DataChallengeReport_RioGrandeAnalysis.ipynb

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   "source": [
    "# Connecting NDVI, Groundwater, and Stream Disconnection During Drought: Case Study along the Rio Grande\n",
    "\n",
    "Danielle Tadych\n",
    "4/10/2023\n",
    "\n",
    "## Introduction\n",
    "    As the southwest faces 30 long years of drought, with severe droughts increasing in length and intensity, western states in the U.S. are turning to groundwater for municipal and agricultural needs. Desert riparian ecosystems also rely on this shallow groundwater and thus riparian vegetation either in the field or through satellite measurements has been used in some studies as a proxy for groundwater levels since groundwater levels are notoriously difficult to measure. For example, numerous studies have been conducted along the San Pedro River basin connecting groundwater with vegetation due to concerns of over pumping of groundwater in the basin and causing riparian areas to decline (Stromberg et al., 1996, 2006, 2007). Another previous study has recorded severe drought in the southwest causing a stream to go from connected to disconnected from the shallow groundwater over time (Fuchs et al., 2019). These sorts of field studies are highly valuable and needed but not feasible to conduct on statewide or regional scales, so many studies have used NDVI from satellite data instead (Alshehri et al., 2020; Barron et al., 2014; Fu & Burgher, 2015; Glenn et al., 2008; Mpakairi et al., 2022; Páscoa et al., 2020).  This method is useful but comes with numerous assumptions and usually the spatial resolution is far too large to determine exact places where groundwater is decreasing. Finer resolution satellite data at more frequent time intervals could be the solution to these research woes. \n",
    "    \n",
    "    This brings me to my research question for this data challenge: can transformation of connected to disconnected streams be seen on satellite images through the use of Normalized Differential Vegetation Index (NDVI)? To conduct this study, we used PlanetLabs 4-band 3-7m resolution satellite products to compare vegetation changes to ground truth stream and groundwater rivers collected from a previous study along the Rio Grande, New Mexico from 2010 to 2017.  NDVI is used as a vegetation proxy and NDVI is plotted against groundwater data. locations along the river were chosen for this study based on areas that showed clear connection and disconnection. The primary conclusions of this study is that there seems to be a general relationship between NDVI, hydraulic head and groundwater seepage; however, further analysis will be conducted to determine the extent of this relationship. If this method is successful, it is our hope to extrapolate the methods used in this study to important rivers and streams in areas of Arizona where many riparian ecosystems are in danger and groundwater levels are suspected to be declining but difficult to track at such fine scales.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'NDVI (-1 to 1)')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10,6))\n",
    "# ax.scatter(NDVI_ts.index, NDVI_ts[0], color='red')\n",
    "ax.scatter(df['NDVI'].values, head_studyareas['Rin_11R'].values, c='black',label = 'NDVI Range')\n",
    "ax.scatter(df['NDVI'].values, head_studyareas['Rin_2R'].values, c='black',label = 'NDVI Range')\n",
    "ax.scatter(df['NDVI'].values, head_studyareas['Rin_12R'].values, c='black',label = 'NDVI Range')\n",
    "# ax2.hlines(y=0.113, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "# ax2.hlines(y=0, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "# ax2.hlines(y=0.0915, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "# ax.axvspan(\"2017-06-10T12:00:00.000Z\", \"2018-06-16T00:00:00.000Z\", color='grey', alpha=0.5, lw=0, label=\"Satellite relaunch gap\")\n",
    "ax.set(title=\"NDVI vs. Head Values at the Rio Grande\")\n",
    "# ax.legend(loc = 'lower left')\n",
    "ax.grid(zorder = 0)\n",
    "ax.set_ylabel('Net Annual Pressure Head (m)')\n",
    "ax.set_xlabel('NDVI (-1 to 1)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'NDVI (-1 to 1)')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10,6))\n",
    "# ax.scatter(NDVI_ts.index, NDVI_ts[0], color='red')\n",
    "ax.scatter(df['NDVI'].values, y['Rin_11R'].values, c='black',label = 'NDVI Range')\n",
    "ax.scatter(df['NDVI'].values, y['Rin_2R'].values, c='black',label = 'NDVI Range')\n",
    "ax.scatter(df['NDVI'].values, y['Rin_12R'].values, c='black',label = 'NDVI Range')\n",
    "# ax2.hlines(y=0.113, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "# ax2.hlines(y=0, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "# ax2.hlines(y=0.0915, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "# ax.axvspan(\"2017-06-10T12:00:00.000Z\", \"2018-06-16T00:00:00.000Z\", color='grey', alpha=0.5, lw=0, label=\"Satellite relaunch gap\")\n",
    "ax.set(title=\"NDVI vs. Seepage Flow Values at the Rio Grande\")\n",
    "# ax.legend(loc = 'lower left')\n",
    "ax.grid(zorder = 0)\n",
    "ax.set_ylabel('Seepage Flow (cms)')\n",
    "ax.set_xlabel('NDVI (-1 to 1)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methods and Study Area\n",
    "\n",
    "This study was completed in conjunction with Fuchs. et al (2019) who provided all groundwater data and study locations along the Rio Grande near Hatch, NM for comparison with calculated NDVI data.  For more information on their study, please read their published work below.\n",
    "\n",
    "Due to time constrains 3 stations were chosen (Rin_11R, Rin_2R, and Rin_12R) due to their wide range of groundwater values and ability to capture the stream connection and disconnection throughout the time period.  QGIS was used to create the study area shapefiles with all the same dimensions.  Areas were also carefully drawn to avoid agricultural fields.\n",
    "\n",
    "### Map of study locations\n",
    "![Logo](https://agupubs.onlinelibrary.wiley.com/cms/asset/107b0e57-eb25-46fd-aa44-37af4815097a/wrcr24069-fig-0002-m.jpg)\n",
    "\n",
    "### Groundwater and Seepage Data Provided For Analysis\n",
    "![Logo](https://agupubs.onlinelibrary.wiley.com/cms/asset/65994f72-4a63-4915-ba31-04718b381ffb/wrcr24069-fig-0008-m.jpg)\n",
    "\n",
    "![Logo](https://agupubs.onlinelibrary.wiley.com/cms/asset/2f0ec751-bfca-410a-85ef-369ca67be62c/wrcr24069-fig-0009-m.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "+ Alshehri, F., Link to external site,  this link will open in a new window, Sultan, M., Karki, S., Link to external site,  this link will open in a new window, Alwagdani, E., Alsefry, S., Alharbi, H., Sahour, H., Link to external site,  this link will open in a new window, & Sturchio, N. (2020). Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia. Remote Sensing, 12(9), 1361. https://doi.org/10.3390/rs12091361\n",
    "+ Barron, O. V., Emelyanova, I., Van Niel, T. G., Pollock, D., & Hodgson, G. (2014). Mapping groundwater-dependent ecosystems using remote sensing measures of vegetation and moisture dynamics. Hydrological Processes, 28(2), 372–385. https://doi.org/10.1002/hyp.9609\n",
    "+ Fu, B., & Burgher, I. (2015). Riparian vegetation NDVI dynamics and its relationship with climate, surface water and groundwater. Journal of Arid Environments, 113, 59–68. https://doi.org/10.1016/j.jaridenv.2014.09.010\n",
    "+ Fuchs, E. H., King, J. P., & Carroll, K. C. (2019). Quantifying Disconnection of Groundwater From Managed-Ephemeral Surface Water During Drought and Conjunctive Agricultural Use. Water Resources Research, 55(7), 5871–5890. https://doi.org/10.1029/2019WR024941\n",
    "+ Glenn, E. P., Huete, A. R., Nagler, P. L., & Nelson, S. G. (2008). Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape. Sensors, 8(4), Article 4. https://doi.org/10.3390/s8042136\n",
    "+ Mpakairi, K. S., Dube, T., Dondofema, F., & Dalu, T. (2022). Spatial Characterisation of Vegetation Diversity in Groundwater-Dependent Ecosystems Using In-Situ and Sentinel-2 MSI Satellite Data. Remote Sensing, 14(13), Article 13. https://doi.org/10.3390/rs14132995\n",
    "+ Páscoa, P., Gouveia, C. M., & Kurz-Besson, C. (2020). A Simple Method to Identify Potential Groundwater-Dependent Vegetation Using NDVI MODIS. Forests, 11(2), Article 2. https://doi.org/10.3390/f11020147\n",
    "+ Stromberg, J. C., Beauchamp, V. B., Dixon, M. D., Lite, S. J., & Paradzick, C. (2007). Importance of low-flow and high-flow characteristics to restoration of riparian vegetation along rivers in arid south-western United States. Freshwater Biology, 52(4), 651–679. https://doi.org/10.1111/j.1365-2427.2006.01713.x\n",
    "+ Stromberg, J. C., Lite, S. J., Rychener, T. J., Levick, L. R., Dixon, M. D., & Watts, J. M. (2006). Status of the Riparian Ecosystem in the Upper San Pedro River, Arizona: Application of an Assessment Model. Environmental Monitoring and Assessment, 115(1–3), 145–173. https://doi.org/10.1007/s10661-006-6549-1\n",
    "+ Stromberg, J. C., Tiller, R., & Richter, B. (1996). Effects of Groundwater Decline on Riparian Vegetation of Semiarid Regions: The San Pedro, Arizona. Ecological Applications, 6(1), 113–131. https://doi.org/10.2307/2269558"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full Analysis Code\n",
    "\n",
    "Data was downloaded throught the planetscope download portal and Placed into the respective Data folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os, glob\n",
    "from osgeo import gdal\n",
    "import subprocess\n",
    "import datetime\n",
    "import xarray as xr\n",
    "import rioxarray as rxr\n",
    "import pandas as pd\n",
    "import geopandas as gp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mosaicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Driver: GTiff/GeoTIFF\n",
      "Files: /Users/danielletadych/Documents/PhD_Materials/github_repos/Tadych_PlanetData_Challenge/Data/RapidEye/1354105_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "       /Users/danielletadych/Documents/PhD_Materials/github_repos/Tadych_PlanetData_Challenge/Data/RapidEye/1354105_2010-03-25_RE5_3A_Analytic_SR_clip.tif.aux.xml\n",
      "Size is 248, 309\n",
      "Coordinate System is:\n",
      "PROJCRS[\"WGS 84 / UTM zone 13N\",\n",
      "    BASEGEOGCRS[\"WGS 84\",\n",
      "        DATUM[\"World Geodetic System 1984\",\n",
      "            ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n",
      "                LENGTHUNIT[\"metre\",1]]],\n",
      "        PRIMEM[\"Greenwich\",0,\n",
      "            ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
      "        ID[\"EPSG\",4326]],\n",
      "    CONVERSION[\"UTM zone 13N\",\n",
      "        METHOD[\"Transverse Mercator\",\n",
      "            ID[\"EPSG\",9807]],\n",
      "        PARAMETER[\"Latitude of natural origin\",0,\n",
      "            ANGLEUNIT[\"degree\",0.0174532925199433],\n",
      "            ID[\"EPSG\",8801]],\n",
      "        PARAMETER[\"Longitude of natural origin\",-105,\n",
      "            ANGLEUNIT[\"degree\",0.0174532925199433],\n",
      "            ID[\"EPSG\",8802]],\n",
      "        PARAMETER[\"Scale factor at natural origin\",0.9996,\n",
      "            SCALEUNIT[\"unity\",1],\n",
      "            ID[\"EPSG\",8805]],\n",
      "        PARAMETER[\"False easting\",500000,\n",
      "            LENGTHUNIT[\"metre\",1],\n",
      "            ID[\"EPSG\",8806]],\n",
      "        PARAMETER[\"False northing\",0,\n",
      "            LENGTHUNIT[\"metre\",1],\n",
      "            ID[\"EPSG\",8807]]],\n",
      "    CS[Cartesian,2],\n",
      "        AXIS[\"(E)\",east,\n",
      "            ORDER[1],\n",
      "            LENGTHUNIT[\"metre\",1]],\n",
      "        AXIS[\"(N)\",north,\n",
      "            ORDER[2],\n",
      "            LENGTHUNIT[\"metre\",1]],\n",
      "    USAGE[\n",
      "        SCOPE[\"Engineering survey, topographic mapping.\"],\n",
      "        AREA[\"Between 108°W and 102°W, northern hemisphere between equator and 84°N, onshore and offshore. Canada - Northwest Territories (NWT); Nunavut; Saskatchewan. Mexico. United States (USA).\"],\n",
      "        BBOX[0,-108,84,-102]],\n",
      "    ID[\"EPSG\",32613]]\n",
      "Data axis to CRS axis mapping: 1,2\n",
      "Origin = (283345.000000000000000,3624600.000000000000000)\n",
      "Pixel Size = (5.000000000000000,-5.000000000000000)\n",
      "Metadata:\n",
      "  AREA_OR_POINT=Area\n",
      "  TIFFTAG_DATETIME=2010:03:25 18:42:25\n",
      "  TIFFTAG_IMAGEDESCRIPTION={\"atmospheric_correction\": {\"aerosol_model\": \"continental\", \"aot_coverage\": 1.0, \"aot_mean_quality\": 1, \"aot_method\": \"map\", \"aot_source\": \"mod09cma\", \"aot_status\": \"Data Found\", \"aot_std\": 0.0394, \"aot_used\": 0.123, \"atmospheric_correction_algorithm\": \"6Sv2.1\", \"atmospheric_model\": \"water_vapor_and_ozone\", \"luts_version\": 3, \"ozone_coverage\": 1.0, \"ozone_mean_quality\": 260, \"ozone_method\": \"fixed\", \"ozone_source\": \"mod09cmg\", \"ozone_status\": \"Data Found\", \"ozone_std\": 0.0012, \"ozone_used\": 0.32, \"satellite_azimuth_angle\": 278.99, \"satellite_zenith_angle\": 7.49, \"solar_azimuth_angle\": 163.991, \"solar_zenith_angle\": 31.436, \"sr_version\": \"2.1\", \"water_vapor_coverage\": 1.0, \"water_vapor_mean_quality\": 1, \"water_vapor_method\": \"fixed\", \"water_vapor_source\": \"mod09cma\", \"water_vapor_status\": \"Data Found\", \"water_vapor_std\": 0.017, \"water_vapor_used\": 0.67}}\n",
      "Image Structure Metadata:\n",
      "  COMPRESSION=LZW\n",
      "  INTERLEAVE=BAND\n",
      "  PREDICTOR=2\n",
      "Corner Coordinates:\n",
      "Upper Left  (  283345.000, 3624600.000) (107d18'43.84\"W, 32d44'16.57\"N)\n",
      "Lower Left  (  283345.000, 3623055.000) (107d18'42.55\"W, 32d43'26.44\"N)\n",
      "Upper Right (  284585.000, 3624600.000) (107d17'56.24\"W, 32d44'17.44\"N)\n",
      "Lower Right (  284585.000, 3623055.000) (107d17'54.95\"W, 32d43'27.31\"N)\n",
      "Center      (  283965.000, 3623827.500) (107d18'19.39\"W, 32d43'51.94\"N)\n",
      "Band 1 Block=256x256 Type=UInt16, ColorInterp=Red\n",
      "  Min=750.000 Max=1787.000 \n",
      "  Minimum=750.000, Maximum=1787.000, Mean=1248.792, StdDev=110.677\n",
      "  NoData Value=0\n",
      "  Metadata:\n",
      "    STATISTICS_MAXIMUM=1787\n",
      "    STATISTICS_MEAN=1248.7921699149\n",
      "    STATISTICS_MINIMUM=750\n",
      "    STATISTICS_STDDEV=110.67692951211\n",
      "    STATISTICS_VALID_PERCENT=38.03\n",
      "Band 2 Block=256x256 Type=UInt16, ColorInterp=Green\n",
      "  Min=1049.000 Max=2421.000 \n",
      "  Minimum=1049.000, Maximum=2421.000, Mean=1670.389, StdDev=148.792\n",
      "  NoData Value=0\n",
      "  Metadata:\n",
      "    STATISTICS_MAXIMUM=2421\n",
      "    STATISTICS_MEAN=1670.388999451\n",
      "    STATISTICS_MINIMUM=1049\n",
      "    STATISTICS_STDDEV=148.79163676264\n",
      "    STATISTICS_VALID_PERCENT=38.03\n",
      "Band 3 Block=256x256 Type=UInt16, ColorInterp=Blue\n",
      "  Min=1508.000 Max=3245.000 \n",
      "  Minimum=1508.000, Maximum=3245.000, Mean=2163.307, StdDev=223.523\n",
      "  NoData Value=0\n",
      "  Metadata:\n",
      "    STATISTICS_MAXIMUM=3245\n",
      "    STATISTICS_MEAN=2163.306992863\n",
      "    STATISTICS_MINIMUM=1508\n",
      "    STATISTICS_STDDEV=223.52309666755\n",
      "    STATISTICS_VALID_PERCENT=38.03\n",
      "Band 4 Block=256x256 Type=UInt16, ColorInterp=Undefined\n",
      "  NoData Value=0\n",
      "Band 5 Block=256x256 Type=UInt16, ColorInterp=Undefined\n",
      "  NoData Value=0\n"
     ]
    }
   ],
   "source": [
    "# Get meta data on the raster images\n",
    "!gdalinfo ~/Documents/PhD_Materials/github_repos/Tadych_PlanetData_Challenge/Data/RapidEye/1354105_2010-03-25_RE5_3A_Analytic_SR_clip.tif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Show all the downloaded Rapid Eye files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data/RapidEye/1354105_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2012-10-17_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2013-01-13_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2013-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2013-10-03_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2013-11-02_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2013-12-01_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2013-12-17_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2014-01-03_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2014-03-21_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2014-03-22_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2014-07-26_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2014-10-12_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2015-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2016-02-04_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2016-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2016-06-10_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2016-06-14_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2017-01-04_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2017-02-23_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2017-10-08_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2017-10-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354105_2017-11-10_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2012-02-15_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2012-10-17_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2013-01-13_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2013-02-01_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2013-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2013-10-03_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2013-11-02_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2013-12-01_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2013-12-17_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2014-01-03_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2014-03-21_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2014-03-22_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2014-07-26_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2014-10-12_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2015-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2016-01-11_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2016-02-04_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2016-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2016-06-10_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2016-06-14_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2017-01-04_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2017-02-23_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2017-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2017-10-08_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2017-10-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354106_2017-11-10_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2012-02-15_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2012-10-17_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2013-01-13_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2013-02-01_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2013-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2013-10-03_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2013-11-02_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2013-12-01_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2013-12-17_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2014-01-03_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2014-03-21_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2014-03-22_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2014-07-26_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2014-10-12_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2015-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2016-01-11_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2016-02-04_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2016-02-04_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2016-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2016-06-10_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2016-06-14_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2017-01-04_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2017-02-23_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2017-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2017-10-08_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2017-10-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354107_2017-11-10_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2012-02-15_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2012-10-17_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2013-01-13_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2013-02-01_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2013-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2013-10-03_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2013-11-02_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2013-12-01_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2013-12-17_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2014-01-03_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2014-03-21_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2014-03-22_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2014-07-26_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2014-10-12_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2015-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2016-01-11_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2016-02-04_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2016-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2016-06-10_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2016-06-14_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2017-01-04_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2017-02-23_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2017-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2017-10-08_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2017-10-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354205_2017-11-10_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2012-02-15_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2012-10-17_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2013-01-13_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2013-02-01_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2013-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2013-10-03_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2013-11-02_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2013-12-01_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2013-12-17_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2014-01-03_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2014-03-21_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2014-03-22_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2014-07-26_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2014-10-12_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2015-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2016-01-11_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2016-02-04_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2016-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2016-06-10_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2016-06-14_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2017-01-04_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2017-02-23_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2017-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2017-10-08_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2017-10-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354206_2017-11-10_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2012-02-15_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2012-10-17_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2013-01-13_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2013-02-01_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2013-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2013-10-03_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2013-12-01_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2013-12-17_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2014-01-03_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2014-03-21_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2014-03-22_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2014-10-12_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2016-02-04_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2016-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2016-06-14_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2017-01-04_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2017-02-23_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2017-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2017-10-08_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2017-10-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354305_2017-11-10_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2012-02-15_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2012-10-17_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2013-01-13_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2013-02-01_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2013-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2013-10-03_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2013-11-02_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2013-12-01_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2013-12-17_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2014-01-03_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2014-03-21_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2014-03-22_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2014-10-12_RE4_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2015-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2016-01-11_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2016-02-04_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2016-04-14_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2016-06-10_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2016-06-14_RE1_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2017-02-23_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2017-05-20_RE3_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2017-10-08_RE2_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2017-10-25_RE5_3A_Analytic_SR_clip.tif\n",
      "Data/RapidEye/1354306_2017-11-10_RE2_3A_Analytic_SR_clip.tif\n"
     ]
    }
   ],
   "source": [
    "ls Data/RapidEye/*RE*_3A_Analytic_SR_clip.tif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Show all the downloaded Planet Scope files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data/PlanetScope/503430_1354105_2017-05-14_100a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/503430_1354106_2017-05-14_100a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/503430_1354205_2017-05-14_100a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/503430_1354206_2017-05-14_100a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/503430_1354305_2017-05-14_100a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/510227_1354106_2017-05-14_1015_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/510227_1354107_2017-05-14_1015_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/513509_1354106_2017-05-21_0e3a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/513509_1354107_2017-05-21_0e3a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/513613_1354106_2017-05-21_101f_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/513613_1354205_2017-05-21_101f_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/513613_1354206_2017-05-21_101f_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/513613_1354305_2017-05-21_101f_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/514439_1354105_2017-05-21_0f35_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/514439_1354106_2017-05-21_0f35_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/514439_1354205_2017-05-21_0f35_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/514439_1354206_2017-05-21_0f35_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/514439_1354305_2017-05-21_0f35_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/514439_1354306_2017-05-21_0f35_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/522890_1354105_2017-05-29_0f34_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/522890_1354106_2017-05-29_0f34_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/522890_1354205_2017-05-29_0f34_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/522890_1354206_2017-05-29_0f34_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/522890_1354305_2017-05-29_0f34_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/523093_1354106_2017-05-29_0f52_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/523093_1354107_2017-05-29_0f52_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/535683_1354105_2017-06-07_1004_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/535683_1354106_2017-06-07_1004_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/535683_1354205_2017-06-07_1004_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/535683_1354206_2017-06-07_1004_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/535683_1354305_2017-06-07_1004_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/535683_1354306_2017-06-07_1004_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/535851_1354106_2017-06-07_102e_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/535851_1354107_2017-06-07_102e_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538456_1354105_2017-06-09_102c_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538456_1354106_2017-06-09_102c_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538456_1354205_2017-06-09_102c_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538456_1354206_2017-06-09_102c_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538456_1354305_2017-06-09_102c_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538456_1354306_2017-06-09_102c_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538462_1354105_2017-06-09_102a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538462_1354106_2017-06-09_102a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538462_1354205_2017-06-09_102a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538462_1354206_2017-06-09_102a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538462_1354305_2017-06-09_102a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538462_1354306_2017-06-09_102a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538507_1354105_2017-06-09_1002_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538507_1354106_2017-06-09_1002_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538507_1354206_2017-06-09_1002_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538779_1354106_2017-06-09_1029_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/538779_1354107_2017-06-09_1029_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/582563_1354106_2017-06-27_0f1b_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/582563_1354107_2017-06-27_0f1b_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/582563_1354206_2017-06-27_0f1b_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583108_1354106_2017-06-27_0e30_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583108_1354107_2017-06-27_0e30_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583108_1354206_2017-06-27_0e30_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583109_1354105_2017-06-27_1023_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583109_1354106_2017-06-27_1023_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583109_1354205_2017-06-27_1023_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583109_1354206_2017-06-27_1023_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583109_1354305_2017-06-27_1023_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583109_1354306_2017-06-27_1023_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583342_1354106_2017-06-27_1009_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583342_1354107_2017-06-27_1009_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583342_1354206_2017-06-27_1009_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583803_1354105_2017-06-27_1010_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583803_1354106_2017-06-27_1010_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583803_1354205_2017-06-27_1010_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/583803_1354206_2017-06-27_1010_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/613150_1354106_2017-07-10_101b_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/613150_1354107_2017-07-10_101b_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/613150_1354206_2017-07-10_101b_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/613459_1354107_2017-07-10_103a_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/613528_1354105_2017-07-10_0f43_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/613528_1354106_2017-07-10_0f43_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/613528_1354205_2017-07-10_0f43_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/613528_1354206_2017-07-10_0f43_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/613528_1354305_2017-07-10_0f43_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/614609_1354205_2017-07-10_103b_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/614609_1354305_2017-07-10_103b_BGRN_SR_clip.tif\n",
      "Data/PlanetScope/614609_1354306_2017-07-10_103b_BGRN_SR_clip.tif\n"
     ]
    }
   ],
   "source": [
    "ls Data/PlanetScope/*SR_clip.tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-01-00\n",
      "2010-01-01\n",
      "2010-01-02\n",
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      "2010-01-08\n",
      "2010-01-09\n",
      "2010-01-10\n",
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      "2010-01-12\n",
      "2010-01-13\n",
      "2010-01-14\n",
      "2010-01-15\n",
      "2010-01-16\n",
      "2010-01-17\n",
      "2010-01-18\n",
      "2010-01-19\n",
      "2010-01-20\n",
      "2010-01-21\n",
      "2010-01-22\n",
      "2010-01-23\n",
      "2010-01-24\n",
      "2010-01-25\n",
      "2010-01-26\n",
      "2010-01-27\n",
      "2010-01-28\n",
      "2010-01-29\n",
      "2010-01-30\n",
      "2010-01-31\n",
      "2010-02-00\n",
      "2010-02-01\n",
      "2010-02-02\n",
      "2010-02-03\n",
      "2010-02-04\n",
      "2010-02-05\n",
      "2010-02-06\n",
      "2010-02-07\n",
      "2010-02-08\n",
      "2010-02-09\n",
      "2010-02-10\n",
      "2010-02-11\n",
      "2010-02-12\n",
      "2010-02-13\n",
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      "2010-02-18\n",
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      "2010-02-25\n",
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      "2010-02-27\n",
      "2010-02-28\n",
      "2010-02-29\n",
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      "2010-02-31\n",
      "2010-03-00\n",
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      "2010-03-03\n",
      "2010-03-04\n",
      "2010-03-05\n",
      "2010-03-06\n",
      "2010-03-07\n",
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      "2010-03-09\n",
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     ]
    }
   ],
   "source": [
    "for i in years:\n",
    "    year = str(i)\n",
    "    for j in months:\n",
    "        # month = str(j)\n",
    "        month = format(j, '02')\n",
    "        for k in days:\n",
    "            day = format(k, '02')\n",
    "            print(year+\"-\"+month+\"-\"+day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ls -1 Data/RapidEye/*RE*_3A_Analytic_SR_clip.tif > tiff_test_list.txt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Data/RapidEye/1354206_2010-10-27_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354107_2010-03-25_RE5_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354205_2010-10-27_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354206_2010-12-09_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354305_2010-03-25_RE5_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354205_2010-12-09_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354306_2010-03-25_RE5_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354106_2010-12-09_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354105_2010-12-09_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354106_2010-10-27_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354105_2010-10-27_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354305_2010-12-09_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354206_2010-03-25_RE5_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354306_2010-12-09_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354205_2010-03-25_RE5_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354107_2010-10-27_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354305_2010-10-27_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354107_2010-12-09_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354306_2010-10-27_RE1_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354106_2010-03-25_RE5_3A_Analytic_SR_clip.tif',\n",
       " 'Data/RapidEye/1354105_2010-03-25_RE5_3A_Analytic_SR_clip.tif']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = glob.glob('Data/RapidEye/*2010*RE*_3A_Analytic_SR_clip.tif')\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished for  2010\n",
      "Finished for  2011\n",
      "Finished for  2012\n",
      "Finished for  2013\n",
      "Finished for  2014\n",
      "Finished for  2015\n",
      "Finished for  2016\n",
      "Finished for  2017\n"
     ]
    }
   ],
   "source": [
    "# Getting the date ranges\n",
    "years = range(2010,2018)\n",
    "months = range(1,13)\n",
    "days = range(0,32)\n",
    "\n",
    "# Use this to make it stop printing that there aren't images available\n",
    "gdal.PushErrorHandler('CPLQuietErrorHandler')\n",
    "\n",
    "# Mosaicing for loop for Rapid Eye\n",
    "for i in years:\n",
    "    year = str(i)\n",
    "    for j in months:\n",
    "        # month = str(j)\n",
    "        month = format(j, '02')\n",
    "        for k in days:\n",
    "            day = format(k, '02')\n",
    "            # print(year+\"-\"+month+\"-\"+day) #Un-comment this if you want to see which days hve errors\n",
    "            monthly_files_list = glob.glob('Data/RapidEye/*'+year+\"-\"+month+\"-\"+day+'*_3A_Analytic_SR_clip.tif')\n",
    "            # This code was for writing all the files into a text file\n",
    "            # with open(year+'-'+month+'_tif_list.txt', 'w') as f:\n",
    "            #     for line in items:\n",
    "            #         f.write(line)\n",
    "            #         f.write('\\n')\n",
    "            files_to_mosaic = monthly_files_list # However many you want.\n",
    "            g = gdal.Warp('outputfiles/mosaic_'+year+'-'+month+\"-\"+day+'.tif', files_to_mosaic, format=\"GTiff\",\n",
    "                  options=[\"COMPRESS=LZW\", \"TILED=YES\"]) # if you want\n",
    "            g = None # Close file and flush to disk\n",
    "            # !gdal_merge.py -n 0 -v -o 'mosaic_year'+'-'+month+'_test.tif' --optfile year+'-'+month+'2010_tif_list.txt'\n",
    "    print('Finished for ',year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-01-00\n",
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      "2010-09-21\n",
      "2010-09-22\n",
      "2010-09-23\n",
      "2010-09-24\n",
      "2010-09-25\n",
      "2010-09-26\n",
      "2010-09-27\n",
      "2010-09-28\n",
      "2010-09-29\n",
      "2010-09-30\n",
      "2010-09-31\n",
      "2010-10-00\n",
      "2010-10-01\n",
      "2010-10-02\n",
      "2010-10-03\n",
      "2010-10-04\n",
      "2010-10-05\n",
      "2010-10-06\n",
      "2010-10-07\n",
      "2010-10-08\n",
      "2010-10-09\n",
      "2010-10-10\n",
      "2010-10-11\n",
      "2010-10-12\n",
      "2010-10-13\n",
      "2010-10-14\n",
      "2010-10-15\n",
      "2010-10-16\n",
      "2010-10-17\n",
      "2010-10-18\n",
      "2010-10-19\n",
      "2010-10-20\n",
      "2010-10-21\n",
      "2010-10-22\n",
      "2010-10-23\n",
      "2010-10-24\n",
      "2010-10-25\n",
      "2010-10-26\n",
      "2010-10-27\n",
      "2010-10-28\n",
      "2010-10-29\n",
      "2010-10-30\n",
      "2010-10-31\n",
      "2010-11-00\n",
      "2010-11-01\n",
      "2010-11-02\n",
      "2010-11-03\n",
      "2010-11-04\n",
      "2010-11-05\n",
      "2010-11-06\n",
      "2010-11-07\n",
      "2010-11-08\n",
      "2010-11-09\n",
      "2010-11-10\n",
      "2010-11-11\n",
      "2010-11-12\n",
      "2010-11-13\n",
      "2010-11-14\n",
      "2010-11-15\n",
      "2010-11-16\n",
      "2010-11-17\n",
      "2010-11-18\n",
      "2010-11-19\n",
      "2010-11-20\n",
      "2010-11-21\n",
      "2010-11-22\n",
      "2010-11-23\n",
      "2010-11-24\n",
      "2010-11-25\n",
      "2010-11-26\n",
      "2010-11-27\n",
      "2010-11-28\n",
      "2010-11-29\n",
      "2010-11-30\n",
      "2010-11-31\n",
      "2010-12-00\n",
      "2010-12-01\n",
      "2010-12-02\n",
      "2010-12-03\n",
      "2010-12-04\n",
      "2010-12-05\n",
      "2010-12-06\n",
      "2010-12-07\n",
      "2010-12-08\n",
      "2010-12-09\n",
      "2010-12-10\n",
      "2010-12-11\n",
      "2010-12-12\n",
      "2010-12-13\n",
      "2010-12-14\n",
      "2010-12-15\n",
      "2010-12-16\n",
      "2010-12-17\n",
      "2010-12-18\n",
      "2010-12-19\n",
      "2010-12-20\n",
      "2010-12-21\n",
      "2010-12-22\n",
      "2010-12-23\n",
      "2010-12-24\n",
      "2010-12-25\n",
      "2010-12-26\n",
      "2010-12-27\n",
      "2010-12-28\n",
      "2010-12-29\n",
      "2010-12-30\n",
      "2010-12-31\n",
      "finished for  2010\n",
      "2011-01-00\n",
      "2011-01-01\n",
      "2011-01-02\n",
      "2011-01-03\n",
      "2011-01-04\n",
      "2011-01-05\n",
      "2011-01-06\n",
      "2011-01-07\n",
      "2011-01-08\n",
      "2011-01-09\n",
      "2011-01-10\n",
      "2011-01-11\n",
      "2011-01-12\n",
      "2011-01-13\n",
      "2011-01-14\n",
      "2011-01-15\n",
      "2011-01-16\n",
      "2011-01-17\n",
      "2011-01-18\n",
      "2011-01-19\n",
      "2011-01-20\n",
      "2011-01-21\n",
      "2011-01-22\n",
      "2011-01-23\n",
      "2011-01-24\n",
      "2011-01-25\n",
      "2011-01-26\n",
      "2011-01-27\n",
      "2011-01-28\n",
      "2011-01-29\n",
      "2011-01-30\n",
      "2011-01-31\n",
      "2011-02-00\n",
      "2011-02-01\n",
      "2011-02-02\n",
      "2011-02-03\n",
      "2011-02-04\n",
      "2011-02-05\n",
      "2011-02-06\n",
      "2011-02-07\n",
      "2011-02-08\n",
      "2011-02-09\n",
      "2011-02-10\n",
      "2011-02-11\n",
      "2011-02-12\n",
      "2011-02-13\n",
      "2011-02-14\n",
      "2011-02-15\n",
      "2011-02-16\n",
      "2011-02-17\n",
      "2011-02-18\n",
      "2011-02-19\n",
      "2011-02-20\n",
      "2011-02-21\n",
      "2011-02-22\n",
      "2011-02-23\n",
      "2011-02-24\n",
      "2011-02-25\n",
      "2011-02-26\n",
      "2011-02-27\n",
      "2011-02-28\n",
      "2011-02-29\n",
      "2011-02-30\n",
      "2011-02-31\n",
      "2011-03-00\n",
      "2011-03-01\n",
      "2011-03-02\n",
      "2011-03-03\n",
      "2011-03-04\n",
      "2011-03-05\n",
      "2011-03-06\n",
      "2011-03-07\n",
      "2011-03-08\n",
      "2011-03-09\n",
      "2011-03-10\n",
      "2011-03-11\n",
      "2011-03-12\n",
      "2011-03-13\n",
      "2011-03-14\n",
      "2011-03-15\n",
      "2011-03-16\n",
      "2011-03-17\n",
      "2011-03-18\n",
      "2011-03-19\n",
      "2011-03-20\n",
      "2011-03-21\n",
      "2011-03-22\n",
      "2011-03-23\n",
      "2011-03-24\n",
      "2011-03-25\n",
      "2011-03-26\n",
      "2011-03-27\n",
      "2011-03-28\n",
      "2011-03-29\n",
      "2011-03-30\n",
      "2011-03-31\n",
      "2011-04-00\n",
      "2011-04-01\n",
      "2011-04-02\n",
      "2011-04-03\n",
      "2011-04-04\n",
      "2011-04-05\n",
      "2011-04-06\n",
      "2011-04-07\n",
      "2011-04-08\n",
      "2011-04-09\n",
      "2011-04-10\n",
      "2011-04-11\n",
      "2011-04-12\n",
      "2011-04-13\n",
      "2011-04-14\n",
      "2011-04-15\n",
      "2011-04-16\n",
      "2011-04-17\n",
      "2011-04-18\n",
      "2011-04-19\n",
      "2011-04-20\n",
      "2011-04-21\n",
      "2011-04-22\n",
      "2011-04-23\n",
      "2011-04-24\n",
      "2011-04-25\n",
      "2011-04-26\n",
      "2011-04-27\n",
      "2011-04-28\n",
      "2011-04-29\n",
      "2011-04-30\n",
      "2011-04-31\n",
      "2011-05-00\n",
      "2011-05-01\n",
      "2011-05-02\n",
      "2011-05-03\n",
      "2011-05-04\n",
      "2011-05-05\n",
      "2011-05-06\n",
      "2011-05-07\n",
      "2011-05-08\n",
      "2011-05-09\n",
      "2011-05-10\n",
      "2011-05-11\n",
      "2011-05-12\n",
      "2011-05-13\n",
      "2011-05-14\n",
      "2011-05-15\n",
      "2011-05-16\n",
      "2011-05-17\n",
      "2011-05-18\n",
      "2011-05-19\n",
      "2011-05-20\n",
      "2011-05-21\n",
      "2011-05-22\n",
      "2011-05-23\n",
      "2011-05-24\n",
      "2011-05-25\n",
      "2011-05-26\n",
      "2011-05-27\n",
      "2011-05-28\n",
      "2011-05-29\n",
      "2011-05-30\n",
      "2011-05-31\n",
      "2011-06-00\n",
      "2011-06-01\n",
      "2011-06-02\n",
      "2011-06-03\n",
      "2011-06-04\n",
      "2011-06-05\n",
      "2011-06-06\n",
      "2011-06-07\n",
      "2011-06-08\n",
      "2011-06-09\n",
      "2011-06-10\n",
      "2011-06-11\n",
      "2011-06-12\n",
      "2011-06-13\n",
      "2011-06-14\n",
      "2011-06-15\n",
      "2011-06-16\n",
      "2011-06-17\n",
      "2011-06-18\n",
      "2011-06-19\n",
      "2011-06-20\n",
      "2011-06-21\n",
      "2011-06-22\n",
      "2011-06-23\n",
      "2011-06-24\n",
      "2011-06-25\n",
      "2011-06-26\n",
      "2011-06-27\n",
      "2011-06-28\n",
      "2011-06-29\n",
      "2011-06-30\n",
      "2011-06-31\n",
      "2011-07-00\n",
      "2011-07-01\n",
      "2011-07-02\n",
      "2011-07-03\n",
      "2011-07-04\n",
      "2011-07-05\n",
      "2011-07-06\n",
      "2011-07-07\n",
      "2011-07-08\n",
      "2011-07-09\n",
      "2011-07-10\n",
      "2011-07-11\n",
      "2011-07-12\n",
      "2011-07-13\n",
      "2011-07-14\n",
      "2011-07-15\n",
      "2011-07-16\n",
      "2011-07-17\n",
      "2011-07-18\n",
      "2011-07-19\n",
      "2011-07-20\n",
      "2011-07-21\n",
      "2011-07-22\n",
      "2011-07-23\n",
      "2011-07-24\n",
      "2011-07-25\n",
      "2011-07-26\n",
      "2011-07-27\n",
      "2011-07-28\n",
      "2011-07-29\n",
      "2011-07-30\n",
      "2011-07-31\n",
      "2011-08-00\n",
      "2011-08-01\n",
      "2011-08-02\n",
      "2011-08-03\n",
      "2011-08-04\n",
      "2011-08-05\n",
      "2011-08-06\n",
      "2011-08-07\n",
      "2011-08-08\n",
      "2011-08-09\n",
      "2011-08-10\n",
      "2011-08-11\n",
      "2011-08-12\n",
      "2011-08-13\n",
      "2011-08-14\n",
      "2011-08-15\n",
      "2011-08-16\n",
      "2011-08-17\n",
      "2011-08-18\n",
      "2011-08-19\n",
      "2011-08-20\n",
      "2011-08-21\n",
      "2011-08-22\n",
      "2011-08-23\n",
      "2011-08-24\n",
      "2011-08-25\n",
      "2011-08-26\n",
      "2011-08-27\n",
      "2011-08-28\n",
      "2011-08-29\n",
      "2011-08-30\n",
      "2011-08-31\n",
      "2011-09-00\n",
      "2011-09-01\n",
      "2011-09-02\n",
      "2011-09-03\n",
      "2011-09-04\n",
      "2011-09-05\n",
      "2011-09-06\n",
      "2011-09-07\n",
      "2011-09-08\n",
      "2011-09-09\n",
      "2011-09-10\n",
      "2011-09-11\n",
      "2011-09-12\n",
      "2011-09-13\n",
      "2011-09-14\n",
      "2011-09-15\n",
      "2011-09-16\n",
      "2011-09-17\n",
      "2011-09-18\n",
      "2011-09-19\n",
      "2011-09-20\n",
      "2011-09-21\n",
      "2011-09-22\n",
      "2011-09-23\n",
      "2011-09-24\n",
      "2011-09-25\n",
      "2011-09-26\n",
      "2011-09-27\n",
      "2011-09-28\n",
      "2011-09-29\n",
      "2011-09-30\n",
      "2011-09-31\n",
      "2011-10-00\n",
      "2011-10-01\n",
      "2011-10-02\n",
      "2011-10-03\n",
      "2011-10-04\n",
      "2011-10-05\n",
      "2011-10-06\n",
      "2011-10-07\n",
      "2011-10-08\n",
      "2011-10-09\n",
      "2011-10-10\n",
      "2011-10-11\n",
      "2011-10-12\n",
      "2011-10-13\n",
      "2011-10-14\n",
      "2011-10-15\n",
      "2011-10-16\n",
      "2011-10-17\n",
      "2011-10-18\n",
      "2011-10-19\n",
      "2011-10-20\n",
      "2011-10-21\n",
      "2011-10-22\n",
      "2011-10-23\n",
      "2011-10-24\n",
      "2011-10-25\n",
      "2011-10-26\n",
      "2011-10-27\n",
      "2011-10-28\n",
      "2011-10-29\n",
      "2011-10-30\n",
      "2011-10-31\n",
      "2011-11-00\n",
      "2011-11-01\n",
      "2011-11-02\n",
      "2011-11-03\n",
      "2011-11-04\n",
      "2011-11-05\n",
      "2011-11-06\n",
      "2011-11-07\n",
      "2011-11-08\n",
      "2011-11-09\n",
      "2011-11-10\n",
      "2011-11-11\n",
      "2011-11-12\n",
      "2011-11-13\n",
      "2011-11-14\n",
      "2011-11-15\n",
      "2011-11-16\n",
      "2011-11-17\n",
      "2011-11-18\n",
      "2011-11-19\n",
      "2011-11-20\n",
      "2011-11-21\n",
      "2011-11-22\n",
      "2011-11-23\n",
      "2011-11-24\n",
      "2011-11-25\n",
      "2011-11-26\n",
      "2011-11-27\n",
      "2011-11-28\n",
      "2011-11-29\n",
      "2011-11-30\n",
      "2011-11-31\n",
      "2011-12-00\n",
      "2011-12-01\n",
      "2011-12-02\n",
      "2011-12-03\n",
      "2011-12-04\n",
      "2011-12-05\n",
      "2011-12-06\n",
      "2011-12-07\n",
      "2011-12-08\n",
      "2011-12-09\n",
      "2011-12-10\n",
      "2011-12-11\n",
      "2011-12-12\n",
      "2011-12-13\n",
      "2011-12-14\n",
      "2011-12-15\n",
      "2011-12-16\n",
      "2011-12-17\n",
      "2011-12-18\n",
      "2011-12-19\n",
      "2011-12-20\n",
      "2011-12-21\n",
      "2011-12-22\n",
      "2011-12-23\n",
      "2011-12-24\n",
      "2011-12-25\n",
      "2011-12-26\n",
      "2011-12-27\n",
      "2011-12-28\n",
      "2011-12-29\n",
      "2011-12-30\n",
      "2011-12-31\n",
      "finished for  2011\n",
      "2012-01-00\n",
      "2012-01-01\n",
      "2012-01-02\n",
      "2012-01-03\n",
      "2012-01-04\n",
      "2012-01-05\n",
      "2012-01-06\n",
      "2012-01-07\n",
      "2012-01-08\n",
      "2012-01-09\n",
      "2012-01-10\n",
      "2012-01-11\n",
      "2012-01-12\n",
      "2012-01-13\n",
      "2012-01-14\n",
      "2012-01-15\n",
      "2012-01-16\n",
      "2012-01-17\n",
      "2012-01-18\n",
      "2012-01-19\n",
      "2012-01-20\n",
      "2012-01-21\n",
      "2012-01-22\n",
      "2012-01-23\n",
      "2012-01-24\n",
      "2012-01-25\n",
      "2012-01-26\n",
      "2012-01-27\n",
      "2012-01-28\n",
      "2012-01-29\n",
      "2012-01-30\n",
      "2012-01-31\n",
      "2012-02-00\n",
      "2012-02-01\n",
      "2012-02-02\n",
      "2012-02-03\n",
      "2012-02-04\n",
      "2012-02-05\n",
      "2012-02-06\n",
      "2012-02-07\n",
      "2012-02-08\n",
      "2012-02-09\n",
      "2012-02-10\n",
      "2012-02-11\n",
      "2012-02-12\n",
      "2012-02-13\n",
      "2012-02-14\n",
      "2012-02-15\n",
      "2012-02-16\n",
      "2012-02-17\n",
      "2012-02-18\n",
      "2012-02-19\n",
      "2012-02-20\n",
      "2012-02-21\n",
      "2012-02-22\n",
      "2012-02-23\n",
      "2012-02-24\n",
      "2012-02-25\n",
      "2012-02-26\n",
      "2012-02-27\n",
      "2012-02-28\n",
      "2012-02-29\n",
      "2012-02-30\n",
      "2012-02-31\n",
      "2012-03-00\n",
      "2012-03-01\n",
      "2012-03-02\n",
      "2012-03-03\n",
      "2012-03-04\n",
      "2012-03-05\n",
      "2012-03-06\n",
      "2012-03-07\n",
      "2012-03-08\n",
      "2012-03-09\n",
      "2012-03-10\n",
      "2012-03-11\n",
      "2012-03-12\n",
      "2012-03-13\n",
      "2012-03-14\n",
      "2012-03-15\n",
      "2012-03-16\n",
      "2012-03-17\n",
      "2012-03-18\n",
      "2012-03-19\n",
      "2012-03-20\n",
      "2012-03-21\n",
      "2012-03-22\n",
      "2012-03-23\n",
      "2012-03-24\n",
      "2012-03-25\n",
      "2012-03-26\n",
      "2012-03-27\n",
      "2012-03-28\n",
      "2012-03-29\n",
      "2012-03-30\n",
      "2012-03-31\n",
      "2012-04-00\n",
      "2012-04-01\n",
      "2012-04-02\n",
      "2012-04-03\n",
      "2012-04-04\n",
      "2012-04-05\n",
      "2012-04-06\n",
      "2012-04-07\n",
      "2012-04-08\n",
      "2012-04-09\n",
      "2012-04-10\n",
      "2012-04-11\n",
      "2012-04-12\n",
      "2012-04-13\n",
      "2012-04-14\n",
      "2012-04-15\n",
      "2012-04-16\n",
      "2012-04-17\n",
      "2012-04-18\n",
      "2012-04-19\n",
      "2012-04-20\n",
      "2012-04-21\n",
      "2012-04-22\n",
      "2012-04-23\n",
      "2012-04-24\n",
      "2012-04-25\n",
      "2012-04-26\n",
      "2012-04-27\n",
      "2012-04-28\n",
      "2012-04-29\n",
      "2012-04-30\n",
      "2012-04-31\n",
      "2012-05-00\n",
      "2012-05-01\n",
      "2012-05-02\n",
      "2012-05-03\n",
      "2012-05-04\n",
      "2012-05-05\n",
      "2012-05-06\n",
      "2012-05-07\n",
      "2012-05-08\n",
      "2012-05-09\n",
      "2012-05-10\n",
      "2012-05-11\n",
      "2012-05-12\n",
      "2012-05-13\n",
      "2012-05-14\n",
      "2012-05-15\n",
      "2012-05-16\n",
      "2012-05-17\n",
      "2012-05-18\n",
      "2012-05-19\n",
      "2012-05-20\n",
      "2012-05-21\n",
      "2012-05-22\n",
      "2012-05-23\n",
      "2012-05-24\n",
      "2012-05-25\n",
      "2012-05-26\n",
      "2012-05-27\n",
      "2012-05-28\n",
      "2012-05-29\n",
      "2012-05-30\n",
      "2012-05-31\n",
      "2012-06-00\n",
      "2012-06-01\n",
      "2012-06-02\n",
      "2012-06-03\n",
      "2012-06-04\n",
      "2012-06-05\n",
      "2012-06-06\n",
      "2012-06-07\n",
      "2012-06-08\n",
      "2012-06-09\n",
      "2012-06-10\n",
      "2012-06-11\n",
      "2012-06-12\n",
      "2012-06-13\n",
      "2012-06-14\n",
      "2012-06-15\n",
      "2012-06-16\n",
      "2012-06-17\n",
      "2012-06-18\n",
      "2012-06-19\n",
      "2012-06-20\n",
      "2012-06-21\n",
      "2012-06-22\n",
      "2012-06-23\n",
      "2012-06-24\n",
      "2012-06-25\n",
      "2012-06-26\n",
      "2012-06-27\n",
      "2012-06-28\n",
      "2012-06-29\n",
      "2012-06-30\n",
      "2012-06-31\n",
      "2012-07-00\n",
      "2012-07-01\n",
      "2012-07-02\n",
      "2012-07-03\n",
      "2012-07-04\n",
      "2012-07-05\n",
      "2012-07-06\n",
      "2012-07-07\n",
      "2012-07-08\n",
      "2012-07-09\n",
      "2012-07-10\n",
      "2012-07-11\n",
      "2012-07-12\n",
      "2012-07-13\n",
      "2012-07-14\n",
      "2012-07-15\n",
      "2012-07-16\n",
      "2012-07-17\n",
      "2012-07-18\n",
      "2012-07-19\n",
      "2012-07-20\n",
      "2012-07-21\n",
      "2012-07-22\n",
      "2012-07-23\n",
      "2012-07-24\n",
      "2012-07-25\n",
      "2012-07-26\n",
      "2012-07-27\n",
      "2012-07-28\n",
      "2012-07-29\n",
      "2012-07-30\n",
      "2012-07-31\n",
      "2012-08-00\n",
      "2012-08-01\n",
      "2012-08-02\n",
      "2012-08-03\n",
      "2012-08-04\n",
      "2012-08-05\n",
      "2012-08-06\n",
      "2012-08-07\n",
      "2012-08-08\n",
      "2012-08-09\n",
      "2012-08-10\n",
      "2012-08-11\n",
      "2012-08-12\n",
      "2012-08-13\n",
      "2012-08-14\n",
      "2012-08-15\n",
      "2012-08-16\n",
      "2012-08-17\n",
      "2012-08-18\n",
      "2012-08-19\n",
      "2012-08-20\n",
      "2012-08-21\n",
      "2012-08-22\n",
      "2012-08-23\n",
      "2012-08-24\n",
      "2012-08-25\n",
      "2012-08-26\n",
      "2012-08-27\n",
      "2012-08-28\n",
      "2012-08-29\n",
      "2012-08-30\n",
      "2012-08-31\n",
      "2012-09-00\n",
      "2012-09-01\n",
      "2012-09-02\n",
      "2012-09-03\n",
      "2012-09-04\n",
      "2012-09-05\n",
      "2012-09-06\n",
      "2012-09-07\n",
      "2012-09-08\n",
      "2012-09-09\n",
      "2012-09-10\n",
      "2012-09-11\n",
      "2012-09-12\n",
      "2012-09-13\n",
      "2012-09-14\n",
      "2012-09-15\n",
      "2012-09-16\n",
      "2012-09-17\n",
      "2012-09-18\n",
      "2012-09-19\n",
      "2012-09-20\n",
      "2012-09-21\n",
      "2012-09-22\n",
      "2012-09-23\n",
      "2012-09-24\n",
      "2012-09-25\n",
      "2012-09-26\n",
      "2012-09-27\n",
      "2012-09-28\n",
      "2012-09-29\n",
      "2012-09-30\n",
      "2012-09-31\n",
      "2012-10-00\n",
      "2012-10-01\n",
      "2012-10-02\n",
      "2012-10-03\n",
      "2012-10-04\n",
      "2012-10-05\n",
      "2012-10-06\n",
      "2012-10-07\n",
      "2012-10-08\n",
      "2012-10-09\n",
      "2012-10-10\n",
      "2012-10-11\n",
      "2012-10-12\n",
      "2012-10-13\n",
      "2012-10-14\n",
      "2012-10-15\n",
      "2012-10-16\n",
      "2012-10-17\n",
      "2012-10-18\n",
      "2012-10-19\n",
      "2012-10-20\n",
      "2012-10-21\n",
      "2012-10-22\n",
      "2012-10-23\n",
      "2012-10-24\n",
      "2012-10-25\n",
      "2012-10-26\n",
      "2012-10-27\n",
      "2012-10-28\n",
      "2012-10-29\n",
      "2012-10-30\n",
      "2012-10-31\n",
      "2012-11-00\n",
      "2012-11-01\n",
      "2012-11-02\n",
      "2012-11-03\n",
      "2012-11-04\n",
      "2012-11-05\n",
      "2012-11-06\n",
      "2012-11-07\n",
      "2012-11-08\n",
      "2012-11-09\n",
      "2012-11-10\n",
      "2012-11-11\n",
      "2012-11-12\n",
      "2012-11-13\n",
      "2012-11-14\n",
      "2012-11-15\n",
      "2012-11-16\n",
      "2012-11-17\n",
      "2012-11-18\n",
      "2012-11-19\n",
      "2012-11-20\n",
      "2012-11-21\n",
      "2012-11-22\n",
      "2012-11-23\n",
      "2012-11-24\n",
      "2012-11-25\n",
      "2012-11-26\n",
      "2012-11-27\n",
      "2012-11-28\n",
      "2012-11-29\n",
      "2012-11-30\n",
      "2012-11-31\n",
      "2012-12-00\n",
      "2012-12-01\n",
      "2012-12-02\n",
      "2012-12-03\n",
      "2012-12-04\n",
      "2012-12-05\n",
      "2012-12-06\n",
      "2012-12-07\n",
      "2012-12-08\n",
      "2012-12-09\n",
      "2012-12-10\n",
      "2012-12-11\n",
      "2012-12-12\n",
      "2012-12-13\n",
      "2012-12-14\n",
      "2012-12-15\n",
      "2012-12-16\n",
      "2012-12-17\n",
      "2012-12-18\n",
      "2012-12-19\n",
      "2012-12-20\n",
      "2012-12-21\n",
      "2012-12-22\n",
      "2012-12-23\n",
      "2012-12-24\n",
      "2012-12-25\n",
      "2012-12-26\n",
      "2012-12-27\n",
      "2012-12-28\n",
      "2012-12-29\n",
      "2012-12-30\n",
      "2012-12-31\n",
      "finished for  2012\n",
      "2013-01-00\n",
      "2013-01-01\n",
      "2013-01-02\n",
      "2013-01-03\n",
      "2013-01-04\n",
      "2013-01-05\n",
      "2013-01-06\n",
      "2013-01-07\n",
      "2013-01-08\n",
      "2013-01-09\n",
      "2013-01-10\n",
      "2013-01-11\n",
      "2013-01-12\n",
      "2013-01-13\n",
      "2013-01-14\n",
      "2013-01-15\n",
      "2013-01-16\n",
      "2013-01-17\n",
      "2013-01-18\n",
      "2013-01-19\n",
      "2013-01-20\n",
      "2013-01-21\n",
      "2013-01-22\n",
      "2013-01-23\n",
      "2013-01-24\n",
      "2013-01-25\n",
      "2013-01-26\n",
      "2013-01-27\n",
      "2013-01-28\n",
      "2013-01-29\n",
      "2013-01-30\n",
      "2013-01-31\n",
      "2013-02-00\n",
      "2013-02-01\n",
      "2013-02-02\n",
      "2013-02-03\n",
      "2013-02-04\n",
      "2013-02-05\n",
      "2013-02-06\n",
      "2013-02-07\n",
      "2013-02-08\n",
      "2013-02-09\n",
      "2013-02-10\n",
      "2013-02-11\n",
      "2013-02-12\n",
      "2013-02-13\n",
      "2013-02-14\n",
      "2013-02-15\n",
      "2013-02-16\n",
      "2013-02-17\n",
      "2013-02-18\n",
      "2013-02-19\n",
      "2013-02-20\n",
      "2013-02-21\n",
      "2013-02-22\n",
      "2013-02-23\n",
      "2013-02-24\n",
      "2013-02-25\n",
      "2013-02-26\n",
      "2013-02-27\n",
      "2013-02-28\n",
      "2013-02-29\n",
      "2013-02-30\n",
      "2013-02-31\n",
      "2013-03-00\n",
      "2013-03-01\n",
      "2013-03-02\n",
      "2013-03-03\n",
      "2013-03-04\n",
      "2013-03-05\n",
      "2013-03-06\n",
      "2013-03-07\n",
      "2013-03-08\n",
      "2013-03-09\n",
      "2013-03-10\n",
      "2013-03-11\n",
      "2013-03-12\n",
      "2013-03-13\n",
      "2013-03-14\n",
      "2013-03-15\n",
      "2013-03-16\n",
      "2013-03-17\n",
      "2013-03-18\n",
      "2013-03-19\n",
      "2013-03-20\n",
      "2013-03-21\n",
      "2013-03-22\n",
      "2013-03-23\n",
      "2013-03-24\n",
      "2013-03-25\n",
      "2013-03-26\n",
      "2013-03-27\n",
      "2013-03-28\n",
      "2013-03-29\n",
      "2013-03-30\n",
      "2013-03-31\n",
      "2013-04-00\n",
      "2013-04-01\n",
      "2013-04-02\n",
      "2013-04-03\n",
      "2013-04-04\n",
      "2013-04-05\n",
      "2013-04-06\n",
      "2013-04-07\n",
      "2013-04-08\n",
      "2013-04-09\n",
      "2013-04-10\n",
      "2013-04-11\n",
      "2013-04-12\n",
      "2013-04-13\n",
      "2013-04-14\n",
      "2013-04-15\n",
      "2013-04-16\n",
      "2013-04-17\n",
      "2013-04-18\n",
      "2013-04-19\n",
      "2013-04-20\n",
      "2013-04-21\n",
      "2013-04-22\n",
      "2013-04-23\n",
      "2013-04-24\n",
      "2013-04-25\n",
      "2013-04-26\n",
      "2013-04-27\n",
      "2013-04-28\n",
      "2013-04-29\n",
      "2013-04-30\n",
      "2013-04-31\n",
      "2013-05-00\n",
      "2013-05-01\n",
      "2013-05-02\n",
      "2013-05-03\n",
      "2013-05-04\n",
      "2013-05-05\n",
      "2013-05-06\n",
      "2013-05-07\n",
      "2013-05-08\n",
      "2013-05-09\n",
      "2013-05-10\n",
      "2013-05-11\n",
      "2013-05-12\n",
      "2013-05-13\n",
      "2013-05-14\n",
      "2013-05-15\n",
      "2013-05-16\n",
      "2013-05-17\n",
      "2013-05-18\n",
      "2013-05-19\n",
      "2013-05-20\n",
      "2013-05-21\n",
      "2013-05-22\n",
      "2013-05-23\n",
      "2013-05-24\n",
      "2013-05-25\n",
      "2013-05-26\n",
      "2013-05-27\n",
      "2013-05-28\n",
      "2013-05-29\n",
      "2013-05-30\n",
      "2013-05-31\n",
      "2013-06-00\n",
      "2013-06-01\n",
      "2013-06-02\n",
      "2013-06-03\n",
      "2013-06-04\n",
      "2013-06-05\n",
      "2013-06-06\n",
      "2013-06-07\n",
      "2013-06-08\n",
      "2013-06-09\n",
      "2013-06-10\n",
      "2013-06-11\n",
      "2013-06-12\n",
      "2013-06-13\n",
      "2013-06-14\n",
      "2013-06-15\n",
      "2013-06-16\n",
      "2013-06-17\n",
      "2013-06-18\n",
      "2013-06-19\n",
      "2013-06-20\n",
      "2013-06-21\n",
      "2013-06-22\n",
      "2013-06-23\n",
      "2013-06-24\n",
      "2013-06-25\n",
      "2013-06-26\n",
      "2013-06-27\n",
      "2013-06-28\n",
      "2013-06-29\n",
      "2013-06-30\n",
      "2013-06-31\n",
      "2013-07-00\n",
      "2013-07-01\n",
      "2013-07-02\n",
      "2013-07-03\n",
      "2013-07-04\n",
      "2013-07-05\n",
      "2013-07-06\n",
      "2013-07-07\n",
      "2013-07-08\n",
      "2013-07-09\n",
      "2013-07-10\n",
      "2013-07-11\n",
      "2013-07-12\n",
      "2013-07-13\n",
      "2013-07-14\n",
      "2013-07-15\n",
      "2013-07-16\n",
      "2013-07-17\n",
      "2013-07-18\n",
      "2013-07-19\n",
      "2013-07-20\n",
      "2013-07-21\n",
      "2013-07-22\n",
      "2013-07-23\n",
      "2013-07-24\n",
      "2013-07-25\n",
      "2013-07-26\n",
      "2013-07-27\n",
      "2013-07-28\n",
      "2013-07-29\n",
      "2013-07-30\n",
      "2013-07-31\n",
      "2013-08-00\n",
      "2013-08-01\n",
      "2013-08-02\n",
      "2013-08-03\n",
      "2013-08-04\n",
      "2013-08-05\n",
      "2013-08-06\n",
      "2013-08-07\n",
      "2013-08-08\n",
      "2013-08-09\n",
      "2013-08-10\n",
      "2013-08-11\n",
      "2013-08-12\n",
      "2013-08-13\n",
      "2013-08-14\n",
      "2013-08-15\n",
      "2013-08-16\n",
      "2013-08-17\n",
      "2013-08-18\n",
      "2013-08-19\n",
      "2013-08-20\n",
      "2013-08-21\n",
      "2013-08-22\n",
      "2013-08-23\n",
      "2013-08-24\n",
      "2013-08-25\n",
      "2013-08-26\n",
      "2013-08-27\n",
      "2013-08-28\n",
      "2013-08-29\n",
      "2013-08-30\n",
      "2013-08-31\n",
      "2013-09-00\n",
      "2013-09-01\n",
      "2013-09-02\n",
      "2013-09-03\n",
      "2013-09-04\n",
      "2013-09-05\n",
      "2013-09-06\n",
      "2013-09-07\n",
      "2013-09-08\n",
      "2013-09-09\n",
      "2013-09-10\n",
      "2013-09-11\n",
      "2013-09-12\n",
      "2013-09-13\n",
      "2013-09-14\n",
      "2013-09-15\n",
      "2013-09-16\n",
      "2013-09-17\n",
      "2013-09-18\n",
      "2013-09-19\n",
      "2013-09-20\n",
      "2013-09-21\n",
      "2013-09-22\n",
      "2013-09-23\n",
      "2013-09-24\n",
      "2013-09-25\n",
      "2013-09-26\n",
      "2013-09-27\n",
      "2013-09-28\n",
      "2013-09-29\n",
      "2013-09-30\n",
      "2013-09-31\n",
      "2013-10-00\n",
      "2013-10-01\n",
      "2013-10-02\n",
      "2013-10-03\n",
      "2013-10-04\n",
      "2013-10-05\n",
      "2013-10-06\n",
      "2013-10-07\n",
      "2013-10-08\n",
      "2013-10-09\n",
      "2013-10-10\n",
      "2013-10-11\n",
      "2013-10-12\n",
      "2013-10-13\n",
      "2013-10-14\n",
      "2013-10-15\n",
      "2013-10-16\n",
      "2013-10-17\n",
      "2013-10-18\n",
      "2013-10-19\n",
      "2013-10-20\n",
      "2013-10-21\n",
      "2013-10-22\n",
      "2013-10-23\n",
      "2013-10-24\n",
      "2013-10-25\n",
      "2013-10-26\n",
      "2013-10-27\n",
      "2013-10-28\n",
      "2013-10-29\n",
      "2013-10-30\n",
      "2013-10-31\n",
      "2013-11-00\n",
      "2013-11-01\n",
      "2013-11-02\n",
      "2013-11-03\n",
      "2013-11-04\n",
      "2013-11-05\n",
      "2013-11-06\n",
      "2013-11-07\n",
      "2013-11-08\n",
      "2013-11-09\n",
      "2013-11-10\n",
      "2013-11-11\n",
      "2013-11-12\n",
      "2013-11-13\n",
      "2013-11-14\n",
      "2013-11-15\n",
      "2013-11-16\n",
      "2013-11-17\n",
      "2013-11-18\n",
      "2013-11-19\n",
      "2013-11-20\n",
      "2013-11-21\n",
      "2013-11-22\n",
      "2013-11-23\n",
      "2013-11-24\n",
      "2013-11-25\n",
      "2013-11-26\n",
      "2013-11-27\n",
      "2013-11-28\n",
      "2013-11-29\n",
      "2013-11-30\n",
      "2013-11-31\n",
      "2013-12-00\n",
      "2013-12-01\n",
      "2013-12-02\n",
      "2013-12-03\n",
      "2013-12-04\n",
      "2013-12-05\n",
      "2013-12-06\n",
      "2013-12-07\n",
      "2013-12-08\n",
      "2013-12-09\n",
      "2013-12-10\n",
      "2013-12-11\n",
      "2013-12-12\n",
      "2013-12-13\n",
      "2013-12-14\n",
      "2013-12-15\n",
      "2013-12-16\n",
      "2013-12-17\n",
      "2013-12-18\n",
      "2013-12-19\n",
      "2013-12-20\n",
      "2013-12-21\n",
      "2013-12-22\n",
      "2013-12-23\n",
      "2013-12-24\n",
      "2013-12-25\n",
      "2013-12-26\n",
      "2013-12-27\n",
      "2013-12-28\n",
      "2013-12-29\n",
      "2013-12-30\n",
      "2013-12-31\n",
      "finished for  2013\n",
      "2014-01-00\n",
      "2014-01-01\n",
      "2014-01-02\n",
      "2014-01-03\n",
      "2014-01-04\n",
      "2014-01-05\n",
      "2014-01-06\n",
      "2014-01-07\n",
      "2014-01-08\n",
      "2014-01-09\n",
      "2014-01-10\n",
      "2014-01-11\n",
      "2014-01-12\n",
      "2014-01-13\n",
      "2014-01-14\n",
      "2014-01-15\n",
      "2014-01-16\n",
      "2014-01-17\n",
      "2014-01-18\n",
      "2014-01-19\n",
      "2014-01-20\n",
      "2014-01-21\n",
      "2014-01-22\n",
      "2014-01-23\n",
      "2014-01-24\n",
      "2014-01-25\n",
      "2014-01-26\n",
      "2014-01-27\n",
      "2014-01-28\n",
      "2014-01-29\n",
      "2014-01-30\n",
      "2014-01-31\n",
      "2014-02-00\n",
      "2014-02-01\n",
      "2014-02-02\n",
      "2014-02-03\n",
      "2014-02-04\n",
      "2014-02-05\n",
      "2014-02-06\n",
      "2014-02-07\n",
      "2014-02-08\n",
      "2014-02-09\n",
      "2014-02-10\n",
      "2014-02-11\n",
      "2014-02-12\n",
      "2014-02-13\n",
      "2014-02-14\n",
      "2014-02-15\n",
      "2014-02-16\n",
      "2014-02-17\n",
      "2014-02-18\n",
      "2014-02-19\n",
      "2014-02-20\n",
      "2014-02-21\n",
      "2014-02-22\n",
      "2014-02-23\n",
      "2014-02-24\n",
      "2014-02-25\n",
      "2014-02-26\n",
      "2014-02-27\n",
      "2014-02-28\n",
      "2014-02-29\n",
      "2014-02-30\n",
      "2014-02-31\n",
      "2014-03-00\n",
      "2014-03-01\n",
      "2014-03-02\n",
      "2014-03-03\n",
      "2014-03-04\n",
      "2014-03-05\n",
      "2014-03-06\n",
      "2014-03-07\n",
      "2014-03-08\n",
      "2014-03-09\n",
      "2014-03-10\n",
      "2014-03-11\n",
      "2014-03-12\n",
      "2014-03-13\n",
      "2014-03-14\n",
      "2014-03-15\n",
      "2014-03-16\n",
      "2014-03-17\n",
      "2014-03-18\n",
      "2014-03-19\n",
      "2014-03-20\n",
      "2014-03-21\n",
      "2014-03-22\n",
      "2014-03-23\n",
      "2014-03-24\n",
      "2014-03-25\n",
      "2014-03-26\n",
      "2014-03-27\n",
      "2014-03-28\n",
      "2014-03-29\n",
      "2014-03-30\n",
      "2014-03-31\n",
      "2014-04-00\n",
      "2014-04-01\n",
      "2014-04-02\n",
      "2014-04-03\n",
      "2014-04-04\n",
      "2014-04-05\n",
      "2014-04-06\n",
      "2014-04-07\n",
      "2014-04-08\n",
      "2014-04-09\n",
      "2014-04-10\n",
      "2014-04-11\n",
      "2014-04-12\n",
      "2014-04-13\n",
      "2014-04-14\n",
      "2014-04-15\n",
      "2014-04-16\n",
      "2014-04-17\n",
      "2014-04-18\n",
      "2014-04-19\n",
      "2014-04-20\n",
      "2014-04-21\n",
      "2014-04-22\n",
      "2014-04-23\n",
      "2014-04-24\n",
      "2014-04-25\n",
      "2014-04-26\n",
      "2014-04-27\n",
      "2014-04-28\n",
      "2014-04-29\n",
      "2014-04-30\n",
      "2014-04-31\n",
      "2014-05-00\n",
      "2014-05-01\n",
      "2014-05-02\n",
      "2014-05-03\n",
      "2014-05-04\n",
      "2014-05-05\n",
      "2014-05-06\n",
      "2014-05-07\n",
      "2014-05-08\n",
      "2014-05-09\n",
      "2014-05-10\n",
      "2014-05-11\n",
      "2014-05-12\n",
      "2014-05-13\n",
      "2014-05-14\n",
      "2014-05-15\n",
      "2014-05-16\n",
      "2014-05-17\n",
      "2014-05-18\n",
      "2014-05-19\n",
      "2014-05-20\n",
      "2014-05-21\n",
      "2014-05-22\n",
      "2014-05-23\n",
      "2014-05-24\n",
      "2014-05-25\n",
      "2014-05-26\n",
      "2014-05-27\n",
      "2014-05-28\n",
      "2014-05-29\n",
      "2014-05-30\n",
      "2014-05-31\n",
      "2014-06-00\n",
      "2014-06-01\n",
      "2014-06-02\n",
      "2014-06-03\n",
      "2014-06-04\n",
      "2014-06-05\n",
      "2014-06-06\n",
      "2014-06-07\n",
      "2014-06-08\n",
      "2014-06-09\n",
      "2014-06-10\n",
      "2014-06-11\n",
      "2014-06-12\n",
      "2014-06-13\n",
      "2014-06-14\n",
      "2014-06-15\n",
      "2014-06-16\n",
      "2014-06-17\n",
      "2014-06-18\n",
      "2014-06-19\n",
      "2014-06-20\n",
      "2014-06-21\n",
      "2014-06-22\n",
      "2014-06-23\n",
      "2014-06-24\n",
      "2014-06-25\n",
      "2014-06-26\n",
      "2014-06-27\n",
      "2014-06-28\n",
      "2014-06-29\n",
      "2014-06-30\n",
      "2014-06-31\n",
      "2014-07-00\n",
      "2014-07-01\n",
      "2014-07-02\n",
      "2014-07-03\n",
      "2014-07-04\n",
      "2014-07-05\n",
      "2014-07-06\n",
      "2014-07-07\n",
      "2014-07-08\n",
      "2014-07-09\n",
      "2014-07-10\n",
      "2014-07-11\n",
      "2014-07-12\n",
      "2014-07-13\n",
      "2014-07-14\n",
      "2014-07-15\n",
      "2014-07-16\n",
      "2014-07-17\n",
      "2014-07-18\n",
      "2014-07-19\n",
      "2014-07-20\n",
      "2014-07-21\n",
      "2014-07-22\n",
      "2014-07-23\n",
      "2014-07-24\n",
      "2014-07-25\n",
      "2014-07-26\n",
      "2014-07-27\n",
      "2014-07-28\n",
      "2014-07-29\n",
      "2014-07-30\n",
      "2014-07-31\n",
      "2014-08-00\n",
      "2014-08-01\n",
      "2014-08-02\n",
      "2014-08-03\n",
      "2014-08-04\n",
      "2014-08-05\n",
      "2014-08-06\n",
      "2014-08-07\n",
      "2014-08-08\n",
      "2014-08-09\n",
      "2014-08-10\n",
      "2014-08-11\n",
      "2014-08-12\n",
      "2014-08-13\n",
      "2014-08-14\n",
      "2014-08-15\n",
      "2014-08-16\n",
      "2014-08-17\n",
      "2014-08-18\n",
      "2014-08-19\n",
      "2014-08-20\n",
      "2014-08-21\n",
      "2014-08-22\n",
      "2014-08-23\n",
      "2014-08-24\n",
      "2014-08-25\n",
      "2014-08-26\n",
      "2014-08-27\n",
      "2014-08-28\n",
      "2014-08-29\n",
      "2014-08-30\n",
      "2014-08-31\n",
      "2014-09-00\n",
      "2014-09-01\n",
      "2014-09-02\n",
      "2014-09-03\n",
      "2014-09-04\n",
      "2014-09-05\n",
      "2014-09-06\n",
      "2014-09-07\n",
      "2014-09-08\n",
      "2014-09-09\n",
      "2014-09-10\n",
      "2014-09-11\n",
      "2014-09-12\n",
      "2014-09-13\n",
      "2014-09-14\n",
      "2014-09-15\n",
      "2014-09-16\n",
      "2014-09-17\n",
      "2014-09-18\n",
      "2014-09-19\n",
      "2014-09-20\n",
      "2014-09-21\n",
      "2014-09-22\n",
      "2014-09-23\n",
      "2014-09-24\n",
      "2014-09-25\n",
      "2014-09-26\n",
      "2014-09-27\n",
      "2014-09-28\n",
      "2014-09-29\n",
      "2014-09-30\n",
      "2014-09-31\n",
      "2014-10-00\n",
      "2014-10-01\n",
      "2014-10-02\n",
      "2014-10-03\n",
      "2014-10-04\n",
      "2014-10-05\n",
      "2014-10-06\n",
      "2014-10-07\n",
      "2014-10-08\n",
      "2014-10-09\n",
      "2014-10-10\n",
      "2014-10-11\n",
      "2014-10-12\n",
      "2014-10-13\n",
      "2014-10-14\n",
      "2014-10-15\n",
      "2014-10-16\n",
      "2014-10-17\n",
      "2014-10-18\n",
      "2014-10-19\n",
      "2014-10-20\n",
      "2014-10-21\n",
      "2014-10-22\n",
      "2014-10-23\n",
      "2014-10-24\n",
      "2014-10-25\n",
      "2014-10-26\n",
      "2014-10-27\n",
      "2014-10-28\n",
      "2014-10-29\n",
      "2014-10-30\n",
      "2014-10-31\n",
      "2014-11-00\n",
      "2014-11-01\n",
      "2014-11-02\n",
      "2014-11-03\n",
      "2014-11-04\n",
      "2014-11-05\n",
      "2014-11-06\n",
      "2014-11-07\n",
      "2014-11-08\n",
      "2014-11-09\n",
      "2014-11-10\n",
      "2014-11-11\n",
      "2014-11-12\n",
      "2014-11-13\n",
      "2014-11-14\n",
      "2014-11-15\n",
      "2014-11-16\n",
      "2014-11-17\n",
      "2014-11-18\n",
      "2014-11-19\n",
      "2014-11-20\n",
      "2014-11-21\n",
      "2014-11-22\n",
      "2014-11-23\n",
      "2014-11-24\n",
      "2014-11-25\n",
      "2014-11-26\n",
      "2014-11-27\n",
      "2014-11-28\n",
      "2014-11-29\n",
      "2014-11-30\n",
      "2014-11-31\n",
      "2014-12-00\n",
      "2014-12-01\n",
      "2014-12-02\n",
      "2014-12-03\n",
      "2014-12-04\n",
      "2014-12-05\n",
      "2014-12-06\n",
      "2014-12-07\n",
      "2014-12-08\n",
      "2014-12-09\n",
      "2014-12-10\n",
      "2014-12-11\n",
      "2014-12-12\n",
      "2014-12-13\n",
      "2014-12-14\n",
      "2014-12-15\n",
      "2014-12-16\n",
      "2014-12-17\n",
      "2014-12-18\n",
      "2014-12-19\n",
      "2014-12-20\n",
      "2014-12-21\n",
      "2014-12-22\n",
      "2014-12-23\n",
      "2014-12-24\n",
      "2014-12-25\n",
      "2014-12-26\n",
      "2014-12-27\n",
      "2014-12-28\n",
      "2014-12-29\n",
      "2014-12-30\n",
      "2014-12-31\n",
      "finished for  2014\n",
      "2015-01-00\n",
      "2015-01-01\n",
      "2015-01-02\n",
      "2015-01-03\n",
      "2015-01-04\n",
      "2015-01-05\n",
      "2015-01-06\n",
      "2015-01-07\n",
      "2015-01-08\n",
      "2015-01-09\n",
      "2015-01-10\n",
      "2015-01-11\n",
      "2015-01-12\n",
      "2015-01-13\n",
      "2015-01-14\n",
      "2015-01-15\n",
      "2015-01-16\n",
      "2015-01-17\n",
      "2015-01-18\n",
      "2015-01-19\n",
      "2015-01-20\n",
      "2015-01-21\n",
      "2015-01-22\n",
      "2015-01-23\n",
      "2015-01-24\n",
      "2015-01-25\n",
      "2015-01-26\n",
      "2015-01-27\n",
      "2015-01-28\n",
      "2015-01-29\n",
      "2015-01-30\n",
      "2015-01-31\n",
      "2015-02-00\n",
      "2015-02-01\n",
      "2015-02-02\n",
      "2015-02-03\n",
      "2015-02-04\n",
      "2015-02-05\n",
      "2015-02-06\n",
      "2015-02-07\n",
      "2015-02-08\n",
      "2015-02-09\n",
      "2015-02-10\n",
      "2015-02-11\n",
      "2015-02-12\n",
      "2015-02-13\n",
      "2015-02-14\n",
      "2015-02-15\n",
      "2015-02-16\n",
      "2015-02-17\n",
      "2015-02-18\n",
      "2015-02-19\n",
      "2015-02-20\n",
      "2015-02-21\n",
      "2015-02-22\n",
      "2015-02-23\n",
      "2015-02-24\n",
      "2015-02-25\n",
      "2015-02-26\n",
      "2015-02-27\n",
      "2015-02-28\n",
      "2015-02-29\n",
      "2015-02-30\n",
      "2015-02-31\n",
      "2015-03-00\n",
      "2015-03-01\n",
      "2015-03-02\n",
      "2015-03-03\n",
      "2015-03-04\n",
      "2015-03-05\n",
      "2015-03-06\n",
      "2015-03-07\n",
      "2015-03-08\n",
      "2015-03-09\n",
      "2015-03-10\n",
      "2015-03-11\n",
      "2015-03-12\n",
      "2015-03-13\n",
      "2015-03-14\n",
      "2015-03-15\n",
      "2015-03-16\n",
      "2015-03-17\n",
      "2015-03-18\n",
      "2015-03-19\n",
      "2015-03-20\n",
      "2015-03-21\n",
      "2015-03-22\n",
      "2015-03-23\n",
      "2015-03-24\n",
      "2015-03-25\n",
      "2015-03-26\n",
      "2015-03-27\n",
      "2015-03-28\n",
      "2015-03-29\n",
      "2015-03-30\n",
      "2015-03-31\n",
      "2015-04-00\n",
      "2015-04-01\n",
      "2015-04-02\n",
      "2015-04-03\n",
      "2015-04-04\n",
      "2015-04-05\n",
      "2015-04-06\n",
      "2015-04-07\n",
      "2015-04-08\n",
      "2015-04-09\n",
      "2015-04-10\n",
      "2015-04-11\n",
      "2015-04-12\n",
      "2015-04-13\n",
      "2015-04-14\n",
      "2015-04-15\n",
      "2015-04-16\n",
      "2015-04-17\n",
      "2015-04-18\n",
      "2015-04-19\n",
      "2015-04-20\n",
      "2015-04-21\n",
      "2015-04-22\n",
      "2015-04-23\n",
      "2015-04-24\n",
      "2015-04-25\n",
      "2015-04-26\n",
      "2015-04-27\n",
      "2015-04-28\n",
      "2015-04-29\n",
      "2015-04-30\n",
      "2015-04-31\n",
      "2015-05-00\n",
      "2015-05-01\n",
      "2015-05-02\n",
      "2015-05-03\n",
      "2015-05-04\n",
      "2015-05-05\n",
      "2015-05-06\n",
      "2015-05-07\n",
      "2015-05-08\n",
      "2015-05-09\n",
      "2015-05-10\n",
      "2015-05-11\n",
      "2015-05-12\n",
      "2015-05-13\n",
      "2015-05-14\n",
      "2015-05-15\n",
      "2015-05-16\n",
      "2015-05-17\n",
      "2015-05-18\n",
      "2015-05-19\n",
      "2015-05-20\n",
      "2015-05-21\n",
      "2015-05-22\n",
      "2015-05-23\n",
      "2015-05-24\n",
      "2015-05-25\n",
      "2015-05-26\n",
      "2015-05-27\n",
      "2015-05-28\n",
      "2015-05-29\n",
      "2015-05-30\n",
      "2015-05-31\n",
      "2015-06-00\n",
      "2015-06-01\n",
      "2015-06-02\n",
      "2015-06-03\n",
      "2015-06-04\n",
      "2015-06-05\n",
      "2015-06-06\n",
      "2015-06-07\n",
      "2015-06-08\n",
      "2015-06-09\n",
      "2015-06-10\n",
      "2015-06-11\n",
      "2015-06-12\n",
      "2015-06-13\n",
      "2015-06-14\n",
      "2015-06-15\n",
      "2015-06-16\n",
      "2015-06-17\n",
      "2015-06-18\n",
      "2015-06-19\n",
      "2015-06-20\n",
      "2015-06-21\n",
      "2015-06-22\n",
      "2015-06-23\n",
      "2015-06-24\n",
      "2015-06-25\n",
      "2015-06-26\n",
      "2015-06-27\n",
      "2015-06-28\n",
      "2015-06-29\n",
      "2015-06-30\n",
      "2015-06-31\n",
      "2015-07-00\n",
      "2015-07-01\n",
      "2015-07-02\n",
      "2015-07-03\n",
      "2015-07-04\n",
      "2015-07-05\n",
      "2015-07-06\n",
      "2015-07-07\n",
      "2015-07-08\n",
      "2015-07-09\n",
      "2015-07-10\n",
      "2015-07-11\n",
      "2015-07-12\n",
      "2015-07-13\n",
      "2015-07-14\n",
      "2015-07-15\n",
      "2015-07-16\n",
      "2015-07-17\n",
      "2015-07-18\n",
      "2015-07-19\n",
      "2015-07-20\n",
      "2015-07-21\n",
      "2015-07-22\n",
      "2015-07-23\n",
      "2015-07-24\n",
      "2015-07-25\n",
      "2015-07-26\n",
      "2015-07-27\n",
      "2015-07-28\n",
      "2015-07-29\n",
      "2015-07-30\n",
      "2015-07-31\n",
      "2015-08-00\n",
      "2015-08-01\n",
      "2015-08-02\n",
      "2015-08-03\n",
      "2015-08-04\n",
      "2015-08-05\n",
      "2015-08-06\n",
      "2015-08-07\n",
      "2015-08-08\n",
      "2015-08-09\n",
      "2015-08-10\n",
      "2015-08-11\n",
      "2015-08-12\n",
      "2015-08-13\n",
      "2015-08-14\n",
      "2015-08-15\n",
      "2015-08-16\n",
      "2015-08-17\n",
      "2015-08-18\n",
      "2015-08-19\n",
      "2015-08-20\n",
      "2015-08-21\n",
      "2015-08-22\n",
      "2015-08-23\n",
      "2015-08-24\n",
      "2015-08-25\n",
      "2015-08-26\n",
      "2015-08-27\n",
      "2015-08-28\n",
      "2015-08-29\n",
      "2015-08-30\n",
      "2015-08-31\n",
      "2015-09-00\n",
      "2015-09-01\n",
      "2015-09-02\n",
      "2015-09-03\n",
      "2015-09-04\n",
      "2015-09-05\n",
      "2015-09-06\n",
      "2015-09-07\n",
      "2015-09-08\n",
      "2015-09-09\n",
      "2015-09-10\n",
      "2015-09-11\n",
      "2015-09-12\n",
      "2015-09-13\n",
      "2015-09-14\n",
      "2015-09-15\n",
      "2015-09-16\n",
      "2015-09-17\n",
      "2015-09-18\n",
      "2015-09-19\n",
      "2015-09-20\n",
      "2015-09-21\n",
      "2015-09-22\n",
      "2015-09-23\n",
      "2015-09-24\n",
      "2015-09-25\n",
      "2015-09-26\n",
      "2015-09-27\n",
      "2015-09-28\n",
      "2015-09-29\n",
      "2015-09-30\n",
      "2015-09-31\n",
      "2015-10-00\n",
      "2015-10-01\n",
      "2015-10-02\n",
      "2015-10-03\n",
      "2015-10-04\n",
      "2015-10-05\n",
      "2015-10-06\n",
      "2015-10-07\n",
      "2015-10-08\n",
      "2015-10-09\n",
      "2015-10-10\n",
      "2015-10-11\n",
      "2015-10-12\n",
      "2015-10-13\n",
      "2015-10-14\n",
      "2015-10-15\n",
      "2015-10-16\n",
      "2015-10-17\n",
      "2015-10-18\n",
      "2015-10-19\n",
      "2015-10-20\n",
      "2015-10-21\n",
      "2015-10-22\n",
      "2015-10-23\n",
      "2015-10-24\n",
      "2015-10-25\n",
      "2015-10-26\n",
      "2015-10-27\n",
      "2015-10-28\n",
      "2015-10-29\n",
      "2015-10-30\n",
      "2015-10-31\n",
      "2015-11-00\n",
      "2015-11-01\n",
      "2015-11-02\n",
      "2015-11-03\n",
      "2015-11-04\n",
      "2015-11-05\n",
      "2015-11-06\n",
      "2015-11-07\n",
      "2015-11-08\n",
      "2015-11-09\n",
      "2015-11-10\n",
      "2015-11-11\n",
      "2015-11-12\n",
      "2015-11-13\n",
      "2015-11-14\n",
      "2015-11-15\n",
      "2015-11-16\n",
      "2015-11-17\n",
      "2015-11-18\n",
      "2015-11-19\n",
      "2015-11-20\n",
      "2015-11-21\n",
      "2015-11-22\n",
      "2015-11-23\n",
      "2015-11-24\n",
      "2015-11-25\n",
      "2015-11-26\n",
      "2015-11-27\n",
      "2015-11-28\n",
      "2015-11-29\n",
      "2015-11-30\n",
      "2015-11-31\n",
      "2015-12-00\n",
      "2015-12-01\n",
      "2015-12-02\n",
      "2015-12-03\n",
      "2015-12-04\n",
      "2015-12-05\n",
      "2015-12-06\n",
      "2015-12-07\n",
      "2015-12-08\n",
      "2015-12-09\n",
      "2015-12-10\n",
      "2015-12-11\n",
      "2015-12-12\n",
      "2015-12-13\n",
      "2015-12-14\n",
      "2015-12-15\n",
      "2015-12-16\n",
      "2015-12-17\n",
      "2015-12-18\n",
      "2015-12-19\n",
      "2015-12-20\n",
      "2015-12-21\n",
      "2015-12-22\n",
      "2015-12-23\n",
      "2015-12-24\n",
      "2015-12-25\n",
      "2015-12-26\n",
      "2015-12-27\n",
      "2015-12-28\n",
      "2015-12-29\n",
      "2015-12-30\n",
      "2015-12-31\n",
      "finished for  2015\n",
      "2016-01-00\n",
      "2016-01-01\n",
      "2016-01-02\n",
      "2016-01-03\n",
      "2016-01-04\n",
      "2016-01-05\n",
      "2016-01-06\n",
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      "finished for  2016\n",
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      "2017-10-14\n",
      "2017-10-15\n",
      "2017-10-16\n",
      "2017-10-17\n",
      "2017-10-18\n",
      "2017-10-19\n",
      "2017-10-20\n",
      "2017-10-21\n",
      "2017-10-22\n",
      "2017-10-23\n",
      "2017-10-24\n",
      "2017-10-25\n",
      "2017-10-26\n",
      "2017-10-27\n",
      "2017-10-28\n",
      "2017-10-29\n",
      "2017-10-30\n",
      "2017-10-31\n",
      "2017-11-00\n",
      "2017-11-01\n",
      "2017-11-02\n",
      "2017-11-03\n",
      "2017-11-04\n",
      "2017-11-05\n",
      "2017-11-06\n",
      "2017-11-07\n",
      "2017-11-08\n",
      "2017-11-09\n",
      "2017-11-10\n",
      "2017-11-11\n",
      "2017-11-12\n",
      "2017-11-13\n",
      "2017-11-14\n",
      "2017-11-15\n",
      "2017-11-16\n",
      "2017-11-17\n",
      "2017-11-18\n",
      "2017-11-19\n",
      "2017-11-20\n",
      "2017-11-21\n",
      "2017-11-22\n",
      "2017-11-23\n",
      "2017-11-24\n",
      "2017-11-25\n",
      "2017-11-26\n",
      "2017-11-27\n",
      "2017-11-28\n",
      "2017-11-29\n",
      "2017-11-30\n",
      "2017-11-31\n",
      "2017-12-00\n",
      "2017-12-01\n",
      "2017-12-02\n",
      "2017-12-03\n",
      "2017-12-04\n",
      "2017-12-05\n",
      "2017-12-06\n",
      "2017-12-07\n",
      "2017-12-08\n",
      "2017-12-09\n",
      "2017-12-10\n",
      "2017-12-11\n",
      "2017-12-12\n",
      "2017-12-13\n",
      "2017-12-14\n",
      "2017-12-15\n",
      "2017-12-16\n",
      "2017-12-17\n",
      "2017-12-18\n",
      "2017-12-19\n",
      "2017-12-20\n",
      "2017-12-21\n",
      "2017-12-22\n",
      "2017-12-23\n",
      "2017-12-24\n",
      "2017-12-25\n",
      "2017-12-26\n",
      "2017-12-27\n",
      "2017-12-28\n",
      "2017-12-29\n",
      "2017-12-30\n",
      "2017-12-31\n",
      "finished for  2017\n"
     ]
    }
   ],
   "source": [
    "gdal.PushErrorHandler('CPLQuietErrorHandler')\n",
    "# Mosaicing for loop for PlanetScope SR\n",
    "for i in years:\n",
    "    year = str(i)\n",
    "    for j in months:\n",
    "        # month = str(j)\n",
    "        month = format(j, '02')\n",
    "        for k in days:\n",
    "            day = format(k, '02')\n",
    "            # print(year+\"-\"+month+\"-\"+day)\n",
    "            monthly_files_list = glob.glob('Data/PlanetScope/*'+year+\"-\"+month+\"-\"+day+'*RE*_3A_Analytic_SR_clip.tif')\n",
    "            # This code was for writing all the files into a text file\n",
    "            # with open(year+'-'+month+'_tif_list.txt', 'w') as f:\n",
    "            #     for line in items:\n",
    "            #         f.write(line)\n",
    "            #         f.write('\\n')\n",
    "            files_to_mosaic = monthly_files_list # However many you want.\n",
    "            g = gdal.Warp('outputfiles/mosaic_'+year+'-'+month+\"-\"+day+'.tif', files_to_mosaic, format=\"GTiff\",\n",
    "                  options=[\"COMPRESS=LZW\", \"TILED=YES\"]) # if you want\n",
    "            g = None # Close file and flush to disk\n",
    "            # !gdal_merge.py -n 0 -v -o 'mosaic_year'+'-'+month+'_test.tif' --optfile year+'-'+month+'2010_tif_list.txt'\n",
    "    print('finished for ',year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
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      "Filename: Data/RapidEye/1354206_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 1808x5041x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283415.000000,3648600.000000)   LR:(292455.000000,3623395.000000)\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "\n",
      "Processing file     2 of    21,  4.762% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354107_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "File Size: 1641x2739x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(307415.000000,3617120.000000)   LR:(315620.000000,3603425.000000)\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
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      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "\n",
      "Processing file     3 of    21,  9.524% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354205_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 777x5040x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(280700.000000,3648600.000000)   LR:(284585.000000,3623400.000000)\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "\n",
      "Processing file     4 of    21, 14.286% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354206_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 1808x5041x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283415.000000,3648600.000000)   LR:(292455.000000,3623395.000000)\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "\n",
      "Processing file     5 of    21, 19.048% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354305_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "File Size: 538x627x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(281895.000000,3650535.000000)   LR:(284585.000000,3647400.000000)\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "\n",
      "Processing file     6 of    21, 23.810% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354205_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 777x5040x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(280700.000000,3648600.000000)   LR:(284585.000000,3623400.000000)\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "\n",
      "Processing file     7 of    21, 28.571% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354306_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "File Size: 974x612x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283415.000000,3650455.000000)   LR:(288285.000000,3647395.000000)\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "\n",
      "Processing file     8 of    21, 33.333% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354106_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 5034x3324x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283415.000000,3624600.000000)   LR:(308585.000000,3607980.000000)\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "\n",
      "Processing file     9 of    21, 38.095% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354105_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 248x309x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283345.000000,3624600.000000)   LR:(284585.000000,3623055.000000)\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "\n",
      "Processing file    10 of    21, 42.857% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354106_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 5034x3324x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283415.000000,3624600.000000)   LR:(308585.000000,3607980.000000)\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "\n",
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      "Filename: Data/RapidEye/1354105_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 248x309x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283345.000000,3624600.000000)   LR:(284585.000000,3623055.000000)\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "\n",
      "Processing file    12 of    21, 52.381% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354305_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 538x627x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(281895.000000,3650535.000000)   LR:(284585.000000,3647400.000000)\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "\n",
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      "Filename: Data/RapidEye/1354206_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "File Size: 1808x5041x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283415.000000,3648600.000000)   LR:(292455.000000,3623395.000000)\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "\n",
      "Processing file    14 of    21, 61.905% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354306_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 974x612x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283415.000000,3650455.000000)   LR:(288285.000000,3647395.000000)\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "\n",
      "Processing file    15 of    21, 66.667% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354205_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "File Size: 777x5040x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(280700.000000,3648600.000000)   LR:(284585.000000,3623400.000000)\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "\n",
      "Processing file    16 of    21, 71.429% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354107_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 1641x2739x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(307415.000000,3617120.000000)   LR:(315620.000000,3603425.000000)\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "\n",
      "Processing file    17 of    21, 76.190% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354305_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 538x627x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(281895.000000,3650535.000000)   LR:(284585.000000,3647400.000000)\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "\n",
      "Processing file    18 of    21, 80.952% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354107_2010-12-09_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 1641x2739x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(307415.000000,3617120.000000)   LR:(315620.000000,3603425.000000)\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "\n",
      "Processing file    19 of    21, 85.714% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354306_2010-10-27_RE1_3A_Analytic_SR_clip.tif\n",
      "File Size: 974x612x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283415.000000,3650455.000000)   LR:(288285.000000,3647395.000000)\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "\n",
      "Processing file    20 of    21, 90.476% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354106_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "File Size: 5034x3324x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283415.000000,3624600.000000)   LR:(308585.000000,3607980.000000)\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "\n",
      "Processing file    21 of    21, 95.238% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354105_2010-03-25_RE5_3A_Analytic_SR_clip.tif\n",
      "File Size: 248x309x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283345.000000,3624600.000000)   LR:(284585.000000,3623055.000000)\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n"
     ]
    }
   ],
   "source": [
    "# !gdal_merge.py -n 0 -v -o mosaic_test.tif --optfile tiff_test_list.txt\n",
    "!gdal_merge.py -n 0 -v -o mosaic_2010_test.tif --optfile 2010_tif_list.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.remove(\"tiff_test_list.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing file     1 of     7,  0.000% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354105_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "File Size: 248x309x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283345.000000,3624600.000000)   LR:(284585.000000,3623055.000000)\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "Copy 0,0,248,309 to 529,5187,248,309.\n",
      "\n",
      "Processing file     2 of     7, 14.286% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354106_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "File Size: 5034x3324x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283415.000000,3624600.000000)   LR:(308585.000000,3607980.000000)\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "Copy 0,0,5034,3324 to 543,5187,5034,3324.\n",
      "\n",
      "Processing file     3 of     7, 28.571% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354107_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "File Size: 1641x2739x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(307415.000000,3617120.000000)   LR:(315620.000000,3603425.000000)\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "Copy 0,0,1641,2739 to 5343,6683,1641,2739.\n",
      "\n",
      "Processing file     4 of     7, 42.857% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354205_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "File Size: 777x5040x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(280700.000000,3648600.000000)   LR:(284585.000000,3623400.000000)\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "Copy 0,0,777,5040 to 0,387,777,5040.\n",
      "\n",
      "Processing file     5 of     7, 57.143% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354206_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "File Size: 1808x5041x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283415.000000,3648600.000000)   LR:(292455.000000,3623395.000000)\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "Copy 0,0,1808,5041 to 543,387,1808,5041.\n",
      "\n",
      "Processing file     6 of     7, 71.429% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354305_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "File Size: 538x627x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(281895.000000,3650535.000000)   LR:(284585.000000,3647400.000000)\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "Copy 0,0,538,627 to 239,0,538,627.\n",
      "\n",
      "Processing file     7 of     7, 85.714% completed in 0 minutes.\n",
      "Filename: Data/RapidEye/1354306_2013-05-19_RE2_3A_Analytic_SR_clip.tif\n",
      "File Size: 974x612x5\n",
      "Pixel Size: 5.000000 x -5.000000\n",
      "UL:(283415.000000,3650455.000000)   LR:(288285.000000,3647395.000000)\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n",
      "Copy 0,0,974,612 to 543,16,974,612.\n"
     ]
    }
   ],
   "source": [
    "# this helps merge files\n",
    "!gdal_merge.py -v Data/RapidEye/1354105_2013-05-19_RE2_3A_Analytic_SR_clip.tif Data/RapidEye/1354106_2013-05-19_RE2_3A_Analytic_SR_clip.tif Data/RapidEye/1354107_2013-05-19_RE2_3A_Analytic_SR_clip.tif  Data/RapidEye/1354205_2013-05-19_RE2_3A_Analytic_SR_clip.tif Data/RapidEye/1354206_2013-05-19_RE2_3A_Analytic_SR_clip.tif Data/RapidEye/1354305_2013-05-19_RE2_3A_Analytic_SR_clip.tif Data/RapidEye/1354306_2013-05-19_RE2_3A_Analytic_SR_clip.tif -o 2013-05-19_merged.tif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract the data from the red and near-infrared bands "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio\n",
    "import numpy as np\n",
    "\n",
    "filepath = \"outputfiles/\"\n",
    "outputpath = 'outputfiles/ndvi/'\n",
    "# filename = \"data/20160831_180302_0e26_3B_AnalyticMS_SR.tif\"\n",
    "# filename = filepath+\"1354107_2013-05-19_RE2_3A_Analytic_SR_clip.tif\"\n",
    "# filename = '2013-05-19_merged.tif'\n",
    "filename = 'mosaic_2010-03-25.tif'\n",
    "\n",
    "# Load red and NIR bands - note all PlanetScope 4-band images have band order BGRN\n",
    "with rasterio.open(filepath+filename) as src:\n",
    "    band_red = src.read(3)\n",
    "\n",
    "with rasterio.open(filepath+filename) as src:\n",
    "    band_nir = src.read(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform the NDVI calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you're going to calculate NDVI through subtraction and division of the values stored in the NumPy arrays. This calculation will give you NDVI values that range from -1 to 1. Values closer to 1 indicate a greater density of vegetation or higher level of \"greenness.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Allow division by zero\n",
    "np.seterr(divide='ignore', invalid='ignore')\n",
    "\n",
    "# Calculate NDVI. This is the equation at the top of this guide expressed in code\n",
    "ndvi = (band_nir.astype(float) - band_red.astype(float)) / (band_nir + band_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.2560877709392561, 0.6313497822931785)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check range NDVI values, excluding NaN\n",
    "np.nanmin(ndvi), np.nanmax(ndvi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save an NDVI image "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you're going to save the calculated NDVI values to a new image file, making sure the new image file has the same geospatial metadata as the original GeoTIFF we downloaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set spatial characteristics of the output object to mirror the input\n",
    "kwargs = src.meta\n",
    "kwargs.update(\n",
    "    dtype=rasterio.float32,\n",
    "    count = 1)\n",
    "\n",
    "# Write band calculations to a new raster file\n",
    "with rasterio.open(outputpath+filename+'_ndvi.tif', 'w', **kwargs) as dst:\n",
    "        dst.write_band(1, ndvi.astype(rasterio.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['outputfiles/mosaic_2010-03-25.tif',\n",
       " 'outputfiles/mosaic_2010-10-27.tif',\n",
       " 'outputfiles/mosaic_2010-12-09.tif',\n",
       " 'outputfiles/mosaic_2012-02-15.tif',\n",
       " 'outputfiles/mosaic_2012-10-17.tif',\n",
       " 'outputfiles/mosaic_2013-01-13.tif',\n",
       " 'outputfiles/mosaic_2013-02-01.tif',\n",
       " 'outputfiles/mosaic_2013-05-19.tif',\n",
       " 'outputfiles/mosaic_2013-05-20.tif',\n",
       " 'outputfiles/mosaic_2013-10-03.tif',\n",
       " 'outputfiles/mosaic_2013-11-02.tif',\n",
       " 'outputfiles/mosaic_2013-12-01.tif',\n",
       " 'outputfiles/mosaic_2013-12-17.tif',\n",
       " 'outputfiles/mosaic_2014-01-03.tif',\n",
       " 'outputfiles/mosaic_2014-03-21.tif',\n",
       " 'outputfiles/mosaic_2014-03-22.tif',\n",
       " 'outputfiles/mosaic_2014-07-26.tif',\n",
       " 'outputfiles/mosaic_2014-10-12.tif',\n",
       " 'outputfiles/mosaic_2015-04-14.tif',\n",
       " 'outputfiles/mosaic_2016-01-11.tif',\n",
       " 'outputfiles/mosaic_2016-02-04.tif',\n",
       " 'outputfiles/mosaic_2016-04-14.tif',\n",
       " 'outputfiles/mosaic_2016-06-10.tif',\n",
       " 'outputfiles/mosaic_2016-06-14.tif',\n",
       " 'outputfiles/mosaic_2017-01-04.tif',\n",
       " 'outputfiles/mosaic_2017-02-23.tif',\n",
       " 'outputfiles/mosaic_2017-05-20.tif',\n",
       " 'outputfiles/mosaic_2017-10-08.tif',\n",
       " 'outputfiles/mosaic_2017-10-25.tif',\n",
       " 'outputfiles/mosaic_2017-11-10.tif']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = glob.glob('outputfiles/*.tif')\n",
    "items.sort()\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010 : 3\n",
      "2011 : 0\n",
      "2012 : 2\n",
      "2013 : 8\n",
      "2014 : 5\n",
      "2015 : 1\n",
      "2016 : 5\n",
      "2017 : 6\n"
     ]
    }
   ],
   "source": [
    "years = range(2010,2018)\n",
    "for y in years:\n",
    "    year = str(y)\n",
    "    yearcount = glob.glob('outputfiles/*'+year+'*.tif')\n",
    "    print(year,':',len(yearcount))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NDVI files exported.\n"
     ]
    }
   ],
   "source": [
    "# For loop to standardize this\n",
    "\n",
    "for i in items:\n",
    "    mosaic = i\n",
    "    # Load red and NIR bands - note all PlanetScope 4-band images have band order BGRN\n",
    "    with rasterio.open(mosaic) as src:\n",
    "        band_red = src.read(3)\n",
    "    with rasterio.open(mosaic) as src:\n",
    "        band_nir = src.read(4)\n",
    "    # Allow division by zero\n",
    "    np.seterr(divide='ignore', invalid='ignore')\n",
    "    # Calculate NDVI. This is the equation at the top of this guide expressed in code\n",
    "    ndvi = (band_nir.astype(float) - band_red.astype(float)) / (band_nir + band_red)# check range NDVI values, excluding NaN\n",
    "    np.nanmin(ndvi), np.nanmax(ndvi)\n",
    "    # Set spatial characteristics of the output object to mirror the input\n",
    "    kwargs = src.meta\n",
    "    kwargs.update(\n",
    "        dtype=rasterio.float32,\n",
    "        count = 1)\n",
    "\n",
    "    # Write band calculations to a new raster file\n",
    "    with rasterio.open(mosaic+'_ndvi.tif', 'w', **kwargs) as dst:\n",
    "            dst.write_band(1, ndvi.astype(rasterio.float32))\n",
    "\n",
    "print(\"NDVI files exported.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the NDVI values on the image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the last two steps, you'll use [Matplotlib](https://matplotlib.org/) to visualize the NDVI values you calculated for the PlanetScope scene. First you'll view a map of the NDVI values; then you'll generate a histogram of NDVI values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "\"\"\"\n",
    "The NDVI values will range from -1 to 1. You want to use a diverging color scheme to visualize the data,\n",
    "and you want to center the colorbar at a defined midpoint. The class below allows you to normalize the colorbar.\n",
    "\"\"\"\n",
    "\n",
    "class MidpointNormalize(colors.Normalize):\n",
    "    \"\"\"\n",
    "    Normalise the colorbar so that diverging bars work there way either side from a prescribed midpoint value)\n",
    "    e.g. im=ax1.imshow(array, norm=MidpointNormalize(midpoint=0.,vmin=-100, vmax=100))\n",
    "    Credit: Joe Kington, http://chris35wills.github.io/matplotlib_diverging_colorbar/\n",
    "    \"\"\"\n",
    "    def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):\n",
    "        self.midpoint = midpoint\n",
    "        colors.Normalize.__init__(self, vmin, vmax, clip)\n",
    "\n",
    "    def __call__(self, value, clip=None):\n",
    "        # I'm ignoring masked values and all kinds of edge cases to make a\n",
    "        # simple example...\n",
    "        x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]\n",
    "        return np.ma.masked_array(np.interp(value, x, y), np.isnan(value))\n",
    "\n",
    "\n",
    "# Set min/max values from NDVI range for image (excluding NAN)\n",
    "# set midpoint according to how NDVI is interpreted: https://earthobservatory.nasa.gov/Features/MeasuringVegetation/\n",
    "min=np.nanmin(ndvi)\n",
    "max=np.nanmax(ndvi)\n",
    "mid=0.1\n",
    "\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# diverging color scheme chosen from https://matplotlib.org/users/colormaps.html\n",
    "cmap = plt.cm.RdYlGn \n",
    "\n",
    "cax = ax.imshow(ndvi, cmap=cmap, clim=(min, max), norm=MidpointNormalize(midpoint=mid,vmin=min, vmax=max))\n",
    "\n",
    "ax.axis('off')\n",
    "ax.set_title('Normalized Difference Vegetation Index'+str(filename), fontsize=18, fontweight='bold')\n",
    "\n",
    "cbar = fig.colorbar(cax, orientation='horizontal', shrink=0.65)\n",
    "\n",
    "fig.savefig(outputpath+filename+\"ndvi-fig.png\", dpi=200, bbox_inches='tight', pad_inches=0.7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a histogram of NDVI values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=(10,10))\n",
    "ax = fig2.add_subplot(111)\n",
    "\n",
    "plt.title(str(filename)+\" NDVI Histogram\", fontsize=18, fontweight='bold')\n",
    "plt.xlabel(\"NDVI values\", fontsize=14)\n",
    "plt.ylabel(\"# pixels\", fontsize=14)\n",
    "\n",
    "\n",
    "x = ndvi[~np.isnan(ndvi)]\n",
    "numBins = 20\n",
    "ax.hist(x,numBins,color='green',alpha=0.8)\n",
    "\n",
    "fig2.savefig(outputpath+filename+\" ndvi-histogram.png\", dpi=200, bbox_inches='tight', pad_inches=0.7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Special Rio Grande Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['outputfiles/mosaic_2014-01-03.tif',\n",
       " 'outputfiles/mosaic_2016-02-04.tif',\n",
       " 'outputfiles/mosaic_2010-03-25.tif',\n",
       " 'outputfiles/mosaic_2017-05-20.tif',\n",
       " 'outputfiles/mosaic_2016-06-10.tif',\n",
       " 'outputfiles/mosaic_2016-04-14.tif',\n",
       " 'outputfiles/mosaic_2013-01-13.tif',\n",
       " 'outputfiles/mosaic_2013-11-02.tif',\n",
       " 'outputfiles/mosaic_2016-06-14.tif',\n",
       " 'outputfiles/mosaic_2017-10-08.tif',\n",
       " 'outputfiles/mosaic_2015-04-14.tif',\n",
       " 'outputfiles/mosaic_2014-07-26.tif',\n",
       " 'outputfiles/mosaic_2016-01-11.tif',\n",
       " 'outputfiles/mosaic_2017-02-23.tif',\n",
       " 'outputfiles/mosaic_2012-02-15.tif',\n",
       " 'outputfiles/mosaic_2014-10-12.tif',\n",
       " 'outputfiles/mosaic_2012-10-17.tif',\n",
       " 'outputfiles/mosaic_2017-10-25.tif',\n",
       " 'outputfiles/mosaic_2013-12-17.tif',\n",
       " 'outputfiles/mosaic_2014-03-22.tif',\n",
       " 'outputfiles/mosaic_2010-10-27.tif',\n",
       " 'outputfiles/mosaic_2017-01-04.tif',\n",
       " 'outputfiles/mosaic_2014-03-21.tif',\n",
       " 'outputfiles/mosaic_2013-05-19.tif',\n",
       " 'outputfiles/mosaic_2010-12-09.tif',\n",
       " 'outputfiles/mosaic_2013-12-01.tif',\n",
       " 'outputfiles/mosaic_2017-11-10.tif',\n",
       " 'outputfiles/mosaic_2013-05-20.tif',\n",
       " 'outputfiles/mosaic_2013-02-01.tif',\n",
       " 'outputfiles/mosaic_2013-10-03.tif']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab out dates\n",
    "items = glob.glob('outputfiles/*.tif')\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the Shapefile\n",
    "filename = \"DataChallenge_Mask_Reprojected.shp\"\n",
    "filepath = os.path.join('Data/Shapefiles/', filename)\n",
    "study_areas = gp.read_file(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_list = []\n",
    "for i in items:\n",
    "    date = i[-14:-4]\n",
    "    parsed_date = datetime.datetime.strptime(date, \"%Y-%m-%d\")\n",
    "    date_list.append(parsed_date)\n",
    "dt_array = np.array(date_list, dtype='datetime64')\n",
    "dt_array.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2010-03-25T00:00:00.000000', '2010-10-27T00:00:00.000000',\n",
       "       '2010-12-09T00:00:00.000000', '2012-02-15T00:00:00.000000',\n",
       "       '2012-10-17T00:00:00.000000', '2013-01-13T00:00:00.000000',\n",
       "       '2013-02-01T00:00:00.000000', '2013-05-19T00:00:00.000000',\n",
       "       '2013-05-20T00:00:00.000000', '2013-10-03T00:00:00.000000',\n",
       "       '2013-11-02T00:00:00.000000', '2013-12-01T00:00:00.000000',\n",
       "       '2013-12-17T00:00:00.000000', '2014-01-03T00:00:00.000000',\n",
       "       '2014-03-21T00:00:00.000000', '2014-03-22T00:00:00.000000',\n",
       "       '2014-07-26T00:00:00.000000', '2014-10-12T00:00:00.000000',\n",
       "       '2015-04-14T00:00:00.000000', '2016-01-11T00:00:00.000000',\n",
       "       '2016-02-04T00:00:00.000000', '2016-04-14T00:00:00.000000',\n",
       "       '2016-06-10T00:00:00.000000', '2016-06-14T00:00:00.000000',\n",
       "       '2017-01-04T00:00:00.000000', '2017-02-23T00:00:00.000000',\n",
       "       '2017-05-20T00:00:00.000000', '2017-10-08T00:00:00.000000',\n",
       "       '2017-10-25T00:00:00.000000', '2017-11-10T00:00:00.000000'],\n",
       "      dtype='datetime64[us]')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['outputfiles/ndvi/mosaic_2010-03-25.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2010-10-27.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2010-12-09.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2012-02-15.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2012-10-17.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2013-01-13.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2013-02-01.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2013-05-19.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2013-05-20.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2013-10-03.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2013-11-02.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2013-12-01.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2013-12-17.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2014-01-03.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2014-03-21.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2014-03-22.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2014-07-26.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2014-10-12.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2015-04-14.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2016-01-11.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2016-02-04.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2016-04-14.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2016-06-10.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2016-06-14.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2017-01-04.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2017-02-23.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2017-05-20.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2017-10-08.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2017-10-25.tif_ndvi.tif',\n",
       " 'outputfiles/ndvi/mosaic_2017-11-10.tif_ndvi.tif']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Open in xarray\n",
    "\n",
    "ndvi_tifs = glob.glob('outputfiles/ndvi/*.tif')\n",
    "ndvi_tifs.sort()\n",
    "ndvi_tifs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mask crs: EPSG:32612\n",
      "data crs: EPSG:32613\n"
     ]
    }
   ],
   "source": [
    "# Testing to see if coordinate systems match\n",
    "ndvi_file_toclip = xr.open_dataset(ndvi_tifs[17])\n",
    "mask = study_areas\n",
    "print(\"mask crs:\", mask.crs)\n",
    "print(\"data crs:\", ndvi_file_toclip.rio.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mask crs: EPSG:32613\n"
     ]
    }
   ],
   "source": [
    "# Skip this if they are both in the same coordinate system and change info in next cell\n",
    "mask_reproject = mask.to_crs(epsg=32613)\n",
    "print(\"mask crs:\", mask_reproject.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clipping\n",
    "clipped = ndvi_file_toclip.rio.clip(mask_reproject.geometry, mask_reproject.crs)\n",
    "# clipped.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3160, 4990)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clipped_np = clipped['band_data'].to_numpy()\n",
    "clipped_np.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n",
      "(1, 3160, 4990)\n"
     ]
    }
   ],
   "source": [
    "ndvi_file_1 = xr.open_dataset(ndvi_tifs[0])\n",
    "# ndvi_file['band_data']\n",
    "ndvi_data_1 = ndvi_file_1['band_data']\n",
    "ndvi_array_1 = ndvi_data_1.to_numpy()\n",
    "\n",
    "all_data = []\n",
    "for i in ndvi_tifs:\n",
    "    ndvi_file = xr.open_dataset(i)\n",
    "    clipped = ndvi_file.rio.clip(mask_reproject.geometry, mask_reproject.crs)\n",
    "    ndvi_data = clipped['band_data']\n",
    "    ndvi_array = ndvi_data.to_numpy()\n",
    "    # # dummy_array = np.ndarray(shape=ndvi_array_1,dtype=float)\n",
    "    # # ndvi_array.read.direct(dummy_array)\n",
    "    # # np.place(dummy_array, dummy_array==ndvi_array.attrs.get('_FillValue'),np.nan)\n",
    "    # ndvi_array=np.ndarray(shape=ndvi_data.shape,dtype=float)\n",
    "    # ndvi_data.read_direct(ndvi_array)\n",
    "    # np.place(ndvi_array,ndvi_array==ndvi_data.attrs.get('_FillValue'), np.nan)\n",
    "    all_data.append(ndvi_array)\n",
    "    print(ndvi_array.shape)\n",
    "    # all_data.append(ndvi_file)\n",
    "# all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 3160, 4990)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combo = np.vstack(all_data)\n",
    "combo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.06839687, 0.07827039, 0.07583418, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.07864597, 0.05376344, 0.06229661, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.04583439, 0.01787394, 0.05179795, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ..., 0.12614259,\n",
       "         0.10476191, 0.04962153],\n",
       "        [       nan,        nan,        nan, ..., 0.11536748,\n",
       "         0.08584905, 0.05160744],\n",
       "        [       nan,        nan,        nan, ..., 0.09662751,\n",
       "         0.12414109, 0.07085916]],\n",
       "\n",
       "       [[0.21866295, 0.2521861 , 0.20318726, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.18223584, 0.20993087, 0.17650676, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.14185022, 0.16591929, 0.16630669, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ..., 0.38183933,\n",
       "         0.30962098, 0.18342541],\n",
       "        [       nan,        nan,        nan, ..., 0.36550194,\n",
       "         0.30046403, 0.17309813],\n",
       "        [       nan,        nan,        nan, ..., 0.35623267,\n",
       "         0.37254903, 0.26174498]],\n",
       "\n",
       "       [[0.09474347, 0.07906388, 0.05541347, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.07868556, 0.05228951, 0.05616183, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.07407407, 0.05018687, 0.04616144, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ..., 0.12383178,\n",
       "         0.12618296, 0.12377711],\n",
       "        [       nan,        nan,        nan, ..., 0.11785533,\n",
       "         0.11302585, 0.06386555],\n",
       "        [       nan,        nan,        nan, ..., 0.11089589,\n",
       "         0.12154408, 0.08402585]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.14802982, 0.1309131 , 0.16142705, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.11321349, 0.08457711, 0.12443946, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.11016226, 0.104708  , 0.09722558, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ..., 0.248671  ,\n",
       "         0.15045089, 0.04468865],\n",
       "        [       nan,        nan,        nan, ..., 0.28747568,\n",
       "         0.15397082, 0.10533275],\n",
       "        [       nan,        nan,        nan, ..., 0.3745401 ,\n",
       "         0.2983871 , 0.21292374]],\n",
       "\n",
       "       [[0.14582501, 0.08431897, 0.04245133, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.05926623, 0.13429952, 0.10467706, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.07970008, 0.11163896, 0.08440959, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ..., 0.19244823,\n",
       "         0.09968521, 0.01830443],\n",
       "        [       nan,        nan,        nan, ..., 0.28103682,\n",
       "         0.16470589, 0.07775965],\n",
       "        [       nan,        nan,        nan, ..., 0.2570481 ,\n",
       "         0.11673469, 0.08883554]],\n",
       "\n",
       "       [[0.13654473, 0.10951526, 0.05664264, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.06841612, 0.08756039, 0.07059477, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.03472222, 0.07624795, 0.07847478, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ..., 0.25353926,\n",
       "         0.17713568, 0.08290452],\n",
       "        [       nan,        nan,        nan, ..., 0.24761905,\n",
       "         0.21157822, 0.16733068],\n",
       "        [       nan,        nan,        nan, ..., 0.29340512,\n",
       "         0.2766252 , 0.24931507]]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_main = clipped['x']\n",
    "lon_main = clipped['y']\n",
    "\n",
    "lon_main = lon_main.to_numpy()\n",
    "lat_main = lat_main.to_numpy()\n",
    "# lat_main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Main data shape:  (30, 3160, 4990)\n",
      "Longitude shape:  (3160,)\n",
      "Latitude shape:  (4990,)\n",
      "Data Shape:  (30,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Main data shape: \", combo.shape)\n",
    "print('Longitude shape: ', lon_main.shape)\n",
    "print(\"Latitude shape: \", lat_main.shape)\n",
    "print('Data Shape: ', dt_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 30, lon: 3160, lat: 4990)&gt;\n",
       "array([[[0.06839687, 0.07827039, 0.07583418, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.07864597, 0.05376344, 0.06229661, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.04583439, 0.01787394, 0.05179795, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ..., 0.12614259,\n",
       "         0.10476191, 0.04962153],\n",
       "        [       nan,        nan,        nan, ..., 0.11536748,\n",
       "         0.08584905, 0.05160744],\n",
       "        [       nan,        nan,        nan, ..., 0.09662751,\n",
       "         0.12414109, 0.07085916]],\n",
       "\n",
       "       [[0.21866295, 0.2521861 , 0.20318726, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.18223584, 0.20993087, 0.17650676, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.14185022, 0.16591929, 0.16630669, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ..., 0.19244823,\n",
       "         0.09968521, 0.01830443],\n",
       "        [       nan,        nan,        nan, ..., 0.28103682,\n",
       "         0.16470589, 0.07775965],\n",
       "        [       nan,        nan,        nan, ..., 0.2570481 ,\n",
       "         0.11673469, 0.08883554]],\n",
       "\n",
       "       [[0.13654473, 0.10951526, 0.05664264, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.06841612, 0.08756039, 0.07059477, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.03472222, 0.07624795, 0.07847478, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ..., 0.25353926,\n",
       "         0.17713568, 0.08290452],\n",
       "        [       nan,        nan,        nan, ..., 0.24761905,\n",
       "         0.21157822, 0.16733068],\n",
       "        [       nan,        nan,        nan, ..., 0.29340512,\n",
       "         0.2766252 , 0.24931507]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2010-03-25 2010-10-27 ... 2017-11-10\n",
       "  * lon      (lon) float64 3.627e+06 3.627e+06 3.627e+06 ... 3.611e+06 3.611e+06\n",
       "  * lat      (lat) float64 2.854e+05 2.854e+05 2.854e+05 ... 3.104e+05 3.104e+05\n",
       "Attributes:\n",
       "    description:  Calculated NDVI</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 30</li><li><span class='xr-has-index'>lon</span>: 3160</li><li><span class='xr-has-index'>lat</span>: 4990</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b496857a-3988-48f3-8990-3264a5d04521' class='xr-array-in' type='checkbox' checked><label for='section-b496857a-3988-48f3-8990-3264a5d04521' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0684 0.07827 0.07583 0.05968 0.03526 ... 0.2356 0.2934 0.2766 0.2493</span></div><div class='xr-array-data'><pre>array([[[0.06839687, 0.07827039, 0.07583418, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.07864597, 0.05376344, 0.06229661, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.04583439, 0.01787394, 0.05179795, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ..., 0.12614259,\n",
       "         0.10476191, 0.04962153],\n",
       "        [       nan,        nan,        nan, ..., 0.11536748,\n",
       "         0.08584905, 0.05160744],\n",
       "        [       nan,        nan,        nan, ..., 0.09662751,\n",
       "         0.12414109, 0.07085916]],\n",
       "\n",
       "       [[0.21866295, 0.2521861 , 0.20318726, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.18223584, 0.20993087, 0.17650676, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.14185022, 0.16591929, 0.16630669, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ..., 0.19244823,\n",
       "         0.09968521, 0.01830443],\n",
       "        [       nan,        nan,        nan, ..., 0.28103682,\n",
       "         0.16470589, 0.07775965],\n",
       "        [       nan,        nan,        nan, ..., 0.2570481 ,\n",
       "         0.11673469, 0.08883554]],\n",
       "\n",
       "       [[0.13654473, 0.10951526, 0.05664264, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.06841612, 0.08756039, 0.07059477, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.03472222, 0.07624795, 0.07847478, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ..., 0.25353926,\n",
       "         0.17713568, 0.08290452],\n",
       "        [       nan,        nan,        nan, ..., 0.24761905,\n",
       "         0.21157822, 0.16733068],\n",
       "        [       nan,        nan,        nan, ..., 0.29340512,\n",
       "         0.2766252 , 0.24931507]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-fabc57c1-54d6-48ba-afee-fccf38e42043' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fabc57c1-54d6-48ba-afee-fccf38e42043' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-03-25 ... 2017-11-10</div><input id='attrs-9318efae-5440-4369-a7e2-dd35cef80619' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9318efae-5440-4369-a7e2-dd35cef80619' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12d12e20-46ae-41e9-9f39-3858cffaf343' class='xr-var-data-in' type='checkbox'><label for='data-12d12e20-46ae-41e9-9f39-3858cffaf343' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-03-25T00:00:00.000000000&#x27;, &#x27;2010-10-27T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-09T00:00:00.000000000&#x27;, &#x27;2012-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-10-17T00:00:00.000000000&#x27;, &#x27;2013-01-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-05-19T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-05-20T00:00:00.000000000&#x27;, &#x27;2013-10-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-11-02T00:00:00.000000000&#x27;, &#x27;2013-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-12-17T00:00:00.000000000&#x27;, &#x27;2014-01-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-03-21T00:00:00.000000000&#x27;, &#x27;2014-03-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-07-26T00:00:00.000000000&#x27;, &#x27;2014-10-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-04-14T00:00:00.000000000&#x27;, &#x27;2016-01-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-02-04T00:00:00.000000000&#x27;, &#x27;2016-04-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-10T00:00:00.000000000&#x27;, &#x27;2016-06-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-04T00:00:00.000000000&#x27;, &#x27;2017-02-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-20T00:00:00.000000000&#x27;, &#x27;2017-10-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-25T00:00:00.000000000&#x27;, &#x27;2017-11-10T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.627e+06 3.627e+06 ... 3.611e+06</div><input id='attrs-76f7a50e-94a8-4a5c-aee7-7f957220e2e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76f7a50e-94a8-4a5c-aee7-7f957220e2e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8300973-034d-4774-831f-add3a60eb8bd' class='xr-var-data-in' type='checkbox'><label for='data-d8300973-034d-4774-831f-add3a60eb8bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3626577.5, 3626572.5, 3626567.5, ..., 3610792.5, 3610787.5, 3610782.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.854e+05 2.854e+05 ... 3.104e+05</div><input id='attrs-5c21aa76-c1a3-4efc-bdb3-6de8c38af884' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c21aa76-c1a3-4efc-bdb3-6de8c38af884' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-befe14a7-2203-4178-bf39-acb2d755a5a8' class='xr-var-data-in' type='checkbox'><label for='data-befe14a7-2203-4178-bf39-acb2d755a5a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([285427.5, 285432.5, 285437.5, ..., 310362.5, 310367.5, 310372.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2923268c-13fe-48ce-bfa8-a4ecf4e9e241' class='xr-section-summary-in' type='checkbox'  ><label for='section-2923268c-13fe-48ce-bfa8-a4ecf4e9e241' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2260ced6-44fd-4264-9bc9-22030135fc0a' class='xr-index-data-in' type='checkbox'/><label for='index-2260ced6-44fd-4264-9bc9-22030135fc0a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2010-03-25&#x27;, &#x27;2010-10-27&#x27;, &#x27;2010-12-09&#x27;, &#x27;2012-02-15&#x27;,\n",
       "               &#x27;2012-10-17&#x27;, &#x27;2013-01-13&#x27;, &#x27;2013-02-01&#x27;, &#x27;2013-05-19&#x27;,\n",
       "               &#x27;2013-05-20&#x27;, &#x27;2013-10-03&#x27;, &#x27;2013-11-02&#x27;, &#x27;2013-12-01&#x27;,\n",
       "               &#x27;2013-12-17&#x27;, &#x27;2014-01-03&#x27;, &#x27;2014-03-21&#x27;, &#x27;2014-03-22&#x27;,\n",
       "               &#x27;2014-07-26&#x27;, &#x27;2014-10-12&#x27;, &#x27;2015-04-14&#x27;, &#x27;2016-01-11&#x27;,\n",
       "               &#x27;2016-02-04&#x27;, &#x27;2016-04-14&#x27;, &#x27;2016-06-10&#x27;, &#x27;2016-06-14&#x27;,\n",
       "               &#x27;2017-01-04&#x27;, &#x27;2017-02-23&#x27;, &#x27;2017-05-20&#x27;, &#x27;2017-10-08&#x27;,\n",
       "               &#x27;2017-10-25&#x27;, &#x27;2017-11-10&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-640d7580-335a-4582-a76a-0790b2fed210' class='xr-index-data-in' type='checkbox'/><label for='index-640d7580-335a-4582-a76a-0790b2fed210' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([3626577.5, 3626572.5, 3626567.5, 3626562.5, 3626557.5, 3626552.5,\n",
       "              3626547.5, 3626542.5, 3626537.5, 3626532.5,\n",
       "              ...\n",
       "              3610827.5, 3610822.5, 3610817.5, 3610812.5, 3610807.5, 3610802.5,\n",
       "              3610797.5, 3610792.5, 3610787.5, 3610782.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=3160))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f07cf4e4-bf7d-4ad0-825a-cc1093712ee6' class='xr-index-data-in' type='checkbox'/><label for='index-f07cf4e4-bf7d-4ad0-825a-cc1093712ee6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([285427.5, 285432.5, 285437.5, 285442.5, 285447.5, 285452.5,\n",
       "              285457.5, 285462.5, 285467.5, 285472.5,\n",
       "              ...\n",
       "              310327.5, 310332.5, 310337.5, 310342.5, 310347.5, 310352.5,\n",
       "              310357.5, 310362.5, 310367.5, 310372.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=4990))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dd47450a-23fb-4a90-80e1-aea7d0defe56' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd47450a-23fb-4a90-80e1-aea7d0defe56' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Calculated NDVI</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 30, lon: 3160, lat: 4990)>\n",
       "array([[[0.06839687, 0.07827039, 0.07583418, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.07864597, 0.05376344, 0.06229661, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.04583439, 0.01787394, 0.05179795, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ..., 0.12614259,\n",
       "         0.10476191, 0.04962153],\n",
       "        [       nan,        nan,        nan, ..., 0.11536748,\n",
       "         0.08584905, 0.05160744],\n",
       "        [       nan,        nan,        nan, ..., 0.09662751,\n",
       "         0.12414109, 0.07085916]],\n",
       "\n",
       "       [[0.21866295, 0.2521861 , 0.20318726, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.18223584, 0.20993087, 0.17650676, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.14185022, 0.16591929, 0.16630669, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ..., 0.19244823,\n",
       "         0.09968521, 0.01830443],\n",
       "        [       nan,        nan,        nan, ..., 0.28103682,\n",
       "         0.16470589, 0.07775965],\n",
       "        [       nan,        nan,        nan, ..., 0.2570481 ,\n",
       "         0.11673469, 0.08883554]],\n",
       "\n",
       "       [[0.13654473, 0.10951526, 0.05664264, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.06841612, 0.08756039, 0.07059477, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.03472222, 0.07624795, 0.07847478, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ..., 0.25353926,\n",
       "         0.17713568, 0.08290452],\n",
       "        [       nan,        nan,        nan, ..., 0.24761905,\n",
       "         0.21157822, 0.16733068],\n",
       "        [       nan,        nan,        nan, ..., 0.29340512,\n",
       "         0.2766252 , 0.24931507]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2010-03-25 2010-10-27 ... 2017-11-10\n",
       "  * lon      (lon) float64 3.627e+06 3.627e+06 3.627e+06 ... 3.611e+06 3.611e+06\n",
       "  * lat      (lat) float64 2.854e+05 2.854e+05 2.854e+05 ... 3.104e+05 3.104e+05\n",
       "Attributes:\n",
       "    description:  Calculated NDVI"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_xr = xr.DataArray(\n",
    "    combo,\n",
    "    coords={'time':dt_array,'lon': lon_main,'lat': lat_main}, \n",
    "    dims=[\"time\",\"lon\", \"lat\"],\n",
    "    attrs=dict(\n",
    "        description=\"Calculated NDVI\")\n",
    ")\n",
    "data_xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_xr2 = data_xr.rio.set_spatial_dims('lat','lon')\n",
    "data_xr2.rio.to_raster(r\"NDVI_time_attempt2.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f98a0f65d90>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_xr2[0,:,:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9870a672e0>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Study area mean\n",
    "NDVI_ts = data_xr2.mean(('lon','lat'))\n",
    "NDVI_ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NDVI</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-03-25</th>\n",
       "      <td>0.045241</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-27</th>\n",
       "      <td>0.180760</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-09</th>\n",
       "      <td>0.068149</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-15</th>\n",
       "      <td>0.062190</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-17</th>\n",
       "      <td>0.114276</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-13</th>\n",
       "      <td>0.059894</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-01</th>\n",
       "      <td>0.054340</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-19</th>\n",
       "      <td>0.080961</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-20</th>\n",
       "      <td>0.088244</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-03</th>\n",
       "      <td>0.150722</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-02</th>\n",
       "      <td>0.107308</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-01</th>\n",
       "      <td>0.052648</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-17</th>\n",
       "      <td>0.054423</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>0.070468</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-21</th>\n",
       "      <td>0.050059</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-22</th>\n",
       "      <td>0.058936</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-26</th>\n",
       "      <td>0.075831</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-12</th>\n",
       "      <td>0.144066</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-14</th>\n",
       "      <td>0.077225</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>0.089095</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-04</th>\n",
       "      <td>0.087477</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-14</th>\n",
       "      <td>0.065878</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-10</th>\n",
       "      <td>0.075915</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-14</th>\n",
       "      <td>0.081862</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>0.055315</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-23</th>\n",
       "      <td>0.073782</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-20</th>\n",
       "      <td>0.096551</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-08</th>\n",
       "      <td>0.118029</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>0.103093</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-10</th>\n",
       "      <td>0.089606</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                NDVI  Year\n",
       "time                      \n",
       "2010-03-25  0.045241  2010\n",
       "2010-10-27  0.180760  2010\n",
       "2010-12-09  0.068149  2010\n",
       "2012-02-15  0.062190  2012\n",
       "2012-10-17  0.114276  2012\n",
       "2013-01-13  0.059894  2013\n",
       "2013-02-01  0.054340  2013\n",
       "2013-05-19  0.080961  2013\n",
       "2013-05-20  0.088244  2013\n",
       "2013-10-03  0.150722  2013\n",
       "2013-11-02  0.107308  2013\n",
       "2013-12-01  0.052648  2013\n",
       "2013-12-17  0.054423  2013\n",
       "2014-01-03  0.070468  2014\n",
       "2014-03-21  0.050059  2014\n",
       "2014-03-22  0.058936  2014\n",
       "2014-07-26  0.075831  2014\n",
       "2014-10-12  0.144066  2014\n",
       "2015-04-14  0.077225  2015\n",
       "2016-01-11  0.089095  2016\n",
       "2016-02-04  0.087477  2016\n",
       "2016-04-14  0.065878  2016\n",
       "2016-06-10  0.075915  2016\n",
       "2016-06-14  0.081862  2016\n",
       "2017-01-04  0.055315  2017\n",
       "2017-02-23  0.073782  2017\n",
       "2017-05-20  0.096551  2017\n",
       "2017-10-08  0.118029  2017\n",
       "2017-10-25  0.103093  2017\n",
       "2017-11-10  0.089606  2017"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NDVI_ts_df = NDVI_ts.to_dataframe(name='NDVI')\n",
    "NDVI_ts_df['Year'] = pd.DatetimeIndex(NDVI_ts_df.index).year\n",
    "NDVI_ts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'NDVI (-1 to 1)')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12,6))\n",
    "# ax.scatter(NDVI_ts.index, NDVI_ts[0], color='red')\n",
    "ax.scatter(NDVI_ts_df.index, NDVI_ts_df['NDVI'], c='green')\n",
    "# ax.axvspan(\"2017-06-10T12:00:00.000Z\", \"2018-06-16T00:00:00.000Z\", color='grey', alpha=0.5, lw=0, label=\"Satellite relaunch gap\")\n",
    "ax.set(title=\"NDVI Values for the Rio Grande over time\")\n",
    "ax.legend(loc = 'upper right')\n",
    "ax.grid(zorder = 0)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('NDVI (-1 to 1)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monitoring station</th>\n",
       "      <th>Rin_10R</th>\n",
       "      <th>Rin_1R</th>\n",
       "      <th>Rin_11R</th>\n",
       "      <th>Rin_9R</th>\n",
       "      <th>Rin_2R</th>\n",
       "      <th>Rin_8R</th>\n",
       "      <th>Rin_3R</th>\n",
       "      <th>Rin_7R</th>\n",
       "      <th>Rin_4R</th>\n",
       "      <th>Rin_5R</th>\n",
       "      <th>Rin_12R</th>\n",
       "      <th>Rin_6R</th>\n",
       "      <th>Rin_13R</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Sum</th>\n",
       "      <th>Std dev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.021</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.003</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.011</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Monitoring station  Rin_10R  Rin_1R  Rin_11R  Rin_9R  Rin_2R  Rin_8R  \\\n",
       "0              2010.0    0.040   0.016    0.021  -0.001   0.001   0.035   \n",
       "1              2011.0    0.054   0.039    0.061   0.059   0.046   0.049   \n",
       "2              2012.0    0.101   0.089    0.113   0.113   0.113   0.061   \n",
       "3              2013.0    0.058   0.064    0.098   0.113   0.080   0.045   \n",
       "4              2014.0    0.074   0.087    0.113   0.113   0.097   0.063   \n",
       "5              2015.0    0.050   0.055    0.088   0.109   0.056   0.036   \n",
       "6              2016.0    0.092   0.099    0.113   0.113   0.095   0.056   \n",
       "7              2017.0    0.052   0.045    0.065   0.089   0.042   0.020   \n",
       "8                 NaN      NaN     NaN      NaN     NaN     NaN     NaN   \n",
       "\n",
       "   Rin_3R  Rin_7R  Rin_4R  Rin_5R  Rin_12R  Rin_6R  Rin_13R   Mean    Sum  \\\n",
       "0   0.020   0.030   0.014   0.019    0.003  -0.018    0.000  0.012  0.141   \n",
       "1   0.044   0.050   0.035   0.024    0.033   0.007    0.004  0.038  0.451   \n",
       "2   0.096   0.082   0.081   0.056    0.074   0.035    0.031  0.079  0.944   \n",
       "3   0.063   0.053   0.058   0.042    0.072   0.016    0.034  0.061  0.737   \n",
       "4   0.093   0.075   0.090   0.061    0.113   0.024    0.042  0.081  0.972   \n",
       "5   0.063   0.045   0.060   0.037    0.083   0.014    0.025  0.056  0.672   \n",
       "6   0.096   0.067   0.091   0.062    0.082   0.012    0.048  0.078  0.935   \n",
       "7   0.034   0.053   0.044   0.035    0.011  -0.016    0.027  0.037  0.449   \n",
       "8     NaN     NaN     NaN     NaN      NaN     NaN      NaN    NaN    NaN   \n",
       "\n",
       "   Std dev  \n",
       "0    0.014  \n",
       "1    0.018  \n",
       "2    0.027  \n",
       "3    0.026  \n",
       "4    0.028  \n",
       "5    0.026  \n",
       "6    0.029  \n",
       "7    0.025  \n",
       "8      NaN  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading in KC's data\n",
    "seepage = pd.read_csv('Data/From_KC/Flow_values.csv')\n",
    "seepage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monitoring station</th>\n",
       "      <th>Rin_10R</th>\n",
       "      <th>Rin_1R</th>\n",
       "      <th>Rin_11R</th>\n",
       "      <th>Rin_9R</th>\n",
       "      <th>Rin_2R</th>\n",
       "      <th>Rin_8R</th>\n",
       "      <th>Rin_3R</th>\n",
       "      <th>Rin_7R</th>\n",
       "      <th>Rin_4R</th>\n",
       "      <th>Rin_5R</th>\n",
       "      <th>Rin_12R</th>\n",
       "      <th>Rin_6R</th>\n",
       "      <th>Rin_13R</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Sum</th>\n",
       "      <th>Std dev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.859</td>\n",
       "      <td>1.015</td>\n",
       "      <td>1.772</td>\n",
       "      <td>0.771</td>\n",
       "      <td>1.101</td>\n",
       "      <td>1.003</td>\n",
       "      <td>1.242</td>\n",
       "      <td>1.366</td>\n",
       "      <td>1.803</td>\n",
       "      <td>1.621</td>\n",
       "      <td>2.548</td>\n",
       "      <td>2.345</td>\n",
       "      <td>2.014</td>\n",
       "      <td>1.550</td>\n",
       "      <td>18.601</td>\n",
       "      <td>0.539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.982</td>\n",
       "      <td>-0.289</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.897</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.465</td>\n",
       "      <td>2.005</td>\n",
       "      <td>1.967</td>\n",
       "      <td>1.948</td>\n",
       "      <td>1.047</td>\n",
       "      <td>12.559</td>\n",
       "      <td>0.693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.181</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.970</td>\n",
       "      <td>1.177</td>\n",
       "      <td>1.647</td>\n",
       "      <td>1.642</td>\n",
       "      <td>1.616</td>\n",
       "      <td>0.703</td>\n",
       "      <td>8.434</td>\n",
       "      <td>0.699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>0.095</td>\n",
       "      <td>-0.223</td>\n",
       "      <td>-0.178</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.941</td>\n",
       "      <td>1.706</td>\n",
       "      <td>1.228</td>\n",
       "      <td>0.463</td>\n",
       "      <td>5.554</td>\n",
       "      <td>0.643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>0.296</td>\n",
       "      <td>-0.248</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.727</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>1.623</td>\n",
       "      <td>1.213</td>\n",
       "      <td>0.238</td>\n",
       "      <td>2.857</td>\n",
       "      <td>0.631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-0.181</td>\n",
       "      <td>-0.199</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>-0.317</td>\n",
       "      <td>0.558</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.440</td>\n",
       "      <td>1.729</td>\n",
       "      <td>1.365</td>\n",
       "      <td>0.416</td>\n",
       "      <td>4.987</td>\n",
       "      <td>0.651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.543</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.855</td>\n",
       "      <td>0.422</td>\n",
       "      <td>1.022</td>\n",
       "      <td>0.960</td>\n",
       "      <td>1.184</td>\n",
       "      <td>1.645</td>\n",
       "      <td>1.948</td>\n",
       "      <td>1.458</td>\n",
       "      <td>0.818</td>\n",
       "      <td>9.818</td>\n",
       "      <td>0.664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.604</td>\n",
       "      <td>1.166</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>0.533</td>\n",
       "      <td>1.223</td>\n",
       "      <td>1.055</td>\n",
       "      <td>1.055</td>\n",
       "      <td>1.385</td>\n",
       "      <td>1.402</td>\n",
       "      <td>2.440</td>\n",
       "      <td>2.305</td>\n",
       "      <td>1.630</td>\n",
       "      <td>1.205</td>\n",
       "      <td>14.458</td>\n",
       "      <td>0.721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Monitoring station  Rin_10R  Rin_1R  Rin_11R  Rin_9R  Rin_2R  Rin_8R  \\\n",
       "0                2010    0.859   1.015    1.772   0.771   1.101   1.003   \n",
       "1                2011    0.466   0.548    0.982  -0.289   0.287   0.620   \n",
       "2                2012    0.139   0.109    0.181  -0.341  -0.341   0.722   \n",
       "3                2013    0.095  -0.223   -0.178  -0.341  -0.341   0.470   \n",
       "4                2014    0.017  -0.341   -0.341  -0.341  -0.341   0.296   \n",
       "5                2015    0.132  -0.181   -0.199  -0.341  -0.317   0.558   \n",
       "6                2016    0.364   0.104    0.543  -0.341   0.019   0.855   \n",
       "7                2017    0.694   0.604    1.166  -0.341   0.533   1.223   \n",
       "\n",
       "   Rin_3R  Rin_7R  Rin_4R  Rin_5R  Rin_12R  Rin_6R  Rin_13R   Mean     Sum  \\\n",
       "0   1.242   1.366   1.803   1.621    2.548   2.345    2.014  1.550  18.601   \n",
       "1   0.751   0.897   1.380   1.465    2.005   1.967    1.948  1.047  12.559   \n",
       "2   0.300   0.753   0.970   1.177    1.647   1.642    1.616  0.703   8.434   \n",
       "3   0.085   0.587   0.684   0.936    0.941   1.706    1.228  0.463   5.554   \n",
       "4  -0.248   0.357   0.280   0.727   -0.026   1.623    1.213  0.238   2.857   \n",
       "5  -0.105   0.648   0.454   0.936    0.440   1.729    1.365  0.416   4.987   \n",
       "6   0.422   1.022   0.960   1.184    1.645   1.948    1.458  0.818   9.818   \n",
       "7   1.055   1.055   1.385   1.402    2.440   2.305    1.630  1.205  14.458   \n",
       "\n",
       "   Std dev  \n",
       "0    0.539  \n",
       "1    0.693  \n",
       "2    0.699  \n",
       "3    0.643  \n",
       "4    0.631  \n",
       "5    0.651  \n",
       "6    0.664  \n",
       "7    0.721  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head = pd.read_csv('Data/From_KC/Groundwater_head_values.csv')\n",
    "head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monitoring station</th>\n",
       "      <th>Rin_11R</th>\n",
       "      <th>Rin_2R</th>\n",
       "      <th>Rin_12R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>1.772</td>\n",
       "      <td>1.101</td>\n",
       "      <td>2.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.287</td>\n",
       "      <td>2.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>0.181</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>1.647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>-0.178</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>0.941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>-0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>-0.199</td>\n",
       "      <td>-0.317</td>\n",
       "      <td>0.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.019</td>\n",
       "      <td>1.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017</td>\n",
       "      <td>1.166</td>\n",
       "      <td>0.533</td>\n",
       "      <td>2.440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Monitoring station  Rin_11R  Rin_2R  Rin_12R\n",
       "0                2010    1.772   1.101    2.548\n",
       "1                2011    0.982   0.287    2.005\n",
       "2                2012    0.181  -0.341    1.647\n",
       "3                2013   -0.178  -0.341    0.941\n",
       "4                2014   -0.341  -0.341   -0.026\n",
       "5                2015   -0.199  -0.317    0.440\n",
       "6                2016    0.543   0.019    1.645\n",
       "7                2017    1.166   0.533    2.440"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Case study stations\n",
    "seepage_studyareas = seepage[['Monitoring station','Rin_11R','Rin_2R','Rin_12R']]\n",
    "\n",
    "head_studyareas = head[['Monitoring station','Rin_11R','Rin_2R','Rin_12R']]\n",
    "head_studyareas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Rin_11R</th>\n",
       "      <th>Rin_2R</th>\n",
       "      <th>Rin_12R</th>\n",
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       "      <th>2010</th>\n",
       "      <td>1.772</td>\n",
       "      <td>1.101</td>\n",
       "      <td>2.548</td>\n",
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       "      <th>2011</th>\n",
       "      <td>0.982</td>\n",
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       "      <td>2.005</td>\n",
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       "      <th>2012</th>\n",
       "      <td>0.181</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>1.647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>-0.178</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>0.941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>-0.341</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>-0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>-0.199</td>\n",
       "      <td>-0.317</td>\n",
       "      <td>0.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.543</td>\n",
       "      <td>0.019</td>\n",
       "      <td>1.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1.166</td>\n",
       "      <td>0.533</td>\n",
       "      <td>2.440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rin_11R  Rin_2R  Rin_12R\n",
       "Year                          \n",
       "2010    1.772   1.101    2.548\n",
       "2011    0.982   0.287    2.005\n",
       "2012    0.181  -0.341    1.647\n",
       "2013   -0.178  -0.341    0.941\n",
       "2014   -0.341  -0.341   -0.026\n",
       "2015   -0.199  -0.317    0.440\n",
       "2016    0.543   0.019    1.645\n",
       "2017    1.166   0.533    2.440"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seepage_studyareas = seepage_studyareas.rename(columns= {'Monitoring station':'Year'})\n",
    "head_studyareas = head_studyareas.rename(columns= {'Monitoring station':'Year'})\n",
    "seepage_studyareas = seepage_studyareas.set_index('Year')\n",
    "head_studyareas = head_studyareas.set_index('Year')\n",
    "head_studyareas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NDVI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.180760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.114276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.150722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.144066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.077225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.089095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.118029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NDVI\n",
       "Year          \n",
       "2010  0.180760\n",
       "2012  0.114276\n",
       "2013  0.150722\n",
       "2014  0.144066\n",
       "2015  0.077225\n",
       "2016  0.089095\n",
       "2017  0.118029"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NDVI_ts_df = NDVI_ts_df.set_index('Year')\n",
    "NDVI_ts_df\n",
    "NDVI_df_year_min = pd.pivot_table(NDVI_ts_df, index=[\"Year\"], values=['NDVI'], dropna=False, aggfunc=np.min)\n",
    "NDVI_df_year_max = pd.pivot_table(NDVI_ts_df, index=[\"Year\"], values=['NDVI'], dropna=False, aggfunc=np.max)\n",
    "NDVI_df_year_mean = pd.pivot_table(NDVI_ts_df, index=[\"Year\"], values=['NDVI'], dropna=False, aggfunc=np.mean)\n",
    "\n",
    "NDVI_df_year_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Net Annual Pressure Head (m)')"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12,6))\n",
    "# ax.scatter(NDVI_ts.index, NDVI_ts[0], color='red')\n",
    "ax2 = ax.twinx()\n",
    "ax2.scatter(head_studyareas.index, head_studyareas['Rin_11R'], c='blue', label = 'Pressure Head Range')\n",
    "ax2.scatter(head_studyareas.index, head_studyareas['Rin_2R'], c='blue')\n",
    "ax2.scatter(head_studyareas.index, head_studyareas['Rin_12R'], c='blue')\n",
    "ax.scatter(NDVI_df_year_max.index, NDVI_df_year_max['NDVI'], c='green',label = 'NDVI Range')\n",
    "ax.scatter(NDVI_df_year_min.index, NDVI_df_year_min['NDVI'], c='green')\n",
    "ax2.hlines(y=2.0, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "ax2.hlines(y=0, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "ax2.hlines(y=0.325, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "ax.text(2016.5, 0.156, 'Connected ^Losing', style='italic', bbox={\n",
    "        'facecolor': 'white', 'alpha': 0, 'pad': 5})\n",
    "ax.text(2016.5, 0.150, 'Connected v Gaining', style='italic', bbox={\n",
    "        'facecolor': 'white', 'alpha': 0, 'pad': 5})\n",
    "ax.text(2016.5, 0.078, 'Transition', style='oblique', bbox={\n",
    "        'facecolor': 'white', 'alpha': 0.0, 'pad': 5})\n",
    "ax.text(2016.5, 0.063, 'Disconnected', style='oblique', bbox={\n",
    "        'facecolor': 'white', 'alpha': 0, 'pad': 5})\n",
    "ax.set(title=\"NDVI vs. Head Values at the Rio Grande over time\")\n",
    "ax.legend(loc = [0,0.86])\n",
    "ax2.legend(loc = [0,0.8])\n",
    "ax.grid(zorder = 0)\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('NDVI (-1 to 1)')\n",
    "ax2.set_ylabel('Net Annual Pressure Head (m)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Seepage Flow (cms)')"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12,6))\n",
    "# ax.scatter(NDVI_ts.index, NDVI_ts[0], color='red')\n",
    "ax2 = ax.twinx()\n",
    "ax2.scatter(seepage_studyareas.index, seepage_studyareas['Rin_11R'], c='purple', label = 'Seepage Range (cms)')\n",
    "ax2.scatter(seepage_studyareas.index, seepage_studyareas['Rin_2R'], c='purple')\n",
    "ax2.scatter(seepage_studyareas.index, seepage_studyareas['Rin_12R'], c='purple')\n",
    "ax.scatter(NDVI_df_year_max.index, NDVI_df_year_max['NDVI'], c='green',label = 'NDVI Range')\n",
    "ax.scatter(NDVI_df_year_min.index, NDVI_df_year_min['NDVI'], c='green')\n",
    "ax2.hlines(y=0.113, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "ax2.hlines(y=0, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "ax2.hlines(y=0.0914, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "ax.text(2016.5, 0.047, 'Connected ^Losing', style='italic', bbox={\n",
    "        'facecolor': 'white', 'alpha': 0, 'pad': 5})\n",
    "ax.text(2016.5, 0.156, 'Transition', style='oblique', bbox={\n",
    "        'facecolor': 'white', 'alpha': 0.0, 'pad': 5})\n",
    "ax.text(2016.5, 0.182, 'Disconnected', style='oblique', bbox={\n",
    "        'facecolor': 'white', 'alpha': 0, 'pad': 5})\n",
    "# ax.axvspan(\"2017-06-10T12:00:00.000Z\", \"2018-06-16T00:00:00.000Z\", color='grey', alpha=0.5, lw=0, label=\"Satellite relaunch gap\")\n",
    "ax.set(title=\"NDVI vs. Seepage Flow Values at the Rio Grande over time\")\n",
    "ax.legend(loc = [0,0.86])\n",
    "ax2.legend(loc = [0,0.8])\n",
    "ax.grid(zorder = 0)\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('NDVI (-1 to 1)')\n",
    "ax2.set_ylabel('Seepage Flow (cms)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7k/m9jgv50j0qv046r7xmwxt0qh0000gn/T/ipykernel_3184/3983479622.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = NDVI_DF2.append({'Year':2011, 'NDVI':np.nan}, ignore_index=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>NDVI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>0.098050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>0.088233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>0.081068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>0.079872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>0.077225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.080045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>0.089396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year      NDVI\n",
       "0  2010.0  0.098050\n",
       "1  2012.0  0.088233\n",
       "2  2013.0  0.081068\n",
       "3  2014.0  0.079872\n",
       "4  2015.0  0.077225\n",
       "5  2016.0  0.080045\n",
       "6  2017.0  0.089396\n",
       "7  2011.0       NaN"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NDVI_DF2 = NDVI_df_year_mean.reset_index()\n",
    "# NDVI_DF2 = NDVI_DF2.append([2011,np.nan])\n",
    "\n",
    "df = NDVI_DF2.append({'Year':2011, 'NDVI':np.nan}, ignore_index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'NDVI (-1 to 1)')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UqFGuex516NBBl156qRYsWKA77rhDCxYsUFxcnOuDcrGUlBSlpKSosLBQH330kWbOnKkhQ4YoISFB3bp1C/j4ZdGkSRPVqlWrxOXYmzRpIklasWKFjh8/LpvN5jZztG3bNrf+xR/GvV3gnp+fX+b79AwfPlwPPPCAXnzxRUVERGjr1q166KGHXCH2/fff14EDB5Sbm+v6q7+kMi0aIP3xIdbbB/c/fxhv0qSJLrzwQv373//2up8zw/eIESM0YsQIHT9+XBs2bNC0adN0zTXX6Ntvvy1xtu3VV19VRESE3nrrLbcP2uGwjHjxzz47O7vMs4XetGnTxvW71rNnT9WqVUv333+/nnzySU2YMKHE5zVu3FinTp3STz/95BaeDMNQfn6+a7bWm7i4OHXs2FE5OTk6ceKE2/f2oosukvRHGPLG2x9KyjvevGncuLE++eQTj/Y//+4U/xymTJmi1NRUr/tq3759wHUAqFjMOAEIqSuvvFJ9+vTRgw8+WOKHn5LMmDFD27dvV3p6usdfhIcNG6aaNWvq2Wef1SuvvKK//vWv5frAKP3xYfrjjz/Wxo0btWrVKg0bNsxtFa8zRUVFKTk52XUBeXlWLiura665Rrt27VLjxo29zlAVB53iD5PFsxLSHx9gn3/+ebf9de3aVdHR0XrllVfc2j/88EOvf/0vSVxcnK666iotXbpUTz/9tGrUqKFhw4a5tnurR5KeffbZMu0/ISFBX3zxhVvb+++/7zGerrnmGv3vf/9T27ZtvX5/zgxOxerUqaP+/ftr6tSpOnnypL766qsS6zCZTKpZs6bbmPj999/dThktFhUV5dcMlL/9/6xfv36qWbOmdu3aVeIMZiAmTpyoc889V7NmzdKxY8dK7Fd8iuyfly1fvny5jh8/7vMU2qlTp+rQoUMaP36819Nz/VHe8eZN7969dezYMb355ptu7UuWLHF73L59e7Vr106ff/55iT+HevXqBVwHgIrFjBOAkHvkkUeUlJSkgwcPuk6bOtMvv/yijz76SJJ0/Phx7dixQ6+++qrsdrsGDRqkGTNmeDwnJiZGAwYM0IIFC2QYRlDu3XTjjTdq/PjxuvHGG1VYWOgxg/XAAw9o//79+utf/yqLxaJffvlFWVlZbtdPSH9cM5WcnBzwdR0lycjI0PLly9WzZ0+NGzdOF154oU6fPq29e/dq7dq1uueee3TZZZepT58+ioyM1I033qiJEyfqxIkTeuaZZ3TkyBG3/Z1zzjmaMGGC/vWvf+m2227T3//+d+3bt0/Tp08v86l6xUaOHKnVq1frhRdeUL9+/VynX0l/XDN1zjnnaNSoUZo2bZoiIiL0yiuvlPk+X0OHDtU///lPPfDAA0pOTtb27dv11FNPqUGDBm79HnzwQeXk5Kh79+4aO3as2rdvrxMnTmjPnj1as2aN5s2bJ4vFottvv121atVSjx49FBsbq/z8fM2cOVMNGjQodWbk6quv1pw5czRkyBD94x//0OHDh/XYY495fECXpE6dOunVV1/VsmXL1KZNG0VHR6tTp04l7rtTp07Kzc3VqlWrFBsbq3r16vk1M5GQkKAHH3xQU6dO1ffff6+rrrpK55xzjn788Ud98sknqlOnjtffI18iIiL08MMPa9CgQcrKyirxxtZ9+vRRv379NGnSJB09elQ9evTQF198oWnTpikxMVFDhw4t9Tg33nijvvrqK/373//W559/ruHDh6tdu3Y6ffq09u3b5wqnZQkd5R1v3txyyy16/PHHdcstt+jf//632rVrpzVr1ujdd9/16Pvss8+qf//+6tevn4YPH64WLVro559/1tdff60tW7bo9ddfD7gOABUshAtTADjLnLmq3p8NGTLEkOR1VT39/xXkTCaTUbduXaN9+/bG0KFDjXfffbfU461cudKQZDRq1Mg4ceKE1z5lWVXPW509evTw2PbWW28Z/fv3N1q0aGFERkYazZo1MwYMGGDY7Xa3firjinTDhg0z6tSpU+J2byut/fbbb8b9999vtG/f3oiMjDQaNGhgdOrUyRg3bpyRn5/v6rdq1Sqjc+fORnR0tNGiRQvj3nvvNd5++21DkrFu3TpXv9OnTxszZ8404uPjjcjISOPCCy80Vq1aZSQnJ/u1KtzJkyeN5s2be12hzDAM48MPPzS6detm1K5d22jatKlx2223GVu2bPFYkczbqnqFhYXGxIkTjfj4eKNWrVpGcnKysW3bNo9V9QzDMH766Sdj7NixRuvWrY2IiAijUaNGRlJSkjF16lTjt99+MwzDMBYtWmT07t3baN68uREZGWnExcUZgwYNMr744gufr/PFF1802rdvb0RFRRlt2rQxZs6cacyfP9+QZOzevdvVb8+ePUbfvn2NevXqGZLcVqDzZtu2bUaPHj2M2rVru42fkn6nilfBO/NnaRiGsWLFCqN3795G/fr1jaioKKNVq1ZGWlqa8d///rfU4xfv7/XXX/e6/bLLLjPOOecc14qSf15VzzAM4/fffzcmTZpktGrVyoiIiDBiY2ONO++802NFvtJs2LDBGDx4sGGxWIyIiAijdu3aRocOHYw777zT2Lx5s1tfb6t0FivreCvpd9DbONy/f79x/fXXG3Xr1jXq1atnXH/99caHH37osU/DMIzPP//cGDRokNGsWTMjIiLCiImJMa644gpj3rx5Zf5eAKh8JsMo55w3AAAAAFRzXOMEAAAAAD4QnAAAAADAB4ITAAAAAPhAcAIAAAAAHwhOAAAAAOADwQkAAAAAfDjrboB7+vRpHThwQPXq1XPdPRwAAADA2ccwDB07dkxxcXGqUaP0OaWzLjgdOHDA7Y71AAAAAM5u+/btk8ViKbXPWRec6tWrJ+mPb079+vVDXA1CpaioSGvXrlXfvn0VERER6nIAD4xRhDvGKMIdYxRlcfToUcXHx7syQmnOuuBUfHpe/fr1CU5nsaKiItWuXVv169fnzRRhiTGKcMcYRbhjjMIfZbmEh8UhAAAAAMAHghMAAAAA+EBwAgAAAAAfCE4AAAAA4APBCQAAAAB8IDgBAAAAgA8EJwAAAADwgeAEAAAAAD4QnAAAAADAB4ITAAAAAPhAcAIAAAAAHwhOAAAAAOADwQkAAAAAfKgZ6gIAAAAAnB2cTqfsdrscDodiY2NltVplNptDXVaZEJwAAAAAVDibzab09HTt37/f1WaxWJSVlaXU1NQQVlY2nKoHAAAAoELZbDalpaW5hSZJysvLU1pammw2W4gqKzuCEwAAAIAK43Q6lZ6eLsMwPLYVt2VkZMjpdFZ2aX4hOAEAAACoMHa73WOm6UyGYWjfvn2y2+2VWJX/CE4AAAAAKozD4Qhqv1AhOAEAAACoMLGxsUHtFyoEJwAAAAAVxmq1ymKxyGQyed1uMpkUHx8vq9VayZX5J6TBaebMmbrkkktUr149NWvWTNddd5127NhR6nNyc3NlMpk8vr755ptKqhoAAABAWZnNZmVlZUmSR3gqfpyZmRn293MKaXBav369xowZo48++kg5OTk6deqU+vbtq+PHj/t87o4dO+RwOFxf7dq1q4SKAQAAAPgrNTVV2dnZatGihVu7xWJRdnZ2lbiPU0hvgPvOO++4PV6wYIGaNWumzz77TD179iz1uc2aNVPDhg0rsDoAAAAAwZKamqqUlBTZ7XY5HA7FxsbKarWG/UxTsZAGpz/79ddfJUmNGjXy2TcxMVEnTpxQhw4ddP/996t3795e+xUWFqqwsND1+OjRo5KkoqIiFRUVBaFqVEXFP3vGAMIVYxThjjGKcMcYDV89evRw/fv06dM6ffp0yGrxZ3yYDG93ogoBwzCUkpKiI0eOlLqG+44dO7RhwwYlJSWpsLBQixcv1rx585Sbm+t1lmr69OmaMWOGR/uSJUtUu3btoL4GAAAAAFVHQUGBhgwZol9//VX169cvtW/YBKcxY8Zo9erV2rhxoywWi1/PHThwoEwmk958802Pbd5mnOLj43Xo0CGf3xxUX0VFRcrJyVGfPn0UERER6nIAD4xRhDvGKMIdYxRlcfToUTVp0qRMwSksTtW7++679eabb2rDhg1+hyZJ6tq1q15++WWv26KiohQVFeXRHhERwS8RGAcIe4xRhDvGKMIdYxSl8WdshDQ4GYahu+++W2+88YZyc3PVunXrgPazdevWsL9hFgAAAICqK6TBacyYMVqyZIlWrlypevXqKT8/X5LUoEED1apVS5I0ZcoU5eXl6aWXXpL0xxrvCQkJ6tixo06ePKmXX35Zy5cv1/Lly0P2OgAAAABUbyENTs8884wkqVevXm7tCxYs0PDhwyVJDodDe/fudW07efKkJkyYoLy8PNWqVUsdO3bU6tWrNWDAgMoqGwAAAMBZJuSn6vmycOFCt8cTJ07UxIkTK6giAAAAAPBUI9QFAAAAAEC4IzgBAAAAgA8EJwAAAADwgeAEAAAAAD4QnAAAAADAB4ITAAAAAPhAcAIAAAAAHwhOAAAAAOADwQkAAAAAfCA4AQAAAIAPBCcAAAAA8IHgBAAAAAA+EJwAAAAAwAeCEwAAAAD4QHACAAAAAB8ITgAAAADgA8EJAAAAAHwgOAEAAACADwQnAAAAAPCB4AQAAAAAPhCcAAAAAMAHghMAAAAA+EBwAgAAAAAfCE4AAAAA4APBCQAAAAB8IDgBAAAAgA8EJwAAAADwgeAEAAAAAD4QnAAAAADAB4ITAAAAAPhAcAIAAAAAHwhOAAAAAOADwQkAAAAAfCA4AQAAAIAPBCcAAAAA8IHgBAAAAAA+EJwAAAAAwAeCEwAAAAD4QHACAAAAAB8ITgAAAADgA8EJAAAAAHwgOAEAAACADwQnAAAAAPCB4AQAAAAAPhCcAAAAAMAHghMAAAAA+EBwAgAAAAAfCE4AAAAA4APBCQAAAAB8IDgBAAAAgA8EJwAAAADwgeAEAAAAAD4QnAAAAADAB4ITAAAAAPhAcAIAAAAAHwhOAAAAAOADwQkAAAAAfCA4AQAAAIAPBCcAAAAA8IHgBAAAAAA+1Ax1AQAAVHVOp1N2u10Oh0OxsbGyWq0ym82hLgsAEEQEJwAAysFmsyk9PV379+93tVksFmVlZSk1NTWElQEAgimkp+rNnDlTl1xyierVq6dmzZrpuuuu044dO3w+b/369UpKSlJ0dLTatGmjefPmVUK1AAC4s9lsSktLcwtNkpSXl6e0tDTZbLYQVQYACLaQBqf169drzJgx+uijj5STk6NTp06pb9++On78eInP2b17twYMGCCr1aqtW7fqvvvu09ixY7V8+fJKrBwAcLZzOp1KT0+XYRge24rbMjIy5HQ6K7s0AEAFCOmpeu+8847b4wULFqhZs2b67LPP1LNnT6/PmTdvnlq2bKnMzExJ0vnnn6/Nmzfrscce0/XXX+/Rv7CwUIWFha7HR48elSQVFRWpqKgoSK8EVU3xz54xgHDFGA1/Gzdu1OHDh1WrVq0S+xw6dEgbNmzQ5ZdfXomVVQ7GKMIdYxRl4c/4MBne/lQWIt99953atWunL7/8UhdccIHXPj179lRiYqKysrJcbW+88YYGDRqkgoICRUREuPWfPn26ZsyY4bGfJUuWqHbt2sF9AQAAAACqjIKCAg0ZMkS//vqr6tevX2rfsFkcwjAMjR8/XpdffnmJoUmS8vPz1bx5c7e25s2b69SpUzp06JBiY2Pdtk2ZMkXjx493PT569Kji4+PVt29fn98cVF9FRUXKyclRnz59PMI2EA4Yo+Fv48aNuvrqq332W716dbWdcWKMIpwxRlEWxWejlUXYBKe77rpLX3zxhTZu3Oizr8lkcntcPGn253ZJioqKUlRUlEd7REQEv0RgHCDsMUbDV8+ePdW4cWPl5eV5vc7JZDLJYrGoZ8+e1XppcsYowh1jFKXxZ2yExQ1w7777br355ptat26dLBZLqX1jYmKUn5/v1nbw4EHVrFlTjRs3rsgyAQBwMZvNrtPG//yHu+LHmZmZ1To0AcDZJKTByTAM3XXXXbLZbHr//ffVunVrn8/p1q2bcnJy3NrWrl2rLl268NcEAEClSk1NVXZ2tlq0aOHWbrFYlJ2dzX2cAKAaCempemPGjNGSJUu0cuVK1atXzzWT1KBBA9cqRVOmTFFeXp5eeuklSdKoUaP01FNPafz48br99tu1adMmzZ8/X0uXLg3Z6wAAnL1SU1OVkpIiu90uh8Oh2NhYWa1WZpoAoJoJaXB65plnJEm9evVya1+wYIGGDx8uSXI4HNq7d69rW+vWrbVmzRqNGzdOTz/9tOLi4vTEE094XYocAIDKYDabPf4vAwBULyENTmVZCX3hwoUebcnJydqyZUsFVAQAAAAAnsJicQgAAAAACGcEJwAAAADwgeAEAAAAAD4QnAAAAADAB4ITAAAAAPhAcAIAAAAAHwhOAAAAAOADwQkAAAAAfCA4AQAAAIAPBCcAAAAA8IHgBAAAAAA+EJwAAAAAwAeCEwAAAAD4QHACAAAAAB8ITgAAAADgA8EJAAAAAHwgOAEAAACADwQnAAAAAPCB4AQAAAAAPhCcAAAAAMCHmv4+YceOHVq6dKnsdrv27NmjgoICNW3aVImJierXr5+uv/56RUVFVUStAAAAABASZZ5x2rp1q/r06aPOnTtrw4YNuuSSS5SRkaGHHnpIN998swzD0NSpUxUXF6dHHnlEhYWFFVk3AAAAgCrG6XQqNzdXS5cuVW5urpxOZ6hLKrMyzzhdd911uvfee7Vs2TI1atSoxH6bNm3S448/rv/85z+67777glIkAAAAgKrNZrMpPT1d+/fvd7VZLBZlZWUpNTU1hJWVTZmD086dOxUZGemzX7du3dStWzedPHmyXIUBAAAAqB5sNpvS0tJkGIZbe15entLS0pSdnR324anMp+qVJTSVpz8AAACA6sfpdCo9Pd0jNElytWVkZIT9aXt+Lw5R7JNPPlFubq4OHjyo06dPu22bM2dOuQsDAAAAUPXZ7Xa30/P+zDAM7du3T3a7Xb169aq8wvwUUHB6+OGHdf/996t9+/Zq3ry5TCaTa9uZ/wYAAABwdnM4HEHtFyoBBaesrCy9+OKLGj58eJDLAQAAAFCdxMbGBrVfqAR0A9waNWqoR48ewa4FAAAAQDVjtVplsVhKPDPNZDIpPj5eVqu1kivzT0DBady4cXr66aeDXQsAAACAasZsNisrK0uS52U9xY8zMzNlNpsrvTZ/BHSq3oQJE3T11Verbdu26tChgyIiIty222y2oBQHAAAAoOpLTU1Vdna21/s4ZWZmhv1S5FKAwenuu+/WunXr1Lt3bzVu3JgFIQAAAACUKjU1VSkpKbLb7XI4HIqNjZXVag37maZiAQWnl156ScuXL9fVV18d7HoAAAAAVFNmszmslxwvTUDXODVq1Eht27YNdi0AAAAAEJYCCk7Tp0/XtGnTVFBQEOx6AAAAACDsBHSq3hNPPKFdu3apefPmSkhI8FgcYsuWLUEpDgAAAADCQUDB6brrrgtyGQAAAAAQvgIKTtOmTQt2HQAAAAAQtgK6xqksDMOoqF0DAAAAQKUqc3A6//zztWTJEp08ebLUfjt37tSdd96pRx55pNzFAWcDp9Op3NxcLV26VLm5uXI6naEuCUA1wnsMAARHmU/Ve/rppzVp0iSNGTNGffv2VZcuXRQXF6fo6GgdOXJE27dv18aNG7V9+3bdddddGj16dEXWDVQLNpvN6x20s7KyqsQdtAGEN95jACB4yhycrrjiCn366af68MMPtWzZMi1ZskR79uzR77//riZNmigxMVG33HKLbr75ZjVs2LACSwaqB5vNprS0NI/TWvPy8pSWlqbs7Gw+2AAIGO8xABBcfi8O0b17d3Xv3r0iagHOGk6nU+np6V6vBTQMQyaTSRkZGUpJSZHZbA5BhQCqMt5jACD4KmxxCAAls9vtbqfO/JlhGNq3b5/sdnslVgWguuA9BgCCj+AEhIDD4QhqPwA4E+8xABB8BCcgBGJjY4PaDwDOxHsMAAQfwQkIAavVKovFIpPJ5HW7yWRSfHy8rFZrJVcGoDrgPQYAgo/gBISA2WxWVlaWJHl8sCl+nJmZyUXbAALCewwABF+Zg9PRo0fL/AXAt9TUVGVnZ6tFixZu7RaLhWWCAZQb7zEAEFxlXo68YcOGJU75/xl3JQfKJjU1VSkpKbLb7XI4HIqNjZXVauWvwACCgvcYAAieMgendevWuf69Z88eTZ48WcOHD1e3bt0kSZs2bdKiRYs0c+bM4FcJVGNms1m9evUKdRkAqineYwAgOMocnJKTk13/fvDBBzVnzhzdeOONrrZrr71WnTp10nPPPadhw4YFt0oAAAAACKGAFofYtGmTunTp4tHepUsXffLJJ+UuCgAAAADCSUDBKT4+XvPmzfNof/bZZxUfH1/uogAAAAAgnJT5VL0zPf7447r++uv17rvvqmvXrpKkjz76SLt27dLy5cuDWiAAAAAAhFpAM04DBgzQzp07de211+rnn3/W4cOHlZKSom+//VYDBgwIdo0AAAAAEFIBzThJf9wH4uGHHw5mLQAAAAAQlgIOTpJUUFCgvXv36uTJk27tF154YbmKAgAAAIBwElBw+umnnzRixAi9/fbbXrdzA1wAAAAA1UlA1zhlZGToyJEj+uijj1SrVi298847WrRokdq1a6c333wz2DUCAAAAQEgFNOP0/vvva+XKlbrkkktUo0YNtWrVSn369FH9+vU1c+ZMXX311cGuEwAAAABCJqAZp+PHj6tZs2aSpEaNGumnn36SJHXq1Elbtmwp8342bNiggQMHKi4uTiaTSStWrCi1f25urkwmk8fXN998E8jLAAAAAIAyCSg4tW/fXjt27JAkXXTRRXr22WeVl5enefPmKTY2tsz7OX78uDp37qynnnrKr+Pv2LFDDofD9dWuXTu/ng8AAAAA/gjoVL2MjAw5HA5J0rRp09SvXz+98sorioyM1MKFC8u8n/79+6t///5+H79Zs2Zq2LCh388DAAAAgEAEFJxuuukm178TExO1Z88effPNN2rZsqWaNGkStOJKkpiYqBMnTqhDhw66//771bt37xL7FhYWqrCw0PX46NGjkqSioiIVFRVVeK0IT8U/e8YAwhVjFOGOMYpwxxhFWfgzPkyGYRiBHujkyZPavXu32rZtq5o1y3VLKJlMJr3xxhu67rrrSuyzY8cObdiwQUlJSSosLNTixYs1b9485ebmqmfPnl6fM336dM2YMcOjfcmSJapdu3a5agYAAABQdRUUFGjIkCH69ddfVb9+/VL7BhScCgoKdPfdd2vRokWSpG+//VZt2rTR2LFjFRcXp8mTJ/tddFmCkzcDBw6UyWQqcRl0bzNO8fHxOnTokM9vDqqvoqIi5eTkqE+fPoqIiAh1OYAHxijCHWMU4Y4xirI4evSomjRpUqbgFNA00ZQpU/T5558rNzdXV111lav9yiuv1LRp0wIKToHq2rWrXn755RK3R0VFKSoqyqM9IiKCXyIwDhD2GKMId4xRhDvGKErjz9gIKDitWLFCy5YtU9euXWUymVztHTp00K5duwLZZcC2bt3q10p+AAAAAOCvgILTTz/95LqP05mOHz/uFqR8+e233/Tdd9+5Hu/evVvbtm1To0aN1LJlS02ZMkV5eXl66aWXJEmZmZlKSEhQx44ddfLkSb388stavny5li9fHsjLAAAAAIAyCSg4XXLJJVq9erXuvvtuSXKFpeeff17dunUr8342b97stiLe+PHjJUnDhg3TwoUL5XA4tHfvXtf2kydPasKECcrLy1OtWrXUsWNHrV69WgMGDAjkZQAAAABAmQQUnGbOnKmrrrpK27dv16lTp5SVlaWvvvpKmzZt0vr168u8n169eqm0tSn+fE+oiRMnauLEiYGUDAAAAAABqxHIk7p3764PPvhABQUFatu2rdauXavmzZtr06ZNSkpKCnaNAAAAABBSAd98qVOnTq7lyAEAAACgOvMrOB09erRM/bg/EgAAAIDqxK/g1LBhw1JXzTMMQyaTSU6ns9yFAQAAAEC48Cs4rVu3zvVvwzA0YMAAvfDCC2rRokXQCwMAAACAcOFXcEpOTnZ7bDab1bVrV7Vp0yaoRQEAAABAOAloVT0AAAAAOJsQnAAAAADAh3IHp9IWiwAAAACA6sCva5xSU1PdHp84cUKjRo1SnTp13NptNlv5KwMAAACAMOFXcGrQoIHb45tvvjmoxQAAAABAOPIrOC1YsKCi6gAAAACAsMXiEAAAAADgA8EJAAAAAHwgOAEAAACADwQnAAAAAPCB4AQAAAAAPgQcnBYvXqwePXooLi5OP/zwgyQpMzNTK1euDFpxAAAAABAOAgpOzzzzjMaPH68BAwbol19+kdPplCQ1bNhQmZmZwawPAAAAAEIuoOD05JNP6vnnn9fUqVNlNptd7V26dNGXX34ZtOIAAAAAIBwEFJx2796txMREj/aoqCgdP3683EUBAAAAQDgJKDi1bt1a27Zt82h/++231aFDh/LWBAAAAABhpWYgT7r33ns1ZswYnThxQoZh6JNPPtHSpUs1c+ZMvfDCC8GuEQAAAABCKqDgNGLECJ06dUoTJ05UQUGBhgwZohYtWigrK0s33HBDsGsEAAAAgJDyOzidOnVKr7zyigYOHKjbb79dhw4d0unTp9WsWbOKqA8AAAAAQs7va5xq1qypO++8U4WFhZKkJk2aEJoAAAAAVGsBLQ5x2WWXaevWrcGuBQAAAADCUkDXOI0ePVr33HOP9u/fr6SkJNWpU8dt+4UXXhiU4gAAAAAgHAQUnAYPHixJGjt2rKvNZDLJMAyZTCY5nc7gVAcAAAAAYSCg4LR79+5g1wEAAAAAYSug4NSqVatg1wEAAAAAYSug4PTSSy+Vuv2WW24JqBgAAAAACEcBBaf09HS3x0VFRSooKFBkZKRq165NcAIAAABQrQS0HPmRI0fcvn777Tft2LFDl19+uZYuXRrsGgEAAAAgpAIKTt60a9dOs2bN8piNAgAAAICqLmjBSZLMZrMOHDgQzF0CAAAAQMgFdI3Tm2++6fbYMAw5HA499dRT6tGjR1AKAwAAAIBwEVBwuu6669wem0wmNW3aVFdccYX+85//BKMuAAAAAAgbAQWn06dPB7sOAAAAAAhbQbnGyel0atu2bTpy5EgwdgcAAAAAYSWg4JSRkaH58+dL+iM09ezZUxdffLHi4+OVm5sbzPoAAAAAIOQCCk7Z2dnq3LmzJGnVqlXas2ePvvnmG2VkZGjq1KlBLRAAAAAAQi2g4HTo0CHFxMRIktasWaO///3v+stf/qKRI0fqyy+/DGqBAAAAABBqAQWn5s2ba/v27XI6nXrnnXd05ZVXSpIKCgpkNpuDWiAAAAAAhFpAq+qNGDFCgwYNUmxsrEwmk/r06SNJ+vjjj3XeeecFtUAAAAAACLWAgtP06dN1wQUXaN++ffr73/+uqKgoSZLZbNbkyZODWiAAAAAAhFpAwUmS0tLS3B7/8ssvGjZsWLkLAgAAAFA9OZ1O2e12ORwOxcbGymq1VplLfQK6xumRRx7RsmXLXI8HDRqkxo0by2Kx6IsvvghacQAAAACqB5vNpoSEBPXu3VtDhgxR7969lZCQIJvNFurSyiSg4PTss88qPj5ekpSTk6OcnBy9/fbbuuqqqzRhwoSgFggAAACgarPZbEpLS9P+/fvd2vPy8pSWllYlwlNAp+o5HA5XcHrrrbc0aNAg9e3bVwkJCbrsssuCWiAAAACAqsvpdCo9PV2GYXhsMwxDJpNJGRkZSklJCevT9gKacTrnnHO0b98+SXJbjtwwDDmdzuBVBwAAAKBKs9vtHjNNZzIMQ/v27ZPdbq/EqvwX0IxTamqqhgwZonbt2unw4cPq37+/JGnbtm0699xzg1ogAAAAgKrL4XAEtV+oBBScHn/8cSUkJGjfvn2aPXu26tatK+mPFzt69OigFggAAACg6oqNjQ1qv1AJKDhFRER4XQQiIyOjvPUAAAAAqEasVqssFovy8vK8XudkMplksVhktVpDUF3ZBXSNkyQtXrxYl19+ueLi4vTDDz9IkjIzM7Vy5cqgFQcAAACgajObzcrKypL0R0g6U/HjzMzMsF4YQgowOD3zzDMaP368+vfvr19++cW1IETDhg2VmZkZzPoAAAAAVHGpqanKzs5WixYt3NotFouys7OVmpoaosrKLqDg9OSTT+r555/X1KlT3ZJhly5d9OWXXwatOAAAAADVQ2pqqvbs2aN169ZpyZIlWrdunXbv3l0lQpMU4DVOu3fvVmJiokd7VFSUjh8/Xu6iAAAAAFQ/ZrNZvXr1CnUZAQloxql169batm2bR/vbb7+tDh06lLcmAAAAAAgrAc043XvvvRozZoxOnDghwzD0ySefaOnSpZo5c6ZeeOGFYNcIAAAAACEVUHAaMWKETp06pYkTJ6qgoEBDhgxRixYtlJWVpRtuuCHYNQIAAABASPkdnE6dOqVXXnlFAwcO1O23365Dhw7p9OnTatasWUXUBwAAAAAh5/c1TjVr1tSdd96pwsJCSVKTJk0CDk0bNmzQwIEDFRcXJ5PJpBUrVvh8zvr165WUlKTo6Gi1adNG8+bNC+jYAAAAAFBWAS0Ocdlll2nr1q3lPvjx48fVuXNnPfXUU2Xqv3v3bg0YMEBWq1Vbt27Vfffdp7Fjx2r58uXlrgUAgEA5nU7l5uZq6dKlys3Ndd3fEABQfQR0jdPo0aN1zz33aP/+/UpKSlKdOnXctl944YVl2k///v3Vv3//Mh933rx5atmypesmu+eff742b96sxx57TNdff32Z9wMAQLDYbDalp6dr//79rjaLxaKsrKwqc28SAIBvAQWnwYMHS5LGjh3rajOZTDIMQyaTqcL+0rZp0yb17dvXra1fv36aP3++ioqKFBER4fGcwsJC12mFknT06FFJUlFRkYqKiiqkToS/4p89YwDhijFaNaxatUpDhw6VYRiqVauWq/3nn3/W0KFDJUkDBw4MVXkVijGKcMcYRVn4Mz4CvgFuKOTn56t58+Zubc2bN9epU6d06NAhxcbGejxn5syZmjFjhkf72rVrVbt27QqrFVVDTk5OqEsASsUYDW9ms1lLliwptc+aNWsqqZrQYIwi3DFGUZqCgoIy9/U7OB07dkzffvutioqKdOmll6pJkyb+7qJcTCaT22PDMLy2F5syZYrGjx/venz06FHFx8erb9++ql+/fsUVirBWVFSknJwc9enTx+tMJRBqjNHwt3HjRl199dU++61evVqXX355JVRUuRijCHeMUZRF8dloZeFXcPriiy/Uv39/5efnyzAM1a9fX9nZ2bryyiv9LjIQMTExys/Pd2s7ePCgatasqcaNG3t9TlRUlKKiojzaIyIi+CUC4wBhjzEavvLz8/X777+XqV91/hkyRhHuGKMojT9jw69V9SZPnqyWLVvKbrdr8+bNSk5O1l133eV3gYHq1q2bx3Tr2rVr1aVLF34h4BUrXQGoKN5ODy9PPwBAePNrxmnz5s1as2aNunTpIkl68cUX1axZM/3222+qW7eu3wf/7bff9N1337ke7969W9u2bVOjRo3UsmVLTZkyRXl5eXrppZckSaNGjdJTTz2l8ePH6/bbb9emTZs0f/58LV261O9jo/orbaWr6nqxNoDKY7VaZbFYlJeX5zpt/Ewmk0kWi0VWqzUE1QEAgs2vGadDhw6pZcuWrseNGzdW7dq19dNPPwV08M2bNysxMVGJiYmSpPHjxysxMVEPPPCAJMnhcGjv3r2u/q1bt9aaNWuUm5uriy66SA899JCeeOIJliKHB5vNprS0NLfQJEl5eXlKS0vTqlWrQlQZgOrCbDYrKytLkud1tsWPMzMzZTabK702AEDw+TXjZDKZdOzYMUVHR0uSa/nxY8eOuV1YVdZFF3r16uX1r3TFFi5c6NGWnJysLVu2+FM2zjJOp1Pp6elex1bxmJ08ebIeffTREFQHoDpJTU1Vdna219ntzMxM7uMEANWIX8HJMAz95S9/8WgrnjGq6Ps4AWVht9s9ZprOZBhGqdsBwB+pqalKSUmR3W6Xw+FQbGysrFYrM00AUM34FZzWrVtXUXUAQeNwOEJdAoCzjNlsVq9evUJdBgCgAvkVnJKTkyuqDiBoWMEKAAAAwebX4hBAVVC80lVJN0UuXukKAAAAKCuCE6qdsqx0NWvWrEqvCwAAAFUXwQnVUvFKVy1atHBrt1gsys7O5j5OAAAA8Itf1zgBVUlpK10VFRWFujwAAABUIQQnVGusdAUAAIBgKHNw8ucmfjabLaBiAAAAACAclTk4NWjQoCLrAAAAAICwVebgtGDBgoqsAwAAAADCFqvqAQAAAIAPAS8OkZ2drddee0179+7VyZMn3bZt2bKl3IUBAAAAQLgIaMbpiSee0IgRI9SsWTNt3bpVl156qRo3bqzvv/9e/fv3D3aNAAAAABBSAQWnuXPn6rnnntNTTz2lyMhITZw4UTk5ORo7dqx+/fXXYNcIAAAAACEVUHDau3evunfvLkmqVauWjh07JkkaOnSoli5dGrzqAAAAACAMBBScYmJidPjwYUlSq1at9NFHH0mSdu/eLcMwglcdAAAAAISBgILTFVdcoVWrVkmSRo4cqXHjxqlPnz4aPHiw/va3vwW1QAAAAAAItYBW1Xvuued0+vRpSdKoUaPUqFEjbdy4UQMHDtSoUaOCWiAAAAAAhFpAwalGjRqqUeP/JqsGDRqkQYMGBa0oAAAAAAgnAQWnDRs2lLq9Z8+eARUDAAAAAOEooODUq1cvjzaTyeT6t9PpDLggAAAAAAg3AS0OceTIEbevgwcP6p133tEll1yitWvXBrtGAAAAAAipgGacGjRo4NHWp08fRUVFady4cfrss8/KXRgAAAAAhIuAZpxK0rRpU+3YsSOYuwQAAACAkAtoxumLL75we2wYhhwOh2bNmqXOnTsHpTAAAAAACBcBBaeLLrpIJpNJhmG4tXft2lUvvvhiUAoDAAAAgHARUHDavXu32+MaNWqoadOmio6ODkpRAAAAABBOAgpOrVq1CnYdAAAAABC2AgpOkvTee+/pvffe08GDB3X69Gm3bZyuBwAAAKA6CSg4zZgxQw8++KC6dOmi2NhYt5vfAgAAAEB1E1BwmjdvnhYuXKihQ4cGux4AAAAACDsB3cfp5MmT6t69e7BrAQAAAICwFFBwuu2227RkyZJg1wIAAAAAYSmgU/VOnDih5557Tv/973914YUXKiIiwm37nDlzglIcAAAAAISDgILTF198oYsuukiS9L///c9tGwtFAAAAAKhuAgpO69atC3YdAAAAABC2ArrGCQAAAADOJgHNOB0/flyzZs0q8Qa433//fVCKAwAAAIBwEFBwuu2227R+/XoNHTqUG+ACAAAAqPYCCk5vv/22Vq9erR49egS7HgAAAAAIOwFd43TOOeeoUaNGwa4FAAAAAMJSQMHpoYce0gMPPKCCgoJg1wMAAAAAYSegU/X+85//aNeuXWrevLkSEhI8boC7ZcuWoBQHAAAAAOEgoOB03XXXBbkMAAAAAAhfAQWnadOmlbjt1KlTARcDAAAAAOEoaDfA3b59u+655x61aNEiWLsEAAAAgLBQruD022+/6YUXXlC3bt104YUX6uOPP9bkyZODVRsAAAAAhIWATtXbuHGjXnjhBS1fvlytW7fW9u3btX79eu7rBAAAAKBa8mvGafbs2TrvvPN0ww03qGnTptq4caO++OILmUwmnXPOORVVIwAAAACElF8zTvfdd58mTZqkBx98UGazuaJqAgAAAICw4teM04MPPqjXX39drVu31qRJk/S///2vouoCAAAAgLDhV3C677779O2332rx4sXKz89X165d1blzZxmGoSNHjlRUjQAAAAAQUgGtqpecnKxFixbJ4XDozjvvVFJSkpKTk9W9e3fNmTMn2DUCAAAAQEiVaznyevXqadSoUfr444+1detWXXrppZo1a1awagMAAOXkdDqVm5urpUuXKjc3V06nM9QlAUCVFLQb4Hbq1EmZmZnKy8sL1i4BAEA52Gw2JSQkqHfv3hoyZIh69+6thIQE2Wy2UJcGAFVO0IJTsYiIiGDvEgAA+MlmsyktLU379+93a8/Ly1NaWhrhCQD8FPTgBAAAQsvpdCo9PV2GYXhsK27LyMjgtD0A8APBCQCAasZut3vMNJ3JMAzt27dPdru9EqsCgKqN4AQAQDXjcDiC2g8AEGBwMpvNOnjwoEf74cOHZTaby10UAAAIXGxsbFD7AQACDE7ezpmWpMLCQkVGRparIAAAUD5Wq1UWi0Umk8nrdpPJpPj4eFmt1kquDACqrpr+dH7iiSck/fGG+8ILL6hu3bqubU6nUxs2bNB5553nVwFz587Vo48+KofDoY4dOyozM7PEN/Lc3Fz17t3bo/3rr7/2+7gAAFRXZrNZWVlZSktLk8lkcvuDZ3GYyszM5CwRAPCDX8Hp8ccfl/THjNO8efPc3nAjIyOVkJCgefPmlXl/y5YtU0ZGhubOnasePXro2WefVf/+/bV9+3a1bNmyxOft2LFD9evXdz1u2rSpPy8DAIBqLzU1VdnZ2UpPT3dbKMJisSgzM1OpqakhrA4Aqh6/gtPu3bslSb1795bNZtM555xTroPPmTNHI0eO1G233Sbpj79+vfvuu3rmmWc0c+bMEp/XrFkzNWzYsEzHKCwsVGFhoevx0aNHJUlFRUUqKioKvHhUacU/e8YAwhVjFMEwcOBADRgwQJs2bVJ+fr5iYmLUrVs3mc3mco8txijCHWMUZeHP+DAZJV2wVAYnT57U7t271bZtW9Ws6VcG08mTJ1W7dm29/vrr+tvf/uZqT09P17Zt27R+/XqP5xSfqpeQkKATJ06oQ4cOuv/++72evlds+vTpmjFjhkf7kiVLVLt2bb9qBgAAAFB9FBQUaMiQIfr111/dzmjzxr+08//9/vvvuuuuu7Ro0SJJ0rfffqs2bdpo7NixiouL0+TJk33u49ChQ3I6nWrevLlbe/PmzZWfn+/1ObGxsXruueeUlJSkwsJCLV68WH/961+Vm5urnj17en3OlClTNH78eNfjo0ePKj4+Xn379vX5zUH1VVRUpJycHPXp00cRERGhLgfwwBhFuGOMItwxRlEWxWejlUVAwWny5Mn6/PPPlZubq6uuusrVfuWVV2ratGllCk7F/rzij2EYJa4C1L59e7Vv3971uFu3btq3b58ee+yxEoNTVFSUoqKiPNojIiL4JQLjAGGPMYpwxxhFuGOMojT+jI2AliNfsWKFnnrqKV1++eVuIadDhw7atWtXmfbRpEkTmc1mj9mlgwcPesxClaZr167auXNnmfsDAAAAgL8CCk4//fSTmjVr5tF+/PjxEmeL/iwyMlJJSUnKyclxa8/JyVH37t3LXMvWrVu5gR/85nQ6JUnZ2dnKzc11PQYAAAC8CSg4XXLJJVq9erXrcXFYev7559WtW7cy72f8+PF64YUX9OKLL+rrr7/WuHHjtHfvXo0aNUrSH9cn3XLLLa7+mZmZWrFihXbu3KmvvvpKU6ZM0fLly3XXXXcF8jJwlrLZbOrUqZMkaeTIka4FR2w2W4grAwAAQLgK6BqnmTNn6qqrrtL27dt16tQpZWVl6auvvtKmTZu8roZXksGDB+vw4cN68MEH5XA4dMEFF2jNmjVq1aqVJMnhcGjv3r2u/idPntSECROUl5enWrVqqWPHjlq9erUGDBgQyMvAWchmsyktLU3R0dFu7Xl5eUpLS1N2djb3NgEAAICHgGacunfvrg8++EAFBQVq27at1q5dq+bNm2vTpk1KSkrya1+jR4/Wnj17VFhYqM8++8xtkYeFCxcqNzfX9XjixIn67rvv9Pvvv+vnn3+W3W4nNKHMnE6n0tPT5W0F/uK2jIwMTtsDAACAh4BmnCSpU6dOruXIgarAbrdr//79JW43DEP79u2T3W5Xr169Kq8wAAAAhL2AZpyAqsjhcAS1HwAAAM4efs041ahRw+eqeSaTSadOnSpXUUBFKOvqi6zSCAAAgD/zKzi98cYbJW778MMP9eSTT3q9fgQIB1arVRaLRXl5eV63m0wmWSwWWa3WSq4MAAAA4c6v4JSSkuLR9s0332jKlClatWqVbrrpJj300ENBKw4IJrPZrKysLKWlpXnMnBY/zszMlNlsDkV5AAAACGMBX+N04MAB3X777brwwgt16tQpbdu2TYsWLVLLli2DWR8QVKmpqcrOzlZcXJxbu8ViYSlyAAAAlMjv4PTrr79q0qRJOvfcc/XVV1/pvffe06pVq3TBBRdURH1A0KWmpurLL7+UJM2fP1/r1q3T7t27CU0AAAAokV+n6s2ePVuPPPKIYmJitHTpUq+n7gFVQfHpeGlpaYqIiAhxNQAAAAh3fgWnyZMnq1atWjr33HO1aNGiEu/jZLPZglIcAAAAAIQDv4LTLbfc4nM5cgAAAACobvwKTgsXLqygMgAAAAAgfAW8qh4AAAAAnC0ITgAAAADgA8EJAAAAAHwgOAEAAACADwQnAAAAAPCB4AQAAAAAPhCcAAAAAMAHv+7jBAAAPDmdTtntdjkcDsXGxspqtcpsNoe6LABAEBGcAAAoB5vNpvT0dO3fv9/VZrFYlJWVpdTU1BBWBgAIJk7VAwAgQDabTWlpaW6hSZLy8vKUlpYmm80WosoAAMFGcAIAIABOp1Pp6ekyDMNjW3FbRkaGnE5nZZcGAKgABCcAAAJgt9s9ZprOZBiG9u3bJ7vdXolVAQAqCsEJAIAAOByOoPYDAIQ3ghMAAAGIjY0Naj8AQHgjOAEAEACr1SqLxSKTyeR1u8lkUnx8vKxWayVXBgCoCAQnAAACYDablZWVJUke4an4cWZmJvdzAoBqguAEAECAUlNTlZ2drRYtWri1WywWZWdncx8nAKhGuAEuAADlkJqaqpSUFNntdjkcDsXGxspqtTLTBADVDMEJAIByMpvN6tWrV6jLAABUIE7VAwAAAAAfCE4AAAAA4APBCQAAAAB8IDgBAAAAgA8sDgGEmNPpZDUuAACAMEdwAkLIZrMpPT1d+/fvd7VZLBZlZWVx/xcAAIAwwql6QIjYbDalpaW5hSZJysvLU1pammw2W4gqAwAAwJ8RnIAQcDqdSk9Pl2EYHtuK2zIyMuR0Oiu7NAAAAHhBcAJCwG63e8w0nckwDO3bt092u70SqwIAAEBJCE5ACDgcjqD2AwAAQMUiOAEhEBsbG9R+AAAAqFgEJyAErFarLBaLTCaT1+0mk0nx8fGyWq2VXBkAAAC8ITgBIWA2m5WVlSVJHuGp+HFmZib3cwIAAAgTBCcgRFJTU5Wdna0WLVq4tVssFmVnZ3MfJwAAgDDCDXCBEEpNTVVKSorsdrscDodiY2NltVqZaQIAAAgzBCcgxMxms3r16hXqMgAAAFAKTtUDAAAAAB8ITgAAAADgA8EJAAAAAHwgOAEAAACADwQnAAAAAPCB4AQAAAAAPhCcAAAAAMAHghMAAAAA+EBwAgAAAAAfCE4AAAAA4APBCQAAAAB8qBnqAgAAQMVxOp2y2+1yOByKjY2V1WqV2WwOdVkAUOUQnAAAqKZsNpvS09O1f/9+V5vFYlFWVpZSU1NDWBkAVD2cqgcAQDVks9mUlpbmFpokKS8vT2lpabLZbCGqDACqJoITAADVjNPpVHp6ugzD8NhW3JaRkSGn01nZpQFAlRXy4DR37ly1bt1a0dHRSkpKkt1uL7X/+vXrlZSUpOjoaLVp00bz5s2rpEoBAKga7Ha7x0zTmQzD0L59+3z+nwsA+D8hDU7Lli1TRkaGpk6dqq1bt8pqtap///7au3ev1/67d+/WgAEDZLVatXXrVt13330aO3asli9fXsmVAwAQvhwOR1D7AQBCHJzmzJmjkSNH6rbbbtP555+vzMxMxcfH65lnnvHaf968eWrZsqUyMzN1/vnn67bbbtOtt96qxx57rJIrBwAgfMXGxga1HwAghKvqnTx5Up999pkmT57s1t63b199+OGHXp+zadMm9e3b162tX79+mj9/voqKihQREeHxnMLCQhUWFroeHz16VJJUVFSkoqKi8r6McnE6ndq0aZPy8/MVExOjbt26sURsJSn+2Yd6DAAlYYyiPLp27apzzz1XBw4c8Hqdk8lkUosWLdS1a9eAxxhjFOGOMYqy8Gd8hCw4HTp0SE6nU82bN3drb968ufLz870+Jz8/32v/U6dO6dChQ17/cjZz5kzNmDHDo33t2rWqXbt2OV5B8NSuXVtHjx7Vu+++G+pSzjo5OTmhLgEoFWMUgSrL2RjB+H+HMYpwxxhFaQoKCsrcN+T3cTKZTG6PDcPwaPPV31t7sSlTpmj8+PGux0ePHlV8fLz69u2r+vXrB1p2uaxatUpDhw71+Ctg8WtYvHixBg4cGIrSzhpFRUXKyclRnz59vM5UAqHGGEUwrFq1SpMmTVJeXp6rzWKxaNasWeX+f4YxinDHGEVZFJ+NVhYhC05NmjSR2Wz2mF06ePCgx6xSsZiYGK/9a9asqcaNG3t9TlRUlKKiojzaIyIiQvJLVLxEbEnp1mQyKSMjQykpKZy2VwlCNQ6AsmKMojxSU1OVkpIiu90uh8Oh2NhYWa3WoP7/whhFuGOMojT+jI2QLQ4RGRmppKQkj+nTnJwcde/e3etzunXr5tF/7dq16tKlS5X5hWCJWABAZTKbzerVq5duvPFG9erViz/KAUCAQrqq3vjx4/XCCy/oxRdf1Ndff61x48Zp7969GjVqlKQ/TrO75ZZbXP1HjRqlH374QePHj9fXX3+tF198UfPnz9eECRNC9RL8xhKxAAAAQNUT0mucBg8erMOHD+vBBx+Uw+HQBRdcoDVr1qhVq1aS/ggPZ97TqXXr1lqzZo3GjRunp59+WnFxcXriiSd0/fXXh+ol+I0lYgEAAICqJ+SLQ4wePVqjR4/2um3hwoUebcnJydqyZUsFV1VxrFarLBaL8vLySlwi1mKxyGq1hqA6AAAAAN6E9FS9s5HZbFZWVpYkz5UAix9nZmZyDjoAAAAQRghOIZCamqrs7Gy1aNHCrd1isSg7O1upqakhqgwAAACANyE/Ve9sVRlLxAIAAAAIDoJTCBUvEQsAAAAgvHGqHgAAAAD4QHACAAAAAB8ITgAAAADgA8EJAAAAAHwgOAEAAACADwQnAAAAAPCB4AQAAAAAPhCcAAAAAMAHghMAAAAA+EBwAgAAAAAfaoa6AKAiOZ1O2e12ORwOxcbGymq1ymw2h7osAAAAVDEEJ1RbNptN6enp2r9/v6vNYrEoKytLAwcODGFlAAAAqGoITqiWbDab0tLSZBiGW3teXp7S0tKUnZ3NzBMAAADKjGucUO04nU6lp6d7hCZJrrZJkyZJkrKzs5Wbmyun01mpNQIAAKBqITih2rHb7W6n5/2ZYRjKy8uTJI0cOVK9e/dWQkKCbDZbZZUIAACAKobghGrH4XD4/ZziU/gITwAAAPCG4IRqJzY21u/nFJ/Cl5GRwWl7AAAA8EBwQrVjtVplsVhkMpn8ep5hGNq3b5/sdnsFVQYAAICqiuCEasdsNisrK0uS/A5PUmCn+gEAAKB6IzihWkpNTVV2drZatGjh93MDOdUPAAAA1RvBCdVWamqq9uzZo3Xr1mnJkiX673//W+opfCaTSfHx8bJarZVcKQAAAMIdN8BFtWY2m9WrVy/X46ysLKWlpXmEp+LHmZmZ3BgXAAAAHphxwlml+BS+uLg4t3aLxaLs7GylpqaGqDIAAACEM4ITzjqpqan68ssvJUnz58/XunXrtHv3bkITAAAASsSpejgrFZ+Ol5aWpoiIiBBXAwAAgHDHjBMAAAAA+EBwAgAAAAAfCE4AAAAA4APBCQAAAAB8IDgBAAAAgA8EJ5yVnE6nJCk7O1u5ubmuxwAAAIA3BCecdWw2mzp16iRJGjlypHr37q2EhATZbLYQVwYAAIBwRXDCWcVmsyktLU15eXlu7Xl5eUpLSyM8AQAAwCuCE84aTqdT6enpMgzDY1txW0ZGBqftAQAAwEPNUBdwNjt58qTmzp2rXbt2qW3btho9erQiIyNDXVa14nQ6Zbfb5XA49OOPP2r//v0l9jUMQ/v27ZPdblevXr1CUmNsbKysVqvMZnOlHR8AAAC+EZxCZOLEiZozZ47b7MaECRM0fvx4zZ49O4SVVR82m03p6emlhiVvHA5HBVXkyVuNFotFWVlZSk1NrbQ6AAAAUDpO1QuBiRMn6tFHH/U4JczpdOrRRx/VxIkTQ1RZ9VF8LZO/oUmSYmNjK6AiTyXVyPVWAAAA4YfgVMlOnjypOXPmlNpnzpw5OnnyZCVVVP2Udi1TaUwmk+Lj42W1Wiuosv/D9VYAAABVC8Gpks2dO9fnh2Gn06m5c+dWUkXVj91u93umyWQySZIyMzMr5foiXzWeeb0VAAAAQo/gVMl27doV1H7wFMg1ShaLRdnZ2ZV2XVFZa6zM660AAABQMhaHqGRt27YNaj948ucapfnz54dkJbuy1lhZ11sBAACgdMw4VbLRo0f7/IBuNps1evToSqqo+rFarbJYLK7T7/7MZDLJYrFIktLS0tSrV69KX/67LDVW1vVWAAAA8I3gVMkiIyM1fvz4UvuMHz+e+zmVg9lsVlZWliR5BJPix7Nmzar0us5Ulhor63orAAAA+EZwCoHZs2fr3nvv9fhQbDabde+993IfpyBITU1Vdna2WrRo4dZefC3TwIEDQ1TZ//FVI/dxAgAACB9c4xQis2fP1r/+9S/NnTtXu3btUtu2bTV69GhmmoIoNTVVKSkpstvtcjgcbtcyFRUVhbo8SaXXCAAAgPBBcAqhyMhIZWRkhLqMas1sNqtXr16hLqNUVaFGAACAsx2n6gEAAACADwQnAAAAAPCB4AQAAAAAPhCcAAAAAMAHghMAAAAA+EBwAgAAAAAfCE4AAAAA4APBCQAAAAB8IDgBAAAAgA8EJwAAAADwgeAEAAAAAD4QnAAAAADAB4ITAAAAAPhQM9QFVDbDMCRJR48eDXElCKWioiIVFBTo6NGjioiICHU5gAfGKMIdYxThjjGKsijOBMUZoTRnXXA6duyYJCk+Pj7ElQAAAAAIB8eOHVODBg1K7WMyyhKvqpHTp0/rwIEDqlevnkwmU6jLQYgcPXpU8fHx2rdvn+rXrx/qcgAPjFGEO8Yowh1jFGVhGIaOHTumuLg41ahR+lVMZ92MU40aNWSxWEJdBsJE/fr1eTNFWGOMItwxRhHuGKPwxddMUzEWhwAAAAAAHwhOAAAAAOADwQlnpaioKE2bNk1RUVGhLgXwijGKcMcYRbhjjCLYzrrFIQAAAADAX8w4AQAAAIAPBCcAAAAA8IHgBAAAAAA+EJwAAAAAwAeCE6qcuXPnqnXr1oqOjlZSUpLsdnup/devX6+kpCRFR0erTZs2mjdvnkefzMxMtW/fXrVq1VJ8fLzGjRunEydOlOu4OLuFYpxOnz5dJpPJ7SsmJiborw3VQ7DHaFFRkR588EG1bdtW0dHR6ty5s955551yHxdnr1CMUd5HUSoDqEJeffVVIyIiwnj++eeN7du3G+np6UadOnWMH374wWv/77//3qhdu7aRnp5ubN++3Xj++eeNiIgIIzs729Xn5ZdfNqKiooxXXnnF2L17t/Huu+8asbGxRkZGRsDHxdktVON02rRpRseOHQ2Hw+H6OnjwYIW/XlQ9FTFGJ06caMTFxRmrV682du3aZcydO9eIjo42tmzZEvBxcfYK1RjlfRSlITihSrn00kuNUaNGubWdd955xuTJk732nzhxonHeeee5td1xxx1G165dXY/HjBljXHHFFW59xo8fb1x++eUBHxdnt1CN02nTphmdO3cuZ/U4G1TEGI2NjTWeeuoptz4pKSnGTTfdFPBxcfYK1RjlfRSl4VQ9VBknT57UZ599pr59+7q19+3bVx9++KHX52zatMmjf79+/bR582YVFRVJki6//HJ99tln+uSTTyRJ33//vdasWaOrr7464OPi7BWqcVps586diouLU+vWrXXDDTfo+++/D9ZLQzVRUWO0sLBQ0dHRbn1q1aqljRs3BnxcnJ1CNUaL8T6KkhCcUGUcOnRITqdTzZs3d2tv3ry58vPzvT4nPz/fa/9Tp07p0KFDkqQbbrhBDz30kC6//HJFRESobdu26t27tyZPnhzwcXH2CtU4laTLLrtML730kt599109//zzys/PV/fu3XX48OEgv0pUZRU1Rvv166c5c+Zo586dOn36tHJycrRy5Uo5HI6Aj4uzU6jGqMT7KEpHcEKVYzKZ3B4bhuHR5qv/me25ubn697//rblz52rLli2y2Wx666239NBDD5XruDi7hWKc9u/fX9dff706deqkK6+8UqtXr5YkLVq0KCivCdVLsMdoVlaW2rVrp/POO0+RkZG66667NGLECJnN5nIdF2evUIxR3kdRmpqhLgAoqyZNmshsNnv8tengwYMef2UqFhMT47V/zZo11bhxY0nSP//5Tw0dOlS33XabJKlTp046fvy4/vGPf2jq1KkBHRdnr1CN0xo1PP8OVqdOHXXq1Ek7d+4MxktDNVFRY7Rp06ZasWKFTpw4ocOHDysuLk6TJ09W69atAz4uzk6hGqPe8D6KMzHjhCojMjJSSUlJysnJcWvPyclR9+7dvT6nW7duHv3Xrl2rLl26KCIiQpJUUFDg8aHTbDbL+GPxlICOi7NXqMapN4WFhfr6668VGxsb6MtBNVRRY7RYdHS0WrRooVOnTmn58uVKSUkJ+Lg4O4VqjHrD+yjchGJFCiBQxcuTzp8/39i+fbuRkZFh1KlTx9izZ49hGIYxefJkY+jQoa7+xcuTjhs3zti+fbsxf/58j+VJp02bZtSrV89YunSp8f333xtr16412rZtawwaNKjMxwXOFKpxes899xi5ubnG999/b3z00UfGNddcY9SrV49xCg8VMUY/+ugjY/ny5cauXbuMDRs2GFdccYXRunVr48iRI2U+LlAsVGOU91GUhuCEKufpp582WrVqZURGRhoXX3yxsX79ete2YcOGGcnJyW79c3NzjcTERCMyMtJISEgwnnnmGbftRUVFxvTp0422bdsa0dHRRnx8vDF69Gi3N1JfxwX+LBTjdPDgwUZsbKwRERFhxMXFGampqcZXX31VkS8TVViwx2hubq5x/vnnG1FRUUbjxo2NoUOHGnl5eX4dFzhTKMYo76MojckwSjjHAwAAAAAgiWucAAAAAMAnghMAAAAA+EBwAgAAAAAfCE4AAAAA4APBCQAAAAB8IDgBAAAAgA8EJwAAAADwgeAEAAAAAD4QnAAA1dr777+v8847T6dPnw51KQE7ePCgmjZtqry8vFCXAgBnLYITAKBchg8fLpPJpFmzZrm1r1ixQiaTyfU4NzdXJpNJJpNJNWrUUIMGDZSYmKiJEyfK4XC4+nXq1Em33Xab12MtXbpUERER+vHHH137++WXX0qtb+LEiZo6dapq1Cj5v7yvvvpK119/vRISEmQymZSZmenzdS9cuFANGzb02a8snnvuOfXq1Uv169f3+pqaNWumoUOHatq0aUE5HgDAfwQnAEC5RUdH65FHHtGRI0d89t2xY4cOHDigTz/9VJMmTdJ///tfXXDBBfryyy8lSSNHjtRrr72mgoICj+e++OKLuuaaa9S8efMy1fXhhx9q586d+vvf/15qv4KCArVp00azZs1STExMmfYdTAUFBbrqqqt03333ldhnxIgReuWVV8r0PQYABB/BCQBQbldeeaViYmI0c+ZMn32bNWummJgY/eUvf9ENN9ygDz74QE2bNtWdd94pSRo6dKgKCwv1+uuvuz1v7969ev/99zVy5Mgy1/Xqq6+qb9++io6OLrXfJZdcokcffVQ33HCDoqKifO43NzdXI0aM0K+//uqaRZs+fbok6ciRI7rlllt0zjnnqHbt2urfv7927txZ6v4yMjI0efJkde3atcQ+nTp1UkxMjN544w2f9QEAgo/gBAAoN7PZrIcfflhPPvmk9u/f79dza9WqpVGjRumDDz7QwYMH1bhxY6WkpGjBggVu/RYsWKDmzZurf//+Zd73hg0b1KVLF7/qKYvu3bsrMzNT9evXl8PhkMPh0IQJEyT9ceri5s2b9eabb2rTpk0yDEMDBgxQUVFRuY976aWXym63l3s/AAD/EZwAAEHxt7/9TRdddFFA1+Gcd955kqQ9e/ZIkm699VZt2LBB33//vSTJMAwtXLhQw4cPl9lsLvN+9+zZo7i4OL/r8SUyMlINGjSQyWRSTEyMYmJiVLduXe3cuVNvvvmmXnjhBVmtVnXu3FmvvPKK8vLytGLFinIft0WLFq7vEQCgchGcAABB88gjj2jRokXavn27X88zDEOSXItJ9O3bVxaLxTXr9P7772vPnj0aMWKEX/v9/fff3U7T27t3r+rWrev6evjhh/3any9ff/21atasqcsuu8zV1rhxY7Vv315ff/11ufdfq1Ytr9d+AQAqHsEJABA0PXv2VL9+/Upd5MCb4lCRkJAgSapRo4aGDx+uRYsW6fTp01qwYIF69uypdu3a+bXfJk2auC2mEBcXp23btrm+Ro0a5df+fCkOgN7az1xhMFA///yzmjZtWu79AAD8R3ACAATVrFmztGrVKn344Ydl6v/777/rueeeU8+ePd1CwYgRI7R//37ZbDbZbDa/FoUolpiY6Db7VbNmTZ177rmur0aNGvm9z2KRkZFyOp1ubR06dNCpU6f08ccfu9oOHz6sb7/9Vueff37Axyr2v//9T4mJieXeDwDAfwQnAEBQderUSTfddJOefPJJr9sPHjyo/Px87dy5U6+++qp69OihQ4cO6ZlnnnHr17p1a11xxRX6xz/+oYiICKWlpfldS79+/bRx40af/U6ePOmahTp58qTy8vK0bds2fffddyU+JyEhQb/99pvee+89HTp0SAUFBWrXrp1SUlJ0++23a+PGjfr888918803q0WLFkpJSSlxX/n5+W7H+/LLL7Vt2zb9/PPPrj4FBQX67LPP1LdvXz++AwCAYCE4AQCC7qGHHirxtLX27dsrLi5OSUlJmjVrlq688kr973//U4cOHTz6jhw5UkeOHNENN9yg2rVr+13HzTffrO3bt2vHjh2l9jtw4IASExOVmJgoh8Ohxx57TImJiSXeiFf6Y2W9UaNGafDgwWratKlmz54t6Y/V/5KSknTNNdeoW7duMgxDa9asUURERIn7mjdvnhITE3X77bdL+uOUx8TERL355puuPitXrlTLli1ltVr9+RYAAILEZJT0PxsAANXAxIkT9euvv+rZZ58NdSnlcumllyojI0NDhgwJdSkAcFZixgkAUK1NnTpVrVq18rgeqSo5ePCg0tLSdOONN4a6FAA4azHjBAAAAAA+MOMEAAAAAD4QnAAAAADAB4ITAAAAAPhAcAIAAAAAHwhOAAAAAOADwQkAAAAAfCA4AQAAAIAPBCcAAAAA8IHgBAAAAAA+/D+eFbcXZa8JegAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10,6))\n",
    "# ax.scatter(NDVI_ts.index, NDVI_ts[0], color='red')\n",
    "ax.scatter(df['NDVI'].values, head_studyareas['Rin_11R'].values, c='black',label = 'NDVI Range')\n",
    "ax.scatter(df['NDVI'].values, head_studyareas['Rin_2R'].values, c='black',label = 'NDVI Range')\n",
    "ax.scatter(df['NDVI'].values, head_studyareas['Rin_12R'].values, c='black',label = 'NDVI Range')\n",
    "# ax2.hlines(y=0.113, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "# ax2.hlines(y=0, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "# ax2.hlines(y=0.0915, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "# ax.axvspan(\"2017-06-10T12:00:00.000Z\", \"2018-06-16T00:00:00.000Z\", color='grey', alpha=0.5, lw=0, label=\"Satellite relaunch gap\")\n",
    "ax.set(title=\"NDVI vs. Head Values at the Rio Grande\")\n",
    "# ax.legend(loc = 'lower left')\n",
    "ax.grid(zorder = 0)\n",
    "ax.set_ylabel('Net Annual Pressure Head (m)')\n",
    "ax.set_xlabel('NDVI (-1 to 1)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rin_11R</th>\n",
       "      <th>Rin_2R</th>\n",
       "      <th>Rin_12R</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010.0</th>\n",
       "      <td>0.021</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011.0</th>\n",
       "      <td>0.061</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>0.113</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013.0</th>\n",
       "      <td>0.098</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014.0</th>\n",
       "      <td>0.113</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015.0</th>\n",
       "      <td>0.088</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016.0</th>\n",
       "      <td>0.113</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>0.065</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Rin_11R  Rin_2R  Rin_12R\n",
       "Year                            \n",
       "2010.0    0.021   0.001    0.003\n",
       "2011.0    0.061   0.046    0.033\n",
       "2012.0    0.113   0.113    0.074\n",
       "2013.0    0.098   0.080    0.072\n",
       "2014.0    0.113   0.097    0.113\n",
       "2015.0    0.088   0.056    0.083\n",
       "2016.0    0.113   0.095    0.082\n",
       "2017.0    0.065   0.042    0.011"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = seepage_studyareas['Rin_11R'].values\n",
    "y = seepage_studyareas.iloc[:-1,:]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'NDVI (-1 to 1)')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/vkhPT3fWExwc7PEe11tuucXje30ZbzfccIOsVqvz9eWXXy5Jzq/vL7/8ot27d6t///4uM7kNGjRQhw4dXPbl7ecZgJMIVwCcbr/9drVu3Vrjxo1zuz/HW0X/edevX9/ZZrPZdOmll2r+/PmSpO+++04bN27U4MGDS7xEqyR33323/v33X7355puSTv4gmpub67wkUJI6deqkd9991/kDalRUlFq0aKFFixad1TElKTg4WD169NCTTz6plStXaufOnYqPj9f777/vvK9sz549OnDggEJCQlx+kAoODlZeXp7zcqui5byvuuoqt36LFy929isSFBTkDI5F6tWrJ0nav3+/JGnHjh2y2WzKyclRenq67Ha7vvrqKz3//POS5LyEp6i/p1XvTm/bs2ePvvvuO7caq1evLsMw3Or0pFKlSmrTpo3LnyZNmhTb/++//5ZhGB7vcSsaW0Xn0KFDB1WpUkUfffSRfvvtN23fvt0Zrr788ksdOXJEH330kRo2bKjY2Ngz1pqUlKQLLrjAOV4zMzO1Z88el4UsZs6cqXvvvVdt27bV0qVL9cUXX+irr77S9ddfb9plUt6OD4vFoo8//lg9evTQ008/rdatW6tOnTp68MEHdfjw4RKP0b9/fz333HMaOnSoVq5cqfXr1+urr75SnTp1SvVyr9PHsXRyUYlTj7lnzx4tX77c7dwvu+wySfJq3DVq1EhfffWV1q9fr7fffltXXHGFpk6d6vzcKI4v48+TouB4+iMu+vfv7/zlQnErpHo6pq/j7fSvb2hoqCT37/+iz49Tnd7m7ecZgJNYfgaAk8Vi0VNPPaVu3bq5rYjnDcMwtHz5clWtWlVt2rRx2Xb33XdrzJgxWr9+vd544w1VqlRJgwYNOutamzdvrquvvlrz58/XPffco/nz56t+/frq3r27S7+EhAQlJCQoPz9fX3zxhaZOnar+/fsrJiZG7du3P+vjF6lVq5ZSUlK0evVq/fDDD+rVq5dq166tWrVq6cMPP/T4nurVq0s6eR+XJC1ZskQNGjQ447FOnDih/fv3u/zglJeX56xDkt59910dPXpUGRkZLvs8fWGDov6enteUl5fnMntVu3ZtVa5cudgl9YvOw0wXXXSRKlWqpNzcXLdtu3fvdjluSEiIrrnmGn300UeKiopSvXr11LJlSzVs2FDSyYUCPv74Y6+frVS5cmX169dPL730knJzczVv3jxVr17duaiDJL322muKj4/X7NmzXd57pjAjyblCXX5+vvOHXsk9LPgyPho0aOCcCfz111/11ltvaeLEiTp+/LjmzJnj8T0HDx7U+++/rwkTJmjMmDHO9vz8fOc9Pv5Uu3ZtXX755XryySc9bj/1FzjFKVqoRToZUrt06aLLLrtMKSkp6t27t8us1Kl8GX+eFH2Gvvfeexo1apSzvW7duqpbt66kk58Dnp5z5ekXTucy3jwp+v4v+vw41elt3n6eATiJmSsALrp27apu3bpp8uTJOnLkiE/vnTRpkjZv3qzk5GTnD5BFBg4cqKCgIL3wwgt6/fXXdd1113kVKEoyePBgffnll1q7dq2WL1/uXGzAk9DQUHXu3FlPPfWUJJ1xCeTTFRQUFPub6qLLcop+2Ovdu7f2798vh8PhNltz6oxNjx49FBQUpN9//91jv9MDqiS9/vrrLq+LnslUtLpf0Q9mp/7QbhiGXnrpJZf3tW3bVqGhoW435n/xxRduv23v3bu3fv/9d9WqVctjjaXxwNGqVauqbdu2ysjIcPnNfGFhoV577TVFRUXp0ksvdbZ37dpVGzZs0NKlS52X/lWtWlXt2rXTs88+q927d3t1SWCRIUOGyOFwaPr06crMzNTtt9+uKlWqOLdbLBaXr7F0ckY2Ozv7jPsu+np99913Lu3Lly93eX0240OSLr30Uo0fP14tW7Z0PpzbE4vFIsMw3M7j5Zdfdlts5PSZjzPxtb8nvXv31g8//KBGjRp5PHdvwtXpatWqpWnTpmnPnj169tlni+3n6/g73c0336zmzZtrypQpJT5E3VvnMt48adKkiSIiIrRo0SKXS7n/+OMP5+W1Rbz9PANwEjNXANw89dRTiouL0969e52X4JzqwIEDzmV9jx49ql9++UVvvvmm7Ha7brvtNk2aNMntPfXq1VOvXr00f/58GYZhyrOC+vXrp9TUVPXr10/5+fluM2GPPfaYdu3apeuuu05RUVE6cOCA856Hzp07O/sFBQWpc+fO+vjjj4s91sGDBxUTE6Nbb71VXbt2VXR0tI4cOaLVq1crPT1dzZo1U2JioqSTl1e+/vrr6tWrl5KTk3X11VcrODhYu3bt0qeffqqEhATdfPPNiomJ0eTJkzVu3Dht3bpV119/vS666CLt2bNH69evV9WqVV2+liEhIfq///s/HTlyRFdddZXWrVunJ554Qj179nQ+jLdbt24KCQlRv379NHr0aP3777+aPXu2/v77b5fzqVmzplJTUzV16lRddNFFuvnmm7Vr1y5NmjRJERERLkvLp6SkaOnSperUqZNGjhypyy+/XIWFhdqxY4dWrVqlhx56SG3btj3rf8fiTJ06Vd26dVOXLl00atQohYSEaNasWfrhhx+0aNEil9/wX3fddXI4HPr444/1yiuvONu7du2qCRMmyGKx6Nprr/X62G3atNHll1+utLQ0j+O1d+/eevzxxzVhwgR17txZv/zyiyZPnqzY2NgzrkbZq1cv1axZU0OGDNHkyZMVFBSkBQsWaOfOnS79vB0f3333ne6//37deuutuuSSSxQSEqJPPvlE3333ncuM1Olq1KihTp06afr06apdu7ZiYmK0Zs0azZ07VxdeeKFL3xYtWkiSXnzxRVWvXl1hYWGKjY31eHmfJLVs2VLSyXuMBg4cqODgYDVp0sSnWY7JkycrKytLHTp00IMPPqgmTZro33//1fbt25WZmak5c+YoKirK6/0VueuuuzRz5kzNmDFD9913n2rUqOGxny/j73RWq1XvvvuuevTooauvvlrDhg1TfHy8LrroIh04cEBffvmlvv3222KXaT/duYw3TypVqqTHH39cQ4cO1c0336xhw4bpwIEDmjhxottlgd5+ngH4//lpIQ0AAeDU1QJP179/f0OSx9UC9f+v9maxWIxq1aoZTZo0MQYMGGCsXLmyxOMtW7bMkGTUrFnTbRWtU/d/ptUCPdV56spzRd5//32jZ8+eRmRkpBESEmLUrVvX6NWrl2G321366bTV8TzJz883ZsyYYfTs2dO4+OKLjdDQUCMsLMxo1qyZMXr0aGP//v0u/QsKCowZM2YYV1xxhREWFmZUq1bNaNq0qXHPPfcYW7Zscen77rvvGl26dDFq1KhhhIaGGg0aNDCSkpKMjz76yNln4MCBRtWqVY3vvvvOiI+PNypXrmzUrFnTuPfee40jR4647G/58uXO40ZGRhr//e9/jQ8++MBttbfCwkLjiSeeMKKiooyQkBDj8ssvN95//33jiiuucFvx7siRI8b48eONJk2aGCEhIcYFF1xgtGzZ0hg5cqSRl5dX4teuqPaSeFqtzTAMw263G9dee61RtWpVo3Llyka7du2M5cuXu72/sLDQqF27tiHJyMnJcbZ//vnnhiSjdevWJR7fk/T0dEOS0bx5c7dt+fn5xqhRo4zIyEgjLCzMaN26tfHuu+8aAwcOdFlpzTDcVws0jJOrt3Xo0MGoWrWqERkZaUyYMMF4+eWXPa6wd6bxsWfPHmPQoEFG06ZNjapVqxrVqlUzLr/8cuN///ufy4pxnuzatcu45ZZbjIsuusioXr26cf311xs//PCD0aBBA2PgwIEufdPS0ozY2FjDarV6/Lc63dixY4369esblSpVchl7xX2Pd+7c2e378M8//zQefPBBIzY21ggODjZq1qxpxMXFGePGjXMb9572d/rnV5EVK1YYkpyrSZ7r+CvOwYMHjSlTphhXXXWVUaNGDSMoKMioW7eu0a1bN+P55583jh496uxbtFrg22+/7bYfb8db0XlMnz7dbR+exuHLL79sXHLJJUZISIhx6aWXGvPmzfM4hn35PAMqOothePmAEgCA3wwaNEhLlizx+VJNX23btk1NmzbVhAkT9Mgjj5TqsQAAON9wWSAAVFDffvutFi1apA4dOqhGjRr65Zdf9PTTT6tGjRqmXLYJAEBFQ7gCgAqqatWq+vrrrzV37lwdOHBAF1xwgeLj4/Xkk096XKIdAACUjMsCAQAAAMAELMUOAAAAACYgXAEAAACACQhXAAAAAGACFrTwoLCwULt371b16tVLfEggAAAAgPObYRg6fPiw6tevr0qVSp6bIlx5sHv3bkVHR/u7DAAAAAABYufOnYqKiiqxD+HKg+rVq0s6+QWsUaOGn6uBPxQUFGjVqlXq3r27goOD/V0O4BHjFIGOMYpAxxiFNw4dOqTo6GhnRigJ4cqDoksBa9SoQbiqoAoKClSlShXVqFGDD1sELMYpAh1jFIGOMQpfeHO7EAtaAAAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJgvxdAEp2/PhxzZo1S7///rsaNWqkESNGKCQkxN9loQwxBgCUNofDIbvdrtzcXEVERMhms8lqtfq7LAAodwhXAWz06NGaOXOmHA6Hs23UqFFKTU3V008/7cfKUFYYAwBKW0ZGhpKTk7Vr1y5nW1RUlNLT05WYmOjHygCg/OGywAA1evRoTZ8+3eWHaunkbxenT5+u0aNH+6kylBXGAIDSlpGRoaSkJJdgJUk5OTlKSkpSRkaGnyoDgPKJcBWAjh8/rpkzZ5bYZ+bMmTp+/HgZVYSyxhgAUNocDoeSk5NlGIbbtqK2lJQUt1/wAACKR7gKQLNmzTrjf2YOh0OzZs0qo4pQ1hgDAEqb3W53m7E6lWEY2rlzp+x2exlWBQDlG+EqAP3++++m9kP5wxgAUNpyc3NN7QcAIFwFpEaNGpnaD+UPYwBAaYuIiDC1HwCAcBWQRowYccYlcK1Wq0aMGFFGFaGsMQYAlDabzaaoqChZLBaP2y0Wi6Kjo2Wz2cq4MgAovwhXASgkJESpqakl9klNTeVZR+cxxgCA0ma1WpWeni5JbgGr6HVaWhrPuwIAHxCuAtTTTz+t//73v27/qVmtVv33v//lGUcVAGMAQGlLTEzUkiVLFBkZ6dIeFRWlJUuW8JwrAPARDxEOYE8//bSeeOIJzZo1S7///rsaNWqkESNGMFtRgTAGAJS2xMREJSQkyG63Kzc3VxEREbLZbMxYAcBZIFwFuJCQEKWkpPi7DPgRYwBAabNarYqPj/d3GQBQ7nFZIAAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACbwe7iaNWuWYmNjFRYWpri4ONnt9mL75ubmqn///mrSpIkqVaqklJQUj/2WLl2q5s2bKzQ0VM2bN9c777xTStUDAAAAwEl+DVeLFy9WSkqKxo0bp2+++UY2m009e/bUjh07PPbPz89XnTp1NG7cOF1xxRUe+2RnZ6tv374aMGCAvv32Ww0YMEC33Xabvvzyy9I8FQAAAAAVXJA/Dz5z5kwNGTJEQ4cOlSSlpaVp5cqVmj17tqZOnerWPyYmRunp6ZKkefPmedxnWlqaunXrprFjx0qSxo4dqzVr1igtLU2LFi3y+J78/Hzl5+c7Xx86dEiSVFBQoIKCgrM/QZRbRf/u/PsjkDFOEegYowh0jFF4w5fx4bdwdfz4cW3YsEFjxoxxae/evbvWrVt31vvNzs7WyJEjXdp69OihtLS0Yt8zdepUTZo0ya191apVqlKlylnXgvIvKyvL3yUAZ8Q4RaBjjCLQMUZRkmPHjnnd12/hat++fXI4HAoPD3dpDw8PV15e3lnvNy8vz+d9jh07Vqmpqc7Xhw4dUnR0tLp3764aNWqcdS0ovwoKCpSVlaVu3bopODjY3+UAHjFOEegYowh0jFF4o+iqNm/49bJASbJYLC6vDcNwayvtfYaGhio0NNStPTg4mG+0Co4xgPKAcYpAxxhFoGOMoiS+jA2/havatWvLarW6zSjt3bvXbebJF/Xq1TN9nzi/ORwO2e125ebmKiIiQjabzd8lAQAAoBzy22qBISEhiouLc7vGNSsrSx06dDjr/bZv395tn6tWrTqnfeL8lZGRoZiYGHXp0kX9+/dXly5dFBMTo+XLl/u7NAAAAJQzfr0sMDU1VQMGDFCbNm3Uvn17vfjii9qxY4eGDx8u6eS9UDk5OVq4cKHzPZs2bZIkHTlyRH/++ac2bdqkkJAQNW/eXJKUnJysTp066amnnlJCQoKWLVumjz76SGvXri3z80Ngy8jIUFJSkgzDcGnPycnRgAED9MYbb/ipMgAAAJRHfg1Xffv21f79+zV58mTl5uaqRYsWyszMVIMGDSSdfGjw6c+8atWqlfPvGzZs0BtvvKEGDRpo+/btkqQOHTrozTff1Pjx4/Xoo4+qUaNGWrx4sdq2bVtm54XA53A4lJyc7BasJLm0ORwOrsEGAACAV/y+oMWIESM0YsQIj9sWLFjg1ubph+HTJSUlKSkp6VxLCwie7geyWq3+Lqvcs9vt2rVrV7Hbi8ZZdna2unTpUlZlAQAAoBzze7hC8TIyMpScnOwSAqKiopSenq7ExEQ/Vlb+5ebmetXvXB4LAAAAgIrFbwtaoGRF9wOdPruSk5OjpKQkZWRk+Kmy80NERIRX/erVq1fKlQAAAOB8QbgKQN7cD5SSkiKHw1HWpZ03bDaboqKiin3+WVF7+/bty7IsAAAAlGOEqwDkzf1AO3fulN1uL8Oqzi9Wq1Xp6emS3B86fepr7m8DAACAtwhXAcjb+4G87QfPEhMTtWTJEkVGRrq0R0VF6dVXX/VTVQAAACivWNAiAHl7P5C3/VC8xMREJSQkuK3IWFhYqMzMTH+XBwAAgHKEcBWAiu4HysnJ8XjflcViUVRUlGw2mx+qO/9YrVbFx8e7tBUWFvqnGADwAx77AQDm4LLAAOTN/UBpaWn8xwcAOGcZGRmKiYlRly5d1L9/f3Xp0kUxMTGsSgsAZ4FwFaBKuh9oyZIlPOcKAHDOeOwHAJiLywIDWHH3AzFjBQA4V2d67IfFYlFKSooSEhL4fwcAvES4CnCe7gcCAOBc+fLYD/4fAgDvcFkgAAAVEI/9AADzEa4AAKiAeOwHAJiPcAUAQAVU9NiP01elLWKxWBQdHc1jPwDAB4QrAAAqIB77AQDmI1wBAFBB8dgPADAXqwUCAFCB8dgPADAP4QoAgAqOx34AgDm4LBAAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATBPm7ACCQrV27Vnl5eYqIiJDNZpPVavV3SQAAAAhQhCvAg+XLl8tqteqGG27QP//8I0mKiopSenq6EhMT/VwdAAAAAhGXBQKnycjI0IABA9zac3JylJSUpIyMDD9UBQAAgEBHuAJO4XA4lJycLMMw3LYVtaWkpMjhcJR1aQAAAAhwhCvgFHa7Xbt27Sp2u2EY2rlzp+x2exlWBQAAgPKAcAWcIjc319R+AAAAqDgIV8ApIiIiTO0HAACAioNwBZzCZrMpKipKFovF43aLxaLo6GjZbLYyrgwAAACBjnAFnMJqtSo9Pd3jtqLAlZaWxvOuAAAA4IbnXAGnKe45VlFRUUpLS+M5VwAAAPCIcAV40KdPH2VmZmrFihXKy8tTRESEbDYbM1YAAAAoFuEKKME111yj4OBgf5cBAACAcoB7rgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAEzAc66AEqxdu5aHCAMAAMArhCvAg+XLl8tqteqGG27QP//8I0mKiopSenq6EhMT/VwdAAAAAhGXBQKnycjI0IABA9zac3JylJSUpIyMDD9UBQAAgEBHuAJO4XA4lJycLMMw3LYVtaWkpMjhcJR1aQAAAAhwhCvgFHa7Xbt27Sp2u2EY2rlzp+x2exlWBQCly+FwaPXq1Vq0aJFWr17NL5AA4CxxzxVwitzcXFP7AUCgy8jIUHJysssvlrjHFADODjNXwCkiIiJM7QcAgSwjI0NJSUluM/bcYwoAZ4dwBZzCZrMpKipKFovF43aLxaLo6GjZbLYyrgwAzMU9pgBgPsIVcAqr1ar09HSP24oCV1paGs+7AlDucY8pAJiPe66A0xR3j0FUVJTS0tK4BwHAeYF7TAHAfIQrwIM+ffooMzNTK1asUF5eniIiImSz2ZixAnDe4B5TADAf4QoVnsPhkN1uV25urjNEFbnmmmsUHBzsx+oAoHQU3WOak5Pj8b4ri8WiqKgo7jEFAB9wzxUqtIyMDMXExKhLly7q37+/unTpopiYGC1fvtzfpQFAqTr1HtPTF/HhHlMAODt+D1ezZs1SbGyswsLCFBcXd8YbZ9esWaO4uDiFhYWpYcOGmjNnjluftLQ0NWnSRJUrV1Z0dLRGjhypf//9t7ROAeVUSUsQDxgwwE9VAUDZSUxM1JIlSxQZGenSHhUVpSVLlnCPKQD4yK+XBS5evFgpKSmaNWuWOnbsqBdeeEE9e/bU5s2bdfHFF7v137Ztm3r16qVhw4bptdde0+eff64RI0aoTp06uuWWWyRJr7/+usaMGaN58+apQ4cO+vXXXzVo0CBJ0v/+97+yPD0EMG+WIC7qx2WBAM5niYmJSkhIcLs8mhkrAPCdX8PVzJkzNWTIEA0dOlTSyRmnlStXavbs2Zo6dapb/zlz5ujiiy9WWlqaJKlZs2b6+uuvNWPGDGe4ys7OVseOHdW/f39JUkxMjPr166f169cXW0d+fr7y8/Odrw8dOiRJKigoUEFBgSnnisCydu1a7d+/X5UrV/a4PSwsTJK0bt067jdAwCr6fOJzCmbo2LGj8++FhYUqLCw8530yRhHoGKPwhi/jw2/h6vjx49qwYYPGjBnj0t69e3etW7fO43uys7PVvXt3l7YePXpo7ty5KigoUHBwsK655hq99tprWr9+va6++mpt3bpVmZmZGjhwYLG1TJ06VZMmTXJrX7VqlapUqXIWZ4fyYNGiRWfsc/jwYWVmZpZBNcDZy8rK8ncJQIkYowh0jFGU5NixY1739Vu42rdvnxwOh8LDw13aw8PDlZeX5/E9eXl5HvufOHFC+/btU0REhG6//Xb9+eefuuaaa2QYhk6cOKF7773XLcSdauzYsUpNTXW+PnTokKKjo9W9e3fVqFHjHM4SgWrt2rW64YYbit1euXJlzZs3T9WrV2fmCgGroKBAWVlZ6tatG5evIiAxRhHoGKPwRtFVbd7w+1Lsp69QZBiGW9uZ+p/avnr1aj355JOaNWuW2rZtq99++03JycmKiIjQo48+6nGfoaGhCg0NdWsPDg7mG+081alTJ9WqVavEJYglqUOHDowBBDw+qxDoGKMIdIxRlMSXseG3cFW7dm1ZrVa3Waq9e/e6zU4VqVevnsf+QUFBqlWrliTp0Ucf1YABA5z3cbVs2VJHjx7Vf/7zH40bN06VKvl9gUQEgKIliJOSkmSxWFwC1qkBnhu6AQAA4C2/JY2QkBDFxcW5XeOalZWlDh06eHxP+/bt3fqvWrVKbdq0cSbKY8eOuQUoq9UqwzA8zlCg4ippCeJXX33VT1UBAACgvPLrZYGpqakaMGCA2rRpo/bt2+vFF1/Ujh07NHz4cEkn74XKycnRwoULJUnDhw/Xc889p9TUVA0bNkzZ2dmaO3euy8IEffr00cyZM9WqVSvnZYGPPvqobrzxRmYh4Ka4JYgLCwtZyAIAAAA+8Wu46tu3r/bv36/JkycrNzdXLVq0UGZmpho0aCBJys3N1Y4dO5z9Y2NjlZmZqZEjR+r5559X/fr19cwzzziXYZek8ePHy2KxaPz48crJyVGdOnXUp08fPfnkk2V+figfrFar4uPjXdrMWIIYAAAAFYvfF7QYMWKERowY4XHbggUL3No6d+6sjRs3Fru/oKAgTZgwQRMmTDCrRAAAAAA4I1Z3AAAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABEHn8ub8/HyFhoaaVQsADxwOh+x2u3JzcxURESGbzSar1ervsgAAAHAan2auVq5cqUGDBqlRo0YKDg5WlSpVVL16dXXu3FlPPvmkdu/eXVp1AhVSRkaGYmJi1KVLF/Xv319dunRRTEyMMjIy/F0aAAAATuNVuHr33XfVpEkTDRw4UJUqVdJ///tfZWRkaOXKlZo7d646d+6sjz76SA0bNtTw4cP1559/lnbdwHkvIyNDSUlJ2rVrl0t7Tk6OkpKSCFgAAAABxqvLAqdMmaIZM2bohhtuUKVK7nnstttuk3Tyh7709HQtXLhQDz30kLmVAhWIw+FQcnKyDMNw22YYhiwWi1JSUpSQkMAlggAAAAHCq3C1fv16r3YWGRmpp59++pwKAiDZ7Xa3GatTGYahnTt3ym63Kz4+vuwKAwAAQLHOebVAh8OhTZs26e+//zajHgCScnNzTe0HAACA0udzuEpJSdHcuXMlnQxWnTt3VuvWrRUdHa3Vq1ebXR9QIUVERJjaDwAAAKXP53C1ZMkSXXHFFZKk5cuXa9u2bfr555+VkpKicePGmV4gUNocDodWr16tRYsWafXq1XI4HP4uSTabTVFRUbJYLB63WywWRUdHy2azlXFlAAAAKI7P4Wrfvn2qV6+eJCkzM1O33nqrLr30Ug0ZMkTff/+96QUCpam4pc6XL1/u17qsVqvS09MlyS1gFb1OS0tjMQsAAIAA4nO4Cg8P1+bNm+VwOPThhx+qa9eukqRjx47xgx7KlZKWOh8wYICfqvp/EhMTtWTJEkVGRrq0R0VFacmSJUpMTPRTZQAAAPDEq9UCTzV48GDddtttioiIkMViUbdu3SRJX375pZo2bWp6gUBpONNS56f2Cw4OLsvSXCQmJiohIUF2u125ubmKiIiQzWbjFxkAAAAByOdwNXHiRLVo0UI7d+7UrbfeqtDQUEknL2MaM2aM6QUCpcGbpc4lKTs7W126dCmrsjyyWq0stw4AAFAO+ByuJCkpKcmtbeDAgedcDFBWvF3CPC8vr5QrAQAAwPnirMLV+vXrtXr1au3du1eFhYUu22bOnGlKYUBp8nYJ86LFWwAAAIAz8TlcTZkyRePHj1eTJk0UHh7uspJZcctGA4GmaKnznJwcj/ddFY3l9u3bl3VpAAAAKKd8Dlfp6emaN2+eBg0aVArlAGWjaKnzpKQkWSwWl4B16i8JWDgCAAAA3vJ5KfZKlSqpY8eOpVELUKZKWur81Vdf9VNVAAAAKK98DlcjR47U888/Xxq1AGUuMTFR27dv16effqo33nhDn376qbZt26Y+ffr4uzQAAACUMz5fFjhq1CjdcMMNatSokZo3b+72DKCMjAzTigPKgqelzk9fqAUAAAA4E5/D1QMPPKBPP/1UXbp0Ua1atVjEAgAAAAB0FuFq4cKFWrp0qW644YbSqAcAAAAAyiWf77mqWbOmGjVqVBq1AAAAAEC55XO4mjhxoiZMmKBjx46VRj0AAAAAUC75fFngM888o99//13h4eGKiYlxW9Bi48aNphUHAAAAAOWFz+HqpptuKoUyAAAAAKB88zlcTZgwoTTqAAAAAIByzed7rr766it9+eWXbu1ffvmlvv76a1OKAgAAAIDyxudwdd9992nnzp1u7Tk5ObrvvvtMKQoAAAAAyhufw9XmzZvVunVrt/ZWrVpp8+bNphQFAAAAAOWNz+EqNDRUe/bscWvPzc1VUJDPt3ABAAAAwHnB53DVrVs3jR07VgcPHnS2HThwQI888oi6detmanEAAAAAUF74PNX0f//3f+rUqZMaNGigVq1aSZI2bdqk8PBwvfrqq6YXCAAAAADlgc/hKjIyUt99951ef/11ffvtt6pcubIGDx6sfv36uT1QGAAAAAAqirO6Sapq1ar6z3/+Y3YtAAAAAFBueXXPVXZ2ttc7PHr0qH788cezLggAAAAAyiOvwtVdd92lbt266a233tKRI0c89tm8ebMeeeQRNW7cWBs3bjS1SAAAAAAIdF5dFrh582a98MILeuyxx3THHXfo0ksvVf369RUWFqa///5bP//8s44eParExERlZWWpRYsWpV03AAAAAAQUr8JVcHCw7r//ft1///3auHGj7Ha7tm/frn/++UdXXHGFRo4cqS5duqhmzZqlXS8AAAAABCSfF7Ro3bq1WrduXRq1AAAAAEC55fNDhAEAAAAA7ghXAAAAAGACwhUAAAAAmIBwBQAAAAAm8DlcHTt2rDTqAAAAAIByzefVAi+88EK1adNG8fHx6ty5s6655hpVrVq1NGoDAAAAgHLD55mrNWvW6MYbb9TGjRt166236qKLLlK7du00ZswYffDBB6VRIwAAAAAEPJ/DVfv27TVmzBh9+OGH+vvvv/XZZ5+padOm+r//+z/17t27NGoEAAAAgIDn82WBkvTzzz9r9erVWrNmjVavXq2CggL16dNHnTt3Nrs+AAAAACgXfA5X9erVU0FBga699lrFx8frkUceUcuWLUujNgAAAAAoN3y+LLBevXo6cuSIduzYoR07dmjXrl06cuRIadQGAMB5w+FwaPXq1Vq0aJFWr14th8Ph75IAACbzOVxt2rRJe/bs0bhx43TixAk9+uijqlOnjtq2basxY8aURo0AAJRrGRkZiomJUZcuXdS/f3916dJFMTExysjI8HdpAAATndVDhC+88ELdeOONGjdunB555BHddttt2rhxo6ZPn252fQAAlGsZGRlKSkrSrl27XNpzcnKUlJREwAKA84jP4eqdd95RcnKyrrjiCtWtW1f33nuvjh49qv/973/67rvvSqNGAADKJYfDoeTkZBmG4batqC0lJYVLBAHgPOHzghb33HOPOnXqpGHDhik+Pl4tWrQojboAACj37Ha724zVqQzD0M6dO2W32xUfH192hQEASoXPM1d79+7VkiVLdP/995sSrGbNmqXY2FiFhYUpLi5Odru9xP5r1qxRXFycwsLC1LBhQ82ZM8etz4EDB3TfffcpIiJCYWFhatasmTIzM8+5VgAAfJGbm2tqPwBAYDur51w5HA69++67+umnn2SxWNSsWTMlJCTIarX6tJ/FixcrJSVFs2bNUseOHfXCCy+oZ8+e2rx5sy6++GK3/tu2bVOvXr00bNgwvfbaa/r88881YsQI1alTR7fccosk6fjx4+rWrZvq1q2rJUuWKCoqSjt37lT16tXP5lQBADhrERERpvYDAAQ2n8PVb7/9pl69eiknJ0dNmjSRYRj69ddfFR0drRUrVqhRo0Ze72vmzJkaMmSIhg4dKklKS0vTypUrNXv2bE2dOtWt/5w5c3TxxRcrLS1NktSsWTN9/fXXmjFjhjNczZs3T3/99ZfWrVun4OBgSVKDBg1KrCM/P1/5+fnO14cOHZIkFRQUqKCgwOvzwfmj6N+df38EMsZp4GvXrp0aN26s3bt3e7zvymKxKDIyUu3atTsv/x0Zowh0jFF4w5fxYTE8fdqXoFevXjIMQ6+//rpq1qwpSdq/f7/uvPNOVapUSStWrPBqP8ePH1eVKlX09ttv6+abb3a2Jycna9OmTVqzZo3bezp16qRWrVopPT3d2fbOO+/otttu07FjxxQcHKxevXqpZs2aqlKlipYtW6Y6deqof//+evjhh4udWZs4caImTZrk1v7GG2+oSpUqXp0PAAAAgPPPsWPH1L9/fx08eFA1atQosa/PM1dr1qzRF1984QxWklSrVi1NmzZNHTt29Ho/+/btk8PhUHh4uEt7eHi48vLyPL4nLy/PY/8TJ05o3759ioiI0NatW/XJJ5/ojjvuUGZmprZs2aL77rtPJ06c0GOPPeZxv2PHjlVqaqrz9aFDhxQdHa3u3buf8QuI81NBQYGysrLUrVs35wwoEGgYp+XH8uXL9fDDDysnJ8fZFhUVpWnTpqlPnz5+rKx0MUYR6Bij8EbRVW3e8DlchYaG6vDhw27tR44cUUhIiK+7k8VicXltGIZb25n6n9peWFiounXr6sUXX5TValVcXJx2796t6dOnFxuuQkNDFRoa6tYeHBzMN1oFxxhAecA4DXyJiYlKSEiQ3W5Xbm6uIiIiZLPZfL5XubxijCLQMUZREl/Ghs/hqnfv3vrPf/6juXPn6uqrr5Ykffnllxo+fLhuvPFGr/dTu3ZtWa1Wt1mqvXv3us1OFalXr57H/kFBQapVq5akkzcFBwcHu/yH1axZM+Xl5en48eNnFQABADhXVquV5dYB4Dzn81LszzzzjBo1aqT27dsrLCxMYWFh6tixoxo3buxyL9SZhISEKC4uTllZWS7tWVlZ6tChg8f3tG/f3q3/qlWr1KZNG2ei7Nixo3777TcVFhY6+/z666+KiIggWAEAAAAoNT7PXF144YVatmyZtmzZop9//lmGYah58+Zq3LixzwdPTU3VgAED1KZNG7Vv314vvviiduzYoeHDh0s6eS9UTk6OFi5cKEkaPny4nnvuOaWmpmrYsGHKzs7W3LlztWjRIuc+7733Xj377LNKTk7WAw88oC1btmjKlCl68MEHfa4PAAAAALx1Vs+5kqRLLrlEl1xyyTkdvG/fvtq/f78mT56s3NxctWjRQpmZmc6l03Nzc7Vjxw5n/9jYWGVmZmrkyJF6/vnnVb9+fT3zzDPOZdglKTo6WqtWrdLIkSN1+eWXKzIyUsnJyXr44YfPqVYAAAAAKIlX4erUlfTOZObMmT4VMGLECI0YMcLjtgULFri1de7cWRs3bixxn+3bt9cXX3zhUx0AAAAAcC68ClfffPONVzsraZU/AAAAADifeRWuPv3009KuAwAAAADKNa/vudq6datiY2OZnUKFsnbtWuXl5VW4Z9IAAADAd14vxX7JJZfozz//dL7u27ev9uzZUypFAf62fPlySdINN9yg/v37q0uXLoqJiVFGRoafKwMAAECg8jpcGYbh8jozM1NHjx41vSDA3zIyMjRgwAC39pycHCUlJRGwAAAA4JHPDxEGzmcOh0PJycluv0yQ/t8vGFJSUuRwOMq6NAAAAAQ4r8OVxWJxu9+K+69wvrHb7dq1a1ex2w3D0M6dO2W328uwKgAAAJQHXi9oYRiGBg0apNDQUEnSv//+q+HDh6tq1aou/bhkCuVZbm6uqf0AAABQcXgdrgYOHOjy+s477zS9GMDfIiIiTO0HAACAisPrcDV//vzSrAMICDabTVFRUfrrr788brdYLIqKipLNZivjygAAABDoWNACOIXValV6errHbUX3GKalpfG8KwAAALjxeuYKqCgSExM9tkdFRSktLa3Y7QAAAKjYCFeAB3369FFmZqZWrFihvLw8RUREyGazMWMFAACAYhGugBJcc801Cg4O9ncZAAAAKAe45woAAAAATHBW4erVV19Vx44dVb9+ff3xxx+STt7kv2zZMlOLAwAAAIDywudwNXv2bKWmpqpXr146cOCAHA6HJOnCCy9UWlqa2fUBAAAAQLngc7h69tln9dJLL2ncuHEuN/e3adNG33//vanFAQAAAEB54XO42rZtm1q1auXWHhoaqqNHj5pSFAAAAACUNz6Hq9jYWG3atMmt/YMPPlDz5s3NqAkAAAAAyh2fl2L/73//q/vuu0///vuvDMPQ+vXrtWjRIk2dOlUvv/xyadQIAAAAAAHP53A1ePBgnThxQqNHj9axY8fUv39/RUZGKj09Xbfffntp1AgAAAAAAe+sHiI8bNgwDRs2TPv27VNhYaHq1q1rdl0AAAAAUK6cVbgqUrt2bbPqAAAAAIByzedw1apVK1ksFrd2i8WisLAwNW7cWIMGDVKXLl1MKRAAAAAAygOfVwu8/vrrtXXrVlWtWlVdunRRfHy8qlWrpt9//11XXXWVcnNz1bVrVy1btqw06gUAAACAgOTzzNW+ffv00EMP6dFHH3Vpf+KJJ/THH39o1apVmjBhgh5//HElJCSYVigAAAAABDKfZ67eeust9evXz6399ttv11tvvSVJ6tevn3755Zdzrw4AAAAAygmfw1VYWJjWrVvn1r5u3TqFhYVJkgoLCxUaGnru1QEAAABAOeHzZYEPPPCAhg8frg0bNuiqq66SxWLR+vXr9fLLL+uRRx6RJK1cuVKtWrUyvVgAAAAACFQ+h6vx48crNjZWzz33nF599VVJUpMmTfTSSy+pf//+kqThw4fr3nvvNbdSAAAAAAhgZ/WcqzvuuEN33HFHsdsrV6581gUBAAAAQHnk8z1XAAAAAAB3Ps9cORwO/e9//9Nbb72lHTt26Pjx4y7b//rrL9OKAwAAAIDywueZq0mTJmnmzJm67bbbdPDgQaWmpioxMVGVKlXSxIkTS6FEAAAAAAh8Poer119/XS+99JJGjRqloKAg9evXTy+//LIee+wxffHFF6VRIwAAAAAEPJ/DVV5enlq2bClJqlatmg4ePChJ6t27t1asWGFudQAAAABQTvgcrqKiopSbmytJaty4sVatWiVJ+uqrr3hwMAAAAIAKy+dwdfPNN+vjjz+WJCUnJ+vRRx/VJZdcorvuukt333236QUCAAAAQHng82qB06ZNc/49KSlJUVFRWrdunRo3bqwbb7zR1OIAAAAAoLw4q4cIn6pdu3Zq166dGbUAAHDecjgcstvtys3NVUREhGw2m6xWq7/LAgCY6KzC1S+//KJnn31WP/30kywWi5o2baoHHnhATZo0Mbs+AADKvYyMDCUnJ2vXrl3OtqioKKWnpysxMdGPlQEAzOTzPVdLlixRixYttGHDBl1xxRW6/PLLtXHjRrVo0UJvv/12adQIAEC5lZGRoaSkJJdgJUk5OTlKSkpSRkaGnyoDAJjN55mr0aNHa+zYsZo8ebJL+4QJE/Twww/r1ltvNa04AADKM4fDoeTkZBmG4bbNMAxZLBalpKQoISGBSwQB4DxwVs+5uuuuu9za77zzTuXl5ZlSFAAA5wO73e42Y3UqwzC0c+dO2e32MqwKAFBafA5X8fHxHv8TWLt2rWw2mylFAQBwPih6LqRZ/QAAgc3nywJvvPFGPfzww9qwYYNzlcAvvvhCb7/9tiZNmqT33nvPpS8AABVVRESEqf0AAIHN53A1YsQISdKsWbM0a9Ysj9skyWKxyOFwnGN5AACUXzabTVFRUcrJyfF435XFYlFUVBRXfgDAecLnywILCwu9+kOwAgBUdFarVenp6ZJOBqlTFb1OS0tjMQsAOE/4HK5O9e+//5pVBwAA56XExEQtWbJEkZGRLu1RUVFasmQJz7kCgPOIz+HK4XDo8ccfV2RkpKpVq6atW7dKkh599FHNnTvX9AIBACjvEhMTtX37dn366ad644039Omnn2rbtm0EKwA4z/gcrp588kktWLBATz/9tEJCQpztLVu21Msvv2xqcQAAnC+sVqvi4+PVr18/xcfHcykgAJyHfA5XCxcu1Isvvqg77rjD5T+Gyy+/XD///LOpxQEAAABAeeFzuMrJyVHjxo3d2gsLC1VQUGBKUQAAAABQ3vgcri677DKPDxF+++231apVK1OKAgAAAIDyxufnXE2YMEEDBgxQTk6OCgsLlZGRoV9++UULFy7U+++/Xxo1AgAAAEDA83nmqk+fPlq8eLEyMzNlsVj02GOP6aefftLy5cvVrVu30qgRAAAAAAKezzNXktSjRw/16NHD7FoAAAAAoNw6q4cIHzhwQC+//LIeeeQR/fXXX5KkjRs3Kicnx9TiAAAAAKC88Hnm6rvvvlPXrl11wQUXaPv27Ro6dKhq1qypd955R3/88YcWLlxYGnUCAAAAQEDzeeYqNTVVgwYN0pYtWxQWFuZs79mzpz777DNTiwMAAACA8sLncPXVV1/pnnvucWuPjIxUXl6eKUUBAAAAQHnjc7gKCwvToUOH3Np/+eUX1alTx5SiAAAAAKC88TlcJSQkaPLkySooKJAkWSwW7dixQ2PGjNEtt9xieoEAAAAAUB74HK5mzJihP//8U3Xr1tU///yjzp07q3HjxqpevbqefPJJnwuYNWuWYmNjFRYWpri4ONnt9hL7r1mzRnFxcQoLC1PDhg01Z86cYvu++eabslgsuummm3yuCwAAAAB84fNqgTVq1NDatWv1ySefaOPGjSosLFTr1q3VtWtXnw++ePFipaSkaNasWerYsaNeeOEF9ezZU5s3b9bFF1/s1n/btm3q1auXhg0bptdee02ff/65RowYoTp16rjNmv3xxx8aNWqUbDabz3UBAAAAgK/O6iHCknTttdfq2muvPaeDz5w5U0OGDNHQoUMlSWlpaVq5cqVmz56tqVOnuvWfM2eOLr74YqWlpUmSmjVrpq+//lozZsxwCVcOh0N33HGHJk2aJLvdrgMHDpRYR35+vvLz852vi+4pKygocF7+iIql6N+df38EMsYpAh1jFIGOMQpv+DI+vA5XX375pf766y/17NnT2bZw4UJNmDBBR48e1U033aRnn31WoaGhXu3v+PHj2rBhg8aMGePS3r17d61bt87je7Kzs9W9e3eXth49emju3LkqKChQcHCwJGny5MmqU6eOhgwZcsbLDCVp6tSpmjRpklv7qlWrVKVKFa/OB+enrKwsf5cAnBHjFIGOMYpAxxhFSY4dO+Z1X6/D1cSJExUfH+8MV99//72GDBmiQYMGqVmzZpo+fbrq16+viRMnerW/ffv2yeFwKDw83KU9PDy82CXd8/LyPPY/ceKE9u3bp4iICH3++eeaO3euNm3a5O2paezYsUpNTXW+PnTokKKjo9W9e3fVqFHD6/3g/FFQUKCsrCx169bNGdqBQMM4RaBjjCLQMUbhDU8rpRfH63C1adMmPf74487Xb775ptq2bauXXnpJkhQdHa0JEyZ4Ha6KWCwWl9eGYbi1nal/Ufvhw4d155136qWXXlLt2rW9riE0NNTjjFtwcDDfaBUcYwDlAeMUgY4xikDHGEVJfBkbXoerv//+22XWaM2aNbr++uudr6+66irt3LnT6wPXrl1bVqvVbZZq7969brNTRerVq+exf1BQkGrVqqUff/xR27dvV58+fZzbCwsLJUlBQUH65Zdf1KhRI69rBAAAAABveb0Ue3h4uLZt2ybp5P1SGzduVPv27Z3bDx8+7FOqCwkJUVxcnNs1rllZWerQoYPH97Rv396t/6pVq9SmTRsFBweradOm+v7777Vp0ybnnxtvvFFdunTRpk2bFB0d7XV9AAAAAOALr2eurr/+eo0ZM0ZPPfWU3n33XVWpUsVlmfPvvvvO51mh1NRUDRgwQG3atFH79u314osvaseOHRo+fLikk/dC5eTkaOHChZKk4cOH67nnnlNqaqqGDRum7OxszZ07V4sWLZIkhYWFqUWLFi7HuPDCCyXJrR0AAAAAzOR1uHriiSeUmJiozp07q1q1anrllVcUEhLi3D5v3jy3lfzOpG/fvtq/f78mT56s3NxctWjRQpmZmWrQoIEkKTc3Vzt27HD2j42NVWZmpkaOHKnnn39e9evX1zPPPOP2jCsAAAAAKGteh6s6derIbrfr4MGDqlatmqxWq8v2t99+W9WqVfO5gBEjRmjEiBEety1YsMCtrXPnztq4caPX+/e0DwAAAAAwm88PEb7gggs8ttesWfOciwEAAACA8srrBS0AAAAAAMUjXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYI8ncBAABUBA6HQ3a7Xbm5uYqIiJDNZpPVavV3WQAAExGuAAAoZRkZGUpOTtauXbucbVFRUUpPT1diYqIfKwMAmInLAgEAKEUZGRlKSkpyCVaSlJOTo6SkJGVkZPipMgCA2QhXAACUEofDoeTkZBmG4batqC0lJUUOh6OsSwMAlALCFQAApcRut7vNWJ3KMAzt3LlTdru9DKsCAJQWwhUAAKUkNzfX1H4AgMBGuAIAoJRERESY2g8AENgIVwAAlBKbzaaoqChZLBaP2y0Wi6Kjo2Wz2cq4MgBAaSBcAQBQSqxWq9LT0yXJLWAVvU5LS+N5VwBwniBcAQBQihITE7VkyRJFRka6tEdFRWnJkiU85woAziM8RBgAgFKWmJiohIQE2e125ebmKiIiQjabjRkrADjPEK4AACgDVqtV8fHx/i4DAFCKuCwQAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATBDk7wIAAAAAoIjD4ZDdbldubq4iIiJks9lktVr9XZZXCFcAAAAAAkJGRoaSk5O1a9cuZ1tUVJTS09OVmJjox8q8w2WBAAAAAPwuIyNDSUlJLsFKknJycpSUlKSMjAw/VeY9whUAAAAAv3I4HEpOTpZhGG7bitpSUlLkcDjKujSfEK4AAAAA+JXdbnebsTqVYRjauXOn7HZ7GVblO7+Hq1mzZik2NlZhYWGKi4s74xdszZo1iouLU1hYmBo2bKg5c+a4bH/ppZdks9l00UUX6aKLLlLXrl21fv360jwFAAAAAOcgNzfX1H7+4tdwtXjxYqWkpGjcuHH65ptvZLPZ1LNnT+3YscNj/23btqlXr16y2Wz65ptv9Mgjj+jBBx/U0qVLnX1Wr16tfv366dNPP1V2drYuvvhide/eXTk5OWV1WgAAAAB8EBERYWo/f/HraoEzZ87UkCFDNHToUElSWlqaVq5cqdmzZ2vq1Klu/efMmaOLL75YaWlpkqRmzZrp66+/1owZM3TLLbdIkl5//XWX97z00ktasmSJPv74Y911110e68jPz1d+fr7z9aFDhyRJBQUFKigoOOfzRPlT9O/Ovz8CGeMUgY4xikDHGA0c7dq1U+PGjbV7926P911ZLBZFRkaqXbt2Zf7v5cvx/Baujh8/rg0bNmjMmDEu7d27d9e6des8vic7O1vdu3d3aevRo4fmzp2rgoICBQcHu73n2LFjKigoUM2aNYutZerUqZo0aZJb+6pVq1SlShVvTgfnqaysLH+XAJwR4xSBjjGKQMcYDQwzZsw4Y5+VK1eWQSWujh075nVfv4Wrffv2yeFwKDw83KU9PDxceXl5Ht+Tl5fnsf+JEye0b98+j9OEY8aMUWRkpLp27VpsLWPHjlVqaqrz9aFDhxQdHa3u3burRo0avpwWzhMFBQXKyspSt27dPIZ2IBAwThHoGKMIdIzRwLN8+XI9/PDDLrf0REVFadq0aerTp49faiq6qs0bfn+IsMVicXltGIZb25n6e2qXpKefflqLFi3S6tWrFRYWVuw+Q0NDFRoa6tYeHBzMN1oFxxhAecA4RaBjjCLQMUYDR2JiohISEmS325Wbm6uIiAjZbDZZrVa/1eTL2PBbuKpdu7asVqvbLNXevXvdZqeK1KtXz2P/oKAg1apVy6V9xowZmjJlij766CNdfvnl5hYPAAAAoFRYrVbFx8f7u4yz4rfVAkNCQhQXF+d2jWtWVpY6dOjg8T3t27d3679q1Sq1adPGJVFOnz5djz/+uD788EO1adPG/OIBAAAA4DR+XYo9NTVVL7/8subNm6effvpJI0eO1I4dOzR8+HBJJ++FOnWFv+HDh+uPP/5QamqqfvrpJ82bN09z587VqFGjnH2efvppjR8/XvPmzVNMTIzy8vKUl5enI0eOlPn5AQAAAKg4/HrPVd++fbV//35NnjxZubm5atGihTIzM9WgQQNJJx8Sduozr2JjY5WZmamRI0fq+eefV/369fXMM884l2GXTj6U+Pjx40pKSnI51oQJEzRx4sQyOS8AAAAAFY/fF7QYMWKERowY4XHbggUL3No6d+6sjRs3Fru/7du3m1QZAAAAAHjPr5cFAgAAAMD5gnAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACYL8XQAAAAAAFHE4HLLb7crNzVVERIRsNpusVqu/y/IK4QoAAABAQMjIyFBycrJ27drlbIuKilJ6eroSExP9WJl3uCwQAAAAgN9lZGQoKSnJJVhJUk5OjpKSkpSRkeGnyrxHuAIAAADgVw6HQ8nJyTIMw21bUVtKSoocDkdZl+YTwhUAAAAAv7Lb7W4zVqcyDEM7d+6U3W4vw6p8R7gCAAAA4Fe5ubmm9vMXwhUAAAAAv4qIiDC1n78QrgAAAAD4lc1mU1RUlCwWi8ftFotF0dHRstlsZVyZbwhXAAAAAPzKarUqPT1dktwCVtHrtLS0gH/eFeEKAAAAgN8lJiZq1KhRqlTJNaJUqlRJo0aN4jlXAAAAAOCNjIwMzZgxw225dYfDoRkzZvCcKwAAAAA4k5Kec1WE51wBAAAAwBnwnCuTzJo1S7GxsQoLC1NcXNwZv2Br1qxRXFycwsLC1LBhQ82ZM8etz9KlS9W8eXOFhoaqefPmeuedd0qrfAAAAADniOdcmWDx4sVKSUnRuHHj9M0338hms6lnz57asWOHx/7btm1Tr169ZLPZ9M033+iRRx7Rgw8+qKVLlzr7ZGdnq2/fvhowYIC+/fZbDRgwQLfddpu+/PLLsjotAAAAAD44X55zZTFKurCxlLVt21atW7fW7NmznW3NmjXTTTfdpKlTp7r1f/jhh/Xee+/pp59+crYNHz5c3377rbKzsyVJffv21aFDh/TBBx84+1x//fW66KKLtGjRIo915OfnKz8/3/n60KFDio6O1r59+1SjRo1zPk+UPwUFBcrKylK3bt0UHBzs73IAjxinCHSMUQQ6xmjgcDgcatmypXbv3u3xviuLxaLIyEh99913Zb4c+6FDh1S7dm0dPHjwjNkgqIxqcnP8+HFt2LBBY8aMcWnv3r271q1b5/E92dnZ6t69u0tbjx49NHfuXBUUFCg4OFjZ2dkaOXKkW5+0tLRia5k6daomTZrk1r5q1SpVqVLFyzPC+SgrK8vfJQBnxDhFoGOMItAxRgPDjBkzzthn5cqVZVCJq2PHjnnd12/hat++fXI4HAoPD3dpDw8PV15ensf35OXleex/4sQJ7du3TxEREcX2KW6fkjR27FilpqY6XxfNXHXv3p2ZqwqK32ShPGCcItAxRhHoGKOBZ/ny5Xr44YeVk5PjbIuKitK0adPUp08fv9R06NAhr/v6LVwVOf0JzIZhuLWdqf/p7b7uMzQ0VKGhoW7twcHBfKNVcIwBlAeMUwQ6xigCHWM0cCQmJiohIUF2u125ubmKiIiQzWYr80sBT+XL2PBbuKpdu7asVqvbjNLevXvdZp6K1KtXz2P/oKAg1apVq8Q+xe0TAAAAQOCwWq2Kj4/3dxlnxW+rBYaEhCguLs7tGtesrCx16NDB43vat2/v1n/VqlVq06aNM1EW16e4fQIAAACAGfx6WWBqaqoGDBigNm3aqH379nrxxRe1Y8cODR8+XNLJe6FycnK0cOFCSSdXBnzuueeUmpqqYcOGKTs7W3PnznVZBTA5OVmdOnXSU089pYSEBC1btkwfffSR1q5d65dzBAAAAFAx+DVc9e3bV/v379fkyZOVm5urFi1aKDMzUw0aNJB08iFhpz7zKjY2VpmZmRo5cqSef/551a9fX88884xuueUWZ58OHTrozTff1Pj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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10,6))\n",
    "# ax.scatter(NDVI_ts.index, NDVI_ts[0], color='red')\n",
    "ax.scatter(df['NDVI'].values, y['Rin_11R'].values, c='black',label = 'NDVI Range')\n",
    "ax.scatter(df['NDVI'].values, y['Rin_2R'].values, c='black',label = 'NDVI Range')\n",
    "ax.scatter(df['NDVI'].values, y['Rin_12R'].values, c='black',label = 'NDVI Range')\n",
    "# ax2.hlines(y=0.113, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "# ax2.hlines(y=0, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "# ax2.hlines(y=0.0915, xmin = 2009.5, xmax = 2017.5,color='grey',linestyles='--')\n",
    "# ax.axvspan(\"2017-06-10T12:00:00.000Z\", \"2018-06-16T00:00:00.000Z\", color='grey', alpha=0.5, lw=0, label=\"Satellite relaunch gap\")\n",
    "ax.set(title=\"NDVI vs. Seepage Flow Values at the Rio Grande\")\n",
    "# ax.legend(loc = 'lower left')\n",
    "ax.grid(zorder = 0)\n",
    "ax.set_ylabel('Seepage Flow (cms)')\n",
    "ax.set_xlabel('NDVI (-1 to 1)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Failed Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;NDVI&#x27; (time: 30, lat: 4990, lon: 3160)&gt;\n",
       "array([[[       nan,        nan,        nan, ..., 0.04583439,\n",
       "         0.07864597, 0.06839687],\n",
       "        [       nan,        nan,        nan, ..., 0.01787394,\n",
       "         0.05376344, 0.07827039],\n",
       "        [       nan,        nan,        nan, ..., 0.05179795,\n",
       "         0.06229661, 0.07583418],\n",
       "        ...,\n",
       "        [0.09662751, 0.11536748, 0.12614259, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.12414109, 0.08584905, 0.10476191, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.07085916, 0.05160744, 0.04962153, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ..., 0.14185022,\n",
       "         0.18223584, 0.21866295],\n",
       "        [       nan,        nan,        nan, ..., 0.16591929,\n",
       "         0.20993087, 0.2521861 ],\n",
       "        [       nan,        nan,        nan, ..., 0.16630669,\n",
       "         0.17650676, 0.20318726],\n",
       "...\n",
       "        [0.2570481 , 0.28103682, 0.19244823, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.11673469, 0.16470589, 0.09968521, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.08883554, 0.07775965, 0.01830443, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ..., 0.03472222,\n",
       "         0.06841612, 0.13654473],\n",
       "        [       nan,        nan,        nan, ..., 0.07624795,\n",
       "         0.08756039, 0.10951526],\n",
       "        [       nan,        nan,        nan, ..., 0.07847478,\n",
       "         0.07059477, 0.05664264],\n",
       "        ...,\n",
       "        [0.29340512, 0.24761905, 0.25353926, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.2766252 , 0.21157822, 0.17713568, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.24931507, 0.16733068, 0.08290452, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2010-03-25 2010-10-27 ... 2017-11-10\n",
       "  * lon      (lon) float64 3.611e+06 3.611e+06 3.611e+06 ... 3.627e+06 3.627e+06\n",
       "  * lat      (lat) float64 2.854e+05 2.854e+05 2.854e+05 ... 3.104e+05 3.104e+05\n",
       "Attributes:\n",
       "    description:  Calculated NDVI</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'NDVI'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 30</li><li><span class='xr-has-index'>lat</span>: 4990</li><li><span class='xr-has-index'>lon</span>: 3160</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-571853ef-9b44-40c8-9c07-b5b10f7a551d' class='xr-array-in' type='checkbox' checked><label for='section-571853ef-9b44-40c8-9c07-b5b10f7a551d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[       nan,        nan,        nan, ..., 0.04583439,\n",
       "         0.07864597, 0.06839687],\n",
       "        [       nan,        nan,        nan, ..., 0.01787394,\n",
       "         0.05376344, 0.07827039],\n",
       "        [       nan,        nan,        nan, ..., 0.05179795,\n",
       "         0.06229661, 0.07583418],\n",
       "        ...,\n",
       "        [0.09662751, 0.11536748, 0.12614259, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.12414109, 0.08584905, 0.10476191, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.07085916, 0.05160744, 0.04962153, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ..., 0.14185022,\n",
       "         0.18223584, 0.21866295],\n",
       "        [       nan,        nan,        nan, ..., 0.16591929,\n",
       "         0.20993087, 0.2521861 ],\n",
       "        [       nan,        nan,        nan, ..., 0.16630669,\n",
       "         0.17650676, 0.20318726],\n",
       "...\n",
       "        [0.2570481 , 0.28103682, 0.19244823, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.11673469, 0.16470589, 0.09968521, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.08883554, 0.07775965, 0.01830443, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ..., 0.03472222,\n",
       "         0.06841612, 0.13654473],\n",
       "        [       nan,        nan,        nan, ..., 0.07624795,\n",
       "         0.08756039, 0.10951526],\n",
       "        [       nan,        nan,        nan, ..., 0.07847478,\n",
       "         0.07059477, 0.05664264],\n",
       "        ...,\n",
       "        [0.29340512, 0.24761905, 0.25353926, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.2766252 , 0.21157822, 0.17713568, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.24931507, 0.16733068, 0.08290452, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-9313accc-f1f2-4721-893c-13cc37bdbe69' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9313accc-f1f2-4721-893c-13cc37bdbe69' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-03-25 ... 2017-11-10</div><input id='attrs-bc07842f-6ce5-4761-a7d1-ad378036d637' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc07842f-6ce5-4761-a7d1-ad378036d637' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18216aab-e420-4437-813e-871ccb2f658a' class='xr-var-data-in' type='checkbox'><label for='data-18216aab-e420-4437-813e-871ccb2f658a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-03-25T00:00:00.000000000&#x27;, &#x27;2010-10-27T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-09T00:00:00.000000000&#x27;, &#x27;2012-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-10-17T00:00:00.000000000&#x27;, &#x27;2013-01-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-05-19T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-05-20T00:00:00.000000000&#x27;, &#x27;2013-10-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-11-02T00:00:00.000000000&#x27;, &#x27;2013-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-12-17T00:00:00.000000000&#x27;, &#x27;2014-01-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-03-21T00:00:00.000000000&#x27;, &#x27;2014-03-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-07-26T00:00:00.000000000&#x27;, &#x27;2014-10-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-04-14T00:00:00.000000000&#x27;, &#x27;2016-01-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-02-04T00:00:00.000000000&#x27;, &#x27;2016-04-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-10T00:00:00.000000000&#x27;, &#x27;2016-06-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-04T00:00:00.000000000&#x27;, &#x27;2017-02-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-20T00:00:00.000000000&#x27;, &#x27;2017-10-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-25T00:00:00.000000000&#x27;, &#x27;2017-11-10T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.611e+06 3.611e+06 ... 3.627e+06</div><input id='attrs-323be21d-6230-4fa0-86d5-e1a21b380183' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-323be21d-6230-4fa0-86d5-e1a21b380183' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05fc4022-65ba-4001-b7f1-0d87ac3ba607' class='xr-var-data-in' type='checkbox'><label for='data-05fc4022-65ba-4001-b7f1-0d87ac3ba607' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3610782.5, 3610787.5, 3610792.5, ..., 3626567.5, 3626572.5, 3626577.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.854e+05 2.854e+05 ... 3.104e+05</div><input id='attrs-4c846649-7a93-4e17-b444-258394d36822' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c846649-7a93-4e17-b444-258394d36822' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5a8e0d3-7fe6-4f8a-ab98-f338d4c88c3d' class='xr-var-data-in' type='checkbox'><label for='data-c5a8e0d3-7fe6-4f8a-ab98-f338d4c88c3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([285427.5, 285432.5, 285437.5, ..., 310362.5, 310367.5, 310372.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b9a97c5-b2ac-4103-b840-3b9e19fe26a4' class='xr-section-summary-in' type='checkbox'  ><label for='section-8b9a97c5-b2ac-4103-b840-3b9e19fe26a4' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0352a555-341e-403b-8161-479d8271d713' class='xr-index-data-in' type='checkbox'/><label for='index-0352a555-341e-403b-8161-479d8271d713' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2010-03-25&#x27;, &#x27;2010-10-27&#x27;, &#x27;2010-12-09&#x27;, &#x27;2012-02-15&#x27;,\n",
       "               &#x27;2012-10-17&#x27;, &#x27;2013-01-13&#x27;, &#x27;2013-02-01&#x27;, &#x27;2013-05-19&#x27;,\n",
       "               &#x27;2013-05-20&#x27;, &#x27;2013-10-03&#x27;, &#x27;2013-11-02&#x27;, &#x27;2013-12-01&#x27;,\n",
       "               &#x27;2013-12-17&#x27;, &#x27;2014-01-03&#x27;, &#x27;2014-03-21&#x27;, &#x27;2014-03-22&#x27;,\n",
       "               &#x27;2014-07-26&#x27;, &#x27;2014-10-12&#x27;, &#x27;2015-04-14&#x27;, &#x27;2016-01-11&#x27;,\n",
       "               &#x27;2016-02-04&#x27;, &#x27;2016-04-14&#x27;, &#x27;2016-06-10&#x27;, &#x27;2016-06-14&#x27;,\n",
       "               &#x27;2017-01-04&#x27;, &#x27;2017-02-23&#x27;, &#x27;2017-05-20&#x27;, &#x27;2017-10-08&#x27;,\n",
       "               &#x27;2017-10-25&#x27;, &#x27;2017-11-10&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fc251645-23d0-4036-a8bf-3e2b6202c4ad' class='xr-index-data-in' type='checkbox'/><label for='index-fc251645-23d0-4036-a8bf-3e2b6202c4ad' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([3610782.5, 3610787.5, 3610792.5, 3610797.5, 3610802.5, 3610807.5,\n",
       "              3610812.5, 3610817.5, 3610822.5, 3610827.5,\n",
       "              ...\n",
       "              3626532.5, 3626537.5, 3626542.5, 3626547.5, 3626552.5, 3626557.5,\n",
       "              3626562.5, 3626567.5, 3626572.5, 3626577.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=3160))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-645191b0-a056-4e73-8a3a-ded512b3a4db' class='xr-index-data-in' type='checkbox'/><label for='index-645191b0-a056-4e73-8a3a-ded512b3a4db' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([285427.5, 285432.5, 285437.5, 285442.5, 285447.5, 285452.5,\n",
       "              285457.5, 285462.5, 285467.5, 285472.5,\n",
       "              ...\n",
       "              310327.5, 310332.5, 310337.5, 310342.5, 310347.5, 310352.5,\n",
       "              310357.5, 310362.5, 310367.5, 310372.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=4990))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ca4d38f6-a213-4b98-b28c-777cd4279f0f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ca4d38f6-a213-4b98-b28c-777cd4279f0f' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Calculated NDVI</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'NDVI' (time: 30, lat: 4990, lon: 3160)>\n",
       "array([[[       nan,        nan,        nan, ..., 0.04583439,\n",
       "         0.07864597, 0.06839687],\n",
       "        [       nan,        nan,        nan, ..., 0.01787394,\n",
       "         0.05376344, 0.07827039],\n",
       "        [       nan,        nan,        nan, ..., 0.05179795,\n",
       "         0.06229661, 0.07583418],\n",
       "        ...,\n",
       "        [0.09662751, 0.11536748, 0.12614259, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.12414109, 0.08584905, 0.10476191, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.07085916, 0.05160744, 0.04962153, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ..., 0.14185022,\n",
       "         0.18223584, 0.21866295],\n",
       "        [       nan,        nan,        nan, ..., 0.16591929,\n",
       "         0.20993087, 0.2521861 ],\n",
       "        [       nan,        nan,        nan, ..., 0.16630669,\n",
       "         0.17650676, 0.20318726],\n",
       "...\n",
       "        [0.2570481 , 0.28103682, 0.19244823, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.11673469, 0.16470589, 0.09968521, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.08883554, 0.07775965, 0.01830443, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ..., 0.03472222,\n",
       "         0.06841612, 0.13654473],\n",
       "        [       nan,        nan,        nan, ..., 0.07624795,\n",
       "         0.08756039, 0.10951526],\n",
       "        [       nan,        nan,        nan, ..., 0.07847478,\n",
       "         0.07059477, 0.05664264],\n",
       "        ...,\n",
       "        [0.29340512, 0.24761905, 0.25353926, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.2766252 , 0.21157822, 0.17713568, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.24931507, 0.16733068, 0.08290452, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2010-03-25 2010-10-27 ... 2017-11-10\n",
       "  * lon      (lon) float64 3.611e+06 3.611e+06 3.611e+06 ... 3.627e+06 3.627e+06\n",
       "  * lat      (lat) float64 2.854e+05 2.854e+05 2.854e+05 ... 3.104e+05 3.104e+05\n",
       "Attributes:\n",
       "    description:  Calculated NDVI"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndvi3 = data2.transpose('time', 'lat', 'lon')\n",
    "# ndvi3 = data2\n",
    "# ndvi3 = ndvi.rio.set_crs(\"epsg:4326\")\n",
    "\n",
    "ndvi3.rio.set_spatial_dims('lon', 'lat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# Write an nc file for remapping\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m ndvi3\u001b[39m.\u001b[39;49mrio\u001b[39m.\u001b[39;49mto_raster(\u001b[39mr\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mNDVI_time_attempt2.nc\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m      3\u001b[0m \u001b[39m# ndvi3.rio.to_raster(r\"NDVI_time.tif\")\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[39m# ndvi3.to_netcdf()\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/planet/lib/python3.8/site-packages/rioxarray/raster_array.py:1146\u001b[0m, in \u001b[0;36mRasterArray.to_raster\u001b[0;34m(self, raster_path, driver, dtype, tags, windowed, recalc_transform, lock, compute, **profile_kwargs)\u001b[0m\n\u001b[1;32m   1127\u001b[0m out_profile \u001b[39m=\u001b[39m {\n\u001b[1;32m   1128\u001b[0m     key: value\n\u001b[1;32m   1129\u001b[0m     \u001b[39mfor\u001b[39;00m key, value \u001b[39min\u001b[39;00m out_profile\u001b[39m.\u001b[39mitems()\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1140\u001b[0m     )\n\u001b[1;32m   1141\u001b[0m }\n\u001b[1;32m   1142\u001b[0m rio_nodata \u001b[39m=\u001b[39m (\n\u001b[1;32m   1143\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencoded_nodata \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencoded_nodata \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnodata\n\u001b[1;32m   1144\u001b[0m )\n\u001b[0;32m-> 1146\u001b[0m \u001b[39mreturn\u001b[39;00m RasterioWriter(raster_path\u001b[39m=\u001b[39;49mraster_path)\u001b[39m.\u001b[39;49mto_raster(\n\u001b[1;32m   1147\u001b[0m     xarray_dataarray\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_obj,\n\u001b[1;32m   1148\u001b[0m     tags\u001b[39m=\u001b[39;49mtags,\n\u001b[1;32m   1149\u001b[0m     driver\u001b[39m=\u001b[39;49mdriver,\n\u001b[1;32m   1150\u001b[0m     height\u001b[39m=\u001b[39;49m\u001b[39mint\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mheight),\n\u001b[1;32m   1151\u001b[0m     width\u001b[39m=\u001b[39;49m\u001b[39mint\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mwidth),\n\u001b[1;32m   1152\u001b[0m     count\u001b[39m=\u001b[39;49m\u001b[39mint\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcount),\n\u001b[1;32m   1153\u001b[0m     dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[1;32m   1154\u001b[0m     crs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcrs,\n\u001b[1;32m   1155\u001b[0m     transform\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtransform(recalc\u001b[39m=\u001b[39;49mrecalc_transform),\n\u001b[1;32m   1156\u001b[0m     gcps\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_gcps(),\n\u001b[1;32m   1157\u001b[0m     nodata\u001b[39m=\u001b[39;49mrio_nodata,\n\u001b[1;32m   1158\u001b[0m     windowed\u001b[39m=\u001b[39;49mwindowed,\n\u001b[1;32m   1159\u001b[0m     lock\u001b[39m=\u001b[39;49mlock,\n\u001b[1;32m   1160\u001b[0m     compute\u001b[39m=\u001b[39;49mcompute,\n\u001b[1;32m   1161\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mout_profile,\n\u001b[1;32m   1162\u001b[0m )\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/planet/lib/python3.8/site-packages/rioxarray/raster_writer.py:292\u001b[0m, in \u001b[0;36mRasterioWriter.to_raster\u001b[0;34m(self, xarray_dataarray, tags, windowed, lock, compute, **kwargs)\u001b[0m\n\u001b[1;32m    290\u001b[0m                 rds\u001b[39m.\u001b[39mwrite(data, \u001b[39m1\u001b[39m, window\u001b[39m=\u001b[39mwindow)\n\u001b[1;32m    291\u001b[0m             \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 292\u001b[0m                 rds\u001b[39m.\u001b[39mwrite(data, window\u001b[39m=\u001b[39mwindow)\n\u001b[1;32m    294\u001b[0m \u001b[39mif\u001b[39;00m lock \u001b[39mand\u001b[39;00m is_dask_collection(xarray_dataarray\u001b[39m.\u001b[39mdata):\n\u001b[1;32m    295\u001b[0m     \u001b[39mreturn\u001b[39;00m dask\u001b[39m.\u001b[39marray\u001b[39m.\u001b[39mstore(\n\u001b[1;32m    296\u001b[0m         encode_cf_variable(xarray_dataarray)\u001b[39m.\u001b[39mdata\u001b[39m.\u001b[39mastype(numpy_dtype),\n\u001b[1;32m    297\u001b[0m         \u001b[39mself\u001b[39m,\n\u001b[1;32m    298\u001b[0m         lock\u001b[39m=\u001b[39mlock,\n\u001b[1;32m    299\u001b[0m         compute\u001b[39m=\u001b[39mcompute,\n\u001b[1;32m    300\u001b[0m     )\n",
      "File \u001b[0;32mrasterio/_base.pyx:448\u001b[0m, in \u001b[0;36mrasterio._base.DatasetBase.__exit__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mrasterio/_base.pyx:439\u001b[0m, in \u001b[0;36mrasterio._base.DatasetBase.close\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/planet/lib/python3.8/contextlib.py:531\u001b[0m, in \u001b[0;36mExitStack.close\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    528\u001b[0m             \u001b[39mraise\u001b[39;00m\n\u001b[1;32m    529\u001b[0m     \u001b[39mreturn\u001b[39;00m received_exc \u001b[39mand\u001b[39;00m suppressed_exc\n\u001b[0;32m--> 531\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mclose\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    532\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Immediately unwind the context stack.\"\"\"\u001b[39;00m\n\u001b[1;32m    533\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__exit__\u001b[39m(\u001b[39mNone\u001b[39;00m, \u001b[39mNone\u001b[39;00m, \u001b[39mNone\u001b[39;00m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Write an nc file for remapping\n",
    "ndvi3.rio.to_raster(r\"NDVI_time.nc\")\n",
    "# ndvi3.rio.to_raster(r\"NDVI_time.tif\")\n",
    "# ndvi3.to_netcdf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mask crs: EPSG:4326\n"
     ]
    }
   ],
   "source": [
    "mask_reproject_easymore = mask.to_crs(epsg=4326)\n",
    "print(\"mask crs:\", mask_reproject_easymore.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file written.\n"
     ]
    }
   ],
   "source": [
    "mask_reproject_easymore.to_file(\"mask_easymore.shp\")\n",
    "print('file written.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
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       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Variable (band: 1, y: 9422, x: 6984)&gt;\n",
       "[65803248 values with dtype=float32]\n",
       "Attributes:\n",
       "    AREA_OR_POINT:  Area</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Variable</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span>band</span>: 1</li><li><span>y</span>: 9422</li><li><span>x</span>: 6984</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f911140d-5e49-4c8c-8beb-89f071839f03' class='xr-array-in' type='checkbox' checked><label for='section-f911140d-5e49-4c8c-8beb-89f071839f03' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[65803248 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-0e8afed8-332b-4357-93c9-db6a7e11cdef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0e8afed8-332b-4357-93c9-db6a7e11cdef' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Variable (band: 1, y: 9422, x: 6984)>\n",
       "[65803248 values with dtype=float32]\n",
       "Attributes:\n",
       "    AREA_OR_POINT:  Area"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 3160, lon: 4990)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 3.611e+06 3.611e+06 3.611e+06 ... 3.627e+06 3.627e+06\n",
       "  * lon      (lon) float64 2.854e+05 2.854e+05 2.854e+05 ... 3.104e+05 3.104e+05\n",
       "Data variables: (12/30)\n",
       "    Band1    (lat, lon) float32 ...\n",
       "    Band2    (lat, lon) float32 ...\n",
       "    Band3    (lat, lon) float32 ...\n",
       "    Band4    (lat, lon) float32 ...\n",
       "    Band5    (lat, lon) float32 ...\n",
       "    Band6    (lat, lon) float32 ...\n",
       "    ...       ...\n",
       "    Band25   (lat, lon) float32 ...\n",
       "    Band26   (lat, lon) float32 ...\n",
       "    Band27   (lat, lon) float32 ...\n",
       "    Band28   (lat, lon) float32 ...\n",
       "    Band29   (lat, lon) float32 ...\n",
       "    Band30   (lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:       CF-1.5\n",
       "    GDAL:              GDAL 3.6.3, released 2023/03/07\n",
       "    history:           Mon Apr 10 16:30:19 2023: GDAL Create( NDVI_time_attem...\n",
       "    GDAL_description:  Calculated NDVI</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e542706d-90fa-4f5e-bdbb-3921accb3cb8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e542706d-90fa-4f5e-bdbb-3921accb3cb8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 3160</li><li><span class='xr-has-index'>lon</span>: 4990</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-92852fa0-c1a3-4912-846a-eb39ea72056e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-92852fa0-c1a3-4912-846a-eb39ea72056e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.611e+06 3.611e+06 ... 3.627e+06</div><input id='attrs-c4a7b3f5-c5aa-4e37-a02d-1d919c57612e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4a7b3f5-c5aa-4e37-a02d-1d919c57612e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6453c6e-d21b-4db1-9fe0-84a8e1303c17' class='xr-var-data-in' type='checkbox'><label for='data-a6453c6e-d21b-4db1-9fe0-84a8e1303c17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([3610782.5, 3610787.5, 3610792.5, ..., 3626567.5, 3626572.5, 3626577.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.854e+05 2.854e+05 ... 3.104e+05</div><input id='attrs-38f91a64-aaec-458a-b6c1-2099ad6d89db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38f91a64-aaec-458a-b6c1-2099ad6d89db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bf71dea-1735-442b-a78d-70bfa9848d57' class='xr-var-data-in' type='checkbox'><label for='data-2bf71dea-1735-442b-a78d-70bfa9848d57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([285427.5, 285432.5, 285437.5, ..., 310362.5, 310367.5, 310372.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-38d51fa1-f1eb-46b0-9964-39b866734924' class='xr-section-summary-in' type='checkbox'  ><label for='section-38d51fa1-f1eb-46b0-9964-39b866734924' class='xr-section-summary' >Data variables: <span>(30)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Band1</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45914abe-705d-405f-be22-1175de6ea498' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45914abe-705d-405f-be22-1175de6ea498' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91e526a6-fecf-4182-a9b9-ce34265cfcf9' class='xr-var-data-in' type='checkbox'><label for='data-91e526a6-fecf-4182-a9b9-ce34265cfcf9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 1</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band2</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a277d8e8-ab12-43d9-913c-8f441e9b45ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a277d8e8-ab12-43d9-913c-8f441e9b45ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58a253b1-a572-4772-a2db-1f06b490ea45' class='xr-var-data-in' type='checkbox'><label for='data-58a253b1-a572-4772-a2db-1f06b490ea45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 2</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band3</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-acd38a24-9882-4e98-8f48-3e2f0b27e165' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acd38a24-9882-4e98-8f48-3e2f0b27e165' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97256d4a-86a6-4a89-9416-39b80b24ee06' class='xr-var-data-in' type='checkbox'><label for='data-97256d4a-86a6-4a89-9416-39b80b24ee06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 3</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band4</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0c1929f1-45e7-4b36-839c-188f22214a73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c1929f1-45e7-4b36-839c-188f22214a73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0e1f935-4430-4757-bd54-d8c645718d7b' class='xr-var-data-in' type='checkbox'><label for='data-c0e1f935-4430-4757-bd54-d8c645718d7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 4</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band5</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ceae25ca-62e2-4374-8b0d-59e3bd0cd19b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ceae25ca-62e2-4374-8b0d-59e3bd0cd19b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a34f8c6-3690-40e3-901e-7211b3bac6cb' class='xr-var-data-in' type='checkbox'><label for='data-2a34f8c6-3690-40e3-901e-7211b3bac6cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 5</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band6</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-83ee30fa-3b4d-446b-88c6-b290e8c10dc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83ee30fa-3b4d-446b-88c6-b290e8c10dc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13b8a96a-7114-4147-a2fa-653e6db35aab' class='xr-var-data-in' type='checkbox'><label for='data-13b8a96a-7114-4147-a2fa-653e6db35aab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 6</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band7</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-43472c2c-1fa4-405d-8758-231f5c65f8cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43472c2c-1fa4-405d-8758-231f5c65f8cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b315116a-229a-491b-a1e6-73499c02c2e0' class='xr-var-data-in' type='checkbox'><label for='data-b315116a-229a-491b-a1e6-73499c02c2e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 7</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band8</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-de5fcc62-e082-4ee4-a7d2-41bd6ac6f01a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de5fcc62-e082-4ee4-a7d2-41bd6ac6f01a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26a57146-772e-4209-9a37-08ee34ccef47' class='xr-var-data-in' type='checkbox'><label for='data-26a57146-772e-4209-9a37-08ee34ccef47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 8</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band9</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d2623ef5-ec03-408a-8888-d8d2b7afc177' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2623ef5-ec03-408a-8888-d8d2b7afc177' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-143174ed-ca8e-4833-8cc5-884ab56f3d64' class='xr-var-data-in' type='checkbox'><label for='data-143174ed-ca8e-4833-8cc5-884ab56f3d64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 9</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band10</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2dfa9a82-159d-4ca9-a9e1-2ffff7556a47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2dfa9a82-159d-4ca9-a9e1-2ffff7556a47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4ecea07-1eab-4a2d-912e-ebdae5eba6b8' class='xr-var-data-in' type='checkbox'><label for='data-c4ecea07-1eab-4a2d-912e-ebdae5eba6b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 10</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band11</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d524411c-1621-42fd-8d20-987ddec3709d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d524411c-1621-42fd-8d20-987ddec3709d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f64eb8c-926e-4b97-aee4-51cde6c0bcff' class='xr-var-data-in' type='checkbox'><label for='data-6f64eb8c-926e-4b97-aee4-51cde6c0bcff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 11</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band12</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e4ab860d-e535-4b99-a183-9eb053256800' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4ab860d-e535-4b99-a183-9eb053256800' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57badd94-2257-4b40-b824-f153028eed47' class='xr-var-data-in' type='checkbox'><label for='data-57badd94-2257-4b40-b824-f153028eed47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 12</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band13</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-893294cb-d3a3-4fd1-9637-411eaee8ad03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-893294cb-d3a3-4fd1-9637-411eaee8ad03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-809aad84-9208-4304-abac-a9733e95be8a' class='xr-var-data-in' type='checkbox'><label for='data-809aad84-9208-4304-abac-a9733e95be8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 13</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band14</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1ebc1a4c-39ab-402c-b822-14aa7dddd5e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ebc1a4c-39ab-402c-b822-14aa7dddd5e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-222ee104-cc7c-4ba1-8f5d-57c6574f5358' class='xr-var-data-in' type='checkbox'><label for='data-222ee104-cc7c-4ba1-8f5d-57c6574f5358' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 14</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band15</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f33c054c-2297-4a61-94bb-32f50a28deea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f33c054c-2297-4a61-94bb-32f50a28deea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41796cde-3868-465b-b7ab-cdb875382b2d' class='xr-var-data-in' type='checkbox'><label for='data-41796cde-3868-465b-b7ab-cdb875382b2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 15</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band16</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e6cdb3f6-160e-4b0d-a9cf-5414a1a9e2a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6cdb3f6-160e-4b0d-a9cf-5414a1a9e2a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d6e3d91-2147-45f7-973e-63edb163e897' class='xr-var-data-in' type='checkbox'><label for='data-5d6e3d91-2147-45f7-973e-63edb163e897' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 16</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band17</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d3dd12a-b4d6-4a81-b0e9-bbd827ee6aed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d3dd12a-b4d6-4a81-b0e9-bbd827ee6aed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce2f35e3-e783-4554-a021-d0c71a69c7ca' class='xr-var-data-in' type='checkbox'><label for='data-ce2f35e3-e783-4554-a021-d0c71a69c7ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 17</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band18</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5b40f13c-cfb9-41a7-9fb4-8c99539e869a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b40f13c-cfb9-41a7-9fb4-8c99539e869a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd0ff0c7-2c3b-40a5-91e1-9e24081f88b2' class='xr-var-data-in' type='checkbox'><label for='data-cd0ff0c7-2c3b-40a5-91e1-9e24081f88b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 18</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band19</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca8d58e0-ae47-4117-b1eb-24a3fa4dd697' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca8d58e0-ae47-4117-b1eb-24a3fa4dd697' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-727fb956-99d8-41de-83d2-8938ab51e959' class='xr-var-data-in' type='checkbox'><label for='data-727fb956-99d8-41de-83d2-8938ab51e959' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 19</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band20</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe19964c-73c8-43fd-9ae2-87f77ad04c58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe19964c-73c8-43fd-9ae2-87f77ad04c58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0472737-7d33-45b8-9a1b-54f850e69d9c' class='xr-var-data-in' type='checkbox'><label for='data-f0472737-7d33-45b8-9a1b-54f850e69d9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 20</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band21</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-990f6909-4d4c-4066-881a-a83382d0478c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-990f6909-4d4c-4066-881a-a83382d0478c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-702ba632-d1c4-4c7a-b201-dcf8dd601434' class='xr-var-data-in' type='checkbox'><label for='data-702ba632-d1c4-4c7a-b201-dcf8dd601434' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 21</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band22</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-de0c75dd-449e-4d92-9889-06ff0ac505f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de0c75dd-449e-4d92-9889-06ff0ac505f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6361551d-5d8d-4b7e-ab21-cbf36d5063f0' class='xr-var-data-in' type='checkbox'><label for='data-6361551d-5d8d-4b7e-ab21-cbf36d5063f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 22</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band23</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-087b7530-33c8-4b83-b2ae-bd9e2b341994' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-087b7530-33c8-4b83-b2ae-bd9e2b341994' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b4fde45-55b5-47a1-ac86-a2536506ee82' class='xr-var-data-in' type='checkbox'><label for='data-9b4fde45-55b5-47a1-ac86-a2536506ee82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 23</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band24</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4deefd21-2ffa-449a-b0c7-3bf90f7df8a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4deefd21-2ffa-449a-b0c7-3bf90f7df8a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93a78a64-4474-49e5-b35c-47a1793b1dcb' class='xr-var-data-in' type='checkbox'><label for='data-93a78a64-4474-49e5-b35c-47a1793b1dcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 24</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band25</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ab27bbd0-f587-423b-b475-3dafb6dbefeb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab27bbd0-f587-423b-b475-3dafb6dbefeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5db2b33-edb9-40e9-8fc6-22c59692fe0a' class='xr-var-data-in' type='checkbox'><label for='data-f5db2b33-edb9-40e9-8fc6-22c59692fe0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 25</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band26</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca52a52b-9689-42fe-a794-0aa41e13acdf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca52a52b-9689-42fe-a794-0aa41e13acdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14cf9e74-1afc-417e-9459-ce734dfcf2b0' class='xr-var-data-in' type='checkbox'><label for='data-14cf9e74-1afc-417e-9459-ce734dfcf2b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 26</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band27</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c176eab6-c2e9-42df-a4c1-3c25098e773e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c176eab6-c2e9-42df-a4c1-3c25098e773e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f871f7b0-9715-4eb0-a0cf-743cadde9bc0' class='xr-var-data-in' type='checkbox'><label for='data-f871f7b0-9715-4eb0-a0cf-743cadde9bc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 27</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band28</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-85d6dce8-144d-4596-8458-e2ec5e9b824d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85d6dce8-144d-4596-8458-e2ec5e9b824d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fc73a14-2801-42a3-bd5f-5a3aca677fd5' class='xr-var-data-in' type='checkbox'><label for='data-1fc73a14-2801-42a3-bd5f-5a3aca677fd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 28</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band29</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-61171a03-daa2-4c2d-810a-f9331f5dfc36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61171a03-daa2-4c2d-810a-f9331f5dfc36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e7f1d86-c0d9-4810-aa71-da72bf9d8394' class='xr-var-data-in' type='checkbox'><label for='data-4e7f1d86-c0d9-4810-aa71-da72bf9d8394' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 29</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Band30</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a008ae7e-1128-4f83-82f5-a50c12adfd4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a008ae7e-1128-4f83-82f5-a50c12adfd4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad7b6f9e-cb30-48b9-8b98-066daacc5aca' class='xr-var-data-in' type='checkbox'><label for='data-ad7b6f9e-cb30-48b9-8b98-066daacc5aca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GDAL Band Number 30</dd></dl></div><div class='xr-var-data'><pre>[15768400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a98475b-54bf-4310-ba7c-e4fc412ea941' class='xr-section-summary-in' type='checkbox'  ><label for='section-4a98475b-54bf-4310-ba7c-e4fc412ea941' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d91f6dd2-0c6c-4842-87a4-ece89fedc27b' class='xr-index-data-in' type='checkbox'/><label for='index-d91f6dd2-0c6c-4842-87a4-ece89fedc27b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([3610782.5, 3610787.5, 3610792.5, 3610797.5, 3610802.5, 3610807.5,\n",
       "              3610812.5, 3610817.5, 3610822.5, 3610827.5,\n",
       "              ...\n",
       "              3626532.5, 3626537.5, 3626542.5, 3626547.5, 3626552.5, 3626557.5,\n",
       "              3626562.5, 3626567.5, 3626572.5, 3626577.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=3160))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b4d36894-4eac-4dfb-9dce-b4ba642bc459' class='xr-index-data-in' type='checkbox'/><label for='index-b4d36894-4eac-4dfb-9dce-b4ba642bc459' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([285427.5, 285432.5, 285437.5, 285442.5, 285447.5, 285452.5,\n",
       "              285457.5, 285462.5, 285467.5, 285472.5,\n",
       "              ...\n",
       "              310327.5, 310332.5, 310337.5, 310342.5, 310347.5, 310352.5,\n",
       "              310357.5, 310362.5, 310367.5, 310372.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=4990))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f08dc89e-ba0a-481d-b676-dfb67bd50718' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f08dc89e-ba0a-481d-b676-dfb67bd50718' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.5</dd><dt><span>GDAL :</span></dt><dd>GDAL 3.6.3, released 2023/03/07</dd><dt><span>history :</span></dt><dd>Mon Apr 10 16:30:19 2023: GDAL Create( NDVI_time_attempt2.nc, ... )</dd><dt><span>GDAL_description :</span></dt><dd>Calculated NDVI</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 3160, lon: 4990)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 3.611e+06 3.611e+06 3.611e+06 ... 3.627e+06 3.627e+06\n",
       "  * lon      (lon) float64 2.854e+05 2.854e+05 2.854e+05 ... 3.104e+05 3.104e+05\n",
       "Data variables: (12/30)\n",
       "    Band1    (lat, lon) float32 ...\n",
       "    Band2    (lat, lon) float32 ...\n",
       "    Band3    (lat, lon) float32 ...\n",
       "    Band4    (lat, lon) float32 ...\n",
       "    Band5    (lat, lon) float32 ...\n",
       "    Band6    (lat, lon) float32 ...\n",
       "    ...       ...\n",
       "    Band25   (lat, lon) float32 ...\n",
       "    Band26   (lat, lon) float32 ...\n",
       "    Band27   (lat, lon) float32 ...\n",
       "    Band28   (lat, lon) float32 ...\n",
       "    Band29   (lat, lon) float32 ...\n",
       "    Band30   (lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:       CF-1.5\n",
       "    GDAL:              GDAL 3.6.3, released 2023/03/07\n",
       "    history:           Mon Apr 10 16:30:19 2023: GDAL Create( NDVI_time_attem...\n",
       "    GDAL_description:  Calculated NDVI"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check written ndvi file\n",
    "filename = 'NDVI_time_attempt2.nc'\n",
    "ndvi_dataset = xr.open_dataset(filename)\n",
    "ndvi_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EASYMORE version 0.0.4 is initiated.\n"
     ]
    }
   ],
   "source": [
    "# %% Now remapping following this tutorial\n",
    "# https://github.com/ShervanGharari/EASYMORE/blob/main/examples/Chapter1_E1.ipynb\n",
    "# # loading EASYMORE\n",
    "from easymore.easymore import easymore\n",
    "\n",
    "# initializing EASYMORE object\n",
    "esmr = easymore()\n",
    "\n",
    "# specifying EASYMORE objects\n",
    "# name of the case; the temporary, remapping and remapped file names include case name\n",
    "esmr.case_name                = 'ndvi_remapping'              \n",
    "# temporary path that the EASYMORE generated GIS files and remapped file will be saved\n",
    "esmr.temp_dir                 = '../temporary/'\n",
    "\n",
    "# name of target shapefile that the source netcdf files should be remapped to\n",
    "# It needs me to reproject my shapefile to WGS84 (epsg:4326)\n",
    "#       Note: It said please\n",
    "esmr.target_shp = 'mask_easymore.shp'\n",
    "\n",
    "\n",
    "# name of netCDF file(s); multiple files can be specified with *\n",
    "esmr.source_nc                = 'NDVI_time.nc'\n",
    "\n",
    "# name of variables from source netCDF file(s) to be remapped\n",
    "esmr.var_names                = [\"NDVI\"]\n",
    "# rename the variables from source netCDF file(s) in the remapped files;\n",
    "# it will be the same as source if not provided\n",
    "esmr.var_names_remapped       = [\"NDVI\"]\n",
    "# name of variable longitude in source netCDF files\n",
    "esmr.var_lon                  = 'lon'\n",
    "# name of variable latitude in source netCDF files\n",
    "esmr.var_lat                  = 'lat'\n",
    "# name of variable time in source netCDF file; should be always time\n",
    "esmr.var_time                 = 'time'\n",
    "# location where the remapped netCDF file will be saved\n",
    "esmr.output_dir               = 'outputfiles/remapped/'\n",
    "# format of the variables to be saved in remapped files,\n",
    "# if one format provided it will be expanded to other variables\n",
    "esmr.format_list              = ['f4']\n",
    "# fill values of the variables to be saved in remapped files,\n",
    "# if one value provided it will be expanded to other variables\n",
    "esmr.fill_value_list          = ['-9999.00']\n",
    "# if required that the remapped values to be saved as csv as well\n",
    "esmr.save_csv                 = True\n",
    "esmr.complevel                 =  9\n",
    "# if uncommented EASYMORE will use this and skip GIS tasks\n",
    "#esmr.remap_csv                = '../temporary/ERA5_Medicine_Hat_remapping.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no author name is provide and the author name is changed to (author name)!\n",
      "EASYMORE will remap variable  NDVI  from source file to variable  NDVI  in remapped NeCDF file\n",
      "EASYMORE detects that target shapefile is in WGS84 (epsg:4326)\n",
      "EASYMORE detects that no field for ID is provided in sink/target shapefile\n",
      "arbitarary values of ID are added in the field ID_t\n",
      "EASYMORE detects that either of the fields for latitude or longitude is not provided in sink/target shapefile\n",
      "calculating centroid of shapes in WGS84 projection;\n",
      "for better appximation use the easymore equal area centroid function to preprocess target shapefile\n",
      "EASYMORE detects that no field for latitude is provided in sink/target shapefile\n",
      "latitude values are added in the field lat_t\n",
      "EASYMORE detects that no field for longitude is provided in sink/target shapefile\n",
      "longitude values are added in the field lon_t\n",
      "it seems everything is OK with the sink/target shapefile; added to EASYMORE object target_shp_gpd\n",
      "EASYMORE will save standard shapefile for EASYMORE claculation as:\n",
      "../temporary/ndvi_remapping_target_shapefile.shp\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'NDVI'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[26], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# execute EASYMORE\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m esmr\u001b[39m.\u001b[39;49mnc_remapper()\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/planet/lib/python3.8/site-packages/easymore/easymore.py:80\u001b[0m, in \u001b[0;36measymore.nc_remapper\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     78\u001b[0m target_shp_gpd\u001b[39m.\u001b[39mto_file(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtemp_dir\u001b[39m+\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcase_name\u001b[39m+\u001b[39m\u001b[39m'\u001b[39m\u001b[39m_target_shapefile.shp\u001b[39m\u001b[39m'\u001b[39m) \u001b[39m# save\u001b[39;00m\n\u001b[1;32m     79\u001b[0m \u001b[39m# check the source NetCDF files\u001b[39;00m\n\u001b[0;32m---> 80\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcheck_source_nc()\n\u001b[1;32m     81\u001b[0m \u001b[39m# find the case\u001b[39;00m\n\u001b[1;32m     82\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mNetCDF_SHP_lat_lon()\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/planet/lib/python3.8/site-packages/easymore/easymore.py:350\u001b[0m, in \u001b[0;36measymore.check_source_nc\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    348\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    349\u001b[0m     ncid      \u001b[39m=\u001b[39m nc4\u001b[39m.\u001b[39mDataset(nc_names[\u001b[39m0\u001b[39m])\n\u001b[0;32m--> 350\u001b[0m     var_dim   \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(ncid\u001b[39m.\u001b[39;49mvariables[\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mvar_names[\u001b[39m0\u001b[39;49m]]\u001b[39m.\u001b[39mdimensions)\n\u001b[1;32m    351\u001b[0m     lat_dim   \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(ncid\u001b[39m.\u001b[39mvariables[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvar_lat]\u001b[39m.\u001b[39mdimensions)\n\u001b[1;32m    352\u001b[0m     lon_dim   \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(ncid\u001b[39m.\u001b[39mvariables[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvar_lon]\u001b[39m.\u001b[39mdimensions)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'NDVI'"
     ]
    }
   ],
   "source": [
    "# execute EASYMORE\n",
    "esmr.nc_remapper()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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