dev_samir_xarray.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build a validation ndvi and weather dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data_path = '/mnt/e/DATA/DEV_inputs_test'\n",
    "\n",
    "size = 10\n",
    "\n",
    "# Original sets\n",
    "ndvi_path = data_path + os.sep + 'ndvi_' + str(size) + '.nc'\n",
    "rain_path = data_path + os.sep + 'rain_' + str(size) + '.tif'\n",
    "ET0_path = data_path + os.sep + 'ET0_' + str(size) + '.tif'\n",
    "\n",
    "# Validation sets\n",
    "val_ndvi_path = data_path + os.sep + 'xls_NDVI_' + str(size) + '.nc'\n",
    "rain_path = data_path + os.sep + 'xls_Rain_' + str(size) + '.tif'\n",
    "val_ET0_path = data_path + os.sep + 'xls_ET0_' + str(size) + '.tif'\n",
    "\n",
    "val_outputs = data_path + os.sep + 'xls_outputs_' + str(size) + '.nc'\n",
    "\n",
    "# Modspa excel file\n",
    "xls_file_path = '/home/auclairj/GIT/modspa-pixel/SAMIR_xls/SAMIRpixel_Reference_Simonneaux2012.xls'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get input data\n",
    "modspa_excel = pd.read_excel(xls_file_path, sheet_name=0, header=10, index_col=0)\n",
    "modspa_excel = modspa_excel.loc[:, ~modspa_excel.columns.str.contains('^Unnamed')]\n",
    "\n",
    "# Dates\n",
    "dates = modspa_excel.index\n",
    "\n",
    "# Open empty dataset to get structure and reindex with correct dates\n",
    "empty_dataset = xr.open_dataset(ndvi_path)\n",
    "empty_dataset = empty_dataset.reindex(time = dates)\n",
    "\n",
    "# Transpose dimensions\n",
    "empty_dataset = empty_dataset.transpose('time', 'y', 'x')\n",
    "\n",
    "# Get the numpy array for 'ndvi'\n",
    "zero_values = empty_dataset['ndvi'].values\n",
    "\n",
    "# Transpose the numpy array for 'ndvi'\n",
    "zero_values = zero_values.transpose([0,2,1])\n",
    "empty_dataset['ndvi'] = empty_dataset.ndvi.transpose('time', 'y', 'x')\n",
    "\n",
    "# Assign the transposed numpy array back to 'ndvi'\n",
    "empty_dataset.ndvi.values = zero_values\n",
    "\n",
    "# Drop ndvi to get empty dataset\n",
    "empty_dataset = empty_dataset.drop_vars('ndvi')\n",
    "\n",
    "# Datasets\n",
    "ndvi_val = empty_dataset.copy(deep = True)\n",
    "rain_val = empty_dataset.copy(deep = True)\n",
    "ET0_val = empty_dataset.copy(deep = True)\n",
    "outputs_val = empty_dataset.copy(deep = True)\n",
    "\n",
    "# Inputs\n",
    "ndvi_val['NDVI'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'uint8'))\n",
    "rain_val['Rain'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'uint16'))\n",
    "ET0_val['ET0'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'uint16'))\n",
    "\n",
    "# Outputs\n",
    "for var in modspa_excel.columns[0:-2]:\n",
    "    outputs_val[var] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'int16'))\n",
    "\n",
    "for x in ndvi_val.coords['x'].values:\n",
    "    for y in ndvi_val.coords['y'].values:\n",
    "        # Inputs\n",
    "        ndvi_val['NDVI'].loc[{'x' : x, 'y' : y}] = np.round(modspa_excel['NDVI'].values * 255)\n",
    "        rain_val['Rain'].loc[{'x' : x, 'y' : y}] = np.round(modspa_excel['Rain'].values * 1000)\n",
    "        ET0_val['ET0'].loc[{'x' : x, 'y' : y}] = np.round(modspa_excel['ET0'].values * 1000)\n",
    "\n",
    "        # Outputs\n",
    "        for var in list(outputs_val.keys()):\n",
    "            outputs_val[var].loc[{'x' : x, 'y' : y}] = np.round(modspa_excel[var].values * 100)\n",
    "\n",
    "# Add precip\n",
    "outputs_val['Rain'] = rain_val['Rain'].copy(deep = True) / 10\n",
    "\n",
    "# Reorganize dimension order\n",
    "ndvi_val = ndvi_val.transpose('time', 'y', 'x')\n",
    "rain_val = rain_val.transpose('time', 'y', 'x')\n",
    "ET0_val = ET0_val.transpose('time', 'y', 'x')\n",
    "\n",
    "# Save datasets\n",
    "# Inputs\n",
    "ndvi_val.to_netcdf(val_ndvi_path, encoding = {\"NDVI\": {\"dtype\": \"u1\", \"_FillValue\": 0}})\n",
    "rain_val.Rain.rio.to_raster(rain_path, dtype = 'uint16')\n",
    "ET0_val.ET0.rio.to_raster(val_ET0_path, dtype = 'uint16')\n",
    "\n",
    "# Create encoding dictionnary\n",
    "for variable in list(outputs_val.keys()):\n",
    "    # Write encoding dict\n",
    "    encoding_dict = {}\n",
    "    encod = {}\n",
    "    encod['dtype'] = 'i2'\n",
    "    encoding_dict[variable] = encod\n",
    "\n",
    "# Outputs\n",
    "outputs_val.to_netcdf(val_outputs, encoding = encoding_dict)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare `modspa-pixel` and `modspa-excel` outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# Settings for plots\n",
    "plt.style.use('default')\n",
    "rcParams['mathtext.fontset'] = 'stix'\n",
    "rcParams['font.family'] = 'STIXGeneral'\n",
    "rcParams.update({'font.size': 18})\n",
    "# Date format\n",
    "date_plot_format = mdates.WeekdayLocator(interval=6)\n",
    "date_format = mdates.DateFormatter('%Y-%m')\n",
    "\n",
    "def plot_time_series(ds1: xr.Dataset, var: str, ds2:xr.Dataset = None, label1: str = 'Dataset1', label2: str = 'Dataset2', scale_factor1: float = 1000, scale_factor2: float = 1000, unit: str = 'mm', title: str = 'variable comparison') -> None:\n",
    "    \"\"\"\n",
    "    Plot times series of a uniform modspa dataset.\n",
    "    Select first pixel (upper left corner) and plot\n",
    "    its value over time.\n",
    "\n",
    "    ## Arguments\n",
    "    1. ds1: `xr.Dataset`\n",
    "        first dataset to plot\n",
    "    2. var: `str`\n",
    "        name of variable to plot\n",
    "    3. ds2: `xr.Dataset` `default = None`\n",
    "        second dataset to plot, optional\n",
    "    4. label1: `str` `default = 'Dataset1'`\n",
    "        label for first dataset\n",
    "    5. label2: `str` `default = 'Dataset2'`\n",
    "        label for second dataset, optional\n",
    "    6. scale_factor1: `float` `default = 1000`\n",
    "        scale factor for first dataset to\n",
    "        divide the time series in order to\n",
    "        plot the correct variable values\n",
    "    7. scale_factor2: `float` `default = 1000`\n",
    "        scale factor for second dataset to\n",
    "        divide the time series in order to\n",
    "        plot the correct variable values\n",
    "    8. unit: `str` `default = 'mm'`\n",
    "        unit of value\n",
    "    9. title: `str` `default = 'variable comparison'`\n",
    "        title of plot\n",
    "\n",
    "    ## Returns\n",
    "    `None`\n",
    "    \"\"\"\n",
    "    \n",
    "    plt.figure(figsize = (14,7))\n",
    "    plt.grid(color='silver', linestyle='--', axis = 'both', linewidth=1)\n",