{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb55881f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import matplotlib.colors as Normalize\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
    "import matplotlib.ticker as mticker\n",
    "import matplotlib\n",
    "import xarray as xr\n",
    "import netCDF4\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import glob\n",
    "import dask\n",
    "import os\n",
    "import re\n",
    "import warnings\n",
    "import gc\n",
    "\n",
    "import wrf\n",
    "from wrf import (getvar, interplevel, to_np, latlon_coords, get_cartopy,\n",
    "                 cartopy_xlim, cartopy_ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6cd0c7b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_in_monthly_data(month, hour_interval, climate_state, var_name):\n",
    "    \"\"\"\n",
    "    Reads in all NetCDF files for a given month and combines them into one dataset.\n",
    "\n",
    "    Parameters:\n",
    "        month (str): month you want data for, e.g., '04'\n",
    "        hour_interval (str): '1hr' or '3hr'\n",
    "        climate_state (str): 'current', 'future', or 'future_urban'\n",
    "        var_name (str): Variable name you want to load in, e.g., \"RAINNC\" for accumulated precipitation\n",
    "\n",
    "    Returns:\n",
    "        xarray.Dataset of full month of data\n",
    "    \"\"\"\n",
    "\n",
    "    # Determine the file path based on the input parameters\n",
    "    if hour_interval == '3hr':\n",
    "        #file_path = f'/pscratch/sd/y/yuwei/Climate_Impact/long-term/data/{climate_state}/{hour_interval}/wrfout_d01_2017-{month}*'\n",
    "        file_path = f'/pscratch/sd/y/yuwei/Climate_Impact/long-term/data/{climate_state}/3hr/wrfout_d01_2017-04-01_00:00:00'\n",
    "    elif hour_interval == '1hr':\n",
    "        file_path = f'/pscratch/sd/y/yuwei/Climate_Impact/long-term/data/{climate_state}/{hour_interval}/wrfout_hourly_d01_2017-{month}*'\n",
    "\n",
    "        # also need to grab xlat and xlon from the 3hr files\n",
    "        file_path2 = f'/pscratch/sd/y/yuwei/Climate_Impact/long-term/data/{climate_state}/3hr/wrfout_d01_2017-04-01_00:00:00'\n",
    "        ncfile2 = netCDF4.Dataset(file_path2, 'r') \n",
    "        # Extract latitude and longitude\n",
    "        lats = ncfile2.variables['XLAT'][:]  # Latitude\n",
    "        lons = ncfile2.variables['XLONG'][:]  # Longitude\n",
    "\n",
    "    # Use glob to find all matching files for the month\n",
    "    file_list = sorted(glob.glob(file_path))\n",
    "\n",
    "    array_list = []\n",
    "    for file in file_list:\n",
    "        print(file)\n",
    "        # Read file\n",
    "        ncfile = netCDF4.Dataset(file, 'r') \n",
    "        # Check if REFL_10CM is the variable we want\n",
    "        if var_name == 'REFL_10CM':\n",
    "            # Extract REFL_10CM variable directly\n",
    "            ref_data = ncfile.variables['REFL_10CM'][:]\n",
    "            times_char_array = ncfile.variables['Times'][:]\n",
    "\n",
    "            ref_data = ref_data.max(axis=1) # Get max dbz for vertical column\n",
    "\n",
    "            # Decode times (convert to string or datetime64)\n",
    "            times = decode_times(times_char_array)\n",
    "            \n",
    "            # Convert to xarray DataArray with time, lat, lon as dimensions\n",
    "            ref_da = xr.DataArray(\n",
    "                ref_data, \n",
    "                dims=[\"Time\", \"south_north\", \"west_east\"], \n",
    "                coords={\"Time\": times, \"XLAT\": ([\"south_north\", \"west_east\"], lats[0]), \"XLONG\": ([\"south_north\", \"west_east\"], lons[0])},\n",
    "                name=\"REFL_10CM\",\n",
    "                attrs={\"Description\": \"Composite ref for vertical columns (dbz)\"}\n",
    "            )\n",
    "            \n",
    "            \n",
    "            # Convert to xarray Dataset\n",
    "            data = ref_da.to_dataset(name=\"REFL_10CM\")\n",
    "            #print(data)\n",
    "        else:\n",
    "            # For other variables, use wrf-python getvar\n",
    "            data = getvar(ncfile, var_name).to_dataset(name=var_name)\n",
    "        \n",
    "        array_list.append(data)\n",
    "        ncfile.close()\n",
    "\n",
    "    print('done')\n",
    "    \n",
    "    # Combine all datasets along the Time dimension\n",
    "    combined_ds = xr.concat(array_list, dim='Time')\n",
    "    \n",
    "    return combined_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71690ce0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/pscratch/sd/y/yuwei/Climate_Impact/long-term/data/current/3hr/wrfout_d01_2017-04-01_00:00:00\n",
      "done\n",
      "/pscratch/sd/y/yuwei/Climate_Impact/long-term/data/future_urban/3hr/wrfout_d01_2017-04-01_00:00:00\n",
      "done\n",
      "/pscratch/sd/y/yuwei/Climate_Impact/long-term/data/current/3hr/wrfout_d01_2017-04-01_00:00:00\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "# Read in land use categories\n",
    "c_land_use = read_in_monthly_data('04', '3hr', 'current', 'LU_INDEX')\n",
    "fu_land_use = read_in_monthly_data('04', '3hr', 'future_urban', 'LU_INDEX')\n",
    "land_mask = read_in_monthly_data('04', '3hr', 'current', 'LANDMASK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad0d81c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from scipy.ndimage import label, binary_fill_holes, binary_dilation\n",
    "from skimage.measure import regionprops\n",
    "\n",
    "def define_metro_area(land_use, min_size=80):\n",
    "    \"\"\"\n",
    "    Identify contiguous urban areas (LU_INDEX = 13) and label them.\n",
    "    Keeps only regions larger than `min_size` (in grid cells).\n",
    "    Ensures small interior patches don't form new regions.\n",
    "    \"\"\"\n",
    "    tb = land_use[\"LU_INDEX\"].values\n",
    "    frame = tb[0, :, :]\n",
    "\n",
    "    # Binary urban mask\n",
    "    mask = frame == 13\n",
    "\n",
    "    # Fill small internal holes before labeling\n",
    "    mask = binary_fill_holes(mask)\n",
    "\n",
    "    # Label contiguous regions (8-connectivity)\n",
    "    labeled, _ = label(mask, structure=np.ones((3, 3)))\n",
    "\n",
    "    # Filter by region size\n",
    "    props = regionprops(labeled)\n",
    "    for prop in props:\n",
    "        if prop.area < min_size:\n",
    "            labeled[labeled == prop.label] = 0\n",
    "\n",
    "    # Fill internal holes again after filtering\n",
    "    labeled = binary_fill_holes(labeled > 0).astype(int)\n",
    "    \n",
    "    # Reassign consistent integer labels (avoid fragmented re-labeling)\n",
    "    labeled, _ = label(labeled, structure=np.ones((3, 3)))\n",
    "\n",
    "    return labeled\n",
    "\n",
    "metro_areas = define_metro_area(c_land_use)\n",
    "metro_areas_future = define_metro_area(fu_land_use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b928aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Create periphery \n",
    "def expand_labeled_regions(label_array: np.ndarray, buffer_size: int = 5) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Expand each labeled region in a labeled 2D array by a buffer of N grid cells.\n",
    "    Each region keeps its original label value in the expanded area.\n",
    "    \n",
    "    Args:\n",
    "        label_array: 2D integer array where 0 = background, >0 = region labels\n",
    "        buffer_size: number of grid cells to expand each region\n",
    "    \n",
    "    Returns:\n",
    "        2D integer array with expanded regions.\n",
    "    \"\"\"\n",
    "    expanded = np.zeros_like(label_array, dtype=np.int32)\n",
    "\n",
    "    labels = np.unique(label_array)\n",
    "    labels = labels[labels > 0]  # ignore background\n",
    "\n",
    "    # Define circular structuring element\n",
    "    from scipy.ndimage import generate_binary_structure, iterate_structure\n",
    "    struct = generate_binary_structure(2, 1)\n",
    "    struct = iterate_structure(struct, buffer_size)\n",
    "\n",
    "    for lbl in labels:\n",
    "        mask = label_array == lbl\n",
    "        #print(lbl)\n",
    "        dilated = binary_dilation(mask, structure=struct)\n",
    "        expanded[dilated] = lbl  # keep label ID\n",
    "\n",
    "    return expanded\n",
    "\n",
    "# Suburbs (10km buffer)\n",
    "expanded_metro_10km = expand_labeled_regions(metro_areas, buffer_size=5)\n",
    "expanded_metro_future_10km = expand_labeled_regions(metro_areas_future, buffer_size=5)\n",
    "\n",
    "# Rural (10km buffer around suburbs)\n",
    "expanded_metro_20km = expand_labeled_regions(metro_areas, buffer_size=10)\n",
    "expanded_metro_future_20km = expand_labeled_regions(metro_areas_future, buffer_size=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b196f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create metro periphery and rural areas\n",
    "def define_periphery(expanded_labels: np.ndarray, urban_labels: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Compute periphery (ring) regions for each labeled urban area.\n",
    "    The periphery keeps the same label as its parent region.\n",
    "\n",
    "    Args:\n",
    "        expanded_labels: 2D array of expanded urban areas (each labeled)\n",
    "        urban_labels:    2D array of original urban areas (each labeled)\n",
    "\n",
    "    Returns:\n",
    "        2D integer array: periphery region labels (0 = non-periphery)\n",
    "    \"\"\"\n",
    "    # Periphery = expanded area minus the core\n",
    "    periphery = np.zeros_like(expanded_labels, dtype=np.int32)\n",
    "\n",
    "    # Get unique region IDs\n",
    "    labels = np.unique(urban_labels)\n",
    "    labels = labels[labels > 0]\n",
    "\n",
    "    for lbl in labels:\n",
    "        expanded_mask = expanded_labels == lbl\n",
    "        core_mask = urban_labels == lbl\n",
    "\n",
    "        ring = np.logical_and(expanded_mask, ~core_mask)\n",
    "        periphery[ring] = lbl  # assign same label\n",
    "\n",
    "    # Mask water areas\n",
    "    tb = land_mask[\"LANDMASK\"].values\n",
    "    frame = tb[0, :, :]\n",
    "    mask = frame != 0 # Water\n",
    "    periphery = np.where(mask, periphery, 0)\n",
    "\n",
    "    return periphery\n",
    "\n",
    "metro_periphery = define_periphery(expanded_metro_10km, metro_areas) # Periphery (suburbs)\n",
    "metro_rural = define_periphery(expanded_metro_20km, expanded_metro_10km) # Rural\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "77adcb7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 1GB\n",
      "Dimensions:  (Time: 240, south_north: 1080, west_east: 1210)\n",
      "Coordinates:\n",
      "    XLONG    (south_north, west_east) float32 5MB ...\n",
      "    XLAT     (south_north, west_east) float32 5MB ...\n",
      "    XTIME    (Time) float32 960B ...\n",
      "  * Time     (Time) datetime64[ns] 2kB 2017-04-01 ... 2017-04-30T21:00:00\n",
      "Dimensions without coordinates: south_north, west_east\n",
      "Data variables:\n",
      "    T2       (Time, south_north, west_east) float32 1GB ...\n"
     ]
    }
   ],
   "source": [
    "######### Load in temp data ##########\n",
    "month='04'\n",
    "c_temp = xr.open_dataset(f'/pscratch/sd/d/dbrooks/acc2017_analysis/temp_data/Current/temp_data_month{month}.nc')\n",
    "print(c_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ba6a5e09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def plot_urban_areas(ds1: xr.Dataset, ds2: xr.Dataset) -> None:\n",
    "    \"\"\"\n",
    "    Plot only urban areas (LU_INDEX = 13) from WRF output on a map.\n",
    "    \"\"\"\n",
    "    lats = ds1[\"XLAT\"].values\n",
    "    lons = ds1[\"XLONG\"].values\n",
    "\n",
    "    lu1 = ds1[\"LU_INDEX\"].isel(Time=0).values\n",
    "    # Mask non-urban areas\n",
    "    urban_mask1 = np.where(lu1 == 13, 1, np.nan)\n",
    "\n",
    "    lu2 = ds2[\"LU_INDEX\"].isel(Time=0).values\n",
    "    # Mask non-urban areas\n",
    "    urban_mask2 = np.where(lu2 == 13, 1, np.nan)\n",
    "\n",
    "    # Create map\n",
    "    fig, ax = plt.subplots(\n",
    "        figsize=(10, 8),\n",
    "        subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
    "    )\n",
    "    # Add features and title\n",
    "    ax.add_feature(cfeature.STATES, edgecolor=\"gray\", linewidths=0.65, alpha=0.7)\n",
    "    ax.add_feature(cfeature.COASTLINE, edgecolor=\"gray\", linewidths=0.65, alpha=0.7)\n",
    "    #ax_wind.add_feature(USCOUNTIES, linewidth=0.35, alpha=0.5)\n",
    "    gl = ax.gridlines(draw_labels=True, linewidth=0.6, color='gray', alpha=0.5, linestyle='--', zorder=2)\n",
    "    gl.top_labels = False\n",
    "    gl.right_labels = False\n",
    "    gl.left_labels = True\n",
    "    gl.bottom_labels = True\n",
    "\n",
    "    #mesh = ax.pcolormesh(\n",
    "    #    lons, lats, urban_mask2, \n",
    "    #   cmap=\"Reds_r\", shading=\"nearest\"\n",
    "    #)\n",
    "\n",
    "    labels=np.unique(metro_areas[metro_areas > 0])\n",
    "    #ax.contour(lons, lats, metro_areas, levels=[0], colors=\"purple\", linewidths=1)\n",
    "    #ax.contour(lons, lats, expanded_metro_10km, levels=[0], colors=\"red\", linewidths=1)\n",
    "    #ax.contour(lons, lats, expanded_metro_20km, levels=[0], colors=\"green\", linewidths=1)\n",
    "    ax.contourf(lons, lats, metro_periphery, levels=labels, cmap='gist_ncar')\n",
    "    ax.contourf(lons, lats, metro_rural, levels=labels, cmap='plasma_r')\n",
    "    #ax.contourf(lons, lats, expanded_metro_future, levels=labels, cmap='tab20')\n",
    "    #ax.contourf(lons, lats, metro_areas_future, levels=labels, cmap='tab20')\n",
    "    #ax.contour(lons, lats, metro_areas_future, levels=[0], colors=\"red\", linewidths=2)\n",
    "    #ax.contour(lons, lats, expanded_metro_future, levels=[0], colors=\"purple\", linewidths=3)\n",
    "\n",
    "    # Plot urban areas\n",
    "    mesh = ax.pcolormesh(\n",
    "        lons, lats, urban_mask1, \n",
    "        cmap=\"Blues\", shading=\"nearest\"\n",
    "    )\n",
    "\n",
    "    ax.set_extent([-96,-94,29,31])\n",
    "    #ax.set_extent([-89,-86,41,43])\n",
    "\n",
    "    # Add colorbar\n",
    "    cbar = plt.colorbar(mesh, ax=ax, orientation=\"vertical\", pad=0.02, shrink=0.5)\n",
    "    #cbar.set_label(\"Urban Area (1 = Urban, NaN = Non-urban)\", fontsize=12)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Example usage:\n",
    "plot_urban_areas(c_land_use, fu_land_use)\n"
   ]
  }
 ],
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