Photovoltaic_Fault_Detector/Code_Example/.ipynb_checkpoints/Example Detection AllInOne-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import os\n",
    "import argparse\n",
    "import json\n",
    "import cv2\n",
    "import sys\n",
    "sys.path += [os.path.abspath('../keras-yolo3-master')]\n",
    "sys.path += [os.path.abspath('../')]\n",
    "from utils.utils import get_yolo_boxes, makedirs\n",
    "from utils.bbox import draw_boxes\n",
    "from tensorflow.keras.models import load_model\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from panel_disconnect import disconnect\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define Image Path and output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_path   = '../../Data/Ocoa/001/' \n",
    "output_path  = '../../Data/Ocoa/001_detect/'\n",
    "makedirs(output_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Model (Panel Detector, Soiling, Diode, Cell Damage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "## Config of trained model, change this for use different trained model\n",
    "config_path_panel  = 'config_full_yolo_panel_infer.json' #Panel\n",
    "config_path_soiling  = 'config_full_yolo_fault_1_infer.json' #Soiling\n",
    "config_path_diode  = 'config_full_yolo_fault_4_infer.json'  #Diode\n",
    "config_path_cellD  = 'config_full_yolo_fault_2_infer.json'  #Cell Damage\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Parameters of detection\n",
    "net_h, net_w = 416, 416 # a multiple of 32, the smaller the faster\n",
    "obj_thresh, nms_thresh, nms_thresh_panel = 0.5, 0.45, 0.3\n",
    "\n",
    "####################################################\n",
    "#####   Load the model Panel Detector  #############\n",
    "####################################################\n",
    "with open(config_path_panel) as config_buffer:\n",
    "        config_panel = json.load(config_buffer)\n",
    "\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = config_panel['train']['gpus']\n",
    "infer_model_panel = load_model(config_panel['train']['saved_weights_name'])\n",
    "\n",
    "####################################################\n",
    "#####   Load the model Soiling  #############\n",
    "####################################################\n",
    "with open(config_path_soiling) as config_buffer:\n",
    "        config_soiling = json.load(config_buffer)\n",
    "\n",
    "infer_model_soiling = load_model(config_soiling['train']['saved_weights_name'])\n",
    "\n",
    "####################################################\n",
    "#####   Load the model Diode  #############\n",
    "####################################################\n",
    "with open(config_path_diode) as config_buffer:\n",
    "        config_diode = json.load(config_buffer)\n",
    "\n",
    "infer_model_diode = load_model(config_diode['train']['saved_weights_name'])\n",
    "\n",
    "####################################################\n",
    "#####   Load the model Cell Damage  #############\n",
    "####################################################\n",
    "with open(config_path_cellD) as config_buffer:\n",
    "        config_cellD = json.load(config_buffer)\n",
    "\n",
    "infer_model_cellD = load_model(config_cellD['train']['saved_weights_name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Image paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_paths = []\n",
    "\n",
    "if os.path.isdir(input_path):\n",
    "    for inp_file in os.listdir(input_path):\n",
    "        image_paths += [input_path + inp_file]\n",
    "else:\n",
    "    image_paths += [input_path]\n",
    "\n",
    "image_paths = [inp_file for inp_file in image_paths if (inp_file[-4:] in ['.jpg', '.png', 'JPEG'])]\n",
    "\n",
    "# the main loop\n",
    "times = []\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detection of images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../Data/Ocoa/001/DJI_0027.jpg\n",
      "Elapsed time = 5.246682167053223\n",
      "../../Data/Ocoa/001/DJI_0147.jpg\n",
      "Elapsed time = 5.895338535308838\n",
      "../../Data/Ocoa/001/DJI_0166.jpg\n",
      "Elapsed time = 5.632398366928101\n",
      "../../Data/Ocoa/001/DJI_0100.jpg\n",
      "Elapsed time = 5.191319942474365\n",
      "../../Data/Ocoa/001/DJI_0014.jpg\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-12-52ef73f77509>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mboxes_diode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_yolo_boxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfer_model_diode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_h\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_w\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig_diode\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'model'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'anchors'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj_thresh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnms_thresh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mboxes_cellD\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_yolo_boxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfer_model_cellD\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_h\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_w\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig_cellD\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'model'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'anchors'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj_thresh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnms_thresh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mboxes_panel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_yolo_boxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfer_model_panel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_h\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_w\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig_panel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'model'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'anchors'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj_thresh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnms_thresh_panel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mboxes_panel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbox\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mboxes_panel\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mobj_thresh\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[0mboxes_disc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdisconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mboxes_panel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz_thresh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/Rentadrone.cl-ai-test&SomeCode/model-definition/keras-yolo3-master/utils/utils.py\u001b[0m in \u001b[0;36mget_yolo_boxes\u001b[0;34m(model, images, net_h, net_w, anchors, obj_thresh, nms_thresh)\u001b[0m\n\u001b[1;32m    264\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myolos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m             \u001b[0myolo_anchors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manchors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# config['model']['anchors']\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m             \u001b[0mboxes\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mdecode_netout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myolos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myolo_anchors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj_thresh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_h\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_w\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    267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0;31m# correct the sizes of the bounding boxes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/Rentadrone.cl-ai-test&SomeCode/model-definition/keras-yolo3-master/utils/utils.py\u001b[0m in \u001b[0;36mdecode_netout\u001b[0;34m(netout_old, anchors, obj_thresh, net_h, net_w)\u001b[0m\n\u001b[1;32m    201\u001b[0m             \u001b[0mobjectness\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnetout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m             \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjectness\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mobj_thresh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m             \u001b[0;31m# first 4 elements are x, y, w, and h\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/new/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36mless_equal\u001b[0;34m(x, y, name)\u001b[0m\n\u001b[1;32m   4979\u001b[0m       _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(\n\u001b[1;32m   4980\u001b[0m         \u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_context_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"LessEqual\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4981\u001b[0;31m         tld.op_callbacks, x, y)\n\u001b[0m\u001b[1;32m   4982\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4983\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FallbackException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for image_path in image_paths:\n",
    "    image = cv2.imread(image_path)\n",
    "    print(image_path)\n",
    "    start = time.time()\n",
    "    # predict the bounding boxes\n",
    "    boxes_soiling = get_yolo_boxes(infer_model_soiling, [image], net_h, net_w, config_soiling['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
    "    boxes_diode = get_yolo_boxes(infer_model_diode, [image], net_h, net_w, config_diode['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
    "    boxes_cellD = get_yolo_boxes(infer_model_cellD, [image], net_h, net_w, config_cellD['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
    "    #boxes_panel = get_yolo_boxes(infer_model_panel, [image], net_h, net_w, config_panel['model']['anchors'], obj_thresh, nms_thresh_panel)[0]\n",
    "    #boxes_panel = [box for box in boxes_panel if box.get_score() > obj_thresh]\n",
    "    #boxes_disc = disconnect(image, boxes_panel, z_thresh = 1.8)\n",
    "    print('Elapsed time = {}'.format(time.time() - start))\n",
    "    times.append(time.time() - start)\n",
    "    # Draw boxes\n",
    "    draw_boxes(image, boxes_soiling, config_soiling['model']['labels'], obj_thresh, number_color = 0)\n",
    "    draw_boxes(image, boxes_diode, config_diode['model']['labels'], obj_thresh, number_color = 2)\n",
    "    draw_boxes(image, boxes_cellD, config_cellD['model']['labels'], obj_thresh, number_color = 4)\n",
    "    #draw_boxes(image, boxes_disc, ['disc'], obj_thresh, number_color = 5)\n",
    "    \n",
    "    cv2.imwrite(output_path + image_path.split('/')[-1], np.uint8(image))\n",
    "    # write the image with bounding boxes to file\n",
    "    #plt.figure(figsize=(16, 16))\n",
    "    #plt.imshow(image)\n",
    "\n",
    "file = open(output_path + 'time.txt','w')\n",
    "file.write('Tiempo promedio:' + str(np.mean(times)))\n",
    "file.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example One image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../Data/Ocoa/001/DJI_0111.jpg\n",
      "Elapsed time = 5.186281204223633\n",
      "Elapsed time = 5.186866521835327\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f674b4ff610>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = image_paths[60]\n",
    "\n",
    "image = cv2.imread(image_path)\n",
    "plt.figure(figsize=(12, 10))\n",
    "plt.imshow(image)\n",
    "print(image_path)\n",
    "\n",
    "start = time.time()\n",
    "# predict the bounding boxes\n",
    "boxes_soiling = get_yolo_boxes(infer_model_soiling, [image], net_h, net_w, config_soiling['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
    "boxes_diode = get_yolo_boxes(infer_model_diode, [image], net_h, net_w, config_diode['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
    "boxes_cellD = get_yolo_boxes(infer_model_cellD, [image], net_h, net_w, config_cellD['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
    "boxes_panel = get_yolo_boxes(infer_model_panel, [image], net_h, net_w, config_panel['model']['anchors'], obj_thresh, nms_thresh_panel)[0]\n",
    "boxes_panel = [box for box in boxes_panel if box.get_score() > obj_thresh]\n",
    "boxes_disc = disconnect(image, boxes_panel, z_thresh = 1.8)\n",
    "print('Elapsed time = {}'.format(time.time() - start))\n",
    "times.append(time.time() - start)\n",
    "# Draw boxes\n",
    "draw_boxes(image, boxes_soiling, config_soiling['model']['labels'], obj_thresh, number_color = 0)\n",
    "draw_boxes(image, boxes_diode, config_diode['model']['labels'], obj_thresh, number_color = 2)\n",
    "draw_boxes(image, boxes_cellD, config_cellD['model']['labels'], obj_thresh, number_color = 4)\n",
    "draw_boxes(image, boxes_disc, ['disc'], obj_thresh, number_color = 5)\n",
    "\n",
    "print('Elapsed time = {}'.format(time.time() - start))\n",
    "times.append(time.time() - start)\n",
    "plt.figure(figsize=(12, 10))\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Type Fault\n",
    "## 1: Soiling Fault\n",
    "## 2: Affected Cell \n",
    "## 4: Diode Fault\n",
    "## disc: Panel Disconnected"
   ]
  },
  {
   "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
}