{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3185cd90-7614-4ab5-8234-04721abf74e9",
   "metadata": {},
   "source": [
    "An example notebook that reads data and convert it to xarray Dataset with aligned time and space axis.\n",
    "\n",
    "The data used in this notebook is attached with the GitHub repository of `motrainer`. They can be found via [this link](https://github.com/VegeWaterDynamics/motrainer/tree/main/docs/notebooks/example_data). \n",
    "\n",
    "This notebook generates the example dataset `./example1_data.zarr/` for the following example notebooks:\n",
    "\n",
    "- [Prallely training sklearn models with dask-ml](https://vegewaterdynamics.github.io/motrainer/notebooks/example_daskml/)\n",
    "- [Prallely training DNN with Tensorflow](https://vegewaterdynamics.github.io/motrainer/notebooks/example_dnn/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6db8da8-c6e2-4330-85db-5df185df62cb",
   "metadata": {},
   "source": [
    "## Import libraries and set paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47d44c1e-28a4-4d53-934c-1f32e8989269",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt # only for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "06e45086-3e05-4d66-a2a4-e22e067eea40",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_file_path = \"./example_data/example_data.pickle\"\n",
    "nc_file_path = \"./example1_data.nc\"\n",
    "zarr_file_path = \"./example1_data.zarr\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a18a1b54-3f63-4161-9fcf-77e466057225",
   "metadata": {},
   "source": [
    "## Read the data and explore it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b37baf27-a457-453b-affe-808798fed525",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data\n",
    "df_all_gpi = pd.read_pickle(pickle_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc298f63-71d7-4ff7-ab0a-d97717191079",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>1</th>\n",
       "      <td>56.125</td>\n",
       "      <td>11.375</td>\n",
       "      <td>sig      slop      curv    ...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46.125</td>\n",
       "      <td>6.625</td>\n",
       "      <td>sig      slop      curv    ...</td>\n",
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       "      <td>53.375</td>\n",
       "      <td>6.125</td>\n",
       "      <td>sig      slop      curv    ...</td>\n",
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       "      <th>4</th>\n",
       "      <td>49.375</td>\n",
       "      <td>12.375</td>\n",
       "      <td>sig      slop      curv    ...</td>\n",
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       "      <th>5</th>\n",
       "      <td>44.375</td>\n",
       "      <td>0.625</td>\n",
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      ],
      "text/plain": [
       "      lat     lon                                               data\n",
       "1  56.125  11.375                     sig      slop      curv    ...\n",
       "2  46.125   6.625                     sig      slop      curv    ...\n",
       "3  53.375   6.125                     sig      slop      curv    ...\n",
       "4  49.375  12.375                     sig      slop      curv    ...\n",
       "5  44.375   0.625                     sig      slop      curv    ..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_gpi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7631ef10-2ceb-4dd3-b0f7-015ce657898d",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sig</th>\n",
       "      <th>slop</th>\n",
       "      <th>curv</th>\n",
       "      <th>TG1</th>\n",
       "      <th>TG2</th>\n",
       "      <th>TG3</th>\n",
       "      <th>WG1</th>\n",
       "      <th>WG2</th>\n",
       "      <th>WG3</th>\n",
       "      <th>BIOMA1</th>\n",
       "      <th>BIOMA2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime_doy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2007-01-02</th>\n",
       "      <td>-8.774847</td>\n",
       "      <td>-0.118061</td>\n",
       "      <td>-0.001871</td>\n",
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       "      <td>0.055779</td>\n",
       "      <td>0.064610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-03</th>\n",
       "      <td>-8.737255</td>\n",
       "      <td>-0.116761</td>\n",
       "      <td>-0.001753</td>\n",
       "      <td>283.059404</td>\n",
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       "      <th>2007-01-03</th>\n",
       "      <td>-8.791911</td>\n",
       "      <td>-0.118357</td>\n",
       "      <td>-0.002037</td>\n",
       "      <td>284.386143</td>\n",
       "      <td>278.075722</td>\n",
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       "      <td>0.250349</td>\n",
       "      <td>0.335715</td>\n",
       "      <td>0.062280</td>\n",
       "      <td>0.043909</td>\n",
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       "    <tr>\n",
       "      <th>2007-01-05</th>\n",
       "      <td>-7.962205</td>\n",
       "      <td>-0.118063</td>\n",
       "      <td>-0.002072</td>\n",
       "      <td>276.947048</td>\n",
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       "      <td>277.941320</td>\n",
       "      <td>0.305945</td>\n",
       "      <td>0.332280</td>\n",
       "      <td>0.315607</td>\n",
       "      <td>0.052877</td>\n",
       "      <td>0.017596</td>\n",
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       "    <tr>\n",
       "      <th>2007-01-06</th>\n",
       "      <td>-8.607216</td>\n",
       "      <td>-0.118727</td>\n",
       "      <td>-0.002048</td>\n",
       "      <td>276.458553</td>\n",
       "      <td>282.783491</td>\n",
       "      <td>277.956962</td>\n",
       "      <td>0.380480</td>\n",
       "      <td>0.364697</td>\n",
       "      <td>0.280530</td>\n",
       "      <td>0.051309</td>\n",
       "      <td>0.034444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>-8.824627</td>\n",
       "      <td>-0.119621</td>\n",
       "      <td>-0.000872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>-8.578708</td>\n",
       "      <td>-0.121446</td>\n",
       "      <td>-0.001059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>-8.731547</td>\n",
       "      <td>-0.119538</td>\n",
       "      <td>-0.000887</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2020-01-01</th>\n",
       "      <td>-7.358630</td>\n",
       "      <td>-0.122284</td>\n",
       "      <td>-0.000725</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>-9.165778</td>\n",
       "      <td>-0.123732</td>\n",
       "      <td>-0.000753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>7995 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "                   sig      slop      curv         TG1         TG2  \\\n",
       "datetime_doy                                                         \n",
       "2007-01-02   -8.774847 -0.118061 -0.001871  282.495667  277.571790   \n",
       "2007-01-03   -8.737255 -0.116761 -0.001753  283.059404  278.609833   \n",
       "2007-01-03   -8.791911 -0.118357 -0.002037  284.386143  278.075722   \n",
       "2007-01-05   -7.962205 -0.118063 -0.002072  276.947048  277.841682   \n",
       "2007-01-06   -8.607216 -0.118727 -0.002048  276.458553  282.783491   \n",
       "...                ...       ...       ...         ...         ...   \n",
       "2019-12-30   -8.824627 -0.119621 -0.000872         NaN         NaN   \n",
       "2019-12-31   -8.578708 -0.121446 -0.001059         NaN         NaN   \n",
       "2019-12-31   -8.731547 -0.119538 -0.000887         NaN         NaN   \n",
       "2020-01-01   -7.358630 -0.122284 -0.000725         NaN         NaN   \n",
       "2020-01-01   -9.165778 -0.123732 -0.000753         NaN         NaN   \n",
       "\n",
       "                     TG3       WG1       WG2       WG3    BIOMA1    BIOMA2  \n",
       "datetime_doy                                                                \n",
       "2007-01-02    280.432019  0.353169  0.297954  0.316928  0.055779  0.064610  \n",
       "2007-01-03    279.851678  0.224477  0.336283  0.303121  0.057188  0.007182  \n",
       "2007-01-03    285.383157  0.378645  0.250349  0.335715  0.062280  0.043909  \n",
       "2007-01-05    277.941320  0.305945  0.332280  0.315607  0.052877  0.017596  \n",
       "2007-01-06    277.956962  0.380480  0.364697  0.280530  0.051309  0.034444  \n",
       "...                  ...       ...       ...       ...       ...       ...  \n",
       "2019-12-30           NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2019-12-31           NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2019-12-31           NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2020-01-01           NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2020-01-01           NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "\n",
       "[7995 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_gpi.iloc[3][\"data\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9dffc7e-489a-4588-bab1-456ed0fb2db3",
   "metadata": {},
   "source": [
    "## Convert data\n",
    "\n",
    "As seen above, the \"datetime_doy\" values are not unique. While it's possible to have non-unique index values, it's generally not recommended. Having a non-unique index can make certain operations and data manipulation more complex, or even incorrect. These values shows two observation at one day. To avoid duplication, we add a hal-hour shift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ec3de51-f368-4d17-a307-aac3f39ac1c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to make timestamps unique by adding half an hour\n",
    "def make_timestamps_unique(df):\n",
    "    seen_timestamps = set()\n",
    "    new_index = []\n",
    "\n",
    "    for timestamp in df.index:\n",
    "        if timestamp not in seen_timestamps:\n",
    "            new_index.append(timestamp)\n",
    "            seen_timestamps.add(timestamp)\n",
    "        else:\n",
    "            # Timestamp is a duplicate, add half an hour\n",
    "            while timestamp in seen_timestamps:\n",
    "                timestamp += pd.Timedelta(minutes=30)\n",
    "            new_index.append(timestamp)\n",
    "            seen_timestamps.add(timestamp)\n",
    "    \n",
    "    df.index = new_index\n",
    "    df.index.name = \"time\"\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "742333d4-9796-4248-ab19-c06c854d8bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_list = []\n",
    "for index, row in df_all_gpi.iterrows():\n",
    "    \n",
    "    # Filter the nested DataFrame based on location\n",
    "    df = df_all_gpi.iloc[index-1][\"data\"]\n",
    "\n",
    "    # Make timestamps unique\n",
    "    df = make_timestamps_unique(df)\n",
    "\n",
    "    # convert dataframe to dataset\n",
    "    ds = xr.Dataset(df, coords={'latitude': row[\"lat\"], 'longitude': row[\"lon\"]})\n",
    "    ds_list.append(ds)\n",
    "\n",
    "# Create one dataset\n",
    "dataset = xr.concat(ds_list, dim=\"space\")\n",
    "\n",
    "# Add attribute (metadata)\n",
    "dataset.attrs['source'] = 'data source'\n",
    "dataset.attrs['license'] = 'data license'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43bfa92c-aafc-48c4-970d-88979cb1c29d",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "## Inspect output and store it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d5358c5c-3fc1-40e4-8d4a-ac52a7d4e242",
   "metadata": {},
   "outputs": [
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       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 8506, space: 5)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2007-01-02 ... 2020-01-01T01:00:00\n",
       "    latitude   (space) float64 56.12 46.12 53.38 49.38 44.38\n",
       "    longitude  (space) float64 11.38 6.625 6.125 12.38 0.625\n",
       "Dimensions without coordinates: space\n",
       "Data variables:\n",
       "    sig        (space, time) float64 -9.49 -8.494 -9.069 ... -8.071 -8.237\n",
       "    slop       (space, time) float64 -0.1208 -0.1178 -0.121 ... -0.1144 -0.1191\n",
       "    curv       (space, time) float64 -0.001396 -0.001464 ... -0.0006173\n",
       "    TG1        (space, time) float64 280.0 270.4 285.5 277.4 ... nan nan nan nan\n",
       "    TG2        (space, time) float64 274.8 278.4 280.6 283.7 ... nan nan nan nan\n",
       "    TG3        (space, time) float64 280.9 279.7 278.0 278.0 ... nan nan nan nan\n",
       "    WG1        (space, time) float64 0.3249 0.2798 0.2773 0.2867 ... nan nan nan\n",
       "    WG2        (space, time) float64 0.3408 0.2902 0.3373 0.2709 ... nan nan nan\n",
       "    WG3        (space, time) float64 0.3123 0.2916 0.2891 0.3538 ... nan nan nan\n",
       "    BIOMA1     (space, time) float64 0.07079 0.05532 0.04846 ... nan nan nan\n",
       "    BIOMA2     (space, time) float64 0.04366 0.0462 0.03821 ... nan nan nan\n",
       "Attributes:\n",
       "    source:   data source\n",
       "    license:  data license</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b3aad894-9844-4651-8846-2007376038b5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b3aad894-9844-4651-8846-2007376038b5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 8506</li><li><span>space</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-15758844-11ef-4b17-9010-84607a9d4c30' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15758844-11ef-4b17-9010-84607a9d4c30' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2007-01-02 ... 2020-01-01T01:00:00</div><input id='attrs-9ef4d5aa-5015-4282-a14e-f1b2b23e6251' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ef4d5aa-5015-4282-a14e-f1b2b23e6251' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf269dcf-93f6-4729-870f-0afeec2a34c2' class='xr-var-data-in' type='checkbox'><label for='data-bf269dcf-93f6-4729-870f-0afeec2a34c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2007-01-02T00:00:00.000000000&#x27;, &#x27;2007-01-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-01-03T00:30:00.000000000&#x27;, ..., &#x27;2020-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-01T00:30:00.000000000&#x27;, &#x27;2020-01-01T01:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(space)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>56.12 46.12 53.38 49.38 44.38</div><input id='attrs-9c964406-74fa-4b96-89d9-29e8d8020be3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c964406-74fa-4b96-89d9-29e8d8020be3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32a56acb-c22a-4495-88c0-fcaf11f3d122' class='xr-var-data-in' type='checkbox'><label for='data-32a56acb-c22a-4495-88c0-fcaf11f3d122' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([56.125, 46.125, 53.375, 49.375, 44.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(space)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>11.38 6.625 6.125 12.38 0.625</div><input id='attrs-355c70aa-94bd-46ce-9983-28a77f5b8867' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-355c70aa-94bd-46ce-9983-28a77f5b8867' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0c0f42d-08ee-44e9-bb43-f823cab89190' class='xr-var-data-in' type='checkbox'><label for='data-b0c0f42d-08ee-44e9-bb43-f823cab89190' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([11.375,  6.625,  6.125, 12.375,  0.625])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-504c9f4a-a449-46c4-bd93-f5604a3f9209' class='xr-section-summary-in' type='checkbox'  checked><label for='section-504c9f4a-a449-46c4-bd93-f5604a3f9209' class='xr-section-summary' >Data variables: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sig</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.49 -8.494 ... -8.071 -8.237</div><input id='attrs-d2dccfed-1ca7-4063-9903-b367a71f54ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d2dccfed-1ca7-4063-9903-b367a71f54ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f33c34e-b5fa-444c-a722-210e9159a3cf' class='xr-var-data-in' type='checkbox'><label for='data-3f33c34e-b5fa-444c-a722-210e9159a3cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-9.48985745, -8.49424829, -9.06917897, ..., -8.87350746,\n",
       "        -8.55944322,         nan],\n",
       "       [-9.02591086, -8.79194073, -8.24172421, ..., -8.65639331,\n",
       "        -7.39492538, -8.74562845],\n",
       "       [-9.29394782, -8.87621932, -8.96752995, ..., -8.2149011 ,\n",
       "        -9.08360639,         nan],\n",
       "       [-8.77484736, -8.73725534, -8.79191062, ..., -7.35863039,\n",
       "        -9.16577772,         nan],\n",
       "       [-8.24819331, -8.8397918 , -9.10849921, ..., -8.06904894,\n",
       "        -8.07118479, -8.23718386]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slop</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1208 -0.1178 ... -0.1144 -0.1191</div><input id='attrs-e2fc9aa2-fb4c-4480-90b5-e77e875ed72d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2fc9aa2-fb4c-4480-90b5-e77e875ed72d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec339bfa-d9dd-4f83-8cbe-c7c7f3e7b786' class='xr-var-data-in' type='checkbox'><label for='data-ec339bfa-d9dd-4f83-8cbe-c7c7f3e7b786' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.12082425, -0.11783219, -0.12098574, ..., -0.11359581,\n",
       "        -0.11493786,         nan],\n",
       "       [-0.11527791, -0.11782626, -0.11479579, ..., -0.12395586,\n",
       "        -0.12741118, -0.12636578],\n",
       "       [-0.11198183, -0.11500686, -0.11297782, ..., -0.11395541,\n",
       "        -0.1149019 ,         nan],\n",
       "       [-0.11806147, -0.11676077, -0.11835654, ..., -0.1222841 ,\n",
       "        -0.1237319 ,         nan],\n",
       "       [-0.11225743, -0.1164341 , -0.11506161, ..., -0.11670426,\n",
       "        -0.114442  , -0.11907956]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>curv</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.001396 -0.001464 ... -0.0006173</div><input id='attrs-eb0fab31-8d39-44cd-884d-dfa68f92f147' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb0fab31-8d39-44cd-884d-dfa68f92f147' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a48d061c-f982-4416-9caf-6fa105e62f28' class='xr-var-data-in' type='checkbox'><label for='data-a48d061c-f982-4416-9caf-6fa105e62f28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.00139633, -0.00146407, -0.00155133, ..., -0.00147185,\n",
       "        -0.00133919,         nan],\n",
       "       [-0.00192987, -0.00172127, -0.00155783, ..., -0.00127703,\n",
       "        -0.0010599 , -0.00104103],\n",
       "       [-0.00175796, -0.00185267, -0.0018512 , ..., -0.00093651,\n",
       "        -0.00089887,         nan],\n",
       "       [-0.0018711 , -0.00175281, -0.00203733, ..., -0.00072492,\n",
       "        -0.00075293,         nan],\n",
       "       [-0.00105648, -0.00112672, -0.0011477 , ..., -0.00039547,\n",
       "        -0.00081939, -0.00061725]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TG1</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>280.0 270.4 285.5 ... nan nan nan</div><input id='attrs-7e0a8b9e-499c-4d70-82b8-66eb759cf1e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e0a8b9e-499c-4d70-82b8-66eb759cf1e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8d398fd-9928-4097-b44f-4eb8b0ecb8db' class='xr-var-data-in' type='checkbox'><label for='data-d8d398fd-9928-4097-b44f-4eb8b0ecb8db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[280.02112326, 270.43043758, 285.49255157, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [273.95243199, 279.10472356, 275.67491766, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [282.79039682, 285.25875506, 286.80482212, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [282.49566693, 283.05940391, 284.38614309, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [272.8261471 , 280.37911393, 283.709762  , ...,          nan,\n",
       "                 nan,          nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TG2</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>274.8 278.4 280.6 ... nan nan nan</div><input id='attrs-a49228cd-ced6-469f-a1f1-1a5142d3f383' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a49228cd-ced6-469f-a1f1-1a5142d3f383' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6693962c-137c-4996-a437-92455934da35' class='xr-var-data-in' type='checkbox'><label for='data-6693962c-137c-4996-a437-92455934da35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[274.84709093, 278.41673812, 280.57289091, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [278.42437536, 283.31915753, 278.51042955, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [279.21544423, 279.57413371, 284.76804529, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [277.57179007, 278.6098334 , 278.07572192, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [278.54961327, 283.62250125, 286.60718795, ...,          nan,\n",
       "                 nan,          nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TG3</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>280.9 279.7 278.0 ... nan nan nan</div><input id='attrs-fc89ee87-9411-45df-9da8-5d1381d48dae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc89ee87-9411-45df-9da8-5d1381d48dae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fc28b9e-9dc3-4262-be77-23aabed54f3b' class='xr-var-data-in' type='checkbox'><label for='data-1fc28b9e-9dc3-4262-be77-23aabed54f3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[280.94967399, 279.73701754, 277.95140294, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [275.7377006 , 278.96455422, 283.03484774, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [277.36359601, 279.51634776, 281.45332132, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [280.43201923, 279.85167842, 285.38315711, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [280.93250698, 277.34480673, 276.23607919, ...,          nan,\n",
       "                 nan,          nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WG1</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3249 0.2798 0.2773 ... nan nan</div><input id='attrs-baf9bd9b-5667-46b7-ad99-b9c70d629691' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-baf9bd9b-5667-46b7-ad99-b9c70d629691' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9db034e-a381-4492-bec7-fefbce654d6b' class='xr-var-data-in' type='checkbox'><label for='data-e9db034e-a381-4492-bec7-fefbce654d6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.32492937, 0.27983456, 0.27729615, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.28419109, 0.29665461, 0.26357131, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.28657149, 0.27418057, 0.32130955, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.35316888, 0.22447731, 0.37864479, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.32013111, 0.32989414, 0.37377789, ...,        nan,        nan,\n",
       "               nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WG2</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3408 0.2902 0.3373 ... nan nan</div><input id='attrs-29843134-3717-4837-b9f3-04d88df0f956' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29843134-3717-4837-b9f3-04d88df0f956' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14093959-c350-4ed1-a856-14958032eab3' class='xr-var-data-in' type='checkbox'><label for='data-14093959-c350-4ed1-a856-14958032eab3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.34075338, 0.29023887, 0.33732519, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.29739347, 0.31481947, 0.32647732, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.31802497, 0.35188802, 0.27310549, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.29795442, 0.33628332, 0.25034937, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.34499846, 0.3227702 , 0.34632033, ...,        nan,        nan,\n",
       "               nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WG3</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3123 0.2916 0.2891 ... nan nan</div><input id='attrs-29bf186d-b36f-4767-81aa-e30f77037afe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29bf186d-b36f-4767-81aa-e30f77037afe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fb9a9b0-60f8-490f-953a-721dac2b3042' class='xr-var-data-in' type='checkbox'><label for='data-2fb9a9b0-60f8-490f-953a-721dac2b3042' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.31225323, 0.29158948, 0.28909846, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.31240911, 0.28942008, 0.28934139, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.31223442, 0.32122713, 0.25863411, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.31692828, 0.30312131, 0.33571518, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.34937695, 0.33768108, 0.3156577 , ...,        nan,        nan,\n",
       "               nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BIOMA1</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.07079 0.05532 0.04846 ... nan nan</div><input id='attrs-2ad26c5a-4ac6-423b-825f-f1041605451c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ad26c5a-4ac6-423b-825f-f1041605451c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b26afc91-b201-4dfe-8e23-0e53314d0ed3' class='xr-var-data-in' type='checkbox'><label for='data-b26afc91-b201-4dfe-8e23-0e53314d0ed3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.07078687, 0.05531632, 0.04845626, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.06313461, 0.06849046, 0.06948795, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.10973646, 0.09282028, 0.10490551, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.05577876, 0.05718846, 0.06228029, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.06641911, 0.07987195, 0.07466871, ...,        nan,        nan,\n",
       "               nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BIOMA2</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.04366 0.0462 0.03821 ... nan nan</div><input id='attrs-089d6b66-e57f-4507-9c1b-d0e4cb2ece32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-089d6b66-e57f-4507-9c1b-d0e4cb2ece32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db7f95c3-3a2f-4891-b119-0c90559360cf' class='xr-var-data-in' type='checkbox'><label for='data-db7f95c3-3a2f-4891-b119-0c90559360cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.04365577, 0.04620425, 0.03821127, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.05440495, 0.03978223, 0.00253587, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.11810849, 0.13832198, 0.1520621 , ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.06460992, 0.00718201, 0.04390921, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.04191187, 0.04335878, 0.08054077, ...,        nan,        nan,\n",
       "               nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9813f050-c339-4f39-a63d-28ab1e9e82e4' class='xr-section-summary-in' type='checkbox'  ><label for='section-9813f050-c339-4f39-a63d-28ab1e9e82e4' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4a820f92-aaf9-41f5-88a4-a2fa099eaddd' class='xr-index-data-in' type='checkbox'/><label for='index-4a820f92-aaf9-41f5-88a4-a2fa099eaddd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2007-01-02 00:00:00&#x27;, &#x27;2007-01-03 00:00:00&#x27;,\n",
       "               &#x27;2007-01-03 00:30:00&#x27;, &#x27;2007-01-05 00:00:00&#x27;,\n",
       "               &#x27;2007-01-06 00:00:00&#x27;, &#x27;2007-01-08 00:00:00&#x27;,\n",
       "               &#x27;2007-01-08 00:30:00&#x27;, &#x27;2007-01-09 00:00:00&#x27;,\n",
       "               &#x27;2007-01-10 00:00:00&#x27;, &#x27;2007-01-11 00:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2019-12-29 01:00:00&#x27;, &#x27;2019-12-30 00:00:00&#x27;,\n",
       "               &#x27;2019-12-30 00:30:00&#x27;, &#x27;2019-12-30 01:00:00&#x27;,\n",
       "               &#x27;2019-12-30 01:30:00&#x27;, &#x27;2019-12-31 00:00:00&#x27;,\n",
       "               &#x27;2019-12-31 00:30:00&#x27;, &#x27;2020-01-01 00:00:00&#x27;,\n",
       "               &#x27;2020-01-01 00:30:00&#x27;, &#x27;2020-01-01 01:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=8506, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a77869be-c62b-433a-84e7-e4b4e7377b5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a77869be-c62b-433a-84e7-e4b4e7377b5b' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>data source</dd><dt><span>license :</span></dt><dd>data license</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 8506, space: 5)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2007-01-02 ... 2020-01-01T01:00:00\n",
       "    latitude   (space) float64 56.12 46.12 53.38 49.38 44.38\n",
       "    longitude  (space) float64 11.38 6.625 6.125 12.38 0.625\n",
       "Dimensions without coordinates: space\n",
       "Data variables:\n",
       "    sig        (space, time) float64 -9.49 -8.494 -9.069 ... -8.071 -8.237\n",
       "    slop       (space, time) float64 -0.1208 -0.1178 -0.121 ... -0.1144 -0.1191\n",
       "    curv       (space, time) float64 -0.001396 -0.001464 ... -0.0006173\n",
       "    TG1        (space, time) float64 280.0 270.4 285.5 277.4 ... nan nan nan nan\n",
       "    TG2        (space, time) float64 274.8 278.4 280.6 283.7 ... nan nan nan nan\n",
       "    TG3        (space, time) float64 280.9 279.7 278.0 278.0 ... nan nan nan nan\n",
       "    WG1        (space, time) float64 0.3249 0.2798 0.2773 0.2867 ... nan nan nan\n",
       "    WG2        (space, time) float64 0.3408 0.2902 0.3373 0.2709 ... nan nan nan\n",
       "    WG3        (space, time) float64 0.3123 0.2916 0.2891 0.3538 ... nan nan nan\n",
       "    BIOMA1     (space, time) float64 0.07079 0.05532 0.04846 ... nan nan nan\n",
       "    BIOMA2     (space, time) float64 0.04366 0.0462 0.03821 ... nan nan nan\n",
       "Attributes:\n",
       "    source:   data source\n",
       "    license:  data license"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5698dee8-a668-44c5-800c-9357858e36a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe3818fb510>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Time series of one variable at one location\n",
    "sig = dataset.sig.isel(space=0)\n",
    "sig.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "560a1bc8-2095-4186-aa43-c822d37cd22d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# map of one variable at one time\n",
    "time = \"2007-01-02T00:00:00.000000000\"\n",
    "sig = dataset.sig.sel(time=time)\n",
    "lons, lats = xr.broadcast(sig.longitude, sig.latitude)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(lons, lats, c=sig, cmap=\"viridis\", marker=\"o\", s=10)\n",
    "plt.colorbar(label=\"sig\") \n",
    "plt.xlabel(\"longitude\")\n",
    "plt.ylabel(\"latitude\")\n",
    "plt.title(f\"Plot of sig at time {time}\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7b7dac4-f981-4627-9f56-d5c297f2e769",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save data in netcdf format\n",
    "dataset.to_netcdf(nc_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dbaed4c1-61dc-4d07-8094-740bac0e8e07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x7fe2fffda420>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For large dataset, chunk the data and save data in zarr format\n",
    "dataset.chunk({'space':1000})\n",
    "dataset.to_zarr(zarr_file_path)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}