pipeline/Visualize-VEG.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio\n",
    "path = \"s3://planet-snowcover-analysis/VEG/USCATB20140827_chm_3p0m.tif\"\n",
    "with rasterio.Env(AWS_S3_ENDPOINT='s3.us-west-2.amazonaws.com'):\n",
    "    with rasterio.open(path) as f:\n",
    "        img = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-08-26 17:55:29--  https://glad.geog.umd.edu/Potapov/Forest_height_2019/Forest_height_2019_NAM.tif\n",
      "Resolving glad.geog.umd.edu (glad.geog.umd.edu)... 128.8.149.19\n",
      "Connecting to glad.geog.umd.edu (glad.geog.umd.edu)|128.8.149.19|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 5680464301 (5.3G) [image/tiff]\n",
      "Saving to: ‘Forest_height_2019_NAM.tif’\n",
      "\n",
      "Forest_height_2019_ 100%[===================>]   5.29G  22.4MB/s    in 4m 1s   \n",
      "\n",
      "2020-08-26 17:59:31 (22.4 MB/s) - ‘Forest_height_2019_NAM.tif’ saved [5680464301/5680464301]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://glad.geog.umd.edu/Potapov/Forest_height_2019/Forest_height_2019_NAM.tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Driver: GTiff/GeoTIFF\n",
      "Files: Forest_height_2019_NAM.tif\n",
      "Size is 436004, 156004\n",
      "Coordinate System is:\n",
      "GEOGCS[\"WGS 84\",\n",
      "    DATUM[\"WGS_1984\",\n",
      "        SPHEROID[\"WGS 84\",6378137,298.257223563,\n",
      "            AUTHORITY[\"EPSG\",\"7030\"]],\n",
      "        AUTHORITY[\"EPSG\",\"6326\"]],\n",
      "    PRIMEM[\"Greenwich\",0],\n",
      "    UNIT[\"degree\",0.0174532925199433],\n",
      "    AUTHORITY[\"EPSG\",\"4326\"]]\n",
      "Origin = (-161.000499999999988,52.000500000000002)\n",
      "Pixel Size = (0.000250000000000,-0.000250000000000)\n",
      "Metadata:\n",
      "  AREA_OR_POINT=Area\n",
      "Image Structure Metadata:\n",
      "  COMPRESSION=LZW\n",
      "  INTERLEAVE=BAND\n",
      "Corner Coordinates:\n",
      "Upper Left  (-161.0005000,  52.0005000) (161d 0' 1.80\"W, 52d 0' 1.80\"N)\n",
      "Lower Left  (-161.0005000,  12.9995000) (161d 0' 1.80\"W, 12d59'58.20\"N)\n",
      "Upper Right ( -51.9995000,  52.0005000) ( 51d59'58.20\"W, 52d 0' 1.80\"N)\n",
      "Lower Right ( -51.9995000,  12.9995000) ( 51d59'58.20\"W, 12d59'58.20\"N)\n",
      "Center      (-106.5000000,  32.5000000) (106d30' 0.00\"W, 32d30' 0.00\"N)\n",
      "Band 1 Block=436004x1 Type=Byte, ColorInterp=Gray\n",
      "  Description = Layer_1\n",
      "  Metadata:\n",
      "    LAYER_TYPE=athematic\n"
     ]
    }
   ],
   "source": [
    "!gdalinfo Forest_height_2019_NAM.tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-rw-r-- 1 ubuntu ubuntu 5.3G Aug 19 16:44 Forest_height_2019_NAM.tif\n"
     ]
    }
   ],
   "source": [
    "! ls -lah Forest_height_2019_NAM.tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-270422a262e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Load the DEM and read out a np array of the elevation values (meters):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mdem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'Forest_height_2019_NAM.tif'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0melev\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mrasterio/_io.pyx\u001b[0m in \u001b[0;36mrasterio._io.DatasetReaderBase.read\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import rasterio as rio\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import rasterio.plot\n",
    "import os\n",
    "from datetime import datetime as dt\n",
    "\n",
    "# Load the DEM and read out a np array of the elevation values (meters):\n",
    "dem = rio.open(r'Forest_height_2019_NAM.tif')\n",
    "elev = dem.read(1)\n",
    "\n",
    "\n",
    "\n",
    "# Plot this elevation band:\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(elev)\n",
    "\n",
    "title_text = ''\n",
    "plt.title(title_text);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-21ca0e59c090>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Open data and assign negative values to nan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mrio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Forest_height_2019_NAM.tif'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mepsg4326_dem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'meta origin'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mrio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'veg.tiff'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mrasterio/_io.pyx\u001b[0m in \u001b[0;36mrasterio._io.DatasetReaderBase.read\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pyproj\n",
    "from geopandas import GeoDataFrame\n",
    "from shapely.geometry import shape,box\n",
    "from rasterio.mask import mask\n",
    "from rasterio.transform import from_origin\n",
    "# Open data and assign negative values to nan\n",
    "with rio.open('Forest_height_2019_NAM.tif') as origin:\n",
    "    epsg4326_dem = origin.read(1)\n",
    "    print('meta origin',origin.meta)\n",
    "    with rio.open('veg.tiff') as src:\n",
    "        print('ASO Veg origin',src.meta)\n",
    "        #pf = src.read(1, masked=True)\n",
    "        print(box(*src.bounds))\n",
    "        clipped_raster,clipped_transform = mask(origin,[box(*src.bounds)],crop=True,nodata= 0)\n",
    "        print('clipped transform',clipped_transform)\n",
    "        clipped_meta = origin.meta.copy()\n",
    "        clipped_meta.update({\"driver\": \"GTiff\",\n",
    "                 \"height\": clipped_raster.shape[1],\n",
    "                 \"width\": clipped_raster.shape[2],\n",
    "                               \"nodata\": 0,\n",
    "                 \"transform\": clipped_transform})\n",
    "        print(src.meta,\"ds\")\n",
    "        print(clipped_raster[0].shape)\n",
    "        print(src.crs)\n",
    "        print(src.meta)\n",
    "        print(src.shape[1])\n",
    "        print(src.shape[0])\n",
    "        print(\"clipped\")\n",
    "        print(clipped_raster.shape[1])\n",
    "        print(clipped_raster.shape[2])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with rio.open(\"jedi_Tuol_masked.tif\", \"w\", **clipped_meta) as dest:\n",
    "    dest.write(clipped_raster) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open data and assign negative values to nan\n",
    "with rio.open('veg.tiff') as src:\n",
    "    epsg4326_dem = src.read(1, masked=True)\n",
    "ds = rio.open('veg.tiff')\n",
    "print(ds.crs)\n",
    "print(ds.meta)\n",
    "# View object dimensions\n",
    "epsg4326_dem.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "session = boto3.Session(profile_name='esip')\n",
    "# Any clients created from this session will use credentials\n",
    "# from the [dev] section of ~/.aws/credentials.\n",
    "dev_s3_client = session.client('s3')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('USCATB20140827_chm_3p0m.tif', 'wb') as f:\n",
    "    dev_s3_client.download_fileobj('planet-snowcover-analysis', 'VEG/USCATB20140827_chm_3p0m.tif', f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/__init__.py:1067: UserWarning: Duplicate key in file \"/home/ubuntu/.config/matplotlib/matplotlibrc\", line #2\n",
      "  (fname, cnt))\n",
      "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/__init__.py:1067: UserWarning: Duplicate key in file \"/home/ubuntu/.config/matplotlib/matplotlibrc\", line #3\n",
      "  (fname, cnt))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import rasterio as rio\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import rasterio.plot\n",
    "import os\n",
    "from datetime import datetime as dt\n",
    "\n",
    "# Load the DEM and read out a np array of the elevation values (meters):\n",
    "dem = rio.open(r'USCATB20140827_chm_3p0m.tif')\n",
    "elev = dem.read(1)\n",
    "\n",
    "\n",
    "\n",
    "# Plot this elevation band:\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(elev)\n",
    "\n",
    "title_text = ''\n",
    "plt.title(title_text);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       ...,\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'driver': 'GTiff',\n",
       " 'dtype': 'float32',\n",
       " 'nodata': -9999.0,\n",
       " 'width': 17699,\n",
       " 'height': 16780,\n",
       " 'count': 1,\n",
       " 'crs': CRS.from_epsg(32611),\n",
       " 'transform': Affine(3.0, 0.0, 254247.0,\n",
       "        0.0, -3.0, 4229667.0)}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get metadata\n",
    "dem.meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Driver: GTiff/GeoTIFF\n",
      "Files: USCATB20140827_chm_3p0m.tif\n",
      "Size is 17699, 16780\n",
      "Coordinate System is:\n",
      "PROJCS[\"WGS 84 / UTM zone 11N\",\n",
      "    GEOGCS[\"WGS 84\",\n",
      "        DATUM[\"WGS_1984\",\n",
      "            SPHEROID[\"WGS 84\",6378137,298.257223563,\n",
      "                AUTHORITY[\"EPSG\",\"7030\"]],\n",
      "            AUTHORITY[\"EPSG\",\"6326\"]],\n",
      "        PRIMEM[\"Greenwich\",0,\n",
      "            AUTHORITY[\"EPSG\",\"8901\"]],\n",
      "        UNIT[\"degree\",0.0174532925199433,\n",
      "            AUTHORITY[\"EPSG\",\"9122\"]],\n",
      "        AUTHORITY[\"EPSG\",\"4326\"]],\n",
      "    PROJECTION[\"Transverse_Mercator\"],\n",
      "    PARAMETER[\"latitude_of_origin\",0],\n",
      "    PARAMETER[\"central_meridian\",-117],\n",
      "    PARAMETER[\"scale_factor\",0.9996],\n",
      "    PARAMETER[\"false_easting\",500000],\n",
      "    PARAMETER[\"false_northing\",0],\n",
      "    UNIT[\"metre\",1,\n",
      "        AUTHORITY[\"EPSG\",\"9001\"]],\n",
      "    AXIS[\"Easting\",EAST],\n",
      "    AXIS[\"Northing\",NORTH],\n",
      "    AUTHORITY[\"EPSG\",\"32611\"]]\n",
      "Origin = (254247.000000000000000,4229667.000000000000000)\n",
      "Pixel Size = (3.000000000000000,-3.000000000000000)\n",
      "Metadata:\n",
      "  AREA_OR_POINT=Area\n",
      "  Band_1=canopy height (m)\n",
      "Image Structure Metadata:\n",
      "  INTERLEAVE=BAND\n",
      "Corner Coordinates:\n",
      "Upper Left  (  254247.000, 4229667.000) (119d48'20.72\"W, 38d10'53.16\"N)\n",
      "Lower Left  (  254247.000, 4179327.000) (119d47'18.80\"W, 37d43'41.75\"N)\n",
      "Upper Right (  307344.000, 4229667.000) (119d12' 0.06\"W, 38d11'39.67\"N)\n",
      "Lower Right (  307344.000, 4179327.000) (119d11'11.48\"W, 37d44'27.51\"N)\n",
      "Center      (  280795.500, 4204497.000) (119d29'42.78\"W, 37d57'41.93\"N)\n",
      "Band 1 Block=17699x1 Type=Float32, ColorInterp=Gray\n",
      "  Description = canopy height (m)\n",
      "  NoData Value=-9999\n"
     ]
    }
   ],
   "source": [
    "!gdalinfo USCATB20140827_chm_3p0m.tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BoundingBox(left=254247.0, bottom=4179327.0, right=307344.0, top=4229667.0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem.bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clip the area of interest\n",
    "from rasterio.warp import calculate_default_transform, reproject, Resampling\n",
    "from rasterio.mask import mask\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dst_crs='EPSG:4326'\n",
    "transform, width, height = calculate_default_transform(dem.crs, dst_crs, dem.width, dem.height, *dem.bounds)\n",
    "kwargs = dem.meta.copy()\n",
    "kwargs.update({\n",
    "                'crs': dst_crs,\n",
    "                'transform': transform,\n",
    "                'width': width,\n",
    "                'height': height\n",
    "            })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "outpath='veg.tiff'\n",
    "with rio.open(outpath, 'w', **kwargs) as dst:\n",
    "            reproject(\n",
    "                        source=rio.band(dem, 1),\n",
    "                        destination=rio.band(dst, 1),\n",
    "                        src_transform=dem.transform,\n",
    "                        src_crs=dem.crs,\n",
    "                        dst_transform=transform,\n",
    "                        dst_crs=dst_crs,\n",
    "                        resampling=Resampling.bilinear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/__init__.py:1067: UserWarning: Duplicate key in file \"/home/ubuntu/.config/matplotlib/matplotlibrc\", line #2\n",
      "  (fname, cnt))\n",
      "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/__init__.py:1067: UserWarning: Duplicate key in file \"/home/ubuntu/.config/matplotlib/matplotlibrc\", line #3\n",
      "  (fname, cnt))\n"
     ]
    }
   ],
   "source": [
    "# Open raster data\n",
    "import earthpy as et\n",
    "import earthpy.plot as ep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prettier plotting with seaborn\n",
    "sns.set(font_scale=1.5, style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPSG:4326\n",
      "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -9999.0, 'width': 19874, 'height': 14959, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(3.115758480714683e-05, 0.0, -119.80575467105508,\n",
      "       0.0, -3.115758480714683e-05, 38.194353568849955)}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(14959, 19874)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Open data and assign negative values to nan\n",
    "with rio.open('veg.tiff') as src:\n",
    "    epsg4326_dem = src.read(1, masked=True)\n",
    "ds = rio.open('veg.tiff')\n",
    "print(ds.crs)\n",
    "print(ds.meta)\n",
    "# View object dimensions\n",
    "epsg4326_dem.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-- -- -- ... -- -- --]\n",
      " [-- -- -- ... -- -- --]\n",
      " [-- -- -- ... -- -- --]\n",
      " ...\n",
      " [-- -- -- ... -- -- --]\n",
      " [-- -- -- ... -- -- --]\n",
      " [-- -- -- ... -- -- --]]\n"
     ]
    }
   ],
   "source": [
    "print(epsg4326_dem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import earthpy as et\n",
    "import earthpy.spatial as es\n",
    "import earthpy.plot as ep\n",
    "ep.hist(epsg4326_dem, colors=['purple'],\n",
    "        title=\"Distribution of vegetation\",\n",
    "        xlabel='Elevation (meters)',\n",
    "        ylabel='Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Driver: GTiff/GeoTIFF\n",
      "Files: 12660.tif\n",
      "Size is 512, 512\n",
      "Coordinate System is:\n",
      "GEOGCS[\"WGS 84\",\n",
      "    DATUM[\"WGS_1984\",\n",
      "        SPHEROID[\"WGS 84\",6378137,298.257223563,\n",
      "            AUTHORITY[\"EPSG\",\"7030\"]],\n",
      "        AUTHORITY[\"EPSG\",\"6326\"]],\n",
      "    PRIMEM[\"Greenwich\",0],\n",
      "    UNIT[\"degree\",0.0174532925199433],\n",
      "    AUTHORITY[\"EPSG\",\"4326\"]]\n",
      "Origin = (-119.201660156250000,37.822802433527563)\n",
      "Pixel Size = (0.000021457672119,-0.000016950648287)\n",
      "Metadata:\n",
      "  AREA_OR_POINT=Area\n",
      "Image Structure Metadata:\n",
      "  INTERLEAVE=PIXEL\n",
      "Corner Coordinates:\n",
      "Upper Left  (-119.2016602,  37.8228024) (119d12' 5.98\"W, 37d49'22.09\"N)\n",
      "Lower Left  (-119.2016602,  37.8141237) (119d12' 5.98\"W, 37d48'50.85\"N)\n",
      "Upper Right (-119.1906738,  37.8228024) (119d11'26.43\"W, 37d49'22.09\"N)\n",
      "Lower Right (-119.1906738,  37.8141237) (119d11'26.43\"W, 37d48'50.85\"N)\n",
      "Center      (-119.1961670,  37.8184631) (119d11'46.20\"W, 37d49' 6.47\"N)\n",
      "Band 1 Block=512x1 Type=Float64, ColorInterp=Gray\n",
      "Band 2 Block=512x1 Type=Float64, ColorInterp=Undefined\n",
      "Band 3 Block=512x1 Type=Float64, ColorInterp=Undefined\n",
      "Band 4 Block=512x1 Type=Float64, ColorInterp=Undefined\n"
     ]
    }
   ],
   "source": [
    "# crop the area\n",
    "!gdalinfo 12660.tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Driver: GTiff/GeoTIFF\n",
      "Files: veg.tiff\n",
      "Size is 19874, 14959\n",
      "Coordinate System is:\n",
      "GEOGCS[\"WGS 84\",\n",
      "    DATUM[\"WGS_1984\",\n",
      "        SPHEROID[\"WGS 84\",6378137,298.257223563,\n",
      "            AUTHORITY[\"EPSG\",\"7030\"]],\n",
      "        AUTHORITY[\"EPSG\",\"6326\"]],\n",
      "    PRIMEM[\"Greenwich\",0],\n",
      "    UNIT[\"degree\",0.0174532925199433],\n",
      "    AUTHORITY[\"EPSG\",\"4326\"]]\n",
      "Origin = (-119.805754671055084,38.194353568849955)\n",
      "Pixel Size = (0.000031157584807,-0.000031157584807)\n",
      "Metadata:\n",
      "  AREA_OR_POINT=Area\n",
      "Image Structure Metadata:\n",
      "  INTERLEAVE=BAND\n",
      "Corner Coordinates:\n",
      "Upper Left  (-119.8057547,  38.1943536) (119d48'20.72\"W, 38d11'39.67\"N)\n",
      "Lower Left  (-119.8057547,  37.7282673) (119d48'20.72\"W, 37d43'41.76\"N)\n",
      "Upper Right (-119.1865288,  38.1943536) (119d11'11.50\"W, 38d11'39.67\"N)\n",
      "Lower Right (-119.1865288,  37.7282673) (119d11'11.50\"W, 37d43'41.76\"N)\n",
      "Center      (-119.4961418,  37.9613104) (119d29'46.11\"W, 37d57'40.72\"N)\n",
      "Band 1 Block=19874x1 Type=Float32, ColorInterp=Gray\n",
      "  NoData Value=-9999\n"
     ]
    }
   ],
   "source": [
    "!gdalinfo veg.tiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meta origin {'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 19874, 'height': 14959, 'count': 1, 'crs': CRS.from_dict(init='epsg:4326'), 'transform': Affine(3.115758480714683e-05, 0.0, -119.80575467105508,\n",
      "       0.0, -3.115758480714683e-05, 38.194353568849955)}\n",
      "planet origin {'driver': 'GTiff', 'dtype': 'float64', 'nodata': None, 'width': 512, 'height': 512, 'count': 4, 'crs': CRS.from_dict(init='epsg:4326'), 'transform': Affine(2.1457672119140625e-05, 0.0, -119.20166015625,\n",
      "       0.0, -1.6950648287314407e-05, 37.82280243352756)}\n",
      "POLYGON ((-119.190673828125 37.81412370160446, -119.190673828125 37.82280243352756, -119.20166015625 37.82280243352756, -119.20166015625 37.81412370160446, -119.190673828125 37.81412370160446))\n",
      "clipped transform | 0.00, 0.00,-119.20|\n",
      "| 0.00,-0.00, 37.82|\n",
      "| 0.00, 0.00, 1.00|\n",
      "{'driver': 'GTiff', 'dtype': 'float64', 'nodata': None, 'width': 512, 'height': 512, 'count': 4, 'crs': CRS.from_dict(init='epsg:4326'), 'transform': Affine(2.1457672119140625e-05, 0.0, -119.20166015625,\n",
      "       0.0, -1.6950648287314407e-05, 37.82280243352756)} ds\n",
      "(280, 353)\n",
      "EPSG:4326\n",
      "{'driver': 'GTiff', 'dtype': 'float64', 'nodata': None, 'width': 512, 'height': 512, 'count': 4, 'crs': CRS.from_dict(init='epsg:4326'), 'transform': Affine(2.1457672119140625e-05, 0.0, -119.20166015625,\n",
      "       0.0, -1.6950648287314407e-05, 37.82280243352756)}\n",
      "512\n",
      "512\n",
      "clipped\n",
      "280\n",
      "353\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pyproj\n",
    "from geopandas import GeoDataFrame\n",
    "from shapely.geometry import shape,box\n",
    "from rasterio.mask import mask\n",
    "from rasterio.transform import from_origin\n",
    "# Open data and assign negative values to nan\n",
    "with rio.open('out.tiff') as origin:\n",
    "    epsg4326_dem = origin.read(1)\n",
    "    print('meta origin',origin.meta)\n",
    "    with rio.open('12660.tif') as src:\n",
    "        print('planet origin',src.meta)\n",
    "        #pf = src.read(1, masked=True)\n",
    "        print(box(*src.bounds))\n",
    "        clipped_raster,clipped_transform = mask(origin,[box(*src.bounds)],crop=True,nodata= 0)\n",
    "        print('clipped transform',clipped_transform)\n",
    "        clipped_meta = origin.meta.copy()\n",
    "        clipped_meta.update({\"driver\": \"GTiff\",\n",
    "                 \"height\": clipped_raster.shape[1],\n",
    "                 \"width\": clipped_raster.shape[2],\n",
    "                               \"nodata\": 0,\n",
    "                 \"transform\": clipped_transform})\n",
    "        print(src.meta,\"ds\")\n",
    "        print(clipped_raster[0].shape)\n",
    "        print(src.crs)\n",
    "        print(src.meta)\n",
    "        print(src.shape[1])\n",
    "        print(src.shape[0])\n",
    "        print(\"clipped\")\n",
    "        print(clipped_raster.shape[1])\n",
    "        print(clipped_raster.shape[2])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "with rio.open(\"dem.masked.tif\", \"w\", **clipped_meta) as dest:\n",
    "    dest.write(clipped_raster) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPSG:4326\n",
      "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 0.0, 'width': 353, 'height': 280, 'count': 1, 'crs': CRS.from_dict(init='epsg:4326'), 'transform': Affine(3.115758480714683e-05, 0.0, -119.20167141681412,\n",
      "       0.0, -3.115758480714683e-05, 37.82283052760954)}\n"
     ]
    }
   ],
   "source": [
    "dem_m = rio.open('dem.masked.tif')\n",
    "dem_r = dem_m.read()\n",
    "print(dem_m.crs)\n",
    "print(dem_m.meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rasterio import Affine, MemoryFile\n",
    "from rasterio.enums import Resampling\n",
    "import earthpy as et\n",
    "import earthpy.spatial as es\n",
    "import earthpy.plot as ep\n",
    "import numpy\n",
    "scale=1\n",
    "# Open data and assign negative values to nan\n",
    "with rio.open('dem.masked.tif') as origin:\n",
    "        with rio.open('12660.tif') as src:\n",
    "            t = src.transform\n",
    "            # # Load red and NIR bands - note all PlanetScope 4-band images have band order BGRN\n",
    "            planet_ndvi = es.normalized_diff(src.read(3), src.read(4))\n",
    "            # rescale the metadata\n",
    "            transform = Affine(t.a * scale, t.b, t.c, t.d, t.e * scale, t.f)\n",
    "            height = int(src.height / scale)\n",
    "            width = int(src.width / scale)\n",
    "\n",
    "            profile = origin.profile\n",
    "            profile.update(transform=transform, driver='GTiff', height=height, width=width)\n",
    "\n",
    "            #data = raster.read(\n",
    "            #        out_shape=(origin.count, height, width),\n",
    "            #        resampling=Resampling.bilinear,\n",
    "            #    )\n",
    "            data = origin.read(\n",
    "                    out_shape=(origin.count, height, width),\n",
    "                    resampling=Resampling.bilinear,\n",
    "            )\n",
    "            with rasterio.open('dem.masked_res.tif', 'w', **profile) as dataset:  # Open as DatasetWriter\n",
    "                dataset.write(data)\n",
    "            \n",
    "            aug_pla_meta = src.profile\n",
    "            # Change the count or number of bands from 4 to 5\n",
    "            aug_pla_meta['count'] = 5\n",
    "            # Change the data type to float rather than integer\n",
    "            aug_pla_meta['dtype'] = \"float64\"\n",
    "            aug_pla_meta\n",
    "            \n",
    "            #convert to float64\n",
    "            ndvi_64 = np.array(planet_ndvi, dtype=numpy.float64)\n",
    "\n",
    "\n",
    "            # Write band calculations to a new raster file\n",
    "            with rasterio.open('12660_aug.tif', 'w', **aug_pla_meta) as dstr:\n",
    "                    dstr.write_band(1, src.read(1))\n",
    "                    dstr.write_band(2, src.read(2))\n",
    "                    dstr.write_band(3, src.read(3))\n",
    "                    dstr.write_band(4, src.read(4))\n",
    "                    dstr.write_band(5, ndvi_64)\n",
    "            dstr.close()         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ep.hist(ndvi_64, colors=['purple'],\n",
    "        title=\"Distribution of ndvi\",\n",
    "        xlabel='ndvi',\n",
    "        ylabel='Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(blue_64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import rasterio\n",
    "from matplotlib import pyplot\n",
    "aug_pla = rasterio.open(\"12660_aug.tif\")\n",
    "\n",
    "ep.hist(aug_pla.read(5), colors=['purple'],\n",
    "        title=\"Distribution of ndvi\",\n",
    "        xlabel='ndvi',\n",
    "        ylabel='Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPSG:4326\n",
      "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 0.0, 'width': 512, 'height': 512, 'count': 1, 'crs': CRS.from_dict(init='epsg:4326'), 'transform': Affine(2.1457672119140625e-05, 0.0, -119.20166015625,\n",
      "       0.0, -1.6950648287314407e-05, 37.82280243352756)}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem_res = rio.open('dem.masked_res.tif')\n",
    "dem_rs = dem_res.read()\n",
    "print(dem_res.crs)\n",
    "print(dem_res.meta)\n",
    "dem_rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio\n",
    "from matplotlib import pyplot\n",
    "srced = rasterio.open(\"dem.masked_res.tif\")\n",
    "pyplot.imshow(srced.read(1), cmap='pink')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio\n",
    "from matplotlib import pyplot\n",
    "srced = rasterio.open(\"dem.masked.tif\")\n",
    "pyplot.imshow(srced.read(1), cmap='pink')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!gdalinfo dem.masked_res.tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ep.plot_bands(dem_r[0], cmap='PiYG', scale=False,\n",
    "              title=\"PDEM\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!gdalinfo 12660.tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with rasterio.open('12660.tif', 'r') as dst_new:\n",
    "        aug_pla_ras = dst_new.read()\n",
    "        aug_pla_meta = dst_new.profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aug_pla_ras[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ep.plot_bands(aug_pla_ras[0], cmap='PiYG', scale=False,\n",
    "              vmin=-1, vmax=1,\n",
    "              title=\"Planet Band 0\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add stuff\n",
    "\n",
    "# Change the count or number of bands from 4 to 5\n",
    "aug_pla_meta['count'] = 5\n",
    "# Change the data type to float rather than integer\n",
    "aug_pla_meta['dtype'] = \"float64\"\n",
    "aug_pla_meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dem_rs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "#convert to float64\n",
    "dem_rs_64 = numpy.array(dem_rs[0], dtype=numpy.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your the dem to raster object\n",
    "with rio.open('planet_aug.tif', 'w', **aug_pla_meta) as dst_pla:\n",
    "    dst_pla.write(dem_rs_64, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with rasterio.open('planet_aug.tif', 'r') as dst_new:\n",
    "        anew_pla_ras = dst_new.read()\n",
    "        anew_pla_meta = dst_new.profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import environ\n",
    "import sys\n",
    "import io\n",
    "from os.path import expanduser\n",
    "sys.path.append(\"../model/robosat_pink/\")\n",
    "from robosat_pink.config import load_config\n",
    "config_location= '/home/ubuntu/planet-snowcover/experiments/co-train.toml'\n",
    "config = load_config(config_location)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for imagery...(planet-snowcover-imagery/20180528_.*)\n",
      "candidates:\n",
      "['planet-snowcover-imagery/20180423_180737_1009_3B_AnalyticMS_DN_udm_clip.tif',\n",
      " 'planet-snowcover-imagery/20180601_181447_0f32_3B_AnalyticMS_DN_udm_clip.tif',\n",
      " 'planet-snowcover-imagery/20180601_181447_0f32_3B_AnalyticMS_SR_clip.tif',\n",
      " 'planet-snowcover-imagery/20180601_181447_0f32_3B_AnalyticMS_metadata_clip.xml',\n",
      " 'planet-snowcover-imagery/20180601_181447_0f32_metadata.json',\n",
      " 'planet-snowcover-imagery/20180601_181448_0f32_3B_AnalyticMS_DN_udm_clip.tif',\n",
      " 'planet-snowcover-imagery/20180601_181448_0f32_3B_AnalyticMS_SR_clip.tif',\n",
      " 'planet-snowcover-imagery/20180601_181448_0f32_3B_AnalyticMS_metadata_clip.xml',\n",
      " 'planet-snowcover-imagery/20180601_181448_0f32_metadata.json',\n",
      " 'planet-snowcover-imagery/20180601_181449_0f32_3B_AnalyticMS_DN_udm_clip.tif',\n",
      " 'planet-snowcover-imagery/20180601_181449_0f32_3B_AnalyticMS_SR_clip_scaled.tif',\n",
      " 'planet-snowcover-imagery/20180601_181449_0f32_3B_AnalyticMS_metadata_clip.xml',\n",
      " 'planet-snowcover-imagery/20180601_181449_0f32_metadata.json',\n",
      " 'planet-snowcover-imagery/20180601_181450_0f32_3B_AnalyticMS_DN_udm_clip.tif',\n",
      " 'planet-snowcover-imagery/20180601_181450_0f32_3B_AnalyticMS_SR_clip.tif',\n",
      " 'planet-snowcover-imagery/20180601_181450_0f32_3B_AnalyticMS_metadata_clip.xml',\n",
      " 'planet-snowcover-imagery/20180601_181450_0f32_metadata.json',\n",
      " 'planet-snowcover-imagery/20180601_181451_0f32_3B_AnalyticMS_DN_udm_clip.tif',\n",
      " 'planet-snowcover-imagery/20180601_181451_0f32_3B_AnalyticMS_SR_clip.tif',\n",
      " 'planet-snowcover-imagery/20180601_181451_0f32_3B_AnalyticMS_metadata_clip.xml',\n",
      " 'planet-snowcover-imagery/20180601_181451_0f32_metadata.json',\n",
      " 'planet-snowcover-imagery/20190727_184554_29_1063-image.tif',\n",
      " 'planet-snowcover-imagery/20191001_183911_1013.tif',\n",
      " 'planet-snowcover-imagery/20191001_183911_1013_1B_AnalyticMS.tif',\n",
      " 'planet-snowcover-imagery/archive',\n",
      " 'planet-snowcover-imagery/assets-combined-dedup-2019-10-03.csv',\n",
      " 'planet-snowcover-imagery/assets-combined-dedup-cloud-date-constrained-2019-11-08.pkl',\n",
      " 'planet-snowcover-imagery/complete-tiles.csv',\n",
      " 'planet-snowcover-imagery/manifest.json',\n",
      " 'planet-snowcover-imagery/20170224_171306_0e26_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170224_171307_0e26_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170329_093247_0e16_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170329_093248_0e16_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170329_093249_0e16_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170331_093332_0e20_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170331_093333_0e20_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170331_093334_0e20_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170331_093335_0e20_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170331_093336_0e20_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170331_093337_0e20_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170331_093338_0e20_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170402_093245_0e0d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170402_093246_0e0d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170402_093247_0e0d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170516_092457_1030_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170516_092458_1030_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170516_092514_1028_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170516_092515_1028_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170516_092516_1028_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170517_092756_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170517_092757_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170517_092758_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170517_092759_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170517_092800_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170517_092801_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170517_092802_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170517_092803_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20170517_092804_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180330_172048_0f3f_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180330_172049_0f3f_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180330_172052_0f3f_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180330_172053_0f3f_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180330_172054_0f3f_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180422_180809_1033_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180422_180811_1033_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180422_180812_1033_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180422_182001_101c_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180422_182002_101c_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180423_180735_1009_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180423_180736_1009_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180423_180737_1009_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180423_180738_1009_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180423_181001_100c_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180423_181002_100c_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180423_181004_100c_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180524_172141_103d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172142_103d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172143_103d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172144_103d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172145_103d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172146_103d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172147_103d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172148_103d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172149_103d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172326_0f51_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172327_0f51_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172329_0f51_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172330_0f51_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172331_0f51_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172332_0f51_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172333_0f51_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172334_0f51_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172630_0f2d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172631_0f2d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172632_0f2d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172633_0f2d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172634_0f2d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172635_0f2d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172636_0f2d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172637_0f2d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180524_172637_1_0f2d_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_180846_1002_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_180847_1002_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181107_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181108_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181109_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181110_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181111_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181112_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181113_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181114_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181115_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181116_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181319_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181320_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181322_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181323_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181324_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181325_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181326_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181327_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180601_181447_0f32_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180601_181448_0f32_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180601_181450_0f32_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20180601_181451_0f32_3B_AnalyticMS_SR_clip_tiled',\n",
      " 'planet-snowcover-imagery/20190616_185606_63_1057_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20190727_184554_29_1063-image',\n",
      " 'planet-snowcover-imagery/20191001_183911_1013',\n",
      " 'planet-snowcover-imagery/PSScene4Band_20170129_180054_0e0f_analytic_sr',\n",
      " 'planet-snowcover-imagery/archive',\n",
      " 'planet-snowcover-imagery/planet-orders']\n",
      "result:\n",
      "['planet-snowcover-imagery/20180528_180846_1002_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_180847_1002_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181107_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181108_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181109_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181110_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181111_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181112_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181113_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181114_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181115_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181116_1025_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181319_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181320_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181322_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181323_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181324_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181325_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181326_1005_3B_AnalyticMS_SR_clip',\n",
      " 'planet-snowcover-imagery/20180528_181327_1005_3B_AnalyticMS_SR_clip']\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "import s3fs\n",
    "import boto3\n",
    "import io\n",
    "from re import match\n",
    "%matplotlib inline\n",
    "import rasterio as rio\n",
    "\n",
    "p = pprint.PrettyPrinter()\n",
    "\n",
    "fs = s3fs.S3FileSystem(session = boto3.Session(profile_name = config['dataset']['aws_profile']))\n",
    "\n",
    "imagery_searchpath = config['dataset']['image_bucket']  + '/' +  config['dataset']['imagery_directory_regex']\n",
    "print(\"Searching for imagery...({})\".format(imagery_searchpath))\n",
    "imagery_candidates = fs.ls(config['dataset']['image_bucket'])\n",
    "print(\"candidates:\")\n",
    "p.pprint(imagery_candidates)\n",
    "imagery_locs = [c for c in imagery_candidates if match(imagery_searchpath, c)]\n",
    "print(\"result:\")\n",
    "p.pprint(imagery_locs)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "planet-snowcover-imagery:20180528_180846_1002_3B_AnalyticMS_SR_clip/15/5534/12660.tif\n",
      "planet-snowcover-imagery:20180528_180846_1002_3B_AnalyticMS_SR_clip/15/5534/12661.tif\n",
      "planet-snowcover-imagery:20180528_180846_1002_3B_AnalyticMS_SR_clip/15/5534/12662.tif\n",
      "planet-snowcover-imagery:20180528_180846_1002_3B_AnalyticMS_SR_clip/15/5534/12663.tif\n",
      "planet-snowcover-imagery:20180528_180847_1002_3B_AnalyticMS_SR_clip/15/5532/12669.tif\n",
      "planet-snowcover-imagery:20180528_180847_1002_3B_AnalyticMS_SR_clip/15/5532/12670.tif\n",
      "planet-snowcover-imagery:20180528_180847_1002_3B_AnalyticMS_SR_clip/15/5533/12665.tif\n",
      "planet-snowcover-imagery:20180528_180847_1002_3B_AnalyticMS_SR_clip/15/5533/12666.tif\n",
      "planet-snowcover-imagery:20180528_180847_1002_3B_AnalyticMS_SR_clip/15/5533/12667.tif\n",
      "planet-snowcover-imagery:20180528_180847_1002_3B_AnalyticMS_SR_clip/15/5533/12668.tif\n",
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      "planet-snowcover-imagery:20180528_180847_1002_3B_AnalyticMS_SR_clip/15/5533/12670.tif\n",
      "planet-snowcover-imagery:20180528_180847_1002_3B_AnalyticMS_SR_clip/15/5534/12664.tif\n",
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      "planet-snowcover-imagery:20180528_180847_1002_3B_AnalyticMS_SR_clip/15/5534/12670.tif\n",
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      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5474/12629.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5474/12630.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5474/12631.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5474/12632.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5474/12633.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5474/12634.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5475/12628.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5475/12629.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5475/12630.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5475/12631.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5475/12632.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5475/12633.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5475/12634.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5475/12635.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5476/12628.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5476/12629.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5476/12630.tif\n",
      "planet-snowcover-imagery:20180528_181322_1005_3B_AnalyticMS_SR_clip/15/5476/12631.tif\n"
     ]
    }
   ],
   "source": [
    "import earthpy as et\n",
    "import earthpy.spatial as es\n",
    "import earthpy.plot as ep\n",
    "import numpy as np\n",
    "import numpy\n",
    "%matplotlib inline\n",
    "import rasterio as rio\n",
    "from matplotlib import pyplot as plt\n",
    "import rasterio.plot\n",
    "import os\n",
    "from datetime import datetime as dt\n",
    "from rasterio.io import MemoryFile\n",
    "import tempfile\n",
    "#create the files with 5 babds\n",
    "for link in imagery_locs:\n",
    "    #print(link)\n",
    "    #sub_candidates = fs.ls(link)\n",
    "    #print(sub_candidates)\n",
    "    #dev_s3_client.list_objects(link) \n",
    "    s3 = boto3.resource('s3')\n",
    "    s3_client = boto3.client('s3')\n",
    "\n",
    "    bucket=link.partition('/')[0] \n",
    "    my_bucket = s3.Bucket(bucket)\n",
    "    for my_bucket_object in my_bucket.objects.filter(Prefix=link.partition('/')[2]):\n",
    "        #print(my_bucket_object)\n",
    "        print('{0}:{1}'.format(my_bucket.name, my_bucket_object.key))\n",
    "        with rio.open('s3://{0}/{1}'.format(my_bucket.name, my_bucket_object.key)) as src:\n",
    "            #print(src.meta)\n",
    "            # # Load red and NIR bands - note all PlanetScope 4-band images have band order BGRN\n",
    "            planet_ndvi = es.normalized_diff(src.read(3), src.read(4))\n",
    "            aug_pla_meta = src.profile\n",
    "            # Change the count or number of bands from 4 to 5\n",
    "            aug_pla_meta['count'] = 5\n",
    "            # Change the data type to float rather than integer\n",
    "            aug_pla_meta['dtype'] = \"float64\"\n",
    "            aug_pla_meta\n",
    "            \n",
    "            #convert to float64\n",
    "            ndvi_64 = np.array(planet_ndvi, dtype=numpy.float64)\n",
    "\n",
    "            new_bucket = s3.Bucket('planet-snowcover-imagery-augmented')\n",
    "            temp_file = tempfile.TemporaryFile()\n",
    "            #with tempfile.NamedTemporaryFile() as tmpfile:\n",
    "            #tmpfile.write(data)\n",
    "            #with rasterio.open(tmpfile.name) as dataset:\n",
    "            #data_array = dataset.read()\n",
    "            \n",
    "            with tempfile.NamedTemporaryFile() as tmpfile:\n",
    "                with rasterio.open(tmpfile.name,\n",
    "                               'w', **aug_pla_meta) as dstr:\n",
    "                    dstr.write_band(1, src.read(1))\n",
    "                    dstr.write_band(2, src.read(2))\n",
    "                    dstr.write_band(3, src.read(3))\n",
    "                    dstr.write_band(4, src.read(4))\n",
    "                    dstr.write_band(5, ndvi_64)\n",
    "                dstr.close()   \n",
    "            \n",
    "                s3_client.upload_fileobj(tmpfile, new_bucket.name, my_bucket_object.key)  \n",
    "                # Write band calculations to a new raster file\n",
    "               \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "            with rasterio.open('s3://{0}/{1}'.format(new_bucket.name, my_bucket_object.key),\n",
    "                               'w', **aug_pla_meta) as dstr:\n",
    "                    dstr.write_band(1, src.read(1))\n",
    "                    dstr.write_band(2, src.read(2))\n",
    "                    dstr.write_band(3, src.read(3))\n",
    "                    dstr.write_band(4, src.read(4))\n",
    "                    dstr.write_band(5, ndvi_64)\n",
    "            dstr.close()        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "            with MemoryFile() as memfile:\n",
    "                with memfile.open(**aug_pla_meta) as dataset:\n",
    "                    dataset.write(src.read(1), 1)\n",
    "                    dataset.write(src.read(2),2)\n",
    "                    dataset.write(src.read(3),3)\n",
    "                    dataset.write(src.read(4),4)\n",
    "                    dataset.write(ndvi_64,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "2020-02-06 03:22:56   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5470/12657.tif\n",
      "2020-02-06 03:22:57   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5471/12651.tif\n",
      "2020-02-06 03:22:57   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5471/12652.tif\n",
      "2020-02-06 03:22:58   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5471/12653.tif\n",
      "2020-02-06 03:22:59   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5471/12654.tif\n",
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      "2020-02-06 03:23:00   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5471/12656.tif\n",
      "2020-02-06 03:23:00   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5471/12657.tif\n",
      "2020-02-06 03:23:01   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5471/12658.tif\n",
      "2020-02-06 03:23:02   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5472/12651.tif\n",
      "2020-02-06 03:23:02   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5472/12652.tif\n",
      "2020-02-06 03:23:03   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5472/12653.tif\n",
      "2020-02-06 03:23:04   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5472/12654.tif\n",
      "2020-02-06 03:23:04   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5472/12655.tif\n",
      "2020-02-06 03:23:05   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5472/12656.tif\n",
      "2020-02-06 03:23:06   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5472/12657.tif\n",
      "2020-02-06 03:23:07   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5472/12658.tif\n",
      "2020-02-06 03:23:08   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5473/12651.tif\n",
      "2020-02-06 03:23:08   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5473/12652.tif\n",
      "2020-02-06 03:23:09   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5473/12653.tif\n",
      "2020-02-06 03:23:11   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5473/12654.tif\n",
      "2020-02-06 03:23:11   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5473/12655.tif\n",
      "2020-02-06 03:23:12   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5473/12656.tif\n",
      "2020-02-06 03:23:12   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5473/12657.tif\n",
      "2020-02-06 03:23:13   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5473/12658.tif\n",
      "2020-02-06 03:23:14   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5474/12651.tif\n",
      "2020-02-06 03:23:15   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5474/12652.tif\n",
      "2020-02-06 03:23:15   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5474/12653.tif\n",
      "2020-02-06 03:23:16   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5474/12654.tif\n",
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      "2020-02-06 03:23:18   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5474/12657.tif\n",
      "2020-02-06 03:23:19   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5474/12658.tif\n",
      "2020-02-06 03:23:19   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5475/12652.tif\n",
      "2020-02-06 03:23:22   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5475/12653.tif\n",
      "2020-02-06 03:23:23   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5475/12654.tif\n",
      "2020-02-06 03:23:23   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5475/12655.tif\n",
      "2020-02-06 03:23:24   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5475/12656.tif\n",
      "2020-02-06 03:23:25   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5475/12657.tif\n",
      "2020-02-06 03:23:26   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5475/12658.tif\n",
      "2020-02-06 03:23:26   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5476/12652.tif\n",
      "2020-02-06 03:23:27   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5476/12653.tif\n",
      "2020-02-06 03:23:28   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5476/12654.tif\n",
      "2020-02-06 03:23:28   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5476/12655.tif\n",
      "2020-02-06 03:23:29   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5476/12656.tif\n",
      "2020-02-06 03:23:30   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5476/12657.tif\n",
      "2020-02-06 03:23:30   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5476/12658.tif\n",
      "2020-02-06 03:23:31   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5476/12659.tif\n",
      "2020-02-06 03:23:32   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5477/12652.tif\n",
      "2020-02-06 03:23:33   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5477/12653.tif\n",
      "2020-02-06 03:23:33   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5477/12654.tif\n",
      "2020-02-06 03:23:34   10490262 20180528_181325_1005_3B_AnalyticMS_SR_clip/15/5477/12655.tif\n",
      "2020-02-06 03:23:34   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5467/12658.tif\n",
      "2020-02-06 03:23:35   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5467/12659.tif\n",
      "2020-02-06 03:23:36   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5467/12660.tif\n",
      "2020-02-06 03:23:36   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5467/12661.tif\n",
      "2020-02-06 03:23:37   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5467/12662.tif\n",
      "2020-02-06 03:23:38   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5467/12663.tif\n",
      "2020-02-06 03:23:38   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5467/12664.tif\n",
      "2020-02-06 03:23:39   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5468/12658.tif\n",
      "2020-02-06 03:23:40   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5468/12659.tif\n",
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      "2020-02-06 03:23:41   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5468/12661.tif\n",
      "2020-02-06 03:23:42   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5468/12662.tif\n",
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      "2020-02-06 03:23:43   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5468/12664.tif\n",
      "2020-02-06 03:23:44   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5468/12665.tif\n",
      "2020-02-06 03:23:45   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5469/12658.tif\n",
      "2020-02-06 03:23:46   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5469/12659.tif\n",
      "2020-02-06 03:23:46   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5469/12660.tif\n",
      "2020-02-06 03:23:47   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5469/12661.tif\n",
      "2020-02-06 03:23:48   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5469/12662.tif\n",
      "2020-02-06 03:23:48   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5469/12663.tif\n",
      "2020-02-06 03:23:49   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5469/12664.tif\n",
      "2020-02-06 03:23:50   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5469/12665.tif\n",
      "2020-02-06 03:23:50   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5470/12658.tif\n",
      "2020-02-06 03:23:51   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5470/12659.tif\n",
      "2020-02-06 03:23:52   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5470/12660.tif\n",
      "2020-02-06 03:23:52   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5470/12661.tif\n",
      "2020-02-06 03:23:53   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5470/12662.tif\n",
      "2020-02-06 03:23:53   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5470/12663.tif\n",
      "2020-02-06 03:23:54   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5470/12664.tif\n",
      "2020-02-06 03:23:55   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5470/12665.tif\n",
      "2020-02-06 03:23:55   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5471/12659.tif\n",
      "2020-02-06 03:23:56   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5471/12660.tif\n",
      "2020-02-06 03:23:57   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5471/12661.tif\n",
      "2020-02-06 03:23:57   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5471/12662.tif\n",
      "2020-02-06 03:23:58   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5471/12663.tif\n",
      "2020-02-06 03:23:59   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5471/12664.tif\n",
      "2020-02-06 03:24:00   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5471/12665.tif\n",
      "2020-02-06 03:24:00   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5472/12659.tif\n",
      "2020-02-06 03:24:01   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5472/12660.tif\n",
      "2020-02-06 03:24:02   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5472/12661.tif\n",
      "2020-02-06 03:24:02   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5472/12662.tif\n",
      "2020-02-06 03:24:03   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5472/12663.tif\n",
      "2020-02-06 03:24:04   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5472/12664.tif\n",
      "2020-02-06 03:24:04   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5472/12665.tif\n",
      "2020-02-06 03:24:05   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5472/12666.tif\n",
      "2020-02-06 03:24:06   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5473/12659.tif\n",
      "2020-02-06 03:24:07   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5473/12660.tif\n",
      "2020-02-06 03:24:07   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5473/12661.tif\n",
      "2020-02-06 03:24:08   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5473/12662.tif\n",
      "2020-02-06 03:24:09   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5473/12663.tif\n",
      "2020-02-06 03:24:09   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5473/12664.tif\n",
      "2020-02-06 03:24:10   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5473/12665.tif\n",
      "2020-02-06 03:24:11   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5473/12666.tif\n",
      "2020-02-06 03:24:12   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5474/12659.tif\n",
      "2020-02-06 03:24:12   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5474/12660.tif\n",
      "2020-02-06 03:24:13   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5474/12661.tif\n",
      "2020-02-06 03:24:13   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5474/12662.tif\n",
      "2020-02-06 03:24:14   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5474/12663.tif\n",
      "2020-02-06 03:24:15   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5474/12664.tif\n",
      "2020-02-06 03:24:16   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5474/12665.tif\n",
      "2020-02-06 03:24:16   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5474/12666.tif\n",
      "2020-02-06 03:24:17   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5475/12660.tif\n",
      "2020-02-06 03:24:18   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5475/12661.tif\n",
      "2020-02-06 03:24:19   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5475/12662.tif\n",
      "2020-02-06 03:24:19   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5475/12663.tif\n",
      "2020-02-06 03:24:20   10490262 20180528_181326_1005_3B_AnalyticMS_SR_clip/15/5475/12664.tif\n",
      "2020-02-06 03:24:21   10490262 20180528_181327_1005_3B_AnalyticMS_SR_clip/15/5467/12666.tif\n",
      "2020-02-06 03:24:22   10490262 20180528_181327_1005_3B_AnalyticMS_SR_clip/15/5467/12667.tif\n",
      "2020-02-06 03:24:23   10490262 20180528_181327_1005_3B_AnalyticMS_SR_clip/15/5468/12666.tif\n",
      "2020-02-06 03:24:23   10490262 20180528_181327_1005_3B_AnalyticMS_SR_clip/15/5468/12667.tif\n",
      "2020-02-06 03:24:24   10490262 20180528_181327_1005_3B_AnalyticMS_SR_clip/15/5469/12666.tif\n",
      "2020-02-06 03:24:25   10490262 20180528_181327_1005_3B_AnalyticMS_SR_clip/15/5469/12667.tif\n",
      "2020-02-06 03:24:26   10490262 20180528_181327_1005_3B_AnalyticMS_SR_clip/15/5470/12666.tif\n",
      "\n",
      "Total Objects: 2109\n",
      "   Total Size: 22123962558\n"
     ]
    }
   ],
   "source": [
    "!aws s3 ls s3://planet-snowcover-imagery-augmented --recursive --summarize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'planet-snowcover-imagery'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Environment (conda_pytorch_p36)",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
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