IMD_KOLKATA_EXAMPLE_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d54cc320",
   "metadata": {},
   "source": [
    "# IMD Kolkata DWR Example\n",
    "\n",
    "Author: [Hamid Ali Syed](https://syedha.com)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a14081f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
      "## library for working with weather radar data. Py-ART is partly\n",
      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
      "## Science user facility.\n",
      "##\n",
      "## If you use this software to prepare a publication, please cite:\n",
      "##\n",
      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
      "\n",
      "\n",
      "## Cite PyScanCf:\n",
      "\n",
      "## Syed, H. A.,Sayyed, I., Kalapureddy, M. C. R., & Grandhi, K. K.(2021). \n",
      "## PyScanCf – The library for individual sweep datasets of IMD weather radars. \n",
      "## Zenodo. doi:10.5281/zenodo.5574160.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import glob\n",
    "import pyart\n",
    "import shutil\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import xradar as xd\n",
    "import pyscancf as pcf\n",
    "import cartopy.crs as ccrs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f61215",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c540c88",
   "metadata": {},
   "source": [
    "PyScanCf, up until version 1.0.28, was built to directly use data from the folder where it's stored. We did this to sort and organize the data properly, making sure the scans are correctly grouped into a Cf-Radial volume. Initially, IMD radar data came in separate folders based on its type — Type B data in its own folder, separate from Type C data, for instance. This setup was to automate the sorting and aggregating of the data, keeping different types of data well-organized right from the start.\n",
    "\n",
    "However, IMD has recently been mixing Type B and Type C data in the same folder. As we prepare for a new version release, this notebook will guide you through handling mixed data sets. We will organize the files into their appropriate folders. If a data file is for short-range or Type B and requires more than three sweeps for a volume scan, it will go into one folder. Long-range or Type C data will go into another folder. We will name these folders based on the number of sweeps. For example, a folder for Type-B radar mode with 6 sweeps will be called `sweep_6`, and if a Type C volume is made from 2 sweeps, the folder will be named `sweep_2`. This way, we'll move the data into the right folders, and then it will be easy for PyScanCf to make it Cf-radial.\n",
    "\n",
    "Here's our plan:\n",
    "\n",
    "1. Use the `process_file` function to sort the data into the correct folders.\n",
    "2. Use `load_folder_names` to get the names of these folders after sorting.\n",
    "3. Use `est_grid_shape` to determine the best grid shape for processing the data.\n",
    "\n",
    "This method ensures the data is neatly organized and easy to use, simplifying the process for users to handle the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6279466b",
   "metadata": {},
   "source": [
    "## Organize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe52cc75",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_file(file_path, organized_dir):\n",
    "    ds = xr.open_dataset(file_path)\n",
    "    sweep_number = ds.dims['sweep']\n",
    "    \n",
    "    # Create the directory\n",
    "    directory_name = f\"sweep_{sweep_number}\"\n",
    "    os.makedirs(os.path.join(organized_dir, directory_name), exist_ok=True)\n",
    "    \n",
    "    # Move the file to the directory\n",
    "    destination_path = os.path.join(organized_dir, directory_name, os.path.basename(file_path))\n",
    "    shutil.copy2(file_path, destination_path)\n",
    "    \n",
    "    print(f\"File '{file_path}' copied to '{destination_path}'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a58fd30",
   "metadata": {},
   "source": [
    "### Setting Up Directories for Data Organization\n",
    "\n",
    "- `raw_dir`: The directory where your raw radar data is stored.\n",
    "- `organized_dir`: The target directory where the organized data will be placed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb909553",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File 'KOLKATA_RADAR_DATA/CAL220430170207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430170207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430172209-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430172209-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430170207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430170207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430170207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430170207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430174203-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430174203-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430170000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430170000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430180207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430180207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430135206-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430135206-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430122000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430122000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430135206-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430135206-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430180207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430180207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430132207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430132207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430181000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430181000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430174000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430174000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430121207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430121207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430170207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430170207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430170207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430170207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430180000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430180000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430180207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430180207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430132207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430132207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430135206-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430135206-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430115207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430115207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430175000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430175000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430135206-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430135206-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430180207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430180207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430125207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430125207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430113000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430113000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430122207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430122207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430132000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430132000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430114207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430114207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430113206-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430113206-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430112232-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430112232-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430114207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430114207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430172209-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430172209-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430173207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430173207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430181206-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430181206-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430173207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430173207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430114000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430114000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430122207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430122207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430165225-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430165225-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430172209-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430172209-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430170000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430170000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430173207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430173207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430172209-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430172209-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430172209-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430172209-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430173207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430173207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430175206-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430175206-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430114207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430114207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430115000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430115000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430173000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430173000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430114207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430114207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430181206-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430181206-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430134207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430134207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430124000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430124000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430175206-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430175206-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430134207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430134207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430134207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430134207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430181206-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430181206-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430175206-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430175206-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430165225-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430165225-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430165225-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430165225-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430140000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430140000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430112010-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430112010-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430140207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430140207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430140207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430140207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430165225-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430165225-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430134000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430134000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430165225-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430165225-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430115207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430115207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430123000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430123000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430181206-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430181206-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430173000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430173000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430134207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430134207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430175206-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430175206-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430124206-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430124206-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430134207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430134207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430181206-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430181206-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430113206-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430113206-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430175206-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430175206-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430140207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430140207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430140207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430140207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430130000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430130000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430174203-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430174203-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430131000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430131000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430121207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430121207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430120206-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430120206-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430141017-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430141017-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430120000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430120000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430165010-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430165010-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430130206-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430130206-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430131205-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430131205-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430133000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430133000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430124206-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430124206-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430124000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430124000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430134000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430134000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430133207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430133207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430123206-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430123206-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430133207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430133207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430171207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430171207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430165010-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430165010-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430121000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430121000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430141017-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430141017-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430133207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430133207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430133207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430133207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430113000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430113000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430173207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430173207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430135206-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430135206-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430132207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430132207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430125207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430125207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430132207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430132207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430180207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430180207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430112010-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430112010-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430125207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430125207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430125000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430125000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430132000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430132000-IMD-C.nc.1'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File 'KOLKATA_RADAR_DATA/CAL220430121207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430121207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430121207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430121207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430174203-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430174203-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430114000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430114000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430174203-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430174203-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430121000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430121000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430171000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430171000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430132207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430132207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430125207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430125207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430132207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430132207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430125207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430125207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430174203-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430174203-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430174203-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430174203-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430133000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430133000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430123206-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430123206-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430121207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430121207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430180207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430180207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430170207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430170207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430121207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430121207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430174000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430174000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430114207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430114207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430113206-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430113206-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430112232-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430112232-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430113206-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430113206-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430112232-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430112232-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430122207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430122207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430173207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430173207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430122207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430122207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430133207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430133207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430123000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430123000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430131000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430131000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430180000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430180000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430171207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430171207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430172209-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430172209-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430122207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430122207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430122207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430122207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430112232-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430112232-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430113206-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430113206-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430114207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430114207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430122000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430122000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430135000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430135000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430112232-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430112232-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430113206-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430113206-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430120206-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430120206-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430120206-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430120206-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430131205-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430131205-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430181206-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430181206-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430115000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430115000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430124206-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430124206-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430134207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430134207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430124206-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430124206-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430120000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430120000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430115207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430115207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430130206-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430130206-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430165225-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430165225-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430140207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430140207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430115207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430115207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430135000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430135000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430125207-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430125207-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430140207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430140207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430120206-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430120206-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430120206-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430120206-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430172000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430172000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430115207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430115207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430115207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430115207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430124206-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430124206-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430124206-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430124206-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430175206-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430175206-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430175000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430175000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430140000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430140000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430123206-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430123206-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430120206-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430120206-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430130000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430130000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430123206-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430123206-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430141017-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430141017-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430141017-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430141017-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430171000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430171000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430171207-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430171207-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430131205-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430131205-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430130206-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430130206-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430171207-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430171207-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430172000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430172000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430130206-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430130206-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430131205-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430131205-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430123206-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430123206-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430112232-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430112232-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430133207-IMD-C.nc.1' copied to 'KOL_ORG/sweep_6/CAL220430133207-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430123206-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430123206-IMD-C.nc.4'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File 'KOLKATA_RADAR_DATA/CAL220430181000-IMD-C.nc.1' copied to 'KOL_ORG/sweep_2/CAL220430181000-IMD-C.nc.1'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430171207-IMD-C.nc.5' copied to 'KOL_ORG/sweep_6/CAL220430171207-IMD-C.nc.5'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430131205-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430131205-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430130206-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430130206-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430171207-IMD-C.nc.2' copied to 'KOL_ORG/sweep_6/CAL220430171207-IMD-C.nc.2'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430135206-IMD-C.nc' copied to 'KOL_ORG/sweep_6/CAL220430135206-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430125000-IMD-C.nc' copied to 'KOL_ORG/sweep_2/CAL220430125000-IMD-C.nc'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430130206-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430130206-IMD-C.nc.4'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430131205-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430131205-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430141017-IMD-C.nc.3' copied to 'KOL_ORG/sweep_6/CAL220430141017-IMD-C.nc.3'\n",
      "File 'KOLKATA_RADAR_DATA/CAL220430141017-IMD-C.nc.4' copied to 'KOL_ORG/sweep_6/CAL220430141017-IMD-C.nc.4'\n"
     ]
    }
   ],
   "source": [
    "# Example usage\n",
    "raw_dir = \"KOLKATA_RADAR_DATA/\"\n",
    "organized_dir = \"KOL_ORG\"\n",
    "files = glob.glob(\"KOLKATA_RADAR_DATA/*\")\n",
    "\n",
    "# Create 'organized' directory if it doesn't exist\n",
    "os.makedirs(organized_dir, exist_ok=True)\n",
    "\n",
    "for file in files:\n",
    "    process_file(file, organized_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7821c0",
   "metadata": {},
   "source": [
    "### Retrieving Organized Data Folder Names\n",
    "\n",
    "This function, `load_folder_names`, is designed to fetch and return the names of all subdirectories within a specified directory, which in this case is intended to be the directory where the radar data has been organized in the above step i,e., using `process_file` function. This is particularly useful for quickly getting an overview of the different sets of organized data, categorized by sweep numbers, without manually navigating the directory structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "724a290a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sweep_2', 'sweep_6']\n"
     ]
    }
   ],
   "source": [
    "def load_folder_names(organized_dir):\n",
    "    folder_names = next(os.walk(organized_dir))[1]\n",
    "    return folder_names\n",
    "\n",
    "# Example usage\n",
    "folders = load_folder_names(organized_dir)\n",
    "print(folders)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d66f3f",
   "metadata": {},
   "source": [
    "## Creating CfRadial Data\n",
    "\n",
    "Before utilizing the `pcf.cfrad` function to aggregate radar data into CfRadial format, it's essential to define the source and destination directories for the operation. The `input_dir` should be set to the directory where your organized radar data is stored (`organized_dir`), and `output_dir` should be the target directory where you want the aggregated CfRadial data to be saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ca1d27d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of files:  52\n",
      "Total no. of output files: 26.\n",
      "Merging all scans into one Volume\n",
      "Data merging done \n",
      "Total Time Elapsed:  0:00:04.409257\n",
      "Number of files:  162\n",
      "Total no. of output files: 27.\n",
      "Merging all scans into one Volume\n",
      "Data merging done \n",
      "Total Time Elapsed:  0:00:12.493864\n"
     ]
    }
   ],
   "source": [
    "input_dir = organized_dir\n",
    "output_dir = \"KOL_CFRADIAL\"\n",
    "\n",
    "for folder_name in folders:\n",
    "    folder_path = os.path.join(input_dir, folder_name)\n",
    "    sweep_number = int(folder_name.split(\"_\")[1])\n",
    "    if sweep_number > 2:\n",
    "        scan_type = 'B'\n",
    "    elif sweep_number <= 2:\n",
    "        scan_type = 'C'\n",
    "    pcf.cfrad(input_dir=folder_path, output_dir=output_dir, scan_type=scan_type, nf=sweep_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a8a32bdc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# !ls $output_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce97939",
   "metadata": {},
   "source": [
    "## Gridding Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d827533c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def est_grid_shape(height_from=0, height_to=10e3, vert_res=250, max_range=100e3, horz_res=500):\n",
    "    '''\n",
    "    Estimate the grid shape for radar grid data based on given parameters.\n",
    "    \n",
    "    Parameters:\n",
    "    - height_from: Bottom or start height in meters. Default is 0 (surface).\n",
    "    - height_to: Top or end height in meters. Default is 10,000 meters (10 km).\n",
    "    - vert_res: Vertical resolution or gap in meters. Default is 250 meters.\n",
    "    - max_range: Maximum range of the radar in meters. Default is 100,000 meters (100 km).\n",
    "    - horz_res: Horizontal resolution in meters. Default is 500 meters.\n",
    "    \n",
    "    Returns:\n",
    "    A tuple (vert_points, horz_points, horizontal) indicating the number of points vertically and horizontally.\n",
    "    '''\n",
    "    \n",
    "    def _linspace_range(start, stop, step):\n",
    "        # Adjust to include both endpoints by adding a step if stop is not on a step from start\n",
    "        num = int((stop - start) / step) + 1\n",
    "        return num\n",
    "    \n",
    "    # Fix applied here: Changed the second parameter of the first call to `height_to`\n",
    "    vert_points = _linspace_range(start=height_from, stop=height_to, step=vert_res)\n",
    "    \n",
    "    # The horizontal range calculation considers the radar range in both directions\n",
    "    horz_points = _linspace_range(start=-max_range, stop=max_range, step=horz_res)\n",
    "    \n",
    "    return vert_points, horz_points, horz_points"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da4c64a8",
   "metadata": {},
   "source": [
    "### Configuring Radar Data Grid Parameters\n",
    "\n",
    "To accurately grid radar data, defining the grid's spatial dimensions and resolutions is essential. This involves specifying the vertical range (height) of the radar's observation area, the maximum horizontal range (distance from the radar station), and the resolutions (spacing between data points) for both vertical and horizontal dimensions.\n",
    "\n",
    "Here's a step-by-step guide to configuring these parameters and determining the grid shape and limits for radar data processing:\n",
    "\n",
    "1. **Define Vertical Range Parameters:**\n",
    "   - `height_from`: The starting height (in meters) from which the grid begins, typically set to 0.0 meters to start from the surface level.\n",
    "   - `height_to`: The maximum height (in meters) the grid extends to, for example, 10,000 meters (10 km) above the surface.\n",
    "\n",
    "2. **Set Maximum Horizontal Range:**\n",
    "   - `max_rng`: The maximum horizontal range (in meters) from the radar, indicating how far out the radar data will be considered. This example uses 250,000 meters (250 km).\n",
    "\n",
    "3. **Specify Resolutions:**\n",
    "   - `vert_res`: The vertical resolution (in meters), determining the spacing between each vertical data point within the grid. Here, it's set to 500 meters.\n",
    "   - `horz_res`: The horizontal resolution (in meters), which sets the spacing between data points in the horizontal plane. This is also 500 meters in the example.\n",
    "\n",
    "4. **Calculate Grid Shape:**\n",
    "   Use the `est_grid_shape` function, providing it with the previously defined parameters, to calculate the grid shape. This function will return the number of vertical and horizontal points that will make up the grid based on the provided resolutions and range limits.\n",
    "\n",
    "5. **Determine Grid Limits:**\n",
    "   Define the grid limits for the z (height), y, and x dimensions based on the specified parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38840ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "height_from=0.0\n",
    "height_to=10e3\n",
    "max_rng = 200e3\n",
    "vert_res=250\n",
    "horz_res=500\n",
    "\n",
    "# Calculate grid shape\n",
    "grid_shape = est_grid_shape(height_from=height_from, height_to=height_to,\n",
    "                            max_range=max_rng, vert_res=vert_res, horz_res=horz_res)\n",
    "# Calculate grid limits\n",
    "grid_limits = (height_from, height_to), (-max_rng, max_rng), (-max_rng, max_rng) # z, y, x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "798d1152",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 801, 801)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "35d1fec2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0.0, 10000.0), (-200000.0, 200000.0), (-200000.0, 200000.0))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_limits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612bd7cf",
   "metadata": {},
   "source": [
    "The configuration specifies that `grid_limits`, set to ((0.0, 10000.0) for vertical height, and (-200000.0, 200000.0) for both horizontal dimensions, will be organized into a `grid_shape` of (41, 801, 801). This structure indicates a division of the 10 km vertical extent into 41 layers, with each layer having a thickness of 250 meters. Horizontally, the grid extends from -200,000 to 200,000 meters along both the X and Y axes, segmented into 801 columns and 801 rows, with each cell measuring 500 meters across."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f95b997",
   "metadata": {},
   "source": [
    "### Gridding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f0b9105",
   "metadata": {},
   "source": [
    "In the below cell, only 4 volume scans are processed due to the slice `[:4]` on `line 7`. To process the entire dataset, remove `[:4]` from `for cfile in cfiles[:4]:`, changing it to `for cfile in cfiles:`. This adjustment ensures all data files are included in the gridding process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6d0beffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File saved in KOL_GRID as grid_cfrad_CAL220430112010-IMD-C.nc\n",
      "File saved in KOL_GRID as grid_cfrad_CAL220430112232-IMD-C.nc\n",
      "File saved in KOL_GRID as grid_cfrad_CAL220430113000-IMD-C.nc\n",
      "File saved in KOL_GRID as grid_cfrad_CAL220430113206-IMD-C.nc\n"
     ]
    }
   ],
   "source": [
    "# Define the output directory for grids\n",
    "gridded_dir = \"KOL_GRID\"\n",
    "!rm -rf $gridded_dir\n",
    "os.makedirs(gridded_dir, exist_ok=True)\n",
    "# Converting to grids\n",
    "cfiles = sorted(glob.glob(os.path.join(output_dir, \"*.nc\")))\n",
    "for cfile in cfiles[:4]: # remove the [0:1], and keep it like \"cfiles:\" only to transform all the files\n",
    "    radar = pyart.io.read(cfile)\n",
    "    grid = pyart.map.grid_from_radars(radar,grid_shape,\n",
    "                                      (grid_limits),\n",
    "                                      weighting_function='Barnes2',\n",
    "                                      fields=radar.fields.keys(),)\n",
    "\n",
    "    filename = os.path.basename(cfile)\n",
    "    output_path = os.path.join(gridded_dir, f\"grid_{filename}\")\n",
    "    print(f\"File saved in {gridded_dir} as grid_{filename}\")\n",
    "    pyart.io.write_grid(filename=output_path, grid=grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6494b7db",
   "metadata": {},
   "source": [
    "### Load gridded data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07140dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_data = sorted(glob.glob(gridded_dir+\"/*.nc\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89eacb8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['KOL_GRID/grid_cfrad_CAL220430112010-IMD-C.nc',\n",
       " 'KOL_GRID/grid_cfrad_CAL220430112232-IMD-C.nc',\n",
       " 'KOL_GRID/grid_cfrad_CAL220430113000-IMD-C.nc',\n",
       " 'KOL_GRID/grid_cfrad_CAL220430113206-IMD-C.nc']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6069de94",
   "metadata": {},
   "source": [
    "### Read data using xarray directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0f100d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_list = []\n",
    "for file in grid_data:\n",
    "    ds_temp = xr.open_dataset(file)\n",
    "    file_list.append(ds_temp.where(ds_temp.REF<100))\n",
    "ds = xr.concat(file_list, dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "adabc9b2",
   "metadata": {},
   "outputs": [
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       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 8GB\n",
       "Dimensions:                     (time: 4, z: 41, y: 801, x: 801, nradar: 1)\n",
       "Coordinates:\n",
       "  * time                        (time) datetime64[ns] 32B 2022-04-30T11:20:10...\n",
       "  * x                           (x) float64 6kB -2e+05 -1.995e+05 ... 2e+05\n",
       "  * y                           (y) float64 6kB -2e+05 -1.995e+05 ... 2e+05\n",
       "  * z                           (z) float64 328B 0.0 250.0 ... 9.75e+03 1e+04\n",
       "Dimensions without coordinates: nradar\n",
       "Data variables: (12/14)\n",
       "    origin_latitude             (time, z, y, x) float32 421MB 22.57 ... nan\n",
       "    origin_longitude            (time, z, y, x) float32 421MB 88.35 ... nan\n",
       "    origin_altitude             (time, z, y, x) float32 421MB 35.0 35.0 ... nan\n",
       "    projection                  (time, z, y, x) float64 842MB 1.0 1.0 ... nan\n",
       "    ProjectionCoordinateSystem  (time, z, y, x) float64 842MB 1.0 1.0 ... nan\n",
       "    radar_latitude              (nradar, time, z, y, x) float32 421MB 22.57 ....\n",
       "    ...                          ...\n",
       "    radar_time                  (nradar, time, z, y, x) datetime64[ns] 842MB ...\n",
       "    radar_name                  (nradar, time, z, y, x) object 842MB b&#x27;CAL&#x27; ....\n",
       "    REF                         (time, z, y, x) float32 421MB 11.19 ... nan\n",
       "    VEL                         (time, z, y, x) float32 421MB 9.969e+36 ... nan\n",
       "    WIDTH                       (time, z, y, x) float32 421MB 9.969e+36 ... nan\n",
       "    ROI                         (time, z, y, x) float32 421MB 7.406e+03 ... nan\n",
       "Attributes:\n",
       "    instrument_name:  CAL\n",
       "    Conventions:      CF/Radial\n",
       "    history:          created by syed44 on MacBook-Pro-6.local at 2024-04-06T...\n",
       "    field_names:      REF, VEL, WIDTH\n",
       "    volume_number:    0\n",
       "    platform_type:    fixed\n",
       "    instrument_type:  radar\n",
       "    primary_axis:     axis_z</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e6d62cea-7c85-4b5f-9370-29e6a0670ddc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e6d62cea-7c85-4b5f-9370-29e6a0670ddc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>z</span>: 41</li><li><span class='xr-has-index'>y</span>: 801</li><li><span class='xr-has-index'>x</span>: 801</li><li><span>nradar</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-31302439-9cf0-4f45-bc94-26b61fc17e0e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31302439-9cf0-4f45-bc94-26b61fc17e0e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-04-30T11:20:10 ... 2022-04-...</div><input id='attrs-b4d16767-e03a-4fe2-9f4c-219eeda063bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4d16767-e03a-4fe2-9f4c-219eeda063bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6999464d-b3b3-4320-8c16-6b32df1d619a' class='xr-var-data-in' type='checkbox'><label for='data-6999464d-b3b3-4320-8c16-6b32df1d619a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time of grid</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-04-30T11:20:10.000000000&#x27;, &#x27;2022-04-30T11:22:32.000000000&#x27;,\n",
       "       &#x27;2022-04-30T11:30:00.000000000&#x27;, &#x27;2022-04-30T11:32:06.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2e+05 -1.995e+05 ... 2e+05</div><input id='attrs-4b1c3943-52bc-4704-9ddf-013170c101ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b1c3943-52bc-4704-9ddf-013170c101ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3713d4fc-5604-485e-b29f-f9af9da67c2b' class='xr-var-data-in' type='checkbox'><label for='data-3713d4fc-5604-485e-b29f-f9af9da67c2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>X distance on the projection plane from the origin</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-200000., -199500., -199000., ...,  199000.,  199500.,  200000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2e+05 -1.995e+05 ... 2e+05</div><input id='attrs-69ea1b0a-b294-4a65-a6b0-7552d9bf49f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69ea1b0a-b294-4a65-a6b0-7552d9bf49f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e63f95a-7982-4868-8cd9-ace4f01ab1bb' class='xr-var-data-in' type='checkbox'><label for='data-7e63f95a-7982-4868-8cd9-ace4f01ab1bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Y distance on the projection plane from the origin</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-200000., -199500., -199000., ...,  199000.,  199500.,  200000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 250.0 500.0 ... 9.75e+03 1e+04</div><input id='attrs-04d4d67d-1537-466d-a32f-591044f8b33e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04d4d67d-1537-466d-a32f-591044f8b33e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-578a3d0c-fedb-4c54-93d6-d78a5c0cfd63' class='xr-var-data-in' type='checkbox'><label for='data-578a3d0c-fedb-4c54-93d6-d78a5c0cfd63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Z distance on the projection plane from the origin</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_z_coordinate</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array([    0.,   250.,   500.,   750.,  1000.,  1250.,  1500.,  1750.,  2000.,\n",
       "        2250.,  2500.,  2750.,  3000.,  3250.,  3500.,  3750.,  4000.,  4250.,\n",
       "        4500.,  4750.,  5000.,  5250.,  5500.,  5750.,  6000.,  6250.,  6500.,\n",
       "        6750.,  7000.,  7250.,  7500.,  7750.,  8000.,  8250.,  8500.,  8750.,\n",
       "        9000.,  9250.,  9500.,  9750., 10000.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-373b5dc5-539f-45e7-b520-14557d24b183' class='xr-section-summary-in' type='checkbox'  checked><label for='section-373b5dc5-539f-45e7-b520-14557d24b183' class='xr-section-summary' >Data variables: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>origin_latitude</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>22.57 22.57 22.57 ... nan nan nan</div><input id='attrs-e4fd4579-0be4-4666-b68f-059bd4c7ec6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4fd4579-0be4-4666-b68f-059bd4c7ec6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ea1a23c-9d57-48d7-86c2-4d444abb403e' class='xr-var-data-in' type='checkbox'><label for='data-1ea1a23c-9d57-48d7-86c2-4d444abb403e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude at grid origin</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>array([[[[22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         ...,\n",
       "         [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705]],\n",
       "\n",
       "        [[22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         ...,\n",
       "         [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705]],\n",
       "\n",
       "        [[22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "         ...,\n",
       "...\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan]],\n",
       "\n",
       "        [[    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "         ...,\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan]],\n",
       "\n",
       "        [[    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "         ...,\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "         [    nan,     nan,     nan, ...,     nan,     nan,     nan]]]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>origin_longitude</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.35 88.35 88.35 ... nan nan nan</div><input id='attrs-9ac642ce-c395-49c8-9d94-3ec6cf0489a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ac642ce-c395-49c8-9d94-3ec6cf0489a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4cb7925-5641-45ff-a9d1-501c5e0f7e75' class='xr-var-data-in' type='checkbox'><label for='data-d4cb7925-5641-45ff-a9d1-501c5e0f7e75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude at grid origin</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><pre>array([[[[88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         ...,\n",
       "         [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353]],\n",
       "\n",
       "        [[88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         ...,\n",
       "         [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353]],\n",
       "\n",
       "        [[88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "         ...,\n",
       "...\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan]],\n",
       "\n",
       "        [[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "         ...,\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan]],\n",
       "\n",
       "        [[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "         ...,\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "         [   nan,    nan,    nan, ...,    nan,    nan,    nan]]]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>origin_altitude</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>35.0 35.0 35.0 35.0 ... nan nan nan</div><input id='attrs-7c575939-53aa-4c1c-88e3-d549b209fd20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c575939-53aa-4c1c-88e3-d549b209fd20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40e6b715-8cc7-4de0-8e33-1146bf8ae783' class='xr-var-data-in' type='checkbox'><label for='data-40e6b715-8cc7-4de0-8e33-1146bf8ae783' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Altitude at grid origin</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>altitude</dd></dl></div><div class='xr-var-data'><pre>array([[[[35., 35., 35., ..., 35., 35., 35.],\n",
       "         [35., 35., 35., ..., 35., 35., 35.],\n",
       "         [35., 35., 35., ..., 35., 35., 35.],\n",
       "         ...,\n",
       "         [35., 35., 35., ..., 35., 35., 35.],\n",
       "         [35., 35., 35., ..., 35., 35., 35.],\n",
       "         [35., 35., 35., ..., 35., 35., 35.]],\n",
       "\n",
       "        [[35., 35., 35., ..., 35., 35., 35.],\n",
       "         [35., 35., 35., ..., 35., 35., 35.],\n",
       "         [35., 35., 35., ..., 35., 35., 35.],\n",
       "         ...,\n",
       "         [35., 35., 35., ..., 35., 35., 35.],\n",
       "         [35., 35., 35., ..., 35., 35., 35.],\n",
       "         [35., 35., 35., ..., 35., 35., 35.]],\n",
       "\n",
       "        [[35., 35., 35., ..., 35., 35., 35.],\n",
       "         [35., 35., 35., ..., 35., 35., 35.],\n",
       "         [35., 35., 35., ..., 35., 35., 35.],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... nan nan nan nan</div><input id='attrs-9a805996-82bd-48ce-aa71-40eb2337202d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a805996-82bd-48ce-aa71-40eb2337202d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1eb45830-f374-4300-8e78-334df9d14262' class='xr-var-data-in' type='checkbox'><label for='data-1eb45830-f374-4300-8e78-334df9d14262' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>proj :</span></dt><dd>pyart_aeqd</dd><dt><span>_include_lon_0_lat_0 :</span></dt><dd>true</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         ...,\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.]],\n",
       "\n",
       "        [[ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         ...,\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.]],\n",
       "\n",
       "        [[ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ProjectionCoordinateSystem</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... nan nan nan nan</div><input id='attrs-53a12948-c906-447c-b2f4-3ddcaabc250b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53a12948-c906-447c-b2f4-3ddcaabc250b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d68949ac-b884-4cbe-8514-266308cfe57e' class='xr-var-data-in' type='checkbox'><label for='data-d68949ac-b884-4cbe-8514-266308cfe57e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>latitude_of_projection_origin :</span></dt><dd>22.5705</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>88.353</dd><dt><span>_CoordinateTransformType :</span></dt><dd>Projection</dd><dt><span>_CoordinateAxes :</span></dt><dd>x y z time</dd><dt><span>_CoordinateAxesTypes :</span></dt><dd>GeoX GeoY Height Time</dd><dt><span>grid_mapping_name :</span></dt><dd>azimuthal_equidistant</dd><dt><span>semi_major_axis :</span></dt><dd>6370997.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.25</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         ...,\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.]],\n",
       "\n",
       "        [[ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         ...,\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.]],\n",
       "\n",
       "        [[ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_latitude</span></div><div class='xr-var-dims'>(nradar, time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>22.57 22.57 22.57 ... nan nan nan</div><input id='attrs-d42dc395-6f4d-4190-8844-ff8af09d240d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d42dc395-6f4d-4190-8844-ff8af09d240d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-127e8060-04d4-440a-8c30-2d1ef0641609' class='xr-var-data-in' type='checkbox'><label for='data-127e8060-04d4-440a-8c30-2d1ef0641609' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude of radars used to make the grid.</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([[[[[22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          ...,\n",
       "          [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705]],\n",
       "\n",
       "         [[22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          ...,\n",
       "          [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705]],\n",
       "\n",
       "         [[22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          [22.5705, 22.5705, 22.5705, ..., 22.5705, 22.5705, 22.5705],\n",
       "          ...,\n",
       "...\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan]],\n",
       "\n",
       "         [[    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "          ...,\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan]],\n",
       "\n",
       "         [[    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "          ...,\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "          [    nan,     nan,     nan, ...,     nan,     nan,     nan]]]]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_longitude</span></div><div class='xr-var-dims'>(nradar, time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.35 88.35 88.35 ... nan nan nan</div><input id='attrs-4ad568e6-0332-485b-9b1c-c63e88eba939' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ad568e6-0332-485b-9b1c-c63e88eba939' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ac24967-c7db-455b-a826-4057715a3a83' class='xr-var-data-in' type='checkbox'><label for='data-6ac24967-c7db-455b-a826-4057715a3a83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude of radars used to make the grid.</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([[[[[88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          ...,\n",
       "          [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353]],\n",
       "\n",
       "         [[88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          ...,\n",
       "          [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353]],\n",
       "\n",
       "         [[88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          [88.353, 88.353, 88.353, ..., 88.353, 88.353, 88.353],\n",
       "          ...,\n",
       "...\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan]],\n",
       "\n",
       "         [[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "          ...,\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan]],\n",
       "\n",
       "         [[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "          ...,\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "          [   nan,    nan,    nan, ...,    nan,    nan,    nan]]]]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_altitude</span></div><div class='xr-var-dims'>(nradar, time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>35.0 35.0 35.0 35.0 ... nan nan nan</div><input id='attrs-1adc048e-084a-4836-bc5c-0e298868af9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1adc048e-084a-4836-bc5c-0e298868af9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3919c7fe-d0a6-46c2-9a63-c853d1e8b62a' class='xr-var-data-in' type='checkbox'><label for='data-3919c7fe-d0a6-46c2-9a63-c853d1e8b62a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Altitude of radars used to make the grid.</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[[[35., 35., 35., ..., 35., 35., 35.],\n",
       "          [35., 35., 35., ..., 35., 35., 35.],\n",
       "          [35., 35., 35., ..., 35., 35., 35.],\n",
       "          ...,\n",
       "          [35., 35., 35., ..., 35., 35., 35.],\n",
       "          [35., 35., 35., ..., 35., 35., 35.],\n",
       "          [35., 35., 35., ..., 35., 35., 35.]],\n",
       "\n",
       "         [[35., 35., 35., ..., 35., 35., 35.],\n",
       "          [35., 35., 35., ..., 35., 35., 35.],\n",
       "          [35., 35., 35., ..., 35., 35., 35.],\n",
       "          ...,\n",
       "          [35., 35., 35., ..., 35., 35., 35.],\n",
       "          [35., 35., 35., ..., 35., 35., 35.],\n",
       "          [35., 35., 35., ..., 35., 35., 35.]],\n",
       "\n",
       "         [[35., 35., 35., ..., 35., 35., 35.],\n",
       "          [35., 35., 35., ..., 35., 35., 35.],\n",
       "          [35., 35., 35., ..., 35., 35., 35.],\n",
       "          ...,\n",
       "...\n",
       "          ...,\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "         [[nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          ...,\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "         [[nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          ...,\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan]]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_time</span></div><div class='xr-var-dims'>(nradar, time, z, y, x)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-04-30T11:20:10 ... NaT</div><input id='attrs-33917b09-b2b6-45d6-a501-4a9b6b1ea84f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33917b09-b2b6-45d6-a501-4a9b6b1ea84f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d99f283d-6186-4495-b42b-742689177bca' class='xr-var-data-in' type='checkbox'><label for='data-d99f283d-6186-4495-b42b-742689177bca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time in seconds of the volume start for each radar</dd></dl></div><div class='xr-var-data'><pre>array([[[[[&#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;, ...,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;],\n",
       "          [&#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;, ...,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;],\n",
       "          [&#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;, ...,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "           &#x27;2022-04-30T11:20:10.000000000&#x27;],\n",
       "          ...,\n",
       "          [&#x27;2022-04-30T11:20:10.000000000&#x27;,\n",
       "...\n",
       "                                     &#x27;NaT&#x27;],\n",
       "          ...,\n",
       "          [                          &#x27;NaT&#x27;,\n",
       "                                     &#x27;NaT&#x27;,\n",
       "                                     &#x27;NaT&#x27;, ...,\n",
       "                                     &#x27;NaT&#x27;,\n",
       "                                     &#x27;NaT&#x27;,\n",
       "                                     &#x27;NaT&#x27;],\n",
       "          [                          &#x27;NaT&#x27;,\n",
       "                                     &#x27;NaT&#x27;,\n",
       "                                     &#x27;NaT&#x27;, ...,\n",
       "                                     &#x27;NaT&#x27;,\n",
       "                                     &#x27;NaT&#x27;,\n",
       "                                     &#x27;NaT&#x27;],\n",
       "          [                          &#x27;NaT&#x27;,\n",
       "                                     &#x27;NaT&#x27;,\n",
       "                                     &#x27;NaT&#x27;, ...,\n",
       "                                     &#x27;NaT&#x27;,\n",
       "                                     &#x27;NaT&#x27;,\n",
       "                                     &#x27;NaT&#x27;]]]]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_name</span></div><div class='xr-var-dims'>(nradar, time, z, y, x)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>b&#x27;CAL&#x27; b&#x27;CAL&#x27; b&#x27;CAL&#x27; ... nan nan</div><input id='attrs-cd50fab4-565d-4a72-a00c-27a9cc237279' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd50fab4-565d-4a72-a00c-27a9cc237279' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21bf8ad4-c99a-434c-a899-1a55a82c29e5' class='xr-var-data-in' type='checkbox'><label for='data-21bf8ad4-c99a-434c-a899-1a55a82c29e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name of radar used to make the grid</dd></dl></div><div class='xr-var-data'><pre>array([[[[[b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          [b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          [b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          ...,\n",
       "          [b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          [b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          [b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;]],\n",
       "\n",
       "         [[b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          [b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          [b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          ...,\n",
       "          [b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          [b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          [b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;]],\n",
       "\n",
       "         [[b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          [b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          [b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, ..., b&#x27;CAL&#x27;, b&#x27;CAL&#x27;, b&#x27;CAL&#x27;],\n",
       "          ...,\n",
       "...\n",
       "          ...,\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "         [[nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          ...,\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "         [[nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          ...,\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan],\n",
       "          [nan, nan, nan, ..., nan, nan, nan]]]]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>REF</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>11.19 11.22 11.25 ... nan nan nan</div><input id='attrs-6fcdc62f-cb90-4c88-affa-5804b8f1b94a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fcdc62f-cb90-4c88-affa-5804b8f1b94a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c11afa3-1a5f-457d-b818-adc8776bc6c4' class='xr-var-data-in' type='checkbox'><label for='data-8c11afa3-1a5f-457d-b818-adc8776bc6c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Reflectivity</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd></dl></div><div class='xr-var-data'><pre>array([[[[11.189418 , 11.217292 , 11.250817 , ..., 11.12783  ,\n",
       "          11.140183 , 11.173882 ],\n",
       "         [11.21569  , 11.2560215, 11.282754 , ..., 11.0940485,\n",
       "          11.126245 , 11.128018 ],\n",
       "         [11.235323 , 11.259921 , 11.291095 , ..., 11.07241  ,\n",
       "          11.077232 , 11.110162 ],\n",
       "         ...,\n",
       "         [11.711411 , 11.714837 , 11.704006 , ..., 11.325689 ,\n",
       "          11.280603 , 11.261372 ],\n",
       "         [11.673632 , 11.673696 , 11.664402 , ..., 11.396879 ,\n",
       "          11.34543  , 11.314074 ],\n",
       "         [11.616512 , 11.644    , 11.648158 , ..., 11.396717 ,\n",
       "          11.427312 , 11.395867 ]],\n",
       "\n",
       "        [[11.187434 , 11.206663 , 11.241107 , ..., 11.122985 ,\n",
       "          11.142659 , 11.19483  ],\n",
       "         [11.222736 , 11.245202 , 11.263633 , ..., 11.102361 ,\n",
       "          11.128684 , 11.17276  ],\n",
       "         [11.235406 , 11.261125 , 11.277772 , ..., 11.082896 ,\n",
       "          11.108516 , 11.119747 ],\n",
       "...\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan]],\n",
       "\n",
       "        [[       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         ...,\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan],\n",
       "         [       nan,        nan,        nan, ...,        nan,\n",
       "                 nan,        nan]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VEL</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.969e+36 9.969e+36 ... nan nan</div><input id='attrs-c8f1e9e0-c770-40cf-b355-163c774dfe09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8f1e9e0-c770-40cf-b355-163c774dfe09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1f32c91-3ecf-4adc-a098-8a97d2cf0834' class='xr-var-data-in' type='checkbox'><label for='data-e1f32c91-3ecf-4adc-a098-8a97d2cf0834' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean dopper velocity</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>standard_name :</span></dt><dd>radial_velocity_of_scatterers_away_from_instrument</dd></dl></div><div class='xr-var-data'><pre>array([[[[9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         ...,\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36]],\n",
       "\n",
       "        [[9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "...\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]],\n",
       "\n",
       "        [[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         ...,\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WIDTH</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.969e+36 9.969e+36 ... nan nan</div><input id='attrs-9fff26d4-25e0-469e-8805-980924b0ac5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fff26d4-25e0-469e-8805-980924b0ac5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64fe7ca7-d8b5-4048-ac7a-677fe9548b5a' class='xr-var-data-in' type='checkbox'><label for='data-64fe7ca7-d8b5-4048-ac7a-677fe9548b5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Doppler spectrum width</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>standard_name :</span></dt><dd>doppler_spectrum_width</dd></dl></div><div class='xr-var-data'><pre>array([[[[9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         ...,\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36]],\n",
       "\n",
       "        [[9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "         [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36,\n",
       "          9.96921e+36, 9.96921e+36],\n",
       "...\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]],\n",
       "\n",
       "        [[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         ...,\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ROI</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.406e+03 7.397e+03 ... nan nan</div><input id='attrs-e371fa81-9235-4204-a391-6d1dd39d12ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e371fa81-9235-4204-a391-6d1dd39d12ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71bc26da-6104-48f7-a721-91e588ca46cc' class='xr-var-data-in' type='checkbox'><label for='data-71bc26da-6104-48f7-a721-91e588ca46cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Radius of influence for mapping</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>radius_of_influence</dd></dl></div><div class='xr-var-data'><pre>array([[[[7406.4937, 7397.241 , 7388.001 , ..., 7388.001 , 7397.241 ,\n",
       "          7406.4937],\n",
       "         [7397.241 , 7387.9775, 7378.725 , ..., 7378.725 , 7387.9775,\n",
       "          7397.241 ],\n",
       "         [7388.001 , 7378.725 , 7369.4614, ..., 7369.4614, 7378.725 ,\n",
       "          7388.001 ],\n",
       "         ...,\n",
       "         [7388.008 , 7378.732 , 7369.4683, ..., 7369.4683, 7378.732 ,\n",
       "          7388.008 ],\n",
       "         [7397.248 , 7387.9844, 7378.732 , ..., 7378.732 , 7387.9844,\n",
       "          7397.248 ],\n",
       "         [7406.5005, 7397.248 , 7388.008 , ..., 7388.008 , 7397.248 ,\n",
       "          7406.5005]],\n",
       "\n",
       "        [[7418.9937, 7409.741 , 7400.501 , ..., 7400.501 , 7409.741 ,\n",
       "          7418.9937],\n",
       "         [7409.741 , 7400.4775, 7391.225 , ..., 7391.225 , 7400.4775,\n",
       "          7409.741 ],\n",
       "         [7400.501 , 7391.225 , 7381.9614, ..., 7381.9614, 7391.225 ,\n",
       "          7400.501 ],\n",
       "...\n",
       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "                nan],\n",
       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "                nan],\n",
       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "                nan]],\n",
       "\n",
       "        [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "                nan],\n",
       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "                nan],\n",
       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "                nan],\n",
       "         ...,\n",
       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "                nan],\n",
       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "                nan],\n",
       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "                nan]]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1e3d5af2-5961-4687-b13b-79281e77c63e' class='xr-section-summary-in' type='checkbox'  ><label for='section-1e3d5af2-5961-4687-b13b-79281e77c63e' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eea3b729-2fb1-401e-9598-9106060c5347' class='xr-index-data-in' type='checkbox'/><label for='index-eea3b729-2fb1-401e-9598-9106060c5347' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-04-30 11:20:10&#x27;, &#x27;2022-04-30 11:22:32&#x27;,\n",
       "               &#x27;2022-04-30 11:30:00&#x27;, &#x27;2022-04-30 11:32:06&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c9cd4441-e589-4c1d-bab1-2d50ba130884' class='xr-index-data-in' type='checkbox'/><label for='index-c9cd4441-e589-4c1d-bab1-2d50ba130884' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-200000.0, -199500.0, -199000.0, -198500.0, -198000.0, -197500.0,\n",
       "       -197000.0, -196500.0, -196000.0, -195500.0,\n",
       "       ...\n",
       "        195500.0,  196000.0,  196500.0,  197000.0,  197500.0,  198000.0,\n",
       "        198500.0,  199000.0,  199500.0,  200000.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=801))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a1bc5579-b9f6-4320-b9ee-19b06a66e784' class='xr-index-data-in' type='checkbox'/><label for='index-a1bc5579-b9f6-4320-b9ee-19b06a66e784' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-200000.0, -199500.0, -199000.0, -198500.0, -198000.0, -197500.0,\n",
       "       -197000.0, -196500.0, -196000.0, -195500.0,\n",
       "       ...\n",
       "        195500.0,  196000.0,  196500.0,  197000.0,  197500.0,  198000.0,\n",
       "        198500.0,  199000.0,  199500.0,  200000.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=801))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ad1089a9-d8ce-46ac-aa05-944a9eca0a80' class='xr-index-data-in' type='checkbox'/><label for='index-ad1089a9-d8ce-46ac-aa05-944a9eca0a80' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0.0,   250.0,   500.0,   750.0,  1000.0,  1250.0,  1500.0,  1750.0,\n",
       "        2000.0,  2250.0,  2500.0,  2750.0,  3000.0,  3250.0,  3500.0,  3750.0,\n",
       "        4000.0,  4250.0,  4500.0,  4750.0,  5000.0,  5250.0,  5500.0,  5750.0,\n",
       "        6000.0,  6250.0,  6500.0,  6750.0,  7000.0,  7250.0,  7500.0,  7750.0,\n",
       "        8000.0,  8250.0,  8500.0,  8750.0,  9000.0,  9250.0,  9500.0,  9750.0,\n",
       "       10000.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;z&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-401b3d4e-bf6a-483e-9673-718ba6753dd6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-401b3d4e-bf6a-483e-9673-718ba6753dd6' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>instrument_name :</span></dt><dd>CAL</dd><dt><span>Conventions :</span></dt><dd>CF/Radial</dd><dt><span>history :</span></dt><dd>created by syed44 on MacBook-Pro-6.local at 2024-04-06T03:09:04.794234 using Py-ART</dd><dt><span>field_names :</span></dt><dd>REF, VEL, WIDTH</dd><dt><span>volume_number :</span></dt><dd>0</dd><dt><span>platform_type :</span></dt><dd>fixed</dd><dt><span>instrument_type :</span></dt><dd>radar</dd><dt><span>primary_axis :</span></dt><dd>axis_z</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 8GB\n",
       "Dimensions:                     (time: 4, z: 41, y: 801, x: 801, nradar: 1)\n",
       "Coordinates:\n",
       "  * time                        (time) datetime64[ns] 32B 2022-04-30T11:20:10...\n",
       "  * x                           (x) float64 6kB -2e+05 -1.995e+05 ... 2e+05\n",
       "  * y                           (y) float64 6kB -2e+05 -1.995e+05 ... 2e+05\n",
       "  * z                           (z) float64 328B 0.0 250.0 ... 9.75e+03 1e+04\n",
       "Dimensions without coordinates: nradar\n",
       "Data variables: (12/14)\n",
       "    origin_latitude             (time, z, y, x) float32 421MB 22.57 ... nan\n",
       "    origin_longitude            (time, z, y, x) float32 421MB 88.35 ... nan\n",
       "    origin_altitude             (time, z, y, x) float32 421MB 35.0 35.0 ... nan\n",
       "    projection                  (time, z, y, x) float64 842MB 1.0 1.0 ... nan\n",
       "    ProjectionCoordinateSystem  (time, z, y, x) float64 842MB 1.0 1.0 ... nan\n",
       "    radar_latitude              (nradar, time, z, y, x) float32 421MB 22.57 ....\n",
       "    ...                          ...\n",
       "    radar_time                  (nradar, time, z, y, x) datetime64[ns] 842MB ...\n",
       "    radar_name                  (nradar, time, z, y, x) object 842MB b'CAL' ....\n",
       "    REF                         (time, z, y, x) float32 421MB 11.19 ... nan\n",
       "    VEL                         (time, z, y, x) float32 421MB 9.969e+36 ... nan\n",
       "    WIDTH                       (time, z, y, x) float32 421MB 9.969e+36 ... nan\n",
       "    ROI                         (time, z, y, x) float32 421MB 7.406e+03 ... nan\n",
       "Attributes:\n",
       "    instrument_name:  CAL\n",
       "    Conventions:      CF/Radial\n",
       "    history:          created by syed44 on MacBook-Pro-6.local at 2024-04-06T...\n",
       "    field_names:      REF, VEL, WIDTH\n",
       "    volume_number:    0\n",
       "    platform_type:    fixed\n",
       "    instrument_type:  radar\n",
       "    primary_axis:     axis_z"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "321a7857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2803eae40>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['REF'].isel(time=1).sel(z=1000, method='nearest').plot(vmin=0, vmax=75, cmap='SyedSpectral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ba3cb4c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2804cae40>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['REF'].sel(x=slice(-250e3, -100e3), y=slice(-100e3, 100e3)).sel(z=500,\n",
    " method='nearest').isel(time=0).plot(vmin=0, vmax=75, cmap='SyedSpectral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46508194",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds.to_netcdf(\"Merged_KOL_radar.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94c0c69f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}