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Kelvinrr/TES YEAR 25: 6 deg bins per 10lsubs-NIGHT.ipynb

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TES YEAR 25: 6 deg bins per 10lsubs-NIGHT.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": 53,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def binlsubs(val):\n",
" # Make sure the bins is only instantiated once during runtime\n",
" global bins\n",
" if not 'bins' in locals(): bins = np.arange(10,361,10)\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",
"binlsubs = udf(binlsubs, FloatType())\n",
"binlat = udf(binlat, FloatType())\n",
"binlong = udf(binlong, FloatType())"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 11 ms, sys: 75 µs, total: 11 ms\n",
"Wall time: 1.25 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\"]).where(df.incidence.between(95, 180)).where(df.mars_year == 25)\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'), binlsubs(df.lsubs).alias('lsubs')).agg({\"ti_spc\": \"avg\"})"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 823 ms, sys: 145 ms, total: 968 ms\n",
"Wall time: 12min 6s\n"
]
}
],
"source": [
"%%time\n",
"# Load the new dataframe as a pandas dataframe\n",
"dfs = df.toPandas()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(39054, 4)\n"
]
},
{
"data": {
"text/plain": [
"(1462, 4)"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(dfs.shape)\n",
"dfs.query('lsubs == 240').shape"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>long</th>\n",
" <th>lat</th>\n",
" <th>lsubs</th>\n",
" <th>avg(ti_spc)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>30.0</td>\n",
" <td>-84.0</td>\n",
" <td>100.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-120.0</td>\n",
" <td>54.0</td>\n",
" <td>110.0</td>\n",
" <td>259.298646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-72.0</td>\n",
" <td>-6.0</td>\n",
" <td>110.0</td>\n",
" <td>232.401929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-126.0</td>\n",
" <td>12.0</td>\n",
" <td>110.0</td>\n",
" <td>222.500234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-126.0</td>\n",
" <td>-48.0</td>\n",
" <td>120.0</td>\n",
" <td>473.815684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-30.0</td>\n",
" <td>42.0</td>\n",
" <td>120.0</td>\n",
" <td>132.442857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>90.0</td>\n",
" <td>-72.0</td>\n",
" <td>120.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>156.0</td>\n",
" <td>54.0</td>\n",
" <td>120.0</td>\n",
" <td>175.724535</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>24.0</td>\n",
" <td>18.0</td>\n",
" <td>120.0</td>\n",
" <td>273.023736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-54.0</td>\n",
" <td>-6.0</td>\n",
" <td>120.0</td>\n",
" <td>208.188191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-180.0</td>\n",
" <td>-78.0</td>\n",
" <td>120.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>-132.0</td>\n",
" <td>-12.0</td>\n",
" <td>130.0</td>\n",
" <td>214.631879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>-126.0</td>\n",
" <td>-36.0</td>\n",
" <td>130.0</td>\n",
" <td>155.782250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>18.0</td>\n",
" <td>-84.0</td>\n",
" <td>130.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>-78.0</td>\n",
" <td>-6.0</td>\n",
" <td>130.0</td>\n",
" <td>270.434448</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>150.0</td>\n",
" <td>-6.0</td>\n",
" <td>130.0</td>\n",
" <td>68.232676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>-48.0</td>\n",
" <td>48.0</td>\n",
" <td>130.0</td>\n",
" <td>254.682354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>78.0</td>\n",
" <td>-30.0</td>\n",
" <td>130.0</td>\n",
" <td>151.519922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>-108.0</td>\n",
" <td>-6.0</td>\n",
" <td>130.0</td>\n",
" <td>209.602678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-150.0</td>\n",
" <td>30.0</td>\n",
" <td>130.0</td>\n",
" <td>186.045607</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>30.0</td>\n",
" <td>-18.0</td>\n",
" <td>130.0</td>\n",
" <td>253.193222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>72.0</td>\n",
" <td>54.0</td>\n",
" <td>130.0</td>\n",
" <td>259.414689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-162.0</td>\n",
" <td>30.0</td>\n",
" <td>130.0</td>\n",
" <td>173.443968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>90.0</td>\n",
" <td>0.0</td>\n",
" <td>140.0</td>\n",
" <td>121.863304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>138.0</td>\n",
" <td>60.0</td>\n",
" <td>140.0</td>\n",
" <td>148.961370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>-84.0</td>\n",
" <td>-24.0</td>\n",
" <td>140.0</td>\n",
" <td>206.480137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>120.0</td>\n",
" <td>-78.0</td>\n",
" <td>150.0</td>\n",
" <td>0.017817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>126.0</td>\n",
" <td>36.0</td>\n",
" <td>150.0</td>\n",
" <td>47.235836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>-90.0</td>\n",
" <td>-66.0</td>\n",
" <td>150.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-156.0</td>\n",
" <td>-66.0</td>\n",
" <td>150.0</td>\n",
" <td>0.007519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39024</th>\n",
" <td>-66.0</td>\n",
" <td>-54.0</td>\n",
" <td>320.0</td>\n",
" <td>223.541635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39025</th>\n",
" <td>90.0</td>\n",
" <td>36.0</td>\n",
" <td>320.0</td>\n",
" <td>79.018943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39026</th>\n",
" <td>-90.0</td>\n",
" <td>24.0</td>\n",
" <td>320.0</td>\n",
" <td>245.012232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39027</th>\n",
" <td>-6.0</td>\n",
" <td>-12.0</td>\n",
" <td>320.0</td>\n",
" <td>176.243834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39028</th>\n",
" <td>108.0</td>\n",
" <td>6.0</td>\n",
" <td>320.0</td>\n",
" <td>61.295913</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39029</th>\n",
" <td>-66.0</td>\n",
" <td>-48.0</td>\n",
" <td>320.0</td>\n",
" <td>227.356704</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39030</th>\n",
" <td>72.0</td>\n",
" <td>42.0</td>\n",
" <td>320.0</td>\n",
" <td>136.002248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39031</th>\n",
" <td>66.0</td>\n",
" <td>78.0</td>\n",
" <td>330.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39032</th>\n",
" <td>162.0</td>\n",
" <td>-30.0</td>\n",
" <td>330.0</td>\n",
" <td>180.624483</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39033</th>\n",
" <td>-108.0</td>\n",
" <td>-54.0</td>\n",
" <td>330.0</td>\n",
" <td>197.508608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39034</th>\n",
" <td>150.0</td>\n",
" <td>-48.0</td>\n",
" <td>330.0</td>\n",
" <td>247.976374</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39035</th>\n",
" <td>102.0</td>\n",
" <td>6.0</td>\n",
" <td>330.0</td>\n",
" <td>65.513380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39036</th>\n",
" <td>-168.0</td>\n",
" <td>54.0</td>\n",
" <td>330.0</td>\n",
" <td>630.061804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39037</th>\n",
" <td>42.0</td>\n",
" <td>-12.0</td>\n",
" <td>330.0</td>\n",
" <td>287.146537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39038</th>\n",
" <td>96.0</td>\n",
" <td>12.0</td>\n",
" <td>330.0</td>\n",
" <td>81.437273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39039</th>\n",
" <td>138.0</td>\n",
" <td>-66.0</td>\n",
" <td>330.0</td>\n",
" <td>136.455366</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39040</th>\n",
" <td>126.0</td>\n",
" <td>36.0</td>\n",
" <td>340.0</td>\n",
" <td>79.878451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39041</th>\n",
" <td>-138.0</td>\n",
" <td>-24.0</td>\n",
" <td>340.0</td>\n",
" <td>263.888422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39042</th>\n",
" <td>-90.0</td>\n",
" <td>48.0</td>\n",
" <td>340.0</td>\n",
" <td>220.267721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39043</th>\n",
" <td>-42.0</td>\n",
" <td>-54.0</td>\n",
" <td>340.0</td>\n",
" <td>156.189999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39044</th>\n",
" <td>-54.0</td>\n",
" <td>24.0</td>\n",
" <td>330.0</td>\n",
" <td>252.520001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39045</th>\n",
" <td>78.0</td>\n",
" <td>18.0</td>\n",
" <td>330.0</td>\n",
" <td>102.888919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39046</th>\n",
" <td>138.0</td>\n",
" <td>-12.0</td>\n",
" <td>340.0</td>\n",
" <td>56.777241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39047</th>\n",
" <td>-138.0</td>\n",
" <td>-54.0</td>\n",
" <td>350.0</td>\n",
" <td>210.910388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39048</th>\n",
" <td>-120.0</td>\n",
" <td>-30.0</td>\n",
" <td>350.0</td>\n",
" <td>238.371818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39049</th>\n",
" <td>-60.0</td>\n",
" <td>-18.0</td>\n",
" <td>350.0</td>\n",
" <td>278.514644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39050</th>\n",
" <td>-36.0</td>\n",
" <td>36.0</td>\n",
" <td>350.0</td>\n",
" <td>93.071214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39051</th>\n",
" <td>-132.0</td>\n",
" <td>-48.0</td>\n",
" <td>350.0</td>\n",
" <td>207.197370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39052</th>\n",
" <td>120.0</td>\n",
" <td>-12.0</td>\n",
" <td>350.0</td>\n",
" <td>87.102147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39053</th>\n",
" <td>-126.0</td>\n",
" <td>6.0</td>\n",
" <td>350.0</td>\n",
" <td>206.403973</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>39054 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" long lat lsubs avg(ti_spc)\n",
"0 30.0 -84.0 100.0 0.000000\n",
"1 -120.0 54.0 110.0 259.298646\n",
"2 -72.0 -6.0 110.0 232.401929\n",
"3 -126.0 12.0 110.0 222.500234\n",
"4 -126.0 -48.0 120.0 473.815684\n",
"5 -30.0 42.0 120.0 132.442857\n",
"6 90.0 -72.0 120.0 0.000000\n",
"7 156.0 54.0 120.0 175.724535\n",
"8 24.0 18.0 120.0 273.023736\n",
"9 -54.0 -6.0 120.0 208.188191\n",
"10 -180.0 -78.0 120.0 0.000000\n",
"11 -132.0 -12.0 130.0 214.631879\n",
"12 -126.0 -36.0 130.0 155.782250\n",
"13 18.0 -84.0 130.0 0.000000\n",
"14 -78.0 -6.0 130.0 270.434448\n",
"15 150.0 -6.0 130.0 68.232676\n",
"16 -48.0 48.0 130.0 254.682354\n",
"17 78.0 -30.0 130.0 151.519922\n",
"18 -108.0 -6.0 130.0 209.602678\n",
"19 -150.0 30.0 130.0 186.045607\n",
"20 30.0 -18.0 130.0 253.193222\n",
"21 72.0 54.0 130.0 259.414689\n",
"22 -162.0 30.0 130.0 173.443968\n",
"23 90.0 0.0 140.0 121.863304\n",
"24 138.0 60.0 140.0 148.961370\n",
"25 -84.0 -24.0 140.0 206.480137\n",
"26 120.0 -78.0 150.0 0.017817\n",
"27 126.0 36.0 150.0 47.235836\n",
"28 -90.0 -66.0 150.0 0.000000\n",
"29 -156.0 -66.0 150.0 0.007519\n",
"... ... ... ... ...\n",
"39024 -66.0 -54.0 320.0 223.541635\n",
"39025 90.0 36.0 320.0 79.018943\n",
"39026 -90.0 24.0 320.0 245.012232\n",
"39027 -6.0 -12.0 320.0 176.243834\n",
"39028 108.0 6.0 320.0 61.295913\n",
"39029 -66.0 -48.0 320.0 227.356704\n",
"39030 72.0 42.0 320.0 136.002248\n",
"39031 66.0 78.0 330.0 0.000000\n",
"39032 162.0 -30.0 330.0 180.624483\n",
"39033 -108.0 -54.0 330.0 197.508608\n",
"39034 150.0 -48.0 330.0 247.976374\n",
"39035 102.0 6.0 330.0 65.513380\n",
"39036 -168.0 54.0 330.0 630.061804\n",
"39037 42.0 -12.0 330.0 287.146537\n",
"39038 96.0 12.0 330.0 81.437273\n",
"39039 138.0 -66.0 330.0 136.455366\n",
"39040 126.0 36.0 340.0 79.878451\n",
"39041 -138.0 -24.0 340.0 263.888422\n",
"39042 -90.0 48.0 340.0 220.267721\n",
"39043 -42.0 -54.0 340.0 156.189999\n",
"39044 -54.0 24.0 330.0 252.520001\n",
"39045 78.0 18.0 330.0 102.888919\n",
"39046 138.0 -12.0 340.0 56.777241\n",
"39047 -138.0 -54.0 350.0 210.910388\n",
"39048 -120.0 -30.0 350.0 238.371818\n",
"39049 -60.0 -18.0 350.0 278.514644\n",
"39050 -36.0 36.0 350.0 93.071214\n",
"39051 -132.0 -48.0 350.0 207.197370\n",
"39052 120.0 -12.0 350.0 87.102147\n",
"39053 -126.0 6.0 350.0 206.403973\n",
"\n",
"[39054 rows x 4 columns]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Termal Intertia Kernal Density Estimation"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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vVI6dBVwHzAJuAT6VUkrAUOAPEfEwcB9wW0rpt5VzfQH4XETMBXapnFuSSm/5cnjlFdhr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\n3vDhsHDhlvcrg9ZQqeqZOBF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RZ5+FwYOLrkIqj9Gj4S9/gXPOgZNPho9/HFpaiq5KkqTq\nMlRKJbJsGey+e9FVSOXSqxf84z9mjxzZbbdsruWZZ8LUqfDqq0VXJ0nStotUwokeEZHKeN/SwIEw\nfz7svHPRlUjltWYNXHst/Oxn8PDDcOKJWdCcMAHGjYNddsmGz26NDRuy865evfnX2rXZ/M6NG7M/\n27/69IHtt4e+fd/4c9ddYciQ7NW3bz7/TCRJtRcRpJS28v84nZyrjOHKUKkyevXV7JEHa9du/Q+s\nkvLx7LNw221w//1w330wdy6sXw977pk9+7Jfv+yVUvbf7muvZX++8sqbw2K/ftnqs62v9l/3758F\nxR49sr8D2r56VMYtrVuXnfuVV7K/M9asgeefh2eeyV477gj775+9DjwQjjsuC8L+nSJJjcdQuY0M\nlSqjBQuybsiiRUVXImlzXnoJFi/OwuKaNdmrR4+si7jddtmffftuGhb79s0/2KUES5fCY49lr0ce\ngT/9CVauhFNOgb/9Wzj11CzMSpLqn6FyGxkqVUYPPACf/CQ8+GDRlUhqJgsWwM03wy9/CX/9K5x9\ndvZ3zfjxRVcmSdqcaoZKF+qRSsJFeiTlYfjwbCGiO+6AOXNgn33gve+Fww+Hn/wkG84rSWpuhkqp\nJAyVkvI2ZAh88Yswbx585Svw/e/DmDFw5ZXZfFBJUnMyVEol8eyzhkpJtdGzJ7zzndmcy6uuguuu\ng5Ej4bvfzRYVkiQ1F0OlVBLLlsHgwUVXIals3va2bIXbX/wCbrklC5ff+57hUpKaiaFSKgmHv0oq\n0pFHZgv6XH893HgjjB6dDY91WKwkNT5DpVQShkpJ9eDII+G3v4UpU+BXv8rC5Q9+kD0jU5LUmAyV\nUkkYKiXVk6OOyobF/uQn2ZzL0aOzBX0Ml5LUeAyVUkk8+6xzKiXVn2OOgd/9Dq65Bn76Uxg1Ci67\nDFasKLoySVJXGSqlEli/Hl58EQYNKroSSerYccfB73+fLegzcya85S3w938P994LKRVdnSRpcyKV\n8G/qiEhlvG+V19KlcMgh2RBYSWoEzz6bDYe9+mrYsAE+8AF4//th332LrkySmkNEkFKKapzLTqVU\nAs6nlNRoBg+Giy+G2bOzYbHLl2eL/Bx9NPzXf/lLMkmqJ4ZKqQQMlZIaVQQccUQWJBcvhi99Ce67\nD/bbD04+Ga66KhveL0kqjqFSKoFly1ykR1Lj69MH3vUu+PGPYckS+Id/gJtughEj4Kyz4Oc/hzVr\niq5SksrHUCmVwNKlsMceRVchSdXTrx+cc072rMsFC7JQedVVMHw4XHQRPPVU0RVKUnkYKqUSWLLE\nUCmpeQ0cCB/+cPbcy7/+9Y0hs+95Dzz0UNHVSVLzM1RKJWColFQWe+8N//Ef8PTTcPzx8O53Z+Fy\n1qyiK5Ok5mWolErAUCmpbPr3hwsvhHnz4Jhj4O1vh899Dl56qejKJKn5GCqlEjBUSiqrvn3hn/8Z\nHnsMVq6EsWPhhhuKrkqSmkuklIquoeYiIpXxvlVOKcH228MLL2QLW0hSmd19dzb/8phj4Fvfgp12\nKroiSSpGRJBSimqcy06l1ORWrMjCpIFSkuDYY+GRR7LhsYcemi3sI0naNoZKqck59FWSNtW/P1xx\nRbagz2mnwX//dzaqQ5LUPYZKqckZKiWpY+99L9xzD3znO3DBBbB+fdEVSVJjMlRKTc5QKUmdGz06\nC5YtLXDmmbBqVdEVSVLjMVRKTW7pUkOlJG3OwIFw880wbBgcdxwsXlx0RZLUWAyVUpOzUylJW9a7\nN3z/+3DuuVmwfOqpoiuSpMZhqJSa3JIlMHRo0VVIUv2LgIsv/n/t3X20lVWdwPHv74IIovmCoiaC\noZChMqQjvk2GmSQmWhlKNROGpbOsycx8QV1mQ2smXTpTU8vlVGRoJgllYr5AhLhWAaJLQAQRzJEX\nQZxRULn5Buz5Yz80xzsXELjnPuee8/2sddZ5zj7P2fzu2exznt/Z+9kPfOtb8NGPwjPPlB2RJHUM\nncsOQFJ1OVIpSdvn4ovzZZhOOQUeeggGDiw7IkmqbSaVUp0zqZSk7Xf++dCtGwwdCtOmwZFHlh2R\nJNUuk0qpjm3aBC++6PRXSdoR552Xr185dChMnw6HH152RJJUm0wqpTr20kt5VcNddy07EknqmEaO\nhLfegtNOgxkz4NBDy45IkmqPSaVUx5Yvhz59yo5Ckjq2UaPgzTfh1FPhkUf8XJWklqq++mtEnB4R\niyNiSURc2crzXSJiQkQsjYhZEdG74rkxRfnTETF0W3VGxG0R8VxEzI2IJyLCU+vV0JYtg969t72f\nJGnrLroILr00J5arVpUdjSTVlqqOVEZEE/Aj4FRgFfBYRNybUlpcsdsFwCsppX4RcR5wIzAyIgYA\n5wIfAnoB0yKiHxDbqPOylNI91fy7pI5i+XKTSklqK5dcAm+8AR//eB6x3G+/siOSpNpQ7ZHKwcDS\nlDxsXVoAABOHSURBVNKylNI7wATg7Bb7nA2ML7YnAR8rts8CJqSUNqSUngeWFvVtq06vvSkVnP4q\nSW3rqqvgM5/Ji/esW1d2NJJUG6qdgB0ErKh4vLIoa3WflNJG4NWI2KeV175QlG2rzu9GxLyIuDki\ndmmTv0LqoByplKS2N3YsDBkCw4bB+vVlRyNJ5av2Qj3RSll6j/tsqby1RHhznVellNYUyeRPgCuB\n77YW2PXXX//X7SFDhjBkyJDWdpM6NJNKSWp7EfBv/wYXXghnnQX335+vaSlJtWzGjBnMmDGjKnVH\nSi1zvDasPOJ44PqU0unF46uAlFK6oWKfB4t9Ho2ITsDqlFLPlvtGxEPAt8nJ5lbrLMo/Sj6/8qxW\n4krV/LulWrHvvrBwIey/f9mRSFL92bgRvvhFWLsWfvtb6NKl7Igk6b2LCFJKrQ3kbbdqT399DDgs\nIvpERBdgJDC5xT73AaOK7RHA9GJ7MnnBni4R8QHgMGDO1uqMiAOK+wA+BTxVtb9MqnHNzfnWs2fZ\nkUhSferUCcaPh65d4fOfhw0byo5IkspR1aSyOEfya8BUYCF54Z2nI+I7EXFmsds4YN+IWAp8A7iq\neO0i4G5gEfAAcHHKWq2zqOvOiJgPzAd6sIWpr1IjWLECDj44T9OSJFVH585w11353MrRo2HTprIj\nkqT2V9Xpr7XK6a9qBFOnwo03wrRpZUciSfXvL3/JC/cMGAC33OIPepJqX0ea/iqpJC7SI0ntZ7fd\n4He/g7lz4fLLwd+uJTUSk0qpTi1bZlIpSe1pjz3gwQfhD3+Ayy4zsZTUOEwqpTq1fDn06VN2FJLU\nWPbeG6ZPh9mz4ctfzivESlK9M6mU6tTzz5tUSlIZ9t47n9e+fDmMHAlvvVV2RJJUXSaVUp169lk4\n7LCyo5CkxrT77vkcy40b8wI+r7xSdkSSVD0mlVIdam7OBzC9epUdiSQ1rl13hYkT4eij4bjjYPHi\nsiOSpOowqZTq0J//DH37QpM9XJJK1akT3HQTjBkDJ58MU6aUHZEktT0POaU65NRXSaoto0fDpElw\n/vlw3XWwYUPZEUlS2zGplOqQSaUk1Z6TT4YnnoBZs+CUU/KlnySpHphUSnXIpFKSatOBB+YpsMOH\nwzHHwA9+4GVHJHV8JpVSHTKplKTa1dQEV1wBM2fCPffAiSfC/PllRyVJO86kUqpDzz4L/fqVHYUk\naWv694fp0+HLX4bTToMLLoAXXig7KknafiaVUp154w146SU4+OCyI5EkbUtTE3zlK7BkCey3Hwwc\nCFdfDS+/XHZkkvTemVRKdea55+CQQ/Iy9pKkjmGvveB734N58+C//zvPNrn0Uli5suzIJGnbTCql\nOuP5lJLUcR18MPzkJ7BgQf5xcODAfDmSBQvKjkyStsykUqozixfn83QkSR3XQQfBTTflHwr79oVh\nw+AjH4G77oK33y47Okl6N5NKqc489RQcdVTZUUiS2sI++8C118J//VeeDjtuHPTuDddck8skqRaY\nVEp15qmn4Mgjy45CktSWdtkFPvMZmDYNZsyA9eth8GA45RS4/XZobi47QkmNLFJKZcfQ7iIiNeLf\nrfq3YQO87315kYfu3cuORpJUTW+9BffdBz//OfzpT/DpT8OXvgQnnZRXlZWkrYkIUkrRJnU1YnJl\nUql6tXgxnHlmPgdHktQ4Vq+GX/wiJ5ivvQaf/SyMGAHHH1+bCeamTTnmZctg7VpICfbdN58/2rNn\n2dFJjcGkcieZVKpeTZqUDyp++9uyI5EklWXhQpg4Md9eew3OOSf/4Ph3fwddu5YT0wsvwKxZMHt2\nvj3xBOy5Zz4/tEcPiMizbJYuzUnl8OHwj//oauZSNZlU7iSTStWr66+HjRth7NiyI5Ek1YJFi/IP\njlOmwJNP5qmxQ4fCySfny5V06dL2/+amTTmx/eMf8+1Pf8rngJ5wQr4dfzwceyzssUfrr50/HyZM\ngJ/9LK96e8MNcOCBbR+n1OhMKneSSaXq1TnnwLnnwnnnlR2JJKnWrFsHDz8MU6fCzJn5VIkjj4Rj\njsmXourfH/r1gwMOgN13z6OHW/Pmm7ByZb4tWZKT1gUL8v1+++WR0ZNOyvf9+2+7vpZefx2++92c\nXN56a/6Ok9R2TCp3kkml6tUHPwi/+Q0ccUTZkUiSal1zc56GOndunna6ZEm+f/HFfI7jfvvl0cTO\nnfPqsxH5Nc3NOeFrbs7X0+zVCw49NI98HnVUvm/L8yLnzMk/mI4cCf/6r9ufnEpqnUnlTjKpVD16\n4418PbPXXstf/pIk7ajm5nyO4/r1eWXxd97JiWb37nkUs3v3/J3TXosAvfxyPi90wAD48Y+hU6f2\n+XelemZSuZNMKlWPZs6Er38dHn+87EgkSWp7zc3wqU/lkdFx42pzVVupI2nLpNLuKNWJ2bPhuOPK\njkKSpOro3j2vbv7MM3D55WVHI6mSSaVUJx59NK+oJ0lSvereHe6/Hx58ME+DlVQbnP4q1Yk+feD3\nv88r7EmSVM+WLMmryt5zT15hVtL2c/qrpHdZvTqvxNevX9mRSJJUff37w89/DiNG5EuaSCqXSaVU\nBx59NJ9P6TLrkqRGccYZ8NWv5kuNbNhQdjRSYzOplOrA5qRSkqRGMmYMdOsGY8eWHYnU2EwqpTow\nc6aL9EiSGk9TE9x+e16055FHyo5Galwu1CN1cK+/DgceCGvW5FXxJElqNA89BF/5CsybBz16lB2N\n1DG4UI+kv5oxAwYPNqGUJDWu00+H886D0aPBcQOp/ZlUSh3clCnwiU+UHYUkSeX6l3+BVavgllvK\njkRqPE5/lTq4fv1g4kQYNKjsSCRJKtfSpXDiiTB9Ohx1VNnRSLXN6a+SAHjuuXxO5cCBZUciSVL5\n+vWDm2/Olxn5y1/KjkZqHCaVUgf24IMwdGhe/U6SJME//AN8+MPwzW+WHYnUODwUlTqwiRPhnHPK\njkKSpNoRkc+rnDYNfvObsqORGoPnVEod1OrVMGBAvu/atexoJEmqLXPmwPDhMGsW9O1bdjRS7fGc\nSkn84hfw6U+bUEqS1JrBg+Haa/N3ZXNz2dFI9c2RSqkDSgkOPxxuuy2vcidJkv6/lOD88+Htt+GX\nv8xTYyVljlRKDe4Pf4BddoETTig7EkmSalcE3HorLFkCN9xQdjRS/epcdgCStt9NN8Fll/mLqyRJ\n29KtG9x7L5x0EhxwQB65lNS2TCqlDmb2bFi0CD7/+bIjkSSpY+jVC6ZMgSFDoEePvICPpLbj9Fep\nA0kJrrwSrrsOdt217GgkSeo4Dj8cJk+G0aPhoYfKjkaqLyaVUgdy553w+utO3ZEkaUcMHpynwo4a\nla/1LKltuPqr1EEsXw5/+7fwwAP5XpIk7Zh58/IU2AsvhGuugSaHWdSAXP1VajCvvAJnnAFjxphQ\nSpK0swYNgjlz8g+1w4fD6tVlRyR1bCaVUo1buxY++UkYNgy+8Y2yo5EkqT4ceCA88ggcc0xOMm+9\nFTZsKDsqqWOqelIZEadHxOKIWBIRV7byfJeImBARSyNiVkT0rnhuTFH+dEQM3VadEXFIRMyOiGci\n4q6IcHXbDmrGjBllh1ATHn0Ujj46L4N+4421cwkR26f22Ua1zfapfbZR7WuLNurSBf75n/PKsBMm\nwMCBMH48vP32zsfX6OxDjaWqSWVENAE/Aj4BHAF8LiIOb7HbBcArKaV+wPeBG4vXDgDOBT4EDANu\niWxrdd4A3JxS+iCwrqhbHVCjfxA9+SR84Qtw9tlw8835upS1klCC7dMR2Ea1zfapfbZR7WvLNho0\nCB5+GL7/fbjjDnj/++Gii2D6dHjzzTb7ZxqKfaixVHskbzCwNKW0DCAiJgBnA4sr9jkb+HaxPQn4\nYbF9FjAhpbQBeD4ilhb1xVbq/BjwueL144Hrgf+syl8mtYGUYN06+POfYenSPDI5bVpe4fXCC/NU\nnD32KDtKSZLqXwQMHZpvy5bBr36V1zJYuDAnncccky9L0r9/njrbsyfss4+L/EhQ/aTyIGBFxeOV\n5MSw1X1SShsj4tWI2Kcon1Wx3wtFWbRWZ0T0ANamlDZVlL9/S4Gdeea7H29pMdjtKW+LOqw7369Y\nAVOnVqfu91Je7bqbm+HVV3NC2bUrHHpovh17LPz0p3nJc7+kJEkqR58+cMUV+bZ+PcyeDfPnw9y5\ncPfd8OKL8NJL+Ufg7t2hWzfYbbf/u+/cOX+PNzXlZHXzdsvHtTQLqa0tWQKPP152FOWo53bdkqpe\nUiQiPgsMTSldWDz+e+DYlNIlFfs8Veyzqni8eURyLDAzpfTLovynwP1Ap9bqLPafVUyjJSJ6Afen\nlP6mlbi8nogkSZKkhtZWlxSp9kjlSqB3xeNewKoW+6wADgZWRUQnYM+U0tqIWFmUt3xttFZnSul/\nImKviGgqRitb+7eAtnvzJEmSJKnRVXuC3WPAYRHRJyK6ACOByS32uQ8YVWyPAKYX25OBkcXqsB8A\nDgPmbKHOe4vXTC/qoKhzc7kkSZIkqQqqOlJZnCP5NWAqOYEdl1J6OiK+AzyWUvodMA64o5j2+jI5\nSSSltCgi7gYWAe8AF6c8V7e1Ojcv/HMVMCEixgJzi7olSZIkSVVS1XMqJUmSJEn1rSHWl4yIb0XE\npmJV2c1l/xERSyNiXkQMqigfFRFLIuKZiPhiORE3joi4MSKeLtrh1xHxvornxhRt9HREDK0oPz0i\nFhftdGU5kTcu3//yRUSviJgeEYsiYkFEfL0o3zsiphafX1MiYs+K17T6mafqioimiHgiIiYXjw+J\niNlFG90VEZ2L8i4RMaFoo1kR0XvrNWtnRcSeETGx+I5ZGBHH2YdqS0RcGhFPRcSTEXFn0U/sQyWK\niHERsSYinqwo2+5+4/F2dWyhfdrlWLvuk8rIq8B+HFhWUTYMOLRYKfYi4NaifG/gOvJqsscB367s\nGKqKqcARKaVBwFJgDEBEDADOBT4EDANuiawJ+BHwCeAI4HMRcXgpkTcg3/+asQH4ZkppAHAC8NWi\nHa4CpqWUPkg+x3xzf2r1M0/t4hLyaRyb3QDcXLTROuCCovwC4JWijb4P3NiuUTamHwAPpJQ+BPwN\n+XrX9qEaERHvB/4JODqlNJB8ytbnsA+V7TbyMUCl7eo3Hm9XVWvt0y7H2nWfVAL/Dlzeouxs4HaA\nlNKjwJ4RsT/5zZuaUno1pbSO3Aint2ewjSalNK3i2qKzyav2ApwFTEgpbUgpPU/uBIOL29KU0rKU\n0jvABHJ7qn34/teAlNKLKaV5xfZ64Gly3zkbGF/sNp7/a5stfeapioofNc8AflpR/DHg18X2eOBT\nxXZl200CTm2PGBtVROwBfCSldBtA8V3zKvahWtMJ6F6MRnYjr+p/Cvah0qSU/gisbVG8vf3G4+0q\naa192utYu66TyogYDqxIKS1o8dRB5EuZbLayKGtZ/kJRpvYxGnig2N5SW2yp7dQ+fP9rTEQcAgwi\nf1Hsn1JaAznxBHoWu/nZVo7NP2omgIjoAayt+HKv7D9/baOU0kZgXVScsqE21xf4n4i4rZie/OOI\n2A37UM0orl9+M7Cc/H6/CjwBrLMP1Zye77HfeLxdvqoda1f7OpVVFxG/Byp/LQzyF/i1wNXAaa29\nrJXHqZVyinLthK200TUppfuKfa4B3kkp3VWxT0uJ1n8IsY3aj32khkTE7uRf5C9JKa2PiC21he3W\nziLik8CalNK8iBiyuZj/3xap4rl3VYFtVE2dgaOBr6aUHo+IfydP4bMP1YiI2Is8OtKHnFBOJE/R\na8k+VLs83q4h1T7W7vBJZUqptaSRiDgSOASYHxFBHup9IiIGkzPugyt270WeUrESGNKi/OG2j7qx\nbKmNNouIUeQpYh+rKN5SGwXQu5VytY+V+P7XhGI62CTgjpTS5mvyromI/VNKayLiAOClonxL/UnV\ncxJwVkScQZ62twf5PK89I6KpGGmpbIfNbbQqIjoB70sptZxiprazkjyT6fHi8a/JSaV9qHZ8HHgu\npfQKQETcA5wI7GUfqjnb22883m5n7XGsXbfTX1NKT6WUDkgp9U0pfYD8xn04pfQSMBn4IkBEHE+e\nSrEGmAKcFnlFuL3Jo5xTSvoTGkJEnA5cAZyVUnqr4qnJwMhiNbcPAIcBc4DHgMMiok9EdCFf13Ry\ne8fdwHz/a8fPgEUppR9UlE0Gzi+2zwfurShv7TNPVZJSujql1Dul1JfcT6anlP6efOA0othtFO9u\no1HF9gjyYheqkuL//4qI6F8UnQosxD5US5YDx0dE12JwYHMb2YfK13LWxfb2G4+3q+td7dNex9od\nfqRyO/x1uD2l9EBEnBERzwLNwJeK8rURMRZ4vNj/O8UJxKqeHwJdgN/n7wxmp5QuTiktioi7yasm\nvgNcnPJFVTdGxNfIJ3U3AeNSSk+XFHvDSSn5/teAiDgJ+AKwICLmkj+vriavinh3RIwmH5CNgC1/\n5qkUVwETiu+aucC4onwccEdELAVeJn+Jq7q+DtwZEbsAz5H7RSfsQzUhpTQnIiaR+8k7xf2PyeeD\n2YdKEhG/JI8y9oiI5cC3ge8BE99rv/F4u3q20D5X0w7H2pFfK0mSJEnS9qvb6a+SJEmSpOozqZQk\nSZIk7TCTSkmSJEnSDjOplCRJkiTtMJNKSZIkSdIOM6mUJEmSJO0wk0pJkiRJ0g77XwkhWOJV2LD2\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25ef50518>"
]
},
"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": 59,
"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": 60,
"metadata": {},
"outputs": [],
"source": [
"dataframes = [dfs.query('lsubs == {}'.format(lbin)) for lbin in np.arange(10,361,10)]\n",
"arrays = [np.zeros((30,60)) for i in range(len(dataframes))]\n",
"\n",
"for arr, df in zip(arrays, dataframes):\n",
" if len(arr) and not df.empty:\n",
" indices = np.vstack(df['indices'])\n",
" arr[indices[:,1], indices[:,0]] = df['avg(ti_spc)']"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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75CTvSvKO/XVEIQEAAAA2v7OTXDJsX5LkZau0OTPJju7+anffn2RHkrNmzq9W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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25f65c7b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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AAAAmU0gAAAAAJvv/AfPy+hBP/h73AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25f261710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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+/EP5i0996ITvV9V3JjnW3Qeq6qJ7myfb/bqzmX4oAgAAAMAW+9pHXZivfdSF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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25f069ac8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e88f7f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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WrKJfVddV1aGquv2Ec9dW1YGq+uTkuGJjwwQAAADWYi1D9/ckuXyF829srV0yOf5qznEB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\nAQAAYIFI9AEAAGCBSPQBAABggfw/tIsP78Rn+68AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e768b70>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e721a20>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25ef65cc0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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RBwAAgCHr2w5vdAp9AAAAGNATGww/rd4CAAAAgyT6AAAAMKAnNnRfog8AAAALRKIPAAAA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe28bbec7f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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JAAAAAMwcmQAAAAAwYGmOUgFkAgAAAMCCkAkAAAAAA3pp2jOYHEEAAAAAGNCW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\nAAAALIj/H2i4sOICRPDzAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25f4a5908>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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kX5LTq+rUqtqa5KIkS1cpuD7JxZP9C5Psnexfl+SiyaoIpyU5Pckt3f3u7t7R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\nAgUAAADAlEABAAAAMCVQAAAAAEz9/5fsepT/TRYhAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e9449b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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AALqM0HAAmQAAAACwRcgEAAAAgC7mBAAAAAA2G5kAAAAA0CVHZ06ANekEOHzoWCluYrJe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\nH5cE8uYZAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e9b6e80>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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HTk/3l3v44RPdsfsOLHTHtv5mkcVTy92x8wuz3bELe/pO06np/nb83E+/tDv2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\nAMbYiIkBtwuPCAQAAIBdQiYAAAAAjNE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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e7be2e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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lxFwOAAAAAKw7LgcAAACADm4RCAAAAKw7KgEAAACgwzBNDKgSAAAAADYIlQAA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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25ef51f98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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r/WL3zHI5uzKsH9SHrZ6dW+6xD/Vod9Bq+8FgWO8XSyv1/XYwrO+3nz9c38Y7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\nRQBJkiRJkqaERQBJkiRJkqbEf5KMW2vsM+SdAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25f710438>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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1tvwjmbmamQcj4nBEXHVa+JURcV/X9+sEAAAAgA6Z/R8R+OJrr4oXX/sXf6+/7g8/eKZy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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e78be10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Puz0+XsRF59Y31spq/fx1ZK7emW++tb7M9947W8qNjdX3+ZXl+vlr997pcnZ+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\nkqYIiwCSJEmSJE0R/wBxZkVKOoSuvwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e69aef0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e596be0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25f785240>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25f13ba58>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25f309320>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e96fd68>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e9c0e10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e998ef0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25ea37128>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25faa3d30>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e93f6d8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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8X86ef/5UObu6Wt/H11xejsYAh0UcWazv59mpARoewOGl2jE1NlJ/bQ+26XJ2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\nBwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e6e5be0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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hc3iGA6gEAAAAgC1iXSoBHl3cVsotTf2tcpvHlifK2ZVW7+V55OhkKXfe9GK5zc+NXVnO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\n+rYe75xTE/XsoMc6Z9bbzR7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AADq0wfDMCaATAAAAADoYDgAAAABsOioBAAAAoENzi0AA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\nNEbYBJAkSZIkaYz4F3m2AUwLyt0EAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e61bb38>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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AAEAXlwgEAAAA1huVAAAAANAhc3imA6gEAAAAgA1iVSoBPv3AVCn32I4vL7d5Yq4+vvG/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\nD2qinXrT0bMDAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e88f278>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Awd3l7JW7Tpaz80v17fvYsVr2oV4/r84t1M+r199QX9YP37+rnN21vRzN3OLzy9n7Hq1f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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25fa9de10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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4H3qk/nnq/s8fLeVGR+v71NHD9XNyn/+PtMHafEYfekM0J4DhAAAAANDBcAAA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\neM2MiBjtce2bmatnp4qXoT5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mKzPzM5l5e2b+2LloY207AR69aS2bZwjZp1hp9ilWmn2K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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe28bc441d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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9zo9HjtT3z0iPsdEP7q/vozvvrp8fH37gUCluZLRe3z736DnSo9yF9f+/15Yw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\nc4v1272xkfp27hM7Plprjwf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RByLi9yPiW07w0V49EjoBAAAAoMPJJvJ7PC684vlx4RXP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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e791c88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e9a5eb8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25e95a2e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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AAB3MCQAAAABsO3oCAAAAQBePCAQAAICdoY1QJ/rR2RIAAACgk54AAAAA0KGN\n0HAAPQEAAABgh9ATAAAAADp4RCAAAACw7egJAAAAAB1ajM6cAJd1I8Da6qCcHbZWzi6dWilnq8Z6\nTDRxemG5nO2zn4bLq+Xs2upaOdvH7K6Zcvb04tKWlDu7a7qU27uvXubn3XJlOfuedz5Uzl591VQ5\nO96jX9O+XcNydmmlfu1OjJejcWKldl5ERAyGm38TO7VcL7NP/XjydDkap5frt8i1+u0r2rBeLx+8\nek85u3S6T51e2+Dd+2bLZV5xcFc5e+BAva5Z7vERIeuXbQx6lDtT39xYXKpXrn2y1Y8YB/bUr5/D\nx+sHaNjq9VSPSz5W+5wXPc7Hueke+/nx+j13eblH5Vo0PlE/j/scnxyrl9t6/B+K8zMcAAAAANh2\nLuueAAAAALDVPCIQAAAA2Hb0BAAAAIAOozQxoJ4AAAAAsEPoCQAAAAAdPB0AAAAAdogWueFf55OZ\nY5n5/sx8y/rPN2bmuzLz7sz85cycWF8+lZlvysx7MvN/Z+YNF7MtGgEAAADg8vF9EXHXWT//ZET8\nm9bazRExHxHfvr782yPiaGvtORHx0xHxry7mzTUCAAAAQIeWYxv+dS6ZeSgiXh0RP3/W4q+IiF9f\n//6NEfE31r9/zfrPERG/FhEvv5ht0QgAAAAAl4fXRcT3R0SLiMjMgxFxrLU2XP/9gxFx3fr310XE\nAxERrbVBRMxn5oELFWBiQAAAAOiwEY8IfNe73x3vevd7zvv7zPxrEXG4tfaBzLz9M4vXvz57df7i\nd5/1Fmf97rw0AgAAAMAl9uIv+qJ48Rd90RM//8y//XdPfslLI+JrMvPVETEbEXvizFj/fZk5tt4b\n4FBEPLz++gcj4vqIeDgzxyNib2vt2IXWw3AAAAAA6LAZcwK01n6otXZDa+2miPj6iHhba+3vRsQf\nRsTXrr/sWyLizevfv2X951j//dsuZls0AgAAAECHzXxE4Dn8QET8k8z8WEQciIjXry9/fURcmZn3\nRMT/tf66C8rWLjhkoJfMbN/6o4/UsmP1NorlU8vl7FYYDIYXftF5DNcG5ezqylo528fkVH0kytTM\nVDl7enGpnM2x+jigF7/sxnK2eonu2VVf32sP1M+p44vj5ezCqXI0Zqbr2Wv216+D2cmtuYZOr9av\noUGrnRvjWb9fHFuor+/kRL3chdP166BP9bi6Ws8unqpff5n17e3zeWB5uXYP27WrXl/MTNc/I8zN\nlKO9zouZdosiAAAgAElEQVQet5Feds/Ws+Nj9fPi9Ep9g/ftqp1Tq2v1Mo+erGcXFuuf4w7u335/\nkztV/zgVkz0GIz9+tF4/XnmgVt8M6kXGR+8+Wc5OTNbPi2OPLZSzk9O1A/TzP3xVtFb8gLENZGb7\nxMc/vuHv+6xnP3tL9ps5AQAAAKBD69HQfrnZfk2PAAAAQImeAAAAANBhlEY7XLAnQGa+PjMPZ+af\nn7XsRzLzwcx8//rXKy/tagIAAAB9XcxwgDdExF89x/Kfaq19/vrX727wegEAAMBlocXYhn9tlQsO\nB2it/WlmPuMcvxqd/hAAAABwHk/xkX6XtT7ND9+dmR/IzJ/PzH0btkYAAADAJVGdGPDnIuJftNZa\nZv54RPxURHz7+V58xx/+6ye+v/bGl8TTnvnSYrEAAABspYc/+Y749KfesdWrsalGqSdAqRGgtfbY\nWT/+54j4H12vv/XLv79SDAAAAJeZp9/00nj6TX/xh9073vavO17N5eZiGwEyzpoDIDOvba09sv7j\n34qID230igEAAMDlYEf1BMjM/xYRt0fEwcy8PyJ+JCK+PDNviYhhRNwbEd95CdcRAAAA2AAX83SA\nbzzH4jdcgnUBAACAy86O6gkAAAAAO1lro9MI0OcRgQAAAMA2sik9AW7+nCtLueGwlct88IGFcnZl\nabWcXTq1Us5W5Vi9LWf3/l09yq23hrUex/aKq3aXswcOXF0vd3/9cpmdKUdjvHh41wb1MheXxsvZ\niXo0nn/9Ujm7MqgXPD2xVs4uLk/2yPbYWT3MTA1LuT7r26Oaitnp2vpGRMwv1Nd5eaVeT7X6Kseg\nx7W7b299R6+t1bd3drZW7tRE/T4yWb/0YrLHp5+nHajXF/OL9YIX69VjtPqhjeXV+jFare+qWCmW\nmz3+UDfV47wY6/GZaLn+sTP21T/G9TqnDuzdmnKnpur7uXpu7J4rFxmf+8I95Wyfz3FLh+orffWB\n2o76+XKJ28coDQfQEwAAAAB2CHMCAAAAQAc9AQAAAIBtR08AAAAA6DBKPQE0AgAAAEAHjwgEAAAA\nth09AQAAAKDDcISGA+gJAAAAADuEngAAAADQYZQmBtQTAAAAAHYIPQEAAACgwyg9HUAjAAAAAHQw\nHAAAAADYdvQEAAAAgA6GAzxFVx2odTiY6rF2mXvK2dZaObuyUssuLw/KZe7ZU99Rk5P1ziBzM/UL\n4dRSfR9PTdbLHevR92Vqsp696ZrlcnZ1UFvpE6fr58Wu6fr5+MixermPnZwuZ4fDcjRmp+snxuLS\neDm7b26tnJ0Yr19DxxZqx+jUcv3a61Ofzy/U9/H+3fX9tG9XORqDYY/6cVd9e/vs5+jRzTGL0UGP\n6/bg3no9NexxfHp8RIjds/V1nu5x71tcqtdx1WPbN/vYfC28b3e9zIn6pRd7d2/Nfw5O1z9exJ7Z\n+sk8M1XP9jkfry7+nyIiYna6ts6LS/VjO93js+Pqaj3bx7//12/fmoLZVHoCAAAAQIdRmhNAIwAA\nAAB0GKXhACYGBAAAgB1CTwAAAADo0GMam8uOngAAAACwQ+gJAAAAAB3MCQAAAABsqMyczsx3Z+Yd\nmXlnZv7I+vL/mpkfzcw/z8yfz8zxszI/m5n3ZOYHMvOWC5WhEQAAAAA6tMgN/zpnOa0tR8SXt9Zu\njYhbIuJVmXlbRPzX1trzWmsvioi5iPj7ERGZ+aqIeFZr7TkR8Z0R8R8utC2GAwAAAECHzRwO0Fo7\ntf7tdJz5P3trrf3uWS95T0QcWv/+NRHxi+u5d2fmvsy8prV2+HzvrycAAAAAXCYycywz74iIRyLi\n91tr7z3rdxMR8U0R8T/XF10XEQ+cFX9ofdl5aQQAAACADps1HCAiorU2XB8OcCgivigzP+esX/9c\nRPxxa+2d6z+f641a17YYDgAAAACX2B3veXvc8d4/uejXt9ZOZOYfRcQrI+Ku9UkCr2ytfcdZL3sw\nIq4/6+dDEfFw1/tqBAAAAIAOw86/rV+cz/vCl8XnfeHLnvj5DT/32r/0msy8MiJWW2vHM3M2Il4R\nET+RmX8/Ir4qIr7iSZG3RMR3R8SvZOaLI2K+az6ACI0AAAAAcLl4WkS8MTPH4szw/V9prf1OZq5G\nxL0R8a7MbBHxG621H1//3asz8+MRsRgR33ahAi7rRoDJiXpzy97d9ekO+rTyrK7WwjNX1Q/Fntly\nNCbG6xt79ES93LW1Pse2PjPnymo5GuM9ZtCYHBuWs8tr4xd+0Tn0ObYPH62fj8P6pvbKzs3Uw/tn\nVsrZ1qbL2fGx+jFaWKqdF31M9bhj9KlX50/Ws2Nj9fpivMcunqufFr2ug9np+o5eWqnvq13F6291\nrV7mqaV6pXxquV7utVfUD1DrcWwP7l4uZ6cnpsrZxeX6ft7MmbQ/Y9BjH68N6tmJHvVFn2t+10z9\nmh8M68fn2EI926d+nJmq76zq57iJXfV9PL9Qv34metxzZ2c2/9rbCbrG8G9oOa3dGRGff47lkx2Z\n73kqZVzWjQAAAACw1baiYfNS8XQAAAAA2CH0BAAAAIAObQMmBrxc6AkAAAAAO4SeAAAAANBhuEkT\nA24GPQEAAABgh9ATAAAAADqM0tMBNAIAAABABxMDAgAAANuOngAAAADQoZkYEAAAANhu9AQAAACA\nDkNzAgAAAADbjZ4AAAAA0MEjAp9qIeO13JHj9R2dPfo4zPbaK7V13jtXL3H37KCcPb5YPDgRMT1V\njsbuXfUDNDddL/fEQr0fz1p9N8enHpvd9HL7rO+p0/X9tHd3/bod63Hdrg3q5c4v1U/mxaX6Si+c\nrmcXl8rR8n7u82icmR71RZ/zYmyL7tfLq1tT7rGT9Q1eXauXu7xaO0jDYb3M7HFsd9er5FjtUdf0\nqadO9Kinrpo7Xc7unql/Tnj85Ew5O1usb/rUU4/N14/P4qBe8IG99XKXV+vZ6ufziIipHp+Vx8fq\n+2q8x/2ges+dm6lXVH3209x0fT/1qR85P48IBAAAALYdwwEAAACgw9AjAgEAAIDtRk8AAAAA6GBO\nAAAAAGDb0RMAAAAAOnhEIAAAAOwQQ8MBAAAAgO1GTwAAAADoYGJAAAAAYNvREwAAAAA6tBidiQH1\nBAAAAIAdQk8AAAAA6DBKTwfQCAAAAAAdRmliwE1pBBgMa7ncosEKa4PNL3NxqZ49dnK8nB0Wj01E\nxES92FhdrV9Fq4P6eJy9u+vZEwv1df74vSvl7N69tct0crK+rbvm6tnl+qbG5ESPclfr2QN76hdC\ntX6LiJierJ9Tu2bq27uyVssdma9v7PJ0vUJf6VFfTPW4DrLHzX51tZ4d9viUMZb17e1T7lSrldvn\nPnJwb/18XFmr76fFpfpKHz1Rjsb+3fXs0squcnZmqr6fs8dFtKtY7uJy/fhMT5WjsThf39Y+96+t\n+hzXJ3vkeH17d/f4fFKtHk8t9bh/Fe+3ERGnluvbOkr/WeXS0BMAAAAAOoxS44qJAQEAAGCHuGAj\nQGYeysy3ZeZdmXlnZv6j9eVXZOZbM/PuzPy9zNx36VcXAAAANtew5YZ/bZWL6QmwFhH/pLX2ORHx\nxRHx3Zn5vIj4gYj4g9bazRHxtoj4wUu3mgAAAEBfF2wEaK090lr7wPr3CxHxkYg4FBGviYg3rr/s\njRHxNy7VSgIAAMBWaW3jv7bKU5oYMDNvjIhbIuJdEXFNa+1wxJmGgsy8asPXDgAAALbYKE0MeNGN\nAJm5OyJ+LSK+r7W2kE/huS9vfuOPPvH9zZ93ezzvltufwioCAABwuTj++B1x/MgHtno1KLqoRoDM\nnIgzDQD/pbX25vXFhzPzmtba4cy8NiIePV/+Nd/yo71XFAAAgK2378pbY9+Vtz7x84P3vLHj1aNh\nuEk9ATLzUET8YkRcGxGDiPjPrbWfPev3/zQi/lVEXNlaO7q+7Gcj4lURsRgR3/qZ4fznc7GPCPyF\niLirtfYzZy17S0R86/r33xIRb35yCAAAALho55uY/zMNBK+IiPs+8+LMfFVEPKu19pyI+M6I+A8X\nKuBiHhH40oj4OxHxFZl5R2a+PzNfGRE/GRFfmZl3r6/ITzzVrQMAAIDLXWu54V/nLuecE/Nft/7r\n10XE9z8p8po403MgWmvvjoh9mXlN17ZccDhAa+0dETF+nl+/4kJ5AAAA4Kk5a2L+d2fmV0fEA621\nOzM/qwHhuoh44KyfH1pfdvh87/uUng4AAAAAO81GPB3gI3f8UXz0A390Ua89e2L+ODM3wA9HxFee\n66XnWNa5thoBAAAAoMNGTAx48y23x81nPSnvt974z8/5uidPzJ+ZL4yIGyPig3mmG8ChiHh/Zt4W\nEQ9GxPVnxQ9FxMNd63GxEwMCAAAAl95nTczfWvtQa+3a1tpNrbVnxpn/+N/aWns0zkzY/80REZn5\n4oiYb62ddyhAxCb1BFhdq+Vmpuplrg3q2T5dPcbOPb/DJTV+vhkbLsJkjzOgT2vYzEx9R0322d4e\n2XP3tLk4M9P1k/naA7UdfXyxvr4rxWs2ot/5uLRSz85N17MT4/WTed+uemWztFJvh52dHpazw1Yr\nd2K8fk6trdX3cZ86edCjouqzvX0Mety/du2uZ8e34AY21eMedHyxfv1csae+k4+cqFdya4P6+bg6\nqB+fsR7H9mP399jPe+vlXntFrY5bXKqXOaxXq7G0VA/Pn6iXu7JSP6fGevwpcLVHnd7HYFC//uZm\nN7+Om+jxmWjxdI9y9fW+JDZiOMDFOGti/jsz844407X/h1prv3v26sT6f05aa7+Tma/OzI/HmUcE\nftuFynCKAAAAwGXgAhPzf+Y1Nz3p5+95KmVoBAAAAIAOm9UTYDOYEwAAAAB2CD0BAAAAoMNGPB3g\ncqERAAAAADoYDgAAAABsO3oCAAAAQIc+jxG93OgJAAAAADuEngAAAADQYZTmBNAIAAAAAB1GqRHA\ncAAAAADYIfQEAAAAgA5DPQEAAACA7UZPAAAAAOjQRmhSgE1pBNg9W8uNZX1Hr6xlObu8Wo7G3l21\n3MR4fVuHrb6tffbx8mq93KxHexn0eL5nn3Wem6ln1wa13P7d9Y1dXq13Ehofq59T4z36Ju2ZXStn\nr5pdqJc7cbKcvX/xmnL29Eq9+j6yUstdfaB+EZw4VY7GWI9r7+De+vl4arle7mr9dIzZ6foG97mX\n9Ll21wa1dV7donv1wunxcrbP+Tg1UQ9PT9bLPXK8np3pcT6O9ajT73modoz6HJ+jx+on1dh4veDJ\nyT6fp+rZPv+h2b2rfg2trNbL3TVX397qZ8A+9fmwxzXQ5/+bg+JnR3YOPQEAAACgwwh1BNAIAAAA\nAF2GPXoUX25MDAgAAAA7hJ4AAAAA0GGUhgPoCQAAAAA7hJ4AAAAA0GGoJwAAAACw3egJAAAAAB1G\naU4AjQAAAADQoY3QeADDAQAAAGCH0BMAAAAAOoxQRwA9AQAAAGCn0BMAAAAAOpgY8Cmq7rC1YZbL\nXBuUozExXs9WnVqub+vu2foZOb9QL3d6shzdMn2ObZ9zamaqnj29UjtG1VxEv0puZqpPufWCx7Ke\nPbE6U86utXqHqqXV+gm5tFovd3mllnvO05bKZS6t1PfxWI8+a3PT9Qt3MKwfnz718tR4Pbu8Vr/+\ntsJqj3p1ZbWePdoju7paPz675ramXl7psc59PPr4Wjk7N1u7/qZ73G9ni2X2dfr0sJydmKifUzPT\n9cp1vEe9PN3jc8Ja/ZSKQXE3D3pUq5M9/qfV51we9Khb2Rn0BAAAAIAOwxGaFEAjAAAAAHQYpeEA\nJgYEAACAHUJPAAAAAOigJwAAAACw7egJAAAAAB2GI9QVQE8AAAAA2CH0BAAAAIAObbjVa7Bx9AQA\nAACADq21Df86l8x8fWYezsw/f9Ly783Mj2bmnZn5E2ct/8HMvCczP5KZX3Ux26InAAAAAFwe3hAR\n/zYifvEzCzLz9oj46oh4YWttLTOvXF/+/Ij4uoh4fkQciog/yMzntPO1MKzTCAAAAAAdhps0HKC1\n9qeZ+YwnLf6HEfETrbW19dc8vr78NRHxpvXl92bmPRFxW0S8u6sMwwEAAADg8vXciHhZZr4rM/8w\nM79gffl1EfHAWa97aH1ZJz0BAAAAoMMFethfahMRsb+19uLM/MKI+NWIuCki8hyvveCKagQAAACA\nS+zej/xx3PfRt1eiD0TEb0REtNbem5mDzDwYEQ9GxA1nve5QRDx8oTfblEaAoydqud1z9TKPzNcH\nbYyNnatB5eJUG4hmputlHpmvt0qN9RgQsjxZX+fBsL7Oa2vlaK/93Kfx79RSPVvdV9NTPc6po4Ny\n9sD+8XL2aI/r9u5B/WQ+eGCqnD1+op592tX1fXX8ZP2EPH5itZT75MxMuczllXI0bnpabX0jIk6c\nrt/mJsbr+3h1rX79zS/Uz+XVHvXj0fn6dT9Vrm/q+3h+vn5eTEzU9/HRo6fL2WuvrX+wOdbjs8np\n0/Vj28fiQv0YjRe3d6VeZK/Hf/X5C+FgUM/u3l2v4xYW6+fF6lp9nedm6/e+Pvt5ZaWW7fNZeWqq\nHj55sl6hz8zU9zHn1+O/L0+44XlfFjc878ue+Pntb/7x870047P/yv9bEfHyiHh7Zj43IqZaa0cy\n8y0R8UuZ+VNxZhjAsyPiPRdaDz0BAAAAoEPbiFaAi5CZ/y0ibo+Ig5l5f0T8SET8QkS8ITPvjIjl\niPjmiIjW2l2Z+d8j4q6IWI2I77rQkwEiNAIAAADAZaG19o3n+dU3nef1r42I1z6VMjQCAAAAQIet\nnRdwY3lEIAAAAOwQegIAAABAh+EmzQmwGfQEAAAAgB1CTwAAAADo0OcRlZcbjQAAAADQoQ23eg02\njuEAAAAAsEPoCQAAAAAdhiM0HEBPAAAAANgh9AQAAACADqM0MaCeAAAAALBD6AkAAAAAHYbD0ekJ\nsCmNAI8dWS3ljh3PcpmrK/VnOIyP18utnhvLy/VOGYuLa+Xsnj31U+D4ifo+np2pb+9gUI7GwkJ9\nnfvsq7W1eqWxulrLTh8YL5c5GNTX9/jJ+gFaWqpnJyfr59QDDy6Vs0tLtfotImL+WP0YLS/X99Xu\nPVOl3Mc/Vd9Pu3ZNlrM5Vs8uLdXP5auuqJ9Td39ypZzdt6++vY89tlzOPv7oQjk7VrxtzszVzsWI\niNm5+n569JH6tu4/MFfOZtY/X5w8Wb/XP/3a+n6++56T5ez4eI97ffED1cLx+rU3MVFf35nZ+meE\nPvX5qVP182LX7vo11Hr8Z+jo6fr2zszW75vzx2r14/R0vcx9++rX3rFj9XvutdfW6ynOb4RGAxgO\nAAAAADuF4QAAAADQoU8PmMuNngAAAACwQ1ywESAzD2Xm2zLzrsy8MzO/d335j2Tmg5n5/vWvV176\n1QUAAIDNNWxtw7+2ysUMB1iLiH/SWvtAZu6OiD/LzN9f/91PtdZ+6tKtHgAAALBRLtgI0Fp7JCIe\nWf9+ITM/EhHXrf+6Ps0tAAAAbAM7dk6AzLwxIm6JiHevL/ruzPxAZv58Zu7b4HUDAACALdeGbcO/\ntspFPx1gfSjAr0XE9633CPi5iPgXrbWWmT8eET8VEd9+ruwf/+aPPfH9M573srjx+V/Wb60BAADY\nEscfvyOOH/nAVq8GRRfVCJCZE3GmAeC/tNbeHBHRWnvsrJf854j4H+fLf9nf/Gd91hEAAIDLxL4r\nb419V976xM8P3vPGLVybzTFCowEuejjAL0TEXa21n/nMgsy89qzf/62I+NBGrhgAAACwsS7YEyAz\nXxoRfyci7szMOyKiRcQPRcQ3ZuYtETGMiHsj4jsv4XoCAADAlhiliQEv5ukA74iI8XP86nc3fnUA\nAACAS+WiJwYEAACAnai1HdQTAAAAAHay4U4aDrAR1laHpdyhp02Xyzw6XyszIiKzHI3x8Vr4xMm1\ncpl79mxNW87c7LlGiVycPvv41Kn6vpqbq6/zwsKgnH3wvvlydnKqdnxPnpwpl3li/nQ5OzM7Wc6e\nWlwpZ6em69dB9jghJyYvdn7Vv2x5uX5OzR9ZKGenp/eVcieOL5XLXFyonxdHHq8fn8mp+jV/14cW\ny9k9+2fL2cGg/iFjeqa+vc+4aX85O39suZQbDOr36rW1enbfFfXj84zr63XrvffX69Y+58W999fr\nmvHxeh23e89UOXvksVOl3Nxcva65/lD9vLj//tr6RvSrpzLr58XqSv286KP6/4KIfp95q/f6Ptfe\npz9dv4/0MT+/uiXlsn3oCQAAAAAdRmk4QL15FwAAANhW9AQAAACADjvqEYEAAACwk41SI4DhAAAA\nALBD6AkAAAAAHYYmBgQAAAC2Gz0BAAAAoIM5AQAAAIBtRyMAAAAAdGitbfjXuWTmP87MD2Xmn2fm\nL2XmVGbemJnvysy7M/OXM7NXj36NAAAAANBhOGwb/vVkmfn0iPjeiPj81tqL4szw/W+IiJ+MiH/T\nWrs5IuYj4tv7bItGAAAAALg8jEfErvW/9s9GxMMR8eUR8evrv39jRPzNPgWYGBAAAAA6bMbEgK21\nhzPz30TE/RFxKiLeGhHvj4j51tpw/WUPRsTT+5SjEQAAAAAuscP3vTMO3/+/z/v7zNwfEa+JiGdE\nxPGI+NWIeNU5XtqrRWJTGgH27Zss5a49MCiXOT05Xs5es3+1nP34w7Vt7TO3w/49Wc5mPRpz0/Vz\nr0+5J05NlbPXXFE/p9734fr27r1irpz91EceLuVe9vXPKpf52PxMOfupexfL2X1XzJaza2vDC7/o\nPG565q5ydmm5fl7cf99COft3/8+D5ez/eNtSKbd7b/28eNYz69fAgw8vl7OLiyvl7PXP3F/O3nhd\nvU7/oz9+tJx95VdeVc4ePlqvmJ/7jNo9d/+utXKZf/bR+ojGv33bg+Xsn9z3jHL2xbdOl7Ora/Xj\n8/4P1a+hq6+u18vXXFk/Rlcd3FfKvfsdD5XLfMYz6vXUF7yovp9+/w+PlLM3PfuKcnbP7vpn5en6\nR7G4an/9fv3YfP2cGhur3cMefbR2z4yI+KZX1j93fvJI7RqIiHjgcDlKh/NN5PdUXH3DF8fVN3zx\nEz9/6B2ve/JLXhERn2ytHY2IyMzfjIiXRMT+zBxb7w1wKM4MESgzJwAAAABsvfsj4sWZOZOZGREv\nj4gPR8QfRsTXrr/mWyLizX0KMRwAAAAAOrRhvRfLRZfR2nsy89ci4o6IWF3/9z9FxO9ExJsy88fW\nl72+TzkaAQAAAKDDuR7pdym01v55RPzzJy3+VER80UaVYTgAAAAA7BB6AgAAAECHjZgY8HKhJwAA\nAADsEHoCAAAAQIe2SXMCbAY9AQAAAGCH0BMAAAAAOoxSTwCNAAAAANBh2IZbvQobxnAAAAAA2CH0\nBAAAAIAOozQcQE8AAAAA2CH0BAAAAIAOo9QTIFu7tBuTme0lf/2PLmkZAAAAbI13/vbt0VrLrV6P\nSyUz22v+4d0b/r5v/vc3b8l+0xMAAAAAOlzqP55vJo0AAAAA0GE49IhAAAAAYJvREwAAAAA6jNLE\ngHoCAAAAwA6hJwAAAAB0aM2cAAAAAMA2oycAAAAAdBilOQE0AgAAAECHUWoEMBwAAAAAdgg9AQAA\nAKDD0MSAAAAAwHajJwAAAAB0MCcAAAAAsO3oCQAAAAAd2nB05gTQCAAAAAAdDAcA/v/27ibUruoM\n4/j/CUWxLfX7gxqTKIKpoWAG2qqDxiIaKyQKCgkELFSctBiolFQnjgp2UDSQYdMSxCZ+oWaWWCIt\nDtSAiQrRGBTTXEKuVkyLCGLN28HegUMSLuZk37vd5/5/cGDvxcldb+DhrHPWXnttSZIkSRocVwJI\nkiRJkjSD8hGBkiRJkiRpaFwJIEmSJEnSDI65J4AkSZIkSRoaVwJIkiRJkjQDHxEoSZIkSdI84SMC\nJUmSJEnS4LgSQJIkSZKkGfiIwI785997+uxeE8hMqWtmSl0zU+qamVKXzJPUryQrk7yX5P0kG2aj\nj34nAT7d22f3mkBmSl0zU+qamVLXzJS6ZJ6kU6tj1fnrREkWAJuA24FlwNokS7v+v7gngCRJkiRJ\n/bsBOFBVB6vqK2AbsLrrTtwTQJIkSZKkGczRIwIvBw6NnE/RTAx0KlWz+6iDJJPzLAVJkiRJ0kmq\nKn3XMFuSfAQsnoU/PV1Vl430cw9wW1U90J6vA66vqvVddjrrKwEmOQySJEmSpMlWVUvmqKspYNHI\n+ULgcNeduCeAJEmSJEn92w1cnWRxkrOANcD2rjtxTwBJkiRJknpWVV8n+Q2wk+aC/eaqerfrfmZ9\nTwBJkiRJkvTt0NvtAElWJnkvyftJNvRVh4YryeYk00neHmk7P8nOJPuT7Ehybp81ajiSLEyyK8m+\nJO8kebBtN1MaS5Kzk7yeZE+bqUfb9iVJXmsztTWJq/J0WpIsSPJmku3tuZnS2JJ8lOSt9rPqjbbN\nsU+aYL1MAiRZAGwCbgeWAWuTLO2jFg3aX2kyNOr3wN+r6hpgF/DwnFelofof8Nuquha4Efh1+7lk\npjSWqvoSuKWqlgPXAXck+QnwR+BPbaaOAr/qsUwN03pg38i5mdKZOAasqKrlVXX8UWSOfdIE62sl\nwA3Agao6WFVfAduA1T3VooGqqleBz05oXg1saY+3AHfNaVEarKo6UlV72+PPgXdpdmQ1UxpbVX3R\nHp5Nsw9PAbcAz7ftW4C7eyhNA5VkIfAL4M8jzT/HTGl84eTfBI590gTraxLgcuDQyPlU2yadqUuq\nahqaH3XAxT3XowFKsoTmyu1rwKVmSuNql23vAY4ALwMfAEer6lj7lingh33Vp0F6HPgdzYQSSS4E\nPjNTOgMF7EiyO8n9bZtjnzTB+rpnLKdoc4dCSb1L8n3gOWB9VX2exM8mja39YbY8yQ+AF4Afnept\nc1uVhirJncB0Ve1NsuJ4Myd/rzJTOh03VdWRJBcDO5PsxwxJE62vlQBTwKKR84XA4Z5q0WSZTnIp\nQHtqL2YAAAGBSURBVJLLgI97rkcD0m6m9RzwZFW91DabKZ2xqvov8A/gp8B57d444Pin03MzsCrJ\nh8BWmtsAngDONVMaV3uln6r6BHiR5rZdxz5pgvU1CbAbuDrJ4iRnAWuA7T3VomE78QrIduCX7fF9\nwEsn/gNpBn8B9lXVxpE2M6WxJLno+I7aSc4BbqXZzO0V4N72bWZK31hVPVJVi6rqKprvTruqah1m\nSmNK8t12BRxJvgfcBryDY5800VLVz2qfJCuBjTQTEZur6rFeCtFgJfkbsAK4EJgGHqWZwX4WuAL4\nF3BvVR3tq0YNR5KbgX/SfPmp9vUI8AbwDGZKpynJj2k21FrQvp6uqj8kuZJmQ9zzgT3AunaTXOkb\nS/Iz4KGqWmWmNK42Oy/QjHnfAZ6qqseSXIBjnzSxepsEkCRJkiRJc6uv2wEkSZIkSdIccxJAkiRJ\nkqR5wkkASZIkSZLmCScBJEmSJEmaJ5wEkCRJkiRpnnASQJIkSZKkecJJAEmSJEmS5on/A4zf+ykZ\np7msAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe28bc44ba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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75CTvSvKO/XVEIQEAAAA2v7OTXDJsX5LkZau0OTPJju7+anffn2RHkrNmzq9W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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe25f069908>"
]
},
"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
}
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