TES: 6 degree bins per 30 Lsubs over all years.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Creating Thermal Inertia Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/krodriguez/repos/mgs_tes_data\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "print(os.path.abspath('../'))\n",
    "sys.path.insert(0, os.path.abspath('../'))\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "from pytes.io import io_db as iodb\n",
    "\n",
    "# Math stuff\n",
    "from math import floor\n",
    "import numpy as np\n",
    "\n",
    "# MatPlotLib for colors and basic plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "import pyspark.sql.functions as funcs\n",
    "from pyspark.sql.functions import udf\n",
    "from pyspark.sql.functions import UserDefinedFunction\n",
    "from pyspark.sql.types import FloatType\n",
    "from pyspark.sql.types import IntegerType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a spark context and get the reader for mongodb queries\n",
    "sqlContext, dbreader = iodb.spark_from_mongodb(host='smalls',db='tes', collection='point_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binval(val):\n",
    "    # Make sure the bins is only instantiated once during runtime\n",
    "    global bins\n",
    "    if not 'bins' in locals(): bins = np.arange(30,361,30)\n",
    "        \n",
    "    try:\n",
    "        return float(bins[np.digitize(val, bins)])\n",
    "    except:\n",
    "        return float(bins[len(bins)-1])\n",
    "\n",
    "\n",
    "def binlong(val):\n",
    "    # Make sure the bins is only instantiated once during runtime\n",
    "    global bins\n",
    "    if not 'bins' in locals(): bins = np.arange(-180,180,6)\n",
    "    try:\n",
    "        return float(bins[np.digitize(val, bins, right=True)])\n",
    "    except:\n",
    "        return float(bins[len(bins)-1])\n",
    "    \n",
    "def binlat(val):\n",
    "    # Make sure the bins is only instantiated once during runtime\n",
    "    global bins\n",
    "    if not 'bins' in locals(): bins = np.arange(-90,90,6)\n",
    "    try:\n",
    "        return float(bins[np.digitize(val, bins, right=True)])\n",
    "    except:\n",
    "        return float(bins[len(bins)-1])\n",
    "    \n",
    "binval = udf(binval, FloatType())\n",
    "binlat = udf(binlat, FloatType())\n",
    "binlong = udf(binlong, FloatType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12.9 ms, sys: 188 µs, total: 13.1 ms\n",
      "Wall time: 3.59 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Load the collection\n",
    "df = dbreader.load()\n",
    "lsubs = binval(df.lsubs)\n",
    "df = df.select([\"ti_spc\", \"loc\", \"lsubs\"])\n",
    "df = df.withColumn(\"long\", df.loc.coordinates[0]).withColumn(\"lat\", df.loc.coordinates[1]).drop(\"loc\")\n",
    "df = df.groupby(binlong(df.long).alias('long'),binlat(df.lat).alias('lat'), binval(df.lsubs).alias('lsubs')).agg({\"ti_spc\": \"avg\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 909 ms, sys: 246 ms, total: 1.15 s\n",
      "Wall time: 17min 9s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Load the new dataframe as a pandas dataframe\n",
    "dfs = df.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20729, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1717, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dfs.shape)\n",
    "dfs.query('lsubs == 90').shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Termal Intertia Kernal Density Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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i+rU47CDg3ZTSGsAw4Jel1/YHvg+sDewMXBLx2VqFV5bO2ZrzU0pfLX3cXdY3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\nJpWSJEmSpE1mUilJkiRJ2mT/C40EeRmQm/XUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe28bc44828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7.5))\n",
    "dfs['avg(ti_spc)'].plot.density(title='Thermal Inertia KDE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "longbins = np.arange(-180,180,6)\n",
    "latbins = np.arange(-90,90,6)\n",
    "\n",
    "def latlonbin(x):\n",
    "    binx = np.digitize(x['long'], longbins, right=True)\n",
    "    biny = np.digitize(x['lat'], latbins, right=True)\n",
    "    binx = binx if binx < len(longbins) else len(longbins)-1\n",
    "    biny = biny if biny < len(latbins) else len(latbins)-1\n",
    "    \n",
    "    return [binx, biny]\n",
    "    \n",
    "dfs['indices'] = dfs.apply(latlonbin, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframes = [dfs.query('lsubs == {}'.format(lbin)) for lbin in np.arange(30,361,30)]\n",
    "arrays = [np.zeros((30,60)) for i in range(len(dataframes))]\n",
    "\n",
    "for arr, df in zip(arrays, dataframes):\n",
    "    indices = np.vstack(df['indices'])\n",
    "    arr[indices[:,1], indices[:,0]] = df['avg(ti_spc)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe25f3a9b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe25f62c7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe28b707160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe25f2784a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe25f70a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe25f204978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe25f7abe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe25ef92588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe25f75acc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe25f2387b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe25f643198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe25f76ad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for arr in arrays:\n",
    "    plt.figure(figsize=(20,8))\n",
    "    plt.imshow(arr[::-1,::-1], interpolation='nearest', cmap='coolwarm')\n",
    "    plt.colorbar()\n",
    "    plt.show()    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import gdal\n",
    "import numpy as np\n",
    "import osr \n",
    "\n",
    "def save_image(filename, bands, lats, lons):\n",
    "        \"\"\"\n",
    "        Jacked from Pablo on StackOverflow\n",
    "        (http://gis.stackexchange.com/questions/58517/python-gdal-save-array-as-raster-with-projection-from-other-file)\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        filename : string\n",
    "                   The path to save the image to\n",
    "        \"\"\"\n",
    "        dst_filename = filename\n",
    "        x_pixels = bands[0].shape[1]\n",
    "        y_pixels = bands[0].shape[0]  # number of pixels in y\n",
    "        PIXEL_SIZE = 3  # size of the pixel...\n",
    "\n",
    "        driver = gdal.GetDriverByName('GTIFF')\n",
    "\n",
    "        dataset = driver.Create(\n",
    "            dst_filename,\n",
    "            x_pixels,\n",
    "            y_pixels,\n",
    "            len(bands),\n",
    "            gdal.GDT_Float32)\n",
    "\n",
    "        xmin, ymin, xmax, ymax = [min(lons), min(lats), max(lons), max(lats)]\n",
    "        xres = (xmax - xmin) / float(x_pixels)\n",
    "        yres = (ymax - ymin) / float(y_pixels)\n",
    "        geotransform = (xmin, xres, 0, ymax, 0, -yres)\n",
    "\n",
    "        srs = osr.SpatialReference()            # establish encoding\n",
    "        srs.ImportFromEPSG(3857)                # WGS84 lat/long\n",
    "        dataset.SetProjection(srs.ExportToWkt()) # export coords to file\n",
    "        dataset.SetGeoTransform(geotransform)\n",
    "        \n",
    "        for arr, i in zip(bands, range(0, len(bands))):\n",
    "            dataset.GetRasterBand(int(i+1)).WriteArray(arr[::-1,::-1])\n",
    "\n",
    "        dataset.FlushCache()  # Write to disk.\n",
    "        dataset = None"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:autocnet]",
   "language": "python",
   "name": "conda-env-autocnet-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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}