pipeline/stitch-python.ipynb

{
 "cells": [
  {
   "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",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "resultRoot = \"/home/ubuntu/veg-co-validate/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = glob(os.path.join(resultRoot, \"*/*/*_merged.tif\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/data-mask.geojson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172329_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172147_103d_3B_AnalyticMS_SR_clip/preds/20180524_172147_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172147_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172326_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172326_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172326_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172327_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172327_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172327_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172332_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172332_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172332_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172148_103d_3B_AnalyticMS_SR_clip/preds/20180524_172148_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172148_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172144_103d_3B_AnalyticMS_SR_clip/preds/20180524_172144_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172144_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172632_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172632_0f2d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172632_0f2d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172331_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172331_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172331_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172333_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172333_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172333_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172145_103d_3B_AnalyticMS_SR_clip/preds/20180524_172145_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172145_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172143_103d_3B_AnalyticMS_SR_clip/preds/20180524_172143_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172143_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172146_103d_3B_AnalyticMS_SR_clip/preds/20180524_172146_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172146_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172634_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172634_0f2d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172634_0f2d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172330_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172330_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172330_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172633_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172633_0f2d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172633_0f2d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172635_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172635_0f2d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172635_0f2d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172637_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172637_0f2d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172637_0f2d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172142_103d_3B_AnalyticMS_SR_clip/preds/20180524_172142_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172142_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separate into ASOs\n",
    "\n",
    "asoS =  glob(os.path.join(resultRoot, \"*/*/ASO_*_merged.tif\"))\n",
    "#separate into Preds\n",
    "predS = glob(os.path.join(resultRoot, \"*/*/20180524_*_merged.tif\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172329_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172147_103d_3B_AnalyticMS_SR_clip/preds/20180524_172147_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172326_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172326_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172327_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172327_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172332_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172332_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172148_103d_3B_AnalyticMS_SR_clip/preds/20180524_172148_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172144_103d_3B_AnalyticMS_SR_clip/preds/20180524_172144_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172632_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172632_0f2d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172331_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172331_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172333_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172333_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172145_103d_3B_AnalyticMS_SR_clip/preds/20180524_172145_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172143_103d_3B_AnalyticMS_SR_clip/preds/20180524_172143_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172146_103d_3B_AnalyticMS_SR_clip/preds/20180524_172146_103d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172634_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172634_0f2d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172330_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172330_0f51_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172633_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172633_0f2d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172635_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172635_0f2d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172637_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172637_0f2d_3B_AnalyticMS_SR_clip_merged.tif',\n",
       " '/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172142_103d_3B_AnalyticMS_SR_clip/preds/20180524_172142_103d_3B_AnalyticMS_SR_clip_merged.tif']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172329_0f51_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172147_103d_3B_AnalyticMS_SR_clip/preds/20180524_172147_103d_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172326_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172326_0f51_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172327_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172327_0f51_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172332_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172332_0f51_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172148_103d_3B_AnalyticMS_SR_clip/preds/20180524_172148_103d_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172144_103d_3B_AnalyticMS_SR_clip/preds/20180524_172144_103d_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172632_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172632_0f2d_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172331_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172331_0f51_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172333_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172333_0f51_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172145_103d_3B_AnalyticMS_SR_clip/preds/20180524_172145_103d_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172143_103d_3B_AnalyticMS_SR_clip/preds/20180524_172143_103d_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172146_103d_3B_AnalyticMS_SR_clip/preds/20180524_172146_103d_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172634_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172634_0f2d_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172330_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172330_0f51_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172633_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172633_0f2d_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172635_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172635_0f2d_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172637_0f2d_3B_AnalyticMS_SR_clip/preds/20180524_172637_0f2d_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172142_103d_3B_AnalyticMS_SR_clip/preds/20180524_172142_103d_3B_AnalyticMS_SR_clip_merged.tif' mode='r'>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List for the source files\n",
    "src_files_to_mosaic = []\n",
    "\n",
    "# Iterate over raster files and add them to source -list in 'read mode'\n",
    "for fp in predS:\n",
    "    src = rasterio.open(fp)\n",
    "    src_files_to_mosaic.append(src)\n",
    "\n",
    "src_files_to_mosaic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import rasterio\n",
    "from rasterio.merge import merge\n",
    "from rasterio.plot import show\n",
    "import glob\n",
    "import os\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Create 4 plots next to each other\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(ncols=4, nrows=1, figsize=(12, 4))\n",
    "\n",
    "# Plot first four files\n",
    "show(src_files_to_mosaic[0], ax=ax1)\n",
    "show(src_files_to_mosaic[1], ax=ax2)\n",
    "show(src_files_to_mosaic[2], ax=ax3)\n",
    "show(src_files_to_mosaic[3], ax=ax4)\n",
    "\n",
    "# Do not show y-ticks values in last three axis\n",
    "for ax in [ax2, ax3, ax4]:\n",
    "    ax.yaxis.set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa76e393668>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge function returns a single mosaic array and the transformation info\n",
    "mosaic, out_trans = merge(src_files_to_mosaic)\n",
    "\n",
    "# Plot the result\n",
    "show(mosaic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172147_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172326_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172327_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172332_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172148_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172144_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172632_0f2d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172331_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172333_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172145_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172143_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172146_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172634_0f2d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172330_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172633_0f2d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172635_0f2d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172637_0f2d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>,\n",
       " <open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172142_103d_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List for the source files\n",
    "aso_files_to_mosaic = []\n",
    "\n",
    "# Iterate over raster files and add them to source -list in 'read mode'\n",
    "for fp in asoS:\n",
    "    src = rasterio.open(fp)\n",
    "    aso_files_to_mosaic.append(src)\n",
    "\n",
    "aso_files_to_mosaic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Create 4 plots next to each other\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(ncols=4, nrows=1, figsize=(12, 4))\n",
    "\n",
    "# Plot first four files\n",
    "show(aso_files_to_mosaic[0], ax=ax1)\n",
    "show(aso_files_to_mosaic[1], ax=ax2)\n",
    "show(aso_files_to_mosaic[2], ax=ax3)\n",
    "show(aso_files_to_mosaic[3], ax=ax4)\n",
    "\n",
    "# Do not show y-ticks values in last three axis\n",
    "for ax in [ax2, ax3, ax4]:\n",
    "    ax.yaxis.set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from osgeo import gdal\n",
    "outvrt = 'gunnison_stacked.vrt' #/vsimem is special in-memory virtual \"directory\"\n",
    "outtif = 'gunnison_stacked.tif'\n",
    "translateoptions = gdal.TranslateOptions(gdal.ParseCommandLine(\"-of Gtiff -co COMPRESS=LZW\"))\n",
    "\n",
    "outds = gdal.BuildVRT(outvrt, asoS, separate=True)\n",
    "outds = gdal.Translate(outtif, outds, options=translateoptions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa78f03d278>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot first four files\n",
    "show(rasterio.open(outtif))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from osgeo import gdal\n",
    "predoutvrt = 'gunnison_pred_stacked.vrt' #/vsimem is special in-memory virtual \"directory\"\n",
    "predouttif = 'gunnison_pred_stacked.tif'\n",
    "predoutds = gdal.BuildVRT(predoutvrt, predS, separate=True)\n",
    "translateoptions = gdal.TranslateOptions(gdal.ParseCommandLine(\"-of Gtiff -co COMPRESS=LZW\"))\n",
    "predoutds = gdal.Translate(predouttif, predoutds, options=translateoptions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "ename": "JSONDecodeError",
     "evalue": "Expecting value: line 1 column 1 (char 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-133-fa66b7f9daab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mgeojson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/data-mask.geojson'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mcut_by_geojson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredouttif\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'gunnison_pred_stacked_clipped.tif'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgeojson\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-61-34d797bbaaec>\u001b[0m in \u001b[0;36mcut_by_geojson\u001b[0;34m(input_file, output_file, shape_geojson)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;31m# Get coords for bounding box\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mgj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape_geojson\u001b[0m\u001b[0;34m)\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[0m\u001b[1;32m     17\u001b[0m     \u001b[0mmin_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/geojson/codec.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, cls, parse_constant, object_hook, **kwargs)\u001b[0m\n\u001b[1;32m     52\u001b[0m                       \u001b[0mcls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject_hook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobject_hook\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m                       \u001b[0mparse_constant\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_constant\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m                       **kwargs)\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/simplejson/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)\u001b[0m\n\u001b[1;32m    540\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"use_decimal=True implies parse_float=Decimal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0mkw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'parse_float'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\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    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/simplejson/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w, _PY3)\u001b[0m\n\u001b[1;32m    368\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_PY3\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\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    369\u001b[0m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 370\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\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    371\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\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    372\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\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[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/simplejson/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx, _w, _PY3)\u001b[0m\n\u001b[1;32m    398\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mord0\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0xef\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0midx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'\\xef\\xbb\\xbf'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m                 \u001b[0midx\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 400\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\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[0m",
      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
     ]
    }
   ],
   "source": [
    "from shapely.geometry import mapping\n",
    "from rasterio.mask import mask\n",
    "import json\n",
    "geojson='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/data-mask.geojson'\n",
    "\n",
    "cut_by_geojson(predouttif,'gunnison_pred_stacked_clipped.tif',geojson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Driver: GTiff/GeoTIFF\n",
      "Files: gunnison_pred_stacked.tif\n",
      "Size is 20992, 23037\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 = (-107.215576171875000,39.095962936305483)\n",
      "Pixel Size = (0.000021457672119,-0.000016700378284)\n",
      "Metadata:\n",
      "  AREA_OR_POINT=Area\n",
      "Image Structure Metadata:\n",
      "  COMPRESSION=LZW\n",
      "  INTERLEAVE=PIXEL\n",
      "Corner Coordinates:\n",
      "Upper Left  (-107.2155762,  39.0959629) (107d12'56.07\"W, 39d 5'45.47\"N)\n",
      "Lower Left  (-107.2155762,  38.7112363) (107d12'56.07\"W, 38d42'40.45\"N)\n",
      "Upper Right (-106.7651367,  39.0959629) (106d45'54.49\"W, 39d 5'45.47\"N)\n",
      "Lower Right (-106.7651367,  38.7112363) (106d45'54.49\"W, 38d42'40.45\"N)\n",
      "Center      (-106.9903564,  38.9035996) (106d59'25.28\"W, 38d54'12.96\"N)\n",
      "Band 1 Block=20992x1 Type=Int16, ColorInterp=Gray\n",
      "  NoData Value=9999\n",
      "Band 2 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 3 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 4 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 5 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 6 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 7 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 8 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 9 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 10 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 11 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 12 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 13 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 14 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 15 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 16 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 17 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 18 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n",
      "Band 19 Block=20992x1 Type=Int16, ColorInterp=Undefined\n",
      "  NoData Value=9999\n"
     ]
    }
   ],
   "source": [
    "!gdalinfo gunnison_pred_stacked.tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa794223e10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot first four files\n",
    "show(rasterio.open(predouttif))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<open DatasetReader name='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_binary_merged.tif' mode='r'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aso_files_to_mosaic[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16511488,) (16518656,)\n",
      "(11010560,) (11013120,)\n"
     ]
    }
   ],
   "source": [
    "mask_data = aso_files_to_mosaic[0].read(1).flatten()\n",
    "pred_data = src_files_to_mosaic[0].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 != 9999)]\n",
    "        pred_data = pred_data[np.where(pred_data != 9999)]\n",
    "except Exception as e:\n",
    "        print(\"Error filtering nodata. Do input data files have nodata attribute set? [{}]\".format(e))\n",
    "print(mask_data.shape, pred_data.shape)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [11010560, 11013120]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-5a96b6d9f7d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mcompare\u001b[0m \u001b[0;34m=\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[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \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[0m\u001b[1;32m      8\u001b[0m \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[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mbalanced_accuracy_score\u001b[0;34m(y_true, y_pred, sample_weight, adjusted)\u001b[0m\n\u001b[1;32m   1431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1432\u001b[0m     \"\"\"\n\u001b[0;32m-> 1433\u001b[0;31m     \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\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   1434\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdivide\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minvalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\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   1435\u001b[0m         \u001b[0mper_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\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[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight)\u001b[0m\n\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m     \"\"\"\n\u001b[0;32m--> 253\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\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    254\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\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    255\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not supported\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindicator\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \"\"\"\n\u001b[0;32m---> 71\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\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     72\u001b[0m     \u001b[0mtype_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0mtype_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\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[1;32m    234\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 235\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [11010560, 11013120]"
     ]
    }
   ],
   "source": [
    "from glob import glob\n",
    "from sklearn import metrics\n",
    "import json\n",
    "\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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172329_0f51_3B_AnalyticMS_SR_clip_merged.tif'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predS[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pred\n",
      "{'driver': 'GTiff', 'dtype': 'int16', 'nodata': 9999.0, 'width': 3584, 'height': 4609, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(2.1457672119140625e-05, 0.0, -107.138671875,\n",
      "       0.0, -1.6678264160907563e-05, 39.01918369029135), 'tiled': False, 'compress': 'lzw', 'interleave': 'band'}\n",
      "mask\n",
      "{'driver': 'GTiff', 'dtype': 'int16', 'nodata': 9999.0, 'width': 3584, 'height': 4607, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(2.1457672119140625e-05, 0.0, -107.138671875,\n",
      "       0.0, -1.668429992519871e-05, 39.01918369029135), 'tiled': False, 'compress': 'lzw', 'interleave': 'band'}\n"
     ]
    }
   ],
   "source": [
    "from shapely.geometry import mapping\n",
    "from rasterio.mask import mask\n",
    "import json\n",
    "geojson='/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/data-mask.geojson'\n",
    "with open(geojson) as data_file:\n",
    "     geoms= json.loads(data_file.read())\n",
    "all_bounds = [geoms]        \n",
    "with rasterio.open(predS[0]) as src:\n",
    "    out_image, out_transform = mask(src, all_bounds, crop=True) \n",
    "    out_meta = src.meta.copy()\n",
    "    ## Re-arrange bounding box to plot\n",
    "    bounds = (src.bounds.left, src.bounds.right, \\\n",
    "          src.bounds.bottom, src.bounds.top)\n",
    "    # save the resulting raster\n",
    "    out_meta.update({\"driver\": \"GTiff\",\n",
    "            \"height\": out_image.shape[1],\n",
    "            \"width\": out_image.shape[2],\n",
    "            \"transform\": out_transform})\n",
    "    print('pred')\n",
    "    print(src.profile)\n",
    "    with rasterio.open( \"preds_clipped.tif\", \"w\", **out_meta) as dest:\n",
    "            dest.write(out_image) \n",
    "with rasterio.open(asoS[0]) as src:\n",
    "    out_image, out_transform = mask(src, all_bounds, crop=True) \n",
    "    out_meta = src.meta.copy()\n",
    "\n",
    "    # save the resulting raster\n",
    "    out_meta.update({\"driver\": \"GTiff\",\n",
    "            \"height\": out_image.shape[1],\n",
    "            \"width\": out_image.shape[2],\n",
    "            \"transform\": out_transform})\n",
    "    print('mask')\n",
    "    print(src.profile)\n",
    "    with rasterio.open( \"asos_clipped.tif\", \"w\", **out_meta) as dest:\n",
    "            dest.write(out_image)             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-107.138671875, -107.061767578125, 38.94231357077373, 39.01918369029135)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16511488,) (16518656,)\n",
      "(11007488,) (11010560,)\n"
     ]
    }
   ],
   "source": [
    "mask_data = rasterio.open(\"asos_clipped.tif\").read(1).flatten()\n",
    "pred_data = rasterio.open(\"preds_clipped.tif\").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 != 9999)]\n",
    "        pred_data = pred_data[np.where(pred_data != 9999)]\n",
    "except Exception as e:\n",
    "        print(\"Error filtering nodata. Do input data files have nodata attribute set? [{}]\".format(e))\n",
    "print(mask_data.shape, pred_data.shape) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa74ee704a8>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "lsoas = gpd.read_file(geojson)\n",
    "lsoas.head()\n",
    "lsoas.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Re-arrange bounding box to plot\n",
    "bounds = (source.bounds.left, source.bounds.right, \\\n",
    "          source.bounds.bottom, source.bounds.top)\n",
    "## Stack RGB values into a single array\n",
    "pix = np.dstack(rasterio.open(\"preds_clipped.tif\").read(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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]]], dtype=int16)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Set up figure\n",
    "f, ax = plt.subplots(figsize=(6, 6))\n",
    "# Add raster\n",
    "ax.imshow(rasterio.open(\"preds_clipped.tif\").read(1), extent=bounds)\n",
    "# Extract limits from Liverpool from LSOAs\n",
    "w, s, e, n = lsoas.total_bounds\n",
    "# Center figure on Liverpool limits\n",
    "ax.set_xlim((w, e))\n",
    "ax.set_ylim((s, n))\n",
    "# Add polygon layer\n",
    "lsoas.plot(ax=ax, alpha=0.5, color='green', linewidth=3)\n",
    "# Remove axis\n",
    "plt.axis('off')\n",
    "# Display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 4607, 3584)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(out_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9999.0\n"
     ]
    }
   ],
   "source": [
    "# no data values of the original raster\n",
    "no_data=src.nodata\n",
    "print (no_data)\n",
    "# extract the values of the masked array\n",
    "data = out_image[0]\n",
    "# extract the row, columns of the valid values\n",
    "\n",
    "row, col = np.where(data != no_data) \n",
    "snow = np.extract(data != no_data, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rasterio import Affine # or from affine import Affine\n",
    "T1 = out_transform * Affine.translation(0.5, 0.5) # reference the pixel centre\n",
    "rc2xy = lambda r, c: (c, r) * T1  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col</th>\n",
       "      <th>row</th>\n",
       "      <th>snow</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1024</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-107.116688</td>\n",
       "      <td>39.019175</td>\n",
       "      <td>POINT (-107.11669 39.01918)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1025</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-107.116667</td>\n",
       "      <td>39.019175</td>\n",
       "      <td>POINT (-107.11667 39.01918)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    col  row  snow           x          y                     geometry\n",
       "0  1024    0     1 -107.116688  39.019175  POINT (-107.11669 39.01918)\n",
       "1  1025    0     1 -107.116667  39.019175  POINT (-107.11667 39.01918)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = gpd.GeoDataFrame({'col':col,'row':row,'snow':snow})\n",
    "# coordinate transformation\n",
    "d['x'] = d.apply(lambda row: rc2xy(row.row,row.col)[0], axis=1)\n",
    "d['y'] = d.apply(lambda row: rc2xy(row.row,row.col)[1], axis=1)\n",
    "# geometry\n",
    "from shapely.geometry import Point\n",
    "d['geometry'] =d.apply(lambda row: Point(row['x'], row['y']), axis=1)\n",
    "# first 2 points\n",
    "d.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'driver': 'GTiff', 'dtype': 'int16', 'nodata': 9999.0, 'width': 3584, 'height': 4607, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(2.1457672119140625e-05, 0.0, -107.138671875,\n",
      "       0.0, -1.668429992519871e-05, 39.01918369029135), 'tiled': False, 'interleave': 'band'}\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Source shape (4609, 3584) is inconsistent with given indexes 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-117-2017ad206c2d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'preds_clipped_matched.tif'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mrasterio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mout_profile\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m             \u001b[0mdst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\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[0m",
      "\u001b[0;32mrasterio/_io.pyx\u001b[0m in \u001b[0;36mrasterio._io.DatasetWriterBase.write\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Source shape (4609, 3584) is inconsistent with given indexes 1"
     ]
    }
   ],
   "source": [
    "import geopandas as gp\n",
    "from rasterio.features import shapes\n",
    "with rasterio.open('asos_clipped.tif') as msk:\n",
    "    with rasterio.open('preds_clipped.tif') as src:        \n",
    "        aso = msk.read(1)  ## first and only band      \n",
    "        pred = src.read(1)   ## first and only band\n",
    "        #print('mask',msk.profile)\n",
    "        #print('pred', src.profile)\n",
    "        out_profile = msk.profile.copy()\n",
    "        print(out_profile)\n",
    "        out_affine = out_profile.pop(\"transform\")\n",
    "        out_profile[\"transform\"] = out_affine\n",
    "\n",
    "        # then, write the output raster\n",
    "        output='preds_clipped_matched.tif'\n",
    "        with rasterio.open(output, 'w', **out_profile) as dst:\n",
    "            dst.write(pred)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>altitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((-107.11627 39.01918, -107.11627 39.0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((-107.11593 39.01918, -107.11593 39.0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry  altitude\n",
       "0  POLYGON ((-107.11627 39.01918, -107.11627 39.0...       0.0\n",
       "1  POLYGON ((-107.11593 39.01918, -107.11593 39.0...       0.0"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thisGeoData.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale=1\n",
    "from rasterio.enums import Resampling\n",
    "# Open data and assign negative values to nan\n",
    "with rio.open('preds_clipped.tif') as origin:\n",
    "        with rio.open('asos_clipped.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,\n",
    "                           driver='GTiff', \n",
    "                           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('preds_clipped_matched.tif', 'w', **profile) as dataset:  # Open as DatasetWriter\n",
    "                dataset.write(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import rasterio as rio\n",
    "from rasterio.plot import plotting_extent\n",
    "import geopandas as gpd\n",
    "import earthpy as et\n",
    "import earthpy.spatial as es\n",
    "import earthpy.plot as ep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "stack_band_paths= [\"asos_clipped.tif\",\"preds_clipped_matched.tif\"]\n",
    "raster_out_path = os.path.join('.', \"raster.tiff\")\n",
    "array, raster_prof = es.stack(stack_band_paths, out_path=raster_out_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View hist of data with nodata values removed\n",
    "ep.hist(\n",
    "    array,\n",
    "    title=[\n",
    "        \"Band 1 - ASO\",\n",
    "        \"Band 2 - PRED\"\n",
    "    ],\n",
    ")\n",
    "plt.show()\n",
    "\n",
    "# Recreate extent object for the No Data array\n",
    "\n",
    "extent_nodata = plotting_extent(\n",
    "    array[0], raster_prof[\"transform\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [11007488, 11010560]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-130-1c1d2f1dfd2b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mcompare\u001b[0m \u001b[0;34m=\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[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \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[0m\u001b[1;32m     11\u001b[0m \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[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mbalanced_accuracy_score\u001b[0;34m(y_true, y_pred, sample_weight, adjusted)\u001b[0m\n\u001b[1;32m   1431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1432\u001b[0m     \"\"\"\n\u001b[0;32m-> 1433\u001b[0;31m     \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\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   1434\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdivide\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minvalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\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   1435\u001b[0m         \u001b[0mper_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\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[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight)\u001b[0m\n\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m     \"\"\"\n\u001b[0;32m--> 253\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\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    254\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\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    255\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not supported\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindicator\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \"\"\"\n\u001b[0;32m---> 71\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\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     72\u001b[0m     \u001b[0mtype_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0mtype_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\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[1;32m    234\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 235\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [11007488, 11010560]"
     ]
    }
   ],
   "source": [
    "from glob import glob\n",
    "from sklearn import metrics\n",
    "import json\n",
    "\n",
    "mask_data = array[0][np.where(array[0] != 9999)]\n",
    "pred_data = array[1][np.where(array[1] != 9999)]\n",
    "        \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": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(\n",
       "  data=[[[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",
       "\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",
       "  mask=False,\n",
       "  fill_value=-9999,\n",
       "  dtype=int16)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geojson as gj\n",
    "from osgeo import ogr, osr, gdal\n",
    "\n",
    "# Enable GDAL/OGR exceptions\n",
    "gdal.UseExceptions()\n",
    "\n",
    "\n",
    "# GDAL & OGR memory drivers\n",
    "GDAL_MEMORY_DRIVER = gdal.GetDriverByName('MEM')\n",
    "OGR_MEMORY_DRIVER = ogr.GetDriverByName('Memory')\n",
    "\n",
    "\n",
    "def cut_by_geojson(input_file, output_file, shape_geojson):\n",
    "\n",
    "    # Get coords for bounding box\n",
    "    x, y = zip(*gj.utils.coords(gj.loads(shape_geojson)))\n",
    "    min_x, max_x, min_y, max_y = min(x), max(x), min(y), max(y)\n",
    "\n",
    "    # Open original data as read only\n",
    "    dataset = gdal.Open(input_file, gdal.GA_ReadOnly)\n",
    "\n",
    "    bands = dataset.RasterCount\n",
    "\n",
    "    # Getting georeference info\n",
    "    transform = dataset.GetGeoTransform()\n",
    "    projection = dataset.GetProjection()\n",
    "    xOrigin = transform[0]\n",
    "    yOrigin = transform[3]\n",
    "    pixelWidth = transform[1]\n",
    "    pixelHeight = -transform[5]\n",
    "\n",
    "    # Getting spatial reference of input raster\n",
    "    srs = osr.SpatialReference()\n",
    "    srs.ImportFromWkt(projection)\n",
    "\n",
    "    # WGS84 projection reference\n",
    "    OSR_WGS84_REF = osr.SpatialReference()\n",
    "    OSR_WGS84_REF.ImportFromEPSG(4326)\n",
    "\n",
    "    # OSR transformation\n",
    "    wgs84_to_image_trasformation = osr.CoordinateTransformation(OSR_WGS84_REF,\n",
    "                                                                srs)\n",
    "    XYmin = wgs84_to_image_trasformation.TransformPoint(min_x, max_y)\n",
    "    XYmax = wgs84_to_image_trasformation.TransformPoint(max_x, min_y)\n",
    "\n",
    "    # Computing Point1(i1,j1), Point2(i2,j2)\n",
    "    i1 = int((XYmin[0] - xOrigin) / pixelWidth)\n",
    "    j1 = int((yOrigin - XYmin[1]) / pixelHeight)\n",
    "    i2 = int((XYmax[0] - xOrigin) / pixelWidth)\n",
    "    j2 = int((yOrigin - XYmax[1]) / pixelHeight)\n",
    "    new_cols = i2 - i1 + 1\n",
    "    new_rows = j2 - j1 + 1\n",
    "\n",
    "    # New upper-left X,Y values\n",
    "    new_x = xOrigin + i1 * pixelWidth\n",
    "    new_y = yOrigin - j1 * pixelHeight\n",
    "    new_transform = (new_x, transform[1], transform[2], new_y, transform[4],\n",
    "                     transform[5])\n",
    "\n",
    "    wkt_geom = ogr.CreateGeometryFromJson(str(shape_geojson))\n",
    "    wkt_geom.Transform(wgs84_to_image_trasformation)\n",
    "\n",
    "    target_ds = GDAL_MEMORY_DRIVER.Create('', new_cols, new_rows, 1,\n",
    "                                          gdal.GDT_Byte)\n",
    "    target_ds.SetGeoTransform(new_transform)\n",
    "    target_ds.SetProjection(projection)\n",
    "\n",
    "    # Create a memory layer to rasterize from.\n",
    "    ogr_dataset = OGR_MEMORY_DRIVER.CreateDataSource('shapemask')\n",
    "    ogr_layer = ogr_dataset.CreateLayer('shapemask', srs=srs)\n",
    "    ogr_feature = ogr.Feature(ogr_layer.GetLayerDefn())\n",
    "    ogr_feature.SetGeometryDirectly(ogr.Geometry(wkt=wkt_geom.ExportToWkt()))\n",
    "    ogr_layer.CreateFeature(ogr_feature)\n",
    "\n",
    "    gdal.RasterizeLayer(target_ds, [1], ogr_layer, burn_values=[1],\n",
    "                        options=[\"ALL_TOUCHED=TRUE\"])\n",
    "\n",
    "    # Create output file\n",
    "    driver = gdal.GetDriverByName('GTiff')\n",
    "    outds = driver.Create(output_file, new_cols, new_rows, bands,\n",
    "                          gdal.GDT_Float32)\n",
    "\n",
    "    # Read in bands and store all the data in bandList\n",
    "    mask_array = target_ds.GetRasterBand(1).ReadAsArray()\n",
    "    band_list = []\n",
    "\n",
    "    for i in range(bands):\n",
    "        band_list.append(dataset.GetRasterBand(i + 1).ReadAsArray(i1, j1,\n",
    "                         new_cols, new_rows))\n",
    "\n",
    "    for j in range(bands):\n",
    "        data = np.where(mask_array == 1, band_list[j], mask_array)\n",
    "        outds.GetRasterBand(j + 1).SetNoDataValue(0)\n",
    "        outds.GetRasterBand(j + 1).WriteArray(data)\n",
    "\n",
    "    outds.SetProjection(projection)\n",
    "    outds.SetGeoTransform(new_transform)\n",
    "\n",
    "    target_ds = None\n",
    "    dataset = None\n",
    "    outds = None\n",
    "    ogr_dataset = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/preds/20180524_172329_0f51_3B_AnalyticMS_SR_clip_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[0])\n",
    "# Load the DEM and read out a np array of the elevation values (meters):\n",
    "dem = rio.open(results[0])\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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/veg-co-validate/planet-snowcover-analysis:planet-snowcover-2020-03-01-13-37-37-330:planet-snowcover-imagery-veg:20180524_172329_0f51_3B_AnalyticMS_SR_clip/mask/ASO_3M_SD_USCOGE_20180524_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[1])\n",
    "# Load the DEM and read out a np array of the elevation values (meters):\n",
    "dem = rio.open(results[1])\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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Environment (conda_pytorch_p36)",
   "language": "python",
   "name": "conda_pytorch_p36"
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}