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Photovoltaic_Fault_Detector/GPS_Panel/.ipynb_checkpoints/ClassifierPanel_KML-checkpoint.ipynb

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Creating Classifier
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{
"cells": [
{
"cell_type": "code",
Classifier SVM
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"execution_count": 54,
Creating Classifier
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"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from matplotlib import path\n",
"import matplotlib.patches as patches\n",
"from skimage import draw\n",
"import scipy.ndimage as ndimage\n",
"import Utils\n",
"import georasters as gr\n",
"import cv2\n",
"from Utils import doubleMADsfromMedian\n",
"from skimage.transform import resize\n",
"import pickle\n",
Classifier SVM
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"import simplekml\n",
"\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.svm import SVC\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"import time\n",
"import tensorflow as tf\n",
"\n",
"\n",
"def classifier(base_model, recognizer, labels, image_input):\n",
" weight, height, dim = base_model.input.shape[1], base_model.input.shape[2], base_model.input.shape[3]\n",
" Im_resize = cv2.resize(image_input, (weight, height), interpolation = cv2.INTER_AREA)\n",
" vec = base_model.predict(tf.keras.backend.expand_dims(Im_resize,0)).flatten()\n",
" prob = recognizer.predict_proba([vec])[0]\n",
" return labels.classes_[np.argmax(prob)], prob[np.argmax(prob)]\n",
" "
Creating Classifier
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parameters"
]
},
{
"cell_type": "code",
Classifier SVM
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"execution_count": 55,
Creating Classifier
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"metadata": {},
"outputs": [],
"source": [
Classifier SVM
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"path_T = \"Pampa/THERMAL_01.tif\"\n",
"path_String = \"Pampa/BP-A-1.tif\"\n",
"ZonaPV = 'BP-A-1'\n",
Creating Classifier
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"path_kml_panel = 'Pampa/KML/Paneles_' + ZonaPV +'_classifier.kml'\n",
"path_dict = 'Pampa/KML/Mesa_' + ZonaPV + '.pickle'\n",
Classifier SVM
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"path_new_dict = 'Pampa/KML/Mesa_' + ZonaPV + '_classifier.pickle'\n",
Creating Classifier
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"\n",
"GR_String = gr.from_file(path_String)\n",
"GR_T = gr.from_file(path_T)\n",
"geot_T = GR_T.geot\n",
"## Load List in coordinate latitud and longitude ###\n",
"with open(path_dict, \"rb\") as fp:\n",
" L_strings_coord = pickle.load(fp)"
]
},
Classifier SVM
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load Classifier"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"path_dataset = './Classifier/Data_set_2/Data_prueba_0/' \n",
"output_recognizer = path_dataset + \"model_SVM/recognizer.pickle\"\n",
"output_label = path_dataset + \"model_SVM/le.pickle\"\n",
"\n",
"\n",
"img_width, img_height = 224, 224\n",
"base_model = tf.keras.applications.Xception(input_shape=(img_height, img_width, 3), weights='imagenet', include_top=False)\n",
"\n",
"x = base_model.output\n",
"x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
"base_model = tf.keras.models.Model(inputs=base_model.input, outputs=x)\n",
"recognizer_SVM = pickle.loads(open(output_recognizer, \"rb\").read())\n",
"le = pickle.loads(open(output_label, \"rb\").read())"
]
},
Creating Classifier
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Classifier each panel"
]
},
{
"cell_type": "code",
Classifier SVM
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"execution_count": 77,
Creating Classifier
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
Classifier SVM
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"10\n"
Creating Classifier
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]
},
{
Classifier SVM
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"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f2df4b97090>"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
Creating Classifier
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}
],
"source": [
"\n",
Classifier SVM
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"epsilon = -2\n",
Creating Classifier
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"matrix_expand_bounds = [[-epsilon, -epsilon],[+epsilon, -epsilon], [+epsilon, +epsilon], [-epsilon, +epsilon]]\n",
"\n",
Classifier SVM
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"for string_key in ['10']:# L_strings_coord.keys():\n",
Creating Classifier
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" print(string_key)\n",
" string = L_strings_coord[string_key]\n",
" for panel_key in string['panels'].keys():\n",
" panel = string['panels'][panel_key]\n",
" Points = Utils.gps2pixel(panel['points'], geot_T) + matrix_expand_bounds\n",
" \n",
" if not GR_T.raster.data[0,Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]].size == 0:\n",
" Im = np.zeros((img_height, img_width, 3))\n",
" Im[:,:,0] = cv2.resize(GR_T.raster.data[0,Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]], (img_width, img_height))\n",
" Im[:,:,1] = Im[:,:,0].copy()\n",
" Im[:,:,2] = Im[:,:,0].copy()\n",
Classifier SVM
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" panel['status'], panel['prob'] = classifier(base_model, recognizer_SVM, le, Im)\n",
Creating Classifier
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" else:\n",
" print('problem with coords panel: ', string_key, '_', panel_key)\n",
"\n",
"plt.figure(figsize=(6, 6))\n",
"plt.imshow(Im.astype(int))"
]
},
{
"cell_type": "code",
Classifier SVM
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"execution_count": 83,
Creating Classifier
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"metadata": {},
Classifier SVM
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"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f2df758e190>"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
Creating Classifier
2020-11-18 03:05:29 -03:00
"source": [
Classifier SVM
2020-11-25 16:03:59 -03:00
"epsilon = 100\n",
"matrix_expand_bounds = [[-epsilon, -epsilon],[+epsilon, -epsilon], [+epsilon, +epsilon], [-epsilon, +epsilon]]\n",
"\n",
"\n",
"panel = string['panels']['29']\n",
"Points = Utils.gps2pixel(panel['points'], geot_T) + matrix_expand_bounds\n",
"plt.figure(figsize=(6, 6))\n",
"plt.imshow(GR_T.raster.data[0,Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]],cmap = 'gray')\n"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'id': 'Mesa_010',\n",
" 'points': array([[-70.1137603 , -18.88374416],\n",
" [-70.1137603 , -18.8841109 ],\n",
" [-70.11373707, -18.8841109 ],\n",
" [-70.11373707, -18.88374416]]),\n",
" 'panels': {'1': {'id': 1,\n",
" 'points': array([[-70.1137603 , -18.88374416],\n",
" [-70.1137603 , -18.88375319],\n",
" [-70.11373852, -18.88375319],\n",
" [-70.11373852, -18.88374416]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999974582974,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '2': {'id': 2,\n",
" 'points': array([[-70.1137603 , -18.88375319],\n",
" [-70.1137603 , -18.88376222],\n",
" [-70.11373852, -18.88376222],\n",
" [-70.11373852, -18.88375319]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999999994424,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '3': {'id': 3,\n",
" 'points': array([[-70.1137603 , -18.88376222],\n",
" [-70.1137603 , -18.88377156],\n",
" [-70.11373852, -18.88377156],\n",
" [-70.11373852, -18.88376222]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999997602535461,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '4': {'id': 4,\n",
" 'points': array([[-70.1137603 , -18.88377156],\n",
" [-70.1137603 , -18.88378059],\n",
" [-70.11373852, -18.88378059],\n",
" [-70.11373852, -18.88377156]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999999980936,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '5': {'id': 5,\n",
" 'points': array([[-70.1137603 , -18.88378059],\n",
" [-70.1137603 , -18.88378993],\n",
" [-70.11373852, -18.88378993],\n",
" [-70.11373852, -18.88378059]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999779162957,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '6': {'id': 6,\n",
" 'points': array([[-70.1137603 , -18.88378993],\n",
" [-70.1137603 , -18.88379895],\n",
" [-70.11373852, -18.88379895],\n",
" [-70.11373852, -18.88378993]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999999999447,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '7': {'id': 7,\n",
" 'points': array([[-70.1137603 , -18.88379895],\n",
" [-70.1137603 , -18.88380829],\n",
" [-70.11373852, -18.88380829],\n",
" [-70.11373852, -18.88379895]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.999999999128643,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '8': {'id': 8,\n",
" 'points': array([[-70.1137603 , -18.88380829],\n",
" [-70.1137603 , -18.88381732],\n",
" [-70.11373852, -18.88381732],\n",
" [-70.11373852, -18.88380829]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999999976773,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '9': {'id': 9,\n",
" 'points': array([[-70.1137603 , -18.88381732],\n",
" [-70.1137603 , -18.88382666],\n",
" [-70.11373852, -18.88382666],\n",
" [-70.11373852, -18.88381732]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999865502004,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '10': {'id': 10,\n",
" 'points': array([[-70.1137603 , -18.88382666],\n",
" [-70.1137603 , -18.88383569],\n",
" [-70.11373852, -18.88383569],\n",
" [-70.11373852, -18.88382666]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.6644867214270673,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '11': {'id': 11,\n",
" 'points': array([[-70.1137603 , -18.88383569],\n",
" [-70.1137603 , -18.88384472],\n",
" [-70.11373852, -18.88384472],\n",
" [-70.11373852, -18.88383569]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999943922003554,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '12': {'id': 12,\n",
" 'points': array([[-70.1137603 , -18.88384472],\n",
" [-70.1137603 , -18.88385406],\n",
" [-70.11373852, -18.88385406],\n",
" [-70.11373852, -18.88384472]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999930979888187,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '13': {'id': 13,\n",
" 'points': array([[-70.1137603 , -18.88385406],\n",
" [-70.1137603 , -18.88386309],\n",
" [-70.11373852, -18.88386309],\n",
" [-70.11373852, -18.88385406]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9745824938551096,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '14': {'id': 14,\n",
" 'points': array([[-70.1137603 , -18.88386309],\n",
" [-70.1137603 , -18.88387243],\n",
" [-70.11373852, -18.88387243],\n",
" [-70.11373852, -18.88386309]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9898600718156667,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '15': {'id': 15,\n",
" 'points': array([[-70.1137603 , -18.88387243],\n",
" [-70.1137603 , -18.88388145],\n",
" [-70.11373852, -18.88388145],\n",
" [-70.11373852, -18.88387243]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9806793434421213,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '16': {'id': 16,\n",
" 'points': array([[-70.1137603 , -18.88388145],\n",
" [-70.1137603 , -18.88389079],\n",
" [-70.11373852, -18.88389079],\n",
" [-70.11373852, -18.88388145]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.999999837090797,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '17': {'id': 17,\n",
" 'points': array([[-70.1137603 , -18.88389079],\n",
" [-70.1137603 , -18.88389982],\n",
" [-70.11373852, -18.88389982],\n",
" [-70.11373852, -18.88389079]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9924301909160113,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '18': {'id': 18,\n",
" 'points': array([[-70.1137603 , -18.88389982],\n",
" [-70.1137603 , -18.88390916],\n",
" [-70.11373852, -18.88390916],\n",
" [-70.11373852, -18.88389982]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999999942012,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '19': {'id': 19,\n",
" 'points': array([[-70.1137603 , -18.88390916],\n",
" [-70.1137603 , -18.88391819],\n",
" [-70.11373852, -18.88391819],\n",
" [-70.11373852, -18.88390916]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999919790152,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '20': {'id': 20,\n",
" 'points': array([[-70.1137603 , -18.88391819],\n",
" [-70.1137603 , -18.88392722],\n",
" [-70.11373852, -18.88392722],\n",
" [-70.11373852, -18.88391819]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999904970847652,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '21': {'id': 21,\n",
" 'points': array([[-70.1137603 , -18.88392722],\n",
" [-70.1137603 , -18.88393656],\n",
" [-70.11373852, -18.88393656],\n",
" [-70.11373852, -18.88392722]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999959942511066,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '22': {'id': 22,\n",
" 'points': array([[-70.1137603 , -18.88393656],\n",
" [-70.1137603 , -18.88394559],\n",
" [-70.11373852, -18.88394559],\n",
" [-70.11373852, -18.88393656]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9882801857680308,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '23': {'id': 23,\n",
" 'points': array([[-70.1137603 , -18.88394559],\n",
" [-70.1137603 , -18.88395493],\n",
" [-70.11373852, -18.88395493],\n",
" [-70.11373852, -18.88394559]]),\n",
" 'status': '1-Falla',\n",
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" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '24': {'id': 24,\n",
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" [-70.1137603 , -18.88396395],\n",
" [-70.11373852, -18.88396395],\n",
" [-70.11373852, -18.88395493]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999999954068,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '25': {'id': 25,\n",
" 'points': array([[-70.1137603 , -18.88396395],\n",
" [-70.1137603 , -18.88397329],\n",
" [-70.11373852, -18.88397329],\n",
" [-70.11373852, -18.88396395]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999999993415,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '26': {'id': 26,\n",
" 'points': array([[-70.1137603 , -18.88397329],\n",
" [-70.1137603 , -18.88398232],\n",
" [-70.11373852, -18.88398232],\n",
" [-70.11373852, -18.88397329]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.995600030187818,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '27': {'id': 27,\n",
" 'points': array([[-70.1137603 , -18.88398232],\n",
" [-70.1137603 , -18.88399166],\n",
" [-70.11373852, -18.88399166],\n",
" [-70.11373852, -18.88398232]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999993947400787,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '28': {'id': 28,\n",
" 'points': array([[-70.1137603 , -18.88399166],\n",
" [-70.1137603 , -18.88400069],\n",
" [-70.11373852, -18.88400069],\n",
" [-70.11373852, -18.88399166]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9905119422535897,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '29': {'id': 29,\n",
" 'points': array([[-70.1137603 , -18.88400069],\n",
" [-70.1137603 , -18.88401003],\n",
" [-70.11373852, -18.88401003],\n",
" [-70.11373852, -18.88400069]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.7757526418449818,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '30': {'id': 30,\n",
" 'points': array([[-70.1137603 , -18.88401003],\n",
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" [-70.11373852, -18.88401003]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9860332917154839,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '31': {'id': 31,\n",
" 'points': array([[-70.1137603 , -18.88401906],\n",
" [-70.1137603 , -18.88402809],\n",
" [-70.11373852, -18.88402809],\n",
" [-70.11373852, -18.88401906]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999561655515,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '32': {'id': 32,\n",
" 'points': array([[-70.1137603 , -18.88402809],\n",
" [-70.1137603 , -18.88403742],\n",
" [-70.11373852, -18.88403742],\n",
" [-70.11373852, -18.88402809]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999999999699,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '33': {'id': 33,\n",
" 'points': array([[-70.1137603 , -18.88403742],\n",
" [-70.1137603 , -18.88404645],\n",
" [-70.11373852, -18.88404645],\n",
" [-70.11373852, -18.88403742]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999999907283,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '34': {'id': 34,\n",
" 'points': array([[-70.1137603 , -18.88404645],\n",
" [-70.1137603 , -18.88405579],\n",
" [-70.11373852, -18.88405579],\n",
" [-70.11373852, -18.88404645]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999999999699,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '35': {'id': 35,\n",
" 'points': array([[-70.1137603 , -18.88405579],\n",
" [-70.1137603 , -18.88406482],\n",
" [-70.11373852, -18.88406482],\n",
" [-70.11373852, -18.88405579]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.999999992402474,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '36': {'id': 36,\n",
" 'points': array([[-70.1137603 , -18.88406482],\n",
" [-70.1137603 , -18.88407416],\n",
" [-70.11373852, -18.88407416],\n",
" [-70.11373852, -18.88406482]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.90917031183596,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '37': {'id': 37,\n",
" 'points': array([[-70.1137603 , -18.88407416],\n",
" [-70.1137603 , -18.88408319],\n",
" [-70.11373852, -18.88408319],\n",
" [-70.11373852, -18.88407416]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.8419277241921781,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '38': {'id': 38,\n",
" 'points': array([[-70.1137603 , -18.88408319],\n",
" [-70.1137603 , -18.88409253],\n",
" [-70.11373852, -18.88409253],\n",
" [-70.11373852, -18.88408319]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.8482129635733254,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '39': {'id': 39,\n",
" 'points': array([[-70.1137603 , -18.88409253],\n",
" [-70.1137603 , -18.88410156],\n",
" [-70.11373852, -18.88410156],\n",
" [-70.11373852, -18.88409253]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.9999999999999699,\n",
" 'T': 0,\n",
" 'severidad': 'default'},\n",
" '40': {'id': 40,\n",
" 'points': array([[-70.1137603 , -18.88410156],\n",
" [-70.1137603 , -18.88411027],\n",
" [-70.11373852, -18.88411027],\n",
" [-70.11373852, -18.88410156]]),\n",
" 'status': '1-Falla',\n",
" 'prob': 0.999999999978633,\n",
" 'T': 0,\n",
" 'severidad': 'default'}},\n",
" 'status': 'default',\n",
" 'T': 0}"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"string = L_strings_coord['10']\n",
"string"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Save KML Panels"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"kml=simplekml.Kml()\n",
"\n",
"\n",
"for string_key in L_strings_coord.keys():\n",
" \n",
" string = L_strings_coord[string_key]\n",
" points = string['points']\n",
" \n",
" for panel_key in string['panels'].keys():\n",
" panel = string['panels'][panel_key]\n",
" points = panel['points']\n",
" \n",
" pmt = kml.newpolygon(outerboundaryis = points)\n",
" pmt.extendeddata.newdata(name= 'Id integer', value= str(string_key).zfill(3) + '_' + str(panel['id']).zfill(3))\n",
" pmt.extendeddata.newdata(name= 'Id panel', value= str(panel['id']).zfill(3))\n",
" pmt.extendeddata.newdata(name='Zona PV', value= ZonaPV)\n",
" pmt.extendeddata.newdata(name='Cód. Fall', value= 0)\n",
" pmt.extendeddata.newdata(name= 'Tipo falla', value= panel['status'])\n",
" pmt.extendeddata.newdata(name= 'Mesa', value= string['id'])\n",
" \n",
"kml.save(path_kml_panel)\n",
Classifier SVM
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"## Save List in coordinate latitud and longitude ###\n",
"with open(path_new_dict, 'wb') as handle:\n",
" pickle.dump(L_strings_coord, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
Creating Classifier
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"print('Listo')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
Classifier SVM
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"execution_count": 20,
Creating Classifier
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
Classifier SVM
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"<matplotlib.image.AxesImage at 0x7f2df8b29190>"
Creating Classifier
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]
},
Classifier SVM
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"execution_count": 20,
Creating Classifier
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
Classifier SVM
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"image/png": "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
Creating Classifier
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
Classifier SVM
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"plt.imshow(GR_T.raster.data[0,Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]].astype(int), cmap = 'gray')"
Creating Classifier
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Reference in New Issue Copy Permalink