Update Notebooks
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c900add842
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Notebooks/APs.ipynb
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230
Notebooks/APs.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"from pathlib import Path\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"triskele_path = Path('../triskele/python/')\n",
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"sys.path.append(str(triskele_path.resolve()))\n",
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"import triskele"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Specific Utils\n",
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"\n",
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"def DFC_filter(raster):\n",
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" raster[raster > 1e4] = raster[raster < 1e4].max()\n",
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"\n",
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"def show(im, im_size=1, save=None):\n",
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" plt.figure(figsize=(16*im_size,3*im_size))\n",
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" plt.imshow(im)\n",
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" plt.colorbar()\n",
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" \n",
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" if save is not None:\n",
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" plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
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" \n",
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" plt.show()\n",
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"\n",
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"def mshow(Xs, titles=None, im_size=1, save=None):\n",
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" s = len(Xs)\n",
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"\n",
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" plt.figure(figsize=(16*im_size,3*im_size*s))\n",
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"\n",
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" for i in range(s):\n",
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" plt.subplot(s,1,i+1)\n",
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" plt.imshow(Xs[i])\n",
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" \n",
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" if titles is not None:\n",
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" plt.title(titles[i])\n",
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" \n",
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" plt.colorbar()\n",
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" \n",
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" if save is not None:\n",
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" plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
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" \n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"layers_files = [\n",
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" '../Data/phase1_rasters/DEM+B_C123/UH17_GEM051_TR.tif',\n",
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" '../Data/phase1_rasters/DEM_C123_3msr/UH17_GEG051_TR.tif',\n",
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" '../Data/phase1_rasters/DEM_C123_TLI/UH17_GEG05_TR.tif',\n",
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" '../Data/phase1_rasters/DSM_C12/UH17c_GEF051_TR.tif',\n",
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" '../Data/phase1_rasters/Intensity_C1/UH17_GI1F051_TR.tif',\n",
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" '../Data/phase1_rasters/Intensity_C2/UH17_GI2F051_TR.tif',\n",
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" '../Data/phase1_rasters/Intensity_C3/UH17_GI3F051_TR.tif',\n",
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" #'../Data/ground_truth/2018_IEEE_GRSS_DFC_GT_TR.tif'\n",
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"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Define dataset dependent raster filtering"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def DFC_filter(raster):\n",
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" ## Remove extrem values\n",
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" #raster[raster == raster.max()] = raster[raster != raster.max()].max()\n",
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" raster[raster > 1e4] = raster[raster < 1e4].max()\n",
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" #raster[raster == np.finfo(raster.dtype).max] = raster[raster != raster.max()].max()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load rasters data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"layers = list()\n",
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"\n",
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"for file in layers_files:\n",
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" print('Loading {}'.format(file))\n",
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" layer = triskele.read(file)\n",
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" DFC_filter(layer)\n",
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" layers.append(layer)\n",
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"\n",
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"layers_stack = np.stack(layers, axis=2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Display rasters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in range(layers_stack.shape[2]):\n",
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" plt.figure(figsize=(16*2,3*2))\n",
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" plt.imshow(layers_stack[:,:,i])\n",
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" plt.colorbar()\n",
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" plt.title(layers_files[i])\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Attributes filter with TRISKELE !"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"area = np.array([10, 100, 1e3, 1e4, 1e5])\n",
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"sd = np.array([0.5,0.9,0.99,0.999,0.9999])#,1e4,1e5,5e5])\n",
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"moi = np.array([0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99])\n",
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"\n",
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"t = triskele.Triskele(layers_stack[:,:,0], verbose=False)\n",
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"attributes_min = t.filter(tree='min-tree',\n",
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" area=area,\n",
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" #standard_deviation=sd,\n",
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" #moment_of_inertia=moi\n",
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" )\n",
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"attributes_max = t.filter(tree='max-tree',\n",
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" area=area,\n",
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" #standard_deviation=sd,\n",
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" #moment_of_inertia=moi\n",
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" )\n",
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"\n",
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"attributes_min_lbl = ['origin']\n",
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"attributes_min_lbl.extend(['Thickening area {}'.format(x) for x in area])\n",
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"attributes_max_lbl = ['origin']\n",
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"attributes_max_lbl.extend(['Thinning area {}'.format(x) for x in area])\n",
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"\n",
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"attributes_min.shape, attributes_max.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"attributes = np.dstack((attributes_min[:,:,:0:-1], attributes_max))\n",
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"attributes_lbl = attributes_min_lbl[:0:-1]\n",
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"attributes_lbl.extend(attributes_max_lbl)\n",
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"\n",
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"attributes.shape, attributes_lbl"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"figs = list()\n",
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"\n",
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"attributes[0,0,:] = 255 # J'ai honte...\n",
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"\n",
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"for i in range(attributes.shape[-1]):\n",
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" figs.append(attributes[:,:,i])\n",
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"\n",
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"mshow(figs, attributes_lbl, 2)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -16,6 +16,19 @@
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"import triskele"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def show(im):\n",
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" plt.figure(figsize=(16*2,3*2))\n",
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" plt.imshow(im)\n",
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" plt.colorbar()\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -271,19 +284,6 @@
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def show(im):\n",
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" plt.figure(figsize=(16*2,3*2))\n",
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" plt.imshow(im)\n",
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" plt.colorbar()\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -295,12 +295,10 @@
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"cell_type": "markdown",
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"metadata": {},
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"outputs": [],
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"source": [
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"labels.shape"
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"## Scores"
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]
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},
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{
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@ -309,90 +307,37 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"np.arange(238400).reshape(-1, 4768)"
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"scores(actual=labels.reshape(-1), prediction=cv_labels.reshape(-1))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"cell_type": "markdown",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('../Res/classifier_0.pkl', 'wb') as f:\n",
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" pickle.dump(rfc, f)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"Yp = Y.copy()\n",
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"#### Labels\n",
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"\n",
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"Yp[training == False] = rfc.predict(X[training == False])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(16*2,3*2))\n",
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"plt.imshow(Y.reshape(labels.shape))\n",
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"plt.colorbar()\n",
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"plt.show()\n",
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"\n",
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"plt.figure(figsize=(16*2,3*2))\n",
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"plt.imshow(Yp.reshape(labels.shape))\n",
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"plt.colorbar()\n",
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"plt.show()\n",
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"\n",
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"plt.figure(figsize=(16*2,3*2))\n",
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"plt.imshow(Yp.reshape(labels.shape).astype(np.float) - labels)\n",
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"plt.colorbar()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"class cvg:\n",
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" def __init__(self, attributes, ground_truth, order_dim=0, n_test=2): \n",
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" self._tests_left = n_test\n",
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" \n",
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" if attributes.shape[0] != ground_truth.shape[0] or \\\n",
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" attributes.shape[1] != ground_truth.shape[1] :\n",
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" raise ValueError('attributes and ground_truth must have the same 2D shape')\n",
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" \n",
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" def __iter__(self):\n",
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" return self\n",
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" \n",
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" def __next__(self):\n",
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" if self._tests_left == 0:\n",
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" raise StopIteration\n",
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" \n",
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" train_filter = np.arange(attributes.shape) < (Y.size * .50)\n",
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"\n",
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" Xtrain = 42\n",
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" Xtest = 432\n",
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" Ytrain = 12\n",
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" Ytest = 123\n",
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" \n",
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" return (Xtrain, Xtest, Ytrain, Ytest)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"cvg(attributes, labels[:,:-1])"
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" 0 – Unclassified\n",
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" 1 – Healthy grass\n",
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" 2 – Stressed grass\n",
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" 3 – Artificial turf\n",
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" 4 – Evergreen trees\n",
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" 5 – Deciduous trees\n",
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" 6 – Bare earth\n",
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" 7 – Water\n",
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" 8 – Residential buildings\n",
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" 9 – Non-residential buildings\n",
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" 10 – Roads\n",
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" 11 – Sidewalks\n",
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" 12 – Crosswalks\n",
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" 13 – Major thoroughfares\n",
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" 14 – Highways\n",
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" 15 – Railways\n",
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" 16 – Paved parking lots\n",
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" 17 – Unpaved parking lots\n",
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" 18 – Cars\n",
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" 19 – Trains\n",
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" 20 – Stadium seats\n"
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]
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}
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],
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