examples/07_GRACE_capitals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An example of using EASYMORE with source netCDF files longitude between 0 to 360 while the target shapefile is from -180 to 180\n",
    "## Remapping of GRACE to the location of the capital cities around the world\n",
    "## data should be downloaded using the following instructions: https://github.com/podaac/data-subscriber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EASYMORE version 2.0.0 is initiated.\n",
      "EASYMORE version 2.0.0 is initiated.\n",
      "no author name is provided.\n",
      "EASYMORE will remap variable  lwe_thickness  from source file to variable  lwe_thickness  in remapped netCDF file\n",
      "EASYMORE detects that the variables from the netCDF files are identicalin dimensions of the variables and latitude and longitude\n",
      "EASYMORE detects that all the variables have dimensions of:\n",
      "['time', 'lat', 'lon']\n",
      "EASYMORE detects that the longitude variables has dimensions of:\n",
      "['lon']\n",
      "EASYMORE detects that the latitude variables has dimensions of:\n",
      "['lat']\n",
      "--CREATING-REMAPPING-FILE--\n",
      "Started at date and time 2023-12-27 23:29:43.281892\n",
      "EASYMORE detects that target shapefile is in WGS84 (epsg:4326)\n",
      "EASYMORE detects that no field for ID is provided in sink/target shapefile\n",
      "arbitarary values of ID are added in the field ID_t\n",
      "EASYMORE detects that either of the fields for latitude or longitude is not provided in sink/target shapefile\n",
      "calculating centroid of shapes in WGS84 projection;\n",
      "for better appximation use the easymore equal area centroid function to preprocess target shapefile\n",
      "EASYMORE detects that no field for latitude is provided in sink/target shapefile\n",
      "latitude values are added in the field lat_t\n",
      "EASYMORE detects that no field for longitude is provided in sink/target shapefile\n",
      "longitude values are added in the field lon_t\n",
      "EASYMORE detects point(s) as geometry of target shapefile and will apply small buffer to them\n",
      "it seems everything is OK with the sink/target shapefile; added to EASYMORE object target_shp_gpd\n",
      "EASYMORE saved target shapefile for EASYMORE claculation as:\n",
      "./temporary/capitals_GRACE_target_shapefile.shp\n",
      "EASYMORE detects case 1 - regular lat/lon\n",
      "./temporary/capitals_GRACE_source_shapefile.shp\n",
      "EASYMORE created the shapefile from the netCDF file and saved it here:\n",
      "EASMORE detects that target shapefile is outside the boundary of source netCDF file  and therefore correction for longitude values -180 to 180 or 0 to 360 if correction_shp_lon  flag is set to True [default is True]\n",
      "correcting target shapefile\n",
      "correcting source shapefile\n",
      "EASYMORE detects that the shapefile is in region 180 to 360 and correct by moving -360\n",
      "Data has different projections.\n",
      "Converted data to projection of first GeoPandas DatFrame\n",
      "EASYMORE detects that the shapefile is also in region -180 to 180\n",
      "Data has different projections.\n",
      "Converted data to projection of first GeoPandas DatFrame\n",
      "         S_1_name  S_1_ID_t  S_1_lat_t   S_1_lon_t  S_1_order  \\\n",
      "0    Vatican City         1  41.903282   12.453387          1   \n",
      "1      San Marino         2  43.936096   12.441770          2   \n",
      "2           Vaduz         3  47.133724    9.516669          3   \n",
      "3      Luxembourg         4  49.611660    6.130003          4   \n",
      "4         Palikir         5   6.916644  158.149974          5   \n",
      "..            ...       ...        ...         ...        ...   \n",
      "197         Cairo       198  30.051906   31.248022        198   \n",
      "198         Tokyo       199  35.686963  139.749462        199   \n",
      "199         Paris       200  48.868639    2.331389        200   \n",
      "200      Santiago       201 -33.448068  -70.668987        201   \n",
      "201     Singapore       202   1.294979  103.853875        202   \n",
      "\n",
      "                                              geometry       AS1  IDS1  \n",
      "0    POLYGON ((1201581.909 4891518.715, 1201580.944...  1.843254     1  \n",
      "1    POLYGON ((1200461.087 5082604.750, 1200460.122...  1.784270     2  \n",
      "2    POLYGON ((918229.003 5370270.928, 918228.038 5...  1.686858     3  \n",
      "3    POLYGON ((591462.134 5581825.655, 591461.169 5...  1.607632     4  \n",
      "4    POLYGON ((15259303.709 880294.385, 15259302.74...  2.444373     5  \n",
      "..                                                 ...       ...   ...  \n",
      "197  POLYGON ((3015006.408 3664541.779, 3015005.443...  2.138051   198  \n",
      "198  POLYGON ((13483906.679 4270481.045, 13483905.7...  2.008667   199  \n",
      "199  POLYGON ((224948.062 5519460.711, 224947.097 5...  1.631721   200  \n",
      "200  POLYGON ((-6818586.692 -4034280.438, -6818587....  2.062484   201  \n",
      "201  POLYGON ((10020475.036 165191.083, 10020474.07...  2.461202   202  \n",
      "\n",
      "[202 rows x 8 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shg096/Desktop/EASYMORE/easymore-env/lib/python3.9/site-packages/easymore/remapper.py:551: PendingDeprecationWarning: dropping variables using `drop` will be deprecated; using drop_vars is encouraged.\n",
      "  aatr = attr.drop('index')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ended at date and time 2023-12-27 23:36:02.957501\n",
      "It took 379.675609 seconds to finish creating of the remapping file\n",
      "---------------------------\n",
      "------REMAPPING------\n",
      "netcdf output file will be compressed at level 4\n",
      "Remapping ../../GRCTellus.JPL.200204_202011.GLO.RL06M.MSCNv02CRI.nc to ./output/capitals_GRACE_remapped_GRCTellus.JPL.200204_202011.GLO.RL06M.MSCNv02CRI.nc \n",
      "Started at date and time 2023-12-27 23:36:03.020248 \n",
      "Ended at date and time 2023-12-27 23:36:04.347062 \n",
      "It took 1.326814 seconds to finish the remapping of variable(s) \n",
      "---------------------\n",
      "---------------------\n"
     ]
    }
   ],
   "source": [
    "# loading EASYMORE\n",
    "from easymore import Easymore\n",
    "# initializing EASYMORE object\n",
    "esmr = Easymore()\n",
    "\n",
    "config     = {\n",
    "\"case_name\"   : \"capitals_GRACE\",\n",
    "\"temp_dir\"    : \"./temporary/\",\n",
    "\"target_shp\"  : \"./data/target_shapefiles/Capitals_point.shp\", \n",
    "\"source_nc\"   : \"../../GRCTellus.JPL.200204_202011.GLO.RL06M.MSCNv02CRI.nc\", \n",
    "\"var_names\"   : [\"lwe_thickness\"],\n",
    "\"var_lon\"     : \"lon\",\n",
    "\"var_lat\"     : \"lat\",\n",
    "\"var_time\"    : \"time\",\n",
    "\"output_dir\"  : \"./output/\"\n",
    "}\n",
    "\n",
    "esmr = esmr.from_dict(config)\n",
    "\n",
    "# execute EASYMORE\n",
    "esmr.nc_remapper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'lwe_thickness' (time: 191)>\n",
      "[191 values with dtype=float64]\n",
      "Coordinates:\n",
      "  * time     (time) datetime64[ns] 2002-04-17T12:00:00 ... 2020-11-16\n",
      "    ID       float64 182.0\n",
      "Attributes:\n",
      "    long_name:  Liquid_Water_Equivalent_Thickness\n",
      "    units:      cm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "font = {'family' : 'Times New Roman',\n",
    "         'weight' : 'bold',\n",
    "         'size'   : 24}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "capital_name = 'Tehran'\n",
    "\n",
    "ds = xr.open_dataset('./output/capitals_GRACE_remapped_GRCTellus.JPL.200204_202011.GLO.RL06M.MSCNv02CRI.nc')\n",
    "\n",
    "shp = gpd.read_file('./temporary/capitals_GRACE_target_shapefile.shp')\n",
    "shp = shp[shp['name']==capital_name] # get the data for given capital\n",
    "\n",
    "# plot found values from the ID of the city\n",
    "plt.figure(figsize=(15,5))\n",
    "ds_slice = ds.sel(ID = shp.ID_t.iloc[0]) # slice the data for the capital\n",
    "print(ds_slice['lwe_thickness'])\n",
    "\n",
    "# plot the data\n",
    "fig, axes = plt.subplots(1,1,figsize=(15,5), facecolor='white')\n",
    "ds_slice.lwe_thickness.plot(marker='o', color='blue', linewidth=1, markerfacecolor='red',markeredgecolor='red', linestyle='none')\n",
    "plt.ylabel('Liquid Water Equivalent \\n Thickness [cm]')\n",
    "plt.grid()\n",
    "plt.title('GRACE for the city of '+ capital_name)\n",
    "plt.tight_layout()\n",
    "plt.savefig('./fig/GRACE_'+capital_name+'.png')\n",
    "plt.savefig('./fig/GRACE.png')\n",
    "\n",
    "# # plot found values form the lat lon of the city from the source file\n",
    "# plt.figure(figsize=(15,5))\n",
    "# ds = xr.open_dataset(GRACE_file_path)\n",
    "# if shp.lon_t.iloc[0] > 0:\n",
    "#     ds_slice = ds.sel(lat = shp.lat_t.iloc[0], lon = shp.lon_t.iloc[0]     , method ='nearest' )\n",
    "# else:\n",
    "#     ds_slice = ds.sel(lat = shp.lat_t.iloc[0], lon = shp.lon_t.iloc[0]+ 360, method ='nearest' )\n",
    "# ds_slice.lwe_thickness.plot()\n",
    "# plt.grid()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import xarray as xr\n",
    "# import urllib.request\n",
    "# import numpy as np\n",
    "# import pandas as pd\n",
    "# import os\n",
    "# from   datetime import datetime\n",
    "# import time\n",
    "# import hashlib\n",
    "# import requests\n",
    "# from   bs4 import BeautifulSoup\n",
    "\n",
    "# # download GRACE data \n",
    "# pwd = './temporary/' #\n",
    "# url = 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/mascon/RL06/JPL/v02/CRI/netcdf/'\n",
    "\n",
    "# def GRACE_download(url, pwd): # downlaod and unzip\n",
    "    \n",
    "#     # \n",
    "#     response = requests.get(url)\n",
    "#     soup = BeautifulSoup(response.content, \"html.parser\")\n",
    "    \n",
    "#     # \n",
    "#     for link in soup.find_all():\n",
    "#         href = link.get(\"href\")\n",
    "#         if href and href.startswith(\"GRCTellus.JPL.\") and href.endswith(\".nc\"):\n",
    "#             GRACE_name = href\n",
    "#             GRACE_link = url+GRACE_name\n",
    "#         if href and href.startswith(\"GRCTellus.JPL.\") and href.endswith(\".nc.md5\"):\n",
    "#             md5_link = url+href\n",
    "            \n",
    "#     if not os.path.isdir(pwd):\n",
    "#         os.makedirs(pwd)\n",
    "    \n",
    "#     # get the md5\n",
    "#     urllib.request.urlretrieve(md5_link, pwd+'GRACE.nc.md5')\n",
    "    \n",
    "#     # get the md5 value\n",
    "#     with open(pwd+'GRACE.nc.md5', 'r') as f:\n",
    "#         first_line = f.readline().strip()\n",
    "#         first_line_list = first_line.split()\n",
    "#         md5 = first_line_list[0]\n",
    "\n",
    "    \n",
    "#     # remove existing file is exists\n",
    "#     if os.path.isfile(pwd+GRACE_name) and hash_file(pwd+GRACE_name) == md5:\n",
    "#         print('It seems the file exists; perhaps perviously downloaded; will skip downloading')\n",
    "#     else:\n",
    "#         # download the file\n",
    "#         print('Downloading GRACE; this may take a while ...')\n",
    "#         print('Downloading starts at: ', datetime.now())\n",
    "#         urllib.request.urlretrieve(GRACE_link, pwd+GRACE_name)\n",
    "#         print('Downloading ends at: ', datetime.now())\n",
    "#         if not (hash_file(pwd+GRACE_name) == md5):\n",
    "#             sys.exit('the file was not downloaded successfully. You can download manually.')\n",
    "            \n",
    "#     return pwd+GRACE_name\n",
    "\n",
    "\n",
    "# def hash_file(filename):\n",
    "#    \"\"\"This function returns the MD5 hash\n",
    "#    of the file passed into it\"\"\"\n",
    "#    # make a hash object\n",
    "#    h = hashlib.md5()\n",
    "#    # open file for reading in binary mode\n",
    "#    with open(filename,'rb') as file:\n",
    "#        # loop till the end of the file\n",
    "#        chunk = 0\n",
    "#        while chunk != b'':\n",
    "#            chunk = file.read(1024)\n",
    "#            h.update(chunk)\n",
    "#    # return the hex representation of digest\n",
    "#    return h.hexdigest()\n",
    "\n",
    "# # get the link that the file is located\n",
    "\n",
    "# GRACE_file_path = GRACE_download(url, pwd)\n",
    "\n",
    "\n",
    "# # https://github.com/podaac/data-subscriber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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