{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d559d409",
   "metadata": {},
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset\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",
    "from matplotlib.colors import LinearSegmentedColormap, TwoSlopeNorm\n",
    "from scipy.stats import linregress, pearsonr\n",
    "from datetime import datetime\n",
    "\n",
    "import wrf\n",
    "from wrf import (getvar, vinterp, interplevel, to_np, latlon_coords, get_cartopy,\n",
    "                 cartopy_xlim, cartopy_ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5af92c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove boundaries\n",
    "def remove_lateral_boundaries(current_ds,future_ds,future_urban_ds):\n",
    "    # Access the latitude and longitude arrays (XLAT, XLONG)\n",
    "    lats = current_ds['XLAT']\n",
    "    lons = current_ds['XLONG']\n",
    "\n",
    "    # Get the shape of the latitude and longitude arrays\n",
    "    n_lat, n_lon = lats.shape\n",
    "    # Exclude 15 grid cells from each side (latitude and longitude)\n",
    "    lat_slice = slice(15, n_lat - 15)\n",
    "    lon_slice = slice(15, n_lon - 15)\n",
    "\n",
    "    # Subset the data using the grid cell indices\n",
    "    current_ds = current_ds.isel(south_north=lat_slice, west_east=lon_slice)\n",
    "    future_ds = future_ds.isel(south_north=lat_slice, west_east=lon_slice)\n",
    "    future_urban_ds = future_urban_ds.isel(south_north=lat_slice, west_east=lon_slice)\n",
    "\n",
    "    return current_ds,future_ds,future_urban_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14b9b372",
   "metadata": {},
   "outputs": [],
   "source": [
    "monlist = ['04'] # months in the simulation\n",
    "#sim_list = ['future']\n",
    "\n",
    "# Buoyancy\n",
    "filename1 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/current/integrated_buoyancy_month{monlist[0]}.nc'\n",
    "filename2 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future/integrated_buoyancy_month{monlist[0]}.nc'\n",
    "filename3 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future_urban/integrated_buoyancy_month{monlist[0]}.nc'\n",
    "\n",
    "c_b_ds = xr.open_dataset(filename1)\n",
    "f_b_ds = xr.open_dataset(filename2)\n",
    "fu_b_ds = xr.open_dataset(filename3)\n",
    "\n",
    "#c_b_ds,f_b_ds,fu_b_ds = remove_lateral_boundaries(c_b_ds,f_b_ds,fu_b_ds)\n",
    "\n",
    "# Winds\n",
    "filename1 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/wind_data/Current/convective_winds_month{monlist[0]}_updated.nc'\n",
    "filename2 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/wind_data/Future/convective_winds_month{monlist[0]}_updated.nc'\n",
    "filename3 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/wind_data/Future_urban/convective_winds_month{monlist[0]}_updated.nc'\n",
    "\n",
    "c_winds_ds = xr.open_dataset(filename1)\n",
    "f_winds_ds = xr.open_dataset(filename2)\n",
    "fu_winds_ds = xr.open_dataset(filename3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef4df318",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_in_mask(sim, month):\n",
    "    #filename = f'/pscratch/sd/d/dbrooks/acc2017_analysis/masks/{sim}/convective_core_mask_withtimes_month{month}.nc'\n",
    "    filename = f'/pscratch/sd/d/dbrooks/acc2017_analysis/masks/{sim}/convective_wind_mask_updated_{month}.nc'\n",
    "    #filename = f'/pscratch/sd/d/dbrooks/radar_data/{sim}/composite_ref_month{month}.nc'\n",
    "    ds = xr.open_dataarray(filename)\n",
    "    ds=ds.to_dataset(name='conv_wind_mask')\n",
    "    #ds=ds.to_dataset(name='conv_core_mask')\n",
    "    #print(ds)\n",
    "    #ds = ds['__xarray_dataarray_variable__']\n",
    "    #ds = ds['MSKCLD2'] == 1\n",
    "    #ds = ds['mdbz'] > 5 \n",
    "    return ds\n",
    "\n",
    "c_cw_mask = load_in_mask('current',monlist[0])\n",
    "f_cw_mask = load_in_mask('future',monlist[0])\n",
    "fu_cw_mask = load_in_mask('future_urban',monlist[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a8c82b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "select_time_window = False\n",
    "land_only = False\n",
    "\n",
    "if select_time_window == True:\n",
    "    start_time = '2017-06-20T00:00:00'\n",
    "    #end_time = '2017-06-20T00:00:00'\n",
    "    #start_time = '2017-06-01T00:00:00'\n",
    "    end_time = '2017-06-24T06:00:00'\n",
    "\n",
    "    # buoyancy\n",
    "    c_b_ds = c_b_ds.sel(Time=slice(start_time, end_time))\n",
    "    f_b_ds = f_b_ds.sel(Time=slice(start_time, end_time))\n",
    "    fu_b_ds = fu_b_ds.sel(Time=slice(start_time, end_time))\n",
    "\n",
    "    # winds\n",
    "    c_winds_ds = c_winds_ds.sel(Time=slice(start_time, end_time))\n",
    "    f_winds_ds = f_winds_ds.sel(Time=slice(start_time, end_time))\n",
    "    fu_winds_ds = fu_winds_ds.sel(Time=slice(start_time, end_time))\n",
    "\n",
    "    # wind mask\n",
    "    c_cw_mask = c_cw_mask.sel(Time=slice(start_time, end_time))\n",
    "    f_cw_mask = f_cw_mask.sel(Time=slice(start_time, end_time))\n",
    "    fu_cw_mask = fu_cw_mask.sel(Time=slice(start_time, end_time))\n",
    "    \n",
    "\n",
    "if land_only == True:\n",
    "    land_mask = xr.open_dataset('/pscratch/sd/d/dbrooks/acc2017_analysis/masks/landmask.nc')\n",
    "    # Access the latitude and longitude arrays (XLAT, XLONG)\n",
    "    lats = land_mask['XLAT']\n",
    "    #lons = land_mask['XLONG']\n",
    "\n",
    "    # Get the shape of the latitude and longitude arrays\n",
    "    n_lat, n_lon = lats.shape\n",
    "    # Exclude 15 grid cells from each side (latitude and longitude)\n",
    "    lat_slice = slice(15, n_lat - 15)\n",
    "    lon_slice = slice(15, n_lon - 15)\n",
    "\n",
    "    # Subset the data using the grid cell indices\n",
    "    land_mask = land_mask.isel(south_north=lat_slice, west_east=lon_slice)\n",
    "\n",
    "    land_mask = land_mask['LANDMASK'] == 1\n",
    "\n",
    "    # Mask non land areas\n",
    "    c_b_ds = xr.where(land_mask, c_b_ds, float(\"nan\")).transpose('Time', 'south_north', 'west_east') # transpose is to keep dimension order the same\n",
    "    f_b_ds = xr.where(land_mask, f_b_ds, float(\"nan\")).transpose('Time', 'south_north', 'west_east')\n",
    "    fu_b_ds = xr.where(land_mask, fu_b_ds, float(\"nan\")).transpose('Time', 'south_north', 'west_east')\n",
    "\n",
    "    c_winds_ds = xr.where(land_mask, c_winds_ds, float(\"nan\")).transpose('Time', 'south_north', 'west_east','wspd_wdir')\n",
    "    f_winds_ds = xr.where(land_mask, f_winds_ds, float(\"nan\")).transpose('Time', 'south_north', 'west_east','wspd_wdir')\n",
    "    fu_winds_ds = xr.where(land_mask, fu_winds_ds, float(\"nan\")).transpose('Time', 'south_north', 'west_east','wspd_wdir')\n",
    "\n",
    "    c_cw_mask = xr.where(land_mask, c_cw_mask, float(\"nan\")).transpose('Time', 'south_north', 'west_east')\n",
    "    f_cw_mask = xr.where(land_mask, f_cw_mask, float(\"nan\")).transpose('Time', 'south_north', 'west_east')\n",
    "    fu_cw_mask = xr.where(land_mask, fu_cw_mask, float(\"nan\")).transpose('Time', 'south_north', 'west_east')\n",
    "\n",
    "    domain_type = 'Land Only'\n",
    "\n",
    "else:\n",
    "    domain_type = 'Full Domain'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d78b630",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "from scipy.ndimage import label, binary_fill_holes\n",
    "from skimage.measure import regionprops\n",
    "\n",
    "\n",
    "def define_cold_pool_objects(b_ds, threshold=-0.005, min_size=1):\n",
    "    '''\n",
    "    Returns list of arrays (one for each timestep) that contain cold pool objects based on \n",
    "    desired threshold.\n",
    "\n",
    "    b_ds: buoyancy dataset\n",
    "    threshold: buoyancy threshold used to define cold pools\n",
    "    min_size: keep objects only of a certain size (number of grid cells)\n",
    "    '''\n",
    "    tb = b_ds['integrated_b'].values\n",
    "\n",
    "    labeled_results = []\n",
    "\n",
    "    for t in range(tb.shape[0]):\n",
    "        frame = tb[t, :, :]\n",
    "\n",
    "        # Create binary mask\n",
    "        mask = frame < threshold\n",
    "        \n",
    "        # Label contiguous regions (8-connectivity)\n",
    "        labeled, num_features = label(mask, structure=np.ones((3, 3)))\n",
    "        \n",
    "        # Optionally 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",
    "        # Re-label after filtering\n",
    "        labeled, _ = label(labeled > 0)\n",
    "        \n",
    "        labeled_results.append(labeled)\n",
    "        print(t)\n",
    "\n",
    "    return labeled_results\n",
    "\n",
    "    # Now labeled_results is a list of labeled 2D arrays, one per timestep\n",
    "\n",
    "c_labeled_results = define_cold_pool_objects(c_b_ds)\n",
    "f_labeled_results = define_cold_pool_objects(f_b_ds)\n",
    "fu_labeled_results = define_cold_pool_objects(fu_b_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "963d2679",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "\n",
    "######## Filters out cold pool objects to keep only ones which intersect with 30% of the area of given mask ###########\n",
    "\n",
    "def filter_cold_pool_objects(labeled_results, mask_array, min_overlap_frac=0.3):\n",
    "    filtered_labeled_results = []\n",
    "\n",
    "    for t in range(len(labeled_results)):\n",
    "        labeled = labeled_results[t]\n",
    "        mask = mask_array[t, :, :].values  # Already boolean array (True/False)\n",
    "        \n",
    "        labeled_flat = labeled.ravel()\n",
    "        mask_flat = mask.ravel()\n",
    "\n",
    "        # Total pixel count per label\n",
    "        total_counts = np.bincount(labeled_flat)\n",
    "\n",
    "        # Overlap counts per label (where mask is True)\n",
    "        overlap_counts = np.bincount(labeled_flat, weights=mask_flat.astype(int))\n",
    "\n",
    "        # Avoid background (label 0)\n",
    "        valid_labels = []\n",
    "        for obj_id in range(1, len(total_counts)):\n",
    "            total_area = total_counts[obj_id]\n",
    "            overlap_area = overlap_counts[obj_id]\n",
    "            frac = overlap_area / total_area if total_area > 0 else 0\n",
    "            if frac >= min_overlap_frac:\n",
    "                valid_labels.append(obj_id)\n",
    "\n",
    "        valid_mask = np.isin(labeled, valid_labels)\n",
    "        filtered_labeled = labeled * valid_mask\n",
    "\n",
    "        filtered_labeled_results.append(filtered_labeled)\n",
    "        print(t)\n",
    "\n",
    "    return filtered_labeled_results\n",
    "\n",
    "\n",
    "c_filtered_labeled_results = np.array(filter_cold_pool_objects(c_labeled_results, c_cw_mask['conv_wind_mask']))\n",
    "f_filtered_labeled_results = np.array(filter_cold_pool_objects(f_labeled_results, f_cw_mask['conv_wind_mask']))\n",
    "fu_filtered_labeled_results = np.array(filter_cold_pool_objects(fu_labeled_results, fu_cw_mask['conv_wind_mask']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "805dd547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the figure\n",
    "lats = c_b_ds.XLAT\n",
    "lons = c_b_ds.XLONG\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "\n",
    "#cb = ax.pcolormesh(lons, lats, ds2[0,:,:], cmap='jet')\n",
    "#cb = ax.pcolormesh(lons, lats, c_b_ds['integrated_b'][231,:,:], cmap='coolwarm', vmin=-100)\n",
    "\n",
    "#contour = ax.contour(lons, lats, labeled_results[231], levels=[0], colors=\"purple\", linewidths=1.5)\n",
    "contour = ax.contour(lons, lats, f_filtered_labeled_results[20,:,:], levels=[0], colors=\"purple\", linewidths=2)\n",
    "#contour = ax.contour(lons, lats, c_filtered_labeled_results[20,:,:], levels=[0], colors=\"orange\", linewidths=2)\n",
    "contour = ax.contour(lons, lats, f_cw_mask['conv_wind_mask'][20,:,:], levels=[0], colors=\"orange\", linewidths=1.5)\n",
    "\n",
    "#contour = ax.contour(lons, lats, f_cpi_ds['CPI'].max(dim='Time'), levels=[0], colors=\"purple\", linewidths=1.5, label='Wind Mask')\n",
    "\n",
    "winds = c_winds_ds['wspd_wdir10'].sel(wspd_wdir='wspd')\n",
    "\n",
    "#contour = ax.contour(lons, lats, winds[231,:,:], levels=[0,1], colors=\"limegreen\", linewidths=1.5)\n",
    "#cb = ax.pcolormesh(lons, lats, winds[231,:,:], cmap='coolwarm')\n",
    "\n",
    "#ax.set_extent([-96,-90,26,32])\n",
    "\n",
    "# Add labels and title\n",
    "ax.add_feature(cfeature.STATES, edgecolor=\"black\", linewidths=0.35, alpha=0.8)\n",
    "ax.add_feature(cfeature.COASTLINE, edgecolor=\"black\", linewidths=0.35, alpha=0.8)\n",
    "#ax.set_title(f\"Time: {ds2.Time.values}\")\n",
    "ax.set_xlabel(\"Longitude\")\n",
    "ax.set_ylabel(\"Latitude\")\n",
    "#cbar = plt.colorbar(cb, ax=ax, orientation='vertical', fraction=0.05, pad=0.02, shrink=0.9, extend='both')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c675eec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "######### CPI calculation ##########\n",
    "c_cpi = np.sqrt(-2*c_b_ds['integrated_b'])\n",
    "f_cpi = np.sqrt(-2*f_b_ds['integrated_b'])\n",
    "fu_cpi = np.sqrt(-2*fu_b_ds['integrated_b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7250c644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "del c_cw_mask, f_cw_mask, fu_cw_mask\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "646501d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#c_cpi.to_netcdf('/pscratch/sd/d/dbrooks/acc2017_analysis/TS_Cindy/current_cpi.nc')\n",
    "#f_cpi.to_netcdf('/pscratch/sd/d/dbrooks/acc2017_analysis/TS_Cindy/future_cpi.nc')\n",
    "#fu_cpi.to_netcdf('/pscratch/sd/d/dbrooks/acc2017_analysis/TS_Cindy/future_urban_cpi.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eaf2f296",
   "metadata": {},
   "outputs": [],
   "source": [
    "#c_cpi = xr.open_dataarray('/pscratch/sd/d/dbrooks/acc2017_analysis/TS_Cindy/current_cpi.nc')\n",
    "#f_cpi = xr.open_dataarray('/pscratch/sd/d/dbrooks/acc2017_analysis/TS_Cindy/future_cpi.nc')\n",
    "#fu_cpi = xr.open_dataarray('/pscratch/sd/d/dbrooks/acc2017_analysis/TS_Cindy/future_urban_cpi.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "01329bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib widget\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.widgets import Slider\n",
    "\n",
    "import cmweather\n",
    "wind_cmap = cmweather.cm_colorblind.ChaseSpectral\n",
    "#wind_cmap = pyart.graph.cm.LangRainbow12\n",
    "\n",
    "#p_clevs=np.arange(0,61,5)\n",
    "#p_clevs=[1,3,5,7,9,12,15,20,25,30,35,40,45,50]\n",
    "p_clevs=[1,2,3,4,5,6,8,10,12,14,16,20,24]\n",
    "p_cmap = mcolors.ListedColormap(wind_cmap(np.linspace(0.1,0.8,len(p_clevs))))\n",
    "p_norm = mcolors.BoundaryNorm(p_clevs, len(p_clevs))\n",
    "#norm = mcolors.BoundaryNorm(clevs, cmap.N)\n",
    "#p_cmap.set_over(wind_cmap(np.linspace(0.9,0.91,1)))\n",
    "p_cmap.set_over('#feb2fa')\n",
    "p_cmap.set_under('white')\n",
    "\n",
    "def plot_cpi_with_slider(current_radar_da=c_cpi, \n",
    "                           future_radar_da=f_cpi, \n",
    "                           future_urban_radar_da=fu_cpi):\n",
    "    \"\"\"\n",
    "    Plots radar reflectivity data for three simulations with a slider to move through timesteps.\n",
    "\n",
    "    Parameters:\n",
    "        current_radar_da (xarray.DataArray): Current simulation radar data.\n",
    "        future_radar_da (xarray.DataArray): Future simulation radar data.\n",
    "        future_urban_radar_da (xarray.DataArray): Future-Urban simulation radar data.\n",
    "    \"\"\"\n",
    "    # Get lat/lon for georeferencing\n",
    "    lats = current_radar_da.XLAT.values\n",
    "    lons = current_radar_da.XLONG.values\n",
    "    timesteps = current_radar_da.Time.values  # Shared timesteps for all simulations\n",
    "\n",
    "    # Create the 1x3 subplot\n",
    "    fig, axs = plt.subplots(1, 3, figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "    fig.subplots_adjust(bottom=0.2)  # Make room for the slider and its label\n",
    "\n",
    "    \n",
    "    # Initial timestep index\n",
    "    initial_timestep = 0\n",
    "\n",
    "    # Titles for subplots\n",
    "    titles = [\"Current\", \"Future\", \"Future+Urban\"]\n",
    "\n",
    "    # Plot the initial timestep for each simulation\n",
    "    plots = []\n",
    "    datasets = [current_radar_da, future_radar_da, future_urban_radar_da]\n",
    "\n",
    "    for i, ax in enumerate(axs):\n",
    "        radar_data = datasets[i].isel(Time=initial_timestep)\n",
    "        plot = ax.pcolormesh(lons, lats, radar_data, cmap=p_cmap, norm=p_norm, transform=ccrs.PlateCarree())\n",
    "        ax.add_feature(cfeature.STATES, edgecolor=\"black\", linewidth=0.5)\n",
    "        ax.add_feature(cfeature.COASTLINE, edgecolor=\"black\", linewidth=0.5)\n",
    "        ax.set_title(titles[i], fontsize=12)\n",
    "        plots.append(plot)\n",
    "        #ax.set_extent([-96,-92,31,36])\n",
    "        ax.set_extent([-96,-92,27,32])\n",
    "\n",
    "        gl = ax.gridlines(draw_labels=True, linewidth=0.5, color='gray', alpha=0.25, linestyle='--', zorder=2)\n",
    "        gl.top_labels = False\n",
    "        gl.right_labels = False\n",
    "        gl.left_labels = True\n",
    "        gl.bottom_labels = True\n",
    "        gl.xlabel_style = {'size': 8, 'color': 'black'}\n",
    "        gl.ylabel_style = {'size': 8, 'color': 'black'}\n",
    "    \n",
    "    # Add a colorbar for each plot\n",
    "    for i, ax in enumerate(axs):\n",
    "        cbar = fig.colorbar(plots[i], ax=ax, orientation='horizontal', fraction=0.04, pad=0.05, shrink=0.8, extend='both')\n",
    "        cbar.set_label(\"CPI (m s$^{-1}$)\")\n",
    "\n",
    "    # Create the slider\n",
    "    ax_slider = plt.axes([0.25, 0.05, 0.5, 0.03])  # [left, bottom, width, height]\n",
    "    slider = Slider(ax_slider, \"\", 0, len(timesteps) - 1, valinit=initial_timestep, valstep=1)\n",
    "\n",
    "    # Add a label to display the selected time\n",
    "    time_label = plt.text(0.5, 0.1, \"\", transform=fig.transFigure, ha=\"center\", fontsize=12)\n",
    "\n",
    "    def format_time_label(index):\n",
    "        \"\"\"Formats the time label in 'Y-m-d HH:MMz'.\"\"\"\n",
    "        timestep = timesteps[int(index)]\n",
    "        return timestep.astype('M8[s]').item().strftime(\"%Y-%m-%d %H:%Mz\")\n",
    "\n",
    "    # Update function for the slider\n",
    "    def update(val):\n",
    "        timestep = int(slider.val)  # Get the timestep index\n",
    "        for i, plot in enumerate(plots):\n",
    "            # Update data for each subplot\n",
    "            plot.set_array(datasets[i].isel(Time=timestep).values.ravel())\n",
    "        # Update the time label\n",
    "        time_label.set_text(format_time_label(timestep))\n",
    "        # Redraw the figure\n",
    "        fig.canvas.draw_idle()\n",
    "\n",
    "    # Initialize the time label with the first timestep\n",
    "    time_label.set_text(format_time_label(initial_timestep))\n",
    "\n",
    "    # Connect the slider to the update function\n",
    "    slider.on_changed(update)\n",
    "\n",
    "# Call the function in your notebook\n",
    "#plot_cpi_with_slider()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "730bc507",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   ],
   "source": [
    "########## Get dataframes of averaged buoyancy for each cold pool object ############\n",
    "\n",
    "def get_dataframes_of_coldpool_properties(filtered_labeled_results, b_ds, wind_ds):\n",
    "    '''\n",
    "    Returns list of dataframes with cold pool properties. \n",
    "    '''\n",
    "    list_of_dataframes = []\n",
    "\n",
    "    time_values = b_ds['Time'].values\n",
    "    buoyancy_data = b_ds.values  # shape: [T, Y, X]\n",
    "    windspeed_data = wind_ds['wspd_wdir10'].sel(wspd_wdir='wspd').values  # shape: [T, Y, X]\n",
    "\n",
    "    for t, labeled in enumerate(filtered_labeled_results):\n",
    "        buoyancy = buoyancy_data[t]\n",
    "        windspeed = windspeed_data[t]\n",
    "        time = pd.to_datetime(time_values[t])\n",
    "\n",
    "        object_ids = np.unique(labeled[labeled > 0])  # Skip background\n",
    "\n",
    "        data = []\n",
    "        for obj_id in object_ids:\n",
    "            obj_mask = (labeled == obj_id)\n",
    "\n",
    "            # Flattened values within object\n",
    "            wind_vals = windspeed[obj_mask]\n",
    "            cpi_vals = buoyancy[obj_mask]\n",
    "\n",
    "            mean_buoyancy = cpi_vals.mean()\n",
    "            max_buoyancy = cpi_vals.max()\n",
    "            num_grid_cells = obj_mask.sum()\n",
    "            max_wind_speed = np.nanmax(wind_vals)\n",
    "\n",
    "            # Flat indices of object\n",
    "            flat_indices = np.flatnonzero(obj_mask)\n",
    "            #print(np.nanargmax(wind_vals))\n",
    "            wind_max_idx = flat_indices[np.nanargmax(wind_vals)]\n",
    "            cpi_max_idx = flat_indices[np.nanargmax(cpi_vals)]\n",
    "\n",
    "            # 2D positions\n",
    "            y_wind, x_wind = np.unravel_index(wind_max_idx, obj_mask.shape)\n",
    "            y_cpi, x_cpi = np.unravel_index(cpi_max_idx, obj_mask.shape)\n",
    "\n",
    "            pixel_distance = np.hypot(y_wind - y_cpi, x_wind - x_cpi)\n",
    "\n",
    "            data.append({\n",
    "                'object_id': obj_id,\n",
    "                'time': time,\n",
    "                'mean_CPI': mean_buoyancy,\n",
    "                'num_grid_cells': num_grid_cells,\n",
    "                'max_wind_speed': max_wind_speed,\n",
    "                'max_cpi': max_buoyancy,\n",
    "                'distance_maxwind_maxcpi': pixel_distance\n",
    "            })\n",
    "\n",
    "        df = pd.DataFrame(data)\n",
    "        list_of_dataframes.append(df)\n",
    "\n",
    "        print(f\"Processed timestep: {t}\")\n",
    "\n",
    "    return list_of_dataframes\n",
    "\n",
    "\n",
    "c_dfs = get_dataframes_of_coldpool_properties(c_filtered_labeled_results, c_cpi, c_winds_ds)\n",
    "f_dfs = get_dataframes_of_coldpool_properties(f_filtered_labeled_results, f_cpi, f_winds_ds)\n",
    "fu_dfs = get_dataframes_of_coldpool_properties(fu_filtered_labeled_results, fu_cpi, fu_winds_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3e4898d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "c_combined_df = pd.concat(c_dfs, ignore_index=True)\n",
    "f_combined_df = pd.concat(f_dfs, ignore_index=True)\n",
    "fu_combined_df = pd.concat(fu_dfs, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1df7b158",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>object_id</th>\n",
       "      <th>time</th>\n",
       "      <th>mean_CPI</th>\n",
       "      <th>num_grid_cells</th>\n",
       "      <th>max_wind_speed</th>\n",
       "      <th>max_cpi</th>\n",
       "      <th>distance_maxwind_maxcpi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>840</td>\n",
       "      <td>2017-04-01 00:00:00</td>\n",
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       "      <th>3</th>\n",
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       "    </tr>\n",
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       "      <th>117889</th>\n",
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       "      <td>2017-04-30 21:00:00</td>\n",
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       "      <td>6.628929</td>\n",
       "      <td>5.709174</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>117890</th>\n",
       "      <td>7193</td>\n",
       "      <td>2017-04-30 21:00:00</td>\n",
       "      <td>6.427057</td>\n",
       "      <td>9</td>\n",
       "      <td>6.892364</td>\n",
       "      <td>7.071909</td>\n",
       "      <td>1.414214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117891</th>\n",
       "      <td>7195</td>\n",
       "      <td>2017-04-30 21:00:00</td>\n",
       "      <td>1.501057</td>\n",
       "      <td>1</td>\n",
       "      <td>8.977571</td>\n",
       "      <td>1.501057</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117892</th>\n",
       "      <td>7196</td>\n",
       "      <td>2017-04-30 21:00:00</td>\n",
       "      <td>6.946561</td>\n",
       "      <td>2</td>\n",
       "      <td>7.136274</td>\n",
       "      <td>6.978574</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117893</th>\n",
       "      <td>7227</td>\n",
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       "      <td>2.137049</td>\n",
       "      <td>1</td>\n",
       "      <td>8.794775</td>\n",
       "      <td>2.137049</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117894 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        object_id                time  mean_CPI  num_grid_cells  \\\n",
       "0             840 2017-04-01 00:00:00       NaN             412   \n",
       "1             879 2017-04-01 00:00:00  4.687703             101   \n",
       "2            1179 2017-04-01 00:00:00  2.724778             107   \n",
       "3            1347 2017-04-01 00:00:00       NaN             108   \n",
       "4            1380 2017-04-01 00:00:00  4.256419              95   \n",
       "...           ...                 ...       ...             ...   \n",
       "117889       7188 2017-04-30 21:00:00  5.709174               1   \n",
       "117890       7193 2017-04-30 21:00:00  6.427057               9   \n",
       "117891       7195 2017-04-30 21:00:00  1.501057               1   \n",
       "117892       7196 2017-04-30 21:00:00  6.946561               2   \n",
       "117893       7227 2017-04-30 21:00:00  2.137049               1   \n",
       "\n",
       "        max_wind_speed   max_cpi  distance_maxwind_maxcpi  \n",
       "0            11.798527       NaN                18.601075  \n",
       "1             9.049084  9.455496                 3.162278  \n",
       "2             9.842138  4.618817                20.808652  \n",
       "3            15.098543       NaN                 1.000000  \n",
       "4            12.020343  7.851500                 2.236068  \n",
       "...                ...       ...                      ...  \n",
       "117889        6.628929  5.709174                 0.000000  \n",
       "117890        6.892364  7.071909                 1.414214  \n",
       "117891        8.977571  1.501057                 0.000000  \n",
       "117892        7.136274  6.978574                 1.000000  \n",
       "117893        8.794775  2.137049                 0.000000  \n",
       "\n",
       "[117894 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1cf6f2af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#c_combined_df = c_combined_df[c_combined_df['num_grid_cells'] >= 10]\n",
    "#f_combined_df = f_combined_df[f_combined_df['num_grid_cells'] >= 10]\n",
    "#fu_combined_df = fu_combined_df[fu_combined_df['num_grid_cells'] >= 10]\n",
    "\n",
    "#c_combined_df = c_combined_df[c_combined_df['num_grid_cells'] < 10]\n",
    "#f_combined_df = f_combined_df[f_combined_df['num_grid_cells'] < 10]\n",
    "#fu_combined_df = fu_combined_df[fu_combined_df['num_grid_cells'] < 10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "de80c4e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n# Linear regression\\nslope, intercept, r_value, p_value, std_err = linregress(combined_df['mean_CPI'], combined_df['max_wind_speed'])\\nline_x = np.linspace(combined_df['mean_CPI'].min(), combined_df['mean_CPI'].max(), 100)\\nline_y = slope * line_x + intercept\\n\\n# Plot\\nplt.figure(figsize=(6, 6))\\nplt.scatter(combined_df['mean_CPI'], combined_df['max_wind_speed'], color='blue', alpha=0.7, label='Max wind per timestep')\\nplt.plot(line_x, line_y, color='red', label=f'Fit: y={slope:.2f}x+{intercept:.2f}, r={r_value:.2f}')\\nplt.xlabel('Cold Pool Intensity (CPI)')\\nplt.ylabel('Max Wind Speed (m/s)')\\nplt.title('Max Wind Speed vs CPI (One Point Per Timestep)')\\nplt.legend()\\nplt.grid(True)\\nplt.tight_layout()\\nplt.show()\\n\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import linregress\n",
    "'''\n",
    "# Linear regression\n",
    "slope, intercept, r_value, p_value, std_err = linregress(combined_df['mean_CPI'], combined_df['max_wind_speed'])\n",
    "line_x = np.linspace(combined_df['mean_CPI'].min(), combined_df['mean_CPI'].max(), 100)\n",
    "line_y = slope * line_x + intercept\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(combined_df['mean_CPI'], combined_df['max_wind_speed'], color='blue', alpha=0.7, label='Max wind per timestep')\n",
    "plt.plot(line_x, line_y, color='red', label=f'Fit: y={slope:.2f}x+{intercept:.2f}, r={r_value:.2f}')\n",
    "plt.xlabel('Cold Pool Intensity (CPI)')\n",
    "plt.ylabel('Max Wind Speed (m/s)')\n",
    "plt.title('Max Wind Speed vs CPI (One Point Per Timestep)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5fbbf6ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=np.float64(0.9286108541990863), pvalue=np.float64(2.501209450826328e-15))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=np.float64(0.9156233926799697), pvalue=np.float64(3.298678717827241e-14))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=np.float64(0.9142582392237324), pvalue=np.float64(1.6850100676671357e-14))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def binned_mean_wind_vs_cpi(df, sim, bin_width=1.0, wind_threshold=0, percentile=90, n_bootstrap=100):\n",
    "    wspd = df['max_wind_speed'] \n",
    "    cpi = df['max_cpi']  \n",
    "\n",
    "    # Flatten arrays\n",
    "    wspd = wspd.values\n",
    "    cpi = cpi.values\n",
    "\n",
    "    # Filter invalid or thresholded data\n",
    "    mask = (\n",
    "        (wspd > wind_threshold) &\n",
    "        #(cpi > 6) & (cpi < 12) &\n",
    "        ~np.isnan(wspd) &\n",
    "        ~np.isnan(cpi)\n",
    "    )\n",
    "\n",
    "    cpi_filtered = cpi[mask]\n",
    "    wspd_filtered = wspd[mask]\n",
    "    cp_size = df['num_grid_cells'].values[mask]\n",
    "    distance = df['distance_maxwind_maxcpi'].values[mask]\n",
    "\n",
    "    #print(cpi_filtered)\n",
    "\n",
    "    # Bin CPI\n",
    "    cpi_min = np.floor(cpi_filtered.min())\n",
    "    cpi_max = np.ceil(cpi_filtered.max())\n",
    "    bins = np.arange(cpi_min, cpi_max + bin_width, bin_width)\n",
    "    bin_centers = (bins[:-1] + bins[1:]) / 2\n",
    "\n",
    "\n",
    "    mean_size = []\n",
    "    mean_winds = []\n",
    "    std_winds = []\n",
    "    bin_counts = []\n",
    "    mean_distance = []\n",
    "\n",
    "    for i in range(len(bins) - 1):\n",
    "        in_bin = (cpi_filtered >= bins[i]) & (cpi_filtered < bins[i + 1])\n",
    "        bin_winds = wspd_filtered[in_bin]\n",
    "        sizes = cp_size[in_bin]\n",
    "        d = distance[in_bin]\n",
    "        count = len(bin_winds)\n",
    "        bin_counts.append(count)\n",
    "\n",
    "        if count >= 100:\n",
    "            cutoff = np.percentile(bin_winds, percentile)\n",
    "            top_pct = bin_winds[bin_winds >= cutoff]\n",
    "            mean_winds.append(np.mean(top_pct))\n",
    "            std_winds.append(np.std(top_pct))\n",
    "            mean_size.append(np.mean(sizes))\n",
    "            mean_distance.append(np.mean(d))\n",
    "        else:\n",
    "            mean_winds.append(np.nan)\n",
    "            mean_size.append(np.nan)\n",
    "            std_winds.append(np.nan)\n",
    "            mean_distance.append(np.nan)\n",
    "\n",
    "    \n",
    "\n",
    "    mean_winds = np.array(mean_winds)\n",
    "    mean_size = np.array(mean_size)\n",
    "    bin_counts = np.array(bin_counts)\n",
    "    std_winds = np.array(std_winds)\n",
    "    mean_distance = np.array(mean_distance)\n",
    "    #print(mean_winds)\n",
    "    #print(bin_counts)\n",
    "\n",
    "    # Bootstrap regression CI\n",
    "    valid = ~np.isnan(mean_winds)\n",
    "    x = bin_centers[valid]\n",
    "    y = mean_winds[valid]\n",
    "\n",
    "    R = pearsonr(x,y)\n",
    "    print(R)\n",
    "\n",
    "    line_x = np.linspace(x.min(), x.max(), 100)\n",
    "\n",
    "    #################### Bootstrapped resampling #####################\n",
    "    y_bootstrap = []\n",
    "    for _ in range(n_bootstrap):\n",
    "        # Resample (cpi, wspd) pairs\n",
    "        idx = np.random.choice(len(cpi_filtered), size=len(cpi_filtered), replace=True)\n",
    "        cpi_sample = cpi_filtered[idx]\n",
    "        wspd_sample = wspd_filtered[idx]\n",
    "\n",
    "        # Re-bin the resampled data\n",
    "        mean_winds_bs = []\n",
    "        for i in range(len(bins) - 1):\n",
    "            in_bin = (cpi_sample >= bins[i]) & (cpi_sample < bins[i + 1])\n",
    "            bin_winds = wspd_sample[in_bin]\n",
    "\n",
    "            if len(bin_winds) >= 100:\n",
    "                cutoff = np.percentile(bin_winds, percentile)\n",
    "                top_pct = bin_winds[bin_winds >= cutoff]\n",
    "                mean_winds_bs.append(np.mean(top_pct))\n",
    "            else:\n",
    "                mean_winds_bs.append(np.nan)\n",
    "\n",
    "        mean_winds_bs = np.array(mean_winds_bs)\n",
    "        valid = ~np.isnan(mean_winds_bs)\n",
    "\n",
    "        if valid.sum() > 1:\n",
    "            x_bs = bin_centers[valid]\n",
    "            y_bs = mean_winds_bs[valid]\n",
    "            slope, intercept, *_ = linregress(x_bs, y_bs)\n",
    "            y_bootstrap.append(slope * line_x + intercept)\n",
    "\n",
    "    y_bootstrap = np.array(y_bootstrap)\n",
    "\n",
    "    lower = np.percentile(y_bootstrap, 2.5, axis=0)\n",
    "    upper = np.percentile(y_bootstrap, 97.5, axis=0)\n",
    "    #############################################################################\n",
    "    # Final fit (for central line)\n",
    "    slope, intercept, r, *_ = linregress(x, y)\n",
    "    line_y = slope * line_x + intercept\n",
    "    #print(r)\n",
    "\n",
    "    if sim=='Future':\n",
    "        color='#1E88E5'\n",
    "    elif sim=='Future+Urban':\n",
    "        color='#D81B60'\n",
    "    elif sim=='Current':\n",
    "        color='black'\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    fig, ax1 = plt.subplots(figsize=(3, 3))\n",
    "\n",
    "    # Primary Y-axis: Wind speed vs CPI\n",
    "    ax1.scatter(bin_centers, mean_winds, color=color, s=10)\n",
    "\n",
    "    # Only plot band where valid\n",
    "    valid = ~np.isnan(mean_winds) & ~np.isnan(std_winds)\n",
    "    plt.fill_between(\n",
    "        bin_centers[valid],\n",
    "        mean_winds[valid] - std_winds[valid],\n",
    "        mean_winds[valid] + std_winds[valid],\n",
    "        color=color,\n",
    "        alpha=0.2,\n",
    "        label='±1σ'\n",
    "    )\n",
    "\n",
    "    # regression line\n",
    "    ax1.plot(line_x, line_y, color=color, label=f'r={r:.2f}\\nm={slope:.2f}')\n",
    "    #ax1.fill_between(line_x, lower, upper, color=color, alpha=0.15)\n",
    "\n",
    "    ax1.set_xlabel('CPI')\n",
    "    ax1.set_ylabel('Wind Speed (m s$^{-1}$)', color=color)\n",
    "    ax1.tick_params(axis='y', labelcolor=color)\n",
    "    #ax1.set_xlim(0, 18)\n",
    "    #ax1.set_ylim(4, 12)\n",
    "    ax1.grid(True, linestyle='--', alpha=0.5)\n",
    "\n",
    "    '''\n",
    "    # Secondary Y-axis: bin counts (or any other metric)\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.plot(bin_centers, mean_size, color='gray', linestyle='-',linewidth=0.8)\n",
    "    ax2.set_ylabel('Mean Cold Pool Size\\n(# of Grid Cells)', color='gray')\n",
    "    #ax2.plot(bin_centers, mean_distance, color='gray', linestyle='-',linewidth=0.8)\n",
    "    #ax2.set_ylabel('Mean Distance From Max Wind to Max CPI\\n(# of Grid Cells)', color='gray')\n",
    "    ax2.tick_params(axis='y', labelcolor='gray')\n",
    "    ax2.set_ylim(0,200)\n",
    "    #ax2.set_ylim(0,4)\n",
    "    '''\n",
    "    \n",
    "\n",
    "    # Title formatting\n",
    "    if monlist[0] == '04':\n",
    "        month = 'April'\n",
    "    elif monlist[0] == '05':\n",
    "        month = 'May'\n",
    "    elif monlist[0] == '06':\n",
    "        month = 'June'\n",
    "\n",
    "    ax1.set_title(f'{sim}', loc='left')\n",
    "    ax1.set_title(f'({month})', loc='right')\n",
    "\n",
    "    # Legends\n",
    "    ax1.legend(loc='upper left')\n",
    "    #ax2.legend(loc='upper right')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "binned_mean_wind_vs_cpi(c_combined_df, sim='Current', bin_width=0.5, percentile=0)\n",
    "binned_mean_wind_vs_cpi(f_combined_df, sim='Future', bin_width=0.5, percentile=0)\n",
    "binned_mean_wind_vs_cpi(fu_combined_df, sim='Future+Urban', bin_width=0.5, percentile=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "efebb378",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117894\n",
      "[58.71969736 27.56204726  5.23436307  0.56236959]\n",
      "Bin 0-5:\n",
      "  Warming:       -1.03% \n",
      "  Combined:   -0.71% \n",
      "  Urbanization:     0.32% \n",
      "Bin 5-10:\n",
      "  Warming:       2.29% \n",
      "  Combined:   0.62% \n",
      "  Urbanization:     -1.63% \n",
      "Bin 10-15:\n",
      "  Warming:       2.13% \n",
      "  Combined:   5.28% \n",
      "  Urbanization:     3.09% \n",
      "Bin ≥15:\n",
      "  Warming:       14.32% \n",
      "  Combined:   22.84% \n",
      "  Urbanization:     7.46% \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "########### CPI PDFs #############\n",
    "# Define wind speed intensity bins and labels\n",
    "WIND_SPEED_BINS = [(0, 5), (5, 10), (10, 15), (15, np.inf)]\n",
    "#WIND_SPEED_BINS = [(0, 10), (10, 15), (15, 20), (20, np.inf)]\n",
    "BIN_LABELS = [f'{WIND_SPEED_BINS[0][0]}-{WIND_SPEED_BINS[0][1]}', f'{WIND_SPEED_BINS[1][0]}-{WIND_SPEED_BINS[1][1]}', f'{WIND_SPEED_BINS[2][0]}-{WIND_SPEED_BINS[2][1]}',f'≥{WIND_SPEED_BINS[3][0]}']\n",
    "\n",
    "# Functions to calculate relative changes\n",
    "def calculate_relative_change(future, current):\n",
    "    return ((future / current) - 1) * 100\n",
    "\n",
    "def calculate_relative_change_urbanization(future, current, future_urban):\n",
    "    #f = ((future / current) - 1) * 100\n",
    "    #fu = ((future_urban / current) - 1) * 100\n",
    "    #return fu - f\n",
    "    return ((future_urban - future) / future) * 100\n",
    "\n",
    "# Function to calculate frequency (number of occurrences) per intensity bin for wind speeds\n",
    "def calculate_frequency_per_bin(wind_data, bins):\n",
    "    bin_frequencies = []\n",
    "    for lower, upper in bins:\n",
    "        # Count occurrences within each bin across all time steps and grid points\n",
    "        bin_mask = (wind_data >= lower) & (wind_data < upper)\n",
    "        bin_frequency = bin_mask.sum().item()  # Total occurrences in this bin\n",
    "        bin_frequencies.append(bin_frequency)\n",
    "    return bin_frequencies\n",
    "\n",
    "def plot_cpi_frequency_bar(start_time=None, end_time=None):\n",
    "\n",
    "    # Step 1: Use all occurrences of wind speed values for each simulation across all time steps and grid cells\n",
    "    all_wind_current = c_combined_df['mean_CPI'] # Replace with the appropriate variable for wind speed in your dataset\n",
    "    all_wind_future = f_combined_df['mean_CPI']\n",
    "    all_wind_future_urban = fu_combined_df['mean_CPI']\n",
    "\n",
    "    #all_wind_current = c_combined_df['max_cpi'] # Replace with the appropriate variable for wind speed in your dataset\n",
    "    #all_wind_future = f_combined_df['max_cpi']\n",
    "    #all_wind_future_urban = fu_combined_df['max_cpi']\n",
    "\n",
    "    if land_only == True:\n",
    "\n",
    "        domain_type = 'Land Only'\n",
    "\n",
    "    else:\n",
    "        domain_type = 'Full Domain'\n",
    "\n",
    "    print(len(all_wind_current))\n",
    "\n",
    "    # Step 2: Calculate frequency for each bin for each simulation\n",
    "    freq_current = np.array(calculate_frequency_per_bin(all_wind_current, WIND_SPEED_BINS)) / len(all_wind_current) *100\n",
    "    freq_future = np.array(calculate_frequency_per_bin(all_wind_future, WIND_SPEED_BINS))  / len(all_wind_future) *100\n",
    "    freq_future_urban = np.array(calculate_frequency_per_bin(all_wind_future_urban, WIND_SPEED_BINS))  / len(all_wind_future_urban) *100\n",
    "\n",
    "    print(freq_current)\n",
    "\n",
    "    #print(freq_current)\n",
    "    #print(freq_future)\n",
    "    #print(freq_future_urban)\n",
    "\n",
    "    # Step 3: Calculate relative change between simulations\n",
    "    relative_change_future = calculate_relative_change(np.array(freq_future), np.array(freq_current))\n",
    "    relative_change_acc_urban = calculate_relative_change(np.array(freq_future_urban), np.array(freq_current))\n",
    "    relative_change_urban = calculate_relative_change_urbanization(\n",
    "        np.array(freq_future), np.array(freq_current), np.array(freq_future_urban)\n",
    "    )\n",
    "\n",
    "\n",
    "    # Step 4: Plotting\n",
    "    fig, ax1 = plt.subplots(figsize=(4.5, 4))\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.grid(True, axis='y', alpha=0.5, zorder=0)\n",
    "\n",
    "    # Bar width and positions\n",
    "    bar_width = 0.2\n",
    "    x = np.arange(len(WIND_SPEED_BINS))\n",
    "\n",
    "    # Plot bars for each simulation\n",
    "    ax1.bar(x - bar_width, freq_current, width=bar_width, color='black', label='Current',zorder=2)\n",
    "    ax1.bar(x, freq_future, width=bar_width, color='#1E88E5', label='Future',zorder=2)\n",
    "    ax1.bar(x + bar_width, freq_future_urban, width=bar_width, color='#D81B60', label='Future+Urban',zorder=2)\n",
    "\n",
    "    # Primary Y-axis (left)\n",
    "    ax1.set_xlabel('Mean CPI (m s$^{-1}$)')\n",
    "    #ax1.set_ylabel('Frequency (# of Occurrences)')\n",
    "    ax1.set_ylabel('% of Occurrences')\n",
    "    ax1.set_yscale('symlog', linthresh=1)\n",
    "    #ax1.set_yscale('log')\n",
    "    # Optional: Define custom ticks if desired\n",
    "    #custom_ticks = [1, 5, 10,20,40,100]\n",
    "    #ax1.set_yticks(custom_ticks)\n",
    "    #ax1.minorticks_off()\n",
    "    ax1.set_xticks(x)\n",
    "    ax1.set_xticklabels(BIN_LABELS)\n",
    "    ax1.set_ylim(0.1, 350)\n",
    "\n",
    "    # Secondary Y-axis (right) for relative changes\n",
    "    ax2.plot(x, relative_change_acc_urban, color='#FFB507', marker='o', linestyle='-', label='Warming+Urban', linewidth=2)\n",
    "    ax2.plot(x, relative_change_future, color='black', marker='o', linestyle='--', label='Warming', linewidth=2)\n",
    "    ax2.plot(x, relative_change_urban, color='black', marker='s', linestyle=':', label='Urban', linewidth=2)\n",
    "    ax2.set_ylabel('Relative change (%)', color='black')\n",
    "    ax2.set_ylim(-15, 30)  # Adjust based on expected range of relative changes\n",
    "\n",
    "    for i, bin_label in enumerate(BIN_LABELS):\n",
    "        \n",
    "        print(f\"Bin {bin_label}:\")\n",
    "        print(f\"  Warming:       {relative_change_future[i]:.2f}% \")\n",
    "        print(f\"  Combined:   {relative_change_acc_urban[i]:.2f}% \")\n",
    "        print(f\"  Urbanization:     {relative_change_urban[i]:.2f}% \")\n",
    "\n",
    " \n",
    "    # Add legend for secondary y-axis lines\n",
    "    #ax2.legend(loc='upper right', fontsize=8)\n",
    "    #ax1.legend(loc='upper left', fontsize=8)\n",
    "\n",
    "    if monlist[0] == '04':\n",
    "        month = 'April'\n",
    "    elif monlist[0] == '05':\n",
    "        month = 'May'\n",
    "    elif monlist[0] == '06':\n",
    "        month = 'June'\n",
    "        \n",
    "    #ax1.set_title(f'Frequency of CPI in {month}')\n",
    "    ax1.set_title(f'{month}')\n",
    "    #start_time = datetime.strptime(start_time, '%Y-%m-%dT%H:%M:%S')\n",
    "    #end_time = datetime.strptime(end_time, '%Y-%m-%dT%H:%M:%S')\n",
    "    #ax1.set_title(f'{start_time.strftime(\"%Y-%m-%d %Hz\")} to {end_time.strftime(\"%Y-%m-%d %Hz\")}\\n{domain_type}', fontsize=12)\n",
    "    fig.tight_layout()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "plot_cpi_frequency_bar()"
   ]
  }
 ],
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