EX-Climate-download-reanalysis-2020.ipynb

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     "text": [
      "Folium version: 0.11.0\n",
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "from src.tools_dev import Mapdisplay, random_point_in_shp\n",
    "import ee\n",
    "import tensorflow as tf\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point, Polygon\n",
    "from folium.plugins import MarkerCluster\n",
    "import numpy as np\n",
    "from src.tools_dev import get_bound_points, generate_box_buffer, \\\n",
    "                            generate_ee_points, get_landsat_data\n",
    "import geopy.distance\n",
    "import rasterio\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import folium\n",
    "print('Folium version: ' + folium.__version__)\n",
    "\n",
    "# Define the URL format used for Earth Engine generated map tiles.\n",
    "EE_TILES = 'https://earthengine.googleapis.com/map/{mapid}/{{z}}/{{x}}/{{y}}?token={token}'\n",
    "ee.Initialize()\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_features(shpfile):\n",
    "    df = gpd.read_file(shp).to_crs(\"EPSG:4326\")\n",
    "    df = df[df.area==np.max(df.area)].reset_index()\n",
    "    xmin, ymin, xmax, ymax = df.bounds.values[0]\n",
    "\n",
    "    x,y = df.geometry[0].exterior.coords.xy\n",
    "    cords = np.dstack((x,y)).tolist()\n",
    "    g = ee.Geometry.Polygon(cords)\n",
    "    feature = ee.Feature(g)\n",
    "    fc = ee.FeatureCollection([feature])\n",
    "    return(fc,g)\n",
    "\n",
    "def normalize(img):\n",
    "    return (img-np.nanmin(img))/(np.nanmax(img)-(np.nanmin(img)))\n",
    "\n",
    "def clipper(img):\n",
    "    return img.clip(fc)\n",
    "  \n",
    "def addTime(image): \n",
    "    constant = 1000 * 60 * 60 * 24 * 365\n",
    "    return image.addBands(image.metadata('system:time_start').divide(constant))\n",
    "\n",
    "def fill(image):\n",
    "    filled1a = image.focal_mean(2, 'square', 'pixels', 1).blend(image)\n",
    "    return filled1a.set('system:time_start',image.get('system:time_start'))\n",
    "\n",
    "def climate_collection_generator(bounds):\n",
    "    \n",
    "    tc = ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE').filterBounds(bounds)\n",
    "       \n",
    "    return tc \n",
    "\n",
    "def image_collection_export(ic, outbands, BUCKET, PREFIX, DESC, REGION):\n",
    "    task = ee.batch.Export.image.toCloudStorage(\\\n",
    "                                         image = ic,\\\n",
    "                                         description = DESC,\\\n",
    "                                         bucket = BUCKET,\\\n",
    "                                         fileNamePrefix = PREFIX,\\\n",
    "                                         scale = 30,\\\n",
    "                                         region = REGION)\n",
    "    task.start()    \n",
    "    return \n",
    "\n",
    "def ic_annual_aggregate(ic, outbands, years, month_range):\n",
    "    out_images = []\n",
    "    for year in years:\n",
    "        annual_image_group = []\n",
    "        #renamed = [f'{b}_{year}' for b in outbands]\n",
    "        for b in outbands:\n",
    "            renamed = f'{b}_{year}'\n",
    "            if b == 'pr':\n",
    "                image_annual = ic.filter(ee.Filter.calendarRange(int(year),int(year),'year'))\\\n",
    "                    .filter(ee.Filter.calendarRange(int(month_range[0]),int(month_range[1]),'month'))\\\n",
    "                    .select([b])\\\n",
    "                    .sum()\\\n",
    "                    .rename([renamed]).float()\n",
    "            else:\n",
    "                image_annual = ic.filter(ee.Filter.calendarRange(int(year),int(year),'year'))\\\n",
    "                    .filter(ee.Filter.calendarRange(int(month_range[0]),int(month_range[1]),'month'))\\\n",
    "                    .select([b])\\\n",
    "                    .mean()\\\n",
    "                    .rename([renamed]).float()\n",
    "            annual_image_group.append(image_annual)\n",
    "        out_images.append(ee.Image.cat(annual_image_group))\n",
    "    out = ee.Image.cat(out_images)    \n",
    "    return out\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "BUCKETS = ['ikh-nart','rio-mora']\n",
    "\n",
    "for BUCKET in BUCKETS:\n",
    "    if BUCKET == 'rio-mora':\n",
    "        shp = './data/shp/RioMoraNWRBndry_2019/RioMoraNWRBndry_2019.shp'\n",
    "        target_months = (4,10) \n",
    "        #shp = './data/shp/MRW/Mora River Watershed July2011.shp'\n",
    "    elif BUCKET == 'ikh-nart':\n",
    "        shp = './data/shp/IN_boundary/IN_boundary.shp'\n",
    "        target_months = (6,10) \n",
    "\n",
    "    #need an automated way of determining growing season....\n",
    "    fc,g = build_features(shp)\n",
    "    REGION = g.getInfo()['coordinates']\n",
    "\n",
    "    years = np.arange(1986,2021) \n",
    "    # Define a FeatureCollection fc above\n",
    "    #Build landsat collection\n",
    "    outbands = ['pr', 'tmmx', 'tmmn']\n",
    "    terra = climate_collection_generator(bounds=fc) \n",
    "    terra = terra.select(outbands).map(clipper)\n",
    "    #landsat = landsat.map(clipper)\n",
    "    \n",
    "    month_groups = (1,3),(4,6),(7,9),(10,12)\n",
    "    groups = ['JFM', 'AMJ', 'JAS', 'OND']\n",
    "    \n",
    "    #image = ic_annual_aggregate(terra, outbands, years, month_groups[0]) \n",
    "    ######## ALL BANDS #############\n",
    "    for i,m in enumerate(month_groups):\n",
    "        DESC = f'{BUCKET}__{groups[i]}'\n",
    "        PREFIX = f'processed/terra_{groups[i]}'\n",
    "        out = ic_annual_aggregate(terra, outbands, years, target_months)\n",
    "        #export these to bucket\n",
    "        image_collection_export(out, outbands, BUCKET, PREFIX, DESC, REGION)\n",
    "        del out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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