{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# IMD Radar Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "The India Meteorological Department (IMD) provides radar data in a format similar to IRIS/Sigmet, but with some differences in structure and formatting. `radarx` offers specialized support for processing and analyzing this IMD radar data, ensuring compatibility and ease of use despite the variations from standard IRIS/Sigmet formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import subprocess\n",
    "import radarx as rx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {},
   "source": [
    "## Load IMD Radar Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Base URL for the radar data files on GitHub\n",
    "base_url = (\n",
    "    \"https://raw.githubusercontent.com/syedhamidali/pyscancf_examples/main/data/goa16/\"\n",
    ")\n",
    "\n",
    "# List of radar data files to download\n",
    "files = [\n",
    "    \"GOA210516024101-IMD-B.nc\",\n",
    "    \"GOA210516024101-IMD-B.nc.1\",\n",
    "    \"GOA210516024101-IMD-B.nc.2\",\n",
    "    \"GOA210516024101-IMD-B.nc.3\",\n",
    "    \"GOA210516024101-IMD-B.nc.4\",\n",
    "    \"GOA210516024101-IMD-B.nc.5\",\n",
    "    \"GOA210516024101-IMD-B.nc.6\",\n",
    "    \"GOA210516024101-IMD-B.nc.7\",\n",
    "    \"GOA210516024101-IMD-B.nc.8\",\n",
    "    \"GOA210516024101-IMD-B.nc.9\",\n",
    "]\n",
    "\n",
    "# Target directory to save downloaded files\n",
    "target_dir = os.path.join(os.getenv(\"GITHUB_WORKSPACE\", \".\"), \"radarx_data\")\n",
    "os.makedirs(target_dir, exist_ok=True)\n",
    "\n",
    "\n",
    "# Function to download files using curl\n",
    "def download_with_curl(file_name):\n",
    "    file_url = base_url + file_name\n",
    "    file_path = os.path.join(target_dir, file_name)\n",
    "\n",
    "    if not os.path.exists(file_path):\n",
    "        print(f\"Downloading {file_name}...\")\n",
    "        subprocess.run([\"curl\", \"-o\", file_path, file_url], check=True)\n",
    "        print(f\"Downloaded {file_name}\")\n",
    "    else:\n",
    "        print(f\"{file_name} already exists.\")\n",
    "\n",
    "    return file_path\n",
    "\n",
    "\n",
    "# Download the files\n",
    "downloaded_files = []\n",
    "for file_name in files:\n",
    "    file_path = download_with_curl(file_name)\n",
    "    downloaded_files.append(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Number of files: {len(downloaded_files)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "## Read a Sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the first radar file using radarx\n",
    "swp = rx.io.read_sweep(downloaded_files[0])\n",
    "swp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8",
   "metadata": {},
   "source": [
    "We have successfully read one sweep in the above cell. Now, let's create a volume scan."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "## Create a Volume"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10",
   "metadata": {},
   "source": [
    "We have a total of 10 files, each representing a different sweep. Our goal is to combine these individual sweeps into a single CF-Radial volume scan dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11",
   "metadata": {},
   "outputs": [],
   "source": [
    "for file in downloaded_files:\n",
    "    print(os.path.basename(file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12",
   "metadata": {},
   "outputs": [],
   "source": [
    "vol = rx.io.read_volume(downloaded_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13",
   "metadata": {},
   "outputs": [],
   "source": [
    "vol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14",
   "metadata": {},
   "source": [
    "We have successfully generated a volume, which can now be explored further. The volume is stored in a DataTree, a high-level structure that organizes and manages the data within the xarray framework."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15",
   "metadata": {},
   "outputs": [],
   "source": [
    "vol.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = vol[\"volume_0\"].ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18",
   "metadata": {},
   "source": [
    "We have successfully created a volume, and we can export it to CF-Radial formatted NetCDF using `xarray`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can save all these cfradial1 files by iterating over the vol object\n",
    "\n",
    "outdir = \"IMD_OUT\"\n",
    "os.makedirs(outdir, exist_ok=True)\n",
    "for key in vol.children:\n",
    "    ds = vol[key].ds\n",
    "    time_str = ds.time.min().dt.strftime(\"%Y%m%d_%H%M%S\").item()\n",
    "    ds.to_netcdf(os.path.join(outdir, f\"GOA_{time_str}_cfrad1.nc\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.listdir(\"IMD_OUT/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21",
   "metadata": {},
   "source": [
    "## Convert to CF/Radial2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22",
   "metadata": {},
   "source": [
    "We can also convert it to cfradial2 data using to_cfradial2 function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = rx.io.to_cfradial2_volumes(vol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = dtree[\"volume_0\"].ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export all the data in a dtree\n",
    "\n",
    "# for volume in dtree:\n",
    "#     dtree[volume].to_netcdf()"
   ]
  }
 ],
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