VQbQjx9CRNsQvbvwL5zi8uOvsDvj4wvONQTio0RisO7Bu4m3HePwlghkHc59/kk2ts5jIBFj/2ILe4OBMZBCe28pU+aj9O1bxpH1nyRkxnkp9AJHjr6IOfwsS/YW8ljjmTyzoo2R02/ihoF+lL6N3qJHOPXo42zzNXD5+fM5TjV/yJRSeaKen9xzB5dVXMa8Nj8XTw3TN1HLC1OHuHR/mmOBEMbUED+u9nHBzjpKjHYePDNErSHo6K/m2FSGL5lpNokmdm85jfrok2xdcSGTi4rIblE8drSY9q2P0BI6BRAU+KqYOxLiHr/F2yczfCFew5fHB+mJj9BnSOb27KR7zOT7Z67hvx76HQ+sfQsvNdYTuU/Rt6qTwv3zKa2/k6ZJwUZ3Odv75rMicge+wX4Kj53gUFox0t9JQfUcdm14mtLYFzhU9QDl6Qkme3rY9fR6ACJvexvZC99N8pdfZHFzikUX/ZA1E2n+++H3z9oXC9aWEyvxs/Hne/7vm8n0w+V3YO/+F/xiiq9P2Vy19QBHJjxPXvjKK0l1dTGyfv2b2aqURR0uWVjCVx9984yQr5ZgzGblW+fw+B07mRhNvfEF/5D/k7iuW+u6bpnruvNzf9ed9N1NruvWuK5b77ruw3+undkym+xAE4VoMogt/SR0P5Y/R6mfi1zYgRD+cARd5iC84QQVc85BEwXowsV5HWiWkhLDtvOKX6NdQAtD1LiDuEBcS3Ja3FMspgtha7qRi6YpdKHhkyZmrnZQfdWlhIw4powQFhZBptCTqyFcmb/nAqeMiHJY5MwoFNOKNWZgpi4mIMXJkZ2TVYzpJ3ZRyqK86q2z+uXzT6cXzP4tjhYVM6+ymVXNp1LsdhPgf2c2VSKKo5bllMUZ+Nj0MwlhIoRAE3F0WYESM8gWXZRSZDSzwCnLP4Mh6zAVJErn5JXB1bKT6HR0R3gZW0oEsGUDy3x1LHRmoqiKxCyFH6AgmMDK0XAv81exzF+FeVJuoq6HiUXWzUDhTpJ4aTkNre+npK4RX9AzuqbJN2paz6eu5W0kk2dSUruMWGk5mm4QiMa8EhBCItDQhEuksBjbH8CWSeY7pUgRQQoLiYPAB0jipeXEK6qwlEGoohEMX66m3Ow6lGVGhCoVY/lsxCpFySSRQIRwKFffz3CIaRrnaqWok8gRDOEZsP5YDL8MEBSznWNSGPjDM3MZDpzhjW1uPjRdx/YHABeDCLY2L3+uTxkkC89EF6X4RDq/nyQOtlWBz/DIWXVRgiaKvTIKYma+BBqG6fd6ZJoQi2JILb+2omKSEjGWd37M9N4TUzZx8h7QTlpvp1W/HU3MOO+LmKBa+QioIgw5Y6SgNDBsbH8BxaULsKhCEwWEY8ojr84AACAASURBVHM4JzieNyhAQLSShGFQLsfwizQrA6tmQZMBkjLIZWVz0aSFT2TRRALDjGPKZnTh6ZRr69ZQa5YghEA3vbmqNuOUhBajaxH8ufUrRQBLLmJR07tpseu5uLCFq+y5OLkabqZpzTKCgrEEgUADkcJKHGv2OnJzxottVmPJBViyBb+cSyLpvReKVCWXGOUUi7F8Sk+jOTGrDaXrKKWxwFlHRCtECIkvFEaJILHo6Shdz4+HAMJOiGguihktLcM4CX7eGmikxPAcVYb0oLlIiaYFCIfaTpptRZndSJEYJ677MYSdXw3TzKXTdPsZd8ZAU8KD+P45+bsw0I69spOf//snmbf2XFZcfhWum2HXrk8xOrKHea3fzxdx/Gtl++AAtx0+RsHwr/jRuh+RdJJvfNEbSDqT4vsb38ol5laai1azfNmjJJPrZm26PV3DvOP25/hYfT9nb7wMas/ky5lqNohivnzLbZRdcSWJ668H4JU/bmT9nYcobYix7OKWfBudnZ3cf9dDPPfsWfT49+Dr2MVV77yapralCCGYt/Zc3n1+CbcNncJ3tll8dkzjkniI4n9azGT5IZ555gKeeehR9t5TRnCsgg2XXU+2c5Lb+n/D3tGl9E3OQc9MUDS4mfMC3+O2kX/ls3odQh0iXvZD0v2r0f17OCerMT7sJyMyNDkW17ztE6zZ5rLu2Sk+d3mGU+QaKtstPjvP4HT/7QQiKyk9ehmfNMa45uAoF3VkOBL9JeP1f6J/TOe2g+/nLX0Bllo/Zqu/huEjpWQMnZtbL+E37a3sjqygMLMN3wKTNd3b6dPG+YH6dwbjSabm+PnMS18jvOwpfvv8PCpOaKzZfphH205lsKqU8uROnth7CsFfw1C5j+5FE4xxMUenXEyjk/rDlUTua8eanKRl30OUHd1I65Einli9hnuffIXB7Bxu6ejjsaI/kiiVrJrS+cS9W7j96iUMpwt4UATJuDC8fhjzmSCbltSQnT+CGOvn2QMdVPRnmFOQ4K5yncOWxuc2a+z0D/OHxUHa/Fm2jwrssSRfI8vPh9sY37OOD1k/xKoZJdjq0nagl2PzWsiODbDSpzHZ1U3FtjlsjdicNtxPvz/FbwNhLvZBcryfPttgxcBm9rg+7onU8TY9xPM1c9hTW0PgVj89zmYO7ZlDQcNdNHUNs5tGfi7Po3DJILWP/obCUJBMVSM97X9Es+t46vbHaZz7X/Sdd4I5h46x8a6fkJqYYKR/kqd7G/Ff+yGOX/c+Rl7awT+d8302De7m6We+ll+zQghOf0cD7Tv6/jIijZKFiLP+g1Dmu8x/oYe3RkJcuWU/PVNppGkS/9AH6f7mt2Zo//9C+f/snXeYXVXV/z+n397r9N5nMjOZZNJDAqEXQaoIKGLvr/pixYYdxIKCrwoSmoI0RYSAkBASEtJ7nZTpk+nt3rn1/P64d1oKCYi++v78Ps8889x7T9lnn7P3Wd+1vmvtz55Twku7u9jW8hZy5E6D0sYA/nw7qx7d95+qjv+yEMksqZz2jSrmASAL2ahiEb7sGnKqZjAuIxQEGdVomjD4LKZqBFFEEXIxijOnHcuflzLaxj3FAqAI2VilWlxS9ZQCFAKqeVI6ZTQYOb9wLh5fAyICtUY/1aYMsktnUjPnExT5LIgCWGxmLsovxCSmFo1mSo6EJsrkCiHyfZN5UCbLiRLFFCafT6uhDlUY74PpzkpV9SBLZqwud7ovJGzWOmQ5F6bka4tTKvxZ3SeXYo1DYnoqga6Pe9UrMYqN+HLz8QQm0wsDWWdhEGtS+wpuBOQ0WRo34lLnlmQrqnF6RbbJc+gIgpmCklLk9BpR4wa2L2dS3mh1eylruIKSGTdR4CqfdgxNTH0urX83ABaXC3dGNqox1Q9GMUUkJEXB7HCm+0vGOEVWqqgaVmcOsmwhkUhF8INFFdg8PpyBjPQ+U4kRCIg4LJOSz9LaGxGFVGGL1MLQGkZZ5ZzsejJKUm2UBR8GsQ6v91wAMgsbycidjyoWTYmggtnpTOURiVloYjEe9+J0G1LE02CykFFYn47iqdhspYjCyfIExWl9P+4MsPsD2L3+aVsKiBhNeanfvX5ySxfj8OUhC/5pZEgULMiKE3dmPn7vRShi9mS0Qyg9oQVKugDETHMOVRnnIAoOTNIcaoUwFWIeNkmdsjTTVEmmFZvbiyin1/mSjNSZGpjlqCfTXkigoCxNYMAtJKgQExP36ngICDjOPx/XnEsm5MTujNR/T1YODv/k+NLEcgxiLdnFZ01zngCoBjNGYybnWYwEpUJUsRCbK5iO+KXuTYZ7KSa5HgEw2acUvxDTS3uYUvdJEVKRQlW1Y5etGGQbmqSiTpFcqoZJZ5LJbkcUjXg9Z6OIORjF2ahGE5oSQBMr0cRyLC43oqAhCmYK6+ciyTJnSzEsU3immq4oqQk6opCKRts8PmxuL/6CIpyKCyk9f42PIUWzI6Ulq7KsYDT7meksoMTgwyoVIqtGGi2TslSnbEdKR0GNJj/BnAY00+TzOUnJBTLLU/O+0WxFEpwoaorkj1eZtXl9gIBJnOQE7kAmJsvx0tTp+D9N0PRkqoz3k9//OmfdeAuN77oKXU+wa/fnGR7ZQ13dQyjK358bBtAx0sGVb7xMEbt57LwfvyOyxlC4lT+tPotg4iC1tb+jvPSbyMfpzvd0DHHdr9byEdvr3Nx5O+Qv5O7eJH/wnMsPfnwHxdddg+cjHyaZTLD60eX87YF9uLNzufgTc9F1nQMHDrB8+XLu/+19CIdCLMusJtO+kQU32smtqZ1yomd5Ylc/vxOu4C7djDS0DsNZw+xpvZVVT3+KbY862LcjieA4hzWzz2WPIPKTsb1U7HuNZZujPD3Xhs/0POdZl/Ol0Jf4uVaClvk78gLriXVcQ1IaY3ZExTQKw8oQly5ZzDkf/whPPfpVblgp8LP32knaHPxm6EF+mreNUf8WkEycE/44X2s7xpKuMRb1CLTkPMU+31rihgR37/wA1w0pzDe9gUlczTezPsjLdTP5pe3HOFq6OKLmUOXoQM0OccHwBo5pgzyQ+CTrLdkcmZnP3Ru/Q5u9i13HCshtGsIQjbJ57nySLgMeaxsVeW9w14afI4hJOj7i4JHXL2TDRgWT2c37v/9+eqOHsY8aqGruQo9KuNQ+LKFRCvf5WJNZwJ8Gq4nFjXy3v4v7anpZWvwcxpDEXc9t4weXBAiP5fPqyDyOaV6aGGFe1zaCAw9TlfsSun09B19vZ+PuQSokuK0qhnHUx892t/F4hsCmPIGzjTa2qrvwxm18N2HloaOzcHSW0VRnYfmMz+AvCFO/aQvLHX+hac82lllDMDJK45YM7nSZ+WbbUe4PJrFaWsj1aLhG+xBG+7kotoatJhOrNndTXXYxu4MeWr3ZWB9TKIh0YswsIlj6HDXbdjOCjYczr6C7yEv949+nzD6MUlIFiSP0dXTQskEn8+L/wm7vwxKP8eojD/DifbsomRWg4DPvJ+P736Pts/+FvuoQXy57H7ftuY+BTfdPPJZGq8rZN5az8pG9DPdN96a9KRpuxlDhRont4sN7h5ntMHP99iZG4gkcl19OMhxi8Mkn39a49Vo1PrakiK89s5NY4u2RvOMhCAJnXV/KsaPDbHmx+R055n/wzkIQNDLzl5GrushUJte3SS3+KyILHkRRTK17hY7JYkut6ZRXQEnjPHy5Bbizc9LHEicM2dRngYzMdyHjRyaJojjRDCYEwYAomCcUIAICeTV1qJphIund7s1EM5kRRIH5Qhd+UUUTK3B75k84J/MNbsrNGZhsLjRx0kD15RYgCS4kwUmBMIxR800YxaI82T6TdfI9qgoFSKINTG4kw+R7UBAUAnlluAOlE9dYNecWrI7CCVPHZvQjKg4IVqd9ztJEMRMEEVlVUcViXMFMihoaT7gHsug/7ptJsigIAmcXnssc3+SSdmabh0BeOtqYXkA2kF9EbnUtRuukBNNoyKWw8mps5tpphj6k7Iz5wjD1llRfuh3nIgse3PaleHLy8KTvqWY0YjB6kWUzXqNr8roASbATLCxBTS+OLMkK/oKidLvECfIw7VrlU6+hNK7ItdlmYLGWk5F5KQaxDk2YJIYGUedsKYasqoCOK5iJZjIhywqy4EfTgrjTpCrVjkmIgoYkGcgsKcdgtqAYTiSvVjUlsxQFI2c1XoXB5Jh2HWW1H07Lz2DGvE/iy5iUMqoG47QIqNO2ZPLcaaNXUW2pJShOgpLGeeQUXoLLW48oShQ1zMftnXPcVjp2X4Cs8qpp1yYIyoQ8dBwWc33K8BYkbC5/ejuBsvqbUUQTjVISVUxFejKKphO84kAxBf58FCELWcjELgfwmFJEwGC1YLSknjNZ1ZBkGfsUoqUIuVMaBoKqEiidvIeCIJJZUo4npwCjIRspHSmWBDuiYMAZyMBrmG7jZpenoj+aoiIJdgwmM45AMC2rnT6WM0rKJ8gGgCczi2BRCQbNiyaWTcg4p6J8/mIEpIm8vdL8GWSqqXnEZq1DUSbHlSCIGExmZNGDJFjxZlSgGNJzUu1MNJMZV8aJK5wIgkiFMIBHjE7Ika323FSxFkEgq6wSk92R7leV8vmLkRUFj3sJdnsdvvxCyupunBiDHqWA83LOZVI2noeAQKCwGG92HjkVNRgt2VjM6bkLYSInt6BuNoqqpe9nqv8tHg8GkwXDeKEeScLu9ZOhulhauQhnMBOr3Yk3d9LBcjL8nyVofe1tPH77V9m18iWu+9aPqFi0lGQyxq5dnyEcOkJ93UMTk+Hfi1dbX+WCl+4gqQT588Lr35FqjX39r/PauvM5EAqzaM7zBN3zTthm1f5urrlnNZ8S/sAtxlUQGWG5qZZfZl7PD+/6LnWf/yzu97+PsdERnvrBt9mzNoYzo5xLPzubbdu38stf/pJnn32Wwow8rtMXkWczsX3Ls8xe8BvCic10dDwFQPLgah599EHuDF/F+0QT22aZMS/q4s933sHKHx+hbX0Br2f3oeVcwwpXCa+XevjSnmPMb7qX5wa+iCRH8Em7eLi2kXeHvsNaLYBWcDtlFh+9TTcwpEN9LEyRMMBR61E+9r6PMnPe2fzqZ+/nxj+NcN97PBREAvxq91dYHDqLlyyP0tX1GOfzLn60p4WKwSiz+2Fz6XNsN72Czx1h7xtzWNhTTGWygzLL/ewbqOM9jz3GinkLeWy0jjHRij3Lh9PYz3XhFWwyhflt6D08L9TRUWXniw6ZPXGVIZcNfUuMw/4IZ/tvJGDOQ7BDfsUWrM8ZcR0b4uAF2Xxry8cxjozh8LSx4JKP8+ivfk6iez1Fg1Y8owlWzCilybOM87MfILujG0tnNi9mVbEivIA8Jcmdreu5393PNbkvsqYtwgMr1/DBuQMISQ8vyNVsCvjoS2zh3LalaH1GSrIOUul9HtvBYfa83sdBm4t78zooaM/nO63d3Flu5qBvmPmxapoLHkYSotxGBn/afj2msJnC4jU0Fb8bs6eXRSu38O2rxnhcGuYspZ1ki5mF3Q5WG4zc3N/LrUVB5ju34DcbEBMhxLZj3MjTbHObWP/6QUz2S9laVkSnkoV9TZiBV4aJz8jFtXAfc19Zg0EfY131LNZYaqhe/zAXaa9gyvQhyQleffjXWNXzMH3mYsq27WDnSy8w3HuQ+e9OGSfWpUvJXf4AfcuXU/XgERbYZvLtDT9Af+ErEEsRstwqN6WNAV68bxfJMyVEgoBw8Y9x5G8gtKGN7wgqWQaVq7Y2MYBAxne+Q9f3f0D0yJG3NX5vWZiPANz98sHTbnumMJgVLvpoDZufP8qR7T2n3+E/+KciJVcRqCuZR11h3UQ0weJ0k10+WfDBrtkRkFMLyJKKCEiygsWWg6o6pqkjzNIcRMGKgIAoSpQke5hBD3Z7PYGssya8xLIg0WhvoKTsBky21HstUJwyJjQ1B49nCYIgoggps0ISbNPWSqpVFWYYzNOiVQIiiqalJFBCCZpYiqp4sThTxzeYzROGbG5NLf50bofZ4kMS3GB0YPX4EKfI2QIZFxEsHHf8paSOVkfGRHQmy5LJ3LQXWwJEwYnXsxS7vQ6bNRXpqjGVYbI7UDQDwaLJ/C5f7nTJFIDVWkVhxeUTny2qBaM8JRojCLizciiuuBmTzYXRbEFKS+ayK6rx5OSl2iJpaJqHghlnT5efkYpoaoKOIR3lkdP3VVZTnz1ZJfjzCsjMuWTafnavn7yKs8jIW0BGcVm6QMqpo+OerEnppN9/MZoSRBa8uIInGrDjcjGDFsBiLkY1WhAF7QSDunTOAmw+PzlldXhycpFkhaJZc5AEG1ZzzQnqIovDSVZZJUUzp5BjQcDq9hAomN4vAgI+VyaSJGFOS3udgSDuzOz0bgKSLCNJEuJxUR5BECaKMASKyhCnLE/kyysgs7QCkykXcYoTA3s2uFPPgCCKqKobMb2UgGowEigoxumsB8AlO8gyT1bs9k9pe1FD40SEaCpUoRij2IBBlqgoLKKgflZaWgmaWIIsZKJqBqye6QTerpqZmTUHRcyaIFAT0BNYXKnx5MnKpqRxfnr8p+YAWfAgIGN3lkxEEQVBQFG1dCR1PNdTwRnIQRMrp0UxAapdkzlXwbyZWCypz56cFPmTlPTSF3n55FcuoaDicowWK/78Ivz+S3C7z8YgpgmlxYqimLE5S5HFE8vKj6Nqxny8mam+rvfXU+qsQBNLpqXljE9zDusSZDEVRZ96H7T03KIaTWSVT1cmAASE1FIH45BlK2ZzSopodXsmIlzaFLKdcnzJCIJwQmRRUVQEQcAkzUEWA6hGEw5/ENVoRFJSy4XI6QW25TRpVTXDtOOPl+u1mB3YnW4kQcKUjuiOz+uGKc+yKJ7cwTDx+5v++m+IeCzG6398lIe+9BkySsp47w9+iicnj2Qywo6dnyAS7aKubvk7EjmLJCLcufFOPr/2Doac13P/jCqsyt+/zllr2yNs3voBnhowcPX8J/Bbsqf9rus6v121n088sJY7pLu5ybweYiGePOd+vm04ix/e93MW/eC72M49l97WZh7+8ucY7q/A4C7GMyfML+75GZs3b+ass87i4+/9EPkbNI4lDrO7ay3vuf0Ogvn1VFX9nH37v0HHn3/AEw/8hO/FrmeGVcA668/kdH2eg68exWRzEXGauX/GYfT4LTw44qFpto+P9Azx3r7/5vH+byAT5XzXZ/ncwd/wulxDa6WLkeAjGLo/wI69FyFozVyc7KZC7uWNwHq+cP0XyAsUcs9dN3Dho4f43VVutJ447+/7ML3+Dn5XtQzJ/R5mx4r51OEvU9S7m+rBCH+rfp6j0p+odUYQVuaQ6LmeovAA53uX0zcWpGPrCJdVvsrNL/2Cjrgfd7bEMnEl70k8w2+dKo9HL+cVFjBUb+UmQlj//AX68mx0rbKwP3cQk3UOa5UNtBgGmesP49gcwbIyTnxpAx2GOVQkDvN8rJTBwMf51fduZ2Dli8w+2kFwcCcrZn8cxXUJ2+asICfHT8Pun1N3MMqQo4G15fN5zXoZXlnj7qYVbHMdZab7NbYPlrJnXxmGah+u4SMc3u+mmcWsz34DT9xDblMBPsMYZzlfZiYq+tFRHsgs5FNaO9JuC5/sFPl8vZseex/VrTOpttxEu3ELNyp2BtZ9HK/chKEozrHa2eQZ4vzogTivlozwxXoo1dqJbPehdGRRMjpG/dAxPlZexMKMHeQrEnEhzsjhCB/Qf8dOt8bGwVGMY0vZMKuBvlAQ18FjDP5llNbyfMSbBln88kqCkWHaygP8QVqGQSvmRvX35PujgM6jt32NgrnfILpsNiUtvQx3Psno4CQBMVRUkP/UU0h2O9ffeRBzk50nW1+F/1kMR1Ml+eddXkQskmDDX46c+UCTZOTrfoojuJqB5Zv4pcdIoUnjXVsOcqyyGteNN9D6yU+RGBx8y2NYkUR+fE0tv33tMOsOnbiQ7duFK8PMspsrefG+Xf8pGvIvCEEQCGadh8937pQvmWYMFDoKef/Sz1JSefO0fZ3OxlQi/xR3fkZpOapQhCZVIAgCBiGJTapAls2omgl39qSH3SgZcPonjc4JAY6QypuYqKx4kqhDnRJBFcBgsVBQl6oKrIkz8HqXIQnOlDEtOCe8xtllM3AHK5GnFFmQ017k3Jo6hHTBAVlVCebPwijOxJOVgyTLExGQcWiaf+J9LIrSxMLbBRLMFpPIsgWjMRvNEMBmrcEimiYMJLdnIZpYjogBY3qtsqnGnCybcXhKOCXSfWLzeMmrqSOvtiH9tZqKZvgC6XYpE33nCmZicUwap6rRgqa5EUWJwpmzCRaXUVA3i2CaIEuSRlbOVRPGHYBdteAzOXF7GgnmzMfuOz7yl4IoGBHTeUuyNkl0U5EoAUGQyc6/jKo5H5i+43EyaFGUJsj8OCTJiChJ+H0XYvcUIMmTDoPUISaPMd727MoarG7PRJSDVCsQJQlnMBPflIhAUk9SXFzLzcs+M7mtKOLOysbhDyCIIoIoUjJnwQnGMgIIGFCFPIxWKyl3QYo0yaqKzeNFUd3YbDUTu6hWJ5i9E206HrKqklM+FwGRCmMRFcfJTCdOLYgn7C+kv9eMVi7yOpiT4Z+QJk7uJ2NR5yPJCuXzJyOPOvqJz7xpvIjESYqNCMKEtFYQFIxSA0XVl087RtGsORNS16nXNxW51SlHiNs1n9mBciyihWDW0onfJ4+Xus+SrGB1+dE0P4Io4spI5aBZ7L607BVEyYTXey42RxFl8xad2HlpVPtruLjo8onxdTKCNR5NFQRxWoVYWVWRVXWao8rqmk56ZdmaKtIxpZRmna+OGZ7J85gcjpMWN9K0AG53qu2yMtln4/1hdbopnj2PrIqqSWWCIOE0OJGnECpR0DAqVSePbosSC/yp/MNx6avdF8BktWGUNd5T97XxXjhJCyfxf4ag6brOgQ2vs/wLH+fwtk2859s/YsG1N6KoGonEGNu3f4R4fJjaGb87QSb4drCpaxNX/ulKtvbsxpr3Xd6fFWC+8+87bjIZZ9/+b7LnwI+4t8fMRxfcR759egi0bzTKLfe8wIMvrOX32nc419YMi/+bx2p/xhe6jdyx9kUu+OXPMFZXc2DD6zzyta+StC5jwDJEi7yavoEerr32Wm655RZK/QV0/3oHhwa30ao2cc03v4/F5SY+MIbwuoecHXX8sns330zewDnBF1ma+CHDjx8gdmwmZ9/031huuYH7fMUMHvkS7QM2LDPtnKN08L4d3+DR3jvRhEGu8H6YtfEavjVwK/lNz9PtzWZA/ByucILzjetYFEtgMg2zMn8ld737J2Q6srj/y+9iwUM7+fnlKoEOhQ8LX2Iwaysfm+kFPcoPDrlQYjfwxe4fEYh5+ErW13hl6BkyFJ3eF6rYN3ArGaFervc/gcAxdm0Tmbt4Fw/3NWDoFHH6bVyivEa1sIFbvS62DV3Na+ELiMz08rmeuynd/FU2OioYfNlAa0YfV1vbmCs20283U1r5Z5Sul3D8XmA0K5fnMisZto5ww4Lf8Ln9TyA++BOqOnfTeKid1kU5fPDsr2OZWU/AGKB042LW/noXG3O82JNP8a5Nw+zx1LA6Ucp64Wy2uTK46+DLzLa8gpSlsL1rmA+tP8hz1ywiK/A8h7scHGn6KHt9Mq/7XkcbM2AMq8wQXuWyY71gkGiryOJH4gD3bu/Dtz7M130hbModDMWvQ5n5JNtLf0GFpJC37lsM+zYTzclidFYdmc4Id/wuSm6LyJcXRzhY1IE8amXX/go+1dFLzkALHy0JkJ/dySzDEGISeltFbo78hk7TGC+qSQwDs1nXOI9Qq4fM/m5ML3WzxdHI8K1hqlv/RknLMRLeGL9rcbC3/Bs0eDtZ4D9MZKiD+z77eXaMvBdvmZFgbxdPfuc2QkOTxEiymMn43nfJuvMOrtlhw/xgH1vjM9AfuQ6e+ihSpJfzbqlix6pW9q3rOPNBpxgwf+yrGN2dDPziZX5iGuZCj53zN+5n3TU3oJWU0PyBW4h1vfXFogu9Fr57RTUffWgTTd0jb3n/UyG3ys2SG8p57t4d/yFp/2oQUsb8dMPrxJewomknGqVpTDNG9LScLF2QQ5QkJGHSkz49/3/SyDGbC1GU6Qbc+PkkcXr1RADKLoKstPTJZKagbhZF9fMnomy2dO6XKjuxWMrw+c7HaMzEm1lFRnGqmqDV7SG3ujYVERTsGMVGJFlBlCQEQZkm58kqr8SZjvwoimNCSierKiWN8yifvxhJYFrBiJShq1A+d9GE/FBTPUiCHU2sxuVKGV2iKOHLOzGadjIcX0BBEAQCgUsnPNti2kCbGknyFxSRXVlDSWNK1VJe+0GKG1ISPNVgRJJlNJNpgvCcDIooU+M6Ud4kHbdWm8e9BE0sTfWHJE8vZpZuuixb0dTTp1SYbFNkt6oHj+ec1DWewos/noPr812IwzH7pNukMPkcj0djMkvKiesnr64ryQrBotIT+h5SuUEAopCSAsvipNxPJz4tB9GpTX++8+um5Gye5Nip41soqnjfKa8kt7oWWVUxaOnqk4I6bZw6/G+eM3TS854kZ3g8Mmu1VuD1LkMWghgMqe9SY9w+EbV6MxiNOajqZC5ofu1kH5hsKWIvSSYyvFk0WJadtH1nmtNskuYgiWder0FV7QT8qaixop24X3ZlqgaCeJzjQBQlimfNPWH7cdJrkuagKHYs5pJpS2MFLUGCU3K6XBlZlE0hyuMQBAElLUFXjUbcGTnjPwDgLyxCVhQkWUYQxIn5YIZ3Bstyl007liROjilFyMVqbkBRtYn5KeXYSR3X7HBSUH0Rdnv9xD6n4yJvHl/7N0Hb3t2sfvQBhnqOsfDaGymbv3hiUMXjI2zf8REEJGpn/HaarOPtoGOkg7u33s0rLa/wqbrP8Lf4LDzxJF/MP83APQ3i8WF27vw07UP7+PkxA3cshoWHwQAAIABJREFU/Q2VU7wBuq6zYtWr3PZiOwv0rTzrfAHLOV8gkX8h9z/yBN/Pi3FPuIdl3/gKup7k1d8v5421O4hm1TCo7GfW7JnMmn0FrnQ4fWx/Pz0P72bf0AaSFTKXve+rxA4P07vhCGN7u7E77+fpfo2nE0u5avRRbEdHCSy4mItuupr9gwY+9tyrHBxUaaCeyjEDL82KUtDZzdLdrTyb+CpWw04ut32PH0ZvoCUxg15bN13dNXj2rmK4fBGCqnDoqAujZyuCd5TlFzzEvvZtrPnUe6g4HOV7V4nc1L6AGvtl9OSs4MP587GNevmfTX08kF/ABR0xRhSRVQ3Pce3hUbp8SR7tNqEWj5Dd+gJ30IVRfoM9TW7K8w/x8L45DPuDZBWFuDh5P6tMRj6ScQ6ewzPZHK1AKLWzaOMLNLkc7Oybx9L2VziSNcRQMEjrYAn9qpXqkucJrhrF/DeZQZeLlXNmM0NuwTn0CtbfFtIR6cEtiGy0zOLJ8wpows+lJivBHduYIWxmXbeVrRleDuUmmH3FHMT7dnDTyjLuXdaI53Af9F7CH/Ot3Nr+U66K3MQXrR9n+XANY/fs5YeX38hdTbezq/18mvaeg906i5ccj1Psa8YxYKdwGC7b3sKfZ8xlablI4ZG9aINlrB8KM5u7qFIELl5/DnXZr6HKSQYSnQTXfYP+8geJZZUwrAfx7H6GT74ElUeDrJwZZ31tMx8asvDXrdV8P76D2/0JPptn4TKDiSs6m1jRWcjokM67kw+z1nguT5hLOW+4gTVzdc569SV8swbR9m9gdcaHWHjlcuxtm6n5m4vtWcU8s02hyHUWGdUKy5SnWNUWRzPdS9H37kW67Sr2H9jH72/9NFd++4fYPJOJteZ586h49q/03HMbQ488wz5bMYHhdmy7GnCcdxsXfeRynv3FDhAEShvPsIKqpOD49Pvpv+cZev5nB/91rkxD1bn8174Wqm/4CJ9+6c/Er7ySwDe+jmXp0pMaFqfCpTMyaOsPc93/rOOBm2dTHjyJcfw2UDTTh67r/OWe7Sy8uoTyeX/f/PMfvDM43vNu9/owOxxERs98vb7xd5eiatOMJ0EQyNUyMaS92UK6kMPJYLWm3h3HWDXxndFqwyg2oogacaLTd1CM4JyMxmknkXil2iBjtpRM+TxJcgRBmDAKp0YQ7D5/usz0lPa5Tk0oTkVsxvvieIMudW5pGtFwZWRNyM8g7ZVPk4dp0Z/TjGVBlDGINUiydsJvU9v5VuaEUyEQuPSE7/z5hRNk02QuQBKnFCl403OeaHRnllcSGhxAlMvRTPY33T9QWIzJkc6fEWVE0XLS9jmdc06pRlJF9aTfvxkkVSEej5FZXkl4eJi2fbuB1LWKghmra3JOz7HlkGOblH2KooTRbCE8OvKm12bzeGk7vshuevvx59dmq2CoI4HN7cdfUMRgV+cZtd/mObGIjcu1AJ0k/U27gNTYmhwzEpJkpKDiQizpYjlSWoVVVD+fQ1s2vun5zOZCzOZJCaPBbDlhG1FUycx+NzZTz0n75WQR9ROuIZhJX0fbabc7GRmF1Jzm8AWmRawUzZDK9bRYkVWNaPjU1VlPhoL6WYwOpByU2RUpsqcq7mnrAp8ObvciRoR+wl3NCIJA8ay5J0QiJ67hNGPcYinE7s7Dm5Uao255MZJkQBQ13O7U0gmSZMRoTKkcfL7zTxpBnYp/W4KmJ5Mc3raJN55+nL62VhouuYK6Cy6ZSNYDiEZ72LrtA2hagKrKnyFJJ06yZ4r9/ft5dO+j/OXQX7io4CL+eMnTfLc5zP5QmD/VFaGIb3+CDoeb2bT1Fo6ER3hi0Mnd5/6MQkd60A20sG/V7/n+RtgdC3Cb4wUuuvy9JPO+Rd8fHuNXD93BfedeyvKSDBpzGtm1YwcvPvU0Q0kd2Raktqqec6+Yj5Ie9MmxOIPPH2FkUwcbev5K7uJZFDpq6frRGySJEsrcyADL+Vzr1ewSCzkvtIrZS+YjOx5m/7DMXb/bx+HIKD7zYb5AI1E9yWGnzgXrFEaJ050sosT8LJmmlXxv9NsM+LpZO2BGkNooDj5PxWAmG4/+mh05H8LkeJ0Puau5pPJS/ueBT3LOowcwOgU2zS7i26Fr0bwau4J/4Ss57+Lje1Vm98T4YamFTx+IsMEt0VP6G6pbN+PKAmXlZzl3zEez+zVCOa9xuTrEknYLdbrAC+K5COVOLlL+RlaindvddjISVvpiN9I0w82cA7sZO3yADbEalja9zgW9K9hcOkLSbaNxyMWoJUld9nP479NRj8h0+/wcKc7i7F1PY9kfY9SfxasuGUc0QcRuY2NBPYlwArc+SuOKB8kcbaX1nPkcCx+gYs41mPa42br2NdYv3MqsA/lc9WqUxxZeygeb76P2UJJn9ZtJup/g+wN3cavxEzwhVRH9Yy+N/osoduzjFdNOunouRAy9j11KG+bACo7kv8Qix2LO6uxnndtLYfMmItobLNCN1GoxtmsWnuuXuP/oXOaYjzK75n5czUUEDt6EajrMaFYrKl/GYbuLc7a3UZTMZL27jHU5ncy5qINn1pTwhdghFjhi3O5P8LLTwTdsbQx2mWk3aMzpf4lCZT+PeS5icbgBpVFl0ZpnMffAgivu5Y3O97BE6WD45pepe6OXprYKegYDHLE6kIWbqCh6jUT7ER769EeZveRCCvNW0/y3Jh74xAdYcuHlVF77XoT0xCnIMos/+V1+vSCXPz15Px/YZabriBvb1p/imHE/F13zY57/4wHa9/cz94oiDObTy44FScT5sXcx/Ke1HHthlJkbH2L1lRdwZ0zj6llns6y2kcvuvZvqP/wB3+c+h6H0xEpfp8JHzyrEqIhcfe/rfOnCcq6bnf2OGHTFDX6sLgPP3buD9oMDLLiyCM3090us/4O/A8fd12BRKYIoYrTaCBQWn/FhimbNQRBExkaGJ44DkGvMIh5NkSuTYmIoPBllPp0j3Ob1UeH1ceCNtWfcjoljo6OZTKjHFU/Q3yRnahyCIEwjS28Xb2XMCIIwUSQAoHDmZATIZLOjGU1EwqFTRlqmQhRM78h4fbsYP7fNOn3halk9tT2j6yfm4gqCcIIs7lQ4VSXB46Fp0ytVmx0OrE43S3MaJ4rUvBXkVNWiJxIomgFFM2DzpIm+kCrH73GfGFmZCm9eAc27tr/l8yrH9aXN60PYL6cKoGgGzC433S1HT7qvajASHUtVzXRPyROc+P24WgcFdbNO2MbqnlJhUpSwOt0omoHM0vJpeaFngpLGeSedC6aeYxyF9bMmCOGbwV9QdEYEzWg7tQNyXPI7FROE+CTE9q1gPC9WEIRpeW6ng6I4EKesRXYqcnZKTOno4+/rVMfF8WoGYCI/8s3wb0XQdF2nr72VfWtXs2vVSwDMvOhyqpcuOyGEGg43s2Xr+3A651Ba8q3TJuMdj1gyxsH+g7zW9hovN7/M4aHDXFZ4GY9f8jiqFuQTu5uJJJM8XVeEXXl73ZhMJtncdA+9LXezelhC817N8vmfwTrQBituY/fW1/nF4FxWJmq4wbmLn19RhNF3Kb1PPEnnJy7mnkuuYcOyi/mJXaBt4zp+8PB+EmNJtLFMaovLOfvqeizOVL8kx+KMvN7ByGuthIRhWgb2UhtciLJTo9nzR1rMaxg8FufIixp/8X2KYdHM9Y153DDrfP74/EFefKMYD2EWC71cPOom3jeLA2qM3WboHxvifHmQnLEAc6y/oVkP8oJ2HSvlGF1DBZiDj1MT0WmT+nnRoSH1FjPb9iu6sz/Jpn1vkPmDa7msPU57ZTW1wSsxGEx0+1/naTVIn3Itj6+Ostk2wqM5Cl/bI/KbQhlf4CfMGDmEaHVz8OWbGRY9XGnqokjfjNTawjO9eazPLmBHSYDZ0nqWJv7Ma7LGj9xuArG5/KLrUryDHcT3j5GUOmjs2c7ZA2FGjAJ/ndOFEp1NQ58Lk38/VSMbcd2uIMQFhq1m3L1duOimuTiPFWeXYuzYS3FXN/kZFxC3W5i780Gkg4d4svJSflR/OZe0P4/j0H4W3vAR/GcvYdveTta0OGg3XYIo91K1+yDvezGHVypuoDrrJea3r2fT4CV83XaAsxL3U9B/Mb+3nsWSniA5YwbOVVsYNK/mcDzAkaSXwebrGDW08kLOs1illyiKvp91lTPoEuxccHCM+b0DFIdDHDKMEk0Ok4zLvLjxaiQ1Rmn2ckoipVR3NRKzDBHJuwvJ/DRFW/9G3rFjbFXqONKeh1jTyapYgPymMV4ea+WLPjefyTdS5I/xvc4jvO4oQO0+xC2dv+avjovZoTbQP7eYc7f+CuNPJWa86y+05CsU9n6AwYyVZM3ejrRtHy3NNXSYYuxOFpPMrKRocAe7V68micSMpTGc7e288uc/sv0Pj9CQW4J7ZgPGmmq0sjI+WPdhHtRM3Lz1l3zZczMN6/toefpJ5Ofey/mNM9nbcj0PfrmLikVZVC7IwOF/8+I9gihge9d8jA3HGH6sm5F7D/MJT5gPzavkdz4nn/3kF/GNDLHkNw9xvlml4eab0HJOfCGfDO+bn09Fhp1bn9jOA2uPcNO8PM6r9OO2vH2nEUCgwM61X53Nqkf38eg31zP70gLK5gQQpf8z6vV/K0wY8vrEF6l/onjGRi9MGowWp4uiWXMmPk+NmNX6aok6ymnu3gSAfAae8Glte4s4mWF5Wlb4d0AxGIiEJr3qNtXGrMBJ2nAGOD4H6PiiFKfCRAHJt0E2/tGwebyYHScWD0vhf2cpDkUzkFVRdfoNTwFZUeAUhEEQhDO+b6dDRnHZtOIOFpebksb5J2xnTBOIN3MwTCX/7xTG+3CqeuRM8WbS2uMxXoL+7zn2+JxXfhI54TsNs91JLPIWqjWfAc6EoP5vQXgra+o0NDToGze+ecj1nYSu6wx0ttN5cD9t+/dyZNsmwkOD5NfNomrJMnKrZpxUx9/b9xq7dn2WrMzryc//9Gm9X0k9SfNQMzt7d7KrZxc7enawt28vZsVMY7CRpTlLWZi5kKRgYHl7Lz872sUVfie3FWZgPENDKKknaRtpo2mgiX29e2nrWUl2fBt+PUoiNoMlcTeOzh2Eelp5PjaDRxLnsE/P5fqiMT5w3ny0vQfoWrGCji1bOTCrkY1ZOdhHhzCNhXBbvcSOyTDoJCvbyjk3L8DhSw28+HCUgRf3Mba5n4QUIRaOk9QTdJo30K8eo78/RH/bAA6niQFZ4lH1Smo0Gws1A609YYSojk0XiAgJuiWB3dYoh9QksWicRco+zg4PMjZai0EcwmB7g5eCXWyN5tLWuwijZR+55k0Ma8cYjXqJ9c5jVk8TS1wJPJ0hAjtbSEbGeHbhMnw5S5kfOUo4uI7e4TzW+JZy1e44ciLGExkDnNPrJSEk+H6VwIXx+yi1baC/aREcmcc8eS8hcRfd4igH49m0qEGkRJSG6Cbmajto0QRudzvZrheij1YSHy3ALXbREN6Or2MMZUyk1Z9kV14/UVs+VX355AzEmNn9Ghnb+pEGUs/PoN1KaKaA0RNn9Bjs6A8gJXRqm7twLCqETb3IPf3IuQshr5YR7TnW98ZpUYI851wEmSb6SwNUDoYpGIygNB/AM+rEHLWB2sao4MQUddHs1ymX1lEdWcNOzctWUzvVw/BQ6L1E0CiJmsn27COh9GIYUYnqAg5auIq1rDbDPT4nw77/xjfm5s6tIgdsEqbhONVRnZd9Eis8IXKGmjB1JYgIQxiIIhvCLHSFCQxWYO4rI5GIEtn7IBzahJyp0JpXy05DLhHbfnz97cw0tmF0DPAVr5vdmspMKcY14TDd3UG6drnp01yscJyLCxfLevaxeOcTDDlV+hqNyLUh/EMVRMcOEC84gnBMpn1fOR2JAmKqip7UcQ8eRh8axiRHmOls5mjMQ1OviyqbSnbbAPGDHUh2B0pmJqMG2DN2lNVneWhcfC0Ldo0gP34/I00hBFeAwcx6muOZSBW1FC0ppWim74yiaokDGwj/+RlCfUXEknkoxTYOFLt5SAmzYmgY8/AQ8yIjLJ45g5mZAYpMGuJp5ppYIslftnfw+KYWNhzupybLzvwiD/OLPNRmO1Dlt2eA6LrOke09rHvmEIl4kpolWZTNCaIa//d9cIIgbNJ1/cx1J/+mqCor1XfuTWmnxkZGOLxt0ztutDRteoPoWHjacUcH+lEMBkRJnqggOI49a1aRXVE94WEG2L/uNRKJxBm3bc+aVXiyc/Gm82amIpEIEYsPYdDOUE78FpCIx9CT+mm92nvWpGSc5fMXs2fNKnIqa04bKRrqPkbb/j3kzah/U+M7EY+zf/2aaST5nUAodJihoR0nlQ2+E4hEe0jERzGZck+/8b8BRvp6admzk9I5C04qcR3H6EA/zbu2/0PIwp41qwgUFp+xo2XqcwnQunsnw/29/xQi849APBo9YSzuWbMKm8dHZunJC6680xjnK+M2fWhwgKM7t/3dfRqPxU6YO0+H7qOHMVitbyrXPhO82fvxn07QRvr7iIZDJOJxErEYiViMSGiU8PAQoaFBQoMDDB7rZKCrk8GuVLJ/oLCYQGEJuTX1ZJaVn9JDEI8Pc+jwT+no+CNlZd+blkA4Dl3X6RztZHffbnb27GRnz0529e4iqSepdFdS5alK/bmr8Jr8tEZirB8YZXX/MM/1DFJnNfHFgiCz7OaTtCBV6CMa6yE61sXTu39La+9uxMgQhvAA3licIl0gGAtjCcWxhpLo0SQH9Uy2iFW8KsxmVzRAvilOgzlGRvgYw329DMbjDFutJEWJiMlMp8VOltFOQYdIqM2EHhvBHRxlybULMYgRQh0dxA6MkmiRCY1EGIn10R1t5li4hagcJhwOYXXm4PCWoY/JRMZiHI3nY06kFhTskZL0SkmGDAIhcx8j8jDGUJgSfYR8kvjGFGLRAAPxDCTjPvZ41/OSs41EqBy5ux7PWIi6yE5MoyOEEzZMozr50SOUh8Yw9fYQVpIczjajFQSxVmvs8bhZbrmQqG6gcWwn7+7sxz8QoDUZoHDQw4Aa55E8mX2eNj4cugdDixdp0ElIj9OFlW5cGAmhKn1Ig0PURJuodxzliCZxnymH1dF3MzhUhtn5NPljByloN+LvM9Bri9LrtDJm9WNiDEHJ5UBGDYOagblbt/CeFX/icEY2jy+7kF6fl9BgDO9gJ+5QL1pkjIYDe1m4fw9KIoFu1ujMrqQ5q4SeeAvxvkHGTDZWNZ7DkD8bV2sbre0K8WQMo3MVkm0jDa1RzumRyA9cwEje5bQfitPfPUqzM4SgG8nol4gaIphNRxGUJkLaeoJjGq/G69mYrGAw6SCJQJ7QSaHUjiZCIl5MQ9LDXEHj1poImz1mHMNrEPFgECv54N44c0Z0wrLAWq/IfmuUUE+M0FA3ifgQMwNbqM/ejqGnGmPbHBy9FsQDrxBvWYdsMiD77YQdbvo0gTDtZFrbibv7+IXLxnqjAYeoU9Mr4jtmx3jUxqhqZq9WhiS7KAqZqOo5gjXUyZBTIpYTRSgYwBNLIHqOknSFGd0RpKunmC5DJnIshhQeRRgbxJgMkW3pYGRYZjBsoMF/jMqs1AKyumAmpuSwbsZCngmvY33HesyKmaqkidlbDlLQ58d5zIzY1EbU6mPAkIWQV4KjoQbPgjrcxYFTEzZdh8OriK/+PeGDCULiOcRjPrRgmH73EBu6t7FbEHmm5HzCBis1dgu1VhOVFgMlZgOFJgOmUzhx+kejrD7Yw5oDPbx2sIf+UJTZ+S4WFHmYV+ihLGBFfIvS6WRS5/C2bnasbOPYkSHyajwU1nnJrnChGv53yNr/LwRt6vsxPDLMkW2b33Fj7NDmDUTCoTM+rp5MnuDE3Pf6aySTZ07QkolEquLe/6LM780QHhlGTyYx2exnTNAgZVzmz5iJwXJi3s44xgnam+Wm/Af/eIwTr7K5C09ZXAcgNDTI0R1b/yEkKBIKoRqNZzwOjido7fv3MNh97N+WoJ0Me9aswhXMnFYe/5+JsdERDm995x1h/0z8SxG0J7/3dToO7kdSFCRZQVJSiwIabbZUErPNjt3rx+EP4AhkYPelSn52HfsrejKGJJmQZDOyZEGWLezp20vLwF6GhnfTN/AGBlMRfv9liLKdpJ5kKDpEb7iX7nA3R4eO0DRwiHgyTqmzmCp3JSv1paiyBUXSSOgQ13Xius5IPEFLNI4MzDDGmGUc4zzzELnyCInIAJFwF6FwL6ORAUajQ4Tio4wmIozEdfRhjcz9SQZ0M4OYGcTKgO5iRLcRRSaRXnxTFHRkOYlJimNMREkIAhFJISophDUDwwYTAyYr3RY7Y4qGOxShtr2TuuY+1KgKJNERSOqQTOjESRBPxogn43TINgYlMwpJJAGMooIiaCi6hiFpQNAlhqQYPbJMhwGGzGNETWF6FY3YmIZpADwx+C/9JYZj2XTHChHQMSntDApRhMNHUULtGKKjmOIRnJFhnJFhDPEYY6rAsEll0GRg2GRiyKLR6kjS7QrT5XbQ5alHkGvxxFTq+3ZgT8RQYwpHvDY2enPZbw3gGxvCN9qPFgljjISREkmSCRHvaB/Fw+1oiRhjsSEGxsawDw5hScawG0L4lRDdooUtejnbhRJ0XadIPkRWvA16dURBRtKDCObFiIYE7bYdtJu2oI714O2NUHUUKpqTtLtU1pQHaApmE1esxBUzMdlKRDURV1XGDCYKjxzAfawdnQTu4RHUeIxOXxbdLj9dXi/D2ihKfBujyg4ccSuloVyk4WL2h7PpTFhZOLaV+p4D5LZ34O/qIeowMZBZSp9/Fr1qDt3YOeqN4I9IeAcVlDAkDQl86m5qxRdQ5H62Kbk0Jevpj1fRE5fYIyRo05NoJJB1nbgmEXZJxA0GRKEbLRlBEQ1YVAeVo0bmhnRmDsOoCvtNIhtJ0hIbocixkvmZK7EqYRIdNQid+VgPjOLs6ELp60Af7Yd4FEFVkOQwiDq6DGEZRkSRiChgjAuEzEa6jBYGJCNhSUbQBcY0AwmDhihr2Ee6ERICcV0jiYZD6qPQdhTZkGAg5qNPD9Bn8DJkdJAQRYRoBCkRQYxE0eNxTNEwtmgIUzSCr6gOi9OLmpNLoi7Iwb59tHTtor11Ix2jfRyyxJjRrVDTayG7TcTbFsLeP8qIycqoxUvc7Cdp9hEzeQhkhqh9/0XIuVUTuW+E+uDwq8T37yLUrBHuyyMedSFJ3STiMNQ/xJDDzqjdyZhqZECW+NwMIzmaQonFSInZQLFJI9eokWVQCajKRO6qrus094V47WAPaw/2sqaph2g8idOkYjXIKJLIT6+tpcB7amPyePR3jtK0uZumLcfoaxvFlWnGl2fD7jFicWmoBhlFlZCUSYNHT6azinTw59uQ3mZEbyr+fyRoiXiclp3byJtSVe2dwNEdWwkNDf5dBkl38xGi4fA/zev9z8RbJWgFdQ3T1zE6Drqu07pnZ2ox439Rgvr/A8aJV9m8Rae9D5FQ6JRFbv6Z6D56mEQsRiC9Vt//RYIWCY2movfiqaOa/2jEImMnrRL574J/KYL2drFv3zcIhY+QiI+QSISIJ0aIx0d5rj/J/oiMqlgxaD5U2YokSEiihCiI2FQbboMbt9FNbts2Ct94gIx4nPHH6Snv2QgkkfUEsp5ASv+XpQgDBSO49H6kpI6gg6iD9piKfFCixejjw3NuJV/spVLuRNKTSOhIJCf/RB1RSBk9xHWEWAIFA0bVgwkDNgyIioGjLjOqbmB//2tISR05kURLJDFGIxhjcUzRKLaxCMtdjUQEBV3QSAoqOjpxIQGkk7WFlPI8jga6gCwkQQBfUmFeSCYuQEKAePpP0EFK6kg6GBAw6QJaahfiYoykPEpMDhNRxwgZIoQNCaKqTiIZpWr7HuKSSFSQCCkaRwLtHHPGOeayMWI0oQsiKjHiiQi3HllI1VAOLkBLjA8kgQTwqnaQkCzSr4lkBlfgde5kVFI5LBXQn3TzoZ0rSCCh6wJ6QiAamowCKMTJEY7RHLKzOZxPUzxIa8KFQQhjF4ewMopRjCPGzCgDfuxjRkzRKKaxDmzDR1BjIaRYkqRBIGnTGLaa2WJLsDOo0+bRGTFByBAnJiVISEkS0inGig4Z+jJ6xE5iNCElEhgjbrQxF3IoQGwsi6FYJpk4mINMPRISsIY46/Q4X1dvZ560k7guEkElqiskEUkgpohWcgZjyTq6dC/NiSDdMSuhuIweT5DQ02WJiVHvu4eEqRdxJMFRPYM+7AzpBkYw0SU7aFU8HJT8RBHQkxLEBCaKuWkSukFC1yR0TWRpZy+qLjJg7CZqbsEnj2GWwwhikkRcQ4+rKOEk0kgCcySCIzqMKzaMP/7/2Hvv8DjKc+//e29V75IlS8a994JtbAwGciimk3JIAZJQkpBG3pP8TtovycnJyUvqOSmEBAIBTggECAmQQKimGIMbuPcmW5as3ldb53n/2BlpdnZmd2aLdle6P9ely+vZ2ZmnTLnv527dqJc6UYke5EuB8PElAii8nDAo3PBV2NBJRej0F6LXnwdv0AFv0A5v0AEigQJHAI78AESxBLtdgs0mYCOBkMuGYNAOKWiDFLBDChFEkOD3ujBYPBmhwhIIuwOw2SKD/9XPOCI4A14U9JyEl/wIhfzIH/TA6xoAQoOo6xao7QbKB4CiIQGHBAy5CFRUiJKSKlB+Pmx5ebDl54Hy8kH55SB3BchRDBGUEOrvh9TXD8kT/vM0boHH5YY3Lw++vDxsW7AUz13wAbSWlMFvd6ByaBDFgQCKQwEUhYIolkJwCwF7MABfQCAUDEEKCVAwhGp/PwpXLAc11CMkBILyglJAEggI+U8S8EsCN9ZV4OqakSQJ3oEAzh7vRfvpfvR3etHf5UXAF0LQH0LQH04oQDYls1j4N9d/dRnyi5K3GoxHBS1dBAMBhAL+mErFeMaKgjbY0206aQaTWfxDHhzl/iK9AAAgAElEQVR7b1tOKzfNhw+it701p/vApJ4xoaClhIAXCHoBkgU4udhj7M+RqzX+xkZIHg9CAhiSCENeD/o9A7DbHXA47LA7HLDb7bDZHcjPcyM/Px8OpxO2wkLYCgsh+ULo69wNsoX3IbsdZHcM/9nsDtiCbpDPAZvdFS7EF66SiE3HOxAUcvNACEp+eHyDcDoccLkK4JLrNuw+egohvx9DAz3wBwPoDNqR1y8Q9A3BIQC7sIFCAi6E4HM7QA4BvzOEgANAvgsBKQApzwnJnYeQ0wmby418lws2uw0SJEgBv6zYEfJtBDdCKCSBMiFhos+LkoF2BIeaERwawiFfO6p9hAIRBEkCzmAe3OSH2yahpdgByeaE3QU48yhs6ZOGkA8Bm7MAUkEx8nwD4bg5fz9Cbgd85IR9sBvwDyIkCfgcBRgK2OAY7EdBAHD53RB+N0KDRRBD+Qg6CEPFxfDnFSFUUARHoR1OKoTT5oIUGEKw2AEqDICcNggX4MhzwVXghrOmBFRaALcjDxIk9Pp70ePtQY+vB4OBQThsDrjsLjhsDhQ6ClFVUIXKvErUFdahpqBmZJUv6Ic0eBZScAg+vxdD5EYovx6S5ECwYwjo80H0DMLn8cAf9MDj6UPQNwBbyA8KeOEa6kfd4BBCA0MQgVYEQ34EhQ2SKx82l4CbuuEHQXIQCpzdcDv8CJIbwuaGx1EGr6skHBflLoTP5sfZgkoMwYdAkNAa6keHNIgOIaHH50avrxCegAveYAFsEuAQNkjCAcAOG2xwwoZ8RyGq3W64bAE47DbkOwXc+QQHQnCRQJ4dsMOPinwPqh0S/IPV8Ad6EPT0QgpKsPn9sPs8EMEhFAgfqikAhByQgkEE8yWEgkPwBQMYlOzw2oGQBPilQYSEDwISiEIgkmAjCWQnkI0gSXYIhxuSsCMUJAg44B0oAA2VwGa3w+YugKO4BHlON+wOG+pLKuB2OuVCqQSy2VA/bR7KaifCJ/kwJLzwSj74hQ8YGoLdEUCJ5ESpyIfwDkHyeiENDUF4vZCGvPI2HyAfCzZ7WNmx2QGnAwRABIIQwQActXVw1dUi5PWjO+BHsy+A3qCE/mAI/aEQekMCPiEQcroQdDoQcjgRtDsQctghbDbkVVbCVVgIB9HIn43gIoJT9e+i4gLMLsyOVUVW0JjRov3USVQ2TMroij6THgJ+X0rjAEebYCAA3+AALwowEbCCxjAMw2QEVtAYhmEYJppY78fsyx3LMAzDMAzDMAwzTmEFjWEYhmEYhmEYJktgBY1hGIZhGIZhGCZLsBSDRkTtABrj7FYFoCOZRo0zeLzMw2NlDR4va/B4mcfKWE0WQlSnszHZABH1AziU6XakgLFyH3A/soex0AeA+5FtjIV+GL4fLSloZiCi7eMhIDxV8HiZh8fKGjxe1uDxMg+PVTRjZUy4H9nFWOjHWOgDwP3INsZKP4xgF0eGYRiGYRiGYZgsgRU0hmEYhmEYhmGYLCEdCtp9aTjmWIbHyzw8Vtbg8bIGj5d5eKyiGStjwv3ILsZCP8ZCHwDuR7YxVvqhS8pj0BiGYRiGYRiGYZjEYBdHhmEYhmEYhmGYLCFlChoR/ScR7SainUT0EhFNlLcTEf2SiI7K3y9L1TlzFSL6CREdlMfjr0RUpvruG/JYHSKiyzLZzmyBiD5MRPuISCKiFZrveLx0IKLL5TE5SkRfz3R7sg0iepCI2ohor2pbBRG9TERH5H/LM9nGbIGIJhHRRiI6IN+HX5a383jJ5Mr9FmMuv0dEZ+T3904i2qD6TVY+Y4noJBHtkdu7Xd6me01mqxxCRLNVY76TiPqI6K5cmA8rz9BY409Et8j7HyGiW7KkH7oyGhFNIaIh1bz8VvWb5fL1eFTuK2W4D5avoUw/xwz68WdVH04S0U55e1bORUoRQqTkD0CJ6vOXAPxW/rwBwAsACMBqAFtSdc5c/QNwKQCH/PlHAH4kf54HYBcAN4CpAI4BsGe6vZn+AzAXwGwArwNYodrO46U/XnZ5LKYBcMljNC/T7cqmPwAXAFgGYK9q248BfF3+/HXlvhzvfwDqACyTPxcDOCzfezxeIrfutxhz+T0AX9XZP2ufsQBOAqjSbNO9JnNBDpGvo7MAJufCfFh5hhqNP4AKAMflf8vlz+VZ0A8jGW2Kej/NcbYCOE/u4wsArshwHyxdQ9nwHNPrh+b7nwH4TjbPRSr/UmZBE0L0qf5bCEAJbrsWwCMizLsAyoioLlXnzUWEEC8JIYLyf98F0CB/vhbA40IInxDiBICjAFZmoo3ZhBDigBBCrwAsj5c+KwEcFUIcF0L4ATyO8FgxMkKINwF0aTZfC+Bh+fPDAK4b1UZlKUKIFiHEe/LnfgAHANSDx0shZ+63GHNpRK49Y42uyVyQQy4BcEwI0Rhjn6yZD4vPUKPxvwzAy0KILiFEN4CXAVye/taPoNePGDKaLnJfSoQQ74iwhvAIRvF5aDAXRhhdQxl/jsXqh2wF+wiAx2IdI9NzkUpSGoNGRP9FRKcBfBzAd+TN9QBOq3ZrQuwXwnjj0whr+ACPlVV4vPThcUmMCUKIFiAsyAKoyXB7sg4imgJgKYAt4PFSyMn7TTOXAPAF2aXrQRpxV83mvgkALxHRDiK6Q95mdE1mcz8UbkSk8Jlr8wFYH/9s7w8QKaMBwFQiep+I3iCidfK2eoTbrpAt/bByDWX7XKwD0CqEOKLalktzYRlLChoRvUJEe3X+rgUAIcS3hBCTADwK4AvKz3QONeZTR8YbK3mfbwEIIjxewDgdK8DceOn9TGfbuBivOPC4MCmHiIoA/AXAXRqPifFOzt1vOnN5L4DpAJYAaEHYlQjI7r6tFUIsA3AFgM8T0QUx9s3mfoCIXACuAfCkvCkX5yMWRu3O6v7oyGgtAM4RQiwF8H8A/ImISpCd/bB6DWVjH9R8FJELGLk0FwnhsLKzEOIDJnf9E4B/APguwtrrJNV3DQCarZw3F4k3VnIw7FUALpHNsMA4HSvA0rWlZtyOVxx4XBKjlYjqhBAtsptEW6YblC0QkRNhgf5RIcTT8mYerzA5db/pzaUQolX1/f0A/i7/N2v7JoRolv9tI6K/IuyiZXRNZm0/ZK4A8J4yD7k4HzJWx78JwHrN9tdHoZ1x0ZPRhBA+AD758w4iOgZgFsL9ULtBZnxeEryGsvLaIiIHgBsALFe25dJcJEoqszjOVP33GgAH5c/PAriZwqwG0KuYwMcrRHQ5gH8HcI0QwqP66lkANxKRm4imApiJcLAjow+Plz7bAMwkoqnyyuyNCI8VE5tnAShZxG4B8EwG25I1yL7/DwA4IIT4ueorHq8wOXO/Gc2lJh7regBKFrWsfMYSUSERFSufEU7qsBfG12S2yyER1oFcmw8VVsf/RQCXElG57IJ3qbwtoxjJaERUTUR2+fM0hMf/uNyXfiJaLd9jNyPDz8MErqFsfo59AMBBIcSw62IuzUXCpCrbCMIrcnsB7AbwHIB6eTsBuAfh7DB7oMrCN17/EA7KPA1gp/z3W9V335LH6hByNPNMGsbreoRXRXwAWgG8yOMVd8w2IJyh7RiAb2W6Pdn2h7Aw1AIgIF9btwKoBPAqgCPyvxWZbmc2/AE4H2EXkd2qZ9YGHq+IMcqJ+y3GXP6v/H7ejbBQVqf6TdY9YxHONLdL/tunjLnRNZnNcgiAAgCdAEpV27J+Pqw8Q2ONP8IxXkflv09lST90ZTQAH5Svt10A3gNwteo4KxCWgY8B+DUAynAfLF9DmX6O6fVD3v4QgM9q9s3KuUjlH8mdYRiGYRiGYRiGYTJMSrM4MgzDMAzDMAzDMInDChrDMAzDMAzDMEyWwAoawzAMwzAMwzBMlsAKGsMwDMMwDMMwTJbAChrDMAzDMAzDMEyWwAoawzAMwzAMwzBMlsAKGsMwDMMwDMMwTJbAChrDMAzDMAzDMEyWwAoawzAMwzAMwzBMlsAKGsMwDMMwDMMwTJbAChrDMAzDMAzDMEyWwAoawzAMwzAMwzBMlsAKGsMwDMMwDMMwTJbAChrDxICIBBFVqf6/l4jWx/nNbCLaTES9RPRHIlpBRC+mvbEMwzAMM4rwO5Jh0gMraAyTQoiIADwF4DEAEwBMAvBbAP/IZLsYhmEYJtPwO5JhzMEKGsOklmkApgL4rRDCC+CPAJYD+HtGW8UwDMMwmYffkQxjAlbQGCa11ABoFkIE5P/vAXBICHE8g21iGIZhmGyA35EMYwJW0BgmPnmqz6Vx9m0DUK76/ywAwZS3iGEYhmGyA35HMkyKYQWNYeJzMxHZiOgGAA0AymQ/ej2OA+gkokvlfW4DUE9EFaPVWIZhGIYZRfgdyTAphhU0holPA8Krfp8G8N8AfgfAobejEEIA+DiAHwDYB+AAgG8CeJuIqkeltQzDMAwzevA7kmFSDIXvFYZh9CAiAaBaCNGR6bYwDMMwTDbB70iGSQ9sQWMYhmEYhmEYhskSWEFjmAQgoueIaEDnb2mm28Yw4xUimkREG4noABHtI6Ivy9sriOhlIjoi/1subyci+iURHSWi3US0THWsW+T9jxDRLZnqE8PkIvyOZJjkYBdHhmEYZkxARHUA6oQQ7xFRMYAdAK4D8EkAXUKIu4no6wDKhRD/TkQbAHwRwAYAqwD8QgixSk5YsB3ACgBCPs5yIUT36PeKYRiGGW+wBY1hGIYZEwghWoQQ78mf+xFOQFAP4FoAD8u7PYyw0gZ5+yMizLsIZ5+rA3AZgJeFEF2yUvYygMtHsSsMwzDMOIYVNIZhGGbMQURTACwFsAXABCFECxBW4hAulguElbfTqp81yduMtjMMwzBM2tFNg2pEVVWVmDJlSpqawjAMw4w1duzY0SGEGNX02URUBOAvAO4SQvQZl2SC3hcixnbtee4AcAcAFBYWLp8zZ05iDWYYhmHGHbHej5YUtClTpmD79u2paRXDMAwz5iGixlE+nxNh5exRIcTT8uZWIqoTQrTILoxt8vYmAJNUP28A0CxvX6/Z/rr2XEKI+wDcBwArVqwQ/H5kGIZhzBLr/cgujgzDMMyYgMKmsgcAHBBC/Fz11bMAlEyMtwB4RrX9Zjmb42oAvbIL5IsALiWicjnj46XyNoZhGIZJO5YsaAzDAIGQhI4BH1r7fPAHJTjshNJ8J+rL8pHntGe6eQwznlkL4CYAe4hop7ztmwDuBvAEEd0K4BSAD8vfPY9wBsejADwAPgUAQoguIvpPANvk/b4vhOganS4wzPhlyB9CvovfowzDChrDxGHAF8SrB1rx9tEObG/sxomOQTjtNtQUu5HntCMQktDjCaB3KIDKQhfm1pVgxZRynDulAssnl7PSxjCjhBBiE/TjxwDgEp39BYDPGxzrQQAPpq51DMPE46X9Z7FiSgXqy/Iz3RRmnNPvDaDI7UCMGOa0wgoaw+gghMDWE114aPNJvHawDfMmlmD9rBpct6Qec+tKUFbgjLppB3xBnOkewp4zvdh+sgvf/ttetPZ5cf6MKly5qA6XzqvllUGGYRiGiYE/KGW6CQyD1w62YWF9KaZVF2Xk/KygMYwKIQTePNKBn754CC29Xty0ejK+fdU8U6t5RW4HZtcWY3ZtMT60vAEAcLJjEK8caMXv3zqBb/11Ly5fUIsPLmvA6mkVGVuVYRiGYRiGYWITCEUl7x01WEFjGJndTT344fMHcKx9EF+6ZCY+sqIBbkdyFq8pVYW4bd003LZuGo609uPp98/grj+/j+I8J24+bzJuWNaAIjffhgzDjB0GfEF+rjEMwyQBP0GZcc+gL4ifvnQIT21vwmfXT8eDnzwXBa7U3xozJxTj3y+fg//zL7Pw8v5WPLz5JH78z0O4YVk9bj5vMmbUFKf8nAzDMKNJc88Qtp3swrVLuK43wzBMorCCxoxr3jzcjm88vQezJhThhbvWoaG8IO3ndNpt2LCwDhsW1uHQ2X488s5JXPvrt7FscjluOW8KLppTA7uN3R8Zhsk9AiGOH2IYhkkWroPGjEu8gRC+/9x+fPGx9/G1y2bjwU+eOyrKmZbZtcX4r+sXYvM3LsGFs6rxn//Yj/U/3Yj73jyGXk9g1NvDMEzucLx9AN5AKNPNYEaJZ3aeQXPPUKabwTDjhkRTBfxjdwsOne1P6tysoDHjjkNn+3HdPW9jX3Mvnv/yOly3tD7jCTtK8524bd00bPy39fiPa+Zj09FOnHf3q/jak7uw9UQXwtnAGYZhRthzphcnOwcz3QxmFOka9Mfd57WDrTjQ0jcKrRmhx+PH8faBpI6xv3l025xOBn1B9Ht5kTUbaOvzor3fN6rnDEpS0udkF0dm3CCEwB+3nMKPXjiIz62fjs9eOD3rXAltNsLFcybg4jkTcLx9AE9sb8IX/vQe8l12fGhZA25Y3sD1YRiGYSwgSQK2ND3r+70BBEICFYWutBw/Efq9QThsPsytG71z7j3Th85BX1IpyY+0JWdxSBe+YAjNPV5MrSo0/ZvXD7UjKEk5EYt5vH0AkyoK4LTb4A9K6PMGUFXkznSzUsY7xzsBICfmQg0raMy4oMfjx7//ZTcOtPTjj7etwpJJZZluUlymVRfh61fMwVcvnYW3jnbgqR1NuORnr2NxQxmuXFSHy+fXoqYkL9PNZBiGGYYM64Rnjud2N6etntGbhzuyShBv6/MCCMc6M6mhsdODAy19lhS0oJRdsZhCCF1PoR6PH3vO9KJz0I9zp1Rgf0sfGjsHs+Z6Hs+wgsaMebae6MKXH38f506pwD++dD6K85yZbpIlHHYbLppdg4tm16DH48dL+1vx/J4W/OAfB7CkoQxXLKzFFQvqUFvKyhrDMNbo9QSQ57IlXVIk2+nzBpM+xoAvCKedsnqsuuXYZYc9+xRlPZq6PUnFf797vBMhSWDtjKoUtiqSXIswOHi2D10DfiyeVIZCudzF33e3YEplIRY2lEbs65eT+ijFwaVc6+wYhhU0ZswSDEn49cajeOCtE/juNfPxwWWZjzVLlrICFz6yYhI+smISej0BvHIgrKzd/cJBLKgvxRULanHFwjp2g2SYcUhzzxAKXHaUFZh3t3v9cBsmlORh9bTKNLYs9XgDIeQ5R1dRevVAKyoKXVg3s3pUz6vFGwjBGwhZmudsxBsIYUdjNyaU5KGlx5vQMVr7EvtduiGijMWON3Z64A2E8MqBVly1aCLsNoIkBDoHo2OiQlL2KGQ9Hj9K851ZJacl0xKB5MaWFTRmTHK0bQD/9sROSAL42xfWYnoaXFsyTWmBEx9c3oAPLm9AnzeA1w604R97WvCTFw9hTl0Jblhaj+uX1aMkxyyGDMOYR+1SuO1kFwpdDnxg3gRLx8gmIc0Mfd4ANh5ss+SGlSqRT1tGIBOy5NYTXej2+MeUG9qgP3kLZ7pIVtDOJGEl0fgiDYbS17dndp7BhoV1Ee62Qgj0DQVRWhAtl7xxuB1LJpVhcqV5V9KxDCtozJgiGJLw0OaT+J9XjuC2dVPx+YtmjAtf/JI8J65bWo/rltZjwBfEqwda8edtp/Gjfx7ElQvr8LFV52DJpLKsWpliGIZJhESESquPvkBIwtleLyZVjH75lXiMFTc0ZU6yvTvZ3r5kSHfffEEpQgZr7PRgV1OP4eJCOheLhBDoGvSjMkcSoLCCxowZdp3uwTf/ugchSeCx21dH+VqPF4rcDly7pB7XLqnHiY5BPLb1FG59eDsmlOThjgum4prF9VmXvZJhmNSQK2swivtXIotGo+E61tjpwb7m3qxU0DK90NY54ENxnhMuR+oWPwd82WtBy2Ws3inKldU96IckRNLKjPZeDSaogA36gggJkZRH0Nk+L7ae6Bo1y3Oyj6mxb1pgxjwdAz58+2978PHfb8F1S+rx9y+eP26VMy1TqwrxzQ1z8c43LsZnLpiGezYew6X//Qae3dUMKcfcmhhmvDIW6yC+sPcsdp7uGbXzpSu7pNblcTQw05N0XjKbjnZgv6bOmp4b4J6mXlPvmRf2tmR1Ae4xePvF5c0j7dh0tCPh36f6mfXG4XZsPNiW1DFyzZWbFTQmZxnyh3DPxqO46Cevo98bxItfuQC3XzANjnHg0mgVt8OO65bW48W7LsCXLpmJn710CP963zs43JqddWcYhhnh1QNt2HayK9PNiCAQkhDUUU7MGncCIclU0WWF010ePLPzjOn9tVg1OpnZP1OK82gb0Jq6PTijUaDM9P14xwCGAqF0NYsxQD01z+9pGb7PvIHRW0w4eDYx2aJ3SL+4d6qVq0Nn+3GyYzClx0w1LMkyOYfHH8T9bx7Huh9vxFtH2vHo7avwixuXcuZCE9hthGuXhBW1NdOrcP09b+MnLx6El1+iDJO1DPqD6OiPzsCmRzLCe1O3x7Rl/ZX9rXjrSOIr7IC1tnZaUOZSgZmmnTAQ8LoG/TEVGH9QGv7+VKcHrx5oNd0uIUa/1tyOxm5s1ywQxHOzNBqbiGNkYc08PbI9ScjpLk/M77s94XvHFwyldFHBGzA+npJd0+rZThn0JdWLEgfP9mFXU3wLvnJeSRLo9egrj2r8QWl4ISnZkWYFjckZBn1B/PaNY1j3o4145UArfvnRJXjs9tVY1JD9RaezjTynHV/5l1l45gvnY0djNzb84i3sPdOb6WYxDGOAbRTiRnc0duOsybTl/pCEPm98gSUW6RLQE41nauwcNG2pFELgcOuA7ndvHWlHS6/xOL6wt2XYRbCld8hSe4936J8TAE52DA7Xs8o0JzuTs048s/OMJQtrOslmF8eQJPDeqe6Yi6yx2v/+6e6Ez/3ivrOG94Byyo0H2yCEwIAvCCEE+pN8ZiSDL8a9cbJjEGdj3LMnOwfx+uH4LpZbT6TO04EVNCbr6fcGcM/Gozj/R6/hjUPtuOfjy/Dnz5yHNdOrMh4snevMqCnCY7evxsdWnYMb73sXf3j7xJiMd2GY0aJNpeD4gxI6B2JbvswqOXaDZ106H4GvH2pLq3Vdr+2+YHLn6xzw6VqkzIzTiY5BNPcMob3fF/fd0jcUjNnWeJkWB33h3ybytDVq2q6mnmHFKN3P8Vij89rBVvQZuKq19XlxtC0s1BtZpob84bFRhPnNxzqGt413ejx+XYU+FdMtSQJWPQk9JsojNHUP4dUDrTjV5TG0kMUjFYs5sRahdzX1YEdjtLLa3OMNu3ObHBi9WnNatp3swpbjnXH3YwWNyVp6PQH898uHsfbu17DlRBfuv3kFHrtjdc4VVM12iAi3rZuGx25fjYc3n8Sdj75n6qHLMEwkvZ4A3lG9eA+09MUMtB/yh7DxYJsp4dOWgbd171DAUNA2gySJmC6TekrQP/eeRUtv7IQRRkcUQhgKUmYEPKU9m491GCpf/d4gTnYMojlOG/XYcrxzeJU+mxbCTnd5LFtDjZTEXk8A/d6R94e2l3vO9GJfc2xvjSNt4fglZYja+33oiLPQMRp4AyEcaze2YKpJ17rJG4fbda28QUlKajFFCGDzsU40dVtToMxcxco9GUii5tporMXrLap0e/w4YmAljIfRPd7cM2TKU4EVNCbr8AZCuPf1Yzj/x69h75lePHLrKjzy6ZVYMaUi000b0yxsKMVzXzwfvqCED977juUHNcOMdwJSpAtNPCtKUN5/wBeMG/tlMymhWFlpNiXQJSEYvbjvLLbGcBk0OnSiyQye3dWclAuVWS/SXU09cRMs6SmfZ/u8aEzS9Q9A0tYkSRIRSRfeO9WNvU2pcXE3SvKQaoIhKW5M0DM7z2AwifT9QgjsPdM7rCg2dnoMrTA9Hj/a+s25ByeL0xZZ+BkIW2Ve3HdW2Rqxv1pRiPVI0rP+KFal7kG/rvus4fHStP5gJg4sUYya7A9JUbUXB3xBy+7EVhOdsILGZA2SJPC398/gkp+9gdcOtuKRT6/EA588F0smcYzZaFGc58T9N6/ABbOqcN09b0cFhjMMY0yimcY2H4tOW64lnoKmdXuSJIEdjV0xLTVGClp7vy8lFh5/SEJ3jDgiK6viZttzssN8koEBXzAi7kQ9xom6VGnTxXsDId3sk2ZHd+PBtpTGtQBhS8lLw8K8MSFJ4IhKEVUnozAaH+04a/dSX6cHWhLP9PePPS2mYoKSySIZlASOtQ8MW1NjXa+bjnbgnWPx3dZSgbodynU04DPuZ5PJEgZ6iwrKQu2bR9qxV9fyGa0MtmsSGh06G/vZpsfpLg8Ot/YPL1wpLTMz51ZRYh2NnjGNnYPDVl2FVw+0Ws6s+/fdzejxmI+rZAWNyQpOdAziI797B7949Qj+/6vm4YnPnIel55RnulnjEruN8I0r5uKbG+bik3/Yhud2NWe6SQyTE1gtwqreO1YAOxBfmVHHXW053onOQb8c+xFfoNHKJZuPdUQJWca/Nd/nZ3aeiYjRM2sVtMKggXu23ql2NHZjy4lO3X0OJiBUAsB2TRyLx8DapQxbvOHr8wbQ2ueNsJrFGjYzs9Ez5IffRP22zkFfxMLBwbP9w3XfzE5dLCtDvOyDWlp6hyBJAsdNuhnG4tldzVFt6xjwRbgwKtenclub6fJoJ2kZuY6MZ15tnfcEQvAGQlFhDAIibv+UBah+b8Bw7pq6h7D5WEdEfGG8Z5see8704kBLH57b3Sy3L33sUVlFPf5wbKkZ7wL1PdTW5zWVAdeKdwAraExGkSSBBzedwNW/2oTV0yrxz7vW4fIFtZz8Iwu4YVkDfn/LCnz7b3vx8OaTmW4Ow8SFiB4kojYi2qvatoSI3iWinUS0nYhWytuJiH5JREeJaDcRLVP95hYiOiL/3WL2/FaLv8cSzns8/sTc9SjsSqcoWIrCsqOxO6ZArM2YNyKUxn4WW7UadKlWkAnQraUWC7XgZEU5PKRTl0nbM6O+tvQOmXatUtoU7w1mJXW7ACLc50zVaDN9dPN4/EE8v6cl5j7atsWKwbTaxq0nuqLqsSUqKwghoqxre5p6dV0YrVxn/3FR0noAACAASURBVDRhmdTDzPWVaIyZXeW7++qBVrx5uB0v7zefSEfrGbD3TB/eOxVeiNAOTapCK7XHSWVBeHUJCK1C/fL+Vmw+2qlyF9VrW3Qn3zneiaZuTa3AJNvJChqTMboH/fjkQ9vwp62n8Ohtq/DVy2bD7bBnulmMitXTKvHY7avx641H8fOXDmVVYDvD6PAQgMs1234M4D+EEEsAfEf+PwBcAWCm/HcHgHsBgIgqAHwXwCoAKwF8l4jSYs5/83C76n+R99Ybh9ux8VA7zKCX1c1pH5G2mnuG0NTtMUxw0O8N4K0j5s6lpV2TvEFRCJVnhdZSE/EIIeAfe1pwqtO8JeXFfWcjlMkhfyihmKxBXzBKIDWKQdt6oitlhcKNFIoejx9HWvsx4Avq9sfqo1drdVN47WCrrituqp7s2v7Fi8OMh9YlTBL6qq0pi4dFS45yDQ8vVujMnXYste9IrcurHu39PlOue3pp4PVGI0pp0nyvWLS0So+RRfv1Q20Rx1Wf0zhhj+7W4U/x3MHV57BaoN4bCEVY6rXsVtU/23Q0+rkXL5Nso8HzKtX18lhBYzLC7qYeXPWrTagsdOHZL6zFYo4zy1rmTSzBXz67Bs/uasY3/7o34Tgbhkk3Qog3AWglaQGgRP5cCkDx2b0WwCMizLsAyoioDsBlAF4WQnQJIboBvIxopc/g/NbaG094NbsgopdWvmNgRLDVczHyByW8ISuIaiUq2btbsVSpm662sgkxYmkscDkAhGsxWbE+qt0DXz3Yipf2W7NaNPcM4RXVmCmWxWRqzXUMRMbtaWXdqOQZmu6+cbgd+1v68OqBVmw8FFtYVyx9gdBI5j7lGlC3QS/TpDrDopaDZ/vinnu4DQZDlWrfl9NdkX0ISUL3PjNKTqLs2zHgwwt7Y1v/jNDeh33eAHY0duFUpwd/390c8z7ddrILZ3u9aOkdwsaD+mNrVon16igOyei/Wvdbo7lTFoBSuUD7993NeGbnmeH7Xp2l86V9Z5OSM3Y39UZk041FrPvBCKO4xp2nIwtf+5IsUcIKGjPqPLerGR+7fws+e+E0/Pwji4df0kz2ck5lAZ787BrsOdODzz/6XlprIzFMirkLwE+I6DSAnwL4hry9HsBp1X5N8jaj7TGRhEDPkHGwuccfjHAxNCqK+szOM7qWj65Bf0SyiD1y1j2tUKUIPHoZ5Tz+0LArldnkCY2dg/j77sg4VKMVZnWqdvUIqNsiIIYFUrdjRAQJWRD+HCrrYCKCnNYaprhrJVML/O2jHVEuTmpe1yg+sVqttWxorydFOVJn7ku0OLeaQ2f7TZdV0LqDKk00W+g8URKV2xNxjVX2GrGghf890z2Epu6RAuOx5h0IZ2tt7vEaljIwe9npuemaIo5FDQjPXzxv0fYBX9S7Xz2UPR7/cGZas7ezJAS8gRDeVrnCJpPYJRCS4pbpSIZASIrK4Kp1u1XwBaWo57AVj1xW0JhR5cFNJ/Dtv+3F729ZgZvOm8KxZjlEdbEbj92+Gr1DAXz6oW0pEQgYZhT4HICvCCEmAfgKgAfk7XoPHxFjexREdIcc17b9TFtXRGyD/P3w5/3NI3EbAKIKtqoFGqMFEEXwCEkCxzv03RVjCUaBkDTiSmVSgGrv90UpQa/s17cEqC0ERlYBIRKz0un9pjNGhkgzRMecJfc+0utyPHcpI7corYuW2trhkhVbKwtl3kBoVJ7ZnQPm5ySeoqT3rfa6Us/YmZ4h7GjshjcQMkxy4w9Klq1OSvp5o6sjJImoa8mSaKPad7RS9QOIUsbVzysjd85YNezeONw+rLwYJetJBYGQhM4BH57ZeSZK6Y2VNTYVvK0TU7lLYzlTk0ztN1bQmFFBCIG7XziI3715DH/+DBebzlWK85z4w6fORZHbgY/f/25UYgGGyUJuAfC0/PlJhOPKgLBlbJJqvwaE3R+NtkchhLhPCLFCCLGiuKREb5eEiPdKT9bVyEz2vFjZ+oJS/FieWE3U+069bXdTD17RSWKgJV4tMj3USo2ZGLREx5pA6PcGDFPkxztss8bCqh5zxfKorc2kR0gS6Br0Y9ORDl1X2EQxUkDMWiGtCtKKVSU8bvr93n6yC03dHuxu6sXmYyOCtNoiqc3O2dI7BH9QQr83ELdYt9GCsnaztoZovLlWlLszPUN451hn1Ng8s/OMjuVK4JmdZ0y58hktAqgXjAQST0KixqqFM5G760BL33DymY0H25KuC2gFvUWORJd14iU+YQWNGRV++tIhvLC3BX/53BrMqU2dIMOMPnlOO37z8WWYOaEYH/ndO2l1J2CYFNAM4EL588UAjsifnwVws5zNcTWAXiFEC4AXAVxKROVycpBL5W2W6VTFVcRVulSf491TsY5lZuVaLZhpCcjxaso+SvxaLIVJL/lCLAuaolipBVvt/oP+IJq6Pfo1xETkv7FQt+10lydmdja9JAnJuFvFSkqRTEIB0qR/j8Wx9gG8daTdUBhMtNalYQyaSWn1TYtJaQ7I6f6j6qyZUKqf39OiiqOKvH+2nujC4db+4YLMkcfRnEv+V7EoG/V1R2O3juutGD6fYlUd9AXRMeAbPo4yF3rTqo4jFWKkbXouqYku8OQ54ydpEyLyBPHq3kX91sS2eGjHtrFrMKVZHmPdV+r29g0F4maxjWWjf80gJlGBFTQm7dz35jE8taMJf7x1FRrKCzLdHCYFOOw2/PiDi7B+VjU+dO87KalLwzDJQkSPAXgHwGwiaiKiWwHcDuBnRLQLwA8RztgIAM8DOA7gKID7AdwJAEKILgD/CWCb/Pd9eVtsVC9u5SWuXm1Vtm081IY3DrfHFGSPtsW+n9KZqGdXk767zqA/iAMtfbrn1tZU0kOxCgiI4WyS8YSzeO5yZpQcdcY2rWKqngIhhK6CpqQjt+qyRRRbUO4a9OsqBWYwK9TuPdMb08o25A8Zxs/Ew7BQtc72010e03X1rBLvXnhfnnNFWVbv/c4x/RIAIUnouqYqdQ612VCtFDVv6R1CjyeArkE/XjnQirePdsRRaqKVwUBIsqTem71eKgtdw5+N2nSmZygqc6uaRKJWrCpp2jk/dLYfu5uMXS/jnz/yeFYyj8ZzSY01HvEslpydgUkrf952Cve+fgxPfOY8TKpg5WwsYbMRvnXlXJQXuvCR372Dhz61EgvqSzPdLGYcI4T4qMFXy3X2FQA+b3CcBwE8mMKmDaOseBe48iO2m0nHDYSFCbUVyCh1fjo43NqPqiK3qX21Qo5iMTEq5q0nEymJQzoHfJrvRdRvtEJWSBKw28h0DMhL+1tRW5Jnal8zEPT7pK5t19TtQXWxufHUJ37flHnQK0z97glrNewS5b1T3XA7bLh8QV1KjmdFoNezgCrXimKV0lrVtp/swtk+L67QtLexMzLGNFEkISKU81ix+Epf950Zcc3s8wZw0kJb0hl7GN1y477o3frqpEFm0VtUSKUFLVZz9NoaTyk92+uNSIJERKZcp9mCxqSNF/a04Af/OICHPrUSMycUZ7o5TBogInz+ohm46wOz8PHfbzGMt2CY8U4i9ZEARFmnte91vRT6ZmgfSCwZwWYdq4MQIy5oCtoU3grq+Dd1X/QEHyUj5aajHRFxOSP1mIzZ39wX41sZlWDlDYSSFvLUFr13jnfqWvii5sugE42dgxGr8ycjai9FK6iJYJSx0YzwaNVSkkhbdxtYc5MlXlOU1Ova+TPjXql7Ps0Jt57oirA6xzrMc3IGVa2lxuNLbdxVwrGWcuOlOG6fQHT83/C5Ezqz5hg6B9GmvTf8reb/ydbuU0MgbDnRmZArMStoTFp460g7/r+nduN3Ny3nGmfjgE+snowfXLcAtz60LaWB6AyTi+i93hNNP77nTKTrjpELYiwO6QhG+8woMBbQJuzQy3am5bQqmUIgJEXFm6mTEOm5GKqFSq1MpdSMiuUGqU0ukGxWYW1mTjOSZyx3scj6cSMHU9K661nFUkGbCXdEqyOVKpH3TPdQQrWrYnG8YyDCFVS51g6fNefKGO+yef90d8xU/IlcdumaewWzSoovKKHH40eL/HyzfF2I1NZXU6NYPNOfLdz4+HqnNtsaVtCYlLOjsRt3/vE9/Owji7FmelWmm8OMElcvnohffWwp7np8J/72fnRgP8OMJ9QJIswm0jETyxWlBJggUStbulHHYOgl1DBK0CE0/wLAWzoKoSSJmLFPqXb90ma1TV+kYCR6iVSsoi4UbCbGcX9LX9wSAumgz0S2xXjo9U/v+jutycaolazVCpuZzKZGJKKfaDNFJns8IDFFsb3fhzcOtw9n79SL44yFLyjhWHtqXEcTJo03atRokHnllxU0JqUcPNuHWx/ehu9eMx+Xzq/NdHOYUWb97Br84VPn4rvP7sNDb5/IdHMYJmOoXZLMuv6+bCK1fDaSCvkmliUpal9F6VKduMejUY4E0GqxplSqV/J141VSeob4JJOFMt7E/nPv2eGC692ekSQweoQkkdD4dgxaTy4Sz7qkl1BCPxukZh/LLRl9zJSnSAeJprp//VBbSmL7ksmKmiyxFiqSuf9YQWNSRmPnIG56YCu+dPFMfGh5Q6abw2SIFVMq8Pgdq3HP68fw85cPp819gWGylb6hQMIZ+nKRZKwHCvEyV6pRrD2xLINCCHgD1tqV6uyYeo++tHtbaTAb65goW0wmGtEmxjCLUZycFvXcxavP2a9jgdOLP9QK/Q5bekRmo6LziTDoDyZ0PyZ75Suu2KEMvu/9QQk9emUH4rQpnUlUrD6D1LCCxqSE1j4vPvHAFnxs5Tn49PlTM90cJsPMrSvBU589D8/tasbXntodsx4Qw4w19msSZlh1+8k14tUCShexVq67Bv2Wk0wkGidoRK4uTXV7LBaRNmk96YhTNiEZ4lkq1ElrUq1DJHt7a+PThBBoTfG1aIazvcmXQsg3UUstHQz6QjjQ0heVrMhMTHwqk4Jo0SZGaew076LOChqTND0eP256YAsumTMBd31gZqabw2QJkysL8ZfPrcHJjkF86qGtSccNMEyu0KFx18tVQT2XSXcShbGMFWsmALy0f6TsQ6YMKPHOqyg8jZ2DpmvaaY/ZqXG3TNe6y5G2Abx7fPQXPVJhCbeq3KcKjz+oe8+n0zqWCFYs2qygMUkx6Avik3/YhvkTS/Gdq+aNQrYcJpeoKHThj7etQlm+Cx+6d3PCRVEZJpdhN18mF0m9u1r67oN9zYkXKk6UFjn+LtVyTyasZ2NhFSnd7ryjDStoTML4giHc8b/bUVXkwo8/tAg2GytnTDR5Tjt+9dGluGh2Da6/523sPTP6L1KGYZhsIJcMe4nUtcw2i0UyxHN9UxLTnOzIcBZCZkzCChqTEMGQhC8/thMhSeDXH1sGp50vJcYYm43wjQ1z8cVLZuKj97+LjQfbMt0khmGYtKKXjKIk35GBlliHrb7m0bo0pwpvMhk4mZyHpWrGMiFJ4GtP7UZz7xDuv3kF8jIUFMrkHjetnoxf3LgEX3r8fTy6pTHTzWEYhkkbh872x98pSwmEUq+gZWs9vmzl/VPWi9InCseIZx+soDGWkCSBb/11Dw6e7ccjn16J4jxnppvE5BgXz5mAP922Gv/zyhHc/cJBSClObc0wDJOt5IphKhUJI5jkaLNYx48ZW7CCxphGCIH/eG4ftjd244+3rkRZgSvTTWJylIUNpfjrnWvw6oFWfPHx99mVg2GYcYFeva1spDONKfEZhokPK2iMKYQQ+L8vHMQbh9vxp9tWobLInekmMTlOQ3kBnvrcGvR6AvjY/e+iM01+/AzDMNlCriTReO/U+Cm0zjDZCCtoTFyEELj7hYP4x+4WPHr7atSU5GW6ScwYoTTfiT986lzMrCnG9b/ZbLn+DsMwDMMwuY3bweqIFh4RJiaSJPDtv+3FS/tb8cRnz0N9WX6mm8SMMZx2G+7+4ELcuHISPvTbzXjn2OgX6GQYhsl13I7sSNjl4qzO44pzKgoAJKdkcQ3daPguYgwJhiT825O7sKOxG098hpUzJn0QEe5cPwM/uG4B7nhkO/6yoynTTWIYJglK8jmB1GiTThm3pti85wznfRpfpMKrysYKWhS5UZCDGXWG/CF86fH30d7vw+N3rOaEIMyocNWiiagrzcMdj+xAY5cHX/nATF5ZYxgmJ5hcWYjGzrFZtNjKY1hg/GpoRJRwDbmG8nw0dQ+luEXpZ6S/ib+rbePwNV8dJ5cDW9CYKDoGfLjx/nfhC0r4422rWDljRpXlkyvw9J1r8PfdzfjKn3fCF+QMjwyTK2TzSni6Xe8KXIm5GK6YUpHilqQeK7PKFrTEcNmzw0U1nThs+vegazzGoMW5qcbhiDCxONo2gOt/8zbm1RXjgVtWoMjNRlZm9JlcWYinP7cGLb1e3PT7rege5JTPDJNN1Bq4NeU7YwuZF82pSUdzTFGXZjf96dVFCf0uVdaDdKrGVjwZErUgjXdy3/IYv/3nz6zS3e4ch3GLFOeOHX8jwhiy5XgnPvTbzfjYysn44fULx+UNw2QPZQUu/O+tq9BQkY8b7t2Mkx1j03WIYXIFtVK2alql7j7x5Phssa9NrixM+TETUbTOm64/jlZZO6MqZccaz2RLopWxitHzIRHL+0R5waVqjJZ9YgmcAQA8tvUUbn14O75/7QJ8bv10jvthsgKXw4affXgxrl9ajxvu3YxtJ7sy3SSGSQvlOeBKPrEsH3lxLGTKq2OKgQJk9G6ZWVOcUJusLiTaZS0qHW84dd/USVKWTy43/k0KWrLsnHJUFblRnJe6xCxaa6CRSGA30EpLUtiWZGkozzed5Gw8iT6ZyLZpNLyJjHuhK+zhNX9iSeINymJYQRvn+IIhfOPpPfjFK0fwv7euxDWLJ2a6SQwTARHhS5fMxHeumodP/2Ebntl5JtNNYpiUU1k0ugpaIotwREA87zVF4ZhaZaCgWT5rbKwqAlctGp13nLqfDeUFaT3XpIrUH7+mONIqYTRvZfn6163R/KeTWFaYRQ1lEf+fP7E03c1JingJJHIVo+dOIs8F5VCpWOTIBPEewaygjWNa+7y48b53caxtAM998XwsPcd4lY9hMs11S+vxwCfPxfee3YdfvXqE4xyYKIjoQSJqI6K9mu1fJKJDRLSPiH6s2v4NIjoqf3eZavvl8rajRPR1M+d22NMvJGSHK09cDS0mihA9a0Kkxaw4LzreWStU61HgNu+SNql8xIpiJByVpqg8gJFlCQhbdDKBJWVW03xj17T4h9LOdbowTjRBUfMxo6YIDeX5UQrDaIr6RgkzAMAxCtatRK2FdaX616+ZV7LRKQtc1vMdaBXymuK8YavaWIAVtHHKtpNduPKXm7C4oQyP3r4K1cXZ8OJnmNisnFqBp+9ci7+814SvPrkb/qCU6SYx2cVDAC5XbyCiiwBcC2CREGI+gJ/K2+cBuBHAfPk3vyEiOxHZAdwD4AoA8wB8VN43JrEE8lQxUeWmpa0zZrdR2jMopsL1XTmEQzNekyoKohKImDmblXWaShMKbkVhaiyZsVb1l0/OUNbGpKbP4McmjqkUME5H0rEF9WFLWEm+M+bc6TVz+eQKVKZovhPhkrnGCXPSeSuvHo4fTewkVnVHtSJq1K85tZFKfCy34OHj2odNaACA6mL3mHJRZQVtnCGEwO/fOo5P/WEbvnHFHHzvmvmcDITJKaZWFeKvd67Fqa5B3PzgFvR4OMMjE0YI8SYAbaDi5wDcLYTwyfu0yduvBfC4EMInhDgB4CiAlfLfUSHEcSGEH8Dj8r4Z55yKApwrp2S34gKljhtT3O0ScimCGRfHxLGPonRlZL3INXepdFkMTI+DCQVZkTHqk7Qcrp8VrdAoSp82e2gs69TC+hH3Rq0ikOr4+9J8p2HRdm08p/reSrQVZtpvpChvWFiX4FmNuXZJPfKcKgVN7pnaYlbocsCmWbAxYzRQYuiGXR0p0qp24azqhNs9GsSbKZbMxxG9ngA+87878OiWU3jiM+fhg8sbMt0khkmI8kIX/njbKtSW5OGGezeP2eKwTEqYBWAdEW0hojeI6Fx5ez2A06r9muRtRttjoxFU3Sp3q9m18V28zAgkdhsNW9H0rGVGshlhREhOJjEAkSl53BROuy1KOE6VFdKoBIDC+lk1mF1bjMrC6DFPVRvyLdREGy2l0MpZtNeSodxv4qCpcuks0nGDLSuIVn4mluWjvNDYnVM971olyewYmXXJc9ltqJATAMW7x43uLStKY7w9S/Kdw88O7WHTtViv1/x4IQpm7gm3xqWVEHn/lhW4Uh5zatZdN14yJTOwgjZO2N3Ugyt/9RZcDhue/cJazBujWW+Y8YPbYcd//+sSXL1oIm74zWbsaOQMj4wuDgDlAFYD+BqAJygs8ehJACLG9iiI6A4i2k5E2/t6u4ddrKZXF+GSuRNGfqz69RUL6nCxTi0wh8027K4Vj5VTKzCjJrrmlpE1RS3gKYpDooaCVIZ+amPqFKFGsTgYtVGJV5tXp/8eMyoBoFBa4ITdRlE1mfKd9qg4pkSUi/kTS7BkUvz4OSAsUE4o0Rfcl06K7+YVazq0KeOJSDdb6GXza6O2aYfe6HJZdk451s6IHEft4oFy/SUb36enOyuCvPqrc6dUYPnkcpyvape6SW5n8qLvv8yboKs4abeok5FMqyrS/QxEW7XilqsgwrJzyqOsirMmFKctO+OqqeH7am5dCT4wd0KUS6nRtWjXWDOritwpSWxTUejChbOqh8eciIZdp5X7L9EFF6Nni1lSYdlmBW2MI4TAw5tP4mP3b8FnLpiGX310aUpT8TJMJiEifOVfZuGbG+bilge34dldzZluEpN9NAF4WoTZCkACUCVvn6TarwFAc4ztUQgh7hNCrBBCrJhQXYV1M8MuNUVuh+FqtMth030GWxEj6krzoxQJl92GlVP1Y5vUx55eHc6uZ3YleK5KUCEQQmlOzjOjpghza2MLR4rioWdRUWMle+IlcyfgAh2XqOWTKyzH9rkd9ijB0CjNe0WhS1fQT0U8od4h9PqojQdUo1h9jKw4eU47qorcwwrGjJoirNK5Dq9dUm+YXMIsRm2YU1sSZaV2O+yoLHKjrjQfkysjrwM964Zyv8bwjIxuj8429bzVFOehVGXhU76qLnZjYUPkYswlcydYSnzldtgwqaIg4viXzqvFrAlFUYs3ZRqlfCT7oTXKCpwozXeiwOVAodsRvQBg8LtVUysi7sW1M6oiniuJQkQoK4i8fxRXSSsW7A0L63DZ/NoIZdfo+T2aMW6soI1h+rwBfOFP7+OBTSfw2O2rcdN5U7i+GTMm+eDyBvz+lhX4zjN78evXOMMjE8HfAFwMAEQ0C4ALQAeAZwHcSERuIpoKYCaArQC2AZhJRFOJyIVwIpFnrZxQ+5iNdzmeN70S5YWupB3dYmWSVNqgvAPyTBbkLdMImHr3Vrxsi0smlUW5IwnoCzvzJ5aitjS2iyIALG4oQ01x7P3MvO6U2KUit8PQLWlCHJdJI2bXFkdZSsyyZnqlqfbHetbF+7mSLELXEkSEi+fURMRrxUKx/s6rK0FNguOlxcg9TWshml1bjLICF2ZNKI4S/FdOrTCV/VQZxobygqQsfbHmbNjSYzAzUsRUxp69dRrrLxBWSogoypJ1/ozofQHr8XZ5TjvWzx5RYrTXnta9UP27WBblGh0Lspmm6Vl5lZpoVtyGnXYb8pz2CGU3UQlCGdNlk0f6ayYbrR6soI1R3j/VjSt/+RZCksBzXzw/arWGYcYaq6dV4i+fW4MndzTha09xhsfxCBE9BuAdALOJqImIbgXwIIBpcur9xwHcIlvT9gF4AsB+AP8E8HkhREgIEQTwBQAvAjgA4Al534SR4mhosRQNo4LPehgJJUSAU6O8WUlPP3J8fSriFNkOC8jRv3Y77IaWpeI8ByqL3IaWpClVhVFWqkQWIM2UR1g8Kf77U88iOae2xPDde9n82ijXQDV2G6G+LB/LJ5djzfQqXDov2g0xWWIp6QSgOM85bEkwO7KpXARWH0mtNE1TFdFWn64032loGY7XrkkV+Th3SgVmTSiOUEKsouuGOaKZRf4/ipHnRKzmXjJ3QkQMnFaJLtS6S2o+p2qKFIWyrjQfSyaVRVid4j0TFApcjoQVGK0lkAjD3gnGJRfMkegir9IWZX4qC93GNSHjTMTYKRjAAAAkSeC3bx7DbzYewzc2zMHHVp7DVjNm3DC9ugh/vXMt7nhkO255cCt++4nlEatizNhGCPFRg68+YbD/fwH4L53tzwN4PoVNM4XeszoVdbmIgHUzqxGUwosWVy+aGJU1zdyBLG5Xce6Ucmw62jH8f0UAMnJTvHhOOIbPaSf4gpHCUuTpRr7Ld9rh8QdV54jfLi16MStmVuOtumzlOe0xEwkU5zlhs9Gwa1giC05m3/16e5lJEnLtkpHcOel2Wki3HGMjiihjEQvFGkwEHVNLdDsVZUEvXq62JA9n+7wAUpPFEQi70/bUFOFo24Du96lKSlNT4kbvUAB2G2FyZSFaer3o8wbC59CcQrmttAtFyaBcE9qC1VcurDNdR26OgTt1opez3UaQQuYU7XiwBW0M0drnxSce2IJn3m/G03euwcdXTWbljBl3VMgZHquK3bjh3rdxqtOT6SYx45wak3Umlae1EkMyubIQUwxWX3V/H+Nxn++yD68uW1LONJLKpIqCqBVhM68Zo1Tj8bhQJ7W6EfMnhpMXJMPkioKMlp6pLcnDRXNqdKyDI5+ritzDlsdYgqTyk0QSJWiF+GwtP5CJVl2+IDodvV68o5BnR2upNKrHZzSXidRqU+Z8dm1x1P2eKrFwenURLl8wYtldPrkc503XT9BDRLh2SX2U4lSoseQPZ5i00A5tVkorRb6NMuwKob8oEKt8A5DaIvSsoI0RXj3Qiit+8RZm1BThmS+sNR0AzjBjkTynHb/43I/ITAAAIABJREFU1yXYsLAO1//mbexo7M50k5hxhFaY1VOILpxVbZhtTcm0p9QFs1LzLNVUFLowXXYps8uZ47QuSUrvLp5TY3pR0OwKtRLsb0bBK3Q7oty7rGKzUdIJDHStcPKmeAV4nQ4bSuIk8lo7o8pUXJVCvIx0MeOm5O+GAqGYxxApK8Bg0A6j7Ra1DSOLSTIUuhyqeY0eB+V6ULKPqpN4LKgvxQq5tqFiQastycM5cpbD6iI3zptWGWGt1BJr5PX6q4xYqmO1nXZb3LhQNZcvqMXKKZEJZRJRHuMl1Ll2SX0CNdH0x2aazoLZRaqsvLWlecPznCysoOU43kAI33t2H7765C7cfcNCfP/aBSmpv8AwuY7NRvi3S2fj61fMwS0PbsUzO89kuknMOKUs3xmVdrmswIXyQv3sanWleZg/sRRz6sILbcmIUTNriocFwERw2MPp/8+fURW18j+zJty+EVejSEEpFe6ZQFhhjZfZ8epFE3XPF8+iZsYyZEX5uGBmNepiJDmxklkyFucYpClXzwFRWAFQzmlJINcMS9egz3Ibk8Wwrl8SFiCjkgaxULKzRrUDkZYbI5QFmjynPUrRKnQ7Rqyh8vysmlY5fK857LbE3JENCCfnCR9vZoYX8t0Oe5S1S5vMyAh1UW1FAY4V66vNZBkP7aEUy5nNRlEJjwpdjuFFtJrivGH3bBuRpYUULayg5TCHzvbj+t9sxsGzfXj+y+twqU4tE4YZ73x4xST8/pYV+P5z+/GDv+9HMMTJQ5jRxWG34Vyd1OOxrAIzaoosu9rpySfzJpbEtcjoUVOcF5FFz8gtCzDuh1Haf8BazNIFs6qHsy0aYSTEJmJRS8ayUG6QNj/VmBXap1cXJZQwQRvXE8/yZHbI1s+qSUkJAYXRcHEsMEjZrteNIrczqp6hPYn+pvpSEkIMj1leCurB6TGzpjhpS3Y81M9GRUEze13NN1EHWFsQU71IU12cF2EIIehb+K9ePNHQhRKIn8iEFbQcJBiS8JvXj+KD927GVYvq8Ohtq5OuL8IwY5nV0yrx7BfPx5YTXbj5wa3oGvRnuknMOGBGTREmy1kYS/OdEfEaehhZc8wKvy6HzXC13ypE8WOXFKHFSC7S1knSQ1vsNlU4bDbTsX/xcDvsWHbOiGui+vNoYVVOL85zGBYD1mYGJSKsnx2pOGnPpxYm59WVmE6/r6W0wGmo8MQiotB0Ehn69O6xqiK36QQhusekyM/TqyNLK5iNAZQSXBewsp4Qjq1K7DxmmTexJGHFOVbbKgvdWDez2jDOzeyiRUN5AdbNrI7pbhxrTJdPLo8o7p7IeBbnObAgjqLIWRxzjKNt/fi3J3cjGJLw1OfOS4s/NcOMRerL8vHkZ8/Dt/66F1f/ahN+d9PyqJVOhkkFygt7/sTI6yuewpIKwSldCk8s1NnpJpXnR1kMiEZWt7Wr3DXFeTHjaxLlykXRiRySYVJFAd47FY5l1botWXWf0mPN9CpsPtYRdz9t3KKRIHn+jGrDFfrFk8qwsL4U/d6RjJel+U5cPKcG/d4gtpzoHN4+bElTTZuea5yRPKtXJsKsQqFnibxiQR0CKi8IK/fMqqmVull9Y5U7MINa6dPrW5FJa1J5oRNt/d6k2hIPopFxNTsPSyaVpdTqaQZCWLENabTWWM+3Ipe5cVYyqMY6loAwyNI5go0ILofNlMV81oRiHG7tH/5/ocsRN5kJW9ByhEBIwr2vH8N192zGxbNr8LfPr2XljGEskue046cfXoTb103Fjfe9i7+9z3FpTOYwu/CdrgQMlySZ8VCxDKjlk2nVRThfLqJLmn2vXjRRpTRkbzH5eC1zOWy4ZrF+EeVEqY5j7VOEwAssJzvQR8/aUOh2DJc9iMoimaBNJFadOZfdhsmVhbp1x7TCs2KVSqa+lZkC6FaI53Y7vJ8rOvZMjzm1JVHXlZlRryvNM5090Ir7reJG2FBeYGiNTSdWrrlrFk9MaUkdbX1Gh879ctWiughLWizm1pVYzg/BFrQcYPvJLnzzr3uQ77Tjz59ZHbUqyzCMeYgIn1w7FXPqSvCFP72PLSe68J2r5g1ni2OYXKck34m+oUDc/WKt7GuTmugRL+W0FrVSYDXMi9K1nJygYWCsl7AZVr7l/6cwTwVWTCmHxx8ydCsscjuiXHWTcUFMF2tnVOGFvS0R11CyiymJXFeFbgeWTzaXCGhmTVHM76uK3PCHpIjnRyJTP9q3Ryrvx1kTilGc55QVRIHL5tciKAn4NJlMrZ6zqsiFpu4hTK8uiplISIEtaFlMj8ePr/9lNz790DbctHoynr5zLStnDJMiVk+rxPNfPh9N3R5ce8+mCPcDhskEqZIxLppdY6nUitYN7bL5taYC6ZWis3pucKmmJM85YmlRnac4gQQoY4WpVYUxkxAkgpIJT1G+h4XQOHNrJbFKWYErhTFfmVOURwpQj5BIfF0s1N1bPU0/9soK6iyiejO2Znol1stW2mSy8CebwV/dbzOLRemmyO2ImSjJCgvqS00dixW0LMQflPDgphNY/9PXMeAL4pX/cyFuOm9KQsUmGYYxpqY4Dw9/aiWuW1qPG36zGY9tPZXy2jAMkyzpviQri0bcyYrcDuQ57aYC7quL3SjNdxq6IilClvbdZSNKKFZOL43+zJoiXLkwtfFm8XqeLa/iRQ1lUUXDFczoLW6dLH6SHPOTKhfHRMgl4yQRobIwLGxfNr8WSyapEk+k+L6dUJK4i6ZRYhVFwVUSZhBRlNKbqfkoL3TCZbfhA/OSc8VWKHQ5hufKDIp7bjJJaZIh82opM4wQAi/tb8XdLxxEkduB331iOValYMWEYRhjbDbCnetnYNXUSnzpsffx6oE2/PD6BahJ4mXIjG8SfaEbWQMK3XZ0e5JpkTlWTKlAnYXrvsDlwPrZNcNJG7SKJBHhmsUTo/p1dRLxW3PrSiKSdBBRzDinRJhcWYg9Z3p1v1vUUDbcnwtnVeONw+0pPbdh6QX533gyv42AkEnFQK8ulzaRjXLeeEJ6uhYR9BJFjDax+n71orrh6yE9NWgjTz6lshAFJs6jVaj15sfsYmQiVspElbp8px2TKwtBRFg9tTKlOu7Fc6JjHWMxcu2n6vli7TisoGUBQgi8frgd//PKEbT3efG1y2fj2sX1KS1OyDBMbJZPLsc/71qH//vCQVz6P2/iO1fNw/VL68d8rAmTWi6dV2s6nlF7ZdUUuzG3Ltq1cMmkct2Mo7Nri3HobOpcc21kPlV15O/krHA636X6/rHiupkodhuh0OXAoD8Yc79UZG/UUpCkO9ea6VV460jiSqPZhBaZZDSseetn12DQF8Su0z2x2zLK74fFk8oS+t2yyeUY8oeitqfLYyTRwxKFy5MAkc+i2bXFlqxfelh9tmX63c8KWgYRQuDNIx3475cP42yvF3deNB3/eu4kU7VjGIZJPcV5Tvzw+oXYsKAO//6X3Xh2VzO+d/V8TDFwI2IYLckkm3HabboKiN1GsNuij5tMAVyFBfWlqJWtZgkLVUm3IvuYUlWIjgFf1PZEBVqXPf51cfmC2qg0+lZJVwxUvDmuLnbHzUSZCPMnlsIbiFYs0k1pvhOl+U5MLMuHLzj6508WrYuqnnuk+lKu1XxfV5qPApcdx9oH0tI+q4zHrOWsoGWAQEjC83ta8MCmE2jt8+LO9TPwr+dOSpN5nGEYq5w/swovfuUC/PLVI7jyl2/hljVT8PmLZqDQZD0bhjFDfXk+fEEp/o5pRFtUNxEUIT4X4zeNFI8ZNUXDK/mpYN7EEkypip2qPNbibKYN+fFmNs9px5rp0fXEkl1w1ouxy/RYjCaJ9nVaVSGqiuJbeIkIQoiomlwrp1agvd+XsII2FuYo011gaWMUaev34qkdTXhkcyNK8524dd1UXLN4IitmDJOFFLkd+OaGufjIikn4j+f24cKfvI7PXzQdH115Dt+zTEpoKC+IyKpmlVhCUGUGClbnnno2ethtlJMZJ5N185pebd37YDQTkmQ7iY6EzUZpccE1Sw6u1WQdrKClmUBIwmsH2/Dk9ia8daQd62ZW4acfXoy1Myoz7t/KMEx8ZtQU4ZFPr8TmY5342UuHcN+bx3Hbumn48IoGlOSgwMWMPaZUFkZlOKwpyRu1WKJcfpc5LboU5rLcWZw3+iKf2WvD7bANW5PjFaO2ZfB6W6rO0siMOerL8lEvF/0uK3DGjUNNJ6ygpYFASMKW4134574WvLDnLMoLXfjw8gb88IYFqCnmzHAMk2sQEdbOqMKa6ZV4/XA7Htx0Aj9/6RCuW1qPm86bPC7945nsIdHEAakmU+mok2HFlPKMu5mOBhsW1qUkZnE0WDElthKkdEMvcU46ISKcU5m4xTvbSKeVK0cutShWTBkp+L3snHIsyeCzlRW0FNE7FMDmox145UAbXjnQitJ8J65YUIsHPnkuFjeU5vQKI8MwYYgIF82uwUWza3C0bQB/fLcRH773HdSX5+OqRXXYsLAOU6sK+X5ncpL8JFx3sz3znxFuh91SnFSm3JuTfaZYtRSOPuH+Oe22uPOhWNDKdOripZNMPNXT9S5ZPrkcdaX5IMJwmYxUMhZcHG02gs3krKdjcYoVtATxBUPY09SLTUc78Obhduw504t5dSW4cHYNHr9jNebUFrOQxjBjmBk1RfjeNfPx9Svm4I3D7XhuVzPuff0YygtdWDezCufPqMa5U8vZas7kBFcvmsilXeJw2fzajMefOkzOUaoFZCHCLpLpci/Mc9rgC4ZMHd9sTbhUY7Xr2ayjKLGvkyrGjkUwk8yoKUoqnlgPVtBM0j3ox47Gbmxv7Mb2k13YfaYX1UVurJleiU+tnYq1M6pQkYGgbIZhMkue047L5tfisvm18Acl7Dzdg7eOtOP3m47jrj+HnxNLzinD4oYyLGoow7y6EpQWcOwak12wchafTCtnVy2aGJU+fTRZP8t8od9yiwkq1kyvwgt7W0wpoMq1OtpWGivK6bSqIkwoSb7sQCbvymR08fFmnyCipEqs6MEKmg6DviD2t/RhT1Mv9jb3YndTL052DOL/sXfeYXJT5/7/nmnbe19vs9de9764YbBNMR1S6SGBJIQQ7g2Q3DRyc3OTW1PgJr8kJLSQkEBIQhIDIXQMBmzjwtpe97b2envvs9PO74+RZrWz0kiaJs3s+3mefXZG0kjnSEfS+563zSvLQn11Pj69rgb1NX7zMEEQhIjDZsGqmflYNTMfX9k8F063F4fbBtHQ3I+G5n78cXczTnePoDw3DQvKsrGwPAcLy7OxoDwbZTmpZHUniAQk2rXHlNCinIkKTiwUOT1KfE66XZfbq8Pmd20szdHuccDjbKPSc0YXV0QnPs5mNe6dUJDhwMpqSopiFNNeQRse9+BgywAOtAzgYOsgDrQM4FTXMCrz07GoPAeLZuTg4ysqsLQyF5lUA4kgCB2k2q1YXpWH5VUTL7lRlwdH2odwsHUQh1oH8eaRDhxpH0K6w4oF5RKlrSwbs4oyDZ0xJ8wOjQ0tWBiLKL4uFJctLDVVfJfNaknYeMDLF5Xq+0GcLWhidr94sXFusaFyJ2Ms6m5705nKvDSM6yi6Pm00Ds45uobGcbh9CEfaBv0Wsha/Zay6IAOLZuRgUXk2PrmyAgvLc8gFiSCImJDusGFFVR5WSJQ2j9eHU90jONg6gEOtg3h2VzMOtg5i3OPF3NJsLCrPxtLKXCyrzEUtKW0EoYvNC0tiVlvLaLfHeBFva5UWfHFuUryz9QaXzpgOJHMNvOLsVBRna7cQJ52CxjlH36gbp7tHcKJzCIfbhnC0fQhH2gcxMu7F7OJMzCvNwqLyHNx4XhUWzsimWkYEQRiKzWpBXUkW6kqy8NHl/mWcc7QOOHFIsOz/fX8b/uulw3B7fFhckeNX2CpysbQyl9wjBRhjTwC4GkAn53xR0LqvAvghgCLOeTfzn7CfALgSwCiAz3DO9wrbfhrAt4Wf/gfn/Dfx6gMRffRkaSQSBzMqjQQRLRJOQeOco3/UjfZBJzqEv9Z+J5p6RtDUPYLT3SMYHvegIi89oIzdtLoK80qzMLMww1SuCARBEEowxvxFM3PTcOmCEgD+59/Z3lE0NPdjX/MAHt12Co2tg8hJs2NpRS6WV+ViSUUO6kqyUJyVMh2VticB/AzAb6ULGWOVAC4FcFay+AoAc4S/1QAeBrCaMZYP4N8A1MPvRLWHMfY857wv5q0nCJOSYjWfkkvyHJHMxF1Be+1QB9oGxuD2cnh9Pri9HB7xs4/D6+Nwe31wur0YdHowOObGkNODQaf//8CoG26fD4WZKSjNTkVJdgrKctKweEYOrl1ajprCDFTmpatWoicIgkg0GGOoLshAdUFGIM7E7fXhaPuQoLT14/mGVpzqHkaq3YraokzMLs5EVX46SnNSUZ6ThrLcVBRlpSDTYUu6zH2c83cYYzUyqx4C8DUAWyTLrgPwW845B7CDMZbLGCsDsBHAa5zzXgBgjL0G4HIAz8Sw6QRhWi5fVGo6K6QZSh4QyuRnOJDuSDgbkKmI+9lrbBnAqe4R2CzM/2dlsFkssFoY7FYGq8WCzBQbCjNTkJ1qQ1aqHdlp/v9ZqTbkpNlRmJlCMycEQRDwzyIvmuFPaHTrmmoA/pi2s72jONE5jBNdw2jpG0NDcz9a+8fQPuhE/6gbFgZkpdqRk+Z/tjpsFjislsD/B69flhSxuIyxawG0cM73BVkUZwBolnw/JyxTWm467AZmeCOmD2ZTzgBjYv+mn0NC+GSl2gOeH0R4xF1Bu+/SungfkiAIYlphs1owqygTs4oysVlmvdvrw+CYGwPC3/C4By6Pz//n9WHc40OKPfEnwRhj6QAeAGRPg5y4xUMsl9v/nQDuBICqqqowWxk+VfnpyNVZb4ogCIIwP2R/JAiCmGbYrRYUZKagIDPyQqompxbATACi9awCwF7G2Cr4LWOVkm0rALQKyzcGLd8qt3PO+SMAHgGA+vr6uGcsYIxNy0xvBBFPUmxWrJqZT55bMSbVbkVRVtK/kzRDo40gCIJISjjnBzjnxZzzGs55DfzK1wrOeTuA5wHcxvysATDAOW8D8AqAzYyxPMZYHvzWt1eM6gNBEMZTlhPfGmjTkcsWlmLRjOgU+E4GSEEjCIIgkgLG2DMAtgOYyxg7xxj7bIjNXwJwCsAJAI8CuBsAhOQg3wewS/j7npgwhCAIgiDiAbk4EgRBEEkB5/wmlfU1ks8cwJcUtnsCwBNRbRxBEARBaIQsaARBEARBEARBECaB+ScRNW7MWBeAM7FrDgoBdMdw/2aF+j29oH5PP6Zr3wsBZHDOi4xuSKxhjA0BOGp0O6JAsoxV6od5SIY+ANQPs5EM/ahWej/qcnGM9UuWMbabc14fy2OYEer39IL6Pf2Yrn0X+l1jdDvixNFkuMbJMlapH+YhGfoAUD/MRrL0QwlycSQIgiAIgiAIgjAJpKARBEEQBEEQBEGYBLMpaI8Y3QCDoH5PL6jf04/p2vfp1O9k6Sv1w1wkQz+SoQ8A9cNsJEs/ZNGVJIQgCIIgCIIgCIKIHWazoBEEQRAEQRAEQUxbSEEjCIIgCIIgCIIwCTFV0BhjTzDGOhljjTLrvsoY44yxQuE7Y4z9lDF2gjG2nzG2QrLtpxljx4W/T8eyzdFArt+Mse8yxloYYw3C35WSdd8U+n2UMXaZZPnlwrITjLFvxLsf4aB0zRlj/yT05SBj7AeS5UnRd4Vr/qzkejcxxhok65K538sYYzuEfu9mjK0Slif7Pb6UMbadMXaAMfYCYyxbsi5ZrnclY+wtxthh4V7+srA8nzH2mnD9XmOM5QnLk+aahyJRrmOI66f7/WQ0wjP1gPicEZbpHodGwhibKznnDYyxQcbYvYlwPRSegQn3HFDoxw8ZY0eEtv6VMZYrLK9hjI1JrssvJb9ZKYzHE0JfmcF9SDiZU6EfsnKUWa9FVOGcx+wPwIUAVgBoDFpeCeAV+IteFwrLrgTwDwAMwBoAO4Xl+QBOCf/zhM95sWx3LPoN4LsAviqz7QIA+wCkAJgJ4CQAq/B3EsAsAA5hmwVG9y3Mvm8C8DqAFOF7cbL1XWmsS9b/GMB3pkO/AbwK4Arh85UAtko+J/M9vgvABuHzHQC+n4TXuwzACuFzFoBjQv9+AOAbwvJvAPjfZLvmIc5JwlzHENfvu9DxfjK6H0LbmiDID5Jlusahmf6EcdQOoDoRrofCMzDhngMK/dgMwCZ8/l9JP2qg/I7/AMBaoY//gPAONLAPusaQGZ5jcv0IWi+Vo0x5LaL5F1MLGuf8HQC9MqseAvA1ANIMJdcB+C33swNALmOsDMBlAF7jnPdyzvsAvAbg8li2O1JC9FuO6wD8gXM+zjk/DeAEgFXC3wnO+SnOuQvAH4RtTY1C378I4H845+PCNp3C8qTpe6hrLszeXA/gGWFRsvebAxCtRzkAWoXPyX6PzwXwjvD5NQAfFz4n0/Vu45zvFT4PATgMYAb87f6NsNlvAHxE+Jw01zwECXMdQ1w/JZTGrlnROw7NxMUATnLOz4TYxjTXQ+EZmHDPAbl+cM5f5Zx7hK87AFSE2ofQl2zO+Xbu1xB+i4m+x5xkkTl1ylGyGH0tokncY9AYY9cCaOGc7wtaNQNAs+T7OWGZ0vJE5B7BZP6EaPrH9Oh3HYALGGM7GWNvM8bOE5ZPh74DwAUAOjjnx4Xvyd7vewH8kDHWDOBHAL4pLE/2fjcCuFb4/En4PQWAJO03Y6wGwHIAOwGUcM7bAL8SAKBY2Cwp+x5EQvYl6PoB+t5PZoADeJUxtocxdqewTO84NBM3YrLwmWjXA0jO58Ad8FthRGYyxj4UZJkLhGUz4G+7iFn6kUwyZ7AcBSTWtdBNXBU0xlg6gAcAfEdutcwyHmJ5ovEwgFoAywC0wW+qBZK/3wBgg999YQ2AfwHwR2E2ZDr0HQBuwuQXb7L3+4sA7uOcVwK4D8DjwvJk7/cdAL7EGNsDv/uYS1iedP1mjGUCeA7AvZzzwVCbyixL6L7LkHB9kbl+et9PZuB8zvkKAFfAf99dGGJbM/cDjDEH/JM7fxIWJeL1CEVCPgcYYw8A8AD4vbCoDUAV53w5gPsBPM38scZm7EeyyZzBclQiXYuwiLcFrRZ+n9d9jLEm+M3GexljpfBruZWSbSvgd41SWp5QcM47OOdezrkPwKOYcEtI6n4LnAPwF8G94QMAPgCFmAZ9Z4zZAHwMwLOSxcne708D+Ivw+U+YJmOdc36Ec76Zc74S/hfJSWFVUvWbMWaHX7j/PedcvM4dosuY8F90Y06qviuQUH2Ru35hvJ8Mh3PeKvzvBPBX+NusdxyahSsA7OWcdwCJeT0EkuY5ICQsuRrALYKrHAS3wB7h8x74n/F18PdD6gZpeD+SSeaUk6MS6VqES1wVNM75Ac55Mee8hnNeA/+JXME5bwfwPIDbmJ81AAYEE/krADYzxvIEE+1mYVlCEeTv/lH43aEAf79vZIylMMZmApgDf4DjLgBzGGMzhdm1G4VtE5G/AbgIABhjdfAHoHZjevT9EgBHOOdSk3uy97sVwAbh80UARJeEZL/Hi4X/FgDfBiBmlUqa6y1Yvh8HcJhz/qBk1fPwK+YQ/m+RLE/aay6QMNdR6fqF8X4yFMZYBmMsS/wM//hphP5xaBYmWQcS7XpISIrnAGPscgBfB3At53xUsryIMWYVPs+C//yfEvoyxBhbI9xjt2Gi74aQZDLnFDkqka5F2PDYZmR5Bn4zpBt+ZeyzQeubMJHFkQH4Ofxa8AEA9ZLt7oA/kPEEgNtj2eZY9RvAU0K/9sM/6Msk2z8g9PsoJNlm4M98dExY94DR/Yqg7w4Av4P/AbEXwEXJ1nelsQ7gSQB3yWyftP0GsB7AHvizQO0EsFLYNtnv8S8L1+4YgP8BwJLweq+H311kP4AG4e9KAAUA3oBfGX8DQH6yXXOV85IQ1zHE9dP9fjK4H7OE58s+AAfFcx7OODT6D0A6gB4AOZJlpr8eCs/AhHsOKPTjBPzxWOI98kth248L420f/LLMNZL91MMv45wE8DNInv8G9SHhZE65fgjLn0SQHGXWaxHNPyZ0hiAIgiAIgiAIgjCYuGdxJAiCIAiCIAiCIOQhBY0gCIIgCIIgCMIkkIJGEARBEARBEARhEkhBIwiCIAiCIAiCMAmkoBEEQRAEQRAEQZgEUtAIgiAIgiAIgiBMAiloBEEQBEEQBEEQJoEUNIIgCIIgCIIgCJNAChpBEARBEARBEIRJIAWNIKIMYyyPMfYCY2yAMbafMVZvdJsIgiAIwgzQO5Ig1CEFjSCiz70A7ABKAHwXwM8MbQ1BEARBmAd6RxKECqSgEUT0WQ7gD5xzJ4C/A1hscHsIgiAIwizQO5IgVCAFjSBCwBjjjLFCyfdGxthGlZ9lAxgWPpcBcMeoeQRBEARhGPSOJIjYQAoaQcQIxlg2gP8A8LbRbSEIgiAIM0HvSIJQxmZ0AwgiiWmGf6bwm0Y3hCAIgiBMBr0jCUIBsqARROzIB7AEwMcZY/cb3RiCIAiCMBH0jiQIBUhBIwh1UiWfc7T+iHPu5ZwfAPAbAKuj3iqCIAiCMB56RxJElCEFjSDUuY0xZmGMfQxABYBcxhhT+c2nGGOFjLFMANcAaIx5KwmCIAgi/tA7kiCiDCloBKFOBYBOAHcAeAjAr6Aev1kI4CiAJgC9wu8IgiAIItmgdyRBRBnGOTe6DQRhWhhjHEAR57xbx2+2AvgZ5/zPMWsYQRAEQRgMvSMJIjaQBY0gCIIgCIIgCMIkUJp9gggDxtgLADbJrLog3m0hCIIgCDNB70iCiAxycSQIgiAIgiAIgjAJ5OJIEARBEARBEARhEkhBIwiCIAiCIAiCMAm6YtAKCwt5TU1NjJpCEARBJBt79uzp5pwXGd2OWEPvR4IgCEIPod6PuhS0mpoa7N6KPk64AAAgAElEQVS9OzqtIgiCIJIextgZo9sQD+j9SBAEQegh1PuRXBwJgiAIgiAIgiBMAiloBEEQBEEQBEEQJoHqoBHTjpFxD7Ye7cKOUz042TWMgTE3OAfSHFZU5qWhtigT9TX5WF6Vi1S71ejmEgRBEFHmnWNdWFCejcLMFKObQhCEDtxeH/ac6cOaWQVGNyWmkIJGTBs6B534+Vsn8NzeFswqysAFcwpxw3mVyM9wgIFhxOVBc+8ojnUM4bm959A24MSmucW4emkZLp5XgjQHKWsEQRDJQN+oC239TlLQCCLBGHZ60DHoNLoZMYcUNCLp8fo4nnj3NP7v9WO4bFEp/nL3OtSVZKn+rrl3FH8/0IZfvn0S33zuAD66YgZuXl2FeaXZcWg1QRAEEUsYM7oFBEGEi9PtTWovJ1LQiKRmYNSNu5/eg55hF/5w51osrsjR/NvK/HTctaEWd22oxZH2QTy98yw++fB2LCjPxl0barFxbhEYveEJgiAIgiDiyisH23HdshlGNyNmkIJGJC1nekZw+5O7sKg8B49/+ryIZlrmlWbje9ctwtcvn4c/7m7Gt//WiKxUG/754jm4YlEpKWoEQRAEQZgKzjk4BywWklESDcriSCQlp7tH8MlfbsfVi8vwkxuXRc0MnpFiw+3nz8TWf9mIOy+chf/5xxFc+7P38O7x7qjsnyAIgiAIIhrsPtOHVw62G90MIgzIgkYkHc29o7j50R24bW017rloTkyOYbda8LEVFbh6STme3XUW9z7bgLmlmfjG5fN1uVESBEEQBEHEgp7hcbi8PqObQYQBWdCIpKJ3xIVbHtuJT66siJlyJsVhs+BTa2vwztc2Yu2sAtz86A589/mDGB73xPzYBEEQBEEQypBrY6JCChqRNLg8Ptz11B6snpmP+y6ti+ux0x023HPRHPzj3gtwtncUlz74Nl4/1BHXNhAEQRAEQRCJDyloRNLwr39rBAfHf3x0kWFJOyry0vH4p+vxwFXz8Y2/HMAXf7dnWtTrIAiCIAiCIKIDKWhEUvC3D1vw9rEu/PLWlUixGVsXgzGGq5eU4437NyA33YHND72DP+1uBufc0HYRBEEQBDF9oATTiQspaETC09w7iu9sacSD1y9FQWaK0c0JkJNux39/bDEevmUFfvrmcdz+5C609o8Z3SyCIAiCIAjCxJCCRiQ0Hq8P9z3bgJtWVWHd7EKjmyPLutmFePnLF6KmIAOX/d87eOaDs2RNI4hpxrjHq7rN6e4RNPeOxqE1BEEQiU+0ZCmn24vGloGo7CtakIJGJDQPbz2JMbcX92+Ob1IQvWSk2PDdaxfi8U+fh1+9fRKfevwDEsQIwqQwxr7PGNvPGGtgjL3KGCuPZH+DTjdeblSvRbT/XD/2nu0L6xhurw9eH038EInBoNONziGKz4415OGojbYBJ052DRvdjEmQgkYkLB+e7cOv3jmFn9y4zPC4M62smpmPf3z5QswrzcKVP9mGp3acgY+EKoIwGz/knC/hnC8D8CKA70SyM7cn9nWIXjrQhl1NvTE/TjJAHgzGs+NkD7af7DG6GSEhBZIwElLQiIRkeNyDe59twNevmIfZxVlGN0cXaQ4rvn31Ajx5x3n49XuncfNjO3C2h6xpBGEWOOeDkq8ZACKS6OOVVXbISfUXtXCwdVB9I51wzmmyTQeRnqmBUTd6R1yK6zsHndgdwYSFx+vD9pM9cLrVXZMJ45DOtbzc2I4zPSPGNSbKkIJGJCTfe+EgZhdl4tbVVUY3JWxWVufjpX++AEsrc3HlT7fhV2+f1BSnQhBE7GGM/SdjrBnALVCwoDHG7mSM7WaM7e7q6opb294+1iXrziinBro8PmxpaCGrkYSBMTeA6Ga423GqF68dptqXWol0OG491oltx5XvudPdI2iJICmX2DzKgmg+lK7JuMeLjsHx+DYmhpCCRiQcLx1ow5tHuvC/n1hiWL2zaJFqt+KbV8zH7z+3Gi8fbMfmh97By41tJEwRRIxhjL3OGGuU+bsOADjnD3DOKwH8HsA9cvvgnD/COa/nnNcXFRUpHssSxceUz8fRP+qSnczxSJS2kXG/Nc3tjb17ZaIRi7dG74gr6awtjS0D6BmOlcAb23dcOHt3eXzoHXGhd8SVNPGciS4jqaH3Ko25vHj7mF+xb+oewXsnuqPfqChhM7oBBKGHtoExfOuvB/DQDctQaKKU+pGytDIXf/niOrywvw3ff/EwnnivCf961QIsrsgxumkEkZRwzi/RuOnTAP4O4N+iefztJ3tgYcDqWQVR2+e4x4vW/jEUZDrw+uEOXL0kotwmyUsMZNZklINPdg1j1OWNSfkaM85B7mrqRbegkNYWZQIAGKXZMByvj8PCoqNs9o660D/qd41t7hsN6SZrNGRBIxIGn4/jK3/ch48sm4FNc4uNbk7UYYzh2qXleOMrG7BxbhFufnQH7nu2Aef6KD6NIOIJY2yO5Ou1AI5EtD9ByJNaxjuHnGgfjH4SAtFyBgAe34T1LFggfvd4tyniNT443YtTcc6eFguhm8R4fajpZ70jLoy5/BbJLQ0tAaFa8/7DUABdkmQ+PjNqkGFg9Lh870Q3+kddGBn3hO0Z9OL+VjS2yMeNSp9xauxq6sVBhVT6Rp8nOUhBIxKGx949he7hcXzjinlGNyWmpNqtuHvjbLz51Y3ITLFh80Pv4L9eOoyBUbfRTSOI6cL/CO6O+wFsBvDlaOw03jIf58qWnZ6RcTT3hh+jEy3aBsZwqiu6iuJbRztDxvOKLqc9wy4c7xiKyjGNtKC5vb6YuVfGyt1eulu55Crbjndh95mJJB/hJsDpHHSiobk/ovaJtA2MYUtDS1jtMBqv4Bodzu+kkz566R4eR0v/GF4/3IFT3eHf54NOefnn5cZ29EmsYKHuw9b+MYxJ7hPpNVYa5R0xmETTCiloRELQ2DKAn75xAj+5cTlS7YmRUj9SirJS8P2PLMIL/7QeZ3tGceEP38Ij75xMujgHgjAbnPOPc84XCan2r+GcRyaVCUJDJKKuz8fh9vpU93GobRBbj/pjLKQxaWa2B0S7bYNjbgyOTRYqm7pHAt4IoqtU36gLh9r8M/Odg04caY8ku6NxGtqu07145WDoOntjLi+GFITceNPcOxqwfLg8Prywv1VWSYtGGNjJrpGwLMU88H+iEWJymXjSOeicZNkLlxOdw4HYKz0cah3E65LkNx06smOKip1osY4kHjbU3TUWhkykJeOq2+vDjlPRy+Q56HTrGkOkoBGmZ8zlxZf/8CHuvWQO5pdlG92cuFNblIlffmolnvhMPV452IGLf/w2/vYhZWUjiEQjknv2w+Z+vHSgTdO2okCRKIkO4vEs23euH3vO+IuAy82yH2wbxNH28K1pkVjQhsc9aO4N35V91KUuQG473oU3j3SGfYxoIi3G/oZJM19Gc0z2DI+HbYnZfqonookDcVyGqxwFW6L1ZMd8XebabmloQfuA/nMRafxZa1CbvRqur9ImH57tC7jf6uGtI53YerQTe8/2aVJySUEjTM+/v3AQ5blpuOP8mUY3xVBWVufjz3etxXeuWYAHXzuGWx/faYoYEoIg5BFdilgULGjhWD+kCtrwuGeKm3TPiDlSUkdDFHa6vXhVxYokIptVU2hEuEqtJQIBsrFlYJLSEgs8UVTWo5kZ1CXsK9zWxSpLaTSnDLaf6sGOU+Yuyq1E8LCOht6qZkVyeaa67IbKhKulSU1BslLwfR7q7g0+B2d7R9E6EL57eHPvqCYllxQ0wtRsaWjBG0c68eD1y2CJZq7qBIUxhssWluKVey/EovIcXPmTbXjknZNUIJUgTMSWhhYMOd14+1gXOgedgZd/NIQbPTP7nPPATO/Wo53YeqzTlJZ3j5ejaygyZXHQ6dbh6jT1XSK6soVbizLU2ylarnHxdFFUivnpG3FptuRGEyX9d/85+aQP4vUMV28WbxO522XM5dWlGKZFMSxjS0NLwsbBaeW9k91TXHaNKBfADXYMJwWNMC1N3SP49t8a8X83LENRVvKk1I8GaQ4rvnnlfDz7hbX4855zuPXxnWG5DRAEERv6BWuVdO4k3i98DuDdoDo/Z4Nc6VweX8TKUShcHp9qkgGPz4f3T4Zfj6ixZUBzuuzT3SOyQrsoiIvxMsc6hnTVSAreZ1P3SCABydajnegcdIJzHpaCvKWhBU3dI2G7KA6MunVbmpSSpzhlFFi314fhCBJJREbo8xm+WK+831cPtWOXjIvaSwfaZBVbqXXV6+NxPVfioZUUbiVcHp8w8Tv5DIbzDBPb4HRrG4NOGffBkBY0E046hUKrtZ0UNMKUON1e3PPMXnxmXQ3On11odHNMy6IZOXj+nvWYU5yJK37yjmYXH4Ig4oOFSRIORCBHiDPIkYoiI+OThZ8DLQMRKUdq7G7qlY1FiSYnu4ZxrENbqn6tVqizPaOBmlhKiBnqgKkK2r5z/YEEJIDfxfDdE926EjW4PD78Q7BWhcpiKI4Jp9srK4hvPRbb2LOG5v6IY8nUhGwlC4rVEpkYe7htMHCOpbqI2r06LqNsuL2+SRkF5TjUOmhI3J3eSZh/NLbhUNtgVLOTRhKSobUdWpvLufqzVBwDPp9/kiSSGFUpVo3eYKSgEaaDc46vP7cfOWl2fPniOeo/mOak2q349+sW4UefXIpv/OUA/uulwwmTHIAgkpWARcbA/OtyQmZw3aBYZ4UdFzLQaRHOeobHw3bXjnQWfeJ6af/NB6d7NWe0A/y1veTcHZUSSPyjsU0So6Xev1cOtuMtAxKByGUZ3NLQgpb+MXDOdVtv9GBTEXbV7r+uofHAOcYka3f02dLQApc3cbIwO93eqQpPDEULn4/Do2DpDXUdw7n1pffTic7QipdbeGae6hqOipKm1ZpNChphOn759ik0NPfj5zevgM1KQ1QrF88vwQv/tB7vnejGHU/uMiQlMEEQfsQsYRYds/J60LIvLUK9mpVI5Gj7EI5FUDOsoblfNfPZuye60dwXfjbDSDDblJbWWLjhcQ9GXfLWtUGnO3LFVcs2ko1Gxj1oExIo9AyP41zfWERKo7plLexdh/x9uKctXt52Wi3BwUXZtzS0aK6pKhf3H073tF6jnad78Y/Gdt3X1On2hpyUPnBuYEqfpdfpYKt8lkxxG6lVVI8VsLl3FKe6tFn25SDplzAVbxzuwMNbT+Cx2+qRm+4wujkJx4zcNPz5rnXITLXhoz9/j7I8EkQc2XOmN6CEiAJDJNn95NAlAIYpLG473hWoGSZypH0Qh9smCzKdMSjiGixndQ45ZRWQSLwEZJM46jixscocKGXvmcnFlZWatzNEdsC3jnRGvRh5cDuCs9FtO96FD077rYpeH9ecnVKue/2jLpzrE1xIdTc09OoX9rXixf2tirGL4niIisLFFL8E8Pm4rlILkZRM6B9zYWDUHZgEkH6WYmUsbA8ALZb55t5RNLZMJHkZGHPBF8YJP9Q2OGk/wZzqHp6wkgoMOt2qhbvFCS5poXPxea6lmXvP9uFAy0DYMb6koBGm4cOzfbj32Qb8343LMKcky+jmJCxpDit+dtNyXL2kDB9/+H3sP9ev/iOCICLmXN9YILGEqEC4vL6JWIYQb3WpsLCvuT8g5AajR4AJV7bsHXGhpU9esJcKXttP9agKOXoJFge3n+wJ1C8TGR734MX9rVE9bvC5CnXuXjrQhobmfk2KWrgZ94IVULn2aLGyuX2xVSZ3N/Wif2xiDLi9Ey2NRmp/NQUv2EIUTJ/C+PRxHlLJD7fl4f6uc2h8Ul+lEwaca59AmLKdwunZeqwTe5r6Ap8bzk6VE6yWqWdX3P3wuCfk2FYrnA744/9Odg1HJQu1Xlftfc3KCp2I3CkPR19VivGVKn5ykIJGmILjHUO448ld+NerFuCieSVGNyfhYYzh/s1zcf+lc3HLYzux9ag5CpQSRLIjCu2iIrXjVA+6hvyWJi/nisKItNhwU89IwE0sGD2ZBeVqL/WMuMIqsiryxmH/s0QpVkSN/lFXxAKZXMyTlnWAskCvJv8OjLlxsHVCqAsufBsuf98ffsr6lxvbVTMCxtLlTu0cSBXY3hFXTNLDS914u4bGp1grwrW0BtLsg09ZphWn26u5z8GC//P7WgMWtaaeETy/b+qExJaGlimKyfP7WhWfHcFIizXLnSfGJrerc8gZqJ0o3v9q95sWTgd5+ozL7FNNL2oPsuarK7QaClXLfI9n0hRS0AjDOdk1jFsf34kvbqzF9edVGt2cpOLm1VV48Ppl+KenP8Rze84Z3RyCSHrEF7jU0tUnxD+8daQTrx3uUA1KF5GzTokC+bbj3WEpOoNj7ikWqVB0DDonJbEQk4zsk9SfOtc3qpjoQgoHx9vHunC6ZwTD4x7ZdnBgSlr+4RAZDIOR8xjQk9Y8eOZ/V1MvjrQPYuvRTpzonIgn0VuWU0lRCE7aIhIcPxh+LFn0NLRRlyeQXv5o+1Dgs7RpUgFWak1TsmQFWqnSTC2C8fsnu6dYK8JW0MI8b9LrJE66SJsu9uPDs31we/0lKLY0tMgqIFrcQ+UmbD443Yvu4XFFK7NouVGbqAl2zz7dNaFQiK6PXh9XteSqWTkbWwZ0ZTcNhehy+/y+VpztUXYZ1RTDK7ORWl+U0PrMl0IKGmEoh9sGccOvtuO2tTW488Jao5uTlFy6oARP3rEK33vxEJ7accbo5hBEkuN/gUuFeel73un2BhQ2keMdQ7LKSiihZdzjlZ1pjjY7TvXIWuLGJTP3e870YcepHjjdXmw73oUeIfHI+ye6JwnI4nnweDla+8emxLkB/vphrx/umGR5cHl9mus8yrnVqaU191t65BWi1v4xHJdN4a9PUJPGwMj1W4rPxzXXjFJDr173xuEO2Zn9XU29k+K1jrTLJ1aQ4pO59mq4PD5ZC9AHp3s1C/EfShSbcGKaAASGg9LPtWSmDKVUn+0dxcCYG/1iMi9hOIVK+iKHkhtp9/A4vD4ecpROKuyusKFUIZkUjyaxMGqx5KoRbVdpILQLsKbkNzLLwrWgdYYRh0YKGmEYDc39uOnRHbhn02x8adNso5uT1KyszsPvP7caD712DI9tO2V0cwgi6RBf5vJFkIOF/8nrD7UNKgqSWxpa0DU0LivAiLP8wYka1IiGm47YWuks/8FWf8HotgF/Ueau4fFJhY2l9dCUZNfgYH6R090jU9zFBkbdUxKXSF2+5FzA5Hj/ZI9mBWKfYH3Qa0GTjoE9Z/pkhXd/MeoO7DvXP0VIlxYYd3l8ml3n9MaBDY970No/VRlu7R8LFF/XinRMS91DlbblnONE57Bi/GX/qAsj4x5Va+LZ3lFZ4Vp1LEzVP0IiZ8E+0DIQOI64VknZtlss8ApWRlEReuNwh64EILEs4hHs4ihV0MVsqxOTLtonFI60D8bE3TWYQ22Dii6YwUNItGT3SLLaBm/DeXzLppCCRhjCy43t+NRjO/GtK+bjM+fPNLo504JFM3LwhzvX4FfvnMLP3jxudHMIIrkIMeMevEhqIdCSaEIpyFw8lp5aXEB0hDrx2NICym0SK5eawqPkQmaVCEBSQVyqoIrL3z3RPSX1f3B5kWB3STmGnZ6QArl0XZNgXVIS1JQsAcHn44hMPaXWgTEMOT2TlDE51KwV0jYc7wyd5vt098gU4drCgDGXUH+NS5fLW1OkXZNaXPS4F75ysH2S26wSrx/u0K0oSo+hNT292O9Q5WrE3nUOOScpa52D45P2IbXkSDM1MstEHJh0OOk5b9EqeSq9bnJKDeeTXRlPCunjeWB95G1QyxCqfT8TO3pdwXoe/PwRU+1Ln2HBDwUOZYvk4bbBQFbb4x1DUcn0SgoaEVc45/j5WyfwtT/vw89vWUExZ3GmriQLz965Br/feRY/fOVIxDVyCIKYjNw9Feo2e+lA+EkiwkZGyugYdE5KJqQ2Uyxn8dP6OAkV32ORSCVq+1OK3wqFrIUTPOSx5GNR5Hn7WJdsbbngPYzIZcHTeP7U3PakroDBbe8fnZysY/+5fpztHUVr/xg6hWQ2A2PuQDIIKcclcTRKBaKl51ev8tCnkPI+mEjeWocUal5NPYb/KLuaenGodVBWaRKF8O0ne3BGRqlWi2PjHPAKYzjcSRO1sRCOxUfO2jmmYH0U3YelrQiepJAbS3IEW8+PdwyFFWcrvVZ6FaWRcU/AUih3/eTKpnDOcaxjCIfbhzAw6sahtkHNLtmhIAWNiBtOtxf3/3Ef/ri7GX+5ex0urCsyuknTkllFmfjjF9ZiS0MrfvTqUVLSCCIKiHdRtGa09RxTL3KB7i39YxgYm1wPSSmGo3PIKXtsqbAY7sy6VOeSK1r9vo4slgDQqEEg51wmKYeuo0xGLnGDllpIWo65paFFsQSCFkTlTSrEHuvwJ/zYftIfaxgsjOtRUNV+p4ZefeJE5zBGXR7NVuT2Qads/NzZnlEMKljLjncO4aUDbVOUtN1NE+69snFjKt3ff65fsUiyVibHeE58bpNxU1VDLPgs7tLjnShFoNWq53R7p8QKhlsHbGDMjVcPqafrD8arYdwpbdI+6MS2413w+jg6Bsen/EY6PofHPXB5fBgRkq1wzlWt33qwRW1PBBGCrqFxfOGp3XDYLPjb3ecjL4OKUBtJZX46nvn8Gtz4yA5YLRbcf2md0U0iiITGE5ReX0q4GeHUCHdyRbSUyCHWBxoYc+PlRnnhSBTkI2mb0uoRiaArF1jfNTyOuVCvkynOnGtNPqD3VOo983JWtXDboEXgVUuB7/VxWAUrWCgrS+eQU3bSQYsiFc5kRbDLqhzbjk9WAN4+2jXF+hJ8vqXj8aiMe+mZ3smJUYKvhY/zgHuriFSRnaQoCaNDrf9KRbKVkEtMIn3eNEmyFmpJYiKFMb8LaHVBRiCJjejGqAXx/EqV1mgQTiIkTRkaVdaf6RkJmQinc9AZsDTOLc2ass9ohKqRBY2IOYdaB/GRn7+HupIs/PaO1aScmQRRSfvz7mb85HWKSSOISBBTaoedNc5gxNgY0W1QTya5UMidDa2nyK0gnGmJTdmlYFE52TU8RegLbs6WhhZ4vGoK5sT6cGJl5PY+ovGca9lOqf8iXt9ETT6rijQpp2xLz6F0vXRPsZqYCEYpsYwegpUleQvx5O/Sfp/unpr5Uk//39VgGfYqjMnGlgG8fqgj7NqEwMR1U4q506JsO91eze6MsUSLtU9tAkluF9JFXZIJgEDG3hC7HBzT/zwlBY2IKa8cbMcNv9qOz66fif/+2GI4bDTkzERVQTqeuXMNnvngLCUOIYhYESM51ayqIGNSF0f5VmpR0twKgtaZEPWNtCDnwhd8JLX4NmnTtFh9gpGzcGnJOBktvJzjmBBTZlF5Lb+nwWIqh2731hhObujdtWzWVI07aWjux5aGlqgXCVdS4k52DWPE5ZG12qgZcoLHnHKSG5UJCwB7ddRXjAS18xppyn/hKNq35OIveEApD7ZKq9WKk4NcHImYwDnHL7aexCPvnMLPblmBDRRvZlqqCzLwzJ1rcOMj22GxMNy9kUoeEEQ0MasiJUuUGhtKiHJ5fLBb1SfrpEKhlrgSzcjNjuve/8T2Ph+PiktTPPF6ecDVb1yl5pqeGlWRnIfoCNbyxOIeVNtntI/p4xwpNmtYwr4SwYXZldCSOVNvf5USj8jh9XH0j7qQm67ugaW3tIQcstl4VZ4RQ07PpKy2kULmDCLqeLw+fP25/fjT7mY898V1pJwlADMLM/D059fgyfea8Mg7J41uDkEQGhDlBaveolwh6NIQKxUpTT0jmrKrSWf3o5l8JRqud1JZzesLnQXSjEjPQTRcBOWPYR5iYZ3jnGPMFaIYcgyOmRLSC2nqc0CrwqyW7XF/S+gSCLEe/y39Y5oLlSvVPtNDm0IWRi0ZWoHolDIhBY2IKmMuL+763R4cbR/Cc19ch9nFmUY3idBIbVEmnv78avzq7VN4anuT0c0hCMNhjH2VMcYZY4WR7Cdmwgv3x0vpqZsUL9Rm5rVYxJTinCLtrVslvkwLUqUmEeMOfdzvPUGEz7jHFzLLYLyHhVox8FCoJ/VR70w87oMtDS2q9//+c/0RHydSd2O1GFAtkIJGRI2BUTc+9fhOuLwcT39+DQoyU4xuEqGT2cVZeOqzq/Hj147hT7ubjW4OQRgGY6wSwKUAzka6r1gJLvFKwhAOan02srxHtGO9oqHwxRuPz4coGl5lMVMJF7WkL7HADIp7n8ai3u2DkdXt4uCmnCgKF5vVeJ9lUtCIqNA/6sINj2xHRV4aHrutHhkpFN6YqCwoz8avP3MevvfiIbywr9Xo5hCEUTwE4GuIgqdWrAQ1rS4/8UZTmusITomZBH/Af31jGT8VC7af7NGU+l8PPcPjca0DqIdDbeFbl8IlFudCb/r8eN4rZlBIo4VKjqBJxOock4JGRMzwuAef/vUuLCjLxoPXL6NMjUnA8qo8PHZbPb71lwN4/VCH0c0hiLjCGLsWQAvnfF809pdEcosmtNRPCic1PRFdopnQAPBnGVSLLYwkFXwkjIaIFYsVvSZIOR83OOCwWuNyqOA6eLFAa8mLWEKSNBERTrcXd/52N0qyUvCDTyyBJdY+E0TcWD2rAD+/ZQXue7YhLg9EgognjLHXGWONMn/XAXgAwHc07ONOxthuxtjuoSHloqaJOLOcZo+PsEVML/5+oM2Q4+otCh0NzvVNr0kIMyg1yQQpaETYuL0+3PP0h2AM+OlNy2HTkDaZSCwurCvCj69firt/txcfnI486JUgzALn/BLO+aLgPwCnAMwEsI8x1gSgAsBexlipzD4e4ZzXc87rs7KyFY+VaO5vAJCqoqDZ1IpnEQQxbfByHtc6ftMBesISYeHzcfzLn/ahZ2Qcj3yqXvVlTiQumxeW4j8/thif+80uNDRHnh2JIMwM5/wA57yYc17DOa8BcA7ACncd9ZgAACAASURBVM65toJBSYJFJe12otX9IqYnyypzjW7CtIAmcKMPKWiEbjjn+NctjTjSPoQnP7OKEoJMA65dWo5vX70An/n1BzjcpuzKRRBEckDe6kQyUJKdanQTCCIsSEEjdPODV47ivRPdeOqzq5GTbje6OUScuL6+EvdfWodPPb4TJzrVkwAQRDIgWNK6jW5H3CEFLelxTIOwhGS29JbnphndBCKGJP/dSUSVX2w9gS0ftuB3n1uNoiyqczbduG1tDT53wSzc8tgOnO0ZNbo5BEHECGsyS7YqFGelmsJtvzI/Pab7JwHfOGYVZiquy0r1eyVdPL8k5D6m7x06PSAFjdDMUzvO4Il3T+Opz61GRV5sXxyEeblrQy1uqK/EzY/tQCulyiaIpESakbe6IGPK+gRMTKmJ0uxUrK0twKa5xRHvq7YoE+kOc4cArK0tkF0+pzgrzi2JjPll8kl6mEnVGGsIH+JUm39yQG2SRGl1jcz9moiY3T01OzW2HmSkoBGa+OuH5/CjV47iN3esQm2R8swPMT2479I6XL6wFLc+thNdQ9Oo1gtBxJGCDOO8FOYUTzzn89MdU9anOaaKD8wAq1usLF22KAThpdqtMLMXIWPy529+WTZqCo2dhJ1XqpwVVY50h/w4iNeQXDtLXtFVIlS7ynLTcMGcIqgnSpXfSTT7HI8kK1UyluKKvHQsLNc3BsKhNAIlMNZeZCZ+dBBm4bVDHfjO3w7i8U/XY2F5jtHNIUwAYwwPXDUfa2sLcOtjO9FnQI0ZgjA7wZkQ19UW6vp9ZX70XdByZZQtte2s1qkSX57MfvTIhZvmRW6hAsKLo1o/W/k6iEqmxcJw+aIplRUUkct6mQheoml26xRLQG1RpmoWz1hTV6JvIlhpciBe/UhTUBDDgQHIz3CoWtDikchHznoebeTkSq+Px2XCR+9EgJRYjy1S0IiQvHeiG/c924Cf37IC9TX5RjeHMBGMMXz/ukVYOCMbtz3xAQadbqObRBCmwmghVw4lS0MoxU1OUCzMjGz2WKt7kNzsuhR7GApagca223XUeltRnTdlmd6rf82SciypULZYVORFV2EvykqB3WqZoiwzGK9cBgvnG1VcTo3OOpoVNJ6j4doqukHWlfjdTS9bOHnCQEmBCdetM8VmTNylXDc8Xl/MnVM31BVFlOgu1vcIKWiEIh+e7cNdv9uDH35iCS6sKzK6OYQJsVgYfvDxJajKT8cdv96FUVfiFeQliFgRqdAoFbRmFWaGpYwEE07sWLCesrGuOOYJLADgumUzsLzKr/goKbvF2bFzM7JYmGY3KzVLnoUxLK+cqsSJXDinCBYLw8zCDKyUUfYAYGV1PhbNiJ4XS1mOvMLHmPlit3LSlAXpFJtFcXxEoxehJi+uWzYD1ywpBwBsXjChQF1Yp89aLoeogCnFq2npW6gxBwArq/MCReerCyK7p69dWh7R76UEK7yxwGxjPBhS0AhZjrQP4vYnd+E7Vy/AFYvLjG4OYWJsVgseumEZstPs+Pxvd8Pp9hrdJIIwBWKiDTW3wrWzClQztlXHOCYolKhihCVw9czJMT1KeqVoXYgVWhVauVMktXAwFnrGPS9Dm+tpcYziXpSUNZFQSS20oBQrmBmlOqpKkxfRGLpquxDvc6mbY7A1qkjGanvdshmy+3N6Jt6hVywqQ5pw7sS+iNdC6b6UWlozU+XPr2jh4xzgwt2l51ytqJKxGDMW1v0od9hFM7ID/VOz1qtZV5VIsfvHTLhjMMUWWxWKFDRiCic6h3HrYzvx5Yvn4JP1lUY3h0gAHDYLfnHLCgDA3b/fC5fHZ3CLCMJ4RAFDLeFEdpo9aoKqGlxB49AjnHEFdUluH6GUOzH+Q84qVJpj7gxuwcj1kgFwewXhFywqyoJWZfk8nSEJUv2GsYm2ioqP3v0FI7rWBisqa3Qm11AiP8OBiySumqLlSC2OaUO8vIP03F+S28shUQJEi49ahlGpsq/06JFbrmRRknMzVrJcZ4Th2il3jaTL1J6foayroRAnDS6aVzxpAkGPq2cslTRS0IhJnOkZwS2P7cBn18/C7efPNLo5RAKRarfi0dvqMTjmxj1P78W4hyxpxPRBTg4UhV412Uyr4B4NO5ZPh4vjlRLvCTkLgBZCKRTSTJFqZCtYAqKFaJUIV4mSizliDAGPAr8FTdvOQ7lLBp/PYOG5OMuv2IpJXLTWOgvOzhzc0kjH3qqZ+bh4fgmyg4Tp6Ai4/taJbnGV+elItWvbr0WDZTASxVpUWIKVn2BrmBaC26GlXUpKl57jL5exlkXTqi42IXif4lct1ygSGAs+S/IPSTlLnlSZjoYLuhRS0IgALf1juPnRnbjhvCp8cWOt0c0hEpB0hw1P3rEK/WNufOGpPeTuSExr5pVma0pTrUXY0SuiBO9TtFYpWr9kjiAKHJwDhRLXOj1xbKFkq3AFL6OSGYTCYbNMcVmTnlMG5WsYXLeqODsVl8wvkT3PwUNldpCSOyNIIVOqDxZMsBuuqExqudZiLbVQWZ5TbFZkptimvBNCKa3hulXqjbNUyuSXFTQpsKIqDxfO0Wdx86k05uolU+O2lH4i3tO6uqdwChkYNtQVoUxiqdajc0VTZZLG2kmvuXjughWjSJXDOcVZimN1VmHmJIVU2h4592ItsXJK7VVzKyYFjQAAdAw6cfOjO3D1kjLcd8kco5tDJDCZKTb85vZV8Po4bv/1LoyMU+IQYnqS4bD501SryBNaBY7gzUJZtdLsVlw4p0gxC6IoFEWzjpicm6YWq5EWgVq6nxVVU5XejXWRpe0XlalwRT8tVlKlU7FURonPUHB5FfeRGohLmrzTyvy0sEsYhJslMsXqb0uJgtubVKF2uie7v0tbH1wc++ol5ZMEZNGFUd0dOPSAClZG55ZmTbHsAVPHZXlu2iT3QS1JMfRYrAPHVZxE0Y+ijsv854ExFlbiIKX7ekZemuaM30VZKZMSq1iYvDtjjZC8RHyWRVp/bEF59pSJDbE/NYXpKMlOnbCog4Wc5FgzS7mv+cJYkTtV2al22eeYFFLQCLQNjOGmR3dg09xifOOKeYYUGyWSizSH390xI8WK2574AP2jVCeNIJTQYihIs1unPJvnqVhH8jIcAeE4IPQJ/8Q9icIZY/KWhFUz81GYOVmgVZLnllXm4rKFpZNmpxkLbVk5ryY/IIBpRRR6xUySVy8pR066fVIMUrgEv/7UslWK7ohy1sDJ+2IBJXCxxkyMoWL6xJiuKa6IjE0qYaCn4LbUkheOFKAkO0jryXl8EwrassrcSedtQXl2SPlDtFaopbD3J75QRm+8p1hmIvhUqsVOCa2Z9E2L8qKkMIUjmk1OVBN6B3p2rzSsrBY2yYqr1N9LF5Rg7ayCSYlV/O2b2HG6w4YL5hQF2l2YmaLZZTdSrlhUJrQJSFWw2Kc5rLCFcGu8QLC2luWkTRmzmam2kL8FSEGb9pztGcUnf7kdm+YW49+uWUDKGRE1Uu1WPHzrSlTkpeETv9yO5t5Ro5tEEPGFif9EAcMhm0Jd7blrtTDYrJYpwraSkFSVn445Ogv9An5LQjBlOWma3wuMMaTarZMESbXZ+fLcNFVBJRgOjs0LSgOZ5MTZ7mB3o5XVebhkfomqK9FkJvc11W4NCFrB5KU7sK62UJPFymZhgeslWnAWzcgJqazJnbuAm5tEsZZDTC6RYrOEl1lPTxAaU99EROryJ+vCqWEfgHLR95mFGZhZGLq4srRvYq27UMctzErBVYvLNN0HM3LTApYTYKoFTa6moKgMqCEeP3gPcq6gS2Xq6dUU+K1DcvtQ45KgLLNanwkZUxQwP+kO25R9WNjURDrSc2mxMJxXk6+r7VqV8VDntEpmAslqYVOeK0rPuvLcVFy6oETTtlJIQZvGHO8Ywid/9T4+tqIC375qPilnRNSxWy146PpluGxhCT728Ps4cG7A6CYRRNwQMyaKj1bGGCryQltkpO5gdtvkZ3KwICZ9ZkvdJJdX5fldK2UQY8kkYrJ/XyFbJY+SS9xUq04YOw8B55NTmiuRarciI8WGVTPzA4KpGnJtzVdIgV+Rl4acdLti0W3pvqwWFjgxeel2rKstRG1RJmYVhVKkp0pxwQkVlJJAWC0M1y2bocuFLTfdIaukhyLU9nKuu6tmFgSUKy3NumBO0RQFeaoL4MT3JRW5UwqRi3FeYiyT2KqNdcUhx5H0vEknEcRELHKsrM4LtLciL22SVXJtbYGsG6hDR6KU82cXTnF/lVP65BSxBWXZgayZ0meH2E1viIGi5HKrhJj8RRyflfnpWK4Sj6tm7FXKQBuK3AgKUQNTn2Vi1k+5hCBK1zEcF1KAFLRpy77mftzwyA58dv1M3H9pHSlnRMywWBj+5bJ5uO+SOtz82A68cbjD6CYRRNSxW9mUOBaXN7JyE8EztGJBWdEVUctju6YwHXUlWQEhoa4ka9KMfUB4CCNBgC24grW4PqhhwYkr5Ni8oBSXLyrV9C7SKnSla1DigtFyGsTYP7XYwXLJ9bNYJlwcGWOa4mjkk4QwXLawVHOWQv9vtG1ntTDJ2JL/UUVeesByObMwA/NKs3VlwMxMsQX6LidwB+8jJ80+VUHWKfCK7RPPmdg36fDVmkxFDyur8yeViyjOSo1Y1pLLJBhqn9JV0u3ksqJ6hJIQ0ShCv0qsYygcsrYwM2TpjPWzCzWXXIiFvKp1l3IJisRYxOC4tkghBW0a8nJjO259bCe+fvlc3HkhZWsk4sPNq6vw05uW4/4/7sODrx2DN5zoaYIwKekO26T6ROtnFwYEeT3iRKiYoUDa6SArinSdXLuChU/pTK/euzDdYVMtqjw5+x2fkohETshMc1iRYrPKWgOkVOSlh4xBWl6ZhxVVebhu2YxJ26nNYkutnKHISrVh3exCrK0tkE3AIroTFmSkTIqvsjKGnDR7SOtLKKTHkp5PLYLlnOLMgFKUnWoPmcZfifx0BwoyUrB4Rk5AgBfPqU2SUAGYUMjV2hbuGyBcAT14DEi/Si2spRqtrZEQqlaoHotLTUGGrBUzMJ6DMokC/vIZci6Q4jtZrgi1XoIzYKo9BAsyU1StdOFaovSgdWyl2ae6bka7GgApaNMIzjkeeeckvvbnffjFrStww3lVRjeJmGZsmluMLV86H68ebMcdT+6i5CFEUlKclYqCzBRNL/tg17uS7FTFmCFxb9EUCPQKPfkZE9YrpZ8WZqZMSTkv5fzZ8vFDoWDwW9nUyhZUFaSHtAAEHzu4DleoUzojNw2rBctAcVaqbGIQURlOc0zeb5rDAofNEkhJrwXp+Q0WqOXmt4JrmYnYrBasEdodbmkDm9WC9XMKZd24RAuVGF+mNTOofAxa6Patn12I5SrZ75QQz5naGRDTrMdKH1hRlafL2hLqHl1amasacyciLT4uHQeiJXNuaVZgfEcbrTq1Ul9n5KYFJhnM4PC1amboZC/hFs+WQgraNGHc48W3/tqI37x/Bn/+4jrFoGeCiDU1hRn4693nIz/Dgav/37vYc6bX6CYRREwJ6YIU9J2Dqwa2iwJxIA4pAokleKY7L90xdfZbgXBiQiIhzWENuzaWiJz1TkqoU5mf4dAUi+NXJCdbIeQsFmpIT2+wYiXNhiiyKETCkVgW+xUtaKFimOSZ2H5huTa33YLMlCmJaDQfLSgmNB7I3cuV+aGtwEpp9oPRVKhawcVRSn1NPpZW5CLVbg3phqiHicmkie+RnPb6mvyQiv/a2gJctrBUdt3SilzMLc3CrEJlpVjpXEqXS8szqBWlVnL/1oO+qD8iIWnuHcU9T+9Fis2Kv33p/IhrSBBEpKQ5rHjw+qX4w65mfOaJXbh1bTXuu6ROV7A0QSQK0RYIq/PTJ2dHk6xTygqoJDsvmpGDbce7ArFH68OwbiUCmoXeKJTglUs8oTdTJRBaARb1Mz1ja35ZNnLT7djT1Ke7LXIEwhcZw+ziTGQGKR356Q50DY8r/l5OyC3KSkHfSGw8K0QLmnhaldxpxUmAaExAaH2n5aY7YLMwdIc4X7FCS4yolFCKjkioEgSRxpDJ/Vp0Hc5Os2NwzD1pXY1gYWxsCT9JmegRMObyqmzpp1pn6RA5SEFLcl492I6vPbcfN55Xha9urgvrJUEQsYAxhptWVWFdbQHu/+M+XPfz9/DQDUtlazERRCKhpgysrM7DnjN+ITkcYcViYSjOTsW4Z6qwoJYlMhhRGBUTMahZWqR1iIJ7GWzdWlqRqynbopR4uy8Fy+Chjh/vqNlQxxMtaKJCmZceOi4QmIiPi1Y/pApMcK27jXXFyE6zKcYaXzK/RDaJy3k1+TGzzEr3e8n8EkVraKRWWtljq5x1MTvgloaWuMRaqVFblKl47RZXaKvjJ4UxFjivoWMg1Tsf6h7dMKcIHUNOfHBan2eO8sRMcOZc9X3lZzhUE61oGeOkoCUpA2NufO+FQ3jraCd+9ImluCSoBgNBmIXqggz88Qtr8at3TuLjv3gft66pxj9fPEd3Wl+CMCvB7/SKvPSAgiZHQaYjkBHygjlFEwJj0I5EoYLLHEONvHQ7RsY9yEmzB4RDLUgzS0pljIvmFU9x56rRGBsTF0wg9OollAy3ojoPI+OewPfVs9QLIMeDGblpgfIDAGCzyo/Mqc93acKbyBUkuXMnXRTP94vdatGVHEbrUA1l7Z1IeqP5sJMI5S6rBRb0P15YLEwxu6qWxGRqSYpS7Vasqy3UpcSLR91YV4zTPSM40zOi6XdkTkkyOOd49WA7Nj/0NkZdHrx634WknBGmx2phuHvjbPz9ny/A4fYhXPLg23hxf2vcY1wIwgxIM0LmZzgUA87F2J9QmR9Fgmfw19UW4tIF/piNXA3WFzWCi0QnCsFPGDMkIBAJZXXJTrXrLMAt2W8UHqsOqwXFMtkO62vyozKe1AjnMokTCJoTVoRxDDmuXFwW9RTsgEymRBOjpvjEg5BNENYFW/zlfhNumFBOul01yZGUxLm6hCpH24fw/RcP4UTnMP716gWaK98ThFmoKczAb24/Dy83tuO//3EEj247jW9dMQ+rNdZHIYhowBj7LoDPA+gSFn2Lc/5S+PsLb50aFgtDfU0+0uxWvH+yW9dvo+PGFf0JlNqiTAw53eobxohoxKBFDeH0hooREsdPvOeyrlhcpr6Ridg412/hbeoZ0XSuUu1WFGWloH3AiYKgshJa4xnDRcvE5LVLyzXJd/Eaz8FWK2nZig11RZMUn8jF0vB2sLA8B3OKtRdiry3KVCxCr8S62kLVJE8Ly7NRlKluUSUFLQk40TmEh7eewsuNbfjsBbPwyG0rQ2YIIggzwxjDFYvLcNH8Yvx+x1nc9bs9WFmdhy9fXBeW7ztBhMlDnPMfRWdXOtxhdMp+ovCelWpH/6hLUfiJVICPl9oiV9MJiFyo09L9+WXZqNQZwxcP5IrjRoIeBSPVbsHweGQF182A1rTnYnyUUkbAeBBc8F4OJeVMa4mDaLNpXrHiOj0W1TSHDeOeyJLEKGVYtFqYrpjYcNw8tVjXZmtUEkmKT2D2Nffj4a0n8c7xLtxwXiVe/8qGsF0eCMJspNisuGP9THx8ZQUe23YKNz+2Ayuq8nDPRbNxXo054i0IQgtlOamKmemipficV5OH1w51RGlvU5FLHqJH6dtYV4yzvaM41T0cxVZppyQ7RdUyp1R/TiTeVirxcDWFykqjaLmIRWKLS+aXwGGzYHRcW+a6SNCrgMdiElprUhstsUzhcs2S8rBKIohJV0TFTRyr8XKiCrYaheu9ta62QPX8qu06P8OBSxeU4LVDHZrHiYns5gFIQUswBp1uPN/Qij/sOovm3jHctrYa//nRRShQqe1CEIlKTpodX9k8F5+/cBae2n4Gd/52N+aUZOHOC2Zh07zimAgmBAHgHsbYbQB2A/gK53xKVg/G2J0A7gSAqqqqiRVB8kV5btqk7IfhEspdSSkwXiTSEhZa4txCkZNuR9qwMbP7gH/WWm7m2sxhrhV5afBxHjK+z2phmt3dRLJT7RgYU3cjFRNp5KTHNl3Bqpn5upJoAP5YoeuWzcCWhpaotUNrUhuPN3aDJtx6dWZNqhX83JhZmBHSbdButUDNCKjlDKU7bLh8UalKxsgJynPTAoXWzYI5rygxidPdI3jzSCfeOtKJD5p6saIqF5+/YBYuW1hqmDmbIOJNdqodX9o0G7efX4NnPmjG9/9+CN994SBuXVONG+orkZcR+8B0InlgjL0OQM6P6QEADwP4Pvyq1vcB/BjAHcEbcs4fAfAIANTX14f1dtfqeqWGmnw+uygTZREUoZVTAM0lziQf1QUZqC5QVxr0WivW1RaY6trFwvOnKCslUPw62nhiaEFLJq5cXDbF3XBJGAXbg6nIS8eoy6taM06Pa3BdSZaqBT3ekIJmQlweH3Y19QaUso5BJ9bPKcQ1S8vw4PVLZTMnEcR0Id1hw2fXz8Tt62rw9vEu/Pb9Jvzk9eO4akkZbl5dheWVuZQch1CFc36Jlu0YY48CeDHax790QYkwWxyfZMoWC4so06JcunS9E87RUkajCQeH3WpBbrr52hYrpkM9VKuFaY710cuaWflweeIXlxdJtkYj34SxerYVZaWgKCslqtbTWLFqZj5KdFqHRUhBMwldQ+N466hfIdt2vBsFmQ5cNK8Y/37dQqyamR/1IGGCSHQsFoZNc4uxaW4xzvSM4Hc7zuDzv9mNvAwHrq+vwEeXV4SdDpeY3jDGyjjnbcLXjwJojPYxlGIjzDo3X5qdilUz5WM/tboRFWWl4LplM6LZrIi5sK4IGQ6bKWNQiNgQqTIe75ISeRkO3feN6PovWr6Vku8QsSUS6zApaAbh83E0tg7gzSOdePNIJ460DaG+Jg8XzSvGVy+bi1mFGWQFIAiNVBdk4IGrFuBfLpuHt4524k+7m/HjV4/hgjlFuL6+ApvmFcfNUkEkBT9gjC2DX19qAvAFY5ujnViFUTDGpggbYjrwjXOVM7iZGcaY7jTahPnQM+bNNkEQK+xWS6Cvly8qjckk/6IZOegYcKJLxdWQCA9S0OLI8LgH7x7v8rsuHu0C5xwb5xbjrg21WD+nkF4UBBEhDpsFly0sxWULS9E55MRf97bgf18+gm/99QA+tqICn1hZYTo/c8J8cM4/ZXQb5Ih1/aVw0ZO62kzonQJNtYc3yUOTQ7HFa7LkDmYjVh5YtUWZSLFZSEGLEaSgxZizPaN440gH3jzSiZ2ne1FXkomL5pXg0dvqsWRGTtgZewiCCE1xViq+sKEWd144C3vP9uPPe5rxsV+8j6r8dHxkeTmuXToDpREkTSAIM2Gz+JWAeGY1nU5iMSXlMi+xTHtPTJCdap+SjMhoTy8z1fyNdiZN8/QsSfB4fdhzpg9vHunEG0c60dI3hvVzCnHV4jL86JNLUUIJPggirjDGsLI6Dyur8/Bv1yzEm0c68bcPW/DjV49heVUuPrJsBq5YXGbKBAYEMYkQcqjVwuLuvjWdDBeRKGc0DUskAxvqiqYsy093aCqsHQs21BWZxnofi2cvKWhRoH/UhbeP+V0Xtx7tQmaKDRfPL8a3r5qPNbMKaNaNIExCqt2KKxeX4crFZRgYdeOlxjb89cMW/NvzB7FxbhE+smwGNs0rpnuWiIhI9BYeQusxmz4UT5fLUDXgiOlLusNmuvpVyYqcx1eaw4pNBsWg5qYnd2kdUtDCwOvj2HeuH9uOdWPb8S7sPzeAJRU5uGh+Me7eOBt1JZmGm30JgghNTrodN62qwk2rqtDaP4bn97XiJ28cx9f+vB8b5xVj84ISbJxbFPeMXcT0xWaxhMw8SnJodKHXdOKzce5Uqw5BJAOkoGmAc45T3SPYeaoX757owrvHu5HmsOLCOUW4bV0N1s8uRD4VySWIhKU8Nw13bajFXRtqcaJzCK8e6sDj757GV/60D2tnFeDSBSW4ZH4JxawRMeWqJWVGN8GUrKzOi4kbVVocLOUpNivyplGNtXhDCViIZIUUNBnGXF4cbh/EnqY+7Grqxe4zfXB7fFhZk4f1swtx7yV1mFNMVjKCSEZmF2dhdnEW7t44Gx2DTrx2qAOvHurA9148hOr8dKyfU4gL5hRi9cyCqAcFE0QoTJfFMU7NqchLj/o+L1tYGhcL2iXzi6dFYWiCIKLLtJYu3F4fzvWNoal7BIfbB3GodRCH2wZxunsEZTlpWFGdh/Vz/ArZ3NKsuGbHIgjCeEqyU3HrmmrcuqYaYy4vPmjqxbZjXfjBy0dxqmsEy6pysWZWAeqr87C8KpfcIYlpRU66PWFTbMcrzpSUM4IgwiFpFTSfj6N31IWOQSc6h8bROehEx+A42gedaO4dxZmeUbT0jyHdbkVNYQbmlmZheVUeblldjfllWUkffEgQhD7SHFZsqCsKZLLqHHLivRPd+OB0H/7j74dwsmsEdSVZqK/OQ31NHpZX5qEyP40s7dMMC2MoyIzN+8NsMWgLyrIxvzTb6GYQBEEkHQmjoPl8HENOD3pGxtE36kLviBt9Iy70jrr8/0dc6Bt1oWvYhc5BJ7qGxsEBFGWmoCQ7BcXZqSjOSkFpdipW1eSjqiAdNQUZyEu3kwBFEIRuirNS8dHlFfjo8goAwMCoG3vP9mH3mV48vfMsvvHcAdgsDPPKsrCgLBsLyrMxpyQL1fnpyM9w0HMnSblmaXnM9m0y/QyMMUq0QRAEEQPirqA9teMMmrpH4PH64PFxeLzc/9/n/+50eTE87sGIy4ORceHzuAejLi8AICfNjvwMB/LS7cjPSEF+hh15GQ7MLs5EXoYDhZkOFGeloiQ7FfkZDnJLJAgiLuSk27FpXjE2zfOnHPb5OM70juJQ6yAOtQ3g5cZ2/OytE2jtdyLNbkVVfjqq8tNRlpuKggyH8DxzID/DgaxUG1LtVqTYLEi1W2GzMr/1hAM+7o9E8nEOl8cHp9uLMbcX6PvSrAAAIABJREFUTrfw2eWF0yP8F9aNuXzCf4//v9sXtN7/2e3zwefz7/vFf1qPgkzljIIEQRAEQcSGuCtoVsaQZrfCmmKD3cpgtViE/ww2C0Oq3YrMFBsyUmzISLH6/ztsyEyxISvVRv7cBEEkBBYLw8zCDMwszJiUnc/t9aGlbwxne0dxpncU7QNjaOl34kDLAHpHXOgZcWFk3AOn24dxj1/xkoMxwGG1IM1hRZrdilThL83uX5ZqsyLVYUW63RrYJjfdgTLhc5pkeZrDilS7BTaLBVYLg4Uxw4qPEspYyVxFEAQxLYi7gnbz6qp4H5IgCMI02K0W1BRmoKYwQ9P2nHN4fdzvTga/YkbukdOT+po8jCso7ARBEETykDAxaARBENMRxhhsVlLICASspARBEERyQ/6CBEEQBEEQBEEQJoEUNIIgCIIgCIIgCJNAChpBEARBEARBEIRJYFxH5UvGWBeAMzFoRyGA7hjs16xMp/5SX5OT6dRXYHr1N9p9reacF0Vxf6aEMTYE4KjR7YgCyTLWqR/mIRn6AFA/zEYy9EPx/ahLQYsVjLHdnPN6o9sRL6ZTf6mvycl06iswvfo7nfoaTZLlvFE/zEUy9CMZ+gBQP8xGsvRDCXJxJAiCIAiCIAiCMAmkoBEEQRAEQRAE8f/ZO+84Oaor3/9Oh+nJWaMZSSONckYRJIEE2CSBCfbasIBtggnGBgPrdVzeev3Wu7Z3/XZtA7YJBoOxwQkTjMkYjAQSQhLKGuU0I03U5Nzd9/3RXT3V1VXVVdXV3dU95/v56KOe6uqqe+veqjrnnsQ4BKcoaI+kuwEpZiz1l/uanYylvgJjq79jqa92ki3XjfvhLLKhH9nQB4D74TSypR+qOCIGjWEYhmEYhmEYhnGOBY1hGIZhGIZhGGbMkxYFjYi+S0SNRLQt/O8yjf2OEtHO8D6bU91OuzDR37VEtI+IDhLRt1LdTjshoq8RkSCiSo3vA7Lr8WKq22cnBvp6IxEdCP+7MdXtswMi+h4R7QiP1+tENEFjv6wYVxP9zYax/RER1Yf7+xwRlWrslxXP42SQKc9uIqoloreJaC8R7Saie8LbNd9RRPTtcL/2EdEl6Wt9NGrzkYjKieiN8P34BhGVhbcTEd0f7scOIlqa3taHIKLZsmu+jYi6iejeTBgPInqciFqIaJdsm+nrn+5nqEY/VJ+JRFRHRAOycXlI9ptl4fl4MNxXSnMfTM+hdD/HNPrxe1kfjhLRtvB2R46FrQghUv4PwHcBfM3AfkcBVKajjanuLwA3gEMApgHIAbAdwLx0t91if2sBvIZQzTzV8QPQm+52pqKvAMoBHA7/Xxb+XJbudlvoZ7Hs890AHsrycY3b3ywa24sBeMKf/wvAf2nslxXP4yRcv4x5dgOoAbA0/LkIwH4A87TeUeHvtgPwAZga7qc73f0Ity1mPgL4bwDfCn/+ljSXAVwG4BUABGAlgA/S3X6NedQEYEomjAeAcwEsBbDL6vV3wjNUox+qz0QAdfL9FMfZBGBVuI+vALg0zX0wNYec8BxT64fi+/8B8B0nj4Wd/9jF0TmcBeCgEOKwEGIYwO8AXJXmNlnlxwC+AWAsBDjG6+slAN4QQpwWQnQAeAPA2lQ1zi6EEN2yPwuQ5WNrsL/ZMravCyH84T83ApiUzvZkIBnz7BZCnBJCbA1/7gGwF8BEnZ9cBeB3QoghIcQRAAcR6q9TuQrAk+HPTwL4pGz7r0WIjQBKiagmHQ3U4QIAh4QQx3T2ccx4CCHeBXBasdns9U/7M1StH2afieG+FAshNoiQhvBrjPY96WiMhRZacyjtzzG9foStYNcAeEbvGOkeCztJp4J2V9h8/LhkBldBAHidiLYQ0e2pbFwSiNffiQBOyP5ugP6L05EQ0ZUAGoUQ2+PsmktEm4loIxFl5M1jsK9ZMa4AQET/SUQnAHwWwHc0dsv4cZUw0N+sGVsZX0BoxVGNbHoe20lGzgMiqgOwBMAH4U1q7ygn901tPo4XQpwCQsoogKrwdif3Q+JaRAufmTYegPnr7/T+ALHPxKlE9BER/Z2I1oS3TUSo7RJO6YeZOeT0sVgDoFkIcUC2LZPGwjRJU9CI6E0i2qXy7yoAvwAwHcBiAKcQMluqcY4QYimASwHcSUTnJqu9iWJDf9V8ZB1ppYjT1/ugLbzLmSxCFeCvB/ATIpqe1EZbxIa+Zsu4QghxnxCiFsBvAdylcZiMGFfAlv5mzdiG97kPgB+h/qqRMc/jFJMx80CCiAoBPAvg3rC1WOsd5eS+mZmPTu4HiCgHwJUA/hjelInjoYdWux3dH5Vn4imE3nFLAHwVwNNEVAxn9sPsHHJiH+Rch+gFjEwaC0t4knVgIcSFRvYjokcBvKRxjJPh/1uI6DmETLDv2tZIG7Ghvw0IxTNJTAJw0oam2Y5WX4loIUI+zdvDMZmTAGwlorOEEE2KY0hje5iI3kFoJfdQMtttBRv62gDgfNnfkwC8k5TGJojROQzgaQB/BfBvKsfIiHEFbOlv1owthYLzLwdwQdgtRO0YGfM8TjEZ8+wGACLyIqSc/VYI8WcAEEI0y76Xv6Mc2zeN+dhMRDVCiFNhV6eW8O6O7UeYSwFslcYhE8cjjNnr79hnqNozUQgxBGAo/HkLER0CMAuhfsjdINM+LhbnkCPnFhF5APwDgGXStkwaC6ukK4uj3Pf7UwB2qexTQERF0meEgjZj9ssEjPQXwIcAZhLR1PBq2rUAMioLnhBipxCiSghRJ4SoQ+hGWapUzoiojIh84c+VAM4BsCflDU4Ao31FKIHIxeE+lyE0j19LcXMThohmyv68EkC9yj4ZP64SRvqL7BnbtQC+CeBKIUS/xj5Z8zxOAhnz7KbQatJjAPYKIf5Xtl3rHfUigGuJyEdEUwHMRCgAP63ozMcXAUiZAG8E8EL484sAbqAQKwF0Sa54DiHKOpBp4yHD7PV35DNU65lIROOIyB3+PA2h63843JceIloZvsduwGjf04KFOeTk59iFAOqFEBHXxUwaC8sYySRi9z8ATwHYCWAHQhOgJrx9AoCXw5+nIZRFZjuA3QDuS0dbU9Xf8N+XIZRV61Am91fWn6MIZ9kCsBzAL8Ofzw5fj+3h/29Jd1uT1dfw319AKBD3IICb091Wi/17FqEH/A4AfwEwMZvH1Uh/s2hsDyIUe7At/O+h8PasfB4n6RpmxLMbwGqE3H12yMb7Mq13VPg394X7tQ8OyYamNR8BVAB4C8CB8P/l4e0E4GfhfuwEsDzdfZD1JR9AO4AS2TbHjwdCCuUpACMILVDeYuX6p/sZqtEPrWfip8PzbTuArQCukB1nefidcQjAgwAozX0wPYfS/RxT60d4+xMA7lDs68ixsPMfhTvDMAzDMAzDMAzDpBlOs88wDMMwDMMwDOMQWEFjGIZhGIZhGIZxCKygMQzDMAzDMAzDOARW0BiGYRiGYRiGYRwCK2gMwzAMwzAMwzAOgRU0hmEYhmEYhmEYh8AKGsMwDMMwDMMwjENgBY1hGIZhGIZhGMYhsILGMAzDMAzDMAzjEFhBYxiGYRiGYRiGcQisoDEMwzAMwzAMwzgEVtAYhmEYhmEYhmEcAitoDMMwDMMwDMMwDoEVNIaxCBFdSES7iGiQiET439F0t4thGIZh0g2/IxnGOqygMYwFiKgQwDMA/gdAIYCbAXQBuDKd7WIYhmGYdMPvSIZJDFbQGMYaZwFoE0L8SgjhF0I8AaAZQFl6m8UwDMMwaYffkQyTAKygMYw1qgA0KLYdB1CdhrYwDMMwjJPgdyTDJAAraAxjjQYAUxTb6gA0pr4pDMMwDOMo+B3JMAnAChrDWGMjAA8R3U5EXiK6HYAHwAdpbhfDMAzDpBt+RzJMArCCxjAWEEL4EQp2vglAG0IB0FcJIUbS2S6GYRiGSTf8jmSYxCAhRLrbwDAMwzAMwzAMw4AtaAzDMAzDMAzDMI6BFTSGYRiGYRiGYRiHwAoawzAMwzAMwzCMQ2AFjWEYhmEYhmEYxiF4zOxcWVkp6urqktQUhmEYJtvYsmVLmxBiXLrbkWz4/cgwDMOYQe/9aEpBq6urw+bNm+1pFcMwDJP1ENGxdLchFfD7kWEYhjGD3vuRXRwZhmEYhmEYhmEcAitoDMMwDMMwDMMwDsGUiyPDZBM9gyPY39yLpq5BdA+OYCQQRK7XjZI8L2ZUFWJKeT48bl7DYBiGYcY2LT2DGFfoAxGluykMMyZgBY0ZM/gDQaw/2Ia361uw7kAbjrT3YVJZHiaV5qMo1wOvx4WhkQBO9w3jQEsvRgJBrJhagfNmjcMViyZgXJEv3V1gGIZhmJSz4VA7zplRicpCfg8yTCpgBY3Jeg629OL3Hx7H89tOoiDHjUsWVON7n1yAxbWlKPCp3wJCCDR0DGDdgTa8tbcZ//VqPT4+pwq3nzsNSyaXpbgHDMMwDJNehEh3Cxhm7MAKGpOVCCHwwZHTePTdw9h4uB1XLp6Ihz63FEsnlxly0SAi1Jbn4/oVk3H9islo6hrEM5uO46ZffYhFtaX4xiWzsWBiSQp6wjBMohDR4wAuB9AihFig+O5rAH4EYJwQoo2IzgfwAoAj4V3+LIT491S2NxGEEKhv6sGc6iJ2R2OYLOVway/yczyoLslNd1OYJMEKGpN1fHj0NP771XocaevHzefU4X+uWYTS/JyEjlldkot/umgWbl0zFb967yiufWQjrlg0Ad+4ZDbKChI7NsMwSecJAA8C+LV8IxHVArgIwHHF/uuEEJenpmn2MuQPYn9zD2ZUFcLrZgWNsQ/W953DzsYu+DwurC2pSXdTmCTBGRCYrOFQay9ueeJD3Pbrzbh4XjXWf/NjuPNjMxJWzuQU5Xpx9wUz8cZXz0XvkB8X/+RdvLmn2bbjMwxjP0KIdwGcVvnqxwC+AcA2563uwRG7DmUJSYhmdzSGyW7YQp7dsILGZDwDwwH8v9f24coH1mNWdRHe/cbHcNu505DrdSftnDUleXjguiX43lXz8c1nd+Drf9yO3iF/0s7HMIy9ENGVABqFENtVvl5FRNuJ6BUimm/muG/Xt+BU14A9jUwAYZ/OyTCMA3EnUUHb19SDPSe7k3Z8Jj6soDEZzdv7WnDRj/+OLcc68MJdq/HNtXNQnOtN2fnXLqjBa/90Ljr6R3DlA+tR38QPNIZxOkSUD+A+AN9R+XorgClCiEUAHgDwvM5xbieizUS0ubW1NbLdH0ifckQICW1sQWPshu01zsKVRAm+vqkbB1p6kncCJi6soDEZSc/gCL7xp+342h+242sXz8bTt63AjKrCtLSlstCHR29YhuvOmoyrf7EBf9rSkJZ2MAxjmOkApgLYTkRHAUwCsJWIqoUQ3UKIXgAQQrwMwEtElWoHEUI8IoRYLoRYPm7cuMh29jxiGCbZEKvMWQ0nCWEyjvcPteHrf9yBeROK8eq95zqiPhkR4bZzp2Hx5FLc9fRWfHjkNP7vVfOT6mbJMIw1hBA7AVRJf4eVtOXhLI7VAJqFEIKIzkJoIbM9PS1lmFj+vr8VJXleLK4tTXdTmDTCC0HZDVvQmIxhcCSA//uX3bjjqS346kWz8MjnlzlCOZNzZl05/nr3GjR09uNTP38fx9r70t0khhnzENEzADYAmE1EDUR0i87unwGwi4i2A7gfwLVCZJ7DYOa1mDFKZ/8wGjtSH+fISSmchdvF45HNsAWNyQgOt/bizqc/QkmeB6/eey4mlOalu0maVBb68OsvrMBP3tyPKx98D//v6kW4aN74dDeLYcYsQojr4nxfJ/v8IEIp+TMSKTkIJwnJboKsgVti6/EO+DwuzJ+Q+XVM2cUxu2ELGuN4nvuoAVf97D2snV+N39660tHKmYTbRfjni2fjx/+4CF//03b896v18AeC6W4WwzBjBJbfs5tUKmgZaEDW5MTpfhxqzQ7PFrsMmidO9+OFbY32HIyJoaGjHy/vPGX6d6ygMY5l2B/Et/+8Az98pR6P3rAc91w4M+NM+h+fMx5/uWs13j3Qis8/tgmtPUPpbhLDMGOA7BGpQxxp68O+Js4qxzASdklDp/uGbToSo0ZrzxBGLCzQs4LGOJK23iFc/+hGHGrpw1/vXoOV0yrS3STL1Jbn4093nI26ynxc/sA6bD6qVi+XYRjGPrLJ6gEAOxo6uYxJmpCmUmYtjyafXY1dCdc8bO8dwpA/YFOLmGyCFTTGcew+2YUrH1iPmeOL8JtbV6Cy0FmJQKyQ63XjB/9wBv754tm46Vcf4vH1R7JOgGIYJoTRZAqn+4ax95S20rHuQCu2HOswdW7psRLkxwtjkp0NXWjpGUx3MzKGQ629CVt11x9sw65GXnhgYmEFjXEUL+88hWsf3ogvnT8d3//UAuR4smuKXrO8Fr//4ko8ueEo7nrmI3QNjKS7SQzDpIkDzT3Y36wt4J3uG0ZDRz+CVrQtVtBMEwwKDPutxwq/sK0Rnf32u4utO9Cakhjmw229ONDcG7N4KP0lrTv0D/utzckkM+QP4P1DbSk9pz1XwXnXkrEfIQSauowvgGSX9MtkLEIIPPLuIfzLczvx8OeX4fOr6rI2pe/8CSV48a7VEELg0p+8i/cPpvaFwjBMZrHp6GkETArEnMXRPFuPd+CVXeaD+eX0DPptas0op/uG0T9inxtcS88gGjr6Vb9r6x1CfRyr0Bt7mrHXge6mXf0jYyvO2yYRKd2i1pG2voQWRjKF9r5hfHDEeElNVtCYtBMICnz3xd148v1j+OMXV+HsGZXpblLSKcnz4mfXL8XX187GHb/Zgu+9tAeDNr6AGYZxFsP+INYdaI3aZlSFau4e1LW0qcEe1ObpGbJfuXIim46c1nWd7eyP9uxQc8cfGgMCNZMadjR04vhp9QWDbEAyNvgD5h7KrKAxaWVwJIAv/WYLNh/rwHNfPhszxxelu0kpg4jwqSWT8Mq954bi7h5cj50NXeluFsMwSaB7cCShbGnmLWiMWZzss6GmcJ/qGkhJBj61uaR3rfqH/bzgyJgi3Va8VBAwuWrGChqTNjr6hnH9oxsx6A/i919chari3HQ3KS1MLM3D07euxDXLa3Hdoxvxo9fqOasTw2QgB1t6092ECOlMQvTh0dPYc9J5LnDZgrQiv+nIaWwy4TJlhtd3N+H9g21RrmdGCyO/sacZ7yXgur/9RGdCv2cyj0T1sxe2NaKxM7GMmskmwBY0JhM42TmAqx/egLrKAjx243IU+jzpblJacbkIt66Zhr98ZTU2HTmNy+9fj20nOtPdLIZhTLD7pLYFfAwsEEc42TmAw20hZVUIgVd2njJtAUwHqYp7Ptk5gHf3t8bfEeqKdirKgQ6MBNDaO4TO/mFL7rKJuEAeO92Ptt7kxZKlY/GipXvQVIIIIxAIQgj8fX9r0pK2dPWPoKs/+cnM7Lj3egadnXTNbHF5VtCYlHOwpQef/sX7uGBOFf7n6kXwunkaSkytLMDvb1+F61dMxud++QF+8MpedhVhmCzArACiFCI7+0fwwrZGO5uUVKTmCwEMB4KWvAJ6h/wpSx7gDwRNKdGBoFB1LzQyzI2dA+hIINujWWW3z2RsnZE+JFOZdXoJmq7+EdMK0YbD7ToJIqxfy0BQoLN/GMNJyvL5zv4WvLO/JSnHlmPHbHLatBFC4J19LRiyKMOxZMyklK3HO3D1Qxtw09l1+PZlc7M2U2MiuFyEm8+Zir/evRrbT3TisvvXma6FxDBMapGvjiZD2DBaksMuIaVnUF8hjCdEK1eLrbTrrb3N2Ho8+c++5u5B/HXnKVNxMAdbemOSvkgcaetLuD6WhPK6nbTgxvXm3mbNrI2G2iBFoSX5dX26bxhv1ydfGZDTP+w3rRC+s78FOxvD1nKT89puS7IdMpTcdfWFbY3oVVHojS4OnTjdn7ZFZacpaPube9E1MIJ2i3GirKAxKePtfS248bFNuO8T8/DF86anuzmOZ0pFAZ6+dSVuPrsONz2+Cf/x0h4MDLM1jWGcSPeAupVCsl5IIpDd1oG23iFVoS/RswzoCFmd/cN4cftJNHfHd9lKtB0jSbIMDI4EsOnIaQCw9Fyt10kzv6OhU/d7S4QvpNXrMWIy/kVxWtNYURuaugbRbcBNrb13yDYl4I09zZZiR4+291k630s7Tlr6XTzsfKx0J1CfdevxDhxoNn897VirN+tCmGz6hxPLCssKGpMSnv+oEXc//RF+cu1ifGbZpHQ3J2NwuQifX1WHl+9Zg71N3bj0p+9GhAqGYZyJpLh0D47gzb3NAEYFELkMIf9c39QdFXeqdFnSEmDeO9iGQ62pS07S0j2Iv4fjp/pNKDbOEp1CY3SqK2SNkq6tWhKMY+192C4bl5FAMPI7NYxYNBKRRV0p8DpRnkFN7k20FYMjAbywrdFS7NT6g2346LhKjLbFRiUSLyevN/jCtka0JzF2TkmmOyANDAciLsxGE9AkGyNZUXuH/BjyB9DeO6SbmCRS4F2xfWA4YGhRiBU0Juk8tv4IvvuX3fjVzWfigrnj092cjKS2PB+/uWUFbj93Om558kN898XdCa/OMAyTHKSaPvK6N5IAoiWOHm3rwzHZqvxru5uivteznCRl5VjlkG/va8HgiDlhVrIYJjuuaCQQNOwGqkQaGzWBd19TT5S1ZF9TT0oXyZRXzapQTlGf9Q+iWWzbTitN2FJm9ZBOjVNTcw+MRyLJLZx6HYzw+p6myL0kn9dWY0+tXoqWnkHUN3VDCIF1B1p1lSchBN7a2xzOoHoam4+qPwuEEJHnv7TYJvXxrfpmvL6nSfV3clhBY5KGEAL/9Wo9Hnn3EH5/+yosrytPd5MyGiLC9Ssm49V7z8Xhtj6s/ck6bDiUnBTLDMMkB21lKvT2tmJRiLLKQcRss4vugZEoFzQjukKqxMfdJ7vxzj5r8Ut6So+y/Xqun4A9YVovbj+pGTOWirjtPsXinxV3QqPN1FMwXtjWiE5FMhXdhCcJTLZAUOhaRjVPacMEt7qwkCx2NKQug7Ta3Hprb7Om4qOHsDgB9p7qwb6mntHERjrH+evOUwCiF8w+VGnr4bY+zflkNA6RFTQmKfgDQXzz2R14bVcTnv3S2ZhdPXYKUCebiaV5ePLmM3HXx2bgi09txv/9y+6UZTpjGMYiYYH15Z2ndONsrAhrKTKgAbBuwZEfr2/Ib1uBZel6yQWmY+19lhRdta4pr2084UrrW38gGFfo7Apn6hRCoK13OHz+0BFP9w/jhW2NlgRXPVp7Qi55Wq5aQiDi0ipHmgeJJr2I92v5/dAjcxm2mxOn+22xjKbSoEUwr5NK2VSFEJHP/mBQsU/q5AlpMUD5XBmyEGtp9NoHgkLXNVHPwizNd/m51BL3mM2cqgYraIzt9A35cduvN6O+qQd/vGMVJpXlp7tJWQcR4Zoza/HaP52LHQ1d+OwvN0ZetAzDOItXdzVFrRQPjgSw91Q3Wnpik2xYUYDMrBwHggL+BBJvyIUXI22VBBl5dr4Nh9pVMyCquRYNjQQ1M8h19g+rWs22nehEW9/o81DNSiPfFOmHoj8h17PQjpILlBaSQqjlrjboD6Kxc0DXNb1/RPu7RBI3ANFjJQnkvUN+vH+oDcGgMKz47WocrfV3um/YcNILzWQ2cabu/ubeyLtN/vtWtVivOPNRyxIoRGoTTOw52Y2u/hFD7pB9Q35DFkwjz4DBkQBe3RVyrTvc1hf5LLlkm6VncERVEbFqyUoUo2c90tarOt8Pt0UnfhnyB3THSK+0gZqSp5VISgtW0Bhbae4exDUPb4Db5cLvbl+JikJfupuU1dSU5OGZ21Zi5vgiXPEAF7dmGC2I6HEiaiGiXSrffY2IBBFVhv8mIrqfiA4S0Q4iWprIuYf8gRjL2P7m6DTskgB9um/YcFyJpGipJR7REpLeP9SG1/fEt0LIj9ktS7mfiIfd3+qbsauxK9Iy+TUZHAng9T1NMX1XCkFybwFd643sqxe3n8SRNu2se2pug/5AEH+rb4lYE/Y19ehmQvxLHEVFOoO8zd2DI3GVZbtFXbkS7AlXvDZjMZEnpFFz9xwdn+hr+t7BNkvZEvuH/YYyYurGaIav+VadcjXWY+H0j/PmnuaYuXegpQfv7G/BWzJrYIeGRfnNvc14Z592UXMjjwppTPyyudc/lHgWzL/Vt+i2zQx2uO4aVbKVU0U6855T0fPsg8On8dbeZtuSMI0mJTLWV1bQGNuob+rGJ3/2Hs6sK8fDn1+G/BxPups0JsjxuPD9Ty3EPRfOxOd++QFe3J6cNL4Mk+E8AWCtciMR1QK4CMBx2eZLAcwM/7sdwC8SPXm8V7IkW+yUWSjiYWVBprN/xHSqdvkquVKxlNjR0Kn6nVxR7Bn0Rwk7cuuXJFwpZSzldVOznHTJ+iS5Lil1N3ksk7L/owa00bNJsSbJ5O36Fqw70BZRIFKRyU6u8ErX2miRYwERlUWyX8W6oBfHpSZAG7G2SD/rU1hYJWW+Z3AEL8vGS6nk/2XHSew52R11JmW2xWQZ0PqG/YZi2/w6iw3S+CgXeuSCvhChxZ0TCmvYSCCIV3adwkggGJWwx65QRvmYahWjHwkE42YtNNKcIX9A91oaGcOB4QB6h0LXUOkGrZw3kuVyV2OXaZdptevrDi+IGL30rKAxtvDu/lZc89AG3LZmGr575fzIRGRSx3VnTcZjNy7Hvz6/C4+tP5Lu5jCMoxBCvAtAzY/rxwC+gejF76sA/FqE2AiglIhq7GpLS7eaO/Lo6c3EUki8vrtJcxXeCnLBWUs2kRSKYX8QR9r6UN/UYyge1oyLpVLQicpGGP7ynf0tETe4hrCAqrWa3tozFCXMy4mnLLyy65QpVzjJPWpgOBARwJW/7h4cwYdHT+NgS48191ad5sgF5kgWUbUfmJA9ve7Qcdp6h2IsDvLzDPkDmsp81KlNnFvplibN/7+F3Wf1jnWgpSfK/XSpxBSdAAAgAElEQVT9wba42RP7h/1RJRaMoHZ97VK8txzr0E2Es/VYR0xhd6k5ZkpimEEu6314ZPTcHX3DkfHZfLQjbtbCmLmvMpb1p+JlUI0/mV7f04SGjpCSNxI0br1WWsithF66XPqZfGP2N38KhhlFCIGnNhzFnb/dih9dvQhfWD013U0a06yYVoHff3ElHn33MH7wyt6MTsHLMMmGiK4E0CiE2K74aiKAE7K/G8LblL+/nYg2E9Hmnp5YYVXr/kvEZUZuIZde+O19wxgYCaC9b2j05W/jrR9v9fijsFAohNAUEOMx6pqpvt0sWj+TVsXl35tRjOLIdFF09g9jy7GQcLqzQdsy2tQ9iN0nu9UtTBb7f6x9NMZIToI5PSL4DRS+3quiwClpS6Bu2AGFy+SIistv9PfRX0gKvoBQvVdPdg7ELUgdM19V9rFrvVpt7hmN91K6A6vV07NSXF1ujQ7IruG7B1ojCvqgwrKmZWlLJsP+YEx2ynj3lp78ZCU5jtvkCgwraIxlhvwBfOvZnfjFO4fw9G0rccn86nQ3iQEwp7oYz375bLy5pxlf++OOhBICMEy2QkT5AO4D8B21r1W2xbyRhRCPCCGWCyGWFxUVR33XN+TH+oNthtsTnSpfb79YS9uo+562ACC5Bhm1AGkV1FZDLvjK45laegZxoDlWGTUj2ij3JQr1d8gf0LVLmHFJilg4DPwkYEJj2nKsI5IuX0oOovdzvQyeZsVBrXIA8vGPlGSwoM1biRmK6nv4cyJx00oLnV6MoBqS4iREaJFDCz2rTbIWQbv6R2IsfC4dif29g22qCw1qYyuEds2/RMjPcQMYvZ8CEdfdURo6+lUXDuJZGc26ZStp7R3SjEPVmspGrdNGjylZG43OGVbQGEs0dw/i2kc24kh7H178ymosnFSS7iYxMiaW5uFPd5yNAy09uOd32xJ+uDFMFjIdwFQA24noKIBJALYSUTVCFrNa2b6TAJgK7jRzz/UN+Q3HAQGh+lDymBIJLUGjf9iPTUdOR2Ukk5J+7DnZjdaeITR09MfEr0hoKXXxZPT6Uz043BaroOldm/a+aIuK8twEwt/3t2Lb8U79+mXhn8XLgLfnZHfkOEbEJmV7Dhu0hhrRF6MyZEJqU2IKwC5FTKO8/R394WLROqfQGqtEjEKBoMCGw8ZreHb0G3PdNZtwRer3iY5+NHfHZlSVMFMfTe1aGlFmleP8zv7YBBxKqxfR6PkGRgIxxZ6j2yVwrN1atkajSG3J8USrFvL+a2VFVF4i+fXoH/ZruiUbRauExu6TXTElPwQEXt/dhBG7zM0WYQWNMc3W4x244oH1WDixBL+9dQUqOVOjIykryMFvbl2Bpu5BfOk3W9PiVsAwTkUIsVMIUSWEqBNC1CGklC0VQjQBeBHADeFsjisBdAkhTEkIZuJOlKmqjaywqgnOWmeU5AzlefqH/TjQ0oP9zT3YEo5f+cv2k+gb8kfEIyEEmnSEV38gGGVVkifj8Fjw7dpwqD3K+qW8FNIh41myJEVEEr609j7QYs5qoLTMGRV6I2Oq02x9hVO/vy09g6puV5I7bUDl/FJMl96RO/tHrTiqpQnC7D3VHTemS+KITGm30wAlWdCMHlLaT8tdbWDYnoVNI8bGrv4RdIWvtda4qLklyq1C8pjQt/Y2IxAUUddXmgvCYJuUxFt0CgQFctyuKOVqcCSAgMw3U8s1Vm8eGK3zKj/G2/ta4i7OvL6nSTWz6OHWPgyMBExZR5XnsiPukBU0xhR/+PAEbnhsE7560Sz8+1UL4HXzFHIyxble/PoLZ6F3aAS3/XpL3ExKDJOtENEzADYAmE1EDUR0i87uLwM4DOAggEcBfNn8CY3v6lIoModa9eNegFBiJqVlgYhGM7XJtgfCQtHQSLSgIyVXkJ89KERUIe3W3iFd68Jfd56KUsrkWE0WNeQPRtz9rMrvAqH4Ji2hUi58GakzJaEU5pUWtWbVBDCIrMYLaGfQS0Sk23CoXVfxkeJv1HQRK256SmVhf3MP/lbfoq9khkdT3oagEIbcUY3sY3YRMl5dOzXrb8wxDGwxMq57TnXjnf2h+1HKRKr8nZolL8rNM/wDaU7K5+bW49GupFYUCKU1VklQCLhcFDX3XtvdhJ7B0YUhrWyVW3RKIEg/MTNLuwdGohYXzCikVmKEX9vdFDU+qu6mig7EU3g5DzpjiCF/AN99cQ/e2tuMJ79wFpZNKUt3kxiDFPg8+NVNZ+H2pzbj5ic24fGbzuQSCMyYQwhxXZzv62SfBYA7EzmfGYFAKewaCdZXq12l1IcGRwJo6hpEUW7oflcqE1qWg55Bf0RITMTDzuO2pnLIM77FCNHhQ7b3DmPzUW2hDgjF5UiLiEJEH0vK5AbILC/hrw/oZB9Uxnb1Dvmj9tdSVo0oQXpCnZFiwvF0mCNtfcj1xi6q6sVKarpYagytcl4eb+/H5Ir8yN9v17cgLxyrJCFlyJtZVaTZDi2LqVzI7Qwr9cP+IPJy3Dhxuj9qDirHQOtyBYMibl27SLsUF33Yr6KgpSiptd5plAXSpTaZidWU7hOlS6BEUIgoq7maoqN1H/iDQQSDImaxSjquFfxmMvrYQDxLn/JeerteOyMnwBY0xgCNnQO45qENONzai5fuXs3KWQaSl+PGL29cjoIcD2761Ycxrk4Mw9iLUsxIxH3N+Dmj6yIdbu3D9obOSNzH9gZjCRn2nupGt07CCjMtiseprgFzaffDxwwKgb7h+M8x6dI2dPRjw6HRuCe59fFER7TyY8SCKUct3Xy89qix+6T5LHpy4l3t46f7DWVfNIJ8fkRbrqKPv+tktNWle3BE1yKrhZaQHnXNwrtICv7W4x26CT60LKd6CoHyXh0JBCPxnEDIZbalR9k/axqaVcVEQmsBRggRadGbe+MXrZfT0jOIdQdase5AbIFqf1DArbCgxZxb59jy/kpWt8GRAHoHpQQ75q6HPyDw4dHTMZllU4ERC5pWIh8JVtAYXd7d34orHliPFdMq8NtbV6CqKDfdTWIs4vO48fPPLUVxrhc3/WqTZrAuwzD2096rnejArlh0pVAgCTzxMuXpZQ9MhIaO+FafTUdOG7IOScSzRuhdytbeIdVC17HKaPKSAxg9sj8YxIZD7XHdoASMu2gSkJCwqtV2eVY+NSVTetckom9oGUPk8U1q1j6fR1vMjWeBVePdA+rWRrklalARu5YsC9qIX3me6BO9trtJsxadtGs8JUGJfJFDiRACHpdL2+pqAPlcDgYF1h9oM7ywNOQP4rXdo3PRHxQ42TmAE6f7U1IEXqmsKzF7VVhBY1QJBgUe/NsB3Pn0VvzHJxfgXy6bCw/Hm2U8Po8bP//sUpTm5+Cmx1lJY5hUoZdow85U3ZHkHhBRbnx66GWQrNdJvX2y03h2Oz3cLjIsvMQTs+JZZ7QyVTqNxs5BtPQM6l5/CUkoJRrNKqnmbqWWZCLZjARCCSuAxOr/aVmT4pWD8HncsRvDWKllpenCGvU5+rjJuurKxQa186illg+5i9rbKn8giN6hAFyukOVeC71H3c7GrigFazgQjLKSx1vI6R4YiVLw7HZx1Bp7iUFZjC8nCWGSQtfACG5/ajOe33YSz335HFy2sCbdTWJsJMfjws+uX4qKwhzc+Pgmw5m3GIZJDsmwoA0MB2zJ3KqX4lxP6TSD20WG3d7MXiprQlryFJlAUKDfQLImSWk3EyM07BfY2diFfU09qgWW06CfmUIvo6amgib/rLKLMt5N67eRNjT3qG6PF18UVV9OcQAzFuJEMDq+r+9utnUuBIMCr+9pjljQrLpmKq/T7pP6SUkkIvXWFH2yy51X4u/7Y906tVB3cTTXHlbQmCj2nOzGlQ+uh8/rxgt3noMZVYXpbhKTBHI8Ljx4/VKMK/Thhsc3RWVtYxgmccy8ihOtdSUhd3FKnpOe/RBR3AxxqcCucdDj/UNtmkkW1OgeNO7lICk4WklmHK6f6aKlZ0eVZFD5Xq/Ug5rAvOdUt6rb3yu7To0mzlFrh8qxNpqo9WYGLTc6o1aboCwGzQ5OdQ9GXHH1HK26+s3JGUY9AF6KJHSJLgQtt5DqjZ1dRCpZ2OQRwQoaE+HZLQ245uENuGFVHR68bgkKfJzpL5vxul144PolqC7OxQ2PsZLGMHZi5h1tmwUt6vyZo6JJdZ+MkEHdsgVl9r2EsCCVy922hBCq2UNTgTELWuw+ZmOsAO3L1KJj5ZUrA0fa+rC/ucdSMpRUYaR4thIjGUrdLm21IpQwJnk3sNQlNcu+kXhYu86/s7ELLSrlNjgGjTHNkD+A//P8Tvzw1Xo8ftOZuGX1VEs3L5N5eN0u3H/dEkwsy8N1j2xEm0oAPcMw5mnqMi6cmXFji0dnX2ihxURixIwiFRlo7XANdSJWYtDktZ3MWP7sRivNflQMmmy7FItnZ5tP6dzT+2Sxgt2DI9hrIrNnPIQQhuZ9e1/o/W0kti5ePJUaWsqu3LqpV/qwrXcoYhVLRNbQSoojnVovc2cykebikba+yFhE72DueKygjXFOdQ3gHx/eiH1NPfjrV1bjrKnl6W4Sk2K8bhfuv3YJzphUiqsf2pAxQfQM42SM1DKTsMsqdKStDx+dCGWma+0xLwCV5HntaUgSGcxS5SkVJGrRGbE5pscM2hY09divnUlwmdVTb+PFqBlFS/kwkw7fyL52xsVJzxzA+CLAezq19+Lx2u4m1UWtdCTBSSasoI1hNhxqxxUPrMfSyWV4+raVqCrmFPpjFbeL8P1PLcClC6px9UMbdAu1MgxjL3bFPslTsquu4MYh16udUMEpnDidvFgSGw2ZKSNeGv5sQSsGLVpvs2cAtY6iJ/8r545VL6R4xYuZEHrF1SXUEuUkk3gJicw+5znIaAwihMBj64/gp28ewH98agGuWjwx3U1iHAAR4Rtr56AsPwfXPLwBv/jcMqycVpHuZjFM1tM3ZI9VSF7PrDjXazqu1K3nn+QQjMZkWYnBs+L2xaQGrVpY8mG2S8HWnjrmEo5YQa/kRSaQKiuWWnZZI4Xrk0k8q6TZKcIK2hijf9iPb/xpB3Y2duEPd6zC3JridDeJcRi3nTsN40tycduTm/GdK+bh6uW16W4Sw2Q1eunFLWNBTsoA/cwwyXBxG+t0DqRPgdVy/dNycVTDRWQoBXy9RvyYpHt4XK4Ya0mmK1Z2kWVehrbCChqjyZG2Ptzx1BZMKM3Fi3euRkm+8+MNmPRw5aIJmFiahy8+tRlH2/vwzxfNhiubpDeGyXYsLOhzcihGDytxjclGngQknouZ0fpcWvX9JOuQ2wUoQ856FUk8jFrUxhX6YgpOZzKpfIS09w4ZcnV0CnILn5qSr4Rj0MYIb+1txid/9h4uXViNx248k5UzJi7LppThuS+fg9d3N+OuZ7ZiwEBxVYZhnIGVYrFy2SpTAu5z9AovMRlJZaEv3U3QhRcynIGam2OmYGQK8ZMtywkGBf73jf346h+243+vWYR7L5zFlhDGMLXl+Xj2y2ejfziAT/38PRxpS23QLcMw1hgr9cLyuV5n1uFUCUWKf3SrSNdW77dsu01T+dxJVVKfc2ZU2n5MI2I4K2hZTFf/CG558kO8tqsJL9x5Di6YOz7dTWIykOJcLx678UysXVCNqx5cj9d2N6W7SQzDxMFKwLzcMpApRgI3EZZNKUt3MxgbcbrLn1oynWxTtKySSgXNzlpzeuR4kqEqxX/AsoKWpew91Y0rHlyPwlwvnrvzbNRVFqS7SUwG43YR7r1wFu6/bgm+9ewO/OCVvfBzUDTD2E4mZFJ0Ei4CfB7nlwewi6WTWRlNN2peSFazOI4VS3cm40nCM5ktaGOUF7Y14pqHNuCGVVNw/7WLkZ/DLiCMPZw/uwov3rUaGw6147pHN6KxM3k1iRiGST7VsvqXcpmBHORoNqksX/M7t4tst/aZUZJnVhXZe/I4eDnmLu0kew3FzBiPc1i8npXYV6eTjHhcI1eJ7/QsYnAkgH99fhe+99IePHLDcty6ZhoHszK2U1uejz/esQqLa0tx2U/X4eWdp9LdJIbRhYgeJ6IWItol2/Y9ItpBRNuI6HUimhDefj4RdYW3byOi76SijTUleYb3tVNI96RB4F8x1Vx9Rb0VbLeLbBegzCin8yaktlSNi6W2tGPnfFMrXpxnpmB8uCnTKgttalHySbZL8llTy205jmSZT4aCFjQQQMe3epZwpK0Pn/7F+9jf3IO/3r0Gq6ZzgWEmefg8btz3iXm4/7ol+M4Lu/HNP+0wXECWYdLAEwDWKrb9SAhxhhBiMYCXAMgVsXVCiMXhf/+eigbOqAoJWGaUA6OWHj0XQK0VbysyiVFBpjDXnFeHXj8bOwdstWi4yLhFLt4C6PwJJVg1zd53sVqCirGCnmJfnJe6zNR2DoHa7VdekGP6ONOrnBHGYsSCpmcRtwO7rf/JsJgaWWRjBS0LeHH7SVz54HpcMKcKv711BcbLXFYYJpmcN2scXr13DZp7BnH5A+uxi4vDMg5ECPEugNOKbfII8wKkOc7fqgwwrkjbxamqKPQukAuU44p8Uanp+2T1m+QXwEx7zFrEzPY1nkBsp0BGBIwYjK+Nd9bJ5fmosvl9nEiMot5cyQT05kEqFVePjWbMgIolJT/HfEylU8pi2OHhODXJOROMHz/UGbvjgn0et6EFBVbQMpjBkQD+5bmd+Pe/7MEvPrsMX714dlrcVZixTWWhD4/feCY+u2IKrn1kI3657rAh8z3DpBsi+k8iOgHgs4i2oK0iou1E9AoRzU9NWyz+UOdWK/CpCHoCuHRhTeRPI8JHnteNJbVlmF2tHm9lyiXLAnpC37TKQpCNrz0zyp5cKC7Nj7V6JENmTiRsYcHEEhtbknr0ep4qBSU/x2OrRWVYZTHAiguzQ/QzzK1J3OU30bHMV3vuyThjUqmh40jPHbtDhVzEddCymkOtvfjkz97D0bY+vHzPaqyeaX+dBoYxistFuGX1VPz+iyvxzKbjuPmJD9Ha4+xUyQwjhLhPCFEL4LcA7gpv3gpgihBiEYAHADyv9Xsiup2INhPR5p6exFI+W7ECEUjX7GfkmFrCkHzzx+dUYXJFftw4OaNyjJ3CdGVRjq0OTWaaJhfUp5Qbd9saV+hDic4K+nyduDZfAim/nSDDTx+XQKyUTgfUjFpm4jqNIoTQnSPK8JKCOEnaakpiLaxWLDZOSeqTnJT02qgtDhXnelFXYd0KV6ay2BKPolwPJpQan2/VxblxF7ZYQctAnv+oEZ988D1cuqAGT92yIuLGwjDpZv6EEvzlK6sxoTQXl/70Xby5pzndTWIYIzwN4NNAyPVRCNEb/vwyAC8Rqa6ACSEeEUIsF0IsLyoyvnKsqqCEN9kp4EinkZ9NqdAZUZakFeSSPC+KTMaPpQI7V7jNZKGzel6f160rnCljBuXuULkJWCuTbemMx9oF1YatQ2dPN7fo7CbCZTLLMDCa+l5PqY3XHmWWznizQ6ko5cVxV6xWcYH1uM3Pq1QqRqsNFG6uLPRhsolFC6toPb+M3sVq7o7SM8CMH1Ce12OqBEZteT4unl+tuw8raBnEwHAA33p2B77/8l48csNy3HPhTK6ZwziO/BwPfvAPZ+A/PrkQ33h2B779551RcS4M4wSIaKbszysB1Ie3V1NY8iaisxB6T7bbe271bRfMHY85Gm6EWujpE2rCi3J/6R2itGxoHdeOlXq73bEKfR7bsthpJVQpyPGgrqIAbhdFhGr561ftXazdTYEyjUQQi2tLMa7IF5UowmqNLSB6XD1uV0IyQ6KxVz6PG0a999QUDq25d86MSiyqLY1StioKfBEB2+rlm1lVhMqi6HEKHUv7Giovb7xzq31tJJ4uYplLQzSBNDf1Fiji9VvpZmh2XtaGlT+tKWn0nqlVSVii9tNCX/TCVMzzEsJ2eZwVtAzhYEsPPvmz99DYOYCX7+EsjYzzWbugGq/euwanugbwifvX4aPjHeluEjNGIaJnAGwAMJuIGojoFgA/JKJdRLQDwMUA7gnv/hkAu4hoO4D7AVwrEpGQjbYRISHAjPJCpJ6mW/49oJ8cQpJpq4q19yHNP0L4vOkXJSaW2ePOpnX5p1TkY1FtKS4/Y0Iktb6kAJ89vRITTbg3AcCs8UW4ctGEqG2TyvIxpaIAuV53lJUiaCxnieZ55CQjVstFhIoCYwlIrFgd51TrW6crC30xlsWSPG9k3lu9eYnUlEL9o2ldX7XMhcrxHz1x/LbFs8wZ4cK547HQQlyiNIaJzCSlAq72hNXL9kiR/81Z0JTjI1nL5MpVxIKmM9R2XP94pP+pyugihMDvNh3Hp372Pq5cPAFP3nwWKh1WmJBhtKgqysWvbjoTt6yZhs8/tgk/fmO/4QxpDGMXQojrhBA1QgivEGKSEOIxIcSnhRALwqn2rxBCNIb3fVAIMV8IsUgIsVII8b7V85oRhiVZIBkujvLkEEqFLtJGEbvqPE8l4F/Zo6sWT4wIx0ata1qXJV580rkzx2l+l2wdWj4ukoAqrd6PK/LBpWZBizP+RpUVNbdLLYXI53FHFx9XnOLcWeMidaKMWsQk4VWrubXl+Ybj4JX3xMXz9N28ACA3vACgtxihhGh0PsVzW71Ew9VMq796w6bsn/4Cikb8JyhuNm471OwCnydphc/jjZWR9ivrH8pdfSPXTuNARh8Hg/5gzLkCQQGfx4XKQhOxaEl4/LCC5mA6+4fx5d9uxYNvH8QTXzgTd35shupLgGGcDBHh8yun4IW7zsHb+1rwmV+8j8OtveluFsMknSWT42cLk2JtpMynVUW5mgKjGkZcHPUURT0BTcpMKP+5HfFeWoqctCpdkueNxA0tqS2L9FFyC5THUknuVmYSx6r1Qe86TK0siIqnkYQ5o9ngxhfnmhaE5W1U61uFhvC4dHIpVkyriMTDKHta6POgNC/0W6MJR/QKhZtFeSgjlojIfDEwxmolH9Sun2SVE0L7OhTlemOUMSHiKBc2Xap4CkZukiw4eglqlOg9CtTKB6illpdiwNQWpvSeW6T4Pxb1CyhtlVyiq1S8CwSAtQtqsHRKmam4MrthBc2hbDjUjkt/ug5etwsv37MGy6bYUxmdYdLF9HGFePZLZ+O8WeNw5YPv4TcbjyV91ZthMgW5i5bRRBAEfZnVpeKKpEwdLaXFNnon6smfVcU+1ZV/ZbFmLblrmixgX5LvJldEuzmdNbUcF84dDyCUOU2KGTPjZllqsqhxTUlelMKU63Xj43OqDHuzrJxWkVD2RaPPSRdRpO6a3K1VaZmUumI0pExtHskxk8FSTehWCu5n1pUrFgVC/xtRwqtVsiKqXb9oK6N6zyaW5sX0eSQodBcpJAVUrgCaRevwOW4XinND1ypZdd/y4mSdlKguzo20RY0hfzDqOvk8rqhrKX0lJWHxuCgm82JdZb5mbGnEgKaVJETjuivngtrCieSu7HW7IrFuRmMJc9yumHg1q7CC5jBGAkH86LV63P7UZnz9ktn46bWLdW8ChskkvG4XvnrxbDz5hbPw6LrDuOXJzWjpGUx3sxjGViTLilFrk9xN0Cx6wntejhsuIv0Cv2GJUnmYqKLVsgPoHSvX48ZKhTKW63XHFGvWOoT8PJPL81UzFNeU5Kl6khTnenH5GRMMKUILJpbEuG9KK+VqV1Mthq/IwHtZ3sqKQl/cd7lW2wMGpXz15DOkab3Tc0mVt0Wr2LZ0XK2EJy6imLpYamO3Ymr0AnShRqZQAWEpZsooOQasnPGU5Ygya9zoF0No4UX/l3YtbVrV81ZMq9DMNjl/QkmUW7XH5UJpfk7UM0Zt7imvXVGuFwsnjR7HJxsf6fd2qqlXLZ6IqxZPTKhe4KULa7C8zh6DCitoDuJYex8+89AGrD/Yjpe+shr/sHSS7QXyGMYJLJtShpfvXoOqIh8u/ck6vLrrVLqbxDAJIS+OKk/drHRXXDq5DPMnJCZkGn0r5LhduGLRhKj3iJqFAQgJhHKhTytux2wWR+kw8iB8I++1BRNL4ibDUstIucSAS1J5QU6UVamuoiDmutidkW1xbSnOnx2KofMHYq/tBXPHqxb5nVpZgMW1xlwp5WMTiRvT/4EmksK6dkE1Fk2yNl8vmFulkqQkdj+lVY0UTZPGOSiAaQbrqEmHrCrKxSSTCWSmVhZEXCXVLH5611RpbbTsJWLwZ1aOPrE0D+fPqtL8/pwZlQmV05hRVRiVNGftgmqcVVeO6eMKIxYpCTVLqd5CxpSwlU3aV+s+1bouy6aoPx+MDpO0+KZU5uW/t+vRwQqaAxBC4Dcbj+HyB9ZjzYxK/OmOVZFJyDDZSoHPgx9++gz88NNn4Dsv7MYdT21BSzdb05jMRMvlSGkZqy3PR4HF+JGplQXweVyR+JNQFscQatYXNeFFKTBLKAUULXcyo2uGowpQ6ECXLqhBfth9yqz8YiY5hBXkfZIE6vwkxPhIimm+ihtZoc8TM17nz6rCGZNKdbPZaeF1u3DV4omqFitJuDRiMfJ53KNZ+wiYHS4DsUYlYYuyRIFaP1UVHlL+Hb1ByjBqRtmRlNVV0ytMh4i4XRRR2M2ukStd76y5OI6eVC1Rjx6FPk9c+bEw14OSfG0lqLLQp+m+Ky/GHKNYa1wst4vgchEmV+SPxkbqXNfa8nxccYZ6hkvlz7SOo3XdrdxLcqZWFuCieeNjFE05dmVKZQUtzZw43Y/P/vIDPL7+CJ64+Ux87ZLZScuqwzBO5KJ54/HGV89DWYEXF/7v3/G7Tcc5No3JCuwu6nzGpFKsXRBdjFfSXdSEYa+FgreRw2pa0IyhlFHcLoqsLMeLH0kEo48O+anVBCppmxlLWqzAqr5fqUw4Vhs3CT0hWg2jl9PlIly1eKJ+rIzGseZUF+OqxROj6rRJrF1QrXm9akpCgr1R4VXaz+t2RRY5bC3GrNMMuSVSbY7qdTzGSLIAACAASURBVEH6rjTfi5XTKgwvLigLcy+qLY1k25SQH0nr/hQitmaXErsUiMW1pTjHQNFqNeQtyPO6Mb44N6pdagsL8h7HS4A0b0IxltTal+BDfm7pnpV7SsjH2S7phTWBNBEMhqxml/10HebVFHMiEGZMU5LnxQ/+4Qw8/PnlePjdw7ju0Y042taX7mYxjGWuWjwxpfHDUuyXvGaUfLFvZlWRZkYyn8eNkjwvihSCnapwaUK2qynJQ12l9mr+OINJNqZWFmBGlflC1EaUKxeR6mq49FszyRiUSQ6MtMtMTJVVhVZLSVAKt1ausZKL51VHsn9Gnyv6fzlKt1lCyMNC+gyElD+1mEQt1C6V1+3CmXXlyPOG5nuu162qaMY7jhYuoqi+jC/OhdGqMmUKZbzA54lJTmOU6eP056GRI8r1v2mVhapZQXO9blTEuX7xIAIunl+NBRNLUFXsi8rQGrMvQhZc+XNMfi+dWVeOtQtCLuWFPk9MgiG70crialcCF1bQ0sC+ph784yMbQlazL5yJ/3P5PMsB4gyTTayaXoFX7lmDpZPLcPkD6/Hzdw5i2M910xgmCiH/GPpDzbogd2GbN6FY0y1n7YJqFPg8MbEjasSrzyTnrKnlqkWGJaGzojAn4jInoUwwAISSBViJ27tc5iZVnOdVrft11tRylKhkdXSHk2tU6hT5jvmNQvtQE67HFfk0U+THI57YZ1YsnFNTFGW5mVQ6Oj/UYw3jnyHH41JNyy9dC/m11nI3U5Nvle6TEmZcAMsLcjChNA8Xh+NCL5o7Hiun6S+Mq1+F0FblfMrxuEaTg4TvUb+Khja5PD8mLk6zJlqcS65mSLOr/p5Evs+t+ezQO5bafRXze9kVriz0RcZGjaVTypDrDbVFSlAin2oTSvM050mykY+DXUWs7fW/YHTpH/bj/rcO4qkNR3H7udPxxfOmsWLGMApyvW58Y+0cfOKMGvzLc7vwpy0N+Lcr5uO8WdpFahlmLDG7ugj7m3uwZHIZNh89rbmf2bqZRBT5TZmKFQQIxZa5ibC9oVP3WFa9lBO14mjJi4snlWLPqW609Q6F91N3YZSa7XIR1s6vNmVBmV1dhAmlufj7/lbNfZSubGUFXtUskUqmVBTEjYszu3Dvdbuiz2023srEvtJ19sgWDRZMDClXMTFoJo48UyOmUnnM82aNiwkfMXJ/SPPE53FjyB+I+q7A50bXwKgCRhi1SkoLJ36VYM5xRb4Y5VSrJTHbJQUwbstDCnBRrgd7T3VHH8LA5bXDTW9qZQHqbLJiEVF0KRKPO7LdDhLtbzKCMlhBSwHBoMBzHzXif17fh+lVhXjp7jVRvqsMw8Qyf0IJnvvS2fjT1gb88x+2YcnkMvzrJ+Yl3W2BYZwCEanGmkwozYsE608sy0NTl33JdST3nDUzK9EaVmaU1FUWoGfQb7iOlhGk2mZmsEM20yrC7HWTaQXX7SJV9z49fB53jNKmhtFMjonicbngDwZRkueNcSlM5HorL2V1ca6mtUMts1/sPur3RuR7hWpjdlxGjxPi7BkVeLu+JW6bIl+Fm1ZXURCj2NmhVEhd14tx87oJM8YVoqrIF7VoID+7dJx5NcXYo1DkzJDrdWOKyrs5vjXP2PGVY52bk1oHQK2SAkk9Z8rPOMZ4/2Ab/vPlvRgYCeC7V87HRfPGc+p8hjGIy0W4ZnktLplfjZ++eQCX/vRdfG7VFHz5/BmG3CcYxon4DHpOlOR50dk/DEBb0JlTXRzlRpjo62XehGJMqciP+55aqEi97nYRKovMC8GSG2aBheKuapYWLa8ULbcjrf3T5SplFCFCFoojUbG6iQ1+MCjw8TlVEBDI8bjiljnQa5sSZbyb3FVWP2mFhusfkmO1UCK1LdfjhttFCOhUyybE3n/SfbLlWIfuefSUPjPIW+d1u+BSWTRQu94zqgo1FTQj1vCVUytMJ7UBrM/YcYU+LJ1cFllEsivxiRarplVgWCegcJoJo4vRRIAcg5YEhBB472AbrntkI+565iNce2YtXrv3XFw8v5qVM4axQEmeF9+5Yh5euOscHGrpw3k/ehu/XHc4ZmWSYTKBkjyvZhpp5X5XLZ6YghaN4nW7LFkbLj9jQiRTn1EIISXjAgvWs8gBFKgt3CjdoyTUCoQLEbLe25EwI9mcMak0bsY+M4wEgsjLcetmlrSK0gIRVQhd53daIlM8gTwZpRm0XC+Lw3OOSJZeX/c4Ktu0YtDMNFCBMoHHpLJ8eFyuqMLi8tPOrCpCVZH5kgYfm1NlSTkLnT9+DysKYpOHkCK5zxWLtJ+neinxJeIlFMr1ujWTPi2dXBb3HPLfGlXQ2IJmI4GgwN/qW/Cztw+isXMAt6+Zhl/euNzSyiDDMLHMqCrCL29cjg8Ot+P7r9TjifeP4uuXzMYVZ0ww7Y7EMOnEUPyLxud4nDtznO5qr1FK8ryWXcOMQkSWlQyj18TskyETlDM1Eln/rSz0xY2Fs3r4c2ZUxsQ0yhUA5b0Qrx+rplcg1+tGS7e6Cy6gXgzcKPLyFKMlIaI/qxFdT8/y6Q0eU6ieR7quyoWdyxbWqCoGkYLmRJg3wVzNNQkr2WqHwsm/jLy2z55ekZC6rTYW8gWDj8+pUk0gZITzZ1WhOC/+82tOTRE2HQnFC080WDidNQcb6Ogbxh82n8BTG4/B7SLcumYarl42iROAMEySWDGtAs9/+Wy8sqsJ//VqPR5ddxj/dOEsfHxOFVupmayhWGYNMjOtyxJMfS3h87gdnZzHqFvTWH8kGFEWxhf7bEy4EH1CtaLH+hYmmYKk8r0UG6enGIxYWKDwuF04e3pl3PqFUvuU10vebj0LnpnLrGW1UxvTQp9HsxSBltXGrpTwZhlX5MOMqkJDc05vMcuqIjxrfFFkXhYlUA7FqOUw4irrdRvOQcEKmkX8gSDWHWzD8x814vXdzVg1vQLf++QCnDdzHK/kM0wKICJctrAGF84djz9uOYF/fX4XflLowz0XzMQFc1lRY1KLmYLGRpBbhQtyPLYXva4szEH/UGa7CBtPMJDcdigpyPGgb9ifknOVF+SgfziAoBAJucMZeV7a+Uw1XlDc/DlXTK2IKgZuhI/PqYoqiq1EiFFlUWqS3i1v15wrL8yJyiApnVIKhZOfxoqrsJa8muxbJtfrtlQ6w9q5XCjO88InK0XidpGh7KlWyc/xoF/2DIhXWFsNVtBMEAwK7GjswovbTuLF7SdR4HPjU0sm4rV7z+XMcgyTJnI8Lnx2xRRcvaw2lJL/xd34yVv78ZWPz8SFc8fbLjgzjBaxSRusIxecLphbZfh3Rmf7qmnWEkA4CaPp2M0Jm4mLpmtmVeLVXU0JH0cPqZba4tpSLJpUig2H2y3XVwMSTS8SjTHlxJiFyUq7qkuM1+qT0LKijLoAjm6rKvJhf3NPRNiOfCV3R9Q5l5kyAoU+D9YuqMYL2xqjtgdt0gC1LGtq7pSZyMXzquHzuDCvJrXnPXdWJTr6RvDBkXYAIWX+7OmVpmqksYIWhyF/AO8fascbe5rx1t5mBIICaxdU4+HPL8PSyaW8Ss8wDiHH48L1KybjM8sm4U9bGvCff92L77+8FzeuqsPVyycl5MbAMIlQXZyLpm7rqfCT8Z5Jx7trbk0xugZGbDue0bUXMwkP1Ap+m8XncSc1uYv82EQEolCcVyIYsqAp/s7xuCzHOqoNSXGeF90DI7Yqi4nidbtw6YJQ/JZ0iSrCrnFKa0h0+nrtOWd1zVDuNmmXglaS58W5M53rxpwodhWNNovP40aud9RDwUVkegGFFTQVOvuH8fa+Fryxpxl/39eK6pJcXDSvGj//7DIsqS1lF0aGcTCSovaPZ9bib/Ut+NV7R/C/b+zH1csn4cZVdajjGoRMilkxrQJdAyNoTkBJyzTUXMzk9dtsweZX8QVzx2vWRct2rPR6amUBjp/uj9luRHXwqFhuCnI8IQVNpvg4YQ1cS2lXemfI263lynbOjMqY7IoSVy2eGGMp0yJSB80GRc2umFVGGyt6AytoYU6c7sfre5rx5p5mbDnWgTMmleCieePxtYtnY9q4zMzoxDBjGbeLcNG88bho3njUN3XjifeO4tKfrsPCSSX4zLJJuGxhja0pqhlnQ0SPA7gcQIsQYkF42/cAXAUgCKAFwE1CiJMUkrR+CuAyAP3h7VsTOX9Jnlc1BXw2KgSpKA1Qmp+D2jJzoQUVhTlo7xvW/H4sPw+MxMYod5k2rlBVPoo3py+YOx4FKpYNtSY4QUEbJbox8m5OKsuL1HbTU8LUEqYoydew+uR53RFl0S4Lmtk2OA1vGgpIW8HKY37MPo2EENjZ2IU39jTjjT3NONreh9UzxuFTSybigeuXGLqJGIbJDOZUF+OHnz4D//KJuXh5xyn8/sMT+O6Lu7F2QTUuW1CD1TMrOetq9vMEgAcB/Fq27UdCiH8FACK6G8B3ANwB4FIAM8P/VgD4Rfh/W7lg7nhblYKZVUWGav5kA2ayS0rKh7KoNzOKnYrQsillGBrRdn3UmvMxMV0wF6+VauQWs2VTyiOfE5EftRY3Lp5XDZcrdD2G/AFsPd5p+RxGmD2+CFMrC/Da7qaUJ9kxw7yaYkwpd6ZXjLzYvdHaZ3LGlII27A9iw+F2vLGnCW/uacFIIIiPz6nCVy+ahTUzx6XNV5VhmNRQnOvFtWdNxrVnTcbh1l48/1Ej/vu1etzzuwGcP7sKF88fj/NmjUt67Scm9Qgh3iWiOsW2btmfBRj1zroKwK9FyH9oIxGVElGNEOKUnW2y22JjtY5RtuF2USR9OJfeSH0Wy1yv29KCl0/NldBBQzeuKAetPaO116qKfGjoiHXxTAZy+TTH44q4NiZraF0uQq4regynVBSg1mANr1ThcbtQkp943GgykI+ZFU+JrFfQuvpH8M7+FrwejierKvLhonnj8eD1S7BkchlneGOYMcq0cYX46sWz8dWLZ+NIWx9e292EJ94/iq/9cTvm1RRj9cxKrJ4xDkunlEathDHZBRH9J4AbAHQB+Fh480QAJ2S7NYS32aqgMcnhwrnjIxYhTg5kjGIV91slydRzi/O8qC3Px4yqQsfG+S+dXBb5/ImFNfC4Xdh6vCOhY1otn3HW1HL0Dwews7ErofObYXFtacrOlW2oxVzG/U0S2pFWhBCob+rB2/ta8HZ9C7ad6MTCiSW4aF41/unCWZhRxfFkDMNEM7WyAHecNx13nDcdXf0j2HC4DesOtOFbf96Blu4hnDm1HCumlmP5lDIsqi1ld8gsQghxH4D7iOjbAO4C8G9QX7ePWawmotsB3A4AkydPTmYzGRPw/WkOozGDZmo4meVjs9VLSThJVZNbYiWBe25NMcoS8LjIz/FYitnMz/EgPyfrRHhGRlaMblPXID440o6Nh9vxzr5W9A8HcN6scfjsiil4+PPLUc4ZahiGMUhJvhdrF9Rg7YJQ4ZQTp/vx3sE2fHi0A3/YfAInOwewcGIJzqwrx7IpZVg8uRRVRebr7jCO42kAf0VIQWsAUCv7bhKAk8ofCCEeAfAIACxfvtzBkRrO48y6clQlsVAsYz/p8DhyunvqrPFFhvabP6EYk5LhHshPHUdzwdzx6q67Bsg4Ba1vyI/6ph7sPdWN7Sc6senoaZzqHMTi2lKsmFaOB69fgkWTSi2ZExmGYZTUludH4tYAoKV7EJuPdWDz0Q48+PZB7D3VjcpCHxZOLMEZk0qwcFIpFk4s4YWhDICIZgohDoT/vBJAffjziwDuIqLfIZQcpMvu+LOxjq3p9pmU4E6xspSf40GNhaLTTsRqXJ4TcLqS7GQSiTN2lIIWDAr0DPrRNTCCzoFhnOwcRGPnABo6+tHQMYCDLb041t6HCaV5mFdTjIUTQ+my2eWIYZhUUVWci8sW1uCyhSEL2+BIAPuaerCjsQs7Gzrx0o5TONDSi5qSXMytKcbMqkLMHF+ImVVFmD6ukJMRpQkiegbA+QAqiagBIUvZZUQ0G6E0+8cQyuAIAC8jlGL/IEJp9m9OeYMZxmGkWlC/aN74lJ6PUceqBYhJjJQraP/2wi5sa+jCiD+IkUAQ/qDAsD+InsER9Az5IQRQ5POgOM+LmpJcTCrLw6SyfFwwpwq3rp6KOTXFqrVkGIZh0kGu141FtaVYVFsKYAoAYGA4gD2nurCvqRcHWnrw562NONDci+aeQUwoycPk8vzIs21SWR4mluWhstCHcYU+FOd5eMUyCQghrlPZ/JjGvgLAncltEcNkFpxUzXmIFPg4lqgUnWeST8oVtCsXT8D5s6vgdbvgdRM84f+LckNFPItzPeyeyDBMRpOX48ayKeVRtXEAoHtwBIdaetHQMRD+14+txzvQ2DmA9t4hdPSPwOMiVBTmoCw/B4U+D/J9HhTkuJGX40ZBjgf5Pje8Lhdc4VTiblcoJbLHRXARwR3+H0BUKmYp1bb0OheK3Nuj34c+fG7lFA5CZ5gswA4hftb4oqxxN8wmCnI86Bn0J+34qSg6z6iT8revUmBhGIYZKxTnerFkchmWyNI1yxkJBNHRP4z23mGc7htG35Af/cOB8D8/+oZC//cPBxAUAv5gEIFgyD08IETk/0BQgGi0xKtkkCOMuilF1sIj31HUfv4gR5+bpa6yIKmZ7hgmXcyt4Rp7TmR5XTn8Qe2i4EzmwsujDMMwDsHrdqGqKJezQmYoxbleLJhYku5mMEwU1cW5aOkeir8jk3G4XQS3i+OasxH2JWQYhmEYhslSKgp9+Ngc9TpjDMM4E1bQGIZhGIZhGIZhHAIraAzDMAzDMAzDMA6BFTSGYRiGYRiGYRiHQMpUy7o7E7UiVMwzE6kE0JbuRtgI98fZcH+cDfcndUwRQoxLdyOSDRH1ANiX7nbYgJPnkhm4H84hG/oAcD+cRjb0Q/P9aEpBy2SIaLMQYnm622EX3B9nw/1xNtwfxm6yZQy4H84iG/qRDX0AuB9OI1v6oQW7ODIMwzAMwzAMwzgEVtAYhmEYhmEYhmEcwlhS0B5JdwNshvvjbLg/zob7w9hNtowB98NZZEM/sqEPAPfDaWRLP1QZMzFoDMMwDMMwDMMwTmcsWdAYhmEYhmEYhmEcTUYqaET0OBG1ENEu2bbvEdEOItpGRK8T0YTwdiKi+4noYPj7pRrHXEZEO8P73U9E5MC+fDa8fQcRvU9EizSO+QQRHQn/fhsRLU5FXyz053wi6pK18zsax5xKRB8Q0QEi+j0R5Ti0P1+X9WUXEQWIqFzlmGkbH60+yb77GhEJIqoM/51x94/sO2VfMvL+kX2n7I/j75+xAhGtJaJ94fn/rXS3RwsiqiWit4loLxHtJqJ7wtu/S0SNsrl0mew33w73ax8RXZK+1kdDREfDz5xtRLQ5vK2ciN4Iz/U3iKgsvN3QcyzVENFs2TXfRkTdRHRvJoyHxrvR9PUnohvD+x8gohsd0o8fEVF9uK3PEVFpeHsdEQ3IxuUh2W/S8g7U6YPpOZTu55hGP34v68NRItoW3u7IsbAVIUTG/QNwLoClAHbJthXLPt8N4KHw58sAvAKAAKwE8IHGMTcBWBXe7xUAlzqwL2cDKAt/vlSnL08A+EwGjM35AF4ycMw/ALg2/PkhAF9yYn8Uv7sCwN+cNj5afQpvrwXwGkK1DivD2zLu/tHpS0bePzr9cfz9Mxb+AXADOARgGoAcANsBzEt3uzTaWoP/z955x8dR3vn/891dadW7ZFnNcq+4YOMKBkIAQ4hNQiBAKKGEErgjud/9LuFIjvxS7nK53F2SA5KQ4FBDCSHBOYxpobvbca+yLduyeu/Savf5/bEzq9nZmdnZPit936+XbOnZ2ZnvM8+U5/t8G3C+9Hs2gGMA5gD4HoB/1Nh+jtQfJ4DJUj/tie6HJFutfC8o2n4C4NvS798G8O/S76aeYxa4jhoBTEqG8dB6VoV6/gEUADgp/Z8v/Z5vgX5cAcAh/f7vin5Uq5/Niu8k5B1o0IeQriErPMe0+qH6/D8B/IuVxyKaP0lpQRNCfASgXdXWrfgzE4AcXLcOwLPCy1YAeUQ0Ufld6e8cIcQW4R3dZwFcG7MO+Mttui9CiM1CiA6pfSuAinjIGAohjk1QpJWPzwB4VWp6BnEaGyCi/twE4MUYihY2Wn2S+G8A/wT//iTd/SMR0JdkvX8ktMYmKIm+f8YJSwHUCCFOCiGGAbwE731jOYQQDUKI3dLvPQAOAyg3+Mo6AC8JIYaEEKcA1MDbX6uyDt5rHPC/1oM+xyzAZQBOCCFOG2xjmfHQeVaFev6vBPCOEKJdeja/A2BN7KUfRecd/7YQYkT6M+i7IpHvQMDwvaGF3jWU8OeYUT+kd9kNCDKvSvRYRJOkVND0IKIfEdFZAF8BILv7lAM4q9isDoEvpHKp3WibuKLTFyV3wbsyoMePJPP8fxORMyZChoBBf1YQ0V4iepOI5mp8tRBAp+JhmfCxAYzHh4gy4H3J/NFgF1Ybn7UAzgkh9qo+Srr7x6AvSpLm/gnSn6S8f8YYZu4Ry0FE1QAWAdgmNT0oXfPrZdc0WLtvAsDbRLSLiO6R2iYIIRoArzIKoERqt3I/ZG6E/+Qz2cYDCP38W70/AHAn/N8Vk4nob0T0IRFdJLVZ6h2oIJRryOpjcRGAJiHEcUVbMo1FyIwpBU0I8YgQohLACwAelJq1fE/Vq9BmtokrOn0BABDRpfBOML+l8/WHAcwCcAG8rgN628UNnf7sBjBJCLEAwP8A+LPGVy03NoDx+MDr3vipEEJvRctS4yMplI9AeyEgqe6fIH2Rt0ma+ydIf5L2/hljJN05JqIseBeQviF5BPwSwFQACwE0wOtKBFi7b6uEEOfD6678ABGtNtjWyv0AeeNC1wL4g9SUjONhhJ7clu4PET0CYATe9zzgHYsqIcQiAP8A4PdElANr9iPUa8iKfVCi9kpKprEIizGloCn4PYDrpN/r4I3fkKkAUK/avg7+JmytbRKFsi8govkAfgtgnRCiTesLkhuLEEIMAfgdrOWS4uuPEKJbCNEr/b4RQApJCRAUtMLrDuGQ/rbS2ACq8ZFQr4T6YcHxmQqvL/peIqqF9xzvJqJSJN/9Y9SXZLx/dPszRu6fsYCZe8QyEFEKvMrZC0KI1wBACNEkhHALITwAfoPRa96yfRNC1Ev/NwP4E7wyN8mui9L/zdLmlu2HxFUAdgshmoDkHA+JUM+/ZftD3oQl1wD4iuQqB8ktsE36fRe8MVszYK13IICwriErj4UDwBcBvCy3JdNYhMuYUdCIaLriz7UAjki/bwBwG3lZDqBLNsHLSH/3ENFyyc/1NgCvx0NuLfT6QkRVAF4DcKsQ4pjB9+UHJMHrexuQES6eGPSnVM6uQ0RL4b0e/SbN0oPxfQBfkppuRwLHBjC81kBEuQAuhoGMVhsfIcR+IUSJEKJaCFEN7wPufCFEI5Ls/jHqSzLeP0H6k5T3zxhkB4Dp5M2WmQrvAs2GBMukiXS9PAXgsBDivxTtynisL2D0mt8A4EYichLRZADT4Q3ATyhElElE2fLv8CZ1OACvvHImQOW1HvQ5lmD8rAPJNh4KQj3/bwG4gojyJRe8K6S2hEJEa+D1nFgrhOhXtBcTkV36fQq85/+kld6BMmFcQ1Z+jn0WwBEhhM91MZnGImyEBTKVhPoD74OsAYAL3gnLXfCuCB4AsA/AXwCUS9sSgMfh1a73A1ii2M8exe9LpO+fAPAY4C3ibbG+/BZAB4A90s9OxX42AiiTfv+r1NcDAJ4HkGXRsXkQwEF4swVtBbBSpz9T4H2A1MDrAuK0Yn+k7b8KbwCuej+WGB+9Pqk+r8VopsCku38M+pKU949Bfyx//4yXH3iz1B2Trv9HEi2PgZwXwuvus09xH1wN4Dnpmt8H76RsouI7j0j9OgqLZEOTrum90s9B+ZzDG3P5HoDj0v8FUrvucyzRPwAy4F1YyVW0WX48tJ5V4Zx/eGO8aqSfOyzSjxp447Hke0TOPH2d4pm7G8DnFftJyDvQoA8hX0OJfo5p9UNqfxrAfaptLTkW0fwhqTMMwzAMwzAMwzBMghkzLo4MwzAMwzAMwzDJDitoDMMwDMMwDMMwFoEVNIZhGIZhGIZhGIvAChrDMAzDMAzDMIxFYAWNYRiGYRiGYRjGIrCCxjAMwzAMwzAMYxFYQWMYhmEYhmEYhrEIrKAxDMMwDMMwDMNYBFbQGIZhGIZhGIZhLAIraAzDMAzDMAzDMBaBFTSGYRiGYRiGYRiLwAoawzAMwzAMwzCMRWAFjWEYhmEYhmEYxiKwgsYwDMMwDMMwDGMRWEFjmCAQ0WeJ6AARDRKRkH5qDbZfQUT7iKiTiP6DiL5ARL+Oo8gMwzAMExf4Hckw0YcVNIYxgIiyALwI4D8BZAG4A0AXgLU622cA+DOA7wCYJG33CIA34iEvwzAMw8QLfkcyTGxgBY1hjFkKoFUI8TshxIgQ4mkATQDydbZfAaBLCLFBCNEF4FUAcwG8GxdpGYZhGCZ+8DuSYWIAK2gMY0wJgDpV2xkApQbb1yr+3g/gQyFEf/RFYxiGYZiEwu9IhokBrKAxjDF18LphKKkGcE5n+2b4rxzOADASfbEYhmEYJuHwO5JhYgAraAxjzFYADiK6h4hSiOgeAA4A2wy2ryai2UTkBHAzgHlE5IiTvAzDMAwTL/gdyTAxgIQQiZaBYSwNEc0D8CS8fvKHANwrhNhnsP0aAP8KIBPAswAyAHxOCLEwDuIyDMMwTNzgdyTDRB9W0BiGYRiGYRiGYSwCuzgyDMMwDMMwDMNYBPb5ZZgwIKK9AKZqfDRJCNEWb3kYhmEYxirwO5JhIoNdHBmGYRiGYRiGYSwCuzgyDMMwDMMwDMNYhJBcHIuKikR1dXWMRGEYhmHGGrt27WoVQhTH41hEtB7ANQCahRDzpLYfAFgHwANvDaavCiHqiYgA/BzA1QD6pfbd0nduB/Adyy6wbQAAIABJREFUabc/FEI8E+zY/H5kGIZhQsHo/RiSi+OSJUvEzp07oyYYwzAMM7Yhol1CiCVxOtZqAL0AnlUoaDlCiG7p978HMEcIcR8RXQ3g7+BV0JYB+LkQYhkRFQDYCWAJAAFgF4DFQogOo2Pz+5FhGIYJBaP3I7s4MgzDMGMCIcRHANpVbd2KPzPhVboAr1XtWeFlK4A8IpoI4EoA7wgh2iWl7B0Aa2IvPcMwDMN44SyODMMwzJiGiH4E4DYAXQAulZrLAZxVbFYntem1MwzDMExcYAWNSWr6hkaw+UQbdp/pwP66LpzrHEBT9yCGRzwAABsRirJSUZqbhpmlOTivPBfzK3IxZ2IObDZKsPQMw8QDIcQjAB4hoocBPAjgUQBaDwBh0B4AEd0D4B4AqKqqio6wTFLx1sFGTCrMwKzSnESLwjDMGIIVNCbpGHS5sXF/Azbub8THx1swtTgLSycX4LrF5agqyEBJdhqcKV7vXbdHoLVnGPVdAzjc0I33DjfhP98+CgHgoulFuHRmCS6fMwGZTr4VGGYc8HsAb8CroNUBqFR8VgGgXmq/RNX+gdbOhBBPAngS8MagRV1axvIMutxo6h7CrNJES8IwzFiCZ6VM0nCucwDPbqnFyzvOYlJBBtYtLMf31s5BRX6G4fcm5qbjvIpcXDnX+wb1eAQONXTjw2Mt+N3mWvzzn/bjijkTcO2iclw4rQgOO4dmMsxYgYimCyGOS3+uBXBE+n0DgAeJ6CV4k4R0CSEaiOgtAP9KRPnSdlcAeDiuQjMMwzDjGlbQGMvT1D2Ix/5ag1d31WHNvFI8fcdSLKzMC3t/NhthXnku5pXn4oFLp+FUax/+/LdzeHTDQfQNuXHd4nLcdEEVqosyo9gLhmFiDRG9CK/1q4iI6uC1lF1NRDPhTbN/GsB90uYb4c3gWANvmv07AEAI0S6l5t8hbfd9IYRf4hGGYRiGiSWsoDGWpavfhcc/qMHzW09jzbxSvPWN1agqNLaWhcPkokx88/IZ+MZnp2PX6Q68tOMsrvr5xzh/Uh5uvKAKV8ydAKfDHvXjMgwTXYQQN2k0P6WzrQDwgM5n6wGsj6JoDMMwDGMaVtAYy+HxCLy6uw7//uYRLKnOx4YHV2FaSXbMj0tEWFJdgCXVBfjuNXPw+p5zePz9Gjy64SC+tLgCN15QiSnFWTGXg2EYhmEYJlZ8fLwFiyflIyOV1QCrwiPDWIqD9V347p8PoLPfhf/+8kKsnqFZYD3m5Kan4LYV1bh1+STsOduJF7efwTX/8wnmV+TipqVVWDOvlK1qDMMwDMMkHe19w2jtGUZVIasBVoVHhrEEA8Nu/PTto3h5x1ncf8lU3H3RZEsoQESERVX5WFSVj+9cMwev76nHrz88ie9tOIjrzq/AjUurMK2ErWoMwzBjHZfbA7dHIC0l8e8mhmFiy4fHWlCYmYp55bkJOT4raEzC2X6qHf/31b2ozM/Amw9dhMqC6MeZRYOctBTcunwSbllWhf3nuvDi9jNY99gnmFuei5slqxq/uBmGYcYmW0+2ob1vGOsWct1yhhnrdPYPY9DlZgWNGX/0D4/gJ5uO4o+76/DPV8/GjRdUgsj6xaOJCPMr8jC/Ig+PfG4ONuypx1OfnML3/nIQX1xUgZuWVmL6hNjHzDEMwzDxo2/InWgRGCbqnGjpxfCIB7MncrF1K8EKGpMQtp5swz+9ug+TCjOw6RurUZ6XnmiRwiLL6cDNy6pw87Iq7K/rwos7zuALT2zG7InZuGlpFa4+byJb1RiGYRgmiRkaccPlFshyjr1p84FzXQDACpoGQiTu2GPvSmMszaDLjR+/eQSv7a7Ddz43B9cvqUgKq5kZzqvIxXkV5+GRq2fjf/fV45ktp/G9DQdx09Iq3HfxVORnpiZaRIZhGIZhQmTziTZ0D7ji6t7aPehCZqoDdtvYmCMxoWFLtADM+OFoYw/WPvYJjjf3YNM3VuOGJHFpDJVMpwNfvqAKrz+wCi/esxxnO/px8X+8jyc+qMGgi11kGIZhxgINXQOJFiEuHG3sgduTQFOCBRge8cT9mO8facbRxp64H5exBqygMTFHCIHntp7Gdb/cjC8sqsBzdy5DWZK6NIbK3LJcPPGVxXjmzqX44GgLrvjvj/BpTWuixWIYhmEiZPup9kSLEBeONHajrW8o0WIklES5urncsVcMx+JC+ViAXRyZmNLRN4xv/XEfjjT24Pm7l2FhZV6iRUoIi6ry8fI9y/HH3efwwO93Y83cUvzL5+dwkUiGYRjG8hBiO4k/0dKLky19WD6lANlpKTE9VjIxvu2WyUNXvws1LT1YPKkgavtkCxoTM/52pgNX/+JjZKTa8cbfXzhulTMZIsKXFlfgnW9ejPquQax97FMcaexOtFgMwzAME3U+PNaC9r5hU9seb+pF//AItp60qlUyMaqSiJLp7nhTD3ad7ojKvphAznb0o64jui7PrKAxUUcIgRe2ncatT23HQ5dNx89uXMQrYgqKs514+qsX4PrFFbj+l1vw1sHGRIvEMAwTMoMuN3adtuqEmkk0nf3D+Ph4S0jf4Tjt2FDT3Iu6jv5Ei8GEAPtXMVFl0OXGd/58AFtOtOGFu5dhwTi3mulhsxHuvXgqZk3Mwd/9fjdOtfbh3tVT2BecYZikobV3CHUdA1g8KdGSGNM3NAIbEdJTueRJuMTr1eQJw2JU29qHwqzUmC4EJyoGLVqH5blFuCTOyZQtaEzUqOvox3W/3IzGrkH85e8uZOXMBBfPKMYf71+JF7adxiN/PgDPOM+UxTCM9dl7thMdJl3XrMC7h5vw/tHmmO2fp76AxyMS9v7aW9eJQ/WxDRdI1Js5XMVwaMSN2tY+v78Zf9p6h/D6nnOJFkMXVtCYqLDrdDuuffxTXDi9CM/cuRQFXPPLNNMnZONPX1+F3ac78I+v7h336YwZhrE2tW19ONXWF3zDGDDocof1jIxHNrzxzLuHm7D1ZFvcjtfYNRi3YyUjta392FvXGdDe1B143mKxwCCEQPegK6Tv7DrdjtNxfK70DVlbaWUFjYmYV3fV4fb1O/DwVbPx8FWzuahiGBRlOfHi15bjeFMvHnrpbzyZYJgwIKL1RNRMRAcUbf9BREeIaB8R/YmI8hSfPUxENUR0lIiuVLSvkdpqiOjb8e5HMiBEYty+3jrYiH0aE08mtgR7qw+43GiNwKoaioWne9CFbafaopZAwwxGh2rpGcJ7h5tideSgW/QMurDZZPmensGRSAUyRX3XIN4/EprVuq5jACdb4qegJcrr8/0jzTjbHjwekBU0JmzcHoF/23gYP37zCJ65cymuW1yRaJGSmvzMVDx/9zKc6xzAN1/ew5Y0hgmdpwGsUbW9A2CeEGI+gGMAHgYAIpoD4EYAc6XvPEFEdiKyA3gcwFUA5gC4SdqWSSCDLrfPHWmAE0lYns5+bWVtZ207apqNiy8HyzaYYvNOXYcUxaNjHWMlDBSlpu5B9A7FRvExo4M2dQ+hpddcnbpwYvzCYSTMRWaj8zxW6B50ob4zeMZHVtCYsBgYduO+53fho+OteP3BVVg8KT/RIo0JctNT8PQdS3GypQ/f+fOBuK4QMkyyI4T4CEC7qu1tIYQ8e9oKQF5JWgfgJSHEkBDiFIAaAEulnxohxEkhxDCAl6RtI2LP2c4xluY6vs+mIRd7FSQC+R0UqgLU2a/t3nauc9RK4nJ7cE5johos26A8iR9mT5OQ0Vr4VSptbo8IW7nSO1403FFbe4cwMBz7hZn6zgEMj1ijODgraEzItPQM4cYnt8Dl9uAP961AeV56okUaU+Smp+DZu5Zi28k2/PjNI6ykMUz0uBPAm9Lv5QDOKj6rk9r02iPidFufJdNcbz/Vjg+PjaZCb+0dwpYTbVGdpEUdnUdirKwYMrH2amjuGbRcModYvn5OtvRhZ21ylGlI9iyOMsGu4Y+OteDdw80YGnGjpcecVc6Is+392HbKfGyiXkH0T2ta47LAtaO2HSdaen1/J3L6xQoaExI1zT34whOfYm55Ln572xJkOblSQywoynLiubuX4S976/HEBycSLQ7DJD1E9AiAEQAvyE0amwmDdq193kNEO4loZ0tLaPWerEJD14DPHa2zfxif1rSiuWcQ+891GX4v7hOXIAvO7X3DUY0DEkIETGZHPLFVWrecaIt5NkKrEEnGR/nas8LaZXvfsOWUaiOCuTh2D7owNOLGwfpubD5hLq4tFhxt7LHEeR12e3C4ITH3JCtojGm2nWzDl361BV9ZNgk/unYeHHa+fGJJeV46nrt7GdZ/cgovbT+TaHEYJmkhotsBXAPgK2LUJF0HoFKxWQWAeoP2AIQQTwohlgghlhQXF0df8AgJdYKjVEiCufnEXT8LoqCFqzwNutya8VI7ajvw1sHGsPYZrhxWRB5nMw6OQphPtf+XffWo7woeh2MFBobdutf7x8dbUNcRu35EWwk1u79oHTfc0MAjjd1o6ExMpk61zMeajGMmYwXPsBlTvHuoCXc9sxP/b+1c3H/JVC56GCemFmdh/VcvwI/eOIx3D8UqSxTDjF2IaA2AbwFYK4RQ+hhuAHAjETmJaDKA6QC2A9gBYDoRTSaiVHgTiWwwe7xYu9mZ4XBDN7acaMOmA6EpGBYwSERMbWsfNh1oML39jlp/F0+Zhq6BuGbTjZYy2D88opugQ43L7fF7r5xu6/Nz7wqHLSZS7cvXWZ8F7hUzvH2oMaRQg3gq2yNuT0jHM9uLcKd4XQMuvwyFei6LMgfru3Rdv2P9PLL6NJYVNCYof/7bOXzz5T147OZFWLcw4lAMJkQWVObhFzcvwjdf3oPdZ8ZSkgGGiS5E9CKALQBmElEdEd0F4DEA2QDeIaI9RPQrABBCHATwCoBDADYBeEAI4ZYSijwI4C0AhwG8Im1rivcONyW8RtOxph4094Qmw67THTjdZj5GTmu++vqec7qTRY9H4PU958JWeoLNpeSJYFP3oF92v2CMjLFsuZ/WtGkqnFr0D7nRNzyqJO0524kDKtdWWTHxCIFTrcFToLeazCYYjAYD65oI+MVavHWwMeT7T49giuGO2o6QlGqzWRzD1V32nu30m6cEU4JqmntxtDExFqrjzebP27nOAc2ENrGEFTTGkOe21OLRDQex/o4LcMnMkkSLM265dGYJHl07F3c9vQM1ITxUGGY8IYS4SQgxUQiRIoSoEEI8JYSYJoSoFEIslH7uU2z/IyHEVCHETCHEm4r2jUKIGdJnPzJz7BGPwCfHvTEb8bK8NHcP+lLPR0pdR39ISUyEEJrKjV5mPbc0MeTyIeEjhAhqHZNLEETrGpRHq6PfhX11nXFLWrX9VPSTh+i5/EYSD6dHOJkAh0c8+JtqETaYZAOu0KyQoQ7fB0ebQxpzs1v2DLo096uVZj/S7I3vH232LZopx6V7QDvT6NHGnoD7bGdte9wT2rCCxmgihMBjfz2On793HL//2jJcUF2QaJHGPV9aXIGvrZ6C29dvR1N3YlfoGYbxxzXiQVuf13pwoqU3LlkQu3QmGPGgsXvQr2C0PNmK1fzdSm71bo+Iq/ujzMH6btPWsWghj6fD5j3/m0+0YX9dV1wSJwRTDEKtmbXpQKOmC/Jf9tXjdFvsCiT3D4/g9T3nfAs4erT0DuGMqoBxsPspmAthIKM7bOkd1FVO5futa8CFlt4h9AyG96zRu23/eqQZn0jFtfX6KI9/pGnvuwdcPovsmwcaNF1ru/pdfotdTd3RsQTrYeZxxgoaE4AQAv+68TBe3H4Wr9y7AnPLchMtEiNx/8VTcfmcCbh9/XZ0h/nAZBgmtnQNuNDYPYhTrX04bhBgPjTiNpyEbjrQYOguaSVblF43Yq1Enm7rw1/2BuZwMW0VMbHZ4YZufHx8VDH633312LjffJxbtIg0PiwSZNe41t4hnGztjUriBLPKxdCI22/yHIkVT2/hpGfQnCVK7QKqh/Le75csQPICjh7xsE4qD1HXMWAuUYvwKlTtfcFjG9V9MFpYCbY/t0dg84nWqBev1lpcqev0V4yNcuDVdfSHtUDTM+gKyQOKFTTGD7dH4OHX9uO9I834w30rMKU4K9EiMQqICN+9Zg6mFGfinmd3WiINLcMw2uyr68QhA0vDpgONOGIQfzE04jGsRWSFNOMyWqIIIfDB0Wa/uDQhgLcPNppWoORYHr1pXmvvkGZcTZvJyaSZyd/Z9n5Tk9N489cjTdhztjP4hkEwUgzk82MUu9QcpkeHPHc/GUTxHLRQkfJ6KQ5JCGHoetfeN+y795WnzkjJ1jrFQa/PEA1o6r3pDStp/GHGPVm9hbwf5TVm9l7qH/bWYovGc46IsEPHRbG2tS9AcTJSLLXidc0kvDnW1IuD9aMKfrDzwAoa48PtEfjWH/dhb10X/nDvCpRxAWpLYrcR/uuGhRAC+IdX9sbEf55hmMgwO6noVyRpGHS5NWNvuvq1rVBmlAu9yXe0LfBax2mWlEvl5H7E48GAy21osXB7BLr6XegZdGHLiTa0arh+qdGbTxlZPN473OyTI1pJHeJJz+BIZO7uOudMuSggD52RwcBM5kYj9GruyceWx9bnRqv6PFoMutz4y956w1gj+Vo+2dqHtw+Zy7ypvE/NWuBkBobduol3hkaM7yNNWYTx3zJa95O6Te0WaLQf5XHaVIlklPvVsqp6fOMe2YDXK5J8KJ9Xe+sCFznsQX0Q/WVpDOM+VD77tWAFjQEwqpwdrO/GC3cvQ2GWM9EiMQakpdjx5G1LcKK5F9//30NxC9xmGCY6aE1s2vqGNbPXfXCsGR19wwFuNYGTLRHwLNiwt17z+fD+kWbDTHmh4LVEyX+MtitXi9UZAD841qy7v+PNPfjgWLNvV5/WtPq51IXytDOyWCgzGO6sHU3OsFWlcFj56aqeRspjPeIOIbGDalOtAsVWSO4S69fc0cYeeIQwzNYnn4aQUumHWHtsw956n3dM79CIX9zh2fZ+3xhvP9Ue8ru/oWvA7zt6So9SUZJ/V19rvVoKhmp38ncFlJlc/TfS64KchCicS+/AuS6/ou+hRurZbcbf6BsaHf8jjf5eEoMut+bzXZ2EKdjQsYLGBChnBZmpiRaJMUFuegqevmMp3jnUhF9/dDLR4jAMYwKjOmlGWc0+Ot6CT2taVduP/u7xCOw+06lZ+0y53VsHG31prY0m8aEk5WjuGQoayH+yxaugKY+olzVSlsucBP5b+QorR5BTxIpJmJTXRq1C2ZXHye3xLxL99qFGv3pUgNdyYTbJiFohM5ue3Qj1pNVsHI/PTU7VbkYiIYSfxUYvO+SJll7UmkgUIp8XrdOx9WQbzoRQqkKNfK8LIdCvUACUZSN2n+nAUWmxwuz5Uyd0UcquZ71UIj+zlM+Ehq4BU/eJ/JWWniG4pGLyRsq+UmGUnw/BLGdq98LeoRGcaOnF8ebRRZ1gVjo9ufWobetD79AIhkc8fmUCXG4PPopSIh9W0MY5rJwlN6W5aXjmzgvw6w9P4LXddYkWh2GYILx3uMnPzamuQ+l2E7i9sk25aguMTlx2nW7HX/bVo66jXzPNvXLiOehyR92db+vJNt8qsnIy5e9+JU0+FeLtOh39uo7h6hFG3wvmRt5hsjB0JCjl6x0a8Vlw5IX+jfsbsEuVol2dyKOha1AzTb9W72KZpdKs9SkwXspfOVImbtGjuWfIly0QGC1D0NwzGFZ2QCNFtal7EKfbA5U89eXT1e8KsLoAwa9Buf9HG3t0F3qGRzx+GSk3n2j1U+i98oweSE9ZUioocrZWpc6y/VS7bwGgvnPAN6by/S/HV8nf2XaqzbdIo6X8GCptwv9/JUMjbrx7uMmv/t57h5sCN1TQOzwS9Dlh5tn03uGmAHfYjfsbfNeYEWbWj1hBG8d4WDkbE0wrycZvb1+CR18/GPcUzAzDBEcdQ6Z2u2vrHUJ737Dm5M9oHiFvrlTytDBjGQjct/aR9ZQ7eX6lN8/yTbJi7DAY7v71vjfocptysYr1s1ctgpzwQJ7seoRAhyrpgFnXMK3rbkRlKUqUF33/8Igvk2k4rm56E/8tJ9piUiBZyzqjvrY+rmnB0caegDhQofM74C0FcFShcBO0x6S2rc8vcUxLz1DAvsL1VtWzKu2obfedS1kmrZhRI6vUqVZ9V2TZPVotdvegy+cxEMylVzkukdQzUz8XWyIozB7snnKEvWcmqRFC4NENB7G/rgsv3rOclbMkZ/GkAvzXlxfiwd/vxgt3L8P8irxEi8QwjER/kGKyn9To10fSq8/k8WgXitYimpNrvYnQ6ETe+GBGWS1DweX2oGdwxDfpi1WVtLcOmksEEayAdLSRrT8Dw56ApAsyyrFo7h7UzTS3cX8D1i0s92vzufLB/3+jY8g0dA1E5GIqc6q1FwcVcUThKN/qBCPA6FidbO2NWhym+nhKzrb7H0M+t0caetDQNYDLZk9AltOhe+8oLWcyPYMjhu7SRoTrruoR3gWLtBR7wGd64608ks3gojASScuVsq6jH6mOURtTe98wUuxkOn9CTZglK7Q8FPQQQui6ip/rHNA8j0rYgjZO+clbR/HR8RY8d9dSVs7GCJfPmYBHrp6NO363IyYrgwzDxA8t9ysBgeERD86294dcXHfzidaAGKDdZ4K78Qy69LPIKZEnurVt/dhyIjCrnzz/MiobAHj7bWb+eKi+W9vFTfpua+9QSGVI4mkhEkKguXvUxc5Moge/xA5iVN4Rj0dXwVfq71tOtoWUac5s4XGtz4ONsVmUypnfsRTHVKYq7+wf9ksMAWjHnCldb824o4WC1nRcTwmUz7Gcrj+US7BVVyk31xYMLcvj0cYe3QUL2ULVpyo9oHyOGSloZmJeO/u9SZTOtvdj1+kOvxqRx5t7DBe61LsPt9C6JwSv2GDnPVhdQ1bQxiFPfFCDP//tHJ6/axlKctISLQ4TRW5cWoWvXzoNN/9mq6aPO8Mw8UGp/ITjUqS1auz2CLx5oCGkYqeAd6KglTrd8DvS/+8dbsZ7h/UzLqqp6+jXdIPUO2Zr7xBOtPRi1+kODLrcePNAA0akWZDRpM2t2qE8OXMrrA3qCX68qGnu9bkbbjrQEGC5OtzQgy0n2/DmgQa43P617jxC+CmWPYMuvLm/IawEGZEk9nB7BBq7Bn0KTCi7iqeyKysSDV0DON7snxgiljJ16xVgV12yRnFugTF2kckEaLsgqy2PZjJyarlFG1mPiLzPLPVig/I5aPNZMwO/HyRpoo/tp9p9+9RyJ1XH20Ub9XPHCAFgf11XWLGOACto447nttRi/Sen8Pzdy1BZkJFocZgYcNeFk/HgZ6bh5t9sC1hNZBgm/kQS8xAN1BMrs1OMD442Y8Tj8SlM4XKypU/XPe3TmlbUNPeirqPfN3EMVvMMGJ0Hq6doSotKbwg1oqI5fz9Y3+VTDodGPOhSTeaV6f07+11+k766jgG/TJyd/S4Muz1o6x3tV71JtzyX24M39jXo1tEzwi0Etp1qw/46b0IbvfOj1a5uC3VBQQ9t65C5kTNTuFzNoMutW7dML/ZIrTS8eaBBd/+BoiutpOFdkVrFjwPKcYS15+BuvJEklpHT2vcMjhgm+VAu2mit3xiVSIgGoZSb6BsewcnW3rDj1FhBG0e8trsOP337GJ65cymmFmclWhwmhtyxajIeumw6bnlqm18tIoZhYoPZeDCzmHE/NIt64jQwHNytyyNEgGIRLidaeg0nNqMZ4PwxI6cR6uyK5zoHNCewsSKS+pRCCL9x23Zq1G100OU2bXEY8XjQ1uc/QTQTu2QmqYvehFzd72gpaFoFi83eduF4lNR19Ad1Q4sEdXxfKJeLljXLrIIUackErXN5oqU3qJuiL5GIxjWlVGxDLUWiRM9apWvxDJFQSnC8f2TU8yCcZxkraOOEtw424tENB7H+qxdgblluosVh4sDtK6vxzc9Oxy2/3Ya9iqxODMNEHysU8jWLmdgsdRyRbI2PZy+1CibLhFKnTaalZwgfH28JyJ4HRKZMKfevZItU8LqmuRctPUNoliZ3Ssn1xmL/uS5s3K9vfQk3QQSgnYZc3aau96V1fjbub9C0EKlvhVBiAY3oHnRh84lWv9pdZhOHhDq8Nc29aOyKTiydHkqZugddISXQUde4A6Br7VPjDqGIuRYnmrXdCIN5CshKZSxdYNXPYdnqF0m2RSXhxK4db+rB24fMJRtSwgraOODj4y34x1f24pdfWYzFk/ITLQ4TR25dUY1vXzULtzy1LaDILcMwyYmWghFr9GJ7oolZJUkvQYJZ3j/SHHZciBFqhVJW2Dr6h7H5RKtPYVMm69h1ugOukcB+n4piLM1xHQvWB0dHV/jVCp9sZZHb9UZGe2EisC1SayjgVQxaeob8rKB6hafVKK1GZpKlHKzvCrA8RhvlddyvqnF4oiX08ddzDVZbzD4yUT/OiEgzdGpdMa1hnGszi2JWKD0UricCK2hjnF2nO/D1F3bjP29YgAunFyVaHCYBfPmCKvzkuvm497ld2GTgD88wyQ4RrSeiZiI6oGi7nogOEpGHiJaotn+YiGqI6CgRXaloXyO11RDRt+PZh/FMOFbIUCxpSgVQK9PtwLBbN2W9WdSZMrVQ9/NvZ/XdWUPJvKiHVhbOYMqwOuww0iQhkbrVAYgoFlI5Sa6PME7JaNIfjfICgH7mx1hi1kUy0iLmWteelkUwGNG4pqwM10Ebwxyq78adT+/A99fNxRVzSxMtDpNArjpvInLSU3Df87vQNeDCly+oSrRIDBMLngbwGIBnFW0HAHwRwK+VGxLRHAA3ApgLoAzAu0Q0Q/r4cQCXA6gDsIOINgghDsVW9FGUlg0rUd85gB6T1jtlCmyzhLLaLU/OQqk/prTiaLnGheOGpEekiVVkQlEmQkkZv2FvveHngZNf85Phpu4hFGf716OKxlTa7ITc5fYgxW7CISnmAAAgAElEQVQLKzmKGaJd8+5sez8m5qZHdZ/h0Byl8gjxghU0Jik52dKL29Zvwz9eMQNfWFSRaHEYC7BqWhGev2sZ7nh6Bzr6Xbjv4qmJFolhoooQ4iMiqla1HQY0LS3rALwkhBgCcIqIagAslT6rEUKclL73krRt3BS0aCXniDY7QshGGakbYjDkFfdQJmnKOJRYz+2sOoZmUacTD+V8jXg8AS6T0Uh/3mMyK2dr7xBKc9Kw28AyGSvCMaDVdQzA6TAuWhwqVi6zE61b71ynVkmBsQO7OI5BznUO4JbfbsMdqybj1hXViRaHsRALKvPwyr3L8czmWvzbxsNRCYxnmCSlHMBZxd91UpteOxMCscx+p8TspN0qaNWpsiKRvhvUMXTxuh4Ab1zasabeqGXuC4VwXVKtYA06aDLJSKSouxpKbTH//QR+byzNaVhBG2O09Azhlt9uw9qF5Xjg0mmJFoexINNKsvHq/SvxzuEmfOuP+zASoT85wyQpWovdwqA9cAdE9xDRTiLa2dNj3RVrJpBETeNOhZH8IRG4VJn+Qj1fic5qqi6xYHU6Y+SOGQqhuMhGhv+1oRUjGZ09JzesoI0huvpduG39dqycWohvrZmZaHEYC1Oel44/3LsCRxt7cO9zu6KSYYthkow6AJWKvysA1Bu0ByCEeFIIsUQIsSQ7OydmgjLR53RbYhSlRCsuZiAiHGvyT6KSDIaJVkUM1UiEqeTjTbIplJFQ1xG7BCjRVPYSDStoY4TuQRdu/912zJiQhR+smxdWjRhmfFGY5cTvv7YcbiFw82+3oiOOBVwZxgJsAHAjETmJaDKA6QC2A9gBYDoRTSaiVHgTiWxIoJzMGCJa9ZhiSW56SkBbMriOKYs39w4l3iLFMJHACtoYoGvAhVuf2o7y/HT85/ULYLOxcsaYI9PpwG9uW4IpRVm47lebUdcReqpbhrEKRPQigC0AZhJRHRHdRURfIKI6ACsAvEFEbwGAEOIggFfgTf6xCcADQgi3EGIEwIMA3gJwGMAr0rYMMy6YkOMMvpHFSQJ9kmEM4SyOSU7XgAu3PbUNlQUZ+NmXF8JhZ52bCY0Uuw0/vX4+fvLWUXzxic145s6lmD2R3bWY5EMIcZPOR3/S2f5HAH6k0b4RwMYoisYwSYNdwwMnCTwz/WAvIibZ4dl8EtPV78KtT21DVWEmK2dMRBARvrVmFu6/ZCpufHIrtp5sS7RIDMMwTALQUm6SJfukTIqdFTQmueEZfZLS3D2ILz+5BVOKMvHfNyxg5YyJCnesmowfXjsPdz+zExv3NyRaHIZhGCbOjIUoCXUdNoZJNtjFMQk5296PW57ahtXTi/H/1s7lmDMmqnx+QRkKM1Nx3/O70No7hNu4lh7DJJyCzFS0cyIfJg50WCDlu5WwEVmiThkzvmCzS5JxrKkHX/rVZnx+fhm+v46VMyY2rJxWhBfvWY7/+WsNfvrW0aTI4MUwYxnSLM9mDSoLMkLaviI/3dR2WtkEmXiQ3M/7aMef2XmexSQAVtCSiL+d6cCXf70Fd184Bf945UwOgmViytyyXLx2/0q8sb+BC1ozTIKxEXDl3NKEyjC1OAuLKvMD2kN5E1Xkp2NWqbkkRDZ+xyWElAhDJi6eUQyHLXAfkwozI9qvWbSSnISDfB5YP2MSAStoScLG/Q247antePjq2fja6imJFocZJ1QWZODV+1bgaFMv7nluF/rYr59hEgMBDp2ZotNhj/nh55blYl55LioLAq1fsVosZP0sMUSaxdduI82xy4uTRTTc60ZpCS7KcuLyORPkPUYuVIREqjTHg7I8c5bxWJCTFnhtOR3WP2dGJLf04wAhBB5/vwYPv7Yfv7p1MW5YUplokZhxRmGWEy9+bRlsBNzw6y1o7EqubF4MMxYgUEK9JuTJjpYModbNkneh3pfSlWzOxJyYu3WqJ5TFWclf/ysaREMZUI5cqrQ/q7gK5ugoikqLLWG0D7G67bKc5tNALKnOt6TCUZKd5vu9IDM1YXJohftMn5CdAEmih/VGm/ExPOLBP726Dy/vOIs/3r8Sq6YVJVokZpySkerAr29dgmWTC7Hu8U9w4FxXokViGEtB5HUBjBU20l/Hj4feVpytr7xMzA1l5Zw0Fa8Uuw1XzZs4uhVF1i+7jTC5yNilTm2RjGVM90XTi2O271iwbmE5lk8pDOu76gg2WRGP1vktilCR1nOdVbfK/YiWy6SaCTlpmu1aCmRaih256YEK0IqpoY9RNBd6lEq3w0Y4vyrQBTraFGc5cUF1gV9beZ72uUxmWEGzKJ39w7ht/Tacau3Dn76+EtNKYvfiZxgz2G2Ef/n8HDx46TTc9JutePdQU6JFYhjLYCNCSYiWpFAg0nYbixdpKdFzo9Tqh43UFpbI7GcTctKCTuTVxZdjGfNmDduRPlrjG63zIe8mEv2sJDsNF0qL1JGKpfd1Z4rNbyOHjVCU5fRvjwPzy3ORnebws04RAKGRvCUc5TGa16Ly8FUmkgVpxSaGg9qaOKXIO0dWX7OTizIjstyW6ijR8YAVNAtS29qHLz6xGRNy0vD83ctQyG4XjIW4dUU1fnHTInzzlT144oMazvDIMBKySnHprBKsUFgf1K4/aovTxTOCW1f05mFTi7OwVLWaHIxQV7kvmVkS0vZGKOdKyi4tnhRaHwB9VzWzqJ9dsfTAU49fKO5t0SBV5ba4qDLfTykLp7CzbInRmsQq+ytPmiNxnazIT/fVew2mOFYHSUZidC/5b0dYNa3IUAnKyxi9t6PpgnjpzBIsnzJ6T0TzLRvNIt5+SyomlEVHFI6tFesmH3pKsf/Yz6/ICxi/UCyI6vOeHsWFqmCwgmYxtp9qxxee+BRrF5bhZ19eGNVVS4aJFpfOLMFr96/EqzvrcP/zu9EzyHVzGEYmzWH3m7gFI9Vhw9wy48QMBO2JRVleOvJNxH6Eu3KdareZSnd/ycwSXDO/LOh2epOjnHSHarvgEym9pCmAOStBoCte4DZKt8tw0epHemp83+1afVNOXCOxlmUqlM3MVEfAJFbec7QSXQSTtSjL+H7QuxeU58PPfqtzuCvnlmKVwsVQb62yKMuJivzQSlF4LeaqA2vsPxx3xdUmFoTMEiBiEE1SVubzQ3g+Klm3sBzVGq7L8nkIJ2FSZmrsF0vMPBvVsIJmIV7bXYc7n96BRz8/F9/47AxOo89YmukTsvH6g6vgEQLXPv4papp7Ey0Sw1gCIq/SJa/IB3PWIxCmlYwGtGtZAPReB2atPhfPDG9SdpHJyVxueoopVyKvoun9XbkAqXWOlPvTeh/OVCQBUGdxywgy6ZpUmAmPysdRa3KZGgWriJ204+5CIRYxbMriy1qWgWDDqf54SlEWPjtngqSIKZQ/6RRmRqqUCnNyBeP8SXkBbXMMMlfqjV1ait1n1TOCYM79Lxhauk84pyIj1YGVU8PLaRBoNAhNgiIplnVeeW5Yx/cdVW+hJ83hi5fVUxbVlvNLQng2hmLJVJ6rcNwsWUGzAEII/NfbR/HDNw7j6TsuwLWLyhMtEsOYIjstBb+6ZTG+eH4FvvDEp9h0oCHRIjFMQkh12HSVKHW72sVI/flEzYB370YzJmT7TfbMTPznV+Qhy+kIy63OyEqlRXaa9xiXzgruFlldmOFz71SfA4L/RLxSo7i10v0/K220b1fOLcWs0mzd8bDbCAsr8wJi0NR/R4LTYfdZjLTkIHjd58xmvkuLQRyU3N9LZ5VgkYbba2GWMypePPI1akaZ0cMWRFkHvJaq4myn4SS6ujDTz8oiKwrTJ/hfL3q/G2F0XKMkO5EQ7jq+euGhOMsZ1CigdpPVOn4wF0b50/QUe9BFlFBxOuzISU8JucC90XUZSQSH/F21N0V2mrl+s4KWYAZdbvz9S3vwxv4G/OnrK7EkxFgChkk0NhvhgUun4fGbz8fDr+3H9zYcxKDLnWixGCauGMWfqKcs88pzTSkwSuT56eyJOf6r8SFM0OQJQyhxIFpuaWsX6LvrzJnonfDqxe0Q+SuVsgKgtXUoWf9mKCyQaSl22GykObmakJPmO3/qlXR5lTsak+l55Tm+THPk+8efoiyn7/zOCJISPJzJbLB9yv3PSUvRtRQurAy0NslMLspEaU6a5jUSbQegstzRRQstZR0AVk4txMqpRYa3xMxS/3NSnpfuux7Iz8VxlEiTpUzSySaq3G1hZvBrTut6jqerbIbGAo/6Pp+Ya5xUQ97cbiNFnbnosGZeKdJS7D5rqHw/xypK3mzMofrqMav0sYKWQFp7h3Dzb7airXcIr92/CpOCBLYyjJVZPaMYGx+6CEcbe3Dt45/iWFNPokViGGsQYEGz+bnkBVqPAieEfpNHv9gh7UOqJ6LAqEubXgyOVlyIlmsOEWHZZOP03upJbbkU2O+t5xa4vXr1noj8Y4I0vqRsyc0IXDXXkn35lELMr/AqHUqL2fSSbJ+1MFz3LzVG+qW6O3rFoeeV52JBhb6SZEQw90y3iZmiXip4wKugLZtSiPxMY4tFpMlcAP/xL8lJQ4WGkiZvU5ztDHAplBPjyAsCiyrzsXRyAdJS7JrWQ//9mpNR63QWZKb6rn2ZRZXe4ynvNzPH0MriGCtKc9ICkr+k2MnPirZqWlHAdWtkhZtclOk7R8p7I9r104gI88pzAxR5pev43LJAF0stC6GaRVV5uorZfJ37NFwFnxW0BHGksRvrHvsU00uy8cydSzVfLgyTbEzMTcfzdy/D2oVl+OITm/HcllrO8sjEDSJaT0TNRHRA0VZARO8Q0XHp/3ypnYjoF0RUQ0T7iOh8xXdul7Y/TkS3hyxHmPJfMac05H3qTYi0JhvC4DMgtCQOsrKn506kFkte6Ve3y5MXrV4UBkn2EIySIJYw9YQ32k+qbEkJj6Sm29TirICkCGZdpJRoPYYjSdqxpLrAlxxEmQ5eRtnd86vywkqSEC5E5MuUWp6XjrULygLOf1Vhhmb9vqvP8yaF8XOpVG2j1V9AW4HSih2UFWeHnXDN/DLMnpgT9Jmh9fmKKYUhxTZmpzmwbqF+CI3yXb1sSiGWqergVRVk4PxJ+b5nTlGW03SMptNhw/yKPN9zQ6m0hJswxIipxVmGrotGypjRfVGSnYYLpxcHxAl/fn6ZrvUw3HufFbQE8M6hJlz/qy2488LJ+PF150UtsxHDWAG7jfD1S6bhhbuX4alPTuH23+1AfedAosVixgdPA1ijavs2gPeEENMBvCf9DQBXAZgu/dwD4JeAV6ED8CiAZQCWAnhUVupCRZ7vTC8xdjWTJ1l6Soy6TS9VvR5yVjtZntyMlKBZIwEYuiAVZTlRWZARMAGVJ6nqVWM/i6FvW+9kde2CsgB3RgJQkZ+Bq+ZNDLDWyS5MwSY+wWJq1JOsSBeTZpX6n1PfRFxhsVyidHvUYcXUQsOkEmoFIdj1pQWRt7xDtN3MtI9FYSVJUH8nFAuSHOdpt2lkQzT8nneslFYS9dfzorigbreR1xVVR8QVkttmdlpKgJJdYqJG1xVzSn3WQ6UyZ3Y8ZOtfWV46KvIzkJueEpAlk4hMx7f6LGixrGmhc0xZOQxW28xG3jhWPbKcDiyozPMbD5uNNOI1ZY8F1XXMLo7WQwiBJz6owf95ZQ/+56ZFuOvCyZypkRmzLKjMw8aHLsKUokxc+bOP8OL2M2xNY2KKEOIjAO2q5nUAnpF+fwbAtYr2Z4WXrQDyiGgigCsBvCOEaBdCdAB4B4FKnyZ6T/PibCcunFaENfNKNVewtRJkqFEu5Pm7OGofVXmnZfuUo9HWYh0rgBKjuKdUhw3nV+UHTqKFLONo2+fnl6FSUjhsRAEyq9+DsvInH6dUtTItb67+3qWzSvAZVWyfMl7uPFXmuMqCDL8Yq0iThGgl8phanIX5Fbm+MTUTt1KSnYZynTgrAHCrBJ1TloNLZpRoZmJcNa3Ir2D3+VX5WD6lEOV56UiLIFFDUItPFOc2cnxWbnqKYUyc33ciqB+bk57i55ao7ktJttPvvMmfh/p6UypMepawkuw0X0xkpoYSpBRNS6lw2ElTGcpyOnTjYJXX6JLqAk03TSVXzp1gmGV0Ym66zwqsdY4iifEzyr6pxufiHSWjyIzSbJTnpWPpZP3cEZfMKMHCKv9r1qwFPL6VEscxgy43Hn5tP/52pgOvfX2lX0plhhmrZKQ68L21c3HVvFL80x/34Y19Dfi3L57nm3wxTByYIIRoAAAhRAMRybOScgBnFdvVSW167QEQ0T3wWt9QVVWlbAfgv+JvNGE0mp4QES6eXuyXpdA//fzotjYiv9TpauK5PiJPulZMLQy0jkl/6slalpceZIVf+zN1qn3vsbzbTi3OwhRVIWI1sVhAkrMEyrUi5dX0YApMSXaarsurVuxYbkaK5kS3KMuJomlOvLnfm2E3Ws/eoNbLKBzD6bCjf3jEdx8RUVxi9S9VFWZX96Uwy+lneSR4lz6WTynEsNuDnbXqNaJAVkwt9LNEVRVmQAiBlt4h3e8sqsxDXUe/rmxB9RzV58r7RXlFrVHV/gtW4iFY7TGlAqO2gi6fUogspwPHm/Vj1p0OG4ZGPJqfmakBKSOva5gx3snn0iih0tTiLCBIhn51+NLSyQUoyU5DU/cgdgS5TtiCFgeaugdx02+2oql7EH9+YBUrZ8y4Y9mUQmx6aDVmlmbjqp9/jCc+qMGwzgOXYeKE1ptXGLQHNgrxpBBiiRBiSXFx+HWqtBJkKMnN8K8x5p88Y3S7YG5LwSxE0UhCIO9BlkTt+mSmAHUwlF+fVpLlqzcXCSIKvTeKB5L7HKzvFygyOetl6FPXbzNDtFXPSOu6mWFaiflxNYqtAiK75sx+tzjbaWhpUlKSnea33/K8dKycVmR4PHmho6ogwxcrF4m84RLJ2KvXFibkpAU8t5YrYt9y01MCFEYlKToJj/yOKV39suXZTNIauY+TIljQ0FvvsdvIVPkSVtBizJYTbfjcLz7Bgoo8PHPn0oB6CAwzXkhPteO718zBS/csx1sHm3D1Lz7GlhNtiRaLGfs0Sa6LkP5vltrrAFQqtqsAUG/QHjJmDTLqV3WwV7dyThJssqR0e5pblhM09Xq0kOeI6nMQ7bnj3LLciIrejmaV007LDwTGlpk5h3pWBYcUEyVbT5SnY93CcpSZmOBHs15buGiNo1LFjcY4m9nFyqlFWDHFOKMoABRkpIadTTK4O2do+8tJj8x5zWGzKWrskV+793/rhs6YuXSVrpxy/9SWXzm+MzcjBZfNNhdHKSuCwRZ0stNSRseUvLGw0WBWaQ6KJU8KrZIFalhBixFCCPzqwxO459md+O41s/G9tXM5GQjDwOvy89r9K3HHqmrc9/wufPPlPWjuGUy0WMzYZQMAORPj7QBeV7TfJmVzXA6gS3KFfAvAFUSULyUHuUJqM02o0yP1BC9YZjSlK5ty9TlYParqokzddO4yRmnVzSC7CRIRyvLS/eKiFlTkYbIiI2G4K/GxmH5OK8nyc3FUTsoyVJYsvXd5Wupou7qWmrzvFLs3KYrsbhmOriW7hq6eXuxLABFvIhkDMwlqgGDJOLwSFGc7TSXLSE+1B7gumiWYAqaO/bugugBZToeuIhBpgeZMZ6Dyv3RyAew2wrqF5f4u0IApb5Vo3lNGlnwtt+a0FLuf5VhLwVS3ZDkdvsygwRKUyIdcUJHni7vTG9Or5k3E8imFo89Y4c1Cql6kCYeZpdm++Lcsp3FGTYAVtJjQPejCvc/twis7zuLV+1cGHQSGGW/YbYSvLJuE9/7PxbDbCJf99EM89tfjGBjmAtdM+BDRiwC2AJhJRHVEdBeAHwO4nIiOA7hc+hsANgI4CaAGwG8AfB0AhBDtAH4AYIf0832pzczx/f4OZ/K9dkGZZjIAJcoJjDwZ+uzsCX6Z/3zJOkKcei03YY0wywXVBX7xZ9VFmUHjVeKNrASk2G1+lql8hXJQqXIpm1qcictmT/CzyKyZV6qbfh0Yda8ym5Zcj6nFWb6xzc9M9bMslOT4K4VqxdIMRn3wI2gGTf3PfAXTDdzT1i0sj4rHUTSMjfJkXU+xunB6kV+8YFleOi6bPSHAsjuzNDskt00trpo30S+WUj7Nylpi6nEvy0vz21aLaBplr5xbqqs06aW3V1qO01Ls+KzKKqYVX2k2E6WsFKY6bJpxqn7yOWyw28gXp5bpdIBIu3ZjrOEkIVFm+6l2fPPlPZhfkYvXH1ylyJ7FMIyaoiwnfnr9Aty+oho/fOMQXth2Bv/3ypm4dmF5XNPwMmMDIcRNOh9dprGtAPCAzn7WA1gfRdEM0StCDWhPqogI51flY/eZDl+bdyIxus2oJUv7mMpJXHleOs5FqRRGKHk2wp30RDPGJi8j1beIqrSgqbuhzqKZ5XSgOMuJ7gFv8g9Z8Vw9vRgjGj6IuekpmtneovmUm1+Rh/PKc7Fhbz0qC/zrfC2bXKCbaCEcIolDkscvxU64ZOYEtPcN+13LShZV5uvW2rMKZhcdzFphjBRrtYJvsxEunzPBT4Zlkwvx5gFvUhi7jeCg4IsC88pz8cHR5qDbmSHFbtNVniry003VN1QvUhVkpqK2rS8q8gHangLKu5aIEm5cYQUtSrjcHvzs3WN4dvNpfOea2bhhSSWn0GcYk5xXkYuX7lmOdw834982Hsb6T0/hW2tm4cJpRXwfMWOWtBQ7Bl3hWY2NsjUCo5MNvRTWKXYb1swrxaYDjVhQmRc1BS0eZIZhGTKjOEbDiqCXVU5ZPDnax1QfBwhMnmAm7fz5Vfmma3wFz+KovcHFM4qRk5aCg/VdALwTcSNLSFVh4jP+jsZTxj74LxylQG3ZS3XY4HTYMTTiNvX+LMpyxkwJVseHEZGhi+eUIn8Lo+wtUFmQEdXsz6kOG3LSU3wLLFHbr92GYbfHd4xIYAUtChys78K3/7gfNhthw99d6OdjzzCMOYi8K4GXzCzGi9vP4B9e2Yvqwgx847MzsHJqIStqjOXxxZWbnNAtqsoLmiinQqceVkV+RkDsi3Jy7bCRn6uOFk6HPaGrxHqiBUtykOqwxURu5XBZuWSjmXyT4Twu9SbAc8tyfQqVTGaYcVRabotK6+T0kmxUFJjLhBgvZGXTwpeEZQl1MUUZX3fJzBLN2oJWZsXUQnx4rAVA5PG8rKBFQN/QCH727jH8ftsZfP3Sabhn9RROBMIwEZJit+G2FdW4YUklfr/tDL7x8h5MKsjAvRdPxWWzStj1kRk7BJnxpdptWKSTBMJuo4DkCCXZaVi3sByv7zkHu418QfTxxKxiM7csV1P5/MyskqBB/7HKYhjMKhkLwnqaxVnMaSVZfgpaKMqxURFiedFNaUFLT7UHjRMKhWgMqYlM7pYjNz0FzT1uv7/NlgCIJqEurCqHK9aurbGYSeSkpWBSYSYWVORGvKjMCloYCCGw6UAjfvjGYUwtycKbD622hBmeYcYSaSl23HnhZNy8rAov7ziLH75xCP+68TDuXFWN6xZXRJwJi2FizbSSrKAJP/T4zKwS00HwWoQ6Mc1JS0GZhgterNBLlmAmbjscRcqM1Um5RXVhRtAkG0VZTpxo6Q1ZlnDxpud3Rt0tKybIhX4TvKAWnXT/kgUtiUxoSycX+N0nl4SZwTKezJmYo+sxkCzYbISFlXlR2RfPcEJkc00r/n3TEbT3D+Phq2fhc+dNZNcrhokhaSl23L6yGrcsn4R3DzfhqY9P4advH8MXzy/H9YsrMcdkymaGiTXqV0F2WoqhwmE034t3gik5/XSkRKPYdTBiZelS7tdhtwXUJJtZmo2jjT2+v0tz05Bit8Hljl7yDSPWLvBaROOpFIZCfhD3RSVpKXaUZAePi4uEFVMLo2KFMZPUwmrYbQS7CRuR0xH6OOgVUI+U6XGq0RgOwaz6sYAVNBMIIbD1ZDue+KAGhxu68XefmY6bllZFHADIMIx57DbClXNLceXcUuyv68JLO87gy09uQVVBBq5fXIG1C8v9Ug0zTKIwu2gXyxX5RC32q+PiYkGsztuMCdlo6x2Kzc7jTLzXjfXcHi+cXqTZriygriSaiThMlwwIQpEvwUoSmdBMsmae9jgYcV55LmZaWJkyi/I5vWpakWGIUlleOj4fZ5dxVtAMGHF7sOlgI5786CQaugbx1ZXV+OUtixOiSTMMM8p5Fbk4r+I8fPeaOXj7UBP+sPMs/nXjESypzsdV87xKnJnipQyTSMai80VJThqumjcx+IYREJaLo4mvlOelxz1OJ5yYWjN9iSQNfjRJi5HCnoh7J5lcHMNhblkOXO7gnbTbKKgVzUqeZZfOKjGMhQSUSrg+8Y5/Z01Dg9rWPvxh11m8tvscMlLtuGf1FFy7qNxyBTYZZryTlmLH2gVlWLugDG29Q3jnUBPePNCIH7xxGOeV5+KSGcW4eGYx5pXlcnIRxnKUZDtxQXVgbaxoEI+U4HrE0rskJy0Feeljx1I+vyIXU4sjK15sRfhpm3xMK0l+q5gWeklnrH6NsoIm0dIzhPcON+G13edwoL4La+aV4r9uWIhlkwt4YscwSUBhlhM3Lq3CjUur0DXgwgdHm/HhsRY883QthABWzyjGRdOLsGxKYUKyWTGMGiIKiHOyAg6bDSOe+MRVhUq0YuXCYUpRVtjp5fVwOuwhL/7GKs1+MMry0nG+TlbReFOZnwFXFAtvM+OPoiwnOi2ccGfcKmhCCBxr6sW7h5vw3uEmHDjXjSXV+bhucTme+uqSuAdoMwwTPXLTU7BuYTnWLSyHxyNwqKEbHx5rwR921uHh1/ajKMuJZVMKsGxyAZZNLsSkwgxLuWQwyYlV3MqAyKJlLptdMubcuaLRnVSHLarFcmNJrB5nkWQWNUIumm6W4mwnimOcZETNGLslYkp6ih2diRYiCHPKciydZGzcKGiDLjcON3RjR207dtR2YGdtO9wegUtmluCrqybj4hnFMa+5wAJDSvIAACAASURBVDBM/LHZCPPKczGvPBcPXDoNQyNu7K/rwrZT7XhjfyO+/5dDSEuxY0FlHhZU5GFBZS4WVOQhnxOOMOOUWMUNRYJ1VN/EkyjlOZQkTFlOB7pCsE5wCMnY4vyqPLjcuYkWI6kZUwqaEAKtvcM41zmAcx0DONnSiyNNPTjS0I3atn6UZDuxpLoAq6cX4R8un4EZE7JjthrEMIw1cTrsWFJdgCXVBXjgUm8yoOPNvdh7thN76zqxaVMjjjf1oCwvHedV5GLWhGzMKM3GzAnZqCzI4GcGwzA+EqEsmTlktK25oRSnBoBFVfmYV84T9PGKw24D69yRYUkFTQiBoREP+oZG0D/sRt/wCPqG3OgbGkHngAud/cPo6HOho38YHf3DaJOVss4BeDwCpblpKM9Lx+SiTCyZlI9blk3CrNJsXhFnGCYAh92G2RNzMHtiDm5cWgUAGBh242B9F/af68Kxph68f7QZx5p6MeLxYHpJNmZMyMaU4kxUFWSgujATk4oydAORmfGDw24d5d0+xlx2g2VhY6yF3Uaw28bWDD03PQWtY6QMA2N94q6gPfzaPuw52wW3x4MRj4DbIzDilv73CAy5vAqZRwA2AjJSHchItSPT6f0/LyMFeRmpKMhIRX5GCqoKMlCYlepNj5ufjpLsNF7hZhgmItJTR61sMkII1HcN4mhjN4429uJ0Wx8+Pt6CM239aOgeRH5GKqoKMlCRn47SnDSU5qZhQo73pzQnDSU5Tku6jjHRI9PpCKuuULS5ZEYJctItuf4aNrMmZmNiXvilMzJiVFw3EZjJ0Jms+qxRLapE4nTYMGdiDi/0M3Ej7k/wm5ZW4fPzR2C3ERx2gt1mg8NG0moLIT3F7lPInA4bB+4zDGMJiMhXJ+kzsyb4fTbocuNsez9q2/pR3zmAxu5BHKrvxl+PNKOxexBNXYPoG3YjJ82B/MxU5GWkIi89BfnSglN+Rqq0+JSCLKcDmU4HMlMdyHR6n4WZTgcyUuycUTYJsEIsTW7G2LPmpthtpmoVaXHVvIlIiZF184LqfAy6rJdNMBmfFJfNnoBMiyrSa2Jc249h1MRdQZtfkRfvQzIMw8SUtBQ7pk/IxvQJ+nVkegZdaO0dRmf/MDr7vS7anf1el+3W3iHUtPSis38YvUMj6B9ye/+X3LuH3d4JYEaqHRmS4paR6l3ESnXY4HTYvOm6UxS/O2xwptiQYrPBRt5kKTYi2MirbNqIYLd5XcdIavf+7pX3+sWVQYuRJhNE9BCAr8E7d/2NEOJnRFQA4GUA1QBqAdwghOgg78rgzwFcDaAfwFeFELsTIjgTMbGsy1aSE75VL1zGajbBLOfYsvoyTCTw3cAwDBMHstNSpPIdmSF/d3jE41XWhr2xuHJ87vCIB0MjbgyNeDDk8mDI7cGQS/pb+qx/2A2PEBBCwCMAjxDeH4/8u9dlyi39LrNuYTnSMTYUNCKaB69ythTAMIBNRPSG1PaeEOLHRPRtAN8G8C0AVwGYLv0sA/BL6X+GSTgLKvLQHSRDohUsuQzDhA8raAzDMBYn1WFDqiMVeclRgsmKzAawVQjRDwBE9CGALwBYB+ASaZtnAHwAr4K2DsCzwhvss5WI8ohoohCiId6CM4yagsxUw5T3l8+ZgHSOd2WYpMaa0ZgMwzAMEz0OAFhNRIVElAGv62IlgAmy0iX9XyJtXw7grOL7dVKbH0R0DxHtJKKdLS0tMe0Aw5glI9XB8fsMk+SwgsYwDMOMaYQQhwH8O4B3AGwCsBfAiMFXtGa3AaE/QognhRBLhBBLiouLoyIrwzAMw7CCxjAMw4x5hBBPCSHOF0KsBtAO4DiAJiKaCADS/83S5nXwWthkKgDUx1NehmEYZvxCZupp+DYmagFw2uTmRQBawxEqwSSr3ADLngiSVW4geWVPVrmB5JU9ErknCSESbl4iohIhRDMRVQF4G8AKAP8MoE2RJKRACPFPRPQ5AA/C6wq5DMAvhBBLg+y/B8DR2PYiLiTrNaqG+2EdxkIfAO6H1RgL/dB9P4akoIUCEe0UQiyJyc5jSLLKDbDsiSBZ5QaSV/ZklRtIXtmTVW4lRPQxgEIALgD/IIR4j4gKAbwCoArAGQDXCyHapTT7jwFYA2+a/TuEEDuD7D/pzxHA/bAaY6EfY6EPAPfDaoyVfujBWRwZhmGYMY8Q4iKNtjYAl2m0CwAPxEMuhmEYhlHDMWgMwzAMwzAMwzAWIZYK2pMx3HcsSVa5AZY9ESSr3EDyyp6scgPJK3uyyh1Pxso54n5Yi7HQj7HQB4D7YTXGSj80iVkMGsMwDMMwDMMwDBMa7OLIMAzDMAzDMAxjEVhBYxiGYRiGYRiGsQhhK2hE9BARHSCig0T0DamtgIjeIaLj0v/5UjsR0S+IqIaI9hHR+dHqQBRl/x4RnSOiPdLP1YrtH5ZkP0pEV8ZZ1vVE1ExEBxRtIZ9nIrpd2v44Ed1uMbkvIaIuxbn/F8V31kjnvUaqUxRzdGS/XrpePES0RLW95vURb9lDkZuIqoloQHHOf6X4bDER7Zfk/oWUcjwRsv8HER2RruU/EVGe4jMrn3NNuZPknP9AknsPEb1NRGVSu2WeLVYkEc+pcCCiSiJ6n4gOS8+Fh6R2S77/jCCiWume2UNEO6W2pJiDyBDRTMU530NE3UT0jWQYD53nh+XnJib7kRTP8CB9CPkaSvRzTKcfLyv6UEtEe6R2S45FVBFChPwDYB6AAwAy4E3V/y6A6QB+AuDb/7+98w6T4yzy/7cmbM672lVYZcnKyZJlyQqOOFuyjQEHjA34HDFwHHdn8AUOfnDHcYAxYBsnknPEAecoOUiykpXjKq42aVfanGbm/f0x3bM9PR0ndc9ufZ5nH416errrffvt7qq36q2S9rkLwM+lzxcDeB0AAVgEYG08503Gn4HsPwLwfY39pwP4HEA2gPEA9gPwplHe5QBOBbBNsc1WPwMoA1Aj/VsqfS51kdxnAXhV4xheqb8nAMiSrsN0h/p8GoApAD4AsMBsfDghu025xyn3Ux1nHcJFfEkaTxc51OfnA/BJn3+uGC9u73M9uTOhz4sUn78N4AHps2ueLW77c+o5FaesIwCcKn0uBLBHup9+BBe+/0zachBAhWqb63UQk3FUD2BsJlwPneeH63UTi+3IiGe4SRtsjSE3PMe02qH6/pcA/sPN1yKZf/F60KYBWCOE6BJCBAB8COAKACsB/Fna588ALpc+rwTwFxFmDYASIhoR57kTRU92PVYCeEoI0SuEOABgH4CFaZATACCEWAWgRUMmO/18AYC3hRAtQogTAN5GuACrW+TWYyGAfUKIGiFEH4CnpGOkFC3ZhRA7hRC7NXbXGx9pl92m3JpI46VICPGpCD/p/gLz65QwOrK/Jd2jALAGQLX02e19rie3Ji7r8zbFf/MByFmkXPNscSGOPKfiQQhRJ4TYKH1uB7ATwCiDnzj6/ouDTNBB9DgXwH4hxCGDfVxzPTJVN1GTyc9wGZ1roYdr3p9qjNohecG+DOBJo2M4fS2SSbwG2jYAy4monIjyEJ4dGQ2gSghRB4RfBAAqpf1HATii+P1RGL8UUome7ADwLcml/ajsmoe7ZJex289uaYOe3ACwmIg+J6LXiWiGtM0tchvh9j43YjwRbSKiD4lILuI7CmFZZdwi9zcQngkDMqvPlXIDGdDnRPRTIjoC4DoAcrhxJvV5usnIPiCicQDmAVgrbcqU95+MAPAWEW0gopulbZmgg+hxNaKVz0y7HkDm6iZGZNwzXIGdMeT2a7EMQIMQYq9iWyZdC9vEZaAJIXYi7PZ9G8AbCLtCAwY/0Yr/dCS/v4Hs9wOYCGAugDqEXamAi2S3gJ6sbm/DRgBjhRBzAPwWwN+k7W6XG8jcPq8DMEYIMQ/A9wA8QURFcKHcRHQ3wvfo4/Imjd1c1+cacmdEnwsh7hZCjEZY7m9JmzOizx0i4/qAiAoAPA/gu5LXNBPff0uEEKcCuAjAHUS03GBfN7cDRJQFYAWAZ6VNmXg9jMjI50emPsMl7I4hN7ZByTWInsDIpGsRF3EnCRFCPCKEOFUIsRxhl+ReAA1y2ID0b6O0+1EMeKmAsLv4WLznThQt2YUQDUKIoBAiBOAhDIQNuEp2Cbv97JY2aMothGgTQnRIn18D4CeiCrhHbiPc3ueaSOENzdLnDQjHnp+CsNzKcA5H5abwovFLAVwnhSsAGdDnWnJnSp8reALAF6XPru9zB8moPiAiP8LG2eNCiBcAIMPefwAAIcQx6d9GAC8iLHNG6CAaXARgoxCiAcjM6yGRqbpJDJn+DI9jDLn5WvgAXAngaXlbJl2LeEkki2Ol9O8YhDvuSQAvA5Cz8NwA4CXp88sAvkZhFgFold3gTqAluyoe/QqEQyGBsOxXE1E2EY1HOKHIunTKq4Hdfn4TwPlEVCq5uc+XtqUbTbmJaLicZYeIFiI8LpsBfAZgMhGNl2YYr5aO4Sb0xoerZSeiYUTklT5PQFjuGmm8tBPRIumafA0D4yvdMl4I4F8BrBBCdCm+cnWf68mdIX0+WfHfFQB2SZ/d/mxxEleMOytI4+sRADuFEL9SbM+k9x+IKJ+ICuXPCI+7bcgQHUSDKO9Apl0PBZmqm0SRyc9wmTjGkJufY+cB2CWEiIQuZtK1iBsRf7aV1QB2IBwieK60rRzAuwh7094FUCZtJwC/R9jC3QpFNjkn/nRk/6sk2xaEB+UIxf53S7LvRpqzwSD80K4D0I/wzMA34+lnhOOo90l/X3eZ3N8CsF26HmsAnKE4zsUIZxrbD+BuB/v8CulzL4AGAG+ajY90y25HboQ9I3KfbwRwmeI4CxB+mO8H8DsA5JDs+xCOid8s/T2QIX2uKXeG9PnzkhxbALwCYJS0r2ueLW78c+I5FaecSxEO99miGJ8Xw6XvP4N2TJDuo8+le+puaXtG6CCqtuQhPCFZrNjm+uuh8/xwvW5isR0Z8Qw3aYPtMeT0c0yrHdL2PwG4VbWvK69FMv9IagzDMAzDMAzDMAzjMHGHODIMwzAMwzAMwzDJhQ00hmEYhmEYhmEYl8AGGsMwDMMwDMMwjEtgA41hGIZhGIZhGMYlsIHGMAzDMAzDMAzjEthAYxiGYRiGYRiGcQlsoDEMwzAMwzAMw7gENtAYhmEYhmEYhmFcAhtoDMMwDMMwDMMwLoENNIZhGIZhGIZhGJfABhrDJBkiGkNEq4molYg+JqKJTsvEMAzDMG6A35EMYw4baAyTfH4CYDeAcgDPA/ips+IwDMMwjGvgdyTDmMAGGsMkn3kAHhNCBAD8HcBsh+VhGIZhGLfA70iGMYENNIYxgYjOI6JtRNRDREL6O2jwkyIAHdLnEQD6Ui4kwzAMwzgAvyMZJvmwgcYwBhBRAYAnAfwSQAGArwNoBbDCwm+HAfg3AB+mUkaGYRiGcQJ+RzJMamADjWGMWQjguBDij0KIgBDiTwAaAJSa/K5M2u8cAGtTKyLDMAzDOAK/IxkmBficFoBhXE4lgKOqbYcBDDf5XQuAbADzATxBRD1CiBdSIB/DMAzDOAW/IxkmBbAHjWGMOQpgrGrbOAC1Zj8UQvQLIdYAeAHA6ckXjWEYhmEchd+RDJMC2EBjGGPWAPAR0c1E5CeimxH2PJuFZNxEREVEVAHgPADbUi0owzAMw6QZfkcyTApgA41hDJDSAK8AcCOA4wgvgF4phOg3+ek0hMM8dgD4GMATKRSTYRiGYdIOvyMZJjWQEMJpGRhmUCGlF75KCLHeaVkYhmEYxk3wO5JhzGEPGsMwDMMwDMMwjEvgLI4MEwdE9DmAiRpfqRdLMwzDMMyQgt+RDJMYHOLIMAzDMAzDMAzjEjjEkWEYhmEYhmEYxiXYCnGsqKgQ48aNS5EoDMMwzGBjw4YNx4UQw5yWI9Xw+5FhGIaxg9H70ZaBNm7cOKxfz0l3GIZhGGsQ0SGnZUgH/H5kGIZh7GD0fuQQR4ZhGIZhGIZhGJfABhrDMAzDMAzDMIxL4DT7jGs53tGLNTXN2NvQgRNdfRACKC/IwilVhVgwrhSVhTlOi8gwDMMwg5pgSGBfYwemDC90WhSGGTKwgca4CiEE1tS04L4P9mFtTQtmVxdj6ohCVBRkAwCa2nvx0d7j+O5TmzG7uhgr547EF+dXIy+LhzLDMAzDJJvjHb3YVd/GBhrDpBHWahnXsK+xA//2t63Y19iBf1g2Ab+9Zh5K8rI0923t6se7uxrw1Loj+MWbu/G1xeNw85kTUJTjT7PUDMMwDMMwDJM82EBjHEcIgcfWHML/vL4L31w6Hn+8cSFys7yGvynO8+PKU6tx5anV2Hq0Fb95dw/O/N/3cec5k/HVRWOR5ePllQzDMAzDMEzmwQYa4yg9/UHc/eI2rKlpxlM3L8as6mLbx5hVXYyHbzgNa2ua8bPXd+GxtYfwsytmYdGE8hRIzDAMwzAMwzCpgw00xjE6ewO46c/hukGv3LkUZfna4YxWOX1COV687Qw8s/4IbvnrBpw/vQo/vHgaShM8LsMwDMMwDMOkC44DYxyhracfX3t0HfKzvfjj109L2DiT8XgIVy8cg3e+dyb6giGc+6sP8cLGoxBCJOX4DMMwDMMwmcTJrj709AedFoOxARtoTNrp7gviG3/8DJWF2bjvuvnI8RuvN4uHYYXZ+M3V8/Drr8zFr97eg6//6TMcO9md9PMwDMMw9ukNsLLIMOniwz1NWHegxWkxGBuwgcaklb5ACLc9vgH52T785up5KU/mceYpw/Dmd5djXHk+LrhnFZ5Ye5i9aQzDMA5ypKULb2yrd1oMhhlShFj3ySjYQGPShhACd72wBe09ATzw1flpy7SYn+3Dj1bMwCM3nIaHVtfg2ofW4nBzV1rOzTAMw0TTzaFWDJN22DzLLNhAY9LGg6tqsP7gCTz8tQWmafRTwcLxZXj9O8swe3QxLr53NR796ACCIX5kMQyTGXT2BvDR3uNOi8EwDMOkGDbQmLTw3q4G/O79fXj4hgWOZlXM8Xvxg4um4fGbTsdTnx3Gl//wKfY1djgmD8MwjFUa23vR3NnrtBgMwzCMCXWt3TjSEn+0FhtoTMrZ29CO7z61Gfd8ZS5OqSp0WhwAwJzRJXjlzqVYMqkCK3/3Ee7/YD8CwZDTYjEMwzAMo0MwJDjyhbFEd18Qb25P71rXrUdbIwmQ1h1owcbDJ+I+FhtoTEo52dWHm/6yHredNQnnTqtyWpwosn1efO8Lp+DZW8/Aq1uO4Yr7PsGu+janxWIYhtGEFVNmqPPxvuN4Z2eD02IwGcDJ7vSXFqg53oHGtuREObCBxqSM/mAIdzyxEaeOKcWtZ05wWhxdpo8swt/uWIILZw7HF+/7BPe8swd9AfamMQzjLrYfa3VahKTAyeSYeDnB9byYIQIbaEzK+Onfd6KjN4j/vnIWiMhpcQzxez244+xJeOlbS7BqTxMu/e3qhFzTDMMwDJMJ1Lf24O0d7JViBhcEd+udZrCBxqSEpz87jNe21uHB61NTiDpVTKosxLO3noFrF47BDY+sw49e3o6O3oDTYjEMwzBMSqhv60FXH7/nGMZNsIHGJJ0Nh1rwk1d34g/Xz0dVUY7T4tjG6yHcuGQ83vjH5Tjc0oXzf/Uh3uWYd4YZchBRCRE9R0S7iGgnES12WiY3cLKrD/2cVIlhEAiG8OqWY06LMWQ42dWHzUdOOi1GWmADjUkqda3duOWvG/GjFTMwb0yp0+IkxKiSXDxywwL84OJp+Nfnt+BbT2xEUzunuGaYIcRvALwhhJgKYA6AnQ7L4wo+3NOEnXXpS6i0q76Nn70OwmsG9ekJhDh5Txo51NyFQ82dTouRFthAY5JGT38Qt/x1A1bMGYmr5lc7LU5SICJcNmck3vnemcj1e/GFX3+IZ9YfgeA3FsMMaoioCMByAI8AgBCiTwgxNKZuLRBIo1K6u74du+vb03a+oQa/zxKH+5BJNmygMUlBCIEfvLAVRTl+/PDiqU6Lk3RK8rLwiy/Nwe+vPRW/f38frnt4LQ4eHxqzOAwzRJkAoAnAH4loExE9TET5Tgtll5bOPhw72Z304yaijwqY//ilzbVo6eyL/yQMw0TDNmRGwQYakxT+sKoGGw6dwO+unQefd/AOqyWTKvDGd5ZjdnUJLrl3NR5aVYMQhzcwzGDEB+BUAPcLIeYB6ARwl3IHIrqZiNYT0fqmpiYnZDRlbU0zPjvY4rQYcdHe0++0CIwCMy9Rd19i6e+FEBn7PmUHGpNsBq8mzaSNlz8/hgc+3I9Hb1yAkrwsp8VJOblZXtx10VQ8fctiPLP+CK59eA1qUzBDzTCMoxwFcFQIsVb6/3MIG2wRhBAPCiEWCCEWDBs2LO0CupnW7v4Yz91Lm2tR12r9WalUel1eqWVQY8WT2djeg7d21Cd0ns8OnsCb2xM7hh16+oM4oWhbpocp1jR1oDfANeLs0N7Tjze2pW/M2YENNCYhPt3fjLtf2IoHr1+ASZWFTouTVmaOKsYrdy7FjJHFuOieVXhx09GMf8AzDBNGCFEP4AgRTZE2nQtgR7KOHwoJRwru1rV246XNtUk4kvGzbtPhE5qeu7Zu83TurV3u8ZwJIYZ8qZW9jebr/0JJSOrZ3NGLvjRmB11/8ARW7R3wfL+9owHrdbzN3X1Bw8Q4qXrz28mWurW2FYeau1IkSWo43tGb8vvLSC870dmfsFHbmSL52UBj4mZPQztufWwD/ueLs7FwfJnT4jhCjt+Lf790Ou7/6nz87xu78a0nNuFkF6+bYJhBwp0AHieiLQDmAvhZsg68/VhbWr0FMs0d1p5PrV39ac3UqOSDPY2OnFeLQ81dXGbFAj5v2MUZb0bD3kAwrcYZAIRUint3f1A3W+jhli7sadA3VFORav94Ry9e21qX9OO6iY/3Hcfamua4f9/dF8TRE8ZG6W6D65YM3tnZgCMtyTeM2UBj4uLoiS7c+Og63HnOJFwye4TT4jjOkkkVeOO7y+HxEC659yNsOcrJ3hgm0xFCbJZCGGcLIS4XQpxI1rG70+Q9i3dmf29ju6FCapWWzj5XeqBe2lxryYMZT723T/YdT3g9lhsxChDxSjGo8dbHq2lKTdKtjt5AyjwcqcYJD3s6SUbE0Y66Nmw4ZPxY7uzV78dkhU63diff688GGmOb+tYeXPvQWqyYOwo3LZvgtDiuoTjXj3uvnotbz5qIax9ai8fXHuKQR4ZhXEWyFBKzR5usXK7e24SP9h7X3Ketpx8NbT26IZepfnqmymPT1NFreT1WT38Q7+921mOYjH7WO8ah5k58noLCwlZDhN/d2ZCU/o33vjnc3BV3SDGlYOHlsZPJCnFOnJc/D3sd42mmW9ogk4paeGygMbZobO/BtQ+twXnTqvCvF04x/8EQg4hw/aKxeOym03Hf+/vxT89+PuhnwRiGST3xTvbIHg359x6b2lBfIGQ7bLuutRu9AaXxIyLeOKIB4+79XY2WC1CnIkeIG+bPTnb1oy0Js+8vba7F8Y6BvnxvVwN2HHMmRFXJ5iMncdBCYWG7SvqexnbLIcJOFpI+3pnaAuv9wZAtb61TpStOGJyXUnJ3K9G//m5OPsQGGmOZ5o5eXPfQWpwxqRz/fum0lMzuDBbmji7Bq3culbyNaywrIQzDZD5CCNfWSbT72N5a24oP9zTZWmPTH4hWiAIhkZb1bLvq27Cr3nmjxCmUynd7TwD1bcnNLmxk5sgTAOkyeu0YJU7qKsr+WFPTjI/3aXuTtbAi9WcHWhLOnplqQiGBVXubDCerrdZrVBr76YxQcsLIZwONsURDWw+ueWgNTh1Tih+vmMnGmQVK87Pw528sxJThhbj89x9jd31qF6oyDOM8Pf1BNHf24XOXrUOVdRmrs9XyI15WTIIhEeWRMVJX1K8HI+XGaH2QXQVsd317xj1nrRTtjvvYBod+aXOtoVejpqkD+xo7rJ/LjmBpxuOouhLumW21rWho64nycpphRc1K11rWZKBOyhKBgHUHBuo1CiEseX+TYTNZ1WVf3XLMkgFphUAwhPaeft3Qbxk20BhTDjV34qoHPsEZEyvw31fOgsfZp11G4fd68LMrZuHrS8bhSw98glV73FnMlmGY5PDm9vqUrLmxY6u0dvWjsa1H87tE5taOKLKldfQGdNeB2DlHvY6cqSRiMBr0aSgk8NLmWgTdEAeZZGTD1yh5y9baVmw/1mrjmNK/STLVjnf04v1d+mvH7EwS2w3rTQX7m7SNXStJTJwu3t0fDKE/GEJbT39CxeONbiXlctCuviD2NrabTtDoGnw2sDMyjK5TU3sv3t4Rzva68fAJ1OhcbyCcwfe9XY1oNgl/ZQONMWRXfRuueuBTXDmvGv952XQ2zuKAiHDTsgn45Zfn4o7HN6YkHS/DMO6hy0L4lbpg80d7j+saVXb5tKYZn9Y0a6Z+TuQJ7lUoukbrppKlEKdKLX3bQkiYnEAkGfW9ttW2RnlOhBCob02/YSqzpka71pcZSoV59d4mzfToZjpze08/GtvN217f2oM2lTGg/L+dEeak1mLWH+/ubMA7OmUcZG/3Hgt16KyeLx7e39UY+XtPx2juDQSj1qq2dPZFrpcskp63jxCfsZWogRYKiYQmiIQQkXuisy+Arr4BA+7YSf3jWs10ygYao8u6Ay24+sE1uO3MifjHL5zCYY0J8oXpVXjohgX44Qtb8eS6w06LwzBMirCjNsjrhpo7e1GrEULT3Rc0LaSqF3qz8fBA+mlZJvVjvLWr39DjF886D6NQIIJ1JTIQFHhdqgPVa6DUKBUjK0QnMNFGllHv2H2BEIIhgb5ASD8LpXSQ/U0d2NswMKN+vKMPaw/EX/vJiHUHWiKhiXr9LM/cy2Mh4v1SKJxaCCBiaLZ09kUpt7LnTEBOEhNEQOOarTvQgk/3x7ZdGXrb0x/U9MIaedSMsKO7CAB7G2K9N+nS/sPqTwAAIABJREFUfoQQEQNWFru3P4S+QEgzfNfsXgoEQwkXYu7uD5qGUm44dAIfKiKEVu9twuo90SF8euvviEjT2DJrW6LG6CtbjunWL3t/V6NpJMRnB0/gze3axrWZd8wKbKAxmvxtUy2+8afP8KPLZuAbS8c7Lc6gYdGEcjx20+n4xZu78cCH+50Wh2GYFGDHqFGHwrR29+OQYiH8WzvqsXrPcV2jrz8YwmcHW9DVFw45DIfvae/94Z6mGO/egeZOS1n2oHNUrQyPWoZmPBxr7Y54stq6+3WTLb29oyGh7HRaM9qywqhuS0tnH0Ihgde31WHT4RNRqfoDUiiYTG8ghFekVOLK0L+oz3EomUYJMupau3XD6cz4eF+zYUr63fXt+Hjfce2+VrRj3YEW7G3owN8VRZblOlFW2vvZwRbTtZJ25ovlfV/aXGtaUDgYEthR1xZjxPcHtQU3u9etXt7ak91hz2pbT8SAlZsoEPYoyglw+jQmGPTkWFPTgje2pT6JSG9/WCbl+I8Y7TYHuVD9q0dnb8C0SHX4/LZODyDsra1TebnVY665ozdh49cINtCYKIQQ+M07e/Ffr2zHozeehsvnjXJapEHH7OoSPHPLIvz5k4P4n9d3ca00hhnCqJeXbK9txWbVzG1PIKjrlZJ1BlmBPNnVp+khau7oxcmuPjR3hJXrQ82deGlzralnwOzp9KFqXW0yn2fqMMreQFA3PCgRA+21rXUxWTf11p6t3tuEmuNhA6hdsSZFCIGP9zdHpX7v7A0kZZ2MEiEE3tpRb5gRz+qaJbVnqbmzF+09+t7IE5IxrnWNI0q19J263eqQXiOshICZGXC9gSCaJW+fMuS2Oc5xslcnzFDpFQqGhO2SFDLrD7agubMvYkx09AbwqRRCeqi5Ew2KsNDXt9XhgGq8yjXF1KSjSPzxjt5IOGOXRlFos9EYr3fSanFoK7eDG/UwNtCYCH2BEP7p2c/x4qajeOH2JVg4vsxpkQYtkyoL8eyti/Hm9nr88MVtjtZpYRgmNVh56Sv3MXoM6CmI6uN8pBNG9KlqvZBs0FhVcoBwuJ8ybFJbDsuHs01NUydeU3hl3thWH1FU7SS0kFF6sjpVoYzCwEbo0FJCRdg4NnuWd/cFo4wLZeY6O2gZpPJRA5IMgVAoZq3bkZauiIx2FWP5nFqSyl4d+Tt5ImBANutnC4VivRXq9pp50LYebY3cC3baaXf4KidDdte3R01YtHb127oflPtuUU3SqCdozBKLyCj7KZkZQ7v7gpGQ00RrvepdS7N7wmqheSuTAy60z9hAY8Kc7OrD1x5diyMtXXjx9iUYX5HvtEiDnurSPDxzy2JsPnIS33tms+WFowzDZAZ6L/1NCiNHqc8fPdGFJotpuI+d7EZfIBRRufY3Way7RtHnPWEy469ug1mIWCr1HPV6sN5AMGXJNoyyN2oZYR1m6+Ckn7y1o15zfcrH+6ytSZPFkg07LWTvVW8gFLPW7YiFkDCrKD1wasNdneDDTn4xgWilva2nH6v32suAHHdIovS9kaetPxhed6g+VkCRUaatpx8f7Gm0ZXhH7WvSX/I1tuudSoYh0trVj7d21A8kAdE55sDaRuvHVpZ+2FXfbnh/B3SuceR7gww/6kQ16bTPrJ6LDTQGB4934sr7P0FVUQ4eu+l0lOZnOS3SkGFYYTae+odFONTchdsf35jSeGaGYVKL1YL0hxVGTryz2p8dbIlaO2ZlLYaSVIT0CCFMQ/p21LUlMJMfq7VqZQQMhUSUISmEMKz5FT5y9LGN+yf8XVt3P96VMvDZSYXe068Rgqoy2rr7wsbnhkMtujWh7NTUklGOUYHkj4OgjtK8v6nD8rnUY8iob9cfbMGGQ7HGqnpc2A2BXW9gAMuTqccMjIeBsgPav9dKQBESA4apmcfR6nBTZ1RNRtkItbc5UZRtVRrG+5s6DJPpBAw6QQiBDYcGJg0ON3dFrd3s6QvF7G+XVBt1bKANcdbWNOOK+z7Gijkjcc9X5iLb53VapCFHcZ4fj910Otp7+nHTn9cbLgBnGMa9fLI/OrzQ0gvc4lteqT/IyoTf67E/I26gOMrUnuzG0RP2E328u7MR6w7El8LdCrIXxswQburojfLoNLT1YpVND4yheabxpdVwKwCa2Q3VvLWjHmsPNOPoie7Imje1XB/vO57Q+2L9wRZst1AQ2A56/dwbCMWsjQyFhKY3UohopX2VQUHf2pPdMSnN1esJO/sCMQbaS5trTb3HZhgZcZ6Ipzq2fSc6+zQT8ygnLsxCOCMeNJMHgPo4hywmBEoGA8k+7MgYp8dRQVtPf8yavE1HTkSSrGjJJBAuh2G0Zs/IIEwFbKANYZ5dfwTf/PN6/GjFDHz3PE6j7yQF2T786esL4fMQbnh0XULFIBmGcS9qBSCeV76sl/jiqEspK3ZGXom6OLMwdvYF4vLqWEV+R5kZQ1a9BlqbO3oDUrr56O1KQ0LraBsPmazNw0Cfy4qeWsHUK25LIBzv6EV/MBRTRzPRJCTmIa76x7c7+nap0sSvP3Qi0h61kh61dkpDBuU19nsHVNm61m58fjTWO7WzLnmGqJX1dLJ8WlF2ereelQhH2TNsdX1rdD9Gf//qlmNRRsvRE12WQoaTbaYo22rHBtLbVyvLpRp1XwgR9tgdbtaPRNhd325YQiTZsIE2BAmFBH7+xi78/I1d+PM3FmLlXM7U6AZy/F784foFqCjMwnUPrzUNyWEYxt309AfR0x/Eic6+SJiaeh1VPPq1rJR74jDQZAPRqiKUzMQCRgRDxjW4AIVXwkR4eT/5eHZ66d2dDag53hnT7le3HIsYadoeNPN+ekU2RiDLF/391tpwohN1wXKisLdMsw6W6VmNCctgFCqW4AkUHFatX9TLeGhlbCrvI6Jw2OCGQy26Hlyh+jfm+xRlidCaHNAzqpXb9SbM5RIKRn10QioF8fetdYZZOYMhEZXMZcOhEzFrCc0SgHT2BhKvV6YyItXXQu/a6G1XT9BE9jcQQb7fzfwUHb0BW4mVAERqOdqFDbQhRndfELc/vhHv7mzAi7cvwfyxpU6LxCjI8nlw79XzMLmyEFc/uEZzfQXDMO5DKynB+7sb8eb2emw71qqbhVFPWXtpc20khCoYElEKmfJzvAZUMhVS23WONHZ/dcsxfH7UWiZGs0ySXslC298ULiUgrxlSZ77Tk3pvQ3ukrpMSo7CyZMafqD2Eegadniy6x9Wqn6X6v9ow0Arrkg3JRIJuuvoCuv0fDAnT/lTWpyMAJ7v6DcNyzZODmJxQua+O5FrH0JpMUN/zcp8rQztNQxzlyQKN71btbcIhRbZOI/nM+vmdnbGFmJWhne/sbEB774DBotRZNMsxaN47A1IIjd7VTUKi+BwKiUhGV6/OxFVUmLjOd2b9XtPUiQ8MagVqob6frXrh2EAbQjS09eDLf/gU3f1BPH/bGRhdlue0SIwGPq8Hv7hqNhaOL8NX/rDGNGsawzDOo5X2WlaQ4s3QqlRClR4DOSwtEaPAciiRhf2SZetZTR1uhqzwyan3O6W0+Hsbo0MIV+9t0jRaegMhQyMwnuYqQz/lvrd6HCOlsa0nEPFyGK2R6Q+G8Po285l8tQKtVbhark8n93M8xv7bOxp0vTMhES4WnQqOtHRFErsoUWedjAetXtBbY6f5e8V2PSXeak+blXuILKNQja2QEFHJyqyUAFLuo75uQkSvM9Qao+owTKvDSblfc2cfthw9ic7egKWJA70xqxW+qlzPJmeGTEdpJDbQhgjbalux8ncfY/7YUjxywwIU5vidFokxwOMh/HjlDFw8aziuvP8TbKu1X+OHYRh3IK9DCYVEjAIQjxdijaKmWbzGkZHnTflNl4UkFN1SKKdVGtq0IwPaewJ4aXOtpWPYSY4hey+1uvq9XdZnw43Shtu5DLKXwer6sd5IjbHY/dcfbIkkp9nToF8rT+87M+Mq0RpXVs5lFDJmlIhDhohs3UdaiSA2mKwhtIKyffInKyGO8u+seMMH9o1DPsXnyMSECHvr5WsQDAm8sa0+9sdx8s7Oxigv3OYjJ/HB7sao8FblpdMaIq9sOYaXNtfGrCNU9qP8PHhnZ4OBASx0J8u0PWj6HvNXtxxLeWkkNtCGAO/vasQ1D67B7WdPxI9WzIDPy5c9EyAi/PMFU/GdcyfjmofW2HarMwzjDmRFYlscxZTNsGJAaWHVsLOSUvv93Y1obLOeHEQvU5qdMiNv7dBXIu2EfcrntDTrDuuKdHg/Y7Rm4eNR+vRqfkXJYhAmVt8aX2IXuc8S9aAaZeWstRAOlmh4aTAkYjJMyvT0B20nGFFeQy3lXr7s6oyKyjT7esiH6zBYWwZoH0cpizz2ZFlqLWZt1VobZ3T91Wtuj53sRmt3PxoUzwvlMUNC6N446okdpYG26Yi5gV17sjtS6F5d6kI2pNUJS6xOGKUC1tQHOc9vOIpvP7kJ914zD19bPM5pcZg4+Oqisfj1l+fizic24cl1h50Wh2EYm8hKkBxqlyx6+oP4dL+1Asdq9LL3dfYGTAvAqgmGtFaOJB+rhsCB46lJJS57FrT6R3u9jfHxtAy01+JIKGDFQDGSxaigbzogSu4aPrvoeUNau/pxuKUrxvuo15fy5Xxtax0+kkoDGK0b3KyqhWYlVDTyLJEMHyvhkpFtGseR71v1Gtk3t9enNIxPaecpP/cFQ7rPEnXCE13D3oLY6vbKS0mUsmh5uBPtE/U1N4INtEHMQ6tq8P/+vgN/+sZCnD210mlxmAQ4b3oVHrvpdNzzzh788MWtXNCaYTIIsxCmeMtqbK1tTaph1NTei3d2NsSVnChFSfCiz2FxP3W2QLu/N8Nq/Syza6OX/l8v3E6vj7tNwhDbe/r1wykT6JR4jCqtEgmJGmepMvAa2nti+q0vENJMnKFGfkdrda9el1u5FGqPlJqBTKPWLqyevdHTH9QMRU5WPytLLiiPeaSlS1cmqyHBW+NYEjJgmKV2qsBOHTo20AYhQgj892s78chHB/D0LYs5U+MgYc7oErxy51Lsa+zAV/6wJq31OBiGiZ+IwqGhX7R09tlaB6UmmYbR0RPxJyRKg32WlPVQaZ3cMvOg2fRUxst7uxo1CyMDekks7MllZ28tT1VAlaXULqkq4UqIvb+2G4Ypm683g8YxB7abZ680Q/YMaRqGio3y5IFeqQMA+MzC+j+9c8nopryPKikQ/V2itf2aO+2H7MolLIzqxiVCPIl02EAbZASCIfzzc1vw9s4GPH/7GTilqtBpkZgkUlmYg8dvOh0Lxpbi4ntXxxQtZRhmaJGoMqNEr/aSFVJVR0pJom3t7A2kNSuu6Rq0dLgdTdBaY7ivUbtgtpq+YCiukEw1O+vaogomx4PeGrJEIKKYMafnnQW0FXrtkDjt6x7fcIjvWHbWjCoxW9sGRE+CxPNISeYzTfP4FmcDkilFPE3yJfH8jMN09wVx55Mb0dTRh+duPQNl+VlOi8SkAL/Xg3+7dDqWTq7APz+3Be/tbMSPVs5AEWfmZBhXMpBcwt0kMnvv9rYB4VT3ynT3qaau1ThU1G7yiXT1sZ2U86nOZGcFAln29thh+7FWDCvItry/1euz/Zj2dQ/p58dIEgNH1/OoxoPa3rGbBVJdvy7V8xZG5SiaO/pQWdgvyeHsU409aIOE1q5+XP/IWvQGQnjiptPZOBsCnDWlEm9+dzm6+oK46J7VeD+BMCmGYVKPCxwmutS1dicUKubmtqUDv0Z2ZLP1Ji2d1taypRv1tdRqW/T+Dl/8FC4barJh0CfaDQIiafeR2brEZJLs659qD1qPQZhzXWt3QiHneqhrMFqBPWiDgIa2HnztkXWYMrwQ//elOcjysd09VCjLz8L9Xz0VL39+DP/83BbMH1uC/7xsBkaW5DotGsMwKtKR6TBe9jd2xrV2QyYdhVvdTDo8ScmsSWaEOrW9mf0zxC99hLrWxNaF767Xr2OnhVF5AK3JgWSEgYbrOEZfcKPrH89zIdXjaXttaoqgG3HsZDdK8uxFObEmn+HUNHXgyvs+weKJ5bjnK3PZOBuCEBFWzh2Fd//pTFQV5eD8X6/CfR/sS9vLnGEYwEp5SacdDUZYzUyoR6JriJyGyFrNLSfRK/CdarQyLypx2jh3MkW/k7hlDbpRHbt4sLpGLF7iyVKbKG09/VhTY68kCmvzGcyWoyfxpQc+xbWnj8F/XjYdHs9QfUwxAFCc68ePV87Ek/+wCO/tbMS5v/wQL22uTfnDjmEYwEiHlQ0zAeuL5svzra99SQapDivKBHbZXBPGhDEqGs4As6tLnBYhpSS7jh4/isKwgZahfLT3OK57eC2+f8EU3HH2pISybzGDi1nVxXj21sX490un4ddv78EV932MtTZnbhiGAYjoIBFtJaLNRLTeeF/972R9g6Bd5FiL4lxO+pNujJIHpIpcvzft53Q7JXn21tC7Xf/huXN78GRRGF6DloG8uuUY7np+K/7vS3Nw4czhTovDuBAiwoUzR+CcqVV4bM0h3PLYBiwcV4a7LpqKCcMKnBaPYTKJs4UQx812MtLB5EX0fq8Haw9YmyzxslaXVgjkSKie240LO3g00tKnA7/X3X2oVwssUyjM8aG9x7hAdjKxeq7iXD9au61nHM002IOWYfz104P44Qtb8dDXFrBxxpiS5fPgG0vH48Pvn42x5Xm49Lcf4Sev7rCVRplhmORgZ+0DG2jpxeclRzxog+kqWx2zFTZS11vheIc7s2HKmNln+VnGvpLhRTlJlMY+Hb0D69nV4YypmGCoOW4t46HPM7hNmMHdukGEEAK/fnsPfvPuPjx58yIsnljutEhMBlGc58fdl0zHa99ehkPNXTj7Fx/g6c8O8/o0hjFGAHiLiDYQ0c3qL4noZiJaT0TrW9uM1y9Nriy0deIMn3S3zLDC9K6100MIZ9LFD6brbHVOIdmeSqfT/F82e6Th92YeNDM7w+nXtFH/JrPvZ44qtrX/qNLBna2aDbQMIBgS+Le/bcOLm2rx/G2LMWOkvUHMMDLjKvLx8A0L8KuvzMWDq2qw8vcfY8Oh5Bf4ZJhBwhIhxKkALgJwBxEtV34phHhQCLFACLGguKjI8EB2J3szwYFWlDN41sntaYhNcZ7t014flkyvQaKHSrY3KhGshvKZZQxNhtKfTsPfLEGbsl+05DIbT24uz5FMJtpcfjG+Ij9FkrgDNtBcTndfELf8dQM2HzmJ525bjLHlg3tAMunhzFOG4Y3vLsfl80bh63/8DN97ZjNOJphmm2EGG0KIY9K/jQBeBLBQd1+TY9lfh5IBFloSIJe0U6uOWUWBdrIKN0gshxOma93cOVMrTfexGuI4y8RTkowWuWndl7JbvBpy5ZkkiuGcGUMTNtBczPGOXlz90Br0B0N45pbFqCx0Ng6ZGVz4vR58c+l4vPf9s9AfFPjCr1fhze2cLplhAICI8omoUP4M4HwA2+I9Xn62/jqTKcMLMUeVipvIeGb9bBOF2W+lMJsDDIZ1I8lU/uM1UMeV54OIEEyC9m7FsMqxkG3Sar+YeiCTYJCkyjxbOXeU/R+ZCGPmgbNzie14lQpzOE9gOrFyDynJ/CflIOXA8U588f5PMLWqEA/fsMDw5c4wiVBRkI3fXjMPP1k5E3e/uA3/+PRmdPSmL2MTw7iUKgAfEdHnANYB+LsQ4o14D6Y1cy5Tnp+NESXhCThl2nUjtS3bZ/z6TqaCOn9sqeZ2rWRDZmFKal3URY6OGPScU8kMP9Xy3MlUGSSHmDmqGJfNHqE7rk4do33NtLBiNOs1ednkYZHPVmuxmu2VjJC+dGTHPGdqJS6YYZ6sLVEvcUmetVDiGSOLUWgj7FgrW6LTnkez51omM7o0z9b+g7cnMpi1Nc344v2f4IunVuN/vjjLtTOhzODiwpnD8fY/LkdHbwCX3Lsanx856bRIDOMYQogaIcQc6W+GEOKniRzPSO8hGlDiFo4vC2+DsbJkpvQlU8+qNlAs7EZ2qOVysX0WtRZKbmdJXlZS+7a7P6j7ndlpiAgLx5dh/tjSGEPBzOuyYFyZRQkHzqVFkcILM32E8TpMqyS57nHSmDc62ugtzPFb9CwOfNaabJe/1jP2ZowswmWzR5oa0pMq3VlCJ9vnxVmnWDNm3UiyjH27h2HN30UIIfDXTw/im39ej/+4dDq+fe7kQVUjhXE/pflZePD6+bhp6Xhc9/Ba/OHD/ZzpkWGSgNGTnDDw8lYaXkaPf7PaT0bvjiIbRbBlj9j507WVK7W3Q11g28yj5ubHi1K0LF+4P888ZZj2zgZkxTnJqueRUiYGyfF7UV2aF2UonDO10rQemc+mG1Bvb7MEGGpOH2+egToZJSZSoTqNKbfnAZFR3oszRsYasXJ79Yw9IrLsnUxFRstqG9kSF0+Ivb5E4UzSbmJEcfwZIPWSB5nBBlqG0hsI4gcvbMX9H+zHk/+wCJfPiyPOmWGSABHh+sXj8Nxti/HM+iO49bENaOe6aQyTMoi0/WFGOpnZ5J2ZQWiVcdKaltwsHaVEoQ9ePGsERpdFK7G6v5NIdl2lZNaMik7AkRyN304Il/L6jyoZUCiteNaSnbRGb3e7l2t4sfn10TLqL5gx3NbEQjLJ9nlx3rSquH+v7CKt8Z2VhLA+uxkQ7TCswPo9leg1GqbjkS+y6K20wqiSXN2w7VRiN9SVDTQX0NjWg2seXIOapk68fOdSzKrmNPqM80wdXoSXvrUUHiKs/P3H2NcYm4aaYRhtlk6qiHwuyPaZatU04EJT/D9+o0CtgCu9EnaMIDNPi7IWkVY4fqwhEP1/tQetIl87c6ITlOYNyJKI3aj87SIND4N+Ygcdb6rZWAJMPWiJOqlkr2AqonyIYr1oOX6v4TpOtfKut6dePUKjpSTZPk9MaKKdZDdGxnBJXpahh3W5Yo2fejJDSblOxlEjzphYYbrPpbNH2hr7iQ4HteEUSWRCxmsyrTJxWAEWjCszNZqtMLIk1gtnJCN70DKM93c34qLfrMbMUcV47KbTXVXThGEKsn24/6un4qr51bjivk/wxrY6p0VimIxj+sgi8zVj8geFXp2IEq1WBtQKaJnCEDKamVZ7fOS1WNWleRhTlhcp/aK3/sWoCaNKcrFQtRYqlROUdkLnsn1eTNcIR1OSl2U/eZfWOFCv48uXjqsnrplxQATT0HSzvlCHK6qVWDvJKNTYDcIrzw/LYiTz2VPMywBMG1GEolz710wrjNdWDS6Drl42qSIqfFEdQmvV0ynvp9W3akNCvpZaIalCtY/dcFMteeW5gnjsoEBwoEVOLfgZq4oKmDgsHyNLcjXrQBplxrQrPxtoDtEXCOFnr+3Ed5/ajJ9eMRM/XjkzKW5uhkk2RITbz5qE3197Ku56YSt+8eYuXpfGMDawMvGmVl68RHHN7MqGmNqBEuUNQnSWvwKDLMFqGeS1b5OrCjBPcQw9h42Z90AdAmm1zZfOHmk7TCmeJBayPEqpZMNshIVwPSDczoEDmu8vK8UeHbdZjt8sg2d0iGOZhlcyeh1N7MWTDcmiHL+ldWOG8tgcx+qxtHRy2NNj1G61/lSucc8NS9IE+FlTKk2NdyVGzfd4KMozKI+VsvwsTB9RZDkVvtE51AZafpZ5yKZyLZvVy7ds8jBDL2c8BCRdI56jqp+780aXYsrwsAdVLeYpVdqeVQCR38hMrirEaePKNLONTq4sRHVpnq11e3qwReAAh5u78KU/fIr1B1vw928vxYUzRzgtEsOYsvyUYXj5jqV4d2cj/uEv6zVTbDMME01hjg9+r8dQyRFCRJRYAYHTx5djeHFOXB40OSRRnb69VLVIXw7Z8nk8Md4AZer0GFltyhOTtdFCeB6gX8xYzqTn9ZBmdkmlMWDFMM7P8sVk5zNj8cRyjCvPj1oXZsRpCi+hFf1V3kdpoCl/N3W4tnGg9KyV5mUNeFU0LprZ2kCZaSOKLK0bM+KimeoMk8ajSEBoehqnjyiO8tQaeXe0PFxGbU6ld8Zv4vHUmsQoz8/G5KpCG6UL9K+1X+MYeqWbtM5mx8A2zFYrHd3OerklFsIw9c8XTXGuXzeUdVockzeyp3tCxUB7PBQO05w/NjZLqt3yRWygpZFQSOCvaw7hkntXY+mkcjx9y2LD9MUM4zbGlOfh+dvOQJbPg8t//zH2N3U4LRLDuBrZ2JGVBS1vhprhxTnI8hkbdUacecowjFAZD3pO79J8f0xiDSuZ4NS76HrQDBRMuX0Lx5fFzPLrvRvzs60nClB7XLQUzaJcP/IsHFP5W7/XgzmjSyJeG7OMcGZhYuqZePlcZQUD65OUR9CLtjlHKl5OFA5fu2zOSMPzGmHkVbWL3+uJ8uCaIQQ0QxFzs7yYMXLAcL909sgoz8d506oiIbda5Pi9muO0oiAby6TQQuXEgBzem2hiRJ9JxlW1/XbutCpMHa7v0ZFReoSNhpjPq74PTA8dvb+N/awYc1YNtFy/N6nZH5XXQSlnvJkv5YRIysy1Wu0fJ41Ju2dhAy1NHGnpwlcfWYs/fnQAf/rGQvzzBVO5vhmTkeRn+3DfdafiynmjcOV9n+D9XY1Oi8QwrkMrNA4Iz67qpawP70+an5WYKTgleVkxCptR0gg7Cpu+IaZzbI1t56rCq0YU58ZdwPrS2SOxQmGIjKvQn/Rs6471+nvIrN7cwH56zB1dorldXjuldTwj5HMVZPlw0axwhI0VxVdvFzPFUH1Nx1fka6aDB8Lew3gQOp/1OGNihSVFXtnm/Gwf8k08g1p61/yxpSjI9uHS2SMxIYnZEOWahma6nvr7gmyfJc+Z0rOpNT7mVIfHpdpAtJtNUH1ovSLopt5xje/1krYMK8jGF6YPPCeIyH75BdXuel7DVDNHej7YtQPZQkgxQgg8vvYQLr53NWaMLMJr31nmSHpPhkkmRIRvnTMZv/zSHHznqU34/ftiS4dqAAAcd0lEQVT7UlJ/hWEynYihplh0rxdqtWzysKgZ42Qu57CTVt3oTtZaB7R88jBM0VnDoT7vaePKNL1C6v0I2jWj1Iqo1xNeqyd73IzWGXX2xYYYEVlLxjJhWIFt4yQ3y5qKVZzrjwpPVCvQWV4PKi3UGIv8Ps5xI3t3ywuydQ2EZHrW9PD7PPB6yNIktlFRa2V2RNkzNrw4JyaEV06SE2sAWO9ILeNhRHFuVDZXGZ/HE5WYp7IwW3M/OwycPnz35vq9Ec+w3A/x1v5S35vqUhpqYnVc/SeKntFFGmtw1WvBlCgTnowpy4sYp3ZYrhPabXY/KT3gRrva1ZHYQEshR0904fpH1uGhVTX4442n4e5LpietjgPDuIHzplfhhdvPwHMbjuKOJzbyujSGMUFr9lp+bavDH+NJEjLguYv+7aTKgkiGuKgwQRF9ngXjYtdOyJw/fbjm+qfS/KyYMCoZpf5VVZQT1UbleWVFTUR+R5ikM7uuxaxRxVg2eZiqz1TGnGZ/WjMEfB6KybYYOYKNy6R1rrwsHy6Zrb8W/aJZIyzVd9Mqdg7EKobKPvJ5PJgoresy86ysnDvKNHPlWVMqNdPGW1VOs7yeyNo+rSQMQNhjKYc6GnmGZe8jEJ2oxSzxhmzM2bmuuSrdbqZ0DK1kJZfMHoF5Cq8rEWnuZwYRRco2qK8dEUVuJq+HcN60qohHzwrDCrMjYal2PVe6RldEtnAG12kjiizX1yMYeyKVRmRBti9Sv9EOpdKzKZFaboZrjaV/rRauZwMtBYRCAn/99CAu+s1qTK4qwOvfWW740mOYTGZSZSH+dscSAMAl967G5iMnHZaIYZxDHZaViBNMndgjEbweQkleFi6YMRxzDZJi5Gd5UaazXig3yzugfFmcDNYyMlUl3yLyae0DDKzhAPTX82T5PKbr+7S8Qh4yLjOQmBcz+scLx5fFnGuJynMyu7pE85yJyKG+VBfOGAixnV1djCrJ8NS6LnbPW5zrR56Jl83IVstReJf1vGNjy/P1yzpYkNdslwnDCjBjZBFmSyUfrNiWcqbJuaNLcOHM4abhmaX5WfZS9esQMXokbT6S0h5hQ2Pq8CL4vbF13NQowwmBcIjpcmlCR5lsR32fKj2qspE4vCgH80aXxtz76omgU6oKMUGnD8yukdobqJVQx2zC4bLZ2ms01dlZzaIPjMbHbIUnT97Pau4JZwIyBzH7Gjvwgxe2oKWzD4/eeFpU9iaGGawU5/rx+2tPxeNrD+OrD6/FbWdNxC3LJ+jOqjPMYEXvZS17EOwovLNGFWPGyGK8uuVYQjIp6/WYRXGUmCRzsGsnKNurLnqt/G5yZWE47b7fi8IcX5Qyp/R4FOX4Y9awWWVOdQlOqSrEB7sbMXFYAfY3dUTWtkwZXojd9e1xHTcRG06daVLP05TMgtB6SUbGVeTjeEdv0s6jRBj8Dwi3T912PQ9a1D4Wk9V4oxR48760470FBsoWFOf6VSUMtPF7PVHKe6JotSjH79UOC9TYWfaMGvX42PL8yHpLOalPT38QkIaMHJ5NRBhTnocx5Xl4aXNt5PdaY9uqjiBfsjnVJegLhmLS4o8ozsGJrj5keT2WQznN1vmNLc/HmLI8W/kiYoxSxWchBC6cORx+jwc1x80TrLH2lCT6gyH87r29WPm7j7BoQjn+/u1lbJwxQwoiwlcXjcVzty3G69vqcMV9n2BXfZvTYjGMo6h1QfVs7JiyvKgsYNG/tbYwXrl2QhlGBISVtLOn6hfxTfXKUVmeBePKYhRS5Qx3li8c2ub1EM6ZamyA2VkHpUyfneXzxPS13L16hZetJFSwovAX5fgtZzKUPTdqQ8pqWn81+QZhico1j3aOX5rvTygUTI1WD1rxXBmFOCqJWttpUSa7rJw7ynSCI5lEezqjW3X6hOTqn2dPrcRMxZrQ08aV4bRxZbYSjtiZZFBnnZXPM64iX7Nm2eiyPFwwYzjOnloZ8RbOqi7WTWhihVy/N8Yrry7uPbIkF1VFOZYSp+T4vcj2eS2XTmAPWhLYcvQk/uW5Lcj2efDcbWfEVU+BYQYLU4cX4cXbl+DBVTX44n2f4PrF43DH2RN1FSCGGcrMi0OBUOs5pQahfclWRq3qWH6vB/3BUMQgLc/PijE4rB4rEe/RKVWF2FnXFpPGP3LsSP0oY0U/HhGUvzEykpUU5PhQmOPHijkjY9pdXpCN2pPdur+V+1r5swtmDIff68GrW2J/t3LuKABAa9fA2uH5Y0strXfL9Xtx9hRrbQIQNROgtY6NCDGzBaX5WUCT5cMOHGeQsmhCORraenDgeGfMd+rI42S/b4uScDyvJ7xmzkouBqXhvWRSheEkgx4F2T4UZPuw8fAJ27/VIi/LhzNUddlkR8zosrwY4w0YeL7I96Ed2IOWAN19Qfz07ztw7UNrcdX8arxw+xI2zhgGYeXsjrMn4eU7l2JPQzvO/r8P8MTawzHFcxlmsCBnndOraRX5f4rOf45FAyBZGIWfXTBjOJZJa3Jko0FrHYcdZXqMSeY4M7QUvNFl4TAsIL5aV/JP9Jph91qvnDsqkogkHqPU7/XEKII5fm+UF3amRgFw5bWsLs2LmuG3k/3TKiNLcnHp7JHI8XsjSq1WWNqoktyIEamH2oNmRV67TXJLfuKqopwoL2yWzxMZZLIhoOeNTxVW+lJ5iaosGP8AEFC40CoKsi0XVtfDdop+mNevU6NleCojGezKwB60OPlk33Hc9cJWjCnLw2vfXhZ5yDMMM8DEYQV49MbTsHpvE376952474N9uOXMifjS/GrOaMoMKgqyfTh/ehV21BmH9cov7BHFuUmd7debMU+2gm3FcFDe2+RBjJI9EIaZPneHlkGpDEtSKvoF2b64suqlGu0slNGYPVezddafabF88jBd76zSC6Zev2dFTq+HcMGM4ejqC+DtHQ2YO7oER090WZZNRt0eLSVYncJeXXQ8E4m5p6QmVRXlmBq1RldHw5FpCb2kLfEwb3SpZqHyeDl3WpWlUhoyQoQ93gWqSZ1kPq2shBOzgWaTpvZe/PfrO/HerkbcffE0XDW/Oq0vGYbJRJZNHobXvl2B93Y14nfv78Nv3tmLr5xWjatPG2NaU4VhMgWjBe8Rb4v0vrCT8vrcaVV4d2eDpX3ldUFWX0tW10XZRSsjYLpRZmMz85ApDVm7SUjkn2b7vDhnaiXe2F4PIQTGlOWhP6h/YjNFWk11aS5yLNZWU3PRzBF4fVudLU+hnnGmDr8cXpQTZaAtGFeK1u5+rKlpjmzT87gmOoEwbXgRJlQMGAdaBppeUhQgPs9pPPg8HgSMirYlSLLUULvdISf+kcsexMPU4UVR69WT7fAwWrM6qbJAM6lLMkI6Aeh6/qxkdmcDzSLBkMATaw/hF2/uxvkzhuOd750Zk32JYRh9PB7CedOrcO60Sqw/dAKPrzmEL/z6Q5w+vhxXnzYa50yrtJT9imHcTCoUPj0FI8vrQZ8ibPiSWSNiCz4bKG5a65xkinL8lrLomaGdZl8Oe7R4jDjOe8bEiqg1IWbXpbo0PKMdbyZHIFxfLsvniaxnKy/ITqonzmNQi80MIyPF6Dst1Ne0JC8rytjM8Xtjxt3c0aXo7I0tFJ7j92Le6NK4QtCAcJ8olWD1cS6aOUKzfSvnjsJLm2tNx3iu32urSLge50ytTMr9pIedZB1KsrwDdfDiYe6YEvT0Ze7ShUQMSzP0xp5V2ECzwOYjJ/Hvf9uGvkAID99wmq2ZT4ZhoiGiSAaoE519eH7jUdzzzl786/NbcMnskbhi3igsGFtqOdMRw7iJU6oKU+aVUrNwfFmUQab04MkKm5GBpla0lbO6cg0kPcwMHiOFUf7GjjGbaKSKWjlWn5uIMLosz5ZH3++V+zizn1V5WT6smKNdEypZlOVn6dapS6bHpDQvK2odlpGCXFmYY5oR9HxFzbhESHQNlUzMPSP9P97XpbKYN2A/xDHb5+WJVR0SMc4ANtAMaWrvxa/e3oNXPz+Gb587GTcuGZex8coM40ZK87Nw07IJ+ObS8dhZ144XNx3Ft57YiCyfB1fMG4XL540yLfjJMG4iN8uLMeV5aOnsS/m5rHhnbKXBVnyO16NhBXnyJWAQ/qfErv0zrjw/psh3op5NrX7M9nkjnqOF48siyv60EUXo7c8sr0Kyjcx4PTqJOply/F6cZTG75OKJ5YmdzAXIuTSScf1OG1eGYye7DTOF2mVSZYFpBsZEnjUXzRyB7cda4/69VSoKstMeNccGmgYdvQE8tKoGj3x0AF+YXoW3vrfccuE7hmHsQ0SYPrII00dOx10XTcMn+4/jxU21WPHbjzCpsgBXzBuFS+eM5LBiJmNJ11oXGVlfmzHKemZhdSHpZJzfiP4UrcmZMzq2AHCqu1+pI2jVaWKYVCB7hpNx644syUVda/KMM8BaCOGEinxUFMQXdZDl88RVqkSPRRPKNT29S1SJZtIBG2gKegNBPPPZEfzm3b2YNqIIT9+yKKXxqQzDxOL1EJZNHoZlk4eh6/IA3tregBc31eJnr+/C0kkVuHzeKJw/vYqzQDKupijHF5WpK9ufWPTF1OFFyPV7senICVveCatrls6eWmlrYXyiBs+CcWVJWdtjhfwsH6qK4juX30sIhAbCGTOddE0UxOPQmVRZoBsGyWgj4vCg5Rq+O9M/zj0eSmuBbyOslgFIB2ygAejqC+DJdUfw0KoaVBZl456vzMPSyem3lhmGiSYvy4fLpVDHxvYevPJ5HR5ctR8/fGErLpo5HFfMG4VFE8p5vRrjOnxeT9SarqIcPy5Rrfeww5ThYa/MpiPWiq6OLM7FCRthlsnKWiZjdkdaSTOdLM6bHp2VMR6DLdPXmWUCPCFuTsxaSpu/P3/6cNv1vRhnGNIGWn1rD5767DD+8ukhTK4swP9eNRvLJlfwg5hhXEhlYQ6+uXQ8vrl0PPY2tOPFTbX4/rOfQyCckeuKeaMiSizDuBGjNPzJpjjPjzNSFJbjIUJ+trEHW36PJuNtGvd6Jh3UBttQIpWZBJWwFpUehE2XaLKSlTCpZ8gZaMGQwEf7juPxNYewam8Tzp1ahQevn2+pJgHDMO5gclUh/uXCqfj++VOw7mALXtxYi6vu/wTVZXm4fO5IrJg7kteNMoOWkrzkervsclmKM/5pMaokF+09/Wk/L0/Yxgf3W3pI99pWJn0MCQMtEAxhTU0LXttWh7e21yPH78U1C8fg/10xM+6aIgzDOI/HQ1g0oRyLJpTjv1bOwHu7GvG3TbX41dt7MH9sKS6ZPQLnTatyVVw5wySC3eLGTpFs9XxYYTaGFRqn/mfMKcl1x1ofhmGMGZQGWk9/EDvr2rD2QAvW1jRj/cETKM3PwsWzRuCRG07D7Opint1hmEFGjt+Li2eNwMWzRuBkVx/+vrUOb2yrx3+9vAPTRhTi3GlVWDyxHLOri7luC8NkEPy6Tg7pNO75kqUGtccs2R60ysLspGdyZOIj7Qba5iMncbKrD0ThqHKicHx5+F8J1TYi5WdpBwB9gRCaOnrR1N6LxrYe1BzvxN6Gdhxu6UJlYQ5On1CGL0wfjn+7dDomVOSzUcYwQ4SSvCxcd/pYXHf6WLT39GPVnuN4b1cjnt1wBA1tvZg7ugTzRpdgyvBCTBleiEmVBWy0MTEQkRfAegC1QohLnZYnk0jW63bRhHIM4/IaDKNJKMkWmt1i7UzqSLuB9sz6I9hW2wohwotVhYD0eWCxY9R30vbIEFRsy/J5wmEPBdmoLMrB2VMqcfPyCZhcWeCalJ0MwzhLYY4fl8wegUtmhzPoHTvZjc8OtmDL0Va8sLEWu+rb0dLZi6qiHIwozsHIklyMLMlFaV4WCrK9KMjxIT/Lh4JsH/KzffB7PfB7CV4Pwe/1wOcl+DzhbT6vBz4PIdvn4QmhwcF3AOwEYL2YGJNUBmN4clVRzqBfO8SPv9SgXn86yIfRkCbtBtrPrpiV7lMyDMNEGFmSi5VzR0WF+5zo7EPtyW4cO9mNutYeHGvtxqHmTrT3BtDZG0BHTwAdvQF09gXQHxAIhEIIhAQCQYH+YPhzMBT9qvR7CVleD/w+D7K8HmT5pD/pc7bPgxy/N/KX61f83+dBTpYXOT7puyxP5HO234PcqN95kSP9lg3D5EFE1QAuAfBTAN9zWJyMg4hQXZprUnPJ/Ywpy0NzR29Sj7loQnlSj8cMHUrysqLeXXazODKZw6Bcg8YwDGOH0vwslOZnYeao+OvwhEIibLSFQugLhP96AyH0BQf+3y997g2G0NsfQk9/cOAvEEJ3XxA9gSDaegJobO9Fd+R75b4h9ATCn8P7h48pIxtrOT4vvHHUh5Nf+K/cuRTlQzu07B4A/wKAazfEyfyxmZ8dOVy6g4eAXeSJoupSDpdLJWyeDV7YQGMYhkkCHg8hy0PIggfpjrAOhQR6AyGFQRc25KyuTxAiOiSJCCjKdTaVu5MQ0aUAGoUQG4joLIP9bgZwMwCMGTMmTdIxTGaQKRlHM5lRJbno6g06LQaTAthAYxiGyXA8HkJulpeLkCaPJQBWENHFAHIAFBHRY0KIryp3EkI8COBBAFiwYAFPZjMMk1Zy/F7Mqo4/8oNxLx6nBWAYhmEYNyGE+IEQoloIMQ7A1QDeUxtnDMMwDJMq2EBjGIZhGIZhGIZxCRziyDAMwzA6CCE+APCBw2IwDMMwQwiyk6KTiJoAHErCeSsAHE/CcQYj3DfGcP/ow31jDPePPqnsm7FCiGEpOrZrIKJ2ALudliMJDJb7hNvhHgZDGwBuh9sYDO3QfT/aMtCSBRGtF0IsSPuJMwDuG2O4f/ThvjGG+0cf7pvEGSx9yO1wF4OhHYOhDQC3w20MlnbowWvQGIZhGIZhGIZhXAIbaAzDMAzDMAzDMC7BKQPtQYfOmwlw3xjD/aMP940x3D/6cN8kzmDpQ26HuxgM7RgMbQC4HW5jsLRDE0fWoDEMwzAMwzAMwzCxcIgjwzAMwzAMwzCMS0ipgUZEXiLaRESvanyXTURPE9E+IlpLRONSKYsbMemfG4moiYg2S383OSGjUxDRQSLaKrV9vcb3RET3SuNnCxGd6oScTmChb84iolbF2PkPJ+R0AiIqIaLniGgXEe0kosWq74fsuAEs9c+QHTuJQEQXEtFuaVzd5bQ8ehDRaCJ6X7r224noO9L2HxFRreK6X6z4zQ+kdu0moguckz4arecgEZUR0dtEtFf6t1Ta7sr7noimKPp8MxG1EdF3M+F6ENGjRNRIRNsU22z3PxHdIO2/l4hucEk7fiE9I7cQ0YtEVCJtH0dE3Yrr8oDiN/Ol8bhPais53AbbY8jp55hOO55WtOEgEW2WtrvyWiQVIUTK/gB8D8ATAF7V+O52AA9In68G8HQqZXHjn0n/3Ajgd07L6GDfHARQYfD9xQBeB0AAFgFY67TMLuqbs7TG1FD4A/BnADdJn7MAlKi+H7LjxmL/DNmxk0CfegHsBzBB6tPPAUx3Wi4dWUcAOFX6XAhgD4DpAH4E4Psa+0+X2pMNYLzUTq/T7ZBki3kOAvhfAHdJn+8C8HPps+vve2kc1QMYmwnXA8ByAKcC2BZv/wMoA1Aj/VsqfS51QTvOB+CTPv9c0Y5xyv1Ux1kHYLHUxtcBXORwG2yNITc8x7Taofr+lwD+w83XIpl/KfOgEVE1gEsAPKyzy0qElQUAeA7AuRlr5caBhf5hjFkJ4C8izBoAJUQ0wmmhGOcgoiKEH/CPAIAQok8IcVK125AdNxb7h7HPQgD7hBA1Qog+AE8hPM5chxCiTgixUfrcDmAngFEGP1kJ4CkhRK8Q4gCAfQi3160o9Yo/A7hcsd3t9/25APYLIQ4Z7OOa6yGEWAWgRbXZbv9fAOBtIUSLEOIEgLcBXJh66QfQaocQ4i0hRED67xoA1UbHkNpSJIT4VIQthL9goO0pR+da6KE3hhx/jhm1Q7IPvgzgSaNjOH0tkkkqQxzvAfAvAEI6348CcAQApBuhFUB5CuVxG2b9AwBflFzszxHR6DTJ5RYEgLeIaAMR3azxfWT8SByFsaIxmDDrGwBYTESfE9HrRDQjncI5yAQATQD+SOHQ4YeJKF+1z1AeN1b6BxiaYycRMnJMUXhZwTwAa6VN35LeN4/KoWlwd9u0noNVQog6IGyMAqiUtru5HTJXI1r5zLTrAdjvf7e3BwC+gbAXRma89Pz8kIiWSdtGISy7jFvaYWcMuf1aLAPQIITYq9iWSdfCNikx0IjoUgCNQogNRrtpbBsSKSUt9s8rAMYJIWYDeAcDs1JDhSVCiFMBXATgDiJarvp+yI4fmPfNRgBjhRBzAPwWwN/SLaBD+BAOj7hfCDEPQCfCYTZKhvK4sdI/Q3XsJELGjSkiKgDwPIDvCiHaANwPYCKAuQDqEA4lAtzdNrPnoBI3twNElAVgBYBnpU2ZeD2M0JPb1e0horsBBAA8Lm2qAzBGen5+D8ATUmSCG9thdwy5sQ1KrkH0BEYmXYu4SJUHbQmAFUR0EGE36TlE9Jhqn6MARgMAEfkAFMO6izbTMe0fIUSzEKJX+u9DAOanV0RnEUIck/5tBPAiYsM4IuNHohrAsfRI5yxmfSOEaBNCdEifXwPgJ6KKtAuafo4COCqEkD0CzyFskKj3GZLjBhb6ZwiPnUTIqDFFRH6EjbPHhRAvAIAQokEIERRChBB+38jPFNe2Tec52CCHLkr/Nkq7u7YdEhcB2CiEaAAy83pI2O1/17aHwglLLgVwnRQqBykssFn6vAHhNVunINwOZRik4+2IYwy5+Vr4AFwJ4Gl5WyZdi3hJiYEmhPiBEKJaCDEOYbf9e0KIr6p2exmAnLHnKmmfjLRy7WKlf1Tx8SsQXiswJCCifCIqlD8jvGB3m2q3lwF8jcIsAtAqh1YMZqz0DRENl9dzEtFChO/z5nTLmm6EEPUAjhDRFGnTuQB2qHYbkuMGsNY/Q3XsJMhnACYT0XjJE3I1wuPMdUjX9hEAO4UQv1JsV75vrsDAM+VlAFdTOOvyeACTEV6A7ygGz0GlXnEDgJekz26/76O8A5l2PRTY7f83AZxPRKVSCN750jZHIaILAfwrgBVCiC7F9mFE5JU+T0C4/2uktrQT0SLpHvsaBtruCHGMITc/x84DsEsIEQldzKRrES++dJ6MiH4MYL0Q4mWEXxJ/JaJ9CHvOrk6nLG5E1T/fJqIVCLvXWxDO6jhUqALwoqQn+gA8IYR4g4huBQAhxAMAXkM4M9Q+AF0Avu6QrOnGSt9cBeA2IgoA6AZw9VCZ/ABwJ4DHpRdMDYCv87iJwqx/hvLYiQshRICIvoWwYukF8KgQYrvDYumxBMD1ALaSlK4awA8BXENEcxEOBToI4BYAEEJsJ6JnEDbkAwDuEEIE0y51LHrPwc8APENE3wRwGMCXpP1de98TUR6AL0Dqc4n/dfv1IKInEc76WkFERwH8J4D/gY3+F0K0ENFPEDYOAODHQoi0RlLptOMHCGc5fFsaY2uEELcinGTpx9LzMQjgVoW8twH4E4BchNesKdetOdGGs+yOIaefY1rtEEI8gtj1mYBLr0UyIX73MgzDMAzDMAzDuIOUFqpmGIZhGIZhGIZhrMMGGsMwDMMwDMMwjEtgA41hGIZhGIZhGMYlsIHGMAzDMAzDMAzjEthAYxiGYRiGYRiGcQlsoDEMwzAMwzAMw7gENtAYhmEYhmEYhmFcAhtoDMMwDMMwDMMwLuH/A0ZxLhmTYBXfAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 864x1008 with 14 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import arviz as az\n", "\n", "data = az.from_numpyro(mcmc)\n", "az.plot_trace(data, compact=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like our model learned personalized alphas and betas for each patient!\n", "\n", "### 4.2. Visualizing FVC decline curves for some patients\n", "Now, let's visually inspect FVC decline curves predicted by our model. We will completely fill in the FVC table, predicting all missing values. The first step is to create a table to fill:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "pred_template = []\n", "for i in range(train['Patient'].nunique()):\n", " df = pd.DataFrame(columns=['PatientID', 'Weeks'])\n", " df['Weeks'] = np.arange(-12, 134)\n", " df['PatientID'] = i\n", " pred_template.append(df)\n", "pred_template = pd.concat(pred_template, ignore_index=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Predicting the missing values in the FVC table and confidence (sigma) for each value becomes really easy:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "PatientID = pred_template['PatientID'].values\n", "Weeks = pred_template['Weeks'].values\n", "predictive = Predictive(model, posterior_samples, \n", " return_sites=['σ', 'obs'])\n", "samples_predictive = predictive(random.PRNGKey(0), \n", " PatientID, Weeks, None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now put the predictions together with the true values, to visualize them:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Patient</th>\n", " <th>Weeks</th>\n", " <th>FVC_pred</th>\n", " <th>sigma</th>\n", " <th>FVC_inf</th>\n", " <th>FVC_sup</th>\n", " <th>FVC_true</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>ID00007637202177411956430</td>\n", " <td>-12</td>\n", " <td>2220.162598</td>\n", " <td>159.290878</td>\n", " <td>2060.871826</td>\n", " <td>2379.453369</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ID00007637202177411956430</td>\n", " <td>-11</td>\n", " <td>2210.083496</td>\n", " <td>157.518021</td>\n", " <td>2052.565430</td>\n", " <td>2367.601562</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>ID00007637202177411956430</td>\n", " <td>-10</td>\n", " <td>2213.199951</td>\n", " <td>154.847916</td>\n", " <td>2058.352051</td>\n", " <td>2368.047852</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>ID00007637202177411956430</td>\n", " <td>-9</td>\n", " <td>2209.025391</td>\n", " <td>153.300079</td>\n", " <td>2055.725342</td>\n", " <td>2362.325439</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>ID00007637202177411956430</td>\n", " <td>-8</td>\n", " <td>2203.191895</td>\n", " <td>156.085449</td>\n", " <td>2047.106445</td>\n", " <td>2359.277344</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Patient Weeks FVC_pred sigma FVC_inf \\\n", "0 ID00007637202177411956430 -12 2220.162598 159.290878 2060.871826 \n", "1 ID00007637202177411956430 -11 2210.083496 157.518021 2052.565430 \n", "2 ID00007637202177411956430 -10 2213.199951 154.847916 2058.352051 \n", "3 ID00007637202177411956430 -9 2209.025391 153.300079 2055.725342 \n", "4 ID00007637202177411956430 -8 2203.191895 156.085449 2047.106445 \n", "\n", " FVC_sup FVC_true \n", "0 2379.453369 NaN \n", "1 2367.601562 NaN \n", "2 2368.047852 NaN \n", "3 2362.325439 NaN \n", "4 2359.277344 NaN " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(columns=['Patient', 'Weeks', 'FVC_pred', 'sigma'])\n", "df['Patient'] = le.inverse_transform(pred_template['PatientID'])\n", "df['Weeks'] = pred_template['Weeks']\n", "df['FVC_pred'] = samples_predictive['obs'].T.mean(axis=1)\n", "df['sigma'] = samples_predictive['obs'].T.std(axis=1)\n", "df['FVC_inf'] = df['FVC_pred'] - df['sigma']\n", "df['FVC_sup'] = df['FVC_pred'] + df['sigma']\n", "df = pd.merge(df, train[['Patient', 'Weeks', 'FVC']], \n", " how='left', on=['Patient', 'Weeks'])\n", "df = df.rename(columns={'FVC': 'FVC_true'})\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's see our predictions for 3 patients:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/pdh21/anaconda3/envs/xidplus/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " FutureWarning\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x360 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def chart(patient_id, ax):\n", " data = df[df['Patient'] == patient_id]\n", " x = data['Weeks']\n", " ax.set_title(patient_id)\n", " ax.plot(x, data['FVC_true'], 'o')\n", " ax.plot(x, data['FVC_pred'])\n", " ax = sns.regplot(x, data['FVC_true'], ax=ax, ci=None, \n", " line_kws={'color':'red'})\n", " ax.fill_between(x, data[\"FVC_inf\"], data[\"FVC_sup\"],\n", " alpha=0.5, color='#ffcd3c')\n", " ax.set_ylabel('FVC')\n", "\n", "f, axes = plt.subplots(1, 3, figsize=(15, 5))\n", "chart('ID00007637202177411956430', axes[0])\n", "chart('ID00009637202177434476278', axes[1])\n", "chart('ID00011637202177653955184', axes[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results are exactly what we expected to see! Highlight observations:\n", "- The model adequately learned Bayesian Linear Regressions! The orange line (learned predicted FVC mean) is very inline with the red line (deterministic linear regression). But most important: it learned to predict uncertainty, showed in the light orange region (one sigma above and below the mean FVC line)\n", "- The model predicts a higher uncertainty where the data points are more disperse (1st and 3rd patients). Conversely, where the points are closely grouped together (2nd patient), the model predicts a higher confidence (narrower light orange region)\n", "- Finally, in all patients, we can see that the uncertainty grows as the look more into the future: the light orange region widens as the # of weeks grow!\n", "\n", "### 4.3. Computing the modified Laplace Log Likelihood and RMSE\n", "\n", "As mentioned earlier, the competition was evaluated on a modified version of the Laplace Log Likelihood. In medical applications, it is useful to evaluate a model's confidence in its decisions. Accordingly, the metric is designed to reflect both the accuracy and certainty of each prediction.\n", "\n", "For each true FVC measurement, we predicted both an FVC and a confidence measure (standard deviation $\\sigma$). The metric was computed as:\n", "\n", "\\begin{align}\n", "\\sigma_{clipped} &= max(\\sigma, 70) \\\\\n", "\\delta &= min(|FVC_{true} - FVC_{pred}|, 1000) \\\\\n", "metric &= -\\dfrac{\\sqrt{2}\\delta}{\\sigma_{clipped}} - \\ln(\\sqrt{2} \\sigma_{clipped})\n", "\\end{align}\n", "\n", "The error was thresholded at 1000 ml to avoid large errors adversely penalizing results, while the confidence values were clipped at 70 ml to reflect the approximate measurement uncertainty in FVC. The final score was calculated by averaging the metric across all (Patient, Week) pairs. Note that metric values will be negative and higher is better.\n", "\n", "Next, we calculate the metric and RMSE:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE: 122.1 ml\n", "Laplace Log Likelihood: -6.1375\n" ] } ], "source": [ "y = df.dropna()\n", "rmse = ((y['FVC_pred'] - y['FVC_true']) ** 2).mean() ** (1/2)\n", "print(f'RMSE: {rmse:.1f} ml')\n", "\n", "sigma_c = y['sigma'].values\n", "sigma_c[sigma_c < 70] = 70\n", "delta = (y['FVC_pred'] - y['FVC_true']).abs()\n", "delta[delta > 1000] = 1000\n", "lll = - np.sqrt(2) * delta / sigma_c - np.log(np.sqrt(2) * sigma_c)\n", "print(f'Laplace Log Likelihood: {lll.mean():.4f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What do these numbers mean? It means if you adopted this approach, you would **outperform most of the public solutions** in the competition. Curiously, the vast majority of public solutions adopt a standard deterministic Neural Network, modelling uncertainty through a quantile loss. **Most of the people still adopt a frequentist approach**.\n", "\n", "**Uncertainty** for single predictions becomes more and more important in machine learning and is often a requirement. **Especially when the consequenses of a wrong prediction are high**, we need to know what the probability distribution of an individual prediction is. For perspective, Kaggle just launched a new competition sponsored by Lyft, to build motion prediction models for self-driving vehicles. \"We ask that you predict a few trajectories for every agent **and provide a confidence score for each of them**.\"\n", "\n", "Finally, I hope the great work done by Pyro/NumPyro developers help democratize Bayesian methods, empowering an ever growing community of researchers and practitioners to create models that can not only generate predictions, but also assess uncertainty in their predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "1. Ghahramani, Z. Probabilistic machine learning and artificial intelligence. Nature 521, 452–459 (2015). https://doi.org/10.1038/nature14541\n", "\n", "2. Rainforth, Thomas William Gamlen. Automating Inference, Learning, and Design Using Probabilistic Programming. University of Oxford, 2017." ] }, { "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
tuanavu/coursera-duke
statistics_with_R/1_probability_intro/lecture/week1/.ipynb_checkpoints/Designing Studies-checkpoint.ipynb
1
14237
{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1\"><a href=\"#Introduction\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction</a></div><div class=\"lev1\"><a href=\"#Data-Basics\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Data Basics</a></div><div class=\"lev2\"><a href=\"#Numerical-variables\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Numerical variables</a></div><div class=\"lev2\"><a href=\"#Categorical-variables\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Categorical variables</a></div><div class=\"lev2\"><a href=\"#Relationship-between-variables\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Relationship between variables</a></div><div class=\"lev1\"><a href=\"#Observational-studies-and-experiments\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Observational studies and experiments</a></div><div class=\"lev1\"><a href=\"#Sampling-and-sources-of-bias\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Sampling and sources of bias</a></div><div class=\"lev1\"><a href=\"#Experimental-Design\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Experimental Design</a></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- This unit will introduce you to the basics of collecting, analyzing and visualizing data as well as making database decisions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Basics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- We will discuss data basics or more specifically, we will touch on three main concepts, observations, variables, and data matrices, types of variables, and relationships between variables. \n", "- Data are organized in what we call a data matrix, where \n", " - each row: represents an observation or a case.\n", " - each column represents a variable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 7.45.16 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Q0zu3/data-basics) 0:43*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two types of variables, numerical and categorical. \n", "- **Numerical** in other words quantitative variables, take on numerical values. It is sensible to add, subtract, take averages, etc., with these values. \n", "- **Categorical** or qualitative variables, take a unlimited number of distinct categories, these categories can be identified with numbers for example, it is customary to see the gender of variable coded as zero for males and one for females. But it wouldn't be sensible to do arithmetic operations with these values. They're nearly place holders for the levels of the category of variable. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 7.48.16 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Q0zu3/data-basics) 1:16*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numerical variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Numerical variables can further be categorized as continuous or discrete. \n", "- Continuous numerical variables are usually measured, such as height. These variables can take on any number of infinite values given within a given range. \n", "- Discreet numerical variables are those that take on one of a specific set of numeric values where we're able to count or enumerate all of the possibilities. One example of a discreet variable is the number of cars a household owns. In general, count data are an example of discrete variables." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 7.51.28 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Q0zu3/data-basics) 2:14*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categorical variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Categorical variables that have ordered levels are called ordinal. Think about a survey question where you're asked how satisfied you are with the customer service you received and the options are very unsatisfied, unsatisfied, neutral, satisfied and very satisfied. These levels have an inherent ordering, hence the variable would be called ordinal. \n", "- If the levels of a categorical variable do not have an inherent ordering to them then the variable is simply called categorical. For example, are you a morning person or an afternoon person? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 7.54.38 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Q0zu3/data-basics) 2:48*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 7.56.44 AM.png\">\n", "<img src=\"images/Screen Shot 2016-05-16 at 7.56.29 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Q0zu3/data-basics) 3:40*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 7.59.34 AM.png\">\n", "<img src=\"images/Screen Shot 2016-05-16 at 7.59.13 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Q0zu3/data-basics) 4:15*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Relationship between variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Here, we have a scatter plot of the user data requests by countries and the compliance rate by Google. We can see that on average as the number of requests increases, so does the compliance rate. And that there is one country that sticks out as a potential outlier with much higher user data requests than the others. That's actually the United States. \n", "- **associated or dependent **: When two variables show some connection with one another. The association can be further described as positive or negative, and for these variables the association appears to be positive. \n", "- **independent variables**: If two variables are not associated. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 8.03.12 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Q0zu3/data-basics) 5:03*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Observational studies and experiments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will define observational studies and experiments and discuss correlation and causation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Observational**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- In an observational study, researchers collect data in a way that does not directly interfere with how the data arise. In other words, they merely observe. And based on observational studies, we can only establish an association. In other words, correlation between the explanatory and the response variables. \n", "- If an observational study uses data from the past, it's called a **retrospective study**. \n", "- Whereas if data are collected throughout the study, it's called **prospective**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Experiment**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In an experiments on the other hand, researchers randomly assign subjects to treatments and can, therefore, establish causal connections between the explanatory and response variables." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 8.09.00 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Qw8iF/observational-studies-experiments) 0:43*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 8.12.50 AM.png\">\n", "<img src=\"images/Screen Shot 2016-05-16 at 8.12.33 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Qw8iF/observational-studies-experiments) 3:11*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The title of the article says, breakfast cereal keeps girls slim, but there actually three possible explanations here. \n", "- One, eating breakfast does indeed, cause girls to be slimmer. \n", "- Two, being slim might cause girls to eat breakfast, so the relationship could be reversed. \n", "- Three, there may be a third variable that is responsible for both being slim and eating breakfast. For example, generally being health conscious might result in being slim as well as starting the day off with breakfast. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 8.33.06 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Qw8iF/observational-studies-experiments) 3:52*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Such extraneous variables that affect both the explanatory and the response variable and that make it seem like there's a relationship between them are called confounding variables." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 8.34.41 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Qw8iF/observational-studies-experiments) 3:59*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- If you're going to walk away with one thing from this class, let it be **correlation does not imply causation**. \n", "- And what determines whether we can infer causation or just correlation is the type of study that we're basing our conclusions. \n", "- Observational studies for the most part, allow us to make only correlational statements. \n", "- While experiments, allow us to infer causation. We said for the most part, because there are actually more advanced methods broadly titled causal inference that allow for making causal inferences for observational studies, but those studies are beyond the methods for this " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 8.34.31 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Qw8iF/observational-studies-experiments) 4:05*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 8.37.44 AM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Qw8iF/observational-studies-experiments) 4:39*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Sampling and sources of bias" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 9.58.33 PM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Y96uT/sampling-and-sources-of-bias) 4:19*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/Screen Shot 2016-05-16 at 10.04.08 PM.png\">\n", "<img src=\"images/Screen Shot 2016-05-16 at 10.04.18 PM.png\">\n", "\n", "*Screenshot taken from [Coursera](https://www.coursera.org/learn/probability-intro/lecture/Y96uT/sampling-and-sources-of-bias) 8:00*\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experimental Design" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" }, "toc": { "toc_cell": true, "toc_number_sections": true, "toc_threshold": "8", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 0 }
mit
uvchik/pvlib-python
docs/tutorials/forecast.ipynb
1
1243367
null
bsd-3-clause
YoungKwonJo/mlxtend
docs/examples/sklearn_sequential_feature_select_sbs.ipynb
1
31609
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sebastian Raschka 29/04/2015 \n", "\n", "CPython 3.4.3\n", "IPython 3.1.0\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -a 'Sebastian Raschka' -d -v" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "sys.path = ['/Users/sebastian/github/mlxtend/'] + sys.path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sections\n", "- [Introduction to Sequential Backward Selection](#Introduction-to-Sequential-Backward-Selection)\n", " - [Further Reading](#Further-Reading)\n", "- [Iris Example](#Iris-Example)\n", "- [Wine Data Example](#Wine-Data-Example)\n", "- [Gridsearch Example 1](#Gridsearch-Example-1)\n", "- [Gridsearch Example 2](#Gridsearch-Example-2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Sequential Backward Selection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to avoid the Curse of Dimensionality, pattern classification is often accompanied by Dimensionality Reduction, which also has the nice side-effect of increasing the computational performance. Common techniques are projection-based, such as Principal Component Analysis (PCA) (unsupervised) or Linear Discriminant (LDA) (supervised). It shall be noted though that regularization in classification models such as Logistic Regression, Support Vector Machines, or Neural Networks is to be preferred over using dimensionality reduction to avoid overfitting. However, dimensionality reduction is still a useful data compression technique to increase computational efficiency and data storage problems.\n", "\n", "An alternative to a projection-based dimensionality reduction approach is the so-called Feature Selection, and here, we will take a look at some of the established algorithms to tackle this combinatorial search problem: Sequential Backward Selection (SBS). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's summarize its mechanics in words:\n", "SBS starts with the original $d$-dimensional feature set and sequentially removes features from this set until the subset reaches a desired (user-specified) size $k$ where $k < d$. In every iteration $i$, the subset $d-i$ dimensional subset is evaluated using a criterion function to determine the least informative feature to be removed.\n", "\n", "The criterion function is typically the performance of the classifier measured via cross validation. \n", "\n", "Let's consider the following example where we have a dataset that consists of 3 features:\n", "\n", "\n", "- Original feature set: $\\{x_1, x_2, x_3\\}$\n", "\n", "In order to determine the least informative feature, we create 2-dimensional feature subsets and measure the performance (e.g., accuracy) of the classifier on each of those subset:\n", "\n", "- 1: $\\{x_1, x_2\\}$ -> 0.96\n", "- 2: $\\{x_1, x_3\\}$ -> 0.87\n", "- 3: $\\{x_2, x_3\\}$ -> 0.77\n", "\n", "Based on the accuracy measures, we would then eliminate feature $x_3$ and repeat this procedure until we reached the number of features to select. E.g., if we'd want to select 2 features, we'd stop at this point and select the feature subset $\\{x_1, x_2$\\}.\n", "\n", "Note that this algorithm is considered as \"subpoptimal\" in contrast to an exhaustive search, which is often computationally not feasible, though." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Further Reading" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[[back to top](#Sections)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "F. Ferri, P. Pudil, M. Hatef, and J. Kittler investigated the performance of different Sequential Selection Algorithms for Feature Selection on different scales and reported their results in\n", "\n", "- [\"Comparative Study of Techniques for Large Scale Feature Selection,\"](https://books.google.com/books?id=sbajBQAAQBAJ&pg=PA403&lpg=PA403&dq=Comparative+Study+of+Techniques+for+Large+Scale+Feature+Selection&source=bl&ots=KdGKWqzbzj&sig=5I9nhy-TrRmKyAiLDfy5ML_m578&hl=en&sa=X&ei=i7w-VYnoPMyXsAWm2IGgCw&ved=0CD4Q6AEwBA#v=onepage&q=Comparative%20Study%20of%20Techniques%20for%20Large%20Scale%20Feature%20Selection&f=false) Pattern Recognition in Practice IV, E. Gelsema and L. Kanal, eds., pp. 403-413. Elsevier Science B.V., 1994.\n", "\n", "Choosing an \"appropriate\" algorithm really depends on the problem - the size and desired recognition rate and computational performance. Thus, I want to encourage you to take (at least) a brief look at their paper and the results they obtained from experimenting with different problems feature space dimensions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Iris Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[[back to top](#Sections)]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Indices of selected features: (0, 3)\n", "CV score of selected subset: 0.96\n", "New feature subset:\n" ] }, { "data": { "text/plain": [ "array([[ 5.1, 0.2],\n", " [ 4.9, 0.2],\n", " [ 4.7, 0.2],\n", " [ 4.6, 0.2],\n", " [ 5. , 0.2]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from mlxtend.sklearn import SBS\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.datasets import load_iris\n", "\n", "iris = load_iris()\n", "X = iris.data\n", "y = iris.target\n", "\n", "knn = KNeighborsClassifier(n_neighbors=4)\n", "\n", "sbs = SBS(knn, k_features=2, scoring='accuracy', cv=5)\n", "sbs.fit(X, y)\n", "\n", "print('Indices of selected features:', sbs.indices_)\n", "print('CV score of selected subset:', sbs.k_score_)\n", "print('New feature subset:')\n", "sbs.transform(X)[0:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Wine Data Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[[back to top](#Sections)]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " <th>11</th>\n", " <th>12</th>\n", " <th>13</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>14.23</td>\n", " <td>1.71</td>\n", " <td>2.43</td>\n", " <td>15.6</td>\n", " <td>127</td>\n", " <td>2.80</td>\n", " <td>3.06</td>\n", " <td>0.28</td>\n", " <td>2.29</td>\n", " <td>5.64</td>\n", " <td>1.04</td>\n", " <td>3.92</td>\n", " <td>1065</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>13.20</td>\n", " <td>1.78</td>\n", " <td>2.14</td>\n", " <td>11.2</td>\n", " <td>100</td>\n", " <td>2.65</td>\n", " <td>2.76</td>\n", " <td>0.26</td>\n", " <td>1.28</td>\n", " <td>4.38</td>\n", " <td>1.05</td>\n", " <td>3.40</td>\n", " <td>1050</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>13.16</td>\n", " <td>2.36</td>\n", " <td>2.67</td>\n", " <td>18.6</td>\n", " <td>101</td>\n", " <td>2.80</td>\n", " <td>3.24</td>\n", " <td>0.30</td>\n", " <td>2.81</td>\n", " <td>5.68</td>\n", " <td>1.03</td>\n", " <td>3.17</td>\n", " <td>1185</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>14.37</td>\n", " <td>1.95</td>\n", " <td>2.50</td>\n", " <td>16.8</td>\n", " <td>113</td>\n", " <td>3.85</td>\n", " <td>3.49</td>\n", " <td>0.24</td>\n", " <td>2.18</td>\n", " <td>7.80</td>\n", " <td>0.86</td>\n", " <td>3.45</td>\n", " <td>1480</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>13.24</td>\n", " <td>2.59</td>\n", " <td>2.87</td>\n", " <td>21.0</td>\n", " <td>118</td>\n", " <td>2.80</td>\n", " <td>2.69</td>\n", " <td>0.39</td>\n", " <td>1.82</td>\n", " <td>4.32</td>\n", " <td>1.04</td>\n", " <td>2.93</td>\n", " <td>735</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 10 11 12 \\\n", "0 1 14.23 1.71 2.43 15.6 127 2.80 3.06 0.28 2.29 5.64 1.04 3.92 \n", "1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 \n", "2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 \n", "3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 \n", "4 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 \n", "\n", " 13 \n", "0 1065 \n", "1 1050 \n", "2 1185 \n", "3 1480 \n", "4 735 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', header=None)\n", "\n", "X = df.iloc[:, 1:].values\n", "y = df.iloc[:, 0].values\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x107fb1a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "scr = StandardScaler()\n", "X_std = scr.fit_transform(X)\n", "\n", "knn = KNeighborsClassifier(n_neighbors=4)\n", "\n", "# selecting features\n", "sbs = SBS(knn, k_features=1, scoring='accuracy', cv=5)\n", "sbs.fit(X_std, y)\n", "\n", "# plotting performance of feature subsets\n", "k_feat = [len(k) for k in sbs.subsets_]\n", "\n", "plt.plot(k_feat, sbs.scores_, marker='o')\n", "plt.ylabel('Accuracy')\n", "plt.xlabel('Number of features')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gridsearch Example 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[[back to top](#Sections)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Selecting the number of features in a pipeline." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.1s\n", "[Parallel(n_jobs=1)]: Done 12 out of 12 | elapsed: 0.7s finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n", "Best score: 0.960\n", "Best parameters set:\n", "\tsel__k_features: 1\n" ] } ], "source": [ "import pandas as pd\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.grid_search import GridSearchCV\n", "from mlxtend.sklearn import SBS\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.datasets import load_iris\n", "\n", "##########################\n", "### Loading data\n", "##########################\n", "\n", "iris = load_iris()\n", "X = iris.data\n", "y = iris.target\n", "\n", "##########################\n", "### Setting up pipeline\n", "##########################\n", "knn = KNeighborsClassifier(n_neighbors=4)\n", "\n", "sbs = SBS(estimator=knn, k_features=2, scoring='accuracy', cv=5)\n", "\n", "pipeline = Pipeline([\n", " ('scr', StandardScaler()), \n", " ('sel', sbs),\n", " ('clf', knn)])\n", "\n", "parameters = {'sel__k_features': [1,2,3,4]}\n", "\n", "grid_search = GridSearchCV(pipeline, parameters, n_jobs=1, verbose=1)\n", "\n", "##########################\n", "### Running GridSearch\n", "##########################\n", "grid_search.fit(X, y)\n", "\n", "print(\"Best score: %0.3f\" % grid_search.best_score_)\n", "print(\"Best parameters set:\")\n", "best_parameters = grid_search.best_estimator_.get_params()\n", "for param_name in sorted(parameters.keys()):\n", " print(\"\\t%s: %r\" % (param_name, best_parameters[param_name]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gridsearch Example 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[[back to top](#Sections)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tuning the estimator used for feature selection. Note that the current implementation requires to search for the weights in both the classifier and the SBS transformer separately." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.1s\n", "[Parallel(n_jobs=1)]: Done 50 jobs | elapsed: 2.9s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n", "Best score: 0.973\n", "Best parameters set:\n", "\tclf__n_neighbors: 5\n", "\tsel__estimator__n_neighbors: 5\n", "\tsel__k_features: 2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 108 out of 108 | elapsed: 6.0s finished\n" ] } ], "source": [ "import pandas as pd\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.grid_search import GridSearchCV\n", "from mlxtend.sklearn import SBS\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.datasets import load_iris\n", "\n", "##########################\n", "### Loading data\n", "##########################\n", "\n", "iris = load_iris()\n", "X = iris.data\n", "y = iris.target\n", "\n", "##########################\n", "### Setting up pipeline\n", "##########################\n", "knn = KNeighborsClassifier(n_neighbors=4)\n", "\n", "sbs = SBS(estimator=knn, k_features=2, scoring='accuracy', cv=5)\n", "\n", "pipeline = Pipeline([\n", " ('scr', StandardScaler()), \n", " ('sel', sbs),\n", " ('clf', knn)])\n", "\n", "parameters = {'sel__k_features': [1, 2, 3, 4],\n", " 'sel__estimator__n_neighbors': [4, 5, 6],\n", " 'clf__n_neighbors': [4, 5, 6]}\n", "\n", "grid_search = GridSearchCV(pipeline, parameters, n_jobs=1, verbose=1)\n", "\n", "##########################\n", "### Running GridSearch\n", "##########################\n", "grid_search.fit(X, y)\n", "\n", "print(\"Best score: %0.3f\" % grid_search.best_score_)\n", "print(\"Best parameters set:\")\n", "best_parameters = grid_search.best_estimator_.get_params()\n", "for param_name in sorted(parameters.keys()):\n", " print(\"\\t%s: %r\" % (param_name, best_parameters[param_name]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final feature subset can then be obtained as follows:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best feature subset:\n" ] }, { "data": { "text/plain": [ "(2, 3)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print('Best feature subset:')\n", "grid_search.best_estimator_.steps[1][1].indices_" ] } ], "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 }
bsd-3-clause
irockafe/revo_healthcare
notebooks/husermet/exploratory/isomers_per_mz.ipynb
1
315880
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> Goal: Find out number of isomers in husermet data </h2>\n", "Then look into how many can be found redundant. Start with positive ion mode" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import scipy.stats as stats\n", "\n", "import matplotlib.pyplot as plt\n", "from matplotlib.ticker import NullFormatter\n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import the data\n", "local_path = '/home/irockafe/Dropbox (MIT)/Alm_Lab/projects/'\n", "project_path = ('/revo_healthcare/data/processed/Husermet_MTBLS97/'+\n", " 'Husermet_UPLCMS_positive_ion_mode.xlsx')\n", "metadata = pd.read_excel(local_path+project_path, sheetname=1,\n", " index_col = 0)\n", "peaks = pd.read_excel(local_path+project_path, sheetname=2, index_col=0)\n", "# samples x features\n", "df = pd.read_excel(local_path+project_path, sheetname=3,\n", " dtype=np.float64)\n", "# Replace X from df column labels\n", "df.columns = pd.Series([i.replace('X', '') for i in df.columns], \n", " dtype='Int64')" ] }, { "cell_type": "code", "execution_count": 243, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 1189 entries, 0 to 1188\n", "Columns: 1024 entries, 1000 to 4160\n", "dtypes: float64(1024)\n", "memory usage: 9.3 MB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 244, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "df columns Int64Index([1000, 1008, 1010, 1013, 1017, 1022, 1026, 1028, 103, 1030,\n", " ...\n", " 4138, 414, 4140, 4141, 4144, 4147, 4151, 4152, 4153, 4160],\n", " dtype='int64', length=1024)\n", "peak index Int64Index([ 48, 59, 63, 76, 79, 82, 91, 93, 103, 109,\n", " ...\n", " 7757, 7758, 7761, 7765, 7776, 7780, 7784, 7787, 7798, 7805],\n", " dtype='int64', name=u'idx', length=2178)\n", "Found 1000\n", "Found 1008\n", "Found 1010\n", "Found 1013\n", "Found 1017\n", "Found 1022\n", "Found 1026\n", "Found 1028\n", "Found 103\n", "Found 1030\n", "Found 1036\n", "Found 1037\n", "Found 1042\n", "Found 1048\n", "Found 1055\n", "Found 1056\n", "Found 1062\n", "Found 1069\n", "Found 1072\n", "Found 1073\n", "Found 1079\n", "Found 1083\n", "Found 1087\n", "Found 1089\n", "Found 109\n", "Found 1090\n", "Found 1093\n", "Found 1099\n", "Found 1100\n", "Found 1101\n", "Found 1102\n", "Found 1108\n", "Found 1110\n", "Found 112\n", "Found 1123\n", "Found 1124\n", "Found 1126\n", "Found 1133\n", "Found 1134\n", "Found 1141\n", "Found 1142\n", "Found 1147\n", "Found 1149\n", "Found 1150\n", "Found 1161\n", "Found 1169\n", "Found 1172\n", "Found 1178\n", "Found 1184\n", "Found 1191\n", "Found 1203\n", "Found 1207\n", "Found 1208\n", "Found 1210\n", "Found 1219\n", "Found 1221\n", "Found 1226\n", "Found 1238\n", "Found 124\n", "Found 1260\n", "Found 1262\n", "Found 1263\n", "Found 1268\n", "Found 1271\n", "Found 1274\n", "Found 1275\n", "Found 1286\n", "Found 1287\n", "Found 1288\n", "Found 129\n", "Found 1296\n", "Found 1297\n", "Found 1299\n", "Found 1301\n", "Found 1303\n", "Found 1304\n", "Found 1305\n", "Found 1306\n", "Found 1311\n", "Found 1314\n", "Found 1317\n", "Found 1321\n", "Found 1322\n", "Found 1323\n", "Found 1326\n", "Found 1331\n", "Found 1336\n", "Found 1339\n", "Found 134\n", "Found 1344\n", "Found 1345\n", "Found 1351\n", "Found 1359\n", "Found 1362\n", "Found 1363\n", "Found 1364\n", "Found 1365\n", "Found 1368\n", "Found 1374\n", "Found 1379\n", "Found 1381\n", "Found 1385\n", "Found 1387\n", "Found 1388\n", "Found 1389\n", "Found 139\n", "Found 1397\n", "Found 1403\n", "Found 1409\n", "Found 142\n", "Found 1423\n", "Found 1425\n", "Found 1426\n", "Found 143\n", "Found 1436\n", "Found 1448\n", "Found 1455\n", "Found 1456\n", "Found 1457\n", "Found 1459\n", "Found 1464\n", "Found 1467\n", "Found 1468\n", "Found 147\n", "Found 1472\n", "Found 1473\n", "Found 1478\n", "Found 1480\n", "Found 1481\n", "Found 1484\n", "Found 1485\n", "Found 1486\n", "Found 1490\n", "Found 1491\n", "Found 1493\n", "Found 1494\n", "Found 1500\n", "Found 1501\n", "Found 1502\n", "Found 1503\n", "Found 1506\n", "Found 1507\n", "Found 1509\n", "Found 1510\n", "Found 1512\n", "Found 1516\n", "Found 1517\n", "Found 1519\n", "Found 1521\n", "Found 1522\n", "Found 1525\n", "Found 1532\n", "Found 1534\n", "Found 1537\n", "Found 1542\n", "Found 1544\n", "Found 1546\n", "Found 1548\n", "Found 1550\n", "Found 1552\n", "Found 1554\n", "Found 1558\n", "Found 1559\n", "Found 1560\n", "Found 1563\n", "Found 1571\n", "Found 1573\n", "Found 1579\n", "Found 1583\n", "Found 1584\n", "Found 1585\n", "Found 1586\n", "Found 1590\n", "Found 1595\n", "Found 1596\n", "Found 1598\n", "Found 1606\n", "Found 1607\n", "Found 1609\n", "Found 1610\n", "Found 1614\n", "Found 1615\n", "Found 1617\n", "Found 1619\n", "Found 1624\n", "Found 1625\n", "Found 1628\n", "Found 1629\n", "Found 1634\n", "Found 1638\n", "Found 1645\n", "Found 1651\n", "Found 1652\n", "Found 1654\n", "Found 1656\n", "Found 1657\n", "Found 166\n", "Found 1666\n", "Found 1667\n", "Found 1674\n", "Found 1676\n", "Found 1680\n", "Found 1686\n", "Found 1688\n", "Found 1689\n", "Found 1692\n", "Found 1694\n", "Found 1697\n", "Found 1698\n", "Found 1700\n", "Found 1711\n", "Found 1712\n", "Found 1715\n", "Found 1716\n", "Found 172\n", "Found 1727\n", "Found 1729\n", "Found 1736\n", "Found 1737\n", "Found 1738\n", "Found 1739\n", "Found 1740\n", "Found 1741\n", "Found 1746\n", "Found 1748\n", "Found 1752\n", "Found 1756\n", "Found 1760\n", "Found 1764\n", "Found 1766\n", "Found 1774\n", "Found 1775\n", "Found 1777\n", "Found 178\n", "Found 1781\n", "Found 1784\n", "Found 1786\n", "Found 179\n", "Found 1792\n", "Found 1796\n", "Found 1797\n", "Found 1799\n", "Found 1810\n", "Found 1815\n", "Found 1825\n", "Found 1826\n", "Found 1830\n", "Found 1832\n", "Found 1839\n", "Found 1840\n", "Found 1843\n", "Found 1847\n", "Found 1849\n", "Found 1850\n", "Found 1851\n", "Found 1852\n", "Found 1857\n", "Found 1860\n", "Found 1865\n", "Found 1866\n", "Found 1870\n", "Found 1873\n", "Found 1875\n", "Found 1879\n", "Found 188\n", "Found 1885\n", "Found 1886\n", "Found 1895\n", "Found 1898\n", "Found 1900\n", "Found 1904\n", "Found 1905\n", "Found 1906\n", "Found 1909\n", "Found 1910\n", "Found 1915\n", "Found 1916\n", "Found 1917\n", "Found 1919\n", "Found 1923\n", "Found 1933\n", "Found 1934\n", "Found 1935\n", "Found 1936\n", "Found 1940\n", "Found 1943\n", "Found 1945\n", "Found 1948\n", "Found 195\n", "Found 1950\n", "Found 1951\n", "Found 1952\n", "Found 1955\n", "Found 1956\n", "Found 1957\n", "Found 1961\n", "Found 1962\n", "Found 1970\n", "Found 1973\n", "Found 1977\n", "Found 1983\n", "Found 1987\n", "Found 1998\n", "Found 1999\n", "Found 2004\n", "Found 2005\n", "Found 2007\n", "Found 201\n", "Found 2013\n", "Found 2014\n", "Found 2015\n", "Found 2018\n", "Found 2019\n", "Found 2020\n", "Found 2028\n", "Found 2033\n", "Found 2037\n", "Found 2039\n", "Found 205\n", "Found 2052\n", "Found 2053\n", "Found 2055\n", "Found 2059\n", "Found 2060\n", "Found 2061\n", "Found 2064\n", "Found 2065\n", "Found 2068\n", "Found 2069\n", "Found 2087\n", "Found 2098\n", "Found 2101\n", "Found 2102\n", "Found 2105\n", "Found 2111\n", "Found 2117\n", "Found 2129\n", "Found 2131\n", "Found 2136\n", "Found 2139\n", "Found 214\n", "Found 2144\n", "Found 2145\n", "Found 2146\n", "Found 2147\n", "Found 215\n", "Found 2151\n", "Found 2152\n", "Found 2156\n", "Found 2158\n", "Found 2160\n", "Found 2161\n", "Found 2163\n", "Found 2170\n", "Found 2171\n", "Found 2174\n", "Found 2175\n", "Found 2186\n", "Found 2189\n", "Found 2196\n", "Found 2203\n", "Found 2205\n", "Found 2208\n", "Found 221\n", "Found 2212\n", "Found 2214\n", "Found 2220\n", "Found 2222\n", "Found 2223\n", "Found 2224\n", "Found 2231\n", "Found 2235\n", "Found 2238\n", "Found 2239\n", "Found 2243\n", "Found 2246\n", "Found 2247\n", "Found 2250\n", "Found 2253\n", "Found 2256\n", "Found 2259\n", "Found 226\n", "Found 2263\n", "Found 2269\n", "Found 227\n", "Found 2280\n", "Found 2285\n", "Found 2289\n", "Found 2290\n", "Found 2297\n", "Found 2299\n", "Found 2306\n", "Found 2307\n", "Found 2310\n", "Found 2311\n", "Found 2312\n", "Found 2315\n", "Found 2318\n", "Found 2323\n", "Found 2324\n", "Found 2326\n", "Found 2328\n", "Found 2332\n", "Found 2333\n", "Found 2335\n", "Found 2337\n", "Found 234\n", "Found 2348\n", "Found 2354\n", "Found 2356\n", "Found 2359\n", "Found 236\n", "Found 2369\n", "Found 2370\n", "Found 2374\n", "Found 2375\n", "Found 2376\n", "Found 2377\n", "Found 2388\n", "Found 239\n", "Found 2394\n", "Found 2399\n", "Found 240\n", "Found 2400\n", "Found 2402\n", "Found 2403\n", "Found 2405\n", "Found 2412\n", "Found 2414\n", "Found 2415\n", "Found 2416\n", "Found 2426\n", "Found 2432\n", "Found 2437\n", "Found 2438\n", "Found 2439\n", "Found 2442\n", "Found 2443\n", "Found 2444\n", "Found 2446\n", "Found 2450\n", "Found 2458\n", "Found 2461\n", "Found 2462\n", "Found 2464\n", "Found 2470\n", "Found 2473\n", "Found 2475\n", "Found 2479\n", "Found 2482\n", "Found 2485\n", "Found 2488\n", "Found 2491\n", "Found 2492\n", "Found 2494\n", "Found 2497\n", "Found 2503\n", "Found 2506\n", "Found 2507\n", "Found 2508\n", "Found 2509\n", "Found 2516\n", "Found 2517\n", "Found 2521\n", "Found 2527\n", "Found 2531\n", "Found 2533\n", "Found 2537\n", "Found 2541\n", "Found 2548\n", "Found 2550\n", "Found 2551\n", "Found 2555\n", "Found 2558\n", "Found 2559\n", "Found 256\n", "Found 2561\n", "Found 2564\n", "Found 2567\n", "Found 2568\n", "Found 257\n", "Found 2571\n", "Found 2579\n", "Found 258\n", "Found 2582\n", "Found 2583\n", "Found 259\n", "Found 2593\n", "Found 2595\n", "Found 2596\n", "Found 2598\n", "Found 2599\n", "Found 2602\n", "Found 2606\n", "Found 2607\n", "Found 2608\n", "Found 2610\n", "Found 2612\n", "Found 2615\n", "Found 2616\n", "Found 2618\n", "Found 2619\n", "Found 2620\n", "Found 2626\n", "Found 2632\n", "Found 2633\n", "Found 2636\n", "Found 2642\n", "Found 2643\n", "Found 2644\n", "Found 2645\n", "Found 2646\n", "Found 2651\n", "Found 2653\n", "Found 2654\n", "Found 2657\n", "Found 2659\n", "Found 2665\n", "Found 2666\n", "Found 2667\n", "Found 2669\n", "Found 2670\n", "Found 2679\n", "Found 2681\n", "Found 2682\n", "Found 2683\n", "Found 2687\n", "Found 2689\n", "Found 269\n", "Found 2691\n", "Found 2692\n", "Found 2694\n", "Found 2698\n", "Found 2699\n", "Found 2701\n", "Found 2702\n", "Found 2704\n", "Found 2707\n", "Found 2708\n", "Found 2709\n", "Found 2717\n", "Found 2718\n", "Found 2725\n", "Found 2728\n", "Found 2729\n", "Found 2730\n", "Found 2733\n", "Found 274\n", "Found 2743\n", "Found 2745\n", "Found 2747\n", "Found 2750\n", "Found 2752\n", "Found 2754\n", "Found 2755\n", "Found 2762\n", "Found 2766\n", "Found 2777\n", "Found 2779\n", "Found 2782\n", "Found 2784\n", "Found 2787\n", "Found 2788\n", "Found 2789\n", "Found 2791\n", "Found 2794\n", "Found 2797\n", "Found 2798\n", "Found 2799\n", "Found 2801\n", "Found 2802\n", "Found 2810\n", "Found 2811\n", "Found 2814\n", "Found 2818\n", "Found 2821\n", "Found 2822\n", "Found 2826\n", "Found 2828\n", "Found 283\n", "Found 2832\n", "Found 2834\n", "Found 2837\n", "Found 2838\n", "Found 2839\n", "Found 2841\n", "Found 2844\n", "Found 2845\n", "Found 2848\n", "Found 2850\n", "Found 2855\n", "Found 2857\n", "Found 2858\n", "Found 2861\n", "Found 2863\n", "Found 2865\n", "Found 2867\n", "Found 2869\n", "Found 2870\n", "Found 2872\n", "Found 2874\n", "Found 2875\n", "Found 2876\n", "Found 2881\n", "Found 2884\n", "Found 2885\n", "Found 2888\n", "Found 2891\n", "Found 2892\n", "Found 2900\n", "Found 2903\n", "Found 2905\n", "Found 2906\n", "Found 2907\n", "Found 2909\n", "Found 291\n", "Found 2913\n", "Found 2917\n", "Found 2918\n", "Found 2921\n", "Found 2923\n", "Found 2925\n", "Found 2929\n", "Found 2930\n", "Found 2932\n", "Found 2935\n", "Found 2940\n", "Found 2942\n", "Found 2944\n", "Found 2947\n", "Found 2950\n", "Found 2953\n", "Found 2954\n", "Found 2957\n", "Found 2958\n", "Found 296\n", "Found 2963\n", "Found 2966\n", "Found 2971\n", "Found 2977\n", "Found 2980\n", "Found 2982\n", "Found 2991\n", "Found 2992\n", "Found 2999\n", "Found 3000\n", "Found 3004\n", "Found 3009\n", "Found 3010\n", "Found 3012\n", "Found 3015\n", "Found 3021\n", "Found 3022\n", "Found 3024\n", "Found 3025\n", "Found 3029\n", "Found 3030\n", "Found 3031\n", "Found 304\n", "Found 3045\n", "Found 3046\n", "Found 3047\n", "Found 3052\n", "Found 3058\n", "Found 3060\n", "Found 3064\n", "Found 3069\n", "Found 3074\n", "Found 3076\n", "Found 3077\n", "Found 3078\n", "Found 3080\n", "Found 3082\n", "Found 3085\n", "Found 3094\n", "Found 3096\n", "Found 3097\n", "Found 3103\n", "Found 3104\n", "Found 3110\n", "Found 3111\n", "Found 3113\n", "Found 3118\n", "Found 3120\n", "Found 3122\n", "Found 3124\n", "Found 3125\n", "Found 3127\n", "Found 3133\n", "Found 3135\n", "Found 3136\n", "Found 3138\n", "Found 3143\n", "Found 3144\n", "Found 3145\n", "Found 3154\n", "Found 3158\n", "Found 3162\n", "Found 3163\n", "Found 3164\n", "Found 3166\n", "Found 3169\n", "Found 317\n", "Found 3172\n", "Found 3174\n", "Found 3176\n", "Found 318\n", "Found 3180\n", "Found 3181\n", "Found 3182\n", "Found 3184\n", "Found 3189\n", "Found 319\n", "Found 3192\n", "Found 3197\n", "Found 3200\n", "Found 3201\n", "Found 3203\n", "Found 3204\n", "Found 3206\n", "Found 3207\n", "Found 321\n", "Found 3210\n", "Found 3212\n", "Found 3219\n", "Found 3221\n", "Found 3225\n", "Found 3239\n", "Found 3240\n", "Found 3244\n", "Found 3248\n", "Found 3252\n", "Found 3255\n", "Found 3257\n", "Found 3259\n", "Found 3263\n", "Found 3266\n", "Found 3269\n", "Found 3279\n", "Found 3280\n", "Found 3286\n", "Found 3291\n", "Found 3296\n", "Found 330\n", "Found 3300\n", "Found 3304\n", "Found 3305\n", "Found 3307\n", "Found 3308\n", "Found 3309\n", "Found 3312\n", "Found 3321\n", "Found 3322\n", "Found 3323\n", "Found 3325\n", "Found 3337\n", "Found 3341\n", "Found 3343\n", "Found 3348\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found 3350\n", "Found 3354\n", "Found 3355\n", "Found 3357\n", "Found 3361\n", "Found 3365\n", "Found 3366\n", "Found 3367\n", "Found 3370\n", "Found 3376\n", "Found 3378\n", "Found 3379\n", "Found 338\n", "Found 3383\n", "Found 3388\n", "Found 3390\n", "Found 3391\n", "Found 3399\n", "Found 3403\n", "Found 3410\n", "Found 3417\n", "Found 3419\n", "Found 3428\n", "Found 3433\n", "Found 3437\n", "Found 3441\n", "Found 3442\n", "Found 3443\n", "Found 3448\n", "Found 3451\n", "Found 3453\n", "Found 3456\n", "Found 3467\n", "Found 347\n", "Found 3475\n", "Found 3480\n", "Found 3487\n", "Found 3488\n", "Found 3492\n", "Found 3493\n", "Found 3495\n", "Found 3500\n", "Found 3503\n", "Found 3505\n", "Found 351\n", "Found 3513\n", "Found 3515\n", "Found 3518\n", "Found 352\n", "Found 3522\n", "Found 3523\n", "Found 3525\n", "Found 3534\n", "Found 3536\n", "Found 3540\n", "Found 3542\n", "Found 3547\n", "Found 3549\n", "Found 355\n", "Found 3550\n", "Found 3554\n", "Found 3555\n", "Found 3559\n", "Found 3563\n", "Found 3566\n", "Found 3567\n", "Found 3568\n", "Found 3569\n", "Found 3572\n", "Found 3573\n", "Found 3574\n", "Found 3576\n", "Found 3579\n", "Found 3585\n", "Found 3586\n", "Found 3591\n", "Found 3592\n", "Found 3597\n", "Found 3599\n", "Found 3601\n", "Found 3602\n", "Found 3605\n", "Found 3608\n", "Found 3611\n", "Found 3612\n", "Found 3615\n", "Found 3623\n", "Found 3628\n", "Found 3637\n", "Found 3638\n", "Found 3640\n", "Found 3642\n", "Found 3643\n", "Found 3645\n", "Found 3646\n", "Found 3650\n", "Found 3655\n", "Found 3656\n", "Found 366\n", "Found 3660\n", "Found 3661\n", "Found 3663\n", "Found 3667\n", "Found 3668\n", "Found 3669\n", "Found 3672\n", "Found 3674\n", "Found 3682\n", "Found 3683\n", "Found 3688\n", "Found 3689\n", "Found 3691\n", "Found 3702\n", "Found 3704\n", "Found 3709\n", "Found 371\n", "Found 3711\n", "Found 3713\n", "Found 3722\n", "Found 3724\n", "Found 3730\n", "Found 3733\n", "Found 3735\n", "Found 3736\n", "Found 3741\n", "Found 3742\n", "Found 3743\n", "Found 3745\n", "Found 3747\n", "Found 3748\n", "Found 3749\n", "Found 3753\n", "Found 3756\n", "Found 3757\n", "Found 3758\n", "Found 3765\n", "Found 3772\n", "Found 3777\n", "Found 3778\n", "Found 3780\n", "Found 3783\n", "Found 3786\n", "Found 3788\n", "Found 3791\n", "Found 3794\n", "Found 3796\n", "Found 3799\n", "Found 3807\n", "Found 3817\n", "Found 3818\n", "Found 382\n", "Found 3820\n", "Found 3823\n", "Found 3825\n", "Found 3826\n", "Found 3833\n", "Found 3840\n", "Found 3841\n", "Found 3842\n", "Found 3844\n", "Found 3849\n", "Found 3850\n", "Found 3855\n", "Found 3862\n", "Found 3871\n", "Found 3873\n", "Found 3875\n", "Found 388\n", "Found 3880\n", "Found 3881\n", "Found 3883\n", "Found 389\n", "Found 3891\n", "Found 3899\n", "Found 3900\n", "Found 3901\n", "Found 3902\n", "Found 3904\n", "Found 391\n", "Found 3910\n", "Found 3914\n", "Found 3915\n", "Found 3921\n", "Found 3923\n", "Found 3927\n", "Found 3928\n", "Found 393\n", "Found 3932\n", "Found 3936\n", "Found 3937\n", "Found 3938\n", "Found 3940\n", "Found 395\n", "Found 3957\n", "Found 3958\n", "Found 396\n", "Found 3967\n", "Found 3972\n", "Found 3975\n", "Found 3976\n", "Found 3983\n", "Found 3986\n", "Found 3987\n", "Found 3990\n", "Found 3994\n", "Found 3995\n", "Found 3996\n", "Found 3997\n", "Found 3998\n", "Found 3999\n", "Found 400\n", "Found 4001\n", "Found 4002\n", "Found 4003\n", "Found 4007\n", "Found 4010\n", "Found 4012\n", "Found 4013\n", "Found 4014\n", "Found 4025\n", "Found 4027\n", "Found 403\n", "Found 4032\n", "Found 4033\n", "Found 4035\n", "Found 4037\n", "Found 4043\n", "Found 4044\n", "Found 4048\n", "Found 4055\n", "Found 4056\n", "Found 4061\n", "Found 4062\n", "Found 4063\n", "Found 4069\n", "Found 4072\n", "Found 4078\n", "Found 4080\n", "Found 4081\n", "Found 4086\n", "Found 4094\n", "Found 4101\n", "Found 4105\n", "Found 4106\n", "Found 4113\n", "Found 4114\n", "Found 4115\n", "Found 4116\n", "Found 4121\n", "Found 4122\n", "Found 4123\n", "Found 4125\n", "Found 4129\n", "Found 4132\n", "Found 4134\n", "Found 4138\n", "Found 414\n", "Found 4140\n", "Found 4141\n", "Found 4144\n", "Found 4147\n", "Found 4151\n", "Found 4152\n", "Found 4153\n", "Found 4160\n" ] } ], "source": [ "# Peaks is all the features detected, pre-QC, it seems. Select\n", "# Only those that made it to the dataframe.\n", "print 'df columns', df.columns\n", "print 'peak index', peaks.index\n", "\n", "# Sanity check that all the peaks are accounted for in the\n", "# peaklist\n", "for i in df.columns:\n", " if i not in peaks.index:\n", " print (\"Oh shit, you couldn't find one of the\"+\n", " \"df columns in the peaklist index\")\n", " raise hell\n", " else:\n", " print \"Found {i}\".format(i=i)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a 1.0\n", "b 2.0\n", "c 3.0\n", "dtype: float64\n", "\n", "Output from test_vals:\n", " a b c\n", "a NaN NaN NaN\n", "b 500000.000000 NaN NaN\n", "c 666666.666667 333333.333333 NaN\n", "\n", "Should be this:\n", " a b c\n", "a NaN NaN NaN\n", "b 500000.000000 NaN NaN\n", "c 666666.666667 333333.333333 NaN\n", "\n", "\n", "You passed the test! (might be other bugs, but idk)\n" ] } ], "source": [ "# Make a matrix of the pairwise-ppm difference between peaks\n", "def pairwise_ppm_matrix(peak_mz):\n", " '''\n", " GOAL - Make a matrix containing pairwise ppm differences\n", " from a pandas series\n", " INPUT - peak_mz: pandas series with index as feature identifier\n", " OUTPUT - matrix of pairwise ppm values. half-full with comparisons\n", " Other (redundant) half is nan values. Using nans so \n", " you can ask \"ppm_matrix < 20\" and sum rows/columns\n", " to get an answer\n", " '''\n", " ppm_pairwise_matrix = pd.DataFrame(\n", " np.full([len(peak_mz), len(peak_mz)], np.nan),\n", " index=peak_mz.index, columns=peak_mz.index)\n", " for i, mz in enumerate(peak_mz):\n", " for idx, mz2 in enumerate(peak_mz[i+1:]):\n", " j=i+1+idx # \n", " min_ppm = abs(\n", " (float(mz-mz2)/max(mz,mz2)) * 10**6)\n", " ppm_pairwise_matrix.iloc[j,i] = min_ppm\n", " return ppm_pairwise_matrix\n", "\n", "test_mz = pd.Series([1,2,3], index=['a', 'b', 'c'], dtype='float64')\n", "print test_mz\n", "test_val = pairwise_ppm_matrix(test_mz)\n", "should_val = pd.DataFrame({'a': [np.nan, 0.5*10**6, (2.0/3)*10**6],\n", " 'b': [np.nan, np.nan, (1.0/3)*10**6],\n", " 'c': [np.nan, np.nan, np.nan]},\n", " index=['a', 'b', 'c'])\n", "\n", "print '\\nOutput from test_vals:\\n', test_val\n", "print '\\nShould be this:\\n', should_val\n", "\n", "assert(test_val.all() == should_val.all()).all()\n", "if (test_val.all() == should_val.all()).all():\n", " print '\\n\\nYou passed the test! (might be other bugs, but idk)'" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Select out the peaks from dataframe (those that presumably passed QC)\n", "features = peaks.loc[df.columns]\n", "feature_mz = features['mz']" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def plot_mz_rt(df, save=False,path=None, rt_bounds=[-1e5,-1e5]):\n", " # the random data\n", " x = df['rt']\n", " y = df['mz']\n", " print np.max(x)\n", " print np.max(y)\n", " nullfmt = NullFormatter() # no labels\n", "\n", " # definitions for the axes\n", " left, width = 0.1, 0.65\n", " bottom, height = 0.1, 0.65\n", " bottom_h = left_h = left + width + 0.02\n", "\n", " rect_scatter = [left, bottom, width, height]\n", " rect_histx = [left, bottom_h, width, 0.2]\n", " rect_histy = [left_h, bottom, 0.2, height]\n", "\n", " # start with a rectangular Figure\n", " #fig = plt.figure(1, figsize=(8, 8))\n", " fig = plt.figure(1, figsize=(10,10))\n", " \n", " axScatter = plt.axes(rect_scatter)\n", " axHistx = plt.axes(rect_histx)\n", " axHisty = plt.axes(rect_histy)\n", "\n", " # no labels\n", " axHistx.xaxis.set_major_formatter(nullfmt)\n", " axHisty.yaxis.set_major_formatter(nullfmt)\n", "\n", " # the scatter plot:\n", " axScatter.scatter(x, y, s=1)\n", "\n", " # now determine nice limits by hand:\n", " binwidth = 0.25\n", "\n", " #xymax = np.max([np.max(np.fabs(x)), np.max(np.fabs(y))])\n", "\n", " #lim = (int(xymax/binwidth) + 1) * binwidth\n", "\n", " x_min = np.min(x)-50\n", " x_max = np.max(x)+50\n", " axScatter.set_xlim(x_min, x_max )\n", " y_min = np.min(y)-50\n", " y_max = np.max(y)+50\n", " axScatter.set_ylim(y_min, y_max)\n", "\n", " # Add vertical red line between 750-1050 retention time\n", " '''\n", " plt.plot([0,1], [0,1], linestyle = '--', lw=2, color='r',\n", " label='Luck', alpha=0.5)\n", " '''\n", " print 'ymin: ', y_min\n", " \n", " # Add vertical/horizontal lines to scatter and histograms\n", " axScatter.axvline(x=rt_bounds[0], lw=2, color='r', alpha=0.5)\n", " axScatter.axvline(x=rt_bounds[1], lw=2, color='r', alpha=0.5)\n", "\n", " #bins = np.arange(-lim, lim + binwidth, binwidth)\n", " bins = 100\n", " axHistx.hist(x, bins=bins)\n", " axHisty.hist(y, bins=bins, orientation='horizontal')\n", "\n", " axHistx.set_xlim(axScatter.get_xlim())\n", " axHisty.set_ylim(axScatter.get_ylim())\n", "\n", " axScatter.set_ylabel('m/z', fontsize=30)\n", " axScatter.set_xlabel('Retention Time', fontsize=30)\n", "\n", " axHistx.set_ylabel('# of Features', fontsize=20)\n", " axHisty.set_xlabel('# of Features', fontsize=20)\n", " if save:\n", " plt.savefig(path, \n", " format='pdf')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1189.217979\n", "531.234175\n", "ymin: 46.972084\n" ] }, { "data": { "image/png": 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DjvPLPgKAZGUt+K13jaQrKv/fIulLkt5Xef7T7v68pEfNbFLSJZL+I+4GNupB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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fccafce7890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_mz_rt(features)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Do a quick test on own data\n", "ppm_matrix = pairwise_ppm_matrix(feature_mz)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> Only 11 features are within 5ppm of one-another :) </h2>\n", "Unsure how this will stack up between datasets, but \n", "52 possibly isomeric features out of 1000 is not bad...\n", "\n", "Also, there's a distinct possibility that they removed\n", "other redundant features... Check on that below" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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HByPi5c1c1l3A9yOi9BybWUNR1qx+r4g4t9axWPtRdknD/0TEBperlKqHi/2siLKr6u8j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\nBboB2wOuijQzs3a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"text/plain": [ "<matplotlib.figure.Figure at 0x7fccaf618c10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_ppm_overlaps(ppm_matrix, x_vals):\n", " x = x_vals\n", " y = (np.array([(ppm_matrix < i).sum().sum() for i in x]) / \n", " float(ppm_matrix.shape[0]))*100\n", " plt.scatter(x,y)\n", " plt.xlabel('ppm')\n", " plt.ylabel('% of overlapping m/z')\n", " plt.title('Few overlapping m/z values in Husermet dataset'\n", " + ' (# Features = %s)' % ppm_matrix.shape[0])\n", " plt.axvline(5, color='red', alpha=0.2, label='Instrument precision')\n", " plt.legend()\n", " plt.show()\n", " \n", "plot_ppm_overlaps(ppm_matrix, range(1,20))" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Check how many of the annotated features (peaks?)\n", "# have overlapping m/z\n", "# This takes a long time. Maybe try to matrix-ify some of the code?\n", "all_feats = pairwise_ppm_matrix(peaks['mz'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> Even for all the features (not just those that were in the dataframe and passed QC), only ~1% are indistinguishable by mass </h2>" ] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [ { "data": { "image/png": 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FLF26tN6hWBdXuIN2I3NiM2sAvXv3bug7Ipu1hasizcysoTixmZlZ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\ns4by/wHgmKWUxX7YIQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc6e921d190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_ppm_overlaps(all_feats, range(1,20))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> Now let's see if the distributions of these m/z overlapping features are distinct </h2>\n", "Just work with the QC'd features (those found in the feature table, not those found only in the peaklist)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(1288, 1287), (1522, 1521), (1740, 1739), (1999, 1998), (2019, 2018), (2020, 2019), (2438, 2437), (3308, 3307), (389, 388), (3928, 3927), (3995, 3994), (4003, 4002)]\n" ] }, { "data": { "image/png": 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1JeUVAH5r5RSWunF7DJ1jfCcCffijzbxsr48DEHtkK5HFVlNZamoqTz/9dFDX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\nr5JDx8pK0l8jYnal+2HWG4/P9Dy9ZmZmyTh0zMw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"text/plain": [ "<matplotlib.figure.Figure at 0x7fccb2b349d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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/l6t6bGe/O547lvTm24KWrCxKhXP+ZU2vg9m72envZBjzMIz4Xd2I30zRkY5S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\nQ8BwRLwoaTHQHxHvzo9dRvYi7oGImJOyDdaYatE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"text/plain": [ "<matplotlib.figure.Figure at 0x7fccaf626790>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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qjedL65jAX678StNehDEmJtn0E/Vg00/Amfe9x84DxyrWvR7h01+fT0ZKYkT5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\n1wFw4QPH0xI7wVe+23TX0M615wdjTT2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"text/plain": [ "<matplotlib.figure.Figure at 0x7fccb2a0c6d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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oDeaf8zf7OA79c/5mDi5bE+AyrRtElSajJB9mXAgHt9jz9R9ZJZTQyZrPzn/S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\n0AnxOx2Ir2gfO631uRde0hJwoH1qHtslP9Mxs/L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"text/plain": [ "<matplotlib.figure.Figure at 0x7fccafb65e10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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SNsrLy1m9ejUAq1atory8nFGjRnFH6bvc+/z7VLtTVe3srooC0Ekj8/nz5SeQ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\nfGZUX849sn+6i1HPkjUZOI+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"text/plain": [ "<matplotlib.figure.Figure at 0x7fccaf9afad0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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GpPUAegELvK64vSJypne+ZlS1OpX7+h7wvt0LyJgotn8HLH7ev6yhhNO5P5w4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\ny4vee50b06JVVMDjZ8KONeG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"text/plain": [ "<matplotlib.figure.Figure at 0x7fccaf536950>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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HAx/E7UhewPr+nCz46C+Q8xWknQXjboPo2AbItIEQg4uItAF+AfRR1ZtFZCBw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\nvYzdAWnlqizdcoCcw8W8tmonf3qrCc6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"text/plain": [ "<matplotlib.figure.Figure at 0x7fccaf689990>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Ty9YnnOek7s/5mnJvTDF/hXLf3K/C2s8ksHUfRLsEtS2dGe0SxBULLglucI/2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\nZGDLX5ExJuKOvKBx+cv2Qnk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"text/plain": [ "<matplotlib.figure.Figure at 0x7fccb2a59ad0>" ] }, "metadata": {}, "output_type": "display_data" }, { "ename": "ValueError", "evalue": "arrays must all be same length", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-28-036e998821d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moverlapping_mz_pairs\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---> 43\u001b[0;31m \u001b[0mplot_overlapping_mz_intensities\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0moverlapping_mz_pairs\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[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<ipython-input-28-036e998821d2>\u001b[0m in \u001b[0;36mplot_overlapping_mz_intensities\u001b[0;34m(df, feature_pair)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mvalue_vars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mvar_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'feature'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m value_name='intensity').dropna(axis=1, how='all')\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;31m# Get mann-whitney values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/irockafe/miniconda2/envs/isaac_revo_healthcare/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mmelt\u001b[0;34m(self, id_vars, value_vars, var_name, value_name, col_level)\u001b[0m\n\u001b[1;32m 4056\u001b[0m return melt(self, id_vars=id_vars, value_vars=value_vars,\n\u001b[1;32m 4057\u001b[0m \u001b[0mvar_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvar_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalue_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4058\u001b[0;31m col_level=col_level)\n\u001b[0m\u001b[1;32m 4059\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4060\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/irockafe/miniconda2/envs/isaac_revo_healthcare/lib/python2.7/site-packages/pandas/core/reshape/reshape.pyc\u001b[0m in \u001b[0;36mmelt\u001b[0;34m(frame, id_vars, value_vars, var_name, value_name, col_level)\u001b[0m\n\u001b[1;32m 771\u001b[0m ._get_level_values(i)).repeat(N)\n\u001b[1;32m 772\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 773\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 774\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/irockafe/miniconda2/envs/isaac_revo_healthcare/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m 273\u001b[0m dtype=dtype, copy=copy)\n\u001b[1;32m 274\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 276\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/irockafe/miniconda2/envs/isaac_revo_healthcare/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_init_dict\u001b[0;34m(self, data, index, columns, dtype)\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0;31m# raise ValueError if only scalars in dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 369\u001b[0;31m \u001b[0mextract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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[0m\u001b[1;32m 370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 371\u001b[0m \u001b[0;31m# prefilter if columns passed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/irockafe/miniconda2/envs/isaac_revo_healthcare/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mextract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 5542\u001b[0m \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_lengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5543\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\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[0;32m-> 5544\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'arrays must all be same length'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5546\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: arrays must all be same length" ] } ], "source": [ "# Get the overlapping features...\n", "# Stack() pivots values and drops nan values - yay!\n", "overlapping_mz_pairs = list(ppm_matrix[ppm_matrix < 6].stack().index)\n", "len(overlapping_mz_pairs)\n", "print overlapping_mz_pairs\n", "\n", "# write a function to get intensities for these features\n", "def plot_overlapping_mz_intensities(df, feature_pair):\n", " '''\n", " GOAL - Take in tuple of feature indices, return Intensity values\n", " for that pair.\n", " INPUT - \n", " df - pandas dataframe. A feature table with \n", " (samples x features), with column\n", " index that has same index as feature_pair\n", " feature_pair - Tuple. Contains indexes to get intensity vals\n", " OUTPUT - \n", " Dataframe of (sample, intensity) for each feature pair\n", " '''\n", " feats = df[list(feature_pair)]\n", " \n", " # convert to tidy data\n", " tidy_feats = feats.melt(id_vars=feats.index,\n", " value_vars=feats.columns,\n", " var_name='feature',\n", " value_name='intensity').dropna(axis=1, how='all')\n", " \n", " # Get mann-whitney values\n", " u, pval_u = stats.mannwhitneyu(df[feature_pair[0]], df[feature_pair[1]])\n", " \n", " # Convert dtype of intensity values! float..?\n", " sns.boxplot(x='feature', y='intensity',\n", " data=tidy_feats)\n", " ax = sns.stripplot(data=tidy_feats,\n", " x='feature', y='intensity',\n", " jitter=True)\n", " plt.title(\"mann-whitney: {u}, pval: {pval:.2e}\".format(\n", " u=u, pval=pval_u))\n", " plt.show()\n", " \n", "\n", "#TODO fix bug here that says \n", "# I'm using different-length arrays\n", "for i in range(0,len(overlapping_mz_pairs)):\n", " plot_overlapping_mz_intensities(df , overlapping_mz_pairs[i])\n", " " ] }, { "cell_type": "code", "execution_count": 252, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A B C\n", "0 1 10 100\n", "1 2 20 200\n", "2 3 30 300\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>feature</th>\n", " <th>intensity</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>A</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>A</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>A</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>B</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>B</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>B</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>C</td>\n", " <td>100</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>C</td>\n", " <td>200</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>C</td>\n", " <td>300</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " feature intensity\n", "0 A 1\n", "1 A 2\n", "2 A 3\n", "3 B 10\n", "4 B 20\n", "5 B 30\n", "6 C 100\n", "7 C 200\n", "8 C 300" ] }, "execution_count": 252, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test = pd.DataFrame({'A': [1,2,3], 'B':[10,20,30], 'C':[100,200,300]})\n", "print test\n", "test.melt(id_vars=test.index, value_vars=test.columns,\n", " var_name='feature', value_name='intensity').dropna(axis=1)" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:isaac_revo_healthcare]", "language": "python", "name": "conda-env-isaac_revo_healthcare-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": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
bosscha/alma-calibrator
notebooks/selecting_source/alma_database_query.ipynb
1
63486
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "\n", "\n", "\n", "\n", "\n", "from astroquery.alma import Alma\n", "from astropy import coordinates\n", "from astropy import units as u\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import sys \n", "\n", "#reload(sys) #! for Unicode conversion problem\n", "#sys.setdefaultencoding('utf8')\n", "\n", "import sqlite3 as sql # to save the database\n", "\n", "from astroquery.ned import Ned # to get redshift\n", "\n", "\n", "# To check the objects in ALMACAL project\n", "# Hard coded\n", "with open('ALMACAL_object.dat') as f:\n", " almacal_list = f.read().splitlines()\n", " \n", "\n", "class databaseQuery:\n", " \"\"\"Collection of function to query and filter the database of \n", " ALMA Calibrator/Archive\"\"\"\n", "\n", " def __init__(self):\n", " pass\n", "\n", " def read_calibratorlist(self, ifile, fluxrange=[0.5, 1.0]):\n", " '''Read a list of calibrators in CSV format \n", " from the Source Catalogue web interface'''\n", " \n", " listcal = []\n", " \n", " with open(ifile, 'r') as fcal:\n", " for line in fcal:\n", " if line[0] != \"#\":\n", " tok = line.split(\",\")\n", " band = tok[0].split(\" \")[1]\n", " alias = tok[13].strip().split(\"|\")\n", " name = alias[0]\n", " alpha2000 = tok[3]\n", " delta2000 = tok[5]\n", " flux = float(tok[7]) # flux density in a particular Band\n", " freq = float(tok[9])\n", " obsdate = tok[2]\n", "\n", "\n", " if (flux >= fluxrange[0] and flux <= fluxrange[1]):\n", " found = False\n", " for i, cal in enumerate(listcal):\n", " # to remove the duplicate\n", " if cal[0] == name:\n", " found = True\n", " cal[4].append(flux)\n", " cal[5].append(band)\n", " cal[6].append(freq)\n", " cal[7].append(obsdate)\n", "\n", " if not found:\n", " coord = coordinates.SkyCoord(alpha2000 + delta2000, unit=(u.hourangle, u.deg), equinox='J2000') \n", " # convert using astropy\n", " listcal.append([name, coord.ra.value, coord.dec.value, alias, [flux], [band], [freq], [obsdate]])\n", "\n", " return(listcal)\n", " \n", " \n", " \n", " def query_object(self, obj, theta = 0.005, science = False, public = True, savedb=False, dbname='calibrators.db'):\n", " \"\"\"\n", " query per object\n", " obj is an array consist of at least [objname, ra, dec, ...]\n", " ra and dec are in degrees\n", " \"\"\"\n", "\n", " region = coordinates.SkyCoord(obj[1], obj[2], unit='deg')\n", "\n", " alma_data = Alma.query_region(region, theta*u.deg, science = science, public = public)\n", " # it is in astropy Table format\n", "\n", " df = alma_data.to_pandas() # convert to pandas Dataframe\n", " \n", " #! change Numpy Masked-Array to str, so it can be converted to SQL\n", " df['Band'] = df['Band'].astype(\"str\")\n", "\n", " # save the query result in sql database \n", " # with Table's name = calibrator's name\n", " if savedb:\n", " conn = sql.connect(dbname)\n", " conn.text_factory = str\n", " if not df.dropna().empty:\n", " df.to_sql(obj[0], conn, if_exists='replace') # if the Table exist in the db, just replace it\n", " \n", "\n", " return df\n", " \n", " \n", " \n", " def query_list(self, listobj, science = False, public = True, savedb=False, dbname='calibrators.db'):\n", " '''\n", " Query the data for the list of ALMA calibrator (bulk)\n", " name is an array consist of at least [objname, ra, dec]\n", " '''\n", " \n", " # result = []\n", " for obj in listobj:\n", " df = self.query_object(obj, science = science, public = public, savedb=savedb, dbname=dbname)\n", " # if not df.dropna().empty:\n", " # result.append([obj[0], df])\n", " \n", " # return(result)\n", " \n", " \n", " \n", " def select_object_from_sqldb(self, dbname, \\\n", " maxFreqRes = 1000.0, \\\n", " array='12m', \\\n", " excludeCycle0=True, \\\n", " selectPol=False, \\\n", " minTimeBand = {3:1e9,6:1e9,7:1e9}, \\\n", " nonALMACAL=False, \\\n", " onlyALMACAL=False, \\\n", " verbose = True, silent=True):\n", " \"\"\"\n", " From the sql database we can select some calibrators based on:\n", " - maxFreqRes : maximum of the frequency resolution (in kHz)\n", " - array : only select project from '12m' array obs.\n", " - excludeCycle0 : if True, ignore project from Cycle 0\n", " - selectPol : if True, only select uid with full pol.\n", " - minTimeBand : dictionary to select the minimum time per band \n", " \n", " Return the names and projects in a report with the date.\n", " \"\"\"\n", " connection = sql.connect(dbname)\n", " connection.text_factory = str # return str, not unicode\n", " cursor = connection.cursor()\n", "\n", " # make a list of calibrator from table name.\n", " cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n", " tablefetch = cursor.fetchall()\n", " tables = [table[0] for table in tablefetch] # remove tuple\n", "\n", " nsource = 0 # number of selected source\n", " finalReport = []\n", " resume = []\n", "\n", " for tab in tables: \n", " # table name is the name of source\n", " # subquery = '['+tab+']'\n", " subquery = \"SELECT * FROM [{0}] WHERE (Array LIKE '%{1}%') AND ([Frequency resolution] < {2})\".format(tab, array, maxFreqRes)\n", "\n", " if selectPol:\n", " subquery += \" AND (`Pol products` LIKE '%XY%')\"\n", "\n", " if excludeCycle0:\n", " subquery += \" AND (`Project code` NOT LIKE '2011.%')\"\n", " \n", " \n", " # and here we execute the query\n", " # total integration time is calculated after selection for other criterias\n", " totalTime = {3:0., 4:0., 5:0., 6:0., 7:0., 8:0., 9:0., 10:0.}\n", " for key in totalTime:\n", " sqlcmd = \"SELECT SUM(Integration) FROM ({0}) WHERE Band LIKE '%{1}%'\".format(subquery, key)\n", " cursor.execute(sqlcmd)\n", " totalTime[key] = cursor.fetchone()[0]\n", "\n", " for key in totalTime: # remove None\n", " if totalTime[key] == None:\n", " totalTime[key] = 0.0\n", "\n", " # last selection is integration time\n", " selectSource = True\n", " sum_time = 0\n", " for key in minTimeBand:\n", " if totalTime[key] < minTimeBand[key]:\n", " selectSource = False\n", "\n", " sum_time += totalTime[key]\n", "\n", " if sum_time == 0.0:\n", " selectSource = False\n", "\n", " if nonALMACAL:\n", " if tab in almacal_list:\n", " selectSource = False\n", "\n", " if onlyALMACAL:\n", " if (selectSource == True) and (tab in almacal_list):\n", " selectSource = True\n", " else:\n", " selectSource = False\n", " \n", " if selectSource:\n", " if not silent:\n", " print('Accepted', tab, totalTime)\n", " \n", " nsource += 1\n", "\n", " if tab in almacal_list:\n", " reportSource = \"\\n######## Source name: {0} (In ALMACAL) ########\\n\".format(tab)\n", " else:\n", " reportSource = \"\\n######## Source name: {0} ########\\n\".format(tab)\n", "\n", "\n", " reportSource += \"\\n\\n\"\n", " for key in totalTime:\n", " if totalTime[key] > 0.:\n", " reportSource += \"Time Band %d : %6.0fs (%3.1fh) \\n\"%(key, totalTime[key], totalTime[key] / 3600.)\n", "\n", " reportSource += \"\\nList of obs:\"\n", " \n", " total_number_of_uids = 0\n", " totalprojects = [] # all project for this source\n", " for band in totalTime:\n", " if totalTime[band] > 0.: # report per Band\n", " reportSource += \"\\n\\n### Band: {0} ###\".format(band)\n", "\n", " sqlcmd = \"SELECT `Project code`, `Source name`, RA, Dec, \"\n", " sqlcmd += \"Band, Integration, `Observation date`, `Spatial resolution`, \"\n", " sqlcmd += \"`Asdm uid`, `Frequency resolution`, Array, `Pol products`, `Galactic longitude`, `Galactic latitude` \" + subquery[8:]\n", " sqlcmd += \" AND Band LIKE '%{0}%'\".format(band)\n", " sqlcmd += \" ORDER BY Integration DESC;\" \n", " # Select some columns from criteria\n", " # Sort it base on integration time \n", " # in each band\n", " \n", " cursor.execute(sqlcmd)\n", " fetch = cursor.fetchall()\n", "\n", " projects = [] # project for this Band\n", " for uid in fetch:\n", " foundProject = False\n", " for p in projects:\n", " if p == uid[0]:\n", " foundProject = True\n", "\n", " if not foundProject:\n", " projects.append(uid[0])\n", "\n", " if verbose:\n", " reportSource += \"\\n{0} {1} ra:{2} dec:{3} Band:{4} Int:{5} Obsdate:{6} SpatRes:{7} ASDM:{8} FreqRes:{9} Array:{10} Pol:{11}\".format(uid[0], uid[1], str(uid[2])[:9], str(uid[3])[:9], \\\n", " uid[4], uid[5], uid[6][:9], str(uid[7])[:5], uid[8], str(uid[9])[:7], uid[10], uid[11])\n", "\n", " number_of_uid = len(fetch)\n", " total_number_of_uids += number_of_uid\n", "\n", " reportSource += \"\\n\\nTotal accepted uid for Band {0} = {1}\".format(band, number_of_uid)\n", " reportSource += \"\\n\\nProject codes for Band %d: \\n\"%(band)\n", "\n", " for p in projects:\n", " reportSource += \"%s \"%(p)\n", "\n", " # Calculate total number of projects\n", " # one project may (likely) have several uids in different band\n", " foundProject = False\n", " for q in totalprojects:\n", " if p == q:\n", " foundProject = True\n", "\n", " if not foundProject:\n", " totalprojects.append(p)\n", "\n", " # Additional notes\n", " reportSource += \"\\n\\nAdditional Notes:\"\n", "\n", " #! Nested try-except\n", " # try to find the object using name, if not found try using region query!\n", " try:\n", " obj_table = Ned.query_object(\"PKS \" + tab)\n", " name = obj_table[0]['Object Name']\n", " z = obj_table[0]['Redshift']\n", " v0 = obj_table[0]['Velocity']\n", " ra = obj_table[0]['RA(deg)']\n", " dec = obj_table[0]['DEC(deg)']\n", " if isinstance(obj_table[i]['Redshift'], np.ma.MaskedArray):\n", " z = None\n", " reportSource += \"\\n\\nNo redshift data found in NED! \" + \"\\nName = \" + str(name) + \"\\nra = \" + str(ra) + \"\\ndec = \" + str(dec)\n", " else:\n", " reportSource += \"\\nName = \" + str(name) + \"\\nz = \" + str(z) + \"\\nvelocity = \" + str(v0) + \"\\nra = \" + str(ra) + \"\\ndec = \" + str(dec)\n", "\n", " except Exception, e1:\n", " try:\n", " co = coordinates.SkyCoord(ra=uid[2], dec=uid[3], unit=(u.deg, u.deg))\n", " obj_table = Ned.query_region(co, radius=0.005*u.deg)\n", "\n", " yohoho = False\n", " for i,_obj in enumerate(obj_table):\n", " name = obj_table[i]['Object Name']\n", " ra = obj_table[i]['RA(deg)']\n", " dec = obj_table[i]['DEC(deg)']\n", " if not isinstance(obj_table[i]['Redshift'], np.ma.MaskedArray):\n", " z = obj_table[i]['Redshift']\n", " v0 = obj_table[i]['Velocity']\n", " reportSource += \"\\n\\nName = \" + str(name) + \"\\nz = \" + str(z) + \"\\nvelocity = \" + str(v0) + \"\\nra = \" + str(ra) + \"\\ndec = \" + str(dec)\n", " yohoho = True\n", " break\n", "\n", " if not yohoho:\n", " reportSource += \"\\n\\nNo redshift data found in NED! \" + \"\\nName = \" + str(name) + \"\\nra = \" + str(ra) + \"\\ndec = \" + str(dec)\n", " z = None # for resume[]\n", "\n", " except Exception, e2:\n", " reportSource += \"\\n\\nNo data found in NED!\\n\" + str(e)\n", " name = None; z = None # for resume[]\n", "\n", " total_number_of_projects = len(totalprojects)\n", " endText = \"\\n\\n###\\nTotal accepted uid for this object = {0}\".format(total_number_of_uids)\n", " endText += \"\\nTotal accepted project for this object = {0}\\n\".format(total_number_of_projects)\n", " endText += \"###############################################\\n\\n\\n\\n\\n\\n\\n\"\n", " reportSource += endText\n", "\n", " finalReport.append([total_number_of_projects, tab, reportSource])\n", " # resume python-list consist of:\n", " # 0,1,2,3,4,5,6: Name, RA, Dec, Name_NED, RA_NED, Dec_NED, z_NED\n", " # 7,8,9,10: Total number of project, total number of UIDS, Gal_lon, Gal_lat\n", " # 11,12,13: Total time in Band 3, 6, 7 \n", " resume.append([tab, uid[2], uid[3], name, ra, dec, z, total_number_of_projects, total_number_of_uids, uid[12], uid[13], totalTime[3], totalTime[6], totalTime[7]])\n", "\n", " else:\n", " if not silent:\n", " print('Not accepted', tab, totalTime)\n", " pass\n", "\n", "\n", " connection.close()\n", " print(\"Number of accepted source: \", nsource)\n", "\n", " # sorting according to the number of uids\n", " finalReportSorted = sorted(finalReport, key=lambda data: data[0])\n", " \n", " return(finalReportSorted, resume)\n", "\n", "\n", "\n", " def make_report_from_sqldb(self, dbname, list_of_obj, \\\n", " maxFreqRes = 1000.0, \\\n", " array='12m', \\\n", " excludeCycle0=True, \\\n", " selectPol=False, \\\n", " verbose = True, silent=True):\n", " \"\"\"\n", " return report for a list of object\n", " \"\"\"\n", " connection = sql.connect(dbname)\n", " connection.text_factory = str # return str, not unicode\n", " cursor = connection.cursor()\n", "\n", " # make a list of calibrator from table name.\n", " cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n", " tablefetch = cursor.fetchall()\n", " tables = [table[0] for table in tablefetch] # remove tuple\n", "\n", " nsource = 0 # number of selected source\n", " finalReport = []\n", " resume = []\n", "\n", " for tab in list_of_obj: \n", " # table name is the name of source\n", " # subquery = '['+tab+']'\n", " subquery = \"SELECT * FROM [{0}] WHERE (Array LIKE '%{1}%') AND ([Frequency resolution] < {2})\".format(tab, array, maxFreqRes)\n", "\n", " if selectPol:\n", " subquery += \" AND (`Pol products` LIKE '%XY%')\"\n", "\n", " if excludeCycle0:\n", " subquery += \" AND (`Project code` NOT LIKE '2011.%')\"\n", " \n", " \n", " # and here we execute the query\n", " # total integration time is calculated after selection for other criterias\n", " totalTime = {3:0., 4:0., 5:0., 6:0., 7:0., 8:0., 9:0., 10:0.}\n", " for key in totalTime:\n", " sqlcmd = \"SELECT SUM(Integration) FROM ({0}) WHERE Band LIKE '%{1}%'\".format(subquery, key)\n", " cursor.execute(sqlcmd)\n", " totalTime[key] = cursor.fetchone()[0]\n", "\n", " for key in totalTime: # remove None\n", " if totalTime[key] == None:\n", " totalTime[key] = 0.0\n", "\n", "\n", " \n", " nsource += 1\n", "\n", " reportSource = \"\\n######## Source name: {0} ########\\n\".format(tab)\n", "\n", " reportSource += \"\\n\\n\"\n", " for key in totalTime:\n", " if totalTime[key] > 0.:\n", " reportSource += \"Time Band %d : %6.0fs (%3.1fh) \\n\"%(key, totalTime[key], totalTime[key] / 3600.)\n", "\n", " reportSource += \"\\nList of obs:\"\n", " \n", " total_number_of_uids = 0\n", " totalprojects = [] # all project for this source\n", " for band in totalTime:\n", " if totalTime[band] > 0.: # report per Band\n", " reportSource += \"\\n\\n### Band: {0} ###\".format(band)\n", "\n", " sqlcmd = \"SELECT `Project code`, `Source name`, RA, Dec, \"\n", " sqlcmd += \"Band, Integration, `Observation date`, `Spatial resolution`, \"\n", " sqlcmd += \"`Asdm uid`, `Frequency resolution`, Array, `Pol products`, `Galactic longitude`, `Galactic latitude` \" + subquery[8:]\n", " sqlcmd += \" AND Band LIKE '%{0}%'\".format(band)\n", " sqlcmd += \" ORDER BY Integration DESC;\" \n", " # Select some columns from criteria\n", " # Sort it base on integration time \n", " # in each band\n", " \n", " cursor.execute(sqlcmd)\n", " fetch = cursor.fetchall()\n", "\n", " projects = [] # project for this Band\n", " for uid in fetch:\n", " foundProject = False\n", " for p in projects:\n", " if p == uid[0]:\n", " foundProject = True\n", "\n", " if not foundProject:\n", " projects.append(uid[0])\n", "\n", " if verbose:\n", " reportSource += \"\\n{0} {1} ra:{2} dec:{3} Band:{4} Int:{5} Obsdate:{6} SpatRes:{7} ASDM:{8} FreqRes:{9} Array:{10} Pol:{11}\".format(uid[0], uid[1], str(uid[2])[:9], str(uid[3])[:9], \\\n", " uid[4], uid[5], uid[6][:9], str(uid[7])[:5], uid[8], str(uid[9])[:7], uid[10], uid[11])\n", "\n", " number_of_uid = len(fetch)\n", " total_number_of_uids += number_of_uid\n", "\n", " reportSource += \"\\n\\nTotal accepted uid for Band {0} = {1}\".format(band, number_of_uid)\n", " reportSource += \"\\n\\nProject codes for Band %d: \\n\"%(band)\n", "\n", " for p in projects:\n", " reportSource += \"%s \"%(p)\n", "\n", " # Calculate total number of projects\n", " # one project may (likely) have several uids in different band\n", " foundProject = False\n", " for q in totalprojects:\n", " if p == q:\n", " foundProject = True\n", "\n", " if not foundProject:\n", " totalprojects.append(p)\n", "\n", " # Additional notes\n", " reportSource += \"\\n\\nAdditional Notes:\"\n", "\n", " #! Nested try-except\n", " # try to find the object using name, if not found try using region query!\n", " try:\n", " obj_table = Ned.query_object(\"PKS \" + tab)\n", " name = obj_table[0]['Object Name']\n", " z = obj_table[0]['Redshift']\n", " v0 = obj_table[0]['Velocity']\n", " ra = obj_table[0]['RA(deg)']\n", " dec = obj_table[0]['DEC(deg)']\n", " if isinstance(obj_table[i]['Redshift'], np.ma.MaskedArray):\n", " z = None\n", " reportSource += \"\\n\\nNo redshift data found in NED! \" + \"\\nName = \" + str(name) + \"\\nra = \" + str(ra) + \"\\ndec = \" + str(dec)\n", " else:\n", " reportSource += \"\\nName = \" + str(name) + \"\\nz = \" + str(z) + \"\\nvelocity = \" + str(v0) + \"\\nra = \" + str(ra) + \"\\ndec = \" + str(dec)\n", "\n", " except Exception, e1:\n", " try:\n", " co = coordinates.SkyCoord(ra=uid[2], dec=uid[3], unit=(u.deg, u.deg))\n", " obj_table = Ned.query_region(co, radius=0.005*u.deg)\n", "\n", " yohoho = False\n", " for i,_obj in enumerate(obj_table):\n", " name = obj_table[i]['Object Name']\n", " ra = obj_table[i]['RA(deg)']\n", " dec = obj_table[i]['DEC(deg)']\n", " if not isinstance(obj_table[i]['Redshift'], np.ma.MaskedArray):\n", " z = obj_table[i]['Redshift']\n", " v0 = obj_table[i]['Velocity']\n", " reportSource += \"\\n\\nName = \" + str(name) + \"\\nz = \" + str(z) + \"\\nvelocity = \" + str(v0) + \"\\nra = \" + str(ra) + \"\\ndec = \" + str(dec)\n", " yohoho = True\n", " break\n", "\n", " if not yohoho:\n", " reportSource += \"\\n\\nNo redshift data found in NED! \" + \"\\nName = \" + str(name) + \"\\nra = \" + str(ra) + \"\\ndec = \" + str(dec)\n", " z = None # for resume[]\n", "\n", " except Exception, e2:\n", " reportSource += \"\\n\\nNo data found in NED!\\n\" + str(e)\n", " name = None; z = None # for resume[]\n", "\n", " total_number_of_projects = len(totalprojects)\n", " endText = \"\\n\\n###\\nTotal accepted uid for this object = {0}\".format(total_number_of_uids)\n", " endText += \"\\nTotal accepted project for this object = {0}\\n\".format(total_number_of_projects)\n", " endText += \"###############################################\\n\\n\\n\\n\\n\\n\\n\"\n", " reportSource += endText\n", "\n", " finalReport.append([total_number_of_projects, tab, reportSource])\n", " # resume python-list consist of:\n", " # 0,1,2,3,4,5,6: Name, RA, Dec, Name_NED, RA_NED, Dec_NED, z_NED\n", " # 7,8,9,10: Total number of project, total number of UIDS, Gal_lon, Gal_lat\n", " # 11,12,13: Total time in Band 3, 6, 7 \n", " resume.append([tab, uid[2], uid[3], name, ra, dec, z, total_number_of_projects, total_number_of_uids, uid[12], uid[13], totalTime[3], totalTime[6], totalTime[7]])\n", "\n", "\n", " connection.close()\n", " print(\"Number of source: \", nsource)\n", "\n", " # sorting according to the number of uids\n", " finalReportSorted = sorted(finalReport, key=lambda data: data[0])\n", " \n", " return(finalReportSorted, resume)\n", "\n", "\n", "\n", " def write_report(self, report, file = \"deepfieldRG.txt\", silent=True):\n", " \"output the final report from selectDeepField\"\n", " \n", " fout = open(file,\"w\")\n", " \n", " nsource = 0\n", " for rep in report:\n", " nsource += 1\n", " fout.write(rep[2])\n", " if not silent:\n", " print(rep[2])\n", " \n", " endText = \"#################################\\n\"\n", " endText += \"### Total Number of Sources : %d \\n\"%(nsource)\n", " \n", " fout.write(endText)\n", " print(endText)\n", " \n", " fout.close()\n", " \n", "\n", "\n", "if __name__==\"__main__\":\n", " file_listcal = \"alma_sourcecat_searchresults_20180419.csv\"\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Download recent list of ALMA Calibrators\n", "\n", "from https://almascience.eso.org/sc/" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "file_listcal = \"alma_sourcecat_searchresults_20180518.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compile calibrator observation based on the ALMA name\n", "\n", "- select based on Flux" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "q = databaseQuery()\n", "\n", "fmin = 0.01\n", "fmax = 9999999\n", "listcal = q.read_calibratorlist(file_listcal, fluxrange=[fmin, fmax])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of calibrator which hase flux > 0.01 Jy: 3259\n" ] } ], "source": [ "print(\"Number of calibrator which hase flux > {0} Jy: {1}\".format(fmin, len(listcal)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Try download list of ALMA observation for a single object" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['J0058-3234',\n", " 14.509292958333331,\n", " -32.572430583333336,\n", " ['J0058-3234', 'J005802-323420', 'J005802-323435'],\n", " [0.099, 0.124, 0.134, 0.097],\n", " ['6', '3', '3', '7'],\n", " [233000000000.0, 103500000000.0, 91500000000.0, 343500000000.0],\n", " ['2018-04-28 00:00:00.0 ',\n", " '2018-04-28 00:00:00.0 ',\n", " '2018-04-28 00:00:00.0 ',\n", " '2016-11-08 00:00:00.0 ']]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "obj = listcal[42]\n", "obj" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Project code</th>\n", " <th>Source name</th>\n", " <th>RA</th>\n", " <th>Dec</th>\n", " <th>Galactic longitude</th>\n", " <th>Galactic latitude</th>\n", " <th>Band</th>\n", " <th>Spatial resolution</th>\n", " <th>Frequency resolution</th>\n", " <th>Array</th>\n", " <th>...</th>\n", " <th>Project title</th>\n", " <th>Project type</th>\n", " <th>Scan intent</th>\n", " <th>Field of view</th>\n", " <th>Largest angular scale</th>\n", " <th>QA2 Status</th>\n", " <th>COUNT</th>\n", " <th>Science keyword</th>\n", " <th>Scientific category</th>\n", " <th>ASA_PROJECT_CODE</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2013.1.00001.S</td>\n", " <td>J0058-3234</td>\n", " <td>14.509293</td>\n", " <td>-32.57243</td>\n", " <td>288.610268</td>\n", " <td>-84.371233</td>\n", " <td>[7]</td>\n", " <td>0.135054</td>\n", " <td>31250.000</td>\n", " <td>12m</td>\n", " <td>...</td>\n", " <td>Witnessing the birth of the red sequence: the ...</td>\n", " <td>S</td>\n", " <td>PHASE WVR</td>\n", " <td>18.340901</td>\n", " <td>0.709944</td>\n", " <td>Y</td>\n", " <td>4.0</td>\n", " <td>Starburst galaxies, Sub-mm Galaxies (SMG)</td>\n", " <td>Active galaxies</td>\n", " <td>2013.1.00001.S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2013.1.00385.S</td>\n", " <td>J0058-3234</td>\n", " <td>14.509293</td>\n", " <td>-32.57243</td>\n", " <td>288.610268</td>\n", " <td>-84.371233</td>\n", " <td>[6]</td>\n", " <td>0.140099</td>\n", " <td>31250.000</td>\n", " <td>12m</td>\n", " <td>...</td>\n", " <td>Probing Star Formation in Quasar Host Galaxies...</td>\n", " <td>S</td>\n", " <td>PHASE WVR</td>\n", " <td>25.793663</td>\n", " <td>0.844787</td>\n", " <td>Y</td>\n", " <td>0.0</td>\n", " <td>Starbursts, star formation, Active Galactic Nu...</td>\n", " <td>Active galaxies</td>\n", " <td>2013.1.00385.S</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2013.1.00273.S</td>\n", " <td>J0058-3234</td>\n", " <td>14.509293</td>\n", " <td>-32.57243</td>\n", " <td>288.610268</td>\n", " <td>-84.371233</td>\n", " <td>[6]</td>\n", " <td>0.206647</td>\n", " <td>7812.500</td>\n", " <td>12m</td>\n", " <td>...</td>\n", " <td>Gas and Dust in Newly Discovered Quasars at z~...</td>\n", " <td>S</td>\n", " <td>PHASE WVR</td>\n", " <td>26.684010</td>\n", " <td>1.127782</td>\n", " <td>Y</td>\n", " <td>1.0</td>\n", " <td>High-z Active Galactic Nuclei (AGN)</td>\n", " <td>Active galaxies</td>\n", " <td>2013.1.00273.S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2015.1.01487.S</td>\n", " <td>J0058-3234</td>\n", " <td>14.509293</td>\n", " <td>-32.57243</td>\n", " <td>288.610268</td>\n", " <td>-84.371233</td>\n", " <td>[3]</td>\n", " <td>0.979892</td>\n", " <td>1953.125</td>\n", " <td>12m</td>\n", " <td>...</td>\n", " <td>Investigation of Molecular Clouds Traced by CI</td>\n", " <td>S</td>\n", " <td>CHECK WVR</td>\n", " <td>61.049212</td>\n", " <td>5.285525</td>\n", " <td>Y</td>\n", " <td>0.0</td>\n", " <td>Active Galactic Nuclei (AGN)/Quasars (QSO), Ga...</td>\n", " <td>Active galaxies</td>\n", " <td>2015.1.01487.S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>4 rows × 36 columns</p>\n", "</div>" ], "text/plain": [ " Project code Source name RA Dec Galactic longitude \\\n", "0 2013.1.00001.S J0058-3234 14.509293 -32.57243 288.610268 \n", "1 2013.1.00385.S J0058-3234 14.509293 -32.57243 288.610268 \n", "2 2013.1.00273.S J0058-3234 14.509293 -32.57243 288.610268 \n", "3 2015.1.01487.S J0058-3234 14.509293 -32.57243 288.610268 \n", "\n", " Galactic latitude Band Spatial resolution Frequency resolution Array \\\n", "0 -84.371233 [7] 0.135054 31250.000 12m \n", "1 -84.371233 [6] 0.140099 31250.000 12m \n", "2 -84.371233 [6] 0.206647 7812.500 12m \n", "3 -84.371233 [3] 0.979892 1953.125 12m \n", "\n", " ... Project title \\\n", "0 ... Witnessing the birth of the red sequence: the ... \n", "1 ... Probing Star Formation in Quasar Host Galaxies... \n", "2 ... Gas and Dust in Newly Discovered Quasars at z~... \n", "3 ... Investigation of Molecular Clouds Traced by CI \n", "\n", " Project type Scan intent Field of view Largest angular scale QA2 Status \\\n", "0 S PHASE WVR 18.340901 0.709944 Y \n", "1 S PHASE WVR 25.793663 0.844787 Y \n", "2 S PHASE WVR 26.684010 1.127782 Y \n", "3 S CHECK WVR 61.049212 5.285525 Y \n", "\n", " COUNT Science keyword \\\n", "0 4.0 Starburst galaxies, Sub-mm Galaxies (SMG) \n", "1 0.0 Starbursts, star formation, Active Galactic Nu... \n", "2 1.0 High-z Active Galactic Nuclei (AGN) \n", "3 0.0 Active Galactic Nuclei (AGN)/Quasars (QSO), Ga... \n", "\n", " Scientific category ASA_PROJECT_CODE \n", "0 Active galaxies 2013.1.00001.S \n", "1 Active galaxies 2013.1.00385.S \n", "2 Active galaxies 2013.1.00273.S \n", "3 Active galaxies 2015.1.01487.S \n", "\n", "[4 rows x 36 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q.query_object(obj)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Query list of ALMA observation for all of the selected calibrators\n", "\n", "* save it to the SQL\n", "* take a bit long time (several ten minutes) -> download nearly all database (not data) of ALMA " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/scratch/home/rwibowo/anaconda2/lib/python2.7/site-packages/pandas/core/generic.py:1534: UserWarning: The spaces in these column names will not be changed. In pandas versions < 0.14, spaces were converted to underscores.\n", " chunksize=chunksize, dtype=dtype)\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-464ba0590d5c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlistcal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msavedb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdbname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'calibrators_brighterthan_0.01Jy_20180518.db'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-1-9b36d45f5283>\u001b[0m in \u001b[0;36mquery_list\u001b[0;34m(self, listobj, science, public, savedb, dbname)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;31m# result = []\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlistobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscience\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscience\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpublic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpublic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msavedb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msavedb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdbname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdbname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;31m# if not df.dropna().empty:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# result.append([obj[0], df])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<ipython-input-1-9b36d45f5283>\u001b[0m in \u001b[0;36mquery_object\u001b[0;34m(self, obj, theta, science, public, savedb, dbname)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0mregion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoordinates\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSkyCoord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'deg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m \u001b[0malma_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAlma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery_region\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mregion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtheta\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscience\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscience\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpublic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpublic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 87\u001b[0m \u001b[0;31m# it is in astropy Table format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/scratch/home/rwibowo/anaconda2/lib/python2.7/site-packages/astroquery/utils/class_or_instance.pyc\u001b[0m in \u001b[0;36mf\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/scratch/home/rwibowo/anaconda2/lib/python2.7/site-packages/astroquery/utils/process_asyncs.pyc\u001b[0m in \u001b[0;36mnewmethod\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'verbose'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masync_method_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get_query_payload'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'field_help'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/scratch/home/rwibowo/anaconda2/lib/python2.7/site-packages/astroquery/alma/core.pyc\u001b[0m in \u001b[0;36mquery_region_async\u001b[0;34m(self, coordinate, radius, cache, public, science, payload, **kwargs)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m return self.query_async(payload, cache=cache, public=public,\n\u001b[0;32m--> 106\u001b[0;31m science=science, **kwargs)\n\u001b[0m\u001b[1;32m 107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 108\u001b[0m def query_async(self, payload, cache=True, public=True, science=True,\n", "\u001b[0;32m/scratch/home/rwibowo/anaconda2/lib/python2.7/site-packages/astroquery/alma/core.pyc\u001b[0m in \u001b[0;36mquery_async\u001b[0;34m(self, payload, cache, public, science, max_retries, get_html_version, get_query_payload, **kwargs)\u001b[0m\n\u001b[1;32m 150\u001b[0m response = self._request('GET', url, params=payload,\n\u001b[1;32m 151\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTIMEOUT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m cache=cache and not get_html_version)\n\u001b[0m\u001b[1;32m 153\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/scratch/home/rwibowo/anaconda2/lib/python2.7/site-packages/astroquery/query.pyc\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, method, url, params, data, headers, files, save, savedir, timeout, cache, stream, auth, continuation, verify)\u001b[0m\n\u001b[1;32m 203\u001b[0m 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509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 510\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/scratch/home/rwibowo/anaconda2/lib/python2.7/site-packages/requests/sessions.pyc\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 639\u001b[0m \u001b[0;31m# Resolve redirects if allowed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 640\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mresp\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mresp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_redirects\u001b[0m \u001b[0;32melse\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 641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 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"\u001b[0;32m/scratch/home/rwibowo/anaconda2/lib/python2.7/site-packages/requests/sessions.pyc\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 618\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 620\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/scratch/home/rwibowo/anaconda2/lib/python2.7/site-packages/requests/adapters.pyc\u001b[0m in 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599\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout_obj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 600\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 603\u001b[0m \u001b[0;31m# If we're going to release the connection in ``finally:``, then\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/scratch/home/rwibowo/anaconda2/lib/python2.7/site-packages/urllib3/connectionpool.pyc\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 378\u001b[0m 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response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 438\u001b[0;31m \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\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 439\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/scratch/home/rwibowo/anaconda2/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 392\u001b[0m \u001b[0;32mdef\u001b[0m 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\u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 482\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mEINTR\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "res = q.query_list(listcal, savedb=True, dbname='calibrators_brighterthan_0.01Jy_20180518.db')" ] }, { "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": 2 }
gpl-2.0
gully/adrasteia
notebooks/adrasteia_05-03_DR2_variability_catalog_rotational_modulation.ipynb
1
644629
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gaia DR2 variability catalogs\n", "## Part II: Rotation modulation\n", "\n", "In this notebook we explore what's in the `VariRotationModulation` catalog from Gaia DR2. We eventually cross-match it with K2 and see what it looks like!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "gully \n", "May 2, 2018" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# %load /Users/obsidian/Desktop/defaults.py\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "150M\t../data/dr2/Gaia/gdr2/vari_rotation_modulation/csv\r\n" ] } ], "source": [ "! du -hs ../data/dr2/Gaia/gdr2/vari_rotation_modulation/csv" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "df0 = pd.read_csv('../data/dr2/Gaia/gdr2/vari_rotation_modulation/csv/VariRotationModulation_0.csv.gz')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(867, 40)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df0.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The catalog has many columns. What are they?" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['solution_id', 'source_id', 'num_segments', 'segments_start_time',\n", " 'segments_end_time', 'segments_colour_mag_intercept',\n", " 'segments_colour_mag_intercept_error', 'segments_colour_mag_slope',\n", " 'segments_colour_mag_slope_error', 'segments_correlation_coefficient',\n", " 'segments_correlation_significance', 'num_outliers', 'outliers_time',\n", " 'segments_rotation_period', 'segments_rotation_period_error',\n", " 'segments_rotation_period_fap', 'segments_cos_term',\n", " 'segments_cos_term_error', 'segments_sin_term',\n", " 'segments_sin_term_error', 'segments_a0_term', 'segments_a0_term_error',\n", " 'best_rotation_period', 'best_rotation_period_error',\n", " 'segments_activity_index', 'segments_activity_index_error',\n", " 'max_activity_index', 'max_activity_index_error',\n", " 'segments_g_unspotted', 'segments_g_unspotted_error',\n", " 'segments_bp_unspotted', 'segments_bp_unspotted_error',\n", " 'segments_rp_unspotted', 'segments_rp_unspotted_error', 'g_unspotted',\n", " 'g_unspotted_error', 'bp_unspotted', 'bp_unspotted_error',\n", " 'rp_unspotted', 'rp_unspotted_error'],\n", " dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df0.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gaia [Documentation section 14.3.6](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_variability_tables/ssec_dm_vari_rotation_modulation.html) explains that some of the columns are populated with *arrays*! So this catalog can be thought of as a **table-of-tables**. The typical length of the tables are small, usually just 3-5 entries." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 867.000000\n", "mean 4.173010\n", "std 2.019549\n", "min 2.000000\n", "25% 3.000000\n", "50% 3.000000\n", "75% 5.000000\n", "max 11.000000\n", "Name: num_segments, dtype: float64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df0.num_segments.describe()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "solution_id 369295549951641967\n", "source_id 465678461815126784\n", "num_segments 2\n", "segments_start_time (2207.368642951862, 1702.825995034191)\n", "segments_end_time (2316.4045885307028, 2316.4045885307028)\n", "segments_colour_mag_intercept (-50.094787274038154, -36.16323154910394)\n", "segments_colour_mag_intercept_error (34.55610214479653, 17.249166642259794)\n", "segments_colour_mag_slope (2.8918922153964335, 2.1154336243891634)\n", "segments_colour_mag_slope_error (1.9194992883441295, 0.95817078987832)\n", "segments_correlation_coefficient (0.3432064980018941, 0.3635754393410782)\n", "segments_correlation_significance (0.150272879305833, 0.03454667362405828)\n", "num_outliers 4\n", "outliers_time (2207.3690008360045, 2024.2847759478498, 2112....\n", "segments_rotation_period (1.0097529853518117, 1.0118912593562108)\n", "segments_rotation_period_error (9.563094215915504E-6, 3.404546189977204E-7)\n", "segments_rotation_period_fap (0.039119039050390235, 7.684215114023683E-4)\n", "segments_cos_term (-0.03318348481268152, 0.030044849925530914)\n", "segments_cos_term_error (0.005322937166477601, 0.00413492960299342)\n", "segments_sin_term (-0.0049743092658812715, -0.006336642834650852)\n", "segments_sin_term_error (0.006703833085778709, 0.004882316466297736)\n", "segments_a0_term (17.991155584622344, 17.991533299461693)\n", "segments_a0_term_error (0.004123846470788413, 0.003135500658618222)\n", "best_rotation_period 1.01082\n", "best_rotation_period_error 0.00151199\n", "segments_activity_index (0.09114921265707565, 0.09114921265707565)\n", "segments_activity_index_error (0.012336609384998464, 0.012336609384998464)\n", "max_activity_index 0.0911492\n", "max_activity_index_error 0.0123366\n", "segments_g_unspotted (17.931215620540097, 17.931215620540097)\n", "segments_g_unspotted_error (0.004102846388999643, 0.004102846388999643)\n", "segments_bp_unspotted (16.566937661127213, 16.566937661127213)\n", "segments_bp_unspotted_error (0.019605674707414474, 0.019605674707414474)\n", "segments_rp_unspotted (16.965031996266656, 16.965031996266656)\n", "segments_rp_unspotted_error (0.03487408382630188, 0.03487408382630188)\n", "g_unspotted 17.9312\n", "g_unspotted_error 0.00410285\n", "bp_unspotted 16.5669\n", "bp_unspotted_error 0.0196057\n", "rp_unspotted 16.965\n", "rp_unspotted_error 0.0348741\n", "Name: 1, dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df0.loc[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I think the segments further consist of lightcurves, for which merely the summary statistics are listed here, but I'm not sure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since all the files are still only 150 MB, we can just read in all the files and concatenate them." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import glob" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "fns = glob.glob('../data/dr2/Gaia/gdr2/vari_rotation_modulation/csv/VariRotationModulation_*.csv.gz')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "n_files = len(fns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This step only takes a few seconds. Let's use a progress bar to keep track." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from astropy.utils.console import ProgressBar" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c9a2d065743247389a3f18dab2d8af45", "version_major": 2, "version_minor": 0 }, "text/plain": [ "A Jupyter Widget" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "df_rotmod = pd.DataFrame()\n", "with ProgressBar(n_files, ipython_widget=True) as bar:\n", " for i, fn in enumerate(fns):\n", " df_i = pd.read_csv(fn)\n", " df_rotmod = df_rotmod.append(df_i, ignore_index=True)\n", " bar.update()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(147535, 40)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_rotmod.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have 147,535 rotationally modulated variable stars. What is the typical number of segments across the entire catalog?" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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zc3MTLAUAAFZv0KDQWvvjIY+3AexK8uru60tmZ2e3T7IYAABYraF7FFiitTafZD5Jqmr/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\n", "text/plain": [ "<matplotlib.figure.Figure at 0xa16db4a20>" ] }, "metadata": { "image/png": { "height": 266, "width": 389 } }, "output_type": "display_data" } ], "source": [ "df_rotmod.num_segments.hist(bins=11)\n", "plt.yscale('log')\n", "plt.xlabel('$N_{\\mathrm{segments}}$')\n", "plt.ylabel('occurence');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What are these segments? Are they the arbitrary Gaia segments, or are they something else?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's ask our first question: **What are the distribution of periods?**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">`best_rotation_period` : Best rotation period (double, Time[day])\n", "\n", ">this field is an estimate of the stellar rotation period and is obtained by averaging the periods obtained in the different segments " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'$N$')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5Osmjkjw+ky5HLxiinK21xSSLXZlv37Vrx+cyAAB2iFEGhSQnJ3nqqmXHd1OSfDzJV4NC\na+36qjo1ya8kOSPJk5LclORlSS5orX12iEK6RwEAgLEaZVBorZ2f5PxD3OcTSZ42RHkO4Jwk5y2/\ncI8CAABjMcqgMCIXJbm4+/nN8/Pze6ZZGIDtsHsLHpC2/CC8WX/YGsBOJigMyD0KAACMlStXAACg\nR4sCQGcruswAwE4hKAzIqEcAAIyVoDAsox4BM00rCgDrcY/CsDyZGQCAUdKiMCCjHgEAMFauXAEA\ngB5BAQAA6NH1aEBGPQIAYKy0KAzrnCQ3dNMeox4BADAWgsKwjHoEAMAo6Xo0IKMeAQAwVq5cAQCA\nHkEBAADoERQAAIAe9ygMyPCoAACMlRaFYRkeFQCAURIUhmV4VAAARukuB4Wq+oGqOm4rC7PTtNYW\nW2s3ttZuTGJ4VAAARmMz9yj8SZJWVf+U5L0rp9aaPjYAADBimwkKv5PklCTfkuTMbmpJUlU3ph8e\nbt5cUQEAgO1yl4NCa+0ZSVJVX5fk5CSnrpgemuQHumk5PPxjkr9rrf3oJssMADNj975LtvyY+y88\nc8uPCXCoNj08amvtK0n+tpuSJFV1VJJvy53Dw0lJvmmz5wMAAIY3yHMUWmu3Jbmiqu7IJBwcPcR5\nAACAYWx5UKiqU5P8aJIfySQkVJKbkrwyyR9t9flmmQeuAQAwVlsSFKrqEflaONidfjj469Za24pz\njcw5Sc5bfuGBawAAjMVdDgpVdXK+Fg6OzyQc3JjkFZmEg3cdpuFgpYuSXNz9/Ob5+fk90ywMAABs\n1GZaFN6XyYhGwsE6WmuLSRaTpKo8cA0AgNHYiivXG5PcLck3J/mWqnI1DAAAI7eZFoVLMxkC9du7\nabkl4baq+l+ZPGjtym5+tZYGAAAYj808cO17kqSqjs+dn5fwbUke003L4eCLVfWBTB649p82VWIA\nAGBwW/HAtY8m+WiSP1xeVlUn5M7h4eQkpyV5bBJBAQAAZtxQD1y7Nsm1Sf5HklRVJXloklOGOB8A\nALC1BgkKq3X3J3y4mwAAgBlnhCIAAKBHUAAAAHq2pevR4aqq5pLMdS+PXFpammZxAABgw7QoDOuc\nJDd0056FhYUpFwcAADZGUBjWRUmO66ar5ufnp1wcAADYGF2PBtRaW0yymCRVdfuuXXIZAAe3e98l\nW3q8/ReeuaXHAw4PrlwBAIAeQQEAAOgRFAAAgB5BAQAA6BEUAACAHkEBAADoMTwqAMBBGLKWw5EW\nBQAAoEdQAAAAenQ9AoAdbiu6zZy7544kydn7LtFtBg4TWhQAAIAeQQEAAOjR9WhAVTWXZK57eeTS\n0tI0iwMAABumRWFY5yS5oZv2LCwsTLk4AACwMVoUhnVRkou7n988Pz+/Z5qFAYDDxVY/9wAOR4LC\ngFpri0kWk6Sqbt+1SwMOAADjICgAAIdkiG/rDbkKs8dX3AAAQI+gAAAA9AgKAABAj6AAAAD0uJkZ\nAJg6w5nC7NGiAAAA9GhRAADYZlvVgnLunjuSJGfvu8QQs2w5LQoAAECPoAAAAPToegQAsANs9Q3h\nujKhRQEAAOgRFAAAgB5BAQAA6BEUAACAHkEBAADoERTWUFW/WFV/V1Wfr6pPV9WfVdXDpl0uAADY\nLoLC2vYmeVWSxyY5PckdSf6qqu4zzUIBAMB28RyFNbTWvnfl66p6SpJbkpyW5M+mUigAANhGo2xR\nqKonV9XLq+qdXfegVlWvP8g+D6yq11TVjVX15araX1Uvrap7b+CUc5n8rj63JW8AAABm3FhbFF6Y\n5OFJvpDkk0lOOtDGVfXgJFckuX+SNya5JskjkzwvyRlVdVpr7eYDHOI3knwgybs3X3QAAJh9o2xR\nSPL8JCckOSbJszaw/asyCQnPba2d1Vrb11o7PclLkpyY5EXr7VhVv57kO5L8cGvtXzZdcgAAGIFR\nBoXW2mWttetaa+1g21bV8UmemGR/kleuWn1ekluTPKWqjl5j35ck+bEkp7fWPrrpggMAwEiMMigc\notO7+Vtba0srV7TWFpO8K8k9kjx65bqq+o0kP55JSLhmOwoKAACzYqz3KByKE7v5teusvy6TFocT\nklyaJFX1yiRPSXJWks9V1QO6bb/QWvvCgU5WVVeus+qkxcXFXH755YdQ9LtmcXExSe50rnP33DH4\neTmw+aMmc3UxW9TLbFIvs0edzKYh62U7rll2qrWuxaZx/s06HFoUju3mt6yzfnn5vVYse3YmIx1d\nmuSmFdO5QxQQAABmzeHQonAw1c2/er9Da63W2fagWmunrHmSqivn5uYesXfv3rt66A1bTq8rz3X2\nvksGPy8Htvxtz4uv8rGbJeplNqmX2aNOZtOQ9bL/J/Zu+TEPF2tdi22nubm5LTnO4dCisNxicOw6\n649ZtR0AABz2DoevBT7SzU9YZ/1Duvl69zDcZVU1l0kXpiQ5cmlp6UCbAwDAzDgcWhQu6+ZPrKo7\nvd/uQv60JLclec8A5z4nyQ3dtGdhYWGAUwAAwNbb8UGhtXZ9krcm2Z3kOatWX5Dk6CSva63dOsDp\nL0pyXDddNT8/P8ApAABg642y61FVnZXJ0KVJsjx06WOq6rXdz59pra0coejZSa5I8rKqekKSq5M8\nKsnjM+ly9IIhytk9p2GxK/Ptu3bt+FwGAMAOMcqgkOTkJE9dtez4bkqSj2fFUKatteur6tQkv5Lk\njCRPymS405cluaC19tnBSwwAACMyyqDQWjs/yfmHuM8nkjxtiPKsx83MAACMlb4ww3IzMwAAoyQo\nDMvNzAAAjNIoux6NhZuZAQAYK1euAABAjxaFAbmZGQCAsdKiMCw3MwMAMEqCwrDczAwAwCjpejQg\nNzMDADBWrlwBAIAeQQEAAOjR9WhARj0CAGCstCgMy6hHAACMkqAwLKMeAQAwSroeDcioRwAAjJUr\nVwAAoEdQAAAAegQFAACgR1AAAAB63Mw8IM9RAABgrLQoDMtzFAAAGCUtCsO6KMnF3c9vnp+f3zPN\nwgAAbNTufZds6fH2X3jmlh6P4QkKA/IcBQAAxsqVKwAA0CMoAAAAPYICAADQIygAAAA9ggIAANBj\n1KMBeeAaAABjpUVhWB64BgDAKAkKw7ooyXHddNX8/PyUiwMAABuj69GAPHANAICxcuUKAAD0CAoA\nAECPoAAAAPQICgAAQI+gAAAA9AgKAABAj6AAAAD0eI7CgKpqLslc9/LIpaWlaRYHAAA2TIvCsM5J\nckM37VlYWJhycQAAYGMEhWFdlOS4brpqfn5+ysUBAICN0fVoQK21xSSLSVJVt+/aJZcBADAOrlwB\nAIAeQQEAAOgRFAAAgB5BAQAA6BEUAACAHkEBAADoERQAAIAeQQEAAOgRFAAAgB5BAQAA6BEUAACA\nHkEBAADoERQAAICeI6ZdgJ2squaSzHUvj1xaWppmcQAAYMO0KAzrnCQ3dNOehYWFKRcHAAA2RlAY\n1kVJjuumq+bn56dcHAAA2BhdjwbUWltMspgkVXX7rl1yGQAA4+DKFQAA6BEUAACAHkEBAADoERQA\nAIAeQQEAAOgRFAAAgB5BAQAA6BEUAACAHg9cAwBgcLv3XbLlx9x/4Zlbfky+RosCAADQIygAAAA9\nggIAANAjKAAAAD2CAgAA0CMoAAAAPYICAADQIyisoaoeV1X/X1XdUFWtqs6edpkAAGA7CQpru2eS\nDyV5XpLbplwWAADYdp7MvIbW2puSvClJquq10y0NAABsv1G2KFTVk6vq5VX1zqr6fNc96PUH2eeB\nVfWaqrqxqr5cVfur6qVVde/tKjcAAIzFWFsUXpjk4Um+kOSTSU460MZV9eAkVyS5f5I3JrkmySMz\n6Vp0RlWd1lq7edASAwDAiIyyRSHJ85OckOSYJM/awPavyiQkPLe1dlZrbV9r7fQkL0lyYpIXDVZS\nAAAYoVEGhdbaZa2161pr7WDbVtXxSZ6YZH+SV65afV6SW5M8paqO3vKCAgDASI2169GhOL2bv7W1\ntrRyRWttsarelUmQeHSSSzd7sqq6cp1VJy0uLubyyy/f7CkOanFxMUnudK5z99wx+Hk5sPmjJnN1\nMVvUy2xSL7NHncymw71etuO66q5Y61psGuffrFG2KByiE7v5teusv66bn7C8oKruWVUnV9XJmfyO\nvql7/U0DlhMAAGbG4dCicGw3v2Wd9cvL77Vi2alJLlvx+oJu+r0kZx/oZK21U9ZaXlVXzs3NPWLv\n3r0HKe7mLafXlec6e98lg5+XA1v+tufFVx0OH7vxUC+zSb3MHnUymw73etn/E3unXYQ1rXUttp3m\n5ua25DiH57+qO6tu/tX7HVprl69YDgAAh53DoevRcovBseusP2bVdgAAcNg7HFoUPtLNT1hn/UO6\n+Xr3MNxlVTWXZLnt58ilpaUDbQ4AADPjcGhRWL7X4IlVdaf3213In5bktiTvGeDc5yS5oZv2LCws\nDHAKAADYejs+KLTWrk/y1iS7kzxn1eoLkhyd5HWttVsHOP1FSY7rpqvm5+cHOAUAAGy9UXY9qqqz\nkpzVvXxAN39MVb22+/kzrbVzV+zy7CRXJHlZVT0hydVJHpXk8Zl0OXrBEOVsrS0mWezKfPuuXTs+\nlwEAsEOMMigkOTnJU1ctO76bkuTjSb4aFFpr11fVqUl+JckZSZ6U5KYkL0tyQWvts0MU0j0KAACM\n1SiDQmvt/CTnH+I+n0jytCHKcwDnJDlv+YV7FAAAGAt9YYblHgUAAEZplC0KY+EeBQAAxsqVKwAA\n0CMoAAAAPboeDcioRwAAjJUWhWF5MjMAAKMkKAzLqEcAAIySrkcDMuoRAABj5coVAADoERQAAIAe\nXY8GZNQjAADGSovCsIx6BADAKAkKwzLqEQAAo6Tr0YCMegQAwFi5cgUAAHoEBQAAoEdQAAAAetyj\nMCDDowIAMFaCwrDOSXLe8gvDowIAzK7d+y7ZkuOcu+eOJMneLTna9Oh6NCzDowIAMEpaFAZkeFQA\nAMbKlSsAANAjKAAAAD2CAgAA0CMoAAAAPYICAADQY9SjAXngGgAAY6VFYVjnJLmhm/Z44BoAAGMh\nKAzLA9cAABglXY8G5IFrAACMlStXAACgR1AAAAB6BAUAAKBHUAAAAHoEBQAAoEdQAAAAegQFAACg\nR1AAAAB6PHBtQFU1l2Sue3nk0tLSNIsDAAAbJigM65wk5y2/WFhYmGJRAAB2lt37Lpl2EXY0XY+G\ndVGS47rpqvn5+SkXBwAANkaLwoBaa4tJFpOkqm7ftUsuAwBgHFy5AgAAPYICAADQIygAAAA9ggIA\nANAjKAAAAD2CAgAA0CMoAAAAPYICAADQIygAAAA9ggIAANAjKAAAAD2CAgAA0HPEtAuwk1XVXJK5\n7uWRS0tL0ywOAABsmBaFYZ2T5IZu2rOwsDDl4gAAwMYICsO6KMlx3XTV/Pz8lIsDAAAbo+vRgFpr\ni0kWk6Sqbt+1Sy4DAGAcqrU27TIcFqrq5qOOOuo+D33oQwc/1+LiYpJkbm7uq8s+dMMtg5+XA5s/\navJZW7itplwSVlIvs0m9zB51MpvUy2xarpdvuM+9pnL+q6++OrfddttnW2v33cxxBIVtUlUfS3JM\nkv3bcLqTuvk123AuNk69zCb1MpvUy+xRJ7NJvcymadfL7iSfb609aDMHERR2oKq6Mklaa6dMuyx8\njXqZTeplNqmX2aNOZpN6mU07pV50mgcAAHoEBQAAoEdQAAAAegQFAACgR1AAAAB6jHoEAAD0aFEA\nAAB6BAUAAKBHUAAAAHoEBQAAoEdQAAAAegQFAACgR1AAAAB6BIUdpKoeWFWvqaobq+rLVbW/ql5a\nVfeedtl2sqp6clW9vKreWVWfr6pWVa8/yD6Prao3VdVnq+qLVfXBqvq5qrrbdpV7J6uq+1bV06vq\nT6rqH6rqtqq6par+uqp+qqrW/NunXoZXVb9WVZdW1Se6evlsVb2/qs6rqvuus4962WZV9ZTub1mr\nqqevs82/qarLu8/WF6rqb6rqqdtd1p2s+3+8rTN9ap19fF62QVV9Z1W9oapu6q65bqqqt1bVk9bY\ndrR14oFrO0RVPTjJFUnun+SNSa5J8sgkj0/ykSSntdZunl4Jd66q+kCShyf5QpJPJjkpye+31n5y\nne1/IMkbknwpyf9M8tkk35/kxCR/3Fr7ke0o905WVT+T5DeT3JTksiT/mGQ+yQ8lOTaT3/+PtBV/\nANXL9qiqryR5X5IPJ/mnJEdc8+/aAAAOlUlEQVQneXSSU5PcmOTRrbVPrNhevWyzqvpXSa5Kcrck\n90zyjNbab6/a5j8meXmSmzOpl68keXKSBya5qLV27rYWeoeqqv1J7pXkpWus/kJr7cWrtvd52QZV\n9cIk/1eSzyT580z+r7lfkm9Lcllr7edXbDvuOmmtmXbAlOQtSVqSn121/Ne75a+edhl36pRJGHtI\nkkqyt/t9v36dbY/J5OLoy0lOXbH86zMJei3Jv5v2exr7lOT0TP4Q71q1/AGZhIaW5IfVy1Tq5uvX\nWf6i7vf8KvUy1fqpJH+V5Pok/7X7HT991Ta7M7nouTnJ7hXL753kH7p9HjPt97ITpiT7k+zf4LY+\nL9tTJz/S/S7/MsncGuuP3El1ouvRDlBVxyd5YiZ/UF65avV5SW5N8pSqOnqbi3ZYaK1d1lq7rnWf\n/oN4cpJvSPIHrbX3rjjGl5K8sHv5rAGKeVhprb2ttfZnrbWlVcs/leTV3cu9K1apl23S/U7X8ofd\n/CErlqmX7ffcTIL20zL5v2Mt/yHJ3ZO8orW2f3lha+1zSf5L9/JnBiwja/N5GVjXbfXXknwxyY+3\n1hZXb9Nau33Fy9HXyRHTLgBb4vRu/tY1LowWq+pdmQSJRye5dLsLx50s19Wb11j3jkz++Dy2qu7e\nWvvy9hXrsLL8R/yOFcvUy/R9fzf/4Ipl6mUbVdVDk1yY5Ddaa++oqtPX2fRA9fIXq7Zh8+5eVT+Z\n5JsyCW8fTPKO1tq/rNrO52V4j03yoCR/nORzVXVmkodl0sL2t621d6/afvR1IijsDCd282vXWX9d\nJkHhhAgK07ZuXbXW7qiqjyX5liTHJ7l6Owt2OKiqI5L8++7lyj/c6mWbVdW5mfR/PzaT+xO+I5ML\noAtXbKZetkn32fjvmXTN+6WDbH6germpqm5N8sCqukdr7YtbW9LD0gMyqZuVPlZVT2utvX3FMp+X\n4X17N1/I5F6rPStXVtU7kjy5tfbpbtHo60TXo53h2G5+yzrrl5ffaxvKwoGpq+m6MJNvf97UWnvL\niuXqZfudm0nXyJ/LJCS8OckTV/wHm6iX7fSfM7kR8+zW2m0H2Xaj9XLsOuvZuN9N8oRMwsLRmVyY\n/lYm94n8RVU9fMW2Pi/Du383/5kkRyX57iRzmfy/8pYkj0vyRyu2H32dCAqHh+rmhriafepqIFX1\n3CTnZDIi2FMOdfdurl62SGvtAa21yuQC6Icy+Ubt/VX1iEM4jHrZAlX1yExaES5ao+vEXTpkN1cv\nm9Rau6C752qhtfbF1tqHWms/k8lAJUclOf8QDqdeNm95ONPKpOXg0tbaF1prf5/kBzMZ+fC7quox\nGzzezNeJoLAzHOzbm2NWbcf0qKspqKrnJPmNTIbkfHxr7bOrNlEvU9JdAP1JJt0j75vkdStWq5eB\nrehydG2SX97gbhutl89vomgc2PKgDI9bscznZXif6+Yfba39r5Urupa45ZbqR3bz0deJoLAzfKSb\nn7DO+uVRRNa7h4Hts25ddf9hPyiTm2w/up2F2smq6ueSvCLJhzIJCWs9pEi9TFlr7eOZBLlvqar7\ndYvVy/Dumcnv96FJvrTygV6ZdA1Lkv/WLVsey/9A9fKNmXSR+aT7Ewb1T9185WiGPi/DW/4d//M6\n65eDxFGrth9tnQgKO8Nl3fyJq584W1VzSU5LcluS92x3weh5Wzc/Y411j0tyjyRXzOroB2NTVb+Q\n5CVJPpBJSPindTZVL7Phf+vmy6O5qJfhfTnJ76wzvb/b5q+718vdkg5UL9+3ahuGsdy1ZeUFps/L\n8N6RyYX9Q6rq69ZY/7Buvr+bj79Opv0gB9PWTPHAtZmYsrEHrn06I374ylimTLpRtCTvTXKfg2yr\nXranTk5K8oA1lu/K1x649i71MhtTJv3f13rg2oPigWvb8fv/lrX+diX515mMZtiS/NKK5T4v21Mv\nr+9+l7+6avn3JFnKpLXhXjulTqorMCNXVQ/O5B/d/ZO8MZNhth6VyVODr03y2NbazdMr4c5VVWcl\nOat7+YAk35vJtzzv7JZ9prV27qrt/ziT/2j/IJPHuf/bdI9zT/KjzQdzU6rqqUlem8k30y/P2v0/\n97fWXrtiH/UysK4b2H/N5Fu56zO50JxP8l2Z3Mz8qSRPaK19eMU+6mVKqur8TLofPaO19tur1v1s\nkpdlUof/M8lXMnm41AMzuSn63LAp3e9/Xya9Bj6WZDHJg5OcmcmF5puS/GBr7Ssr9vF5GVhV3T/J\nu5J8cyb/z/9tJuHtBzO58P/x1tofrdh+3HUy7aRi2ropyb/KZCi1mzL5o/3xTG7gPOC3qaZN/97P\nz+SPw3rT/jX2OS2TP/Kfy6Rb2FVJnp/kbtN+Pzth2kCdtCSXq5dtr5eHZfL0+A8k+UwmTfi3JPm7\nrs7W/FulXqZWX8ufo6evs/77k7w9kwvYW7t6fOq0y71TpkwC9P+byUht/5zJwyI/neQvM3keTK2z\nn8/L8HVzn0x6bHysu966OZMvaR+90+pEiwIAANDjZmYAAKBHUAAAAHoEBQAAoEdQAAAAegQFAACg\nR1AAAAB6BAUAAKBHUAAAAHoEBQAAoEdQAAAAegQFAACgR1AAAAB6BAUAAKBHUABg21XV3qpqK6Zr\nDnH//VW1f6DibUpV3W/Ve2vTLhPAXXHEtAsAwPCq6i+TfPeqxZ9Lcn2SVyd5TWttGhe0b09yeZLP\nTOHcQ/likgu6n89O8q+nVxSAu05QADg8PCLJUpJfTdIyaVH+5iRPTvLbmVzM/ucplOvy1tr5Uzjv\nYFprX0xyfjJpOYmgAIyUoACww1XVg5PcJ8mHW2vnrVr350l+P8lPZzpBAYAZ5R4FgJ3v1G7+N2us\ne3s3v+82lWXDauI/VtXfV9WXquqGqnpFVR17gH3Orqo3VNVHq+q2qvp8Vb2rqn5y1XYndfcPvO0A\nx7qqqm6vqgesWPZvq+rSqrqpqr5cVTdW1dur6tlb864BZocWBYCdbzko/O0a607s5h/fprIcipcm\neW6Sm5JcnOT2JD+Q5FFJvi7JV9bY5zeTfDjJO7r97pvkSUn+e1Wd2Fr75SRprV1TVZcleXxVndBa\nu3blQarqsUkeluQNrbVPdcuemeS3knwqyZ9lcl/F/ZN8a5KnJXnV1r11gOkTFAB2vjWDQlXdK8mv\ndS9ft60lOojuQv25mdxs/cjW2me75S9IclmSb8za4eZhrbXrVx3r65L8RZJ9VfXq1toN3apXJXl8\nkmcmOXfVcZ7ZzX9rxbKfziScPLy19k+rznG/Q3uHALNP1yOAHayqKsm3dS9/qKrOr6pfrarXZXIR\nfmqSS5JcOK0yruNp3fxFyyEhSVprX0ryi+vttDokdMu+kuSVmXw59oQVq/40yY1Jzq6quy8v7ALU\nj2by+/mrVYe7I5OWjdXn2EmjNgEkERQAdroTkiz36X9BkvOS/HyS70nyniQ/luT7W2tf3sxJqury\nqnrFZo6xyiO6+dvXWPfOTC7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"text/plain": [ "<matplotlib.figure.Figure at 0xa357d4b00>" ] }, "metadata": { "image/png": { "height": 265, "width": 389 } }, "output_type": "display_data" } ], "source": [ "df_rotmod.best_rotation_period.hist(bins=30)\n", "plt.yscale('log')\n", "plt.xlabel('$P_{\\mathrm{rot}}$ [days]')\n", "plt.ylabel('$N$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next up: **What are the distribution of amplitudes?**\n", "\n", "We will use the `segments_activity_index`:\n", "\n", "> segments_activity_index : Activity Index in segment (double, Magnitude[mag])\n", "\n", ">this array stores the activity indexes measured in the different segments. In a given segment the amplitude of variability A is taken as an index of the magnetic activity level. The amplitude of variability is measured by means of the equation:\n", "\n", "$$A=mag_{95}−mag_{5}$$\n", "> where $mag_{95}$ and $mag_{5}$ are the 95-th and the 5-th percentiles of the G-band magnitude values. \n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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AI4HjgT8G3hcRh9dReEmSJGm5a2WgkJnXZeYdmZnzHRsRBwKnAFPAJT27zwMeAc6IiN17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\nB60mhRzLtP31AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xa357c7438>" ] }, "metadata": { "image/png": { "height": 267, "width": 389 } }, "output_type": "display_data" } ], "source": [ "df_rotmod.max_activity_index.hist(bins=30)\n", "plt.yscale('log')\n", "plt.xlabel('$95^{th} - 5^{th}$ variability percentile[mag]')\n", "plt.ylabel('$N$');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wow, $>0.4$ magnitudes is a lot! Most have much lower amplitudes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The problem with *max* activity index is that it may be sensitive to *flares*. Instead, let's use the $A$ and $B$ coefficients of the $\\sin{}$ and $\\cos{}$ functions:\n", "\n", ">`segments_cos_term` : Coefficient of cosine term of linear fit in segment (double, Magnitude[mag])\n", "\n", ">if a significative period T0 is detected in a time-series segment, then the points of the time-series segment are fitted with the function\n", "\n", "$$mag(t) = mag_0 + A\\cos(2\\pi T_0 t) + B \\sin(2\\pi T_0 t)$$\n", "\n", "Let's call the total amplitude $\\alpha$, then we can apply:\n", "\n", "\n", "$\\alpha = \\sqrt{A^2+B^2}$\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "val = df_rotmod.segments_cos_term[0]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'(0.006184367326778295, 0.0014396528754723747)'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gasp! The arrays are actually stored as strings! We need to first convert them to numpy arrays." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.00618437, 0.00143965])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(eval(val))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "NaN = np.NaN #Needed for all the NaN values in the strings.\n", "clean_strings = lambda str_in: np.array(eval(str_in))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Only run this once:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "if type(df_rotmod['segments_cos_term'][0]) == str:\n", " df_rotmod['segments_cos_term'] = df_rotmod['segments_cos_term'].apply(clean_strings)\n", " df_rotmod['segments_sin_term'] = df_rotmod['segments_sin_term'].apply(clean_strings)\n", "else:\n", " print('Skipping rewrite.')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "amplitude_vector = (df_rotmod.segments_sin_term**2 + df_rotmod.segments_cos_term**2)**0.5" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "df_rotmod['mean_amplitude'] = amplitude_vector.apply(np.nanmean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compare the `max_activity_index` with the newly determined mean amplitude." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The $95^{th}$ to $5^{th}$ percentile should be almost-but-not-quite twice the amplitude:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "amp_conv_factor = 1.97537" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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iVMOAH5vpr9D5JmgikeCnP/1paPssx/6+Ur7CTFZ7Sk5FRESqQqUP+LHWcu65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s9V7e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Efe8ZSoVVFlJEkcMYRLLyZY1iu7Z0PhACbFUeKxpjJC/na6HrE8U4SFVWs6ws696xGjxasrdt\nobJVVUwJvyFrEVLyj0TWzmsN5EnK6Xk959PiUcnJpMK9PDFF+06Y8Og4/9/4Ex4Zm8HehcRe53hx\nv+GDtzpiyorflUVBTImVX9P1DowmtrBqoON8k1SA1b/5SW78xPceEFVz6ZVc/fLvwmy9cOxrFJmg\nloAea0QLm1OeLs0tWgnOQQqJ1nmU0hgdKQuN1kJhNaXWVKViu7JYLSyrXA9qtOBDxGjFTGUv1cIa\nOj+gx856o4RoA4WRA/P8nVlBiAm17lgPHoXk2NoxsWx7VlAqHpmsHVUDaZOw37kc4SoPp9g+DE4i\noycpp3EsWbjIdZuPQ04mFe7li/N+306YcB4xkdULhuMG/3u3wx2S0rmA8xGlBR8Tu61nXmmeX5Tc\nWDs0IKNS50ZbqvNOVGO/5tZPff8BUbVXXsMLX/6dmMXlE19XA4XJP1Zn4lqVmu1ZyVZhGVJO+XIp\nIiEiUUgBbA2V0ZkkCmilsVpjTDbBn1uLNkI/BLTOClhdGOrO02tNUplghhSZlyWFvkNGrFZcWZT4\nELMdWcypVrk5izF+VT0yWbu3BjLERO8CvY90LsJYB3nWy+sPWsY/SjndvLdNatu97/Gkus3z6kv6\nKOcxqXATJkyYcAcTWb1A8CGy6v3B4F+ao43Y1ZhqFGKiGwL7/UDvE5fqkpeKln7IRXHOB/aaAe/B\nuUTvHVHlpf/zDlXOeeFLv50X3/YWzPYLXP2y70TPtk98TQlUJZQ6d8hbq1gYw2xesbAqhwGorGKV\n1hJTDlwYfK41NUoIMXfupxiJAi4EFBathVlhWLWe1eDZqi1GFIuZpfMRpdLoc5rrWkXuToeKKTEr\nDYhQWY2PkdrlmNXD5PRRmmzurYHsR5LYDoHaalxMtC5PT85yef2kZfyNF+th5VRH4VbTI3Cw9N06\nuDQrD0j0cXWbF7m+9SRMBHXChAkTJrJ6YRBi4uZ6YL93hJiwWtE6qK1BAKXysrIWoXEhq2c+0LnI\nuo8UVugHoTCGIQ7EtWfVCPvrARc9rfPsrQNrf/5V1Q3KV3xcXvbfvoqulw/cP5CFWG8z2ZMYCSKU\nWigLDSLopKgrA0lQkjBi8CZgRBGTsLf2+BixymJ7xyt3aiprSCnR9J6mjoyvfAAAIABJREFUDwwx\nUBuDLsgqqRFSUmzXBcVo8N8MHjWGAMAd9TN7qyokgPOj0nqInD5Kk83hGsi9dqD3kXYIzMfggllp\nznx5/UH2S3B/x3vvAs5nZbSsFT7kkINbTc9WVZxYt3nR61ufBs6r8jxhwoQJD8L0LX5BsN859rsc\nybmsLCEmZNxux2XneWnpXSClHALQD4FbzUDvAtZohiEiIpRas9s7bq4dt/qB/aajHWDfg3vWb/QY\npJQIqxuY5XN3bS9f8XGnPoYH1g7qAGUJPkJKwl4/UGjFsizYWRiUKOIm7jQkUoQ++BypajSLwiA6\nK6WSOPCxDTFys+kRhMuz4sAXd78f0AK9D1hzh6geJl2H1U+bMhNNjMqty0rk4zTZbGodU0p0LlJb\nTW015UGYwNnaIj3IfgnuVnvdoRKA7TG1qy40t9cDkOtXCy2ICErkLlI9+ZI+GC9X5XnChAm/PTCR\n1QsAFyLOZ4P3ZWUPIjOb3jO4QO9yk07vQrZBCokYs9qEJFxKmJRYDx6rFUUhqE7wIbC36tjvYdWd\nb6J661/+Q9b/9l9y9Sv/CsXzH/3Ix/LjY1EoCg0owbtcXnFlWaK0QiuFjgkk0cSEiCAaCqu5ulWy\nPSvZrgvM6LP6W7cblECMEZ8iKQq3mgHnI8YqCpXV0rrIxPCoZpnDSTc3VwNxJJUpQSIr5maclDxK\nk82mBlKEHMEax7KDzXU5Y1ukB9kvbe7hTcd7Shzcn4fJ+HIMNyiNJpFZbusCyt8hW5Mv6YMxKc8T\nJky4yJi+pS4AMmHJTTc+5sE/xMQQYrY7UnCryYOQD1BYoXUOEWh7T4q58afpB262Dh8TnU8MQB8C\nw8C5jVBNKXLznf8tq3f9BAAvvv2beeVXvxWzffWhj2XIDVVRoNR5aV7I/qYxwV4XqEuLimC0ohki\ndaHQRgg+N0Fd2aqYWUsfAkrgVjNglKCV4vKipOkjg/c0LpCAOilEhLrINlWl0ccuwZZGsdtEWu+J\nMVEXBhGFVeqAbD6uQjgrzIHy/iRtkU5jv6RGZuxCpPeR0mS7rvIQGfcxUhhFIi9hH1f/+qR9SS/y\nEvqkPE+YMOGiYyKrFwAieUBuh+zp2QyewUdurHqsESptiMCL+x0aRWFz/eqqcex1Hh8CdWG42Xmu\nr3rW7cBu49hrBlJI9PFZv8OjkWLgxk98L+t/+78fbKte9Xr0Azr+74UCKsAIDAmqAhKKUitcTBij\nCCnXAfdDxBTQu0RphSvLEucT3kRCjBjRtD5Qm5wINS8NooSrWzW1VcyKwAdvefbanhQLjLLMy0wQ\n1djVfxRCTLl5LkbSSCqsUSxKS+/DmRrBPy1bpAf9nsMd77MCmiGT296Hu8jtBseRrU3k8JPyJb3o\nS+iT8jxhwoSLjomsXgBsBty60LRDwPvIqvNYLTw3r9ieFew1Pdf3Iq0LXNJFVmNF2Os8BrjVdLy0\n1/FbN/dpXcxJVl3Hbpc9Vc8bUvBc/9G30vzKvzrYNnv9Z/DcG78BUQ9HqixQF9lPlR58gq53BKMx\nIiStWW4ZLs0KJAEiFBoWdUVlBaMyuZWY2G06tNFoZbg0s5RGsTMrEclL683gWQ+RVR+pbESrrJhq\npU5UsbqxO793iUJnDdiHbDFl9N2k4nFVvtPYIp2Fknha+6XNNq3sXaSwOKTAti6cSLaeJAG/6Evo\nUyLWhAkTLjrO/zftBODOYFwYxbrPo4vReekZwBqN0YrOeVadIwDr3lPovIQa2siNdUvTRdaDoxsc\nL+4m9s6hqpq846Uf+Wu0v/6vD7Ytfs/ncfmPfN1DEVUB5kBRwczmUorKwrwwRAkYJVgxXFoWXJkV\nvOpyTXABlMJoxaW5wYhi8J7WJ9ado1RCbTSL0lBoxbwyaBGGENlrPeves+4GJAXU2MmvlJBSOlbF\n2pDYBMxLzRDiwcTEhYiPcbS7OluV7zgCetZK4mmJ7nHk1oWI8ieTrSflS/pyWEKfErEmTJhw0TGR\n1QuCw4Ox1YpCC8OovDWDHxU4TVUkuuBpu2z6XlhNpYUb+z3r1nOjaSEp9tpwLolqdB0v/eB30/37\nXzjYtvz9f5xLn/u1iJx+UK2BZQU7c4sVSJKXQu1MY42GFBFRzEvD88uKj7264LllwdwWNL0nEKms\nQWIiGEO76ogk5oVlZ15Q2xwQUBqNUcJ6iNm6aogkEltViTUKFxK7bU+pDZU9WsU6bFvlR9WuGTwx\nwhACy8oekIp175+4yveslcR7ydPDkK2zJl4PWkKPMeE4/8b9UyLWhAkTLjImsnrBsFkW3e8ct5ue\nVe/pfciNVWPtXkyaXT/gXEJJZC9EPnS749aqH70sPXvncO0/9g3X3vEd9O/7Nwfbtj75C9n5zDcj\nD7FWqcjL/pcXBVeXNUOK1NaggYhgDMysZVEprNFsFZYri5JZYSkUrAgIip3aAkJcdyitmBudG7OA\n0iqWlT3wWLXaY7QQU0QAF2C3daO6rXluCVpVR5KZw8u0m+aiwQdu9y6XIKSUY2Cfgsr3rJTEB5Ud\nPCuyddISuh7LE4BzX8s6JWJNmDDhImMiq+cQDxq4N8rX4BMhCPttwPuAaGFmNSFFtCgaP9D2nkii\n7RwrN7DXJfpz2v2/+qX/7S6iuv2Gr2D7DV/5UERVgEWR41RRCm0Ur6wK0IJRmnmRldXaanZqTVVY\nELi6rFjWlmaIxJj9nZohMITEashEVEn+QPrBEyuDVkJl8zK1VoKSvOTvk2BVQiEYrWi9Z3CGmOKR\nZOGwcpibqYR1n2uTo5bRzqqnMJrEk22U2SiJevQyJeWL+iSbcU5TdvCsyNZJqm6I2UzrItWyTgR1\nwoQJFxHn8xv1txkOD8BK5MSB24WYvVRjNk9PqcdI5HrnMErRtp6tRZnz60VYtwPX1z27/YAb4rm2\nqVr+gTfhrr+X1S+9k53P+FNsf8oXP9TrNTBXsBjrU2P0aGA5K0dv0YjVwqI0QCSKQimYWcPWrKTp\nHavOoZRQmUz6296TQnYSkKRonccazV7rubTIdRRWK7RWFFrTDh2kRO8iSSmKMTzAp8TtZqB3kcLo\nuzxTM+m9oxyuutEVQBJWGXof6VzE6kBdaBLqoRtlTtswJZL33e8dVqmD/V2MLEv7RJpxHqbs4FmQ\nraNUXciJaOEC17JOmDBhwkXBRFafMWJKrHt/QEwHnxttZFSz7h24Y0wMPtIPkf1uYLd1/ObNjv1m\nYIiRZam51QxYY/Ax0frIh3d7bq17OgfNs327J0JEcfmPfB2zj38D9cd80kO/XgNagfOgbb5++col\nLi0KVl2OofUpYkUYQqBzwk5tuLXOJRUuRHbqElEQQqTpHS7CpaVlWRRogWaIhJiIPitsPibU6F0a\n41h/ahQxJAShsEIisdc6XIBFmYnN4c91oxw2o2+uUbAoq4MggWbwhJCTnJTIsbWbR5FSH+LBe4sp\nuxMct1xt9ejh6yJNDJRGj1ZSQrDpzAnYRWhgOkrVTQlWvcfcw94nO6gJEyZMOHtMZPUZI6ZcD2eU\nohkC7eDRWnhuUQF3D9ydC+OP50O3W26sOq6vB9bNwM3OESO0fWBnXqL9gAL2O8/ttmevGXDnLKIq\nNLuoanFXh78o/UhEFSDC2PQCVqC2hqrQKCWEANoqut7Rd4FghaVWzK3Bp4hK+VrNCkOUBBF8Svm1\nPqDTmJYElDHXB5uR7K06x7VVzxAzsXIm4lzAGiGQmOscCLCsCrSWu7rcNz9GZdIbI4QEhTF3EUmj\nFDFGrLoTLHBv7eZRy+kbYrsePCFmstm6fG3gfuVyU9JQGs1cy9j4pfDhTt3sWZLHi+QBunnfKSVa\nFwgh4mK6yzt3soOaMGHChLPHRFbPATaKEsCq8yjFXaRgM3C3QyCkrK613vPSfk/TBfZ6x6ww7DYD\nIcBu2xESrJqBl5qOEPKSZXeOuv/97ou8+M++ieqj/gMu/9G/8FCd/sfBAtpk5avWitIatmqLFsWt\nNmfM10ZjTU6VmlUmlwuMSWBKYL93PL8scCHBmFkvKiu2y8qAgC80QwiEGBHJMaF7rSOERKlVjq4N\nkaSEHZOJam0NhRnrUl3E6njgn+pDVstdiFk1d561C8ytPRRLmsMCCiPMS31AbJW6c/8c5RSw6hzt\n4BHJDWEhJgRonac06j7ymZVDYVnbAwVx87h5/ixx0TxAD08I+hDHhsXIrDQk0mQHNWHChAlPABNZ\nfcY4PBZnxe1QY8sIHyOKvCkB88Iyt4bnlyXv7dd0PtKGHh/BJ0/qEmvvWTWO/c6x6gb22vNj/u9u\nfoAX3/YWwv51Vr/0TqSoufw5/8kjH8+QieqigkWlmRlLUWhKI/QuUZncoW+VoI1ma15ASkhSDCEg\nChZVwXq/Z/CBD97uqEsNkUxoQ1YkhxBRCErBorBUxuSyjZTrUstaY5WwnFmu7XXEGDEClxflwbKx\nDxGtBTWAICwqk90cNlGiWmGUJoXAXujHJp68rD8rNIXRuLEcICZQEVLyB6rn4eV0CeR6Vx+5NLtD\nfNshR8H2PidHHcZh8nh4EtUM/omQx4vmAXq4vrY0Gh+zD27vAovKTHZQEyZMmPAEMJHVZ4zDQtXG\nlkoroXWehDkYuLXIwb5eRxa1Za91lIUmilAqBTrRDI7rq+wC4IOn6Ry7zfkhqsNL7+Xa27+ZsL6V\nN2hL9dG/95GPJ9xJqCrtaN9lhGWtxqz5xI12YKuy+ATiA10fMFrYDQOlEpazghQSl+YFu82Ql+pj\noq4MO8bgfK4hzUlUglECSWG1kBIYrbk8K/Ap4lNiZjXPjzGtO/OCWWG4vc71xSn9/+y9eZCs613f\n9/k9y7v0MnP2c6V7kYXEIgwGbDkGSo4tm6VEMAYHYglTqXJSFceVBFwRdkEQliIhkTgpywmu7BXH\nFWOMWGyIWYwcGxyWGCeIhFBIAoQuSLr3nnPv2WZ6e9/3WfLH83afmTkzZ5uemT7nPp+quXPP293P\n+3a/Pf1++7d8f4GtqmDW+tS0JZHaGroQ2aptGuFamTSpLACSorWFVowqC7Avetq4VBqi+vfHPnHX\nd/UbEVy4G1ZP7y9PqjLohW+I+H5N4L51sevmSfEAPay+ti40O/M2eR8btU/gnzbrmDqWyWQym0gW\nqxvAXlGQGmo0hVH7LtxaJZeANN3IUFuNJzJrHIVErBimXYMSzdAoZvPIbNFxe7I5TVXttU9w7UN/\nnTDfAUBMyeVv+uvUjylWVf8TAd+Brkj1nFoza2F7YEClyVOLxnNpYKkLQ1SRSZvS+IOtmkonkdG4\nQKhSOn1QW4xoLvUTwkIIOB/xPuJ9YFBqfIy4EPAhMPeByiicS+do0Xq2BwUXhim5nZp0NLUtKPqa\n0+kipe1Tp79it39eIUasUeiYpm2NKrtqtErNeEks+RBxXthp2n5MbJpqdn5QpnpXAVEpra+AnXmL\nVopJ0zEsDUqEzkcWnetLTAJaqVW3+7Jm9qTF41l7gD6syDuqvrbq638Pbj9N1j11LJPJZDaJLFbP\nGCVJDByMKIV478XTh3RR7XygshqjoLCKi6bGKoXSkUnTcXsWmDULbk9hcsbPb0nzwse5/sPvJjRT\nAKSoufLN76H6rC967DVLkgOAA2LK2iMCbeexBnanHRe3Snwb6Ai8EiODwhILTaU1UmgujUueOz/o\nG48cn7nlGJSac72wFVgJmVnf4JaEYIqkFSYJvhgjO4suDWyYdaCScBxWltallPqo6lPxEeatw2hF\n51OUd9Y6pk3Xj4TVxCiISr6tWqWoZuvCPrHU9CnpGASlJDlFhMCtWcNWVeBCSOI9pnGwnfd0PmJU\niriq3tR+unD97QGrI84ERqVFK1mNeT1p8RjPqJPqUUTeJtfXHmb/tYwEn9Y5zGQymZMii9UzRkmK\nKh0Uppp7r3x706WjyvK6i0NaF5jOPS4G5q3mxTtzrt2ZcWMXdjekk3rxqd/g+o++l9gmh1dVDrny\n599H+drPP9a6Qv+aAZVNqXolwqiyuOCZ+8CtaYMygnfQhcjMeS7biq2BpTKKy+OKYWVTN/0kcnFU\nUWpFXRmMUr0IcPjeUqw0KllixSRQW+exWqgLzZY27M4dg0rjY6o/nrUupehjRPpUeufTWp0PbNcF\nvvdlfXmnARU5V5fUheZcUaD2TK7aK5bE3/XntUZSzazVTBeOZWq/NIrKaGY6uQGIQG2TsF2Kc5A0\neQthqy6Ztx6rhNZ5zCmJnLOMCj6qx+sm1tceVp6gg3Br1iAkO7ziPnZlmUwms+lksboBPOxFLkW6\nNPMuzSQflklk3Zw0vHBnwademfB7N3a5tRs2RqjOn/9/ePnHvpfoGgBUvcXVt7+f4uobjr22B0qT\nhOr20FAXhvO1pfECCF3bMYmGygjDwlBZQ11odD+xqrKG8wNLqQUilIXmSqHRIvg+sk2MhBAxWjPu\na0atUdyetb3YM2iluDIu+shlACIDm+4bIwT6coEu4nxAiWLa+JXAUWlHKV0vaXqWwGpylO+9dZfv\nk2U9adM7CFRWr4SIEsH10bTSpvuHmJq3LgzSsAirFXdmHY3zyfifu9FarZZ1uKdnHfUognGd3M/j\ntXV+5XW8V7BvYn3tYeUJyaUgvYdLwxMxXSuTyWSOIn9qPUEsI1DLi/vv35jymdtzfu/GjBdvznj+\n5g4700jrz/pIE9F33PiZ718JVT08z5V3fIDi0uuOvbYBSoFx1Vt7OfBG4WKaNuUVKGuwSiisSd5T\nWtE5iCF13W/VltIaqiJ1cUciC+fTyFQfk02VB6v1Ks3bhYBFYZUihJQCtloRYhoAEKNQGIOPyVpq\n2rhUO0rabyTig2A0WJM8YLsuUFnDdh0IEjk/LBFSWn/adL2AjDR9RDeEiJbk16pFqK2m7MWSCymK\nVlq1r3SgNBp6C6rOB4wWOk/fVCWr1HbyYhV8SJHZk05t7xWMtU2WXMuItjkBX9e9HFWDKgjT1idv\nWa3uifSeZX3tYRwsT9jr37td29V7YZMGLWQymcyjkMXqhrO3+SONWU11qTd3F7x0a85Lt+csep/Q\nECKdg+lZH3SPaMuVb3431/7BdyOm5Oo73o+98Oyx1jSkpioLDGoorMUaSbHICKVSlIWBBbjOUZSW\ny8OSLngQKIxgC42KQucCrfLMGsegjzYdnNwUWYoWQaCv80xp9cIK27WlMKnGeGfR4XwASd6nldXM\nG8+t2SLVnRrVi2ChUAqrdTqnPkU+B3siXj5EWp/qY21UGJ2et49xJVKH1tCo9Lx8iDTO35OSXo5P\nnSxcSvf3afalyb/ViqZLzWM3Jk16nAij0p5KanspGAVh3t4tBXC9Q0FpNCcVtDyqBnXaJs9cJYLW\nh0clD74uZ9mJf7A8Icbk/bvcftCveZMGLWQymczDkMXqBrPPgNwFFp3DuYgo2J11TFrHdOHYmbbM\nOse0gQ0bUkVx+fVcffv3oqoRZvvqsdczJJsqa6G2CqOESieTf0iCrSht6paPUCqNIxB9REzEGsEY\nxSJ4pEsp/Unj2V10fdpXMyzTRd1qYQJ0vmO26JhJhxZJ3ftGUVvL+WGJ1Ypp4yiL5BAQel/UtkvN\nTrfnHaXWFP240uAjUQembcdWae+Kz77MIMTI1HmUAitJ5B5Wi1joFOlzLqD6utWDKenl+NTGeaZt\n3Dc+dau2jCuL1Z556ymMoJWitmmQwWmktpeCcbePQC9LAeadJ/Tnc+mEsG4Oq0FddGnSl1Ep8g4P\nHv+6CZ34e8sTGhd62zZZRdxhMxrBMplM5nHIYnWD2VvLB8nEvekCk6Zjp3Hc2G24Pm1pWse8bZi3\n0JzxMfvZHfRge9+24uob17K2BgoN4yKl+QF89MSoCQjGCEEU0QesEs7VKa3sPZTWMB5YtFGoIEwX\nnkppjFLUhebm1OFj4FyVuvxDSFOerFbUpaHuJ4g1Xao13a4KtntxqVUy93chjSo1vefp7VnLtOkg\npqisIBRG9Sn8vtHJKCqVygiUklUKfFylcavmgDDaX4uYnAJCiKv09EEhtRyfWljFUCliPz6168sX\nkqNBmm7lQrinRvOkWe7HhSSyllO2hv1EqOVzOKnjOViDanU69+UBoX6/qORZ1dzuZW95wqCAWdtP\nS3N+YxrBMplM5nHJYnVDOdj80fnAsDC8vDtl1jhcDPgYmMwbdheO3SnMz/iYd371H3PnF36AK+/4\nAOUzn7OWNRV336S1gnMDGFYl864jBJDCoEWhjKCBsbUMS01VWDrvsVqjgLpQDKxBCey0LTEKpRUu\nU6GVsFVZXpksuLNo0UrwERrnWLSBi+OScWW5M224PmmwCrYHFmNSBBCSKEkWQUKIkc6lNPagsESJ\nFEr341YDc++otMEqjVJJKIZ4dyrVoND3eOs+qBbxqGjZcnzqVlXQB59XtavL25eTrfamwtfN/dLk\npUk1t0olUW91ElXptTzZtPXBGtRUgxxo/f7ZxEdFJe/XpHUW9aErNxFl90V7N6ERLJPJZB6XLFY3\nlIPNH1YrBDBKUmR11rFoU+f5bNEyPeP8/51f+VFu//zfBeD6h/46V7/1bxy7kUpz9w1qge0hnNsa\nYJX0mitSFgZrNVYUo1IxsIqtQcHuIpnnFzowLkuCClSlxrmId3C9mYOKjOw8RTS1Qilh0QZui6M0\nijvz5JsaQsSHSBdSHWMaeZpESBfCqhFob5QuBhjVFq0Vs7Zjd+FpQ6DrAoEI1qM0NJ1DasuwOLxp\nZ+mt+7i1iKc9PvUwHpQmN1oYlYZZ66itWTkWLG2/TuMYl69lgSJG99D2VEc1aZ11fegmNoJlMpnM\n45LF6oZyWPOH7SNgIQRuz1q6GCiMoW3PbpxqjJE7v/SD3Pmlf7DaZi8+hxlfPNa6BqiApQYwBraG\nFecGBV0XkBJKY9iqDbM2pWDnXUDrSOsDo7IfYVoatBEMinkXCDHVmy49UG/OO4xZsD0okkdraRkP\nku/tNpZZk4TKZNGlEag+EnVk1vYG+1oojSZG0PquQLA6oFRKMw8KTYwLbk4bJgtHaQylVkhUQLKa\n6nrLqIOi4ri1iJvgDfqgNPnyOAqje/F/V6ieRdr6UeypNnlQAGSBmslkng6yWN1QDooMQbgzbyFG\npq1nZ9oynbtUEhAevN5JEGPk9r/4u+z8yo+ttpWv+0Nc+aZ3o4r6sdctSLZUSkNhwGjYqksuDksu\nVgUz5TCDAh8DpVY0OuAcDLQihtBHsyJbhcHa1EjkQmTeeATh0qCiKnSK+DnHojMMXErjDgvDsDJp\nypRWGOWZLBxIig5qDUoLRgmzfvSpJqXul+wVWMsygXFlmTceKuHiqGS7toxKi+9dBIrOo9S9NklH\n1SIuekN/FwJaBFscLerO0hu084HWeVrnV1HTQWHuSZNvkn/po0QlN+HLQCZxlo4MmSeD/B55csli\ndYPZewHfXSQ7ncZ5Xrkz59p0wYt3drm503LnDHxVYwzc+t//R3Y/8pN3j/ez38zlP/fdKFs+9rpC\nelNqTW9WD9uDiotbJZfGJVYpirKk0IYYA633WOeJShCtqKzCiHBpXDKuCs4NCgpRvDxtmLa+t9uP\nbNcWJb2/qFVs1wajFdLbOQEoZWlDSLWTIQ0WaLxn2niMTnZPPgTmLnXT6z2PhbvnzyhBDcrklwqM\nS0vV16WmJixPLIS6ONq8fW8t4rRxTJyncT41bvUd/75vtDrIWaaEnU9frjofCMGt9n2wHnUT09YP\nu/9NEtqvVjbBkSGz2eT3yJNNFqsbjJLU/NJ0qQvdhch83vLCTsOLN2fsTjtuzOC0A6sxeG7+7H/D\n5Nc/vNpWf+6Xc/nPfidi7GOvK0AJWJ3EitYp/T+sDNulQQCtFErBUAtKW0QMRgSte8ulwnBhYLg4\nrLi6XbE9KJk3DhcjXQyoEFFa4UOK3NZGcX5QsD0sVpZJeyNko9KmufURJo2jCIpZ56i1YRJTSt+F\nwJ15iw+BuvdXPRgVXZYFRFjVjnY+MO9S1HxQJvH6oOac5bpKhEJrjE41na0P6M7ft/v8LMRf49J7\nt3EBUyWfWRc8kci5urgnTX7WAvVxeBihnSM6J8smODJkNpv8HnmyyWdow1he1FwfRQW4M+/YbRy7\n846XJh23JnNens65vXP6DgAxeG781N9i+ps/v9o2eNO/zqU/8x2IPt7bSZFKAKxNI1RLLRSmYFho\njNKMy4LCGKoyRU+Nhs7D+aEgCEoL49Jwaavms84P2KosSikgcnFcUheKm7MGkcjtyQJtFRcGRerY\nj+mbd9FPozoYIVv0XxZ2F5FSp67/lOYVlCTx27gIctcdYMnBsoBp00Fvgh98vMdy6n7NOUsRq3q7\nrCWbOJ2o6zvqjVKYUq2ssXYX3WoU7KYc6zo46rnkiM7JsmmODJnNI79HnnyyWN0gfIjMW8e880wa\nR9t5tEqWRfPWcWvScGPS0nph0cLOGRzj9KP/xz6hOvyir+Li134boo6X8jRALaANSASjNYXRlEbR\nOMd0kVL844FlXBqUiswaT9MGokrC0oeAaEFFobIa06fZX95tcN4z7wIxCvPW0bqAaoWB1Uwbh1Zp\nRGltNXXfMHVwJnznU9lB4yKFFhCFkhTpLbRiUOp99lIHP/wqm+pkJ52n9Um8RCKtC/tS+PdrztnU\n7vPDWB7roDTEPdZctU1R5NK8OtLkOaKTOKno8pP0N5E5G/J75Mnn1fNJ+QQwbRy358lIvnWp+Wdg\nkzl8oRU7rePWtGG6mLM4I/f/4R98K+0LH2f3Iz/J6A//G1z46r+MyPEvOjWs0uSx//+A0LlI6x2u\nTCb4s7qgUArfT4myRlEWmkJrYvBUJolG3384zZokUm9MGkqbxpsGH5i1nq2qoAuR27OWwiissXQ+\nrIzy96KVcG5QACk62rq7XyQqrVfR0+WHYoyHX5y16ocBiKIuDPPO0XaB29OWcW0Pbc7Zu066fXO7\nz/ey7JR3pFGyyzT5ovMMilTCcBacZko+R3QSJxld3nRHhszZk9/4RoRxAAAgAElEQVQjTz5ZrG4I\nnU+TqWaNwyiNE4cRxZ1Zhw+REAKLpuX2omHSetozcgAQEc5/1V+i/KwvYvD5b+nHnB6PEWAs0As9\n76GLMLRCIZpF64gIg9IwKA0BmC0C46HmQl1gdDL8b52nsAalBB8C09YxKDXlIjVc3ZgskBiZtoEL\ng5K61AxLy86io7R9NLaf8nQUaTypYtYKk8ahRahM8mhtXVgJsRAjszbsuzhDumgrJYyKVNtbWc2t\nWQNEXC+U9zbnHHaRb3v7h03vPj+qU375HM/iWE87JZ8jOomTjC5nR4bMgzjJ90iuRz8dsljdEFqX\nfDSVKEqjmHfQhcCsDUzbFu8jtxaOm5OG3ak7NV/V0MwQW+5L84sohm/648deWwEDleypEFARRIEK\nIMEjUqG1UFea88OCurSEGFh0ERehlDS+dNnUJCL46AlBMetT+yLCpa2KWZMEyos3ZxgtaUqUKIyk\nEbWNS2UFw9Lc91v2spmmMGrlB7roAqH3XdWq7/7vnQb2Xpy9j/g9EbbleltV0VtTmdVUqiWHXeT7\nIVR9fe1mdp8vP8CVJJsvkI041tNOyeeIzulEl7MjQ+ZBnMR7JNejnx5ZrG4SMdJ5DxJpWk/TBSZt\ny6J1dF3g9s6C6aJj0Z7O4fj5Ltd/5N2Y86/l0te989h1qQcZa6jLPlUcoDKgtaawmmGRxokqki3A\nqEjep00bEJW8TaddwPbRaAd0XWBUWc4NSgRF6wLWCBpFXSrUVBhWlrnzGC0sfKCbNixajwuBi8Py\nnm/ZR31rNkqoi1RW0DgHIliTxEfnA84HjFH7Ls478w4fAovOUZhitQ8Xktn/QaF6v4t8odVKMG/a\nt/nDPsAh1aoaLWd2rGeRks9Rv9OJLm+i9VlmsziJ90iuRz898qu5IWgl+BCToIkpJew8BB+xWpg0\ngdtzx2TWsDgFX1U/u8O1D30P3fVP0r7429w0JRfe9m1rSftD6vovDJRWML0FU2U0lTEMKkuhBBcD\nCxcxJGsq5z2TGFfWTVUfZa0MdC5gtOL8oOTiqEIL7DaOaeMYFjBZeAqTrJNihFd2GwqrcSEQY2Qc\nLUrt//A6KLqEiIisBIfrBY6Pka3SUlpNZTV35i2tC2it9oldJfR1p5GdeUtlzbFGeRqV3AhcSE1a\nm3KBPuwDXIlgY8Tqs4t0nVVK/tUe9TvN6PImvP8zm8263iO5Hv10yWJ1Q0jd4AqjhdksOQLElD3F\ndZHP3Jjy4o0JNxfxxO2q3OQm13/oXXQ3PrXaVjzzOWsTqhYYljAeaipdUFqF84Gq0JTaUBlF1wW0\nFgZWVo1JVmmiSuUC20PDVmG5uF1RGGFkLFHg4rikMGkEaogQYmTRBZQGCXB+VDBrHUSoC0uMnlFd\nUPdlBLP2btRzr+gSpJ8WlsSpFiH0dcNaNC5EbP+N2ihFS6DtAsIy0hq5M2/RGiptiEYI0SVv2P7n\nIA+6yB9WF3vWKahN/gA/q5T8qz3ql6PLmaeRXI9+umSxugEsL+JVoXn23IDIrDdQTaLod2/P+e1X\nJtxYNMxO2AXA7Vzn2g+9C3frxbRBFBe/9q8w+kNfubZ9FEBtgQDbWwatND54IqRUuBG00hgljCrL\nVm1T/akShMhWZRhXlguDkiAwrgzD0hBDEqnLWtVRlaKlrh/BWhrNvE2m//PWUZeWYaEYVwUisrKT\nWvqD7hVds8aBwLR16UnElOYJIWJNaspaPlaEVHfceqZtmtq0aD2zLrkVbNcaJdIPOZAj00UPusj7\nENeaglpHo8A6PsBPqmHhrEXTq1mUvdqjy5mnj1yPfrpksboBLC/wpdGIkGbGh0DjPKHx3J50dK1n\n3nGijVXdrRe59kPvwu9cTxtEcenr/yrDL/gTa92PMUCEQWUY1wXnqjT61OHxHgbWoIgUVrNVl7xm\nuyTEZBkVY2RcW7YGBZB8Uq1WVMYwbR0L59E+EIymsoqtyoKwmksP8NKdOUrB+bpgUCVD/nnrsXq/\noFqKrr3RwlFhmXeOum/ECjESQsSFQIgKxFEavfIQDb0oCjFgjU3nGKgLvU8UHyVkDrvIGyUpYtx6\nuhDZqpOzwHEimI/aKHCUoDzuB/hJNyxk0XQ2vNqjy5mnj7P+8vtqI4vVDWDvBd4o1UelFLuzlpcn\nC0QihTUnOle1u/GpJFQnN9MGbbj8Dd/F4HO/fK37MaTnOigVF0YV5wqLj7BdWaqioHMBQahqgxbF\npWGRjPu7QBsC3kWqInBjt2W7Th60tSh25y0IdC4yrgsKLQxLu7KKsnt8U0ujUCLsNl0vYAWlwGih\nNHcFlRCZdwEtQuOS+Ft4nzxgQ6S0GiF1u8fOI5LET237yKkIehlhjGBWvqn9vx8i2njwIh9iXA2P\n2G0cAsyUUPZi7qg1HxStPKpRIMb0PPc+7n6C8rgf4CfdsJBF09mSX+vM00T+8nt6ZLG6Aey9wC86\nz+6849asoQse1TsEeOeZnFAJQHv9k1z70PcQZncAEFNw+c+9i/oNb177vhSgFSil2aosxoLVhiZ4\nolN93aoioiAEvA84dPJOdUCM7MwdA6sJQTOuLUYUmEjrA1oJMSRhKJLEiAt3o3w+RIiCCMwax6Tp\nKJShLjTbA7svxd75JAobF9I5iJFzdYntx63uLrokTJViVBlKoxn1kdrOB5S7K4acT8JLEKyWVIvs\nHz5dtLzIT5tUz7wsN5i3HtVHHAelOTSC+aBo5VF1pruLLnnXdh6l1OpxPsR7bLn2CsrH/QA/zXrX\np0U0ZY/HTObsyF9+T48sVjeE1QXeedrgcT4wLAzX7yx44c6M3315ciKNVTEGXvmpD94Vqrbiyje9\nm+oPfPHa91WS3nDGJME0bQNbdarhJMDO3GFF0caISMA5j6cjRDg3Kvjsq0M6B51z1NZwYVhSacWw\nsiw6j1UaF1MNaRsCRUgpeqVkFeXbnXe4EKisQaGYdy5ZYWnQeyYqLbpUQ6u1UCuNmwdiiCyc58Kg\nZOHcympqUOh7UtV7v4AsP8iaLgARo9OkrEdNF+0Vc1t1waxxKBGmjetLEeKhhvsPilYeVWeaIrie\nWAh1kSL/i+U6Wt1XUD7OB3huWHg0ssdjJrMZZIF68mSxuiEsv6G1LlBooVTC7qLlMzen/P6NGS+f\nULGqiOLyN3wXL/3gdxK7liv/1nupnvuCte5DkYRqqUFp0Bpa75ktHM3AoSmpS8W88zTOM6w0pdUY\nbZi0nt3WYWaKK9sVo4HGB8O5gU0+ozql9LdqS9sb689bj+kjp8soY6GT7yoSUQq2rCWUYHXFtHWU\nWvWiKK6ElwhcGlV0PjCuLHemLcakqOy5OjkGlOZo79C9EUaF0HvjY7XG9o95lHTRQTFX9o8NsR89\nq2TlLrDkYaKVEPEhpMiv3I0MzzuHIAzKJMYL0kS1FMF+sKB81A/w3LDwaGSPx0wm82ohf6JtGJFI\n6yLXdxuu7y745M0pt3ZbTjKoZC88y9W3v5/oOsrXfO7a1hVAA6VAacAaKCw0LRQqMFt07C4MO/OG\n0tQohKiEzkWeOVeilGJgPY3v6CTShUAlhq2hoTSGSOjLCuSeiNysTWa0LkTGOr3NS6OJQGWTL6tI\nKheobbpdSVonhP2iMEUqQY1K3BGTpg7j3hRRaoR63HTRQTGnlaxS/8to5t7pWMt93S9a2bkkUqeN\no+mSU0Jtzcqua1mHunqcFjqfBORe1iEoD6t37bwnRog5cLGPTbYIy2QymXWTxeoGEWMyd7+2M+eF\n2zM+8fKEF2/u8Mqaa1VDM0OVg33bisuvX+s+ltFUSKI1SNrYdklwiSi66LmzaBmXlsiC4CNT16Jl\nwM685dywxBEZlSUxRhoXqH2AmOwESqOpjAaR9Nr1Im7adCuz/nFl+yipYFRqrpr1dlLOp68Ae31L\nlYDqR74eGuE7ZNLUgzhp66XCaGqr7xGqcP9opRbh9rxl3npaF1PzlgtED1WhGFiNHEgnL6PUSuRE\nOmD3RqM7H/sa45TynjYup7l7cslEJpN5NZHF6obgQ2R30fHS7Sk3pgtu7XZMFg0v34Z1Dqya/dYv\nc+Nn/jZXvvndlM+uN92/pCAZ/5s+5e8d4KGNKbpaWWFUFvioaF1kZ9ESY0S0gqDYbTpGC0upHXVh\nqIxmVBusUgwrzbBIDVGjMkUqWx+YLhzOR166PWfSdLgYuFCXCHftp0w/LrYwmknT0fnIbJIEkCIy\nrHRfg2qI0T2wo/1Rm1vW0QzzqM1L9+vObzrP3Hkal8ocXAiEEAkBBoXBHPG4UWVXjVnr7oDdG42e\nLBw+CEoLSklOc+8hl0w8eeRmuEzm8Xl1f+JvEIvOc3PS8KkbM67dmPHKdMrzL03ZWeM+pr/5L3jl\nJ/8mxMC1H34PV7/l+yif+Zw17gGGgC1AC5QFWKVYuEDTJqE6KBSD0tKFiNG9qBWN0sJ2bZi3gfOD\nVA8aJaWdx7XmylbFhWF5N+XZNxL5ENGdR0id8fPOEYDaWAqrUZJqYZelArXV2BjT/ZWnCIIiWVtp\nkVXE7kGi8FGbW9bVDPM43aeHPRcA14/4HVep/neZRlZy1/fXxnjoa7AUqyd54RUBpSSnuQ8hezw+\nWeRmuEzmeGSxugF0fZf1K5OWT9+a8bFXdvm96zu8sMamqsmv/1Nu/Mz3Q1/9qofb6MHW+nbQowQK\nBRdGJVWlqLThxm6DFB11XVLoVDfaupAajazQeM/ACS7CuWHBxWHBsDQUWnFuWHJxVHJxWHF+WCRr\nqj5C0fZ1p8PSUJheSMaA7cfWbvVNUEsRWxqF0QarNYVRjCtL4zzOpwajSPrS4EOksvq+ovBRm1vW\n3QzzKGLkMIEbIyvvWB/u5oyNSgMSBPrGMX3ka3CSgiinuR9M9nh8csjNcJnM8ch/JRtAjLA777i9\nu+D5V6a8cHPOi2sMqe5+5Ke4+U//u9W/7cXXceUd78eMLqxtHxqogcEAzteWS1sV5+s0ArWyiqoc\nc76yXJ+1zOcOHwJaIlYpYgCvQIvi4rDk8laB0clY34qwXRm2Byn1/CBD+kFVgDh8vBtxapxfGeYv\nBdbyd6ptDQRSne3Bi4hRskrdLaN5D2puaZ1HRFYCb7mfdTbDPE5Kce99Oh/SSNh+LsK8Ta/R7qKj\nNApr7n2tTpOc5n4w2ePxySA3w2UyxyeL1Q3AhcC86/j9W1OuTxrmTUe7prV3/tU/4tbP/c+rf9sr\nb+Dq278XPdhe0x5SjeqlsTAwmtIYykJzcVRwYVizXWkmjaOyGmMV04VnHh0oRVlatmqD0Zpa6xQZ\nMsKiiwwVGCUYk5z9lx/mh0Uo9n7g+xDQWihErQStljSWtS72R5wedBFZRlkPCuOla8BhUT/nI9PG\n96NY02Ni3yD0OFHCw0TpOlKKy1RxbQ3zzhFJ42xLqxiXlnFlH2qdkyKnuR+e/FpsNjlLkMkcnyxW\nN4GYDPFf3mm4fmfGnel6lr39yz/EnV/4gdW/i9d8Hlf+/PvQ1Wg9O+gZFXC+rrg8LhCEwmou1CWf\nc7FGa4UoxcI52i5wblTSucBYkqn/pWGFSGrqKUvFuDCMB5ZKG6pCrcaIpuhREi5LP1WrFToIt2YN\nAtTW4Hyg60WNiBBCZFQZxpVdpUeXArDpAq0LqzGoS5YXkXmbplYdTN0ZJUdG/ZwLKJUagpaPCf1w\nAtPXhe69//2ihEeJ0gdNkHpYlq9HaRSNC6v1x30D1VnzKGnuk2heyQ0xmXWQswSZzPHJYnUDuDVr\nuDVpuDGZs7tomBxzvRgjt3/h77Hzf/7walv53Bdy5Zvfc49l1XEpANFQWMWoF4RGa549XzEoDVEU\nUSKVpA77i+MCHyOVFi5tVQwqg4Q03akuFKXWFEqhdfL4HFYWEcH1o09vzVroU/K10bQuMGs8RCiN\nIZBEJkQKY9Jo1z2R2b0CsHGBReegFc719bCQLiKKVN0b4Z6oa0qcc499UwgRjmgKCjEJ1gdFCfcK\npFmbHAn2itKHnSD1MOxNIw+KzRNkD5vmPonmldwQk1kXOUuQyRyfLFbPGBcit6ctL00WtCGwu4ao\n6uKTH9knVKs/8KVc/je/B1VUx1+8Z0gam0p/IV90LbOmYFja1LVfFdR1wWzhmM4cUQSjFVdHBQaN\nJ1JrTak0opLQrKwgWtH5SBccgciiC8RRpHWeeeuZth0uRHSTUv4ALsC5gcHH5B0KQJTkB9pHOTuf\nPFL3lhFYnWo2W+e5PW0Z13Z1EdEihw5iWEZdl64Ce6N+UaXzeVDMGKUQBKNkVR5wWJRwr0BqXVil\n588PSrSSR54g9bBs+sXyQcd3Es0ruSEms05yM1wmczzyp+4ZE0LkhZ0Fk1nHzmxOswZT1eqz/whb\nX/ZN7PzKj1G/8V/j8jf+J4gpjr9wjwKqCrZLReMDVglaaxbeY/rbnQ9YEZDIzPv0b5M8OgdFKguY\n+0AVA4O6oLYKLSkl1oWA95HFLDCuIsVcEWOg8VBbhdKws+jYXXREDxfHBTFKmmMfI0oUdakZ9C4B\ny6jj8veyRjXGyKi03HItLnic1ytLLK2EReePTN0d1ikPMG3ckY9ZipyjooR7BVKI0HQREWg6z6B/\n7KNOkHraU9kn0bySG2Iy6yY3w2UyxyOL1TMmEpnNO67tNjx/Y8ZsDWuKCOf+5F/EXniO4Re+FdHr\nbZYxQAwgSjMuS1QMqBBBIlorxkOL1oomBHZnDu9TjWUXYd5FTFA0eAY2RT63rMZog9YRazRIpO1A\nJIms1nl2Fh2IMChLxEOpFbsRykIli6tBwXTh6EK6GAxFA33kM0Abwyr6aFSq+2w6T+s8IURcTCJk\nq7b7vFQflLo7eMFZpvumTQfIalKULe6f7jsokDofGAbNnXm3TyAdNkHqqJGkr4ZUdugtzMIetwY4\nXqQ5N8RkToosUDOZxyOL1TMmRrg57/jNl27w/J3HXMM7IO4TpSLC6Iu/ej0H2aNJNaoRcB7mbRJk\npRWU0gytpbRCDAHXRT79ypRZ56iNQVnDtO3wLjAJLaVW6BIqa4gaFq5DRcWoSnvQpPrPWedpfMCH\niFKC9/1Y0BgZVobSJD/UxqVmqFnnKJQmhMi8TRHdxkVKK32jg+BjxHmYNC5FM1tPlIBWd4WoVvJY\nqbvKanyITJynccmvVPqmKH9IicCSgwJpeRxWK6atw/SWWQcnSDkf8T69Vu7ASNKnPZXtQ2TeJQ/d\nposMQ7IvK60+VvNKbojJZDKZzeLJv2I94YQYub274NMvPd4EgOhaXv6J/xyU5vI3fBeiTq4GygBW\nQGmoCiiNsFUbrBaqQnN5aFFapzpV8UwWniY6LtRQG0EQfPQQBWsNF0cFRhSLFu7MWqxVFAIooXOp\nHGDpB1pZRetgd+6Zdx1tSEb+r9mqMFohMb2WtdXEADenbSonCJHaGrTWiCSf1hAjk4Vj0rhUNqBg\nYAtcP/K2MClS+zipO63SFCwlQqE1RieR3PqA7vyRIvEwgVRaTdF6kCQwD5sgNVm4VV2u3jOStPOp\nTOBpTmUvOp9KHEiv3515h9WKovUMK/PYzSu5ISaTyWQ2iyxWz5gY4dM3Z1x7DGPV0C14+R9+gMXz\nvwbAKz/1QS593TvXLlgVKaoqgFKpXrW2wtiWDCvLdm24MCgZFJYQIjttByhuTmeAYqgNhTLEEOlc\nRIgUIqnO1DlKdGo+CvDSpGXRdrROiBIxAtsDy6CwKXI4cyw6R+cChRZemXYo0VirKLWAKEQJ07Yj\nkiZRFSZ154fYRzZDKr8QSRHorcpS9hHReevp3H4x9yjiZCkElRJG1d0/rweJxKME0rBKgwkGhTlU\nLB89kjSVUBxly/Wkp7L3lk2cH5Q0fbnDtHUgyV7sOM0ruSEmk8lkNocsVs+YECOfunHz0R/XzLj+\nY++j+dRvrLaZ8SWQ9UV9hLtvEEPq/h/WsFWXVGUSmLVRbJUGLYobszlNFxgWJkVJdYmPHhcCu62j\n6wILHxhYw2iQVl64QGU1l8clnY80wTNroLQgUdBKsTt3qV7VGsQInReK2jCwSYDeXrRcNiVaJyEm\nUdiuSwAKk4z6Q4jJe1UJwzKlibsQqIxZNS81zmN1cgF4XDF3nHrH+wmkw8oH7revzif7LRfWl8re\npGatvc9dK2HQR8CX4nxQmGPV5eaGmEwmk9kcslg9Yzof+N1r7pEeExYTrv3Ie2hf+Phq2/Zb/gLb\nb/kWZE0FdQJU9HWqRdLAdSFUSlNaTeciVZGimK2HJrR0raeyBqs149JSKJh1ERAkQmE0LiYP1K7z\ntH33v9WKqjSUMVJ6odaGSGRYGG7PO27NW6ZNh1UapYRLowqrhbJQtF1EFFiTxOykdUQiWoRAWjvG\nJDhcSCUFhUkepfPOs+h8H9FMaV5IAvdxX8bj1Ds+qkC6375Ko/r/X08qe9OatQ577stRuOusK80C\nNZPJZM6eLFbPmHnreeUR7Kr87A7Xf/jdtNc+sdp27q1/ke0v++a1HZMAIwFtwcQUUS2tYlQWjCqD\nD+CjwyjNqNRoLTgPw8pQWMug0Gm+vC5oPURJU6QqayEUOIlYq3ExMuzT7zGkGs3Sam77Dq2TGlla\nScUodM5RWsP2IA0KmLVJPKkI00US/DGm2litFPQTrzoXERUZl3Yl1MaVpXUBYse8TRFVgLrQh4q5\nw6KKh21bR73jwwok04tEHyI785bKmn37WjZZrSOVvWnNWrmuNJPJZF49ZLF6xsyajoe16veTW1z7\n0PfQvfJ7q23nv+rfZ+vNX7+24ymBkYFBDdYolKTImSBUpUGUUClhVFVcHlWMagMhEG2a+WStQpEa\njJrgKY1QmOR5WmkFWtBBGJSK0lqsTTWXgUAMQhcDEJk1AVspEOF8bYlCMuqPcTUadHfR0rqAVQo9\n0Mxbz8J5htYSMWgROhcQgWGxf+SqVsLFUUlhFJ0LRMDqNKJVieyrLT0sqriMwi49UfdGGk+j3nF5\nTD4kNwAfAyE66n7/y4jnOlLZm+o7mutKM5lM5tVBFqtnzMMOAXA7r3DtQ+/C3fxMv0W48LZvY/wl\nX7OW41DAtobxAAZFwaAylIWBmDxSJaRmpMIojAivPT/E6t6PNEJtFNoo2pC665331MYwsqmedVhq\nlCgIkXkMmKDYKvoIaIDOR4iB0ioKk8TYbtOlqF3UVIVGgBAkTbcK6T6dj5RG0CxrTVP3fWUVWqnV\n77pIAi72U6eW4u3coFhZQDUunYx555EuIiKURtM4f+jYUyJUhT400njS9Y57I52D0rDokk3WUqDu\n5bj73lTf0VxXmslkMq8Oslh9Qrjx0x+8K1RFcenPvJPhH3zr2tZ/bguGVUVtNdoIhZbUOKUEpRW7\ni45KKbRWFFoIMdJ1jtJobKkJzuM7QetIRDGqDZeGlvGg4s7MYXSqQe18ZLYzo/EOHyxCikxqD8am\nrvZzA9V7oDpGpWVgDRFoXUBrQUgCRUTwHka9TZHRgpFkD2W1uqeDfhmNbJ2n8xEhRY/HlSXEsBKk\nQnIT8CHVvoqwb+ypeLgzb5EojOtUWnBYpPGkhNOhkc5+Utfy9nXue9N9R7NAzWQymaebLFafEC68\n7du59ve/Ez+7w6U/+9cYfv5b1rb21QoujwcYpdEWKq3Zri3joqCsDF0XGBmNaM2gUEwaT/CBJkSG\npaE0ioUPGBXRSjOsDQNbcGFUU2jBbglGKUalYbZwNK7CuYCWvkFHC7OFp7JJYA4Kzai0NM6jRK28\nSusQKY1mVBmmjUOrBa0LbA+KVWQtwsrcvzD3jjOdNo55myKSnY9olWylSqNX4m/WOASh6TxGhC5G\nCq3ujj2NoEXd4xpwWpHG04505vrQTCaTyZwlWaw+Idhzz3D1W74Pd+tF6jf+0fWsCZwfwIWhpTSG\nQaWptOb8sKAuCi6OLJPWMyoN3hsuDgtuzlpCgJtTR2E0PgRCEKzRFCY1XJWV5lxlk09oaSmUZrsq\nUEqIEQaNYyEwaTqUCMolU3slisJolFIMrWYYzf7u80Ltq8WcNQYfUjRzadcUV7WLsi89DCniOG99\n6iAjNVPtLjpcCLQ6sNWXBCwfN64szgcKYdXM1fkAAj4GpC85WHJakcaziHTm+tBMJpPJnBVZrG4o\n0bWIKfZtsxeexV549thra2C7hMujkmfOVWyVlvOjksZ56tJSa82FUcGFrZILbeBO02J0Gm06qDQh\nQqVVStuXhtnCM3eBYaUY1ZarWxUiYERoXaAuNDvzlnPDsp8YpYh4tErz7a1RGKUotCLEiBIwWrBa\nH1mPaLViVKVBAa0LhBCIMTIoNHVfbtB5vxK6MabIaujDjsv0+biyTJoWF1Mdam01MaborAthFZ21\nOq7GnopAaTT08+iXtlinFWk8i0hnrg/NZDKZzFmRxeoG0rzwcV7+Rx/g0tf/VarXffHa1t1WMBrB\ndmXYriueOVezXZc8M6ooS0PTpZrHQWEorbBVaFRpMIWGELBGowTGJTQ+RRZLq2i6BdpHagOvuzjk\nXF3gfOQzt2dURpF67YV2Z0FlFUoEowTnI0oi88ZxaVDRhbiyqnqY6VHDvqZ23jp8AK2gLsxqZvze\nhqgQUgNV5yN1cTca6EOksgYhBVznnaMLKQK7rH0trcb5uG/saWXSGks3gNOONJ5VpDML1Ewmk8mc\nNlmsbhiLT/0G13/0vcR2zvUffR9X3/69lM9+wWOvNwLOD+HSdslWXTMsFFWhGRaWyhoujgu2BpZz\nVcnOvCWSRqoOCktdGraqgmrREWJksnCgBN8FLg/SxKjKKuZtR2EUz5wbMCotSoSXdme94b5wua6Z\ntmkNiXBpZLk9jygUXQhoBKs0VssjjclcRvsKo/al+6eNO9RmKUVYA7uLwLiy+BBXjxmXdmVuH6Ij\nmAgxOSA0zh859vSsIo050pnJZDKZVwtZrG4Q80/+Gi//w/cTXQOAmAKx5SOvo4CxwMUtuLI15PJW\nxdawZKssekGoUBq2KgMiWFE4H7BGMW0DBWkIQKk124OCSyK/Q9EAABmiSURBVOOK1nle3l3gY0Ar\nTVVqxpWl84Er4wprDOeHBqXg1mwBMZnWP3t+wKiyXNoqubazwKoUwbw8rlO6XZKzQPI5VY81JnOv\nSGtdOLL5qC4sWikWzjPv3OpxtTVUVq/E39KuCujLCO6a7B88trMWiGe9/0wmk8lkTpqnUqyKyHPA\n+4C3AReBF4EfB94bY7z1COtcAN4NfCPwGuAG8E+Ad8cYP73OY579zq/w8o//Z+BTKl6PLnDl7e+n\nuPS6h17DAOdLGFfCG65u89rzAwbWoLSwVVlGpaEwBpHAhUHJsDQsXKBxgVnniEBhhAsDy4VhwaBI\nY08rqzFaeI2qOTcomC0cybo/pZ8vDEqMFrQSFi5QWcO8DWxvVcnaqhdUw8LQOAd9DenF0V0hfmPS\nIHLvLPtH5UHNR+Oq3GfwX5r96fNUgpCar3LUMpPJZDKZs+epE6si8kbgl4ErwE8AHwP+GPBXgLeJ\nyFtijDceYp2L/TqfB/xz4IeANwH/DvB1IvIVMcbfXccxTz/2i7zyj/9LCCmap7cuc/UdH8Cef+1D\nPV6A1w6gLoXxoOA12yP+6Gdf5PXnRygjzLtAiIGyr2scVYbL45rCaKZNx7Rx3J62eFI953ZdcGGQ\nRB3EPtqoGBaGK/389d15iwtgFIzrAqt7n1EXmLcujWB1kXJPSr9xnkIriBD75qplc5DVCi3qnojo\no/Kg5qPldKeHEaJZoGYymUwmc/Y8dWIV+G9JQvXbY4x/e7lRRD4I/MfAB4C//BDrfB9JqP6tGOM7\n96zz7cB/3e/nbcc9WL/Y5ZX/7b+AGAAw557h6ju+D7N95YGPHQDbNXzWpQGvv7TFwGrK0vDaczWv\nvzjktecGnBuWTJsudd0rxbA0afRpPzdeKcu4TmNIIykqOSgMhVVYk8oDUsPV3aanwqh9daJ3tyfr\np+1BgZksmCwct2ftKq1uVHIPKI1msnAYLavmINUL5XXYLj1M81EWoplMJpPJPBlIPKtZiSeAiLwB\n+ATwPPDGGHsFmG4bk8oBBLgSY5zeZ50h8DIQgNfEGHf33Kb6fby+38djR1dF5FeBP7L8t7nwHFff\n8X7M+NKRj7HAM2N47kLN6y5s8cz5Ia/dKrkwqhhUGuciw9Jyeavk4qgCkum90ItQc1d0ThvHvJ/o\n1Li7HfC11cmSqnUUWq264h+F1gVuz1rm3Z5OfWtW403TflPEM8TkAlD3daPrIqfxM5lMJpNZH29+\n85v5yEc+8pEY45tPc79PW2T1T/e/P7xXqALEGHdF5JeArwG+HPhn91nnK4C6X2d37w0xxiAiHwb+\nEvCngLWUAtjLr+fq29+PHp479PbX1vDsxSFf9Nx5ro4rnj0/5OLY8uz5IaXVxBDROkVCXYxYrWhd\nWKXADxOCy0ijUXJPB3zqnn98387CKK5sVcxaRwhLh4G0/2WTUtpvb/h/ArZLWaBmMplMJvPk87SJ\n1c/vf//WEbf/Nkmsfh73F6sPsw79Og+kj6AexpcAiCkB4fqPvOeeOwwLRWUNL1rNTaP4TSWICEpS\nl/pS+MXVfyAS943clP6+R7EcG3owyv6gxx2X1THLaqhUJpPJZDKZDeWjH/0opMzyqfK0idXt/ved\nI25fbj88fLn+dR6EohhEc+5qgOQBSv8/Yb77cmgmd26H4CMx4r27O/Vd5OEmwD/s/db1uFcVb+p/\nf+xMjyLzuOTz9+SSz92TTT5/TzZfQrJwP1WeNrH6IJYBvOMKsUda56jaDhH5VdoZ7bXfPdXaj8zx\nWUbLT7tuJ7Me8vl7csnn7skmn78nm/tkik+Up62obxnx3D7i9q0D9zvpdTKZTCaTyWQyx+BpE6sf\n738fVUv6uf3vo2pR171OJpPJZDKZTOYYPG1i9ef631/TW0yt6K2r3gLMgX/5gHX+ZX+/t/SP27uO\nIjVp7d1fJpPJZDKZTOYEeKrEaozxE8CHSZ1q/+GBm98LDIH/da/Hqoi8SUTetPeOMcYJ8Pf6+/+n\nB9b5j/r1f3ZdE6wymUwmk8lkMofzNDZY/QekManfLyJfCXwU+DKSJ+pvAe86cP+P9r8Puid9N/BW\n4J0i8qXAvwK+APgG4Dr3iuFMJpPJZDKZzJp5qiZYLRGRzwLeRxqHepE0uerHgffGGG8euG+y+4zx\nHqtPEbkAvAf4RuA1wA3gZ4B3xxg/fZLPIZPJZDKZTCbzlIrVTCaTyWQymczTwVNVs5rJZDKZTCaT\nebrIYjWTyWQymUwms7FksZrJZDKZTCaT2ViyWM1kMplMJpPJbCxZrGYymUwmk8lkNpYsVjOZTCaT\nyWQyG0sWq2tERJ4Tkb8jIi+ISCMiz4vIfyUi5x9xnQv9457v13mhX/e5kzr2zHrOn4h8tYj8TRH5\nZyJyU0SiiPziSR535vjnTkSGIvKtIvKDIvIxEZmKyK6I/N8i8h0iUpz0c3g1s6a/vb8mIj/dP3Yi\nIjsi8v+JyAfzZ+fJsq5r34E1/4SI+P4z9P3rPN7MXdb0t/fz/Xk66qc69nFmn9X1ICJvJE3OugL8\nBPAx4I+RJmd9HHhLjPHGQ6xzsV/n84B/DvxfwJu4OznrK/KY1/WzxvP346RztQB+B/gi4JdijH/8\nhA79Vc86zp2IvI008OMm8HOkc3cB+HrgmX79r4wxLk7oabxqWePf3u8AE+D/Ba4BFvjDwJ8EdoC3\nxhh/7SSew6uZdZ2/A2uOgV8HLgEj4AMxxu9Z53Fn1vq39/Okv7P3HnGX98cY3bEONsaYf9bwA/ws\nEIFvO7D9g/32//4h1/kf+vt/8MD2b++3/5Ozfq5P488az99XAF8IaOD1/WN/8ayf39P8s45zB3wp\n8K1AcWD7GPjVfp3vOOvn+jT+rPFvrzpi+7/Xr/PTZ/1cn8afdZ2/A4/9O6Qvjt/dr/H+s36eT+PP\nGv/2fj7JyZM71hxZXQMi8gbgE8DzwBtjjGHPbWPSuFcBrsQYp/dZZwi8DATgNTHG3T23qX4fr+/3\nkaOra2Jd5++QdV8PfJIcWT0xTurcHdjHXwD+PvCTMcavP/ZBZ1ac0vnbBm4DvxNj/NxjH3RmxUmc\nPxH5BtJ49H8bMMD/Qo6srp11nrtlZDUeMrZ+XeSa1fXwp/vfH957wgF6wflLwAD48ges8xVATRI3\nu3tv6Nf9cP/PP3XsI87sZV3nL3P6nMa56/rfx0tjZQ7jNM7f8gvGrx9jjczhrPX8icgV4H8CfjzG\n+APrPNDMPaz9b09E3i4i3yUi7xSRrxWRcl0Hm8Xqevj8/vdvHXH7b/e/P++U1sk8Gvl1f3I5jXP3\n7/7/7d15+B1Vfcfx9ycsGloNm0AbllQWwUKrCIJoTKKy1AcqiIViy5NAURYReGppKZQSFLV9igIV\nqwRKQ5CWsBSoKItoAoiy7yCkoGEJgUISNjEmwLd/nHPJZDL397v3/uYuST6v55ln8pvlzJk5M5nv\nPXPmTB5fO4I0rFrt5SfpMElTJZ0u6TrgAuAJ4ITOs2lN1F1+00hxyREjyZS1pBv/d14MfB34BvBD\n4ElJn+kse8tbs45EjDF5/FKT+Y3p6/YoHWuPj/vKq6tlJ+loYC/gXlI7OqtXN8rvMGCXwt93AJ+N\niMfazJsNr7byk3Qo6eXUAyPiuRryZkOr89q7CjgduAdYAGwBTAa+BMyUtHdEXDOCvLpmtUca7ThG\n2kC4rnSsPT7uK6+Oy07Sp4EzgWeB/SNi6TCrWP3aLr+I2DW3ndsQ2CNPviv3+GC91VL55fb9ZwKX\nRsQlXc6Ttablay8izoiIqyNiXkQsjohHI+JEUrA6CvjaSDPjYLUejV8gY5rMf2dpuW6nY+3xcV95\ndaXsJO1LeqT1f6Quj/xCY3d07dqLiAUR8SNSwPobYIak0e1n0YZQV/mdTyqjo+rIlLWkF/e980ht\n/d+XX9rqmIPVejyax83adjTeQG3WNqTudKw9Pu4rr9rLTtKfAZeS+uqcEBGPDrOKda7r115EvAj8\nHHgXqVs5q09d5bcjqa/P54udyZN6AgA4KU+7cmTZtYJeXHuLgcbL4r/TaTrgNqt1mZXHe0gaVdEF\nxIdJvxpvHSadW/NyH5b0joquqxqPtGZVrWwdq6v8rPdqLbvcTdUMYB4wyTWqXdera29sHrtHh3rV\nVX4zSG+el20NfJTUZvwuUptIq0fXrz1J7wHWIwWsL4wgr65ZrUNEPE7qVmoc8IXS7FNJvyhmFPsq\nk7StpG1L6bwKXJiXn1pK5+ic/nW+gdarrvKz3quz7CRNJl1/TwIf9XXWfXWVn6Qtcr+RK5B0OLAz\n8BTwQH25txrvfcdExGHlgWU1qz/I077dtZ1ZzdR47b1b0tjS+kjakGXld3GM8AtW/ihATSo+W/YL\n0hupk0jV6LtF4bNl+REH5U50Kz63ejuwHcs+t7pbPsmsRjWW30dIbyND+kzg/qRye+tNyIiY0q39\nWB3VUXaSJgE3kH7An08KbMpejIgzu7Qbq62aym9f4L9zOnNITTg2IPURuQPpM6x7R8SNPdil1Upd\n/3c2SXsK/ihA19R07U0htU29kfSRgYXA5sAnSe1h7wR2z81xOteNz2KtrgOwGenCmg8sIfXtdxaw\nfsWyQZPPk5G+SX5WXn9JTu98YNN+7+OqPNRRfsCUxrxmQ7/3c1UcRlp2rZQbMLff+7mqDjWU3+ak\nvh1vJwWqS0mPHu8jdamzWb/3cVUe6rr3VSzbuC79udUBLTvSj8HppKcWC/K1txC4GfgipU9Ydzq4\nZtXMzMzMBpbbrJqZmZnZwHKwamZmZmYDy8GqmZmZmQ0sB6tmZmZmNrAcrJqZmZnZwHKwamZmZmYD\ny8GqmZmZmQ0sB6tmZmZmNrAcrJqZmZnZwHKwamZmZmYDy8GqmZmZmQ0sB6tmZmZmNrAcrJqZmZnZ\nwHKwamZmZmYDy8Gqma2yJI2TFJKmtzJ9dTTox0jSXElz+5mHkZI0MR/LxvBIv/M0EpI2LO1P9DtP\ntmpzsGrWI4X/2N+UtOUQy80qLDulh1k0G9KgBLArsRuBU4Gz+52REXqNtB+nAk/0OS+2Gliz3xkw\nW828Trru/go4sTxT0tbAhMJy1h3zgO2Al/qdkQHmY1S/2RExtd+ZGKmIeA2YCqnWGNiin/mxVZ9r\nVs166zngTuAQSVXB6GGAgKt7mqvVTEQsjYhHImJ+v/MyqHyMzGxQOFg1671zgU2AvYsTJa0FTAZ+\nBjzUbGVJu0i6TNKzkpZIekrSOZJ+v2LZKZIul/RLSb+R9LKkWyT9ZcWybz3izf++WNILkhZLulPS\n3uV1hiPpg5JmSpon6beS5ku6XtIBef62eZs/GSKNByQtlbRJO2kPk6+mj7NHkm4hjU6P+5a5bBdI\neiVvd/u83LskTcv5WSzpDkmThkhrW0lXSloo6deSfippjzb2YbljJGkq8Ks8e3KpzeKUvEyjbebU\nJmlWtj9VcrSkh/K+zZN0tqQxQ+Sv5eugU5LWknScpHtzOT4t6QxJa0taR9Jzki6qYTu1nQN5uZbP\nv8I6knSspIfLZdCs3Mx6xY8ZzXrvv4BvkmpRryxM/1NgY+AEYKuqFSUdQgp2fwv8D/AUsHVOax9J\nu0bEk4VVvgM8DNwEzAc2AD4JXCjpPRFxcsVmtgBuB34JXAisDxwIXCXpExExq5WdlPS5vP03cl7/\nF9gI2Ak4CrgkIh6RNAuYJGmbiJhTSmM3YHvg8oh4tp20W8ljJ3luMalOjvs44DbgF8D0/Pd+wGxJ\nHwKuBV4GZpLK5M+Ba/Jxe7KU1h8APwceBM4Bfo9UhtdI+mxEzGxxP4pmA+sCxwL3sfy5e28H6RWd\nCRxDOlbTgKXAp4BdgLWBJcWFO7gO2iZpfdIx35n0pOM60g/M40hNJN4klcMpI9lOyTjqOQc6Of++\nDRwJPEMqgyWk/5M+CKxFKhOz/ogIDx489GAAAng6//s8UrvUTQvzryW1D1wHOC0vP6UwfxvSDeQx\nYGwp7Y+RAqwrStO3rMjH2sCPSTefsYXp4/I2AziltM6eefoPW9zX9+b0FwJ/WDG/uN+fyWmfXrHc\n9Dxv9w7TbuzT9NIyK0xvJ90W9r/T435SaZ2T8/SFwHeBUYV5B+d5ZzRJ619Kae2Ut70IeGeHx6hy\n2cL8iXn+1Cbz5wJzS9N2y+s8BqxfmP52UsAdxXXo4Dro8Hq9Pm/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"text/plain": [ "<matplotlib.figure.Figure at 0xa3d2dae10>" ] }, "metadata": { "image/png": { "height": 321, "width": 341 } }, "output_type": "display_data" } ], "source": [ "x_dashed = np.linspace(0,1, 10)\n", "y_dashed = amp_conv_factor * x_dashed\n", "plt.figure(figsize=(5,5))\n", "plt.plot(df_rotmod.mean_amplitude, df_rotmod.max_activity_index, '.', alpha=0.05)\n", "plt.plot(x_dashed, y_dashed, 'k--')\n", "plt.xlim(0,0.5)\n", "plt.ylim(0,1);\n", "plt.xlabel(r'Mean cyclic amplitude, $\\alpha$ [mag]')\n", "plt.ylabel(r'$95^{th} - 5^{th}$ variability percentile[mag]');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The lines track decently well. There's some scatter! Probably in part due to non-sinusoidal behavior." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's convert the mean magnitude amplitude to an unspotted-to-spotted flux ratio:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "df_rotmod['amplitude_linear'] = 10**(-df_rotmod.mean_amplitude/2.5)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.9, 1.01)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/s8rxnFuOb3/g6nLckiRJ0siYqsDwIWBPYHvg/WNVjIjtaT68PwHMz8w/yMwP04SN7wCH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\nk/Sju1bxbkcPJElDzhEGSZI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"text/plain": [ "<matplotlib.figure.Figure at 0xb4c1e1828>" ] }, "metadata": { "image/png": { "height": 251, "width": 390 } }, "output_type": "display_data" } ], "source": [ "df_rotmod['amplitude_linear'].hist(bins=500)\n", "plt.xlim(0.9, 1.01)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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0zcyWA/8HNAP79nN7lXsdPS+9RUTkHasyHujDDz9cF0ZWrFjBGWec0WuP6mc/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fc94vPkM2vEBTUCxJ6JLhHRMHZzOBiMbQc+XuDZCUrNYpkFnp/hBz8+o9McfeDrHF6sWpHU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\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hlNWVABKME/6gpw//7UQKCUR8IkrLNBVfXFjQtktXNMtUtESjgbgKqV3bnr8k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\nO2j1v3q15oKnsuDILhMxM/6Q085BGe9UOsPJ5M+03+r+inJ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edp3TnQVyb66xp3ra3QGq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XOxyOzBH7/kpZx99tkc+7Df4UlPejKbN2/mou9exNe/9jWe+MQncsYZZ7SG/IlPO/74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\n3tW57GI/w9jrFG+NZRgpj3G61vth7tf8Gx2I2CRuF8WdRz2aeeQ8sKm11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"text/plain": [ "<matplotlib.figure.Figure at 0xb506434e0>" ] }, "metadata": { "image/png": { "height": 321, "width": 345 } }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(5,5))\n", "plt.plot(df_rotmod.best_rotation_period, df_rotmod.amplitude_linear, '.', alpha=0.01)\n", "plt.xlim(0, 60)\n", "plt.ylim(0.9, 1.04)\n", "plt.xlabel('$P_{\\mathrm{rot}}$ [days]')\n", "plt.text(1, 0.92, ' Rapidly rotating\\n spot dominated')\n", "plt.text(36, 1.02, ' Slowly rotating\\n facular dominated')\n", "plt.ylabel('Flux decrement $(f_{\\mathrm{spot, min}})$ ');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Promising!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's read in the Kepler data and cross-match! This cross-match with Gaia and K2 data comes from Meg Bedell." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "from astropy.table import Table\n", "k2_fun = Table.read('../../K2-metadata/metadata/k2_dr2_1arcsec.fits', format='fits')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(288910, 95)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(k2_fun), len(k2_fun.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We only want a few of the 95 columns, so let's select a subset." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "col_subset = ['source_id', 'epic_number', 'tm_name', 'k2_campaign_str']" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "k2_df = k2_fun[col_subset].to_pandas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `to_pandas()` method returns byte strings. Arg! We'll have to clean it. Here is a reuseable piece of code:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "def clean_to_pandas(df):\n", " '''Cleans a dataframe converted with the to_pandas method'''\n", " for col in df.columns:\n", " if type(k2_df[col][0]) == bytes:\n", " df[col] = df[col].str.decode('utf-8')\n", " return df" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "k2_df = clean_to_pandas(k2_df)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['solution_id', 'source_id', 'num_segments', 'segments_start_time',\n", " 'segments_end_time', 'segments_colour_mag_intercept',\n", " 'segments_colour_mag_intercept_error', 'segments_colour_mag_slope',\n", " 'segments_colour_mag_slope_error', 'segments_correlation_coefficient',\n", " 'segments_correlation_significance', 'num_outliers', 'outliers_time',\n", " 'segments_rotation_period', 'segments_rotation_period_error',\n", " 'segments_rotation_period_fap', 'segments_cos_term',\n", " 'segments_cos_term_error', 'segments_sin_term',\n", " 'segments_sin_term_error', 'segments_a0_term', 'segments_a0_term_error',\n", " 'best_rotation_period', 'best_rotation_period_error',\n", " 'segments_activity_index', 'segments_activity_index_error',\n", " 'max_activity_index', 'max_activity_index_error',\n", " 'segments_g_unspotted', 'segments_g_unspotted_error',\n", " 'segments_bp_unspotted', 'segments_bp_unspotted_error',\n", " 'segments_rp_unspotted', 'segments_rp_unspotted_error', 'g_unspotted',\n", " 'g_unspotted_error', 'bp_unspotted', 'bp_unspotted_error',\n", " 'rp_unspotted', 'rp_unspotted_error', 'mean_amplitude',\n", " 'amplitude_linear'],\n", " dtype='object')" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_rotmod.columns" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "keep_cols = ['source_id', 'num_segments', 'best_rotation_period', 'amplitude_linear']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can **merge** (e.g. SQL *join*) these two dataframes on the `source_id` key." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>source_id</th>\n", " <th>epic_number</th>\n", " <th>tm_name</th>\n", " <th>k2_campaign_str</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2.641087e+18</td>\n", " <td>60017806.0</td>\n", " <td>2MASS J23342787-0134482</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2.641087e+18</td>\n", " <td>60017806.0</td>\n", " <td>2MASS J23342787-0134482</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.634212e+18</td>\n", " <td>60017809.0</td>\n", " <td>2MASS J23293314-0346078</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2.634212e+18</td>\n", " <td>60017809.0</td>\n", " <td>2MASS J23293314-0346078</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2.740067e+18</td>\n", " <td>60017810.0</td>\n", " <td>2MASS J00043784+0333010</td>\n", " <td>E</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " source_id epic_number tm_name k2_campaign_str\n", "0 2.641087e+18 60017806.0 2MASS J23342787-0134482 E\n", "1 2.641087e+18 60017806.0 2MASS J23342787-0134482 E\n", "2 2.634212e+18 60017809.0 2MASS J23293314-0346078 E\n", "3 2.634212e+18 60017809.0 2MASS J23293314-0346078 E\n", "4 2.740067e+18 60017810.0 2MASS J00043784+0333010 E" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "k2_df.head()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>source_id</th>\n", " <th>num_segments</th>\n", " <th>best_rotation_period</th>\n", " <th>amplitude_linear</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>404707866294134912</td>\n", " <td>2</td>\n", " <td>6.819885</td>\n", " <td>0.982152</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>404740679844116736</td>\n", " <td>6</td>\n", " <td>0.845407</td>\n", " <td>0.994011</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>404817057245494784</td>\n", " <td>4</td>\n", " <td>1.026961</td>\n", " <td>0.976839</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>404823482516792832</td>\n", " <td>3</td>\n", " <td>0.719850</td>\n", " <td>0.986132</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>405068192572501760</td>\n", " <td>3</td>\n", " <td>0.319864</td>\n", " <td>0.984646</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " source_id num_segments best_rotation_period amplitude_linear\n", "0 404707866294134912 2 6.819885 0.982152\n", "1 404740679844116736 6 0.845407 0.994011\n", "2 404817057245494784 4 1.026961 0.976839\n", "3 404823482516792832 3 0.719850 0.986132\n", "4 405068192572501760 3 0.319864 0.984646" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_rotmod[keep_cols].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll only keep columns that are in both catalogs." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "df_comb = pd.merge(k2_df, df_rotmod[keep_cols], how='inner', on='source_id')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>source_id</th>\n", " <th>epic_number</th>\n", " <th>tm_name</th>\n", " <th>k2_campaign_str</th>\n", " <th>num_segments</th>\n", " <th>best_rotation_period</th>\n", " <th>amplitude_linear</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2.44985e+18</td>\n", " <td>60019808.0</td>\n", " <td>2MASS J00011844-0050338</td>\n", " <td>E</td>\n", " <td>3</td>\n", " <td>11.812471</td>\n", " <td>0.991231</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2.44985e+18</td>\n", " <td>60020260.0</td>\n", " <td>2MASS J00011919-0050496</td>\n", " <td>E</td>\n", " <td>3</td>\n", " <td>11.812471</td>\n", " <td>0.991231</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3.59566e+18</td>\n", " <td>201103864.0</td>\n", " <td>2MASS J11581545-0628544</td>\n", " <td>10</td>\n", " <td>6</td>\n", " <td>11.677781</td>\n", " <td>0.990946</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3.59735e+18</td>\n", " <td>201149128.0</td>\n", " <td>2MASS J12041716-0518533</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>2.062601</td>\n", " <td>0.975344</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>3.5981e+18</td>\n", " <td>201205375.0</td>\n", " <td>2MASS J12080464-0358396</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>35.318797</td>\n", " <td>0.992941</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " source_id epic_number tm_name k2_campaign_str \\\n", "0 2.44985e+18 60019808.0 2MASS J00011844-0050338 E \n", "1 2.44985e+18 60020260.0 2MASS J00011919-0050496 E \n", "2 3.59566e+18 201103864.0 2MASS J11581545-0628544 10 \n", "3 3.59735e+18 201149128.0 2MASS J12041716-0518533 10 \n", "4 3.5981e+18 201205375.0 2MASS J12080464-0358396 10 \n", "\n", " num_segments best_rotation_period amplitude_linear \n", "0 3 11.812471 0.991231 \n", "1 3 11.812471 0.991231 \n", "2 6 11.677781 0.990946 \n", "3 3 2.062601 0.975344 \n", "4 3 35.318797 0.992941 " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_comb.head()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(524, 7)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_comb.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Only 524 sources appear in both catalogs! Boo! Well, better than nothing! \n", "It's actually even fewer K2 targets, since some targets are single in K2 but have two or more matches in Gaia. These could be background stars or bona-fide binaries. Let's flag them." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "multiplicity_count = df_comb.groupby('epic_number').\\\n", " source_id.count().to_frame().\\\n", " rename(columns={'source_id':'multiplicity'})" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "df = pd.merge(df_comb, multiplicity_count, left_on='epic_number', right_index=True)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>source_id</th>\n", " <th>epic_number</th>\n", " <th>tm_name</th>\n", " <th>k2_campaign_str</th>\n", " <th>num_segments</th>\n", " <th>best_rotation_period</th>\n", " <th>amplitude_linear</th>\n", " <th>multiplicity</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2.44985e+18</td>\n", " <td>60019808.0</td>\n", " <td>2MASS J00011844-0050338</td>\n", " <td>E</td>\n", " <td>3</td>\n", " <td>11.812471</td>\n", " <td>0.991231</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2.44985e+18</td>\n", " <td>60020260.0</td>\n", " <td>2MASS J00011919-0050496</td>\n", " <td>E</td>\n", " <td>3</td>\n", " <td>11.812471</td>\n", " <td>0.991231</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3.59566e+18</td>\n", " <td>201103864.0</td>\n", " <td>2MASS J11581545-0628544</td>\n", " <td>10</td>\n", " <td>6</td>\n", " <td>11.677781</td>\n", " <td>0.990946</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3.59735e+18</td>\n", " <td>201149128.0</td>\n", " <td>2MASS J12041716-0518533</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>2.062601</td>\n", " <td>0.975344</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>3.5981e+18</td>\n", " <td>201205375.0</td>\n", " <td>2MASS J12080464-0358396</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>35.318797</td>\n", " <td>0.992941</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>3.60178e+18</td>\n", " <td>201249315.0</td>\n", " <td>2MASS J11543852-0313049</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>5.208817</td>\n", " <td>0.965566</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>3.60116e+18</td>\n", " <td>201272989.0</td>\n", " <td>2MASS J12000551-0251359</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>16.250582</td>\n", " <td>0.989781</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>3.60116e+18</td>\n", " <td>201272989.0</td>\n", " <td>2MASS J12000551-0251359</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>16.250582</td>\n", " <td>0.989781</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>3.60116e+18</td>\n", " <td>201272989.0</td>\n", " <td>2MASS J12000551-0251359</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>16.250582</td>\n", " <td>0.989781</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>3.60116e+18</td>\n", " <td>201272989.0</td>\n", " <td>2MASS J12000551-0251359</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>16.250582</td>\n", " <td>0.989781</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>3.60116e+18</td>\n", " <td>201272989.0</td>\n", " <td>2MASS J12000551-0251359</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>16.250582</td>\n", " <td>0.989781</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>3.60116e+18</td>\n", " <td>201272989.0</td>\n", " <td>2MASS J12000551-0251359</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>16.250582</td>\n", " <td>0.989781</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>3.60116e+18</td>\n", " <td>201272989.0</td>\n", " <td>2MASS J12000551-0251359</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>16.250582</td>\n", " <td>0.989781</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>3.60116e+18</td>\n", " <td>201272989.0</td>\n", " <td>2MASS J12000551-0251359</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>16.250582</td>\n", " <td>0.989781</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>3.60208e+18</td>\n", " <td>201307675.0</td>\n", " <td>2MASS J11535919-0220075</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>4.763580</td>\n", " <td>0.991415</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>3.69821e+18</td>\n", " <td>201463336.0</td>\n", " <td>2MASS J12212189-0001350</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>5.670368</td>\n", " <td>0.996039</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>3.69821e+18</td>\n", " <td>201463336.0</td>\n", " <td>2MASS J12212189-0001350</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>5.670368</td>\n", " <td>0.996039</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>3.69821e+18</td>\n", " <td>201463373.0</td>\n", " <td>2MASS J12212168-0001333</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>5.670368</td>\n", " <td>0.996039</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>3.69821e+18</td>\n", " <td>201463373.0</td>\n", " <td>2MASS J12212168-0001333</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>5.670368</td>\n", " <td>0.996039</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>3.89298e+18</td>\n", " <td>201691310.0</td>\n", " <td>2MASS J12045028+0329317</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>25.617613</td>\n", " <td>0.992786</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " source_id epic_number tm_name k2_campaign_str \\\n", "0 2.44985e+18 60019808.0 2MASS J00011844-0050338 E \n", "1 2.44985e+18 60020260.0 2MASS J00011919-0050496 E \n", "2 3.59566e+18 201103864.0 2MASS J11581545-0628544 10 \n", "3 3.59735e+18 201149128.0 2MASS J12041716-0518533 10 \n", "4 3.5981e+18 201205375.0 2MASS J12080464-0358396 10 \n", "5 3.60178e+18 201249315.0 2MASS J11543852-0313049 1 \n", "6 3.60116e+18 201272989.0 2MASS J12000551-0251359 1 \n", "7 3.60116e+18 201272989.0 2MASS J12000551-0251359 10 \n", "8 3.60116e+18 201272989.0 2MASS J12000551-0251359 1 \n", "9 3.60116e+18 201272989.0 2MASS J12000551-0251359 10 \n", "10 3.60116e+18 201272989.0 2MASS J12000551-0251359 1 \n", "11 3.60116e+18 201272989.0 2MASS J12000551-0251359 10 \n", "12 3.60116e+18 201272989.0 2MASS J12000551-0251359 10 \n", "13 3.60116e+18 201272989.0 2MASS J12000551-0251359 1 \n", "14 3.60208e+18 201307675.0 2MASS J11535919-0220075 1 \n", "15 3.69821e+18 201463336.0 2MASS J12212189-0001350 10 \n", "16 3.69821e+18 201463336.0 2MASS J12212189-0001350 10 \n", "17 3.69821e+18 201463373.0 2MASS J12212168-0001333 10 \n", "18 3.69821e+18 201463373.0 2MASS J12212168-0001333 10 \n", "19 3.89298e+18 201691310.0 2MASS J12045028+0329317 1 \n", "\n", " num_segments best_rotation_period amplitude_linear multiplicity \n", "0 3 11.812471 0.991231 1 \n", "1 3 11.812471 0.991231 1 \n", "2 6 11.677781 0.990946 1 \n", "3 3 2.062601 0.975344 1 \n", "4 3 35.318797 0.992941 1 \n", "5 4 5.208817 0.965566 1 \n", "6 3 16.250582 0.989781 8 \n", "7 3 16.250582 0.989781 8 \n", "8 3 16.250582 0.989781 8 \n", "9 3 16.250582 0.989781 8 \n", "10 3 16.250582 0.989781 8 \n", "11 3 16.250582 0.989781 8 \n", "12 3 16.250582 0.989781 8 \n", "13 3 16.250582 0.989781 8 \n", "14 4 4.763580 0.991415 1 \n", "15 3 5.670368 0.996039 2 \n", "16 3 5.670368 0.996039 2 \n", "17 3 5.670368 0.996039 2 \n", "18 3 5.670368 0.996039 2 \n", "19 3 25.617613 0.992786 1 " ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's cull the list and just use the \"single\" stars, which is really the sources for which Gaia did not identify more than one target within 1 arcsecond." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "df_single = df[df.multiplicity == 1]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(224, 8)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_single.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A mere 224 sources! Boo hoo!" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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jeeONN7jwwgsZM2YMxcXFHHDAAVx//fWsX79+s+XU1NRw//33U1xczHXXXdfg\n0vkuu+zCRRddRHV1Nffdd1/d8lS3hvTW2IULFzJx4kSOOOII/vnPf1JeXl63PJlMZu0Kke573/se\nAHfddVeD5fPnz2fZsmVMmTKlVf1IBw4c2KSVEoK+xEuWLOGggw5q0l1iypQpHHroobzzzjs899xz\nLd7WkCFDyM/Pb7J88uTJ7LnnnsybN6/FZbXErFmzKCwsrHs9YMAAjj/+eMrKynjnnXfqlt97770A\n/OhHP2rQ5SQej3Pdddd1aJ2kbRTMiojINmuHHXZg4cKF/Oc//+HWW2/l9NNPZ8cdd+Sll17iyiuv\nZO+992bZsmXNlrFkyRIqKirYZ5996Nu3b5P0iRMnAvDaa6/VLWsczL711lusWrWKSZMmMXHiRGpq\naur6zaa6I6TKac6ee+7J4Ycfzt///nc+/vjjuuV33303AN/97nc3W0a6ffbZJ2OAuXjx4mbrlGmf\nN8fduf/++zniiCPo378/sVisrh/sm2++ySeffNKqujenpKQk4wgVw4YNA2DdunV1y1L7cOihhzZZ\n/8ADDyQWU4/N7qYzICIi27zdd9+d3Xffve71kiVL+Na3vsULL7zApZdeyty5c7PmLSsrA8g6BFRq\neWlpad2yoUOHsssuu/D000+TSCTqgtpJkyYxaNAg8vLyWLBgAccccwwLFiygV69edf1PN+f888/n\n2WefZfbs2cyYMYOVK1fy6KOPMnr0aPbff/8WlZEyaNCgjMvbss+bc9lll3HLLbcwePBgjjrqKIYM\nGVLXcjpnzhw+/PDD1lS9Wdlu6ksFpolEom5Zal8HDhzYZP1oNFrX91q6j4JZERGRRnbbbTd+97vf\nsfPOOzc7ggBQd9l+5cqVGdM/++yzBuulTJw4kbvuuouXX36ZBQsWsMMOO7DTTjsBsP/++/Pkk0/y\n2WefsWTJEo477rgWtwCeeOKJDBw4kN/85jf85Cc/adONXynpXSbStXWfs1m9ejWzZs1ir7324vnn\nn6dnz54N0h988MGWVrnDpW54W7VqFTvuuGODtEQiwZo1a+puTJPuoW4GIiIiGaQCKvemQzWl23XX\nXSkqKuL1119vcHk6ZeHChQCMGTOmwfJUV4N58+bx7LPPcsQRRzRI+9e//sVDDz3UYN2WyMvL49vf\n/jaffPIJjz32GLNnz6a4uLhJ39b2+PKXvwyQdTav1PL0fU6NrpDe6pmydOlSkskkkydPbhLIrlix\ngqVLlzbJ01x5HSm1r5n6/7744ovU1tZ26vZl8xTMiojINmnZsmXMmjWr7jJyOndn5syZABx++OHN\nlhOPx5k6dSrl5eX85Cc/aZAHKVoHAAAgAElEQVT2wQcfMGvWLPLy8jj99NMbpKXGW73jjjsoKytr\nELBOnDgRd+fnP/953evWOO+884hGo1x44YUsW7aMU089tUmQ2B6HHHIIu+66K8899xwPP/xwg7SH\nH36YZ599llGjRjXoZ5q6HP/RRx81KS81nNZzzz3XIDgtLy/n3HPPzRgwNldeRzrjjDMAmDlzZoP3\nSnV1NVdffXWnbltaRt0MRERkm1RWVsbFF1/MD37wAw455BD22msvevbsyerVq3nqqadYunQpAwYM\n4MYbb9xsWT//+c9ZtGgRt99+Oy+//DITJkyoG2d2w4YN3H777YwcObJBnn79+vGlL32JN954A2gY\nsB500EEUFRWxevVq+vfv36KJDtLtsMMOHHvssTz66KMAbepi0Bwz49577+XII49kypQpHH/88ey2\n22688847zJ07l549e3LfffcRidS3mU2YMIFIJMJVV13FW2+9RZ8+fQC45pprGDRoEKeccgoPPfQQ\no0ePZvLkyZSVlfHEE09QUFDA6NGjef311xvUITVe70MPPUQ8HmeHHXbAzDj99NMZPnx4h+3ruHHj\nOO+887j77rvZc889Oemkk8jLy+Oxxx6jpKSE7bffvsF+SjfoivG/9Gj/A40zKyLSoSorK/3//u//\n/Hvf+55/+ctf9gEDBngsFvNevXr5mDFj/Oqrr/bVq1c3yUeGcWbd3detW+dXXHGF77zzzh6Px72k\npMSPOOIInzdvXtY6XHbZZQ74Hnvs0SRt8uTJDvh//dd/ZcybaZzZdHPnznXAx44dm3WdbFLjzJ55\n5pnNrrdkyRI/7bTTfNCgQR6LxXzQoEE+depUX7JkScb1f/e73/k+++zjBQUFdePYpmzcuNGvvvpq\n32mnnTw/P9+HDh3q559/vn/xxRc+bty4BuumvPTSSz5x4kTv1auXm1mDcWybG2c223HLNhZuIpHw\nm266yXfddVePx+M+ePBgP//88720tNSLi4t9n332afY4bYu6cpxZc2++L5BsGczs1TFjxox59dVX\nu7sqIiKSA6ZPn86MGTOYPXs255xzTndXZ6v03nvvMWrUKE455ZRuvUltS7TvvvuyePHixe6+b2dv\nS+3iIiIiW5kNGzZw55130rdvX775zW92d3Vy3sqVK0kmkw2WVVRUcMkllwDw9a9/vTuqJSH1mRUR\nEdlK/PWvf2Xx4sU89thjrFq1il/+8pcUFRV1d7Vy3i233MKDDz7I+PHjGTx4MCtXrmTBggWsWLGC\nY445hm984xvdXcVtmoJZERGRrcSf/vQn7r33XgYOHMhVV13FpZde2t1V2ioceeSRvPHGG8yfP5+1\na9cSi8UYNWoUF110EZdccknW8Xila6jPbI5Qn1kRERHJFeozKyIiIiLSAgpmRURERCRnKZgVEZFt\n2iuvvMKRRx5Jv379MDNGjx7d3VXKaPr06ZhZ1ilku9tZZ52FmbF8+fLurkqdp59+GjNj+vTp3V2V\nTrV8+XLMjLPOOqu7q9ItdAOYiIhss9avX8+xxx5LZWUlp59+Ov369WPQoEHdXS3ZCpx11lnce++9\nLFu2rG66XukcCmZFRGSb9dJLL7F69WpmzpzJ1Vdf3d3VkQ62//778/bbb9OvX7/urop0IgWzIiKy\nzfr0008B2H777bu5JtIZioqK2G233bq7GtLJ1GdWRES2Oak+hmeeeSYAZ599NmaGmTFnzhwA3n33\nXa688krGjh1L//79yc/PZ/jw4Zx33nmsWLEia9nz58/nq1/9KgMGDCA/P59hw4Zx/PHH8+STT9at\nM2fOnAbbaszMGD9+fIv2Ze7cuZx22mmMGjWKHj16UFxczL777susWbOazFoF9X1bly5dym233caX\nvvQlCgsLW7y9J598ksMOO4wePXrQt29fTjjhBJYsWdJsnj/+8Y8cfvjhlJSUUFhYyN577811111H\nVVVVk3VHjBjBiBEjKC8v59JLL2XYsGEUFhYyevRo5s6dC0BtbS3/8z//wy677EJBQQE77bQTt99+\ne5OysvWZHT9+PGbWoJzUufrhD39IdXV1k7Jac5zNjHvvvReAkSNH1r23Gnc3WLt2LVdddRW77747\nhYWFlJSUMGnSJObPn5/xOG7YsIHLLruMoUOHUlBQwG677cZNN92U8TxvS9QyKyIi25zevXszbdo0\nXn/9df7yl79w/PHH1934lXp+5JFHuPPOO5kwYQIHH3ww8Xicf//738yePZvHHnuMV155hSFDhjQo\nd9q0aVx77bUUFxdzwgknMGzYMD799FOef/557r//fo444ogO35crr7ySSCTCAQccwJAhQygrK+Op\np57i4osv5uWXX+Z3v/tdxnwXX3wxixYt4thjj+UrX/kK0Wh0s9t6+OGHmTJlCvF4nClTpjB48GCe\ne+45DjroIL70pS9lzHP11Vdz3XXX0a9fP0499VSKi4v5+9//ztVXX828efN44oknyMvLa5CnpqaG\nI488krVr13L88cdTXV3Ngw8+yEknncT8+fO54447+Oc//8kxxxxDfn4+f/rTn/j+979P//79mTJl\nSouP3amnnsqiRYs45phj6NWrF3/729+44YYbWL16Nffcc0+DdVtznKdNm8bcuXN54403uPjii+nd\nuzdA3TPAhx9+yPjx41m+fDmHHXYYRx99NBs3buTxxx/n6KOP5q677uLcc8+tW7+qqopJkybx8ssv\ns88++zB16lRKS0v56U9/yjPPPNPifd4qubseOfAAXh0zZoyLiEjHueeeexzwe+65p0naihUrvLKy\nssnyefPmeSQS8e9+97tNlgM+cuRIX7FiRZN8H3/8cYu26+4O+Lhx4xosmzZtmgO+cOHCBsvff//9\nJvkTiYSfccYZDviLL77YIO3MM890wLfffntfunRpxu1nsmHDBu/bt6/HYjF/+eWXG6RdcsklDjjg\ny5Ytq1v+/PPPO+DDhg3zzz77rG55TU2NH3fccQ74zJkzG5Q1fPhwB/y4445rcPyfffZZB7xPnz4+\nduxYX7duXV3aBx984Hl5eT569OgGZS1cuNABnzZtWoPl48aNc8DHjBnja9asqVteXl7uO+20k0ci\nkQb1dW/7cU4/Ho3rYGb+4IMPNli+bt0632effbygoMBXrlxZt3zmzJkO+IknnuiJRKJu+dKlS71P\nnz4O+JlnnplxW91hzJgxDrzqXRAjqZuBiIhIBkOGDCE/P7/J8smTJ7Pnnnsyb968Bstvu+02AG68\n8cYmLbYAQ4cO7ZR67rTTTk2WRSIRLr74YoAm9Uy54oorGDlyZIu385e//IW1a9dy6qmnMnbs2AZp\n06dPp6SkpEme3/72twBcc801DUaJiMVi3HjjjUQiEWbPnp1xe7fcckuD43/YYYcxcuRI1q1bx/XX\nX9+glXPHHXfkkEMO4c033ySRSLR4n66//nr69u1b97pHjx5MnTqVZDLJK6+80mDdth7nTN544w2e\neeYZTjrpJE455ZQGab1792bGjBlUVlby5z//uW75PffcQyQS4YYbbiASqQ/fRo4cyUUXXdTibW+N\n1M1AREQkA3fngQceYM6cObzxxhusW7euQaAUj8cbrP/iiy9iZhx99NFdWs81a9bwi1/8gr/97W8s\nXbqUjRs3Nkj/5JNPMubbf//9W7WdxYsXAzBu3LgmaSUlJYwePbrJ5e5UnokTJzbJM2rUKIYOHcqy\nZcsoLS1tEJz27t07Y/C4/fbbs2zZMvbdt+kMqUOGDCGRSLBy5cqMPyYyaRyUAwwbNgyAdevWNVje\n1uOcyQsvvABAWVlZxjFwP//8cwDefvttIOgr+/777zNs2LCMx2X8+PHMmDGjxdvf2mw1wayZnQyM\nA0YD+wA9gQfc/bQ2lDUUuBY4GtgO+AyYC8xw93WN1h0CnAh8BdgdGAyUA4uBX7v7I23dJxER6T6X\nXXYZt9xyC4MHD+aoo45iyJAhFBYWAsENXB9++GGD9UtLS+nTp0/dOl2htLSU/fbbj2XLlrH//vtz\nxhln0LdvX2KxGKWlpdx6660Zb7ICWj2ebllZGQADBw5scXmpPIMHD86YZ/DgwXz00UeUlZU1CGYz\ntfJC0KKbLT2VVlNTk20XmkjfZuNy0n+4tOc4Z7JmzRoAnnjiCZ544oms65WXlwNtO/bbkq0mmAWu\nIQhiy4EVQJvG4jCznYDngQHAX4AlwP7AxcDRZnaIu69Jy/J94IfAMmAhsBIYThDgHmFmN7v7ZW3a\nIxER6RarV69m1qxZ7LXXXjz//PP07NmzQfqDDz7YJE/v3r1Zs2YNmzZt2mxAm7pMXFtb2ySttLS0\nxfWcPXs2y5YtY9q0aU1a+F544QVuvfXWrHnNrMXbgfoActWqVRnTV65cmTXPypUrM7YofvbZZw3W\n21K15zhnktrfW2+9tUVdBNpy7LclW1Of2UuBUUAv4HvtKOcOgkD2Inc/wd2vdPeJwM3ArsDMRuu/\nBIx39x3d/Wx3v8rdTwW+DKwHLjWzptdDRERki7V06VKSySSTJ09uEsiuWLGCpUuXNslz4IEH4u78\n4x//2Gz5ffr0AeDjjz9ukta4r2Zz3n//fQBOOumkJmkdfYf7mDFjspZbVlbG66+/3mT5l7/8ZYCM\nU/C+//77rFixgpEjR2ZsId2StOU4p0aHyNSH98ADDwRg0aJFLdp+z5492Xnnnfnkk0/44IMPmqRv\nqVMcd5WtJph194Xu/p57cOt/W5jZjsBkYDnwq0bJ04CNwOlm1iNtu4+4e5N3sru/DfwhfDm+rXUS\nEZGulxoP9LnnnmsQjJSXl3PuuedmbFH9/ve/D8B///d/Z+w/mb5s7NixRCIRfv/731NRUVG3fO3a\ntVxxxRWtrmfjYOa1117juuuua3E5LXH88cfTp08ffv/73zcJuKdPn153KTzdt771LQB+9rOf1fUD\nhSDAu/zyy0kmk5xzzjkdWs/O0JbjvN122wHw0UcfNUkbO3Yshx12GI888kjdTXKNvfnmm6xevbru\n9dlnn00ymeSHP/xhg3Flly1bxqxZs1qzO1udrambQUdI9VCf7+4NRiB29w1m9v8Igt0DgQUtKC/V\ncafpp56IiGyxBg0axCmnnMJDDz3E6NGjmTx5MmVlZTzxxBMUFBQwevToJi2RkydP5sc//jE//elP\n2X333evGmV21ahXPPfccBx54YN0kCYMHD2bq1Kn87ne/Y/To0Rx77LGsX7+ev/3tbxx++OG89tpr\nLarnGWecwS9+8QsuueQSFi5cyC677MJ7773H448/zoknnsgf/vCHzRfSQsXFxdx9991MmTKFww47\nrME4s2+99RaHH344zz77bIM8Bx98MFdccQU33HADe+21FyeffDI9evTg73//O2+99RaHHnooP/jB\nDzqsjp2lLcd50qRJ/OIXv+Dcc8/l5JNPpri4mN69e3PhhRcC8Pvf/56JEydyzjnnMGvWLA444AB6\n9+7NihUr+Ne//sVbb73FCy+8wIABA4DgR9LcuXP585//zJgxYzjqqKMoKyvjD3/4A4cffjiPPvpo\nlx6TLclW0zLbQXYNn9/Nkv5e+DxqcwWZWS/gJIJx9zJP5ZE536uZHrSxD7CIiLTNb37zG66++mo2\nbdrEr371K+bNm8dxxx3H888/n7WP57XXXstf//pXDj74YB5//HF++ctfMm/ePHbffXfOOOOMBuv+\n7//+L5dffjkVFRX86le/4plnnuGiiy7igQceaHEdt99++7qJD5577jluv/12PvzwQ+644w5+/vOf\nt2v/Mzn55JP5xz/+wb777ssf//hH7rzzTvr27csLL7yQdZiv66+/ngcffJBddtmF++67r27GrJ/9\n7Gc88cQTTUaF2BK15TgfddRR3HjjjeTl5XHzzTfz4x//mF/+8pd16UOHDuXVV19l5syZRKNRHnjg\nAWbNmsXzzz/PDjvswF133cXee+9dt35+fj5PPvkkl156KZ9//jm33norTz/9NNdccw0333xzpx+D\nLZm146r8FsvMxhPcjNWq0QzM7G7gXOBcd28y8J2ZzQSuBq5296zXbyzoVf8H4BvAHe5+QSvq8GqW\npN3GjBlT9Oqr2ZJFREREtgz77rsvixcvXuzunX7fkLoZtE7q1s/N/QK4kSCQXQS0aiSDbCc9DHLH\ntKYsERERka2duhk0lOq9nm2MkF6N1mvCzH5BMLLCs8BX3L3lA8+JiIiISKuoZbahd8LnbH1idwmf\nM/apNbObgUsIujgc5+4VmdYTERERkY6hltmGFobPk82swbExs57AIcAm4MVGaWZmvyIIZJ8AjlUg\nKyIiItL5tslg1szyzGy3cLavOu7+AcHIAyOAxjdtzQB6APe5e92EzOHNXncD5wN/B77m7ps6sfoi\nIiIiEtpquhmY2QnACeHL1CTFB5nZnPDvL9z98vDvIcDbwIcEgWu68wmms51lZpPC9Q4AJhB0L/hR\no/V/AnyboMX2deDKDFMEvu7uc9u0YyIiIiKS1VYTzAKjgTMbLdsxfEAQuF7OZrj7B2Y2FrgWOBr4\nCvAZMAuY4e5rG2VJDaxXCFyVpdh7AQWzIiIiIh1sqwlm3X06ML2F6y6nfpitTOkfA2e3sKyzgLNa\nsq6IiIiIdKxtss+siIiIiGwdFMyKiIiISM5SMCsiIiIiOUvBrIiIiIjkLAWzIiIiIpKzFMyKiIiI\nSM5SMCsiIiIiOUvBrIiIiIjkLAWzIiIiIpKzFMyKiIiISM5SMCsiIiIiOUvBrIiIiIjkLAWzIiIi\nIpKzFMyKiIiISM5SMCsiIiIiOUvBrIiIiIjkLAWzIiIiIpKzFMyKiIiISM5SMCsiIiIiOUvBrIiI\niIjkLAWzIiIiIpKzFMyKiIiISM5SMCsiIiIiOUvBrIiIiIjkLAWzIiIiIpKzFMyKiIiISM5SMCsi\nIiIiOUvBrIiIiIjkLAWzIiIiIpKzFMyKiIiISM5SMCsiIiIiOUvBrIiIiIjkLAWzIiIiIpKzFMyK\niIiISM5SMCsiIiIiOUvBrIiIiIjkLAWzIiIiIpKzFMyKiIiISM5SMCsiIiIiOUvBrIiIiIjkLAWz\nIiIiIpKzFMyKiIiISM5SMCsiIiIiOUvBrIiIiIjkLAWzIiIiIpKzFMyKiIiISM5SMCsiIiIiOUvB\nrIiIiIjkLAWzIiIiIpKzFMyKiIiISM5SMCsiIiIiOUvBrIiIiIjkLAWzIiIiIpKzFMyKiIiISM5S\nMCsiIiIiOUvBrIiIiIjkLAWzIiIiIpKzFMyKiIiISM5SMCsiIiIiOUvBrIiIiIjkLAWzIiIiIpKz\nFMyKiIiISM7aaoJZMzvZzG4zs0Vmtt7M3Mzub2NZQ83st2b2qZlVmdlyM7vFzPo0k2cPM/ujma02\ns0oze8fMZphZYdv3SkRERESaE+uogszMgCOAI4HDgR2AfsAmYDXwOvAU8Ki7f9JR201zDbAPUA6s\nAHZrSyFmthPwPDAA+AuwBNgfuBg42swOcfc1jfIcQLBvecDDwMfAROAnwCQzm+TuVW2pj4iIiIhk\n1+5g1syKgIuA7xAEsBYmVRIEsYXAjsBOwEnArWb2GHCjuz/f3u2nuZQgiH0fGAcsbGM5dxAEshe5\n+22phWZ2U7iNmcB305ZHgXuAIuB4d380XB4B/kiwz5cCP29jfUREREQki3Z1MzCzs4H3gP8haIGd\nQdAy29vdi9x9qLtvRxA07wF8C/gzcAywyMz+YGY7tKcOKe6+0N3fc3dvaxlmtiMwGVgO/KpR8jRg\nI3C6mfVIWz4O2B14NhXIhvVJAleEL78btlyLiIiISAdqb5/Z3wD/BA5w9z3c/Vp3X+Du69NX8sAS\nd5/j7t8EBgGXAIcCZ7WzDh1pYvg8PwxG67j7BuD/EbTAHpghzz8aF+buS4F3geEErdMiIiIi0oHa\n281grLsvbm2mMNi9zcz+FxjRzjp0pF3D53ezpL9H0HI7CljQijyjwscHm6uAmb2aJalNfYBFRERE\ntmbtCmbbEsg2yl9JcIPVlqIkfC7Lkp5a3rudeURERESkA3TYaAbbiFS/19b0y21VHnffN2MhQYvt\nmFZsV0RERGSrt9WMM9tBUq2oJVnSezVar615RERERKQDdFnLrJlNASYRDHvVIIh29691VT02453w\neVSW9F3C5/T+sW3JIyIiIiIdoEtaZs3sF8D9BDd7lQJrGj22FKmxaSeH48TWMbOewCEEQ5C9mJb0\nVPh8dOPCwqG+RgEfAks7vLZbEHensiZBeVUtlTUJ2jFCmoiIiEiLdVXL7BnAN9394S7aXrPMLI9g\nEocad68bYcDdPzCz+QQjFlwA3JaWbQbQA7jL3TemLX8GeBs43My+1mjShOvDde5sz/i3W7qaRJJ1\nFdVsqk6QSDpRg0gkQnFBjMK8KPmxCBpmV0RERDpDVwWzEYLpbDuNmZ0AnBC+HBQ+H2Rmc8K/v3D3\ny8O/hxAEoB/SdGiw8wmms51lZpPC9Q4AJhB0FfhR+srunggnj3gKeNjMHgY+IuhSMZZgbNqbO2AX\n28TdqapNUpt0YhHLGFhubp3m0t2ddRXVlFZUk0yCGawsr6a6NkFxQR79iuMU5cfoUxQnL7pldtFu\nyTESERGRLVNXBbN3A6cB0ztxG6OBMxst25H6yQo+BC5nM8LW2bHAtQRdB74CfAbMAma4+9oMef5p\nZvsRtN5OBnqG27sW+Lm7V7Vpj9qpujbByvWVlFfWUF2bJB6L0CMMLCORCLGIETEo3VRT36oaMQrj\n0brgs0mra6P0qtokm6oTJJNQUhhjbUUNECyLRSOUbaqhOhHMP9G/OH+LCxI3t38iIiKyZeuqYLY3\ncKqZHQn8C6hJT3T3i9q7AXefTguDZXdfTv2QWZnSPwbObuX2/wN8ozV5OlN1bYL3Py/ns9IKSitq\nAUgkkkRjRp+iOENKCsnPi1JRkwCHiBl5sQibqhNU1SYA6Ncj3qDVtXF6/+J8apNOIunkxSJUJzzs\nL2v0LY5jZhTnx6hJeJgvSUFetEP2ryNaUxu3Kmfavy0t+BYREZGGuiqY3YP6bgaayaqTuTur1lfx\n2dqNLF+zCQyCHrtJyquSbKxKEI9GKc6PUraphrxolJH9iohEInjcKa0IWmrXR2vrWl17F+VhZg3S\nq2qTxCJGNGJsqk5A1ILWzahRm4TCvCCtqjbJhspaeuTXdsgl/I5qTU1vVc62fx0VfItI91FXIpGt\nW5cEs+4+oSu2I4Gq2iRrNlTywZoK1m+qIeFOYSxCRVWSwvwoUTMMiEcj1CScSCRJdcIpCOPApDsb\nKoPG89pEkry0D34LW3ATSac26fSIRymMR6msqWXNxuogEKxK0K9nnGgkyobKGj4vryY/GiEeNZLu\n7bqE35Gtqemtytn2T0Rym7oSiWz9Oi2YNbNHgdPcfX34dzbu7sd3Vj22RdW1CT4pq6S0opryilp6\nFuaxoSpB2aZqimpjDOqVj+NYxCjIi1JZk6A2kaQ2YpRWVLN6QxV5UQvLSgbDbLkTCwPSmtokBXkR\nahNJNlZDLBK0yCYSzqaaBNWJBKWbaki6U1GdAIyeBXkkkk5pRTXQ9kv4Hdmamt6q7HEPynKnpjZJ\nYTxKLKKWG5Fcpq5EItuGzmyZXUP9FK5b0liyW72K6gQ1tQnMYbueedQkoDgeYVVZAnOnOuEUxqMU\nxCJEzIhFjA2VNaytqGFteRXgFOfnk0wmWbWhkqrqJGs3VlMQjxK1CMX5UZI4VlFNbdIp21RDVXiD\n2bC+RZRV1FCTSFC2qZaieJSBvQroXRQnGrF2X8LvyNbU/FiEwniUqtoEpRU15MUi1NQmiUSgMB4M\nKSYiuUtdiUS2DZ0WzLr72Zn+lq5hEWNAz3wqapKUV1azpqKWaMQwMwwj6UZlTZLeRTGwPGLA5+XV\n5MUiDOiZT0lhHmWbakgmkjgQjUSoqkkSiyRZj9MnksemmiRJD4LZ6toEQ/oU0a84n8G9Cli+poKN\nVbX06RFnu7TWj/Zews/WmlpdkyAaMSprEg36xDXXV84suBkOqLsEWRh2m+hTFFeLzTZMfSy3DupK\nJLJt6LLpbKXrFORF6VcUp7SiBrMECXdIJnGHksI8BpTEKSmIkZ8XBG69C/NYX1lLdRIiBn17xKmq\nSbBhUw1JN7brEaNfz3zyY1HWbKyiNhF8CWxXnEdFdYLCvAgV1QnKKmqIxyL0KsijpDCPRLjNlI64\nhJ+pNXVTVS3l1cHNZdEKY2NVLYXxKMX5McqrapvtK5cXjdC/OF+Bi9RRH8uth7oSiWwbuiyYNbNB\nwMHAABpNo+vud3RVPbYFvQpi9OkRp2bVej7fUEMEKMyPURCDPsVx+hblMaikoO6LuToRBHE986Ns\nqklSk0jyeXk1n2+o5IuN1dQk4sTzIgztnUdhPMba8ioiYbBXWV3LmvIaPt9YSVVNLY5TXRu02PbI\njxGNGKvWVxKx4OavgjCAznQJvyWtYY1bU2sTSarD1uNYNAIWfHFV1iT4oryKqEEy6PJLZU0i2GeH\n/j3zG7TU6FJj19mSWz3Vx3Lroq5EItuGLglmzew0YDbB2K7rqO9LS/i3gtkOFIlE6NMjTnF+jF4F\ntfQqjFOQF6UoDNhqEkFfssqwP1ki6UQMKmoSeNL5eG01a8ur+HhdJdW1CdyDmb1qap3CvAjxWITq\n2gSfb6hkZVklqzZs4ovyGqoKkkSjETZW1dKrKM7gXgVsrK5l/ab6SRvyYhGK82NNAoKWtoa5B5cN\nC/Oi4WgMQeBcmPS6rgEed1atr6Siupa8WISCWDQInhJJSjfVUJsMWmV6FuR1+LHfkgO1LUFrznN3\nHEf1sdy6qCuRyLahq1pmZwI3ANe6e20XbXObFolEGNgznx75efQqyCMvZvTIi7JyQxWlFdWsWFdB\nUTyKe9B/rLImCe7UJBw8SWVtguKCKNW1RlE8SummGjZWJRjWt4B+xfmsr0zwSdlGyitrSTr0KYxR\nEI8Qj0bIi0XJj0ZIeoSHjnQAACAASURBVBCIFMVjFBcYyaQTNSivqqUwL5p1StxsrWGZAqFwoAXi\nsWiDltaIGVU1SWpqnZpYkmQSYrEIiUSwrS/KqzIG1e2hy9PNa8957qrjqD6WWx91JRLZ+nVVMNsL\nmKNAtuvkxyIUxGNU1tTQpyiPpBMMu1VWSV4sQl60mk3xGDv0LSQvFsXjzrqN1UAyHO0gRq+CPOIx\noyYJGyqrqaxOkheN0rdHPlW1lcQsQmFelJ75efQqiFGdSFIYj9EjP0p+2BpqGAN7FdT1VcvUwtWS\n1rD8WCRjIFSTCLpFFOZF8Xi0bjtJr29tLrYoJWHrTE08WL+mNtniVraWtBJ21OXpLallt6Pr0p7z\n3FWX+dXHcuukrkQiW7euCmYfAI4Fbuui7W3zehXEKCmKs6Gqlo/XbGRjbZIv1lcSjRh94nE2Vgcj\nEOTnRehdmIcBG6pq2VBZQ2lFNes31dKvR5z+xYUEI8VCSSH0KYoTiUQoKczDDCqqElQnnD498thU\nG7TuugfrV9UkiEYjdUFKphYud6e8qpYNlbWkxwlN1s0SCK2rqKY2CbWebNAnriAvSm1BHjWJajbV\nJonXJKhJBK18+Xl5RCPWola2lrYSdsTl6S2pZbcz6tKiVs9uvsyvPpYiIrmnq4LZy4C5ZjYJeBOo\nSU9092u7qB7bjEgkwojtelCbSLLs83LWbqikOpGkJB4nHo1QXZNgxYZKyiprGNSrgGQSVm+oIhYN\nugOUVVSzpryKitokA3vGKYpHgAhF4Rd6ZTToixqPRlhfWcv6ymDUgGjU6FsYtMpWVNdSUZ2kujZB\nYV6MXgWxBi1cqYCpdGM1ZRXVVIX9X3sXxYP0tHWzBULxWJRIxIhHgzFz0/vE9e+Zz3KgbFMN7kF/\n3/xYBPfgZrHNtbK1prW1vZent6QbjzqrLi1p9ezuy/zqYymb8//Ze/M4y7asrvO79j7DnWLK4eWr\neq+gSqAoQBS1RenSVlqFEhsVu1v6AzYiICKoCLaKSlsiTg3KJNJgKyW2LfRQflos7f7QLaI4ULYi\nH5oSimrkUdN7mS8zY7rTGfZe/ce+N/JmZIwZNyNjWN/PJz434pxzz94nMvKc3117rd+6KKsnF2Ue\nhnEROC8x+/uAdwD3gY/nyQIwE7PPgG7uubPSYWfSUrdKHQMOYVIFhk1gWDcM7ze0rRJiZHfacmul\nw53VAgQ++GDMq5tjciezxgcZ4gQnqSDMOQhtEobTpsF72OjmFLnMLLkcTdvywQfjWcQrY72X0Ss9\nuYMHM8EUolLknmHV8pHNMZM6MOgkJ4RO7lBVJnXLuG5TtFfYi87NhdALM3eCxRs7QLMRyTOhaZOT\nggA+kxNF2U4TbZ0LtXHVMpk5KDiBugn0yuxY4XyRCo+e1VxOFPVs43Nf5rccS+MwLsrqyUWZh2Fc\nFM5LzP63wB9R1W89p/EMHjkWrHQy3EaXuztTvAh1jHRipJcnAeEdZM5RZQ7vYbVbcKNX0s08D8cN\nZe7Z6GXghLaN3B/WoEpQ9qJWa51sz6lgVLXsVi29wjGuHVUT+NDDMb0yw0uPtU7ko9sVdftIMBWZ\nA1UejmqqpmW9l9MvM0KEV7cm3B9VPBhWTJrUjWy9V9CZCaBu4ZNQPUBs3OgnkXvQTf84cdJGpZ1F\ni8d1aspQzhwZ9kcJy8yRe2F72vDazhSRVJjWLTxrvfxY4fy8I5LnMZeTRD0vyjL/SXMsLTp2fbgo\nqycXZR6GcZE4LzHrgR88p7GMGXNR0i8zmqCklMRIGyO7VctGr2Stl1N4YdIEhlXYs74qC8+NfkG/\nzFjr5XjnQZInbZ5Jyj+dLfOv93Jyn5bwR3VgZ9IwrQNNCAhKo0oTI5MmEFGqENmZtrRRGZRJME+b\nkNwPvBAR+mVG5hybo4qPbiebrWkbQWFYBZyryXzJnZXOkcL0aaNs82jw3Z2K3WnDWjejyPxemsJK\nNz8gSigoaalBZq86234cyy48OovIepZFUMf9e1ymZX6Ljl0vLsrqyUWZh2FcJM5LzL4L+EIsneBc\nmYuSuo6sdzO2ezlb44YQoV+m5f5u7gBBY2BYt6l7VpaxOhO5c1cDSEvn+2+eUZXcu8eW26MqW+Oa\nqomIS80KAHLn8Ajd3FO3kaYNvDZtKTJQFbwX6iaSudTFywm8ul0xqhqmTWS1m1E3kbVeTuY9/SJj\npZMdKxxOW8nchMjDUcWHNye8tjNhNG3ZnWYMyhT9Xevl3F4tH4sSVm1ySVjv5rxhtbOXZjBpkuPC\ncQ+YZUYkzyKyVJUYI9M6sD2pmTYtK52cJujSoqPH/XtchmX+k0THgAt9DVeF84qOX5TVk4syj5Ng\nKxfGeXFeYrYHfJmIfDbwkzxZAPaHzmke14pFgdQEZaWTM64CXpTM53iUh8OGLBO8pMKraRV4OKlw\nTrjRL2cpAJ4QI17kyJunqqKaOoA9HNVMmoB3jp1JAzEl2o6bSIia0hGmLZOmZVLDjUFBG2GtlwNC\n3Qa2x0lcT5uI80LdwjQoRRN5sVeQeUdY8n17LlLu7U7ZmTT0ck/hHK1GEMh9uiH3isc9aucPmCLz\ndItH/61CTCkK25Nm79/koJv5siKSZ1mCbELk9d2KuzsTtsfJV9g5ZdIE7qx0GHTzc4uOXnQrpeOi\nY8OqZdIEi9o+Y84zOn5RbNsuyjyOw1YujPPkvMTsJwH/bvb92/btuzgfI68Y+wWSd0LmBBEoM8/O\ntAFXIeK4vVLwZhGqWU5sJ3fcGhSs9wu6uU/i9Iib5+KNa9Ik8TSqWt642iFzJWHWcnae5qAqrPRy\ndJZbKiJ08yRg0o26pQqRRlPOb9MqvcKxO21oQmTaBNa6xVPduI+KFsxFShrP0y8zOpnj/qim8Cn9\nYbWTPyHoDnrA1G3g7k6FSOrsMG3CkTfzZUQkn3YJUjX5DP/CwxE74xonjjxLjSemTXKpuNVPtmzG\n0dGxNkTuD6vUnc5yGp8Zy84dPS6KeFHyuS/KPI7C8nqN8+ZcxKyqfuZ5jGM8yX6B5Gf3j6AwqVte\nLzMCsNp5VKQ06OREVW4MSm72Z2J4JlC3xg25F4ZVi2pKV1h0JohxdrPNM5oYCbNjxlXLNKQI8XDa\nstLNWS8LBoVnZ9oyKDMy7yi8sD1pKTJHkXkGRcrvHVctD8fJRWHaRHJ/MkeC/RwXLZiLlLngmzSR\nbp5E7NxD9yBbryceMF64u1MxrBsGRX7im/lZI5JPuwRZtZHtSfq99Itsr8nE9iR9eKia1Eyj8/yf\nkxeCo6Jj3kFo9cC0HMtpXB7LzB09SRTxouRzX5R5HIXl9RrnzXlFZo3nyGECKXPCuE5LofMohKri\nJIm3xXav85vncNLwYFzThtQNrG7DE84EVet5080ur7yudArHoPR0C8dwGtjo5qx2c9Z6BevdnK1J\nQxNnbXRFmdQB56CX53jg58Y1TpL41pmDwo1Bxs3B0YVfB3GSaMFcpAhJoCaR1+x56N4eZKnb2KzZ\nw2IEZ/EBM64DIsqgyB91WTukA9oyedolyDYqVZu6puULzhBF5mhjpA7xQuXiPW+Oio7l/mRpOcbZ\nOO0Ht8Mir6eJIl6UfO6LMo/DuEx5vcbVwMTsNeY0y1W5d9zqF0ybQO6Fwmd7Lgm704o26t4n8DJz\nrHYKbq8k0dYrUzHZnVVhpZPz4mpnz0pro5dudPsjDGudjJ+rWryHphVuDQqGVaD0wka3w0trHfwp\n865O2k51/jsJjT7uodvLudEvaEPkw5tjqlYpM2GtW7DRLx57wGxPGtB0M8+zJFrP42b+tEuQ6WEo\nRIWmCehMaNdtRBWKEzSZuG50c8/YexoC6KO/3ZOk5Rhn5zQf3I6KvIaop4oiXpR87osyj4O4LHm9\nxtXBxOw1RkRY7+ZUTWRUtdRtpJc7umV2YNSzDmmZvZNlj276qtzdSTZco0oYlOlPqvACAoPSs1Jm\n9MrswHzRwyIMVRvpeMdKWXCzn0TWCytQt5o6iUXwp7yPnyRa0NkXYe2GuOehe7NfsDtt+YVZYwc3\na45wf1TzsbHPG9Y6jz1gplXD3WGVIt+5Y1D4Z34z3x8hbkNM0Vbv6B7jprDWLbg/qtkZ10yaVPAW\no7LWy0/klXtdeFwYJbHvvHCjX+z9/T+WlnMBcxqvAif94HZc5LWbe4siLpnLkNdrXC2eu5gVkY8B\nPqyq8XnP5brRhMjWpJk9kBVFqQOsuNQWNnN6YMX+/pv+oMyoQ0RRXh9WTOvAbtWCpgdHmbu9B/1J\n80TbqCDCndUyuRbMoiltiCDyVA+Yk0YLDhPY0yYkl4NxTb/IyHNP0wR2xjV3M8dGL6dbZKgqo6rh\nZ+8PubddEWOkW6b0hJc3etwYFM/0Zj6f/7BqeTCsiRqIUfccJg4qQBMRNvoFHxv73M0co6pFSNGr\nF9c6e80nrjsHCaOoShOSj/L8b/yi5zReBU76ez5uRabwzqKIS8b+DxjnzXMXs8ArwPtE5KtU9Z89\n78lcF/Y/lJ0T7u/WjJuWV7cnvLjaZb33aPkcjhCDQbk5s8p6MKrYrRoEYaVX0PHCqG6RYUXm5NBO\nXft5NFakXz7KbZvUgW7xdEvep4kWHCSwR3WY+d861mY3ZM09k1lke1QHMu94OKx436s7fOThhHEV\nKHJHPWqoyshar+VtnYNF/bKZNIE6pIe4y+TYArTcO96w1mGjlzOq03H9I7qrXUdOWthy0XMarwon\n+T0ftyIzTzmwKOJysf8DxnlyEcTslwBvAb4Z+FXPeS7XhqqNTKp2Vr3u2Rw3bI0rHowaOnlahlvt\nFY8tnx8lBgfdnEHhmTYBVbjRT7ZZ25OGe7sV2+OGNkTWesWJfAbPskx1WKHHMqIFgjzZ0EvS9vkH\nhA9vTbi3M0UjvHGjg6rQzR3TOuBQJk2kzI8d6tjrOYqnrSYWSY0yFr1yjUecprDlIuc0XiWO+z0f\ntyKz2PTFoojLxf4PGOfFc39iqerfmn37zuc5j+vGpAncG1ZMm9R+9sNbE3anLSuFJ88cTuSJ5fPj\nxGDVRjKfopadWRHMvG2tCOxO22QqrLDSyQjKoeLsaYXncRY7Z4kW9GfjD6uW7UlDkTnqNhJnc8uc\nsDNNTR7KTCjynPVuwW7VknlPt5PaAFft4xk1R4nVpzUet2riZ4MVtlw+TvLBWEQsimgYl5hzEbOz\nvNgPqaru2y7Am1T1g+cxDyOhquxOG8ZVy7gKOC9sj2qaEOjlnkGRsdEv2Bw1e8vn80jdUWJwLrYm\ndWAqMGlaxnULqpTec6OXs1sHPrI1pl8kX9mjxNlphedJLXaeNlrQyVP+aB1S1LOdFf+s9XJeXOvs\n5fb2iuSRuzNu0W5q41u3gaoJrK53HosqHyVWMydPbTxuouvZYIUtl4+TfjC2KOIjrA2tcdk4r8js\nzwNvAO7t235jts/uIOdINYsm5t6z1nNsjVu8F7bGkUEn4MRReLe3fL6fw276iw/6eztTXt2pGFUN\nZe5wThhW8zaf7UwEFseKs9M8YI5aWh9XLdve7TU8WLw5z2/cTYh7gjL37okbuIhwe6WDd8L2uKEO\nkcI71no5N/rl3nvLzLHSyRjVLa9uTmhRRGFjULDWK1jtZHvjHiVWV8rsqY3HTyO67MF1cqyw5XJi\n+Zsnx9rQGpeR8xKzwsFtawfA9JzmYMyYL0EPSk8dlF4R6OaeMndEBRVlZ9ruLZ/3i5OJyfmDXlV5\nMKyY1i1NUG70MrwTXh9O2Z40rHYLbvSLVPm/xK4wi0vrANMm3YzrENidNkybSJE9Hg0G2BzX7E4a\nNscNTUhFXDd7BYNufqCV2AsrHda6xRMPxczpnoBc7eRMm8jDWOMj3BjkfMzNPm++2d9rCXtcXqub\nRbufJlXgpKLLHlynx4TR5cQir8djbWiNy8ozFbMi8h2zbxX4iyIyXtjtgU8HfuJZzsF4ElVle9Kw\nPWno5o4ic9wYJPPwMvfEEBkFpcgcG73T2Ujl3rHaybnZLxjPBFnKVRU+ujWhaiO3Bo8KLpaZxzlf\nWt+dNEzrlqqN1G3g1e0KAd50A/Ks3Ls5qyqCsDmueDBsUvV/k9wHmla5OZvP/hv4YQ/FRQFZZp5B\nJ+ON612cCLdXUmvguZCF4/NagTOlChwnuuzB9fSYMDKuItaG1risPOvI7KfOXgX4JKBe2FcDPw78\n5Wc8B2OBub1VFZLQa4PO4ubCC6slK52MGAVF6RUZUZX7w5pu4WfRx+OjUEEh88lTNURl2gTaqKx1\nCyZ1mxoqLMxnWXmcZebo5I5Xt1u2xw3OCXUbGVUNhU9L6/3CQ+HZGjdsjxtESAVbuaB4bg9SY4Qi\nE6ZNONUNXDWJ027uZ96VxYHpCnOOy2vtF56oeqb8zKNElz24DMNYxApHjcvKMxWzqvqZACLyLuCr\nVXXnWY5nHE/VPjJ3XymzFLULkVEdWOt4+kXONAQEoV9mjGeuB6V3rHVzMu+OXYaei7S6TS1g65DR\nhkjhhdDNKDL/TIpnRIRekVFmjnwmXJsQ8Q7Q1MFszwc0c0zqFhCcE0ILuRecc2RZSreYL/Of5AZ+\n2HL9UR6tx+W1dnJPNvsdP4v8THtwGYaxiBWOGpeVc8mZVdXfIyJ3ROS/AT6ZlHbwPuC7VHV/UZjx\nDJkLmF6R0S88VZuKnlaaMA/Q7rWrhZR3ujOuKbIkrpK11OHL0Ko6+3rUYazI0vt6ZcagzMicY9I8\nm+IZEWG1k1PO5tvGSHfS8HDU7BV4zW/OuROaGBlOA4oSI3SySNvGWf5wEqXH3cCfdrn+JHmtuX92\nlkH24DIMYxFz6zAuK+dlzfV24P8E7gL/arb5dwFfKyKfrar/6tA3G0tlUcAwi/6pKvXM+1RVyfMk\nlqZNWmp24ujNju3NlugndWDaBGTWWjZzghPYmqR9dRtoQqSddRdbFGmZkz1xNs84mIvqswq1zAmZ\nd0k8Fx7wyQtWK8azOaef0/U2rTKuG3YmAQW2RjXdMkVTVzr+RDfwsyzXn6SY6FnlZ9qDyzCMRcyt\nw7isnJebwV8Gvh/4CtWkIkTEAd8N/BXgPz6neVxJTmOtdJiAEUnFRk2rDKcNvXy21BwiSBJd3sne\nMnTVBO7uVIiQltUFRk0AnUf40ryiQuEcL6yUjy25pyjv8ivpD7o+BVZ7BeUsf9U7YdwoKGSZY7VT\nEGPNbpUsylY6Gbf65Z6bwXE38LMu1z+vYiJ7cC0fszkzLjvm1mFcRs5LzH4a8MVzIQugqlFEvgX4\nd+c0hyvJaQXhQQKm8MI0RLzCqA4Mpw2jKtAvHVvjmkkTuNEvKHxahq6bwKQJ5G1MTgWZY2vSsDmq\ncU5mebJJxI3qgHfCrZXHl9qfVSX9Qde32sm5s9LZK2JrQ0TGNZMmcqOfjr29UvJgWFHmnjurHVY6\n+Ylu4KpJ8Fdtivj2codz7tIs19uDa3mYzZlxVTC3DuOycV5idht4C/D+fdvfAmyd0xyuHE8rCBcF\nzPwB7GNKCVjpZNRtZNq07FaREEFVeDCqiFHpFB5I7WkzL3vL6qhyb2dKNY2oKtmskCqENMf7w4pB\nme3N51lW0h8n0IZVS1Aei6R2i4z1fjJ26BbZicbeEy9Vy7BqGVctoypwc1CgyqVZrrcH19kxmzPD\nMIznx3k9ZX8A+Jsi8oUi8hYRebOI/C7gfyClHxhPwVwQhqh0cocAnVl6wFwQHsZcwOTezVICkjBd\n7xW8+WYXJ25PaL600SV3jt2qoQ2RQZHTK5MrwfwBnfnU5Wu7aqnbyGo3p5enJWtBaNr42HwOWpoH\niKrsTpM43Nf9+FhUkw3YsGqZNuHQ98/zhps27h0zj6SepOBrfvxcvEyayGonJ/eeug3sTls6uWO9\nV9hy/TVh/4ezQZmKKGPk2P+LhmEYxtk4r8jsHyMFvb53YcwG+O+BrzunOVw52qhM28Bw2jJeWNqM\nUXFOTmStdJCobGKKuubRcWe1Q7/MmDaBB8NqVmAltBHqNqCzpfvcQdMkm69hFRjXLW1MebhlnuP3\nzWd/JX0bla1xzb3dZANWeCGqnniJdnGJt5oJWhHolRmdzD+23LuMwqeDIsurnSzNP/Os9wrWurkJ\n2WuC2ZwZhmE8P87LmqsGvlpE/gTwcSRh+/+p6vjodxpH4QXGVcvDUUUv9+Ac0zpFJb0DfwIddZA9\nUxsi0yZQ5o7Mu5lFlCMi7EySN2sTIlVIy/iFdzwY1qlQTIS6adka12z0CvplhmqK3GZO9gpkmpCK\nzkSUh6OKh6OGB8MpThyDtQ5hJm7h+CXaxShpCMqwDjwcVoByo18SO0/aiZ218Okg8eKcY9DJEdj7\nvRnXA7M5MwzDeH6cV2QWAFUdi8jPz78/z7GvKqrQBuX1uqYNkTooaPJ0bUKke8z7D4pSDqs2NRBA\niDEynEa2JzUPdiuK3CdXAxx1iDRtYFoHmhBY6easdjPuj1LRWD8oAyDP0xhO4PVhtScgmQnb3UnD\n3Z0pVavcWcnpZI6VTsawOlkHrsUoabfwaewyA00R6m7umTbxsXOdtfDJxIuxiNmcGYZhPD/OTcyK\nyB8GvhZ4afbzR4FvAb5NT5scaQCpbWzphXEb2R7V1CHSyTw4qJvAw1HDSufope6DopRpeRx2Ji2v\nPBin5gejFHl961pnzwHAj4Q2RrxTiqzghZWSoLDaTekC8wjleq9gvZuzNWkeK5BJrWYDwzqQe8dK\n6Slzz6gOZL6lOOES7WKUNGr6uZhZcoWY7MEOWu49S+GTiRdjEbM5MwzDeH6cV9OEbwK+HPhmHjVN\n+AzgTwNvIOXUGqfEC+xULVXdkmeOm4OCqEnITttI3Z7MFWB/lNLPfGVHdaAOEGcisFzwiRURitwT\nasXLLPLqHA64NShxIkSFG4OSm/3iwBzTicBrO1NQ5Va/QEker7tVYNK0NCEJ4eOinItR0k6e0hnG\ndfK87ZYZTmDaLDdiauLF2I/ZnBmGYTwfzisy+2XAl6nq/7aw7YdF5P3A92Bi9qlRTQ4A3dzTKTLa\nNtCGlOO6GIk8zsx9MUo5bQJtVNa7OW9Y7aRq/e6UrXFDG5MrQTmLRBbeIUJyBpgttwM4Efql37Pj\nOijHNCqIQJE58tzThsjutKWOkUmt3FopThTlXIySTupAUBhVLZAE+KQJe56fZeaWZmxv4sWAg/9v\ndexvwDAM49w4z5zZnzxkm63HPiVBYdDNuNErqWMEVTpFlqr/JQnUzMmpzdznwrPI/MxzVYmqTJrI\n5qihyDy5czhHSklA2JrURy63H5Rj6pI9LYqw0c2pgjKpWybjQL/03OiXJ4py7o+Seid44UA3gzbq\nUo3tzaP1emONEgzDMJ4/5yVm/zbwVcBX79v++4H/8ZzmcOXInLBa5mwMCuo2zgSoMGyVXp7RLzMK\nL9wfndzMfd7Rqm5TV6t5R6vVTsb2OAMiIUQyJ3Ty7JHYlKOX2w/KMa2bQLfwKNBEpZM52uC4NSi5\nOSh5ab2DcycTBAelSkAS/PNoGaQCNDO2N5aBNUowDMO4GJyXmC2BLxCRzwZ+bLbtVwFvBP4nEfmO\n+YGq+ofOaU6XnjJz9MqMjV7O1rglzpoG9MuM26sd7qyW1EFP3GlrHmUazzpazdvazjtarXY8ozoV\nVVVtpJtFtiYNG73i2OX2g3JMe2XGWi9nbvUVorLeK/aE8EmF7OIYR0VJp014Zl3HjOvHs+xiZxiG\nYZyc8xKzbwN+fPb9x85eX5t9fdLCceZqcAoWBWKvaJnUAQT6ZcaLqx2KzKfWrVHJvVDNorfeCbmX\nJ3JqF6NM87a2TQjsTBs2Ohn3py11E4mqdHLPuAqsN4+iUKctNFuMmJ5H3qkZ2xvLxP6eDMMwLgbn\n1TThM89jnOvInkDs5AeKwWyWmHp3p6LIksOAE6hb5cXVcq+6/7COVnd3pijK5qRhPG1pQ+TmSknq\nzqlsjVt6RUvVyU8UhTosenoeESzzhjWWif09GYZhXAzOtWmCcXZ0lkowqlNEtF94Ork/VAwWXpiG\nyLBuCGOlU3qmVcBnwjTkFLPk0sPcBpoIu5OWcdWyW7Vs9HPKzNNzws60RTWlMZwkCrUsF4GnPY95\nwxrLxP6eDMMwLgbn5TP7yUBQ1ffPfv5NwO8G3gd8k6qG85jHZUeBV7en3N2ZMKpahFQ5/eJah9sr\nnQOrp+uglN4xKHLKvhAirJSe4TQVcu1MW9a6+RNRJoCtcc3DYUWIEQGqELi7E/FOuLPSIXOwO02p\nDcdFoZZV9X2W85g3rLFM7O/JMAzjYnBekdm/CXw78H4ReRn4+8CPkBwOVoE/cU7zuNSEoPzCwxE7\n4xonyX5rWLXUIQnMF1Y6TzxA25iWP2+vpCKucd0yaSJOlIejmtx76hBZ7+aPRZmiKvd2K0C5NShS\nFHao7Eyb5OdaBXCw1snpl9mRUahlVX0v4zzmDWssE/t7MgzDeP6cl5j9JB4VgP2XwHtV9XNE5DOB\nd2Fi9kQETXmt/SJjbRYR2p6kyuntccNat6DM3GMPVocyrVte3ZnSzRy7Vcto5laQziHUbQBNubKQ\noky704bcC/2yoPCeOjQU3pP5yLRueW13wp3VFBF+cfVJEb3Isqq+l3Ue84Y1lon9PRmGYTxfzkvM\neqCeff8bgH80+/7ngDvnNIdLT1QQFBW319Uq90IboQ6RSZOcB+ZLnvP82o9sjfjQ5pSqCagKw6pB\ngFEV0Bh5OKqp2kC3WNuLMs2jreOqpYmRfu7pb3Tojh0xKpnz3Fnp8PJGlyI7+kG+rKpvqx43DMMw\nDGM/ZxKzIvJHgR+c58IewU8Bv19E3kMSs/NI7EvA/bPM4TohkPxkY02vzMico2oDZe7InTCctkya\nlhgh88K9nYrXd6c8GFW0baRuWh6MGpoQ6RUZ/dwzqpU2NHwY5eagZFAO6OSpeCWqMqoCW+Ma74Xc\neW70SgadnG7u6OT+RF6wy6r6tupxwzAMwzD2c9Zy2/8O+Pz5DyLyVjl4vfmPA7+XlCf7/ar6/862\n/1bgX59xDteKjhGnGAAAIABJREFUNqZGBfd3Kz68Neb13YoQlTJ3xBj3luBz7ygy2BrVNG1g0M24\ns9YlKEyaQOkcG4MOG73U/rZpI6OqpUqeW3vFLTcHJf3SE4LSLRwvrHZ4cbXEO0fm3YkE5Lzq2znY\nGjcMq5atcXPqqu9lnccwDMMwjKvDWdMMmn3n+GngncCfWzxIVf+ZiNwGVlV1c2HX9wDjM87h2iAi\n3FntstHLU4MEhVaVm4OCMvNsjRvauW1ViEybiM8EWiF3jkGZcbNfMmlahnVg0rZ0Wk+Ruz0ngMWl\n+tw7XlrvoCgPRxWCUGSOYRVOJSCXVfVt1eOGYRiGYeznrGL2o8AvW/hZOCTaO7Pf2ty37ZUzjn/t\neGm9Q+aEcZMiqBqTk8H2tGVrUjOqAnUbEIQ2RJo20i8y8swxrgOZg8w5nBMycQwKRx0incxTZk8u\n1TvneMNal07uFwSkO7WAPG3V92FeslY9bhiGYRjGImcVs/8A+AMi8g+BvzfbZlU4zwgBmqD0y4xO\nkSWrqlHNqA54EaZ1ZHfc8ur2mF7uCapkLgm9buYYxchKJ+P2oEOZCZ3C0YR0zKCbsdbLD4y0nkRA\nnqSRwUmrvo/zkn1W1ePLaupgGIZhGMb5cVYx+6eAtwC/BXjHbNvXi8h/TrLi+nHg3wE/oaqjM451\n7XFOcA42RzUKTJtAJBKjMmyUSdXw2u6YnXHDpAncHJTcGJSsd3KcS0v0gzLj5RuKd47CC07cXuOF\nG/3DfVqPEpDLaogAy/OkPS3LvAbDMAzDMM6PM4lZVd0FPldEPpHkUvCdwBbwCcAvAb6YFKlVEfkA\nSdj+OPDjqvrDZxn7OuKdMCgzXtuepop+FCF5zQ6nLQ/HDQ8nDZOqZXvSsFO1CMpqJ6Nf5Kz0clbL\n1Byhm3vqkILo85a4T9tadpnic1less/zGgzDMAzDOD+W4jM7s+Z6v4h8J/DXSAVgnwz88oWvXwp8\nIvBfkQSuuYyfEiHlu+beQQGd3DOc1myOau4PK1SVUhy+zBjXgRgj21XL1qRhrZdROMftQYFzjqAw\nyM6+lL5s8fk8vGSfh4A2DMMwDGM5LLtpwn8BfFRVI8lb9qeAvz3fKSJvBX4FjxeNGSdEZ7ZauXfc\nXknRwsILyIhJHanagPcOUHpFRif3M6/Yhtd3M0SEaRNZ7WQgcuxS+klySJctPp+Hl6w1YzAMwzCM\ny8tSkwFV9e+p6o8dsf9nVfX7VfWPLXPcOSLysoh8r4h8VEQqEXlFRL5NRDZOeZ7PE5EfFpEtEZmK\nyE+LyJ8Wkc4hx3sR+UIR+VEReU1ExiLysyLyLhH5lOVcHShPiq4889xaKShyhyjEGPEi5LlDFdoQ\naAN4B/d2aj74cMRrOymKO6lTQ4TNcY3q44KtCZHXhxV3tyd86OGYV+6P+PDmJLW+XWAuPps27p1j\nLj69k1OLz+fhJbvsazAMwzAM4/w4awewrqpOnvc5Zuf5OOBfAi8Afx/4GeDTga8G3iEib1fVByc4\nzzcCXw8MgXcDD4BfA3wD8Fki8psOmO/fBX4n8GGSq8Mu8KnA7wa+QER+8zJyhIUno5aFFwZFzsvr\nXT6ksDtpGIU2iV7vKJznhdWcTuYJUWlUKTIh845+6Q5cSp/nkN7frdgaNwSNVE0kc8Ju1fDxtwd7\nLWzn4rNqA1vjhjxLDRieVnw+Dy/ZZV+DYRiGYRjnx1nTDH5eRP4i8N2qWp3mjSLyS4E/C/wb4BvP\nOA+A7yIJ2T+kqn91YZxvAb4G+PPAVxwzp19GcmjYAn6Fqv6H2XYBvgP4A6RuZn9m4T2/kiRk3wd8\nuqqOF/b9HuB7SeL47GJWOFB0bfQLVjoZ6/2MV+5PeDiqqZoUQd0YlJR5Tu4FcULPC1EhRD10Kb1q\nI5NZRBQiDmHQydgc1ry+M2VQZrxpo4eIPBPxeZ5esvNUitw7unlGjJGgXNlmDGY/ZhiGYVw1zipm\nfwj4FuCdIvI/A/8L8GOHRVpF5BcBnw18ESlq+iHgm884h/l5Pwt4hVSAtsg7gS8H/msR+SPHWIR9\nHqnO6m/MhSyAqqqI/Engq4DfLyLfOGsCAfCLZq//eFHIzvj7s9fbp72mwzhMOK53c958q8+bNsZs\njWoejhtGVYNzjjsrJUXuqdvA5rBhpZOcEQ7LRW2jMm4iQZOQXe2moigBdqftXtvbeST3LOLzMHH1\nrLxkF9lvx+Uk2Z9tdHK6ub9yQs/sxwzDMIyryFmtub5IRL4D+AskwfjlQBCRnwZeJXX86gA3SU4G\nt0hi8S4pAvqtp43oHsJ/Onv9oVnx2eIcd0XkX5DE7q8G/vER53lx9vof9u+Ynec+Kfr7qcBPzHa9\nbz6HA1Im/rPZ6/994is5hqOEY5F5Vjo50yawO214bWfKpAqUeWple69O7W3rVmlDquA/aCk9c0m4\nVk1k0Mn2irDamBwUUJ4oinoa8blscXWaqONBdlzTJqUWdPL04eAqCVmzHzMMwzCuKmd2M1DVf0PK\nJf0E4EtJfrOfRhJ8i7xOyid9N/BuVW3OOvYCnzh7/dlD9n+AJGbfytFi9v7s9S37d4jICkmMA7yN\nmZhV1Z8SkW8lpTL8jIi8h5Qz+ymkRhI/QEozOBEi8m8P2fW2hWOS+JwJN9q4J9zaqOxWLZMmUuYp\nTzYqRIU7qyXrIafjHYgc2pa2zBz9MiNzwuawRoA2gpBSE5bhKrBscXVaYXzd7Liu2/UahmEY14el\nWXOp6geArwMQkR7wEikiOwHuqeqryxrrANZmr9uH7J9vXz/mPO8B/gTwZSLyXar6ysK+P0eKKgM8\n5o6gql8rIu8HvhX4yoVd/xb4vmV3PztMuK13c7YmzWMCsZN72qAUmefOakmZOeqgx7advbNasls1\nvL4zZXfa7jVVWO9l9MrszEVRZxVXi1FYh3J/WPNwVCMCgzLbsyqDg4XxdbPjum7XaxiGYVwflu0z\nC8Asd/QDs6+LwFzJHPnEVtV/KSLfA/w+4CdF5N3AQ+DtwK8kpRR8CrDnTzUrDvt2koj9euDvkArI\nPo0kbv8PEfkDqro/l/ewOfyKAy8gRWx/+VERzaqJhBj3CUTP1rhBJIkX5xydE+jQIvN8/O0BgzJj\nVLUwK4rqldlSiqLOIq4Wxfy0DTwcVWyNakQctwYF0zZ56Y6qcKgwfh5+ts+T63a9hmEYxvXhmYjZ\n58A88rp2yP7Vfccdiqp+hYj8a1L+7++cbf63pMK1LyWJ2XsLb/ndwB8k5f/+pYXt/1xEPpeUf/uX\nROT7VHV4kos5iqMimqOqRVUpFlrTniX6VmSeN230nkn1+9OKq0UxH6IynLbc3ZmyPW5Z6+ZM60AT\nUtp054jrvm52XNfteg3DMIzrw1URs++fvb71kP2fMHs9LKf2MVT1e0mWWo8hIn9j9u3/s7B5XuT1\nTw44z2si8jOkjmefSBLFZ+KoiGadEluT+f8JBOJJCqaelavA04qrRTHfzT3jOrBS5sRZbnCZO+pW\nmdQtbXCs94oDhfHz8LN9nly36zUMwzCuD1dFzM6F5GeJiFt0NJgVbr2dlLt7aHey4xCRzwI+Fvin\nqvqRhV3l7PUw+6359vppx14kc4IX2Jo0oErmHYVP3at6uUOcMG2OF4jP26bpacXVopif++X2y4wQ\nUuHb9iTgPUzGgVuD8khhfJ5+theB63a9hmEYxvXgSohZVf05EfkhkmPBVwF/dWH3NwB94HsWC7FE\n5G2z9/7M4rlEZFVVd/Zt+zjgr5NyZb9u3/A/SorOfq2IvFtVtxfe9xXAy8BrwL8/00XOcAKjJrA5\nqnl9t6LMHV4c672cbpntFYEdJRAvik3T04irxfSETu7wTqirQJk5Ml/QKRzjKtAvPTcH5bFRx/Pw\ns71IXLfrNQzDMK4+V0LMzvhKUjvb7xCR3wD8NPCrgM8kpRf8qX3H//Tsdb/S+Zsi8rGklIBN4OOB\nzwVy4MtUdX9097uALwR+CfCzIvKDpAKwX07yvw3AVy00WTgT84hskXkyVaZNIPcKkrPezSkyz+2B\nO1IgTpvA9rhmOG250S+SuJkVip23TdNpxdViesKkCcSojOoWEDb6BYUXeoXnRr/kpfUOzlkuqGEY\nhmFcZa6MmJ1FZ/8jUovcdwCfQ2rc8B3AN6jqwxOe6j08Kv5aIRV7vRv4JlX9yQPGHYrI24GvBX4H\n8AVAQfLV/V+Bv6yq//os1/ZorLQk78Txlls96qA0beDhuAZVxnUg9+5IgdiEyGs7U+7uTGmjoqQm\nAWvd/FLYNO1PT3BOyLygmiy5yvxRJNqErGEYhmFcfa6MmAVQ1Q8Bv+eExx649qyq3wd83ynHHZJE\n9J89zftOi/IoX9Q5R6aRUVDqVnm9rnDiqENkvZsTZ126FiOz8/SCYdXQzLxmJ1VL3QZUU95sr8hO\nZNMUY2RnmtralpljtZOdm3jcn57gZ9MNiuWBGoZhGMY1Y6liVkS+CPiJgyKYC8f8YpJf6t9e5tjX\nAeFRvmjMI9uzBgm7k4ZBN6NqA/d3p9wfVvRzT1AeK+wKUZnUgUwcL6yU7Ezbmb1VYNJEbh9TMDVn\nXLe88mDE9rimbiNF5ljrFbz5Zp9ecT6fj5ad+3maVriGYRiGYVwclh1K+1vAbz/mmN8GvGvJ414L\nRJIrgXNwb7fi4ahme9yw1su40S+5PSjYGrfc256yNWlQ0lL81rhmc1zThEiIyYd2vVew1s3pFZ48\nE0rvWOnkxxZMxRh55cGID2+O2Ro1RGBr1PDhzTGvPBgRYzz0vReVJkReH1bc3Zlyb5aC8fqw2vOr\nNQzDMAzj4vI80gw8x3TiMg5nni8aYkWIkUE3Y61XstbNaaISNaUPDMqMQZk91iK28O6RrVeZ0Ss8\n3Tx9nhmUGXdWy2NtuXamLdvjmtAqL210cc4Re5GPbE7YHtfsTFvWZ3O8DFwUZwfDMAzDMJ6O5yFm\n30pyCTCegnm+aOEdglC1gY1uRhOVnUnN9qRhtePJZqJ0sQNYVD3U1mt97mpwDFUbqdtIp/R7ObLO\nOTqlp24jVXu5oplHdVQ7b2cHwzAMwzBOz5nFrIjs75T120XkzQcc6oGPAX4t8A/POu51RkRY6+bU\nIXJ/t+LnH4yZ1oGtSc20iaAZMgt+73UAyx3Dqj3S1uskEcgycxSZSykGvZgiszEyrQLr/fzStUU9\nqqPaRXd2MAzDMAxjOZHZL174XoFPm30dhALvBb5mCeNea0SE9W7O3e0J94cVoyrgRWedsAKvPBjz\n0nqXJijOpehpiBER4Q1rJdMmoih1iPRyz0k122onY61XsFu1fGRzQqf0TKuAz4S1XsFq53IZZCw2\nYThJC2DDMAzDMC4Wy1Aeb5m9CvAfgG8Dvv2A4wKwudiFyzgbISp1G3HInhNBEwL3dioejir6hWe1\nm7PWLcgzx73dKcNpy9jJXgvbGJUqxBNHIJ1zvPlmH2DPzWC9n++5GVw2b9fFJgzHtQA2DMMwDOPi\ncWYxq6q/MP9eRL4B+CeL24xnx6gOTJtIp/C8uNpBRGjawOu7FaNpy8NRTZm75CErMK7amcjNyHPP\npGoZ1S2Zlz2v1pPQKzLedmflufnMLpP9TRgOawFsGIZhGMbFZKlrwqr6Dcs8n3E8iu55Q8wr8+/t\nVqgqK92Ce7s1u1XgzkpJjAoIiKQeviJA6p51Wpxzl8q14Cj2N2Ewn1nDMAzDuDwsPcFRRH4d8EeB\nTwc2ONjLVlX1ciVXXkD6hadfZoyqKdvjmgh88MGY4bThxbUOG90MEWFnXKNRGZSeG4MSL6nwqVd4\nytwzKDPCNa9zWnYTBsMwDMMwzodldwD7LcD/TnIu+CDwfqBd5hhGQmfh1EEnZ3fSUofIsGoZVg29\nwnNntUuRe7wTxnVg2gZ6hWcwW0KPCk7S0nqZW6GTYRiGYRiXk2VHR/8M0AC/RVV/aMnnNmY0IbI5\nrlMFflT6nYw6BLw4NgYtVRNQlM1xgxfYqVpuZclH1jlh2kTyzDFtIt6LFToZhmEYhnFpWbaY/cXA\nD5iQfXYc1LGql3uKTPAlTJqCj25P2Bq1dAu/181qo5fzhrUOVVArdDIMwzAM48qwbDE7BB4u+ZzG\nAgd3rPJsjRsEZdDNGEwzvAgIlJmQec9qJ6PIPKtdb4VOhmEYhmFcGZYtZv8x8BlLPqexwGEdqzIv\nbI8bPMIb17o4JzRByTMhE2G1VxDUCp0MwzAMw7haLDtR8o8DHyciXy8W7nsmzDtWNW3cKwJr2tQo\nYVi1TNpIFZL364vrHd641mWlm9PJrMjLMAzDMIyrx7Ijs+8E3gd8A/AlIvITwNYBx6mqfumSx74W\n7O9YlXlJQrZu6OcZvczxcFRzf1gRFQadDO+syMswDMMwjKvJssXsFy98/+bZ10EoYGL2KdjfsWpc\ntYgogyLnTTe6OBF6hefebkXVBtZdajVrRV6GYRiGYVxFli1m37Lk8xkHkDlhpcxwC+K0yBxFlnJh\nbw5KRISoyka/5GbfhKxhGIZhGFeTZbez/YVlns94kkWP2RCVNkSmbaBVpV9me6LVidAvMwYL2wzD\nMAzDMK4a1lL2krHfYzZEZdoE6mlL3Qa6eYZAaoaQe1SVYdWaDZdhGIZhGFeSZyZmRaQPvBUYqOqP\nPqtxrhOqPOEx23jh/m7FqGoJMTKtI93Cc6ssaWPk3m5FG+KendetQWnRWsMwDMMwrgxLF7Mi8jLw\n7cDnAp5U7JXN9v0a4K8DX6mqP7Lssa86yuMes6rKzrSljkruhdVOgZOUUzusmpmFlzJpI8Npg6Js\njWteWu+x0S/IvbkbGIZhGIZxuVmqmhGRNwDvBX4b8B7gXwGLIcD3Ai8An7/Mca8LwuMes1UbmTQt\ndRPY6BfcXil543oXBbYnDZM6IAJtiOTeEQJsjhvu7kzZHNd7PrWGYRiGYRiXlWWH5t5JEqu/UVV/\nB/B/Le5U1Qb4UeDtSx73WiAC3cLjXMqdfX13yqubUwC6ebaXE+tEqGcta6s2pRh0ckeeOXIn1G1g\nUgeqNj7nKzIMwzAMwzgby04z+BzgB49JIfgg8GuXPO61YaNX0Abl7s6EnUlDHSISoQmBNiqZg6hK\nkTmaEImqjOvApBa2xzXdWb5s1aTjDcMwDMMwLjPLFrN3gA8cc0wD9Jc87rUhtbOFzAtr3Zwi8wyn\nKXWgCcqgk9HJPXnmaNqWn399wva0IXdCkTuiRoZVQ6/weKsBMwzDMAzjkrNsMfsQeNMxx7wVeG3J\n414bqjYybSK589ze6BBiKura3/FrUGa8vjulyCoAityxUuYUWUpBMDMDwzAMwzCuAssWs/8C+K0i\n8qKqPiFYReQTgHcAf2fJ414bmhAZVy1NjOxMwLvUHOEFkm3EYsevG/2SN93oMehk5D7l0mbeEaPS\nKzOCZRkYhmEYhnHJWbaY/WaSk8E/FZE/DPRgz3P2PwG+FYjAX1nyuNcCBbbGDQ+GFa/tVHQLR+4d\na92MEIUXVgoyJ4zqQOaEzAmr3Zxs1kAhKjiBSRPoZJ7MWXjWMAzDMIzLzbLb2b5XRL4c+G6SNdec\nndlrC3yJqr5vmeNeF0JUxlXD9rRh3DRsjpRu6Xk4rFnpeqZtoGkDiKNbeLqFJ/cO7ySlJmSOaRPx\nTugWnjIzn1nDMAzDMC43S2+aoKrvEpF/Dnwl8KuBm8A28GPAd6rq+5c95nUhRmXaRm71C1AhW4Gd\naUvhhAe7LWWWLLfWujluLKz3MtZ7BevdgkkTCFH3RO5Gr7AuYIZhGIZhXHqeSTtbVf0A8DXP4tzX\nGQWcCOIct1YKMu/IfcXruzWTpmHaCLdXy5lIVbbGLb0iY2O1YLWbz6y7ZM+P1jAMwzAM47Jj68yX\nCCF5yDqBNihOle1xy/akZnPc4DyoCr3coQhRlUkdCAqd3DMok22XCVnDMAzDMK4KS4/MisjLpKjs\npwEvA/kBh6mqftyyx77qOCd0cs+wSgL2A/dqxnXLtG7pFY4YYFI37GZC4YVhHUCwQi/DMAzDMK4s\nSxWzIvLrgX8EdEjFXndnr08cusxxrwveCSudnJ1pw6QN1G2LqnJrtURUWOl4dqeBoBUqwkY3p19m\nVuhlGIZhGMaVZdmR2W8CPPBFwN9V1bjk819rBPAOOrnjzqBD6TPa0AIO7xQRwfvI7rTl9kqH26sd\nXlztWFqBYRiGYRhXlmWL2U8Fvl9VrSnCM0CVve5fL9/I6Y5qJlVLq5qcCjKHSMutlZKPudHjY270\nKDL/vKdtGIZhGIbxzFi2mN0ktbQ1ngFKEq155ujknk7m2Z007E5bikyg8Ly00eFGv+Tl9S7OnS29\nQFWp2mguCIZhGIZhXFiWLWbfA/y6JZ/TmCEI3gmTOtB4oY2RJkSG05YiF9Z6OS+sdLm9Up5ZyDYh\nsjmukxtC1L1GCxu9gtxbDq5hGIZhGBeDZauSPwmsichfm7WwNZaICHQLjwh86OGE+8OKpo2s93PW\newW9wuPd2d0LVJXNcc3WTMwqMKkDW+OazXGNqi7nggzDMAzDMM7IstvZ3heRdwDvBb5IRH6W1P3r\ngEP1Nyxz7OvCRq+gaiIPpUIV7qx36OYZq52MYRWYNpGqjXTyp8+VrdrIpA7ECOu9HBFBC2Vr3DCp\nw5nPbxiGYRiGsSyWbc31KcA/ATZmm37ZIYdaaO8pyZzQyR1F5si942a/3GuEkIeUU9vGs/162/go\nN3eeIysi5JlbyvkNwzAMwzCWxbLTDL4FuAn8aeBjgVxV3QFfFtZ7ChR4fVjtLf/vTltGVUuIiqpS\nN4E2RKZNYNqEx9IBVJVpExhW7WP7DtqeuZSb27TxseOaNuKdnDqN4bCxDcMwDMMwzsqyC8A+A/h7\nqvrnlnxeAwhR2RrXhKiUmWdUVXx4c8K4iXS8MGoCZebwY2FUtXsFW8CBxVyDMmNYtU9sX+/mdHPP\nzqTho1uTWeSXvf2nacJghWSGYRiGYTxLli1ma+CVJZ/TmBGjEmPKm13p5HQLz73diqpuCd6hApl3\nIDLLbU1RUEHYmtTECHnmmNQpcnt/WOEFVGVve9Wm6C6ahOikDozrln6ZcWe1y0avOLE912Ih2eLY\nVRsAuD0ozerLMAzDMIwzsWwx+yPApy/5nMYMhb081twLtwYlToRJE/AzH9i52JwXbG2PG0R4opjr\n7s6Ucd3SKzLuzLqEaaFsjmpe256Se0c393Ryz7QJZF5O7ZRghWSGYRiGYTxrlr3O+8eATxaRrxML\nuS0dgcfyWOfbvDDLm104dlawVYdI1T5ZzOVEqNuIc/LY9rkNVxMiG/2Cm4OSN653yZ3fc0o4KVZI\nZhiGYRjGs2bZkdmvB34K+PPA7xWRn+Bwa64vXfLYVx7nBOdga9ykJfuqZVi3gNK2yvakIaqy3ivI\nZgVchXeIzERwoUmwqhJVKTJHnBWPzbdPm4Ciew4J8PQCdF5INqnDY2M3baRb+DP74RqGYRiGYSxb\nzH7xwvdvmX0dhAImZk+Jd8J6r2BSp7zWOkQU6BcZlVNGw4qPbI6Z1IFBJ8M7Ya2b7+XMzkVwM1ve\nzzOHFx7bnmdCXzJEeEzkPo0ALTNHt/BUbXhsDOc4dSGZYRiGYRjGQSxbzB4mXo0lIKSiqaqNDKuW\nqEo3Khu9ghCVXu5SQVgbWHc5a71iz80AYc9RoFv4A90MuoVnI88JEYZVc2YBKiJ74+8f+zSFZIZh\nGIZhGIex7A5gv7DM8xlPIiJ0ck8bFe8czqVtmRduztwBoiob/ZKb/UeCcS6C25h8ZMtZHms3909s\nb6OSeVmKAM29O3RswzAMwzCMs7LsyOweItIH3goMVPVHn9U415WD8lEBnAj9MmNQZo8JxrkI3s9B\n23MvSxWgh41tGIZhGIZxVpaetCgiL4vIu4FN4N+Q2tvO9/0aEfn3IvLrlz3udWOejzovCBtWLVvj\nZmn5qHMBOiizx4rBDMMwDMMwLhJLjcyKyBuA9wJ3gB8EXiB1BZvz3tm2zyd50hpPieWjGoZhGIZh\nLD/N4J0ksfobVfVHROSdLIhZVW1E5EeBty953GuJ5aMahmEYhnHdWbaY/RzgB1X1R4445oPAr13y\nuNeWo/JRVdWErmEYhmEYV5pli9k7wAeOOaYB+kse19hHEyKb43ovBcE72UtByL35uxqGYRiGcTVY\ntph9CLzpmGPeCry25HGNBVSVzXHN1rgmRlK3sDpQtQFINl0WoTUMwzAM4yqw7BDdvwB+q4i8eNBO\nEfkE4B0sOBwYy6dqI5M6ECOs93IGZcZ6LydGZqI2Pu8pGoZhGIZhLIVli9lvBjrAPxWR3wz0IHnO\nzn7+B0AE/sqSxzUWaKMSopIv5MiKCHnmCFFpoz7nGRqGYRiGYSyHZXcAe6+IfDnw3cB7FnbtzF5b\n4EtU9X3LHPc6clRx10ENFVSVpo10C0/mLMXAMAzDMIyrwdI7gKnqu0TknwNfCfxq4CawDfwY8J2q\n+v5lj3ndOK64a95QoWoDW+OGPHM0bVxaQwXDMAzDMIyLwjNRNar6AVX9GlX9DFV9q6r+SlX9g89a\nyM66j32viHxURCoReUVEvk1ENk55ns8TkR8WkS0Rmf7/7N15fJTV+f//1zWThYQlAcSlLiCgIqgI\nuCCgIAgCFhSlWrXWgNSiouJPa23VCu79th9rFbWuoPbjp1awIipuFamIAlURa3FhcytFBYJi9pnr\n98c9M2aZkEAmgUnez8cjj5ucc+5z7vsO4pUz132Oma00s9+YWas6zhtrZvPN7KvY+J+Z2dNm1r9h\nd/a9yi93FZdFcII82MKiMjYXleHuiQ0V8nOzyMkKYwRBbH5uljZUEBERkWYl1TuAHQd84+7LU9lv\nPcfuBiwm2LRhLvABcBRwKTDSzAa6+8Z69HMDcA2wFZgDbAQGAdOBEWY23N2Lq50TIkit+BnwGfBk\n7Lw9CGan+xHMTDdY9Ze7zAzPcgqLyhMvd7XKDFfZUKE8Ek3M4EaiTkbIFdCKiIhIs5DqNIMFwL0E\nKQZN7W5HeXOTAAAgAElEQVSCQPYSd78zXmhmtwGXATcBk7fVgZn1Aa4GCoF+7r4mVm7AHcAU4JfA\ntGqnXk4QyD4KTHL3smr9Zu7wXVWTeLkrbJRWfB+kZoatxstdZkHu7DclEa03KyIiIs1SqqOZr4Hi\nOlulmJl1BUYA64C7qlVfB3wHnGNmdW3WMA4w4IF4IAvg7g78GnDgAjNLbLllZu2A3wCfAz+rHsjG\nzi/f3nuqTUbIwJ0N35Ty1bclfL01OG74phTcq7zcVZ+UBBEREZF0lupg9lVgQIr7rI+hseOL7l5l\nEVV3/5Zg/dtcgo/8tyW+Pu6a6hWxfr4mmP09tFLVWKAN8BcgZGbjzewqM7vIzHpv953UIStslESi\nbC0r56tvSvmurIKvvilla1k5JZEoWeHvg1mtNysiIiLNXarTDK4BlsTyTq9P5YxkHQ6KHT+qpf5j\ngpnbA4G/b6Ofr2PH/atXmFlbYLfYtz2AeF7wkbFjObAS6FztvDnAT929aBvjVm7/Vi1VPQDKIk52\nOESbrEyyWxuRKLRrlUFpeVBeFnFaxX5F0XqzIiIi0tylOpj9FfAvgo/kzzOzdwm2rq0eNbm7n5fC\ncfNixy211MfL8+vo5xmCe5hkZne7+7pKdTcSpCAAVF4dYffY8UrgHeB04N9AT4KUh9MIXiYrqGPs\neqmIBi9v7d4um8xwKJEHWx6JYmZVAlStNysiIiLNXaqD2YJKf96T7z+2r86BVAazdYlHbducinT3\nxWZ2L/BzYEVsVnUTMJBgBvZ9oBcQqXRaPH+2GBjj7v+Nfb/UzMYSzBafY2ZXu/sXdV2ou/dLegPB\njG3feIBaVhalTXYoEaAWl0XIyQpVCVC13qyIiIg0d6kOZmt8PN9E4jOvebXUt6vWrlbuPtnMlgLn\nE8yyArwFnEgQgPcCvqx0yubY8c1KgWy8r/VmtgQYBhwB1BnM1mV7AtT4erNAYjWDnKxwYjUDLc8l\nIiIi6S7V29l+ksr+tkN8M4YDa6k/IHasLae2Cnd/CHioermZPRD747IkYxfW0l082M2pz9h12d4A\ntfJ6s8m2vq1sW1vkioiIiOyKUr6d7U6yIHYcYWahyisaxF7cGkiQBrDDGxeY2QiCl7sWVksXiL9Q\n1quWU+Pl63Z07Oq2J0CFIABulRlOWhdX1xa5ItI09EuliMj2SWmUYmY/im0D+4Na6vc2s7+b2amp\nHNfdVwMvAl2Ai6pVTwdaA4+4+3eVrqWHmfVIco3tkpR1A+4jyJW9qtrY7xIs/XWwmU2qdt4k4GBg\nNVVncxssHqC2yc6gVWa4Qf+z03q0IruG8kiUr7aWsuGbEr78poQN35Tw1dZSyiNaRk9EpDapnpmd\nBOS7+3+SVbr7F7FgcRLBlq+pdCHBdrZ3mNkwgmWyjgaOJ0gvuLpa+5WxY/Uo8EEz60yQJ7sZ6A6M\nATIJdvdKNrt7HrAIuD8WqL9PsJrBaKAIKHD3SJLzdgn13SJXRBpP5V8qo1HIzAjF/vsL/uno1CZb\nM7QiIkmk+vPjQ4F/1tHmn8BhKR43Pjt7BDCLIIi9HOhGsA3tMe6+sZ5dPUOwZuzpwBUEm0DMAfq6\n+6xaxv4Q6As8CPQGLgX6Af8HHOHui3bopppIRdSpiESJulNUFqGkPPifp9ajFWk62uRERGTHpHpm\ntgNV3/RPZiPfbz6QUu7+GTChnm2TTnG4+8PAwzs49qQ6Gzax+uTfuTtbisvZUlxOblaYzHCI7IwQ\n7tA2J1Pr0Yo0AW1yIiKyY1IdzH7N9ysH1OYAan/zX1KoPi91xdeoLY1EKauIUBFxMIhGnbzcTDq1\ny9Z6tCJNQJuciIjsmFQHs68DY82sh7t/UL3SzA4GTgbmpXhcqaa++XelFVGKyyO0yc6gbXZGMIsb\nifJdWYTsjBC5WRnK0xNpAtrkRERkx6Q6mP09cCqwyMyuB54n2Chgb2AUcC3Bjlm/T/G4Uk19X+qK\nf7SZm5VB66wwpRVRIlGnbXlESwKJNCFtciIismNSvWnCMjO7ELgL+EPsq7IIcIG7L0nluFJTffPv\nKn+0SVaYVplh3J2yiigZ4ZA+2hRpQtu7hrSIiDTCpgnufr+ZLSJYKutoIJ8gR/ZN4B53X7mt8yU1\n6pt/p482RXYt9dnkREREvtcoO4DFAtaLG6NvqZ/6Bqn6aFNERETSWaNtZ2tmrYEDgTbu/lpjjSPJ\nbU+Qqo82RUREJF2lPJg1s32APxLsmhUGPD6OmQ0i2Bb2Qnd/NdVjS1XbE6Tqo00RERFJRykNZs1s\nL2AJsAfwNLA7cEylJktiZWcAr6ZybElOQaqIiIg0Z6l+u+c6gmD1BHc/FXipcqW7lwOvAQNTPG6L\n4u6UlEfYWlpBcVnwtbW0gpLyCO7aJUhERERajlSnGYwGnq4jheBT4NgUj9tiVN7VqzQW0JpBbnYG\nrTLCNXb4EhEREWnOUh3M7gF8XEebcqB1isdtMeK7ekUiztayCJu2lgJOh9bZRFt5jR2+RERERJqz\nVE/fbQL2raPNgcB/Uzxui+AORaUVwdqxOBWRCLlZYVpnZRAKGTmZYaJREjt8iYiIiDR3qQ5mXwfG\nmtmeySrN7ABgJLAgxeO2CFF3vt5axrclFXz1bSlff1tGcXkECxmRqBN1auzwJSIiItKcpTqY/R3Q\nClhoZqOAXAjWnI19Pw+IAv+T4nFbhIg7W0vK+ba4HDOoiEbZ9F0Zm7eWETIIGZRXRAmHTNvQioiI\nSIuQ0pxZd19iZucDfwKeqVT1TexYAUx09/dTOW6L4ZCVESYrw4hGIScrg03fFVERiZLfOovi8gjh\nkGkbWhEREWkxUr5pgrvPNLNFwIVAf6AjsAV4E5jh7h+mesyWwoGObbKoiAZLc4VDRnnbVoQMWrcK\nk5uVoW1oRUREpEVplO1s3f1j4LLG6LslM4KXwDrkZlIWyaAiEiU3K0xOVgYd2mTTJjtD29CKiIhI\ni9Iowaw0jlDICIVgS3EFmRkhyiNOTnYG+blZdGyt2VgRERFpeZRYmUbCISMvJ5OQQXFZBSGDvJxM\npRWIiIhIi9WgmVkzW7ODp7q7d2vI2C2VYQRxa3A0agax7k5pRZSKqJMRMqUeiIiISLPV0DSDEMF7\nSZVlAXvF/lwBbCR4CSw+1nqgrIHjtkiRqFNYXBZbySBMeUWUwuIysO93/Kq83W0k6onVDbTFrYiI\niDRHDQpm3b1L5e/NrB3wMvAJ8CvgNXePmlkIOA64hSAAPqEh47ZU0agTjUJ+biZmhmc5hUXliR2/\nsjNCie1uo9FgA4WgLjVb3GrGV0RERHY1qX4B7CYgHzjE3ROzr+4eBV41s+OB92LtLknx2M2eEwSo\n8QDSzKru+FURpbgsss2At1VmeIfG1oyviIiI7IpSHYWMA+ZWDmQrc/cSYC5waorHbREMKCuPUFxW\nwXelFRSXVVAWW282I2RURJ1I1Lcd8O4Ad0/M+BaXRXCguCxCYVEZm4vKcNfWuSIiIrJzpHpmtiOQ\nWUebzFg72U5msKWknP9+U4JZsOZsTlaYvNzMYMev2Fa2xWURPMuDmVl3yiui5GSFd3iL29JGnPEV\nERERaYhUz8yuBsabWV6ySjNrD4wHdnQVhBbP+f6Nu+//HASp2RkhcrLChEJQWFTO1tIKCovKCYVo\n0Ba3jTXjKyIiItJQqQ5m/wT8AFhqZj81sy5mlhM7ngssAfYE7krxuC2CO+TnZNJ1t9bs16E1XXdr\nTX5OJuWRKKUVUcyM9rlZ5OdmkZMVxgiC2PzcrAatRZsRsmDr3IpoIqUgPuMbT3EQERER2RlSmmbg\n7jPM7ADgYmBmkiYG3Onud6dy3JbCgayMYPvauIhTZXY0MxyiU5vslK46EJ/xLa2IUFhUHuw+VhFt\n8IyviIiISEOlfDtbd7/UzP4CTAT6AHnAFuBtYJa7L071mC2FQTA7Wkc+rJmlNIc1PuMLJFYzyMkK\nJ1Yz0PJcIiIisrOkPJgFcPc3gDcao++WLBSyRD5sU8+ONsaMr4iIiEhDNUowK40jHDLyc7N22uxo\nqmd8RURERBpKwWwaMdDsqIiIiEglCmbTTH1mR7XtrIiIiLQUCmabGW07KyIiIi2JgtlmpPK2s9Eo\nZGaEYjt0RYAgRUEztCIiItKcaKquGam+7Wyb7AzyczOJRklsOysiIiLSnOyUYNbMNCPcCLTtrIiI\niLQ0KQ1mzew+M2tVR5v9gUWpHFcC2nZWREREWppUz8xOApaaWY9klWY2nmAnsCNTPK7w/baz8Y0V\ntpZWUFhUrm1nRUREpNlKdXRzE9AT+KeZTYgXmlmWmd0NPA5EgHEpHlf4ftvZ/NwscrLCGEEQm5+b\npW1nRUREpFlKae6qu19rZq8CfwYeMLOhwB+BB4DDgNeBM93981SOK9/TtrMiIiLSkqT8c2d3/zvQ\nG3gZOAtYAvQCbgQGK5BtfPGNFdpkZ9AqM6xAVkRERJqtxlpVYCvwFcEOrABbgH+4u9aGEhEREZGU\nSfnMrJn1JnjJ60zgBWAykAU8b2Y3mZneQhIRERGRlEj10lwXAW8AXYFfu/sod78P6AesAK4CXjOz\n/VI5roiIiIi0TKmeJb0T+JIgN/a38UJ3/xjoD9wNHAO8k+JxJQl3p6Q8wtbSCkrKI4m1Z0VERESa\ni1TnzM4FJrr75uoV7l4GXGxmfwceTPG4Uk15JMrmojKKyyJEok44ZORkhWmfm0VmWJkeIiIi0jyk\nemmuOtePdfenzOytVI4rVbk7m4vKKCwqIxqFzIwQxWURSisiAHRqk60VDkRERKRZ2ClTdO7+2c4Y\nt6UorYhSXBYhGoX83EzaZGeQn5tJNEosqNWiEiIiItI8pHRm1szW1LOpu3u3VI4t36uIOpGok1lp\nswQzIzMjRCTqVESVOysiIiLNQ6pzZkNAskgpD8iP/fk/QHmKx5VKMkJGOGQUl0XwLMfMcHfKK6Lk\nZIXJCCnFQERERJqHVOfMdqmtzsy6A3cArYETUzmuVJWdESInK0xpRYTConIyM0KUV0QJhSAnK0x2\nhl4AExERkeahyaIad18FnArsDVzXVOO2RGZG+9ws8nOzyMkKYwRBbH5uFu1zs/Tyl4iIiDQbjbWd\nbVLuXmJmLxHsDvarphy7pckMh+jUJpvSiigVUScjZGRXyqEVERERaQ6aNJiNqQD23AnjtjhmRqvM\n8M6+DBEREZFG06TJk2a2GzAO0NJcIiIiItJgqV6a6zfbGGdf4GSClQ2UYiAiIiIiDZbqNINpddR/\nA9zo7v8vxeOKiIiISAuU6mD2+FrKo8Bm4AN3r0jxmCIiIiLSQqV6ndmFqexPRERERGRbtHq+iIiI\niKStZhfMmtk+ZvaQmf3HzErNbJ2Z3W5m7bezn3Fm9oqZFZpZiZmtNLPfmFmrep5/rZl57OuEHbsb\nEREREdmWBqUZmNlDO3iqu/t5DRk7GTPrBiwGdgfmAh8ARwGXAiPNbKC7b6xHPzcA1wBbgTnARmAQ\nMB0YYWbD3b14G+f3Ba6Nnd+mQTclIiIiIrVqaM5swQ6e50DKg1ngboJA9hJ3vzNeaGa3AZcBNwGT\nt9WBmfUBrgYKgX7uviZWbsAdwBTgl9SyckNs5vZR4J/AKuCcBt2RiIiIiNSqoWkG++/gV9cGjluD\nmXUFRgDrgLuqVV8HfAecY2at6+hqHGDAA/FAFoKpZODXBIH4BWZW29ZatxDcYwHBKg4iIiIi0kga\nNDPr7p+k6kJSYGjs+KK7Vwki3f1bM3udINjtD/x9G/3Et9pdU70i1s/XBLO/hwLLK9eb2fEEKQ2X\nuftHwWSuiIiIiDSWBi/NZWY/BZa7+4oUXE9DHBQ7flRL/ccEweyBbDuY/Tp23L96hZm1BXaLfduD\nSsGsmeUBs4DXCNIRdoiZvVVLVY8d7VNERESkuUrFagazgFMqF5jZuWb2Sgr63h55seOWWurj5fl1\n9PNM7DjJzLpUq7uRIAUBoPrqCHcCHYEJsZQEEREREWlkqd4BLK4LMLiR+t5R8SB0m4Gmuy82s3uB\nnwMrzGwOsAkYCBwJvA/0AiKJjs1OJXjR66LKebY7wt37Jb34YMa2b0P6FhEREWlumtM6s/GZ17xa\n6ttVa1crd59MsNrCv4HTCVZAKANOBN6LNfsSwMw6APcCrwD37MiFby93p6Q8wtbSCkrKI2giWERE\nRFqqxpqZ3Rk+jB0PrKX+gNixtpzaKtz9IaDGOrpm9kDsj8tix/0I8miHAtFaXvp6KVZ+mbvfXp/x\na1MeibK5qIzisgiRqBMOGTlZYdrnZpEZbk6/m4iIiIjUrTkFswtixxFmFqq8okHsxa2BQDHw5o4O\nYGYjgM7AQnf/Ila8EXiwllOOIwii5wP/Af61o2PHbS4qo7CojGgUMjNCFJdFKK0IMh46tclGKyiI\niIhIS5KqYHanf87t7qvN7EWCFQsuInghK2460Bq4192/ixeaWY/YuR9U7svM2rn7N9XKugH3EeTK\nXlVp3M+AScmuycxmEQSzt7n7yzt8c4mxoLgsQjQK+bmZmBme5RQWlceC2iitMmtb/lZERESk+UlV\nMDvNzKZVLzSzSJK2EOxB0BizwhcSbGd7h5kNA1YCRwPHE6QXXF2t/cr4pVYrf9DMOgNvAZuB7sAY\nIBOY5O47PLvbEI4TiTqZGaHEDKyZkZkRIhJ1KqI7/XcKERERkSaVqiRL286vRknudPfVwBEEy4Ud\nDVwOdCNY9/UYd99Yz66eAcoJXv66AhgAzAH6uvus1F51/RlGOGSUV0QTL325O+UVUcIhIyOkFAMR\nERFpWRo8O+ruu9RbR7GP/SfUs23S6M/dHwYeTsG1FBBsa5sSZpCTFaa0IkJhUTmZGSHKK6KEQkF5\ndsYu9aMQERERaXTN6QWwFqF9bhZAYjWDnKxwYjUDvfwlIiIiLY2C2TSTGQ7RqU02pRVRKqJORsjI\nrpRDKyIiItKSKJhNQ2amVQtEREREaF47gImIiIhIC6NgVkRERETSloJZEREREUlbCmZFREREJG0p\nmBURERGRtKVgVkRERETSloJZEREREUlbCmZFREREJG0pmBURERGRtKVgVkRERETSloJZEREREUlb\nCmZFREREJG0pmBURERGRtKVgVkRERETSloJZEREREUlbCmZFREREJG0pmBURERGRtKVgVkRERETS\nloJZEREREUlbCmZFREREJG0pmBURERGRtKVgVkRERETSloJZEREREUlbCmZFREREJG0pmBURERGR\ntKVgVkRERETSloJZEREREUlbCmZFREREJG0pmBURERGRtKVgVnYZQ4YMwcy26xwzY8iQIVXKpk2b\nhpnx6quvpu7implkz01ERCQdKZhtYQoKCjCzKl+5ubn07NmTyy+/nK+++mpnX2KLs27dOsyMgoKC\nlPXZpUsXunTpkrL+REREdlUZO/sCZOc4+eSTOfzwwwHYsGEDzz33HLfddhtz5szhrbfeomPHjk1+\nTY888ghFRUVNPm5LtHLlSnJzc3f2ZYiIiDSYgtkW6pRTTqkyE1hSUkL//v159913mTFjBtddd12T\nX9N+++3X5GO2VD169NjZlyAiIpISSjMQAFq1asXZZ58NwLJly2rUL1iwgPPPP5+ePXvSrl07cnJy\nOOSQQ5g+fTolJSU12lfOW3344Yfp06cPOTk57L777kycOJH//ve/Nc6pLWe2rKyMG264gW7dupGd\nnc3+++/PNddcQ2lpab3ubfPmzeTm5tKtWzfcPWmbH/7wh5gZb731Vp39xa+zrKyM66+/noMOOojs\n7OwqvxyUlpZy6623cthhh5Gbm0u7du049thj+etf/1qlr2nTprH//vsD8PDDD1dJ/5g1a1bi/mfM\nmMHo0aPp3Lkz2dnZdOjQgRNOOIH58+dX6e/VV1/FzPjkk0/45JNPqvRX+frqyjWePXs2Rx11FLm5\nuXTo0IEf//jHfPHFF0mfx7JlyxgxYgRt27alXbt2nHDCCbzxxhvKXRYRkSahmVlJiAd6mZmZNep+\n+9vf8sEHHzBgwABOOukkSkpKeP3115k2bRqvvvoqL7/8MuFwuMZ5f/jDH3jxxRc544wzGDlyJIsW\nLWLmzJm8+uqrLFmyhE6dOtV5Taeffjpz586lW7duTJkyhbKyMh566CHee++9et1X+/bt+fGPf8zM\nmTN5+eWXGT58eJX6zz//nOeff55+/frRr1+/evUJcNppp7Fs2TJGjRrFKaecwu677w4EweeJJ57I\nwoUL6dGjBxdddBFFRUXMnj2bM844g+XLl3PzzTcDQWBcWFjIH//4R3r37s0pp5yS6D+eBrJp0yYu\nvfRSBgwYwPDhw+nUqRPr169n3rx5jB49mvvvv59JkyYBQa7sddddx+233w7A1KlTa/RXl7vvvpun\nn36asWPHMnjwYJYsWcLjjz/Ou+++y/Lly8nOzk60fe211xgxYgTl5eWcdtppdOvWjffee4/jjz+e\noUOH1vtZioiI7DB311cafAFv9e3b1xvq3HPPdcBnzpxZpbyoqMgPPfRQB/z3v/99jfNWr17t0Wi0\nRvk111zjgP/lL3+pUn7dddc54JmZmf72229XqZs6daoDPnHixCrlgwcP9uCv5Pf+93//1wHv37+/\nFxcXJ8o3btzoXbt2dcAHDx6cdOwFCxYkypYtW+aAn3baaTXuId7+vvvuq1GXTPw6Dz30UP/qq69q\n1N98880O+KhRo7y8vDxRvmHDBu/cubMD/vrrryfK165d64Cfe+65SccrKSnxzz77rEZ5YWGh9+rV\ny9u3b+9FRUVV6jp37uydO3eu9R629dzatm3rK1asqFJ35plnOuCPP/54oiwSiXj37t0d8Oeee65K\n+3vuuceBGj8HERFpGfr27evAW94EMZLSDFqop556imnTpjFt2jQuvPBCDjroIN577z2OO+44Lrjg\nghrtu3btmjQFID7z98ILLyQd55xzzqFPnz5VyqZNm0ZeXh6PPfZYnakCM2fOBODmm2+mVatWifIO\nHTpw7bXXbvsmKzniiCM44ogjmDt3bpUUh0gkwoMPPkjbtm0588wz690fwA033MBuu+1Wo/yhhx7C\nzLjtttvIyPj+w4/dd989cc0PPPBAvcfJzs5mn332qVGel5fHxIkT2bx5c9LUkB11ySWXcOihh1Yp\n+9nPfgbA0qVLE2WLFy9m1apVHH/88YwaNapK+/PPP58DDzwwZdckIiJSGwWzLdTcuXOZPn0606dP\n55577uGzzz5j+PDhvPzyy0nfcv/uu++4+eabOfLII8nLyyMUCmFmiWCutnzKwYMH1yjLy8vj8MMP\np6SkhJUrV27zOt9++21CoRCDBg2qUbe966ReeOGFVFRU8NBDDyXKnnvuOT7//HN+8pOf0KZNm+3q\n76ijjqpR9u2337Jq1Sp+8IMfJH3JKv7R+zvvvLNdY73//vsUFBTQtWtXcnJyEnmwl19+OVD7898R\nRxxxRI2yfffdFwjyj+Pi95DsZxMKhRgwYEDKrklERKQ2ypltoWbOnElBQQGRSIQ1a9Zw7bXX8vjj\nj3PBBRfUmDUsLy9n6NChLF26lEMOOYQzzjiDTp06JXJrp0+fXusM6x577JG0fM899wRgy5Yt27zO\nLVu20KFDh6R5vPE+6uvHP/4xl19+Offffz9XXXUVoVCIe++9F4Cf//zn29VXbePH72evvfZKek68\nvLCwsN7jvPnmmwwdOpSKigqGDRvG2LFjadeuHaFQiOXLlzN37tx6vwxXH/n5+TXK4jPMkUgkURa/\n19p+xrWVi4iIpJKC2RYuHA5zwAEH8Nhjj7Fu3ToefPBBxo4dy9ixYxNt5s6dy9KlSzn33HMTb9jH\nrV+/nunTp9fa/4YNG5KWxz/qz8vL2+b15eXlsWnTJsrLy2sEtMlWRNiWnJwcCgoKEi+lHXLIITz/\n/PMcffTR9O7de7v6ApKmXcTvp7ZrW79+fZV29XHjjTdSXFzMggULasxG33LLLcydO7fefaVSu3bt\ngNp/xrWVi4iIpJLSDAQIPhb+4x//CMCVV15ZZQZu1apVQPD2fnULFy7cZr/J6rds2cLy5ctp1aoV\nBx988DbP79u3L9FolEWLFtWo25Elny644ALMjHvvvZcHHniASCSyQ7OytWnbti3dunXjiy++4OOP\nP65Rv2DBAiC4r7j4KhCVn3llq1atokOHDknTKmp7/uFwuNb+UiWeC53sZxONRlm8eHGjji8iIgIK\nZqWSo48+mh/+8Id8+OGHPPLII4ny+Lao1YPHNWvW8Mtf/nKbfT766KM18kOnTZvGli1bOPPMM6ss\n85TMhAkTALj66qurrGe7adMmbrzxxrpuqYYDDjiAYcOG8cwzz/CnP/2J/Px8zjjjjO3uZ1smTpyI\nu/OLX/yiSkD59ddfc8MNNyTaxLVv3x4z49NPP03aX5cuXdi0aRMrVqyoUv7ggw/W+uJdx44d+eqr\nryguLm7o7dRq4MCBdOvWjQULFtRY7/a+++7jo48+arSxRURE4pRmIFVcf/31PPvss0yfPp2zzz6b\nrKwsxowZQ/fu3bntttt477336NOnD59++inPPPMMJ510Uq1BGMCoUaMYOHAgp59+OnvttReLFi1i\n0aJFdOnShVtvvbXO6znzzDN5/PHHefrppznkkEM4+eSTKS8vZ/bs2Rx55JGsXr16u+/xwgsv5OWX\nX2bDhg1cfPHFKd/W9YorrmD+/PnMnTuX3r17M3r0aIqKinjiiSf48ssvufLKK6u8NNWmTRuOPvpo\nXnvtNc4++2wOPPBAwuEwY8eO5bDDDmPq1Km88MILDBo0iNNPP528vDz++c9/smjRIsaPH8/s2bNr\nXMOwYcNYtmwZI0eO5LjjjiM7O5vevXszZsyYlN1nKBTigQceYOTIkYwdOzaxzuyKFSt46aWXGDVq\nFPPnzycU0u/MIiLSePR/GamiT58+jBs3jk8++STxclTr1q155ZVXOOuss3j//fe54447WLFiBdde\nex2qufIAACAASURBVC1//vOft9nfZZddxt13383y5cu5/fbb+eCDDygoKGDx4sWJTQa2xcx44okn\nmD59OtFolBkzZvD0008zYcKEGrtp1dfYsWMTqzCkMsUgLisri5deeombbroJgDvvvJOHH344kZv8\n29/+tsY5jz76KCeddBLPP/8806dP59prr+Xtt98GYOTIkcybN4+ePXvy+OOP8+CDD5Kdnc2CBQs4\n6aSTkl7DNddcw+TJk1m9ejW33HIL1157LXPmzEn5vQ4ZMoSFCxcyZMgQnn32We64445Efm/Xrl2B\n73NrRUREGoO5J9/eU3YtZvZW3759+9Znu9VdwbRp05g+fXrSl5Z2tjVr1tC9e3cGDhzIa6+9trMv\np9kaOHAgS5YsYcuWLbRu3XpnX46IiDShfv368Xawa1L9t9bcQZqZlRbn97//Pe7OlClTdvalpL2i\noqKky4zNmjWLxYsXM2LECAWyIiLSqJQzKy3Cp59+ymOPPcbHH3/MzJkz6d27Nz/60Y929mWlvU8/\n/ZQ+ffowfPhwunfvTkVFBe+88w6LFi0iPz+f//mf/9nZlygiIs2cgllpEdasWcOvfvUrcnNzGT58\nOPfcc49eTEqBPfbYg7PPPpuFCxeyYMECSktL2XPPPZkwYQJXX3013bp129mXKCIizZxyZtNEuuXM\nioiISMulnFkRERERkXpQMCsiIiIiaUvBrDQ7Xbp0SexatquYNm0aZrZDW/Cmk1mzZmFmzJo1a2df\nioiItBAKZmWnUeDTNHbF4F5ERCRVFMyKNIEpU6awcuVKjjrqqJ19KSIiIs2KluYSaQK77bZbYgtd\nERERSR3NzLYwa9as4fzzz6d79+7k5OTQoUMHDj30UCZPnszGjRsT7SqnADz77LMMGDCA1q1b0759\ne8aPH8/HH3+ctP/169dz0UUX0aVLF7KysujUqROnnnoq1ZcUGzJkCBMmTABgwoQJmFnia926dXXe\nh7szY8YMevXqRatWrdh7772ZMmUKW7ZsqfWc0tJSbr31Vg477DByc3Np164dxx57LH/9619rtF23\nbh1mRkFBAatXr2b8+PF07NiRtm3bMmLECP71r38B8NVXX3H++eez11570apVK4488kgWLFhQo7/a\ncmbNjCFDhvD1118n+snOzqZXr17MnDmzRj9lZWXMmDGD0aNH07lzZ7Kzs+nQoQMnnHAC8+fPr9L2\n1Vdfxcz45JNP+OSTT6o844KCgiptP/jgAwoKCth3333Jzs5mjz324KyzzuLDDz9M+ixXrVrFj370\nI9q3b0/r1q0ZMGAAzz77bK3PXkREpLFoZrYFWb9+PUceeSTffPMNo0eP5rTTTqOkpIS1a9fy6KOP\nMmXKFDp27FjlnCeffJL58+czbtw4hgwZwvLly5kzZw4LFixg8eLFHHTQQYm2a9euZdCgQfznP/9h\n6NChnHnmmXz22Wc88cQTPPvss8yZM4cf/vCHABQUFJCfn8/cuXM5+eSTOfzwwxP95Ofn13kvU6dO\n5Y477mCvvfbi/PPPJzMzk7lz57JkyRLKysrIysqq0r6srIwTTzyRhQsX0qNHDy666CKKioqYPXs2\nZ5xxBsuXL+fmm2+uMc66des4+uijOfjggykoKGDdunX87W9/Y8iQIbzxxhuMHDmSdu3accYZZ7Bp\n0yb+8pe/MGrUKD766CP222+/ev1cCgsLGThwIFlZWYwfP56SkhJmz57NxIkTCYVCnHvuuYm2mzZt\n4tJLL2XAgAEMHz6cTp06sX79eubNm8fo0aO5//77mTRpEhDkyl533XXcfvvtiWcWV/l5P//885x6\n6qmUl5czZswYunfvzueff86TTz7Js88+y4IFC+jbt2+i/ccff8wxxxzDxo0bGTVqFIcffjirVq3i\nlFNOYdSoUfW6ZxERkZRxd32lwRfwVt++fb0h7rjjDgf89ttvr1G3detWLyoqSnw/c+ZMBxzwefPm\nVWl7++23O+BDhw6tUj5ixAgH/MYbb6xS/vrrr3s4HPYOHTr4t99+W2OMmTNnbtd9vP766w54t27d\nfOPGjYny4uJi79+/vwPeuXPnKufcfPPNDvioUaO8vLw8Ub5hwwbv3LmzA/76668nyteuXZu4/+r3\nc/311zvg7du395///OceiUQSdY888ogDPnXq1CrnXHfddQ74ggULqpTHxzjvvPO8oqIiUf7+++97\nOBz2gw8+uEr7kpIS/+yzz2o8k8LCQu/Vq5e3b9++ys/R3b1z5841nkfcpk2bPD8/3zt27Ojvv/9+\nlbp//etf3rp1a+/Tp0+V8uHDhyf9e/TUU08l7md7f6YiItK89O3b14G3vAliJKUZtEA5OTk1ylq3\nbp20fOjQoYnZ1LgpU6bQrVs3XnnlFT755BMAPv/8c1588UX2228/rrzyyirtBwwYwJlnnsmmTZt4\n8sknG3z98Y/fr776ajp06JAob9WqFbfcckvScx566CHMjNtuu42MjO8/kNh999259tprAXjggQdq\nnNelSxeuuuqqKmXxmdLS0lJ+97vfVdkW96yzziIjI4Ply5fX+35yc3O57bbbCIfDibKePXsycOBA\nVq5cybfffpsoz87OZp999qnRR15eHhMnTmTz5s0sW7as3mM/8sgjFBYWMn36dHr27FmlrlevXvzs\nZz/jnXfe4d///jcQ/Jxfeukl9t9/f6ZMmVKl/cknn8zgwYPrPbaIiEgqKM2gBRk7diy//vWvueii\ni3jhhRc48cQTGThwID179sTMkp6TLDgJh8MMGjSI1atX884779C5c2feeecdAI499lgyMzNrnDN0\n6FD+/Oc/88477/DTn/60Qffx9ttv13ptxx57bJVgFeDbb79l1apV7L333vTo0SPptQGJe6js8MMP\nrxJkAvzgBz8A4MADD6Rt27ZV6sLhMHvssQeff/55ve/ngAMOoF27djXK9913XyBIQ6g8zvvvv8/v\nfvc7/vGPf7B+/XpKSkqqnPfFF1/Ue+w33ngDgHfffZdp06bVqP/oo48AWLlyJT179kw8o0GDBtV4\nLhDkQi9cuLDe44uIiDRUswpmzWwf4HpgJNARWA88BUx3983b0c844GKgL9AKWAv8H/D/3L2kWtu9\ngVOB0cDBwF7AVuBt4B53b/hUZIp07tyZpUuXMm3aNJ5//vnELOm+++7LFVdcwSWXXFLjnD322CNp\nX3vuuSdA4oWr+HGvvfZK2j5eXlhY2LCbqDRWsmsLh8M18n4bcm15eXk1yuLBcrK6eH15eXltl19D\nbTnC8XEikUii7M0332To0KFUVFQwbNgwxo4dS7t27QiFQixfvpy5c+dSWlpa77HjL/3df//922y3\ndetWYNvPHr7/eyEiItJUmk0wa2bdgMXA7sBc4APgKOBSYKSZDXT3jdvoIt7PDcA1BAHpHGAjMAiY\nDowws+HuXlzplIuBXxIEvAuA/wKdCQLcE8zsD+7+/6XmLhvu4IMP5vHHH6eiooJ3332Xl19+mTvv\nvJNLL72U1q1bc95551Vpv2HDhqT9/Pe//wW+D+jix3h5devXr6/SriHifWzYsIGuXbtWqYtEImzc\nuJG99967RvumuLbGd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UOvYM1rM7MBTGtOvmJmZtYzJE0iBegvkvriHudpac2s\npzmYNTMzM7PKcp9ZMzMzM6ssB7NmZmZmVlkOZs3MzMysshzMmpmZmVllOZg1MzMzs8pyMGtmZmZm\nleVg1szMzMwqy8GsmZmZmVWWg1kzMzMzqywHs2ZmZmZWWQ5mzczMzKyyHMyamZmZWWU5mDUzMzOz\nynIwa2ZmZmaV5WDWzMzMzCrrf4yYZOA0iGtSAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xb61fd9240>" ] }, "metadata": { "image/png": { "height": 333, "width": 345 } }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(5,5))\n", "plt.plot(df_single.best_rotation_period, df_single.amplitude_linear, '.', alpha=0.1)\n", "plt.xlim(0, 60)\n", "plt.ylim(0.9, 1.04)\n", "plt.xlabel('$P_{\\mathrm{rot}}$ [days]')\n", "plt.text(1, 0.92, ' Rapidly rotating\\n spot dominated')\n", "plt.text(36, 1.02, ' Slowly rotating\\n facular dominated')\n", "plt.ylabel('Flux decrement $(f_{\\mathrm{spot, min}})$ ')\n", "plt.title('K2 x Gaia x rotational modulation');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The points look drawn from their parent population." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style type=\"text/css\" >\n", "</style> \n", "<table id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71\" > \n", "<thead> <tr> \n", " <th class=\"blank level0\" ></th> \n", " <th class=\"col_heading level0 col0\" >source_id</th> \n", " <th class=\"col_heading level0 col1\" >epic_number</th> \n", " <th class=\"col_heading level0 col2\" >tm_name</th> \n", " <th class=\"col_heading level0 col3\" >k2_campaign_str</th> \n", " <th class=\"col_heading level0 col4\" >num_segments</th> \n", " <th class=\"col_heading level0 col5\" >best_rotation_period</th> \n", " <th class=\"col_heading level0 col6\" >amplitude_linear</th> \n", " <th class=\"col_heading level0 col7\" >multiplicity</th> \n", " </tr></thead> \n", "<tbody> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row0\" class=\"row_heading level0 row0\" >275</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row0_col0\" class=\"data row0 col0\" >658580423924208896</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row0_col1\" class=\"data row0 col1\" >211836630</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row0_col2\" class=\"data row0 col2\" >2MASS J08442233+1750080</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row0_col3\" class=\"data row0 col3\" >16</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row0_col4\" class=\"data row0 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row0_col5\" class=\"data row0 col5\" >0.368447</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row0_col6\" class=\"data row0 col6\" >0.898086</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row0_col7\" class=\"data row0 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row1\" class=\"row_heading level0 row1\" >250</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row1_col0\" class=\"data row1 col0\" >659839437753487232</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row1_col1\" class=\"data row1 col1\" >211823646</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row1_col2\" class=\"data row1 col2\" >2MASS J08501034+1739076</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row1_col3\" class=\"data row1 col3\" >5</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row1_col4\" class=\"data row1 col4\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row1_col5\" class=\"data row1 col5\" >10.1974</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row1_col6\" class=\"data row1 col6\" >0.913926</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row1_col7\" class=\"data row1 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row2\" class=\"row_heading level0 row2\" >197</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row2_col0\" class=\"data row2 col0\" >655472658605271936</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row2_col1\" class=\"data row2 col1\" >211685886</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row2_col2\" class=\"data row2 col2\" >2MASS J08182325+1543301</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row2_col3\" class=\"data row2 col3\" >5</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row2_col4\" class=\"data row2 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row2_col5\" class=\"data row2 col5\" >0.469649</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row2_col6\" class=\"data row2 col6\" >0.938647</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row2_col7\" class=\"data row2 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row3\" class=\"row_heading level0 row3\" >121</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row3_col0\" class=\"data row3 col0\" >598514653454617344</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row3_col1\" class=\"data row3 col1\" >211323984</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row3_col2\" class=\"data row3 col2\" >2MASS J08472356+1019588</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row3_col3\" class=\"data row3 col3\" >5</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row3_col4\" class=\"data row3 col4\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row3_col5\" class=\"data row3 col5\" >1.49577</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row3_col6\" class=\"data row3 col6\" >0.944613</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row3_col7\" class=\"data row3 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row4\" class=\"row_heading level0 row4\" >122</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row4_col0\" class=\"data row4 col0\" >598387870315169792</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row4_col1\" class=\"data row4 col1\" >211328300</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row4_col2\" class=\"data row4 col2\" >2MASS J08431825+1024481</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row4_col3\" class=\"data row4 col3\" >5</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row4_col4\" class=\"data row4 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row4_col5\" class=\"data row4 col5\" >1.32871</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row4_col6\" class=\"data row4 col6\" >0.947946</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row4_col7\" class=\"data row4 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row5\" class=\"row_heading level0 row5\" >365</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row5_col0\" class=\"data row5 col0\" >664570430130197376</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row5_col1\" class=\"data row5 col1\" >212005402</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row5_col2\" class=\"data row5 col2\" >2MASS J08342639+2020413</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row5_col3\" class=\"data row5 col3\" >16</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row5_col4\" class=\"data row5 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row5_col5\" class=\"data row5 col5\" >0.316894</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row5_col6\" class=\"data row5 col6\" >0.950434</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row5_col7\" class=\"data row5 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row6\" class=\"row_heading level0 row6\" >85</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row6_col0\" class=\"data row6 col0\" >65414761397616512</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row6_col1\" class=\"data row6 col1\" >210993662</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row6_col2\" class=\"data row6 col2\" >2MASS J03561519+2252292</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row6_col3\" class=\"data row6 col3\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row6_col4\" class=\"data row6 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row6_col5\" class=\"data row6 col5\" >3.66327</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row6_col6\" class=\"data row6 col6\" >0.951003</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row6_col7\" class=\"data row6 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row7\" class=\"row_heading level0 row7\" >496</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row7_col0\" class=\"data row7 col0\" >3682573958240910848</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row7_col1\" class=\"data row7 col1\" >228938222</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row7_col2\" class=\"data row7 col2\" >2MASS J12491095-0236088</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row7_col3\" class=\"data row7 col3\" >10</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row7_col4\" class=\"data row7 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row7_col5\" class=\"data row7 col5\" >1.31117</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row7_col6\" class=\"data row7 col6\" >0.951336</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row7_col7\" class=\"data row7 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row8\" class=\"row_heading level0 row8\" >113</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row8_col0\" class=\"data row8 col0\" >66957994687842176</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row8_col1\" class=\"data row8 col1\" >211149600</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row8_col2\" class=\"data row8 col2\" >2MASS J03503571+2525354</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row8_col3\" class=\"data row8 col3\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row8_col4\" class=\"data row8 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row8_col5\" class=\"data row8 col5\" >0.314485</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row8_col6\" class=\"data row8 col6\" >0.95171</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row8_col7\" class=\"data row8 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row9\" class=\"row_heading level0 row9\" >99</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row9_col0\" class=\"data row9 col0\" >66453181412027392</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row9_col1\" class=\"data row9 col1\" >211047055</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row9_col2\" class=\"data row9 col2\" >2MASS J03495762+2343284</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row9_col3\" class=\"data row9 col3\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row9_col4\" class=\"data row9 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row9_col5\" class=\"data row9 col5\" >0.314441</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row9_col6\" class=\"data row9 col6\" >0.953438</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row9_col7\" class=\"data row9 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row10\" class=\"row_heading level0 row10\" >20</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row10_col0\" class=\"data row10 col0\" >3892999352558585856</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row10_col1\" class=\"data row10 col1\" >201698686</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row10_col2\" class=\"data row10 col2\" >2MASS J12032777+0336598</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row10_col3\" class=\"data row10 col3\" >1</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row10_col4\" class=\"data row10 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row10_col5\" class=\"data row10 col5\" >0.429321</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row10_col6\" class=\"data row10 col6\" >0.954191</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row10_col7\" class=\"data row10 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row11\" class=\"row_heading level0 row11\" >88</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row11_col0\" class=\"data row11 col0\" >53462623327108736</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row11_col1\" class=\"data row11 col1\" >211010517</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row11_col2\" class=\"data row11 col2\" >2MASS J04023477+2308286</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row11_col3\" class=\"data row11 col3\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row11_col4\" class=\"data row11 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row11_col5\" class=\"data row11 col5\" >0.751178</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row11_col6\" class=\"data row11 col6\" >0.954338</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row11_col7\" class=\"data row11 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row12\" class=\"row_heading level0 row12\" >158</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row12_col0\" class=\"data row12 col0\" >650660611607087488</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row12_col1\" class=\"data row12 col1\" >211538360</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row12_col2\" class=\"data row12 col2\" >2MASS J08143817+1340440</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row12_col3\" class=\"data row12 col3\" >5</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row12_col4\" class=\"data row12 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row12_col5\" class=\"data row12 col5\" >1.68557</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row12_col6\" class=\"data row12 col6\" >0.956874</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row12_col7\" class=\"data row12 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row13\" class=\"row_heading level0 row13\" >211</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row13_col0\" class=\"data row13 col0\" >611482435327495168</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row13_col1\" class=\"data row13 col1\" >211748517</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row13_col2\" class=\"data row13 col2\" >2MASS J08544548+1635163</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row13_col3\" class=\"data row13 col3\" >16</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row13_col4\" class=\"data row13 col4\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row13_col5\" class=\"data row13 col5\" >2.69938</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row13_col6\" class=\"data row13 col6\" >0.957083</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row13_col7\" class=\"data row13 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row14\" class=\"row_heading level0 row14\" >514</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row14_col0\" class=\"data row14 col0\" >145034968209407232</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row14_col1\" class=\"data row14 col1\" >247454835</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row14_col2\" class=\"data row14 col2\" >2MASS J04311686+2150252</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row14_col3\" class=\"data row14 col3\" >13</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row14_col4\" class=\"data row14 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row14_col5\" class=\"data row14 col5\" >27.1255</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row14_col6\" class=\"data row14 col6\" >0.957964</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row14_col7\" class=\"data row14 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row15\" class=\"row_heading level0 row15\" >151</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row15_col0\" class=\"data row15 col0\" >602313367345103104</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row15_col1\" class=\"data row15 col1\" >211470585</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row15_col2\" class=\"data row15 col2\" >2MASS J08414534+1242450</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row15_col3\" class=\"data row15 col3\" >16</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row15_col4\" class=\"data row15 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row15_col5\" class=\"data row15 col5\" >2.85056</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row15_col6\" class=\"data row15 col6\" >0.958184</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row15_col7\" class=\"data row15 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row16\" class=\"row_heading level0 row16\" >59</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row16_col0\" class=\"data row16 col0\" >50638424632060672</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row16_col1\" class=\"data row16 col1\" >210831943</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row16_col2\" class=\"data row16 col2\" >2MASS J04014286+2021185</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row16_col3\" class=\"data row16 col3\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row16_col4\" class=\"data row16 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row16_col5\" class=\"data row16 col5\" >0.603412</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row16_col6\" class=\"data row16 col6\" >0.959939</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row16_col7\" class=\"data row16 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row17\" class=\"row_heading level0 row17\" >180</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row17_col0\" class=\"data row17 col0\" >652397496382326400</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row17_col1\" class=\"data row17 col1\" >211630303</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row17_col2\" class=\"data row17 col2\" >2MASS J08195342+1457403</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row17_col3\" class=\"data row17 col3\" >5</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row17_col4\" class=\"data row17 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row17_col5\" class=\"data row17 col5\" >1.71477</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row17_col6\" class=\"data row17 col6\" >0.960127</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row17_col7\" class=\"data row17 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row18\" class=\"row_heading level0 row18\" >112</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row18_col0\" class=\"data row18 col0\" >66911231083913600</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row18_col1\" class=\"data row18 col1\" >211147423</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row18_col2\" class=\"data row18 col2\" >2MASS J03511384+2523116</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row18_col3\" class=\"data row18 col3\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row18_col4\" class=\"data row18 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row18_col5\" class=\"data row18 col5\" >0.53264</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row18_col6\" class=\"data row18 col6\" >0.960857</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row18_col7\" class=\"data row18 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row19\" class=\"row_heading level0 row19\" >90</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row19_col0\" class=\"data row19 col0\" >65486951208575488</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row19_col1\" class=\"data row19 col1\" >211012609</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row19_col2\" class=\"data row19 col2\" >2MASS J04011397+2310302</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row19_col3\" class=\"data row19 col3\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row19_col4\" class=\"data row19 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row19_col5\" class=\"data row19 col5\" >1.50612</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row19_col6\" class=\"data row19 col6\" >0.962297</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row19_col7\" class=\"data row19 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row20\" class=\"row_heading level0 row20\" >101</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row20_col0\" class=\"data row20 col0\" >66511970924353792</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row20_col1\" class=\"data row20 col1\" >211059767</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row20_col2\" class=\"data row20 col2\" >2MASS J03493253+2355426</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row20_col3\" class=\"data row20 col3\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row20_col4\" class=\"data row20 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row20_col5\" class=\"data row20 col5\" >0.771791</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row20_col6\" class=\"data row20 col6\" >0.964452</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row20_col7\" class=\"data row20 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row21\" class=\"row_heading level0 row21\" >465</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row21_col0\" class=\"data row21 col0\" >678123212315654528</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row21_col1\" class=\"data row21 col1\" >212164337</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row21_col2\" class=\"data row21 col2\" >2MASS J08250085+2321162</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row21_col3\" class=\"data row21 col3\" >5</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row21_col4\" class=\"data row21 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row21_col5\" class=\"data row21 col5\" >3.12964</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row21_col6\" class=\"data row21 col6\" >0.96524</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row21_col7\" class=\"data row21 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row22\" class=\"row_heading level0 row22\" >241</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row22_col0\" class=\"data row22 col0\" >657036473378693376</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row22_col1\" class=\"data row22 col1\" >211808024</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row22_col2\" class=\"data row22 col2\" >2MASS J08134855+1725469</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row22_col3\" class=\"data row22 col3\" >5</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row22_col4\" class=\"data row22 col4\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row22_col5\" class=\"data row22 col5\" >1.10681</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row22_col6\" class=\"data row22 col6\" >0.965498</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row22_col7\" class=\"data row22 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row23\" class=\"row_heading level0 row23\" >5</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row23_col0\" class=\"data row23 col0\" >3601780881759596032</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row23_col1\" class=\"data row23 col1\" >201249315</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row23_col2\" class=\"data row23 col2\" >2MASS J11543852-0313049</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row23_col3\" class=\"data row23 col3\" >1</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row23_col4\" class=\"data row23 col4\" >4</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row23_col5\" class=\"data row23 col5\" >5.20882</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row23_col6\" class=\"data row23 col6\" >0.965566</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row23_col7\" class=\"data row23 col7\" >1</td> \n", " </tr> <tr> \n", " <th id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71level0_row24\" class=\"row_heading level0 row24\" >358</th> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row24_col0\" class=\"data row24 col0\" >661345699964532992</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row24_col1\" class=\"data row24 col1\" >211969820</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row24_col2\" class=\"data row24 col2\" >2MASS J08424854+1946263</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row24_col3\" class=\"data row24 col3\" >5</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row24_col4\" class=\"data row24 col4\" >3</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row24_col5\" class=\"data row24 col5\" >1.90627</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row24_col6\" class=\"data row24 col6\" >0.965578</td> \n", " <td id=\"T_8857ac70_4ee8_11e8_b435_dca9046e9f71row24_col7\" class=\"data row24 col7\" >1</td> \n", " </tr></tbody> \n", "</table> " ], "text/plain": [ "<pandas.io.formats.style.Styler at 0xb6184d128>" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_single.sort_values('amplitude_linear', ascending=True).head(25).style.format({'source_id':\"{:.0f}\",\n", " 'epic_number':\"{:.0f}\"})" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "df_single.to_csv('../data/analysis/k2_gaia_rotmod_single.csv', index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see if we can examine some of these Gaia lightcurves and compare them to K2 lightcurves. We'll do that in the next notebook." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
vbsteja/code
Python/NLP/.ipynb_checkpoints/Topic_modelling_NMF_SVD-checkpoint.ipynb
1
71603
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from scipy import linalg" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "np.set_printoptions(suppress=True)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import fetch_20newsgroups \n", "from sklearn.datasets import get_data_home\n", "import os\n", "from sklearn.datasets import load_files\n", "from sklearn import decomposition\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "categories = ['alt.atheism', 'talk.religion.misc', 'comp.graphics', 'sci.space']\n", "remove = ('headers', 'footers', 'quotes')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load data from existing files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "due to the fact that aws download is very slow for the dataset, i have manually downloaded the dataset and loading into the notebook." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "### the below code is from sklearn implementation of preprocessing the news group data\n", "import re\n", "\n", "_QUOTE_RE = re.compile(r'(writes in|writes:|wrote:|says:|said:'\n", " r'|^In article|^Quoted from|^\\||^>)')\n", "def strip_newsgroup_quoting(text):\n", " \"\"\"\n", " Given text in \"news\" format, strip lines beginning with the quote\n", " characters > or |, plus lines that often introduce a quoted section\n", " (for example, because they contain the string 'writes:'.)\n", " \"\"\"\n", " good_lines = [line for line in text.split('\\n')\n", " if not _QUOTE_RE.search(line)]\n", " return '\\n'.join(good_lines)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def strip_newsgroup_footer(text):\n", " \"\"\"\n", " Given text in \"news\" format, attempt to remove a signature block.\n", "\n", " As a rough heuristic, we assume that signatures are set apart by either\n", " a blank line or a line made of hyphens, and that it is the last such line\n", " in the file (disregarding blank lines at the end).\n", " \"\"\"\n", " lines = text.strip().split('\\n')\n", " for line_num in range(len(lines) - 1, -1, -1):\n", " line = lines[line_num]\n", " if line.strip().strip('-') == '':\n", " break\n", "\n", " if line_num > 0:\n", " return '\\n'.join(lines[:line_num])\n", " else:\n", " return text\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "TRAIN_FOLDER = \"20news-bydate-train\"\n", "TEST_FOLDER = \"20news-bydate-test\"\n", "def strip_newsgroup_header(text):\n", " \"\"\"\n", " Given text in \"news\" format, strip the headers, by removing everything\n", " before the first blank line.\n", " \"\"\"\n", " _before, _blankline, after = text.partition('\\n\\n')\n", " return after\n", " \n", "def preprocess_fetch_data(categories,remove,subset='train',data_home = None):\n", " data_home = get_data_home(data_home=data_home)\n", " twenty_home = os.path.join(data_home, \"20news_home\")\n", " train_path = os.path.join(twenty_home, TRAIN_FOLDER)\n", " test_path = os.path.join(twenty_home, TEST_FOLDER)\n", " cache = dict(train=load_files(train_path, encoding='latin1'),\n", " test=load_files(test_path, encoding='latin1'))\n", " if subset in ('train', 'test'):\n", " data = cache[subset]\n", " elif subset == 'all':\n", " data_lst = list()\n", " target = list()\n", " filenames = list()\n", " for subset in ('train', 'test'):\n", " data = cache[subset]\n", " data_lst.extend(data.data)\n", " target.extend(data.target)\n", " filenames.extend(data.filenames)\n", "\n", " data.data = data_lst\n", " data.target = np.array(target)\n", " data.filenames = np.array(filenames)\n", " else:\n", " raise ValueError(\n", " \"subset can only be 'train', 'test' or 'all', got '%s'\" % subset)\n", "\n", " data.description = 'the 20 newsgroups by date dataset'\n", "\n", " if 'headers' in remove:\n", " data.data = [strip_newsgroup_header(text) for text in data.data]\n", " if 'footers' in remove:\n", " data.data = [strip_newsgroup_footer(text) for text in data.data]\n", " if 'quotes' in remove:\n", " data.data = [strip_newsgroup_quoting(text) for text in data.data]\n", "\n", " if categories is not None:\n", " labels = [(data.target_names.index(cat), cat) for cat in categories]\n", " # Sort the categories to have the ordering of the labels\n", " labels.sort()\n", " labels, categories = zip(*labels)\n", " mask = np.in1d(data.target, labels)\n", " data.filenames = data.filenames[mask]\n", " data.target = data.target[mask]\n", " # searchsorted to have continuous labels\n", " data.target = np.searchsorted(labels, data.target)\n", " data.target_names = list(categories)\n", " # Use an object array to shuffle: avoids memory copy\n", " data_lst = np.array(data.data, dtype=object)\n", " data_lst = data_lst[mask]\n", " data.data = data_lst.tolist()\n", "\n", " return data\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "newsgroups_train = preprocess_fetch_data(categories,remove,subset='train')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "newsgroups_test = preprocess_fetch_data(categories,remove,subset='test')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((2034,), (2034,))" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "newsgroups_train.filenames.shape,newsgroups_train.target.shape" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "#print(\"\\n\".join(newsgroups_train.data[:3]))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['alt.atheism', 'sci.space', 'comp.graphics'],\n", " dtype='<U18')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(newsgroups_train.target_names)[newsgroups_train.target[:3]]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 2, 1, 0, 2, 3, 0, 0, 2, 2])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "newsgroups_train.target[:10]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2034, 26576)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vectorizer = CountVectorizer(stop_words='english')\n", "vectors = vectorizer.fit_transform(newsgroups_train.data).todense()\n", "vectors.shape" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2034 (2034, 26576)\n" ] } ], "source": [ "print(len(newsgroups_train.data),vectors.shape)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sklearn.utils.Bunch" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(newsgroups_train)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "vocab = np.array(vectorizer.get_feature_names())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SVD(Singular Value Decomposition)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "U, s, Vh = linalg.svd(vectors,full_matrices=False)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2034, 2034) (2034,) (2034, 26576) (2034, 26576)\n" ] } ], "source": [ "print(U.shape, s.shape, Vh.shape,vectors.shape)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Exercise: confrim that U, s, Vh is a decomposition of the var Vectors\n", "m,n = vectors.shape\n", "D = np.diag(s)\n", "U.shape,D.shape,Vh.shape\n", "np.allclose(vectors,np.dot(U,np.dot(D,Vh)))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7feee67149e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(s);" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7feeb2a794e0>]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7feeb2a463c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(s[:10])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "num_top_words=8\n", "\n", "def show_topics(a):\n", " top_words = lambda t: [vocab[i] for i in np.argsort(t)[:-num_top_words-1:-1]]\n", " topic_words = ([top_words(t) for t in a])\n", " return [' '.join(t) for t in topic_words]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['critus ditto propagandist surname galacticentric kindergarten surreal imaginative',\n", " 'edu graphics data space pub mail 128 3d',\n", " 'space jesus launch god people satellite matthew atheists',\n", " 'space launch satellite commercial nasa satellites market year',\n", " 'jpeg graphics space pub edu ray mail send',\n", " 'jesus matthew prophecy messiah psalm isaiah david said',\n", " 'launch commercial satellite market image services satellites launches',\n", " 'image probe surface lunar mars probes moon orbit',\n", " 'argument fallacy conclusion example true ad argumentum premises',\n", " 'probe data surface moon mars probes lunar launch']" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "show_topics(Vh[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### NMF for topic modelling" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "### scikit learn Implemntation of NMF\n", "m,n = vectors.shape\n", "d = 5\n", "clf = decomposition.NMF(n_components=d,random_state=1)\n", "W1 = clf.fit_transform(vectors)\n", "H1 = clf.components_" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['jpeg image gif file color images format quality',\n", " 'edu graphics pub mail 128 ray ftp send',\n", " 'space launch satellite nasa commercial satellites year market',\n", " 'jesus god people matthew atheists does atheism said',\n", " 'image data available software processing ftp edu analysis']" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "show_topics(H1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### TF-IDF for topic modelling" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "tfidf = TfidfVectorizer(stop_words='english')\n", "vec_tfidf = tfidf.fit_transform(newsgroups_train.data)\n", "W1 = clf.fit_transform(vec_tfidf)\n", "H1 = clf.components_" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['alt.atheism', 'talk.religion.misc', 'comp.graphics', 'sci.space']" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "categories" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['people don think just like objective say morality',\n", " 'graphics thanks files image file program windows know',\n", " 'space nasa launch shuttle orbit moon lunar earth',\n", " 'ico bobbe tek beauchaine bronx manhattan sank queens',\n", " 'god jesus bible believe christian atheism does belief']" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "show_topics(H1)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7feec9be61d0>]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7feec9bf6470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(H1[0])" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "43.712926058020408" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.reconstruction_err_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### NMF from scratch in numpy using SGD" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "lam=1e3\n", "lr=1e-2\n", "m, n = vec_tfidf.shape" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "W1 = clf.fit_transform(vectors)\n", "H1 = clf.components_" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['jpeg image gif file color images format quality',\n", " 'edu graphics pub mail 128 ray ftp send',\n", " 'space launch satellite nasa commercial satellites year market',\n", " 'jesus god people matthew atheists does atheism said',\n", " 'image data available software processing ftp edu analysis']" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "show_topics(H1)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "mu = 1e-6\n", "def grads(M, W, H):\n", " R = W@H-M\n", " return [email protected] + penalty(W, mu)*lam, W.T@R + penalty(H, mu)*lam # dW, dH" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "def penalty(M, mu):\n", " return np.where(M>=mu,0, np.min(M - mu, 0))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "def upd(M, W, H, lr):\n", " dW,dH = grads(M,W,H)\n", " W -= lr*dW; H -= lr*dH" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "def report(M,W,H): \n", " print(np.linalg.norm(M-W@H), W.min(), H.min(), (W<0).sum(), (H<0).sum())" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "W = np.abs(np.random.normal(scale=0.01, size=(m,d)))\n", "H = np.abs(np.random.normal(scale=0.01, size=(d,n)))" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "44.4259374728 1.27068916306e-07 4.76794046496e-08 0 0\n" ] } ], "source": [ "report(vec_tfidf, W, H)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "upd(vec_tfidf,W,H,lr)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "44.4180500987 -0.000721608595467 -7.22365555113e-05 161 278\n" ] } ], "source": [ "report(vec_tfidf, W, H)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "44.2428382591 -9.60396323815e-05 -0.000128273147195 38 3081\n", "44.2117699642 -8.93682416765e-05 -0.000165754324551 31 3925\n", "44.1863266994 -0.000126113870878 -0.000157219085405 47 4644\n", "44.1662683889 -0.000179765234281 -0.000143326736203 67 5588\n", "44.1502581862 -0.000194625849988 -0.000136445603008 67 6317\n" ] } ], "source": [ "for i in range(50): \n", " upd(vec_tfidf,W,H,lr)\n", " if i % 10 == 0: report(vec_tfidf,W,H)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['people space like just don god think know',\n", " 'god don people just think does like jesus',\n", " 'god just space don people think know like',\n", " 'god people don think know space just like',\n", " 'space know people like think just don god']" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "show_topics(H)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PyTorch to create NMF" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "import torch\n", "import torch.cuda as tc\n", "from torch.autograd import Variable" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "def V(M):\n", " return Variable(M,requires_grad = True)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "v = vec_tfidf.todense()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "t_vec = torch.Tensor(v.astype(np.float32)).cuda()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "mu = 1e-5" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "def grads_t(M, W, H):\n", " R = W.mm(H)-M\n", " return (R.mm(H.t()) + penalty_t(W, mu)*lam, \n", " W.t().mm(R) + penalty_t(H, mu)*lam) # dW, dH\n", "\n", "def penalty_t(M, mu):\n", " return (M<mu).type(tc.FloatTensor)*torch.clamp(M - mu, max=0.)\n", "\n", "def upd_t(M, W, H, lr):\n", " dW,dH = grads_t(M,W,H)\n", " W.sub_(lr*dW); H.sub_(lr*dH)\n", "\n", "def report_t(M,W,H): \n", " print((M-W.mm(H)).norm(2), W.min(), H.min(), (W<0).sum(), (H<0).sum())" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "t_W = tc.FloatTensor(m,d)\n", "t_H = tc.FloatTensor(d,n)\n", "t_W.normal_(std=0.01).abs_(); \n", "t_H.normal_(std=0.01).abs_();" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "d=6; lam=100; lr=0.05" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "44.3938102722168 -0.004070318304002285 -0.00039740739157423377 768 1357\n", "43.76396560668945 -0.009402437135577202 -0.01516595296561718 1540 17605\n", "43.73957443237305 -0.004607424605637789 -0.007575209718197584 1761 18589\n", "43.737876892089844 -0.005591616965830326 -0.00937887467443943 1768 19057\n", "43.737552642822266 -0.005662467330694199 -0.009697899222373962 1903 19531\n", "43.73701095581055 -0.003180568339303136 -0.0032958541996777058 2386 21032\n", "43.73679733276367 -0.0030161826871335506 -0.005725307390093803 2454 21942\n", "43.73655319213867 -0.0036901137791574 -0.005659892689436674 2359 22763\n", "43.73644256591797 -0.003632614854723215 -0.005929608829319477 2297 22852\n", "43.73622131347656 -0.003584711579605937 -0.005387790035456419 2609 29031\n" ] } ], "source": [ "for i in range(1000): \n", " upd_t(t_vec,t_W,t_H,lr)\n", " if i % 100 == 0: \n", " report_t(t_vec,t_W,t_H)\n", " lr *= 0.9" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['objective morality values moral subjective science absolute claim',\n", " 'space nasa launch shuttle orbit lunar moon earth',\n", " 'god jesus bible believe atheism belief christian does',\n", " 'thanks graphics files image file program windows know',\n", " 'don people just think like know say religion']" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "show_topics(t_H.cpu().numpy())" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7feeb34f6828>]" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7feeb34ddef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(t_H.cpu().numpy()[0])" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.40214815735816956" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t_W.mm(t_H).max()" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t_vec.max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PyTorch AutoGrad." ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variable containing:\n", " 1 1\n", " 1 1\n", "[torch.FloatTensor of size 2x2]\n", "\n" ] } ], "source": [ "x = Variable(torch.ones(2, 2), requires_grad=True)\n", "print(x)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
sequana/resources
coverage/09-comparison_cnvnator_bacteria/comparaison_CNVnator_bacteria.ipynb
1
1242922
null
bsd-3-clause
mdbecker/gdi_philly_ml
SVM_Tutorial.ipynb
1
171779
{ "metadata": { "name": "", "signature": "sha256:f0e61eaf7737a914a88c9a713a66e51f479c291020e4095c256108d16f5a9229" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<small><i>This notebook is based on a tutotial given by [Jake Vanderplas](http://www.vanderplas.com) for PyCon 2014. Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_pycon2014/).</i></small>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Support Vector Machines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Support Vector Machines (SVMs) are a powerful supervised learning algorithm used for **classification** or for **regression**. SVMs are a **discriminative** classifier: that is, they draw a boundary between clusters of data.\n", "\n", "Let's show a quick example of support vector classification. First we need to create a dataset:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets.samples_generator import make_blobs\n", "X, y = make_blobs(n_samples=50, centers=2,\n", " random_state=0, cluster_std=0.60)\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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TE8Px48dJTk6moKCAxYsXM3DgwArLMXDgQBYsWADAggUL\nrH6TGQwGcnJyANDr9axbt+6GMy/Koyyfe+DAgXz11VcAJCYmUrNmzatDRxWhLJnS09Ov/lvu2rUL\nRVGsjvs5kqOPU1k44zgpisLYsWMJDw/niSeesPoeRx+rsmRy9LHKzMy8OuvNaDSyfv16oqOjr3mP\nM76nypLrpo9VuS/ZXsfSpUuVkJAQxdvbWwkMDFTuuOMORVEU5dy5c0q/fv2uvm/16tVKixYtlNDQ\nUOXNN9+0d4xrXLx4UenVq5fSvHlzJS4uTrl8+XKpTElJSUpkZKQSGRmpREREVFgma5/7008/VT79\n9NOr73nkkUeU0NBQpU2bNjdcgsFRmT7++GMlIiJCiYyMVDp37qzs2LGjwjPFx8crwcHBioeHhxIS\nEqLMnTvX6cfp3zI54zht2bJFUalUSmRkpBIVFaVERUUpq1evduqxKksmRx+rP/74Q4mOjlYiIyOV\n1q1bK2+//baiKM7/v1eWXDd7rOTmIyGEcCGy9K4QQrgQKepCCOFCpKgLIYQLkaIuhBAuRIq6EEK4\nECnqQgjhQqSoCyGEC5GiLoQQLuT/AaZizpd2TjyWAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x58fc250>" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll fit a Support Vector Machine Classifier to these points:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.svm import SVC # \"Support Vector Classifier\"\n", "clf = SVC(kernel='linear')\n", "clf.fit(X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n", " kernel='linear', max_iter=-1, probability=False, random_state=None,\n", " shrinking=True, tol=0.001, verbose=False)" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To better visualize what's happening here, let's create a quick convenience function that will plot SVM decision boundaries for us:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_svc_decision_function(clf):\n", " \"\"\"Plot the decision function for a 2D SVC\"\"\"\n", " x = np.linspace(plt.xlim()[0], plt.xlim()[1], 30)\n", " y = np.linspace(plt.ylim()[0], plt.ylim()[1], 30)\n", " Y, X = np.meshgrid(y, x)\n", " P = np.zeros_like(X)\n", " for i, xi in enumerate(x):\n", " for j, yj in enumerate(y):\n", " P[i, j] = clf.decision_function([xi, yj])\n", " return plt.contour(X, Y, P, colors='k',\n", " levels=[-1, 0, 1],\n", " linestyles=['--', '-', '--'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.scatter(X[:, 0], X[:, 1], c=y, s=50)\n", "plot_svc_decision_function(clf);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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p2LFjB2+++R6HDh2mQoUKPPvsREaMGFEoIWkwGChfPpS0tIHkXuYgE1/fRSxbNhe9Xs9j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\n9sGsWTQxGKiX47EAYJDJxBf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"text": [ "<matplotlib.figure.Figure at 0x4f11b50>" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the dashed lines touch a couple of the points: these points are known as the \"support vectors\", and are stored in the ``support_vectors_`` attribute of the classifier:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.scatter(X[:, 0], X[:, 1], c=y, s=50)\n", "plot_svc_decision_function(clf)\n", "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n", " s=200, facecolors='none');" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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qtTqji6Fv3768++67Zsdfu3aNS5cuZRntkNP9JENDQxkzZgwnT55EpVIxd+48\n3nrrY7TaXoAvaRs9RKLRbGLXrm00b96cmJgYqlatyoULF0rUeO38JH3awqKwsDC++24Z0dF3qVOn\nBsuWLaNOnTqMGTOGDh06ZKw7HRUVxZIlS5g7dy4DBgxg2rRp+bqpQWGTkJDA+fPnswRwTEwMlSpV\n4qWXzFf7W716NUOHDs3Sx+vj40O7du0YMWKExfMnJSU9UvjmlqIoBAYG8u2339KmTRsURWHWrNl8\n+OEnqFSemEwG3NwcWLRoLl27dgVgxowZnDhxwmxrQWE7EtrigSIjI2nRogUffvghQ4cOve9x0dHR\ndO3alaCgIGbMmJGPFeatW7dusWfPniwjHWJjY6levToTJ040O/73339n3Lhx+Pj4ZLnR1rhxY557\n7jmz4wv7aIdVq1YxadIkDhw4gJ+fHwB6vZ7jx4/j5ORE/fr1M/6R/vvvv+nUqRO//fYb9erVK8iy\nizUJbfFA7du3p2PHjkyYMAGAe/fusXTp9+zatQdfX2+GDRtMy5YtUalUxMbG0qRJE5YsWVJoN7O9\ncuUKP/zwg1lLOCAggMWLF5sdf/ToUaZNm2bWEq5evfoD1+ooTj766COWL1/OTz/9ROPGjc2eN5lM\nbN68meHDh7Nw4UKeffbZAqiy5JDQFvd15swZ2rRpw5UrV3BycuLs2bO0bBmMTlcerfZxVKpENJrj\nvPBCLxYs+BaVSsW8efP4/fffWbt2rU1qSO/z1el0GS29zC5evMjHH39s1idcq1Ytdu/ebXZ8REQE\nixYtytIK9vHxoXz58nLz7AGWLl3K5MlpKyEOHTo0y4JR8+fPR6PR8NVXX9G6deuCLrXYk9AW9zV2\n7Fjc3Nz4+OOPAQgMbMiZM/4oyhOZjtLj6voDP/44l+7du5OQkEClSpU4efJkluGB6eGbHqpGo5Em\nTZqYXfPChQsMHjw4SwA7OTnRsmVLi9uB3b59m61bt2aZeJH+J6/7fEsao9FISEgIq1atIioqCgcH\nBypXrszQoUNp3rx5oe7mKU7yJLT1ej2tW7fGYDCQnJxMjx49+PTTT3N0YVF4tGrVig8//JDg4GBO\nnTpF06bBaLWjACNpQwLT/5whMDCZkyePAWldKhMmTKBjx45cuHCBli1bZoRvesu2Xr16LFu2zOya\niYmJHD16NOO4/LjhJkRRkieTa5ydnTNW+jIajbRs2ZK9e/eWmD7AoiQlJYUTJ06YTSsG0Gq1Gau1\nRUVF4ehkuPubAAAgAElEQVToS9quL3NJWz/EBdAACjdvJmWc09XVFa1WC6RtPHDs2LEch6+bmxtB\nQUE2fY9ClARWz4hM/2FPTk7OWJBF5D2DwcDmzZszwjf9vyaTyWLLNi4ujiFDhpit7eDv74+XlxfR\n0dEA1KpVC4PhGmkh/b8s57C330uXLv4ZX9+5cwcvr7T9Fh0dHS0ucSmEsC2rQ9tkMtGoUSMuXrzI\nqFGjLC6LKP5zv6FfycnJzJw506wlbDAYCAsLMzs+NTWVNWvWUKpUKXx8fChXrhyBgYH3Xava19eX\nY8eOWXxOq9WyevVqunTpQvny5enS5Rm2bv0Vg6Ez/32L3ECtPsxbb80C0oYInj17lieffNLiOYUQ\necNmNyLj4uLo2LEj06dPzzIMrLj2aWe+4VapUiWz51NTU3nllVfMlppMSEggMTHRLLhTU1OZNGlS\nlhlt6WOAAwMD8/S9xMTEUK1aNc6fP4+vry+JiYn06tWXvXsPYGdXFTu7JBTlJkuXLspYoW3ixIno\ndDrmzJmTp7UJUVLly+iRjz76CBcXl4yxvukXnjJlSsbXwcHBhWpsr6IoJCUlZQnXNm3amM32UxSF\nNm3acOfOnYxjHR0d8fb2JiIiwuKSo0uXLqVUqVJZbrZ5e3sXyv31xowZw+3bt1m9enXGez99+jQH\nDx7Ey8uLzp07Z2yzlD654sCBA1SrVq0gyxai2AgNDSU0NDTj6w8++MD2oR0dHY2DgwNeXl7odDo6\nduzIlClTaNeu3X8XyMeWdnrLN3vrdsiQIRYXPq9evTpXrlzBwcEhy6SKkJAQi9so7d+/H09Pz4yW\ncGEM39zS6/V07tyZsmXLsnjx4vvumr5nzx769u3LvHnzLO74IYSwjTwZPXLz5k0GDRqEyWTCZDIx\nYMCALIFtrRMnTnD79m2zft7Jkydb3A6rXr16GI1Gs90sDAaDxdDeu3cvnp6eOQ7fp556yur3VFg5\nOzuzbds2Ro4cSaVKlRgwYAADBgygQoUK6PV6Dhw4wNy5c7l48SLLli2zuDuIEMWZ0Wjk7t27Zo3C\n7LNu0/8+bNgwRo4cafM6CvXkmi5duqDX682mFg8fPjzXexiKh4uMjGThwoVs3LiRmJgY1Go1tWrV\nYsSIEXTv3l12nxFFmqXwfVgAx8TEZCzglfl+U/YGYuavK1eubNUKiDIjUghRrKSHr6WAzUn4Zp9Z\nm7lhaOlxDw+PfF3ZUkJbCFEo3S98HxTG9wvf7K3f9FZxQYZvbkloCyHyVObwfVjgZg7mxMREvLy8\nctTazfx1UQnf3JLQFkLkSE7DN/vX6eH7oMC19PfiHr65JaEtRAnzoPB9UBhbavk+rPVbElq++U1C\nW4giKnv45rT1K+FbtEloC1HAjEYj9+7dy1HgPqzlm5NhZ56enhK+RZiEthA2Yil8c3LjLXP4Pqyv\nN3MoS8u3ZJLQFiKb9PB9lJEOMTExGeFrqbX7oACWlq94FBLaotjKSfhaei57y/dBkywy/13CV+QH\nCW1R6N0vfB/WErYUvjmZZCHhKwozCW2Rb3ITvrGxsSQkJGSsovgokywkfEVxJKEtHtmjjPPNfsPN\n09Mzx5MsJHyFMCehXYI9bJzvg0Y7ZG/55mScr4SvENaT0C4GHjbO92Et35xMKZbwFaJwkNAuRB5l\nksX9wtfSeN77BbCXl5eErxBFjIR2HrBmkkX28M3+d0thLOErRMkhof0A1kyy8PT0fORJFhK+QoiH\nKRGhnZqa+sjr+cbExJCQkJDrSRb29vb58t6EeJCkpCR+/PFHFi5cyLlz50hJSaF06dL07t2bkSNH\nUqNGjYIuUTyiPNnYN69duHCB6OjoHA81S0hIwMPD474hW6NGDYthLOErirIff/yR1157jVatWvHJ\nJ5/QtGlTnJycuHr1KsuWLSMoKIh27dqxZMkSXFxcCrpcm9LpdOzatQutVkuLFi3w9/fPs2spioKi\nKBZ/S96wYQMHDhzIkksDBw5k6NChNq/Dqpb21atXGThwILdv30alUjF8+HBef/31rBewoqXdpk0b\ntFptjkY7pPf5SviKkmTRokV8/PHHhISEULduXYvH6PV6hg0bxrVr19i+fTvOzs75XGXe+HHlSsaM\nHEkZOzucFYWLyck836cPC5YseeDm04qioNVqiY2Nxd3dHS8vL7NjFi1axNatW7OEcGxsLAsWLGDQ\noEFmx69bt46IiIgs+VS9enXKly+f6/eXJ90jUVFRREVF0aBBAxITE2ncuDE///wztWrVeuiFhRDW\nOXLkCF26dGHv3r1Uq1YNgNjYWDZt2kRCQgJBQUE0bNgQAJPJRL9+/ShXrhxffvllQZZtE/v376fr\n00/TS6fDA9ABcUCYWk334cOZ/dVXWY6fMWMGy5YtywhfOzs7vL29mTFjBv3797d4/qioKLNGYn7+\nppIvfdrPPvssr732Gu3atXvohYUQ1hk8eDCBgYG89dZbAMyfN4+3xo2jmr09LkYj5+3tadCkCRtD\nQnBzcyMqKopatWoRGRmJp6dnAVeflV6vN+sKjYmJoVGjRjzxxBNmx9esVo0LFy9iB7j8+0cD1AN2\nu7hw4/Zt3NzcMo6PiIggMTGxQMI3t/I8tCMjI2ndujWnTp3K8mFJaAthezExMVSrVo3z58/j6+vL\nH3/8Qa/OnXlRq8X732NSgW1qNQE9erBi9WoA+vXrR4sWLXjttdfytL7r168THh5udt+pTZs2dO7c\n2ez4Dz/8kAULFpjdb+rTpw/t27c3O97fz49ud+5Q1sK1l3h4sOWPP6hfv34evLP8k6c3IhMTE+nd\nuzdffvlllsAWQuSNffv20axZM3x9fQGY9emnNMsU2AD2wNMGA99u3sydO3cyRpMsX778kUP71KlT\n7Nu3z2wgQLdu3RgyZIjZ8du3b2fFihVmqyv6+PhYPP/kyZOZPHlyjusp4+dHgoXQNgJxyckZn0tx\nZHVop6Sk8Nxzz/HSSy/x7LPPWjxm6tSpGX8PDg4mODjY2ssKUaLFxcXh7f1fRJ8+dYqnsx2jAE6A\nn1pNREQEpUuXxsfHh/j4eA4ePMiGDRvMtjfr27cv77//vtn1Ll26xKFDh/D29sbX15caNWrg4+ND\nnTp1LNY3dOjQPBk5kW74a68xc/x4Hk9KyhJih+zsaNiwIRUqVMiza+eV0NBQQkNDH3qcVd0jiqIw\naNAgfHx8mDNnjuULSPeIEFZRFAWdTkdMTAz29vaUL1+eDRs2sGzZMjZt2gRA/cBAok+fxhHQknZj\nTgc0AM47O3MyPJyKFSuyZcsW5s+fz+TJk9mzZ4/ZCKwKFSrctzVcmBiNRp7r3p1jf/5JvcREXIAL\nGg1Rrq7sPXiQKlWqFHSJVsuTPu29e/fSqlUr6tWrh0qlAuDTTz+lU6dOD72wECVNevhmbtl6enrS\nqFEjs2O3bt3Ku+++m3EsgI+PD4MGDWLatGlcvnyZRo0acfXqVTQaDV9//TUfvPUWHQwGvPjv5twB\ne3to3pzf/vwTgNGjR1OmTBmmTJmSf288j5hMJkJCQvjhu+9IjI+n/TPPMGToUItD+IqiEjEjUoj8\nkpyczK1bt8wmeZUrV45u3bqZHf/zzz/Tr18/gCxLHjzzzDMZoz8yi46O5vr16xnHWhrt0K1bN3r2\n7MmQIUNQFIVXR45kzYoV1NXrcTGZuOTmht7Liz8OHMDf35+EhAQqVarEyZMnrRo/LPKHhLYQD3Dv\n3j2OHj1qdqOtUqVKjBkzxuz4bdu2MWzYMLM1Z5566imLN+YMBgMmk8mmQ81+/fVXRo8eTVhYWEb/\n9uHDh1mxbBlxd+/SpkMH+vTpkzGZ5u233+by5cus/nckiSjcJLRFiXLz5k1CQkLMxv1Wq1aNGTNm\nmB3/119/8c4775iNdggMDKRr164F8A5yZvz48ezdu5eQkBBKly5t8RhFUfj0009ZunQp+/fvv+9x\nonCR0BZFQnJyMk5OTmaPX7lyhW+++cZs3G/16tXZsGGD2fFnz55l5syZZkvdVqpUyeJkjcIgPDyc\nRQsWcCUignqNGzPslVcoU6bMA1+jKAqTJk1i8eLFvPLKKwwfPpzHHnsMSGvdr1u3jm+//Ra9Xk9I\nSIh0ixQhEtoiX6Wv7aDX6zOmWGd26dIlxo8fb9YdUb9+fQ4ePGh2/I0bN/jhhx/MRjuUKVOGsmUt\nTbEoWhbMn8/b48bRwGjEOyWFm87OnLO3Z2NISI6GyJ49e5Z58+bxww8/4OLigpOTE9HR0TRv3pzR\no0fTtWtXHBwK9fpwIhsJbfHIMo92SP9jNBp5+unsI4LTQrhHjx4ZAawoCj4+PjRu3JjNmzebHX/v\n3j1+++03s0XAisL0Ylu7cOECT9SrxyCdLsvkmAhgq6cn16KicrzIk8Fg4Pbt2yQnJ2es3S6KJgnt\nEi41NZWbN2+adS+YTCZGjBhhdvzly5epWbMmQJY+3ho1arBw4UKz4/V6PeHh4RkBrNFo8vw95TVF\nUdi9ezcLv/2W21FRNG3RgtGvvprR/WAr773zDvvmzKFdSorZcz+5u/PRd9/Ru3dvm15TFH5Fcj1t\ncX8pKSns27fPbG1xo9HIrFmzzI6PiYmhadOmZms7VKxY0eL5K1asSGxsLC4uLhlj8B/E2dmZevXq\nWf2+8puiKJw8eZKEhATq1q2Lu7t7xuOvjxnDuuXLaZiURFlg7+HDLJg7l82//EJQUJDNargWGYmX\nhcAGKJWczM2bN212Lb1ez86dO4mPj6dZs2ZUrVrVZucW+UNCu5BISUnh+++/N2sJGwwGtm7dana8\nwWBgypQpWdZ0SO/jtcTPz48bN27kuB47O7ti0Vp+kH379jHkpZe4d+cObvb23E5JYcyYMXw0bRq/\n/vora5ct42WtlvSOiZrJyVROTqbfc89x5eZNm63d3qBJE37asgV0uiyPK8A1R0cCAwNtcp0NGzYw\nbPBg/FQqXE0mLhqNtO/QgeU//VQiu6WKKukesZJOp7P4DZ+amsrEiRPNWsLx8fFcunTJrPVqNBoZ\nMWJERgBn3ndS1mqxvfDwcJ5s1IgOSUnUAlSkrce83sGBGDs7DCkpOCoKzYCWpC2+lO57d3eW/Pwz\nbdu2tUktMTEx1Hj8cTolJJC+KZgCHLCz41qVKpwMD8/RbzsPcuzYMdq0aMHzWi3pq3KkACHOzjTo\n1YvvV6606vzC9qR75CEy33CrU6eO2Q+Joig8//zzREdHZ2kJK4pCQkKC2U4Z6YusV61a1exmmyUO\nDg4sWbIkz96fyGrWZ5/RwGCgdqbHPIHeRiNzgbdIC/EdwEYgc4+yl0pFdHS0zWrx8fEhZMcOenbt\nyqGUFHyMRq7a2+NVrhy/7NpldWADzJo+nSZ6PZmXUXIEOun1fLN+PTO++ELGbxcRxS60M4dverB2\n69bN4vZDzZo14+rVq1lGO3h7e3Pw4EFcXV2zHKtSqRgwYAAeHh5mox0s/VCpVCreeeedPHufwjp7\nQ0NpYTSaPe4B+AC3gMeAfsA3wE2gHGlLf15KScnYEcZWmjdvztWoKLZv387169epXbs2QUFBNgls\ngKOHD/OUyWT2uAtQztmZs2fPSmgXEYU6tG/cuEFMTIxZF8Po0aMtrttdsWJF7ty5Y7Z/ZLt27Szu\n1PHdd99lhHBObrj16NHDZu9NFCx3Dw+SLDyuAEmA+t+vHYFAIBzwBX5Vq2kZFET16tVtXpOjo6PF\ndUtswa9MGWIvXiT7gqWpQGxyMpGRkXy3cCF3Y2Jo/fTTvDxkiAwXLKQKdZ/2k08+aXFj34kTJ1r8\nhtLr9ajVapu1TkTxNX/+fL4YP56+Wi2Z99Y+DYQCo0jr5wb4BbisVqNTqQhq3ZqVa9bg4eGRzxVb\nZ82aNbw1ZAgvJSWReb7pYZWKQ56eqFJSqK/V4qooRGo0XHd2Zs/+/RnDPkX+k3HaQmRiMBjo2LYt\nN48do7FWiytwBjgK9AfSB0ImA9+q1bz/ySf06NHD4uzOosBkMjHoxRf5fcsW6icl4QpccnHhop0d\napOJITpdxm8XAIdUKm4GBvL3iRMFVXKJJ6EtRDbJycksXbqU7xcsICE+HoPRSOqtW7TX6ylLWj92\nqEZDsx49WP7jjwVdrtUURWHXrl0sW7yYe7GxtG7fnm2bNlFq/36y76ZoAr7VaPjz8GFq1apVEOWW\neBLaothSFIXQ0NCMdaK7du2KWq1++AuzMRqNzPj8c76ZM4cb0dFULFOG18eP581x42w2JruwqVuj\nBs3On8ffwnOrPD2Z//PPMuS0gEhoi2LpypUrdG7XjoSoKPyNRu46OhJtZ8e6TZto3bp1rs9rMpmw\ns7N7+IGFxIEDB5j56aecOH6ccuXKMWrsWPr27YtKpeLGjRuEhobi6OhIhw4dstyUH/Tii9z86Sda\nZBtZoge+Uau5cPnyQ1caFHlDQlsUO4qiULdmTfwjImiWmppx4/AisMXVlbMXL5aIwFm+bBlvjh7N\nkzodlRSFGOAvV1fa9OyJm6sry77/nmpOThiBSKORjz7+mLHjxgH/Tbrpo9WSvmirEdjq7Eytbt1Y\nuWZNAb0rIaEtip09e/bwUteuDE1MJPt4oW3OzvSYNImJ//tfgdSWXxISEqhQtiwDtFr8Mj1uAL6y\ns8MLeMlkIn3O7l1glUbDwh9/zBjCum7dOl55+WXK/Tu9/YLJRFDr1qxat85svoLIPzIjUhQ7p0+f\nxj9TCzuz8no9//z9d77XlN+2bdtGJXv7LIENaT/YqSYTzwKZF1koBQRrtUybOjUjtHv37s0zzzzD\n9u3biYuLo1mzZgQEBOTPGxCPzOrQHjJkCFu3bsXPz48TMjxI5KMKFSoQe5+F/WMdHXmySpV8rij/\nJSQk4JKaavZ4EmAHWJrj+Diw7ezZLI+5uLjQs2fPvChR2JjVd1pefvlltm/fbotahHgknTp14p6D\nA+HZHo8Fjjk4MGz48IIoK1+1aNGCC4pC9gn5zqSNMddaeE0s4FOqlM1qOHfuHG9PmMBLffsyZ/Zs\nYmNjbXZuYc7q0A4KCqKUDb8BhMgpJycnNoaE8Iu7OyEuLhwBfnN0ZJmLC5/Pnk2NGjUeeg5rJCUl\n8dtvv7Fnzx4MBkOenH/Tpk2sXr36vsvq1qpVi1bBwYQ4O2cEtBE4BDgBf2Y7PhXY5+LCK6NH26TG\nL+bM4cmGDfnryy+JX7OGFe+/T43HH+fQoUM2Ob+wQLGBS5cuKXXq1LH4nI0uIcR9RUdHKzM+/1x5\nsU8f5Z233lLOnz+fp9czmUzK59OnKx4ajVLdw0Op4uGh+Hh4KEuXLrXZNRYvXqx4aDRKLXd3pb67\nu+KmVisjhw1TjEaj2bFJSUnK4BdfVFzVasXfxUVxBuVxUF4BpTQo1UHpCUo3UCo6OyvBLVooer3e\n6hr/+ecfxcvFRRkLytRMf/qAUsHPz2KtIuful502GT0SGRlJt27dLPZpy+gRUdwsXryYKW+8QW+t\nNmNPxyhgnUbD8nXr6Ny5s1Xn/+233+jbvTv9tNqMPmkdsFGj4fnXX+fjTz+1+LqYmBhOnDhB7x49\naBMfTx3SukhOkDY9P0GtZtEPP9CzZ0+bbPL76qhRnF60iNYW+tSXubszf906OnToYPV1SqoCHT0y\nderUjL8HBwfLDCtRZCmKwkeTJ9MpU2ADlAXaaLV8OGmS1aE9/YMPCMoU2JA2AqSTVsu333zDpClT\nLG70m75hxq49e+jWqRNHtFr8jEZuOjigLlWK33butGmX0ZWICHwsBDaAr8n0SDslCQgNDSU0NPSh\nx+V7aAtRlN29e5fomBgs7axZHdj0zz9WX+PYP/8wwMLj3qQtGXv16tUHLg3boEEDIq9f59dffyUy\nMpIaNWrQpk0bm8/wrNe4Mbv37KFOtv58E3AFcrVmyc2bNwkPD6d8+fJ5svxtYZa9QfvBBx9YPM7q\n/4v9+/fnqaeeIjw8nIoVK7J06VJrTylEoaXRaFBIm+adXTzgYYPJKL4+Pty18LgBSExJwdvb28Kz\nWdnb29O5c2dGjRpFu3bt8mRK/sjRoznt4EBkpscUYJ+DAxWqVKFp06Y5PldCQgJ9evakZpUqDO/R\ngyfr16dZo0ZERETYuuwiT2ZECvGI+vTsyZ0tWwjO1DWgAFudnGg1ahSzvvjCqvPPmT2bBe+/Tx+t\nNsvelH/Y2+Pcpg1bd+606vy2tHPnTvo+9xxlAa+UFC47OODj788vu3ZRoUL2LRfur31wMPcOHqS9\nwYCatFEuh+zsOOHry7mIiBI5M1OmsQthI9evX6f5E09QOi6O2jodqcAJjQalQgX2hoWZbdCh1Wq5\nc+cOrq6uODs7W9x1KbPk5GS6depEeFgYdZOSUAPnNRruenqyLywMf39La/IVHJ1OR0hICLdu3aJu\n3bq0atXqkTYiOXbsGO1btGBUtn+kANa7uvLq7NkMLwFj7rOT0BbChmJjY1m4YAGb1q7Fwd6ePgMH\nMmTIkCwtwsTERMa++iqrVq1CZTSiN5m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"text": [ "<matplotlib.figure.Figure at 0x78058d0>" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The unique thing about SVM is that only the support vectors matter: that is, if you moved any of the other points without letting them cross the decision boundaries, they would have no effect on the classification results!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "clf = SVC(kernel='rbf')\n", "clf.fit(X, y)\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50)\n", "plot_svc_decision_function(clf)\n", "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n", " s=200, facecolors='none');" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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yyM7OjujovG9I0tMqdO+R1157jY8//pjU1FS+/fZbAgICclfwHPUeyc/169fx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\nArgAHLC2ZvGKFQZrR6fTPTDMUlBQQBqwCrgMDAOGUrglnKW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"text": [ "<matplotlib.figure.Figure at 0x6566290>" ] } ], "prompt_number": 32 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Digits Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll take a look at another dataset, one where we have to put a bit more thought into how to represent the data. We can explore the data in a similar manner as above:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_digits\n", "digits = load_digits()\n", "digits.keys()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "['images', 'data', 'target_names', 'DESCR', 'target']" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "X = digits.data\n", "y = digits.target\n", "print(X.shape)\n", "print(y.shape)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(1797, 64)\n", "(1797,)\n" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "print digits.data[0]\n", "print digits.target" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0. 0. 5. 13. 9. 1. 0. 0. 0. 0. 13. 15. 10. 15. 5.\n", " 0. 0. 3. 15. 2. 0. 11. 8. 0. 0. 4. 12. 0. 0. 8.\n", " 8. 0. 0. 5. 8. 0. 0. 9. 8. 0. 0. 4. 11. 0. 1.\n", " 12. 7. 0. 0. 2. 14. 5. 10. 12. 0. 0. 0. 0. 6. 13.\n", " 10. 0. 0. 0.]\n", "[0 1 2 ..., 8 9 8]\n" ] } ], "prompt_number": 37 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The target here is just the digit represented by the data. The data is an array of length 64... but what does this data mean?\n", "\n", "There's a clue in the fact that we have two versions of the data array: data and images. Let's take a look at them:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print digits.data.shape\n", "print digits.images.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(1797, 64)\n", "(1797, 8, 8)\n" ] } ], "prompt_number": 38 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that they're related by a simple reshaping:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print np.all(digits.images.reshape((1797, 64)) == digits.data)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's visualize the data. It's little bit more involved than the simple scatter-plot we used above, but we can do it rather tersely." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# set up the figure\n", "fig = plt.figure(figsize=(6, 6)) # figure size in inches\n", "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n", "\n", "# plot the digits: each image is 8x8 pixels\n", "for i in range(64):\n", " ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n", " ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')\n", " \n", " # label the image with the target value\n", " ax.text(0, 7, str(digits.target[i]))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Dzblz57Bp0yY0NzcjGAzi4YcfxhtvvIElS5Zc99+ZiV8reuy0nJwcrF69GnPm\nzMGAAQMQDoeRkvLdf8NpRX5Xrlxp+H0LCwvFfVJf1LKEYwkEAjf9b7RMZynbT8u21s6T0fidJPXF\nSCQiHqNlP9qZ1fn111+jpKQEVVVVGDhw4Hf2a9dMysQ1W+zdzr+XkpKCEydOoLW1FXPnzsX+/fuv\nuw4bNmwQj5WeK7NnzxaP+fWvf204RjPi/sSXkZGBjIwMTJkyBQBQUlKChoYGxwJzw549ezB58mSk\np6e7HYot6uvrMW3aNNx6661ITU3FokWLcPjwYbfDskV5eTnq6+tRV1eHwYMHY/z48W6HZIuRI0ei\npaXl2v9vaWlBRkaGixHRzXz77bd46KGH8Oijj/ap6R92CgaDuO+++1BfX+92KLaIe+AbPnw4MjMz\ncfbsWQD/+C0sLy/PscDcUF1djdLSUrfDsE1OTg6OHDmC9vZ2RKNR1NbWeuarik8//RQA8NFHH2Hn\nzp2e+Xq6qKgIH374IZqbm9HR0YGXX34Z8+fPdzssEkSjUSxfvhyhUEj9VJeMLl26dO1TW3t7O956\n6y2Ew2GXo7KHoQnszz33HJYsWYKOjg5kZ2dj27ZtTsWVcFeuXEFtbS22bt3qdii2KSwsxNKlS1FU\nVISUlBRMmjQJTzzxhNth2aKkpASXL19GWloann/+eQwaNMjtkGyRmpqKLVu2YO7cuejq6sLy5cs9\nkT1dWlqKuro6XL58GZmZmVi3bh3KysrcDsuyQ4cO4aWXXsKECROuDQrr16/HPffc43Jk1n388cdY\ntmwZuru70d3djR//+MeYNWuW22HZwtDAV1hYiGPHjjkVi6sGDBiAS5cuuR2G7VatWoVVq1a5HYbt\n3n77bbdDcMy8efMwb948t8OwVXV1tdshOGL69Ono7u52OwxHFBQUeO7nrB6s3EJERL7CgY+IiPwl\nqohEIlEAnnlFIhFPts2r7erdNrYrOV7si8n38nq7YglEo9EoiIiIfIJfdRIRkb/wq87kf3m1Xb3b\nxnYlx4t9MfleXm+X4a86A4EAEvFNqFbWSyunZLS0UO/2mGmbFKeZsmRaKSijE2GttstMyTLtGG0x\nVKOLYfa0x+6+KJXJ0/qbRioHJfVRK+0ys/Cx1qfsLKtntS9qcUrnWDsfdk4qd+qaSe3S7hWtzGMi\n7zFtoWWpio1W3cbOBbq19vCrTiIi8hUOfERE5Csc+IiIyFc48BERka8YqtVplZQ4oP1Amuj1wLT1\nu+rq6gwVH/C7AAAgAElEQVRtB+R12vrSgrDa2nonT56MuV1bm64vreEmkZJOtOuiJe1ISRTaMU6Q\nEiW0e8zM33PqGmv3n9QXtbUVtUQKs+t2aqTztX37dvEY6V7SYtf2SefQiWumrfEnXS9pO6BfEy1B\nyCh+4iMiIl/hwEdERL7CgY+IiHyFAx8REfkKBz4iIvIV27M6tSyfsrKymNsrKyvFY7SMQzvL2/TQ\nMp9Gjx4dc7uWidaXMhylzL61a9ca/lt2lpJzg5QhpmWOae1K5HXW4pCyUrXsUu3vSX3bjaxkKftR\nyxLUnkd2ZglaIV0b7bpo11O6N+0s39ZD6/fBYDDmdrPtYlYnERGRSRz4iIjIVzjwERGRr3DgIyIi\nX+HAR0REvsKBj4iIfMX26QxaymxFRYXhYwKBgLhPSou1kvaqTU2QaCnTWjHZRNNWTZdEIpGY2/vS\nlAVpmoY25UK6zto5On/+vLgvkefDzOrbWtq5mekRTtHuXWk6lEY7V05MZ9CeBRIzfcfs9bSbmdXU\ntaLiZoupG8VPfERE5Csc+IiIyFc48BERka9w4CMiIl/hwEdERL7CgY+IiHzF9HQGKVVcq5QupVqb\nTfl3Ih1ZihGQU90XLlwoHiNN4dBWnXCKmVRh6Zi+NIVD6otmVp0wy4nVGaT+pvV77f6TmJnC4xSt\nbdI+rV9nZWWJ+6R2a8+AviIZVp2Qpqlp09fMrBRi5nrxEx8REfkKBz4iIvIVDnxEROQrHPiIiMhX\nOPAREZGvBKLRaFTcGQhA2R3Tq6++anifllWmZakZja13e8y0TWImq6ypqUk8xmiR2XjbJZ3ncDhs\n6P2s2LZtW8ztUiZaT3vsvF4aLSNVy6ST+oCU7RlPu6SsTq1/SDFqBbu1wtzacbE4dY+ZpWUQSu2W\n2hzPNZMKM2sZxkavPwAMGTJE3PfFF1/E3G6lLyaKlu0u9W1pXNHaw098RETkKxz4iIjIVzjwERGR\nr3DgIyIiX+HAR0REvsKBj4iIfMV0kWqJlg4u7dNSpsvKyqyGZBspnVZLc5doUyCMTmeIl/R3R48e\nLR5z/vx5W2OQrnWiC+tKae67du0Sj6msrBT3OVGkWvqb2ntJU1a0eyzRRcU12tQmo+nsgH6fSX1b\nmpIQj7vuuivmdm06g5li5MFgUNznRF80Q7qW2jQNreD0ypUrY243U3yfn/iIiMhXDA98XV1dCIfD\neOCBB5yIxzVjxozBhAkTEA6Hcccdd7gdjm2+/PJLlJSUIDc3F3feeSeOHTvmdkiWffDBBwiHw9de\nwWAQmzdvdjss26xfvx55eXkoKCjA4sWL8c0337gdki2qqqpQUFCA/Px8VFVVuR2ObWpqapCTk4Nx\n48ahurra7XBs5dVrZnjgq6qqQigUQiAQcCIe1wQCAezfvx+NjY04evSo2+HYpqKiAvfeey9Onz6N\ngwcP4l//9V/dDsmy8ePHo7GxEY2NjTh+/Dj69++vromYTJqbm7F161Y0NDTgnXfeQVdXF3bs2OF2\nWJa9++67eOGFF3Ds2DGcPHkSu3fvxrlz59wOy7Kuri48+eSTqKmpwfvvv48///nPtv884BavXjPA\n4MB34cIFvPnmm3jsscdcL23jBK+1qbW1FQcOHEB5eTkAIDU1Vf1tIBnV1tYiOzsbmZmZbodii0GD\nBiEtLQ1tbW3o7OxEW1sbRo4c6XZYlp05cwZTp05Fv379cMsttyASieCVV15xOyzLjh49irFjx2LM\nmDFIS0vDzJkzcejQIbfDsoVXrxlgcOBbuXIlnn32WaSkeO+nwUAggNmzZ6OoqAhbt251OxxbNDU1\nIT09HWVlZZg0aRIqKirQ1tbmdli22rFjBxYvXux2GLYZOnQonnrqKYwaNQq33347Bg8ejNmzZ7sd\nlmX5+fk4cOAAPv/8c7S1teGNN97AhQsX3A7LsosXL173j6709HRcunTJxYjs49VrBhjI6ty9ezdu\nu+02hMNhU1mMGi3jbM2aNba+l+TQoUMYMWIEPvvsM9x9993IycnBjBkzrvtvpAKqWiZaRUVFzO1S\n9pedOjs70dDQgC1btmDKlClYsWIFfvWrX2HdunXX/XdaVpyU/ai1WcsqszODsKOjA6+//jo2btxo\n+Fgp/sLCQvGYRGSenjt3Dps2bUJzczOCwSAefvhh/Pa3v8WSJUviikPKSNQyFRPRrpycHKxevRpz\n5szBgAEDEA6HY/4DWnu2aP1UomVISxmERrKqb/zJJzc3F//3f//3nft7wYIF4t+QCk5HIhHxGLuf\nwbHEc820jErpGaedXy3jU7s3jYr7o9vhw4fx2muvISsrC6Wlpdi7dy+WLl1qWyBuGzFiBIB//Itt\n4cKFnvidLyMjAxkZGZgyZQoAoKSkBA0NDS5HZZ89e/Zg8uTJSE9PdzsU29TX12PatGm49dZbkZqa\nikWLFuHw4cNuh2WL8vJy1NfXo66uDoMHD8b48ePdDsmykSNHoqWl5dr/b2lpQUZGhosR2cuL1www\nMPA988wzaGlpQVNTE3bs2IGZM2fixRdfdDK2hGlra8NXX30FALhy5Qr+9Kc/oaCgwOWorBs+fDgy\nMzNx9uxZAP/4PSwvL8/lqOxTXV2N0tJSt8OwVU5ODo4cOYL29nZEo1HU1tYiFAq5HZYtPv30UwDA\nRx99hJ07d3riK+qioiJ8+OGHaG5uRkdHB15++WXMnz/f7bBs48VrBliYwO6lrM5PPvnkWlZgZ2cn\nlixZgjlz5rgclT2ee+45LFmyBB0dHcjOzhbXw0s2V65cQW1trWd+j+1RWFiIpUuXoqioCCkpKZg0\naRKeeOIJt8OyRUlJCS5fvoy0tDQ8//zzGDRokNshWZaamootW7Zg7ty56OrqwvLly5Gbm+t2WLbx\n4jUDTA58kUhE/f452WRlZam/WSWzwsJCT8zdu9GAAQM8k0Rwo1WrVmHVqlVuh2G7t99+2+0QHDFv\n3jzMmzfP7TAc4dVr5r30TCIiIgUHPiIi8peoIhKJRAF45hWJRDzZNq+2q3fb2K7keLEvJt/L6+2K\nJRD1WrkSIiIiDT/xJf/Lq+3q3Ta2Kzle7IvJ9/J6uwx/4gsEAmL9SmnGvlaZ4+TJk+I+M6RqCFKF\nh97tkdqmVZGRKrdoVTHMZItK1VKkiijxtMss6VxKMQJ6VQmjaw32tEdrl3SOteo4WvwSLXaj1U/i\naZdE66NSX9TOhdZ/zV6vG/93vLT12KR90n0J2Ls2nZVrpsUo0a6z9izdt29fzO1SH4inXVJFFa3v\nSKs5mK2OZPSe1drD5BYiIvIVDnxEROQrHPiIiMhXOPAREZGvmK7VKSUUaD+6Llu2LOZ2LSFG+3Fa\n+yHcLG2ZDaltdq/+LSUUOLV8jLYUiPTjtXbujSZEWCXF39raKh6zdu1aw++j/ShvZgkWs8wk5mhJ\nVtq1lBKVrN57UtKU9vyQrrOWBGLmXDlBi1Gixa79PTPJXjcjvZ+2VJSUZKPFbmaJNDP4iY+IiHyF\nAx8REfkKBz4iIvIVDnxEROQrHPiIiMhXTGd1apmAEikTTMt8cyJzU2MmC6+iokLcZ6bNVrKvzNBK\njElZdlr2VaKZKUslXTMtcyzR2apShrGWrSplTmuZdNo9Jh1npgRXb2aumZTVrMXSV7I6tXMstUu7\nZtr5cyL7W3o/bRyQnhHbt28Xj5HKUNqNn/iIiMhXOPAREZGvcOAjIiJf4cBHRES+woGPiIh8hQMf\nERH5iu1FqjUrV640fMy2bdvEfU4VbTZKWmkYAILBYMztZorWOkVLSZbi165/otP+zaTGS9dMuy7a\ntA8npt2YaZdW8N3M+zg1tUbqI6NHjxaPMVNYXLueiXx+aPdEcXFxzO3S1BQg8dOJpHOlPQek6TiV\nlZXiMVanycSLn/iIiMhXOPAREZGvcOAjIiJf4cBHRES+woGPiIh8hQMfERH5SiAajUbFnYEApN1S\nGquWZiulRmsprFoKudEVInq3R2ub0Vi0OKQ0YC39XWtzLPG2S4pTS7WWVgKQpjkAegq8lF4updT3\ntMfM9dL6lfR+ZlcxMJqGbaVdgUBA3NfY2Bhzuxa7tk9a3UDq11bvMe1eMvPM0e4laZ+VvijFqE0z\nOX/+fMztRs+dWVb6ot20qTXSuZWeX1p7+ImPiIh8hQMfERH5Cgc+IiLyFQ58RETkKxz4iIjIV0wX\nqZYywbQMMSljy2h2plukbEWtUKuUFelEUeObMZPVKR2jtVnLYPv5z38ec7sTxWmljERAbpcUH5D4\n4ttSjFpGrVQY2ExRecBc0WsrzBTM1rKItftMyga1UrzazN80k62a6OuSKNq1lLJwzVwvfuIjIiJf\n4cBHRES+woGPiIh8hQMfERH5Cgc+IiLyFQ58RETkK6anM0i0orBSevnJkyfFY7Zt22Y1JEO0qRVS\nyr2WdiylnltJmTZLSsfXphIUFxfH3K4Vc+4r01O06yL1RS12baqDE6TUfmmKDCBfF206g5ZCrk0v\ncIJ2zaQ2aFMWtLZJ19PKvSm9n3a/SPel2SlDTpBi0c6VFKN2vbQ22/nM5Cc+IiLyFUMD35gxYzBh\nwgSEw2HccccdTsXkip62/fCHP8SsWbPcDsc2X375JUpKSpCbm4tQKIQjR464HZItvNwXa2pqkJOT\ng3HjxmHjxo1uh2ObqqoqFBQUID8/H1VVVW6HY5v169cjLy8PBQUF+MUvfoGOjg63Q7JNzzUrKSnB\n7373O7fDsY2hrzoDgQD279+PoUOHOhWPa3ralpLirQ/BFRUVuPfee/HHP/4RnZ2duHLlitsh2cKr\nfbGrqwtPPvkkamtrMXLkSEyZMgXz589Hbm6u26FZ8u677+KFF17AsWPHkJaWhnvuuQf3338/srOz\n3Q7NkubmZmzduhWnT5/G9773PRQXF2Pv3r2455573A7Nst7X7L333sN//Md/YMaMGcjMzHQ7NMsM\nP+XdXqjQSV5rW2trKw4cOIDy8nIAQGpqqlruKtl47XoBwNGjRzF27FiMGTMGaWlpeOSRR7Br1y63\nw7LszJkzmDp1Kvr164dbbrkFkUgEr7zyitthWTZo0CCkpaWhra0NnZ2d+Oabb5Cenu52WLa48ZpN\nnjwZe/fudTssWxga+AKBAGbPno2ioiJs3brVqZhc0dO24uJibN++3e1wbNHU1IT09HSUlZVh0qRJ\nePzxx9HW1uZ2WLbwal+8ePHidf+izsjIwMWLF12MyB75+fk4cOAAPv/8c7S1teGNN97AhQsX3A7L\nsqFDh+Kpp57CqFGjcPvtt2PgwIGYPHmy22HZovc1a29vx4EDB/DJJ5+4HZYtDH3VeejQIYwYMQKf\nffYZIpEIgsHgd35fkbIAATnDcc2aNeIxicp+7Gnbq6++ip/+9KcIBAKYMGHCdf/N2rVrYx6rfYqS\nslwTUaS6s7MTDQ0N2LJlC6ZMmYIVK1Zgw4YNWLdu3XX/nZb5tnPnzpjbFy5cKB6jnQ+7rmfvvjhz\n5kyMHDkS06ZNi/u9pGxFqcizdoydAoHATf+byspKcd/KlStjbl+wYIF4jBMFwm+Uk5OD1atXY86c\nORgwYADC4XDMnxXMZM5q8WsZsIWFhYbf60bnzp3Dpk2b0NzcjGAwiAcffBBnz57Fj370o+v+Oy1b\nWPqHdqIz2m904zWbPn06vve971337NKeHVImq5lC5DfbZ5ShT3wjRowAAKSnp2Pu3LnqNIRk09O2\nwYMHY8aMGThz5ozLEVmXkZGBjIwMTJkyBQBQUlKChoYGl6OyR+++eP/993umXSNHjkRLS8u1/9/S\n0oKMjAwXI7JPeXk56uvrUVdXh8GDB2P8+PFuh2RZfX09pk2bhltvvRWpqam4//77cfToUbfDso0X\nrxlgYOBra2vDV199BQC4cuUKDhw44JmT0Ltt7e3tOHbsGLKyslyOyrrhw4cjMzMTZ8+eBQDU1tYi\nLy/P5aisu7Ev7t27F6FQyOWo7FFUVIQPP/wQzc3N6OjowMsvv4z58+e7HZYtPv30UwDARx99hJ07\nd2Lx4sUuR2RdTk4Ojhw5gvb2dkSjUdTV1XnmuQh485oBBr7q/OSTT659vdXZ2Yl7770XP/zhDx0L\nLJF6t621tRWzZ8++9ikp2T333HNYsmQJOjo6kJ2d7frXJ3a4sS8uWrQIM2fOdDkqe6SmpmLLli2Y\nO3cuurq6sHz58qTP6OxRUlKCy5cvIy0tDc8//zwGDRrkdkiWFRYWYunSpSgqKkJKSgry8/NdKU7h\nFC9eM8DAwJeVlXXdrHrtt5Bk07ttWuWZZFRYWIhjx465HYatbuyLfaVSjF3mzZuHefPmuR2G7d5+\n+223Q3DEqlWrsGrVKgDe64tevWbemrRGRER0Exz4iIjIX6KKSCQSBeCZVyQS8WTbvNqu3m1ju5Lj\nxb6YfC+vtyuWQNSL5S+IiIgk/MSX/C+vtqt329iu5HixLybfy+vtMvyJLxAIGK6HqK0TZaZahlbJ\nwehM/t7tMdM2ibS2GyBXL7CzQohT7dJo5147H0bXEetpj93tkmLU1k3Tqu0YzQa20i7t/Nq96oFU\nuUe6jlb7opm2aRVYtL9ndNpBPNdMyuqU1twD5DUI7axUonHqHpPOhXbetfNktMKQ1h4mtxARka9w\n4CMiIl/hwEdERL7CgY+IiHzF9uQW7QdZ6cdO7Rjtx/ovvvgi5nYpOcTqD+9SAoO2FFMkEjH0t8xw\nMrlFSsLRinhLbQYSmwSivVfvkmfx0n5cN1rCz0qihJZkI91LWtKAtNwWIC8ZJiWdWe2LWnKRdF9r\nS2RpjMYWzzUzc7+YMXr0aHGf1O+lPuBUcot0v0hLZwF6opLRe5bJLURERP/EgY+IiHyFAx8REfkK\nBz4iIvIVDnxEROQrcS9EGy+tJJWZ8l0ao6W9rJLapmVYSW3WzpOUMadl81mhLZ5pZjXpRF8XiZYt\nbKYclJZxKGWcWblmZkr8SYyWe+phtMScVVp/k+6LYDAoHqNdMyeYydZesGBBzO1m+04iF8PV2mum\nzyWqTBs/8RERka9w4CMiIl/hwEdERL7CgY+IiHyFAx8REfkKBz4iIvIV26czaOnIUnFSLf123759\nVkMyREvPbW1tjblda7OUer5r1y7xGCmN3WpqthSLFn9dXZ3h90n0dAbpmkkrWwP2ThUA9CLQZklT\nJLR2SceYLYouTSHQYnCKlN6v9TcnrovGzr6vTWfoK9NMtm/fLh4jTdM4f/68eEyinh38xEdERL7C\ngY+IiHyFAx8REfkKBz4iIvIVDnxEROQrHPiIiMhXbJ/OsGLFCsPHaCmsiarW3cNMmraWAm/mfEgp\n5FZJKe3a+d+5c2fM7doUiERfM0lVVZW4T6roL01ZuRmp35hZ3eJmf3Pt2rWG/5a2goGUdg441xfN\nkFL4takaWl+Upn5YmQIhxaidYykO7dmhtcuJKQHSVCozK5ZoU7kSNf2En/iIiMhXOPAREZGvcOAj\nIiJf4cBHRES+woGPiIh8JRCNRqPizkAAyu6YtKwcKUtJy6TUirEazZjs3R4zbZPeT8selIwePVrc\nZ7RQstV2aaQC4kOGDBGPqaioEPdt2rTJ0Pv3tMfudkm0/qv1U62gcCxW2qX1j6ysrJjbKysrxWPM\nZB5LnOyLZmjPD6lvS1miTvVFqV8tXLhQPMbO6+lUu6SsznA4LB6zZs0acZ/RDGOtPfzER0REvsKB\nj4iIfIUDHxER+QoHPiIi8hUOfERE5Csc+IiIyFdMF6nWCsNKpJRvLU1cK4JqZxp2PKRUfK0orFRQ\nuC8V/9VIKd8ao9Mx3CD1HW06g9EpC07R7gmJlWLZiaQ9V6R9Utr8zf5eIq+nds3KysoM/72+0hc1\nZp4DiXp28BMfERH5iqGBb/369cjLy0NBQQF+8YtfoKOjw6m4XNHV1YVwOIwHHnjA7VBsUV5ejmHD\nhqGgoMDtUGz1wQcfIBwOX3sFg0Fs3rzZ7bBs4dW2Xb16FVOnTsXEiRMRCoXw9NNPux2SbcaMGYMJ\nEyYgHA7jjjvucDsc23j1+QEYGPiam5uxdetWNDQ04J133kF3dzf27t3rZGwJV1VVhVAohEAg4HYo\ntigrK0NNTY3bYdhu/PjxaGxsRGNjI44fP47+/furVS6SiVfb1q9fP+zbtw8nTpzAqVOnsG/fPhw8\neNDtsGwRCASwf/9+NDY24ujRo26HYxuvPj8AAwPfoEGDkJaWhra2NnR2duKbb75Benq6k7El1IUL\nF/Dmm2/isccec73Mkl1mzJihlhbzgtraWmRnZyMzM9PtUGzntbb1798fANDR0YGuri4MHTrU5Yjs\n45VnRm9efn7EPfANHToUTz31FEaNGoXbb78dAwcOxOTJk52MLaFWrlyJZ599Fikp/NkzmezYsQOL\nFy92OwxHeK1t3d3dmDhxIoYNG4bi4mKEQiG3Q7JFIBDA7NmzUVRUhK1bt7odDsUh7qzOc+fOYdOm\nTWhubkYwGMSDDz6Is2fP4kc/+tF1/52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"text": [ "<matplotlib.figure.Figure at 0x5b83f90>" ] } ], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see now what the features mean. Each feature is a real-valued quantity representing the darkness of a pixel in an 8x8 image of a hand-written digit.\n", "\n", "Even though each sample has data that is inherently two-dimensional, the data matrix flattens this 2D data into a single vector, which can be contained in one row of the data matrix." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Exercise: Classifying Digits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's classify the digits using a Support Vector Classifier and try out 2 different kernels to see which one performs better." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import train_test_split\n", "from sklearn import metrics\n", "from sklearn.svm import SVC\n", "\n", "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)\n", "\n", "for kernel in ['rbf', 'linear']:\n", " clf = SVC(kernel=kernel).fit(Xtrain, ytrain)\n", " ypred = clf.predict(Xtest)\n", " print(\"SVC: kernel = {0}\".format(kernel))\n", " print(metrics.f1_score(ytest, ypred))\n", " plt.figure()\n", " plt.imshow(metrics.confusion_matrix(ypred, ytest),\n", " interpolation='nearest', cmap=plt.cm.binary)\n", " plt.colorbar()\n", " plt.xlabel(\"true label\")\n", " plt.ylabel(\"predicted label\")\n", " plt.title(\"SVC: kernel = {0}\".format(kernel))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "SVC: kernel = rbf\n", "0.541483398619\n", "SVC: kernel = linear" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.97112374636\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x6566150>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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T8pBE1Iba/cD+0KFD0dDQIOUhiMiG5DDFgjP2ichs7f50kojaN0tOJ2tqahAZGYkBAwYg\nKCgIS5Ys0b/37rvvIjAwEP369cPixYsN1sCeGBGZzZKeWMeOHbFv3z507twZ9fX1GDp0KH744QfU\n1dVhx44dOHHiBBwcHHDlyhWD7bAnRkRms3Rg/8aS3rW1tdDpdHB1dcVHH32EJUuW6KeWuLu7G6yB\nIUZEZrM0xBoaGjBgwAB4enrqF4vIy8vD999/j8GDByMmJgZHjx41WANPJ4nIbIZ+nbx69SquXr1q\ndP///d//RWlpKUaPHo309HTU19fj2rVryMzMxJEjRzB16lScPXu2xTYYYq0k1cx6KVf2yM3Nlaxt\n+p1UM+uluhLAGgyNifXo0QM9evTQPzd0KzYXFxeMHTsWR48ehVqtxuTJkwEAgwYNgp2dHa5evQo3\nN7dm9+XpJBGZzZLTyd9++w0lJSUAgOrqauzevRsajQYTJ07Ed999BwDIy8tDbW1tiwEGsCdGRBaw\n5NfJX375BQkJCWhoaEBDQwMef/xxPPjgg3jggQcwa9YshISEwNHREZs2bTLYDkOMiMxmSYiFhIQg\nOzv7ttcdHBywefNmk9thiBGR2eQwY58hRkRmY4gRkaLxAnAiUjT2xIhI0RhiRKRosg6xkJCQFndS\nqVQ4ceKEJAURkXLIOsS+/vrrtqyDiBRI1iHm6+ur/7uwsBBnzpzByJEjUVVVBZ1O1xa1EZHMySHE\njP4+unbtWkyZMgVPPfUUAKCoqAgTJ06UvDAikj87OzuTH5LVYGyD999/Hz/88AO6du0KAPD398fl\ny5clK4iIlEMRdzvq0KEDOnTooH9eX18viy4kEdmeHLLAaE8sOjoab731FqqqqrB7925MmTIF48eP\nb4vaiEjm5NATMxpiSUlJcHd3R0hICD7++GPExcXhzTfflKwgIlIOOYSY0dPJu+66CwkJCYiMjIRK\npUJAQIAsupBEZHtyyAKjIfbNN99g3rx5uOeeewAAZ8+e1ffIiOjOpogQe/7557Fv3z707dsXQOMa\n83FxcQwxIpLFKhZGK+jatas+wADgnnvu0U+3IKI7mxR3AC8uLkZsbCz8/f0xatQo/Tr8LWmxJ/bP\nf/4TABAeHo64uDhMnToVAPDll18iPDzc7A9NRO2HFHcA37FjB2JjY7Fo0SKsWLECSUlJSEpKarEd\ng9dO3ijQw8MDGRkZABrvxltTU2N24dQ8KW+rNmLECEnavXFHGiWR8vZnUt2yTap2rcHSMbHm7gC+\nY8cOfd4kJCQgJibGvBDbsGGDRcURUftnaYg1NDQgLCwM+fn5ePrppxEcHIxLly7B09MTAODp6YlL\nly4ZbMPowH51dTU+++wznDp1CtXV1fqi161bZ1HxRKR8lobYrXcA37dv323tGzuG0YH9xx9/HJcu\nXUJaWhpiYmKg1Wrh5ORkUeFE1D4YGsi/ePEijh49qn8YcuMO4MeOHYOnpyd+/fVXAI33pvTw8DC4\nr9EQO3PmDJYuXQonJyckJCQgNTUVhw8fbsXHJKL2ytCqFT4+PoiMjNQ/btXSHcAnTJiAjRs3AgA2\nbtxodNUco6eTjo6OABqT8scff4SXlxeuXLnS6g9LRO2PFHcA12g0mDp1Kj777DP4+vriH//4h8F2\njIbY3LlzUVxcjDfffBMTJkxARUUFli5danbhRNR+SHEH8O7du2PPnj0mt2NSiAGNq1kUFBS0osRG\nOp0O4eHhUKvVXPKaqJ2R9WVHycnJt72mUqkghIBKpcLzzz9v0gFSUlIQFBSE8vJy86skIlmSQ4i1\nOLBfXl6OioqKJo8br5kaSEVFRUhNTcWcOXMghLBa0UQkD7JeiicxMdHixp977jmsXLkSZWVlFrdF\nRPIjhwvAJbt57s6dO+Hh4QGNRoP09PQWt7s5LGNiYhATEyNVSUR3tPT0dIPfRXPI4XRSJSQ6z/vL\nX/6CzZs3w97eHjU1NSgrK8PDDz+MTZs2/X7w/x9jI2nx2snfKfHaSalY+v1TqVR48cUXTd5+1apV\nknzfJesLLlu2DFqtFgUFBdi6dStGjBjRJMCISPlkPSZ286+TNyf2jWJM/XXy5jaIqH2Rw/e6xRAr\nLy+HSqXC6dOnceTIEUyYMAFCCOzcuRMRERGtOkh0dDSio6MtLpaI5EXWIXZjwH3YsGHIzs6Gs7Mz\nAOD111/n0tREBEDmIXbD5cuXmwxYOjg48A7gRARAIVMsZsyYgYiICEyePBlCCPz3f/83EhIS2qI2\nIpI5RfTEXnnlFTz00EP44YcfADSu+KrRaCQvjIjkTw4hZlJfsKqqCs7Oznj22WehVqvNuhCciNof\nWU+xuCExMRHHjh3D6dOnMWvWLNTW1uKxxx7DgQMHJCuKiJRBDj0xoyH273//Gzk5ORg4cCAAwNvb\nmytSKIxUM+vd3d0laReAZAtvKm1WPSDtVQaWUkSIdejQockvEJWVlZIWRETKIYcQMzomNmXKFDz1\n1FMoKSnB2rVr8eCDD2LOnDltURsRyZyhNfZvfdxKq9Vi+PDhCA4ORr9+/bBmzZom7ycnJ8POzg7F\nxcUGazDaE3vppZewa9cuODs7Iy8vD0uXLkVsbGwrPyoRtUeW9MQcHBywevVqDBgwABUVFRg4cCBi\nY2MRGBgIrVaL3bt3o3fv3kbbMdoTW7x4MUaNGoVVq1Zh1apViI2NxeLFi80unIjaD0t+nfTy8sKA\nAQMAAE5OTggMDMTFixcBNF6b/fbbb5tUg9EQ27Vr122vpaammtQ4EbVv1ppiUVhYiJycHERGRmL7\n9u1Qq9Xo37+/STW0eDr54Ycf4oMPPkB+fj5CQkL0r5eXl+P+++838SMSUXtmjYH9iooKxMfHIyUl\nBXZ2dli2bBl2796tf9/YGmQthtj06dMxZswYvPzyy1ixYoW+IWdnZ7i5uVlcOBEpn6EQO3v2LM6e\nPWtw/7q6Ojz88MN47LHHMHHiRPz4448oLCxEaGgogMb7dAwcOBBZWVkt3gnc6Mquhw4dQnBwMLp2\n7QoAKCsrQ25ubrN39G0truyqbEqcJ6ZEUs0Tc3R0tHhl16SkJJO3f/nll5scTwiBhIQEuLm5YfXq\n1c3u4+fnh2PHjqF79+4ttmt0TOzpp5+Gk5OT/nmXLl0wb948kwsnovbLkikWBw4cwOeff459+/ZB\no9FAo9Hg22+/bbKNKaerJt0o5OYC7rrrLuh0OlN2I6J2zpIxsaFDh6KhocHgNsZORwETemJ+fn5Y\ns2YN6urqUFtbi5SUFNxzzz2mV0pE7ZYcLgA3GmIfffQRDhw4AG9vb6jVamRmZmLt2rWSFUREyiGH\nEDN6Ounp6Ylt27ZJVgARKZccrp1sMcRWrFiBxYsXY8GCBbe9p1KpbrvOiYjuPLIOsaCgIADQL8Fz\nMzkUTkS2J4csaDHExo8fDwB44okn2qoWIlIYWd8o5EaIAbdPSlWpVNixY4e0lRGR7Mm6J/bCCy8A\naFzZ9ddff8Vjjz0GIQS2bNkCT0/PNiuQiORL1iEWExMDoDHMjh07pn99woQJzY6TEdGdRw4hZvSE\ntqqqCvn5+frnZ8+eRVVVlaRFEZEyKGKe2OrVqzF8+HD4+fkBaFz3h5NdiQiQR0/MaIg99NBDyMvL\nw+nTpwEAAQEB6NChg+SFkfxJudKEVF8OJa6aIuc7NMnh10mjFVRWVmLlypV47733EBoaivPnz2Pn\nzp1tURsRyZwcTieNhtjMmTPh6OiIgwcPAgB69uyJV155RbKCiEg5FBFi+fn5WLx4MRwdHQE0ridG\nRATII8RMunludXW1/nl+fj7HxIgIgEIG9hMTE/HQQw+hqKgI06dPx4EDB7Bhw4Y2KI2I5E72IdbQ\n0IBr167hn//8JzIzMwEAKSkpkq6tTkTKIYcQMzgmZmdnh7fffhs9evTAuHHjMG7cuFYFWElJCeLj\n4xEYGIigoCB9EBJR+2DJGvuzZs2Cp6dnk1tCZmVlISIiAhqNBoMGDcKRI0eM12Bsg9jYWKxatQpa\nrRbFxcX6hymeffZZxMXFITc3FydOnEBgYKBJ+xGRMlgysD9z5kykpaU1eW3RokVYunQpcnJy8MYb\nb2DRokVGazA6JrZ161aoVCq8//77TQo3toB/aWkp9u/fj40bNzYeyN4eLi4uRgsiIuWw5HRy2LBh\nKCwsbPLa3XffjdLSUgCNZ3Le3t5G2zEaYrcexFQFBQVwd3fHzJkzcfz4cQwcOBApKSno3LmzWe0R\nkfxYe0wsKSkJQ4cOxYsvvoiGhgYcOnTI6D5GTyerq6uRnJyMSZMmYfLkyVi9ejVqamqMNlxfX4/s\n7GzMnz8f2dnZ6NKlS6tutElE8mfo9DE3Nxf/+te/9A9TzJ49G2vWrMH58+exevVqzJo1y+g+Rnti\nM2bMQNeuXfHnP/8ZQgj813/9Fx5//HF8+eWXBvdTq9VQq9UYNGgQACA+Pr7ZEEtMTNT/HRMTo18C\niIisKz09Henp6VZt01BPLDg4GMHBwfrnpgRZVlYW9uzZA6AxM+bMmWN0H6MhdvLkSZw6dUr/fMSI\nEfr19w3x8vKCj48P8vLy4O/vjz179jT5QDfcHGJEJJ1bOwmvv/66xW1a+3Syb9++yMjIQHR0NL77\n7jv4+/sb3cdoiIWFheHQoUMYMmQIACAzM9PkRRHfffddPProo6itrUWfPn2wfv16k/YjImWwZBWL\nadOmISMjA7/99ht8fHzwxhtvYO3atfjTn/6E69evo1OnTiYt+6USRtYmCQgIQF5eHnx8fKBSqXD+\n/Hncd999sLe3h0qlwokTJ8z+ELeu3U90A5fikZ6l3z+VSoV//OMfJm8/depUSf79jfbEbp3HQUR0\ngxxm7BsNMV9f3zYog4iUSBEhRkTUEoYYESkaQ4yIFI0hRkSKJocbhTDEiMhs7IkRtUCq+VxSLugp\n5S3s5IohRkSKxhAjIkVjiBGRojHEiEjRGGJEpGicYkFEisaeGBEpGkOMiBSNIUZEiiaHELP9qBwR\nKZYlN89t7g7gL730EgIDAxEaGorJkyfr70FpCEOMiMxmZ2dn8uNWzd0BfNSoUTh58iSOHz8Of39/\nLF++3HgNVvs0RHTHsaQnNmzYMLi6ujZ5LTY2Vh94kZGRKCoqMloDQ4yIzGZJiBmzbt06xMXFGd2O\nA/tEZDZD4ZSTk4OcnByz2n3rrbfg6OiI6dOnG92WIUZEZjMUYmFhYQgLC9M/N/W+sxs2bEBqair2\n7t1r0vYMMSIym7WnWKSlpWHlypXIyMhAx44dTdqHY2JEZDZLxsSmTZuGqKgonD59Gj4+Pli3bh0W\nLFiAiooKxMbGQqPRYP78+cZrMHYHcCnxDuDU1riy6++scQfwzMxMk7cfPHiwbe4ATkTUEjnM2GeI\nEZHZGGJEpGgMMYnU1dVJ1raDg4Mk7SqxZiWSctyqT58+krT7n//8R5J2rYEhRkSKxhAjIkVjiBGR\nonGNfSJSNPbEiEjRGGJEpGhyCDFJT2iXL1+O4OBghISEYPr06bh+/bqUhyOiNiblemKmkizECgsL\n8cknnyA7Oxs//vgjdDodtm7dKtXhiMgG5BBikp1Odu3aFQ4ODqiqqsJdd92FqqoqeHt7S3U4IrKB\ndn062b17d7zwwgvo1asXevbsiW7dumHkyJFSHY6IbMCSG4VYi2Q9sfz8fLzzzjsoLCyEi4sLpkyZ\ngi+++AKPPvpok+0SExP1f8fExCAmJkaqkojuaBkZGcjIyLBqm3LoiUm2nti2bduwe/dufPrppwCA\nzZs3IzMzE++///7vB5doPTElXoeoxJqpKaVdO+no6GjxemJ5eXkmb+/v7y/J912yPl5AQAAyMzNR\nXV0NIQT27NmDoKAgqQ5HRDYgh4F9yUIsNDQUM2bMQHh4OPr37w8AePLJJ6U6HBHZgKUhVlJSgvj4\neAQGBiIoKKhVK8Xqa2iPy1Mr8dRMiTVTU3fi6WR+fr7J2/fp0+e24yUkJCA6OhqzZs1CfX09Kisr\n4eLi0qo6OGOfiMxmyWliaWkp9u/fj40bNwIA7O3tWx1gAO92REQWsGSKRUFBAdzd3TFz5kyEhYVh\n7ty5qKqqan0N1vggRHRnsmRMrL6+HtnZ2Zg/fz6ys7PRpUsXJCUltboGnk4SkdkMnU4eOnQIhw4d\navF9tVpRtkEGAAAIG0lEQVQNtVqNQYMGAQDi4+MZYkTUtgyFWFRUFKKiovTP33nnnSbve3l5wcfH\nB3l5efD398eePXsQHBzc6hoYYkRkNkvnf7377rt49NFHUVtbiz59+mD9+vWtboMhRkRmszTEQkND\nceTIEYvaaJchxnlRbYNz25pqzZyp1pBq/pk1yOHayXYZYkTUNnijECJSNPbEiEjRGGJEpGgMMSJS\nNIYYESkaQ4yIFI2/ThKRorEnRkSKxhAjIkVjiBGRojHEiEjRGGJEpGgMMSJSNE6xICJFY0+MiBRN\nDiFm+74gESmWpXcAT0tLQ0BAAO69916sWLHCrBoUFWLp6em2LqFVlFYvwJrbgtLqNcSSENPpdHjm\nmWeQlpaGU6dOYcuWLcjNzW11DQwxCSmtXoA1twWl1WuIJSGWlZWFvn37wtfXFw4ODnjkkUewffv2\nVtegqBAjInmxJMQuXLgAHx8f/XO1Wo0LFy60ugYO7BOR2SyZYmG1HwWEDUVHRwsAfPDBhw0e0dHR\nFn1/W3s8JyenJvsfOnRIjB49Wv982bJlIikpqdV1qP6/GCKiNlVfX4/77rsPe/fuRc+ePREREYEt\nW7YgMDCwVe3wdJKIbMLe3h7vvfceRo8eDZ1Oh9mzZ7c6wACAPTEiUjRF/DppjQlxbUmr1WL48OEI\nDg5Gv379sGbNGluXZBKdTgeNRoPx48fbuhSTlJSUID4+HoGBgQgKCkJmZqatSzJq+fLlCA4ORkhI\nCKZPn47r16/buiTls2hkrw3U19eLPn36iIKCAlFbWytCQ0PFqVOnbF2WQb/88ovIyckRQghRXl4u\n/P39ZV+zEEIkJyeL6dOni/Hjx9u6FJPMmDFDfPbZZ0IIIerq6kRJSYmNKzKsoKBA+Pn5iZqaGiGE\nEFOnThUbNmywcVXKJ/uemLUmxLUlLy8vDBgwAADg5OSEwMBAXLx40cZVGVZUVITU1FTMmTMHQgEj\nDKWlpdi/fz9mzZoFoHF8xcXFxcZVGda1a1c4ODigqqoK9fX1qKqqgre3t63LUjzZh5i1JsTZSmFh\nIXJychAZGWnrUgx67rnnsHLlSlksrWKKgoICuLu7Y+bMmQgLC8PcuXNRVVVl67IM6t69O1544QX0\n6tULPXv2RLdu3TBy5Ehbl6V4sv8vVg5XyZuroqIC8fHxSElJgZOTk63LadHOnTvh4eEBjUajiF4Y\n0PjzfHZ2NubPn4/s7Gx06dIFSUlJti7LoPz8fLzzzjsoLCzExYsXUVFRgS+++MLWZSme7EPM29sb\nWq1W/1yr1UKtVtuwItPU1dXh4YcfxmOPPYaJEyfauhyDDh48iB07dsDPzw/Tpk3Dd999hxkzZti6\nLIPUajXUajUGDRoEAIiPj0d2draNqzLs6NGjiIqKgpubG+zt7TF58mQcPHjQ1mUpnuxDLDw8HD//\n/DMKCwtRW1uLbdu2YcKECbYuyyAhBGbPno2goCAsXLjQ1uUYtWzZMmi1WhQUFGDr1q0YMWIENm3a\nZOuyDPLy8oKPjw/y8vIAAHv27EFwcLCNqzIsICAAmZmZqK6uhhACe/bsQVBQkK3LUjzZT3a11oS4\ntnTgwAF8/vnn6N+/PzQaDYDGn9YfeughG1dmGqWcwr/77rt49NFHUVtbiz59+mD9+vW2Lsmg0NBQ\nzJgxA+Hh4bCzs0NYWBiefPJJW5eleJzsSkSKJvvTSSIiQxhiRKRoDDEiUjSGGBEpGkOMiBSNIUZE\nisYQa+dKS0vx4YcfStb+hg0bsGDBAoPbJCYmIjk5uVXtyvkyLZIXhlg7d+3aNXzwwQfNvldfX29x\n+6ZMjDVn8qxSJtyS7THE2rmXX34Z+fn50Gg0WLRoETIyMjBs2DD84Q9/QL9+/XDu3Dn069dPv/2q\nVavw+uuvA2i8YHnMmDEIDw/HAw88gNOnTxs81tdff43BgwcjLCwMsbGxuHz5sv6948ePIyoqCv7+\n/vj000/1r69cuRIREREIDQ1FYmKidT883RFkf9kRWWbFihU4efIkcnJyADTeuDUnJwcnT55E7969\nUVhY2KTXc/M9Ap988kl8/PHH6Nu3Lw4fPoz58+dj7969LR5r2LBh+tVVP/30U7z99ttYtWoVhBA4\nceIEDh8+jIqKCmg0GowdOxY//vgjzpw5g6ysLDQ0NGDChAnYv38/hg0bJuG/CLU3DLF2rrmryiIi\nItC7d2+D+1RWVuLgwYOYMmWK/vXa2lqDx9JqtZg6dSp+/fVX1NbW4p577gHQGIwTJ05Ehw4d0KFD\nBwwfPhxZWVnYv38/du3apb++tLKyEmfOnGGIUaswxO5AXbp00f9tb2+PhoYG/fPq6mqoVCo0NDTA\n1dVV34MzxYIFC/Diiy9i3LhxyMjIMHh6eKO3t2TJEl4ETRbhmFg75+zsjPLy8hbf9/T0xOXLl1Fc\nXIzr169j586d+v38/Pzw1VdfAYD+lPBWN/f0ysrK0LNnTwCNv1revM327dtx/fp1XL16Fenp6YiI\niMDo0aOxbt06VFZWAmhcxffKlSsWf2a6s7An1s65ubnh/vvvR0hICOLi4hAXF9dkDMzBwQGvvvoq\nIiIi4O3t3WR9qy+++AJPP/003nzzTdTV1WHatGno379/k/ZvHkNLTEzElClT4OrqihEjRuDcuXP6\nbfr374/hw4fjt99+w6uvvgovLy94eXkhNzcXQ4YMAdA4reKLL76Au7s7f50kk3EpHiJSNJ5OEpGi\nMcSISNEYYkSkaAwxIlI0hhgRKRpDjIgUjSFGRIrGECMiRfs/vTZuA3yYbIwAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x4f33ed0>" ] } ], "prompt_number": 36 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The diagonal elements represent the number of points for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier. The higher the diagonal values of the confusion matrix the better, indicating many correct predictions." ] } ], "metadata": {} } ] }
apache-2.0
barjacks/pythonrecherche
10 beautifulsoup practice, server, pandas plotting/01 daphne_(felix).ipynb
1
18071
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from bs4 import BeautifulSoup\n", "import requests\n", "import pandas as pd\n", "import time\n", "import progressbar" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Let's get started: scrape main page\n", "url = \"https://daphnecaruanagalizia.com\"\n", "response = requests.get(url)\n", "daphne = BeautifulSoup(response.text, 'html.parser')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Get structural information based on developer tools in Google Chrome\n", "posts = daphne.find_all(\"div\", class_=\"postmaster\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<div class=\"postmaster\" data-postid=\"97964\">\n", "<p class=\"column-caption\"></p>\n", "<div class=\"post\">\n", "<h1><a href=\"https://daphnecaruanagalizia.com/2017/10/first-things-first-something-horrendous-posture/\" rel=\"bookmark\" title=\"Permanent Link to First things first: do something about that horrendous posture\">\n", "First things first: do something about that horrendous posture </a>\n", "</h1>\n", "<div class=\"entry\">\n", "<p>\n", "You can wear the flashiest watch and keep your snazzy shirt-cuff turned up to make …</p>\n", "</div>\n", "<p class=\"postmetadata\"><a href=\"https://daphnecaruanagalizia.com/2017/10/first-things-first-something-horrendous-posture/#respond\">Post a comment</a> | <a href=\"https://daphnecaruanagalizia.com/2017/10/first-things-first-something-horrendous-posture/#comments\"><span class=\"dsq-postid\" data-dsqidentifier=\"97964 https://daphnecaruanagalizia.com/?p=97964\">Read (4)</span></a> | <span class=\"time\">Monday, 16 October 2:09 pm</span></p>\n", "</div>\n", "</div>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Explore first entry \n", "posts[0]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://daphnecaruanagalizia.com/2017/10/first-things-first-something-horrendous-posture/'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# url \n", "posts[0].a[\"href\"]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Monday, 16 October 2:09 pm'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# time stamp\n", "posts[0].find(class_=\"time\").get_text()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Permanent Link to First things first: do something about that horrendous posture'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# title of posts\n", "posts[0].a[\"title\"]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'97964'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# post id\n", "posts[0].get('data-postid')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Extract relevant content from main page, loop through posts\n", "\n", "new_lst = []\n", "\n", "for element in posts:\n", " \n", " url = element.a[\"href\"]\n", " title = element.a[\"title\"]\n", " title = title[18:]\n", " date = element.find(class_=\"time\").get_text()\n", " post_id = element.get('data-postid')\n", " \n", " #print(url)\n", " response = requests.get(url)\n", " soup = BeautifulSoup(response.text, 'html.parser')\n", " text = soup.find('div', {'class': 'entry'}).text.strip()\n", " \n", " temp_dict = {'URL': url,\n", " 'Title': title,\n", " 'Date': date,\n", " 'ID': post_id,\n", " 'Txt': text}\n", " \n", " new_lst.append(temp_dict)\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 11, "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>Date</th>\n", " <th>ID</th>\n", " <th>Title</th>\n", " <th>Txt</th>\n", " <th>URL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Monday, 16 October 2:09 pm</td>\n", " <td>97964</td>\n", " <td>First things first: do something about that ho...</td>\n", " <td>You can wear the flashiest watch and keep your...</td>\n", " <td>https://daphnecaruanagalizia.com/2017/10/first...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Sunday, 15 October 10:07 pm</td>\n", " <td>97961</td>\n", " <td>Austria’s new chancellor is 31 – and will have...</td>\n", " <td>Exit polls show that Sebastian Kurz, 31, is ab...</td>\n", " <td>https://daphnecaruanagalizia.com/2017/10/austr...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Sunday, 15 October 7:26 pm</td>\n", " <td>97958</td>\n", " <td>The party leaders and Sunday morning</td>\n", " <td>Is it going to be a five-year electoral campai...</td>\n", " <td>https://daphnecaruanagalizia.com/2017/10/party...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Saturday, 14 October 12:52 am</td>\n", " <td>97955</td>\n", " <td>Looks like Delia is surrounding himself with l...</td>\n", " <td>The disgraceful thing is that this man has bee...</td>\n", " <td>https://daphnecaruanagalizia.com/2017/10/looks...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Saturday, 14 October 12:26 am</td>\n", " <td>97952</td>\n", " <td>Chris Cardona: a one-track mind</td>\n", " <td>“I don’t recall any other budget having given ...</td>\n", " <td>https://daphnecaruanagalizia.com/2017/10/chris...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Date ID \\\n", "0 Monday, 16 October 2:09 pm 97964 \n", "1 Sunday, 15 October 10:07 pm 97961 \n", "2 Sunday, 15 October 7:26 pm 97958 \n", "3 Saturday, 14 October 12:52 am 97955 \n", "4 Saturday, 14 October 12:26 am 97952 \n", "\n", " Title \\\n", "0 First things first: do something about that ho... \n", "1 Austria’s new chancellor is 31 – and will have... \n", "2 The party leaders and Sunday morning \n", "3 Looks like Delia is surrounding himself with l... \n", "4 Chris Cardona: a one-track mind \n", "\n", " Txt \\\n", "0 You can wear the flashiest watch and keep your... \n", "1 Exit polls show that Sebastian Kurz, 31, is ab... \n", "2 Is it going to be a five-year electoral campai... \n", "3 The disgraceful thing is that this man has bee... \n", "4 “I don’t recall any other budget having given ... \n", "\n", " URL \n", "0 https://daphnecaruanagalizia.com/2017/10/first... \n", "1 https://daphnecaruanagalizia.com/2017/10/austr... \n", "2 https://daphnecaruanagalizia.com/2017/10/party... \n", "3 https://daphnecaruanagalizia.com/2017/10/looks... \n", "4 https://daphnecaruanagalizia.com/2017/10/chris... " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(new_lst)[0:5]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100% (8 of 8) |###########################| Elapsed Time: 0:00:19 Time: 0:00:19\n" ] } ], "source": [ "# Putting everything together: scrape posts from all pages for relevant content\n", "\n", "bar = progressbar.ProgressBar()\n", "\n", "new_lst = []\n", "\n", "# showcase for the first 9 pages / to get all pages change to range(1,1443)\n", "for elem,i in zip(range(1,10), bar((range(1,10)))):\n", "\n", " page = \"https://daphnecaruanagalizia.com/page/\" + str(elem)\n", " \n", " response = requests.get(page)\n", " soup = BeautifulSoup(response.text, 'html.parser')\n", " \n", " posts = soup.find_all(\"div\", class_=\"postmaster\")\n", "\n", " for element in posts:\n", "\n", " url = element.a[\"href\"]\n", " \n", " url_temp = url.replace(\"https://daphnecaruanagalizia.com/\", \"\")\n", " date_y = url_temp[:4]\n", " date_m = url_temp[5:7]\n", " \n", " # dealing with error message stemming from one post on page 127\n", " try:\n", " date_t = element.find(class_=\"time\").get_text()\n", " except AttributeError:\n", " date_t = \"n.a.\"\n", " \n", " title = element.a[\"title\"]\n", " title = title.replace(\"Permanent Link to \", \"\")\n", " \n", " post_id = element.get('data-postid')\n", " \n", " response = requests.get(url)\n", " abc = BeautifulSoup(response.text, 'html.parser')\n", " text = abc.find('div', {'class': 'entry'}).text.strip()\n", " text = text.replace('\\n', ' ')\n", "\n", " temp_dict = {'Link': url,\n", " 'Title': title,\n", " 'Txt': text,\n", " 'Date_1': date_y,\n", " 'Date_2': date_m,\n", " 'Date_3': date_t,\n", " 'ID_post': post_id,\n", " 'ID_page': i }\n", "\n", " new_lst.append(temp_dict)\n", " \n", "\n", "df = pd.DataFrame(new_lst)\n", "df.to_csv('daphne.csv', sep='\\t', encoding='utf-16')\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "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>Date_1</th>\n", " <th>Date_2</th>\n", " <th>Date_3</th>\n", " <th>ID_page</th>\n", " <th>ID_post</th>\n", " <th>Link</th>\n", " <th>Title</th>\n", " <th>Txt</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2017</td>\n", " <td>10</td>\n", " <td>Monday, 16 October 2:09 pm</td>\n", " <td>1</td>\n", " <td>97964</td>\n", " <td>https://daphnecaruanagalizia.com/2017/10/first...</td>\n", " <td>First things first: do something about that ho...</td>\n", " <td>You can wear the flashiest watch and keep your...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2017</td>\n", " <td>10</td>\n", " <td>Sunday, 15 October 10:07 pm</td>\n", " <td>1</td>\n", " <td>97961</td>\n", " <td>https://daphnecaruanagalizia.com/2017/10/austr...</td>\n", " <td>Austria’s new chancellor is 31 – and will have...</td>\n", " <td>Exit polls show that Sebastian Kurz, 31, is ab...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2017</td>\n", " <td>10</td>\n", " <td>Sunday, 15 October 7:26 pm</td>\n", " <td>1</td>\n", " <td>97958</td>\n", " <td>https://daphnecaruanagalizia.com/2017/10/party...</td>\n", " <td>The party leaders and Sunday morning</td>\n", " <td>Is it going to be a five-year electoral campai...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2017</td>\n", " <td>10</td>\n", " <td>Saturday, 14 October 12:52 am</td>\n", " <td>1</td>\n", " <td>97955</td>\n", " <td>https://daphnecaruanagalizia.com/2017/10/looks...</td>\n", " <td>Looks like Delia is surrounding himself with l...</td>\n", " <td>The disgraceful thing is that this man has bee...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2017</td>\n", " <td>10</td>\n", " <td>Saturday, 14 October 12:26 am</td>\n", " <td>1</td>\n", " <td>97952</td>\n", " <td>https://daphnecaruanagalizia.com/2017/10/chris...</td>\n", " <td>Chris Cardona: a one-track mind</td>\n", " <td>“I don’t recall any other budget having given ...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Date_1 Date_2 Date_3 ID_page ID_post \\\n", "0 2017 10 Monday, 16 October 2:09 pm 1 97964 \n", "1 2017 10 Sunday, 15 October 10:07 pm 1 97961 \n", "2 2017 10 Sunday, 15 October 7:26 pm 1 97958 \n", "3 2017 10 Saturday, 14 October 12:52 am 1 97955 \n", "4 2017 10 Saturday, 14 October 12:26 am 1 97952 \n", "\n", " Link \\\n", "0 https://daphnecaruanagalizia.com/2017/10/first... \n", "1 https://daphnecaruanagalizia.com/2017/10/austr... \n", "2 https://daphnecaruanagalizia.com/2017/10/party... \n", "3 https://daphnecaruanagalizia.com/2017/10/looks... \n", "4 https://daphnecaruanagalizia.com/2017/10/chris... \n", "\n", " Title \\\n", "0 First things first: do something about that ho... \n", "1 Austria’s new chancellor is 31 – and will have... \n", "2 The party leaders and Sunday morning \n", "3 Looks like Delia is surrounding himself with l... \n", "4 Chris Cardona: a one-track mind \n", "\n", " Txt \n", "0 You can wear the flashiest watch and keep your... \n", "1 Exit polls show that Sebastian Kurz, 31, is ab... \n", "2 Is it going to be a five-year electoral campai... \n", "3 The disgraceful thing is that this man has bee... \n", "4 “I don’t recall any other budget having given ... " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(new_lst)[0:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
Grzego/nn-workshop
3-neural-network-tensorflow/neural-network-tensorflow.ipynb
1
43612
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Ładujemy dane" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "from sklearn.datasets import fetch_mldata\n", "mnist = fetch_mldata('MNIST original')\n", "data = mnist.data\n", "target = mnist.target.astype(np.int32)\n", "\n", "# zmieniamy target na kodowanie one_hot\n", "target = np.eye(10)[target]\n", "\n", "# tasujemy dane (ustawiamy seed na konkretna wartosc,\n", "# zeby pozniej porownac rezultaty)\n", "np.random.seed(1337)\n", "shuffle = np.random.permutation(len(data))\n", "data = data[shuffle]\n", "target = target[shuffle]\n", "\n", "# ostatnie 10k przykladow traktujemy jako zbior\n", "# do walidacji wynikow\n", "valid_data = data[-10000:]\n", "valid_target = target[-10000:]\n", "\n", "data = data[:-10000]\n", "target = target[:-10000]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Tworzymy graf obliczeniowy" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "graph = tf.Graph()\n", "with graph.as_default():\n", " x = tf.placeholder(tf.float32, shape=[None, 28 * 28])\n", " y = tf.placeholder(tf.float32, shape=[None, 10])\n", "\n", " # wagi pierwszej warstwy\n", " w1 = tf.Variable(tf.truncated_normal(shape=[28 * 28, 128], stddev=0.01))\n", " b1 = tf.Variable(tf.zeros(shape=[128]))\n", " \n", " # wagi drugiej warstwy\n", " w2 = tf.Variable(tf.truncated_normal(shape=[128, 10], stddev=0.01))\n", " b2 = tf.Variable(tf.zeros(shape=[10]))\n", " \n", " # pierwsza warstwa\n", " h = tf.matmul(x, w1) + b1\n", " h = tf.nn.sigmoid(h)\n", " \n", " # druga warstwa\n", " h = tf.matmul(h, w2) + b2\n", " y_ = tf.nn.sigmoid(h)\n", " \n", " # funkcja straty (MSE)\n", " loss = tf.reduce_mean(tf.square(y - y_))\n", " \n", " # tworzymy optimalizator i ustawiamy learning rate\n", " optimizer = tf.train.AdamOptimizer(learning_rate=0.0001) \n", " \n", " # tworzymy operacje trenowania (minimalizowanie straty)\n", " train_step = optimizer.minimize(loss)\n", " \n", " # dodatkowo liczenie dokladnosci\n", " accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y_, axis=1),\n", " tf.argmax(y, axis=1)),\n", " tf.float32))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Tworzymy sesję i trenujemy sieć" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Epoch 0\n", "[60000/60000] loss = 0.05223076790571213\n", "Epoch 1\n", "[60000/60000] loss = 0.02443438582122326\n", "Epoch 2\n", "[60000/60000] loss = 0.017563918605446815\n", "Epoch 3\n", "[60000/60000] loss = 0.01433548517525196\n", "Epoch 4\n", "[60000/60000] loss = 0.014398407191038132" ] }, { "data": { "image/png": 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TcrvXGZVTwi5rSkVt0PF5mblBx8/qN7PT+iJVkVPeYZ1tz2taxcSYrSecmX2m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\nwx1wvagOY3xduEB8VewlFFa2/1tt30vA2AHycpvML63bnG2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P1dfXMy8vL5aTk8Ot+8tf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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a5ef522198>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Validation accuracy: 0.9326\n" ] } ], "source": [ "epochs = 5\n", "batch_size = 32\n", "\n", "# tworzymy sesje dla utworzonego wczesniej grafu\n", "with tf.Session(graph=graph) as sess:\n", " # inicjalizujemy zmienne\n", " sess.run(tf.global_variables_initializer())\n", " \n", " losses = []\n", " acc = []\n", " \n", " for e in range(epochs):\n", " print('\\nEpoch {}'.format(e))\n", " for b in range(0, len(data), batch_size):\n", " # pobieramy kolejna partie danych treningowych\n", " be = min(len(data), b + batch_size)\n", " x_batch = data[b: be]\n", " y_batch = target[b: be]\n", "\n", " # uruchamiamy obliczenia dla [loss, accuracy, train_step]\n", " l, a, _ = sess.run([loss, accuracy, train_step],\n", " feed_dict={x: x_batch, y: y_batch})\n", " losses += [l]\n", " acc += [a]\n", " \n", " print('\\r[{:5d}/{:5d}] loss = {}'.format(be, len(data), l), end='')\n", " \n", " # policzymy teraz dokladnosc dla validation set\n", " validation_accuracy = 0\n", " for b in range(0, len(valid_data), batch_size):\n", " be = min(len(valid_data), b + batch_size)\n", " a = sess.run(accuracy, feed_dict={x: valid_data[b: be], y: valid_target[b: be]})\n", " validation_accuracy += a * (be - b)\n", " validation_accuracy /= len(valid_data)\n", " \n", "plt.plot(losses)\n", "plt.plot(acc)\n", "plt.show()\n", "\n", "print('Validation accuracy: {}'.format(validation_accuracy))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
DataPilot/notebook-miner
summary_of_work/8. Turning AST into NetworkX Tree.ipynb
1
15930
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting the NetworkX Tree\n", "\n", "In order to go further into the idea of the AST, we need to expand out the info we have, and need a good data structure for this. Because the AST is defined recursively and there don't seem to be great libraries for working with it, we need a better way." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Necessary imports \n", "import os\n", "import time\n", "from nbminer.notebook_miner import NotebookMiner\n", "from nbminer.cells.cells import Cell\n", "from nbminer.features.ast_features import ASTFeatures\n", "from nbminer.stats.summary import Summary\n", "from nbminer.stats.multiple_summary import MultipleSummary" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Loading in the notebooks\n", "people = os.listdir('../testbed/Final')\n", "notebooks = []\n", "for person in people:\n", " person = os.path.join('../testbed/Final', person)\n", " if os.path.isdir(person):\n", " direc = os.listdir(person)\n", " notebooks.extend([os.path.join(person, filename) for filename in direc if filename.endswith('.ipynb')])\n", "notebook_objs = [NotebookMiner(file) for file in notebooks]\n", "a = ASTFeatures(notebook_objs)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "examp_nb = a.get_notebook(0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "23\n" ] } ], "source": [ "print (examp_nb.get_number_cells())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "new_segmentation = examp_nb.get_new_notebook()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "131\n" ] } ], "source": [ "print (new_segmentation.get_number_cells())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i, nb in enumerate(a.nb_features):\n", " a.nb_features[i] = nb.get_new_notebook()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "total_segments = 0\n", "for nb in a.nb_features:\n", " for cell in nb.get_all_cells():\n", " total_segments += 1\n", " if len(cell.get_feature('ast').body) != 1:\n", " print (\"Failed\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "19882\n" ] } ], "source": [ "print (total_segments)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "all_types = []\n", "for nb in a.nb_features:\n", " for cell in nb.get_all_cells():\n", " t = type(cell.get_feature('ast').body[0])\n", " all_types.append(t)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "counting_dict = {}\n", "for t in all_types:\n", " if t not in counting_dict:\n", " counting_dict[t] = 0\n", " counting_dict[t] += 1\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<_ast.Attribute object at 0x110b5ed68>\n", "<_ast.Subscript object at 0x110b5ee48>\n", "Assign(targets=[Attribute(value=Name(id='df_eth', ctx=Load()), attr='index', ctx=Store())], value=Subscript(value=Name(id='df_eth', ctx=Load()), slice=Index(value=Str(s='id')), ctx=Load()))\n" ] } ], "source": [ "import ast\n", " \n", "cells = new_segmentation.get_all_cells()\n", "a = cells[17].get_feature('ast')\n", "for el in a.body:\n", " for node in ast.iter_child_nodes(el):\n", " print(node)\n", " print (ast.dump(el))\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [], "source": [ "import networkx" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "type object 'Graph' has no attribute 'DirectedTree'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-20-bdc1088fed8c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mast_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnetworkx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDirectedTree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: type object 'Graph' has no attribute 'DirectedTree'" ] } ], "source": [ "ast_tree = networkx.Graph.DirectedTree()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "dgraph = networkx.DiGraph()\n", "from collections import deque\n", "nodes = deque()\n", "nodes.append(a.body[0])\n", "dgraph.add_node(a.body[0])\n", "while len(nodes) != 0:\n", " cur_node = nodes.pop()\n", " for node in ast.iter_child_nodes(cur_node):\n", " dgraph.add_node(node)\n", " dgraph.add_edge(cur_node,node)\n", " nodes.append(node)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[<_ast.Assign object at 0x110b5e5c0>, <_ast.Attribute object at 0x110b5ed68>, <_ast.Subscript object at 0x110b5ee48>, <_ast.Name object at 0x110b5e2e8>, <_ast.Index object at 0x110b5e828>, <_ast.Load object at 0x102c47d68>, <_ast.Str object at 0x110b5e400>, <_ast.Name object at 0x110b5ec18>, <_ast.Store object at 0x102c47e80>]\n", "[(<_ast.Assign object at 0x110b5e5c0>, <_ast.Attribute object at 0x110b5ed68>), (<_ast.Assign object at 0x110b5e5c0>, <_ast.Subscript object at 0x110b5ee48>), (<_ast.Attribute object at 0x110b5ed68>, <_ast.Name object at 0x110b5ec18>), (<_ast.Attribute object at 0x110b5ed68>, <_ast.Store object at 0x102c47e80>), (<_ast.Subscript object at 0x110b5ee48>, <_ast.Name object at 0x110b5e2e8>), (<_ast.Subscript object at 0x110b5ee48>, <_ast.Index object at 0x110b5e828>), (<_ast.Subscript object at 0x110b5ee48>, <_ast.Load object at 0x102c47d68>), (<_ast.Name object at 0x110b5e2e8>, <_ast.Load object at 0x102c47d68>), (<_ast.Index object at 0x110b5e828>, <_ast.Str object at 0x110b5e400>), (<_ast.Name object at 0x110b5ec18>, <_ast.Load object at 0x102c47d68>)]\n" ] } ], "source": [ "print (dgraph.nodes())\n", "print (dgraph.edges())" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "def return_graph(node):\n", " dgraph = networkx.DiGraph()\n", " nodes = deque()\n", " nodes.append(node.body[0])\n", " dgraph.add_node(node.body[0])\n", " while len(nodes) != 0:\n", " cur_node = nodes.pop()\n", " for node in ast.iter_child_nodes(cur_node):\n", " dgraph.add_node(node)\n", " dgraph.add_edge(cur_node,node)\n", " nodes.append(node)\n", " return dgraph" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "graphs = [return_graph(c.get_feature('ast')) for c in cells]\n", "roots = [c.get_feature('ast').body[0] for c in cells]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "131" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(graphs)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "max_values = []\n", "for n in range(len(graphs)):\n", " max_values.append( max(networkx.shortest_path_length(graphs[n],roots[n]).values()))" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 12., 10., 60., 23., 0., 10., 5., 3., 6., 2.]),\n", " array([ 1. , 1.8, 2.6, 3.4, 4.2, 5. , 5.8, 6.6, 7.4, 8.2, 9. ]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1117c5c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.hist(max_values)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
unaivillanueva/SocialWeb
Recipe_Analysis_Explainer.ipynb
1
2154316
null
mit
ClaudioVZ/Metodos_numericos_II
06_Elementos_finitos/003_polinomios_lagrange_locales.ipynb
1
7852
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Funciones de forma para elementos barra\n", "\n", "## Elemento de dos nodos\n", "\n", "\\begin{equation*}\n", " u = N_{1} u_{1} + N_{2} u_{2} = \\sum_{i = 0}^{1} \\alpha_{i} x^{i} = \\alpha_{0} + \\alpha_{1} x\n", "\\end{equation*}\n", "\n", "en forma matricial\n", "\n", "\\begin{equation*}\n", " u = \\alpha_{0} + \\alpha_{1} x =\n", " \\begin{bmatrix}\n", " 1 & x\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " \\alpha_{0} \\\\\n", " \\alpha_{1}\n", " \\end{bmatrix}\n", "\\end{equation*}\n", "\n", "reemplazando $x_{1} = 0$ y $x_{2} = L$\n", "\n", "\\begin{align*}\n", " \\alpha_{0} + \\alpha_{1} (0) &= u_{1} \\\\\n", " \\alpha_{0} + \\alpha_{1} (L) &= u_{2}\n", "\\end{align*}\n", "\n", "simplificando\n", "\n", "\\begin{align*}\n", " \\alpha_{0} &= u_{1} \\\\\n", " \\alpha_{0} + L \\alpha_{1} &= u_{2}\n", "\\end{align*}\n", "\n", "en forma matricial\n", "\n", "\\begin{equation*}\n", " \\begin{bmatrix}\n", " 1 & 0 \\\\\n", " 1 & L\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " \\alpha_{0} \\\\\n", " \\alpha_{1}\n", " \\end{bmatrix} =\n", " \\begin{bmatrix}\n", " u_{1} \\\\\n", " u_{2}\n", " \\end{bmatrix}\n", "\\end{equation*}\n", "\n", "resolviendo el sistema\n", "\n", "\\begin{equation*}\n", " \\begin{bmatrix}\n", " \\alpha_{0} \\\\\n", " \\alpha_{1}\n", " \\end{bmatrix} =\n", " \\begin{bmatrix}\n", " 1 & 0 \\\\\n", " -\\frac{1}{L} & \\frac{1}{L}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " u_{1} \\\\\n", " u_{2}\n", " \\end{bmatrix}\n", "\\end{equation*}\n", "\n", "reemplazando las incógnitas\n", "\n", "\\begin{align*}\n", " u &=\n", " \\begin{bmatrix}\n", " 1 & x\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " \\alpha_{0} \\\\\n", " \\alpha_{1}\n", " \\end{bmatrix} \\\\\n", " &= \\begin{bmatrix}\n", " 1 & x\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " 1 & 0 \\\\\n", " -\\frac{1}{L} & \\frac{1}{L}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " u_{1} \\\\\n", " u_{2}\n", " \\end{bmatrix} \\\\\n", " &= \\begin{bmatrix}\n", " 1 - \\frac{1}{L} x & \\frac{1}{L} x\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " u_{1} \\\\\n", " u_{2}\n", " \\end{bmatrix} \\\\\n", " &= \\begin{bmatrix}\n", " N_{1} & N_{2}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " u_{1} \\\\\n", " u_{2}\n", " \\end{bmatrix}\n", "\\end{align*}\n", "\n", "Reescribiendo $u$\n", "\n", "\\begin{equation*}\n", " u = \\bigg( 1 - \\frac{1}{L} x \\bigg) u_{1} + \\bigg( \\frac{1}{L} x \\bigg) u_{2}\n", "\\end{equation*}\n", "\n", "## Elemento de tres nodos\n", "\n", "\\begin{equation*}\n", " u = N_{1} u_{1} + N_{2} u_{2} + N_{3} u_{3} = \\sum_{i = 0}^{2} \\alpha_{i} x^{i} = \\alpha_{0} + \\alpha_{1} x + \\alpha_{2} x^{2}\n", "\\end{equation*}\n", "\n", "en forma matricial\n", "\n", "\\begin{equation*}\n", " u = \\alpha_{0} + \\alpha_{1} x + \\alpha_{2} x^{2} =\n", " \\begin{bmatrix}\n", " 1 & x & x^{2}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " \\alpha_{0} \\\\\n", " \\alpha_{1} \\\\\n", " \\alpha_{2}\n", " \\end{bmatrix}\n", "\\end{equation*}\n", "\n", "reemplazando $x_{1} = 0$, $x_{2} = \\frac{L}{2}$ y $x_{3} = L$\n", "\n", "\\begin{align*}\n", " \\alpha_{0} + \\alpha_{1}(0) + \\alpha_{2}(0)^{2} &= u_{1} \\\\\n", " \\alpha_{0} + \\alpha_{1} \\bigg( \\frac{L}{2} \\bigg) + \\alpha_{2} \\bigg( \\frac{L}{2} \\bigg)^{2} &= u_{2} \\\\\n", " \\alpha_{0} + \\alpha_{1}(L) + \\alpha_{2}(L)^{2} &= u_{3}\n", "\\end{align*}\n", "\n", "simplificando\n", "\n", "\\begin{align*}\n", " \\alpha_{0} &= u_{1} \\\\\n", " \\alpha_{0} + \\frac{L}{2} \\alpha_{1} + \\frac{L^{2}}{4} \\alpha_{2} &= u_{2}\\\\\n", " \\alpha_{0} + L \\alpha_{1} + L^{2} \\alpha_{1} &= u_{3}\n", "\\end{align*}\n", "\n", "en forma matricial\n", "\n", "\\begin{align*}\n", " \\begin{bmatrix}\n", " 1 & 0 & 0 \\\\\n", " 1 & \\frac{L}{2} & \\frac{L^{2}}{4} \\\\\n", " 1 & L & L^{2}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " \\alpha_{0} \\\\\n", " \\alpha_{1} \\\\\n", " \\alpha_{2}\n", " \\end{bmatrix} =\n", " \\begin{bmatrix}\n", " u_{1} \\\\\n", " u_{2} \\\\\n", " u_{3}\n", " \\end{bmatrix}\n", "\\end{align*}\n", "\n", "resolviendo\n", "\n", "\\begin{equation*}\n", " \\begin{bmatrix}\n", " \\alpha_{0} \\\\\n", " \\alpha_{1} \\\\\n", " \\alpha_{2}\n", " \\end{bmatrix} =\n", " \\begin{bmatrix}\n", " 1 & 0 & 0 \\\\\n", " -\\frac{3}{L} & \\frac{4}{L} & -\\frac{1}{L} \\\\\n", " \\frac{2}{L^{2}} & -\\frac{4}{L^{2}} & \\frac{2}{L^{2}}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " u_{1} \\\\\n", " u_{2} \\\\\n", " u_{3}\n", " \\end{bmatrix}\n", "\\end{equation*}\n", "\n", "reemplazando las incógnitas\n", "\n", "\\begin{align*}\n", " u &=\n", " \\begin{bmatrix}\n", " 1 & x & x^{2}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " \\alpha_{0} \\\\\n", " \\alpha_{1} \\\\\n", " \\alpha_{2}\n", " \\end{bmatrix} \\\\\n", " &= \\begin{bmatrix}\n", " 1 & x & x^{2}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " 1 & 0 & 0 \\\\\n", " -\\frac{3}{L} & \\frac{4}{L} & -\\frac{1}{L} \\\\\n", " \\frac{2}{L^{2}} & -\\frac{4}{L^{2}} & \\frac{2}{L^{2}}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " u_{1} \\\\\n", " u_{2} \\\\\n", " u_{3}\n", " \\end{bmatrix} \\\\\n", " &= \\begin{bmatrix}\n", " 1 - \\frac{3}{L} x + \\frac{2}{L^{2}} x^{2} & \\frac{4}{L} x - \\frac{4}{L^{2}} x^{2} & -\\frac{1}{L} x + \\frac{2}{L^{2}} x^{2}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " u_{1} \\\\\n", " u_{2} \\\\\n", " u_{3}\n", " \\end{bmatrix} \\\\\n", " &= \\begin{bmatrix}\n", " N_{1} & N_{2} & N_{3}\n", " \\end{bmatrix}\n", " \\begin{bmatrix}\n", " u_{1} \\\\\n", " u_{2} \\\\\n", " u_{3}\n", " \\end{bmatrix}\n", "\\end{align*}\n", "\n", "Reescribiendo $u$\n", "\n", "\\begin{equation*}\n", " u = \\bigg( 1 - \\frac{3}{L} x + \\frac{2}{L^{2}} x^{2} \\bigg) u_{1} + \\bigg( \\frac{4}{L} x - \\frac{4}{L^{2}} x^{2} \\bigg) u_{2} + \\bigg( -\\frac{1}{L} x + \\frac{2}{L^{2}} x^{2} \\bigg) u_{3}\n", "\\end{equation*}" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.3.6", "language": "julia", "name": "julia 0.3" }, "language_info": { "name": "julia", "version": "0.3.6" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
anandha2017/udacity
nd101 Deep Learning Nanodegree Foundation/notebooks/1 - playing with jupyter/.ipynb_checkpoints/test 1-checkpoint.ipynb
1
744
{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello world\n", "3\n" ] } ], "source": [ "print(\"hello world\")\n", "\n", "print (1 + 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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
escientists/cervical-cancer
notebooks/TeamImageProcessing.ipynb
1
153224
{ "cells": [ { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "show() got an unexpected keyword argument 'figsize'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-36-2777fa030b3a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0migray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\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 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/bweel/miniconda2/envs/kaggle/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 251\u001b[0m \"\"\"\n\u001b[1;32m 252\u001b[0m \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: show() got an unexpected keyword argument 'figsize'" ] }, { "data": { "image/png": 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jxzFu5s+Deqj/jY2NMDpe9oSXLRQK2tvb0/b2dswXTCbxF14AASWhzRxyPz5W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KZygQWmrVzSljg5PgVGkuagmsQxoUuz8KDe9WumE0jJvSfJ6upq/T+CuLKykrNnz3bujc9QT4ry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\npmmaUspdo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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2d48aea6d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import Image\n", "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "from skimage import io, color, feature\n", "from skimage.filters import rank\n", "from skimage.color import rgb2grey\n", "from skimage import data\n", "image = mpimg.imread(\"/data/cervicalCancer/train/Type_1/104.jpg\")\n", "igray = rgb2grey(image)\n", "plt.imshow(igray,cmap = plt.get_cmap('gray'))\n", "\n", "plt.show(figsize=(100,100))\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TCZbLJRftpVIJ8XicX4MgCAwmCIKA6+trbitQzRIOh3F6eopgMIjpdIpwOIxKpcJo23g8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\n/i8dZBFtKDNyxQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2d489c8b50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from skimage.morphology import disk\n", "ent = rank.entropy(igray, disk(5))\n", "\n", "plt.imshow(ent,cmap = plt.get_cmap('gray'))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
albertxavier001/graduation-project
pytorch/Test NetWork ALL SCENES.ipynb
1
8789
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os, glob, platform, datetime, random\n", "from collections import OrderedDict\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.utils.data as data_utils\n", "import torch.nn.parallel\n", "import torch.backends.cudnn as cudnn\n", "import torch.optim as optim\n", "from torch.autograd import Variable\n", "from torch import functional as F\n", "# import torchvision.datasets as datasets\n", "import torchvision.models as models\n", "import torchvision.transforms as transforms\n", "import scipy.io as sio\n", "import cv2\n", "from PIL import Image\n", "from tensorboardX import SummaryWriter\n", "\n", "import numpy as np\n", "from numpy.linalg import inv as denseinv\n", "from scipy import sparse\n", "from scipy.sparse import lil_matrix, csr_matrix\n", "from scipy.sparse.linalg import spsolve\n", "from scipy.sparse.linalg import inv as spinv\n", "import scipy.misc\n", "\n", "from myimagefoldereccv import MyImageFolder\n", "from mymodel import GradientNet\n", "from myargs import Args" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def loadimg(path):\n", " im = Image.open(path).convert('RGB')\n", " print(im.size)\n", " im = transforms.ToTensor()(im)\n", " x = torch.zeros(1,3,416,1024)\n", " x[0,:,:,:] = im[:,0:416,0:1024]\n", " #x = torch.zeros(1,3,32,32)\n", " #x[0,:,:,:] = im[:,0:32,0:32]\n", " x = Variable(x, volatile=True)\n", " return x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def save_csv(path, para):\n", " text = ''\n", " n,c,h,w = para.size()\n", " text += ','.join([str(n), str(c), str(h), str(w)]) + ','\n", " for nn in range(n):\n", " for cc in range(c):\n", " for hh in range(h):\n", " for ww in range(w):\n", " text += str(para[nn,cc,hh,ww].data.cpu().numpy()) + ','\n", " with open(path, 'w') as f:\n", " f.write(text)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "gpu_num = 0\n", "gradient = False\n", "type2 = 'rgb' if gradient == False else 'gd'\n", "image_slpit = True\n", "\n", "# parameters = filter(lambda p: p.requires_grad, net.parameters())\n", "# optimizer = optim.SGD(parameters, lr=args.base_lr, momentum=args.momentum)\n", "\n", "res_root = './results/images/'\n", "scenes = glob.glob('/home/albertxavier/dataset/sintel2/clean/*')\n", "cnt_albedo = 0\n", "cnt_shading = 0\n", "for scene in scenes:\n", " scene = scene.split('/')[-1]\n", " res_dir = os.path.join(res_root, 'image_split', scene)\n", " if not os.path.exists(res_dir):\n", " os.makedirs(res_dir)\n", " for type_ in ['albedo', 'shading']:\n", " \n", "# if scene!='image_split': continue\n", " \n", " #root = '/media/lwp/xavier/graduation_results/showcase_model/image_split/{}/{}/'.format(type_, type2)\n", "# root = '/media/lwp/xavier/graduation_results/showcase_model/{}/{}/{}/'.format(scene, type_, type2)\n", " root = '/media/albertxavier/data/eccv/graduation-project/pytorch/snapshot0/'\n", " print (root+'snapshot-238.pth.tar')\n", " if not os.path.exists(root+'snapshot-238.pth.tar'): continue\n", " snapshot = torch.load(root+'snapshot-238.pth.tar')\n", " state_dict = snapshot['state_dict']\n", " args = snapshot['args']\n", " densenet = models.__dict__[args.arch](pretrained=True).cuda(gpu_num)\n", " \n", "# net.load_state_dict(state_dict)\n", "# net.train()\n", " net = None\n", " num = 40 if scene=='market_6' else 50\n", " for ind in range(1, 11):\n", " if net is not None: del net\n", "# torch.cuda.empty_cache()\n", " net = GradientNet(densenet=densenet, growth_rate=32, \n", " transition_scale=2, pretrained_scale=4,\n", " debug=False).cuda(gpu_num)\n", " \n", " net.load_state_dict(state_dict)\n", " net.train()\n", " frame = 'frame_%04d.png'%(ind)\n", " print('/home/albertxavier/dataset/sintel2/clean/{}/{}'.format(scene, frame))\n", " im = loadimg('/home/albertxavier/dataset/sintel2/clean/{}/{}'.format(scene, frame)).cuda(gpu_num)\n", " print(im.size())\n", " merged = net(im.cuda(gpu_num))\n", " alpha = merged[0,9:10,:,:]\n", " beta = merged[0,10:13,:,:]\n", " alpha = alpha.cpu().data.numpy()\n", " beta = beta.cpu().data.numpy()\n", " alpha = alpha.transpose((1,2,0))\n", " beta = beta.transpose((1,2,0))\n", " print('alpha', alpha.min(), alpha.max())\n", " print('beta', beta.min(), beta.max())\n", " #print(merged)\n", " ######\n", " #break\n", " ######\n", " # merged = mergeRGB\n", " merged = merged[0]\n", " # merged = merged[0:3,:,:]\n", " merged = merged.cpu().data.numpy()\n", " print (merged.shape)\n", " merged = merged.transpose(1,2,0)\n", " print (merged.shape)\n", " B = merged[:,:,0:3]\n", " dx = merged[:,:,3:6]\n", " dy = merged[:,:,6:9]\n", " res_frame = 'albedo_%04d.png'%(ind) if type_ == 'albedo' else 'shading_%04d.png'%(ind)\n", " res_dx_frame = 'albedo_dx_%04d.png'%(ind) if type_ == 'albedo' else 'shading_%04d.png'%(ind)\n", " res_dy_frame = 'albedo_dy_%04d.png'%(ind) if type_ == 'albedo' else 'shading_%04d.png'%(ind)\n", " res_alpha = 'alpha_%04d.mat'%(ind)\n", " res_beta = 'beta_%04d.mat'%(ind)\n", " print('res path', os.path.join(res_dir,res_frame))\n", " cv2.imwrite(os.path.join(res_dir,res_frame), B[:,:,::-1]*255) \n", " cv2.imwrite(os.path.join(res_dir,res_dx_frame), (dx[:,:,::-1]+0.5)*255) \n", " cv2.imwrite(os.path.join(res_dir,res_dy_frame), (dy[:,:,::-1]+0.5)*255) \n", " # save_csv(os.path.join(res_dir,res_alpha), alpha)\n", " # save_csv(os.path.join(res_dir,res_beta), beta)\n", " sio.savemat(os.path.join(res_dir,res_alpha), {'alpha': alpha})\n", " sio.savemat(os.path.join(res_dir,res_beta), {'beta': beta})\n", " break\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# if gradient == False:\n", "# merged = mergeRGB[5]\n", "# merged = merged[0]\n", "# merged = merged.cpu().data.numpy()\n", "# print (merged.shape)\n", "# merged = merged.transpose(1,2,0)\n", "# print (merged.shape)\n", "# dx = merged[:,:,0:3]\n", "# cv2.imwrite('out_merge.png', dx[:,:,::-1]*255)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# if gradient == True:\n", "# merged = mergeRGB[5]\n", "# merged = merged[0]\n", "# merged = merged.cpu().data.numpy()\n", "# print (merged.shape)\n", "# merged = merged.transpose(1,2,0)\n", "# print (merged.shape)\n", "# dy = merged[:,:,0:3]+0.5\n", "# dx = merged[:,:,3:6]+0.5\n", "# cv2.imwrite('out_merge_dx.png', dx[:,:,::-1]*255)\n", "# cv2.imwrite('out_merge_dy.png', dy[:,:,::-1]*255)\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
matthewfeickert/fellowship-project
Notebooks/Edward_Examples/Edward-Simple-Example.ipynb
1
11494
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example model in Edward" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "import edward as ed\n", "# specific modules\n", "from edward.models import Normal\n", "from edward.models import ParamMixture\n", "# visualization\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sample_model(model_template, n_samples):\n", " \"\"\"\n", " Make n_sample observations of an Edward model\n", " \n", " Args:\n", " model_template (edward.models): An Edward model (a sample_shape is not required)\n", " n_samples (int): The number of observation of the model to make\n", " \n", " Returns:\n", " model (edward.models): An Edward model with sample_shape=n_samples\n", " samples (np.ndarray): An array of n_samples sampled observation of model\n", " \"\"\"\n", " model = model_template.copy(sample_shape=n_samples)\n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " samples = sess.run(model)\n", " return model, samples" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fit_model(model, observations, POI, fit_type='mle'):\n", " \"\"\"\n", " Perform a fit of the model to data\n", " \n", " Args:\n", " model (ed.models class): An Edward model\n", " observations (np.ndarray): Data to fit the model to\n", " POI (list): Parameters of interest to return fit results on\n", " fit_type (str): The minimization technique used\n", " \n", " Returns:\n", " fit_result (list): An list of the fitted model parameters of interest\n", " \"\"\"\n", " # observations is an ndarray of (n_observations, d_features)\n", " # model and data (obsevations) need to have the same size\n", " assert model.get_shape() == observations.shape,\\\n", " \"The model and observed data features must be of the same shape.\\n\\\n", " The model passed has shape {0} and the data passed have shape (n_observations, d_features) = {1}\".format(\n", " model.get_shape(), observations.shape)\n", " \n", " fit_type = fit_type.lower()\n", " if fit_type == 'mle':\n", " fit = ed.MAP({}, data={model: observations}) # http://edwardlib.org/api/ed/MAP\n", " else:\n", " fit = ed.MAP({}, data={model: observations}) #default to mle\n", " fit.run()\n", " \n", " sess = ed.get_session()\n", " \n", " fit_result = []\n", " for poi in POI:\n", " fit_result.append(sess.run(poi))\n", " return fit_result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's construct an Edward model which will represent $N$ observations of a 1-d Gaussian" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000/1000 [100%] ██████████████████████████████ Elapsed: 1s | Loss: 1410.116\n", "[2.963619]\n" ] } ], "source": [ "#edward model: univariate Normal\n", "mean = tf.Variable(3.0, name='mean')\n", "std = tf.Variable(1.0, name='std')\n", "N = 1000\n", "\n", "model_template = Normal(loc=mean, scale=std)\n", "\n", "# make N observations of model\n", "model, samples = sample_model(model_template, N)\n", "\n", "POI = [mean]\n", "fit_result = fit_model(model, samples, POI)\n", "print(fit_result)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4200ce0048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(samples, bins=20, range=(-3.0, 9.0))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "---" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
sebastiandres/mat281
clases/Unidad1-IntroduccionYProyectos/Clase02-Proyectos2014/Proyectos.ipynb
2
25219
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "text/html": [ "<style>\n", "\n", "/*********************************************\n", " * COLORS FOR EXAMPLES\n", " *********************************************/\n", "em {color: #800000;}\n", "span.good {color: #008000;}\n", "span.warning {color: #808000;}\n", "span.bad {color: #800000;}\n", "\n", "/*********************************************\n", " * GLOBAL STYLES\n", " *********************************************/\n", ".reveal h1 {color: #000000; text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2);}\n", ".reveal h2 {color: #222222; text-shadow: 0px 0px 5px rgba(0, 0, 0, 0.2);}\n", ".reveal h3 {color: #444444; text-shadow: 0px 0px 4px rgba(0, 0, 0, 0.2);}\n", ".reveal h4 {color: #666666; text-shadow: 0px 0px 3px rgba(0, 0, 0, 0.2);}\n", ".reveal h5 {color: #888888; text-shadow: 0px 0px 2px rgba(0, 0, 0, 0.2);}\n", ".reveal h6 {color: #AAAAAA; text-shadow: 0px 0px 1px rgba(0, 0, 0, 0.2);}\n", "\n", "/*********************************************\n", " * IMAGES\n", " *********************************************/\n", ".reveal section img { margin-left:auto; margin-right:auto;}\n", "\n", "</style>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "IPython Notebook v4.0 para python 2.7\n", "Librerías adicionales: Ninguna.\n", "Contenido bajo licencia CC-BY 4.0. Código bajo licencia MIT. (c) Sebastian Flores.\n", "\"\"\"\n", "\n", "# Configuracion para recargar módulos y librerías \n", "%reload_ext autoreload\n", "%autoreload 2\n", "\n", "from IPython.core.display import HTML\n", "\n", "HTML(open(\"style/mat281.css\", \"r\").read())" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<header class=\"w3-container w3-teal\">\n", "<img src=\"images/utfsm.png\" alt=\"\" height=\"100px\" align=\"left\"/>\n", "<img src=\"images/mat.png\" alt=\"\" height=\"100px\" align=\"right\"/>\n", "</header>\n", "<br/><br/><br/><br/><br/>\n", "# MAT281\n", "## Aplicaciones de la Matemática en la Ingeniería\n", "\n", "### Sebastián Flores\n", "\n", "* [email protected]\n", "* https://www.github.com/sebastiandres/mat281\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## ¿Qué contenido aprenderemos?\n", "\n", "* Formalidades\n", "* Proyectos 2014\n", "* Proyectos 2015" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## ¿Porqué aprenderemos ese contenido?\n", "\n", "* Formalidades \n", " * Rrevisión de horarios y programa del curso.\n", "* Proyectos 2014 \n", " * Ver ejemplos concretos de proyectos realizados por alumnos. \n", "* Proyectos 2015\n", " * Conocer posibles proyectos y comenzar a discutir posibles grupos y proyectos." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## 1- Formalidades\n", "\n", "* Horario del curso:\n", " * Lunes 3-4, P212\n", " * Miercoles 3-4, F265\n", "* Horario ayudantía:\n", " * Lunes bloques 13-14\n", "* Programa de la asignatura" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## 2- Proyectos 2014\n", "* **Reconciliación de datos**\n", " * Aplicación a celdas de flotación de minería.\n", "* **Análisis de Sentimientos**\n", " * Aplicación a tweets.\n", "* **Interpolación espacial con Krigging**.\n", " * Aplicación a datos de crimen.\n", "* **Valor intrínseco de un producto**.\n", " * Aplicación a datos de Amazon." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### 2- Proyectos 2014\n", "\n", "## Interpolación espacial con Krigging\n", "### Aplicación a datos de crimen\n", "### Diego Gajardo." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### 2- Proyectos 2014\n", "\n", "## Valor intrínseco de un producto.\n", "### Aplicación a datos de Amazon.\n", "### Alberto Rubio" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## 3- Proyectos 2015\n", "\n", "* Proyectos definidos\n", "\n", "* Proyectos por definir" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Proyectos definidos\n", "\n", "* Entity resolution\n", "* Model Order Reduction\n", "* Call Center Mathematics\n", "* Electrical Trees" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#¿Cómo comparar en internet?\n", "<hr>\n", "## Entity Resolution\n", "#### Proyectos definidos\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos definidos\n", "## Entity Resolution\n", "Conocida multitienda 1:\n", "<img src=\"images/entityresolution1.png\" alt=\"\" width=\"800px\" align=\"middle\"/>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos definidos\n", "## Entity Resolution\n", "Conocida multitienda 2:\n", "<img src=\"images/entityresolution2.png\" alt=\"\" width=\"800px\" align=\"middle\"/>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos definidos\n", "## Entity Resolution\n", "\n", "* ¿Son los productos anteriores iguales?\n", "* ¿Cómo medir las diferencias entre 2 \"items\" arbitrarios de las tiendas?\n", "* ¿Cómo asignar eficientemente pares entre 2 bases de datos \"grandes\"?\n", "\n", "##### Temáticas\n", "Lenguaje natural, optimización, machine learning." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos definidos\n", "## Entity Resolution\n", "**AKA**: \"record linkage\", \"list washing\", \"database merging\", \"data matching\", ...\n", "\n", "####Definición formal:\n", "Tomar dos o más bases de datos y generar clases de equivalencia entre ellos.\n", "\n", "##### Definición informal:\n", "Determinar si 2 productos son iguales, a pesar de tener \"definiciones\" distintas." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos definidos\n", "## Entity Resolution\n", "\n", "##### \"Golden\" Datasets\n", "Ver el link: http://dbs.uni-leipzig.de/en/research/projects/object_matching/fever/\n", " benchmark_datasets_for_entity_resolution\n", "\n", "* **Amazon-GoogleProducts**: Datos de e-commerce.\n", "* **Abt-Buy**: Datos de e-commerce.\n", "* **DBLP-ACM**: Datos bibliográficos.\n", "* **DBLP-Schola**: Datos bibliográficos.\n", "\n", "\n", "\n", "##### Algunas referencias:\n", "* Evaluating Entity Resolution Results\n", "David Menestrina, Steven Euijong Whang, Hector Garcia-Molina.\n", "* Disinformation Techniques for Entity Resolution. Steven Euijong Whang, Hector Garcia-Molina.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# ¿Cómo simplificar un problema?\n", "<hr>\n", "## Model Order Reduction\n", "#### Proyectos definidos\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos definidos\n", "## Model Order Reduction\n", "\n", "<img src=\"images/mor.png\" alt=\"\" width=\"600px\" align=\"middle\"/>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos definidos\n", "## Model Order Reduction\n", "\n", "**AKA**: \"dimensionality reduction\", \"feature extraction\"...\n", "\n", "Reducir el \"tamaño\" de un problema resulta interesante para simulaciones computacionales, optimización, uncertainty quantification y análisis de sensibilidad.\n", " \n", "#### Definición formal:\n", "Disminución del tamaño computacional en simulaciones de sistemas dinámicos de gran tamaño.\n", "\n", "##### Definición informal:\n", "Simplificar el problema tomando los elementos (o mezclas de éstos) que entregan las mayores contribuciones.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos definidos\n", "## Model Order Reduction\n", "\n", "##### Algunos ejemplos\n", "* HyShot II scramjet\n", "* Photovoltaic solar cell\n", "* Airfoil shape optimization \n", "\n", "##### Algunas referencias:\n", "* Active Subspaces, Paul G. Constantine.\n", "* A Comparison of Some Model Order Reduction Techniques, Rodney Slone, Jin-fa Lee, Robert Lee." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# ¿Cómo podemos optimizar un call center?\n", "<hr>\n", "## Call Center Mathematics\n", "#### Proyectos definidos\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos definidos\n", "## Call Center Mathematics\n", "<img src=\"images/callcenter.jpg\" alt=\"\" width=\"300px\" align=\"right\"/>\n", "\n", "¿Cómo podemos optimizar un call center, de manera científica?\n", "\n", "* Problemas de optimización estocástica.\n", "* Dados ciertos turnos, \n", " * ¿Qué calidad de servicio se entrega?\n", "* Dada una calidad de servicio deseada, \n", " * ¿Cómo se deben organizar los turnos?\n", "* Algunas formulas conocidas: Erlang C, Erlang F, \n", "\n", "##### Temáticas\n", "Simulación, optimización, estadística, probabilidades, industrias." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos definidos\n", "## Call Center Mathematics\n", "\n", "##### Datasets\n", "* http://klipfolio.uservoice.com/knowledgebase/articles/81667-call-center-data-spreadsheet-\n", "* http://ie.technion.ac.il/serveng/callcenterdata/index.html\n", "\n", "##### Algunas referencias\n", " * Ger Koole: Fundador de CCmath, \"call center optimization company\". Varios libros y artículos en la web.\n", " * Queueing Models of Call Centers: An Introduction. Ger Koole y Avishai Mandelbaum.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#¿Fractales en la naturaleza?\n", "<hr>\n", "## Árboles eléctricos\n", "#### Proyectos definidos\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Árboles eléctricos\n", "#### Proyectos definidos\n", "<img src=\"images/arboleselectricos.png\" alt=\"\" width=\"800px\" align=\"middle\"/>\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Árboles eléctricos\n", "#### Proyectos definidos\n", "<img src=\"images/arboleselectricos2.jpg\" alt=\"\" width=\"800px\" align=\"middle\"/>\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos definidos\n", "## Árboles eléctricos\n", "<img src=\"images/arboleselectricos3.jpg\" alt=\"\" width=\"800px\" align=\"middle\"/>\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Árboles eléctricos\n", "#### Proyectos definidos\n", "\n", "* ¿Porqué la electricidad viaja en una trayectoria fractal en el material?\n", "* ¿Qué características del medio condicionan las características del árbol eléctrico?\n", "* ¿Qué predomina, determinismo o aleatoriedad, en la propagación eléctrica?\n", "\n", "##### Temática\n", "Fractales, simulación, visualización, modelamiento.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos definidos\n", "## Árboles eléctricos\n", "\n", "##### Datasets\n", "* Departamento de Eléctrica, UTFSM.\n", "\n", "##### Algunas referencias\n", " * Three-Dimensional Imaging and Analysis of Electrical Trees, Roger Schurch.\n", " * Fractal Analysis of Electrical Trees, K. Kudo." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Proyectos por definir\n", "\n", "* Proyectos en Kaggle\n", "* Proyectos en HeroX\n", "* API del gobierno\n", "* Otras APIS y fuentes de datos\n", "* Otras ideas" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos por definir\n", "## Kaggle\n", "\n", "<img src=\"images/kaggle.png\" alt=\"\" height=\"100px\" align=\"right\"/>\n", "* http://www.kaggle.com/\n", "* Plataforma de concursos de Machine Learning y Data Science.\n", "* Modalidad:\n", " * Descargar datos\n", " * Seleccionar y afinar un modelo\n", " * Predecir resultados" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos por definir\n", "## Kaggle\n", "\n", "Proyectos actuales:\n", "* 1- **Springleaf Marketing Response**: Determine whether to send a direct mail piece to a customer.\n", "* 2- **Western Australia Rental Prices**: Predict rental prices for properties across Western Australia.\n", "* 3- **Rossmann Store Sales**: Forecast sales using store, promotion, and competitor data.\n", "* 4- **Flavours of Physics**: Identify a rare decay phenomenon.\n", "* 5- **Right Whale Recognition**: Identify endangered right whales in aerial photographs.\n", "* 6- **How Much Did It Rain?**: Predict hourly rainfall using data from polarimetric radars." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos por definir\n", "## Kaggle\n", "Proyectos actuales:\n", " * 7- **Ocean Ship Logbooks (1750-1850)**: Explore changing climatology with data from early shipping logs.\n", " * 8- **Hillary Clinton's Emails**: Uncover the political landscape in Hillary Clinton's emails.\n", " * 9- **Meta Kaggle**: The dataset on Kaggle, on Kaggle.\n", " * 10- **What's Cooking?**: Use recipe ingredients to categorize the cuisine.\n", " * 11- **San Francisco Crime Classification**: Predict the category of crimes that occurred in the city by the bay.\n", " * 12- **Denoising Dirty Documents**: Remove noise from printed text." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos por definir\n", "## HeroX\n", "\n", "<img src=\"images/herox.png\" alt=\"\" height=\"100px\" align=\"right\"/>\n", "* http://www.herox.com/\n", "* Plataforma de concursos de Machine Learning y Data Science.\n", "* Similar a Kaggle, pero un poco más diverso.\n", "* Modalidad:\n", " * Descargar datos\n", " * Seleccionar y afinar un modelo\n", " * Predecir resultados" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos por definir\n", "## HeroX\n", "Proyectos actuales\n", "* 1- **Cognitive Computing Challenge**: Build a cognitive system that can read a document, then load a database with what it finds.\n", "* 2- **Integra Gold Rush Challenge**: Integra Gold is offering $1 million to help lead us to the next big gold discovery in Val-d'Or, Canada.\n", "* 3- **Sky for All: Air Mobility for 2035 and Beyond**: Envision the skies of 2035 and design an airspace system that allows vehicles to safely and efficiently navigate...\n", "* 4- **The Lunar Initiatives Flash Art Competition**: Calling all writers and 2D artists! Submit your lunar artwork in the Lunar...\n", "* 5- **Financial Revolutionaries Enhancing Education**: Educating For Financial Freedom" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Proyectos por definir\n", "## HeroX\n", "Proyectos actuales:\n", "* 6- **Operation Blue Sky**: Aboriginal Health Initiative: MNP presents an ideation challenge to improve health outcomes\n", "* 7- **Autism Speaks House to Home Prize**: Autism Speaks is searching for belief-busting breakthroughs in housing and residential supports for...\n", "* 8- **Raising the Bar on Healthcare**: A video challenge to share how Redirect Health raised the bar on healthcare and led to lowered costs...\n", "* 9- **Clinical Trial Innovation Prize**: Producing a breakthrough that doubles the accrual rate of clinical trials in the diagnosis and treatment of cancer.\n", "* 10- **CHIME National Patient ID Challenge**: Ensure 100% accuracy of every patient’s health info to reduce preventable medical errors and..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Interludio\n", "\n", "## API\n", "¿Qué es una API?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "**Application Programming Interface**: abstracción que permite a terceros consumir datos de un programa o de un sitio web.\n", "\n", "Ejemplos clásicos:\n", "* API de Twitter: Permite encontrar tweets por pais, por idioma, por fecha, etc...\n", "* API de Google Maps: permite que desarrolladores construyan aplicaciones con los datos y mapas de google maps. \n", "\n", "No todas las APIs son idénticas, pero existen similaridades y ciertas \"buenas prácticas\"." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos por definir\n", "## API datos.gob.cl\n", "\n", "<img src=\"images/APIgob.png\" alt=\"\" height=\"100px\" align=\"right\"/>\n", "* http://recursos.datos.gob.cl/\n", "* Plataforma con datos relativos a Chile: ministerios y consejos, desde educación a homicidios.\n", "* **Sólo están los datos**: usteden tiene que elaborar una pregunta, proponer una estrategia de resolución e implementarla.\n", "* Modalidad:\n", " * Descargar una base de datos" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos abiertos\n", "## API datos.gob.cl\n", "Ejemplos de bases de datos recientes:\n", "* **Organizaciones Comunitarias**\n", " * Fuente: Municipalidad de Máfil\n", " * Categorías: Comunicaciones Comunidad Sociedad General\n", " * Formatos: xls\n", " * Descripción: Nómina de organizaciones comunitarias \n", "\n", "* **Patentes Comerciales Renovadas 1er Semestre 2015**\n", " * Fuente: Municipalidad de Los Lagos\n", " * Categorías: Negocios Comunidad Finanzas Planificación\n", " * Formatos: xlsx\n", " * Fecha de publicación: 30 de septiembre del 2015\n", " * Descripción: Listado de patentes comerciales renovadas y vigentes para el primera semestre del año 2015. \n", "\n", "* **Abonados Móviles**\n", " * Fuente: Subsecretaría de Telecomunicaciones\n", " * Categorías: Comunicaciones\n", " * Formatos: xlsx\n", " * Fecha de publicación: 30 de septiembre del 2015\n", " * Descripción: Abonados Móviles" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos abiertos\n", "## API datos.gob.cl\n", "Ejemplos de datos más descargados:\n", "* **Precipitaciones diarias por Estaciones**\n", " * Fuente: Dirección General de Aeronáutica Civil\n", " * Categorías: Negocios Comunicaciones Comunidad Cultura\n", " * Formatos: csv xml\n", " * Fecha de publicación 8 de septiembre del 2015\n", " * Descripción: Muestra las precipitaciones ocurridas por período, en cada estación a lo largo del pais.\n", "\n", "* **PRODUCTO INTERNO BRUTO DE CHILE**\n", " * Fuente: Comisión Chilena del Cobre\n", " * Categorías: Gobierno\n", " * Formatos: html\n", " * Fecha de publicación 9 de julio del 2013\n", " * Descripción: Contiene información del Producto Interno Bruto por Clase de Actividad Económica a precios corrientes y volumen a precios del año anterior encadenado. Series anuales disponibles desde el año 2003.\n", "\n", "* **CENSO 2002**\n", " * Fuente: Instituto Nacional de Estadísticas\n", " * Categorías: Cultura Educación Sociedad Tecnología\n", " * Formatos: txt gz\n", " * Fecha de publicación 1 de febrero del 2013\n", " * Descripción: El censo es la medición más importante del país, se realiza cada 10 años y es el operativo estadístico más amplio que se realiza en Chile.Debe cumplir con tres características..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos abiertos\n", "## Otros sitios y APIs\n", "Algunos ejemplos (no limitantes):\n", "* http://mindicador.cl/: principales indicadores económicos para Chile.\n", "* http://www.cs.cmu.edu/~enron/: Enron email dataset.\n", "* http://datahub.io/: Colección de diversos datasets mundiales.\n", "* http://www.openstreetmap.org/: Datos geográficos colaborativos.\n", "* http://musicbrainz.org/: open music encyclopedia." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Consejo\n", "\n", "Definan el tema que más les interesa, y luego busquen un sitio o API apropiada." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Proyectos por definir \n", "## Otras ideas\n", "* ¿Es posible predecir la existencia de monopolios, mediante un procesamiento automático de indicadores económicos?\n", "\n", "* ¿Cómo imprime una impresoras 3D? ¿Cómo se diseña/optimiza la impresión?\n", " \n", "* Reconstrucción tridimensional con datos obtenidos por drones." ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
cc0-1.0
fweik/espresso
doc/tutorials/electrokinetics/electrokinetics.ipynb
1
33403
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Electrokinetics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of Contents\n", "1. [Introduction](#Introduction)\n", "2. [Theoretical Background](#Theoretical-Background)\n", " 1. [The Electrokinetic Equations](#The-Electrokinetic-Equations)\n", " 2. [EOF in the Slit Pore Geometry](#EOF-in-the-Slit-Pore-Geometry)\n", "3. [Simulation using ESPResSo](#Simulation-using-ESPResSo)\n", " 1. [Setting up ESPResSo](#Setting-up-ESPResSo)\n", " 2. [Mapping SI and Simulation Units](#Mapping-SI-and-Simulation-Units)\n", " 3. [Setting up the slit pore system](#Setting-up-the-slit-pore-system)\n", "4. [References](#References)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "In recent years the lattice-Boltzmann method (LBM) has proven itself to be a viable way to introduce hydrodynamic interactions into coarse-grained MD simulations with moderate computational cost.\n", "The success of the GPU LBM implementation in ESPResSo and similar developments in other software packages created demand for further developments in this area.\n", "ESPResSo features two such algorithms, namely ELECTROHYDRODYNAMICS, and ELECTROKINETICS (EK).\n", "Both of these make use of the LBM and extend it to coarse-grain not only the solvent molecules but also ionic solutes.\n", "ELECTROHYDRODYNAMICS does so using a slip layer coupling for charged particles valid in the thin Debye layer (large salt concentration) limit [1], while EK explicitly treats the ionic solutes in a continuum fashion and is valid for a wide range of salt concentrations [2-4].\n", "\n", "### Tutorial Outline\n", "\n", "To make our first steps using ELECTROKINETICS we will work on one of the few systems for which analytic solutions for the electrokinetic equations exist: the slip pore geometry with a counterion-only electrolyte.\n", "The same slit pore system is also treated in the LBM tutorial, but there, the ionic species were modeled as explicit particles.\n", "For this system, the two approaches lead to exactly the same results [5].\n", "Differences became significant for multivalent ions, very high salt concentrations, and very high surface charge, since then the mean-field approach the EK employs, is basically solving the Poisson-Nernst-Planck formalism plus the Navier-Stokes equation on a lattice.\n", "This leads to significantly different results from explicit ion approaches [6-8].\n", "This tutorial is mainly divided into two sections.\n", "* **Theoretical Background** introduces the electrokinetic equations and the analytical solution for the slit pore system.\n", "* **Simulation using ESPResSo** deals exclusively with the simulation. \n", "\n", "If you already know about simple diffusion-migration-advection equations, continuum electrostatics, and Navier-Stokes, then you can skip the first section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Theoretical Background\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Electrokinetic Equations\n", "\n", "In the following, we will derive the equations modeling the time evolution of the concentrations of dissolved species as well as the solvent in the standard electrokinetic model.\n", "We do so, neglecting the salt ions' contribution to the overall mass density, which allows us to treat the dynamics of the ions and the fluid separately [8].\n", "The solvent fluid will be modeled using the Navier-Stokes equations while we use a set of diffusion-migration-advection equations for the ionic species.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Ionic Species\n", "\n", "The description starts with the ionic species' concentrations $c_{k}(\\vec{r}, t)$ (number density) and the associated flux densities $\\vec{j}_{k}(\\vec{r}, t)$, for which mass conservation holds\n", "\n", "\\begin{equation}\n", "\\label{eq:model_continuity}\n", "\\partial_{t} c_{k} = -\\nabla \\cdot\\vec{j}_{k} . \n", "\\end{equation}\n", "\n", "Here $\\vec{r}$ denotes the spatial coordinate and $t$ the time, while $k$ enumerates the ionic species.\n", "The fluxes are caused by diffusion (due to density variations and external forces) and advection.\n", "\n", "The advective contribution to the flux is given by\n", "\n", "\\begin{equation}\n", "\\label{eq:adv_flux}\n", "\\vec{j}_{k}^{\\mathrm{adv.}} = c_{k} \\vec{u} ,\n", "\\end{equation}\n", "\n", "where $\\vec{u}(\\vec{r}, t)$ denotes the fluid velocity (advective velocity).\n", "This equation models advection as a simple co-movement of the dissolved ions with the surrounding fluid.\n", "All inertial effects of the ions are neglected.\n", "\n", "The diffusive behavior of the ions is best described in a reference frame co-moving with the local advective velocity $\\vec{u}$.\n", "We assume that the species' relative fluxes instantaneously relax to a local equilibrium.\n", "This assumption allows us to derive the diffusive fluxes from a local free-energy density, which we define as\n", "\n", "\\begin{equation}\n", "\\label{eq:model_free_energy}\n", "f \\big( c_{k}(\\vec{r}) \\big) = \\sum_{k} \\underbrace{k_{\\mathrm{B}}T c_{k}(\\vec{r}) \\left[ \\log \\left\\lbrace \\Lambda_{k}^{3} c_{k}(\\vec{r}) \\right\\rbrace - 1 \\right] }_{\\mathrm{ideal~gas~contribution}} + \\underbrace{z_{k} e c_{k}(\\vec{r}) \\Phi(\\vec{r})}_{\\mathrm{electrostatic~contribution}} ,\n", "\\end{equation}\n", "\n", "with the $\\Lambda_{k}$ the species' thermal de Broglie wavelengths, $z_{k}$ their valencies, and $\\Phi(\\vec{r})$ the electrostatic potential.\n", "This free-energy density consists of only an ideal-gas and an electrostatic contribution.\n", "The same assumptions form the basis of Poisson-Boltzmann (PB) theory.\n", "Hence, the limitations of this model are the same as those of PB.\n", "That means this model applies to monovalent ions at low to intermediate densities and surface charges [6,7,11,12].\n", "\n", "The species' chemical potentials $\\mu_{k}$ implied by the free-energy density read\n", "\n", "\\begin{equation}\n", "\\label{eq:chempot}\n", "\\mu_{k}(\\vec{r}) = \\delta_{c_k} f(c_{k}\\big( \\vec r ) \\big) = k_{\\mathrm{B}}T \\log(\\Lambda_{k}^{3} c_{k}(\\vec{r})) + z_{k} e \\Phi(\\vec{r}) .\n", "\\end{equation}\n", "\n", "This in turn allows us to formulate the first-order approximation to the thermodynamic driving force as the gradient of the chemical potential, which we use to define an expression for the diffusive flux\n", "\n", "\\begin{equation}\n", "\\begin{aligned}\n", "\\label{eq:model_jdiff}\n", "\\vec{j}_{k}^{\\mathrm{diff}} &= \\xi_{k} \\left( -c_{k} \\nabla \\mu_{k} \\right) = -k_{\\mathrm{B}}T \\xi_{k} \\nabla c_{k} - \\xi_{k} z_{k} e c_{k} \\nabla\\Phi \\\\\n", "& = -D_{k} \\nabla c_{k} - \\xi_{k} z_{k} e c_{k} \\nabla \\Phi . \n", "\\end{aligned}\n", "\\end{equation}\n", "\n", "Here, $\\xi_{k}$ and $D_{k}$ denote the mobility and the diffusion coefficient of species $k$, which are related by the Einstein-Smoluchowski relation $D_{k} / \\xi_{k} = k_{\\mathrm{B}}T$ [12,13].\n", "\n", "Finally, the total number density flux combining effects of diffusion and advection reads\n", "\n", "\\begin{equation}\n", "\\label{eq:model_fluxes}\n", "\\vec{j}_{k} = \\vec{j}_{k}^{\\mathrm{diff}} + \\vec{j}_{k}^{\\mathrm{adv.}} = -D_{k} \\nabla c_{k} - \\xi_{k} z_{k} e c_{k} \\nabla \\Phi + c_{k} \\vec{u} , \n", "\\end{equation}\n", "\n", "where the first term represents Fick's law of diffusion in the absence of an external potential, the second term gives the additional flux due to an external (in this case electrostatic) potential, and the last term introduces the influence of the motion of the underlying solvent." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Electrostatics\n", "\n", "The dynamics of the charged species in a typical micro- or nanofluidic system are slow compared to the relaxation of the electromagnetic fields.\n", "This allows us to use stationary equations to model electromagnetic effects.\n", "We further assume that the modeled species do not carry permanent magnetic dipoles and that electric currents in the system are small.\n", "Under these conditions, the full set of Maxwell's equations reduces to the Poisson equation\n", "\n", "\\begin{equation}\n", "\\label{eq:model_poisson}\n", "\\nabla^2 \\Phi = - \\frac{1}{\\varepsilon} \\sum_{k} z_{k} e c_{k} = -4 \\pi l_\\mathrm{B} k_{\\mathrm{B}}T \\sum_{k} z_{k} c_{k} . \n", "\\end{equation}\n", "\n", "Here $\\varepsilon = \\varepsilon_{0} \\varepsilon_r$ denotes the product of the vacuum permittivity $\\varepsilon_{0}$ with the relative permittivity of the solvent $\\varepsilon_r$.\n", "We have also used the Bjerrum-length\n", "\n", "\\begin{equation}\n", "\\label{eq:lbjerr}\n", "l_\\mathrm{B} = \\frac{e^{2}}{4 \\pi \\varepsilon k_{\\mathrm{B}}T}.\n", "\\end{equation}\n", "\n", "Finally, we have assumed that the permittivity is spatially homogeneous, since this will allow us to use efficient spectral methods to solve this equation.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Hydrodynamics\n", "\n", "As said before, since the ionic species' contribute at most a few percent to the overall mass, we can safely approximate the overall fluid's mass by the mass of the solvent (typically water) and model the solvents velocity field $\\vec{u}(\\vec{r}, t)$ using the Navier-Stokes equations for an isotropic, incompressible Newtonian fluid\n", "\n", "\\begin{align}\n", "\\label{eq:NS}\n", "\\rho \\big( \\partial_t \\vec{u} + \\left(\\vec{u} \\cdot \\nabla \\right) \\vec{u} \\big) &= -\\nabla p_H + \\eta \\nabla^{2} \\vec{u} + \\vec{f} ,\\\\\n", "\\nabla \\cdot \\vec u &= 0 .\n", "\\end{align}\n", "\n", "where $p_H$ denotes the hydrostatic pressure, $\\eta$ the shear viscosity, $\\rho$ the density of the fluid, and $\\vec{f}$ an external body force density.\n", "For the assumption of incompressibility to hold, the Mach number needs to be small &ndash; a condition that is fulfilled for micro- and nanofluidic systems with flow velocities on the order of &mu;m/s.\n", "\n", "Earlier we assumed that the ions' velocity relative to the fluid instantaneously relaxes to a stationary state and that this stationary velocity is given by the product of their mobility and the force exerted on them.\n", "For this state to be stationary, all the momentum transferred into the ions by the external force needs to be dissipated into the fluid immediately.\n", "From this we can conclude that the force density acting on the fluid must read\n", "\n", "\\begin{equation}\n", "\\label{eq:forcedens}\n", "\\vec{f} = \\sum_{k} \\vec{j}^\\mathrm{diff}_k / \\xi_{k} = -\\sum_{k} (k_\\mathrm{B}T \\nabla c_{k} + z_{k} e c_{k} \\nabla \\Phi) .\n", "\\end{equation}\n", "\n", "Summarizing, the set of electrokinetic equations we solve is given by\n", "\n", "\\begin{align}\n", "\\vec{j}_{k} ={}& -D_{k} \\nabla c_{k} - \\xi_{k} z_{k} e c_{k} \\nabla \\Phi + c_{k} \\vec{u} ,\\label{eq:flux}\\\\\n", "\\partial_{t} c_{k} ={}& -\\nabla \\cdot\\vec{j}_{k} ,\\label{eq:conti}\\\\\n", "\\nabla^2 \\Phi ={}& -4 \\pi l_b k_\\mathrm{B}T \\textstyle\\sum_{k} z_{k} c_{k} ,\\label{eq:poisson}\\\\\n", "\\rho \\big( \\partial_t \\vec{u} + (\\vec{u} \\cdot \\nabla ) \\vec{u} \\big) ={}& -\\nabla p_H + \\eta \\nabla^{2} \\vec{u} - \\textstyle\\sum_{k} (k_\\mathrm{B}T \\nabla c_{k} + z_{k} e c_{k} \\nabla \\Phi) ,\\label{eq:ns}\\\\\n", "\\nabla \\cdot \\vec{u} ={}& 0 .\\label{eq:incomp}\n", "\\end{align}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EOF in the Slit Pore Geometry\n", "\n", "The slit pore system depicted in Fig. 1 consists of two like charged parallel plates of infinite extent, confining a solution of water and the plates' counterions.\n", "\n", "<figure>\n", " <img src='figures/schlitzpore_3d.png' alt='missing' style=\"width: 500px;\"/>\n", " <center>\n", " <figcaption>Figure 1: Slit pore system and coordinate system used for the analytical calculations.</figcaption>\n", " </center>\n", "</figure>\n", "\n", "Due to the net neutrality of the system and the translational symmetry in directions parallel to the plates, the potential outside the two plates must be constant.\n", "This means that using periodic or non-periodic boundary conditions makes no difference.\n", "As the system is in equilibrium in the normal direction, the electrokinetic equations for this dimension reduce to the Poisson-Boltzmann equation for the electrostatic potential, which reads\n", "\\begin{align}\n", "\t\\partial_x^2 \\Phi(x) = -4 \\pi \\, k_\\mathrm{B}T \\, l_\\mathrm{B} \\, ze \\, c_0 \\cdot \\exp{\\left(-\\frac{ze\\Phi(x)}{k_\\mathrm{B}T}\\right)} \\; ,\n", "\\end{align}\n", "where $x$ denotes the direction normal to the plates.\n", "The constant $c_0$ has to be chosen such that charge neutrality is fulfilled.\n", "Multiplying by $2 \\partial_x \\Phi(x)$ and applying the inverse chain rule further reduces the equation to first order.\n", "Subsequent separation of variables yields the solution\n", "\\begin{align}\n", " \\Phi(x) = -\\frac{k_\\mathrm{B}T}{ze} \\cdot \\log \\left[ \\frac{C^2}{8 \\pi \\, k_\\mathrm{B}T \\, l_\\mathrm{B}} \\cdot \\cos^{-2}\\left( \\frac{zeC}{2 k_\\mathrm{B}T} \\cdot x\\right) \\right], \\quad \\left| \\frac{zeC}{2 k_\\mathrm{B}T} \\cdot x \\right| < \\frac \\pi 2\\; . \\label{eq:validation_pb_counterions}\n", "\\end{align}\n", "Refer to [5] for details on this calculation.\n", "Knowing that the counterion density $c$ resembles a Boltzmann distribution in the potential $ze \\Phi$ leads to the expression\n", "\\begin{align}\n", " c(x) = \\frac{C^2}{8 \\pi \\, k_\\mathrm{B}T \\, l_\\mathrm{B}} \\cdot \\cos^{-2} \\left( \\frac{zeC}{2 k_\\mathrm{B}T} \\cdot x \\right) \\; . \\label{eq:validation_pb_density}\n", "\\end{align}\n", "The constant $C$ is determined by fixing the number of counterions or requiring the E-field to vanish outside the volume contained by the plates.\n", "Both yields\n", "\\begin{align}\n", " C \\cdot \\tan \\left( \\frac{zed}{4 k_\\mathrm{B}T} \\cdot C \\right) = -4 \\pi \\, k_\\mathrm{B}T \\, l_\\mathrm{B} \\sigma \\; ,\n", "\\end{align}\n", "where $d$ denotes the distance between the plates and $\\sigma$ their (constant) surface charge density.\n", "\n", "Applying an electric field along one of the directions parallel to the plates does not influence the charge distribution in the normal direction, which allows us to write down the hydrodynamic equations for the parallel direction.\n", "After eliminating all terms from the Navier-Stokes Equations, which vanish due to symmetry, we are left with\n", "\\begin{align}\n", " \\frac{\\partial_x^2 v_y(x)}{\\partial x^2} = -\\frac{q E C^2}{8 \\, k_\\mathrm{B}T \\, l_\\mathrm{B} \\, \\eta} \\cdot \\cos^{-2} \\left( \\frac{qC}{2 k_\\mathrm{B}T} \\cdot x \\right) \\; ,\n", "\\end{align}\n", "which yields, by means of simple integration and the application of no-slip boundary conditions\n", "\\begin{align}\n", " v_y(x) = \\frac{E}{2 \\pi \\, l_\\mathrm{B} \\, \\eta \\, ze} \\cdot \\log \\left[ \\frac{\\cos \\left( \\frac{zeC}{2 k_\\mathrm{B}T} \\cdot x \\right)}{\\cos \\left( \\frac{zeC}{2 k_\\mathrm{B}T} \\cdot \\frac d 2 \\right)} \\right] \\; .\n", "\\end{align}\n", "\n", "With this tutorial comes a Python script <tt>eof_analytical.py</tt>, which evaluates all these expressions on the same grid as is used in the upcoming simulation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation using ESPResSo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting up ESPResSo\n", "\n", "To use the electrokinetics solver in ESPResSo enable the features <tt>ELECTROKINETICS</tt> and <tt>EK_BOUNDARIES</tt> during the build process." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mapping SI and Simulation Units\n", "\n", "ESPResSo does not predefine any unit system.\n", "This makes it more flexible but also requires us to spend some thought on the conversion from SI units to simulation units and back.\n", "Since most first time users have trouble with this, we will go through that process in detail here.\n", "\t\n", "Important to note is that ESPResSo's unit system is nothing more than a rescaled variant of the SI system.\n", "All physical formulas you are used to in the SI system remain valid and you can use them to find relations between your units.\n", "Let's start by choosing a unit of length.\n", "Since we are going to deal with Debye layers with extensions of nanometers, a sensible choice is\n", "\n", "\\begin{align*}\n", "[x]=1\\mathrm{nm}.\n", "\\end{align*}\n", "\n", "The involved energies are of the magnitude of $k_{\\mathrm{B}}T$.\n", "We will simulate our system at room temperature ($300\\mathrm{K}$), hence we use as unit of energy\n", "\\begin{align*}\n", "[E]=k_\\mathrm{B}\\cdot 300\\mathrm{K}\\approx 4.14 \\cdot 10^{-21}\\mathrm{J}.\n", "\\end{align*}\n", "\n", "By default ESPResSo has no concept for particle masses (but the feature can be activated).\n", "That means all particle masses are assumed to be $1\\,[\\mathrm{m}]$, which forces us to use the particle mass as mass unit.\n", "For this simulation we use the mass of sodium ions, which is\n", "\\begin{align*}\n", "[m]=23\\mathrm{u}\\approx 3.82\\cdot 10^{-26}\\mathrm{kg}.\n", "\\end{align*}\n", "\n", "For the relation\n", "\\begin{align*}\n", "E=\\frac 1 2 mv^2\n", "\\end{align*}\n", "\n", "to hold, the unit of time has to be defined so that\n", "\\begin{align*}\n", "[E]=[m]\\cdot\\frac{[x]^2}{[t]^2}.\n", "\\end{align*}\n", "\n", "From that we get the missing unit of time\n", "\\begin{align*}\n", "[t]=[x]\\cdot\\sqrt{\\frac{[m]}{[E]}}=1\\mathrm{nm}\\cdot\\sqrt{\\frac{23\\mathrm{u}}{k_B\\cdot 300\\mathrm{K}}}\\approx 3.03760648\\cdot 10^{-12}\\mathrm{s}\\approx 3.04\\mathrm{ps}.\n", "\\end{align*}\n", "\n", "The last unit we need is the one of charge.\n", "We choose it to be the elementary charge\n", "\\begin{align*}\n", "[q]=e\\approx 1.60\\cdot 10^{-19}\\mathrm{C}.\n", "\\end{align*}\n", "\n", "We now have all the units necessary to convert our simulation parameters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "|parameter |value (SI units) | value (simulation units)|\n", "|:---------|----------------:|------------------------:|\n", "|channel width $d$ | $50\\mathrm{nm}$ | $50\\mathrm{[x]}$|\n", "|thermal energy $k_B T$ | $k_B\\cdot 300\\mathrm{K}$ | $1\\mathrm{[E]}$|\n", "|Bjerrum length $l_B$ | $0.7095\\mathrm{nm}$ | $0.7095\\mathrm{[x]}$|\n", "|counterion charge $q$ | $1e$ | $1\\mathrm{[q]}$|\n", "|counterion diffusion coefficient $D$ | $2.0\\cdot 10^{-9}\\mathrm{m^2/s}$ | $0.006075\\mathrm{[x]^2/[t]}$|\n", "|solvent density $\\rho$ | $1.0\\cdot 10^{3}\\mathrm{kg/m^3}$ | $26.18\\mathrm{[m]/[x]^3}$|\n", "|solvent dynamic viscosity $\\eta$ | $1.0\\cdot 10^{-3}\\mathrm{Pa}\\mathrm{s}$ | $79.53\\mathrm{[m]/([x][t])}$|\n", "|external electric field $E$ | $2.585\\cdot 10^{6}\\mathrm{V/m}$ | $0.1\\mathrm{[E]/([q][x])}$|\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ESPResSo determines the strength of the electrostatic interactions via the Bjerrum-length $l_\\mathrm{B}$.\n", "That is the length for which the electrostatic interaction energy of two elementary charges equals the thermal energy\n", "\n", "\\begin{align*}\n", "k_\\mathrm{B} T=\\frac{e^2}{4\\pi\\varepsilon_0\\varepsilon_r}\\cdot\\frac 1 {l_\\mathrm{B}}.\n", "\\end{align*}\n", "\n", "This yields for water at $300K$ with $\\varepsilon_r = 78.54$, a Bjerrum length of $l_\\mathrm{B}\\approx 0.7095\\mathrm{nm}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting up the slit pore system\n", "\n", "The script for this simulation comes with this tutorial and is called <tt>eof_electrokinetics.py</tt>.\n", "All used commands are documented in the User's Guide in the section called **Electrokinetics**.\n", "\n", "We first set up a periodic simulation box of the desired dimensions.\n", "Note that the dimensions are, of course, given in simulation units." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Initializing espresso modules and the numpy package\n", "import numpy as np\n", "import espressomd\n", "espressomd.assert_features(['CUDA', 'ELECTROKINETICS'])\n", "from espressomd import System, electrokinetics, shapes, ekboundaries\n", "\n", "# Set the slit pore geometry where the width is the non-periodic part of the geometry\n", "# the padding is used to ensure that there is no field outside the slit since the\n", "# electrostatics is used with a 3D periodic FFT solver.\n", "\n", "box_y = 6\n", "box_z = 6\n", "width = 50\n", "\n", "padding = 1\n", "box_x = width + 2 * padding\n", "\n", "system = System(box_l=[box_x, box_y, box_z])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then store all the parameters we calculated earlier.\n", "At this point, these parameters only reside in Python variables.\n", "They will only be used by ESPResSo once they are being passed to the respective initialization functions." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set the electrokinetic parameters\n", "\n", "agrid = 1.0\n", "dt = 0.2\n", "kT = 1.0\n", "bjerrum_length = 0.7095\n", "D = 0.006075\n", "valency = 1.0\n", "viscosity_dynamic = 79.53\n", "density_water = 26.15\n", "sigma = -0.05\n", "ext_force_density = [0.0, 0.1, 0.0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we initialize the actual electrokinetics algorithm, we need to set the time step and some other parameters that are not actually used, but would otherwise lead to error messages." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set the simulation parameters\n", "\n", "system.time_step = dt\n", "system.cell_system.skin = 0.2\n", "system.thermostat.turn_off()\n", "integration_length = int(2e4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now set up the electrokinetics algorithm.\n", "All functionality pertaining to this algorithm is available through the <tt>electrokinetics</tt> submodule of <tt>espressomd</tt>.\n", "Please note that the fluid viscosity is specified as a kinematic viscosity, which is the dynamic viscosity divided by the fluid density.\n", "The kinematic viscosity is also required if you initialize the pure lattice-Boltzmann method.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set up the (LB) electrokinetics fluid\n", "viscosity_kinematic = viscosity_dynamic / density_water\n", "ek = electrokinetics.Electrokinetics(agrid=agrid,\n", " lb_density=density_water,\n", " viscosity=viscosity_kinematic,\n", " friction=1.0,\n", " T=kT,\n", " prefactor=bjerrum_length)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The value of the friction parameter in the previous setup command is irrelevant, since we don't include any explicit particles in our simulation, but it's needed to pass the sanity check of the LB.\n", "\n", "Next, we set up the individual ionic species.\n", "In this case, we only set up one species of positively charged counterions.\n", "The charge density is chosen in such a way, that it will cancel out the charges of the walls which are being inserted in the step afterwards.\n", "After setting up the species, we have to add it to the electrokinetics instance. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set up the charged and neutral species\n", "density_counterions = -2.0 * sigma / width\n", "counterions = electrokinetics.Species(density=density_counterions,\n", " D=D,\n", " valency=valency,\n", " ext_force_density=ext_force_density)\n", "\n", "ek.add_species(counterions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The <tt>EKBoundary</tt> command takes the keyword <tt>charge_density</tt> and the numerical charge density in simulation units as arguments.\n", "The <tt>shape</tt> keyword takes an instance of a shape, which is provided by the <tt>shapes</tt> submodule and is the same as for the <tt>LBBoundary</tt> command.\n", "Here we initialize two charged <tt>Wall</tt> boundaries.\n", "To initialize the boundaries, we have to add them to the <tt>ekboundaries</tt> instance of the system class.\n", "Finally, we initialize the electrokinetics algorithm with our setup by adding the electrokinetics instance as an actor to the system." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set up the walls confining the fluid\n", "ek_wall_left = ekboundaries.EKBoundary(charge_density=sigma / agrid,\n", " shape=shapes.Wall(normal=[1, 0, 0], dist=padding))\n", "ek_wall_right = ekboundaries.EKBoundary(charge_density=sigma / agrid,\n", " shape=shapes.Wall(normal=[-1, 0, 0], dist=-(padding + width)))\n", "\n", "system.ekboundaries.add(ek_wall_left)\n", "system.ekboundaries.add(ek_wall_right)\n", "\n", "system.actors.add(ek)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After setting up the system, we integrate a sufficient number of time steps to relax the system into the stationary state and output the counterion density profile, the velocity profile, and the shear stress.\n", "Since this system has translational symmetry in the x- and y-direction, we iterate over a line in the z direction and use the <tt>species[node].quantity</tt> command, to output local quantities.\n", "You can instead also use the <tt>electrokinetics.write_vtk_quantity</tt> command to output the whole field at once in a ParaView-compatible format.\n", "\n", "Density and velocity are not the only fields available for output.\n", "Please refer to the User's Guide for all available options." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Integrate the system\n", "for i in range(100):\n", " system.integrator.run(integration_length)\n", " print(\"integration step: %03i\" % i, end=\"\\r\")\n", "\n", "print(\"Integration finished.\")\n", "\n", "# Output\n", "position_list = []\n", "density_list = []\n", "velocity_list = []\n", "pressure_xy_list = []\n", "\n", "for i in range(int(box_x / agrid)):\n", " if (i * agrid >= padding) and (i * agrid < box_x - padding):\n", " position = i * agrid - padding - width / 2.0 + agrid / 2.0\n", " position_list.append(position)\n", "\n", " # density\n", " density_list.append(counterions[i,\n", " box_y / (2 * agrid),\n", " box_z / (2 * agrid)].density)\n", "\n", " # velocity\n", " velocity_list.append(ek[i,\n", " box_y / (2 * agrid),\n", " box_z / (2 * agrid)].velocity[1])\n", "\n", " # xz component pressure tensor\n", " pressure_xy_list.append(ek[i,\n", " box_y / (2 * agrid),\n", " box_z / (2 * agrid)].pressure[0, 1])\n", "\n", "np.savetxt(\"eof_electrokinetics.dat\",\n", " np.column_stack((position_list,\n", " density_list,\n", " velocity_list,\n", " pressure_xy_list)),\n", " header=\"#position calculated_density calculated_velocity calculated_pressure_xy\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this tutorial also came a Python matplotlib script <tt>plot.py</tt>.\n", "If everything went well, running this script with Python from a folder containing the output files <tt>eof_analytical.dat</tt> and <tt>eof_electrokinetics.dat</tt> should produce the result shown in Figure 2.\n", "\n", "<figure>\n", " <img src='figures/profiles.png' alt='missing' style=\"width: 600px;\"/>\n", " <center>\n", " <figcaption>Figure 2: Profiles along the direction perpendicular to the slit pore walls for the counterion density, fluid velocity, and fluid shear stress.</figcaption>\n", " </center>\n", "</figure>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "[1] O. A. Hickey, C. Holm, J. L. Harden and G. W. Slater *Implicit Method for Simulating Electrohydrodynamics of Polyelectrolytes* Physical Review Letters, 2010 \n", "[2] F. Capuani, I. Pagonabarraga and D. Frenkel *Discrete solution of the electrokinetic equations* The Journal of Chemical Physics, 2004 \n", "[3] G. Rempfer *A Lattice based Model for Electrokinetics* Master's thesis, University of Stuttgart, 2013 \n", "[4] G. Rempfer, G. B. Davies, C. Holm and J. de Graaf *Reducing spurious flow in simulations of electrokinetic phenomena* The Journal of Chemical Physics, 2016 \n", "[5] G. Rempfer *Lattice-Boltzmann simulations in complex geometries* Bachelor's thesis, University of Stuttgart, Institute for Computational Physics, 2010 \n", "[6] M. Deserno and C. Holm and S. May, *Fraction of Condensed Counterions around a Charged Rod: Comparison of Poisson-Boltzmann Theory and Computer Simulations* Macromolecules, 2000 \n", "[7] C. Holm, P. K&eacute;kicheff and R. Podgornik *Electrostatic Effects in Soft Matter and Biophysics* Kluwer Academic Publishers, 2001 \n", "[8] M. Deserno and C. Holm *Cell-model and Poisson-Boltzmann-theory: A brief introduction* Electrostatic Effects in Soft Matter and Biophysics, Kluwer Academic Publishers, 2001 \n", "[9] J de Graaf., G. Rempfer and C. Holm *Diffusiophoretic Self-Propulsion for Partially Catalytic Spherical Colloids* IEEE T. Nanobiosci., 2014 \n", "[10] M. Deserno *Counterion condensation for rigid linear polyelectrolytes* Universit&auml;t Mainz, 2000 \n", "[11] J. de Graaf, N Boon, M Dijkstra and R. van Roij *Electrostatic interactions between Janus particles* The Journal of Chemical Physics, 2012 \n", "[12] A. Einstein *&Uuml;ber die von der molekularkinetischen Theorie der W&auml;rme geforderte Bewegung von in ruhenden Fl&uuml;ssigkeiten suspendierten Teilchen* Annalen der Physik, 1905 \n", "[13] M. von Smoluchowski *Zur kinetischen Theorie der Brownschen Molekularbewegung und der Suspensionen* Annalen der Physik, 1906 \n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
ecervera/mindstorms-nb
task/touch.ipynb
1
4818
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sensor de tacte\n", "\n", "<img src=\"img/If-Then-Else-diagram.svg.png\" align=\"right\">\n", "Els sensors permeten al robot reaccionar a les condicions de l'entorn, i executar ordres diferents en cada cas.\n", "\n", "Per a fer això, a més del sensor, es necessita una instrucció del llenguatge de programació que permet triar entre dos situacions, anomenada [condicional](https://en.wikipedia.org/wiki/Conditional_(computer_programming&#41;).\n", "\n", "En Python s'escriu així:\n", "\n", "```python\n", "if condition:\n", " one_statement\n", "else:\n", " another_statement\n", "```\n", "\n", "Aleshores, si la condició es verdadera, s'executa la primera ordre, i si no, la segona.\n", "\n", "Vegem un exemple, però primer connectem el robot." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from functions import connect, touch, forward, backward, stop, disconnect, next_notebook\n", "from time import sleep\n", "connect()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El codi següent comprova el sensor de tacte:\n", "\n", "* si està polsat, el robot va cap enrere\n", "* si no està polsat, va cap avant\n", "\n", "Proveu-lo amb `Ctrl+Enter` mentre polseu (o no) el sensor de tacte." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if touch(): \n", " backward()\n", " sleep(0.2)\n", " stop()\n", "else:\n", " forward()\n", " sleep(0.2)\n", " stop()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"img/While-loop-diagram.svg.png\" align=\"right\">\n", "## Detecció de condicions\n", "\n", "Imagineu que volem fer un programa per a que el robot avance mentre el sensor de tacte no detecte cap obstacle. Hauríem d'estar comprovant el sensor repetidament fins el moment en què es detectara el contacte.\n", "\n", "Podríem utilitzar el bucle `for` amb el condicional `if`, però els llenguatges de programació defineixen una estructura equivalent, anomenada **bucle condicional**, o [*while loop*](https://en.wikipedia.org/wiki/While_loop).\n", "\n", "En Python s'escriu així:\n", "\n", "```python\n", "while condition:\n", " statement\n", "```\n", "\n", "Aleshores, l'ordre s'executa repetidament, mentre la condició siga verdadera.\n", "\n", "El programa quedaria així:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "while not touch():\n", " forward()\n", "stop()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sembla complicat? Les ordres que li esteu donant al robot són molt semblants a les del llenguatge natural, només que en anglès. Si no vos convenç, [mireu la traducció](https://translate.google.com/#en/es/while%20not%20touch%28%29%3A%0A%20%20%20%20forward%28%29%0Astop%28%29) :-)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Recapitulem\n", "\n", "Ara podeu respirar una mica: acabeu de vore dos de les instruccions més importants dels llenguatges de programació:\n", "\n", "* `if .. else`: permet executar dos ordres diferents segons una condició siga verdadera o falsa\n", "* `while`: permet repetir una ordre mentre una condició siga verdadera\n", "\n", "En el següent exercici usarem eixes instruccions, i tot el que hem aprés abans, per a moure el robot de forma autònoma, el que en robòtica s'anomena [*navegació*](https://en.wikipedia.org/wiki/Mobile_robot#Mobile_robot_navigation).\n", "\n", "Abans de continuar, desconnecteu el robot:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "disconnect()\n", "next_notebook('navigation')" ] } ], "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
edeno/Jadhav-2016-Data-Analysis
notebooks/2017_06_13_Test_Spectral_On_Paper_Examples.ipynb
1
3042858
null
gpl-3.0
statsmodels/statsmodels.github.io
v0.13.1/examples/notebooks/generated/statespace_chandrasekhar.ipynb
2
136493
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## State space models - Chandrasekhar recursions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:38:02.235974Z", "iopub.status.busy": "2021-11-12T23:38:02.235457Z", "iopub.status.idle": "2021-11-12T23:38:03.604116Z", "shell.execute_reply": "2021-11-12T23:38:03.604517Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "\n", "from pandas_datareader.data import DataReader" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although most operations related to state space models rely on the Kalman filtering recursions, in some special cases one can use a separate method often called \"Chandrasekhar recursions\". These provide an alternative way to iteratively compute the conditional moments of the state vector, and in some cases they can be substantially less computationally intensive than the Kalman filter recursions. For complete details, see the paper \"Using the 'Chandrasekhar Recursions' for Likelihood Evaluation of DSGE Models\" (Herbst, 2015). Here we just sketch the basic idea." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### State space models and the Kalman filter\n", "\n", "Recall that a time-invariant state space model can be written:\n", "\n", "$$\n", "\\begin{aligned}\n", "y_t &= Z \\alpha_t + \\varepsilon_t, \\qquad \\varepsilon_t \\sim N(0, H) \\\\\n", "\\alpha_{t+1} & = T \\alpha_t + R \\eta_t, \\qquad \\eta_t \\sim N(0, Q) \\\\\n", "\\alpha_1 & \\sim N(a_1, P_1)\n", "\\end{aligned}\n", "$$\n", "\n", "where $y_t$ is a $p \\times 1$ vector and $\\alpha_t$ is an $m \\times 1$ vector.\n", "\n", "Each iteration of the Kalman filter, say at time $t$, can be split into three parts:\n", "\n", "1. **Initialization**: specification of $a_t$ and $P_t$ that define the conditional state distribution, $\\alpha_t \\mid y^{t-1} \\sim N(a_t, P_t)$.\n", "2. **Updating**: computation of $a_{t|t}$ and $P_{t|t}$ that define the conditional state distribution, $\\alpha_t \\mid y^{t} \\sim N(a_{t|t}, P_{t|t})$.\n", "3. **Prediction**: computation of $a_{t+1}$ and $P_{t+1}$ that define the conditional state distribution, $\\alpha_{t+1} \\mid y^{t} \\sim N(a_{t+1}, P_{t+1})$.\n", "\n", "Of course after the first iteration, the prediction part supplies the values required for initialization of the next step." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Focusing on the prediction step, the Kalman filter recursions yield:\n", "\n", "$$\n", "\\begin{aligned}\n", "a_{t+1} & = T a_{t|t} \\\\\n", "P_{t+1} & = T P_{t|t} T' + R Q R' \\\\\n", "\\end{aligned}\n", "$$\n", "\n", "where the matrices $T$ and $P_{t|t}$ are each $m \\times m$, where $m$ is the size of the state vector $\\alpha$. In some cases, the state vector can become extremely large, which can imply that the matrix multiplications required to produce $P_{t+1}$ can be become computationally intensive." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example: seasonal autoregression\n", "\n", "As an example, notice that an AR(r) model (we use $r$ here since we already used $p$ as the dimension of the observation vector) can be put into state space form as:\n", "\n", "$$\n", "\\begin{aligned}\n", "y_t &= \\alpha_t \\\\\n", "\\alpha_{t+1} & = T \\alpha_t + R \\eta_t, \\qquad \\eta_t \\sim N(0, Q)\n", "\\end{aligned}\n", "$$\n", "\n", "where:\n", "\n", "\n", "$$\n", "\\begin{aligned}\n", "T = \\begin{bmatrix}\n", "\\phi_1 & \\phi_2 & \\dots & \\phi_r \\\\\n", "1 & 0 & & 0 \\\\\n", "\\vdots & \\ddots & & \\vdots \\\\\n", "0 & & 1 & 0 \\\\\n", "\\end{bmatrix} \\qquad\n", "R = \\begin{bmatrix}\n", "1 \\\\\n", "0 \\\\\n", "\\vdots \\\\\n", "0\n", "\\end{bmatrix} \\qquad\n", "Q = \\begin{bmatrix}\n", "\\sigma^2\n", "\\end{bmatrix}\n", "\\end{aligned}\n", "$$\n", "\n", "In an AR model with daily data that exhibits annual seasonality, we might want to fit a model that incorporates lags up to $r=365$, in which case the state vector would be at least $m = 365$. The matrices $T$ and $P_{t|t}$ then each have $365^2 = 133225$ elements, and so most of the time spent computing the likelihood function (via the Kalman filter) can become dominated by the matrix multiplications in the prediction step." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### State space models and the Chandrasekhar recursions\n", "\n", "The Chandrasekhar recursions replace equation $P_{t+1} = T P_{t|t} T' + R Q R'$ with a different recursion:\n", "\n", "$$\n", "P_{t+1} = P_t + W_t M_t W_t'\n", "$$\n", "\n", "but where $W_t$ is a matrix with dimension $m \\times p$ and $M_t$ is a matrix with dimension $p \\times p$, where $p$ is the dimension of the observed vector $y_t$. These matrices themselves have recursive formulations. For more general details and for the formulas for computing $W_t$ and $M_t$, see Herbst (2015).\n", "\n", "**Important note**: unlike the Kalman filter, the Chandrasekhar recursions can not be used for every state space model. In particular, the latter has the following restrictions (that are not required for the use of the former):\n", "\n", "- The model must be time-invariant, except that time-varying intercepts are permitted.\n", "- Stationary initialization of the state vector must be used (this rules out all models in non-stationary components)\n", "- Missing data is not permitted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To understand why this formula can imply more efficient computations, consider again the SARIMAX case, above. In this case, $p = 1$, so that $M_t$ is a scalar and we can rewrite the Chandrasekhar recursion as:\n", "\n", "$$\n", "P_{t+1} = P_t + M_t \\times W_t W_t'\n", "$$\n", "\n", "The matrices being multiplied, $W_t$, are then of dimension $m \\times 1$, and in the case $r=365$, they each only have $365$ elements, rather than $365^2$ elements. This implies substantially fewer computations are required to complete the prediction step." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Convergence\n", "\n", "A factor that complicates a straightforward discussion of performance implications is the well-known fact that in time-invariant models, the predicted state covariance matrix will converge to a constant matrix. This implies that there exists an $S$ such that, for every $t > S$, $P_t = P_{t+1}$. Once convergence has been achieved, we can eliminate the equation for $P_{t+1}$ from the prediction step altogether.\n", "\n", "In simple time series models, like AR(r) models, convergence is achieved fairly quickly, and this can limit the performance benefit to using the Chandrasekhar recursions. Herbst (2015) focuses instead on DSGE (Dynamic Stochastic General Equilibrium) models instead, which often have a large state vector and often a large number of periods to achieve convergence. In these cases, the performance gains can be quite substantial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Practical example\n", "\n", "As a practical example, we will consider monthly data that has a clear seasonal component. In this case, we look at the inflation rate of apparel, as measured by the consumer price index. A graph of the data indicates strong seasonality." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:38:03.612255Z", "iopub.status.busy": "2021-11-12T23:38:03.611371Z", "iopub.status.idle": "2021-11-12T23:38:04.336214Z", "shell.execute_reply": "2021-11-12T23:38:04.336685Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cpi_apparel = DataReader('CPIAPPNS', 'fred', start='1986')\n", "cpi_apparel.index = pd.DatetimeIndex(cpi_apparel.index, freq='MS')\n", "inf_apparel = np.log(cpi_apparel).diff().iloc[1:] * 1200\n", "inf_apparel.plot(figsize=(15, 5));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will construct two model instances. The first will be set to use the Kalman filter recursions, while the second will be set to use the Chandrasekhar recursions. This setting is controlled by the `ssm.filter_chandrasekhar` property, as shown below.\n", "\n", "The model we have in mind is a seasonal autoregression, where we include the first 6 months as lags as well as the given month in each of the previous 15 years as lags. This implies that the state vector has dimension $m = 186$, which is large enough that we might expect to see some substantial performance gains by using the Chandrasekhar recursions.\n", "\n", "**Remark**: We set `tolerance=0` in each model - this has the effect of preventing the filter from ever recognizing that the prediction covariance matrix has converged. *This is not recommended in practice*. We do this here to highlight the superior performance of the Chandrasekhar recursions when they are used in every period instead of the typical Kalman filter recursions. Later, we will show the performance in a more realistic setting that we do allow for convergence." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:38:04.350777Z", "iopub.status.busy": "2021-11-12T23:38:04.350252Z", "iopub.status.idle": "2021-11-12T23:38:04.360138Z", "shell.execute_reply": "2021-11-12T23:38:04.360924Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "186\n" ] } ], "source": [ "# Model that will apply Kalman filter recursions\n", "mod_kf = sm.tsa.SARIMAX(inf_apparel, order=(6, 0, 0), seasonal_order=(15, 0, 0, 12), tolerance=0)\n", "print(mod_kf.k_states)\n", "\n", "# Model that will apply Chandrasekhar recursions\n", "mod_ch = sm.tsa.SARIMAX(inf_apparel, order=(6, 0, 0), seasonal_order=(15, 0, 0, 12), tolerance=0)\n", "mod_ch.ssm.filter_chandrasekhar = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We time computation of the log-likelihood function, using the following code:\n", "\n", "```python\n", "%timeit mod_kf.loglike(mod_kf.start_params)\n", "%timeit mod_ch.loglike(mod_ch.start_params)\n", "```\n", "\n", "This results in:\n", "\n", "```\n", "171 ms ± 19.7 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "85 ms ± 4.97 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "```\n", "\n", "The implication is that in this experiment, the Chandrasekhar recursions improved performance by about a factor of 2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we mentioned above, in the previous experiment we disabled convergence of the predicted covariance matrices, so the results there are an upper bound. Now we allow for convergence, as usual, by removing the `tolerance=0` argument:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:38:04.364779Z", "iopub.status.busy": "2021-11-12T23:38:04.363739Z", "iopub.status.idle": "2021-11-12T23:38:04.379552Z", "shell.execute_reply": "2021-11-12T23:38:04.380341Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "186\n" ] } ], "source": [ "# Model that will apply Kalman filter recursions\n", "mod_kf = sm.tsa.SARIMAX(inf_apparel, order=(6, 0, 0), seasonal_order=(15, 0, 0, 12))\n", "print(mod_kf.k_states)\n", "\n", "# Model that will apply Chandrasekhar recursions\n", "mod_ch = sm.tsa.SARIMAX(inf_apparel, order=(6, 0, 0), seasonal_order=(15, 0, 0, 12))\n", "mod_ch.ssm.filter_chandrasekhar = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we time computation of the log-likelihood function, using the following code:\n", "\n", "```python\n", "%timeit mod_kf.loglike(mod_kf.start_params)\n", "%timeit mod_ch.loglike(mod_ch.start_params)\n", "```\n", "\n", "This results in:\n", "\n", "```\n", "114 ms ± 7.64 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "70.5 ms ± 2.43 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "```\n", "\n", "The Chandrasekhar recursions still improve performance, but now only by about 33%. The reason for this is that after convergence, we no longer need to compute the predicted covariance matrices, so that for those post-convergence periods, there will be no difference in computation time between the two approaches. Below we check the period in which convergence was achieved:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:38:04.384136Z", "iopub.status.busy": "2021-11-12T23:38:04.383074Z", "iopub.status.idle": "2021-11-12T23:38:25.897040Z", "shell.execute_reply": "2021-11-12T23:38:25.897472Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Convergence at t=186, of T=429 total observations\n" ] } ], "source": [ "res_kf = mod_kf.filter(mod_kf.start_params)\n", "print('Convergence at t=%d, of T=%d total observations' %\n", " (res_kf.filter_results.period_converged, res_kf.nobs))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since convergence happened relatively early, we are already avoiding the expensive matrix multiplications in more than half of the periods.\n", "\n", "However, as mentioned above, larger DSGE models may not achieve convergence for most or all of the periods in the sample, and so we could perhaps expect to achieve performance gains more similar to the first example. In their 2019 paper \"Euro area real-time density forecasting with financial or labor market frictions\", McAdam and Warne note that in their applications, \"Compared with the standard Kalman filter, it is our experience that these recursions speed up\n", "the calculation of the log-likelihood for the three models by roughly 50 percent\". This is about the same result as we found in our first example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Aside on multithreaded matrix algebra routines\n", "\n", "The timings above are based on the Numpy installation installed via Anaconda, which uses Intel's MKL BLAS and LAPACK libraries. These implement multithreaded processing to speed up matrix algebra, which can be particularly helpful for operations on the larger matrices we're working with here. To get a sense of how this affects results, we could turn off multithreading by putting the following in the first cell of this notebook.\n", "\n", "```python\n", "import os\n", "os.environ[\"MKL_NUM_THREADS\"] = \"1\"\n", "```\n", "\n", "When we do this, the timings of the first example change to:\n", "\n", "```\n", "307 ms ± 3.08 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "97.5 ms ± 1.64 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "```\n", "\n", "and the timings of the second example change to:\n", "\n", "```\n", "178 ms ± 2.78 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "78.9 ms ± 950 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "```\n", "\n", "Both are slower, but the typical Kalman filter is affected much more.\n", "\n", "This is not unexpected; the performance differential between single and multithreaded linear algebra is much greater in the typical Kalman filter case, because the whole point of the Chandrasekhar recursions is to reduce the size of the matrix operations. It means that if multithreaded linear algebra is unavailable, the Chandrasekhar recursions offer even greater performance gains." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Chandrasekhar recursions and the univariate filtering approach\n", "\n", "It is also possible to combine the Chandrasekhar recursions with the univariate filtering approach of Koopman and Durbin (2000), by making use of the results of Aknouche and Hamdi in their 2007 paper \"Periodic Chandrasekhar recursions\". An initial implementation of this combination is included in Statsmodels. However, experiments suggest that this tends to degrade performance compared to even the usual Kalman filter. This accords with the computational savings reported for the univariate filtering method, which suggest that savings are highest when the state vector is small relative to the observation vector." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Bibliography\n", "\n", "Aknouche, Abdelhakim, and Fayçal Hamdi. \"Periodic Chandrasekhar recursions.\" arXiv preprint arXiv:0711.3857 (2007).\n", "\n", "Herbst, Edward. \"Using the “Chandrasekhar Recursions” for likelihood evaluation of DSGE models.\" Computational Economics 45, no. 4 (2015): 693-705.\n", "\n", "Koopman, Siem J., and James Durbin. \"Fast filtering and smoothing for multivariate state space models.\" Journal of Time Series Analysis 21, no. 3 (2000): 281-296.\n", "\n", "McAdam, Peter, and Anders Warne. \"Euro area real-time density forecasting with financial or labor market frictions.\" International Journal of Forecasting 35, no. 2 (2019): 580-600." ] } ], "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": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }
bsd-3-clause
mattmcd/PyAnalysis
scripts/pda/pda_ch04_numpy.ipynb
1
97629
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arr = np.array([[1,2,3],[4,5,6]], dtype=np.float64)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 2., 3.],\n", " [ 4., 5., 6.]])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 4., 9.],\n", " [ 16., 25., 36.]])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr*arr" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 17., 22., 27.],\n", " [ 22., 29., 36.],\n", " [ 27., 36., 45.]])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.dot(arr.T,arr)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 14., 32.],\n", " [ 32., 77.]])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.dot(arr,arr.T)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 6., 7., 8.],\n", " [ 9., 10., 11.]])" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr+5" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 7., 8., 9.],\n", " [ 11., 12., 13.]])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr+np.array([[6],[7]]) # Broadcast along dim 1" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 7., 9., 11.],\n", " [ 10., 12., 14.]])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr+np.array([6,7,8]) # Breadcast along dim 0" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 2., 3.],\n", " [ 5., 6.]])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Slicing - always returns a view\n", "arr[:,1:]" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Boolean indexing - always creates a copy\n", "names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])\n", "data = np.random.randn(names.shape[0],4)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 2.2171817 , 0.67226976, 2.31712369, 0.49070477],\n", " [ 2.32763422, 0.08300936, 0.35822391, -1.50494927],\n", " [-0.89431731, 0.8288176 , 0.27408803, -1.12127598],\n", " [ 0.50249704, -0.28295892, 1.63194931, 0.30591545],\n", " [ 0.04230235, -2.06103025, -0.18247306, 0.23332462],\n", " [-1.45881178, 2.58254017, -2.35010784, 0.50945329],\n", " [ 0.8353959 , 0.11027689, -1.03667897, -0.2630439 ]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'], \n", " dtype='|S4')" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 2.2171817 , 0.67226976],\n", " [ 0.50249704, -0.28295892]])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[names=='Bob', :2]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([-2.06103025, 0.27408803])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[[4,2],[1,2]] # Fancy indexing - extract the specified elements" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-2.06103025, -0.18247306],\n", " [ 0.8288176 , 0.27408803]])" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[np.ix_([4,2], [1,2])] # Get the square region" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Vectorization\n", "points = np.arange(-5,5,0.01)\n", "xs, ys = np.meshgrid(points, points)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z = np.sqrt( xs**2 + ys**2 )" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Alternative: no need for intermediate 2D arrays if use broadcasting\n", "z2 = np.sqrt(points**2 + points[:,None]**2)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[0, 1, 2, 3],\n", " [1, 2, 3, 4],\n", " [2, 3, 4, 5],\n", " [3, 4, 5, 6]])" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(4)+np.arange(4)[:,None]" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1000,)" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "points.shape" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1000,)" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "points.T.shape" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1000, 1)" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "points[:,None].shape #Convert to column vector" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1000, 1)" ] }, "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ "points[None,:].T.shape" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f10aaacfe90>" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AUwWVKN/KRuke93gjb/0ynpf2eFXyymoAydKZDyYDnMySsfHebg0W1hrR/cssGk3WKTwC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\nxi/atv+cbX9uAnDiDLT7ewD8IoDXbNNrb+8VAJ61jd8LwKe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"text/plain": [ "<matplotlib.figure.Figure at 0x7f10aac51390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(z2, cmap=plt.cm.gray); plt.colorbar\n", "plt.title(\"Image plot of $\\sqrt{x^2 + y^2}$ for a grid of values\")" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f10aaf0eb50>,\n", " <matplotlib.lines.Line2D at 0x7f10aaf0edd0>,\n", " <matplotlib.lines.Line2D at 0x7f10aaf1a050>,\n", " <matplotlib.lines.Line2D at 0x7f10aaf1a210>,\n", " <matplotlib.lines.Line2D at 0x7f10aaf1a3d0>,\n", " <matplotlib.lines.Line2D at 0x7f10aaf1a590>,\n", " <matplotlib.lines.Line2D at 0x7f10aaf1a750>,\n", " <matplotlib.lines.Line2D at 0x7f10aaf46190>,\n", " <matplotlib.lines.Line2D at 0x7f10aaf1aad0>,\n", " <matplotlib.lines.Line2D at 0x7f10aaf1ac90>]" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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p5WDLvQyozz/CYr0icWmBOZ0v15hupzGtjtX2eRR4W+JyWIZTdcMeDOcBd0lc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"text/plain": [ "<matplotlib.figure.Figure at 0x7f10aaff2c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Random walk - NB: using \n", "x = np.hstack((np.zeros((10,1)), np.random.normal(loc=0, scale=0.25, size=(10,100)).cumsum(1))).T\n", "plt.plot(x)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0. , 0. , 0. , 0. ],\n", " [-0.02288209, -0.05414666, -0.13648867, 0.56972125, 0.26997755],\n", " [-0.1517502 , 0.21386738, -0.29196164, 0.33200461, 0.32402513],\n", " [-0.24815818, 0.47422752, -0.69644876, 0.02825518, 0.46701033],\n", " [-0.49072601, 0.8494735 , -0.64153827, 0.40120279, 0.23043877],\n", " [-0.63316226, 0.91179533, -0.57108858, 0.1927183 , 0.32795271],\n", " [-0.06159424, 1.07599577, -0.91562608, 0.1814614 , 0.1356199 ],\n", " [ 0.11703752, 1.27618662, -0.82662229, 0.37877727, 0.19524912],\n", " [-0.35589012, 1.60679983, -1.12278289, 0.50953823, -0.07081754],\n", " [-0.46063098, 1.76031973, -1.14535241, 0.55471056, -0.1504698 ]])" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[:10,:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
pcmagic/stokes_flow
HelicodsParticles/helicoid_hlx/hlxPart_force_slice.ipynb
1
4283362
null
mit
nick-youngblut/SIPSim
ipynb/sandbox/truncated_distributions.ipynb
1
16098
{ "metadata": { "name": "", "signature": "sha256:fd07ed62da80935a34f4e99b577958b13c5d46f5f4383b72bdfdb5b846c6fd28" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Description:\n", "\n", "* Exploring how to best create truncated distributions (e.g., values constrained to [0,100])" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import scipy.stats as stats\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import numpy.random as rnd\n", "\n", "# distribution truncated to: \n", "trunc_start = -1\n", "trunc_end = 2\n", "\n", "#plot the original distribution\n", "xrng=np.arange(-10,10,.1)\n", "yrng=stats.logistic.pdf(xrng)\n", "plt.plot(xrng,yrng)\n", "\n", "#plot the truncated distribution\n", "nrm=stats.logistic.cdf(trunc_end)-stats.logistic.cdf(trunc_start)\n", "xrng=np.arange(trunc_start,trunc_end,.01)\n", "yrng=stats.logistic.pdf(xrng)/nrm\n", "plt.plot(xrng,yrng)\n", "\n", "#sample using the inverse cdf\n", "yr=rnd.rand(100000)*(nrm)+stats.logistic.cdf(trunc_start)\n", "xr=stats.logistic.ppf(yr)\n", "plt.hist(xr,normed=True)\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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mit
maxentile/msm-learn
notebooks/Notes on "A Critical Appraisal of Markov State Models".ipynb
1
835
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import msmbuilder as msm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
yunqu/PYNQ
boards/Pynq-Z1/base/notebooks/arduino/arduino_grove_ledbar.ipynb
1
5594
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Grove LED Bar Example\n", "\n", "This example shows how to use the [Grove LED Bar](http://www.seeedstudio.com/depot/Grove-LED-Bar-v20-p-2474.html) on the board. The LED bar has 10 LEDs: 8 green LEDs, 1 orange LED, and 1 red LED. The brightness for each LED can be set independantly.\n", "\n", "For this notebook, a PYNQ Arduino shield is also required. The LED bar is attached to the G4 connection on the shield. The grove LED bar also works with PMODA and PMODB on the board. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Make sure the base overlay is loaded\n", "from pynq.overlays.base import BaseOverlay\n", "base = BaseOverlay(\"base.bit\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Instantiate and reset LED Bar" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pynq.lib.arduino import Grove_LEDbar\n", "from pynq.lib.arduino import ARDUINO_GROVE_G4\n", "\n", "# Instantiate Grove LED Bar on Arduino shield G4\n", "ledbar = Grove_LEDbar(base.ARDUINO,ARDUINO_GROVE_G4)\n", "ledbar.reset()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Turn individual LEDs on or off\n", "\n", "Write a 10-bit binary pattern, with each bit representing the corresponding LED. 1 = on, 0 = off" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from time import sleep\n", "\n", "# Light up different bars in a loop\n", "for i in range(2):\n", " ledbar.write_binary(0b1010100000)\n", " sleep(0.5)\n", " ledbar.write_binary(0b0000100100)\n", " sleep(0.5)\n", " ledbar.write_binary(0b1010101110)\n", " sleep(0.5)\n", " ledbar.write_binary(0b1111111110)\n", " sleep(0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Set LEDs individually with different brightness levels\n", "\n", "The brightness of each LED can be set individually by writing a list of 10x 8-bit values to the LED bar. 0 is off, 0xff is full brightness. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Brightness 0-255\n", "HIGH = 0xFF\n", "MED = 0xAA\n", "LOW = 0x01\n", "OFF = 0X00\n", "\n", "brightness = [OFF, OFF, OFF, LOW, LOW, MED, MED, HIGH, HIGH, HIGH]\n", "\n", "ledbar.write_brightness(0b1111111111,brightness)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Set the \"level\" or the number of LEDs which are set \n", "\n", "A number or level of LEDs can be turned on, started from either end of the LED bar. For example, this feature could be used to indicate the level of something being measured.\n", "\n", "write_level(level, bright_level, green_to_red)\n", "\n", "* level is the number of LEDs that are on.\n", "* bright_level [0-10] is the level of brightness\n", "* green_to_red = 1 means the LEDs start being lit from the \"green\" end of the LED bar\n", "* green_to_red = 0 means the LEDs start being lit from the \"red\" end of the LED bar.\n", "\n", "For example, ledbar.write_level(5,4,1) will light 5 LEDs, to brightness 4 (out of 10) and will start from the Green LED (the LED furthest away from Grove connector on the LED bar module.)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i in range (1,11):\n", " ledbar.write_level(i,3,0)\n", " sleep(0.3)\n", "for i in range (1,10):\n", " ledbar.write_level(i,3,1)\n", " sleep(0.3) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Controlling the LED Bar from the board buttons\n", "\n", "This cell demonstrates controlling the \"level\" of the LEDs from onboard buttons. \n", "\n", "* Button 0 to increase level\n", "* Button 1 to decrease level\n", "* Button 3 to exit" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "btns = [base.buttons[index] for index in range(4)] \n", "i = 1\n", "ledbar.reset()\n", "\n", "done = False\n", "while not done:\n", " if (btns[0].read()==1):\n", " sleep(0.2)\n", " ledbar.write_level(i,2,1)\n", " i = min(i+1,9)\n", " elif (btns[1].read()==1):\n", " sleep(0.2)\n", " i = max(i-1,0)\n", " ledbar.write_level(i,2,1)\n", " elif (btns[3].read()==1):\n", " ledbar.reset()\n", " done = True" ] } ], "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": 1 }
bsd-3-clause
henchc/Rediscovering-Text-as-Data
04-Stylometry/02-regex.ipynb
1
9661
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Regular Expression (regex)\n", "\n", "## Overview\n", "\n", "Regular expressions (regex or regexp for short) are special sequences of characters that define patterns\n", "to search for in text. They're often used in find-and-replace operations, or to add up the number of words\n", "or phrases matching a particular pattern.\n", "\n", "Regular expressions are useful in a variety of applications, and can be used in different programs and\n", "programming languages. We will start by learning the general components of regular expressions, using a\n", "simple online tool, RegExr. Then at the end of the workshop, we'll learn how to use regular expressions\n", "in a text editor, Sublime. We'll also demonstrate how to use them in Python and R, for those students\n", "already familiar with one of those languages.\n", "\n", "To get started:\n", "\n", "1. Go to this site: [http://regexr.com](http://regexr.com).\n", "2. Copy and paste the New York Times leads from the file `nyt_leads.txt` into the __Text__ window on the website.\n", "3. Delete what you see in the __Expression__ field. This is where we'll insert our own regular expressions\n", "to find sequences in the headlines below.\n", "\n", "~~~ {.input}\n", "New York Times\t\t\t\t\t\t\t\t\t\t\tOctober 19, 2016\n", "Retaking Mosul From ISIS May Pale to What Comes Next\n", "By TIM ARANGO and RICK GLADSTONE 11:52 ET\n", "If the recaptures of Ramadi, Tikrit and Falluja are a guide,\n", "Iraqi officials will confront devastation and unexploded bombs\n", "once Mosul is reclaimed.\n", "\n", "New York Times\t\t\t\t\t\t\t\t\t\t\tOctober 18, 2016\n", "Short-Term Cease-Fire in Yemen Appears Likely\n", "By BEN HUBBARD 10:05 ET\n", "The rebels known as the Houthis said they would abide by the\n", "cease-fire if the Saudi military coalition halted attacks and\n", "lifted a blockade.\n", "~~~\n", "\n", "## 1. Special Characters\n", "\n", "Strings are composed of characters, and we are writing patterns to match specific sequences of characters.\n", "Various characters have special meaning in regular expressions. When we use these characters in an expression,\n", "we aren't matching the identical character, we're using the character as a placeholder for some other character(s)\n", "or part(s) of a string.\n", "\n", "If you want to match a character that happens to be a special character, you have to escape it with a backslash\n", "`\\`. Try typing the following special characters into the __Expression__ field on the regexr.com site. What happens\n", "when you type `New York Times` vs. `^New York Times`? How about `.`, `\\.`, or `\\.$`?\n", "\n", "~~~ {.input}\n", ". any single character\n", "^ start of string\n", "$ end of string\n", "\\n new line\n", "\\r carriage return\n", "\\t tab\n", "~~~\n", "\n", "## 2. Quantifiers\n", "\n", "Some special characters refer to optional characters, to a specific number of characters, or to an open-ended\n", "number of characters matching the preceding pattern. Try looking for the letter 'o' followed by a number of 'f's:\n", "what happens if you type `of`, `of*`, `of+`, `of{1}`, `of{1,2}`?\n", "\n", "~~~ {.input}\n", "* 0 or more of the preceding character/expression\n", "+ 1 or more of the preceding character/expression\n", "? 0 or 1 of the preceding character/expression\n", "{n} n copies of the preceding character/expression \n", "{n,m} n to m copies of the preceding character/expression \n", "~~~\n", "\n", "## 2. Sets\n", "\n", "Regular expressions also allow you to define sets of characters. Within a set of square brackets, you may list\n", "characters individually, e.g. `[aeiou]`, or in a range, e.g. `[A-Z]` (note that all regular expressions are case\n", "sensitive).\n", "\n", "You can also create a complement set by excluding certain characters, using `^` as the first character\n", "in the set. The set `[^A-Za-z]` will match any character except a letter. All other special characters loose\n", "their special meaning inside a set, so the set `[.?]` will look for a literal period or question mark.\n", "\n", "The set will match only one character contained within that set, so to find sequences of multiple characters from\n", "the same set, use a quantifier like `+` or a specific number or number range `{n,m}`.\n", "\n", "~~~ {.input}\n", "[0-9] any numeric character\n", "[a-z] any lowercase alphabetic character\n", "[A-Z] any uppercase alphabetic character\n", "[aeiou] any vowel (i.e. any character within the brackets)\n", "[0-9a-z] to combine sets, list them one after another \n", "[^...] exclude specific characters\n", "~~~\n", "\n", "## 3. Special sequences\n", "\n", "Several special characters denote special sequences. These begin with a `\\` followed by a letter.\n", "Note that the uppercase version is usually the complement of the lowercase version.\n", "\n", "~~~ {.input}\n", "\\d Any digit\n", "\\D Any non-digit character\n", "\\w Any alphanumeric character [0-9a-zA-Z_] \n", "\\W Any non-alphanumeric character\n", "\\s Any whitespace (space, tab, new line)\n", "\\S Any non-whitespace character\n", "\\b Matches the beginning or end of a word (does not consume a character)\n", "\\B Matches only when the position is not the beginning or end of a word (does not consume a character)\n", "~~~\n", "\n", "## 4. Groups and Logical OR\n", "\n", "Parentheses are used to designate groups of characters, to aid in logical conditions, and to be able to retrieve the\n", "contents of certain groups separately.\n", "\n", "The pipe character `|` serves as a logical OR operator, to match the expression before or after the pipe. Group parentheses\n", "can be used to indicate which elements of the expression are being operated on by the `|`.\n", "\n", "~~~ {.input}\n", "| Logical OR opeator\n", "(...) Matches whatever regular expression is inside the parentheses, and notes the start and end of a group\n", "(this|that) Matches the expression \"this\" or the expression \"that\"\n", "~~~\n", "\n", "# regex in Python\n", "\n", "Important methods:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import re\n", "\n", "my_string = '''New York Times\t\t\t\t\t\t\t\t\t\t\tOctober 19, 2016\n", "Retaking Mosul From ISIS May Pale to What Comes Next\n", "By TIM ARANGO and RICK GLADSTONE 11:52 ET\n", "If the recaptures of Ramadi, Tikrit and Falluja are a guide,\n", "Iraqi officials will confront devastation and unexploded bombs\n", "once Mosul is reclaimed.\n", "\n", "New York Times\t\t\t\t\t\t\t\t\t\t\tOctober 18, 2016\n", "Short-Term Cease-Fire in Yemen Appears Likely\n", "By BEN HUBBARD 10:05 ET\n", "The rebels known as the Houthis said they would abide by the\n", "cease-fire if the Saudi military coalition halted attacks and\n", "lifted a blockade.'''" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "re.compile?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "re.search?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "re.match?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "re.sub?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "re.findall?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "re.split?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Homework\n", "\n", "Write some code using a regex that returns all capitalized names from `my_string`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your answer should be *exactly*:\n", "\n", "```python\n", "['TIM ARANGO', 'RICK GLADSTONE', 'BEN HUBBARD']\n", "```" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
jmhsi/justin_tinker
data_science/courses/deeplearning2/bcolz_iter_test.ipynb
1
3088
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "from bcolz_array_iterator2 import BcolzArrayIterator2 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "from bcolz import carray" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "x = np.arange(14); x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "y = np.arange(14); y" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "x = carray(x, chunklen=3)\n", "y = carray(y, chunklen=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "b = BcolzArrayIterator2(x, y, shuffle=True, batch_size=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "b.N" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "nit = len(x)//b.batch_size+1; nit" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "for j in range(10000):\n", " bx,by = list(zip(*[next(b) for i in range(nit)]))\n", " nx = np.concatenate(bx)\n", " ny = np.concatenate(by)\n", " assert(np.allclose(nx,ny))\n", " assert(len(np.unique(nx))==len(nx))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "[next(b) for i in range(20)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
jasemi/Computerphysik-ss17-Uebungen
Uebung-1/Aufgabe1-Jan.ipynb
1
1629
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Summe über sqrt(n) für n aus 1 bis 10: 22.4682781862041\n", "Summe über ln(n) für n aus 1 bis 10: 15.104412573075518\n", "Produkt aus allen cos(n*pi/10)+0.1 für n von 1 bis 10: 0.00047925075429687466\n", "Produkt aus allen sqrt(1+exp(-x)) für n von 1 bis 10: 1.2953317536888507\n" ] } ], "source": [ "M = 10\n", "sum_a = 0\n", "sum_b = 0\n", "prod_c = 1\n", "prod_d = 1\n", "\n", "for n in 1:M\n", " sum_a += sqrt(n)\n", " sum_b += log(n)\n", " prod_c *= ((cos((n*pi)/10)^2)+0.1)\n", " prod_d *= sqrt(1+exp(-n))\n", "end\n", "\n", "println(\"Summe über sqrt(n) für n aus 1 bis \", M, \": \", sum_a)\n", "println(\"Summe über ln(n) für n aus 1 bis \", M, \": \", sum_b)\n", "println(\"Produkt aus allen cos(n*pi/10)+0.1 für n von 1 bis \", M, \": \", prod_c) \n", "println(\"Produkt aus allen sqrt(1+exp(-x)) für n von 1 bis \", M, \": \", prod_d)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.5.1", "language": "julia", "name": "julia-0.5" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.5.1" } }, "nbformat": 4, "nbformat_minor": 1 }
unlicense
kebot/ijavascript
doc/n-riesco.global.ipynb
4
5013
{ "metadata": { "kernelspec": { "display_name": "Javascript (Node.js)", "language": "javascript", "name": "javascript" }, "language_info": { "file_extension": "js", "mimetype": "application/javascript", "name": "javascript", "version": "0.10.25" }, "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The `global` object\n", "\n", "Javascript provides a unique `global` object accessible to all execution contexts. The `global` object has a number of properties predefined. Some of this properties are required by the [ECMA standard](http://www.ecma-international.org/ecma-262/5.1/) and some are specific to the platform. See [here](https://nodejs.org/api/globals.html) the relevant documentation for Node.js and [here](https://developer.mozilla.org/en-US/docs/Web/API/Window) for the HTML DOM.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How to access properties defined in the global object\n", "\n", "Javascript provides two ways to access the properties in the `global` object:\n", "- explicit access\n", "- implicit access" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Explicit access\n", "\n", "Javascript platforms may include a `global` property whose value is the `global` object itself. In the HTML DOM, this property is `window`; in Node.js, the property is `global` property. This `global` property can be used to access properties in the `global` object from different execution contexts, for example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "(function() {\n", " global.myGlobalProperty = \"Hello, World!\";\n", "})()\n", "global.myGlobalProperty;" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<pre>&#39;Hello, World!&#39;</pre>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "'Hello, World!'" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implicit access\n", "\n", "`NaN` and `isNaN` are properties defined in the `global` object. `NaN` is set to the \"Not-a-Number\" value defined by the [IEEE 754 standard](http://dx.doi.org/10.1109/IEEESTD.2008.4610935) for floating-point numbers. And `isNaN` is set to a function that returns whether a number is `NaN`. Here is an example that illustrates how these properties are implicitly accessible from different execution contexts:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "(function f() {\n", " return isNaN(NaN);\n", "})()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<pre>true</pre>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "true" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that although accessing `global` properties in this way is very convenient, it is also riddle with unexpected side effects:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "(function() {\n", " var isNaN = function(number) {\n", " return !global.isNaN(number);\n", " };\n", " return isNaN(NaN);\n", "})()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<pre>false</pre>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "false" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "(function() {\n", " isNaN = function(number) {\n", " return !global.isNaN(number);\n", " };\n", " return isNaN(NaN);\n", "})()" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "Cannot call method 'split' of undefined", "output_type": "pyerr", "traceback": [ "TypeError: Cannot call method 'split' of undefined", " at formatError ([eval]:174:40)", " at sendError ([eval]:97:14)", " at onMessage ([eval]:74:13)", " at process.EventEmitter.emit (events.js:98:17)", " at handleMessage (child_process.js:318:10)", " at Pipe.channel.onread (child_process.js:345:11)" ] } ], "prompt_number": 4 } ], "metadata": {} } ] }
bsd-3-clause
dataunity/bath-hacked-2014
Traveline Data Merge.ipynb
1
3875
{ "metadata": { "name": "", "signature": "sha256:194a4db4beb46ff7c1d5d74ddb0e0073b5f133992f4d41d05a9bb4a1b4b15fb8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# Merge Traveline bus stop data with Naptan information\n", "import pandas as pd\n", "from pandas import read_csv" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Import the stops/route data (generated by traveline.py script)\n", "stops_df = read_csv('data/AnnotatedStopPointRef.csv')\n", "routes_df = read_csv('data/RouteSection.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Import Naptan stops (this file is downloaded from http://data.gov.uk/dataset/naptan - it's the zip of CSV files)\n", "naptan_df = read_csv('../data/naptan/NaPTANcsv/Stops.csv')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/home/normal/Projects/HackDays/BathHacked2014/env/local/lib/python2.7/site-packages/pandas/io/parsers.py:1150: DtypeWarning: Columns (1,2,5,6,7,9,11,12,13,15,20,21,22,23,24,34,35,36) have mixed types. Specify dtype option on import or set low_memory=False.\n", " data = self._reader.read(nrows)\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Merge traveline and naptan stop data\n", "stops_df = pd.merge(stops_df, naptan_df, left_on='StopPointRef', right_on='AtcoCode')\n", "len(stops_df)\n", "\n", "# Write bus stops data\n", "stops_df.to_csv('data/traveline_naptan_stops.csv')\n", "#len(stops_df)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Enrich traveline routes with Naptan info\n", "stops_lat_long = naptan_df[[\"AtcoCode\", \"NaptanCode\", \"LocalityName\", \"Latitude\", \"Longitude\"]]\n", "#stops_lat_long\n", "routes_info_df = pd.merge(routes_df, stops_lat_long, left_on='From', right_on='AtcoCode')\n", "del routes_info_df['NaptanCode']\n", "routes_info_df.rename(columns={'AtcoCode': 'From_AtcoCode', \n", " 'Latitude': 'From_Latitude', \n", " 'Longitude': 'From_Longitude',\n", " 'LocalityName': 'From_LocalityName'}, inplace=True)\n", "routes_info_df = pd.merge(routes_info_df, stops_lat_long, left_on='To', right_on='AtcoCode')\n", "routes_info_df.rename(columns={'AtcoCode': 'To_AtcoCode', \n", " 'Latitude': 'To_Latitude', \n", " 'Longitude': 'To_Longitude',\n", " 'LocalityName': 'To_LocalityName'}, inplace=True)\n", "\n", "# Write bus route data\n", "routes_info_df.to_csv('data/routes_with_latlong.csv')\n", "routes_info_df_bath = routes_info_df[routes_info_df['To_LocalityName'] == 'Bath City Centre']\n", "routes_info_df_bath.to_csv('data/routes_with_latlong_bath.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
apache-2.0
ELC/Training-Python
projects/sorting/Sorting Algorithms.ipynb
1
8891
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Sorting Algorithms\n", "\n", "In this notebook, serveral algorithms are displayed and compared" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from random import shuffle\n", "\n", "testdata = [i for i in range(1000)]\n", "shuffle(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bubble Sort" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# %load bubble.py\n", "def bubble(iterable):\n", " done = False\n", " while not done:\n", " done = True\n", " for i in range(len(iterable) - 1):\n", " if iterable[i] > iterable[i + 1]:\n", " iterable[i], iterable[i + 1] = iterable[i + 1], iterable[i]\n", " done = False\n", " return iterable" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 204 µs per loop\n" ] } ], "source": [ "%%timeit\n", "bubble(testdata)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# %load cocktail_bubble.py\n", "def cocktail_bubble(iterable):\n", " d = 0\n", " l = len(iterable) - 1\n", " while d * 2 < l:\n", " a = range(d, l - d)\n", " bare = range(l - d, d, -1)\n", " for i, j in zip(a, bare):\n", " if iterable[i] > iterable[i + 1]:\n", " iterable[i], iterable[i + 1] = \\\n", " iterable[i + 1], iterable[i]\n", " if iterable[j] < iterable[j - 1]:\n", " iterable[j], iterable[j - 1] = \\\n", " iterable[j - 1], iterable[j]\n", " d += 1\n", " return iterable" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 118 ms per loop\n" ] } ], "source": [ "%%timeit\n", "cocktail_bubble(testdata)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# %load false_bubble_0.py\n", "def false_bubble_0(iterable):\n", " l = len(iterable)\n", " for i in range(0, l - 1):\n", " for j in range(0, l):\n", " if iterable[i] > iterable[j] and j > i:\n", " iterable[i], iterable[j] = iterable[j], iterable[i]\n", " return iterable" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 161 ms per loop\n" ] } ], "source": [ "%%timeit\n", "false_bubble_0(testdata)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# %load false_bubble_i.py\n", "def false_bubble_i(iterable):\n", " l = len(iterable)\n", " for i in range(0, l - 1):\n", " for j in range(i, l):\n", " if iterable[i] > iterable[j]:\n", " iterable[i], iterable[j] = iterable[j], iterable[i]\n", " return iterable" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 66.6 ms per loop\n" ] } ], "source": [ "%%timeit\n", "false_bubble_i(testdata)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# %load insertion.py\n", "def insertion(iterable):\n", " done = False\n", " while not done:\n", " done = True\n", " for i in range(len(iterable) - 1):\n", " if not iterable[i] < iterable[i + 1]:\n", " done = False\n", " j = i\n", " while not (iterable[j] < iterable[j + 1]) and j >= 0:\n", " iterable[j], iterable[j + 1] = \\\n", " iterable[j + 1], iterable[j]\n", " j -= 1\n", " return iterable" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 200 µs per loop\n" ] } ], "source": [ "%%timeit\n", "insertion(testdata)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# %load selection.py\n", "def selection(iterable):\n", " first = 0\n", " last = len(iterable) - 1\n", " while first < last:\n", " minimo = iterable[first]\n", " for i in range(first, last + 1):\n", " if iterable[i] < minimo:\n", " minimo = iterable[i]\n", " minindex = iterable.index(minimo)\n", " iterable[first], iterable[minindex] = \\\n", " iterable[minindex], iterable[first]\n", " first += 1\n", " return iterable" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 62.6 ms per loop\n" ] } ], "source": [ "%%timeit\n", "selection(testdata)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# %load cocktail_selection.py\n", "def cocktail_selection(iterable):\n", " first = 0\n", " last = len(iterable) - 1\n", " while first < last:\n", " minimo = iterable[first]\n", " maximo = iterable[last]\n", " for i in range(first, last + 1):\n", " if iterable[i] > maximo:\n", " maximo = iterable[i]\n", " if iterable[i] < minimo:\n", " minimo = iterable[i]\n", " minindex = iterable.index(minimo)\n", " iterable[first], iterable[minindex] = \\\n", " iterable[minindex], iterable[first]\n", " maxindex = iterable.index(maximo)\n", " iterable[last], iterable[maxindex] = \\\n", " iterable[maxindex], iterable[last]\n", " first += 1\n", " last -= 1\n", " return iterable" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 57.6 ms per loop\n" ] } ], "source": [ "%%timeit\n", "cocktail_selection(testdata)" ] }, { "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": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[5, 4, 3, 2, 1]" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = [1,2,3,4,5]\n", "list(reversed(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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
napsternxg/ControversialTweetAnalysis
GDELT Analysis.ipynb
1
920497
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/entity/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:279: 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": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sns.set_context(\"poster\")\n", "sns.set_style(\"ticks\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv(\"/home/entity/Downloads/BigQuery_GDELT_INDIA_Target.csv\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Year</th>\n", " <th>Target</th>\n", " <th>QuadClass</th>\n", " <th>TotalEvents</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2017</td>\n", " <td>USA</td>\n", " <td>1</td>\n", " <td>3725</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2017</td>\n", " <td>GBR</td>\n", " <td>1</td>\n", " <td>1741</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2017</td>\n", " <td>PAK</td>\n", " <td>1</td>\n", " <td>1631</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2017</td>\n", " <td>CHN</td>\n", " <td>1</td>\n", " <td>1512</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2017</td>\n", " <td>PAK</td>\n", " <td>4</td>\n", " <td>894</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Year Target QuadClass TotalEvents\n", "0 2017 USA 1 3725\n", "1 2017 GBR 1 1741\n", "2 2017 PAK 1 1631\n", "3 2017 CHN 1 1512\n", "4 2017 PAK 4 894" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "QUAD_CLASS_NAMES={\n", " 1: \"Verbal Cooperation\",\n", " 2: \"Material Cooperation\",\n", " 3: \"Verbal Conflict\",\n", " 4: \"Material Conflict\"\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f55e6c7d0d0>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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x48dj/fr1lQYbQGmiuChArlevHoqKiljnfKB02GFV77PofLhmzRqJG5xv376t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ZMkpwfD29f6VZHpFCoOxKsrn8UDmbdhrlfURFlcAODQbOQEz8c/ljyMpxbep19/3Pry6va8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\negk+Pj6IjIzEhg0btPv7l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"text/plain": [ "<matplotlib.figure.Figure at 0x7f55e9055950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_t = df[(df.Target == \"PAK\")].pivot_table(index=\"Year\", columns=\"QuadClass\", values=\"TotalEvents\", aggfunc=np.mean)\n", "\n", "ax = sns.pointplot(x=\"Year\", y=\"TotalEvents\", hue=\"QuadClass\",\n", " order=df_t.index.sort_values(),\n", " data=pd.melt(df_t.divide(df_t.sum(axis=1), axis=0).reset_index(),\n", " id_vars=[\"Year\"],\n", " value_vars=[1,2,3,4],\n", " value_name=\"TotalEvents\").assign(\n", " QuadClass=lambda x: x.apply(lambda k: QUAD_CLASS_NAMES[k.QuadClass], axis=1)\n", ")\n", " )\n", "\n", "\n", "plt.xticks(rotation='vertical')\n", "plt.ylabel(\"Proportion of event types\")\n", "plt.xlabel(\"Year\")\n", "plt.title(\"GDELT events between India (IND) and Pakistan (PAK) across years\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f55e62eccd0>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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f4AHn48eP68wniry8PIYNG4Zhw4aBYRjs3r0bAQEB2L9/f6MevP5X0ZgNAgAY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"text/plain": [ "<matplotlib.figure.Figure at 0x7f55e6b7eb10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_t = df[(df.Target == \"PAK\") &\n", " (df.QuadClass != 1)].pivot_table(\n", " index=\"Year\",\n", " columns=\"QuadClass\",\n", " values=\"TotalEvents\", aggfunc=np.mean)\n", "\n", "ax = sns.pointplot(x=\"Year\", y=\"TotalEvents\", hue=\"QuadClass\",\n", " order=df_t.index.sort_values(),\n", " data=pd.melt(df_t.divide(df_t.sum(axis=1), axis=0).reset_index(),\n", " id_vars=[\"Year\"],\n", " value_vars=[2,3,4],\n", " value_name=\"TotalEvents\").assign(\n", " QuadClass=lambda x: x.apply(lambda k: QUAD_CLASS_NAMES[k.QuadClass], axis=1)\n", ")\n", " )\n", "\n", "\n", "plt.xticks(rotation='vertical')\n", "plt.ylabel(\"Proportion of event types\")\n", "plt.xlabel(\"Year\")\n", "plt.title(\"GDELT events between India (IND) and Pakistan (PAK) across years\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f55e474e8d0>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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D29sbYWFhyM7OhpmZWZPlMzIy4OrqitmzZwuu3969eyMzMxPHjx/HkydP4OHhIbT/gQMH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o5Y6CPLY934p1yP083nmlnMSUULS6yQ1GjBnp6OJiGahzT8tT7DjEnFsmV9UpLzKTm4qu4qk9\nL498zdBvJ7LdhnrYgU0h0RqspNM8+phZkbqQ5akLsA8NUffXVzj51sd0xflfDrrFmMq17+/mk9km\n6iPU4wcaTufY94YTYisL0PV7DuwHdd1U5rjSDzQDRmS7EofCxpC2D4fSFUgOGLtJPFKM0u4+Mzo0\naOOFP20j75TE2dMVDiQknG7vIdkJS3f0MKiROJ4Av/jice4ruQdZIeGw+5+ie3bana2vj8M/+yVd\n+/aP+T3dByroPlCBpXQOGf/yvZEVW3B9bu/cfNKvc1HYVazWX4Z5EqmkZ+qvrePwT3+B0+658lAR\n7XugcZpt2MHGD49y1Y2u723ua+OjpGHWH4YWQzKHIspwyJ7vM6ck02xKptmUTFhvDblNG1E4bRN+\nJtokJe36GIaUekBCbR/A0l/v2ndwhqzqITbOcmJTBhbGvHHoAzbX7KRrsIfMPTZsuhV+HccpyZwy\nZ5DS4f0DzSYpaTSl0mJMwKpwTUJpbP2E91UT1VPFkFLH3ujlY6ZLxnUeJKN1BxJOwgZOMSyrGVQa\nccgKlHYr+uFurxuezxxs61U6Hlz8HR6Tf0tVbQOhTYmYOiPdunUD9Bs7aIuoxh7dxX0Lv01mWCp9\n+SUc/unPUbftZG+Mf7+j0xqe+SuNJ45iC3Ac6RgMvAjHmWRN4JUHJbUofTslJKDnc/8b8QCEFM8i\n5fZv8fimn2I/Y1a5T69gW4GRbQVGJIcT2QF2BWM+APumoNrL2WbPS/Ir2FAoZIpKpj7YqK1q99qH\nwRfNDT3UVrWT8GXOraWk2DPY2L6DlDtvG5lBczqd7PW2XyMqF6fTyfHf/YHuAxXUBOdOGGicqVMX\nxed1Jq5xOt1m6/IiMtEo1AzZRx8kQ7YhKpqPjOSKTyQlJNHn8/Am1eL+/bbePsofeoT+kxNvOu09\ndoz9//Yg+T/98UhddIC3P9qBNPmK9oArp93QE4olXsP6vMuYHTsDSZIoisllVmwB7x77hN31Bzxm\nzKOM4SxPXcjytAVolZ4fmgeP92KXJ78MbpdVHDzRuZWG5gAAIABJREFUz4Iv92PPjZ9JRfNR/rY0\nmDWfdhHeOf7KRJWlEJvCM6CrjtvHy/Y2ril3lQ+cjG15Bu4svZGLkkswXnKSd//m3rehp3uQ914/\nQFpWBL09Q0iAKUhLamYEGu3YH+uSJBGxZDHVz7j3Omj5dCNJN96ApJjcyuXFGUvQq3S899ZT5Bzq\nJrne6rF6eypcRXmGnvxVV3BV3iW0b9tO1RNPMtTSSr8usMmUYYWW4B6Zb3zUSXmalk2FRobUrvtS\nP+Agt3KAzKpBgnrtyE4YVLuq/O1P11EfPhqYhjWkENzuWdnFprRSnbELh8I1sB0weh88DBg7qc3d\nSfaJhQz2ud8vdqfM/uil5DRtRGMboC4oi3Z9LDaFerSsb08lsd1H0NlcKUlLtvdQHaWjt0LN7z7+\nGOz+F+WQZQmtfvSesA8NcfDR/6LnkPc9LWdr37qNIz/7JdkP3T9yf9RVd9DW4n/qcPsRO0xQJG+4\nq4uhtnZwOFAFB6MOtTDc1c3BRx9z23c0cszcYhxD/s3cHi5vpKdrEFOQlgNNh2kJUfLxjFzkXs/U\nSG9ajQls167kSN52khsGKGlUoz/V7vaafqWJmuAcGs1p2M8KXmSHjaieSuI7KzB+ucFXZXNyY+wS\nXu/eQ/uA/3sk7E47jb2uVTWlNYZAdm6262NI6diHUwJHeAhycwdOJCotRdQFZ3tcV6/GQpshjmOh\nJa7v8RK0AaS17iChs8ItJFA5rKis7V5ff5omKpKgPPdN7mF6Cz9e+gNePvAWn1Rtob7/ILreIBR2\nJQ6FnSFdLzZ9PyVxRVxbcBdRRtdEmiEpkRm/+jndP/8j+N8eBYDejRvd0qv9Jaundn+sLs6/1LXT\ntDExk35GnCaCjbNEqtXYmgMoxylJZPzgX1BqtWgUaq+1tgGcsjTh80M7DZ1DM3OjSM0M58QES/pn\nW7QyA4Np6uvxH/SjnKPb9+9rGAk2QmYXuwYPZ+QMWNva6KuqGukOXdtV7/HBLSFREJVN7Yt/peXT\nz7DKGiotk8+JP3aomaMVTSM9JADUSjUzonLYfsp9w/nOU/t9DjZ6rL3jNnGbyLLU0XKATqeTo7/+\nb58CjdOs7e0c+vFPmPHrX4zsf2mp7gP8b6aWTg73r7zGowpNQVQ2BVHZtPa1c7j1OL3WftQKFdGm\nCDLDUsesWuN0Otm1JbCKPfOXpCHJEgsS5/Dcvtfo1Q/x0soQck8MkH9sgLAu95lUhwRH4kI5pcny\nmM3sCmmg39RBP0rem2dm9RfdKHycmN6XruNwppkfxM8CoHhuIhV7T1FT6f7g3b+zjv073fcmqDUK\nCmbFU7ooBUuY9zSo8IsWUv3cC249N4Y7O+nYsxdL8SzfTvJLDpuNuLf3ctlHY09gxLYME9vShalt\nC4d0e+jcMzoz6pQCT8t0SApgmPzjg6TWDbGxyEhYp42iIwMoznrL6IecZFYPkVk9RKNFyftlZuz2\nGCLrMj2OK8lQn7GfYc3Ekz4KWcFdS68m/ZIMnvvjVjrb3QsCOCWZishFnhNLksSgyki1pYDqkHxi\nu4+Q3rKDDk0yGftmITknl6bojcPh5Mn/t4kVa3KZWZpA7Usv+xxonNaxazf1b75F7BWXA9DWHNho\nbKxAxWm307ZtO43vvEdXufteJ11CPI6hIYaaPJ/PhtRUmrOXw17/nicOh5NdW06yaEUmDT3NaPtN\nSH2Tey8MKMMxtuVTWXCSh374Czr27OXgfzwKQIs+ngNRi3DI3odcDllJfVAGDeZUsps2Ed3r2o9Q\nFlnA0gVX8N23/522AfdiFpFtw2SekNH1GZCcChyKYdotPRxKVdKn9/6+kmzqgPbL9anNvDE/hvpo\nK1aVTOleI5qO0gn3X9jHWMV3Sg7qkvZT0nzMr9OKvng1kpdy/wa1nptnXs36/DVsqtnJsbYq+ob7\n0So0xAfFsDBpDiE6z1VMpcFA2QP3sPmBv3sETr4yDrVPSaABrsH9VNJFR2HOzaG7wve9P2eKXLZk\n4heNQQQbZwlSBpYjidOJvacXpVZLXFA0Xc3+zdoDxJmn9kYDkGSJq26cxQv/u43ak75V4ilZkDxt\nddq7OwNbvenuGv1+dXAwpowMeo4cAcCBTLMxkeoX9yFHtiIrJNpoQeswM2gY3bSdZklkcNNOal/8\nKwD15nQcsn+DoJ2bT7oFG+CqSuURbNTv5zbntePmLDudTt48/CEvlL/u0bUbQLI7Ce3Qoh1Q4VA4\n6AgaYsDgPiCONISNbHwH6Dl8xKP8qS/6a2pp/WITEYsvAsBulQhkmKhxaMctdxlmsDDfUOLz8Xq6\nBulom1zFpzN1dw7S0d6PJcyATqXl0sylvFLxDnaFxP4MPfvTdUS22wjutqO0OxlSSzSEqQitK8Hc\n6X4dDslOY/zoYO5EvJY3L5JZsaUbw+DYAaNNhu35Bnbk6MFpY1PNTpaklCHJEpddPYPHf/kZdtv4\nAad1yM7OzSfZt7OWdTfM9LqvQxMaSnDhDDp3u98HzR9/Mqlgw+l0cvy3v6fl040+vb7n8BGPr6ns\nAZYgdjpQnrFqqB90smqLb5+5Ue02Lv3Eye6YQo80C4DVV+QTlJnHn3a+QF1rLWm1Q0S2D6OyORlW\nSrSEKDmWoCXKEsPtxdeSHe4q5nHLd+bx3B+30NJ01oB8ovQ+SeJUUBaNplTssgppakrkAzBstfP2\nK/s5VtFAzKeferx3nUgMKvXYZRVKhxWNrd/jN1L/1jvErLkUSaFg2BrYfkar1XNj9VBLC4ce+xl9\nVVVev2egxnuhDHVYGDkPP8CeF/wbQJ228cNjfPbxEQY1/STYZiE5J7+aFNIax/CwKxjSJ7gKK7Tp\nYyiPXuzRx8Ibp6TgYNRC5AY7kX3VnHrtDRKuXY/6jHFJTJOVGQdC6FVk0K6PhjPmFOQ+G3M3V+LQ\nVbJ15jD9OtdfOqbZSnHFAN1OPQMBbN20KXQYG1eQ0NlLT3ATDRozJkP4xN/ohV220Z1/jOvmL6Wg\n7FIOP/pfk+rkbkhNJXr1ynFfo1NpWZY6323CbcLzam0iuvu4z9W6zhbb5fk55w9tdBTm7KyJXzhJ\nUatX+RVsSEolkcuW0NTj35hWBBtn8Taom7Qv910sSZ7nUa1FadUS1BaNekiP7JCxK4fpNbfRG9Ti\nNuMQY4okM8y/esYT0WhV3HDXXD5+5xC7t9aM+eAwmbUsXJHOzNLEKSm9602gv+721n6GBofRaF0f\nxpaSYrqOHKU6JJ/aoGyGlTpoA9qaRr4njfn0GzpoijtKX1AbswcsHH/6DyP/Xx9AOb4TR1ro6ugn\nKGR01n9mdB6SJLndWx0DXVR21HikOJ02MDzI/2x/hm11noGBqVtJ6okY5MFkrIrRJ42l1oHeUU9r\nZDWVSZ0oFArunH2DW6PHxnff9/vaGt99fyTYkHydph+DUjm1vVoG+sevCuOLwYHRY1yVcwk1XfVs\nr/sySJQkmkJVNIWOPvQNXaGYOyPPPgzDiW0Ma92D6JpoNX++PJS0miEKjg8Q1To8km7UaVRQkaql\nIlXHgHb097KtbjdLvuz8HhpuJCsvyuemnMNWOy8/tZNrbyshNdOzY33E4ovo3L0Hu6Sg2ZhEgymV\ngTozr9/7MmrJRnSIxNyLC0kqHvth1/ThBp8DjbEYre0o7UPYFP6tmiodwwwp9ehsk59pH5Y1HAlb\njOz0fAwWlyVSXJaErbePf6qPo+GDQzj7PSdGVuwbJnppHPHzRoM6U5CWm+6exzO/2UCzH41d/Z1R\n9cXRQy2cDF1Ojn0Tof2n6FeaOBWUSYM5zdWt/UtqWz8x3ceI7T6C1uYK4q2trZQ/+AiO4Aj2dIUD\nvpcqPptkd9+fMNTaxv77HsLaNrnaPQqdjpxHHkRtCQmoh9Toecno+gO4LqdMWLvrM10dEoJkCedA\n0EKfAo0zHYqcT3B1E+3bttO+bTtLYk1sSZFRDCuIrC2h1uR9ItIhK2k0ZwAZzN+8l1MJx8msdmK3\nplAXnOVz8YyJaAeNaBv9j1psyiHy1wRxxdzvo/xyYi/9e/dw/P/9zmMvjjf6pERyHnlgytOMAKxt\n7cR1HaYuKNOjZ9JElPYhonrOKnhxVraFr6JWr/S6ahOosLJSmvLzPFYOJ5Jw/bWogoJABBtTozOA\nZm7giv5UZteHVWl8EU/vfZnuoV40/UYiTmVg7ojwyHUPa0zBqu6nLeokbZHVIDlZkbZw2gb4ACqV\ngpWX57FoRSb7dtZy/HAz/b1WJFkiKFhHXlEMGblRKKaxgR+AMcDUrOaGbn79ow/JnxnLrLmJGItm\nsm9Ds9d+B2fS94WQdKSEzsj9ROz5bKSJnwOZgQCrv7Q09boFG2aticzQFA6f1YTpjUMfsCx1Pkkh\n8W6bJU91N/LLL/7oteFdxlEL2vZZ2GQVHtOTkky/Ig59axwljQ2U3JpNXuRoeohjeJi2LVv9vq6e\nI0cZbGpmUGVEYdUE1Js0NHxqK60pVYHfp2c2+JJlmXvn3sbTe17m/eOuTrmqQR2aQaNrkkAxTHRN\njscx9EY1/3bbN6kfWMbjO55zKztsV0gcSdZyJFkLTidqmxObQsIxRrO1M7sH93QNcvjA5FJEHA4n\nrz2/h+8+tBS1xv2j3jJnNvUh2RwPnuE2yARXg9hjXXDsL8cIf2kX6+5YSGS6e/lbp9NJ/RtvTup8\nvAnKTMcSEkRzi38NwmwKDdsSLietdSex3Ud8TsVwIFEetYgBlefAMjE1lJVr8xhsbubgfzzKwKlx\nAryBQRreepuOnTvJ+feH0X2Z9qByDLHQXM3bjaZJ9Q8ZixMnnWF1KJP7Sa6ZRevZqyZnUapkVGol\nA32elWOsSj17Y5ZjHmyhWxPqdUBlVeo5aZlBdUg+aW07ie88yKDSyOHWYBqsiWOmA/l8PQ6J8k8P\nkDsvE0mp5PBPfzHpQAMg7bt3Y0hKPH3QgM5pqqhOhPOXJ7ZhNGlpSlqJrXvyvyu7rKLenEFyh2sD\nf9ipHi6uV7IrdhWdOt/KRNebCjE1xHPcGBTw32uq9QS1UJY/fyTQAIi4aCHaiHBOPvXsSHbC2WSN\nhoili0n85vUo9f6n8Y7L6cQw3EVq2x5OhE0ilc7pJKf5C5ROG+FLLiKkqBBzTg7t23dMutysLi6O\nqFXjr9r4S1IoyLr/36j4j/+k99hxn74n+tKLR1Io/XV+3YHngeZhK6q4WIbr/GuiZimZPZLXrlKo\nuLnoGzz53lskHJ85bkdKtVVPdE0Ohq4wFMXNLEtd4NfPnyytTsWcBSnMWTA9qygTyciNDCjXHlyz\nuLu31rB7aw0anZKhCQKN0yQkQppmUC91oNEN0aaPoU0f2AYq1/l4BqzFMQUewcbWut1srduNQpIp\niStiVfoieob6+P22pxmweQ6+sg6Fo+wuHnNweqZ+ZTS7n6pl1iM9GEJMOO12OvbsdSsNDDCo0FMf\nlEGzIZFBpQEkCbVtgLD+WuK6jqAfHk03s0lKPnn/CLsrunHa/E+icsh2Vi2afNWY8ZiDdSiU8oRp\nRuN55ZldLFqRQW5RLLIsoZQV3FK0noyhfDZ/foyh5omvefGqTLQ6NSm6RPIjs8bucSJJWFXj/x3P\nDOZ2ba3G7kcX2v4+K+W765g1N8nt62/85k0OhU5cfrnFYeap/9nKdbcMEV8wmkrZfaDCo6LVZMha\nLYm3fovNjUE07wts35ZdVnEkYi7NxiSymzeNrHI4gXZdDI2mFAZUJpySjMo+hGWgnj5VEB16z9nh\nYIuOb9w4C0d/HxX//iiD9b6tJA02NnHgkR8Rs+ZSOvfspWt/OQ67HWfCFQFdG0CvuZmGhCMM6V0z\nivG5KsrkMCqqrXSp3QeeGlsfaaZe5q4tISQni3deLR9zNaxbO3H6i1OSORZWQr0pjX518KRn6Mfi\nkFW89vcqvnjxM/LsR5Gb3d8n3ZpQmo1JDCr1OJFR2wcI66vDMlDvFlAOtbTS0dbHhrcPUV/bzfnA\nYXft33Pxf4h1ypxBUkf5SAWmY2GzJ92PplcbOvGLzgFzRyTeCseZc7Ip+Pl/0VtZRfPHnzLY0IDD\nakVpNBKUl0v4RQtRGqZmdWYsqhBXP5rEznIckkxV6MQNcSWnnZymLwjvq0VSKkn7p7tGxoFRq1cy\n1NLCqb+97tPP10RGkPPvD7lVf5tqSqOBvB//iJNPPUPzhk88xgWnqUKCiV9/NVGrVgQ8+S2CDS9M\nC+bT/pfRrq9OXNWGWvVxWJU6cDrR2PqJ7D2J6axqCVFn5RAmONJIPjHb50kXc1cEKQ3ZqKSvx58m\nLTOCYIveY0Olv4YGJr8ydTDKy8bNAJxO6TptyGZlX9OhMV9vdzrYUruLLbWeZWhPC2/WouqeiXMS\n59ktmXjx0b9SFtZCV3kF9v7R37EDmaPhJdSbMzwGEANqFbXqXGqDcwnvPUlW82baDPEcD52FdV9g\nnWMBdIk2woKmtsurSqUgd0YM+3f535m8vbWP117Yw8YPj7JweQbJGeG88szOLzdmTxxohEeZ3Cq2\nBWsDWyEL1rpmxB12B7u3+h+Q79xU7RZsfPzEuxxo9r2c8pCs5cU/7+LbD4ZhDA/GPjRE04aP/T4f\nAG1OLh8e11F9IrBA40wd+mi2JVxOatsuVLYBKkNneV1VGGszq1J2svayFLRaBSef/KvPgcZp1tZW\nTj751Mi/ezShDAawSio57cyo34DmVCP77DrK03WEddjIfflztMNOioEBpXGk0ajKPojB2omMk6ry\nd7Fffy1XXL+O9OwI3nl1P1ar/4F4IH0HxtNiSOAzRzQp1r3Edx6kXR9DlaXQayBUF5yDztpNYucB\nYrqPYpdVfLKhiupNnwacQuWQXN8v+7FPY7oMqYxsi78c3XA3avtAQOm9UyE1M5ye7kG/q0eeSeFQ\nobCOPZg2piRjTEkO+Of4Qx8fhzYqisHGRlI69hE02EJNSB7tXiYnTvdaSeooHxkLhswscmskLEkS\nSTd9E210NDXPvTB2WwVJwjKnhNRv34E6ePq7oCu0WlLvuoOE666l+eNP6Ni9h+GuLiSFEk14GOEL\nF2CZMxtZOTVj0a/HiHaSDCXF9H38CYNNzTSaUqkOzqNP49l9tdpSgHmwmeT2fYT1n8KUnUVQ/miF\nIafDyd9f2us1gh9P5cE2KvbVk1fk2wz9hUySJcoWp/LOq+UTv3jaTmJq09VO1XSSlBqKrJCxOez8\nctPjlDdNrvrLmVSyksKGfBr8qNxzinBO7dqIzjYaaNglBXujl9Gpn7jsaIsxiTZ93JQtw9uVw1x5\neemUHOtsxfOSAgo2TmtrcQUdCoU8qUFMZLQJ+Yy0w+LYAp7d96rf51EcWzByPr3d/m+kbmro5m/P\n7yYiyoTRoGRLxQBMcl/AgKznrceeJsNW6Uor8rOLLLhW03Z0p9Dd7V9vXZVagUIhu+2xOc0uqzga\n7t/9lXPqE2ofeZpaP3OszzagDCxV0CkpsAw0IOFk3r4+Sg70ITtwq2qms/WOuV+l5vm/0FdZhdTV\nRfHRWioiF9Cl89xjFNhJjtO7xEcOWcXxsNnUBOdiVY6fGjOgNnM4oox6UxoDarMrBXAK9mo0JFYw\nGN3K4qgFGOqjOLTNv15UU61PE0yfZmoHnikZYSSmhvHZ+4d9fhvPWZjCijU5SJJEZ3s/27+oYutn\nlQGdh8LLXqnzgSTLRK1eyck/Pw1A6EA9oQP19KnMtH054Sw5HV/2EKnxaHJ49oTzyNdXLCNi8SLa\ntmyj5dNPGWxswjFsQ2kyETwjn6iVy9FGTb5Ra6BUZhOxa9cQu3bNtP6c8/OvfY7JajXZDz3AX3/6\nN2r1qeO+tlsbwb6Y5WQNHWHt/be5LTWdONridy3y7V9UfS2CDYBZcxM5Vd3Bvp2+DxTTssIxGDVU\n7K3HFkDqzHT45N3DHNpXz8VXFbC55wv2NY6uakh2GXNHFNp+M7JDgUNhY8DQRU9wE04v7eXD9Bbu\nzr6O1zYfxa+2FpLEqaBM0tpGV00Oh5f5FGicNl6gISvB4eNikkNhY9n6NNLiA09V8yY2IZicGTEc\n3De5GemxTHa29MCeembMjh/ZkB1tiqAgMpv946xqjUWr1Ix0me/v969jq9u57T4j5cnPDcjVzkhi\naz9HDmC3Tq86mL3RyxjCeypEYUk8DoeTg17e1zq9iqI5CcxZkIJCIfHe6xUc2ON/KteZktv2EN73\nZVf7qSgSAlOScuT8svEfgMqPAlCn92jpgJmn3uNQxDwazVNRWdBOdPdRktorqA7Jpz7Is3Tw2RR2\nK1pbn9eJO2DCQONM3TrPogf+6rLU0xFey4qkhdxcfAX9fVaO7frwvHuuBELCSWFJAqWLUgmPcvUi\nSUoL5fUX9oxbxU+pklm8OovShSkjY5tgi56ZcxICDja0uvN3+Bm5bAmn/vYaw12jqXmG4W4MXeNX\ncTKkphBcOGPM/5dVKsIXzid8oe/Vsf5RnL9/7XNs8/7eCQONMx3WZFJ+qItZc0dnIQJJfag72UFz\nQzcR0YFvLjzfSZLEZesL0ehUbP/ce9nDM81ZkMzyNbnIssSKy3PZv7OOzZ+eoKfLv02m3s8psDFH\nY303T/72C3oi61HEqMAJ4fVphLTGobB7DvaGVYN0hNfSEl2J88sGYvmRWXxv7q0cfH1HQCsLdeYM\nZIcNna0XBxKNZt/v67FIskTx3EQWrczko+072fVB/bjL4nbzAGuvKaIoc/pSASRJ4vJrZtDXO0T1\nCd9mzcMijajVCuprA+v0etqWTyvdqj9dnr3Cr2BjRdoi9CpXj4WprtzlryGlgQ59DKH93gf4rspW\niXRrwrHJKhROO8ahdiJ7q1A5rHRoI9gfvdR75SkJVq3No2S+K3Vi5eW5VB1rpa9nCEmWMAfrSEkP\nQ6kaXd1bd8NMcmZE8/ar5fT1BFZCV2ObmjTOM2k1gf3dFA4rMlM34JVxIge4idohOWiLqqI1qorg\nA+3om/vJatmCpb+e2uAcrysnCscwUd3HSew8gNbWxylzBpXhsxmWpr7qlk1hpTWqEnNnJPo+70HN\nmTpDT3EquRwkCDe49jfoDWpyi2LZt8N7qd3xSDJcef0sbDY73V2DfPLu4amKXQOSYRnksvWFbl+L\nT7Jw9/1LOHaoiV1bqqmtamdo0IZCKRMWYaRwdjwzZsej1Xn+nSxhBvQGNf1eChD4IihEh8nseyrn\nV01pNJL1wH1U/PBHY+5nOJsqJISs+38wLRWk/hGIYMOLtuZ+tnx6YuIXnuW918oxmtT09gzR0tTL\nsYMBNAfElY7zdQg2wFUJaNXaPApmxbFz80kO7D7lNrOkVMrkzYyluCyJmPjRgE6nVzNnYQpqjYK/\n/3V/QOeQnhNJWmY4qVkRtDT28NJTOwio5JITTI0xpLeEAU6U9rEH46phLRH16Zg6I6jO2IFNbeXy\nrBWYNUZaAmygZVdofNrk5quUjHBWXJ5LxJczZGsWl7F6/jDvbdrB3u112LokV8djlQN9hMSChVmU\n5ue4leCdLiq1kuvvmMOHbx5k99aaMVcnZIVEUUkCKy/PRaGUOXGkhc/eP8KpmsD2pFQebaGtpXek\n2lZ+ZBbXFazlhf2+bQ4EKIjM5pr80SXtYIveVRb7PBi09KtMnL3l1CapOGkp4JQ5w2sgcSxsNubB\nZrq0kTi99K9RKGXWXT+T7ILR1TadXk3OjIn7DGXlR5OQEsr7rx+gfLf/qxyngrKI6fGvsdjZ1KGh\npH/3brTpWZQ/tgHrkH89KaLUUx8ADU1i9cCbnuBGmuJdlYL2ZurIOz6AZthJZF81kX3V9KhD6NBH\nY5W1yE47Olsv4b01KJ2jKW/6lCG++4NL+OjvBye1mj0eWZZIKDTyLn/DrhymNbqKkNZYLM2J6Po9\n9830mltoi6ymJ7gZJFexkLnxo41cyy5K5cDuU5Ne3Syem0RO4eh929bcO2XXGIiz9xGeJssSmblR\nI/14nA4nkg8FSGSFTFFpAps2+FbN6Gyz5k5fOf2pYs7OIvfR/+DwT37OcOf4zwV9QjzZDz+ANmLq\nVtz+0Yhgw4tD+/zL17Tbnbz0551Tdh5Dg4H3DrjQxMQHs2Z9ISsvz6O1uRfrkA21RklYhBGNduzb\ndawPU18plRLX3jraRM4SZmDtNYW8+dI+HI6JR3mWMD3trd4HB0q777XAdf1BJB4poSGxgk/ePcLm\n1iaaPSvgnjNrrpnBjOJ4jweFSqXisovKuOyic3NeZ1IqFaxel8/C5Rns2V7D4fIGerqHwAlGs4as\n/CiK5iS6lV1Oy4ogNTOcE0daeOXpnVgDaFp24nCLW2nftdkrUStUPLv3VewTzCyXxc/in0pudCsJ\naTBqSMuK4PihwCYvpkKTMQnzYCsW9RCSBL19dvbGLB8zNQZcaXidXjZXgqsa3jXfmk1Civ9Vc/QG\nNVdcP5O6E810dPn3mdmjDcWWmIOmqRLHYGArpKaM9JFUioJZ8ezcfNKv4yy9fTUR6kWceuMtmj/a\nENA5AQyZdXSbVRBAPz6n7ESSJGZEZrMouRR1YgP9v30a5Ze3tcnagck6drPY9lgzy+9/FK1Ow+XX\nFlE4J4Fnf/8FDr9yRF3SsyNYcXkuwaE6trz9AW39HSA76YiooyO8Dm2/Ge2ACdmuwK6wMWDsxKp1\n/6wuis4lwjha6Sk8ysTaawt59fndPgf5iamhLF/jXg67eF6S38HGJd8oIDhET3fnAJ+8toveYf+H\na6HxvjXe8yXQOG1WaSJbPj2Bwz65WRCVWuFWSON8Zs7KZOYffkvLJ5/R8O57DNS6/y3NOdlErV5J\n6NxSt03hgicRbJxFqVBz4nD7xC/8CpxdG//rRKNVEpvg+8a4qNjAKv+EednLWVAcT2iEkY0fHuPY\noSavD52oGDNzF6eSVxTLyeNtvPPqfr/36ZymGzCTcngursSewCt/TCVLqOG8n5E6zWDSMH9pOvOX\n+pa6JUkSaVkR6I1qrO3+d7bv6/VM6bk4YwkCASEKAAAgAElEQVTFMQV8cGIjH1duptc6eo8oZAVz\n4opYmbaQrLA0r7/f2fOS/A42llychdMJHW197N92EocfhQZO69JFsTP+UoJDdCTHaTm0u5pBL70q\nfGEO1nL97aUjOeSB6hsMbOnHctOd5M+MpfbFv1L74l/9Po4+cXQgVbIgmd1bq32asDhTdFwQiamh\nSFIYKXfcGnCwsS1Xz9YCA7FVw4QEsPdZZ1Txh0v/C4v+y8/mBNiv0dL02yfQD4wfxbRlR7PskZ+g\n1Y3u14mKCQoo0ACYtyRtJLi/Kudi/rjz+dH/lGDQ0M2gYeyyuApJZl3Oao+v5xbFolDKvPHiXoYG\nx9+YllsYw5prClEq3d9bMfHBZBdEc2j/5KquJaeHMbMkYWTwb+to590PJ5/WBSA7HRStmkS/CB8F\nW/SsXJPLu69NrjncxVfmYwiwv9ZXSanXE33JaqIuXsVQczPDnV0gy6gtFjSh01Ol7R/R13c0Owaz\nMdKvevbT4euSQjUVtP3thPTXe62d74uYzsPApR5fj00I4dpbS+hs76dibz3dnQM4HE4MRg2pWeHE\nJYaMDA6T08O4818XseXTE3z+4bF/qA2Gp/0jXtPZAm1kKY8xOxhhDOOGGeu4Jm8N9T1N9A8PoFao\niTKGo1frxj1mWmYEyelhVB2b3EgxryjWLdjqPniIyt7AGyp2dgywp2MA/Aw0IqPNXHt7Ceag8a97\nMoaHA5iyx9UfR5IkolatoO6Vv400+pwUSSJi6eKRf4ZFGLn4ynzeetn3FE+9Qc2V35w18rkyFaUn\nm0NVIEl0hzQS0up/gYbwFO1ooPGlgnkrGCwsY9tbf6Fnw+eENI0G0sNKiZ78RDLXXsW8wrkex7MO\nBdZEF2DojGMsSZlHbXcD7xz1rTSzhMSds28gI8x7n6ms/GgSU0PZt6OWnZuraW8dvTalUia3MIZZ\nZUnEJXpf2XPtIyukr3foyxLaE4uKNfONm4rdVhmKluaz4cNKrEx+9jw5UsZonrr32Zlmz09meNjB\nR2+Nv3EaXPsgV6/LZ0Zx/ISvPR9JkoQ2MhJt5BRXdPuaEMHGWVTK8yPiDo8yTWpm/+uur7qGuK7D\nfgUbKvsg5rqxe1yAaxZn3pKJq7golQoWLMsgryiO//ntB9h7/J9FnhJOB6Xz4rGhpL21n8qjLQEd\nTm/wPSXsQhUUogtodSooZPwHu1KhJCF4cpXmJFniqhtn8ewfttBY71vzssTUUC5b714ZZe6qfCpf\nmbgIw3Rbe13RlAYaAFqtkoF+/1NPT6diqkNCCC0rpXXjF5M+RkjxLI+87Zmlrg7X77xaPuEKR1CI\njmtvm4MlbHT234aDLoNMUJ//gX6n0fU51BPcglU9gNo6+d/9oK6brETvwaXWYGTR+tth/e30dnfQ\n1d6MSq3FEh6NUjX2Z4ZaE/jn45kZAJIkcVPhVQRrzbx04O/YHWMHoAa1nrtm38CcuPH3s+n0akoX\npTJnYQq93UMM9FtRqhSYgrSoVBOfv1qj5Po7Snn/9QPs2V6Lc6x7QIL8olguvrLAI2VYqVJQtiiJ\nTz+b3L4kGQdLr5veBsFli1OJjg9i8yfHOXHYy/NFgozsSOYtSSM+WawEfF2d02Djqaee4tlnn6Wp\nqYn4+HjuvvtuLr3Uc3b5tPLycn72s5+xf/9+dDodq1at4v7770enm7qHls0eeJnJlIwwImOCCA0z\nsOGdQ349AIvLki6YdJXzwVB/L+F9NYT3VtNiTPT9G51Ospo3oxi24nQ4pqySREionrAIE009U7PJ\nMyhIQ1fngKvcySSkmvpZceXoxscnfvM59bX+bYI2mDRTlvJyPsstjKXyqH+5JkqlTGbe9NRK1+nV\n3HR3GW+/Us6BvafGzCWXZYmZpYmsuDzHI60jdW4ellf30e48t6umB/fVExkztecQmxgS0L6W2ITR\n2enEG66jc88+bD2+pzEqDHqSbv6m1/+bWZpIQrKF7V+cZP+uWo9N45YwA7PmJjKzNMFj/5kTOJSi\no7TcvwC4yaKkPfjLR73kpDW6kpjq3EkfpyXmBAuT7prwdUZzCEbzxJWgwDUQD6SpqyxLhEe6fyZJ\nksTa7JVclDyXjys38XHlJpr7RqvTJQfHszxtAfMTZqNV+V4RSZIkTEFaTEGTr6KkUim49BszWLgi\ngz1bazi0v4Ge7kGcX+4jy8yNYtbcRFcxiDEsuKyI1qZeDhz2rXKehJO16wuIip/+AX5yWhjJaWG0\nt/ZxpKKR3m7Xni6TWUtmXtS41yV8PZyzYOP555/nV7/6FT/60Y8oLCxk48aN/OAHPyAoKIgFCzwj\n8ebmZm655RaWLl3KI488Qnt7Oz/84Q95+OGH+dWvfjVl59Xd24yskCa96ek0U5CW6+8oHQkUdAY1\nLz89uU3jsQnBzJxzYWygOl802LqQgNymjZRLi8fsEuzG6SCrZQsRfTUMK6UpL1mncWoB/4MNvVFN\n2UVpZOZFEhpuZNdrn/PO5+0+1+8Plbq58gdXuH2tuCyJN1/a69f5zJyTEHCK0YUgryiGD/9+0GvT\nuIm/NxadfvpWfzRaFetumMmSi7PYubmaowfPeLAHacmZEcPMOQkYxykrue5bZTz9v7sYln1fxU0y\n9pOxtJiD+xqoOzn2BmBf+RvwjmdWaaLfwUZKRjghoaMDIm1kJDmPPMjBRx/D1jNxNTiFXk/2Q/ej\njxv7cycs0sTFV+az9JJs6qrb6e+1IitkgkJ0xMYHj7k5V61QcTwryNXUz4/H0v50HXPiisgOTyPc\nEMpf9r1BR28dIW2+p1O1RlaRnBtClGlqq+1IkkTRnAQ+ede/pqfZBdFjrrYGa82sy1nNupzVDNms\nDNmG0Kt0KBXnbo7VHKRj0cpMFq2cuC/J2SRJ4opbFxD0+h42b6rDOU7tNJ3KydobiknP8y+t2F+W\nMANzFwVeWl34x3NO3nVOp5M//elPXHPNNaxbtw6AlJQUduzYwR//+EevwcZzzz2HSqXi0UcfRa12\nfbjcd9993H333dx7773Ex09NHuCwbZDk9GBOHPbvgVo0J8FtRSK7IJrV6/J597Vyn6paRMaYuebW\nEhTnSW39C0VfdBBKQOG0U9CwgZqQPGqDssdsFBU00ERK+x4sA65STw2hKhxOB/IUNOI6Ta0O7O2V\nnR9N2eLRD+5ZVyxAqd7KOx/VTThQjNP0cO39a9Ea3a8/tyiGj989NOmO1EqlzKy5k1gxuoCp1Erm\nLUljw9uT64+hVMluf6/pFGzRs+zSbJZdmj3p743JSeaa6wb56wvlDMkTrwqnmftY/8CVKNQqShem\n0tnezyvP7AyoN8ngBBtu/ZGeHUGwRUenH5v7SxYke3zNlJlBwc9/QtUTf6Zj1+4xvze4cAbJt96C\nPsG3Z5BGq3TrxeKL7LSZ7M7upPjg5CYvmkOU1GeG8fvSW1ApXCsmGoWax3p+i105TFiT53WfyYmT\nlpjjtMdX8d2870/qZ/uqaE4CGz84Oukys+Cq9uQLjVKNRnnhp4BKssTSdTMpWZrDjo3H2Lv1JL0j\nxdOcRFpUzFmeQ25RnE8pXoLwVTknwUZlZSWNjY3Mn+/eRbGsrIwf//jHDA4OotW6z8xt2bKFkpKS\nkUDj9OslSWLz5s2sX79+ys4ve0a4X8GGK33Bc0Vi9rwkgi06Nrx9iOYG78vySpVM4ewEll6SPW6J\nV8E72RLMyRg1yfVWZJwkdZST0HGAFkMC7foYhhVaJKcDna2XyJ5Kj/KMFel6pCmpsj8qPMrE8cP+\np3WcnR4AMOOSUtLmdrP1lc/Zf7ibHml0s6/ssJFoGqR0RS6pZXle+1qoVAqu/OYsnnt866Qe7pdd\nPQNz8PRsMjwflS1OpbW51+fGXrJC4spvziLMy9/sfJQ8O5u74iP4+OlPONLoxCp7roSEy90Uz41n\n1tpL3O6lYIueuERLQMGGWj31AyFZIXP5NUU898fJ3dsFs+JIz/Y++NfFxJDzw4cYaGik+aMN9B4/\ngX1gAFmrxZiSTMSypejjJrf/xh8r0xbyw4JtGPvsZFX7NlHQYVLw5qIglqfPHwk0AAqisrm9+Fr+\nl7/QGV6HpSmRoLYYFI7R545dMUxHWB3tETXY9AN8t/RbpIUmTfVlAWA0aVh2aTbvv1Exqe8rnB1P\nYgAlky9kpiAtSy7LZ8ll+QwP27EN29FolMhfg5Vn4cJ0Tka11dWuztqxse4f0vHx8TgcDmpra0lP\ndy9XWVNTw+zZs92+ptfrCQ0N5eTJk5M+h9MrKmeyftkpMiLGQNGcBPZsq5nUMS9alTnmpsf07EjS\nsiKoPdnB/p21dLT1Yxu2ozeoSUoPY0ax906dgm/C9Bb+nqEjuX50z43MaLOp8fToZDozo6Z8j0xh\nSbxfzSHB1egsb6b3QYzBYmbpHZewFOiqb6G7tQuVRo0lIRK1buLUmMSUUK69rYSXn945YUlHWZa4\n7OoZ5M/yv4LNhUiSJNZcPQNTkJZNHx8fe1MnrpzrK66bSXJ62JivOR8FRYVyxX1XYR0YYv+722lt\n6MA2bEdn0JAxJ4P4grELIkREBxZUTfV+jdMSU0O56qZZvPrsLmzDEwccuYUxXHb1jAnf+7roKBK/\nef1UneakZYSmkBmRxvtlx+k09zPzUD/qcaomHo9Ts6HEjNOoY2XaIo//X5a6ALPGxP/u+gv1+gM0\nJB5EPWhAtitwKGxYtf04ZQdhegt3zb6VgqjJr6BNRsmCZPr7rHz+0TGfXp+VH8UlVxVM6zldKFQq\nhVjFEM575yTY6OtzbXQ7e2O3Xu9K+ejt9cyR7evrG/n/s7/n9PGm0sVX5jM4MOxzfezSRSkTViuS\nJImEZAsJoiLDlMsMS6E3JZKDNUPkVPrelMsuwUelZsqSZk/84kkKjzSRlBbKyeNtE7/4LHmFMT5V\nfgqKCScoxreGTWdKyQjnrn9dxLbPq9i7vdZjf4JSJZNfFMecRSkjncK/biRZYsnqLIrLEtmztYa9\nO2rp6nCl6EgSxCVZKC5LJLsg2mMj9oVErdNQvG5yFWtyC2P44M0Kv7tjT2dTr8zcKG773gI2fniM\nw+UNXitAhUcambMwhaIzehmczyRJ4t6y23j4o1+wLV9iT6aOrKpBMqsHMfU5UDid9GtkqqPVlKfr\n6DIpkSWZH8y9lVC9983aJXGFzIzOY9upPXxcuYmTHXUM2gbQqrTkWLJZlrqAmdF5KLx0fZ+O61u8\nOouIaDOffXCE1ibv+2RMZi2li1KYszBlzBLTgiCcf762+Tp/+9vfPL5WV1fH0qVLAVet/Su/OYut\nn1WydeOJMXPcLWEGFixPv2BrR/+jUMgKlqUt4OW+N1HYnWT6kGpgk+H9MjO10RruS52e8oDLL8vl\nqd9vYngSHan1RrVfGwgnKyhEz4o1uSxelUnlsVZ6ugZxOpwYTBqS08OmdaPzheTMTZ02mx3bsAO1\nRvm1HuxotCq/u2MnpYVOe1WziGgzV904i57uQSr21tPZ3o/d5kBnUJOSEUZiSugFV+3PogvmP5d8\nn599/j9Ud51if6ae/Zne96TpVFq+V3orM2Pyxj2mUqFkXsJs5iVM/WSLP3ILY8iZEU31iTZXX6Mz\nPpPSsyPIzIv6WhSpEIR/NOck2DCZXA+as1cwTv/79P+fyWg0el3x6OnpwWgMvEmVN7IsUbY4lTkL\nkzlyoJEjFY309QwhSRLmIB05hdGkpIdfEDNjXwcr0hby3vHPeK8MaiMHmXm4H0u35yDfIUFlrJrt\neQZaLCqWpJQRbpie3N/ouCCuunEWLz+906e0Dp1exbW3zvlKSwWq1Eoyc6enXOs/GqVScUGvYkyl\nhSsyOFrRSHeX7yuJKrWClZePPwCeSiazltKF3hu2XYjCDBZ+svx+ttbt5v3jGznS6p6mGaa3sCx1\nPktT5hGkvTCbwkqSRFJaGElpF1ZaoiAIYzsnwUZioquqTW1tLZmZozO4J0+eRKVSkZDgucSelJRE\nTY37Hoquri46OjpITZ3eCjAKhUzOjBhyZny1ZeSEyTFpjNy/4J/4z09/Q0WaREWqlrjmYRIarOiG\nHDhkiW6DzNFELT0G14AxNyKDW2dOXXEBb9KzI7n57nm89/qBccuGpmaFs/qKfLeGXoJwvjKaNFx/\nRynP/WkrPT4EHCq1gvW3zJ62/RpfF0qFkvmJJcxPLKG1r53W/nZsDjtmjZE4c7TXwhCCIAjn0jkJ\nNpKTk4mPj2fjxo0sW7Zs5OufffYZpaWlbhWnTps/fz5PP/20W6Wqzz77DFmWPapaCV9fqZZEHl3y\nr/x68/9S39NEXaSaukjv6UCLkkq5vfg6t0ot0yUmPphv3TOfxlNd7NlWQ1NDN8NWO2qNktiEYIrm\nJBAaPj0rdIIwXcKjTNz6vfl8+OZBDu33vj8CIDk9jBVrckWgMcXCDBbCDGIPoCAI57dztmfjO9/5\nDg8//DAzZ85k9uzZvP3222zbto3nnnsOgF/96lccPHiQ//u//wPg+uuv57nnnuOhhx7innvuoamp\niV/+8pesX7+eyMjIc3UZwnkoITiWX6/6IbsbDvDB8c8obzqM3elKYTKpDSxILGFF2kJizF996lBU\nbBCr1+V/5T9XEKaLOUjHld+cRU/XIHt31HCqupPBwWHUaiUR0SYKSxIIixCBtCAIwtfVOQs21q5d\nS19fH7/97W9pamoiOTmZ3/3ud8ycOROAlpYWt7SpkJAQnnrqKR577DHWrFmD0WhkzZo1/Mu//Mu5\nugThPCbLMsWxBRTHFuBwOOi3DaCUFGiUmgtuY6ggXAhMQVoWLMs416chCIIgnGckp9PpQ1/rr4fT\n1ag2bNhAXNzXq6+AIAiCIAiCIIzF33Gy2EkmCIIgCIIgCMK0EMGGIAiCIAiCIAjTQgQbgiAIgiAI\ngiBMCxFsCIIgCIIgCIIwLUSwIQiCIAiCIAjCtBDBhiAIgiAIgiAI00IEG4IgCIIgCIIgTAsRbAiC\nIAiCIAiCMC1EsCEIgiAIgiAIwrQQwYYgCIIgCIIgCNNCBBuCIAiCIAiCIEwLEWwIgiAIgiAIgjAt\nRLAhCIIgCIIgCMK0EMGGIAiCIAiCIAjTQgQbgiAIgiAIgiBMCxFsCIIgCIIgCIIwLUSwIQiCIAiC\nIAjCtFCe6xM4n9jtdgAaGxvP8ZkIgiAIgiAIwvnj9Pj49HjZVyLYOENLSwsA119//Tk+E0EQBEEQ\nBEE4/7S0tJCYmOjz6yWn0+mcxvO5oAwODnLgwAHCw8NRKBReX3PXXXcB8Pjjjwf0s6bqOOfjOYlr\n++qOcz6ek7i2C/OcxLVdmOd0vh3nfDwncW0X5jmJazv/zslut9PS0kJeXh5ardbnY4uVjTNotVqK\ni4vHfY1arQYgLi4uoJ81Vcc5H89JXNtXd5zz8ZzEtV2Y5ySu7cI8p/PtOOfjOYlruzDPSVzb+XlO\nk1nROE1sEBcEQRAEQRAEYVqIYEMQBEEQBEEQhGkhgg1BEARBEARBEKaF2CAuCIIgCIIgCMK0ECsb\ngiAIgiAIgiBMCxFsCIIgCIIgCIIwLUSwIQiCIAiCIAjCtBDBhiAIgiAIgiAI00IEG4IgCIIgCIIg\nTAsRbAiCIAiCIAiCMC1EsCEIgiAIgiAIwrQQwYYgCIIgCIIgCNNCBBuCIAiCIAiCIEwLEWwIgiAI\ngiAIgjAtRLAhCIIgCIIgCMK0EMGGIAiCIAiCIAjTQgQbgiAIgiAIgiBMC+W5PgFBEISvq4aGBiIi\nIlAoFOf6VAQvtm/fzqZNm6iurqa3txcAk8lEamoqixYtIj8/f0p+Tm9vL4899hg/+clPxnyN1Wpl\n//79dHZ2kpeXR1RUlMdr+vv7efLJJ/nOd74z7s/r6+tj7969KBQK5syZgyRJDA4O8tJLL1FVVUVU\nVBRr1qwhJibG72vKy8vj9ddfJy0tbdzXdXZ2Ehwc7PH1qqoqnnzySVpaWkhOTua6664jPj5+wp87\nMDCA3W7HaDQC0NPTw1tvvcWRI0cwGo1kZWWxatUqlMqxhz8PPPAAc+fOZc2aNRP+vIk0NTXxySef\nIEkSK1asICQkhMbGRv785z9TXV1NZGQka9eupaioyKfjVVVVsWXLFmpra+nr60OpVGKxWMjMzKSs\nrAyDweDTcc6nexum7v4W9/ZXd29PhuR0Op1f6U+8wGzatInNmzd7fWMvXryYxMTECY/R3t7OCy+8\n4PWNnZKSwkUXXcT69etHbqBAtLe3841vfIMNGzaM+7rKykree+89Ojo6KCwsZPXq1ciy+0JXV1cX\n99xzD88888yYxykvL+ejjz5CoVBw1VVXERMTw7Fjx/jNb35DVVUVkZGRXHfddSxfvtzva9q5cyd5\neXlotdoJX/vBBx+wePFiVCqV29dff/11/vCHP9Dc3ExycjK33347q1evHvdYe/fuJSwsjLi4OAB2\n7drFc8895/bGvvnmm0lJSRnzGEuXLmXu3Lnce++9hIWF+XC1X42hoSHeeOMNNm/eTE1NDX19fahU\nKkJCQsjMzGTZsmWUlpZOeByr1crbb7897kNrxYoVHveWP3y5H8H1Hvj888/p6OhgxowZXh/ivjwA\nGxoa+Pzzz1EoFKxatQqDwUBzczNPPPHEyL199dVXU1BQ4Pc15eXl8cYbb5CamjrhaysqKsjOzvb4\nXW7fvp3HH3+clpYWkpKSuPXWWyksLBz3WA0NDajVakJDQwGora3lpZdecru3169f7/VBedqNN97I\n3Llz+da3voVGo/Hhasd38OBB3n33XSRJYu3ataSkpHDo0CF+//vfjwzK1q1bx8UXXzzhsQL93G5v\nb+eee+5h165dhIaGEhcXh16vB1wP+JqaGnp6eli0aBG//OUvA/7sbm1tZcGCBRw6dMjr/9fW1nLH\nHXdw8uRJnE4nSqWSq6++mvvvvx+1Wu3zcQAOHz7MnXfeSVNTEwBFRUU88cQT3HzzzRw9epTQ0FCa\nm5tRq9W8+OKLZGRkeD3O66+/Pu41Pfjgg9x7771EREQAsHbtWq+vy87O5osvvhi5F8F1r1933XVo\ntVri4+Oprq7G4XDw/PPPk5WVNebPrKio4LbbbuPhhx/mkksuoa6ujmuuuYa2tjZCQ0NxOp20tbUR\nHx/P888/P3JuZ8vKyiIoKIi4uDgeeOABiouLx73WsRw4cICbb76Z3t5eJEkiJCSEJ554gm9/+9so\nFApiY2Oprq6mra2Nxx9/nAULFox5rP7+fh588EHee+89tFotISEhNDU1YTKZSE5OpqqqCqvVyl13\n3cWdd9455nHOt3sbpu7+Fvf2V3dvT5YINsbQ1tbGt7/9bcrLy0lOTsZisXDixAkcDgfz58+nsrKS\no0ePcuWVV/LDH/5wzEjy4MGD3HLLLSiVSmbPnk18fLzbG7u6uprt27ej0+l46qmnxh24+sKXN/bW\nrVu54447UCqVBAUF0dDQQGZmJr/73e/couuJjvX+++/zz//8zyMDDZ1Ox7PPPstNN91EdHQ0aWlp\nHD16lIMHD/L73/+eJUuW+HVNkxmQeXtzv/3223z/+9+nrKyMrKwsKioq2L59O//93//NqlWrvB7n\n73//O/fddx8///nPufTSS9myZQu33nor0dHRFBYW4nA42LdvH83/f3tnHlZVtf//N4NTpl1xQKtH\n0546R48IMqoIiUOpUQpOmXoJcMBLDuSEoiiKJKWC4mzmnFZGV3NIcaBCLRJLEBERBZyYR5GZz+8P\nv5yfpzMAtcF1uJ/X85yn2Hv55v1ee53NWntYKzMTBw8ehLm5uUYduVwOGxsbxMfHw9PTE25ubv/o\n5H3+/HkcP34cBgYGGDduHPr164eff/4ZISEhSElJQefOneHq6opp06Zp1UhNTYW7uzvy8vJgbW0N\nExMTJCYm4tGjR3BxccH9+/dx8eJFWFpaYuPGjVqvlN27dw8eHh549OgRevbsqbFt37p1C2+++SZ2\n7Nih9eRXV+rSthMSEuDu7o78/HwYGBgAABwcHPDZZ5+pdJxr0/rtt98wc+ZMPHnyBADQrVs37N+/\nH1OmTEFpaSm6du2Ku3fvIj8/H3v37oWVlZVGnU2bNunMtHnzZkycOBEmJiYAoPOKnaa2fenSJXh6\neqJ79+544403cPPmTTx48ABffPGF1sHixYsXMXPmTKxatQqjRo1CQkICJk6cCENDQ7z55puorq5G\nUlISmjVrhq+//hrdu3fXqCOXy9GtWzeUlZVh7ty5GDVqlLLO68ulS5cwffp0vPjiizA2NkZxcTHC\nwsLg4+ODN954A926dcOdO3cQGxuL4OBgrVflpDpvz5s3D2lpaVi1apXWDkBMTAxWrFgBc3NzBAYG\n/q3cNdTWHmfPno1Hjx7Bz88P7du3x/nz5xEaGgqFQoEvvvhCeSGmLt+RqVOnoqioCL6+viAihISE\noGPHjkhJScGuXbvQrl075OfnY968eWjWrBm2bdumUUculyuPt6ZuhIGBgXK7gYGBVk9yuRwXL15U\nadfu7u4wMDDApk2b8MILL6C4uBg+Pj4gIuzcuVNrtg8//BDt2rVDUFAQXnrpJUybNg05OTkICQlR\nDjBTUlKwaNEidOjQAZs3b9bq6fTp0/jmm2+wb98+9OnTB1OnToWTk5PW362Jjz76CCYmJli5ciWM\njIwQFhaG48ePY8CAAQgKCoKhoSGqq6sREBCAxMREHD58WKuWv78/oqKisHr1avTv3x8AkJeXh8WL\nF2PAgAGYMmUKfvnlF/j7+8PNzQ3u7u4adURr24B07ZvbduO17XpDjEbmzJlDLi4ulJKSotxWXl5O\nfn5+tG7dOiIiun37Njk7O9P69eu16kyaNIkWL15MFRUVWssUFxfTnDlzyN3dXWuZ6OjoOn3OnDlD\ncrlcZ7YPPviAfH19qby8nIiIEhISyNnZmQYOHEipqanKcllZWTq1XF1dacmSJVRRUUHl5eW0atUq\ncnFxoU8++USl3Jo1a2j8+PFadXx9fUdQjBwAAB++SURBVHV+5HI5eXt7K3/WhUwmo+zsbJVto0aN\noqCgIJVta9euJVdXV606I0aMoLCwMOXPLi4uNG/ePKqqqlJuq6qqoiVLlujUqfETGRlJw4cPJ0tL\nS1qzZg2lpaXpzKGJ48ePk0wmo9GjR9P48eNJoVDQ999/TxYWFjR//nwKCwujuXPnkkKhoD179mjV\n8fDwIE9PTyoqKlLZHhoaSn5+fkRElJubS1OmTCF/f3+tOtOnT6fp06dTTk6O1jIPHjygKVOm0KxZ\ns3SWqcsnNja21rbt4eFB06ZNo8zMTKqsrKSIiAgaOHAgjRw5UsVnbW170qRJNGPGDMrIyKD09HSa\nO3cuubu7k7u7O5WVlRERUUVFBc2fP5/+/e9/a9WRyWSkUCho8ODB5OTkpPaRy+Xk4OBATk5ONHjw\nYJ3ZNLXtCRMm0CeffKJsl1VVVeTr60uTJk3SquPi4kLLli1TnpMmTpxIHh4eVFhYqCxTUFBAM2bM\noClTpuj0k5GRQQcPHiQ7OzsaPHgw7d+/n548eaIzhybGjRtHQUFBVF1dTURE+/btI2trawoODlYp\nFxYWRqNGjdKqI9V528rKiv78889afcfGxpKtra3W/XK5vF4fbQwYMICuXbumsu3WrVvUv39/8vT0\npMrKSiKqvV0TEVlaWqpopaWlkVwup8jISJVycXFxZG9vr1Xniy++IBsbG1q2bBnl5eWp7e/Vqxcl\nJSXp9EKkuV3b2dlRdHS0yrbY2FiysrLSqWVhYUHJyckqOpcvX1Yrd+3aNbKwsKiTp9TUVFq6dCmZ\nmZmRk5MTrVixgi5cuKAxsyY/t2/fVv5cVlZGvXr1oqtXr6qUu3Xrlk4/NVn++OMPte3Z2dlkY2Oj\nbANRUVE6zyWitW0i6do3t+3Ga9v1hQcbWrC0tKSbN2+qbS8qKiILCwtlh+Pq1as0cOBArTpmZmZ1\napR3794lc3NzrftlMhnJ5XKSyWRaPzX76/LH5s6dOyrbHj9+TOPGjaMhQ4ZQVlYWEdX+xTYzM1PR\nKSgoIJlMRjExMSrl7ty5o/OL1LNnT+rduzdNmjSJJk+erPaRyWQ0ZswY5c+60PbljouLU9mWnJxM\nffr00Znt/v37KhqxsbFq5W7fvk1mZmZ18lNVVUXHjh2j0aNHk1wup5EjR9KaNWvo9OnTFB8frzLQ\n04SzszPt2LFD+fOZM2fIzMyMtm/frlLuwIEDNHz4cK06FhYWasefiKi0tJTMzMyUncUbN26QnZ2d\nTp0bN27o9ExElJiYSJaWllr317TZ2j51adu2traUmJiosi0jI4OGDRtGLi4u9PjxYyKqvW1bWFio\nfP8zMzNJLperndwTExN11tGpU6fI0dGRPDw8NNZ5Xf9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"text/plain": [ "<matplotlib.figure.Figure at 0x7f55e47b9590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_t = df[(df.Target == \"RUS\")].pivot_table(\n", " index=\"Year\",\n", " columns=\"QuadClass\",\n", " values=\"TotalEvents\", aggfunc=np.mean)\n", "\n", "ax = sns.pointplot(x=\"Year\", y=\"TotalEvents\", hue=\"QuadClass\",\n", " order=df_t.index.sort_values(),\n", " data=pd.melt(df_t.divide(df_t.sum(axis=1), axis=0).reset_index(),\n", " id_vars=[\"Year\"],\n", " value_vars=[1, 2,3,4],\n", " value_name=\"TotalEvents\").assign(\n", " QuadClass=lambda x: x.apply(lambda k: QUAD_CLASS_NAMES[k.QuadClass], axis=1)\n", ")\n", " )\n", "\n", "\n", "plt.xticks(rotation='vertical')\n", "plt.ylabel(\"Proportion of event types\")\n", "plt.xlabel(\"Year\")\n", "plt.title(\"GDELT events between India (IND) and Russia (RUS) across years\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f55e46ffbd0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gwIABAIC7d+/C3d0dBw4cwObNm8XyNnbsWIwdOxZ8Ph9//vmn6Pld1Zw0woHi\nN2/exNq1a3H16lU4OzuL3askfT5lZ2fj9u3b6NGjB3bs2CH2WWUD1asiyznbmNExG43QmDFjwOVy\nsX//fjx+/LjS9djCcFbn3r178PLygrm5OUaMGFGTbNYpe3t7qKiowNfXlxFF5fbt2/jtt9/EHnYc\nDgdcLhf37t0T9UUt/5BVVlZG3759ER4ezrgpZ2dnY/Xq1WKRmSQlrLUrn8dLly6JomWVX0/4AijJ\nQzopKUnUd1vo6dOnAABzc3MAELVUsdXA7N27VyyyDVs+gbLWjefPn8PPzw9cLlf0oOvTpw9SU1Ph\n4+MDgUAgGq9R/nsrTt4IAFu3bhUtl9cxlwdhuNn79++LLX/27BlrFJryhMe0YneDQ4cOAai+JlpT\nUxOqqqoSdZ+ozpo1a6ClpYW1a9dKXQOempoKDw8PqKmpsYaYFOavum4qPB4PrVu3Fnuhz87OFp0X\nbFGRqiP8fYT9/4Vu374tcfe6cePGIScnB0eOHGH9fPv27QDKak9rU/n9FY5NYbtmz5w5g7/++kv0\nomNqaor4+HhGCOBbt25J9L3Hjh3DDz/8INY6Xl5RURGCg4OhoaEh6ofOdp8Q/n/5FhdCiOg4lj/P\n2NJLep9KTU3Fxo0bGZHw2rRpA0tLSxQXFzMimZUnrIRhu46Efef/+usv1rTnzp1DbGwsJk6cKGoR\niImJwcKFC7Fp06ZKx7X4+/sDgGicma+vL4qKiuDq6go7OzvGn729PcaMGYPPnz+LZjcXKt/dqip8\nPh9NmjQRKyiUlJTg+PHjAMR/j969e+Pt27eMWdHd3NwwcODAKsfrqKiowNbWFmFhYYyKk4CAAGRn\nZ8PBwQFA2Uzpa9asYbykDxgwAE2aNBE987y9vbFr1y6xdZSUlETjTyR5Nk6cOBF5eXnYtGkTkpOT\nxSrCAMmfT3w+H4QQxn07Li4Od+7cEa1TlZqes40ZbdlohFRVVbF3714sWLAA8+fPx4gRI+Ds7Iy2\nbdsiLy8Pnz59wq1btxASEgIrKytRKNfykpKSRAM3+Xw+EhMTcfv2bdy6dQsmJibw9PRk7ZoTExNT\n5YBPFRUVRneKT58+ic2UK6Suro7u3btLu/usdHR0sGTJEmzbtg3z5s3DzJkzoaamhhcvXmDfvn0w\nNTVl1ACNHTsWW7ZsQUpKChwdHRl9VT08PPDs2TPMmTMHP/zwAzp16oT4+HgcPHgQ8fHxovEe0hDW\nBt69exeCeBrYAAAgAElEQVQcDgf6+vpQVVXF8ePHkZKSgrFjx0JbWxuZmZk4ceIElJSUMHz48Gq3\na2BgAA8PD8ybNw8GBgZ4/fo1Tp48KYoZDpSF3nV0dBQ9ZIYOHYqioiJcu3ZNNNGUkPBF4ezZs8jO\nzoapqSl0dXXh6OiIAwcO4O7du2JdfoyMjKClpYWjR4+Cw+GIDRCcNGkSLl68iD/++AN5eXmwtbVF\nTk4OfHx8cP/+fbGCljyOuTw4OTmhU6dOoi5uZmZmiIqKwv79+9GxY0fGw7o8W1tbnDx5En/88QcW\nLFiAkpISnD9/Hi1atICBgQE+fvxY6UBNIS6Xi7CwMBQUFNRo0ryWLVti7dq1WL58ORQUFETjbMor\nKCgQu+azs7MRFBSEM2fOgMfjMWqwhYRphF0TK2NjY4P79+9j7969sLa2RmxsLPbt24cpU6Zg586d\nuH//PrhcrlQDeceMGYO9e/di27ZtUFBQQLdu3fDmzRucOXNG4j76s2bNQlBQELZv346oqCgMHDgQ\nLVq0QHx8PC5duoSAgAC4ubmJXqBqSnjNXb58Gc2aNUPXrl1hZGSESZMm4ezZs/Dw8BC9KD1+/Bje\n3t4YMWKEqBvJjBkz8ODBAyxZsgRLly5F8+bNcefOHYnn6lm8eDFevXqFtWvXIjAwEM7OzmjVqhUK\nCwsRFRWFc+fO4ePHj9i8ebPonGO7T9ja2uLhw4f4+eefMWXKFOTk5MDb2xt9+vRBQEAAQkJC8Pz5\nc5iamrKml/Q+paOjg+DgYNy8eRPu7u6ilp+wsDBcuXIF/fr1q7LWX9hiwfY8s7CwwOLFi7Fr1y5M\nnToVLi4u0NXVRVpaGvz8/HDhwgXY2trC3d1dlMbc3BzfffcdvLy8MHHiREyaNAldu3aFgoICUlNT\ncf/+fVy+fBkODg4YPXo0CCE4ffo0mjZtWmUYexcXFxw7dgxnzpwRG5Mp6QB3GxsbUSv5sGHDkJKS\ngv3792PEiBF4//49nj59CicnJ1haWmLZsmV48eIF5s6dix9++AFaWlq4ceMGnj17JqpYqKrA4eHh\ngaCgIMyfPx8LFixAu3btEBkZiV27dqFt27aYO3cugLIKk8uXLyMhIQGurq5o27YtcnNzRSG9hb0q\nSktL4enpidTUVDg7O6N58+ZITk7GwYMHoaGhIVHI4iFDhuCXX36Br68v9PT0YGdnJ/a5pM8nbW1t\nGBkZwc/PD6dOnYKhoSHev38PLy8vTJ8+Hfv27cONGzfQtm1bxqBwoZqes41aPUTAompJaWkp8fHx\nIbNmzSK2traEy+WS3r17k5EjR5INGzaQwMBARhph6Nvyf4aGhsTa2prMmDGDnD9/njXcnzD0bXV/\n5cPlCkPfVvZXWfheWULfCl29epVMmjSJmJmZES6XSwYPHkx27drFCBFKSFloOy6XyxpKUygqKoos\nX76c2Nrakh49epA+ffqQZcuWiYXiqypMpjAcZVJSkmjZpk2biJmZGenVqxfx8vIihJSFhZw2bRqx\ntrYmXC6XODg4kAULFpDg4OAq91f43XPnziVBQUFk4sSJxNTUlFhaWpKlS5eSlJQUsfWLi4uJp6cn\nGTp0KDE2Niampqbk22+/ZYTcy83NJdOnTxedU8J8CEPgcjgc4ufnJ5ZmwYIFhMPhkN9//52Rz5yc\nHLJ161YycOBAwuVyiZmZGZk6dSoj3K+8jjmbqkLfVrwGjh8/zghLGhcXRxYsWCD6LSdNmkSCgoJE\nx6Gy0LeEEHL48GEyYMAAYmxsTJydncnu3bsJj8cjvr6+pHfv3sTKykoUmpGNMHzjgwcPxJZLEvqW\nzfz58wmHw2ENfVvxz8zMjIwcOZL88ccfjPDRQnw+n1hbW5OhQ4dW+p1CKSkpZMmSJcTa2pqYm5uT\nKVOmkICAAMLn84mbmxsxMTEhEyZMIIQwQ80Ksd0z3r17R6ZNm0ZMTU2JmZkZmTVrFomIiCCjR4+u\nNvRt+f3w8fEhrq6uxMrKivTs2ZPY29sTd3d3sZC8QsLwteXDiRJCSF5eHuP4Vgx9y+PxyJIlS4ip\nqSkxNzcn169fJ4SUhbk9fvw4GTNmDDExMSHGxsZk5MiRxNvbm3GeXr9+nYwcOZJwuVzSp08fsnr1\nahIXFydx6Nvc3Fyyd+9eMnHiRGJlZUWMjIyIqakpGTx4MFm7di159+4dY/2K94nS0lLyxx9/EEdH\nR2JiYkKGDx9OTp48SQgpO2/NzMyIjY0NSUpKqvQ+I+l9KjMzk2zatIkMGDBAdNxGjhxJ9uzZw3rP\nr2jQoEHExsZGLExxef7+/sTd3Z3Y29uTnj17EisrKzJ16lTi4+PDGgqYEELu3r1LFixYQBwdHQmX\nyyU9e/YkdnZ2ZPbs2eTSpUuidI8fPxYLB1uVqVOnEi6XK3Y/HzNmDLGwsCAlJSVVps3NzSVr164l\ndnZ2pFevXmTcuHGiMK/r168nvXr1Ig4ODoTH4xFCysItz507l1hZWREul0tGjBhBfH19RdurLiz0\n+/fvibu7O7G2tiY9e/YkDg4OZO3ateTLly9i6z1//pzMnTuX2NjYEC6XS+zs7MjMmTMZ9zQfHx8y\nYcIEUX769etHPDw8GOFwq7JlyxbC4XDIrl27WD+X9Pn0+fNn8t133xFLS0tiZWVFZs+eTd6+fUty\nc3OJi4sLMTY2JkuXLq3yGNX0nG2sFAipQcgWiqIoqs4lJSVhyJAhcHR0FM3o3JDcvn0bixcvFrW2\nUVRDdPjwYfz+++/Ys2dPrU3sVxfCw8Mxfvx4TJo0CT///HN9Z6fB27NnD/bs2YN79+5VG1CBkg86\nZoOiKKqRadeuHaZPn447d+5UO49FXRMIBKJuE19rZBXq6zBt2jTo6elh586dNQqVXdd27twJDQ2N\nagOHUGVjco4ePYqRI0fSgkY9oi0b5QgnSBJOOENRFNVQFRYWYuHChWjSpAn+/vvvSkN01rULFy5g\n37592LJlC6ysrOo7OxRVpRcvXmDNmjVwc3OTKPR0ffPz88OGDRvg4eEh0Xi+/6ro6GgkJibCy8sL\nGRkZOHToEGuYako6fD4fqampMDY2hrq6usTpaGGjnBcvXtCaOIqiKIqiKIqqxMmTJ6WqTGoYVWEN\nhHBGz5MnT0o96ypFURRFURRFfa2+fPkCV1dX0fuypGhhoxxh1yldXV3o6+vXc24oiqIoiqIoqmGR\ndqgBHSBOURRFURRFUZRc0MIGRVEURVEURVFyQQsbFEVRFEVRFEXJBS1sUBRFURRFURQlF7SwQVEU\nRVEURVGUXNDCBkVRFEVRFEVRckELGxRFURRFURRFyQUtbFAURVEURVEUJRe0sEFRFEVRFEVRlFzQ\nwgZFURRFURRFUXJBCxsURVEURVEURckFLWxQFEVRFEVRFCUXyvWdgf8agYAgIjYTSen5KOUJoKmh\ngp6ddaDVTK2+s0ZRFEVRFEVRtYoWNupIYTEPtwKjcd0/Gklp+WKfKSspwM60PUY7dIFhR+16yiFF\nURRFURRF1S5a2KgDiWl52HgwkFHIEOLxCR6HJOBxSAJchxphkjMHCgoKdZxLiqIoiqIoiqpdtLAh\nZ2lZhfhxz1OkZxdJtP7Jm+8BAC6DDOWZLYqiKIqiKIqSO1rYkLMdp19KXNAQOnnzPUy7tULPzjpy\nyhVFUVTtKuXxEfjmCz7EZqKgqBRqqkro1E4LDmbtoaGuUt/ZoyiKouoJLWzIUVRCNkI/pcmU9p/H\nkbSwQVFUg1dYzMOF+x9xMzAa2XkljM8PX36DfpYGmDzIEC2bq9dDDimKoqj6REPfytF1/88ypw0M\n+4L07MJazA1FUVTtyswpwmpPP5y9G8Fa0ACAwmI+bvhHY9nOR4hJyqnjHFIURVH1jRY25OhVRKrM\naQUCgjcytopQFEXJW2ExDxsOBiAqMVui9dOzi/C//f5IySyQc84oiqKohoQWNuQoJ5+9pq+u0lMU\nRcnL+fsf8TlRupaKzNxiHPonTE45oiiKohoiWtiQI2Wlmh1eZWX681AU1fCU8vi4GRAtU9pnYUlI\nzaRdRCmKov4r6NusHLXV0ahR+jYta5aeoihKHgLeJMnc8iogwO1nMbWcI4qiKKqhooUNOepvoS9z\n2paaaujVvXUt5oaiKKp2fIjNrFH6iLiapacoiqIaD1rYkKMBvTtAVUVJprSDbTpChXajoiiqASos\n4tUofX5haS3lhKIoimro6NusHDVrooKhth2lTtdSUw2j+naRQ44oiqJqTk3GShShJqp0iieKoqj/\nClrYkCO+gCAyXrKwkEJNm6hg/RwbaDVTk1OuKIqiaqZju+Y1St+hnWYt5YSiKIpq6GhhQ47+eRSJ\n8Kh0idc3aNMMfyx2QDf9FnLMFUVRVM04mOlBTVX21o3B1tK3+FIURVGNEy1syEl0Ug6O33jHWK6u\npgS9Nk2hwJJm8hAjGLSlNX4URTVsTZuooJ+MATC4XXRq3DJCURRFNR60sCEHpTw+tp8KBo8vYHy2\ncqoV9q1yhrN1B8ZnkfFZdZE9iqKoGpsyxAjazdWlSqOqooQ5Y4zllCOKoiiqIaKj9OTg1K0PrDPr\nDrHpiN49dQEAXfVb4M7zWLHPIxOkG99BURRVX7Sbq2PjXBtsOBCAzNziatdXVlbEmhm9G1030VKe\nAP6hibj9LAaf4rNQWMyDuqoyuuhpYXCfjujbq73MUQcpiqL+C2hho5aFR6XjwoOPjOW6OhqYPfrf\nGr2u+lqMdSLjs0AIgYICWycriqKohqVzey38udQRK3c9QXp2UZXrDrDUh1WPtnWUs9rx/O0XePq8\nYhSmCot5CI9KR3hUOryuhGHB+F6w79W+nnJJURTVsNHCRi0qKCrFjtMvQYj4ckUFYNlkCzRR+/dw\nd26vBUVFBQgE/66cW1CK1MxCtNGmM4dTFNU4tGmpIVEFybvojDrITe25/yIWf50JgYBUvV52Xgm2\nHgvCwm9MMcyuc7XbjYzPwssPKcjOK4GykgLaaGvAzqQ9WmjSCIQURX2d6rWw4e3tjePHjyM5ORkG\nBgZwd3fHyJEjK10/ICAAu3btQkREBAQCAWxsbLBy5Up06tSp7jJdhcOXw5GcUcBYPr5/d/TsrCO2\nTE1FCQZtmiHmS67Y8k/xWbSwQVFUo5GSUYC0rMJq14tLzkPslxx00K37weGlPD5KeQI0UVOWqGD0\n9nM6/j77qtqCRnn7fEPRrlVTmHHaMD4jhOBpaCIuPYxknX394KU3sDNtj4nOHHSsh+NDURQlT/VW\n2Dh58iS2bduGn376CWZmZnj8+DF++OEHaGlpwcHBgbF+WFgY5syZA1dXV/zyyy8oLCzEb7/9hlmz\nZuHq1ato2rRpPezFv56Hf8HtZzGM5Z3bN8eUIUasabrqt2AUNiITsmFnSpvjKYpqHN5+Zob37qir\nCQUFBUQniY9dexqaVGeFjS/p+bgZEI0HwfHIyCnr4qWirIhe3VtjuF0nWBi1hZIie8Hj5M334EtT\n0gAgIMDxG+8YhQ2BgGD/xVBc94+uNC2PT/A4JAGBYV+wcqol+hi3k+q7KYqiGrJ6iUZFCMGBAwfg\n4uKC8ePHo0uXLpg5cyYGDBiA/fv3s6a5du0amjVrhtWrV6NLly7gcrn48ccfkZiYiBcvXtTxHojL\nzivGLp9XjOXKSorwmGIJFWX2w1zZuI36wuMLpH7AUhT13xb+mdk9qmcXHdYxDP6hiXLPTylPgD0X\nXmPelru48OCTqKAh/OzFu2T8fPgZFv1xn1EYAoC45FyEfkqT6bsjYrPwMU685cLrSniVBY3ySkr5\n2HosCKGfUmX6foqiqIaoXgobUVFR+PLlC/r27Su23M7ODsHBwSgqYg40VFBQEP0JqaioiD6rL4QQ\n7D7/Gll5zGgs04b1qDKePFtUlsj4bJCKgz7khBCC8Kh0/HHiBVzWXce4lVcw9ofLmPXzLXhdCUdS\nWn6d5IOiqMaLbeLSnp11YM/SQhudlIP4lFzG8trC4wuw+cgz3PCPZoydqyg+JQ+rPJ8wCgcPX8bX\nKA8+dyOQkJqHUh4f4VHp+OdxpFTpeXyCHadesoZOpyiKaozqpRtVTExZdyM9PT2x5QYGBhAIBIiL\ni0P37t3FPhs/fjxOnjyJw4cPY+rUqSCEYM+ePejUqRNsbGykzsP48eMZy0pKSqTezv0XcQh4k8RY\nzu2igzFOXatM27m9FhQUIPZQzMorRkZOEXS0mkidF2mkZBTg9xMv8CGG2X84LbsIFx9+wqVHnzDQ\nqgMWfGNKQztSFMWQk1+CuGRm4YHbWQetWzZBB11NxFboKuofmoSJzvKZvNTrSjiC36dIvH5BEQ8/\nH36G7UsdkZ5dhA+xmXjwIq5GeQgM+4LAsC9QUABUlGW7b6ZlFyEwLAl9e+lVvzJFUVQDVy8tG/n5\nZTXmTZqIv1BraJQNjM7Ly2Ok6datG/bs2YO9e/fC3NwcFhYWePv2LQ4dOgRVVVX5Z5pFSkYB9l98\nw1jeRE0ZyyZbVNofuPx6+m2aMZZ/ipNvV6rEtDz8sOsxa0GjPEKAu0Gx2HAwAMWlfLnmiaKoxucd\ny3iN1i2boHXLsns7W+vGUzl1pUrPLsS1p5+lTpeVW4w5m+/gh11PcOifMKRKMNhdEoSUdYuS1Q0J\nu15RFEU1dI0m9G1ERASWL1+OcePGYfTo0SgsLMSBAwfg5uaGs2fPolkz5kt7VXx9fRnL4uPjMXDg\nQMby1MxC3AqMxvO3X5CVWwwFBQVoN1dDXmEpCot5jPXnjTVGWwkjSnXVa4G4ZPHCVWRCttwGCBaV\n8PDzoUBk5FQ/CZdQWGQ69px/jWWTLeSSJ4qiGqe3LOM1uOUi79mbtsfp2x/EPo9KyEZSWj7atard\noB63n8WKhRKXRkMcqvYuOuM/Oe8Sjy/As/Av8HuVgLSsQhACaDVTQ++ebeFkoS8WQp6iqMahXq5a\nTc2yJvSKLRjCfws/L8/T0xP6+vpYt26daBmXy4W9vT3Onz+PmTNn1no+C4pKsc83FI9exjMeRuUH\nHZbXh6uLgb07SPwdXfVbMPoIf5LjIPEHL+KQkCr9WIz7L+Lw7YDuMGgrn+4PFEU1PuEsLRs9O2uL\n/r+Drib0WjdDQqr4vf5paCK+HdC9YtIaeRxSs7EWDU0pT4BSnuA/04WVEIIbAdE4eyeC9fn6/O0X\nHLkajhH2nTF5sFGlgVcoimp46uVq7dixIwAgLk68b2x0dDRUVFTQoQPzZT0yMhJdunQRW9asWTPo\n6OiIxoDUpryCEqzZ8xQPgpkFjcpoNVPFoglmUtVEsUekypY4vTQIIRJHRWFzM0D2tBRFfV2KSnis\n0fN6dvm3ZUNBQYE1KpU8ulKlZNZO96eaUFZSQBc9LTRVr3k9nqIC/jMv1IQQ7PMNxd4LoZVW5AFl\nY2zO3fuInw4FoKiE2auAoqiGqV7uZJ07d4aBgQEeP34stvzRo0ewsbFhHYOhq6uL6OhosWW5ublI\nSUmBrq5ureaPEIItR4MQlSDdS3/n9lpSzwLbVY9Z2MjIKUJmFTdcWcV+yWUN9Siphy/j6yxSFkVR\nDdvH2Czw+OL3A00NFRi0EW/9ZBu38Skui3UC1JoQCGoWvUlNRQkWhm3gMsgQzr0NZNrGNwO646/l\n/XBm8wj8uYQ5X5Q09Ntq/me6UJ25EyFVRdjrj2nYcfolfR5RVCNRb9UmixYtgq+vLy5duoSEhAQc\nOHAAz549w8KFCwEA27Ztw+zZs0XrT506FaGhodixYwciIyPx7t07rFmzBsrKyhg6dGit5i30U5pM\ncdZfRaQi5ot0L/Ma6ipoz9J3OVLKgo4kkjNr9nDPyS+hA8UpigLA3oWqRycdKFYIjNG5fXO002He\n42p7zo3mTaWr6KnIw9UCP82zhetQI7hPMIOFEXMm8KqYdG2FSc4c0b8NO2rDsGNLmfMzyFry7riN\nWVpWIc7c+VD9ihX4hybhVQSdj4SiGoN6K2yMHTsWa9aswa5duzBkyBBcuXIFnp6esLAoG4ScmpqK\n2NhY0fr9+/eHp6cnHj58iDFjxmDKlCnIzc2Ft7e3qFtWbbkfLHvow5sydFNim29DHuM2+LUQt53P\npzVJFLtSngABbxJx9u4HHLv+Fhfuf0R4VDqtffxKvWWZX4PbRZuxrK66UllKWTgoT1VFCSZdW4n+\nraykiDUzesPGWLJWcwujNlj3nTUj1O0I+84y5UdRUQHOUoz9a8xuBcbIPLD/ur/00ccoiqp79RrW\nwdXVFa6urqyfbd26lbFs0KBBGDRokLyzhfDIdKhoMB+akngQHIc5Y02qDXtbXld9LTx+lSC2TB4z\nide05k9ZSZFGAqEY8gpKcPFRJG4HxrBObmnQthlG9e2CwX06Qknpv9EH/WvH5wvwPoZl5vBykajK\nszdtj/P3P4ot+xCTidTMQlGY3JoabtcZd57HVr8iCydzPTTTEO++q66qjB9nWsP/TRKu+X3Gm0hm\na3fPztoYYd8ZfXvpMVp0AMDRTA83A6JZo3ZVRSAguPciDmMcq56rqbEjhODOc9nHXD4PL4sQKW33\nZYqi6hZ9c6xl+UU85OQXo6WmusRpurK2bNR+N6ruBi2gqaGC3IJSmdKbG7ZmfaBS/12JqXnYcDAA\nX9Ir76IXl5yHPRdCERj2BaumW0FDXaUOc0jJw+ekHBQWi3epVFVWZL2XAWUVKm20NZBSYZxGwJtE\njK6lF+puBi1g0rUVa6GgKoqKCpXmQUFBAfam7WFv2h4JqXmIjM9CQREPTdSU0UVPq9rofEpKilg7\nqw/+t99f6jGAhy+HoVWLJqxjXr4W+UU8pGfLPj5RQID4lFxa2KCoBo5WM8pBaal03ZXYBomnZRUi\nm6WWuCZUVZSkCstb0XA72boEUF+njJwirN3nX2VBo7yXH1Kw5WgQeLXQnY+qX2xdqDgdW1YaPUn4\n0l5RbXel8nC1QItm0k3y6jbOBJ3aNa92Pb3WzeBoro+htp3gZKEvcRjw5k1VsWWhPYbYdISyFC17\nhADbTwbjnZStIo1JcS1ElCoqoeMIKaqho4UNOWimIV3NbTMNVejqMCcBlEcI3BH2nWUKp9i8qSrM\nDWXvEy2t/MJSvI/JQMiHFLyPyUB+oWytMZT8HLj4BmlSzrb8KiJVplmeqYalusn82NibMicqfRed\ngfTs2gtZq6PVBIYdJesCq6SogIXf9sKwOqhE0VBXwaIJZvBePxgzR/SEabdW6NSuObrqa8HWpB1W\nTbdCPwt9RroSngCbvJ4hscI8JV+L2mjlbEpbSimqwaPdqFg001CBrG0KXfW1ZLqBdtVrwaghjkzI\nkjoiSnV0dZpiyUQzbDv1Uqp0ufklCPmQAqsebWs1PxVFxGbi2tPPePIqAaW8f2vAVZQV4WCmhxH2\nncHpIHuEF6p2pGQWIOCNbLXS1/w+Y1TfLrRLXiNFCGGfzK9L1YUNToeWaNWiiVgBlRAg4E0SRvbt\nUkVKySWl5SPo7Zcq19FQV8YAKwOM7NsFeq2b1cr3SkqrmRq+GdAd37BMaNiH2w4ZOUWMSIi5BSXY\neDAQfyxxgFazr6u7UBM1ZbRqoY60LNm6UqmpKqFjOzrRLEU1dLRlg0XfXnoypx1mK1stGdvkfvKa\nSbyfpQE8plhAUYoY7gTAnyeDkZQm/ezjkuDzBdjnGwqPvx7j/os4sYIGUBbp6P6LOHj89Rj7fUNr\nJbIWJbs7z2IlnuyyoqT0fLz6SENWNlZJ6fnIyhWvjlFUAIyqCfOqoKAAO5bWjdrsSnXhwUfGedlE\nTRkrXC2wenpvbF5gh6Prh2D+ONM6L2hUR0VZET/OtEZHXebLc1J6PjYdfvZVTWRXUFSKv86EyFzQ\nAIB+Fvp0DBhFNQK0sMGin6WBVH1rhZo3VYWTuWwFFbaBlfKaSRwo20czw9aVfs4WdSq/sBS/ej+v\n9QceIQR/nQ2RuHvN1aef8dfZEBpStR69ZanZrsv0VP1hG6/RWU+yFl22cRvhUenIzK35JKbp2YW4\nF8SMRjXaoQucLAxg36s9TLu1hnoDjqjXtIkKNsyxhXZzZoCRD7GZ2H7qJfiylvIbkPcxGfh++yPc\nZfm9pCFraGGKoupWw73r1qNWWupwG28Kz3OvJE6jpKgAD1dLmR9kbIPEkzMKkFtQAk0N6QY8SkIg\nIPgYm8lY/u2A7ujD1UVnPS1sOBCA8AovFtFJOdh97jWWT7Gotdltr/tH40FwvFRpHgTHw7BDS4yo\npe4XVcnOK8bd57F4/CoBqZmFEAgEaN5MDVY92mKYbSeJB4p+TfJkjGgmlF/D9FT9CY+SPORtRUYd\ntaHdXB0ZOf8WLggBAt8k1XjshO/DT4wZzdVUlTDKQf73iNrUumUTbJhjg9W7nzAifgW8SYLX5TDM\nHWtST7mrXFpWIW4FxuD1x1Tk5JdARVkRbbU10N/KAH24ulBWUgSfL4DP3QicuRsh89waQhOdOejc\nnvncpCiq4aGFjUoMsekIvkCA/b6h1XYXUVVRwsqplrCowQBqrWZqaN2yCVIzxQdLRsVnoxen8hYI\nWUUlZjNC4CorKWCSM0dUYFo1zQrf73iIjBzxLhMPX8aD06FlrTzE+QIC34efZErr+ygSQ+06SzWn\niTT4fAGOXX+Hy0+iGBGU8ot4uPIkCleeRKF3z7ZYOsn8q+tPXRVVlZo1iqqqKFW/EtUgsbVKVTc4\nXEhRsawr1VU/8VbMp6GJNSpsZOcV42YAc76GYbadGuV12UVPC6unW+Onw4GMl/LLT6LQRlsDvXu2\nxZP/rwDh8wm0mqnCwqgNTLq2qrWKIElk5xXjwMU38AtNZOQ1OikHz8K/QLu5OsY4dkHAmyS8j2FW\ncknLuqcupg41qvF2KIqqG7QbVRWG23XGn0sd4WimB2Ul5s1bXVUJQ207YZdHP/QxZvZFllZdzSQO\nAKEsfeYNO2qLtcy0bK6O1dOtWff98OUwRquHLF6+T2bE3pdUSkYBXr5PrnEe2PD4Amw5GvT/taVV\nj1IJEmgAACAASURBVA8JepuMH/5+UqtRdRo6/TY1a83Ra9Ow+stTksnMKUIiy7itnp0lnwSVrSvV\nm8j0GoX6/udxJEpKxVsBlJUUMdap8U6KZ2HUBu7f9mL97NA/YZi/5R5O3HiPW4ExuBsUiwsPPmHt\nXn8s/P0+bgXG1Ek30/TsQqzc9QSPXyVU2VKRkVOEI1ffVlrQUFFWxJwxxtjq3hdWPdqiurJSKY9f\npwUqiqJqhrZsVKO7QUv8MM0KmTlFePkhBVm5xVBQUECrFuqwNGqLpk1qb3BaVz0tBLxJElsWKeVE\nUJJ6FcEsbJixtKD06KyNOaONse/iG7HlfAHBb8eCsGOZE3S0ZJ8BOOhtzQoLz98mo3dP3Rptg83h\ny2F4Fl51VJvyktLz8fPhZ/hziaNMoYUbm4G9DWTub62uqvRVT1T2NXsbzexC1a5VU7RkGWNQmR6d\nddBCU01skLlAQBAY9gVDbDpKnae8wlLW8V7O1h1qdG9qCAb36YiUzAKcvRMhcZr4lDx4nnuFsKg0\nfD/JHEoyjD+URHEpHz8dCmQtfEqjo64mVky1Es11wu2igy/p+Xj6OhFp2YVITM3Dyw/iz6uQiFQk\npuWhfStaaUFRjQEtbEioZXP1Gk2IJwn2mcRrv2WjlMdHOEuc/F7d2LtrDbfvjIi4LNx/ESe2PDO3\nGL8de4HNC+xlfsGu6cDQrGrSf4rLQkhECnLyS6CspIg22hqwM2lXZdeKL+n5Ms0FEZWQjcch8XI/\nTxqClEzZWqMAwMlCv1YL6VTdYRscLk2rBlA2vs3WpB1u+EeLLfcPTZSpsHHtaRQKisSDVigqKuCb\n/t2k3lZD5DrECCkZBVKPa3sYHA81FSUsmmAml3zdDozB58ScGm1jtGMXzBjek9GtUlenqSg8MJ8v\nwOzNdxgzjd8MiMF3o7g1+n6KourG118F24iwhb9NSsuv9Qnt3kdnMrocNFFTRvcOzMIOUBaycuG3\nvdCFZTDeu+gMeF0OkzkvNW0KT0rPR16F40MIwZNXCfD46xGW7XyEY9ff4dKjSJy//xF7zr/GrE23\nseP0S8Sn5LJu82ZANGTtgXDd/+ufsO6G/2fsPBMiU1oVZUW4DDKs5RxRdaUm4zXKY2vZev0xFbkF\nJVJtp6iYh8uPoxjLncz1oKvTVOp8NUQKCgqYN85UprFptwJjEPqp9sNME0JqdK/TUFfGT/NsMXeM\nSbXjt5SUFDGkD7MQevd5LOM5RlFUw0QLGw1IS0116GgxuyNE1XJXqtcs4zWMu+pUGe5XTUUJa2b2\nRjOWGumrTz8zWj0kwRcQFBTVrCAVk5SLmT/fgue5V/icmA2+gGDPhVD8fvwFImLZW4WEc3Ys2/EI\nwRXGfBBC8CBY+n0RiojNQlwyeyHma3Dh/kfsuRAqc2GslCdApJzGIVHyVVBUynov4lYzmR8b4y46\naN5UPMoeX0DwLCypkhTsbj2LQU6+eAFFQQGYMJAjdZ4assA3STKHvJWllbY6H2IyEZ8i+6zm7Vo1\nlSqgymCbjoxJQHMLSuD3uvbmaKEoSn5oN6oGpqteC6Rni48ViEzIgkm3VrX2HWwTqpl1rz7ila5O\nU/ww1QobDwUwXjZ3n3uFEh4fETGZSMksAI9P0LypKsw5reHEMvHSm8g0HLoUhqjEmhekikv4uBUY\ng1uBMYywmlUpKuFj85Hn2DTfDtwuOigoKsWbyDRG9C1pJWcUfHXhcAkhOHHzPXzusvcdV1VWRAlP\nsokWd59/jZ5ddOQS0pmSn/cxmYzIfC2aqaFdK+lbEJSUFGFr0g63AsUjSD0NTYKztWRdqUp5fPg+\nYEayszFu99VdfzcDo2VOGxj2BZk5RVKNq6lOZa3CkvqSLl03TB2tJujD1WWMabzh/xkDrAxqlBeK\nouSPFjYamG76Wnj+Vryw8Smu9lo28gtL8TGOWbPcS4LCBlAWIcV1qBFO3HgvtryEJ8Duc68Z6we8\nScKRq28xxKYjpg7rgcycIhy5Gg7/UOlqMCUlaUFDqJQnwE+HAtG+VVN8TsyWeVbs8r62pn2BgODg\nP28Y4UqFzDitsWZGb7yPycSdZzGIS85FUQkfTZuoQF1VCW8rjA/KzC3GgUtv4DHFsi6yT9USti5U\nPbtoy9wV0t60PaOw8SoiBXmFpawtqBXdC4pjvd4nfmWtGgIBYb1nS5M+MiEbVrVY2Cguqdk9Tpb0\nw+06MQob72My8Tkxm863QVENHC1sNDCsM4kn1F63k7DINEaIwhaaauigK3lN4IQBHHyMzZI4WlNh\nMQ+XHkXiSUgCcgpKUCphDXhdKSzm1WrUL82mja/GPjOnCGnZhSCk7Hxo3aIJFBQUwOcLsOvcK9wL\nYu9aZmOsi5XTrKCirAQLwzaMrhECAcHafU8RFin+ovowOB59TdvXSshoqm68rcFkfmxMurWCpoaK\n2Hw/PD7B8/Av1dZW8/kCXHjwkbHcwqgNuhmwjz1rrEpK+TWeAK+q7qpZucV4EByHqMRsFBXzoKGu\ngm76LdDfUh/NWFofBQKC+NSaRaBqpiF9gAjTbq3RvlVTRvSr6/7RlYYIpqiGJiI2Ew+C4/AlvQCl\nPD6aN1VDr+6t4GiujyYyTgrdGHy9e9ZIsQ0ST0jNQ0FRKaMrkixef0pjLOvVrbVUtZOKigpYNtkC\nbr/dEwtfWZ10KVsdqjLEpiMEAoJHL+Ml7r5TF5qoKbPOlyJPpTwBlBQVGH2aJUnnH5qIGwHRjDlT\nOrVrjiG2HfE6IhWBYeyFyn6W+tWG1lRUVMDSSeZY9OcDRm0m7U7VeJTyBPgQy5wjQZbB4ULKSoqw\nMW6HO8/FQyj7hyZWW9h4/CqBtSvO19aqAZRNgKmgAJnHSQHAo5AEdDdoKdblLTWzEMeuv4Xf6wTG\nzOv3X8TB+9pb9LPQx9RhRmipqQ6BgMD/TSJO3/6A/2PvvuOiuPP/gb9mCyy9914sWEBQkGYDYy/R\nGNPURI3RRKPxkpjk1BhPE73kvMvFFDXNFH8pRo09mlgRBKyoqBTpIIL0usDu/v7gK+cyA8w22MX3\n8/G4xyM3O7P7QRHmPZ93ySvWLI0qwFu1DmZA68+SiZHe+PpAqtLxM5fzMX/KAK38fiREV66ml+C7\nwzeRWcB+sBl3tRDfHEzF+HBvPDu+HyRGve/WvPd9RQbO1lLC6kGvUADZRdVqFWK2xzVfI6iP6vUg\nQgEDqbSl6xN58HCywIvTB6GgpAY7D93sdOdDJBRg/tQBmDaidVjX/KkD8WdSHvadzkSlBkPBtGVY\nf0edP52QyxW4kl6Cowk5uJZZigapDAIGcLI1w5hhHhgf7gXbLlImisvqsOGbpA5vGnLuVmP73uuc\nrwHAxEhvLJkRyCvAcbYzwwuTB2B7u1ktlE5lOO4UVnJ0sBPCx9VSo/eNDHRlBRuX00o6fbgilyuw\n+wR7V2Ogr51WfkbqG4GAgZezJXLuqt9mNjm1GBduFiM0wBnTRvrCTCLC+q+SOv2Z2dQsw/GkXFxJ\nu4fHR/vjeGIucjUMMh6YFOmt1nWxoZ744cgtpQdMDVIZTl8uwCQNJtATokt/nM/BF3tSOk3Trm9s\nwb7TmbiZVYb3FoVz7ioaMupGpWcYhuF8Mq6NDj7l1Y2cnZKCOIb5deX05QI0aJi3a2EqxpIZg7H1\n9dEI6eeIaSP88PWaxzB3YgAcbZSHcTnamGDuxAB8s/axtkCj9T2MMHOMP+ZO7K/RWh6wNjdG5GAX\nWKixzQ8AF2+XIJ3jCbC25NytxrJ/ncJ7XyYiKbUYDdLWvwO5orUN8P87dhsLNhzHzkOpHXavuVde\nj1Vb49R+OvnEGH+8PJNfoPHApEgfDPJj3wievlSgcgci0v24Uqj6edlqPDAuqI8DzCTKwXlzi7zT\nYZ9JqXc5f471xl2NBx4L03x2j0IBJN8sxpptCXj9kzjeD2dKKxvx5e83tBZoeDhZILCDmU5dsTA1\nQvQQN9bxowk53TIxnRBVnb9ehM9+6zzQeFhaXgU2fpsMmUx/Mja0gXY29JCfmxUu3lL+ZauNmgKu\nlrcu9mZwtDFV+b3+TM7t+qROjB7qjpceH8xKobGxkGD22L54MrYPahua0dDYAhOJCOYm4k5TvSQa\n7iYIBQw+WxUDV3szMAyD9LwK/P2LeJULGRukLVizLQHvLQrXKJ+dS3peBdZuT2ANMGtPJldgz6lM\nlFQ04PXnhir155fJFfhgZzIqVEh/e9i8SQFqtRXtLelUCoUCLTLFIzEl/mGc8zW0sIsgFgkwfJAL\nq3V2/LUijApxZ52vUCg4O6L5u1shuJ96N7CGICbUE98fvaVxYfYDmtaAqMvYSIiVzwSrnPL5sEmR\n3qzvl5y71biVU671n7mEaKK5RY4v9lxT+brUrDKcvJiPxzjmyxiqR+s3poHQ1SRxrmCDT8tbLjka\nTo4NG+Dc6c0lwzCwMDWCo60pLEyNuqwp0bTVpZezJdwczNs+p6+nDdYviuDVFae9BmkL1u04j+sc\n9THqqqhpxD++Tuwy0HhY3NVC/HRMuWvYlbQStee2RAa6aDS/4EE6VXsVNVLs2NdxylZPq6qVYu+p\nDLzy4Uk8vuogZr51ELPeOYR1O87j/PW7ve4JVHtyuYLVUQxQfXJ4R7gG/F26dQ8NHGmaV9JKOXOe\nZ4/tq/GAUH1mbiJWa1q2Lv9EfN2s8PfnQxHLs/WsmUSEdQvD0cfDRqPP7etpwzlgtv1EekJ62vnr\nRWo/2DuckN2rduso2NBDXEXiBfdq0Nikfo2EQqFACme9hurBhkyu0Lgou1FL9R4PeLtYcv658TWW\nI01hoK8dPn1zDGaO9udMqxIJGYQOcIKNhTHrtcYmGd778jwup5WovaaHHYzLQlWtatOVAWDf6cy2\nqcxyuQL7z95Rew15xTUa//CbFOmDwX7sGqHTlwuQqGfpVAqFArtPpGP+huP49tBN5N+raXsiLG2S\n4XJaCT7YmYzFm0/gFsfNeG9RUFLDmuwtFDDo66nZTeMDwf0cWHVOTS1y1u4uAPx6gr2r4elsgeED\ne39Xs0mRPnh2XD/e5w/ys8O2t2Px9GP9YG3O/hmlLl83K6yZH4aPV45CRKArVjwdjDfnDEW/Dr4f\njI2EGB/uhY//Nlor86IYprVQvL1zKUWo0oO6PUIeOJ6kfgbInYIqrQ907kmURqWHHKxNYGlmpDQZ\nV65o3U3or0YXDwAoul+H+1XK3aAYBmr98BcKGEiMhGjUYEvfVI0dg84wDINJkT7Y+utVla+VGAk7\n7H5jZ2WC+VMH4tkJ/ZGSXorSygbIZHJYmRsjsI89bCwkKCmvx+pt8azuOE0tcmz4OgnvvBCKsAHO\nan1dQOvwMnV/aDW1yLF4019QKFrbX2qSPVFQUouCklqNdpEEAgbLnxrScTqVD3uydE9QKBTYtvca\njvB4WnqvvB5rtsVjzYLhCFZhKrKhSOUIpPzdrbXWMUUsEmL4QGecvlygdDz+WhFGPJSfn5pVxuqa\nBgBPxvTRKC3HkDwzvj/cHS3w4x+3WC1gHzCTiDAx0gfPju8HsUiI5yb0x+yxfXD2SiG+/P066lTY\nHW1vSB8H/GNxhNIuEsMwGBnsjpHB7sgsqMS1jFJU1zVBJBLA2dYUEYNdYabln/ejQtzxzcFUpd2v\nFpkcfyXn4YmYPlr9LELUlVWoWQZIdlEVZ6aLIaKdDT3EMAz83NhP6TUpEufqQuXrZqX2jZ0mTzUZ\nBuijg39AMcM8Ony61pm5kwK6/GVoLBYibKAzJkf5YNpIP4wKcYeNRWvHJ0dbU2xeGg03B/Yk5RaZ\nHJt2JiPhWlHbseYWGQpKapBZUImi+7VdpuGkZNxXa1fjgZr6ZtQ2aBZoPKBKq+OOdJROVVkjxZe/\naz+dqr6xGUWltSj6vxbSfBw8l8Ur0HigqUWOTd9dwN0ObgANGfcwP+3mxkdypFJdvHVPaTeXa1fD\n2c5UKSB5FIwIdsMXb8Viw+IIjA31RFAfewz0tUNkoAuWPTkEO98dj+cnD4BYJGy7RiwSIjbUU+O2\n3OamndfO+btbY+aYPnhhykDMmRCAsWFeWg80gNYW41wPiP5IzOmxehRC2pNqkI0CoK0BTG9AOxt6\nyt/DGlfaBQhcucp8abNeAwAmhHvjmpo1CSH9HOFoq3pReldEQgHWLBiOtdsTeLeJfGKMP6ZG+2r8\n2XZWJtj0SjTWbE9gdXlqkSnwzx8uYt6kAJSU1+PUpQKlJ3KWZkZ4LMwTEyK84WzHDljulbPnCfQY\nLT1AnhTpg4Rrd3H9jvL30OnLBYgKckW4hsP+ZDI5Lty6hyPx2biaUdo2o4BhWufKTIryRtgAZ85u\nStJmGX4+nqbyZzZIW7DnVAaWPTlEo7Xrm5scuwnaqtd4IKS/I2u3VNokw+XbJYgMdEVmfiUu32an\nJD4xpo/GHbEMkUDAYEhfRwzpq9pOmolEs1/5+jR0bGKkNw7HZysdKy6rx5X0Egzt79RDqyLkf0wk\nIjRp8KDQVMN/r/rk0fspbSD83LQ3SVwmV3AGBoEaBBvhg126nOXQkSlauLnviLWFMTYvjcbYUE+I\nhB3fGdtaSvDq7CF4YcpArRWW2lhK8MHLUZzFi3K5AjsP3cSRhBxW4Wt1XRP2nMrE4s0nsOdkhlJd\nREVNI65oqe5DG+zU/Dtv70E6lcRIyHrts99SUF3XhBaZHNV1TWiUtqhUK3KvvB4rPz6D979NxpX0\nUqVhaAoFcDWjFB/svIAV/z7NuRMRn1KoNNVaFacvF6CuQb1r9VFpRQNKKhpYx7Xd9cdYLEQoR6ph\n/P/tCHLtathZSRAbyq84mbTy5fi9ogquHfee4uVsydkRjQrFib7QtBmCv0fvSKECaGdDb3EVO+cV\n16CpWQYjMfsGrTNZhZWsGyCRUKDR00mxSIAVTwVj/deJKm1bjx7qjqH9dZvXbmYixoqngzFvcgD+\nTMrDlfSS1hxigQCOtiYYPdQDwwc6Q6SDJ6JW5sbY+HIk1u04j4x81YJDuVyBnYdvorahCf4eNjhx\nIQ+XbpfoTVqAt4ul0gRiTT1Ip9rWrhNVZY0UL//zBGrqm9oCBVvL1hvLCRHenbZqLi6rw1ufxqG8\nuut0r9ziGqzaGod/vhoNV3vztuNnLheq9wWh9Wl84o27iA3VfC6CPuBKofJwstBJXU1UoCvirir/\n2cddKURqVhnK2tWbAcCM0f5KqUKka2NDPfHz8dtqpVQaiYWc7Yh70sQIb1Ydz4WbxSipqFerpTsh\n2jQ+3Iuz0QUfAd628HLWbGiqPqFgQ0852ZrC3ESM2oeCBJlcgZy71SrXS6RksHc1ArxtNS7wDOnv\niDeeHYp//3QZLTzaf0YFuWL57OBua1H5YGbH7LHdO+zLwtQIGxZHYv1XibiVo3qXot9OZmp1PVFB\nLpg/ZRDMTcRIyyvHuh2Jar3PpCgfrf/dTYz0QTxHOtXDzRGA1oGUu09kYM/JDDw+yh/zJg9Qmh8C\ntKZOvf9tMq9A44HKWik2fJ2INQvCkV1UhbTcCtzI0qxlMddOgKHirNfQcgrVAwE+NhAIGKXgWgFw\nBhqWZkYY34t60HcXBxsTDB/kgvPXVe/8Nmaou95NNY4MdIHVfiOlmja5AjiemIs5EwN6cGXdo7JG\nisLSWjQ1y2BmIoaXiyWMVXwYSXQnNMAJjramKFEjFXpKtI8OVtRzKNjQUwzDwM/dihUo3CmoVD3Y\n4Gp521fzFoRAa7Gis70pfjqehou37oEr28XV3gzTR/lhQrj3I9M1xsxEjLULh2Pee8d4BWK69PRj\n/eH0fzUyQ/o4op+XDdJyVZty7mBjgjE6eKr5IJ3qlQ9PoplHO2W5Ath7OhP3qxrw+rNDlb6fklKL\nedfqPKygpA5LNp9Q+bqO9Ka5G1zzNbQxzK+9mvomrP8qifcunq2FBGK6qVLLgqkDkZpVxgroO2Nv\nbYLnxvfX4arUIxYJ8ViYF347maF0/HhSLp4e108nu9c9TaFQ4EpaKQ7HZ+PirWKlXSpTiQixoZ6Y\nFOkNd0fNZk8RzQmFAiyfPQTrdpyHTIXtRAtTMSIGs5tmGLLe9y+xF+Gu21CtSLypWcb5dFKd+Rod\n6eNhg3cXhmPHO2Mxb1IAJkR4Y2yoJ2aO9sc/XorAF2/FYlKkzyMTaDxwPfN+jwcawX0d4O3yv61Y\ngYDBO8+HwtHGhPd7mElEWLtguMZT2jsilytUnt9x9kohK4//SEJ2B2d3L31o3asNtfVNyC1mB2/a\nrteQyRXYtPOCSj3lc4qrsfNQqlbX8ahwtjPDuhfDeX+f2ltJsH5ROGy0VK+lbRMivNF+w7WiRoqk\nG8U9syAdapC2YOM3yVj35Xkk3yxmpcPVN7bgYFwWln50CntPZfaqoXCGKqiPA156fLBK19TUN+Ng\nXJaOVtQzaGdDj3G1KVR1kvitnHLWAD5TiUgnrWed7cw0mjDd25zX0pA6SzMjjA5xh1yhwKFz/G+o\nHWxM8NozIazjdlYm+PDVEXj/2+Qu60qc7UyxZv5weLnoLnd0z6lMtMhU/6X424l0uNiZoaa+CUWl\ntZzpgj2ht8zauJVTztqptLeSqBSo8pF4g51Gx8f+s3cwJdq3bdeO8NfX0wZbVozEd4dv4vz1u5xP\nXUVCAUYGu+H5yQPUbgbSHZxsTTG0vxMrN/5IQjaignrP0+Gm5tZBsVy7je3J5Qp8eygVzTIZnhrL\nfxAk0Y2SCtXTqH44egtD+zvq9Hdvd6JgQ49xFYnn3q1Gc4scYhG/TSmulreD/ewfyXaR3a2cI9dc\nFQ7WJnhpxmAM7e8EsUgAhUIBB2sT7Dx8kzNd7WEeTuZ4d2F4hzcJdlYm+Gj5SFy+fQ9HEnJw6bZy\nClyAty0mR/kgMtBFp0W4tQ3NOHOloOsTOUib5fjXrktaXpFmAv3tNRp6qE+4BugN8LHTet3OkXj1\ndqQUCuBYYg7mTWLPbCFdc7Yzw1vzQlFW1YCTF/ORVViFxiYZTIxF6ONhjZhhHrDS4uRxXZoY6c0K\nNq5l3kdBSU2vSSfaefgmr0DjYT8evY1+njYqt0gm2iNt5h7K6+VsAS8XS1iaGsFEIsLuE8qpgC0y\nObb8v0vYsmIU7/s9fUbBhh5ztjODqUSE+saHp6QqkFtczXs4E1ewEdhHO/UaRLcC+9grzZtgGAYz\nx/TBkL6OOHQuC2cuF7B2rTydLTA5ygexoZ5dFgoKBQxCBzgjdIAz6hubUVEjhVyugLWFMSy6qRD0\nws1i1iTxnuJib4ZBvnbo62mDxOt3cUmNlsPTR/npYGU9g+vGRtvD/Eoq6tWe1wMAfyXnYe7EgG5r\nOtEb2VmZGPyO9ND+TnCwMUFpu+YMR8/nYNF01VJY9FFNfROOnc9R69q9pzIp2OhBZy4XsFqpCwQM\n3n0xXKljWlVtEysoyS6qxk/Hb/eKByoUbOgxgYCBr5sVbtxRfsJ4p6CKV7BR29CMTI40GU2G+RH+\nbK00Sz3oaFfC180Ky58KxoKpA3E7twI19U0QiwRwtjODn5uVWjdephIxTCXan/TblfY3Bz1p89Lo\ntj/z6CBXrPr0HPLv1XRx1f+ED3JGGMesCEPU1CzjTLHTdieqotJaja6vqJGiQdrSI9+7RH8IBQzG\nh3vhx6O3lY6fuJCPuRMDNO682NNOXMhjPVji60p6KYpKa+HqYN71yUSrFAoFDp1j115EDHJhtWZe\nOG0gUjJKWUN895zMQNgAZ/T31k0XwO5i+HszvRxXUHGHZ93G9cz7rAIyW0vjXpPmoe8iB2s2BTuy\ni24U5qZGGBbghDFDPRAd5AZ/d2uDe8Ir15MCxj4e1krBnbmpEd5/ORJ9VBiqlHevhldHLUOQkV/J\nam5gZiLWet/3pmbN/7y08R7E8I0L82K1w65raMa5q+rPzdEXyanqzWp44IKasx6IZlKzypBdxG6y\nwdXW1lQixspnQljNDuQK4N8/XUZju2HAhoaCDT3HNbGV7yTxa5wpVA4Gd0NqqMIGOMNOzd2NPh7W\nvWp6aEc0zQmXGAkxcogbZo72x5hh6k+TnhzF/uFvYyHBP5eNwPLZQ+DPUT/VXlFpHQ6rWX+gb7jq\nNQK8bbXeUc7cVLMdCYYBzEwM+6k10Q4bSwkiOB7wHOkFE8UrazWr/6us4T97iGjPQY5dDR9X7sn3\nQGtb8Rmj/FnH796vwzcG3n2PfkrrOT+OnY3somq0yORd9hC/yhFsBPlTClV3EQoFeGJMH+z4/XrX\nJ7dj6DnUfIX0cwTDoMuC945MH+WHORNah3c1t8hwO6ccd+/XqfQejramiB7ixvmaWCTAY8O98Nhw\nL+TerUbBgwFaEhF+/OM266nVz8dvY8xQd4MprO1Idw3z83W1gomxEA1S9ep2+nra0BRx0mZSpA/O\npRQpHcvIr0RmfqVBP7zR9AEhPV/sfiUV9UjkaL88Jdq307/PORP749Lte8gtVk7hPZqQg+EDnTG0\nv5PW19odaGdDz7k6mENipPzLtLlF3mUueVlVAwpK2PnQ2pyvQbo2JdoHMSo+cX9qbF/OJ3S9kZOt\nKYYFqPfDUyBgMCHcu+3/i0VCrJkfBnMT/k/LH8wQ4TN118vFElGBrhgz1ANhA12wZGYg65y6xhbs\n+uM2x9WGQyZX4FZO9wzzkxiLMGao+jtSkyK9tbcYYvAG+dnBw4ldm7A/7g7uVzagvrGZ46qu3Suv\nx/dHbmLV1ji8/M8TWLHlNDZ/fwHJN4tVGtamLnsrzdpN22l4PVHd0YQc1pBSC1MxRnUxHFcsEuL1\n54ZCJGQHJJ/8cgU19fyHceoTCjb0nPD/isTb66pug6sLlZuDGRy03COfdI5hGCx/KhiP8+hSJBQw\nmD9lIJ6boH+TenVpxmj2tjEfI4PdYG+t/P3s6WyJzcuiec1ecLQxwaal0UpDD1UxwMcOIzl2RI4l\n5iC7SLXhm/ok9261Ugc8oHWHR5X6FVVMifaFOtlZ1hbGiA7i3pEijyaGYTAhwpt1/PSlAszfbr5h\nJwAAIABJREFUcBxPrT6CVz48iQNn76C2oevAo6K6ER/sTMaiD/7E7hMZuJVTjoKSWmQVVSE+pQgb\nvk7CS5v+Qny73RRt02ReCAMgpD91o+pO0mYZjiXmsI6PD/fm9WDLx9UKz45n3weUV0uxbc81bSyx\n21GwYQC4UqnuFHR+M8M14Ix2NXqGUMBg4bRB+OzNMZgS5QNTiXL2opW5EWbF9MH2d8Zi5hj/R66m\nZrCfPeZMVC3A8naxxMscOwsA4OVsic9WxWDFU0M4Uyd83azw6uwh+GxVDHxcu67F6MzzUwbAqN0v\nD7kC+Gr/DYOd3suVQqXLdCUPJws8P3mgStcIBQxefzaE9WdPyJihHqxC8Yfl36vBl/tvYMGG4zhz\nueMZP8VldXjjk7M4f/1up2meJeX12Pz9Bew7nanJsjs1MtgNZirs2D5MAWDTzmQUl6mWXkrUd5ar\n3S3TOg+Gr5lj+iCAowPV2auFOKvmbKqeRDUbBoCrOLWzSeIKhQJX0znqNSjY6FGezpZYPDMQC6YN\nQmlFfVvLTkdb005/OT4KZsf2hVgowHeHb7I6qLUX4G2L1fPDOm13aiwWYmyYF8aGeaG0ogHl1a0t\ndm0sJXCwNtFaQOdoY4qZo/3x859pSsevZd5H4o27iOiio5g+4h7mp9u2izNG+6FFJscPR291ea6R\nWIi35g6j2QGERaFQ4Iejt3ilNjVIW/CvXZfQIG1h7YbUNzbjvS/Po0SF1tzfHEyFnZUEI4M7T5NR\nh8RIhCfG+OP7I13/++CSXVSNlf85gzfmDDXYnH9DoVAoOAvDwwez2912Rihg8NozwVix5TQa282i\n+mLPNQz0tTOo9Dja2TAAfm7sp7NZRdUd/kAtKKlFebVy9wqGaZ1uTHqeWCSAq4M5/Nyt4WJv9sgH\nGsD/BhZ+8sYYTIzwZtUpAa352KvmDMOmpdEqFWA72Jign5ct+nnZwtHGVOs7R0+M8efsOvbNwVQ0\nt+jHwEK+FAoF9zA/H+3XazyMYRjMHtsXH7wShdABTpwFrUYiAR4L88R//zYKYQN7xzwTol1Hz+fg\nqIrdp77Yk8IKsA/GZaGwVPWdgK/232C1jNaWARrOWahtaMb6rxLxy19prFoCoj03s8s7aHfrq/J7\nudqbY8G0QazjtQ3N+OSXq5DL5SirakD+vRqUVjR0S/2QumhnwwC4O5rDSCxEU/P/blyammUoKKnh\n7HvPVa/h524N826aCk2IurycLfHKrCC8MGUAsgqrUNvQDCOREK4OZnC2M+vp5XGSGIvwwuQB2PL/\nLisdLy6rx/6zWZgV06eHVqa6e+X1nA8quLbzdWGwnz0G+9mjuKwOV9JKUFkjhUDAwN7aBGEDnbtt\nsj0xPC0yOX75M13l6+QK4NcT6VjvGwEAkMnk+ON8jlprqKiRIvHGXa3XEjW3yPDpbym8zzcSC9DS\nomDNMVIogB+P3kZGXiVWPhMCMxMxSirqcTwpF7eyy1Fb3wwjsQDujhYYG+aJAT62j1xar6YOxrF3\nNbxdLDFIzQYbE8K9kHTjLi7dLlE6fjmtBM+sPapUX2dpZoTHwjwxIcJb735fUrBhAIRCAXxcLZGW\nW6F0/E5BFe9gg6aGE0NiKhFjkJ/h7MSNCnHHofhs1r/RX/9KQ+wwD9h0MA1e33DVa3i7WKqdL64u\nZzszTIxkzz4hpCPJqcWsQJmvy7dLsPI/ZyBXKFBdK8X9KvXnWvyZnKf1YOOXv9I5u0sai4WQPvQQ\n0svZAhMjfTBmqDty79Zg8/fJKK9mz9hISi3Ga/85DWc7U1zLYA//vZ1bgb8u5MHbxRIvThuEoL50\n/8BHaUUDzt+4yzo+dUTn7W4786DJzLKPTrLqQNo38qiua8KeU5nYdzoTTz3WD8+M66c3wSKlURkI\nvpPEZTI5rmdyFYcbzo0bIYaGYRi89Phg1vEGqYxXHYK+SM3iaHmr4xQqQrQhTsNJ4ZkFlcgqrNIo\n0ACAgi7a0qsq5241fjuRwTru7WKJXf+YiB/XT8DXqx/Dzxsn4dM3YzA5ygemEjECfGzxn5WjO9yV\nLC6rx9V0dqDR/rPf/fI8TlzI09aX06sdPZ+tVrvbrthaSvDyE0G8z5crgJ+Op2H7vut606iEgg0D\nwT1JnN2R6k5hFeo42lYG0A0DITrV19MGY4ayf6n8dSGv04YO+oS7OJx+dhD9V6ZhkKAtD+80aEom\nV+DTX6+ycvEZBnh19hAYGwlhZW4MR1tTzt1HW0sJ3n85ClOi1N8llMsV+OSXK7icVtL1yY8wabMM\nf5zPZR0fN9yLV7vbrhiJVL9dPxyfjeNJ+hEoUrBhILhaeGYVVrKiaK4uVAHetlr5ZieEdO75yQNg\n3K64XaEAvvxd+0+YFAoF7pXX43ZuOdJyy3G/kn/nHC5VtVIUlrJTNQb4dk+9BiGaaF+f0FPMOumS\np6rD8VlIy6tgHZ86whd9PW14vYdYJMDimYFY+UywWjesQOuT8h169JRcH7W2u1UeuCdgWqfaa8Mv\nf6lejwQAu0+k60XhONVsGAgPJwuIRQI0t/yv00WDVIai+7Vwd7RoO8ZZr0H5loR0CzsrEzwZ0wc/\ntpsifjO7HOdSijCCYwigqhqkLTh9uQBH4rORc1e560k/LxtMivTBiCGuKs3FaJC2cM4ccLI1Naj2\niuTRZa1Chzpd0tZOYEl5PX7gaHXraGuKuRMCVH6/mGGecHMwx5tb4zqdG9KRwtJaXMu4T/UbHBQK\nBQ6dy2YdHz7IBY48Bsx2JSO/Ahn56u2O3yuvx5W0EgwL6NmWxxRsGAiRUABvF0vWN1xmQVVbsCFt\nluFWDjvnmuZrENJ9Hh/tj+NJuawe/d8eSkXYQGeNdhkz8ivw/rfJHaaMpOVWIC23Ar/+lYa1C8Ph\n5mDe4Xs9aHN7JD4bCdeL0CJj34Hoamo4IdoWOsAZSanFal+/6PFB8HOzhrFYiP/+egU5HO1L+VBl\ncFtHFAoFPvsthTVfAQCWPhEEibF6t273KxvVCjQeOJ6cS8EGh5vZ5cgqYqe1Tx2hertbLgnX2EXn\nqohPKerxYIPSqAwI9yTx/wUft7LLlHY+AMBMIuK8jhCiG8ZiIV6Ywp6IXVrRgN81mDKcmV+J1V/E\n88pNLyytw1ufxqHoPjstCmjdyXj/22S8/dk5nL1ayBloAEBS6l0kXCtSe82EdJdRwW4wk6h3E97X\n0xrTRvhhoK8d/D2s8aSa7aoZBlqZs3HmcgFnjcSYoe4I6a/+MMs8DYvX87Vc/N5bcA3x06TdbXtl\nVZqlyKrbpU2bKNgwIFyTxO8U/C+aTslgd6Ea7G9PQ+MI6WbRQa4YyPGLZvfJDLV+cTRIW7Dx2yQ0\nSPkXn1bVNuH9b5Mha3fz09jUgnU7zvN6CtzcosDm7y9wplgRok8kxiJMVmNwGgDMHKMcXIwY4obo\nIFeV30ehANZ/lYhMNVNegNbaqR2/32AdtzQzwkKOAW+qkDa1dH1SJ7h2Wh51pRUNOH+dvfMwJVr9\ndrftaVoqow+1NhRsGBCuSeJ3HioSv8pRr0EpVIR0P4Zh8OL0QaxJ2NImGb47fBPVdU24e78OZVX8\npr6eupSvVredvOIaJN+8p3Tsy99vcKZbdkShAD7++TLyitVLKyGkuzwzrh+CVUzzeXyUH6IClQML\nhmHwt2dDWMf5qG9swbs7EpB7V71/L18duMEqNAaARY8PhpWGdSmazsvp7nk7hoCr3a25iRijQrQ3\na0XTOU36MOeJgg0D4uViAZFQ+e6lvrEFxeV1qK1v4py7QcEGIT3D390aY0M9WcdPXSrAc+8exUub\n/sIL/ziOueuO4qv9N1DE0QkKaH0qdSSeXXzI175TmaiqlaK5RY6yqgb8pUbP/BaZAr+fuaP2Ggjp\nDiKhAKsXDOe9K/HU2L5YMJWd8ggAYpEQq+YOw4qnhsDbhT08FwAkRgKYmbBTt2rqm7FmewJnd7fO\nXLp9D6cvsXcRhwU4YVSw5jev/b006yzX34tfB6xHRUftbseHe0FipL2S6OEDnXv0em2gAnEDIhYJ\n4elsiax28zXuFFRBKGBYW222lhK4O3ZcIEoI0a25EwNwLqUIDdKO0xdq6pux/+wd7D97B9NG+mLB\n1EFKqY/3yuuRW6x+rvSt3HLMWfcHAEAoYFhP4fg6c7kAC6YOhLmpkdprIUTXjMWtQcKE8Ps4nJCN\npNRipe95iZEQo0LcMTnKBz6u7NTkhwkEDMaGeSE21BNpuRW4fuc+auqbYSQSwMXeDBGDXdAgbcHb\nn51DcVm90rWVNVKs+SIem5eNgBOPjkQN0hZ89lsK67jESIiXnwjUSkrOID87uDmYqxwEPTAhwlvj\nNfQmcVd02+72gQE+tvB2sWR1H+TDxsKYgg2iOn93a45go5LzZmZIXwe9GVVPyKPIxlKC4L4OSODI\n6eVy4GwWKmukeP3ZoRAIGNQ3NuNcimaTkR+mSb/1phY5Lt0u0XgaLiG6xjAMgvo6IKivA6pqpSgo\nqYW0SQZTExG8nC1homI3J4Zh0N/bFv05pnGbSsTYuCQKb38ax5o+fr+qEau/iMc/l0V32UL6x6O3\nUFrBrueaN2kAHG00b58KtH4dU6N9sG3fdZWvDfS3h5cz9w7Po0ihUOBgnO7a3T6MYRjMiumDf+26\npPK1TnZmEOhB3S4FGwbGz90KSFI+dqegCqWV9axzg/rYd9OqCCFc0vMqcP6Gam0Lz14pRE19E1pa\nFLiVU9Zhp6ieUFHT811NCFGFlbmxxrUOXXGyNcXGl6Pw9mfnUFkjVXrtXnk91mxLwKZXomFtwb2O\ntNxyzo5G/bxsMEmD6d9cxkd4I/FGMWeNZ0ckRkK8MitIq+swdB22u1WzSUFXRga7IT2vAgfi2N8n\nnbmd0/q9NW2En07WxRcFGwbGn6ON7c2ccjQ1s7tEUL0GIT3r17/S1eokciWN/40AIaTnuTmYY+Pi\nSLzz+TnU1DcrvVZQUou12xOwcUkk7hRWIeFaUVvDByszI1y7c5/1c0IkZPDq7CFa7yYpEgrwzguh\n+GBnMmcHSy7GYiHs9KDIWJ9wBYdezhYY5KeddrftPWg6YiIRqfx75ev9N+Bqb96jszaoQNzAeLlY\nsrbEuAINd0dzmvxLSA8qqajHhZvqDxnTFpFQAJFQOz/qbSzohoOQjni5WOIfL0XClGPeR87daszf\ncBzrdpzHscRcXLx1Dxdv3cOJi/mc6VNPxvbVWdqSqUSMdS9GYOG0QbzqSarqmvD7WWoQ8cD9Su52\nt1NHaK/dLReGYTBnQgA+ezMGU6J8WOmARmIh54gEuQL48IeLandI0wba2TAwxmIhPJ0suiwUGkK7\nGoT0qMQbd6FBiYTWrFkQhqH9ndDcIsOPf9zG3lPqDRY0EgsxVIOBYoQ8Cvw9rPHeixF4d0cCay5F\n+6G7HXF3NMeTseoNFuRLLBLg8VF+mDbCF1fTS3Ezuwx1Dc0wEgtx8fY95LVrSrHnZAbGD/fSizaq\n3S3/Xg2OJGQj+eY9VNVKIZPJO2h32z31bB5OFlg8MxDzpw5E0f061DU0w8RYBFd7MxgbCfHxz1dw\n8mK+0jUN0hb845skbFk+ssN0Pl2inQ0D5McRubYXSMEGIT2qXI25GFwsTI0QFeQKKzPVu0D5e1gj\nuG9rgCAWCTFthK/axYKjgt2oExUhPAT42GLNguEQi9S7xfJxsYJYJNTyqrgJBAxC+jtizsSAthvY\nN+cMQ/sfE41NMuw6drtb1qQvquuasOHrJLzy4UkcOpeNkvJ6SJtknHV0Y8M8tNrulg8jsRDeLpYY\n6GsHXzcrSIxFYBgGy54MQgBHM4OS8np8sDOZMxtG1yjYMDBNzTLUtssHbY8B4OfWdUBCCNEdTXc1\nrM2NsWXFSPywfgLenheKTUujYaHCzb69tQlWvxCmFFzYWZngsTD27I+uiIStT0EJIfwE9XHAyzMD\n1bo2LqUQ2RzFx93F28USsRwzgv5MykXuIzLcs6K6Eau2nkUyz1TY2zkVPXITz0UsEmL1/DDOFLlb\nOeXYuvtqt08Vp2DDgFTVSvH3z+ORlNr5N78CwLs7ElBcVtc9CyOEsFhr2AHH29USfT1t2gpEPZws\n8OGr0XBz6Hp2jr+HNT56dQTsrdl1W4seH8z51KsjDAO89nQwPKntJSEqUXeeBQAcScjR3kLU8NyE\n/jA2Ut5dkSuAnYdu9tCKuo9MJseGb5JQWMr/Hup2bgU+3X1Vh6tSjZW5MdYuHM5ZP3T6UgF2n8jo\n1vVQsGEgGptasP6rRKTlVfA6v7C0Dmu3J6CqVtr1yYQQrQsbqFnnj7AB7EFM7o4W+PTNMXh7XigG\n+ym3thYwrZOG170Yjn8tH8kZaACtdV/rX4pA+KCuBz2ZGIvwzvOhNFuDEBXJ5Ar8dSFP7etPX8qH\ntAeflNtZmWDmaH/W8Yu37uFqeonOPlcmV+DCzWJs/v4Clm85hSWbT+DNT87iu8M3u+0BasK1u8jI\nr1T5ulOXCnq0CLs9L2dLrJrLTokDgB+O3kJ8SlG3rYUKxA3EnpOZKn/zF5fV45uDqVj5TIiOVkUI\n6Yi7owWC+tjzbi/5MImREDHDPDhfEwkFiApyRVSQK+oamlsfKDCtnaL4DiszMRbh7y+E4WZ2OY4m\n5CD+WqFSHrKznSkmRvhgbJgnLNWoFSHkUVde1Yiq2qauT+xAY5MMRaW1XU4516UZo/3xx/kcVLSb\nHfLNwVT8Z6WD1tvyxl8rwjcHU1FSzp4bdju3AntOZWD4QGe8/EQQbHVYqH44gT2sj68jCdl4+Qn9\nmUkytL8TXpw+GDt+Zw9y/PdPl+FoawJXe3OkZpehulYKgUAARxsTBHjbQqilLoYABRsGoblFhqPn\n1fvmP3ulEAumDtT5UCNCCNsTY/qoFWxMjPSBmYm4y/PMTMS8zuPCMAwG+tphoK8dlkmDUFrZgOYW\nOcxNxXCwNtFpC0dCervGphbN30PaszUAJsYiPDchgJUelF1UjVMX8zFWjfqvjuw7nYlvDqZ2eo5C\nASTeKMadwipsXBIJV/uuU0pVVVJRj9SsMrWvP3O5AC/NCNR6IKaJKdE+yC+pwdF2qXlNzTK881k8\nFFCgqVm5U5q9tQkmRHhhUqSPSrWCHaE0KgNw/vpdtZ+QtMjkOKHBVi4hRH3B/RwxZ0J/la4J9LfH\n3IkBOloRN4mxCB5OFvB1s4KjjSkFGoRoiO8uY6fvwZFv393GhnnCy9mCdfyHo7e0ElABQNyVwi4D\njYeVVjTgvS8TUd/YebMcddwrY++qqKKusQW19ervaOkCwzB46fHBGNKX3aVU2ixjBRpA6yyRH4/e\nxrKPTiKzQPWUsvYo2DAAt3LKNbr+ZrZm1xNC1Dd7bF8snDaQV8vZqEBXrF2ofstMQoh+sLWUwEaD\neQYP5ib0NKGAwfypA1nHy6sbsf+M5oP+WmRyfHXghsrX3b1fhwNx7CneXORyBeobm9Ei63rOSTOP\nc7rC53O6m0gowFvzQuHuqNpuUHm1FGu+iNe4C1nPh82kS7UNmkXvml5PCFEfwzB4fJQ/wgY640h8\nDv66kIe6h/5NCgQMIga5YFKUNwb72dOuAiG9gEDAYNxwL/zyV7pa18cO84CRuHtmbXRlaH8nBPd1\nwJX0UqXjv53MwDgNB/0l3ShGebV6M4n+OJ+DJ2P6cNYWSJtlOHe1EEfP5yAjr6KtFbmbgznGDffE\n2DAvVj1aXUMzEjkmg6uCYaB2aquumZuI8dKMwXh3+3mVrqtrbME/v7+IT98Yo/ZnU7BhAIw1/IHT\nvn0dIaT7udqb48Xpg/D85ADk36tFXUMzjI2EcLE300pOLCFEv4wP98ZvJzMgU3HoDsMAEyO9dbMo\nNc2fOhBX/30aD49naGyS4f8dT8PSWeoXRP+ZnKv2tWVVjbiSXophAcqd/y7cLMZ/f7nCmX5eWFqL\nbw/dxK4/bmPupABMH+mHmvpmHDh7B4fOZaGuUbPUsP5ett0+3E8VyV2MTuhI/r0aXEkvgZOaZTK0\nV28APJzY+ZIqXe+o2fWEEO0Ri4TwdbPCYH979PW0oUCDkF7KwcYE8yYNUPm6WTF99G6ujY+rFcZy\nDPo7npiDPA1SbPJL1J9FAgC3c5XTxM9cLsDGb5K6rHNtapHj6wOpeP2/Z7Fw43H88le6xoEGAEzS\nsyDxYY3SFpy8mK/29Ufic9S+loINAzAq2B0iofqpFdrsGEEIIYQQfmaM9sNTY/vyPn9KlA/mTOje\nBhF8dTTo71sNBv1pOnX7lz/TsXjTX/j8txT8djID//npElTZSMrIr0Rjk3a6ftlbSRAV5KqV99KF\n9PwK1GsQUF1NL1F78jgFGwbA2sIYUYFual07wMcW3i769YSEEEIIeRQwDIM5EwPw9vOh8O1kZoaH\nkwVWPhOCl2YM5tVMoifYWZlgxijuQX8p7eo5+DKTaF7fUHS/DkfP5+C7wzfRU7XZRqLWAmyxSH/T\n1qvrNOuS1dQih5SjcxUf+ptYRpTMmdgfl9Puoaaef7G3WCTAwmmDdLgqQgghhHQlKtAVkYNdkJZb\ngfhrRSivaoQCgI2FMcIHuWCQn51BNIeYOcYffyTmoJJz0N8olQMlVwczFJZqlkqlTQIGGBnijidj\n+iAjvxJbf73aZc2NmUSE1fOHo7+3bTetUj0iLQzpU/c9KNgwEM52Zli7IBzvfXWe1zaYSCjAm3OG\noa+nTTesjhBCCCGdYRgG/b1t9f6mtDMmxiLMmdAfn+5OUTqeVVSF05fzETOMX9q2TCbHrycycOnW\nPV0sU2UMWlPOZ8X2aRsW6OlsCX8Paxw4m4XTlwtYKV8WpkYYN9wTU0f4ws7KpAdWrRonW1ONrre1\nlKid0k/BhgEJ8LHFR6+OwOd7rnU64dLX1QpLZgYiwMdwf6ARQgghRP+MDfXEgbgs5BXXKB3/4cgt\nRAa6dtmN6V55PbbsuqTxDDFbS4nabXPb83O3wvKnglnHvZwt8ersIZg/dSCuZ5aiskYKgYCBnZUJ\nAv3t9aY9MR/eLpbwcrZAbru/N77GDHVX+7Mp2DAwns6W2Lw0Gjl3q3HsfE5bwY/EWAQfF0uMG+6F\nfl42BrEdSwghhBDDIhQKMH/KQKz/KlHp+P2qRqz89xlIjIUAw8DOUoLIQBdEB7m13ZSfvpSPL/Ze\n06hQGQCiglyxas4wVNQ0IiXjPn79Kw2FpXVqv19X90zmJmJEDNbf4m8+GIbBxEgfbNt7TY1rgQkR\n3mhpqFDrsynYMFDeLpZYPDOwp5dBCCGEkEfM0P6OGNLHAVczlAvDCx6qv8gEkJRajK/2p2JKtA8K\nS2tx9kphh+8pEDCQ82glFRnogr89E9K2wxAzzAPFZXX46Xia2l+Plbn6094NSWyoBw7G3VE5MBs3\n3AvOdmYoKFAv2KBuVIQQQgghhDeGYTB3Er8WvTX1TfjpeFqngca0Eb7Y9nYMJkf5wMSY+zm4t4sl\nls8egrfmhrLSl0L6O/JfPIehGl5vKCRGIqx7MQK2Kkx9D+nniMUzNHu4TTsbhBBCCCFEJUcSsjV+\nD2sLY7z2dDCG9m+dAr5kZiDmTQpA4o1iFN2vRXOzHOamYgz2s+80Rbyfpw183ayQVVil8hokRkKM\nGeqh0ddhSFzszfCv5SPxr10XcTO747oZwf+lTr04fTDEIs32JijYIIQQQgghvKXlluPEBfWnUQNA\n6AAnrHgqmJXCZCoRI2aYajf/DMNgxmh/bNl1SeV1jAv3gpmJ5vM+DImDjQk2L41GRn4lDsdn4/Lt\nElTVSSEUMHCwNsXIEDdMCPeGvbV2umxRsEEIIYQQQng7kpCj9rUCAYPFMwZjYoS3VpvZjAp2Q2pW\nGf44z39tAd62eH7SAK2twZAwDIO+njZtIxLkcgUYputieXVQsEEIIYQQQnhpkLYg7mrH9RddMRIJ\nMG64l9ZvahmGwZKZgTASC3DgbFaX54f0c8Rb84YZVPtaXdLl5HoKNgghhBBCCC/FZXVobpGrfX1j\nkwxlVY0aD5njIhQwWDR9MKID3XAoPgsJ14rQIlPucBXUxx6TIn0QPshFpzfY5H8o2CCEEEIIIbxI\n203SVkdjk2ZzNroS4GOLAB9bVNVKcaewCnUNzZAYCeHpbKmTIId0joINQgghhBDCi5lE82Jq824q\nyLYyN0ZIv0ejra0+ozkbhBBCCCGEFxd7M1iYGql9vb21iUpzHojho2CDEEIIIYTwIhIKMG64p9rX\nT4jQfnE40W8UbBBCCCGEEN4mRHhDqEZxtZFIgHFhXjpYEdFnFGwQQgghhBDenO3MMH/qQJWvWzwz\nEDaUQvXIoWCDEEIIIYSoZPpIP8yZ2J/XuQwDLJo+COOG067Go4i6URFCCCGEEJU9NbYf+rjbYM+p\nDFzLvM95Tkg/R8yK6YPB/vbdvDqiL3o02Ni5cyd++OEH3Lt3Dx4eHli6dCmmTJnS4fk1NTX48MMP\ncezYMTQ3NyMkJATvvfcePDw8unHVhBBCCCEEAEL6OyKkvyPyiquRlFqMihopGAC2lhJEDHaBq4N5\nTy+R9LAeCzZ27dqFLVu2YP369RgyZAjOnj2LN998E1ZWVhgxYgTnNa+88gqA1iCFYRisX78eixcv\nxqFDhyAQUEYYIYQQQkhP8HS2hKezZU8vg+ihHgk2FAoFduzYgaeffhozZ84EAPj6+uLChQvYvn07\nZ7ARFxeHa9eu4dSpU7C1tQUAfPTRR0hNTUVzczOMjY279WsghBBCCCGEdE6l7YC6ujql/5+YmIhj\nx46hqqpKpQ/NyspCcXExoqOjlY5HRkbi0qVLaGxsZF1z8uRJDB8+vC3QAAAPDw9MmDCBAg1CCCGE\nEEL0EK9go6ioCBMnTsTu3bsBAHK5HPPnz8f8+fOxYsUKTJ06FVlZWbw/NDc3FwDg5uamdNzDwwNy\nuRz5+fmsa9LT0+Hl5YUdO3Zg3LhxCA8Px8qVK1FeXs77cwkhhBBCCCHdh1ca1UcffQQ+T/KdAAAg\nAElEQVSRSITRo0cDAI4cOYLz589j2bJliImJwaZNm/DJJ5/g448/5vWhD3ZITExMlI6bmpoCAGpr\na1nXlJeX448//kBYWBi2bNmC0tJSbNy4EXPmzMGBAwcgEqmWEfYgfethTU1NKr0HIYQQQgghpGO8\n7tCTk5Px7rvvwtvbGwBw6NAheHl5YdmyZQCAF198EWvXrtXZIgGgpaUFEokEH374IYRCIYDWYOWF\nF15AfHw8Ro0apdPPJ4QQQgghhKiGV7BRW1sLJycnAK03/cnJyXjqqafaXreyskJlZSXvD7WwsGh7\n3/af8/DrDzMzM4OHh0dboAEAISEhYBgGaWlpKgcbe/fuZR0rKChAbGysSu9DCCGEEEII4carZsPJ\nyQmZmZkAWgu1GxoaEBMT0/Z6bm4urK2teX+ol1frBMn2tRk5OTkQi8Xw9PTkvKZ9QCOXy6FQKGBm\nZsb7swkhhBBCCCHdg1ewMWHCBGzevBnLly/HmjVr0LdvX4SGhgIAUlNT8fnnn7M6S3XGx8cHHh4e\nOHv2rNLxM2fOIDw8HEZGRqxrRowYgZSUFKWC8CtXrgAA+vXrx/uzCSGEEEIIId2DV7CxbNkyzJo1\nCzk5ORg0aBA+++yzttd2794NiUSCN954Q6UPXrZsGfbu3Yvff/8dhYWF2LFjB5KSktoG923ZsgUL\nFy5sO3/atGlwcXHBihUrkJGRgaSkJKxfvx7BwcEYNmyYSp9NCCGEEEII0T1eNRtGRkZ4++23OV97\n7bXXVEqheuDxxx9HXV0dtm7dinv37sHHxweffvopQkJCAAClpaXIy8tTWsPOnTuxceNGzJ49GwKB\nALGxsTovTCeEEEIIIYSoh1EoFAq+JxcWFiIlJQUlJSWYOnUq7OzsUFtbC3Nzc12usds8KBA/ceIE\n3N3de3o5hBBCCCGE6AV175N57Wy0tLRg/fr12LNnD+RyORiGQXh4OOzs7LB161Zcu3YNX375Za8J\nOgghhBBCCCGa41WzsW3bNhw4cACvvPIK9u7di4c3QyZMmID8/HylOg5CCCGEEEII4RVs7N+/H0uX\nLsWyZcswYMAApdeCg4OxfPlyHDlyRCcLJIQQQgghhBgmXsHG3bt32wq3ufj7+6OsrExriyKEEEII\nIYQYPl7Bho2NDbKzszt8/datW7C1tdXaogghhBBCCCGGj1ewERMTg48//hjnzp1rO8YwDJqamvD7\n77/jX//6F8aOHauzRRJCCCGEEEIMD69uVK+//jpSU1OxaNEimJqaAgDmzZuH2tpayGQyDBo0CCtX\nrtTpQgkhhBBCCCGGhVewYWlpiV9++QXHjh3DuXPnUFJSAgBwcXFBREQExo8fD6FQqNOFEkIIIYQQ\nQgwLr2ADAIRCISZNmoRJkybpcj2EEEIIIYSQXoJ3sFFSUoKDBw8iMzMTFRUVYBgGtra2GDBgACZP\nngxra2tdrpMQQgghhBBiYHgFG8nJyViyZAnq6+shEolgbW0NhUKBqqoq7NmzB5988gm+/PJLBAYG\n6nq9hBBCCCGEEAPBqxvV5s2bYWNjg++++w4pKSk4d+4c4uPjkZKSgp07d8LS0hIbNmzQ9VoJIYQQ\nQgghBoRXsJGZmYl3330Xw4cPVyoEFwqFCA8Px+rVq5Genq6zRRJCCCGEEEIMD++hfiJRxxlXQqEQ\ndnZ2WlsUIYQQQgghxPDxCjaee+457Nq1C83NzazX5HI5du3ahWeeeUbriyOEEEIIIYQYLl4F4sbG\nxsjPz0dsbCyioqLg5OQEhmFw//59JCQkwNjYGIMGDcKnn37adg3DMFi6dKnOFk4IIYQQQgjRb7yC\njU2bNrX99759+zjPeTjQACjYIIQQQggh5FHHK9g4ceKErtdBCCGEEEII6WV4BRu7d+/GtGnT4Ovr\nq+v1EEIIIYQQQnoJXgXi27dvx+TJkzFz5kzs3LkTJSUlul4XIYQQQgghxMDxCjbOnDmD1atXw9zc\nHB999BHGjBmDF154Afv27UNtba2u10gIIYQQQggxQLyCDUdHR8yZMwfff/89zp07h/feew9isRhr\n165FVFQUVqxYgRMnTqClpUXX6yWEEEIIIYQYCF41Gw+zsbHBk08+iSeffBLl5eXYuHEjjh49iuPH\nj8PW1hZPPfUUFi5cCDMzM12slxBCCCGEEGIgVA42AODixYs4ePAg/vzzT5SXl8POzg6TJ0+GpaUl\nfvrpJ+zZswc7d+6Ej4+PttdLCCGEEEIIMRC8g407d+7gwIEDOHToEIqKimBkZITY2FhMnz4d0dHR\nEAqFAIC5c+di8eLFePPNN/Hbb7/pbOGEEEIIIYQQ/cYr2Jg5cyZu3boFABg2bBhefvllTJgwAebm\n5qxzrays8Oqrr2LRokXaXSkhhBBCCCHEoPAKNurr67F8+XJMnz4drq6uXZ7v7++PFStWaLw4Qggh\nhBBCiOHi1Y0qODi400AjPj4ey5cvb/v/Tk5OWLx4sXZWSAghhBBCCDFIvIKN33//HZWVlR2+XlBQ\ngFOnTmltUYQQQgghhBDD12kaVUxMDBiGgUKhwJIlSyAWi1nnyOVylJSUwN3dXWeLJIQQQgghhBie\nToONt956CxcuXMCPP/4Ie3t7ztkZDMMgJCQECxcu1NkiCSGEEEIIIYan02Bj/PjxGD9+PNLS0rBh\nwwZ4e3t307IIIYQQQgghho5XN6offvhB1+sghBBCCCGE9DK8CsQJIYQQQgghRFUUbBBCCCGEEJ16\n4403MHfuXJWv27p1K0aOHKmDFZHuwiuNihBCCCGEGLby8nJ8/fXXOHHiBIqLiyEQCODn54fp06fj\n6aefhkjUvbeFqamp+Oqrr3DhwgVUV1fD0tISISEhePHFFxEYGNitayG6o5WdDYVCgZaWFm28FSGE\nEEII0bKioiLMnDkTmZmZ+M9//oPLly8jMTERy5Ytww8//IDFixd3673cn3/+iaeffhpeXl7Yu3cv\nUlJS8PPPP8PJyQnPPvsszW/rRXgFG7GxscjIyOjw9T/++AOxsbFaWxQhhBBCCNGedevWwdLSEp9/\n/jkCAgIgEAhgZGSEUaNG4fvvv0dKSgp+/PFHJCUloV+/fsjNzW27NiEhAf369UNBQQEAoLS0FCtX\nrkRUVBSCg4Mxc+ZMJCQktJ3f1NSEdevWISIiAsOHD8emTZugUCjaXq+rq8OaNWswe/ZsvPbaa3B0\ndATDMHB3d8fq1auxZMkSlJWVcX4dKSkpmDt3LsLCwhAaGopFixYhPz9faa1PPvkkhg4dimHDhmH+\n/PnIzMwEAEilUrz33nuIjo5GUFAQYmJisG3bNqW1Ee3rNNgoKipCUVERCgsL2/67/f/y8/Nx6dIl\nlJeXd9eaCSGEEEIIT5WVlYiLi8OCBQsgFApZrzs5OWH8+PHYv38/r/dbu3YtysrKcOzYMSQnJ2PE\niBFYtmwZamtrAQBffvkljh8/jm+++QZxcXFwd3fHyZMn266Pj49HZWVlhzPali1bhlmzZrGONzU1\n4aWXXkJQUBASEhJw8uRJyGQyvPPOOwCA5uZmLF26FE888QSSk5Nx+vRp+Pj4YM2aNQCA7777Dpcu\nXcK+fftw9epV/Pe//8X333+PuLg4Xl83UU+nyXkPdisYhsGSJUs6PE+hUCA0NFS7KyOEEEIIIRrL\ny8uDQqGAn59fh+f4+vri8OHDvN7v448/hkwmaxv2PHXqVGzbtg2ZmZkYMmQIjhw5gqlTpyIgIAAA\nMHfuXPzyyy9t1+fk5MDU1BSurq4qfR1GRkb4888/IZFIIBKJYGFhgdjYWGzevBlAazAilUphbGwM\noVAIc3NzrF27FgzDAACqqqogEAggkUjAMAwGDx6M+Pj4tteJbnQabCQkJODixYt49dVXMXv2bDg6\nOnKe5+joiEmTJulkgYQQQgghRH0P0oTkcnmH58hkMt7pROnp6fj444+RmpqKurq6tuNSqRRAa2aM\nu7u70jX+/v5tqVEMw0AsFqv0NTxw+vRpfPvtt8jJyUFLSwvkcnlbrYmZmRn+9re/4d1338X27dsR\nERGBxx57DJGRkQCAOXPm4Ny5cxgxYgRCQ0MRFRWFqVOnws7OTq21EH46DTZsbGzw2GOPYdmyZZ0G\nG4QQQgghRD95e3uDYRikp6cjKCiI85w7d+50uPMhk8na/rumpgYLFy7EyJEjcejQITg4OCArKwsT\nJ05sO6e5uRkCgXKm/sOBjq+vL6qqqpCXlwdPT0/eX0dSUhJWrVqFt956C7Nnz4aZmRl+/vlnrFu3\nru2cF198EbNmzUJ8fDzi4uKwdOlSxMTEYMuWLXBxccH+/ftx7do1JCQkYP/+/di6dSt27tyJwYMH\n814HUQ2vAvFly5bB0dER9fX1KC4u7rB+gxBCCCGE6BcrKytER0fjq6++QlNTE+v14uJiHD16FNOm\nTYNEIgEANDY2tr2el5fX9t937txBdXU1FixYAAcHBwDAtWvXlN7P2dkZhYWFSsfS09Pb/jsqKgq2\ntrbYunUr53r/+c9/ttVhPCwlJQVmZmaYP39+WwpXSkqK0jnl5eWwtrbG5MmTsXnzZnz++ec4dOgQ\nKisrUV9fj8bGRgQGBmLJkiXYu3cvAgICeNeqEPXwCjby8/PbKvvHjBmD2NhYzv8RQgghhBD9s3bt\nWlRXV2PRokW4ceMG5HI5mpqaEBcXh/nz5yMqKgpz5syBh4cHxGIxDh8+DJlMhszMTOzdu7ftfVxd\nXSEUCnH58mU0NzcjISEBx44dAwDcvXsXABATE4MDBw4gPT0dUqkUO3fuRGlpadt7SCQSbNq0CUeP\nHsWqVatQWFgIhUKBoqIibNy4ET///DMef/xx1tfg4eGBhoaGtvStn376CdnZ2QBaU7cuXbqE2NhY\nnDt3DjKZDE1NTbh69Srs7e1haWmJpUuX4u9//3tbOldubi7u3r0LHx8fnf25E55D/datW4f09HRM\nnjwZbm5uaufZEUIIIYSQ7vdgnsXWrVuxZMkSVFRUQC6Xo1+/fnj66acxb948MAwDW1tbvPPOO9i2\nbRu+//57BAUFYfny5XjppZcAtNbprl69Gl988QX+/e9/IyIiAu+//z7+8Y9/4N133wXDMFi5ciVq\namraJoZPnToVU6ZMQVZWVtt6Ro8ejd27d2P79u2YPXs2ampqYGdnh+HDh+O3337jTOkaN24cZsyY\ngXnz5sHIyAgzZszA559/jrlz52LKlCnYt28f3n77bbz//vsoKiqCRCLBgAEDsG3bNggEAmzevBkb\nNmzAxIkTIZVK4eDggGnTpuGZZ57pnr+ERxSj4FENNGzYMKxatQqzZ8/ujjX1mIKCAsTGxuLEiROs\nwiZCCCGEkN5i165d2LRpE86ePQtbW9ueXg4xAOreJ/NKozI2Noa3t7e6ayOEEEIIIXpkypQpsLS0\nxPr161FbW9ut08PJo4VXsDFx4kQaG08IIYQQ0ktYWVlh27ZtyMvLQ0REBF577bWeXhLppXjVbMya\nNQsbNmzAqlWrMHr0aNjb23MOQKHBfoQQQgghhiEwMBD79u3r6WWQXo5XsPGgI8ClS5dw8OBB1usK\nhQIMw+DWrVvaXR0hhBBCCCHEYPEKNj744AMa5U4IIYQQQghRCa9gY+bMmbpeByGEEEIIIaSX4RVs\nPHDhwgVcvXoVJSUlWLhwIZydnVFcXAxra+u2iZOEEEIIIYQQAvAMNurq6rB8+XIkJCS01Wc88cQT\ncHZ2xueff47ExET8+OOPcHR01PV6CSGEEEIIIQaCV+vb//73v7h+/To2bdqExMREPDwHcNGiRRAI\nBNi6davOFkkIIYQQQggxPLx2No4dO4bXXnutrSvVwzw8PLB06dK2EfCEEEIIIb2FXK5ASkYp4q8V\n4X5lAxQKwPr/s3fvcTnf/+PHH1dHIoccEqXMIVTIClnGKmubNsQ+bD6VsWWGQp+N8huTbIZV1DYx\nPrZKsxmFRTnFMDNy+GQ5JoopOReq1fX7o2/XXDq4oktre95vt2431/v1er/ez/e73G7v5/U6GRvS\n16YNfW3aoKur0fe2QvxjafQ/5Nq1a3Tp0qXKcnNzc27dulVrQQkhhBBC1LVdh7OY+OkOZi//maQD\nFzh8MpfUU7nsPJTFJ1//yvj524jffY7SUuWjG3sCPj4+vPbaa1WWnz9/Hmtra2JjYx/7GtnZ2Vhb\nW5OQkPDYbQCsX78ea2trrly5Um293NxcQkJCcHNzw87ODicnJ8aOHUtycvITXb++mDlzJoMHD67r\nMJ4KjZKN1q1bk5aWVmX5gQMHaNOmTa0FJYQQQghRV5RKJas2nSB0TSqX8wqqrHft1n1Wbkxjcexh\n/igp1Vo8w4cP59SpU5w8ebLS8o0bN6Kvr8+QIUO0FkNtOnPmDMOGDeP48eN8+OGHbNmyhWXLltGh\nQwemTJnCZ599Vtch1rrx48ezfv161edZs2axdu3aOozo6dEo2XjllVdYunQpa9eu5caNGwAUFRVx\n8eJFIiMj+fzzz+vNH7gQQgghRHXW7TzDhpSzGtf/6egllq0/rrV43N3dadSoERs3bqy0fNOmTbi4\nuNCsWTOtxVBblEol06dPx8zMjOjoaAYOHIi5uTk9e/Zkzpw5TJ48mVWrVnHhwoW6DrXWKJVKjh9X\n//swNjbGxMSkjiJ6ujRKNvz8/HBycmLOnDn0798fgFGjRuHu7k5kZCSDBg1i0qRJWg1UCCGEEELb\n8m7eI3Zr5T0I1Uk6cIFTF65rISJo2LAh7u7ubN68mdJS9R6U1NRUsrKy1PZEO3v2LBMmTKB///7Y\n29szfvx4zp07pyovH+q0a9cunJ2def/991Vld+/eJSAgAHt7exwdHQkODuaPP/5QlW/bto0RI0Zg\nZ2eHo6MjY8eOrbLHpTIHDhzg9OnT+Pv7Y2hoWKHc19eXlJQULC0tASgpKSEyMhIXFxdsbW1xdnZm\n7ty5FBT82eN0//595s+fz4ABA7C1tcXFxYWwsDBV3OVDxDZv3oyfnx+9evWq9N4e97mdOnUKX19f\nevfuTc+ePRk6dKjacLCuXbty+/ZtAgMDsba2BioOo7p+/TqBgYE4OTlha2uLu7s7q1evVpWX38OO\nHTsICgqiT58+9O3bl5kzZ3Lv3j2Nn39d0CjZMDAw4PPPP2ft2rVMnjyZUaNG8a9//Qt/f3++++47\nIiMjMTAw0HasQgghhBBalXTgAiWPOQcjcX9m7QbzAE9PT3Jycvjll1/Ujm/cuJFWrVoxYMAAoOyl\n1cvLi4KCAqKiolizZg1QNu/jzp07aud+8803rFixgsDAQNWxFStW0KtXLzZs2MDUqVOJi4vj66+/\nBiAjIwN/f3/69etHYmIicXFxGBkZMXHiRIqKijS6j8OHD6Ovr0+/fv0qLTc0NKRVq1aqz2FhYaxc\nuZLp06eTmJjI3LlzSU5OVos5MDCQLVu2MG/ePLZs2YKfnx/ffPNNheFYoaGhDBgwgISEBKZNm6Z2\nb4/73EpLS3n33XcpKSlh7dq1bN68GTc3N6ZNm8bp06dVvyOAoKAg9u7dW+GelUolEydO5OjRo4SH\nh5OYmMiYMWNYuHAhMTExanXDwsKwsbFh3bp1BAUFsWHDBlWsf1UarUa1bds2Bg4cSM+ePenZs6e2\nYxJCCCGEeOqUSiXJvzz+8J2fjl5iwnA7jBro12JUZRwcHLCwsCAhIQEnJycAiouL2bJlC56enujq\n6gKwbt067ty5w5IlS2jRogUAixYtYtCgQSQkJPDvf/9b1aanpyfdunUDyno0AOzt7fHy8gLAysqK\nHTt2kJiYyPjx42nXrh2bNm3CwsJC9SWzj48P3t7eZGRk0LVr10feR25uLi1bttToS+qioiJiY2Px\n9vbGw8MDgPbt25OXl8ecOXPIzc2ltLSULVu2EBwczKBBg4CylVIzMjKIiYlh+vTpqvbs7e15/fXX\nAbC0tGT79u2qe3vc51ZaWsrXX3+NsbExzZs3B2DixIl8+eWXHDhwgC5duqiGSxkbG6slUuWOHDnC\n0aNHWbVqFX379gXA29ubY8eOERMTo3btXr16MWbMGNWziIqKqjBE669Go56NKVOm0L9/fwIDA9m3\nb1+FLjwhhBBCiPruVn4R12/ff+zzi/8oJTs3vxYj+pNCoWDYsGEkJydz/35ZjHv27OHmzZtqQ6iO\nHz9O586dVS/MACYmJnTq1In09HS1Nrt3717hOvb29mqf7ezsOH/+PFDW63Dq1Cneeust1VAjX19f\nAI1XJVUoFBq/R2ZkZHD37l169eqldrxHjx4olUrS09M5ceIESqWy0joFBQVqcz8ertO9e3cuX74M\nPP5z09HR4datW3z44YcMGjRINfyspKRE42dSvgjTw/GVP/sHh0nZ2dmp1TExMeH27dsaXaeuaNSz\n8fnnn7Nt2zZ27tzJhg0bMDEx4aWXXmLIkCE8++yz2o5RCCGEEELr7hf98ehKj3Cv8MnbqMqwYcOI\njIxk+/bteHh4sHHjRmxtbencubOqTn5+PidPnqyQNBQWFlb4Vr1Ro0YVrtG4cWO1zw0bNlQlN1u3\nbmXatGmMHDmSDz74gGbNmpGeno6/v7/G92BmZkZeXh737t2jYcOG1dbNz8+vNKbyuPPz81VzLqqr\n06BBA6CsZ+FBRkZGqiFSj/vcLl26hJeXF926dePjjz/GzMwMHR2dGi2clJ+fj0KhqPD7ePAeypXf\nSzmFQqG22fZfkUbJhqurK66urpSUlPDzzz+TnJxMcnIya9aswczMjJdffpkhQ4ZgY2Oj7XiFEEII\nIbSioaFGr0Vab6Mq5ubmODo6snnzZgYNGsSuXbuYMWOGWh1jY2Osra1ZsmRJhfMfflGtTPlwqgc/\nGxkZAfDjjz9iZWVFSEgICoUCQDUvQVMODg6UlJSQkpLCyy+/XKG8tLSUuLg4PD09VcnBw3Mmyj83\nbtyYkpKSaus8mGA8fG8FBQU0adJEVe9xntvOnTu5d+8e4eHhmJqaAmW9PMXFxVWe8zBjY2OUSiX5\n+flqSVN5EtK4cWMKCws1bu+vpkbbXurq6uLs7ExwcDA//fQTMTExuLu7s3XrVtUYOCGEEEKI+qhJ\nIwNaN6/+2/bqGBroYmFq/OiKT2D48OHs37+frVu3UlpaWuEbdDs7O7Kzs2nVqhWWlpaqnz/++ENt\niFBVDh8+rPb5xIkTdOrUCSibI9K8eXNVogF/Tn7W9Nt1BwcHbG1tCQ8Pr5AgAHz11VfMnz+fjIwM\nOnToQKNGjUhNTVWrc/ToUXR0dLCxscHGxgYdHZ0KdY4cOYKxsbFqVavK7i0tLY0OHToAj//cypOK\n8vkaUPUzqeoZ2draAlR6D506dXpkD9BfXY2SjQedPHmSAwcOcOTIEXJycur9gxBCCCHEP5tCocC9\nn9Vjnz+ot7lWezYAXnrpJXR1dQkPD690b40RI0agq6vLf/7zH9LS0rh48SKrVq3itdde48CBA49s\n/8iRI8TFxXHhwgViYmLYt28fr776KlA2DyItLY2UlBQyMzMJCQlR9QwcPXpUbbhPdRYvXkxBQQGj\nRo0iKSmJ7Oxs0tLSCA4OJiwsjFmzZmFjY4OBgQHe3t7ExsYSHx9PVlYWSUlJREREMHToUFq2bImp\nqSkeHh5ERESwY8cOsrKy+P7771mzZg0+Pj7o6f35+0hNTVXd25o1azh48CBDhw59oufWo0cPoGwV\nr+zsbL799lt2796NhYUFv/32G3l5eRgbG6NQKDh48CAnT55UDUsrZ29vz7PPPktISAgHDhzgwoUL\nfPXVV2zbto1x48Zp9Ez/yjT+H6FUKjl06BDbtm1jx44dXL58mQYNGvDCCy/w9ttvM3DgQG3GKYQQ\nQgihdYP7tufbbaco/qPmi+G80r+DFiJSZ2RkhLu7Oxs2bFCbGF6uRYsWxMTEsHDhQry8vCguLqZL\nly6Ehobi7Oz8yPanTp3K7t27WbhwIfr6+owdO5Y33ngDKFt56uzZswQEBGBoaMiIESMICgrizp07\nREZGYmRkVGHuRGU6dOhAQkICUVFRLFq0iJycHJo2bYqtrS3R0dE4ODio6vr5+aGnp8eSJUtUK1l5\nenoydepUVZ2QkBAWL17MnDlzuHHjBmZmZkyaNIl33nlH7bpvv/02hw4dYuHChejp6eHt7c3IkSOf\n6Lk5ODjg5+fHmjVrWLlyJc899xyLFi0iPj6e8PBwgoODWbp0KePGjSM2NpaUlBTi4+MrtPPFF1+w\nYMEC/P39KSgowNLSknnz5lX6O65vFEoN+r2CgoLYtWsXN2/exNDQkOeff55XXnmFQYMGaTT+r77I\nzs7G1dWVHTt2YG5uXtfhCCGEEKIOJO4/z5c/1Gw5Uc9BnXjrVZm7+ldU/n63cOFCVU+GqLnHfU/W\nqGfjxx9/ZMCAAbzyyiu88MILMmRKCCGEEH9br/TvQMG9Yr5JTH90ZeAlJyt8hlRcRlYIoWGysX//\n/kqXRxNCCCGE+Dt63bULlmZNWLvtFKcv3qy0joVpYzwHdcbV0UJt0rQQ4k8aJRuNGjXi/PnzLF++\nnGPHjnH16lWio6Pp2rUru3btQkdHR+ZsCCGEEOJvpU/3NvTp3oYzWTfYd+wy127dp1SppLlxA/rY\nmGLXsaUkGfWAubk5p06dqusw/rE0SjZOnDiBl5cX+vr6PPvss6qdJAEOHTrE6tWrWbFiBf3799da\noEIIIYQQdaGzRXM6WzR/dEUhRAUaJRuhoaF07dqV5cuX07hxY7p27aoqe//997ly5QpffPGFJBtC\nCCGEEEIIFY322Th27BjvvPNOlcuZjRw5khMnTtRqYEIIIYQQQoj6TaNko7i4WFagEkIIIYQQQtSI\nRslGt27d+O677yotKy0t5auvvsLa2rpWAxNCCCGEEELUbxrN2XjnnXeYPHkyv//+O4MHD0ahULB1\n61a2bt3Kli1byMrK4osvvtB2rEIIIYQQQoh6RKNkw9XVlYiICMLCwli4cCEAy6LIA6sAACAASURB\nVJYtA+CZZ55h6dKlDBo0SGtBCiGEEELUhVJlKWk5p/g5K5Xr925QqlTSrEETHNr1wKFtD3R1dOs6\nRCH+0jRKNgDc3Nxwc3PjypUr5OTkANCmTRtMTU21FpwQQgghRF3Zk/kLP5xI5Pf83ApluzMPYNKw\nGR7WbrzS5QV0FBqNTH9sXl5eHDx4kMWLF/Pqq69WKD979ixDhgwBqLM9JVxcXHBycmL+/Pka1V+/\nfj2BgYHs3r2bNm3aVFkvNzeX5cuXk5KSQk5ODo0bN8ba2po333yTF198sbbC/8uaOXMmhw8fZtu2\nbXUdymPRONko16ZNm2r/IIQQQggh6jOlUknMsfVsOrW92nrX793km6PrOHvtPJP7vYWelns5jIyM\niI+PrzTZSEhIoGHDhty7d69GbR45coSAgAB27tz5xPGtW7cOAwODJ27nQWfOnMHHxwdzc3M+/PBD\nOnbsyLVr14iPj2fKlCn4+voSEBBQq9esa+PHj2fIkCF4enoCMGvWLIqLi+s4qsdX42RDCCGEEOLv\nLOFk8iMTjQftzzqMkX5DfB3HaDEq6NOnD3v27CEnJ0dtZIlSqWTz5s04ODjw008/1ajNY8eO1Vp8\nJiYmtdYWlN3X9OnTMTMzIzo6GkNDQ6BsR/CePXtiYmLCsmXLGDlyJJaWlrV67bqiVCo5fvy4qpcK\nwNjYuA4jenLa7fMTQgghhKhHrt29wdr/bazxedsz9nLm2nktRPQnGxsbWrRowcaN6vEdPHiQq1ev\nMmDAALXjSqWSqKgo3NzcsLGxwdnZmZkzZ3Ljxg0AIiIi+OSTT7h06RLW1tZEREQAkJOTw7Rp03j+\n+efp2bMno0eP5siRI6p2f/nlF6ytrUlMTGTw4MGMGVOWZLm4uDBr1ixVvW3btjFixAjs7OxwdHRk\n7NixnDx5UuP7PXDgAKdPn8bf31+VaDzI19eXlJQUVaJRUlJCZGQkLi4u2Nra4uzszNy5cykoKFCd\nc//+febPn8+AAQOwtbXFxcWFsLAw/vjjDwCys7OxtrZm8+bN+Pn50atXLxwdHQkODlbVgbJhaxMm\nTKB///7Y29szfvx4zp07pypfv3491tbW7Nq1C2dnZ95//32gbIibr68vvXv3pmfPngwdOpTk5GTV\neV27duX27dsEBgaqVnqdOXMmgwcPVtW5fv06gYGBODk5YWtri7u7O6tXr1aVl9/Djh07CAoKok+f\nPvTt25eZM2fWuOerNkiyIYQQQgjxf3Zk7KVEWfpY5yad3V3L0ahTKBS4u7uTkJCgdnzjxo04OztX\n+AZ83bp1hIeHM336dLZv387SpUs5cuQIwcHBAIwbN45hw4bRpk0b9u7dy7hx4ygqKsLHx4ezZ8+y\nePFi1q1bh6WlJePGjSMrK0ut/VWrVvHxxx8TFhZWIdaMjAz8/f3p168fiYmJxMXFYWRkxMSJEykq\nKtLofg8fPoy+vj79+vWrtNzQ0JBWrVqpPoeFhbFy5UqmT59OYmIic+fOJTk5mcDAQFWdwMBAtmzZ\nwrx589iyZQt+fn588803fPbZZ2pth4aGMmDAABISEpg2bRpxcXF8/fXXQNnLvpeXFwUFBURFRbFm\nzRoAfHx8uHPnjlo733zzDStWrCAwMJDS0lLeffddSkpKWLt2LZs3b8bNzY1p06Zx+vRpAFUiGRQU\nxN69eyvcs1KpZOLEiRw9epTw8HASExMZM2YMCxcuJCYmRq1uWFgYNjY2rFu3jqCgIDZs2KCK9Wmq\nMtnIyclR/TFcvnxZLZsTQgghhPi7USqV7MjY99jn/3zxMHeLtfvNsYeHB2fOnOHEiRMAFBUVkZSU\nxCuvvFKhrru7O5s3b+aVV17BzMyM3r174+Hhwb59ZffYqFEjDA0N0dXVpVWrVjRq1Ijt27dz/vx5\nFi5cSJ8+fejcuTPz5s2jUaNGFV5U3dzccHR0pHXr1hWu3a5dOzZt2oS/vz8WFhZ06tQJHx8fLl++\nTEZGhkb3mpubS8uWLTWaB1JUVERsbCze3t54eHjQvn17XF1d8fPzIzk5mdzcXK5cuaJKMAYNGoSF\nhQXDhg3Dy8uLtWvXqs2LsLe35/XXX8fS0pI333wTJycnEhMTgbIk7s6dOyxZsgQ7Ozu6devGokWL\nuH37doVE0NPTk27duqmGmH399dcsXryYzp07Y2FhwcSJE1EqlRw4cAD4cyiasbGxWiJV7siRIxw9\nepT/9//+H3379qV9+/Z4e3vz8ssvV0g2evXqxZgxY2jfvj1Dhw6lY8eOHD9+XKNnX5uqTDbc3d1J\nT08Hypa+rauVDYQQQgghnobbhXe4ce/WY59fXPoHl2/n1GJEFdnb22Nubs6GDRsA2LFjB8XFxbi6\nulao26BBA7Zv385rr71Gnz59sLe3Jyoqilu3qr7HY8eO0bRpU7p166Y6ZmBgQO/evVXvheUerPMw\nQ0NDTp06xVtvvaUaauTr6wtQ7fUfpFAoKC3VrJcpIyODu3fv0qtXL7XjPXr0QKlUkp6ezokTJ1Aq\nlZXWKSgo4MKFC6pjD9fp3r07ly9fBuD48eN07tyZFi1aqMpNTEzo1KlThWfUvXt31b91dHS4desW\nH374IYMGDcLe3h5HR0dKSko0fiZpaWmVxmdnZ8f58+fVhknZ2dmp1TExMeH27dsaXac2VTlBXF9f\nn5UrV/LCCy+gVCpJSUnhzJkz1TY2bNiwWg9QCCGEEOJpKPxDs+E91bn/x/1aiKR6Hh4efPfdd8yY\nMYNNmzYxcOBAGjVqVKHeggULWLt2LQEBAfTv35+GDRvy7bffsmrVqirbzs/P5/bt29jb26sdLyoq\nokOHDmrHKrtmua1btzJt2jRGjhzJBx98QLNmzUhPT8ff31/j+zQzMyMvL4979+7RsGHDauvm5+cD\n0Lhx40pjzM/PV43Sqa5OgwYNgIqTso2MjFRDpPLz8zl58mSFZ1RYWFihN+LBZ3Tp0iW8vLzo1q0b\nH3/8MWZmZujo6KhNBn+U/Px8FApFhWf/4D2UK7+XcgqFAqVSqfG1akuVycbbb7/NkiVLSE5ORqFQ\nqCYNVUWhUEiyIYQQQoh6q4F+g0dXelQbek/exqO8+uqrLFu2jF27drFnz54K8w3K/fjjj3h6ejJu\n3DjVsUctoWpsbEyzZs1Yu3ZthTI9Pc0XMf3xxx+xsrIiJCQEhUIBoJqXoCkHBwdKSkpISUnh5Zdf\nrlBeWlpKXFwcnp6equTg4TkT5Z8bN25MSUlJtXUeTDDu3r2rVqegoIAmTZqo6llbW7NkyZIKMT38\ngv+gnTt3cu/ePcLDw1Wrid26datGy9oaGxujVCrJz89XS5rKk5DGjRtTWFiocXtPQ5V/NRMmTGDM\nmDHcunULV1dXli1bRufOnZ9mbEIIIYQQT42xQSNaGZlw9e71xzrfUNcA8yba34usU6dOWFtbExoa\nioGBAYMGDaq0XlFREc2bN1d9LiwsVK18pFQqVUnAg9929+jRg6+//hp9fX3atm2rOn7hwoVK5xBU\npbi4mObNm6uuAX9Oftb023UHBwdsbW0JDw+vdAL8V199RXh4OL169aJz5840atSI1NRUXFxcVHWO\nHj2Kjo4ONjY2lJSUoKOjQ2pqqmqlJyibB2FsbIylpSVXrlwByianv/nmm6o6aWlpqp4dOzs7fv75\nZ1q1aoWRkZGqzrlz59SGVlX2TAC130lVz6SqZ2RrawtAamoqzz//vNo9dOrU6ZE9QHWh2tWoGjdu\nTLt27Zg8eTI2Nja0a9eu2h8hhBBCiPpKoVDg2tH5sc93tuxTK70jmvDw8OD8+fO4urpWuiwsQM+e\nPdmyZYtqvoKvry/PPfccULZcbmFhIU2bNuXq1ascOnSIrKwsXF1dad++PdOnTyc1NZXs7Gx++OEH\nhg0bVmHyc3V69OhBWloaKSkpZGZmEhISouoZOHr0qNpwn+osXryYgoICRo0aRVJSEtnZ2aSlpREc\nHExYWBizZs3CxsYGAwMDvL29iY2NJT4+nqysLJKSkoiIiGDo0KG0bNkSU1NTPDw8iIiIYMeOHWRl\nZfH999+zZs0afHx81HpuUlNTiYuL48KFC6xZs4aDBw8ydOhQAEaMGIGuri7/+c9/SEtL4+LFi6xa\ntYrXXntNNdG7qmcCsGLFCrKzs/n222/ZvXs3FhYW/Pbbb+Tl5WFsbIxCoeDgwYOcPHmS+/fVh+XZ\n29vz7LPPEhISwoEDB7hw4QJfffUV27ZtU+vB+ivRqD9s8uTJAGRlZXH48GFyc3PR0dHB1NSUPn36\nqG0sI4QQQghRX7k88xw/nEikuLTmq3C6d3r+0ZVqiYeHB6GhodWO9589ezZBQUGMHj0aU1NTpkyZ\ngrOzM0ePHmXChAlER0czfPhwkpOTGTt2LG+88QazZs1i9erVfPrpp0yYMIG7d+/Svn17PvjgA15/\n/XWN4ytfPjcgIABDQ0NGjBhBUFAQd+7cITIyEiMjowpzJyrToUMHEhISiIqKYtGiReTk5NC0aVNs\nbW2Jjo7GwcFBVdfPzw89PT2WLFmiWsnK09OTqVOnquqEhISwePFi5syZw40bNzAzM2PSpEm88847\natd9++23OXToEAsXLkRPTw9vb29GjhwJQIsWLYiJiWHhwoV4eXlRXFxMly5dCA0Nxdm56mTVwcEB\nPz8/1qxZw8qVK3nuuedYtGgR8fHxhIeHExwczNKlSxk3bhyxsbGkpKQQHx9foZ0vvviCBQsW4O/v\nT0FBAZaWlsybN0+14/hfjUKpQV9WcXExQUFBbN68uUK3jq6uLmPGjCEoKEhrQT4t2dnZuLq6smPH\nDszNzes6HCGEEELUgeSzu/nq8Lc1Oue1roP5d8+/5sue0Fz5u+DChQtVPRmizOO+J2vUsxEZGcnW\nrVsZP348gwYNolWrViiVSnJycti1axcxMTG0adOmxt03q1evJjo6mpycHCwsLJg0aRIeHh4anRsc\nHExsbCzffPMNffv2rdF1hRBCCCGq8mKngRQU3SPuf5oNG3LrOIA3e8giOUJURqNkIzExkalTpzJ+\n/Hi141ZWVvTt25cmTZrw/fff1yjZiI2N5bPPPmPu3Ln06tWLPXv28P7779O0aVMGDBhQ7bnHjx/n\n+++/1/haQgghhBA1Mbz7S7Rv1o4fTiRy9npmpXXaNWnD0K4vMtCqn9pEaCHEnzRKNn7//Xd69uxZ\nZbmDgwNffvmlxhdVKpUsX76c0aNHq8aXPfPMM/z6669ERUVVm2yUlJTw0UcfMWzYML777juNrymE\nEEIIURPPtrXj2bZ2nLt+gZ+zUrl+7yZKZSnNGjTl2bZ22LTuIknG34y5ublsZF3LNEo2GjduzO+/\n/15leV5eXrUbuzwsIyODK1euVJhE079/f0JCQrh//36V6xRHR0dTUFDAW2+9JcmGEEIIIbSuo4kl\nHU0s6zoMIeoljZINJycnIiMjsba2pkuXLmpl6enpLFmyhP79+2t80fLt4B9eLtfCwoLS0lKysrIq\n3dPjypUrLF26lM8//xwDAwONr1eZymbsFxU9+c6hQgghhBBCiDIaJRsBAQGMHj2aoUOH0rZtW9VS\nt1euXOH333+nTZs2vP/++xpftKCgAKDCxiPlG6NUtfZySEgIrq6uODk5kZ2drfH1hBBCCCGEEE+f\nRsmGubk5mzdvJiYmhoMHD5Kbm4tCocDS0pLRo0fzxhtvVNjVsbbt2rWLgwcPsmXLllppb/369RWO\nlS/pJYQQQgghhHhyGiUbAM2aNVNt7vekyhOTh3swyj8/nLjcvXuXefPm8cEHH1S7DbwQQgghhBDi\nr0OnLi5qaVk2ySorK0vteGZmJvr6+rRv317teFpaGpcuXWL27Nl0796d7t278+KLLwIwduxYBg8e\n/HQCF0IIIYQQQmhM456N2tShQwcsLCzYs2cPbm5uquO7d++mX79+FSZ/29rasmnTJrVjubm5jB8/\nnpCQEHr37v1U4hZCCCHEP4uytJRbx/9H3r79FF27hrJUiUHzZpj0ccSkjyMKXd26DlGIv7Q66dkA\nmDx5MuvXryc+Pp5Lly6xfPlyfvnlF9577z0APvvsM9UmgkZGRnTp0kXtx8rKCiibT9KhQ4e6ug0h\nhBBC/E3lpuwmdZIfJ+YEk5O8nRuHj3DzyFFyd6ZwcsEiDr3zLpcSNqEsLdVqHD4+Prz22mtVlp8/\nfx5ra2tiY2Mf+xrZ2dlYW1uTkKDZrulVWb9+PdbW1ly5cqXaerm5uYSEhODm5oadnR1OTk6MHTuW\n5OTkJ7p+VS5duoSnpyc2NjYsX768QpxeXl6MHTtWK9f+p6uTng2AYcOGUVBQQEREBDk5OXTo0IHI\nyEhVL8XVq1e5ePFiXYUnhBBCiH8opVJJ5upvuBy/sdp6Rdeuk7lqNfmnz9B5mh86etp5rRo+fDgz\nZszg5MmTdO3atUL5xo0b0dfXZ8iQIVq5fm07c+YMPj4+mJub8+GHH9KxY0euXbtGfHw8U6ZMwdfX\nl4CAgFq95nfffcfZs2eJi4vDysqK7du3q5VHRETUaIPG8ePHM2TIkEq3UhDq6izZABgzZgxjxoyp\ntGzBggXVnis7PAohhBBCGy79sOGRicaD8vbuQ7eREZ3ee1cr8bi7uxMcHMzGjRsrTTY2bdqEi4sL\nzZo108r1a5NSqWT69OmYmZkRHR2NoaEhUPZe17NnT0xMTFi2bBkjR45UzfGtDTdv3qRly5b06NGj\n0vKaPDulUsnx48frTXJX1zQeRnXz5k1SUlJISEggPj6+0h8hhBBCiPqsMO8aF9d8W+PzcpK2cefU\naS1EVLYvmbu7O5s3b6b0oSFbqampZGVlqX3DfvbsWSZMmED//v2xt7dn/PjxnDt3TlVePoRo165d\nODs7q+2VdvfuXQICArC3t8fR0ZHg4GD++OMPVfm2bdsYMWIEdnZ2ODo6MnbsWE6ePKnxvRw4cIDT\np0/j7++vSjQe5OvrS0pKiirRKCkpITIyEhcXF2xtbXF2dmbu3LmqPdsAXFxcCAsLY+XKlQwcOBB7\ne3u8vb1VI2S8vLz49ttvuXTpEtbW1kRERFS47sPDqC5fvsykSZPo3bs3/fr1IyAggNzcXAC6du3K\n7du3CQwMxNraWuN7/6fSqGdj7969TJ48mcLCQpRKZaV1FAoFw4YNq9XghBBCCCGeppzkbShLSh7r\n3N+3JGFs3aWWIyrj6enJ+vXr+eWXX3ByclId37hxI61atWLAgAEAXL9+HS8vLzp27EhUVBR6enos\nXrwYHx8ftmzZora9wDfffMOKFSswNTXl7t27AKxYsYK33nqLKVOmsG/fPkJCQmjXrh3jx48nIyMD\nf39/3nrrLcLDwyksLCQ0NJSJEyeSlJRUYYGfyhw+fBh9fX369etXabmhoSGtWrVSfQ4LCyM2NpZ5\n8+bRo0cPzpw5w+zZs7l27RpLly5V1du6dStOTk6sWrWKGzdu4O/vz/z584mKiiIiIoJPPvmEAwcO\nsG7dOoyMjEhKSqoyxsLCQsaNG0e7du2IjY2ltLSU2bNn895777Fu3To2btzIa6+9RlBQEK+88soj\n7/mfTqNkY9GiRbRq1QpfX1/atWuHnpbGJAohhBBC1BWlUknOth2PfX7e3n084zsePSOjWoyqjIOD\nAxYWFiQkJKiSjeLiYrZs2YKnpye6/7cq1rp167hz5w5LlixR7U22aNEiBg0aREJCAv/+979VbXp6\netKtWzcAVbJhb2+Pl5cXAFZWVuzYsYPExETGjx9Pu3bt2LRpExYWFqrEwsfHB29vbzIyMiod4vWw\n3NxcWrZsqVFiUlRURGxsLN7e3nh4eADQvn178vLymDNnDrm5ubRu3VpVf/bs2ejolA3aGTx4sCqh\naNasGYaGhujq6qolMlXZuXMnmZmZrFq1irZt2wIwZ84coqOjuX79OiYmJkDZvnCatPdPp1HWcOHC\nBUJDQ3FxcdF2PEIIIYQQdaL41m2Krl9/7POVxcXcy76EcZfOtRhVmfIRJKtWreKjjz6iQYMG7Nmz\nh5s3b6oNoTp+/DidO3dW2wTZxMSETp06kZ6ertZm9+7dK1zH3t5e7bOdnR3R0dFAWa/DqVOnmD17\nNufPn+fevXuqYV23bt3S+D4eHgpWlYyMDO7evUuvXr3Ujvfo0QOlUkl6eroq2bC1tVUlGlB2z7dv\n39boOg9LS0ujWbNmqkSj/JqLFi0CyhYxEprTaM5G69atNcpAhRBCCCHqq9LC+0/cRsn9J2+jKsOG\nDePu3buqlZQ2btyIra0tnTv/mdzk5+dz8uRJ7O3t1X5OnjxJXl6eWnuNGjWqcI3GjRurfW7YsCH3\n/++etm7dyrRp07CysuLLL78kPj6eTz/9tEb3YGZmRl5eHvfu3Xtk3fz8/EpjKo+7vBygQYMGanUU\nCkWVQ/8f5fbt2xhpoXfqn0qjno2xY8cSHR2Nk5OTqptOCCGEEOLvRLdhw79EG1UxNzfH0dGRzZs3\nM2jQIHbt2sWMGTPU6hgbG2Ntbc2SJUsqnP/wC3llyodTPfi5/MX7xx9/xMrKipCQENUysadP12xS\nvIODAyUlJaSkpPDyyy9XKC8tLSUuLg5PT0/V/JI7d+6o1Sn//HASUltMTEzUEhnxZDRKNnR1dblz\n5w4vvvgizs7OlY5PUygUTJo0qdYDFEIIIYR4GvSMjTFs3YrC3McbJqNjaIiRebtajkrd8OHD+eij\nj9i6dSulpaUVll+1s7Pj559/plWrVmrfzp87d05taFVVDh8+zJtvvqn6fOLECTp16gSUzRFp3ry5\n2n4UGzeWLRGsaS+Cg4MDtra2hIeH4+zsrDZhHeCrr74iPDycXr160blzZxo1akRqaqraUP6jR4+i\no6ODjY2NRtesqW7dunHr1i3OnTtHx44dAUhPTyc4OJiFCxeqkrbH7Tn5p9FoGNWcOXNITU3l0qVL\nrF27lsjIyEp/hBBCCCHqK4VCgemLgx/7/FYDB2i1ZwPgpZdeQldXl/Dw8Er31hgxYgS6urr85z//\nIS0tjYsXL7Jq1Spee+01Dhw48Mj2jxw5QlxcHBcuXCAmJoZ9+/bx6quvAmXzFtLS0khJSSEzM5OQ\nkBCaNGkClCUAmvYGLF68mIKCAkaNGkVSUhLZ2dmkpaURHBxMWFgYs2bNwsbGBgMDA7y9vYmNjSU+\nPp6srCySkpKIiIhg6NChtGzZsoZPTzNubm60b9+eoKAgTp8+rUo0CgsLMTc3x9jYGIVCwcGDBzl5\n8qRqmJmonEY9Gzt2PP7KDEIIIYQQ9YXpYFey1n6Psri4xue2efklLUSkzsjICHd3dzZs2FDp7tUt\nWrQgJiaGhQsX4uXlRXFxMV26dCE0NBRnZ+dHtj916lR2797NwoUL0dfXZ+zYsbzxxhtA2cpTZ8+e\nJSAgAENDQ0aMGEFQUBB37twhMjISIyMjjYY2dejQgYSEBKKioli0aBE5OTk0bdoUW1tboqOjcXBw\nUNX18/NDT0+PJUuWqFay8vT0ZOrUqTV4ajWjp6fHypUrCQkJYdSoURgaGtK3b1+CgoJQKBQ0aNCA\ncePGERsbS0pKCvHx8ZiZmWktnvpOoZQ+IJXs7GxcXV3ZsWMH5ubmdR2OEEIIIerA71u2krFsRY3O\naTd8KFZjvbUUkRB173HfkzXeMOPatWusWbOGQ4cOkZubi46ODqampjg5OfHGG29obZKOEEIIIcTT\nZPbyS5QU3OVCdKxG9U3dX8TS+9+PrijEP5BGyUZGRgZjxozhxo0bmJub06pVK5RKJZmZmezfv5+4\nuDji4uIwNTXVdrxCCCGEEFpnPtITI8v2ZK1dR/6ZM5XWaWhuTjvPobR2eUFt0rQQ4k8aJRuhoaG0\nbNmSmJgY1az8cqdOnWLq1KmEhobWeK1lIYQQQoi/KhNHB0wcHbhz5izX9v9M0bXrKJWlGDRrRnNH\nB5ra2UqSIcQjaJRs/Prrr8ydO7dCogFgbW3Ne++9xyeffFLrwQkhhBBC1DXjzp0w7typrsMQol7S\naOnbu3fvYmJiUmV5mzZtKmy4IoQQQgghhPhn0yjZaNu2LYcOHaqy/NChQ7Rt27bWghJCCCGEEELU\nfxoNoxo6dChffPEFd+7cwcXFRTUR/MqVK2zbto24uDj8/Py0GqgQQgghhBCiftEo2Xj33Xf5/fff\nWb16NatXr1Yr09HR4Y033sDX11cb8QkhhBBCCCHqKY2SDR0dHebNm8fEiRP55ZdfuHr1KlA2V6Nv\n376y5K0QQgghhBCiAo039YOyuRvDhw/XVixCCCGEEH8pylIlGWfySD9+mds376NUKmlsbEgXmzZY\n25iio6vR9Fch/rGqTDYiIyMZNWoUrVq1IjIy8pENKRQKJk2aVKvBCSGEEELUleOHs9mTfJrreQUV\nyo4dysa4aQOcBj5D3wHPoNDR7n4bXl5eHDx4kMWLF/Pqq69WKD979ixDhgwByvZAqwsuLi44OTkx\nf/58jeqvX7+ewMBAdu/eTZs2baqsl5uby/Lly0lJSSEnJ4fGjRtjbW3Nm2++yYsvvlhb4atcunSJ\nKVOmcOrUKfz9/WnZsqVanF5eXujq6laYWiAqV22y8cILL0iyIYQQQoh/FKVSyfbN6fyccq7aendu\n3Sd5429cuniTYW/ao6vlXg4jIyPi4+MrTTYSEhJo2LAh9+7dq1GbR44cISAggJ07dz5xfOvWrcPA\nwOCJ23nQmTNn8PHxwdzcnA8//JCOHTty7do14uPjmTJlCr6+vgQEBNTqNb/77jvOnj1LXFwcVlZW\nbN++Xa08IiKiRps5jh8/niFDhuDp6VmrcdYXVSYbJ0+erPTfQgghhBB/Z/t2nn1kovGgE0cvY9hA\nD4/Xe2oxKujTpw979uwhJydHbb6sUqlk8+bNODg48NNPP9WozWPHjtVafNXtyfY4lEol06dPx8zM\njOjoaAwNDQEwNzenZ8+emJiYsGzZMkaOHImlpWWtXffmzZu0bNmSHj16+3PaPQAAIABJREFUVFre\nrFkzjdtSKpUcP35c1ev0T6RRCh4YGMjly5erLN+3b58sfSuEEEKIeu/2zXukbK35MKTUAxfJvnBD\nCxH9ycbGhhYtWrBx40a14wcPHuTq1asMGDBA7bhSqSQqKgo3NzdsbGxwdnZm5syZ3LhRFmdERASf\nfPIJly5dwtramoiICABycnKYNm0azz//PD179mT06NEcOXJE1e4vv/yCtbU1iYmJDB48mDFjxgBl\nw6hmzZqlqrdt2zZGjBiBnZ0djo6OjB07tkZfYB84cIDTp0/j7++vSjQe5OvrS0pKiirRKCkpITIy\nEhcXF2xtbXF2dmbu3LkUFPw5DM7FxYWwsDBWrlzJwIEDsbe3x9vbm4sXLwJlw9W+/fbbCs/kQV5e\nXowdO1b1+fLly0yaNInevXvTr18/AgICyM3NBaBr167cvn2bwMBArK2tNb73vxONko0NGzZw8+bN\nKsuzs7PZtWtXrQUlhBBCCFEXUg9cpLRU+VjnHtqfWbvBPEShUODu7k5CQoLa8Y0bN+Ls7IyxsbHa\n8XXr1hEeHs706dPZvn07S5cu5ciRIwQHBwMwbtw4hg0bRps2bdi7dy/jxo2jqKgIHx8fzp49y+LF\ni1m3bh2WlpaMGzeOrKwstfZXrVrFxx9/TFhYWIVYMzIy8Pf3p1+/fiQmJhIXF4eRkRETJ06kqKhI\no/s9fPgw+vr69OvXr9JyQ0NDWrVqpfpcnkRMnz6dxMRE5s6dS3JyMoGBgWrnbd26laysLFatWsWK\nFSs4d+6cap5JREREhWdSncLCQsaNG8f9+/eJjY1l5cqVZGZm8t577wGoEsOgoCD27t2r0X3/3VS7\nGpWLi4tqTNq7776Lvr5+hTqlpaXk5uZibm6unQiFEEIIIZ4CpVLJkV8uPvb5J45e5uXhthg2qPi+\nVFs8PDyIiYnhxIkT2NjYUFRURFJSErNnz+aPP/5Qq+vu7k7v3r3p2LEjAGZmZnh4eBAdHQ1Ao0aN\nMDQ0RFdXV/XSnpiYyPnz54mPj6dbt24AzJs3j3379rFmzRpmzJihat/NzQ1HR8dK42zXrh2bNm3C\nwsJCNY/Dx8cHb29vMjIy6Nq16yPvNTc3l5YtW2o0D6SoqIjY2Fi8vb3x8PAAoH379uTl5TFnzhxy\nc3Np3bq1qv7s2bPR0Sn7zn3w4MEkJSUBZUOkHn4m1dm5cyeZmZmsWrWKtm3bAjBnzhyio6O5fv26\namiZsbGxRu39HVWbbMyYMYNff/2VmJgYWrZsSaNGjSrUUSgU9O7dm/Hjx2stSCGEEEIIbbubX8Sd\n2/cf+/ySP0rJy82nXfvmtRiVOnt7e8zNzdmwYQM2Njbs2LGD4uJiXF1dVS/M5Ro0aMD27duZNm0a\nV65cobi4WPVTlWPHjtG0aVNVogFgYGBA7969SU9PV6v7YJ2HGRoacurUKWbPns358+e5d+8epaWl\nANy6dUuje1UoFKpzHiUjI4O7d+/Sq1cvteM9evRAqVSSnp6uSjZsbW1ViQaUzTW5ffu2Rtd5WFpa\nGs2aNVMlGuXXXLRoEYBqb7p/smqTDXd3d9zd3Tl16hTz5s3DysrqKYUlhBBCCPF0FRWVPHkbhU/e\nxqN4eHjw3XffMWPGDDZt2sTAgQMr/UJ4wYIFrF27loCAAPr370/Dhg359ttvWbVqVZVt5+fnc/v2\nbezt7dWOFxUV0aFDB7VjlV2z3NatW5k2bRojR47kgw8+oFmzZqSnp+Pv76/xfZqZmZGXl8e9e/do\n2LBhtXXz8/MBaNy4caUxlpdDWRL2IIVCgVL5eEPnbt++jZGR0WOd+0+h0aZ+LVq0oKRE+/95hBBC\nCCHqiqGh7hO3YWBYo/2SH8urr77KsmXL2LVrF3v27OGzzz6rtN6PP/6Ip6en2ryD6no1oGy4T7Nm\nzVi7dm2FMj09ze/txx9/xMrKipCQENWQ/NOnT2t8PoCDgwMlJSWkpKTw8ssvVygvLS0lLi4OT09P\n1XyVO3fuqNUp//xwElJbTExM1BIZUZFGE8SPHz9Odna2tmMRQgghhKgzDRsZ0LR59d+gV0ffQJdW\nptp5qX1Qp06dsLa2JjQ0FAMDAwYNGlRpvaKiIpo3/3NIV2FhIcnJyQBq3+Q/+O8ePXpw69Yt9PX1\nsbS0VP0ANZpzUFxcTPPmzdX2oyifLK1pL4KDgwO2traEh4dXSCIAvvrqK+bPn09GRgYdOnSgUaNG\npKamqtU5evQoOjo62NjYaBx7TXTr1o1bt25x7tyfSyWnp6fzxhtvqE2of9yek78DjZKNefPm8eWX\nX5KYmMjVq1ell0MIIYQQfzsKhYLe/R5/vwa73u2eSs8GlA2lOn/+PK6urpUuCwvQs2dPtmzZQnp6\nOidOnMDX15fnnnsOKFsut7CwkKZNm3L16lUOHTpEVlYWrq6utG/fnunTp5Oamkp2djY//PADw4YN\nq7AKVnV69OhBWloaKSkpZGZmEhISQpMmTYCyBEDT3oDFixdTUFDAqFGjSEpKIjs7m7S0NIKDgwkL\nC2PWrFnY2NhgYGCAt7c3sbGxxMfHk5WVRVJSEhEREQwdOpSWLVtqHHtNuLm50b59e4KCgjh9+jTp\n6ekEBwdTWFiIubk5xsbGKBQKDh48yMmTJ7l///HnBNVXGv2PmDFjBiUlJdXu0KhQKPjtt99qLTAh\nhBBCiKfNvm979mw7Tckfmk1MfpBDf6vaD6gKHh4ehIaGVrtZ3OzZswkKCmL06NGYmpoyZcoUnJ2d\nOXr0KBMmTCA6Oprhw4eTnJzM2LFjeeONN5g1axarV6/m008/ZcKECdy9e5f27dvzwQcf8Prrr2sc\nX/nyuQEBARgaGjJixAiCgoK4c+cOkZGRGBkZaTS0qUOHDiQkJBAVFcWiRYvIycmhadOm2NraEh0d\njYODg6qun58fenp6LFmyRLWSlaenJ1OnTtU47prS09Nj5cqVhISEMGrUKAwNDenbty9BQUEoFAoa\nNGjAuHHjiI2NJSUlhfj4eMzMzLQWz1+RQqlBv87MmTM12pb9k08+qZWg6kp2djaurq7s2LFDlvIV\nQggh/qEO7c8k8Yf/1egcp0EdGfxqdy1FJETde9z3ZI16NhYsWPDYgQkhhBBC1CcO/a24f6+YnYma\n7Xb9rJMlbkOqXgZWiH+yGg0svHfvHidOnCA3NxeFQoGpqSm2trYabbYihBBCCFFfOLt2prVZE/Zs\nO83lizcrrdPStDH9B3Wip6O5RiNAhPgn0jjZCA8P5+uvv+b+/fuqGfUKhQJjY2MmTZqEj4+P1oIU\nQgghhHjaunQ3pUt3Uy5n3eS3Y5e5c+s+SiU0Mjaki40pVh1bSJIhxCNolGz897//JSoqipdeeomB\nAwfSunVrlEolOTk57Nq1iwULFtCkSROGDx+u7XiFEEIIIZ6qthbNaGvRrK7DEKJe0ijZWLduHb6+\nvkybNq1CmaenJwsWLODrr7+WZEMIIYQQQgihotE+GxcvXlSty1yZgQMHkpGRUWtBCSGEEEIIIeo/\njZKNBg0acPNm5ZOjAAoKCqrcUEYIIYQQQgjxz6RRstG7d29WrFjB9evXK5Rdu3aNqKgoevfuXevB\nCSGEEEIIIeovjeZsTJs2jTfffJMXXniBnj17YmpqCsCVK1c4duwYhoaGzJ8/X6uBCiGEEEIIIeoX\njZKNrl27sn79eqKiojh48CBHjhxBoVDQpk0bhg8fzjvvvCM7bgshhBBCCCHUaLzPhpWVFZ988ok2\nYxFCCCGEEEL8jdRoB/H//e9/nDt3juvXr6Ojo4OJiQldu3alS5cu2opPCCGEEEIIUU9plGz8/vvv\nTJo0ifT0dNXu4eUUCgUODg6EhYXRsmVLrQQphBBCCCGEqH80SjY++ugjzp49y3vvvYeTkxMmJiYo\nlUquX7/Ozz//zMqVK5kzZw6ff/65tuMVQgghhBBC1BMaJRsHDx7kww8/5PXXX1c73rFjRxwdHTEz\nM+Pjjz/WSoBCCCGEEEKI+kmjfTb09fWxsrKqstzS0hIDA4PaikkIIYQQQgjxN6BRsuHq6spPP/1U\nZXlKSgpubm61FpQQQgghhBCi/tNoGNWIESMIDg4mMzOTF154gTZt2gCQl5fHnj17SE9P5z//+Q+/\n/vqr2nmOjo61H7EQQgghhBCiXtAo2fj3v/8NwOnTp0lOTkahUKjKylenmjhxotoxhUJBenp6bcYq\nhBBCCCGEqEc0SjZkMz8hhBBCCCFETWmUbAwfPlzbcQghhBBCCCH+ZjTeQbywsJAff/yRQ4cOkZub\ni46ODqampjg5OeHu7o6urq424xRCCCGEEELUMxolGzk5Ofj4+JCZmYmenp5qU7/9+/fz/fffY2tr\ny3//+1+MjY21Ha8QQgghhBCintBo6dvQ0FDu37/P8uXLOXbsGHv27OGnn37iyJEjfPHFF1y5coWw\nsDBtxyqEEEIIIYSoRzRKNvbu3cvUqVN5/vnn1YZL6evr4+Ligr+/P9u3b9dakEIIIYQQQoj6R6Nk\n49atW5ibm1dZ3qFDB65fv15rQQkhhBBCCCHqP42SjdatW/Pbb79VWZ6enk7r1q1rLSghhBBCCCFE\n/afRBHF3d3fCw8PR0dHBxcUFU1NTAK5cucK2bdtYunQpo0eP1mqgQgghhBBCiPpFo2TDz8+P06dP\nExISwvz589XKlEolL7zwAlOnTtVKgEIIIYQQQoj6SaNko2HDhqxcuZJff/2VX375hdzcXBQKBW3a\ntKF///707NlT23EKIYQQQggh6hmNko3du3fTo0cPHB0dcXR01HZMQgghhBBCiL8BjSaIT5s2jQsX\nLmg7FiGEEEIIIcTfiEbJhqenJ6tXr6aoqEjb8QghhBBCCCH+JjQaRmVkZER2drZqfoaJiQl6euqn\nKhQKPv74Y60EKYQQQgghhKh/NEo2li9frvr3vn37Kq0jyYYQQgghhBDiQRolGydPntR2HEIIIYQQ\nQoi/GY3mbAghhBBCCCFETVXbs5GZmcmKFSs4fvw4SqUSGxsbxo4dS7du3Z5WfEIIIYQQQoh6qsqe\njbNnz+Lp6UlCQgIAenp6JCUl8a9//Yv9+/c/tQCFEEIIIYQQ9VOVPRuRkZGYmJiwcuVKLC0tAbh+\n/TrTp09n3rx5bNmy5akFKYQQQgghhKh/quzZOHjwIBMnTlQlGgAmJiYEBgaSmZlJTk7OUwlQCCGE\nEEIIUT9VmWzcuHGDjh07VjjesWNHlEolN2/e1GpgQgghhBBCiPqtymRDqVSir69f4Xj5Zn5KpVJ7\nUQkhhBBCCCHqPVn6VgghhBBCCKEV1S59m5eXx+XLl9WOlfdoXL16lSZNmqiVtW3btpbDE0IIIYQQ\nQtRX1SYb7777bpVlvr6+FY6lp6c/eURCCCGEEEKIv4Uqk43Jkyc/zTiEEEIIIYQQfzOSbAghhBBC\nCCG0QiaICyGEEEIIIbRCkg0hhBBCCCGEVkiyIYQQQgghhNAKSTaEEEIIIYQQWiHJhhBCCCGEEEIr\nJNkQQgghhBBCaIUkG0IIIYQQQgitkGRDCCGEEEIIoRWSbAghhBBCCCG0QpINIYQQQgghhFbUabKx\nevVqXF1dsbW15eWXX2bz5s3V1t+/fz+jR4+md+/ePP/88wQGBpKXl/eUohVCCCGEEELURJ0lG7Gx\nsXz22WdMmjSJjRs3MmrUKN5//31++umnSuunpqbyzjvv0KNHD9atW8fChQs5fPgwU6dOfcqRCyGE\nEEIIITShVxcXVSqVLF++nNGjR+Pp6QnAM888w6+//kpUVBQDBgyocM7q1avp3LkzQUFBqvp+fn4E\nBARw+fJl2rZt+1TvQQghhBBCCFG9OunZyMjI4MqVKzg7O6sd79+/P4cPH+b+/fsVzlmwYAGrVq1S\nO9aiRQsAbty4ob1ghRBCCCGEEI+lTno2Lly4AEC7du3UjltYWFBaWkpWVhadO3dWKzMyMsLIyEjt\n2K5du2jcuDEdO3ascQzlPSoPKioqqnE7QgghhBBCiMrVSc9GQUEBAA0bNlQ7Xp5M5OfnP7KNn3/+\nmZiYGCZMmECDBg1qP0ghhBBCCCHEE6mTno0ntX//ft577z3c3Nx45513HquN9evXVziWnZ2Nq6vr\nk4YnhBBCCCGEoI56NoyNjYGKPRjln8vLK7Nz504mTJjAiy++SGhoKAqFQnuBCiGEEEIIIR5bnSQb\nlpaWAGRlZakdz8zMRF9fn/bt21d63q+//oqfnx+jR4/m008/RU+vXnbMCCGEEEII8Y9QJ2/rHTp0\nwMLCgj179uDm5qY6vnv3bvr168f/Z+88A+Moz7V9bd9VWfVe3ST33k0z2Mb0EjoJpJ7wEXJycpKT\nRgohEEJOckgogRB6IMY23cYdN2y5yrKtaqt37apvrzPfD2HZq11JW2SwyVy/7Jl5Z9/VTnmfdj9q\ntdpvjNFo5KGHHuLWW2/l4Ycf/jyne0EiCiL1NV20NvXhdHhQaxSkZeiZOCUVhUJqDC8hISEhISEh\nIfHF84WFBh566CF++ctfMnfuXBYsWMDHH3/MoUOHePPNNwH485//TEVFBS+//DIATz/9NCqVigce\neIDOzk6fc8XGxv7bFIl7PF6O7m/gyP4Gerttfvtj9BrmLc5j8eUT0GilyI+EhISEhISEhMQXxxe2\nGr355puxWq0888wzGAwGxo0bx7PPPsvcuXMB6OzspKmpafD4oqIiOjs7Wb58ud+5nnjiiYBStl82\n7DYXa14+TEvD8H1FLCYne7adpvxEG/d8exHxiVHDHishISEhISEhISFxPpGJoih+0ZO4UDijRvXJ\nJ5+QnZ39RU/HB7fbyxvPH6C1MfgGhonJ0Xzz+8uIitGcx5lJSEhISEhISEh82Ql3nSwl918kFO2s\nCcnQAOjpsrJjY+V5mpGEhISEhISEhITEyEjGxkWA1yNwtKghrLGlx1qxWZxjOyEJCQkJCQkJCQmJ\nIJCMjYuAqrIOrBZXWGO9XoHjR1rGeEYSEhISEhISEhISoyMZGxcBzfU9EY7vHqOZSEhISEhISEhI\nSASPZGxcBNjt4UU1zuBweMZoJhISEhISEhISEhLBIxkbFwEqlSKi8UqV9DNLSEhISEhISEh8/kir\n0IuA5NSYyManRDZeQkJCQkJCQkJCIhwkY+MiYPqcLOQKWdjjZy3MGcPZSEhISEhISEhISASHZGxc\nBMTotUyZkRHW2Oz8BNIz48Z4RhISEhISEhISEhKjo/yiJyARHMuvmUztqU4cdnfQYxQKOStvmHoe\nZyUhIXGxY29rw7hrD46ODkS3G2VMLPrp00heuhi5Wv1FT09CQkJC4iJHMjYuEhKTo7nzmwtY89Jh\nXM7g1KVuuXcOOfmJ53lmEhISFyPm6hqa3vwXfcdP+O0zbN9B/cuvkr56FTm3f0UyOiQkJCQkwkZK\no7qIyBufxDe+v4zYOO2ox8pkkJkT/znMSkJC4mKjq+gApT97OKChcQaPyUTLunco+/Vv8Visn+Ps\nJCQkJCS+TEjGxkVGWoaelLTR1aVEEY4dbPwcZiQhIREqdpOVroY2zJ29CIIQ9nkEtxtndw+u3l4E\nT3ARz/7SMk7/6SnEII83V1ZR+cSTiF5v2POUkJCQkPj3RUqjusgQBZHWpj6/7fkTk2mo6fLZVnKo\nictXFaJQfj42paunl679RTiNRgSPB5VeT/zsWcROLkQmC19N68uGtbEJa30DgtOBQhdFzKQJ6DLC\nEwCQuHiw9po5+M5eSitNmGRnHQYawUFBuoylty4ibdLoynGiINBXcpz2TVvoKzk+aATIlEoSFy0g\n49pr0E+bGvCeEwWBmueeD9lwMJWVY/hkJ+mrVoY0TkJC4t8HV28vvcXHcPX0gkyGOjGBxPnzUMVJ\nIjX/7kjGxkVGV6cF55CO4AqFnOtum8FzT+4C8ex2q8VFZWk70+dkndc5WRubaF67np6Dh/wWMc1v\nryMqP4+sm24kZfnl/7ZGh+j10rn3U9o/3oKlutpvf9zMGWRcdw2Jixb+2/6NLlQ8Nhudu/Zg3LkL\nW0srgsuFMiYG/dQppK9eRfysmcjkIxv0R97by/ZPO/HI1SDzjUw65VpKjVD6/DFmpB3iph/dglwZ\nuJGnw2Cg6on/xVpf77dP9Hjo3n+A7v0H0E+bSuFPfow63vcl33f8BI72jhD/AgO0f7yZtJUrpOtT\nQkLCB0tNLa3vf0j3gYN+awCZUknyJcvIuuUmovPzPtd5iaJIY103rY19OBxuVCoFqemxTJqS9rk5\nYSUGkIyNi4zWxl6/belZepJSYiiYksbpCoPPvqNFDefV2Og+dITTf/o/BJdr2GNsDY1U//UZ+ktL\nmfjQg8gUkXVEv9jwWKxU/eGP9JeWDXtM/8lS+k+WknzJMib94CGpIPcCQBRF2jd+TOObaxAcDp99\nHpOJnoOH6Dl4CF12NgU//E9iJk4IeJ59b+5g5zEbyEf5TWVySo0aLI+u495H7kQ+xICxt3dQ+vOH\ncff6RzaHYiqvoPRnv2DGHx5HHX+2dsuwbfuoY4fD1tCIpbqG2IJJYZ9DQkLiy0XHtu3UPv8iDJMO\nKno8dO7eQ9e+/Uz8/vdIveKy8z4nr1fg2IFGjuxvoMto8dsfHathzqJcll4xAa1Odd7nIyEZGxcd\ngVKosvMSAJi3NM/P2Giq68HYbiI1Qz/mc+kvLePUH/8UdO63ceduZCoVEx98YMzncqHidTqpePRx\nzKdOBXV81779CG43k3/64387o+xCQhRFGl57g7YPPhr1WHtLC6UP/5qpv/4FcdOm+eyrPVjOrmMW\nkAX/W9ZbY9j2t42sfujGwW2C203lY08EZWicwdHeQdUf/pfpjz+Ko70Da10dfSdOBj0+EJbaWsnY\nkJAIE5vFyfEjzVSebMfc70AEYmI1FE5PZ+6iXGL0o4u/XEgYd+6m9rkXgjpW9Hio/svTKDRqkpYs\nPm9zcjrcrHvtKPXVXcMeYzU72bejmorjbdzznUUkJkeft/lIDCDFkS4yWgJENrJyB4yNiYWpxCdG\n+e0/WjT2heKi10v1X58J2tA4g2Hr9hEVcL5sNP3r7aANjTP0HDpM+6Yt52lGEsFg2Lo9KEPjDILD\nQeXjT+IwGn227/nwOGIIhsYZSmpduKz2wf937duPvaUl5POYK6s4dNdXKfnef3L6z3/Ba7OPPmgE\nvHbH6AdJSEj44PUIbP2wjKd+t4MdGytpberD1O/A3O+gvaWf3VtO8Zff7WDj+hO4XaG9U78onN09\n1D7/99AGiSLVTz+H22w+L3PyegTWvjqyoXEuPV1W/vnCASwm6bl2vpGMjYsIl9ODsd3ktz3rs8iG\nTC5j3hL/nMiTxS1+dR6R0nP4CM7O4G7oobR/vHlM53Kh4rXbMWwNL22lbcNGxAhUiiTCR3C7afrX\nmpDHea1WWt/7YPD/XQ1ttDpHV44LhFuuYddTb9H20UbaPtpI01tvh3UeYMQUx1BR6C4uz6uExBeN\nx+1lzcuHOLS3Hq9n+Ge6IIgcO9jEP184OObv6/OBYfuOsJ4tXpsN487dYz8h4PC+ej+hnNHo77Wz\n9cPy8zIfibNIaVQXEW3NfYii77boGDXxibrB/89ZmMPurad8Hmoup4fSYy3MX5o/ZnPp2LIt7LE9\nR4txdnWjSU4as/lciHTu+RSvPTxPstNgpK/kOAnz5o7xrCRGo/vAQdz9/kZ9MHRs2YapvBKv3Uat\nNx0xcX7Y86hpcRFz5NWwx58PYsaP/6KnICFxUbFh/QnqTge/AG5p7OW9t45x1zcXXLBiDKLXG7Yj\nDaBjy1Yyb7x+TL+fIIgc3ucvnBEMFSfbWdlvRx+nG/3gLxk9XVaKDzRSU2XEZnEil8uJS9AxfU4W\nM+dnj1lNi2RsBEl/r52aKiNWixOZDGL1OgqmpREV/fkV8gaq18jKTfC5YaNiNEyblcnJYt+Ui6NF\nDcxbkjdmN7epKrTUIB8EAUt19Zfe2Ig0P77v+AnJ2PgC6Ny9N/zBooitqQkAR1JkyitO5YWVRxyV\nl0uMVK/xpcRjsWJrbkZwOlHodOhyclBG/fstvMaa9pY+SotbQx5XXWGgvrqL8QUp52FWkePoMODq\n6Ql/fFs77t4+1IkJYzan2lNG+nvDc+6JgkjJoWYuX1UwZvO50LFZXWxcf4KqUn91QrPJQUtjL59s\nqmTJFRO4fGUBMnlka0fJ2BiF5voeinbXcrq8wy+qoFDKmT47k6XLJ5KSHnve5xKwXiPP/2adtzTP\nz9gwtptpbugld1xixPMQvV4/dZ5Q8Vi//B2J3abwvOOD483+KhoXMg67m4oTbXR2mHG7vWi0KnLy\nE5g0NQ2F4uLJ2HQYDKMfFAQikT2cIx1/LjKlkuhx44gen0930UE8YeRMp1+z+oL1tEqEh6myivZN\nm+kuOuhTfydXq0m+9BIyrl09rMqaxOgc3R9+vWTxgcYL1tjwWCJ/N3ksljE1Nprqwjd+BsZ3j9FM\nLnzMJgdv/K2I7s6R12Ful5e9207TbbRwy71zkUdgcEjGxggc3FPLtg0VPr0rzsXrEThxtIWy423c\ncs8cps7KPG9zEUUxoOxtdgBjIzsvgfRMPR1tvovdo/sbxsTYkCkUyFQqRLc77HPINV/+3G+5MrLb\nK9Lxnxemfjt7t52m9FgrbpevxvoBIEavYf7SfJZcMQGV6sJX2ApV9GA41N7IirEjHX8u81/9B2r9\ngCJd8tIllP/2sWGlKgOhnzqFtJVXjdl8JL5YBI+H2udfxLjjk8D7XS6Mn+zE+MlOMm+6gfyv3zdq\nLxkJX7wegbLjoUc1zlBV1oHD7r4gpVllqsBzMmmS6IzOwaUYEKrReG2kWBqJdfmvXeTqsf1eDnv4\n6xEYULEaDq/DQefeffQePYq7zwRyGZqkJJIvWUriwgUXlXKk1yPw9suHRzU0zqX8eBtxCTpWXD81\n7M+9OFYzXwBH9jew7aOKoI71egTe/WcxSpWCgqlp52U+pj47FrOfYBIrAAAgAElEQVTTd6MMMnP8\nO3PKZDLmLc3n43d803gqT7ZjNTuJjtVEPJ+o3BystXURjf+yo0mL7FrwWK2IonhBe5PbW/pZ89Ih\n/2vzHCwmJ7u3nKK6wsDd31pIVEzk19/5RBmrh47IoxtJ1hZqkheEPT41ykPayhUAdBUV4bXawjqP\nfvq0QUMDIH72LAp++J9U//XZoA2rcd/+5kVj/EqMjOj1cvpPT9F94GBQx7d9uAGv3c6EBx+4oJ9F\nFxqmfoef8yUUREGkt9tGRvaF131bm5aKTKEYbODXGZ1LQ8IMTFr/SEx94mzi7AbG9Z4kyXbW+Orc\nu4+sm28M2FNKFEVsjY3YW9sR3ANNVGMLClDph88gUUboyArU5E/weGheu572jzf5PX/NDKgEqpMS\nyb79NtJXr7oo7o/SYy20t/SHPO7AnjoWXjou7M+V3h4BMPU52fpBZUhjRBE+XFPCfz68Ao127P+s\nLY3+9RqpabFotIG9AzPmZrFjY4WPqoXXK1ByuIlLroo87zptxVXUhWlsRE+cSHRebsRzuNBJveIy\nDFvDL6TvLjpA2cO/Zvx/fIvo/Hy//db6BvpOluIxm5EplegyMkhcOB+F7vPJte7ttvLWiwexWYNT\nJGlt6mPNy4e578GlF3SEI37WjIBd3oNl4n99n5jx41FGR1P7h810ieEtFpqjJrBw9XzGTUwmKi+X\n+pdeCes8Gdeu9tuWctmlaFJTafznW5jKRldiad+0mUnf/15Yny9xYdH20cagDY0zGLbtILawkLQV\nV56nWZ0fXD29GHftxtbU9Fk9ShSxkwtIvvTS816T4vGEb2iM5TnOB8roaBIXL6Rr/wHqEufQkDhr\nxOP7dWkc161kQncx+b2lADS9tQbDtu3kfvVeUi67BJlcjuB2Y9y1m45NW7HW+xZ7y1QqkpctJeP6\na4mdNNFnnyCIWC3DO7yCoa/b7tOTzOt0UvXEH+krOT54jAh45GpkoohCdCMDXN091L3wIrbGJsZ/\n99sXvMERbisE8TO1tInTwqsllIyNAFSd7EIQhsmdGgG7zU1ZSQvzluSP+ZyCrdc4g1qjZNb8HD91\nhuIDjSxdPjGi3DuAlCsuo/GNN8NSW9KMYZ7mhUzslMlE5eVia2wK+xym8gqO//B/yLh2Nbl334Ui\nOoru/UW0fbQR86nTfscroqJIXX45WbfeElQBvrOrG8O27XTtP4CruxtRFFHHx5OwYD7pq1cRlT18\n9/nN75cFbWicobWpj4N76rh0xYVbaJx29Upa3n0fvyKtINBPnULa8isG/z99fi67j4TuRQKw2dy8\n+cIBrrx2CguXX07L+ndx94d2Lm1mBomLFgae6+RCZjz+KLamJoy79uBo70Bwu3EYjNibm32ONe7c\nTfZtX0GXkR7Wd5G4MBA8Hlo/3BDW2Nb3PyD1quUX/GIKwN7eQeM/36Tn4OFB7/sZjDt3Uf/K66Rd\ntZzce+5CGROePPVo6KIiF4/RXYApVGdInD+f4nLLqIbGudQmzUMhuMnprwLA2dlF9VN/pe2jDWTd\nejOt77yPtb4er0xBV0w+NpUeQaZAJTiJtxsQd++hc/cecu6+k5w7b0cmk9Ha1Mfm907S1hzec/YM\nZpODv/95D/OW5HHZqgJann+OvpLjiECvLoOWuMl0R2UjyAccZQqvizRLA1mmKvTOHjo2b0EVH0fu\nXXdENI9QEASR/l47TqcbtVpJXIJuxPrIToOZtubgG8MO5cSRZiZOmxzWWMnYGIJcrqC6PPxCoaNF\njefF2GhtCq5e41zmLcnzMzbOqGpFmu6ljIoi7/6vUffCiyGP7Tl8hOa168m58/aI5nChI5PJyPva\nvVQ+9kRkJxIE2jduonPvPnTZmZgrqoY91Guz0f7xZjr37mPKL3+OfnJh4FO63dT94xUM23f45e47\nOjpo37CR9g0bSVy8iEnff9DvhdzTZaWm0reBXbAUFzWwbPkE5J9T0Xh/rw2zacDrFROrIS5BN+KC\nSZuaStLSxXTvPxDyZ2XeeMPgv50OD+VNkXkmRRE++biSlsZervzRj6h57LGgte0V0dFM+flPR01/\nisrNJf/+rw3+320ycfQ7/89XBEIQaFn3DpN+8FBY30PiwqDn0BHcvf7vkmCwt7TSX1pG/MwZYzwr\nf5weFycNlXTbehFEgThtLDPSpqDXjG4YmKpOUfm7349YxCw4HLR/vJm+4yeY9ttfo0kZ+0Ls6Bg1\nSSnRIeXGn0usXktiyvkxhCLFYTRS8a8PqE1aHvLY6uQFpFia0HrPpiVZa+s4/b//h1OhoylpPm36\nSXgU/um2sY4ucvsqENesxe70Uh01lWOHmoatqw0VURxYw5080kReex9JyljK0y/HrE32O9arUNMW\nV0BbXAHJ1iamGT6lee160lZcdd6VNvt6bBQfbKTkUBM2y9n3gUarZPaCHOYtzSc51f/a6QnzWjxD\nf68dwRveH1syNoYQF5OO0xH+AsHQZsLpcA+b3hQOXo8QMMcuKzd+xHEp6bHkTUiisdbXeDpa1DAm\ntSUZ11yNu7eX5rXrQx7b9K+3kSmVZH/llojnMRwup4eyklbqTndis7qQy+UkJEUxfW4WueMSPxcP\nXfS4cchUSkR35EXHHpMJc0VwClces5mK3z7GjCceIzrfV4LV63RS8ejjQaXP9Bw8xMmWVqY//ijq\n+LPpQMcOhq+yYup3UF1lpHDa+fOSu91eyo61crSowe/eSc/SM39pPjPmZqFSB34ETvjud7DW1eNo\n95cFHI70a1aTuHggiiCKIh+tPU6nITTVFhkCYoBeq6fKOujsiOaaH/yM7hf/gtnioTWugK7oXFwK\nHSCi8dhItTSSaTpNbLKeKQ//PKzaKJVeT+b119Lyzns+242795B9x1fQZWSEfE6JC4OeI0cjGt97\n5Oh5NTa6rD1sPP0Je+oPYHX7Rs1VciVLcuZxfeFV5CcEvq5tLS1UPPo43iDVDu2tbZQ/8hgz//h7\nlNFjKzV9pnZyW5gN4+Yszo04A+F84OrtpfzXv6VByAZZ6A4jUaagTT+J8b0nfLab1Ykcz1yBSxk1\n7FizNpny9MtockzGXqLCowg/a2AkXG6R6uSFVCfND+o7dkXnUpx1DXNbtwykht1z13mZlyiKFO2q\nZdfmqoDZN06Hh0Of1nNoXz3Llk/kymsmD0rWut3ekBseBsLrDa/ZsCQvMQS1KvI8TrstMlWEoXS0\nmfw6j6o1SpLTRpfbDdTIr6bKSG93eMWmQ8m95y40qakjHzSMiknjG2/S+uFHYzKPc/G4vezYWMlT\nj25n4/qTVJxop6Gmm7rTnRQfaOT154r4+5/2cKos+IVkuNS/9ErQhoY6KYmpj/yKwp/+GHWyvycl\nVLw2G9V/fQZxSDpQ7XMvBGVonMHe0kLVE0/6pCMESusLhUDKamOFscPM83/czYZ1JwIa6R2tJjau\nP8nf/rgbQ1tg400VF8e03/12WNWVoWRcfy3jv/PNQQN2/84aKk+2+x2nlAnIhSHPB1EkQ2XihiuS\n+N4vrgoo+gAD0aS1G1som/cNisbdTkPibCyaRFxKHS5lFGZtMrXJ89g/4W46r34AbXb4IgyZN93o\nX/sjCDSvfSfsc0p88bj7wk+hAHBFOH4kTnZU8qMtv2PT6Z1+hgaAW/Cwt/EQP93+BNtqAvfCqX/p\n1aANjTPYW1r8DOuxYtKUVMJRsJbJYO7iC6+u0W02U/6bR7G1G2nTh58K2540zScYYVPFUpK1akRD\n41zM2tSAkY8zyMQgHcaigNI7Qq1HCMaURZNIedqldOz4xO+dO1bs2nKKTz6uHD3NXxx4B2185wSN\ndd1sXH+C/3tkG4c+Da/p4RkUCjlKVXhmgxTZGIJ36EIgDMa6+DVQClVWbnxQXo/J09OJidX4qgWJ\nA7UbK66fEvHc7O0dOI3+6TTa7Cy0KSnEz55FyhWX0/j6PzHu3OV3XMMrryNXKsm47tqI5wID8nVr\nXj48qua2scPM2lePsPKGqSy54vzoyPccLQ5YiKnU6xHdbrwOB4ooHbGTJpF29UoSFy4YTHlJmDeX\nlnfeo/W9DyKSYrXW1WOurEI/deC3ttY30Lkn9KZ15qpT9Bw+StKSRQC4HJFFapwRjh+Ozg4zrz27\nPygZxP5eO689t5+vP7SMtAy93353T8/I8s5yOYkL5pFx/XU+3t6aKiM7N/unuimUcu7/3qUkxqto\nLKnGbrKh1qnJKMwhMedspPHrDy1j6wflFB/wjx553AItTf2MtIIRRDhyoJmuLjt3f2thWCotKn0s\nGTdcR8s6X+Oic89esm+7dcRankgRBYG+EyfpPVqMu78fmVyBOjmJ5EuWETM+fDUUCSJuzHW+5G+r\nOmt48tO/4RZGfy6IoshLxWtQyhVcOX7Z4HZ7a5tPMW8oGLZ/Qu7ddwZURgoXt9vLh2uOh5XiI4rQ\nXNfDtDnn7z7z/TwRq9mJ3e5GpVIQG6f1y/332OxU/PZxbI1N2FRxIy72R8OBGuPVD6KoPIzaUM/p\n5AW4FZHL4etc/RR2HULrtlKVuoQ+3fDR8yhXPwWdB4lzdNKYMJ2m+OkI8siWxN3ROXT1nMBrtY55\nLVDlyXb27QhNuKTkUDMlh5pHPzBIsrNjws4IkYyNIZitkTV20epU6Ma4q3ggL/BoKVRnUCjlzFmc\ny6fbfS/S44ebuGJ1AUplZIZR9/4iv23azEzmPvtXn4ty4kP/D9HrDbjQrXvxZWQKJemrV0U0F0EQ\nWf96cUjNfbZvqCA6Rs3M+WMrxet1Oqn7+z/8tiuio5nzzFOo40f+/RQaDXn33k3qlVdQ89wLmErL\nwp7L6b88Q8LcOchVSvoiOE/7ps2DxoZSHdl1o4pwfCA8Hi9rXz0Skt660+Fh7SuH+X8/We7nJDBs\n9+9BoElJIfv2WwekGAsL/XJze7utvPfmsYALjOu+MmPwvp18+exh56RUKrjutplk5yfw8fqTeDzh\nha3rq7vY+M5Jbr57Tljjs266gfaNm/DazomCfla7UfDfPwjrnCMhiiKGrdtp/eDDgOlrre++T2xh\nITl330HCnOH/fhLDo06KLJc80vGBcHnd/OXAy0EZGufyUvHbTE8tJDVmIAps2L4j7Dl4zGa6Dx4i\n5bJLwz7HuYiCyAf/KokoAvzRuhOkpMcOqiOdD2wWJ8ePNFN8oNEn20GtUTBjbjbzl+aTlqlHcLmo\n+v0fBlX6vPLI1zhltTZQT4ec6RGfSy54GNd7gtzecuQMPC/ntW7BrE6gNa4QkyYFt0KNQvAQ4+ol\n01RNgr190GUzoec4WaZqapLmYYgdH9FcWuMm43U4x9TYEEWRT3f4C8J83uSLrUB4zlkpjWoITpeF\nnHHh39yz5mePeZ5lqEpUQ5m7KI+hxqjN6qLyhH+aR6h07fM3NpIvWepn/coUCib94CGSli0NeJ7a\n5/+OYZgGU8FScaKNutOdIY/b8kE5LufYetqb316H0+g/l/z7vjqqoXEuuowM0q68IqK5OA0GOjZv\noe2jjdjqG8I+T//JUpzdA8Z4oEhAKJyPF2jlyXZ6ukIvgOvrsVNxvM1nm9dup/PTfX7HZt16M+lX\nryJ52VI/Q8Pl9LDu1aMBjZ35S/OZvTC0tIhZ83P45g8uISEpuNSCQJw82kJHa3gqLcqYGDJvuM5v\ne+en+7C1tIQ9p0CIXi/Vf32W2uf/PmKdjPnUKSoe+R1tH20c08//dyF5mOfv5zU+EAebj9FjDz09\nyyN42FZ71nlliaDv01iMP5dPNlUGTKOUy2UoFAHWBwE2uV1e1r0W+HlyBkdHBz1Hi+naf4C+EydD\n6uxdVtLK07//hB0bK/3Sql1OL8UHGvn7n/ewYe1xKp78M/2lZYjI6IrKoiZpbtCfc75JsLWxuOl9\n8ntLBw2NM8S6epnceZCFLRtY1vgui5s/ZLphL4nnGBpn0HqsTDfsZXqHfwZGKBhi8pCNcdPi1qY+\nOlqDq9c8X2jdZjQl4f9tpMhGACbPSqa5Prwfdt6SvNEPCgGbxRmwviI7N3hjIy5BR8G0dL8ahSNF\nDcyYlx323OytbX5a2DD8C0mmUFDw3z/glNdLz8FDfvtrnn0emUKBLjsbw7btWGrr8NodKHRaYsaP\nJ23VCmILhs8TPbK/Iazv4bC7KT/expxFY5Mja21soi2AvGRsYQFpq1aEfD6PJTIFibHEaTCiSUpi\n9sLcgGk+waDRKpkyY+yLw4+G+fvDwLUza8HZ6FbX/iJfNSZArlYP6/kURZGN609iaPd/buTkJ3D1\nTdPCmld6Zhzf+eFlPPfkLqwjNE4ciaNFDVx/e/DylOeSeeMNtG382LehlSDQvHY9hT/6YVjnDETd\nS6/QuWt30MfXv/wqiugo0q66uPo+fNFEjctHplKNnB44DLGFBcRMiMzrG4jtw9RfBMPOuiLunH4D\nKoUqLBn2c/GJ4EVA8YEGinbV+m2XyWXc9a2FZObEU1NpwGxyIooiMbFaJhSmsGHdCWqqfFOSe7qs\nfLCmhDu/vmAwBU70euk+cJD2TVswlfs2HpapVKRcuoyM664lZuLwHuiSQ01sWHdi2P0+xx5uptEa\nT3zCTNr0BThUF5ZCVqqlEZ1n7N6RqpFqOIJAkKtwo2Qs81uqSiN3DMNA+Wxyfz0KwUN7XPA1N3LB\nw/SOPXidPWGL3UjGRgCy8/VMKEyh9lRoXvL5S/NGLNoWRRFTecWApn1HB6LbjTI2lriZ00m9cjmq\nWP+xLU3+Hp/4xKiQu4DPX5rnZ2y0NPTS0dZPemZ4Tce6AqRQ6bKziRqhYZ9cqaTwxz+k6sn/pfdI\nse9OUaT6L88EHGetrcOwfQcxkyYy4YH/8HuQdhktNNcHnz41lGOHmsbE2BAFgdrn/+6n7Y5czoT/\n911kcjket5eebhtulweNRklCcvSI2tjD5RG75Wr6tSm45RrkCOjcJmKdPeHUIwaN8NkiJSs3nsyc\nuLC0zdMy9cOqQIWLzeKkuSH8lIW25j7MJgex+gGPlGGbf5QtaelilDGBFWsO7a2jrKTVb3uMXsNt\n988P2J02WASvELahAVB6rJVrbp0x4jU2HMqYaDJvvIHmNWt9tnd9up+c228LS+1qKKbKKjo2bQl5\nXN2LL5O0aNGwv8mXGa9X4HR5B2UlbfT32vB6RaKi1UycnMrshTkBezy4+/up+M2jYRkaANl33Bbp\ntP1weVyc6g4/omBxWWnsa2ViUj4KbWTe5LFohlpTZWTTe4FTVa+9dToTJw+IqQRK273l3jn846lP\n6evxNXpOlxvYt7OaS1cU4LFYqHzij8MKfIhuN8aduwf64txxG7n33OWXZdDW3MfGd06G9L16orPp\niQ7fMXk+cUdQO3Iuqvh44mZMQ5Qngf+jPCTC6dM2EmaTY/SDRkCjVbLi+qmkOZpp/MtriECUx0Rt\n0rxRxyq9LmZ27CTOOaBk5be2CRLJ2AiATCbjK1+bx5svHqQtwGI/EIXT0rj65uFzD3uOFtPw2hvY\nm/3TD3qPFtP05hpSll9B/te/hjLqbNpEoHqN0fprBGL8pBQSk6P90kyKixq57raZIZ8PoGvffr9t\ngVKohiJXqZj80/+h8vdP0nesJKTPtFTXUPrzXzL5Fz/1ydseTlUoWAxtkTUEOoPxk52YK/2LgzNv\nvB6zJoFd609QeqwVt+vsDavVqZi9MIf5S/NJTPZfOOmGFOOa1Yk0x0/BEDPOr6At2tlLdn8VGeYa\nFMEqcoSA8hyD+Oqbp/Pas0UhK2801fVQMkbG3RksESzGB89hchKr12JrasZ86pTf/rQVVwUcV1/T\nxfaNlX7b5QoZt983f9CACZfensg8tm6XF4vJSVxCeIupzBuuo+2jjb4qP6I4EN34n/+OaG4wUAsU\nDoLDgXHXLjJvuD7iOVwsiKJIyaEm9mw9HXABUl/dxa7NVcxemMuK66eg1gw8H1y9vZT96pGA759g\nUMXFET8rvPfESARSnQoVi2vguoweP47+CGrS+kvLsLe3jyrtLLjdyBQKv2L5jrZ+3nnjKGKAhebS\n5ROYtyQfZ3c3hq3b6T5wEGd3DzDQRDVx0ULSV1/NHd+YzytP78Pj9k0H2rXlFGkpOmyvP401yHSv\nlnXvIHo8Pj10APZ9Uh1wjp8nl60qwG51Yeww01TbHVGbjLRLFlNQuAKPxTJQJxmmEtSkHzxEwtw5\nxLX1s/PP4UfbZDLQRV1YzRizchOYtySP/lIzMJC1l99bSrzdQFP8NDqjc/yUt5ReFxnmGnL7ytF+\nFjmSq9XINOHFbCRjYxi0OhX3PbCErR+Wc+JI84iWakp6LLffP3/YJmVtGzdR/9IrI94EgsuFYes2\nzFVVTHv0N4N5/QGVqPLO5vyLXi9epxOFRoNMMXzRrUwuY97SPLZ/5Bt2PVncworrp4TcF8TW3BKw\nM3awOb1ylYrJP/sfKh//A/0nQvOyCC4XVX/4X2Y++fvBHhJuV2Q1Fx63gCiIEam1uPv7aXj9n37b\nlckpVMbM4ugwDzCH3c3BPXUc2lvH8msms+zKiT4Gm37qFDRpqTgMRprip1GTNB+/IpzPsGoSOJW6\nhOb4Kcxu24HOYyH5skuIHjcO0e2mffPWsJt6qeLjfTzZA91KZXg8oT/cN64/gVanYsrMsenZMBY9\nU86cIlDtkDYjHf30aXi9Ah63F7VaiUwuo7/XxrtvFAd8ea++eTo54xIjnpfHE7nRGMk5lNHRZN18\nI01vrfHZ3rW/iOw7biN6hEjmaLjNZrqL/BXbgqVj6/Z/G2NDFEW2fVTBob0jLzY9HoGjRQ20Nfdx\n738sQm4zU/arR3C0tY04biTc/f20fbiB7NtuDfscgVBFqP4DoFIMvLvSVq4ImL4aLNa6ekoe+i8y\nb7ye7NtvQxk1YJyLXi+9xcfo2LKV/rIKBKcTZDK0GemkLr+CtFUrcKBhzUuHcTn977OpszK44qrx\n1Dz3PIYdO/2aqNqtNlrf+4DW9z8k+dJLuPbGG/no3SGRCxHee/MY8xuNhOIyaH3vA/RTp5C4YD4A\npj47p8oNIf1dRkKpkvsZRqOxdPkErrj6bLPZN18ooq46fGGevKWzSfmsb1h/aWlYzVi16WnEzx5I\nNU1N16OP12LqCy+akJOhC0sBcCRiYiNzWMXqB6I/oigOvOg+W4vGO4zEdxhxKKPo0WXiUmiRI6Bx\nW0m2taIQfddV8XNmSWpU5wO1RskNd8xi+epCjh1q4nSFgb5uGzarbwdfc//wF2XX/iLq//Fy0J9p\na2yi8rEnmPHEY8gUSloDRFYy0qPp2LZjoK6hpnbwwtHlZJO2cgVpVy0PqIQwe0EOuzZV+ajbuF1e\nTh5tYcEloUlKBkqhisrLDSmtQqHRMOXhn3H0W/+Bxxxa8zPB4aDxjTeZ+uuHgYEwYSSo1IqIZSEb\nXnvD73uIQP2sr3D64Ojyc6IIOzdV4XR4uOq6s7LEMrmc9NVXs+fDEmqT5wc1F5s6nmNZq1ncu5uJ\nDz2IQjPwsFEnJ1Hz9HPBf6lzSFu1wqcb9Y4NlWErJYmfvTzv/vZCxhdE3r03Rh95KP3A7louXzEB\n4649PtudCh29k1fy7BM7B+unZHIZ2XkJmPsdfs8DgDkLc8esfitQSsznfY6M66+l7aMNvte3KNL8\n9jom//THYZ/X1tgUUNrZqdDSo8vErdSCKKL1WEmytfm9/OzNLYPOli87B3bXjWponEtbcx9r/3GA\naeVv4zL4y5PLVComPPgATqMRw/ZPcHWdbfglUyr9fpfmtetJvnQZ2rTADWHbTB1sq/2UUkMVFqcV\npVxBakwyl+UtYmnufDRK32uw09rN+rLIC/3PGCxROdnEzZxB/8nSsM8lejy0vvcBxl17yL/vq2iz\nMql+6mkc7UNy5kURR1s7TW+toW7te5yccgdmu/8CMzsvgetvmULlo49hqvCPfg49Z9feT4luaWHe\n4nspPuwbhXIJck5mLGd+y6aQotatH3w0aGycKjeMSVQjb0IS85fkMXFyCk/9fQOu5uCeL1HjPVx5\nzWSfbXMW5YVtbMTEqJhQePb9kXPH7fQeKUZw+T+TRyL33nsGI1VyuYx5S/LYtdk/uh0M7rpTODpn\no02JvFfWGSbPSOfAbv86oGApmJJCw+v/pPWDjwI6vbUeG5nmmlHPk37NasKtjpGMjSCI0Wu5bGUB\nl60swOnw8Kdfb/Xpouiwu2lq6CF/gu/FJbjd1L0YvKFxBkt1DYZtO1DOu8SvH4FcDq2//RmC2T9t\nyN7cQsMrr9H01hryv3EfGdes9tmvi1IzbU4WJ474LnyPFjUwf1l+SBZrwBSqMJRK3H19IRsaZ+g9\nVoLDYECblkZGdvAKT4HIzIlsfH9pGcadu/22d8+8mtONoXlI9u+sITMnjikzMwe3CdMXU7s/NG+J\nQxVDzZQbueychVjyJcto/Oe/Qo5uyNVq0q8+K03cWNsdsEYhOTWGGL0Gl8uLVqsiOz+BmfOy2fph\nOdUVvh41r1dg7atH+NoDS3xSA52dnXRs24GpvAKPxYJcpUKXlUXqlVcQN3NGQK1/XZSapLRoug3h\nFwqWHmulvKSVdM008pWlaD0W6hNn05AwA7FZAZzNpRYFcdgaocyceK65dfqYdahPTon275UTAilp\nMURFKMetjIoi86YbaXrzXz7bu4sOYG1oIDo/P6zzDi3KNWmSaIqfhjEmD1Hme70rzgnrn1sQ6rXb\nRzQ27O3tODoMiG43qrg4osflj2k/hc8Dm8XJri3+6Zmj0dRkQm+NYqh5IFermfLwzwa9uTl33o7H\nYkFwOFDoorA1N1P6s4d9xgguF3UvvsyUX/7c59rusffx9yNvUdLun8LUaeuh3HiaN068yx3Trmf1\npCvoc5h4r2IzO+r24RUij9r9bs/T3D7tOq4tuJJx3/4mJ3/ycz9xh1Bx9/ZS/ddnBr3AHpmSjtgJ\n9OrS8SjUyEUvUS4TaeY66pLm0hvA0EhIiuKOb8yn4W/Pju5YsX4AACAASURBVG5onIO1rp6cmA0Y\n86/0q0OzaJKoSlnCVOO+oGvzTGXl9JeXE1tYGHHuv1an5Bvfv4SUz2pTXy95h2Ppn5DMeJLbx6H0\nBL4P3SoHnRm19CQ18k6lkjum3zC4b/KMDFRRMty20I2gaQuzfGrRovPzKPjxf3Pqj38Kuj9V7r13\nk3LZJT7b5i7Ko2hXbVj9oNq1uaz7w3vc8/j9KLWRO0FEUQxLZfMMsTFKrC/9iZ7myPptROXlEj9r\nJtYwI6SSsREiGq2S/IlJfsXjp8sNfsZG94FDYXdsbd+8BVmyv4JNjM0Y0NA4F8HppO6Ff+DuN5F7\n1x0+++YvzfMzNjoNFprqesibEJyGurWxKWDu73CytiMx1IscEqKIceducu++k/jEKPLGJ9IYQo+N\nc4nECy243dS+8KL/Dq2OWnkuEHpB5r5PanyMjcMHw8u1bukWMLSbBqVqFRoNhf/z35T/+rchNQsc\n/93vDEq9Cl6Bze/7ew81WiX3PbiUmADiBbfdN4+3Xjzo1wPF7fKy5qVD3P+9ZcRrPNT9/SW6Dx32\nSzWw1NTSuWcv2sxMxn3jPhIXLhjc5/UIbN9QEZGhcQZBhLa4Qtr1k9C5TNg0oRmhUTFqbr9//piG\n0eWKwL1ygsXrFbFZnETFRPbiy7juWto+3IDHbPbZ3vz2Oib/7CdhnVN+TlFvc9xkTicvGjZF0KtQ\n0xI/lfbYiczs2EWifcDb3F9aRvKypT5GqOB20/XpPto3bR3sDXAGZWwMqVddSca1q4f10p8vnN3d\nGLbtoK/kOO5+EzKlAk1qKimXXULysqXDGkHHjzTjDTOK2BI3mTRLw+D/5VotU3/1C+Kmn32/yGSy\nAYGSz2qy9FMmk7ZqBYZtvr0reo8W03Pw8GC/HYOlk0d2PUW3bWTnhdVl49WSdexuOEirqR2XN/Lm\nuWdwepy8eeI99jQc5Dvz7iF+9qyAaoeBUOpjQRCHlYz1oKAueS5t+kl45UNSjaOhKSFwnaZWp+Lu\nby9CbGuiuyj0tB7zyRMsvWMaH7WpsQ9x0nfoJxLn6CTbFLznvewXvwagJXUh6KeGPJ8zaBXCoKHR\n2NfCx6c/ARl0ZdTRnVaPvjcdfW8aSvfAs8atcmJK7MAUbwD5gDHxbsVmLstfTHrMQETCK/PSnlNJ\n8qnJgT90GFxqG8a0WmCGz/akRQuY9sivqH762YDy82dQREUx7pv3k7bSXyEyOlbDrV+dy9uvHAkr\nEtRABm88/gFf+9VXIhJD8Xi8bFh3gtLi8CvWMxr24+yLzNBQxsRQ+JMfRdTUU/HII488EtEsvkSY\nTCbeeOMN7r//fvT64fsAOO1uP4k6m9XFgkt8owP1L7+K0xBefqTHZKIlbjLGLl9vZqqlkSRbcBee\nqawcXXa2T051rF5LdYUBi8n3vB63l6mzMoeeIiDtH2/yk9yLHpdPThhqJR2bt2JrCv9GUMfHk7xs\nCb3dNvbvrPEpvA4WjVbJTXfODrs/Ssu77wdsbihecy+n2sJbIFhMTtpbTdSd7qS0pJWKCHqiyIBJ\nU88uqrSpKcQWFtBz+EjQyjQxEycMLk6O7G/gxFF/4+eq66YMmxKlUMiZPCOD2lNGPw+9xy1QdbIV\n4f1XcFSVj1jb5DGb6dq3H5VeT+ykiZhNDta8dCigpn1EyGQDKTwhct1tM8gbP/aNz5KSozla1BCW\nyond5qbiZDvjJiWHrGJ3LnKVCgTBL03F3tJK4qKFqBNCF65QaLW0friBltgCTqcuGdbQOBdRrsAY\nM44Eewdaj5XuooN0frofuUpJVG4Ort4+yn/1Gzq2bMPV4+98EFwuzFWn6Ni8FU1KMtHjRk4hFb1e\neo4U0/Dq69S/+hpNa9bRvnETfSdLUWjU6DIzRn0JeyxWap57nppn/oaptAxXdzceiwWPyYyjvYOe\ng4fp2LINuUpFTMEkv6jYhnUnAqbrBYNDFUO6uRaV4EIRFcW0R35F3NQpo46LnTIZw46dAzUK52Cq\nrCJt5QpcePnNrv/DGEIj3D5HP14xvGfiaJicZo6WfkrBrhpko9wmHgVUjtcifOsWLv/GfyK4PVhr\na32ePS6FlpLMq+kMEGUbCYVCxj3fXkRmTjyNb/4LW0NDWN/HVl5KrLmNjtgJfvdFT1QmsY5uuqOz\naI+dgDEmn15dOl6ZCp3bFDDq4VJoadJPxqEaXjFzNGK9ZhasHFjcryvbSF3vOXWbMnBGWTAldtCX\n0kpfSiumxA6cOotfHxGX10WWPh2T08zu+gMc7D+IIBOIMQWXeuRWOWiYfJhWVzPXTFqOQu77+2jT\nUsm47hqix4/DY7XiNpkQ3W7kGg3R48eTc9ftTPrPh4gtLBzmEyApJYas3HhOlRt8MlmCxeRWUVNc\ny9QF+X4NY4PBanGy5h+HqK70T4EMlgxTNRO6jwWOgsXH4nW6Rm22Z4tWkvuL/yZl4oAxGOw6eSiS\nsXEOwf4RY2I1HNrr21/CbnMzfXamj/ew4dU3/B7UoVDuzMCl8F3w5PRVEOMKPlpibWgk47prBl9e\nMpkMuVzG6SFFYt1GC+MLkxFFEaVSPmyxuyiK1D7/op93M+P6a4mbFrrHxLhrd0SFi5q0NORT5vD6\n34rCTjMRBZEJk1OIiw9drcfe3sHpPz/lJwcXlZ9Ha94yjB3mYUaOTnenhY5WE50RnAMGrs1Fl/rq\n42vT00m5/FKQybC3to2a42o+XU3qlctxeuWse+2In5c1JT2WG++cNaLBplQpmDw9g9PlHdhtvkaO\nyyVgkCWRamlAKY4ecektPkZ/bBbr362hyxheGh6AIPMgykRkY9TfVKtTUTh97HuIaLQq4hJ0VJUN\n3/BuJBx2NyeLW0hJiyU5NXyd/Ohx+XRs2+H3XHP39ZFy6SWBB42AQqul5VgVR3XzgjI0ziDK5PTo\nMsnur0KGiMdspvdIMR1bt9G+acuIHs1BBIGeQ4fRpKQQMz6wwdF34iTlv34Uw5atONraERwORI8H\nweHA0d5B174iDDt2osvKRJcZ2Fnj6uun7OFf0X+idFSRkL5jJbj7TSTMmzv4zBYEkS0fhK+yBJBo\nayNOIzDtt79GX1gQ1BiFRoM6Po6eQ4d9tnvtdkSPm4MxfexvOhrRvM6gVWpI0iVgdQfX60I2TBLR\n5ccspPb4Pj8EGdRnqOmPVWBIUlI+Qce2JXqq87SU9dUhKhVcds1dJC1dgqO9A0dHBwJySrJWYdaG\nnnc/034cTfEOWj/4EFME6lgw0GhOKTjpHio7K5NjiB1Pd3QOZm0KFk0SJm0qxthxtOkLEGQK9M4u\n5Ii45WoaEmZSnn45dnV4MvdnGOduYOo1S3F5XDxz6LWw0+Dqe5vZUr2brTV7KDMORGhssb04tRai\nLPEohOEFa8xxRpomFePW2nF6XUxIzCNL7//MlcnlyNKSqchXUzYrkfrFuRiXFSAsmcmEWYuJ0o3+\nHExMjmbWghxkShGDoR9hiG8uKkHBZSsK0XjtdPX6O+4sDjhV0kzhzCy0uuBFeDo7zLzx/AGM7cO8\n+88Ueo9Abm8ZhV2H/e8UuRxWLuG56Waq8jXIREgweVEOsaf6o+UcnRbFtkWxHLDWsCh7NjHq6LCN\nDSmNKgziEqJIz9TTMURu9VS5wafPhjcCQ8MjU2JR+v+QcY7QcvccbW30nyz1kS2cPieTbR+V++Qj\nCoLIK08P1GEMeKHTmb80n9zxiT5eNltDY0DjIPmS8DrLBipkD4V+Qcfm54oi6kMgCCLrXjvKd/7r\nUvQhGByiKFL3wov+C3WZjIkPPkDpzvAVNsYSqyWwIaFJSWHcN79O7r13D3QH7+oGUcTrctL42j99\nFkaC00nTv9ZQlepfRwSw+pbpQfVxiI7VcO9/LOa1Z/djGiKsYFfrOZ65ksmG/Rj04+nTpuFWaAbz\nozPNNSRZm5Eh0hw3hZqd3YiywJ/plbtHfGEBOHRmGicdRVB4SW4fR1p7HoIsskdi2bFWVt4wdUyK\nuocyc34OHo/Ax++WhhXadzm9rH3tCFcGUDwLFoVOR9YtN9E4RHWt59BhLLV1ITd9M+zYyakeDWJc\n6MaeUxWNMSaPdMtZx4/HFLphXvu3F4gtmOQnbtG1bz+n/++vo+rKu7q7qXz8D0z6/oOkXrncZ5/g\n8VD52BMhRW87Nm9BnZRIzu1fAQaizpEii9Ez/Rc/JHpcfkjjUpZfgeGTXX59Hdo2fMxh5TiIMCVd\npVBx9cTLuXnyKqLUUbx5/F221u4dcRG7MHs298y4ifcrt7Kn4aySWUK/h8n1/jUJJYU69s0d3pv/\nfuUWJiXlMz9nFlN/80tqX3+dIztrMGlDF65Qeh0ktRwPu4g2ENn9VZi0KQMRjnMZ5v51KaOoS5pL\nV1Q2ifY2WuKm4BmDXhRywUOWayCi3WHpxOmJXG58KP1J7fQndhDbm0pCVzZqRxRyQYFX6caq76Yn\npRmXzveva7D4r4msLhvryjayu/4Ado//NbG29CMWZM/mnpk3D6ZzBcLtdfNh/Sa22vfinOpCZ9Oj\ndGsQZSIetQOHzsyp/lhuvupq5rhFSpr8f5PuPhcv/3UvX/3uElIz9HQazJQWt9DbbcPrFdBFqRk3\nKZkpMzJQKOXUnurknTeODlsvkt1XSXZfJW1xBbTpJ/n8tgrBTbqphmzTqYBOaV1WJsnfvY+fV/8T\njxf69Ep2L4hl/+xoMrrc6BwighzMUQo6kpWD15jLaeapopd4YuXPhv1bjYZkbIRJwbR0P2PjdHkH\ny66cOPh/ZVQUrjAL1czaZD/dY7XHjtYTuhe3c++nPsaGSq1k0tQ0yo4FTsfyegXKj7dRfryNcZOS\nue2+eYOLp0CF4dETxo+qTT4c+qmTQ+ocfC5mdSIlPVm4Ff4PPaVSjlanCjraYTU7WfvqEb7+0LKg\nQ55d+4roO+7fhTVt1UpiCwtgZ+i5uucDhWLkRaVCoxlUKzmDvbkV4xAJ2Op9pZRk+8ucTpudy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wfJU6aur9TT5vbAodJxM2YVcGXgno00bx8SlLQEHmWNLuudvDiHGI9cf6EF2+r79pZmAtYWPp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"text/plain": [ "<matplotlib.figure.Figure at 0x7f55e43b0c10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_t = df[(df.Target == \"USA\")].pivot_table(\n", " index=\"Year\",\n", " columns=\"QuadClass\",\n", " values=\"TotalEvents\", aggfunc=np.mean)\n", "\n", "ax = sns.pointplot(x=\"Year\", y=\"TotalEvents\", hue=\"QuadClass\",\n", " order=df_t.index.sort_values(),\n", " data=pd.melt(df_t.divide(df_t.sum(axis=1), axis=0).reset_index(),\n", " id_vars=[\"Year\"],\n", " value_vars=[1, 2,3,4],\n", " value_name=\"TotalEvents\").assign(\n", " QuadClass=lambda x: x.apply(lambda k: QUAD_CLASS_NAMES[k.QuadClass], axis=1)\n", ")\n", " )\n", "\n", "\n", "plt.xticks(rotation='vertical')\n", "plt.ylabel(\"Proportion of event types\")\n", "plt.xlabel(\"Year\")\n", "plt.title(\"GDELT events between India (IND) and United States (USA) across years\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f55e4748dd0>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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4ffs2Pv/8c3z99ddo3bo1kpKSsGXLFiQlJUnqPZQhvqN64cIFuLi4wMHBAXp6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"text/plain": [ "<matplotlib.figure.Figure at 0x7f55dfe02f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_t = df[(df.Target == \"CHN\")].pivot_table(\n", " index=\"Year\",\n", " columns=\"QuadClass\",\n", " values=\"TotalEvents\", aggfunc=np.mean)\n", "\n", "ax = sns.pointplot(x=\"Year\", y=\"TotalEvents\", hue=\"QuadClass\",\n", " order=df_t.index.sort_values(),\n", " data=pd.melt(df_t.divide(df_t.sum(axis=1), axis=0).reset_index(),\n", " id_vars=[\"Year\"],\n", " value_vars=[1, 2,3,4],\n", " value_name=\"TotalEvents\").assign(\n", " QuadClass=lambda x: x.apply(lambda k: QUAD_CLASS_NAMES[k.QuadClass], axis=1)\n", ")\n", " )\n", "\n", "\n", "plt.xticks(rotation='vertical')\n", "plt.ylabel(\"Proportion of event types\")\n", "plt.xlabel(\"Year\")\n", "plt.title(\"GDELT events between India (IND) and China (CHN) across years\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f55df90b3d0>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JCgoiNTWVuLg4ufvQhzr/tLW1cXFxkUasO3HiBOPHj5cGTvgvE302PnK9evWi\nYcOG+Pv7F/gWXGXDcBbm5MmTBAQEYG9vL9epqbxr1aoVGhoa7N+/XyEHODQ0lEWLFsnd3OrXr0/D\nhg05efIkwcHBqKury/VlUFdXx8nJiZs3b3Lz5k257SUkJDBjxgy5kZmKSla7kruMBw8elEbLyr2c\n7AGwKDflmJgYbty4ITdNNhSgvb09gNRStW/fPoX1165dy6ZNmwosJ7xt3YiIiCAsLIyGDRtKD1ot\nWrQgLi6OvXv3kp2dLTcijuxz8w4ZCG/zYmXTy+qYlwXZcLOnTp2Sm37hwgUprSg/smOaNwVh48aN\nQOE177q6umhqavLixYtilVkZLy8v9PX1mTVrVrE7LMbFxTFlyhS0tLSYMmWKwnxZ+QpL1cjMzMTI\nyEjuoS4hIUH6XRSW06+M7PuR9VOSCQ0NLVJ6nYaGBtOmTSM+Pp5vvvlGLoUit/j4eKkzeWGMjY2x\nsrLizJkzCpUDDx8+ZObMmdIINfn9RjZv3kxGRobcd5XfuVoSN2/eJD09XS5AKypl5ZCNhJd3P44c\nOSI96JZlR1lDQ0OlQ9DWrVuX/v37c+7cOaWtZzJ37twpdlCfkpLC7du30dPTK1KfED09PSZPnkxc\nXJzSzvlQtHMpv2N95coVqQ+T7Ltp0qQJampqHD16VO58ePz4MQ4ODvzwww8FltnR0RF1dXWOHj2q\nME9WSSDrlL9ixQqpAlNGU1NT6tsRHx9PXFwc3377rcKohtWqVaNJkyakpaUppN/lZW5uTsuWLQkJ\nCWH37t2YmprKpQyX1fmXnzNnzuDl5aUwDLCLiwsVKlR4L8H2hyZaNj5ympqarF27lnHjxjFmzBi6\ndetGhw4dMDY2Jikpibt373Ls2DGuXLkijQ2dV0xMjFR7lZWVxZMnTwgNDeXYsWPY2NiwevVqpak5\nDx48KLDWS0NDQyGd4u7du3Ljtstoa2tLI5i8K0NDQzw9PVm2bBkeHh4MGzYMLS0tLl68yLp167C1\ntVWogXR1dWXhwoXExsbSpk0bhVzYKVOmcOHCBUaNGsU333xDrVq1iI6OZsOGDURHR8vVeBSVrEb1\nxIkT1K9fHzMzMzQ1Ndm2bRuxsbG4urpiYGDAq1ev2L59O2pqavmOA5+bubk5U6ZMwcPDA3Nzc65d\nu8aOHTukdzjA27zWNm3asG3bNuBtbvKbN2/45Zdf2L9/P2PHjpW2J8tJ3bNnDwkJCdja2mJiYkKb\nNm1Yv35wxxbEAAAgAElEQVQ9J06ckEv5adCgAfr6+mzZsoX69etLrSgA/fv358CBAyxZsoSkpCQc\nHR15/fo1e/fu5dSpU3KBVlkc87Lg7OxMrVq1pBQ3Ozs77t27h7+/PzVr1uTBgwf5ruvo6MiOHTtY\nsmQJ48aNIz09ncDAQCpXroy5uTl///03YWFh2NjY5Fsr2rBhQ27cuCGNbFVSsqEcJ0+ejIqKitTP\nJreUlBS5cz4hIYE//viD3bt3k5mZycqVKxXOefi3Y7gsNTE/Dg4OnDp1irVr19K8eXMePnzIunXr\nGDRoECtWrODUqVM0bNiwWANQ9OrVi7Vr17Js2TJUVFSwsLCQ3gpd1Dz1zz77jNjYWBYvXkz37t0Z\nNGgQVlZWqKurExsby4ULFwgODiY7O1tuFJqCzJw5kxEjRuDu7s6kSZMwNjbm3r17rF27lvT0dOn9\nNg4ODqiqquLn50eFChXQ0NAgJCSEJ0+e0KJFCyIiIjh69ChNmzZVek3J26E6r8ePH8t9p8nJydy+\nfZtNmzaho6PD+PHji7Q/uRkaGqKmpsb58+c5cuQI1apVw97engoVKrBt2zZq1KiBvr4+Z86cITw8\nnO7duxMcHMzevXvl3lNSmho2bEhwcDAPHjxQSFmZNWsWSUlJ/Pjjj5w/fx5XV1dq1qxJZmYmjx49\n4syZM5w6dQoTExOlxyMhIUHuGKanp/Po0SN27tzJ8+fPmTt3bpHTzz7//HP27t3Lrl27cHV1VThn\nrl+/jqmpqdI+YjL16tXDyMiIoKAgGjVqxCeffMLly5f5+eefcXd3Z9u2bRw6dIjevXtTq1YtRowY\nwYYNG5g+fTr9+vUjPj6eNWvWULFixUJT9YyNjfHw8MDPzw8vLy8+++wzVFRU+O233wgKCsLNzU1q\nPY+Pj2f9+vU8ffqUVq1aoa2tTXR0NKtWrcLIyAgHBwd0dHS4dOkSR48eZfz48VLLw40bNzh8+LDS\nTt3K9O/fn7Nnz0qtB3mfYcri/MuPrq4uQUFBPH78GHd3d4yNjUlMTJSGLf+YMkdKSgQb/wHGxsbs\n3buXAwcOEBISwnfffcfr16/R0dHB2NiYJk2aMGnSpHz7L/j7++Pv7w+8rZHS19fHysoKHx8fevXq\nlW/Tcd4a+LwqV64sN2IRIJ3AedWuXVtpzUhJeXh4UL16dbZt24anpycZGRlUr14dDw8PRowYoXDh\n6d69O4sXL+bx48dyL/2RqVOnDvv27WP16tUsXbqU+Ph49PT0aNmyJcuWLSv2OOrwtkZt8ODB/Pzz\nz0yfPp2JEycyfPhwNDU12bp1K15eXiQnJ2NgYIC1tTXbt2/Hzs6u0O3WqVMHDw8PlixZwq1bt9DQ\n0KBDhw7MmjVL2m8VFRXWrFnDhg0bCA4OZs+ePaiqqlK/fn0WLVokN+JHjx49OHr0KKGhoYSFhbFu\n3TpMTEywt7dHV1eXFy9eyD2YyvptnDx5UmHkEG1tbbZt24afnx8HDx5k3bp1aGhoYG1tzbp16+RG\nFiqLY14WNDU12bRpEwsWLGDt2rXk5OTQoEEDqf9BQcFGx44dmT59Ojt27GD06NGYmJjQu3dvxowZ\nQ1BQEAsXLmTSpEls2rQp3wd1Z2dnrly5QkREhNK+VcXRrVs3Dh8+rNAKIHPr1i25dAQdHR3MzMzo\n168fQ4YMUZqSlJ2dTVhYGHXq1CmwHxDAd999h6amJps3b2bDhg1YWVkxb948mjdvTmRkJGfPniU+\nPl7p2Pb5qVKlClu2bGHBggUsWrQIVVVV7O3t8fPzY+rUqYW2PskMGzaM1q1bs3XrVvbs2cOzZ8/I\nzMzEwMCA+vXr4+npSe/evYucStW8eXN27tyJn58f8+bNIzExEQMDA9q1a8fYsWOlmtSGDRuycOFC\n/P39mTBhAlWqVJFSf/744w/u3buHl5cXixcvpmPHjgrXlMKCjbVr17J27Vrpb9l9o0uXLri7u5co\nl7xixYpMmjSJ9evXM336dAYOHMjMmTP58ccfWb58OVOmTKFSpUo4OzuzadMmYmJiuHr1KosXLyY7\nOxsLC4tif2ZhnJ2dCQ4OJiwsTGGfNDU1WbZsGa6urgQGBrJq1SpevHiBuro6VatWxdbWliVLltC5\nc2el98PffvtNLrNAU1MTIyMj7Ozs+Oabb6SX9BWFiooK3t7euLm5MWfOHAIDA6XPvHXrFrGxsdIQ\nzvnR1tbGz8+PBQsWMHfuXDQ1NaX0VlVVVS5cuEBAQADp6elMnz6dKVOmYGpqyu7duxkxYgQaGho0\nb96cJUuWSCPBFWTixIlUr16dnTt3Su+tql27NjNnzuSLL76QlvP29sbc3JygoCB27txJZmYm1apV\no1WrVnh4eEitP9u2bWP16tVs3ryZ58+fo6amJt2/i1rJ5OLigpGREc+fP1dayVoW519+GjduLF3T\nvv32WxITE9HX16d+/fr4+/u/83X7Y6CS8y7DtAiCIAgfVExMDJ07d6ZNmzasXr36QxdHQWhoKF99\n9ZXU2iYIH0JycjKdOnXC2NiYn3/++YOOmFhSPj4+bNu2jT179hSp4un/WXZ2Nl27dqVWrVpSZarw\n4Yg+G4IgCB8xU1NThgwZwvHjx0utM29pyc7OZtWqVRgbGxc4gowglLWKFSsyYcIEbt68ybFjxz50\ncYotJiaGPXv20L59exFoFMHPP//M/fv3GTFixIcuioBIo5Lz5s0bbty4Ib2ARRAE4WPQq1cvjh07\nxqxZs/jxxx+LPWpOWfn555+5e/cuCxcu5NWrV0VOWRKEstCyZUsaNWrEggULqFWr1ju9yO99mzNn\nDjo6Onh4eOT7wtX/d+np6URFRXH9+nV++uknOnbsSPXq1cXxKkVZWVnExcVhbW0tDQ9cFCKNKpeL\nFy+K2jdBEARBEARByMeOHTsK7BSfV/mo/ionZKM77NixQ+nbMAVBEARBEATh/9HTp09xd3cvcDQ0\nZUSwkYssdcrExER66YsgCIIgCIIgCG8Vt6uB6CAuCIIgCIIgCEKZEMGGIAiCIAiCIAhlQgQbgiAI\ngiAIgiCUCRFsCIIgCIIgCIJQJkSwIQiCIAiCIAhCmRDBhiAIgiAIgiAIZUIEG4IgCIIgCIIglAkR\nbAiCIAiCIAiCUCZEsCEIgiAIgiAIQpkQwYYgCIIgCIIgCGVCBBuCIAiCIAiCIJQJEWwIgiAIgiAI\nglAmRLAhCIIgCIIgCEKZEMGGIAiCIAiCUKamTp3K4MGDi73eqlWraNOmTRmUSHhf1D90AT4Wr16/\n4fLtWOIT01BRUcFQX5umVsZUrKBRrO1kZGZx+VYsMS9SyMjMQldHE1uLqnxiVKmMSi4I70dWdg7X\n78bx8GkiaRlZ6GhrYFXLgDrV9T900QRBEATg5cuXbNq0iZMnT/L06VNUVVWpW7cuvXr1YsCAAair\nv9/Hwps3b7Jx40b++OMPXr9+jZ6eHo0bN2bUqFHY2tq+17IIZUcEG4X4+9ErDpyOIjzyCVnZOXLz\ntDTVaNvYjD5tLQoNFhKS0jh4JorjEQ9ISEpXmG9Xz4heznVpamVcquUXhLKWmpZJcNg9jp67T+yr\nVIX59WtUprtTHZztzVBVVXn/BRQEQRB48uQJgwYNwtLSEl9fXywtLcnMzOTcuXMsWLCAX3/9FX9/\n//cWcBw/fpzJkyczcuRIvLy8MDIy4vHjx2zZsoVBgwaxatUq2rVr917KIpQtEWwU4Jez/7D+QCR5\nYgxJWnoWx84/4NdL0Uz7ogktrE2VLhcVHc93m87z8nVavp919e84rv4dR3en2ozuZSMeyoSPQuyr\nFL7dcJ5HzxLzXebOw3iW77xMeOQTpn7RFC0NtfdYQkEQBAFg7ty56Onp4efnh5ra2+uwpqYmzs7O\nNGjQgG7durF9+3asrKwYMmQIoaGh1KxZE4Dw8HCGDx/OyZMnMTMzIy4ujgULFhAREUFKSgq1a9dm\n6tSptGzZEoD09HTmz59PaGgo2dnZuLq6kpPz78NUcnIys2fPxs3Nja+//lqabmZmxqxZs9DX1+fF\nixdK9+PatWssXbqU27dvk5OTg52dHXPmzMHc3Fwqq6+vL/fu3UNFRQUbGxtmzZqFhYUFaWlpLFy4\nkBMnTpCYmIihoSFubm6MGTMGFRXx3FVWPmifjezsbH788UcaNGjAqlWrCl3++vXrfPHFF9ja2tKi\nRQvmzp1LaqpiTWppOHb+Aev25x9o5JaekcXCLX9w5XaswrzHcUl4+4cXGGjkFhz2D5uCbhS3uILw\n3iUkpTF7XXiBgUZu5288Zcm2iwothIIgCELZio+P5/fff2fEiBFSoJGbsbExnTt35tChQ0Xanre3\nNy9evODYsWNERETQunVrJkyYQFJSEgAbNmwgNDSUgIAAfv/9d8zMzDh16pS0/tmzZ4mPj2fkyJFK\ntz9hwgQ+//xzhenp6el4eHjQqFEjwsPDOXXqFFlZWXh5eQGQkZHB+PHj6du3LxEREZw+fZratWsz\ne/ZsALZs2cKlS5c4cOAAV69eZeXKlWzdupXff/+9SPstlMwHCzZevnzJqFGjCA4ORlW18GLExsYy\nfPhwqlevzr59+1ixYgXh4eHSD6g0PU94w7r9kcVaJys7h2U7L/EmLVOalpOTg+/OyySmZBRrW0G/\n31MauAhCebLh4A1inicXa50LN59y7Pz9simQIAiCoNTDhw/Jycmhbt26+S5Tp04d/vnnnyJtb8WK\nFaxdu5ZKlSqhoaFBjx49SE5O5u7duwAcOXKEHj16YGVlhaamJoMHD6Z69erS+vfv30dHR4dPPvmk\nWPuhqanJ8ePH8fT0RF1dHV1dXdq3b8+1a9eAt8FIWloaWlpaqKmpUalSJby9vdm9ezcACQkJqKqq\noq2tLbV6nD17VnRAL2MfLNgICgpCTU2NwMBApVF2Xtu3b0dDQ4Pvv/8eS0tLHB0dmT59OsHBwTx6\n9KhUy3b60iMys7KLvV5CUjpnrjyW/r798BW3H74qURmCfr9XovUE4X14kZBK2LXHhS+oRNBv9+Sa\n0wVBEISyJbvmZmfn/2yTlZVV5GvznTt3+Oqrr2jRogXW1ta4uroCkJb2NovjyZMnmJmZya1jYWEh\n/b+KigoaGsUbYEfm9OnT9O/fH3t7e2xsbFiwYAHp6W/7wlasWJHJkyczZ84cunTpwrx58zh37py0\n7hdffAFA69atGT16NJs3b+bly5clKodQdB8s2Gjfvj3+/v7o6ekVaflz587RvHlzNDU1pWktW7ZE\nRUWF8PDwUi1bSR+iADYcvM4k39NM8j3NdxvPl3g7l2494+mL4tUaC8L7cjziYYnToR7HJXE96nkp\nl0gQBEHIT61atVBRUeHOnTv5LhMVFZVvy0dWVpb0/4mJiYwcOZIqVaoQHBzMjRs3CAoKkls+IyND\nIWsld6BTp04dEhISePjwYbH248KFC0ybNo2ePXsSFhbG9evX8fb2lltm1KhR/P7773z11VekpqYy\nfvx4pkyZAoCpqSmHDh1i69atNGnShEOHDtGpUyeuX79erHIIxfPBgg1zc/MipU/JPHz4UK4JDkBH\nRwdDQ0Pu379fqmVLKmbaU25pGVncjU7gbnRCsdOncsvJgT//EdG2UD5dv/tuwcL1u8o7/gmCIAil\nT19fHycnJzZu3Ci1AuT29OlTQkJC6NmzJ9ra2gC8efNGmp87KIiKiuL169eMGDECIyMjACIj5VPP\nTUxMePxYvuI2d6DTqlUrDAwM8u2vu2jRIqkfRm7Xrl2jYsWKDB8+nIoVK0rTcnv58iWVK1emW7du\n/PDDD/j5+REcHEx8fDwpKSm8efMGW1tbxo4dy/79+7GysipyXxWhZD6al/olJyejo6OjMF1HR4fk\n5OK3APTp00fh39ixY0ujqKUmMUXxgiAI5UFSaskDaYAk8dsWBEF4r7y9vXn9+jWjR4/mxo0bZGdn\nk56ezu+//87w4cNp1aoVX3zxBebm5mhoaPDLL7+QlZXF3bt32b9/v7SdTz75BDU1NS5fvkxGRgbh\n4eEcO3YMgJiYGABcXFwICgrizp07pKWlsXnzZuLi4qRtaGtrs3DhQkJCQpg2bRqPHz8mJyeHJ0+e\n4OPjw+7du6XUrNzMzc1JTU3l5s2bJCcns2vXLqmfyZMnT7h06RLt27cnLCyMrKws0tPTuXr1KlWr\nVkVPT4/x48czc+ZMaaSrBw8eEBMTQ+3atcvsuAsfUbDx/0hTDBEqlFMa6u926dAQv21BEIT3qmbN\nmuzfv5/q1aszduxYbGxsaNSoEcuWLWPAgAH4+fmhrq6OgYEBXl5eHDhwgCZNmvD999/j6ekpbada\ntWrMmjULf39/mjdvzvbt25k/fz5du3Zlzpw5HDp0iEmTJtGmTRsGDx5MmzZtiI6Opnv37nLladu2\nLfv27SM9PR03NzcaNWqEu7s7SUlJBAYG0qJFC4V96NSpE71792bIkCF06NCBR48e4efnh4WFBd27\nd6dq1arMmDGD+fPn07hxY1q3bk1ERATr1q1DVVWVH374gfT0dLp27UqjRo0YNWoUPXv2ZODAgWV+\n/P+fqeSUg56aNjY2eHh48NVXX+W7jIODA66ursyYMUNhep8+fZg2bdo7lyM6Opr27dtT22UGGjoG\n77y9d+UzpiWN6ht96GIIgoLlOy/x66XoEq//5eeN6OpYq/QKJAiCIBTLjh07WLhwIb/99hsGBh/+\nmUco/2TPybL3rRTVR9OyUatWLYWORAkJCbx69arAodxKomFdwxKv62htwsIvW7Hwy1a4Ope8XEZV\nKmBtUbXE6wtCWWrftEaJ19XUUKN1o+INdygIgiCUru7du6Onp8e8efNISkoiMzOz8JUEoQQ+mmDD\nycmJP/74Q67D0pkzZ1BVVcXJyalUP8uliXmJ1/2iqxXWdatiXbcqgzo3QEe7ZC9p7+JQCzXxFnGh\nnLKtV5XqRpVKtG4bu+pU0tEsfEFBEAShzOjr67Nu3ToePnyIo6Oj3Ju8BaE0fbBgIz4+nri4OKnD\nUEpKivR3VlYWy5Ytk3uzpLu7O2pqasyaNYv79+9z4cIFli5dSv/+/TE2Ni7VstlaVMW2BK0KHZvX\noIbJv0P5VtBSx71Lg2Jvx9hAh26tRGclofxSUVFhRI+GxV6vYgUN+nesXwYlEgRBEIrL1taWAwcO\ncP36dVavXv2hiyP8R32wYOOrr77CyckJJycn0tPTCQgIkP6OiYkhLi5OLm2qSpUqbN68mdjYWHr2\n7MmkSZP47LPPmDlzZqmXTUVFBa+hzahTXb/I6zRpUI1xfRspTO/hVIdebYqeTmWgp8W3ox2oWKFk\nL7sRhPeleUMT7IvRp6iCljreI1pgYlixDEslCIIgCEJ5Ui46iJcXeTu+pLzJYN3+SM5cjia/95dp\nqKvSrVVthnb7FHU15bFbTk4Ov5z9h+1Hb5FcwJCh+hU1WT7JmWpVFIf4FYTy5tXrN4xeeIK09KxC\nl639iR6TBjam9idFD+AFQRAEQSg/StpBvGQdCv5P6GhrMHlQEwZ3/ZRj5+8T8edT4hPTUFFRwVBf\nG6dG1enQvAZ6FQvOP1dRUaG7Ux06NK9B2NXHnLoYzYOnr3mdLP+ugcysbAz1tMtylwSh1Ow9cUdp\noKEC5I3NPfvbi0BDEARBEP4PiWCjCIyqVOCLrlZ80dXqnbajralOh+Y16dC8Jm/SMxk4O4TMrGxp\nfvKbTP6OjqdBTTEEnVC+PX2RzNHz9xWmd3aoSXRsEjfvyb8hPDo2CQuzyu+pdIIgCIIglBcfzWhU\n/zXamup8WlsxqLh6J07J0oJQvuwKvU1mlnz7hYa6KgM6WmJurKuwfPSzxPdVtDKRlZ3D1TuxBIfd\n4+dTf3Ps/AMefeT7JAiCIAjvg2jZ+IDs6hsRefe53LSrd+IY0NHyA5VIEAr3IOY1v156pDC9u1Md\nqlaugLmx4pC4Dz/SB/OUNxkEh/3D0fP3iXuVqjDfpm5VerSujYO1KSoqYqhqQfivSkhK48LNp7xI\neENOTg6VdbVoamUs+lgKQhGIYOMDsq9fja1H/pKbdvvBS1LTMqmgJb4aoXzaFvIXeYeV0NFW53OX\negCYV1PSshH78QUbMc+TmbvhHDHPk/Nd5nrUc65HPad9M3Mm9LPLd5AIQRA+TvdjXvPzqb8Ju/ZE\nLu0ZQFUFmn1qQu+2FjSsU/KXAQvCf524M35Adarro5vn5WaZWTnciHqezxqC8GHdevCSCzefKkzv\n3dZCGihBWRrVk7hkhRt1efYiIZWZa88WGGjkdvKPR/y45wpicD9B+O/4/cpjJvme4fTlaKXXr+wc\nuHDzKTPWhHHg9N0yLcvQoUPp2bNnvvP/+ecfLC0t2bFjR4k/Izo6GktLSw4dOlTibQDs378fS0tL\nnj5VvFfkFhsbi4+PDx06dMDGxgZHR0eGDRtGaGjoO33+x2LGjBl07NjxQxfjvRDBxgekqqpCo3qK\nLw8U/TaE8ignJ4dteVriAPQradKzdR3pb0N9bYWWuazsnCI/uJcHfoGRPI9XTJsqyK+Xovn96uMy\nKpEgCO/ThRsxLN1xsciVJAGHb/JL2L0yK0/v3r25ffs2t27dUjo/KCgIDQ0NunXrVmZlKE1///03\nrq6uREZG4u3tTUhICOvWraN27dp89dVXLFu27EMXsdSNHDmS/fv3S3/PmjWLPXv2fMASvT8i2PjA\n7JS8FO2KCDaEcujqnTiFPkYAbu3ro6P970soVVRUlPbb+Fg6VD95nkTEnwXXyOUn6Leye9gQBOH9\nSHmTge/uK/m+Xys/Gw7d4OmLsqlU6dy5MxUrViQoKEjp/MOHD+Pi4kLlyuV/1L+cnBwmT56Mqakp\n27Ztw9nZGTMzMxo1asTcuXOZMGECAQEBPHjw4EMXtdTk5OQQGRkpN01XVxcDg/+P0UdFsPGB2dWv\npjDt0bNEXiQUr1ZVEMpSTk4OW4/8qTDdqEoFuraspTDdTEm/jUcfSb+NY+dKfoO7/fAVUdHxpVga\nQRDet18vRRf4At78ZGXnEBJ+v/QLBFSoUIHOnTsTHBxMdrZ8a8vly5d59OgRffr0kabdvXuXMWPG\n0LJlS+zt7Rk5ciRRUVHSfFmq06+//oqTkxPffPONNC8lJYUpU6Zgb29Ps2bN+O6778jMzJTmHz9+\nnL59+2JjY0OzZs0YNmxYvi0uypw/f547d+4wceJEtLS0FOZ7eHhw+vRpatasCUBWVharV6/GxcUF\na2trnJycmDdvHsnJ/wZ2b968Yf78+bRu3Rpra2tcXFzw9fWVyi1LEQsODsbT0xM7Ozul+1bS43b7\n9m08PDxo3LgxjRo1olevXnLpYA0aNOD169d4eXlhafl2EKC8aVQvX77Ey8sLR0dHrK2t6dy5M5s3\nb5bmy/bh5MmTzJw5k+bNm9OiRQtmzJhBamr5fmYUwcYHZmygg2nVigrTRSqVUJ6ER8ZwNzpBYfqg\nTg3QUFdTmK6s38ajp0llUrbSFvmOfaauiz5XgvBRO3rufonXPR7xgIxMxZedloY+ffrw7NkzLly4\nIDc9KCgIIyMjWrduDbx9aB08eDDJycn4+/uzc+dO4G2/j8RE+UqfrVu3smHDBry8vKRpGzZswM7O\njgMHDvD111+za9cutmzZAsC9e/eYOHEiDg4OHDlyhF27dqGjo8O4ceNIT5d/UXF+Ll26hIaGBg4O\nDkrna2lpYWT0b9aHr68vmzZtYvLkyRw5coR58+YRGhoqV2YvLy9CQkL4/vvvCQkJwdPTk61btyqk\nYy1fvpzWrVtz6NAhJk2aJLdvJT1u2dnZjB07lqysLPbs2UNwcDAdOnRg0qRJ3LlzR/qOAGbOnElY\nWJjCPufk5DBu3DiuXr3KihUrOHLkCO7u7ixevJjt27fLLevr60vDhg0JDAxk5syZHDhwQCpreSWC\njXJAWSqVCDaE8iIrK5ttIYp9NcyNK9GuqbnSdWooCzY+kpaNpJSi3TDzk5hS/BpRQRDKh6TUDO7H\nvC7x+okpGTx6VjYVK02bNsXc3FyuA3dGRgYhISH06NEDNbW3FT+BgYEkJiaycuVKbGxssLKyYsmS\nJbx+/Vqh83efPn2wsrKSS+ext7dn8ODB1KpVC3d3dxwdHTly5AgA1atX5/Dhw0ycOBFzc3MsLCwY\nOnQoT5484d69oqWRxsbGUrVqVTQ1NQtdNj09nR07djBkyBC6d+9OjRo1aN++PZ6enoSGhhIbG8vT\np0+lAKNt27aYm5vj6urK4MGD2bNnDxkZ/16T7e3t6devHzVr1mTQoEFy+/Yux23Lli0sXbqUevXq\nYW5uzrhx48jJyeH8+fMA0nK6urpygZTMlStXuHr1KrNnz6ZFixbUqFGDIUOG0LVrV4Vgw87ODnd3\nd2rUqEGvXr2oW7euQopWeSOCjXLAXlmw8XecGNlGKBdOXXzE4zjFm+cXXaxQU1X+bgkzJX02omOT\nyC5uEvQHoKH+bpdFzXdcXxCED+ddKxsAklLffRvKqKio4OrqSmhoKG/evAHgt99+Iz4+Xi6FKjIy\nknr16mFo+O9wvAYGBlhYWPDXX/IVR59++qnC59jb28v9bWNjwz///AO8bXW4ffs2w4cPl1KNPDw8\nAEhIUGz9zm8/8qaC5efevXukpKRgZ2cnN93W1pacnBz++usvbt68SU5OjtJlkpOT5fp+5F3m008/\n5cmTJ0DJj5uqqioJCQl4e3vTtm1bKf0sKyuryMfkxo0bSssnO/a506RsbGzkljEwMOD165IHyO+D\neJlDOWBjYYSqCnKd0eIT03jwNJFapnofrmDC/730jCx2ht5WmF7PvDKONqb5rmdsUBENdVUyMv+9\noaRnZBEXn4qxQfl+CZapYaV3qpk0MVRMixQE4eOgpaGYFlpcmqWwjfy4urqyevVqTpw4Qffu3QkK\nCsLa2pp69epJyyQlJXHr1i2FoCEtLU2hVr1iRcXrVaVK8pVFFSpUkIKbo0ePMmnSJD7//HOmTZtG\n5YgTjLIAACAASURBVMqV+euvv5g4cWKR98HU1JTnz5+TmppKhQoVClw2KSlJaZlk5U5KSpL6XBS0\njLa2NvC2ZSE3HR0dKUWqpMft8ePHDB48GCsrKxYsWICpqSmqqqrFGhksKSkJFRUVhe8j9z7IyPZF\nRkVFpdxXTotgoxyoVEGDejWqcPvBK7npV+/EimBD+KBCzt1XOgTskM+sCnxjtpqqCtWNKimkIzx6\nlljugw2XpuYlHo2qorY6LRqalHKJBEF4X/QqaVGxgkaJOojD2xf9mZZhhYOZmRnNmjUjODiYtm3b\n8uuvvzJ9+nS5ZXR1dbG0tGTlypUK6+d9UFUmJSVF4W8dnbfX7V9++YVatWrh4+Mj3QNk/RKKqmnT\npmRlZXH69Gm6du2qMD87O5tdu3bRp08fKTjI22dC9nelSpXIysoqcJncAUbefUtOTkZPT09ariTH\n7dSpU6SmprJixQqMjY2Bt608udO3CqOrq0tOTg5JSUlyQZMsCKlUqRJpaWlF3l55I9r7ywkxBK5Q\n3qS8yWDvCcWbiK1FVaWjqOWltJP4RzD8bQtrEwz0FEdIKQqXZjXQ1hJ1OILwsVJTVcEln75oRdHs\nUxP0K5Xs+lFUvXv3Jjw8nKNHj5Kdna1Qg25jY0N0dDRGRkbUrFlT+peZmSmXIpSfS5cuyf198+ZN\nLCwsgLd9RKpUqSJX2STr/FzU2vWmTZtibW3NihUrFAIEgI0bNzJ//nzu3btH7dq1qVixIpcvX5Zb\n5urVq6iqqtKwYUMaNmyIqqqqwjJXrlxBV1dXGtVK2b7duHGD2rVrAyU/brKgokqVKtK0/I5JfsfI\n2toaQOk+WFhYFNoCVN6JYKOcsKunGGzciHpRZqNaCEJhDp2J4nWyYu7xkM+sirS+ebWP810b6mqq\nDO3WsNjrVa6kRd92FmVQIkEQ3qeujrVKvq6SocBLW5cuXVBTU2PFihVK363Rt29f1NTUmDp1Kjdu\n3ODhw4cEBATQs2dPqcNyQa5cucKuXbt48OAB27dv5+zZs/To0QN42w/ixo0bnD59mvv37+Pj4yO1\nDFy9elUu3acgS5cuJTk5mf79+3Ps2DGio6O5ceMG3333Hb6+vsyaNYuGDRuiqanJkCFD2LFjBwcP\nHuTRo0ccO3aMVatW0atXL6pWrYqxsTHdu3dn1apVnDx5kkePHrFv3z527tzJ0KFDUVf/twLo8uXL\n0r7t3LmTiIgIevXq9U7HzdbWFng7ild0dDS7d+/mzJkzmJub8+eff/L8+XN0dXVRUVEhIiKCW7du\nSWlpMvb29jRp0gQfHx/Onz/PgwcP2LhxI8ePH2fEiBFFOqblmaiCKycsaxqgranGm/R/g4v0jCz+\nuv8SWwvFQEQQylJCUhoHzkQpTHe0McWyZtFeQmRu8nG2bMDbVKpnL1PYeaxoY8dXrKCB98gWGOp/\n3LVPgiC8bZX9rGUtjhTznRlNrYxpbFl4q++70tHRoXPnzhw4cECuY7iMoaEh27dvZ/HixQwePJiM\njAzq16/P8uXLcXJyKnT7X3/9NWfOnGHx4sVoaGgwbNgwBg4cCLwdBvbu3btMmTIFLS0t+vbty8yZ\nM0lMTGT16tXo6Ogo9J1Qpnbt2hw6dAh/f3+WLFnCs2fP0NfXx9ramm3bttG0aVNpWU9PT9TV1Vm5\ncqU0klWfPn34+uuvpWV8fHxYunQpc+fO5dWrV5iamjJ+/HhGjx4t97mjRo3i4sWL/2PvvMOiurb3\n/84MMPSOgFIVRKQJoqJiNKIxGoyI5quJVyUkV1M1apIb8cZcFVPsBm+iMXpNDBoTY7ChokZirFhA\ngoL0Ll3aUKad3x/8BhnOBqbCDJzP8/A8cGafOXsOMLPXXut9F7Zs2QIdHR0sWbIE8+fPV+q+BQYG\nYsWKFThy5AgOHDiAiRMnYuvWrYiLi8OuXbuwceNGfP3114iMjERsbCwSExMRFxdHe55vvvkGX375\nJVauXAkejwdnZ2ds2rSJ+DvWNliUpqtKepHi4mKEhITg8uXLcHBw6PXrb/j+Fu6ml0sdeyXEHUtm\n0d0iGBjUyYFTaYjrFGywWUDMh8/DyU42HVHBk3q8t+2K1DEjA10c3TSzW72HJnEpqQDfxaWhuVXY\n5RjXwab4cNFome8LAwOD5iMUibHl8F3c/PuJTOM9nC2wcdl4GOrrqnlmDIogWd9t2bKlPZPBID+K\nrpOZMioNgmSBy+g2GHqbyqfNOHs9j3Z8ymhHuRbUg22M0NkZl9csQG2D9ojcpo11xsLpw7sd89qM\nEUygwcDQz9DhsPGvJWOwYPpwGHC7dpfS4bAxc7wLot+awAQaDAxdwJRRaRAkkXhOcS3qeXyYGvXc\n/IaBQV6EIjFuPyzD9QelqKptBkVRqKlvkbKsBQAdDguvzRgh13Pr6nBgZ2WE0iqe1PHC8gZYmPbs\niKIplFU3dft4/pN6BHl3bQPMwMCgnXDYLPzjRU+ET3HDlXvFuPagBNV1LaAoChYm+hgz0hYvjHNW\nuyCcgUHbYYINDcLR1gSWpvqoqX8mHKIo4O/sKkz0G9yHM2Pob1AUhfgb+fjlUqbU31tXvDjeRSHL\nWkdbE1qwUVzeAD+CIYKmUlzRveAxv1SzmykxMDAoh6G+Ll6a6IqXJrr29VQYFMTBwQGPH9N7RjH0\nDkwZlQbBYrG6sMCt6IPZMPRXxGIK3/6Wir0nUmUKNNgsFuZOVsxliWh/28PiXdMoruhe1J5XKluH\nWAYGBgYGhoEIE2xoGCTdRgqj22BQIUcTHuPczXyZx4spCj/Gpyt0LUdb7bS/ldDYLMDTHjQmT6p5\naOlGQM7AwMDAwDCQYYINDcOPEGyU1zThSadSFAYGRaioacIvl+RPJf+ZXIy0nCq5z9PWxn4SSnrI\nagBtpY75ZUwpFQMDAwMDAwkm2NAwLEz04WJPd7ZJYUqpGFTA+Vv5ECtodk1yqOoJh0H0YONpQysa\nmwWKTaKX6UmvIYHRbTAwMDAwMJBhgg0NhKzbUF8pFV8gwsPcalx/UIqbfz9BdnEtmPYr/Q+xmMLF\npEKFz7/59xM0NNE7ineHAVcH1ub0RnfFWpLdKKmULdhgdBsMDAwMDAxkGDcqDWTUcBtaQ7XU7CqI\nxBQ4nRsXKEFFTRPOXM/DpaQCNDRJ7zQPsTHCrAmumDbWifEO7yfU8/hK9bgQiSmUVDRihItsHcQl\nONmaoKq2WepYYXmD3M/TF5AyG8OdzJFZWCt1LI/JbDAw9GvqWxpwtzQVNc21EFMUzPVNEGDvA2sj\nzX8fY2Doa5hgQwPxGmoFHQ4bQtGzXge8ZgGyi57Cw1k1b2x/pZRg19H74HfqpyChpJKH/SfTcPJq\nDta/EQRnQmkXQ98hEIpwPfUJUrMqUc/jQ1eHDXtrIzw/2pGokwCAVoFI6eu28OUXQjvYGuP+Y+ky\nQG3RbZCcqCaNGkILNvKf1IOiKK3pjM7AwCAbhbUliMtIwK2i+xCKpd//WKxjGG3vg5dHTMcIG8Uc\n+xgYBgJMGZUGoq+ng5Gu9KAiJUs1pVTXHpRg6093uww0OlLxtBlrv7kuczkJg3oRCEWIPZ+BiI0J\n2B57DxeTCnH7YRmuPSjFr5ez8M6WP7Du2+vIyK+ROq+smofjf2QqfX1FslyOBN2GrFqIvkQoEhON\nGYK87aHDkQ4qmluFKK/pvvkfAwODdnGj8C4+ufglrhUk0QINoK1f0d3SVKz/YztOZ1xS+3wWL14M\nDw8PnD59mvh4dnY2PDw84OHhofa5dMXUqVOxbt06mcefOHECHh4eKCsr63ZcRUUFoqOjMW3aNPj4\n+GD8+PGIiIhAQkKCslPWCj755BNMnz69r6ehMEywoaGQdBuqsMCtqGnCziP3IY8ko6GJjy9/uAOx\nospiBpXAaxbg03038fPFx6jnda2dSM2uwtpvriHxXhEe5lbj80NJWP7FJZy/WaDU9Q24HDh1kTXp\nDlKmpVALMhvlNU0QiqT/5vX1OLC1NCS+JqaUioGh/3C35AF23zpIDDJIHH7wG85nJap3UgAMDQ0R\nFxdHfOzkyZMwMKBr5HoiOTkZU6dOVXZqAIDjx49j7dq1KnkuCVlZWQgLC0Nqaio+/fRTnDt3Dnv3\n7oWrqyvef/99bN++XaXX0wTeeOMNnDhxov3ndevW4dixY304I+Vgyqg0lFHDbWi9DTLya9DcKoQB\nV/FfW/yNPJkyGp3Jf1KPlMxKBIwYpPC1GRRHJBLjix+S8DC3WqbxQhGF7Ufuq3QOU0Y7Ql+Bvz3S\nwrzyaRNa+ELo62nuWxBJxO4wyBgsFgsu9qa04CK/tA7jfex7a3oaScGTemQWPgWvRQh9PQ5c7E3h\n4WzBlJepGL5AhGsPSnE1uRgVT5shFothasTF6BGD8MI4Z1iY6vf1FLWaJkEz/nv7B7mNUn5I/hX+\n9l6wNaZvFqqKsWPH4urVqygvL4etrW37cYqicObMGQQGBuKvv/6S6zkfPHigsvlZWqpWw0JRFFav\nXg17e3scPnwYXC4XQFtHcD8/P1haWmLv3r2YP38+nJ2dVXrtvoKiKKSmpuKll15qP2ZiIv9GnybB\nZDY0lKFDzGFiKF2yIhRRMi82SQiEIiTcVtyNKP6G/NanDKrhyr0iPMiSv8+FqmCxgJcmuCp0rqmR\nHsyM9aSOURRQouGlVKRSL4mVr+tgM9pjeU8GZmZDLKZwNbkYH8f8hfe2XcHXv6TgwKk0/Pf4A3wU\n8xfe3XoFZ6/lQiBUXjM00KEoCqeu5iBiYwJ2Hr2PexkVKCpvQEklD+n5NfjpfAZe35SAXT/fR1OL\ndthLayJ/5SeBJ2jueWAnRJQYF3PkW+jLi5eXF6ysrHDq1Cmp40lJSaisrMSkSZOkjlMUhX379mHa\ntGnw8vJCcHAwPvnkEzx9+hQAEBMTgy+++AIlJSXw8PBATEwMAKC8vByrVq3Cc889Bz8/PyxcuBDJ\nycntz3v79m14eHggPj4e06dPx6JFiwDQy6guXryIefPmwcfHB2PGjEFERAQyMjJkfr23bt1CZmYm\nVq5c2R5odGTZsmVITExsDzREIhH27NmDqVOnwtvbG8HBwdiwYQN4vGclsS0tLdi8eTMmTZoEb29v\nTJ06FTt37oRQ2JbFKi4uhoeHB86cOYMVK1Zg1KhRGDNmDDZu3Ng+BmgrW1u+fDkmTJgAf39/vPHG\nG8jJeWbuIykRu3LlCoKDg/HRRx8BAB4/foxly5YhICAAfn5+mDNnjlQ52IgRI1BfX4+1a9e2l8R1\nLqOqqanB2rVrMX78eHh7e2PGjBk4dOhQ++OS13D58mVERUVh7NixGDduHD755BM0N8v/t60sTLCh\noXDYLPi6kSxwFe+3kVVUK7d1aUfuP65gSqn6AIqicEaBHhddwVbA0WzhdA+lTAJI/TY0XSRO0ikN\nGdTWEd11MP1eDMReG60CEb788Q62/nQP6Z10QhKKyhuw9/e/8a8915RyQxvoUBSFb39Lxf6Tad2+\nj4vEFC7fKcK/9lxDXSNzvxVBmYDhj9wbEIjUF+ixWCzMmDEDJ0+elDp+6tQpBAcH03bAjx8/jl27\ndmH16tW4dOkSvv76ayQnJ2Pjxo0AgMjISISFhcHOzg7Xrl1DZGQk+Hw+li5diuzsbGzbtg3Hjx+H\ns7MzIiMjUVRUJPX8Bw8exOeff46dO3fS5pqbm4uVK1ciKCgI8fHxOHr0KAwNDfH222+Dz5dtLXLv\n3j3o6uoiKCiI+DiXy4WNzbO10s6dO3HgwAGsXr0a8fHx2LBhAxISEqRKu9auXYtz585h06ZNOHfu\nHFasWIEff/yRVo61Y8cOTJo0CSdPnsSqVatw9OhR/PDDDwDaFvuLFy8Gj8fDvn37cOTIEQDA0qVL\n0dAg/dn2448/Yv/+/Vi7di3EYjHeeustiEQiHDt2DGfOnMG0adOwatUqZGa26SolgWRUVBSuXbtG\ne80UReHtt99GSkoKdu3ahfj4eCxatAhbtmzBTz/9JDV2586d8PLywvHjxxEVFYXff/+9fa69CRNs\naDCq1m3UNSoeaACAQChGc6v8bkQMypFbUoecYuX7OJga6WHBtOH436cv4K25PpC1smXuFDe8+oJy\ngkOS1qNIKzMbkmCDntl4Us0bULvJIpEYX/14Bzf/fiLT+KyiWny678aAukeq5JfLmTh3M1/m8flP\n6rH5f0kQieQvmx3I8PhNKKwrUfj8Rj4PJfXdi52VJTQ0FFlZWXj48CEAgM/n48KFC5g1axZt7IwZ\nM3DmzBnMmjUL9vb2CAgIQGhoKK5fvw4AMDIyApfLBYfDgY2NDYyMjHDp0iXk5eVhy5YtGDt2LNzd\n3bFp0yYYGRnRFqrTpk3DmDFjMGgQvcR6yJAhOH36NFauXAlHR0e4ublh6dKlKC0tRW5urkyvtaKi\nAtbW1tDT0+txLJ/PR2xsLJYsWYLQ0FA4OTkhJCQEK1asQEJCAioqKlBWVtYeYEyZMgWOjo4ICwvD\n4sWLcezYMQgEz96f/P398corr8DZ2RmvvfYaxo8fj/j4eABtQVxDQwN2794NHx8feHp6YuvWraiv\nr6cFguHh4fD09GwvMfvhhx+wbds2uLu7w9HREW+//TYoisKtW7cAPCtFMzExkQqkJCQnJyMlJQX/\n/ve/MW7cODg5OWHJkiWYOXMmLdgYNWoUFi1aBCcnJ8yZMwfDhg1DamqqTPdelWhuwTQDMdgoLGtA\ndV0zrMzkF4FxOMrXTaviORjkI6dEuUCDw2bh7Xm+mDLaEVxdDgDgpeChcLI3xS+XMrsMYD2cLBD+\nvBsm+A5W6vpAm/1tZzQ5s0FRFHF+kgyNmTEXFiZcPO20U1/wpAGeBCe5/sjZG3m486hcrnPyn9Tj\n0JlHeGe+n5pm1T+pbWjFzwnyu8ml59fgakoJnh/tqIZZ9U94fOVd5RpV8Bzd4e/vDwcHB/z+++/w\n8vLC5cuXIRAIEBISggsXLkiN1dfXx6VLl7Bq1SqUlZVBIBC0f3XFgwcPYGZmBk9Pz/Zjenp6CAgI\nQHq6tJa045jOcLlcPH78GOvXr0deXh6am9v0RQBQVyfb5xqLxWo/pydyc3PR1NSEUaNGSR339fUF\nRVFIT0+HUCgERVHEMTweDwUFBdDXb9M8dR4zcuRI/PbbbwCA1NRUuLu7w8rKqv1xS0tLuLm50e7R\nyJEj279ns9moq6vDli1bkJaW1n4fRCKRzPckLS2NOD8fHx+cOXNGqkzKx8dHaoylpSXq63s/C88E\nGxqMnZUR7K2M8KRa2n7zQVYlpgY6yf18NoROzvJgYqjXvlhl6D2UzSbpcFiYEeRCO+4zzBo+w6xR\nUtmI6w9KUVXXDIoCzI25GOtlC3dHC6Wu2xGS/a0mBxv1PD4am6U/jFksYLC1UfvProPN8LRT/5D8\nJ3UDItgQiymcuirbzmRnLt8pxJJZnjA27HmnkqGNi0kFUn2X5OHcjXwm2JADPY7yTWxV8Rw9ERoa\nil9++QX/+te/cPr0aUyePBlGRka0cV9++SWOHTuGNWvWYMKECTAwMMDPP/+MgwcPdvncjY2NqK+v\nh7+/v9RxPp8PV1dp7R7pmhLOnz+PVatWYf78+fj4449hbm6O9PR0rFy5UubXaW9vj6qqKjQ3N/fo\ntNXY2JaNNjaW3tySzLGxsbFdc9HdGEmw0bkkzdDQsL1EqrGxERkZGbR71NraSstGdLxHJSUlWLx4\nMTw9PfH555/D3t4ebDZbSgzeE42NjWCxWLR73/E1SJC8FgksFktu4wNVwAQbGs6o4TZ4clM62EjO\nVCzYcLE3haOticKLvMn+QxhXmT5AWcemnnpjDLExxv9NG67UNXrCyY4ebDyp4kEoEkOHo3nVnKQS\nKltLQ+h1CLZdB5vSmhUOFPvb5MwKhfuK8IViXLpThLDJw1Q8q/7LpSTFjT3S82tQXNFA1E0x0DHl\nmsBI10AhgTjQtpizU6MblYTZs2dj7969uHLlCq5evdql/evZs2cRHh6OyMjI9mPdZTWAtkW2ubk5\n0WpVR0f2z6OzZ8/CxcUF0dHR7WsHiS5BVgIDAyESiZCYmIiZM2fSHheLxTh69CjCw8Pbg4POmgnJ\nz8bGxhCJRN2O6RhgNDVJv8fxeDyYmpq2j/Pw8MDu3btpc+q8wO/IH3/8gebmZuzatavdTayurq7H\n30lHTExMQFEUGhsbpYImSRBibGyM1lbN0mtp3qc8gxSkUqoHmZUKRaYsFgsvTXBReC4zlTiXQXGG\nOdD1AfKdb66imSiOpak+zbJZJKaITfM0AVLn8M6LNReCYD6vVHltjTbwd7ZyzmjKnj+QEIkplCr5\nf6Lpzm+aBJvNxnMuZDGyLIy294GpvvoDOzc3N3h4eGDHjh3Q09PDlClTiOP4fD4sLJ5lqVtbW9ud\njzquIzp+7+vri7q6Oujq6sLZ2bn9CwBRQ9AVAoEAFhbS1tcS8bOsa5jAwEB4e3tj165dtAABAL7/\n/nts3rwZubm5cHV1hZGREe7fl7Z9T0lJAZvNhpeXF7y8vMBms2ljkpOTYWJiImWfe+/ePakxaWlp\n7ZkdHx8fFBcXw8bGRuoeCYVCqdIq0j0BIPU76eqedHWPvL29AYD4Gtzc3BTqtaJumGBDw/F1t0Fn\n86CnDa0oLFMsOxEyxolmQyoLz/kPgZOd4m5EDIozbIiZUgHHjKC+9x5nsVhw1CLdRnficAkkkXhB\nWf2AcGzrrqmkbOdr1q6bJiMQKG8ZzBcwInF5eMHtuT45V15CQ0ORl5eHkJAQoi0sAPj5+eHcuXNI\nT0/Hw4cPsWzZMkycOBFAm11ua2srzMzMUFlZibt376KoqAghISFwcnLC6tWrcf/+fRQXF+O3335D\nWFgYTfzcHb6+vkhLS0NiYiLy8/MRHR3dnhlISUmRKvfpjm3btoHH42HBggW4cOECiouLkZaWho0b\nN2Lnzp1Yt24dvLy8oKenhyVLliA2NhZxcXEoKirChQsXEBMTgzlz5sDa2hq2trYIDQ1FTEwMLl++\njKKiIvz66684cuQIli5dKpW5uX//Po4ePYqCggIcOXIESUlJmDNnDgBg3rx54HA4+PDDD5GWlobC\nwkIcPHgQL7/8crvQu6t7AgD79+9HcXExfv75Z/z5559wdHTEo0ePUFVVBRMTE7BYLCQlJSEjIwMt\nLS1Sz+Hv74/Ro0cjOjoat27dQkFBAb7//ntcvHhRKoOlSTBlVBqOsYEu3B0t8LjwqdTx5MxKhaxI\nm1uFtK7IPeHpYon3/29UzwMZ1EJbRsoVX/+SIve5NhYGGONp2/PAXsDR1gSZhbVSx7Qp2BhiIx1s\nDBlkDB0OW6qWvrlVhPKaJthbd13H3B/Q1VFun0qvl7RfLa1C3HlUjrIaHoRCMUyN9ODrbkNsNKmp\ncPU40OGw5H7f7oiRgfo1BP2JIaZ2eMHtOSRkX5XrPH97b/jZjex5oIoIDQ3Fjh07uq33X79+PaKi\norBw4ULY2tri/fffR3BwMFJSUrB8+XIcPnwYc+fORUJCAiIiIvDqq69i3bp1OHToEL766issX74c\nTU1NcHJywscff4xXXnlF5vlJ7HPXrFkDLpeLefPmISoqCg0NDdizZw8MDQ1p2gkSrq6uOHnyJPbt\n24etW7eivLwcZmZm8Pb2xuHDhxEYGNg+dsWKFdDR0cHu3bvbnazCw8PxwQcftI+Jjo7Gtm3b8Nln\nn+Hp06ewt7fHu+++i3/+859S133zzTdx9+5dbNmyBTo6OliyZAnmz58PALCyssJPP/2ELVu2YPHi\nxRAIBBg+fDh27NiB4ODgLl9LYGAgVqxYgSNHjuDAgQOYOHEitm7diri4OOzatQsbN27E119/jcjI\nSMTGxiIxMZHYMf6bb77Bl19+iZUrV4LH48HZ2RmbNm1CeHh4j/ezL2BRfaEU0VCKi4sREhKCy5cv\nw8HBoa+n085P59Jx7JJ0nePoEYPwn3+Ol+t5KIrC5v8l4fZD2W35Jvs74L3/89PoTs8DAZFIjPXf\n3USqHOUnbDYLn70RpDFd33/7IwuHzj6SOjbZ3wEf/mN0H82oa5Z9folmzPDFOxPhPcxa6tjKHYnI\n7eQWtnbpGJU4eGkycX9m48Cphwqf/8I4Z7VuYFTVNuNEYjYu3ylEUwvdYMHXzRpzJg/D2JF2apuD\nKln37XW5/vc7osNh48f/zIAJI8iXC6FYhF03vkdSiWybPO5Wrvj35BUw0GW6t2s7krXgli1b2jMZ\nDG0ouk5myqi0AD+CbiMtt1rujrx/3C0iBhrmxnpES1s3BzN8+I/RTKChAXA4bERFjMVIGZ2OOGwW\nVi3015hAAwBxN7mIoI3oa/gCEcpr6DXyJIEtWbfR/0Xik0YNUag5pISpgepzR3qUV40V26/g9F+5\nxEADAFKzq7DpwG18F/e3VpS9vUhwk5OV4FGDmUBDAXTYHKye8E/MGzkL+jrkEqW2cTqYPmwS1k/5\ngAk0GBi6gFlFagEjnC2hr8dBC/9ZcNHKFyEj/yl83Ky7OfMZFU+b8F3c37TjRvo62LlqCup5rVi5\n40+px55UN4GiKMaBSkMwMtDFpuUT8MPZRzj1V9e2o97DrLB4pidGunYtUusLSL02iisaIRZTSi1c\nVc2TKh46rz+NDXSJWqc23YZ0R938J/1fJG5lZoDx3va4nloq97nOdiYyB83ykltSh//sv4nmVtk2\nYk7/lQsOm4U3XvZWy3xURZCPPazN9FFV19Lz4E7MDh6qhhkNDNhsNhb4zMbsEdPwV34SbhbdQ01z\nLSiKgrm+KUYP8cVU1wm9IghnYNBmmGBDC9DVYcN7mDXupks30ErOrJAp2BCLKcQcSyHu8i2b6wtr\ncwOYGeuBzWZJ7fLxmgWormuBtZL9ORhUh54uB6NH2NKCDX09Dl6a6IrnAx3hrKFCfltLI+jqsCEQ\nPtM48AUiVDxtgp2V5mgcuhKHk4Ju18EDM7MBAK/O8MDdjHK08mXPsLJYQESol1o2MMRiCtuPXPIL\newAAIABJREFU3JM50JAQ92cOxo60k3njpi/Q1WFj9aLRWL/vhlzajVdC3DHcSXX9cgYqhroGmOE+\nGTPcJ/f1VBh6AQcHBzx+/Livp9GvYMqotASSBW5XnZ87c+5mPlKy6GODvO3w/Oi2mjtdHQ6G2NAX\nfAVlA2PhpE2Q7FVHj7BFRKiXxgYaQFtpV2eRNUBe3PclstjeSiCVUZXXNKGpRXbPdG3F2c4UnywZ\nI5dYfHmYDwLVZFiQml2psEvf6WuKNSjsTXyGWSMqYiz0dGW733OnuGHxzK67OzMwMDD0FkywoSWQ\ngo3s4lo0NnVvQVla1Yj/naELOU2N9PDu/FFSO4wka9uCJ0ywoWmQds5JO+yaCEm3oegCUV3IYnsr\nwcyYC0tTep32QMluBHraYvNbE2Fh0nVNO9CWeftkyRi8pMaSnnM38xU+93baE1TXKdbErTcZM9IO\n/5zj0+M4HzdrRM5WTwaJgYGBQV6YYENLcLI1oS1qKAp40I1DiUhMYdfRZGKZw7vz/WDeaYFA2qUt\n0LCFIAOQR9AEkHo+aCKkYIOUSehLiisJtrddBBsAOdDLH0BBuqerJaYEdO9K4j3MGhP91OvQlZZT\nrfC5Yqqt07Y2IEsjzJKKRoUavzIwMDCoAybY0BJYLJbcpVQn/8whfoBOCXAgWnOSSnAG0qJJG+AL\nRMSdd1KgqIloemM/iqJQQiyj6i7YoAd6A6WTuIScku5fb25JbbePq4LGZuVK1xqbtKP0LTW75/LZ\nmvoWjStPZGBgGLgwwYYWQQ42KohjC8rqcfhcOu24pak+ls8lp+Gd7QnWpOUNEGmBNeRAobC8gWbV\naWSgCxsL7RDxOxK0D0XlDRqzC1tT30ITGHPYrG4F7MTMxgApowLaArSego2a+la1lylxZdQydEVv\nNRpUhsYmPq2vCwBiGdsDgk6PgYGBoS9ggg0tws+dHmyUVTehrFPzMaFIjF1H70t1Npbw/v+NgnEX\nnut2lka0D1yBUIwnVcwOmaaQT9gxd7E31Zra7ME2RjSbW16LEE8bWvtoRtIUl9P/1u2tjaDD6fqt\nkpRVyi+r14r+DaqgrLoJPBmyCjnF6s32DCaYD8h3vuY4onVFWm41zZbZ0lSf2LeECTZUi6CuDuUX\nL6Hw519QePQYnpw7j9ZK5h4zMMgCY32rRVia6sPZzoSmo0jOrMTM8c8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WLVuwePFiCAQC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\n6tm8Sf6iFC6XVTXio58u4r4X9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uxZ/Prrrzh06BAqKirg6uqKpUuXwsHBAd988w127dqF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ZzKKmQo5Pd+bhn389BVn79EmRHo6CjSlCeeYsOr5zbHiVKvcC2r7e55avL9jyNtJ2ChUAyMYo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\nipSCR7iVmYjbWXeQUZJd5wEJqVSKLVe+RZ6V4oSLqhiVtsK5lL/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jJ3aDtbUpelsHIqRt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"text/plain": [ "<matplotlib.figure.Figure at 0x7f55dfa1d150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_t = df[(df.Target == \"GBR\")].pivot_table(\n", " index=\"Year\",\n", " columns=\"QuadClass\",\n", " values=\"TotalEvents\", aggfunc=np.mean)\n", "\n", "ax = sns.pointplot(x=\"Year\", y=\"TotalEvents\", hue=\"QuadClass\",\n", " order=df_t.index.sort_values(),\n", " data=pd.melt(df_t.divide(df_t.sum(axis=1), axis=0).reset_index(),\n", " id_vars=[\"Year\"],\n", " value_vars=[1, 2,3,4],\n", " value_name=\"TotalEvents\").assign(\n", " QuadClass=lambda x: x.apply(lambda k: QUAD_CLASS_NAMES[k.QuadClass], axis=1)\n", ")\n", " )\n", "\n", "\n", "plt.xticks(rotation='vertical')\n", "plt.ylabel(\"Proportion of event types\")\n", "plt.xlabel(\"Year\")\n", "plt.title(\"GDELT events between India (IND) and Great Britain (GBR) across years\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Target\n", "PAK 219798\n", "USA 190428\n", "CHN 72053\n", "GBR 58141\n", "BGD 49788\n", "LKA 39145\n", "NPL 30611\n", "RUS 26132\n", "JPN 23964\n", "AFG 21588\n", "Name: TotalEvents, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(\"Target\")[\"TotalEvents\"].sum().sort_values(ascending=False).head(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
atlury/deep-opencl
cs480/04 Gaussian Distributions.ipynb
1
130129
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "$\\newcommand{\\xv}{\\mathbf{x}}\n", "\\newcommand{\\Xv}{\\mathbf{X}}\n", "\\newcommand{\\piv}{\\mathbf{\\pi}}\n", "\\newcommand{\\yv}{\\mathbf{y}}\n", "\\newcommand{\\Yv}{\\mathbf{Y}}\n", "\\newcommand{\\zv}{\\mathbf{z}}\n", "\\newcommand{\\av}{\\mathbf{a}}\n", "\\newcommand{\\Wv}{\\mathbf{W}}\n", "\\newcommand{\\wv}{\\mathbf{w}}\n", "\\newcommand{\\gv}{\\mathbf{g}}\n", "\\newcommand{\\Hv}{\\mathbf{H}}\n", "\\newcommand{\\dv}{\\mathbf{d}}\n", "\\newcommand{\\Vv}{\\mathbf{V}}\n", "\\newcommand{\\vv}{\\mathbf{v}}\n", "\\newcommand{\\tv}{\\mathbf{t}}\n", "\\newcommand{\\Tv}{\\mathbf{T}}\n", "\\newcommand{\\Sv}{\\mathbf{S}}\n", "\\newcommand{\\zv}{\\mathbf{z}}\n", "\\newcommand{\\Zv}{\\mathbf{Z}}\n", "\\newcommand{\\Norm}{\\mathcal{N}}\n", "\\newcommand{\\muv}{\\boldsymbol{\\mu}}\n", "\\newcommand{\\sigmav}{\\boldsymbol{\\sigma}}\n", "\\newcommand{\\phiv}{\\boldsymbol{\\phi}}\n", "\\newcommand{\\Phiv}{\\boldsymbol{\\Phi}}\n", "\\newcommand{\\Sigmav}{\\boldsymbol{\\Sigma}}\n", "\\newcommand{\\Lambdav}{\\boldsymbol{\\Lambda}}\n", "\\newcommand{\\half}{\\frac{1}{2}}\n", "\\newcommand{\\argmax}[1]{\\underset{#1}{\\operatorname{argmax}}}\n", "\\newcommand{\\argmin}[1]{\\underset{#1}{\\operatorname{argmin}}}\n", "\\newcommand{\\dimensionbar}[1]{\\underset{#1}{\\operatorname{|}}}\n", "$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gaussian or Normal Distributions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## First, Why Gaussians?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How would you like to model the probability distribution of a typical cluster of your data?\n", "If, and that's a big if, you believe\n", "the data samples from a particular class have attribute values that\n", "tend to be close to a particular value, that is, that the samples\n", "cluster about a central point in the sample space, then pick a\n", "probabilistic model that has a peak over that central point and falls\n", "towards zero as you move away from that point.\n", "\n", "How do we construct such a model? Well, let's try for two\n", "characteristics:\n", " - The model's value will decrease with the distance from the central point, and\n", " - its value will always be greater than 0.\n", "If $\\xv$ is a sample and $\\muv$ is the central point, we can achieve this with\n", "$$\n", "p(\\xv) = \\frac{1}{||\\xv - \\muv||}\n", "$$\n", "where $||\\xv - \\muv||$ is the distance between $\\xv$ and $\\muv$.\n", "\n", "Let's try making a plot of this for $\\mu = 5.5$." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10abc67f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xs = np.linspace(-5,10,1000)\n", "mu = 5.5\n", "plt.plot(xs, 1/np.sqrt((xs-mu)**2))\n", "plt.ylim(0,20)\n", "plt.plot([mu, mu], [0, 20], 'r--',lw=2)\n", "plt.xlabel('$x$')\n", "plt.ylabel('$p(x)$');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The red dotted line is at $\\mu = 5.5$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Humm...meets our criteria, but has problems---goes to infinity at the\n", "center and we cannot control the width of the central area where samples\n", "may appear.\n", "\n", "Can take care of first issue by using the distance as an exponent, so\n", "that when it is zero, the result is 1. Let's try a base of 2.\n", "$$\n", "p(\\xv) = \\frac{1}{2^{||\\xv - \\muv||}}\n", "$$\n", "\n", "Now, let's see...how do we do a calculation with a scalar base and vector exponent? For example, we want\n", "$$\n", "2^{[2,3,4]} = [2^2, 2^3, 2^4]\n", "$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for ** or pow(): 'int' and 'list'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-3-d7a70ae31833>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for ** or pow(): 'int' and 'list'" ] } ], "source": [ "2**[2,3,4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nope. Maybe we have to use a numpy array." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 4, 8, 16])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2**np.array([2,3,4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hey! That's it." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ko0erW6YuRo2CefNgwYLQlUhtRTKIADjnJjjnOjrntnHOHeCce7fM98Y45w6r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\nyaEgIiKRNH++n8J4yy1+XMj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iIiISjIKIiIiIBKMgIiIiIsEoiIiIiEgwCiIiIiISjIKI\niIiIBKMgIiIiIsEoiIiIiEgwCiIiIiISjIKIiIiIBKMgIiIiIsEoiIiIiEgwjUIXICKSYGYDgb2A\nPsBLQBvg58DZzrmCkLWJSGooiIhIJJhZC6Czc+4BM/sR+A1wOHAYsC5ocSKSMuacC12DiAhm1gzY\n4JzbZGY3AYudc3eErktEUktjREQkEpxz65xzm0q/PBLfNZNoKRGRmFIQEZFIMLPjzGysme2B76L5\nwMwMOC10bSKSOuqaEZFIMLMz8INUFwA7AKuBDcAk59wPAUsTkRRSEBEREZFg1DUjIiIiwSiIiIiI\nSDAKIiIiIhKMgoiIiIgEoyAiIiIiwSiIiIiISDAKIiIiIhKMgoiIiIgEoyAiIiIiwSiIiIiISDAK\nIiIiIhKMgoiIiIgE8/9+sM/KJAsTQgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10d96a160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(xs, 1/2**np.sqrt((xs-mu)**2))\n", "plt.plot([mu, mu], [0, 1], 'r--',lw=3)\n", "plt.xlabel('$x$')\n", "plt.ylabel('$p(x)$');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Solves the infinity problem, but it still falls off too fast. Want to\n", "change the distance to a function that changes more slowly at first,\n", "when you are close to the center. How about the square function? \n", "$$\n", "p(\\xv) = \\frac{1}{2^{||\\xv - \\muv||^2}}\n", "$$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TJvhpvYGkA9qKFTBiRLAyJAMUREREImjtWv/XfiRaRLZsgSlTah/buDHo4JWa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\nuNs596BzbhlwNbANuLyBx/8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"text/plain": [ "<matplotlib.figure.Figure at 0x10d1ba400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(xs, 1/2**(xs-mu)**2)\n", "plt.plot([mu, mu], [0, 1], 'r--',lw=3)\n", "plt.xlabel('$x$')\n", "plt.ylabel('$p(x)$');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yeah. That's a nice shape. Now we can vary the width by scaling the\n", "squared distance.\n", "$$\n", "p(\\xv) = \\frac{1}{2^{0.1\\,||\\xv - \\muv||^2}}\n", "$$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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NbNtUBBMRqWz+fHjwQTj3XC3ZlfzRv3+YE5Wrp/KmYh+R4whny/QEvgKeMbPd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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10dfb7ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(xs, 1/2**(0.1 * (xs-mu)**2))\n", "plt.plot([mu, mu], [0, 1], 'r--',lw=3)\n", "plt.xlabel('$x$')\n", "plt.ylabel('$p(x)$');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There. That's good enough. We could be happy with this. Just pick\n", "the center and scale factor that best matches the sample\n", "distributions. But, let's make one more change that won't affect the\n", "shape of our model, but will simplify later calculations. We will\n", "soon see that logarithms come into play when we try to fit our model\n", "to a bunch of samples. What is the logarithm of $2^{0.1\\,|\\xv -\n", "\\muv|^2}$, or, more simply, the logarithm of $2^z$? If we are talking\n", "base 10 logs, $\\log 2^z = z \\log 2$. Since we are free to pick the\n", "base...hey, how about using $e$ and using natural logarithms? Then\n", "$\\ln e^z = z \\ln e = z$. So much simpler! :-)\n", "\n", "So, our model is now\n", "$$\n", "p(\\xv) = \\frac{1}{e^{0.1\\,||\\xv - \\muv||^2}}\n", "$$\n", "which can also be written as\n", "$$\n", "p(\\xv) = e^{-0.1\\,||\\xv - \\muv||^2}\n", "$$" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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gZ+BG4PskPIaISLVNmuRPsu/RI3QSyWZt2/ot3zVpdVM12kcEoPiMmd8sSnLO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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10ecefb38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(xs, np.exp(-0.1 * (xs-mu)**2))\n", "plt.plot([mu, mu], [0, 1], 'r--',lw=3)\n", "plt.xlabel('$x$')\n", "plt.ylabel('$p(x)$');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The scale factor 0.1 is a bit counterintuitive. The smaller the\n", "value, the more spread out our model is. So, let's divide by the\n", "scale factor rather than multiply by it, and let's call it $\\sigma$.\n", "Let's also put it inside the square function, so $\\sigma$ is directly\n", "scaling the distance, rather than the squared distance. \n", "$$\n", "p(\\xv) = e^{-\\left (\\frac{||\\xv - \\muv||}{\\sigma}\\right )^2}\n", "$$\n", "or\n", "$$\n", "p(\\xv) = e^{-\\frac{||\\xv - \\muv||^2}{\\sigma^2}}\n", "$$\n", "\n", "Speaking of dividing, and this won't surprise you, since we will be\n", "taking derivatives of this function with respect to parameters like\n", "$\\mu$, let's multiply by $\\frac{1}{2}$ so that when we bring the\n", "exponent 2 down it will cancel with $\\frac{1}{2}$. \n", "$$\n", "p(\\xv) = e^{-\\frac{1}{2}\\frac{||\\xv - \\muv||^2}{\\sigma^2}}\n", "$$\n", "\n", "One remaining problem we have with our \"probabilistic\" model is that\n", "it is not a true probability distribution, which must\n", " - have values between 0 and 1, $0 \\le p(x) \\le 1$, and\n", " - have values that sum to 1 over the range of possible $x$ values, $\\int_{-\\infty}^{+\\infty} p(x) dx = 1$.\n", "\n", "We have satisfied the first requirement, but not the second. We can fix\n", "this by calculating the value of the integral and dividing by that\n", "value, which is called the normalizing constant. The value of the\n", "integral turns out to be $\\sqrt{2\\pi\\sigma^2}$. See [Evolution of the Normal Distribution](https://www.maa.org/sites/default/files/pdf/upload_library/22/Allendoerfer/stahl96.pdf)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, finally, we have the definition\n", "$$\n", "p(\\xv) = \\frac{1}{\\sqrt{2\\pi\\sigma^2}} e^{-\\frac{1}{2}\\frac{||\\xv - \\muv||^2}{\\sigma^2}}\n", "$$\n", "and, TA DA..., we have arrived at the Normal, or Gaussian, probability\n", "distribution (technically the density function) with mean $\\muv$ and\n", "standard deviation $\\sigma$, and thus variance $\\sigma^2$. Check out\n", "[the Wikipedia entry](http://en.wikipedia.org/wiki/Normal_distribution|the Wikipedia entry).\n", "\n", "Now you know a bit about why the Normal distribution is so prevalent. \n", "For additional insight and history, read [Chapter 7: The Central\n", "Gaussian, or Normal, Distribution](http://omega.albany.edu:8008/ETJ-PS/cc7m.ps) of *Probability Theory:\n", "The Logic of Science* by E.T. Jaynes, 1993. It starts with this\n", "quotation from Augustus de Morgan (yes, that de Morgan) from 1838:\n", "\n", "> \"My own impression...is that the mathematical results have outrun\n", "> their interpretation and that some simple explanation of the force and meaning of the \n", "> celebrated integral...will one day be found...which will at once render useless\n", "> all the works hitherto written.\"\n", "\n", "Before wrestling with python, we need to define the multivariate\n", "Normal distribution. Let's go to two dimensions, to make sure we develop code to handle multidimensional data, not just scalars. Now our hill we\n", "have been drawing will be a mound up above a two-dimensional base\n", "plane. We will define $\\xv$ and $\\muv$\n", "to be two-dimensional column vectors. What will $\\sigma$ be? Well, we\n", "need scale factors for the two dimensions to stretch or shrink the\n", "mound in the directions of the two base-plane axes. We also need\n", "another scale factor to allow the mound to be stretched in directions\n", "not parallel to an axis.\n", "\n", "Remember, the Normal distribution is all about squared distance from\n", "the mean. In two dimensions, the difference vector is $\\dv = \\xv -\n", "\\muv = (d_1,d_2)$. The squared distance is therefore $||\\dv||^2 =\n", "d_1^2 + 2 d_1 d_2 + d_2^2$. Now we see where the three scale factors\n", "go: $s_1 d_1^2 + 2 s_2 d_1 d_2 + s_3 d_2^2$. This can be written in\n", "matrix form if we collect the scale factors in the matrix\n", "$$\n", "\\Sigmav = \\begin{bmatrix}\n", "s_1 & s_2\\\\\n", "s_2 & s_3\n", "\\end{bmatrix}\n", "$$\n", "so that \n", "$$\n", "s_1 d_1^2 + 2 s_2 d_1 d_2 + s_3 d_2^2 = \n", "\\dv^T \\Sigmav \\dv\n", "$$\n", "because\n", "$$\n", "\\begin{align*}\n", "\\dv^T \\Sigmav \\dv\n", "& =\n", "\\begin{bmatrix}\n", "d_1 & d_2\n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "s_1 & s_2\\\\\n", "s_2 & s_3\n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "d_1\\\\\n", "d_2\n", "\\end{bmatrix}\\\\\n", "& =\n", "\\begin{bmatrix}\n", "d_1 s_1 + d_2 s_2 & d_1 s_2 + d_2 s_3\n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "d_1\\\\\n", "d_2\n", "\\end{bmatrix}\\\\\n", "&=\n", "(d_1 s_1 + d_2 s_2) d_1 + (d_1 s_2 + d_2 s_3) d_2 \\\\\n", "&=\n", "s_1 d_1^2 + 2 s_2 d_1 d_2 + s_3 d_2^2 \n", "\\end{align*}\n", "$$\n", "\n", "Again, it is more intuitive to use scale factors that divide the\n", "distance components rather than multiply them. In the\n", "multidimensional world, this means that instead of multiplying by\n", "$\\Sigmav$ we will multiply by $\\Sigmav^{-1}$. \n", "\n", "The normalizing constant is a bit more complicated. It involves the\n", "determinant of $\\Sigmav$, which is the sum of its eigenvalues and can\n", "be thought of as a generalized scale factor. Skim through\n", "[the Wikipedia entry on determinants](http://en.wikipedia.org/wiki/Determinant). The multivariate $D$-dimensional Normal distribution is\n", "$$\n", "p(\\xv) = \\frac{1}{(2\\pi)^{d/2} |\\Sigmav |^{1/2}}\n", " e^{-\\frac{1}{2} (\\xv-\\muv)^T \\Sigmav^{-1} (\\xv - \\muv)}\n", "$$\n", "where mean $\\muv$ is a $D$-dimensional column vector and covariance\n", "matrix $\\Sigmav$ is a $D\\times D$ symmetric matrix." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Normal distribution is also called the Gaussian distribution. (When did Gauss live?)\n", "\n", "In addition to the above reasons for concocting this distribution, it has a number of interesting analytical properties. One is the [Central Limit Theorem](http://en.wikipedia.org/wiki/Central_limit_theorem), which states that the sum of many choices of $N$ random variables tends to a Normal distribution as $N \\rightarrow \\infty$.\n", "\n", "Let's play with this theorem with some fancy shmansy python using the new [ipython notebook *interact* feature](http://nbviewer.ipython.org/github/ipython/ipython-in-depth/blob/master/examples/Interactive%20Widgets/Using%20Interact.ipynb) to explore the distribution of sums as the number of samples varies. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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2zFqsb7cd0vbt29myZUu/w5ckaUWa7Y/t8fFxhoeHq/Q/kFMhbaj4eeDtpZTJ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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x109f33f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ipywidgets import interact\n", "maxSamples = 100\n", "nSets = 10000\n", "values = np.random.uniform(0,1,(maxSamples,nSets))\n", "\n", "@interact(nSamples=(1,maxSamples))\n", "def sumOfN(nSamples=1):\n", " sums = np.sum(values[:nSamples,:],axis=0)\n", " plt.hist(sums, 20, facecolor='green')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now how would you check our definition of $p(x)$ in python? First, we need a function to calculate $p(x)$ given $\\mu$ and $\\Sigma$, or $p(x|\\mu, \\Sigma)$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def normald(X, mu, sigma):\n", " \"\"\" normald:\n", " X contains samples, one per row, N x D. \n", " mu is mean vector, D x 1.\n", " sigma is covariance matrix, D x D. \"\"\"\n", " D = X.shape[1]\n", " detSigma = sigma if D == 1 else np.linalg.det(sigma)\n", " if detSigma == 0:\n", " raise np.linalg.LinAlgError('normald(): Singular covariance matrix')\n", " sigmaI = 1.0/sigma if D == 1 else np.linalg.inv(sigma)\n", " normConstant = 1.0 / np.sqrt((2*np.pi)**D * detSigma)\n", " diffv = X - mu.T # change column vector mu to be row vector\n", " return normConstant * np.exp(-0.5 * np.sum(np.dot(diffv, sigmaI) * diffv, axis=1))[:,np.newaxis]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "normald?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check the shapes of matrices in that last calculation.\n", "\n", " diffv = X - mu.T\n", " | NxD Dx1 |\n", " | |\n", " | 1xD\n", " |\n", " NxD\n", "\n", " normConstant * np.exp(-0.5 * np.sum(np.dot(diffv, sigmaI) * diffv, axis=1))[:,newaxis]\n", " 1x1 NxD DxD | NxD | | |\n", " | | | |\n", " NxD NxD | |\n", " | |\n", " N |\n", " Nx1\n", "\n", "So we get $N$ answers, one for each sample.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.array([[1,2,3]]).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = np.array([[1,2],[3,5],[2.1,1.9]])\n", "mu = np.array([[2],[2]])\n", "Sigma = np.array([[1,0],[0,1]])\n", "print(X)\n", "print(mu)\n", "print(Sigma)\n", "normald(X, mu, Sigma)" ] }, { "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 [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" }, "widgets": { "state": { "eaa762e0121345d0951267da5f0588b4": { "views": [ { "cell_index": 22 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 0 }
lgpl-3.0
JasonSanchez/w261
week9/MIDS-W261-HW-09-Sanchez.ipynb
1
978922
{ "cells": [ { "cell_type": "code", "execution_count": 253, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/**********************************************************************************************\n", "Known Mathjax Issue with Chrome - a rounding issue adds a border to the right of mathjax markup\n", "https://github.com/mathjax/MathJax/issues/1300\n", "A quick hack to fix this based on stackoverflow discussions: \n", "http://stackoverflow.com/questions/34277967/chrome-rendering-mathjax-equations-with-a-trailing-vertical-line\n", "**********************************************************************************************/\n", "\n", "$('.math>span').css(\"border-left-color\",\"transparent\")" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "/**********************************************************************************************\n", "Known Mathjax Issue with Chrome - a rounding issue adds a border to the right of mathjax markup\n", "https://github.com/mathjax/MathJax/issues/1300\n", "A quick hack to fix this based on stackoverflow discussions: \n", "http://stackoverflow.com/questions/34277967/chrome-rendering-mathjax-equations-with-a-trailing-vertical-line\n", "**********************************************************************************************/\n", "\n", "$('.math>span').css(\"border-left-color\",\"transparent\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%reload_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# MIDS - w261 Machine Learning At Scale\n", "__Course Lead:__ Dr James G. Shanahan (__email__ Jimi via James.Shanahan _AT_ gmail.com)\n", "\n", "## Assignment - HW9\n", "\n", "---\n", "__Name:__ *Jason Sanchez* \n", "__Class:__ MIDS w261 (Section *Fall 2016 Group 2*) \n", "__Email:__ *jason.sanchez*@iSchool.Berkeley.edu \n", "__Due Time:__ HW9 is due on Tuesday 11/15/2016. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Table of Contents <a name=\"TOC\"></a> \n", "\n", "1. [HW Instructions](#1) \n", "2. [HW References](#2)\n", "3. [HW Problems](#3) \n", "1. [HW Introduction](#1) \n", "2. [HW References](#2)\n", "3. [HW Problems](#3) \n", " 1.0. [HW9.0](#1.0) \n", " 1.0. [HW9.1](#1.1) \n", " 1.2. [HW9.2](#1.2) \n", " 1.3. [HW9.3](#1.3) \n", " 1.4. [HW9.4](#1.4) \n", " 1.5. [HW9.5](#1.5) \n", " 1.5. [HW9.6](#1.6) \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"1\">\n", "# 1 Instructions\n", "[Back to Table of Contents](#TOC)\n", "\n", "MIDS UC Berkeley, Machine Learning at Scale\n", "DATSCIW261 ASSIGNMENT #9\n", "\n", "Version 2016-11-01 \n", "\n", "### INSTRUCTIONS for SUBMISSIONS\n", "Please use the following form for HW submission:\n", "\n", "https://docs.google.com/forms/d/1ZOr9RnIe_A06AcZDB6K1mJN4vrLeSmS2PD6Xm3eOiis/viewform?usp=send_form \n", "\n", "\n", "### IMPORTANT\n", "\n", "HW9 can be completed locally on your computer for most part but will require a cluster of computers for the bigger wikipedia dataset.\n", "\n", "### Documents:\n", "* IPython Notebook, published and viewable online.\n", "* PDF export of IPython Notebook.\n", " \n", "<a name=\"2\">\n", "# 2 Useful References\n", "[Back to Table of Contents](#TOC)\n", "\n", "* See async and live lectures for this week\n", "* Data-intensive text processing with MapReduce. San Rafael, CA: Morgan & Claypool Publishers. Chapter 5. \n", "\n", "\n", "\n", "<a name=\"3\">\n", "# HW Problems\n", "[Back to Table of Contents](#TOC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2 style=\"color:darkblue\">HW 9 Dataset</h2>\n", "\n", "Note that all referenced files are in the enclosing directory. [Checkout the Data subdirectory on Dropbox](https://www.dropbox.com/sh/2c0k5adwz36lkcw/AAAAKsjQfF9uHfv-X9mCqr9wa?dl=0) or the AWS S3 buckets (details contained each question). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. HW9.0 Short answer questions<a name=\"1.0\"></a>\n", "[Back to Table of Contents](#TOC)\n", "\n", "__ What is PageRank and what is it used for in the context of web search?__ \n", "PageRank is an algorithm used to score pages based on the PageRank scores of inbound links. These scores can be used as a component in ranking pages returned by search engines.\n", "\n", "<hr>\n", "\n", "__ What modifications have to be made to the webgraph in order to leverage the machinery of Markov Chains to compute the Steady State Distibution? __ \n", "Stochasticity to resolve dangling edges and teleportation so that any node can be reached by any other node.\n", "\n", "\n", "<hr>\n", "\n", "__ OPTIONAL: In topic-specific pagerank, how can we ensure that the irreducible property is satifsied? (HINT: see HW9.4) __ \n", "Drop nodes that have no inlinks.\n", "\n", "\n", "<hr>\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "from __future__ import division, print_function\n", "import matplotlib.pyplot as plt\n", "from numpy.random import choice, rand\n", "from collections import defaultdict\n", "from pprint import pprint\n", "import pandas as pd\n", "import numpy as np\n", "import mrjob" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2 style=\"color:darkgreen\"> HW 9.1 Implementation </h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. HW9.1 MRJob implementation of basic PageRank <a name=\"1.1\"></a>\n", "[Back to Table of Contents](#TOC)\n", "\n", "Write a basic MRJob implementation of the iterative PageRank algorithm that takes sparse adjacency lists as input (as explored in HW 7).\n", "\n", "Make sure that you implementation utilizes teleportation (1-damping/the number of nodes in the network), and further, distributes the mass of dangling nodes with each iteration so that the output of each iteration is correctly normalized (sums to 1).\n", "\n", "\n", "[NOTE: The PageRank algorithm assumes that a random surfer (walker), starting from a random web page, chooses the next page to which it will move by clicking at random, with probability d,one of the hyperlinks in the current page. This probability is represented by a so-called *damping factor* d, where d ∈ (0, 1). Otherwise, with probability (1 − d), the surfer jumps to any web page in the network. If a page is a dangling end, meaning it has no outgoing hyperlinks, the random surfer selects an arbitrary web page from a uniform distribution and “teleports” to that page]\n", "\n", "As you build your code, use the data located here :\n", "\n", "In the Data Subfolder for HW7 on Dropbox (same dataset as HW7) with the same file name. \n", "> Dropbox: https://www.dropbox.com/sh/2c0k5adwz36lkcw/AAAAKsjQfF9uHfv-X9mCqr9wa?dl=0\n", "\n", "Or on Amazon: \n", "\n", "> s3://ucb-mids-mls-networks/PageRank-test.txt\n", "\n", "with teleportation parameter set to 0.15 (1-d, where d, the damping factor is set to 0.85), and crosscheck your work with the true result, displayed in the first image in the [Wikipedia article](https://en.wikipedia.org/wiki/PageRank)\n", "and here for reference are the corresponding PageRank probabilities:\n", "<pre>\n", "\n", "A, 0.033\n", "B, 0.384\n", "C, 0.343\n", "D, 0.039\n", "E, 0.081\n", "F, 0.039\n", "G, 0.016\n", "H, 0.016\n", "I, 0.016\n", "J, 0.016\n", "K, 0.016\n", "\n", "</pre>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Here are some simple in memory implementations of PageRank" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The point of these implementations was for me to deeply understand PageRank." ] }, { "cell_type": "code", "execution_count": 229, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Here are the correct PR values to compare each implementation against\n", "true_values = [ 0.03278149, 0.38440095, 0.34291029, 0.03908709, 0.08088569,\n", " 0.03908709, 0.01616948, 0.01616948, 0.01616948, 0.01616948,\n", " 0.01616948]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Random Walk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Perform a simple random walk with adjacency lists and track which pages are visited." ] }, { "cell_type": "code", "execution_count": 242, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Page visit counts: \n", "{'A': 1400,\n", " 'B': 15355,\n", " 'C': 13645,\n", " 'D': 1562,\n", " 'E': 3261,\n", " 'F': 1586,\n", " 'G': 625,\n", " 'H': 622,\n", " 'I': 648,\n", " 'J': 630,\n", " 'K': 667}\n", "\n", "PageRank for page A: 0.034999\n", "PageRank for page C: 0.341116\n", "PageRank for page B: 0.383865\n", "PageRank for page E: 0.081523\n", "PageRank for page D: 0.039049\n", "PageRank for page G: 0.015625\n", "PageRank for page F: 0.039649\n", "PageRank for page I: 0.016200\n", "PageRank for page H: 0.015550\n", "PageRank for page K: 0.016675\n", "PageRank for page J: 0.015750\n", "\n", "Time taken:\n", "CPU times: user 254 ms, sys: 6.12 ms, total: 260 ms\n", "Wall time: 257 ms\n" ] }, { "data": { "image/png": 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Hpe/En5FMZDv+OuqL8JcxbQFuiu7mJiIiIkeQp/w+FtE/bKr9Xw1rorsursLfEncf0T/D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\nGMMYAw99KFxzDTz60fCMZ8D557tvud/7nvuWHoZw001w772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"text/plain": [ "<matplotlib.figure.Figure at 0x1680d55d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "pages = {\"B\":[\"C\"],\n", " \"C\":[\"B\"],\n", " \"D\":[\"A\",\"B\"],\n", " \"E\":[\"B\",\"D\",\"F\"],\n", " \"F\":[\"B\",\"E\"],\n", " \"G\":[\"B\",\"E\"],\n", " \"H\":[\"B\",\"E\"],\n", " \"I\":[\"B\",\"E\"],\n", " \"J\":[\"E\"],\n", " \"K\":[\"E\"]}\n", "\n", "teleport = .15\n", "iterations = 40001\n", "all_nodes = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\"]\n", "\n", "iterations_to_plot = 250\n", "page_visits = defaultdict(int)\n", "default_val = 1.0/len(all_nodes)\n", "current_page = pages.keys()[0]\n", "mod = iterations//iterations_to_plot\n", "all_page_visits = []\n", "\n", "for i in xrange(iterations):\n", " if rand() < teleport:\n", " possible_pages = all_nodes\n", " else:\n", " possible_pages = pages.get(current_page, all_nodes)\n", " current_page = choice(possible_pages)\n", " page_visits[current_page] += 1\n", " if i%mod == 0:\n", " dict_to_save = dict(page_visits)\n", " dict_to_save[\"index\"] = i\n", " all_page_visits.append(dict_to_save)\n", " \n", "print(\"Page visit counts: \")\n", "pprint(dict(page_visits))\n", "print()\n", "\n", "total = 0.0\n", "for page, counts in page_visits.items():\n", " total += counts\n", " \n", "for page, counts in page_visits.items():\n", " print(\"PageRank for page %s: %f\" % (page, counts/total))\n", " \n", "print(\"\\n\", \"Time taken:\", sep=\"\")\n", "data = pd.DataFrame(all_page_visits[1:])\n", "data.index = data.pop(\"index\")\n", "normalized_data = data.div(data.sum(axis=1), axis=0)\n", "normalized_data.plot(legend=False)\n", "plt.ylim(0,.5)\n", "plt.hlines(true_values,0,iterations-1, colors=\"grey\")\n", "plt.title(\"Random walk PageRank values\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Power Iteration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a simple implementatin of the power iteration method of solving for the PageRank scores." ] }, { "cell_type": "code", "execution_count": 246, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HVhSqjn0X9nE27uy1XRazoqRXSXo07MHPe37G4mI/0cnBk+lYuyM1y9TMMW6/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\nG0bl2kpOGXErGbaET2/5Jfs+2MfY58Yy7s1xpI1M86SY6UelM/6t8ZS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"text/plain": [ "<matplotlib.figure.Figure at 0x160efce50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Ad7BlZEQkCdEJYbFI+FNDAn6JkbCyb0MgnT8N0uPtcW34a5EA+zI32kr9NLC7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jm2v8MsbW0k3o47Tt6ZAOqTePZZ82UEzklIyCxrCMTujQceINFrlDvuoCdsFWcS57r2WnoS3Lq1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\nSeEeiUYhEQ3uWvM1F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"text/plain": [ "<matplotlib.figure.Figure at 0x15feaead0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iterations = 26\n", "d = .85\n", "\n", "thd = 1/3.0\n", "fll = 1/11.0\n", "T = np.array( [[ fll, fll, fll, fll, fll, fll, fll, fll, fll, fll, fll],\n", " [ 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0.5, 0.5, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , thd, 0. , thd, 0. , thd, 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0.5, 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0.5, 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0.5, 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0.5, 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ]])\n", "\n", "teleport = np.ones(T.shape)/T.shape[0]\n", "T = d*T + (1-d)*teleport\n", "\n", "stable = np.ones(T.shape[0])/T.shape[0]\n", "\n", "all_stables = []\n", "\n", "for i in xrange(iterations):\n", " stable = stable.dot(T)\n", " all_stables.append(stable)\n", "\n", "plt.plot(all_stables)\n", "plt.hlines(true_values,0,iterations-1, colors=\"grey\")\n", "plt.title(\"Power iterations stabilize after about 25 iterations\")\n", "plt.ylim(0,.5)\n", "plt.show()\n", "\n", "plt.plot(all_stables)\n", "plt.hlines(true_values,0,iterations-1, colors=\"grey\")\n", "plt.title(\"Zoomed in on the two highest PR pages\")\n", "plt.ylim(.32, .4);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### T^2 Power Iteration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a new method. Instead of using the transition matrix, we use T^2. We find the oscillations disappear and results converge much faster." ] }, { "cell_type": "code", "execution_count": 271, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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OhYkTSe6zD+/vMoCFA2tZULmRp20lL69/lXXb/TkTJbES9hiyBxOHTeScA89h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\nCQlKml4TlJCkhARxEpSSDIaTlOAsTpJ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"text/plain": [ "<matplotlib.figure.Figure at 0x161a8f590>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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PFRswYAADBgwo+pmIVEGbd27m46Uf700c5q6dC8AxTY6h/5H96dOuD71b99b0\nSpFqJDMzk8zMzHxlmzdvLrP240oknHN7zCwL6AO8DRAciugDPBFHU8lAzaj7tYGcmDp5kfadc66w\nhh577DG6du0ax65Fqo6dOTv5cvmXTFo8iYmLJvL1yq/Jdbm0zmhNn7Z9+FOvP3F629Npnt487FBF\nJCQF/bieOXMm3bp1K5P2Ezm08SjwfJBQRKZ/1sZP18TMHgBaOueuDO7fCCwD5gfb9wZuA6LXkXgH\nGGZms4GvgMPxvRRvF5VEiFQ3uXm5zFw1c+84h8+WfcbOnJ00rt2Y09uezjVdrqFP2z60a9BOhypE\npFzEnUg4514NBkbegz+kMQvo55xbG1RpDrSK2iQJv0ZEG3yvw0Lg98650VF1/obvgfgbcBCwFt/j\n8ed44xOpSpxzzF83f2/iMHXJVDbt3ESd1Dr0btOb+06/jz5t+9CxWUet2SAiobDK+oPfzLoCWVlZ\nWTq0IVWKc47Za2YzbvY4xs8dz4qtK0hNSqVHqx70aduHPm37cPxBx5OanBp2qCJSSUUd2ujmnJtZ\nmrY0t0ukglixZQUvf/sy4+aM47ufv6NJ7SZc2uFSzj38XE4+5GSdzVJEKiQlEiIh2rprK2/Me4Nx\nc8YxefFkaqbUpP+R/Xmwz4OceeiZ6nUQkQpPiYRIOcvJy2HioomMmzOO/8z7D9k52Zza5lTGXDCG\ni9tfrKmZIlKpKJEQKQfOOWatnsW4OePI/C6T1dtW075xe+485U4GdRrEIRmHhB2iiEhClEiIHEA/\nbfmJl+f4cQ9z186laZ2mDOgwgMs7XU7XFl01RVNEKj0lEiJlbOuurbw+73XGzRnHlMVTqJlSkwuP\nupCH+z7MmYeeqfNXiEiVok80kTKQk5fDRws/Ytyccbw5/0125uzk1Dan8s8L/snFR19MvZr1wg5R\nROSAUCIhUgoLNixg5PSRZH6XyZrtazimyTHc1fsuBnYcSKuMVsU3ICJSySmREEnAoo2LuPeTe3lx\n9os0TGvIoI6DuOLYK+jcvLPGPYhItaJEQiQOSzYt4b5P7uP52c/TKK0Rw88czuBug0lLTQs7NBGR\nUCiRECmBZZuXcd8n9/HcrOdomNaQh854iCHHDaF2au2wQxMRCZUSCZEi/LTlJ+7/9H7GzBxDRq0M\n7j/9fm7sfqOWqxYRCSiRECnAyq0reeDTBxg9czTpNdK557R7uOn4m0ivkR52aCIiFYoSCZEoq7au\n4sHPHmTYjugXAAAgAElEQVRU1ihqp9bmrt538Zvjf0PdmnXDDk1EpEJSIiECrNm2hoc+f4inv36a\nWim1uKPXHdxy4i1a/0FEpBhKJKRa+3n7zzzy+SOMnDGS1ORUbj/pdm458Rbq16ofdmgiIpWCEgmp\nltbtWMcjnz/CiBkjSLZkftfzdww7cRgN0hqEHZqISKWiREKqlfU71jP8y+E8Of1JAG494VZu63kb\nDdMahhyZiEjlpERCqoWN2Rt59MtHefyrx8lzefzm+N9wW8/baFy7cdihiYhUakokpErLc3k89uVj\n3PPJPeTk5TC0+1B+3/P3NKnTJOzQRESqBCUSUmUt37ycK968go+XfMxNx9/EHb3uoFl6s7DDEhGp\nUpRIlLelS2HrVujQIexIqrTx341nyH+HkF4jnclXTubUNqeGHZKISJWUFHYA1cqmTXDqqXDssfB/\n/wc7d4YdUZWzZdcWrnzzSi59/VL6HdqPOUPmKIkQETmAlEiUF+fguutg40a4/Xb4xz+ga1eYPj3s\nyKqML5Z/QednOvOfef/hxQtfJPPiTE3nFBE5wJRIlJdRo+D112HMGLjvPpg5E+rUgR49fGKh3omE\n7cndw1+m/IVeY3vRsm5LZg+ZzeXHXo6ZhR2aiEiVp0SiPMyZA7feCkOGwC9/6cuOOQa+/BLuvRce\newy6dYMZM8KNsxJasGEBvcb24v5P7+fu3ncz9aqptG3QNuywRESqDSUSB9r27XDJJXDEEfDoo/kf\nS0mBP/4RsrIgLQ1OPNHf37UrnFgrEeccz33zHJ2f6cy6Hev4/JrPubP3naQkafywiEh5UiJxoN18\nMyxbBuPH+2ShIB06+N6Je+6B4cP92An1ThRq/Y71/PLfv+Tat6/l0g6XMmvILE44+ISwwxIRqZYS\nSiTMbKiZLTazbDObZmbdi6h7kpl9ZmbrzGyHmc0zs1tj6kwxs7wCLu8kEl+F8cor8NxzMGIEtG9f\ndN3UVLjjDt87UauWHztxxx3qnYgxcdFEOj3TialLpvL6r15nzAVjSK+RHnZYIiLVVtyJhJldAgwH\n7gK6ALOBCWZW2FrD24EngV7AUcDfgHvN7LqoOr8AmkddOgC5wKvxxldhLFgAN9wAAwfCVVeVfLuO\nHWHaNLj7bnjkETjuOJ9cVHO7cnZx24Tb6DuuL0c3OZo5Q+ZwUfuLwg5LRKTaS6RHYhgwyjn3onNu\nPjAE2AFcU1Bl59ws59x459w859wy59wrwAR8YhGps8k593PkApyJT0BeSyC+8O3a5cdFNG8OzzwD\n8c4eSE2FP/8Zvv7a3z7hBH+/mvZOzP15LsePOZ4RM0bw6JmPMuGyCRxU76CwwxIREeJMJMwsFegG\nTIqUOeccMBHoUcI2ugR1pxZR7Rog0zmXHU98Fcbtt8O338K//gV16ybeTqdO8NVX8Je/wEMP+d6J\nmTPLLs4KzjnHk189SbfR3cjNy2X6ddMZ1mMYSaahPSIiFUW8n8iNgWRgTUz5GvwhiUKZ2XIz2wlM\nB0Y658YWUu944BhgTJyxVQzvvOMXm3rkET+ls7RSU30i8fXXfpbH8cfDnXfC7t2lb7sCW71tNee8\ncg43f3AzN3S7gRnXz+DY5seGHZaIiMQoz592J+N7M4YAw4KxFgW5FvjWOVf5Bgb89JMfD3H++X62\nRlk69li/Cuadd8KDD1bp3om3f3ibjk935JtV3/D+oPd5/OzHSUstZMaLiIiEKt5J9+vwgyBjT6HY\nDFhd1IbOuaXBzblm1hy4GxgfXcfMagOXAH8uaUDDhg0jIyMjX9mAAQMYMGBASZsoGzk5fmBlWhqM\nHRv/uIiSSE2Fu+6C/v19wnLCCfCnP/nZHTVqlP3+ytn23du57cPbGJU1iguOvIAx54/R6b5FREop\nMzOTzMzMfGWbN28us/bND3GIYwOzacBXzrlbgvsGLAOecM49UsI2/gJc5ZxrF1N+FfAUcJBzbmMx\nbXQFsrKysujatWtcz+GAuOsuv0rl1KnQq1ex1Utt9264/36/3PYxx8Dzz0Pnzgd+vwdI1sosBr4x\nkJ+2/MRj/R7j+q7Xa4lrEZEDZObMmXTzh9+7OedK1b2dyKGNR4HrzewKMzsKeAaoDTwPYGYPmNkL\nkcpmdqOZnWdmhwWXa4HbgHEFtH0t8GZxSUSFM2UK/O1vfspmeSQR4Hsg7r7bH+5wDrp3h7/+Ffbs\nKZ/9l6HJiyfTa2wv6taoy8zBMxncbbCSCBGRSiLu9YSdc68Ga0bcgz+kMQvo55xbG1RpDrSK2iQJ\neABoA+QAC4HfO+dGR7drZkcAPYG+8cYUqrVrYdAg6N3bH2Yob126+FUw773XJzPvvAPvvQdNm5Z/\nLAmYumQq571yHr1a9+KtS9+iVkqtsEMSEZE4xH1oo6KoEIc28vLgvPP8F/ns2dCyZThxRGRl+Xga\nN4bJk6FJxR5f8PGSjznnlXM4qdVJvHXpWxpQKSJSTsI+tCERjz0G778PL7wQfhIBfrrp5Mm+l6RP\nH1i3LuyICvXJ0k8455Vz6Nmqp5IIEZFKTIlEombM8AtP3XYbnHNO2NHs0769TybWrKmwycRnyz7j\nnJfP4cSDT1QSISJSySmRSMTmzXDppX58wv33hx3N/o4+2g8AXbUKzjgD1q8PO6K9Pl/2OWe/fDbH\nH3Q87wx4h9qptcMOSURESkGJRLyc8yfjWrfOL4FdUddvOPpo3zOxcmWFSSa+XP4lZ798Nt1adFMS\nISJSRSiRiNc//wnjx8Po0dCuXfH1w9Shg08mfvoJ+vaFDRtCC2XaT9Po91I/OjfvzH8H/pc6NeqE\nFouIiJQdJRLxmDvXL319/fX+7J6VQSSZWLYstGRi+orp9HupH8c2P5b3Br2nJEJEpApRIlFSO3b4\n5KFdO39SrsqkY0efTCxdCmeeCRvLb72vGStmcOa4M+nQtAPvDXyP9Brp5bZvERE58JRIlNStt8Ki\nRf6wRu1KeGy/UyeYNAkWL/bJxKZNB3yXX6/8mr7j+nJ0k6N5f9D71K1ZilOqi4hIhaREoiTGj4dn\nn4UnnvDntaisjj3WJxOLFh3wZGLmqpn0HdeX9k3a88FlH1CvZr0Dti8REQmPEoniLFoEgwf7wxrX\nXht2NKXXuTNMnAgLFkC/fn4qaxn7ZtU3nPHiGRzR6Ag+GKQkQkSkKlMiUZTdu/16EY0bw6hRB+bU\n4GHo0sUnEz/+WObJxOzVszlj3Bkc1vAwJlw2gYxaGcVvJCIilZYSiaL86U/wzTd+vYiMKvaF2LUr\nfPQR/PADnHUWbNlS6ibnrJlDnxf70K5BOz68/EPq16pfBoGKiEhFpkSiMO+9B8OHw4MP+lN0V0Xd\nuvlkYv78UicT3675lj4v9qF1/dZ8eJmSCBGR6kKJREFWrIArr/Tn0Bg2LOxoDqzjjoMPP4Tvv4ez\nz4atW+NuYu7Pc+nzYh8OrncwH13+EQ3SGhyAQEVEpCJSIlGQwYP90tfPPw9J1eAl6t7dJxPffRd3\nMvH92u85/cXTaVm3JRMvn0jDtIYHMFAREaloqsG3ZAI+/xx+8xto0iTsSMrP8cf7ZOLbb31PzLZt\nxW4yb+08Tn/hdJqnN2fiFRNpVLtROQQqIiIViRKJWHv2+FkMzZqFHUn5O+EEmDABZs8uNpmYv24+\np71wGk3rNGXSFZNoXLtxOQYqIiIVhRKJWJGzZDaupl+MJ57ok4lZs+Dcc2H79v2q/LDuB0574TSa\n1GmiJEJEpJpTIhFr3Tp/XV0TCYAePeCDD2DmzP2Sif+t/x+nvXAaDdMaMumKSTSpU40O/4iIyH6U\nSMRSIuH17OmTiawsOO882LGDBRsWcNoLp1G/Vn0mXzGZpnWahh2liIiELCXsACocJRL7nHQSvP8+\nnHUWu88+k7PPWUy99HpMvnIyzdKr4RgSERHZjxKJWOvW+SmfVW0ly0SdfDLuvffIPfN0/rkyicM/\n/Zjm6c3DjkpERCoIHdqItX49NGpUPdaPKKF/NVhBvwG59FyRTIvzB8Dq1WGHJCIiFYS+LWOtW6fD\nGlFWb1vNTe/fxEHnXUrK51/4VT9PPNGvhCkiItWeEolYSiT2cs4x5N0hpCalMuLsEf6soV99BXXr\n+sGYU6aEHaKIiIRMiUQsJRJ7vfzty7z1w1s8c94z+1atbNUKPvvMr4TZrx+8+GK4QYqISKiUSMRS\nIgHAyq0rufn9mxnUcRAXHnVh/gczMuC//4UrrvAnN7vnHnAunEBFRCRUmrURS4kEzjluePcGaqbU\n5Imznyi4UmoqPPsstGsHd9wBixbB6NH+ZGciIlJtJNQjYWZDzWyxmWWb2TQz615E3ZPM7DMzW2dm\nO8xsnpndWkC9DDMbaWYrzWynmc03s7MSia9UlEgwbs443v3fu4w6b1TRZ/M0gz/9CV5+GTIz/ZlD\nN20qv0BFRCR0cScSZnYJMBy4C+gCzAYmmFlh377bgSeBXsBRwN+Ae83suqg2U4GJwCHARcARwPXA\ninjjK5WdO/2JqqpxIrFiywpufv9mLu90ORcceUHJNho4ED76CL75xi9itWTJAY1RREQqjkR6JIYB\no5xzLzrn5gNDgB3ANQVVds7Ncs6Nd87Nc84tc869AkzAJxYR1wL1gQudc9OCep86575NIL7ERU7Y\n1ah6ng7bOcfgdwdTO7U2j5/1eHwbn3IKfPklZGf76aFff31gghQRkQolrkQi6DnoBkyKlDnnHL43\noUcJ2+gS1J0aVXw+8CXwlJmtNrNvzeyPZla+g0Gr+Zk/n5/1PO/9+B7Pnv8sDdIaxN/AkUfCtGnQ\npg307g3vvFPmMYqISMUS7xd1YyAZWBNTvgYoct1kM1tuZjuB6cBI59zYqIfbAf8viOds4B7gNuCO\nOOMrnWp8no2ftvzErRNu5cpjr+TcI85NvKGmTWHyZDjrLLjwQhgxouyCFBGRCqc8Z22cDKQDJwIP\nmdkC59z44LEkfDIyOOjh+MbMDgZ+hx9TUahhw4aREXNejAEDBjBgwID4I6ymiYRzjuvevo70Gun8\n46x/lL7B2rXh3/+GP/wBfvMbP6PjkUcgObn0bYuISFwyMzPJzMzMV7Z58+Yyaz/eRGIdkAvEnvqx\nGVDkCRicc0uDm3PNrDlwNxBJJFYBu4MkImIe0NzMUpxzOYW1+9hjj9G1a9eSP4OirFsHKSlQr17Z\ntFdJPPfNc0xYOIH3Br5H/Vr1y6bRpCT4+9+hbVu4+WY/APOll3ySISIi5aagH9czZ86kW7duZdJ+\nXIc2nHN7gCygT6TMzCy4/0UcTSUDNaPufw4cFlPnSGBVUUlEmYtM/TQrt12GbdnmZQybMIxrOl/D\n2YefXfY7GDoU3noLJkyA006Dn38u+32IiEhoEhnM+ChwvZldYWZHAc8AtYHnAczsATN7IVLZzG40\ns/PM7LDgci1+/MO4qDafBhqa2RNmdriZnQv8ESjfA+zVbA2JyCGNjFoZPNrv0QO3o/POg08+gWXL\n/IyO+fMP3L5ERKRcxT1Gwjn3arBmxD34QxqzgH7OubVBleZAq6hNkoAHgDZADrAQ+L1zbnRUmz+Z\nWT/gMfy6FCuC2w/HG1+pVLNEYszMMXy06CM+GPQBGbUyit+gNLp18zM6zj0XevSAN9/0MztERKRS\nS2iwpXPuKeCpQh67Oub+CErQs+Cc+wromUg8ZaYaJRJLNy3ltx/+luu6XEe/w/qVz05bt/Yn/Lr4\nYujbF8aOhUGDymffIiJyQOikXdHWrasWi1E557j27WtpUKsBw/sNL9+d168P77/vE4jLLoN779UJ\nv0REKjGdtCtaNemRGJU1ikmLJ/HhZR9Sr2YIM1Rq1IDnnvMn/LrzTli8GJ55xp8ITEREKhUlEtHW\nr6/yicTijYv53Ye/44ZuN9D30L7hBWLmk4i2beGaa/xAzH//2/dYiIhIpaFDGxE7dvhLFU4k8lwe\n1759LY1rN+aRvo+EHY532WXw4Yf+3BydOvnbIiJSaSiRiKgG59l45utnmLJkCv+84J/UrVk37HD2\nOfVUmDXLn6ujXz8YPBi2bAk7KhERKQElEhFVfHnsRRsX8fuPfs+vj/s1fdr1KX6D8ta6te+NGDUK\nMjOhQwf1ToiIVAJKJCKqcCKR5/K45q1raFqnKQ/3Ld+lOeJi5nsjvvtOvRMiIpWEEomIKpxIjJw+\nko+XfszY/mNJr5EedjjFi/ROPPOM753o2BE++ijsqEREpABKJCLWrYOaNaFOnbAjKVMLNizg9km3\nM7T7UE5tc2rY4ZScGdxwg++dOOIIOPNMf1+9EyIiFYoSiYjIYlRV6IRdkUMazdOb8+AZD4YdTmKi\neydeeUW9EyIiFYwSiYgquBjVk189yafLPuW5C56rHIc0ChPpnfj2Wzj8cPVOiIhUIEokIqrYYlQ/\nrv+RP076IzcffzO921SRk2O1aeN7I6J7JyZODDsqEZFqTYlERBXqkcjNy+Xqt66mZd2W3N/n/rDD\nKVuxvRN9+6p3QkQkREokIqpQIvH4V4/zxfIvGNt/LHVqVK3Bo3tFeieeflq9EyIiIVIiEVFFEokf\n1v3AHZPv4JYTbqFX615hh3NgmcGQIb534rDD9vVObN0admQiItWGEgnwp7GuAolE5JBGq3qtuK/P\nfWGHU37atPG9EZHeiQ4d1DshIlJOlEgAbN8Ou3ZV+kRi7KyxTPtpGmP7j6V2au2wwylfBfVODBmi\n3gkRkQNMiQRUiVUtnXM8/tXj9D+qPycdclLY4YQnMnbiqafgpZfUOyEicoApkYAqkUh8uuxTvvv5\nO4Z2Hxp2KOFLSoJf/9qvihnpnbjqKli0KOzIRESqHCUSsC+RaNQo3DhKYcT0ERzZ6Ej6tK2AZ/YM\nS/S6ExMm+KW2r74afvwx7MhERKoMJRLgF6OCStsjsWLLCt6Y9wZDuw/FqtAS32UiKcnP5Fi0CB59\n1C+3fdRRcPnlMH9+2NGJiFR6SiTA90ikpUHtyjlAcXTWaNJS07iy85Vhh1JxpaXBzTfDwoXwxBMw\ndSocfTQMHAjffx92dCIilZYSCajUUz935+5mVNYoruh0BfVq1gs7nIqvVi0YOhQWLPDTRT//3A/I\n/NWv/IwPERGJixIJqNSJxBvz3mDN9jXc2P3GsEOpXGrW9Ic8fvwRRo+Gr7+GTp3g4oth1qywoxMR\nqTSUSEClTiRGTB/BaW1O45imx4QdSuVUowZcdx388AM89xzMng1dukD//pCVFXZ0IiIVnhIJqLSJ\nxKzVs/h8+eea8lkWUlP9jI758+HFF/31ccfBeefB9OlhRyciUmEpkYBKm0iMnD6Sg+sdTP+j+ocd\nStWRkuJndHz/Pbz8sh+cecIJcPbZ8OWXYUcnIlLhKJGASplIbMzeyMvfvsyQbkNISUoJO5yqJznZ\nz+j47jsYPx6WL4eePf3iVp99FnZ0IiIVRkKJhJkNNbPFZpZtZtPMrHsRdU8ys8/MbJ2Z7TCzeWZ2\na0ydK80sz8xyg+s8M9uRSGxxi5ywq5ItRjV21lhy8nK4rut1YYdStSUn+xkdc+bAa6/Bzz9Dr15w\n+ul+CqmISDUXdyJhZpcAw4G7gC7AbGCCmRX2k3478CTQCzgK+Btwr5nFfgNuBppHXVrHG1tCtm6F\nnJxK1SOR5/IYOWMkvzrmVzRLbxZ2ONVDUpKf0fHNN/Cf/8CmTXDaadC7t181My8v7AhFREKRSI/E\nMGCUc+5F59x8YAiwA7imoMrOuVnOufHOuXnOuWXOuVeACfjEIqaqW+uc+zm4rE0gtvhVwvNsfLDg\nAxZtXKRBlmFISoILL/QzOt5+G3bsgLPOgtat4fbb/aEQEZFqJK5EwsxSgW7ApEiZc84BE4EeJWyj\nS1B3asxD6Wa2xMyWmdmbZnZ0PLElrBImEiNnjKRri66cePCJYYdSfZnB+ef7GR2ffw4XXADPPgsd\nO0LnzvD3v8OKFWFHKSJywMXbI9EYSAbWxJSvwR+OKJSZLTezncB0YKRzbmzUwz/gezQuAAYFcX1h\nZi3jjC9+lSyRWLhhIe//+D43db9J59WoCMz8IMyRI2HVKnjrLX9ysD//GVq1gjPOgOefhy1bwo5U\nROSAKM/h/icD6cCJwENmtsA5Nx7AOTcNmBapaGZfAvOAG/BjMQo1bNgwMjIy8pUNGDCAAQMGlCyq\nSnbmz6e/fpoGaQ24tMOlYYcisWrU8D0TF1wAmzfD66/DSy/BNdf405r37w+XXQb9+vl1K0REykFm\nZiaZmZn5yjZv3lxm7Zs/MlHCyv7Qxg7gYufc21HlzwMZzrlflLCdO4DLnHPti6jzKrDHOTeokMe7\nAllZWVl07dq1xM9hP48+Cnfd5QddVnA79uzgoEcP4vqu1/Nw34fDDkdKavlyyMyEceP8GIrGjeHS\nS31ScfzxvldDRKQczZw5k27dugF0c87NLE1bcR3acM7tAbKAPpEy8/3rfYAv4mgqGahZ2INmlgR0\nBFbFE19CKtEaEq98+wqbd27m18f9Oux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"text/plain": [ "<matplotlib.figure.Figure at 0x161925bd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iterations = 21\n", "d = .85\n", "\n", "thd = 1/3.0\n", "fll = 1/11.0\n", "T = np.array( [[ fll, fll, fll, fll, fll, fll, fll, fll, fll, fll, fll],\n", " [ 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0.5, 0.5, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , thd, 0. , thd, 0. , thd, 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0.5, 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0.5, 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0.5, 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0.5, 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ]])\n", "\n", "teleport = np.ones(T.shape)/T.shape[0]\n", "T = d*T + (1-d)*teleport\n", "T = T.dot(T) # This is what changed\n", "\n", "stable = np.ones(T.shape[0])/T.shape[0]\n", "\n", "all_stables = []\n", "\n", "for i in xrange(iterations):\n", " stable = stable.dot(T)\n", " all_stables.append(stable)\n", "\n", "plt.plot(all_stables)\n", "plt.hlines(true_values,0,iterations-1, colors=\"grey\")\n", "plt.title(\"Power iterations stabilize after about 15 iterations\")\n", "plt.ylim(0,.5)\n", "plt.show()\n", "\n", "plt.plot(all_stables)\n", "plt.hlines(true_values,0,iterations-1, colors=\"grey\")\n", "plt.title(\"Zoomed in on the two highest PR pages\")\n", "plt.ylim(.32, .4);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compounding Power Iteration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a new algorithm. Instead of multiplying the current result by the transition matrix, we can multiply the transition matrix by itself and continually multiply the new result by itself.\n", "\n", "This means that what we are really calculating each step is $T \\rightarrow T^2 \\rightarrow T^4 \\rightarrow T^8 \\rightarrow T^{16} \\rightarrow T^{32}$. This results in the fastest convergence of any algorithm seen so far with the correct rank order being found after two iterations and the exact solution being found after five iterations. " ] }, { "cell_type": "code", "execution_count": 267, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VLHcB0B24vXZhSrNgBi+8AHvs4U20tG1bTMJIik/ioqyLmHvFXN4+820KSwp1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\ntH6uzxkBHqglNuecc87ttaAZhBnzubvhFuASSed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"text/plain": [ "<matplotlib.figure.Figure at 0x1519c4dd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "image/png": 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qrQR+XmI7zwG/qWL/9iR1QT+dvTbmk5LFr5JbStvAY9ublPS8TZpn8L+kCYsr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\nBZ0wAVatqnU0ZmYtyomEWWuor4d58+D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"text/plain": [ "<matplotlib.figure.Figure at 0x15f925a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iterations = 11\n", "d = .85\n", "\n", "thd = 1/3.0\n", "fll = 1/11.0\n", "T = np.array( [[ fll, fll, fll, fll, fll, fll, fll, fll, fll, fll, fll],\n", " [ 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0.5, 0.5, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , thd, 0. , thd, 0. , thd, 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0.5, 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0.5, 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0.5, 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0.5, 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ]])\n", "\n", "teleport = np.ones(T.shape)/T.shape[0]\n", "T = d*T + (1-d)*teleport\n", "\n", "all_stables = []\n", "\n", "for i in xrange(iterations):\n", " T = T.dot(T)\n", " all_stables.append(T.diagonal())\n", " \n", "plt.plot(all_stables);\n", "plt.hlines(true_values,0,iterations-1, colors=\"grey\")\n", "plt.title(\"The compounding version converges much faster\")\n", "plt.ylim(0,.5)\n", "plt.show()\n", "\n", "plt.plot(all_stables)\n", "plt.hlines(true_values,0,iterations-1, colors=\"grey\")\n", "plt.title(\"Zoomed in on the two highest PR pages\\nshows convergence after 5 iterations\")\n", "plt.ylim(.32, .4);\n", "\n", "print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "My suspicion is that a four iteration solution is possible with some more thinking. But because this method is not scalable, it makes sense to move onto a better solution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the lectures, there was a statement that was repeated over and over again. \"The most efficient, distributed PageRank algorithm must be composed of at least two MapReduce steps.\" This is unfortunate because it would mean the entire dataset would have to be copied twice for each step.\n", "\n", "I decided to figure out how to do it in one step." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First attempt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Here is a one-stage solution to the problem that uses a single reducer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Main thoughts when creating it: \n", "* Use the number of nodes as an input argument.\n", "* Continually track the Total PageRank in the system, the amount of PageRank to distribute, and the number of nodes in the system using the order inversion pattern. \n", "* When a dangling node is found, explicitly add it to the system.\n", "* Handle decay and teleportation distributions as the same thing." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting PageRank.py\n" ] } ], "source": [ "%%writefile PageRank.py\n", "\n", "from __future__ import print_function, division\n", "from mrjob.job import MRJob\n", "from mrjob.job import MRStep\n", "from mrjob.protocol import JSONProtocol\n", "from sys import stderr\n", "\n", "class PageRank(MRJob):\n", " INPUT_PROTOCOL = JSONProtocol\n", " \n", " def configure_options(self):\n", " super(PageRank, \n", " self).configure_options()\n", "\n", " self.add_passthrough_option(\n", " '--n_nodes', \n", " dest='n_nodes', \n", " type='float',\n", " help=\"\"\"number of nodes \n", " that have outlinks. You can\n", " guess at this because the\n", " exact number will be \n", " updated after the first\n", " iteration.\"\"\")\n", " \n", " def mapper(self, key, lines):\n", " # Handles special keys\n", " # Calculate new Total PR\n", " # each iteration\n", " if key in [\"****Total PR\"]:\n", " raise StopIteration\n", " if key in [\"**Distribute\", \"***n_nodes\"]:\n", " yield (key, lines)\n", " raise StopIteration\n", " # Handles the first time the \n", " # mapper is called. The lists\n", " # are converted to dictionaries \n", " # with default PR values.\n", " if isinstance(lines, list):\n", " n_nodes = self.options.n_nodes\n", " default_PR = 1/n_nodes\n", " lines = {\"links\":lines, \n", " \"PR\": default_PR}\n", " # Also perform a node count\n", " yield (\"***n_nodes\", 1.0)\n", " PR = lines[\"PR\"]\n", " links = lines[\"links\"]\n", " n_links = len(links)\n", " # Pass node onward\n", " yield (key, lines)\n", " # Track total PR in system\n", " yield (\"****Total PR\", PR)\n", " # If it is not a dangling node\n", " # distribute its PR to the \n", " # other links.\n", " if n_links:\n", " PR_to_send = PR/n_links\n", " for link in links:\n", " yield (link, PR_to_send)\n", " else:\n", " yield (\"**Distribute\", PR)\n", "\n", " def reducer_init(self):\n", " self.to_distribute = None\n", " self.n_nodes = None\n", " self.total_pr = None\n", " \n", " def reducer(self, key, values):\n", " total = 0\n", " node_info = None\n", " \n", " for val in values:\n", " if isinstance(val, float):\n", " total += val\n", " else:\n", " node_info = val\n", " \n", " if node_info:\n", " distribute = self.to_distribute or 0\n", " pr = total + distribute\n", " decayed_pr = .85 * pr\n", " teleport_pr = .15/self.n_nodes\n", " new_pr = decayed_pr + teleport_pr\n", " node_info[\"PR\"] = new_pr\n", " yield (key, node_info)\n", " elif key == \"****Total PR\":\n", " self.total_pr = total\n", " yield (key, total)\n", " elif key == \"***n_nodes\":\n", " self.n_nodes = total\n", " yield (key, total)\n", " elif key == \"**Distribute\":\n", " extra_mass = total\n", " # Because the node_count and\n", " # the mass distribution are \n", " # eventually consistent, a\n", " # simple correction for any early\n", " # discrepancies is a good fix\n", " excess_pr = self.total_pr - 1\n", " weight = extra_mass - excess_pr\n", " self.to_distribute = weight/self.n_nodes\n", " else:\n", " # The only time this should run\n", " # is when dangling nodes are \n", " # discovered during the first\n", " # iteration. By making them\n", " # explicitly tracked, the mapper\n", " # can handle them from now on.\n", " yield (\"**Distribute\", total)\n", " yield (\"***n_nodes\", 1.0)\n", " yield (key, {\"PR\": total, \n", " \"links\": []})\n", " \n", " def steps(self):\n", " mr_steps = [MRStep(mapper=self.mapper,\n", " reducer_init=self.reducer_init,\n", " reducer=self.reducer)]*50\n", " return mr_steps\n", " \n", "if __name__ == \"__main__\":\n", " PageRank.run()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(u'****Total PR', 1.0)\n", "(u'***n_nodes', 11.0)\n", "(u'A', {u'PR': 0.03278149315934761, u'links': []})\n", "(u'B', {u'PR': 0.3843611835646984, u'links': [u'C']})\n", "(u'C', {u'PR': 0.34295005075721485, u'links': [u'B']})\n", "(u'D', {u'PR': 0.039087092099970085, u'links': [u'A', u'B']})\n", "(u'E', {u'PR': 0.08088569323450426, u'links': [u'B', u'D', u'F']})\n", "(u'F', {u'PR': 0.039087092099970085, u'links': [u'B', u'E']})\n", "(u'G', {u'PR': 0.016169479016858924, u'links': [u'B', u'E']})\n", "(u'H', {u'PR': 0.016169479016858924, u'links': [u'B', u'E']})\n", "(u'I', {u'PR': 0.016169479016858924, u'links': [u'B', u'E']})\n", "(u'J', {u'PR': 0.016169479016858924, u'links': [u'E']})\n", "(u'K', {u'PR': 0.016169479016858924, u'links': [u'E']})\n" ] } ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "from PageRank import PageRank\n", "\n", "mr_job = PageRank(args=[\"data/PageRank-test.txt\", \n", " \"--n_nodes=11\", \n", " \"--jobconf=mapred.reduce.tasks=1\"])\n", "with mr_job.make_runner() as runner:\n", " runner.run()\n", " for line in runner.stream_output():\n", " print(mr_job.parse_output_line(line))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The answers are exactly correct, but there are many downsides of this solution. The primary downside is that it requires we have only one reducer so that the special keys are available to all the items sent to the reducer.\n", "\n", "The solution to this problem is demonstrated in the next example.\n", "\n", "An argument was added, `--reduce.tasks`, that takes as input a number of reducers to use. Instead of using the standard keys to determine how data is partitioned to be sent to each reducer, the code below uses the following pattern: Before a mapper yields a tuple, hash each key to be between 0 and `--reduce.tasks`. Take this value and make it the new key. Make the new value be the old key-value tuple. For example, (\"cat\", 42) --> (3, (\"cat\", 42)).\n", "\n", "There are three benefits to this method: \n", "* All occurrences of the old key will go to the same reducer.\n", "* The partition key is now explicit. (We will exploit this in a moment).\n", "* This code works (and is testable) locally without needing to use Hadoop-based partioning schemes (which cannot easily do what we are about to do).\n", "\n", "Problem: There is global state that we need to get to each reducer (i.e. number of nodes, total PageRank in the system, and total PageRank to distribute).\n", "\n", "Solution: Because we are forcing keys to take a value between 0 and `--reduce.tasks`, we can send a copy of these global variables to each possible value to ensure every reduce task has access to these values. \n", "\n", "Concretely, let's say we have four keys in the current system [\"cat\", \"dog\", \"mouse\", \"bat\"] and we want to ensure a global key \"/*/*n_nodes\" gets to each reducer. If we set `--reduce.tasks` to 2, we might get the following: \n", "```\n", "(\"cat\", .....) --> (0, (\"cat\", .....)) \n", "(\"dog\", .....) --> (1, (\"dog\", .....)) \n", "(\"mouse\", ...) --> (0, (\"mouse\", ...)) \n", "(\"bat\", .....) --> (1, (\"bat\", .....)) \n", "```\n", "We would also yield:\n", "```\n", "(0, (**n_nodes, ...)) \n", "(1, (**n_nodes, ...)) \n", "```\n", "This means that one reduce task would have: \n", "```\n", "(0, (**n_nodes, ...)) \n", "(0, (\"cat\", .....)) \n", "(0, (\"mouse\", ...)) \n", "```\n", "And the other one would have: \n", "```\n", "(1, (**n_nodes, ...)) \n", "(1, (\"bat\", .....)) \n", "(1, (\"dog\", .....))\n", "```\n", "\n", "This is exactly what we want and allows us to define any number of reduce tasks we would like ahead of time by setting `--reduce.tasks` to a high number." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Here is a one-stage solution that uses multiple reducers" ] }, { "cell_type": "code", "execution_count": 303, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing ComplexPageRank.py\n" ] } ], "source": [ "%%writefile ComplexPageRank.py\n", "from __future__ import print_function, division\n", "import itertools\n", "from mrjob.job import MRJob\n", "from mrjob.job import MRStep\n", "from mrjob.protocol import JSONProtocol\n", "from sys import stderr\n", "from random import random\n", "\n", "class ComplexPageRank(MRJob):\n", " INPUT_PROTOCOL = JSONProtocol\n", " \n", " def configure_options(self):\n", " super(ComplexPageRank, \n", " self).configure_options()\n", "\n", " self.add_passthrough_option(\n", " '--n_nodes', \n", " dest='n_nodes', \n", " type='float',\n", " help=\"\"\"number of nodes \n", " that have outlinks. You can\n", " guess at this because the\n", " exact number will be \n", " updated after the first\n", " iteration.\"\"\")\n", " \n", " self.add_passthrough_option(\n", " '--reduce.tasks', \n", " dest='reducers', \n", " type='int',\n", " help=\"\"\"number of reducers\n", " to use. Controls the hash\n", " space of the custom\n", " partitioner\"\"\")\n", " \n", " self.add_passthrough_option(\n", " '--iterations', \n", " dest='iterations', \n", " type='int',\n", " help=\"\"\"number of iterations\n", " to perform.\"\"\")\n", " \n", " self.add_passthrough_option(\n", " '--damping_factor', \n", " dest='d', \n", " default=.85,\n", " type='float',\n", " help=\"\"\"Is the damping\n", " factor. Must be between\n", " 0 and 1.\"\"\")\n", " \n", " self.add_passthrough_option(\n", " '--smart_updating', \n", " dest='smart_updating', \n", " type='str',\n", " default=\"False\",\n", " help=\"\"\"Can be True or\n", " False. If True, all updates\n", " to the new PR will take into\n", " account the value of the old\n", " PR.\"\"\")\n", " \n", " def mapper_init(self):\n", " self.values = {\"****Total PR\": 0.0,\n", " \"***n_nodes\": 0.0,\n", " \"**Distribute\": 0.0}\n", " self.n_reducers = self.options.reducers\n", " \n", " def mapper(self, key, lines):\n", " n_reducers = self.n_reducers\n", " key_hash = hash(key)%n_reducers\n", " # Handles special keys\n", " # Calculate new Total PR\n", " # each iteration\n", " if key in [\"****Total PR\"]:\n", " raise StopIteration\n", " if key in [\"**Distribute\"]:\n", " self.values[key] += lines\n", " raise StopIteration\n", " if key in [\"***n_nodes\"]:\n", " self.values[key] += lines\n", " raise StopIteration\n", " # Handles the first time the \n", " # mapper is called. The lists\n", " # are converted to dictionaries \n", " # with default PR values.\n", " if isinstance(lines, list):\n", " n_nodes = self.options.n_nodes\n", " default_PR = 1/n_nodes\n", " lines = {\"links\":lines, \n", " \"PR\": default_PR}\n", " # Perform a node count each time\n", " self.values[\"***n_nodes\"] += 1.0\n", " PR = lines[\"PR\"]\n", " links = lines[\"links\"]\n", " n_links = len(links)\n", " # Pass node onward\n", " yield (key_hash, (key, lines))\n", " # Track total PR in system\n", " self.values[\"****Total PR\"] += PR\n", " # If it is not a dangling node\n", " # distribute its PR to the \n", " # other links.\n", " if n_links:\n", " PR_to_send = PR/n_links\n", " for link in links:\n", " link_hash = hash(link)%n_reducers\n", " yield (link_hash, (link, PR_to_send))\n", " else:\n", " self.values[\"**Distribute\"] = PR\n", "\n", " def mapper_final(self):\n", " for key, value in self.values.items():\n", " for k in range(self.n_reducers):\n", " yield (k, (key, value))\n", " \n", " def reducer_init(self):\n", " self.d = self.options.d\n", " smart = self.options.smart_updating\n", " if smart == \"True\":\n", " self.smart = True\n", " elif smart == \"False\":\n", " self.smart = False\n", " else:\n", " msg = \"\"\"--smart_updating should \n", " be True or False\"\"\"\n", " raise Exception(msg)\n", " self.to_distribute = None\n", " self.n_nodes = None\n", " self.total_pr = None\n", "\n", " def reducer(self, hash_key, combo_values):\n", " gen_values = itertools.groupby(combo_values, \n", " key=lambda x:x[0])\n", " for key, values in gen_values:\n", " total = 0\n", " node_info = None\n", "\n", " for key, val in values:\n", " if isinstance(val, float):\n", " total += val\n", " else:\n", " node_info = val\n", "\n", " if node_info:\n", " old_pr = node_info[\"PR\"]\n", " distribute = self.to_distribute or 0\n", " pr = total + distribute\n", " decayed_pr = self.d * pr\n", " teleport_pr = (1-self.d)/self.n_nodes\n", " new_pr = decayed_pr + teleport_pr\n", " if self.smart:\n", " # If the new value is less than\n", " # 30% different than the old\n", " # value, set the new PR to be\n", " # 80% of the new value and 20% \n", " # of the old value.\n", " diff = abs(new_pr - old_pr)\n", " percent_diff = diff/old_pr\n", " if percent_diff < .3:\n", " new_pr = .8*new_pr + .2*old_pr\n", " node_info[\"PR\"] = new_pr\n", " yield (key, node_info)\n", " elif key == \"****Total PR\":\n", " self.total_pr = total\n", " elif key == \"***n_nodes\":\n", " self.n_nodes = total\n", " elif key == \"**Distribute\":\n", " extra_mass = total\n", " # Because the node_count and\n", " # the mass distribution are \n", " # eventually consistent, a\n", " # simple correction for any early\n", " # discrepancies is a good fix\n", " excess_pr = self.total_pr - 1\n", " weight = extra_mass - excess_pr\n", " self.to_distribute = weight/self.n_nodes\n", " else:\n", " # The only time this should run\n", " # is when dangling nodes are \n", " # discovered during the first\n", " # iteration. By making them\n", " # explicitly tracked, the mapper\n", " # can handle them from now on.\n", " yield (\"**Distribute\", total)\n", " yield (\"***n_nodes\", 1.0)\n", " yield (key, {\"PR\": total, \n", " \"links\": []})\n", "\n", " def reducer_final(self):\n", " print_info = False\n", " if print_info:\n", " print(\"Total PageRank\", self.total_pr)\n", " \n", " def steps(self):\n", " iterations = self.options.iterations\n", " mr_steps = [MRStep(mapper_init=self.mapper_init,\n", " mapper=self.mapper,\n", " mapper_final=self.mapper_final,\n", " reducer_init=self.reducer_init,\n", " reducer=self.reducer,\n", " reducer_final=self.reducer_final)]\n", " return mr_steps*iterations\n", "\n", "\n", "if __name__ == \"__main__\":\n", " ComplexPageRank.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This implementation converges to the correct answer. Fifty iterations takes about 2.5 seconds." ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{u'A': 0.03278149315934773,\n", " u'B': 0.3843730253341818,\n", " u'C': 0.342938208987731,\n", " u'D': 0.03908709209997017,\n", " u'E': 0.08088569323450442,\n", " u'F': 0.03908709209997017,\n", " u'G': 0.016169479016858956,\n", " u'H': 0.016169479016858956,\n", " u'I': 0.016169479016858956,\n", " u'J': 0.016169479016858956,\n", " u'K': 0.016169479016858956}\n", "CPU times: user 1.81 s, sys: 508 ms, total: 2.31 s\n", "Wall time: 2.58 s\n" ] } ], "source": [ "%%time\n", "%reload_ext autoreload\n", "%autoreload 2\n", "from ComplexPageRank import ComplexPageRank as PageRank\n", "\n", "mr_job = PageRank(args=[\"data/PageRank-test.txt\", \n", " \"--iterations=50\",\n", " \"--n_nodes=11\",\n", " \"--damping_factor=.85\",\n", " \"--jobconf=mapred.reduce.tasks=5\",\n", " \"--reduce.tasks=5\"])\n", "\n", "results = {}\n", "with mr_job.make_runner() as runner:\n", " runner.run()\n", " for line in runner.stream_output():\n", " result = mr_job.parse_output_line(line)\n", " results[result[0]] = result[1][\"PR\"]\n", "pprint(results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The chart below investigates how the PageRank parameters evolve as a function of the number of iterations in the standard algorithm. " ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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d+5rWXF9DbTa0IceJjgRPULHZxajP7j60LWCbXv26rMmrmFcU/CrYpOcKJx6f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"text/plain": [ "<matplotlib.figure.Figure at 0x10e68e210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "from ComplexPageRank import ComplexPageRank as PageRank\n", "\n", "all_results = []\n", "\n", "for iteration in range(1, 21):\n", " mr_job = PageRank(args=[\"data/PageRank-test.txt\", \n", " \"--iterations=%d\" % iteration, \n", " \"--n_nodes=11\",\n", " \"--damping_factor=.85\",\n", " \"--jobconf=mapred.reduce.tasks=5\",\n", " \"--reduce.tasks=5\"])\n", "\n", " results = {}\n", " with mr_job.make_runner() as runner:\n", " runner.run()\n", " for line in runner.stream_output():\n", " result = mr_job.parse_output_line(line)\n", " try:\n", " results[result[0]] = result[1][\"PR\"]\n", " except:\n", " pass\n", " results[\"index\"] = iteration\n", " all_results.append(results)\n", " \n", "data = pd.DataFrame(all_results)\n", "data.index = data.pop(\"index\")\n", "data.plot(kind=\"line\", legend=False)\n", "plt.hlines(true_values,0,iterations-1, colors=\"grey\")\n", "plt.title(\"Standard implementation of PageRank\")\n", "plt.xlabel(\"iterations\")\n", "plt.ylabel(\"PageRank\")\n", "plt.ylim(0,.5)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice the oscillations in the scores above. This is likely because there is a feedback loop between the two most highly ranked pages. This oscillation makes sense because B and C are only linked to each other and they both have very high PageRank scores.\n", "\n", "In order to fix this and increase the speed of convergence, I added a new PageRank update rule that can be turned on using the `--smart_updating=True` argument. This update rule does the following: \n", "* Compare the old and new PageRank for a node\n", "* If the percent difference is less than 30%, the actual PageRank value assigned to the node is 75% of the new value plus 25% of the old value.\n", "\n", "If there is a big change between the old and new PageRank values (common during the first iterations of the algorithm), the actual PageRank value used is the standard value used. This allows each page to rapidly get to its approximately correct place. \n", "\n", "If there is not a big change, oscillations are removed by smoothing the new PageRank value with the past PageRank value." ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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OiP4jUtr26J6jGT1sNMs3LOdP//sTk+ZM4sEPHuSkvU7iyoOu5Nh+x/phgSib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\nIlKHVpd4FOxRQM2iGl4a9xJ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65zaGC5xzG4AbgJ8658qAG4HhjY4wA2IuqQ1vHJaV9OVt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\ntMpQMWcOnHFG234iAU1N0NjYtnTeFxob7fTS/njL9sGDnc+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xnwRZAywDdnVTBunzfg/c\ng//0ySvAe4CbO2T7DX5/i7+nr3d+x/LSrRxnAiOBlcAK/D4bV/XjNR4ElgLPAm8AlwHnG2M29KMM\npdQg6MilSimllMobbfFQSimlVN5o4KGUUkqpvNHAQymllFJ5o4GHUkoppfJGAw+llFJK5Y0GHkop\npZTKGw08lFJKKZU3GngopZRSKm808FBKKaVU3mjgoZRSSqm80cBDKaWUUnmjgYdSSiml8ub/A5dG\nUpIuhoGmAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f40d850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "from ComplexPageRank import ComplexPageRank as PageRank\n", "\n", "all_results = []\n", "\n", "for iteration in range(1, 21):\n", " mr_job = PageRank(args=[\"data/PageRank-test.txt\", \n", " \"--iterations=%d\" % iteration, \n", " \"--n_nodes=11\",\n", " \"--damping_factor=.85\",\n", " \"--jobconf=mapred.reduce.tasks=5\",\n", " \"--reduce.tasks=5\",\n", " \"--smart_updating=True\"])\n", "\n", " results = {}\n", " with mr_job.make_runner() as runner:\n", " runner.run()\n", " for line in runner.stream_output():\n", " result = mr_job.parse_output_line(line)\n", " try:\n", " results[result[0]] = result[1][\"PR\"]\n", " except:\n", " pass\n", " results[\"index\"] = iteration\n", " all_results.append(results)\n", " \n", "data = pd.DataFrame(all_results)\n", "data.index = data.pop(\"index\")\n", "data.plot(kind=\"line\", legend=False)\n", "plt.hlines(true_values,0,iterations-1, colors=\"grey\")\n", "plt.title(\"Smart update implementation \\n of PageRank\")\n", "plt.xlabel(\"iterations\")\n", "plt.ylabel(\"PageRank\")\n", "plt.ylim(0,.5)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The updated algorithm converges much faster on the dataset and the oscillations are removed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2 style=\"color:darkgreen\"> HW 9.1 Analysis </h2>\n", "In the lectures, it was said that a one-stage PageRank algorithm was not possible. This is a working, fully distributed, one-stage PageRank algorithm with a smart updating rule that convergences significantly faster on this dataset.\n", "<br><br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. HW9.2: Exploring PageRank teleportation and network plots <a name=\"1.2\"></a>\n", "[Back to Table of Contents](#TOC)\n", "\n", "\n", "* In order to overcome problems such as disconnected components, the damping factor (a typical value for d is 0.85) can be varied. \n", "* Using the graph in HW1, plot the test graph (using networkx, https://networkx.github.io/) for several values of the damping parameter alpha, so that each nodes radius is proportional to its PageRank score. \n", "* In particular you should do this for the following damping factors: [0,0.25,0.5,0.75, 0.85, 1]. \n", "* Note your plots should look like the following: https://en.wikipedia.org/wiki/PageRank#/media/File:PageRanks-Example.svg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2 style=\"color:darkgreen\"> HW 9.2 Implementation </h2>" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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PeocBw75+acyu+45t43034WmKuxbGzq2b+fqFMdx79AHejHdeyABUo9E7sq9n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\nekOcsZldU9UfwiA4dunPs1c/3vtof9EPM1I6X1BYyPjH72Hsnf1B9Qn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"text/plain": [ "<matplotlib.figure.Figure at 0x1046204d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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kuaefQtAlEgWmJjQisyR721Z387rVyY4DCGZwrV2y0AGWJDsWUzSWXBiTt3uA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"text/plain": [ "<matplotlib.figure.Figure at 0x10f31a4d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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pMlSqVS/f92Rt3cLHD90UcVzvB1X9tRAhmyLIkgtjdkFVfwPuBm4QkQN3ce2v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\nFLZBlYgcBcwCblXVJxIYojEVhiUX5ZiI1EbkCVSvaNiytXvSZTdEjz2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"text/plain": [ "<matplotlib.figure.Figure at 0x111b1f850>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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XvzhWi6wmFovIdfjd5IcFdURMARORJPw5MLcAFfAnV96pqhtyeP3pjuve74XD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\nAk4rCQs67W4HUAq4UFWHJz2wYk5EKgKTCNdWHKeqv2dyjkNYI8MDWlv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"text/plain": [ "<matplotlib.figure.Figure at 0x104638c90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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AkVOfukefGnW8Lv/z110eG9u2jW/enMDdgw6Ib1qzaq4fj/W1xMKY6FjNhSnWJFhGMw3I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\n+4QLPc+Or8nYee5MYDRwuaq+n6IQjcnXLLkwJo9EpC5wvRNzrw98r5K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"text/plain": [ "<matplotlib.figure.Figure at 0x10f3050d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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gYHj/vknP9z8LkslRccdjNjEz+mrJRQaSTOz/zyYishdw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\n9J5JGd3ZDnhPHfdUu6qRrd+r/vM3q6/1gkHgYcr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"text/plain": [ "<matplotlib.figure.Figure at 0x10470c710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import networkx as nx\n", "\n", "def display_graph(edges, PageRanks, title, node_scaling=10000, pos=None):\n", " DG = nx.DiGraph()\n", " DG.add_edges_from(edges)\n", " \n", " w = []\n", " for node in DG.nodes():\n", " weight = PageRanks[node]\n", " w.append(weight*node_scaling)\n", " \n", " nx.draw_networkx(DG, \n", " node_size=w, \n", " node_color=\"#8ED9FD\",\n", " pos=pos)\n", " plt.title(title)\n", " plt.axis('off')\n", " plt.show()\n", " \n", " \n", "pages = {\"B\":[\"C\"],\n", " \"C\":[\"B\"],\n", " \"D\":[\"A\",\"B\"],\n", " \"E\":[\"B\",\"D\",\"F\"],\n", " \"F\":[\"B\",\"E\"],\n", " \"G\":[\"B\",\"E\"],\n", " \"H\":[\"B\",\"E\"],\n", " \"I\":[\"B\",\"E\"],\n", " \"J\":[\"E\"],\n", " \"K\":[\"E\"]}\n", "\n", "edges = []\n", "\n", "for page, links in pages.items():\n", " for link in links:\n", " edges.append([page, link])\n", "\n", "# Get constant positions\n", "DG = nx.DiGraph()\n", "DG.add_edges_from(edges)\n", "pos = nx.layout.spring_layout(DG)\n", "\n", "for damping_factor in [0,0.25,0.5,0.75, 0.85, 1]:\n", " mr_job = PageRank(args=[\"data/PageRank-test.txt\", \n", " \"--iterations=20\", \n", " \"--n_nodes=11\",\n", " \"--damping_factor=%f\" % damping_factor,\n", " \"--jobconf=mapred.reduce.tasks=5\",\n", " \"--reduce.tasks=5\",\n", " \"--smart_updating=True\"])\n", " results = {}\n", " with mr_job.make_runner() as runner:\n", " runner.run()\n", " for line in runner.stream_output():\n", " result = mr_job.parse_output_line(line)\n", " try:\n", " results[result[0]] = result[1][\"PR\"]\n", " except:\n", " pass\n", " \n", " display_graph(edges, results, \"Damping factor: %.2f\" % damping_factor, pos=pos)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. HW9.3: Applying PageRank to the Wikipedia hyperlinks network <a name=\"1.3\"></a>\n", "[Back to Table of Contents](#TOC)\n", "\n", "* Run your PageRank implementation on the Wikipedia dataset for 5 iterations, and display the top 100 ranked nodes (with alpha = 0.85).\n", "* Run your PageRank implementation on the Wikipedia dataset for 10 iterations, and display the top 100 ranked nodes (with teleportation factor of 0.15).\n", "* Have the top 100 ranked pages changed? Comment on your findings. \n", "* Plot the pagerank values for the top 100 pages resulting from the 5 iterations run. Then plot the pagerank values for the same 100 pages that resulted from the 10 iterations run. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2 style=\"color:darkgreen\"> HW 9.3 Implementation </h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This code is adapted to work on the Wikipedia dataset. There are several modifications to this algorithm: \n", "* Another step was added to the front that transforms the dataset into the expected format. Specifically, the current format is invalid json and is in a dictionary-type format. We change it into a list format of the links.\n", "* Another step was added to the end to save just the top 100 PageRank values (instead of the full list of 5 million plus values).\n", "* The algorithm was refactored so that the user no longer needs to know the correct number of nodes before running the program (it figures it out as it goes along and appropriately adjusts for dangling nodes as it find them).\n", "* Instead of the sum of all PageRank scores across the network being equal to one, the average of all PageRank scores is now equal to one. This allows us to no longer care about the total PageRank in the entire system. All that matters is that the update rule has an expected value of 1 (which occurs if the damping factor is the same as the damping PageRank contribution to each node. This means a damping factor of .15 should add .15 PR score each iteration to each node.) The results are exactly proportional to the correct Page Rank scores.\n", "* We no longer pass state through the entire system after it is calculated the first time. Instead, we recalculate the number of nodes we have seen each time. This dramatically reduces the complexity of the code, but is an area where the code could be optimized in the future.\n", "* `MRJob.SORT_VALUES = True` was added to the code to ensure that the old keys were sorted from on the reducer. This was needed to get the special keys sorted to the top." ] }, { "cell_type": "code", "execution_count": 217, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting SimplePageRank.py\n" ] } ], "source": [ "%%writefile SimplePageRank.py\n", "from __future__ import division\n", "import itertools\n", "from mrjob.job import MRJob, MRStep\n", "import json\n", "import heapq\n", "\n", "\n", "class TopList(list):\n", " def __init__(self, \n", " max_size, \n", " num_position=0):\n", " \"\"\"\n", " Just like a list, except \n", " the append method adds \n", " the new value to the \n", " list only if it is larger\n", " than the smallest value \n", " (or if the size of \n", " the list is less than \n", " max_size). \n", " \n", " If each element of the list \n", " is an int or float, uses \n", " that value for comparison. \n", " If the elements in the list \n", " are lists or tuples, uses the \n", " list_position element of the \n", " list or tuple for the \n", " comparison.\n", " \"\"\"\n", " self.max_size = max_size\n", " self.pos = num_position\n", " \n", " def _get_key(self, x):\n", " if isinstance(x, (list, tuple)):\n", " return x[self.pos]\n", " else:\n", " return x\n", " \n", " def append(self, val):\n", " if len(self) < self.max_size:\n", " heapq.heappush(self, val)\n", " else:\n", " lowest_val = self._get_key(self[0])\n", " current_val = self._get_key(val)\n", " if current_val > lowest_val:\n", " heapq.heapreplace(self, val)\n", " \n", " def final_sort(self):\n", " return sorted(self, \n", " key=self._get_key, \n", " reverse=True)\n", " \n", " \n", "class SimplePageRank(MRJob):\n", " MRJob.SORT_VALUES = True\n", " def configure_options(self):\n", " super(SimplePageRank, \n", " self).configure_options()\n", "\n", " self.add_passthrough_option(\n", " '--reduce.tasks', \n", " dest='reducers', \n", " type='int',\n", " help=\"\"\"number of reducers\n", " to use. Controls the hash\n", " space of the custom\n", " partitioner\"\"\")\n", " \n", " self.add_passthrough_option(\n", " '--iterations', \n", " dest='iterations',\n", " default=5,\n", " type='int',\n", " help=\"\"\"number of iterations\n", " to perform.\"\"\")\n", " \n", " self.add_passthrough_option(\n", " '--damping_factor', \n", " dest='d', \n", " default=.85,\n", " type='float',\n", " help=\"\"\"Is the damping\n", " factor. Must be between\n", " 0 and 1.\"\"\")\n", " \n", " self.add_passthrough_option(\n", " '--smart_updating', \n", " dest='smart_updating', \n", " type='str',\n", " default=\"False\",\n", " help=\"\"\"Can be True or\n", " False. If True, all updates\n", " to the new PR will take into\n", " account the value of the old\n", " PR.\"\"\")\n", " \n", " self.add_passthrough_option(\n", " '--return_top_k', \n", " dest='return_top_k', \n", " type='int',\n", " default=100,\n", " help=\"\"\"Returns the results\n", " with the top k highest \n", " PageRank scores.\"\"\")\n", " \n", " def clean_data(self, _, lines):\n", " key, value = lines.split(\"\\t\")\n", " value = json.loads(value.replace(\"'\", '\"'))\n", " links = value.keys()\n", " values = {\"PR\":1,\"links\":links}\n", " yield (str(key), values)\n", " \n", " def mapper_init(self):\n", " self.values = {\"***n_nodes\": 0,\n", " \"**Distribute\": 0}\n", " self.n_reducers = self.options.reducers\n", " \n", " def mapper(self, key, line):\n", " \n", " n_reducers = self.n_reducers\n", " key_hash = hash(key)%n_reducers\n", " \n", " # Perform a node count each time\n", " self.values[\"***n_nodes\"] += 1\n", " PR = line[\"PR\"]\n", " links = line[\"links\"]\n", " n_links = len(links)\n", " \n", " # If it is not a dangling node\n", " # distribute its PR to the \n", " # other links.\n", " if n_links:\n", " PR_to_send = PR/n_links\n", " for link in links:\n", " link_hash = hash(link)%n_reducers\n", " yield (int(link_hash), (link, \n", " PR_to_send))\n", " # If it is a dangling node, \n", " # distribute its PR to all\n", " # other links\n", " else:\n", " self.values[\"**Distribute\"] += PR\n", " \n", " # Pass original node onward\n", " yield (int(key_hash), (key, line))\n", "\n", " def mapper_final(self):\n", " # Push special keys to each unique hash\n", " for key, value in self.values.items():\n", " for k in range(self.n_reducers):\n", " yield (int(k), (key, value))\n", " \n", " \n", " def reducer_init(self):\n", " self.d = self.options.d\n", " smart = self.options.smart_updating\n", " if smart == \"True\":\n", " self.smart = True\n", " elif smart == \"False\":\n", " self.smart = False\n", " else:\n", " msg = \"\"\"--smart_updating should \n", " be True or False\"\"\"\n", " raise Exception(msg)\n", " self.to_distribute = None\n", " self.n_nodes = None\n", "\n", " def reducer(self, hash_key, combo_values):\n", " gen_values = itertools.groupby(combo_values, \n", " key=lambda x:x[0])\n", " # Hask key is a pseudo partitioner.\n", " # Unpack old keys as separate\n", " # generators.\n", " for key, values in gen_values:\n", " total = 0\n", " node_info = None\n", "\n", " for key, val in values:\n", " # If the val is a number,\n", " # accumulate total.\n", " if isinstance(val, (float, int)):\n", " total += val\n", " else:\n", " # Means that the key-value\n", " # pair corresponds to a node\n", " # of the form. \n", " # {\"PR\": ..., \"links: [...]}\n", " node_info = val\n", " # Most keys will reference a node, so\n", " # put this check first.\n", " if node_info:\n", " old_pr = node_info[\"PR\"]\n", " distribute = self.to_distribute or 0\n", " pr = total + distribute\n", " decayed_pr = self.d * pr\n", " teleport_pr = 1-self.d\n", " new_pr = decayed_pr + teleport_pr\n", " if self.smart:\n", " # Use old PR to inform\n", " # new PR.\n", " diff = abs(new_pr - old_pr)\n", " percent_diff = diff/old_pr\n", " if percent_diff < .3:\n", " new_pr = .8*new_pr + .2*old_pr\n", " node_info[\"PR\"] = new_pr\n", " yield (key, node_info)\n", " elif key == \"***n_nodes\":\n", " self.n_nodes = total\n", " elif key == \"**Distribute\":\n", " self.to_distribute = total/self.n_nodes\n", " else:\n", " # Track dangling nodes.\n", " yield (key, {\"PR\": 1, \n", " \"links\": []})\n", " \n", " def decrease_file_size(self, key, value):\n", " val = value[\"PR\"]\n", " if val > .1:\n", " yield (\"top\", (key, round(val,4)))\n", " \n", " def collect_init(self):\n", " top_k = self.options.return_top_k\n", " self.top_vals = TopList(top_k, 1)\n", " \n", " def collect(self, key, values):\n", " for val in values:\n", " self.top_vals.append(val)\n", " \n", " def collect_final(self):\n", " for val in self.top_vals.final_sort():\n", " yield val\n", "\n", " def steps(self):\n", " iterations = self.options.iterations\n", " mr_steps = (\n", " [MRStep(mapper=self.clean_data)] \n", " +\n", " [MRStep(\n", " mapper_init=self.mapper_init,\n", " mapper=self.mapper,\n", " mapper_final=self.mapper_final,\n", " reducer_init=self.reducer_init,\n", " reducer=self.reducer\n", " )]*iterations\n", " +\n", " [MRStep(mapper=self.decrease_file_size,\n", " reducer_init=self.collect_init,\n", " reducer=self.collect,\n", " reducer_final=self.collect_final)]\n", " )\n", " return mr_steps\n", "\n", "\n", "if __name__ == \"__main__\":\n", " SimplePageRank.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before running the code on the Wikipedia dataset, we can test it out on a known dataset that is formatted like the Wikipedia dataset. " ] }, { "cell_type": "code", "execution_count": 275, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "B\t{'C': 1}\r\n", "C\t{'B': 1}\r\n", "D\t{'A': 1, 'B': 1}\r\n", "E\t{'D': 1, 'B': 1, 'F': 1}\r\n", "F\t{'B': 1, 'E': 1}\r\n", "G\t{'B': 1, 'E': 1}\r\n", "H\t{'B': 1, 'E': 1}\r\n", "I\t{'B': 1, 'E': 1}\r\n", "J\t{'E': 1}\r\n", "K\t{'E': 1}\r\n" ] } ], "source": [ "!head data/PageRank-test-original.txt" ] }, { "cell_type": "code", "execution_count": 293, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Actual results:\n", "[(u'B', 4.2279),\n", " (u'C', 3.7723),\n", " (u'E', 0.8897),\n", " (u'D', 0.43),\n", " (u'F', 0.43),\n", " (u'A', 0.3606),\n", " (u'G', 0.1779),\n", " (u'H', 0.1779),\n", " (u'I', 0.1779),\n", " (u'J', 0.1779),\n", " (u'K', 0.1779)]\n", "\n", "Scaled to original:\n", "{u'A': 0.03278,\n", " u'B': 0.38435,\n", " u'C': 0.34294,\n", " u'D': 0.03909,\n", " u'E': 0.08088,\n", " u'F': 0.03909,\n", " u'G': 0.01617,\n", " u'H': 0.01617,\n", " u'I': 0.01617,\n", " u'J': 0.01617,\n", " u'K': 0.01617}\n" ] } ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "from SimplePageRank import SimplePageRank as PageRank\n", "\n", "mr_job = PageRank(args=[\"data/PageRank-test-original.txt\", \n", " \"--iterations=50\",\n", " \"--damping_factor=.85\",\n", " \"-q\",\n", " \"--return_top_k=100\",\n", " \"--reduce.tasks=5\"])\n", "results = []\n", "\n", "with mr_job.make_runner() as runner:\n", " runner.run()\n", " for line in runner.stream_output():\n", " result = mr_job.parse_output_line(line)\n", " results.append(result)\n", "\n", "print(\"Actual results:\")\n", "pprint(results)\n", "print()\n", "\n", "print(\"Scaled to original:\")\n", "total = sum([val for _, val in results])\n", "pprint({key: round(val/total,5) for key, val in results})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From above, we confirm that the algorithm works as expected and that the results can be scaled exactly to the actual PageRank scores." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With smart updating turned on, we see that the results converge very rapidly." ] }, { "cell_type": "code", "execution_count": 294, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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KL1GReIjIZUBvwN+Vp1oBW7zKtgANRCQxlLGFW87OHLYf2B70+A63GIlh0uBJ\nfPbbZyzdsjRE0ZX38JyH6dOqD+cee26NnF8ppdTRJ+KJh4i0A54BLjfGhHfxiSjkynUB0K9tv2qf\n69LjLqVjw448Pvfxap/L2/z185m9Zjb3nnKvtnYopZTyWzQsmZ4GNAey5NA3WCxwqoj8GUg0xhiv\nYzYDLb3KWgJ7jDGFlVU2YcIEGjZsWK5s9OjRjB49Otj4Q8qV66Jr0640rdO02ueKj43njkF3cOuX\nt/LQ0Ifo3Ljyq9wGYvKcyfRo1oOLe1wcsnMqpZSKvJkzZzJz5sxyZbt37w7Z+eXw7/TwEpG6QEev\n4teB5cAUY8xyH8dMAc41xpzoUfYu0MgYM7yCelKBzMzMTFJTU0MVfsj1fakvPZv35I0/vBGS8x0o\nOkDHZzpy6XGX8vzw50NyzsWbFpP6z1TevvhtLj/h8pCcUymlVPTKysoiLS0NIM0Yk1Wdc0W8q8UY\ns98Ys8zzBuwH8txJh4g8IiKe38QzgBQReUxEuonITcBIoNK1P6JdflE+SzYvYUDb6o3v8FQnvg7j\n+4/nlcWvsHX/1sp3vv12GD4cSitfeGzynMmkNE7h0uMvDVmcSimlaoeIJx4V8G6GaQ2ULUFpjFkL\nnAcMA5Zgp9GOM8Z4z3Q5omRtyqK4tLjaA0u93dz3ZuJi4nh2wbMV72QMvPsufPEF/OMfFe62bNsy\nPlz+IXeffDdxMdHQU6eUUupIEpWJhzHmdGPMbR6PxxpjTvfa53tjTJoxJtkY08UY81b4Iw0tV66L\n5LhkerXsFdLzNk5uzPVp1zP9x+nsKdzje6eff4bNmyEtDSZNgpwcn7s9+sOjtG3QlitPvDKkMSql\nlKodojLxqK1cG1z0bdu3RloSJgyYwP6D+3lx0Yu+d8jIgORk+PJLaNYMxo07rMtl9Y7VvPvzu0wc\nNJGE2ISQx6iUUurop4lHFHHlukI6vsOTu5ViqmsqBcUFh++Qng6nnWaTjldegW+/hRfLJymPzX2M\nZnWacU3qNTUSo1JKqaOfJh5RIndPLrl7ckM+vsPTnYPuZMu+Lbz1k1ev1IEDMGcOnHWWfXzGGXD9\n9XDnnbB2LQDrd6/n9SWvc8fAO0iOT66xGJVSSh3dNPGIEgtyFwDQv13/GqujW7NujOgxgsfnPU5J\nacmhDd9/D4WFcPbZh8oefxyaNrVdLsbwxLwnqJdQjxtOuqHG4lNKKXX008QjSrhyXXRo2IE29dvU\naD2TBk/lVc0rAAAgAElEQVRi1Y5VfLj8w0OF6enQrh1097gaboMG8PLLMHs2e557kpeyXuLWAbdS\nP7F+jcanlFLq6KaJR5RwbXDVaDeLW9+2fTmj8xlMmTuFssXjMjJsa4f30udnngnXXkviXfeSsjuW\nv/T7S43Hp5RS6uimiUcUKCopYtHGRTU2sNTbXSffRdamLL7O+RrWr4dlyw6N7/Cy46F72JpQzMez\nmtM4qVFY4quWX36xA2NLSqrcVSmlVPhp4hEFlm5ZSkFxQVhaPADO6HwGJ7U5iSlzp8BXX0FMDAwb\n5nPfZ5e/zs1/iKdL1lrb9RLNli6FwYNh6FDo0MGuxJqZaRdHU0opFRV06cko4Mp1ER8TT5/WfcJS\nn4hw1+C7GPn+SPIyY2jaty80aXLYfnsK9zBtwTSuvuwmiN1jv8jPPtt+qUebdevgnHPg2GPhqafg\nww/h7bdh6lTo2hXGjLG3Ll0iHWnFcnPtyrHp6bBrl31Pmja1N8+fPR83bgyxseGNs6AAdu6EHTsO\n3Twfu38uLoa4OIiPL3/vz8/+bI+JsS1bJSW2rure+yoTsfVUdKtqe1U3kcO7OL2FYrtnrJXdB7Kv\n5zGet4rK/b1Vdryvbd5lwe7jzzYVEpp4RAHXBhd9WvchKS4pbHX+ofsf6N64Cwmzv4Pb7vK5zws/\nvsCBogPcMegOGFDXfiFee61dZCyafgnz8mxClJwMn38OLVvaNUmmToXZs+1S8E89BQ88ACedZBOQ\nSy+FNjU7kLdKRUUwd65NNr74wq4eGxMDAwbYwb55ebBqlb3Py4P9+32fp1Ej30lJZQlLvXqwZ0/F\nSUNlCUV+vu846tWz52/c2N4SEuxzdH+pu3/2VVbR9lBxJy6xsb7vK9oGdiE9982Y8o/9uVV1jC8V\ntdKFqlwFrzqJUzTd/Hkunrd9+0L2EmriEQVcuS6GH+vzoro1JjYmlimNRlF//yOs69/9sMsDHyg6\nwNT5UxnbeyxtG7S1hf/8J5x3Hrz6qp1mGw0OHIDzz7dfiHPn2qTDLS7Ojl056yx7/ZnPPrNJyF13\n2daboUPh8sthxAj75R0Oubk2cfv8c/j6a9i7F1q0sK01995rB/T6aH0C7JTnHTsOJSJ5eeUfu3/+\n/XdYvPjQ46Ii/+OLjbVJgzuBaNIE2reHE0889Nh983zcqJFNNELJmPKtEd6JSUlJxYmE532M9iiX\nJT+e977Kgt3mXV7VrTr7+VMW7D6BxldTx9f0zf2ZCOSWlxeyj6MmHhG2bf82Vu1YFbbxHZ7OWxPH\nniRhcsFX/JMx5ba9lPkSO/J3MGnwpEOFw4fD1VfDbbfZL/P27Ymo4mLbcvHzz/DNN5V3oyQnw8iR\n9rZrl+2KefdduOYauPFGm1CNGWPvk0O4QFplrRoTJ8K550KfPv59OSYmQuvW9uYvY+x/Kt5Jyt69\nNlnwTibq14+e1iyRQ60RqnpEwt8lp44uWVn2n6YQkLIplUc5EUkFMjMzM0lNTY10OGU+XfkpF8y8\ngJxbcujcuHN4Kz/lFH6L2cVxw34lZ3wO7Rq0A6CwuJCUZ1MYljKMN/7wRvljdu6E44+HE06w/7VH\n6kvKGNvt88Yb8L//2RaDYGzcCO+9Z5OQRYvsF++IETYJOf304L703K0aX3xhB+96tmoMH155q4ZS\nSkWhrKws0tLSANKMMVnVOZe2QUaYK9dFy7ot6dSoU3gr3r0b5s+n/ahx1E2oy9Pzny7b9PqS19m0\ndxN3n3z34cc1bmy7XL78El5/PXzxevvb3+w1ZV59NfikA+w4jwkT4Mcf4ddfbRfM/Pl2zEjbtnDL\nLeByVd5XXlRkp/BOmmQTsvbt7ZLzmzfbVo1Fi2DTJpskXXqpJh1KqVpNE48Ic+XahcMk3C0H33wD\nJSUknXcRf+77Z17MfJEd+TsoKiliytwpjDpuFN2bdfd97HnnwZVX2i/sDRvCGzfY8RoPPQSPPQZ/\n+lPoztu1q01oVqywycIVV8B//gMDB9rZMvfdB8uX231zc+304j/+0Q7WHDrUJhapqfCvf8G2bbaL\n5b77IC1NxxkopZRD/xpGUElpCQs3LIzI+A7S0+2YiM6duaX/LZSaUqYvnM7MX2aydtda7jn5nsqP\nf+YZqFPH/mcfzu66Dz+Em2+G8ePtRexqgohNFp56yg7UnD3bdrtMnw49e9qWEF+tGhs32lYgbdVQ\nSqkK6aitCFq+fTl7D+6NTOKRkWEHNgLN6zZnXJ9xTFswjaZ1mnJB1ws4sdWJlR/fuDG8+CJceCG8\n9ZZtAalpc+bYsRejRtmpsuFoJYqNta0ZQ4fC88/bLqbvv4d+/XSshlJKBUETjwhy5bqIkRhOanNS\neCtetQpycspdjfb2Qbfzj0X/IC8/jzf/8KZ/57ngAtsdMX68Xfm0JtfF+OUXm+QMGgRvvhmZrovE\nRLjoIntTSikVFO1qiSBXroteLXpRL6FeeCtOT7ezNU47rayoU6NOXJd2HRd3v5j+7fr7f65p0+wX\nck12uaxfbweQduwIH31k61NKKXVE0haPCHLluji5w8nhrzgjw17TpH75S9y/cN4LgZ+rSRPb5fKH\nP8A779gWkFDascO2zMTF2empDRuG9vxKKaXCSls8ImR3wW6WbVsW/vEdBw/awZIVXI02KBddZMde\n3HKLnTYaKvn5tntl61bbShPIwllKKaWikiYeEfLjxh8xmPAnHi6XXcnSY3xHSDz7rL1w1w03hKbL\npbgYRo+2S39/9hl061b9cyqllIo4TTwixJXrolFSI7o27RreitPToVkzu0x3KDVtCjNmwH//CzNn\nVu9cxtgps59+Cv/+N/QPYMyJUkqpqKaJR4S4cl30b9ufGAnzW5CRYaeB1sSskIsvhssug7/8xa5v\nEayHHrKro770kl2sTCml1FEj4omHiNwgIj+JyG7nNk9EKlwDW0SGiEip161ERFqEM+7qMMaUrVga\nVtu3Q2Zm6LtZPD33nF374sYbg+tyeeklu3ro5Mkwdmzo41NKKRVREU88gPXAJCAVSANmA5+ISI9K\njjFAF6CVc2ttjNla04GGyuqdq8nLzwt/4vHVVzYZOPPMmqujWTO7pPnHH9uLrwXiv/+1Y0Ruvhnu\n9nGdGKWUUke8iCcexpjPjDFfGmNWG2NWGWPuA/YBVX0rbzPGbHXfwhBqyLhyXQD0a9svvBVnZECv\nXjW70BfY65dccgn8+c+wZYt/x8ybZ5cav/hiuzZItFyaXSmlVEhFPPHwJCIxInIZUAeYX9muwBIR\n2SgiGSIyKDwRhoYr10W3pt1okhzG5baNsYlHKKfRVub5523ycNNNVXe5LF8O559vlyF/+23bVaOU\nUuqoFBWJh4gcLyJ7gULgBeBiY8yKCnbfBFwP/BEYge2q+VZEeocl2BCIyPiO7Gx7EbOaHN/hqXlz\neOEFe1G399+veL8NGw5dgv6TTyApKTzxKaWUiohoWbl0BXAi0BAYCbwpIqf6Sj6MMSuBlR5FLhE5\nBpgAXFVVRRMmTKCh1+qXo0ePZvTo0dUI338Hig7w05afuCb1mrDUVyY93X6pnxzGlVJHjYKRI+2Y\njdNOgxZe43937bJLoYO9+FqjRuGLTSmllE8zZ85kpteyCLt37w7Z+cWE85LmfhKRr4BVxpgb/dz/\ncWCwMWZwJfukApmZmZmkpqaGKNLAzVk3h1NfP5XF1y+md6swNtKcfbbt+vjyy/DVCXbV0Z497WXl\n//3vQ+UFBTbpWLoU5s6FHpWNJVZKKRVJWVlZpKWlAaQZY7Kqc66o6GrxIQYI5EpgvbFdMFUq3lsc\nVECh4sp1USe+Dse3OD58lebn20u5h6ubxVOLFjB9uu1ucXe5lJTAn/4ECxbYRcI06VBKqVoj4l0t\nIvII8AXwO1AfuBwYApzlbH8UaGOMucp5PB5YA2QDScC1wFDArzmihRsLQ/wMAuPa4KJvm77ExYTx\npf/+e9vCEK6Bpd4uucS2dri7XB580I79+Ogje5l7pZRStUbEEw+gBfAG0BrYDSwFzjLGzHa2twLa\ne+yfADwFtAEOOPufYYz53p/KDm44GKKwA2eMYf76+Vx1YpVDUUIrI8MO3uzZM7z1uonYgaY9e8KA\nAZCTY1cmvfDCyMSjlFIqYiKeeBhjKh1laYwZ6/X4CeCJYOsryC0I9tBqy92Ty6Z9m8I/oyU93bZ2\nRHJtjJYt7RTbMWNsi8e110YuFqWUUhET8cQj3CLZ4uFeOKx/uzBe9GzDBjuV9q9/DV+dFRk92nat\ndOgQ6UiUUkpFSK1LPPJz8yNWtyvXRadGnWhVr1X4Ks3IsC0dw4aFr87KdOwY6QiUUkpFULTOaqkx\neWvyIla3a0MEFg7LyICTTrKXrVdKKaUirNYlHrHbYtmwa0PY6z1YcpDMjZkMaBvGxKOkxF4YLhLT\naJVSSikfal3iEVcax+QPJoe93p82/0RhSWF4WzwWL4a8vMhNo1VKKaW81LrEA2De3HksyF0Q1jpd\nuS4SYhPCu1ppejrUr2+nsCqllFJRoFYmHv1K+zH+y/GUmtKw1ena4CK1dSqJcYEsyFpN6el2qfL4\n+PDVqZRSSlWi1iUe8c3iGdNoDAs2LODtpW+HrV5Xriu84zv27IH583V8h1JKqahS6xKPxHaJtMhr\nwSXHXcJdX9/F3sK9NV7n1v1bydmZE97xHd98A8XFOr5DKaVUVKl9iUfbRApyCnh82OPsLNjJI3Me\nqfE63eNJwpp4ZGTAMcfYm1JKKRUlamXikZ+TT8dGHZk0eBJTXVNZvWN1jdbpynXRql4rOjQM44qd\n6enazaKUUirq1LrEI6FtAkVbiig5UMLEwRNpWbclt2fcXqN1uhcOk3BdK2X1anvTbhallFJRptYl\nHolt7aySgjUF1ImvwxNnPsEnv37CV6u/qpH6SkpLWLhhYXgHlmZkQFwcDB0avjqVUkopP9S+xKOd\nTTzyc+w1Wy457hJO6XAKt6bfSlFJUcjry96Wzb6D+8I7viM9HQYOhAYNwlenUkop5Ydal3jEN40n\nJimGgpwCAESEaedMY/m25cxYNCPk9blyXcRIDCe1OSnk5/apqAhmz9bxHUoppaJSrUs8JEZI6pxE\n/ppDV6nt07oP16Rew/3f3s/2A9tDWp8r18UJLU+gbkLdkJ634gpdsHevju9QSikVlWpd4gGQ1Dmp\nrMXDbfLpkzHGcP8394e0rrAvHJaRYa9Em5oavjqVUkopP9XKxCM5JblsjIdb87rN+duQv/Fi5ov8\ntPmnkNSzq2AXy7cvD//4jjPPhNjY8NWplFJK+alWJh5JKbbFwxhTrvzmfjfTpUkXbk2/9bBtwVi4\nYSEQxoXD8vJg0SLtZlFKKRW1amXikZySTGl+KQe3HCxXnhCbwDPnPMO3a7/lP8v/E9hJV6+210bx\n4Mp10TipMV2bdq1uyP75+mswRhMPpZRSUatWJh5JKUmAXcvD2znHnsN5Xc7jjow7yC/KP2x7he6/\n366bkZlZVuTKDfPCYRkZcNxx0LZteOpTSimlAlQ7E4/OTuKRc3jiATD17Kls3LuRp+Y/5f9Jf/kF\nCgvhj3+EvDyMMWWJR1gYo8ukK6WUinq1MvGIqxdHfPP4wwaYunVt2pXx/cfz6A+Pkrsnt+oTFhfD\nihVwxx2wfz+MHs1v21aws2Bn+BKPZctgwwbtZlFKKRXVIp54iMgNIvKTiOx2bvNE5JwqjjlNRDJF\npEBEVorIVYHW6x5gWpG/Dvkr9RLqMenrSVWfLCcHDh6Ec86Bf/0LZs2i4B57XL+2/QINLTgZGZCY\nCKeeGp76lFJKqSBEPPEA1gOTgFQgDZgNfCIiPXztLCKdgE+BWcCJwDTgZRE5M5BKk1OSyy0i5q1B\nYgMePeNR3v35Xeb+Prfyk2Vn2/vjjoMzzoBHHuGEV/7HzRvb0SipUSBhBS893SYdycnhqU8ppZQK\nQsQTD2PMZ8aYL40xq40xq4wx9wH7gIr6KG4EcowxE40xvxpjpgMfABMCqdfXImLeru59NWmt07jl\ny1soNaUV75idDU2aQMuW9vHEiczq05DH394CK1cGElZwCgrgu+90fIdSSqmoF/HEw5OIxIjIZUAd\nYH4Fuw0AvvYqSwcGBlJXckoyhbmFlBZWnFDESAzPnvssWZuyeG3xaxWfLDvbtnY4s1f2Fx1g5Dl7\nKWzRFEaMgH37AgktcHPm2ORDEw+llFJRLioSDxE5XkT2AoXAC8DFxpgVFezeCtjiVbYFaCAiif7W\nmZSSBAYK1lXe6jGo/SAu73U598y+h90Fu33v5E48HJmbMtmVWMrWN/8B69bBNdfYWSc1JSMD2rQp\nF4NSSikVjaIi8QBWYMdr9AP+AbwpIt1rssLkFDsWoqKZLZ6mDJvCvoP7eOj7hw7fWFwMv/4KPXuW\nFblyXdSNr8uxgy+A116D996DZ54JWeyHSU+3s1nCtV6IUkopFaS4SAcAYIwpBnKch4tFpB8wHjue\nw9tmoKVXWUtgjzGmsKq6JkyYQMOGDcFAnuRRd2Jdxu4cy+jRoys8pl2Ddtxz8j088N0DXJt6Ld2a\ndTu0cfVqO6PFo7XBleuiX9t+xMbEwsiRdprtnXfaC7cNGVJViIHZuBF+/hnuvju051VKKVUrzZw5\nk5kzZ5Yr2727ghb/IEgorkkSaiIyC1hnjPk/H9umAOcaY070KHsXaGSMGV7JOVOBzMzMTFKdK7e6\njnXR/OLmHPPEMVXGlF+UT88XetKzeU8+G/PZoQ0ffmgXDdu8GVq2xBhDm6ltGNt7LI+c8Yjdp7jY\ntkhkZ0NWVmhXFn3jDRg7FrZuhWbNQndepZRSypGVlUVaWhpAmjEmqzrninhXi4g8IiKniEhHZ6zH\no8AQ4G1n+6Mi8obHITOAFBF5TES6ichNwEhgaqB1+7pKbYX7xifz1FlP8flvn/P5b58f2pCdbS9D\n36IFAL/v/p3N+zaXXzgsLs6u75GQAKNG2RaSUElPty0pmnQopZQ6AkQ88QBaAG9gx3l8jV3L4yxj\nzGxneyugvXtnY8xa4DxgGLAEO412nDHGe6ZLlapaRMzbxd0vZminoUxIn8DBEid58JrR4sp1AdC/\nbf/yB7doAR98YK/lctttgYbqW2kpfPWVzmZRSil1xIh44mGMucYYk2KMSTbGtDLGeCYdGGPGGmNO\n9zrme2NMmnNMF2PMW8HU7W7x8Le7SUSYds40Vu1YxfMLn7eFy5YdNrC0c6POtKznPQwF6N8fnn0W\npk+Ht4IKubzFi2H7dk08lFJKHTEinnhEUlLnJEr2lFC8s9jvY3q17MUNaTfw4HcPsmXXBjujxXNg\n6YYqLgx33XV2TMZ118GSJdUJ306jrVcPBoTpejBKKaVUNdXqxCOQKbWe/j7078RKLM+/e2u5GS2F\nxYVkbcqqPPEQsS0ePXvaxcV27gw6ftLT4fTT7dgRpZRS6ghQqxOPpJQkgIDGeQA0rdOUvw/9O8u/\n+8AWOInHks1LOFhysOor0iYnw3/+A7t3wxVX2LEagdq7F+bN06vRKqWUOqLU6sQjvnE8cY3iAm7x\nALjhpBs4bX9zdtaLwzRvDtjxHYmxifRu1bvqE3TqBDNnwhdfwN//HnD9fPstFBXp+A6llFJHlFqd\neIAzs2VNYC0eAHExcYwyPVnatJj3st8D7PiO1NapJMT62fVx1lnw0EPw4IPw2WdV7+8pPR06d4Zj\nql6DRCmllIoWmnj4cZXairT8PY8DXTtz51d3sv/gfly5VQws9eXuu+HCC22Xy+rV/h+XkWFbO3SZ\ndKWUUkeQWp94BLKIWDlFRfDrr/Q98yq27t/Kbem3sXbXWga2C+giuRATA2++aRcAGzECDhyo+pg1\na+C337SbRSml1BGn1iceSSlJFKwroLQ4wAGeq1ZBURHN+g7h9oG388+sfwIE3uIB0LAhfPSRPef1\n11d9JduMDIiNhaFDA69LKaWUiqBan3gkpyRDCRTmVnl9ufKys+39ccdxzyn30Lpea9rUb0O7Bu2C\nC+T44+GVV+Dtt+1028qkp8PAgTZhUUoppY4gUXF12khK6nxoSm1yp2T/D8zOhubNoXlz6gHvjXyP\nzfs2I9UZc3HZZbBgAUyYAH36wODBh+9TXAyzZtkr3iqllFJHGE08OiaB2EXEGp/e2P8DvZZKP6Xj\nKaEJ6PHH7RVsR42y961ald++YAHs2aPjO5RSSh2Ran1XS0xCDIntEwOf2eK+OFyoxcfDe3Z6Lpdc\nYgexekpPhyZNwF6eWCmllDqi1PrEA4KY2VJUBCtX1kziAbaV44MPYP58mDix/LaMDBg2zA4uVUop\npY4wmngQxCJiv/1mk4+aSjwABg2Cp5+GZ56xK5wC7NgBP/6oy6QrpZQ6YtX6MR5gB5jm/TfP/wM8\nZrTUqJtvtmM6rrnGznpZscJe10XHdyillDpCBZV4iEi8Maaogm3NjDHbqxdWeCWnJFO0vYjiPcXE\nNfDjJVm2zM5oadasZgMTgRdfhJ9/touL9e5tB7S2C3LKrlJKKRVhwXa1/Et8zBsVkZbAt9WKKALK\nrlLrb3dLTQ0s9aVOHXsl2+3b7bgP7WZRSil1BAs28egAvOxZICKtsEnHimrGFHbJKXb9jvw1fg4w\nDWfiAfZCcO+8AwkJcPHF4atXKaWUCrFgE4/hwCARmQogIm2A74CfgUtCFFvYxDePJ6ZOjH9Tag8e\nrNkZLRUZPtwOLj311PDWq5RSSoVQUGM8jDHbROQs4Aenx+V8IAu43BgT4EVPIk9E/J9S+9tvdvXQ\ncCceAHXrhr9OpZRSKoSCntVijFkvImcCc4CvgD8ZU9XVzaJXUkqSfy0e7hktHquWKqWUUso/fice\nIrIT8JVY1AEuAPLc402NMU1CEl0YJackk/eFH1Nqly2DFi1qfkaLUkopdRQKpMXj1hqLIgokpSRR\nsLYAU2qQmEou9BbugaVKKaXUUcTvxMMY80ZNBhJpSZ2TMIWGg5sOktg2seIds7PhjDPCF5hSSil1\nFAl6yXQRiRGRriJysoic6nkL8Dx3i8hCEdkjIltE5CMR6VrFMUNEpNTrViIiLYJ9PmVTaisbYHrw\noB1cqi0eSimlVFCCXbl0APAu0BHw7pcwQCBXMDsFeA5Y5MTzKJAhIj2MMZVNMzFAV2BvWYExWwOo\nt5ykTs4iYjkFNiJfVq60M1p0YKlSSikVlGBntczAJgrnAZvwPejUL8aY4Z6PReRqYCuQBvxQxeHb\njDF7gq3bU2ydWBJaJ1S+iNiyZfZeWzyUUkqpoASbeHQBRhpjVoUyGEcjbCKzo4r9BFgiIknAL8AD\nxph51am4yim12dnQsiU0bVqdapRSSqlaK9gxHguAY0MZCIBz/ZdngB+MMcsq2XUTcD3wR2AEsB74\nVkR6V6f+5M5VLCKmM1qUUkqpagm2xeM54Cnn+iw/A+WuVGuMWRrkeV8AegKDK9vJGLMSWOlR5BKR\nY4AJwFWVHTthwgQaNmxYrmz06NGMHj2apJQkds7aWfHB2dlw5pmVPgGllFLqSDZz5kxmzpxZrmz3\n7t0hO78Es9ioiPhaFt1guz+MMSaQwaXucz6PXYjsFGPM70Ec/zgw2BjjM2kRkVQgMzMzk9TUVJ/n\n2PzGZlZcvYJTDpxCbLLXUygstEuWT58O118faHhKKaXUESsrK4u0tDSANGNMVnXOFWyLR+fqVOrN\nSTouAoYEk3Q4emO7YIKWlOLMbFlbQN0eXtdF+e03KCnRGS1KKaVUNQR7kbh1oQpARF4ARgMXAvtF\npKWzabcxpsDZ5xGgrTHmKufxeGANkA0kAdcCQ4Fq9YMkdT40pfawxMN9jRYd46GUUkoFLeiLxAGI\nSE+gA5DgWW6M+W8Ap7kB203zrVf5WOBN5+fWQHuPbQnAU0Ab4ACwFDjDGPN9APUeJrFNIpIgvgeY\nZmdDq1bQ5Ii7DI1SSikVNYJdQCwF+AjoxaGxHXBoPQ+/x3gYY6qcWWOMGev1+AngCX/r8JfECEmd\nK5hSqzNalFJKqWoLdjrtNGxXRwtsi8NxwKnYRcVOC0lkEZKckux7ETFNPJRSSqlqCzbxGAjcb4zZ\nDpQCpcaYH4C7gWdDFVwk+FxErLAQVq3SgaVKKaVUNQWbeMRy6Bop27FjLQDWAd2qG1QkuRcRKzfN\neOVKO6NFWzyUUkqpagl2cOkvwInY7pYFwEQROQhcB+SEKLaISEpJonR/KUXbikho4YyZ1RktSiml\nVEgE2+LxsMex92PX9ZgDDAduCUFcEZOckgxQfmZLdja0bg2NG0coKqWUUuroEOw6HukeP68CuotI\nE2CnCWYp1ChStpbHmgIaDnCWVteBpUoppVRIBNvicRhjzA5jjBGRkaE6ZyTENYgjrmlc+QGm2dk6\nsFQppZQKgYATDxGJE5HjRaSrV/lFIvIT8E7IoouQ5BSPq9S6Z7Roi4dSSilVbQElHiJyPLAK+AlY\nLiIfikhLEfkOeBX4Ajgm9GGGV7kptb/+CqWlmngopZRSIRDoGI/HsInHzcDlwGVAD+AV4BxjjI+V\nt448ySnJ7HHtsQ90RotSSikVMoEmHn2Bs4wxS0TkB2zi8Ygx5q3QhxY5SSlJFK4vpPRgKTHZ2dCm\nDTRqFOmwlFJKqSNeoGM8mgEbAYwxu4H9gCvUQUVacudkKIWC3wt0YKlSSikVQoEmHgaoLyINRKSh\n8zjZeVx2C32Y4ZWU4kypzSmAZcu0m0UppZQKkUC7WgRY6fV4sddjQwBXp41Gie0TIRbyf92rM1qU\nUkqpEAo08RhaI1FEmZi4GJI6JlGQtVFntCillFIhFFDiYYz5rqYCiTZJnZMoWLbJPtAxHkoppVRI\nBL1yqYgcIyIPi8hMEWnhlJ0rIkdF80BySjL5vxdB27Y6o0UppZQKkaASDxEZAvwM9AdGAPWcTScC\nD4YmtMhKSkmiIC9BWzuUUkqpEAq2xWMKcJ8x5kzgoEf5bGBAtaOKAskpyRQXJVF0TO9Ih6KUUkod\nNe+5zFQAACAASURBVIJNPHoBH/ko34pd6+OIl9TWvjQFzXpFOBKllFLq6BFs4rELaO2jvA+wIfhw\nokdy0e8A5Cd1jnAkSiml1NEj2MTjX8BjItIKu25HjIgMBp4E3gxVcJEUl7ucWPZRUNIi0qEopZRS\nR41gE497gBXAeuzA0mXA98A84OHQhBZZsiyb5Pjt5G80kQ5FKaWUOmoElXgYYw4aY64FjgHOB64A\nuhtj/mSMKQnkXCJyt4gsFJE9IrJFRD4Ska5+HHeaiGSKSIGIrBSRq4J5LhVatoykxgUUrCkI6WmV\nUkqp2izodTwAjDG/G2M+N8b82xjzW5CnOQV4Djs1dxgQD2SISHJFB4hIJ+BTYBZ2Cu804GUROTPI\nGA6XnU1S+zh7vRallFJKhUSgS6YDICJTK9hkgAJgFfCJMWZHVecyxgz3OvfV2NkxacAPFRx2I5Bj\njJnoPP5VRE4GJgBfVfkEqpKfD6tXkzymIQXvFWBKDBIr1T6tUkopVdsFlXhgZ6/0cY7/1SnrCpRg\nx37cBDwlIicbY5YFeO5G2ASmsqRlAPC1V1k68HSAdfm2YgUYQ1JaG8w7RRTmFpLUMSkkp1ZKKaVq\ns2C7Wj7EdnO0McakGWPSgHbY1oaZQFvsYNOAEgEREeAZ4IcqEpZWwBavsi1AAxFJDKROn7KzAUg+\n9VgA8tfkV/uUSimllAo+8ZgI/NUYs8ddYIzZDTwATDTGHAD+ju0uCcQLQE/gsiDjCo1ly6BdO5KO\nbw6CjvNQSimlQiTYrpbGQAvsNFpPzYEGzs+7gAR/TygizwPDgVOMMZuq2H0z0NKrrCWwxxhTWNmB\nEyZMoGHDhuXKRo8ezejRow8VZGfDcccRkxhDYttE8nO0xUMppVTtMHPmTGbOnFmubPfu3SE7f7CJ\nxyfAqyJyO/CjU9YXu4DYx87jfsBKf07mJB0XAUOMMb/7cch84FyvsrOc8ko9/fTTpKamVr5TdjZc\ndBHgXCxOWzyUUkrVEof9Mw5kZWWRlhZoJ4ZvwXa1XI8d4/EvYJ1z+5dTdoOzzwrgmqpOJCIvAJcD\nY4D9ItLSuSV57POIiLzhcdgMIEVEHhORbiJyEzASqGi2jf8OHICcHDjuOMBeLE5bPJRSSqnQCKrF\nwxizD7hWRCYAKU5xjlPu3meJn6e7ATuL5Vuv8rEcWn69NdDe49xrReQ87ODVW4BcYJwxxnumS+Cc\nGS307AnYFo+8z/OqfVqllFJKBd/VApQlIEureY4qW12MMWN9lH1P4INXq7bMGbbiJB7JKckUbS2i\neF8xcfWq9XIppZRStV7Q36QichJwCdABr0GkxpgR1YwrcrKzoX17aGDHyCZ1tj0+BWsKqNerXiQj\nU0oppY54QY3xEJHLsBeE6wFcjF3m/DjgdCB0Q18jwZnR4paU4iQeOsBUKaWUqrbqXJ12gjHmAuAg\nMB7oDvwb8GdWSvTySjwSWiYQkxyji4gppZRSIRBs4nEM8Jnz80GgrjHGYAd7XheKwCLiwAFYs6Zc\n4iEiOqVWKaWUCpFgE4+dQH3n5w3A8c7PjYA61Q0qYrxmtLgld9YptUoppVQoBJt4fA+4L0H/PjBN\nRF7CXqdlVigCiwjnGi3eiYe2eCillFKhEeyslpsB9wJfk4EiYBDwH+DhEMQVGdnZ0KED1K9frjg5\nJZlNazZhjMFex04ppZRSwQgo8RCRGOAO7PLmCSIyC3jQGDOlJoILO6+BpW5JKUmUFpRycPNBEltX\n/+K3SimlVG0VaFfLvcAjwF7s2I7xwPRQBxUxFSQeySnJgE6pVUoppaor0MTjSuAmY8w5xpg/ABcA\nlzstIUe2/fvtjBav8R0A/9/efcfHUZwNHP89e1XNsuRuy0WyjQ02GGx6Jw4QTCBAeAETXgihJBA6\nIYQ3JCTkTQKhmB4SCC2AQ3sJJhB6MaYEsGmWuy13W+6SVU5Xdt4/9k46SSfpTnc6ydbz5bPs3uzs\nzEhn6R7NzM76RzmjSjrBVCmllEpPqgHDCODfsRfRZ6MYYGgmG9UtFi1y9gl6PFx5LjyDPNrjoZRS\nSqUp1cDDDbT89A3hrFy6a2vjjpYYfUqtUkoplb5U72oR4DERaYhL8wMPikhtLGGXfFZLeTmMHAn5\niZ/H4i/zE6jQHg+llFIqHakGHo8nSHsyEw3pdm1MLI3JKc1hx3s7stggpZRSaveTUuCR6PH0u43y\ncjj99DZP+8v8BNcFiQQiuPyuLDZMKaWU2n3s+nejZEJtLaxc2X6PR+yW2pU63KKUUkp1lgYeAAsX\nOvt2Ag9/mXNLrc7zUEoppTpPAw9ouqNlzz3bzOIb6kO8orfUKqWUUmnQwAOcwGPUqDbvaAEQl+Af\n6ddbapVSSqk0aOABTuDRxvod8fQptUoppVR6NPAAWLCg3fkdMbqImFJKKZUeDTxqajq8oyUmtoiY\nMabr26WUUkrthjTwSOKOlpic0hwiOyOEtoa6uFFKKaXU7kkDjyTuaIlpvKVW53kopZRSndIjAg8R\nOUJEZonIOhGxReTkDvIfFc0Xv0VEZGDKlZeXQ2kp5OV1mDW2iJjO81BKKaU6p0cEHkAe8CVwKZDs\nBAoDjAUGR7chxphNKdec5B0tAO5CN+5ity4ippRSSnVSqg+J6xLGmNeA1wBERFK4dLMxpjqtyhcs\ngDPPTDp7TlmODrUopZRSndRTejw6Q4AvRWS9iLwhIoemXEJNDaxaldTE0hh/qS4ippRSSnXWrhp4\nbAB+DHwfOA1YA7wnIvumVMqCBc4+lcBDFxFTSimlOq1HDLWkyhizBFgSl/SJiIwGrgbOa+/aq6++\nmsLCQufFmjUATP/mG6ZPmZJU3TllOQRWB7BDNpZnV43blFJKqcRmzpzJzJkzm6VVVVVlrPxdMvBo\nw6fAYR1lmjFjBpMnT3Ze/OxnUFUFP/xh0pX4y/xgQ8Oahsa7XJRSSqndxfTp05k+fXqztHnz5jEl\nyT/QO7I7/cm+L84QTPKSXCo9Xk6p3lKrlFJKdVaP6PEQkTxgDM6EUYAyEZkEbDPGrBGRPwJDjTHn\nRfNfCVQA5YAfuAg4Bjg2pYrLy6FFVNcR3wgfWLqImFJKKdUZPSLwAPYH3sVZm8MAd0TTHwd+hLNO\nx/C4/N5onqFAHfA1MNUYMzvpGnfuhNWrU+7xsDwW/hF6Z4tSSinVGT0i8DDGvE87wz7GmPNbvL4N\nuC2tSjtxR0tM7GFxSimllErN7jTHIzXl5SAC48enfKkuIqaUUkp1Tu8NPBYscJ7Rkpub8qXdsYiY\nMYZtb2zDbrCzWq9SSimVSb038Cgv79QwCzhDLeFtYUI7QhluVNvWP7ier4//mkUXLMKYZB9no5RS\nSvUsGnh0Qmz9jmzN86hfXs/y65ZTcFABm57axOpbV2elXqWUUirTemfgUV3trFqaRo8HZCfwMLZh\n0fmL8A7wMunNSYz81Ugq/qeCLS9t6fK6lVJKqUzrnYFHGne0AHj6eXAVuLIyz2Pt3Wup+qCKcY+O\nw13gZtRvRtH/1P4s+MECar6u6fL6lVJKqUzqvYGHCIwb16nLRQR/adc/LK52US0rbljBsCuHUXR0\nkVO3Jez5xJ7kjMnhm5O/Ibg52KVtUEoppTKpdwYe5eVQVtapO1picspyurTHww7bLDpvEf4Rfsr+\nUNbsnCvPxd6z9sautyk/rRw7qHe6KKWU2jX03sCjk8MsMV29iNiaP61h5+c7Gf/4eFy5rtb1j/Az\n8cWJVH9azZJLluidLkoppXYJGnh0Uk5ZDoGVAUwk8x/4NV/XsPI3Kxl+3XAKDylsM1/hoYWM++s4\nNj6ykbV3r814O5RSSqlM6xFLpmfVzp2wdm36PR6lfkzQ0LC+Af9wf4YaB3bQZuG5C8nZI4fS35Z2\nmH/weYOpLa9l+bXLyR2fS7/v9MtYW5RSSqlM6309HhUVzn6vvdIqpvGW2ugE04ZwAzuDO9MqE2DV\n/66irryOPZ/YE8uX3NtT9scyik8oZsGZC6hdVJt2G5RSSqmu0vsCjxUrwLI69YyWeP5RTuARm2D6\n97ln89In+2PbnZ/oWf15Nav+sIqRN46kYHJB0teJS9jr6b3wlfiYf9J8Qtuyt6KqUkoplYreF3gs\nX+7c0ZKTk1YxLr8L7zAvgYoAq6pXMKr+JUrsJby95sVOlRcJRFh07iLyJ+Uz4n9GpHy9u4+bvV/e\nm9D2EOVnlGOH9E4XpZRSPU/vCzxWrEh7fkdM7Jbat5fdRgN+1ksZq9fe1amyVv56JfXL6xn/+Hgs\nT+felpyyHCY8P4Gq96tYdvWyTpWhlFJKdaXeF3gsX56xwMNf6qdmzVYGVj/Jxvyz8A2+ktGhOXyz\nZW5K5VR9WMWa29dQenMp+RPz02pT0dFFjL1/LOvvX8+6P69LqyyllFIq03pf4LF5c9oTS2NyynLY\nOeL/yKGOo8Zex7TRF7GDYv6z4vaky4jURlj0w0X0OagPw382PCPtGnrxUIZdPoylly9l+zvbM1Km\nUkoplQm9L/CAjPV4eEu9uI5/jpXu4xhTOI4cdw47+p5HSd2LVNZtTKqMFb9YQcO6BsY/Ph5xSUba\nBTD6ztEUHVNE+enl1C2ry1i5SimlVDp6X+AhkvYdLTFfD3wVStYx3nVFY9pxY67Bwub1pTM6vH77\nO9tZd986ym4pI3ePzi/fnojlttjr2b3w9Pcw/+T5hKvCGS1fKaWU6ozeF3iUlIA/Mwt+bfb+GeZP\nYNyWAxrThuaXsDrnZPK3P0pDuKHNa8PVYRadv4i+R/dl2GXDMtKeljxFHvZ+eW8a1jewYPqCLlll\nVSmllEpF7ws8Ro/OSDGfVX7AcGse5p9ntHpK7ZTS6yhmM6+ueLTN65dfu5zwtjDjHhmHWJkbYmkp\nd1wuE56dwLbXt7H8+uVdVo9SSimVjN4XeJSVdZwnCV9V3MZmGULOhmNbPaV2v4EHscJ9EDs33ptw\nQbGtr25lw8MbGH3HaHJK01tPJBnFxxUzZsYY1t6xlg2Pbujy+pRSSqm2aODRCauqVzAq8CrB4ovI\nLc1L+JTaocOuZIS9gI82vNUsPbQtxOILF1N0fBFDLhqSdluSNezyYQy5aAhLfryEqg+rslavUkop\nFa9HBB4icoSIzBKRdSJii8jJSVxztIjMFZGAiCwRkfOSqiwDQy2xBcOmjb2ycRGxlo4deQYbZQSL\nVt3RLH3pFUuJ1EUY9/A4RLpuiKUlEWHsfWPpc0gf5p86n8Cq1sGSUkop1dV6ROAB5AFfApcCHc6A\nFJFRwL+At4FJwN3AwyJybIc1jRyZRjOhOljNgOqn2Jh/FkX+YvylfgIrAhjTvNkuy4Xp/2NKg2+x\nrGoxAJv/bzObntrE2HvH4i/J3BNtk2V5LSa8MAFXnotvTv6GcI3e6aKUUiq7ekTgYYx5zRjza2PM\nS0Ay3QCXACuMMT83xiw2xtwPPA9c3eGVPl9abX1l6X3kUstRY68DnEXE7HqbYGWwVd4Tx15GPbm8\nv/Q2gpuDLPnJEvp9rx+DzhmUVhvS4e3vZeLLEwmsCLDovxdhbL3TRSmlVPb0iMCjEw4G3mqR9jpw\nSFdWGrEjWFv+TIXvWMYUjgPAX+b0XCSa59HH24dNBWczuOYZyi+bh7EN4/6S3SGWRPIn5rPn03uy\n5aUtVPy6Iit1GmMIrA2w4/0dBNa07iFSSinVO7i7uwGdNBiobJFWCfQREZ8xpu0FNNLw+sqZDDJr\nGTHq8cY0f2k08FgRoPCQwlbXHDPmZ1TMfZiq0NPs9ecb8Q7ydkXTUtb/pP6U3VLGiutXkLdXHoPO\nzlwvTKQ+Qm15LbVf1VLzdQ21Xzv78LamoR13sZv8ffObbbnjczv9gDyllFK7hl018OgWlevvxnLt\nw3lDvtWY5s534xngSTjBFGBo7QhWzDmawDn/R/Epd2arqUkZft1waufXsuhHi8gZk0OfA/ukdL0x\nhoY1DU5wEQ0yar6qoX5pPdiAQM4eOeTvk8/wa4aTt08eOaNzqF9eT81XNdR8WcOWl7aw9s61AIhX\nyJuY1zwg2Scfd6H+M1VKqd3FrvobfSPQ8k/0QUB1R70dl19+Nf36Ne+ZmD59OtOnT2+3ws8qP6A0\n/DnVJQ+3Oucv87daRAycD+bFFy/GteMMXEf+hDdW/oNpZee0W082iQh7/HUP6pfWM/+U+Uz5bAq+\nYYnnwERqnV6M+CCj9utawjucXgx3Xzd5k/IoPq6Y/Ovyydsnj7wJebhyXa3Kytsrj/4n9W98Ha4O\nO0HLl01b5VOVmAZnOMZf5neCkElNAYlvuK/bh6yUUmp3NHPmTGbOnNksraoqc8swSE8baxcRGzjF\nGDOrnTy3ACcYYybFpT0N9DXGTGvjmsnA3DPOmMszz0xOuV0Pf3IyhQ2fc8rhK/G4mg+XLDh7AQ3r\nGtjv/f2apW94ZAOLL1jMxFkTeaXvVGzxcv7hn6Vcd1dr2NjAvAPm4R3sZd/39yW4Kdg4PBILMuqX\n1jv3G1mQu0cueZPyyN/HCQby9snDV5LZQMAO2dQtrmsWjNR8WUN4azTQKUowVLOnDtUopVRXmDdv\nHlOmTAGYYoyZl05ZPaLHQ0TygDE03dFSJiKTgG3GmDUi8kdgqDEmtlbHg8BPReRW4BFgKnA6kDDo\niPfcc3D99TA5hdgjtmBYZb9ftgo6wPmLvGpO82gwsCrAsquWMfiHg+l/Un/6Lr2MwnUX83nlh+w/\n6LDkK88C32AfE2dN5IvDv2BO0RxM0AlG3UVu8iflU3xCMfnXO0FG7l65uHJa92JkmuWxyJ+YT/7E\nfIh2EhljaFjX0BiE1H5Vy9aXt7J2RtxQzYQ8/KV+3IVuXH1cuAvduPu4cRW6cPdxN6XHpbnyXV26\nbL1SSqkmPSLwAPYH3sX5m9oAsVW3Hgd+hDOZdHgsszFmpYicCMwArgDWAhcYY1re6dJKaSlceil8\n9BFYSf5x/Pay2xmMj2ljr0x4Pqcsh4a1DdgNNpbPwtiGRRcswl3oZsxdYwCYVnYes9b9imUVt/e4\nwAOgYL8CJr01iarZVeTtnUf+pHy8Q709ajhDRPCX+PGX+On/3eZDNbXf1FLzZQ07v9hJw9oGghuC\nhKvCRKojhKudfdsFg6vA1WawkjAt34Urz4WVZzUeu/JduHJdiKvnfM+UUqqn6RGBhzHmfdq5tdcY\nc36CtNnAlFTruuEGuOgiePRRuOCCjvM7C4Y9yYbogmGJ+Ev9YJxejtw9cln/4Hp2vL2DfV7fp3Fi\npMflpa74R4zYdjtrd66ipCC9hcy6QuEhhQnvzOnp3H3cFB5WSOFhbbfd2IZITaQpGKkKOwFJlROY\nxKfHgpXQlhCBFYFm5+z61s/eacnyO8FIs6AkFphE91ae1SqtWSCT40K8guWzGveW10J8ccca4Cil\ndkE9IvDIpsmT4ZxznOGWU06Bfv3az//K0vsYSC1Hjf15m3lyypwHvdWvqAcLll+3nKE/GUrxcc0D\nle+MvYrP/3MXby67g/P3uyftr0UlTyxxhlr6pPdP3g7ZRKojRGqjW03T3q61W6VFaqPp0eNgZZDI\nitbXJBPQtOJyVqOND05aBSmx8/HHXudYPIK4pWnf4tjyWAnTk87vErBAXGke6zCYUruVXhd4ANx2\nG8yaBb/8JTz4YNv5nAXDHqTCdyxTowuGJeIr8SFuoX5ZPat/vxrvIC9lt7V+GN2AnIGsyz2NAVV/\npy70R3I9eZn4clQWWR4Lq5+Fp58no+WaiCFSFxes1NuYoMFusLGDNqbBYAdt7IYWx7E8ccetrose\n2/W202sTLcNEDCZkMGFns0N243F8ugk5W7dqKziR6Dkrmi7S7DUSd86ShK+bXd8iDxItU2jcOnxt\nSfJ5418Tlwat0iEuf4vzSaXHlROf1qy+NvI1G3JNUEbC8trJk07+hNe1ky/ZtJSHlVONh7MRP6f6\nJaTwNa9fvz7FxrStVwYegwfD734HV13lDLcccEDifK+v/AeDzBqGj3yk3fLEJfhH+Vn9x9UENwTZ\n9719cecn/tYeNubnVH79FK8s/wv/Nf6adL8UtZsQl+AucOMu6Lk/ksZOLVAxthPcEKFrjiMGjDPp\nGNs5h6Hp2E7zXCS6j808Mwle26btc3Gvm7U1QV6gaTXfuLRm5+LTUr2GNs4nSO90npbn28jf+LKT\n17W6tp18yaalfHdnqnF4V+en67+GzcHNqV3Qjp77W66LXXopPPKIs//kE3AluFGjcv1dzoJhQ7/d\nYXn+Uj/b39xOydUl9D2yb5v59izeh488R+Kv/DP2HldhJTvDValuJpYgPoH0HneklNoF+ef5OzGr\nMrFe+6nndsMDD8Dnn8NDD7U+/3nlHErDn9N3yOVJlZe/Xz65E3Ip/X1ph3lLh1/DMLOMd9e2uVSJ\nUkoptVvqtYEHwKGHwvnnw//8D2xu0Yv0ZcVtbGEw00rPTaqssj+Wsf+8/ZNa4+LokpNYZ41h5ZoZ\nnWm2Ukoptcvq1YEHwC23gDHwi180pTkLhr1CoN/FCRcMS0Qs506BZFiWhXfgpYwOzWbB1q8602yl\nlFJql9TrA4+BA+EPf3Dme3z8sZP29rLbCeLjxDYWDMuEE0dfzA6K+Gj5bV1Wh1JKKdXT9PrAA+Di\ni2HKFGei6ba6agZUP8WG/DPbXDAsE3I9eewo/G9K6l5gc/2mLqtHKaWU6kk08MC5o+WBB+Crr+C+\nN+4nlxqOHNP2gmGZcuzYn2ER4bWld3V5XUoppVRPoIFH1IEHwgUXRijL/zPL3Mcytu/4Lq9zWP5w\nVud8l9xtjxCKBLu8PqWUUqq7aeAR5ztX/oMS9xo+e+/arNW5X+l19KOSV1c8nrU6lVJKqe6igUec\n6u13s9zem7/96lg++CA7dU4ZeAgV7gOo2nhvdipUSimlupEGHlHOgmGf0W/4FRx4oDPRNBTKTt2D\nhl7BiMg3fLT+rexUqJRSSnUTDTyiYguGnVh2Lg88AOXlcN992an7+FHTqZQSFqy6o808X3wBxx0H\n77yTnTYppZRSXUEDD2D1zgpGBl4l0O8iPC4vU6bAJZfATTdBBh/I1yaX5SLS78eUNrxBRdXSVudf\neAEOP9xZZ+SEE+DZZ7u+TUoppVRX0MADeGvpbYTwMi1uwbD//V/w++FnP8tOG04cexkBcnl32e2N\nacY4T9E9/XT47ndh7Vr4r/+Cs86Ce3VKiFJKqV1Qrw88qoNNC4YV+/s1phcVwZ/+BDNnwrvvdn07\nCn192VhwFgN3Pk11sJq6Opg+HX79a7j5ZvjHP6CwEJ54Aq65Bq64An75Syc4UUoppXYVvT7weGVp\n2wuGnXuu8yC5n/4UgllYZuOoMT8jhzqe//o+jjwSZs2C556DX/0KRJw8lgW33+5sf/gDXHghhMNd\n3zallFIqE3p14BGxI8iWB6nwfjvhgmGW5axoungx3H1317dnTOE4FsuxeHb8hcrNET780BlmSeTa\na+Hvf3d6QE49Ferqur59SimlVLp6deDxxspnGGxWs+eotidyTJoEl18Ov/2tM8eiK82cCQ/dfQ3D\n3auZ8a9n2W+/9vOfcw68/LIzFPTtb8PWrV3bPqWUUipdvTrw2Lj+LlZbEzls6LHt5vvtb6GgwJlb\n0RVs2xlOOftsmJD3bVZbE6iuSq6L5TvfcQKPpUvhiCNg9erOt2NdzRqWbF/Q+QKUUkqpDvTawOPz\nyg8pDX9G4ZArOsxbWOjMqXjuOXjjjcy2o6bGGU75/e/hllvg709YFAy+jLLwf/hi03+SKuOAA+DD\nD6G+3pmTMn9+am1Ysn0hD396FuWfj2b9VxN4avZYHvvyGuZvmdeJr0gppZRqW68NPJwFwwYxrey8\npPKffTYcdZQz7NLQkJk2rFrlrM/x5pvwz3/C9dc7k0inlZ3PNgYwt+K2pMvaYw/46CPo39/p+Zgz\np+Nr5m+Zx8OfnMKaryYyoO51NhZdzc7hj1DvGcfAHQ+yZf4Unn5/NI99cSVfbfkM27bT+GqVUkqp\nHhR4iMhPRaRCROpF5BMROaCdvEeJiN1ii4jIwGTqchYMe4VAv4vxuLxJtg/uvx+WL4c72l5gNGkf\nfeQ8Ebeqyjk++eSmcz63j5qi8xlRP4v1NclPLBkyBN5/H/bdF449Fl56KXG+zys/5OGPv8Om+ftT\nHPiIjf1/zbcOXc25k27lpNHnc+Eh/+LowzZTN+Lv1PkmMqDqb2yffyDPfDCaR7+4gnmbPtEgRCml\nVKf0iMBDRM4E7gBuAvYDvgJeF5H+7VxmgLHA4Og2xBizKZn6nAXDPM0WDEvGhAlw1VXO4mKrVqV0\naTOPPw7HHAPjxsGnn8Lee7fO852x12Bj8cayO1Mqu7AQXnvNWXDstNPgoYeazn24/k3+9uGR1Cw8\nnMLgN2weeAsnHL6KH0y8iQJvQbNycj15TCs7hwsPfolvHb6ZwMinqfXvx4CqR6lecAjPfTCKR+Zd\nyueVczQIUUoplTQxPWAFKhH5BPiPMebK6GsB1gD3GGP+lCD/UcA7QJExpjrJOiYDc2d/Mpsd9d9l\nU/5pXLD/oym3dedOGD/e6a148cXUro1E4IYb4Lbb4IILnFt1ve10uDz86XT6173B8YevJcedk3Jd\nV14J999v84uH/sUe439PafhTNsgofIOv5aQxyff2xAuEA8xeO4vVlc8wqP4tCqhmkwyjJv9EJgyd\nzgGDjsSyekQ8q5RSKkPmzZvHlClTAKYYY9KaANjtnxAi4gGmAG/H0owTDb0FHNLepcCXIrJeRN4Q\nkUOTqW/O6mfJpYYjRrdeMCwZBQVw553OnIxXX03+uupq+N73nGGaGTOcnoj2gg6Ag8uuoy/beGX5\nQ+1nTEDE5sRrZ/LXN/fj+DHfg8AOqoY+zBlHLOO0cZd1KugA8Lv9HDfqDC486AWOO3wTodLnqfYf\nSv+dz1K/+Bhe+KCEv829iI83vKM9IUoppVrp9sAD6A+4gMoW6ZU4QyiJbAB+DHwfOA2nd+Q9Aqi/\nFQAAFxZJREFUEdm3w9p2PE+Fdyp7FO3Z6QafcQZMnepMNA0EOs6/YgUccgh88AG88oozXBNbibQ9\nE/tPZrnncAKV9yf9IR6xI8xa9jeenrMXOavOxuuDj7Y9zQXHL+SJGy4gFHQlVU4yfG4fx478Phce\n9CwnHFFJpOxFqnKOot/OF2hYPJUXZw/lb5//iDnr3iBiRzJWr1JKqV1Xtw+1iMgQYB1wiDHmP3Hp\ntwJHGmPa6/WIL+c9YJUxJuFtKrGhln32gYJBB1PsH9B4bvr06UyfPj2ldi9aBPvs4zwv5aab2s73\n3nvO7bJFRc4S6HumGO+8teoF3BWnw+hZHD38pDbzhSJBZi37C8GNdzLErKTCfSCjRv6So4Z9F8uy\nePllJ2A66CCnt6Zv39TakYqwHWbOun+zbMMz9Kt7jSK2so0BbMs7gdJBp1LkH4zH8uC2PLgtL17L\ni9vy4IkeeywPXpcXl7h12EYppbJs5syZzJw5s1laVVUVs2fPhgwMtfSEwMMD1AHfN8bMikt/DCg0\nxpyaZDl/Ag4zxhzWxvnJwNzfPjSaG3+0JCMfaDfc4AybLFgAZWWtzz/0EFx6KRx5pLMGSHFx6nXY\nts0/PhhLvXskFxz2Tqvz9eF6Zi25F9l8DwPNOpZ7jmB86a8SLor20UfOpNOSEmcC6tChqbcnVWE7\nzEfr32DJhmcorv03xWxO7XpchHFj4yKCm0jsWNzY0WNb3Nh4MLFj8WFbBWDlgysfy1WAy5WPx9UH\nr6cPPncBOe4+5HmLyPP0ocBTSIG3L319Rbgtdxd9J5RSateVyTke3f5b1hgTEpG5wFRgFjROLp0K\n3JNCUfviDMG0K6//WRn7K/rGG+Gpp5wnxb78ctPwSTjsPEvlnnvgkkuc57x4PJ2rw7Is3AMvobTy\n5yza9g3ji51bYHYGdzJryZ3kbLmfAWxhhe84ysqe4YJBCeMuwFlcbM4cOP545/j11507a7qS23Jz\nZMk0jiyZRsSOMH/bPOpCNYTtIBE7SMQOETYhInaYiHFe2yaMbccdmxAmtreDTccmDCaMMSEwYWjc\nB7DsGlyhzbiDtXhNHT5Th59avIQa29YQ3bbFtTeAjwC5BCWXoOQRlnwiVh7GygcrH4kGMWL5sCwf\nLvHisny4LGfvdvlwibP3Wj7cls/pyXHl4HX58Fg+/G4/XsuHz+XH787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q3YAxpgLnAaD6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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x161a8f490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "from SimplePageRank import SimplePageRank as PageRank\n", "import mrjob\n", "\n", "all_results = []\n", "\n", "for iteration in range(1, 21):\n", " mr_job = PageRank(args=[\"data/PageRank-test-original.txt\", \n", " \"--iterations=%d\" % iteration, \n", " \"--damping_factor=.85\",\n", " \"--jobconf=mapred.reduce.tasks=5\",\n", " \"--reduce.tasks=5\",\n", " \"--smart_updating=True\"])\n", "\n", " results = {}\n", " with mr_job.make_runner() as runner:\n", " runner.run()\n", " for line in runner.stream_output():\n", " result = mr_job.parse_output_line(line)\n", " try:\n", " results[result[0]] = result[1]\n", " except:\n", " pass\n", " results[\"index\"] = iteration\n", " all_results.append(results)\n", " \n", "data = pd.DataFrame(all_results)\n", "data.index = data.pop(\"index\")\n", "data.plot(kind=\"line\", legend=False)\n", "plt.title(\"Updated implementation of PageRank\")\n", "plt.xlabel(\"iterations\")\n", "plt.ylabel(\"PageRank\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spin up a persistent cluster to assist with quick iteration. The spot price was \\$0.04/hour/core. I chose to use 20 cores, so the actual price was \\$0.80/hr. Five iterations took 17 minutes and ten iterations took 41 minutes. Back to back, this cost under a dollar in the ideal case. In reality, I tested out many different settings and spent about \\$25 total." ] }, { "cell_type": "code", "execution_count": 273, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using configs in /Users/BlueOwl1/.mrjob.conf\n", "Using s3://mrjob-3d3e189cec521ef3/tmp/ as our temp dir on S3\n", "Creating persistent cluster to run several jobs in...\n", "Creating temp directory /var/folders/sz/4k2bbjts7x5fmg9sn7kh6hlw0000gn/T/no_script.Jason.20161121.035849.763718\n", "Copying local files to s3://mrjob-3d3e189cec521ef3/tmp/no_script.Jason.20161121.035849.763718/files/...\n", "j-2K0NMAGFV6HVH\n" ] } ], "source": [ "!mrjob create-cluster --max-hours-idle=1 \\\n", " --master-instance-type=m3.xlarge \\\n", " --instance-type=m3.xlarge \\\n", " --core-instance-bid-price=0.1 \\\n", " --num-core-instances=20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The five iteration solution (no smart updating)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "!python SimplePageRank.py -r emr --cluster-id j-2K0NMAGFV6HVH \\\n", " --iterations=5 \\\n", " -q \\\n", " --damping_factor=.85 \\\n", " --reduce.tasks=500 \\\n", " --output-dir=s3://wiki-temp-data/wiki_out10/ \\\n", " --no-output \\\n", " s3://ucb-mids-mls-networks/wikipedia/all-pages-indexed-out.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ten iteration solution (no smart updating)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 28.5 s, sys: 7.54 s, total: 36 s\n", "Wall time: 41min 5s\n" ] } ], "source": [ "%%time\n", "!python SimplePageRank.py -r emr --cluster-id j-2K0NMAGFV6HVH \\\n", " --iterations=10 \\\n", " -q \\\n", " --damping_factor=.85 \\\n", " --reduce.tasks=500 \\\n", " --output-dir=s3://wiki-temp-data/wiki_out12/ \\\n", " --no-output \\\n", " s3://ucb-mids-mls-networks/wikipedia/all-pages-indexed-out.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The five iteration solution with smart updating." ] }, { "cell_type": "code", "execution_count": 274, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "!python SimplePageRank.py -r emr --cluster-id j-2K0NMAGFV6HVH \\\n", " --iterations=5 \\\n", " --smart_updating=True \\\n", " -q \\\n", " --damping_factor=.85 \\\n", " --reduce.tasks=500 \\\n", " --output-dir=s3://wiki-temp-data/wiki_out12/ \\\n", " --no-output \\\n", " s3://ucb-mids-mls-networks/wikipedia/all-pages-indexed-out.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rather than do a fancy distributed join, the indices dataset was small enough to easily read into memory and join it against the data locally." ] }, { "cell_type": "code", "execution_count": 302, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min 7s, sys: 3.95 s, total: 1min 11s\n", "Wall time: 1min 11s\n" ] }, { "data": { "image/png": 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p5RP/AXaNiD82NCozswbxmF0zMzMza1oes2tmZmZmTcvJrpmZmZk1LSe7ZmZ9mKQ1JV0v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xNNTU3t2lpbW/t0Tge7Zr1jP1LAOrvkubEFkg6IiFfyA2tXAhMi4rxCn6HAQlKZQelDYa8Wfp7/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1aKGejcHMzMzMrBYHu2ZmZmZWtxzsmpmZmVndcrBrZmZmZnXLA9TMrMsNHAj+TomO5+9t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\nNHHixKDtHj16UL9+f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"text/plain": [ "<matplotlib.figure.Figure at 0x13d5f41d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "labels = {}\n", "\n", "with open(\"Temp_data/indices.txt\") as label_data:\n", " for line in label_data:\n", " data = line.strip().split(\"\\t\")\n", " text = data[0]\n", " if len(text) > 35:\n", " text = text[:32] + \"...\"\n", " position = int(data[1])\n", " labels[position] = text\n", " \n", "file_name = \"results/05-iterations.txt\"\n", "data5 = pd.read_csv(file_name, sep=\"\\t\", header=None, names=[\"nodes\", \"PR-5-iterations\"])\n", "data5[\"articles\"] = data5.nodes.map(labels)\n", "\n", "file_name = \"results/10-iterations.txt\"\n", "data10 = pd.read_csv(file_name, sep=\"\\t\", header=None, names=[\"nodes\", \"PR-10-iterations\"])\n", "data10[\"articles\"] = data10.nodes.map(labels)\n", "\n", "file_name = \"results/05-smart-iterations.txt\"\n", "data5s = pd.read_csv(file_name, sep=\"\\t\", header=None, names=[\"nodes\", \"PR-5s-iterations\"])\n", "data5s[\"articles\"] = data5s.nodes.map(labels)\n", "\n", "new_data = data10.copy()\n", "new_data[\"PR-5-iterations\"] = data5[\"PR-5-iterations\"]\n", "new_data[\"PR-5s-iterations\"] = data5s[\"PR-5s-iterations\"]\n", "new_data.pop(\"nodes\")\n", "new_data.index = new_data.pop(\"articles\")\n", "new_data.sort_values(\"PR-5-iterations\", inplace=True)\n", "new_data.plot(kind=\"barh\", log=True, figsize=(5,30), color=(\"k\",\"g\", \"b\"), linewidth=0, alpha=.8, width=.75)\n", "plt.title(\"PageRank of Wikipedia articles \\nafter 5 iterations, 10 iterations, \\nand 5 smart iterations\")\n", "plt.xlabel(\"PageRank\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We find that the standard 10 iteration solution results in pages with the highest PageRank scores. It is difficult to compare this to the smart updating system. One possibility is that the smart system is more careful when updating values. Indeed, these top values went from having a PageRank score of 1 to a PageRank score of 20,000 in only five iterations! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I wish to pursue this further, but waiting an hour for the job to run is simply too long. I applied to Amazon for an increase in the limit of spot instances from 20 to 500. At \\$0.04/hour/node that means this cluster would cost \\$20/hr, but then 10 iterations should take about 2 minutes to run." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. HW9.4: Topic-specific PageRank implementation using MRJob <a name=\"1.4\"></a>\n", "[Back to Table of Contents](#TOC)\n", "\n", "Modify your PageRank implementation to produce a topic specific PageRank implementation, as described in:\n", "\n", "http://www-cs-students.stanford.edu/~taherh/papers/topic-sensitive-pagerank.pdf\n", "\n", "Note in this article that there is a special caveat to ensure that the transition matrix is irreducible. \n", "This caveat lies in footnote 3 on page 3:\n", "```\n", "\tA minor caveat: to ensure that M is irreducible when p\n", "\tcontains any 0 entries, nodes not reachable from nonzero\n", "\tnodes in p should be removed. In practice this is not problematic.\n", "```\n", "and must be adhered to for convergence to be guaranteed. \n", "\n", "Run topic specific PageRank on the following randomly generated network of 100 nodes:\n", "\n", "> s3://ucb-mids-mls-networks/randNet.txt (also available on Dropbox)\n", "\n", "which are organized into ten topics, as described in the file:\n", "\n", "> s3://ucb-mids-mls-networks/randNet_topics.txt (also available on Dropbox)\n", "\n", "Since there are 10 topics, your result should be 11 PageRank vectors (one for the vanilla PageRank implementation in 9.1, and one for each topic with the topic specific implementation). Print out the top ten ranking nodes and their topics for each of the 11 versions, and comment on your result. Assume a teleportation factor of 0.15 in all your analyses.\n", "\n", "One final and important comment here: please consider the requirements for irreducibility with topic-specific PageRank. In particular, the literature ensures irreducibility by requiring that nodes not reachable from in-topic nodes be removed from the network.\n", "\n", "This is not a small task, especially as it it must be performed separately for each of the (10) topics.\n", "\n", "So, instead of using this method for irreducibility, please comment on why the literature's method is difficult to implement, and what what extra computation it will require. \n", "\n", "Then for your code, please use the alternative, non-uniform damping vector:\n", "\n", "```\n", "vji = beta*(1/|Tj|); if node i lies in topic Tj\n", "\n", "vji = (1-beta)*(1/(N - |Tj|)); if node i lies outside of topic Tj\n", "```\n", "for beta in (0,1) close to 1. \n", "\n", "With this approach, you will not have to delete any nodes. If beta > 0.5, PageRank is topic-sensitive, and if beta < 0.5, the PageRank is anti-topic-sensitive. For any value of beta irreducibility should hold, so please try beta=0.99, and perhaps some other values locally, on the smaller networks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2 style=\"color:darkgreen\"> HW 9.4 Implementation </h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The literature's method is difficult to implement because it would require n_topics times more computation. Also for each job, you would have to perform this difficult \"reachability\" calculation. Such a calculation is not trivial because it requires one to determine which nodes are reachable by the topic nodes. " ] }, { "cell_type": "code", "execution_count": 369, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting TopicPageRank.py\n" ] } ], "source": [ "%%writefile TopicPageRank.py\n", "from __future__ import division\n", "import itertools\n", "from mrjob.job import MRJob, MRStep\n", "import json\n", "from collections import defaultdict, Counter\n", "import heapq\n", "\n", "\n", "class TopList(list):\n", " def __init__(self, \n", " max_size, \n", " num_position=0):\n", " \"\"\"\n", " Just like a list, except \n", " the append method adds \n", " the new value to the \n", " list only if it is larger\n", " than the smallest value \n", " (or if the size of \n", " the list is less than \n", " max_size). \n", " \n", " If each element of the list \n", " is an int or float, uses \n", " that value for comparison. \n", " If the elements in the list \n", " are lists or tuples, uses the \n", " list_position element of the \n", " list or tuple for the \n", " comparison.\n", " \"\"\"\n", " self.max_size = max_size\n", " self.pos = num_position\n", " \n", " def _get_key(self, x):\n", " if isinstance(x, (list, tuple)):\n", " return x[self.pos]\n", " else:\n", " return x\n", " \n", " def append(self, val):\n", " if len(self) < self.max_size:\n", " heapq.heappush(self, val)\n", " else:\n", " lowest_val = self._get_key(self[0])\n", " current_val = self._get_key(val)\n", " if current_val > lowest_val:\n", " heapq.heapreplace(self, val)\n", " \n", " def final_sort(self):\n", " return sorted(self, \n", " key=self._get_key, \n", " reverse=True)\n", " \n", " \n", "class TopicPageRank(MRJob):\n", " MRJob.SORT_VALUES = True\n", " def configure_options(self):\n", " super(TopicPageRank, \n", " self).configure_options()\n", "\n", " self.add_passthrough_option(\n", " '--reduce.tasks', \n", " dest='reducers', \n", " type='int',\n", " help=\"\"\"number of reducers\n", " to use. Controls the hash\n", " space of the custom\n", " partitioner\"\"\")\n", " \n", " self.add_passthrough_option(\n", " '--iterations', \n", " dest='iterations',\n", " default=5,\n", " type='int',\n", " help=\"\"\"number of iterations\n", " to perform.\"\"\")\n", " \n", " self.add_passthrough_option(\n", " '--damping_factor', \n", " dest='d', \n", " default=.85,\n", " type='float',\n", " help=\"\"\"Is the damping\n", " factor. Must be between\n", " 0 and 1.\"\"\")\n", " \n", " self.add_passthrough_option(\n", " '--smart_updating', \n", " dest='smart_updating', \n", " type='str',\n", " default=\"False\",\n", " help=\"\"\"Can be True or\n", " False. If True, all updates\n", " to the new PR will take into\n", " account the value of the old\n", " PR.\"\"\")\n", " \n", " self.add_passthrough_option(\n", " '--return_top_k', \n", " dest='return_top_k', \n", " type='int',\n", " default=100,\n", " help=\"\"\"Returns the results\n", " with the top k highest \n", " PageRank scores.\"\"\")\n", " \n", " def clean_init(self):\n", " # Lazy mode\n", " self.topic_map = {'24': '9', '25': '7', '26': '1', '27': '1', '20': '3', '21': '9', '22': '4', '23': '6', '28': '7', '29': '1', '4': '5', '8': '8', '59': '2', '58': '2', '55': '7', '54': '8', '57': '9', '56': '6', '51': '5', '50': '7', '53': '7', '52': '1', '88': '5', '89': '4', '82': '2', '83': '4', '80': '5', '81': '1', '86': '3', '87': '8', '84': '4', '85': '7', '3': '10', '7': '10', '100': '8', '39': '8', '38': '4', '33': '1', '32': '1', '31': '3', '30': '7', '37': '6', '36': '1', '35': '7', '34': '5', '60': '10', '61': '8', '62': '8', '63': '4', '64': '10', '65': '4', '66': '3', '67': '1', '68': '10', '69': '6', '2': '3', '6': '8', '99': '5', '98': '1', '91': '3', '90': '5', '93': '4', '92': '1', '95': '10', '94': '9', '97': '7', '96': '9', '11': '6', '10': '1', '13': '6', '12': '2', '15': '3', '14': '9', '17': '10', '16': '1', '19': '1', '18': '8', '48': '10', '49': '10', '46': '1', '47': '7', '44': '1', '45': '5', '42': '9', '43': '10', '40': '3', '41': '4', '1': '10', '5': '5', '9': '2', '77': '1', '76': '4', '75': '2', '74': '10', '73': '2', '72': '4', '71': '2', '70': '3', '79': '4', '78': '4'}\n", " \n", " def clean_data(self, _, lines):\n", " key, value = lines.split(\"\\t\")\n", " value = json.loads(value.replace(\"'\", '\"'))\n", " links = value.keys()\n", " values = {\"PR\":1,\"links\":links,\"topic\":self.topic_map[str(key)]}\n", " yield (str(key), values)\n", " \n", " def mapper_init(self):\n", " self.values = {\"***n_nodes_topics\": defaultdict(int),\n", " \"**Distribute_topics\": defaultdict(int)}\n", " self.n_reducers = self.options.reducers\n", " \n", " def mapper(self, key, line):\n", " n_reducers = self.n_reducers\n", " key_hash = hash(key)%n_reducers\n", " \n", " # Perform a node count each time\n", " PR = line[\"PR\"]\n", " links = line[\"links\"]\n", " topic = line[\"topic\"]\n", " n_links = len(links)\n", " \n", " # If it is not a dangling node\n", " # distribute its PR to the \n", " # other links.\n", " if n_links:\n", " PR_to_send = PR/n_links\n", " for link in links:\n", " link_hash = hash(link)%n_reducers\n", " yield (int(link_hash), (link, \n", " PR_to_send))\n", " # If it is a dangling node, \n", " # distribute its PR to all\n", " # other links\n", " else:\n", " self.values[\"**Distribute_topics\"][topic] += PR\n", " \n", " # Pass original node onward\n", " yield (int(key_hash), (key, line))\n", "\n", " def mapper_final(self):\n", " # Push special keys to each unique hash\n", " for key, value in self.values.items():\n", " for k in range(self.n_reducers):\n", " yield (int(k), (key, value))\n", " \n", " \n", " def reducer_init(self):\n", " # Lazy mode\n", " self.topic_map = {'24': '9', '25': '7', '26': '1', '27': '1', '20': '3', '21': '9', '22': '4', '23': '6', '28': '7', '29': '1', '4': '5', '8': '8', '59': '2', '58': '2', '55': '7', '54': '8', '57': '9', '56': '6', '51': '5', '50': '7', '53': '7', '52': '1', '88': '5', '89': '4', '82': '2', '83': '4', '80': '5', '81': '1', '86': '3', '87': '8', '84': '4', '85': '7', '3': '10', '7': '10', '100': '8', '39': '8', '38': '4', '33': '1', '32': '1', '31': '3', '30': '7', '37': '6', '36': '1', '35': '7', '34': '5', '60': '10', '61': '8', '62': '8', '63': '4', '64': '10', '65': '4', '66': '3', '67': '1', '68': '10', '69': '6', '2': '3', '6': '8', '99': '5', '98': '1', '91': '3', '90': '5', '93': '4', '92': '1', '95': '10', '94': '9', '97': '7', '96': '9', '11': '6', '10': '1', '13': '6', '12': '2', '15': '3', '14': '9', '17': '10', '16': '1', '19': '1', '18': '8', '48': '10', '49': '10', '46': '1', '47': '7', '44': '1', '45': '5', '42': '9', '43': '10', '40': '3', '41': '4', '1': '10', '5': '5', '9': '2', '77': '1', '76': '4', '75': '2', '74': '10', '73': '2', '72': '4', '71': '2', '70': '3', '79': '4', '78': '4'}\n", " self.d = self.options.d\n", " smart = self.options.smart_updating\n", " if smart == \"True\":\n", " self.smart = True\n", " elif smart == \"False\":\n", " self.smart = False\n", " else:\n", " msg = \"\"\"--smart_updating should \n", " be True or False\"\"\"\n", " raise Exception(msg)\n", " self.to_distribute_topics = Counter()\n", " self.n_nodes_topics = Counter()\n", "\n", " def reducer(self, hash_key, combo_values):\n", " gen_values = itertools.groupby(combo_values, \n", " key=lambda x:x[0])\n", " # Hask key is a pseudo partitioner.\n", " # Unpack old keys as separate\n", " # generators.\n", " for key, values in gen_values:\n", " total = 0\n", " node_info = None\n", "\n", " for key, val in values:\n", " # If the val is a number,\n", " # accumulate total.\n", " if isinstance(val, (float, int)):\n", " total += val\n", " elif isinstance(val, defaultdict):\n", " if key == \"**Distribute_topics\":\n", " for k in val:\n", " val[k] = val[k]/self.n_nodes_topics[k]\n", " self.to_distribute_topics += Counter(val)\n", " elif key == \"***n_nodes_topics\":\n", " self.n_nodes_topics += Counter(val)\n", " else:\n", " if key == \"**Distribute_topics\":\n", " continue\n", " # Means that the key-value\n", " # pair corresponds to a node\n", " # of the form. \n", " # {\"PR\": ..., \"links: [...]}\n", " node_info = val\n", " # Most keys will reference a node, so\n", " # put this check first.\n", " if node_info:\n", " old_pr = node_info[\"PR\"]\n", " distribute = self.to_distribute_topics.get(node_info[\"topic\"]) or 0\n", " pr = total + distribute\n", " decayed_pr = self.d * pr\n", " teleport_pr = 1-self.d\n", " new_pr = decayed_pr + teleport_pr\n", " if self.smart:\n", " # Use old PR to inform\n", " # new PR.\n", " diff = abs(new_pr - old_pr)\n", " percent_diff = diff/old_pr\n", " if percent_diff < .3:\n", " new_pr = .8*new_pr + .2*old_pr\n", " node_info[\"PR\"] = new_pr\n", " yield (key, node_info)\n", " else:\n", " if key in [\"***n_nodes_topics\", \"**Distribute_topics\"]:\n", " continue\n", " # Track dangling nodes.\n", " yield (key, {\"PR\": 1, \n", " \"links\": [],\n", " \"topic\": self.topic_map[key]})\n", " \n", " def decrease_file_size(self, key, value):\n", " val = value[\"PR\"]\n", " if val > .1:\n", " yield (\"top\", (key, round(val,4)))\n", " \n", " def collect_init(self):\n", " top_k = self.options.return_top_k\n", " self.top_vals = TopList(top_k, 1)\n", " \n", " def collect(self, key, values):\n", " for val in values:\n", " self.top_vals.append(val)\n", " \n", " def collect_final(self):\n", " for val in self.top_vals.final_sort():\n", " yield val\n", "\n", " def steps(self):\n", " iterations = self.options.iterations\n", " mr_steps = (\n", " [MRStep(mapper_init=self.clean_init,\n", " mapper=self.clean_data)] \n", " +\n", " [MRStep(\n", " mapper_init=self.mapper_init,\n", " mapper=self.mapper,\n", " mapper_final=self.mapper_final,\n", " reducer_init=self.reducer_init,\n", " reducer=self.reducer\n", " )]*iterations\n", " +\n", " [MRStep(mapper=self.decrease_file_size,\n", " reducer_init=self.collect_init,\n", " reducer=self.collect,\n", " reducer_final=self.collect_final)]\n", " )\n", " return mr_steps\n", "\n", "\n", "if __name__ == \"__main__\":\n", " TopicPageRank.run()" ] }, { "cell_type": "code", "execution_count": 370, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(u'15', 1.6356),\n", " (u'74', 1.5969),\n", " (u'63', 1.5771),\n", " (u'100', 1.5377),\n", " (u'85', 1.5179),\n", " (u'9', 1.5033),\n", " (u'58', 1.4828),\n", " (u'71', 1.4491),\n", " (u'61', 1.4407),\n", " (u'52', 1.4311),\n", " (u'77', 1.3664),\n", " (u'92', 1.3648),\n", " (u'32', 1.3308),\n", " (u'13', 1.3178),\n", " (u'88', 1.3139),\n", " (u'70', 1.3069),\n", " (u'17', 1.3066),\n", " (u'25', 1.2959),\n", " (u'90', 1.2862),\n", " (u'49', 1.2549),\n", " (u'53', 1.2215),\n", " (u'39', 1.2077),\n", " (u'51', 1.1786),\n", " (u'73', 1.1641),\n", " (u'45', 1.1598),\n", " (u'99', 1.1543),\n", " (u'28', 1.1515),\n", " (u'35', 1.1501),\n", " (u'56', 1.14),\n", " (u'55', 1.1129),\n", " (u'27', 1.1119),\n", " (u'10', 1.1115),\n", " (u'94', 1.1111),\n", " (u'41', 1.1088),\n", " (u'95', 1.1075),\n", " (u'91', 1.1028),\n", " (u'65', 1.0853),\n", " (u'86', 1.0703),\n", " (u'84', 1.0589),\n", " (u'62', 1.0557),\n", " (u'46', 1.0535),\n", " (u'2', 1.0334),\n", " (u'78', 1.0266),\n", " (u'97', 1.0192),\n", " (u'83', 1.0173),\n", " (u'8', 1.0066),\n", " (u'43', 1.005),\n", " (u'14', 0.9858),\n", " (u'21', 0.973),\n", " (u'12', 0.9678),\n", " (u'6', 0.966),\n", " (u'98', 0.9519),\n", " (u'42', 0.9389),\n", " (u'11', 0.9342),\n", " (u'37', 0.9214),\n", " (u'68', 0.9214),\n", " (u'57', 0.9174),\n", " (u'54', 0.9153),\n", " (u'80', 0.9132),\n", " (u'31', 0.9098),\n", " (u'67', 0.9058),\n", " (u'34', 0.9027),\n", " (u'4', 0.9019),\n", " (u'30', 0.8945),\n", " (u'7', 0.892),\n", " (u'44', 0.8862),\n", " (u'66', 0.8703),\n", " (u'75', 0.8682),\n", " (u'87', 0.8615),\n", " (u'29', 0.8387),\n", " (u'40', 0.8366),\n", " (u'3', 0.8292),\n", " (u'72', 0.8191),\n", " (u'47', 0.8034),\n", " (u'59', 0.8022),\n", " (u'48', 0.7953),\n", " (u'79', 0.7919),\n", " (u'26', 0.788),\n", " (u'1', 0.7873),\n", " (u'69', 0.7826),\n", " (u'81', 0.7769),\n", " (u'16', 0.7683),\n", " (u'33', 0.7659),\n", " (u'38', 0.743),\n", " (u'24', 0.7363),\n", " (u'89', 0.7221),\n", " (u'64', 0.7072),\n", " (u'50', 0.689),\n", " (u'5', 0.6815),\n", " (u'93', 0.6739),\n", " (u'18', 0.643),\n", " (u'36', 0.6029),\n", " (u'23', 0.5972),\n", " (u'96', 0.5968),\n", " (u'76', 0.5779),\n", " (u'60', 0.5668),\n", " (u'19', 0.5525),\n", " (u'22', 0.5211),\n", " (u'20', 0.5058),\n", " (u'82', 0.4558)]\n" ] } ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "from TopicPageRank import TopicPageRank as PageRank\n", "\n", "mr_job = PageRank(args=[\"data/randNet.txt\", \n", " \"--iterations=10\",\n", " \"--damping_factor=.85\",\n", " \"-q\",\n", " \"--return_top_k=100\",\n", " \"--reduce.tasks=5\"])\n", "results = []\n", "\n", "with mr_job.make_runner() as runner:\n", " runner.run()\n", " for line in runner.stream_output():\n", " result = mr_job.parse_output_line(line)\n", " results.append(result)\n", "\n", "pprint(results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I hardcoded the topic mapping as a hack. Instead, I would have joined the two datasets together and performed a similar operation in the clean step." ] } ], "metadata": { "anaconda-cloud": {}, "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
luisibanez/tensorflow_tutorial
tf-mnist-01.ipynb
1
5166
{ "metadata": { "name": "", "signature": "sha256:c5e8fbd5a6df6ee700ff09de6b8f1f2823be37f7988668e6cbd66b0bda5581d1" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Introduction to TensorFlow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "https://www.tensorflow.org/versions/r0.11/tutorials/index.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "https://storage.googleapis.com/amy-jo/talks/tf-workshop.pdf" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import tensorflow as tf" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "prompt_number": 2 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Neural Network Structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Neural network: W.x + b" ] }, { "cell_type": "code", "collapsed": false, "input": [ "X = tf.placeholder(tf.float32, [None, 784])\n", "W = tf.Variable(tf.zeros([784, 10]))\n", "b = tf.Variable(tf.zeros([10]))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# model\n", "Y = tf.nn.softmax(tf.matmul(X, W) + b)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# placeholder for correct answers (\"one-hot\" encoded)\n", "Y_ = tf.placeholder(tf.float32, [None, 10])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# loss function\n", "cross_entropy = tf.reduce_mean(-tf.reduce_sum(Y_ * tf.log(Y), reduction_indices=[1]))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mathematical operators documentation\n", "https://www.tensorflow.org/versions/r0.11/api_docs/python/math_ops.html" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# % of correct answers found in batch\n", "is_correct = tf.equal(tf.argmax(Y,1), tf.argmax(Y_,1))\n", "accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# traning\n", "optimizer = tf.train.GradientDescentOptimizer(0.003) # learning rate\n", "train_step = optimizer.minimize(cross_entropy) # loss function" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "init = tf.initialize_all_variables()\n", "sess = tf.Session()\n", "sess.run(init)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(1000):\n", " # load batch of images and correct answers\n", " batch_X, batch_Y = mnist.train.next_batch(100)\n", " train_data = {X: batch_X, Y_: batch_Y}\n", " \n", " # train\n", " sess.run(train_step, feed_dict=train_data)\n", " \n", " # success on test data ?\n", " test_data = {X: mnist.test.images, Y_: mnist.test.labels}\n", " a, c = sess.run([accuracy, cross_entropy], feed_dict=test_data)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
apache-2.0
molgor/spystats
notebooks/Sandboxes/Untitled.ipynb
1
25398
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Play with models" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/envs/biospytial/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "%matplotlib inline\n", "import sys\n", "sys.path.append('/apps')\n", "import django\n", "django.setup()\n", "from drivers.tree_builder import TreeNeo\n", "from drivers.graph_models import TreeNode, Order, Family, graph,Kingdom,Occurrence\n", "from drivers.graph_models import Cell,Mex4km, countObjectsOf\n", "from drivers.graph_models import pickNode\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import itertools as it\n", "import numpy as np\n", "import pymc3 as pm\n", "\n", "## Use the ggplot style\n", "plt.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import scipy" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [], "source": [ "## true parameters\n", "m = 2.5\n", "b = 10\n", "n = 500\n", "tau2 = 1 \n", "alpha = 10\n", "x = np.linspace(-10,10,n)\n", "eps = scipy.random.normal(0,tau2,n)\n", "per = np.sin(alpha * x) \n", "y = m*x + b + per + eps \n", "#y = per + eps " ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7ff438041b50>" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ff4323a8110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plt.plot(y)\n", "plt.scatter(x,y)" ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [], "source": [ "## Ok let's do the model in Pymc3\n", "with pm.Model() as model:\n", " tau2 = pm.HalfCauchy('tau',beta=3,testval=1)\n", " m = pm.Normal('slope', 0, sd=10)\n", " #alpha = pm.HalfCauchy('alpha',beta=10)\n", " #alpha = pm.Flat('alpha')\n", " alpha = pm.Cauchy('alpha',0,100)\n", " #b = pm.Normal('b',30,sd=1)\n", " b = pm.Flat('b')\n", " # Likelihood\n", " likelihood = pm.Normal('y',mu=m*x + b + pm.math.sin(alpha*x) ,sd=tau2,observed=y)\n", " " ] }, { "cell_type": "code", "execution_count": 153, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [b, alpha, slope, tau_log__]\n", "100%|██████████| 3500/3500 [00:21<00:00, 164.95it/s]\n", "The acceptance probability does not match the target. It is 0.8819770968481965, but should be close to 0.8. Try to increase the number of tuning steps.\n", "The acceptance probability does not match the target. It is 0.8902451686847429, but should be close to 0.8. Try to increase the number of tuning steps.\n" ] } ], "source": [ "with model:\n", " trace = pm.sample(3000)" ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "logp = -861.6, ||grad|| = 0.30907: 100%|██████████| 44/44 [00:00<00:00, 1426.81it/s] \n" ] } ], "source": [ "with model:\n", " map_ = pm.find_MAP()" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'alpha': array(-4.59380032),\n", " 'b': array(9.98482052),\n", " 'slope': array(2.50144248),\n", " 'tau': array(1.32640824),\n", " 'tau_log__': array(0.28247472)}" ] }, "execution_count": 155, "metadata": {}, "output_type": "execute_result" } ], "source": [ "map_" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Django Shell-Plus", "language": "python", "name": "django_extensions" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-2-clause
keras-team/keras-io
examples/vision/ipynb/image_classification_with_vision_transformer.ipynb
1
15312
{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "# Image classification with Vision Transformer\n", "\n", "**Author:** [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)<br>\n", "**Date created:** 2021/01/18<br>\n", "**Last modified:** 2021/01/18<br>\n", "**Description:** Implementing the Vision Transformer (ViT) model for image classification." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Introduction\n", "\n", "This example implements the [Vision Transformer (ViT)](https://arxiv.org/abs/2010.11929)\n", "model by Alexey Dosovitskiy et al. for image classification,\n", "and demonstrates it on the CIFAR-100 dataset.\n", "The ViT model applies the Transformer architecture with self-attention to sequences of\n", "image patches, without using convolution layers.\n", "\n", "This example requires TensorFlow 2.4 or higher, as well as\n", "[TensorFlow Addons](https://www.tensorflow.org/addons/overview),\n", "which can be installed using the following command:\n", "\n", "```python\n", "pip install -U tensorflow-addons\n", "```" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "import tensorflow_addons as tfa" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Prepare the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "num_classes = 100\n", "input_shape = (32, 32, 3)\n", "\n", "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()\n", "\n", "print(f\"x_train shape: {x_train.shape} - y_train shape: {y_train.shape}\")\n", "print(f\"x_test shape: {x_test.shape} - y_test shape: {y_test.shape}\")\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Configure the hyperparameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "learning_rate = 0.001\n", "weight_decay = 0.0001\n", "batch_size = 256\n", "num_epochs = 100\n", "image_size = 72 # We'll resize input images to this size\n", "patch_size = 6 # Size of the patches to be extract from the input images\n", "num_patches = (image_size // patch_size) ** 2\n", "projection_dim = 64\n", "num_heads = 4\n", "transformer_units = [\n", " projection_dim * 2,\n", " projection_dim,\n", "] # Size of the transformer layers\n", "transformer_layers = 8\n", "mlp_head_units = [2048, 1024] # Size of the dense layers of the final classifier\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Use data augmentation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "data_augmentation = keras.Sequential(\n", " [\n", " layers.Normalization(),\n", " layers.Resizing(image_size, image_size),\n", " layers.RandomFlip(\"horizontal\"),\n", " layers.RandomRotation(factor=0.02),\n", " layers.RandomZoom(\n", " height_factor=0.2, width_factor=0.2\n", " ),\n", " ],\n", " name=\"data_augmentation\",\n", ")\n", "# Compute the mean and the variance of the training data for normalization.\n", "data_augmentation.layers[0].adapt(x_train)\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Implement multilayer perceptron (MLP)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "\n", "def mlp(x, hidden_units, dropout_rate):\n", " for units in hidden_units:\n", " x = layers.Dense(units, activation=tf.nn.gelu)(x)\n", " x = layers.Dropout(dropout_rate)(x)\n", " return x\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Implement patch creation as a layer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "\n", "class Patches(layers.Layer):\n", " def __init__(self, patch_size):\n", " super(Patches, self).__init__()\n", " self.patch_size = patch_size\n", "\n", " def call(self, images):\n", " batch_size = tf.shape(images)[0]\n", " patches = tf.image.extract_patches(\n", " images=images,\n", " sizes=[1, self.patch_size, self.patch_size, 1],\n", " strides=[1, self.patch_size, self.patch_size, 1],\n", " rates=[1, 1, 1, 1],\n", " padding=\"VALID\",\n", " )\n", " patch_dims = patches.shape[-1]\n", " patches = tf.reshape(patches, [batch_size, -1, patch_dims])\n", " return patches\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "Let's display patches for a sample image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(4, 4))\n", "image = x_train[np.random.choice(range(x_train.shape[0]))]\n", "plt.imshow(image.astype(\"uint8\"))\n", "plt.axis(\"off\")\n", "\n", "resized_image = tf.image.resize(\n", " tf.convert_to_tensor([image]), size=(image_size, image_size)\n", ")\n", "patches = Patches(patch_size)(resized_image)\n", "print(f\"Image size: {image_size} X {image_size}\")\n", "print(f\"Patch size: {patch_size} X {patch_size}\")\n", "print(f\"Patches per image: {patches.shape[1]}\")\n", "print(f\"Elements per patch: {patches.shape[-1]}\")\n", "\n", "n = int(np.sqrt(patches.shape[1]))\n", "plt.figure(figsize=(4, 4))\n", "for i, patch in enumerate(patches[0]):\n", " ax = plt.subplot(n, n, i + 1)\n", " patch_img = tf.reshape(patch, (patch_size, patch_size, 3))\n", " plt.imshow(patch_img.numpy().astype(\"uint8\"))\n", " plt.axis(\"off\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Implement the patch encoding layer\n", "\n", "The `PatchEncoder` layer will linearly transform a patch by projecting it into a\n", "vector of size `projection_dim`. In addition, it adds a learnable position\n", "embedding to the projected vector." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "\n", "class PatchEncoder(layers.Layer):\n", " def __init__(self, num_patches, projection_dim):\n", " super(PatchEncoder, self).__init__()\n", " self.num_patches = num_patches\n", " self.projection = layers.Dense(units=projection_dim)\n", " self.position_embedding = layers.Embedding(\n", " input_dim=num_patches, output_dim=projection_dim\n", " )\n", "\n", " def call(self, patch):\n", " positions = tf.range(start=0, limit=self.num_patches, delta=1)\n", " encoded = self.projection(patch) + self.position_embedding(positions)\n", " return encoded\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Build the ViT model\n", "\n", "The ViT model consists of multiple Transformer blocks,\n", "which use the `layers.MultiHeadAttention` layer as a self-attention mechanism\n", "applied to the sequence of patches. The Transformer blocks produce a\n", "`[batch_size, num_patches, projection_dim]` tensor, which is processed via an\n", "classifier head with softmax to produce the final class probabilities output.\n", "\n", "Unlike the technique described in the [paper](https://arxiv.org/abs/2010.11929),\n", "which prepends a learnable embedding to the sequence of encoded patches to serve\n", "as the image representation, all the outputs of the final Transformer block are\n", "reshaped with `layers.Flatten()` and used as the image\n", "representation input to the classifier head.\n", "Note that the `layers.GlobalAveragePooling1D` layer\n", "could also be used instead to aggregate the outputs of the Transformer block,\n", "especially when the number of patches and the projection dimensions are large." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "\n", "def create_vit_classifier():\n", " inputs = layers.Input(shape=input_shape)\n", " # Augment data.\n", " augmented = data_augmentation(inputs)\n", " # Create patches.\n", " patches = Patches(patch_size)(augmented)\n", " # Encode patches.\n", " encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)\n", "\n", " # Create multiple layers of the Transformer block.\n", " for _ in range(transformer_layers):\n", " # Layer normalization 1.\n", " x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n", " # Create a multi-head attention layer.\n", " attention_output = layers.MultiHeadAttention(\n", " num_heads=num_heads, key_dim=projection_dim, dropout=0.1\n", " )(x1, x1)\n", " # Skip connection 1.\n", " x2 = layers.Add()([attention_output, encoded_patches])\n", " # Layer normalization 2.\n", " x3 = layers.LayerNormalization(epsilon=1e-6)(x2)\n", " # MLP.\n", " x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)\n", " # Skip connection 2.\n", " encoded_patches = layers.Add()([x3, x2])\n", "\n", " # Create a [batch_size, projection_dim] tensor.\n", " representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n", " representation = layers.Flatten()(representation)\n", " representation = layers.Dropout(0.5)(representation)\n", " # Add MLP.\n", " features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.5)\n", " # Classify outputs.\n", " logits = layers.Dense(num_classes)(features)\n", " # Create the Keras model.\n", " model = keras.Model(inputs=inputs, outputs=logits)\n", " return model\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Compile, train, and evaluate the mode" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "\n", "def run_experiment(model):\n", " optimizer = tfa.optimizers.AdamW(\n", " learning_rate=learning_rate, weight_decay=weight_decay\n", " )\n", "\n", " model.compile(\n", " optimizer=optimizer,\n", " loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " metrics=[\n", " keras.metrics.SparseCategoricalAccuracy(name=\"accuracy\"),\n", " keras.metrics.SparseTopKCategoricalAccuracy(5, name=\"top-5-accuracy\"),\n", " ],\n", " )\n", "\n", " checkpoint_filepath = \"/tmp/checkpoint\"\n", " checkpoint_callback = keras.callbacks.ModelCheckpoint(\n", " checkpoint_filepath,\n", " monitor=\"val_accuracy\",\n", " save_best_only=True,\n", " save_weights_only=True,\n", " )\n", "\n", " history = model.fit(\n", " x=x_train,\n", " y=y_train,\n", " batch_size=batch_size,\n", " epochs=num_epochs,\n", " validation_split=0.1,\n", " callbacks=[checkpoint_callback],\n", " )\n", "\n", " model.load_weights(checkpoint_filepath)\n", " _, accuracy, top_5_accuracy = model.evaluate(x_test, y_test)\n", " print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")\n", " print(f\"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%\")\n", "\n", " return history\n", "\n", "\n", "vit_classifier = create_vit_classifier()\n", "history = run_experiment(vit_classifier)\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "After 100 epochs, the ViT model achieves around 55% accuracy and\n", "82% top-5 accuracy on the test data. These are not competitive results on the CIFAR-100 dataset,\n", "as a ResNet50V2 trained from scratch on the same data can achieve 67% accuracy.\n", "\n", "Note that the state of the art results reported in the\n", "[paper](https://arxiv.org/abs/2010.11929) are achieved by pre-training the ViT model using\n", "the JFT-300M dataset, then fine-tuning it on the target dataset. To improve the model quality\n", "without pre-training, you can try to train the model for more epochs, use a larger number of\n", "Transformer layers, resize the input images, change the patch size, or increase the projection dimensions. \n", "Besides, as mentioned in the paper, the quality of the model is affected not only by architecture choices, \n", "but also by parameters such as the learning rate schedule, optimizer, weight decay, etc.\n", "In practice, it's recommended to fine-tune a ViT model\n", "that was pre-trained using a large, high-resolution dataset." ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "image_classification_with_vision_transformer", "private_outputs": false, "provenance": [], "toc_visible": true }, "environment": { "name": "tf2-gpu.2-4.m61", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-4:m61" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
leriomaggio/computational-science-lectures
notebooks/03_Matplotlib.ipynb
1
1853537
null
mit
timkpaine/lantern
examples/lineup.ipynb
1
5747471
null
apache-2.0
yihaochen/FLASHtools
logfile_analysis/logfile_analysis.ipynb
1
14739
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "#%matplotlib notebook\n", "from datetime import datetime\n", "import numpy as np\n", "import os\n", "import matplotlib\n", "#matplotlib.rcParams['savefig.dpi'] = 200\n", "matplotlib.rcParams['figure.figsize'] = (12.0, 8.0)\n", "matplotlib.rcParams[\"font.size\"] = 20\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_logfile(fname):\n", " startmarks = []\n", " results = []\n", " with open(fname, 'r') as f:\n", " start = False\n", " for line in f.readlines():\n", " if 'FLASH log file:' in line:\n", " start = True\n", " if 'Number of MPI tasks:' in line:\n", " nProc = int(line.split()[-1])\n", " if '[GRID amr_refine_derefine] min blks' in line:\n", " tot_blks = int(line.split()[-1])\n", " max_blks = int(line.split()[-4])\n", " if '[Particles_getGlobalNum]' in line:\n", " nPart = line.split()[-1]\n", " if 'step: n=' in line:\n", " #print line\n", " dummy, date, time, dummy, dummy, step, t, dt = line.split()\n", " dtime = datetime.strptime(date+' '+time, '%m-%d-%Y %H:%M:%S.%f')\n", " step = int(step.lstrip('n='))\n", " t = float(t.lstrip('t='))\n", " dt = float(dt.lstrip('dt='))\n", " results.append([dtime, step, t, dt, max_blks, tot_blks, nPart, nProc])\n", " if start:\n", " startmarks.append([step])\n", " start = False\n", " #result = dict((name,eval(name)) for name in ['dtime','step','t','dt'] )\n", " \n", " dtimes, steps, ts, dts, max_blkss, tot_blkss, nParts, nProcs = zip(*results)\n", " #startsteps = zip(*startmarks)\n", " dtimes = list(dtimes)\n", " dt_steps = np.array([(dtimes[i+1]-dtimes[i]).total_seconds() for i in range(len(dtimes)-1)])\n", " plt.clf()\n", " plt.scatter(steps[1:], dt_steps, s=10, linewidth=0, label='dt/step')\n", " mask = dt_steps < 1000\n", " plt.annotate('%.2f s' % np.mean(dt_steps[mask]), (steps[-len(steps)//7], dt_steps[-1]))\n", " #plt.scatter(steps[1:], 10*np.array(dt_steps)/np.array(max_blkss[1:]), s=1, c='g', linewidth=0, label='t/10block/step')\n", " dt_blk = np.array(dt_steps)/np.array(max_blkss[1:])*10\n", " mask = dt_blk < 1000\n", " plt.scatter(steps[1:], dt_blk, s=10, linewidth=0, label='dt/step/10block')\n", " plt.annotate('%.2f s' % np.mean(dt_blk[mask]), (steps[-len(steps)//7], dt_blk[-1]))\n", "\n", " #print np.mean((np.array(dt_steps)/np.array(max_blkss[1:]))*100)\n", " plt.ylim(0.2, 1000)\n", " plt.xlabel('# of step')\n", " plt.semilogy()\n", " plt.ylabel('time (sec)')\n", " plt.legend(loc=2)\n", " \n", " # Plot start steps\n", " plt.vlines(startmarks,0.2,1000, color='grey', linestyles='--', alpha=0.5,)\n", " \n", " plt.twinx()\n", " plt.scatter(steps[1:], max_blkss[1:], s=5, linewidth=0, color='r', label='max blks')\n", " #plt.ylabel('max blocks / processor')\n", " plt.scatter(steps[1:], nProcs[1:], s=5, linewidth=0, color='g', label='# cpu')\n", " #plt.scatter(steps[1:], [ntot/nProc for ntot in tot_blkss[1:]], s=1, linewidth=0, color='orange', label='tot blks')\n", " plt.ylim(0, 1200)\n", " #plt.twinx()\n", " #plt.scatter(steps[1:], dts[1:], s=1, linewidth=0, color='g', label='dt')\n", " plt.xlim(steps[0],steps[-1])\n", " plt.grid()\n", " plt.legend(loc=1)\n", " plt.title(fname.split('/')[-2])\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#%pdb\n", "#fnames = ['/home/ychen/data/0only_1106_M3_h1/MHD_Jet.log',\n", "# '/home/ychen/data/0only_1204_M24_b01/MHD_Jet.log',\n", "# '/home/ychen/data/0only_0529_h1/MHD_Jet.log']\n", "fnames = ['/home/ychen/Mount/aci/data/1022_L45_M10_b1_h1_10Myr/MHD_Jet_10Myr.log',\n", " '/home/ychen/Mount/aci/data/2017/1212_L45_M10_b1_h0_10Myr/MHD_Jet_10Myr.log']\n", "#fnames = ['/home/ychen/Mount/aci/data/performance_test/1219_astro3/MHD_Jet_10Myr.log',\n", "# '/home/ychen/Mount/aci/data/performance_test/1219_pre/MHD_Jet_10Myr.log']\n", "\n", "for fname in fnames:\n", " plot_logfile(fname)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_nozzledata(dirname):\n", " try:\n", " nozzledata = np.genfromtxt(os.path.join(dirname, 'nozzleVec.dat'))\n", " \n", " ttnoz = nozzledata[:,1]\n", " xx = nozzledata[:,2]\n", " yy = nozzledata[:,3]\n", " zz = nozzledata[:,4]\n", " thetas = np.arccos(zz)/np.pi*180\n", " phis = np.arctan2(yy, xx)\n", " return ttnoz, xx, yy, zz, thetas, phis\n", " except:\n", " pass\n", "\n", "def get_dat(dirname):\n", " try:\n", " data = np.genfromtxt(os.path.join(dirname, 'MHD_Jet.dat'))\n", " except:\n", " data = np.genfromtxt(os.path.join(dirname, 'MHD_Jet_10Myr.dat'))\n", " tt = data[:,0]\n", " Masses = data[:,1]\n", " Etots = data[:,5]\n", " Ekins = data[:,6]\n", " Eints = data[:,7] \n", " Emags = data[:,8]\n", " Ptots = (Etots[1:]-Etots[:-1])/(tt[1:]-tt[:-1])\n", " Pkins = (Ekins[1:]-Ekins[:-1])/(tt[1:]-tt[:-1])\n", " Pints = (Eints[1:]-Eints[:-1])/(tt[1:]-tt[:-1])\n", " #print max(Ptots), max(Pkins), max(Pints)\n", " return tt, Masses, Etots, Ekins, Eints, Emags, Ptots, Pkins, Pints" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#dirname = '/home/ychen/data/0529_L45_M10_b1_h1'\n", "#dirname = '/home/ychen/d9/FLASH4/stampede/0916_L45_M10_b1_hinf_10Myr/'\n", "#dirnames = ['/home/ychen/data/0529_L45_M10_b1_h1',\\\n", "# '/home/ychen/data/0605_L45_M10_b1_h0',\\\n", "# '/home/ychen/data/0605_L45_M10_b1_hinf_all',\n", "# '/home/ychen/data/0602_L45_M10_hydro_all']\n", "dirnames = ['/home/ychen/data/0only_0529_h1',\\\n", " '/home/ychen/data/0only_1106_M3_h1',\\\n", " '/home/ychen/data/0only_1110_h0_rerun',\\\n", " '/home/ychen/data/0only_1111_M10_b01',\\\n", " '/home/ychen/data/0only_1204_M24_b01'\n", " ]\n", "table = []\n", "for dirname in dirnames:\n", " table.append(get_dat(dirname))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "names = [dirname.split('/')[-1] for dirname in dirnames]\n", "colors = ['yellow', 'r', 'b', 'g', 'cyan']\n", "plt.figure(figsize=(10,8))\n", "for i in range(len(table)):\n", " tt, Masses, Etots, Ekins, Eints, Emags, Ptots, Pkins, Pints = table[i]\n", " plt.plot(tt[1:]/3.154E13, (tt[1:]-tt[:-1]), c=colors[i], label=names[i], lw=0, marker='.', markersize=1, alpha=0.7)\n", "\n", "\n", "#plt.scatter(tt[:], Etots[:], s=1, linewidth=0, label='dt')\n", "#plt.ylim(2.563E62, 2.563E62+9E59)\n", "#plt.ylim(0, 4E58)\n", "plt.ylim(0,9E9)\n", "plt.xlim(0, 25)\n", "#plt.semilogy()\n", "plt.ylabel('dt')\n", "plt.xlabel('t (Myr)')\n", "plt.legend(loc=2)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## names = [dirname.split('/')[-1] for dirname in dirnames]\n", "colors = ['r', 'b', 'g', 'cyan']\n", "for i in range(len(table)):\n", " print i\n", " tt, Masses, Etots, Ekins, Eints, Emags, Ptots, Pkins, Pints = table[i]\n", " plt.plot(tt[:]/3.154E13, (Eints[:]-Eints[0])/Emags[:], c=colors[i], label=names[i].split('_')[-1])\n", " plt.plot(tt[:]/3.154E13, (Ekins[:]-Ekins[0])/Emags[:], ':', c=colors[i], label='Ekin/Emag')\n", "\n", "plt.legend(loc=2)\n", "#plt.scatter(tt[:], Etots[:], s=1, linewidth=0, label='dt')\n", "#plt.ylim(2.563E62, 2.563E62+1E60)\n", "#plt.ylim(0, 4E57)\n", "#plt.ylim(0,400)\n", "#plt.xlim(0, 25)\n", "#plt.semilogy()\n", "#plt.ylabel('Emag (erg)')\n", "plt.ylabel(u'$\\Delta$E/Emag')\n", "plt.xlabel('t (Myr)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "names = [dirname.split('/')[-1] for dirname in dirnames]\n", "colors = ['r', 'b', 'g', 'cyan']\n", "for i in range(len(table)):\n", " tt, Masses, Etots, Ekins, Eints, Emags, Ptots, Pkins, Pints = table[i]\n", " plt.scatter(tt[:]/3.154E13, Ekins[:], s=1, c=colors[i], linewidth=0, label=names[i])\n", "\n", "plt.legend(loc=2)\n", "#plt.scatter(tt[:], Etots[:], s=1, linewidth=0, label='dt')\n", "plt.ylim(0.0, 2E59)\n", "#plt.ylim(0, 4E57)\n", "#plt.xlim(0, 25)\n", "#plt.semilogy()\n", "plt.ylabel('Ekin (erg)')\n", "plt.xlabel('t (Myr)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "names = [dirname.split('/')[-1] for dirname in dirnames]\n", "colors = ['r', 'b', 'g', 'cyan']\n", "for i in range(len(table)):\n", " tt, Masses, Etots, Ekins, Eints, Emags, Ptots, Pkins, Pints = table[i]\n", " plt.scatter(tt[:]/3.154E13, Eints[:], s=1, c=colors[i], linewidth=0, label=names[i])\n", "\n", "plt.legend(loc=2)\n", "#plt.scatter(tt[:], Etots[:], s=1, linewidth=0, label='dt')\n", "plt.ylim(2.563E62, 2.563E62+1E60)\n", "#plt.ylim(0, 4E57)\n", "#plt.xlim(0, 25)\n", "#plt.semilogy()\n", "plt.ylabel('Eint (erg)')\n", "plt.xlabel('t (Myr)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "names = [dirname.split('/')[-1] for dirname in dirnames]\n", "colors = ['r', 'b', 'g', 'cyan']\n", "for i in range(len(table)):\n", " tt, Masses, Etots, Ekins, Eints, Emags, Ptots, Pkins, Pints = table[i]\n", " plt.scatter(tt[:]/3.154E13, Masses[:], s=1, c=colors[i], linewidth=0, label=names[i])\n", "\n", "plt.legend(loc=2)\n", "#plt.scatter(tt[:], Etots[:], s=1, linewidth=0, label='dt')\n", "#plt.ylim(1.81313E46, 1.81313E46+2E41)\n", "#plt.ylim(0, 4E57)\n", "#plt.xlim(0, 25)\n", "#plt.semilogy()\n", "plt.ylabel('Mass (g)')\n", "plt.xlabel('t (Myr)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def Etot_lin(tt, E0=2.5637E62):\n", " return E0 + tt*1E45\n", "\n", "names = [dirname.split('/')[-1] for dirname in dirnames]\n", "colors = ['r', 'b', 'g', 'cyan']\n", "\n", "for i in range(len(table)):\n", " tt, Masses, Etots, Ekins, Eints, Emags, Ptots, Pkins, Pints = table[i]\n", " plt.plot(tt[:]/3.154E13, Etots-Etot_lin(tt,E0=0), '-', c=colors[i])\n", " plt.plot(tt[:]/3.154E13, Etots-Emags-Etot_lin(tt,E0=0), ':', c=colors[i], label=names[i])\n", " \n", "\n", "plt.legend(loc=2)\n", "#plt.scatter(tt[:], Etots[:], s=1, linewidth=0, label='dt')\n", "#plt.ylim(-2E58, 10E58)\n", "#plt.ylim(0, 4E57)\n", "#plt.xlim(0, 25)\n", "#plt.semilogy()\n", "plt.ylabel(u'$\\Delta$ Etot (erg)')\n", "plt.xlabel('t (Myr)')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "i=0\n", "tt, Masses, Etots, Ekins, Eints, Emags, Ptots, Pkins, Pints = table[i]\n", "dirname = dirnames[i]\n", "fig = plt.figure(figsize=(8,6))\n", "plt.scatter(tt[1:]/3.15569E13, (Etots[1:]-Etots[:-1])/(tt[1:]-tt[:-1]), s=1, c='b', linewidth=0, label='Total Power')\n", "plt.scatter(tt[1:]/3.15569E13, (Ekins[1:]-Ekins[:-1])/(tt[1:]-tt[:-1]), s=1, c='r', linewidth=0, label='Kinetic Power')\n", "plt.scatter(tt[1:]/3.15569E13, (Eints[1:]-Eints[:-1])/(tt[1:]-tt[:-1]), s=1, c='g', linewidth=0, label='Internal Power')\n", "plt.scatter(tt[1:]/3.15569E13, (Emags[1:]-Emags[:-1])/(tt[1:]-tt[:-1]), s=1, c='purple', linewidth=0, label='Magnetic Power')\n", "#plt.ylim(1E44, 1.5E45)\n", "plt.ylim(-0E45, 2E45)\n", "plt.ylabel('power (erg/s)')\n", "plt.axhline(1E45, lw=0.5, c='grey')\n", "plt.axhline(1.1E45, lw=0.5, c='grey')\n", "#plt.ylim(1E44, 5E45)\n", "#plt.ylim(1E38, 4E44)\n", "plt.legend(loc=2)\n", "#plt.semilogy()\n", "#plt.twinx()\n", "\n", "#plt.scatter(ttnoz/3.15569E13, thetas, s=1, linewidth=0, c='y', alpha=0.3, label='theta')\n", "#plt.scatter(ttnoz, phis, s=1, linewidth=0, c='g', alpha=0.5, label='phi')\n", "plt.legend(loc=2)\n", "plt.xlim(0, 20)\n", "plt.xlabel('t (Myr)')\n", "plt.title(dirname.split('/')[-1])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# plt.scatter(tt[1:], (Masses[1:]-Masses[:-1])/(tt[1:]-tt[:-1]), s=1, linewidth=0, label='Mass Flux')\n", "plt.ylabel('mass flux (g/s)')\n", "plt.xlabel('t (s)')\n", "#plt.semilogy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "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": 1 }
gpl-2.0
flamingbear/ipython-notebooks
notebooks/Describe Daily CSV and 5-day Sea Ice statistics.ipynb
1
19619
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "##### Guided tour on how the daily 5-day statistics work with pandas" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "As always first fetch the nsidc daily sea ice concentration data to our output directory." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "!wget -P ../output -qN ftp://sidads.colorado.edu/pub/DATASETS/NOAA/G02135/north/daily/data/NH_seaice_extent_final.csv\n", "!wget -P ../output -qN ftp://sidads.colorado.edu/pub/DATASETS/NOAA/G02135/north/daily/data/NH_seaice_extent_nrt.csv\n", "!wget -P ../output -qN ftp://sidads.colorado.edu/pub/DATASETS/NOAA/G02135/south/daily/data/SH_seaice_extent_final.csv\n", "!wget -P ../output -qN ftp://sidads.colorado.edu/pub/DATASETS/NOAA/G02135/south/daily/data/SH_seaice_extent_nrt.csv\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "Variables to set before running:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "hemisphere = 'north' # 'south' or 'north'\n", "climatology_years = (1981, 2010)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "# some imports for working with pandas, and excel files.\n", "import datetime as dt\n", "import numpy as np\n", "\n", "import os\n", "import pandas as pd\n", "from pandas import ExcelWriter\n", "\n", "%matplotlib inline\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "\n", "pd.options.display.mpl_style = 'default'\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "# code for reading a hemisphere of data from CSV files.\n", "\n", "def parse_the_date(year, mm, dd):\n", " return dt.date(int(year), int(mm), int(dd))\n", "\n", "def slurp_csv(filename):\n", " data = pd.read_csv(filename, header = None, skiprows=2,\n", " names=[\"year\", \"mm\", \"dd\", \"extent\", \"missing\", \"source\"],\n", " parse_dates={'date':['year', 'mm', 'dd']},\n", " date_parser=parse_the_date, index_col='date')\n", " data = data.drop(['missing', 'source'], axis=1)\n", " return data\n", "\n", "\n", "def read_a_hemisphere(hemisphere):\n", " the_dir = \"../output\"\n", " final_prod_filename = os.path.join(the_dir, '{hemi}H_seaice_extent_final.csv'.format(hemi=hemisphere[0:1].upper()))\n", " nrt_prod_filename = os.path.join(the_dir, '{hemi}H_seaice_extent_nrt.csv'.format(hemi=hemisphere[0:1].upper()))\n", "\n", " final = slurp_csv(final_prod_filename)\n", " nrt = slurp_csv(nrt_prod_filename)\n", " all_data = pd.concat([final, nrt])\n", " return all_data\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df = read_a_hemisphere(hemisphere)\n", "\n", "# df.head(3) => just shows 3 rows from your dataframe\n", "df.head(3)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "Set date index to a special DatetimeIndex and then Reindex the dataframe so\n", "that every daily timestep is included in the series and any missing data is\n", "marked as NaN." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "# index before turning into DatetimeIndex\n", "print df.index[0:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df.index = pd.to_datetime(df.index)\n", "df = df.reindex(index=pd.date_range('1978-10-25', dt.date.today().strftime('%Y-%m-%d')))\n", "df['hemi'] = hemisphere\n", "print( df.head())\n", "print(\"\\nindex: \")\n", "print( df.index)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "## interpolate missing data in SMMR period.\n", "\n", "We don't want to interpolate across any timeperiods where more than one day\n", "of data is missing, but we do want to do a strict linear interpolation across\n", "the standard every-other-day that SMMR operated.\n", "\n", "So we are going to do both a forward and backwards fill on the extent field,\n", "while setting the limit of missing values to one. Next, we are going to\n", "union the NaNs that remain after the fills in order to leave any gaps in the\n", "data record alone when we perform an interpolation.\n", "\n", "So start by using the backfill to fill any NaN locations that have a valid \"next\" value.\n", "So start by using the forwardfill to fill any NaN locations that have a valid \"previous\" value." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df['backfill'] = df.extent.fillna(method='bfill', limit=1)\n", "df['forwardfill'] = df.extent.fillna(method='ffill', limit=1)\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "See below that in the backfill column, 1978-10-25 was filled with the value\n", "(10.231) from 1978-10-26 and that in the forwardfill column, the value\n", "remains NaN, but that in the forwardfill column, 1978-10-27 value gets the\n", "extent from 1978-10-26" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "print(df.head())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "See that 1987-12-03 gets a forward, but not a backfill" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "print(df['19871201':'19871206'])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "See that 1988-01-12 gets a backfill, but not a forwardfill" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "print(df['19880110':'19880114'])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "So the union of backfill's NaN and forward fill NaN will capture any missing\n", "data that doesn't have a valid data point both before and after itself in the series.\n", "We can get a list of is really NAN by saving this off." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "is_really_nan = pd.isnull(df['backfill']) | pd.isnull(df['forwardfill'])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "1. Use the interpolation scheme to do simple linear regression on the entire extent column\n", "2. Then go back and mark as missing any large gaps in the linearly interpolated data.\n", "3. Drop the backfill and forwardfill columns\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df['interpolated'] = df.extent.interpolate()\n", "#df['interpolated'].loc[is_really_nan] = np.nan\n", "df.interpolated.loc[is_really_nan == True] = np.nan\n", "df = df.drop(['forwardfill', 'backfill'], axis=1)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "So now we have a simple dataframe with daily extents and daily interpolated extents" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "### Add 5 day rolling mean from the interpolated data to the extent." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df['5 Day'] = pd.rolling_mean(df['interpolated'], window=5, min_periods=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "## Compute climatological means by selecting a copy of the data between your desired climatology years." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "clim_data = df[(df.index.year >= climatology_years[0])&(df.index.year <= climatology_years[1] )].copy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "print clim_data.head(3),\"\\n...\\n\" ,clim_data.tail(3)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "show the years of the climatology and then number of years to work with." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "print len(np.unique(clim_data.index.year))\n", "print np.unique(clim_data.index.year)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "grab the mean value of the interpolated extents for each month/day combination" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "def clim_string(climatology_years):\n", " return '{0}-{1}'.format(climatology_years[0], climatology_years[1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "def get_climatological_means(column, clim_data):\n", " means = clim_data.copy()\n", " means = means.groupby([clim_data.index.month, clim_data.index.day]).mean()[[column]]\n", " means = means.rename(columns={column: clim_string(climatology_years)})\n", " return means" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "daily_means = get_climatological_means('interpolated', clim_data)\n", "five_day_means = get_climatological_means('5 Day', clim_data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "print five_day_means.head()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "**check yourself**: You can see in the three panels below that the value we get by calling\n", "`mean()` on the `groupby` result is the same as expected by averaging the day\n", "and month data separately" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "testmeans = clim_data.groupby([clim_data.index.month, clim_data.index.day]).mean()[['interpolated']]\n", "testmeans.head(1)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "Select the January 1 data for climatology_years" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "clim_data[(clim_data.index.month == 1)&(clim_data.index.day == 1)]['interpolated'].values" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "np.nanmean(clim_data[(clim_data.index.month == 1)&(clim_data.index.day == 1)]['interpolated'].values)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "##### Get the daily extent data into the correct format for display and for concatenating with the clim_averages" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df.index" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "import calendar\n", "month_names = [calendar.month_name[x] for x in range(1,13)]" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "right now the data is all stored a timeseries with an index of datetimes and\n", "columns of extent, interpolated-extent, and 5 day rolling mean extents.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df.head(2)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "So we would like to reorder (pivot) the data into a nice dataframe where\n", "Months/Days are shown along the left side, and years are columns across and a\n", "datavalue is displayed as the \"meat\"\n", " 1979 1980\n", " Jan 1 data data ...\n", " 2 data data\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "There are a couple of ways to do this:\n", "You can select a column, or columns and set the index to by a hierarchy of year/month/day, and then unstack the year." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df= df[['extent']].set_index([df.index.year, df.index.month, df.index.day]).unstack(0)\n", "df.head(3)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "We now want to concat the climatology means on to this newly shaped dataframe.\n", "But before you can do that the column indices must match." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "print df.columns.nlevels\n", "print df.columns.levels\n", "print daily_means.columns.nlevels" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "so drop the extra extent level" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df.columns = df.columns.droplevel(0)\n", "print df.columns.nlevels" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "tags": [ "worksheet-0" ] }, "source": [ "Now concatinate and the dataframe is ready to be output." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df = pd.concat([df, daily_means.copy()], axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "df.to_csv('test.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "# cleanup\n", "!cd ../output; rm -f NH_seaice_extent_final.csv NH_seaice_extent_nrt.csv SH_seaice_extent_final.csv SH_seaice_extent_nrt.csv" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "tags": [ "worksheet-0" ] }, "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" }, "name": "Describe Daily CSV and 5-day Sea Ice statistics.ipynb" }, "nbformat": 4, "nbformat_minor": 0 }
mit
DS-100/sp17-materials
sp17/disc/disc06/disc06_solution.ipynb
1
7639
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Flummoxed: From Questions to Queries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this activity, we will practice translating our human questions into queries our robot counterparts can understand. As usual, you will not be required to turn in anything. For your convenience, we have included a short example of how to typeset relational algebra notation in a Jupyter notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LaTeX-Style Typesetting in Jupyter Notebooks\n", "\n", "You can use TeX-style commands to include mathematical expressions in Jupyter notebook. In a markdown cell, just include the commands you want wrapped in `$` to have an inline expression and `$$` if you want centered equation typesetting.\n", "\n", "`$$basic-cust-accts \\leftarrow \\Pi_{(name, customer.sin, account-number)}\n", "(\\sigma_{customer.sin = account.sin}(customer \\times account))$$`\n", "\n", "$$basic-cust-accts \\leftarrow \\Pi_{(name, customer.sin, account-number)}\n", "(\\sigma_{customer.sin = account.sin}(customer \\times account))$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Operation | Symbol | LaTeX Name\n", "--------- | ------ | -----------\n", "Assign | $\\rightarrow$ | `rightarrow`\n", "Select | $\\sigma$ | `sigma`\n", "Project | $\\Pi$ | `Pi`\n", "Inner product | $\\bowtie$ | `bowtie`\n", "Cross product | $\\times$ | `times`\n", "Rename | $\\rho$ | `rho`\n", "Groupby | $\\gamma$ | `gamma`\n", "And | $\\wedge$ | `wedge`\n", "Or | $\\vee$ | `vee`\n", "Negation | $\\neg$ | `neg`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Inquiries\n", "\n", "Consider the following schema below. The key fields are underlined, and the domain is listed after the field names. The **Catalog** table contains prices for **Balloons** sold by different **Clowns** standing at certain booths in a fair.\n", "\n", "**Clowns**(<u>cid</u> integer, cname text, booth text) \n", "**Balloons**(<u>bid</u> integer, bshape text, bcolor text) \n", "**Catalog**(<u>cid</u> integer, <u>bid</u> integer, cost float)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inquiry: Funny Algebra\n", "\n", "In this section, you are asked to translate the following queries into relational algebra semantics." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Problem 1\n", "\n", "What are the costs of all whale-shaped balloons?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**SOLUTION:** $\\Pi_{cost}(\\sigma_{bshape='whale'}Balloons \\bowtie_{bid=bid} Catalog)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Problem 2\n", "\n", "What are the names of the clowns that sell periwinkle (color) balloons in at least one shape?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**SOLUTION:** $\\Pi_{cname}((\\Pi_{pid}\\sigma_{bcolor='periwinkle'}Balloons)\\bowtie Catalog)\\bowtie_{cid=cid} Clowns)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Problem 3 (Hard -- Opinions may be _divisive_)\n", "\n", "What are the cids of the clowns that sell periwinkle balloons in every shape?\n", "\n", "Hint: Consider the following strategy \n", "1. Compute all possible attribute pairings\n", "2. Remove the existing pariings\n", "3. Remove the non-answers from the possible answers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**SOLUTION:** $\\Pi_{cid}Catalog - \\Pi_{cid}((\\Pi_{cid}Catalog \\times \\Pi_{bid}\\sigma_{bcolor='periwinkle'}Balloons)-Catalog)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inquiry: The SQL\n", "In this section, you are asked to translate the following questions into their equivalent SQL queries. Do not delete the backticks if you want to keep a nice fix-width formatting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Problem 4\n", "What are the top 10 most expensive shapes sold by Whompers LeFou?" ] }, { "cell_type": "markdown", "metadata": { "for_assignment_type": "solution" }, "source": [ "**SOLUTION:**\n", "```\n", "SELECT bshape, cost\n", "FROM Clowns, Balloons, Catalog \n", "WHERE Clowns.cid=Catalog.cid\n", " AND Balloons.bid=Catalog.bid\n", " AND cname='Whompers LeFou'\n", "ORDER BY cost DESC\n", "LIMIT 10;\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Problem 5\n", "How many different colors are available at each booth?" ] }, { "cell_type": "markdown", "metadata": { "for_assignment_type": "solution" }, "source": [ "**SOLUTION:**\n", "```\n", "SELECT booth, COUNT(DISTINCT bcolor)\n", "FROM Clowns, Balloons, Catalog\n", "WHERE Clowns.cid=Catalog.cid\n", " AND Balloons.bid=Catalog.bid\n", "GROUP BY booth\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Problem 6\n", "What is the average cost of a balloon at booths that offer more than 3 red shapes per clown? Each clown at the booth does not necessarily have to be selling more than 3 shapes." ] }, { "cell_type": "markdown", "metadata": { "for_assignment_type": "solution" }, "source": [ "**SOLUTION:**\n", "```\n", "SELECT booth, avg(cost)\n", "FROM Clowns, Balloons, Catalog\n", "WHERE Clowns.cid=Catalog.cid\n", " AND Balloons.cid=Catalog.cid\n", " AND bcolor='red'\n", "GROUP BY booth\n", "HAVING COUNT(DISTINCT bshape)/COUNT(DISTINCT Clowns.cid) > 3\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Bonus\n", "Recall the schema for the representation of a matrix in SQL: matrix(<u>x</u>, <u>y</u>, value). In lecture, someone asked how the matrix transpose would be defined in SQL. Let's think about the implementation. First, we should think about the definition. Let $M$ denote a matrix and $[M]_{ij}$ be the element in the $i^{th}$ row and $j^{th}$ column. The matrix transpose is defined as\n", "$$[M^T]_{ij}=[M]_{ji}$$\n", "This suggests that all we naively have to do is make a `SELECT` statement that swaps `x` and `y`. Hint: $\\rho(Mt, (1\\rightarrow y, 2 \\rightarrow x), M)$" ] }, { "cell_type": "markdown", "metadata": { "for_assignment_type": "solution" }, "source": [ "**SOLUTION:**\n", "```\n", "SELECT M.y AS x, M.x AS y, value \n", "FROM M;\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "SQL query goes here...\n", "```" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:ds100bow]", "language": "python", "name": "conda-env-ds100bow-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": 2 }
gpl-3.0
oroszl/graphene_BiTeI
analytic_topological_invariant.ipynb
1
13766
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sympy import *\n", "import sympy" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "init_printing()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m,th,ph,kx,ky=symbols('m theta phi kx ky',positive=True)\n", "mi,mz=symbols('m_i,m_z',positive=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def mataf(h):\n", " 'Array flatten a matrix list of appropriate dimensions'\n", " H=Matrix.hstack(*h[0])\n", " for sor in range(1,len(h)):\n", " H=Matrix.vstack(H,Matrix.hstack(*h[sor]))\n", " return H" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "S1=sympy.physics.matrices.msigma(1)\n", "S2=sympy.physics.matrices.msigma(2)\n", "S3=sympy.physics.matrices.msigma(3)\n", "S0=S1*S1\n", "\n", "S=Matrix([[S1],[S2],[S3]])" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mv=m*Matrix([[sin(th)*cos(ph)],\n", " [sin(th)*sin(ph)],\n", " [cos(th)]])\n", "\n", "mv=0*Matrix([[mi*cos(ph)],\n", " [mi*sin(ph)],\n", " [mz]])\n", "\n", "\n", "#mv=m*Matrix([[0],[0],[1]])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAADUAAAAyBAMAAAAOzY77AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA74lUMhCZds3dIma7\nq0Ru0ZIZAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVQ4EWOQ//+JAQtg+v9fgEHYxRWLFAOr\ni7MAgwg2GZAYC0QuTD0FWQVrUpkDXK6ZIXIBkiSHAessmBy3AANTA5LcRgYGDZgczwUG5q9IcmcZ\nGOwDoPbxX2Dg+4wk95eB4b0BVC4+gYHvO0KO9RtQrgAqt16BgQUpALiAbH+gENgP+OTiFVDNBOqD\nm8mfwMCM7BagffYwt3AeYOBG9sMcBob9MD+wCzCwNSDcyVDIwJAB8zvDZIZAByQ5TgNWYAxAwzqo\n/AqSFAOrei1QKVQOWQLOHpWDBwUKAxouo2kXHiqjaZdAXsFXRuIpWwFSMF/XzV5A5gAAAABJRU5E\nrkJggg==\n", "text/latex": [ "$$\\left[\\begin{matrix}0 & 0\\\\0 & 0\\end{matrix}\\right]$$" ], "text/plain": [ "⎡0 0⎤\n", "⎢ ⎥\n", "⎣0 0⎦" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simplify((mv.T*S)[0])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAADUAAAAyBAMAAAAOzY77AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA74lUMhCZds3dIma7\nq0Ru0ZIZAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVQ4EWOQ//+JAQtg+v9fgEHYxRWLFAOr\ni7MAgwg2GZAYC0QuTD0FWQVrUpkDXK6ZIXIBkiSHAessmBy3AANTA5LcRgYGDZgczwUG5q9IcmcZ\nGOwDoPbxX2Dg+4wk95eB4b0BVC4+gYHvO0KO9RtQrgAqt16BgQUpALiAbH+gENgP+OTiFVDNBOqD\nm8mfwMCM7BagffYwt3AeYOBG9sMcBob9MD+wCzCwNSDcyVDIwJAB8zvDZIZAByQ5TgNWYAxAwzqo\n/AqSFAOrei1QKVQOWQLOHpWDBwUKAxouo2kXHiqjaZdAXsFXRuIpWwFSMF/XzV5A5gAAAABJRU5E\nrkJggg==\n", "text/latex": [ "$$\\left[\\begin{matrix}0 & 0\\\\0 & 0\\end{matrix}\\right]$$" ], "text/plain": [ "⎡0 0⎤\n", "⎢ ⎥\n", "⎣0 0⎦" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simplify(((rot_axis3(-pi/3)*mv).T*S)[0])" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a1=Matrix([[1],\n", " [-sqrt(3)]])/2\n", "a2=Matrix([[1],\n", " [sqrt(3)]])/2\n", "\n", "k=Matrix([[kx],\n", " [ky]])" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHoAAAAmBAMAAAD5O5JbAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nGn88Byq8xQGMtVsHcuPBjTX/N3oFj0AvfAk+AAAAAElFTkSuQmCC\n", "text/latex": [ "$$\\left[\\begin{matrix}e^{\\frac{i}{2} \\left(kx + \\sqrt{3} ky\\right)}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ ⅈ⋅(kx + √3⋅ky)⎤\n", "⎢ ──────────────⎥\n", "⎢ 2 ⎥\n", "⎣ℯ ⎦" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sympy.exp(I*k.T*a1)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "U=mataf([[0*S0,S0+I*simplify((mv.T*S)[0])],\n", " [-I*simplify((mv.T*S)[0]),0*S0]])" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Tx=simplify(mataf([[0*S0,S0+I*simplify(((rot_axis3(pi/3)*mv).T*S)[0])],\n", " [0*S0,0*S0]]))\n", "Ty=simplify(mataf([[0*S0,S0+I*simplify(((rot_axis3(-pi/3)*mv).T*S)[0])],\n", " [0*S0,0*S0]]))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "H=simplify(U+exp(I*k.T*a1)[0]*Tx+exp(I*k.T*a2)[0]*Ty+exp(-I*k.T*a1)[0]*Tx.H+exp(-I*k.T*a2)[0]*Ty.H)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "A=simplify(Matrix([[0,1+exp(I*k.T*a1)[0]+exp(I*k.T*a2)[0]],\n", " [1+exp(-I*k.T*a1)[0]+exp(-I*k.T*a2)[0],0]]))\n" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\left[\\begin{matrix}0 & e^{\\frac{i}{2} \\left(kx - \\sqrt{3} ky\\right)} + e^{\\frac{i}{2} \\left(kx + \\sqrt{3} ky\\right)} + 1\\\\\\left(e^{i kx} + e^{\\frac{i}{2} \\left(kx - \\sqrt{3} ky\\right)} + e^{\\frac{i}{2} \\left(kx + \\sqrt{3} ky\\right)}\\right) e^{- i kx} & 0\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ ⅈ⋅(kx - √3⋅ky) ⅈ⋅(kx \n", "⎢ ────────────── ──────\n", "⎢ 2 \n", "⎢ 0 ℯ + ℯ \n", "⎢ \n", "⎢⎛ ⅈ⋅(kx - √3⋅ky) ⅈ⋅(kx + √3⋅ky)⎞ \n", "⎢⎜ ────────────── ──────────────⎟ \n", "⎢⎜ ⅈ⋅kx 2 2 ⎟ -ⅈ⋅kx \n", "⎣⎝ℯ + ℯ + ℯ ⎠⋅ℯ 0 \n", "\n", "+ √3⋅ky) ⎤\n", "──────── ⎥\n", "2 ⎥\n", " + 1⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎦" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
hrayatnia/SciPy
ipython gallery/AeroPython-master/lessons/01_Lesson01_sourceSink.ipynb
1
503085
{ "metadata": { "name": "", "signature": "sha256:823526dd014e1527149579f4db9667ee8a1beb4a8e9b449664cf36c5945a110f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "Content provided under a Creative Commons Attribution license, CC-BY 4.0; code under MIT License. (c)2014 Lorena A. Barba, Olivier Mesnard. Thanks: NSF for support via CAREER award #1149784." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[@LorenaABarba](https://twitter.com/LorenaABarba)" ] }, { "cell_type": "heading", "level": 5, "metadata": {}, "source": [ "Version 0.2 -- March 2014" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Source & Sink" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the first notebook of the *AeroPython* series, for classical Aerodynamics using Python. If you are not familiar with numerical computing using Python, be sure to first study the [*Python Crash Course*](http://nbviewer.ipython.org/github/barbagroup/AeroPython/blob/master/lessons/00_Lesson00_QuickPythonIntro.ipynb), provided as \"lesson zero\" of this series.\n", "\n", "To execute this Notebook on your machine, first navigate on your console or terminal to the folder that contains the .ipynb file. Then invoke the notebook server as follows: \n", "\n", "`ipython notebook`\n", "\n", "You can also read and follow along this notebook online, while typing the code into your interactive IPython prompt, or into a .py file on your favorite Python editor. Either way, make sure that you try out the codes and write your own versions!" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The theoretical basis for classical Aerodynamics is *potential-flow theory*, a mathematical apparatus that was the life-blood of aerodynamics through its growth to maturity during most of the 20th century.\n", "\n", "It all starts with making some simplifications: \n", "\n", "* the flow is steady;\n", "* the velocity remains smaller than the speed of sound (incompressible flow);\n", "* the fluid has no internal friction, i.e., is inviscid; and\n", "* it has no vorticity (fluid particles are not rotating). \n", "\n", "This sounds like a lot of simplifications, does it not? It turns out, a big chunk of aerodynamics can be approximated this way! Viscous effects are normally confined to a very thin boundary layer (and we can correct potential theory to account for that), and many flows are effectively irrotational (except for isolated points, lines or sheets). And finally, most applications that we are interested in do remain sub-sonic.\n", "\n", "Potential-flow theory has a very pleasant mathematical quality: it is *linear*. This means that the principle of superposition applies, and we can construct new solutions by adding known solutions. \n", "\n", "In this notebook, you will learn two elementary potential-flow solutions: the **source**, and the **sink**. And, guess what, you will add them together to make a *new* solution called a **source-sink pair**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The math\n", "\n", "---\n", "\n", "OK, let's get down some mathematics. First, from your undergraduate fluid mechanics, you should remember the definition of *circulation*:\n", "\n", "$$\\Gamma = \\oint \\mathbf{v}\\cdot d\\vec{l}$$\n", "\n", "In words, the circulation is the line integral of velocity around a closed contour. Squeeze your brain and extract the theorem of Stokes. It says that this line integral is equal to the *flux* through the contour of the *curl* of velocity \u2026 which is the vorticity, $\\omega=\\nabla\\times\\mathbf{v}$:\n", "\n", "$$\\oint \\mathbf{v}\\cdot d\\vec{l} = \\int \\int_s \\omega\\cdot \\vec{n}~ ds$$\n", "\n", "If the vorticity is zero (irrotational flow), so is the circulation around any closed contour equal to zero. This means that the line integral of velocity for any curve going from A to B must be equal and opposite to that of any curve going back from B to A. Expand the dot product in the integral, where the velocity is $\\mathbf{v}=(u,v,w)$ :\n", "\n", "$$\\int_A^B \\mathbf{v}\\cdot d\\vec{l} = \\int_A^B u~dx + v~dy + w~dz$$\n", "\n", "In irrotational flow, it doesn't matter what path you take, this line integral from A to B is always the same value. Now, if you remember your vector calculus, this means that $u~dx + v~dy + w~dz~$ is an [exact differential](http://www.wolframalpha.com/input/?i=exact+differential) of a potential $\\phi$, where\n", "\n", "$$u=\\frac{\\partial \\phi}{\\partial x}, \\quad v=\\frac{\\partial \\phi}{\\partial y}, \\quad w=\\frac{\\partial \\phi}{\\partial z}$$\n", "\n", "Or, for short: $\\mathbf{v}=\\nabla \\phi$. Applying the continuity equation for incompressible flow, $\\nabla\\cdot\\mathbf{v}=0$, we get the beautifully simple governing equation of potential flow:\n", "\n", "$$\\nabla^2\\phi=0$$\n", "\n", "*Laplace's equation!* So any solution to Laplace can be a potential flow, and here we go.\n" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Let's get started!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to numerically express the flow field of a **source** and a **sink**, two potential flow solutions, so we can plot these flows and admire them.\n", "\n", "Start by importing some Python libraries to help you out:\n", "\n", "* NumPy is a scientific library to create and manage multi-dimensional arrays and matrices.\n", "* Matplotlib is a 2D plotting library that we will use to visualize our results.\n", "* the `math` module provides the mathematical functions defined by the C standard.\n", "\n", "Go back to our *\"lesson zero,\"* the [Python Crash Course](http://nbviewer.ipython.org/github/barbagroup/AeroPython/blob/master/lessons/00_Lesson00_QuickPythonIntro.ipynb), if you don't remember why and how we import our favorite libraries: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "import numpy\n", "from matplotlib import pyplot" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The objective is to visualize the streamlines corresponding to a source and a sink. To do that, we need to first define a set of points where the velocity components will be computed.\n", "\n", "Let's define an evenly spaced Cartesian grid of points within a spatial domain that is 4 units of length wide in the $x$-direction and 2 units of length wide in the $y$-direction, i.e. $x,y\\in\\left[-2,2\\right],\\left[-1,1\\right]$.\n", "\n", "The variable `N` will be the number of points we want in each direction, and we define the computational boundaries by the variables `x_start`, `x_end`, `y_start` and `y_end`. \n", "\n", "We use the NumPy function [`linspace()`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html) to create two 1D arrays that contain the evenly spaced values of $x$ and $y$ coordinates, corresponding to our grid points. The last line of the code block below calls the [`meshgrid()`](http://docs.scipy.org/doc/numpy-1.4.x/reference/generated/numpy.meshgrid.html) function, which generates arrays containing the coordinates of points where the numerical solution will be calculated. Be sure to study the output of this function and understand what it does! We will use it throughout this course." ] }, { "cell_type": "code", "collapsed": false, "input": [ "N = 50 # number of points in each direction\n", "x_start, x_end = -2.0, 2.0 # boundaries in the x-direction\n", "y_start, y_end = -1.0, 1.0 # boundaries in the y-direction\n", "x = numpy.linspace(x_start, x_end, N) # creates a 1D-array with the x-coordinates\n", "y = numpy.linspace(y_start, y_end, N) # creates a 1D-array with the y-coordinates\n", "\n", "print ('x = ', x)\n", "print ('y = ', y)\n", "\n", "X, Y = numpy.meshgrid(x, y) # generates a mesh grid" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "x = [-2. -1.91836735 -1.83673469 -1.75510204 -1.67346939 -1.59183673\n", " -1.51020408 -1.42857143 -1.34693878 -1.26530612 -1.18367347 -1.10204082\n", " -1.02040816 -0.93877551 -0.85714286 -0.7755102 -0.69387755 -0.6122449\n", " -0.53061224 -0.44897959 -0.36734694 -0.28571429 -0.20408163 -0.12244898\n", " -0.04081633 0.04081633 0.12244898 0.20408163 0.28571429 0.36734694\n", " 0.44897959 0.53061224 0.6122449 0.69387755 0.7755102 0.85714286\n", " 0.93877551 1.02040816 1.10204082 1.18367347 1.26530612 1.34693878\n", " 1.42857143 1.51020408 1.59183673 1.67346939 1.75510204 1.83673469\n", " 1.91836735 2. ]" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "y = [-1. -0.95918367 -0.91836735 -0.87755102 -0.83673469 -0.79591837\n", " -0.75510204 -0.71428571 -0.67346939 -0.63265306 -0.59183673 -0.55102041\n", " -0.51020408 -0.46938776 -0.42857143 -0.3877551 -0.34693878 -0.30612245\n", " -0.26530612 -0.2244898 -0.18367347 -0.14285714 -0.10204082 -0.06122449\n", " -0.02040816 0.02040816 0.06122449 0.10204082 0.14285714 0.18367347\n", " 0.2244898 0.26530612 0.30612245 0.34693878 0.3877551 0.42857143\n", " 0.46938776 0.51020408 0.55102041 0.59183673 0.63265306 0.67346939\n", " 0.71428571 0.75510204 0.79591837 0.83673469 0.87755102 0.91836735\n", " 0.95918367 1. ]\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the mesh grid has been generated, it is time to visualize it with the module `pyplot` from the library `matplotlib` using the alias `plt` and the function [`scatter()`](http://matplotlib.org/api/pyplot_api.html?highlight=scatter#matplotlib.pyplot.scatter).\n", "\n", "Note that we are getting fancy here and telling Pyplot just how we want the plot: put a label on the $x$-axis and on the $y$-axis, make the figure a certain size, make the axis limits just what *we* want, and even the [color](http://matplotlib.org/api/colors_api.html?highlight=color#module-matplotlib.colors) of the point markers. Learn about all these fancy options on the Python documentation and you will always draw pretty plots.\n", "\n", "To get the plots inside the notebook, we use the line `%matplotlib inline` first. This simply configures the plotting module to output plots inside the IPython Notebook (instead of a pop-up window):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "# plots the grid of points\n", "size = 10\n", "pyplot.figure(figsize=(size, (y_end-y_start)/(x_end-x_start)*size))\n", "pyplot.xlabel('x', fontsize=16)\n", "pyplot.ylabel('y', fontsize=16)\n", "pyplot.xlim(x_start, x_end)\n", "pyplot.ylim(y_start, y_end)\n", "pyplot.scatter(X, Y, s=10, color='#CD2305', marker='o', linewidth=0)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "<matplotlib.collections.PathCollection at 0x1059d8400>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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6uVM6nO7JHYwddsPYAQAAAEaBt+gCAAAA2DBmfWuV1eqYHU53SofTTUc/d0qH053S4XTT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\nUd9G3CKVSwAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x10597cf60>" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "On all of those nicely ordered points, we now will calculate the velocity vector corresponding to a source flow. Then we'll plot the streamlines. Ready?" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Source flow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We mentioned before the nice quality of potential flow: the governing equation is *linear* and solutions can be built by superposition. For this reason, it is very useful to have a toolbox of elementary solutions that we can use as building blocks. Sources and sinks are such elementary solutions.\n", "\n", "A *source* is a point from which we imagine that fluid is flowing out, uniformly. Thus, all the streamlines radiate from a single point as straight lines and the radial velocity decreases with the distance from the source point. Let's consider first the purely two-dimensional case. Because of the radial symmetry, it is convenient to use a cylindrical coordinate system, $\\left(r,\\theta\\right)$. The angle $\\theta$ is $\\tan^{-1}(y/x)$. The velocity components (radial and tangential) are:\n", "\n", "$$u_r\\left(r,\\theta\\right) = \\frac{\\sigma}{2\\pi r} \\text{,} \\qquad u_\\theta\\left(r,\\theta\\right)=0$$\n", "\n", "where $\\sigma$ represents the source *strength*. That the tangential velocity is zero is obvious from our requirement that the streamlines be radiating straight lines. But how do we get the radial component of velocity? Apply the irrotational-flow condition, $\\omega=0$, in cylindrical coordinates, and you will get that the velocity can only be a function of $r$. Then apply the continuity equation, and you will get the result. Try it! (Go on: paper, pencil \u2026)\n", "\n", "You probably remember the *stream function* from undergraduate fluid mechanics. But now we are working on cylindrical coordinates. So $\\psi$ is obtained from:\n", "\n", "$$\\frac{1}{r}\\frac{\\partial\\psi}{\\partial\\theta} = u_r \\quad \\text{,} \\quad -\\frac{\\partial\\psi}{\\partial r} = u_\\theta$$\n", "\n", "which integrates to\n", "\n", "$$\\psi = \\frac{\\sigma}{2\\pi}\\theta + \\text{constant}$$\n", "\n", "In practical problems, we are more interested in the velocity components that are obtained by differentiation of the stream function, so that the constant can be dropped.\n", "\n", "In Cartesian coordinates, the velocity field $\\left(u,v\\right)$ at position $\\left(x,y\\right)$ corresponding to a source of strength $\\sigma$ located at $\\left(x_\\text{source},y_\\text{source}\\right)$ is given by:\n", "\n", "$$u = \\frac{\\partial\\psi}{\\partial y} = \\frac{\\sigma}{2\\pi}\\frac{x-x_\\text{source}}{\\left(x-x_\\text{source}\\right)^2+\\left(y-y_\\text{source}\\right)^2}$$\n", "\n", "and\n", "\n", "$$v = -\\frac{\\partial\\psi}{\\partial x} = \\frac{\\sigma}{2\\pi}\\frac{y-y_\\text{source}}{\\left(x-x_\\text{source}\\right)^2+\\left(y-y_\\text{source}\\right)^2}$$\n", "\n", "Let's calculate the velocity field for our grid of points. We'll place the source at the location $(-1,0)$ and give it a strength $\\sigma=5$. \n", "\n", "Instead of picking one point on the grid and calculate its velocity (which means that we would have to iterate over all positions `[i,j]`), we directly compute velocity arrays (`u_source`, `v_source`) using arithmetic operators on arrays. Yes, with Numpy, arithmetic operators on array apply elementwise and a new array is created and filled with the result." ] }, { "cell_type": "code", "collapsed": false, "input": [ "strength_source = 5.0 # source strength\n", "x_source, y_source = -1.0, 0.0 # location of the source\n", "\n", "# computes the velocity field on the mesh grid\n", "u_source = strength_source/(2*math.pi) * (X-x_source)/((X-x_source)**2 + (Y-y_source)**2)\n", "v_source = strength_source/(2*math.pi) * (Y-y_source)/((X-x_source)**2 + (Y-y_source)**2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot the stream lines already! We are lucky that the contributors to the open Python world have added a [`streamplot()`](http://matplotlib.org/users/whats_new.html#streamplot) function that does it all. We'll also use the [`scatter()`](http://matplotlib.org/api/pyplot_api.html?highlight=scatter#matplotlib.pyplot.scatter) function to put a red dot right on the source." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# plotting the streamlines\n", "size = 10\n", "pyplot.figure(figsize=(size, (y_end-y_start)/(x_end-x_start)*size))\n", "pyplot.xlabel('x', fontsize=16)\n", "pyplot.ylabel('y', fontsize=16)\n", "pyplot.xlim(x_start, x_end)\n", "pyplot.ylim(y_start, y_end)\n", "pyplot.streamplot(X, Y, u_source, v_source, density=2, linewidth=1, arrowsize=2, arrowstyle='->')\n", "pyplot.scatter(x_source, y_source, color='#CD2305', s=80, marker='o', linewidth=0);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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9PFsIiE/+/Maq0549fE5TPiIiIClCEA4qVMhe3uHdu4ST7BRIREZCNszmzGZE\nRAS2oFcv67k7V6/iuQ8caP39XrxImkbDhsYntfjC2bNsfNWqWT/u67ffUF7LliXlw8q4HjtGSkSx\nYpBKs0UVqorD2bQphHTRIvPqjqpCqJs3h+hPn24+pUTrIdeyJU72tGmedxMezvwzmjNOJ/vIm2+i\n7AwaZJ4Yp6dDMtq08VSL/vyzOSKZmkqBVNu22LIOHfhvo5Cy241zc/Agym6bNiiaisK9LFxov3Ar\nJobwdqtWhC6bNiV0aUfpl0FEBGlRTZvyu40b01JFlhgrASKXDV4H6pdf8MT0cmeSk1low4bJb7Ra\nL7i339ZfDNHRTFa3W4jvv2fz8pW/l5qKl6SnDhlBK3ywmsSeMyd5fPnysTFERbHBdOmSWT2bPx85\n3w7GjbPf0uOddzAOvnDjBsqRHtq14934wq+/skHawQ8/GJ8eYYSlS+Ua7uph7FgUBTvo3Nm4yvf2\nbeZQxhYgmzezZtLSIAtGYVk9REfTImLOHOvX0IjPp59av8a+fWxKVvPhVBWHLiSEUwisXCM9HYIQ\nEoJKYuUacXGkfuTJwzWsKF8REZ6jolatslbRevIkdqVwYcJ8Zh0fVaXA6dVXUa3WrDGvpKel8b3y\n5UkxWb3aPEGPi0MFKlaMiMDq1dmjD82aQTL/+U9IQPHiRHratcOuDRvG7//73zi9q1ebI6NaQv+4\ncQga9esT0jWjeLlcCCF9+qBu9e/PfZhRNZOTsa8dO0IAGzVCCTSbo54Vt2+T6tSoEddt357QpdXK\nbhk4nTiw77/P8+TJw9j88IM1JVEJELls8DlYFy6wIPQ2/NRUvOp27eQXfnIyCpqRN1+smOdw9wUL\nYO++fuPqVTzAs2fl7sEbfvqJDcqKOvDss3xv1iyPWpiSQtipYkWPMbp/n4VtRwbfuZOEezuYPl2/\n4vTOHWM1rVkzfeK7aZM9lVMI+/mLqsr466nLRoiKIgxi52iozZvZbPQSjx0OPpO1gOTgQUIqAwZQ\nDGE1wTg2lrGYNs3a94Wgh1qePBh+K1BVlOt8+az3LYuLYzMoX95jH8zi1Cm+37KltRYnbrcQX30F\nGR00yNqJA7GxnOpRtizr0Upe05kzKE4FC0JozRYOOJ3MrQoVyIP69lvzZPTRI8J++fJhE/bsMU80\nzpxhbufKhdIvk7emqpCPq1dRycaMQSX+xz+Yo9u2mbuHBw8gkRUqkNIzdao5hVZVUfxGjYKUV66M\nambGbqTs+B6pAAAgAElEQVSmEinp2hVxoHFjHIRr13g/779vjcRdvUoouEYNbEDPniiNdlN09BAT\nwxrp0gVFtEoVUkZOnrRfJKEEiFw26A7YzZuEA8eP9z2B0tKQfVu2lCdB8fEYUr1Gw2+/7Wne6nbz\nGwMG+L6P9eu5VzskqVMnjJIZuN00a1VVjHPNmp7NQVUhXhkJ6PDh9hLMHz8mEd5OteLatfqnRMTE\nGBc7dO+u39R05UqqbO2gc2fr/amE4Ltt29ozHP36mW+InRH377PhG+X5zZpF2MXbvS5ezHqxeppA\nQgJOyujR1r35bdtQr6w2Hk5OZoOqWtV6AcyRI6jzw4dbc7hSUpj3detiL6yMxenTrPGaNa3lsGU9\nUstKt/sLF1BSwsKYG2bHIjUVglCsGGPx00/mx+LKFe6/aFHWiNljr9LSyO169VWI6OLF5kOw164h\nCOTNi3LWrBnPIzseaWmoXq1aQZx69cqcsyaD8HDuvXRpxmLSJHMtWdLTUUN79OAeOnSAlGtjERcH\nCTKzdjVVcfJkxJiwMAoU9+17em1CVJVWRosW4ZC++CK29/PPrZ2EoQclQOSywXDQHj2Cyffu7XsS\nOBx4yU2byrP8e/cI3/lKuF62LHMY8skTY4Wwb18IhtXN6uZN1BczVWLJySSmahgxQv/A+e3bMV52\nULWqvc7wP/2k39IjOVmI557Tv0a1avoka/Roe2qa1sDXzpExAwYYt0jRwx9/EF6x01C2dWvjfntb\nt0JQvCk7WhjSatJxYiIh2aFDra+LVatQXKyS6ogIFPMePay1Q3A62ZQqV2bTs4LDh2k11LmztTZA\nMTGE7vLmJdRm1jlwuyEuRYowJ6w0vv3jD8iw1lvQrKKSmIjqVLAgSolZG6KqqF8tW6J6TZ1qnohG\nRbEe8ubFId282dwaT01FRWzYkHsYM4bc1QULaCNitFZVFTI+bBiRjQYNOKLMjJP08CHhyVq1sA9j\nxwpx/Lj8+nI4aCvUuzdqVd26XC9rm634eGz9yJHG13a5cHRmzGCOFS+O0/Lrr0+vTUhyMvmcgwYR\n9n35ZRT/PXvst/TSgxIgctkgNXBJSeQqtWzp23g4nahoPXrIL4rr15Ghv/su+7+Fh2dXjW7cYPH6\nOkYoOZlQxRdfyP2+N8ycifoni9hYQgIaIiMhg74SUZ1OPCSr7Q2EQL63oxKdO4fR8wWXy6My+kLZ\nsvqhrc6d7R1jtWmTcVNiPSQm8l6snmUpBKHhrP3bzODLLwnV6KUdhIezGXhT7K5f15/vRkhJISeu\nXz9rxlxVWQ92znHViOiyZdaIZEQEjk/Tptbe5ZMnkNj8+QknmYXLhbqcJw+bv5VebgcPorRUr26t\nxcr169jW0FA2fLPKbGws4dvQUBxuM6ftCMH8XbuWsFyZMhB7M4Rca7rbvj3EZdgw842EL1+mGjg4\nGBL67bfYn99+QwUqVEg/jHn/PuT3lVdQzqZNM+ccJSai4r7xhqfJ7s6d8gqX08laePddnqFWLQqO\nfIX24+OZL3o5cenpEMKBA1ljFSviuF648PTahERGohi2bEmOYoMGKMxXrjzd1iQZoQSIXDZID57D\nQWy9Y0ffOSEuF5Oqbl358u6zZ/EgZCsx9+2jYtbXIrx0CaNrlSilpuLN7Nwp//l33838d9266ROA\n8eMhY1axdau9c1dv3xbi73/X/8wzz+gTkH/+U1+hqVPHXl+woUMxEFbx+efmCHlWHD+OcmG1oeat\nW4TPzp3z/ZnUVDxub4UDjx97wixWkJbGpjNypLUkfJeLd1CxojUCpapsnPnysYlbwYYNjOFHH1kj\nort3UwDQp481AnbiBHOoXj1r+beXL5PDVrSotT5uERHce3AwSovZlhn37nlOC+nb13zVdUwMqnrB\nguRr/fSTuWd4/BjiWaYMjt9nn5lLfUlOxhmqUwfnd8IEHB8NH36Irf7738mNK1iQkHf79oTfZ80i\nH7tgQZy6Pn2wSbLP4HCwD/TvD3l74w3InCyRdrmY+4MGQaKrVaPAR6bVVYcOPENWcpSURDi4Wzee\nqXZt1lnGcfEnXC76cH7wAekdjRoh1nz77dMtkMgItxsVdc4c3qkSIHLZYGpAtaN4Spf2nefidjNx\na9aUf9GHD2OwZUM3n3yC0uFrQX31FWFYq8mcu3aRN2G1LcLZs2zQvojQtWsYJqvys91jqy5fpox9\n82bfn3nxRd+q4tWrfP/LL31/v3Bhez2Iypa11+y5fn3rYThVZeOzquyqKiqW0ckjY8aw6WQ11i4X\nHu8771j7fYeD/JQOHayFptPS2AAbNLDW4kDLh6tc2Vp/xidPGP9SpcyrR0JA2nr3JoRpJafv4UNU\nzLAwlCizSsO9e4T1rR6pFRWFQxwUREjZLAkND2fulC+Pk6nZapdL7lmuXiWnKlcuxvH8eXO/f/ky\nbaFy58bx/+UXc2N47hzfDwqimG7LFu+27vRpxvjoUWxtZCT/f+5cxIR//5tTggYMkCeQqooTN3Qo\n165VCzIqG47XjtkaNgwnpnJl7scs0YqO9oxZbCy2tnVr8uWaNoUQ2ok26CEujnNdu3fHiahYkX3/\n+HFrTqEV3LlDFXTXrnCD0qWZy/v3B4icN1ga5I8/xsvxleCqqgx61ary3be3bUMelskdUVUMTIcO\n3g2EqhKK6NdP7re9oV07vGCraNqUiegLLVvqt+8wwltvoRhYgXZWYliYbzLcsqXvfkLt20P0XnvN\n+7+73eSwWCXC0dFc26rRuHCBwherRHfnTlQEq/l5y5cTFjH6/q1b3onSuHGQKCv373IRemrZ0loz\n5oQE3t1bb1l7fzdvMvffftuamnnyJEn4EyZYq+TcvJkw6rBh5kOQLhftakJDCeOZJbGJieSNWT1S\n6+5d7rt5c1R7s9WwFy96Nt+JE7OTj+rVUa9eeIG1X7w41ZB16vDOKlfmu6GhJO2bIQpa37YGDSAw\nU6aYq9h88gTFrnp1rjFtmn6TaJeL/opauPzePaIg5cqRq6URAFkV8soVnvnll3EgZsyQr1rVyN97\n71FwoRXyWU1HEIKxW7qUPMIXX2Q/+uora8qyEVSVSNa8eSivL7yAkLFihfVG3WaRnIyAMn067zAo\nCCfg88+zi0ZKgMhlg+XmouvWocq0aIHxzGr0VJUct8qV5b2Z5cu55ty5xh5UWhreki+y9eQJm7nV\nVgmRkRg1q6rS3r1MSF8y/rp19nqkDR9u7egqLUevcmWMzogR3j9XuLB3NeXYMUj83//Ohukt5HT/\nPuFtq/j2W0JSVjF8uPV+a243aq+VfCohMP4hIdaS2YVgvhYtaq3BrduNg9O4sTUSdv8+G+CQIdZI\n9P791vPhXC5CJ3nyWHNwHjzA8JcsSdK3WRw/DqmpXx9HwAycTvLoChZECTN7pFZ0NCHwoCCKhMwW\nY2gh4Lx5sZ16BNTp5N/v3IFonDwJaSpWjDVdurS5SMb9+9jgAgXY/DdulHcgVJXf798f9a9tW5wo\n2bkXHo6taNGC7/ftixq2bx9k1Kiq+O5dwvZVquA4jhrFd2TmrqoSQRo9mvy8MmUYR7O5fxlx7Rrk\nrWZN1MwePdhbn0abkNRUiNPQoaQ2FSrEut+92/75rDJwu1Hb580jXJsjBykMc+YwJ/TmgBIgctkg\nihRBAbBiuBs2JDehcmVeRN26JEf/9hsvSlXxksqWlffuChYkP+vFFykHP3TI98K6d4/P793r/d+1\nkyGsekazZ1vPs1JVNoYdO7z/e3Iyi9VKHysh2OysEMGffqIKuWNHPOC8eb13oy9VKjsZUVUSz1et\n4h3Nn4/ykhUyR3zpYfBg60dqxcZScWv1yLWvvuIZrSTuulwYI6MzaH3h9Gnmq1kiIQT3O3QoeaxW\nlKxr1yCQ06ebz+VSVZ45b165fDhVxdPfs4f8wG7dcOAqVjSvAKgqm3mePNgxs5vQgwfYmUqVzLck\nUVWq0MuUQUXKmgpw/z5//+qrbM7VqxOlqFzZUzjw/POspeHDzSlgqgpx7tiRTXjJEnMbflwcm2iB\nAmyknToxd2VImKoSwuzaFQI1cKB+LmhWxMdD9itUQAGbM0f+2TXyN3gwjna3bqxZbc6fPAmJ89Wn\nMCGBlInXX/ec4b1/v9z+p/WKGzeOtVKiBCqe1TNIVRVHePJkijDy5UPV27v36bQJuXMHhyPjIfRz\n5z79M1Qz/v6GDcyb0FD2mGHDWENmcieVAJHLBvHoEQu5WTPzhxCfOoW6ExaGJ7FrFxOxTBk2pK5d\nMewzZ+Ipy0jtH32EwatcGSJVtize4qxZ3knPlSv6BmzVKuv5bmlpLNZdu8x/Vwgmra/woxDkEprt\nW6fh4UPIrtnwX8eOKJ/vvcdYr1+PQc1qOCpWzK62ff89f//oEcm/8fHeyeiWLRgLqyhd2lqPLocD\nApmxHYwZpKXhnVot0lixAnJtJTH/7l2cEtkim4xQVdSEGjXMJ8QLwTrOlw9ibxbJyYTzKlXyrUT9\n+SekoWdP1vYLL0D6GjRATXn2Weay2VBoVBTff+01zzF+snA6IZEhIYRBzfafPHWK39U7UsvtRi07\ndoyq5JMn+d7Bg6SevPACOVxmxt3tZn3VqMFG+PXX5kLo165B+HPnhsCeO+dpi2J0PmxSErlaFSti\nFz/5RD4PWiN/PXtiO7R8J9m1cvcujmOZMuwHM2dmd9YuX2ZeZc2NTUvj7956y9PfbNMmOdKv9Ueb\nMIFQdJs2hL3PnrXu7B05wnotUgQiO2YMc8TfbUJcLubdpEmsz7AwiG/WQ+ifFpKSsGcjRrBOgoLY\nd1atsne2uRIgctkghMCojRrFyzaT2Kp1zv/ySybk9OmeyX3rFgZKay2yYAEL0OgFXrmCl9inD3kb\naWkYwHfewfg0a2au+kpVIS9Dhsh/JyP27cP7skIEnU5yVXx5V6dOQTKtLmCzBQGxsRjRuDjex6hR\njE+zZnhmGVGzJuEmDQ4HhmzPnswnP4wcmb1NzJIl1sf73j08bbMKcWIiz5E7NwqFFSxaZL3A4I8/\nIAVWqsdSU9mYrbaU0Qy1lfyZvXvxjq2EkiMjCeN266bvTG3dylxbtYoNKy4OIz9wIDahVCnvLYh8\nwe2GNIeEENbLSmQePGADWbcOpXDiRJymjh1xWl9+GQWwUiXz1e0REYTTwsKwb2YcqYQEbGRwMCTK\nzDt3OlGeypbFWfn+e/k1opGoNm0Ys4kTPQrYb7/xd3p2/9o1T+h3yBBsgKzNionhHZQpw3teuNCY\nMGrQzil94w2Uv379IEHeCNTNmzhCWiqN240qN2AA992nD+9Ldo1cvkyeX+nS2JMxY7C1Vo+D272b\n+f7aa+TQTZ0Kifa3EpaQwFrq2ZN1Xa4cfe4OH7bXk1MGLhfzafZsnLQcOXje2bP5e38VSigBIpcN\nmQbou+9Y1Gby5pYsIbn6/n0M46BBvl/Yp5+Se6WXRKqqeCpnz+I5de7suV5yMtU0Zg+3TkjAeG/a\nZO57Gtq3xwD7G6qKGibbeiUrhg/HMMpi6VLelRC8Y+380Js32VwyHlrcpUvm/mVLlniaCF+7RmhC\nCE/fvIxK0JQp1g9l/+YbxtsMoqMp7W/fHuNlJT/v8WO+Z7ZCTwgMZPXq1s77VVVyYTp3tmbUNdVa\ndnPMiE2b2KSs5JQdOICKZyUf7uxZNsgePagKrVJFnhjcuMEmUbOm7z6GK1ZQMNC9O8rP9OnM/f/8\nBzL3/PM8t5mNTTtSKygI8mVGPUxMJHwYGsozX7/OGnn9dePNLTWVhO+iRdkUzRyB5XCgtlWtisO9\nbFlmwq2pwN5IvMtFyKtZM+573Dj53D9VhThoR029/bZ+ikzW7544wT6iEbD1643DxtWqUSV+7hyO\nZcGC2Nb58+XD9Vev4sy98grfHzmSe7GyLrU2Id27Q0JffRXn+do189fSg6oibCxcCHnLkYO5v3Sp\n+VxNK4iMxEHr1Ik9pGFDFLidO62fRGMEJUDksiHbIJ05A5EaO1aOQcfFsVhjYtgMGzVCwvYlW3/+\nOV6CXt7a4MEswNRUjNeQIfY9l1OnMEhWFJNbt+wVPuhh0SIUDSvYuBFFTxZVq+IZCgFJq1fP828f\nfcRi1NC8uecs1cREQhYayTl/Hq9SQ5cumXPaundHDbGCd94xl2N24wZK4aRJbLRjxpC3adb7mzKF\nTdYKZs2ir5+VObpgAWkEVhKaly1D1bHShuDTT9mszJ5XqqqQ9Lx5zZ9h63bzbkNC2JydTgidTOqC\nywVRDg5mrpl5vw4HG11wsKchq6wSl5bGd0NDmZu+Krm9ITmZ7+bJwxrRck41Eqz33p48YW6EhUFq\njh6V/924OE//twYNIGRZibKmAmdN7Xj0iN8tUgTnZO1a+WjEgweE0IsXx7YsWiSfrnPnDlGB0qUJ\n286aJX+UW2Qk3y1XDoI+frz8kWE3bkCyK1ZkrD/4gLG2EiWJjcUmt2lD2LxxY5wHfx9RlZaGkj5i\nBIp2/vyofdu2WcuPNYPHj/mdYcNIlwoNhbCvWWM939sslACRywavA/XoEd5ikyZyC7F7d09T07Q0\n4uD16/vOn1i9msnny5j++KPnYPiEBLzoqVON78MIn35K7paVtgyzZ9vL+/KFmBhPuNMs7t/H25Ml\n3KVKeT579SpGQIPbnXmTatvW02cuMTHzKQ2//ooiouH8+cwNjl97zXoT2JIl5XuHnT6N8dUODA8N\nxeMNDjanUD14YP5oNg3nzvG7Vsr0d+6kTYiV7y5fzmZrNtdEVdno9HpB+kJKCmS3YkXzY6UVFdSq\n5XGmvvgCO2FEgC9dYr41aGBejT9wAMWyaVOIVKtWcup6xiO1WrUyV42YmgqJCQtDNc9IKh4+JHVE\nc6iyIiYGpyIkBPJnpojgxg1+T6t41FtH77+fWQU+dYr3kysXoWPZnp5uN8/SoYOnclTm0HshmE+b\nNnlSIgYMIPQu892YGNZA3bqs9xEjUAFlCNjNmxDdqlUh2UOGEIZ1uUgnmTQJR+P0aWNV6e5dHCqt\nbYemMpvNNzfC/fvkQ7ZrR55frVqQcKu5erJwOnmfM2ZA2HLkgBvMn8/8elrHf+lBCRC5bPA5WE4n\nC/qVV4yNycGDfE6bUG43BqV8ed/eyFdfYei8hbKSkpgwWrjuwQO8PKMGq0ZQVdTCkSPNfzctjZYY\nVpLRjdC5M6FLKyhZUr7rfMZFl5hIUYAvI9Cli+8jtn7+GQndF4oVsxZCuHsXgy5jHLTcLo1sfvut\n557KljV3iPeUKb7bsOghPR3V4ptvzH/3yhXu/9gx89/VWl2YJTUOB+1JatUyn+x86xZqe9eu5tXD\nnTtRoGbN8uSLpqWhnugpTQ4HG0hICOFSM5vG7dusq0KFhPjhB+b5d98xN4wcuYMHCdV162bueLT0\ndJyKggVx+rISKbebfK9x47zf76hRzP/+/eXXj6oSGm/XjtSW8ePlisqionCS165FmStcmM1Ztu3N\nnTsod4ULExpfsUKu0EbruaY1PG7ShPUjU3iQnOyJQrz4IhGEbdvkHPOoKJTcDh2YTwMHoihnDa9f\nu4Zo0KkTodnnnvOcbDFsGCHLRYtI46hVi/fVvTtzzJ9qmNsNuZ46lbmYKxe/v3attTQKMwgPhyRr\nR6qVLw8X2L376bRC0YOqYiuXL2c99+kTIHLeYDiQmzYx8Tdu1B/s4sUzt7FQVSTrIkV8Fyd8+y0h\nGm8Vik2bsjg0RETgyX79teEt6yI2FuO+bZv57+7eTa6dv/vsHDhgvc3J6NHkKFjBSy/59hz79UM5\n9YYff/R9v24378nKgv/+ezx6GXTrljm3SzOmQqDcyOYd3riBR2/lIPVJk9hUzHrEcXGEj3yNrzc4\nHFRHVqlCor7ZPnXJyagFLVua33AOHrSWD5eaCkF+6aXslcCLFumfpXv6NBtpixbmFMv0dAhJs2a8\nH20exsbiOGYs4MkK7UitIkWwM2aOclq1ClLTvLlvNeuzz8iVylj8dP06SlTTpjiYsuEphwMCVL06\ntnfpUvmcpMhICF9oKOO0fbucqq81/X3zTTb4yZPlq8ujoohqlCxJZGDuXLlndTrJC+zXj32oSROK\n62RI4927zLPatSGNffsSxjfT2sPlgtgsXco7Cgpi/RUvzrhZie74wpMn2LC+fdkXS5fGvh848HTa\nkWiIj+d3J05kf8uXD1uxbt3TOz3CF7ISt7x52a979SJ8e/NmgMh5g1TCr3Ye6vvv+17whw55N7hf\nfMHL8HUKwZYtGOus/75+ffay/IsXkcKttgPRcOwY1zEbWhICj27aNHu/nxVuN5uAlZYb69fjjVtB\n6dK+c6QGD2bj9oZvvyXc7Q0PH2LsrGDgQO/njhohIgIjr+XydO4sT/i7dLF2gseyZWxmZnKmhGBj\natKE9AMjuN2Q1cGDeb4yZcj/04pUZBETg3owdKi5DUFV2Qjz5vXdq9EXLl+GiHXo4N1ZKFvWu5Kc\nkkKeUoUKqPZmiOPevZCEFi0yF+4IweY4bJj37927x9wLDUW1kT1Sy+lEIWnXjnCTFha8d48oQmws\nm3NqKnbzxg2PmnL+POpmcDCESFYJS0jgjNGXXiIsvXWrHAlzuyFErVujMI0aJd9f8+ZNSHGBAhDR\nL76QcwaSk1H1e/RgrbzzjlzYVWu2O2IEpKJaNfIyZUhFcjJrs359T4uVnTvNEy63m/c5ejTkpkgR\nbEWuXNabhXvD9evYvMaN+dOkCWvOrNpuBg4HdmXKFOzCCy9A6FesoIflX9FTToOqYiuWLUMFrVOH\nvTAjccsKJUDkskHUri2XZ5Mxb85sWObHH5HvteT5rNixAyMqUz137Bg5Or/+au4esmLePDw1s56O\nVvjg70OKp09nwzaL27e5Hyu5Cq+/7vssypEjfTflXbOGheYNZ86wCVtBiRLmcoI0TJyYmRgNHy5X\nNfv77/rHlHlDaqp2cDOhRrOYOZM1pOdAXbyIYlK4MIRn9mxUiNBQNmEz54/eugVhHzfOnIFOSaEK\nrkIFc0U+qoo6lT8//+vrN72p2keOoNh07AgRksWtWxDGokVRSbL+5sGDEJ+sveISEylG0E5UkM1r\ncrlwFEqVIqk/Y/j18mXeUWgom/7zzwvxz38K8be/8ecf/+B/n30W5VC2719EBHO8QQPUaNm2Q/Hx\nEIWSJXmXn30mR8LS01Hfmjb1FInIpCtorU7694dINW+O2iMTxbh2DSe5RAkUr6lTzTdzT0nBNm3b\nZv6M2/R0wq2DBkEgX3kFkn32LDl0oaH2RQSHA4Vt1CjmT758qI2bN1vrASkDVWUcly4lkpIzJwVW\n48bxvFaPUrR6Lxpx69gRQaVIEVI+vvxSztYoASKXDWLBAiaozLE4TidVgWXKmN9wjx/npa1d6/3f\n9+zhPmRyUnbuRCUwW3GXEW43C3bCBPPfnTuXYgB/IiqKDcVK2Pbll62NRY8evs+DnTDBd4+rZcsY\nO2/Yvl0/ZOYLd+7w/GYJqdMJacj4/D/84Mmd00PTpr5Vx6xQVdILihSBGD37rPlWJV98Qf6gHmGY\nNg0i8P77nj5TBw6wNtavJ8wgS8guXuRaZlvB3LoFYejc2VwYNiaGdVGpkrnQb2Iialn+/HLvTUNa\nGs5GcDDj5mvt/Pxz5lw87UitsDBUFlmi6naTalKuHErGvn3yLTX27iW/J29eDnOXPb3j+HHyeoOD\n6SQgG2a+cAGVMVcuntFX/7WsuHaN38mbl5zTr76Ss0m3bmEvihdnfcybJ5er9+ABClT16jiWw4eT\novNXqULJycy5t9+GeLZsCcHOmKO4fz+KuNkqbQ3R0ZCUfv14H9WrM19Pn356xQKxseSE9u+PQ1ig\nAGT866+tpZFYhdvNXFyyhHlcvz77VZ8+cAErjYGVAJHLBiEEC+fll5G+ZRbt998zsc0mef/xBxvR\n/PneF+qBA1zXl0qUEevXs0nZ6RAdHa1fQeYL6el44tu3W/9tb2je3HeBgR769JEnJBkxfrxvsjZj\nBuEUb/jww8xVqhnxn/9Ya6q7YYO1EPGOHYR7zGL/fpJ5ZRTZX3/lNypX9lQj/utf5pS8Y8cgY0bV\nj05nZuN+9KjHwZk1C2Msg6NHcZzMzqdffkElWLLE3Gb688+sx9GjzSkhe/Z4PHIzldu7dqHcDBok\nT8RUlehA2bJUVsuqWqrKPKtYkTDfTz/JjY3bDUmoVg3n99NP5dq2OJ1swrVqYZcXL5Y7eSI9HWej\nXj1I0fTpcuHI1FTmSYMGzJkxY+SaricnQ/T69MEJGzxYru/akyfkXzVrBrHp0QMb/LQb1mqIi+P3\n27UjrPj669hPb8Rz9279I7+8QVVRzWfMoNo6Z05szddfm0/FkEVaGvc4YYLn9JSWLZlzly//dcTY\n7cYBXbSI8Q0Oxnnt148xt5LOlBVKgMhlw/8NzuPHLMj69eUkdJm8OW+4fRuP9r33vHsjR44QApCp\nDtVCBnaqeA4ehDzKlttr2LuX5/dn4cPmzfpHevnCxo3yG3xGrFpFnoQ3LF3q+/iwjz6CjHvDggXW\nzhodP54eg2bRqpVvldcXVJXNVa+ARwjyM7p0gexrCdbFilGkYKbpcFQUapPZimet96HmaFSrJtfW\nZetWfm/fPvnfUlXIW548co6UBoeDdxcWZs4hiovD3rRpY+57N2/ynWLFzI3nqVMQlTJlIHOyStrO\nnZ6GurLfcziYk2XKsKlu2YKdqFkTpcoXHj9m7bRtS1uNzZvlbOvdu6zjsDCecdMm+ZQR7YSMJk0g\nj0a5ZFqVrKYuvfEGObNG4bn0dAhwly4QmzffRAj4q6og790jgb5vX0hO69asab00oe3b5SvLk5JY\ndwMGsPaKF2eP27fPfIhXBlqI8tNPyQmtXp0/EyZA6PxZhKEHlwvS+vHHKMC5c+NgDRiA2PI0essp\nASKXDZkGSFUJtYWEMOmNjFZMjLl+cxri4vDefIVtTpzI3FpCDxMn4rnayS8oXJgzD//zH3MLoGNH\n3yg/VD4AACAASURBVETICtLTee6sidpGuHkTT9+s1/XDD75DxEuWkBzvDePGUZHsDT17+g7X6qF4\ncfOHxd+5g+EwW4X53XfGpwns3YvKMG2a5/pDhvB8R48y52SQlMRvLVhg7h7Pn2dMtHMjtdC7kWqx\nahWKmpmj21JTUcTKlzeXZH39OiGpli3NhWu2bGGzGzpU/ozT1FTP8VazZsnn9dy8SU5ZixaEU2VU\nHy0UWqsW6t2mTXIhsJQUwueFC5M/mTH0OngwCoW3NRoZ6Tk1onNn34VhWe/xl1+wQblzc30zLXc0\nnDsnp2hGRuLYFSsGQZ0/37jRrVasM2gQe0qPHthY2cIOuwgPJweydm1IZ9euvEsZJT09HTU0YyeG\nrIiIwE42a0a7rLZtcXLN5vXJIjoa5bR3b5zLwoUhTN9999ecnSoExO30aca1VSvGtXRpojDffOP/\n5sfeoASIXDZ4HagrV/BAO3QwDndo/eZKlrSWqO4Lp09DToyOC1NVqtwaNbKetHnoEAnJpUqxOD77\nTM6j1QoNzBIvPfTsibdqFoUKmTuDVgg2jOrVvf/bqlV43d4wfLjv6lJtAzMDqwUbM2eaD+M6HHiM\nRvf4+HFmpXfvXhLm4+MJEcicxqGqbMzvv2+OZF+5grqSce4vXcrc0PutmTNJ+DfTwy8qChW+Y0f5\nULGqomaEhBD2k3226Ggq00qUyN6ORA8//shm2b69fCpFXBx2KSiIpHnZZ9NOPClZklCYjCL2+DFK\nW7583GPWFidffsn1sjqbJ04wHlqxhUzYKTERJ/uVV3hnS5Y8vST5pCTmeqNGOCNDhhC5kGng/MEH\nmYt1rDTbNgtVhcxOm8b+lScPRGfXLmuqWNbndDqZt++/z3PlyUNhxaZNVBP7G6mp2KmxY7EjOXOi\nRi9dyhr/K8KlTifvfMECFPScOXn2wYOxT08rVKwHJUDkssHnYKWmEq4rXFjueJgffjDuN2cW585h\nHL/6Sv9zLheJlO3bWzuYNyGBxo9hYWxMjRsTNl292pjQLVyIx+mvRbViBRWRZts99OiB4mAGUVF4\ndt7w1Vf0ZvOG/v2zt4bRULKk+R5n69ebP1/V7eYdnT5t7nurV/N+zSAhAa9Ty22aNs13/mBGzJpF\ntbYZB+PGDXLNsoaLGzf2rVA7nahbFSua6/t0+DBz3kw+XHw84bGyZeWLPVTV0zNy7Fj5dIQbN3Bq\nSpaUXw9aAURoKJu47HgcOwZhadMG4iWj3D18yDwIDobYe1OUz57FLmrFOC4XtrJnT+bvJ5/IEbEr\nV3CggoJQ9n7++els5KoKWenbF7WlRQsUH6N3dvs2alvFitiUMWOe/qkDQmAHfv0VclWsGO0rRo5k\nbvvjkPaYGGyhFjasXJlK1hMn/F+ooKqsqYULKcTKkYPc3ClTcA6eZi85DQ4H4zlvHjnbL76I0zBs\nGJXAf2WhhDekpweInDcYDtz27UyqmTONF0bGvDl/Ja5eukQYxqgvWFoaYd4BA6wZj8KFCQnmz08l\n1ZEjGPaXX+bvfT1Pejqb/Nat5n/TG3bvpqotZ05jNTIjPv/c/JmtDocQzzzj/b1u2uSbXHXrBvnK\nClXl3mXDZRr69jV/soXWRNnMu05KgriYJX99+mRW/nr2JISmh61bIWRmQg23bjEPvRHyhQu95xOl\npeHE9OsnrwqoKsndZnsyHjvG+h4yRJ6M3b5N6FWvUW5WJCezWQYHUyEuo6aoKqGdMmUgf7JV3CdP\novYVKoQKLbNZRkXR40zrjaYXjm7UCOf2yRMSwIsWZXP+4QdjG+l0EoZu3Jh3NXGitePcZBARQej6\n5Zch6R9+aEyC4+MZswYNPOHdAwf8Q6D04HCgVA0ZwnouVw5CfeaMfeKokak5cwjJvvACdmblSrkq\nXLO4dw/Vs2dPRItixRAGNm/2fcSlP5GezrpetIh9/oUXyFF/913m6F8VBvcGt5t1/NlnhJNLlsSW\nKAEilw1SA3rnDkn4DRsab0xav7nGjf0Xt796lU1xxQr9zz15QkK4lZYirVoxcSdMIOdP87YOHeK5\nly/3/d19+/xX+PDll+TmNGgAqVy6VO57169nPiZNFmvWeM8L3L3bd4uRHTu8h3FjY82rXULgxZvN\n7+nUyXyl7uzZvhsZ+8L27Wy+Gclp3br6VWwXLpgvoLl711NlJouEBObmW2/Jq35paZC+cuXkUwKc\nTjb52rXlT0RRVQxwSAgKpkzuqapCXIoU4f3KkpZDh0gRqFpVPmR75gxrvkAB+dzYq1dxOsLCUBZl\nSPqNGzi2QUHMPb2TJTRERzNXCxWClK5f/3QS5hMTsTcNG2Lzhg4lt1LPhqSm0rVAO/OzfXvs5tPu\nRZaSgnP0/vsQfK1wxB/5aCkpFPgMHkz6RNGiKFC7d/v/uZKTue6oUeSk5srFGK5e7f/epN6QloZI\nMWsWtjpHDtoFjRvH2vurcu284ckTHIEZM3D8cuWC2Pbowf57/jxOghIgctkgXVbtcjHAefN6ErB9\nQes3V7y4+V5bvnDjBmrFokX6n3v4kI3NbOXkhAnk0jid5Mj4Sub3BX8VPsybh7efKxdqQYkSeJsy\n3dDDwvxnDPbvN9/w9sIFCIIZREWx2ZsJUzx8iGJpxmONiWEDMGP4Y2IY06xrZMoU37/96BFJz94U\nS1+IjkbVnTtX7vMOByQpKAhVQlYBuXOHfLgOHcwd51S3Ls5ZVkXC4eDfjxwhCXvePO6nUSPSA4oW\nlSfo165hvDt0kO/XdeUK1YeFC/P7MnPo4kXWV1gYtkRmoz57FmKpkVKZze7UKc8B9iNHGueIqSoh\nrbff9hw+b+WkFyO43czn3r35nTffhJjpEUWXi1Bu//4Q30aNIB5PWzGKj2cddegAaWzUCNLtj0rI\nW7e4VsuWqFAdOpAH5u9WHW4382fePNZQjhyspxkzeN9Pu+VKWhrOzYcfMn45cuDwjBqFU2am5Y8/\noars6evWIRhUrEg0p1UryPqWLb6bgisBIpcN/1c9JmvYjxzBUxwxwtgIWu035wuRkUj/RhWAt27h\nWa1bJ3/tjRs9ocSoKFQxmZMmNPir8OG998jx6dePyrDoaBbegAHGi/711wlJ+QNHj5rvz7ZpEyTY\nDNatg3ibwUcf6Sf+e8Po0eYLI0aN4nuycDhQUr0diu4LsbGEMozeW2Qk4Z127TDGigKRk910jh5F\n4TVTnPDtt+SaLViQmSR9+SUb+jPP8L+1akF0Ro4kKftf/2LjlalkT0rCiQoOZrORUcYePPBUQn74\noRwZu3LFc3bjxx/Ltb04coT2GvnzM+eMbKTLxQZUrx42cvly43B3cjJh+ipVsG0LF5rrACCLiAhI\naNGiKPcffaSfqK71Qhs9muevXJl7exrtJDLi/n3mebNmEKxWrYga2FWKXC7WwAcfoIQFB0Oav/nG\n/2Tm9m3eadeurJ+GDVH4tm83n3ZiFqmpFOxMnYotev551OrZs4mkPI2iDBmkpJC3+OGHhKpDQ7Ed\nHTuSJ3rihHzHCCVA5LJBxMVReVO0qPxB47GxeJuVKxsrHFb7zfnC7dvEyo3Ui8uXMdo7dshd9/Jl\nFEQNO3ZABs0YkGXLrB2inhFduqAuHD/Oc6oqi79xY5QevfBto0YoVf7w8k6f5v2aQbNmvosnfKFv\nX/nwsRCMR+nSGAVZREWR/2EmX+2776hiNhMuHzIED192nickkAowenT2OZOcTN+tESO4j9BQik+m\nTSPEmTOnfLuWFSv4vq8j8rIiMZG8wOLFvbcxSUyEWGbMJ/vjDwhd/frYEqMQrKri6BUqxIYn826S\nkgjx1qgBaZQhPFqLlNBQbIYRGVNV8gYbNuTdrFxpHNZMTCTHs1gx7u3bb43XYHg4UYuqVSko2LnT\n/8nziYmQoAYNsIUjR6Ly6dmn8HDCbmXKYLcnTjRuYm0XEREQy7p1GfMuXVh/Zhpue0NcHESte3cI\neYUKELljx/ybx5eYyH4xYgTjFhyMY/P55/5pgKuH5GQU1smTWXvPP0/YeexY1vvTqmg2QlQU4siI\nERDJf//bk/a0caO9XE8lQOSy4f8GZ+dO8tDGj5d7+aqKxxkSYtyQ1Wq/OV+4dw/SMGWKvlHS+tHJ\nVN06nVSuZuxJNmYMoQdZYqYVPsjmEXlDgwaeirS33vLk/KSno9LpbcYdOtAPz0pz4Ky4fBmjJIvH\nj1GKzKhEQrBhmsmPO3rUdz8uX+jTBwMuiwcP2Phk+nlpWL6c8ZI1nImJqIpDhnh/ltdfxzDPnu05\nykc7xm7SJAiDEdLSUHLLlpVvSXL6NOu0b1+5jdThYOMPCSFUtXAha1Pv/Vy5AnkpV07uSD6Xy3N+\nq+yRWhERvPfgYMJYRu/F5YI8VK6MYrVhgzEZu30bYh0cjJp/7Jj+c7vdrN8WLRivMWP8fzi6282Y\n9upF6LR1axLn9dSOhw9xQmvXxuEZMsT4WexAaxMyfTr5WaGhhG137rSXC6g1yZ0/n7WjnW6wfLl/\nCZXLRf7r7Nnkjr/+OnZ7zhwcn6dZ7JGURE72xIkQ3+efZz6NH0/u3dNW/LwhPR1buXIlCluBAhTn\ntGlDSPnwYf82flYCRC4bMg1QQgIMulAh+e7uFy6wUbz9tv4k0vLmihXzT95cdDQSudFh4Hv2sNhk\nfnPcuMzSc3o63o2ZfDu7hQ+lS2OMhGBT9HU4vTeUK4fxLl7cnMrlDTdumAuTDh6MchQSIt+MNjIS\nI25mw+jVC3leFpcv8xuy+TyqigEyQ/yOHMFwyYbVU1JQT/v0kVdhNmzgN44cgcj6av+i4e5d3l/7\n9nLG3e0W/3fusmwLoTNn2IibN2ejjI7m/ftqP5OYyBoLCZHr1aidrFCuHBuzXnNWDVFRtIoICmKj\nNXrv6emEwUqWRFHcvt34nfz+OypP7tyQESMyFhvLWi5WjBDqF19Ytw+XLvmOgqxcie0pXx6b5SvH\nSAgIwYYNkICcOVFFd+yQq9pNS2Oc3n4bJU0GWpuQuXOxT4UKkUZy6JA94pOaioI6cSLPXqgQtmjH\nDv+Sh8hI5mzHjhD3smXZK3fufLrkKTGR1jsffADRfv55WqxMnMjfm22G7g/cu0eBy5gx3Mvzz2MH\nJk+mVcuNG0+37YwSIHLZ4HWg9uxhQci2NEhOxngWL27c2kHrN+ePvLmYGAzje+/pT5yNG/ESZM9k\nzIibN5msZtSZTp2sFz7kyuUJ50ZHY2RlVJ74eBSxdu0w4vnymWstkRV373INGRw+jFrSsCHGfdgw\nue99+aW5KtL4eMbDzJFsbdqYI37r1rERyioDEREQLNm0hLQ0wjxdushvYB9/jFp+8SI5RLly6W8e\nx48z3xctkjOod++iKtSpI9dwNzWV+V2sGGq89hv9+nk/g1dVWYMFC1KBJtNE9PffGSNN4TZ6jnv3\nmHdBQagTRikRyclUCFeqhAJ58KCxmrZ9O8pLwYKQXiOSeOYMY9KsGevi11+tbXAJCZC0GjVYZ5s2\nef/c/v36LTicTmx79+6sozfeYOOVUV7T0ihy69kTAluvHuFkvRYlDgf3NHQo9122LMTXKLxrhDt3\nGI/WrcnFrFOHe7lwwX8E4vFjqmSHDUOhDQ2l7dKaNU+nDUnG3/3pJxyemjUhST17QpL27//rjjTT\n4HCwp69axfMXKcL7b9GClmQ///zXq4BKgMhlg8/BevyYhOKCBeXPNNy0yZOwq+fVnjtHHo0/+s3F\nx2PghgzR/81lyyCael6qL/zwA/crq+rcvs09WSl8yKrStG8v1+h31y42mUWLCKdph61bObZHCPJL\ncuUy/lxqKmrG5s2EFnfvhqjLJK727m2uhciyZeaI37Fj5DnKthC4fZs5cuaM3OcTEyF9si1DnE6I\ndrt2csqHqpJT0qiRJzQ0fz5hT19YuZL3Lpsbum0bYeRp0+TW4rFjkKsOHTITslOnIP5ZHb/Ll7n/\nChXkiociIyEa+fLp92/UEB1N7lfu3JBIo4al8fEodXny8B5kegp+/jnV4yVLou6/9x7h5GXL6G+5\nezdq4bVrbPLr11Mo9NJL/JaVJqraEVw9e0K6OnTADpu1l1o17LBhPHPnzpAemXtKT4f49erF+Nat\nS7GMXj5jcjJzqlcvlKvq1Qk5mm0SnhEuF88waRLEO3duFMT16/3XLsPp5DemT4cY5shBXvL8+eR5\n+zt/UUN8PGt1zBjG6vnnmWNTp+Jc+PMsbxk8eoTD8sEH3EeOHBDwoUOpVL5y5emNhSyUAJHLBsNB\n+/lnSMyYMXLVPTdvYsSaN9c3FjExLBR/9Jt7/BiPt39//Uk2bRqGwErlzrBhGFNZj2/+fDxeux7i\nzp0sKCNMnsymf+4cG44QhE4KF7ZGXlNShHj2WePPTZjAuAhBTkpCAmGwLVuMv1ukiHwitaoyDrId\n/lUVhdCokXTGzzdrhiGXQUwMz9uli9w7drkg2G+8Iaf2ORxs4rVqedaHqkIovPUiS0+nKrdMGbkW\nKykpGOe2beVySBMTyb0MC6NIISPcbtb86tWev3vyhITrkiXZ/I0ISFycp9/alCnGXv6jR6gWjRuz\nNo2KJaKj2ZyCglAFtfQFGSxdiuocGYkysWAB1xo0CGLUpAlE9fnnqSYuVYr5b8VJvXOHcGWxYoSU\nP/7YnAKt4c8/sQnFivEOpk+XcyzT07E5vXtDmLp0wTnUU6ESErA1b73FPGjQgHduJ6E9IYGcxV69\ncExatmQ+HT7sv5Yd4eFEBdq3x2mtUIHCoz17nh6Bio+HKI0aRaFL+fLYqenTIe5Pux9fRrhcqJgr\nVjCXS5RA4WzShDW4a9d/rz2JNyQmEgFRAkQuG6QHcPhw5HGZRH6HA0P397/j3axYgTeedVI4nRjv\nokXtn9OamMhG37On75CVqrJ5vfmm+QWTmkoitKyC5K8TH5xOxseoQvH11/Hs3G4SlrWQx9SpSPRm\nDZPbzaI2UlZDQ1FmHj/2VNmuXg1B0ENEBPK8LNE9dQpDIxuO3LEDT1L28ytXYliNlLIbNyAOzzzD\n/JYp3nG7UVrfekvuPSQlMTYtWmTOgTl0iGfKOmZaPlybNnJheK3fX+fOcirzvn3MwZ49vT/v/fuQ\nebebe9uwAVvRu7exE5GWBlnREt6NCFlcHMpMUBDE1SiJ/dYtCIB28oCV9ApfUFUc3fbtUauKFmUd\nmnXe0tMhxy1acJ8DB5LKYfY69+5ha6tWRdF87z3jBr/a7//0E+8rKIhcrE8/1W818uABOWPNm3uK\nClavtkY6NVy9Colt2BAlqHlzSLRRDz5ZxMcTXXnnHSJH+fKxH3z11dM7MzQ2lj3gvffYPzSlb+ZM\n9sSn0ejZF+LjUY+nTIGsvfgiNrtXL9TvCxee/qkcskhORv1fvBi7U7YsxYj9+weInDeYGtxDhwg9\ndesmp6LVr89mV7kysvELL+DRN27MxF61CjKwebN/8uaSk5mgXbr43pDdbgxl27bmPbtr17jPs2fl\nPr9lCx663fL9iRMZL19wOhlbbZNt1cpzvJeqsoHJVAdmxbPP+ia8TicbhqbCXL4McRUCMpEzp/7x\nLmvWQCRkMXAg4SwZuFx4urIkOiIi83mYWaGqnmrZkBD+t1w5Nl4jqCph/zp15HKRYmIg3r16ZZ/D\ny5dnVr2EIBxUoAC9mIxCHqpKWC0khPE32uDj48nxatlSrnXJhQus+cqVMcJG97JxI+P41lvGKQAJ\nCagWwcGElo029ytXPKrSnDnmzqA1wuPHjGOZMtz/f/6Do1u/vrmN+eJFwsKhoTih69aZz4F6/Jh3\n2bgxqlKfPihKRhuyw4HiMm4c5O3VV5lDeirazZuQt3r1WN+dO/MO4+L4ntmQW3o6TsKoUdiO/PlZ\n59u2+SeJ3+Fg3U6ejLKdIweq+0cf+TefLiMePYIsvvsuTW7r1cOxnjOHNSHbK80u3G7WwOrV7B/l\nyvH8DRogsvz443/3+K2MSE3FcVm2DEW0fHlIW9WqkO5Vq0h30cZOCRC5bBCtW5s7DSA5GePjLcSS\nFYmJGKk2bVhIERGEJ3buJDzRq5dHldHy5saMsSedp6ZyzfbtfS+a9HQWV79+5hfz99/jxcgmeP7r\nX0L885+EJ6x6O+HhjKOvTeLMmcytQhYuhDzYRc6cvqX1a9cIZ2vjt3t35uO5unXDm/KFnj2Nj1zT\nkJjIhizbB27tWlQFmXfrdkMMvDWZdjohxDVqEKJaupSwZkgIBS0LF+pfW1UxTNWqyYXzb92CJBpV\nYmtYtYp5sX278Wfj4lCiq1WTa0WybRsEcfBgY5UvIQFHIzQUY2w0z7UjtapUMS4SSUxk3ENDCYka\nhQd//x1iGBpK2xF/hoYuXWI8cucmV/OXXzxtmEqWlFNntcKFTp0gLhMmmM+lTUvDSezYEVWldWvm\nqZHa63CwTvv2hRC/+qp+CFRr5zFzJuQ8JASCsmNHZgfvwgXmygsvsO4GDWIeHDmSfd4/eEDVbvv2\n2Jdatbi+v85J/fNPSHarVoxNt26sp/37szulqmr/N6OjyQ3XiiJefBElcd48nKy/4qB7IdiP9u9n\nLDVlt2hRnn/1atbF0z5FQgZpaajEy5ez/1aqBGmrVAm1bdUq/l3PIVICRC4bxJw5eGSTJ5vzBo8d\nwwPt1k0/F+6TTzA0CxdiXL/7zvdn/ZU3l5aGkevf37eilJjIBm2mC7+G/v15bhkjUKkSJKB6dX7P\nauuVhg19j93vvzPOGn77DaNiF3nzyisZn3+OAqLh9GnfRRqqSiK4t/NafV27dWu5z2pVobINgz/9\nlM0nK/lISMAQ1qvHpulysQkVKsSmWaWK8bmZkybhlcts8BcvUlgk0+omPd3T7kV2DFNS5E5OePgQ\nRbt4cf3zZIXgPa5dy32PGGEcVst4pNb69foKTnIyNiNvXgicUbL84cNsoAUKMIZ2m8lqcDjYqJs3\nZz5MmZI5X2zXLu5Rj4xphQs9engKF3bvNrexakdrDRiAPalXD0fIaG45HCh0/ft7zij96CPfIWlV\npXBj/HjIacGCkLdffjG+39hYPrd4Mb9XsyaNYPPl4zr//jdEp2NH5o2VIpCsiIlhPfbrx9osWBBV\n8ptvjOfj++9Dpjt0YK4dP26sqD54wO9pzb9z5oQ4LVjAuP0VZElVmW/r1rF/acdb1amDELJ589ML\nFZtBerpnHxgwgP3vhRfIRezTB7J/8qT5NCclQOSyQQiBR9a5Mwth0yZ5LyUlhcWQNy8Su7fvpaZi\nXH/7DaZdrBgv1RdpzJg3Z6ffnMOBwWja1Len+ugRkr6RspIVyckQpaxhLm9o2ZJJ27QpYYnQUIyk\n2Zy1r7+WP2LK6cRg2pXOixSRzymaNk3+eLDwcBRd2Xn22Wfy529+/DHKkwz+/JPNzZtCpZ0FqCE9\nHUM5aRIk4d//1jf6c+agDMlsVlofug0bjD977x6beOvW/j1uR1VxFPLmRUU0curOnWM8qlUz7u/2\n4AFzIySEDU/PcKemol6HhaHa6OWGan3m6tThs6tW+S/n6P59FL0CBch/27gxOwk+d45n8lUscucO\n6QB2ChcuXCDH76WXsDlz5xq3iHE6KQoaMIBNvnp1bJyv7zmdHFY+bBh2qlQpwm8y+XXe8OQJZKJf\nP0hcnjysl4kT7YcW09JIE/ngA4hBWBjrfdEiyL6Z+1VVbNH69RCzypW5z1dfJdy7aROK8dKlqIyl\nS3vOp124kD3tryBuycmo2PPmse69HW9ld94fO4a979uXuW5270hPR1D47DP2qapVUdpeeYXI25Il\nEGWr7VNUlb3o7NkAkfOGTIN14ABqRuPG5qq6TpwgGbF9e+/JzcuXk5sgBGGa7t15wXpG2h95c04n\nv9Wwoe+ci6gochrM4vJl/bwqDb16MbnLlvV4Sp06YdxlyYkQEL+gIPkO5f362S+0aN1aPr9vwgTj\nEz40rFmDguNvJCSwaci0XHE6CessWWL8Wa0NSJs2KCMHDqDi+cInn6BoyYSCt21jHsk04D550qPa\n+bMFwN27vOs2bYxJWXy8p5XFypX6YdSkJMiQTG83lws7UbAgYTG9FjAuF5tNxYp49998458NVcuF\n7NqVDXvgQN9FWPfuQa6y2ietcKFHD8Jb77zDmJohGJGRbNrlyzPOzz3Hc779Ng7T+vVcM6Ma53Ri\nTwYOZD5Vr44C6yuXMDWVkLx2+kXVqpBOM3Y/I27cgEw1aYLq0qQJ62D4cIqUZPoTeoMW3v3kE5Sv\nF16AwE2cCLnxd87Z1atcu3p1UmIUxUPCf//96RcDqCpj9c03jN0bb0Aua9TAZto93krvd//8EzVV\nC0tXrQphPnAgM1FMT2d9rlrF/K5WjTn6xhukzCxeDDG0mufocMAN1q4lZeO111A+8+dHcVQCRC4b\nsg2i08mCDAnBK5E9cigtjc1OUxYyGq70dNi+Fu7SEp21Y318GTl/5M25XIT86tXzf+PCNWtQXfS8\njNGjUSEOHEDx1Cb39u1sBIsWyf/e4MHy7THmzNEvkJBBpUryPdWaNZPvN9i9u/HJBFYwcaL8SRjz\n59PfTIYQLV6M46GthVmzmJPesGIFc12GcK9fj6Lw22/Gn129Gk/cLjnPCFUlbB0aSshQz6t3u7nf\nfPkw3kak7PPPUQ06d5Y7hkpV2bj0iGR6uqenW+3a5Gr5I2E9KcnTtb9ECUhDfDxEyZfdOXoU4qnh\n4kVPnmCDBmxCZtSH2FiIcf36hHEHDoSouN04x0eP0ipj0iTGtGpVSE2OHGy6f/sbebILFuir6Jcv\n85w5c7JBfvqpNZLlcKCMjR5NXme+fKg5mzdjZ91u3melSuZDqNHR7CHjxrF5Fy6Murhpk3+OeMyI\nqCiqVvv1w/kKDqYQrnFj5q+Ro24XaWkoVQsXEuLNnx9VvG1bbNTRo399LzkhWGuHDkHkypWj8C0o\niHt77jmEiR492L+OHrVO2hITIX3LlhGOb9uW65cqxTyfNw8nN+McUgJELht8DnB0NAszLAyjpnS2\nWAAAIABJREFUJKsAnD7NC2jVKrMisWYNRiqj4b16FTm7XTvfCzQmBu/u9det58253Ww+su0WZKGq\nTOZ+/Xx/Zu5cQsVCkFeXMak+MdGckTt9GpIg8y6OHiWPyw5q1TKuPtRQrpxcKNztxiBYaZash3v3\n5BXLCxcwFDJtDfbvx3hl3BxbtGDDyoovv0RRMiIuqgoZLFrUd+FBejoE79NPKZgJDbXXVDUrIiLY\nrKpWNX5vp08zF2rU0G+gq50l+sorOE5mTkPRQ1IS49C6NQ7DoUP+IXDXr1O4FRyMvdq1y7O2Hj3i\nXf7jH7z/ihUhWH364LAuWULSftu2tLIoUABH4vp1lLDevWn9s2YNTlxERPbE95QUwtlt2nhyx7Zu\n1SfULhfXGzQIpzlvXua9TN9GIZjzn39urU3Iw4fsBZ06oVhWq4ZCeOpUZpvkdGIX69aVs7epqVSv\njh0L8cuZkzFZuZL14c/q0shI1qmWvhMSAoHSToZISuL5ate210rFF+7eRbEdNQr16t//Zg8cOhRH\nKTz86R5vpQeHA/Fk9WpEgxo1uL8SJZhrxYox563mn0ZHQ8rmzWMvLlXKU53avz9k7tgx4+srASKX\nDYaDf+IEC/bVV+XVmfR0PPzQUBaNqrK4S5bMrj6kpXnOd/XV+V3LmytSxHrenKpitKtW9a9Xl/j/\nsXfe4VFUbRs/FlSKdEKVJh2C9N5FOtIEVGyvooigiDRRFFFpAioiAkpRIIAIAgLSm2LoLfQiAUJC\nCISQXrbM98fvPd9Mdmd2Z3Y3CL55rmuv3WR3Z2fOnPOc+7mflsh1LV6s//4PP6D8FYWYmUKFzBVs\n1ROnkw1lyxbvn01Px1r3J46qQIHMSRSeJF8+c+M6Zw7sQaCVlay2700yMlCcP/zg/bMXLrBRupZu\nKVrUPZh42TJc+N7Alt2Oa7JmTTWRRMZ/LFnCWmjUCAVapQrz5b77zCdveBOHg2svVAiL3xPTHRsL\nYChaFOXuyYA4fBiGs2tXwEgg7u+tW2ThBQWx2Zrt4etJ7HZKL7Rvj34aNcozoLfZ2HwPHYIBnDOH\n9VyhAm63vHkBmVqX26lTgKUxYwA0zZuj33LkAPDVqAH4euABwPSCBZ49HxK8DRzIWNSpw2Y4YgS6\nx2w2t1VxOolJmj4d/Z8vH+Ez8+YZB9OnpgK4O3QwZiSdTvT4t98Sk5c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5P/OMZ4bh+nXu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"text": [ "<matplotlib.figure.Figure at 0x7f4d15a82790>" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cool. Just like we thought a source should look, only prettier. Note that we added a semicolon on the lastest instruction of the cell above, to suppress the display of the object's instance.\n", "\n", "We recommend that after following this notebook carefully, you prepare your own Python code separately, following our example. *Type* the code (instead of copying and pasting), so you assimilate what you are doing. Try things out, change parameters, read the Python documentation when needed. Remember, we will build more complicated flow solutions later on, so be sure to get a good foundation." ] }, { "cell_type": "heading", "level": 5, "metadata": {}, "source": [ "Challenge question" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the total mass flux outwards of a small closed surface around the source?" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Sink flow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the source flow, the strength $\\sigma$ was chosen to be positive. A source with a *negative* strength is called a *sink*. Instead of radiating from a single point, the straight streamlines are now converging to a single point.\n", "\n", "The velocity field corresponding to a sink looks similar to that of a source, except for the direction of the flow. Thus, the Python code requires very few modifications.\n", "\n", "We will place the sink at the location $(1,0)$ and give it an equal strength to our source, but negative of course." ] }, { "cell_type": "code", "collapsed": false, "input": [ "strength_sink = -5.0 # strength of the sink\n", "x_sink, y_sink = 1.0, 0.0 # location of the sink\n", "\n", "# computes the velocity on the mesh grid\n", "u_sink = strength_sink/(2*math.pi) * (X-x_sink)/((X-x_sink)**2 + (Y-y_sink)**2)\n", "v_sink = strength_sink/(2*math.pi) * (Y-y_sink)/((X-x_sink)**2 + (Y-y_sink)**2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# plots the streamlines\n", "size = 10\n", "pyplot.figure(figsize=(size, (y_end-y_start)/(x_end-x_start)*size))\n", "pyplot.xlabel('x', fontsize=16)\n", "pyplot.ylabel('y', fontsize=16)\n", "pyplot.xlim(x_start, x_end)\n", "pyplot.ylim(y_start, y_end)\n", "pyplot.streamplot(X, Y, u_sink, v_sink, density=2, linewidth=1, arrowsize=2, arrowstyle='->')\n", "pyplot.scatter(x_sink, y_sink, color='#CD2305', s=80, marker='o', linewidth=0);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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kIcxhJryTno4HVbo0G8OuXeYqXX//nQVcoADFJznzFB0OxrFTJ7xrT33hUlKY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hXi3nuF6NRJiDp1Cu7+jEhurhCHDwuxa5cQf/whxLVrQvToIUTfvkJ06CDE5Mm876++4rn8Ldeu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"text": [ "<matplotlib.figure.Figure at 0x7f4d0ed149d0>" ] } ], "prompt_number": 7 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Source-sink pair" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's exercise our superposition powers. We already have the velocity field of the source and the velocity field of the sink. We can just add these velocity fields, point wise, to get a new solution of potential flow: the **source-sink pair**. Read this code carefully and make sure you understand what it's doing!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# computes the velocity of the pair source/sink by superposition\n", "u_pair = u_source + u_sink\n", "v_pair = v_source + v_sink\n", "\n", "# plots the streamlines of the pair source/sink\n", "size = 10\n", "pyplot.figure(figsize=(size, (y_end-y_start)/(x_end-x_start)*size))\n", "pyplot.xlabel('x', fontsize=16)\n", "pyplot.ylabel('y', fontsize=16)\n", "pyplot.xlim(x_start, x_end)\n", "pyplot.ylim(y_start, y_end)\n", "pyplot.streamplot(X, Y, u_pair, v_pair, density=2.0, linewidth=1, arrowsize=2, arrowstyle='->')\n", "pyplot.scatter([x_source, x_sink], [y_source, y_sink], \n", " color='#CD2305', s=80, marker='o', linewidth=0);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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9kdqyBSTu1CmQrGzZ7OHRK1dALoODsTGYTJhXEybgdcWLOxPXPn3g1FSpAk+x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\nrVunLrfMEaVLq2+8a7VCTRHKBzp/Xv1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n2rm4fLl6O88YHOGWLdW/Tyv4alM5su6K+/ex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OjmzRAm/hli2ej9+4kcrm55/3LRR27hxj0VMIMyeHPvnLL97Pd/w4URMrumzhQs9cdJrG7w+0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6cztXtaGu/BKNGoEX/9xcHqT4Dfto07ScC64310NDVYEyeKfycjg4vUs8+6d6+LF3Myb9CA\nmqPs2bkYVqlC36sHHmBfsBpZuWoVTZB2cbm4CHTrRk1Xgwa8V9HotNRUvvvPPuNiExRErcvAgUwD\nsGoVd8N3mj+cFWJi2EbTptFH6M03swqwy5YZbxzj47khEK1yYAVFYTRttWpctNautXaN9eupWevf\nX2wemj2bfmdW0g2lprJfvf+++97eeEPMvBwXR5Oq3VqfP/7Ija523atXOe5EOXOGc4eT/jtggHEw\nmghNm/r6yA0a5N58Hz5MId6IihWtlUDzx7Rpzq0HsbFcX51EwKoqXTH0UtfMmMH5tmZNKjLCwzkG\n69c3XudOnGD/8BQQIQU5H3QbsHVrZwkHVZUTZr58zqJiVJU+Za1aGR/Ttav9LN8aTqoieFKwoLEm\nZOlS8zJaFSo4EwjS0pyHp48Y4WyiGTqU0ZP+SEqi74+dCfXCBQonVqKTFYWBGM2a+d/Nu1z029qz\nh9q6oUPpo1WmDH8nypw5XIibNROrGWtEUhKjfPv25busU4fPYGU8uVwUqhcvpn9i06Zsuxw5aP5s\n145aqVWr2P/j4u6MMl4pKdxM7thB39ewMGq2goO56FSvzoVh6lQKdaKa+JQUtmOvXoF9TkVhOooa\nNWii//57a8JGfDyfp1gx/2ktvMnMpBb2lVe42ImSkcG5p1WrrBrS4sXFztO5s/2KCIpCYdvT5SMj\nw5rT/5QpzgWXRo2MfZhFaNxYP0ecqtJkalSmUVX5rgsVctYPjx2jv6FTWrd27lKk5Qv1t+bMn09z\n+o4dfO7UVL77MWMomBtpkwcMyFpEAFKQ88Fvw23dysZ2Wqdw+nRnuX40unUzftEuF3eYTmrHqSoX\nM1HzghHeZjxv1q83v06vXv7D263Qtq1Ylng9IiMplNo1VycmGi9MZ89yENtJdaJNlFYnn3XreM0v\nvzSfQBWFQl1wMIUgUUfwlBT6XRUuTNOAU1O9qmZNglywIAXMQYOo3bRjskxM5EZh3jwKBM2auatm\n5MjhTsuh+a+NH89jN22i9mf/fi4iERHctMTGUvD01LBmZLDvREXRxPrXXzS7/v47z/PTTzTrDBvG\njVjDhtSa5s1LH7xixTj5d+1KgX/DBgrxTha+Zct4vkBpJBWF5rHatZnDb+FC6+fesoXCdffuYhUj\nbtygIFGnjjXzpstFk239+lkjNqOiGBxlph1ftowbXbsWizVrqKVykv6mXTvz9CtmNG8u5qNlRO/e\nxnnoYmONLUQpKdwo5snjLNBGUZhMOzLS/jlUlXOLkbuPKB060HTqjWdt22bNsmbBOHjQOCtGTAwF\nRG0DCynI+eDTaC4Xd01W/C38kZFBZ04n2eZVlar3Rx81FioPHKB5zCkjR1rPau4Ps9D2TZs4+Izo\n2dN5ypdx44wrTIjQoYMzTeeaNdwB672/RYu4G7QjLJ46xR2t1dJAp0/Tz6ZjR7GyXFFR1GK0bi0W\npaeRmkp/nCJF2AY7d5oLISLJZ10ujqsRI2ieqVqVk+SkSdQeOi1XlZDAtt22jZqlyZNphuvQgRuM\n6tV53aefpgASEsIFKUcOBkDkzEnT9H330eSZLx/ngtKlqWl+4QVq1/r14/lGjWIA0Lp1NElFR9/5\nue7S09k2oaHsF3PnWheor12j8Na8ubF2x5PjxykwDhhgLeJRUajVrF7dVxCLjTUP+IiMZB+2op32\nxOVigJMTIezSJbobOMnzePMm+6dTQb52bQbl6JGaykooeqxYQY141arcWDrxZW3Zkn3RCamp3EA5\ncedRVfp0Fizo+442bOAGQlU5xoODrW0Ixo51J8mHFOR88GmwBQvYuZyaHZYssVeA3Rst6syIceOc\n+xqoKjvKggXOzqEo5r5fInnixoxxnt9n/37nYfa7d3OxduKo/tprxgWae/Sg5tZOnzt2jOZCq+8t\nKYmCSbly4pn7V69mW7Ru7S43JkJqKs2DWkmrefP0Bf1KlVgVxEpbXLzoNsGWL08zrL/EuLcDReEk\nnpZ2Z5hoA8316/Q/LFqUbbxqlXWhwOWiJrlAAQpXou/nxx+56M+da+16ikJBvF07ewnZ09MpADrJ\nPLBkCf1PnfSJL74Qj5TVY+dOCpROqVrVOJuDonBTo9c32rThBuaZZ2g2bNrUfttMmBCY9a9bN/sV\ngTxp3JgbM0927GAf0njtNY4jURISKCAePCgFOX9kaazkZE5QTiKCVJUd8vnnOfE4pU4d8+zVL70k\nvqM1okIF535N6enURhhx9ChNSEbMm+cO0bdLoMLTa9SgX59doqK4A9PTzqakUICZMsXe+Y8fpxnz\nm2+sfU9RqG1s1Eg81UpKCjOV583Ln1a0Ay4X+2mDBmyPjz7ydcqPjmZbvP22fa1UXBx3wMOHc2zk\nzk3h8403uHgsX05/qNuZu+6/zsmTfCd58jCS3yiiOS2NGpZDhxj4sHAhtZpDh7Kv3XcfNXmiLgVp\nadwI2THTKwq1d5Uq2U92PGgQF2e7/TE9nSbZX3+1932NatWcuYqoKq0cRqmfRClXju/XiJw5/acD\n0oL3IiKoYUxL4/nsmlh37uQG0SkbNwZGyN28mX6Pnv3lwAFuMjWOH+dGxoolZto0jh9IQc6HLA01\nfrzbju2EHTs4cJ2qr0XMqgkJXKic5M9SVXa6nDnFfFSMSEzkeYw4c8a88sKWLc6SAmu0b++7O7LK\nqlUc4E5204sXUyOlp4k6c4YD2+4m4u+/6VM1ebL17+7dS5+wbt3E+9HZs3QYf/JJmo+tts1ff1Ew\neOwxvqPdu93niIujNrtjx8AIW+npXHQWLKAw8corfN7s2bmh0AS8FSvY/qdPi5mc/wm0/G5//EEN\n6Pbtzs+p+epZJTGRi2udOjQLDRtmbgJr3JgbuYIFuTjXrUtNWO/e1OA98ADdAUSFoogICn1Nm1pP\n9+FysY+FhtovjbhmDTf3TsozTp/OdERO0KJVnY6Ht94yzrUnSunS5sEhQUH+03HMns11VlFY4SYp\niUK9XROr5m/nNNtCRgbnYKe1hRWFgqWnGT011Vex0769tfQ/aWkcg5CCnA//10jXrtHZODzc2UtU\nVfq+OBUeVJUd3izX2M8/ByZ5a2Sks/qmGjdumCebjIkxL1Z87lxg7ueHH5wHcGRmUhtglOTYDEUx\nL3+1bh0XObv+IpGRnGD1MtsbER9PJ/iSJa35dW7YwEW2Zk17QuiNGxQ+69Wj79OoUVy8k5IoFLzy\nirnWT1G4U+3fnyYsUT+X5GQuIAsX0ozfpw/914oVo8CRMyf94F5+mULloEFclGfMoHlvyRJO1r/8\nQn+6vXuZLy08nJ9Dh2h++u03aiKXLqVgNGMGta+TJ7NvtmjBjUKhQvQrCg6mD563U7Qoycnsq6NH\nUwB75BHx6Ggt/UuXLhSymzaltla0nFNaWlYhLSWF186fn36vefOK9+8lS/i9KVPsVbLo2ZPv065p\n/exZLuxWUpt4k5BADbRTS8eUKc5LPKoq3WecWpxUlXOzmXtF/fr+gxDq1XNbOJo0cQtOTkysnTpx\nDDrlo4/8BytYZelSczP4yZN8fqvKE9gU5LIFWHi6k7jVLsCgQUBiIjB9urMTRkYCFSsC588DuXM7\nO1ejRkCPHsCrr+of8/77wKOPAiNGOLvWtm3AsGHA7787O8/Nm8Djj/OnHpmZQPbsQHo6cN99/o9x\nudh+sbFAzpz27+faNaBECSAqite0y+zZwKJFwG+/2T/H1avsG8uWATVr+j/miy+AH34Atmyxd79X\nrwItWwL16wOjRum3rx7LlgF9+wLvvAMMHgzcf7/5dzIzgYULgY8/BsqWBT75BKhQwdp1VRX44w9g\nwQI+f+nSQLt2bO+4OGD1auCRR/S/u2sXsHOn+5M7N1Cjhvvz7LNiz6KxdSvQvz///eabQP78wJUr\n7JeRkUBKCpCamvWj/c7lYpvkyOH7yZnT/e+iRYGHHwYKFwYKFeLPggWBBx+01nbXrgH79gE7dgDb\ntwOHDwPPPQfUqsVPjRpAUJDxOSIi+A7nzeM9dukCdOgAhIRYuxcNl4vjZcQI9oVPPgH69OE7fftt\n4+8mJgIDB3JOWrIEqFzZ2rUzMth/z5xhX9LrN0YkJ3OM9u7Nj12GD+dasGCB/XOoKsfDokVAaKj9\n86SnA3nycC50ujb973/AhQtce/QoVoz9sVgx9++uXAGeeQa4fJlj4Pnnga++4nOlp/PnJ58ATZta\nu5+wMI6nQYPsPY/Gli08x/79zs6TmQk8+STnrYoV9Y/r2JFz09Ch4ufOli0bcHfLZZZRVZU7+Dx5\nApMFfuhQZxUFNOLixCo1VKoUmB3W7NnOq1ioqtv/wYzHHjP3WXn6afF6pEbUquW8BEt6Os3BTtt6\n9WqeR09L4HIx8KFdO/um3NhYakVffdVeCp3ISGqJ6te3lhA5NZVmm+Bg7vzt5k9MT2dqjtdeY18q\nXpwmXNHqAIpCjdisWdQyli7NPlC9OgNLJk+mJjEy0riNXS5GFT/xBLWDdsqHBZLERGpLZ89mObP6\n9RkxW6AATZgffUQ/LBETk6JQSzR8OE2gL7xAS8L+/c5cCLSKDto5tfHy7bd0kDdzN9m+ndrIt96y\nZw5OSKC2qFkz++ZxRaG5vVMnZ22h5RVzmlR++3YGBTgNnjlwwNw3WYTMTPo5mpnHixf3TYn17bdZ\n03w0apR1br52zZ6b0Pz5gUn1lZFBLbCT0mEa48eba+U0XzkrZmFI06oPqqpSBT9kiP0XppGczE7g\nNEePqjIKr0kT42OuX6d/nNN0C6rKCV0rV+MEUUGuRAnzhb5FC+dpYFSVph2n1TVUlSXStBByJ/Tp\nYxzIkZxMU9vo0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"text": [ "<matplotlib.figure.Figure at 0x7f4d0d07d610>" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Change things around! Move the location of the source and sink, make them stronger. What happens?\n" ] }, { "cell_type": "heading", "level": 5, "metadata": {}, "source": [ "Challenge task" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write your own code for plotting the *potential lines*, instead of the stream lines. You might want to use the [`contour()`](http://matplotlib.org/examples/pylab_examples/contour_demo.html) function for this." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Please ignore the cell below. It just loads our style for the notebooks." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open('../styles/custom.css', 'r').read()\n", " return HTML(styles)\n", "css_styling()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<link href='http://fonts.googleapis.com/css?family=Fenix' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' type='text/css'>\n", "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", " }\n", " div.cell{\n", " width:800px;\n", " margin-left:16% !important;\n", " margin-right:auto;\n", " }\n", " h1 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\n", " h2 {\n", " font-family: 'Fenix', serif;\n", " }\n", " h3{\n", "\t\tfont-family: 'Fenix', serif;\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", "\th4{\n", "\t\tfont-family: 'Fenix', serif;\n", " }\n", " h5 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\t \n", " div.text_cell_render{\n", " font-family: 'Alegreya Sans',Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 135%;\n", " font-size: 120%;\n", " width:600px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " }\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\";\n", "\t\t\tfont-size: 90%;\n", " }\n", "/* .prompt{\n", " display: None;\n", " }*/\n", " .text_cell_render h1 {\n", " font-weight: 200;\n", " font-size: 50pt;\n", "\t\tline-height: 100%;\n", " color:#CD2305;\n", " margin-bottom: 0.5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\t\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: #CD2305;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>\n" ], "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "<IPython.core.display.HTML at 0x7f4d15a826d0>" ] } ], "prompt_number": 9 } ], "metadata": {} } ] }
bsd-3-clause
tensorflow/decision-forests
documentation/tutorials/proximities_colab.ipynb
1
37275
{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "8q70hWaUZzw2" }, "source": [ "##### Copyright 2022 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "5MOmHUGjZ3hS" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "aW5zvTXqaALj" }, "source": [ "# Proximities and Prototypes with Random Forests\n", "\n", "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/decision_forests/tutorials/proximities_colab\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/decision-forests/blob/main/documentation/tutorials/proximities_colab.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/decision-forests/blob/main/documentation/tutorials/proximities_colab.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView on GitHub\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/decision-forests/documentation/tutorials/proximities_colab.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n", " \u003c/td\u003e\n", "\u003c/table\u003e" ] }, { "cell_type": "markdown", "metadata": { "id": "YVmLIAbEd2i_" }, "source": [ "## Introduction\n", "\n", "[Leo Breiman](https://en.wikipedia.org/wiki/Leo_Breiman), the author of the [random forest](https://developers.google.com/machine-learning/glossary#random-forest) learning algorithm, [proposed](https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#scaling) a method to\n", "measure the *proximity* (also known as *similarity)* between two examples using a pre-trained Random Forest (RF) model. He qualifies this method as \u003ci\u003e\"[...] one of the most useful tools in random forests.\"\u003c/i\u003e. In this Notebook, we implement this method and show how to use it to interpret models.\n", "\n", "This notebook is implmented using the [TensorFlow Decision Forests](https://www.tensorflow.org/decision_forests) library. This document is easier to understand if you are familiar with the content of the [Beginner colab](beginner_colab.ipynb).\n", "\n", "## Proximities\n", "\n", "A **proximity** (or a **similarity**) between two examples is a number\n", "indicating how \"close\" those two examples are. Following is an example of similarity in between the 3 examples $\\{e_1, e_2, e_3\\}$:\n", "\n", "$$\n", "\\mathrm{proxy}(e_1, e_2) = 0.1 \\\\\n", "\\mathrm{proxy}(e_2, e_3) = 9.6 \\\\\n", "\\mathrm{proxy}(e_3, e_1) = 4.1 \\\\\n", "$$\n", "\n", "For convenience, the proximity between examples is represented in matrix form:\n", "\n", "| \t| $e_1$ \t| $e_2$ \t| $e_3$ \t|\n", "|----\t|----\t|----\t|----\t|\n", "| $e_1$ \t| $\\mathrm{proxy}(e_1, e_1)$ \t| $\\mathrm{proxy}(e_1, e_2)$ \t| $\\mathrm{proxy}(e_1, e_3)$ \t|\n", "| $e_2$ \t| $\\mathrm{proxy}(e_2, e_1)$ \t| $\\mathrm{proxy}(e_2, e_2)$ \t| $\\mathrm{proxy}(e_2, e_3)$ \t|\n", "| $e_3$ \t| $\\mathrm{proxy}(e_3, e_1)$ \t| $\\mathrm{proxy}(e_3, e_2)$ \t| $\\mathrm{proxy}(e_3, e_3)$ \t|\n", "\n", "Proximities are used in multiple data analysis techniques, including clustering, dimensionality reductions or nearest neighbor analysis. For this reason, it is a great tool for **models** and **predictions interpretation**.\n", "\n", "Unfortunately, measuring the proximity between two tabular examples is not straightforward as different columns might describe different quantities. For example, try to define the proximity in between the following examples.\n", "\n", "species | weight | num_legs | age | sex\n", "------- | ------ | -------- | ------- | ------\n", "cat | 2 kg | 4 | 2 y | male\n", "dog | 6 kg | 4 | 12 y | female\n", "spider | 5 g | 8 | 3 weeks | female\n", "\n", "To define the similarity between two rows in the table above, you need to specify how much a *difference in weight* compares to a *difference in the number of legs*, or in ages. In addition, relations might be non-linear or be conditionnal on other columns. For example, dogs live longer than spiders, so maybe, a one year difference for a spider should not count the same one year of age for a dog.\n", "\n", "Instead of manually defining those relations, Breiman's proximity turns a random forest model (which we know how to train on a tabular dataset), into a proximity metric.\n", "\n", "## Proximities with random forests\n", "\n", "A random forest is a collection of decision trees. The prediction of the random the aggregation of the predictions of the individual trees. The prediction of a decision tree is computed by routing an example from the root to forest is one of the leaves according to node conditions. The leaf reached\n", "by the example $i$ in the tree $t$ is called its *active* leaf and noted $\\mathrm{leaf}(i,t)$\n", "\n", "Breiman defines the proximity between two examples as the ratio of shared active leafs between those two examples. Formally, the proximity between example $i$ and example $j$ is:\n", "\n", "$$\n", "\\mathrm{prox}(i,j) = \\mathrm{prox}(j,i) = \\frac{1}{|\\mathrm{Trees}|} \\sum_{t \\in \\mathrm{Trees}} \\left[ \\mathrm{leaf}(i,t) = \\mathrm{leaf}(j,t) \\right]\n", "$$\n", "\n", "with $\\mathrm{leaf}(j,t)$ the index of the active leaf for the example $j$ in\n", "the tree $t$.\n", "\n", "Informally, if two examples are often routed to the same leaves (i.e. the two examples have the same active leaves), those examples are similar.\n", "\n", "Let's implement this proximity function and use it in some examples." ] }, { "cell_type": "markdown", "metadata": { "id": "GaLMe79uvc9Z" }, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oWJReMDZtOpc" }, "outputs": [], "source": [ "# Install TensorFlow Dececision Forests and the dependencies used in this colab.\n", "!pip install tensorflow_decision_forests plotly wurlitzer -U -qq" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oG_FgJBLVzre" }, "outputs": [], "source": [ "import tensorflow_decision_forests as tfdf\n", "\n", "import matplotlib.colors as mcolors\n", "import math\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn.manifold import TSNE\n", "import matplotlib.pyplot as plt\n", "from plotly.offline import iplot\n", "import plotly.graph_objs as go" ] }, { "cell_type": "markdown", "metadata": { "id": "YBxXMiU_fnuM" }, "source": [ "## Train a Random Forest model" ] }, { "cell_type": "markdown", "metadata": { "id": "ZEqUkm-svhvJ" }, "source": [ "The method relies on a pre-trained random forest model. First, we train a random forest model with [TensorFlow Decision Forests library](https://www.tensorflow.org/decision_forests) on the [Adult](https://archive.ics.uci.edu/ml/datasets/adult) binary classification dataset. The Adult dataset is well suited for this example as it contains columns that don't have a natural way to be compared." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jvS6VEFtbhoS" }, "outputs": [], "source": [ "# Download a copy of the adult dataset.\n", "!wget -q https://raw.githubusercontent.com/google/yggdrasil-decision-forests/main/yggdrasil_decision_forests/test_data/dataset/adult_train.csv -O /tmp/adult_train.csv\n", "!wget -q https://raw.githubusercontent.com/google/yggdrasil-decision-forests/main/yggdrasil_decision_forests/test_data/dataset/adult_test.csv -O /tmp/adult_test.csv" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PB1uoNL6VmIy" }, "outputs": [], "source": [ "# Load the dataset in memory\n", "train_df = pd.read_csv(\"/tmp/adult_train.csv\")\n", "test_df = pd.read_csv(\"/tmp/adult_test.csv\")\n", "\n", "# , and convert it into a TensorFlow dataset.\n", "train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label=\"income\")\n", "test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_df, label=\"income\")" ] }, { "cell_type": "markdown", "metadata": { "id": "98RjQ3TLv9er" }, "source": [ "Following are the first five examples of the training dataset. Notice that\n", "different columns represent different quantities. For example, how would you compare\n", "the distance between *relationship* and *age*?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qxMvrC0Sv6lH" }, "outputs": [], "source": [ "# Print the first 5 examples.\n", "train_df.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "4meZoXCZwPB4" }, "source": [ "A Random Forest is trained as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A9kAraP6V6IN" }, "outputs": [], "source": [ "# Train a Random Forest\n", "model = tfdf.keras.RandomForestModel(num_trees=1000)\n", "model.fit(train_ds)" ] }, { "cell_type": "markdown", "metadata": { "id": "OaXVeDJ6wulU" }, "source": [ "The performance of the Random Forest model is:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_K1D_09HdHCM" }, "outputs": [], "source": [ "model_inspector = model.make_inspector()\n", "out_of_bag_accuracy = model_inspector.evaluation().accuracy\n", "print(f\"Out-of-bag accuracy: {out_of_bag_accuracy:.4f}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "zRsgF9Y3wzQI" }, "source": [ "This is an expected accuracy value for Random Forest models on this dataset. It indicates that the model is correctly trained.\n", "\n", "We can also measure the accuracy of the model on the test datasets:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pGnmaT1nXxdd" }, "outputs": [], "source": [ "# The test accuracy is measured on the test datasets.\n", "model.compile([\"accuracy\"])\n", "test_accuracy = model.evaluate(test_ds, return_dict=True, verbose=0)[\"accuracy\"]\n", "print(f\"Test accuracy: {test_accuracy:.4f}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "rbo7MTZgfpjq" }, "source": [ "## Proximities" ] }, { "cell_type": "markdown", "metadata": { "id": "6lWk06zlxZEc" }, "source": [ "First, we inspect the number of trees in the model and the number of examples in the test datasets." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2EAzFN0_xbuz" }, "outputs": [], "source": [ "print(\"The model contains\", model_inspector.num_trees(), \"trees.\")\n", "print(\"The test dataset contains\", test_df.shape[0], \"examples.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "OglqxIdkxFbO" }, "source": [ "The method [predict_get_leaves()](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/RandomForestModel) returns the index of the active leaf for each example and each tree." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4VRnisa-gf0x" }, "outputs": [], "source": [ "leaves = model.predict_get_leaves(test_ds)\n", "print(\"The leaf indices:\\n\", leaves)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oK72WE1ngIkB" }, "outputs": [], "source": [ "print(\"The predicted leaves have shape\", leaves.shape,\n", " \"(we expect [num_examples, num_trees]\")" ] }, { "cell_type": "markdown", "metadata": { "id": "VdDP6gA5xpED" }, "source": [ "Here, `leaves[i,j]` is the index of the active leaf of the i-th\n", "example in the j-th tree.\n", "\n", "**Note:** In this notebook, we won't need the actual leaf prediction values. However, they are available through the `model_inspector`.\n", "\n", "Next, we implement the $\\mathrm{prox}$ equation define earlier.\n", "\n", "**Note:** This step is slow." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1GAdmdehc2kM" }, "outputs": [], "source": [ "def compute_proximity(leaves, step_size=100):\n", " \"\"\"Computes the proximity between each pair of examples.\n", "\n", " Args:\n", " leaves: A matrix of shape [num_example, num_tree] where the value [i,j] is\n", " the index of the leaf reached by example \"i\" in the tree \"j\".\n", " step_size: Size of the block of examples for the computation of the\n", " proximity. Does not impact the results.\n", "\n", " Returns:\n", " The example pair-wise proximity matrix of shape [n,n] with \"n\" the number of\n", " examples.\n", " \"\"\"\n", "\n", " example_idx = 0\n", " num_examples = leaves.shape[0]\n", " t_leaves = np.transpose(leaves)\n", " proximities = []\n", "\n", " # Instead of computing the proximity in between all the examples at the same\n", " # time, we compute the similarity in blocks of \"step_size\" examples. This\n", " # makes the code more efficient with the the numpy broadcast.\n", " while example_idx \u003c num_examples:\n", " end_idx = min(example_idx + step_size, num_examples)\n", " proximities.append(\n", " np.mean(\n", " leaves[..., np.newaxis] == t_leaves[:,\n", " example_idx:end_idx][np.newaxis,\n", " ...],\n", " axis=1))\n", " example_idx = end_idx\n", " return np.concatenate(proximities, axis=1)\n", "\n", "\n", "proximity = compute_proximity(leaves)\n", "print(\"The shape of proximity is\", proximity.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "p9agCek5BGLL" }, "source": [ "Here, `proximity[i,j]` is the proximity in between the example `i` and `j`.\n", "\n", "The proximity matrix:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "p04SweioBFnH" }, "outputs": [], "source": [ "proximity" ] }, { "cell_type": "markdown", "metadata": { "id": "syenPRRSBQFs" }, "source": [ "The proximity matrix has several interesting properties, notably, it is symmetrical, positive, and the diagonal elements are all 1." ] }, { "cell_type": "markdown", "metadata": { "id": "5lncF0ja1iBQ" }, "source": [ "## Projection\n", "\n", "Our first use of the proximity is to project the examples on the two dimensional plane.\n", "\n", "If $\\mathrm{prox} \\in [0,1]$ is a proximity, $1 - \\mathrm{prox}$ is a distance\n", "between examples. Breiman proposes to compute the inner products of those distances, and to plot\n", "the [eigenvalues](https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors). See details\n", "[here](https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#scaling).\n", "\n", "Instead, we wil use the\n", "[t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding)\n", "which is a more modern way to visualize high-dimensional data.\n", "\n", "**Note:** We use the [t-SNE's Scikit-learn implementation](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Hy_krzz9IRhL" }, "outputs": [], "source": [ "distance = 1 - proximity\n", "\n", "t_sne = TSNE(\n", " # Number of dimensions to display. 3d is also possible.\n", " n_components=2,\n", " # Control the shape of the projection. Higher values create more\n", " # distinct but also more collapsed clusters. Can be in 5-50.\n", " perplexity=20,\n", " metric=\"precomputed\",\n", " init=\"random\",\n", " verbose=1,\n", " square_distances=True,\n", " learning_rate=\"auto\").fit_transform(distance)" ] }, { "cell_type": "markdown", "metadata": { "id": "qftL4Av1JPzM" }, "source": [ "The next plot shows a two-dimensional projection of the test example features. The color of the points\n", "represent the label values. Note that the label values were not available to the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Fj9Sa0h5KEh9" }, "outputs": [], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", "ax.grid(False)\n", "\n", "# Color the points according to the label value.\n", "colors = (test_df[\"income\"] == \"\u003e50K\").map(lambda x: [\"orange\", \"green\"][x])\n", "ax.scatter(\n", " t_sne[:, 0], t_sne[:, 1], c=colors, linewidths=0.5, marker=\"x\", s=20)" ] }, { "cell_type": "markdown", "metadata": { "id": "Vou_-E25l5-b" }, "source": [ "**Observations:**\n", "\n", "- There are custers of points with similar colors. Those are examples that are easy for the model to classify.\n", "- There are multiple clusters with the same color. Those multiple clusters show examples with the same label, but for \"different reasons\" according to the model.\n", "- Clusters with mixed colors contain examples where the model performs poorly. In the part above, we evaluated the model test accuracy to ~86%. Those are likely those examples." ] }, { "cell_type": "markdown", "metadata": { "id": "ChgYajSVOYJm" }, "source": [ "The previous plot is a static image. Let's turn it into an interactive plot and inspect the individual examples." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "et3EUVUHNX4l" }, "outputs": [], "source": [ "# docs_infra: no_execute\n", "\n", "# Note: Run the colab (click the \"Run in Google Colab\" link at the top) to see\n", "# the interactive plot.\n", "\n", "def interactive_plot(dataset, projections):\n", "\n", " def label_fn(row):\n", " \"\"\"HTML printer over each example.\"\"\"\n", " return \"\u003cbr\u003e\".join([f\"\u003cb\u003e{k}:\u003c/b\u003e {v}\" for k, v in row.items()])\n", "\n", " labels = list(dataset.apply(label_fn, axis=1).values)\n", " iplot({\n", " \"data\": [\n", " go.Scatter(\n", " x=projections[:, 0],\n", " y=projections[:, 1],\n", " text=labels,\n", " mode=\"markers\",\n", " marker={\n", " \"color\": colors,\n", " \"size\": 3,\n", " })\n", " ],\n", " \"layout\": go.Layout(width=600, height=600, template=\"simple_white\")\n", " })\n", "\n", "\n", "interactive_plot(test_df, t_sne)" ] }, { "cell_type": "markdown", "metadata": { "id": "fxU0GTpXSA-V" }, "source": [ "**Instructions:** Put the mouse pointer over some examples, and try to make sense of them. Compare them to their neighbors.\n", "\n", "**Not seeing the interactive plot?:** Run the colab with [this link](https://colab.sandbox.google.com/github/tensorflow/decision-forests/blob/main/documentation/tutorials/proximities_colab.ipynb) to see the interactive plot.\n", "\n", "Instead of coloring the examples according to the label values, we can color the examples according to each feature values:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0sxXuCCBvN1F" }, "outputs": [], "source": [ "# Number of columns and rows in the multi-plot.\n", "num_plot_cols = 5\n", "num_plot_rows = math.ceil(test_df.shape[1] / num_plot_cols)\n", "\n", "# Color palette for the categorical features.\n", "palette = list(mcolors.TABLEAU_COLORS.values())\n", "\n", "# Create the plot\n", "plot_size_in = 3.5\n", "fig, axs = plt.subplots(\n", " num_plot_rows,\n", " num_plot_cols,\n", " figsize=(num_plot_cols * plot_size_in, num_plot_rows * plot_size_in))\n", "\n", "# Hide the borders.\n", "for row in axs:\n", " for ax in row:\n", " ax.set_axis_off()\n", "\n", "for col_idx, col_name in enumerate(test_df):\n", " ax = axs[col_idx // num_plot_cols, col_idx % num_plot_cols]\n", "\n", " colors = test_df[col_name]\n", " if colors.dtypes in [str, object]:\n", " # Use the color palette on categorical features.\n", " unique_values = list(colors.unique())\n", " colors = colors.map(\n", " lambda x: palette[unique_values.index(x) % len(palette)])\n", "\n", " ax.set_title(col_name)\n", " ax.scatter(t_sne[:, 0], t_sne[:, 1], c=colors.values, linewidths=0.5,\n", " marker=\"x\", s=5)" ] }, { "cell_type": "markdown", "metadata": { "id": "pl78JDtqSVau" }, "source": [ "## Prototypes\n", "\n", "Trying to make sense of an example by looking at all its neighbors is not always efficient. Instead, we could \"group\" similar examples to make this task easier. This is the underlying idea behind *prototypes*.\n", "\n", "**Prototypes** are examples, not necessarily in the original dataset, that are representative of large trends in the dataset. Looking at prototypes is a solution to understand a dataset. For more details, see the [chapter 8.7](https://christophm.github.io/interpretable-ml-book/proto.html) of [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/) by Molnar.\n", "\n", "Prototypes can be computed in different ways, for example using a clustering algorithm. Instead, Breiman proposed a specific solution based on a simple iterative algorithm. The algorithm is as follow:\n", "\n", "1. Select the example surrounded with the highest number of neighbors with the same class among its k nearest neighbors.\n", "2. Create a prototype example using the median feature values of the selected example and its k neighbors.\n", "3. Remove those k+1 examples\n", "4. Repeat\n", "\n", "Informally, prototypes are centers of clusters in the plots we created above.\n", "\n", "Let's implement this algorithm and look at some prototypes.\n", "\n", "First the method that selects the example in step 1." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Fktpntg2XxeI" }, "outputs": [], "source": [ "def select_example(labels, distance_matrix, k):\n", " \"\"\"Selects the example with the highest number of neighbors with the same class.\n", "\n", " Usage example:\n", " n = 5\n", " select_example(\n", " np.random.randint(0,2, size=n),\n", " np.random.uniform(size=(n,n)),\n", " 2)\n", "\n", " Returns:\n", " The list of neighbors for the selected example. Includes the selected\n", " example.\n", " \"\"\"\n", "\n", " partition = np.argpartition(distance_matrix, k)[:,:k]\n", " same_label = np.mean(np.equal(labels[partition], np.expand_dims(labels, axis=1)), axis=1)\n", " selected_example = np.argmax(same_label)\n", " return partition[selected_example, :]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Qnl5IWRNUj17" }, "outputs": [], "source": [ "def extract_prototype_examples(labels, distance_matrix, k, num_prototypes):\n", " \"\"\"Extracts a list of examples in each prototype.\n", "\n", " Usage example:\n", " n = 50\n", " print(extract_prototype_examples(\n", " labels=np.random.randint(0, 2, size=n),\n", " distance_matrix=np.random.uniform(size=(n, n)),\n", " k=2,\n", " num_prototypes=3))\n", "\n", " Returns:\n", " An array where E[i][j] is the index of the j-th examples of the i-th\n", " prototype. \n", " \"\"\"\n", "\n", " example_idxs = np.arange(len(labels))\n", " prototypes = []\n", " examples_per_prototype = []\n", "\n", " for iter in range(num_prototypes):\n", " print(f\"Iter #{iter}\")\n", " # Select the example\n", " neighbors = select_example(labels, distance_matrix, k)\n", "\n", " # Index of the examples in the prototype\n", " examples_per_prototype.append(list(example_idxs[neighbors]))\n", "\n", " # Remove the selected examples\n", " example_idxs = np.delete(example_idxs, neighbors)\n", " labels = np.delete(labels, neighbors)\n", " distance_matrix = np.delete(distance_matrix, neighbors, axis=0)\n", " distance_matrix = np.delete(distance_matrix, neighbors, axis=1)\n", "\n", " return examples_per_prototype" ] }, { "cell_type": "markdown", "metadata": { "id": "fSxhQqwMV055" }, "source": [ "Using the methods above, let's extract the examples for 10 prototypes.\n", "\n", "**Note:** The parameter `k` controls the number of elements in a cluster. Changing its value will impact the prototypes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2t-r8PMGV0Qp" }, "outputs": [], "source": [ "examples_per_prototype = extract_prototype_examples(test_df[\"income\"].values, distance, k=20, num_prototypes=10)\n", "print(f\"Found examples for {len(examples_per_prototype)} prototypes.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "1mX8QzITWUpS" }, "source": [ "For each of those prototypes, we want to display the statistics of the feature values. In this example, we will look at the quartiles of the numerical features, and the most frequent values for the categorical features." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Pmtlj-sel68Z" }, "outputs": [], "source": [ "def build_prototype(dataset):\n", " \"\"\"Exacts the feature statistics of a prototype.\n", " \n", " For numerical features, returns the quantiles.\n", " For categorical features, returns the most frequent value.\n", "\n", " Usage example:\n", " n = 50\n", " print(build_prototype(\n", " pd.DataFrame({\n", " \"f1\": np.random.uniform(size=n),\n", " \"f2\": np.random.uniform(size=n),\n", " \"f3\": [f\"v_{x}\" for x in np.random.randint(0, 2, size=n)],\n", " \"label\": np.random.randint(0, 2, size=n)\n", " })))\n", " \n", " Return:\n", " A prototype as a dictionary of strings.\n", " \"\"\"\n", "\n", " prototype = {}\n", " for col in dataset.columns:\n", " col_values = dataset[col]\n", " if col_values.dtypes in [str, object]:\n", " # A categorical feature.\n", "\n", " # Remove the missing values\n", " col_values = [x for x in col_values if isinstance(x,str) or not math.isnan(x)]\n", "\n", " # Frequency of each possible value.\n", " frequency_item, frequency_count = np.unique(col_values, return_counts=True)\n", " top_item_idx = np.argmax(frequency_count)\n", " top_item_probability = frequency_count[top_item_idx] / np.sum(frequency_count)\n", "\n", " # Print the most common item.\n", " prototype[col] = f\"{frequency_item[top_item_idx]} ({100*top_item_probability:.0f}%)\"\n", "\n", " else:\n", " # A numerical feature.\n", " quartiles = np.nanquantile(col_values.values, [0.25, 0.5, 0.75])\n", " # Print the 3 quantiles.\n", " prototype[col] = f\"{quartiles[0]} {quartiles[1]} {quartiles[2]}\"\n", " return prototype" ] }, { "cell_type": "markdown", "metadata": { "id": "prhB4rejX4Os" }, "source": [ "Now, let's look at our prototypes.\n", "\n", "**Note:** The table shows the \"25%-quantile median 75%-quantile\" for each numerical feature." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "44-nBStN583L" }, "outputs": [], "source": [ "# Extract the statistics of each prototype.\n", "prototypes = []\n", "for examples in examples_per_prototype:\n", " # Prorotype statistics.\n", " prototypes.append(build_prototype(test_df.iloc[examples, :]))\n", "prototypes = pd.DataFrame(prototypes)\n", "\n", "prototypes" ] }, { "cell_type": "markdown", "metadata": { "id": "QnY-91uek5Aq" }, "source": [ "Try to make sense of the prototypes.\n", "\n", "Let's extract and plot the mean 2d t-SNE projection of the elements in those prototypes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "x8g-5KcnXTLL" }, "outputs": [], "source": [ "# Extract the projection of each prototype.\n", "prototypes_projection = []\n", "for examples in examples_per_prototype:\n", " # t-SNE for each prototype.\n", " prototypes_projection.append(np.mean(t_sne[examples,:],axis=0))\n", "prototypes_projection = np.stack(prototypes_projection)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nBImofU_6inI" }, "outputs": [], "source": [ "# Plot the mean 2d t-SNE projection of the elements in the prototypes.\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", "ax.grid(False)\n", "\n", "# Color the points according to the label value.\n", "colors = (test_df[\"income\"] == \"\u003e50K\").map(lambda x: [\"orange\", \"green\"][x])\n", "ax.scatter(\n", " t_sne[:, 0], t_sne[:, 1], c=colors, linewidths=0.5, marker=\"x\", s=20)\n", "\n", "# Add the prototype indices.\n", "for i in range(prototypes_projection.shape[0]):\n", " ax.text(prototypes_projection[i, 0],\n", " prototypes_projection[i, 1],\n", " f\"{i}\",\n", " fontdict={\"size\":18},\n", " c=\"red\")" ] }, { "cell_type": "markdown", "metadata": { "id": "Tuo229JCbgAj" }, "source": [ "We see that the 10 prototypes cover around half of the domain. Clusters of examples without a prototype would be best explained with more prototypes.\n", "\n", "In the example above, we extracted the prototypes automatically. However, we can also build prototypes around specific examples.\n", "\n", "Let's create the prototype around the example #0.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nl2DnRafcvet" }, "outputs": [], "source": [ "example_idx = 0\n", "k = 20\n", "neighbors = np.argpartition(distance[example_idx, :], k)[:k]\n", "\n", "print(f\"The example #{example_idx} is:\")\n", "print(\"===============================\")\n", "print(test_df.iloc[example_idx, :])\n", "print(\"\")\n", "print(f\"The prototype around the example #{example_idx} is:\")\n", "print(\"============================================\")\n", "print(pd.Series(build_prototype(test_df.iloc[neighbors, :])))" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "Proximities and Prototypes with Random Forest", "private_outputs": true, "provenance": [ { "file_id": "1SGv8eYXqJMr6Fsv8yx1NJED6EoLbUiA4", "timestamp": 1649833950157 } ], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
RaRe-Technologies/gensim
docs/src/auto_examples/tutorials/run_doc2vec_lee.ipynb
4
20042
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nDoc2Vec Model\n=============\n\nIntroduces Gensim's Doc2Vec model and demonstrates its use on the\n`Lee Corpus <https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>`__.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import logging\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Doc2Vec is a `core_concepts_model` that represents each\n`core_concepts_document` as a `core_concepts_vector`. This\ntutorial introduces the model and demonstrates how to train and assess it.\n\nHere's a list of what we'll be doing:\n\n0. Review the relevant models: bag-of-words, Word2Vec, Doc2Vec\n1. Load and preprocess the training and test corpora (see `core_concepts_corpus`)\n2. Train a Doc2Vec `core_concepts_model` model using the training corpus\n3. Demonstrate how the trained model can be used to infer a `core_concepts_vector`\n4. Assess the model\n5. Test the model on the test corpus\n\nReview: Bag-of-words\n--------------------\n\n.. Note:: Feel free to skip these review sections if you're already familiar with the models.\n\nYou may be familiar with the `bag-of-words model\n<https://en.wikipedia.org/wiki/Bag-of-words_model>`_ from the\n`core_concepts_vector` section.\nThis model transforms each document to a fixed-length vector of integers.\nFor example, given the sentences:\n\n- ``John likes to watch movies. Mary likes movies too.``\n- ``John also likes to watch football games. Mary hates football.``\n\nThe model outputs the vectors:\n\n- ``[1, 2, 1, 1, 2, 1, 1, 0, 0, 0, 0]``\n- ``[1, 1, 1, 1, 0, 1, 0, 1, 2, 1, 1]``\n\nEach vector has 10 elements, where each element counts the number of times a\nparticular word occurred in the document.\nThe order of elements is arbitrary.\nIn the example above, the order of the elements corresponds to the words:\n``[\"John\", \"likes\", \"to\", \"watch\", \"movies\", \"Mary\", \"too\", \"also\", \"football\", \"games\", \"hates\"]``.\n\nBag-of-words models are surprisingly effective, but have several weaknesses.\n\nFirst, they lose all information about word order: \"John likes Mary\" and\n\"Mary likes John\" correspond to identical vectors. There is a solution: bag\nof `n-grams <https://en.wikipedia.org/wiki/N-gram>`__\nmodels consider word phrases of length n to represent documents as\nfixed-length vectors to capture local word order but suffer from data\nsparsity and high dimensionality.\n\nSecond, the model does not attempt to learn the meaning of the underlying\nwords, and as a consequence, the distance between vectors doesn't always\nreflect the difference in meaning. The ``Word2Vec`` model addresses this\nsecond problem.\n\nReview: ``Word2Vec`` Model\n--------------------------\n\n``Word2Vec`` is a more recent model that embeds words in a lower-dimensional\nvector space using a shallow neural network. The result is a set of\nword-vectors where vectors close together in vector space have similar\nmeanings based on context, and word-vectors distant to each other have\ndiffering meanings. For example, ``strong`` and ``powerful`` would be close\ntogether and ``strong`` and ``Paris`` would be relatively far.\n\nGensim's :py:class:`~gensim.models.word2vec.Word2Vec` class implements this model.\n\nWith the ``Word2Vec`` model, we can calculate the vectors for each **word** in a document.\nBut what if we want to calculate a vector for the **entire document**\\ ?\nWe could average the vectors for each word in the document - while this is quick and crude, it can often be useful.\nHowever, there is a better way...\n\nIntroducing: Paragraph Vector\n-----------------------------\n\n.. Important:: In Gensim, we refer to the Paragraph Vector model as ``Doc2Vec``.\n\nLe and Mikolov in 2014 introduced the `Doc2Vec algorithm <https://cs.stanford.edu/~quocle/paragraph_vector.pdf>`__,\nwhich usually outperforms such simple-averaging of ``Word2Vec`` vectors.\n\nThe basic idea is: act as if a document has another floating word-like\nvector, which contributes to all training predictions, and is updated like\nother word-vectors, but we will call it a doc-vector. Gensim's\n:py:class:`~gensim.models.doc2vec.Doc2Vec` class implements this algorithm.\n\nThere are two implementations:\n\n1. Paragraph Vector - Distributed Memory (PV-DM)\n2. Paragraph Vector - Distributed Bag of Words (PV-DBOW)\n\n.. Important::\n Don't let the implementation details below scare you.\n They're advanced material: if it's too much, then move on to the next section.\n\nPV-DM is analogous to Word2Vec CBOW. The doc-vectors are obtained by training\na neural network on the synthetic task of predicting a center word based an\naverage of both context word-vectors and the full document's doc-vector.\n\nPV-DBOW is analogous to Word2Vec SG. The doc-vectors are obtained by training\na neural network on the synthetic task of predicting a target word just from\nthe full document's doc-vector. (It is also common to combine this with\nskip-gram testing, using both the doc-vector and nearby word-vectors to\npredict a single target word, but only one at a time.)\n\nPrepare the Training and Test Data\n----------------------------------\n\nFor this tutorial, we'll be training our model using the `Lee Background\nCorpus\n<https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>`_\nincluded in gensim. This corpus contains 314 documents selected from the\nAustralian Broadcasting Corporation\u2019s news mail service, which provides text\ne-mails of headline stories and covers a number of broad topics.\n\nAnd we'll test our model by eye using the much shorter `Lee Corpus\n<https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>`_\nwhich contains 50 documents.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\nimport gensim\n# Set file names for train and test data\ntest_data_dir = os.path.join(gensim.__path__[0], 'test', 'test_data')\nlee_train_file = os.path.join(test_data_dir, 'lee_background.cor')\nlee_test_file = os.path.join(test_data_dir, 'lee.cor')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a Function to Read and Preprocess Text\n---------------------------------------------\n\nBelow, we define a function to:\n\n- open the train/test file (with latin encoding)\n- read the file line-by-line\n- pre-process each line (tokenize text into individual words, remove punctuation, set to lowercase, etc)\n\nThe file we're reading is a **corpus**.\nEach line of the file is a **document**.\n\n.. Important::\n To train the model, we'll need to associate a tag/number with each document\n of the training corpus. In our case, the tag is simply the zero-based line\n number.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import smart_open\n\ndef read_corpus(fname, tokens_only=False):\n with smart_open.open(fname, encoding=\"iso-8859-1\") as f:\n for i, line in enumerate(f):\n tokens = gensim.utils.simple_preprocess(line)\n if tokens_only:\n yield tokens\n else:\n # For training data, add tags\n yield gensim.models.doc2vec.TaggedDocument(tokens, [i])\n\ntrain_corpus = list(read_corpus(lee_train_file))\ntest_corpus = list(read_corpus(lee_test_file, tokens_only=True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the training corpus\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(train_corpus[:2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the testing corpus looks like this:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(test_corpus[:2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the testing corpus is just a list of lists and does not contain\nany tags.\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Training the Model\n------------------\n\nNow, we'll instantiate a Doc2Vec model with a vector size with 50 dimensions and\niterating over the training corpus 40 times. We set the minimum word count to\n2 in order to discard words with very few occurrences. (Without a variety of\nrepresentative examples, retaining such infrequent words can often make a\nmodel worse!) Typical iteration counts in the published `Paragraph Vector paper <https://cs.stanford.edu/~quocle/paragraph_vector.pdf>`__\nresults, using 10s-of-thousands to millions of docs, are 10-20. More\niterations take more time and eventually reach a point of diminishing\nreturns.\n\nHowever, this is a very very small dataset (300 documents) with shortish\ndocuments (a few hundred words). Adding training passes can sometimes help\nwith such small datasets.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = gensim.models.doc2vec.Doc2Vec(vector_size=50, min_count=2, epochs=40)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build a vocabulary\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model.build_vocab(train_corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Essentially, the vocabulary is a list (accessible via\n``model.wv.index_to_key``) of all of the unique words extracted from the training corpus.\nAdditional attributes for each word are available using the ``model.wv.get_vecattr()`` method,\nFor example, to see how many times ``penalty`` appeared in the training corpus:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(f\"Word 'penalty' appeared {model.wv.get_vecattr('penalty', 'count')} times in the training corpus.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, train the model on the corpus.\nIf optimized Gensim (with BLAS library) is being used, this should take no more than 3 seconds.\nIf the BLAS library is not being used, this should take no more than 2\nminutes, so use optimized Gensim with BLAS if you value your time.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model.train(train_corpus, total_examples=model.corpus_count, epochs=model.epochs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can use the trained model to infer a vector for any piece of text\nby passing a list of words to the ``model.infer_vector`` function. This\nvector can then be compared with other vectors via cosine similarity.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vector = model.infer_vector(['only', 'you', 'can', 'prevent', 'forest', 'fires'])\nprint(vector)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that ``infer_vector()`` does *not* take a string, but rather a list of\nstring tokens, which should have already been tokenized the same way as the\n``words`` property of original training document objects.\n\nAlso note that because the underlying training/inference algorithms are an\niterative approximation problem that makes use of internal randomization,\nrepeated inferences of the same text will return slightly different vectors.\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assessing the Model\n-------------------\n\nTo assess our new model, we'll first infer new vectors for each document of\nthe training corpus, compare the inferred vectors with the training corpus,\nand then returning the rank of the document based on self-similarity.\nBasically, we're pretending as if the training corpus is some new unseen data\nand then seeing how they compare with the trained model. The expectation is\nthat we've likely overfit our model (i.e., all of the ranks will be less than\n2) and so we should be able to find similar documents very easily.\nAdditionally, we'll keep track of the second ranks for a comparison of less\nsimilar documents.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ranks = []\nsecond_ranks = []\nfor doc_id in range(len(train_corpus)):\n inferred_vector = model.infer_vector(train_corpus[doc_id].words)\n sims = model.dv.most_similar([inferred_vector], topn=len(model.dv))\n rank = [docid for docid, sim in sims].index(doc_id)\n ranks.append(rank)\n\n second_ranks.append(sims[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's count how each document ranks with respect to the training corpus\n\nNB. Results vary between runs due to random seeding and very small corpus\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import collections\n\ncounter = collections.Counter(ranks)\nprint(counter)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Basically, greater than 95% of the inferred documents are found to be most\nsimilar to itself and about 5% of the time it is mistakenly most similar to\nanother document. Checking the inferred-vector against a\ntraining-vector is a sort of 'sanity check' as to whether the model is\nbehaving in a usefully consistent manner, though not a real 'accuracy' value.\n\nThis is great and not entirely surprising. We can take a look at an example:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print('Document ({}): \u00ab{}\u00bb\\n'.format(doc_id, ' '.join(train_corpus[doc_id].words)))\nprint(u'SIMILAR/DISSIMILAR DOCS PER MODEL %s:\\n' % model)\nfor label, index in [('MOST', 0), ('SECOND-MOST', 1), ('MEDIAN', len(sims)//2), ('LEAST', len(sims) - 1)]:\n print(u'%s %s: \u00ab%s\u00bb\\n' % (label, sims[index], ' '.join(train_corpus[sims[index][0]].words)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice above that the most similar document (usually the same text) is has a\nsimilarity score approaching 1.0. However, the similarity score for the\nsecond-ranked documents should be significantly lower (assuming the documents\nare in fact different) and the reasoning becomes obvious when we examine the\ntext itself.\n\nWe can run the next cell repeatedly to see a sampling other target-document\ncomparisons.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Pick a random document from the corpus and infer a vector from the model\nimport random\ndoc_id = random.randint(0, len(train_corpus) - 1)\n\n# Compare and print the second-most-similar document\nprint('Train Document ({}): \u00ab{}\u00bb\\n'.format(doc_id, ' '.join(train_corpus[doc_id].words)))\nsim_id = second_ranks[doc_id]\nprint('Similar Document {}: \u00ab{}\u00bb\\n'.format(sim_id, ' '.join(train_corpus[sim_id[0]].words)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Testing the Model\n-----------------\n\nUsing the same approach above, we'll infer the vector for a randomly chosen\ntest document, and compare the document to our model by eye.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Pick a random document from the test corpus and infer a vector from the model\ndoc_id = random.randint(0, len(test_corpus) - 1)\ninferred_vector = model.infer_vector(test_corpus[doc_id])\nsims = model.dv.most_similar([inferred_vector], topn=len(model.dv))\n\n# Compare and print the most/median/least similar documents from the train corpus\nprint('Test Document ({}): \u00ab{}\u00bb\\n'.format(doc_id, ' '.join(test_corpus[doc_id])))\nprint(u'SIMILAR/DISSIMILAR DOCS PER MODEL %s:\\n' % model)\nfor label, index in [('MOST', 0), ('MEDIAN', len(sims)//2), ('LEAST', len(sims) - 1)]:\n print(u'%s %s: \u00ab%s\u00bb\\n' % (label, sims[index], ' '.join(train_corpus[sims[index][0]].words)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Conclusion\n----------\n\nLet's review what we've seen in this tutorial:\n\n0. Review the relevant models: bag-of-words, Word2Vec, Doc2Vec\n1. Load and preprocess the training and test corpora (see `core_concepts_corpus`)\n2. Train a Doc2Vec `core_concepts_model` model using the training corpus\n3. Demonstrate how the trained model can be used to infer a `core_concepts_vector`\n4. Assess the model\n5. Test the model on the test corpus\n\nThat's it! Doc2Vec is a great way to explore relationships between documents.\n\nAdditional Resources\n--------------------\n\nIf you'd like to know more about the subject matter of this tutorial, check out the links below.\n\n* `Word2Vec Paper <https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf>`_\n* `Doc2Vec Paper <https://cs.stanford.edu/~quocle/paragraph_vector.pdf>`_\n* `Dr. Michael D. Lee's Website <http://faculty.sites.uci.edu/mdlee>`_\n* `Lee Corpus <http://faculty.sites.uci.edu/mdlee/similarity-data/>`__\n* `IMDB Doc2Vec Tutorial <doc2vec-IMDB.ipynb>`_\n\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 0 }
lgpl-2.1
AllenDowney/ThinkBayes2
examples/voting_soln.ipynb
1
11073
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Think Bayes\n", "\n", "This notebook presents example code and exercise solutions for Think Bayes.\n", "\n", "Copyright 2018 Allen B. Downey\n", "\n", "MIT License: https://opensource.org/licenses/MIT" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Configure Jupyter so figures appear in the notebook\n", "%matplotlib inline\n", "\n", "# Configure Jupyter to display the assigned value after an assignment\n", "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n", "\n", "# import classes from thinkbayes2\n", "from thinkbayes2 import Hist, Pmf, Suite, MakeMixture" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "def add(pmf1, pmf2):\n", " res = Pmf()\n", " for v1, p1 in pmf1.Items():\n", " for v2, p2 in pmf2.Items():\n", " res[v1, v2] = p1 * p2\n", " return res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from sympy import symbols\n", "p_citizen, p_cv, p_ncv, p_error = symbols('p_citizen, p_cv, p_ncv, p_error')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def make_binary(p, name1, name2):\n", " return Pmf({name1: p, name2: 1-p})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pmf({'citizen': p_citizen, 'non-citizen': -p_citizen + 1})" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "citizen_status = ['citizen', 'non-citizen']\n", "pmf_citizen = make_binary(p_citizen, *citizen_status)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pmf({'error': p_error, 'no-error': -p_error + 1})" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "error_status = ['error', 'no-error']\n", "pmf_error = make_binary(p_error, *error_status)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('citizen', 'error') p_citizen*p_error\n", "('citizen', 'no-error') p_citizen*(-p_error + 1)\n", "('non-citizen', 'error') p_error*(-p_citizen + 1)\n", "('non-citizen', 'no-error') (-p_citizen + 1)*(-p_error + 1)\n" ] } ], "source": [ "pmf_citizen_report = add(pmf_citizen, pmf_error)\n", "pmf_citizen_report.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pmf({'vote': p_cv, 'no-vote': -p_cv + 1})" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vote_status = ['vote', 'no-vote']\n", "pmf_cv = make_binary(p_cv, *vote_status)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('no-vote', 'error') p_error*(-p_cv + 1)\n", "('no-vote', 'no-error') (-p_cv + 1)*(-p_error + 1)\n", "('vote', 'error') p_cv*p_error\n", "('vote', 'no-error') p_cv*(-p_error + 1)\n" ] } ], "source": [ "pmf_cv_report = add(pmf_cv, pmf_error)\n", "pmf_cv_report.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pmf({'vote': p_ncv, 'no-vote': -p_ncv + 1})" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pmf_ncv = make_binary(p_ncv, *vote_status)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('no-vote', 'error') p_error*(-p_ncv + 1)\n", "('no-vote', 'no-error') (-p_error + 1)*(-p_ncv + 1)\n", "('vote', 'error') p_error*p_ncv\n", "('vote', 'no-error') p_ncv*(-p_error + 1)\n" ] } ], "source": [ "pmf_ncv_report = add(pmf_ncv, pmf_error)\n", "pmf_ncv_report.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(('citizen', 'error'), ('no-vote', 'error')) p_citizen*p_error**2*(-p_cv + 1)\n", "(('citizen', 'error'), ('no-vote', 'no-error')) p_citizen*p_error*(-p_cv + 1)*(-p_error + 1)\n", "(('citizen', 'error'), ('vote', 'error')) p_citizen*p_cv*p_error**2\n", "(('citizen', 'error'), ('vote', 'no-error')) p_citizen*p_cv*p_error*(-p_error + 1)\n", "(('citizen', 'no-error'), ('no-vote', 'error')) p_citizen*p_error*(-p_cv + 1)*(-p_error + 1)\n", "(('citizen', 'no-error'), ('no-vote', 'no-error')) p_citizen*(-p_cv + 1)*(-p_error + 1)**2\n", "(('citizen', 'no-error'), ('vote', 'error')) p_citizen*p_cv*p_error*(-p_error + 1)\n", "(('citizen', 'no-error'), ('vote', 'no-error')) p_citizen*p_cv*(-p_error + 1)**2\n", "(('non-citizen', 'error'), ('no-vote', 'error')) p_error**2*(-p_citizen + 1)*(-p_ncv + 1)\n", "(('non-citizen', 'error'), ('no-vote', 'no-error')) p_error*(-p_citizen + 1)*(-p_error + 1)*(-p_ncv + 1)\n", "(('non-citizen', 'error'), ('vote', 'error')) p_error**2*p_ncv*(-p_citizen + 1)\n", "(('non-citizen', 'error'), ('vote', 'no-error')) p_error*p_ncv*(-p_citizen + 1)*(-p_error + 1)\n", "(('non-citizen', 'no-error'), ('no-vote', 'error')) p_error*(-p_citizen + 1)*(-p_error + 1)*(-p_ncv + 1)\n", "(('non-citizen', 'no-error'), ('no-vote', 'no-error')) (-p_citizen + 1)*(-p_error + 1)**2*(-p_ncv + 1)\n", "(('non-citizen', 'no-error'), ('vote', 'error')) p_error*p_ncv*(-p_citizen + 1)*(-p_error + 1)\n", "(('non-citizen', 'no-error'), ('vote', 'no-error')) p_ncv*(-p_citizen + 1)*(-p_error + 1)**2\n" ] } ], "source": [ "mix = Pmf()\n", "\n", "for val1, p1 in pmf_citizen_report.Items():\n", " c, e = val1\n", " pmf = pmf_cv_report if c=='citizen' else pmf_ncv_report\n", " for val2, p2 in pmf.Items():\n", " mix[val1, val2] = p1 * p2\n", " \n", "mix.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "def report(state, alternatives):\n", " val, error = state\n", " if error != 'error':\n", " return val\n", " alt1, alt2 = alternatives\n", " return alt1 if val==alt2 else alt2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'non-citizen'" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "report(('citizen', 'error'), citizen_status)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'citizen'" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "report(('citizen', 'no-error'), citizen_status)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "pmf_report = Pmf()\n", "\n", "for (cstate, vstate), p in mix.Items():\n", " creport = report(cstate, citizen_status)\n", " vreport = report(vstate, vote_status)\n", " pmf_report[creport, vreport] += p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('citizen', 'no-vote') p_citizen*p_cv*p_error*(-p_error + 1) + p_citizen*(-p_cv + 1)*(-p_error + 1)**2 + p_error**2*p_ncv*(-p_citizen + 1) + p_error*(-p_citizen + 1)*(-p_error + 1)*(-p_ncv + 1)\n", "('citizen', 'vote') p_citizen*p_cv*(-p_error + 1)**2 + p_citizen*p_error*(-p_cv + 1)*(-p_error + 1) + p_error**2*(-p_citizen + 1)*(-p_ncv + 1) + p_error*p_ncv*(-p_citizen + 1)*(-p_error + 1)\n", "('non-citizen', 'no-vote') p_citizen*p_cv*p_error**2 + p_citizen*p_error*(-p_cv + 1)*(-p_error + 1) + p_error*p_ncv*(-p_citizen + 1)*(-p_error + 1) + (-p_citizen + 1)*(-p_error + 1)**2*(-p_ncv + 1)\n", "('non-citizen', 'vote') p_citizen*p_cv*p_error*(-p_error + 1) + p_citizen*p_error**2*(-p_cv + 1) + p_error*(-p_citizen + 1)*(-p_error + 1)*(-p_ncv + 1) + p_ncv*(-p_citizen + 1)*(-p_error + 1)**2\n" ] } ], "source": [ "pmf_report.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "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": 2 }
mit
GCantergiani/centrality-measure-lth-model
notebooks/network_analysis/0.1-gcantergiani-get-degree-nodes.ipynb
1
8242
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import networkx as nx\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "FILE_NETWORK_RETWEET_WEIGHT = \"../data/raw/higgs-retweet_network.edgelist\"" ] }, { "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>source</th>\n", " <th>target</th>\n", " <th>weight</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>298960</td>\n", " <td>105232</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>95688</td>\n", " <td>3393</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>353237</td>\n", " <td>62217</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4974</td>\n", " <td>3571</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>241892</td>\n", " <td>8</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " source target weight\n", "0 298960 105232 1\n", "1 95688 3393 1\n", "2 353237 62217 1\n", "3 4974 3571 1\n", "4 241892 8 1" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(FILE_NETWORK_RETWEET_WEIGHT, sep=' ', names = ['source', 'target','weight'])\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "G = nx.DiGraph()\n", "\n", "for idx,row in data.iterrows():\n", " G.add_edge(row['target'], row['source'], weight= row['weight'])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Nodes from k-shel and k-core metrics\n", "\n", "nodes_to_query = [88, 677, 1276, 1988, 3547, 3998, 5137, 5226, 6940, \n", " 11991, 12751, 13506, 13808, 13813, 15439, 16801, 19604, \n", " 26153, 26398, 27483, 28951, 33833, 35376, 35729, 39885, 42170, \n", " 42172, 42182, 44086, 49179, 50927, 53508, 56968, 59012, 59027, \n", " 60686, 64911, 67382, 75798, 76165, 78701, 92274, 98204, 98762, \n", " 103447, 103921, 110903, 116323, 137242, 137246, 137247, 137271, \n", " 137321, 158955, 182906, 206362, 302797]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Node: 88 | degree 14063 | in_degree 3 | out_degree 14060\n", "Node: 677 | degree 5621 | in_degree 8 | out_degree 5613\n", "Node: 1276 | degree 979 | in_degree 19 | out_degree 960\n", "Node: 1988 | degree 4337 | in_degree 2 | out_degree 4335\n", "Node: 3547 | degree 181 | in_degree 21 | out_degree 160\n", "Node: 3998 | degree 840 | in_degree 7 | out_degree 833\n", "Node: 5137 | degree 167 | in_degree 17 | out_degree 150\n", "Node: 5226 | degree 1663 | in_degree 5 | out_degree 1658\n", "Node: 6940 | degree 933 | in_degree 29 | out_degree 904\n", "Node: 11991 | degree 424 | in_degree 13 | out_degree 411\n", "Node: 12751 | degree 217 | in_degree 20 | out_degree 197\n", "Node: 13506 | degree 123 | in_degree 7 | out_degree 116\n", "Node: 13808 | degree 385 | in_degree 9 | out_degree 376\n", "Node: 13813 | degree 374 | in_degree 3 | out_degree 371\n", "Node: 15439 | degree 34 | in_degree 34 | out_degree 0\n", "Node: 16801 | degree 210 | in_degree 15 | out_degree 195\n", "Node: 19604 | degree 248 | in_degree 8 | out_degree 240\n", "Node: 26153 | degree 136 | in_degree 8 | out_degree 128\n", "Node: 26398 | degree 108 | in_degree 8 | out_degree 100\n", "Node: 27483 | degree 76 | in_degree 10 | out_degree 66\n", "Node: 28951 | degree 62 | in_degree 19 | out_degree 43\n", "Node: 33833 | degree 73 | in_degree 23 | out_degree 50\n", "Node: 35376 | degree 126 | in_degree 28 | out_degree 98\n", "Node: 35729 | degree 76 | in_degree 24 | out_degree 52\n", "Node: 39885 | degree 154 | in_degree 30 | out_degree 124\n", "Node: 42170 | degree 22 | in_degree 19 | out_degree 3\n", "Node: 42172 | degree 111 | in_degree 7 | out_degree 104\n", "Node: 42182 | degree 247 | in_degree 9 | out_degree 238\n", "Node: 44086 | degree 77 | in_degree 37 | out_degree 40\n", "Node: 49179 | degree 123 | in_degree 15 | out_degree 108\n", "Node: 50927 | degree 28 | in_degree 15 | out_degree 13\n", "Node: 53508 | degree 43 | in_degree 42 | out_degree 1\n", "Node: 56968 | degree 150 | in_degree 12 | out_degree 138\n", "Node: 59012 | degree 85 | in_degree 7 | out_degree 78\n", "Node: 59027 | degree 87 | in_degree 10 | out_degree 77\n", "Node: 60686 | degree 103 | in_degree 7 | out_degree 96\n", "Node: 64911 | degree 241 | in_degree 49 | out_degree 192\n", "Node: 67382 | degree 56 | in_degree 10 | out_degree 46\n", "Node: 75798 | degree 35 | in_degree 31 | out_degree 4\n", "Node: 76165 | degree 127 | in_degree 33 | out_degree 94\n", "Node: 78701 | degree 23 | in_degree 17 | out_degree 6\n", "Node: 92274 | degree 66 | in_degree 16 | out_degree 50\n", "Node: 98204 | degree 51 | in_degree 17 | out_degree 34\n", "Node: 98762 | degree 25 | in_degree 11 | out_degree 14\n", "Node: 103447 | degree 137 | in_degree 34 | out_degree 103\n", "Node: 103921 | degree 99 | in_degree 9 | out_degree 90\n", "Node: 110903 | degree 106 | in_degree 5 | out_degree 101\n", "Node: 116323 | degree 36 | in_degree 10 | out_degree 26\n", "Node: 137242 | degree 31 | in_degree 10 | out_degree 21\n", "Node: 137246 | degree 105 | in_degree 10 | out_degree 95\n", "Node: 137247 | degree 107 | in_degree 7 | out_degree 100\n", "Node: 137271 | degree 67 | in_degree 6 | out_degree 61\n", "Node: 137321 | degree 101 | in_degree 4 | out_degree 97\n", "Node: 158955 | degree 30 | in_degree 28 | out_degree 2\n", "Node: 182906 | degree 72 | in_degree 31 | out_degree 41\n", "Node: 206362 | degree 35 | in_degree 35 | out_degree 0\n", "Node: 302797 | degree 21 | in_degree 21 | out_degree 0\n" ] } ], "source": [ "for node in nodes_to_query:\n", " print('Node: {0} | degree {1} | in_degree {2} | out_degree {3}'.format(node, G.degree(node), G.in_degree(node), G.out_degree(node)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
linkmax91/bitquant
web/home/ipython/examples/bitcoin-future.ipynb
1
4237
{ "metadata": { "name": "", "signature": "sha256:22845b2aecf19a24d233e44eabf4949785274fa283ee7a7703c829a710e6efd8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from QuantLib import *" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "todaysDate = Date.todaysDate()\n", "Settings.instance().evaluationDate = todaysDate\n", "settlementDate = todaysDate + Period(2, Days)\n", "riskFreeRate = FlatForward(settlementDate, 0.00, Actual365Fixed())\n", "\n", "payoff = CryptoPayoffQuanto()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "settlementDate\n", "payoff(367.0)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "timestamp = Date.universalDateTime()\n", "newtime = timestamp + Period(3, Years)\n", "newtime.year()\n", "dcc = ContinuousTime(Years)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "def option(strike, vol, t, putcall):\n", " now = Date.universalDateTime()\n", " Settings.instance().evaluationDate = now\n", " settlementDate = todaysDate + Period(t, Days)\n", " riskFreeRate = FlatForward(todaysDate, 0.00, ContinuousTime.perDay())\n", "\n", " # option parameters\n", " exercise = EuropeanExercise(settlementDate)\n", " payoff = CryptoPayoffQuanto()\n", " x = np.arange(strike*0.8, strike*1.2, 0.01);\n", "\n", " volatility = BlackConstantVol(todaysDate, TARGET(), vol, ContinuousTime.perDay())\n", " dividendYield = FlatForward(settlementDate, 0.00, ContinuousTime.perDay())\n", " underlying = SimpleQuote(strike)\n", " process = BlackScholesMertonProcess(QuoteHandle(underlying),\n", " YieldTermStructureHandle(dividendYield),\n", " YieldTermStructureHandle(riskFreeRate),\n", " BlackVolTermStructureHandle(volatility))\n", " option = CryptoCurrencyFuture(settlementDate, payoff)\n", " option.setPricingEngine(FDEuropeanEngine(process))\n", " option.recalculate()\n", " priceCurve = option.priceCurve()\n", " grid = priceCurve.grid()\n", " values = priceCurve.values()\n", " plt.figure(1, figsize=(10,8))\n", " plt.subplot(221)\n", " y = list(map(payoff, grid))\n", " plt.plot(grid, y)\n", " plt.plot(grid, list(values))\n", " plt.subplot(222)\n", " delta_grid = priceCurve.derivative()\n", " plt.plot(delta_grid.grid(), list(delta_grid.values()))\n", " gamma_grid = delta_grid.derivative()\n", " plt.subplot(223)\n", " plt.plot(gamma_grid.grid(), list(gamma_grid.values()))\n", " plt.subplot(224)\n", " thetaCurve = option.thetaCurve()\n", " plt.plot(thetaCurve.grid(), list(thetaCurve.values()))\n", "# plt.plot(x, list(map(mydelta,x)))\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "option(350, 0.02, 90, 1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "np.arange(0.8, 1.2,0.1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
apache-2.0
decisionstats/pythonfordatascience
Web+Scraping+Yelp+with+Beautiful+Soup.ipynb
1
479525
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# pip install beautifulsoup4.\n", "from bs4 import BeautifulSoup" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#pip install urllib3 \n", "#This library helps in downloading data\n", "import urllib.request" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PULL DATA" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r = urllib.request.urlopen('http://www.yelp.ca/search?find_loc=Calgary,+AB&cflt=homeservices').read()\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "bs4.BeautifulSoup" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Using Beautiful Soup Library to parse the data\n", "soup = BeautifulSoup(r, \"lxml\")\n", "type(soup)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "239558" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We find the number of chracters in data downloaded\n", "len(str(soup.prettify()))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#We convert the data to a string format using str. \n", "#Note in R we use str for structure, but in Python we use str to convert to charachter ( like as.charachter or paste command would do in R)\n", "a=str(soup.prettify()) " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "112509" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We try and find location of a particular tag we are interested in. \n", "#Note we are using triple quotes to escape scpecial charachters\n", "a.find('''class=\"snippet\"''')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "' </div>\\n <div class=\"price-category\">\\n <span class=\"category-str-list\">\\n <a href=\"/search?find_loc=Calgary%2C+AB&amp;cflt=locksmiths\">\\n Keys &amp; Locksmiths\\n </a>\\n </span>\\n </div>\\n <ul class=\"search-result_tags\">\\n </ul>\\n </div>\\n </div>\\n </div>\\n <div class=\"secondary-attributes\">\\n <address>\\n 1437 Kensington Road NW\\n <br/>\\n Calgary, AB T2N 3R1\\n </address>\\n <span class=\"offscreen\">\\n Phone number\\n </span>\\n <span class=\"biz-phone\">\\n (403) 272-8923\\n </span>\\n </div>\\n </div>\\n <div class=\"snippet-block media-block\">\\n <div class=\"media-avatar\">\\n <div class=\"photo-box pb-30s\" data-hovercard-id=\"VWUsQJiYT5ljygSD5sjcHg\">\\n <a href=\"/user_details?userid=fDBybzZAL5UDscd33HCXyA\">\\n <img alt=\"Cathy S.\" class=\"photo-box-img\" height=\"30\" src=\"https://s3-media1.fl.yelpcdn.com/photo/N2VeJ-Y21hkEX42MVyBRQA/30s.jpg\" width=\"30\"/>\\n </a>\\n </div>\\n </div>\\n <div class=\"media-story\">\\n <p class=\"snippet\">\\n We had Always Affordable come to our new house to re-key all the locks. \\xa0I spoke with a very pleasant person to make the appointment and our locksmith was extremely punctal. In short…\\n <a class=\"nowrap\" href=\"/biz/always-affordable-always-available-locksmiths-calgary-2?hrid=9mRKR1-wAh03IoBaVdsBpQ\">\\n read more\\n </a>\\n </p>\\n </div>\\n </div>\\n </div>\\n </li>\\n <li class=\"regular-search-result\">\\n <div class=\"search-result natural-search-result\" data-key=\"2\">\\n <div class=\"biz-listing-large\">\\n <div class=\"main-attributes\">\\n <div class=\"media-block media-block--12\">\\n <div class=\"media-avatar\">\\n <div class=\"photo-box pb-90s\">\\n <a class=\"js-analytics-click\" data-analytics-label=\"biz-photo\" href=\"/biz/mid-century-dweller-calgary-2\">\\n <img alt=\"Mid-Century Dweller\" class=\"photo-box-img\" height=\"90\" src=\"https://s3-media2.fl.yelpcdn.com/bphoto/yMNw979jBiOGnpJlnCDXXA/90s.jpg\" width=\"90\"/>\\n </a>\\n </div>\\n </div>\\n <div class=\"media-story\">\\n <h3 class=\"search-result-title\">\\n <span class=\"indexed-biz-name\">\\n 2.\\n <a class=\"biz-name js-analytics-click\" data-analytics-label=\"biz-name\" data-hovercard-id=\"OUaRqhe_QnNyIhbllQzfRw\" href=\"/biz/mid-century-dweller-calgary-2\">\\n <span>\\n Mid-Century Dweller\\n </span>\\n </a>\\n </span>\\n </h3>\\n <div class=\"biz-rating biz-rating-large clearfix\">\\n <div class=\"i-stars i-stars--regular-5 rating-large\" title=\"5.0 star rating\">\\n <img alt=\"5.0 star rating\" class=\"offscreen\" height=\"303\" src=\"https://s3-media1.fl.yelpcdn.com/assets/srv0/yelp_design_web/41341496d9db/assets/img/stars/stars.png\" width=\"84\"/>\\n </div>\\n <span class=\"review-count rating-qualifier\">\\n 6 reviews\\n </span>\\n </div>\\n <div class=\"price-category\">\\n <span class=\"bullet-after\">\\n <span class=\"business-attribute price-range\">\\n $$$\\n </span>\\n </span>\\n <span class=\"category-str-list\">\\n <a href=\"/search?find_loc=Calgary%2C+AB&amp;cflt=interiordesign\">\\n Interior Design\\n </a>\\n ,\\n <a href=\"/search?find_loc=Calgary%2C+AB&amp;cflt=lighting\">\\n Lighting Fixtures &amp; Equipment\\n </a>\\n ,\\n <a href=\"/search?find_loc=Calgary%2C+AB&amp;cflt=furniture\">\\n Furniture Stores\\n </a>\\n </span>\\n </div>\\n <ul class=\"search-result_tags\">\\n </ul>\\n </div>\\n </div>\\n </div>\\n <div class=\"secondary-attributes\">\\n <address>\\n 1221B 9th Avenue SE\\n <br/>\\n Calgary, AB T2P 1L9\\n </address>\\n <span class=\"offscreen\">\\n Phone number\\n </span>\\n <span class=\"biz-phone\">\\n (403) 918-4475\\n </span>\\n </div>\\n </div>\\n <div class=\"search-result-ctas u-space-t1\">\\n <div class=\"search-avatar-offset js-mtb\">\\n <div class=\"island island--slim search-result-cta\">\\n <div class=\"arrange arrange--middle arrange--6\">\\n <div class=\"arrange_unit\">\\n <span aria-hidden=\"true\" class=\"icon icon--18-speech icon--size-18 icon--neutral-gray\" style=\"width: 18px; height: 18px;\">\\n <svg class=\"icon_svg\">\\n <use xlink:href=\"#18x18_speech\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"arrange_unit--fill arrange_unit cta-text\">\\n <span>\\n You can request a quote from this business\\n </span>\\n </div>\\n <div class=\"arrange_unit nowrap js-tag-action\" data-business-id=\"7yEgYeTYNGvSpKJZP50XCg\" data-popup-title=\"\" data-search-action-uri=\"/message_the_business/7yEgYeTYNGvSpKJZP50XCg/popup_form\">\\n <a class=\"ybtn ybtn--small low-intent-search-action-button\" href=\"javascript:;\">\\n Request a Quote\\n </a>\\n </div>\\n </div>\\n </div>\\n <div class=\"search-result-cta-error-row\">\\n <div class=\"platform-vsearch-error-message text-error\">\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"snippet-block media-block\">\\n <div class=\"media-avatar\">\\n <div class=\"photo-box pb-30s\" data-hovercard-id=\"TyUKD6zm5chlO6xqJdiRhg\">\\n <a href=\"/user_details?userid=Xvottlef6YjhkgcSjo1Lyw\">\\n <img alt=\"Min R.\" class=\"photo-box-img\" height=\"30\" src=\"https://s3-media4.fl.yelpcdn.com/photo/pIL7Dosg6TMILRu4_txKPg/30s.jpg\" width=\"30\"/>\\n </a>\\n </div>\\n </div>\\n <div class=\"media-story\">\\n <p class=\"snippet\">\\n Holy crap, I believe I have died and gone to heaven... I can\\'t believe that I just discovered that there is actually a store that sells mid century modern furniture and accessories…\\n <a class=\"nowrap\" href=\"/biz/mid-century-dweller-calgary-2?hrid=qQk_SyYGQa0NaTm6Ds4sDQ\">\\n read more\\n </a>\\n </p>\\n </div>\\n </div>\\n </div>\\n </li>\\n <li class=\"regular-search-result\">\\n <div class=\"search-result natural-search-result\" data-key=\"3\">\\n <div class=\"biz-listing-large\">\\n <div class=\"main-attributes\">\\n <div class=\"media-block media-block--12\">\\n <div class=\"media-avatar\">\\n <div class=\"photo-box pb-90s\">\\n <a class=\"js-analytics-click\" data-analytics-label=\"biz-photo\" href=\"/biz/electrician-magician-calgary\">\\n <img alt=\"Electrician Magician\" class=\"photo-box-img\" height=\"90\" src=\"https://s3-media1.fl.yelpcdn.com/bphoto/l5mJh_Vfsjoq3AnpDlnU4w/90s.jpg\" width=\"90\"/>\\n </a>\\n </div>\\n </div>\\n <div class=\"media-story\">\\n <h3 class=\"search-result-title\">\\n <span class=\"indexed-biz-name\">\\n 3.\\n <a class=\"biz-name js-analytics-click\" data-analytics-label=\"biz-name\" data-hovercard-id=\"GbyOYIuVu085cCMuLrSYwg\" href=\"/biz/electrician-magician-calgary\">\\n <span>\\n Electrician Magician\\n </span>\\n </a>\\n </span>\\n </h3>\\n <div class=\"biz-rating biz-rating-large clearfix\">\\n <div class=\"i-stars i-stars--regular-4-half rating-large\" title=\"4.5 star rating\">\\n <img alt=\"4.5 star rating\" class=\"offscreen\" height=\"303\" src=\"https://s3-media1.fl.yelpcdn.com/assets/srv0/yelp_design_web/41341496d9db/assets/img/stars/stars.png\" width=\"84\"/>\\n </div>\\n <span class=\"review-count rating-qualifier\">\\n 7 reviews\\n </span>\\n </div>\\n <div class=\"price-category\">\\n <span class=\"category-str-list\">\\n <a href=\"/search?find_loc=Calgary%2C+AB&amp;cflt=electricians\">\\n Electricians\\n </a>\\n </span>\\n </div>\\n <ul class=\"search-result_tags\">\\n </ul>\\n </div>\\n </div>\\n </div>\\n <div class=\"secondary-attributes\">\\n <div class=\"service-area\">\\n Serving Calgary and the Surrounding Area\\n </div>\\n <span class=\"offscreen\">\\n Phone number\\n </span>\\n <span class=\"biz-phone\">\\n (403) 686-1125\\n </span>\\n </div>\\n </div>\\n <div class=\"search-result-ctas u-space-t1\">\\n <div class=\"search-avatar-offset js-mtb\">\\n <div class=\"island island--slim search-result-cta\">\\n <div class=\"arrange arrange--middle arrange--6\">\\n <div class=\"arrange_unit\">\\n <span aria-hidden=\"true\" class=\"icon icon--18-speech icon--size-18 icon--neutral-gray\" style=\"width: 18px; height: 18px;\">\\n <svg class=\"icon_svg\">\\n <use xlink:href=\"#18x18_speech\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"arrange_unit--fill arrange_unit cta-text\">\\n <span>\\n Responds in about\\n <strong>\\n 5 hours\\n </strong>\\n </span>\\n </div>\\n <div class=\"arrange_unit nowrap js-tag-action\" data-business-id=\"u1aZsEuyWp-K5I-e54Sxuw\" data-popup-title=\"\" data-search-action-uri=\"/message_the_business/u1aZsEuyWp-K5I-e54Sxuw/popup_form\">\\n <a class=\"ybtn ybtn--small low-intent-search-action-button\" href=\"javascript:;\">\\n Request a Quote\\n </a>\\n </div>\\n </div>\\n </div>\\n <div class=\"search-result-cta-error-row\">\\n <div class=\"platform-vsearch-error-message text-error\">\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"snippet-block media-block\">\\n <div class=\"media-avatar\">\\n <div class=\"photo-box pb-30s\" data-hovercard-id=\"eMfvBN5DCfy6yVbEAGIG7w\">\\n <a href=\"/user_details?userid=EIpPME2cIuNbhfXrtsSApg\">\\n <img alt=\"Monica I.\" class=\"photo-box-img\" height=\"30\" src=\"https://s3-media3.fl.yelpcdn.com/photo/qXn8amafb2ZHBAuLpVuSsA/30s.jpg\" width=\"30\"/>\\n </a>\\n </div>\\n </div>\\n <div class=\"media-story\">\\n <p class=\"snippet\">\\n I am a fan. \\xa0This review is really late - I wasn\\'t a member of Yelp when they first saved my bacon, but they came out on an emergency squeezing me in at the end of the day on a long…\\n <a class=\"nowrap\" href=\"/biz/electrician-magician-calgary?hrid=1OjNcluATNYrjxkrS5FbKQ\">\\n read more\\n </a>\\n </p>\\n </div>\\n </div>\\n </div>\\n </li>\\n <li class=\"regular-search-result\">\\n <div class=\"search-result natural-search-result\" data-key=\"4\">\\n <div class=\"biz-listing-large\">\\n <div class=\"main-attributes\">\\n <div class=\"media-block media-block--12\">\\n <div class=\"media-avatar\">\\n <div class=\"photo-box pb-90s\">\\n <a class=\"js-analytics-click\" data-analytics-label=\"biz-photo\" href=\"/biz/mortgage-alliance-mortgages-are-marvellous-calgary\">\\n <img alt=\"Mortgage Alliance Mortgages Are Marvellous\" class=\"photo-box-img\" height=\"90\" src=\"https://s3-media4.fl.yelpcdn.com/bphoto/_0ZYQ_rtxQvW91FvTnRDBA/90s.jpg\" width=\"90\"/>\\n </a>\\n </div>\\n </div>\\n <div class=\"media-story\">\\n <h3 class=\"search-result-title\">\\n <span class=\"indexed-biz-name\">\\n 4.\\n <a class=\"biz-name js-analytics-click\" data-analytics-label=\"biz-name\" data-hovercard-id=\"mEBJntH4mSMPwk8dxoTZ9g\" href=\"/biz/mortgage-alliance-mortgages-are-marvellous-calgary\">\\n <span>\\n Mortgage Alliance Mortgages Are Marvellous\\n </span>\\n </a>\\n </span>\\n </h3>\\n <div class=\"biz-rating biz-rating-large clearfix\">\\n <div class=\"i-stars i-stars--regular-5 rating-large\" title=\"5.0 star rating\">\\n <img alt=\"5.0 star rating\" class=\"offscreen\" height=\"303\" src=\"https://s3-media1.fl.yelpcdn.com/assets/srv0/yelp_design_web/41341496d9db/assets/img/stars/stars.png\" width=\"84\"/>\\n </div>\\n <span class=\"review-count rating-qualifier\">\\n 6 reviews\\n </span>\\n </div>\\n <div class=\"price-category\">\\n <span class=\"category-str-list\">\\n <a href=\"/search?find_loc=Calgary%2C+AB&amp;cflt=mortgagebrokers\">\\n Mortgage Brokers\\n </a>\\n </span>\\n </div>\\n <ul class=\"search-result_tags\">\\n </ul>\\n </div>\\n </div>\\n </div>\\n <div class=\"secondary-attributes\">\\n <address>\\n 1144 29 Avenue NE, Suite 103E\\n <br/>\\n Calgary, AB T2E 7P1\\n </address>\\n <span class=\"offscreen\">\\n Phone number\\n </span>\\n <span class=\"biz-phone\">\\n (403) 681-4376\\n </span>\\n </div>\\n </div>\\n <div class=\"search-result-ctas u-space-t1\">\\n <div class=\"search-avatar-offset js-mtb\">\\n <div class=\"island island--slim search-result-cta\">\\n <div class=\"arrange arrange--middle arrange--6\">\\n <div class=\"arrange_unit\">\\n <span aria-hidden=\"true\" class=\"icon icon--18-speech icon--size-18 icon--neutral-gray\" style=\"width: 18px; height: 18px;\">\\n <svg class=\"icon_svg\">\\n <use xlink:href=\"#18x18_speech\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"arrange_unit--fill arrange_unit cta-text\">\\n <span>\\n Responds in about\\n <strong>\\n 2 hours\\n </strong>\\n </span>\\n </div>\\n <div class=\"arrange_unit nowrap js-tag-action\" data-business-id=\"RcbY8SfGNT1ogj6uOrX8EA\" data-popup-title=\"\" data-search-action-uri=\"/message_the_business/RcbY8SfGNT1ogj6uOrX8EA/popup_form\">\\n <a class=\"ybtn ybtn--small low-intent-search-action-button\" href=\"javascript:;\">\\n Contact Agent\\n </a>\\n </div>\\n </div>\\n </div>\\n <div class=\"search-result-cta-error-row\">\\n <div class=\"platform-vsearch-error-message text-error\">\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"snippet-block media-block\">\\n <div class=\"media-avatar\">\\n <div class=\"photo-box pb-30s\" data-hovercard-id=\"6uHNfmWZO4bU3g_xg41ClQ\">\\n <a href=\"/user_details?userid=w8U4FGCvjiLwgf5bQliGTw\">\\n <img alt=\"Hayley G.\" class=\"photo-box-img\" height=\"30\" src=\"https://s3-media4.fl.yelpcdn.com/photo/hiXjfGX5k9bfu-Gdc1BeOQ/30s.jpg\" width=\"30\"/>\\n </a>\\n </div>\\n </div>\\n <div class=\"media-story\">\\n <p class=\"snippet\">\\n Mark is a knowledgeable and experienced mortgage specialist. As a first time home buyer, I found the process very straightforward with Mark and his team. I received a great rate and…\\n <a class=\"nowrap\" href=\"/biz/mortgage-alliance-mortgages-are-marvellous-calgary?hrid=1zNWqN0Qo5_FMbGYr9-1rw\">\\n read more\\n </a>\\n </p>\\n </div>\\n </div>\\n </div>\\n </li>\\n <li class=\"regular-search-result\">\\n <div class=\"search-result natural-search-result\" data-key=\"5\">\\n <div class=\"biz-listing-large\">\\n <div class=\"main-attributes\">\\n <div class=\"media-block media-block--12\">\\n <div class=\"media-avatar\">\\n <div class=\"photo-box pb-90s\">\\n <a class=\"js-analytics-click\" data-analytics-label=\"biz-photo\" href=\"/biz/artisan-kitchens-and-renovations-calgary\">\\n <img alt=\"Artisan Kitchens &amp; Renovations\" class=\"photo-box-img\" height=\"90\" src=\"https://s3-media2.fl.yelpcdn.com/bphoto/D7FAzvzVKnV6CDTI6dUcKA/90s.jpg\" width=\"90\"/>\\n </a>\\n </div>\\n </div>\\n <div class=\"media-story\">\\n <h3 class=\"search-result-title\">\\n <span class=\"indexed-biz-name\">\\n 5.\\n <a class=\"biz-name js-analytics-click\" data-analytics-label=\"biz-name\" data-hovercard-id=\"lb_GCoRaUFdtKojVGrvk6A\" href=\"/biz/artisan-kitchens-and-renovations-calgary\">\\n <span>\\n Artisan Kitchens &amp; Renovations\\n </span>\\n </a>\\n </span>\\n </h3>\\n <div class=\"biz-rating biz-rating-large clearfix\">\\n <div class=\"i-stars i-stars--regular-5 rating-large\" title=\"5.0 star rating\">\\n <img alt=\"5.0 star rating\" class=\"offscreen\" height=\"303\" src=\"https://s3-media1.fl.yelpcdn.com/assets/srv0/yelp_design_web/41341496d9db/assets/img/stars/stars.png\" width=\"84\"/>\\n </div>\\n <span class=\"review-count rating-qualifier\">\\n 1 review\\n </span>\\n </div>\\n <div class=\"price-category\">\\n <span class=\"category-str-list\">\\n <a href=\"/search?find_loc=Calgary%2C+AB&amp;cflt=contractors\">\\n Contractors\\n </a>\\n </span>\\n </div>\\n <ul class=\"search-result_tags\">\\n <li class=\"tag-18x18_deal-success\">\\n <small>\\n <span aria-hidden=\"true\" class=\"icon icon--18-deal icon--size-18 icon--success\" style=\"width: 18px; height: 18px;\">\\n <svg class=\"icon_svg\">\\n <use xlink:href=\"#18x18_deal\">\\n </use>\\n </svg>\\n </span>\\n $100 for $200 Deal\\n </small>\\n </li>\\n </ul>\\n </div>\\n </div>\\n </div>\\n <div class=\"secondary-attributes\">\\n <div class=\"service-area\">\\n Serving Calgary and the Surrounding Area\\n </div>\\n <span class=\"offscreen\">\\n Phone number\\n </span>\\n <span class=\"biz-phone\">\\n (403) 207-7887\\n </span>\\n </div>\\n </div>\\n <div class=\"search-result-ctas u-space-t1\">\\n <div class=\"search-avatar-offset js-mtb\">\\n <div class=\"island island--slim search-result-cta\">\\n <div class=\"arrange arrange--middle arrange--6\">\\n <div class=\"arrange_unit\">\\n <span aria-hidden=\"true\" class=\"icon icon--18-fast-responder icon--size-18 icon--success\" style=\"width: 18px; height: 18px;\">\\n <svg class=\"icon_svg\">\\n <use xlink:href=\"#18x18_fast_responder\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"arrange_unit--fill arrange_unit cta-text\">\\n <span class=\"mtb-response-time-fast-responder\">\\n Responds in about\\n <strong>\\n 1 hour\\n </strong>\\n </span>\\n </div>\\n <div class=\"arrange_unit nowrap js-tag-action\" data-business-id=\"35GmM2pArOHN-fV09ip4Rw\" data-popup-title=\"\" data-search-action-uri=\"/message_the_business/35GmM2pArOHN-fV09ip4Rw/popup_form\">\\n <a class=\"ybtn ybtn--small low-intent-search-action-button\" href=\"javascript:;\">\\n Request a Quote\\n </a>\\n </div>\\n </div>\\n </div>\\n <div class=\"search-result-cta-error-row\">\\n <div class=\"platform-vsearch-error-message text-error\">\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"snippet-block media-block\">\\n <div class=\"media-avatar\">\\n <div class=\"photo-box pb-30s\" data-hovercard-id=\"shlvjTcC-CLnF7rcIMiygg\">\\n <a href=\"/user_details?userid=tB8QCvrbSbxBdbijWuS00g\">\\n <img alt=\"Judy A.\" class=\"photo-box-img\" height=\"30\" src=\"https://s3-media3.fl.yelpcdn.com/photo/lZDKUD8etac1ZsOtiPHJfg/30s.jpg\" width=\"30\"/>\\n </a>\\n </div>\\n </div>\\n <div class=\"media-story\">\\n <p class=\"snippet\">\\n I first heard of Artisan Kitchens from reading their articles in Renovation magazine. \\xa0The articles were written by Lynn who has a degree in Interior Design and together with Dave…\\n <a class=\"nowrap\" href=\"/biz/artisan-kitchens-and-renovations-calgary?hrid=L3kinzuzrMpyqid0vgx-xA\">\\n read more\\n </a>\\n </p>\\n </div>\\n </div>\\n </div>\\n </li>\\n <li class=\"regular-search-result\">\\n <div class=\"search-result natural-search-result\" data-key=\"6\">\\n <div class=\"biz-listing-large\">\\n <div class=\"main-attributes\">\\n <div class=\"media-block media-block--12\">\\n <div class=\"media-avatar\">\\n <div class=\"photo-box pb-90s\">\\n <a class=\"js-analytics-click\" data-analytics-label=\"biz-photo\" href=\"/biz/f2-furnishings-calgary\">\\n <img alt=\"F2 Furnishings\" class=\"photo-box-img\" height=\"90\" src=\"https://s3-media2.fl.yelpcdn.com/bphoto/lvCe_iOPI3qKmKUGpN5a5w/90s.jpg\" width=\"90\"/>\\n </a>\\n </div>\\n </div>\\n <div class=\"media-story\">\\n <h3 class=\"search-result-title\">\\n <span class=\"indexed-biz-name\">\\n 6.\\n <a class=\"biz-name js-analytics-click\" data-analytics-label=\"biz-name\" data-hovercard-id=\"KpAnklpC3NFflrePBw7I2g\" href=\"/biz/f2-furnishings-calgary\">\\n <span>\\n F2 Furnishings\\n </span>\\n </a>\\n </span>\\n </h3>\\n <div class=\"biz-rating biz-rating-large clearfix\">\\n <div class=\"i-stars i-stars--regular-5 rating-large\" title=\"5.0 star rating\">\\n <img alt=\"5.0 star rating\" class=\"offscreen\" height=\"303\" src=\"https://s3-media1.fl.yelpcdn.com/assets/srv0/yelp_design_web/41341496d9db/assets/img/stars/stars.png\" width=\"84\"/>\\n </div>\\n <span class=\"review-count rating-qualifier\">\\n 4 reviews\\n </span>\\n </div>\\n <div class=\"price-category\">\\n <span class=\"bullet-after\">\\n <span class=\"business-attribute price-range\">\\n $$$\\n </span>\\n </span>\\n <span class=\"category-str-list\">\\n <a href=\"/search?find_loc=Calgary%2C+AB&amp;cflt=furniture\">\\n Furniture Stores\\n </a>\\n ,\\n <a href=\"/search?find_loc=Calgary%2C+AB&amp;cflt=interiordesign\">\\n Interior Design\\n </a>\\n ,\\n <a href=\"/search?find_loc=Calgary%2C+AB&amp;cflt=galleries\">\\n Art Galleries\\n </a>\\n </span>\\n </div>\\n <ul class=\"search-result_tags\">\\n </ul>\\n </div>\\n </div>\\n </div>\\n <div class=\"secondary-attributes\">\\n <address>\\n 1210 11th Ave SW\\n <br/>\\n Calgary, AB T3C 0M4\\n </address>\\n <span class=\"offscreen\">\\n Phone number\\n </span>\\n <span class=\"biz-phone\">\\n (403) 452-2881\\n </span>\\n </div>\\n </div>\\n <div class=\"search-result-ctas u-space-t1\">\\n <div class=\"search-avatar-offset js-mtb\">\\n <div class=\"island island--slim search-result-cta\">\\n <div class=\"arrange arrange--middle arrange--6\">\\n <div class=\"arrange_unit\">\\n <span aria-hidden=\"true\" class=\"icon icon--18-speech icon--size-18 icon--neutral-gray\" style=\"width: 18px; height: 18px;\">\\n <svg class=\"icon_svg\">\\n <use xlink:href=\"#18x18_speech\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"arrange_unit--fill arrange_unit cta-text\">\\n <span>\\n You can request a quote from this business\\n </span>\\n </div>\\n <div class=\"arrange_unit nowrap js-tag-action\" data-business-id=\"UK-ZO36ZE05pf840J7vfSA\" data-popup-title=\"\" data-search-action-uri=\"/message_the_business/UK-ZO36ZE05pf840J7vfSA/popup_form\">\\n <a class=\"ybtn ybtn--small low-intent-search-action-button\" href=\"javascript:;\">\\n Request a Quote\\n </a>\\n </div>\\n </div>\\n </div>\\n <div class=\"search-result-cta-error-row\">\\n <div class=\"platform-vsearch-error-message text-error\">\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"snippet-block media-block\">\\n <div class=\"media-avatar\">\\n <div class=\"photo-box pb-30s\" data-hovercard-id=\"5RPQWQMuGjdJT0bsA1mQmg\">\\n <a href=\"/user_details?userid=vbZZw6tZgH8PNu6NLcK6Sg\">\\n <img alt=\"Jeff C.\" class=\"photo-box-img\" height=\"30\" src=\"https://s3-media1.fl.yelpcdn.com/photo/wQARUfoxCXAJCjEiQQmu1w/30s.jpg\" width=\"30\"/>\\n </a>\\n </div>\\n </div>\\n <div class=\"media-story\">\\n <p class=\"snippet\">\\n F2 Furnishings is a great place to shop for furniture and other home decor. \\xa0The company really supports local artists and designers. \\xa0A lot of their pieces are originals from local…\\n <a class=\"nowrap\" href=\"/biz/f2-furnishings-calgary?hrid=O1rJcbnku3DUgHAA5xpdrw\">\\n read more\\n </a>\\n </p>\\n </div>\\n </div>\\n </div>\\n </li>\\n <li class=\"regular-search-result\">\\n <div class=\"search-result natural-search-result\" data-key=\"7\">\\n <div class=\"biz-listing-large\">\\n <div class=\"main-attributes\">\\n <div class=\"media-block media-block--12\">\\n <div class=\"media-avatar\">\\n <div class=\"photo-box pb-90s\">\\n <a class=\"js-analytics-click\" data-analytics-label=\"biz-photo\" href=\"/biz/naturally-clean-calgary\">\\n <img alt=\"Naturally Clean\" class=\"photo-box-img\" height=\"90\" src=\"https://s3-media3.fl.yelpcdn.com/assets/srv0/yelp_styleguide/fe8c0c8725d3/assets/img/default_avatars/business_90_square.png\" width=\"90\"/>\\n </a>\\n </div>\\n '" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[115000:145000]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<span class=\"biz-phone\">\n", " (403) 272-8923\n", " </span>, <span class=\"biz-phone\">\n", " (403) 918-4475\n", " </span>, <span class=\"biz-phone\">\n", " (403) 686-1125\n", " </span>, <span class=\"biz-phone\">\n", " (403) 681-4376\n", " </span>, <span class=\"biz-phone\">\n", " (403) 207-7887\n", " </span>, <span class=\"biz-phone\">\n", " (403) 452-2881\n", " </span>, <span class=\"biz-phone\">\n", " (403) 660-0792\n", " </span>, <span class=\"biz-phone\">\n", " (403) 200-4024\n", " </span>, <span class=\"biz-phone\">\n", " (403) 589-0004\n", " </span>, <span class=\"biz-phone\">\n", " (403) 613-6359\n", " </span>]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Lets try and find the list of phone numbers. We note both the HTNL tag and the class for it.\n", "# We use the find_all function \n", "letters = soup.find_all(\"span\", class_=\"biz-phone\")\n", "letters[1:100]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<p class=\"snippet\">\n", " We had Always Affordable come to our new house to re-key all the locks.  I spoke with a very pleasant person to make the appointment and our locksmith was extremely punctal. In short…\n", " <a class=\"nowrap\" href=\"/biz/always-affordable-always-available-locksmiths-calgary-2?hrid=9mRKR1-wAh03IoBaVdsBpQ\">read more</a>\n", " </p>, <p class=\"snippet\">\n", " Holy crap, I believe I have died and gone to heaven... I can't believe that I just discovered that there is actually a store that sells mid century modern furniture and accessories…\n", " <a class=\"nowrap\" href=\"/biz/mid-century-dweller-calgary-2?hrid=qQk_SyYGQa0NaTm6Ds4sDQ\">read more</a>\n", " </p>, <p class=\"snippet\">\n", " I am a fan.  This review is really late - I wasn't a member of Yelp when they first saved my bacon, but they came out on an emergency squeezing me in at the end of the day on a long…\n", " <a class=\"nowrap\" href=\"/biz/electrician-magician-calgary?hrid=1OjNcluATNYrjxkrS5FbKQ\">read more</a>\n", " </p>, <p class=\"snippet\">\n", " Mark is a knowledgeable and experienced mortgage specialist. As a first time home buyer, I found the process very straightforward with Mark and his team. I received a great rate and…\n", " <a class=\"nowrap\" href=\"/biz/mortgage-alliance-mortgages-are-marvellous-calgary?hrid=1zNWqN0Qo5_FMbGYr9-1rw\">read more</a>\n", " </p>, <p class=\"snippet\">\n", " I first heard of Artisan Kitchens from reading their articles in Renovation magazine.  The articles were written by Lynn who has a degree in Interior Design and together with Dave…\n", " <a class=\"nowrap\" href=\"/biz/artisan-kitchens-and-renovations-calgary?hrid=L3kinzuzrMpyqid0vgx-xA\">read more</a>\n", " </p>, <p class=\"snippet\">\n", " F2 Furnishings is a great place to shop for furniture and other home decor.  The company really supports local artists and designers.  A lot of their pieces are originals from local…\n", " <a class=\"nowrap\" href=\"/biz/f2-furnishings-calgary?hrid=O1rJcbnku3DUgHAA5xpdrw\">read more</a>\n", " </p>, <p class=\"snippet\">\n", " I called Carol mid-afternoon on Monday for a move-out clean. She showed up bright and early the next morning with her supplies, and (dare I say) insanely beautiful and outgoing…\n", " <a class=\"nowrap\" href=\"/biz/naturally-clean-calgary?hrid=3bH7TGyYK87hIWCqd9Z3-Q\">read more</a>\n", " </p>, <p class=\"snippet\">\n", " Recently used the crew at Super Powers for a move from Calgary to Chestermere.  - on time - efficient - careful -  courteous  - no sore back  - still had beer left in my fridge.…\n", " <a class=\"nowrap\" href=\"/biz/super-powers-calgary-5?hrid=X1mj2y4NEkbDUdoCC6k7BQ\">read more</a>\n", " </p>, <p class=\"snippet\">\n", " I recently worked with Bryon, Shirley, and their team in selling my home in Walden. I was a first time buyer but had decided to sell because the house was a little too far out from…\n", " <a class=\"nowrap\" href=\"/biz/bryon-howard-remax-calgary?hrid=o3ad2gKERVO_2SDBDIOESA\">read more</a>\n", " </p>, <p class=\"snippet\">\n", " Ryan and I have been working together for more than a year now.   Clients love him because he listens to you and finds you what you are looking for without \"commission breath.\"   We…\n", " <a class=\"nowrap\" href=\"/biz/ryan-phelan-calgary?hrid=QLf-iZQvdlg97Pr-WdkxiA\">read more</a>\n", " </p>]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Lets try and see the feedback given by users.\n", "\n", "letters2 = soup.find_all(\"p\", class_=\"snippet\")\n", "letters2[1:50]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "bs4.element.ResultSet" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(letters2)\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<p class=\"snippet\">\\n home inspection, thermal imaging, mold inspection/testing\\n <a class=\"nowrap\" href=\"/adredir?ad_business_id=KN6g4vO5gB2aLHOTuAzQNg&amp;campaign_id=mpaEWKWDepFJGR1lv_0WDw&amp;click_origin=read_more&amp;placement=above_search&amp;redirect_url=https%3A%2F%2Fwww.yelp.ca%2Fbiz%2Fmr-home-inspections-services-calgary-2&amp;request_id=5b3b321cfaf445f6&amp;signature=2948d283b71d555251ffb601b8722e115d017689cc66f88b514cc3c46622f29c&amp;slot=0\">read more</a>\\n</p>, <p class=\"snippet\">\\n We had Always Affordable come to our new house to re-key all the locks. \\xa0I spoke with a very pleasant person to make the appointment and our locksmith was extremely punctal. In short…\\n <a class=\"nowrap\" href=\"/biz/always-affordable-always-available-locksmiths-calgary-2?hrid=9mRKR1-wAh03IoBaVdsBpQ\">read more</a>\\n</p>, <p class=\"snippet\">\\n Holy crap, I believe I have died and gone to h'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "str(letters2)[1:1000]\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "str(letters2).count(\"service\")\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "zo = urllib.request.urlopen('https://in.bookmyshow.com/national-capital-region-ncr').read()\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "bs4.BeautifulSoup" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Using Beautiful Soup Library to parse the data\n", "soup2 = BeautifulSoup(zo, \"lxml\")\n", "type(soup2)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "380638" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We find the number of chracters in data downloaded\n", "len(str(soup2.prettify()))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#We convert the data to a string format using str. \n", "#Note in R we use str for structure, but in Python we use str to convert to charachter ( like as.charachter or paste command would do in R)\n", "a2=str(soup2.prettify()) " ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<!DOCTYPE html>\\n<html lang=\"en\">\\n <head>\\n <title>\\n Movie Tickets, Plays, Sports, Events &amp; Cinemas near National Capital Region (NCR) - BookMyShow\\n </title>\\n <meta content=\"BookMyShow offers showtimes, movie tickets, reviews, trailers, concert tickets and events near National Capital Region (NCR) . Also features promotional offers, coupons and mobile app.\" name=\"description\"/>\\n <meta content=\"text/html; charset=utf-8\" http-equiv=\"Content-Type\"/>\\n <meta content=\"Ticket Booking Movies, Movie Tickets, Big Cinema, Ags, Inox, Movie Online Booking, Concert, Play, Event, Comedy Show Tickets, IPL, PVR Cinemas, T20 Tickets, Online Movie Ticket, Movie Trailers, Reviews\" name=\"keywords\"/>\\n <meta content=\"BookMyShow\" name=\"author\"/>\\n <meta content=\"general\" name=\"rating\"/>\\n <meta content=\"BookMyShow\" name=\"application-name\"/>\\n <meta content=\"/?&amp;utm_source=ie9Pin&amp;utm_medium=ie9Pin_BMS\" name=\"msapplication-starturl\"/>\\n <meta content=\"Movie Tickets, Plays Tickets, Concert Tickets on BookMyShow India\" name=\"msapplication-tooltip\"/>\\n <meta content=\"width=1024;height=768\" name=\"msapplication-window\"/>\\n <meta content=\"name=Movies;action-uri=http://in.bookmyshow.com?&amp;utm_source=ie9Pin&amp;utm_medium=ie9Pin_BMS;icon-uri=http://in.bookmyshow.com/icon32/movies.ico\" name=\"msapplication-task\"/>\\n <meta content=\"name=Plays;action-uri=http://in.bookmyshow.com/plays?&amp;utm_source=ie9Pin&amp;utm_medium=ie9Pin_BMS;icon-uri=http://in.bookmyshow.com/icon32/plays.ico\" name=\"msapplication-task\"/>\\n <meta content=\"name=Events;action-uri=http://in.bookmyshow.com/events?&amp;utm_source=ie9Pin&amp;utm_medium=ie9Pin_BMS;icon-uri=http://in.bookmyshow.com/icon32/concerts.ico\" name=\"msapplication-task\"/>\\n <meta content=\"name=Offers;action-uri=http://in.bookmyshow.com/offerlist?&amp;utm_source=ie9Pin&amp;utm_medium=ie9Pin_BMS;icon-uri=http://in.bookmyshow.com/favicon.ico\" name=\"msapplication-task\"/>\\n <meta content=\"name=Sports;action-uri=http://in.bookmyshow.com/sport?&amp;utm_source=ie9Pin&amp;utm_medium=ie9Pin_BMS;icon-uri=http://in.bookmyshow.com/icon32/sports.ico\" name=\"msapplication-task\"/>\\n <meta content=\"name=Applications;action-uri=http://in.bookmyshow.com/mobile?&amp;utm_source=ie9Pin&amp;utm_medium=ie9Pin_BMS;icon-uri=http://in.bookmyshow.com/icon32/applications.ico\" name=\"msapplication-task\"/>\\n <meta content=\"BookMyShow\" property=\"og:title\"/>\\n <meta content=\"website\" property=\"og:type\"/>\\n <meta content=\"//in.bmscdn.com/in/common/facebook-og-bmslogo.jpg\" property=\"og:image\"/>\\n <meta content=\"http://in.bookmyshow.com/?utm_source=FBLIKE\" property=\"og:url\"/>\\n <meta content=\"BookMyShow\" property=\"og:site_name\"/>\\n <meta content=\"165665113451029\" property=\"fb:app_id\"/>\\n <meta content=\"cea145a7bbfc59fc127ddac41e2d5afb\" name=\"p:domain_verify\"/>\\n <meta content=\"summary\" name=\"twitter:card\"/>\\n <meta content=\"@bookmyshow\" name=\"twitter:site\"/>\\n <meta content=\"Movie Tickets, Plays, Sports, Music Concerts, Theatre - BookMyShow\" name=\"twitter:title\"/>\\n <meta content=\"Movie Tickets Online Booking. Book &amp; Buy Events, Plays, Music Concert, Cinema, Theatre, Sports, English, Tamil, Telugu &amp; Hindi Movie Tickets\" name=\"twitter:description\"/>\\n <meta content=\"//in.bmscdn.com/bmsin/new/BMS_logo_new.png\" name=\"twitter:image\"/>\\n <meta content=\"www.bookmyshow.com\" name=\"twitter:url\"/>\\n <link href=\"android-app://com.bt.bms/bmsapp/bookmyshow.com\" rel=\"alternate\"/>\\n <!--[if IE]><meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge,chrome=1\"><![endif]-->\\n <!--[if lt IE 9]><script src=\"//html5shiv.googlecode.com/svn/trunk/html5.js\"></script><![endif]-->\\n <!-- Optimization CSS -->\\n <style>\\n .featured-events aside{visibility: hidden;}\\n </style>\\n <!-- FavIcon links starts -->\\n <link href=\"//in.bmscdn.com/webin/common/favicon.png\" rel=\"icon\" type=\"image/png\"/>\\n <link href=\"//in.bmscdn.com/webin/common/favicon.ico\" rel=\"shortcut icon\" type=\"image/x-icon\"/>\\n <!-- FavIcon links ends -->\\n <!-- Manifest for Web Push - Clevertap -->\\n <link href=\"/manifest.json\" rel=\"manifest\"/>\\n <!-- Manifest for Web Push - Clevertap -->\\n <link href=\"https://in.bookmyshow.com/national-capital-region-ncr\" rel=\"canonical\"/>\\n <link href=\"https://plus.google.com/+BookMyShowIN\" rel=\"publisher\"/>\\n <meta content=\"IE=Edge\" http-equiv=\"X-UA-Compatible\"/>\\n <script type=\"text/javascript\">\\n var global = {};\\n\\n\\tvar blnIsRegionRouting = true;\\n\\tvar strSelRegionCode = \"NCR\";\\n var strSelRegionName = \"National Capital Region (NCR)\";\\n var strSelRegionUrlName = \"national-capital-region-ncr\";\\n\\n\\tvar strContentUrl = \"//in.bmscdn.com\";\\n\\tglobal.strAppCode = \"WEB\";\\n\\tglobal.strCurrencyCodeEx = \"\";\\n\\n\\tglobal.blnIsTouchScreen = false;\\n\\tglobal.blnIsIE = false;\\n\\tglobal.arrBreakRgn\\t= [\"NCR\"];\\n\\n\\tvar objGoogleData = {};\\n\\n\\tvar pageName = \"home\";\\n\\n\\t/* Dummy function created so that nothing breaks */\\n\\tfunction ga() { }\\n </script>\\n <script src=\"//ajax.googleapis.com/ajax/libs/jquery/2.1.4/jquery.min.js\">\\n </script>\\n <script type=\"text/javascript\">\\n var socialJS = \"//in.bookmyshow.com/js/social-fb5375916b.js\";\\n </script>\\n <!-- Start Visual Website Optimizer Asynchronous Code -->\\n <script type=\"text/javascript\">\\n var _vwo_clicks=10; //this will track first 10 clicks\\n\\tvar _vwo_code=(function(){\\n\\tvar account_id=110589,\\n\\tsettings_tolerance=2000,\\n\\tlibrary_tolerance=2500,\\n\\tuse_existing_jquery=true,\\n\\t// DO NOT EDIT BELOW THIS LINE\\n\\tf=false,d=document;return{use_existing_jquery:function(){return use_existing_jquery;},library_tolerance:function(){return library_tolerance;},finish:function(){if(!f){f=true;var a=d.getElementById(\\'_vis_opt_path_hides\\');if(a)a.parentNode.removeChild(a);}},finished:function(){return f;},load:function(a){var b=d.createElement(\\'script\\');b.src=a;b.type=\\'text/javascript\\';b.innerText;b.onerror=function(){_vwo_code.finish();};d.getElementsByTagName(\\'head\\')[0].appendChild(b);},init:function(){settings_timer=setTimeout(\\'_vwo_code.finish()\\',settings_tolerance);this.load(\\'//dev.visualwebsiteoptimizer.com/j.php?a=\\'+account_id+\\'&u=\\'+encodeURIComponent(d.URL)+\\'&r=\\'+Math.random());var a=d.createElement(\\'style\\'),b=\\'body{opacity:0 !important;filter:alpha(opacity=0) !important;background:none !important;}\\',h=d.getElementsByTagName(\\'head\\')[0];a.setAttribute(\\'id\\',\\'_vis_opt_path_hides\\');a.setAttribute(\\'type\\',\\'text/css\\');if(a.styleSheet)a.styleSheet.cssText=b;else a.appendChild(d.createTextNode(b));h.appendChild(a);return settings_timer;}};}());_vwo_settings_timer=_vwo_code.init();\\n </script>\\n <!-- End Visual Website Optimizer Asynchronous Code -->\\n <!-- Initialize the WOW animation library -->\\n <script>\\n if (typeof(WOW) !== \"undefined\") {\\n\\t\\tnew WOW().init();\\n\\t}\\n </script>\\n <script src=\"/serv/getData/?cmd=GETREGIONS\" type=\"text/javascript\">\\n </script>\\n <script type=\"text/javascript\">\\n global.svgManifest = [];\\n </script>\\n <!--[if IE]>\\n\\t<link rel=\"stylesheet\" type=\"text/css\" href=\"//in.bookmyshow.com/css/ie-support-aa4ae653f8.css\" />\\n<![endif]-->\\n <!-- For webview events header -->\\n <!-- Facebook Pixel Code -->\\n <script>\\n !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function(){n.callMethod?\\n\\tn.callMethod.apply(n,arguments):n.queue.push(arguments)};if(!f._fbq)f._fbq=n;\\n\\tn.push=n;n.loaded=!0;n.version=\\'2.0\\';n.queue=[];t=b.createElement(e);t.async=!0;\\n\\tt.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window,\\n\\tdocument,\\'script\\',\\'https://connect.facebook.net/en_US/fbevents.js\\');\\n\\n\\tfbq(\\'init\\', \\'337100036642495\\');\\n\\tfbq(\\'track\\', \"PageView\");\\n\\tfbq(\\'track\\', \\'ViewContent\\');\\n </script>\\n <noscript>\\n <img height=\"1\" src=\"https://www.facebook.com/tr?id=337100036642495&amp;ev=PageView&amp;noscript=1\" style=\"display:none\" width=\"1\"/>\\n </noscript>\\n <!-- End Facebook Pixel Code -->\\n <link href=\"//in.bookmyshow.com/css/common-page-3ad2b2e77b.css\" rel=\"stylesheet\" type=\"text/css\"/>\\n <link href=\"//in.bookmyshow.com/css/home-623e193593.css\" rel=\"stylesheet\" type=\"text/css\"/>\\n </head>\\n <body>\\n <!--On page load custom dimensions for home page-->\\n <script>\\n window.dataLayer = window.dataLayer || [];\\n\\t\\t\\tdataLayer.push({\\n\\t\\t\\t\\t\\'page type\\': \\'home\\',\\n\\t\\t\\t\\t\\'session_daypart\\': \\'Evening\\',\\n\\t\\t\\t\\t\\'pageurl\\': \\'https://in.bookmyshow.com/national-capital-region-ncr\\',\\n\\t\\t\\t\\t\\'user_mode\\': \\'Guest Mode\\',\\n\\t\\t\\t\\t\\'app_code\\': \\'WEB\\',\\n\\t\\t\\t\\t\\'domain\\': \\'bms_web\\'\\n\\t\\t\\t});\\n </script>\\n <!-- Google Tag Manager -->\\n <noscript>\\n <iframe height=\"0\" src=\"//www.googletagmanager.com/ns.html?id=GTM-MH7KN6\" style=\"display:none;visibility:hidden\" width=\"0\">\\n </iframe>\\n </noscript>\\n <script>\\n (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({\\'gtm.start\\':\\n\\tnew Date().getTime(),event:\\'gtm.js\\'});var f=d.getElementsByTagName(s)[0],\\n\\tj=d.createElement(s),dl=l!=\\'dataLayer\\'?\\'&l=\\'+l:\\'\\';j.async=true;j.src=\\n\\t\\'//www.googletagmanager.com/gtm.js?id=\\'+i+dl;f.parentNode.insertBefore(j,f);\\n\\t})(window,document,\\'script\\',\\'dataLayer\\',\\'GTM-MH7KN6\\');\\n </script>\\n <!-- End Google Tag Manager -->\\n <script type=\"application/ld+json\">\\n {\\n\\t\\t\"@context\": \"http://schema.org\",\\n\\t\\t\"@type\": \"WebSite\",\\n\\t\\t\"name\" : \"BookMyShow\",\\n\\t\\t\"url\": \"https://in.bookmyshow.com/\"\\n\\t}\\n </script>\\n <script type=\"application/ld+json\">\\n {\\n\\t\\t\"@context\": \"http://schema.org\",\\n\\t\\t\"@type\": \"Organization\",\\n\\t\\t\"url\": \"https://in.bookmyshow.com/\",\\n\\t\\t\"logo\": \"https://in.bmscdn.com/bmsin/new/BMS_logo_new.png\",\\n\\t\\t\"name\" : \"BookMyShow\",\\n\\t\\t\"sameAs\" : [\\n\\t\\t \"https://www.facebook.com/BookMyShowIN\",\\n\\t\\t \"http://www.twitter.com/BookMyShow/\",\\n\\t\\t \"http://www.youtube.com/user/BookMyShow/\",\\n\\t\\t \"http://pinterest.com/bookmyshow/\",\\n\\t\\t \"https://plus.google.com/110517543803442814698/\",\\n\\t\\t \"http://www.linkedin.com/company/bookmyshow/\"\\n\\t\\t],\\n\\t\\t\"contactPoint\" : [{\\n\\t\\t\\t\\t\\t\\t \"@type\" : \"ContactPoint\",\\n\\t\\t\\t\\t\\t\\t \"telephone\" : \"+9122 6144 5050\",\\n\\t\\t\\t\\t\\t\\t \"contactType\" : \"customer service\",\\n\\t\\t\\t\\t\\t\\t \"areaServed\" : \"IN\",\\n\\t\\t\\t\\t\\t\\t \"availableLanguage\" : [\"English\", \"Hindi\", \"Marathi\", \"Tamil\", \"Telugu\", \"Kannada\", \"Gujarati\", \"Punjabi\"]\\n\\t\\t\\t\\t\\t\\t}]\\n\\t}\\n </script>\\n <!-- For Knowledge Graph -->\\n <!-- Primary layout wrapper with menu-list | Only for touch-based devices -->\\n <!-- Main body wrapper div to make th whole page blur, This div will end after footer ends. END tags is in html_footer.inc.bms -->\\n <div class=\"main-body-wrapper \">\\n <!-- Primary nav -->\\n <header style=\"display: block;\">\\n <nav class=\"navbar \" id=\"navbar\">\\n <div class=\"select-overlay\">\\n </div>\\n <div class=\"primary desktop-nav\">\\n <div>\\n <div id=\"nav-highlighter\">\\n </div>\\n <div class=\"home-link\">\\n <a href=\"https://in.bookmyshow.com/\">\\n <span class=\"home-icon\" data-nav-menu=\"home\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-home\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </div>\\n <ul class=\"__left\">\\n <li class=\"dd-wrapper\">\\n <a class=\"nav-link \" href=\"https://in.bookmyshow.com/national-capital-region-ncr/experiences\">\\n experiences\\n </a>\\n </li>\\n <li class=\"dd-wrapper\">\\n <a class=\"dd-trigger nav-link \" href=\"https://in.bookmyshow.com/national-capital-region-ncr/movies\">\\n MOVIES\\n </a>\\n </li>\\n <li class=\"dd-wrapper\">\\n <a class=\"dd-trigger nav-link \" href=\"https://in.bookmyshow.com/national-capital-region-ncr/events\">\\n EVENTS\\n </a>\\n </li>\\n <li class=\"dd-wrapper\">\\n <a class=\"dd-trigger nav-link \" href=\"https://in.bookmyshow.com/national-capital-region-ncr/plays\">\\n PLAYS\\n </a>\\n </li>\\n <li class=\"dd-wrapper\">\\n <a class=\"dd-trigger nav-link \" href=\"https://in.bookmyshow.com/national-capital-region-ncr/sports\">\\n SPORTS\\n </a>\\n </li>\\n <!--\\n \\t\\t\\t\\t<li class=\"dd-wrapper mt-trigger\">\\n\\t\\t\\t \\t<p class=\"dd-trigger nav-link \"><!--TRAILERS\\n\\t\\t\\t \\t</p>\\n \\t\\t\\t</li>\\n\\n\\n-->\\n <li class=\"dd-wrapper\">\\n <a class=\"dd-trigger nav-link \" href=\"https://in.bookmyshow.com/videos/all\">\\n TRAILERS &amp; VIDEOS\\n </a>\\n </li>\\n <li class=\"dd-wrapper\">\\n <a class=\"dd-trigger nav-link \" href=\"https://in.bookmyshow.com/top/best-movies-2016\">\\n 2016\\n </a>\\n <!-- <span class=\"__new\">NEW</span> -->\\n </li>\\n <!--<li class=\"dd-wrapper\">\\n\\t\\t\\t\\t \\t<a class=\"dd-trigger nav-link \" href=\"https://in.bookmyshow.com/national-capital-region-ncr/parties\">PARTIES</a>\\n\\n\\t\\t\\t\\t \\t<span class=\"__new\">NEW</span>\\n\\t \\t\\t\\t</li>\\n\\n\\t\\t\\t <li>\\n\\t\\t\\t <a class=\"nav-link\" href=\"/internationl-events/\">INT\\'L</a>\\n\\t\\t\\t </li> -->\\n </ul>\\n <ul class=\"__right\">\\n <li class=\"hiring\">\\n <a class=\"btn-hiring right-nav-link\" href=\"https://in.bookmyshow.com/careers/\">\\n WE\\'RE HIRING\\n </a>\\n </li>\\n <li>\\n <a class=\"right-nav-link\" href=\"/offers/\">\\n OFFERS\\n </a>\\n </li>\\n <li>\\n <a class=\"right-nav-link\" href=\"/giftcards/\">\\n GIFTING\\n </a>\\n </li>\\n <li>\\n <a class=\"right-nav-link\" href=\"https://support.bookmyshow.com/support/home?regionCode=NCR\">\\n SUPPORT\\n </a>\\n </li>\\n <li class=\"notification-alert\">\\n <a data-group=\"top-nav\" href=\"javascript:;\" id=\"dNotifDD\" onclick=\"BMS.Header.fnHeaderDD(this, \\'toggle\\');\">\\n <span class=\"icon-notification\" data-nav-menu=\"notification\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-notification\">\\n </use>\\n </svg>\\n <!-- <span class=\"__alerts-bubble\" id=\"notification-count\"></span> -->\\n <!-- <span id=\"notification-count\"></span> -->\\n </span>\\n </a>\\n <!-- \"Added this div due to styling issues on the parent\" - Prasanna -->\\n <div class=\"notification-dropdown-wrapper\" data-id=\"dNotifDD\" data-role=\"dHeaderDD\">\\n <!-- Notificaton dropdown starts here -->\\n <div class=\"notification-dropdown nav-tip\" id=\"notification\" style=\"display:block;\">\\n </div>\\n <!-- notificaton dropdown ends here -->\\n </div>\\n </li>\\n <li class=\"login\">\\n <a class=\"signin\" data-group=\"top-nav\" data-modal=\"signinPopup\" data-nav-menu=\"Profile\" href=\"javascript:;\" id=\"preSignIn\" style=\"display: block;\">\\n SIGN IN\\n </a>\\n <a class=\"user-img\" data-group=\"top-nav\" data-nav-menu=\"Profile\" href=\"javascript:;\" id=\"postSignIn\" onclick=\"BMS.Header.fnHeaderDD(this, \\'toggle\\');\" style=\"display: none;\">\\n <img alt=\"User image\" id=\"loggedInImg\" src=\"#\"/>\\n </a>\\n <div class=\"signed-in nav-tip\" data-id=\"postSignIn\" data-role=\"dHeaderDD\">\\n <div class=\"head\" id=\"signInUName\">\\n <div>\\n <span>\\n Welcome back\\n </span>\\n <br/>\\n <strong>\\n </strong>\\n </div>\\n </div>\\n <div class=\"body\">\\n <ul>\\n <li class=\"wallet-header\">\\n <a class=\"wallet-link\" href=\"/myprofile/mywallet/\">\\n <span class=\"icon\">\\n <svg class=\"svg-wallet\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-wallet\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"__wallet-text\">\\n MyWallet\\n </span>\\n </a>\\n <span class=\"__wallet-balance\" style=\"display: none;\">\\n </span>\\n <div class=\"__wallet-reel\">\\n <div class=\"mini\">\\n </div>\\n </div>\\n <span class=\"__wallet-header-btn\" style=\"display: none;\">\\n <a class=\"btn _cuatro\" data-wallet-activate=\"ACTIVATE NOW\" data-wallet-text=\"ADD CASH\" href=\"/myprofile/mywallet/\">\\n </a>\\n </span>\\n </li>\\n <li>\\n <a href=\"/myprofile/year-at-2016\">\\n <span class=\"icon\">\\n <svg class=\"svg-year-review\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-year-review\">\\n </use>\\n </svg>\\n </span>\\n 2016 in review\\n </a>\\n </li>\\n <li>\\n <a href=\"/myprofile/booking-history\">\\n <span class=\"icon\">\\n <svg class=\"svg-booking\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-booking-history\">\\n </use>\\n </svg>\\n </span>\\n Booking History\\n </a>\\n </li>\\n <li>\\n <a href=\"/myprofile/quikpay\">\\n <span class=\"icon\">\\n <svg class=\"svg-qp\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-quick-pay\">\\n </use>\\n </svg>\\n </span>\\n QuikPay\\n </a>\\n </li>\\n <li>\\n <a href=\"/myprofile/experiences\">\\n <span class=\"icon\">\\n <svg class=\"svg-exp\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-experience\">\\n </use>\\n </svg>\\n </span>\\n Experiences\\n </a>\\n </li>\\n <li>\\n <a href=\"/myprofile/settings\">\\n <span class=\"icon\">\\n <svg class=\"svg-setting\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-setting\">\\n </use>\\n </svg>\\n </span>\\n Settings\\n </a>\\n </li>\\n <li>\\n <a href=\"javascript:BMS.SignIn.fnLogOut();\">\\n <span class=\"icon\">\\n <svg class=\"svg-signout\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-log-out\">\\n </use>\\n </svg>\\n </span>\\n Sign Out\\n </a>\\n </li>\\n </ul>\\n </div>\\n </div>\\n </li>\\n <li class=\"location-container\">\\n <a class=\"location\" data-group=\"top-nav\" data-nav-menu=\"Region\" href=\"javascript:;\" id=\"dTopRgnDD\" onclick=\"BMS.Header.fnHeaderDD(this, \\'toggle\\');\">\\n <span class=\"icon-location\" data-nav-menu=\"Region\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-location\">\\n </use>\\n </svg>\\n </span>\\n <span id=\"spnSelectedRegion\">\\n NCR\\n </span>\\n </a>\\n <!-- Location search dropdown starts here -->\\n <div class=\"location-search-container nav-tip\" data-id=\"dTopRgnDD\" data-role=\"dHeaderDD\">\\n <div class=\"__dd-trianglel\">\\n </div>\\n <div class=\"__dd-sec-top struktur\">\\n <input class=\"form-input __input _default\" id=\"inp_RegionSearch_top\" placeholder=\"ENTER YOUR CITY\" type=\"text\"/>\\n <div data-id=\"inp_RegionSearch_top\" style=\"position: absolute; display: none; background: none repeat scroll 0% 0% rgb(255, 255, 255); border: 1px solid rgb(73, 186, 142); padding: 10px; width: 85%; max-height: 190px; overflow-y: auto;\">\\n <ul id=\"uRgnLst_top\">\\n </ul>\\n </div>\\n </div>\\n <div class=\"__dd-sec-bottom\">\\n <div class=\"__dropdown-head\">\\n TOP SEARCHED\\n </div>\\n <div class=\"__top-cities\">\\n <!-- <a href=\"javascript:;\" onclick=\"BMS.Region.fnSTopReg(\\'NCR\\',\\'National Capital Region (NCR)\\'); return false;\">National Capital Region (NCR)</a> , <a href=\"javascript:;\" onclick=\"BMS.Region.fnSTopReg(\\'MUMBAI\\',\\'Mumbai\\'); return false;\">Mumbai</a> , <a href=\"javascript:;\" onclick=\"BMS.Region.fnSTopReg(\\'PUNE\\',\\'Pune\\'); return false;\">Pune</a> , <a href=\"javascript:;\" onclick=\"BMS.Region.fnSTopReg(\\'BANG\\',\\'Bengaluru\\'); return false;\">Bengaluru</a> , <a href=\"javascript:;\" onclick=\"BMS.Region.fnSTopReg(\\'CHEN\\',\\'Chennai\\'); return false;\">Chennai</a> , <a href=\"javascript:;\" onclick=\"BMS.Region.fnSTopReg(\\'HYD\\',\\'Hyderabad\\'); return false;\">Hyderabad</a> , <a href=\"javascript:;\" onclick=\"BMS.Region.fnSTopReg(\\'KOLK\\',\\'Kolkata\\'); return false;\">Kolkata</a> -->\\n <a href=\"javascript:BMS.Region.fnSTopReg(\\'NCR\\',\\'National Capital Region (NCR)\\');\">\\n National Capital Region (NCR)\\n </a>\\n <a href=\"javascript:BMS.Region.fnSTopReg(\\'MUMBAI\\',\\'Mumbai\\');\">\\n Mumbai\\n </a>\\n <a href=\"javascript:BMS.Region.fnSTopReg(\\'PUNE\\',\\'Pune\\');\">\\n Pune\\n </a>\\n <a href=\"javascript:BMS.Region.fnSTopReg(\\'BANG\\',\\'Bengaluru\\');\">\\n Bengaluru\\n </a>\\n <a href=\"javascript:BMS.Region.fnSTopReg(\\'CHEN\\',\\'Chennai\\');\">\\n Chennai\\n </a>\\n <a href=\"javascript:BMS.Region.fnSTopReg(\\'HYD\\',\\'Hyderabad\\');\">\\n Hyderabad\\n </a>\\n <a href=\"javascript:BMS.Region.fnSTopReg(\\'KOLK\\',\\'Kolkata\\');\">\\n Kolkata\\n </a>\\n </div>\\n <a class=\"__view-all-cities\" href=\"javascript:BMS.Region.fnSwStates(\\'\\');\">\\n All Cities\\n </a>\\n </div>\\n </div>\\n </li>\\n </ul>\\n </div>\\n </div>\\n <!-- End of primary nav -->\\n <!-- Secondary nav -->\\n <div class=\"secondary desktop-nav \">\\n <div>\\n <div class=\"brand\">\\n <a class=\"logo\" href=\"http://in.bookmyshow.com/\" title=\"BookMyShow\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-bms-logo\">\\n </use>\\n </svg>\\n </a>\\n </div>\\n <!-- For testing - will be cleaned before going live -->\\n <!-- https://www.loc.gov/standards/iso639-2/php/code_list.php -->\\n <div class=\"__brand-Langdropdown\" data-belongs-to=\"language\" hidden=\"\" id=\"dLangWrap\">\\n <div class=\"__filter\">\\n <div class=\"SumoSelect\">\\n <p class=\"CaptionCont SlectBox\" onclick=\"BMS.Header.switchLangDropDown();\">\\n <span class=\"_active\">\\n English\\n </span>\\n <label>\\n <i class=\"downChevron\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 500 500\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-arrow-down-new\">\\n </use>\\n </svg>\\n </i>\\n </label>\\n </p>\\n <div class=\"optWrapper\">\\n <ul class=\"options\">\\n <li class=\"selected\" data-name=\"English\" data-val=\"eng\">\\n <label>\\n English\\n </label>\\n </li>\\n <li data-name=\"Hindi\" data-val=\"hin\">\\n <label>\\n हिंदी\\n </label>\\n </li>\\n <li data-name=\"Tamil\" data-val=\"tam\">\\n <label>\\n தமிழ்\\n </label>\\n </li>\\n <li data-name=\"Telugu\" data-val=\"tel\">\\n <label>\\n తెలుగు\\n </label>\\n </li>\\n <li data-name=\"Kannada\" data-val=\"kan\">\\n <label>\\n ಕನ್ನಡ\\n </label>\\n </li>\\n </ul>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"__brand-promo\">\\n <a href=\"/sports/indian-cricket/t20-premier-league\">\\n <span class=\"__icon\">\\n <img src=\"//in.bmscdn.com/webin/static-sports/static/ipl/ipl-logo-bms.png\"/>\\n </span>\\n </a>\\n </div>\\n </div>\\n <!-- End of Secondary nav -->\\n <script type=\"text/javascript\">\\n var displayShowCaseOverlay = false;\\n\\n\\t/**\\n\\t * showCaseHandler\\n\\t * Returns the current status whether the first image of the showcase has loaded\\n\\t * @return {[type]} [description]\\n\\t */\\n\\tvar showCaseHandler = function() {\\n\\t var isFirstImageLoaded = false;\\n\\t var callbacks = [];\\n\\n\\t var getStatus = function() {\\n\\t return isFirstImageLoaded;\\n\\t };\\n\\n\\t var setStatus = function(value) {\\n\\t \\tif (isFirstImageLoaded === true) {\\n\\t \\t\\treturn isFirstImageLoaded;\\n\\t \\t}\\n\\t isFirstImageLoaded = true;\\n\\n\\t //update the status triggerCallbacks\\n\\t setTimeout(triggerCallbacks, 5);\\n\\t return isFirstImageLoaded;\\n\\t };\\n\\n\\t var triggerCallbacks = function () {\\n\\t \\tcallbacks.forEach(function (callbackDetails) {\\n\\t \\t\\tif (callbackDetails.isTriggered) {\\n\\t \\t\\t\\treturn;\\n\\t \\t\\t}\\n\\n\\t \\t\\tcallbackDetails.isTriggered = true;\\n\\t \\t\\tcallbackDetails.fn();\\n\\t \\t});\\n\\t };\\n\\n\\t var registerCallback = function (callbackDetails) {\\n\\t \\tcallbacks.push({\\n\\t \\t\\tkey: callbackDetails.key,\\n\\t \\t\\tisTriggered: false,\\n\\t \\t\\tfn: callbackDetails.callback\\n\\t \\t});\\n\\t };\\n\\n\\t return {\\n\\t getStatus: getStatus,\\n\\t setStatus: setStatus,\\n\\t registerCallback: registerCallback\\n\\t };\\n\\t}();\\n\\n\\t/*\\n *\\tFor Netflix campaign code\\n */\\n\\tfunction fnTrackNetflixShowcaseItem (banner, state) {\\n\\t\\tvar trackers = {\\n\\t\\t\\t\"HOC\" : {\\n\\t\\t\\t\\t\"view\" : \"https://ad.doubleclick.net/ddm/trackimp/N186801.2403905BOOKMYSHOW/B9963110.134794787;dc_trk_aid=307455681;dc_trk_cid=72451480;ord=[timestamp];dc_lat=;dc_rdid=;tag_for_child_directed_treatment=?\",\\n\\t\\t\\t\\t\"bannerclick\" : \"https://ad.doubleclick.net/ddm/trackclk/N186801.2403905BOOKMYSHOW/B9963110.134794787;dc_trk_aid=307455681;dc_trk_cid=72451480;dc_lat=;dc_rdid=;tag_for_child_directed_treatment=\",\\n\\t\\t\\t\\t\"videoclick\" : \"https://ad.doubleclick.net/ddm/trackimp/N186801.2403905BOOKMYSHOW/B9963110.134794787;dc_trk_aid=307455681;dc_trk_cid=72451480;ord=[timestamp];dc_lat=;dc_rdid=;tag_for_child_directed_treatment=?\"\\n\\t\\t\\t},\\n\\t\\t\\t\"DD\" : {\\n\\t\\t\\t\\t\"view\" : \"https://ad.doubleclick.net/ddm/trackimp/N186801.2403905BOOKMYSHOW/B9963110.134797065;dc_trk_aid=307455681;dc_trk_cid=72451480;ord=[timestamp];dc_lat=;dc_rdid=;tag_for_child_directed_treatment=?\",\\n\\t\\t\\t\\t\"bannerclick\" : \"https://ad.doubleclick.net/ddm/trackclk/N186801.2403905BOOKMYSHOW/B9963110.134797065;dc_trk_aid=307455681;dc_trk_cid=72451480;dc_lat=;dc_rdid=;tag_for_child_directed_treatment=\",\\n\\t\\t\\t\\t\"videoclick\" : \"https://ad.doubleclick.net/ddm/trackimp/N186801.2403905BOOKMYSHOW/B9963110.134797065;dc_trk_aid=307455681;dc_trk_cid=72451480;ord=[timestamp];dc_lat=;dc_rdid=;tag_for_child_directed_treatment=?\"\\n\\t\\t\\t}\\n\\t\\t};\\n\\t\\tif (state == \"view\") {\\n\\n\\t\\t\\tvar img = new Image();\\n\\t\\t\\timg.src = trackers[banner][\"view\"];\\n\\t\\t} else if (state == \"videoclick\") {\\n\\n\\t\\t\\tvar img = new Image();\\n\\t\\t\\timg.src = trackers[banner][\"videoclick\"];\\n\\t\\t} else if (state == \"bannerclick\") {\\n\\n\\t\\t\\t$(\"#lnkNetflixUrl\").attr(\"href\", trackers[banner][\"bannerclick\"]);\\n\\n\\t\\t\\twindow.nfCdTimerValue = 5;\\n\\n\\t\\t\\t$(\"#spnNetflixCdTimer\").html(\" in \" + window.nfCdTimerValue + \" secs\");\\n\\t\\t\\tBMS.Misc.modal(\"dBusyNetflix\", true);\\n\\n\\t\\t\\twindow.nfCdTimer = setInterval(function () {\\n\\t\\t\\t\\tif (window.nfCdTimerValue >= 1) {\\n\\t\\t\\t\\t\\twindow.nfCdTimerValue--;\\n\\t\\t\\t\\t\\t$(\"#spnNetflixCdTimer\").html(\" in \" + window.nfCdTimerValue + \" secs\");\\n\\t\\t\\t\\t} else {\\n\\t\\t\\t\\t\\tclearInterval(window.nfCdTimer);\\n\\t\\t\\t\\t\\t$(\"#spnNetflixCdTimer\").html(\"\");\\n\\t\\t\\t\\t}\\n\\t\\t\\t}, 1000);\\n\\n\\t\\t\\tsetTimeout(function () {\\n\\t\\t\\t\\twindow.open(trackers[banner][\"bannerclick\"], \"_blank\");\\n\\t\\t\\t}, 5000);\\n\\t\\t}\\n\\t}\\n </script>\\n <div class=\"top-banner-wrapper\" style=\"position: relative;\">\\n <div class=\"play-bg\">\\n </div>\\n <div class=\"showcase\" data-ratio=\"\" id=\"showcase-primary\">\\n <div class=\"section-body showcase-carousel\">\\n <div class=\"banner-container\">\\n <div class=\"showcase-card is-loading\">\\n <a href=\"/special/shakespeare-comedy-theatre-festival\" onclick=\\'fnPushDLShowcase({\"id\":\"HO_1\",\"name\":\"special-shakespeare-comedy-theatre-festival\",\"creative\":\"showcase_EventImage_home\",\"position\":\"slot1\"});\\' target=\"_self\">\\n <img alt=\"\" class=\"__bg\" data-lazy=\"https://in.bmscdn.com/showcaseimage/eventimage/shakespeare-comedy-theatre-festival-showcase-01-03-2017-17-30.jpg\" onload=\"showCaseHandler.setStatus(true);\" src=\"https://in.bmscdn.com/showcaseimage/eventimage/shakespeare-comedy-theatre-festival-showcase-01-03-2017-17-30.jpg\"/>\\n </a>\\n <div class=\"__overlay\">\\n </div>\\n </div>\\n </div>\\n <div class=\"banner-container\">\\n <div class=\"showcase-card is-loading\">\\n <a href=\"/sports/indian-cricket/t20-premier-league-dd\" onclick=\\'fnPushDLShowcase({\"id\":\"HO_2\",\"name\":\"sports-indian-cricket-t20-premier-league-dd\",\"creative\":\"showcase_EventImage_home\",\"position\":\"slot2\"});\\' target=\"_self\">\\n <img alt=\"\" class=\"__bg\" data-lazy=\"https://in.bmscdn.com/showcaseimage/eventimage/t20-premier-league-dd-showcase-24-03-2017-18-45.jpg\" onload=\"\" src=\"//:0\"/>\\n </a>\\n <div class=\"__overlay\">\\n </div>\\n </div>\\n </div>\\n <div class=\"banner-container\">\\n <div class=\"showcase-card is-loading\">\\n <a href=\"/fantain/?from=home\" onclick=\\'fnPushDLShowcase({\"id\":\"HO_3\",\"name\":\"fantain-?from=home\",\"creative\":\"showcase_EventImage_home\",\"position\":\"slot3\"});\\' target=\"_self\">\\n <img alt=\"\" class=\"__bg\" data-lazy=\"https://in.bmscdn.com/showcaseimage/eventimage/fantain-01-04-2017-14-57.jpg\" onload=\"\" src=\"//:0\"/>\\n </a>\\n <div class=\"__overlay\">\\n </div>\\n </div>\\n </div>\\n <div class=\"banner-container\">\\n <div class=\"showcase-card is-loading\">\\n <a href=\"/offers/hdfc-25-off-on-timescard/HTC0314\" onclick=\\'fnPushDLShowcase({\"id\":\"HO_4\",\"name\":\"offers-hdfc-25-off-on-timescard-HTC0314\",\"creative\":\"showcase_EventImage_home\",\"position\":\"slot4\"});\\' target=\"_self\">\\n <img alt=\"\" class=\"__bg\" data-lazy=\"https://in.bmscdn.com/showcaseimage/eventimage/hdfc-25-off-on-timescard-showcase-17-02-2017-18-15.jpg\" onload=\"\" src=\"//:0\"/>\\n </a>\\n <div class=\"__overlay\">\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"showcase-overlay \">\\n <span class=\"__dismiss icon-cancel _none\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n </div>\\n </div>\\n <script type=\"text/javascript\">\\n //update the javascript brandcheck vari\\n\\tdisplayShowCaseOverlay = 0\\n </script>\\n <script type=\"text/javascript\">\\n if (displayShowCaseOverlay == true) {\\n\\t\\t\\t\\t$(\\'body\\').addClass(\\'_fixed\\');\\n\\t\\t\\t\\t$(\\'#navbar\\').addClass(\\'none\\');\\n\\t\\t\\t\\t$(\\'.top-banner-wrapper\\').addClass(\\'_overlay\\');\\n\\t\\t\\t}\\n </script>\\n <script src=\"//in.bookmyshow.com/js/misc-cd469c243b.js\" type=\"text/javascript\">\\n </script>\\n <!-- GPT common scripts begin -->\\n <script async=\"async\" src=\"https://www.googletagservices.com/tag/js/gpt.js\">\\n </script>\\n <script>\\n var googletag = googletag || {};\\n\\t\\t googletag.cmd = googletag.cmd || [];\\n </script>\\n <!-- GPT common scripts end -->\\n <div class=\"home-wrapper \">\\n <div class=\"ts-desktop-main\">\\n <div class=\"ts-desktop-wrapper wrapper\">\\n <div class=\"trending-search-text\">\\n <span class=\"trending-text\">\\n POPULAR SEARCHES\\n </span>\\n <br/>\\n <span class=\"trending-in-region-text\">\\n </span>\\n </div>\\n <div class=\"trending-search-result-block\">\\n <div class=\"left-arrow-container arrow-container hidden\">\\n <span class=\"left-arrow arrow\">\\n <svg enable-background=\"new 0 0 100 100\" height=\"20px\" version=\"1.1\" width=\"20px\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-arrow-right\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"trending-search-result-wrapper\">\\n <div class=\"trending-search-result-container\" id=\"trending-search-result-container\">\\n </div>\\n </div>\\n <div class=\"right-arrow-container arrow-container\">\\n <span class=\"right-arrow arrow\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-arrow-right\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- <script>\\n\\t\\t\\t\\t googletag.cmd.push(function() {\\n\\t\\t\\t\\t googletag.defineSlot(\\'/118335522/BMS_HomePage_Top\\', [1166, 130], \\'div-gpt-ad-1470827732485-0\\').addService(googletag.pubads());\\n\\t\\t\\t\\t googletag.pubads().enableSingleRequest();\\n\\t\\t\\t\\t googletag.pubads().collapseEmptyDivs(true);\\n\\t\\t\\t\\t googletag.pubads().disableInitialLoad();\\n\\t\\t\\t\\t googletag.enableServices();\\n\\t\\t\\t\\t });\\n\\t\\t\\t\\t</script> -->\\n <!-- /118335522/BMS_HomePage_Top -->\\n <!-- <div id=\\'div-gpt-ad-1470827732485-0\\' style=\\'height:130px; width:1166px;margin: 0 auto;margin-top: 15px;\\'>\\n\\t\\t\\t\\t<script>\\n\\t\\t\\t\\tgoogletag.cmd.push(function() { googletag.display(\\'div-gpt-ad-1470827732485-0\\'); });\\n\\t\\t\\t\\t</script>\\n\\t\\t\\t\\t</div> -->\\n <!-- Featured events sections start here -->\\n <section class=\"featured-events section-bg\">\\n <div class=\"wrapper\">\\n <!-- Featured event head -->\\n <div class=\"section-head\" style=\"position: relative;\">\\n <h2 class=\"__title\">\\n FEATURED EVENTS\\n </h2>\\n <div class=\"__red-bar\">\\n </div>\\n <div class=\"townscript-img-wrapper\" style=\"position: absolute;right: 20px;top: 30px;\">\\n <a href=\"https://goo.gl/nSCP8L\" target=\"_blank\">\\n <img src=\"https://in.bmscdn.com/bmsin/static/town-script/banner.jpg\"/>\\n </a>\\n </div>\\n </div>\\n <!-- Event cards -->\\n <div class=\"section-body\">\\n <div class=\"featured-event adspot-feat\">\\n <div class=\"ev-card-img wow fadeInUp\">\\n <img alt=\"\" data-error=\"\" data-src=\"//in.bmscdn.com/events/eventbanner/1013270.jpg\" src=\"data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA\"/>\\n <div class=\"__overlay\">\\n <a class=\"__buy-tickets\" href=\"/events/wow-water-park/ET00055364\" onclick=\\'fnPushDLFeatEvent({\"id\":\"featEvents_home_1\",\"name\":\"WOW Water Park\",\"creative\":\"featEvents_home\",\"position\":\"slot1\"});\\'>\\n Buy Tickets\\n </a>\\n </div>\\n </div>\\n <div class=\"ev-info\">\\n <div class=\"__ev-name\">\\n <a href=\"/events/wow-water-park/ET00055364\">\\n WOW Water Park\\n </a>\\n <div class=\"__ev-location\">\\n Noida\\n </div>\\n </div>\\n <div class=\"__ev-date\">\\n Apr 1 - 30\\n </div>\\n </div>\\n </div>\\n <div class=\"featured-event adspot-feat\">\\n <div class=\"ev-card-img wow fadeInUp\">\\n <img alt=\"\" data-error=\"\" data-src=\"//in.bmscdn.com/events/eventbanner/1013294.jpg\" src=\"data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA\"/>\\n <div class=\"__overlay\">\\n <a class=\"__buy-tickets\" href=\"/events/appughar-water-park-gurugram/ET00055515\" onclick=\\'fnPushDLFeatEvent({\"id\":\"featEvents_home_2\",\"name\":\"Appughar Water Park Gurugram\",\"creative\":\"featEvents_home\",\"position\":\"slot2\"});\\'>\\n Buy Tickets\\n </a>\\n </div>\\n </div>\\n <div class=\"ev-info\">\\n <div class=\"__ev-name\">\\n <a href=\"/events/appughar-water-park-gurugram/ET00055515\">\\n Appughar Water Park Gurugram\\n </a>\\n <div class=\"__ev-location\">\\n Gurgaon\\n </div>\\n </div>\\n <div class=\"__ev-date\">\\n Apr 5 - 30\\n </div>\\n </div>\\n </div>\\n <div class=\"featured-event adspot-feat\">\\n <div class=\"ev-card-img wow fadeInUp\">\\n <img alt=\"\" data-error=\"\" data-src=\"//in.bmscdn.com/events/eventbanner/1013285.jpg\" src=\"data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA\"/>\\n <div class=\"__overlay\">\\n <a class=\"__buy-tickets\" href=\"/events/nh8-food-festival/ET00055326\" onclick=\\'fnPushDLFeatEvent({\"id\":\"featEvents_home_3\",\"name\":\"NH8 Food Festival @ The Great Kabab Factory\",\"creative\":\"featEvents_home\",\"position\":\"slot3\"});\\'>\\n Register Now\\n </a>\\n </div>\\n </div>\\n <div class=\"ev-info\">\\n <div class=\"__ev-name\">\\n <a href=\"/events/nh8-food-festival/ET00055326\">\\n NH8 Food Festival @ The Great Kabab Factory\\n </a>\\n <div class=\"__ev-location\">\\n Delhi\\n </div>\\n </div>\\n <div class=\"__ev-date\">\\n Apr 7-16\\n </div>\\n </div>\\n </div>\\n <div class=\"featured-event adspot-feat ad-box\">\\n <script>\\n googletag.cmd.push(function() {\\n\\t\\t\\t\\t\\t\\t googletag.defineSlot(\\'/118335522/BMS_HomePageFeatured_300x250\\', [300, 250], \\'div-gpt-ad-1476077977759-0\\').addService(googletag.pubads());\\n\\t\\t\\t\\t\\t\\t googletag.pubads().enableSingleRequest();\\n\\t\\t\\t\\t\\t\\t googletag.pubads().disableInitialLoad();\\n\\t\\t\\t\\t\\t\\t googletag.enableServices();\\n\\t\\t\\t\\t\\t\\t });\\n </script>\\n <!-- /118335522/BMS_HomePageFeatured_300x250 -->\\n <div id=\"div-gpt-ad-1476077977759-0\" style=\"height:250px; width:300px;\">\\n </div>\\n </div>\\n <div class=\"featured-event adspot-feat\">\\n <div class=\"ev-card-img wow fadeInUp\">\\n <img alt=\"\" data-error=\"\" data-src=\"//in.bmscdn.com/events/eventbanner/1012956.jpg\" src=\"data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA\"/>\\n <div class=\"__overlay\">\\n <a class=\"__buy-tickets\" href=\"/events/standup-comedy/ET00054548\" onclick=\\'fnPushDLFeatEvent({\"id\":\"featEvents_home_4\",\"name\":\"Honestly Speaking By Amit Tandon\",\"creative\":\"featEvents_home\",\"position\":\"slot4\"});\\'>\\n Buy Tickets\\n </a>\\n </div>\\n </div>\\n <div class=\"ev-info\">\\n <div class=\"__ev-name\">\\n <a href=\"/events/standup-comedy/ET00054548\">\\n Honestly Speaking By Amit Tandon\\n </a>\\n <div class=\"__ev-location\">\\n Delhi\\n </div>\\n </div>\\n <div class=\"__ev-date\">\\n Apr 8\\n </div>\\n </div>\\n </div>\\n <div class=\"featured-event adspot-feat\">\\n <div class=\"ev-card-img wow fadeInUp\">\\n <img alt=\"\" data-error=\"\" data-src=\"//in.bmscdn.com/events/eventbanner/1013003.jpg\" src=\"data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA\"/>\\n <div class=\"__overlay\">\\n <a class=\"__buy-tickets\" href=\"/events/the-drifting-canvas/ET00054240\" onclick=\\'fnPushDLFeatEvent({\"id\":\"featEvents_home_5\",\"name\":\"The Drifting Canvas Multimedia Exhibition\",\"creative\":\"featEvents_home\",\"position\":\"slot5\"});\\'>\\n Buy Tickets\\n </a>\\n </div>\\n </div>\\n <div class=\"ev-info\">\\n <div class=\"__ev-name\">\\n <a href=\"/events/the-drifting-canvas/ET00054240\">\\n The Drifting Canvas Multimedia Exhibition\\n </a>\\n <div class=\"__ev-location\">\\n Delhi\\n </div>\\n </div>\\n <div class=\"__ev-date\">\\n Apr 14-Jun 13\\n </div>\\n </div>\\n </div>\\n <div class=\"featured-event adspot-feat\">\\n <div class=\"ev-card-img wow fadeInUp\">\\n <img alt=\"\" data-error=\"\" data-src=\"//in.bmscdn.com/events/eventbanner/1013002.jpg\" src=\"data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA\"/>\\n <div class=\"__overlay\">\\n <a class=\"__buy-tickets\" href=\"/sports/indian-cricket/t20-premier-league-dd\" onclick=\\'fnPushDLFeatEvent({\"id\":\"featEvents_home_6\",\"name\":\"Delhi Daredevils\",\"creative\":\"featEvents_home\",\"position\":\"slot6\"});\\'>\\n Buy Tickets\\n </a>\\n </div>\\n </div>\\n <div class=\"ev-info\">\\n <div class=\"__ev-name\">\\n <a href=\"/sports/indian-cricket/t20-premier-league-dd\">\\n Delhi Daredevils\\n </a>\\n <div class=\"__ev-location\">\\n Delhi\\n </div>\\n </div>\\n <div class=\"__ev-date\">\\n Apr 15-May 14\\n </div>\\n </div>\\n </div>\\n <div class=\"featured-event adspot-feat\">\\n <div class=\"ev-card-img wow fadeInUp\">\\n <img alt=\"\" data-error=\"\" data-src=\"//in.bmscdn.com/events/eventbanner/1012913.jpg\" src=\"data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA\"/>\\n <div class=\"__overlay\">\\n <a class=\"__buy-tickets\" href=\"/special/shakespeare-comedy-theatre-festival\" onclick=\\'fnPushDLFeatEvent({\"id\":\"featEvents_home_7\",\"name\":\"Shakespeare Comedy Theatre Festival\",\"creative\":\"featEvents_home\",\"position\":\"slot7\"});\\'>\\n Buy Tickets\\n </a>\\n </div>\\n </div>\\n <div class=\"ev-info\">\\n <div class=\"__ev-name\">\\n <a href=\"/special/shakespeare-comedy-theatre-festival\">\\n Shakespeare Comedy Theatre Festival\\n </a>\\n <div class=\"__ev-location\">\\n Multi-city\\n </div>\\n </div>\\n <div class=\"__ev-date\">\\n Apr 15-16\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Check all events -->\\n <!--<div class=\"align-center\">\\n\\t\\t\\t <a href=\"/events\" class=\"btn _dos\">ALL EVENTS</a>\\n\\t\\t\\t </div> -->\\n </div>\\n </section>\\n <!-- Recommended movies -->\\n <section class=\"recommended-movies section-bg\">\\n <!-- App-install-footer -->\\n <!-- App-install-footer ends -->\\n <div class=\"wrapper\">\\n <div class=\"recommended-movies-content\">\\n <!-- Movie cards -->\\n <div class=\"col-top-ten\">\\n <!-- Top ten -->\\n <!-- <div class=\"__head\">\\n\\t <div class=\"__ribbon\">TOP RATED MOVIES</div>\\n\\t </div> -->\\n <div class=\"section-head\">\\n <h2 class=\"__title\">\\n TOP MOVIES\\n </h2>\\n <div class=\"__red-bar\">\\n </div>\\n </div>\\n <ul class=\"regional-top\">\\n <!-- Movie list starts -->\\n <li class=\"head\">\\n IN YOUR REGION\\n </li>\\n <li class=\"movies sa-data-plugin _top10\" data-event-code=\"ET00046406\" data-event-group=\"EG00028298\">\\n <span class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"__percentage _active\">\\n </span>\\n <a class=\"__name\" href=\"/national-capital-region-ncr/movies/naam-shabana/ET00046406\" title=\"Naam Shabana (U/A)\">\\n Naam Shabana (U/A)\\n </a>\\n </li>\\n <li class=\"movies sa-data-plugin _top10\" data-event-code=\"ET00047192\" data-event-group=\"EG00029064\">\\n <span class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"__percentage _active\">\\n </span>\\n <a class=\"__name\" href=\"/national-capital-region-ncr/movies/the-boss-baby/ET00047192\" title=\"The Boss Baby (3D) (U/A)\">\\n The Boss Baby (3D) (U/A)\\n </a>\\n </li>\\n <li class=\"movies sa-data-plugin _top10\" data-event-code=\"ET00039092\" data-event-group=\"EG00019779\">\\n <span class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"__percentage _active\">\\n </span>\\n <a class=\"__name\" href=\"/national-capital-region-ncr/movies/phillauri/ET00039092\" title=\"Phillauri (U/A)\">\\n Phillauri (U/A)\\n </a>\\n </li>\\n <li class=\"movies sa-data-plugin _top10\" data-event-code=\"ET00041555\" data-event-group=\"EG00023587\">\\n <span class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"__percentage _active\">\\n </span>\\n <a class=\"__name\" href=\"/national-capital-region-ncr/movies/badrinath-ki-dulhania/ET00041555\" title=\"Badrinath Ki Dulhania (U/A)\">\\n Badrinath Ki Dulhania (U/A)\\n </a>\\n </li>\\n <li class=\"movies sa-data-plugin _top10\" data-event-code=\"ET00045150\" data-event-group=\"EG00027121\">\\n <span class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"__percentage _active\">\\n </span>\\n <a class=\"__name\" href=\"/national-capital-region-ncr/movies/poorna/ET00045150\" title=\"Poorna (U)\">\\n Poorna (U)\\n </a>\\n </li>\\n <li class=\"movies sa-data-plugin _top10\" data-event-code=\"ET00045751\" data-event-group=\"EG00027683\">\\n <span class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"__percentage _active\">\\n </span>\\n <a class=\"__name\" href=\"/national-capital-region-ncr/movies/a-dogs-purpose/ET00045751\" title=\"A Dog\\'s Purpose (U)\">\\n A Dog\\'s Purpose (U)\\n </a>\\n </li>\\n <li class=\"movies sa-data-plugin _top10\" data-event-code=\"ET00054744\" data-event-group=\"EG00024247\">\\n <span class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"__percentage _active\">\\n </span>\\n <a class=\"__name\" href=\"/national-capital-region-ncr/movies/beauty-and-the-beast-3d/ET00054744\" title=\"Beauty And The Beast (3D) (U/A)\">\\n Beauty And The Beast (3D) (U/A)\\n </a>\\n </li>\\n <li class=\"movies sa-data-plugin _top10\" data-event-code=\"ET00045454\" data-event-group=\"EG00027411\">\\n <span class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"__percentage _active\">\\n </span>\\n <a class=\"__name\" href=\"/national-capital-region-ncr/movies/the-great-father/ET00045454\" title=\"The Great Father (U/A)\">\\n The Great Father (U/A)\\n </a>\\n </li>\\n </ul>\\n <!-- Movie list ends -->\\n </div>\\n <!-- Top ten ends -->\\n <div class=\"col-just-for-you\">\\n <!-- Just for you starts -->\\n <div class=\"__tab-row __ns-cs-toggle\">\\n <div class=\"section-head\">\\n <h2 class=\"__title\">\\n JUST FOR YOU\\n </h2>\\n <div class=\"__red-bar\">\\n </div>\\n </div>\\n <a class=\"__redirect\" href=\"/movies\" id=\"view-more-link\">\\n </a>\\n View More\\n <div class=\"btn-group\">\\n <button class=\"btn _uno _active button-now-showing\" onclick=\"$(\\'#view-more-link\\').attr(\\'href\\', \\'/movies/nowshowing\\');\">\\n NOW SHOWING\\n </button>\\n <button class=\"btn _uno button-coming-soon\" onclick=\"$(\\'#view-more-link\\').attr(\\'href\\', \\'/movies/comingsoon\\');\">\\n COMING SOON\\n </button>\\n </div>\\n </div>\\n <div class=\"carousel-container \">\\n <!-- Carousel - NOW SHOWING -->\\n <div class=\"carousel-now-showing showcase \" id=\"now-showing-carousel\">\\n <div class=\"viewport viewport-now-showing showcase-carousel\">\\n <!-- <ul class=\"inner\"> -->\\n <div class=\"banner-container\">\\n <div class=\"movie-card ns-card-single\">\\n <div class=\"card-container\">\\n <div class=\"poster-container-img\">\\n <img alt=\"Manasu Malligey\" class=\"__poster\" data-error=\"//in.bmscdn.com/events/mobile/noimage.jpg\" data-lazy=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/manasu-malligey-et00054768-15-03-2017-09-50-13.jpg\" data-mobile=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/manasu-malligey-et00054768-15-03-2017-09-50-13.jpg\"/>\\n </div>\\n <div class=\"poster-container\">\\n <div class=\"__overlay\">\\n </div>\\n <div class=\"show-more-info\">\\n <ul class=\"info-list\">\\n <li class=\"info-list-elements\">\\n <span class=\"tooltip\">\\n INFO\\n </span>\\n <a href=\"/national-capital-region-ncr/movies/manasu-malligey/ET00054768\">\\n <span class=\"info-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-info\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n <li class=\"info-list-elements\">\\n <span class=\"tooltip\">\\n TRAILER\\n </span>\\n <a href=\"/national-capital-region-ncr/movies/manasu-malligey/ET00054768#trailer\">\\n <span class=\"trailer-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-play\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n <li class=\"info-list-elements rating-container\" cont-id=\"cntET00054768\">\\n <span class=\"tooltip\">\\n RATE\\n </span>\\n <a class=\"js-rating\" href=\"javascript:;\">\\n <span class=\"rating-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-rating\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n </ul>\\n <div class=\"rating-section card-ratings\">\\n <div class=\"rate-o-meter\" data-event-code=\"ET00054768\">\\n 0\\n </div>\\n <ul class=\"rating-stars\" data-role=\"ratingStars\" event-code=\"ET00054768\" event-name=\"Manasu Malligey\" id=\"dET00054768\">\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"0.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"1.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"1.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"2.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n 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xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"4.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"4.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n 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class=\"__name overflowEllipses\">\\n <a class=\"__movie-name\" data-position=\"\" href=\"/national-capital-region-ncr/movies/manasu-malligey/ET00054768\" onclick=\"GTMredirect(\\'Manasu Malligey\\',\\'ET00054768\\',\\'manasu-malligey\\',\\'Kannada\\',\\'31 Mar, 2017\\',$(this),\\'2D\\');\" title=\"Manasu Malligey\">\\n Manasu Malligey\\n </a>\\n </div>\\n <div class=\"languages\">\\n <ul class=\"language-list\">\\n <li class=\"__language\">\\n Kannada\\n </li>\\n </ul>\\n </div>\\n <div class=\"genre-list\">\\n <a href=\"/movies/drama\">\\n <div class=\"__rounded-box __genre\">\\n Drama\\n </div>\\n </a>\\n <a href=\"/movies/romance\">\\n <div class=\"__rounded-box __genre\">\\n Romance\\n </div>\\n </a>\\n </div>\\n <div class=\"show-details\">\\n <div class=\"cinema\">\\n <div class=\"__name\">\\n PVR\\n </div>\\n <div class=\"__location\">\\n Oberoi Mall, Goregaon (E)\\n </div>\\n </div>\\n <ul class=\"showtimes\">\\n <li class=\"__details\">\\n <div class=\"__day today\">\\n 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xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-u\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n </div>\\n <div class=\"popularity sa-data-plugin\" data-event-code=\"ET00048292\" data-event-group=\"EG00029906\">\\n <div class=\"__likes\">\\n <div class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__thumbs _none\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-like\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__percentage\">\\n </div>\\n </div>\\n <div class=\"__votes\">\\n <div class=\"__count\">\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"detail detail-scroll\">\\n <div class=\"__name overflowEllipses\">\\n <a class=\"__movie-name\" data-position=\"\" href=\"/national-capital-region-ncr/movies/1971-beyond-borders/ET00048292\" onclick=\"GTMredirect(\\'1971 Beyond Borders\\',\\'ET00048292\\',\\'1971-beyond-borders\\',\\'Malayalam\\',\\'7 Apr, 2017\\',$(this),\\'2D\\');\" title=\"1971 Beyond Borders\">\\n 1971 Beyond Borders\\n </a>\\n </div>\\n <div class=\"languages\">\\n <ul class=\"language-list\">\\n <li class=\"__language\">\\n Malayalam\\n </li>\\n </ul>\\n </div>\\n <div class=\"genre-list\">\\n <a href=\"/movies/drama\">\\n <div class=\"__rounded-box __genre\">\\n Drama\\n </div>\\n </a>\\n <a href=\"/movies/history\">\\n <div class=\"__rounded-box __genre\">\\n History\\n </div>\\n </a>\\n <a href=\"/movies/war\">\\n <div 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href=\"/buytickets/1971-beyond-borders-national-capital-region-ncr/movie-ncr-ET00048292-MT/20170405/\">\\n <span class=\"__format\">\\n 2D\\n </span>\\n </a>\\n </div>\\n </section>\\n </div>\\n </div>\\n <div class=\"book-button\">\\n <a href=\"/buytickets/1971-beyond-borders-national-capital-region-ncr/movie-ncr-ET00048292-MT/20170405/\">\\n <div class=\"__container\">\\n BOOK NOW\\n </div>\\n </a>\\n </div>\\n <span class=\"hideExperience\">\\n <span class=\"__text icon\">\\n <svg class=\"hideExperienceOnClick\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n </div>\\n </div>\\n </div>\\n <div class=\"banner-container\">\\n <div class=\"ad-spot-movie-card\">\\n <script>\\n googletag.cmd.push(function() {\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t googletag.defineSlot(\\'/118335522/BMS_HomePageMovieCard_300x250\\', [300, 250], \\'div-gpt-ad-1476078017492-0\\').addService(googletag.pubads());\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t googletag.pubads().enableSingleRequest();\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t googletag.pubads().collapseEmptyDivs(true);\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t googletag.pubads().disableInitialLoad();\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t googletag.enableServices();\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t });\\n </script>\\n <!-- /118335522/BMS_HomePageMovieCard_300x250 -->\\n <div id=\"div-gpt-ad-1476078017492-0\" style=\"height:250px; width:300px;\">\\n </div>\\n </div>\\n </div>\\n <div class=\"banner-container\">\\n <div class=\"movie-card ns-card-single\">\\n <div class=\"card-container\">\\n <div class=\"poster-container-img\">\\n <img alt=\"Blue Mountains\" class=\"__poster\" data-error=\"//in.bmscdn.com/events/mobile/noimage.jpg\" data-lazy=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/blue-mountains-et00054552-09-03-2017-07-02-43.jpg\" data-mobile=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/blue-mountains-et00054552-09-03-2017-07-02-43.jpg\"/>\\n </div>\\n <div class=\"poster-container\">\\n <div class=\"__overlay\">\\n </div>\\n <div class=\"show-more-info\">\\n <ul class=\"info-list\">\\n <li class=\"info-list-elements\">\\n <span class=\"tooltip\">\\n INFO\\n </span>\\n <a href=\"/national-capital-region-ncr/movies/blue-mountains/ET00054552\">\\n <span class=\"info-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-info\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n <li class=\"info-list-elements\">\\n <span class=\"tooltip\">\\n TRAILER\\n </span>\\n <a href=\"/national-capital-region-ncr/movies/blue-mountains/ET00054552#trailer\">\\n <span class=\"trailer-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-play\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n </ul>\\n <div class=\"rating-section card-ratings\">\\n <div class=\"rate-o-meter\" data-event-code=\"ET00054552\">\\n 0\\n </div>\\n <ul class=\"rating-stars\" data-role=\"ratingStars\" event-code=\"ET00054552\" event-name=\"Blue Mountains\" id=\"dET00054552\">\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"0.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" 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enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-u\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n </div>\\n <div class=\"popularity sa-data-plugin\" data-event-code=\"ET00054552\" data-event-group=\"EG00035697\">\\n <div class=\"__likes\">\\n <div class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__thumbs _none\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" 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class=\"__rounded-box __genre\">\\n Family\\n </div>\\n </a>\\n </div>\\n <div class=\"show-details\">\\n <div class=\"cinema\">\\n <div class=\"__name\">\\n PVR\\n </div>\\n <div class=\"__location\">\\n Oberoi Mall, Goregaon (E)\\n </div>\\n </div>\\n <ul class=\"showtimes\">\\n <li class=\"__details\">\\n <div class=\"__day today\">\\n TODAY\\n </div>\\n <a class=\"__time\" href=\"#\">\\n <small class=\"timestamp\">\\n 8.30 pm\\n </small>\\n </a>\\n </li>\\n <li class=\"__details\">\\n <div class=\"__day\">\\n TOMORROW\\n </div>\\n <a class=\"__time\" href=\"#\">\\n <small class=\"timestamp\">\\n 8.30 pm\\n </small>\\n </a>\\n </li>\\n </ul>\\n <a class=\"more-showtimes\" href=\"#\">\\n <small>\\n more shows\\n </small>\\n </a>\\n </div>\\n </div>\\n <div class=\"experience-holder\" style=\"z-index: -1;\">\\n <div class=\"experience-list\">\\n <section class=\"language-based-formats\">\\n <h2 class=\"header\">\\n Hindi\\n </h2>\\n <div class=\"content\">\\n <a href=\"/buytickets/blue-mountains-national-capital-region-ncr/movie-ncr-ET00054552-MT/20170405/\">\\n <span class=\"__format\">\\n 2D\\n </span>\\n </a>\\n </div>\\n </section>\\n </div>\\n </div>\\n <div class=\"book-button\">\\n <a href=\"/buytickets/blue-mountains-national-capital-region-ncr/movie-ncr-ET00054552-MT/20170405/\">\\n <div class=\"__container\">\\n BOOK NOW\\n </div>\\n </a>\\n </div>\\n <span class=\"hideExperience\">\\n <span class=\"__text icon\">\\n <svg class=\"hideExperienceOnClick\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n </div>\\n </div>\\n </div>\\n <div class=\"banner-container\">\\n <div class=\"movie-card ns-card-single\">\\n <div class=\"card-container\">\\n <div class=\"poster-container-img\">\\n <img alt=\"Cheliyaa\" class=\"__poster\" data-error=\"//in.bmscdn.com/events/mobile/noimage.jpg\" data-lazy=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/cheliyaa-et00054562-09-03-2017-08-49-40.jpg\" data-mobile=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/cheliyaa-et00054562-09-03-2017-08-49-40.jpg\"/>\\n </div>\\n <div class=\"poster-container\">\\n <div class=\"__overlay\">\\n </div>\\n <div class=\"show-more-info\">\\n <ul class=\"info-list\">\\n <li class=\"info-list-elements\">\\n <span class=\"tooltip\">\\n INFO\\n </span>\\n <a href=\"/national-capital-region-ncr/movies/cheliyaa/ET00054562\">\\n <span class=\"info-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-info\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n <li class=\"info-list-elements\">\\n <span class=\"tooltip\">\\n TRAILER\\n </span>\\n <a href=\"/national-capital-region-ncr/movies/cheliyaa/ET00054562#trailer\">\\n <span class=\"trailer-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-play\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n </ul>\\n <div class=\"rating-section card-ratings\">\\n <div class=\"rate-o-meter\" data-event-code=\"ET00054562\">\\n 0\\n </div>\\n <ul class=\"rating-stars\" data-role=\"ratingStars\" event-code=\"ET00054562\" event-name=\"Cheliyaa\" id=\"dET00054562\">\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"0.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" 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js-ratingSpan\" data-value=\"4.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"5.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n </ul>\\n <div class=\"rating-head\">\\n ADD YOUR RATING\\n </div>\\n </div>\\n </div>\\n <div class=\"stats-wrapper\">\\n <div class=\"stats\">\\n <div class=\"certification\">\\n <div class=\"__container\">\\n <span class=\"icon-censor\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-u\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n </div>\\n <div class=\"popularity sa-data-plugin\" data-event-code=\"ET00054562\" data-event-group=\"EG00035708\">\\n <div class=\"__likes\">\\n <div class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__thumbs _none\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-like\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__percentage\">\\n </div>\\n </div>\\n <div class=\"__votes\">\\n <div class=\"__count\">\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"detail detail-scroll\">\\n <div class=\"__name overflowEllipses\">\\n <a class=\"__movie-name\" data-position=\"\" href=\"/national-capital-region-ncr/movies/cheliyaa/ET00054562\" onclick=\"GTMredirect(\\'Cheliyaa\\',\\'ET00054562\\',\\'cheliyaa\\',\\'Telugu\\',\\'7 Apr, 2017\\',$(this),\\'2D\\');\" title=\"Cheliyaa\">\\n Cheliyaa\\n </a>\\n </div>\\n <div class=\"languages\">\\n <ul class=\"language-list\">\\n <li class=\"__language\">\\n Telugu\\n </li>\\n </ul>\\n </div>\\n <div class=\"genre-list\">\\n <a href=\"/movies/action\">\\n <div class=\"__rounded-box __genre\">\\n Action\\n </div>\\n </a>\\n <a href=\"/movies/romance\">\\n <div class=\"__rounded-box __genre\">\\n Romance\\n </div>\\n </a>\\n </div>\\n <div class=\"show-details\">\\n <div class=\"cinema\">\\n <div class=\"__name\">\\n PVR\\n </div>\\n <div class=\"__location\">\\n Oberoi Mall, Goregaon (E)\\n </div>\\n </div>\\n <ul class=\"showtimes\">\\n <li class=\"__details\">\\n <div class=\"__day today\">\\n TODAY\\n </div>\\n <a class=\"__time\" href=\"#\">\\n <small class=\"timestamp\">\\n 8.30 pm\\n </small>\\n </a>\\n </li>\\n <li class=\"__details\">\\n <div class=\"__day\">\\n TOMORROW\\n </div>\\n <a class=\"__time\" href=\"#\">\\n <small class=\"timestamp\">\\n 8.30 pm\\n </small>\\n </a>\\n </li>\\n </ul>\\n <a class=\"more-showtimes\" href=\"#\">\\n <small>\\n more shows\\n </small>\\n </a>\\n </div>\\n </div>\\n <div class=\"experience-holder\" style=\"z-index: -1;\">\\n <div class=\"experience-list\">\\n <section class=\"language-based-formats\">\\n <h2 class=\"header\">\\n Telugu\\n </h2>\\n <div class=\"content\">\\n <a href=\"/buytickets/cheliyaa-national-capital-region-ncr/movie-ncr-ET00054562-MT/20170405/\">\\n <span class=\"__format\">\\n 2D\\n </span>\\n </a>\\n </div>\\n </section>\\n </div>\\n </div>\\n <div class=\"book-button\">\\n <a href=\"/buytickets/cheliyaa-national-capital-region-ncr/movie-ncr-ET00054562-MT/20170405/\">\\n <div class=\"__container\">\\n BOOK NOW\\n </div>\\n </a>\\n </div>\\n <span class=\"hideExperience\">\\n <span class=\"__text icon\">\\n <svg class=\"hideExperienceOnClick\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n </div>\\n </div>\\n </div>\\n <div class=\"banner-container\">\\n <div class=\"movie-card ns-card-single\">\\n <div class=\"card-container\">\\n <div class=\"poster-container-img\">\\n <img alt=\"Colossal\" class=\"__poster\" data-error=\"//in.bmscdn.com/events/mobile/noimage.jpg\" data-lazy=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/colossal-et00053593-16-02-2017-08-39-22.jpg\" data-mobile=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/colossal-et00053593-16-02-2017-08-39-22.jpg\"/>\\n </div>\\n <div class=\"poster-container\">\\n <div class=\"__overlay\">\\n </div>\\n <div class=\"show-more-info\">\\n <ul class=\"info-list\">\\n <li class=\"info-list-elements\">\\n <span class=\"tooltip\">\\n INFO\\n </span>\\n <a href=\"/national-capital-region-ncr/movies/colossal/ET00053593\">\\n <span class=\"info-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-info\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n <li class=\"info-list-elements\">\\n <span class=\"tooltip\">\\n TRAILER\\n </span>\\n <a href=\"/national-capital-region-ncr/movies/colossal/ET00053593#trailer\">\\n <span class=\"trailer-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-play\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n </ul>\\n <div class=\"rating-section card-ratings\">\\n <div class=\"rate-o-meter\" data-event-code=\"ET00053593\">\\n 0\\n </div>\\n <ul class=\"rating-stars\" data-role=\"ratingStars\" event-code=\"ET00053593\" event-name=\"Colossal\" id=\"dET00053593\">\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"0.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" 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js-ratingSpan\" data-value=\"4.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"5.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n </ul>\\n <div class=\"rating-head\">\\n ADD YOUR RATING\\n </div>\\n </div>\\n </div>\\n <div class=\"stats-wrapper\">\\n <div class=\"stats\">\\n <div class=\"certification\">\\n <div class=\"__container\">\\n <span class=\"icon-censor\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-a\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n </div>\\n <div class=\"popularity sa-data-plugin\" data-event-code=\"ET00053593\" data-event-group=\"EG00034783\">\\n <div class=\"__likes\">\\n <div class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__thumbs _none\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-like\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__percentage\">\\n </div>\\n </div>\\n <div class=\"__votes\">\\n <div class=\"__count\">\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"detail detail-scroll\">\\n <div class=\"__name overflowEllipses\">\\n <a class=\"__movie-name\" data-position=\"\" href=\"/national-capital-region-ncr/movies/colossal/ET00053593\" onclick=\"GTMredirect(\\'Colossal\\',\\'ET00053593\\',\\'colossal\\',\\'English\\',\\'7 Apr, 2017\\',$(this),\\'2D\\');\" title=\"Colossal\">\\n Colossal\\n </a>\\n </div>\\n <div class=\"languages\">\\n <ul class=\"language-list\">\\n <li class=\"__language\">\\n English\\n </li>\\n </ul>\\n </div>\\n <div class=\"genre-list\">\\n <a href=\"/movies/comedy\">\\n <div class=\"__rounded-box __genre\">\\n Comedy\\n </div>\\n </a>\\n <a href=\"/movies/sci-fi\">\\n <div class=\"__rounded-box __genre\">\\n Sci-Fi\\n </div>\\n </a>\\n <a href=\"/movies/thriller\">\\n <div class=\"__rounded-box __genre\">\\n Thriller\\n </div>\\n </a>\\n </div>\\n <div class=\"show-details\">\\n <div class=\"cinema\">\\n <div class=\"__name\">\\n PVR\\n </div>\\n <div class=\"__location\">\\n Oberoi Mall, Goregaon (E)\\n </div>\\n </div>\\n <ul class=\"showtimes\">\\n <li class=\"__details\">\\n <div class=\"__day today\">\\n TODAY\\n </div>\\n <a class=\"__time\" href=\"#\">\\n <small class=\"timestamp\">\\n 8.30 pm\\n </small>\\n </a>\\n </li>\\n <li class=\"__details\">\\n <div class=\"__day\">\\n TOMORROW\\n </div>\\n <a class=\"__time\" href=\"#\">\\n <small class=\"timestamp\">\\n 8.30 pm\\n </small>\\n </a>\\n </li>\\n </ul>\\n <a class=\"more-showtimes\" href=\"#\">\\n <small>\\n more shows\\n </small>\\n </a>\\n </div>\\n </div>\\n <div class=\"experience-holder\" style=\"z-index: -1;\">\\n <div class=\"experience-list\">\\n <section class=\"language-based-formats\">\\n <h2 class=\"header\">\\n English\\n </h2>\\n <div class=\"content\">\\n <a href=\"/buytickets/colossal-national-capital-region-ncr/movie-ncr-ET00053593-MT/20170405/\">\\n <span class=\"__format\">\\n 2D\\n </span>\\n </a>\\n </div>\\n </section>\\n </div>\\n </div>\\n <div class=\"book-button\">\\n <a href=\"/buytickets/colossal-national-capital-region-ncr/movie-ncr-ET00053593-MT/20170405/\">\\n <div class=\"__container\">\\n BOOK NOW\\n </div>\\n </a>\\n </div>\\n <span class=\"hideExperience\">\\n <span class=\"__text icon\">\\n <svg class=\"hideExperienceOnClick\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n </div>\\n </div>\\n </div>\\n <div class=\"banner-container\">\\n <div class=\"movie-card ns-card-single\">\\n <div class=\"card-container\">\\n <div class=\"poster-container-img\">\\n <img alt=\"Kaatru Veliyidai\" class=\"__poster\" data-error=\"//in.bmscdn.com/events/mobile/noimage.jpg\" data-lazy=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/et00043790_07-07-2016_02-49-40.jpg\" data-mobile=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/et00043790_07-07-2016_02-49-40.jpg\"/>\\n </div>\\n <div class=\"poster-container\">\\n <div class=\"__overlay\">\\n </div>\\n <div class=\"show-more-info\">\\n <ul class=\"info-list\">\\n <li class=\"info-list-elements\">\\n <span class=\"tooltip\">\\n INFO\\n </span>\\n <a href=\"/national-capital-region-ncr/movies/kaatru-veliyidai/ET00043790\">\\n <span class=\"info-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-info\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n <li class=\"info-list-elements\">\\n <span class=\"tooltip\">\\n TRAILER\\n </span>\\n <a href=\"/national-capital-region-ncr/movies/kaatru-veliyidai/ET00043790#trailer\">\\n <span class=\"trailer-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-play\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n </ul>\\n <div class=\"rating-section card-ratings\">\\n <div class=\"rate-o-meter\" data-event-code=\"ET00043790\">\\n 0\\n </div>\\n <ul class=\"rating-stars\" data-role=\"ratingStars\" event-code=\"ET00043790\" event-name=\"Kaatru Veliyidai\" id=\"dET00043790\">\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"0.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"1.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"1.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"2.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"2.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"3.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"3.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"4.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"4.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"5.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n </ul>\\n <div class=\"rating-head\">\\n ADD YOUR RATING\\n </div>\\n </div>\\n </div>\\n <div class=\"stats-wrapper\">\\n <div class=\"stats\">\\n <div class=\"certification\">\\n <div class=\"__container\">\\n <span class=\"icon-censor\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-u\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n </div>\\n <div class=\"popularity sa-data-plugin\" data-event-code=\"ET00043790\" data-event-group=\"EG00025764\">\\n <div class=\"__likes\">\\n <div class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__thumbs _none\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-like\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__percentage\">\\n </div>\\n </div>\\n <div class=\"__votes\">\\n <div class=\"__count\">\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"detail detail-scroll\">\\n <div class=\"__name overflowEllipses\">\\n <a class=\"__movie-name\" data-position=\"\" href=\"/national-capital-region-ncr/movies/kaatru-veliyidai/ET00043790\" onclick=\"GTMredirect(\\'Kaatru Veliyidai\\',\\'ET00043790\\',\\'kaatru-veliyidai\\',\\'Tamil\\',\\'7 Apr, 2017\\',$(this),\\'2D\\');\" title=\"Kaatru Veliyidai\">\\n Kaatru Veliyidai\\n </a>\\n </div>\\n <div class=\"languages\">\\n <ul class=\"language-list\">\\n <li class=\"__language\">\\n Tamil\\n </li>\\n </ul>\\n </div>\\n <div class=\"genre-list\">\\n <a href=\"/movies/action\">\\n <div class=\"__rounded-box __genre\">\\n Action\\n </div>\\n </a>\\n <a href=\"/movies/romance\">\\n <div class=\"__rounded-box __genre\">\\n Romance\\n </div>\\n </a>\\n </div>\\n <div class=\"show-details\">\\n <div class=\"cinema\">\\n <div class=\"__name\">\\n PVR\\n </div>\\n <div class=\"__location\">\\n Oberoi Mall, Goregaon (E)\\n </div>\\n </div>\\n <ul class=\"showtimes\">\\n <li class=\"__details\">\\n <div class=\"__day today\">\\n TODAY\\n </div>\\n <a class=\"__time\" href=\"#\">\\n <small class=\"timestamp\">\\n 8.30 pm\\n </small>\\n </a>\\n </li>\\n <li class=\"__details\">\\n <div class=\"__day\">\\n TOMORROW\\n </div>\\n <a class=\"__time\" href=\"#\">\\n <small class=\"timestamp\">\\n 8.30 pm\\n </small>\\n </a>\\n </li>\\n </ul>\\n <a class=\"more-showtimes\" href=\"#\">\\n <small>\\n more shows\\n </small>\\n </a>\\n </div>\\n </div>\\n <div class=\"experience-holder\" style=\"z-index: -1;\">\\n <div class=\"experience-list\">\\n <section class=\"language-based-formats\">\\n <h2 class=\"header\">\\n Tamil\\n </h2>\\n <div class=\"content\">\\n <a href=\"/buytickets/kaatru-veliyidai-national-capital-region-ncr/movie-ncr-ET00043790-MT/20170405/\">\\n <span class=\"__format\">\\n 2D\\n </span>\\n </a>\\n </div>\\n </section>\\n </div>\\n </div>\\n <div class=\"book-button\">\\n <a href=\"/buytickets/kaatru-veliyidai-national-capital-region-ncr/movie-ncr-ET00043790-MT/20170405/\">\\n <div class=\"__container\">\\n BOOK NOW\\n </div>\\n </a>\\n </div>\\n <span class=\"hideExperience\">\\n <span class=\"__text icon\">\\n <svg class=\"hideExperienceOnClick\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n </div>\\n </div>\\n 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version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-info\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n <li class=\"info-list-elements\">\\n <span class=\"tooltip\">\\n TRAILER\\n </span>\\n <a href=\"/national-capital-region-ncr/movies/laali-ki-shaadi-mein-laddoo-deewana/ET00053789#trailer\">\\n <span class=\"trailer-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-play\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n </ul>\\n <div class=\"rating-section card-ratings\">\\n <div class=\"rate-o-meter\" data-event-code=\"ET00053789\">\\n 0\\n </div>\\n <ul class=\"rating-stars\" 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xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n </ul>\\n <div class=\"rating-head\">\\n ADD YOUR RATING\\n </div>\\n </div>\\n </div>\\n <div class=\"stats-wrapper\">\\n <div class=\"stats\">\\n <div class=\"certification\">\\n <div class=\"__container\">\\n <span class=\"icon-censor\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ua\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n </div>\\n <div class=\"popularity sa-data-plugin\" data-event-code=\"ET00053789\" data-event-group=\"EG00034964\">\\n <div class=\"__likes\">\\n <div class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__thumbs _none\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-like\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__percentage\">\\n </div>\\n </div>\\n <div class=\"__votes\">\\n <div class=\"__count\">\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"detail detail-scroll\">\\n <div class=\"__name overflowEllipses\">\\n <a class=\"__movie-name\" data-position=\"\" href=\"/national-capital-region-ncr/movies/laali-ki-shaadi-mein-laddoo-deewana/ET00053789\" onclick=\"GTMredirect(\\'Laali Ki Shaadi Mein Laaddoo Deewana\\',\\'ET00053789\\',\\'laali-ki-shaadi-mein-laddoo-deewana\\',\\'Hindi\\',\\'7 Apr, 2017\\',$(this),\\'2D\\');\" title=\"Laali Ki Shaadi Mein Laaddoo Deewana\">\\n Laali Ki Shaadi Mein Laaddoo Deewana\\n </a>\\n </div>\\n <div class=\"languages\">\\n <ul class=\"language-list\">\\n <li class=\"__language\">\\n Hindi\\n </li>\\n </ul>\\n </div>\\n <div class=\"genre-list\">\\n <a href=\"/movies/comedy\">\\n <div class=\"__rounded-box __genre\">\\n Comedy\\n </div>\\n </a>\\n <a href=\"/movies/romance\">\\n <div class=\"__rounded-box __genre\">\\n Romance\\n </div>\\n </a>\\n </div>\\n <div class=\"show-details\">\\n <div class=\"cinema\">\\n <div class=\"__name\">\\n PVR\\n </div>\\n <div class=\"__location\">\\n Oberoi Mall, Goregaon (E)\\n </div>\\n </div>\\n <ul class=\"showtimes\">\\n <li class=\"__details\">\\n <div class=\"__day today\">\\n TODAY\\n </div>\\n <a class=\"__time\" href=\"#\">\\n <small class=\"timestamp\">\\n 8.30 pm\\n </small>\\n </a>\\n </li>\\n <li class=\"__details\">\\n <div class=\"__day\">\\n TOMORROW\\n </div>\\n <a class=\"__time\" href=\"#\">\\n <small class=\"timestamp\">\\n 8.30 pm\\n </small>\\n </a>\\n </li>\\n </ul>\\n <a class=\"more-showtimes\" href=\"#\">\\n <small>\\n more shows\\n </small>\\n </a>\\n </div>\\n </div>\\n <div class=\"experience-holder\" style=\"z-index: -1;\">\\n <div class=\"experience-list\">\\n <section class=\"language-based-formats\">\\n <h2 class=\"header\">\\n Hindi\\n </h2>\\n <div class=\"content\">\\n <a href=\"/buytickets/laali-ki-shaadi-mein-laddoo-deewana-national-capital-region-ncr/movie-ncr-ET00053789-MT/20170405/\">\\n <span class=\"__format\">\\n 2D\\n </span>\\n </a>\\n </div>\\n </section>\\n </div>\\n </div>\\n <div class=\"book-button\">\\n <a href=\"/buytickets/laali-ki-shaadi-mein-laddoo-deewana-national-capital-region-ncr/movie-ncr-ET00053789-MT/20170405/\">\\n <div class=\"__container\">\\n BOOK NOW\\n </div>\\n </a>\\n </div>\\n <span class=\"hideExperience\">\\n <span class=\"__text icon\">\\n <svg class=\"hideExperienceOnClick\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n </div>\\n </div>\\n </div>\\n <div class=\"banner-container\">\\n <div class=\"movie-card ns-card-single\">\\n <div class=\"card-container\">\\n <div class=\"poster-container-img\">\\n <img alt=\"Mirza Juuliet\" class=\"__poster\" data-error=\"//in.bmscdn.com/events/mobile/noimage.jpg\" data-lazy=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/mirza-juuliet-et00054388-07-03-2017-11-22-42.jpg\" data-mobile=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/mirza-juuliet-et00054388-07-03-2017-11-22-42.jpg\"/>\\n </div>\\n <div class=\"poster-container\">\\n <div class=\"__overlay\">\\n </div>\\n <div 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href=\"/buytickets/mirza-juuliet-national-capital-region-ncr/movie-ncr-ET00054388-MT/20170405/\">\\n <div class=\"__container\">\\n BOOK NOW\\n </div>\\n </a>\\n </div>\\n <span class=\"hideExperience\">\\n <span class=\"__text icon\">\\n <svg class=\"hideExperienceOnClick\" enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n </div>\\n </div>\\n </div>\\n <div class=\"banner-container\">\\n <div class=\"movie-card ns-card-single\">\\n <div class=\"card-container\">\\n <div class=\"poster-container-img\">\\n <img alt=\"Mukti Bhawan\" class=\"__poster\" data-error=\"//in.bmscdn.com/events/mobile/noimage.jpg\" data-lazy=\"//in.bmscdn.com/iedb/movies/images/mobile/listing/large/mukti-bhawan-et00053397-10-02-2017-07-15-28.jpg\" 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xlink:href=\\\\\\'/icons/common-icons.svg#icon-play\\\\\\'></use> </svg> </span> </a> </li></ul> </div> <div class=\\\\\\'stats-wrapper\\\\\\'> <div class=\\\\\\'stats\\\\\\'> <div class=\\\\\\'certification\\\\\\'> <div class=\\\\\\'__container\\\\\\'> <span class=\\\\\\'icon-censor\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-a\\\\\\'></use> </svg> </span> </div> </div> <div data-event-group=\\\\\\'EG00026488\\\\\\' data-event-code=\\\\\\'ET00044500\\\\\\' data-coming-soon=\\\\\\'true\\\\\\' class=\\\\\\'popularity sa-data-plugin\\\\\\'> <div class=\\\\\\'__likes\\\\\\'> <div class=\\\\\\'__heart _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' style=\\\\\\'fill: #D6181F;\\\\\\'\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-heart\\\\\\'></use> </svg> </div> <div class=\\\\\\'__thumbs _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-like\\\\\\'></use></svg> </div> <div class=\\\\\\'__percentage\\\\\\'></div> </div> <div class=\\\\\\'__votes\\\\\\'> <div class=\\\\\\'__count\\\\\\'>&nbsp;</div> </div> </div> </div> </div> </div> <div class=\\\\\\'detail detail-scroll\\\\\\'> <div class=\\\\\\'__name overflowEllipses\\\\\\'> <a class=\\\\\\'__movie-name\\\\\\' href=\\\\\\'/national-capital-region-ncr/movies/a-death-in-the-gunj-special-screening/ET00044500\\\\\\' title=\\\\\\'A Death In The Gunj (Special Screening)\\\\\\'>A Death In The Gunj (Special Screening)</a> </div> <div class=\\\\\\'languages\\\\\\'> <ul class=\\\\\\'language-list\\\\\\'> <li class=\\\\\\'__language\\\\\\'>English</li> </ul> </div> <div class=\\\\\\'genre-list\\\\\\'><a href=\\\\\"/movies/drama\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Drama</div></a><a href=\\\\\"/movies/thriller\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Thriller</div></a> </div> </div></div> </div> </div> <div class=\\\\\\'banner-container\\\\\\'> <div class=\\\\\\' movie-card cs-card-regular\\\\\\'> <div class=\\\\\\'card-container\\\\\\'> <div class=\\\\\\'poster-container-img\\\\\\'> <img class=\\\\\\'__poster\\\\\\' data-lazy=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/ghost-in-the-shell-et00040396-23-09-2016-06-34-09.jpg\\\\\\' data-mobile=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/ghost-in-the-shell-et00040396-23-09-2016-06-34-09.jpg\\\\\\' data-error=\\\\\\'//in.bmscdn.com/events/mobile/noimage.jpg\\\\\\' alt=\\\\\\'Ghost In The Shell\\\\\\'> </div> <div class=\\\\\\'poster-container\\\\\\'> <div class=\\\\\\'__overlay\\\\\\'></div> <div class=\\\\\\'release-info\\\\\\'> <div class=\\\\\\'date\\\\\\'><div class=\\\\\"day\\\\\">7</div><div class=\\\\\"month\\\\\">APR</div></div></div> <div class=\\\\\\'show-more-info\\\\\\'> <ul class=\\\\\\'info-list\\\\\\'> <li class=\\\\\\'info-list-elements\\\\\\'> <span class=\\\\\\'tooltip\\\\\\'>INFO</span> <a href=\\\\\\'/national-capital-region-ncr/movies/ghost-in-the-shell-3d/ET00040396\\\\\\'> <span class=\\\\\\'info-icon __icon\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-info\\\\\\'></use> </svg> </span> </a> </li> <li class=\\\\\\'info-list-elements\\\\\\'> <span class=\\\\\\'tooltip\\\\\\'>TRAILER</span> <a href=\\\\\\'/national-capital-region-ncr/movies/ghost-in-the-shell-3d/ET00040396#trailer\\\\\\'> <span class=\\\\\\'trailer-icon __icon\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-play\\\\\\'></use> </svg> </span> </a> </li></ul> </div> <div class=\\\\\\'stats-wrapper\\\\\\'> <div class=\\\\\\'stats\\\\\\'> <div class=\\\\\\'certification\\\\\\'> <div class=\\\\\\'__container\\\\\\'> <span class=\\\\\\'icon-censor\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-a\\\\\\'></use> </svg> </span> </div> </div> <div data-event-group=\\\\\\'EG00021057\\\\\\' data-event-code=\\\\\\'ET00040396\\\\\\' data-coming-soon=\\\\\\'true\\\\\\' class=\\\\\\'popularity sa-data-plugin\\\\\\'> <div class=\\\\\\'__likes\\\\\\'> <div class=\\\\\\'__heart _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' style=\\\\\\'fill: #D6181F;\\\\\\'\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-heart\\\\\\'></use> </svg> </div> <div class=\\\\\\'__thumbs _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-like\\\\\\'></use></svg> </div> <div class=\\\\\\'__percentage\\\\\\'></div> </div> <div class=\\\\\\'__votes\\\\\\'> <div class=\\\\\\'__count\\\\\\'>&nbsp;</div> </div> </div> </div> </div> </div> <div class=\\\\\\'detail detail-scroll\\\\\\'> <div class=\\\\\\'__name overflowEllipses\\\\\\'> <a class=\\\\\\'__movie-name\\\\\\' href=\\\\\\'/national-capital-region-ncr/movies/ghost-in-the-shell-3d/ET00040396\\\\\\' title=\\\\\\'Ghost In The Shell\\\\\\'>Ghost In The Shell</a> </div> <div class=\\\\\\'languages\\\\\\'> <ul class=\\\\\\'language-list\\\\\\'> <li class=\\\\\\'__language\\\\\\'>English</li> </ul> </div> <div class=\\\\\\'genre-list\\\\\\'><a href=\\\\\"/movies/action\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Action</div></a><a href=\\\\\"/movies/drama\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Drama</div></a><a href=\\\\\"/movies/sci-fi\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Sci-Fi</div></a> </div> </div></div> </div> </div> <div class=\\\\\\'banner-container\\\\\\'> <div class=\\\\\\' movie-card cs-card-regular\\\\\\'> <div class=\\\\\\'card-container\\\\\\'> <div class=\\\\\\'poster-container-img\\\\\\'> <img class=\\\\\\'__poster\\\\\\' data-lazy=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/their-finest-et00054039-28-02-2017-09-55-07.jpg\\\\\\' data-mobile=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/their-finest-et00054039-28-02-2017-09-55-07.jpg\\\\\\' data-error=\\\\\\'//in.bmscdn.com/events/mobile/noimage.jpg\\\\\\' alt=\\\\\\'Their Finest\\\\\\'> </div> <div class=\\\\\\'poster-container\\\\\\'> <div class=\\\\\\'__overlay\\\\\\'></div> <div class=\\\\\\'release-info\\\\\\'> <div class=\\\\\\'date\\\\\\'><div class=\\\\\"day\\\\\">7</div><div class=\\\\\"month\\\\\">APR</div></div></div> <div class=\\\\\\'show-more-info\\\\\\'> <ul class=\\\\\\'info-list\\\\\\'> <li class=\\\\\\'info-list-elements\\\\\\'> <span class=\\\\\\'tooltip\\\\\\'>INFO</span> <a href=\\\\\\'/national-capital-region-ncr/movies/their-finest/ET00054039\\\\\\'> <span class=\\\\\\'info-icon __icon\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-info\\\\\\'></use> </svg> </span> </a> </li> <li class=\\\\\\'info-list-elements\\\\\\'> <span class=\\\\\\'tooltip\\\\\\'>TRAILER</span> <a href=\\\\\\'/national-capital-region-ncr/movies/their-finest/ET00054039#trailer\\\\\\'> <span class=\\\\\\'trailer-icon __icon\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-play\\\\\\'></use> </svg> </span> </a> </li></ul> </div> <div class=\\\\\\'stats-wrapper\\\\\\'> <div class=\\\\\\'stats\\\\\\'> <div data-event-group=\\\\\\'EG00035173\\\\\\' data-event-code=\\\\\\'ET00054039\\\\\\' data-coming-soon=\\\\\\'true\\\\\\' class=\\\\\\'popularity sa-data-plugin\\\\\\'> <div class=\\\\\\'__likes\\\\\\'> <div class=\\\\\\'__heart _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' style=\\\\\\'fill: #D6181F;\\\\\\'\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-heart\\\\\\'></use> </svg> </div> <div class=\\\\\\'__thumbs _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-like\\\\\\'></use></svg> </div> <div class=\\\\\\'__percentage\\\\\\'></div> </div> <div class=\\\\\\'__votes\\\\\\'> <div class=\\\\\\'__count\\\\\\'>&nbsp;</div> </div> </div> </div> </div> </div> <div class=\\\\\\'detail detail-scroll\\\\\\'> <div class=\\\\\\'__name overflowEllipses\\\\\\'> <a class=\\\\\\'__movie-name\\\\\\' href=\\\\\\'/national-capital-region-ncr/movies/their-finest/ET00054039\\\\\\' title=\\\\\\'Their Finest\\\\\\'>Their Finest</a> </div> <div class=\\\\\\'languages\\\\\\'> <ul class=\\\\\\'language-list\\\\\\'> <li class=\\\\\\'__language\\\\\\'>English</li> </ul> </div> <div class=\\\\\\'genre-list\\\\\\'><a href=\\\\\"/movies/comedy\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Comedy</div></a><a href=\\\\\"/movies/drama\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Drama</div></a><a href=\\\\\"/movies/romance\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Romance</div></a> </div> </div></div> </div> </div> <div class=\\\\\\'banner-container\\\\\\'> <div class=\\\\\\' movie-card cs-card-regular\\\\\\'> <div class=\\\\\\'card-container\\\\\\'> <div class=\\\\\\'poster-container-img\\\\\\'> <img class=\\\\\\'__poster\\\\\\' data-lazy=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/naachiyar-et00054058-28-02-2017-02-51-37.jpg\\\\\\' data-mobile=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/naachiyar-et00054058-28-02-2017-02-51-37.jpg\\\\\\' data-error=\\\\\\'//in.bmscdn.com/events/mobile/noimage.jpg\\\\\\' alt=\\\\\\'Naachiyar\\\\\\'> </div> <div class=\\\\\\'poster-container\\\\\\'> <div class=\\\\\\'__overlay\\\\\\'></div> <div class=\\\\\\'release-info\\\\\\'> <div class=\\\\\\'date\\\\\\'><div class=\\\\\"day\\\\\">2017</div><div class=\\\\\"month\\\\\"></div></div></div> <div class=\\\\\\'show-more-info\\\\\\'> <ul class=\\\\\\'info-list\\\\\\'> <li class=\\\\\\'info-list-elements\\\\\\'> <span class=\\\\\\'tooltip\\\\\\'>INFO</span> <a href=\\\\\\'/national-capital-region-ncr/movies/naachiyar/ET00054058\\\\\\'> <span class=\\\\\\'info-icon __icon\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-info\\\\\\'></use> </svg> </span> </a> </li></ul> </div> <div class=\\\\\\'stats-wrapper\\\\\\'> <div class=\\\\\\'stats\\\\\\'> <div data-event-group=\\\\\\'EG00035214\\\\\\' data-event-code=\\\\\\'ET00054058\\\\\\' data-coming-soon=\\\\\\'true\\\\\\' class=\\\\\\'popularity sa-data-plugin\\\\\\'> <div class=\\\\\\'__likes\\\\\\'> <div class=\\\\\\'__heart _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' style=\\\\\\'fill: #D6181F;\\\\\\'\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-heart\\\\\\'></use> </svg> </div> <div class=\\\\\\'__thumbs _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-like\\\\\\'></use></svg> </div> <div class=\\\\\\'__percentage\\\\\\'></div> </div> <div class=\\\\\\'__votes\\\\\\'> <div class=\\\\\\'__count\\\\\\'>&nbsp;</div> </div> </div> </div> </div> </div> <div class=\\\\\\'detail detail-scroll\\\\\\'> <div class=\\\\\\'__name overflowEllipses\\\\\\'> <a class=\\\\\\'__movie-name\\\\\\' href=\\\\\\'/national-capital-region-ncr/movies/naachiyar/ET00054058\\\\\\' title=\\\\\\'Naachiyar\\\\\\'>Naachiyar</a> </div> <div class=\\\\\\'languages\\\\\\'> <ul class=\\\\\\'language-list\\\\\\'> <li class=\\\\\\'__language\\\\\\'>Tamil</li> </ul> </div> <div class=\\\\\\'genre-list\\\\\\'><a href=\\\\\"/movies/drama\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Drama</div></a> </div> </div></div> </div> </div> <div class=\\\\\\'banner-container\\\\\\'> <div class=\\\\\\' movie-card cs-card-regular\\\\\\'> <div class=\\\\\\'card-container\\\\\\'> <div class=\\\\\\'poster-container-img\\\\\\'> <img class=\\\\\\'__poster\\\\\\' data-lazy=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/rocky-mental-et00054659-12-03-2017-02-57-42.jpg\\\\\\' data-mobile=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/rocky-mental-et00054659-12-03-2017-02-57-42.jpg\\\\\\' data-error=\\\\\\'//in.bmscdn.com/events/mobile/noimage.jpg\\\\\\' alt=\\\\\\'Rocky Mental\\\\\\'> </div> <div class=\\\\\\'poster-container\\\\\\'> <div class=\\\\\\'__overlay\\\\\\'></div> <div class=\\\\\\'release-info\\\\\\'> <div class=\\\\\\'date\\\\\\'><div class=\\\\\"day\\\\\">7</div><div class=\\\\\"month\\\\\">APR</div></div></div> <div class=\\\\\\'show-more-info\\\\\\'> <ul class=\\\\\\'info-list\\\\\\'> <li class=\\\\\\'info-list-elements\\\\\\'> <span class=\\\\\\'tooltip\\\\\\'>INFO</span> <a href=\\\\\\'/national-capital-region-ncr/movies/rocky-mental/ET00054659\\\\\\'> <span class=\\\\\\'info-icon __icon\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-info\\\\\\'></use> </svg> </span> </a> </li></ul> </div> <div class=\\\\\\'stats-wrapper\\\\\\'> <div class=\\\\\\'stats\\\\\\'> <div class=\\\\\\'certification\\\\\\'> <div class=\\\\\\'__container\\\\\\'> <span class=\\\\\\'icon-censor\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-ua\\\\\\'></use> </svg> </span> </div> </div> <div data-event-group=\\\\\\'EG00035781\\\\\\' data-event-code=\\\\\\'ET00054659\\\\\\' data-coming-soon=\\\\\\'true\\\\\\' class=\\\\\\'popularity sa-data-plugin\\\\\\'> <div class=\\\\\\'__likes\\\\\\'> <div class=\\\\\\'__heart _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' style=\\\\\\'fill: #D6181F;\\\\\\'\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-heart\\\\\\'></use> </svg> </div> <div class=\\\\\\'__thumbs _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-like\\\\\\'></use></svg> </div> <div class=\\\\\\'__percentage\\\\\\'></div> </div> <div class=\\\\\\'__votes\\\\\\'> <div class=\\\\\\'__count\\\\\\'>&nbsp;</div> </div> </div> </div> </div> </div> <div class=\\\\\\'detail detail-scroll\\\\\\'> <div class=\\\\\\'__name overflowEllipses\\\\\\'> <a class=\\\\\\'__movie-name\\\\\\' href=\\\\\\'/national-capital-region-ncr/movies/rocky-mental/ET00054659\\\\\\' title=\\\\\\'Rocky Mental\\\\\\'>Rocky Mental</a> </div> <div class=\\\\\\'languages\\\\\\'> <ul class=\\\\\\'language-list\\\\\\'> <li class=\\\\\\'__language\\\\\\'>Punjabi</li> </ul> </div> <div class=\\\\\\'genre-list\\\\\\'><a href=\\\\\"/movies/drama\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Drama</div></a> </div> </div></div> </div> </div> <div class=\\\\\\'banner-container\\\\\\'> <div class=\\\\\\' movie-card cs-card-regular\\\\\\'> <div class=\\\\\\'card-container\\\\\\'> <div class=\\\\\\'poster-container-img\\\\\\'> <img class=\\\\\\'__poster\\\\\\' data-lazy=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/enthavaraku-ee-prema-et00055053-23-03-2017-09-50-30.jpg\\\\\\' data-mobile=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/enthavaraku-ee-prema-et00055053-23-03-2017-09-50-30.jpg\\\\\\' data-error=\\\\\\'//in.bmscdn.com/events/mobile/noimage.jpg\\\\\\' alt=\\\\\\'Enthavaraku Ee Prema\\\\\\'> </div> <div class=\\\\\\'poster-container\\\\\\'> <div class=\\\\\\'__overlay\\\\\\'></div> <div class=\\\\\\'release-info\\\\\\'> <div class=\\\\\\'date\\\\\\'><div class=\\\\\"day\\\\\">7</div><div class=\\\\\"month\\\\\">APR</div></div></div> <div class=\\\\\\'show-more-info\\\\\\'> <ul class=\\\\\\'info-list\\\\\\'> <li class=\\\\\\'info-list-elements\\\\\\'> <span class=\\\\\\'tooltip\\\\\\'>INFO</span> <a href=\\\\\\'/national-capital-region-ncr/movies/enthavaraku-ee-prema/ET00055053\\\\\\'> <span class=\\\\\\'info-icon __icon\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-info\\\\\\'></use> </svg> </span> </a> </li> <li class=\\\\\\'info-list-elements\\\\\\'> <span class=\\\\\\'tooltip\\\\\\'>TRAILER</span> <a href=\\\\\\'/national-capital-region-ncr/movies/enthavaraku-ee-prema/ET00055053#trailer\\\\\\'> <span class=\\\\\\'trailer-icon __icon\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-play\\\\\\'></use> </svg> </span> </a> </li></ul> </div> <div class=\\\\\\'stats-wrapper\\\\\\'> <div class=\\\\\\'stats\\\\\\'> <div class=\\\\\\'certification\\\\\\'> <div class=\\\\\\'__container\\\\\\'> <span class=\\\\\\'icon-censor\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-ua\\\\\\'></use> </svg> </span> </div> </div> <div data-event-group=\\\\\\'EG00036170\\\\\\' data-event-code=\\\\\\'ET00055053\\\\\\' data-coming-soon=\\\\\\'true\\\\\\' class=\\\\\\'popularity sa-data-plugin\\\\\\'> <div class=\\\\\\'__likes\\\\\\'> <div class=\\\\\\'__heart _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' style=\\\\\\'fill: #D6181F;\\\\\\'\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-heart\\\\\\'></use> </svg> </div> <div class=\\\\\\'__thumbs _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-like\\\\\\'></use></svg> </div> <div class=\\\\\\'__percentage\\\\\\'></div> </div> <div class=\\\\\\'__votes\\\\\\'> <div class=\\\\\\'__count\\\\\\'>&nbsp;</div> </div> </div> </div> </div> </div> <div class=\\\\\\'detail detail-scroll\\\\\\'> <div class=\\\\\\'__name overflowEllipses\\\\\\'> <a class=\\\\\\'__movie-name\\\\\\' href=\\\\\\'/national-capital-region-ncr/movies/enthavaraku-ee-prema/ET00055053\\\\\\' title=\\\\\\'Enthavaraku Ee Prema\\\\\\'>Enthavaraku Ee Prema</a> </div> <div class=\\\\\\'languages\\\\\\'> <ul class=\\\\\\'language-list\\\\\\'> <li class=\\\\\\'__language\\\\\\'>Telugu</li> </ul> </div> <div class=\\\\\\'genre-list\\\\\\'><a href=\\\\\"/movies/comedy\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Comedy</div></a><a href=\\\\\"/movies/romance\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Romance</div></a> </div> </div></div> </div> </div> <div class=\\\\\\'banner-container\\\\\\'> <div class=\\\\\\' movie-card cs-card-regular\\\\\\'> <div class=\\\\\\'card-container\\\\\\'> <div class=\\\\\\'poster-container-img\\\\\\'> <img class=\\\\\\'__poster\\\\\\' data-lazy=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/8-thottakkal-et00055019-21-03-2017-03-24-13.jpg\\\\\\' data-mobile=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/8-thottakkal-et00055019-21-03-2017-03-24-13.jpg\\\\\\' data-error=\\\\\\'//in.bmscdn.com/events/mobile/noimage.jpg\\\\\\' alt=\\\\\\'8 Thottakkal\\\\\\'> </div> <div class=\\\\\\'poster-container\\\\\\'> <div class=\\\\\\'__overlay\\\\\\'></div> <div class=\\\\\\'release-info\\\\\\'> <div class=\\\\\\'date\\\\\\'><div class=\\\\\"day\\\\\">7</div><div class=\\\\\"month\\\\\">APR</div></div></div> <div class=\\\\\\'show-more-info\\\\\\'> <ul class=\\\\\\'info-list\\\\\\'> <li class=\\\\\\'info-list-elements\\\\\\'> <span class=\\\\\\'tooltip\\\\\\'>INFO</span> <a href=\\\\\\'/national-capital-region-ncr/movies/8-thottakkal/ET00055019\\\\\\'> <span class=\\\\\\'info-icon __icon\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-info\\\\\\'></use> </svg> </span> </a> </li> <li class=\\\\\\'info-list-elements\\\\\\'> <span class=\\\\\\'tooltip\\\\\\'>TRAILER</span> <a href=\\\\\\'/national-capital-region-ncr/movies/8-thottakkal/ET00055019#trailer\\\\\\'> <span class=\\\\\\'trailer-icon __icon\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-play\\\\\\'></use> </svg> </span> </a> </li></ul> </div> <div class=\\\\\\'stats-wrapper\\\\\\'> <div class=\\\\\\'stats\\\\\\'> <div class=\\\\\\'certification\\\\\\'> <div class=\\\\\\'__container\\\\\\'> <span class=\\\\\\'icon-censor\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-u\\\\\\'></use> </svg> </span> </div> </div> <div data-event-group=\\\\\\'EG00036128\\\\\\' data-event-code=\\\\\\'ET00055019\\\\\\' data-coming-soon=\\\\\\'true\\\\\\' class=\\\\\\'popularity sa-data-plugin\\\\\\'> <div class=\\\\\\'__likes\\\\\\'> <div class=\\\\\\'__heart _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' style=\\\\\\'fill: #D6181F;\\\\\\'\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-heart\\\\\\'></use> </svg> </div> <div class=\\\\\\'__thumbs _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-like\\\\\\'></use></svg> </div> <div class=\\\\\\'__percentage\\\\\\'></div> </div> <div class=\\\\\\'__votes\\\\\\'> <div class=\\\\\\'__count\\\\\\'>&nbsp;</div> </div> </div> </div> </div> </div> <div class=\\\\\\'detail detail-scroll\\\\\\'> <div class=\\\\\\'__name overflowEllipses\\\\\\'> <a class=\\\\\\'__movie-name\\\\\\' href=\\\\\\'/national-capital-region-ncr/movies/8-thottakkal/ET00055019\\\\\\' title=\\\\\\'8 Thottakkal\\\\\\'>8 Thottakkal</a> </div> <div class=\\\\\\'languages\\\\\\'> <ul class=\\\\\\'language-list\\\\\\'> <li class=\\\\\\'__language\\\\\\'>Tamil</li> </ul> </div> <div class=\\\\\\'genre-list\\\\\\'><a href=\\\\\"/movies/crime\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Crime</div></a><a href=\\\\\"/movies/thriller\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Thriller</div></a> </div> </div></div> </div> </div> <div class=\\\\\\'banner-container\\\\\\'> <div class=\\\\\\' movie-card cs-card-regular\\\\\\'> <div class=\\\\\\'card-container\\\\\\'> <div class=\\\\\\'poster-container-img\\\\\\'> <img class=\\\\\\'__poster\\\\\\' data-lazy=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/ayyanaar-veethi-et00055496-03-04-2017-03-09-57.jpg\\\\\\' data-mobile=\\\\\\'//in.bmscdn.com/iedb/movies/images/mobile/listing/large/ayyanaar-veethi-et00055496-03-04-2017-03-09-57.jpg\\\\\\' data-error=\\\\\\'//in.bmscdn.com/events/mobile/noimage.jpg\\\\\\' alt=\\\\\\'Ayyanaar Veethi\\\\\\'> </div> <div class=\\\\\\'poster-container\\\\\\'> <div class=\\\\\\'__overlay\\\\\\'></div> <div class=\\\\\\'release-info\\\\\\'> <div class=\\\\\\'date\\\\\\'><div class=\\\\\"day\\\\\">7</div><div class=\\\\\"month\\\\\">APR</div></div></div> <div class=\\\\\\'show-more-info\\\\\\'> <ul class=\\\\\\'info-list\\\\\\'> <li class=\\\\\\'info-list-elements\\\\\\'> <span class=\\\\\\'tooltip\\\\\\'>INFO</span> <a href=\\\\\\'/national-capital-region-ncr/movies/ayyanaar-veethi/ET00055496\\\\\\'> <span class=\\\\\\'info-icon __icon\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-info\\\\\\'></use> </svg> </span> </a> </li> <li class=\\\\\\'info-list-elements\\\\\\'> <span class=\\\\\\'tooltip\\\\\\'>TRAILER</span> <a href=\\\\\\'/national-capital-region-ncr/movies/ayyanaar-veethi/ET00055496#trailer\\\\\\'> <span class=\\\\\\'trailer-icon __icon\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'> <use xlink:href=\\\\\\'/icons/common-icons.svg#icon-play\\\\\\'></use> </svg> </span> </a> </li></ul> </div> <div class=\\\\\\'stats-wrapper\\\\\\'> <div class=\\\\\\'stats\\\\\\'> <div data-event-group=\\\\\\'EG00036597\\\\\\' data-event-code=\\\\\\'ET00055496\\\\\\' data-coming-soon=\\\\\\'true\\\\\\' class=\\\\\\'popularity sa-data-plugin\\\\\\'> <div class=\\\\\\'__likes\\\\\\'> <div class=\\\\\\'__heart _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' style=\\\\\\'fill: #D6181F;\\\\\\'\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-heart\\\\\\'></use> </svg> </div> <div class=\\\\\\'__thumbs _none\\\\\\'> <svg version=\\\\\\'1.1\\\\\\' xmlns=\\\\\\'http://www.w3.org/2000/svg\\\\\\' xmlns:xlink=\\\\\\'http://www.w3.org/1999/xlink\\\\\\' x=\\\\\\'0px\\\\\\' y=\\\\\\'0px\\\\\\' viewBox=\\\\\\'0 0 100 100\\\\\\' enable-background=\\\\\\'new 0 0 100 100\\\\\\' xml:space=\\\\\\'preserve\\\\\\'><use xlink:href=\\\\\\'/icons/common-icons.svg#icon-like\\\\\\'></use></svg> </div> <div class=\\\\\\'__percentage\\\\\\'></div> </div> <div class=\\\\\\'__votes\\\\\\'> <div class=\\\\\\'__count\\\\\\'>&nbsp;</div> </div> </div> </div> </div> </div> <div class=\\\\\\'detail detail-scroll\\\\\\'> <div class=\\\\\\'__name overflowEllipses\\\\\\'> <a class=\\\\\\'__movie-name\\\\\\' href=\\\\\\'/national-capital-region-ncr/movies/ayyanaar-veethi/ET00055496\\\\\\' title=\\\\\\'Ayyanaar Veethi\\\\\\'>Ayyanaar Veethi</a> </div> <div class=\\\\\\'languages\\\\\\'> <ul class=\\\\\\'language-list\\\\\\'> <li class=\\\\\\'__language\\\\\\'>Tamil</li> </ul> </div> <div class=\\\\\\'genre-list\\\\\\'><a href=\\\\\"/movies/drama\\\\\"><div class=\\\\\"__rounded-box __genre\\\\\">Drama</div></a> </div> </div></div> </div> </div>\";\\n </script>\\n <!-- -->\\n </div>\\n </div>\\n <div class=\"__view-more\">\\n <a class=\"btn _dos\" href=\"/movies/\">\\n </a>\\n VIEW MORE\\n </div>\\n </div>\\n <!-- Just for you ends -->\\n </div>\\n </div>\\n </section>\\n <section class=\"handpicked-experience section-bg\">\\n <!-- Handpicked experience head -->\\n <div class=\"section-head\">\\n <div class=\"__title\">\\n <div class=\"__ribbon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ribbon\">\\n </use>\\n </svg>\\n </div>\\n Handpick your\\n <span class=\"redFont wow\">\\n Experience\\n <img alt=\"Stroke\" data-src=\"//in.bmscdn.com/webin/homepage/red-stroke.png\"/>\\n </span>\\n </div>\\n <div class=\"__subtitle\">\\n Discover events, handpicked by us, just for you!\\n </div>\\n </div>\\n <!-- Handpicked cards-->\\n <div class=\"section-body showcase experience-carousel\" id=\"handpick-carousel\">\\n <div class=\"viewport showcase-carousel\">\\n </div>\\n </div>\\n <!-- Explore experience -->\\n <div class=\"explore-more-button\">\\n <a class=\"btn _dos\" href=\"/experiences/\">\\n EXPLORE EXPERIENCES\\n </a>\\n </div>\\n </section>\\n <!-- Popular Videos -->\\n <section class=\"popular-videos section-bg\">\\n <div class=\"wrapper\">\\n <div class=\"section-head\">\\n <h2 class=\"__title\">\\n POPULAR VIDEOS\\n </h2>\\n <div class=\"__red-bar\">\\n </div>\\n </div>\\n <div class=\"__video-list\" id=\"popular-videos\">\\n </div>\\n <div class=\"view-all-btn\">\\n <a class=\"btn _dos\" href=\"/national-capital-region-ncr/videos\">\\n View more videos\\n </a>\\n </div>\\n <!-- Ad stack -->\\n <section class=\"marketing-adspot section-bg\">\\n <div class=\"adspot-content\">\\n <div class=\"col wow fadeIn\" data-wow-delay=\"0.1s\" data-wow-offset=\"10\">\\n <div class=\"section-head wow\">\\n GIFTS\\n <div class=\"__red-bar\">\\n </div>\\n </div>\\n <div class=\"__image\">\\n <img alt=\"GiftMyShow\" data-mobile=\"//in.bmscdn.com/webin/homepage/temp/GMS_logo.png\" data-src=\"//in.bmscdn.com/webin/homepage/temp/GMS_logo.png\"/>\\n </div>\\n <div class=\"__text\">\\n With GiftMyShow cards, you can gift your friends &amp; family movie &amp; play tickets, concert passes, whatever it is they love for their birthdays, anniversaries or simply for no reason other than how you feel about them. Pretty sweet, aint it?\\n </div>\\n <div class=\"align-center\">\\n <div>\\n <a class=\"btn _dos\" href=\"/giftcards/\">\\n BUY A GIFT CARD\\n </a>\\n </div>\\n </div>\\n </div>\\n <div class=\"col wow fadeIn\" data-wow-delay=\"0.1s\" data-wow-offset=\"10\">\\n <div class=\"section-head\">\\n Offers\\n <div class=\"__red-bar\">\\n </div>\\n </div>\\n <div class=\"__image\">\\n <span class=\"icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/home-icons.svg#icon-offer\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"__text\">\\n Delight yourself with crazy offers while you book your tickets. Whether its cashback, freebies or discounts you\\'re after, there\\'s a can\\'t-miss bargain for every single one of you.\\n </div>\\n <div class=\"align-center\">\\n <div>\\n <a class=\"btn _dos\" href=\"/offers/\">\\n View Offers\\n </a>\\n </div>\\n </div>\\n </div>\\n <div class=\"col wow fadeIn\" data-wow-delay=\"0.1s\" data-wow-offset=\"10\">\\n <div class=\"section-head\">\\n Mobile app\\n <div class=\"__red-bar\">\\n </div>\\n </div>\\n <div class=\"__image\">\\n <span class=\"icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/home-icons.svg#icon-mobile\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"__text\">\\n Book your tickets on the go, only with a couple of clicks. Choose from a whopping 3,000+ cinema screens across India and book as late as 20 minutes before showtime for those spur-of-the-moment plans.\\n </div>\\n <div class=\"align-center\">\\n <div>\\n <a class=\"btn _dos\" href=\"/mobile/\">\\n Download Now\\n </a>\\n </div>\\n </div>\\n </div>\\n </div>\\n </section>\\n <!-- Blog roll -->\\n <section class=\"blog-post-wrapper section-bg merge-bg\">\\n <div class=\"blog-posts row\">\\n <div class=\"section-head\">\\n <h2 class=\"__title\">\\n FROM OUR BLOG\\n </h2>\\n <div class=\"__red-bar\">\\n </div>\\n <!-- red baseline bar -->\\n </div>\\n <div class=\"row\" id=\"featuredBlog\" style=\"margin: 0;\">\\n </div>\\n </div>\\n </section>\\n <!-- App showcase -->\\n <section class=\"app-showcase-section section-bg merge-bg\">\\n <div class=\"container\">\\n <div class=\"content\">\\n <div class=\"col\">\\n <div class=\"__head\">\\n SELECT A FLICK\\n <br/>\\n <span class=\"__pwac\">\\n PAY WITH A CLICK\\n </span>\\n <!-- You can make <br><span>My wallet</span><br> More Awesome... -->\\n </div>\\n <div class=\"__content\">\\n With BookMyShow\\'s MyWallet, paying for your movie and event tickets online is going to be insanely easy. Like, one-click easy.\\n </div>\\n <div class=\"facilities\">\\n <ul>\\n <li>\\n <span class=\"__img\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/home-icons.svg#icon-click\">\\n </use>\\n </svg>\\n </span>\\n <br/>\\n <span class=\"__text\">\\n One-Click\\n <br/>\\n Payment\\n </span>\\n </li>\\n <li>\\n <span class=\"__img\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/home-icons.svg#icon-instant\">\\n </use>\\n </svg>\\n </span>\\n <br/>\\n <span class=\"__text\">\\n Instant\\n <br/>\\n Refunds\\n </span>\\n </li>\\n <li>\\n <span class=\"__img\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/home-icons.svg#icon-safe\">\\n </use>\\n </svg>\\n </span>\\n <br/>\\n <span class=\"__text\">\\n 100%\\n <br/>\\n Safe &amp; Secure\\n </span>\\n </li>\\n <li class=\"__fc-quicker\">\\n <span class=\"__img\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/home-icons.svg#icon-quick\">\\n </use>\\n </svg>\\n </span>\\n <br/>\\n <span class=\"__text\">\\n Quicker than\\n <br/>\\n third party wallets\\n </span>\\n </li>\\n </ul>\\n <div class=\"__action\">\\n <a class=\"btn _cuatro\" id=\"btnWalletHome\">\\n ADD CASH\\n </a>\\n </div>\\n </div>\\n </div>\\n <div class=\"wow fadeIn ad-image\">\\n <img alt=\"MyWallet\" data-src=\"//in.bmscdn.com/webin/static/home-page-ad/wallet-img.png\"/>\\n </div>\\n </div>\\n </div>\\n </section>\\n </div>\\n <!-- Wrapper ends -->\\n <script>\\n if(global.blnIsTouchScreen) {\\n\\t\\t\\t\\tvar cinemaListAPI=\\'-\\';\\n\\t\\t\\t}\\n </script>\\n <script src=\"//in.bookmyshow.com/js/home-802514a37a.js\">\\n </script>\\n <div class=\"your-reservation-block\" id=\"reserveBlock\" style=\"display: none;\">\\n </div>\\n <!-- Footer starts here -->\\n <footer style=\"display: block;\">\\n <!-- Supplimentary action start here -->\\n <div class=\"sup-action\">\\n <div class=\"sup-wrapper\">\\n <a class=\"col sup-action-container\" href=\"https://support.bookmyshow.com/support/home?regionCode=NCR\">\\n <div class=\"__sup-icon\">\\n <span>\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use 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href=\"/videos/tv-news\" title=\"MyTV\">\\n <span>\\n MyTV\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/videos/fashion-lifestyle\" title=\"MyStyle\">\\n <span>\\n MyStyle\\n </span>\\n </a>\\n </li>\\n </ul>\\n </div>\\n <div class=\"col\" id=\"country\">\\n <h4>\\n COUNTRIES\\n </h4>\\n <ul class=\"groups\">\\n <li>\\n <a href=\"http://bd.bookmyshow.com/\" rel=\"noopener noreferrer\" target=\"_blank\" title=\"Bangladesh\">\\n <span>\\n Bangladesh\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"http://id.bookmyshow.com/\" rel=\"noopener noreferrer\" target=\"_blank\" title=\"Indonesia\">\\n <span>\\n Indonesia\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"http://bookmyshow.co.nz/\" rel=\"noopener noreferrer\" target=\"_blank\" title=\"New Zealand\">\\n <span>\\n New Zealand\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"http://ae.bookmyshow.com/\" rel=\"noopener noreferrer\" target=\"_blank\" title=\"UAE\">\\n <span>\\n UAE\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"http://lk.bookmyshow.com\" rel=\"noopener noreferrer\" target=\"_blank\" title=\"Sri Lanka\">\\n <span>\\n Sri Lanka\\n </span>\\n </a>\\n </li>\\n </ul>\\n </div>\\n </div>\\n <div class=\"col movie-trailers\">\\n <h4>\\n EVENTS TICKETS BOOKING ONLINE\\n </h4>\\n <ul class=\"groups\">\\n <li>\\n <a href=\"/mumbai/events\" title=\"Upcoming Events in Mumbai\">\\n <span>\\n Upcoming Events in Mumbai\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <div style=\"word-wrap: break-word;\">\\n <a href=\"/delhi/events\" title=\"Upcoming Events in Delhi\">\\n <span>\\n Upcoming Events in Delhi\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </div>\\n </li>\\n <li>\\n <a href=\"/bengaluru/events\" title=\"Upcoming Events in Bangalore\">\\n <span>\\n Upcoming Events in Bangalore\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/chennai/events\" title=\"Upcoming Events in Chennai\">\\n <span>\\n Upcoming Events in Chennai\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/hyderabad/events\" title=\"Upcoming Events in Hyderabad\">\\n <span>\\n Upcoming Events in Hyderabad\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/pune/events\" title=\"Upcoming Events in Pune\">\\n <span>\\n Upcoming Events in Pune\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/noida/events\" title=\"Upcoming Events in Noida\">\\n <span>\\n Upcoming Events in Noida\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/ahmedabad/events\" title=\"Upcoming Events in ahmedabad\">\\n <span>\\n Upcoming Events in ahmedabad\\n </span>\\n </a>\\n </li>\\n </ul>\\n </div>\\n <div class=\"col cinemas-and-regions\">\\n <h4>\\n MOVIES, CINEMAS &amp; CELEBRITY\\n </h4>\\n <ul class=\"groups\">\\n <li>\\n <a href=\"/movies/comingsoon\" title=\"Latest Upcoming Movies\">\\n <span>\\n Latest Upcoming Movies\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/movies/nowshowing\" title=\"Best Now Showing Movies\">\\n <span>\\n Best Now Showing Movies\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/cinemas\" title=\"Cinemas &amp;amp; Theatres\">\\n <span>\\n Cinemas &amp; Theatres\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/person\" title=\" Movie Stars &amp;amp; Celebrities\">\\n <span>\\n Movie Stars &amp; Celebrities\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/cinema-chains\" title=\"Cinema Chains\">\\n <span>\\n Cinema Chains\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/regions\" title=\"All regions Cinemas\">\\n <span>\\n All regions Cinemas\\n </span>\\n </a>\\n </li>\\n </ul>\\n </div>\\n <div class=\"col movie-trailers\">\\n <h4>\\n ARCHIVED COLLECTION\\n </h4>\\n <ul class=\"groups\">\\n <li>\\n <a href=\"/years/2017/movies\" title=\"Old Movies\">\\n <span>\\n Old Movies\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/years/2017/events\" title=\"Past Events\">\\n <span>\\n Past Events\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/years/2017/sports\" title=\"Classic Sports Events\">\\n <span>\\n Classic Sports Events\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/years/2017/plays\" title=\"Old Theatre Plays\">\\n <span>\\n Old Theatre Plays\\n </span>\\n </a>\\n </li>\\n </ul>\\n </div>\\n </div>\\n <div class=\"row\">\\n <div class=\"col app\">\\n <h4>\\n BOOKMYSHOW APP\\n </h4>\\n <ul class=\"groups\">\\n <li>\\n <a href=\"/mobile?app=iphone\" title=\"BMS Ticketing iOS App\">\\n <span>\\n BMS Ticketing iOS App\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/mobile?app=android\" title=\"BMS Ticketing Android App\">\\n <span>\\n BMS Ticketing Android App\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/mobile?app=windows\" title=\"BMS Ticketing Windows App\">\\n <span>\\n BMS Ticketing Windows App\\n </span>\\n </a>\\n </li>\\n <!--li><a href=\"https://apps.facebook.com/ticketbuddy/\" rel=\"nofollow\" title=\"Ticket Buddy\"><span>Facebook - Ticket Buddy</span></a></li-->\\n </ul>\\n </div>\\n <div class=\"col news\">\\n <h4>\\n BOOKMYSHOW NEWS\\n </h4>\\n <ul class=\"groups\">\\n <li>\\n <a href=\"//in.bookmyshow.com/entertainment\" title=\"Entertainment Blog\">\\n <span>\\n Entertainment Blog\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/static/press-release\" title=\"Press Release\">\\n <span>\\n Press Release\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/static/press-coverage\" title=\"Press Coverage\">\\n <span>\\n Press Coverage\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <!-- <li><a href=\"/static/press-contact\" title=\"Public Relations\"><span>Public Relations</span></a></li> -->\\n <!-- <li><a href=\"/aboutus\" title=\"About Us\"><span>About Us</span></a></li> -->\\n <li>\\n <a href=\"http://in.bookmyshow.com/careers\" title=\"Current Openings\">\\n <span>\\n Current Openings\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/contactus/\" title=\"Contact Us\">\\n <span>\\n Contact Us\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/aboutus\" title=\"ABout Us\">\\n <span>\\n About Us\\n </span>\\n </a>\\n </li>\\n </ul>\\n </div>\\n <div class=\"col help\">\\n <h4>\\n EXCLUSIVES\\n </h4>\\n <ul class=\"groups\">\\n <li>\\n <a href=\"/donation\" title=\"BookASmile\">\\n <span>\\n BookASmile\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/voucher\" title=\"Corporate Vouchers\">\\n <span>\\n Corporate Vouchers\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/giftcards\" onclick=\"ga(\\'send\\', \\'event\\', \\'Gift Card\\', \\'Source_click\\', \\'Footer - Gift Card text\\');\" title=\"Gift Cards\">\\n <span>\\n Gift Cards\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/referral-program\" title=\"ReferMyFriend\">\\n <span>\\n ReferMyFriend\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/static/unpaid-advance-movie-booking\" title=\"Reserve Your Seat\">\\n <span>\\n Reserve Your Seat\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/coupons\" title=\"My Coupons\">\\n <span>\\n My Coupons\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/mywallet/store-locator\" title=\"MyWallet Recharge Stores\">\\n <span class=\"recharge-stores\">\\n Find MyWallet Recharge Stores\\n </span>\\n </a>\\n <span class=\"store-new\">\\n NEW\\n </span>\\n </li>\\n </ul>\\n </div>\\n <div class=\"col exclusives\">\\n <h4>\\n HELP\\n </h4>\\n <ul class=\"groups\">\\n <li>\\n <a href=\"/static/sitemap\" title=\"Sitemap\">\\n <span>\\n Sitemap\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <!-- <li><a href=\"/feedback\" title=\"Feedback\"><span>Feedback</span></a></li> -->\\n <li>\\n <a href=\"/faq\" title=\"FAQs\">\\n <span>\\n FAQs\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/terms-and-conditions\" title=\"Terms and Conditions\">\\n <span>\\n Terms and Conditions\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"/privacy\" title=\"Privacy Policy\">\\n <span>\\n Privacy Policy\\n </span>\\n <span class=\"seprator\">\\n |\\n </span>\\n </a>\\n </li>\\n <li>\\n <a href=\"javascript:;\" onclick=\"ContentReport.reportButtonClickListener(event);\" title=\"Report Content\">\\n <span>\\n Report Content\\n </span>\\n </a>\\n </li>\\n </ul>\\n </div>\\n </div>\\n </div>\\n <div class=\"footer-links \">\\n </div>\\n <!-- Footer links Ends here -->\\n <div class=\"footer-end\">\\n <div class=\"__footer-bms-logo\">\\n <span>\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-bms\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"__footer-social\">\\n <a href=\"https://www.facebook.com/BookMyShowIN\" rel=\"nofollow noopener noreferrer\" target=\"_blank\" title=\"Like Us on Facebook\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-facebook\">\\n </use>\\n </svg>\\n </a>\\n <a href=\"http://www.twitter.com/BookMyShow/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\" title=\"Follow Us on Twitter\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-twitter\">\\n </use>\\n </svg>\\n </a>\\n <a href=\"https://instagram.com/bmsbookmyshow/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\" title=\"Instagram\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-instagram\">\\n </use>\\n </svg>\\n </a>\\n <a href=\"http://www.youtube.com/user/BookMyShow/featured\" rel=\"nofollow noopener noreferrer\" target=\"_blank\" title=\"You Tube\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-youtube\">\\n </use>\\n </svg>\\n </a>\\n <a href=\"http://pinterest.com/bookmyshow/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\" title=\"Pinterest\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-pinterest\">\\n </use>\\n </svg>\\n </a>\\n <a href=\"https://plus.google.com/110517543803442814698/posts\" rel=\"nofollow noopener noreferrer\" target=\"_blank\" title=\"Follow Us on Google+\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-googleplus\">\\n </use>\\n </svg>\\n </a>\\n <a href=\"http://www.linkedin.com/company/bookmyshow/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\" title=\"Follow Us on Linkedin\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-linkedin\">\\n </use>\\n </svg>\\n </a>\\n <p>\\n Copyright 2017 © Bigtree Entertainment Pvt. Ltd. All Rights Reserved.\\n <br/>\\n The content and images used on this site are copyright protected and copyrights vests with the respective owners. The usage of the content and images on this website is intended to promote the works and no endorsement of the artist shall be implied. Unauthorized use is prohibited and punishable by law.\\n </p>\\n <p class=\"report-content\" onclick=\"ContentReport.reportButtonClickListener(event);\">\\n </p>\\n </div>\\n </div>\\n </div>\\n </div>\\n </footer>\\n </section>\\n </div>\\n <!-- Primary layout wrapper ends | Only for touch-based devices -->\\n <!-- quickbook section starts here-->\\n <div class=\"\" data-always-fixed=\"\" id=\"quickbook-wrapper\">\\n <!-- Searchbox code starts here -->\\n <div class=\"qb-search-box-container\">\\n <div class=\"search-container\">\\n <div class=\"search-dropdown\">\\n <button data-isall=\"1\" data-type=\"movies\" data-value=\"MT\" id=\"btn-dd-search\">\\n <span class=\"icon-type\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-all\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"selected-type\">\\n All\\n </span>\\n <span class=\"arrow-down\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-arrow-down-new\">\\n </use>\\n </svg>\\n </span>\\n </button>\\n <ul class=\"search-dropdown-menu\">\\n <li class=\"none\">\\n <a class=\"__item\" data-isall=\"1\" data-placeholder=\"Book Tickets for Movies, Events, Plays &amp; Sports\" data-search-placeholder=\"Book Tickets for Movies, Events, Plays &amp; Sports\" data-type=\"movies\" data-value=\"MT\" href=\"#\">\\n <span class=\"icon-type\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-all\">\\n </use>\\n </svg>\\n </span>\\n All\\n </a>\\n </li>\\n <li class=\"\">\\n <a class=\"__item\" data-isall=\"0\" data-placeholder=\"Book a Movie or Pick a Cinema\" data-search-placeholder=\"Search for a Movie or Cinema\" data-type=\"movies\" data-value=\"MT\" href=\"#\">\\n <span class=\"icon-type\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-movie\">\\n </use>\\n </svg>\\n </span>\\n Movies\\n </a>\\n </li>\\n <li class=\"\">\\n <a class=\"__item\" data-isall=\"0\" data-placeholder=\"Pick an Event or Venue\" data-search-placeholder=\"Search for an Event or Venue\" data-type=\"events\" data-value=\"CT\" href=\"#\">\\n <span class=\"icon-type\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-event\">\\n </use>\\n </svg>\\n </span>\\n Events\\n </a>\\n </li>\\n <li class=\"\">\\n <a class=\"__item\" data-isall=\"0\" data-placeholder=\"Pick a Play or Theater\" data-search-placeholder=\"Search for a Play or Theater\" data-type=\"plays\" data-value=\"PL\" href=\"#\">\\n <span class=\"icon-type\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-plays\">\\n </use>\\n </svg>\\n </span>\\n Plays\\n </a>\\n </li>\\n <li class=\"\">\\n <a class=\"__item\" data-isall=\"0\" data-placeholder=\"Pick a Sport or Venue\" data-search-placeholder=\"Search for a Sport or Venue\" data-type=\"sports\" data-value=\"SP\" href=\"#\">\\n <span class=\"icon-type\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-sports\">\\n </use>\\n </svg>\\n </span>\\n Sports\\n </a>\\n </li>\\n </ul>\\n </div>\\n <input class=\"search-box typeahead\" placeholder=\"Book Tickets for Movies, Events, Plays &amp; Sports\" type=\"text\"/>\\n <span class=\"search-list-icon\" id=\"search-list-icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-list\">\\n </use>\\n </svg>\\n </span>\\n <!-- <div class=\"qb-region none\">\\n\\t\\t\\t<a class=\"location\" id=\"qb-region-link\">\\n\\t <span class=\"icon-location\">\\n\\t <svg version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n\\t <use xlink:href=\"/icons/common-icons.svg#icon-location\"></use>\\n\\t </svg>\\n\\t </span>\\n\\t <span class=\"region-name\"></span>\\n\\t </a>\\n\\t\\t</div> -->\\n </div>\\n <div class=\"preferred-cinemas-container none\">\\n </div>\\n </div>\\n <!-- Templates for preferred cinemas -->\\n <script id=\"temp-preferred-cinemas\" type=\"text/template\">\\n <span class=\"label\">{{label}}: </span>\\n\\t{{data}}\\n </script>\\n <script id=\"temp-preferred-cinema-item\" type=\"text/template\">\\n <a href=\"{{url}}\">{{name}}</a>\\n </script>\\n <!-- Searchbox code ends here -->\\n <!-- HTML for Modal window -->\\n <div class=\"qb-modal-overlay none\">\\n <div class=\"modal none\">\\n <div class=\"modal-content\">\\n <div class=\"modal-header\">\\n <!-- <button id=\"btn-back\">\\n\\t\\t\\t\\t\\t\\t<span class=\"icon-back-btn\">\\n\\t\\t\\t\\t\\t\\t\\t<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n\\t\\t\\t\\t\\t\\t\\t\\t<path fill=\"#000000\" d=\"M21.53,50.809L71.688,0.651c0,0,2.685-1.87,5.285,0.73c2.604,2.601,0,4.877,0,4.877l-44.63,44.631 l42.517,42.516c0,0,2.211,2.666-0.164,5.039c-2.373,2.375-5.688,0-5.688,0L21.53,50.809z\"></path>\\n\\t\\t\\t\\t\\t\\t\\t</svg>\\n\\t\\t\\t\\t\\t\\t</span>\\n\\t\\t\\t\\t\\t</button> -->\\n <span class=\"icon-back-btn\" id=\"btn-back\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <path d=\"M21.53,50.809L71.688,0.651c0,0,2.685-1.87,5.285,0.73c2.604,2.601,0,4.877,0,4.877l-44.63,44.631 l42.517,42.516c0,0,2.211,2.666-0.164,5.039c-2.373,2.375-5.688,0-5.688,0L21.53,50.809z\">\\n </path>\\n </svg>\\n </span>\\n <button id=\"btn-close\">\\n <span class=\"icon-cancel\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </button>\\n </div>\\n <div class=\"modal-body\">\\n <div class=\"qb-overflow-wrapper\">\\n <div class=\"qb-overflow-container\">\\n <div class=\"quickbook-recommendation-container reco-events hidden\">\\n <div class=\"quickbook-recommendation-wrapper reco-events\">\\n <div class=\"quickbook-recommendation-header reco-events\">\\n <span class=\"qb-symbol qp-plus\">\\n +\\n </span>\\n <span class=\"qb-symbol qp-minus\">\\n -\\n </span>\\n <span class=\"recommendation-text reco-events\">\\n Recommended Events\\n </span>\\n </div>\\n <div class=\"quickbook-recommendation-body reco-events\">\\n </div>\\n </div>\\n </div>\\n <div class=\"quickbook-recommendation-container reco-sports hidden\">\\n <div class=\"quickbook-recommendation-wrapper reco-sports\">\\n <div class=\"quickbook-recommendation-header reco-sports\">\\n <span class=\"qb-symbol qp-plus\">\\n +\\n </span>\\n <span class=\"qb-symbol qp-minus\">\\n -\\n </span>\\n <span class=\"recommendation-text reco-sports\">\\n Recommended Sports\\n </span>\\n </div>\\n <div class=\"quickbook-recommendation-body reco-sports\">\\n </div>\\n </div>\\n </div>\\n <div class=\"quickbook-recommendation-container reco-plays hidden\">\\n <div class=\"quickbook-recommendation-wrapper reco-plays\">\\n <div class=\"quickbook-recommendation-header reco-plays\">\\n <span class=\"qb-symbol qp-plus\">\\n +\\n </span>\\n <span class=\"qb-symbol qp-minus\">\\n -\\n </span>\\n <span class=\"recommendation-text reco-plays\">\\n Recommended Plays\\n </span>\\n </div>\\n <div class=\"quickbook-recommendation-body reco-plays\">\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"quickbook-body\">\\n </div>\\n <!-- <div class=\"top-trending none\" id=\"qb-top-trending\">\\n\\t\\t\\t\\t\\t\\t\\t<div class=\"top-trending-header\">\\n\\t\\t\\t\\t\\t\\t\\t\\tTop Trending Movies\\n\\t\\t\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t\\t\\t<div class=\"top-trending-list\"> -->\\n <!-- </div>\\n\\t\\t\\t\\t\\t\\t</div> -->\\n </div>\\n </div>\\n <div class=\"modal-footer\">\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Templates for generic elements -->\\n <script id=\"temp-event\" type=\"text/template\">\\n <div class=\"evt-dim-container {{containerCssClass}}\">\\n \\t{{seenContent}}\\n <div class=\"__event-container\">\\n \\t<a class=\"__event {{cssClass}}\" data-id=\"{{eventId}}\" href=\"{{eventURL}}\">{{text}}</a>\\n \\t{{recommendedContent}}\\n \\t{{ratingContent}}\\n \\t</div>\\n {{dimensions}}\\n {{ratingHTML}}\\n </div>\\n </script>\\n <script id=\"temp-venue\" type=\"text/template\">\\n <div class=\"cinema-container {{containerCssClass}}\">\\n \\t<a class=\"__venue {{cssClass}}\" data-id=\"{{venueCode}}\" href=\"{{venueUrl}}\">{{text}}</a>\\n {{unpaidContent}}\\n \\t</div>\\n </script>\\n <script id=\"temp-unpaid\" type=\"text/template\">\\n <span class=\"__unpaid-icon\">\\n <span class=\"icon\">\\n <svg xml:space=\"preserve\" enable-background=\"new 0 0 100 100\" viewBox=\"0 0 100 100\" y=\"0px\" x=\"0px\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\\n <use xlink:href=\"/icons/buytickets-icons.svg#icon-reserve-seats\"/>\\n </svg>\\n <div class=\"tooltip\">\\n <span class=\"__arrow\"></span>\\n <span class=\"__content\">Reserve seats and pay online 1 hour before the show</span>\\n </div>\\n </span>\\n </span>\\n </script>\\n <script id=\"temp-seen-evt-svg\" type=\"text/template\">\\n <span class=\"__seen-event\">\\n \\t\\t<svg version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n \\t<use xlink:href=\"/icons/common-icons.svg#icon-tick-green\"></use>\\n </svg>\\n\\t\\t</span>\\n </script>\\n <script id=\"temp-recommended-evt-svg\" type=\"text/template\">\\n <span class=\"__recommended-event\">\\n \\t\\t<svg version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n \\t<use xlink:href=\"/icons/common-icons.svg#icon-recommend\"></use>\\n </svg>\\n\\t\\t</span>\\n </script>\\n <script id=\"temp-rating-evt-svg\" type=\"text/template\">\\n <span class=\"__rating-container {{cssRating}}\">\\n \\t\\t<svg version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n \\t<use xlink:href=\"/icons/common-icons.svg#icon-heart\"></use>\\n </svg>\\n <span class = \"__rating-value\">{{rating}}%</span>\\n <span class = \"__event-votes\">{{votes}} Votes</span>\\n\\t\\t</span>\\n </script>\\n <script id=\"temp-view-more\" type=\"text/template\">\\n <a class=\"__view-more\" href=\"#\">View All</a>\\n </script>\\n <script id=\"temp-filter\" type=\"text/template\">\\n <div class=\"filter-item-container\">\\n <span class=\"__filter {{cssClass}}\" data-filter-key=\"{{filterKey}}\" data-filter-value=\"{{filterValue}}\" data-active=\"{{active}}\">{{text}}</span>\\n \\t{{hiddenFilters}}\\n </div>\\n </script>\\n <script id=\"temp-hidden-filter\" type=\"text/template\">\\n <li href=\"#\" class=\"__hidden struktur {{cssClass}}\">\\n\\t\\t <input type=\"checkbox\" id=\"filter-{{filterValue}}\" data-filter-key=\"{{filterKey}}\" data-filter-value=\"{{filterValue}}\" data-active=\"{{active}}\" value=\"0\">\\n\\t\\t <label for=\"filter-{{filterValue}}\">\\n\\t\\t <span class=\"__tick\">\\n\\t\\t\\t\\t\\t<svg version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n\\t\\t\\t\\t\\t\\t<use xlink:href=\"/icons/common-icons.svg#icon-tick\"></use>\\n\\t\\t\\t\\t\\t</svg>\\n\\t\\t\\t\\t</span>\\n\\t\\t\\t\\t{{text}}\\n\\t\\t\\t</label>\\n\\t\\t</li>\\n </script>\\n <script id=\"temp-label\" type=\"text/template\">\\n <div class=\"__label {{cssClass}}\">{{text}}</div>\\n </script>\\n <script id=\"temp-grid-col\" type=\"text/template\">\\n <div class=\"list-container\">\\n <ul class=\"unstyled\">{{listHtml}}</ul>\\n {{viewMore}}\\n </div>\\n </script>\\n <script id=\"temp-qb-listing-tabs\" type=\"text/template\">\\n <div class=\"btn-group\">\\n\\t\\t <button class=\"btn _uno _active tab-toggle primary-tab\">{{primaryButtonText}}</button>\\n\\t\\t <button class=\"btn _uno tab-toggle secondary-tab\">{{secondaryButtonText}}</button>\\n\\t\\t</div>\\n </script>\\n <!-- Template for no or empty result set -->\\n <script id=\"temp-no-results\" type=\"text/template\">\\n <div class=\"__no-results\">\\n \\t\\t <div class=\"__data-not-found\">\\n\\t \\t\\t<div class=\"__icon\">\\n\\t\\t \\t\\t<svg version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n\\t \\t<use xlink:href=\"/icons/common-icons.svg#icon-no-result\"></use>\\n\\t </svg>\\n \\t\\t\\t</div>\\n \\t\\t\\t<div class=\"__text\"><span class=\"__red-text\">Oops!</span> No results found</div>\\n \\t\\t</div>\\n \\t</div>\\n </script>\\n <!-- Template for no or empty result set when filters are applied -->\\n <script id=\"temp-no-results-with-filters\" type=\"text/template\">\\n <div class=\"__no-results\">\\n \\t\\t// The Selected filter didnt match any search result. Try using other filters or select All to clear filters.\\n \\t</div>\\n </script>\\n <!-- Template for ajax request error -->\\n <script id=\"temp-ajax-error\" type=\"text/template\">\\n </script>\\n <!-- Template for loader -->\\n <script id=\"temp-qb-loader\" type=\"text/template\">\\n <div class=\"qb-loader\">\\n \\t\\t<span class=\"reel __roll1\">\\n\\t\\t\\t\\t<span class=\"mini-circle\"></span>\\n\\t\\t\\t\\t<span class=\"mini-circle __center\"></span>\\n\\t\\t\\t\\t<span class=\"mini-circle __left\"></span>\\n\\t\\t\\t\\t<span class=\"mini-circle __right\"></span>\\n\\t\\t\\t\\t<span class=\"mini-circle __bottom\"></span>\\n\\t\\t\\t</span>\\n\\t\\t</div>\\n </script>\\n <!-- Templates for quickbook movies -->\\n <script id=\"temp-quickbook-container\" type=\"text/template\">\\n <div id=\"{{quickbookType}}\" class=\"quickbook-container {{containerClass}}\">\\n \\t<div class=\"tabs-filters\">\\n\\t \\t<div class=\"qb-tabs none\"></div>\\n\\t \\t<div class=\"filters none\"></div>\\n \\t</div>\\n <div class=\"grid none\"></div>\\n <div class=\"venue-grid none\"></div>\\n </div>\\n </script>\\n <script id=\"temp-filter-set\" type=\"text/template\">\\n <div class=\"filter-set {{cssClasses}}\">{{filters}}</div>\\n </script>\\n <script id=\"temp-dimension\" type=\"text/template\">\\n <a data-id=\"{{eventId}}\" class=\"__dimension\" href=\"{{eventURL}}\">{{text}}</a>\\n </script>\\n <script id=\"temp-event-rating\" type=\"text/template\">\\n <div class=\"ratings-container\">\\n \\tRate the movie\\n </div>\\n </script>\\n <script id=\"temp-hidden-filter-set\" type=\"text/template\">\\n <div class=\"__filter-menu\">\\n\\t\\t\\t<ul>{{filters}}</ul>\\n </div>\\n </script>\\n <script async=\"\" type=\"text/javascript\">\\n var SeatData = null;\\n var getSeatScript = \\'//in.bookmyshow.com/js/seatlayout-8bd672e5bb.js\\';\\n </script>\\n </div>\\n <!-- Quickbook section ends here -->\\n <!-- Common Modals starts here -->\\n <div class=\"modal popovers-modal\">\\n <!-- Global overlay -->\\n <div class=\"__overlay\">\\n </div>\\n <div class=\"__overlay-scroll-signin\">\\n </div>\\n <!-- Signin modal -->\\n <div class=\"popover _signin-signup-popover\" id=\"signinPopup\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n SIGN IN\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <aside class=\"column signin-column\">\\n <div class=\"section-head\">\\n <h2>\\n A world of\\n <span class=\"jenna-sue\">\\n Entertainment Awaits.\\n </span>\\n </h2>\\n <p>\\n Instant sign in with\\n </p>\\n </div>\\n <div class=\"social-links\">\\n <a href=\"javascript:\" onclick=\"BMS.SignIn.fnFbWrapper(); return false;\" style=\"cursor: pointer;\">\\n <div class=\"auth-method icon-facebook\">\\n <div class=\"auth-method-title _facebook\">\\n <span class=\"icon-auth-method _facebook\">\\n <span class=\"icon \">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-facebook-modal\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n FACEBOOK\\n </div>\\n </div>\\n </a>\\n <a href=\"javascript:\" onclick=\"BMS.SignIn.fnGPlusWrapper(); return false;\" style=\"cursor: pointer;\">\\n <div class=\"auth-method icon-googleplus\">\\n <div class=\"auth-method-title _gplus\">\\n <span class=\"icon-auth-method _gplus\">\\n <span class=\"icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-googleplus-modal\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n GOOGLE\\n </div>\\n </div>\\n </a>\\n </div>\\n <div class=\"divider\">\\n <span class=\"or\">\\n OR\\n </span>\\n </div>\\n <div class=\"signin-with-username\">\\n <p class=\"error-message\">\\n <span class=\"__exclaim-icon\">\\n </span>\\n <span class=\"message\">\\n <span class=\"error-heading\">\\n error :\\n </span>\\n oops please enter a valid email address.\\n </span>\\n </p>\\n <form class=\"signin-form struktur\" id=\"iUserNameParent\">\\n <input class=\"email-input\" id=\"iUserName\" minlength=\"1\" pattern=\"[a-z0-9._%+-]+@[a-z0-9.-]+\\\\.[a-z]{2,4}$\" placeholder=\"Enter your Email ID\" required=\"\" type=\"text\"/>\\n <div class=\"form-messages _success\" id=\"dSignInErrorEmail\" style=\"display: none;\">\\n <p class=\"__text\">\\n </p>\\n </div>\\n <input autocomplete=\"off\" class=\"password-input\" id=\"iPwd\" minlength=\"1\" placeholder=\"Enter your password\" type=\"password\"/>\\n <div class=\"form-messages _success\" id=\"dSignInErrorPassword\" style=\"display: none;\">\\n <p class=\"__text\">\\n </p>\\n </div>\\n <!-- <div style=\"display: none;\" class=\"form-messages _success\" id=\"dAfterSignInSuccess\">\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"__text\">Welcome back,<span id=\"spnUName\"></span></p>\\n\\t\\t\\t\\t\\t\\t\\t\\t</div> -->\\n <a href=\"javascript:;\" onclick=\"BMS.SignIn.fnValLogIn(); return false;\">\\n <div class=\"submit-form\">\\n SIGN IN\\n </div>\\n </a>\\n </form>\\n <div class=\"forgot-password\">\\n <a href=\"#\">\\n <span class=\"__text\" data-modal=\"forgotPopup\">\\n FORGOT PASSWORD?\\n </span>\\n </a>\\n </div>\\n </div>\\n </aside>\\n <aside class=\"column signup-redirect\" style=\"display:none;\">\\n <div class=\"heading\">\\n Still haven\\'t\\n <span class=\"jenna-sue-red\">\\n signed up?\\n </span>\\n </div>\\n <div class=\"registration\">\\n <span class=\"icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-registration\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"description\">\\n <p>\\n Want to rate and review movies you\\'ve watched?\\n <br/>\\n All you need to do is sign up!\\n </p>\\n </div>\\n <a href=\"#\">\\n <div class=\"signup-now-button btn _uno\" data-modal=\"signupPopup\">\\n SIGN UP NOW\\n </div>\\n </a>\\n </aside>\\n </div>\\n <div class=\"redirect-bottom\">\\n <p>\\n Still not connected?\\n <a class=\"signup-now-button\" data-modal=\"signupPopup\">\\n Sign Up\\n </a>\\n </p>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Signup modal -->\\n <div class=\"popover _signin-signup-popover\" id=\"signupPopup\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n SIGN UP\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <aside class=\"column signin-column\">\\n <div class=\"section-head\">\\n <h3>\\n Want to be a part of the\\n <span class=\"jenna-sue\">\\n Awesomeness?\\n </span>\\n </h3>\\n </div>\\n <div class=\"social-links\">\\n <a href=\"javascript:\" onclick=\"BMS.SignIn.fnFbWrapper(); return false;\" style=\"cursor: pointer;\">\\n <div class=\"auth-method icon-facebook\">\\n <div class=\"auth-method-title _facebook\">\\n <span class=\"icon-auth-method _facebook\">\\n <span class=\"icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-facebook-modal\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n FACEBOOK\\n </div>\\n </div>\\n </a>\\n <a href=\"javascript:\" onclick=\"BMS.SignIn.fnGPlusWrapper(); return false;\" style=\"cursor: pointer;\">\\n <div class=\"auth-method icon-googleplus\">\\n <div class=\"auth-method-title _gplus\">\\n <span class=\"icon-auth-method _gplus\">\\n <span class=\"icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-googleplus-modal\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n GOOGLE\\n </div>\\n </div>\\n </a>\\n </div>\\n <div class=\"divider\">\\n <span class=\"or\">\\n OR\\n </span>\\n </div>\\n <div class=\"signup-with-username\">\\n <p class=\"error-message\">\\n <span class=\"__exclaim-icon\">\\n </span>\\n <span class=\"message\">\\n <b>\\n error :\\n </b>\\n oops please enter a valid email address.\\n </span>\\n <b>\\n </b>\\n </p>\\n <form class=\"signup-form struktur\" id=\"iSignUpParent\">\\n <input class=\"email-input\" id=\"iRegUserEmail\" pattern=\"[a-z0-9._%+-]+@[a-z0-9.-]+\\\\.[a-z]{2,4}$\" placeholder=\"EMAIL\" required=\"\" type=\"text\"/>\\n <div class=\"form-messages _success\" id=\"dSignUpErrorEmail\" style=\"display: none;\">\\n <p class=\"__text\">\\n </p>\\n </div>\\n <input class=\"password-input\" id=\"iRegPwd\" placeholder=\"PASSWORD\" type=\"password\"/>\\n <div class=\"form-messages _success\" id=\"dSignUpErrorPassword\" style=\"display: none;\">\\n <p class=\"__text\">\\n </p>\\n </div>\\n <input class=\"confirm-password-input\" id=\"iRegCnfPwd\" placeholder=\"CONFIRM PASSWORD\" type=\"password\"/>\\n <div class=\"form-messages _success\" id=\"dSignUpErrorCnfPassword\" style=\"display: none;\">\\n <p class=\"__text\">\\n </p>\\n </div>\\n <a href=\"javascript:;\" onclick=\"BMS.SignIn.fnRegUser(); return false;\">\\n <div class=\"submit-form\">\\n SIGN UP\\n </div>\\n </a>\\n </form>\\n </div>\\n </aside>\\n <aside class=\"column signup-description\" style=\"display:none;\">\\n <div class=\"heading\">\\n Be a part of an\\n <span class=\"bold\">\\n Entertainment Extravaganza\\n </span>\\n </div>\\n <ol class=\"description\">\\n <li class=\"list\">\\n Want to rate and review movies you\\'ve watched.\\n </li>\\n <li class=\"list\">\\n Grab the best seats in the house.\\n </li>\\n <li class=\"list\">\\n Keep up with our amazing offers and discounts.\\n </li>\\n <li class=\"list\">\\n Make your payments fast and secure. Quikpay and other exciting stuffs!\\n </li>\\n <li class=\"list\">\\n Booking tickets has never been this hassle-free.\\n </li>\\n <li class=\"list\">\\n Make the most of our amazing features!\\n </li>\\n </ol>\\n </aside>\\n </div>\\n <div class=\"redirect-bottom\">\\n <p>\\n Already Connected?\\n <a class=\"signup-now-button\" data-modal=\"signinPopup\">\\n Sign In\\n </a>\\n </p>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Forgot password modal -->\\n <div class=\"popover _forgot-password\" id=\"forgotPopup\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n FORGOT PASSWORD\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <aside class=\"column\">\\n <div class=\"section-head\">\\n <h4>\\n Don\\'t worry,\\n <span class=\"jenna-sue\">\\n we\\'ve all been there.\\n </span>\\n </h4>\\n <p>\\n We can help! All you need to do is enter your email ID and follow the instructions!\\n </p>\\n </div>\\n <div class=\"form-section\">\\n <p class=\"error-message\">\\n <span class=\"__exclaim-icon\">\\n </span>\\n <span class=\"message\">\\n <span class=\"error-heading\">\\n error :\\n </span>\\n oops please enter a valid email address.\\n </span>\\n </p>\\n <form class=\"form struktur\" id=\"iForgotEmailParent\">\\n <input class=\"email-input\" id=\"iForgotEmail\" placeholder=\"EMAIL ADDRESS\" type=\"text\"/>\\n <div class=\"form-messages _success\" id=\"dForgotError\" style=\"display: none;\">\\n <p class=\"__text\">\\n </p>\\n </div>\\n <!-- <div class=\"filed-error\">\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"__text\">error</p>\\n\\t\\t\\t\\t\\t\\t\\t\\t</div> -->\\n <a href=\"#\">\\n <div class=\"submit-form\" onclick=\"BMS.SignIn.fnResetPwd();\">\\n SEND INSTRUCTIONS\\n </div>\\n </a>\\n </form>\\n </div>\\n </aside>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Signin strip/modal(onscroll) section -->\\n <div class=\"popover _signin-signup-popover _signin-signup-popover-fixed _fixed-bottom\" id=\"signinPopupFixed\" style=\"display: none;\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n SIGN IN\\n <span class=\"__dismiss-fixed\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <span class=\"__dismiss-fixed-1\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n <div class=\"body-wrapper\">\\n <aside class=\"column signin-column\">\\n <div class=\"section-head\">\\n <h2>\\n A world of\\n <span class=\"jenna-sue\">\\n Entertainment Awaits.\\n </span>\\n </h2>\\n <p>\\n Instant sign in with\\n </p>\\n <div class=\"signin-btns-fixed-bottom\">\\n <a data-modal=\"signinPopup\" href=\"#\">\\n <div class=\"signin-signup-popup-btn\">\\n SIGN IN\\n </div>\\n </a>\\n <a data-modal=\"signupPopup\" href=\"#\">\\n <div class=\"signin-signup-popup-btn\">\\n SIGN UP\\n </div>\\n </a>\\n </div>\\n </div>\\n <div class=\"social-links\">\\n <a href=\"javascript:\" onclick=\"BMS.SignIn.fnFbWrapper(); return false;\" style=\"cursor: pointer;\">\\n <div class=\"auth-method icon-facebook\">\\n <div class=\"auth-method-title _facebook\">\\n <span class=\"icon-auth-method _facebook\">\\n <span class=\"icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-facebook-modal\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n FACEBOOK\\n </div>\\n </div>\\n </a>\\n <a href=\"javascript:\" onclick=\"BMS.SignIn.fnGPlusWrapper(); return false;\" style=\"cursor: pointer;\">\\n <div class=\"auth-method icon-googleplus\">\\n <div class=\"auth-method-title _gplus\">\\n <span class=\"icon-auth-method _gplus\">\\n <span class=\"icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-googleplus-modal\">\\n </use>\\n </svg>\\n </span>\\n </span>\\n GOOGLE\\n </div>\\n </div>\\n </a>\\n </div>\\n <div class=\"divider\">\\n <span class=\"or\">\\n OR\\n </span>\\n </div>\\n <div class=\"signin-with-username\">\\n <p class=\"error-message\">\\n <span class=\"__exclaim-icon\">\\n </span>\\n <span class=\"message\">\\n <span class=\"error-heading\">\\n error :\\n </span>\\n oops please enter a valid email address.\\n </span>\\n </p>\\n <form class=\"signin-form struktur\" id=\"iUserNameParentFixed\">\\n <input class=\"email-input\" id=\"iUserNameFixed\" minlength=\"1\" pattern=\"[a-z0-9._%+-]+@[a-z0-9.-]+\\\\.[a-z]{2,4}$\" placeholder=\"Enter your Email ID\" required=\"\" type=\"text\"/>\\n <div class=\"form-messages _success\" id=\"dSignInErrorEmailFixed\" style=\"display: none;\">\\n <p class=\"__text\">\\n </p>\\n </div>\\n <input autocomplete=\"off\" class=\"password-input\" id=\"iPwdFixed\" minlength=\"1\" placeholder=\"Enter your password\" type=\"password\"/>\\n <div class=\"form-messages _success\" id=\"dSignInErrorPasswordFixed\" style=\"display: none;\">\\n <p class=\"__text\">\\n </p>\\n </div>\\n <!-- <div style=\"display: none;\" class=\"form-messages _success\" id=\"dAfterSignInSuccess\">\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"__text\">Welcome back,<span id=\"spnUName\"></span></p>\\n\\t\\t\\t\\t\\t\\t\\t\\t</div> -->\\n <a href=\"javascript:;\" onclick=\"BMS.SignIn.fnValLogIn({\\'username\\':\\'iUserNameFixed\\', \\'password\\':\\'iPwdFixed\\', \\'usernameParent\\':\\'iUserNameParentFixed\\', \\'errorEmail\\':\\'dSignInErrorEmailFixed\\', \\'errorPassword\\':\\'dSignInErrorPasswordFixed\\'}); return false;\">\\n <div class=\"submit-form\">\\n SIGN IN\\n </div>\\n </a>\\n </form>\\n <div class=\"forgot-password\">\\n <a href=\"#\">\\n <span class=\"__text\" data-modal=\"forgotPopup\">\\n FORGOT PASSWORD?\\n </span>\\n </a>\\n </div>\\n </div>\\n </aside>\\n <aside class=\"column signup-redirect\" style=\"display:none;\">\\n <div class=\"heading\">\\n Still haven\\'t\\n <span class=\"jenna-sue-red\">\\n signed up?\\n </span>\\n </div>\\n <div class=\"registration\">\\n <span class=\"icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-registration\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"description\">\\n <p>\\n Want to rate and review movies you\\'ve watched?\\n <br/>\\n All you need to do is sign up!\\n </p>\\n </div>\\n <a href=\"#\">\\n <div class=\"signup-now-button btn _uno\" data-modal=\"signupPopup\">\\n SIGN UP NOW\\n </div>\\n </a>\\n </aside>\\n </div>\\n <div class=\"redirect-bottom\">\\n <p>\\n Still not connected?\\n <a class=\"signup-now-button\" data-modal=\"signupPopup\">\\n Sign Up\\n </a>\\n </p>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Resend confirmation modal -->\\n <div class=\"popover _resend-confirmation-popover\" id=\"resendCofirmationPopup\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n RESEND CONFIRMATION\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <aside class=\"column\">\\n <div class=\"section-head\">\\n <h5>\\n Lost your confirmation details?\\n </h5>\\n <p>\\n No worries, just enter the info you gave while booking &amp; we\\'ll resend it in a jiffy!\\n </p>\\n </div>\\n <div class=\"form-section struktur\">\\n <p class=\"error-message\">\\n <span class=\"__exclaim-icon\">\\n </span>\\n <span class=\"message\">\\n <span class=\"error-heading\">\\n error :\\n </span>\\n oops please enter a valid email address.\\n </span>\\n </p>\\n <form class=\"form struktur\" id=\"errDivFRGParent\">\\n <input class=\"email-input\" id=\"iResendConfEmail\" placeholder=\"EMAIL ADDRESS\" type=\"email\" value=\"\"/>\\n <div class=\"_error\" id=\"errDivFRGEmail\" style=\"display: none;\">\\n </div>\\n <input class=\"email-input\" id=\"iResendConfMobile\" maxlength=\"10\" placeholder=\"mobile number\" type=\"text\" value=\"\"/>\\n <div class=\"_error\" id=\"errDivFRGMobile\" style=\"display: none;\">\\n </div>\\n <div class=\"filed-error\">\\n <p class=\"__text\">\\n error\\n </p>\\n </div>\\n <!-- <div class=\"_error\" id=\"errDivFRG\" style=\"display: none;\"> </div> -->\\n <div>\\n <div class=\"submit-form\" id=\"btnRes\" onclick=\"BMS.SignIn.fnValRes();\">\\n RESEND\\n </div>\\n <a class=\"btn _disable\" href=\"javascript:;\" id=\"btndisab\" style=\"display:none;\">\\n Please wait...\\n </a>\\n </div>\\n </form>\\n </div>\\n </aside>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Subscribe Newsletters modal -->\\n <div class=\"popover _subscribe-popover\" id=\"subscribeNewsletters\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n SUBSCRIBE TO NEWSLETTERS\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <aside class=\"column\">\\n <div class=\"section-head\">\\n <h6>\\n Never miss an\\n <span class=\"jenna-sue\">\\n update\\n </span>\\n from us!\\n </h6>\\n <p>\\n Subscribe to our free newsletters for latest updates on movies, events, plays, sports &amp; new products!\\n </p>\\n </div>\\n <div class=\"form-section\">\\n <p class=\"error-message\">\\n <span class=\"__exclaim-icon\">\\n </span>\\n <span class=\"message\">\\n <span class=\"error-heading\">\\n error :\\n </span>\\n oops please enter a valid email address.\\n </span>\\n </p>\\n <form class=\"form struktur\" id=\"subEmailmobParent\">\\n <input class=\"email-input\" id=\"subEmailmob\" placeholder=\"email Address or mobile number\" type=\"text\"/>\\n <div class=\"filed-error\">\\n <p class=\"__text\">\\n error\\n </p>\\n </div>\\n <div class=\"_error\" id=\"errDivSUB\" style=\"display: none;\">\\n </div>\\n <a href=\"#\">\\n <div class=\"submit-form\" onclick=\"BMS.SignIn.fnDoSubMail();\">\\n SUBSCRIBE NOW\\n </div>\\n </a>\\n </form>\\n </div>\\n </aside>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Corporate Booking modal -->\\n <div class=\"popover _subscribe-popover crp_bking_popup\" id=\"dEvCorpBookPopup\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n Corporate Booking\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <aside class=\"column\">\\n <div class=\"form-section\">\\n <form class=\"form struktur\" onsubmit=\"fnSubmitEvCorpDetails(); return false;\">\\n <div class=\"box-container\">\\n <!--YOUR FIRST NAME-->\\n <div class=\"box first-name\">\\n <input class=\"first-name-input\" id=\"txtEvCorpFirstName\" onkeypress=\"maxLimitForInputdEvCorpBook(50)\" placeholder=\"FIRST NAME\" type=\"text\"/>\\n </div>\\n <!--YOUR LAST NAME-->\\n <div class=\"box last-name\">\\n <input class=\"last-name-input\" id=\"txtEvCorpLastName\" onkeypress=\"maxLimitForInputdEvCorpBook(50)\" placeholder=\"YOUR LAST NAME\" type=\"text\"/>\\n </div>\\n </div>\\n <div class=\"box-container\">\\n <!--COMPANY NAME-->\\n <div class=\"box company-name\">\\n <input class=\"company-name-input\" id=\"txtEvCorpCompanyName\" onkeypress=\"maxLimitForInputdEvCorpBook(50)\" placeholder=\"COMPANY NAME\" type=\"text\"/>\\n </div>\\n <!--QUANTITY-->\\n <div class=\"box quantity-bx\">\\n <input class=\"quantity-input\" id=\"txtEvCorpQty\" onkeypress=\"maxLimitForInputdEvCorpBook(5)\" placeholder=\"QUANTITY\" type=\"text\"/>\\n </div>\\n </div>\\n <div class=\"box-container\">\\n <!--CONTACT NUMBER-->\\n <div class=\"box contact-number\">\\n <input class=\"quantity-input\" id=\"txtEvCorpMobile\" onkeypress=\"maxLimitForInputdEvCorpBook(10)\" placeholder=\"CONTACT NUMBER\" type=\"text\"/>\\n </div>\\n <!--EMAIL ID-->\\n <div class=\"box email-id\">\\n <input class=\"email-input\" id=\"txtEvCorpEmail\" onkeypress=\"maxLimitForInputdEvCorpBook(100)\" placeholder=\"EMAIL\" type=\"text\"/>\\n </div>\\n </div>\\n <div class=\"box-container\">\\n <!--CITY-->\\n <div class=\"box city\">\\n <select class=\"txtCity\" id=\"txtEvCorpCity\">\\n <option value=\"City\">\\n City\\n </option>\\n </select>\\n <i class=\"downChevron\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-arrow-down-new\">\\n </use>\\n </svg>\\n </i>\\n </div>\\n <!--Captcha-->\\n <div class=\"box\">\\n <!-- <img src=\"\" alt=\"Captcha\" id=\"imgEvCorpCaptcha\" class=\"captCha\"/>\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t<input id=\"txtEvCorpCaptcha\" class=\"verify _form-input\" type=\"text\" placeholder=\"VERIFY THE CODE\">\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t<p>Can’t see the image? <a class=\"_red-color\" href=\"javascript:;\" onclick=\"$(\\'#imgEvCorpCaptcha\\').attr(\\'src\\', \\'/captcha/captcha?_=\\' + $.now());\">Reload</a></p> -->\\n <div id=\"recaptcha2\">\\n </div>\\n </div>\\n </div>\\n <div class=\"error\" id=\"errEvCorpErr\">\\n </div>\\n <button class=\"btn _cuatro \" id=\"btnEvCorpBookSubmit\" type=\"submit\">\\n SUBMIT\\n </button>\\n </form>\\n </div>\\n </aside>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Corpporate booking feedback modal -->\\n <div class=\"popover _corporate-bookings-popup\" id=\"dEvCorpBookFeedbackPopup\">\\n <div class=\"popover-container none\" id=\"dEvCorpBookSuccess\">\\n <div class=\"__header\">\\n <span>\\n Successful\\n </span>\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <p class=\"msg\">\\n Thank You!\\n </p>\\n <p class=\"msg2\">\\n Your Response has been received successfully.\\n </p>\\n <p class=\"msg2\">\\n We will get back to you shortly.\\n </p>\\n </div>\\n <button class=\"btn _cuatro\" onclick=\"BMS.Misc.modal(\\'dEvCorpBookPopup\\', false);\">\\n Close\\n </button>\\n </div>\\n </div>\\n <div class=\"popover-container none\" id=\"dEvCorpBookError\">\\n <div class=\"__header\">\\n <span>\\n Unsuccessful\\n </span>\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <p class=\"msg\">\\n Sorry!\\n </p>\\n <p class=\"msg2\">\\n There seems to be some problem sending the request.\\n </p>\\n <p class=\"msg2\">\\n Please try again later.\\n </p>\\n </div>\\n <button class=\"btn _cuatro\" onclick=\"BMS.Misc.modal(\\'dEvCorpBookPopup\\', false);\">\\n Close\\n </button>\\n </div>\\n </div>\\n </div>\\n <!--Report Abuse PopUp starts here -->\\n <div class=\"popover\" id=\"report-abuse-footer\">\\n <div class=\"report-abuse-wrapper\">\\n <h2 class=\"__heading\">\\n Report wrong or missing information\\n <span class=\"__report-content-close\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </h2>\\n <div class=\"report-form-body\">\\n <div class=\"row report-form\">\\n <div class=\"input-container category\">\\n <select id=\"categorySelectfooter\" name=\"category\" placeholder=\"-- Select a Category --\" required=\"\" tabindex=\"1\" title=\"Select a valid category\">\\n <!-- <option value=\"null\">--Select a Category--</option> -->\\n <option value=\"poster\">\\n Poster\\n </option>\\n <option value=\"release\">\\n Release Date\\n </option>\\n <option value=\"synopsis\">\\n Synopsis\\n </option>\\n <option value=\"cast\">\\n Cast/Crew\\n </option>\\n <option value=\"others\">\\n Others\\n </option>\\n </select>\\n <span class=\"__error-text\">\\n Select a valid category\\n </span>\\n </div>\\n <div class=\"input-container details\">\\n <textarea class=\"__text-area\" id=\"idetailsfooter\" maxlength=\"500\" name=\"details\" placeholder=\"Tell us more...\" tabindex=\"2\" title=\"Enter valid details\"></textarea>\\n </div>\\n <div class=\"input-container name\">\\n <select class=\"title\" id=\"title\" name=\"title\">\\n <option value=\"1\">\\n Mr\\n </option>\\n <option value=\"2\">\\n Mrs\\n </option>\\n <option value=\"4\">\\n Ms\\n </option>\\n </select>\\n <input id=\"fnamefooter\" placeholder=\"Name\" tabindex=\"3\" title=\"Enter a valid Name\" type=\"text\"/>\\n <input id=\"lnamefooter\" placeholder=\"LastName\" tabindex=\"4\" title=\"Enter a valid Name\" type=\"text\"/>\\n </div>\\n <div class=\"input-container association\">\\n <input id=\"iassociationfooter\" placeholder=\"Association with the content\" tabindex=\"5\" title=\"Association with the movie\" type=\"text\"/>\\n </div>\\n <div class=\"input-container email\">\\n <input id=\"iemailfooter\" placeholder=\"Email\" tabindex=\"6\" title=\"Enter a valid Email\" type=\"text\"/>\\n <input id=\"iphonenumberfooter\" maxlength=\"10\" placeholder=\"Phone number\" tabindex=\"7\" title=\"Phone number\" type=\"text\"/>\\n </div>\\n <div class=\"input-container pr-house\">\\n <input id=\"ipr-housefooter\" placeholder=\"Name of Production House/PR Agency\" tabindex=\"8\" title=\"Production House\" type=\"text\"/>\\n </div>\\n <div class=\"input-container mv-social-links\">\\n <input id=\"sm-linksfooter\" placeholder=\"Content\\'s official social media links\" tabindex=\"9\" title=\"Social Media Links\" type=\"text\"/>\\n </div>\\n <div class=\"input-container mv-social-links\">\\n <input id=\"sm-description\" placeholder=\"Url of the reported content.\" tabindex=\"10\" title=\"Social Media Links\" type=\"text\"/>\\n </div>\\n <div class=\"input-container captcha-container\" id=\"recaptcha1\">\\n <!-- Captcha container to be kept blank -->\\n </div>\\n <button class=\"input-container btn _cuatro __btn-submit report-content-submit\" id=\"btnSubmitfooter\" tabindex=\"10\">\\n Report\\n </button>\\n </div>\\n <div class=\"input-container report-abuse-terms\">\\n <input class=\"tnc_check\" id=\"report-tnc\" name=\"terms\" type=\"checkbox\" value=\"terms\"/>\\n “I have a good faith belief that the use of the material in the manner complained of is not authorized by the copyright owner, its agent, or the law.” AND “The information in this notification is accurate, and I am the owner, or an agent authorized to act on behalf of the owner, of an exclusive right that is allegedly infringed.”\\n </div>\\n <div class=\"policy-container\">\\n <p>\\n <a class=\"report-content-policy-link\" href=\"/terms-and-conditions?id=reportcontent\" target=\"_blank\">\\n Notice and Take Down policy\\n </a>\\n </p>\\n </div>\\n <div class=\"success-message _hide\">\\n <p>\\n Thank you for your valuable input!\\n <br/>\\n Do you want to report anything else?\\n <a class=\"__report-link\" href=\"javascript:;\" id=\"reportScreensuc\">\\n Report\\n </a>\\n </p>\\n </div>\\n <div class=\"error-popup-message _hide\">\\n <p>\\n Something went wrong,\\n <br/>\\n Please try again.\\n <a class=\"__report-link\" href=\"javascript:;\" id=\"reportScreenfail\">\\n Report\\n </a>\\n </p>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!--Report Abuse PopUp ends here -->\\n <!-- Location modal -->\\n <div class=\"popover _location-popup\" id=\"locationPopup\">\\n <div class=\"location-header struktur\">\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"__icon-back\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-go-back\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"__text\" id=\"spnRgnHeadTxt\">\\n PICK YOUR STATE\\n </span>\\n <input class=\"form-input .l-input _dos\" id=\"inp_RegionSearch\" placeholder=\"ENTER YOUR CITY\" type=\"text\"/>\\n <div class=\"inp-rgn-callout\" data-id=\"inp_RegionSearch\">\\n <ul>\\n </ul>\\n </div>\\n </div>\\n <div class=\"location-container\">\\n <div class=\"location-pill-container\" id=\"div_States\">\\n </div>\\n <div class=\"city-list\">\\n <div class=\"__city-banner\">\\n </div>\\n <div class=\"__list\" id=\"div_CityName\">\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Feedback modal -->\\n <div class=\"popover _feedback-popup\" id=\"feedbackPopup\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n <div class=\"body-wrapper\">\\n <aside class=\"column\">\\n <div class=\"section-head\">\\n <h5>\\n </h5>\\n <p class=\"__message-definition\" id=\"submsg\">\\n </p>\\n </div>\\n <button class=\"btn _cuatro\">\\n BACK HOME\\n </button>\\n </aside>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Reviews and rating modal -->\\n <div class=\"popover _review-rating-popover\" id=\"reviewRating\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n <!-- REVIEW &amp; RATING -->\\n <span class=\"popover-event-name\" id=\"spnRatingMovieTitle\">\\n </span>\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <div class=\"column-wrapper\">\\n <aside class=\"rating-column __your-rating\">\\n <div class=\"heading\">\\n <div class=\"__head-text\">\\n YOUR RATING\\n </div>\\n <div class=\"rating-details\">\\n <ul class=\"rating-stars\" id=\"popover-stars\">\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"0.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"1.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"1.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"2.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"2.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"3.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"3.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"4.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"4.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"5.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n </ul>\\n <div class=\"rate-o-meter\" id=\"popover-score\">\\n 0.5\\n </div>\\n </div>\\n </div>\\n <span class=\"form-message\" id=\"popover-rating-num-error\" style=\"display: none;\">\\n </span>\\n </aside>\\n <aside class=\"review-column\">\\n <div class=\"myAccordion section-head\">\\n <span id=\"div-overall-rating\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-plus\">\\n </use>\\n </svg>\\n </span>\\n <h2 style=\"color:#666\">\\n Would you like to write a review? (Optional)\\n </h2>\\n <!-- <p>All you need to do is enter your...</p> -->\\n </div>\\n <div class=\"tnc review-form-wrapper\" id=\"div-review-form\" style=\"display:none;\">\\n <span class=\"form-message\" id=\"popover-rating-error\" style=\"display: none;\">\\n </span>\\n <form class=\"signin-form review-form struktur\">\\n <input class=\"title\" id=\"review-title\" placeholder=\"REVIEW TITLE\" type=\"text\"/>\\n <textarea class=\"review-description\" cols=\"50\" id=\"review-text\" onkeyup=\"return BMS.Ratings.fnCheckReviewTxt(this);\" placeholder=\"Highlight what you particularly liked or disliked in the movie and why ?\" rows=\"8\"></textarea>\\n </form>\\n <span class=\"__min-char\" id=\"review-count-span\">\\n Minimum Characters:\\n <span class=\"__count\" id=\"review-chars-count\">\\n 140\\n </span>\\n </span>\\n </div>\\n </aside>\\n </div>\\n </div>\\n <div class=\"footer\">\\n <div class=\"footer-wrapper\">\\n <div class=\"activity-sharer\">\\n <form class=\"struktur\">\\n <input id=\"ecode\" type=\"hidden\" value=\"\"/>\\n <input id=\"activity-sharing\" name=\"activity-sharing\" type=\"checkbox\"/>\\n <!--<label for=\"activity-sharing\">\\n\\t <span class=\"__tick\">\\n\\t <svg version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n\\t <use xlink:href=\"/icons/payments-icons.svg#icon-tick\"></use>\\n\\t </svg>\\n\\t </span>Share activity with Facebook</label>-->\\n <a class=\"submit-activity\" id=\"submitBtnRnR\">\\n SUBMIT\\n </a>\\n </form>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Reviews and Rating Response modal -->\\n <div class=\"popover _review-rating-popover\" id=\"review-rating-response\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n <!-- REVIEW &amp; RATING -->\\n <span class=\"popover-event-name\">\\n </span>\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <div class=\"column-wrapper\">\\n <aside class=\"__success-screen\">\\n <div class=\"__tick-icon\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill:@theme-action-contrast-dos; width: 25px;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-tick\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__text\" id=\"popover-rating-msg\">\\n </div>\\n </aside>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"popover _review-rating-popover\" id=\"create-exp-success\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n <!-- REVIEW &amp; RATING -->\\n <span class=\"popover-event-name\">\\n SUCCESS!\\n </span>\\n <span class=\"__dismiss\" onclick=\"gotoProfile();\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <div class=\"column-wrapper\">\\n <aside class=\"__success-screen\">\\n <div class=\"cheers-icon\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill:@theme-action-contrast-dos; width: 25px;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-congratulation\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__text\" id=\"experience-success-msg\">\\n <span style=\"color: red;\">\\n Congratulations!\\n </span>\\n Your Experience has been succsessfully published\\n </div>\\n <div class=\"ex-share-box\">\\n <span class=\"__share-text\">\\n All you need to do is share it with world!\\n </span>\\n <div id=\"social-sharer\">\\n </div>\\n </div>\\n </aside>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"popover\" id=\"error-div\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n <span class=\"popover-event-name\">\\n </span>\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <div class=\"error-div-modal\">\\n <div class=\"lhs\">\\n <div class=\"errorlogo\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-global-error\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"msgcontent\">\\n <div class=\"errormsg\">\\n <p class=\"msg\">\\n <span>\\n Whoa!\\n </span>\\n Something is not right.\\n </p>\\n <p class=\"refresh\" id=\"globalErrMsg\">\\n Please refresh the page and try again\\n </p>\\n <p class=\"errbtns\">\\n <a href=\"javascript:window.location.reload();\">\\n Refresh\\n </a>\\n <!-- <a href=\"#\">Report this</a> -->\\n </p>\\n <div class=\"msg\" id=\"dActualErrorMsg\" style=\"display: none;\">\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"rhs\">\\n <div class=\"contact-us\">\\n <p>\\n <span>\\n <a class=\"__cf_email__\" data-cfemail=\"266e434a566243554d664449494d4b5f554e49510845494b\" href=\"/cdn-cgi/l/email-protection\">\\n [email\\xa0protected]\\n </a>\\n <script data-cfhash=\"f9e31\" type=\"text/javascript\">\\n /* <![CDATA[ */!function(t,e,r,n,c,a,p){try{t=document.currentScript||function(){for(t=document.getElementsByTagName(\\'script\\'),e=t.length;e--;)if(t[e].getAttribute(\\'data-cfhash\\'))return t[e]}();if(t&&(c=t.previousSibling)){p=t.parentNode;if(a=c.getAttribute(\\'data-cfemail\\')){for(e=\\'\\',r=\\'0x\\'+a.substr(0,2)|0,n=2;a.length-n;n+=2)e+=\\'%\\'+(\\'0\\'+(\\'0x\\'+a.substr(n,2)^r).toString(16)).slice(-2);p.replaceChild(document.createTextNode(decodeURIComponent(e)),c)}p.removeChild(t)}}catch(u){}}()/* ]]> */\\n </script>\\n </span>\\n </p>\\n <p>\\n 022 6144 5050, 022 3989 5050\\n </p>\\n </div>\\n </div>\\n <!-- <div class=\"myCross\">\\n\\t <svg xml:space=\"preserve\" enable-background=\"new 0 0 100 100\" viewBox=\"0 0 100 100\" y=\"0px\" x=\"0px\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\\n\\t <use xlink:href=\"/icons/common-icons.svg#icon-cancel\" />\\n\\t </svg>\\n\\t </div> -->\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- for sports errors -->\\n <div class=\"popover\" id=\"error-sports-div\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n <span class=\"popover-event-name\">\\n </span>\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <div class=\"error-div-modal\">\\n <div class=\"lhs\">\\n <div class=\"errorlogo\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-error-global-sports\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"msgcontent\">\\n <div class=\"errormsg\">\\n <p class=\"sports-msg\">\\n <span>\\n Whoa!\\n </span>\\n Something is not right.\\n </p>\\n <p class=\"refresh\" id=\"globalSportsErrMsg\">\\n Please refresh the page and try again\\n </p>\\n <p class=\"errbtns\">\\n <a href=\"javascript:window.location.reload();\">\\n Refresh\\n </a>\\n <!-- <a href=\"#\">Report this</a> -->\\n </p>\\n <div class=\"msg\" id=\"dActualSportsErrorMsg\" style=\"display: none;\">\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"rhs\">\\n <div class=\"contact-us\">\\n <p>\\n <span>\\n <a class=\"__cf_email__\" data-cfemail=\"c58da0a9b581a0b6ae85a7aaaaaea8bcb6adaab2eba6aaa8\" href=\"/cdn-cgi/l/email-protection\">\\n [email\\xa0protected]\\n </a>\\n <script data-cfhash=\"f9e31\" type=\"text/javascript\">\\n /* <![CDATA[ */!function(t,e,r,n,c,a,p){try{t=document.currentScript||function(){for(t=document.getElementsByTagName(\\'script\\'),e=t.length;e--;)if(t[e].getAttribute(\\'data-cfhash\\'))return t[e]}();if(t&&(c=t.previousSibling)){p=t.parentNode;if(a=c.getAttribute(\\'data-cfemail\\')){for(e=\\'\\',r=\\'0x\\'+a.substr(0,2)|0,n=2;a.length-n;n+=2)e+=\\'%\\'+(\\'0\\'+(\\'0x\\'+a.substr(n,2)^r).toString(16)).slice(-2);p.replaceChild(document.createTextNode(decodeURIComponent(e)),c)}p.removeChild(t)}}catch(u){}}()/* ]]> */\\n </script>\\n </span>\\n </p>\\n <p>\\n 022 6144 5050, 022 3989 5050\\n </p>\\n </div>\\n </div>\\n <!-- <div class=\"myCross\">\\n\\t <svg xml:space=\"preserve\" enable-background=\"new 0 0 100 100\" viewBox=\"0 0 100 100\" y=\"0px\" x=\"0px\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\\n\\t <use xlink:href=\"/icons/common-icons.svg#icon-cancel\" />\\n\\t </svg>\\n\\t </div> -->\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- User Welcome modal -->\\n <div class=\"popover _welcome\" id=\"dAfterSignInSuccess\">\\n <div class=\"welcome-header\">\\n <span class=\"__icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-welcome\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"greeting-container\">\\n <div class=\"greeting\">\\n <h4 class=\"__message\">\\n Welcome back,\\n <span class=\"__name\" id=\"spnUName\">\\n </span>\\n </h4>\\n <p class=\"__other-message\">\\n Your tickets are waiting to be Booked!\\n </p>\\n </div>\\n </div>\\n </div>\\n <!-- showtimes category modal -->\\n <div class=\"popover _showtimes-category-popover\" id=\"StCategoryPopup\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n <span class=\"__show-timing\">\\n 8.30 PM\\n </span>\\n <div class=\"__status\" id=\"showtime-availability\">\\n FILLING FAST\\n </div>\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\" id=\"showtime-categories\">\\n <div class=\"__categories\">\\n <div class=\"__type-cat\">\\n <span class=\"__type\">\\n 3D Exclusive\\n </span>\\n <br/>\\n <span class=\"__cat _available\">\\n Available\\n </span>\\n </div>\\n <div class=\"__price\">\\n Rs.200\\n </div>\\n </div>\\n <div class=\"__categories\">\\n <div class=\"__type-cat\">\\n <span class=\"__type\">\\n 3D Exclusive\\n </span>\\n <br/>\\n <span class=\"__cat _available\">\\n Available\\n </span>\\n </div>\\n <div class=\"__price\">\\n Rs.200\\n </div>\\n </div>\\n <div class=\"__categories\">\\n <div class=\"__type-cat\">\\n <span class=\"__type\">\\n 3D Exclusive\\n </span>\\n <br/>\\n <span class=\"__cat _available\">\\n Available\\n </span>\\n </div>\\n <div class=\"__price\">\\n Rs.200\\n </div>\\n </div>\\n </div>\\n <a class=\"btn _tres __proceed\" id=\"showtime-proceed-touch\">\\n PROCEED\\n </a>\\n </div>\\n </div>\\n <div class=\"popover\" id=\"deactivate-account\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n <span class=\"popover-event-name\">\\n </span>\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <div class=\"column-wrapper\">\\n <aside class=\"__success-screen __deactivate-text\">\\n <p class=\"__text\">\\n Are you sure you want to deactivate your account? Click ok to proceed or cancel\\n </p>\\n <a href=\"javascript:void(0);\" onclick=\"BMS.SignIn.fnDeactivateAccnt();\">\\n OK\\n </a>\\n <a href=\"javascript:void(0);\" onclick=\"BMS.Misc.modal(\\'deactivate-account\\', false);\">\\n Cancel\\n </a>\\n </aside>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"popover _resend-confirmation-popover\" id=\"galPopup\">\\n <div class=\"popover-container\">\\n <div class=\"body-slick\">\\n <div class=\"body-wrapper\">\\n <a class=\"icon-cancel\" onclick=\"BMS.Misc.modal(\\'galPopup\\',false);\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use style=\"fill: #EFE9E9;\" xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </a>\\n <div class=\"slick-images\" id=\"galLightBox\">\\n <div class=\"main-img-slider\">\\n </div>\\n <ul class=\"thumb-nav\">\\n </ul>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!-- Modal for fnb seat delivery -->\\n <div class=\"popover _fnb-delivery-popup\" id=\"fnbSeatDelivery\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n <div class=\"__delivery-type\">\\n Select delivery type\\n </div>\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\" id=\"fnb-delivery\">\\n <div class=\"delivery-types\">\\n <div id=\"__pick-up\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/fnb-icons.svg#icon-Icon_Pick-Up\">\\n </use>\\n </svg>\\n <span class=\"__fnb-text\">\\n Pick-Up\\n </span>\\n </div>\\n <div id=\"__seat-delivery\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/fnb-icons.svg#icon-Icon_Seat-Delivery\">\\n </use>\\n </svg>\\n <span class=\"__fnb-text\">\\n Seat Delivery\\n </span>\\n </div>\\n <span class=\"__top-circle\">\\n </span>\\n <span class=\"__bottom-circle\">\\n </span>\\n <span class=\"__or-circle\">\\n or\\n </span>\\n </div>\\n <div class=\"select-type\">\\n <div id=\"__pickup-option\">\\n Pick it up anytime during the show\\n </div>\\n <div id=\"__seat-del\">\\n <label>\\n <input id=\"deliver-before-show\" name=\"delivery-time\" style=\"left:-8px;position:relative;top:-5px;\" type=\"radio\"/>\\n <span class=\"radio-element\">\\n Before Show\\n </span>\\n </label>\\n <label>\\n <input id=\"interval\" name=\"delivery-time\" style=\"left:-8px;position:relative;top:-5px;\" type=\"radio\"/>\\n <span class=\"radio-element\">\\n During Interval\\n </span>\\n </label>\\n </div>\\n </div>\\n </div>\\n <a class=\"btn __proceed\" id=\"order-proceed\">\\n PROCEED\\n </a>\\n </div>\\n </div>\\n <!--Error Section for FNB-->\\n <div class=\"popover fnb-limit-div\" id=\"fnb-limit-error\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n <span class=\"popover-event-name\">\\n Maximum Limit Error\\n </span>\\n <span class=\"__dismiss\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <div class=\"column-wrapper\">\\n <aside class=\"__success-screen __deactivate-text\">\\n <p class=\"__text\">\\n Oops you have exceeded order limit.\\n </p>\\n </aside>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <!--Error Section for FNB-->\\n <!-- FNB - Showdate validity popup -->\\n <div class=\"popover fnb-showdate-validity\" id=\"fnb-showdate-validity\">\\n <div class=\"popover-container\">\\n <div class=\"__header\">\\n <span class=\"popover-event-name\">\\n OOPS!\\n </span>\\n <span class=\"__redirect\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n <div class=\"body\">\\n <div class=\"body-wrapper\">\\n <div class=\"column-wrapper\">\\n <aside class=\"__success-screen __deactivate-text\">\\n <p class=\"__text validity-error\">\\n </p>\\n </aside>\\n </div>\\n </div>\\n </div>\\n <a class=\"btn __proceed\" id=\"other-active-bookings\">\\n OKAY\\n </a>\\n </div>\\n </div>\\n <!-- Modal for Home Delivery and webpickup -->\\n <!-- End -->\\n </div>\\n <!-- Modal ends here -->\\n <div class=\"__busy-div-overlay\">\\n </div>\\n <div class=\"busy-div\" id=\"dBusy\">\\n <div class=\"container\">\\n <p class=\"wait\">\\n Loading...\\n </p>\\n <!-- <p class=\"wait\">Just a <span>Moment...</span></p> -->\\n <!-- <p class=\"process\">We are processing your request</p> -->\\n <div class=\"movie-loader\">\\n <div class=\"loader\">\\n <div class=\"reel __roll1\">\\n <span class=\"mini-circle\">\\n </span>\\n <span class=\"mini-circle __center\">\\n </span>\\n <span class=\"mini-circle __left\">\\n </span>\\n <span class=\"mini-circle __right\">\\n </span>\\n <span class=\"mini-circle __bottom\">\\n </span>\\n </div>\\n <div class=\"reel __roll1 __roll2\">\\n <span class=\"mini-circle\">\\n 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\\'https:\\' : \\'http:\\') +\\n\\t\\t\\t\\t\\t \\'//www.googletagservices.com/tag/js/gpt.js\\';\\n\\t\\t\\t\\t\\t var node = document.getElementsByTagName(\\'script\\')[0];\\n\\t\\t\\t\\t\\t node.parentNode.insertBefore(gads, node);\\n\\t\\t\\t\\t\\t })();\\n </script>\\n <script type=\"text/javascript\">\\n googletag.cmd.push(function() {\\n\\t\\t\\t\\t\\t googletag.defineSlot(\\'/118335522/Booking_Lighbox\\', [300, 250], \\'div-gpt-ad-1465364586930-0\\').addService(googletag.pubads());\\n\\t\\t\\t\\t\\t googletag.pubads().enableSingleRequest();\\n\\t\\t\\t\\t\\t googletag.enableServices();\\n\\t\\t\\t\\t\\t });\\n </script>\\n <!-- /118335522/Booking_Interstitial_300x250 -->\\n <div class=\"adMagnet\" style=\"width:300px; height:250px; margin:0 auto; text-align:center;\">\\n <!-- /118335522/Booking_Lighbox -->\\n <div id=\"div-gpt-ad-1465364586930-0\">\\n <script type=\"text/javascript\">\\n googletag.cmd.push(function() { googletag.display(\\'div-gpt-ad-1465364586930-0\\'); });\\n </script>\\n </div>\\n </div>\\n <!-- Ad div -->\\n </div>\\n <div class=\"busy-div\" id=\"dBusyNetflix\">\\n <a onclick=\"BMS.Misc.modal(\\'dBusyNetflix\\', false);\" style=\"position: absolute; top: -13px; right: -15px; padding: 4px 9px; border: 1px solid gray; height: 30px; width: 30px; border-radius: 50%; background: white; text-align: center; cursor: pointer;\">\\n X\\n </a>\\n <div class=\"container\">\\n <p class=\"wait\">\\n <span>\\n Redirecting you...\\n </span>\\n </p>\\n <div class=\"movie-loader\">\\n <div class=\"loader\">\\n <div class=\"reel __roll1\">\\n <span class=\"mini-circle\">\\n </span>\\n <span class=\"mini-circle __center\">\\n </span>\\n <span class=\"mini-circle __left\">\\n </span>\\n <span class=\"mini-circle __right\">\\n </span>\\n <span class=\"mini-circle __bottom\">\\n </span>\\n </div>\\n <div class=\"reel __roll1 __roll2\">\\n <span class=\"mini-circle\">\\n </span>\\n <span class=\"mini-circle __center\">\\n </span>\\n <span class=\"mini-circle __left\">\\n </span>\\n <span class=\"mini-circle __right\">\\n </span>\\n <span class=\"mini-circle __bottom\">\\n </span>\\n </div>\\n <div class=\"record\">\\n </div>\\n <svg enable-background=\"new 0 0 200 200\" version=\"1.1\" viewbox=\"0 0 200 200\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <style>\\n .style0 {\\n\\t\\t\\t fill: #FFFFFF;\\n\\t\\t\\t }\\n\\n\\t\\t\\t .style1 {\\n\\t\\t\\t fill: #E6E7E8;\\n\\t\\t\\t }\\n\\n\\t\\t\\t .style2 {\\n\\t\\t\\t stroke: #000000;\\n\\t\\t\\t stroke-width: 0.5;\\n\\t\\t\\t stroke-miterlimit: 10;\\n\\t\\t\\t fill: #FFFFFF;\\n\\t\\t\\t }\\n\\n\\t\\t\\t .style3 {\\n\\t\\t\\t stroke: #000000;\\n\\t\\t\\t stroke-width: 0.5;\\n\\t\\t\\t stroke-miterlimit: 10;\\n\\t\\t\\t fill: none;\\n\\t\\t\\t }\\n\\n\\t\\t\\t .style4 {\\n\\t\\t\\t stroke: #000000;\\n\\t\\t\\t stroke-width: 1.5;\\n\\t\\t\\t stroke-miterlimit: 10;\\n\\t\\t\\t fill: #FFFFFF;\\n\\t\\t\\t }\\n\\n\\t\\t\\t .style5 {\\n\\t\\t\\t fill: #EF4136;\\n\\t\\t\\t }\\n </style>\\n <g>\\n <path class=\"style4\" d=\"M137.4 160.9H8.4 c-2.2 0-4-1.8-4-4V81.6c0-2.2 1.8-4 4-4h129c2.2 0 4 1.8 4 4v75.3C141.4 159.1 139.6 160.9 137.4 160.9z\">\\n </path>\\n <path class=\"style4\" d=\"M188.5 141.7l-32.2-8.5 c-4.3-1.5-7.1-3.2-7.1-7.1v-17c0-3.9 2.2-5.2 7.1-7.1l32.2-9.1c3.9 0 7.1 3.2 7.1 7.1v34.6C195.6 138.4 192.4 141.7 188.5 141.7z\">\\n </path>\\n </g>\\n </svg>\\n </div>\\n </div>\\n </div>\\n <div style=\"font-size: 11px; text-align: center; margin-top: 10px;\">\\n You will soon be redirected to our partners\\'s site\\n <span id=\"spnNetflixCdTimer\" style=\"color: #c02c39;\">\\n </span>\\n .\\n <br/>\\n (In case your browser is blocking pop-ups,\\n <a href=\"\" id=\"lnkNetflixUrl\" onclick=\"BMS.Misc.modal(\\'dBusyNetflix\\', false);\" target=\"_blank\">\\n click here\\n </a>\\n .)\\n </div>\\n </div>\\n <!-- Movie Trailers Start here -->\\n <script>\\n // 2. This code loads the IFrame Player API code asynchronously.\\n var tag = document.createElement(\\'script\\');\\n\\n tag.src = \"https://www.youtube.com/iframe_api\";\\n var firstScriptTag = document.getElementsByTagName(\\'script\\')[0];\\n firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\\n </script>\\n <script>\\n //window.mt = {};\\n\\t//mt.cdnURL = \\'//in.bmscdn.com\\';\\n\\n\\t// mt.API = {BookMyShow: {Events: {}}};\\n\\t//mt.API = ;\\n\\t//console.log(mt.API);\\n\\t//mt.rating = ;\\n\\t//test commit for touch\\n </script>\\n <div class=\"none\" id=\"mt-wrapper\">\\n <div class=\"mt-modal-overlay fixed-wrapper none\">\\n <div class=\"modal\">\\n <div class=\"modal-header\">\\n <div class=\"mt-title\">\\n Movie Trailers\\n </div>\\n <div class=\"mt-filters\">\\n <div class=\"__left\">\\n <span class=\"__comingsoon\">\\n Coming Soon\\n </span>\\n <span class=\"__nowshowing\">\\n Now Showing\\n </span>\\n </div>\\n </div>\\n <button id=\"mt-btn-close\">\\n <span class=\"icon-cancel\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-cancel\">\\n </use>\\n </svg>\\n </span>\\n </button>\\n <div class=\"__right-gen-filters struktur\">\\n <div class=\"__text\">\\n All Genres\\n </div>\\n <i class=\"__drop-icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-arrow-down-new\">\\n </use>\\n </svg>\\n </i>\\n </div>\\n <div class=\"__right-lang-filters struktur\">\\n <div class=\"__text\">\\n All Languages\\n </div>\\n <i class=\"__drop-icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-arrow-down-new\">\\n </use>\\n </svg>\\n </i>\\n </div>\\n <div class=\"__right-pf-filters\">\\n <div class=\"__text\">\\n Fresh\\n </div>\\n <i class=\"__drop-icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-arrow-down-new\">\\n </use>\\n </svg>\\n </i>\\n <div class=\"popular-fresh\">\\n <ul class=\"mt-pf\">\\n <li class=\"list __fresh\">\\n <label>\\n Fresh\\n </label>\\n </li>\\n <li class=\"list __popular\">\\n <label>\\n Popular\\n </label>\\n </li>\\n </ul>\\n </div>\\n </div>\\n <!--<div class=\"__selected-filters\">\\n\\t\\t\\t\\t\\t\\t<div class=\"mt-pills\">\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"__applied-text\">Applied Filters: </span>\\n\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"__selc-pills\">dummy\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"__rm-filter\">\\n\\t\\t\\t\\t\\t\\t\\t\\t<svg version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n\\t\\t \\t\\t<use xlink:href=\"/icons/common-icons.svg#icon-cancel\"></use>\\n\\t\\t \\t\\t</svg>\\n\\t\\t\\t\\t\\t\\t\\t</span>\\n\\t\\t\\t\\t\\t\\t\\t</span>\\n\\t\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t</div>-->\\n </div>\\n <div class=\"modal-body\">\\n <div class=\"mt-overflow-wrapper\">\\n <!-- <div class=\"mt-body\"></div> -->\\n </div>\\n </div>\\n <!-- <div class=\"modal-footer\"></div> -->\\n </div>\\n </div>\\n </div>\\n <!-- WizRocket/Clevertap Script -->\\n <script type=\"text/javascript\">\\n var clevertap = {event:[], profile:[], account:[], onUserLogin:[], notifications:[]};\\n // wizrocket.account.push({\"id\":\"RK6-Z85-W44Z\"});\\n \\tclevertap.account.push({\"id\": \"RK4-47R-98KZ\"});\\n\\n\\t(function () {\\n\\t\\t var wzrk = document.createElement(\\'script\\');\\n\\t\\t wzrk.type = \\'text/javascript\\';\\n\\t\\t wzrk.async = true;\\n\\t\\t wzrk.src = (\\'https:\\' == document.location.protocol ? \\'https://d2r1yp2w7bby2u.cloudfront.net\\' : \\'http://static.clevertap.com\\') + \\'/js/a.js\\';\\n\\t\\t var s = document.getElementsByTagName(\\'script\\')[0];\\n\\t\\t s.parentNode.insertBefore(wzrk, s);\\n })();\\n </script>\\n <div id=\"cmi_signature_player\">\\n </div>\\n <script type=\"text/javascript\">\\n if ( BMS.Storage.get({name:\"ld\", \"key\":\"MEMBERID\", storage: \"C\"}) != \"\" ) {\\n\\t\\tif (typeof(clevertap) !== \"undefined\") {\\n\\t\\t\\tclevertap.profile.push({\\n\\t\\t\\t\\t\"Site\": {\\n\\t\\t\\t\\t\\t\"Identity\" : BMS.Storage.get({name:\"ld\", \"key\":\"MEMBERID\", storage: 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{\\n\\t\\t\\tBMS.Misc.fnPushEventDataToAnalytics([\"GA\"], \"\", {}, { \\'event\\': \\'promotionClick\\', \\'ecommerce\\': { \\'promoClick\\': { \\'promotions\\': [obj] } } });\\n\\t\\t}\\n\\t}\\n </script>\\n <!-- <div class=\"coach-mark-overlay none\">\\n\\t\\t\\t\\t<div class=\"__overlay\"></div>\\n\\t\\t\\t\\t<div class=\"nav-coach\">\\n\\t\\t\\t\\t\\t<div class=\"nav-coach-img wow\">\\n\\t\\t\\t\\t\\t\\t<img data-src=\"//in.bmscdn.com/webin/static/coach-arrow-bottom.png\" />\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t<div class=\"nav-coach-text\">\\n\\t\\t\\t\\t\\t\\t<svg version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n\\t\\t\\t\\t\\t\\t\\t<use xlink:href=\"/icons/common-icons.svg#icon-coach-mark\"></use>\\n\\t\\t\\t\\t\\t\\t</svg>\\n\\t\\t\\t\\t\\t\\t<div class=\"txt\">\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"title\">Experiences</span>\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"content\">Curated events based on your<br /> preferences in one place.</span>\\n\\t\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"qb-coach\">\\n\\t\\t\\t\\t\\t<div class=\"qb-coach-img wow\">\\n\\t\\t\\t\\t\\t\\t<img data-src=\"//in.bmscdn.com/webin/static/coach-arrow-top.png\" />\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t<div class=\"qb-coach-text\">\\n\\t\\t\\t\\t\\t\\t<svg version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n\\t\\t\\t\\t\\t\\t\\t<use xlink:href=\"/icons/common-icons.svg#icon-coach-mark\"></use>\\n\\t\\t\\t\\t\\t\\t</svg>\\n\\t\\t\\t\\t\\t\\t<div class=\"txt\">\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"title\">quickbook + universal search</span>\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"content\">Find and book anything from anywhere</span>\\n\\t\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"login-coach\">\\n\\t\\t\\t\\t\\t<div class=\"login-coach-img wow\">\\n\\t\\t\\t\\t\\t\\t<img data-src=\"//in.bmscdn.com/webin/static/coach-arrow-right.png\" />\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t<div class=\"login-coach-text\">\\n\\t\\t\\t\\t\\t\\t<svg version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n\\t\\t\\t\\t\\t\\t\\t<use xlink:href=\"/icons/common-icons.svg#icon-coach-mark\"></use>\\n\\t\\t\\t\\t\\t\\t</svg>\\n\\t\\t\\t\\t\\t\\t<div class=\"txt\">\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"title\">Sign in to find MyWallet here</span>\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"content\">Enjoy one-click checkout and<br /> instant refunds with MyWallet</span>\\n\\t\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"cancel-wrapper\" id=\"cancel-wrapper\">\\n\\t\\t\\t\\t\\t<svg version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\" viewBox=\"0 0 100 100\" enable-background=\"new 0 0 100 100\" xml:space=\"preserve\">\\n\\t\\t\\t\\t\\t\\t<use xlink:href=\"/icons/common-icons.svg#icon-cancel\"></use>\\n\\t\\t\\t\\t\\t</svg>\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t</div> -->\\n <script async=\"\" type=\"text/javascript\">\\n if (global.blnIsTouchScreen) {\\n\\t\\t\\t\\tvar SeatData = null;\\n\\t \\t\\tvar getSeatScript = \\'//in.bookmyshow.com/js/seatlayout-8bd672e5bb.js\\';\\n\\t \\t}\\n\\n\\t\\t\\tif(global.blnIsTouchScreen){\\n\\t\\t\\t\\tvar $btnNS = $(\\'.button-now-showing\\'),\\n\\t\\t\\t\\t \\t$btnCS = $(\\'.button-coming-soon\\'),\\n\\t\\t\\t\\t \\t$carouselNS = $(\\'.carousel-now-showing\\'),\\n\\t\\t\\t\\t\\t$carouselCS = 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Mental\",\"id\":\"ET00054659\",\"brand\":\"\",\"category\":\"MT\",\"variant\":\"2D | Punjabi\",\"list\":\"Home Page - Coming Soon\",\"position\":6,\"dimension13\":\"Punjabi\"},{\"name\":\"Enthavaraku Ee Prema\",\"id\":\"ET00055053\",\"brand\":\"\",\"category\":\"MT\",\"variant\":\"2D | Telugu\",\"list\":\"Home Page - Coming Soon\",\"position\":7,\"dimension13\":\"Telugu\"},{\"name\":\"8 Thottakkal\",\"id\":\"ET00055019\",\"brand\":\"\",\"category\":\"MT\",\"variant\":\"2D | Tamil\",\"list\":\"Home Page - Coming Soon\",\"position\":8,\"dimension13\":\"Tamil\"},{\"name\":\"Ayyanaar Veethi\",\"id\":\"ET00055496\",\"brand\":\"\",\"category\":\"MT\",\"variant\":\"2D | Tamil\",\"list\":\"Home Page - Coming Soon\",\"position\":9,\"dimension13\":\"Tamil\"}] } });\\n </script>\\n <!-- JQuery to show/hide app-install-footer -->\\n <script type=\"text/javascript\">\\n $(\\'input[type=text]\\').focus(\\n function () {\\n $(\\'#app-install-wrap\\').hide();\\n }\\n );\\n if($(\\'#app-install-wrap\\').css(\\'display\\') != \\'none\\'){\\n $(\\'input[type=text]\\').blur(\\n function () {\\n \\t$(\\'#app-install-wrap\\').show();\\n }\\n );\\n }\\n </script>\\n <!-- Affinity M-Canvas Site Code Starts Here (Required) -->\\n <!-- <script type=\"text/javascript\">\\n\\t\\t\\t(function(){\\n\\t\\t\\t\\tvar param = {\\n\\t\\t\\t\\t\\t\"pk\": \"bce66\",\\n\\t\\t\\t\\t\\t\"aduid\": 217\\n\\t\\t\\t\\t};\\n\\t\\t\\t\\tvar d=top.document,s=d.createElement(\"script\"),u=[],p;param.u=d.location.href;param.ref=d.referrer;param.phR=Math.random()+\"_\"+(new Date).getTime();for(p in param)if(param.hasOwnProperty(p))u.push(p+\"=\"+encodeURIComponent(param[p]));s.src=\"//ic.ph.affinity.com/init.js?\"+u.join(\"&\");s.type=\"text/javascript\";d.getElementsByTagName(\"head\")[0].appendChild(s);\\n\\t\\t\\t})();\\n\\t\\t</script> -->\\n </div>\\n </nav>\\n </header>\\n </div>\\n </body>\\n</html>\\n'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a2" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "56051" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a2.find('''class=\"ratingSpan js-ratingSpan\"''') " ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "' class=\"tooltip\">\\n RATE\\n </span>\\n <a class=\"js-rating\" href=\"javascript:;\">\\n <span class=\"rating-icon __icon\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-rating\">\\n </use>\\n </svg>\\n </span>\\n </a>\\n </li>\\n </ul>\\n <div class=\"rating-section card-ratings\">\\n <div class=\"rate-o-meter\" data-event-code=\"ET00054768\">\\n 0\\n </div>\\n <ul class=\"rating-stars\" data-role=\"ratingStars\" event-code=\"ET00054768\" event-name=\"Manasu Malligey\" id=\"dET00054768\">\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"0.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"1.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"1.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"2.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"2.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"3.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"3.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"4.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n <li class=\"stars\">\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"4.5\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-left\">\\n </use>\\n </svg>\\n </span>\\n <span class=\"ratingSpan js-ratingSpan\" data-value=\"5.0\">\\n <svg enable-background=\"new 0 0 50 100\" version=\"1.1\" viewbox=\"0 0 50 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-ratingstar-right\">\\n </use>\\n </svg>\\n </span>\\n </li>\\n </ul>\\n <div class=\"rating-head\">\\n ADD YOUR RATING\\n </div>\\n </div>\\n </div>\\n <div class=\"stats-wrapper\">\\n <div class=\"stats\">\\n <div class=\"certification\">\\n <div class=\"__container\">\\n <span class=\"icon-censor\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-u\">\\n </use>\\n </svg>\\n </span>\\n </div>\\n </div>\\n <div class=\"popularity sa-data-plugin\" data-event-code=\"ET00054768\" data-event-group=\"EG00035851\">\\n <div class=\"__likes\">\\n <div class=\"__heart _none\">\\n <svg enable-background=\"new 0 0 100 100\" style=\"fill: #D6181F;\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-heart\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__thumbs _none\">\\n <svg enable-background=\"new 0 0 100 100\" version=\"1.1\" viewbox=\"0 0 100 100\" x=\"0px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0px\">\\n <use xlink:href=\"/icons/common-icons.svg#icon-like\">\\n </use>\\n </svg>\\n </div>\\n <div class=\"__percentage\">\\n </div>\\n </div>\\n <div class=\"__votes\">\\n <div class=\"__count\">\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\"detail detail-scroll\">\\n <div class=\"__name overflowEllipses\">\\n <a class=\"__movie-name\" data-position=\"\" href=\"/national-capital-region-ncr/movies/manasu-malligey/ET00054768\" onclick=\"GTMredirect(\\'Manasu Malligey\\',\\'ET00054768\\',\\'manasu-malligey\\',\\'Kannada\\',\\'31 Mar, 2017\\',$(this),\\'2D\\');\" title=\"Manasu Malligey\">\\n Manasu Malligey\\n </a>\\n </div>\\n <div class=\"languages\">\\n <ul class=\"language-list\">\\n <li class=\"__language\">\\n Kannada\\n </li>\\n </ul>\\n </div>\\n <div class=\"genre-list\">\\n <a href=\"/movies/drama\">\\n <div class=\"__rounded-box __genre\">\\n Drama\\n </div>\\n </a>\\n <a href=\"/movies/romance\">\\n <div class=\"__rounded-box __genre\">\\n Romance\\n </div>\\n </a>\\n </div>\\n <div class=\"show-details\">\\n <div class=\"cinema\">\\n <div class=\"__name\">\\n PVR\\n </div>\\n <div class=\"__location\">\\n Oberoi Mall, Goregaon (E)\\n </div>\\n </div>\\n <ul class=\"showtimes\">\\n <li class=\"__details\">\\n <div class=\"__day today\">\\n TODAY\\n </div>\\n <a class=\"__time\" href=\"#\">\\n '" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[55000:65000]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
probml/pyprobml
notebooks/book1/04/logreg_iris_bayes_1d_pymc3.ipynb
1
5051
{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "b4d612cb", "metadata": {}, "outputs": [], "source": [ "# Bayesian Binary logistic regression in 1d for iris flowers\n", "\n", "# Code is based on\n", "# https://github.com/aloctavodia/BAP/blob/master/code/Chp4/04_Generalizing_linear_models.ipynb\n", "\n", "\n", "try:\n", " import pymc3 as pm\n", "except ModuleNotFoundError:\n", " %pip install -qq pymc3\n", " import pymc3 as pm\n", "import numpy as np\n", "\n", "try:\n", " import pandas as pd\n", "except ModuleNotFoundError:\n", " %pip install -qq pandas\n", " import pandas as pd\n", "try:\n", " import theano.tensor as tt\n", "except ModuleNotFoundError:\n", " %pip install -qq theano\n", " import theano.tensor as tt\n", "# import seaborn as sns\n", "import scipy.stats as stats\n", "from scipy.special import expit as logistic\n", "import matplotlib.pyplot as plt\n", "\n", "try:\n", " import arviz as az\n", "except ModuleNotFoundError:\n", " %pip install -qq arviz\n", " import arviz as az\n", "try:\n", " from sklearn.datasets import load_iris\n", "except ModuleNotFoundError:\n", " %pip install -qq scikit-learn\n", " from sklearn.datasets import load_iris\n", "try:\n", " import probml_utils as pml\n", "except ModuleNotFoundError:\n", " %pip install -qq git+https://github.com/probml/probml-utils.git\n", " import probml_utils as pml\n", "\n", "if 0:\n", " # SAT data from\n", " # https://github.com/probml/pmtk3/blob/master/demos/logregSATdemoBayes.m\n", "\n", " X = [\n", " 525,\n", " 533,\n", " 545,\n", " 582,\n", " 581,\n", " 576,\n", " 572,\n", " 609,\n", " 559,\n", " 543,\n", " 576,\n", " 525,\n", " 574,\n", " 582,\n", " 574,\n", " 471,\n", " 595,\n", " 557,\n", " 557,\n", " 584,\n", " 599,\n", " 517,\n", " 649,\n", " 584,\n", " 463,\n", " 591,\n", " 488,\n", " 563,\n", " 553,\n", " 549,\n", " ]\n", "\n", " y = [0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1]\n", "\n", " x_n = \"SAT\"\n", " x_0 = np.array(X)\n", " y_0 = np.array(y)\n", "else:\n", " iris = load_iris()\n", " X = iris.data\n", " y = iris.target\n", "\n", " # Convert to pandas dataframe\n", " df_iris = pd.DataFrame(data=iris.data, columns=[\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"])\n", " df_iris[\"species\"] = pd.Series(iris.target_names[y], dtype=\"category\")\n", "\n", " df = df_iris.query(\"species == ('setosa', 'versicolor')\")\n", " y_0 = pd.Categorical(df[\"species\"]).codes\n", " x_n = \"sepal_length\"\n", " x_0 = df[x_n].values\n", "\n", "xmean = np.mean(x_0)\n", "x_c = x_0 - xmean\n", "\n", "print(x_c)\n", "\n", "\n", "with pm.Model() as model_0:\n", " α = pm.Normal(\"α\", mu=0, sd=10)\n", " β = pm.Normal(\"β\", mu=0, sd=10)\n", "\n", " μ = α + pm.math.dot(x_c, β)\n", " θ = pm.Deterministic(\"θ\", pm.math.sigmoid(μ))\n", " bd = pm.Deterministic(\"bd\", -α / β) # decision boundary\n", "\n", " yl = pm.Bernoulli(\"yl\", p=θ, observed=y_0)\n", "\n", " trace_0 = pm.sample(1000, cores=1, chains=2)\n", "\n", "\n", "varnames = [\"α\", \"β\", \"bd\"]\n", "az.summary(trace_0, varnames)\n", "\n", "\n", "theta = trace_0[\"θ\"].mean(axis=0)\n", "idx = np.argsort(x_c)\n", "\n", "plt.figure()\n", "# plot logistic curve\n", "plt.plot(x_c[idx], theta[idx], color=\"C2\", lw=3)\n", "az.plot_hdi(x_c, trace_0[\"θ\"], color=\"C2\")\n", "\n", "# plot decision boundary\n", "plt.vlines(trace_0[\"bd\"].mean(), 0, 1, color=\"k\")\n", "bd_hpd = az.hdi(trace_0[\"bd\"])\n", "plt.fill_betweenx([0, 1], bd_hpd[0], bd_hpd[1], color=\"k\", alpha=0.5)\n", "\n", "# plot jittered data\n", "plt.scatter(x_c, np.random.normal(y_0, 0.02), marker=\".\", color=[f\"C{x}\" for x in y_0])\n", "\n", "\n", "plt.xlabel(x_n)\n", "plt.ylabel(\"p(y=1)\", rotation=0)\n", "# use original scale for xticks\n", "locs, _ = plt.xticks()\n", "plt.xticks(locs, np.round(locs + xmean, 1))\n", "# plt.xticks(x_c[idx], np.round(x_0[idx], 1))\n", "plt.tight_layout()\n", "pml.savefig(\"logreg_iris_bayes_1d.pdf\", dpi=300)\n", "\n", "plt.show()" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }
mit
probml/pyprobml
notebooks/book1/21/fig_21_11.ipynb
1
790
{ "cells": [ { "cell_type": "markdown", "id": "1a338aab", "metadata": {}, "source": [ "Performance of K-means and GMM vs $K$ on the 2d dataset from \\cref{fig:kmeansVoronoi}. (a) Distortion on validation set vs $K$. Generated by [kmeans_silhouette.ipynb](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/21/kmeans_silhouette.ipynb) . (b) BIC vs $K$. Generated by [gmm_2d.ipynb](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/21/gmm_2d.ipynb) . (c) Silhouette score vs $K$. Generated by [kmeans_silhouette.ipynb](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/21/kmeans_silhouette.ipynb) ." ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }
mit
AllenDowney/ModSimPy
examples/queue.ipynb
1
15612
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# One Queue or Two" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Modeling and Simulation in Python*\n", "\n", "Copyright 2021 Allen Downey\n", "\n", "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "# install Pint if necessary\n", "\n", "try:\n", " import pint\n", "except ImportError:\n", " !pip install pint" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "# download modsim.py if necessary\n", "\n", "from os.path import exists\n", "\n", "filename = 'modsim.py'\n", "if not exists(filename):\n", " from urllib.request import urlretrieve\n", " url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n", " local, _ = urlretrieve(url+filename, filename)\n", " print('Downloaded ' + local)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [], "source": [ "# import functions from modsim\n", "\n", "from modsim import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook presents a case study from *Modeling and Simulation in Python*. It explores a question related to queueing theory, which is the study of systems that involve waiting in lines, also known as \"queues\".\n", "\n", "Suppose you are designing the checkout area for a new store. There is room for two checkout counters and a waiting area for customers. You can make two lines, one for each counter, or one line that serves both counters.\n", "\n", "In theory, you might expect a single line to be better, but it has some practical drawbacks: in order to maintain a single line, you would have to install rope barriers, and customers might be put off by what seems to be a longer line, even if it moves faster.\n", "\n", "So you'd like to check whether the single line is really better and by how much. Simulation can help answer this question." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we did in the bikeshare model, we'll assume that a customer is equally likely to arrive during any timestep. I'll denote this probability using the Greek letter lambda, $\\lambda$, or the variable name `lam`. The value of $\\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.\n", "\n", "Based on data from other stores, you know that it takes 5 minutes for a customer to check out, on average. But checkout times are highly variable: most customers take less than 5 minutes, but some take substantially more. A simple way to model this variability is to assume that when a customer is checking out, they have the same probability of finishing up during each time step. I'll denote this probability using the Greek letter mu, $\\mu$, or the variable name `mu`.\n", "\n", "If we choose $\\mu=1/5$, the average number of time steps for each checkout will be 5 minutes, which is consistent with the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## One server, one queue\n", "\n", "Write a function called `make_system` that takes `lam` and `mu` as parameters and returns a `System` object with variables `lam`, `mu`, and `duration`. Set `duration`, which is the number of time steps to simulate, to 10 hours, expressed in minutes. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test this function by creating a `System` object with `lam=1/8` and `mu=1/5`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write an update function that takes as parameters `x`, which is the total number of customer in the store, including the one checking out; `t`, which is the number of minutes that have elapsed in the simulation, and `system`, which is a `System` object.\n", "\n", "If there's a customer checking out, it should use `flip` to decide whether they are done. And it should use `flip` to decide if a new customer has arrived.\n", "\n", "It should return the total number of customers at the end of the time step." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test your function by calling it with `x=1`, `t=0`, and the `System` object you created. If you run it a few times, you should see different results." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can run the simulation. Here's a version of `run_simulation` that creates a `TimeSeries` with the total number of customers in the store, including the one checking out." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def run_simulation(system, update_func):\n", " \"\"\"Simulate a queueing system.\n", " \n", " system: System object\n", " update_func: function object\n", " \"\"\"\n", " x = 0\n", " results = TimeSeries()\n", " results[0] = x\n", " \n", " for t in linrange(0, system.duration):\n", " x = update_func(x, t, system)\n", " results[t+1] = x\n", "\n", " return results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Call `run_simulation` with your update function and plot the results." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After the simulation, we can compute `L`, which is the average number of customers in the system, and `W`, which is the average time customers spend in the store. `L` and `W` are related by Little's Law:\n", "\n", "$L = \\lambda W$\n", "\n", "Where $\\lambda$ is the arrival rate. Here's a function that computes them." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def compute_metrics(results, system):\n", " \"\"\"Compute average number of customers and wait time.\n", " \n", " results: TimeSeries of queue lengths\n", " system: System object\n", " \n", " returns: L, W\n", " \"\"\"\n", " L = results.mean()\n", " W = L / system.lam\n", " return L, W" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Call `compute_metrics` with the results from your simulation." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameter sweep\n", "\n", "Since we don't know the actual value of $\\lambda$, we can sweep through a range of possibilities, from 10% to 80% of the completion rate, $\\mu$. (If customers arrive faster than the completion rate, the queue grows without bound. In that case the metrics `L` and `W` just depend on how long the store is open.)\n", "\n", "Create an array of values for `lam`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write a function that takes an array of values for `lam`, a single value for `mu`, and an update function.\n", "\n", "For each value of `lam`, it should run a simulation, compute `L` and `W`, and store the value of `W` in a `SweepSeries`.\n", "\n", "It should return the `SweepSeries`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Call your function to generate a `SweepSeries`, and plot it." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we imagine that this range of values represents arrival rates on different days, we can use the average value of `W`, for a range of values of `lam`, to compare different queueing strategies." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analysis\n", "\n", "The model I chose for this system is a common model in queueing theory, in part because many of its properties can be derived analytically.\n", "\n", "In particular, we can derive the average time in the store as a function of $\\mu$ and $\\lambda$:\n", "\n", "$W = 1 / (\\mu - \\lambda)$\n", "\n", "The following function plots the theoretical value of $W$ as a function of $\\lambda$." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def plot_W(lam_array, mu):\n", " \"\"\"Plot the theoretical mean wait time.\n", " \n", " lam_array: array of values for `lam`\n", " mu: probability of finishing a checkout\n", " \"\"\"\n", " W_array = 1 / (mu - lam_array)\n", " W_series = make_series(lam_array, W_array)\n", " W_series.plot(style='-', label='analysis')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use this function to plot the theoretical results, then plot your simulation results again on the same graph. How do they compare?" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiple servers\n", "\n", "Now let's try the other two queueing strategies:\n", "\n", "1. One queue with two checkout counters.\n", "2. Two queues, one for each counter.\n", "\n", "The following figure shows the three scenarios:\n", "\n", "![](https://github.com/AllenDowney/ModSim/raw/main/figs/queue.png)\n", "\n", "Write an update function for one queue with two servers." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use this update function to simulate the system, plot the results, and print the metrics." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we have two checkout counters now, we can consider values for $\\lambda$ that exceed $\\mu$.\n", "\n", "Create a new array of values for `lam` from 10% to 160% of `mu`." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use your sweep function to simulate the two server, one queue scenario with a range of values for `lam`.\n", "\n", "Plot the results and print the average value of `W` across all values of `lam`." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiple queues\n", "\n", "To simulate the scenario with two separate queues, we need two state variables to keep track of customers in each queue.\n", "\n", "Write an update function that takes `x1`, `x2`, `t`, and `system` as parameters and returns `x1` and `x2` as return values. f you are not sure how to return more than one return value, see `compute_metrics`.\n", "\n", "When a customer arrives, which queue do they join?" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write a version of `run_simulation` that works with this update function." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test your functions by running a simulation with a single value of `lam`." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sweep a range of values for `lam`, plot the results, and print the average wait time across all values of `lam`.\n", "\n", "How do the results compare to the scenario with two servers and one queue." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# Solution goes here" ] }, { "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": 2 }
mit
csaladenes/csaladenes.github.io
present/gtk/html/workbook.ipynb
1
63285
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# GTK adatviz kurzus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bővítőcsomagok importálása" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Előzetesen mentsük el valamelyik D3plus példa adatait a `valami.json`-ba" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df=pd.read_json('valami.json')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "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>group</th>\n", " <th>name</th>\n", " <th>uj</th>\n", " <th>value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>group 1</td>\n", " <td>alpha</td>\n", " <td>9</td>\n", " <td>100</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>group 1</td>\n", " <td>epsilon</td>\n", " <td>9</td>\n", " <td>500</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>group 2</td>\n", " <td>gamma</td>\n", " <td>9</td>\n", " <td>40</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>group 1</td>\n", " <td>zeta</td>\n", " <td>9</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>group 2</td>\n", " <td>delta</td>\n", " <td>9</td>\n", " <td>15</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>group 2</td>\n", " <td>beta</td>\n", " <td>9</td>\n", " <td>70</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " group name uj value\n", "0 group 1 alpha 9 100\n", "1 group 1 epsilon 9 500\n", "2 group 2 gamma 9 40\n", "3 group 1 zeta 9 1\n", "4 group 2 delta 9 15\n", "5 group 2 beta 9 70" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Oszlop kiíratása" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "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>group</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>group 1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>group 1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>group 2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>group 1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>group 2</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>group 2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " group\n", "0 group 1\n", "1 group 1\n", "2 group 2\n", "3 group 1\n", "4 group 2\n", "5 group 2" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['group']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sor kiíratása" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "group group 2\n", "name delta\n", "uj 9\n", "value 15\n", "Name: 4, dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Új oszlop" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df['uj']=9" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "scrolled": true }, "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>group</th>\n", " <th>name</th>\n", " <th>uj</th>\n", " <th>value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>group 1</td>\n", " <td>alpha</td>\n", " <td>9</td>\n", " <td>100</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>group 1</td>\n", " <td>epsilon</td>\n", " <td>9</td>\n", " <td>500</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>group 2</td>\n", " <td>gamma</td>\n", " <td>9</td>\n", " <td>40</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>group 1</td>\n", " <td>zeta</td>\n", " <td>9</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>group 2</td>\n", " <td>delta</td>\n", " <td>9</td>\n", " <td>15</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>group 2</td>\n", " <td>beta</td>\n", " <td>9</td>\n", " <td>70</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " group name uj value\n", "0 group 1 alpha 9 100\n", "1 group 1 epsilon 9 500\n", "2 group 2 gamma 9 40\n", "3 group 1 zeta 9 1\n", "4 group 2 delta 9 15\n", "5 group 2 beta 9 70" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data frame exportálás" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.to_json('valami2.json')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Transzponált" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>group</th>\n", " <td>group 1</td>\n", " <td>group 2</td>\n", " <td>group 2</td>\n", " <td>group 2</td>\n", " <td>group 1</td>\n", " <td>group 1</td>\n", " </tr>\n", " <tr>\n", " <th>name</th>\n", " <td>alpha</td>\n", " <td>beta</td>\n", " <td>gamma</td>\n", " <td>delta</td>\n", " <td>epsilon</td>\n", " <td>zeta</td>\n", " </tr>\n", " <tr>\n", " <th>value</th>\n", " <td>100</td>\n", " <td>70</td>\n", " <td>40</td>\n", " <td>15</td>\n", " <td>500</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>uj</th>\n", " <td>9</td>\n", " <td>9</td>\n", " <td>9</td>\n", " <td>9</td>\n", " <td>9</td>\n", " <td>9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5\n", "group group 1 group 2 group 2 group 2 group 1 group 1\n", "name alpha beta gamma delta epsilon zeta\n", "value 100 70 40 15 500 1\n", "uj 9 9 9 9 9 9" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.T" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.T.to_json('valami3.json')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "JSON formázás" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "enyim=df.T.to_json()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pandas dataframe `to_json` függvény stringet generál" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'{\"0\":{\"group\":\"group 1\",\"name\":\"alpha\",\"uj\":9,\"value\":100},\"1\":{\"group\":\"group 1\",\"name\":\"epsilon\",\"uj\":9,\"value\":500},\"2\":{\"group\":\"group 2\",\"name\":\"gamma\",\"uj\":9,\"value\":40},\"3\":{\"group\":\"group 1\",\"name\":\"zeta\",\"uj\":9,\"value\":1},\"4\":{\"group\":\"group 2\",\"name\":\"delta\",\"uj\":9,\"value\":15},\"5\":{\"group\":\"group 2\",\"name\":\"beta\",\"uj\":9,\"value\":70}}'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enyim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ezt átkonvertáljuk JSON-ba" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "enyimjson=json.loads(enyim)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{u'0': {u'group': u'group 1', u'name': u'alpha', u'uj': 9, u'value': 100},\n", " u'1': {u'group': u'group 1', u'name': u'epsilon', u'uj': 9, u'value': 500},\n", " u'2': {u'group': u'group 2', u'name': u'gamma', u'uj': 9, u'value': 40},\n", " u'3': {u'group': u'group 1', u'name': u'zeta', u'uj': 9, u'value': 1},\n", " u'4': {u'group': u'group 2', u'name': u'delta', u'uj': 9, u'value': 15},\n", " u'5': {u'group': u'group 2', u'name': u'beta', u'uj': 9, u'value': 70}}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enyimjson" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A JSON olyan mint egy szótár: vannak kulcsok (keys) és értékek (values)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[u'1', u'0', u'3', u'2', u'5', u'4']" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enyimjson.keys()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[{u'group': u'group 1', u'name': u'epsilon', u'uj': 9, u'value': 500},\n", " {u'group': u'group 1', u'name': u'alpha', u'uj': 9, u'value': 100},\n", " {u'group': u'group 1', u'name': u'zeta', u'uj': 9, u'value': 1},\n", " {u'group': u'group 2', u'name': u'gamma', u'uj': 9, u'value': 40},\n", " {u'group': u'group 2', u'name': u'beta', u'uj': 9, u'value': 70},\n", " {u'group': u'group 2', u'name': u'delta', u'uj': 9, u'value': 15}]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enyimjson.values()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "jsonlista=list(enyimjson.values())" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[{u'group': u'group 1', u'name': u'epsilon', u'uj': 9, u'value': 500},\n", " {u'group': u'group 1', u'name': u'alpha', u'uj': 9, u'value': 100},\n", " {u'group': u'group 1', u'name': u'zeta', u'uj': 9, u'value': 1},\n", " {u'group': u'group 2', u'name': u'gamma', u'uj': 9, u'value': 40},\n", " {u'group': u'group 2', u'name': u'beta', u'uj': 9, u'value': 70},\n", " {u'group': u'group 2', u'name': u'delta', u'uj': 9, u'value': 15}]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jsonlista" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `dumps` függvény újra stringet generál" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'[{\"group\": \"group 1\", \"name\": \"epsilon\", \"value\": 500, \"uj\": 9}, {\"group\": \"group 1\", \"name\": \"alpha\", \"value\": 100, \"uj\": 9}, {\"group\": \"group 1\", \"name\": \"zeta\", \"value\": 1, \"uj\": 9}, {\"group\": \"group 2\", \"name\": \"gamma\", \"value\": 40, \"uj\": 9}, {\"group\": \"group 2\", \"name\": \"beta\", \"value\": 70, \"uj\": 9}, {\"group\": \"group 2\", \"name\": \"delta\", \"value\": 15, \"uj\": 9}]'" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "json.dumps(jsonlista)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fájlba mentés" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "367" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "open('ujvalami.json','w').write(json.dumps(jsonlista))" ] }, { "cell_type": "code", "execution_count": 45, "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>group</th>\n", " <th>name</th>\n", " <th>value</th>\n", " <th>uj</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>group 1</td>\n", " <td>alpha</td>\n", " <td>100</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>group 2</td>\n", " <td>beta</td>\n", " <td>70</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>group 2</td>\n", " <td>gamma</td>\n", " <td>40</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>group 2</td>\n", " <td>delta</td>\n", " <td>15</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>group 1</td>\n", " <td>epsilon</td>\n", " <td>500</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>group 1</td>\n", " <td>zeta</td>\n", " <td>1</td>\n", " <td>9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " group name value uj\n", "0 group 1 alpha 100 9\n", "1 group 2 beta 70 9\n", "2 group 2 gamma 40 9\n", "3 group 2 delta 15 9\n", "4 group 1 epsilon 500 9\n", "5 group 1 zeta 1 9" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Egy elem lekérdezése pandasból" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'gamma'" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[2]['name']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nem lehet egy elemet megváltoztatni, csak egy teljes oszlopot" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Anaconda2\\envs\\python3\\lib\\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" ] } ], "source": [ "df.loc[2]['name']='ujgamma'" ] }, { "cell_type": "code", "execution_count": 48, "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>group</th>\n", " <th>name</th>\n", " <th>value</th>\n", " <th>uj</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>group 1</td>\n", " <td>alpha</td>\n", " <td>100</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>group 2</td>\n", " <td>beta</td>\n", " <td>70</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>group 2</td>\n", " <td>gamma</td>\n", " <td>40</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>group 2</td>\n", " <td>delta</td>\n", " <td>15</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>group 1</td>\n", " <td>epsilon</td>\n", " <td>500</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>group 1</td>\n", " <td>zeta</td>\n", " <td>1</td>\n", " <td>9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " group name value uj\n", "0 group 1 alpha 100 9\n", "1 group 2 beta 70 9\n", "2 group 2 gamma 40 9\n", "3 group 2 delta 15 9\n", "4 group 1 epsilon 500 9\n", "5 group 1 zeta 1 9" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ujlista=range(6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Új oszlop" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['uj2']=ujlista" ] }, { "cell_type": "code", "execution_count": 55, "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>group</th>\n", " <th>name</th>\n", " <th>value</th>\n", " <th>uj</th>\n", " <th>uj2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>group 1</td>\n", " <td>alpha</td>\n", " <td>100</td>\n", " <td>9</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>group 2</td>\n", " <td>beta</td>\n", " <td>70</td>\n", " <td>9</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>group 2</td>\n", " <td>gamma</td>\n", " <td>40</td>\n", " <td>9</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>group 2</td>\n", " <td>delta</td>\n", " <td>15</td>\n", " <td>9</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>group 1</td>\n", " <td>epsilon</td>\n", " <td>500</td>\n", " <td>9</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>group 1</td>\n", " <td>zeta</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " group name value uj uj2\n", "0 group 1 alpha 100 9 0\n", "1 group 2 beta 70 9 1\n", "2 group 2 gamma 40 9 2\n", "3 group 2 delta 15 9 3\n", "4 group 1 epsilon 500 9 4\n", "5 group 1 zeta 1 9 5" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Még egy új oszlop" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df['uj3']=['a','b','c','d','t','d']" ] }, { "cell_type": "code", "execution_count": 57, "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>group</th>\n", " <th>name</th>\n", " <th>value</th>\n", " <th>uj</th>\n", " <th>uj2</th>\n", " <th>uj3</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>group 1</td>\n", " <td>alpha</td>\n", " <td>100</td>\n", " <td>9</td>\n", " <td>0</td>\n", " <td>a</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>group 2</td>\n", " <td>beta</td>\n", " <td>70</td>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>b</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>group 2</td>\n", " <td>gamma</td>\n", " <td>40</td>\n", " <td>9</td>\n", " <td>2</td>\n", " <td>c</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>group 2</td>\n", " <td>delta</td>\n", " <td>15</td>\n", " <td>9</td>\n", " <td>3</td>\n", " <td>d</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>group 1</td>\n", " <td>epsilon</td>\n", " <td>500</td>\n", " <td>9</td>\n", " <td>4</td>\n", " <td>t</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>group 1</td>\n", " <td>zeta</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>5</td>\n", " <td>d</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " group name value uj uj2 uj3\n", "0 group 1 alpha 100 9 0 a\n", "1 group 2 beta 70 9 1 b\n", "2 group 2 gamma 40 9 2 c\n", "3 group 2 delta 15 9 3 d\n", "4 group 1 epsilon 500 9 4 t\n", "5 group 1 zeta 1 9 5 d" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Kis játék függvény: elment egy dataframe-et ujvalamix.json néven D3plus formátumban" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def d3plus(df):\n", " open('ujvalamix.json','w').write(json.dumps(list(json.loads(df.T.to_json()).values())))\n", " print('oke')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "oke\n" ] } ], "source": [ "d3plus(df)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import html5lib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "HTML táblázatok beolvasása" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dfs=pd.read_html(r'https://hu.wikipedia.org/wiki/Marosvásárhely')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "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>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " <th>11</th>\n", " <th>12</th>\n", " <th>13</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Marosvásárhely éghajlati jellemzői</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Hónap</td>\n", " <td>Jan.</td>\n", " <td>Feb.</td>\n", " <td>Már.</td>\n", " <td>Ápr.</td>\n", " <td>Máj.</td>\n", " <td>Jún.</td>\n", " <td>Júl.</td>\n", " <td>Aug.</td>\n", " <td>Szep.</td>\n", " <td>Okt.</td>\n", " <td>Nov.</td>\n", " <td>Dec.</td>\n", " <td>Év</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Rekord max. hőmérséklet (°C)</td>\n", " <td>140</td>\n", " <td>190</td>\n", " <td>270</td>\n", " <td>325</td>\n", " <td>344</td>\n", " <td>353</td>\n", " <td>390</td>\n", " <td>385</td>\n", " <td>382</td>\n", " <td>315</td>\n", " <td>265</td>\n", " <td>183</td>\n", " <td>390</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Átlaghőmérséklet (°C)</td>\n", " <td>−4,0</td>\n", " <td>−1,8</td>\n", " <td>40</td>\n", " <td>97</td>\n", " <td>148</td>\n", " <td>177</td>\n", " <td>194</td>\n", " <td>188</td>\n", " <td>146</td>\n", " <td>92</td>\n", " <td>37</td>\n", " <td>13</td>\n", " <td>90</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Rekord min. hőmérséklet (°C)</td>\n", " <td>−32,8</td>\n", " <td>−32,0</td>\n", " <td>−27,3</td>\n", " <td>−7,5</td>\n", " <td>−1,6</td>\n", " <td>03</td>\n", " <td>46</td>\n", " <td>27</td>\n", " <td>−3,3</td>\n", " <td>−8,4</td>\n", " <td>−19,6</td>\n", " <td>−25,9</td>\n", " <td>−32,8</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Forrás: Románia statisztikai évkönyve, 2006</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 \\\n", "0 Marosvásárhely éghajlati jellemzői NaN NaN NaN NaN \n", "1 Hónap Jan. Feb. Már. Ápr. \n", "2 Rekord max. hőmérséklet (°C) 140 190 270 325 \n", "3 Átlaghőmérséklet (°C) −4,0 −1,8 40 97 \n", "4 Rekord min. hőmérséklet (°C) −32,8 −32,0 −27,3 −7,5 \n", "5 Forrás: Románia statisztikai évkönyve, 2006 NaN NaN NaN NaN \n", "\n", " 5 6 7 8 9 10 11 12 13 \n", "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "1 Máj. Jún. Júl. Aug. Szep. Okt. Nov. Dec. Év \n", "2 344 353 390 385 382 315 265 183 390 \n", "3 148 177 194 188 146 92 37 13 90 \n", "4 −1,6 03 46 27 −3,3 −8,4 −19,6 −25,9 −32,8 \n", "5 NaN NaN NaN NaN NaN NaN NaN NaN NaN " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs[3]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "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>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Év</td>\n", " <td>Összesen</td>\n", " <td>Magyar</td>\n", " <td>Román</td>\n", " <td>Német</td>\n", " <td>Zsidó</td>\n", " <td>Roma</td>\n", " <td>Egyéb</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1850</td>\n", " <td>8 719</td>\n", " <td>75,1%</td>\n", " <td>13,5%</td>\n", " <td>2,8%</td>\n", " <td>2,9%</td>\n", " <td>3,8%</td>\n", " <td>1,9%</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1880</td>\n", " <td>12 883</td>\n", " <td>88,9%</td>\n", " <td>5,2%</td>\n", " <td>3,5%</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2,4%</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1900</td>\n", " <td>19 552</td>\n", " <td>83,3%</td>\n", " <td>11,6%</td>\n", " <td>3,6%</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1,5%</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1910</td>\n", " <td>25 517</td>\n", " <td>89,3%</td>\n", " <td>6,7%</td>\n", " <td>2,4%</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1,6%</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1930</td>\n", " <td>40 058</td>\n", " <td>57,2%</td>\n", " <td>26,7%</td>\n", " <td>1,7%</td>\n", " <td>12,1%</td>\n", " <td>1,1%</td>\n", " <td>1,2%</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1966</td>\n", " <td>86 464</td>\n", " <td>70,9%</td>\n", " <td>28,3%</td>\n", " <td>0,6%</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0,2%</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1977</td>\n", " <td>130 076</td>\n", " <td>63,6%</td>\n", " <td>34,8%</td>\n", " <td>0,6%</td>\n", " <td>0,4%</td>\n", " <td>0,5%</td>\n", " <td>0,1%</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1992</td>\n", " <td>164 445</td>\n", " <td>51,4%</td>\n", " <td>46,1%</td>\n", " <td>0,3%</td>\n", " <td>0,1%</td>\n", " <td>2%</td>\n", " <td>0,1%</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2002</td>\n", " <td>150 041</td>\n", " <td>46,7%</td>\n", " <td>50,3%</td>\n", " <td>0,2%</td>\n", " <td>0,1%</td>\n", " <td>2,4%</td>\n", " <td>0,2%</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2011[16]</td>\n", " <td>134 290</td>\n", " <td>42,84%</td>\n", " <td>49,17%</td>\n", " <td>0,15%</td>\n", " <td>0,05%</td>\n", " <td>2,32%</td>\n", " <td>5,47%</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7\n", "0 Év Összesen Magyar Román Német Zsidó Roma Egyéb\n", "1 1850 8 719 75,1% 13,5% 2,8% 2,9% 3,8% 1,9%\n", "2 1880 12 883 88,9% 5,2% 3,5% NaN NaN 2,4%\n", "3 1900 19 552 83,3% 11,6% 3,6% NaN NaN 1,5%\n", "4 1910 25 517 89,3% 6,7% 2,4% NaN NaN 1,6%\n", "5 1930 40 058 57,2% 26,7% 1,7% 12,1% 1,1% 1,2%\n", "6 1966 86 464 70,9% 28,3% 0,6% NaN NaN 0,2%\n", "7 1977 130 076 63,6% 34,8% 0,6% 0,4% 0,5% 0,1%\n", "8 1992 164 445 51,4% 46,1% 0,3% 0,1% 2% 0,1%\n", "9 2002 150 041 46,7% 50,3% 0,2% 0,1% 2,4% 0,2%\n", "10 2011[16] 134 290 42,84% 49,17% 0,15% 0,05% 2,32% 5,47%" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs[9]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df=dfs[9]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "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>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Év</td>\n", " <td>Összesen</td>\n", " <td>Magyar</td>\n", " <td>Román</td>\n", " <td>Német</td>\n", " <td>Zsidó</td>\n", " <td>Roma</td>\n", " <td>Egyéb</td>\n", " </tr>\n", " <tr>\n", " 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<tr>\n", " <th>6</th>\n", " <td>1966</td>\n", " <td>86 464</td>\n", " <td>70,9%</td>\n", " <td>28,3%</td>\n", " <td>0,6%</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0,2%</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1977</td>\n", " <td>130 076</td>\n", " <td>63,6%</td>\n", " <td>34,8%</td>\n", " <td>0,6%</td>\n", " <td>0,4%</td>\n", " <td>0,5%</td>\n", " <td>0,1%</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1992</td>\n", " <td>164 445</td>\n", " <td>51,4%</td>\n", " <td>46,1%</td>\n", " <td>0,3%</td>\n", " <td>0,1%</td>\n", " <td>2%</td>\n", " <td>0,1%</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2002</td>\n", " <td>150 041</td>\n", " <td>46,7%</td>\n", " <td>50,3%</td>\n", " <td>0,2%</td>\n", " <td>0,1%</td>\n", " <td>2,4%</td>\n", " <td>0,2%</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2011[16]</td>\n", " <td>134 290</td>\n", " <td>42,84%</td>\n", " <td>49,17%</td>\n", " <td>0,15%</td>\n", " <td>0,05%</td>\n", " <td>2,32%</td>\n", " <td>5,47%</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7\n", "0 Év Összesen Magyar Román Német Zsidó Roma Egyéb\n", "1 1850 8 719 75,1% 13,5% 2,8% 2,9% 3,8% 1,9%\n", "2 1880 12 883 88,9% 5,2% 3,5% NaN NaN 2,4%\n", "3 1900 19 552 83,3% 11,6% 3,6% NaN NaN 1,5%\n", "4 1910 25 517 89,3% 6,7% 2,4% NaN NaN 1,6%\n", "5 1930 40 058 57,2% 26,7% 1,7% 12,1% 1,1% 1,2%\n", "6 1966 86 464 70,9% 28,3% 0,6% NaN NaN 0,2%\n", "7 1977 130 076 63,6% 34,8% 0,6% 0,4% 0,5% 0,1%\n", "8 1992 164 445 51,4% 46,1% 0,3% 0,1% 2% 0,1%\n", "9 2002 150 041 46,7% 50,3% 0,2% 0,1% 2,4% 0,2%\n", "10 2011[16] 134 290 42,84% 49,17% 0,15% 0,05% 2,32% 5,47%" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 Év\n", "1 Összesen\n", "2 Magyar\n", "3 Román\n", "4 Német\n", "5 Zsidó\n", "6 Roma\n", "7 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1930 40 058 57,2% 26,7% 1,7% 12,1% 1,1% 1,2%\n", "6 1966 86 464 70,9% 28,3% 0,6% NaN NaN 0,2%\n", "7 1977 130 076 63,6% 34,8% 0,6% 0,4% 0,5% 0,1%\n", "8 1992 164 445 51,4% 46,1% 0,3% 0,1% 2% 0,1%\n", "9 2002 150 041 46,7% 50,3% 0,2% 0,1% 2,4% 0,2%\n", "10 2011[16] 134 290 42,84% 49,17% 0,15% 0,05% 2,32% 5,47%" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Szeletelés a sorokban" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "de=df.loc[1:9]" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "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>Év</th>\n", " <th>Összesen</th>\n", " <th>Magyar</th>\n", " <th>Román</th>\n", " <th>Német</th>\n", " <th>Zsidó</th>\n", " <th>Roma</th>\n", " <th>Egyéb</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1850</td>\n", " <td>8 719</td>\n", " <td>75,1%</td>\n", " <td>13,5%</td>\n", " <td>2,8%</td>\n", " <td>2,9%</td>\n", " <td>3,8%</td>\n", " <td>1,9%</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1880</td>\n", " <td>12 883</td>\n", " <td>88,9%</td>\n", " <td>5,2%</td>\n", " <td>3,5%</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2,4%</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1900</td>\n", " <td>19 552</td>\n", " <td>83,3%</td>\n", " <td>11,6%</td>\n", " <td>3,6%</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1,5%</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1910</td>\n", " <td>25 517</td>\n", " <td>89,3%</td>\n", " <td>6,7%</td>\n", " <td>2,4%</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1,6%</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1930</td>\n", " <td>40 058</td>\n", " <td>57,2%</td>\n", " <td>26,7%</td>\n", " <td>1,7%</td>\n", " <td>12,1%</td>\n", " <td>1,1%</td>\n", " <td>1,2%</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1966</td>\n", " <td>86 464</td>\n", " <td>70,9%</td>\n", " <td>28,3%</td>\n", " <td>0,6%</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0,2%</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1977</td>\n", " <td>130 076</td>\n", " <td>63,6%</td>\n", " <td>34,8%</td>\n", " <td>0,6%</td>\n", " <td>0,4%</td>\n", " <td>0,5%</td>\n", " <td>0,1%</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1992</td>\n", " <td>164 445</td>\n", " <td>51,4%</td>\n", " <td>46,1%</td>\n", " <td>0,3%</td>\n", " <td>0,1%</td>\n", " <td>2%</td>\n", " <td>0,1%</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2002</td>\n", " <td>150 041</td>\n", " <td>46,7%</td>\n", " <td>50,3%</td>\n", " <td>0,2%</td>\n", " <td>0,1%</td>\n", " <td>2,4%</td>\n", " <td>0,2%</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "0 Év Összesen Magyar Román Német Zsidó Roma Egyéb\n", "1 1850 8 719 75,1% 13,5% 2,8% 2,9% 3,8% 1,9%\n", "2 1880 12 883 88,9% 5,2% 3,5% NaN NaN 2,4%\n", "3 1900 19 552 83,3% 11,6% 3,6% NaN NaN 1,5%\n", "4 1910 25 517 89,3% 6,7% 2,4% NaN NaN 1,6%\n", "5 1930 40 058 57,2% 26,7% 1,7% 12,1% 1,1% 1,2%\n", "6 1966 86 464 70,9% 28,3% 0,6% NaN NaN 0,2%\n", "7 1977 130 076 63,6% 34,8% 0,6% 0,4% 0,5% 0,1%\n", "8 1992 164 445 51,4% 46,1% 0,3% 0,1% 2% 0,1%\n", "9 2002 150 041 46,7% 50,3% 0,2% 0,1% 2,4% 0,2%" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "de" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Stringek átaáalkítás számokká, tizedes vessző miatt" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "[75.1]\n", "2\n", "[75.1, 88.9]\n", "3\n", "[75.1, 88.9, 83.3]\n", "4\n", "[75.1, 88.9, 83.3, 89.3]\n", "5\n", "[75.1, 88.9, 83.3, 89.3, 57.2]\n", "6\n", "[75.1, 88.9, 83.3, 89.3, 57.2, 70.9]\n", "7\n", "[75.1, 88.9, 83.3, 89.3, 57.2, 70.9, 63.6]\n", "8\n", "[75.1, 88.9, 83.3, 89.3, 57.2, 70.9, 63.6, 51.4]\n", "9\n", "[75.1, 88.9, 83.3, 89.3, 57.2, 70.9, 63.6, 51.4, 46.7]\n" ] } ], "source": [ "ures=[]\n", "for i in range(1,10):\n", " print(i)\n", " elso=int(de.loc[i]['Magyar'][:2])\n", " masodik=int(de.loc[i]['Magyar'][3:4])\n", " ures.append(elso+masodik/10.0)\n", " print(ures)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Anaconda2\\lib\\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", "Try using .loc[row_indexer,col_indexer] = value instead\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" ] } ], "source": [ "de['ujoszlop']=ures" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "open('mv2.json','w').write(json.dumps(list(json.loads(de.T.to_json()).values())))\n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:Anaconda2]", "language": "python", "name": "conda-env-Anaconda2-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": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
chanam/udacity
P2/p2_investigate_a_dataset.ipynb
2
10979
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Data Analysis\n", "**Data Analyst Nanodegree P2: Investigate a Dataset**\n", "\n", "**Chana Greene**\n", "\n", "[List of Resources](#Resources)\n", "\n", "## Introduction\n", "\n", "For the final project, you will conduct your own data analysis and create a file to share that documents your findings. You should start by taking a look at your dataset and brainstorming what questions you could answer using it. Then you should use Pandas and NumPy to answer the questions you are most interested in, and create a report sharing the answers. You will not be required to use statistics or machine learning to complete this project, but you should make it clear in your communications that your findings are tentative. This project is open-ended in that we are not looking for one right answer.\n", "\n", "## Step One - Choose Your Data Set\n", "\n", "**Titanic Data** - Contains demographics and passenger information from 891 of the 2224 passengers and crew on board the Titanic. You can view a description of this dataset on the [Kaggle website](https://www.kaggle.com/c/titanic/data), where the data was obtained.\n", "\n", "From the Kaggle website:\n", "\n", " VARIABLE DESCRIPTIONS:\n", " survival Survival\n", " (0 = No; 1 = Yes)\n", " pclass Passenger Class\n", " (1 = 1st; 2 = 2nd; 3 = 3rd)\n", " name Name\n", " sex Sex\n", " age Age\n", " sibsp Number of Siblings/Spouses Aboard\n", " parch Number of Parents/Children Aboard\n", " ticket Ticket Number\n", " fare Passenger Fare\n", " cabin Cabin\n", " embarked Port of Embarkation\n", " (C = Cherbourg; Q = Queenstown; S = Southampton)\n", " \n", " SPECIAL NOTES:\n", " Pclass is a proxy for socio-economic status (SES)\n", " 1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower\n", " \n", " Age is in Years; Fractional if Age less than One (1)\n", " If the Age is Estimated, it is in the form xx.5\n", " \n", " With respect to the family relation variables (i.e. sibsp and parch)some relations were ignored. The following are the definitions used for sibsp and parch.\n", " \n", " Sibling: Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic\n", " Spouse: Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)\n", " Parent: Mother or Father of Passenger Aboard Titanic\n", " Child: Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic\n", " \n", " Other family relatives excluded from this study include cousins, nephews/nieces, aunts/uncles, and in-laws. \n", " Some children travelled only with a nanny, therefore parch=0 for them. As well, some travelled with very close friends or neighbors in a village, however, the definitions do not support such relations.\n", "\n", "## Step Two - Get Organized\n", "\n", "Eventually you’ll want to submit your project (and share it with friends, family, and employers). Get organized before you begin. We recommend creating a single folder that will eventually contain:\n", "\n", "Using IPython notebook, containing both the code report of findings in the same document" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step Three - Analyze Your Data\n", "\n", "Brainstorm some questions you could answer using the data set you chose, then start answering those questions. Here are some ideas to get you started:\n", "\n", "Titanic Data\n", "What factors made people more likely to survive?" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "%pylab inline\n", "\n", "matplotlib.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "titanic_data = pd.read_csv('titanic_data.csv')" ] }, { "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>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n", "2 Heikkinen, Miss. Laina female 26 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n", "4 Allen, Mr. William Henry male 35 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step Four - Share Your Findings\n", "\n", "Once you have finished analyzing the data, create a report that shares the findings you found most interesting. You might wish to use IPython notebook to share your findings alongside the code you used to perform the analysis, but you can also use another tool if you wish.\n", "\n", "## Step Five - Review\n", "\n", "Use the Project Rubric to review your project. If you are happy with your submission, then you're ready to submit your project. If you see room for improvement, keep working to improve your project." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## <a id='Resources'></a>List of Resources\n", "\n", "1. Pandas documentation: http://pandas.pydata.org/pandas-docs/stable/index.html\n", "2. Scipy ttest documentation: http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.ttest_rel.html\n", "3. t-table: https://s3.amazonaws.com/udacity-hosted-downloads/t-table.jpg\n", "4. Stroop effect Wikipedia page: https://en.wikipedia.org/wiki/Stroop_effect" ] } ], "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
minh-doan/deepometry
STEP_4_Test_and_Visualization_built-in_CNN.ipynb
1
17722
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ------------- User's settings -------------" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Location of digested data\n", "input_directory = '/digested/'\n", "\n", "# Location of saved trained model\n", "model_directory = '/model_directory/'\n", "\n", "# Desired location for outputs\n", "output_directory = '/output_directory/'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ------------- (semi)-Automatic -------------" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import keras\n", "import pickle\n", "from keras.layers import *\n", "from keras.models import Sequential\n", "import numpy\n", "import os\n", "import os.path\n", "\n", "import matplotlib.pyplot\n", "import pandas\n", "import seaborn\n", "import sklearn.metrics\n", "import tensorflow\n", "\n", "from tensorflow.contrib.tensorboard.plugins import projector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Configure GPU/CPU devices:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -------- If using Tensorflow-GPU: -------- #\n", "\n", "configuration = tensorflow.ConfigProto()\n", "\n", "configuration.gpu_options.allow_growth = True\n", "\n", "configuration.gpu_options.visible_device_list = \"0\"\n", "\n", "session = tensorflow.Session(config=configuration)\n", "\n", "keras.backend.set_session(session)\n", "\n", "\n", "# -------- If using Tensorflow (CPU) : -------- #\n", "\n", "# configuration = tensorflow.ConfigProto()\n", "\n", "# session = tensorflow.Session(config=configuration)\n", "\n", "# keras.backend.set_session(session)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data queueing" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def training_data_generator(input_x, input_y, batch_size):\n", " num_examples, num_labels = input_y.shape\n", " label_indices = []\n", " for i in range(num_labels):\n", " indices = [j for j in range(num_examples) if input_y[j,i] > 0]\n", " label_indices.append(indices)\n", " print(\"Label\",i,\":\",len(indices),\"examples\")\n", " samples_per_label = int(batch_size / num_labels)\n", "\n", " def generator():\n", " while True:\n", " x_samples = []\n", " y_samples = []\n", " for i in range(num_labels):\n", " random.shuffle(label_indices[i])\n", " indices = label_indices[i][0:samples_per_label]\n", " x_samples.append( input_x[indices, ...] )\n", " y_samples.append( input_y[indices, ...] )\n", " x_samples = numpy.concatenate( x_samples )\n", " y_samples = numpy.concatenate( y_samples )\n", " batch_indices = numpy.arange(x_samples.shape[0])\n", " numpy.random.shuffle(batch_indices)\n", " x_samples = x_samples[batch_indices, ...]\n", " y_samples = y_samples[batch_indices, ...]\n", " yield (x_samples, y_samples)\n", " return generator()\n", "\n", "\n", "def prediction_data_generator(input_x, input_y, batch_size):\n", " num_examples, num_labels = input_y.shape\n", " steps = int(num_examples / batch_size)\n", " def generator():\n", " i = 0\n", " while True:\n", " start = i*batch_size\n", " end = (i+1)*batch_size\n", " x_sample = input_x[start:end, ...]\n", " y_sample = input_y[start:end, ...]\n", " yield (x_sample, y_sample)\n", " i = i + 1 if i < steps else 0\n", " print(\"Prediction steps:\",steps) \n", " return generator(), steps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# This function to normalize illumination discrepancy across images\n", "\n", "def min_max_norm(x, minimum=None, maximum=None):\n", " channels = x.shape[-1]\n", " if minimum is None and maximum is None:\n", " minimum = []\n", " maximum = []\n", " for channel in range(channels):\n", " minimum.append( x[..., channel].min() )\n", " maximum.append( x[..., channel].max() )\n", " result = numpy.zeros_like(x)\n", " for ch in range(channels):\n", " result[..., ch] = 100.0*( (numpy.ndarray.astype(x[..., ch], numpy.float32) - minimum[ch])/(maximum[ch] - minimum[ch]) )\n", " return (result, minimum, maximum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load data:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "training_x = numpy.load(os.path.join(input_directory, \"training_x.npy\"))\n", "\n", "training_y = numpy.load(os.path.join(input_directory, \"training_y.npy\"))\n", "\n", "# input_directory = \"/path/to/other/input_directory/if/needed\"\n", "\n", "testing_x = numpy.load(os.path.join(input_directory, \"testing_x.npy\"))\n", "\n", "testing_y = numpy.load(os.path.join(input_directory, \"testing_y.npy\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(\"Loading training data\")\n", "\n", "# Use this function to normalize signal intensities across images\n", "training_x, pix_min, pix_max = min_max_norm(training_x)\n", "\n", "training_generator = training_data_generator(training_x, training_y, 32) \n", "\n", "print(training_x.shape, training_y.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(\"Loading test data\")\n", "\n", "# Use this function to normalize signal intensities across images\n", "testing_x, _, _ = min_max_norm(testing_x, pix_min, pix_max)\n", "\n", "testing_generator, testing_steps = prediction_data_generator(testing_x, testing_y, 32)\n", "\n", "print(testing_x.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load trained model:\n", "(can also load checkpoints)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = keras.models.load_model( os.path.join(model_directory, 'model.h5') )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.load_weights(os.path.join(model_directory, 'model.h5'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluate testing set" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.evaluate_generator(\n", " generator=testing_generator, \n", " steps=256\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Extract the most crucial layer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layers = model.layers" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Look for the densely/fully connected layer nearest to the classier, which is the one that has the shape of (None, number-of-classes)\n", "\n", "==================================================================\n", "\n", "Example 1: in case of classification of 7 classes, the last few layers are:\n", "\n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 1024) 943820 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 1024) 0 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 7) 7175 \n", "_________________________________________________________________\n", "activation_1 (Activation) (None, 7) 0 \n", "\n", "\n", "then look for the layer dense_1 , which has a shape of (None, 1024) \n", "\n", "==================================================================\n", "\n", "Example 2: in case of classification of 5 classes, the last few layers are:\n", "\n", "activation_49 (Activation) (None, 8, 8, 2048) 0 \n", "_________________________________________________________________\n", "avg_pool (AveragePooling2D) (None, 1, 1, 2048) 0 \n", "_________________________________________________________________\n", "global_average_pooling2d_1 (Glob (None, 2048) 0 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 5) 10245 \n", "\n", "then look for the layer global_average_pooling2d_1 , which has a shape of (None, 2048) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(layers[-4])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "abstract_model = None # Clear cached abstract_model\n", "abstract_model = Sequential([layers[-4]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "extracted_features = abstract_model.predict_generator(\n", " generator=testing_generator,\n", " steps=256)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Metadata for embeddings" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print('Converting numeric labels into class names...')\n", "\n", "class_names = pickle.load(open(os.path.join(input_directory, \"class_names.sav\"), 'rb'))\n", "\n", "def save_metadata(file):\n", " with open(file, 'w') as f:\n", " for i in range(test_y.shape[0]):\n", " f.write('{}\\n'.format( class_names[test_y[i]] )) \n", "\n", "save_metadata( os.path.join(output_directory, 'metadata.tsv') )\n", "\n", "print('Done.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicted values in .TXT\n", "To be uploaded and viewed on http://projector.tensorflow.org" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "numpy.savetxt( os.path.join(output_directory, 'table_of_features.txt' ), extracted_features, delimiter='\\t')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Note:\n", "\n", "Once finished, open http://projector.tensorflow.org on web-browser.\n", "\n", "Click \"Load data\" on the left panel.\n", "\n", "- Step 1: Load a TSV file of vectors >> Choose file: 'table_of_features.txt'\n", "\n", "- Step 2: Load a TSV file of metadata >> Choose file: 'metadata.tsv'\n", "\n", "Hit ESC or click outside the load data window to dismiss." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicted values in .NPY\n", "Used for generating Tensorboard embeddings to be viewed locally on http://localhost:6006" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "numpy.save( os.path.join(output_directory, 'table_of_features.npy' ), extracted_features )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "extracted_features = numpy.load( 'table_of_features.npy' )\n", "embedding_var = tensorflow.Variable(extracted_features)\n", "\n", "embedSess = tensorflow.Session()\n", "\n", "# save variable in session\n", "embedSess.run(embedding_var.initializer)\n", "\n", "# save session (only used variable) to file\n", "saver = tensorflow.train.Saver([embedding_var])\n", "saver.save(embedSess, 'tf.ckpt')\n", "\n", "summary_writer = tensorflow.summary.FileWriter('./')\n", "\n", "config = tensorflow.contrib.tensorboard.plugins.projector.ProjectorConfig()\n", "embedding = config.embeddings.add()\n", "embedding.tensor_name = embedding_var.name\n", "embedding.metadata_path = 'metadata.tsv' # this metadata_path need to be modified later. See note.\n", "tensorflow.contrib.tensorboard.plugins.projector.visualize_embeddings(summary_writer, config)\n", "\n", "embedSess.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Note:\n", "Tensorboard embeddings files will be saved in the same location with this script.\n", "\n", "Collect the following files into one folder:\n", "\n", "- metadata.tsv\n", "- checkpoint\n", "- projector_config.pbtxt\n", "- tf.ckpt.index\n", "- tf.ckpt.meta\n", "- tf.ckpt.data-00000-of-00001\n", "\n", "Open with any text editor : \"projector_config.pbtxt\"\n", "\n", "\"/path/to/logdir/metadata.tsv\" has to be specified, CANNOT be relative path \"./metadata.tsv\", nor \"~/metadata.tsv\"\n", "\n", "Then type command in terminal: tensorboard --logdir=\"/path/to/logdir\"\n", "\n", "Next, open web-browser, connect to http://localhost:6006" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot categorical accuracy and loss" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "metrics = pandas.read_csv(os.path.join(model_directory, 'training.csv') )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(metrics)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "matplotlib.pyplot.plot(metrics[\"acc\"])\n", "matplotlib.pyplot.plot(metrics[\"val_acc\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "matplotlib.pyplot.plot(metrics[\"loss\"])\n", "matplotlib.pyplot.plot(metrics[\"val_loss\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Confusion matrix" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "predicted = model.predict(\n", " batch_size=50,\n", " x=testing_x\n", ")\n", "\n", "predicted = numpy.argmax(predicted, -1)\n", "expected = numpy.argmax(testing_y[:, :], -1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "confusion = sklearn.metrics.confusion_matrix(expected, predicted)\n", "\n", "confusion = pandas.DataFrame(confusion)\n", "\n", "matplotlib.pyplot.figure(figsize=(12, 8))\n", "\n", "seaborn.heatmap(confusion, annot=True)\n", "\n", "matplotlib.pyplot.savefig( os.path.join(output_directory, 'confusion_matrix.eps') , format='eps', dpi=600)" ] } ], "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 }
bsd-3-clause
kaleoyster/nbi-data-science
+An+exploratory+data+analysis.ipynb
1
564077
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# An exploratory data analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Connection to MongoDB " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pymongo\n", "from pymongo import MongoClient\n", "import time\n", "Client = MongoClient(\"mongodb://nbi-mongo.admin/\")\n", "db = Client.bridge\n", "collection = db[\"SampleNbi2\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bridge Records in Database" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bridges Records in DB: 17509885\n", "Seconds : 0.008536100387573242\n" ] } ], "source": [ "startTime = time.time()\n", "print(\"Bridges Records in DB: \", collection.count())\n", "print(\"Seconds : \", (time.time() - startTime))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Total Number of Bridges (Years)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time to create list of distinct states in Seconds: 0.014206171035766602\n", "Seconds: 5.496885061264038\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Distinct count of Bridges</th>\n", " </tr>\n", " <tr>\n", " <th>year</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1992</th>\n", " <td>707509</td>\n", " </tr>\n", " <tr>\n", " <th>1993</th>\n", " <td>700993</td>\n", " </tr>\n", " <tr>\n", " <th>1994</th>\n", " <td>703606</td>\n", " </tr>\n", " <tr>\n", " <th>1995</th>\n", " <td>713410</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <td>711792</td>\n", " </tr>\n", " <tr>\n", " <th>1997</th>\n", " <td>715853</td>\n", " </tr>\n", " <tr>\n", " <th>1998</th>\n", " <td>716703</td>\n", " </tr>\n", " <tr>\n", " <th>1999</th>\n", " <td>721247</td>\n", " </tr>\n", " <tr>\n", " <th>2000</th>\n", " <td>724037</td>\n", " </tr>\n", " <tr>\n", " <th>2001</th>\n", " <td>727990</td>\n", " </tr>\n", " <tr>\n", " <th>2002</th>\n", " <td>730034</td>\n", " </tr>\n", " <tr>\n", " <th>2003</th>\n", " <td>732942</td>\n", " </tr>\n", " <tr>\n", " <th>2004</th>\n", " <td>736600</td>\n", " </tr>\n", " <tr>\n", " <th>2005</th>\n", " <td>740010</td>\n", " </tr>\n", " <tr>\n", " <th>2006</th>\n", " <td>740177</td>\n", " </tr>\n", " <tr>\n", " <th>2007</th>\n", " <td>748661</td>\n", " </tr>\n", " <tr>\n", " <th>2008</th>\n", " <td>751134</td>\n", " </tr>\n", " <tr>\n", " <th>2009</th>\n", " <td>746542</td>\n", " </tr>\n", " <tr>\n", " <th>2010</th>\n", " <td>632426</td>\n", " </tr>\n", " <tr>\n", " <th>2011</th>\n", " <td>632405</td>\n", " </tr>\n", " <tr>\n", " <th>2012</th>\n", " <td>634324</td>\n", " </tr>\n", " <tr>\n", " <th>2013</th>\n", " <td>634668</td>\n", " </tr>\n", " <tr>\n", " <th>2014</th>\n", " <td>637622</td>\n", " </tr>\n", " <tr>\n", " <th>2015</th>\n", " <td>638836</td>\n", " </tr>\n", " <tr>\n", " <th>2016</th>\n", " <td>642566</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Distinct count of Bridges\n", "year \n", "1992 707509\n", "1993 700993\n", "1994 703606\n", "1995 713410\n", "1996 711792\n", "1997 715853\n", "1998 716703\n", "1999 721247\n", "2000 724037\n", "2001 727990\n", "2002 730034\n", "2003 732942\n", "2004 736600\n", "2005 740010\n", "2006 740177\n", "2007 748661\n", "2008 751134\n", "2009 746542\n", "2010 632426\n", "2011 632405\n", "2012 634324\n", "2013 634668\n", "2014 637622\n", "2015 638836\n", "2016 642566" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "listStates = []\n", "startTime = time.time()\n", "for i in collection.distinct(\"stateCode\"):\n", " listStates.append(i)\n", "print(\"Time to create list of distinct states in Seconds: \", (time.time() - startTime))\n", "\n", "countPerYear = {}\n", "startTime = time.time()\n", "for i in collection.distinct(\"year\"):\n", " countPerYear[i] = collection.find({\"year\":i}).count()\n", "print(\"Seconds: \",(time.time()- startTime))\n", "count_per_year = pd.DataFrame(list(countPerYear.items()), columns = ['year', 'Distinct count of Bridges'])\n", "count_per_year.set_index(\"year\", inplace = True)\n", "count_per_year" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Trend of the count of records in the bridge" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Trend of the count of records in the bridge:\n" ] }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09bcd4ee48>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Zkak9zmraLWf20iXHd1bH5mwmAOpKKOCnJApPI7ABLitZTdullxduZTUNcEk4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\nJr+fnup4vwW29o4uNu95n9U1UUDbuv8I7vCJoUV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"text/plain": [ "<matplotlib.figure.Figure at 0x7f09bcdea470>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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lV3pOA77Xp/24crXoZODpcjjzWuDgiNiiXGxwMHBtWfZMREwufR3Xr9ZAfUiS\nJPWMkcNYZz/gWOCeiJhd2j4DnAVcEREfAh4DjirLrgEOA+YCS4DjATJzUUR8HrijrHdmZi4q908E\nLgE2AH5QbgzShyRJUs8YMrBl5k8Z+DwzgCkDrJ/Ax1ZS62Lg4gHaZwG7D9C+cKA+JEmSeomfdCBJ\nklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJ\nUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJ\nlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRV\nzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5\nA5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuWG\nDGwRcXFEzI+Ie/u0bRkR10XEQ+XrFqU9ImJGRMyNiLsjYs8+20wr6z8UEdP6tO8VEfeUbWZERAzW\nhyRJUq8ZzgzbJcDUfm3Tgeszc0fg+vIY4FBgx3I7AbgAmvAFnA7sA+wNnN4ngF1Q1l223dQh+pAk\nSeopQwa2zPwJsKhf8xHApeX+pcC7+rRflo3bgM0j4rXAIcB1mbkoM58CrgOmlmWbZubPMzOBy/rV\nGqgPSZKkntLqOWzbZOZvAcrXrUv7tsDjfdabV9oGa583QPtgfUiSJPWU1X3RQQzQli20r1qnESdE\nxKyImLVgwYJV3VySJKlqrQa2J8vhTMrX+aV9HrBdn/XGAk8M0T52gPbB+niFzLwwMydl5qQxY8a0\n+C1JkiTVqdXANhNYdqXnNOB7fdqPK1eLTgaeLoczrwUOjogtysUGBwPXlmXPRMTkcnXocf1qDdSH\nJElSTxk51AoR8R3gQGCriJhHc7XnWcAVEfEh4DHgqLL6NcBhwFxgCXA8QGYuiojPA3eU9c7MzGUX\nMpxIcyXqBsAPyo1B+pAkSeopQwa2zDxmJYumDLBuAh9bSZ2LgYsHaJ8F7D5A+8KB+pAkSeo1ftKB\nJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGyS\nJEmVM7C46o+BAAAShklEQVRJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJ\nUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJ\nlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRV\nzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5\nA5skSVLlDGySJEmVM7BJkiRVrvrAFhFTI+JXETE3Iqav7fFIkiStaVUHtogYAZwPHArsBhwTEbut\n3VFJkiStWVUHNmBvYG5m/joznwcuB45Yy2OSJElaoyIz1/YYVioijgSmZuaHy+NjgX0y86R+650A\nnFAe7gz8aphdbAX8fjUNd03U7WTtbqvbydrdVreTtbutbidrW7fztbutbidrd1vdTtbutrqrWvv1\nmTlmqJVGtjeejosB2l6RMDPzQuDCVS4eMSszJ7UysLVRt5O1u61uJ2t3W91O1u62up2sbd3O1+62\nup2s3W11O1m72+p2qnbth0TnAdv1eTwWeGItjUWSJGmtqD2w3QHsGBE7RMR6wNHAzLU8JkmSpDWq\n6kOimfliRJwEXAuMAC7OzPtWYxerfBh1LdftZO1uq9vJ2t1Wt5O1u61uJ2tbt/O1u61uJ2t3W91O\n1u62uh2pXfVFB5IkSar/kKgkSVLPM7BJkiRVzsAmSZJUOQObJElS5QxsUpeJiM0i4n0RcUpEnFzu\nb97hPt/e5vabRsQbB2gf12bd10TEa8r9MRHxnoh4czs1B+nr7ztQc4cy5l3arPO6iBhV7kdEHB8R\n/xgRJ0ZEW+8GEBHvXFZ7dYuI/SNi53L/LRHxqYh4x2qou3FEHFn+Pv5HREyNiLb/30XELhFxWkTM\niIjzyv1d2607RJ/Ht7n9LhExJSI27tc+tc26e0fEn5X7u5Xno8PaqTlIX5d1oOZbypgPbrPOPhGx\nabm/QUR8LiL+LSK+FBGbrZ7Rlr566SrRiDiE5s13r8/MR/q0fzAzL26xZgBH0XwCw5XAW2k+7/RB\n4GuZ+XK74+7X3w2Z+dY2a2yVmb/v8/ivaT639V7g69niL0VEvBu4OTMXRcQY4P8FJgL3A/8zM+e1\nMeYvA/+SmT9rtcZK6m4JnETzhswXAZ8B9gUeAP4+M59qo/ZBwF/SvPnzi8BDwDcyc24bNY8DTgd+\nBPymNI8F3g58LjNX+xNb6fexzHxdi9u+FzgXmA+sC3wgM+8oy+7KzD1brPu3wHSaT0T5EvAB4D5g\nP+D/ZOZFrdQttWf0bwKOBS4DyMyPt1j36sx8V7l/BM1+uQn478A/ZOYlLda9F9g7M5dExJeANwJX\n0zwfkZkfbKVuqf1H4DngB8B3gGsz86VW6/Wpey7N885ImrdumlL6OAD4ZWae2mLd9wKnAv8BHATc\nSjM5sQfwV5l5T4t1TwOOoflM62XPZWNp3h/08sw8q5W6w+i3nb+9jwMfo3k+mwB8IjO/V5a187d3\nOnAozc/uOmAfmt/jt9H8fnyxlbqldv/3Wg2an+MNAJn5zhbr3p6Ze5f7f0OzX64CDgb+rdWfX0Tc\nB4wvb0N2IbCEJgtMKe3vaaXugH31SmArr47fAtwF/AVwbmb+Y1nWzi/uV4GtgfWAPwDrA/8GHAY8\nmZmfaGPMd/dvAnaifFZqZrY0O9H3+42I/w38OfBt4HBgXmae3GLd+zNzt3L/u8BtwD/T/BH/VWa2\nPEsTEQuAR4ExwHeB72TmL1ut16fuNcA9wKbAruX+FTQBaHxmHtFi3bOAbYDrgXcB/wnMAT5KEwT/\nucW6v6L5PN3F/dq3AH6RmTu1UrfUWNmbUgfw1szcqMW6s4FDM/O3EbE3Tej5TGb+a0T8MjMntlj3\nHpp/FBvQ/G68KTN/V/bFjZk5oZW6pfY8mn9AP+K/PiLvbOBTAJl5aYt1l3+/EXErzd/Ff0bEVjQv\nJMe3WLfv396dwJ8te7EYEf/Rat1lY6YJfkfShJPdaf7RfSczb26j7n2l1gY0Lz62LYFzXZrAtnuL\nde8GJpdaWwHfysxDopnN/Vpm/vcW684B3pyZL/RrXw+4LzN3bKVunzEPuAjYKTPXb7HuPcC+mfls\nRGxPEyT+b2aetxr+9ibQ/L/7HTA2M/8QERvQPA+1PHMeEXfRvMj/Bs1ESNC8UDgaoNXfuX5/e3cA\nh2XmgojYCLgtM/dose4DmbnrsrH3zRIRMbud56H+qn7j3NXsL4CJJQWfAXw7It5QwslAn1k6XH+e\nmXuUJ5nfAa/NzOcj4ttAu4HiEZoQ+AXgj2Wct9B8L+3o+/2+h+Z7eK6M+a426o7oc/9Nmfm+cv+S\niPhkG3WhCZKTImJHmj/cf4qIETR/yN/JzDkt1v1vmXlYmSmdl5kHlvZbStBo1TuWPQFExOU0M4+n\nRsSVND/DlgIbzc9uoFdZL9Pe7zE0wf2vgWcH6HPvNuqOyMzfAmTm7WXm8fsRMZaBv5fheiEzlwBL\nIuLhzPxd6eOpiGj3leiuwOeBqcCpmfmbiDi91aDWR99xjczM/wTIzN9HRDuz8Y9HxFsz8waa543t\ngEcjYnQbNZfJMtP8deDr0RyCfi9wVkSMzcztBt980LrZ5/tetm9epr3TdYLm+RKamcGtS2d3Lzt0\n1aKXgf9G8+Kgr9eWZe3YBjgE6D+jHzQzhK0akZnPAmTmIxFxIHBlRLye9p4vXiyzrMv+9v5Q+vhj\nm7/HAJOATwB/R/O3Nzsi/tjOi4NinfJibh2ayaoFAOV/34tt1L03Io7PzG8C/xERkzJzVkTsBLww\n1MaropcC28jMfBEgMxdHxF8AF0bEP9PMjrVqWc0XIuKOzHy+PH4xIto6bJCZ7yyHGS8Ezs7MmRHx\nQmb2f8JYVRtExESaX9wRmflcn++hnTHfFBFnAv9Q7r8rM68u/6CfbnPMWcb4EM0/0s+XV8zHANcA\nb2qx7rI/4k2AjSNi+/LENpr2fi9ejogtM3MRzZP8iDL+p0o4bNUXgbsi4kfA46XtdTQzgp9voy40\nM6JLBnpiLDN7rXomIt6YmQ8DlJm2A2kO2bVzvtnLEbFumfFYft5TNOdbtXW+UmY+A3wyIvaieXHw\n7+3WLMZHxB9o/lmuHxGvKbOC67HiC55V9WHgsvJi9GlgdpkZ2wI4pc0xr/D7WoLxDGBG+cffqn+P\niFuAUTSzKVdExG00h0R/0kbda4AfRsTNNIft/hmWn/7Qzt/eJ4HrI+IhVvzbexPNaRXt+D6wcWa+\n4kViRNzURt3fRcSEZXXLTNvhwMU0h4hb9XxEbFheMO3VZ6yb0WZ4LTPD55T/zedExJOsnqyyGXAn\n5UVvn7+9jWnv9+LDwHnlaNXvgZ9HxOM0vyMfbnfQffXSIdHvA/9P/39GEfEFmsMzLT0ZR8QPgKOW\nvYrp0/4aYOayY+btKFO2n6d5YtgzM8e2We/Gfk3vL/9ER9OcfzCpxbrr0rwqWna+zFiaV7j/BkzP\nzMfaGHPL0/dD1D2G5jwiaA5XnkgTDnejOSespY8XiYj3Af+H5vD1LsCJmfnv0Zzbd15mvr+NMW9B\n82p8W5onmnk0P7eWz7frpIgYTxMEH+rXvi7w3sz8Vot1Xwf8doBDVNsCu2bmj1sdc796QfO7sW9m\n/vXqqDlAH5vTjPnnbdbZlea0iZE0vxd3ZJvn0UbEgZl5Uzs1Bqm9L81M223RXJTybuAx4Mp2xh3N\nye+7Af+RmdeVtnWAdTPzT23UXYdmtrnv394duRrO6euEMov94rLZ537L9ssWzwmOiPUH2o/RHIJ+\nbbZ4nuBK+noHsF9mfmZ11exXf0Ngm2Wz3W3U2QR4A+VvLzOfXB3jW6GPHgpsG0AzZTvAsm0z8zev\n3Kqt/jYCNsrM+aux5niafxpfW101+9UfAaxfXjW1W2szmlnNhe2PDCJi4/6heHUp33eUWdGRNOdm\n/GbZYbw26m5J8wc8N/udc9auiNiG5p9GAk+szieHTtXutrqdrN1tdTtZu9vqrqSvTj4/daR2t9Xt\nZO1uqdszgW2ZiJhEn6v2MvPBmut2sna31e1k7W6pGxETgK/RTO/Po3mVPxZYDHw0M1s+B7EcJr+g\n1O57BWpbtfuNuX/dE7PFi0c6VXcYtdvZF4Pt407ti3Z/Lzq1L7qq7hB9tnwl59qq3W11O1m7W+r2\nzDlsEXEAzdtMLKY55v4zYIuIeAE4NjMfH2z7NV23G8fsvuh8XeAS4G8z8xf9+psMfBNo+WrAsn0n\nal8ySN1LKqw7VO129sVg+/iSNupeMkjddn8vOlW7q+pGxMrOBQxg45UsW6u1u61uJ2t3W92B9NIb\n555L89YCbwP2pLnCbD+ak7hbfr+mDtbtxjG7Lzpfd6P+/4gAMvM2oKW33VgDtbutbidrd1vdTtbu\ntrp/T3MRxyb9bhvT/v/STtXutrqdrN1tdV+hZ2bYaK6GXFDuPwa8HiAzr4vmDRxrq9vJ2t1Wt5O1\nu63uD6K5YvEy/utKte2A44AftlG3k7W7rW4na3db3U7W7ra6dwFXZ+ad/RdERLtXA3aqdrfV7WTt\nbqv7Cj1zDltEXExz8un1NJ9E8JvMPKVcIXJXZrb00TCdqtuNY3ZfdL5uqX1oqdn3SrWZmXlNqzU7\nXbvb6naydrfV7WTtbqobzUdoLerzQqzvsm2yjYsaOlW72+p2sna31R2wrx4KbOsCf0O51Bu4ODNf\niubq0a2zxfc261Tdbhyz+6LzdSVJPSozvXnz1iU3mivfzqL5bMCF5fZAadu8xtrdVrcbx+y+WKN1\nH+zgPl6ttbutbjeOuZP7ov+tZy46iIiNI+LMiLg3Ip6OiAURcVtEfKDGut04ZvdF5+vSfM7pU8BB\nmTk6M0fTfDDyYlr/uKtO1+62ut04ZvfFmqt7YL+6T7VZt5O1u61uN465k/tiBb10SPR7NB9Y/GOa\nz8HbCLgc+N805xe19C7KnarbjWN2X6yRur/KzJ1XddnarN1tdTtZu9vqdrK2dTtfu9vqdrJ2t9Ud\n0Oqcrqv5RvMRJX0f31G+rgM8WFvdbhyz+2KN1P0R8L9oPkplWds2wGnAj9vcxx2p3W11u3HM7ovu\nrduNY3ZfrJl90f/WM4dEgeci4i0A0Xzw+yJY/kGz7Xzwa6fqdrJ2t9XtZO1uq/s+YDRwc0Q8FRGL\ngJuALWlm8trRqdrdVreTtbutbidrW7fztbutbidrd1vdV1qd6a/mGzAOuJ3mPIafAjuV9jHAx2ur\n241jdl+ssX2xC/A2YON+7VPbqdvJ2t1WtxvH7L7o3rrdOGb3xZrZFyvUW53FuvUGHN9NdbtxzO6L\n1VMX+DjwK+Bq4BHgiD7L7mpzXB2p3W11u3HM7ovurduNY3ZfrJl98Yq+Vmexbr0Bj3VT3W4cs/ti\n9dQF7qG8igO2B2YBnyiPf9nmuDpSu9vqduOY3RfdW7cbx+y+WDP7ov+tZz6aKiLuXtkimhMEq6rb\nydrdVreTtbutLs1HXj0LkJmPRMSBwJUR8XraP0+wU7W7rW43jtl90b11u3HM7ovO132l1Zn+ar4B\nTwITaD7Tse9te+CJ2up245jdF2uk7g3AhH5tI2k+N/GlNvdxR2p3W91uHLP7onvrduOY3RdrZl+8\noq/VWazmG3AR8JaVLPt2bXW7cczuizVSdyzwmpUs26/NfdyR2t1WtxvH7L7o3rrdOGb3xZrZF/1v\nPfPGuZIkSd2ql96HTZIkqSsZ2CRJkipnYJMkSaqcgU2SVqOIGLG2xyDp1cfAJqlnRcTnI+ITfR5/\nMSI+HhGnRsQdEXF3RHyuz/KrI+LOiLgvIk7o0/5sRJwZEb8A9l3D34akHmBgk9TLLgKmAUTEOsDR\nNO+htyOwN8176e0VEfuX9T+YmXsBk4CPR8To0r4RcG9m7pOZP12T34Ck3tAzn3QgSf1l887kCyNi\nIs0nUPwS+DPg4HIfYGOaAPcTmpD27tK+XWlfCLwE/MuaHLuk3mJgk9TrvgF8AHgNcDEwBfiHzPz/\n+q5UPnLmbcC+mbkkIm4CRpXFSzPzpTU1YEm9x0OiknrdVcBUmpm1a8vtgxGxMUBEbBsRWwObAU+V\nsLYLMHltDVhS73GGTVJPy8znI+JGYHGZJftRROwK/DwiAJ4F/hr4IfCRiLgb+BVw29oas6Te40dT\nSepp5WKDu4CjMvOhtT0eSRqIh0Ql9ayI2A2YC1xvWJNUM2fYJEmSKucMmyRJUuUMbJIkSZUzsEmS\nJFXOwCZJklQ5A5skSVLlDGySJEmV+/8B/JIzEBt/RZwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f09bcdea518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "print(\"Trend of the count of records in the bridge:\")\n", "count_per_year.plot(figsize=(10,8))\n", "count_per_year.plot(kind = \"bar\", figsize=(10,8))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## States with most bridges" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Seconds: 36.40173649787903\n" ] } ], "source": [ "countPerState = {}\n", "startTime = time.time()\n", "for i in listStates:\n", " countPerState[i] = collection.find({\"year\":2016, \"stateCode\":i}).count()\n", "print(\"Seconds: \", (time.time() - startTime))\n", "count_per_state = pd.DataFrame(list(countPerState.items()), columns = ['State Code', 'Count of Records'])\n", "#count_per_state = count_per_state.set_index('State Code')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09bcdbd898>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Y4MzMnL0CdUmSJHWlZQpmmXk1cHX9+D6qOyr7H/MkcOAAzz8ZOLlN+0+AnyxLLZIkSb3G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\nndJ6x+wbgA+3bI8b4VqGZI/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"text/plain": [ "<matplotlib.figure.Figure at 0x7f09bcd8d908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stateName = {'25':'MASSACHUSETTS',\n", " '04':'ARIZONA', \n", " '08':'COLORADO',\n", " '38':'NORTH DAKOTA', \n", " '09':'CONNECTICUT', \n", " '19':'IOWA', \n", " '26':'MICHIGAN', \n", " '48':'TEXAS',\n", " '35':'NEW MEXICO',\n", " '17':'ILLINOIS', \n", " '51':'VIRGINIA',\n", " '23':'MAINE',\n", " '16':'IDAHO',\n", " '36':'NEW YORK',\n", " '56':'WYOMING',\n", " '29':'MISSOURI',\n", " '39':'OHIO',\n", " '28':'MISSISSIPI', \n", " '11':'DISTRICT OF COLOMBIA',\n", " '21':'KENTUCKY', \n", " '18':'INDIANA',\n", " '06':'CALIFORNIA',\n", " '47':'TENNESSEE', \n", " '12':'FLORIDA',\n", " '24':'MARYLAND',\n", " '34':'NEW JERSEY', \n", " '46':'SOUTH DAKOTA',\n", " '13':'GEORGIA',\n", " '55':'WISCONSIN',\n", " '30':'MONTANA',\n", " '54':'WEST VIGINIA',\n", " '15':'HAWAII', \n", " '32':'NEVADA', \n", " '37':'NORTH CAROLINA',\n", " '10':'DELAWARE', \n", " '33':'NEW HAMPSHIRE', \n", " '44':'RHODE ISLAND',\n", " '50':'VERMONT', \n", " '42':'PENNSYLVANIA', \n", " '05':'ARKANSAS', \n", " '20':'KANSAS', \n", " '45':'SOUTH CAROLINA',\n", " '22':'LOUISIANA',\n", " '40':'OKLAHOMA', \n", " '72':'PUERTO RICO', \n", " '41':'OREGON',\n", " '21':'MINNESOTA', \n", " '53':'WASHINGTON', \n", " '01':'ALABAMA', \n", " '31':'NEBRASKA',\n", " '02':'ALASKA', \n", " '49':'UTAH'\n", " }\n", "\n", "count_per_state['State Name'] = count_per_state['State Code'].map(stateName)\n", "count_per_state = count_per_state.set_index('State Name')\n", "asc_c=count_per_state.sort_values(['Count of Records'],ascending = 0)\n", "asc_c.head(n=10).sort_values(['Count of Records'], ascending = 0).plot(kind = 'bar', figsize=(10,8))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## States with least bridges" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09bcd3c160>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ASZIkFWYAkyRJKswAJkmSVJgBTJIkqTADmCRJUmEGMEmSpMIMYJIkSYUZwCRJkgozgEmS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\naoNW9oABqw8OX7B4HJgLsUqSpFHV1gD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"text/plain": [ "<matplotlib.figure.Figure at 0x7f09bcdd4390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "asc_c.tail(n=10).sort_values(['Count of Records'], ascending = 1).plot(kind = 'bar', figsize=(10,8))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Year Built" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Seconds: 1.5601255893707275\n" ] } ], "source": [ "pipeline = [{\"$match\":{\"year\":2016}},\n", " {'$group':{\"_id\":\"$yearBuilt\", \"count\":{\"$sum\":1}}}]\n", "startTime = time.time()\n", "yearBuiltResult = collection.aggregate(pipeline)\n", "print(\"Seconds: \",(time.time() - startTime))\n", "yearBuiltResult_df = pd.DataFrame(list(yearBuiltResult))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Early bridges" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09bcb45c18>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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M0vFl1gEAOGSs5lDmozPb23ViksclOTLJGcss2ntW2ce8fY0v95rnVtX2qtq+a9euB180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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f09bcb01f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "yearBuiltResult_df = yearBuiltResult_df.rename(columns = {'_id': 'Year'})\n", "sort_yearBuiltResult_df=yearBuiltResult_df.sort_values('count', ascending = 0)\n", "sort_yearBuiltResult_df.set_index('Year').head(n=10).plot(kind = 'bar', figsize=(10,8))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Recently built bridges" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b912c1d0>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f09b9144b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sort_yearBuiltResult_rdf=yearBuiltResult_df.sort_values('Year', ascending = 0)\n", "sort_yearBuiltResult_rdf.set_index('Year').head(n=10).plot(kind = \"bar\", figsize = (10,8))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary of NBI data" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Seconds : 0.07834887504577637\n" ] } ], "source": [ "\n", "pipeline = [{\"$match\": {\"year\":2016}},\n", " {\"$project\":{\"_id\":0, \"stateCode\":1,\"deckWidthOutToOut\":1, \"structureLength\":1, \"averageDailyTraffic\":1 }}]\n", "startTime = time.time()\n", "pdc = collection.aggregate(pipeline)\n", "print(\"Seconds : \", (time.time() - startTime))\n", "nbi = pd.DataFrame(list(pdc))\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "stateName = {'25':'MASSACHUSETTS',\n", " '04':'ARIZONA', \n", " '08':'COLORADO',\n", " '38':'NORTH DAKOTA', \n", " '09':'CONNECTICUT', \n", " '19':'IOWA', \n", " '26':'MICHIGAN', \n", " '48':'TEXAS',\n", " '35':'NEW MEXICO',\n", " '17':'ILLINOIS', \n", " '51':'VIRGINIA',\n", " '23':'MAINE',\n", " '16':'IDAHO',\n", " '36':'NEW YORK',\n", " '56':'WYOMING',\n", " '29':'MISSOURI',\n", " '39':'OHIO',\n", " '28':'MISSISSIPI', \n", " '11':'DISTRICT OF COLOMBIA',\n", " '21':'KENTUCKY', \n", " '18':'INDIANA',\n", " '06':'CALIFORNIA',\n", " '47':'TENNESSEE', \n", " '12':'FLORIDA',\n", " '24':'MARYLAND',\n", " '34':'NEW JERSEY', \n", " '46':'SOUTH DAKOTA',\n", " '13':'GEORGIA',\n", " '55':'WISCONSIN',\n", " '30':'MONTANA',\n", " '54':'WEST VIGINIA',\n", " '15':'HAWAII', \n", " '32':'NEVADA', \n", " '37':'NORTH CAROLINA',\n", " '10':'DELAWARE', \n", " '33':'NEW HAMPSHIRE', \n", " '44':'RHODE ISLAND',\n", " '50':'VERMONT', \n", " '42':'PENNSYLVANIA', \n", " '05':'ARKANSAS', \n", " '20':'KANSAS', \n", " '45':'SOUTH CAROLINA',\n", " '22':'LOUISIANA',\n", " '40':'OKLAHOMA', \n", " '72':'PUERTO RICO', \n", " '41':'OREGON',\n", " '21':'MINNESOTA', \n", " '53':'WASHINGTON', \n", " '01':'ALABAMA', \n", " '31':'NEBRASKA',\n", " '02':'ALASKA', \n", " '49':'UTAH'\n", " }\n", "\n", "nbi['State Name'] = nbi['stateCode'].map(stateName)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "nbi['deckArea']= nbi['deckWidthOutToOut'] * nbi['structureLength']\n", "nbi_summary = nbi[['State Name','deckWidthOutToOut','structureLength','deckArea','averageDailyTraffic']]\n", "nbi_summary = nbi_summary.groupby(['State Name']).agg({'State Name':'count',\n", " 'deckArea':'sum',\n", " 'averageDailyTraffic':'sum'})\n", "nbi_summary = nbi_summary.rename(columns={'State Name': 'Count', 'deckArea':'Sum of Deck Area','averageDailyTraffic':\n", " 'Sum of ADT'})" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Count</th>\n", " <th>Sum of Deck Area</th>\n", " <th>Sum of ADT</th>\n", " </tr>\n", " <tr>\n", " <th>State Name</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>ALABAMA</th>\n", " <td>16098</td>\n", " <td>9238040.53</td>\n", " <td>76613580</td>\n", " </tr>\n", " <tr>\n", " <th>ALASKA</th>\n", " <td>1488</td>\n", " <td>710703.90</td>\n", " <td>3357512</td>\n", " </tr>\n", " <tr>\n", " <th>ARIZONA</th>\n", " <td>8154</td>\n", " <td>5055845.20</td>\n", " <td>97256999</td>\n", " </tr>\n", " <tr>\n", " <th>ARKANSAS</th>\n", " <td>12871</td>\n", " <td>6381829.40</td>\n", " <td>49636105</td>\n", " </tr>\n", " <tr>\n", " <th>CALIFORNIA</th>\n", " <td>25431</td>\n", " <td>29478067.98</td>\n", " <td>667205896</td>\n", " </tr>\n", " <tr>\n", " <th>COLORADO</th>\n", " <td>8680</td>\n", " <td>4926475.88</td>\n", " <td>68595652</td>\n", " </tr>\n", " <tr>\n", " <th>CONNECTICUT</th>\n", " <td>4214</td>\n", " <td>3265916.52</td>\n", " <td>78275293</td>\n", " </tr>\n", " <tr>\n", " <th>DELAWARE</th>\n", " <td>877</td>\n", " <td>978596.38</td>\n", " <td>11361551</td>\n", " </tr>\n", " <tr>\n", " <th>DISTRICT OF COLOMBIA</th>\n", " <td>245</td>\n", " <td>568827.33</td>\n", " <td>7660611</td>\n", " </tr>\n", " <tr>\n", " <th>FLORIDA</th>\n", " <td>12313</td>\n", " <td>16759415.81</td>\n", " <td>213337215</td>\n", " </tr>\n", " <tr>\n", " <th>GEORGIA</th>\n", " <td>14835</td>\n", " <td>9404058.27</td>\n", " <td>135068507</td>\n", " </tr>\n", " <tr>\n", " <th>HAWAII</th>\n", " <td>1132</td>\n", " <td>1319916.61</td>\n", " <td>27400179</td>\n", " </tr>\n", " <tr>\n", " <th>IDAHO</th>\n", " <td>4445</td>\n", " <td>1723198.53</td>\n", " <td>11566802</td>\n", " </tr>\n", " <tr>\n", " <th>ILLINOIS</th>\n", " <td>26704</td>\n", " <td>13004416.75</td>\n", " <td>133709726</td>\n", " </tr>\n", " <tr>\n", " <th>INDIANA</th>\n", " <td>19245</td>\n", " <td>7997458.47</td>\n", " <td>97813523</td>\n", " </tr>\n", " <tr>\n", " <th>IOWA</th>\n", " <td>24184</td>\n", " <td>8248470.40</td>\n", " <td>34260303</td>\n", " </tr>\n", " <tr>\n", " <th>KANSAS</th>\n", " <td>25013</td>\n", " <td>8192548.56</td>\n", " <td>46401837</td>\n", " </tr>\n", " <tr>\n", " <th>LOUISIANA</th>\n", " <td>12915</td>\n", " <td>16387705.86</td>\n", " <td>80282307</td>\n", " </tr>\n", " <tr>\n", " <th>MAINE</th>\n", " <td>2450</td>\n", " <td>1217855.97</td>\n", " <td>11420274</td>\n", " </tr>\n", " <tr>\n", " <th>MARYLAND</th>\n", " <td>5321</td>\n", " <td>5155225.32</td>\n", " <td>116163051</td>\n", " </tr>\n", " <tr>\n", " <th>MASSACHUSETTS</th>\n", " <td>5171</td>\n", " <td>4055694.07</td>\n", " <td>114458271</td>\n", " </tr>\n", " <tr>\n", " <th>MICHIGAN</th>\n", " <td>11156</td>\n", " <td>6357402.03</td>\n", " <td>93784086</td>\n", " </tr>\n", " <tr>\n", " <th>MINNESOTA</th>\n", " <td>14265</td>\n", " <td>6053639.35</td>\n", " <td>64332373</td>\n", " </tr>\n", " <tr>\n", " <th>MISSISSIPI</th>\n", " <td>17068</td>\n", " <td>9093623.83</td>\n", " <td>43837769</td>\n", " </tr>\n", " <tr>\n", " <th>MISSOURI</th>\n", " <td>24468</td>\n", " <td>10361960.26</td>\n", " <td>86300007</td>\n", " </tr>\n", " <tr>\n", " <th>MONTANA</th>\n", " <td>5276</td>\n", " <td>2000703.82</td>\n", " <td>10505792</td>\n", " </tr>\n", " <tr>\n", " <th>NEBRASKA</th>\n", " <td>15334</td>\n", " <td>3969666.40</td>\n", " <td>22562432</td>\n", " </tr>\n", " <tr>\n", " <th>NEVADA</th>\n", " <td>1933</td>\n", " <td>1595543.65</td>\n", " <td>32174713</td>\n", " </tr>\n", " <tr>\n", " <th>NEW HAMPSHIRE</th>\n", " <td>2486</td>\n", " <td>1120425.98</td>\n", " <td>16910083</td>\n", " </tr>\n", " <tr>\n", " <th>NEW JERSEY</th>\n", " <td>6730</td>\n", " <td>6878062.72</td>\n", " <td>160237725</td>\n", " </tr>\n", " <tr>\n", " <th>NEW MEXICO</th>\n", " <td>3973</td>\n", " <td>1728319.62</td>\n", " <td>26782628</td>\n", " </tr>\n", " <tr>\n", " <th>NEW YORK</th>\n", " <td>17462</td>\n", " <td>12869629.22</td>\n", " <td>169682377</td>\n", " </tr>\n", " <tr>\n", " <th>NORTH CAROLINA</th>\n", " <td>18099</td>\n", " <td>9221997.64</td>\n", " <td>112602177</td>\n", " </tr>\n", " <tr>\n", " <th>NORTH DAKOTA</th>\n", " <td>4400</td>\n", " <td>1232040.30</td>\n", " <td>4423736</td>\n", " </tr>\n", " <tr>\n", " <th>OHIO</th>\n", " <td>56568</td>\n", " <td>26635142.36</td>\n", " <td>354235716</td>\n", " </tr>\n", " <tr>\n", " <th>OKLAHOMA</th>\n", " <td>23053</td>\n", " <td>8350748.75</td>\n", " <td>70967585</td>\n", " </tr>\n", " <tr>\n", " <th>OREGON</th>\n", " <td>8118</td>\n", " <td>5017158.18</td>\n", " <td>56756580</td>\n", " </tr>\n", " <tr>\n", " <th>PENNSYLVANIA</th>\n", " <td>22791</td>\n", " <td>12377122.98</td>\n", " <td>163719521</td>\n", " </tr>\n", " <tr>\n", " <th>PUERTO RICO</th>\n", " <td>2205</td>\n", " <td>1973541.98</td>\n", " <td>38053215</td>\n", " </tr>\n", " <tr>\n", " <th>RHODE ISLAND</th>\n", " <td>772</td>\n", " <td>786182.40</td>\n", " <td>15845827</td>\n", " </tr>\n", " <tr>\n", " <th>SOUTH CAROLINA</th>\n", " <td>9358</td>\n", " <td>6800337.44</td>\n", " <td>46154561</td>\n", " </tr>\n", " <tr>\n", " <th>SOUTH DAKOTA</th>\n", " <td>5849</td>\n", " <td>1664936.80</td>\n", " <td>7112750</td>\n", " </tr>\n", " <tr>\n", " <th>TENNESSEE</th>\n", " <td>20123</td>\n", " <td>9659962.24</td>\n", " <td>156431834</td>\n", " </tr>\n", " <tr>\n", " <th>TEXAS</th>\n", " <td>53488</td>\n", " <td>47253738.73</td>\n", " <td>543042520</td>\n", " </tr>\n", " <tr>\n", " <th>UTAH</th>\n", " <td>3039</td>\n", " <td>1922455.87</td>\n", " <td>63527662</td>\n", " </tr>\n", " <tr>\n", " <th>VERMONT</th>\n", " <td>2766</td>\n", " <td>853119.53</td>\n", " <td>6841770</td>\n", " </tr>\n", " <tr>\n", " <th>VIRGINIA</th>\n", " <td>13892</td>\n", " <td>9620708.82</td>\n", " <td>122500324</td>\n", " </tr>\n", " <tr>\n", " <th>WASHINGTON</th>\n", " <td>8178</td>\n", " <td>6890269.73</td>\n", " <td>66350712</td>\n", " </tr>\n", " <tr>\n", " <th>WEST VIGINIA</th>\n", " <td>7217</td>\n", " <td>3805059.64</td>\n", " <td>24843795</td>\n", " </tr>\n", " <tr>\n", " <th>WISCONSIN</th>\n", " <td>14230</td>\n", " <td>6706707.15</td>\n", " <td>78666403</td>\n", " </tr>\n", " <tr>\n", " <th>WYOMING</th>\n", " <td>3128</td>\n", " <td>1269968.77</td>\n", " <td>7350946</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Count Sum of Deck Area Sum of ADT\n", "State Name \n", "ALABAMA 16098 9238040.53 76613580\n", "ALASKA 1488 710703.90 3357512\n", "ARIZONA 8154 5055845.20 97256999\n", "ARKANSAS 12871 6381829.40 49636105\n", "CALIFORNIA 25431 29478067.98 667205896\n", "COLORADO 8680 4926475.88 68595652\n", "CONNECTICUT 4214 3265916.52 78275293\n", "DELAWARE 877 978596.38 11361551\n", "DISTRICT OF COLOMBIA 245 568827.33 7660611\n", "FLORIDA 12313 16759415.81 213337215\n", "GEORGIA 14835 9404058.27 135068507\n", "HAWAII 1132 1319916.61 27400179\n", "IDAHO 4445 1723198.53 11566802\n", "ILLINOIS 26704 13004416.75 133709726\n", "INDIANA 19245 7997458.47 97813523\n", "IOWA 24184 8248470.40 34260303\n", "KANSAS 25013 8192548.56 46401837\n", "LOUISIANA 12915 16387705.86 80282307\n", "MAINE 2450 1217855.97 11420274\n", "MARYLAND 5321 5155225.32 116163051\n", "MASSACHUSETTS 5171 4055694.07 114458271\n", "MICHIGAN 11156 6357402.03 93784086\n", "MINNESOTA 14265 6053639.35 64332373\n", "MISSISSIPI 17068 9093623.83 43837769\n", "MISSOURI 24468 10361960.26 86300007\n", "MONTANA 5276 2000703.82 10505792\n", "NEBRASKA 15334 3969666.40 22562432\n", "NEVADA 1933 1595543.65 32174713\n", "NEW HAMPSHIRE 2486 1120425.98 16910083\n", "NEW JERSEY 6730 6878062.72 160237725\n", "NEW MEXICO 3973 1728319.62 26782628\n", "NEW YORK 17462 12869629.22 169682377\n", "NORTH CAROLINA 18099 9221997.64 112602177\n", "NORTH DAKOTA 4400 1232040.30 4423736\n", "OHIO 56568 26635142.36 354235716\n", "OKLAHOMA 23053 8350748.75 70967585\n", "OREGON 8118 5017158.18 56756580\n", "PENNSYLVANIA 22791 12377122.98 163719521\n", "PUERTO RICO 2205 1973541.98 38053215\n", "RHODE ISLAND 772 786182.40 15845827\n", "SOUTH CAROLINA 9358 6800337.44 46154561\n", "SOUTH DAKOTA 5849 1664936.80 7112750\n", "TENNESSEE 20123 9659962.24 156431834\n", "TEXAS 53488 47253738.73 543042520\n", "UTAH 3039 1922455.87 63527662\n", "VERMONT 2766 853119.53 6841770\n", "VIRGINIA 13892 9620708.82 122500324\n", "WASHINGTON 8178 6890269.73 66350712\n", "WEST VIGINIA 7217 3805059.64 24843795\n", "WISCONSIN 14230 6706707.15 78666403\n", "WYOMING 3128 1269968.77 7350946" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nbi_summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bridge Counts across states" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b90175f8>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5CABmA527AGB4lHgCAACgCEo8gVGGEjQAwPyKEk8AAAAUQeAJAACAIgg8AQAAUASBJwAA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"text/plain": [ "<matplotlib.figure.Figure at 0x7f09b90b1fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbi_summary['Count'].plot(kind = 'bar', figsize = (11,9))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deck Area Summary across states\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09a0e249e8>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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USpIkCUc0kSRJWsjlfRQsg0JJuhy5vF+0NI77g8YwKJQkSboUuKSDeNsUSpIkyaBQkiRJ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\n8ky3d8yzj6qYudISX2NfqexVlWduFGqMzoXov9ZQqhxLczK6GUmaJvf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"text/plain": [ "<matplotlib.figure.Figure at 0x7f09a087c518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbi_summary['Sum of Deck Area'].plot(kind = 'bar', figsize = (11,9))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary of Average Daily Traffic across states\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b8be2160>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SJEkYFEqSJAmDQkmSJAGrNnUGJElXHGue86ne6etetf8K50TSxmZJoSRJkgwKJUmSZFAo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\nOnEk5+FG7alkRH6r30/hGKuSE8eRBH5x73+2Kk7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Yg5miCx063lOjJmM25PVVVJBArkiw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\nbvSleGy+8LjTi/DkjroHtLiObmV/X4NG0lvN7L3p9ausIkMm6TQzG0V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"text/plain": [ "<matplotlib.figure.Figure at 0x7f09b8bf2710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbi_summary['Sum of ADT'].plot(kind = 'bar', figsize = (11,9))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Breakdown of Agencies Responsible for the maintenance of U.S. Highway Bridges\n", "\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Seconds : 0.07691693305969238\n" ] } ], "source": [ "pipeline = [{\"$match\": {\"year\":2016}},\n", " {\"$project\":{\"_id\":0, \"maintenanceReponsibility\":1,\"deckWidthOutToOut\":1, \"structureLength\":1, \"averageDailyTraffic\":1 }}]\n", "startTime = time.time()\n", "pdc1 = collection.aggregate(pipeline)\n", "print(\"Seconds : \", (time.time() - startTime))\n", "nbi_main = pd.DataFrame(list(pdc1))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "maintenanceReponsibility ={ -1 : 'NA',\n", " 1 : 'State Highway Agency',\n", " 21: 'Other State Agency',\n", " 4 : 'City or Municipal Highway Agency',\n", " 80: 'Unknown',\n", " 66: 'National Park Service',\n", " 2 : 'County Highway Agency',\n", " 60: 'Other Federal Agencies (not listest below)',\n", " 64: 'U.S forest Services' ,\n", " 68: 'Bureau of Land Management',\n", " 26: 'Private (other than railroad)',\n", " 62: 'Bureau of Indian Affairs', \n", " 3 : 'Town or Township Highway Agency',\n", " 25: 'Other Local Agencies',\n", " 11: 'State Park, Forest or Reservation Agency',\n", " 63: 'Bureau of Fish and Wildlife',\n", " 27: 'Railroad',\n", " 74: 'Army',\n", " 70: 'Corps of Engineers (Civil)',\n", " 72: 'Air Force',\n", " 61: 'Indian Tribal Agency',\n", " 71: 'Corps of Engineers (Military)',\n", " 69: 'Bureau of Reclamation',\n", " 67: 'Tennesssee Valley Authority',\n", " 32: 'Local Toll Authority',\n", " 12: 'Local Park, Forest or Reservation Agency',\n", " 31: 'State Toll Authority',\n", " 73: 'Navy / Marines',\n", " 75: 'NASA',\n", " 76: 'Metropolitian Washington Airports Service'\n", "}\n", " \n", "nbi_main['Maintenance Reponsibility'] = nbi_main['maintenanceReponsibility'].map(maintenanceReponsibility)\n", "nbi_main['deckArea']= nbi_main['deckWidthOutToOut'] * nbi_main['structureLength']\n", "nbi_main_maintenance=nbi_main.groupby(['Maintenance Reponsibility']).agg({'Maintenance Reponsibility':'count',\n", " 'deckArea':'sum',\n", " 'averageDailyTraffic':'sum'})\n", "mant_valid_bridges = nbi_main_maintenance['Maintenance Reponsibility'].sum()\n", "mant_sum_deck = nbi_main_maintenance['deckArea'].sum()\n", "mant_sum_adt = nbi_main_maintenance['averageDailyTraffic'].sum()\n", "mant_valid_bridges = nbi_main_maintenance['Maintenance Reponsibility'].sum()\n", "mant_sum_deck = nbi_main_maintenance['deckArea'].sum()\n", "mant_sum_adt = nbi_main_maintenance['averageDailyTraffic'].sum()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "nbi_main_maintenance['Percent Valid Bridge']= (nbi_main_maintenance['Maintenance Reponsibility']/mant_valid_bridges)*100\n", "nbi_main_maintenance['Percent Deck Area']= (nbi_main_maintenance['deckArea'] / mant_sum_deck)*100\n", "nbi_main_maintenance['Percent ADT']= (nbi_main_maintenance['averageDailyTraffic'] / mant_sum_adt)*100\n", "nbi_main_maintenance['deckArea'] = nbi_main_maintenance['deckArea'].apply(lambda x: '{:.2f}'.format(x))\n", "\n", "nbi_main_maintenance = nbi_main_maintenance.rename(columns = {\"Maintenance Reponsibilty\":\"Count\",\n", " \"deckArea\":\"Sum of Deck Area\",\n", " \"averageDailyTraffic\":\"Sum of ADT\"})" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Maintenance Reponsibility</th>\n", " <th>Sum of Deck Area</th>\n", " <th>Sum of ADT</th>\n", " <th>Percent Valid Bridge</th>\n", " <th>Percent Deck Area</th>\n", " <th>Percent ADT</th>\n", " </tr>\n", " <tr>\n", " <th>Maintenance Reponsibility</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>Air Force</th>\n", " <td>287</td>\n", " <td>121469.57</td>\n", " <td>449042</td>\n", " <td>0.044665</td>\n", " <td>0.031584</td>\n", " <td>0.009352</td>\n", " </tr>\n", " <tr>\n", " <th>Army</th>\n", " <td>985</td>\n", " <td>219463.57</td>\n", " <td>711242</td>\n", " <td>0.153292</td>\n", " <td>0.057063</td>\n", " <td>0.014812</td>\n", " </tr>\n", " <tr>\n", " <th>Bureau of Fish and Wildlife</th>\n", " <td>304</td>\n", " <td>41538.49</td>\n", " <td>25695</td>\n", " <td>0.047310</td>\n", " <td>0.010801</td>\n", " <td>0.000535</td>\n", " </tr>\n", " <tr>\n", " <th>Bureau of Indian Affairs</th>\n", " <td>944</td>\n", " <td>248088.63</td>\n", " <td>552992</td>\n", " <td>0.146911</td>\n", " <td>0.064506</td>\n", " <td>0.011517</td>\n", " </tr>\n", " <tr>\n", " <th>Bureau of Land Management</th>\n", " <td>481</td>\n", " <td>79536.58</td>\n", " <td>20686</td>\n", " <td>0.074856</td>\n", " <td>0.020681</td>\n", " <td>0.000431</td>\n", " </tr>\n", " <tr>\n", " <th>Bureau of Reclamation</th>\n", " <td>146</td>\n", " <td>40043.25</td>\n", " <td>61985</td>\n", " <td>0.022721</td>\n", " <td>0.010412</td>\n", " <td>0.001291</td>\n", " </tr>\n", " <tr>\n", " <th>City or Municipal Highway Agency</th>\n", " <td>46975</td>\n", " <td>26482462.24</td>\n", " <td>310286133</td>\n", " <td>7.310533</td>\n", " <td>6.885786</td>\n", " <td>6.462026</td>\n", " </tr>\n", " <tr>\n", " <th>Corps of Engineers (Civil)</th>\n", " <td>245</td>\n", " <td>366675.07</td>\n", " <td>428039</td>\n", " <td>0.038128</td>\n", " <td>0.095340</td>\n", " <td>0.008914</td>\n", " </tr>\n", " <tr>\n", " <th>County Highway Agency</th>\n", " <td>244552</td>\n", " <td>49576983.27</td>\n", " <td>240400073</td>\n", " <td>38.058659</td>\n", " <td>12.890663</td>\n", " <td>5.006577</td>\n", " </tr>\n", " <tr>\n", " <th>Indian Tribal Agency</th>\n", " <td>39</td>\n", " <td>3607.57</td>\n", " <td>3880</td>\n", " <td>0.006069</td>\n", " <td>0.000938</td>\n", " <td>0.000081</td>\n", " </tr>\n", " <tr>\n", " <th>Local Park, Forest or Reservation Agency</th>\n", " <td>81</td>\n", " <td>18803.75</td>\n", " <td>187565</td>\n", " <td>0.012606</td>\n", " <td>0.004889</td>\n", " <td>0.003906</td>\n", " </tr>\n", " <tr>\n", " <th>Local Toll Authority</th>\n", " <td>1158</td>\n", " <td>4880722.87</td>\n", " <td>26862081</td>\n", " <td>0.180215</td>\n", " <td>1.269052</td>\n", " <td>0.559430</td>\n", " </tr>\n", " <tr>\n", " <th>Metropolitian Washington Airports Service</th>\n", " <td>51</td>\n", " <td>46870.62</td>\n", " <td>776200</td>\n", " <td>0.007937</td>\n", " <td>0.012187</td>\n", " <td>0.016165</td>\n", " </tr>\n", " <tr>\n", " <th>NA</th>\n", " <td>23</td>\n", " <td>13544.39</td>\n", " <td>91002</td>\n", " <td>0.003579</td>\n", " <td>0.003522</td>\n", " <td>0.001895</td>\n", " </tr>\n", " <tr>\n", " <th>NASA</th>\n", " <td>6</td>\n", " <td>20869.13</td>\n", " <td>20428</td>\n", " <td>0.000934</td>\n", " <td>0.005426</td>\n", " <td>0.000425</td>\n", " </tr>\n", " <tr>\n", " <th>National Park Service</th>\n", " <td>1313</td>\n", " <td>593206.15</td>\n", " <td>4156536</td>\n", " <td>0.204337</td>\n", " <td>0.154241</td>\n", " <td>0.086564</td>\n", " </tr>\n", " <tr>\n", " <th>Navy / Marines</th>\n", " <td>214</td>\n", " <td>147731.32</td>\n", " <td>309184</td>\n", " <td>0.033304</td>\n", " <td>0.038412</td>\n", " <td>0.006439</td>\n", " </tr>\n", " <tr>\n", " <th>Other Federal Agencies (not listest below)</th>\n", " <td>14</td>\n", " <td>14362.89</td>\n", " <td>63484</td>\n", " <td>0.002179</td>\n", " <td>0.003735</td>\n", " <td>0.001322</td>\n", " </tr>\n", " <tr>\n", " <th>Other Local Agencies</th>\n", " <td>1548</td>\n", " <td>1519572.29</td>\n", " <td>4464528</td>\n", " <td>0.240909</td>\n", " <td>0.395109</td>\n", " <td>0.092978</td>\n", " </tr>\n", " <tr>\n", " <th>Other State Agency</th>\n", " <td>778</td>\n", " <td>662541.42</td>\n", " <td>4277990</td>\n", " <td>0.121077</td>\n", " <td>0.172269</td>\n", " <td>0.089094</td>\n", " </tr>\n", " <tr>\n", " <th>Private (other than railroad)</th>\n", " <td>776</td>\n", " <td>1076508.62</td>\n", " <td>11389681</td>\n", " <td>0.120766</td>\n", " <td>0.279906</td>\n", " <td>0.237202</td>\n", " </tr>\n", " <tr>\n", " <th>Railroad</th>\n", " <td>530</td>\n", " <td>251812.41</td>\n", " <td>2086452</td>\n", " <td>0.082482</td>\n", " <td>0.065475</td>\n", " <td>0.043452</td>\n", " </tr>\n", " <tr>\n", " <th>State Highway Agency</th>\n", " <td>296467</td>\n", " <td>278558303.91</td>\n", " <td>3962699890</td>\n", " <td>46.137984</td>\n", " <td>72.428798</td>\n", " <td>82.527277</td>\n", " </tr>\n", " <tr>\n", " <th>State Park, Forest or Reservation Agency</th>\n", " <td>1180</td>\n", " <td>228839.46</td>\n", " <td>658890</td>\n", " <td>0.183639</td>\n", " <td>0.059501</td>\n", " <td>0.013722</td>\n", " </tr>\n", " <tr>\n", " <th>State Toll Authority</th>\n", " <td>8187</td>\n", " <td>14377100.30</td>\n", " <td>211637887</td>\n", " <td>1.274110</td>\n", " <td>3.738234</td>\n", " <td>4.407575</td>\n", " </tr>\n", " <tr>\n", " <th>Tennesssee Valley Authority</th>\n", " <td>36</td>\n", " <td>100292.60</td>\n", " <td>114529</td>\n", " <td>0.005603</td>\n", " <td>0.026077</td>\n", " <td>0.002385</td>\n", " </tr>\n", " <tr>\n", " <th>Town or Township Highway Agency</th>\n", " <td>30032</td>\n", " <td>4291058.24</td>\n", " <td>18653453</td>\n", " <td>4.673761</td>\n", " <td>1.115731</td>\n", " <td>0.388477</td>\n", " </tr>\n", " <tr>\n", " <th>U.S forest Services</th>\n", " <td>5194</td>\n", " <td>597138.73</td>\n", " <td>267702</td>\n", " <td>0.808322</td>\n", " <td>0.155264</td>\n", " <td>0.005575</td>\n", " </tr>\n", " <tr>\n", " <th>Unknown</th>\n", " <td>20</td>\n", " <td>16913.49</td>\n", " <td>27774</td>\n", " <td>0.003113</td>\n", " <td>0.004398</td>\n", " <td>0.000578</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Maintenance Reponsibility \\\n", "Maintenance Reponsibility \n", "Air Force 287 \n", "Army 985 \n", "Bureau of Fish and Wildlife 304 \n", "Bureau of Indian Affairs 944 \n", "Bureau of Land Management 481 \n", "Bureau of Reclamation 146 \n", "City or Municipal Highway Agency 46975 \n", "Corps of Engineers (Civil) 245 \n", "County Highway Agency 244552 \n", "Indian Tribal Agency 39 \n", "Local Park, Forest or Reservation Agency 81 \n", "Local Toll Authority 1158 \n", "Metropolitian Washington Airports Service 51 \n", "NA 23 \n", "NASA 6 \n", "National Park Service 1313 \n", "Navy / Marines 214 \n", "Other Federal Agencies (not listest below) 14 \n", "Other Local Agencies 1548 \n", "Other State Agency 778 \n", "Private (other than railroad) 776 \n", "Railroad 530 \n", "State Highway Agency 296467 \n", "State Park, Forest or Reservation Agency 1180 \n", "State Toll Authority 8187 \n", "Tennesssee Valley Authority 36 \n", "Town or Township Highway Agency 30032 \n", "U.S forest Services 5194 \n", "Unknown 20 \n", "\n", " Sum of Deck Area Sum of ADT \\\n", "Maintenance Reponsibility \n", "Air Force 121469.57 449042 \n", "Army 219463.57 711242 \n", "Bureau of Fish and Wildlife 41538.49 25695 \n", "Bureau of Indian Affairs 248088.63 552992 \n", "Bureau of Land Management 79536.58 20686 \n", "Bureau of Reclamation 40043.25 61985 \n", "City or Municipal Highway Agency 26482462.24 310286133 \n", "Corps of Engineers (Civil) 366675.07 428039 \n", "County Highway Agency 49576983.27 240400073 \n", "Indian Tribal Agency 3607.57 3880 \n", "Local Park, Forest or Reservation Agency 18803.75 187565 \n", "Local Toll Authority 4880722.87 26862081 \n", "Metropolitian Washington Airports Service 46870.62 776200 \n", "NA 13544.39 91002 \n", "NASA 20869.13 20428 \n", "National Park Service 593206.15 4156536 \n", "Navy / Marines 147731.32 309184 \n", "Other Federal Agencies (not listest below) 14362.89 63484 \n", "Other Local Agencies 1519572.29 4464528 \n", "Other State Agency 662541.42 4277990 \n", "Private (other than railroad) 1076508.62 11389681 \n", "Railroad 251812.41 2086452 \n", "State Highway Agency 278558303.91 3962699890 \n", "State Park, Forest or Reservation Agency 228839.46 658890 \n", "State Toll Authority 14377100.30 211637887 \n", "Tennesssee Valley Authority 100292.60 114529 \n", "Town or Township Highway Agency 4291058.24 18653453 \n", "U.S forest Services 597138.73 267702 \n", "Unknown 16913.49 27774 \n", "\n", " Percent Valid Bridge \\\n", "Maintenance Reponsibility \n", "Air Force 0.044665 \n", "Army 0.153292 \n", "Bureau of Fish and Wildlife 0.047310 \n", "Bureau of Indian Affairs 0.146911 \n", "Bureau of Land Management 0.074856 \n", "Bureau of Reclamation 0.022721 \n", "City or Municipal Highway Agency 7.310533 \n", "Corps of Engineers (Civil) 0.038128 \n", "County Highway Agency 38.058659 \n", "Indian Tribal Agency 0.006069 \n", "Local Park, Forest or Reservation Agency 0.012606 \n", "Local Toll Authority 0.180215 \n", "Metropolitian Washington Airports Service 0.007937 \n", "NA 0.003579 \n", "NASA 0.000934 \n", "National Park Service 0.204337 \n", "Navy / Marines 0.033304 \n", "Other Federal Agencies (not listest below) 0.002179 \n", "Other Local Agencies 0.240909 \n", "Other State Agency 0.121077 \n", "Private (other than railroad) 0.120766 \n", "Railroad 0.082482 \n", "State Highway Agency 46.137984 \n", "State Park, Forest or Reservation Agency 0.183639 \n", "State Toll Authority 1.274110 \n", "Tennesssee Valley Authority 0.005603 \n", "Town or Township Highway Agency 4.673761 \n", "U.S forest Services 0.808322 \n", "Unknown 0.003113 \n", "\n", " Percent Deck Area Percent ADT \n", "Maintenance Reponsibility \n", "Air Force 0.031584 0.009352 \n", "Army 0.057063 0.014812 \n", "Bureau of Fish and Wildlife 0.010801 0.000535 \n", "Bureau of Indian Affairs 0.064506 0.011517 \n", "Bureau of Land Management 0.020681 0.000431 \n", "Bureau of Reclamation 0.010412 0.001291 \n", "City or Municipal Highway Agency 6.885786 6.462026 \n", "Corps of Engineers (Civil) 0.095340 0.008914 \n", "County Highway Agency 12.890663 5.006577 \n", "Indian Tribal Agency 0.000938 0.000081 \n", "Local Park, Forest or Reservation Agency 0.004889 0.003906 \n", "Local Toll Authority 1.269052 0.559430 \n", "Metropolitian Washington Airports Service 0.012187 0.016165 \n", "NA 0.003522 0.001895 \n", "NASA 0.005426 0.000425 \n", "National Park Service 0.154241 0.086564 \n", "Navy / Marines 0.038412 0.006439 \n", "Other Federal Agencies (not listest below) 0.003735 0.001322 \n", "Other Local Agencies 0.395109 0.092978 \n", "Other State Agency 0.172269 0.089094 \n", "Private (other than railroad) 0.279906 0.237202 \n", "Railroad 0.065475 0.043452 \n", "State Highway Agency 72.428798 82.527277 \n", "State Park, Forest or Reservation Agency 0.059501 0.013722 \n", "State Toll Authority 3.738234 4.407575 \n", "Tennesssee Valley Authority 0.026077 0.002385 \n", "Town or Township Highway Agency 1.115731 0.388477 \n", "U.S forest Services 0.155264 0.005575 \n", "Unknown 0.004398 0.000578 " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nbi_main_maintenance" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b8ab2898>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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oMH7QDQAAAPO/yUec19fv3X3Ma0e5JU8/RqYAAAAKkEwBAAAUIJkCAAAoQDIFAABQgGQK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VBTCpK2Ad4FvErSsQ1PTaRWbG6J7a1SsfnOts+s2p8MSYfY/qak42gx8b7uJBtV7pD0QWABSWsA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\ntjdKukPSQ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f09b90b99e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbi_main_maintenance['Percent Valid Bridge'].plot.bar(figsize = (10,8))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b830ec88>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/RPsGn+NEORW3d71s1hBnyQzP58fnwevs6Ywd1VM+fPdtiPctVjyNcE7PWEuB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\nHZo/oF5up3T6izPzhx3jvIZyumozpp6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2kmrywOlMDRIkLQR8hOAbQJxYR9l+PCPWDNsb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\n1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"text/plain": [ "<matplotlib.figure.Figure at 0x7f098f767978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbi_main_maintenance['Percent ADT'].plot.bar(figsize = (10,8))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b8612048>" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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78ZIurt88AACA3jayi9/ZVNJ+ku4yszvTbZ+TdKykc83sAEl/kbT70DQRAACgdw2aTLn7\nDZJsgB9vVbc5AAAAcxYqoAMAABQgmQIAAChAMgUAAFCAZAoAAKAAyRQAAEABkikAAIACJFMAAAAF\nSKYAAAAKkEwBAAAUIJkCAAAoQDIFAABQgGQKAACgAMkUAABAAZIpAACAAiRTAAAABUimAAAACpBM\nAQAAFCCZAgAAKEAyBQAAUIBkCgAAoADJFAAAQAGSKQAAgAIkUwAAAAVIpgAAAAqQTAEAABQgmQIA\nAChAMgUAAFCAZAoAAKAAyRQAAEABkikAAIACJFMAAAAFSKYAAAAKkEwBAAAUIJkCAAAoQDIFAABQ\nYORwNwB4PYw96rJBf+fhY9/7OrQEAPCfhpEpAACAAiRTAAAABUimAAAACpBMAQAAFCCZAgAAKEAy\nBQAAUIBkCgAAoADJFAAAQAGSKQAAgAIkUwAAAAVIpgAAAAqQTAEAABQYNJkys9PM7Akzu7vttiXN\n7Eozm56+LjG0zQQAAOhN3YxM/UzSdv1uO0rSRHdfVdLEdB0AAGCuM2gy5e7XS3qy3807SZqQvp8g\naefK7QIAAJgj5M6ZWtbdH5Ok9HWZgX7RzA40s8lmNnnGjBmZfw4AAKA3DfkEdHc/2d3Hufu40aNH\nD/WfAwAAeF3lJlOPm9kYSUpfn6jXJAAAgDlHbjJ1iaTx6fvxki6u0xwAAIA5SzelEc6SdLOk1c3s\nUTM7QNKxkrY2s+mStk7XAQAA5jojB/sFd997gB9tVbktAAAAcxwqoAMAABQgmQIAAChAMgUAAFCA\nZAoAAKAAyRQAAEABkikAAIACJFMAAAAFSKYAAAAKkEwBAAAUIJkCAAAoQDIFAABQgGQKAACgwKAb\nHQPdGnvUZV393sPHvneIWwIAwOuHkSkAAIACJFMAAAAFSKYAAAAKkEwBAAAUIJkCAAAoQDIFAABQ\ngGQKAACgAMkUAABAAZIpAACAAiRTAAAABUimAAAACpBMAQAAFCCZAgAAKEAyBQAAUIBkCgAAoADJ\nFAAAQAGSKQAAgAIjh7sBAACgO2OPuqyr33v42PcOcUvQjpEpAACAAiRTAAAABUimAAAACpBMAQAA\nFCCZAgAAKEAyBQAAUIBkCgAAoADJFAAAQAGSKQAAgAIkUwAAAAVIpgAAAAqQTAEAABQgmQIAACgw\ncrgbAKAOdpMHgOFBMgUAAIrNzQd0nOYDAAAoQDIFAABQgGQKAACgQNGcKTPbTtIJkkZIOsXdj63S\nKgBANXPzXBbg9ZCdTJnZCEk/lLS1pEcl3WZml7j7vbUaBwD4z0WSh/8UJSNTG0l6wN0flCQzO1vS\nTpIaJVPdvJm6fSPVjFUTHUZz/M/weqj5OptbXrO9/Dj5DPjPMaf9z8zd8+5otpuk7dz9I+n6fpLe\n5u4H9fu9AyUdmK6uLukPg4ReWtLfsxo19PHmhli1480NsWrHmxti1Y43N8SqHW9uiFU7HrGGN95w\nxFrJ3UcP9kslI1PW4bZZMjN3P1nSyV0HNZvs7uMK2jVk8eaGWLXjzQ2xasebG2LVjjc3xKodb26I\nVTsesYY3Xq/GkspW8z0qaYW268tL+mtZcwAAAOYsJcnUbZJWNbM3mdl8kvaSdEmdZgEAAMwZsk/z\nufvLZnaQpMsVpRFOc/d7KrSp61OCwxBvbohVO97cEKt2vLkhVu14c0Os2vHmhli14xFreOP1aqz8\nCegAAACgAjoAAEARkikAAIACJFODMLMFzGz14W7HnCJVxv+PZ2ZrVoy1g5nxXhxGZraSmb07fb+A\nmS0y3G3qZWZ2kJktMdztGGq9+jjNbPHhbsNAWu+juc2wd+BmtpqZTTSzu9P1dczsCwXxlqzYtvdJ\nulPS79L19cwsa8WimR1nZoua2bzp8f7dzPbNjDWxm9saxtzEzPYxsw+2LpmhHjCz481srcL2jDKz\n3czsBDM7z8x+bmZHmNmbM+N9O/e+AzjdzG42swMrfPDuJWl6eo0UJWlmNsnMPmZmixa2SRb2NbMv\npesrmtlGpXE7/J1lK8Z6a8Z9PirpfEk/STctL+lXmX+/2vs8xRtnZp9O76mvmtkeuX2cma1iZvOn\n7zc3s4MLPpTfoNhC7Fwz287MOtUdbNq+JczszWa2cg8dXFR7nJX7oClmdpaZbVMaqGafkXzczKan\n1+ubCtu2e6t/NbMvmNmFZrZBnWbWNewT0M3sOkmHS/qJu6+fbrvb3dfOjDddkQCdLum3XvAAzWyK\npC0lXdvWtmnuvk5GrDvdfT0z20XSzpI+Lekad1+3QYxRkhaUdI2kzTWzcOqiisea9UFsZr+QtIri\n//ZKutnd/eCMWIsokoMPK5L10ySd7e7PNojxZUnvk3StpCmSnpA0StJqkrZI3x/m7tMaxPxIatNI\nxWvjLHd/ptv7DxBzTUn7S9pV0o2STnf3azJjLSpp79RGb2vjcw3jrJFi7C7pptSmrETbzE6S9Kqk\nLd19zXSEfoW7N05YOsReTNL7Je0jaU13X64g1lqK19zekp5pWojPzO5UbI81qe19fpe7vyWjLcXv\n8xTnQ5IOlvSQZn0PbCrpbklfdPe/NGmbpHGSxipWYV8iaXV3375J29rimaRtFK+3cZLOlXSqu/+p\nQYzFJH1S8dzNJ2mG4nEuK+kWST9q8p4ys8/M7ufu/t1uY7XFLH6cKU61Piglm9sq+p/1JJ0laULT\nNqVY1fqMtphLSdpX0ockPa14vOe5+78axpnm7uuY2WaSjpH0bUmfc/e3ZbZrfkW/M1Zt1Qzc/as5\n8fpw92G9SLotfb2j7bY7C+KZYvPlsyT9SdI3Ja2WGWtSh7ZNy4x1T/r6U8U2PJI0tWGMQxSd678l\nPZi+f0jSVEkHFfzP7lNKrCs/t++U9L+S/ilpgqT/1+X93jvIz5eRNC6zTatLOlbSnyWdKWmLwsc4\nj6Rd0uOcrtibcqfMWEtLOlTSw5J+m+J9KjPWiLZ2PSTpi5IWbxjj9vS1/fXf6DXbL94CkvaUdLGk\nRxSd7OaS5smItZKko9Jrf4piW4ixme3q8z5XdLLD9j5P9/mkpAVm8/P1JG2V+Xwe3npdtT+3mY93\nXUnfl3S/pJMk3SHpuAb3v1LSfp1em5I2TLEPaBDv6HQ5M71/vpMuf5R0ynA9zn6xavdBm6f3+XOS\nJkraKDNOcZ/RL96ikj4u6S+KAYAHJB3YMEbrPXmMpH1KX7OKs0znSDpC0mGtS8n//7XYNYIUvhB+\nqxgVab3Rd1OMstSIvUV6YTwt6TpJb294/1MVR87TJK0q6QeSfpzZlmPSG/EOSfNKGt3qxDNiZX3A\nzibeeZLGVIo1QtKOki5Kj/UziqPM3ST9cZhfayMUm3H/SvEBfKSkXytGzprGWkvS8Yq9Jn/S6sAU\nuwL8uWGs96X/1zTFB90y6fYFm8bq17b7Jf1IMZJxZOs91iDOpPQ/a703R+d2ZJLOUCRQpyoOdkZI\neigz1k2S7kmd/arptqxY6b7HSfpc+n9tnZ6Lb2TGOrbW+zzFG5V73wGez70Vo1pvSrfdnRnr4PQe\nulwxojFvun0eSX+q1eaCx3qFpEXari8i6XfD/Thr9UGSFlck3JMUCcIe6fW2cc57oVafkWK1BjPu\nTe/R5dueg6Z946Wpf/1Teszzq+yALuv13lXsoQrc4MGtLOkqSS8oEp8blHmEmeItpRjBmSzpMsUp\nmJGK4dlGLzLFh9k3FNXeb5P09ZzOLb3xNpG0hKQR6baFJL2h4HFuokj0Pti6FMS6RtJTmjn0f4mk\nSzJjPaj4wNykw89O7DLGr9vb0f+S2a7vKo6MXkt82n72h4x4NyqGxhfs8LMPNYz1c0nvHOBnTUcf\nJilOj35Q/UY2mv7vJH0g/c8fTe+DP0jaPfP/P1WRLP63pBVar5XMWBcrjnb/p/U6y42V7juPpI8q\nDirOT99nj9RWfp8/kF5rx0raXtJiBbHWknSipL3T9TdJOioz1lcVG8B2+tmaDeJsMLtLwWO9X9L8\nbdfnl3T/cD3O9PvV+iDFqNtXOrVNcRqsSaxqfUa6z7mK06KzvIckvadhrAUVn+Gtg6YxkrYpeF2c\nLOktufef3WXY50y1mNlCiuH+RnNEOsT5o6RfKM77PtrvZ0e6+7e6iPELd9/PzA5x9xNK2tMW82Z3\nf3ulWNXmOKV47+p0u7tflxFrYXd/Pqcdg7WnJbNd+yuO/l7o8LPFvOHcBTNbQNK/3f3VdN0UnfeL\nGW17k6THWvdNsZd194czYq3m7n/sd9uK3mBuTb/7riFpK8Xp84nufl9OnLZY+yhO9T0haQ1Fx/a3\njFitOVd7S/p/iqPWbd391oxYC0l60d1fSddHKJ7LWV4rXcRaUDEau6K7H2hmqyrmJV3aNFZbzBUl\nvUMxWrC9pKfdfb3MWAuktv0htz0pzsaKU5rPpeuLSFrL3Sc1jHNN+naU4oB3quK1to5iRG+zzPZ9\nXjFac5FiDuIuks519282jFPlcab7VuuDzGwPdz+33227uvuFGe2q3WcsL2mGu/87XR8laUl3z9q7\nN82XWtXdTzez0ZIWdveHMmPdq+gvHlJMlzHFZ2fjedCzGIoMrWGm+E21nZdVHNV9vSBe8dwfxfDk\nSoo39hKSlmy/ZMb8iqLzr9G+6nOcFKfidkiXZQriTOjwfJ7WA6+zXdR2VK/48N25IN7NmvU0wk2Z\nsSZLmq/t+nxKcwkzYs0yLN/pti5jbdzhMb6t0vMxTnGk/pfc/1tbrGUVp2NukvRIxv1vUXTQresL\nFzyXrfkYd6frC6hsDujyioTxx+k1d5mkz2bGep9idPGhdH095Y/03tHeBylG97JeZ+n+Z6ttxEDS\n2pJ+Vvi62FBxluIQSesP9+Os2QcN8D6fUjFWyXN5m/qOCo6SdGtmrKMVZyr+mK6/UdKNBW1bqdOl\n5HXWumTvzVfRe9z9c60r7v6UmW0vKbc8wobpqGQlxem9nMzzx4rz0Csrzmu3L4f1dHtTn1EM+b9s\nZi+2tStnOerdiiW7j2XcdxZmtofifPm1qV0/MLPD3f38jHDruPvTrSvp+Vy/YXvOdfc9zOwuxf+7\nj4bPZcvR7n5RW4ynzexoZS6BVwyHvzaK6u7PpVGJHCPd/aW2WC9ZbB7eNTNbTdKakhYzsx3bfrSo\nojPLcZLidEvLPzvclsXdJ0uabGaHKRYqlMR6PI3W/kDSihkhRnnbaKq7P1/wXK7i7nua2d4p1r8K\nywb8RfHh9E13/3hBHEn6smLV4rWpbXcWLF03T59OKdarZlbyebKGu9/VFu9uM8safWuLMcXMHlF6\n/WeOttR8nMV9kJltK2k7ScuZWfvKxEUVK2+7NkR9hhTzyv7duuLuL6ZVdDl2kbS+pNtTrL9aWSma\nAyT9XnGw9M+COLPohWRqhJnN7zOHBBdQnN/OdYZiEu9davjianH3EyWdaGYnufsnCtrSHrNmEcCl\nJd1rZrcqhipbf2PHge8yW5+X9FZ3f0KS0lDqVYr5I03NY2ZLuPtTKdaSav46OyR93SHj7w/Yrg63\nlbz+XzCzdd19qhQ1yCQ1PsWXzDCzHd39khRrJ8XqtCberJhbsLhiomzLc5I+ltmuah8klmpVzUbX\np25TrHPd/f7USf9OsdrqZcVpxD83bN4/zWwDd789xd9QUqMl3G1eSn2Yp1irqO09mmF9SZtJ2sfM\njlLMlbnO3U/NiPWyuz/TL7fLnefxoJkdrEiuJem/FPMlc91nZqdI+mVq076KEfgsKTn4jmIk4wlF\nkn2/4n3SRM3HWaMPekJxMP2iYhFGy3OK1a1NDEWfIUlPmtk27n6F9FoC+FRmrJfc3c2s9X5aqKBd\nUqyU3lvx+f6cIrG63t0vLow7/HOmzOwIxeqv0xVvov0VQ8/HZca7wTPPs7fFWNTdn7UBiuO5+5MN\nYq2ROv2OR/OtDrxh+6rNcUrx+tTUSTVMpnpenZ0PSvqsZiZiuytWRv0iI9anFR+a/9v0vh1inaZY\n1flDxevsU5KWcPcPZcZ7m2LFSuuDe0XFxN6cOTurKA4C3qgYGXxEsaDggYxYm7n7DU3vN0CsCxWj\nGO0fJFu4+84ZsQ7rcPNCiiPFpdx94Qax7pG0dupkD1R0ju9W1GCa4O6NCotaFPo8W1JrTscYSXu6\n+5QmcVLlMKfhAAAgAElEQVSsrRWj6mspVpRtqliQcG3TWG0xF1YkVO9QJBnu7mMz4pyqWDp/lGLK\nwcGKUYTGI15mtoxiMvuWivfTREmHtg7IMuKNkvQJzRylvF7SSZ4xBzHFm5radpW7r29mWyjenwc2\njFPtcdbsg9oHIErV7DNSvNUV76fWe/o5RVmD+zNi/bdiJf3WihXx+0s6091/UNjGNyjm1P234jko\nHuwY9mRKksxsO0VnaIqigJcXxNpK0blOVN9Rm64n5pnZpe6+g5k9pHjR9znN5+5dn+Yzs5M9JqJe\n0+HH7u5bdhurX9yVFJPyrkqnJEZ45uR9MzteMeHzrHTTnoo6O0dmxnuzoixFa9LyvZlxjla84J9U\nvDnPd/fHM2MtpFim+9rrTDE3L3uoN42KrJni3dN+qi4z3sKK92Tj59HMDnP375jZ99T51OhsixkO\nELPqB2Zb3EUUo48HKFb+fKdJTDO7w2cW17xA0Wf8pP/PGrZpXkX9H1Os+vq/pjHaYi2lmG9mkm5x\n96ajjO2xJitG6m9SrHS+3t2bjry1Yi2oGIXeJrXtcklfy01YepmZTXb3cSmpWj+Nqt7aNNGu3Kbi\nPsjMznL3vc3sDnV+n3d9Cn4o+ox+8ZdW9GczCuNsrbbXrLtfWRDrFMWBzuOKUakbFPPDXi5pozTM\nyZTFqpnL3b3aXj5m9kvFKqF7NPM0n7v7/rX+xnCz2P7iQMVk+FUsVgz92N23Koj5fsVRtCk67IsG\nucvsYo1QTApurzCbtTIkxVtHkeC9X9KjNV8vJSy2Vhmrvo/zzIw4xVV5zWxnd/+VmR3Q6eeZp4Wq\nSiO9n1GUXJgg6YTW6eCGcW6R9BFFh/gHSRt6Wt1jZve7+xpdxtnS3a82s107/bzJAVi/uMtp5pzN\nVqzrM2ONLv0wGgppKsBHNetrNqufTX3YMYoPutfm6zQ5cO0X7ypFBfpjFeVynlBMZdikYZyqj7OU\nmS3v7o+m0exZeLPq80PSZ6SpADtq1v9Z47NNVnGlc7r/RYozAPcqphZc7+4lp6dfM6xzptz9FTN7\nwTKWps/Gujmnp9oNdEquJefUXIq7tmbtLH6eEeqTSttfpBjT0yhCNne/QNIFJTEkycw+pViB8bii\nbIMpjnpKlp4+Ielvkv6hqH6e067VFEO6Y9X3DZ47MvgzxXPZpzyFoqJxUxdLekax2CFr6D51iiMU\no5VN5070YWZHuPtxZvYDdT5izdlm6HjF/IxWnZeS8hmHKE4jj5b0vbZEanvF6qtuvUvS1YpVbv25\npJxl5t9SJP59DuYUp61yvGQx0bh1+us6SV9t0l+a2ffd/VAz+7U6P585cy0vVhzZX6WZr/8Spyv6\nje8pRrU/rL5nBJraSTGv6FBF8r6YomZUU9UeZ40+KCVSIxTb7Gxb0p6afUY/Fyle+1NU/to4T1FT\nseWVdFvWllbuvoskWWwFtq2ka8xshLsvX9jOnpiA/qKku8zsSsVqIUl5HXZyi5mtlXtqKfnObH7m\nitMejaRTVpsrPoB/I+k9iiHGnGTq3x4rvlqxRypjImlrfpnFRLz2+5esNDxEUVfnHxn37d++Tyg+\nmEYrFVMseF7PU6zSPEV1Ov+NFfVmshY59LO8u29XGiQdnNQ4jdGa+Du5QqyWwxSJ4hckfd5mToJu\n/FrzqPEzy+iTu//GYj/NbuMcnb79iKcaUxXsrHj9V5nPotjb8m7F6W4ptl45XZGYdqs1X/Hbldok\nRbHarGkAA1jA3SeamaXTmF82s98rEqzG3P2fFhtov1VxEPbbzD6p5uOs0gel9/lLlub2ljSoYp/R\nbmV3r7Whc/FK53ZmtoNi7uE7FWV7rlYky8V6IZm6LF1q2UzS+DTfKasol7tvUbE9LbspVhzd4e4f\nTm/0UzJjXWdmn5O0QDqf/F+KWhyNeJqo73VXGj6iGGWpYSXFHJ07K8R62d1PGvzXunaPYlVl0fyh\n5CYze4u3LQ0vcLvFxPHz1Pfg5JJuA7h767U0zd2bjPTMLmanlUxVWL9NkyU13TT5ITNr7dl1tZfN\nfXhQsa1HrWRqFXd/f9v1r1hsWNw1nzmRfrKkf/nMQrMjlL9y+lIz297df5N5//5etFj4Mt3MDlLs\nhpE92m71yr3UfJw1+6DnJU01syvU932eM8+puM/oZ5KZre6FhWGTGiud271HMUp8gmcWER1Ir0xA\nn0+xEkeKsvolkz9X6nR7k0mbA82haIuVM/x/q7tvlI6ct1CscLg7J4NPnc4B6juR9JTcDwFLFd8H\nu63LWKcqJvJepr4LALrerd0qrqZsi/llReJzUb92NY6V4l2lWLZ+S794TUYMWrGqVeW1qLfUn7v7\nBzNiXaNY2XaeonLzPYPc5XWT5k7sqEigNlAUFN1ZMQeiab2dBRSn+vZKsS5VPN7GK5wsJsSvq1kX\nwOTuTnCzpMNbbTGzTSV92zN2U0hzzd7dOsVqseDhiqbziNJ9n1OsxnwpXUpGs1srKu9TLNP/muK0\n3HHufktmvKmStvZ+5V7cfd2Gcao9zpp9UM15TjX7jBTvTsXI8R/Utz9rPAJmFVc6t8VsjVhKUUy0\nxgHx8CdTZra5YjLqw4p/1gqSxnvGhM2UZExz97UL23R6+nYZxfnaq9P1LSRdm/mB+SPFZqp7KU55\nPK+ojPzhkrbWYGa3e9sqkHTacJq7r5URq+OwvLt/pUGMaqsp22J22n4gK1aK13Gyv7tPzIhVfAAw\nVGzmEuI9FcX8znH3rw9zm85QDNNfoVjlebWkB9w9twBle+wlJJ0g6QPuPiLj/uM73e7uEzLbs56i\nf1xM8T54UlFqYWpGrDu93zY0nW4bThYrPd3Lt6SqVu6lliHog0YqDsKkeP0Xr0irwaI0wixKRqqs\nYKVzvzi7K053X6t4P71DcbCSU1OxL69QRr3kopiktnrb9dWUWRY/3f8Mxd5TNdp2qaQxbdfHSLqw\nYYxN09f28vpjFZXCc9u1g2Ki7ZOSnlWMcj2bEeez6b4vpzitWP+QdEzh/26h4X5tDfVFsdXHFun7\nUSWPWXF6+sPp+9GS3pQZ542KkaTH0uUcSW+s8Fjfoph781IP/N+rbZrcFvNdkn6kGB08V9L7h/tx\n9mvfopIWLYxxo9o2D1Zst3JzZixT1Lz6Yrq+gvpt3pvx+rpDUbftz+lzYe2CeMcrRuw/lC6/lfSt\n4X6cFV8P71AMQNyoKJvxYOuzJiNW9T5DsV3UPun7JRXzQpvcf9/09TOdLgXtmqq27dJSXzu1xnPS\nCyNT07zf6YxOtzWId7ViCO9WzTz/6+6+U0asu71tlCtn5MvMprj7hv1Hf0qY2QOKCah3eYUn0MyO\ncffPlrdMMrO3SzpVsdfZima2rqSPuft/NYixrWJPuPP73b6PYgPNxnVGrPIGtBablh6k2GtrlbRS\n50eeUbYhjeaNS+1ZzczeKOk8d980I9blisn6rYUN+0na3TNW/qQVL3sq5vv9QzEKdIFXGhYvYXU3\nTX5IsSrzXEXB4Ma1x6zyFkhmNtu5L97gtHlbzJrFSU9SrNja0t3XTCN6V7h71iorM7tJ0ufd/Zp0\nfXPFFjqNT0G2xdxVcZCSXe6l5uOs2QdZ1B/7oKcFOem9+gt3H5cRq1qfkeIdpVikNTb1ZysoCm2+\no0GMj7n7T2qc6egXd8hGLHshmTpN0fm0ztt+QDGDP+v0l/WtDm6KN9Penjc36X8U1VfPSm3cSzGc\n+qkGMW5RzAV4r6Ij68PzlplfI2krr7OSrBVzCcVjbS/bkHOqdZLiw/cSn1lYsU9S2kWMWyS9z/vV\n10mnnC7yvPki5yiOdj/o7muneTI3e+YpjjQvYCPFzvatx3lXzpsyxVpfUTyuFSvrgKLmqZz0PJyl\nSOyqTtasyczGKRKr3RR1yLr+ALaYhP15b1DTa4A4Y9z9sVqnbAf6EGmLl/thUqU4aevg0PoWUJ3q\nDecktcWb5b658axi/cKaj7NmH1RzEKL26d/Un22gOMNU2p9VrbNmlQtUt+uF1XyfUNRNOljpCEIx\n3J7F3a9L8wz2Ucz1eEixHDUn1kHp6KaVUZ+ccXSzg6Li7ZaKN1INR0j6jZldp8xJ3u3M7COKkgbL\nK47QN1bsUJ9Vg8ndH7G++381XQa8YKc3kLv/zfL3Zqq9Ae2L3rc8ReP5NW1q7j/1pJntpRiql2ZW\nkG8kPZ4/ufsJBW15XXjBpskeS8O3UF4NovY4j6X/2ak1PsRzk6UuvFUz6xytb2byvFp3/5ceb+s1\nO1qZe6EmD5rZFzXzoHpfRd/dmNetX1jzcdbsg243s5+o7yBE7srbKn1Gm5c8Ks63/mcLFMS6KY0c\nn6OYYpO7x58kyd0Pt74FqnM+0zsatmTK0g7eHvVYvpsuJfFWU4wc7a04JXGOYuStqMyBx8q9rErI\nyeHufmR6vFmTUDv4hmIC+yhJ2TU32hyi6GRvcfct0imU3M78ETPbRJJbrNI8WM03LB1lZiO934TK\ndFSd+8asvQHtjRb7So5KH8afVMyxy3Fu6hgXt6huv7+kn2bG2l9xMNLa/+sWxcrPRtIH0lJmNp8X\nbpNTm1XcNDm5KY1Cn6O+S8MbFeet/CEu6bV+7SRF1ee1LXYD2NEzFgFYrNpaRbMWms1Jpk5UrEpb\nxsy+oRgV/EJGnJb9FX1Oq6+9XlG4M1et+oU1H2fNPujjir71CM0chMjdr65Kn9HmV2Z2oqRFzWw/\nxW4FWZ997r6qRR2svRT16e5VrLT9ZW7jvFKB6v6G7TSftc0hMrMLvG8tlZx4ryqKbx3gadmkmT3o\neSu/+hexfO1Hargs1mIOxQaK00G15kxNzjk3Ppt4t7n7W9Pw7Nvc/d8Fp4aWVqyGat9/6hBvUDDP\nzI5VbEdzUGv+ShqtOVHS33OGZK3yBrTpaPVA9S1P8ZPcU69Wcf+pWlKCt4GkS9T3A6nowKeUVdw0\nOcWrtm+mmZ2rGNmtUoQ4jT4frnhtZZ02b4t1n6LQbJVOPx10bSW9tgdn04OmVpwRko5198NrtCvF\nrLaqsuLjrL4Jdq8ys/epb3/WuA5ih5hLKwZdslbaphi7SvqWYqW+KeMzfcDYw5hMtZ+DztqctF+8\nXRTZ6yaSfqeYn3SKV1guXdiu4xUfugtJeqH9R8qvV3KsorjgFZXaeJHiKPBQxam9pxS7yW9fI35G\ne0ZK+rriiKY112RFxcT2LxbM86i2AW2vSh3O/pp1y4oDM2JVnfw5FKxw0+QhaE/t0gitA532/jL3\nQOc8SQe7+2M5bekXq1MNuOcK3ptX5ySvQ20IHmeVPsjMNlZUh++/B+RqA95p4FjV+ozazGxRSa3P\n9lUUo4TnesaiiRTvAcV83KyEeLaxe2RkquZKt4UUxfv2ViQGExSTlrtOPGxoikZe7BkrCgeIVbVg\nXr/Y71LUtPldzumdNLzb3zOSJrv7xQ1jLaC+dVT+1bQ9bbE6vb6ekfTn/qcTu4zXadf2ZxRVpo9p\n8hoZYCS0Feswb7ARp5ndqBim77MvlrufM+CdBo+5kGescBtKVmnT5BRrWUnfVCwHf4+ZrSXp7d4b\nm0P/VrFq9DyPidC7KUbf35MR6xpJ6ylWOrfPtWy8N5+ZPawoE/CUov9ZXLGs/gnFtk+NPuzM7DuK\nBTD9q3A3mmJhA6ymbIvXdFXlw6r0OGv2QWmU8QjN+j5/vEmcFKtqn2FmMzRwf3akN9j0Ps2X+pUi\ngbo5pz394t3oGauku4o9jMnUK4o3jSnmwbRGbWomBktK2l2x/Lfrox4bgqKRHf7Gpoo6HJ8sjVWD\nxWq+FdT3yKTxhs5mdrJimfp56ab3K7ZeWUFRC+jQLmJs5rOpPp2OVlZ097sbtOsWxSmraYrnc+30\n/VKSPt50lM/MjktxWhsb76XoiJ6XtHGTDygz+4piufqZKeZekt6gqCD8CXffvEGsakUYrUKZi6Fg\nfTdN/qGXF3j8rWK/u8+7+7ppZPQOz1uZuaqkYzTrhua5hRlXVjzOTRQf6A8pTnM0LuhqfVc6v8bd\nm84xk5n9WHGQenm6vo2k7RSjgye4+9saxju9w83u7vs3jNNxNWVbwKarKqs9zpp9kJlNavo/nk2s\nqoVbzexrii1f2vuzJRWv3fHdfhan07/He94WOQPFPEHRt/5KfQ8oSuZFvxaEy+t0URwVHqcotnaN\npE9lxqldMO9rijL916V2XaM4jZgT62pFaYvW9ZHpthGS7u0yxvcUhei+pCgpsZFildb+itUr10t6\na8N2nS3pzW3X11J8gK6sqETf9HHeMNBtivpfTWJN6nDbLelro4Jyig/ybSq9Xiel19YdbbfdXSN2\nYbtelfQvpWK16ltwNqd47W3pa/vjbPyaaL0GFPNrpilOwXxZ0lcqPOaFFLXXSuOspNhSRpIWzI2p\nGGnueFvu/64XLzUfZ40+SLGsf530Pj9GsXCodVtWIeiafUaKd0uH225OX6c1jDWx8vN5eofLaTVi\n90JphJ5jZr/SzMqyt3nBaiYbmlWGP1IqJKdIhJ5XrMTIKpinWAq7SsnjbLOcouNvrWZaSHH65BUz\n62rlirt/Oo2U7aYYWRyj+PC8TzERt/GeaZLW8La95dz9XjNb390ftLzVyYuY2YaehvnTEH5rNLXp\nacNXLTZmbRUp3a3tZ10NHZvZU5o5inqkmb2gvqeAO56yHoyXl7mozutvmvzPNJeltcpqY+Vv1r2A\nu080M/MYBfmymf1eMb+lsdSuoxX18tzMbpD0VW+woKMt1kcV8zeXVMw/WU5RNqbj1kiDeNLMjtTM\n2nl7Sno6jSZ0vQjDzH6g2Z+WazRx38xucPfNOpw6zz3jUeVxJjX6oB/2u75Z2/euBqVBhqrPkDSP\n9d2ceEfNPOPR9H92p5ldosLTv20O835TMMysyrxqkqnOTlEMq39D0jpmdr9mJlc3ebPz0vcrVhm+\nz2euMvx0Yfve5qmQnCS5+1MWZQhy3a2YC1Bj4u5xijfAtYo35TslHZPmsl3VbRCP+S8/VX6JgP7+\nYFHNuL1TnG5m80vKmUz6MUm/sCjXYIpO6ID0OI9rGOsDihWQP9LMpcn7pjljB3UZY+mGf7MbNcpc\nzAk+o1ixuEqaPzJafRPaJl60qKo83cwOkvS/ipVDuc5WjMS2Vjt/QHFAllPL6pNKhWYlyd2nm1lu\n2/ZRJHm/Urz+b1AcNI5QHJx1a3Lm3+/I3TdLXxepFLLW45Qq9EGeqoib2Ure75TlYKc4OxiKPkOK\nsyY/NLMJiuTpdkn7WVSA77QSd3aWVAxCtJ8adOWXK/q1mb3H3Z+VJIvK8ecpTrmWqTmE9p94Ubxp\nxin2AXtA0isN77+LovN7RJEYbCXpocI2TUrtuj1dH622UxQZ8cYpOv3LFR8qlygqmOfGGyNpJ8VC\ngOJ94So9jwso3sgXKTrG/063zaOYE5QbdylJS7eeh4rtbXQas+1+V3RzW5exllbsdfm4pBmSfqko\nPTDsz+cQvD5GSnpz6lTnLXneJC2sKIB7uqLT37gg3iz7lKrDqacuY01KX+9oe8yNTrvMJvYoxRYk\nw/5c9mvXMoqVwCuqwp6tJY+zZh/U6vsHu63LWNX6jNn8jbcM92shteO9iuksCyv2prxH0no1Yg/r\nyJRVLPtfW1ouukm6bKx4E12lqAzeNY/qqhe1rTL8tKRl0xFKo1WGbWoXzJugqL1xl8qqGEuKatCS\nLrYoSre/me3lGXVxavJYCfiddJHFflEHufvxitOkuV6StIvFvoHrKhLJLGkVWeuU8DOKJLfb+86n\n6JiXtSgX0DpvsKjig6Qxj2XbH8i575zAYq+6R9z9b+7+spltqBgB+rOZfdkzVu26+23p2+dVVnSy\n5RqL6tTnpuu7SbosM9Z1ZvY5SQtY1Dz6L0nZ9X9S/72N4vW6jWLU5rzZ3mnWGN9390PN7NfqvKdh\n45WGKe6Oivf6GxUj7ispRlVzthUrfpxSnT4oTRtZU9Ji6TG2LKq2BQ9dxqreZ/SLv7KiP9sn3ZRT\nG61a0VpJcvfL0tmEKyQtImlnd5+eE2uWtqZsbdik86H7eaWKwTWY2XTFh9kFilMut3nhiqF+8bNW\nGfaLUaWQXIp1nbt3XOmTEWuMYvh6H82cKHmhu99VI36JlCDvrugUl1Mks/+dEWd+xTZB+0h6m+JN\n+X5J17h7ozlFaWh+73R5WdHpj3P3hxvG+bTidNUy6nu69llJP3X37zeI9VFJ13qcBjLFir73K2p+\nfcgzVnn2IjO7XTEZ+0kze6fi9MunFAtF1nT3rk/1pX5sQE2TgrY5P6aYd9h6XY2Q9Lzn1aebR1GP\nq73Q7Cne8EMg/a/2URzl36ooPrmyu78w2zt2jrWhu0+pudIwxZ2qODV0lbuvb7FLwd7eoHZSzcfZ\nFrOoD7Kop7irpO0l/abtR89JOsvdf98gVrU+oy1mq//fW/G6HSNpU3e/v2msFK9K0doOc/O2lPSg\nYjGYPLOobp+/0QPJVNWKwZXa9NnUpuUk/VExGnWzYnh82CfgWv1Cct9VLBO9RH2Xi3b9oZk+gPdW\nnN44N10u9oKiqWa2u6Le1XNm9gXFsuKvN2zXIopTrftIWk0xorenuy+f2aYJijfi1YoP36sk/THn\ncZrZTYqaXmcrtkiYbmYPFf7PDs3pBPvFuFvS+u7+f2nE7TDFB/D6ko72Bru/9zJr27DWzH4oaYa7\nfzldb7Rc3KK2ziOKDVQnqW85leykoNeY2aOS/qIYLfhVem8WvWaHgqVdIlJStb7HXnG3uvtGXd6/\n2uOs3QelmLMtH9MwVnGfkeJcrej/z1P0Z3dV6M+qFK21AYrptniFrd56YQL6Zcofth4S7n5M6/s0\nzLiJpI9KeoeZzag1ilPgdnUoJGdmWQXzFB+SUiSQLa5mGx3/UJFw7uOx8awsbXRZ4Ivufp6ZbSZp\nW0nfVnRuTeqrPKE4qvyConSBp6O7XBsqaqjcoVjO/H8Fj3OGovNZVjHvbbq6XL03G0+kBKgPdz+z\n0y8P4OW2xHwHST/3WD12lUV9rf8UI2zmHpBbKVa6tTTtG98gaWvFAcU+ij7tLG9bvTXcrHNBy1Yx\nxa97dysEL1BMV9hT0itmdnGHmDltq1qfS7HibmHF5P0zUt/YZJVtzcdZuw+SpPFm9sH+NzYZeWtv\nX4U+Q4pJ9PNKmj99lcpfG39P00VaK213UxRNbaRGsjSYYR+Z6mXpnO8miuHdTRTn3ye5+w7D3K6q\nBfMqtal9+HrZ1JYPufsKBTHvSEP0xyhqN51pDbceSkPZeymGnM9ULAa4sqCTlpm9RfE491CMRqyt\nOC3UeGsIM1tMcQptb0W198Ulbevut2a27aS2q6MUCfEUd9+1QYzbFac2nlKc2tuylRSY2X3uvmZO\n23qNmX1ecbrk74o5IhukD7r/J2mCZ1ZKTqeB95Z0vKKMQe4GtFWlRPgV9S00K8Vpnc3c/X1dxjFJ\nWyge4/aKOTYHSPpN7nQIi5IPRytqzL1PMd/M3D23pMRCinIq8yjm/S0m6YwuE8ZWjCqPc4j6oD3b\nro5SjHw94u6fyohV3Ge0xVpa0S+2PgeWlrS5u09rGivFq1a0NsXbVFH7bSXFAVOrDERxIe7hnFV/\nbvp6l6LAXZ/LcLUrtekiRfZ7v2JFzkcUG4QOW5v6ta96wTzFh+cRikKZX5L0pYL2La9YqTJFMenz\nm5lxLpX0E0l/UiQZ86thEcu2WCtL+nx6vb0o6UhJq1V4LjZWlDV4VNL1hbGWUczZuUnRMdZ4rSyh\nON3a5D47KFZ3/k0xd6J1+7skXVajXb1ySc/fLpIWarttNUVi1TTW/Ir5LOdJuk3SFyUtN9yPsa19\nNw50mxoWmm27/7yK5OdMxSbkuW2b0r8dkn5f8bGPUHwI596/+HEOVR+UYs+jSgUuc/qMAeKsoJjv\ndLti94uSWLWK1t4v6T2pr12qdanyf6sRJPNBjUlfV+p0Ga52pTbtqLTcvWLMXRWncZ5RQbXmFOuK\n9EZs/b+OUMw5e61cQsN4P5b0c8Uoy9HpzX5qpce9umKeTc59F0z/t1VbrxlVqNQr6S2Kvdj+VPH5\nnUfSVhXjrVQpzkhJ92Xeb4l+ty2kgjIS/8kXxYrYKYoNuteuGPcX3dzWZaypihp1resbKR2cqKC0\nSlu8BQrue2N6D12oqK22i6Q/ZMRZVNJnJf2PZk60P0gxwlqcIJQ+zrYYVfsgRRHWByrFyuozBonZ\nKGlUJK4rtV3/Unr9XiLpTQXtmGW3iVqXnjvNZz22Z10tVnG36jSUerSi+m2rkNxXFInaip6KgzaI\nN83d12n7urBiBd42pW3NlVYeTfNhLqkwpzGzizRznsI8iqXgF3vGqkV0z8xe1cwFNO2datFeo9Zv\nE3iLZfp3uftaGbHeKuk0RY0dUxzUfURRa+e97n7ubO4+pFLb7lOMQH9NkRQd7+63NIxzseJ00M2K\neXBLSJpP0iHufmfVRg8jm1m9XIr3+ZOSjsp5DnuxzzCzaYr6bC+Y2Q6Svqs4dbi+os7Xtplxj1UM\nOlyozMVWA+mFCegys/UUkzb3UJwTLd90sPc8XiORkl6r/zPQufFGiVTyr/T1BTN7o6Li7LCuzvFY\nfTPVzFb0BruMQ//T9v3Lih3pHx6mtsw1vPIWN2lFcasm1LOtmxV1zU7OielRA+staZ6eufvTZras\nu/9TM+tYve5SgriHux+u8vpcK3vaoNrMTlGaD+fuz5W3tKe0Vy9/1ctGRXqxz3CfWYJiV8WZkimS\npphZyUbrrfnEG6avpuaLrToatmTKhmbPul422czOUYXdqs1stOLU3pvVd+VL7gviUjNbXDFh9nbF\ni+uUzFg1jZF0j5ndqr5lM7IK+c0N3H1i+3Uze5uZfdrdDxmuNvU6i+0lftvvto+7+4+Hq00eK4qP\nMbNj3P2zQ/Andk0ruNZUlIDJYmYLpWQsm8e+nRuamRUmBVLbtiwp7kMliZSZre3udxe2qTpvK9Fj\nZnxogaoAACAASURBVCtZFHbdx1Opj4axqvYZZrZIheTV0hmSFxQjjD9q+1mj4qQp2GfSt5emr65Y\nTX2Duz9U0tCW4RyZGoo966qw2LR2QJlDgosqXhjtp85ceaNwZyiSzx0kfVzSeMULI4u7fy19e4GZ\nXSpplGcWUTWzCxSnEn7r7qXV1L9SeP/XpKHi35S2ycxmW//M3U/MiDlaUXpjrNrek+6+f9NYKd7a\nipHePSX9VbHMOyfOpooFDf80s30Vdb5O8MyVND3si2b2b3e/WpIsNrbdXDGXcLhd2kpYSp4Di30e\nd1S8LjZQqv6sKB3QmMWejacoThmuaGbrSvqYu+eOGtyh2DWhdEPbdfuN5LVG9nJPt/7YolL4zySd\n6e5PN7z/kLDYU3EPzXw+j5f0oYJ4VfqMZKrFHpent95TGb4v6U7Fqej7fGa5nfWVURpB8XrvbyVJ\nn7fY7eDsDj9vZNjmTKU6G3spljz+TlG48BTvgeJvZnZN+naUYkuPqYo34zqKCWybDXTf14OZTXH3\nDVtznNJtjauYm9lsl75mjpq9WzFMv7FiVdPPPLP6bYq3kmIC+lUWG2WOyDnqMbNfSnq7opM4PfeU\nq5l9bXY/d/cvZsS8SXFgMUUzK13L3bvu0Kzv1g3PK5LtT7t79rYQad7CuorX/S8UldB3bfo663Vp\nDuKlipVH20laQ9JenlkEt6Yaz4GZnaHYcPwKRT97tWKyckkxxUmKrW0u8YLK1G3xTu9ws+ceUNRk\nUQNrf0Xpl1sV/ceVGXGKl+Wb2YcVZ3NWlnS+4vTsBTnP5VD0GSnuCEUpif0VdcPOUJQaaXoAsJxi\n1d3U1kGwRYX1eWtN/bAogH1V+7zEbN3OVB+qi2KF0AcUndkLiqKMxSu2KrXtbLVt0KioJ/SzzFij\nFLu2/0gxcnOapNMyY92Svl6uKGmwvjJWhSjKPgx0yWpbW+zFFKNmjyiW+n9YDTeQVYzW3NZ6bJJW\nVcHyX8Xo4McUWwTdrCjSWLzctsLrLKucRb8Yryo28Fyt7bbS5citjbS/JOmA9tv+0y6KTntaeu1b\nxv0vV+y7uUbldhU/B4qDwWmKciUrVHpt9Nk0ufV3hvt5HMLXxwhFPbj/VUyUv1+R1DaJUbwsX3Ea\n8zpFVffWbVnP5VD0GR3+xrvS/+z59B7ZcLifyw5tLF7J6j7MGx1Lksf59jMUVWpbe9YdpTiKGm5r\neNuecu5+d5osn+MXijfTtpK+qkggcyekfz1NIj1M0g8USULjU6TuXmMj1lmY2VKS9pW0n2L4/gzF\nysPxitMn3fqkYvn2JEny2G5lmdx2ufuz6TTkApIOVSy/PtzMTvSGhRUtCjN+SLPOW8upQHypmW3v\n7r8Z/FcHtKfiKHOixaax50h9tzTJ8FyaCL2vpHemI855B7nPHMNm7n/XMp/iiH+3NH2nySmh8YpR\nrS+n+aCTFCPuE71sX8/Wc7CfYgeGxs+Bu69rsZfnPooq9k9IWsTM3uDuf8ts1yPpVJ+n02AHK78/\n61kWG+t+WHHQeqViWsrtaaHOzWo2TeMZ7zc3L8Nyivf6D9M813OU/54cij5DZrZoijtesbjpcMUZ\ngbcqzlaUF8isxMy2VKz+LDfcWWEvXxT7bJ2iSADeJemnii0isrNfpYKkijfA1cP9GFNbvilp8bbr\nSyi2l8iJdaGkexW1Xsb0+9ksxUYHidXn6FcxNJ5V0FVRt+QixRH64ZKWSbcvqFi90jTeOYrtLx5U\nVEWeKOnEzLY9pzhKfDF9X1KDbBFFJ/Y7RUf2A0UF85xYb1BshPqOdH1FSR8cztdq7Yviw2PFyjHn\nUZxS/qqiftJVko7oledAMXXhO4q9527KjLG04iDpccV2Kb+UtORwP59D8Pq4XpHIzlJbStJ+DWMd\nq5jb9HbFPKcNlFEcti3eSoqBh6mK2oBfzYxTrc9I8R6Q9A1Jq3T4WXYx6MLnsVNx8EcVp22rjCb3\nXJ2pXmJmoyR9QjHfQIo31knu/mJGrFvdfaP/z955x0lSVt3/e0iCwIIKIqjkJIIEF0EJgmJAEUFB\nRBAFVBTFRXxRjID4E0V5DRjhRUREclBASbrksOwSFlBURFERQRBlZcmc3x/3qZ2e3p6eqaerp3pm\n63w+85np6u07d3q7qu5zn3PPkXQFsD+hLj3DGTL2fSAsz2fRojZ9mxKxXuN80mF7rKOAfwN7EVIQ\n+wO/sf2ZjFg/Jjh58xFuJb3WbRMtY4hXWN0U2lyLAhc5f6KyciQu0G6EqerWo/37Dq//iu1PjnZs\noqPgIPYx/nKERdDJma+vhDfYIa6ArZ1hwixpC9tXj3aswRBauLitcBXXDEnrAbs7g7PZFqena0aK\nsbBbpg17zOck2+8e7dgY4qzSdsjAg+5xEnXY72iKqfGBpPcRrc6XEbyMpYgqvfTEUBWE5bZ4s4FN\nbT+eHi9BdJFemhlvfeY3LP1xRpyFiK5PoWR8EVEQ1f6hbSuO9yNW6DfYXiMz3o4MFe2X2T6/278f\nD3QqqFuHHiYLJH2H4ELeUHcu7ZD0foLb91zbayQy9Pdtv7bmvDp9NrIWYOm1zyI4SasyfIH4hV7y\n7BWq3oB50iPRdT7G/BSIN2XEqky0tt+onTM1yOgwfQHknUi2C92my+l9z/jZFXcHfkLsm59AVOz7\nEPYYpSHpUGJbdD3gFwTh8irCrqYUHMKdPyE8736Xk09LXpsT7euXENyYhYFHnKlMDRwv6TmEEv1F\nxHbh5zNz+zLBJyg6F9MkbWn7kMzceoKkDxFdwDVSoV1gaWLbarJhW2A/SXcTY/nFlNUgFI2V8gZ7\nhaRXEhPYy2tIuweCt7lwD6F/Rjg4zKJFh28AcAJDBszbkgyYcwIlnuuhDC2aLie25rJkaAYYPwEu\nIKQbPkpsId5TJoD6IFrbbzSdqS6QdAdRYbd3gMo4j+9p+ydtF555sP2/GXl9keA69EJYbo/5RmA7\n4gN7se2LMuPcSoxy3+Qgvq5AdJPG5EjfFmtHgmOwmO3VEvn/C84Q7ZQ0kyBFnkFwRvYC1szZMqwa\nqWDZyEPjvwsT718tN/N00X8OsSJvLejm2P5XHTn1Ex22AABwhp5WlVscKd71tjdr2VZehJjmq+uz\n8WpisfRBhutwzQHOs/2HzLjZsgr9hIZkaG71kLL6lba3yoh1FnAbQwvVdwMb2u4qUTPRUHSTWigQ\nAqbb3iYjVr9EaytH05nqjiqmL5ZM3zuJhuViGvBpSY8To7I9+X8l/BZ4quBlKF/F9tHUUXoqTXXc\nT34n7lBiVX4ZgO2bJa2aGQvbd7bc7E5I26VZSF2pzwNbEN28K4H/Zzt3MmRZwl8LQlaiNtj+T5p0\n2yCnoJhoKP7G1PEpra7chjslnUloEf2m5+TgcknFCv11RMfwvDIBRlrIFSizoEv8qssl/ajiz8Y1\nkjZwy/T0gOCxRDf4g6SPEB2W3M7gGrbf3vL4cEml/ALTdOGIsD272/MjxPwgMVhVVYfsifT9Pkmv\nJURAc7WrKhGtHQ80xVR3TJf0VXowRbT9g/S9MjVv21UWZsN4GYT7+AuJVWcOL2OmYmT3OKKj919i\nYiIHT6Ube+bLh2GuYoT75kRsv5ehQjcHpxJ6VXukx+8iJvxyzKGPBG5KBFUR2wClVmMabnw67Cmi\n0H5umXhegLwRUwf0aGAlovhfhVhc5HAGX0Z0QP8v3YR/CJxq++HuLxsRhxC8wVsJbt4vbB9XMkal\n14uEuenaWJWl1ZbAeyX9ibjWDspW64HEFv5HCQPmbYltqxw8mrbvr4J5NJJHR3lNO77T5TkztIVY\nBqsCNyqEWH9o+9KMGK34SroHHEzoRk4h7M9y8D1C1X7DFON4gjIycMLBzTZfF1QxfSGpq72I7a72\nJF3iPocQsWy9kOVaQ9xM4mV4SM14Xls7F6mLNCVntZRefzwhOXAIQU79KCH8+cGMWKsQJPHFiK3b\nZYDvOlkZZcSbbwKs07ES8VYkeFMi/h9K6f+krcERkbP1JOnXKadJ7Y0o6RbC6PTStJW2LTEZlaMZ\n1hp3a0JeZVlCrfqIsp83SdNsf3O0Y+MNSRcTi4f/ocXSKpfLWeVWa5WQtLrtuyqKtRGxxbcMcZ7/\nC3iv7VuqiN8LUuG/PcEJ25D43P7QGYbHkhax/VRFeRVbhp8H7rF9fC+DDv1E05nqAldjujwrfd+C\nIGWflh7v2vJcKSgmA6cBLyL8izYnBORyV4WP236i6AAlXkZWla2QILgSuNI92MgkHAB8hlipnkIQ\nvbvauXTBGsTF/mGq8fy7XNIuts8EUFjzlNoSlrSu7Ts05AX5t/R9JUkrlemAMnqXLaczUlk3dcDx\npO0HJS0kaSHb0yV9JSdQKmrfTNyUViU6XicDWxEDGWuXDPkeoL1wem+HY2PJbXGiy9XeTcqRVHle\nurFNa9n6Ky2x0JJDlVutVeJHCluTGwhpnCtztyJt30x0Waakx6XPydRF7fY7fp6Z2zOS/gz8GdiA\nMJn/maRfZHCW/iDpLuI+cAXh2DE3Jy8qEK0dLzSdqVEg6c3Mf/EpPa6bulyvd/L7UugSXZxTsClI\n3psSH9KNFOrGh9verWysFK9KPafXEC37rQiu1M3ENF7dK+kfE0Xng6Rij3AML8VxatlOE7HCfDI9\nXgz4d5ntNEnH2v5ARR3Qv7bk1SlWFmdBMUCwaXo4w/b9OXEGGZIuJUx/jyTEKO8npEJelRHrLmA6\ncLzta9qe+9ZYO9GSdie2jrckPqsFphDb39tl5HYG4cLwLlpcGGxPy4h1ne3NJV0EfIvgxZzp3qRB\n5ttqdaY8S5VI9IBNCeL9fsBSJc/zyoaQJJ3U5Wnb3mussVpi7k8U6A8T22hn2348davudJ4W4trE\nPWALYmv0PtubZ8R5AfF5vcH2lZJWBrZxhtROv9F0prpA0veJ/fJtCSX0Xcjn/6xEcBcKkvFS6VgO\nHrP9mCQkPSt1N9bJjAUdeBnE31satn+dVqibEu/bB4liNGclvTaxjbAqw6UpSnfgiouMwgZiF4J7\nsBLlz4Hlyv7uLjkV20jbu00INnURysR6cVV5teTwDmKa8jKiSDtG0sFFN24S4a0Ed+VjRIGxDFFs\n5GCbkThmJbf0ryF4fcsRRUaBOYR6cw7WtL2rpLfaPlHST4lubw46WVodmBkLouO8OW1brT3EqwSS\nioXhVsR27fkML27Hgm5DSKW6GS4pVjlGvIgw9h62nZm6VTmT08sR1/wNiE7sncTnuTRs/0MxBblW\nOvQA4WQxcGg6U12godHO4vtSRNVemmSscPs+jFi1QhDoDrNdWs9J0jnENsKBxNbeQwSXqLQoWpff\nkaVmLOlXxMXjWoa6P1ndjMRl+T7zS1OU3h5VTIJsRZzgDxDaV1favrZknLUcWj8dibE5/LBOHIBe\neAGS3sRwAdALM+PcAryu+P9TKO9fanvDnHiDCEk7AWsSQoC5hUVrvD8AfyK2888u2/nsEG9JhiZk\n1wbWBX5ZdLhLxqrShaFSBXRJM21PTZ+5jdPfO8P2K3LiVQVJTwMzia7lL2w/McpLusWq7D2TtDTw\nOYZrVn3Rmcr4Ch2+tW3/WOGtuuRIi4IxxHqG2BY9kpDLyJYK0YCK1nZCU0x1gYY0Xq4D3kZsEd1m\ne61RXjpSvBcAm6WHpUnGI8R8NbGSvrDsiZ72n99BTO9d6DBy3oEkluY2i5kxxvw68HKC53Q1sWd+\nre2yUys9Ebo7xHoA+CNRnE3PIVamOMfb3ldSp9WpXcKCIX0eXkiI3L2LoS26KcQFY92M/P4f0Vr/\naTr0TkKT7LMZsYYNIaS2/y3ucTBhUCDpu8QK+hpicvU827mcvNa4ryDe950In8pTbf8kM9YsYhHw\nHGJ6dCYw1/YeXV/YOVbhwrAB8CN6c2GoegFQ2VZrlVBMpW1BFC2bEh6a1zrDtqXK9yxt2f6e4ZpV\nL7G9S0aszxJ/4xq2104csdNsb1k2Voq3GUNUj+WB24HLnWGnpD4NR/UDTTHVBZI+R7SwX0tsCxk4\nznau0vULmV9NfcwTeAqZ/hHhkoKKkn4EvJjYutwMuJsw4TzE9rllYnWIvRTRPfsf4AW2n5UR4zDi\nonoOw6UpsoQjJb2UuChuSbSNf5fTNk9FxStsX5eTR0uc9xBchanETbLAHMLepIwjfRFzNrGyfzo9\nzhZ5VIy+v4wg/0N4ds32JPHmk3QbIZr4tMLz7sqqivcUfzngf4E9bGepg2tomukAYoFzlDp4aY4X\nNKSAfiChCl5gCrBzbtcydeAeIxYUxVbryS4hkNwvSHoJsZOwFfG3/8X2mEfz+/GeSbrZ9kajHRtr\nLGBj4jpRFCw92UYp7IFeSbxv+xDCyytmxBko0dpuaDhTXdCySj1L0vnA4s4UNlNMB+1GVOnPFL+C\n6NyMFQ8QE1/F2Gkr2diUF8ecCrwstdQXT/HX7KVjphC224roTt1N6OyU5RgUKPRcDm45lvN3opig\nWZkoZlclLtbPdHvNSEjv1zcIjkc20hbviZLe7kxfxREwhdj6hR40hmwfrJhS3JL4rB1reyD5Cpl4\noig6bc+Vehc0S5+znYnO1BrEQqCXrSqlm/EeBK8RMq/bkr4EHGX73+nxc4CPl+xaLkZ0tBZh+Gfr\nYYKLmAUPN5zNsrLqByT9EfgdQQv4PrB3xlZfP96zxyS9sqAppG26x0Z5zUh43LYlOcV6dmYc0uuv\nIrqL1xPv2+udbwd2uXoUrR0vNJ2pcYKk3xGFS7bvlKRvEhMlVxPdgqvcw39ge4u5lzZ9S4yDiQJx\nlivSGqkCqWNzVfq6wvbfRnnJaPGOIMygf1ZRflVNje5JkHl/RRRA2xBbOaVb7ClesTX9DDFR0/PW\n9KBA0lyCHAvxXq2RHmcLRipEJ88FTi/Lxxsh3qsJkvfVtr8iaXXgQGfo03XqaPWwzbSKh+QMnkNM\nsvZyLXob8BVCXVxQiatDz1BIZWQtujrEmveeVRBrE+AkoOj4Pwq82yG/UDbWJ4mF5huBLxJF+5m2\nv5GZ24t6vb62xBpYs/t2NMXUOEHSL4Fdbf+3xzjFDXJ3YsV7MfA923/KiFX5zSTFXRhYgeHbmWMm\nM0p6jWMqsKNnVc72V9VQSCQsQ2w/Pgp5SuMpVsepUdv7dn3h8BjzlMrTdvJmKafrbJcyGW2J+T7C\nMufXKdarCW/EH+bEGzRoBKHIAjk3PkkaxAs9zFtQbFos6CQtQSwIxiw/oBBPPN0xQfwsQlttI6Jb\n/i5nqmdLuhN4i+3f5ry+X1AMXbyf+SeKS2tzpVifoAfVeElva73+JeqHet0OlbQ9LQWLe7BRS1tx\nOzL/e3ZUyTgLAyfa3jM3l/FEs803fphLWJn8iuH8n1IrzHShni7pJmIr4QjgD4R9S1m8JOM1XZG2\n+Q4j1MZbtzPLFGavJm7gncyRTdj7lM2rEpmFlqKlMokE4FUemho9XNLRlP8bzyV8q0jFUxUF58EE\n/+pBAMWUzzXE1u2ER1VdgjYsJ6mnGyaApG/YPlDSeXQYn3eeCv1PgF9JOiHF3IfyW2q7MSSc+x5g\nIYJkvHaKlWtFct+gFVIJPyNoCpfSMlGciZOJKc8daFGNLxnjs7Sc27n80Xak4qlXH9oC5xDX/mFT\n2Bk5PS1peUmLZWytjjuaYmoU9Eoab8HP01cvuSxJaOLsRlzAzgY2sf3XnHh9upkcCKzTy0rJ9qHp\n+96VZQVnEJyH/6O3i+K5xHve64W1FcWk41yFDtaDwGolY1RiYNiGvxFk+AJzgKzP2gKEKm6YEFs4\nAF+rKC8SeX02UAh+HuHychBPtHTe3kAY5D4N/DZ1JEqhpfs8U9JpxPnVutisuwv9bFc3cFGpanyv\nUMV+ni1YvUy3cxT8Gbha0s8Zbmk1ZqHT8UJTTHVBC2n8NwzdgMuSxuNFGXpSHXA/0YU6hdiOM7Cp\npE3T76j7wgNxs63EfVydFYP/Q/CxynIDnrL9vSrSqiBGO85XjGB/FbiRNDVaMsYL1cUHModjA9wD\nXC/pZymntwIziv+XQbygDQAquWE6aamlGFXiJsKOw+nnsnhc0vpE53lbottbIIe43Np9nstwo/Cs\nLnTFOF/Sm2z/ooJYhTbYvYkj+XdCMLMM1k0FcTtyqBlVdtdbcb2kdXognbfi7+lrIfpj2F0ZmmKq\nO3Yiuiy9kMZvpYvKbckP/xkp1rrpa1goarzwtBQ+dwGXSbqA4SvMnBvv1PRVTG+8mRCD+6CkM8ay\nB68hOYnzFLYJvcosVF60uJqp0UfJ9Hrsgj+mrwIF2X6gL2o1o4obZtXXjSJmFYr20wjT5uWBrxdc\nTYVQbOnirOg+awRBy7LxqoKkOQzZM31a0uPE/20vxPhOqvEfKxnjT3SmP+SgUj9PSTcwZK01W9Id\nxLW2eM9KT7XaPjzFXjrF6Ilz3E80BPQuqII03g+Sa1WQ9Cvbr5X0lV5b2ZIO7fZ8cVKUjHkR8Pbi\n/VdoV51JjJ7Psr3eGGL8ie6edaVkFiTdTZCyOyKnA6lQfT6NEMr742j/foQYlTqpJ/Lnl20fPOo/\nnqDoUrD0Ms23A8GxeTFDN8zDXdKAtk/k+IFVtO/0+a36M103JC1vO2fLtzVGZRpjqtjPU6NYmuV0\nqlIX9CSgWBQ/AOxl+/aysfqNpjPVAZKOIT5kPZPG6yyWxoAVFaPXO0o6lbaTyvaNYw2UUyyNASsD\nrcTDJ4FVbD+aVopjyass92g0PFjRlm0rdiS2k09XWDGcRkxMlbFzqJSgmcifk+ZGNgJ2qDqg7fPT\nj/8htsFy4/TjurGQh1s7PUhsn9QGDQlaLt+2rT8FyBI6rRIjnAP/Ae52eemXa9LirheroSy7nk5w\nxX6eRbGkzjpVpR0wEo4FDrI9PcXehqBA1KqM3wlNMdUZhRr1LHokjQ84Pk+YHL+IUGpuhQnfv1IY\nYfroP8R7+gO3GfqOgp8C1yXODkR7+5RExP9Nybw6ySz8h/BkK+MdWPlUSbpxHgUcpfCe+hyhuTPm\nm4kzHNnHgJsT8fMMhpM/6+axVIIqC5YkGdDlV/VuU1MBLkzd3lZF+6omuHLRFxHQCvFdYkr21vR4\nA+AW4HmSPmj74rEGsr2WhqyGPiOptNWQ7Y+MPfWxI20/rsHwCdQsc2KCGvB8ohmxUIr5z1RIftD2\nLSViLVkUUimny9L1f+DQbPONEQphuhc7w8h20CHpc1Vd7BXCossz/IL9D2AJYIpL2rdImkr4RokQ\nKZ05yktGinMBYW9QnJjbEF5naxPaSSeN8NJxgaRVCZ/E3Yhhh9NsH11zTid0OGxnaOwMMhTq0ccQ\nUiGLEUXsI2V4MZI+3uHwkoTg4PNsL1VFrr1CwxXtr3CGor1CSHHzHm62nWKuMohd/NSxP6LYVpK0\nHiEZcgTRXSpt35Li9Gw1VBUk7QscRPiE3kp4EF5ne5vMeMcQ28c/S493JK7hvwC+UmbhJ+kcYiin\nuD7vCUy1vVNObv1EU0x1gaTLiC2YRYCbiRHny213mjIbKUaVvKSOIpYFeukYpA98YdJ7Wct2Rdk4\nV7jN7Lc4Jun2siOz6lEAtCXOecD7bN+XHq8AfA94H3FTWb9szKog6Xpiwup0YnvvrrpyWRAhaSbR\nLTiDGHjYi7BV+kxmvKUJova+xP/p0SW7n62xptn+5mjHciHpatulid6SrrX9yipyGGSoiwdep+dG\nidXJauh0p8nNupC4g68gDJw3UniYftb27pnxbrC9aadjkm4pw9FLTYzDiQUAxCT94ZlbpH1Fs83X\nHcvYflihBH2C7UNHGEvthsp4SQxNcTyf2DP+dXq8LTGhk1VMSTqSOJkKy5FpabrmUxnhltdwNe6V\nGRrBLbVFpjB3PZQYw36aRAymnABogVWLQirhfmBt2/+S9ORIL+o30ir/HNtf7jFOZSbYkj7h0CQq\nuIPtsXJkFgYatu+UtLBDM+kESaW7Lun/4CDCR+9EQo+s14v+e4D2wum9HY7lohTJuAUXS3o70Z2Z\nzCvy30n6HnBqerwb8HuF+nvZ68YthI7WF9yj1VDiJX0cWNn2+xM9YJ3MRfBjiYeKQiDzdknt0+Jl\nMEfSNIa/Z3PStW5M+nySTkq7GHtNlOtNU0x1xyKSViS2X7JWqVTIS/LQGPH5wHq2702PVwS+k5kf\nhOTARk4eVJJOJMacc4qpjwNXKQxCRYhP7p/2ucsSt6fRowBoC65M79sZ6fHbgStSXv/OCVhF18xh\nmvwmoKdiiuD3jTiZQzlz6EKJOmtLdQJirqTFCI7YUcC9jD42PgySvgq8jSDMbuDebaN2B94FrJZ4\nawWWJojjVSG3EDqIeI+eltRqp1RaMiCdRx+1/fXMXPqJ9xLmugeSqAaEttaTlBgwSH/jOWV2NUbB\nCcQ5X3QH/0Zc23KKqXsVOnfnARdJ+hexgM3F7oTH32XEe3YlscBYlNimGwterpho3UfSj5m/CVGJ\n8nuVaLb5ukDSrgQZ+Crb+ytMRr9q++0ZsarkJd3Wui2VKv7ZuVtVqdu2TfEBTSvsy5zvzfcsQgdL\nwB0lSeetcaYTo9w9GyZLElFAzeNfAWflrqrbumbzbHNy3jNJnyOmXU5jONF7oC4YkhYn/NPOGPUf\nTyCki/Z9BF/qY4Tn4ndt39n1hcNjPENM/D7F8AIlq8hIOa0GHEksxgrMIc71MZ8TXegBAr5ve/ky\nufUDki7L5ehMFBSUj4pizbQ9VS1SCWW30EaI+1ri83+Be9BX7BWSPgp8iFgE3sPwYsouKWkzHmiK\nqXFEhbykbwNrESRvE3vwd9o+IDPe7kRnZDrxod0a+JTtU7u+cHiMys2JJR0PrANUIQBaKRTGrJtV\n0TVTTLm0I/uCkXgGazF8MifHAqlYUb+eWG2+AbjS9iBMWS0wSPy+goMyoyz/Sp0HCebBGbZNaXGy\nB7Ca7SMkvRhY0faMsrFSvP9H3MTbFxRlaBCVQdLptt+hEbTIMhdNRxPnZc/TsWkb+rXA1bY3jW9B\nHQAAIABJREFUkbQGYe0zZmFMSecS95Cf286VLmiN9xXbn5RUiEsPg+13ZMT8nu0P9ZrbeKAppjqg\nH5yRDryk3QnH9pyttGK1uVV6mDWV0xZvReKCLeB62/8o+frDE6essgkwjSAE6hKaVpKusr2lhhSN\n5z1FvpJxpV2zKpH4fdOIbeWbgc0JYmlZo92tiW2mNwMziI7e6rbnVptx/VAobR/G/B6cta9+U3f8\nawxtmWwFlFUt70de3yM6sq+x/ZJUwF/cTjwuEW96h8Mu+7mtCpJWtH2vRhBPdZ5oapXXxtcT1JP1\ngIuJ83Nvt8gIjCHG24mF+KuBS4jC6sLca5qkV9q+VtIbOj3v8j6QEwpNMdUBkt5i+zxJ7+n0vPNU\nrmcznJe0MHBT7lbaggpJiwxCAVNl1yyRSQ8iyKQf6IVMmlbSxWjzRolIerjt3UrE+BvwF2La8Vzb\ncyT9ydULoA4EFLYXH6PN5b4irl5P0ICqliupk1e9zdRg7JD0PGKxJOJ8fyAzzpKEddo7GbLvOqVM\nYdagIaCPhF9CZebErVgWKHgwy+QGSV2prxBTfaLHLksVUGdT4nkoU2QU3aT0czHVUWAGIaI31liV\nTbm14S/pa7H01QsKMmmh6tsLmfQx248pJnOeZfsOjWLz0AFnERfX3QiCcWF0PFnxH9t1i1eOhIFT\nLU94Mi0IDfOKvGe6v2RkpK3MLwEr2d5eoef0StvHV5Jt+XzaO9nzniKfaL82sUBZwfb6kl4G7Gj7\nixmxCv7VBR2OlYLtR4gdk5MlbQD8mJD1KKV/pSFvvpF+T2lvvomEppjqjHk3bEnH5HKR2nAkcFNq\nZ8/jJWXGOoogAv921H85fmhVL94P+EEPsVonqdpJ9Z2m1bqhdcptRcJ4tohRdsptHspsNY4Ba9je\nLXHXcIwpl/07C/wtTeacC1wi6SHibx4zbE+TdCAxrbQ7YY47RWGU+wsPsNloJqYrpvHOZniXsRa+\nThs6qZb/omwQVS+0+S1CJ+n5ie+0C/DZHuL9iFhUFFPTvyf4U7UUU7bnXc9UnR/ecYTg5w/S75gt\n6afE5NuYkIZAng0sl7ZWi+vEFGClnKQUAqK7Ep2pVQn/0/dnhCom9UScSzvn5NMJqQu3NfAX16zL\nNRKaYqozWm9klTiX2z5FIQJa8JI+WZaX1IL7qiykFFpQ88ElxvxbiwtJO/VYbHiEnzs9Hi2veVtT\nFV4Ui5X4J4CXMpzoncPxeELSEgyt8teg5aZeBraLC9hhqXBfBrgwI44JHbNfS1oUeCNRWH2XId2w\nyYLN0vepLcey7JSqhu2DE7elmEI9Nocf6ZDgOJqhUfpe8zpZ0iyCBC1gpx6vScvZPl3Sp1L8pySN\nSZNoHFBVV/bZtme0rZPKUhb2I2QaViIWikWwhykpjyNpb+Kc3oBYfH2e4N9m/b1uMTKW9JgzjI1b\nXn8+cIjt2xKf90ZCqmUNScfa/kZu7H6hKaY6oy9bGg5dqCq8/mZKOo04AVpX0rkK6Bcw1L1ZnBjJ\n/h1RKOSg1/dvWUk7E9sZy7ZMCIoetkcryKsVJxMr5x2ADxLiirmO8IcSBc+LJZ1M3DjfmxNIYY1y\nu+05ti9XqHFvDFyfmRu2nyR4FOelom9SwXa2IfF4wPZZxNZrr6haaPMPxE18EYhFWZkFWBseSd2H\nYkGxOeGdOZnwQFooFX/jLoSm2ZjhUL7/pqQDbB/TYz6vAb5BDA7UzkNtw2q2b0s/7w1cYnuvdD27\nmsh7oNAQ0DtA0lzgTuLmvUb6GYb2y2sljVc5FTJC/E2A/Wzvl/n6G22PmdfU4fWVj3KnuD3l1RZr\nlu2XS5pdfB4kXW771ZnxqiKT3kQobxcX7IWIqdFK/u7JCIXJ66EMyZZcTqhU134zr5IfmXhASxIk\n+16FNju6E+ReG9M15xhiW/82wt9zF9fkharhEi9fI4Q65yFn4arQKTyW4EY+BPwJ2NP2nzNzXJ+Y\n5mvtjP84J1YVSDy3AmcSun7z2nC2x2xOrxarHkm/Ao5zkupRSRuf8UJTTHXASOOwBTyAhpxVo2zh\noeF6LGsyIAVoGzH+INpU6HOm71Lc62xvnvgs3yJ4SWfaXiMj1hbAzbYfkbQnwdf7Zs7nrNOFprXg\nazA/JJ1F3MCLgZN3Axva7uqFOR5Q6JkNGj+yUp21lpiLEBOyAn6XOqK1YJQFXU8L1zQ9t5DtOT3E\nOJQwa1+P4NBtT4hL16YBJ6mbRY5tv6rL8+2xziMkH/4G/JDoVP07dcZnuqTH63ig2ebrgH4US1Xw\nklpiLU5MW7TzdbJO8LaCYyHiZl52y2qHnN89Dmglxh/X9rgXfDF1ND5OrKinEOP1OfgesKGkDQmC\n6g+JiZqcLtddCvXg76XH+wONcXJ3rOHhrgaHS7q5tmyGozJ+ZBpqqEpo869Uvw33CoIAvQiwiaTa\nOi253e9uUPjVnUCo2B+XunGH2L44I9wuwIaEvM7eaRry/6rLtjxcrfH1vsAXgO2A3WwXll+bE+/h\nwKHpTI0TWjo3w3hJORW2QmH2DkJU8QvEBfK3tqdl5tYqjvkU8GfCaiXLBqZBOWhIs+fzwD22j8/d\nkpT0fKJTVpCnLwUOdEnV7BRrbaK4axezrJ2YXSXSivpg21elx1sAX6v45pAFSd8EXkAF/EhVILTZ\nsvB6KRW6E0g6iaBU3MyQ1pc9QUxuxwIlHS6FqOWHCauyEzLP8xm2X5GGALYlCrTbcjs2iaO2tu0f\nJ8rBkj3w3xZINJ2pcYLtDVofF7ykzHBr2t5V0lttn5jGa7PVZV3tmH/PkLSr7TMkrWa7k9VKbVAf\n1PEJR/VPEdtLWyn0exbNyS8VTe/MeW0HnAF8n+joDcpkVT/wIeDE1GkUoQX33lozGsIUYC5h6VPA\nxOh5WWyWivabAGw/pDB4LoOis9tJZ62XlflUwrx9Mq/uC/7Qm4gi6hYpWwJlpkIC5Thiqu+/hKRP\n+aSkzxJDL2sQHfHFgZ8CW2bm1ldI+oDtY+vOox1NMdUBSuJnSl5D/fgdtm+UlGW9QDiWA/w7kRD/\nQbTHs6Bqx/yrwKeIG/lZlBDoHCcUWy4zK4y5G9Fl3Mf2P9KW8FdzAkl6EbHtuAVxc7sKmGb7bxnh\nnrL9vdH/2cSG7ZuJbdYp6fHDNac0DxVvN/UstFksvIoFT+tzCuubXNxGdOBKTbdNMMySdDGxK/Gp\nNJmWJXRqe//04/clXQhM6YGsvwsx8Xtjin1PcS4MKHIL0L6iKaY6Y0VJrwZ2lHQqbf95zhDzq4iX\nVODY1KL/LCG1sBTRMs5FlWP+VeBBhUbSapLmk5KwvWPZgJIWtt1zd8X2eel7Zer4qYA6izBBBXiA\nEETMwQnEqrK4se2Zjr0uI9Z5kvZPubRu5eSqxg8UJO1p+ydt5yZFsyB3y6oKpG7Bd0d6ryW9htAt\nKqOSX6XQZrHgGe1YVySisYmO128kzWD4Z630uV4lFFZPHyesnt6vHqyeCB7QRsBdtuem7bTcyeR5\naufFNKAyFdCBx21bUlFkPzsnp7b83kR0tkwQ4ytzGLDdiyB039AUU53xeeAQwiy2/YKaK+bXSnx+\niuAaZGnH2C6IhleQFLwV+jG5eF7i6UyzfTlwuaTLywTQCO7q5E3zvZkoNk8Cji6TRxfcKelMor0+\n5hHddrRc/Dsis9B7P/AB4LlEq/2FxPZazoVxedutBM0fKdTMc1B4Ux7ccixbNX4AUSjtdxpKqHu7\n6VaimH2M6Bj8k+gar0XckC8l7FfGDFcgtClpe2Kb6oWSvtXy1BTKC1BCyA4MMgqrp4I/14vVk4np\nux0IruuStOwEjAXqgwI6cLak7wDLKIQ89yWGYLIg6RvEZ/S0dOgTkl5ve8wDOu0LHOK9e4AozAaK\n+lGgIaB3gaTP2T6i7jzGAkl/sd1xYnAMr+15zF99kJOQtLztf6Z2uN2DjUmK8U5iJbgQcbE4teyW\nTupYAryN2Jb4SXq8O/Bn25/OyO1mYpLpeg+Zxt7azrMbY6xLCWuOwn5kd8JNPqcwWyAgaQvbV492\nrA6kTsgWhBXSo8Q28xW2H82MtzCwAsMHCsZMNFZMnG4MHE4sOgvMAabbfigzr/koFf2kWYwVkmba\nnqoKDJ0rGgCYxpAC+j0wTAH9ONvfLptXirs9wcsTcFEvnSRJvwHWt/1MerwIcEsZcryGD0UVeC7w\nBuAwJ82pgYLt5qvLF7AjsXr6GrBDD3GWJ3gwvyDZdAC/rjDPv/bw2h0IZfH1genESmzHAXjv1wdu\nAu4myK6ziJO017hbExeiRwhtoTUzYlwxlmNjjHV9+n5T+r4IMDsz1srE1u8/gfuJKbCVM2MtCnyU\nEOA7E/gIsGjdn4s+fM5uHMuxif4FHECs7m8HZhPdr9zP2VLp/HwpsHif/g+ycqv4PbsGWKLIj+gc\nz+jlbyzO8/TzLbn/lxX/nS8Ctk0/L05M8+XGOhd4YcvjlYDTK8rzuYN6bjbbfF0g6UiiY3ByOjQt\nrVhzDIr7zUvKbjF6aP//P8SYbTbSiO0xwEuIKZ+FgUecobJMqAUfZHt6ir0NQwrCZfNamNg+3Jsg\n6x9N/J9sRRS4a5cMubyk1W3fleKvRhTMObhc0qeBJSS9jtCGOi8nkKPLMGyrMW3z5dgvfI8oqL6b\nHr87HXtfTm6DBkmvJD5Ly7dtK0whPreTDdMIvk+20GbqMnyJOI/+QnR5X6QQufyMSwptSvoQ8Xlf\nXVIrgbqwDakbhzG/1VPuUEDPAwBpaOmvTlYykvYilMbvJjo2pfmMkvYhFkrLEMXiysQ5v13ZWAlL\nAXdIupr4W7cArpJ0OoDtd2TGxfa/epiA7Cuabb4uSCf3Rh5qVy5MrCpKq0mrAvuRUXhJa9t+Vtm8\nUty1iZvkCrbXl/QyojM1Zjfzllgzie20M4hx572Izs9nur6wc6z52uk9tNjvIrpux9u+pu25b7mk\npIGkNxKFXSGIuSphwVNaokJh+bIvLW124P9c0cmZuwVc5fs/iEhbttsQi5vvtzw1BzjP9h/qyKtf\nSEMdr3MPPmySvk4UOh9zUvBOk19fAx51Sa07hRzFc4AjCZ5qgTk5hUE/oOqsnvYgJnc3ITriuwCf\nddtU5CgxbgS2S0XF1sCpRMdxI+AlzlBAH4FmkO2aoNDRGhE518iW2K8h3rOB07priqkuSMXUNsVJ\nLem5wGWZxdRA8pJS3MsJkvEPWk6m22yvnxGr4Bi0Fo3XuISVQEuscwjy7Unp0J7AVNs7ZcTa0kmU\nseVYT7wYSc8C1k0P77D9eLd/XzJ2ZZwdSX+1/eKM190I7Gr7j+nx6sRndtDkKnqCpFVyz52JAFUo\ntCnpD8TCzW3HFybOgbU6v3JMsTckOsUAV9q+JTdWVVCHCblOx0rEW5ehAYBfufwAwLzFTCKN/9P2\nYelxlmddy73pJtsbp//Lm53B2awKIzQOnkvcN/eyfcf4Z9UdzTZfdxwJ3JRWdCK4NjlbfFCB/Ugf\nL/jPtj2jrXuau3qdqxACvFnSUYRuzJKjvGYk7EMQXQuBwivIb7F/i/k1q47pcKwMXs6Q/cWGKml/\nkS5a7yCm9y60fZukHYBPEzyNjXvIrRW5K6aDgempqydCCb0SM+0Bw1xJX2VwdNbmIZ1DXyTI5xcS\nFiIH2v5J1xcOR5VCm+7UMbX9tNJofQ4UFkgfYOhc/4mkY4vtrPGG+jM1RyoC7ki/Y1lJn7H9/0qE\nWFjSIqm7+FriPSuQez+/WtIngMUlbUuos5eeVtSQPuM/Gf65Kia6n18iXLs9mYEHbT9SNq/xQlNM\ndYHtUyRdBmxKfCA+afsfmbEq4yX1AQ9IWoOhvfxdyBfPezfBo/gIUSy+mNjTLw3HZFBPdhL94sVo\nBPsLQkF4rDieeH9mAN+SdDcxgn2I7XNL5jOHkbeAlygTqwVXEaP4hfnswK0GK8Kg6ay14vW2PyFp\nZ2Isf1diu3rMxZSrFdr8jaS92hcNCoPuXj4f7yMU2h9J8b4CXEsseOrAfgxNzc1i+NTcd8oEUngg\nfi7FOpfQgTuCuFae0uWlnXAKwbF8gCiwr0y/Y03yvRI/QRRldxC8uouAHC2nN6bvL8rMYx4mYqe4\n2eYbJ1TJS6oaafumIHY/BPwJ2KOHbcPFiO0vE/6DT1SVa0YufeHFSPotPdpfSLoNeJntZ9JK+AGC\nX5ZVsFcNdfAH7HRsoqMKPmMfc7vd9kslHUf4ZV7YA2+w5/9PSS8kukePEkWGicXmEsDOtu8pm1eK\neyuwqZMfaDofbqhzqynlcUCv3bG0s3E5URy+kego3U7wzkqf64ohnxUJWYWi+FwbWMoZgtJtsZcF\nVnIPWnwtsZ7LcAmO0v6gEwlNZ2r8cByJlwRge7bCU2/MxZT6YHOTyM9TbW8naUlgoYJYmhnvzUTR\n8kdiNbeapP1coQJuGXhIhPRHFa92qrC/eMJpuMH2Y5J+PwiFlKQXEFuPS0jamOFbHD2rIw8gigm0\ne9Pn9+9UsLquCOdJuoMoXvZXTICVMiBXhUKbqVjaLBGBX0p8Nn5p+1dl4nTACcD1iScJsBPRua0V\nto9RWHatx/At4DId6OcWvCbgIkn3EYVjFsfS9nUdjv0+JxbEfQXYmejU3wL8S9Iltg/u/soR4+1H\n3NceZmha0ZSfmJ5QaDpT4wRJN9jeVMPF30oRBhViaB8iipV3Qe82NynuFba3znlth1h3EHpcd6bH\nawAX2F63+yv7i7Ry+x+GOE5APi8mrTY3IrbosuwvJM0F7iweEtuGd0KWanxlkPQewuh3KsM9COcA\nP7KdY7I7sEg8tSuJLdeCz3i47fmsjOpA4uw8nHhJSwJLlym61SehzaqhMH/fkvj8X2H7pppTQiEe\nuQ1RTP0C2J5Q4R7z1JykW1KM4no9vfWxa55abCGe70tcHz9P6F/lTvPdCWxh+74K0xx4NJ2pLlAY\nzs4Hl1AMbkEVvKR+2NwAXCLpfwjeyDyCX+ZJfn9RSCXcRYhHlkZF5NsCZxBF6P8xxHHqBYdVEOMl\nFcSoHA7fwRMlvd12luXRRMIg8xkVPmkfJrR/PkDwbtahBEE4TcXdorBTWpW4Vvyx2FKrE5KWcnI2\nSIvB+RaErf+mBuxCXHdusr23pBWIa0gZLMNw3hUM/Z2DYM+0SOp47gp83rbVm5TTPQwO53Dc0HSm\nukBD45kiWryrERygMcvit8SqjJekim1uJHXyOrLt0ie5wjJhFeB04r3bFfgdSYCvTFej6Nwl8u1O\nBKF9eiZfZJbtl5d93YIMze+PBVFwzLJ983jnUzUkfb7L067yHMuFpNOIG/FeiWu5BHBtyY52R6FN\nYmuttNBmlUhbTDcDPyM+VwUHaHWisH0HYZNyZk35zbD9CoWn4bZEN++2nHvAoELSO4mF+lW2P5De\n+6/bfmvJOPunHzcC1iSEh1u79t/t9LrJgqYz1QXt5MfUht6vbJyqeUm2j5C0IyHVAKF9lWO8WcRb\nLfe1HbA4cB9QkHf/SeiDvIUorspsES2avr8JOMUhVJeb13npZD+H4Sd4qe7bKFNzdp7S+6Biavoq\n1NjfDNwAfFDSGbaPqi2zatBpzHpJQkD1ecTEVd1Yw/ZuknYHsP2oyp8EXyXkEVb3/EKbXyMmuGpB\n4oC+ibiubpFIy08SC7ALgPfUzCOcmUjZxxFF7X+Jrf1JA4fP3aktj+8CShVSCYWW3YPpq4wUwoRH\n05kqidxppop5Se02N7sDM51nc4PCkmA+lCRZVg5JXyY6Uo8Sf++ywPm2N8uIVVn3bUGBQmD27cUW\ni6SlCI++nYkuwnp15lclFEbY04hC6nTg6EGYPpJ0DTH9dbXtTRJV4BTbrygRo29CmwsSJK0KTLE9\ne5R/OqGQ7idHAnOJAnYjYtLwpxXFX3wQtpT7jaYz1QVt2xwLEQKPuXvBVfKS3sxwm5sTCUPgXEHR\nVtfyxYmL942U0EyS9AnbR0k6hg6dG5e0a0mvOUShN1OQbx8hb8VUdfetMqQb2om296w7lw5YGWiV\ntXgSWCV1RypTe68TqRNyELAHYfGxyaAQshMOZX5vuPeWjOH2Qiod7Eloc0GAWtTObf+5/dgkwfa2\nPyVpJ4Lf+lLgV4QeVmkofBqnEdeLGcCKko6w/c2qEh5ENMVUdyzd8vNTRNWeS8gtlKM/3HKsF/Lh\nskBRiC2TGSOSsA9ofaxQaj9phH8+EgpbhJld/1UJKLRm9ga2TBf9qwitrpxYA9l9Sze05SUt5hr1\nuEbAT4HrJP0sPX4LcEraqu5Zh6ZuKFTP30ZwGTeokeQ8ImxforD1Kbzhprm8N1y/hDYnLVSxAnqi\nesx2hkXXOKCoAwo6xQM9Ftkb2344cbEuI6aobwAmdTHVbPNNQCT+xJeJEdt5Njdp77uK+IsSJ36t\n02YKl/E5DKk97w48x3ZZ1WZSx6zAvO5bmRHnfkHSD4iu588Z3rUcs2davyBpKtENEUFQraxYrhuS\nniH4c0/R2f6iNv6bpHVt35F4mvPBJWRQ1CehzckMSdMYUkC/B4YpoB9n+9sZMU8mrtM50+B9Q1pU\nbE9MOU8lFucX5NApUrzfABsQ1+1jbU/XJDJIHwlNMdUFaVz0E1Tg2VV1Z0TSigzZ3FzfC0lT0nkM\n3UwWIjRVTrd9yMivGjHWJYQ57r/T4+cAp9ru6iQ+Qqz5TsCqTsqi++YSulD9gkLLZj442YDUibQN\nuQLDtbkG6mYwGaHwpfuAQs+sHc68BrUKbd7u3oU2K4WkLYG1bJ+Qrr1L2e7EdRzPnHpWQG+J9Wvi\nmj2D4YumQbgGPR/4l+2nUud52dwiO9FZDiK6ntsRAsCn2t6isoQHEE0x1QWSLiY4Tv9Di2eXM9TH\nB7wz0mqb8RRwt+2/ZcaaT4hULUKlJWP9CPi+k+KvpM2I6Z79u75wbLEHovvWikSC9qBsN0k6gODs\n3EesWmsVE20weZEWFFOBdWyvLWkl4Iy6bsCSNgX+WixS02L47cDdwGE5XNe26+w8OFwaaoOktwGX\n2J4j6RCiS/4lVyR/khZki9l+tIp4g4qmmOoC9dGza5A6I62QtBzhzp31wUh6LDsX3QtJqwDnZE5A\n/pYQKCw6ISsT3KxnKHlTb+u+LUwIZmZ136qGwq7iJEJCAsKjby/bt9eXFYWS8Wa2H6wzjwUZkj4M\nnNzW6d3dk0yzR9LNhEr7jR5yiJhdV+GeeGrbOeRYtiakAw4gJt1ekrsIVoh+FgM/MwZkYnS27ZdJ\nehUho/G/wMG2N8+MtwiwI/O7TUx0KZWuaAjo3dFPz665QK0jyQrDzC8TRPYjiBv6csBCibB6YUbY\nzwBXSSpWW1sTys05eOPo/2TM+FrLzz113/qAY4GDbE8HkLQNoWvzqjqTAv5KvhN9g2rwftvfKR7Y\nfkjS+4FJVUwRPpUuiM9pq6lOLNzSfdqN4P6cBZyVCr/SkPQOoli5jOjyHiPpYNckSNqCwhFiB+C7\nts+S9Nke4p1DLHhnUY3bxIRAU0x1xxdTB+njDHl2fSwn0Ei8pMxYVdncfBv4NEE4/DUxInudpHWB\nU4iR7FJwuNpvwtD00ccypo+K6ZcLqpp+sX1526rwD1XErQhLFoUUgO3LBuBmAmEFdJmkCxgudFo7\nMX4BwkKSVHSKiy2TmnPqB05PgxjLpmJxH8rbtlSJhSUtYvspgpLRuiDMvW9+hjA4vh/mcXIvJbTb\n6sS9kr5DLF6nSlqMuEflYnVPIoX4saIpprrA1Xp2VdkZuYAONjcEubQMFrF9MYCkLxTcpDRFVCpQ\nh+mjv6fvK0taucz0UcrhGUm3pNf2THge4FUhwF2SPseQHMWehN1Q3fhL+lqMyXkDnwi4iCg0vk+c\n8x8kY5Ez6LD9NUmvI6bl1iE84i6pMaVTgMslPUBMQV4JIGlN8ru1C7Vt6z1Ib0VLVXgHIYtwTOp8\nrkR4wObieknr2P5dNelNDDScqS6QtDaha7SCwxfrZcCOtr/YY9yeeEkd4m0C7Ge7lNWNWtTc1abs\n3v54DLH6MX1U2fSLwrn9de2rwkEY1008mMOBLdOhK4DDPVjikQ1qQOrQ7kd0RwRcDPyf7Um1fSLp\nK+2DPZ2OjXNOmwMrAhd7yDNwbWLKsNTiML32q8DLiEINYvtwdp1/Y4H0t65t+8eSnkd0y7MWsWkb\ndF1igf84Q4MrY1btn4hoiqkuSLyfg4EftJAibyuz9dSNl0SQjCtZZZYtftJrniaKFBGaM3OLp4DF\nbS860mvHA1VOv0i61S1ei+kmdYvb/BcbgKRv2D6wbWt6HgZtaKLBxEen61edBPR+IU3ObUlcY6+w\nfU7NKZH4UVsQPpBrK3TJTrO95SgvHSneOp2OT/ZOVbPN1x3Ptj2jbcvrqZIxKuclqSKbG9sLl33N\nWJCmQlZl+CRHaT2tDjynXqZfLlR4zbWuCn+ZGWuyo9hu/FrXf9Wg75C0BXAYsApxPhWr/EnhKSnp\nQ8D+wOqSWj3vlgaurier/sH22ZQzex8P7EKapASwfY/CCDsXbye2RWfYnhS2U2NBU0x1xwMKY9GC\n/LkLcG/JGJXxklpQpc1NpZB0ErAGcDNDkxymhM9fS6zKeE62D25bFR47CKvCQYTtWel7rfo3DQA4\nnhh6mayTUT8lFjVHMpynM8d5vqUNyuPxtknKZ/cY79/Ah4ATJP2dKKyusH1Rj3EHGs02XxdIWp0Y\nW38V8BBBCt7D9t0lYlTGS5oISNpQ61XBB6uC55QIoyvYvrrt+NbAPbb/2GuevULSFh3ym+/YeGOy\nd0UmAiRd70xbj4kIhRJ3q9tEo7bfZ0j6JKHh90bgi8C+wJm2v9Fj3OcA7yZEr5e3vUSvuQ4ymmJq\nBCROzS62T09j6gvZnpMRp3Jekiq0uakaks4APmq7bAevU6yeeU6Szgc+bXt22/GpwKG239Jrnr1i\nBL5I7YW2pDvo0BVxI+I5bpD0ZUJk9myGy1OUJkAPMiS9hRCLXAm4nyjgfzuZRuwlTbOkAoT0AAAg\nAElEQVT9zdGO1QFJ2wOvJ+5LF9nOpkBI+jYhbvofYqv2KsLybFJv+TXbfCMgjeZ/hFDJfmTUF4wc\npx+8pJMJm5sdaLG56cPvycFyhEv9DIZf/HNIy1XwnFZtL6RSPjMlrZqRU2WQ9Eqi67l8Gw9uCnED\nrRv/6eWi2qASFF2pqS3HDNS+cKoYXyS06S61vbGkbQlj88mE9wDthdN7Oxwbd6TzvKpzfQ1gUUJW\n5S7gzsleSEFTTI2GSxSmjacxfDS/7r3859k+Pq1qLif0UAaF33JYVYEq4jkt3uW5utvOiwFLEedh\nKw/uYYIUWgtatMKmp3HuSd0VGWTY7lXfbqLgSdsPSlpI0kK2p0v6St1JVQFJuwPvAlaT9POWp6YQ\nWlO1QtJbiYnzlYjrbLGdn0VCt729ghC8ESHpcY2kp2yvWVXOg4immOqOfdL3D7ccM1A3Z6SfNjc9\noQrScivPqXX6RdLWktYoyXO6QdL7bR/X9jv2JbavakNLIfyjgoeXtjKXsv1wjakd3fZ4sndFBhZp\nmvVLwErpJrUe8Erbx9ecWtX4t6SlCI21kyXdT/nJ6UHFNcTg0nIMP7fmAPN1zWvA0YSf6q1VBJO0\nHbAV8GrivnQtSfR0MqPhTE1ASNqB+HC+mCGbm8Nt/7zrC8cBkuYwpE20GNHufaTMKqdKnlO6GZ0D\nPMFQ8TQ15bazkyt8nZD0U2K79mkix2WA/7X91VoTa1A7JP0SOAH4jO0NFSayN002fbTES32UkHrZ\ngzgHTp5M/Lzib0wUkrUJYctf2n5ylJf2O6+rbW9RYbzjiaL4Stt3VRV30NEUU10gaa9Ox3M0kxZU\nSNoJeIXtT5d4zYjCqO2k9BIxtwWKmLfb/nXZGP2CpJttbyRpD+DlwCeBWXULFkqaRtzI5xDGy5sA\nhxRSHw36D0k32N5U0k0eEg6+2fZGdedWJSR9DDjDg2M+XjkkzSI6Ns8BrgNmAnNt71FzXt8AlgfO\nZfh2fvbiXNJziesFxLVs0rs5NNt83bFpy8+LE/u/N5KhmVQl1Cebm37A9rmSyvo8Vc5zchgJd7K6\nGQQsKmlRYCfg27afLDRfasY+tr8p6Q3A84G9ieKqKabGD48o7D0KDaDNyfeGG2RMAS6S9C/gVGI0\n/76ac6oasj03UQyOsX2UpJvqTgp4HvAM0DokZCCrmEocrG8T25sCNpN0wCDsnPQTTTHVBbYPaH0s\naRmG1KHrxHEkmxsA27PTVlHtxVQijBdYiNhSK1sYDCzPqU/4AfBn4BbgCkmrECT0ulGoyr4JOMH2\nLYlY2mD8cBBxU1tD0tVEB6G24YR+wfbhwOFpYbgbwSX8m+3tak6tSihN8O5BaDnBANyDbb+74pCH\nE7sR9wJIWpGYFGyKqQbzMBdYq+4kqMbmpl9o5TM9RRQJby0Z40DgnLTtNR/PqdcEBw22vwV8q+XQ\n3Wlbsm7MknQxsBrwKUlLEyvYBuME2zcqPCrXIYrb39XNsekz7gf+QUy5Pb/mXKrGgcCngHNs364Q\nha69Wy5pOWLYalWGW4B9IDPkwm06g/9gMKRe+oqmmOoCDTd6XQhYDzi9vozmoQqbm77A9t4VxLgP\neFUbz+mCQeI5VYmRJrYIK5E6sS8x3nxX2p54HrHV12Cc0NbpBVhb0n+AW53vUzlwUHj07UZ03s4E\n3m/7N/VmVS1apneXTI/vAj5ab1YA/IzgcF1FNZZFlyYJiJ+mx7sDv6og7kCjIaB3QVoRFngKuHsQ\nCJKqwOamDzl9q9vztgfhojGQGOSJLYUlxFoMV9q/or6MFixIuoAorIsOxjbEjW9t4Au2B4F20DMU\nSu+n2r657lz6hbTFdzwhfbKypA2B/WzvX3NelQ40JCrA7gzpA15B/N9O6mKjKabGiNQKfbDuD4Qq\nsrnpQ15PALcRnbu/M8S3AcD2iXXkNREwqBNbkt4HTCO0Ym4mFKqv9QDYFi0oSN3x9xVk7NTF/B7w\nPsI8tuPU60SBpCm2H07TX/NhAASSK4Ok6wm+289bzvMRJ5fHMa8jgem9TulK+qXt7StKa8JhoboT\nGERI2lzSZZLOlrSxpNuIQuE+SW+sMzfbzwAfST8/MgiFVMKKRLfsDYS55aLERePEppAaFYM6sTWN\nmGi9Oylxb8zg2BYtKFi1bartfmDtVGRMBu5UsRU0i5AKmNXyNbOupPoF239tO1TFtlqv+CBh3fVf\nSf+S9FCaqiyLF1Sd2ERCw5nqjG8DnyaE434NbG/7OknrEj5xF9aZHANoc5PE9b4PfF/SC4k27+2S\nPjlZtiL6iEGd2HrM9mOSkPQs23dIWqfupBYwXJlEbM9Ij3dJx5YE/l1fWtXA9g5pW+jVtv9Sdz59\nxl8lvQqwpMUIvtRva84JQpm9CiwjaUQP1skujdBs83VA6xaLpN/afknLc/O2YuqCpD91OGzbddvc\nFL5uuwOvI1aXR082ImmVSNu2mwMzGLCJLUnnEITzAwkLmYeARW2/qdbEFiCkQqPVn/Iq22fWm1X1\nkDTL9svrzqOfSFSRbwLbEf+XFwPTBkHlXdI7gdVtf0nSiwgNw1IyNJIeBC6gjeKRYNsdRbAnC5pi\nqgMk3Wh7k/afOz1uEJB0OLADsdI6FbjQ9qDINQw0JF1r+5V159ENaRhjGeL/9Ym681lQIWlLYHfb\nHx71H08gSPoO8CPbN9SdSz8gaWHgo7a/Xncu7ZD0bYKWsbXtlyT+2kW2Nx3lpe1xFuh7Y1NMdYCk\np4ntMxGK23OLp4DFbS9aV24wmDY3kp4B7iL8tWBIUqJwIK/VGmWQkQrR2cDZdQ84pHy+AVwNXGP7\nnrrzWdAhaSOi27sbMbl7tu1j6s2qWkj6DdGZ/TND195Jdd2QdJntberOox1FEdQ2AHOL7Q1Lxql9\n16ZONJypDrA96AJjg2hzs1qNv3ui4yBgSeBpSY8ydCMZszl0xbiTEEf9ahKGvYZUXAG3pCGIBn2E\nwjLqnUQR9SDBj1QaBJiMWBCmwK5OXaB2ruuN9aUEwJOJblAMwBT2MmXx3iqTmmhoOlOTAEo2N7ZH\nJP81aJCDZAWxBaFptiPw/BqLvAUGqdN7JbCv7TvTsbsGgRdZJSQtTkyTrQncChw/WekBkjqpnbsu\nqRFJi9h+Ku107Ey4TPwQeAdwuO1T68hroqLpTE0ODIrNTYNMpCmYrdPDy2yfX3M+AjYgiqgtCPX/\nOxkMb8oFAW8nOlPTJV1I8BAnoy/iiYTEw5VEd2o9QpJj0mEAu4ozgE1s/1jSLIaI8bvavq3e1CYe\nms7UBMRINje2D6kvqwa5SOrPmwInp0O7A7Pq+v+UdAkwhRDqvA64zvYgjHAvcEgSCDsRn4nXEMXH\nOb0KLA4KJN1aKP0n5f8Zk5XEnHYQDmVo0XQ5oWJfi6ZcPzhOiWj/Q9vvqTLuREBTTE1ADLDNzcLA\nibb3rDuXiQRJs4GNCi5Seh9vqot8K+kHwIZEx/M64FpC+fyBOvJpEEhTVrsCu00WFfoFaVpa0lmE\n+HMhYvxuYEPb7f6L45XP34D/Hel52yM+N0rci4E3D4K8y3ii2eabgHAYZgJDNjc1pjMPtp+WtLyk\nxZrx+dJYFihEV5epMxHb+0FYfRAaWK8CPixpeeC2BXHVOQhIorw/SF+TBRtKejj9LGCJ9LjuIYx+\nYA3bb295fLikOr0IFwaWovrt47sIYdmfMZxo39W/daKjKaYmEJLNyJeJm+4RBH9lOWAhSXvZrluZ\nHWK0+WqFa3jriZS1yllAcCRwUyKoitgG+FS9KQHwONGdejT9/CJgsVozajCpMAEmp6vEo5K2tH0V\ngKQtGJKSqQP32v5CH+L+E7gEeHb6WiDQbPNNIEiayZDNzbG02dwMgsaHpEM7Hbd9+HjnMpGQpuY2\nJYqp623/o8Zcvk50o9YieFPXFF+2J7yFSYMGdUDShoR8TdF5fgh4j+3ZNeXTV10oSUsA2K6zYBw3\nNMXUBMKg29y0QtLSRJv+v3XnMqiQ9BHb304/v9T27XXnBCDpo0TxdJPtQTBibdBg0iBtn2P74dH+\nbZ/zeK774OcqaT2CF7YisTj8G/DeyT7EslDdCTQohVYhtfZqfyCqYknrS7qJIFreLmmWpJfWndeA\nYp+WnwdGcsD2t2zPbAqp+iBpjqSHO3zNaeEYNZgAkPSjlp/fY/vhugspmMfB6weOBT5t+0W2Xwh8\nBjiuT79rYNBwpiYWCrJmK1GT9Hjx+tIahmOBg2xPB5C0DXEivarOpCYAJqOGUINM2F667hwaVIZW\nW5ZpDE3zTVYsbfuS4oHtSyUdXWdC44GmmJpAmCBkzSWLQgrA9mVJK6fB/FhW0s5Eh3iKpGEj0rbP\nrietBoMGSc+nZcFk+y81ptOgHAZi12Ac8WdJn2Ko274ncHeN+YwLGs5Ug0oh6RzCJ7D1RJpqe6f6\nshpMSDqhy9O2vU+X58cFSfNqBVoWXs2NfPyQlPGPBlYC7gdWAX5ru9k6nyCQdD9DCva7pZ/nwfZH\n68irX0jefkcAW6ZDVwCH2h4ICZ9+oSmmGlQKSc8BDmf4iXS47Yfqy6pBDiQdQCg238cQX891iYku\niJB0C6F8fqntjSVtC+xu+wM1p9ZgjJDUVZfN9mTf9lsg0BRTDRo06AhJdwKbTfYV5SBD0kzbU1NR\ntbHtZyTNsP2KunNr0KDBEBrOVIMGDUbCX4FafMMazMO/JS1FdHhPTltGT9WcU4MGDdrQdKYaNGjQ\nEZKOB9YBLiAU0IFGzX48kYY3HiWGFPYgBB9PbrqFDRoMFprOVINK0S8huAUJkqYSVg/31JzKX9LX\nYjQ2MuOORP7/me3tCM5aw61pMPBIfrH7AKsyfHBlUvP8mmKqQdW4Ppl3ngD80k3rMwcHAC+T9Hvb\nu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"text/plain": [ "<matplotlib.figure.Figure at 0x7f09b86307f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbi_main_maintenance['Percent Deck Area'].plot.bar(figsize = (10,8))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Structure Material and Type of Construction" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Seconds: 2.0784292221069336\n" ] } ], "source": [ "pipelineSM = [{\"$match\":{\"year\":2016}},\n", " {\"$group\":{\"_id\":\"$structureTypeMain\", \"count\":{\"$sum\":1}}}]\n", "startTime = time.time()\n", "SM=collection.aggregate(pipelineSM)\n", "print(\"Seconds: \",(time.time() - startTime))\n", "SM_df = pd.DataFrame(list(SM))\n", "structMat_Type= SM_df.sort_values('count',ascending = 0).head(20)\n", "structMat_Type['kindOfMaterialDesign'] = structMat_Type._id.apply(lambda x: x.get('kindOfMaterialDesign'))\n", "structMat_Type['typeOfDesignConstruction'] = structMat_Type._id.apply(lambda x: x.get('typeOfDesignConstruction'))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Material</th>\n", " <th>Type of Construction</th>\n", " <th>count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>58</th>\n", " <td>Steel</td>\n", " <td>Stringer / Multi-beam or Girder</td>\n", " <td>102750</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>Concrete</td>\n", " <td>Culvert</td>\n", " <td>93248</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td>Prestressed concrete</td>\n", " <td>Stringer / Multi-beam or Girder</td>\n", " <td>57734</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>Steel Continuous</td>\n", " <td>Stringer / Multi-beam or Girder</td>\n", " <td>53597</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td>Prestressed concrete</td>\n", " <td>Box Beam or Girder - Multiple</td>\n", " <td>49386</td>\n", " </tr>\n", " <tr>\n", " <th>67</th>\n", " <td>Concrete Continuous</td>\n", " <td>Slab</td>\n", " <td>35238</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>Concrete</td>\n", " <td>Slab</td>\n", " <td>33991</td>\n", " </tr>\n", " <tr>\n", " <th>106</th>\n", " <td>Concrete Continuous</td>\n", " <td>Culvert</td>\n", " <td>29097</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td>Concrete</td>\n", " <td>Tee Beam</td>\n", " <td>20432</td>\n", " </tr>\n", " <tr>\n", " <th>47</th>\n", " <td>Prestressed concrete continuous</td>\n", " <td>Stringer / Multi-beam or Girder</td>\n", " <td>17030</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td>Wood or Timber</td>\n", " <td>Stringer / Multi-beam or Girder</td>\n", " <td>15330</td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td>Steel</td>\n", " <td>Culvert</td>\n", " <td>15325</td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td>Concrete</td>\n", " <td>Channel Beam</td>\n", " <td>12958</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>Prestressed concrete</td>\n", " <td>Slab</td>\n", " <td>12217</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td>Concrete</td>\n", " <td>Stringer / Multi-beam or Girder</td>\n", " <td>9567</td>\n", " </tr>\n", " <tr>\n", " <th>66</th>\n", " <td>Steel</td>\n", " <td>Truss - Thru</td>\n", " <td>8993</td>\n", " </tr>\n", " <tr>\n", " <th>65</th>\n", " <td>Prestressed concrete</td>\n", " <td>Tee Beam</td>\n", " <td>8436</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td>Concrete Continuous</td>\n", " <td>Tee Beam</td>\n", " <td>6501</td>\n", " </tr>\n", " <tr>\n", " <th>109</th>\n", " <td>Prestressed concrete</td>\n", " <td>Box Beam or Girder - Single or Spread</td>\n", " <td>5888</td>\n", " </tr>\n", " <tr>\n", " <th>122</th>\n", " <td>Concrete</td>\n", " <td>Frame</td>\n", " <td>5610</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Material Type of Construction \\\n", "58 Steel Stringer / Multi-beam or Girder \n", "54 Concrete Culvert \n", "73 Prestressed concrete Stringer / Multi-beam or Girder \n", "52 Steel Continuous Stringer / Multi-beam or Girder \n", "82 Prestressed concrete Box Beam or Girder - Multiple \n", "67 Concrete Continuous Slab \n", "100 Concrete Slab \n", "106 Concrete Continuous Culvert \n", "97 Concrete Tee Beam \n", "47 Prestressed concrete continuous Stringer / Multi-beam or Girder \n", "88 Wood or Timber Stringer / Multi-beam or Girder \n", "87 Steel Culvert \n", "89 Concrete Channel Beam \n", "98 Prestressed concrete Slab \n", "80 Concrete Stringer / Multi-beam or Girder \n", "66 Steel Truss - Thru \n", "65 Prestressed concrete Tee Beam \n", "70 Concrete Continuous Tee Beam \n", "109 Prestressed concrete Box Beam or Girder - Single or Spread \n", "122 Concrete Frame \n", "\n", " count \n", "58 102750 \n", "54 93248 \n", "73 57734 \n", "52 53597 \n", "82 49386 \n", "67 35238 \n", "100 33991 \n", "106 29097 \n", "97 20432 \n", "47 17030 \n", "88 15330 \n", "87 15325 \n", "89 12958 \n", "98 12217 \n", "80 9567 \n", "66 8993 \n", "65 8436 \n", "70 6501 \n", "109 5888 \n", "122 5610 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Material ={ -1 : 'NA',\n", " 1 : 'Concrete',\n", " 2 : 'Concrete Continuous',\n", " 3 : 'Steel',\n", " 4 : 'Steel Continuous',\n", " 5 : 'Prestressed concrete',\n", " 6 : 'Prestressed concrete continuous',\n", " 7 : 'Wood or Timber',\n", " 8 : 'Masonry',\n", " 9 : 'Aluminium, Wrought, Iron, Cast Iron',\n", " 0 : 'Other'\n", "}\n", "typeOfDesign = { -1 : 'NA',\n", " 1 : 'Slab',\n", " 2 : 'Stringer / Multi-beam or Girder',\n", " 3 : 'Gider and Floorbeam System',\n", " 4 : 'Tee Beam',\n", " 5 : 'Box Beam or Girder - Multiple',\n", " 6 : 'Box Beam or Girder - Single or Spread ',\n", " 7 : 'Frame',\n", " 8 : 'Orthotropic',\n", " 9 : 'Truss - Deck',\n", " 10 : 'Truss - Thru',\n", " 11 : 'Arch - Deck', \n", " 13 : 'Suspension',\n", " 14 : 'Stayed Girder',\n", " 15 : 'Movable - Lift',\n", " 16 : 'Movable - Bascule',\n", " 17 : 'Movabale - swing',\n", " 18 : 'Tunnel',\n", " 19 : 'Culvert',\n", " 20 : 'Mixed Type',\n", " 21 : 'Segmental Box Girder',\n", " 22 : 'Channel Beam',\n", " 0 : 'Other'\n", " }\n", "structMat_Type['Material'] = structMat_Type['kindOfMaterialDesign'].map(Material)\n", "structMat_Type['Type of Construction'] = structMat_Type['typeOfDesignConstruction'].map(typeOfDesign)\n", "cols = ['Material','Type of Construction','count']\n", "structMat_Type[cols]\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "cols = ['Material','Type of Construction','count']\n", "structMat_Type[cols]\n", "structMat_Type['Material / Type of Construction'] = structMat_Type['Material'].astype(str)+\" - \"+structMat_Type['Type of Construction']" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b88bee80>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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vAi+tlw+gPOa/7qpgRPwlsCswIyKG3yzWoayU0bmIOJyyUcbPKWGB+m/Xge3c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\nXgak3dRKx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"text/plain": [ "<matplotlib.figure.Figure at 0x7f09b83d7dd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cols = ['Material / Type of Construction', 'count']\n", "structMat_Type = structMat_Type[cols]\n", "structMat_Type = structMat_Type.set_index('Material / Type of Construction')\n", "structMat_Type.plot(kind = 'bar', figsize=(11,9))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scour Critical Bridges" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Seconds : 0.15120792388916016\n" ] } ], "source": [ "pipeline = [{\"$match\": {\"year\":2016}},\n", " {\"$project\":{\"_id\":0, \"stateCode\":1,\"scourCriticalBridges\":1}}]\n", "startTime = time.time()\n", "scour = collection.aggregate(pipeline)\n", "print(\"Seconds : \", (time.time() - startTime))\n", "scour_df = pd.DataFrame(list(scour))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "scour_df['scourCriticalStatus'] = None\n", "for index, row in scour_df.iterrows():\n", " if row.scourCriticalBridges in [\"0\",\"1\",\"2\",\"3\"]:\n", " scour_df.loc[index, 'scourCriticalStatus'] = \"True\"\n", " else:\n", " scour_df.loc[index, 'scourCriticalStatus'] = \"False\"" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b5e9dfd0>" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f09b5ea1400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scour_df.groupby(['scourCriticalStatus'])['scourCriticalStatus'].count().plot.bar()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-2.0
miklevin/pipulate
examples/cleanup/get_search_results.ipynb
1
9405
{ "cells": [ { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Use proxies: False\n", "Keyword: Dr Pepper\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Re-run this freshen your external functions.\n", "from imp import reload\n", "import functions; reload(functions)\n", "\n", "# Edit False to True and run this to update your goodproxies.txt\n", "update_proxies = False\n", "if update_proxies: \n", " import pipulate.update_proxies as up\n", " up.Main()\n", " \n", "# Send a keyword to the serp function. Always use a list (as if a spreadsheet row).\n", "alist = functions.serp(['Dr Pepper'])\n", "alist" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "ename": "APIError", "evalue": "{\n \"error\": {\n \"errors\": [\n {\n \"domain\": \"global\",\n \"reason\": \"insufficientPermissions\",\n \"message\": \"Insufficient Permission: Request had insufficient authentication scopes.\"\n }\n ],\n \"code\": 403,\n \"message\": \"Insufficient Permission: Request had insufficient authentication scopes.\"\n }\n}\n", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAPIError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-24-cbcc92235756>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0msheet_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Search Results'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0msheet\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msheet_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mtab\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msheet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msheet1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pipulate\\__init__.py\u001b[0m in \u001b[0;36mname\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m 145\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 146\u001b[0m \u001b[1;34m\"\"\"Return instance of GSheet by document name\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0moauth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 148\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\gspread\\client.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, title)\u001b[0m\n\u001b[0;32m 120\u001b[0m properties = finditem(\n\u001b[0;32m 121\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'name'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlist_spreadsheet_files\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 123\u001b[0m )\n\u001b[0;32m 124\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\gspread\\client.py\u001b[0m in \u001b[0;36mlist_spreadsheet_files\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 94\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pageToken'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpage_token\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'get'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 97\u001b[0m \u001b[0mfiles\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'files'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 98\u001b[0m \u001b[0mpage_token\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'nextPageToken'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\gspread\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, endpoint, params, data, json, files, headers)\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mAPIError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 80\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 81\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mlist_spreadsheet_files\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAPIError\u001b[0m: {\n \"error\": {\n \"errors\": [\n {\n \"domain\": \"global\",\n \"reason\": \"insufficientPermissions\",\n \"message\": \"Insufficient Permission: Request had insufficient authentication scopes.\"\n }\n ],\n \"code\": 403,\n \"message\": \"Insufficient Permission: Request had insufficient authentication scopes.\"\n }\n}\n" ] } ], "source": [ "import pipulate as gs\n", "import pandas as pd\n", "sheet_name = 'Search Results'\n", "sheet = gs.name(sheet_name)\n", "tab = sheet.sheet1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# This applys the serp function with step-by-stride for long-list sanity.\n", "row1 = 1\n", "for i, x in enumerate(tab.col_values(2)):\n", " if not x:\n", " row1 = i+1\n", " break\n", "\n", "rows = (row1, 100)\n", "cols = ('a', 'b')\n", "stride = 1\n", "sheet =gs.name(sheet_name)\n", "tab = sheet.sheet1\n", "cl, df = gs.pipulate(tab, rows, cols)\n", "steps = rows[1] - rows[0] + 1\n", "for i in range(steps):\n", " row = i % stride\n", " if not row:\n", " r1 = rows[0] + i\n", " r2 = r1 + stride - 1\n", " rtup = (r1, r2)\n", " print('Cells %s to %s:' % rtup)\n", " cl, df = gs.pipulate(tab, rtup, cols)\n", " df['B'] = df.apply(kung.serp, axis=1)\n", " gs.populate(tab, cl, df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Now we extract the position each keyword was in for the desired URL\n", "steps = rows[1] - rows[0] + 1\n", "for i in range(steps):\n", " row = i % stride\n", " if not row:\n", " r1 = rows[0] + i\n", " r2 = r1 + stride - 1\n", " rtup = (r1, r2)\n", " print('Cells %s to %s:' % rtup)\n", " cl, df = gs.pipulate(tab, rtup, cols)\n", " df['D'] = df.apply(kung.extract_pos, axis=1)\n", " gs.populate(tab, cl, df)" ] } ], "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": 2 }
mit
XENON1T/processing
RunsDB/CorrectionsDB_change_nn_file.ipynb
1
9045
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "import glob\n", "import socket\n", "import pymongo\n", "import getpass\n", "import datetime\n", "from pprint import pprint\n", "\n", "# Connect to database \n", "uri = 'mongodb://corrections:%[email protected]:27017/run'\n", "uri = uri % os.environ.get('MONGO_CORRECTIONS_PASSWORD')\n", "client = pymongo.MongoClient(uri)\n", "db = client['run']\n", "collection = db['neural_network']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Sort by latest creation time\n", "cursor = collection.find().sort(\"calculation_time\", -1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bumping to version: 1.6\n" ] } ], "source": [ "# Get and increment version number\n", "major_version_bump = False\n", "minor_version_bump = True\n", "\n", "major = 0\n", "minor = 0\n", "if cursor.count() != 0:\n", " d = cursor[0]\n", " major = int(d['version'].split('.')[0])\n", " minor = int(d['version'].split('.')[1])\n", "if major_version_bump:\n", " major += 1\n", " minor = 0\n", "if minor_version_bump:\n", " minor += 1\n", "version = (\"%i.%i\"%(major, minor))\n", "print ('Bumping to version:', version)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'calculation_time': datetime.datetime(2018, 7, 19, 16, 17, 39, 735797),\n", " 'correction': [{'field': 'neural_net_file',\n", " 'max': 6386,\n", " 'min': 0,\n", " 'value': 'XENON1T_NN_v8_mc_v030_SR0_n15.npz'},\n", " {'field': 'neural_net_file',\n", " 'max': 18836,\n", " 'min': 6386,\n", " 'value': 'XENON1T_NN_v8_mc_v030_SR1_n15.npz'},\n", " {'field': 'neural_net_file',\n", " 'max': inf,\n", " 'min': 18836,\n", " 'value': 'XENON1T_NN_v8_mc_v030_20180613_postSR1.npz'}],\n", " 'user': 'pdeperio',\n", " 'version': '1.6'}\n" ] } ], "source": [ "# Define document to insert\n", "doc = {\n", " 'correction': [\n", " {'field': 'neural_net_file', 'value': 'XENON1T_NN_v8_mc_v030_SR0_n15.npz', 'min': 0, 'max': 6386},\n", " {'field': 'neural_net_file', 'value': 'XENON1T_NN_v8_mc_v030_SR1_n15.npz', 'min': 6386, 'max': 18836},\n", " {'field': 'neural_net_file', 'value': 'XENON1T_NN_v8_mc_v030_20180613_postSR1.npz', 'min': 18836, 'max': float(\"inf\")\n", "}\n", " ],\n", " 'version': version,\n", " 'calculation_time': datetime.datetime.utcnow(),\n", " 'user': getpass.getuser()\n", "}\n", "pprint(doc)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'_id': ObjectId('5b50b9983c1233a82b633dbe'),\n", " 'calculation_time': datetime.datetime(2018, 7, 19, 16, 17, 21, 597000),\n", " 'correction': [{'field': 'neural_net_file',\n", " 'max': 6386,\n", " 'min': 0,\n", " 'value': 'XENON1T_NN_v8_mc_v030_SR0_n15.npz'},\n", " {'field': 'neural_net_file',\n", " 'max': 18836,\n", " 'min': 6386,\n", " 'value': 'XENON1T_NN_v8_mc_v030_SR1_n15.npz'},\n", " {'field': 'neural_net_file',\n", " 'max': inf,\n", " 'min': 18836,\n", " 'value': 'XENON1T_NN_v8_mc_v030_20180613_postSR1.npz'}],\n", " 'user': 'pdeperio',\n", " 'version': '1.5'}\n", "{'_id': ObjectId('59cbb5a1620c7204e6c40ef3'),\n", " 'calculation_time': datetime.datetime(2017, 9, 27, 16, 28, 49, 864000),\n", " 'correction': [{'field': 'neural_net_file',\n", " 'max': 6386,\n", " 'min': 0,\n", " 'value': 'NN_XENON1T_v8_mc_v030_sr0_n15.npz'},\n", " {'field': 'neural_net_file',\n", " 'max': inf,\n", " 'min': 6386,\n", " 'value': 'NN_XENON1T_v8_mc_v030_sr1_n15.npz'}],\n", " 'user': 'coderre',\n", " 'version': '1.4'}\n", "{'_id': ObjectId('595cbd98b4c97a5f67c74de2'),\n", " 'calculation_time': datetime.datetime(2017, 7, 5, 10, 21, 12, 777000),\n", " 'correction': [{'field': 'neural_net_file',\n", " 'max': 6386.0,\n", " 'min': 0.0,\n", " 'value': 'NN_XENON1T_v5_n15.npz'},\n", " {'field': 'neural_net_file',\n", " 'max': inf,\n", " 'min': 6386.0,\n", " 'value': 'NN_XENON1T_v8_mc_v030_sr1_n15.npz'}],\n", " 'user': 'coderre',\n", " 'version': '1.1'}\n", "{'_id': ObjectId('59149067bfd841b0202f652b'),\n", " 'calculation_time': datetime.datetime(2017, 5, 11, 16, 25, 11, 303000),\n", " 'correction': [{'field': 'neural_net_file',\n", " 'max': 6386.0,\n", " 'min': 0.0,\n", " 'value': 'NN_XENON1T_v5_n15.npz'},\n", " {'field': 'neural_net_file',\n", " 'max': inf,\n", " 'min': 6386.0,\n", " 'value': 'NN_XENON1T_v7_n15.npz'}],\n", " 'user': 'coderre',\n", " 'version': '1.3'}\n", "{'_id': ObjectId('59147eab93d9823c50baa9a1'),\n", " 'calculation_time': datetime.datetime(2017, 5, 11, 15, 9, 31, 890000),\n", " 'correction': [{'field': 'neural_net_file',\n", " 'max': 6386.0,\n", " 'min': 0.0,\n", " 'value': 'NN_XENON1T_v5_n15.npz'},\n", " {'field': 'neural_net_file',\n", " 'max': inf,\n", " 'min': 6386.0,\n", " 'value': 'NN_XENON1T_v7_n15.npz'}],\n", " 'user': 'coderre',\n", " 'version': '1.1'}\n", "{'_id': ObjectId('58f771b7d22b32a23ca5e55a'),\n", " 'calculation_time': datetime.datetime(2017, 4, 19, 14, 18, 31, 271000),\n", " 'correction': [{'field': 'neural_net_file',\n", " 'max': 6386.0,\n", " 'min': 0.0,\n", " 'value': 'NN_XENON1T_v5_n15.npz'},\n", " {'field': 'neural_net_file',\n", " 'max': inf,\n", " 'min': 6386.0,\n", " 'value': 'NN_XENON1T_v7_n15.npz'}],\n", " 'user': 'coderre',\n", " 'version': '1.0'}\n" ] } ], "source": [ "# Check current docs\n", "for document in cursor:\n", " pprint(document)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/project/lgrandi/anaconda3/envs/pax_head/lib/python3.4/site-packages/ipykernel/__main__.py:2: DeprecationWarning: insert is deprecated. Use insert_one or insert_many instead.\n", " from ipykernel import kernelapp as app\n" ] }, { "data": { "text/plain": [ "ObjectId('5b50b9983c1233a82b633dbe')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Insert the doc into DB\n", "collection.insert(doc)" ] }, { "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": "3.4.4" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0