OSGeoLive-Notebooks/OpenDataCube/dc_load_esa_worldcover.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8784196f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datacube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c0adfbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "dc = datacube.Datacube(config='/home/user/.config/datacube/datacube.conf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a0118381",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1 datasets\n"
     ]
    }
   ],
   "source": [
    "datasets = dc.find_datasets(product=\"esa_worldcover_2021\")\n",
    "\n",
    "print(f\"Found {len(datasets)} datasets\")\n",
    "\n",
    "# Load the first dataset\n",
    "ds = dc.load(datasets=datasets[0:1])\n",
    "\n",
    "#ds = dc.load(product=\"esa_worldcover_2021\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7921c073",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:         (time: 1, latitude: 12874, longitude: 8912)\n",
      "Coordinates:\n",
      "  * time            (time) datetime64[ns] 2021-07-02\n",
      "  * latitude        (latitude) float64 43.17 43.17 43.17 ... 42.1 42.1 42.1\n",
      "  * longitude       (longitude) float64 20.22 20.22 20.22 ... 20.96 20.96 20.96\n",
      "    spatial_ref     int32 4326\n",
      "Data variables:\n",
      "    classification  (time, latitude, longitude) uint8 0 0 0 0 0 0 ... 0 0 0 0 0\n",
      "Attributes:\n",
      "    crs:           EPSG:4326\n",
      "    grid_mapping:  spatial_ref\n"
     ]
    }
   ],
   "source": [
    "print(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "68ff4906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f75d4597eb0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.classification.plot(size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "68cc04b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import colors as mcolours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26fd1658",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmap = mcolours.ListedColormap(\n",
    "  [\n",
    "      np.array([0, 0, 0]) / 255,\n",
    "      np.array([0, 100, 0]) / 255,\n",
    "      np.array([255, 187, 34]) / 255,\n",
    "      np.array([255, 255, 76]) / 255,\n",
    "      np.array([240, 150, 255]) / 255,\n",
    "      np.array([250, 0, 0]) / 255,\n",
    "      np.array([180, 180, 180]) / 255,\n",
    "      np.array([240, 240, 240]) / 255,\n",
    "      np.array([0, 100, 200]) / 255,\n",
    "      np.array([0, 150, 160]) / 255,\n",
    "      np.array([0, 207, 117]) / 255,\n",
    "      np.array([250, 230, 160]) / 255,\n",
    "  ]\n",
    ")\n",
    "\n",
    "bounds = [-5, 5, 15, 25, 35, 45, 55, 65, 75, 85, 92, 98, 105]\n",
    "norm = mcolours.BoundaryNorm(np.array(bounds), cmap.N)\n",
    "cblabels = [\n",
    "  \"no data\",\n",
    "  \"tree cover\",\n",
    "  \"shrubland\",\n",
    "  \"grassland\",\n",
    "  \"cropland\",\n",
    "  \"built up\",\n",
    "  \"bare/sparse vegetation\",\n",
    "  \"snow and ice\",\n",
    "  \"permanent water bodies\",\n",
    "  \"herbaceous wetland\",\n",
    "  \"mangroves\",\n",
    "  \"moss and lichen\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc88d065",
   "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></th>\n",
       "      <th>name</th>\n",
       "      <th>dtype</th>\n",
       "      <th>units</th>\n",
       "      <th>nodata</th>\n",
       "      <th>flags_definition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <th>measurement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>esa_worldcover_2021</th>\n",
       "      <th>classification</th>\n",
       "      <td>classification</td>\n",
       "      <td>uint8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'data': {'bits': [0, 1, 2, 3, 4, 5, 6, 7], 'v...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              name  dtype units  nodata  \\\n",
       "product             measurement                                           \n",
       "esa_worldcover_2021 classification  classification  uint8     1       0   \n",
       "\n",
       "                                                                     flags_definition  \n",
       "product             measurement                                                        \n",
       "esa_worldcover_2021 classification  {'data': {'bits': [0, 1, 2, 3, 4, 5, 6, 7], 'v...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measure_list = dc.list_measurements()\n",
    "measure_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c8354d8",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'_Dataset_PlotMethods' object has no attribute 'imshow'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_31877/3468690862.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_colorbar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: '_Dataset_PlotMethods' object has no attribute 'imshow'"
     ]
    }
   ],
   "source": [
    "#im = ds.plot.imshow(cmap=cmap, norm=norm, add_colorbar=legend, **plot_kwargs)\n",
    "##  lulc_plot\n",
    "##   supposedly, that function comes from dea-africa / data_cube_utilities repo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d0737b2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable   Type              Data/Info\n",
      "--------------------------------------\n",
      "bounds     list              n=13\n",
      "cblabels   list              n=12\n",
      "cmap       ListedColormap    <matplotlib.colors.Listed<...>object at 0x7f75dc25ead0>\n",
      "datacube   module            <module 'datacube' from '<...>es/datacube/__init__.py'>\n",
      "datasets   list              n=1\n",
      "dc         Datacube          Datacube<index=Index<db=P<...>er:***@:5432/datacube)>>>\n",
      "ds         Dataset           <xarray.Dataset>\\nDimensi<...>rid_mapping:  spatial_ref\n",
      "mcolours   module            <module 'matplotlib.color<...>es/matplotlib/colors.py'>\n",
      "norm       BoundaryNorm      <matplotlib.colors.Bounda<...>object at 0x7f75dc25eb60>\n",
      "np         module            <module 'numpy' from '/us<...>kages/numpy/__init__.py'>\n"
     ]
    }
   ],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8902b8fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8.9\n"
     ]
    }
   ],
   "source": [
    "print(datacube.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca6987c9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "python3"
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    "name": "ipython",
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   "file_extension": ".py",
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