0_recap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "625.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4Bfw7cM1q13pF3X8JPN2FWpf78hl0Zkl4gs4sCYfdLAmH3SwJh90sCYfdLAmH3SwJh90sCYfdLIn/B+oP2faTJb0tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr = np.arange(50*50, dtype=np.float32).reshape((50,50))\n",
    "\n",
    "arr[arr > 50*50/2] = np.nan\n",
    "#arr[arr.T >= 50*50/2] = np.nan\n",
    "#arr[(arr.T + arr) > 50*50] = np.nan\n",
    "\n",
    "plt.imshow(arr)\n",
    "\n",
    "np.nanmean(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>bits</th>\n",
       "      <th>values</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cloud</th>\n",
       "      <td>6</td>\n",
       "      <td>{'0': False, '1': True}</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cloud_shadow</th>\n",
       "      <td>5</td>\n",
       "      <td>{'0': False, '1': True}</td>\n",
       "      <td>Cloud shadow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dry</th>\n",
       "      <td>[7, 6, 5, 4, 3, 1, 0]</td>\n",
       "      <td>{'0': True}</td>\n",
       "      <td>No water detected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high_slope</th>\n",
       "      <td>4</td>\n",
       "      <td>{'0': False, '1': True}</td>\n",
       "      <td>High slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nodata</th>\n",
       "      <td>0</td>\n",
       "      <td>{'0': False, '1': True}</td>\n",
       "      <td>No data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>noncontiguous</th>\n",
       "      <td>1</td>\n",
       "      <td>{'0': False, '1': True}</td>\n",
       "      <td>At least one EO band is missing over over/unde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sea</th>\n",
       "      <td>2</td>\n",
       "      <td>{'0': False, '1': True}</td>\n",
       "      <td>Sea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>terrain_or_low_angle</th>\n",
       "      <td>3</td>\n",
       "      <td>{'0': False, '1': True}</td>\n",
       "      <td>terrain shadow or low solar angle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wet</th>\n",
       "      <td>[7, 6, 5, 4, 3, 1, 0]</td>\n",
       "      <td>{'128': True}</td>\n",
       "      <td>Clear and Wet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       bits                   values  \\\n",
       "cloud                                     6  {'0': False, '1': True}   \n",
       "cloud_shadow                              5  {'0': False, '1': True}   \n",
       "dry                   [7, 6, 5, 4, 3, 1, 0]              {'0': True}   \n",
       "high_slope                                4  {'0': False, '1': True}   \n",
       "nodata                                    0  {'0': False, '1': True}   \n",
       "noncontiguous                             1  {'0': False, '1': True}   \n",
       "sea                                       2  {'0': False, '1': True}   \n",
       "terrain_or_low_angle                      3  {'0': False, '1': True}   \n",
       "wet                   [7, 6, 5, 4, 3, 1, 0]            {'128': True}   \n",
       "\n",
       "                                                            description  \n",
       "cloud                                                            Cloudy  \n",
       "cloud_shadow                                               Cloud shadow  \n",
       "dry                                                   No water detected  \n",
       "high_slope                                                   High slope  \n",
       "nodata                                                          No data  \n",
       "noncontiguous         At least one EO band is missing over over/unde...  \n",
       "sea                                                                 Sea  \n",
       "terrain_or_low_angle                  terrain shadow or low solar angle  \n",
       "wet                                                       Clear and Wet  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datacube\n",
    "import xarray as xr\n",
    "\n",
    "dc = datacube.Datacube(app=\"recap\")\n",
    "\n",
    "query = {'lat': (-35.25, -35.35),\n",
    "         'lon': (149.05, 149.17),\n",
    "         'time':('2001-01-01', '2001-06-01')}\n",
    "\n",
    "\n",
    "ds = dc.load(product='wofs_albers', **query)\n",
    "\n",
    "from datacube.storage import masking\n",
    "\n",
    "masking.describe_variable_flags(ds, with_pandas=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 25, x: 492, y: 500)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2001-01-03T23:40:40.500000 ... 2001-05-27T23:40:29.500000\n",
       "  * y        (y) float64 -3.953e+06 -3.953e+06 ... -3.966e+06 -3.966e+06\n",
       "  * x        (x) float64 1.542e+06 1.542e+06 1.542e+06 ... 1.555e+06 1.555e+06\n",
       "Data variables:\n",
       "    water    (time, y, x) int16 0 0 0 0 0 0 0 0 0 0 0 ... 8 8 8 8 8 8 8 8 8 8 8\n",
       "Attributes:\n",
       "    crs:      EPSG:3577"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f3312ee57f0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.water.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3312d5f630>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#water\n",
    "\n",
    "plt.imshow(ds.water.isel(time=0).values == 128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f330acfccf8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#High slope\n",
    "\n",
    "plt.imshow(ds.water.isel(time=0).values == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "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": 4
}