pipeline/create-test-analytics-ndvi-validate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "candidates:\n",
      "['planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_171624_0f49_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_171625_0f49_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_181943_101b_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_181945_101b_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_181946_101b_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_181947_101b_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_181948_101b_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_182420_0f28_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_182421_0f28_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_182422_0f28_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_182423_0f28_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_182424_0f28_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_182425_0f28_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_182532_0f17_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_182533_0f17_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_182534_0f17_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_182535_0f17_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_182536_0f17_3B_AnalyticMS_SR',\n",
      " 'planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/planet-snowcover-imagery-ndvi-validate:20190705_182537_0f17_3B_AnalyticMS_SR']\n"
     ]
    }
   ],
   "source": [
    "# NDVI validate\n",
    "# read thru and combine with run summary\n",
    "#build a pipeline to assemble all the images\n",
    "#Amazon S3/planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656\n",
    "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",
    "import numpy as np\n",
    "p = pprint.PrettyPrinter()\n",
    "\n",
    "fs = s3fs.S3FileSystem(session = boto3.Session(profile_name = 'esip'))\n",
    "#dem only\n",
    "imagery_searchpath = 's3://planet-snowcover-experiments/planet-snowcover-2020-04-17-16-32-32-656/'\n",
    "#print(\"Searching for imagery...({})\".format(imagery_searchpath))\n",
    "imagery_candidates = fs.ls(imagery_searchpath)\n",
    "print(\"candidates:\")\n",
    "#p.pprint(imagery_candidates)\n",
    "# filter only the test scenes tiles\n",
    "clipped_scenes = [link for link  in imagery_candidates if link.endswith('AnalyticMS_SR')]\n",
    "p.pprint(clipped_scenes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imagepath: planet-snowcover-imagery-ndvi-validate/20190705_171624_0f49_3B_AnalyticMS_SR\n",
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      "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
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      "Imagepath: planet-snowcover-imagery-ndvi-validate/20190705_171625_0f49_3B_AnalyticMS_SR\n",
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      "Imagepath: planet-snowcover-imagery-ndvi-validate/20190705_181943_101b_3B_AnalyticMS_SR\n",
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      "Imagepath: planet-snowcover-imagery-ndvi-validate/20190705_181944_101b_3B_AnalyticMS_SR\n",
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      "Imagepath: planet-snowcover-imagery-ndvi-validate/20190705_181945_101b_3B_AnalyticMS_SR\n",
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      "Imagepath: planet-snowcover-imagery-ndvi-validate/20190705_181946_101b_3B_AnalyticMS_SR\n",
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      "Imagepath: planet-snowcover-imagery-ndvi-validate/20190705_182420_0f28_3B_AnalyticMS_SR\n",
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      "0...10...20...30...40...50...60...70...80...90...100 - done.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('/home/ubuntu/planet-snowcover/experiments/ASO_Tuolumne/scripts')\n",
    "for link in clipped_scenes:\n",
    "    !python summarize.py --aws_profile esip {link} ~/ndvi-validate --mask_loc planet-snowcover-snow/ASO_3M_SD_USCATE_20190705_4326_binary \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 10304\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    2259 Mar  5 04:36 aftertrain.py\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    7409 Feb 27 23:33 compare.py\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    3952 Feb 27 23:33 get-modis.py\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    8868 Feb 27 23:33 get_sentinel.py\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  322120 May  4 04:45 output-dah.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu     680 Mar 12 14:42 output.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  315242 Apr 20 21:40 output-ndvi.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu 3625676 May 27 15:34 output-ndvi_validate.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    1673 Mar  5 04:42 output_summarize_veg.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu   13396 May 28 16:42 output-veg_validate.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  306443 Mar 25 12:02 prediction_base_job_test_only.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  314434 Mar 18 05:36 prediction_base_test_only.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  318913 May  5 04:59 prediction_dem_job_test.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu 2332511 Mar 13 03:04 prediction_veg_dem_job.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  332866 Mar 18 05:32 prediction_veg_dem_job_test.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu 2263394 May  4 06:38 prediction_veg_job.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  319397 Mar 18 05:25 prediction_veg_job_test_only.log\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    3738 Mar 12 17:20 run_prediction.py\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    2737 Feb 27 23:33 sagetrain.py\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    6068 Mar  5 04:36 summarize.py\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    1021 Mar 26 05:28 veg_gdal.vrt\n"
     ]
    }
   ],
   "source": [
    "ls -l"
   ]
  },
  {
   "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": [
    "import os\n",
    "from glob import glob\n",
    "\n",
    "import pandas as pd \n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultRoot = \"/home/ubuntu/ndvi-validate/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = glob(os.path.join(resultRoot, \"*/*/*_merged.tif\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR/preds/20190705_181944_101b_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182424_0f28_3B_AnalyticMS_SR/preds/20190705_182424_0f28_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182424_0f28_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_171624_0f49_3B_AnalyticMS_SR/preds/20190705_171624_0f49_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_171624_0f49_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182533_0f17_3B_AnalyticMS_SR/preds/20190705_182533_0f17_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182533_0f17_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182422_0f28_3B_AnalyticMS_SR/preds/20190705_182422_0f28_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182422_0f28_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181943_101b_3B_AnalyticMS_SR/preds/20190705_181943_101b_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181943_101b_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182420_0f28_3B_AnalyticMS_SR/preds/20190705_182420_0f28_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182420_0f28_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182535_0f17_3B_AnalyticMS_SR/preds/20190705_182535_0f17_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182535_0f17_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181945_101b_3B_AnalyticMS_SR/preds/20190705_181945_101b_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181945_101b_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_171625_0f49_3B_AnalyticMS_SR/preds/20190705_171625_0f49_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_171625_0f49_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182537_0f17_3B_AnalyticMS_SR/preds/20190705_182537_0f17_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182537_0f17_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182425_0f28_3B_AnalyticMS_SR/preds/20190705_182425_0f28_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182425_0f28_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182532_0f17_3B_AnalyticMS_SR/preds/20190705_182532_0f17_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182532_0f17_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182536_0f17_3B_AnalyticMS_SR/preds/20190705_182536_0f17_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182536_0f17_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182421_0f28_3B_AnalyticMS_SR/preds/20190705_182421_0f28_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182421_0f28_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181948_101b_3B_AnalyticMS_SR/preds/20190705_181948_101b_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181948_101b_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181947_101b_3B_AnalyticMS_SR/preds/20190705_181947_101b_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181947_101b_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182534_0f17_3B_AnalyticMS_SR/preds/20190705_182534_0f17_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182534_0f17_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181946_101b_3B_AnalyticMS_SR/preds/20190705_181946_101b_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181946_101b_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182423_0f28_3B_AnalyticMS_SR/preds/20190705_182423_0f28_3B_AnalyticMS_SR_merged.tif',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182423_0f28_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif']"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dirs = glob(os.path.join(resultRoot, \"*\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182424_0f28_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_171624_0f49_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182533_0f17_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182422_0f28_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181943_101b_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182420_0f28_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182535_0f17_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181945_101b_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_171625_0f49_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182537_0f17_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182425_0f28_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182532_0f17_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182536_0f17_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182421_0f28_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181948_101b_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181947_101b_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182534_0f17_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181946_101b_3B_AnalyticMS_SR',\n",
       " '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182423_0f28_3B_AnalyticMS_SR']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dirs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182424_0f28_3B_AnalyticMS_SR/preds/20190705_182424_0f28_3B_AnalyticMS_SR_merged.tif\n",
      "[[9999 9999 9999 ... 9999 9999 9999]\n",
      " [9999 9999 9999 ... 9999 9999 9999]\n",
      " [9999 9999 9999 ... 9999 9999 9999]\n",
      " ...\n",
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     "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",
    "print(results[2])\n",
    "# Load the DEM and read out a np array of the elevation values (meters):\n",
    "dem = rio.open(results[2])\n",
    "elev = dem.read(1)\n",
    "print(elev)\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": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182424_0f28_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif\n",
      "[[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"
     ]
    },
    {
     "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",
    "print(results[3])\n",
    "# Load the DEM and read out a np array of the elevation values (meters):\n",
    "dem = rio.open(results[3])\n",
    "elev = dem.read(1)\n",
    "print(elev)\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": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Driver: GTiff/GeoTIFF\n",
      "Files: /home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif\n",
      "Size is 13824, 7167\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 = (-119.542236328125000,38.134556577054127)\n",
      "Pixel Size = (0.000021457672119,-0.000016894786483)\n",
      "Metadata:\n",
      "  AREA_OR_POINT=Area\n",
      "Image Structure Metadata:\n",
      "  COMPRESSION=LZW\n",
      "  INTERLEAVE=BAND\n",
      "Corner Coordinates:\n",
      "Upper Left  (-119.5422363,  38.1345566) (119d32'32.05\"W, 38d 8' 4.40\"N)\n",
      "Lower Left  (-119.5422363,  38.0134716) (119d32'32.05\"W, 38d 0'48.50\"N)\n",
      "Upper Right (-119.2456055,  38.1345566) (119d14'44.18\"W, 38d 8' 4.40\"N)\n",
      "Lower Right (-119.2456055,  38.0134716) (119d14'44.18\"W, 38d 0'48.50\"N)\n",
      "Center      (-119.3939209,  38.0740141) (119d23'38.12\"W, 38d 4'26.45\"N)\n",
      "Band 1 Block=13824x1 Type=Int16, ColorInterp=Gray\n",
      "  NoData Value=9999\n"
     ]
    }
   ],
   "source": [
    "!gdalinfo {results[1]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Driver: GTiff/GeoTIFF\n",
      "Files: /home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR/preds/20190705_181944_101b_3B_AnalyticMS_SR_merged.tif\n",
      "Size is 13824, 7168\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 = (-119.542236328125000,38.134556577054127)\n",
      "Pixel Size = (0.000021457672119,-0.000016890791833)\n",
      "Metadata:\n",
      "  AREA_OR_POINT=Area\n",
      "Image Structure Metadata:\n",
      "  COMPRESSION=LZW\n",
      "  INTERLEAVE=BAND\n",
      "Corner Coordinates:\n",
      "Upper Left  (-119.5422363,  38.1345566) (119d32'32.05\"W, 38d 8' 4.40\"N)\n",
      "Lower Left  (-119.5422363,  38.0134834) (119d32'32.05\"W, 38d 0'48.54\"N)\n",
      "Upper Right (-119.2456055,  38.1345566) (119d14'44.18\"W, 38d 8' 4.40\"N)\n",
      "Lower Right (-119.2456055,  38.0134834) (119d14'44.18\"W, 38d 0'48.54\"N)\n",
      "Center      (-119.3939209,  38.0740200) (119d23'38.12\"W, 38d 4'26.47\"N)\n",
      "Band 1 Block=13824x1 Type=Int16, ColorInterp=Gray\n",
      "  NoData Value=9999\n"
     ]
    }
   ],
   "source": [
    "!gdalinfo {results[0]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR/preds/20190705_181944_101b_3B_AnalyticMS_SR_merged_crop.vrt'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reproject_image_to_master(results[1],results[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Driver: VRT/Virtual Raster\n",
      "Files: /home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR/preds/20190705_181944_101b_3B_AnalyticMS_SR_merged_crop.vrt\n",
      "Size is 13824, 7167\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 = (-119.542236328125000,38.134556577054127)\n",
      "Pixel Size = (0.000021457672119,-0.000016894786483)\n",
      "Corner Coordinates:\n",
      "Upper Left  (-119.5422363,  38.1345566) (119d32'32.05\"W, 38d 8' 4.40\"N)\n",
      "Lower Left  (-119.5422363,  38.0134716) (119d32'32.05\"W, 38d 0'48.50\"N)\n",
      "Upper Right (-119.2456055,  38.1345566) (119d14'44.18\"W, 38d 8' 4.40\"N)\n",
      "Lower Right (-119.2456055,  38.0134716) (119d14'44.18\"W, 38d 0'48.50\"N)\n",
      "Center      (-119.3939209,  38.0740141) (119d23'38.12\"W, 38d 4'26.45\"N)\n",
      "Band 1 Block=128x128 Type=Int16, ColorInterp=Undefined\n",
      "  Min=0.000 Max=0.000 \n",
      "  NoData Value=9999\n"
     ]
    }
   ],
   "source": [
    "!gdalinfo {reproject_image_to_master(results[1],results[0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9999.0\n",
      "9999.0\n",
      "EPSG:4326 EPSG:4326\n",
      "(99076608,) (99076608,)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Target is multiclass but average='binary'. Please choose another average setting.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-109-f901a9cff681>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mbalanced_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbalanced_accuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcompare\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mprfs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprecision_recall_fscore_support\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcompare\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'binary'\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     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m these_metrics = {\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mprecision_recall_fscore_support\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight)\u001b[0m\n\u001b[1;32m   1045\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1046\u001b[0m             raise ValueError(\"Target is %s but average='binary'. Please \"\n\u001b[0;32m-> 1047\u001b[0;31m                              \"choose another average setting.\" % y_type)\n\u001b[0m\u001b[1;32m   1048\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mNone\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1049\u001b[0m         warnings.warn(\"Note that pos_label (set to %r) is ignored when \"\n",
      "\u001b[0;31mValueError\u001b[0m: Target is multiclass but average='binary'. Please choose another average setting."
     ]
    }
   ],
   "source": [
    "mask = rio.open(results[1])\n",
    "preds = rio.open(reproject_image_to_master(results[1],results[0]))\n",
    "print(mask.nodata)\n",
    "print(preds.nodata)\n",
    "\n",
    "print(mask.crs, preds.crs)\n",
    "mask_data = mask.read(1).flatten()\n",
    "pred_data = preds.read(1).flatten()\n",
    "\n",
    "print(mask_data.shape, pred_data.shape)\n",
    "compare = (mask_data, pred_data)\n",
    "\n",
    "balanced_acc = metrics.balanced_accuracy_score(*compare)\n",
    "prfs = metrics.precision_recall_fscore_support(*compare, average='binary')\n",
    "\n",
    "these_metrics = {\n",
    "        \"balanced_accuracy\" : balanced_acc,\n",
    "        \"precision\": prfs[0],\n",
    "        \"recall\": prfs[1],\n",
    "        \"f_score\": prfs[2]\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "base=results[0].split(\"preds\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_region = base +\"data-mask.geojson\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR/data-mask.geojson'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate key in file '/home/ubuntu/.config/matplotlib/matplotlibrc' line #2.\n",
      "Duplicate key in file '/home/ubuntu/.config/matplotlib/matplotlibrc' line #3.\n",
      "Traceback (most recent call last):\n",
      "  File \"compare-metrics.py\", line 28, in <module>\n",
      "    from osgeo import gdal\n",
      "ModuleNotFoundError: No module named 'osgeo'\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('/home/ubuntu/planet-snowcover/experiments/ASO_Tuolumne/scripts')\n",
    "!python compare-metrics.py  prediction  /home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X offset 0, Y offset 0\n",
      "Xmax -119.24560546875, Ymin 38.013483381195826\n",
      "Reading 13824 cols, 7166 rows\n"
     ]
    }
   ],
   "source": [
    "from osgeo import gdal\n",
    "\n",
    "# given that a geotransform object is in this form:\n",
    "#adfGeoTransform[0] /* top left x */\n",
    "#adfGeoTransform[1] /* w-e pixel resolution */\n",
    "#adfGeoTransform[2] /* 0 */\n",
    "#adfGeoTransform[3] /* top left y */\n",
    "#adfGeoTransform[4] /* 0 */\n",
    "#adfGeoTransform[5] /* n-s pixel resolution (negative value) */\n",
    "\n",
    "smaller_raster = gdal.Open(results[0])\n",
    "larger_raster  = gdal.Open(results[1])\n",
    "\n",
    "gt = smaller_raster.GetGeoTransform()\n",
    "lt = larger_raster.GetGeoTransform()\n",
    "\n",
    "SmlMaxX = gt[0] + (gt[1] * smaller_raster.RasterXSize)\n",
    "SmlMinY = gt[3] + (gt[5] * smaller_raster.RasterYSize)\n",
    "Xoff = int((gt[0] - lt[0])/lt[1]) # cols to skip\n",
    "Yoff = int((gt[3] - lt[3])/lt[5]) # rows to skip\n",
    "Cols = int((SmlMaxX - gt[0])/lt[1])\n",
    "Rows = int((SmlMinY - gt[3])/lt[5])\n",
    "\n",
    "# print out some numbers so you can check manually\n",
    "print(\"X offset {}, Y offset {}\".format(Xoff,Yoff))\n",
    "print(\"Xmax {}, Ymin {}\".format(SmlMaxX,SmlMinY))\n",
    "print(\"Reading {} cols, {} rows\".format(Cols,Rows))\n",
    "\n",
    "band = larger_raster.GetRasterBand(1)\n",
    "data = larger_raster.ReadAsArray(Xoff,Yoff,Cols,Rows) # read the larger raster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9999.0\n",
      "9999.0\n",
      "EPSG:4326 EPSG:4326\n",
      "(99076608,) (99090432,)\n",
      "(49545216,) (49552896,)\n",
      "X offset 0, Y offset 0\n",
      "Xmax -119.24560546875, Ymin 38.01347164232713\n",
      "Reading 13824 cols, 7168 rows\n",
      "<class 'numpy.ndarray'>\n",
      "(49552896,) (49552896,)\n",
      "(49552896,) (49552896,)\n",
      "{'mask': '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif', 'preds': '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR/preds/20190705_181944_101b_3B_AnalyticMS_SR_merged.tif', 'metrics': {'balanced_accuracy': 1.0, 'precision': 1.0, 'recall': 1.0, 'f_score': 1.0}}\n"
     ]
    }
   ],
   "source": [
    "prediction('/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR','','mask')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9999.0\n",
      "9999.0\n",
      "EPSG:4326 EPSG:4326\n",
      "(99076608,) (99090432,)\n",
      "(49545216,) (49552896,)\n",
      "X offset 0, Y offset 0\n",
      "Xmax -119.24560546875, Ymin 38.01347164232713\n",
      "Reading 13824 cols, 7168 rows\n",
      "<class 'numpy.ndarray'>\n",
      "(49552896,) (49552896,)\n",
      "(49552896,) (49552896,)\n",
      "{'mask': '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif', 'preds': '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_181944_101b_3B_AnalyticMS_SR/preds/20190705_181944_101b_3B_AnalyticMS_SR_merged.tif', 'metrics': {'balanced_accuracy': 1.0, 'precision': 1.0, 'recall': 1.0, 'f_score': 1.0}}\n",
      "9999.0\n",
      "9999.0\n",
      "EPSG:4326 EPSG:4326\n",
      "(81788928,) (81815552,)\n",
      "(47974912,) (47995392,)\n",
      "X offset 0, Y offset 0\n",
      "Xmax -119.24560546875, Ymin 37.77939244280755\n",
      "Reading 13312 cols, 6146 rows\n",
      "<class 'numpy.ndarray'>\n",
      "(47995392,) (47995392,)\n",
      "(47995392,) (47995392,)\n",
      "{'mask': '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182424_0f28_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif', 'preds': '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182424_0f28_3B_AnalyticMS_SR/preds/20190705_182424_0f28_3B_AnalyticMS_SR_merged.tif', 'metrics': {'balanced_accuracy': 1.0, 'precision': 1.0, 'recall': 1.0, 'f_score': 1.0}}\n",
      "9999.0\n",
      "9999.0\n",
      "EPSG:4326 EPSG:4326\n",
      "(14683648,) (14680064,)\n",
      "(11537920,) (11536896,)\n",
      "X offset 0, Y offset 0\n",
      "Xmax -119.72900390625, Ymin 37.89219146024844\n",
      "Reading 3584 cols, 4097 rows\n",
      "<class 'numpy.ndarray'>\n",
      "(11537920,) (11537920,)\n",
      "(11537920,) (11537920,)\n",
      "{'mask': '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_171624_0f49_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif', 'preds': '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_171624_0f49_3B_AnalyticMS_SR/preds/20190705_171624_0f49_3B_AnalyticMS_SR_merged.tif', 'metrics': {'balanced_accuracy': 1.0, 'precision': 0.0, 'recall': 0.0, 'f_score': 0.0}}\n",
      "9999.0\n",
      "9999.0\n",
      "EPSG:4326 EPSG:4326\n",
      "(85184000,) (85184000,)\n",
      "(48230912,) (48230912,)\n",
      "(48230912,) (48230912,)\n",
      "{'mask': '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182533_0f17_3B_AnalyticMS_SR/mask/ASO_3M_SD_USCATE_20190705_4326_binary_merged.tif', 'preds': '/home/ubuntu/ndvi-validate/planet-snowcover-experiments:planet-snowcover-2020-04-17-16-32-32-656:planet-snowcover-imagery-ndvi-validate:20190705_182533_0f17_3B_AnalyticMS_SR/preds/20190705_182533_0f17_3B_AnalyticMS_SR_merged.tif', 'metrics': {'balanced_accuracy': 0.5767513874815117, 'precision': 0.39216665796880995, 'recall': 0.9719203944714581, 'f_score': 0.5588423000273137}}\n",
      "9999.0\n",
      "9999.0\n",
      "EPSG:4326 EPSG:4326\n",
      "(81735680,) (81788928,)\n",
      "(48731648,) (48760832,)\n",
      "X offset 0, Y offset 0\n",
      "Xmax -119.212646484375, Ymin 37.909533909951826\n",
      "Reading 13312 cols, 6143 rows\n",
      "<class 'numpy.ndarray'>\n",
      "(48758272,) (48760832,)\n",
      "(48758272,) (48760832,)\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "Masks and predictions are different shapes. Are you sure they're in the same CRS/extent?",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-125-bbdcf0627892>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresults_dirs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mask'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-96-bddc3c5b0e4d>\u001b[0m in \u001b[0;36mprediction\u001b[0;34m(directory, plot, mask_dir)\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;31m#mask = reproject_like_vrt(mask, preds)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m     \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred_data\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     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     results = {\n",
      "\u001b[0;32m<ipython-input-82-08c187b934bf>\u001b[0m in \u001b[0;36mcompute_metrics\u001b[0;34m(true, pred)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcompute_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mtrue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Masks and predictions are different shapes. Are you sure they're in the same CRS/extent?\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mcompare\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: Masks and predictions are different shapes. Are you sure they're in the same CRS/extent?"
     ]
    }
   ],
   "source": [
    "for path in results_dirs:\n",
    "    prediction(path,'','mask')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reproject_image_to_master ( master, slave, res=None ):\n",
    "    \"\"\"This function reprojects an image (``slave``) to\n",
    "    match the extent, resolution and projection of another\n",
    "    (``master``) using GDAL. The newly reprojected image\n",
    "    is a GDAL VRT file for efficiency. A different spatial\n",
    "    resolution can be chosen by specifyign the optional\n",
    "    ``res`` parameter. The function returns the new file's\n",
    "    name.\n",
    "    Parameters\n",
    "    -------------\n",
    "    master: str \n",
    "        A filename (with full path if required) with the \n",
    "        master image (that that will be taken as a reference)\n",
    "    slave: str \n",
    "        A filename (with path if needed) with the image\n",
    "        that will be reprojected\n",
    "    res: float, optional\n",
    "        The desired output spatial resolution, if different \n",
    "        to the one in ``master``.\n",
    "    Returns\n",
    "    ----------\n",
    "    The reprojected filename\n",
    "    TODO Have a way of controlling output filename\n",
    "    \"\"\"\n",
    "    slave_ds = gdal.Open( slave )\n",
    "    if slave_ds is None:\n",
    "        raise IOError( \"GDAL could not open slave file %s \" % slave)\n",
    "    slave_proj = slave_ds.GetProjection()\n",
    "    slave_geotrans = slave_ds.GetGeoTransform()\n",
    "    data_type = slave_ds.GetRasterBand(1).DataType\n",
    "    n_bands = slave_ds.RasterCount\n",
    "\n",
    "    master_ds = gdal.Open( master )\n",
    "    if master_ds is None:\n",
    "        raise IOError (\"GDAL could not open master file %s \" % master)\n",
    "    master_proj = master_ds.GetProjection()\n",
    "    master_geotrans = master_ds.GetGeoTransform()\n",
    "    w = master_ds.RasterXSize\n",
    "    h = master_ds.RasterYSize\n",
    "    if res is not None:\n",
    "        master_geotrans[1] = float( res )\n",
    "        master_geotrans[-1] = - float ( res )\n",
    "\n",
    "    dst_filename = slave.replace( \".tif\", \"_crop.vrt\" )\n",
    "    dst_ds = gdal.GetDriverByName('VRT').Create(dst_filename,\n",
    "                                                w, h, n_bands, data_type)\n",
    "    dst_ds.GetRasterBand(1).SetNoDataValue( 9999.0 )\n",
    "    dst_ds.SetGeoTransform( master_geotrans )\n",
    "    dst_ds.SetProjection( master_proj)\n",
    "\n",
    "    gdal.ReprojectImage( slave_ds, dst_ds, slave_proj,\n",
    "                         master_proj, gdal.GRA_NearestNeighbour)\n",
    "    dst_ds = None  # Flush to disk\n",
    "    return dst_filename\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_extent(small,large):\n",
    "\n",
    "    # given that a geotransform object is in this form:\n",
    "    #adfGeoTransform[0] /* top left x */\n",
    "    #adfGeoTransform[1] /* w-e pixel resolution */\n",
    "    #adfGeoTransform[2] /* 0 */\n",
    "    #adfGeoTransform[3] /* top left y */\n",
    "    #adfGeoTransform[4] /* 0 */\n",
    "    #adfGeoTransform[5] /* n-s pixel resolution (negative value) */\n",
    "\n",
    "    smaller_raster = gdal.Open(small)\n",
    "    larger_raster  = gdal.Open(large)\n",
    "\n",
    "    gt = smaller_raster.GetGeoTransform()\n",
    "    lt = larger_raster.GetGeoTransform()\n",
    "\n",
    "    SmlMaxX = gt[0] + (gt[1] * smaller_raster.RasterXSize)\n",
    "    SmlMinY = gt[3] + (gt[5] * smaller_raster.RasterYSize)\n",
    "    Xoff = int((gt[0] - lt[0])/lt[1]) # cols to skip\n",
    "    Yoff = int((gt[3] - lt[3])/lt[5]) # rows to skip\n",
    "    Cols = int((SmlMaxX - gt[0])/lt[1])\n",
    "    Rows = int((SmlMinY - gt[3])/lt[5])\n",
    "\n",
    "    # print out some numbers so you can check manually\n",
    "    print(\"X offset {}, Y offset {}\".format(Xoff,Yoff))\n",
    "    print(\"Xmax {}, Ymin {}\".format(SmlMaxX,SmlMinY))\n",
    "    print(\"Reading {} cols, {} rows\".format(Cols,Rows))\n",
    "\n",
    "    band = larger_raster.GetRasterBand(1)\n",
    "    data = larger_raster.ReadAsArray(Xoff,Yoff,Cols,Rows) # read the larger raster\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "from sklearn import metrics\n",
    "import json\n",
    "def compute_metrics(true, pred):\n",
    "    print(true.shape, pred.shape)\n",
    "    assert true.shape == pred.shape, \"Masks and predictions are different shapes. Are you sure they're in the same CRS/extent?\"\n",
    "\n",
    "    compare = (true, pred)\n",
    "\n",
    "    balanced_acc = metrics.balanced_accuracy_score(*compare)\n",
    "    prfs = metrics.precision_recall_fscore_support(*compare, average='binary')\n",
    "\n",
    "    these_metrics = {\n",
    "        \"balanced_accuracy\" : balanced_acc,\n",
    "        \"precision\": prfs[0],\n",
    "        \"recall\": prfs[1],\n",
    "        \"f_score\": prfs[2]\n",
    "    }\n",
    "\n",
    "    return these_metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction(directory, plot, mask_dir):\n",
    "    \"\"\"\n",
    "    Compare ASO in <directory> to predictions found in <directory>.\n",
    "\n",
    "    Looks for *merged.tif files.\n",
    "    \"\"\"\n",
    "\n",
    "    dirfiles = os.listdir(directory)\n",
    "    assert mask_dir in dirfiles, f\"Mask directory '{mask_dir}' not found in {directory}\"\n",
    "    assert \"preds\" in dirfiles, f\"Prediction directory not found in {directory}\"\n",
    "\n",
    "    mask_file = glob(os.path.join(directory, mask_dir, \"*merged*.tif\"))[0]\n",
    "    mask = rio.open(mask_file)\n",
    "    print(mask.nodata)\n",
    "\n",
    "    preds_file = glob(os.path.join(directory, \"preds\", \"*merged*.tif\"))[0]\n",
    "    preds = rio.open(preds_file)\n",
    "    print(preds.nodata)\n",
    "\n",
    "    print(mask.crs, preds.crs)\n",
    "    mask_data = mask.read(1).flatten()\n",
    "    pred_data = preds.read(1).flatten()\n",
    "    print(mask_data.shape, pred_data.shape)\n",
    "    # remove nodata\n",
    "    try:\n",
    "        mask_data = mask_data[np.where(mask_data != mask.nodata)]\n",
    "        pred_data = pred_data[np.where(pred_data != preds.nodata)]\n",
    "    except Exception as e:\n",
    "        print(\"Error filtering nodata. Do input data files have nodata attribute set? [{}]\".format(e))\n",
    "        sys.exit(1)\n",
    "    print(mask_data.shape, pred_data.shape)\n",
    "    if not (mask_data.shape==pred_data.shape):\n",
    "        if mask_data.shape < pred_data.shape:\n",
    "            mask_r = match_extent(mask_file,preds_file).ravel()\n",
    "            print(type(mask_r))\n",
    "            mask_data = mask_r.flatten()\n",
    "            pred_data = preds.read(1).flatten()\n",
    "        if mask_data.shape > pred_data.shape:    \n",
    "            preds_r = match_extent(preds_file,mask_file).ravel()\n",
    "            print(type(preds_r))\n",
    "            mask_data = mask.read(1).flatten()  \n",
    "            pred_data = preds_r.flatten()    \n",
    "            \n",
    "            \n",
    "\n",
    "        mask_data = mask_data[np.where(mask_data != mask.nodata)]\n",
    "        pred_data = pred_data[np.where(pred_data != preds.nodata)]\n",
    "        print(mask_data.shape, pred_data.shape)\n",
    "        #mask = reproject_like_vrt(mask, preds)\n",
    "        \n",
    "    metrics = compute_metrics(mask_data, pred_data)\n",
    "\n",
    "    results = {\n",
    "        \"mask\": mask_file,\n",
    "        \"preds\": preds_file,\n",
    "        \"metrics\" : metrics\n",
    "    }\n",
    "    print(results)\n",
    "    outfile = os.path.join(directory, \"aso_metrics.json\")\n",
    "    with open(outfile, 'w') as o:\n",
    "        json.dump(results, o)\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import os\n",
    "os.chdir('/home/ubuntu/planet-snowcover/experiments/ASO_Tuolumne/scripts')\n",
    "!python compare.py --help"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scipy==1.1.0\n",
      "  Using cached https://files.pythonhosted.org/packages/a8/0b/f163da98d3a01b3e0ef1cab8dd2123c34aee2bafbb1c5bffa354cc8a1730/scipy-1.1.0-cp36-cp36m-manylinux1_x86_64.whl\n",
      "Requirement already satisfied: numpy>=1.8.2 in /home/ubuntu/anaconda3/lib/python3.6/site-packages (from scipy==1.1.0)\n",
      "Installing collected packages: scipy\n",
      "  Found existing installation: scipy 1.0.0\n",
      "    Uninstalling scipy-1.0.0:\n",
      "      Successfully uninstalled scipy-1.0.0\n",
      "Successfully installed scipy-1.1.0\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 20.1.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install scipy==1.1.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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