{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e5b9b1d8-cbb5-459e-bef4-50a40abe94da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "nc = Dataset('path to grid_conc_*.nc file')\n",
    "lat = nc.variables['latitude'][:]\n",
    "lon = nc.variables['longitude'][:]\n",
    "spec001_mr = nc.variables['spec001_mr'][:,:,:,:,:,:]  #nageclass, pointspec, time, height, latitude, longitude\n",
    "nc.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6bb966ed-5ba8-43da-9743-24f8768916cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1, 24, 3, 180, 360)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(spec001_mr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2723656-1f36-41ca-88ff-f2a164812675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fad94edff10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(lon, lat, spec001_mr[0,0,0,0,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da5655a6-72c9-4846-8fee-35503fd5f1b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb4455cf-39ea-4c13-9634-10c3cc657bbd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "MSC-Modelling-WS2022",
   "language": "python",
   "name": "modell2022"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}