main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import csv\n",
    "import datetime\n",
    "from glob import glob\n",
    "import os\n",
    "import exifread\n",
    "import scipy.ndimage.interpolation\n",
    "from pysolar.solar import *\n",
    "import scipy.io as sio\n",
    "\n",
    "import matplotlib as mpl\n",
    "mpl.style.use('classic')\n",
    "mpl.rc(\"figure\", facecolor=\"white\")\n",
    "\n",
    "# User defined functions\n",
    "from normalize_array import *\n",
    "from readCoverage import *\n",
    "from nearest import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MOD data\n",
    "\n",
    "This imports the MOD data from MODIS satellite images. The various parameters viz., day number, hour, minute, average cloud mask value, and center cloud mask value are pre-computed using a MATLAB script *something.m*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(268, 5)\n",
      "The total number of MOD observations is 268\n"
     ]
    }
   ],
   "source": [
    "test = sio.loadmat('./cloud_mask/CloudMask_2015_MOD.mat', squeeze_me=True, struct_as_record=False)\n",
    "\n",
    "data_array = test[\"DATA\"]\n",
    "print (data_array.shape)\n",
    "day_number1 = data_array[:,0]\n",
    "hour1 = data_array[:,1]\n",
    "minute1 = data_array[:,2]\n",
    "avg_cmask1 = data_array[:,3]\n",
    "cen_cmask1 = data_array[:,4]\n",
    "\n",
    "print ('The total number of MOD observations is', len(day_number1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MYD data\n",
    "\n",
    "Just like MOD data, the MYD data are also extracted from the MODIS satellite images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(265, 5)\n",
      "The total number of MYD observations is 265\n"
     ]
    }
   ],
   "source": [
    "test = sio.loadmat('./cloud_mask/CloudMask_2015_MYD.mat', squeeze_me=True, struct_as_record=False)\n",
    "    \n",
    "data_array = test[\"DATA\"]\n",
    "print (data_array.shape)\n",
    "\n",
    "day_number2 = data_array[:,0]\n",
    "hour2 = data_array[:,1]\n",
    "minute2 = data_array[:,2]\n",
    "avg_cmask2 = data_array[:,3]\n",
    "cen_cmask2 = data_array[:,4]\n",
    "\n",
    "print ('The total number of MYD observations is', len(day_number2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenating MOD & MYD data\n",
    "\n",
    "We combine both MOD and MYD data into a single data series. This is because, we analyze all the observations together in a day-wise fashion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "day_series = np.concatenate([day_number1, day_number2])\n",
    "hour_series = np.concatenate([hour1, hour2])\n",
    "minute_series = np.concatenate([minute1, minute2])\n",
    "avg_cmask_series = np.concatenate([avg_cmask1, avg_cmask2])\n",
    "cen_cmask_series = np.concatenate([cen_cmask1, cen_cmask2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting MODIS observations into datetime objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of combined MODIS observations is 533\n"
     ]
    }
   ],
   "source": [
    "YEAR = 2015\n",
    "datetime_MODIS_series = []\n",
    "for i,item in enumerate(day_series):\n",
    "    \n",
    "    date_object = datetime.datetime(YEAR, 1, 1) + datetime.timedelta(item - 1)\n",
    "    sw = datetime.datetime(date_object.year,date_object.month,date_object.day,int(hour_series[i]),int(minute_series[i]),0)\n",
    "    datetime_MODIS_series.append(sw) \n",
    "    \n",
    "print ('The total number of combined MODIS observations is',len(datetime_MODIS_series))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Coverage value from ground-based sky cameras\n",
    "\n",
    "The coverage ratio from ground-based sky-cameras are pre-computed using our cloud segmentation algorithm. The coverage results are archived in text files inside the folder `coverage_data`.\n",
    "\n",
    "Please refer the refer the repository <a href=\"https://github.com/Soumyabrata/cloud-radiative-effect\">cloud-radiative-effect</a> for the cloud coverage computation. \n",
    "\n",
    "The following section combines the results from all the individual-day results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Index all the files\n",
    "global_files = []\n",
    "start_dir = './coverage_data/'\n",
    "\n",
    "# Check for all text files.\n",
    "pattern  = \"*.txt\"\n",
    "for direc,_,_ in os.walk(start_dir):\n",
    "    global_files.extend(glob(os.path.join(direc,pattern)))\n",
    "\n",
    "\n",
    "datetime_series = []\n",
    "COV_series = []\n",
    "for one_file in global_files:\n",
    "    \n",
    "    (datetime_COV,COV_values) = readingCOVERAGE(one_file)  \n",
    "    datetime_series = np.concatenate([datetime_series, datetime_COV])\n",
    "    COV_series = np.concatenate([COV_series, COV_values])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine data from cloud mask and cloud coverage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating for the first time\n",
      "Computation complete\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile('./cmask_coverage_result/2015data.txt'):\n",
    "    print ('Computed file exists')\n",
    "else:\n",
    "    print ('Calculating for the first time')\n",
    "    \n",
    "    file_name = './cmask_coverage_result/2015data.txt'\n",
    "    text_file = open(file_name, \"w\")\n",
    "\n",
    "    # Header line\n",
    "    text_file.write(\"date,timeMODIS,timeImage,cloudMaskForMODISTime,coverageForMODISTime\\n\")    \n",
    "\n",
    "    # Parsing the datetime objects of MODIS\n",
    "    for robin,particular_date in enumerate(datetime_MODIS_series):\n",
    "\n",
    "        # Check time is from MODIS, and it checks sky camera image timestamps\n",
    "        check_time = particular_date\n",
    "        (time_found,diff_ts) = nearest(check_time,datetime_series) \n",
    "\n",
    "        if abs(diff_ts)<900: #15 mins\n",
    "            #print ('check time=',check_time,' and found time=',time_found)\n",
    "            #print ('within 15 mins---adding')\n",
    "            location_index = np.where( datetime_series == time_found)\n",
    "            coverage_item = COV_series[location_index]\n",
    "            cmask_item = avg_cmask_series[robin]\n",
    "            text_file.write(\"%s,%s,%s,%s,%s\\n\" %(particular_date, check_time, time_found, cmask_item, coverage_item[0])) \n",
    "\n",
    "    text_file.close() \n",
    "    print ('Computation complete')    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data points of *combined* cloud mask and cloud coverage values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_path = \"./cmask_coverage_result/2015data.txt\"\n",
    "input_file = csv.DictReader(open(file_path))\n",
    "\n",
    "\n",
    "final_cmask = []\n",
    "final_coverage = []\n",
    "\n",
    "for row in input_file:\n",
    "    \n",
    "    c_mask = float(row[\"cloudMaskForMODISTime\"])\n",
    "    coverage_image = float(row[\"coverageForMODISTime\"])\n",
    "    \n",
    "    final_cmask.append(c_mask)\n",
    "    final_coverage.append(coverage_image)\n",
    "    \n",
    "    \n",
    "final_cmask = np.array(final_cmask)\n",
    "final_coverage = np.array(final_coverage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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U2xI5z3RGhPfoLEikaSiH/FEOhSLtPFJlIKLHp/wk+qMc8kc5FIq080i3iUQ0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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ea3470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NO_OF_BINS = 4   \n",
    "box_mask = [[] for _ in range(NO_OF_BINS)]\n",
    "mask100 = np.multiply(final_cmask,100)\n",
    "  \n",
    "for i,item in enumerate(mask100):\n",
    "    whichBin = int(item/(100/NO_OF_BINS))\n",
    "    if whichBin==NO_OF_BINS:\n",
    "        whichBin = NO_OF_BINS - 1\n",
    "    \n",
    "    box_mask[whichBin].append(final_coverage[i])\n",
    "    \n",
    "    \n",
    "labelList = []\n",
    "for ll in np.arange(1,1+NO_OF_BINS):\n",
    "    labelList.append(str(int((100/NO_OF_BINS)*(ll-1)+1)) + '-' + str(int((100/NO_OF_BINS)*ll)))   \n",
    "    \n",
    "\n",
    "fig = plt.figure(2,figsize=(5, 4))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(box_mask,whis=5)\n",
    "ax.set_xticklabels(labelList)\n",
    "ax.set_ylabel('Cloud Coverage (from images)', fontsize=14)\n",
    "ax.set_xlabel('Cloud Mask (from MODIS)', fontsize=14)\n",
    "ax.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.savefig('./figs/result.pdf', format='pdf')\n",
    "plt.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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}