aggr_lai.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import netCDF4 as nc\n",
    "import rasterio\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'lai'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# File paths\n",
    "file_paths = [\n",
    "    r'data\\lai\\lai.daily.0d50_CentralEurope.2018.nc',\n",
    "    r'data\\lai\\lai.daily.0d50_CentralEurope.2019.nc',\n",
    "    r'data\\lai\\lai.daily.0d50_CentralEurope.2020.nc',\n",
    "    r'data\\lai\\lai.daily.0d50_CentralEurope.2021.nc'\n",
    "]\n",
    "\n",
    "# Load the data\n",
    "data_sets = [xr.open_dataset(fp) for fp in file_paths]\n",
    "\n",
    "# Assuming the variable name is consistent and known, typically it might be named 'temperature' or similar\n",
    "# We'll check the first dataset for the variable name to use\n",
    "variable_name = list(data_sets[0].data_vars)[0]  # Use the first variable available\n",
    "variable_name\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine the datasets along the time dimension and calculate the maximum for each grid cell across the years\n",
    "combined_data = xr.concat(data_sets, dim='time')\n",
    "summer_data = combined_data.sel(time=combined_data['time'].dt.month.isin([4, 5, 6, 7, 8, 9]))\n",
    "\n",
    "min_lai = combined_data['lai'].min(dim='time')\n",
    "summer_mean_lai = summer_data['lai'].mean(dim='time')\n",
    "\n",
    "\n",
    "min_lai.to_netcdf('data/aggregated_over_years_model_input/lai_min.nc')\n",
    "summer_mean_lai.to_netcdf('data/aggregated_over_years_model_input/lai_sum_mean.nc')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = xr.open_dataset('data/aggregated_over_years_model_input/lai_sum_mean.nc')\n",
    "\n",
    "# Plot the spatial distribution of the 'lai' variable\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.pcolormesh(data.longitude, data.latitude, data.lai, shading='auto')\n",
    "plt.colorbar(label='Leaf Area Index (LAI)')\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Spatial Distribution of Leaf Area Index (LAI)')\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "babadormann",
   "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}