ncfile_read_and_plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4263f030",
   "metadata": {},
   "source": [
    "# code for reading and ploting netcdf file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77dfaa4f",
   "metadata": {},
   "source": [
    "##  1. Using netCDF4 library "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "76791598",
   "metadata": {},
   "outputs": [],
   "source": [
    "#imporing required libraries \n",
    "\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "15f2e0df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Dataset'>\n",
       "root group (NETCDF4_CLASSIC data model, file format HDF5):\n",
       "    Conventions: CF-1.4\n",
       "    start_time: 2019-05-31T00:00:00Z\n",
       "    end_time: 2019-05-31T23:59:59Z\n",
       "    temporal_resolution: daily\n",
       "    nco_openmp_thread_number: 1\n",
       "    NCO: netCDF Operators version 4.7.8 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)\n",
       "    title: MYD08_D3_6_1_Cloud_Fraction_Mean (60E_0N_110E_40N)\n",
       "    history: Sat Dec  4 17:39:49 2021: /usr/bin/ncatted -a title,global,o,c,MYD08_D3_6_1_Cloud_Fraction_Mean (60E_0N_110E_40N) -a valid_range,,d,, -O -o ./subsetted.MYD08_D3_6_1_Cloud_Fraction_Mean.20190531.60E_0N_110E_40N.nc ./subsetted.MYD08_D3_6_1_Cloud_Fraction_Mean.20190531.60E_0N_110E_40N.nc\n",
       "    dimensions(sizes): time(1), lat(40), lon(50), latv(2), lonv(2), nv(2)\n",
       "    variables(dimensions): float64 MYD08_D3_6_1_Cloud_Fraction_Mean(time, lat, lon), int32 dataday(time), float32 lat(lat), float64 lat_bnds(lat, latv), float32 lon(lon), float64 lon_bnds(lon, lonv), int32 time(time), int32 time_bnds(time, nv)\n",
       "    groups: "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading file and extracting the parameter of interests\n",
    "\n",
    "file = nc.Dataset('g4.subsetted.MYD08_D3_6_1_Cloud_Fraction_Mean.20190531.60E_0N_110E_40N.nc','r')\n",
    "file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e783615",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MYD08_D3_6_1_Cloud_Fraction_Mean\n",
      "dataday\n",
      "lat\n",
      "lat_bnds\n",
      "lon\n",
      "lon_bnds\n",
      "time\n",
      "time_bnds\n"
     ]
    }
   ],
   "source": [
    "for var in file.variables:\n",
    "    print(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6127e12f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lats = file.variables[\"lat\"][:]\n",
    "lons = file.variables[\"lon\"][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb57cd1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploing the contour plot\n",
    "\n",
    "lon, lat = np.meshgrid(lons, lats) # create a matrix of coordinates\n",
    "dummy = plt.contourf(lon, lat, file['MYD08_D3_6_1_Cloud_Fraction_Mean'][0,:,:])\n",
    "cbar = plt.colorbar(dummy)\n",
    "#plt.colorbar()\n",
    "plt.xlabel('lon')\n",
    "plt.ylabel(' lat')\n",
    "cbar.set_label('your label here')\n",
    "#plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66396ffa",
   "metadata": {},
   "source": [
    "## 2. Using xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ae09a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# all other library do required \n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a63e685e",
   "metadata": {},
   "outputs": [],
   "source": [
    "xfile =xr.open_dataset('path to file')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08bcd683",
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in xfile.variables:\n",
    "    print(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "824ce4cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "lats = xfile.variables[\"lat\"][:]\n",
    "lons = xfile.variables[\"lon\"][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f27eb0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "lon, lat = np.meshgrid(lons, lats) # create a matrix of coordinates\n",
    "plt.contourf(lon, lat, file['variable'][0,:,:])\n",
    "plt.colorbar()\n",
    "plt.xlabel('lon')\n",
    "plt.ylabel('lat')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b8d67b78",
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "\n\nAmbiguous 'iris' package.\nPlease use either:\n\n**illumon-iris** - Python integration for Illumon Iris, a time-series database\n  - pip install illumon-iris\n  - https://pypi.python.org/pypi/illumon-iris\n  - https://deephaven.io/\n\n**SciTools Iris** - Python library for analysing and visualising meteorological and oceanographic data sets\n  - http://scitools.org.uk/iris/\n\n**info.gianlucacosta.iris** - A general-purpose library for Python\n  - pip install info.gianlucacosta.iris\n  - https://pypi.python.org/pypi/info.gianlucacosta.iris\n\n**irisapi** - Iris is a highly configurable and flexible service for paging and messaging\n  - pip install irisapi\n  - https://pypi.org/project/irisapi/\n  - https://iris.claims/\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_6217/1951808917.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0miris\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/iris/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m raise RuntimeError(\"\"\"\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mAmbiguous\u001b[0m \u001b[0;34m'iris'\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mPlease\u001b[0m \u001b[0muse\u001b[0m \u001b[0meither\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: \n\nAmbiguous 'iris' package.\nPlease use either:\n\n**illumon-iris** - Python integration for Illumon Iris, a time-series database\n  - pip install illumon-iris\n  - https://pypi.python.org/pypi/illumon-iris\n  - https://deephaven.io/\n\n**SciTools Iris** - Python library for analysing and visualising meteorological and oceanographic data sets\n  - http://scitools.org.uk/iris/\n\n**info.gianlucacosta.iris** - A general-purpose library for Python\n  - pip install info.gianlucacosta.iris\n  - https://pypi.python.org/pypi/info.gianlucacosta.iris\n\n**irisapi** - Iris is a highly configurable and flexible service for paging and messaging\n  - pip install irisapi\n  - https://pypi.org/project/irisapi/\n  - https://iris.claims/\n"
     ]
    }
   ],
   "source": [
    "import iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c678bb29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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