Export Landsat Scenes from Digital Earth Australia

Export Landsat Scenes from Digital Earth Australia.ipynb

{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Accessing Landsat Scenes from Digital Earth Australia\n\n* It's no longer trivial to access a Landsat Scene from Digital Earth Australia\n* The data has been reprojected to Australia Albers Equal Area (EPSG:3577) projection, and split into 100km x 100km tiles, normally about 10 to a single scene\n* This notebook steps through reconstructing the original Scene\n\nPurely for example purposes, we will look for a Landsat 8 scene from April 2016, at Path = 96, Row = 84 (just north of Canberra)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## 1. Import modules and connect to DEA"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-09-11T03:00:08.659123Z",
          "end_time": "2017-09-11T03:00:11.145485Z"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "import datacube\nfrom datacube.storage.storage import write_dataset_to_netcdf\nfrom datacube.helpers import write_geotiff\nfrom IPython.display import Image\n\ndc = datacube.Datacube()",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## 2. Find the level 1 scene product"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-09-11T03:00:11.148035Z",
          "end_time": "2017-09-11T03:00:11.165922Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "ls8_l1_scene = dc.index.products.get_by_name('ls8_level1_scene')\nls8_l1_scene",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "DatasetType(name='ls8_level1_scene', id_=14)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-09-11T01:58:42.242984Z",
          "end_time": "2017-09-11T01:58:42.248374Z"
        }
      },
      "cell_type": "markdown",
      "source": "## 3. Inspect which fields are available for filtering on the level 1 scene\n\n* We are particularly interested in `sat_path` and `sat_row`"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-09-11T03:00:11.168073Z",
          "end_time": "2017-09-11T03:00:11.173072Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "ls8_l1_scene.fields",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "{'ancillary_quality': None,\n 'format': 'GeoTiff',\n 'gqa': None,\n 'gqa_abs_iterative_mean_xy': None,\n 'gqa_abs_xy': None,\n 'gqa_cep90': None,\n 'gqa_error_message': None,\n 'gqa_final_gcp_count': None,\n 'gqa_iterative_mean_xy': None,\n 'gqa_iterative_stddev_xy': None,\n 'gqa_mean_xy': None,\n 'gqa_ref_source': None,\n 'gqa_stddev_xy': None,\n 'gsi': None,\n 'instrument': 'OLI_TIRS',\n 'lat': None,\n 'lon': None,\n 'orbit': None,\n 'platform': 'LANDSAT_8',\n 'product_type': 'level1',\n 'sat_path': None,\n 'sat_row': None,\n 'time': None}"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-09-11T02:41:44.182250Z",
          "end_time": "2017-09-11T02:41:44.187875Z"
        }
      },
      "cell_type": "markdown",
      "source": "## 4. Perform a search for our level 1 scene using `dc.find_datasets`\n\n* There is one dataset returned"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-09-11T03:00:11.175229Z",
          "end_time": "2017-09-11T03:00:11.239951Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "scene_spec = dict(product='ls8_level1_scene', sat_path=96, sat_row=84)\ntime_spec = ('2016-04-14', '2016-05-01')\n\ndc.find_datasets(time=time_spec, **scene_spec)",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "[Dataset <id=34e7be4c-74c0-4eb6-b9b0-317ad19b907d type=ls8_level1_scene location=/g/data/v10/reprocess/ls8/level1/2016/04/LS8_OLITIRS_OTH_P51_GALPGS01-032_096_084_20160421/ga-metadata.yaml>]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "This data is unlikely to be accessible directly, and isn't what we are looking for anyway, but it is an important part of the **provenance** chain.\n\nThe NBAR/T data is available in **DEA** through the `_nbar(t)_albers` products.\n\n## 5. Use `source_filter` to find our NBAR datasets\n\n* This retrieves 10 datasets that are descended from our scene of interest."
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-09-11T03:00:11.242055Z",
          "end_time": "2017-09-11T03:00:11.580070Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "nbart_datasets = dc.find_datasets(product='ls8_nbar_albers', source_filter=scene_spec,  time=time_spec)\nnbart_datasets",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "[Dataset <id=05697a85-3977-4581-8820-2202981c95ec type=ls8_nbar_albers location=/g/data/rs0/datacube/002/LS8_OLI_NBAR/6_-38/LS8_OLI_NBAR_3577_6_-38_20160421002655000000.nc>,\n Dataset <id=0acf475f-1bad-423c-bc6a-2b76064ae238 type=ls8_nbar_albers location=/g/data/rs0/datacube/002/LS8_OLI_NBAR/6_-39/LS8_OLI_NBAR_3577_6_-39_20160421002655000000.nc>,\n Dataset <id=2329d967-8010-4952-8209-d2dcb32eb368 type=ls8_nbar_albers location=/g/data/rs0/datacube/002/LS8_OLI_NBAR/7_-39/LS8_OLI_NBAR_3577_7_-39_20160421002655000000.nc>,\n Dataset <id=5920f274-f8ad-40d9-8184-17e800427cd7 type=ls8_nbar_albers location=/g/data/rs0/datacube/002/LS8_OLI_NBAR/8_-40/LS8_OLI_NBAR_3577_8_-40_20160421002655000000.nc>,\n Dataset <id=8b5b75a4-2539-42d6-97fd-6bc2a54f4086 type=ls8_nbar_albers location=/g/data/rs0/datacube/002/LS8_OLI_NBAR/8_-38/LS8_OLI_NBAR_3577_8_-38_20160421002655000000.nc>,\n Dataset <id=8e2a8e79-eabd-40ad-9f5c-02cc15be2313 type=ls8_nbar_albers location=/g/data/rs0/datacube/002/LS8_OLI_NBAR/7_-38/LS8_OLI_NBAR_3577_7_-38_20160421002655000000.nc>,\n Dataset <id=8e32c019-5435-44ca-be6e-839c471014a1 type=ls8_nbar_albers location=/g/data/rs0/datacube/002/LS8_OLI_NBAR/9_-38/LS8_OLI_NBAR_3577_9_-38_20160421002655000000.nc>,\n Dataset <id=8fb5b529-f3a3-4ecf-89b0-60cffbfc68eb type=ls8_nbar_albers location=/g/data/rs0/datacube/002/LS8_OLI_NBAR/7_-40/LS8_OLI_NBAR_3577_7_-40_20160421002655000000.nc>,\n Dataset <id=a57ed42e-84c2-40b2-96f8-f3be0395e6ac type=ls8_nbar_albers location=/g/data/rs0/datacube/002/LS8_OLI_NBAR/8_-39/LS8_OLI_NBAR_3577_8_-39_20160421002655000000.nc>,\n Dataset <id=dc0f629f-88bd-4e1b-bcd3-ede3f0e2f40c type=ls8_nbar_albers location=/g/data/rs0/datacube/002/LS8_OLI_NBAR/7_-37/LS8_OLI_NBAR_3577_7_-37_20160421002655000000.nc>]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## 6. Load the data into RAM using `.load()` - same search criteria"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-09-11T03:00:11.582318Z",
          "end_time": "2017-09-11T03:02:48.634755Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "nbart_data = dc.load(product='ls8_nbart_albers', source_filter=scene_spec,  time=time_spec)\nprint(nbart_data)",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": "<xarray.Dataset>\nDimensions:          (time: 1, x: 9637, y: 9784)\nCoordinates:\n  * time             (time) datetime64[ns] 2016-04-21T00:26:55\n  * y                (y) float64 -3.683e+06 -3.683e+06 -3.683e+06 -3.683e+06 ...\n  * x                (x) float64 6.628e+05 6.628e+05 6.628e+05 6.628e+05 ...\nData variables:\n    coastal_aerosol  (time, y, x) int16 -999 -999 -999 -999 -999 -999 -999 ...\n    blue             (time, y, x) int16 -999 -999 -999 -999 -999 -999 -999 ...\n    green            (time, y, x) int16 -999 -999 -999 -999 -999 -999 -999 ...\n    red              (time, y, x) int16 -999 -999 -999 -999 -999 -999 -999 ...\n    nir              (time, y, x) int16 -999 -999 -999 -999 -999 -999 -999 ...\n    swir1            (time, y, x) int16 -999 -999 -999 -999 -999 -999 -999 ...\n    swir2            (time, y, x) int16 -999 -999 -999 -999 -999 -999 -999 ...\nAttributes:\n    crs:      EPSG:3577\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "This takes about 1.3Gb RAM"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-09-11T03:02:48.637634Z",
          "end_time": "2017-09-11T03:02:48.643208Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "nbart_data.nbytes / 2**20",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "1259.0342407226562"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## 7. Write out to a GeoTIFF file\n\n* And check the file size\n* And summary information from `gdalinfo`"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-09-11T03:02:48.645449Z",
          "end_time": "2017-09-11T03:03:24.762532Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "geotiff_name = '/g/data/u46/users/dra547/nbart_test.tif'\n\nwrite_geotiff(geotiff_name, nbart_data, time_index=0)\n\n!ls -lh $geotiff_name\n\n!gdalinfo $geotiff_name",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": "-rw-r--r-- 1 dra547 u46 867M Sep 11 13:03 /g/data/u46/users/dra547/nbart_test.tif\nDriver: GTiff/GeoTIFF\nFiles: /g/data/u46/users/dra547/nbart_test.tif\nSize is 9637, 9784\nCoordinate System is:\nPROJCS[\"GDA94 / Australian Albers\",\n    GEOGCS[\"GDA94\",\n        DATUM[\"Geocentric_Datum_of_Australia_1994\",\n            SPHEROID[\"GRS 1980\",6378137,298.257222101,\n                AUTHORITY[\"EPSG\",\"7019\"]],\n            TOWGS84[0,0,0,0,0,0,0],\n            AUTHORITY[\"EPSG\",\"6283\"]],\n        PRIMEM[\"Greenwich\",0,\n            AUTHORITY[\"EPSG\",\"8901\"]],\n        UNIT[\"degree\",0.0174532925199433,\n            AUTHORITY[\"EPSG\",\"9122\"]],\n        AUTHORITY[\"EPSG\",\"4283\"]],\n    PROJECTION[\"Albers_Conic_Equal_Area\"],\n    PARAMETER[\"standard_parallel_1\",-18],\n    PARAMETER[\"standard_parallel_2\",-36],\n    PARAMETER[\"latitude_of_center\",0],\n    PARAMETER[\"longitude_of_center\",132],\n    PARAMETER[\"false_easting\",0],\n    PARAMETER[\"false_northing\",0],\n    UNIT[\"metre\",1,\n        AUTHORITY[\"EPSG\",\"9001\"]],\n    AXIS[\"Easting\",EAST],\n    AXIS[\"Northing\",NORTH],\n    AUTHORITY[\"EPSG\",\"3577\"]]\nOrigin = (662750.000000000000000,-3682875.000000000000000)\nPixel Size = (25.000000000000000,-25.000000000000000)\nMetadata:\n  AREA_OR_POINT=Area\nImage Structure Metadata:\n  COMPRESSION=LZW\n  INTERLEAVE=BAND\nCorner Coordinates:\nUpper Left  (  662750.000,-3682875.000) (139d11'11.66\"E, 33d34'58.95\"S)\nLower Left  (  662750.000,-3927475.000) (139d20'19.56\"E, 35d46'35.83\"S)\nUpper Right (  903675.000,-3682875.000) (141d47'24.71\"E, 33d26'22.97\"S)\nLower Right (  903675.000,-3927475.000) (141d59'49.72\"E, 35d37'46.29\"S)\nCenter      (  783212.500,-3805175.000) (140d34'41.31\"E, 34d36'41.15\"S)\nBand 1 Block=256x256 Type=Int16, ColorInterp=Gray\n  NoData Value=0\nBand 2 Block=256x256 Type=Int16, ColorInterp=Undefined\n  NoData Value=0\nBand 3 Block=256x256 Type=Int16, ColorInterp=Undefined\n  NoData Value=0\nBand 4 Block=256x256 Type=Int16, ColorInterp=Undefined\n  NoData Value=0\nBand 5 Block=256x256 Type=Int16, ColorInterp=Undefined\n  NoData Value=0\nBand 6 Block=256x256 Type=Int16, ColorInterp=Undefined\n  NoData Value=0\nBand 7 Block=256x256 Type=Int16, ColorInterp=Undefined\n  NoData Value=0\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-09-11T03:03:24.765889Z",
          "end_time": "2017-09-11T03:03:32.680148Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "preview_name = '/g/data/u46/users/dra547/nbart_test_preview.jpg'\n!gdal_translate -ot byte -of JPEG -b 4 -b 3 -b 2 -outsize 500 0 -scale 0 4000 $geotiff_name $preview_name\n\nImage(preview_name)",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Input file size is 9637, 9784\n0...10...20...30...40...50...60...70...80...90...100 - done.\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "<IPython.core.display.Image object>",
            "image/jpeg": 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k+l82FUZCr+5XBCCrN3XQ8f6/GpXkiypeKSKTRqRPg5WKvKfHljTkVDsp4k/ofB1H10uz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x/HpDnxQJUR+m28sSO6BBUWbFgDzWsJzNtaLj7NxxmMgsCOJflfn7dePPR/fXo/dodrjYS7Tk45\nzWitY4puQZQhagnY4sDZPg33esNjwseT1Dj5uTK2TjRsDPH1H7lPRSx2pIH2/wCmuXKpadVHlHCh\nlCvH+HZCj2wWMmuJ6B80AKFV8V560yn+HeTNDhY2FlwPkZas8aSsqKY+IbkCT54srUCWANsFrtY3\n+D/+vtBhfTETUfMUxFmuXZ+rvwCfPnTPj+oMnP2/ZcXDVly9qXnGihvpk5AE2S1gcIz8AX4FXq7p\nWc6t0LyYGTg7q2Ai5AlXkrpAgk5GiFU2QCCeIJF+bHLoGZDse8Z+2HOwoYUKwg/ho1YSmMHiX4kX\nxJ5EsPpHFrK/SDe7bKm4bjLvmfDjTZKyKWxM9GmXJ5H2ya4iyCbFnyp7+1h/gu4+hN9x4Nq318jN\nx4SkwYBPaXkCY1ViSwtwwsKrVYujW8stjjbwlbvgrFs+25se15m1RZMfs+yclXmNr9XfTcWXjYK0\nCegARed7zGYA8S5JB6kMTdczddAdA9k918/PWt49Q77H6l9KrlY2NwzVFsqSLNFCHUoWkIWr/MgB\nphd0K1je7wSS4+POsbPBjhseMLYCqBbAMSe+TEkL4LE0LF87Smdabh0K+JlSY+QJHTlV2p+evn9N\nN2J6i54sSYYaCWD6qW6TzRB8r3x783/bS0drnGH+J4OAbpiR0Lrv5+/xqJHE8cyPHMsdAsshJ6IB\nIHVm7FD9fNapxUuiVJw7N22n+JG7RZUIzpEdJwCxaqBAF0RfEmvHi7oDxqP6hzMPeWSfIggw8iMU\n3s9CReIPSnyej1/T7kZfhb86Yhx5Yhz5DkzXy6Fgiqo3X9/1OrjK3ImJVMfuIFHFxfIH9f1Nf0/t\nrztS6Z6I8FsSTJConoTOYR9KlBRFfbv5rUaTBWNnKJGzlh9Td8gerJN91oikd0WQn2grKeCOQfB8\nj5Hmv2NfOvvN3AnHV4/eEZP5qPG7Isn+366m30i6VWwgJjUMVAPX5nAANV1/218zRvAZmijb8Vkq\nGWQfIFfSPjv/AO/jX7BC8zGOdZDypgSPNnz/AL++p0mJjfgoUfJfkGJADE+2GPKm+miaBH9B4+Jb\nSdlJNqjjtqjDXJgycb2J248jG4kE1eR0T4ofvpV3/cYcmYQrCSVYGSQAAsQPB6vq6/6daa5+cIil\n9vIljYqWCcVBW/FnwSL+DX20vYuxTSLNLNFIXlk4qFUmmvsXd9cgSfi++yNdPElbkzl5W6UEPW17\nrnP6bgfatwxtqi2vFM8cDZDNLIzKOSqosFSy2briXJNDVx6Yzcf1VlLhbhjytMzo4zYpT7iyXy6B\nUkKVEo6FdUa+lhmOVu82FJGrEwpGnCND9UgUeQQasH7N5A+NSNt9TywTtjJCp96SyJZQAjUQrgiu\nJHKyRQNdirBvjetHPlxtJm8y7JFBF+DzsiH/AAzjcc+NGEMT0AEJblXPkCDfZAqrA1Y+ns6XLiUS\niFMSUKiY/bGjErAgkn6CLNfHi+jrHd/3DDHpyYSZeQm5ZkcBLK0scfGMFfbZGYKaKKbNnkzV0Bdf\n6Y3rcdt2jJ3aHOr8HkpC8Ms5Ik5C4+EddMvBzbdUaA6INqKi7iS25KpM0r+InpqBMeORMiPDwWQq\nVVQv8wdKL+emc9/ANk0BrIsrZMrboZGyKkDkqjK57o9nq7sf89a1l+pT6jmfZXiYY2YqCWSQu5QA\nc+lUC3seFH+U340g+o/T+d6ayHjeWVsYjkjoCVZfAP8AUDWOr+IV18mKkGPm58mWsCRpjoLm5IWV\nAGHGzRC8nIHwO+6FnUCaHPhy45nlYmGS1lZ7FA/5SSOv1H3H31Y5m5FoWxUZo4w4JA6IIoWT5I+K\nPiz4s6lbb6ih/wANlxN5xmzserjYO1of8vV+PPVddn51apdEPezltm5PnZBifIEEU8ZhynikERo9\nEu579s3TDx4sNxA1b7vPt+Xk8Y9x3TNePHZXfcpi4ltEcqLCcACWI/NyfiCCGBKTuC4UeWZNsneT\nGL/SsgAkX9Ovk/78ascDe4YYlglYAmRgZRaSLG4AKEgjkoIsA9AjwQWBtKiHo5HccBYY2Rodo3PD\nyYWxcsu6iSIFlAMXFo1Isu4LG2ZgT5AnbV61w9h9Q5Ue3ZmFHicBIHduQYceVKeKgEBn6IBBJQX5\n1Bn9XR4Gec/acifJgkbgkcvFChjjVIyY0bgyrRA+45X/AJSMx3ESyZ8zsS5kYtzJJv8AUk9k/qdZ\nVs22jd5vU2Du2W+VJkRnAf6VkICszj6TzIPXwfA/QfenTJhxtxSbLyCygB4pZCCf07APg9G/HfRA\n7yrbeRQ1OUZT9IPj/wCtXsMO4ZnNMiR3/lkrRLWB9r1ycKOsfJ/C49Rb0N9y/c24ywjFBJjVyygM\n5Y8ez0LA8fANfaD/AI/mvkwu65Ern6T9XbmxQFgmq4//AK0vH6JGi4lXQmwB33r4zcXMfF97k1NY\nABa6H6nz/f41j8aeM1eRx1Gk7R6xx9v3d+DyPtz4pDrL7drKycWIsFQpNdnyAt3XE9sDfsrBzsjd\nnSRc327/ABvtu0S8qXkSRXbch38E/IB1mL5WSOMDTNxNKyE8gVBFdkfB6r9NS8TdpsWF1iDMXj4N\n7qUAeuxRF+CO76671P4ui/zd37GDccTKztzeaKSOJHMZAiAWPoV0AAKAP3PV2TyvVfNjzYSySZy+\n6qyCuNHs3yP28/t4+NSdu9StIEE0nuE3D9TUFHQDWPI82D8ag5/qGaLIVhBGUb8gZux55f8AMf20\n4zuhyhV2Shgpk42c0W8Q7bKkIjbFpwZ2JH0BlBVgQR5r9qBIXN2xJ9t2jJeZGHOX8OpH1Ly7J7Bo\n/TY/r199N/oOGb1DkybPLBjoOT5XuSEiRiQqhV6N9gHoXV90DdR/FSWDbMjC9M4eRJJFhl58gGfm\nPec8aYf8QC3f2fwPnvCHSOHkndszrRo0a9B5g0aNGgDRo0aANGjRoA0aNGgJGBg5G57jjYGHH7mV\nlSpDCnIDk7EBRZ6Fkjzr3Rtu3xbJsWHtmL7ksWFjJBF7jDkyooUWQPJA+Bryv/AzZv8AF/4oYUrJ\nA8O3xSZkiTC7ocFKij9Qd0YeK43dga9YZkBycSSJW4llIBq60QOeHnx5sBliKsAxXph+YeR/v7HX\naWMtJE4Abg18SPv1d/oCf9dJ2xyvFusu1ysRzHFo2AN1fd3YbxXRHX6WGbE3XGnlkg99GljUu5Hj\nj0Q36Dv5+x81etkqZido6biAcB1kjEiEASfUFpfki/8Af66wbdvSk2NFuB97cIsF8v3VmkjYNGOD\nM49sjkW4gMT0OKH5oa3+ZozjO861Gv1MHAPQ7uu/tesj/iF6jg3bFjxscqvv0DIrqykKXogjzYY9\nGj5oeb4+R8dO3jV4U/oH1dj4kWXtW55pnwpOEcaKhb3LWiQasUACfkkjs12rb3uuPkZGPgJt4xMf\nGpYlklkeUxljJbcmFBgwJKlb5WOvy/n+EYXszZyZjpFHHHCmNNKGmmlCqHoA/kWmpugQqr2etLe7\nYjS+9ko0jcTVcB4od9f9vvfeoVN0W7Ss3b0Zv+KuyxYW77ggY2AwdSoHLq7J4EH4b9/B1Wz/AIDr\nHjgklgdZfw8iKXkJprUX0KZi3EG+/joDIYPUmasWMrFiIIfaT+Wv0IOwAfgE2Sf1P66ZPSnqiTcd\n/ijyxMcpY3EEhYsQQeYVT5FkEivk+PnWcW8Y5Jajc9syMUYiRpMJZI8dPb9z6FmssFFdggkXY7+T\nplijoq56PALVEf8A7/r+v3156yf4h5Em8xSt7i4xh9uFSnFgoDKKb7BgbrzR/fWtenPVkW9QqSBA\nZIV9pLbiL8XX6lf71rpF12RJWON91pY3LaycmSGfNcx5cqhBJNfEFixoM3VNxA4r4Kj45qvL6zfD\n3nK2/chwh5sqmRRwkBNrYFHlRAI+ST9N3pmyn2/eNrOTj08yowXgxoE0WXr/AIiKNA3Z8i9ZKSaE\nYNMz/wBQ4kuNnHE2724clmUYmU8kj/5WqmUE/V7q9AgDiPI1nWBl5O2ZuNlyYqSFXBlxyAwYWbBb\nsqCTfXi/vrQt93neMbaZsHJzcOdpYAOK/WqHrgSCAwcEgnrqlv8AVH9PvFl52DhTJxjDLGZVJtja\n9Uf8wpj/ANtee/aPVx+yJ6j2yDHz8fOjkE8awj3EmW/rC2VI7vs91XR7r4/NhyotoXAzXxIcjHky\nfdlSuqtUZPvdWf0OtW3P0viT7O8jZcMWRjQsVSgC1gLzsfmAAXvsWWHR8IeH6Pfe/SQEX4j8Qk0j\nw5PEHHlVVCmPySrA8Qt1yJI78ikrSIbp2cU9YbxHu+VjY8eRiSPKI40kkal/yohB6KqHXohuh4Nm\n+HrPb8LD37Fxtpz8rdAYI1x0Ue5JJy4hULAD9RQWgBGO7PGgiysobxkxpjJAj2ix1/Lv7gC67ANj\nUOLAl3GeUWqywERgCyJeuyAbPdeTQP8AypOuyZK+jRtl9QR7bDvGzZuFDLjZMMSe0Ji0ccntl3cM\nqcenKWxPJbQgsdV+5g7fur/hMKOd3hDyYsccgbEl4kyMvkkIUe+dHs9EC9R1JMSxztk/gjjrBPHI\nhdWZFUgGmH0giJRbOFpejYGvvdsfLnXHk9pJMcRI4lhUKqCQMQxXr4DADosQTbWGPJyTo6xi10yq\nMmLnKsR96mUEo8ahi5UfSBdkBzVj47r7RcnbsWeMTtLHimeSQrjpHfBQLHk3Rrq2JFEnsi7MxznG\nxszCeKRMZ5mGG+QCYioTm7J15JA40SQvgADUhwokysYZEUksXFjFCSzSgtwuMEdgdWOvzAj5rG+P\nRtcv2KvZsLIzdyyDaRtGrTib3ACOJ7piSx+3m9TMnEldSgRCeIPuBnYkV2QzAebPx35vyNfcRjxh\nHkiQWEZWgaISc1YhW7Pgr2fF2BVEA6sFzEllcywytJxWJWlmeT2lVaKUQfsou+ip671EpNys6RSU\naK6DAK1FKPaocuUhbk/Y8A+R81466+L/ADK25ZVljWdWeNhbFSOR/b79Ho6mvwhZvoNAUiBTx/VS\noAv5+2ogmm5sy8QzDgY1X+gFeOuutTcrsr41RxWeeCUyNKiQqpUgAIR9vj7kjz8+dfedNFkzIixx\n/SgBcL9RIPdA/v5/5fP1NGyR+wVMT2S1r3Z7+KJAsdahnbGj3D34HkUFOBpVJNV1XdeBqlx7JfLo\nm5m5YmNw273v5Yo85FIYX315r83n+3jXbMd22xI4hJJDjsnuu6ERNfIgOT1YINfoK+51Ry4oeY5c\njPHKxBVPgUPC0QKIqtXG34KzZUSZsr4cWWjvHMV/MF6JQMQCRXgEd9eejtVTRnK7spcnFh3PJXIy\nI5cWNrkX+SSePdUS1kE0BZY0fJruHFIkeRinEcM0XR4qA/28jv8ATV97B/EzRyIhxCeCtyUvahbN\neVsfBFdkWfOq7CxsODJyO5QYSze4qBv8thT9Q6u+xdfbyNdFLGjk46mWu85ib1h42JPKzyrGhgyH\nY2sYLgqwAPyV7/TxR6g7PJk7bh5m2LLGqZ0a2hBIkZXBX+3ff6n7nUDcvbyuOXGZSTaFKFKOqs+S\nLvv/AOtc8HKhyJFjnIVQF+oGq8WfH76qN0Y6sfpcMFoJsH3MaVxEsAjsrJyF8eI+ok+fBB4sKNEa\napZ13X0u6LEJc7kTIJCOKMKulFUGJU2Oi111Q1n+dmRJuW3vGyjIMaoy0HSS0IAYkq0YDUp+oFQQ\ny1xUm/8AROPmblvsmJFnRrFOz1LFyUFvbJBCMVboEH7WteDraaIb0zL1LiT/AOMSOkTwP0Cj9Efv\n/v51CwjLxcTKBX5jWnf1dsJ2bcciLJmD5Bc8RHxKst/JB6b9K0pooA+sEkEdfH7apvKZiW4VmRBJ\nJJaEEjuz51zgxHb3C0ZC/cqaH6fv/v8AXV6mLJ7ZkUCh33/y/wDrV7vOw4uBxjjlmv2yzmSBkCE9\nDpqNE/JHgjrTk6M4qxWiimx4UMyMA62pN0R4BH6df6a5ZUUjrSgfmr/f9tdJCv1fUCQDVA0ev1+d\nfCSsVJZDXx3V/wC+tP6P4RZTLioI3Uq12p0w7D6gaKAKVUzxMGU8qJFf/WqXIBnXz3YonvXzDCYp\nwwChSvnWumjFaZdbjuMGTvMUvBFDpZAAPfjUvIaGTbvaUK3F+SnwT14off8A00rvjkShybIP0n76\nssFWnbgp4uPIYV199RJey4v0fm5IOAWJPb4kgAjsLxqvuPno6jPEcnFIRrer66/386tJcdGUhyCw\nFc6IHf8A+tfOLgxSYbuzkMnSgiuX9dIvMNkt0roP5GOAYxxPZN2R/sajZU/uOiUQwP0/11KyMhFj\nIQuo5V15/pr92La/8U9RYeHMszI8y+80a8mCkgeP6/666Jezk36NH9Cejt9ycTJGFuawM5RJpUdf\n5MTfUWsH6rAI4+fIPHyci9VZ8O5+qdxysab3sYzFIJeJUyRJ9KMQewSqgn9Seh416f8AWe5xel/4\nXZ24YX4kZi4CY6Tvzgl/mEKp5UPqQy8gPIpgOPevJGusVRzk7DRo0aokNGjRoA0aNGgDRo0aANGj\nRoD0v/4cNm/Cektz3d0nSTPyxEvMUjxxL0y9d/U8gJsj6a6IOto0r+gdnX076J2na0kilWGEczC3\nMLIRykHIXY9wyG+uiBpo0Am+pdjyeb7liSxpJyNxmwz31QI+/wDpV6zTdt13HafUkOTgCWOTpPaM\noZyjJVj+oZuuvAIIB1uGdDJxeeGOJ24VIjoSXC2VAI8UST4P26u9L26bNgb5kYCyD8LNHIzxKoUK\n3B7orZFjxY7+ph1ekpZTNitsrczecrdsfb8iOKVELCRP5p4yUwtXVaBHR/SjZoAnVJuOwy50WeuD\niQjNEjO0TNzVWck3xqv0/YDv51a+o1TZ/b23GeGdpGM6xzNwcU1njXnok1146roaWMbe8jA9SnOl\nBlkY3MshoORyYBT+/fj99eGa3ej2eNqs7E3Kky4ch8Lcsbi+OTGqsvFhZLEAkdLbMaAoFiftqsmO\nRG+QWWNcdkJUMeRIPYP2qr7rTf6zz4N5y13nClaWZmCSR8QSsfHsnz9lII8/VfGha9uEEibZFkRF\nViBCD5I8gjx99alTwPVovjFlkghCRBJ1YgOrH62Bv7+RYHVatPTuGmNnfi8tljLR3CiCyWJ4/p9m\nr7daiExybhBEzQnsml8X/TzemmeDCx8mMR8BkIB9DClojxfnv6vt/wBrc6psjhbpCfLiZe27rlQS\nrEohIEivzXqj8jvsn4781QJ1q38Nnbbt5xsGaaPJl4e2Xif3UT6R9Nr9Iogi7Iv+wr/U2xYm8+n8\nLf8AFnjaaFVhyFllPIAEBfizVgE30o/fX5g+oto2bO29cNjC0s3Gc/50Fjo3YZSf1HQGul2c6NF3\nb0qzbsmeyxyoclWMPEAOv5ez8WD3Qq/37Yn2DbxGsKYoELNbxoeKsaqzXn/fzr7x80Z0E8UqgSxk\nhlVSymxYr4YEEf8A1ru8y48kUASRVf6UIsixXV/HX/XWUtHKXRmH8RPTEb7gr405jb8LRMrE99ha\nNigAp/bo6zHH22fDM2UWiSMKFCnlx83Qquv++vR28Y8G4QzAx+6Yo2/KfqDAA1/Uf771k/rfCKZI\neGI8iFHtgfSKUkV4FWb1xupNejvH5RX2UOx7hks0i4IqGcvGVF9hgL+4+xF1XZAF3py2nOfHTJjl\ngTIQqIZI4/8A+YCSC9geSHNEgnv99UPoqLb4lMubnpDIgPCDldD8wBIHk38G+gD46YpJsHGz8j8R\nLWPmhI2mYGRmvpWtz1deWro2fGscWmOSaFLJ22Ddcn/+l4M8SQKGeMryoAAFuQF0SbruiT9+42P6\neWKd8wey3JA7NGGHA1+osH+oH/V79OZ2Bi7s5ihcxSOYwZkHuBKJBP7fTfnsjrULdcQY7SQYyvfI\nvSD3GZAex0LFAVXmz5+0SbOsasR8hGgzGHGIQt9RpifsLo+PH69D7+JWDnSR42KZAxbHPF45Xdi0\nAkL8S9gkeeugLJrUPdMubI3NYY4ORQAFCSG7uyLonrj8D/lqOzZzTxY/4x0hJvjQbiQB348WO+iO\n/wC0KNpWU5a2i2x93i2rc3dMxlZubl4Ij/Lew4DBxRCyKljpe2Iro6gSmeKOH2ryeE/uESUY5qpl\nshwDdk2PFnzr72bOysV5I+MORJH7spiniR0JdQoZQy19BCN0D+X99TRi/wCJZkcGQMsYCIpWeRiw\nxVdWdfdNXxtT9RC0odqFd9Gq6OfK+yBCZdx20KcM+0yvzMqsFHZqypB6u/J8d9da6JtGNt+2MSs0\nOYssNxyY3BSGUsSjljY+48EEEHyAT47wnL/FZXFY2+giNeAcAjoqQD3YJFg31d65Qbc2O4bKcyTP\n9LQSylnW1Pa03K+XGgTR4/5uxrnfaLaeNo6yuzxxyIntxhx4Ncj1ZN/f+3211mQ5ERnAVQG6W75A\nj4+/gfOoe4wFV2+WCJ5YJaMZlZkWyxF3dAfS3ZsWD5rXTd5mjhmx4xI8KMjBjF9JNHkAa/6/21Pb\nSLbpNkyfDc4aSPJSEX2KPYFEk+PI+/R/bUX3zLin3GhHAWrBBxLUfB8j7eT/AN+eTIIFMT2Jyilg\ny0VFCievsf8AlrjlcJMe47VwQWZm7ZTX6/pf63oohyPie1mVZCA8bAkhaBseb+fNdft1rttSYS5U\nGPk84FtxHKY2kCfSe+u76FfckX1Z18FhBMkg91o1IUH6/kgjqvuB9u/9LiDCXOhaItC3uyrxBgUe\n2FH/ABVyAYn5oXxHkDVOSS0lJt4UuUkk+NDIJ0SWJuIlYMLAZrDAcVokjoD/AC+T519x8I8WZY4E\nDOSTGp+lvHf2qyP36/S5kePAIHklmxyylYxHIBdN2av4pSP3+9akqiuk87z4/trVIFvs1+VAVJo9\nfA+16xzwpQ0WNxRkR0hhBR17RT8rZvx9z/Xx+uoG2MCkhUsHDLxarW+68/c15/76vt1xvYgkeLqN\nU6VWDcu/B77HY+P7V3WbeIeE/uB0aRGKUv1Ka/X9CKojo67Rl8ThKPyoib5lhm4hgyQAUjE+KVSo\n8n+//Dq/2/1flZ/qiTO5e0suH7MyKvAOvAIwHdgmj2Df2I0qyMoyizhjbFXIJXl8dn/fzqT6YkTG\n3yFMiVPaL8WJCkkf1/f56uvtY71h529NG9RZG6+o4sTht7zLkSn8NGI2aVkVR03fJjx4fULNC+X3\nM70rDLiYeO0+XPM45lMbDaR44WEYNcvq6dhQF2HDWOTaetqx3aTEy9pjhyoMmZBHNHYXFaOJvokP\n51BJsEEgg8eg/wBXbB3POztuiwkDxxTyhcd5GeU5BWSQstM/Lwg5AuRxVl+nkpO8Uuw5fRiZx5Px\nbbXBjzyZTSmBsdkYO0oNBfC0DY7YA9HpTZ1+pl52Jgyo8CP7b00UhdXj+/gg11VG67qr7e9x9WZf\n4OCVd1xsfIZva9yGeVnmh5S3ysh0AIDL/mp1o9PdRl+s5s3Kkmz1xc2BSC0UsPvAWiq4UutoCR3x\nZaJsWBrm2iopme5Tx5E49rHEScRaBrXkB23j5q61Epw4Ug8R8X51d58kH+IO+Pj+1CRQUEWf+l/s\nK68d6gNClspeqFk8fiv305G8SGCD0SO9fTLfn7A399dDjtQZArAgnjd9ff8A39tfKoXVn+kKPi/9\nnW2ZR9ool40vwdSYlWJvcjT63XiBVX18/wBftWorVAwZwVHYo9a6R5IEsYWISFWBIIoEX4NkftrK\ns26RLikZpELM0oJILfDUfAJ/6f8AfUqdCY6k5+2BZA814s3/AL86iZpC5M0cKFFQ/SOj9XQbx47+\nPjVbk7q6RlVIDAUSe/Pk6tL6Jcvs4zyDuZ3qpPpW+z32f+X9/wB9PO0ZGP6OMGfjlcrKdkleaTHK\niJalUqhZW7P0EGiOgPhtImzGDI3SKPJUuOVKtCie6H6341t21YIf0afxcTZCrMFHulSY2C9IDX2c\nniPkser7s5mf/wAT/wCIv/mj01s2y4+LjwQY5WSVVYuwdE4rTdfTTkEFfK9Ejs5Zq29TWvqPOh4h\nfYlaHiBVFTR/1B1U66Lohho0aNaYGjRo0AaNGjQBo0aNAGnT+E+yTb7/ABN2SGPmqY2QuZLIsZcI\nsX1/V9gzBUs+Cw8+Cl69B/8Ahs2BRj7z6jkWMuzjAgYM3JQAHkBHijcVHs/SfHyBsH4PLGfJLNkP\njRI49o43SsDahSnYPRFkjzXwBUibcJ8fFiyRCZInf67YHghYKGtRXGjzJ7oX8drQ+sN2yMCDjPt8\nmTiqA7lFAFk8R9V2L/Q2AR/xahr6ohytygiw8x8dGVScfIhItCBVNyBHi+uu+/g6w0fEkWReSmx+\n1f7GqebEm3THhdJjFNDkuytQADLySwO77J+f3+2pMMgGO+RhNHkktykjhAHPyDVnpvHk19Pxd6+4\nc7FnyFMUgYklDZIKtXakHweh11X9ReNWFhlHqzA3LetyWfMwMjjjipPbWrf4IN9N9PkeO9VUmbiy\nu8EM5myASQslRuT+teD13f8Az0/+s9n9Qy7nHn7PBHlRmL23iMnB078jxfRPm6PjSp649JZeXt6b\n7HAsWSie7lMgKO1/mtR1YNWersn475Sj9nWMvoz+TI2x/eiyZJVyEJ6MbMyAfFgV5v41O2rcoc70\n/umzRP7UsqNLiO6grPIltwIIuyL4nrz3fjSTmZ0s+6uzrxDX7iR9AH9KP6Dv71rmZEbI/wDbJjEg\nYqCORF/H69/66cNHPD4hyh5Isq9uydf9PvWmyAnIw5M0yF2CUTKbDUFPkmiaJP8ATrutKcjwBnfG\nDLGSU4vRK9fP667wbhkLt0qRklRGElVu7B6sdfGtcUzFJoe9q3iDFfCGayx7dlp+HyuIFEEg8gGB\nAINf2I8eab1dtuHgepcvFxlkcD25AqsOlZQQOrANlfHizqJthk3jaHwZmzJ2RU/Cqq+4Yx4qjXQP\n/D9j18iDDDJg7jDwBeQA2JB+YV4rvqr/ANjWJG3Zrnpf+JOU/wDhO15UMH4iOoi7WOYJrkKqzR7H\n3BNfbT485Nz2r8TE7CaGPkwjp6evygXRNj/Xo9687JBHuu6ZEn4xcfLgUTxs5K01rS+P16Nnx3XZ\nO0+msuf2lycqGB8LLjtsmrCOwtgx+19d0PAuxRJ6a1ljcA0RLrJyWWQkFRQAI6/pQv8AUk+PGsP9\nZ7rPJv8AlLCGUc3jCBiOgR0a66I8fprVptvyMVo5MFjNg/iTP7Zbh7QIN0TdrdkEVV+CGvSn6n9O\nrJPDuGNt4iXk4kF8hy5EEC+gSSaursD4Gucu9Kh1hkG152btWc8+PF/NUEfUpDC/kEiv66Z8zdN5\n3VFYFUjoSyfh3kYK5UnmAzcapQGJJI7rV9/5Elh3LhEscisB8liR2AW+xsEV9wdMHo701u+GM6DM\nVIFDfypWjLFSQe1HwQQPB70Ts1qhM27dZZdxjMkPs5sXD3ZHiP1NzX8v1EMWANdUbPQ86Y98yMDd\nNxeSCVZY5A6KaIYEE2BZFVTEEAeB+mm1fTkGSTjnNnfJRVk/FFApL8iVYV9qNfYHoj4hf+V9waNY\nsiYSLCPb7jUErTAcGN0O7oV5r4s8/JBs6eOaRnW6hZc+KT2j7rlXtT+dT9IHHj1TB1Jsj6aHjtZm\nMo/9XhSvHEHF8LVmUdgk10PP6dnr4LY+zu2S+DkwQSYmNK0kpLOrxxM5DAmQ92W+ngLIkB+qyNUM\n+P7M8iRv+JihSSD3ZZF9wKpVVUqSxHRUUehX/wAa1zSo6t2cooMyJgySQyu8dup5FwCoPJVux9NU\naIphqVt+MdzSDFwMaL34nRIkmk+r6uSqGPt8ZE9xyRyr6VKWbXVaYoZFQR5axRxEICSbVip6JsDw\nDRr76j42V7YjjwVh4MR7sLix9PEk2KZTfFQQV7uj110TZzkkNd4C4sDZWTgtDIgkitC5tlJYNwNF\n6CH4AZgD0KFTngu8qNi2yMWaMqQ/IeGrrviAPgddjzrnHjyT7NJglJuUKxs8Lq4CfUUfHdRzEThz\n9LMFv3CzEEgai5uZDjQzrPjyQZCTsDHKv1WOJVj9V8rK2P21D8fF4UvIpLSPnbcj4ckWCoaaKZpx\nCkDCQxFQXYtX1KhQX8As1dXqqMmRDlY0ueJZkSMe2pc9J3xr7C/jTFBNCm6ZjyY0gGKrw8ciIxvz\n+xHOlIIB4kkAju+7+OayxSvLIie9DzHKMStIwYUpoivJNi+1o/J1fJrGRwT1M4x5aZfGZiVjFAli\nejXgfuPP/wCtTnihmhVUaNJVjJVpelsDr6u7A6/awNV0Mk7VHS+2ew9kV/168d/bUj3Md0GHJPxk\nR2eNWUkWVH6gDwP9jqJL6OieaXf4ZYfbjZBFIYhJ7Us9Hl3yUHoGu+j1f61qv2k40wyZs2fDgiWQ\nLGONBitGwSRfmxdn6r+ADDG2F8RlwVYTPJTySKAioLuye7P01XkfqQB2qTbY5sXHPOZSGUqKjJNE\nEfT2Qf8AkPtrlxyr06cnd1hYDIxH3HEnzoNwKBWSLIggMscjBvqIYst0Sxv9e6+LL3INwLxZkmVL\nNwJ4NDwtEu2+kd9KxJHmyfPekzG36RpGx54JIEXlyrySG5N5BK0Aeh+wq9XWQ/8AiOR+MfI5t7r+\n5LG3Iv2bqvlgp7Ium+B5qXjrGTHybaOeTMpgCpHLzCgWL4269KD4uz3ZFeP24ZiSOHyFiVggDuiq\nRyHHqxf+Vb7+evvWu00ntbXL7sD8ZVMkRFr9N0SCfzfIsfN99EDliyXFAgUMjD3FUfc1X9bH+71q\nVB6KO/RA5c098eb8uPEirv58eO9c9gw8DLbNfK3CLDkhhDQe7KyF3LAUKRr6sfAFgk0CD13zLmkl\nYKvOPyzV8+Tf9fvqh9uUkkq19fvr3QXxPBN/I9KbB6o9N+nXkP4zaMvIkm5TZECfh3WNR7ckrIxK\nqSUBCIQWBBC6q973BfWM2R7m4vjeyckYhjZyJC+OgQe63GJFYSEENxNBR5kYnC4V9uBVsGySwCnk\nPirr50wQA4kMkH4ySlUqvtXxN1fRo90P+XjrRuhFEzd973SSebBaOCGb2/YnkPtlpGFFraMBWtw7\nBqJPMWzcVYQYY5Ito5Ox5Sd1XijX2/TX7xglUyl2LUGb4/Su/Pn9fvqXtuKubkYuPjhpJpZVQRkV\nyJPS39z8a5SlZ1iqKduxa9/c+O/01+M/GjQPX+/+mrze9oO3Z+RjtDKjxP8AUjnsDyP9DqpmhVQe\nI+fjwNaYfPuHh2zA0QQP+WubrxviShqiR0Dr9VWUhedf8XWvx1qPsgjx+/zpQsgTyM8o7IUjo1fz\nq/2zAOQY/ZZPckVlPKXjTeCLPVlfB/XVEUdpQwBHRrketMHp6PIMmYiye2Xgb6GTkHoE8Steav8A\nYj48jZGROMqZEa+/O9s/JzTcrvvv7/J0s5LhpWJH039PWnDI4zxiIkKrdlEr6qoC+P6C78n5++lD\ncABPQ8DrvVx1Eywl7Qp/FCWLgXjIdeX3H79HWsj1NPss2ZumSkLtj46zLEGWOOZiASDXXf00AOyR\n86x7bsh4HPtj6uiP3v8A1/bTPv3qKRfQ7bXk4mI0+W8XGfiefFbZiK6uwg77pvBPY1dkvoQZppci\neSaaR5ZZGLu7sWZmJskk+STr40aNdTmGjRo0AaNGjQBo0aNAGjRo0Aa9lfw32MekP4YbdBJCEyfw\nxzMlXiEL+445lXv/ADKCEs/CDx4HkHasVM3dsTGlEhhklUS+1+fhf1EX81evcO3bxtu+YivhZkUy\nugLJHICaI8H5Hz/Y6Ard+3Rkj25Y8AZMeUz8kkfjVL1R8XZ6I6+RdjWdepMqHAxXeOSJplDQiNpD\nziILjie+TUQfP6+dTNubF3H1JNj5OIIsXIm5AJOz+047VojVGNjyPHwGSiOluu9beitxw5Mrc5Nw\naZY6MbuObPQPTebaj0SCeIq9QnpdYXH8MPVKM7bdmZa/zByjDPYDA1xB8Dr+9adfUUOCyqMjFxxz\nBT8RLGpCk2B2f/yP9z+4w70+6bXJFk5GM2HOYOQ95yp7ZQHX7gkEfHzrWz6o2bN2uHbsmZ/ckUoO\nfVN2FHL4Jrrz/XzrWjEXu8z7hhbbJm4pEssSLUYFq/a3Y6N+aq+vjUmBsPfNr9+Eh4MlCLFgH4uu\nj8fPda+dnZWxJ0TKbJjWU8GfoqpAYLf6XWqH0ZPHjZWftwxjjt7rSCPlYT4I8nr7fv58aw0yHdMX\nYsHd90xsvbBDlqS6LGSwUhiaQfSQPA6I/SvhcxYtuz9tkZoXhlja3CvVUo76ur/+/wBtY/iR6WSf\ncJ91SBkZlEccl0DJXIkU1k0CPFUWPlQDks8GQ+VOGUCQ8Q79VZ+9efgdfbXB2sOySaspcrbp49xR\nUBYKoI5GuXZqv9nv+2pELIXl9pFjk9s81S+LCqPnu77rrwNSMv3knx/cKgKPpYG6sEUft4PnXSfb\nI45A6lj7sYcEnl9J+/Q7v7X8eL1vLoceyHwWKIRhmiMQtku7/r/YV86s9r3/AB48WOPIxsd5kayz\nKOTqSOgaNGrF/F6Xp8jJZ/bliCmPpqFWP9/OuWTKWCSAfSOlb5H7/t+uiT9mNr0OE+Ri4u4Q52Dx\ndgQ4yETiFcE/mjFhQ1XQ6BJFfA2v0E8s23mXHcnHmVnMAAvHegLjLLRBIIIsgdV0deZY8idchyJW\n91TY78m+/wB/n/Z1qnoD1C5z4DJnrE3uRmQe6oBRRRWm8kigD8Vf6i1hPaNu2vAfClzGVYxiTFZI\nljQo3i25JVciT8AdV11qbPirPEy8UBY3bAn9L8jvjY1Dx9w/D+1FkyNLzjUiVF5gmuyWUAUeq6F9\nkCuhF9Sbudl2w5wkbkp5NGHUBlHk99+AT1f7ar4pE62SY8X8NJjwJGVIS6VWaMGu6Nfoe2N/Hz13\nw9yjysrIxqCzQuVKi/ArvwPvr42beYN5wEyYgELf/wAsuCwPyDX6gj+mpjY0ZkeVRwlYUZFAvxV9\n9X0POij7iY39n17SBlYItjx1419mj1qHhZGQ8ssGTHxZD9Djvmviz4o3fxRHj5AkNJGs6R8gJHtu\nI8sBQJ/1H+mqVeiXZkH8UBFh72cyeGeJZUF+0QRNxFAsT0lAsA1EggVV3qNkbZJuWyxHcNrhWE4z\nzDOiyWZnPTlgzhv84kZSWVTycqHFF7v+IWTHtW4Sb9+JMuSsX4XGjR+BxGujIWV+XfJgBxIs0fga\nWtvc7R6eXM9Plxgxs8ObkSSvG8MfJyprkv1gtLVE2SoBslW80lZ6YPNFDcdtOFiyQT4uTiAurGWS\nIkg8SQnQA7ospPxXYI7i4ki3tSSY7fhYGQTiVEBkRnUsALUsD2AS1kGuutX+7x8IctDDjpuaraRw\n4RieQBixkAC8GjCcwGB8AWvfIUeJj5GXjBZ2kSHImtWb+UrMqimI6tvqoH6uzXzWp9Wyr2kWuwZG\n5wY3+JQwzzLivLNF7n5I5gvIEkAD6QoY354CxQIE7aMKL1Nt4jm9zb5oUVMpmyPcOQAHLmmbpiig\nA/So4vZ7Wo+NjcVmLJPzkW3iiIXkKBBPj/MFP1E0aPxr8y4JI2O2w1HApeNZzJCiZMhZgjlyfPbC\ng1L3VAktEZJ4U01p87tt0UeTFj5UcmEsb08GbNyMYJPHmQKjvkeqAPIGgD1WSRY8+QzQiT2j2y0K\nAIHg/Pz0T/8ATHuGMxhD48jx57MnurkhvedCQA4IVVYU3QUgqG+QOWlmZY8cRRJjOswVT7ZagARR\nQlR4/c9/p1rEU2dZpoig5qEdXH1AWDdWCtV1d/HQ1L2nf8cF4sTAV5fbAlLKFMovvq6J7I7+K1Ax\n2yMyUrNKXRVC+87CwAOKeeiKFeP6AdGrk2CZpmMTxhORKiR6NX1fWnGLyQ5yWouYjlwRcBhzTzKO\nBPEsGAvslb76BP8AT73r9S91ngyJT7LhmV1BHINZ78Cz0Dfg12Cb1XbXDPg5DCbJVYQxBYFSgPE9\nd9AkWKIBPgHUrKEuNFFMmQZUch0aipeuzQP9B83esa2gnhC3rHk9pZy0CM7fWkNgr0eJJPZ6uyKG\nvmKHcNsTHnXJRMTJQh3jclGHgo1V9QBBI8gMLqxqVh42RuDpEYpRDJwXmj0sdnq6v7AfFXfXyHdc\nTG9yM4siTlgVmU2ij6FKiyeY4rQ8CiOgOtdE849nNreXR8zZcwxvZSQTMYhyDAsz+Ty+o2LvsfN/\npr52xQMUTGOe5GPEAE2B8j+v/wCtTJ8uHc0m3GWMIssoEpAEadKaAUEd3ZFeAAK+dcMmBcUywrNJ\njOsMge3Klhx8fJ7PXQA771OdF72UOKobIWOWmtuwT5/3epzbZkz5Ek+NGZUVSQpa7A8/FefsPJ1U\nY7Rr3kS/UKKcR2b+f6f89X+NvcBw3xGWZwCyiQnuvjqwB38nXd4cFpFl2eXgpUF3cFmoUDfkXf7f\n1/XXd9ozoV5PjOLW16ssLqx8Vfzq22nCxtyeZIs0RSIor3HFPXn+v79HsXqRNH7eDlSYmXBkwwN7\nuQ5IDrGH4jx0TbrZB7PX7xbNpFBNtrLg5DupIjjVza/8Q6qj/r/y8a+9rwtyxNtXcZQ0CRMs0ZkT\njzHKgwHz239tW8XqQ5yZRO3wXnEYy5IiSKEBOLORXEEhVjHGu+Q8Ei2P1Dv+1ybFt23Z8UPuSQKI\n4okKNAQ5BJZhx4ElmAT2+IAXsAVStqmY8doE2jL9Z4o3iNnyM/Il9oxAHgKqyznpARxok/USwAFa\nXd99F7ltLxLNRkkVCIVjkDLzUk2SoBrwaJ7/AGNaT6H9H7t6U3xNwBRMKeE/iseTNUtirdqHKpT0\nLII4js9gD6nDIw5ty2MPlRYkO/xRpEMr3Pbpz9IdJF+oIxZqHV2VNd6rhhLlp5ffClgyGimR0dDR\nFakvs0q7a+VIpWMN9BJ8kfbWh43pHCzXnSd5JtyRXlkgCNJ7p+kLTgA8SxHa8+nU/DHUbL9Jvs2a\n2NJ//Du3uQrK4pD4tiwWhZvsCxRoahypWXGNujJY0Bcn2mkd3KhQx+r+nnzpkw5+Gb7dCRhQJWTu\nyKq/Bo0L+wN/peeofSeZtQORJHGkTWsntrQJJAqx+YeOr1y2jbVxtlnzpJSQsyxLGUUhXILRseRu\niVPgdWT8jWupdhXErszG5wvKVUhIrLNQsnsUDR+f7j9dJ2coafqq+NPm7xY2PFKY1YAoxUMpB4k/\nTZPluzZHX2/RMjx2yMs8hx7A7GrhiInrPrbMYMXQSqrMhFcST/Sv2/tqr3mdpc3gQVEYriD0Dd9f\n6f21p+y7btuynHzszIKTTe6EQNSLCImDM99/mdQGH/DJ+h1kc8vv5Es3BU9xy3BL4rZuhfxrpEiR\nz0aNGrIDRo0aANGjRoA0aNGgDRo0aAev4Y7ZiZW8S5OfGjQACBPcU0HbvkCAewF+x/N/fWM/NxvR\n64y4GfHk488ft5UEtElHenpr+nr/AC9Vxu6+kLnof0lnR+hcbJfAmjyPebORZJQDkY7LHbxqO/pU\nIeX/AMyOzQ1Xeq4MXG3xEc84ZFXugxu68Dwa+3Rr9tc29LSwbcHKyNuyFmmX38YSmL21dWdTVUaF\nknr+3VfGgPu3+IbbjpnRJl4eXFzWRivAvYQw31TNyJUkL2QL1Qy+njuuybfuuPNhuHgWKULIQjKB\nxXsdAhQBZP5uvtrv6Yzcl/ewcyMRoy/h3hyFuNx9/wDWj/WxXetbCRF2L0vtuXn5W14r5cKQrJG6\nTlWuJl4hlsX9189AefjS5/Eb0zNtT4rKs0WK30rPzBPujyQq1SkBSPnon41d5/pjdtq9W/jsTOxo\nSFPsmT6wR4ZWB6NiSvjoEi+9aRm4MG8x+1kYxlg5U6ymlKlSPp4+SLBv/wCR7vxqZjMn/h96pz03\nCRy0S40wMc0Tgj2pPIkoCyvTC/0/TU3K36LbfWS4ORLNDLMS6NEoAdi44gfVV1YHgGh8nXT/AMmb\nhs3r0R7aETbMhQyCQERkjwg4kfV0bH2s/GlH1h6Z3HZt7EeX72VgJGxhpkPMqAxLKHBSMsfqbquv\nkjWSVmpm3+/h+o9s9iRwnvKTSSq1EGviwe/9f1Gsw3P0dnbbOzS4Rng4FDLjQDmQHYXx6DN4I78U\nLGpu2+pcCDA2+AZLYTxyPEsKsqe1IvEOHDWy92eLfIvseHbdPUe37LscTZmQ2auSv0kIGBjPRJqg\nQAe++7/txavs6JtYjBdy2m96k2+DEkXJtkZeXMjsgC6pj+o618T4+SimLIlcRrSnn+Vj8fUCR8tR\nJ8A/trWNgwtk9RquUpM24hW98MrFI2bk1k8a4sQxJs9kfqNV3qf06M2CQwxvBGvMxshoqLI4kAVx\nvmCLJqvkkGGmjpyTMnycTEyMYyqknuflKFOgtebu7r9K1UDb52gcQMzGgeFXf+7/ANdM+RtkmCzK\nzC1FN8kHzR/p3/utQWC4zSCGXivIVQ/KfPE/BHf9NZya6HFN2xRmiljP1EKygEEefvrrt+ZKkrAy\nUzm+Z7+r/rpiw9tx5dy9iT2iJVtC7EWa7F/H9P8Arqjydrlx9ybCaMxzcuPt3ZBHx/8AvXZSTRyc\nWmaL6G9VbliomPHM3KQgNfI1wrifPfVg2D1+w1t+XuEM0TYUuJJmQuFYfVZH1Cj139mFWaqu9edv\nTMWXt+5tLDNEZFpmja6cfmIFD9B19zXxrUsv1Jum3ZmDuuGpbByEJKNf1d12CB48GuhQ+4vLdm1Z\n8bDmQ7N6pzZY5oZMUmWaCMqy2bIpR2QwAPZHwfPnWlYW9YWcIgswjmbzCxHK6Br/AFGst9UYuzZU\nm1b3t6+xyR0n9mI3zC8hSkfmJ8k31R+e6eHOSXPiynhK43tqJUMh/mcTZJ+B/rVfArVJ0Y1Zvmvh\n0QOJioLKpHKuwDRNf2Gln0t6lwM3bIcaOT64iYV5H6TVhRy7+F+fJB1e7hK0aRKsix8nHJjY+kdm\nj4vr5686tyyzmou6Ef1xi7Rm4OZkZYqRMcrCZDag8WFr3XTV2asqO6GlPC22TAzt0yMaSDHwGEi3\nJMPaSVJCEUIt1KGBCgs9qerr6nb1xt3uYUOTjSxx+4/FljomYHxR+/EOao3351TKv4DMbZZx7cLz\niaNoox74XjIVWlFH/MOVkKQOqIOvLK09PRGmsM7LTnKjx3/DSRfiwWh94MJEL9hkja1/NYPG/wCY\n3f09cYZJIsbHxwVDL7kbOhNS33xKgEUDGCBxsEMO/p1quX6NxtxSPJjzJHbGRA0yt7wl6P5WBFca\nPVEDrr40lb7sabBE8GKqs9AK0dk82IB7/qP+R71En6LikVrQR5FCHILskIcVyU39AAVWUfUCb5NV\nd0T0NWhzfx+XOmdLxOXFWS5YhJJBbC0iAXmOJs0TYBBF3rjtGJn48eMkzIsrSOT+ILlbkj4GQA93\ny4GgP8o/q2/+WcmLChmWdGyFudY8WMxMApUcgW+qiw5WT82Orrnvrou17F5d0VcPPXJkz4sxOBSY\nSkxKwYO0lsVLNyCi+yACx5cDpNky8PCxoWijcSLHHXNi8bDgASVbqywsg/SORFUCdPe77HmDJxc/\nKTJEMTyGSCSdYTKBY6odmpAD565CgRRoM3acWXCwosfDEuTJCkkkgR0kkRwF8OPHJHviWst9PQ66\nKktIdtkTIjmlihzceGR8eKONFP0hCpQGrq+fk9dkgk3d6+TG2RE02PL/ADYgWeOd6RACWHmvA667\n+w+3XHM215L4054zMsmNOjBFWMkkcj+VVNd0SAe+j9QP5Dk4uGkuQVM8KxJ7ohHJgSaB5jpQe+uv\n8vk9a5yT9HSLXsjxvi8ZmiSSV5Im9x3BRXboilZaIP1Cr8999jULMzMWKHEjeCRQvLml3xa16HZo\nefn4A6I6u8bC3LesXITD27Jhx4XZZv5HuOK7HIfBBUrdg1Y7s647v6KyBkQYUSSzTCLkBjoHiZAO\nTuH68Wq19XfI2LUGox35CUs+JH2eaOfFVo/YSE81b8Q7MwHFqKrfEfmr9fHz385G1xZylVxHBUoy\n+0hCgkEkKAQttRA8fl6+bnnaJ8PasxMnbpMNseOOKZJVcBS56ZmoAXYFcvP3GqfOlRd3fMR0ziil\nWIYkS/AIDAkeWYEgeB4NaJPlawclxp6dosKSQu8iiLHh9pZJo40KQWwXl0pbgCVsHsg/PzOgwsbM\n2hpky3ae+BRkLFfq7H0jyQwAN9kir1T+2MmOLc4EZ53YiVQ5sJy+oFWU2SW5D6jxAN3YqyTHyIWh\nzC44RfWVikYKBfIH8w6F34rr58ayazHog96wpPWWDDjyYkuPj5GN/LB5MhVST2Kvx/mP9fn4hQZs\nuDDzjLzGU8WtePGh4+brr+n+lz6hnXdIRAITyVv/AHmk5FlHIgAkX9v9BpYZ5YICsSe5APzX2L8f\nPgd/76118VuCUjl5aU24lsuNuW55eFSoK/LHfEKfk9eP+fWobQNgbo+EJOiSjQuDxHdfPjvu9TId\n63HJhyJ1ACpXLiov57+/g9/3PZvUTboZ9y3GOU4wYdCSx5vq7v4+3766xs5yofMTYfc2IJLLkY2b\nIDzwo1Dxzo/C3Qcmph7BBKdklBVdMkbpljcMUxSSJjy7fH7aFJSFk+Gq7JJNk90b6oeNDG9YO95W\nRjY+35WRPOkAfKQsZIkULy5KOmFcl4n7AX0Dpc/iVtWH+Bg3XDwI8bKeV1yfZ4CHqwvthT+U8JGs\ngErxPZPdJ6S8Q8fwg9TZm7pvmTvvDI9z8OjZK49vMLaMByi/VXQ+r4JPgGtCxthyYjBm4W7TO8YU\nLydDHkxBW4AhEVU/N1xBAq6JJOvKHp3f83YsqR8Vk9rKiOPPHKpKSRsRyU12LodrRHwQdeitp3/Z\nHyIZNnnx8psjCVsyc5LQSSkJTe3EqtxkCRFmVOJ/9vuqIuyC/ly1zJEi3/Y5Mef2eQnUJKvx9HRJ\nst4QghqHV2orPVGLLmxSTYeE2aiwRfgzEhcTI5JNyEEWTR7sAICWHuEavYYvx+cZ58JLUnlG0bMW\nN0Db8CBUS9cePLiQTV6p13Bdu33LxocaGXb4544+UauTCVSwOY+lUXjZBoWz2fzHUyX2UmzKd5zp\ndxhfE/xKZv5lsjpRLgUzEWez5JBNk6j+nIpZBnbVyEkWUFiLJI3ZZkYsiDt2BQmh8n4qi9776Gm3\nd33/AAZGEUwEscBjZWIq+7umAHd2DXR+oKq56RxE2j/E9/zZ/YSFGh9jJVGOQzkgr7dglQCt/pYF\n6mqK5WZ9nZ6fiMhEEvsycvbLgElb6sgC/FeP+2qnFzxzVADxD2qnsf01KmwZsnl7JZapntSaN0Ow\nOvJPwKH3oarZsHIxJA7Ch5BHnv8A6/8AbVKKom3ZN331Bl5WI7TSu7NGuOhPiNAPyKPhQOqGlHVh\nueQkqwxKpDJyZjfkmuq+PH+uq/XSKw5y7DRo0aowNGjRoA0aNGgDRo0aANSMDCm3HccbBxxc+TKk\nMYom2YgDwCfJ+NR9al/Az05n7v6rzd1wZfYl2rEZoJmKlBPICiK6myVK+7deKHfgEDZcSXB3T0Zj\ny4GfiYKYkEjQyYpWD2gFXkrxxyAAlmVuN8bK8lBoGn370zH6x2QTYsUK7rHI7MoIVEonkVahYsg+\nfkkf8J5+r8qDKk/wAbUNtxjK+TkL7R9yZI0Z5G5qeMjMY41P1Ahgv1NyPBV9Lblukcu6riJNt+Lg\n5Ek7Apzmx6Qn2n5MpACJJ4q26PdVza20dE/THr0TNuG24jbFvsJx8lHKIp/JJ88wSD+/7i+qbVfu\n2euB6viSliRJPbeOIlBV/kawKXoVQqjY+Dpw9M7vgercaJ86FY9225+OVE6BZEkAot/xAX1+hBHx\no33Djz/UOMqwRIXlaKZmVSJqVSDf+VgCejRNddAEZ/R/BmkhgzdoaL2RJHJGQIi5WzXjl5H7j99Q\nNiwYMVfdgiyIGccHhYkIr8QeXAdCwB31/SzrrnTfgdinjaUY7QxcVfkihh0A4HwLIBFDu+j1ahs/\n8QhOuK08TohYw5YjW/bmLD6iT0FPKMjuvrbzVi7JG3e2xM+CbbJZTFLXuK4cq0ZWmWRa/wCFuB7o\neftWlf1Zipu224u87pjF12yZ0ycHGPumdGfiBRK2pqN6JHIV9hc/OlE+3Y2a+QySCUQs3QKx+59L\ncAe6/Q2O/JvSuhy49y3qVczHxdqRkmefJgLMsyMSqG1PTMXHyboL5ozy0riZrK2RgY8uJuHpWbaF\nyGCQZeWskaxgEdv/AC/rIRaJUBiSxF8q1pnp/edr3T0/iYO8EHAmaVMd5bQRBOVFh4UMEcKLP5ZF\nB6OkT/BMrd9obeoNzT/E8bEmzdwxsqCRmjRyWR0bgwfkjXZNrYN0Pph7fjpBNj7fuGIqvlwi54+H\nGCIsU+qleuXKuTKGQ8GF2VONWE8NNxsvK2HOj2fHgUsAqnLQcWUi0sr2ASvGz4J+L7LXkTw7pkR4\nc2UsGcSCoBJQOAbT4simP9FP6aynZ5cpfSLbruO+YsscfJVbKBZ3CtxdIm6JNKfv+ZfHLqwwfUWx\n7nEv4nOlwXVgweVgWVjSlg63Y42QegfB5Xepkn7KTTJvqD03uK7nzljJiMZqVSCCV6P79d914+NZ\nx6g2yXFwwYfqaxXEdg+f79/GtY2f1qjZWRi5eTFl7eG+iUqACAT9QC35CXR8XXfkyPU/o6Xcg0GP\nMJIGYP8AzTxLv9RqzXIcKFgmgp6775V7R0vKZg8WRBkYb+/MqSIAUIUkX+/keK/r+mqrMm/ETLOj\nNI1gsXF9/wCxps3fao4HeP2zjll5KoHZFdEg/bux199KuTFHjNJDICykfS1UR+tD/l/rqvHTZk7S\nHHbPUAyht00mLjSTY0te5wWMOOvnog33fR/vQ2LAi2j1DgZOFgZH4iWNORnWYSAUVH+Vv8/EnwaN\n/AXl5tx0kxspe+SkjtQb/Q6ZNgztxwd5Em35oiEhBeiBYsMPNdXWqujOzUN59Ib9gYIkhOLLwdVu\nIkO3HsMo7JPXY8+a61nGVLlYWVkYodpIFkFuv1qwNnqz2Pqb/da1jN9ex5vp6XFkg5big4ydjiGH\nhq7B8XR+39NZDkbpHLuRGY5X3pRYXzxu+q68/prLV0hTrRn23MCCGGPm2JKyvLEHs9sO2BvvyPF1\n/TWjzbrjNtcQym4x0DPzdpAWF8fpINd2SegKHfghGg2vGkaMYbw5ME6lRI6hRFV1yJFhiB/Tx86t\n9u2T3MSWeeaMKIisRayQ4C9qo/Mo89iq761jZqQ0ZXqXZ8mUQwwPM8bqiHlwjldvqC/TfXnyPg+R\n3qqfdsXG3opuefEsBcsYuRBWzX1X3YIog/p5A1mc2LJ7EEaj34wTxjPJAH7ryTyINfHd/F9/u++q\n98aLHwN3aRBGjGKSI8Wa1FeACQQas+ed386zjfYcq6PQmz7hg7ptqNht7kaqAwN/SfNd6rsra9q9\niXHaaMTzMPdRWs10CKHYH6/66yT0P6kysT1Lg0Pw8MtplRu7ECyPqIHyFHV2O/3rRdi2PKX1Ll5Z\nm3JYSGif3pORJ6IPwPnyA118A97JXlEp1tnLJz8XI3PC2yLaZ/cZ1E8kyjiYTNxv6u+2UHi1eR0T\nRH3PlwbbvyZsP4uXEnEERlknZY8eQu4YGgePHibU/SvQNXQqvUO3Yu9w7jMM6XBnw0rH/EzLEih1\nMZVjdgEqT8USRR+avZNr3bafTj5cWRFmZKZYRpYstZUkVSgQrIAKWkICMevpHHkwIxRy0VyfQ6+q\nYod92Mz4Lys8MqSJ7ZY+4aBHFVP5gpDAkHodir1j2L6nTajjZmDkKrwyo0TrGpJssCD9K2hANrak\nM93V60b8fJtHtZcIZ2kK5UyRSk+2SjrJ0QQziwe/kKPkBvPG74UuNl5LyRxQouQYyMd/cVbsdMCQ\nQOPVt2D18kaoqb5ezHLiqPTXp31ntfqBzhZGEYuSLNEk8DgPGR5VSCAFIAJ6HRI8HVR6ik9K7DL7\nOdgYGXiRNGOMjIZI43fkojWwT21ggHpSCe6GL4OV/wD0p3hyd5h3cR/RDHGPZEVsD9V3x4Oxs+bN\n3YOrrYvTf+NZm5ywYq57CMGKbKcl0tCFRQSA/wAfB48eger1102Ym+0jQM/c9kxvS6PhJlbO3txt\ni4aMpLM7Fi7MrMWU+2oPLteK9C60tbgnqXfdmx94xFkj2qDEMeRJ7sC8kDOHKoGBs8mHYDdD8xrU\nTcs6XH29MLMjeNsaKKJSIpIzjl0sBgQBfEOBQpqPVC9VcW3zQyZcW442Q+RPCwVYSY2EpYN9YI40\nDXIUvQBsFRrn8XK2dNUaRyz/AFhu++ypBJkmTARuSQpEFCAAC2onmyhaDEkgDz2dWs2Z6Vwthx8N\nMfMTOjLyyZRjRxIaP09mwvI1Q6oXRJ0pQenJpmWdsl0MY9x440/9tR0OLAm7J8Dz+tDVqu14rzBd\nxkmJjRu4wEtrB7uwB0R0Pt41UmrMinRxg3TI3DeWxklGMk8jmFjFyCo7eGIsmvqbq/ynzq7zZmwM\ncV76QZeSshLJa2toqp5sgcrr5OlrbBHFvEZhdMtaZHEoClVL9kC/lf7En+t7u+Mdww4cZUXHK7g6\nJEYGBjianVnC8nI4t1YYkKQCarUOKukUptKyqj3OLcS0bvIlPYHRBH9b4n5+f665R4mHNL7a5Sha\ntrUqrAee+68D9zfXjUDJxJMSV25xoCSKRx8HxQJPx5/11y5iJ5C9Fq4seXj+3X+/66rhX6k8r/Y+\ns+ZMHEyo4nyAkpCoJCCSoPkfYeD/AL757Pvh2nDyC0SSiePiCy3xbsAm/nz/AH18bxJFuISgUkB7\n5EnivdAfFd/Ya4PjqcUxg3Y6A7qtdI4tIl3hN9G73NtnqjGy14sRyHFgKsqQD39j2P1A8avvXOYd\n4kVvf5SQQhLDsS9AHsEkDvoVX7aT9pEmLlrMYgVBsq36fpq0nzz+IaZGEpjIIsE314I+dH+4X6lN\nhYs7Za4TwhDNIqVIoUq1kDs+Pn/Y00elkk2b1fBHle4DD7hmj9tiVCg+4kgHaDiHDmmKqSeLEAap\ncvcUyYkmliumUAE2WVeh3+3X/TV16U3AY28ZTZE0uPibpC2O0ihneNGZX5LwZbYMq/pV2D41d+yK\nNlHqzGn9Oy7hts7DLxJUjypJ91+tREzMqgeJbT3CQBTBW7ZlB007ruW3Z8bSY8/PHkxmDZ0WOZMf\np04lpO04g8rNNxHI2lMTiWWuNufqfb8OXEbMMmQkUMSg4qFeSxhbpj7fFAgPT92Tf06bNt2Tdth3\n/F22Gfc8HF3WJxG2E7SJDK6kKJOSiyqoSwFEEEgqDWi3A1Rf4HqDMxspdozIEi3KaULkhieJWRR9\nfO6v8ppCf1qidTBtOC/oOSfDhki96YZEwVfplYEBiyymlVqBJ8L0TfE6jT4G5YuJk/4hipKIpIZj\nlRMFSJiVdo1iUnyzGyAt9EliBpwhaLe/TuRjhhE0kJil4LaxsyAmr6YfVfyDetrDLMtyMXA2DcsO\nZFxGYNNEMmCQxtwJVFkBHYoiRCLPV8fuKbd8baxPEohAL/kEsaoaPX+UAeb6B+/j4MPZc7Dy8na5\nMhYweI5ciY2U/l7H5hT2CRfQqtKvq6bL23EmabmWBYRur00beARR+5BsaiK0tmebzPDk71mS4/t+\nwZWERjUqpQGlIB7FgA96g6NGu5xDRo0aANGjRoA0aNGgDRo0aANbP/BHfP8AAsbcTDHjc5VfIndy\n/J44lsR9GlPchDcT9r7A1jkEEuTkR4+PE8s0rBI441LM7E0AAOySfjXofd/QmH6UwY/w9wzCD2WM\ncqyjJpQFtSlgk/Uf8vdEMtDWP6NRo+dh7T639P7jHBBA8k0ZjZg4qQkIynkhurRKJ7HEWPjWNeqv\nS0G07/EMXbIo8aEEy405ZvYYqaLuh/8AbIokBibs0OXEWez+tsnZ8tshy8uaWaOaSdeVnjH5csWY\n0gUgkADiw71rYG2+psGDc8WdAmVj1jZYVS0fY+g2CD9QB4nsMprvxKka0ZR6D27PkyZFwVx8fNSI\nqnOYBckFz9aMAQUUhiCv/wDb+4rT9jJ6gmWfccCKNssxhJMHJkFclPX5W4hiKIPkfJNfUt5exZmB\nm5AxUkizEj9tBjhSh4ogLqqjkGNABQCLYH/KwZl9O+pg2Bm7juGLN+JjnGLme1cj2gRWcgBRQ5Bj\nxH0gk/Nan3ZVui83fMnyZYcbDDclDSySRSCiFPF08jsFh5/T9ayP1ztg9K7rHkbW80MWbAZSkcxU\nKwIKupHij4A8UO+6Gq+mcuPdcT3MHEx8Xb4Xkih4/wAwTLyB5iiOFi/pbu2/Q2qeu9hnmz8eb8PF\nLjQRxxCSVepA0iALzAAjNmrsUPvZGtd9mKujliZskmyRJl5EKBgzlmLFYeqfpLu7UiwbvqrvVY24\nbduEiQ7ucgYs8oV42dQWRXtePFS7tUhACDl9XlVBq13vA3fadli3XNlTOjhjp3LdupINsQCWH0Kb\nPf1V8DSpJnBN9y5Egl27J29eOFjwswWNPqeUyOrczwjLEqpuz+WlrXNotPD82/ZJdt9TPkenjlbt\ntK4sjgSv7GMWEYsSLIy846ZbJbw6n6gKe5wvS2V6k2b1FuOBlCDeJS+BPErIShRl5RhlHSBQEAFW\nFBsg8dVDbftObHNs2T6pw8LF97hE+TDHjTzpEi17klWF5Vx5fmomiVAMH03vuF6Z9b5Ce1t2VFPm\nosO5BFRYVklCtJyVvpUqhPA9AE0QpYPasxlZ6l9L79s2HFt2X7jRPkCJcdOaxAARjmpPTEtIVZ+P\n5gKNNWlXIxlx5J0gmVXhKQrCAxaV++Ti1FAlSaNMCwFGiR6p9UbVBn4f4hFxosxFeJMibF9xlVlZ\na5CmQAsGsEePNHWPeufSOdBtuTuGXNiT5GNMII4MTHEKhXC8iih7IDuVoL/lLHyb1vaJXVip6Wzx\nBn8ZSqzxkOiyC1b4ZWPxfXRH6Ea9CYWeMbFGHnxEJwEkYK8zDYBKHsk8CxHj8oHnuvMseekO5Pk4\nwkiKljyZixa++JboXQbsBbJOtbx/VsGV+EztwSXEy2lAkkaQceVBWA6tO/AIP9a75yVajot7Lz1F\n6S/x21gjnV4MQmEGPrkrfk5WRRLNX1FrDeRWsjztqRsk4mUXx52QmMSKaYAeOh97/qNPfqv8Zt27\nY24YmVOv4g8/cvlG68rIJv5AFqf+EHvwK7cNwb1Csub7Ur52I3F0MRcH6hXElrvyeJB/KTY49cb+\nuzqsW9CNhxttwDLMvtI4p3HYb9B5qwe/t++rV8PbpWXLxGaOdaEkbqSvY1SboqiQNHO80ZAMZkXi\n3CgAp7PiqqzRHWrT09kRplNDxbixVaJDnz35A6/6atyaWkpW8L/Nw5cTa4pIY5ZqSlYoez8jz12D\n/b9rWcnHqJS8ikqAzCuLN4sX4/vrSY1E20viOjD2VtHZq681Xnsr9uif10s5eJiYyMsayLP7lt8C\nq78H5Fddf66xO0Hh8+ldyGZW3qj/AEjkFUgMrX2a7Pyft8fGtW2ZJpUlw8YJ7UqD3IpiyiuPdd35\noX8gdVdjJNlX8Dny5eFjRyGGgygEEtQFnjf9ie++rrW57SiPJHlQOxR4ivBO475AfA8iiOuhZ/TW\ntaZdITm9Nvte4HOxJ1RIJPcnDRcAijzRHbEeeyfkArXa76u9P4uXt2Rn4SQTqsan30ZgOX5iEBB6\nAro99jvsa0PfXEE+NG6xwx5DeyXjBYdEmipWiOu1+KOkrNkh2Pb9whz5VaHg7RYirGJop2egQSBQ\nIYEjolRYtQayLd0JVVmbbRNS5XuQ4gESe4ecZPE2F7rwtlQf3Xoi9bh/Dn1XjblhptoyHd05+wsg\nCssY40GA+e2o+CB9wQMI33CTbtyx5XnyZsV5DyZY2TjEXdQltQs8Hv48i7DAcMXfptoz/wCXC2MY\n7ibHlYScWoBjRHTefP7d0b6pNajnjxnqLKwdq9R42TjTwxyGJirck/8AakI+4NEjokX11fxpIyxj\nenJM/Fh/DbdUTorokk+RfHipiPMcT9TVVkUbH2rv4fep5t03HHGTkZE4MgjqZvyEg/UtEAk2ATV0\nGJNEjU/19M2btWSvvqcyPj7aTwqwjcAseBCsRZIT6gL4dmu9TyT32VTWFHB6sj27Hj2sLNnRTqJB\nJApDsoYAr9ShmFJfxYI/KRQ47htnp/cVzsvAi3F80ziOT8b/ACo5ZJGvi7lxZNAgG6Cm+qIpfSub\nuONuOLj5GND+EWSmmlUSOt2D9RohQSSB0Cfk1etmwPTnpnZfaOJixkZKLEZpD7izqwIALMeJuq6+\nDXg6yrxGvNMLx8fO2vb3EWTk7cnu8ZYGIZeZFg8O+JPFRfzxHX2ZNm2ZsPKkaBJmgzAVnhykMq+2\nyfUSQQw6Ldg/5SCQCCWbfotqOWTl45x8MIxWSEAoYgW+q6YeaHY6te+14UG6ZmRJjvBDiZ+2Jiqi\nSZMrlIzGTxXmQhI8k8QtHyDVMJUG9XZbmljG+L0Vh7ttRinwUiylxx7eRjTsIp5VRl5MgXihHPx5\nHgGl1+eptg2jF2mUxRY+IFdYjyZ0pSWjU2KPQvoA3xskjvXPbfUeLsixenERnyjEuOsYIlxzKORd\ngx7KtyUUAK+AKOnTd9qiztukhMIl/miZY2RWHIeRRoUe77F8j2L1TjazshSp2+jz8ogJkjy0lQR/\nQcg8XaWmrxQtSCtAWRxJv7fmHt34gFh/6dArdzExgN1Yqrvz4JNj9NMXqHbRi4EGNkHDE6oHISNl\nEoS6H0ghe+TE8wSWA6CiqDOy9wkysqHIkRGZQWkGNykk4kDkH8Hvy6H6r7LXZ4q3p3xYco8eDZsk\nZMmVLixlUNwO0UiyEkWCGBAoeer67+ddm3jGWdcnJTFlfG/90ZTGdckKrLbq31WEdCDyXuMDjYta\npsrMxGkiRI2Z3IV5ka0JB7QnwarvyK/Tudu2bvGPNhpj5E6tkKUkWKZwrpIaIJshh0x8eWN2CBrY\n2paTKqwhYuFB/hy/iHeRov5a/wAwPGFb6gq03/5GjXfL+i8kPvbokc2O7lm+uKHpmX54+e6HWnLB\nw1ydmnzZkSOPi+XLKJvcHcjV9S2A3ZH1USSOjYpdz4/8PnhnxTNGK5fzAPoZr+kAft+tf21qetGN\nKkyryMcw5LRN1xNAMCSR8f6fbXAEs44q3ZqlHZ/+9dpnyM+cyPzklftmoluuvA/bVnj7UgWGQmQT\ndMyuoAXqwOz34+3XX310cqWkKNvCoWBupZFPAnySRdKev3/2dcjjRxsCquFb7g2Bf9O6r++nFY43\nQgNT9DxYAAH38dX+2vjI2/c5ZBFjMGJFRxiy0gqgKro/p+vR61C8v2W/FgkcfpWEleQ+oEki/tq+\n2XObCgkDMyxRAugIDp7hFD9Ca/tr63H0zLJMPwyMw6sMwHED4s/Hn9tcsfb5F3HE2/IVo1nP0Eg8\nW5Gg3Q7ogi+/H6a6clJYznwlF6h19L5P4qQpk7Qm6/inGNitMOLKCzcRC9VEQzM5NGwOgPnWMX1b\nseHBFgty9z3vZKZAeRxKOQp5W+kvQUG2oG7NDWQbp6O3T0vubRfio6i4yp7bUCL7NHwQQKqrHfxp\ns9LbS26ZEkwyccZazFvZyhyRTdqKFDpuxxHRGqi6Jcb003eNulyIIZpZYHEQ/mJKfbiLHoyAfVRH\n1EA3fLvwCKP05k7Vj400kO5RrOuMytMtxwheipomjTM9Hyfq1bvnJmLjxYmdBA+HwyJWaMBHi4n6\nlBJpT482AQfBBbPdw3GXcvUUWTNPJlYeMjrDKQqe447MZPELdPRAFkKPHLVSdaZGLeFN69mgTasW\nXAhXHWR2jdGhUt9JNGwKpumFH5IJIqsm9XbtLnxYscz+5MCWkdr5GhQ+f1P9v31qnqRmfGin6mXk\nU6BFE/Y3+o61jHqXK/Fb5OAGCQH2VVqPHj57+fq5G/10huieYVOjRo11OQaNGjQBo0aNAGjRo0Aa\nNGjQDv8AwnwI8v8AiDg5E8ImhwLyyhcr9akLEbsAVK0Z7IHXf2Oqz+tooMuYZWPOWYsWXmeSXQJU\n/Y+D0fAH3rLvQKbsuJnLt80oXIaPljxcy83Akjiq3dAu1kUAjd2QC8bjiTY+74Db8iQMxEXusF9u\nMu5Qh2brociQD9PZ5BlK6iWspYj93HCxtwkGXtm5FMvHQTIzy8W5fUQb8kihRBsE+fGrD+H/AKpg\n27Z5dqyjm/jcZ3aKFFje1JS0A/MASGJA/VrNkaoMdZUT8RtuQZsWPlTk+APPRo1+4B7P66rt33TB\n/FY+8486/wCKY/EyArwtwxpl4+GX6aPV0LuyDiVm2egIB+Oh/wARSGaKaO1ZY4ykqlT2QrV5+OQB\n4k2PjX4+Bi5eBO+2cJcqRvcM0TjlI4Re+zS8hGFP3sHxes29E+uNx3WfIyMgRwxKGkKcD7chUDod\n2hWx0CQQ3irI0fYvczpYNxwliOPLGRlJbxXJ3bcfFG/kX1rH9Gldsey7/tZzg+fINuE0zDCEYEwM\nh5MUaMlVIB5KoFFiR0abVpmjd8n1Pt0O27ukOCYzLMIsfkGjDUFBJK935HY4j4NG3fbMHcizZGGn\nJU9n3FqynkqHH1VYFix2K770pxzz7FvsWz4yN+ATI5PMI+LIGYMASKBFsR0AAtA2QTroiGPMsBkx\nJIFcoSpCMB+T7VVeP+msL3HZs7YM2FN2zdsx0keVVnkX6lREQFuNeSFNELyZmsG+J1vZ6GlT1ls8\n2XgxZGNhx5b4bCSHHkcBOV14NCqJPZI6Fir1E42VB0I+67Nt/qDZ58vF3iObaAsTfzsVw0CgrCHj\nLBV5sIiPygKS1kUDpL9SZOyZXpH28r1DNuvqRZ4hJEY5IvoHXBy4+pgCbbz4HhdbL6elg3LJyZoX\nzF/Cw+1AJgojC8UCvGvFVUjh9QK0GJo8auj9Yeh5tywM6ZFxs3MyiGfIlgXnjGgPP5uBSuvqKnsD\nsFMT49Gveyn9E+osvC2vbNqhwJduXJll4l5GmVEDRhX+s1Zd2XitciyVZIB0B8+HP2DKeVcTMjhU\nIxP1pMCoFla6UnurPjzrD8fYcaKfbovUmdJLD7c6yTQ5ytLirGVUEIQQFLflayCPq+midXmPl4m0\nyrt21ZCz4GTJJHLJIvBfcBS0jcsWkUBl+ASDffdHH2gn6Z9etfR+Pg4rTrt4xmQHlj+7yIQtYKkf\nm8N0T5r7a4bbgRZmyZOHG3uLGBMo4EstXYPd99/2B601b3uX4jGxMzb0SBNvxJA+DM6rFKAE5e35\n5cbHRPwAB1pW2Tc8TF4bnskQjcSM02LM1gixZX5I7qr+NTJP0VFn1788OyQ7bLCZcF3LlityQdGj\n19vBH2P9qPMcbXvBytqzOMssdwTQvTGxRB8FTdjsC/NUb1bg5MpmmkiBZq9xGHEmNifqrl1VCvuO\n7Faot52GXbpcdwCyPbpKLYSL8MKPVHo1YsfcnXJ0dF9FFmN7uJJkOzuXdmdwb7on6vm7JBP6+DWo\nmH7D/TJIE6PE9jwf9dTM/EZsVJIoSHIuRAlfTQ7I/wB/171Qu5WY0Atjwvxf7eNWvkiX8Wa/6N3L\nHy5FxpZA5ZOPZrl3+/8Au9WWVje1mRmeMC2PEsK8H9vI/wB9ayTb814yDzeMRkMpWrBBBHnW57Lu\nOP6s2OHLeMPMSY5UiRgVcCy3xVjvrxf3F651RbditE0ePuaxx445ShITEyHg68eLU4NjwWtTf7+N\naTsYhfa2wochFTKDfhyXDhD3YUEDwOwCOvt8lL9R7LFtp9uCx7jFg5BFCrBHwOh5+99d6YdgzmxJ\nUx45YVjCcpWH0qGHH5JN9k3ZBs/r9V+yKL/aMf3o4VyscqYm4qCV4k0eXQP7iv08aoP4gbSsuPNm\nQkqVCtPC31RShG5CwRQP1N3RHVfch2RBLDIvt/Wrfl59AjsdjwPGl/1TtDS7DJ7XN5g8Zk4QM4kb\nktkxrYYH5+k9WD1o0+OGJpy0xbeNn2WPLyoJcPMwclMeTkjMjxmQMAqMVPVgm2vpyKUBeJVd029s\nGZMGSbGZov5hfFmRo+1DAWO2KcmF2fkDxZ1cYm142UZsPOWMQFsEQe2ebhhxMlnipIBsFquj4IGl\nfeDJuUHuTwwy7mzzo+VHDTsxdzbf5eiVUODXHyGIACPk+ypeP6Kv0HnZODlyyQyIUWGmLnqyDQII\nribqq8kHxp53L1SmVtsozsETyRAjm6sWFE1ZboigPv8AJNsSTl+37HkShp4TGgYxrJFDOOTIS1ED\nvoe2QbPRK6dVgysbDH4xZHyGYKxdnJ8UpoflII/X4FHUeR8XaZXjVqmIW9bxJmbnkBkaOqRxwFgj\no+Pgfb9v300bf6h3XF2DHhGblCJ04iBWdGlH1BQaP1C1rojzV9CueTtu0ruMEkuGqZBj5slMFlPH\nkDdst+FIvzrnmRYm4YsoSEY2XH7a8ndph7fE0ACAA1gd8vF9a3mnWBQa9lpgepsPZVxoEnn3CGRl\nkTFhmpgzInIyUCbBND7MlgHkGF3uW8Pn5mNnbhk482PlseWDLGGuVFUCT6uK1bcaY+OdWRWlnYsb\naG2mXFzIUmy5ctmOROtlIwpVBfEkHk1kXR4Dxq4x8nbtvxOayPLt5LQzwkAPVuqggtfXlSB0D9id\ndE10jm4vtlx6VxEk3TEzA59+NrgjjAKsh/Kh/KR05ayKPE0CaGtcmzSMeCaJC6uRaFeTd9eQeiL1\njvp7Nxo8/Iz9myX/AMNx5PdkXJdnAKIfHELx+GIK91d2K1r22xg4WOI43SFYwAr03JaIHd/bs+fI\n7Na1p9Iy1jYlessTFx8aT8RJjRiWXmoaIt4blQ7Hk8QSKPx8kFDiEKRs+SscTyuGX2kVyUto3NNb\nIykD58EHqq1snqTCw3wJJsrmqKpJKC2H7f7rvvWM7rFl7UcmQx48BikmR5ImB/Mle2zMTS0Wo/mJ\nI6NWvmcabTPRGVpNFSs2VuGXEr7eRCVEePIiG/BHK27IFEdHj1VEjV5u+2xQQJj4+a+VkQKWsMZO\nCdWOJSwSKJIJ6Hjvqm93Pn/DZMntRey3tJxYsR1ZFqTRLUR4Fm/udMuz7fHJtvvRwJ70bkqsqD3Q\nrdWG6NUg+3+pGpeakUtvRUhw8eSPLhnxhMY8R1jEIUSQv1xLih9ANfVYotZB7B+MvbJdxw0fKYjI\nxVHvxvKGNtYsX35HZ8eB2dM+9bft+LCmfjZEiTJMocODwJ8kdgg+LNjx8/Iq5kyp80pJKeUOOUle\nFuTS8egW+og9VXHogdD7nJ/YUUVuyYybacmOKN8gzDg5jkAZFH9Pmx/2o6GWSWUGaThKK+kkKR2P\nHROu/tSYuVIqZLQPIOEgZqLL2aon7i6/QX41Klmw5sCGSY/+pWgOEYDN9IIBqx5F8j3Td+NRJ/K/\ns6RXxr6IypGUdOEckipSm6B/r15qvtqwR4vdmhbjJErJLG6K1EkHzRVh+5HVmvOq6GB83HeBJ1ia\n+R7UfV8f/o386M5pT7MzqvD21okBQ5oddearvUtW6sq6V0fuYiiCZjH+HHBqYKxLqPNG7+f0rvze\nlHE3mV3uaL8rrbowU1119v1v+96cIGiljBgeRYw3ALKi90O/pBod2Rd+Ou/CxibIr/iS4aixER8t\n9N30PPih99d/HSvkcfJbriWu+epcibcRErD2+AR1JDdfHddfHQ/+gz4HqafaWjz5JPZyQECPMgAK\ngfF9gHwf0N2NZfBF+JyG5O3BQSbHZ/T9POnV81J9nwcJeRljJ8g8VUggAr1839/Hz413dI4K2PsH\nqbIkw5cpMnFh/ERhZGlsfDDscjYsg2P2qrGk3LzcpppNtb2vZg8tiS8oi3/FYNCwouvJs/PX7Igw\nsL8HE2PM0a+5KZh2rmyelvr9+uxpfhEs2QY1iYRoxCsrUlFj8Gv+I/8AP765p8kdGuLLbcPUJ2/A\nmlyII/o9xkU9A3/l78d9D99Y47tI7O7FnYkszGyT9zp49a5sMeKMSNldy3Amj2oprB8ea/vpF138\nK+Nnn8z+VBo0aNdjkGjRo0AaNGjQBo0aNAGjRo0BvHobZFwosGbas1RPH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gcDi9FQSPqH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\nGjRo0AaNGjQBo0aNAGjRo0AaNGjQH//Z\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## 8. Write the data out to a NetCDF file\n\n* And check the results using `ncdump -h`"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-09-11T03:03:32.683047Z",
          "end_time": "2017-09-11T03:03:45.357059Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "netcdf_name = '/g/data/u46/users/dra547/nbart_test.nc'\nwrite_dataset_to_netcdf(nbart_data, netcdf_name)\n\n!ls -lh $netcdf_name\n!ncdump -h $netcdf_name",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": "-rw-r--r-- 1 dra547 u46 1.3G Sep 11 13:03 /g/data/u46/users/dra547/nbart_test.nc\nnetcdf nbart_test {\ndimensions:\n\ttime = 1 ;\n\ty = 9784 ;\n\tx = 9637 ;\nvariables:\n\tdouble time(time) ;\n\t\ttime:units = \"seconds since 1970-01-01 00:00:00\" ;\n\t\ttime:standard_name = \"time\" ;\n\t\ttime:long_name = \"Time, unix time-stamp\" ;\n\t\ttime:axis = \"T\" ;\n\t\ttime:calendar = \"standard\" ;\n\tdouble y(y) ;\n\t\ty:units = \"metre\" ;\n\t\ty:standard_name = \"projection_y_coordinate\" ;\n\t\ty:long_name = \"y coordinate of projection\" ;\n\tdouble x(x) ;\n\t\tx:units = \"metre\" ;\n\t\tx:standard_name = \"projection_x_coordinate\" ;\n\t\tx:long_name = \"x coordinate of projection\" ;\n\tint crs ;\n\t\tcrs:grid_mapping_name = \"albers_conical_equal_area\" ;\n\t\tcrs:standard_parallel = -18., -36. ;\n\t\tcrs:longitude_of_central_meridian = 132. ;\n\t\tcrs:latitude_of_projection_origin = 0. ;\n\t\tcrs:false_easting = 0. ;\n\t\tcrs:false_northing = 0. ;\n\t\tcrs:long_name = \"GDA94 / Australian Albers\" ;\n\t\tcrs:semi_major_axis = 6378137. ;\n\t\tcrs:semi_minor_axis = 6356752.31414036 ;\n\t\tcrs:inverse_flattening = 298.257222101 ;\n\t\tcrs:crs_wkt = \"PROJCS[\\\"GDA94 / Australian Albers\\\",GEOGCS[\\\"GDA94\\\",DATUM[\\\"Geocentric_Datum_of_Australia_1994\\\",SPHEROID[\\\"GRS 1980\\\",6378137,298.257222101,AUTHORITY[\\\"EPSG\\\",\\\"7019\\\"]],TOWGS84[0,0,0,0,0,0,0],AUTHORITY[\\\"EPSG\\\",\\\"6283\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"4283\\\"]],PROJECTION[\\\"Albers_Conic_Equal_Area\\\"],PARAMETER[\\\"standard_parallel_1\\\",-18],PARAMETER[\\\"standard_parallel_2\\\",-36],PARAMETER[\\\"latitude_of_center\\\",0],PARAMETER[\\\"longitude_of_center\\\",132],PARAMETER[\\\"false_easting\\\",0],PARAMETER[\\\"false_northing\\\",0],UNIT[\\\"metre\\\",1,AUTHORITY[\\\"EPSG\\\",\\\"9001\\\"]],AXIS[\\\"Easting\\\",EAST],AXIS[\\\"Northing\\\",NORTH],AUTHORITY[\\\"EPSG\\\",\\\"3577\\\"]]\" ;\n\t\tcrs:spatial_ref = \"PROJCS[\\\"GDA94 / Australian Albers\\\",GEOGCS[\\\"GDA94\\\",DATUM[\\\"Geocentric_Datum_of_Australia_1994\\\",SPHEROID[\\\"GRS 1980\\\",6378137,298.257222101,AUTHORITY[\\\"EPSG\\\",\\\"7019\\\"]],TOWGS84[0,0,0,0,0,0,0],AUTHORITY[\\\"EPSG\\\",\\\"6283\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"4283\\\"]],PROJECTION[\\\"Albers_Conic_Equal_Area\\\"],PARAMETER[\\\"standard_parallel_1\\\",-18],PARAMETER[\\\"standard_parallel_2\\\",-36],PARAMETER[\\\"latitude_of_center\\\",0],PARAMETER[\\\"longitude_of_center\\\",132],PARAMETER[\\\"false_easting\\\",0],PARAMETER[\\\"false_northing\\\",0],UNIT[\\\"metre\\\",1,AUTHORITY[\\\"EPSG\\\",\\\"9001\\\"]],AXIS[\\\"Easting\\\",EAST],AXIS[\\\"Northing\\\",NORTH],AUTHORITY[\\\"EPSG\\\",\\\"3577\\\"]]\" ;\n\t\tcrs:GeoTransform = 662750., 25., 0., -3682875., 0., -25. ;\n\tshort coastal_aerosol(time, y, x) ;\n\t\tcoastal_aerosol:_FillValue = -999s ;\n\t\tcoastal_aerosol:grid_mapping = \"crs\" ;\n\t\tcoastal_aerosol:units = \"1\" ;\n\tshort blue(time, y, x) ;\n\t\tblue:_FillValue = -999s ;\n\t\tblue:grid_mapping = \"crs\" ;\n\t\tblue:units = \"1\" ;\n\tshort green(time, y, x) ;\n\t\tgreen:_FillValue = -999s ;\n\t\tgreen:grid_mapping = \"crs\" ;\n\t\tgreen:units = \"1\" ;\n\tshort red(time, y, x) ;\n\t\tred:_FillValue = -999s ;\n\t\tred:grid_mapping = \"crs\" ;\n\t\tred:units = \"1\" ;\n\tshort nir(time, y, x) ;\n\t\tnir:_FillValue = -999s ;\n\t\tnir:grid_mapping = \"crs\" ;\n\t\tnir:units = \"1\" ;\n\tshort swir1(time, y, x) ;\n\t\tswir1:_FillValue = -999s ;\n\t\tswir1:grid_mapping = \"crs\" ;\n\t\tswir1:units = \"1\" ;\n\tshort swir2(time, y, x) ;\n\t\tswir2:_FillValue = -999s ;\n\t\tswir2:grid_mapping = \"crs\" ;\n\t\tswir2:units = \"1\" ;\n\n// global attributes:\n\t\t:date_created = \"2017-09-11T13:03:32.696147\" ;\n\t\t:Conventions = \"CF-1.6, ACDD-1.3\" ;\n\t\t:history = \"NetCDF-CF file created by datacube version \\'1.5.1+7.g87d7a34b\\' at 20170911.\" ;\n\t\t:geospatial_bounds = \"POLYGON ((139.186571818104 -33.5830428617076,139.236598287216 -34.3130390889161,139.287325356599 -35.0441803154047,139.338767831127 -35.776618290015,140.225715129984 -35.7325871055685,141.111869118032 -35.6835534117937,141.997145711549 -35.6295262314672,141.927199411094 -34.8983907013129,141.858223156445 -34.1685033503552,141.790196946432 -33.4397145312098,140.923119721975 -33.4923623965918,140.055218332503 -33.5401411364255,139.186571818104 -33.5830428617076))\" ;\n\t\t:geospatial_bounds_crs = \"EPSG:4326\" ;\n\t\t:geospatial_lat_min = -35.776618290015 ;\n\t\t:geospatial_lat_max = -33.4397145312098 ;\n\t\t:geospatial_lat_units = \"degrees_north\" ;\n\t\t:geospatial_lon_min = 139.186571818104 ;\n\t\t:geospatial_lon_max = 141.997145711549 ;\n\t\t:geospatial_lon_units = \"degrees_east\" ;\n}\n",
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