{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "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",
    "\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": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_in_mask(sim, month):\n",
    "    filename = f'/pscratch/sd/d/dbrooks/masks/{sim}/convective_core_mask_withtimes_month{month}.nc'\n",
    "    #filename = f'/home/dbrooks/wind_analysis/radar_data/{sim}/composite_ref_month{month}.nc'\n",
    "    #print(ds)\n",
    "    ds = xr.open_dataset(filename)\n",
    "    ds = ds['__xarray_dataarray_variable__']\n",
    "    #ds = ds['mdbz'] > 5 \n",
    "    return ds\n",
    "\n",
    "#current_mask = load_in_mask('current','04')\n",
    "#future_mask = load_in_mask(sim_list[1],monlist[0])\n",
    "#future_urban_mask = load_in_mask(sim_list[2],monlist[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_in_monthly_data(month, hour_interval, climate_state, var_name, mask2d, num_bins=5000):\n",
    "    \"\"\"\n",
    "    Reads in all NetCDF files for a given month, extracts updraft and downdraft\n",
    "    vertical velocity data, and computes the 99th percentile per vertical level.\n",
    "\n",
    "    Parameters:\n",
    "        month (str): Month you want data for, e.g., '04'\n",
    "        climate_state (str): 'current', 'future', or 'future_urban'\n",
    "        var_name (str): Variable to load, e.g., \"wa\"\n",
    "        mask2d (xarray.DataArray): 2D mask to apply to the data\n",
    "        num_bins (int): Number of bins for histogram per level\n",
    "\n",
    "    Returns:\n",
    "        Two xarray Datasets containing 99th percentile values of downdrafts and updrafts.\n",
    "    \"\"\"\n",
    "\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}/{hour_interval}/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",
    "    file_list = sorted(glob.glob(file_path))\n",
    "\n",
    "    # Define vertical levels (these should match your dataset)\n",
    "    levels = np.linspace(0.5, 15, 59)\n",
    "\n",
    "    # Histogram bins for all levels\n",
    "    bin_edges = np.linspace(-10, 20, num_bins)  # Adjust range based on dataset\n",
    "\n",
    "    # Initialize histogram count arrays for each level\n",
    "    downdraft_counts = np.zeros((len(levels), len(bin_edges) - 1))\n",
    "    updraft_counts = np.zeros((len(levels), len(bin_edges) - 1))\n",
    "\n",
    "    for i, file in enumerate(file_list):\n",
    "        print(f\"Index: {i}, Processing: {file}\")\n",
    "\n",
    "        # Open the file lazily\n",
    "        ncfile = netCDF4.Dataset(file, 'r')\n",
    "\n",
    "        # Read vertical velocity data\n",
    "        data = getvar(ncfile, var_name)\n",
    "    \n",
    "        # Apply the mask\n",
    "        mask = mask2d[i, :, :]\n",
    "        expanded_mask = mask.expand_dims(dim={\"bottom_top\": data.sizes[\"bottom_top\"]})\n",
    "        data = xr.where(expanded_mask, data, float(\"nan\"))\n",
    "\n",
    "        # Get height levels\n",
    "        height = getvar(ncfile, \"height_agl\", units=\"km\")\n",
    "\n",
    "        ##### Downdrafts #####\n",
    "        #downdrafts = data.where(data < -1) * -1  # Mask for downdrafts\n",
    "        #downdrafts = interplevel(downdrafts, vert=height, desiredlev=levels)\n",
    "\n",
    "        ##### Updrafts #####\n",
    "        updrafts = data.where(data > 2)  # Mask for updrafts\n",
    "        updrafts = interplevel(updrafts, vert=height, desiredlev=levels)\n",
    "\n",
    "        # Flatten data per level and update histograms\n",
    "        for level_idx, level in enumerate(levels):\n",
    "            #level_downdrafts = downdrafts.sel(level=level).values.flatten()\n",
    "            level_updrafts = updrafts.sel(level=level).values.flatten()\n",
    "\n",
    "            # Update histograms per level\n",
    "            #downdraft_counts[level_idx] += np.histogram(level_downdrafts[~np.isnan(level_downdrafts)], bins=bin_edges)[0]\n",
    "            updraft_counts[level_idx] += np.histogram(level_updrafts[~np.isnan(level_updrafts)], bins=bin_edges)[0]\n",
    "\n",
    "        ncfile.close()\n",
    "\n",
    "    print(\"Estimating final 99th percentiles per level...\")\n",
    "\n",
    "    # Compute the 99th percentile per level\n",
    "    def compute_percentile(bin_edges, bin_counts, percentile=0.99):\n",
    "        cdf = np.cumsum(bin_counts, axis=1) / np.sum(bin_counts, axis=1, keepdims=True)\n",
    "        return np.array([bin_edges[np.searchsorted(cdf[i], percentile)] for i in range(len(bin_counts))])\n",
    "\n",
    "    #final_downdraft_99 = compute_percentile(bin_edges, downdraft_counts, 0.99)\n",
    "    final_updraft_99 = compute_percentile(bin_edges, updraft_counts, 0.99)\n",
    "\n",
    "    # Store as xarray Dataset\n",
    "    #final_downdraft_ds = xr.Dataset({\"wa\": ([\"level\"], final_downdraft_99)}, coords={\"level\": levels})\n",
    "    final_updraft_ds = xr.Dataset({\"w_99\": ([\"level\"], final_updraft_99)}, coords={\"level\": levels})\n",
    "\n",
    "    print(f\"Completed processing for {month}, {climate_state}\")\n",
    "\n",
    "    return final_updraft_ds\n",
    "\n",
    "def read_in_monthly_data2(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): what variable you want to load in, e.g. \"wspd_wdir10\"\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}/{hour_interval}/wrfout_d01_2017-06-01_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",
    "    # 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",
    "        ##-- read file            \n",
    "        ncfile = netCDF4.Dataset(file,'r')\n",
    "        #print(ncfile) \n",
    "        print(file)\n",
    "        data = getvar(ncfile,var_name)\n",
    "\n",
    "        # Interpolate to standard levels\n",
    "        height = getvar(ncfile, \"height_agl\", units=\"km\")\n",
    "        data = interplevel(data, vert=height, desiredlev=[3,8])\n",
    "        print(data)\n",
    "        lapse_rate = (data[1,:,:] - data[0,:,:]) / 5\n",
    "\n",
    "        data = lapse_rate.to_dataset(name='3_8km_lapse_rate')\n",
    "        #data3['mcin'] = data2\n",
    "        #print(data3)\n",
    "        array_list.append(data)\n",
    "        ncfile.close()\n",
    "\n",
    "    '''\n",
    "    array_list=[]\n",
    "    for file in file_list:\n",
    "        ##-- read file            \n",
    "        ncfile = netCDF4.Dataset(file,'r')\n",
    "        #print(ncfile) \n",
    "        print(file)\n",
    "        #data = getvar(ncfile,var_name)\n",
    "        data = getvar(ncfile,'uvmet')\n",
    "        data2 = getvar(ncfile,'uvmet10')\n",
    "\n",
    "        # Interpolate to standard levels\n",
    "        height = getvar(ncfile, \"height_agl\", units=\"km\")\n",
    "        data = interplevel(data, vert=height, desiredlev=[1,6,10])\n",
    "        #data2 = interplevel(data2, vert=height, desiredlev=[0.01,1,3,6,10,12])\n",
    "        #print(data2)\n",
    "\n",
    "        # 0-1km shear\n",
    "        u_1 = data[0,0,:,:] - data2[0,:,:] # u component\n",
    "        v_1 = data[1,0,:,:] - data2[1,:,:] # v component\n",
    "\n",
    "        bulk_shear_1 = np.sqrt(u_1**2 + v_1**2)\n",
    "\n",
    "        #print(data)\n",
    "\n",
    "        # 6-10km shear\n",
    "        u_6_10 = data[0,2,:,:] - data[0,1,:,:] # u component\n",
    "        v_6_10 = data[1,2,:,:] - data[1,1,:,:] # v component\n",
    "\n",
    "        bulk_shear_6_10 = np.sqrt(u_6_10**2 + v_6_10**2)\n",
    "\n",
    "        # 3-12km mean speed\n",
    "        data3 = getvar(ncfile,'wspd',units='m s-1')\n",
    "        data3 = interplevel(data3, vert=height, desiredlev=np.arange(3,12.1,1))\n",
    "        mean_speed = data3.mean(dim='level')\n",
    "        \n",
    "        #data = data.to_dataset(name=var_name)\n",
    "        dataset = bulk_shear_1.to_dataset(name='bulk_shear_0_1km')\n",
    "        dataset['bulk_shear_6_10km'] = bulk_shear_6_10\n",
    "        dataset['3_12km_mean'] = mean_speed\n",
    "        #data['uvmet10'] = data2\n",
    "        #data3['mcin'] = data2\n",
    "        #print(data3)\n",
    "        array_list.append(dataset)\n",
    "        ncfile.close()\n",
    "        '''\n",
    "\n",
    "    print('done')\n",
    "    combined_ds = xr.concat(array_list, dim='Time')\n",
    "\n",
    "    #combined_ds = combined_ds.to_dataset(name='vert_velo_mask')\n",
    "    #combined_ds[var].attrs['projection'] = str(combined_ds[var].attrs['projection'])\n",
    "    #combined_ds['mcin'].attrs['projection'] = str(combined_ds[var_name].attrs['projection'])\n",
    "\n",
    "    return combined_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ds2 = read_in_monthly_data2('06', '3hr','current', 'tc')\n",
    "#print(ds2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "monlist = ['06'] # months in the simulation\n",
    "sim_list = ['current','future','future_urban']\n",
    "w_direction = 'downdraft' # 'updraft' or 'downdraft'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "filename1 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/current/{w_direction}_freq_bylevel_UPDATED_month{monlist[0]}.nc'\n",
    "filename2 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future/{w_direction}_freq_bylevel_UPDATED_month{monlist[0]}.nc'\n",
    "filename3 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future_urban/{w_direction}_freq_bylevel_UPDATED_month{monlist[0]}.nc'\n",
    "\n",
    "#filename1 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/current/{w_direction}_freq_bylevel_redone_month{monlist[0]}.nc'\n",
    "#filename2 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future/{w_direction}_freq_bylevel_redone_month{monlist[0]}.nc'\n",
    "#filename3 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future_urban/{w_direction}_freq_bylevel_redone_month{monlist[0]}.nc'\n",
    "\n",
    "c_freq_ds = xr.open_dataset(filename1)\n",
    "f_freq_ds = xr.open_dataset(filename2)\n",
    "fu_freq_ds = xr.open_dataset(filename3)\n",
    "\n",
    "filename1 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/current/{w_direction}_sum_bylevel_UPDATED_month{monlist[0]}.nc'\n",
    "filename2 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future/{w_direction}_sum_bylevel_UPDATED_month{monlist[0]}.nc'\n",
    "filename3 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future_urban/{w_direction}_sum_bylevel_UPDATED_month{monlist[0]}.nc'\n",
    "\n",
    "c_sum_ds = xr.open_dataset(filename1)\n",
    "f_sum_ds = xr.open_dataset(filename2)\n",
    "fu_sum_ds = xr.open_dataset(filename3)\n",
    "'''\n",
    "filename1 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/current/{w_direction}_std_bylevel_redone_month{monlist[0]}.nc'\n",
    "filename2 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future/{w_direction}_std_bylevel_redone_month{monlist[0]}.nc'\n",
    "filename3 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future_urban/{w_direction}_std_bylevel_redone_month{monlist[0]}.nc'\n",
    "\n",
    "c_std_ds = xr.open_dataset(filename1)\n",
    "f_std_ds = xr.open_dataset(filename2)\n",
    "fu_std_ds = xr.open_dataset(filename3)\n",
    "\n",
    "'''\n",
    "filename1 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/current/{w_direction}_iqr_bylevel_UPDATED_month{monlist[0]}.nc'\n",
    "filename2 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future/{w_direction}_iqr_bylevel_UPDATED_month{monlist[0]}.nc'\n",
    "filename3 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future_urban/{w_direction}_iqr_bylevel_UPDATED_month{monlist[0]}.nc'\n",
    "\n",
    "c_iqr_ds = xr.open_dataset(filename1)\n",
    "f_iqr_ds = xr.open_dataset(filename2)\n",
    "fu_iqr_ds = xr.open_dataset(filename3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray (level: 59)> Size: 472B\n",
      "array([3.27816014, 3.68614354, 4.09705685, 4.36794528, 4.59592385,\n",
      "       4.69091233, 4.7112558 , 4.70490173, 4.6234777 , 4.51476051,\n",
      "       4.41121315, 4.28734153, 4.17299274, 4.10156946, 4.05153547,\n",
      "       4.02577169, 4.03073397, 4.04410646, 4.07720892, 4.0921512 ,\n",
      "       4.11244224, 4.12229748, 4.12017589, 4.12854254, 4.11958981,\n",
      "       4.11160894, 4.08796393, 4.07686723, 4.05805649, 4.0587338 ,\n",
      "       4.0500801 , 4.04370897, 4.03416567, 4.05731272, 4.07082496,\n",
      "       4.09875031, 4.12863268, 4.1712304 , 4.22079735, 4.27912068,\n",
      "       4.35149088, 4.43571779, 4.52895786, 4.62491239, 4.73792084,\n",
      "       4.83799272, 4.93409611, 5.0243921 , 5.09953355, 5.17021011,\n",
      "       5.25013553, 5.29589036, 5.32913015, 5.35413615, 5.33745208,\n",
      "       5.29685234, 5.23534498, 5.15059223, 5.03081702])\n",
      "Coordinates:\n",
      "  * level    (level) float64 472B 0.5 0.75 1.0 1.25 ... 14.25 14.5 14.75 15.0\n",
      "<xarray.DataArray (level: 59)> Size: 472B\n",
      "array([3.310462  , 3.73609173, 4.15004113, 4.42628199, 4.65881812,\n",
      "       4.75354162, 4.77589023, 4.76400254, 4.67199456, 4.56217528,\n",
      "       4.45155158, 4.32516745, 4.20250398, 4.12072214, 4.05839682,\n",
      "       4.01885868, 4.0195159 , 4.02828472, 4.05272893, 4.06694515,\n",
      "       4.09142138, 4.10212213, 4.10052244, 4.10260535, 4.09468947,\n",
      "       4.08012917, 4.04478813, 4.0322491 , 4.00993561, 4.0061892 ,\n",
      "       3.99153978, 3.97882574, 3.97033207, 3.98552821, 3.98848442,\n",
      "       4.00286146, 4.02388536, 4.05728794, 4.10020396, 4.14164014,\n",
      "       4.20777563, 4.28148065, 4.35023285, 4.42303852, 4.51539863,\n",
      "       4.59203323, 4.68117342, 4.7776091 , 4.86404507, 4.95693713,\n",
      "       5.05184921, 5.11361103, 5.16911503, 5.21514132, 5.23789624,\n",
      "       5.23910696, 5.21231377, 5.14819002, 5.05026535])\n",
      "Coordinates:\n",
      "  * level    (level) float64 472B 0.5 0.75 1.0 1.25 ... 14.25 14.5 14.75 15.0\n"
     ]
    }
   ],
   "source": [
    "percentile = 90 # choose from 50,70,90,or 99\n",
    "\n",
    "c_99_mean = c_sum_ds[f'w_{percentile}_sum'].sum(dim='Time') / c_freq_ds[f'w_{percentile}_freq'].sum(dim='Time')\n",
    "f_99_mean = f_sum_ds[f'w_{percentile}_sum'].sum(dim='Time') / f_freq_ds[f'w_{percentile}_freq'].sum(dim='Time')\n",
    "fu_99_mean = fu_sum_ds[f'w_{percentile}_sum'].sum(dim='Time') / fu_freq_ds[f'w_{percentile}_freq'].sum(dim='Time')\n",
    "\n",
    "print(c_99_mean)\n",
    "print(f_99_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quartiles\n",
    "c_q_25 = c_iqr_ds[f'w_{percentile}_q25']\n",
    "c_q_75 = c_iqr_ds[f'w_{percentile}_q75']\n",
    "\n",
    "f_q_25 = f_iqr_ds[f'w_{percentile}_q25']\n",
    "f_q_75 = f_iqr_ds[f'w_{percentile}_q75']\n",
    "\n",
    "fu_q_25 = fu_iqr_ds[f'w_{percentile}_q25']\n",
    "fu_q_75 = fu_iqr_ds[f'w_{percentile}_q75']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
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       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
       "  );\n",
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       "  );\n",
       "  --xr-background-color: var(\n",
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       "    var(--pst-color-on-background, white)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
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       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
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       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
       "  );\n",
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       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
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       "    var(--pst-color-on-background, #111111)\n",
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       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
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       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
       "  );\n",
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       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "  line-height: 1.6;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-obj-name,\n",
       ".xr-group-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-group-name::before {\n",
       "  content: \"📁\";\n",
       "  padding-right: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-name,\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "  margin-block-start: 0;\n",
       "  margin-block-end: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "  border: 2px solid transparent !important;\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0) !important;\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-top: 4px;\n",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
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       "\n",
       ".xr-group-box {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 0px 20px auto;\n",
       "  width: 100%;\n",
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       "\n",
       ".xr-group-box-vline {\n",
       "  grid-column-start: 1;\n",
       "  border-right: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "  width: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-hline {\n",
       "  grid-column-start: 2;\n",
       "  grid-row-start: 1;\n",
       "  height: 1em;\n",
       "  width: 20px;\n",
       "  border-bottom: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-group-box-contents {\n",
       "  grid-column-start: 3;\n",
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       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "  padding-right: 5px;\n",
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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
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       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
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       ".xr-var-item > div,\n",
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       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "  border-color: var(--xr-background-color-row-even);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  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",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\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-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\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",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;w_90_q25&#x27; (level: 59)&gt; Size: 472B\n",
       "[59 values with dtype=float64]\n",
       "Coordinates:\n",
       "  * level    (level) float64 472B 0.5 0.75 1.0 1.25 ... 14.25 14.5 14.75 15.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;w_90_q25&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 59</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b8f444f4-fe3e-4d4c-b980-6da620fcce1e' class='xr-array-in' type='checkbox' checked><label for='section-b8f444f4-fe3e-4d4c-b980-6da620fcce1e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[59 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-3a4f0b46-51ce-4ec9-9fc0-9bdd4ae680b5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3a4f0b46-51ce-4ec9-9fc0-9bdd4ae680b5' class='xr-section-summary' >Coordinates: <span>(1)</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'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.75 1.0 ... 14.5 14.75 15.0</div><input id='attrs-10c63b13-26d1-4e88-9132-2ba807c7d537' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10c63b13-26d1-4e88-9132-2ba807c7d537' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1e80e39-79bb-48c8-9bc6-6714be07ad5b' class='xr-var-data-in' type='checkbox'><label for='data-a1e80e39-79bb-48c8-9bc6-6714be07ad5b' 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'></dl></div><div class='xr-var-data'><pre>array([ 0.5 ,  0.75,  1.  ,  1.25,  1.5 ,  1.75,  2.  ,  2.25,  2.5 ,  2.75,\n",
       "        3.  ,  3.25,  3.5 ,  3.75,  4.  ,  4.25,  4.5 ,  4.75,  5.  ,  5.25,\n",
       "        5.5 ,  5.75,  6.  ,  6.25,  6.5 ,  6.75,  7.  ,  7.25,  7.5 ,  7.75,\n",
       "        8.  ,  8.25,  8.5 ,  8.75,  9.  ,  9.25,  9.5 ,  9.75, 10.  , 10.25,\n",
       "       10.5 , 10.75, 11.  , 11.25, 11.5 , 11.75, 12.  , 12.25, 12.5 , 12.75,\n",
       "       13.  , 13.25, 13.5 , 13.75, 14.  , 14.25, 14.5 , 14.75, 15.  ])</pre></div></li></ul></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'w_90_q25' (level: 59)> Size: 472B\n",
       "[59 values with dtype=float64]\n",
       "Coordinates:\n",
       "  * level    (level) float64 472B 0.5 0.75 1.0 1.25 ... 14.25 14.5 14.75 15.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_q_25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_99_freq = c_freq_ds[f'w_{percentile}_freq'].sum(dim='Time')\n",
    "f_99_freq = f_freq_ds[f'w_{percentile}_freq'].sum(dim='Time')\n",
    "fu_99_freq = fu_freq_ds[f'w_{percentile}_freq'].sum(dim='Time')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "Values at 6km and 12km for Downdrafts (June) - IQR\n",
      "============================================================\n",
      "\n",
      "--- 6.0 km ---\n",
      "Current:        4.120 m/s\n",
      "Warming:         4.101 m/s\n",
      "Warming+Urban:   4.098 m/s\n",
      "\n",
      "Relative Changes:\n",
      "Warming Effect:                -0.48%\n",
      "Combined Effect:                 -0.53%\n",
      "Urbanization Effect:       -0.05%\n",
      "\n",
      "--- 12.0 km ---\n",
      "Current:        4.934 m/s\n",
      "Warming:         4.681 m/s\n",
      "Warming+Urban:   4.699 m/s\n",
      "\n",
      "Relative Changes:\n",
      "Warming Effect:                -5.13%\n",
      "Combined Effect:                 -4.77%\n",
      "Urbanization Effect:       0.38%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############### Mean profile ################\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",
    "def compute_updraft_uncertainty(mean, std, freq, stat_type='se', q1=None, q3=None):\n",
    "    \"\"\"\n",
    "    Compute uncertainty bounds for updraft/downdraft profiles.\n",
    "    \n",
    "    Parameters:\n",
    "    - mean: xr.DataArray of mean values at each level\n",
    "    - std: xr.DataArray of standard deviations at each level\n",
    "    - freq: xr.DataArray of frequencies (sample sizes) at each level\n",
    "    - stat_type: 'iqr', 'std', or 'se' (standard error)\n",
    "    - q1: xr.DataArray of 25th percentile (required if stat_type='iqr')\n",
    "    - q3: xr.DataArray of 75th percentile (required if stat_type='iqr')\n",
    "    \n",
    "    Returns:\n",
    "    - lower: lower bound\n",
    "    - upper: upper bound\n",
    "    \"\"\"\n",
    "    if stat_type == 'iqr':\n",
    "        if q1 is None or q3 is None:\n",
    "            raise ValueError(\"q1 and q3 must be provided when stat_type='iqr'\")\n",
    "        lower = q1\n",
    "        upper = q3\n",
    "    elif stat_type == 'std':\n",
    "        lower = mean - std\n",
    "        upper = mean + std\n",
    "    elif stat_type == 'se':\n",
    "        se = std / np.sqrt(freq)\n",
    "        lower = mean - se\n",
    "        upper = mean + se\n",
    "    else:\n",
    "        raise ValueError(f\"stat_type must be 'iqr', 'std', or 'se', got '{stat_type}'\")\n",
    "    \n",
    "    return lower, upper\n",
    "\n",
    "def plot_updraft_profile(stat_type='iqr'):\n",
    "    \"\"\"\n",
    "    Plot updraft/downdraft vertical profile with selectable uncertainty measure.\n",
    "    \n",
    "    Parameters:\n",
    "    - stat_type: 'iqr' (interquartile range), 'std' (standard deviation), or 'se' (standard error, default)\n",
    "    \"\"\"\n",
    "    fig, ax1 = plt.subplots(figsize=(3, 3))\n",
    "    ax2 = ax1.twiny()\n",
    "\n",
    "    # Mean lines\n",
    "    ax1.plot(c_99_mean, c_99_mean.level, label=f\"Current\", color=\"black\", linewidth=2.5)\n",
    "    ax1.plot(f_99_mean, f_99_mean.level, label=f\"Future\", color=\"#1E88E5\", linewidth=2.5)\n",
    "    ax1.plot(fu_99_mean, fu_99_mean.level, label=f\"Future+Urban\", color=\"#D81B60\", linewidth=2.5)\n",
    "\n",
    "    # Relative changes\n",
    "    rel_change_acc = calculate_relative_change(f_99_mean, c_99_mean)\n",
    "    rel_change_acc_urban = calculate_relative_change(fu_99_mean, c_99_mean)\n",
    "    re_change_urban = calculate_relative_change_urbanization(f_99_mean, c_99_mean, fu_99_mean)\n",
    "\n",
    "    ax2.plot(rel_change_acc_urban, c_99_mean.level, color=\"#FFB507\", linestyle='-', linewidth=2.5)\n",
    "    ax2.plot(rel_change_acc, c_99_mean.level, color=\"black\", linestyle='--', linewidth=2.5)\n",
    "    ax2.plot(0, -100, label=f\"ACC Impact\", color=\"black\", linestyle='--', linewidth=2.5)\n",
    "    ax2.plot(re_change_urban, c_99_mean.level, label=f\"Urbanization Impact\", color=\"black\", linestyle=':', linewidth=2.5)\n",
    "    ax2.plot(np.zeros(17),np.arange(0,17,1), color=\"gray\", alpha=0.6)\n",
    "\n",
    "    # Compute uncertainty bounds based on stat_type\n",
    "    if stat_type == 'iqr':\n",
    "        # Use pre-computed quartiles\n",
    "        c_lower, c_upper = c_q_25, c_q_75\n",
    "        f_lower, f_upper = f_q_25, f_q_75\n",
    "        fu_lower, fu_upper = fu_q_25, fu_q_75\n",
    "    else:\n",
    "        # Get standard deviations for std or se\n",
    "        c_std_var = c_std_ds[f'w_{percentile}_std']\n",
    "        f_std_var = f_std_ds[f'w_{percentile}_std']\n",
    "        fu_std_var = fu_std_ds[f'w_{percentile}_std']\n",
    "        \n",
    "        c_lower, c_upper = compute_updraft_uncertainty(c_99_mean, c_std_var, c_99_freq, stat_type)\n",
    "        f_lower, f_upper = compute_updraft_uncertainty(f_99_mean, f_std_var, f_99_freq, stat_type)\n",
    "        fu_lower, fu_upper = compute_updraft_uncertainty(fu_99_mean, fu_std_var, fu_99_freq, stat_type)\n",
    "\n",
    "    # Plot uncertainty bands\n",
    "    ax1.fill_betweenx(c_99_mean.level, c_lower, c_upper, color='black', alpha=0.1)\n",
    "    ax1.fill_betweenx(f_99_mean.level, f_lower, f_upper, color=\"#1E88E5\", alpha=0.1)\n",
    "    ax1.fill_betweenx(fu_99_mean.level, fu_lower, fu_upper, color=\"#D81B60\", alpha=0.1)\n",
    "\n",
    "    # Month label\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",
    "    if w_direction == 'updraft':\n",
    "        title='Updraft'\n",
    "    else:\n",
    "        title='Downdraft'\n",
    "\n",
    "    # Labels and legend\n",
    "    ax1.set_xlabel(f\"{title} Speed (m s$^{{-1}}$)\")\n",
    "    ax1.set_ylabel(\"Height (km)\")\n",
    "    ax1.set_ylim(0,15)\n",
    "    \n",
    "    # Dynamic x-axis limits to include both mean and uncertainty bounds\n",
    "    all_means = [c_99_mean, f_99_mean, fu_99_mean]\n",
    "    all_lowers = [c_lower, f_lower, fu_lower]\n",
    "    all_uppers = [c_upper, f_upper, fu_upper]\n",
    "    \n",
    "    x_min = min(\n",
    "        min(l.min().values for l in all_lowers),\n",
    "        min(m.min().values for m in all_means)\n",
    "    )\n",
    "    x_max = max(\n",
    "        max(u.max().values for u in all_uppers),\n",
    "        max(m.max().values for m in all_means)\n",
    "    )\n",
    "    x_range = x_max - x_min\n",
    "    x_min = x_min - 0.05 * x_range\n",
    "    x_max = x_max + 0.05 * x_range\n",
    "    ax1.set_xlim(x_min, x_max)\n",
    "    \n",
    "    ax1.grid(alpha=0.75,axis='both', linestyle=':')\n",
    "\n",
    "    ax2.set_xlim(-10,10)\n",
    "    ax2.set_xlabel('Relative Change (%)')\n",
    "\n",
    "    # Print values at 6km and 12km\n",
    "    print(f\"\\n{'='*60}\")\n",
    "    print(f\"Values at 6km and 12km for {title}s ({month}) - {stat_type.upper()}\")\n",
    "    print(f\"{'='*60}\")\n",
    "\n",
    "    for height in [6.0, 12.0]:\n",
    "        print(f\"\\n--- {height} km ---\")\n",
    "        print(f\"Current:        {c_99_mean.sel(level=height, method='nearest').values:.3f} m/s\")\n",
    "        print(f\"Warming:         {f_99_mean.sel(level=height, method='nearest').values:.3f} m/s\")\n",
    "        print(f\"Warming+Urban:   {fu_99_mean.sel(level=height, method='nearest').values:.3f} m/s\")\n",
    "        print(f\"\\nRelative Changes:\")\n",
    "        print(f\"Warming Effect:                {rel_change_acc.sel(level=height, method='nearest').values:.2f}%\")\n",
    "        print(f\"Combined Effect:                 {rel_change_acc_urban.sel(level=height, method='nearest').values:.2f}%\")\n",
    "        print(f\"Urbanization Effect:       {re_change_urban.sel(level=height, method='nearest').values:.2f}%\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "# Choose stat_type: 'iqr' (interquartile range), 'std' (standard deviation), or 'se' (standard error, default)\n",
    "# Examples:\n",
    "#   plot_updraft_profile(stat_type='iqr')  # Q1 to Q3 range (uses pre-computed quartiles)\n",
    "#   plot_updraft_profile(stat_type='std')  # Mean ± std deviation\n",
    "#   plot_updraft_profile(stat_type='se')   # Mean ± standard error (default)\n",
    "\n",
    "plot_updraft_profile(stat_type='iqr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "Frequency values at 6km and 12km for Updrafts (June)\n",
      "============================================================\n",
      "\n",
      "--- 6.0 km ---\n",
      "Current:        21112 occurrences\n",
      "Future:         22338 occurrences\n",
      "Future+Urban:   21972 occurrences\n",
      "\n",
      "Relative Changes:\n",
      "Warming Effect:                5.81%\n",
      "Combined Effect:                 4.07%\n",
      "Urbanization Effect:       -1.64%\n",
      "\n",
      "--- 12.0 km ---\n",
      "Current:        7213 occurrences\n",
      "Future:         9856 occurrences\n",
      "Future+Urban:   9659 occurrences\n",
      "\n",
      "Relative Changes:\n",
      "Warming Effect:                36.64%\n",
      "Combined Effect:                 33.91%\n",
      "Urbanization Effect:       -2.00%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############### Frequency profile ################\n",
    "fig, ax1 = plt.subplots(figsize=(3, 3))\n",
    "\n",
    "ax2 = ax1.twiny()\n",
    "\n",
    "# w>0\n",
    "ax1.plot(c_99_freq, c_99_freq.level, label=f\"Current\", color=\"black\", linewidth=2.5)\n",
    "ax1.plot(f_99_freq, f_99_freq.level, label=f\"Future\", color=\"#1E88E5\", linewidth=2.5)\n",
    "ax1.plot(fu_99_freq, fu_99_freq.level, label=f\"Future-Urban\", color=\"#D81B60\", linewidth=2.5)\n",
    "\n",
    "rel_change_acc = calculate_relative_change(f_99_freq, c_99_freq)\n",
    "rel_change_acc_urban = calculate_relative_change(fu_99_freq, c_99_freq)\n",
    "re_change_urban = calculate_relative_change_urbanization(f_99_freq, c_99_freq, fu_99_freq)\n",
    "\n",
    "ax2.plot(rel_change_acc_urban, c_99_mean.level, color=\"#FFB507\", linestyle='-', linewidth=2.5)\n",
    "ax2.plot(rel_change_acc, c_99_freq.level, label=f\"ACC Impact\", color=\"black\", linestyle='--', linewidth=2.5)\n",
    "ax2.plot(re_change_urban, c_99_freq.level, label=f\"Urbanization Impact\", color=\"black\", linestyle=':', linewidth=2.5)\n",
    "ax2.plot(np.zeros(17),np.arange(0,17,1), color=\"gray\", alpha=0.6)\n",
    "\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",
    "if w_direction == 'updraft':\n",
    "    title='Updraft'\n",
    "else:\n",
    "    title='Downdraft'\n",
    "\n",
    "# Labels and legend\n",
    "#fig.suptitle(f\"Frequency of {title} Speeds >99th Percentile ({month})\")\n",
    "ax1.set_xlabel(\"# of Occurrences\")\n",
    "ax1.set_ylabel(\"Height (km)\")\n",
    "#ax1.legend(loc=\"center\", bbox_to_anchor=(0.14, 0.6), fontsize=10, title='Simulations')\n",
    "#ax1.legend(loc=\"center\", bbox_to_anchor=(0.86, 0.22), fontsize=10, title='Simulations')\n",
    "ax1.set_ylim(0,15)\n",
    "#ax1.set_xlim(0,3000)\n",
    "ax1.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))\n",
    "#ax1.invert_xaxis()\n",
    "ax1.grid(alpha=0.75,axis='y', linestyle=':')\n",
    "\n",
    "#ax2.legend(loc=\"center\", bbox_to_anchor=(0.185, 0.45),  fontsize=10, title='Relative Change')\n",
    "#ax2.legend(loc=\"center\", bbox_to_anchor=(0.815, 0.07),  fontsize=10, title='Relative Change')\n",
    "ax2.set_xlim(-20,100)\n",
    "ax2.set_xlabel('Relative Change (%)')\n",
    "ax2.grid(alpha=0.75, linestyle=':')\n",
    "\n",
    "# Print values at 6km and 12km\n",
    "print(f\"\\n{'='*60}\")\n",
    "print(f\"Frequency values at 6km and 12km for {title}s ({month})\")\n",
    "print(f\"{'='*60}\")\n",
    "\n",
    "for height in [6.0, 12.0]:\n",
    "    print(f\"\\n--- {height} km ---\")\n",
    "    print(f\"Current:        {c_99_freq.sel(level=height, method='nearest').values:.0f} occurrences\")\n",
    "    print(f\"Future:         {f_99_freq.sel(level=height, method='nearest').values:.0f} occurrences\")\n",
    "    print(f\"Future+Urban:   {fu_99_freq.sel(level=height, method='nearest').values:.0f} occurrences\")\n",
    "    print(f\"\\nRelative Changes:\")\n",
    "    print(f\"Warming Effect:                {rel_change_acc.sel(level=height, method='nearest').values:.2f}%\")\n",
    "    print(f\"Combined Effect:                 {rel_change_acc_urban.sel(level=height, method='nearest').values:.2f}%\")\n",
    "    print(f\"Urbanization Effect:       {re_change_urban.sel(level=height, method='nearest').values:.2f}%\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cmweather\n",
    "wind_cmap = cmweather.cm_colorblind.ChaseSpectral\n",
    "#wind_cmap = pyart.graph.cm.LangRainbow12\n",
    "\n",
    "p_clevs=[0.001, 0.01, 0.05, 0.1,0.3,0.5,1,2,4,8]\n",
    "#p_clevs=[0.0001, 0.0005, 0.001,0.003,0.005,0.01,0.02,0.04,0.08,0.12]\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_updraft_histogram(ds: xr.Dataset, sim):\n",
    "    \"\"\"\n",
    "    Plots a normalized 2D histogram of updraft frequency versus height and updraft speed bins.\n",
    "\n",
    "    Parameters:\n",
    "    - ds: xarray.Dataset containing dimensions `level`, `bin_edge`, and variable `w_count`\n",
    "    \"\"\"\n",
    "    level = ds['level'].values\n",
    "    bin_edge = ds['bin_edge'].values\n",
    "    w_count = ds['w_count'].values\n",
    "\n",
    "    # Normalize across bins at each level (row-wise normalization)\n",
    "    w_count_sum = np.sum(w_count, axis=1, keepdims=True)\n",
    "    #w_count_sum = np.sum(w_count, keepdims=True)\n",
    "    #print(w_count_sum)\n",
    "    w_freq = (w_count / w_count_sum) * 100  # Convert to percentage\n",
    "\n",
    "    # Plot\n",
    "    fig, ax = plt.subplots(figsize=(4, 4))\n",
    "\n",
    "    #norm = plt.Normalize(vmin=0.01, vmax=np.nanmax(w_freq))\n",
    "\n",
    "    pcm = ax.pcolormesh(bin_edge, level, w_freq, cmap=p_cmap, norm=p_norm, shading=\"auto\")\n",
    "    #pcm = ax.contourf(bin_edge, level, w_freq, cmap=p_cmap, levels=p_clevs, norm=p_norm, shading=\"auto\", extend='both')\n",
    "    cbar = plt.colorbar(pcm, ax=ax, fraction=0.055, pad=0.01, aspect= 30, extend='both', label=\"Normalized Frequency (%)\")\n",
    "\n",
    "    if w_direction=='updraft':\n",
    "        title='Updraft'\n",
    "    else:\n",
    "        title='Downdraft'\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",
    "    ax.set_xlabel(f\"{title} velocity (m s$^{-1}$)\")\n",
    "    ax.set_ylabel(\"Height above ground (km)\")\n",
    "    ax.set_title(f\"{sim}\", loc='left')\n",
    "    ax.set_title(f\"({month})\", loc='right')\n",
    "    ax.grid(True, alpha=0.5, linestyle='--')\n",
    "    ax.set_xlim(1,35)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "#plot_updraft_histogram(c_hist_ds, sim='Current')\n",
    "#plot_updraft_histogram(f_hist_ds, sim='Future')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "############### Diverging colormap #####################\n",
    "\n",
    "#clevs3 = [-1,-0.5,-0.1, -0.05, -0.01, 0.01, 0.05, 0.1,0.5,1] # precip levels\n",
    "clevs3 = [-0.1,-0.05,-0.01, -0.005, -0.001, 0.001, 0.005, 0.01,0.05,0.1] # precip levels\n",
    "\n",
    "def create_custom_diverging_colormap(levels):\n",
    "    \"\"\"\n",
    "    Creates a custom diverging colormap with cool colors (blues) on one end and \n",
    "    warm colors (yellows, oranges, reds) on the other end.\n",
    "\n",
    "    Parameters:\n",
    "    - levels (int): The number of intervals or levels in the colormap.\n",
    "\n",
    "    Returns:\n",
    "    - colormap: A matplotlib colormap object.\n",
    "    \"\"\"\n",
    "\n",
    "    # Ensure levels is an odd number for symmetry around the white midpoint\n",
    "    \n",
    "    import colormaps\n",
    "    diff_cmap1 = colormaps.rdbu_11_r\n",
    "    cmap = diff_cmap1[1:10]\n",
    "    #cmap = mcolors.ListedColormap([diff_cmap1(i / (len(clevs) - 1)) for i in range(len(clevs) - 1)])\n",
    "\n",
    "    # Extract individual colors from the base colormap\n",
    "    colors = [diff_cmap1(i / (len(levels) - 1)) for i in range(len(levels))]\n",
    "\n",
    "    cmap.set_over(colors[-1])   # Upper bound color\n",
    "    cmap.set_under(colors[0])  # Lower bound color\n",
    "\n",
    "    return cmap\n",
    "\n",
    "# Create a ListedColormap from the custom RGBA values\n",
    "diff_cmap = create_custom_diverging_colormap(levels=clevs3)\n",
    "\n",
    "# Create a normalization for the contour levels\n",
    "diff_norm = mcolors.BoundaryNorm(clevs3, diff_cmap.N)\n",
    "\n",
    "def plot_updraft_difference_histogram(current: xr.Dataset, future: xr.Dataset, future_urban: xr.Dataset, sim):\n",
    "    \"\"\"\n",
    "    Plots a normalized 2D histogram of updraft frequency versus height and updraft speed bins.\n",
    "\n",
    "    Parameters:\n",
    "    - ds: xarray.Dataset containing dimensions `level`, `bin_edge`, and variable `w_count`\n",
    "    \"\"\"\n",
    "    level = current['level'].values\n",
    "    bin_edge = current['bin_edge'].values\n",
    "    w_count1 = current['w_count'].values\n",
    "\n",
    "    w_count2 = future['w_count'].values\n",
    "    w_count3 = future_urban['w_count'].values\n",
    "\n",
    "    # Normalize across bins at each level (row-wise normalization)\n",
    "    w_count_sum1 = np.sum(w_count1, axis=1, keepdims=True)\n",
    "    #print(w_count_sum)\n",
    "    w_freq1 = (w_count1 / w_count_sum1) * 100  # Convert to percentage\n",
    "\n",
    "    w_count_sum2 = np.sum(w_count2, axis=1, keepdims=True)\n",
    "    #print(w_count_sum)\n",
    "    w_freq2 = (w_count2 / w_count_sum2) * 100  # Convert to percentage\n",
    "\n",
    "    w_count_sum3 = np.sum(w_count3, axis=1, keepdims=True)\n",
    "    #print(w_count_sum)\n",
    "    w_freq3 = (w_count3 / w_count_sum3) * 100  # Convert to percentage\n",
    "\n",
    "    if sim=='ACC':\n",
    "        freq_diff=w_freq2-w_freq1\n",
    "    elif sim=='Urbanization':\n",
    "        freq_diff = (w_freq3-w_freq1) - (w_freq2-w_freq1)\n",
    "\n",
    "    # Plot\n",
    "    fig, ax = plt.subplots(figsize=(4, 4))\n",
    "\n",
    "    #norm = plt.Normalize(vmin=0.01, vmax=np.nanmax(w_freq))\n",
    "\n",
    "    pcm = ax.pcolormesh(bin_edge, level, freq_diff, cmap=diff_cmap, norm=diff_norm, shading=\"auto\")\n",
    "    #pcm = ax.contourf(bin_edge, level, freq_diff, cmap=diff_cmap, levels=clevs3, norm=diff_norm, shading=\"auto\", extend='both')\n",
    "    cbar = plt.colorbar(pcm, ax=ax, fraction=0.055, pad=0.01, aspect= 30, extend='both', label=\"Normalized Frequency Difference (%)\")\n",
    "\n",
    "    if w_direction=='updraft':\n",
    "        title='Updraft'\n",
    "    else:\n",
    "        title='Downdraft'\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",
    "    ax.set_xlabel(f\"{title} velocity (m s$^{{-1}}$)\")\n",
    "    #ax.set_ylabel(\"Height above ground (km)\")\n",
    "    ax.set_title(f\"{sim}\", loc='left')\n",
    "    ax.set_title(f\"({month})\", loc='right')\n",
    "    ax.grid(True, alpha=0.5, linestyle='--')\n",
    "    ax.set_xlim(1,35)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "#plot_updraft_difference_histogram(c_hist_ds, f_hist_ds, fu_hist_ds, sim='ACC')\n",
    "#plot_updraft_difference_histogram(c_hist_ds, f_hist_ds, fu_hist_ds, sim='Urbanization')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "\n",
    "def compute_histogram_stats_above_percentile(ds: xr.Dataset, var_name='w_count', percentile=90):\n",
    "    \"\"\"\n",
    "    Compute mean and standard deviation for each level using only bins above a given percentile.\n",
    "\n",
    "    Parameters:\n",
    "    - ds: xarray.Dataset with 'level' and 'bin_edge' dimensions\n",
    "    - var_name: name of the histogram variable (e.g., 'QRAIN_hist')\n",
    "    - percentile: percentile threshold (e.g., 90 means keep top 10% bins per level)\n",
    "\n",
    "    Returns:\n",
    "    - mean: xr.DataArray of mean values at each level\n",
    "    - std: xr.DataArray of standard deviation at each level\n",
    "    \"\"\"\n",
    "    import numpy as np\n",
    "    import xarray as xr\n",
    "\n",
    "    counts = ds[var_name]  # (level, bin_edge)\n",
    "    bin_edges = ds['bin_edge'].values\n",
    "\n",
    "    # Bin centers\n",
    "    bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])\n",
    "    bin_centers_da = xr.DataArray(\n",
    "        bin_centers,\n",
    "        dims=[\"bin_edge\"],\n",
    "        coords={\"bin_edge\": ds['bin_edge'].isel(bin_edge=slice(0, -1)).values}\n",
    "    )\n",
    "\n",
    "    # Truncate counts to match bin centers\n",
    "    counts = counts.isel(bin_edge=slice(0, -1))\n",
    "\n",
    "    # Prepare outputs\n",
    "    level_dim = counts.dims[0]\n",
    "    means = []\n",
    "    stds = []\n",
    "\n",
    "    for level in counts[level_dim]:\n",
    "        hist = counts.sel({level_dim: level})\n",
    "        total = hist.sum().item()\n",
    "\n",
    "        if total == 0:\n",
    "            means.append(np.nan)\n",
    "            stds.append(np.nan)\n",
    "            continue\n",
    "\n",
    "        cdf = hist.cumsum(dim=\"bin_edge\") / total\n",
    "        threshold = percentile / 100.0\n",
    "\n",
    "        # Create a mask: keep bins where CDF >= threshold\n",
    "        mask = cdf >= threshold\n",
    "\n",
    "        # Apply mask\n",
    "        counts_above = hist.where(mask, 0)\n",
    "        bin_values = bin_centers_da.where(mask, 0)\n",
    "\n",
    "        numerator = (counts_above * bin_values).sum().item()\n",
    "        denominator = counts_above.sum().item()\n",
    "\n",
    "        if denominator == 0:\n",
    "            means.append(np.nan)\n",
    "            stds.append(np.nan)\n",
    "            continue\n",
    "\n",
    "        mean = numerator / denominator\n",
    "\n",
    "        # Variance\n",
    "        var = ((counts_above * (bin_values - mean) ** 2).sum().item()) / denominator\n",
    "        std = np.sqrt(var)\n",
    "\n",
    "        means.append(mean)\n",
    "        stds.append(std)\n",
    "\n",
    "    level_vals = ds[level_dim].values\n",
    "    mean_da = xr.DataArray(means, dims=[level_dim], coords={level_dim: level_vals})\n",
    "    std_da = xr.DataArray(stds, dims=[level_dim], coords={level_dim: level_vals})\n",
    "\n",
    "    return mean_da, std_da\n",
    "\n",
    "\n",
    "\n",
    "def plot_updraft_mean_profile(c_q=c_hist_ds, f_q=f_hist_ds, fu_q=f_hist_ds):\n",
    "\n",
    "    fig, ax1 = plt.subplots(figsize=(3, 3))\n",
    "\n",
    "    ax2 = ax1.twiny()\n",
    "\n",
    "    c_mean, c_std = compute_histogram_stats_above_percentile(c_q)\n",
    "    f_mean, f_std = compute_histogram_stats_above_percentile(f_q)\n",
    "    fu_mean, fu_std = compute_histogram_stats_above_percentile(fu_q)\n",
    "\n",
    "    # w>0\n",
    "    ax1.plot(c_mean, c_mean.level, label=f\"Current\", color=\"black\", linewidth=2.5)\n",
    "    ax1.plot(f_mean, f_mean.level, label=f\"Future\", color=\"#1E88E5\", linewidth=2.5)\n",
    "    ax1.plot(fu_mean, fu_mean.level, label=f\"Future+Urban\", color=\"#D81B60\", linewidth=2.5)\n",
    "\n",
    "    ax1.fill_betweenx(c_mean.level, c_mean - c_std, c_mean + c_std, color='black', alpha=0.1)\n",
    "    ax1.fill_betweenx(f_mean.level, f_mean - f_std, f_mean + f_std, color=\"#1E88E5\", alpha=0.1)\n",
    "    ax1.fill_betweenx(fu_mean.level, fu_mean - fu_std, fu_mean + fu_std, color=\"#D81B60\", alpha=0.1)\n",
    "\n",
    "    rel_change_acc = calculate_relative_change(f_mean, c_mean)\n",
    "    rel_change_urban = calculate_relative_change_urbanization(f_mean, c_mean, fu_mean)\n",
    "\n",
    "\n",
    "    ax2.plot(rel_change_acc, c_mean.level, label=f\"ACC Impact\", color=\"black\", linestyle='--', linewidth=2.5)\n",
    "    ax2.plot(rel_change_urban, c_mean.level, label=f\"Urbanization Impact\", color=\"black\", linestyle=':', linewidth=2.5)\n",
    "    ax2.plot(np.zeros(17),np.arange(0,17,1), color=\"gray\", alpha=0.6)\n",
    "\n",
    "\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",
    "    # Labels and legend\n",
    "    #fig.suptitle(f\"{percentile}th Percentile {title} Thresholds For Each Height by Simulation ({month})\")\n",
    "    ax1.set_xlabel(f\"Updraft Speed (m s$^{{-1}}$)\")\n",
    "    #ax1.set_ylabel(\"Height AGL (km)\")\n",
    "    #ax1.legend(loc=\"center\", bbox_to_anchor=(0.14, 0.6), fontsize=8)\n",
    "    #ax1.legend(loc=\"center\", bbox_to_anchor=(0.86, 0.22), fontsize=8)\n",
    "    #ax2.legend(loc='upper center', fontsize=10, ncol=2)\n",
    "    ax1.set_ylim(0,15)\n",
    "    ax1.set_xlim(0,20)\n",
    "    #ax1.invert_xaxis()\n",
    "    ax1.grid(alpha=0.75,axis='y', linestyle=':')\n",
    "\n",
    "\n",
    "    #ax2.legend(loc=\"center\", bbox_to_anchor=(0.185, 0.45),  fontsize=8)\n",
    "    #ax2.legend(loc=\"center\", bbox_to_anchor=(0.815, 0.07),  fontsize=8)\n",
    "    ax2.set_xlim(-20,10)\n",
    "    ax2.set_xlabel('Relative Change (%)')\n",
    "    ax2.grid(alpha=0.75, linestyle=':')\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "\n",
    "plot_updraft_mean_profile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gmvf2S/fhe8kRwWJvb8+DBw++6riMvswf9+Y+RsjkCMzp1UehUKRZh68ZrQDkzZs3zQfyZyG5N5reeqXY2Nhss3xJ695cuHCBZs2aUaNGDZYtW4adnR3a2tqsW7dOnETOCT6tq1KpBFTzLHny5EmV/9MPWEb43LOd1rl/++23dEeDn37sMuv9NDc3p0mTJqJg2bVrFwkJCRma9/gaOnTowPDhw9m8eTPjx49n06ZNlC9fPs2O1veQfC27dOlCt27d0sxTqlSpTDtfVn8nc2TyvkmTJqxatYorV65QuXLlz+bNnz8/SqWS58+fq01+BgQEEB4eLloifQ3pvTjJZT19+lStN5OYmMirV6/UPvTm5uapLMVA1Xv4tCeUXeTPn5+nT5+mSn/y5Im4H76ux21tbY2BgUG65UqlUvLly/dNdQXSLDc5Pa17m9xD/5hnz55hYGCAtbU1oPr4ppUvvXOlxe7du9HT0+PYsWPo6uqK6evWrcvQ8dm1uNPFxQUAGxubz3ZEkq9lRq5Lcu/10+f70xF78nOura2daZ2g5PY8ePDgs4YqoBpNNG/enBs3brB582bKlCmT4ZGzUqnk5cuX4igFVM8RoGZFZWFhQePGjdm8eTOdO3fm0qVLLFy48OsalQGsra0xNjZGoVB89bVMvrcvXrxItS+ttIzyPc9wjpgbjx49GkNDQ3r37k1AQECq/d7e3ixatAiARo0aAaS6mfPnzwegcePGX33+ZJXSpy9O3bp10dHR4e+//1aT3GvWrCEiIkLtXC4uLly9epXExEQxzcvLi3fv3n11fTKLRo0acf36da5cuSKmxcTEsGrVKpycnERdenrtTwuZTEb9+vXZv3+/moogICCALVu2UK1aNVHd8jXY2dnh5ubGpk2bUtXjv//+4+rVq2mu17ly5Yra0P7du3fs37+f+vXrI5PJkMlkeHh4sG/fPt6+fSvme/z4MceOHctw/WQyGRKJRK2X/vr16wy7qPmaa/w9eHh4YGJiwowZM0hKSkq1P1k9mHy9N2zYoGaRdOLEiVRzb/nz50cmk6Uy4162bJnato2NDe7u7qxcuRJ/f/90z/011K9fH2NjY2bOnJnKZPjT3nTDhg2xsrJi9uzZnDt37qtHK0uWLFEre8mSJWhra1OnTh21fF27duXRo0eMGjUKmUxGhw4dvrJVX0Ymk9G6dWt2796dpjbnc9fS3t6eEiVKsHHjRqKjo8X0c+fOcf/+/W+u0/c8wzkyYnFxcWHLli20b9+eYsWKqa28v3z5Mjt37hTXiJQuXZpu3bqxatUqwsPDqVmzJtevX2fDhg20aNGCWrVqffX53dzckMlkzJ49m4iICHR1dalduzY2NjaMGzeOqVOn0qBBA5o1a8bTp09ZtmwZFSpUUHtwe/fuza5du2jQoAHt2rXD29ubTZs2iT2unGDs2LFs3bqVhg0bMmTIECwsLNiwYQOvXr1i9+7dokGCi4sLZmZmrFixAmNjYwwNDalYsWK6cw3Tp0/nxIkTVKtWjQEDBqClpcXKlStJSEhgzpw531zf+fPn4+HhgZubG927d8fe3p7Hjx+zatUq7OzsGDduXKpjSpQogYeHh5q5McDUqVPFPFOnTuXo0aNUr16dAQMGIJfLWbx4McWLF+fevXsZqlvjxo2ZP38+DRo0oFOnTgQGBrJ06VIKFiyYoTI+94xlJiYmJixfvpyuXbtStmxZOnTogLW1NW/fvuXQoUNUrVpV/IDOnDmTxo0bU61aNXr27EloaKh4XT7+IJmamtK2bVsWL16MRCLBxcUFLy+vNHX8S5cupVq1apQsWZI+ffrg7OxMQEAAV65cwcfHh7t37351exYsWEDv3r2pUKECnTp1wtzcnLt37xIbG8uGDRvEvNra2nTo0IElS5Ygk8no2LFjhs+jp6fH0aNH6datGxUrVuTIkSMcOnSI8ePHiyPfZBo3boylpSU7d+6kYcOGmX4Pk5k1axZnzpyhYsWK9OnTB1dXV0JDQ7l16xYnT55MZYr+MTNmzKB58+ZUrVqVHj16EBYWxpIlSyhRooTavf0ako1khgwZgoeHx9cJ1W+yJcsknj17JvTp00dwcnISdHR0BGNjY6Fq1arC4sWL1cwXk5KShKlTpwoFChQQtLW1hXz58gnjxo1TyyMIKvO+xo0bpzrPpybAgiAIq1evFpydnUUT1I9NFJcsWSIULVpU0NbWFmxtbYVff/1VCAsLS1XuvHnzBAcHB0FXV1eoWrWqcPPmzXTNjdMzq00LQBg4cOAX86Vl8uzt7S20adNGMDMzE/T09IRffvlF8PLySnXs/v37BVdXV0FLSytDpse3bt0SPDw8BCMjI8HAwECoVauWcPnyZbU8X2NunMzVq1eFJk2aCObm5oKWlpbg4OAg9O7dW/Dx8UmVN/m6bNq0SShUqJCgq6srlClTJk3z0nPnzgnlypUTdHR0BGdnZ2HFihWiSW9aZabFmjVrxPMULVpUWLduXZplpGVuLAiff8Y+JbncZFPXT0k2171x40aa+8+cOSN4eHgIpqamgp6enuDi4iJ0795dzTRbEARh9+7dQrFixQRdXV3B1dVV2LNnj9CtWzc1k1RBEISgoCChdevWgoGBgWBubi7069dPePDgQZrPire3t+Dp6SnkyZNH0NbWFhwcHIQmTZoIu3bt+mL90zNtPnDggFClShVBX19fMDExEX755Rdh69atqdp9/fp1ARDq16+f5nVJi27dugmGhoaCt7e3UL9+fcHAwECwtbUVJk+erLbM4GMGDBggAMKWLVsyfJ70vkeCkP53ISAgQBg4cKCQL18+QVtbW8iTJ49Qp04dYdWqVWKetMyNBUEQtm3bJhQtWlTQ1dUVSpQoIRw4cEBo3bq1ULRo0VTHpvWOAsLkyZPFbblcLgwePFiwtrYWJBLJV5keSz4UqEFDrkcikTBw4EA1FYaG76d79+6cPXs21zpl/Rx3797Fzc2NjRs3ZthS61sYPnw4a9as4f379xgYGGTZeTIbNzc3rK2tOXHiRLaeV+M2X4MGDT8sq1evxsjIiFatWmXZOeLj49m0aROtW7fOtUIlKSkJuVyulnb27Fnu3r2r5mIqu8gx78YaNGjQ8K0cPHiQR48esWrVKgYNGpQlzjgDAwM5efIku3btIiQkRM1/YW7D19eXunXr0qVLF+zt7Xny5AkrVqwgT5489O/fP9vroxEsGjRo+OEYPHgwAQEBNGrUSM1wIzN59OgRnTt3xsbGhr///jtTQ0xkNubm5pQrV45//vmHoKAgDA0Nady4MbNmzcLS0jLb66OZY9GgQYMGDZmKZo5FgwYNGjRkKhrBokGDBg0aMhWNYNGgIYdJDoL1sbnvpwHjNGj4kdAIFg0a0uHx48diGNisds2iQcPPhEawaNCQDps2bRI9Bn8ckjiz6dq1K3Fxcd/kUFWDhtyIRrBo0JAGgiCwZcsWOnXqRKNGjVIF3MoMkkMLy2Qy9PT0ss0jsgYNWY1GsGjQkAaXLl3i9evXdOjQgQ4dOnD+/Hl8fHxS5XNycqJJkyYcP34cNzc39PT0cHV1Zc+ePWr5kudRzp07x4ABA7CxsRFjzac1x6JBw4+MRrBo0JAGmzdvxsXFhQoVKtC0aVMMDAzYunVrmnmfP39O+/btadiwITNnzkRLS4u2bdum6Z9pwIABPHr0iEmTJjF27NisboYGDTmCZuW9Bg2fkJSUxM6dO0VXGPr6+jRr1ozNmzczatSoVPmfPXvG7t27RX9VvXr1omjRoowZM4Z69eqp5bWwsODUqVPpRvDToOFnQDNi0aDhE44cOUJISIhafI+OHTty9+5dHj58mCq/vb09LVu2FLdNTEzw9PTk9u3bvH//Xi1vnz59NEJFw0+PRrBo0PAJmzZtokCBAujq6vLixQtevHiBi4sLBgYGaU7iFyxYMNXEe3LI20/nTdILpqZBw8+ERhWmQcNHREZGcvDgQeLj4ylUqFCq/Vu2bOHPP//8ZgsufX39762iBg25Ho1g0aDhI/bs2UN8fDzLly/HyspKbd/Tp0+ZOHEily5dolq1amL6ixcvEARBTdg8e/YMUFmNadDw/4ZGsGjQ8BGbNm3C2dk5zRgWCQkJzJo1i82bN6sJFj8/P/bu3StO3kdGRrJx40bc3NzEBZYaNPw/oZlj0aDhA35+fpw5c4ZmzZqluV9XVxcPDw927txJUlKSmF64cGF69erFuHHjWLhwIdWqVSMgIIBZs2ZlV9U1aMhVaASLBg0f2LZtG0qlkqZNm6abp2nTpoSEhHDkyBExrVChQmzfvp3Dhw8zduxYkpKS2L59Ox4eHtlRbQ0ach2aQF8aNHwHTk5OlChRAi8vr5yuigYNuQbNiEWDBg0aNGQqGsGiQYMGDRoyFY1g0aBBgwYNmYpmjkWDBg0aNGQqmhGLBg0aNGjIVDSCRYMGDRo0ZCo//cp7pVKJn58fxsbGmgh9GjRo+CkQBIGoqCjs7e2RSnPf+OCnFyx+fn7ky5cvp6uhQYMGDZnOu3fvxEikuYmfXrAYGxsDqhtgYmKSZp7Dhw+LsTe6du2KZ3gxzG8+AOBcu1b0npYSrGn79u307dsXAGdnZ65evYquri67dj2h9MRFAMTpG1D87B/ITA3UzvP69WsaNWqEr68vAK6urnh5eWFpaZlmvZRKJf7+/tjZ2X1TryReLrDuZjTv7vjQcfUa9D7EWNcuXwjH1QO5GQy/HQonJE4JgI4MJpWR0MzNFJlx9nnhTaudoaGhnD59mtjYWIoVK0apUqXQ1dX9YlkbNmxg2LBhKJWqNlWsWJFt27ZhYWGRZv6oBCW3fBO57ZfILd8k7gckkqhIu2w9bQmT65jQOK+EyD2XCd90Fvm74HTrItHRwmpie8zapfgV+957+qPw/9JOyJm2RkZGki9fPvH7ltv46a3CIiMjMTU1JSIiIl3BEhcXh7W1NTExMVhbW/PkwEWC2swEwNfUEpfzM8hvqQOohqDu7u6cP38egOnTpzNhwgTi5QJbmyymyoPbAMR3rkfp2Z1Sncvb2xt3d3cxfnqpUqU4ffp0msIlWY2XGcPd+IdvedduNsqIWAAMahbHYc1QAqIVLFj0H2a3H1PhzTMcw4JQSKUEVC5DXGt3LCsVJK+ZFrZGMrSkWaNKzMx2gipQV9u2bYn5IEgLFy7MhAkTMDQ0RF9fHwMDA/T19TEzM6Nw4cJqKtIEucAd/0QuvU7g4psE7vglofjkDfmtujGDKhuBUiDm3APi//Mm4ck7Eh77kPQ2KFV9TLvWwnZqJyQ6Wmm2VREZS9yN5xhULorU4MvC80cgs+9pbiYn2pqR71pOohEsH2jTpg27d+8G4Ny5c2jNvITlfZXr8/P9PekzsZaY9/79+5QpUwaFQoG+vj6PHz8mf/787D3pQ8FeU9FRyJHLZBQ89ye6TrapzvXixQtq1qyJn58fAG5ubpw6dSpVrzqzH9i4Oy/x6TAXZXQ8ANoFbJH7hyHEJ6Z7zHNre7aVr8mVwiWoml+XKXVNcbbI3IFuVryYt27donHjxqkiOH6Kq6srU6dOpU2bNmnuj05Qcs0nkX0P4zjwOE5M71DKgIm1TTDWVa+vIiqOxKc+ROy+QsS/Z8R0vfIFcVg5EKm1idhWiVIg+vhtAn7fjCIgHO0CtjjuGouWrdm3NzyXoBEsWUtuFyw/9x3/Cj4OLbt//34KjkpxRGiz/wxRCUpxu2TJkgwaNAhQjXb69++PUqmkWW0HzlRWqT20FAoeeS5BGZuQ6lwFCxbkzJkz2NnZAXDnzh2qVasmjoKyCn03Zxw2Dkeirxp9Jb0KUBMqglTCU7t8hOulqPAKBfnx+5GtdLtwlPMv42i9KZgnQUmpys5tlC1blitXrlCsWLHP5nv06BFt27blxIkTae430pVSx0WPv5uaMc49Re2w7V4sVZYHsOBiJAFRCuRKVf9MZqyPfvlC5JnpSZ4FvZDoqoRw/M0XvG40lbj/XqB8H07IvH28rDQKv75LUQSEA6r78bru70TsusxP3t/T8JOjGbF8IDQ0FCsrKwRBwM3NjVu3bnG94jjM/AJQIuHl7tk0rmgt5o+IiKBo0aJij3jOnDmMGjWKU3fCkHaajn1kKABJdcpRfP3ANC3Snjx5Qq1atdR61e3atWPu3Lk4OjpmWU8o5tIjfHv8jRCbgMzWDCP3khjWKolBNVcSDfV59T6esH3X0Nt1BiPvd+JxF51dWeLeFKzM2NLBkmI22plSn6zs8cXGxuLl5UVgYCBxcXHExsaK///77z8uX74MqATRjRs3vnj+PfejGXU0Arky9f001JFgqivFVE+CqZ6Uyo669NAPIvjXpcj9VM8DWjJQKOELr51BzeLYzuyGjqP1Z/PlVjQjlqwlt49YNILlI8qWLcvt26o5ktu3byNseojBtpMAHOntybAptdTynzhxAg8PDwRBQEtLiwsXLlCpUiXmrHtC3WkLMUxSjVZ0BjWnwNgWaZ7z+fPntGvXjjt37ohp+vr6jBkzhpEjRxIeHp4lD6w8OBJlRAzaznnSNcMWBIHwf88QOHETfOiRx2rrsPmX2pypVJV/O9vimgnCJac+QkqlknLlyonXfseOHbRt2zZVvqioKObNm8fRo0e5e/cuckM7TGoNxrBMaySyz6sFnS1kzKskw2rqP8Rdfaq+UyrBqG5pTDu7o1ciP4FTthB18Ia4W6Kvg9WoVpj3rItES/bd7c1ONIIla9EIlhzma27A0qVLRRVX+/btWdl3Au895wNwomR5+noNQEem/hGeMGECM2bMAFQu1G/fvo2hsSnTpl6k09p1SFFdXsulv2LV/Jc0z6tQKFizZg0TJkwgODjFysjR0ZHu3bvTpEkTypQpg5ZWzhjxRZ+8g//Qf1BGxIhp78ys2FSnMaPGV6W47fcJl5z8CB09epSGDRsCqkn+hw8fitdZqVSyadMmxowZk+Zcjcw8H8ZVeqBtXRCpvikWdvnRN7clMl6pZlkmlUDfMnp0u3CUqPUnkdiYYN65FmYdaqBtZ65WZvSJOwSM34jcP0xM069QCLu/+6KdTz1Ucm5GI1iyFo1gyWG+5gbExcXh5OREYGAgUqmUx3fuI286H5lcwXtjM6SHZlDdWd0MVy6X4+7uzqVLlwBo3bo1O3fuJDxeYFn/XbQ5pQoIlaSjg8uB8eiXyJ/u+cPDw5k2bRqLFy9GLper7TMyMqJy5cpUq1aNWrVqUaVKFWSy7OvFykOjCJ67l4hNZ9XUOP85F8F6YjsKV3Akr6kM2TdYjuXkR+hTK79Vq1bRp08frly5wogRI7h69apa/oIFC1K2bFnxTy6X06JFCxITVXNVySrR58FJjDoczm3/lPkoFwst/nLXwVo3Eoe8edNtqyIqjuDZuwnfcFq81lJjfWxndcOkecWsuAyZjkawZC0awZLDfO0NmDlzJuPHjwfg119/pc87Rwxuq1QY+2eOZ3TXQqmOefv2LW5uboSFqXqZ27Zto3379jwNTORslxW4P1Kp1xJsLSlxfjpSQ73P1uHJkycMHz6co0ePppunTJkybNiwgZIlS36xTZlJ/IM3+E/cTOLN52rp1/MX5mDZKoSWKoqzlTYFLbWo7qTLL3l1vihscvojdPnyZapWrQqATCbD1dWV+/fvq+Vp0aIF8+bNw9nZOdXxW7ZsoXPnzuL2rl27aN26NXKlwD83opl3IUocwUglUMVBoGw+I5wttXG20MLJXAtTvdTtjr3xnPdDVpH00VoZh43DMapdKjOanaXk9D3NTjSCJTUawfIJERER2NnZERcXh62tLbfGrCJq3l4A1jZuzYzljdP8UO7du5dWrVoBqrmamzdvIpFIOPkwihjPORQNUK1bMepSC4dZnl+shyAI3Lt3j4MHD3L//n0uXrwomicno62tzZQpUxg9enS2qskEQSBo11VeT96OaWSE2r7XFjbsLV2FU0XdSNTSxtJAikchPRoW0aOyoy7astTXLjd8hNq3b8+OHTtSpRcrVoxFixZRr169NI5KYdq0aUyePBlQzZGdO3eOChUqAPAsKImRh8O59z59azprQylV8uvSqIgeNQvooq+dssYlYPy/RO1TjZx0XPLgdPIPJNq5e21zbrin2YVGsKRGI1jSoEWLFuzfvx+A62v2YDLpAADHi5ah6PK+1CuUesQhCAK//PILN2/eBOD06dPUqqWa7B+z6hmdZvyFnlz1YbH/ZzDGDcp+sR4fP7ASiYTXr19z9uxZ/vrrLx49eiTmq1ChAuvXr8fV1TVD7css4iPjuLHwFEZ7zmAQHKq2L1JXHx9zK2J09IjS0ydGV48kA31snS2p9GtNXJ1T7kVu+AjFxcUxZ84c5s+fT2RkJCVKlGDYsGF4enqirf3lOSRBEPD09GTTpk0A2NracuTIEaKionj+/DlPnj3nQqwL/nnqgNbnR6z62hJqO+vSsIg+tV10MdCW8K7VTOJuqEaJNlM7Yd7r84Iup8kN9zS70AiW1GgESxqsW7eOnj17AjB25Ci67QlDkignQs+AFZMmsNbTPs3jtm/fTocOHQBo1KgRhw4dAuBFSBJ/DznAwHMf4qLrauO4YzT65Qp+th7pPbAJCQlMmTKFOXPmiK5LdHV1mTZtGoMGDcLAwCC9IrMEQa4g+thtwtYcJ+768y/mv+BSnDsjejK8mgkulmmvRs8pIiMjeffuHa6url/ttDQhIYF69epx4cKF9DPJtNGycETbyhktK2fVf2tndOyKI9U3TZVdT0vC6BrGdNIO5G2TPwCQmhrifHEWMnOjDNVLGZeARCZDopN9o5zcdE+zGo1gSY1GsKRBUFAQefLkQalUUqRIEU7VG070XpUqYmXVhgxa3irN1edyuZyCBQvy5s0bAB48eEDx4sUBGHM4DMd5G6n79A4ASlNDXA5ORMc5T7r1+NIDe+3aNbp168bTpylmrNra2lSsWBF3d3fq169PtWrVstWrc/zdV4T+c5yY0/dE9zFpMaRtP57ZOdK6uD5DqhgijQn8KT5CISEhVKpUiRcvXqS5X0tLCz09PbV7IggC0XEJ6DlXwaBkY6wrtiVO0FE7rns5Q/p67SR6l8pIxKxHXWz/6MznEBLlhG8+S9CMnUhkUkw71MC8T320HdL2TZeZaARL1qIRLDnMt96AGjVqiD3PJ0cvIvReg0QQCDUw4tTiKUz0SPvlXLRoEcOGDQNUoxYvLy8kEglBMQo6bHxP7w3rKOvjDYA8nw2u5/9MV1+ekQc2Li6OSZMmMW/evDRXa/fu3ZuVK1fmyMstyBUoo+JQRMSijIwl/NhtIhap1Iq+phbM9GjPM9u86MigRSGB32rbYGusLR4be/UpEl1tDCqkNpjIzbx8+ZJhw4aRlJREoUKFKFy4MIUKFaJQoULkzZuXwEB1ISoIAhMnThTN1pHKaDFgGnlr9+TAsxSPD87yaBavmYf2Bws0vdJO6BTNi26RvOgWcUCnsANCbDwxZx8Qc/4hsVeeIHzi+UFmZYLT6eloWWSt80KNYMlaNIIlh/nWGzB37lxGjx4NwPLly2lwPYmE47cAmNe+Byv/qp7mSCA6OpqiRYuKHox37twp+qGKiFcydpcfzeYuwTkkAADjub2w71gtVTnwdQ/stWvX+Oeffzh37hzPn6urowYPHszChQtz/AUXkuS8rjeJxBf+ACgkUjZXcGdreXcUMhm6WtDLUU5771sodp8X13LYLeuPSbMfw8z2S6R3TwVBYPTo0fz1119imqWlJZ2mbeZQTEnkH+RLxxtn6HH15HfVocDF2eg42XxXGV9CI1iyltwuWHL0jp8/f56mTZuKk9P79u1LN2///v2RSCQsXLgwW+pWo0YN8ffFixexbF9V3C559w4PAtK28DEyMmLRokXi9pAhQ4iIUFlOmepJWdrZgUsdW4n7Xy44LM6TfA8VK1Zk9erVPHv2jHfv3qkJksWLF+Pg4EDPnj3ZtWuXWJ/sRqKthcP6YeiVdgJAJijxvH6aRbtXUuP5fUYf2ELTEVNJWLJfbYFg0LTtafpc+5mQSCTMmTOHJUuWiK7QQ0JCWDywAfKdvXGSv0BXJrC9XA22lauBn0naYQA+RmZjikmbKtjO6iam6RS2Rzv/j+kmRsOPQ44KlpiYGEqXLs3SpUs/m2/v3r1cvXoVe/u0J82zgrJly6Kvr1oMeeHCBQxrlkShr7LmqfLyMaceRad7bKtWrWjSpAkA/v7+TJw4UdwnlUgYNLAsz+xVwces/fw5vu5mptY9b968DB06lDVr1ohp79+/Z926dbRt2xYrKytq1arF7NmzuXLliri4LzvQcbLBcd8ELIc1A5nq8Ssc4MvEo9uo7v0QmaASsgqJhBBD1QdW/j6M0OVHsq2OOYVEImHgwIE8efKE9u3bi+lvbxzh/MSavJhQBMv7KzjnXp7u3UbSrP9kBrb7lcUerfFvWQcD9xIY1i6F9cR25D8+DZf/FmC3sA9JviFiWabtsnfOTcP/JzkqWBo2bMj06dPVPAt/iq+vL4MHD2bz5s0ZMvvMLLS1talUqRKgWgDpE+iPXl03AIwS43l3/H66x0okEpYsWSJaZy1dupTr16+L+/MYa2Hav6G4HbP6KG/D5anK+V66d+/O4cOHadq0qZqlmFwu5+zZs4wdO5YqVapgamqKu7s7EydO5OjRo2Ick6xCoq2F1W8tcdw7Hu0C6mEF4s1M2FmpFp7dfmNUy17IP4y6gpYdJuGjD+THCILA1bcJTD4ZwabbMcQlff8IMCext7dn27ZtnDx5kkaNGqXMxSTG8N/WGZwZUYqY3UMQpIk8t83LwcJl6Za3NsMbdiN2/iAs+jdEzzUfEokEQaEkcqdqwh+ZFJPWVb65XsqY+Mxonob/A3L1KiulUknXrl0ZNWqUaF31JRISEkhISFGbREZGimV9rcqpatWqnDmjiqlx/vx5mrWogP9BlXWY8407vA2rTF7TtC9hvnz5xMWLgiDQv39/bty4IfYWq3pW4MayfZi/D6SEzyvmL77NzDFl0dVK6U0qlUoEQfguVZmHhwceHh7Ex8dz7tw5jhw5wuHDh/H29hbzJO87d+4cANbW1uzatYtq1dKe+8ksdN0K4HhkMiF/HyTq8Vus2lXHuH4Z8iukGN+OZc3NGA6UqkSrO5eRJiRxx2M65q0rka9tJXSK5UUpwNHnCSy7Gs2jwBTBvP1eDDs6Wqpdy9zC19zTWrVqUatWLXx9fdm0aRPr1q0T589Cbuwm9MFJLBtPwqC8ysT9hk8i9dYEYWUgpYi1FkWttCjn84KC71VqRZmZIdHnHmBYtzQyk4ybpAtyBQEj1hC17xoGtUthO7vbF2PGZMaz+6OQE23N7dc110zeSyQS9u7dS4sWLcS0mTNncubMGY4dO4ZEIsHJyYlhw4aJVldpMWXKFKZOnZoq/dGjR18dxvPs2bN07doVgJ49ezJlwu9EeMxCOy6BKF09bqwaS9Mi6fvrSkpKonHjxjx+/BhQhUD+2AVLzL7/YNY+ALwt8+DV35PRjY3R+jCOFASBsLAwzM3NM1V9IQgCr1694sqVK9y4cYPr16/z7t07tTx6enqsWrVKXOSZlaTXzvB4WHUxni7TF2Aar266HOmSl2kNO3BPYv5pcQAca6vESCfNXTnK99xTQRC4efMm27dvZ9++fWIHyq58U/J3W4ZfTGoFRI3n95l4dJt6orYM2S8uyGoVR8vdFYnR5xdsJsw7hHznRz7TTA3QHdccLff0F+Rm1bObG8mJtkZFReHq6pprJ+9zrWD577//aNy4Mbdu3RLnVjIiWNIaseTLl4+wsLCvvgGhoaFYW6smOitXrszFixd51GMZ2qf+A2DfiIGMGvb5FfQrV65kwIABAIwdO5Y///xT3CfIFTyrPxXpC5UFWYy2LvdaNaTLjIZItbWyNZa2j48PFy9eZPXq1Zw9exZQqQM3bNigpu/PCr7UzqsnvQmYtoMib7yRffS4BhmZMrZ5d95ZqFs41XDSYUPbL09u5wSZdU+Dg4Pp1KkTp06dAmD4qHE4NR/L5beJPA1KIjTug/NKpZKu10/R4OFNLGNTzwtKzY2wmdYJo2a/pPlRDF93iqDJW9Ksg0n7alhP7oDUSD/VPk3M+6wlMjISc3NzjWD5Ep8KloULFzJixAi1G6VQKJBKpeTLl4/Xr19nqNzvNcsrWLAg3t7e6OnpERkZSeTeawSPUE2KH65ck+E7u3/2+ICAAOzt7VEqlRQuXJgnT56ovcDxD9/yqt1cpBEpL32YowOlFndHr4xztpsxJiQk0KVLF3bt2gWo7svKlSvp06dPlp0zI+aasYlKFnv58W7PDVrfuigGUos1MCD2r4GM9bfkbbjK0+NBTytK2amGK4mvAojcdxXDmiXQL+uSZW3IKJlpmvrixQuKFy9OYmIiWlpa3L9/n6JFiyIIAg9e+jPiz6Vcex6MXuGaGBSqgav/W2q8eEAN7wdYRkeplWXkUQbzP7ryUGnAwwA51Zx0yHPnEb49FomxeKyndCTu6lOij94Sj9POb43d331TeZHQmBtnLRpz42+ka9eu3Lt3jzt37oh/9vb2jBo1imPHjmVbPZIdCcbHx/Pw4UNM65RC+CAYXJ8+xifi85Putra2VK9eHYBnz57x8OFDtf16xR0pfHEmEY1SzJnN3/ryrvmfBIzZgPCZ1etZga6uLtu2baNXr16Aapjft29f5syZk+Ey4uPj8fHxydTwugY6Usa0ykuP2Y3ZOnwQAfnyqtJjY7EYvpA8t1XXtbqTrihUok/c4U3DKYTM28fbFn8SPH8fgiJ366a/hoIFC4prreRyOYMHD0ahULBs2TKqlinKyXUziLq4iqC1nQnePYr7ltYsr9GETt1HM7R1b567pgja6GO3eVx9PCsnnmDyiXAGzLnH237LRKFiMbgJFr3rY796EHnm9UTywUN30psg3racQcTOi9l/ATTkWnJ0xBIdHS26vihTpgzz58+nVq1aWFhY4OjomCp/RlRhn/K9kn3+/PmMHDkSgNWrV9O7d2+u1pqK+fPXADxdP41mdfN9tozFixczZMgQACZNmpTmHBDAga330Zq9BZfglKBSEltTnLwmoWOXvaodQRAYNWoU8+bNE9PKly9PmzZtMDAwwNDQEENDQ3R0dHj79i3Pnz/n+fPnPHv2jLdv36JUKunQoQNbtmz5ot75W3p8yug4fPssIfaCyhmnQiJlddUGNO5QgjLvvIk5/1AVsfGTx9u4SQXslv+aY3r/zO7dxsbGUqxYMd6+fZvmfmtra+bPn4+pqSm//7WS98X7oZu/nLi/2osHDD57APO4FEvAAGMzDBLjMU5QWYHpNCiP06pfkXxU38TXgfgPXUX8fyojEImuNk6nposLLzUjlqwlt49YclSwnD17Ns3J4W7durF+/fpU6TkhWM6fP0/NmjUBGDBgAEuXLuX21L0YrFa5JrnSvT3dpzf4bBm+vr7ky5cPQRCwsbHB29sbI6O0HQiuvBzOo8Un6Xb1pBja2LBOaRzWD832j6EgCMycOZMJEyZ8cxkfe3lOj299MYVEOf7D/yFq/7XP5ossUgDj56+RfOh97548mnfWtiQpoIStNpUcdXCz10EvG6zIsuIjtGfPHlq3bp0qvWfPnsyZMwdLS5X7IUEQ2HfgION2PiHWtYMYVtk4LpYBF7yo8/RuqjIe2+bjz059mNjQkmbF9NV9nMkVvKo9kaSXqo6Q7dwemHWskWXtzK1oBEtqcvSOu7u7IwhCqr+0hArA69evv0qoZAYlSpQQfyersexrFRPTdB++/GIZDg4OYiz1wMBAunfvnq65YL8qZpj2rEefLkMJNVAJn5hTd4nckf2qBolEwvjx45k9e3aGjzExMVGLDTN9+vSsqBoAEh0t7Bb3JbBt2i7k/UwsWF69EW3q9WJtxZQ878884vDTeE68iGfBpSjabw2h1EJ/2m8JZv6FSK69S8hUNV5W07JlSyZMmCB+1MqXL8/Zs2dZs2aNKFRAdT9bNm/G439HMdL5GcLL88Tc2s3rQxOZ/HodkaM9xHVFgpaMq4VLMLlJFwLlMoYcDKfv3jACo1NiLku0ZGjnTSlf361ANrVYQ24n10zeZxWZIdnz5s2Lr68vhoaGhIWFIZMLPC4yAC2lAj8LK2rdm/vFMl68eEGZMmWIjlZN0o8ePTrdD3aiQqDd5mB0L91j2iFVfA8M9XA+9QfaeXMm7vnDhw/x9fUlJiaGmJgYYmNjiYmJIS4uDjs7O9HRorW1NUqlkmLFiolrLi5dukSVKukvzMuMHt/TA3fwW3CQYB1DbuR14ZytC+9NU9SHzkF+rNim8vBwybkYUxt3+Wx5JfNoM7qGMdWddDN1pJiVvduXL18SHR1NyZIlM1RnHx8fGjZsyIMHDwCVifnmfzfRpGxVZFYmhMl0mXQigkNPUxZG5jOVsa2jJXlNtVDGxPOi1BCEhCS08pjjfGOeeF7NiCVrye0jFo1gyQBdunRh8+bNgMpvWNWqVTlZZTL5Pui1ra4uxDJv6lgan3LkyBGaNGkijlZWrlxJ375908wbEK2g3eZgWu/agcdjlRWOVqWiOO8Ypabrzq18HNPm49g0aZEVL2a8XOB9lAKfCAWB0Qp0pQLObcYgjYwBEwOMLi9ELki46ZvIlbcJXH2biOHTV3S8eY68YcE8sM/PmcKl0a9UhFG1zCjnkDmLYnLbBzciIoLWrVuLZssSiYQFCxbQs2dPHjx4wIMHDzj2PJFbRrVR6KqecQcTlXAxPX6V9x8sJE271iLPTE8EhZKEp77E3XpBeFg4Du1roWNjllPNyxY0giU1GsGSAdauXStaSU2dOpVJkyaxt9cGXI+dBSB2zq+U6fRLhspavny5uK5FJpNx6NAhPDw80szrE55E13/eMu2fxdhEqxxHGk/qhH3f3B09EFSLQwsWLChOKq9YsYIePXqgo5P6A51dL6ZvnyVEH1GtQXLcP0E0kU144kPQzF3EnEo9xxBiYMTSmk2R1i1H25IGuLvoYqL77XXMbYIFIDExkT59+rBx48Z088iMbbHpswNtG9U1szWEDcfWI72tGpUaVFWph+PuvEL4yPWLRFcL8971sRrdGoksd7Q3s9EIltT8nHc6k6lTp474+/jx4wBolU2JERJx5VmGy/r1118ZPnw4oFqX07p1a27cuJFmXnsTGbOb6LKhacrEbOjMHcQ/8fmq+ucE2trajB07Vtzu378/BQsWZNGiRcTFxeVInZI/fgC+PRYRdewW70et43X9SWkKFQDL2GgmHN2G9PgNBh8Mw23Re9ptCWb51SgeByb9UHMx6aGjo8P69evVnKV+iiIqgIDVbUkKVAmSgBiQ33sl7o+99JjYS4/VhAqAkCAndOlh/PotRRmXfc5ONeQsmhFLBnF1deXx48dIpVICAwN55Sdg2HA0UgSirCwod2tuhlVUCoWCtm3bsnfvXgCsrKy4fPkyhQqpB7RK7gkl6Nvg1X0dDW6r3GpE57enzKkpSPWyzynnt5CYmEjHjh3Zs2ePWrqLiwvLly+nXj3VyCu7enzKmHjeNPmDxOd+ae7XsjPHclhzjJtWIPb8Q8K2nCPuvMpgQyGRMsOjHRcKlVQ7Jo+RlCbF9PmtujH62l+ue24csXzMunXrmDFjBlZWVpQsWZKSJUtSokQJSpYsyaFDhxgydgr6bZahm7c0PS8fo8N/59WO17IzR6+MC3plnQn39kG+4yp8WDukV84Fh3VDszzIWHajGbGkRiNYMsjo0aOZO1c1Sb9p0yaatenIgdqzKP9G1YOz3zQCY/eSnytCjfj4eOrXry9GqcyfPz8HDhygVKlSYp6PH9hzT6JJ7PAnTqGBACT+4kqBQR4Y1iiORCt9f2W5gcuXLzNr1iwOHjyolt6xY0fmz5+PjY1Ntr2YivAY/PotJfbSYzFNaqyPxcDGmPeqh1Q/RVUnKJUEjNtIxGaVc06lVMrfHbtz2CL1Cv4ydtqsaWOBpcHn70VuFyxfIjAwkCnTprP9tRlG1ftT0vcVrv5v8dHTooBjACNGdMTS0lJsp+nzUPz7LxdHMtoFbMn774gsDzSWnWgES2p+vCc7h0iOrwLg5eWFsa6Ut3VSLJ3erDnzVeXp6elx4MAB0SnlmzdvqFSpUrp67lquJrwd3Z3ED2sPdK4/wtdzAS9+GUnQnztIeOb7tU3KNqpUqcKBAwe4e/eu6IUAYOvWrRQtWpQVK1Zkm7dWmZkh1uuHc6x8FYIMTThZvgrmJ2ZgOaixmlABkEil2M70xLSDqs5SpZKRJ3Zzto0+U+qa4O6si+4HOXLbP4mW/wbzKjTzwx/kJmxsbFi25G+uzO9CoSdLuGNswPbyNblUoir/GjSmULMR9OvXT3S8alizBI67xyL74A056VUAr+tP4l2nvwj+ay/Rp++hCEs/tpGGHxPNiCWDyOVyrK2tCQ8Px8zMjMDAQLwexZOn7XgsYqNRyqQUujkfLesvW4d9jJ+fH82aNeO///4T0wYMGMDixYvF/ck9IYVSYPaks9TZtguz+NSuXvRKF8ByWDOM6rl9czuzGqVSyfr16xk1ahShoaFietGiRXFzc8PS0hILCwvMzc2xsLCgSJEiVKxYMVNNfr0exzHwQEqEyir5ddjc3hJpOucQlEp8PRcSc1YVg8ewTmnsVw5EqqfN48Akuu8M4X20SjCa60tZ29qCsulYkf3oI5ZPOXLuGiOPJxBjkuIrLOryOsIO/o57zZosWbKE4sWLk+Qbgo/nAhKfpt0B0nbOg0nLSlgOa/bDeUPWjFhSoxEsX0GnTp3YunUroDI7dqtQmQXtN9L2hkpVYvZ7B2z7pW3h9Tni4+MZOnQoq1atEtMWLlzI4MGDUz2wiQqBdVfCubL1FjXu/0fF10/R+qS3b1TfDZupndHOlzNrXjJCUFAQo0aNYsOGDV/MW6JECQYPHkznzp0xNDT87nOPPBTGrgfqBgRe3awomSd9k2J5YASv605EEarqXesUssduYW/0ShfAP1JBt50hPA1WjVb0tCSsaGFOLZfU7uh/NsECqmdy1N537PNOWRgbfmw2kWf+RkdHhylTpjBq1CgkMYkE/bFNNUoJTDs8ttPp6egWdsiuqmcKGsGSmp/jyc4m3N3dxd83btzAUEeK0KiimPZu339pHPVl9PT0WLlypVoo4TFjxnD/fuoolToyCf2qmTN7ljv3RvehY8+xLKvRGG+rPGKe6ON3eFVrAiFLvBASc6dqxtramvXr13PmzBk17wZp8eDBA/r160fevHn57bffePXq1Wfzf4nqBXTVtl0stHAy/3zMOy0bU/Is7INEV5Uv8bkfb5pNJ/ivveTRF9jVxYoq+VWCKV4u0HtPKLsfZK8D0ZxCRyZhURtH5jQ0I3msYVZ/FHpFapOYmMj48eOpWLkql597Ez/BE6tL83C+Ohe7Zf0xrJ0ypygzN8qxBcAaMhfNiOUruHXrFuXKqRz4DRs2jAULFnDPL4HgehNwiAhBIZVS+M7C77J6GTFiBAsWLABUPfW9e/fi7Oycbk/o+PM4Rh+JICxWgfvze/S7cFgt7oZOQTts/uyCYdX0gzLlNAqFgkePHqGrq0tERAShoaGEhYUREBDAjh07uHz5slp+iURC8eLFMTAwQE9PDz09PXR1dTE0NKRt27a0atXqi+dcdjWKXffjaFREj4GVjTJk0QWQ8NQX/2GrSbj/RkzTKZoXi18botugPMNPRHP4o5XqzV31mVrXFHN9Vfk/24glMjKSx48fU758eWQyGYsvR/HXBZVLfqkyicSoYKR6Jkh1U0aa+lqwoqUFNZ10eNv8T+Jvq9wi2czoirln7Rxpx/egGbGkRiNYvoKkpCTOnTtHuXLlMDdPiVy4usNaalxUWXfFTe6BW58a33yO+Ph4KlasyL179wCVQ841a9Ygk6VvbRQQpWDk4XAuvE7AICGebtdO0vzeVaQf3VrTTjWxmdwBqeHnowXmBF96MW/dusXixYvZunWrWhC39Pjzzz8ZP358VlQVACFJTsjfXoT8fVA0pQWQWRpj0qkmq/OXY9WbFLWataGUGR6m1C+k/1MJlmvXrtG0aVOCgoKoWrUqW7ZsIV++fPTfF8bRZyrhKg/3RRkfhU6eomrHaksFtpp6YzJtHQA6RRxwOjY111s4poVGsKRGI1gygaPb7lLgt4UAPC9TiiYHh39XeQ8fPqRcuXLiR7RPnz4sXboUbe30160oBYF/b8cy40wk8XIBlyA/hpw5QLGAlJDD2vmtsV/2K3qlc5ezwIy+mEFBQaxevZq1a9fi6+tLfHx8unmTPSRkJfH3XvN+zHq10QsAMikRlUsxvGQjfLRSeuotXPWZXs+YiOD3P7xgOXToEG3btlVb7GpmZsbWrVupVqs+ffeG8p9PPPHXNxJyaSM6lo5ole2I1NAaXcey6CUlsvbfBVjFRAKQd8tvGNYornaOhOd+RO65gkGlIhjW/Ly6NCfRCJbUaARLJhAfL+dByaEYx8USp62Dw7X5WNt83yTz6tWr1fyI1a9fnx07dmBq+nmrs5ehckYcCuO2XxISQUnjBzfoe+koekmqVc8yS2OcTv7x1dZrWck3u80XBJKSkoiPjyc+Pp5169aprfZfvnw5/fv3z4oqq9Uh7sZzwtefIurwfyBP8f4rK56fP3sN5PSrJDGtYj5tZlRJwNnxxxYsNjY2BAUFAWBkZCQ6V7WwsCAkJASlUomPjw958+Zl3rx5LFiwgLVr1/Lb6LEEuA2hkbY9449tByDRMQ8lLs0QrcGSfILxm7OPuH2XkSgFlDraFLrxF1qWue8DChrBkhY/7pOdi9DT0yK4YmkA9JMSubH+yneXmey7KXmUcvz4capWrfrFkMzOFlrs6mzF6BrGaMmkeJWsSL+Og3hiq4q4qAiJwu+3dT+FKxKJRIKOjg4mJibY2NgwZswYtcBkgwYNyvJooxKJBINfCmO/7Fdcrs7FcnhzcZ/i4RumbFzOsqJRGOuoPprX3iXRcJeEX5YFUnt1IM02BtFpWzC/7gtl460YQmIV6Z0qV/HxQt5kofJxuiAISKVSYmJiWL9+PbNnz6Z+/fpcu3KJ+pznSehTFBLV50fn7XtODtlCjH8EVwdt4FnVscTvuSTGz5EmJnFs/bcZxmjIGTSCJZPI372m+Nvl/NVMKbNz585s2bIFCwuV+/eHDx9SsWJFrl37fGArLamEgZWNOdHLhiZF9fA3tWRSk66E6atGUXGn7nJ05nES5D++cPmUESNGiBE/k13nJLuFz2q08phjNbIF9qsHiWnxd15RcPBstvudwgrVqDFRISEoRol3qJy7/klcepPI4afx/H4iggpLAui+M4S9D2OJScy9YZT379+Pp6enuK2lpcW4ceM4fPgwgLjgddy4cbi4uNCiRQukUimGhoaULV2S0IdLaaa4TYRSNZpz3HuSl5V+w3zfWbQUKuGaIEux1Ht7+C6nvdNXfWrIXWhUYZnEy5cvOdL0N+pGqFRMTmdnoFvQ7rvKTB5ix8TE0LRpUzG+iZ6eHlOnTsXT05M8efJ8oRR4HJjEwktRhB27wx9e/wIQr6XNlD5DmdW3MEWsc9bnWGarEhQKBW3atGHfvn0AODo6snTpUiIjIwkJCSE0NJSQkBAx35ciXH4LsZefEDB+I4kv/MU0wdqMfY2acjRvYRIELaITBaISBNJ7AfW1JdQrqMfgKkYUtsqdfuF27drFpUuX6Nmzp+hFAlT39PDhwwwePJjNmzeL8Xi6devG27dvGT9+PMNHjOD9y0COmNbBRJpiAh6vpc3xClUROtel4ajp6MTFE6FnQM9fx7Ozqw3FbHLXtdCowlKjESzfiCAIaiuEz5w5w4AuPfhLXoaC2maY92+IzcR233WOjx/Y8PBwWrVqxblz58T9UqmUevXq0adPH1q1avXFFcsPA5J4OHgtpS+rRlSPbfMxu3s/9nTLg51JzlnjZMWLGRMTQ82aNdU8GqRH7dq1mT17NuXLl8+UcycjJMoJXXWUkAUHEBJS5lmkZZywH98Ow8rFEASB2CSB12EKDj6OY/+jOPyi1NVhujIYWd2E3hUMkUlz96p0pVKJl5cXTZo0wcPDg1KlSjFz5kx0dHTYtm0bkyZN4urVq1hYWKBUKnFxcaEcxfgjyRaFVMoNt+IUmdKFim7WyKQSfPosIeZDqIMhbfsTWciJfZ5W2BrlHusxjWBJjUYV9g18KlQUCgW1atWi16/92Bz3BIDIXZcQkjJvcaKFhQXHjx8X48KA6oE+duwYbdq0oUGDBl+cfyluq02bdd0Q8qkcABYLeMfArRvou/4doT+Ibj+jGBoacvDgQfLly/fFvKdPn6ZixYqMHTv2s5ZmX4tERwvLQU1wOvOn2kJA5e3X+LSdQ9CMnSBXYKgjpbitNmPdTbj0qw07O1nSxc0AMz3VM5aggBlnI2m9KZhnQUnpnS5XEBsby7///ouVlRV3797ljz/+QEdHh6SkJP7++28GDx4sqnbDwsKIjo4msaQWHsEHqeG/jZ4HR7J9w5/IPxibGNVKGQW1un0Jv0g5E4+nvWpfQ+5BI1i+kunTp1O9enW2bduGj48qLkryGpMGLZrySC+OeEGOIjiSqKO31I4VBEEtZvjXoqOjwz///MPjx4+ZMGEC+fPnF/cdP36cEiVKiC5n0kNqqEf+pX1BW1XnCm+fM2LFYkYvfkhkQu7V6X8LdnZ2nDt3jtGjRzNx4kQWLFjAxo0bOXToEFevXmXjxo24uKg8FSuVSmbPnk25cuXSjY/zreg4WuOwYRj2qwaKMeUBQpcd5m2bWST5BItpUomE8uZKxsQ/xev+brZ5raC0j2oB4W3/JBqtD2LhxSgSFblT0WBkZMTOnTtZuXIlxsbGtGzZEoAdO3YQEBDA4MGDxbx//vknHh4e7Nmzh4bdWhMmV3kq+Pvvv6levTqvXr3CqH4ZJAYqNZn7i/t0uX6aE8/jeROWOz1KaFChUYV9JefOncPDw4N27drx6NEjatSoQenSpXFzc2PHjh0kvgui92lVr1KniANOx6chkUmJS1Ky834cJ1/Es7GdZYbO9aUhtlKp5ODBgwwcOBBfX5VzP4lEwtq1a+nevftny4698oR3fZfCB8+ycdo67GvXnjHT3TO8Cj2zyMlFg0lJScybN4/JkyeTmKjqJUulUho3boynpydNmjRBTy/zFpUqkuS8nb+bxOUnRNNkqakBtjM8QakkyusmMWfvq6nOlHksGNznN56Hp7yqha20WNbcnEK5dO5FqVTy8OFDli1bxvLly1mzZg1Pnz5lzpw5AFy5coURI0Ywbdo06tWrhyAIrFq1iqFDh4rrt8zMzFi3bh119Bzx67MEPnyqZtVvS4HOVZlUJ3eYzGtUYanRCJZvoEGDBsydOxdbW1vWrFnD1atXCQ4ORiaTsXDhQiwnHyL+9ksEIM/CPpi1UU1cKpQC/faGUctFl85uX17nktEHNjIykiFDhogOHSUSCatWraJ3796fLT/JN4SX3f+Gx2/FtEt169B5VUd0dbJPh50bVqM/ePCAbt26ceuW+ijTzMyM9u3b4+npSeXKlb/b825yWy0C43k/aBVJb4MydJzV3/1Ya+XKimvRyD8MLK0MpGztaJkrJ/aDg4Nxd3fH29ubzZs3Y2VlxcCBA1m4cCH29va0aNECT09Phg8fjoGBgXjc7du3adu2Ld7e3mLayJEjGetSm5A/dwKQKJXxR7uerJtRCUOdnFe6aARLajSC5RvYtGkTO3fuZP/+/WJaWFgY2tranD9/HtswBZG/H8A+UY62ozUFzs5A0JYhlUi445fIlFMR7Otq/cXzfM0DKwgCw4YN4++//xbTlixZwoABAz77MVTGJfJk6Dpkh1NMpL1di1J67QAc8mZPpL/cIFhANXqZP38+ixcvFkeAH1OoUCGGDh1Kv3790NL6vNPK9Pi4rUJUPAFj1hPlpa56k1mbQO2yXFCaUmWn6hnTK+2Eo9ckHgfJGe4VxpMglSrI0kDKtg6WFM5hy75PGTZsGIsWLQJUHZ0FCxbg4ODAtGnTKF26NLa2tvz1119pHhsZGUnv3r3ZuXOnmNayRUv+dmlMzLaLAEToGfBu7lBatyyc9Y35AhrBkgbCT05ERIQACBEREZlabs2aNYW4uDhBEATh8OHDQq1atQQtLS2hTp06QpXBKwWnqc+EoR12CEtqLxTurrsgCIIgJMqVwvmXccLIQ6FCXKLyi+dQKBTCu3fvBIVCkaE6KZVKYeTIkQIg/pUuXVpYs2aNkJiY+Nnjbs09JDzM21N44tBdeOLQXTjjOkL4c9k94WlQ+sdlFl/bzqxGLpcLJ0+eFDw9PQVDQ0O16wkI/fr1++ayP22rUqkUwjafFV43ny68n/CvEHP5sRAVmyS4LfIXHGf6CAfdxon3JOb6M0EQBCE8TiE0Xh8oOM7yFRxn+Qo1Vr4XFMovP0/ZSVxcnNCmTRu16/bvv/8KgiAIsbGxYr707rlSqRSWLFkiaGlpicfXrukuPGgxU7weTxy6C961JgiB07cLMVeeCMokeba07VNy4vnNqu9aZqEZsXwjcrkcLS0tunTpgpeXF4MHD2bs2LEYGhryLjiaGgse4/7wFoJFPsKsrRFcnXC21OLq2wR6lDOiX0WjL57jW3pCgiAwbtw4Zs+erZZevnx5NmzYgKtr+l6OL+28i864VZh8CCKWKJWxsnojElrV5NdKxukGr/pecsuIJS2io6PZu3cv69at48wZVZRQmUyGt7e3mvFERslIWyPilVRaFkBskkC9x7cYdXI3AOb9PLD5vQMAt/0SafGvatLfQl/KrcG2uSpAllKpxNfXl1WrVjF9+nRAZUzx7NkzjIy+/Ownc/z4cdq0aUNUlMpjcpNa9RgVURbbgIBUeaWmBhi6l8RiYCP0XB0zpyEZQDNiSU3ueot/ILS0tDhz5gyCIPD06VP++OMPMQhVPisjXIRXRBtJGXd8J7/vWstouwhqFNBlawcrupUzJCBKkSWWPRKJhJkzZ7Jz504qVkyJFXPz5k3Kli3L3LlzUSjStkyr2rY0eb0mEeai+mDqKBUMPneQ8os30HmtD522BfM4MHebu2Y2RkZGdO3aldOnTzN8uMq5qEKhENU8WYGpnpR/WltgqCPhaoGiKD8IjJhT98Q8q66nuFHpVcEwVwmVZCQSCVOnTqV5c5WbG39//1Qdni9Rv359Tp06JX48vc6cYJZjMBsr1+VRnnwoSWm3MiKWqP3XeNvkD8LWnPgp3Bb9qGgEy3dw7NgxzM3NsbVNMSH19/dn1KhRPN8+Ef/StXllYYNxQjzOB05RzFqbBZeiqLo8gCmnIui7JzRL1o9IJBLatGnD1atXuXDhAkWLqlyWJyQkMHr0aGrUqCGu4v8Uu6K2VDwxEaMe9cQ09xf3WbptKe9vvqbR+iAmHAv/YXxaZSbjxo0TLcT++ecfIiKybj1F1fy6XB9gy8bejhiUV4X9TXzhT+LrQO76J4oxX6wNpfQsp+rQKAUBhTL3fUznzp0r+rz766+/ePPmTZr5vL291cJVJ1OhQgWOHj0qjnSO7FvDSetIhrXtT/ve49jQqj0GTX5BaqoyAhAS5QRO3oJvz7+Rh0ZlUas0fA6NYPkO2rVrx/Hjx/H39+fixYtMnjyZIUOG4Ofnx/7dO/i1ljWJFqYkSmVMjban2cYgdGUSjvSwZkpdUwqYazHjTGSW1rFatWrcunWLkSNHir3ay5cvU7p0af7+++80e3USHS0c/uiE/epBSE30AXCICOXvHStof/0MW25F4b4qkDU3oknKpespsgJra2vRP1ZUVBSrV69W2y8IAjdv3mTo0KHkz5+fWrVqZWjlf3oY6Uop66CDUZ3SYtr9nf8x+ki4uO1oJmPu+ShabwrGdcF7yi0J4MTz3OVTq1ChQgwZMgRQxRv62AN1MtevX6dy5co4OTkxYcIEwsLC1PZXrlyZw4cPixZk11cOQRL8lAh9QzY7lGJLx0643FqIed+U0OAxJ+7wpv5kYq88ycLWaUgLzRzLdzJr1iyOHz+Oj48PdevWpXz58jRu3BhbW1v8/f2R/XOelTdi+C9fQaYXiafEkPrisf6RCkYdCeefVhboaadWZWS27vbixYt0795dzZSzRYsWrFmzRlwN/SmJb4Pw/3UZ8Xdfi2mPbfMx06Md700tcLPTZlUri+9ysZGb51g+5cmTJxQrVgwAc3Nz5syZQ4UKFdixYwfbt29Xu7agWhOze/duWrRoAXxbWxOe+PC67u8APMzjyIg2fRAk6R8rAbZ2tKSyo266ebKaT9sZHh5OoUKFCA5WzQvdu3dP9C0WEhKCi4uL2giwWLFi3Lp1K9UaotOnT9O4cWPi4+PRzlOMPIMOI9HSAaWCjS0NqFnUkuhTd3k//B8UoR/UhVIJjgcmou/mnC1tzQ40cyw/OWPHjmXXrl08e/aMZcuW0bFjRw4dOoSbmxvt2rdnmv8tzuXJg+f1U+itO4QyTrUILyBKJVQKW2mlKVSygmrVqnH37l0GDUrxvrtv3z5Kly7N+fPn0zxGx9GafHvGYzGoMXzwU1Us4B3Lty3B/dld7vgn0fLfYN6G/3+shC5atKgoJMLCwujTpw9ubm7MmDEjlVAB1Uene/fuvHz58pvPqVPEAe38Kjc8xd+/pdONs5/NL0CumwszMzOjW7du4vazZ8/E3/fv31dzvQ/w/PnzNKOF1q5dmxMnTlCtWjUUgU+JOP1hrksqo9uWd9x//hajOqVxOj4NgyofolYqBSJ3XMz8RmlIlxwVLOfPn6dp06bY29sjkUhEb7SgWlMwZswYSpYsiaGhIfb29nh6euLn55dzFU4HCwsLcUI3b968zJ07l19//ZXly5bh5GzNKwNtFBIpiqBIfDddYNPtGCaeiMDWSMr4Wtnb2zA0NGTx4sUcPHhQHKX4+PhQq1YtpkyZglyeWkBIdbWxHtsGx30TxA+cYWIC44/tYOTJ3ST6htDp30Be/5+42VizZg2tWrVKlS6VSqlduzarV68mMDCQtm3bAhAREUH79u0zFFY5LSQSCbYzPeGDKrPrtdOUf/OMGk66jKxuzMa2FtwebEsLV33xmFJ5ssaC73t49OiR+Lt06dKiG506deqoGZTY29uzb9++dIPaVatWjQsXLhAYGMiibuVQ+KgWtQqGNjSaf42T1x5yW27A3TF9ET50hl6ffUpYXIrLInlQBKErjhCy7DCC/P9vvjCryVFV2JEjR7h06RLlypWjVatW7N27V+wNRkRE0KZNG/r06UPp0qUJCwtj6NChKBQKbt68meFzZNeQ8eLFi0yaNInffvuNRo0aqe0r2mgINoXa4BgTR7CFJYWrFKBsPj3aldTHQEdKQJQCW+PUqqSsHmL7+PjQpUsXNY/J1atXZ/Pmzek6b1RGxxEwYRORuy+rpyMh0tAQs3wWGOY1RzuvFWZda6FbxOGL9fiRVGEfc+HCBaZNm4ZCoaB169a0bt1aLYxBZGQk5cqV48WLFwAMGTKEBQsWfHNbQxZ7ETxbZXosNTXEfll/DGoUF+fOqiwPwDdSgZ6WhPvD8qAjyzlLsU/vqVKpxNLSkvDwcKytrXn69Cndu3fnwIED4jHVq1fn999/p27dukgkEp4+fcro0aOpWrUqv/32W7rX6+oDb9rvigd981T7lm1dQsFgfxQSCavmTGdWaTlha04QtfcqQqKqI2Q1uhWWQ5pmWluzg9yuCss1cywSiURNsKTFjRs3+OWXX3jz5g2OjhmzU8+OGyAIAl26dKFy5cpqaiYALy8vli5dymC7ZtwO0qZwgC+lO/9C/oENOPkiniVXonAw0cLWSEq7UgaUtkvpaWbHA6tQKJg5cyZTpkwRe41mZmbMnTuXnj17pr/WYtdlAib8ixCT9kSxxFCP/PsnoFs07xfr8KMKloxw+/ZtKlWqJPoh27lzJ5UqVfqmtgpKJX69FxN9/I6YplPEAfNe9YisU4Hq68MBqJBXh/mNzQiIVvA+SklgtIL85lrUdtFFmk1myZ/e00ePHlG8uCqmvaOjIzKZjFevXgGqd3/SpEn8/vvvokNXQRCoXLmyGNSubdu2bNy4MV2/bV53Axl4JBE+mXsadPYAze6rynhtYYNTaGCqY7XyWuJ8eQ6Sb3z2NIIlNd/mlyKHiIiIQCKRYGZmlm6ehIQENZVDZKTK6kqpVIpR7bICS0tL3r9/L57j9OnT7N+/nytXrtC1a1dq1WmES6OpoBR4ukXJYJ1ShCllDKpkRFkHbU57JzD9dATbO6Y4qFQqlQiCkKX1lkgkjB8/npo1a9KlSxfevn1LeHg4ffr0Yfv27WzZsgVLy9ROM41bVUK3rDMRm84Q8yqI54+CMYyIxCImCpmgJGBCDwoWts9Q3bOjnTlF6dKlmT9/vtjh6N27NwcOHMhQgLa0sJnfi6QOf5HwQGWym/jUl4DR65Gb7KRXwbJ4lazIDR9zqq9M/QGt7qTDhjbm2bLm5dN7euHCBXHf27cpvuksLCz4999/adCggXgcwKlTp9Qipe7cuZPY2Fi1Ec7HNCppRYf/LrD+aiDK2HDsTbX4tWsbihgUhQ+C5WOhIjUxQGpqgPxdMHKfEGIuPcagarFMaWt2kNvflR9mxBIfH0/VqlUpWrQomzdvTrecKVOmMHXq1FTpjx49wtg463xfvXr1isGDB+Pg4MD79++xsrLC2dmZunXrigsVE5cc4/bJNyyo04raYa/pM74MEq0UFVjPIxLGVhQo/MFASxAEwsLCMDfPno9BeHg4U6ZMYffu3WKao6Mj//zzj2gJlR4RCTD0lATvEAGzuGgi9Ayp5CilewkBV6u0j1G+DEDqbJvt7cxuBEFgwIABeHl5AeDs7MzJkyfFtR1fXZ5cgeL8Y5K2XUF5763avkSZFn82aM8V57Q9LOxqrsQu4wvfv5lP7+ny5cuZMWOGWh43NzdWrFiBg0NqdenKlSvFFfvJ6OjopGkgkUxISAhubm6AqqN3584dlAERxLach+TD+p4gS0scelRGq5EbiktPSfhd5Y9Mu2t1dAbWT6/or2prdhAVFYWrq2uuHbH8EIIlKSmJ1q1b4+Pjw9mzZz97IdMaseTLl4+wsLAsvwH+/v7s37+ffPnykT9/fpycnEhKSmLTpk1ERUVRv1ZdjqzxQR4WS5cbZ7D8rQUWH3S7ex7Gsf1eLGtbm4seW5VKJf7+/tjZ2WWriujEiRN4enoSGKjq4RkaGrJ+/fo0J6w/JjxOyfBD4Zx9laiWXjW/DoMqGVIxn4744kVsPU/g2I2YD2iIxaiWOdLO7CQyMpKqVauKE9jbt2+nTZs2311u/N1XhK89SdTBG6Ib/iQtLfYM6ofSrSC2RjL+uRFDcKyqh7u5nTlV8me9GfKnz254eDht2rRBKpXi4uKCQqFgyZIl6OikbWTw5s0bnJ1TzIMlEglLly6lX79+6Z7zzz//ZNKkSQAMHDhQdMi6csIxFDeec65gCRr1LEe38qoOZrxvCO8qjwbAoEZxHDaNyJS2ZgeRkZGYm5trBMuXSE+wJCUl0a5dO16+fMnp06fTVMt8jpzSRb57945x48axd+9eSpQogbu7O1evXkWv9mQq3XmnMhnVlhG3dRJesabc8kuke1kjmrnqixEqc3Lu4d27d7Rs2VJtgd+kSZOYMmXKZ3tlgiCw60Ecf52P5H20+nC9nIM2w6oaUyHCj/fD12Dxa0Nizj1AGROPML4pDgUL/LSCBVR+rzw8VAv4atasydmzZzOtbHlQBIGTtxB14Dqg8pvluHscukXz8u/tGDHq4qwGpnQs/eWQDd9LWs+uIAhERkZiZGSEVCr9Yu9+3bp1lChRgoEDBzJmzBhat26dbl6FQkHevHl5//49UqmUFy9eUKBAAV6Fyqm7JhC5Esz0JFz51Ra5AOtuxvDP9SjWLPsT07hYZJbGuNxZ9E0jDs0cS2py9VucLFSeP3/OyZMnv1qo5BReXl5UrVoVQRB4+PAh165dY/bs2SxduhRvrxkcq1SdvaUr80edtsza7UtSkpKR1U1o5qqPXCnkCnVQvnz5uHDhAl26dBHTpk2bRq9evdI0SU5GIpHQtqQB5/vZMsvDFEezFFXff75JXHoRS+CEf9EpYINpu2rYL+2Prms+5AdvISTKf2r/TvXq1RPd65w7d4579+594YiMo2Vtit2iPhjULAGo/Gb5dJlPkm8IjqYp9+BdeM6Z1kokEkxNTZHJZBl6xnv06EGFChW4evXqZ4UKqOZw3r9/D0CzZs0oUKAAoArpnBy/pnUJA1bfiKHq8gDmX4wiMhG8rewAUIREoQgI//bGaVAjRwVLdHQ0d+7c4c6dO4BqnuLOnTu8ffuWpKQk2rRpw82bN9m8eTMKhYL379/z/v170cImN5KQkMCJEyeYMGECmzdvxsnJSdxnY2NDybzGTK6nR3g+BxwiQmhx4SSe9y5QwlabeRci6bc3lOVXozjjnfNuOfT19dm4cSN//fWX+CFYt24drVq1+qxwAdDVktDRzZAzfWxY1NSMwlZa6MqgRyUTrH5rhSI8huC/9gJg+VtLtNpURKKjlSuEalYhkUgYOHCguD19+vRMnYSVaGvhsGogeqVVH1X5+zB8Os8jn17KOU55x3PuZTzyXOhTLD0yMgr4eF6wXbt2AFx9m8Dxj9zbbLwVoxIoCSltf2FtL/6+eepFZlRXAzksWG7evEmZMmUoU6YMACNGjKBMmTJMmjQJX19fDhw4gI+PD25ubtjZ2Yl/ly9f/kLJOYeuri7nz5+nWrVqYlpUVBT//vsvlStXpmDBgjQsa8+UQSXoeeM0ZXxesvlKGNWXv+fym0SaFdNHKah6WrkhrrlEImHkyJHs2LFD1IeXKlUqw4GutKQSWrgacKSzGfs9rcljrIVhrZLk/XcEcTdfEHfrw2RskoLgmbt4P3YDgVO2ZlVzcpyuXbuKRiQ7d+6kZcuWokv4zEBqqIfDxmFoF1A5Rk184Y/Z1bskBwR9EiTHc2coFZcGMOlEBK9Cf45FrQ8fPhR/V6miith67Z16BzTpg3yVSaBdSQPO9bXBunTKeq2HN1IHd9PwbeSoYHF3d0cQhFR/69evx8nJKc19giDg7u6ek9X+IrVr12bGjBmcOnWKadOm0a9fPw4fPsy4ceOYO3cuAHol8iPt1YApjTpzolBpZofcZHcXK5q7GjCwsjGFLLXYdDs2h1uSQps2bThy5Ahjxozhjz/++GL+kJAQte3YY7cx/yvFmk9qqIfM0hhFcCTywAjiB6/n0dW33C9eAnlAGD7dFooL2H4mjI2N+eOPP8Re+IEDB6hSpYq4piMz0LI0wfbPruJ24pUnjK9lgrFOymgwOFbJhlsxNN0QxLuIH/86ly1bVvx9+vRpAPKbp150bGcs5WxfG+Y2MsPJXAtnuxRvBVb6P+9oObvJ1XMsPyqTJ0+mUqVKDBs2jLdv31KjRg0GDRpEx44dAcR5hOh2ddFDwfxdq3DedICEJz4AxCUpSVRAabvcFW62du3azJo164vqqidPnlCqVCm12BvGjcqhjI7Dt88S4h+9JfrUXWLO3keirUXYiiP4CHqMa9iFmfH5ONOjE1JDXYSkH/+DlxatW7fm8OHD4nqsBw8eUKFCBS5dupRp59CvUBCJjmpUGXv5MT3KGXFzcB5WtjSnURE9dD98c6MSBf48nbUetr+VuLg4li9fzq1bt76Y9+M5mEGDBjFw4EAcFD58+qTWLaiHo1nKaPt5SMoz9vFclIbvQyNYsgATExMGDx7M/fv3+eeff+jVqxdVq1YVA4Elf5ifxGmTUDAfJglxIFfgP2QVRx5G03ePymV4CdvcJVgyQmxsLN27d6dx48acOnVKnFOQaGthv+xX9Eo7ETR9BxHbL2IzRSVo33vdZlrNVuzrasm/7S24fvQZQZefo4z9Nt9aPwL16tXj2rVrFC6sitkeEhJC8+bNCUgjMuK3INXXRa+sCwBJb4JI8glGT0tCg8L6LG9hwdUBtlgbql7/I8/iufg6d11rQRBo3bo1AwYMoFq1al8c0VWsWJFChQoBqmdw2bJl/FKqCHqR6s4/3Z3VV+57fyRYHEw0giWz0AiWLEYQhHQXwrUvbcAbYwsu1ajBgZIVmZK3EnsOeFPVSZe5jczQ1frxhubJHmxXrVrF8ePHCQ0NZerUqeIkteWgJtgv7Y/9ygGYtK5C0Nw9vO3ShCRTI7xDFdhJkxh66yShHlWIMjImySeY2CtPCN90JieblSUULlyYa9euUatWLUAlXH799ddMK1/07guEbzqrts/CQMboGilmqlNPReSqCf01a9Zw5MgRQDVyGTNmzGfzS6VSTp8+zaBBg8QOHID/tT3ib22pQBXHlHUzQTEKAmNSDBu0ctC32s+GRrBkMZ9TG0klEmZ4mPGuZT1OFitDkQBf6hw4SNOoN5jr/3i3RqlU0rp1axQKBcuXLwdg69atdOnSRe06yMyNSP6E6Rayp15DF8ZWFPjzTCRPR/6Lvost1YbWQt8vgLdtZxO5+zLRx27ztvVM5D+ZSaiZmRnbt2/HykrlnmDv3r3pui35Wgyqpqy+D11yiIhPHIe2KalPIUuVWuhZsDxXudpfv3692vbu3bu/aA2aN29eFi9ezLt375g7dy6Ojo7EP03pkJhEv8RAJ+W9uuWbSKIsRS0WseMiijB19/0avo0f7+v1gxMbqz4hX9NZj0ltHNhaKJRWdy9T5p03cSNXIn8flk4JuY+kJNUHSSqV0qFDB/bt28fmzZvFOQMXFxdOnDjBli1bOHHiBADnXiaw4noMUiM9AiduooT3MwYc2I7ypR8WfTx48jaGM33Wccu5CFOrtSDvvyPQcbEj+nTmrf3ILVhbW7N48WJxe8iQIamek29Bv0JBjDzKiNvvh/9DlNcNcTsmUcA3UrWuxVBHQgHz3OM6sH59dfcqVatWTXeV/qeYm5vz22+/4e3tzeLfBxB9czvysHfY+R1Vy/cwIInb+Vx4b2wGqPyu+XSZjyIqLlPa8P+MRrBkE/PmzaNSpUq4ubmlWgQokUgw7+eBQS1VRD1FSBR+g1chKHK3o7lkDhw4wMiRI8VtBwcHChYsSFyc6gUdPHgww4YN486dOwwZMoS//voLVxttDj+NZ2+zlhi4lyTp9EN8tQ14MulXrulacW3aAfRN9fllSXfM9aV02xkCOlrIfYJzqplZSvv27alduzagcmfyqV+tb0EilWK/ehBmnipVG0oBv0EriTqmmgzf8zCO2CTVs9jSVR8j3dzzORg7diyVKlXC09OTmjVrMm/evK8uQ0tLi3r16hK6awR+sythGqM+T/MwMIlELW3GtugJVqrYL/F3X+HbfRHKuNw15/SjkXuepJ8cLy8vrl27xvPnz3nw4EGq/RKpFLtFfdDKo4opEXflCUELMkclktXUr1+fR48e0a9fP0JCQnjw4AEnTpwgODiYuXPncubMGU6dOsWcOXPYu3cvx44dw4BYtnSw5NLrBMbkqcSICi252Lo5ZYtbcmz1NSq+eUaNf/uT31yLMTVNcAn0I/roLYzql/lyhX5Akn1hJc/HzZ07Vy3K4jeXK5ViM70Lph2qqxLkCvz6LyPq1F02344R83Upk/VuXr4GHR0dLl26xIYNGzh79iwVKlTI9HM8ClBN3EfZWOG4dSRSM9U1iLv2FL8+S39aq8TsQCNYsomPHTiuWbMmzTxaFsbYLesPMtVtCfvbi8Bz6XtzzS0YGxtz5MgRtLW1adu2LUOGDKF37964uLiwYsUK1qxZg52dnZg3PDyc8PBwDLVhQzsLepYzYHA5WN/GggnHw3EyUGLbsRpSI9Uag303Qim50wujDjXQLZk/J5uapRQtWlQc+SUmJjJo0KBMcXEjkUqxnd0dk9aqhYMkKfDtswT9208BlQ+3Yja5zwLxe/1u+fv7c/LkSXH7zJkz4vUMi1PiF6VSAxaz0Ua/WD7ybR6J1EhlNRZz9j7B8/Z91/n/n9EIlmyic+fO6OurPpQrV67k6dOnaeYz+KUwYT1UHo+lCCT9uQdFOsG0chtLlixhzZo17N27l6lTp9KvXz9GjBghhg0AGDp0KAULFiRfvnwqR4SAq2k8rlbg/WEVeMeK5sScvU/iqwDO3w3BbPxK8ruYY9GmCn7RSnwj5bwN/zl7kxMnThSD2J04cYLDhw9nSrkSmZQ883pi3FTV85ckyhl1YhfaCjntShpkyjlymsDAQBYuXEiHDh3Inz8/9vb29O3bV9z//v17ypQpw/r163n6PmUOy+6DmbFe6QI4bBgO2qrt0BVHkYfkzjU+uR2NYMkmLCwsGDBgAKCKLdO9e/d0/W0V+q0xDwsUBMAsMpIb072yrZ7fS4ECBTA1NSU6OppChQrRuXNncd/UqVOJj48XV+736NGDnj17Urt2bU6fPo1SCdGJAorqpTDrVJMXHecRMHoD1jb6FJ3nyQW5CR23hjDnXBRdd4Rw9uWPIXC/BkNDQ/766y9x+/fff880x5wSLRl2f/cVA1pZx0RS58lttUWCPyrXr1+nRIkSDB8+nO3bt6sFE/uYu3fv0qNHD5q7l0UmqNp9/Hk8IbGq0YtBxcKYdf0wJyVXEHvuYZrlaPg8GsGSjUybNk1cxHX16lW1D8jHWBppYzmtC0lSVc/JePsJYl4HZVs9Mwt/f38WLFhAVFQUU6dOZdeuXQwZMgRdXV26d+/OvXv3GD16NDNmzOD333/H7+ElqubXpd2WYFY4/8LQxj2IHNcV103DOBcgYfLJCAZWNmZRU3NmeJgx7VQkvpE//kfxU1q3bi36z7t9+zbz58/PNMerEm0trMakrFLveu00gTuvkBibex27fokDBw7g7u5OUFDKO2JgYCC+a8l8PHIOfPuc8MsbAIhLElhzI2W+ybhBOfF39JmfzwoxO9AIlmzEwMCADRs2iLrjSZMmcf/+/TTz1nB35Ea1qgDoyOXcGv1jOWY0MjJi//79XLhwgW7dunHs2DH++ecf6tevzz///IO3tzfnzp2jWLFieHh40KBBA2JjYxlT04RBlY35Ja8OUzyd6eVujU+kgrnnIhlY2Yh2xXQInrePYkdOU9BKC2kqpx0/PlKpVC164m+//YajoyO///477969++7y9cu6qI1aBh3czotfRhI0axdJ734sq7tly5bRsmVL0QKxZs2a3Lp1i4iICFq2bCnmGz9+PFevXuXy5cu0a9cOmUxG5LnlCHKV9deGWzGEx6msMPXLFxTnWmLPPUDI5WGAcyXCT05ERIQACBERETldFZExY8YIgAAIbm5uQkJCQpr57j8PE64WHCg8ceguPHHoLviffZTNNc0cIiIihOjoaEEQBOHIkSOCubm58PDhQ7X9dnZ2wv79+1MdK1cohUbrAoXZZyMEZWKS8LrpH8ITh+7CH83XCjXmvxYS5cpsa0dmoFAohHfv3gkKheKz+ZRKpdCpUyfxOUn+k8lkQsuWLYVHj77vWUh4EyjcrzlRfLbEv7w9hHfdFgjxT3y+q/yMtvNbUSqVau8RIHTs2FGIj48XBEH1TJmYmAiAoKOjI/j5+akd/+bNGyFPnjyCeYtZguMsX8Fxlq8w/0LKN8Kn59/iNYm95f3ZumR1W9MiN37XPkYzYskBpk6dSvHixQG4c+cOK1asSDOfq7MJ/zWrK26/HbURZdyPp7IwMTFBX18VGfPQoUOMGDECV9eUVeH9+/enSpUqNGnSJNWxsUkCBcxl9KtohERbC4PKRThXsATnnIvTb+cmkm69QPkTBgeTSCRs2rSJM2fO0LZtWzFMgUKhSDeE99eg42hN0VNTmd65L2cLlUSebIElCMScvMu7NrNQROQe79qfcuHCBTUnp2PHjmXTpk3o6qrCLu/du5fISNXEe8uWLUWrxGQcHR3p3LkzkeeWIihU6tQtd1Paq1faSfwde/lxVjXjp0UjWHIAXV1dNZcVixYtQqFIO7Jf5d5leWGjCkZk6veeN3/szI4qZjrJoWitrKzU5guGDBlCdHQ0ffr0SfM4uRJehSk4+UI1UX+pSUO21mtC4wfXcfV7w/thq+EHsZr7WiQSCe7u7uzYsYM3b94wZcoUDAxUFlx+fn7fXb6WTIpZ1aLMaNCBzt1HozW4BVp2qnVUirBowtYc/+5zZBUfxwOqV68eM2fOVDNPzpcvJc7K0aNH01Qh+vn5oQh7hzxMtS82UdVBkQeEE/ZPStu1Ha0zvf4/OxrBkkOUL1+eevXqAfDy5Uu8vNK2/LI2lvJiWFcSPvg0Stx4kpjzP66lSvPmzdm3bx99+/alQ4cOnD59milTpoghez/FXF/K7IZmLL0SzaD9ocy9FMOwFnlpZBiNvjyJpDdBP3VgsGTs7e2ZPHkyDg4OAOk6Nv1a9LVVc1RhhsbIezYi366xoKUyGgn753iuHbX88ssvmJqqVsv/999/qTpmtWvXplOnTgBERETQs2dP9u3bx9y5c+nbty+1atVi505VJ03LNA8AdsYyBKUS/xH/oAhV+Qwz8iiDcZPMX5z5s6MRLDnI8OHDxd8LFixIN1/Hli5srN5A3PYd9s8P6yzPzc2NkydPUqhQIZo3b8758+dxc3NDJkvfZXkJW212dLJkrLsJuztb0by4IU4LeyIxVE2wRmy7ILop+dlJ9suWUcGiUCi4fft2uua3yYIFVNZROvltMG2jWkipjIwjbPWx76xx1qBy16LqmIWGhnL9+vVUeZYsWYK9vWq0f/LkSVq2bMno0aNZvXo1Z8+eRS6XI9U3Q6KtWl9mry/wfvga0cRYZmuG7dweP3W47KxCI1hyEA8PD4oUKQLAuXPnOHXqVJr5rAxlWPasw01H1doWITCc96PWZdr6huzGxsaGUaNG0bFjRywsLDIU993KUEZeUy3yGKsEkE5+GzGeC0DA6PXIgyKyrM65hYwIFkEQOHz4MD179sTOzo6yZctSsGBBtmzZkiqv3kehGeLlqufJYkjTlFHLmhMowmNSHZcbaNiwofg72cX+x5ibm7N27dp0j7e2tsa9STsADBLi6b5uDZHJHqAlEuwW9EbLwjhzK/1/gkaw5CBSqZTRo0eL2927d1ezxf+YfpWMWdawDZF6qt5V9NFbRGw9ny31zGo+1o0/ePCA0NDQDB1n2qG66L1XERLFs19X/fSmocmCxdfXl4MHD3L79m18fHxISEgQBUr58uVp3Lgx69atE5+npKQkunXrlipglv5HgiU5RryOozWmbVWm7sqoOEJXqXsFzi00aJAyit+9e3eaHRQPDw82b95Mjx49mDx5Mps3b+b69euEhYURGBjIuBmLAOh/4RAOT54DINHVxn7lAAxrFM+ehvyEaARLDtO9e3fRq62Pjw8dOnRIc0W+hYGMFjXtmFu3jZgWPHfvTxVl8d27d9SvX59KlSrx/PnzL+aXSCQYTfMk2tgIANnVRwTN3//DjuQyQokSJcTfzZo1o2zZsuTLlw89PT2MjIxo3LhxuqF85XJ5KrVO+bwprujnX4xi0aUoBEHAckhT0bVJ2OrjyANz32jQ3t6eSpUqAfDo0SM2btyYZr5OnTqxdu1apkyZQqdOnahQoYIYFvroU9X6F9uocDF/3o3DMW5UPkvr/rOjESw5jFQqZdOmTeTJo5pAPH36NBMmTEgz78BKRviXKc4lZ9XiNkVQBKGrcqcO/Fvo06cP/v7+PH/+nEqVKnH+/JdHZIZ2pqxr1RHlh4WSYQsP4D9oZa5V33wvCxYsQE9PL819H8dwKVOmDHv27KFnz55iWtu2bXFyclI7pmp+HYZWMRK351+M4s8zkWjltcSsszsAQlwiIYtyp6ftj8MLjBs3juho9bnH27dv07t3b5o0aZJqtBadoOTQU5VFYexHUSe18lmlOk/iy/f4dFtI0KxdP3XHJbPQCJZcgJ2dHbt27RJNKOfMmcOuXbtS5TPQkbKsuTmbqnugkKhuXcCSw8iDfw5HeStWrBDX94SGhlK3bt10e6HJaEkldO1fjrVVUgJDRe2/hnfdScRefpKl9c0JSpUqxX///ceiRYuYOHEiffv2pWXLllSrVo0iRYpQrVo1du3axc2bN0lISBDnGHR1ddVW8ycjkUgYUd2EibVSwhSvvhHDyMPhGA1ogsRAtS4kfPM5El8HZk8jv4JatWqJK+zfv3/PrFmzUCgU7Nu3j5o1a1K2bFnWrFnDoUOH6Nevn9qxh57Gi/FoLO1S2q/8xDBGGR2HT/dFxJy6S+iSQ8T+wFaZ2YVE+MnFb2RkJKampkRERGBiYvLlA3KQxYsXM2TIEEDlEuXq1auYmppib2+vNg9x6EkcT4ato8kDVTTAyBY1qbCke05UOVNQKpX4+flhb29PVFQU7du359ixlJHYn3/+ybhx4z5rnTPcK4zgfdcZemYfxgmqXqggkaDToz5OE1oj1c0dbuE/buv3uoX/HE+ePKF8+fLExKhGbqtXr6Z3796fPWbr3RjGHY0Qw0YXtdZisc8FhFUqU3jjFpWwX9Iv/QI+IrvaCeDt7U2xYsVISkpCV1cXBwcHXr58mSqfRCLh9evXovfoNpuDueGjmlc6FHsZ7TWHAMi7aQSG7qqge4Ig4D9wBVEHUqzOjJtXxH5pf3E7O9uaTG7/rmlGLLmIQYMGid6Ao6Oj6dChQ5oTko2L6mM4uBnxWqqPpcGBC7x9HJCtdc0qTE1N8fLy4tdffxXTJkyYoBa6Ny1mNjCjZNfKDO82lNt5nQGQCAJJa4/xX/uFKP/Pgjb17dtXFCpdu3alV69eXzymY2lDljY3F02QnwTJ6aJbFsxVqrKofVdJ9PbPukp/Iy4uLgwbNgyAhIQENaFStGhR8udXxfARBIGtW1Vrnt6Gy0WhUshSC/t8KR9neUC4+Dv6yH9qQgUg+uh/KH/SRbmZhUaw5CIkEgmrVq2iZElVb+nBgwdqgYo+pl8jBx7UrQmAllLJmSU/h4UYqNYoLF26VM1lx5gxYz4bUVFPS8LomiYcGFWYt7MGs6FGQ9E7tMnNR3j13vh/oxsXBIGLFy8CqhXoy5cvz/BajMZF9TngaUUhS5VaNgBdnlmmuEORh0RlfoUzgQkTJojzlKCKanrkyBGuXLmitoYnPl4lEF6FpnQ06hXSQ9clpY1ha06IYcHTUjMLciVC4v9XR+Vr0QiWXIaBgQEzZ84Ut1evXp1mPolEQsMxKX7ErC7d5v77H8+PWHpIJBJGjx7N0KFDgZQYNum5vknGXF/KaHczRq9pw4kR/UXhUuTUBa5O3pvl9c4NxMbGikK0UKFCGBp+Xdjhwlba7O1qhaOZDOuocFy8XwCg5WCJfrmCmV7fzMDU1JRLly6xZMkSHjx4wLFjx2jQoAEzZswQr0WFChXUFiUnoyMDg5rFxeikCY/eEbH9AgBmnWpi3tcDPhLMViNbIDM3SlWOhhQ0giUX0rBhQ3Hh5NWrVzl48GCa+SwK2RJVSKUvLhjsz4od3j9dr3zGjBkULKj6mF25cuWzHgo+xkxfyvBh5Xk3OGURpcXag/isSXsR6s9EVFTKqMLI6Ns+gMa6UhY1MafB4/+QfXimYhpWRiLLvZ8MZ2dnBg4cKBqAvHjxgoULFwIq44WdO3emOx8hkUqxmdJJ3A6eswdFeAwSLRk2kzrguG8Cxk0rYDmiORaDGmd5W350cu9T8n+MVCpl0qRJ4nayo8a0yN+ukvjb9Nx/nHn586xrAdUIbv369aIqZ+LEiTx5knFrrwa/1eZc8xSvydFTNhN5MLX7j5+Jj5+VbxUsAGXyaNHG+zYAComE8foliEz4cRagjh49WlxQOnLkSHGuJT0MKhYW/YIpgiPx678M4cPcnH45F+yXD8BqRAsk2TRB/yOjuUK5lI4dO1KnTh0A3r59m6apKIBZ81/E37We3WPeuQgUyp9r1FK1alVRhZGQkEDXrl1FXfmXkEgktJzTgn0VVfNREkHAb/AqIvdfy7L65jQfCxZj4293SRJ76TH6wWEA3HQsxH2MmXnmxzBtv3z5Mnv3qlSfefLkYezYsenm/XiQb/17e2QWKmEce/ERwQv2Z2k9f1Y0giWXIpFIWLZsmRhfYsWKFWoL4JLRtrdEv4IqBKtjWBDFjp1lw62fb3Hg9OnTKVy4MAA3b96kd+/eGVb7WRvKcP2jHUdcVSFnJXIF/gNXELLowE+nOgTQ0UlZTf/posCvIcknJZrkA3snAO74/xjzeA8fpqw1adSoUSoBa6yb8um77Zck/tZ2sMRiUMoIN/7u66yr5E9MjgqW8+fP07RpU+zt7ZFIJOzbt09tvyAITJo0CTs7O/T19albt26GXH38LBQsWJBmzZoBKtff27dvTzOf1bg2CB9URT2uHGff1oe8Dvu5rFb09fXZvn27GI9k8+bNqZ6Xz9GkmD6vBnbksGuKq47guXsJ/H3zTydcihUrJqp9Tp06RXDwt4Ub1nNzFn+X8HutKtsmd6wH+hItWrQQPRRs376dsLAwtf2l7bRxNFMZdpx/ncCLEJVwUSYkEb4hZR7OzLNWNtX45yJHBUtMTAylS5dm6dKlae6fM2cOf//9NytWrODatWsYGhri4eGRYTXIz0CXLl3E3ytXrkwzj8EvhbH8VeXpVUupZPShrUze4fPTRVZ0c3NT81b7+++/f9FKLBmJRML0BubsadWa1VU8xPTw9acIX/9zTehLJBLatVN57VUoFOzZs+ebytEtlheZrRkApX1foa2QUyqPzucPyiVYW1vTo0cPQPWd+fTdkUkleJZJsZZLHuWHrz1J0huV406DKkUxql8mm2r8c5GjgqVhw4ZMnz5ddMnwMYIgsHDhQiZOnEjz5s0pVaoUGzduxM/P76t6qj86ZcqUoVSpUgBcu3aNu3fvppnPanQrdH5RqYqsoyOov2EzG27kzjUH30O7du1Ex4MPHz5MdxSXFsa6UhY3s2BvhRr8VaeVmB44ZesPHTwtLdq3by/+/ppr9DESiUT08KsnT6K43xtK2/0YIxaAESNGiEYfixYtIiFB3bClXSkDcTHorvtxhPmGpfhEk0iwntJRE4vlG8m1cyyvXr3i/fv31K2bslbD1NSUihUrcuXKlXSPS0hIIDIyUu0PVG4XfsQ/QM0Vx8qVK9PMJ0glOCzth9JCZU5Z/u0LXv51kBfBiTnehoz8CYKQ4XzTpk0Tr8fkyZNJSEjI8HlK5dHit+rGHHctx/ay1VWFKJT49V9KwtvAXNXW7/lzc3PDxcUFgLNnz+Lv7/9N5Rh85Dr+l3fPKWoly1Xt/Nyfs7MzLVq0AFR+xDZt2qS231gHWrmq1GXxCXIejtyEMlqlDTFpXw2donlzdVtzM1pfzpIzvH//HgBbW1u1dFtbW3FfWsycOZOpU6emSvf390/XZDe3IggCYWFh1K5dG319feLi4ti8eTPDhw9HX18/zWMM/mhD7KB1SAWBdlfPMH5lcf7oZIVhLu5oJrcTyFAPsVixYlSpUoXLly/z4sULli1bRtu2bTN8vsYOcNpOwrrK9ckXFkSVV09QRsbhu/IQOr/W++Z2ZISvbev30KBBA5YuXYpSqWTXrl1paga+RJSDmdj7LB7uT0hgxly6ZGc7P0eVKlVE67CzZ8/i4eGhtt/VBPSS5Iw7th3rVx/M2A10kXetgp+fX4bOkRNt/XitUm4k1wqWb2XcuHGMGDFC3I6MjCRfvnzY2dnlSmdtnyO5V2JnZ0fbtm3ZuHEjkZGR3Lhxgw4dOqR9kL09/vf9iV5xBB2lgpZeXswv2p9lLcxz7bD+43Zm1InfzJkzqVlTZUK8fPlyBg0a9Nnwxp/yR4MkPNaFsLB2SyqtnYVUEODsE+ymeWbpdfqWtn4ryYIF4PXr12KY3q/hXFAYyUeZGmhluIzsbOfn+Hh+qVOnTqnqf+PEW+bs+Yeigb6qBG0Zef7qgXHJwhk+R060NVkTk1vJtaqwZL8/AQHqzhUDAgLUfAJ9iq6uLiYmJmp/oFp0+CP+SSQSpFKpOBEJsHHjxs8eYzeiOdhZAFD2nTexR/5jw+24HG9LRtqZ0b8aNWpQq5bKYuf58+fs3Lnzq44vaqNLqTzahBsYcdehAABJbwJJevQu17X1W/8qVKggPjO3b9/+pjLu+qbMS5gaauXKdqb3d+XKFVFtXrJkSRo0aKC2/909P1rO/VsUKhJjffJuGoFps4q59p5+/JebybW1K1CgAHny5FGLAx8ZGcm1a9eoXLlyDtYsZ6hRo4YYpOnEiRP4+vqmm1dqoIv99M7idv8Lh1h2KpiQ2IxZUP0ofOydYPr06Rm2EEumdQmV6fL5QiXFtKiDNzKncrkAW1tbsYd+69atbzKrvvsuxQLTzCgX61PTYO7cueLv3377TW0kKg+KIKLjLOwiVSqsOEsz8u8dj2FV12yv589IjgqW6Oho7ty5w507dwDVhP2dO3d4+/YtEomEYcOGMX36dA4cOMD9+/fx9PTE3t5enJD7f0IqldKtWzdANfT+UgAso/plMKxTGgCrmCjanjvG/Au5Wy/7tdSsWZPq1VUT8I8fP04zONrnaO6qj7YULroUFwOnRXnd+KnWtZQtWxZQrYP62sWSCXKBp+9TBIue3o+jOX/69CkHDqgsvBwcHFKpjoPWnUI3SmVi/NrSFru9E9Atmjfb6/mzkqOC5ebNm5QpU4YyZVS24iNGjKBMmTJiT3T06NEMHjyYvn37UqFCBaKjozl69Gi6oVl/djw9PcXf8+bN48WLF+nmlUgk2EzrBB8CXLW4d5V7xx7/UL6evoREIlEbtUybNu2rrGXM9aXUdtEjQt+QOx9iuCS9DSLmVNom3T8i5cunLAhdvnz5Vx0bHKsgXpnSy0/yC820emU1u3alhBAeOnSomjcCgDCv/wCVD7QzQ3tj65w6HLGGbydHBYu7uzuCIKT6W79+PaD6cEybNo33798THx/PyZMnRbce/484OzvTvHlzAEJCQqhXr95nLVd08ttgPa6NuD3o5D5OP/mxLOO+RJ06dahSpQoAjx49+upRS35z1YT/wZIVxbTguXsRcrk5Z0bp2rWr6BZowYIF6a6DSos8RjJM8prz2sIGgPhb3sTff50V1cx0Pm5n06ZN1fYp45OQvVFZlr62tCXM3CJb6/b/QK6dY9GQNmvXrqVEiRKAytLHw8OD0ND0e5LmPeoiL6pyre8UGkjI8qPZUs/sQiKRMGXKFHF76tSpXzVq0ZWpeuSXnYuRVER1nRIeviX68H+ZWs+cwtnZmQkTJgCqVfj9+vXL8PWRSSUMqWrC/tIpc5ohSw/9EKrCe/fuASpjnuSwC8kkPveFD4G8XlrZ8Tz453J/lBvQCJYfDAsLC44dO0aBAipLpgcPHtCkSRMxDO2nSGRSnOb1QPlh4vKXIyeIfJ77wst+D3Xr1v3mUYuO1gdVj0RCeM9mYnrEtguZWsecZPTo0WJ8n2vXrqXrGigtmrnq4125PJG6qnVT0V43CV+Xu13gxMXFiT4FXV1d0dJSnxtKeOwj/n5pmYc34XISFblfWP5IaATLD4i9vT3Hjx8XF49euXKF1q1bi7EnPsWwtBOPPoQx1lHI8R654YfodWYUiUTC5MmTxe2vGbUkj1gAIsoWE31jxV5/9tOEn9XV1WXFihXi9rhx4z67yPhjtKQSBrhbsKxmisffwClbiLmQe13gPHr0SLz/yWG+Pybh0Tvx9yurPMiV/HROW3MajWD5QSlYsCDHjh3D1NQUgGPHjjFs2LB081uPbE6AsRkABreeEn3sdjbUMvuoV6+eaIb+6NEjTpw4kaHj9LRTBEusXOV4EECITSDh8bv0DvvhcHd3F60KP+cpOy2aFNXnUbmyHCumsjBDKRC2+nhWVDNT+NhlflqCJfF1yto4q+gIAAKifi5T/JxGI1h+YEqXLo2Xl5do8bLsf+yddXQUVxuHn93NZuNGXCC4Q3AtGtzd3V2K034tFHctTpHi7lBKcSvuLsESEuIuu/P9MckkKQkkEIV5zsnJ7syd2XtX5jf3vvb776xZsybJtpUKm7O9frwR8/VvOxBivp0fk0Kh4Mcff5Seb968OUXHOZvFR+u/8ItBP3d8CqGYD1k7ujm1JHTTDw8PT/Fxb4O05PT1puqz+At2nABnRZ4/fy49TsrZx7hWCenx0JP7aHf3AkVzyJfCtER+N7M5VatWTbRmPmrUqCSN+WqVglo9K3DXQazTofLwInDHuQzrZ0bQqFEjKdPCnj17UnTxzG8dv/7++EMMKov4Ur5a/2+rYFrCQnFxdW0+2T5Kx9yzQbRY/JKBW9djHCVG4ZvUK41l33qfOTrzePnypfQ4Lqg4jmitwFQrN6kuj75OS6+ThwhsPonQc/czsJffNqkWlhcvXrBhwwZ+++03xo8fz7x58zh58uR3VSMlq9G9e3e6dOkCQEBAQLJljJsVNeKvBg2k529n7kUXnj0qAqYEAwMDWrVqBYhJ+g4dOvTZY5zNVVLq9McfolFZxtfo0AZ8W67ZKRUWQRA48CCcWqu9+f1sAGMPbsYxSLxZic7vjMOiPlm67vt/hcV70hZeNpyE986L9Njhy457ESyu0ZRDReNT3kQ9fseb9rN522cJ0a+/rDCaTDwp/nZs2rSJ8uXLkzdvXsaOHcvevXs5e/Ysq1evpn79+tjZ2TFw4EA8PDzSs78yyTBt2jQp4/GSJUsSLQfEoVIqaNm5BJdcRQ8hvQ8BBGz/tmYtHTt2lB5v2bLls+2VCgX5c4izllcBWmJM4y+4uu9wxuIZpKXdFl8G7/fHM0jL4FMHKPlWjNhXWptRcNNwlMZZO0A5LsNAjhw5UFx4iv+qv4i8/RL/4Sup9ftaDKMi0dNXUXBhD3Lu/ylRpcyQI9d4UWMCvks+f1MikzwpEpZSpUqxaNEiunfvjoeHB56enly7do1z585x//59goKC2LdvHzqdjrJly7Jjx4707rfMf3B2dpZsDNHR0UydOjXJdnXzG/Bvw7rS81ebL2RI/zKKmjVrSt5yBw4ckOIZPkWB2OUwAdj3Nt7mEvk0ZWnTswuvX8c7IyQnLCMO+XP5tTiLbXjvCg3vXwVAodHDee1Q1I450r+jX8H169elcbq6uhK043yi/VWeP6DTnfNsap+DRoUMMSydl5z7J2I/pweoxc9eiIzmw4ydhF/9fsqgpzUpEpYZM2Zw+fJlBg4ciIuLy0f7NRoNNWrUYPny5Tx8+JA8efIkcRaZ9GbMmDGSl9iWLVsICAj4qI1CoaBl28J4WNoAYHjvGR/+fZaR3UxXVCoV/fr1A0SB7dy580eVA/9L40LxtW2mvzYh2kS86IYcvvbNeIYFBASwatUqAPT09BKleonjqW80F1+JolI22Ith5+Lv2u3n9MSwdN6M6ewXEhAQQOvWrSVX+iZNmqAw/LiUcvuW+SnnrJGeR9x4TsDGkxAd78yiNDdGz1GOyP9SUiQs/y2O8yly5MhBmTJlvrhDMl+OqampZGsJDw/nzz//TLJdrbwG3HavIT2/OPVgRnQvw5gwYYLkZnrnzh1+/vnnT7avmdeA8TVEo3+kWp8/SlYTdwgCPjN3pWtfM4r58+cTGCi61nbv3h1n548TLm67LS6VGUVG8PPRLSiixdgOi+61MWuR9TOK9+vXT1oGK1++POPHj+dDpZKJ2ij6NyZ/ezF9T8z7ADyHr+JVs6lE3HoptTFtXA7XY79m+dlZVuaLLXDe3t7cvXuX27dvJ/qTyVzi7tYBli9fnmQgpEKhoNPEWgQYiYbqvNdvceTkm4/aZVc0Gg2bNm2S3LDnzJnD6dOnP3lM/womDKsseoTtK1ERHxNx5hf69y3C/n2cvh1OZ/z9/VmwYAEgzlbiUrwkJEorsOtuOAgCo//ZjaGXaMA2KJkbm5/bZWR3v4gHDx6wfft2ACwtLdm+fTv6+voczJFXmp17tXIn/8SWAAQd+JcXNScQtDN+KVi/oBMu28bguHwgamc5KeXXkGphuXbtGsWKFcPBwYESJUrg5uZGqVKlpP8ymUuxYsWoUqUKIAaKXbiQtA3FwdqA6NY1AFAJAvcXHMXrGwoSK168ONOmTQNEL6eePXsmm5kgjhFVTela2ohoPTUbKtSStvtM25GtMxUsWLBAqjjYo0ePj1xwAY4/icA3TEeTO5ep8lSMV1GaG+O4fCBKTdavw5Iws8DPP/9Mrly50AkC+55pGdR+EN17jKLkjA4IUTF4jf4DzwHL0AWJ7uhKc2Nsp3TG9dgkjKoUzqwhfFOkWlh69uxJgQIFuHDhAs+fP+fFixeJ/stkPglnLXHr6klRcVQ9YtSi4brOzcusXH493fuWkYwYMUKq1/L8+fNkg0fjUCgU/FLbnBp5NBwvVEq60424+pSIm6mrZZJVePDgAXPmzAHE2cqECROSbHf0sRgu0P5q/MzOYWFv1C5Z/879jz/+kITF0NCQ7t27A/DIJwavEB1RemryF7fHwlCJ/6pjBG45Ix1r2qwCuc9Ox7J7bRR6KS9tLfNpUi0sz58/Z9asWVSoUAFXV1dy5cqV6E8m82ndurVkxN+1a1eygYJ6VqaYdRLL+2q0MTT5fTW3jj3MsH6mN0qlkhkzZkjPx44d+8kyAyDmxlrS1BI9tYrLueOjy3WB2dP1eNKkSZKbcd++fZOcrQBoYq+p5uHiOPXzOWDi7pYBPfxyoqOjGTp0KD179iQqSnQ66Nu3L5aWlgAY68en63nmF0OMTkAXFu/IYTWoIQ5L+qFnZZqxHf8OSLWw1K5dO1U1HWQyHkNDQylQMCQkhAMHDiTb1unnNgSUEcuxGkVHIQxaSPjtlxnRzQyhcuXK9OjRAxBLWw8ZMuSzx5hqlAgC2MWWrQVQ57JNtz6mFx4eHlKmZxsbG2bOnJls2+ZFRU+4CLVolxK0WbsejY+PD3Xq1GHx4sXStoEDBzJr1izpeU4LParnFr2/3gRqOfIoIpFnmxAVk6hcsUzakWphWb16NWvXrmXSpEns2rWL/fv3J/qTyRp06hRf8/5TebOUGjVum4fywFX8wRlGRPCyw5xsVS3wc8yZMwdbW1EYdu/ezd69ez/ZXqsTiNaBY2Dse6BUoHbKfh5CixYtQqsV7WaDBg3CxMQk2baVc+njbK4iMlZYYkKybiaNW7duUbZsWckhQ19fn1WrVrF06dKPKkX2LR8/5mknA9EWiw+FCL+afAVWma8j1UWsL168yPnz5zly5MhH+xQKhfRFlslcqlevjoODA56enhw+fBh/f39pieC/aIw1qBYO5k7/BRT39EAZGMqH+ftwmN0jg3udPlhZWbFw4UI6dOgAiBfZmjVrSsuF/yWuNod9bBoTtVMOFPrZp947iLOzOPuaRqNhwIABn2yvVChoV8KICD3RUB8V8unYn8zi7du31K9fX0r77+DgwK5du6hUqRJBkTruv48mXw49rI3Ftb2Au38T9TwM/TxVeBesY/q1SPrlcyDqqScRdz3QhUehTCLWRebrSPWMZciQIXTu3BlPT090Ol2iP1lUsg4qlYr27dsD4lp0XLnn5GhU2pwt3bsRqi8uHQRuO0fUK5/07maG0a5dOxo2bAjAu3fvmD17drJtI2PAJCIc00jxrj07LoPNmjWL4OBgALp27SrN2D5F2+JGhMfe8SsjorKkJ9yAAQMkUSlXrhxXr16VyiX02e1Huy2+lF3ynmYbfJh0+DXtBv+PDztHI0SLn+WWu9Foi8fOWqK1RNyVU1ClB6kWFl9fX0aMGCGlzZDJunTt2lV6PHHiRO7fTz57q1KhoH9dB3a5ia7KCp0O/wTeM9kdhULBokWLpOf//vtvsm19w3QosuBFNaUcPXpUcrVWKpWMGDEiRcd5h2gJ1oi2FpWgQ+uftZJwhoeHc/SoWFrbzs6OQ4cO4ejoKO33DhFvbAXgpmc0a++osB5wALuB+1Go4/ObaRNWlPyGSkdkJVItLC1btuTkyZPp0ReZNMbNzU1aAgkPD6dDhw6fzELtnk/D69qV0MYaNN9vPpfljbipIU+ePNIa/KcqKL4NiiHY0IgAA/EiG/Uk++QM8/DwoFOnTtJsY+rUqRQu/PnYDEEQmH46iPdmFtK2mCyW5ffKlStSLFKjRo2wsbFJtL9+AcOkDkNlEu8y7fRqL5q7sWERSgUGJVzTpa/fO6kWlgIFCjB+/Hi6d+/O3LlzWbRoUaI/mazF3LlzKVq0KAC3b99mzJgxybZVKBSMbJGTK7nE4kga3wC8/76TIf3MCBQKhTTTfv/+fbLt3gaKd7GvrMTlo5j3AWgDw5Jtn1WIjIykdevWUj2epk2bfvLzTsjZl5Fc8IjCyyw+P1ZWSx9/9uxZ6XFcfFJC2hSPT6xpa6LE5fkWwh8cRxcVjqDTEXB0OhVi7hH5UMwyoSnikuUzNWdXUm2RXL16NSYmJpw+ffqjNBkKhYKhQ4emWedkvh5DQ0O2bt1KuXLliIiIYPHixdSpU4cmTZok2d7NUZ/T9SvD8kcA3F52kjr1SibZNjtiZ2fH69ev+fDhA1qtFpXq46C4N0FxwmJDiXcvAYh6+g7DMvkysqupZvjw4Vy9KmYjzpMnD+vXr0eZgropOkFg+ikxMt8rwYwlKwtL1apVP9qfx0qPMk5qrr2NxjtEh+beWXzO7wM9DUp9I3Rh/hQtNAEE8SYhq3+e2ZkvKvSV3J8ceZ81KVasGPPmzZOe9+jRgw8fkr9otBlQQcoj5nD9Lh4v/JNtm92Im7HodDq8vb2TbPM2KPGMBSDycdZeDjt79qwUfW5gYMCuXbuwsLBI0bEHH0Rw31tMOGnsGr+8FP0m6whLQECAlJ7I3t6evHmTzrTcpHD8cpifdWwhr5hIdGHid9g1JD49jWFZWVjSi1QLy927d5Pd97n4AJnMo3///lLNc19fX3799ddk2zrm0OBbszwAap2WBzuuZUAPM4bcuXNLjxPeASdEF2u39zGOd0fW+gana7++loRjmT59Om5ubik6ThAEfr8UP7auReNdb4UsYtjW6XR06dJF8nJzd3dPMrDxjlcUSy/GOxzoGX/sXu/oGz8mw3L506G3MvAFwlKvXj0pNXVCdu3alSgoTyZroVAo+P333zE2Fmciy5cv/6SXmH2LCtJj1akb6d29DCNOXAG2bt2aZJtyzuLFVV8bI23L6rEOCYt4xSUhTQmnnkfywEccZykHNflexq86ZJU7+ilTpnDwoFjawcrKit9+++2jNsGROnrs9MMnVHQ2yWulx4qepWnQoAFqtThLGTv8R/QeiU4b6pw2cgbjdCTVwtK7d2/c3d0TedVs27aNrl27fjZWQiZzcXBwYNy4cQBotVpGjRqVbNuCNfLywVjMoeRw/zG6kKTzjWU3atSoIS2HHT58WKpRkpAqucRYHk1MlLRNYaT5qF1WIqGwJFWMLzmW/xt/hz+wqJLABKWqjSpnfqbfw4cPS7NrpVLJ1q1bk8x3tvRiiCQq+c0i2dXZmma1K3L48GG8vb15/vw5P7fuiRApiqhR5UIfnUMm7Ui1sEyaNImGDRvi7u6On58fmzdvpkePHmzYsIE2bdqkRx9l0pCRI0dKRZ6OHDnC8ePHk2xnbKDH/cKiN5k6JoaAE9+Gd5hKpZK+p5GRkezbt++jNrktVTiYKjFIkGY/q89YXr16BYjpTVISDAlw810Ul2IrRuaxVFJk6SZi3vgCYFixYKansXn27NlHrtN16tT5qN2rgBhWXhadD4SYSJ6v6IKZfnwckoWFBblz5ybs/ANpW1YQzW+ZLyr0tXjxYkqWLEnFihXp06cPW7ZskZIeymRtjIyMEmX8nT59erJtAyvHe4N57b2Srv3KSOIyEgBScaiEKBQKqubSYBATLyxJlbjNSsTNWJydnVPkCQbw++X42crPHhcJ/Utc8lRaGOOwsE/adzIVRERE0LBhQ6m8dosWLRg7dmySbScceIc29lIWdHYlL24lnXIq7EJ85m55xpK+pOgb+N9Ek/v376dly5ZERETQoUMHFAqFnIQyG9GhQwecnJwAOHPmTLKpO6yrFyZII3rZKP7+duq/V6xYUTL+JmUvBKicS4NhVHy+LGUWXgrTarXSBTils5VLryI5FluDpdnL2zitj//tOizqm+mzlZcvX/L4cXzlznXr1iVpsL/0KpKz78SoCW2wN0GnlgBiHZr/EnlPnNWpc9miZ5903jyZtCFFcSwJDZ7/Ze3ataxduxZI+ySUWq2WX3/9lT///BMvLy8cHR3p3r07P/30k5zu+itQKpW4urry9u1btFotWq0WPb2PvwpNi5swt1w1up47hlIQeDPxT/LsGpft33sPDw9JTPPnT9ozyM1Rn+eR8XYllYVxhvTtS1AqlSgUCgRBSNHvzzdMy/CDovut2+tnDDiyU9pnPaYlJrVKpFtfU0pcloSoqChcXV0xMzP7qI1WJzD5nyDpecBfsxAiQ7C2tqZLly6J2urCIhHCxWU/PUcrZNKXFM1Y/ptsMrm/tE5COXPmTJYtW8aSJUt48OABM2fOZNasWYlqMMh8GQnTi8cVSfovVkYqDLvX4a25+EOM+fcxoX9n/1o89+7dkx7HZSX4L66WKqyiEgpL8innMxuFQiF9nsl9lnFodQJD9/vjGawj9wcvJh/ZjDLWrdi8U3WshjRO9/6mBH19fUqWFJdiX758mbQn6t1w7r0XlyuL2OqxeGA9unTpwvHjx7G3t0/UNuZDvADpWcuFvdKbL7KxZBQXLlygWbNmNGrUCFdXV1q3bk3dunU/mUBQJmUkFJZP1YLvVdmCP6rWl56/n7ELQZe984cljMUqVqxYkm2UCgX2QnxeNX/9rJ36Q6MRl+oiIz+d7n7euWDOeURhHRLI9APrMYjN4Gxcxw27qV2y1Gy0QYMG0uM4b8Y4QiJ1zDoTLxb/q21Oj+7d2LBhQ5IxPNoEwqLK8fHsRyZtSZGwJOfvnxSvX7/m/PnzX9yhhFSuXJkTJ05Ia623bt3i3Llzib5w/yUyMpKgoKBEf5DyWVdW+xMEIV3Om1BYIiIikm1nZ6LAoVkZHtqKNpmYR28I3Hsp24wzqb+EwlK4cOFk21lGizMWrULJnSBllh5rnLBERUUl2+bks3CWxAYQ9rz4F1Yh4m9D45Yb+8V9EZSKTPlMnz9/TpkyZWjatGmichzDhw+XEk1u376d8+fPS/t+vxQsuRfXy6+hgrMa398P87zKWF53nsuHRQcIvfSImPBIdDodUQnS0yhzmKb5+5+R39+4v6xMimwsy5YtY9KkSfTo0YMmTZp8lC01MDCQ8+fP8+eff3L8+HHWrFmTJp0bN24cQUFBFCpUCJVKhVarZerUqZ8MxJw+fTqTJk36aLunpychIVkrDfjnEAQBf39xLTyt7yTjgsZAjENq2bJlsm075IMZVeswefc6ALym7yTYzTHNil+l5ziT4tGjR9JjU1NT3r37OF2LVge6YFFYQvU13H/tR8Gkk+emivQaq6Gh2Lm3b99y//79j9K5CAJM+VsBiK9Z6cNLaZ9qWlu8AnwhIM26k6pxTp06levXr3P9+nVu3rzJhg0byJdPDM7s27cvU6dOBeDgwYPkypWLyBhYf00ci55SoGfhcN69Cyds1VEEn2CiPbwJOxV786Cvh7KIE7rHntLrhZioiEziM8+IsaYVcVkIsiopujKcPn2a/fv3s3jxYsaPH4+xsTF2dnYYGBjg7++Pl5cX1tbWdO/enbt376ZZrZbt27ezadMmNm/eTNGiRbl58ybDhw/H0dGRbt26JXnM+PHjGTlypPQ8KCgIFxcXHBwckjQAZmXi7kocHBxS7EKaUnr37s3u3bsBmDFjBt26dcPUNOm1Z0egaBNjrl/JS+nXz1B4+mP09yMse38cU/AlpOc4P4VKpUqU4iUhC8+HUDIm1ltOqaRtOTtsjD9OWJla0musTZs2ZcmSJURGRnL48OGPgl/PvozkaUCswd5BjZWdGZEfAkClxLFwPhSqtH3fUzPOmzdvSo9fv35Ny5Yt2bt3L1WrVk0UwFq6dGkcHR3Z/yCckGhxe9PChpQvaAHA2yK5CDv9n5RTUTHobsYX89IUy4lz5zooNWrSisz4/satxGRVUnzL2bRpU5o2bcqHDx84d+4cHh4ehIeHY21tTalSpShVqlSav6mjR49m3LhxUtxB8eLF8fDwYPr06ckKi0ajkZYFEqJUKjP0opVWKBSKdOl7gwYNaNq0Kfv378fT05Np06Yxc+bMZNu3LGHC0Cr1Wbr1d5QI+C06gEW7H1CZGyV7TGpIr3EmRZyTiUqlSvL1Tj+PYOGFENbGPjfRKLAzTbsLUXqMdciQISxduhRBEJg/fz5Dhw7FwCDeLrTmanza/77lTVCfshLdb7U6dD5BqNPBUyol4wwJCeHWrcQOIf7+/tStW5eNGzdy7NgxAPT09Khbty7vgnUsvBC/8tCuhLF0fpN6pSRhMSyfH7WrHeGXHxHtIVZCNW1eEfvZ3VEapr3reEZ+f4Esfy1L9VqGtbX1J92P05KwsLCP3kCVSpXl1xezC/Pnz+fYsWNERkYyf/58evbsScGCBZNsW9RWD1XhnJwoWJI6j26iCwjFb8lBbCa2zeBefz0JheW/vA2KYegBfxJG9qiz9m8YEOsktWzZkl27duHl5cX69evp168fAA+8ozn9QjTqu5irqF/AAB+bBAk2/YLTRVhSwr///it9Hh06dMDX15e//vqLyMhI2raN/25VrlyZ1xFGdN/xQbKtFLDWo7xLvK3QpE4pvCdsBEAXEoHDvF4ARHv6owsOQ1PAKaOG9d2TpX8yTZo0YerUqRw6dIiXL1+yZ88e5s2bR4sWLTK7a98EefLkYfTo0YDoGTZs2LBkgyUVCgUtixqyvqI7USrxfsR/7fEslVo9pcTEiPmi/issgiAwZL8/ARHie2CkzjoeUilh/Pjx0uOZM2cSExODNjCMF31+Z9H2ZczZtYo5h9bj1WcxgZviaynpgjIvD1xcKnyA+vXrc/DgQXr06PFRO7cG3Wi7OV5UcluqWN3SCmUCm4bawRKDkuLSZuT911I9GbWDpSwqGUyWFpbFixfTunVrBg4cSOHChRk1ahT9+vVLMrupzJcxfvx4KWnhsWPHmDhxYrJtmxcxwsfMkj0lKwEgRMbgu+RQhvQzLYkzsEZHRyeKvXobpOXaW9H12tZEibWJKKC6iOhsUaK5TJkyVKtWDRAzCjx9+pSHmy6Q99pNCr1/Q4l3L7G88YCQY4mzVQtpHH+WGhJm2H78+DFqtZo1a9Z8VNbhuqk7oVGi4JdxUrO7szW5LBMvuAgxWhSa+G3h15+lX8dlPkmWFhZTU1MWLFgg2XOePXvGlClTErnKynwdRkZGLF++XLrYTp8+nd9//z3Jtg5mKuoVMGBbmeqEqsV16qDt54jxyl6FwIoUKQKIrum3b9+Wtuur4u9+i9iqMSwsJusUwiKlcrZZHU9P0ftJpVJhb2/PPwGftieoXW0zNT1+wuWuqVOncuXKFRQKBb/88gvr168nV65ctBnyG69CRMEobq9mcztrrIz+M9uMjuH9xI2E//sEEPOdGVVMellXJv3J0sIikzE0bNgwUTaDwYMHc+rUqSTbjvrBlDBDQw4WFwuBCVEx+K36KyO6mWbE3dWDmCstDmtjJfqx1yvPIC1GleITFYZfjE9gmFV58uQJT56IF9YqVapgaGLOdiHeQ1NTNh/57i0l7/UF5Lk4m9ynp+F6Ykq6GLOT486dO4mM9c2bN5fciQGWLl0qPe7atSsvX77Eoc4gaVvPssYY/GeJMsbLn9ftZ8cv7+mpcFo5GD07i/QZhMxnSbWwTJ48mbCwsI+2h4eHM3ny5DTplEzGM2jQIGmNXhAEunXrlmStkvzWaloVM2S3WxXJ1hLw5ym0wdmnXssPP/wgPU4oLEqFAiczUVneBGkxrFhA2hd2MT72Jaty+PBh6XGjRo04/SKCV3omvDe1ACDq/muUJgbo2ZqjdrFGP69Dmrrdfo69e/dSqlQp3Nzc6N69uxRXNnz4cCwtxaSQW7duxdfXVzrGN0zL4Ufid8vCQEHD/wQThV14yMsGvxJ+OTZhpZ4K+zk95ezFmcwX1WNJKtAwLCwsycBEmezDlClTqFGjBiDW9xg2bFiS7UZUNSXUzJS/C7kBIIRGELzvUgb18uspXry4FED4119/JbpRcjYXxTI0SiAslyPK2OSTYZceZflUNnFVFkGche6/L16QH9rFL+lFPX6bKX3z9vamT58+kk1r/fr1lClThuPHj2NoaCgZ7CMjI/njjz+k4zZeDyMq1gTUprgRBnrxsxW/FUd53X4WWh8xpkPPwZKcu8Zh3rpyBo1KJjlSLSyCICQZXXrr1i2srOSsodkZpVLJunXrpEDS9evXc/ny5Y/aOZnp0aWUMQeLlZe2Be26mGH9/FpUKhWlS5cGxDiKa9euSfssDOK/295hAoal8wKgCwyVvIyyIrt27eLvv/8GxAqSTnkKc/yp6GIcah4fGBz1zCvJ49Ob9evX8+FD4vfv8ePH1K1bl0qVKiUKK4gL3N1yM5QF5+MjzDu5xWeYDtx6Bp/ftoEu1oOvWlFyHf0VwzJZo5zy906KhcXS0hIrKysUCgUFChTAyspK+jM3N6dOnTqJDHEy2ZNcuXIlKgQ2e/bsJNsNqmTCOydnXluIdcPDbzxHF/bpBIhZBUEQePZM9BhSKBSSV1xkjCDFexjoKXAyVRL1NDYViFqFXo6smRV3//79iYqX9e/fn023woiIEXAM8KXundgibUoFmiIpL1uclvw32Wfx4sWlx5cvX2bOnDnS86pVq7LheijjjgVK8UStixmS2yp2NnnqDu9j41UArIY2xnnjSPTk5JJZhhQHSC5YsABBEOjZsyeTJk3C3Dw+wEpfXx9XV1cqVaqULp2UyVh69uzJ5MmT8fLyYvfu3Tx58uSjuiU5jFS0KW7ELefcuAR8gBgt4VeeYFw96WzBWYmrV6/i4SGm+XB3d5dqqN97H01QpHgpq5vfAP3X74l+JUZtG1UoiNIkDZKFpTFHjx6lTZs2UmxO9+7dGTFqLD+s9MEmOIAZ+/5AL0p0obboXhv9PPafOl26Ub9+fWrWrMnJkycBaNWqFT/99BO//fZboqSgzZo1o1Cbn/j5eLx9r1dZYybWNCPirgcfZu4i9GR8mWyLbrWwGSNXr81qpFhY4lKo5M6dm8qVKydKYijzbaHRaBg+fDjjxo1DEATmzp3L8uXLP2rXrIghi5zy0PiueEccdvFhthCWXbt2SY/btGkjPb7pGV/LpLyLPqEn4u1GxrUzv/jVfzlx4gQtWrSQarB07NiR1atXs/dBJFrvAObuWYt9kOgKrp/fEetRmRdYrFAomD9/PmXKlEGr1TJt2jRu377NrVu32L9/P6tWrcLV1ZXinacy6Z94G+7AiiaMqWZKwNq/8f5lc6JzGlUviu2vHTJ6KDIpINU2lurVq6NSqXj8+DHnzp3jzJkzif5kvg369esnJaVct24d79+//6hNaUc1PoXySs+Dzmd9l1xBECRhUSqVidIT3fSMr0vj5qAm5ER8jEtWqKqYkHPnztG0aVMiIsR6Kq1bt2b9+vUolUq2nvRi5p61OAWK3lVqV1tcto5GZZY2ed2+lJIlS0oJYqOioujXrx8KhYLmzZtz6NAh3LrNZPqZeEeKIZVEUVEoFBjXKAaxiTL1nHJgP7cnzutHoFCnTYZtmbQl1cJy6dIl8uXLR+HChalWrRo1atSQ/mrWrJkefZTJBCwsLKRcU5GRkUkGTSoUCmqUt+OVpVgzI+r2iyxvZ3n8+DFPnz4FxHiWuHofAHe8xDt/jQryG+sIvyLGhKhz2aLOpCWkpIiOjqZ9+/aSN1vTpk3ZvHkzKpWKWSf86bB+Pbn8xSU8PRdrXLaPyTIxHX1HTMTUUXTjPn36NBcvik4fD32imXoyPmPv8Com/PiDqeQopJ/XAav+9bH9tQO5z0zHvN0PKPS+Ptu0TPqQamHp378/ZcuW5e7du/j5+eHv7y/9+fn5pUcfZTKJYcOGSfm0Vq9eLa3jJ6ReAQPuOuQCQKnVEXHX46M2WYk3b+Ij6CtWrCg91gkCbwJFv9bcVnrg8R5iS/YaViiQpSor/vXXX7x9K7oN//DDD2zfvh09PT0mnQhCNWcbRb1eARBjbYHLtjGoHXNkZncljj0KxX1dAJZDT2I3+DDmP/TFwNIBgK23wiRDff9yRoyoavbRe24zvg2WvetmaOyNzJeRamF58uQJ06ZNo3DhwlhYWGBubp7oT+bbwdnZmcaNxRro796949Chj/OCFbVT89IxPsFfxO2XGdW9LyJh0GfC76tvmE6Kl3AwVRH1PN4tN7MM3smxYcMG6fHo0aPRaDRsvxOO54ZTNLkrlu3WqfXIu34o+jltkjtNhnLlTST993xAUIqioHEuiXmjX2h9QEOPnb6cufAWo6gINHowsLLs3ZXdSbWwVKhQQVpKkPn2iVsOA5I04OspFaiKuUrP/a8+z4hufTEJCyQlFBav4PhEjA6mKqJfxNuU9HOnTeG6tODNmzfs27cPABsbG+rXr8/7EC0btz5k8KkDUjvHWd2kTL+ZzUOfaDpv9kKnEEVFGxT/3moFMN5zmvE71jHmr50s/GcLJuEfZ/aQyV6kyPKVMFHfkCFD+PHHH/Hy8qJ48eIfeYeVKJG1jJwyX0fdunXJlSsXHh4eHDt2jLt3734Uk+BSJidRShX6Oi2hN19kUk9TRsIZS8KKop4JhMXeVEXU8wTCkidrCItWq6VHjx5ERop2rC5duqBWq/l1nw8DDu9EXyeOwaJ7bczbVM3Mrkq8CYyh3UYvIgTxOhH+6CSTK4ZTu2U39twL58WeqzS5c5n/Ne5MsIERW27v4f1Pf5JjWBM51X02JkXC4ubmhkKhSFSro2fPntLjuH0KhSJRGnKZ7I9KpWLQoEGMGTMGQRCYOHGidMccR1lXI57bOFDo/Rs0b7zRBoVlugdSciQ3Y/EMjk/X4miqTGQrUrvaZkznkkEQBPbv389PP/0kxXw4OTkxceJEZp0Jwn7DQfJ9iM1qXNAZ2/+1/9TpMgxBgEH7/AmIFi8zka+u09b8Jv37ikG3Y6qrCYq05JlBKRrWyUUBazXFxo/g3eAVBKz/B9tJHWUDfTYlRcLy4kXWvguVSV+GDBnCggULePfuHUeOHCEgIEDKtQVQylGfZXZOFHovGsYjbr/EuGqRTOrtp0nogJBwtu0TEn9D5HLhGlGPROO4pmjODM3++19OnjzJhAkTuHQpPqZGqVSy5o91TL+k5O2ey/xyXXTzF1RKnOf3QqGfNVxwr7+H2+/F9zX6wwsKP1/NggM7E7VRKhWY/nWJCWMbo7IUb0bspnbmVbOpBLldzDIzL5nUkSIbS65cuVL8J/PtYWBgIAUSRkdHf2TEN9EoCc4X/9kHXs26BZb09OIvuglFxj9cnLEYRkVisXy3tN1mXOuM61wCBEGgT58+1KpVK5GoVKhQgWMnTrMtsBQXzngw6u/4YE+7n9thUMI1E3qbNJvuxa9wBP41i0WzfkN4H4jP1O3EfBBnjibubpjUKsG7wcsRYr3w9GzMsRrYkIjbWdvDUCZ5Un1rs3///iS3KxQKDAwMyJcvH7lzZw2joUza0bJlSxYuXAiIkeudOnVKtN+0bB6IvR77XH6GY0Z3MIUknKVER8cHRPrFCkuHq6dQfBDtMCZ13TCuWZzMYMeOHaxevVp6XrRoUaZOnUrt+o3ptsOfuy+CWHR4M8bRor3FtFkFLHrVyfB+6nQ6wsPDMTY2TrT9kU80l73EZawYv1fUdlVSwCAHr9vNItrDByEqBusxLVEaG5BjZHO8f9nM296LcV43HIDIx29RyG7F2ZZUC0vz5s0/srdAYjtL1apV2bt3r1RjQSb7U6VKFWxtbfH29ubo0aOEhoYmupgUKO1EiL4BJlERcOdFslmwM5tkhSVMR16fd7S6cR4Ahb4eNv/LnHQhgYGBiUoWLFy4kEGDBqFFSa+dflx9E8mEf/bg6ucNgH5BJ+xn98jw9zssMoaKo7bia5wf/ZhQHMxUFHW1pXQBZy54REjtgs6tYsLcsWgDQrHqXx+TBmV43XomSgtjcgxtgtrRCruZ3XjXezGvO81FoVSgC43AcdXgDB2PTNqRanfj48ePU65cOY4fP05gYCCBgYEcP36cChUqcPDgQc6cOYOvry+jRo1Kj/7KZBIqlUpKfxIeHs6xY8cS7S+b04DHdqIXj0FAEDGeWTNYVpegpkrCpbCAkGjG/rUDdaxnlWX/+uhnktF+/PjxeHmJcTRNmjRhyJAh6FAydL8/Z15G0unKSWo8ERMxKk0NcVo1GKVRxtqBdDodjaYdI9C5FnqWLuhsCvFWk5+/PM2ZcTqYMy9F0daG+VPW2JOKFStiVC4/Jg3KoJfDDPu5PQnacZ7ALWfQRUajZ2VKzt0TsJnYBvP21XDZNV7OVpyNSbWwDBs2jHnz5lG7dm1MTU0xNTWldu3azJ49m9GjR1OlShUWLFjA8ePH06O/MplIo0aNpMdXrlxJtM/JTI83DvHuoVGP32VYv1JKWFhYotQ0Tk5if6O1AvkuXpFmAJpiObEe3ixT+nj69GmWLVsGgJGREYsXLyZaB4P2+3PkcQRNbl+i2+UTUnv7+b0zPIAzMjKSLl26cO91wGfbhlxYy/hRw6XncWJhWDovNv9rj9/SQ0TcfEGMlz8fFuzHoEhOTBuVzZKzXZmUk+qlsGfPniXy/4/DzMyM58/F4Lj8+fN/VNRHJvtTtGhR6fHjx48/2h9kHx/lHfXSG+OPWmQukydP5uXLlwDUrFlTKlH86E0YHS7+I7Wz/a1zpnhWhYWF0atXL+n59OnTsXfKycC9/hx/GkH1x7cZdDq+SqTN/9pjWr90hvbR39+fFi1acPr0acyqW2JYUMwP+GNFFeF3DzNlwSp0htaozB3QRYag9+QotWrN/Og8gk6Haf3SCBFRvBvwOwqlEtOm5T9qJ5M9SfWMpUyZMowePRofHx9pm4+PD2PGjKFcuXKAmPYlrniSzLeDq6urZKN49OjjGvARDvHCEv7842zImcmdO3eYO3cuINYPWrZsmXRX7LXmBDYhosHet1wxjMrlT/Y86cnPP/8sFSCrUqUKffoPov9eP44/jaD0q6eMOb4TZWxGLashjbHqWy9D++fh4UHVqlU5ffo0ADqPC9K+HQ9hQO9uHF03G4O351GcX8fAR49pW6VWIk+8OBSxFSP1c9uhCwzFemzLLBN/I/P1pFpY1qxZw4sXL3B2diZfvnzky5cPZ2dnXr58KXmxhISE8NNPP6V5Z2UyF5VKRb58YunXp0+ffhQMq3WKt0lkJWHR6XT069dPsqlMmDCBggULAqANCsNmm2gv0qFAMyxzapZcunSJ+fPnA6J794pVaxi4P4B/nkVS4P0bfjm8SbL/mHeohvWYlhnavxs3blCxYkXu378PiOlk/t66nNKO4o3GqwAtow4HULlyZS7sO8ph59b0Mi3GYIPkvep0kdH4rf4Lx1VD5HiVb4xUz/cLFizI/fv3+euvv6TlkIIFC1KnTh2pbnXCGhcy3xYFCxbkwYMHREZG4uHhQZ48eaR9ajtzIvTUGMREE/3SOxN7mZiNGzdK6dkLFCjAuHHjpH1+K45iECrmpjpZsCTdK7tmeP8ePHhAu3btJE/LyZMns9HDjn+ehZEjJJCpB9ZjGC2m9DepVxq76V0z1AZx7tw5GjVqJGUtKFCgAEeOHCFPnjwsKhRDo3U+BEYIHHscwf4L3pScdhhrnT4ABjffEu3pj9rhYw9RpUaNw7xeck2Vb5Av+kSVSiX169enfv36ad0fmSxO3J0+iMthCYXF0lgPX2NTnAL9EPyCM6N7SXLgQHxyxqVLl6LRiB5U2sAwfFYdRwVEK1VcalyPgfqpnsR/FVFRUTRt2pRXr8RU9+XKlaNAwwEMPRgEgsCof/ZgHpuU0bBCARyW9MvQNCfHjh2jRYsWhIeHA1C5cmX2799PjhxiKn7fUB0qhQIQcAz4gPOQjUR6i/ZVhb05uXaOT1JU4pBF5dskRZ/qokWL6Nu3LwYGBixatOiTbYcOHZomHZPJmhQoUEB6/PjxYxo0aCA9NzNQoBfrzqvLQheMOKcSlUpFjRo1pO2vVxxHFSbGWxwvXIpuTV0zvG+///67lC28QIECLFm3g15/iaLc6O6/lPEQi43p2VngtHoISkP9DOvb7t27ad++vRTvU69ePXbv3o2RkZh65e+nEQza509EjEBhz1dMOfwnJmGhAKjsLNCf0wl1FknbL5OxpOjXP3/+fDp16oSBgYG0DpwUCoVCFpZvnP/OWBJibqBEPyY2fiEJg21mIAiCZBDPlSuXZEjWhoTjv+YvjACtQkFgh7rUzmuQoX3z9/dn8uTJgPjb2bRlKzNvGBMUGcUPT+8y+Gx86hz7OT1RWZpkWN82bNhAjx49pLifVq1asWnTJmm2t+F6KL/8HYhOgCrP7jHhr+2oY21Y+gWdcFw/HB8ikj2/zLdNqpNQygkpv28+JSxmGiXqWIO+NotkpfXz85NsA3nz5pW2n5t7HPtY28rFIiUZ3i5vksenJ1OnTsXf3x8QU+BfiMjHv2+CqfPgOj+e2I0y1uZi0a1WhqaWWbp0KYMHx0e9d+vWjdWrV6Onp4dOEJh5Opjll0NAEGh58wL9zh9BEdtXoyqFcVw5GIWpAbzLerFMMhnDF99WRkVF8eLFC/LmzZukO6HMt4m1tTVWVlb4+fklMWNRoNaKd63RqqzxnYibrQCSPcjPOxSDTWIArw4FeUY1wcIwY20rV69eZfHixYDoBdZ55BR+3OdNv39P0urmeamdebsfsJ3cKbnTpDlbtmxJJCqDBw9m4cKFKJVKHnhHM/1UEKdfRKKJjmLkiT3UfBJfq8msVWUxtYy+XqIMBzLfH6n+NcUFcRkZGVG0aFHJ6DhkyBBmzJiR5h2UyXrE1TH5bxCsJiYaTZyw6GecLeBTJEw9kz9/fgRB4MaANViEhQDwvFQJatbJk9zh6cI///xDrVq1iIoSPb1GDRvBy7XXWLN+biJRsejpjt3s7ihUGSd6CRNfjh8/nkWLFqFUKpl3NogGf/hw+kUkNsEBzN+1MpGoWA1tjP2C3lkmZb9M5pLqb+z48eO5desWp06dwsAgfk3a3d2dbdu2pWnnAN6+fUvnzp3JkSMHhoaGFC9enKtXr6b568ikjHfv3knLoWXKlEm0L/hB/NJHlEvmV1189uwZM2eKUd9KpZLWrVvzdu1JnC/fACBE34ASszI2KG/VqlX07NmT0FDRyP1D5Sp0e2FBtV37MIsUPa8UGjXW41qLha6UGTuTioiIt4tMmTIFhULB8ScRLLwQggAUe/uSZdt/J5+PWFhMYWyA46rB2IxpJadhkZFI9e3F3r172bZtGxUrVkz0RSpatGiiZYe0wN/fnypVqlCzZk2OHDmCjY0NT548kbMmZyKnTp2SHtesWTPRvojY4lgAijyZmzg/JiaGzp07Sxfw3r17Yxeq5PmULcRZf6716UDfwhmXZ2vmzJmJYmjatGnDgkJNCV4bn/vrRZVy1J7fDrVjjgzr16d4H6JlzJEAABre/ZehZw6ijLWjqXPZ4rR2KJqCcglhmcSkWlh8fHywtf0462toaGia37HMnDkTFxcX/vjjD2mbXOslczl58qT0+L/CwvP4GYtB4cy92EyfPl0qkJU3b15mT57G69ZzUEWLS3UH3CrSZXCVDOvPqlWrEonKyJEj+blSS94PF5eeYpRKJjTtzoz/VURtmzXqkOgEgVGHAoj2D+HHc0eo9+C6tM/ohyI4/j4gQz3VZLIPqZ5nly1bNlEFwTgxWb16NZUqVUq7niEWFStbtixt2rTB1taWUqVKsWrVqjR9DZnUEScs+vr6H33emhfxwmJR1DlD+5WQK1euMGnSJECMXfnzzz8JnbkX3QsxFf0TGwciBrXCzjRjPNe2b99O//79pedjx46lR6N+vB29Ttq2tFpjDKsUpnAmikp0dDTBwfGBrav+DUV99DJr/lyQSFQse9fFeeNIWVRkkiXVM5Zp06bRoEED7t+/T0xMDAsXLuT+/ftcuHBBSk6XVjx//pxly5YxcuRIJkyYwJUrVxg6dCj6+vp069YtyWMiIyOJjIyUnse5mup0umznqaLT6RAEIcv0OyQkRFruLF26NAYGBlLfBEHA/JW4FBaqr8HF1SrF/U7LccbExNCnTx8pj9mECRMo5ZKPl9tXABCm1mdu4w5srWqeIe/r2rVr6devn/haCiWtxy3juX0N/AYtwy529nS4aFmimv/A7w0sMu2z/vvvvxkxYoSUC6xo2W5YjFzE2Nfxy9tKU0NsJnXErHVlBMQMxcmR1b676UlmjDWrv6+pFpaqVaty8+ZNZsyYQfHixfnrr78oXbo0Fy9epHjxtPW11+l0lC1blmnTpgFQqlQp7t69y/Lly5MVlunTp0t3qwnx9PQkJCQkTfuX3giCIMU5ZAXDaMLCWFFRUbxLEKfg+8wP81gRf+nsgm3ge94Fpuy8aTnOVatWcevWLUC0+/Xo0YO3O89I+3e5VaFaRSuiU9G/L2X16tXSd1Ghb0zxUfu5alKAaVvWYRccAMAjBxeUwxvxS7EIwvy9CPNP3z79l1evXvHbb79x9OhRANQo6Ve4E3281Wi08aKiql0U/eENCbExIyQF8SlZ7bubnmTGWBPOLLMiX+QbmDdv3gxZknJwcKBIkSKJthUuXJhdu3Yle8z48eMZOXKk9DwoKAgXFxccHBySrCOTlYm7K3FwcJASfGY2pqamBAcHExoaiqNjvIH+zuaHxDntKssWSLTvc6TVON+8ecO8efMA8Qe+atUqcuXKxc1TO6XaME9LFGONuz2G6vS7AAiCwOTJk6WoepWpHUXGHCFAbUePC39ROnYWEG1uSrUdQ2icM+MN9VFRUUydOpXZs2dLM/wyZvmY7lCHnEHhgHgTEZrDknyzO2Pi7paq82fF7256kRljjVuJyaqkWFhSOpC0vHhXqVLloyC8x48fkytXrmSP0Wg0UtqJhCiVymz5BVcoFFmq71ZWVgQHB+Pv75+oT8EX4z+n/HWLprq/aTHOH3/8UZqV9u3bl0qVKhHsF4r+dbFvPibm9OxUCGNN+tpW/ve///Hbb78BoLYtQL4R+wlUmPLD07t0uBa7XKxSkmf1QIxcMz6Xlk6no3PnztINmoWJHRPzt6OJdxCEiC7PWoWCZw1q0HBeG1Qmhl/0Olntu5ueZPRYs/p7mmJhsbCw+OQ0TxAEFArFRzU6voYRI0ZQuXJlpk2bRtu2bfn3339ZuXIlK1euTLPXkEkdlpaWeHh44O/vL33mgRE6bB+LiR6jVXoUrp4vw/t17Ngxdu7cCYi1QqZPnw7A3tXXKB/7nXxVsigd8n/ZRTKlLFy4UBIVg4K1cOmxhlD0qfj8AeOPbZfaWU9ojVGlQunal6QQBIHRo0eza9cuFPpG1C/fn5/fR2LlHX/j+MTOCZ8RHeneqfA3v4wlkz6kWFgSupkKgkDDhg1ZvXq1VDc8PShXrhx79uxh/PjxTJ48mdy5c7NgwQI6dcq4FBcyiYmbkUZHRxMREYGhoSH/XnxDzkA/APzz5kSVgRl4QXR1HzRokPR8zpw5WFpacuNxIHZ/xnswVuxaPl0vlCdPnmTkyJEo9I2xbDYVkzJtiBQEWt84S+8Lx6TcX3oNS2HRu2669SM5oqOjGTBgAGv+WIdx2fZUK96RmSf2ox+bLSFMrc+Dto2oNb4B9hZZw+VZJnuSYmGpXr16oucqlYqKFSsmqseRHjRu3JjGjRun62vIpJw4I6VGo5EyL8QcvCTtV/xQIsP7NHr0aMlbrVKlSnTp0gWvgCie9P6dkn5iwbEgRzvK1i2abn149+4d7du3R4cSm86rMCxQHXVMNMNP7qPOwxtSO5NmFdCNbpDhM4Hg4GBat2nD2deQc/Ax6gaEMeDUIUlU3hYvTKFFPSiVX05zL/P1yIl9ZFJFnCeYo6MjCoUCQRDIcUpMsaNDgWnzChnanyNHjrBs2TIAjIyMWL9+PZExAkd6r6Pyc7HCaZiREUU3D0+3olIxMTG0b98eb29vrFrOwrBAdSxDg5l8ZBMFPV9L7XKMaIblsMZ4enmlSz+S4927d9TtPoZol14MM4mi6ZG9WIXFe0gqfyhOzQ1D5aJbMmmG/E2SSTGRkZH4+voCogcMQMTNF5i/9wHgtnNu3Atm3B3vhw8f6Nmzp/R87ty55MuXj/WjD1D5kliKOEapwmHlIIzypV/qlokTJ3L27FlMqw3ApHwn8vh48tvBjdiEiP7MCgN9HBb0xrRxuQyPPzh54zk/zTtND2Vl6vz9l5QkNA7jWiVwXDFQFhWZNOWrvk2yYe/7wtPTU3oc504ctPuitO1sETfaZlD6eUEQGDBgAF6xd/8NGjSgX79+HFhxmfLb9sY3/F8X7GsUSfokacC+ffuYNWsWRsWbYNnwJ8q8esJPh7dgHC268Oo5WuG0digGxZL3ZEwPorQCvxx4g27jBVb9exZVrH0HQFAqMG1YBqs+9TAsk/GOFjLfPikWlpYtWyZ6HhERQf/+/TE2Nk60fffu3WnTM5ksx8uXL6XHDg4O6MIiCdor2lciVXq8KlMiw242li9fLnmBWVlZsWbNGh5tOE+eaeuki+iHDvWp0rv6p07zVZw8eZKOHTtiXK4jVs2m0ezWRfqfPYxKEGclBqXy4LRmKHq25unWh6TQ6gR6zbtCw93xMTMAMYYacnSqTo5edVC7WGdon2S+L1IsLHE1OOLo3LlzmndGJmvz119/SY9LlSpFwObT6PzFtfpz+YpSKE/GXEAvXrzIsGHDpOcrV65Ec/YZiolriPNl8qhQijoz26RbHw4ePEjrNm0wqDaUHNUHMej0QRrduyLtN6lXGoclfVEafhxTlZ6ER0QwYtBWBp25imW4mNlZB6i6VafQ2LaozIwytD8y3ycpFpaEGYZlvk8OHjwIiEugDRs2JKjb79K+baWrMalg+teMf//+Pa1btyY6OhoQY52a16rH/UpjiXNyPlu2Ip039Uq3Wibbtm2jS++BWLRbjp1rZX7et46Sb+NLdlsNaoj12FYZXkvl0b2H7O6/gREv4pcsA/SV5FzSB4eGFTO0LzLfN7LFTiZFeHh4cOfOHQDKly9PDrURz+6K1UOf2Djg7+xAlVzpe3cuCAK9e/eWPNOqVavGzJkzuT9qE/ohYv36kwVLUvn37hgYpM9Xe9WqVQyetBibAQcpoFPzy/ZlOATF5onS6GE3qwfmrSqny2t/iuv7/sZj9A5ahkVJ217mtqXWngmorTN2KU5GRhYWmRQRN1sBMbYo7PwD6fkNl3zUy2+IWpW+9pX169dL/bC1tWX79u0E3XuH3m4xyWS4Wh+Gt6aEY/oI3Lx58/jflivY9d9Lg8d3GHz6oBQHorIxw2n1UAzL5E2X104OQRB4OW8negsOUUQQ3/9opYonrarTfF5n2cFGJlOQhUUmRWzfHp+OpEmTJoRuiC8Pfd0lH8MKpe8y2Lt37xLZVVatWoV5tB43eizBKtZYf9rdncGN0icTxO7du/ll+02sW82mz7mjtE5Qm96gpCuOqwZneNVHXXgkb0asIergFTSIAvLawpr34zrTqXPJDO2LjExCZGGR+SynTp3izBlxVpA/f36K5SvIk4PLUSCmAfHKlzvdl8GGDx8uJULt2rUr9d0qcafxNKw+iKlk3lpZ03JaY1TKtL1DFwSBPXv20G/Gn9g2mcPYo9uo/vSutN+iWy1s/tcepSZjU6BEvfnAw86L0DyND8DcU7IST9vVY1WnnBnaFxmZ/yILi8xniUtFD2Lm3pAj11GERgBwOn9xBlS3StdlsEOHDrFjxw4ArK2tmT10PA+bTMPENwCAd+ZWGKwciYtN2iaY9PDwYPDgwfx124t8ndcyef8Ginl6iDtVSuymd8WiY/q5MyfHi38e4jdwKSaxmZzD1PpMr90CCirZ0DGnvPwlk+nIwiLzSd6+fSuVonZycqJDhw7cbzFb8sC6Vb4cK0qmnwtraGgoAwcOlJ7//tNUPnRfhsZfnL14WNoQvGg4rSo6pNlrRkdHs3DhQn755ReiTZwp3GUDc/dtwCXgAwAKIw2OywdiUitj86IJgsD9+UdgwS5MYiP435lZMbZYfh4cHcHrORcx0JNFRSbzkYVF5pOsX79eSkPSs2dPYl5/kOqbvLawpmn7Yuin42zll19+4dUr0fvMvVZtShz1glhReWZtz+PfBjO8ZsqLin2Oa9eu0atXL27duoXKwolCHVcz59guSVRUNuY4bxiOQXHXNHvNlKANCuPZ0DXo/R1fe/62sytDg/7Fa8c8+vXrh4WFRYb2SUYmOWRhkUkWrVbLmjVrADF2pWfPnlxbepI4E/XNsmUZWTT9Ziv79+9nwYIFgJhNeX7hlrBXDEJ8a56Df8YOYlbjtBOVixcvUrt2bcLDw1Ga2pK/8x/M+ecQOf3FXGgqF2tybR+b4VHr2oBQXrSZifAg3p5yqlp1Zl2Zg9fTewAMGTIkQ/skI/MpZGGRSZY9e/bw/LlYwMvd3R1zY2v8dosVEGOUSsoMqI4yndbzT5w4QevWraXCcVtqDkYdKypahYINzdqwvLVTmr3+/fv3adSokSgqJtYU672VGSeOkdv3PQBKB6vMEZXgcF51mos2VlSCNQZsbdWOD4G7eB4rKnXq1KFo0fQrCSAjk1qydn1LmUxDEARmzZolPR81ahTHJh/BOFI02t8vV5Ya5e3S5bVv3bpFixYtpOj6NZX6UOSOmFVZh4LVDVoyfkRpDNKobv3r16+pV6+eWG7ZyJKS/XYy/cRf5IkVFYWdBbm2j8lwUdGFRfKm63yibolR/f6GxkztNoAiZYJYv3IJAMbGxvz++++fOo2MTIYjz1hkkuTUqVNcuSLOENzc3LByrYDFkQmAOGOo+GuzdPE+ev36NQ0bNiQ4OBiA5WV7UOWVKDA6FPzRuBVDptUjt1XafHX9/PyoX78+b968QaExpVjfbfz29zHy+sbWTLG1wHXHWPRzp4+IAtzxiuLPG2HkMFZS2EZNgRxK7P69jf+C/UQ9fANAkIEhv7buhbvLc4b36Sodu3jxYvLlkzMUy2QtZGGRSZKEs5UfR4/l7xl/0SJCTJvi+0MZihRPO9tGHAEBATRo0EBK2TKveCdqeIrBjzoUbGzSkmEz6+FkljZf27CwMJo2bcr9+/dBpU+5zqv45Z9/4kXFxpzcO8einyf9arkERujovsOPD2E6lDottR7dpv210xBr1wEI0TdgQvMeKH33M7rnr9L21q1b071793Trm4zMlyILi8xH3Lx5k6NHjwLg6upKpEVVapydAYBOoaDM/5ql+Wt6enoyaNAg7t0T7QbT8regoZ8YdKhDwZZmrRg+sz62Jqo0eb03b97QoUMHzp8/j8LAjOrN5zLj9FksYjMCY22W7qISESMw6nBArKjomHzwT8p7PE7U5omNIwtqNef5k5282B8v9p06dWL58uVyzIpMlkQWFpmP+Pnnn6XHA0aMw2DGn1IKdqF2aYwKOafp6128eJGWLVvi7S3Wp2+fqzotw8TEiToU7GzZmpEz62ORRkXELl26RLNmzfD29kbPJi+1Gs1k+unjGEeJxbl0zrbk3TQc/bxpFxvzX/zDdfTdE8DVt2LSyB5X/0kkKk9y5mZdqepcccmHybP9kqhYWFiwbNky2rdvn259k5H5WmRhkUnE+fPnpUSPzs7OmAfko+zzPwEIMzel+My0rcPz8OFD6tSpQ3h4OABVC1RiXFRBQLzg7mrSjKGz6mNukDaisnXrVrp3705kZCQGBWtRr9oYJv21C4MY0Y4TXTwPhbeMRGVh/JkzfTlvg2HsYV9e+Iseb9VfPaTtv6fEnSolTquHULCOG7WjdXTp1Z9dG1cBYnG18+fPkzt37nTrm4xMWiALi4yEIAhMmDBBej5s5DRK/r5Hem79W2f07CzS7PW0Wi09evSQRMW9VhMmfiiEQahoX7hUsjT9ZjdKE1ERBIFJkyYxadIkUCgwqzGYRvmaMP7odvR14gVeWakIRdYPRWmUfnnPbryNoN8xBf6RWgyiIhl85S/qXr8k7bce0xKTOm6Eh4czbtw4SVSMjIw4cOCALCoy2QJZWGQkTpw4ISWbLFCgAC7nQqQlsPflSlCtZfk0fb2FCxdy6ZJ4Uc1doBi99Wrg4CvWfHlta4/72l7YmHz9VzQ8PJyePXuydetWFIbm5GizgGYKa0Ye3RZfRrhuaVyW9U+3ZJLv3r2j5/Rt3LNtglLfiFKvnvLjP7uwDQ6S2pg0LItl//rs3r2bH3/8USoFrVAo2Lx5M2XKlEmXvsnIpDWysMhIJIyHGN5mAm7rTgEQptFQelG3NH2tmzdvMnHiRABUprb0LTUSt3OiqIVqDMi7bghOdmkT1T958mS2bt0KgFXT3yhiV5LRmxdJ+01bVcJhbi8UemnjGPBfjvx1gh7rH6Ep3pbSb57T8uZ5Kr58JO0PF2IIbluaG+WsmF+5Mv/++6+0T6VSsXjxYpo1S3uHCRmZ9EIWFhlAtHXs3bsXgCIuhSi9I77eSmDf5pi4WKXZa92/f586deoQERGB0sSGkfVn0+LcSWm/8cxe5C6Rdu7MPXv2ZNasWRgaGrKkXR7W+TtxvEQ56ty+gkX32thO7phuZYR3HTzG4IMhaEo0p+eFY7S/dibR/suRnvzkf4HXC/786Fh3d3cWLlxIkSJF0qVvMjLphSwsMgBMmTIFQRBQomCmQ3Ms3oqxHE8LFqD+qDpp9jpPnz7F3d2dDx8+gFKPLg1n0Ot8vKhoh7akSOuyafJagiCgUCjInz8/kydPpnHjxpQsWZJaIVoi63fF/FIpzFpWSpPXSop1u44y4YIB+rmLU+XpvcSiYmvGs4p2bH76jtfHgxMdV6JECX799VeaN28uuxPLZEtkYZHh0aNHbNmyBYCxBdqSP1ZUPpiYUWJ1f1SqtFki8vDwoHbt2nh6egJQt+U0hv97WdqvGdwU1zFNvuo1dOFRKNQqFHqqRBfluGU3QIyFMTGBdBSVhZsOM/u+A2pbOxwCfRl1Ype0Tz2wDrlGtaGgvpr6unH8/PPPLF++nCpVqjBixAhq1KghC4pMtkYWFhmmTJmCTqejsmleuoSKxbK0CiUffu5FldyWafIaz58/x93dXUqBX6Zuf8bf88QwWnQrDq5Zknyjm6b4fN7e3gwcOBBXV1esra3pOXg02389SvG7d1AaaSjewg2LLjXTpO+p4X2IltHrLnDSLx8qMxPUMdH879BGKUbGpGl5dF1+kOw5SqWSqVOnMnXq1Azvq4xMeiELy3fOo0eP2Lx5M2YKfWZa10QZFQPAyYYNGNixeJq9Rs2aNaWZStEKjRkb5IhDkJg5OSSvC3a/pDz32Js3b2jUqBHNmjWjQoUKDJs4jXd3Xej0+DZRE7qy88x7jOYexMorGJfu1dGzMU+TcXyKKK3A3LPBrLwchI68KGM9loed3EZeX9F9Wp3HHrsZXfEK8k/3/sjIZCaysHzH6HQ6+vXrh06nY4yDOzaxonIzVz6azWyaJssxL168SLT8Va56J34OdqXAe1FUwkxMKPrnED4oIlN8znfv3lGyZEkmT54MgMrJjZMTNvEkVzRtGuQjX61CzLW3oNWxw5hYGmDTu65kb0kPXvrHMGS/P7e9oolLGC5oo2lz5zB1Hz4AQGGgj9OKgShNDEEWFplvnGyVNn/GjBkoFAqGDx+e2V35JlixYgWnT5+mjL4trZW2AISqNYRO6IKzhf5njv48b968oVatWrx9+xaAmnUHM9PHjgLvxecRGgNc/xiCvlOOT53mIz58+MCtW7cA0UCvNs2BrakNqkvPOHToEA5mKrp3K87a0jXwXnyYiFsv0kVUdILA7rthNFrnEysqoBC0BJ1aiuFsd3pdivess5vaGU1hlzTvg4xMViTbCMuVK1dYsWIFJUpkbJ3xbxUPDw/GjBmDGiW/2cV7fe2tVY9O9b7+Avj+/Xtq164tBfk1qTWU6c90OAb6ARBsbk7uvROwrFQgRed79uwZPj7iklLDhg3JkbsEvVr2IOatL7XzGnC7aUPscuTHc/IWYmJiKGanpmr7Ulwt4UbQP7e/ejwJCYvSsfFGKO6rfRhxKICQKDEDc25LFfMqBxF1bBZz9YuiihaDL83aVcW83Q9p2gcZmaxMtlgKCwkJoVOnTqxatYopU6ZkdneyPYIg0KdPH0JCQhhoVoo8OjHa/IGdCxVG1kWj93V3976+vri7u/P4sZhUsXvVoYx8Goa+Vlxq83e0x233KAycUzZTGT9+PEePHsXGxgYbGxsKdV9E/6BiCEH+nGw4lWJtKvJLn6bMvNeAZocPEbDgANajWuBsriJYF41K/+u/5pEP3/D+sTc7BTvWvVQSGCEk2l8nZzTzW9ihfh7FAZfWOGkNABBcrbGbkrb51WRksjrZQlgGDRpEo0aNcHd3l4UlDdi8eTPHjx8nv54F/c1KgiB6gf3doTWLi3xdtHtQUBD169fn7t27AIwvN5RuL+LTlrwvWoDK24ail8Ikj8uWLePUqVOcOHGCyMhI6o9aRaWZaynirMJncGd+XX6XidsuEHLjLvfe3uJD7R+xW7+X+zc9ee8TSSFtMObt2n3VmD7svYzPkBUoBYFGQFErW5b90IgbOfMR8ewCQWeWs+bRCSJsSjJO44aTIIpKsC6Kw4Wj+Z9h+uUek5HJimR5Ydm6dSvXr1+Xqhl+jsjISCIj4w3BQUHiRU2n06HT6dKlj+mFTqdDEIQ07beHhweDBw+mkNqKNTYN0I+98d5Zqgq9OxZEEAQEQfj0SZIhLCyMxo0bc/XqVQz1DJlffCA13vnGv3bNitRa1R2lvjrRmD41TpVKRatWrbCwsABg0IB+BE2Yy4mYCIaULcKKn634aZkFPf45wYxyVcn/a2X218iNwf0X5FOFU2JAdZSGmi9+D31Ctdyfsg/HBO+Jq583fS4dpMWew0R7ivVj6hnkYqLaDaUgzvYeRPkx0v80Vjfy8VMKx/ot8b2MEzJnrFn9fc3SwvL69WuGDRvG8ePHMTAwSNEx06dPFzPY/gdPT09CQkLSuovpiiAI+PuLHkRpYXyOioqidevWGAfHsN62KeYKcQnsiY0DQR1rYq3zIbZ44xedu1evXpw9exZHEydW5GxDfu/4OvX329Sl/MgqeH3w+ejYT40zMDCQJUuW0LZtW5RKJTExoGeZF8fTh1m9ejUNGzZkTM9izFJZ8uveDUSc+ZcGpXJBqbwAePn7whc6YXkEwaiTCvobmOGIZ6J9+57/RXSAKCqjareh2yMjSVS2hz5iSsC/RKGle9OmUkXMz431W+J7GSdkzljjSndnVbK0sFy7dg1vb29Kly4tbdNqtZw5c4YlS5YQGRn5UVT4+PHjGTlypPQ8KCgIFxcXHBwcMDMzy7C+pwVxdyUODg4o0yCX1ejRo7lx4wZLrWphrhS9vu7Z52RL755saOn8xbaVmJgYOnbsyKlTp7A3smOTYwscAgMACNPXEPBzD1p0K5fs8QnH+fz5c7Zv386gQYMwNzenZc9h/Htbj3VdJjDx2Fr6O+ox8EML8nm+xGjnPRx798YRqNnUjLd+5amo1cPU8evzjF19E8XA4/4ERAjccXRNlDTytouCVZdEr7Tfug2m3aUYdEIEAKpGpSjbrA0zHj2iaNGiuLu7JzvWtPhMsyrfyzghc8YatxKTVcnSwlK7dm3u3LmTaFuPHj0oVKgQY8eOTTLViEajQaP5eE1bqVRmyy+4QqFIk74fPHiQefPmUcPAmdqGOQHwNTZldrse7OjghKH+l6Vt0el09O/fn127dmGlNmG9c2scQsWZ4XsLK6xWDKFmFdfPnkehUHDv3j0aNmyIp6cngiDQtMcIpi17wMRzD9GPUTO9QU8m/r2B8bWtGHK8NP2vXcV73AbsZ3Xn0atQyj94hqJRoS9+rwSdjqCdF3j0zyPWRdpg4JAHzCzxLV0ELv0FOgGqFqDzzv8BUMsyL22vgy5EFBVj95I4LR1EPj0VDRo2/ORYs+v3MTV8L+OEjB9rVn9Ps7SwmJqaUqxYsUTbjI2NyZEjx0fbZZLn1atXdOvWDQ0qfspRTdq+smoDZra2x9n8y74GgiAwYsQI/vjjD0wV+qzL3YVcoWL9Fi8LK3LtHo9rAesUn+/Zs2f89ttvVK1alQYNGuBtUph+an2MJ3Vhzs1oOu3bzE/V23BW6YOpnSvbOk3EZMsGztefS1s/X1ybuWHWtMIXjSXmfQCew1YSdu4B5sCw2O2BlhY41C+J2bzeCJHR9N+9BFNBj2EW5WlrVADBTxRRw3L5cVw2IN1S78vIZCeytLDIfD1hYWG0aNECPz8/Zjk0whlxCeymcx5KdKtMtdwps139F0EQGDlyJIsWLcJIocfa/D0oECpWgvxgYobJ2h9TJSoAzZs3x8fHBzs7O5YuXcqAAQMo+ssMKrkXon0VE46EBNP26ilqt2tEzR+7IaiUPO/wM8ILf8roh2FYJu8XjSXy8VvedJ1PzBvfj/aZ+wcQtuU02pvPOVrFGIe/n3HcrhVGyviCYIaVCuG0ajBK2ftLRgbIhsJy6tSpzO5CtkEQBHr27Mn169fpbVOFpkobACL01Fzo0JJ5Vb/M5iQIAqNHj2bBggXoo2JFgV4UDxE98QIMjAhdNJwq5b/MzmFnZwdA/fr1Y0sJT6BssW04m5vzyiWYQ5EVab3/Os/reHL2xgca+78g36gWX/RaAGEXH/K212J0QWEA+JiY80fFOjTPEY7b2+eEX3mKEBlN5IPX1HwANU1LSscqjA2wmdAGiy410q2ei4xMdiTbCYtMylm+fDnbtm2jhkVRRurnl7Zva9Oeqf0Lo1Km3lgvCALjxo1j7ty5qFHye6HelAsWRSVYY4D3nKE0qfv1ddkFQaBr165otVoaN26MUqmkf//+5Bv5I5vmKmjcYSal9RToTWz1xa8RtO8ynsNXQ7QYuPnExpFfm3XF3dWbf59dYnnwdbzDHjKfctioDKXjtErI0d2dHEMaZ0iCSxmZ7IYsLN8od+/eZeTIkeQzdmaueSWUMeLF82B1d0ZNqo6JJvV32IIgMHHiRGbNmoWt0oj5+TpTJlZUwtX6vJ06iGbNC6bpOAoVKkRAQAAbNmykTZvWXHsbzU4Lezpqo8i5YjDGNVKfgVkQBPyWHubDjJ3Stsu5CrCgSXsCj05g5umtidr30vNjlXUdrJWGPHHV0GDrVPRdbL56bDIy3yqysHyDhIeH06FDB9QxSpa7NMM4TDSoXylUnA5L22JllHoDsyAI/PLLL0yfPp0aRq7MtKmNeahYSyVSpceLX/vTov3XOVQIgsCWW6H84KKHfUwEMWYaFi1axMGDB3F3dyciRmDTJX9+9r9Jrj9HYlQx9SImRMXg+eMagvdckrYdLlqWrY2aYXzyF278R1QsLS1xKV2aIyVyUaNaNZo2a/zNx2XIyHwtsrB8YwiCwKBBg3h89wF/FOyLc4goKi9tHai0rh+OFurPnCHpc44ePZq5c+fS1bIs44yKoYzRAqJNwmdiD1p0KfmZsySPn58fdx89Y8Y1I47eC2HBuZ0U0wWSc+9PbNiwAbVa7LOBnoJZLWzRazoAxRfm//JbfiSRqPxR0Z2TlcrhtaIFr+6L2Yjt7e1ZsmQJZcqUIVeuXLKQyMikEllYvjEWL17M+j/WsShvZ8qEiF5agQZG2K8eQl7nlOXnSkhMTAx9+/Zl3R9/MM6xPt0V9tK+i3kL4zC7By3Kf/myUEhICAPGT+e8aWOMcwTz66FN5H/1hEjAe9IW7Kd3TdRerVKA6su+trrwKPzXHAfE3GhT6rfnXys1z38qhxAhBpzp6+uzZcsWatSo8cVjkpH53pGF5Rvin3/+YeTIkfzs1Aj3CHG5K0JPzftpA2lS2iHV54uIiKB9+/Yc3neAhbk7UDcq3p12T5VaNJjflpKOqXexDQwM5MCBA+zatYt/nkdi1nIeZkoNk/avx+3tC0AsjGXaoEyqz/0pgnaeR+srpsI4nb845/MVxfuPrpKo1K1bl7lz58oxUjIyX4nsI/mN8OLFC9q0aUNfi/J0QIwf0SqUnB/cgyZti6b6fEFBQTRo0ID9+/YxP383SVS0CiW7WrSix5qOqRaVEydO0KhRI2xsbOjSpQv/BLhg3n4lpgo10/f9IYmK0sQA500/Ylwt9f1ODkGrw2/FUen5jtJViQnyIuLJKYoUKcKRI0c4duyYLCoyMmmAPGP5BoiIiKBFixbUjrJlmHm8QXtPmzaMHlEx1efz9vamQYMGXL9+nQmFuuEeLGb2jdBTc7J/N0aOqoxBKvOKrVu3jl69eqHT6VDoG5GjwyKMSzYlR0ggvx3YSL4PYpJHpbkRzn/+iGGpPKnu96cIOXad6JfeAFx3zsszG0eiz69kyaKF9OvXDz09+acgI5NWyL+mbE6csd75cRC/WlWXtm+t1YCBv9VBX5U6AXj48CGNGjXi+fPndCvRk66+YoI9rULJ7R97MmxwhVQbs9evX0+PHj0AMChYG5tWM1GY2lPnwXX6nz2EaWRsAsccpjhvGYVBkZypOv/n8Pfy5s6IxdjFTtB3lq4KwKafO1KnXMoqWMrIyKQcWViyObNnz2bt2rXUqDwMvw8CNqFB7CldheYzm2NjnDq34hs3blCrVi0CAgKoW2kYY17Hp+a+26MVnb9AVHbs2EHPnj1BocSq5SxMynXAOiSQYQc2UMHjsdRO4WCB86ZRGBRwStX5P8fRo0e533sejRDP+9Tagas581PeSU8WFRmZdEIWlmzMnj17GDduHMZl2/O86Rj6h4dR6/Et6v1Un5IO+qk615s3b2jcuDEBgYGUqvM/pj99j0oQZysvGteizaQGqRaVQ4cO0bFjR3Q6HZZNp2BStj0N7l6h7/kjGEfFF2MzbVERbf+a6OdLvYNBcjx79oyRI0ei//cDplhWAcSlvJl122CqUTKnkVWavZaMjExiZON9NuXatWt06tQJTb4fsGoxE4BgQyPcRtajUZHUuRWHhITQpEkT3nm9J2/zOcx/Eyxd+H0rFKfe0k6pFpV//vmHVq1aERMTg1mtYTiWasfU/esZcXKvdG6VnQVOa4div7APCvOvK4mccCwTJkygSJEi2P3zQhIVgEU1muGRw47ZDS3IZSnfU8nIpBfyryubIeh0vH33jiZNmhBj7opdp5UoYuM6epU1pmdZk1SdT6vV0rFjR27evIl9i1nMehWIfZBYDS88rwsVNw5CoUrd/ceFCxdo2rQpkZGRGJfvRDm3Tvy8bSn2wQFSG7N2VbH9XwdU5kZpUmZVq9WydetWxowZw/t3nvxkXoEOJoWk/btLVubvwqXoVdaYBgUNP3EmGRmZr0UWlmyCLjwKlAoCQoNp2LAh70O02A/agNLAFIB6BQyYWDN12YoFQWDo0KEcOHAA87pjGRVmTFGv+wDEWJtTdOsIlEapcyl++vQp9evXJ0yrwrLZVKo6V2XKzpVotGKuMpW1GQ7ze2NcM/U5vpLi3bt3rF27ltWrV+Ph4QHADMuqNDfKJ7VZW6kuW8tUo5SjmnE1slcVURmZ7IgsLNmA92PWIYREEH79GRujHhP5xBubXmvRsxBT07s5qFnY2CLV2YonTpzI77//jmn1gXSwrUjjk/sA0OmrybtxOGoHy1SdT6vV0rVrV6Idy+HYeh7FQ8KYvG+dJCoGpfPiuHwgasevs29otVqOHTvGypUrOXjwIFqtVto3yLSkJCpalYo5tVpyopAbFgYKfm9mlWovORkZmdQj21iyONE7LhH13Av7BX34u4CAzeswuhbvQTmFOQgCTmYqVreywlCduo9y5syZTJ8+HZOK3ahSsjODTh+U9jnO6oZBcddU93X27NlcexOBTedVFAyPYur+9RhGi4kqjd1L4rJj7FeLytWrVylQoACNGjVi3759kqgoFAomVmzBELNSAAgKBVPqteNEITc0KljZ0gpHM7m6o4xMRiDPWLI4urf+mDcpz/HT/9B/03wK1BhBH4OcNLh/FU8nJ9a2tk21W/GaNWsYN24cRqVaUcB9LP/b9jtqnXiBtuxdF/PWVT5zho+5desWv85Zhk3/feQOCmT6vnWSkd7ohyI4Lh+EUpP6BJgJOXfuHA0bNiQ4ON4N2sXRibENO1LPIh+6XVcQEGdHqyrX43zeoigVsKSZJRVc5OqOMjIZhSwsWRylsxV+5+7Qef80DIs1JKL+KJZpY/jt4EZWPv2LQjb9UnW+AwcO0LdvXwyL1se++Uz+t/cPrMLEuu1GVYtg81PbVPcxMjKSzt17Y9lpFS5aJdP3rsI8QqzIaFg+P05rhqI0+DpROXHiBE2bNiUsLAwrpQHdC/9Ah9zlMH/qj+6ID1p8pLaHipZjZykxCHJ6PXPq5peN9TIyGYm8FJbF0VUryJt/rjJNXYUcbRYAEKPSQzu6PY7maoRUeFSdP3+edu3aoc5dCZv2Sxl16gCFvN8CoOdijeOyASj0Ujf7iYqKolff/rwvORgnU2dm7VlLjlihMijpitO64al2APgvhw4dolGjRoSHhdHLpBhnHNvRN8AJ0xvv0AWHS+10CgXHCpdmSfUmoFAwppop7UumPqOzjIzM1yHPWLIgAVvOEPPWF738Dvy0eQEHvU+wrEh/1m9bzsy6bSlf0praJ/4GjTrFtdZv375N48aNiTZxxL7TCvpcPkmtx7cAUBjq47RmKCrL1Lkq+/r60rJ1W+67dMLRtRIzdq3CNiQQAP1Czjj/+SMqs6+LT9mxYwcdO3bEXKfHzBx1qGrgBEL8fqWFMcY1irPRJA8bDXITbCi+Xq+yxgysmLrxfAtotVqio6MzuxvodDpiYmKIiIhAmcLvaHYlPceqVqtRqbKfbVAWliyGz7QdhF97inGtEjz+dT1FPN4QUP83JpZvSveLxxl5+QhFgq0RFGC/JGXLYM+ePaNevXoERSux672eXjev0ObGOXGnUoHDkn4YFHFJVT9DQkKoW78Rrwr3wSZfdabtXoNLwAcA1LntcNkyKtVClRBBEPjjjz/o06cPFdV2zLSuFl93XqHAomtNTJtXxLB0XvY9iuD3AwEAqBQwoqopgyqZfHcFukJCQnjz5g2CIHy+cTojCAJarZawsLBv/nNIz7EqFAqcnZ0xMcleN0mysGQhIp+8I/TkHVy2j+H45XN0u7uRASX60swvHNXbFxxyr0c/dyX2uSxSHLTo6elJ3bp18frgj22vLQx5cJ+WNy9I++2md8O0XulU9TM6OpoW7TrzuvhgLPNUZfK+dVJ2Yj0HS1y2jELPxjxV54zjwYMH/Pnnn2zevJk3Lz0YblaavqbxMS8qW3McFvbB+Acxpb5fmJZJfwdJ+5e3sPwubSparZY3b95gZGSEjY1Npl/MBUEgOjoatVqd6X1Jb9JrrIIg4OPjw5s3b8ifP3+2mrnIwpKFUDtboynsjNej53Tp0oWIMq1Y32wk3S8dZ+DZQzgMn4BjnpTfuXz48IG6devy/PlzrNsuYvRLL5rc/VfabzutCxadqn/iDB8jCAJdBozibv7BmNoW4H8H/6S4pxiYqLIywXnLaNTO1qk6p7e3txSTcuPGDQBMFfpstG5AKY2t1M64RnHsF/RGzzo+yPG3f4LwCxftTI0KGnyXogKi2AuCgI2NDYaGmf8eCIKASqX6boQlvcZqY2PDy5cviY6OloVF5gtRKtDFaLndZSbB5gWxbToZnVLJ2sr1aKgfQIHXLyFPygpR+fn5Ubt2be7evUuOOmP55YOOOo9EUREUChzm9MC83Q+p6l5MTAx9Rv/GObvu5I1RMHTfOkrGFecyNcR5049oUplI8syZMzRr1oyAgABpm7FCzQIbd0qpRVHRqZTkGNsam/71EtmUjj4OZ/c90XhvplHwq/uXzZK+Jb71i/j3Rnb9PL9tq1o2IcYnkMinnig1auYbPeNFkD9H9MriHCQawvuUM8JOF4E2MDRF5wsODqZ+/frcvn2bnHXGszTQiDqPbgIgKJU4LuqTalHx9vamZuM2nFG40/fODVZsWSyJikKjxmnd8FQHVe7fv5969eolEpWmJatwulhPqsSKSpDGkGEt+9JIKMW662FERIv2gxNPIxiy31867qda5tiaZJ87OhmZbxl5xpLJeP64Bq1vMBE3nvM2lxEn/9rE1i6L6PkhjCXbfuduGTdq3AlFZWmCWdMKnz1feHg4TZo04cqVK+Qs141VnjHk8hcFQKevxnlpv1TXkr906RJtOnSnSulRTD+0E5vQeJuG2sUa+wV9MKqQutom69evp1evXlLk/A8//MCKLj+imnMUIVQs/BWsMWByw448sneBEB2/ngji98shNC9iyLproUTFZnJpWtiQtsUzf/lH5mNiYmKYNm0aW7ZsQU9PDz09PcqXL8+sWbOwsLDI8P7cvHmThw8f0r59+wx/7e8JWVgykcAd54h64kmu/T9xdvdhTvWeSvc8zbiqM2F9xSq8KFSQ8a5+mFlbYN7s86ISFRVFmzZtOH36NHZ5fmBFhB25/MVyvNEWpuTdMAzD0nlT3D+dTsesWbNYMGkmcwr0pNzl8/E79fXIMbAhVoMaoTRMXe2XefPm8eOPP0rPO7bvwK85a6GbtFfyJI7I7cjgH9rjaZ4DJzMVb4NEFfEO0bHy3/iZW9PChsxvbJFtlwy+dXr37o2fnx8XL17E0tISQRDYuXMnfn5+KRaWmJiYRKWj//s8Ndy8eZO9e/fKwpLOyMKSiQiR0RhVLMjz589p0bcroTbFGVCiFvXvXeODuSVDR7hhpviAmaPjZ+NV4hJAHjp0CCurPKwxrkRuP1FUQnNYUuzAePRz2qS4b15eXnTp0oWLZ6+wJXcXCvj6xe+sWozcM7qg72qb/AmSGq8g8NNPPzFt2jQAzBT6TG/Ug4bBjkRuihct02YVeNCzHZ5/i/aTvuVNKOesz8ILwRx7HCG1a1LIgPmNLdBLZfJNmYzh6dOn7Nixg1evXmFpKSY0VSgUtGnThlOnTtGyZUtu3rwJwN27d2ncuDEvX77k5cuXuLm50a9fP44fP07Xrl3Zu3cvJUqU4MqVKxgaGvLPP/8wZ84ctm/fTkxMDLa2tqxYsYJcuXLx66+/8uDBA8LCwnj27Bn29vbs3LmTmJgY/ve//xEYGIibmxsVK1Zk+fLlmfgOfbvIwpKJ6Oe2x3fjSTpu/I2AGA32bRexyyQHRlGRTLp2GLccbnj6fj4mQafT0a9fP7Zt24aJoRVrnVqQL1ZUgs3NKLZnbKpE5fz587Rs2ZKgSDVLiw+mgKcYne9rbIrhL51w61A+1TOEkJAQhgwZwrp163DTt6GrcRHqmeRGdSOSSMSlOlRKbH5uh2kPd56cD0l0fFE7NStbWHHvfTSbb4ViZ6JiYEUTWVSSoWzZsnh5eaXb+e3t7bl69eon29y8eZP8+fNjbZ06L0GAwMBAihYtysyZYhG7vXv38vjxY86cOYNarWbz5s08evSIixcvolKp2LhxIwMHDuTQoUMAXL58mWvXrpEjRw7at2/PihUrGD9+PJMnT2bv3r3s3bs31X2SSTmysGQCYZcfo/UPwbBWcY69f8DcwKKMadGStyY5AHjfvh72h30RorWfvYBHRUXRrVs3tm7dikZtzMoCPSn0QQxUDDQ2Id/OsRjksUtx344cOUKrVq0wsirIeueGlIwVlTB9DfqrRlCqWu5Uj/fs2bN079adfJ5attk0oqR+rMglyEajyGWNxdTu7DTKxfqVPrwJik+Fr0nwLS1qp2ZqXYtU9+F7w8vLi7dv32Z2N74YtVpN586dE23r3LkzarWYc27v3r1cuXKFMmVEe2HC0gkA9evXJ0cO8fdUqVIl7ty5kwG9lolDFpYMxnPkGrQfghC0Om6NXc6Ee4foV3kcK04cY00EBOdx4afTt1AaG6AyNYRg/2TPFRoaSuvWrTl69CgqlYbFxQdR+n3sTMXAkBwbRmFV2DFF/dLpdMyfP59x48aRM687y/SKkev9OwAi9dSYLhlIvlSKSkREBD///DPnF29ihllpSuRIPGtSWZuhaVwBn2pl2BRqzJHrKkKjgxK1KWanpk4+g1S9row4o8js87u5ufHkyRN8fX2li3wcenp6icQgIiIi0X4jI6OP0qMkjD4XBIHx48fTt2/fJF/bwCD+O6NSqYiJiflsf2XSjiwtLNOnT2f37t08fPgQQ0NDKleuzMyZMylYsGBmd+2L+DB3L7qAEJw3jGDnxs14HNmPe7l+bG3UmyevntDu1nlK2QSjVoLj4v6fLNnr5+dH48aNuXjxIgqVmlmlhlPN8z0AEXpqDJcPI2+FXCnq17t37+jWrRt///03Zcp0Z1GQCTli07MEm5jgsm4YdhXzfeYsibl27Rq/dupPiw+W9Laul/h9cHHiRMWq7HMuyocYFdyI2xO/7Fcjj4beZY2p6qqRDfNfwOeWqTKCfPny0apVK3r16sW6deuwsLBAEAR2796Ng4MDHh4e+Pj4YGNjw8aNG1N17ubNmzN37lxat26NlZUV0dHR3L17l1KlSn3yODMzMwIDA79mWDIpIEsLy+nTpxk0aBDlypUjJiaGCRMmULduXe7fv4+xcfbKWhvzPoDw60+x/V8Hbt26Rbf+fehvXQt3AxeuCjpuu+Zn7OjyFHA1RIjRfvJc7969o169ety9exdU+kwrN4ZGr98AEK1UoZ3Tn5LuKRPfvXv30rt3b/z9A+lTcRQDvYIwjBbtG/62NhTd+SOmqVhKi46OZtHYSajWn2eOYTFIMNl4aWPP6gp1+Ne1ICgU8J+bSEO1glbFDOlRxph8Ob4uzb5M1mDNmjVMnTqVChUqoKenh06no1q1asycOZMxY8ZQvnx57OzsaNCgQarO26lTJ3x9falZsyYgeor17Nnzs8JSu3Zt5syZQ4kSJahcubJsvE8nFEJWyFiXQnx8fLC1teX06dNUq1YtRccEBQVhbm5OYGAgZmaZW+886rkXgbooKrhX5+V7P5q0XUvTF8+Z3KgTU3MFUe/lXWzGtUYQBBQKBTqdjnfv3uHo6CgtCzx//hx3d3devHiBQm3A5IrjafNcNH5rFQqCf+1FhV6fL9QVGhrKyJEjWblyJbmNnZibqxVFAuOXoXwL5KbczhGorUxTPL77d++xt9VomoRYo6+ID1YU7M053agJ0w0LIijilzccTVW4WqlwtVBhrw6lS0V7rIyz9L3OV5PUZ5oWRERE8OLFC3Lnzp1oGSizkHOFpQ3Jfa5Z6bqWFNnqVxw3hbWySr68bWRkJJGRkdLzoCDxYqnT6T65tJQhOFvRoWFDXrzywLrLWsKtnFG8eEZFF31+2LEHVdPyUh8FQUCn00n/QfySNW7cmBcvXqBnZMmMMj/S+PlTAHQoCJ3QlXI9Kn12nG/fvqVevXo8ePCAWg6VmaUpikkCUflQvwoVFnVGaaCfovdMq9WyeO581HOP0UqTE2J/W+EGSqxGtmZqjtIcfSnOwozUCja1s6SgtRpDtdhQp9Ph6RmCmYbM/4zSmf9+pml93ri/rERW6096ktZjjfs8/3v9yuq/k2wjLDqdjuHDh1OlShWKFUs+X9b06dOZNGnSR9s9PT0JCQlJ4oiMY/LkyZw4cQKzWsMwKlIXv+AArMND+N+m39EWcySsdkHC3r2T2guCgL+/aLxXKBRMmTKFBw8eoLHMyYIifaiZQFR8BjclT5N8vEtwfFL4+PjQpk0bnj97xpD87RkQZoAytn7HWytrhNHNKFjTFS+/Dykak4eHB/8bPoZeL80po8kJgBaBoPqFCOnVhv7XDPB8Gb+0N9BNi63wAX8fiHNL+O84v2XSa6wxMTFSLZaskqzwezKYp9dYo6Oj0Wq1eHt7JwoKTVieOyuSbYRl0KBB3L17l3Pnzn2y3fjx4xk5cqT0PCgoCBcXFxwcHDJ1yrhz505WrVqFQf7qmLuPAiBSrU/+AG9M3YriOL3nR8fE3ZU4ODhw4cIFVq5cialDcRbnbEXFl88AiFEqYXIPqnat/Nk++Pn50a1bN968eMPvRQdSMyBM2nenaDEq/tEXR/uU26727dvHuG79WWhYhdwaMQFktB7YLx3ARdsiTD8VTHTsjZWFgYI5Dc2pnffjZZqE4/weikJB2o81IiKCsLAw1Gq15JKbFchKfUlv0mOsWq0WlUqFra3tR0thWZlsISyDBw/m4MGDnDlzBmdn50+21Wg0aDQfl8JVKpWZdtF6/fo1/fr1Q2XpTI72S6Uo+r517HAs1AnTpuWT7ZtCoSA8PJyePXuisc7H7y4tKffmJQBRenqYLhqAa9PP11MJDAykQYMGPH/yhj+KDKR07F2zDgU3WjSg5bxWGKg///4IOh3Rrz5we89xzv22gLXGNbGOLcAVbmbE0SH9Ofjaltd34++oSjuqWdLMEiez5L9uCoUiUz+jjCQ9xqpUKlEoFNJfZpNwSSgr9Cc9Sc+xxn2e//2+ZPXfSZYWFkEQGDJkCHv27OHUqVPkzp364LzMRqfT0a1bNwJDwrHrvwWVsZjawj2fhkGVTFBW/nyW4bFjx/LSN4IZbsMo9+Q+ABEaDfZrhmFXo/Bnj/fz86Np06a8eubPhgK9KOorLnOFqfV5N6EnHft8Og9ZjHcgfiuPEXHjGZH3XqELicAU6GsUvyTpYWnDT0278T7IEohf+upTzpix1c1Qq77ti4uMjEw8WVpYBg0axObNm9m3bx+mpqZSigpzc/MsUcwoJfz8889cP3Wew3m68ff7APY6xOBkrWFeI0uUn7m70el0TJkyhT92HWeG2zCaxIpKjEqF04YRWFf5vEvxnj17GDBgAD+oizPfwh2zWFEJ0RjA0mE0ql/ok8cH7b/M+wkb0QUkn7L/as58TKvXnhAD8TPRqKCUkz59y5skufQlIyPzbZOlhWXZsmUA1KhRI9H2P/74g+7du2d8h1LJtm3bmDZtGgtd25E7Ipo+F47hEuRL3Q19MTf49FQ2MjKSbt26ceD0HRYX6EX1WFHRocBiZs/PikpAQABDhgzh1Ja9zHJpRoVoNUSJ3nKBxibYrB9J7orJzwBj/ILxnriR4ANXEm0PMoAbCLwqVoPntk48s3bAz8qK0o76VMypT8WcGko56mOgJ89QZGS+V7K0sGRnN8Xr16/To0cPmlqVpl60eCcfrDHA/sdmFLb9tJFPEAS6du3KsQvPWeXShjIeTwDQKpWYzemNU9tKnzz++PHj9OjRA1df2OvYBpPo+PfxdqlSuC/vhqVT8tUWQ/66gdfYdWh94g2Epo3LcbKYhiF/e2NaqZu0vXZeDQuaWGKmydprvjIZT4ECBdBoNNLqQtmyZVm9enWSbQMCAli+fDnjxo3LyC7KpBNZWliyK+/fv6dZs2ZYROvxs21piHVFPNOhNSPcnT57/PTp0zl1+TXrrOtS+J1YTz5KrcZu+SBs65VM9rjQ0FDGjh3L0qVLaWdajJ9zlEVPJ4qKt4k5t3q2pd+PlZK1d2gDw/D+dTNBO+JT2CstjLGb1oV7LqaMWvEA00rxEdIDKpgwupopKjnDsEwybN269bPR8CAKy4wZM75IWL6mPotM+iB/GmlMdHQ0rVq14u2bN2zM3xvTMFFULhZzo9dPNT/rNXLw4EEWLdjIOrvG5PERY1LCDQ3JuXE4VhWTr9J4+fJlunTpwrMnTxljW4Oealdp34XchdH+1pNB1XMk+/qhZ+7h9eMaYjzjk14a13HDelpX1tz2ZvauEPRzizMlhS6GeU2saVnMKEXviUzG03i9Dz4hn04N9DXYmKg42C3lpRgATp06xfDhw5OswdK/f3+Cg4Nxc3NDT0+Pq1evUqNGDYYPH07z5s0BaN26NY0bN6Z79+50794dpVLJ06dP8fb25uHDh2zcuJElS5YQHR2NiYkJixcvpmTJ5G/EZNIPWVjSmKlTp3L+/Hkm5G1H2VhR8TYxp+j8rp+1qxw+fJhJo5axzaou9v6ikT3I1JQC20dhXjxnksdERUXx22+/MX36dBwxYrNrZ9yi4wPk9pWpSpnZHahdIGkREAQBv2VH+DB9J8QtPRpreNogD4tionmw4AEKK1cwFVO7KML92dw1J5XzyKKSlfEJ0eIVkvnR2e3bt5eWwn755Zdk2y1fvhw3NzdJdFLCtWvXOHfuHKamppw/f54tW7Zw5swZNBoNZ8+epWPHjty7d+9rhyDzBcjCkob8888/TJ48mX65m9E1QvwxaRUKPH7sSufClp889s9Nmzmw6AKrI5wwiBFtGwFmZlgs74lp0aRjd27dukW3bt14dvs+PSxLM9CkOEaxEYlahZItDZrSdVpD8lsnbdPRRUTzfuw6gnZdkLbd0YQyRj+ACFUH1A75STi/0XreYWv3PFTOk/L8YTKZg41J+kbfp/T8CZfCTp06laZ9aNOmDaaxNzz79u3j1q1bVKgQ7zrv5+dHeHh4tvEg/ZaQhSWNeP/+PZ06daKqYxVGRMWLyMUubenZ+9NrzHMXrSR021smv4136X2b04XiGwcTqYn6qH1MTAwzZsxg1ZS5dDAsSBunTpgIgFYUlXdmVvzbrxOj+7sla1SP8Q7kbe/FRFx/Jm1bpgliR9PB6NkXJKEUGQU+poH1O7nrPagAACEpSURBVEaPrYmDg0MK3g2ZzCa1y1QZwedqsKS2/X/rs3Tr1k0qey2TuciuPGmATqejS5cuhIfpMdXITdp+sXF9uk+pl6xdIyYmhsHDfsNpvQftPF5J29/UrEC1Ez+RI/fHJV3v3btHpzK1MJ73N4etm9HDuLAoKrH8XaIcEZt+Yuyw0kmKii48Cv81x3lZ/xdJVCIVAj+7lWRP38Xo2ce7MRc0DWdjazPuT63OvJEdZVGR+Sry5Mkj1WABEtVgMTMzIzw8nKio+BupfPnycfnyZQBevHjxyXROTZs25c8//+TVK/F3pNPpskRNmu8VecaSBsyYMYO/T51lXtlx2L56CcDTfPlov6h1sh5T3t7edOk2hqFvbcnnJ/7QYpRKIka0pdbwulLa/DgiIiKY8ctkzNdcZLJBXkhg4ohU6fF3oVKEta1F33b5sU1imUIXHkXA+hP4rTiayI34vb4evzTvw3O7+OW2Ug56jK5uTuWc+t98Og6ZjMPR0THZGixWVlZ07dqVEiVKYGJiwtWrVxkzZgzt2rWjePHiFC1aNNEy13/54YcfmDVrFi1atCAmJoaoqCgaNWpE2bJlM2JoMv8hW9Vj+RLSu27Bhg0b6Nl/CH2qTGT4gwcAhGk0OB2bjG0+2ySP2blzJ8tm7uN/wVbYh4gX+UAjIxxXDsG5RnwkfFztjoCAACa27cVAfxdcVfH2DX9DY/aXqIh3g6qMaOJEUTs1PqFabIxFYdEJAkqFgqiX3rztvZioh28S9eNczrwsdm+Dv7F4TjtDLb/UtaZhQYMMFZT0qlGSFZHrsXx7yPVYPkaesXwFixcvZsTEqQyq/hsD70j1dTGa0CFJUfH29mbgoKE4vHJmsZc+KkEUFX9TMwruGYtlocT16QVBYMvmzTycvokpxqXQj40/CdXXsKpKfV5ULsegSmruHV1Lp5GR6Jdqi6utGWVdTRlV1QSFTiD46HW8xq5HFyjabwTgaNgLtlZuiOcPnQFQC1GMrGZJz/JmcsS8jIzMVyMLyxeyf/9+Rv62kOHVfqXPzcvS9qgeDSjW8+PqlsePH2dQ//H8bF6H8t5e0va3uXNRduNgzFwT21Pev3/P2AFDqXA2gDHG8dmLH9s6srBxB7o2z81iNyPGjPqRR16h2FT5EcsnO/E+dYuTjeaiPX2HNru2o/0Qv+yln9eea41yMmLsOtRX3+PwQ2eKW4SzrrMr1sZZo4aHjIxM9kcWli/g8ePHdB04mpEVxtDzxkVpu3JgM4pPaJ6orSAIzJ49m/3rLvGHqiLWsaKiQ8H7dnWpMaM1SnX8x/D06VNWTZpNwMHL9DQpjp0mfhaztnAxrpdxZmLXPLhYqnnzyoNbt27RaMKfvPXWMcq1CFVWzqXtm+mcqtGF4goT8iMKi0ldN+wX9sVJo6L+yf107NgR67Km1C3oIEfOy8jIpCmysKSS4OBgmrdqS+Myw+l5LV5UDH5sRa4RjRO1DQkJoUev/ti8cmJlsCUqQVyOCjQ2wWROb2o0iY8Kvrbvby7OWIvT81B6qq3AuJS4bgV80BjQ1yKAF7eWYmo/nh67/KmTI5pfg25z8/xl6v+2irNVWtF3/T7qqpx5+eRvzMo143auvJQq74h5myoY1yqBQqlEBRw5ciTd3ycZGZnvl2/bUprGeHt706h5G4o6dWLMzevSduMxbT4SlZMnT1KvVheaPsvFsNfeqATRw+t1wfwU+GcyRWJFxeP6PbaX7orJoE3Uea2hiNoq0Xmu5MzP+jGj6N69BvduXMIpxoumeybz4EkAd9adp4rSjnv3j2ERHsol14I0MsrNFaUfNYsbc7lZI5xWDMK4dkmpuJiMjIxMeiPPWFLIhQsXaNtrON2d29Hl7l1pu65ZFZyGxLtNent7M3rMBEzuGbPUxwbDmPga9F5t61JrVluUeiqC/QM52PMXCv7rS0lF4o/hka0T5/IW5XmpEtSubM6yHxwI921D6MJDuJ88xSn/e+Qs2YYThUpR5+0VFobcobteEPtatGRrm15QsyKOxTVoXykJjtRhKmcelpGRyUDkK04KWLlyJS16TGSKRRO6PIgXlYi65Sk4rxsKhQKtVsuSRUsYXHck7a6aM9QzCMOYaED0+mLRYKrP6wBKBXv+t4ALxftS+kogxrGiEqQxZE2lunTuNoo/Rw3HfVpztowpRJN8EPbPbd43mkLA+n/ooHTAUxtK6XeXuFKxIg1Pb8IovxM6q1u8NTBjxrUPFKvagON+TlTPrZFFRSbTKFCgAIUKFcLNzQ03Nzd69+6dbNu47MaZiUKhICAgINE2V1fXZPOXnTp1Cjc3t3TvV3ZEnrF8hg0bNrBgxTn+VJcl5+vnAOgUCvRGtaHE0PooFArOnz7H5vEbae5vQJ1gNeAntkPB2/pVqTa3PWozQ07vOsjz8RuoHGZMXISjDgWHi5VlXcW6VC5pxYryJpR20gcgyjuAiJ+3E3r8jthWEDA2NqZSuQq8dn6LhY0xJ8MNmTd3Dhs2bCDywz3W5yiBabGBlDAzpFtp4wx/v2RkEpLV0ua/fPmS7t27f3XespjYUhgySSMLyyc4cOAA0xf9xZpgS3KEidmGw40McVo2AJvaxfH0eM0fAxZT+WkYQ8OigPh0FG+cnHCe1oWC+Q3ZMGE6umMPqRhpQmUh/mL/0M6ZpTWaULxWfvZWNEXP/xU3Nm7j6qkblDFxwvi+N4qgMKm9cbWi2M/szuSIANq1aU33Os9Z9pceoxpVwrRVKRZa3KV69epEawW5xrxMliO7p813dXWlXbt2nDx5kvz589OnTx9iYmLo1q0b165dw8DAgDVr1uDm5oaXlxcdOnQgKCiIiIgIatasyaJFi1Aqlaxbt44///wTGxsb7t69i0ajYfv27eTJkyfN+prZyMKSBIIgsGr1GnauvcHyUBtyhAUAEOhkT4ktw9HktmXt/1bguvU2rcLCEh37xCUnNiOaU7qOK/OHTqDU3+/5QWUOxCfMCzAw4o8qdbFo9wOrK5thGO3PkuYdaPBUj8IqAwoD8FJqr7QwxvbXDpi1qoxCoeD+7rPcuXOHhX/sRKgyjC03g+ngZkJ1t+roBFlUZOBlw0lovQPT7fwqW3NcDyefBj+Oby1tvq+vL5cvX0ahUHDq1Cnu3bvHggULWL16NXv27KF9+/Y8ePAACwsLDhw4gImJCVqtlmbNmrF9+3bat28PwJUrV7h58ya5c+dm3LhxzJw5kxUrVqRpXzMTWVj+w5s3b+jffxy1fPOw+HUYIApHoKMdpY9M4OErD45WnUMjjw+JDFS3cuXG/sfm1G9ahN07d7G9xDC6C3nRV8WXAA40MOJ44VJou9fn5zr22BnBjsWrCZy7j47Yw39iFKNUelwoWIzuG7qgsRe9xa5fv86mTZuYuu4I7yzK07W0EeWcNdIxym88fYZMytB6BxLj5f/5hulMVkib//79e2nWExUVxatXryTbSIkSJdiwYcMnXydhmpbu3bsneu7q6krt2rWJjo6mbdu29OvXj9evX2Ntbc3YsWM5d+4cgiDg7e1NsWLFJGGpVKkSuXPnlh4vXrz4q9+PrIQsLAnYtnUbu2cd5adgcyzDX0vbfQvkptjq3kz5aSmVTnrRJDj+TvCBnQOKUa1o1LQQm1auZXuh4bjHONNXyCu1eWlly45KNSnZsTz9yppjoRHYOX8lvosPUkNrg57CXmp7ybUgV3IV4JGDC/kr5CSvZTTkiBen0qVLs2vXLgRB+OZzMMl8OSpb8883yuDzZ1ba/IQG+E/ZWGxsbPD19cXCwkLa9uHDB2xt49MzJXzNpFAoFCgUCubNm4e3tzeXL1/GwMCAkSNHJup/wrxfKpXqm7PZyMKCaIgbP/Z/5DwZxWRviJulhOlrEIa2wMM2hGNdFtLu1QcpHiVKqeKCeyXq/FSPg1MW88cv76gWoaa6Lpd0Xh0KdpapSlSfpsysZYm5vsDuWb8TtOI4VbRWKLEjrpJWkIEhi2o040z+4lTNpc+S2ubkz6Hi3bt3SS5tyaIi8ylSskyV0SRMm29jY5Ns2nx9fdF5JS5tfqtWraS0+a1bt07y3E2bNqVTp07079+fnDlzotPpuH79eqqyG9erV48VK1Ywa9YsQHTcyZMnzyfLRbx8+ZKTJ09StWpVdu7ciZ2dHc7Ozvj7+2Nvb4+BgQFeXl7s2LGDVq1apbgv2Z3vXlh8fX3p1XkUvd7koIC/r7T9WfHCvCppiM+247h/CKd0eLwt5XkOa14VMSPm4S3e1PmXepFRiG9lfKLo1xbWXCpeGDPdW1SzJ7Prp2hyhMVQMiwGyCG1C9IYsrdkJfaVrEQOB1NW1zTHPZ/mo7T5MjLZnayeNn/BggUMHz6cEiVKoFQqsbe3Z8eOHZ88pmjRoqxfv56hQ4ei0WjYsmULCoWCYcOG0bp1a4oWLYqjoyPu7u4p7se3wHefNr9Dpy40f+CCm7cnAGFqfYLHdmbH2fUMPK1NZEeJUSrZm9OSZi98UCs+jg8JMjDkpr0z1V4++Wy//IxM2FWqKkeKl6dwblMaFTKgk5uxlMEYvp908t/LOEFOm/8tIqfN/5jvfsYyf+5smjcaxFJ9W8KMDHH5YxilyuXCsIwBN66sokxYNDFKJY8L5KXE5PZEHPmDx4/9KKovZiPWKpRcy5mXDzXLULpzZRa7l6IaPyT7eu/MrDhStiLKltWpWciUn101mBt82xdTGRmZ74vvXljs7e1Ztvon3twOo1ajEhhbi14m5cqV43K1A1xS56fuuMa0yCXWsa+nq8e1sx5EWuTnfclSuLUoT7viVtJMo2ydH3h6OYZoK1Oe2JYgxtoMjYsN1gWcsc5nR96cpsy01vvm7+JkZGS+X757YQEoVaoUSQUHD149+aNtVatWpeqpqsmea+0ff6Rl12RkZGSyHfIajIzMN8Q3bjL97siun6c8Y5GR+QaIMxzHufJm9lJrnEFbq9Vmel/Sm/QaqyAI+Pj4oFAoUKvVaXbejEAWFhmZbwCVSoWzszNv3rzh5cuXmd0dBEFAq9WiUqm+C2FJr7EqFAqcnZ1RqbJX6XBZWGRkvhFMTEzInz8/0dHRmd0VdDod3t7e2Nrafhcu5Ok1VrVane1EBbKJsCxdupTZs2fj5eVFyZIlWbx4MeXLl8/sbsnIZDlUKlWWuBDpdDr09PQwMDD4LoTlexlrSsny78K2bdsYOXIkv/zyC9evX6dkyZLUq1cPb2/vzO6ajIyMjEwSZHlhmTdvHn369KFHjx4UKVKE5cuXY2RkxNq1azO7azIyMjIySZCll8KioqK4du0a48ePl7YplUrc3d25ePFiksdERkYSGRkpPQ8MFDMRBwQEZLvcWzqdjqCgIIyMjL7pKfb3Mk74fsb6vYwTMmesQUFBQNZ1R87SwvLhwwe0Wi12dnaJttvZ2fHw4cMkj5k+fTqTJk36aHuuXLmSaC0jIyOTfQkODsbcPH1LJHwJWVpYvoTx48czcuRI6blOp8PPz48cOXJkO7fHoKAgXFxceP36dZZMNJdWfC/jhO9nrN/LOCFzxioIAsHBwTg6OmbI66WWLC0s1tbWqFQq3r9/n2j7+/fvsbe3T/IYjUaDRqNJtC1h4Z7siJmZ2Tf/44TvZ5zw/Yz1exknZPxYs+JMJY4svfipr69PmTJlOHHihLRNp9Nx4sQJKlWqlIk9k5GRkZFJjiw9YwEYOXIk3bp1o2zZspQvX54FCxYQGhpKjx49MrtrMjIyMjJJkOWFpV27dvj4+PC///0PLy8v3NzcOHr06EcG/W8RjUbDL7/88tHS3rfG9zJO+H7G+r2ME76vsaaUb76CpIyMjIxMxpKlbSwyMjIyMtkPWVhkZGRkZNIUWVhkZGRkZNIUWVhkZGRkZNIUWVgymTNnztCkSRMcHR1RKBTs3bs30X5BEPjf//6Hg4MDhoaGuLu78+TJk/+3d6dBTV1tHMD/QQhbADcELBhQWUQhLiik1loVpNahuMyUsS7gOiiMwlCqLVq0LuBSFaxVa1X0g6AyUtG6ViDVKqgQZDVqCoI2liogiyDb835wuNNr8MViLBLPbyYz3HPOPfd5yAwPuSfJ6ZxgX0NUVBRGjhwJExMT9OnTB1OmTIFCoeCNqa+vR1BQEHr16gWRSITp06erfTi2K9i1axdcXV25D8xJpVKcOXOG69eWPF8UHR0NgUCAkJAQrk1bcl29ejUEAgHv4eTkxPVrS56awgpLJ6utrYVEIsHOnTvb7N+0aRNiY2Oxe/duZGRkwNjYGN7e3qivr/+PI309MpkMQUFBSE9Px4ULF9DY2IiJEyeitraWGxMaGoqTJ0/i2LFjkMlk+PPPPzFt2rROjLpjrK2tER0djczMTNy4cQPjx4+Hr68v8vPzAWhPnv90/fp17NmzB66urrx2bcp18ODBUKlU3OPy5ctcnzblqRHEvDUAUFJSEnfc0tJClpaWtHnzZq6tsrKS9PX1KT4+vhMi1JyysjICQDKZjIie56Wnp0fHjh3jxhQWFhIAunr1ameFqTE9evSgn376SSvzrK6uJnt7e7pw4QKNHTuWli1bRkTa9ZxGRkaSRCJps0+b8tQU9orlLVZUVISHDx/C09OTazMzM4O7u/tLtw3oKlq3M+jZsycAIDMzE42NjbxcnZyc0K9fvy6da3NzMxISElBbWwupVKqVeQYFBWHy5Mm8nADte07v3LmDvn37on///pg5cyZKSkoAaF+emvDWf/L+Xfbw4UMAaHPbgNa+rqilpQUhISEYPXo0hgwZAuB5rkKhUO0LQ7tqrrm5uZBKpaivr4dIJEJSUhKcnZ2RnZ2tVXkmJCQgKysL169fV+vTpufU3d0dcXFxcHR0hEqlwpo1azBmzBjk5eVpVZ6awgoL858LCgpCXl4e7x61tnF0dER2djaePHmCxMRE+Pv7QyaTdXZYGlVaWoply5bhwoULMDAw6Oxw3qhJkyZxP7u6usLd3R1isRhHjx6FoaFhJ0b2dmK3wt5irVsD/JttA952wcHBOHXqFFJTU2Ftbc21W1paoqGhAZWVlbzxXTVXoVCIgQMHYsSIEYiKioJEIkFMTIxW5ZmZmYmysjIMHz4curq60NXVhUwmQ2xsLHR1dWFhYaE1ub6oe/fucHBwwN27d7XqOdUUVljeYnZ2drC0tORtG1BVVYWMjIwut20AESE4OBhJSUlISUmBnZ0dr3/EiBHQ09Pj5apQKFBSUtLlcm1LS0sLnj17plV5TpgwAbm5ucjOzuYebm5umDlzJveztuT6opqaGiiVSlhZWWnVc6oxnf3ugXdddXU1yeVyksvlBIC2bt1Kcrmc7t27R0RE0dHR1L17dzpx4gTl5OSQr68v2dnZUV1dXSdH/u8sXryYzMzMKC0tjVQqFfd4+vQpNyYwMJD69etHKSkpdOPGDZJKpSSVSjsx6o5ZsWIFyWQyKioqopycHFqxYgUJBAI6f/48EWlPnm3557vCiLQn17CwMEpLS6OioiL6/fffydPTk3r37k1lZWVEpD15agorLJ0sNTWVAKg9/P39iej5W45XrVpFFhYWpK+vTxMmTCCFQtG5QXdAWzkCoAMHDnBj6urqaMmSJdSjRw8yMjKiqVOnkkql6rygO2jevHkkFotJKBSSubk5TZgwgSsqRNqTZ1teLCzakqufnx9ZWVmRUCik9957j/z8/Oju3btcv7bkqSnsa/MZhmEYjWJrLAzDMIxGscLCMAzDaBQrLAzDMIxGscLCMAzDaBQrLAzDMIxGscLCMAzDaBQrLAzDMIxGscLCvJU++ugj3k6Er+PHH3+EjY0NdHR0sH37do3M2dnS0tIgEAjUvp/qRRcvXsSgQYPQ3Nz83wQGoKCgANbW1rxN3Jh3CyssTIe87A9/XFyc2teHd6aqqioEBwdj+fLlePDgARYtWvTKRauoqAiff/45+vbtCwMDA1hbW8PX1xe3bt1684FryJdffomVK1eiW7duGpnPzs4Ov/766/8d4+zsDA8PD2zdulUj12S6HlZYmC6poaHhlcaVlJSgsbERkydPhpWVFYyMjF7pvMbGRnh5eeHJkyc4fvw4FAoFjhw5AhcXl3ZfJbwtLl++DKVSienTp2tkvpycHFRUVGDs2LHtjp07dy527dqFpqYmjVyb6VpYYWHeqICAAEyZMgVr1qyBubk5TE1NERgYyCsMtbW1mDNnDkQiEaysrPDdd9+pzWNra4u1a9dizpw5MDU1xaJFiwAAy5cvh4ODA4yMjNC/f3+sWrUKjY2NAJ6/enJxcQEA9O/fHwKBAAEBAZDJZIiJiYFAIIBAIEBxcbHa9fLz86FUKvHDDz/Aw8MDYrEYo0ePxrp16+Dh4QEAKC4uhkAgQEJCAt5//30YGBhgyJAhavuu5OXlYdKkSRCJRLCwsMDs2bPx6NEjrr+lpQVRUVGws7ODoaEhJBIJEhMTeXOcPn0aDg4OMDQ0xLhx49qM+UUJCQnw8vLi7ZWyevVqDB06FPv370e/fv0gEomwZMkSNDc3Y9OmTbC0tESfPn2wfv16tflOnDiBjz/+GHp6erh37x58fHzQo0cPGBsbY/DgwTh9+jQ31svLC+Xl5Vq3Bw3zalhhYd64ixcvorCwEGlpaYiPj8fx48exZs0arj88PBwymQwnTpzA+fPnkZaWhqysLLV5tmzZAolEArlcjlWrVgEATExMEBcXh4KCAsTExGDv3r3Ytm0bAMDPz4+7bXPt2jWoVCrExMRAKpVi4cKFUKlUUKlUsLGxUbuWubk5dHR0kJiY2O76RHh4OMLCwiCXyyGVSuHj44PHjx8DACorKzF+/HgMGzYMN27cwNmzZ/HXX3/hs88+486PiorCoUOHsHv3buTn5yM0NBSzZs3i/iiXlpZi2rRp8PHxQXZ2NhYsWIAVK1a0+3u/dOkS3Nzc1NqVSiXOnDmDs2fPIj4+Hvv27cPkyZNx//59yGQybNy4EStXrkRGRgbvvOTkZPj6+gJ4vlnbs2fP8NtvvyE3NxcbN26ESCTixgqFQgwdOhSXLl1qN05GC3X2t2AyXdOL32Lb6sCBA2RmZsYd+/v7U8+ePam2tpZr27VrF4lEImpubqbq6moSCoV09OhRrv/x48dkaGjIm18sFtOUKVPajWvz5s00YsQI7rh1O4KioqJ2Y3/R999/T0ZGRmRiYkLjxo2jb7/9lpRKJddfVFREACg6Oppra2xsJGtra9q4cSMREa1du5YmTpzIm7e0tJQAkEKhoPr6ejIyMqIrV67wxsyfP59mzJhBRERfffUVOTs78/qXL19OAKiiouKl8ZuZmdGhQ4d4bZGRkWRkZERVVVVcm7e3N9na2lJzczPX5ujoSFFRUdzx/fv3SSgUctdzcXGh1atXv/TaRERTp06lgICA/zuG0U5sa2LmjZNIJLy1DalUipqaGpSWlqKyshINDQ1wd3fn+nv27AlHR0e1edr67/vIkSOIjY2FUqlETU0NmpqaYGpqqpG4g4KCMGfOHKSlpSE9PR3Hjh3Dhg0bkJycDC8vL14+rXR1deHm5obCwkIAwM2bN5Gamsr7b76VUqlEY2Mjnj59ypsPeL6GNGzYMABAYWEh7/fz4jVfpq6urs0tg21tbWFiYsIdW1hYoFu3btDR0eG1lZWVccfJycn44IMPuDdmLF26FIsXL8b58+fh6emJ6dOnw9XVlXcdQ0NDPH36tN04Ge3DboUxHWJqaoonT56otVdWVsLMzOyNXNPY2Jh3fPXqVcycOROffPIJTp06BblcjoiIiFde2H8VJiYm8PHxwfr163Hz5k2MGTMG69ate+Xza2pquFtY/3zcuXMHH374IWpqagAAv/zyC6+/oKBAbZ3l3+rduzcqKirU2vX09HjHAoGgzbaWlhbuODk5GZ9++il3vGDBAvzxxx+YPXs2cnNz4ebmhh07dvDmKC8vh7m5+WvlwHRNrLAwHeLo6NjmOkhWVhYcHBx4bTdv3kRdXR13nJ6eDpFIBBsbGwwYMAB6enq8+/kVFRW4fft2uzFcuXIFYrEYERERcHNzg729Pe7du9fueUKhsEOf6xAIBHByclL7fEZ6ejr3c1NTEzIzMzFo0CAAwPDhw5Gfnw9bW1sMHDiQ9zA2NoazszP09fVRUlKi1t+69jNo0CBcu3btpdd8mWHDhqGgoOBf5/mimpoapKamcusrrWxsbBAYGIjjx48jLCwMe/fu5fXn5eVxr7qYdwsrLEyHLF68GLdv38bSpUuRk5MDhUKBrVu3Ij4+HmFhYbyxDQ0NmD9/PgoKCnD69GlERkYiODgYOjo6EIlEmD9/PsLDw5GSkoK8vDwEBATwbsu8jL29PUpKSpCQkAClUonY2FgkJSW1e56trS0yMjJQXFyMR48e8f4zb5WdnQ1fX18kJiaioKAAd+/exb59+7B//361P7A7d+5EUlISbt26haCgIFRUVGDevHkAnt9OKy8vx4wZM3D9+nUolUqcO3cOc+fORXNzM0xMTPDFF18gNDQUBw8ehFKpRFZWFnbs2IGDBw8CAAIDA3Hnzh2Eh4dDoVDg8OHDiIuLazdPb29vXL58ud1x7Tl79iwcHBxga2vLtYWEhODcuXMoKipCVlYWUlNTuWIKPH/H3IMHD+Dp6fna12e6oM5e5GG6rmvXrpGXlxeZm5uTmZkZubu7U1JSEm+Mv78/+fr60jfffEO9evUikUhECxcupPr6em5MdXU1zZo1i4yMjMjCwoI2bdqktsAuFotp27ZtajGEh4dz8/r5+dG2bdt4bx5oa/FeoVCQh4cHGRoaqvW1+vvvv2np0qU0ZMgQEolEZGJiQi4uLrRlyxZukbt18f7w4cM0atQoEgqF5OzsTCkpKby5bt++TVOnTqXu3buToaEhOTk5UUhICLW0tBDR8+2nt2/fTo6OjqSnp0fm5ubk7e1NMpmMm+PkyZM0cOBA0tfXpzFjxtD+/fvbXbx//PgxGRgY0K1bt7i2yMhIkkgkvHGtz9E//fP3P2vWLIqIiOD1BwcH04ABA0hfX5/Mzc1p9uzZ9OjRI65/w4YN5O3t/dLYGO3GtiZm3qiAgABUVlbi559/7uxQNK64uBh2dnaQy+UYOnRoZ4fTpvDwcFRVVWHPnj0dOr+pqQkWFhY4c+YMRo0a9UrnNDQ0wN7eHocPH8bo0aM7dF2ma2O3whhGi0VEREAsFrd5u+9VlJeXIzQ0FCNHjnzlc0pKSvD111+zovIOY69YmDeKvWJhmHcPKywMwzCMRrFbYQzDMIxGscLCMAzDaBQrLAzDMIxGscLCMAzDaBQrLAzDMIxGscLCMAzDaBQrLAzDMIxGscLCMAzDaBQrLAzDMIxG/Q/6JmAuUD46qQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##################### w histogram plots ######################\n",
    "\n",
    "# Extract data from xarray\n",
    "c_w_count = c_hist_ds['w_count'].values\n",
    "bin_edges = c_hist_ds['bin_edge'].values\n",
    "levels = c_hist_ds['level'].values\n",
    "\n",
    "f_w_count = f_hist_ds['w_count'].values\n",
    "fu_w_count = fu_hist_ds['w_count'].values\n",
    "\n",
    "# Create the meshgrid (without error this time!)\n",
    "X, Y = np.meshgrid(bin_edges, levels)\n",
    "\n",
    "# Define frequency intervals for the contours\n",
    "#contour_levels = np.arange(1, 1001, 200)\n",
    "contour_levels = [10,100,500,2000]\n",
    "\n",
    "\n",
    "# Plot the filled contour plot\n",
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "\n",
    "# Add a colorbar\n",
    "#cbar = plt.colorbar(contour, ax=ax)\n",
    "#cbar.set_label('Frequency of Updrafts')\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",
    "# Add labels and title\n",
    "ax.set_xlabel('Updraft Speed (m/s)')\n",
    "ax.set_ylabel('Height (km)')\n",
    "ax.set_title(f'Contour Plot of Updraft Frequency by Height\\n {month}')\n",
    "ax.set_xlim(2,55)\n",
    "ax.set_ylim(0,15)\n",
    "ax.grid(True, alpha=0.35)\n",
    "\n",
    "# add contour lines for current\n",
    "contour_lines = ax.contour(\n",
    "    X, Y, c_w_count, \n",
    "    levels=contour_levels, \n",
    "    colors='black', \n",
    "    linewidths=2\n",
    ")\n",
    "ax.clabel(contour_lines, inline=True, fontsize=8)\n",
    "\n",
    "# add contour lines for future\n",
    "contour_lines = ax.contour(\n",
    "    X, Y, f_w_count, \n",
    "    levels=contour_levels, \n",
    "    colors='#1E88E5', \n",
    "    linewidths=2\n",
    ")\n",
    "ax.clabel(contour_lines, inline=True, fontsize=8)\n",
    "\n",
    "# add contour lines for future\n",
    "contour_lines = ax.contour(\n",
    "    X, Y, fu_w_count, \n",
    "    levels=contour_levels, \n",
    "    colors='#D81B60', \n",
    "    linewidths=2,\n",
    ")\n",
    "ax.clabel(contour_lines, inline=True, fontsize=8)\n",
    "\n",
    "ax.plot(-1,-1,label='Current', color='black', linewidth=2)\n",
    "ax.plot(-1,-1,label='Future', color='#1E88E5', linewidth=2)\n",
    "ax.plot(-1,-1,label='Future+Urban', color='#D81B60', linewidth=2)\n",
    "ax.legend(loc='lower right', fontsize=8)\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'w_90' (level: 59)> Size: 472B\n",
      "[59 values with dtype=float64]\n",
      "Coordinates:\n",
      "  * level    (level) float64 472B 0.5 0.75 1.0 1.25 ... 14.25 14.5 14.75 15.0\n"
     ]
    }
   ],
   "source": [
    "c_thresholds = xr.open_dataset(f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/current/{w_direction}_percentiles_month{monlist[0]}.nc')\n",
    "f_thresholds = xr.open_dataset(f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future/{w_direction}_percentiles_month{monlist[0]}.nc')\n",
    "fu_thresholds = xr.open_dataset(f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future_urban/{w_direction}_percentiles_month{monlist[0]}.nc')\n",
    "\n",
    "print(c_thresholds['w_90'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "################ Threshold profile ##################\n",
    "fig, ax1 = plt.subplots(figsize=(3, 3))\n",
    "\n",
    "#ax2 = ax1.twiny()\n",
    "\n",
    "# w>0\n",
    "ax1.plot(c_thresholds[f'w_{percentile}'], c_thresholds.level, label=f\"Current\", color=\"black\", linewidth=2.5)\n",
    "#ax1.plot(f_thresholds[f'w_{percentile}'], f_thresholds.level, label=f\"Future\", color=\"#1E88E5\", linewidth=2.5)\n",
    "#ax1.plot(fu_thresholds[f'w_{percentile}'], fu_thresholds.level, label=f\"Future-Urban\", color=\"#D81B60\", linewidth=2.5)\n",
    "\n",
    "#rel_change_acc = calculate_relative_change(f_thresholds[f'w_{percentile}'], c_thresholds[f'w_{percentile}'])\n",
    "#rel_change_urban = calculate_relative_change_urbanization(f_thresholds[f'w_{percentile}'], c_thresholds[f'w_{percentile}'], fu_thresholds[f'w_{percentile}'])\n",
    "\n",
    "\n",
    "#ax2.plot(rel_change_acc, c_99_freq.level, label=f\"ACC Impact\", color=\"black\", linestyle='--', linewidth=2.5)\n",
    "#ax2.plot(re_change_urban, c_99_freq.level, label=f\"Urbanization Impact\", color=\"black\", linestyle=':', linewidth=2.5)\n",
    "#ax2.plot(np.zeros(17),np.arange(0,17,1), color=\"gray\", alpha=0.6)\n",
    "\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",
    "if w_direction == 'updraft':\n",
    "    title='Updraft'\n",
    "else:\n",
    "    title='Downdraft'\n",
    "\n",
    "# Labels and legend\n",
    "#fig.suptitle(f\"{percentile}th Percentile {title} Thresholds For Each Height by Simulation ({month})\")\n",
    "ax1.set_xlabel(f\"{title} Speed (m s$^{{-1}}$)\")\n",
    "ax1.set_ylabel(\"Height (km)\")\n",
    "#ax1.legend(loc=\"center\", bbox_to_anchor=(0.14, 0.6), fontsize=10)\n",
    "#ax1.legend(loc=\"upper left\", fontsize=10)\n",
    "#ax1.legend(loc=\"center\", bbox_to_anchor=(0.86, 0.22), fontsize=10, title='Simulations')\n",
    "ax1.set_ylim(0,15)\n",
    "ax1.set_xlim(2,4)\n",
    "#ax1.invert_xaxis()\n",
    "ax1.grid(alpha=0.75,axis='both', linestyle=':')\n",
    "\n",
    "\n",
    "#ax2.legend(loc=\"center\", bbox_to_anchor=(0.185, 0.45),  fontsize=10, title='Relative Change')\n",
    "#ax2.legend(loc=\"center\", bbox_to_anchor=(0.815, 0.07),  fontsize=10, title='Relative Change')\n",
    "#ax2.set_xlim(-20,20)\n",
    "#ax2.set_xlabel('Relative Change (%)')\n",
    "#ax2.grid(alpha=0.75, linestyle=':')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "########################## Hydrometeor Profile #########################\n",
    "monlist=['06']\n",
    "day_type='allclouds'\n",
    "#day_type='full'\n",
    "\n",
    "#filename1 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/hydrometeor_data/current/q_vars_spatial_mean_month{monlist[0]}.nc'\n",
    "#filename2 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/hydrometeor_data/future/q_vars_spatial_mean_month{monlist[0]}.nc'\n",
    "#filename3 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/hydrometeor_data/future_urban/q_vars_spatial_mean_month{monlist[0]}.nc'\n",
    "\n",
    "filename1 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/hydrometeor_data/current/q_var_histogram_{day_type}_month{monlist[0]}.nc'\n",
    "filename2 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/hydrometeor_data/future/q_var_histogram_{day_type}_month{monlist[0]}.nc'\n",
    "filename3 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/hydrometeor_data/future_urban/q_var_histogram_{day_type}_month{monlist[0]}.nc'\n",
    "\n",
    "#filename1 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/hydrometeor_data/current/q_var_histogram_{day_type}_convcores_month{monlist[0]}.nc'\n",
    "#filename2 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/hydrometeor_data/future/q_var_histogram_{day_type}_convcores_month{monlist[0]}.nc'\n",
    "#filename3 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/hydrometeor_data/future_urban/q_var_histogram_{day_type}_convcores_month{monlist[0]}.nc'\n",
    "\n",
    "c_q = xr.open_dataset(filename1)\n",
    "f_q = xr.open_dataset(filename2)\n",
    "fu_q = xr.open_dataset(filename3)\n",
    "\n",
    "filename1 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/cloud_stuff/current/melting_level_hgt_month{monlist[0]}.nc'\n",
    "filename2 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/cloud_stuff/future/melting_level_hgt_month{monlist[0]}.nc'\n",
    "filename3 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/cloud_stuff/future_urban/melting_level_hgt_month{monlist[0]}.nc'\n",
    "\n",
    "c_ml = xr.open_dataset(filename1)\n",
    "f_ml = xr.open_dataset(filename2)\n",
    "fu_ml = xr.open_dataset(filename3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "################ Hydrometeor profile ##################\n",
    "import cmweather\n",
    "wind_cmap = cmweather.cm_colorblind.ChaseSpectral\n",
    "#wind_cmap = pyart.graph.cm.LangRainbow12\n",
    "\n",
    "p_clevs=[0.001, 0.01, 0.05, 0.1,0.3,0.5,1,2,4,8]\n",
    "#p_clevs=[0.0001, 0.0005, 0.001,0.003,0.005,0.01,0.02,0.04,0.08,0.12]\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_hydrometeor_histogram(ds: xr.Dataset, sim, q_var):\n",
    "    \"\"\"\n",
    "    Plots a normalized 2D histogram of hydrometeor frequency versus height and hydrometeor bins.\n",
    "\n",
    "    Parameters:\n",
    "    - ds: xarray.Dataset containing dimensions `level`, `bin_edge`, and variable `q_count`\n",
    "    - q_var: str of the hydrometeor variable you want e.g. QCLOUD, QRAIN, etc.\n",
    "    \"\"\"\n",
    "    level = ds['level'].values\n",
    "    bin_edge = ds['bin_edge'].values\n",
    "    if q_var == 'QTOTAL':\n",
    "        q_count = sum(c_q[var] for var in c_q.data_vars).values\n",
    "        print(q_count)\n",
    "    else:\n",
    "        q_count = ds[f'{q_var}_hist'].values\n",
    "\n",
    "    # Normalize across bins at each level (row-wise normalization)\n",
    "    w_count_sum = np.sum(q_count, axis=1, keepdims=True)\n",
    "    #w_count_sum = np.sum(w_count, keepdims=True)\n",
    "    #print(w_count_sum)\n",
    "    w_freq = (q_count / w_count_sum) * 100  # Convert to percentage\n",
    "\n",
    "    # Plot\n",
    "    fig, ax = plt.subplots(figsize=(4, 4))\n",
    "\n",
    "    #norm = plt.Normalize(vmin=0.01, vmax=np.nanmax(w_freq))\n",
    "\n",
    "    pcm = ax.pcolormesh(bin_edge, level, w_freq, cmap=p_cmap, norm=p_norm, shading=\"auto\")\n",
    "    #pcm = ax.contourf(bin_edge, level, w_freq, cmap=p_cmap, levels=p_clevs, norm=p_norm, shading=\"auto\", extend='both')\n",
    "    cbar = plt.colorbar(pcm, ax=ax, fraction=0.055, pad=0.01, aspect= 30, extend='both', label=\"Normalized Frequency (%)\")\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",
    "    ax.set_xlabel(f\"{q_var} (g kg$^{{-1}}$)\")\n",
    "    ax.set_ylabel(\"Height above ground (km)\")\n",
    "    ax.set_title(f\"{sim}\", loc='left')\n",
    "    ax.set_title(f\"({month})\", loc='right')\n",
    "    ax.grid(True, alpha=0.5, linestyle='--')\n",
    "    ax.set_xlim(0,5)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "#plot_hydrometeor_histogram(c_q, 'Current', q_var='QTOTAL')\n",
    "#plot_hydrometeor_histogram(f_q, 'Future', q_var='QTOTAL')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hydrometeor difference plots\n",
    "\n",
    "def plot_hyhdrometeor_difference_histogram(current: xr.Dataset, future: xr.Dataset, future_urban: xr.Dataset, sim, q_var):\n",
    "    \"\"\"\n",
    "    Plots a normalized 2D histogram of updraft frequency versus height and updraft speed bins.\n",
    "\n",
    "    Parameters:\n",
    "    - ds: xarray.Dataset containing dimensions `level`, `bin_edge`, and variable `w_count`\n",
    "    \"\"\"\n",
    "    level = current['level'].values\n",
    "    bin_edge = current['bin_edge'].values\n",
    "\n",
    "    if q_var == 'QTOTAL':\n",
    "        w_count1 = sum(current[var] for var in current.data_vars).values\n",
    "        w_count2 = sum(future[var] for var in future.data_vars).values\n",
    "        w_count3 = sum(future_urban[var] for var in future_urban.data_vars).values\n",
    "    else:\n",
    "        w_count1 = current[f'{q_var}_hist'].values\n",
    "        w_count2 = future[f'{q_var}_hist'].values\n",
    "        w_count3 = future_urban[f'{q_var}_hist'].values\n",
    "    \n",
    "\n",
    "    # Normalize across bins at each level (row-wise normalization)\n",
    "    w_count_sum1 = np.sum(w_count1, axis=1, keepdims=True)\n",
    "    #print(w_count_sum)\n",
    "    w_freq1 = (w_count1 / w_count_sum1) * 100  # Convert to percentage\n",
    "\n",
    "    w_count_sum2 = np.sum(w_count2, axis=1, keepdims=True)\n",
    "    #print(w_count_sum)\n",
    "    w_freq2 = (w_count2 / w_count_sum2) * 100  # Convert to percentage\n",
    "\n",
    "    w_count_sum3 = np.sum(w_count3, axis=1, keepdims=True)\n",
    "    #print(w_count_sum)\n",
    "    w_freq3 = (w_count3 / w_count_sum3) * 100  # Convert to percentage\n",
    "\n",
    "    if sim=='ACC':\n",
    "        freq_diff=w_freq2-w_freq1\n",
    "    elif sim=='Urbanization':\n",
    "        freq_diff = (w_freq3-w_freq1) - (w_freq2-w_freq1)\n",
    "\n",
    "    # Plot\n",
    "    fig, ax = plt.subplots(figsize=(4, 4))\n",
    "\n",
    "    #norm = plt.Normalize(vmin=0.01, vmax=np.nanmax(w_freq))\n",
    "\n",
    "    pcm = ax.pcolormesh(bin_edge, level, freq_diff, cmap=diff_cmap, norm=diff_norm, shading=\"auto\")\n",
    "    #pcm = ax.contourf(bin_edge, level, freq_diff, cmap=diff_cmap, levels=clevs3, norm=diff_norm, shading=\"auto\", extend='both')\n",
    "    cbar = plt.colorbar(pcm, ax=ax, fraction=0.055, pad=0.01, aspect= 30, extend='both', label=\"Normalized Frequency Difference (%)\")\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",
    "    ax.set_xlabel(f\"{q_var} (g kg$^{{-1}}$)\")\n",
    "    ax.set_ylabel(\"Height AGL (km)\")\n",
    "    ax.set_title(f\"{sim}\", loc='left')\n",
    "    ax.set_title(f\"({month})\", loc='right')\n",
    "    ax.grid(True, alpha=0.5, linestyle='--')\n",
    "    ax.set_xlim(0,5)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "#plot_hyhdrometeor_difference_histogram(c_q, f_q, fu_q, sim='ACC', q_var='QTOTAL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "################ Hydrometeor profile ##################\n",
    "\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",
    "def _compute_single_histogram_stats(ds: xr.Dataset, var_name: str, stat_type: str = 'iqr'):\n",
    "    \"\"\"\n",
    "    Helper function to compute mean and uncertainty measure for a single histogram variable.\n",
    "    \n",
    "    Parameters:\n",
    "    - ds: xarray.Dataset with histogram data\n",
    "    - var_name: variable name (e.g., 'QRAIN')\n",
    "    - stat_type: 'iqr', 'std', or 'se' (standard error)\n",
    "    \n",
    "    Returns:\n",
    "    - mean: weighted mean\n",
    "    - lower: lower bound (Q1 for iqr, mean-std for std, mean-se for se)\n",
    "    - upper: upper bound (Q3 for iqr, mean+std for std, mean+se for se)\n",
    "    \"\"\"\n",
    "    counts = ds[f'{var_name}_hist']  # (level, bin_edge)\n",
    "    bin_edges = ds['bin_edge'].values\n",
    "\n",
    "    # Estimate bin centers\n",
    "    bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])\n",
    "    bin_centers_da = xr.DataArray(\n",
    "        bin_centers,\n",
    "        dims=[\"bin_edge\"],\n",
    "        coords={\"bin_edge\": ds['bin_edge'].isel(bin_edge=slice(0, -1)).values}\n",
    "    )\n",
    "\n",
    "    # Truncate counts to match bin centers\n",
    "    counts = counts.isel(bin_edge=slice(0, -1))\n",
    "\n",
    "    # Weighted mean per level\n",
    "    numerator = (counts * bin_centers_da).sum(dim='bin_edge')\n",
    "    denominator = counts.sum(dim='bin_edge')  # Sample size per level (sum across bins)\n",
    "    mean = numerator / denominator\n",
    "\n",
    "    if stat_type == 'iqr':\n",
    "        # Compute Q1 (25th percentile) and Q3 (75th percentile) from histogram\n",
    "        cdf = counts.cumsum(dim='bin_edge') / denominator\n",
    "        \n",
    "        # Find Q1 (25th percentile)\n",
    "        q1_indices = (cdf >= 0.25).argmax(dim='bin_edge')\n",
    "        lower = bin_centers_da.isel(bin_edge=q1_indices)\n",
    "        \n",
    "        # Find Q3 (75th percentile)\n",
    "        q3_indices = (cdf >= 0.75).argmax(dim='bin_edge')\n",
    "        upper = bin_centers_da.isel(bin_edge=q3_indices)\n",
    "        \n",
    "    elif stat_type == 'std':\n",
    "        # Compute standard deviation\n",
    "        variance = ((counts * (bin_centers_da - mean)**2).sum(dim='bin_edge')) / denominator\n",
    "        std = np.sqrt(variance)\n",
    "        lower = mean - std\n",
    "        upper = mean + std\n",
    "        \n",
    "    elif stat_type == 'se':\n",
    "        # Compute standard error\n",
    "        variance = ((counts * (bin_centers_da - mean)**2).sum(dim='bin_edge')) / denominator\n",
    "        std = np.sqrt(variance)\n",
    "        n_per_level = denominator\n",
    "        se = std / np.sqrt(n_per_level)\n",
    "        lower = mean - se\n",
    "        upper = mean + se\n",
    "    else:\n",
    "        raise ValueError(f\"stat_type must be 'iqr', 'std', or 'se', got '{stat_type}'\")\n",
    "\n",
    "    return mean, lower, upper\n",
    "\n",
    "def compute_histogram_stats(ds: xr.Dataset, var_name: str, stat_type: str = 'iqr'):\n",
    "    \"\"\"\n",
    "    Compute mean and uncertainty bounds from a histogram for each vertical level.\n",
    "\n",
    "    Parameters:\n",
    "    - ds: xarray.Dataset with 'level' and 'bin_edge' dimensions\n",
    "    - var_name: name of the histogram variable (e.g., 'QRAIN_hist')\n",
    "    - stat_type: 'iqr', 'std', or 'se' for uncertainty measure\n",
    "\n",
    "    Returns:\n",
    "    - mean: xr.DataArray of mean values at each level\n",
    "    - lower: xr.DataArray of lower bound values at each level\n",
    "    - upper: xr.DataArray of upper bound values at each level\n",
    "    \"\"\"\n",
    "    # For additive variables, compute mean of each component first, then sum\n",
    "    if var_name == 'QTOTAL':\n",
    "        component_vars = ['QCLOUD', 'QRAIN', 'QICE', 'QSNOW', 'QGRAUP', 'QHAIL']\n",
    "        means = []\n",
    "        lowers = []\n",
    "        uppers = []\n",
    "        for var in component_vars:\n",
    "            if f'{var}_hist' in ds:\n",
    "                comp_mean, comp_lower, comp_upper = _compute_single_histogram_stats(ds, var, stat_type)\n",
    "                # Replace NaN with 0 for levels with no data\n",
    "                comp_mean = comp_mean.fillna(0)\n",
    "                comp_lower = comp_lower.fillna(0)\n",
    "                comp_upper = comp_upper.fillna(0)\n",
    "                means.append(comp_mean)\n",
    "                lowers.append(comp_lower)\n",
    "                uppers.append(comp_upper)\n",
    "        mean = sum(means)\n",
    "        lower = sum(lowers)\n",
    "        upper = sum(uppers)\n",
    "        return mean, lower, upper\n",
    "    \n",
    "    elif var_name == 'QICE':\n",
    "        #ice_vars = ['QICE', 'QSNOW', 'QGRAUP', 'QHAIL']\n",
    "        ice_vars = ['QICE']\n",
    "        means = []\n",
    "        lowers = []\n",
    "        uppers = []\n",
    "        for var in ice_vars:\n",
    "            if f'{var}_hist' in ds:\n",
    "                comp_mean, comp_lower, comp_upper = _compute_single_histogram_stats(ds, var, stat_type)\n",
    "                means.append(comp_mean)\n",
    "                lowers.append(comp_lower)\n",
    "                uppers.append(comp_upper)\n",
    "        mean = sum(means)\n",
    "        lower = sum(lowers)\n",
    "        upper = sum(uppers)\n",
    "        return mean, lower, upper\n",
    "    \n",
    "    elif var_name == 'QGRAUP+QHAIL':\n",
    "        ice_vars = ['QGRAUP', 'QHAIL']\n",
    "        means = []\n",
    "        lowers = []\n",
    "        uppers = []\n",
    "        for var in ice_vars:\n",
    "            if f'{var}_hist' in ds:\n",
    "                comp_mean, comp_lower, comp_upper = _compute_single_histogram_stats(ds, var, stat_type)\n",
    "                means.append(comp_mean)\n",
    "                lowers.append(comp_lower)\n",
    "                uppers.append(comp_upper)\n",
    "        mean = sum(means)\n",
    "        lower = sum(lowers)\n",
    "        upper = sum(uppers)\n",
    "        return mean, lower, upper\n",
    "    \n",
    "    else:\n",
    "        # Single variable - use helper function\n",
    "        return _compute_single_histogram_stats(ds, var_name, stat_type)\n",
    "\n",
    "\n",
    "def plot_hydrometeor_mean_profile(q_var: str, c_q=c_q, f_q=f_q, fu_q=fu_q, stat_type='iqr'):\n",
    "\n",
    "    '''\n",
    "    if q_var == 'QRAIN' or q_var == 'QCLOUD':\n",
    "        c_q = c_q.sel(level=c_q.level <= 10)\n",
    "        f_q = f_q.sel(level=f_q.level <= 10)\n",
    "        fu_q = fu_q.sel(level=fu_q.level <= 10)\n",
    "    '''\n",
    "\n",
    "    fig, ax1 = plt.subplots(figsize=(3, 3))\n",
    "\n",
    "    ax2 = ax1.twiny()\n",
    "\n",
    "    c_mean, c_lower, c_upper = compute_histogram_stats(c_q, q_var, stat_type)\n",
    "    f_mean, f_lower, f_upper = compute_histogram_stats(f_q, q_var, stat_type)\n",
    "    fu_mean, fu_lower, fu_upper = compute_histogram_stats(fu_q, q_var, stat_type)\n",
    "\n",
    "    # w>0\n",
    "    ax1.plot(c_mean, c_mean.level, label=f\"Current\", color=\"black\", linewidth=2.5)\n",
    "    ax1.plot(f_mean, f_mean.level, label=f\"Future\", color=\"#1E88E5\", linewidth=2.5)\n",
    "    ax1.plot(fu_mean, fu_mean.level, label=f\"Future+Urban\", color=\"#D81B60\", linewidth=2.5)\n",
    "\n",
    "    # Plot uncertainty bounds (IQR, std, or se)\n",
    "    ax1.fill_betweenx(c_mean.level, c_lower, c_upper, color='black', alpha=0.1)\n",
    "    ax1.fill_betweenx(f_mean.level, f_lower, f_upper, color=\"#1E88E5\", alpha=0.1)\n",
    "    ax1.fill_betweenx(fu_mean.level, fu_lower, fu_upper, color=\"#D81B60\", alpha=0.1)\n",
    "\n",
    "    rel_change_acc = calculate_relative_change(f_mean, c_mean)\n",
    "    rel_change_acc_urban = calculate_relative_change(fu_mean, c_mean)\n",
    "    rel_change_urban = calculate_relative_change_urbanization(f_mean, c_mean, fu_mean)\n",
    "\n",
    "    if q_var == 'QRAIN' or q_var == 'QCLOUD':\n",
    "        rel_change_acc = rel_change_acc.sel(level=c_q.level <= 10)\n",
    "        rel_change_acc_urban = rel_change_acc_urban.sel(level=f_q.level <= 10)\n",
    "        rel_change_urban = rel_change_urban.sel(level=fu_q.level <= 10)\n",
    "\n",
    "    ax2.plot(rel_change_acc_urban, rel_change_acc.level, label=f\"ACC+Urban\", color=\"#FFB507\", linestyle='-', linewidth=2.5)\n",
    "    ax2.plot(rel_change_acc, rel_change_acc.level, label=f\"ACC Impact\", color=\"black\", linestyle='--', linewidth=2.5)\n",
    "    ax2.plot(rel_change_urban, rel_change_acc.level, label=f\"Urbanization Impact\", color=\"black\", linestyle=':', linewidth=2.5)\n",
    "    ax2.plot(np.zeros(17),np.arange(0,17,1), color=\"gray\", alpha=0.6)\n",
    "\n",
    "    # melting level\n",
    "    c_ml_mean = c_ml['melting_level_km'].mean(dim=['Time','south_north','west_east']).values\n",
    "    f_ml_mean = f_ml['melting_level_km'].mean(dim=['Time','south_north','west_east']).values\n",
    "    fu_ml_mean = fu_ml['melting_level_km'].mean(dim=['Time','south_north','west_east']).values\n",
    "\n",
    "    # Compute dynamic x-axis limits for ax1 (hydrometeor concentrations)\n",
    "    # Include both uncertainty bounds AND mean values to ensure everything is visible\n",
    "    all_means = [c_mean, f_mean, fu_mean]\n",
    "    all_lowers = [c_lower, f_lower, fu_lower]\n",
    "    all_uppers = [c_upper, f_upper, fu_upper]\n",
    "    \n",
    "    # Find min considering both lower bounds and means\n",
    "    x_min = min(\n",
    "        min(l.min().values for l in all_lowers),\n",
    "        min(m.min().values for m in all_means)\n",
    "    )\n",
    "    # Find max considering both upper bounds and means\n",
    "    x_max = max(\n",
    "        max(u.max().values for u in all_uppers),\n",
    "        max(m.max().values for m in all_means)\n",
    "    )\n",
    "    \n",
    "    # Add some padding (5% on each side)\n",
    "    x_range = x_max - x_min\n",
    "    x_min = max(0, x_min - 0.05 * x_range)  # Don't go below 0\n",
    "    x_max = x_max + 0.05 * x_range\n",
    "\n",
    "    ax1.plot([x_min, x_max], np.full(2,c_ml_mean), color=\"black\", linestyle='--')\n",
    "    ax1.plot([x_min, x_max], np.full(2,f_ml_mean), color=\"#1E88E5\", linestyle='--')\n",
    "    ax1.plot([x_min, x_max], np.full(2,fu_ml_mean), color=\"#D81B60\", linestyle='--')\n",
    "    #print(c_ml_mean)\n",
    "\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",
    "    # Labels and legend\n",
    "    #fig.suptitle(f\"{percentile}th Percentile {title} Thresholds For Each Height by Simulation ({month})\")\n",
    "    ax1.set_xlabel(f\"{q_var} (g kg$^{{-1}}$)\")\n",
    "    ax1.set_ylabel(\"Height AGL (km)\")\n",
    "    #ax1.legend(loc=\"center\", bbox_to_anchor=(0.14, 0.6), fontsize=8)\n",
    "    #ax1.legend(loc=\"center\", bbox_to_anchor=(0.86, 0.22), fontsize=8)\n",
    "    #ax2.legend(loc='upper center', fontsize=10, ncol=2)\n",
    "    ax1.set_ylim(0,15)\n",
    "    ax1.set_xlim(x_min, x_max)  # Dynamic x-axis limits\n",
    "    #ax1.invert_xaxis()\n",
    "    ax1.grid(alpha=0.75,axis='y', linestyle=':')\n",
    "\n",
    "\n",
    "    #ax2.legend(loc=\"center\", bbox_to_anchor=(0.185, 0.45),  fontsize=8)\n",
    "    #ax2.legend(loc=\"center\", bbox_to_anchor=(0.815, 0.07),  fontsize=8)\n",
    "    ax2.set_xlim(-40,70)\n",
    "    ax2.set_xlabel('Relative Change (%)')\n",
    "    ax2.grid(alpha=0.75, linestyle=':')\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "\n",
    "# Choose stat_type: 'iqr' (default), 'std' (standard deviation), or 'se' (standard error)\n",
    "# Examples:\n",
    "#plot_hydrometeor_mean_profile(q_var='QTOTAL', stat_type='iqr')  # Interquartile range (default)\n",
    "plot_hydrometeor_mean_profile(q_var='QTOTAL', stat_type='std')  # Mean ± std dev\n",
    "#plot_hydrometeor_mean_profile(q_var='QTOTAL', stat_type='se')   # Mean ± standard error\n",
    "\n",
    "#plot_hydrometeor_mean_profile(q_var='QTOTAL')\n",
    "\n",
    "plot_hydrometeor_mean_profile(q_var='QCLOUD', stat_type='std')\n",
    "plot_hydrometeor_mean_profile(q_var='QRAIN', stat_type='std')\n",
    "plot_hydrometeor_mean_profile(q_var='QICE', stat_type='std')\n",
    "plot_hydrometeor_mean_profile(q_var='QSNOW', stat_type='std')\n",
    "plot_hydrometeor_mean_profile(q_var='QGRAUP', stat_type='std')\n",
    "plot_hydrometeor_mean_profile(q_var='QHAIL', stat_type='std')\n",
    "#plot_hydrometeor_mean_profile(q_var='QGRAUP+QHAIL')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "################ Buoyancy ###################\n",
    "c_buoyancy = xr.open_dataset(f'/pscratch/sd/d/dbrooks/vertical_motion/current/buoyancy_vertical_mean_month{monlist[0]}.nc') \n",
    "f_buoyancy = xr.open_dataset(f'/pscratch/sd/d/dbrooks/vertical_motion/future/buoyancy_vertical_mean_month{monlist[0]}.nc')\n",
    "fu_buoyancy = xr.open_dataset(f'/pscratch/sd/d/dbrooks/vertical_motion/future_urban/buoyancy_vertical_mean_month{monlist[0]}.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 230kB\n",
      "Dimensions:    (Time: 240, level: 59)\n",
      "Coordinates:\n",
      "    XTIME      (Time) float32 960B ...\n",
      "  * Time       (Time) datetime64[ns] 2kB 2017-06-01 ... 2017-06-30T21:00:00\n",
      "  * level      (level) float64 472B 0.5 0.75 1.0 1.25 ... 14.25 14.5 14.75 15.0\n",
      "Data variables:\n",
      "    B_total    (Time, level) float32 57kB ...\n",
      "    B_thermal  (Time, level) float32 57kB ...\n",
      "    B_vapor    (Time, level) float32 57kB ...\n",
      "    B_cl       (Time, level) float32 57kB ...\n"
     ]
    }
   ],
   "source": [
    "print(c_buoyancy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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58o5fQ8bhq7COvwOLp9zBushzcERRn55wGdUbARN6w9rRWqPP5KmGd81ahsEaosLCQjg5Oem7OjqBEIL//vsPH3/8MWJiYuo8x83NDVOmTMGUKVMQHByssUtDFEoUSu4h5eh1KKNvwu1BZqPXKAUCZHfwAgnsDq9RvdAltBuE1i13MZRKJeLj4xEQEMDZL6jKNSsoKMC0adNgadm67m9rUlRUBGdnZ9MwRFxzzZ5GtdfXgwcPsGnTJvz666+Qy+te8Oru7o5JkyZhypQpGDp0aLPGWeT5Utw8moj8iBtwiL8N8ePyxq8RilDQuQPMBnZHlzG+cBvUBQILzV0OlVYurzdT9YgUCgXGjx/Pu2YaYrCGiKuuWX1kZ2dj69at+P777xsMx9mmTRtMmjQJkydPxrBhw5rVwyCEIF2SjjtHrgMXb6J9WjrMmMan7cvNzFDSzRs2QT3QeXQv2Pf1BmXGzR6OpvCuWcswWEPEZdcMqL5x4+LiEBgYqHbTlpeXY+/evfj9998RHR3dYKQDV1dXtqcUEhLS7Ju/pKgC144kofhcEuxvpMArP6fxiwDIzc1R3qsznIb1gNfIXrDs3QGUqHZvrT6tXELlmuXn52P69Om8a6YhBmuITME1q6yshJWVVb3uSnZ2Nv7++28cPHgQUVFRDRolsViMESNGYPTo0QgLC6s3NG9T6nU3pRi3T95ExcVkuN1OYUPcNobc0gKVvbvAZagPPIb3ZA1TU7QaO6oekVKpxLhx43jXTEMM1hCZmmvWGDk5Ofj777/x559/4vz5841u6dKtWzeEhYUhLCwMw4cPh62tbbM+V6YkuJaYj9Qzt6CQJMMz5R7alxQ26VqFhQXkfp3hOswHLsN6wtKvA2ddOd41axkGa4hMwTWTSCQYOHCgxjdtbm4uDh06hIMHDyIyMrJRo2RmZoYhQ4awvaWAgIBmzx0qrKAhic3Fw7O3IIhLRrf0VLjX2LutIRQW5qD9OsN1WA84DvWBpZ83KHNufGFVrlleXh5eeeUV3jXTEIM1RKbgmsnl8hZH9MvPz8exY8dw5swZREREoLCw8d6Ki4sLRo0ahbCwMIwcORLt27dv1mcTQnCvUInYK4+QH3kbFgkp6PEwrcmGSWlhDqZ3J7iG9IB9kA+s+nQyWsOk6hExDINnn32Wd800hDdEekK1T1XNbWdaCk3TiI+Px+nTp3HmzBlcvHiR3R20Iby9vRESEoJhw4YhJCQE3t7ezaqTkiG4mavA1bhs5J+/DZsbKej1MB1tS4savxgAbW4G9OkC19AesB3iA0t/43kqpzJEhBCEh4fzhkhDDNYQ8a5ZyykrK8O5c+dw5swZnD59Wm33lYZo166dmmHq3r17swxTlZIg/pEcsXGPkP1vAtzvZaJ3VjraSZs2xkRbmkPYtytchvnAekgPWPp1rPOpnCHAu2YtQ2NDFBUVhQ0bNuDq1avIzs5W2+lVoVDgo48+wokTJ5CamgqxWIyRI0di3bp17M6vjcH3iHRHamoqIiIicPr0aZw9exalpaVNus7NzY01SsOGDYOvr69GY0wqrZVKCleyFLh2LRclMclwuX0PvbPS4VlS0KRyaCsLmAd2hdOwnrAe4gML3w6ghIYRVoTvEbUMjQ3RyZMnERMTg379+mHSpElqhkgqlWLy5Ml466234O/vj+LiYrz77rugabretVVPY0qGSBtjRM1FNbfn/PnziIqKQnR0dJMNk6OjI4YOHYqQkBAEBwcjICCgwS9efVoLK2hcypDjWkIeSmOS0e7uPfhlpTX5qRxtYwmrAd0hHtoD1sE9YOHTHpSe4h3xY0Qto0WuGUVRaoaoLmJjYzFgwABkZGTUucmeTCZTi9VTWloKT09P5OXlwdXVlbN7qiuVSsTGxiIwMBBmZmZ610RRFOLj43H+/HlcuHABUVFRKC5u2qCztbU1BgwYgKCgIAQHB2PAgAFwcHBg60vTdKNaBQIBskppXEitRGJiPiou3UGn1Pvwy0xr8hgT42gHu6E9YTu0JyyGdIdVhzattk88wzA4fvw48vLyMGPGDFanId57Lf0+FRcXa9010/nPh1QqBUVR9UZbXLt2LcRiMfvy9PQEAGRmVi/SzMjIQEZGBoBq10KVn5KSguzs6qiFycnJyMvLA1Ad+6eoqPrGTUhIYJdLxMfHo6ysDED1JpGVlZUAAIlEArlcDpqmIZFIQNM05HI5JBIJgOpIi6reXFlZGeLj4wFUR6lLSEgAUO0zq2IN5eXlITk5GUD1hETVnu+ZmZlITU1lNWVlZSEoKAgPHjwwCE1CoRCdO3fG8OHDcfjwYSQnJ+PgwYPYunUrxo8f3+B4XUVFBSIjI7FmzRqEh4fDxcUFvr6+mDt3LjZt2oTr168jKCgI9+7da1BTe7EIXZTJWPG8CzZ/PwZtlw3GnR8/wvaPP8DmsEk44xOAXDuHeushKC7D46MS5C7ZhQdBy5EctBwPFv+IpO1/g5ZWNKudNLn3VKFORCKRQd97Lf0+6WJMU6c9oqqqKgQFBcHHxwf79u2r85z6ekSqwWqu9ogIIZDJZDA3N4dAIDB4TQzD4N69e4iMjERUVBSioqKQlZXV5HulXbt2CA4ORlBQEIYOHYqePXtCJBI1WVOVXIGb+QwuZsiQdC0bwqt30PNBGvpkpcG1XNro5xMBBaFvR4iH9YTtMF+YB1TPYdJmj+jEiROgaRrjxo1j29QQ772Wfp9KS0uNxzVTKBR44YUXkJmZicjIyCZX2JSemhnz+itCCNLT0xETE4OYmBhER0fj5s2bTd4F2M7ODoMGDUJwcDCCg4MxcOBA2NjYNPnzqxQEsZkyXEirQvK1R7CPT0bAw/vok5kKW3lVo9czluawHtgNdqG+sH3GD2ad3Fs0VsevNWsZOjFECoUCL774IlJTU/Hff//B2dm5yWXySzyMl+LiYly6dIk1TFeuXEFVVeNGAQCEQiH69u3LGqagoCC0adOmyZ9dWEEjJl2OmNQKZF5Og2fyHQQ8vI9e2Q+aFFkA7V3gMNIfNs/4wXqwDwRWmgW/55d4tAytGyKVEUpJScG5c+fg6uqqUZmm9NSM6zF65HI5rl27hujoaJw/fx4SiQT5+U1bQAsAXbt2ZQ1TcHAwunbt2qT/FSEE6cU0LqTLcPlOKUov34VP6j30fXgPnQsajyzAWJjBakgPiEf6V/eWPF0avYaPR9QyNDZE5eXl7MS4gIAAfP311xg+fDicnJzg4eGByZMn49q1azh27JjaL5qTk1OTtlgxJdeM61ELVai09unTB2lpaaw7d+HCBdy9e7fJ5bi6uqoZpsamDbCfzxAkZisQnSFDfFIhcCUZ/g/uod+De3BrwvgSOnnAcVS1UbIa0LXO2d58hMaWobEhioyMxPDhw2vlv/baa/jkk0/q3R7n3LlzCA0NbbR83jUzLfLz89XGmeLi4pq0LAUArKysMHDgQAwbNgyhoaEYNGgQrKysGr2uTMYgJkOGyPtVSJFkwPtWMgZk3IXvowwIScMLiBlrS9iG9II4vB9sRvpDaF8d45t3zVqGwS7xMAXXjOs7W6jQRGtFRQViY2MRHR2N6OhoXLx4sckTLc3NzTFw4ECEhIQgNDQUgwcPhrV1w5sBEEKQUqDEuVQZLt8qBrl0C33T7mJA+h04Vj5u+FqREJZBPeEwth8sR/ZGhCSG38WjmRisIeLSvmZ1YQp7faloiVbVdtUqwxQdHc3OfWkMMzMz9O/fH6GhoQgJCcGQIUMajcv0WM7gYoYckfcqkH4xDR1u3cbA9DvolpsFAer/qihFFK6P94bSxRqTX58Omw7uGuk0JkxqXzPeNeOpjwcPHrBG6fz587h161aTrhOJRAgMDGQNU1BQEOzs7Oo9nxCC+0VKRKbKcPl6AahLNxGYdgf90+/CRqG+VRMtAK4NqX463PdiIUS9vOE8PhB24f1g7t30p3/GgEm5ZsXFxfXOxuYCprAfvApda83Pz0dUVBTOnz+PyMhI3Lhxo0nXCYVCBAYGsrGZBg0a1KBLJa1i8N/9Kvx7swzSqFvof/cmBqfehriqopYhEtYYaqK6tYfz5MGwf34wzDyMv5dfUlICR0dH0zBEpuCaJSUlwdfX1yRcs9bUWlhYyBqm8+fPIyEhoUkTLW1tbTF8+HDWMHXr1q3eMa0qBUFMhgynbz/Go8hk9L6dCGenB7BUKmoZIhWEomAx2AdOU4bALrwfBLaND6wbIrxrxsPTDIqLi3HhwgVERkbi/PnziI+Pb5Jh8vLyYo3SiBEj6p2YSzMEVx5U4MypUyh9JIXDdQZD792CRwORKhkLM9iO7gvHyUNgM6yXwcZZqgveNeMQDMMgLy8Pbm5uJuGaGZLWkpISREdHIzIyEhEREUhMTGz0Goqi0K9fP9YwDRkyRG1enOrxfUVFBXoEj8PZVCWunktF52uJGH43oWGj5GQPp+cHQjx5SHWMJQN/isq7ZhyCpmkkJyfDx8fHJFwzQ9aak5ODf//9FxEREThz5gxychqffW1jY4PRo0djwoQJGDt2LBwcHHDixAmUlJRg6tSpsLCwACEEiTkK/J1Ugbv/JqP/9WsYdu8G7GT1L3sR+bSH8+sjYD9pMARa2O5bF/CuGQ+PjiGEICkpiTVKUVFRbNiO+hAIBAgODkZYWBg6dOiAl156qdaERgVNcCFdhn8SSlF8JgEhN+PRP+NuvevgiJ01nF4eCodXn4F5Rzet6dMGvGvGIRiGQXZ2Njw8PAzCXdElxqy1qqoKMTExrGFSxQR6GqFQiPDwcADVcX7GjRuHCRMmYNCgQbV6geUyBmdSqnDqSgEs/ovDM8nX0TPnYZ3lEoqCdWhvOM0cAZtQX71FoKwJ75pxCJqmkZKSgq5duxqku6JNuKQ1Ly8P//77L44dO4YTJ05AKq1eq1bTEJ08eRI0Xd3TcXV1ZY3SqFGjaoU6yS2n8XdSBc6cfYA+kisIvxUHcVVFnZ8t8HKD86wREE8JhlDc8IxxXcK7Zjw8BoRcLseFCxdw9OhR/PPPP+jVqxcAdUNUExsbG0ycOBHTp0/HqFGj1Nw3miH4774Mv0pKYPZvHJ5LvIRueY/q/mAbSzi/FQbH2aPZtW6tCe+acQiGYZCZmYn27dsbnbuiKaagVaFQYN++fUhJScG///6LK1euNHi+q6srpk6diunTp2PgwIFqT8ruFSrwy7XHSDxzB6OuXcKwlKQ6x5IosTWc3wmH46yRENi03mp/3jXjEDRNIzU1FZ06dTJ6d6UxTEGrKgxIWVkZJk+ejIKCAvzzzz84evQozp49C7lcXu+1nTt3xrRp0zB9+nR0796dzS+XMfj7ZiX+jspBj+jLGHdDApfHZbWup5zs4DJvLBxmDNc4oFtz4F0zHh4DpaEwIGVlZTh+/Dh+++03nDx5ssEwJ/369cP06dMxbdo0Np4XIQTnUmXYFFmCNhfi8MqV/+qclyRwE8NlwXg4TAvR6dbdvGvGIRiGQUZGBjp06MBZd0WFKWhVGSJVj6i+IICFhYU4ePAg9u3bh+jo6HrLMzMzw4svvoh58+axrhshBKdTqrD5XDE6RV/BtNjIOjcOMO/WFu5fzYJV385a01cTXbhm3LwreHgMFGdnZ7zzzju4cOEC0tLS8MUXX7CD3DVRjTkNHjwY/fv3x549eyCTyTCmmxWOv+WB0SvC8MmCxdg2bCyKrNVDm8jvPsKDiWuQ99nvYCpltco2RAy2R8S7ZjzGREsiNBJCcOPGDezbtw+//fZbvfGWnJ2d8dZbb+Gdd95Bhw4doGQIDt2sxLZzhegXHYOp16JgX6U++dKsgxvcv5oJ68E+zRf3FLr4jhpsj6iux59cQjW3hus6AdPRSghBaWmpxjopioKfnx++/PJLZGRk4MSJE3j22WdrnVdYWIh169ahU6dOeP7553Ex+gKm9LbGyTnt4TQnHG/OeA9nu/urXaPIyMPDKV8iZ8VeMOUNzxBvKrpoR4M1RIa+8K+lUBQFCwsLzusETEeraiPClugUCAQIDw/H8ePHkZKSgvfee69WyGSGYXD48GGEhIRg1KhRSIyPxQfDxdg7uwMOTZuGleNmIN9Gvaci/eUcMiZ+AcWjpm3f3RC6aEeDNURcHdRUIRAI4OXlxXmdgGlptbW11ZrOLl264Ouvv0ZWVhZ++OEH+Pr61jrn33//xcCBAzFx4kQICu7g2GuuGDQ9EHNmvIsTvQLVzpUnZ+LBhM9Rdavu5SRNRRftqHGJUVFRGD9+PNq2bQuKonD48GG144QQrFq1Ch4eHrCyssLIkSPZPbg1gevdeNWKdK7rBExHq2qTAG3rtLGxwezZs5GYmIjz589j8uTJtYzBkSNH4O/vj1mvvYIJHnk4MNsTJ16cgqUTZyHf9kmPSplTjIcvrMXjCzebXR+DcM0eP34Mf39/fPfdd3UeX79+PbZs2YLt27dDIpGw4RKauuOnClPoxnN5c8WamIpWiqJgbm6uM50URWHYsGE4ePAgkpKSMHnyZLXjhBD89ttv8PHxweaP5mLHGMAmuAcWTHkb952fBPNnyiqROWMTpH/GNLseWoe0AADk0KFD7HuGYYi7uzvZsGEDm1dSUkIsLCzI/v376yyjqqqKSKVS9vXw4UMCgBQVFRFCCKFpmtA0TQghRKlUapxmGIYQQohCoWgwzTBMrbRKU31ppVLJ1lHTNK+JW5oUCgU5cuQIOXr0KFEoFK2mKTY2lowZM4YAqPVydXUlB/78myw4Wkh8PrtP9g36nCS3e13tJT11VeN2kkqlBACRSqVEW2jV2UtLS0NOTg5GjhzJ5onFYgwcOBCXLl2q85q1a9dCLBazL09PT7YsAMjIyEBGRgaA6vAKqkebKSkpyM7OBgAkJycjLy8PAJCUlISiouoBuYSEBJSUlAAA4uPjUVZWPT0+Li6OjTEjkUggl8tB0zQkEglomoZcLodEIgEAVFZWIi4uDkD1DFlVGIiSkhIkJCQAqN75MikpCUD16uzk5GQAQHZ2NuuWZmZmIjU1ldWUlpaGmzdv4v79+5zRVF87ZWVl4ebNm7h16xZnNNXVTkVFRcjPz2e3T2oNTR07dsS6detw4cIFDBo0CDXJz8/H1MmTkP7TLIzpqsBH41/F6R591c7Jnr8DsnvZGt17unDNWjSPiKIoHDp0CBMnTgQAXLx4EUFBQXj06BE8PDzY81588UVQFIUDBw7UKkMmk0EmezLpqrS0FJ6enigoKICzszMYpjoKuUAgAE3T7JOJpqZVTzGUSiWEQmG9aaDa962ZFolEIITUm2YYBkKhEAzDgBCiUZphGFajUCjkhKb62okQ0qhWY9P0dJphGBw/fhxVVVV4/vnnWZ2tqYmmaZw5cwZLly5lDa4KDw8PvPjFERzKaYv/RR3DxMTL7DGzTu5of+QDiMQ2Tbr3pFIp92ZWW1hYwN7eXu0FgN3WRSAQsANzqsbVJK3yZ0UiUYNpiqJqpQE0mFbdOAKBQOO0SCSCu7s7zMzMOKOpvvo2RauxaaorLRAIYG1tzZbR2ppUMZHi4uKwYsUKtQHt7OxsfDNzADxKr2H70GcR374Te0yRmoO8hT9BNfLT2L1nEE/NGsLdvXpALDc3Vy0/NzeXPdZUuP6ERdV957pOwHS0EkJQVFSkd50WFhb44osvcPHiRbXV/AAgWT8ZZtL7WDPmJeTYP4lu8fhsAkr/vvx0UXViEE/NGsLb2xvu7u44e/Ysm1daWgqJRILBgwdrVJYpPGFp164d53UCpqOVoihYW1sbjM6BAwciPj4e77///pM60XLc/24KypkKfPLsdFSJnmwoWbjlHxC6jg3ZnsIgJjSWl5fj+vXruH79OoDqQeXr16/jwYMHoCgKCxcuxOeff46jR4/ixo0bePXVV9G2bVt2HKnJFeP45DeBQAAXFxfO6wRMS6ulpaVB6bSyssJXX32F//77j41mwZQXIPfPJUh19cAh/yHsuYrUHJT903BAN8BAJjTGxcUhICAAAQEBAIBFixYhICAAq1atAgAsXboU8+fPx+zZs9G/f3+Ul5fj1KlTsLTULIKcvru3uoamaVy7do3zOgHT0aoalDdEnaGhoYiKimIfIlXejoAsPRZ/BwSp94q2HgNhGu4VGdxTM12gWtlbUlJSa40NlyD/PwvXwcHBYLryusIUtKpW38vlckyYMIF92GJopKWlYdSoUbh//z4sOg5Am3cO4a3ok5gS/yQ2kuefy2E9qHu9ZUilUjg4OHDrqVl9cPWGVUFRFBwdHTmvEzAtrbqcWa0NvL29ER0dDX9/f8jSr6DqfgwO9Rmidk7l1XsNlmEQY0StRUPhNLmAUqlEbGws53UCpqOVEIL8/HyD1+nu7o5Tp07Bzc0NlXfOocBWjFw7B/Z4VUJ6g9frQp/BGiKuBllXIRQK0b17d87rBExHK0VRcHBwMAqd7u7u+OWXXyBLrV7xcKdNe/ZYVUJag9fqQp/BGiJD7t5qA4qiYG9vz3mdgGlpNTMzMxqdYWFheHf6GDBVZbjr1o7NV2YVgi6te5NHgHfNOIVSqcTly5c5rxMwHa2EEOTl5RmVzjWrP4NIXopKs6ZvQ8S7ZhxCKBTCz8+P8zoB09FKURScnJyMSqeZmRmcHB1g/pRxoSzqf+qnC3262/yohRhL97a5qGbhmgKmpLXmWjJjQSy2gzmtYN8zIIBZ/caGd804hFKpRExMDOd1AqajlRCC3Nxco9MpU1IQVz4ZE6pilPXuJALwrhmnEAqFCAwM5LxOwHS0UhQFV1dXo9IprWKQWUqjZ3YGm3dXWdJgeGeTempmChjTDdtSTEWrsbllN3IUsFTI0TU/m827KsvFvXsNT2rUNgZriAxxvY42qRmVj+uYilbVUzNj0pmQLYdPzkMIyZP1ZVflDRsigw8Dok24/gsqFAoxcOBAzusETEcrRVFwc3MzKp0R96oQmpKolhcvz2NDxdYF75pxDGP65WwppqLVwNaQN8j9QiVup5cj9O4TQySRZaOYkWkcyLClGKwh4vqNS9M04uLiOK8TMB2tqrVmxqLzr6QKhKYkwlohZ/MOPL4LAGjXrl19l5mWa6aKz8tVRCIRgoKCOK8TMB2tFEWhTZs2RqFTThP8eeMxxt94EgitmK5CRGX107P27dvXd6lO9BmsITKmLm5zIISgoqKC8zoB09KqVCqNQufBGxXomnADXfMfsXlHKu9DgepB644dO9Z7rS70GawhMpbubXOhaRqJiYmc1wmYjlZDCZ7fGDIlwfcXpJh5KYLNU1AEe8pvAQBcXFzg7+9f7/W60GewfUhj6N62BJFIVGtDPK5iKlpVT80M/d7940YF/K5cgWdJAZu373EysunHAIBx48Y1+GSMd804BCEEpaWlnNcJmJZWhUJh0DrLZQx+PpuLVy//y+YpLUX4viSefT9+/PgGyzAK14ymaaxcuRLe3t6wsrJC586dsXr1ao0rb+jd25ZC0zTu3LnDeZ2A6WhVxeY2ZJ3fXCzD86ePw7HyMZt32DIHUlL95MzS0hKjRo1qsAyjcM2+/PJLfP/999izZw969eqFuLg4zJw5E2KxGAsWLGh6xQy8e9tSRCIR+vfvr+9qtAqmolW11sxQ7927+QrE/3Uda25fY/OU7Rzw2ZVf2Pevvvoq7OzsGixHF/q0XuLFixfx3HPPYezYsQCqR9/379+PK1ca3y+pJobcvdUGprCzhQpT0iqXyw3y3iWEYPXxPMw/e/hJHkVhl2M25HjSw1m0aFGTytI2WnfNhgwZgrNnz+Lu3eqJUQkJCYiOjkZ4eHid58tkMpSWlqq9gGpfGwAYhgHz//ss0TStcVr1T6v5WLWuNCGkVhpAg2lVF5VhGI3TSqUSaWlpUCgUnNFUX32botXYNNWVZhgGpaWlbBmGpGl/wmME7D8C97ISqKDH+2NTxEH2/bhx49C9e/dG7z3Ve22idUO0fPlyvPTSS/Dx8YGZmRkCAgKwcOFCTJ8+vc7z165dC7FYzL48PT0BAA8fPgQAZGRkICOjepJVamoqGyclJSUF2dnVK4aTk5PZtTFJSUkoKioCUG0ES0pKAADx8fEoKysDUL1JZGVlJQBAIpFALperLcyUy+WQSCQAgMrKSsTFxQEAysrKEB9fPahXUlKChIQEAEBRURGSkpIAAHl5eUhOTgYAZGdns+EUMjMzkZqaymrKzMxE37592TQXNNXXTnl5eejbty+b5oKmutqppKQEtra2EAqFBqXp6p1MnNohwdibsWDxcMLiW0fVjMrrr79eS1Nd7aSTtXREy+zfv5+0b9+e7N+/nyQmJpK9e/cSJycnsnv37jrPr6qqIlKplH09fPiQACAFBQWEEEJomiY0TRNCCFEqlRqnGYYhhBCiUCgaTDMMUytNCGkwrVQq2TpqmlYoFCQ/P5/I5XLOaKqvvk3Ramyank4rFApy+PBh8scffxCZTGYwmhiGIW/vTCWXu8wlye1eJ8ntXie3288kMd/9RgCwr9GjRzf53isuLiYAiFQqJdpC62NES5YsYXtFANC7d29kZGRg7dq1eO2112qdb2FhAQsLi1r5qv21a+6zXdMSa5quOcCmSZqiqHrTqvJr1rGpaUIIsrKy4OjoyB4zdk311Zem6SZrNRZNT6cZhgFFUewMckPRtP96OQbvPgCHGk/JHN4Kwys/f8W+pygKGzZsaPJ9SIxhjKiiokLtHwJUC9DUrzSmUArNQSgUwt/fn/M6AdPRamjB89OLlYj9+gwGpd95ktmtPU60KcPVq1fZrNdffx29e/ducrlGETx//PjxWLNmDby8vNCrVy/Ex8fj66+/xqxZszQqRxcDYoYEwzDIy8uDm5tbLcPNNUxJa2VlpUHcuwqaYO3O25h3/iSbx5iJ4PTFVCwdF8rmWVtbY/Xq1RqVrQt9WjdEW7duxcqVK/G///0PeXl5aNu2Ld5++22sWrVKo3J00f0zJAghKCwshKurq76ronNMRSshBFVVVQZx734XWYhJv/4Kc/pJoHu3lVPxwa6tKCwsZPNWrlzZYMiPutCFPooYwn+tBqWlpRCLxZBKpbC3t9d3dXh4moRSqcTJk9W9j/DwcL1OaozLlCPyrZ8x/oaEzROG+qFo/hAMGjyYNSQ+Pj5ISEiAuXnTN1cEdPMdNdh+siF0b3UJwzDIysrivE7AtLRWVFToVae0isGeDRfUjJDSyR7tv3oNb82erdab2bZtm8ZGCNDNd9NgDZGBddS0DiEEZWVlnNcJmI5WQoheZ1YTQrB2fxpePfaXWn7Hb2dj064fkJj4JCTstGnTMHz48GZ/jrYxzEUxMI2nZj4+PvquRqtgKlopioKDg4Pe7t2D18sRuO0X2Msq2Tzbt8PxyMMcn376KZvn5OSETZs2NftzTCp4Pte78QzD4MGDB5zXCZiW1vLycr3oTCtS4vaaw+hdY6NExtcb7ksnYvbs2ZDJZGz+xo0b4ebm1uzP4l0zDkEIgUwm47xOwHS0kv9fh9XaOpUMwZYtcZgiOfckz9oSnXfMwc5dPyMyMpLNHzFiRJ0TizWBd804hFAoRNeuXfVdjVbBVLRSFAV7e/tWv3d3RuTihf37IaxhINpveB3ZpAJLlixh86ysrPDDDz+0OAIC75pxCIZhkJaWxnmdgGlpLSsra1WdN7JlsFqzF66PS9k8sxeCYT9hAN566y2Ul5ez+V988QU6d+7c4s80KdeMh4enYWRKgr8+OY3Bqbef5Hm2Qce1r2DXrl2IiHgSHD8oKAjz58/XRzWbhMG6ZlxfCiAQCODt7a3varQKpqTVzs6u1e7dvUfS8PzpY+x7pUiIbj/OQUG5FIsXL2bzLS0t8fPPP2vNpdKFPoP9thty3F9tQNM0UlJSOK8TMB2t5P83CWgNnSl5Mjh/uReWSgWbJ146GZa9OmDRokUoLi5m8z/99FN069ZNa59tUju9cj2kKEVRsLCw4LxOwHS0UhQFgUCgc50MITjx4VH0evTkUf1j/25o904YIiIisG/fPjbfz88P7733nlY/Xxf6eNdMTwgEAnh5eem7Gq2CKWm1tbXV+b179Hganjl9mn0vs7CA7/Y3IZPLMWfOHDafoijs2LEDZmZmWv183jXjEDRNIzk5mfM6AdPRSlphO6Hix0pQq/fCnHnyGbYfvQxzT1ds2rQJ9+/fZ/P/97//YeDAgVqvA++acQiKomBnZ8d5nYDpaKUoCubm5jrVeeTz0/DJeuKSFQT0hPfrw5CZmYnPP/+czXdzc8OaNWt0UgfeNeMQAoFA4zgwxoopabW2ttbZvZuQkAu/A/+w76vMzNHn29dBURSWLl2KiooK9ti6desgFot1Ug/eNeMQNE3j5s2bnNcJmI5WQgiKi4t1opNmCO4s2wcb+ZM1Y8o5z8GmgysuXryI/fv3s/n9+/dv8TKOBuvCu2bcgaIoODs7c14nYDpaKYqCpaWlTnSe3H0V/kk32Pe5Xp7o+/4YEELUlnEA1VFSdelR8K4ZhxAIBHB3d9d3NVoFU9JqZWWl9Xu3uEQGm02/s+9pikLnr18HJRTg77//xsWLF9lj06dP18kAdU1414xD0DSNhIQEzusETEcrIQRFRUVa13l65VG0LX4SZzpndDDaDeoEhUKB5cuXs/nm5uZqA9a6gnfNOARFUWjXrh3ndQKmo5WiKFhbW2tV5834bPT458masTJrGwxePxUA8NNPP7G7uQLA/Pnz0bFjR619dn3ooh11YoiysrLwyiuvwNnZGVZWVujduze7dW6TK2YCrpmLiwvndQKmpdXS0lJrOgkhuLtsn9oyDvLeZFg52aCiogKfffYZm+/g4IAPP/xQK5/bGEbhmhUXFyMoKAhmZmY4efIkbt26hY0bN8LR0VGjcrjejadpGteuXeO8TsB0tBJCUFBQoDWd5/bEouetm+z7zE7eCHwnBADw3XffITs7mz22bNkyjb9jzUUX7aj1weovv/wSnp6e2LVrF5vXnJXXXP/1VK1I57pOwHS0qgKjaUNnRbkMZl8dYN/TlACdN7wKiqIglUqxbt069pi7u3urhvgwih7R0aNHERgYiClTpsDNzQ0BAQHYuXNnvefLZDKUlpaqvYAnwZcYhmHTNE1rnFaFtVQqlQ2mCSG10gAaTKt+GRiG0ThNCIGjo2Oz9Bmqpobq25hWY9NUV5oQws6sbqmmsysOwa2kCCrSRwejfb/2AIANGzagqOjJsQ8++AA2NjY60VRXOxnFGFFqaiq+//57dO3aFadPn8acOXOwYMEC7Nmzp87z165dC7FYzL48PT3ZcgAgIyMDGRkZbF5mZiYAICUlhe2aJicnIy8vDwCQlJTENlJCQgJKSkoAAPHx8SgrKwMAxMXFobKyeqcDiUQCuVwOmqYhkUhA0zTkcjkkkup9oSorK9nxrbKyMsTHxwMASkpKkJCQAAAoKipCUlISACAvLw/JyckAgOzsbHYwMTMzU01TamoqYmNjce/ePc5oqq+dsrKyEBsbi1u3bnFGU13tVFRUhJycHCiVyhZpOvXbv/A+8i9UlNjYIeCjcMTHxyM3N1dtBw4vLy8MHjxYZ5rqaieVkdcqRMuYmZmRwYMHq+XNnz+fDBo0qM7zq6qqiFQqZV8PHz4kAEhhYSEhhBCapglN04QQQpRKpcZphmEIIYQoFIoG0wzD1EoTQhpMK5VKto7NSUulUqJQKDijqb76NkWrsWl6Oq1QKMiRI0fIn3/+SeRyebM10UoliRj6KUlu9zr7+m/bBfacefPmEQDsa8+ePTrTVF87lZSUEABEKpUSbaH1MSIPDw/07NlTLa9Hjx7466+/6jzfwsICFhYWtfJVW/bW9EdrRpjTNF1zC2BN0hRF1ZtWlV+zjpqkn96ulwua6qtvU7Uak6aaaYZh1Ba9NlfT5W8j4Zmaxubf9fHB2LeHgKIoPHjwAD/88AN7rHfv3pg+fXqz9TX3+2QUrllQUBDu3Lmjlnf37l106NBBo3J00v0zIJRKJS5fvsx5nYDpaCWEIC8vr9k6i9PyYbnlIPu+SmSGbl+9yhqPJUuWQKF48ih/7dq1etntRhftqHVD9N577+Hy5cv44osvcO/ePfz222/YsWMH5s6dq1E5prCdkJ+fH+d1AqajlaIoODk5NUsnIQTxs3+EdY2NEO+8EI7ufTwAAP/99x/+/vtv9tiwYcPw7LPPtrzSzcAothPq378/Dh06hP3798PX1xerV6/G5s2bMX36dI3K4WfhcgdT0ioSiZql8+rmCLS7fZd9f6+9F8atGQ+gugfy7rvvsscoisLmzZv19v80mkWv48aNw7hx41pUBte78UqlEhKJBAMHDlQbK+AipqK1pmumic7C249gtuXJGGqVyAwuX70Ba8vqMr7//nv2aR8AvPnmmwgICNBexTXEKFwzbcH1brxQKERgYCDndQKmo5WiKLi6umqkU1lRhduvboWlQs7m3ZgyDgODq2N8p6enY8WKFewxsViss8iLTcUoXDOepsP1L2ZNTEWrJm4LIQTRb+2Ca3YOm3fLuwue++xZ9vhbb72Fx48fs8dXr14NV1dX7VXYQDBYQ8T1dUk1J7FxHVPRqnLNmqoz/tuz8Dh/hX1faGOH7jvfgZ1VtUv2008/4d9/n0xsDAoK0vihjy4wqTAgXP8FFQqFGDhwIOd1AqajlaIouLm5NUln2tnbtdaSFX/yJnr6OAMA7t+/j/fff589rtqt1RDW6/GuGcfgeg+hJqailfz/OrKGyEl8iOK3t8KcfjLoGzt5PMa+7AegernK5MmT2XWXQLVLps3dWg0NgzVEXL9xaZpGXFwc53UCpqOVEIL8/PwGdZZmFiFt2ibYVFWyedd7++PFLyew7xcsWIDr16+z74cOHar13VpbglGEAdEWXH7MC1TrCwoK0nc1WgVT0UpRFNq0aVPvvSuTViD+hY1wL3myL/1dL2+M+O0dWJlX9wl2796NH3/8kT3epk0bHDhwwKDcWl18Nw22R9SULq4xQwhBRUUF53UCpqW1ZpiPmlQWPUbMuA1wz3rE5mW6uMFv/7twcbQEAERFReGdd95hjwsEAuzfvx8eHh66r7gG6KIdDdYQcb0bT9M0EhMTOa8TMB2tpJ7g+eX5pbg8dh3apaWzeUU2dnDb/R46dKjeBPHGjRuYMGECZDWWeHz++ecYPnx4q9RdE3jXjEOIRCIMGjRI39VoFUxFq+qpWc17V/pIivjnvkTbGmFdpVbWoLa9i959qrdYSk9Px+jRoyGVStlzpkyZgmXLlrVe5TWAd804BCEEpaWlnNcJmJZWhULB6ixML0TCuLXwqGGEim3sIPxpMQaN6AwAyM/Px+jRo9XiTz/zzDP45ZdfDOJRfV3wrhmHoGkad+7c4bxOwHS0EkJQUlICmqZx78J9pIR/hjZ5uezxQlt7WO9Zgn7DqmO4Z2ZmYtiwYbh798li1z59+uDQoUN1xugyFHTRjhQxsJ+p0tJSiMViSKXSWsG0eHgMFaVSiZMnTwIAXAqcYPPpHljU2AYo394Bzr8uQY++bQFUh2YdNWoUG7YVADp16oSYmBiD3xVXF99Rg+0RGZh91DqEEBQXF3NeJ2BaWh9evg/xxz+qGaGHbu5wP7iCNUKJiYkYOnSomhHy9vZGRESEwRshwMRcM9XOAVyFYRikpaVxXidgGlorSyqQGZEE+/uZavm3e/ZEwJmV6NLLDUD1I/qQkBDk5j5x2Xr16oXo6Gh06tSpVevcXHTRjgZriAxpApcuEAqF6Nu3L+d1AtzXej/6Pq6NWg27vEK1/MTRzyD82CI4u1iDEILvvvsOI0aMYHf3AKoDCZ4/fx5t27Zt5Vo3H5Naa8blX0+gWl9BQQHndQLc1UoIwfnPj6Ny+lq4Fhaw+QqBEMnzpmPyj6/AwlwImUyGN998E/PmzVMLKjZ8+HCcPXsWzs7O+qh+s9FFOxrsZB2ujycQQpCVldVq2wTrEy5qLcuR4tKsHeiQeAsAQP//T3qlmTmkX8/Hc5P8AQBZWVl44YUX2P3XVLz66qv44YcfYGlp2ar11ga6+G4arCHiajdehVAohL+/v76r0SpwTWvCH1dR9fFedCgrVcsvdnKC2zAfDJnQCwBw5swZvPbaa8jJeRL4TCgU4uuvv8b8+fONNoa3Lr6bBmuIuNaNfxqGYZCXlwc3NzeDnbimLbii9XF+GaLn/4KO0bGo2Y+RCUW4O3MivAJoyGRVqKiowKpVq/DNN9+oXe/s7IyDBw8a5LINTTDKwep169aBoigsXLhQo+tMwTUrLCzkvE6AG1qv77uCm0M/RMfoWLX8LGc3yHevwMSPRoOigJycHAQHB9cyQn369EFcXJzRGyHACF2z2NhY/PDDD/Dz89P4WlNwzXr16qXvarQKxqy1PEeKS3P3wEsSD6sa+Qwo3Bg5DKO+ngpHJyvI5XLcuHEDsbGxuHXrlloZ8+bNw/r162FlZQUuYFRPzcrLyzF9+nTs3LmzWYOUpuCaZWVlcV4nYJxaCcPg8pazSB76Abwk8WrHspxdkf/dYry4+3U4Olnh9u3bGDlyJC5fvqy2/MHd3R0nTpzA1q1bOWOEACNzzebOnYuxY8di5MiRDZ4nk8lQWlqq9gKe7J3EMAwrnKZpjdOqbmTNODF1pQkhtdIAGkyrbjqGYZqVLisrg1Kp5Iym+urbFK2GpCn98n1EhnwGx/W/wq6yAipoisL1Mc/A/9wnCB7vA5lMhlWrVsHf3x8XL15ETZ577jkkJCQgPDzcIDQ1pZ2aeu8Zzczq33//HdeuXcPatWsbPXft2rUQi8Xsy9PTEwDw8OFDAEBGRgY7FT41NRWZmdUzV1NSUtgVy8nJycjLywMAJCUloaioCACQkJDATh6Lj49HWVkZACAuLg6VldWhOiUSCeRyudpOE3K5nH3cWllZibi4OABAWVkZ4uOrfx1LSkqQkJAAACgqKmI3wMvLy0NycjIAIDs7GykpKQCqFzimpqaymjIzM+Hj48OmuaCpvnbKy8uDj48PmzZUTVUlFTg5aycqpqxB27Qnyy8A4IGLG+6tnY2pP85Aft4DHDp0CH369MHq1avV9qMXiUTYvn07Vq5cCTMzM71r0qSdmnrv6cI10/qi14cPHyIwMBARERHs2FBoaCj69OmDzZs31zpfJpOpBYMqLS2Fp6cnCgoK4OzszFpjgUAAmqZBUZRGaYFAAIqioFQqIRQK600D1Ra/ZlokErG/PnWlGYaBUCgEwzAghGiUZhgGjx49goeHB4RCISc01ddOhJBGtepTE03TiN91EfRXB+FQXqZ2f1aJzJD8/GiM+vhZONhbQCqVYunSpWrhXIHqcZPZs2cjICAAr732GqvTmNqpqfeeVCqFo6OjVhe9at0QHT58GM8//7ya1awpRiaTNWhRVSt7i4qKODUB7mlomkZqaio6derE+YF5Q9aadTUDyYt/QfuU+7WO3ezZC93Xv4JefdzBMAx+/vlnrFixAgUFBWrnubm5YdOmTbC1tUVZWRkmT55s0GE8WkpxcTGcnJy0aoi0/tRsxIgRuHHjhlrezJkz4ePjg2XLljX5RjS0G1bbCIVCdO3aVd/VaBUMUWtFYTkuLj+Itqei0Z6oD77mih1RvnAqJr45AAKKgkQiwbx581g3qSZvvPEG1q9fD3t7e5w8eRL29vYmce9qG60bIjs7O/j6+qrl2djYwNnZuVZ+QxjTE5bmwDAMMjIy0KFDB6Oe5NcUDEkroRlc2fofhNsOw7PisdoxuUCIm8+OwKgvJsLZyQq5ublYvnw5du/eXaucbt264YcffkBoaCiAJw9XysrKTOLe1TYGO7Oah0fbpJ+/g4zlv8L9YWatYzd9eqDTF9Px0oB2UCgU2LRpEz755BO1TQ6B6h/VlStXYuHChZx2v1qbVjFEkZGRGl+j719OXSMQCODt7a3varQK+tZallkEyZLf4XkhFk+HHct2dEbFwhcxcVZ/CCgKZ86cwcKFC3H79u1a5UyfPh3r169vMGSHnZ2dSdy72sZge0Rcj29syAO42kZfWukqOS6tOwnbvSfgKZerHaswM8ed50djzCfj4GBvzu41f+TIkVrl9OnTB1u3bkVwcHCDn0cIQVlZGft0i6uY1HZCxroyualQFAULCwvO6wT0o/XWX1dR8tnvanGCVFzv2xd+a1/CS71cUV5ejg8++AQbN26E/Clj5eTkhM8//xyzZ89ukgFVPRnmepvqQp/BGiJT6N56eXnpuxqtQmtqLUzOxvVFv6B94m08HW4s3b0tBMtfxosvVK97++2337BkyRI8evRI7TyBQIA5c+bgs88+g5OTk0afb2traxL3rrYxWENkCq5ZSkoKunbtahKuma610lVyxHx6FI6/nUZ7Wql2TGpljfRXJmDs0hGwsRIhPj4e8+fPR0xMTK1yQkND8c033zRroTYhBFKplHfNmoHB/rdMoXtrZ2fHeZ2A7rXe+ScBBSt/RZunJhrSlADXhwUhaO1kDPCyR2FhIRYv+gg7duyo9Qja09MTGzduxOTJk5tdT4qiYG5uzvk25V0zDiEQCNCuXTt9V6NV0JXW0qxiSBb+Cq9L1+Dy1LHkjp3g8ul0TBvRCTRNY9u2bfjoo49QXFysdp6lpSWWLl2KZcuWwdrausV1sra2Nol7V9sYrCEyBdcsOTkZPj4+JuGaaVMrIQSSzf/C/Nu/4SWrUjtWbG2Dh29MwvhFobA0E+DSpUuYM2cOu0i0JpMmTcLGjRvRsWPHFtdJVS/VTq+8a6YZBvvfMoXurbOzM+d1AtrVWpxRiKuzf4TnzWS1fAYUrgcNwpD1UzGogxglJSVY9MEH2L59e62wFT169MA333yDUaNGtbg+NaEoCpaWlpxvU9414xACgcAodvXUBtrQSghB7M4LEK3fD88q9V5QRpu2MP/4Fbw03gcAcPDgQSxYsEAtaD0A2Nvb45NPPsG8efPYEB3axsrKyiTuXW1jsIbIFFyzpKQk+Pr6moRr1hKtpTlSXHpnFzrGqbtXcqEIt14ci3GfjoWNtRkyMjIwd+5cHD9+vFYZr7zyCr766iu0adOm2ToaQ7W1Nu+aaY7B/rdMoXvbrl07zusEWqY18XACqpb9iI6Py9Xy0z3aw+XrNzB1aEcwDIMtW7ZgxYoVqKioUDuvc+fO2L59e6ORQrUBRVGwtrbmfJvyrhmHEAgEcHF5+lkPN2mOVoam8e/yQ/DcfwIWeDLGoxQIcHNCGMZ++TxsbMyRlpaGWbNm1VrPKBKJsGzZMnz44YetGi/a0tLSJO5drZep9RK1hCm4ZteuXeO8TkBzrcXZpTgT/hU67D8OQQ0jlOXihrIdy/Dit1NhbW2GHTt2wM/Pr5YRGjJkCK5fv47PP/+8VY0QIQQFBQWcb1OTcs1M4VfF29ub8zoBzbTe+PcOyhZsh3dpiXp+8GCE7XgN9vYWyMzMxJtvvonTp0+rnWNtbY3169djzpw5evm/UhQFe3t7zrepSQ1Wm4KfzeVQuDVpqtaITefgsWkfnJknv7hVIjNkLXgZL7wXCoqicPjwYcycOZMNtq9i6NCh2LVrFzp37qzl2msGP7O6eRis6VZFvOMqSqUSsbGxnNcJNK6VYRgcXvQnvDbuhVkNI5Tt7Arhbx/i2UXDQdM0lixZgueff17NCFlYWGDjxo04d+6c3o0QIQT5+fmcb1Nd6DPYHhHXH2kLhUJ0796d8zqBhrVWyWgcf/1n+F5Q3xfsbl9/DN31FhycbZCdnY2pU6fiwoULaucMGDAAe/bsgY+Pj07r31QoioKDgwPn29SodnptKabQvbW3t+e8TqB+rUVFlTg5flMtI3Rv8hiMPbwADs42iIyMREBAQC0jtHTpUsTExBiMEVJhZmbG+TblXTMOoVQqcfnyZc7rBOrWmv2oDJfGrkfPWzfZPJoS4NHCaRi7eSooisLWrVsxYsQI5ObmsueIxWIcPnwYX375pcFNGiSEIC8vj/NtyrtmHEIoFMLPz4/zOoHaWjMfSHHzha/QJftJEPsqMzMo187G8JcCoVQq8e6772Lbtm1q5fTp0wd//vmn3seC6oOiKDg5OXG+TY3CNVu7di369+8POzs7uLm5YeLEibhz547G5ZhC99YUZuEC6loz7hcheeI6dKxhhMqsrGGzawn6vRSIkpISjB07tpYReuONN3Dx4kWDNUIqRCIR59vUKFyz8+fPY+7cubh8+TIiIiKgUCgQFhaGx48fN35xDUyhexsTE8N5ncATrXdv5iB10jp45j1ZjCq1tYPHgWXoEtoV9+/fx+DBg3HmzBn2uEAgwJYtW7Bz585WnZzYHAghyM3N5XybGoVrdurUKbX3u3fvhpubG65evYphw4bVOl8mk0Emk7Hvn95HSht7dRvi/uMURSEwMJDVyAVNDbWTg5UXsqduQNuSIrZti+3F6PDHUrj1aIPLly9j/Pjxats529nZ4ffff8ezzz5rkJpqplW4uLhAKBQabTs15d4zCtfsaaRSKQDUG4R87dq1EIvF7MvT0xMAkJGRwf5VpVNTU5GZWd2lT0lJQXZ2NgAgOTkZeXl5AICkpCQUFVXf7AkJCeyck/j4eJSVlQEA4uLiUFlZCQCQSCSQy+WgaRoSiQQ0TUMul0MikQAAKisr2a2Gy8rKEB8fDwAoKSlhg20VFRUhKSkJAJCXl4fk5OpYOdnZ2UhJSQEAZGZmIjU1VU2TUChEWloapzTV1U6Ss0komb4R7jWMUL6DA5x2z4GHb1ts2bIFzzzzjJoR6tChA77//nt2J1VD01RXOxUXF7M7gRhjO2ly72kbijwdNUqLMAyDCRMmoKSkBNHR0XWeU1ePyNPTE3l5eXB1deVsj0g1yS8wMBBmZmac0FRXOyXFpKJ81mY4Pi5j2zjPxQXd/1wCl04u+OGHHzBv3jy1GNKDBg3CkSNH2IFfQ9NUV5phGBw/fhx5eXmYMWMG26bG0k6a3HvFxcVwdnaGVCqFvb1984zDU+jUEM2ZMwcnT55EdHQ02rdv36RrSktLIRZXR9gTi8W6qpreUd1YqpuSi9yMvIeKtzbBvvJJaI7sNu7wO7IUju0c8MEHH2DdunVq10ycOBH79u3TSvzo1kSpVOLkyZMghCA8PFxngdcMAalUCgcHB60aIp09vp83bx6OHTuGqKioJhshU6PmryDXSDxxC/S8LbCXP+ntPmzXHv2PLIGNizVmzZqF3bt3q10zd+5cfPPNN0b9P9Hh7zqn0foYESEE8+bNw6FDh/Dff/81e89zUwilEBcXx0md1/68BvxvM6xrGKH77T3R9+himIvNMGnSpFpGaP369di6davRG6H8/HxOtmlNjCIMyNy5c/Hbb7/hyJEjsLOzY+MGi8VijR6/GtqsWW0jEokQFBSk72ponSu7L8F61U9qi1fvd+mCoYcXgYYMYWFhauOFIpEIe/bswbRp0/RRXa1CURTatGljEveuttF6j+j777+HVCpFaGgoPDw82NeBAwc0KofrXVxCCCoqKjil89K352C7cqeaEbrj64uQfxYjr/ARQkJC1IyQtbU1jh07xgkjpEKpVHKqTetCF/q0btq0VUlT6N4mJiYiMDDQ6H9BCSG4sPIQ2uz+Ry3/Zv9+eHb/O0hJuY2wsDC1XTWcnJxw4sQJDBw4sLWrqzMIISgqKgJN05werDYK10xbGPuXszFEIhEGDRqk72q0GEZJI/Lt3Wh3Wn16RuKwIDy3ZyYkly+yUzhUtG/fHmfOnEGPHj1auba6haIouLm5mcS9q20MdvW9KXRvS0tLjVqnskKGc5O+qWWE4seF4flfZuHI4b8xatQoNSPUs2dPxMTEcM4IqVAoFEbdpk1BF/oM1hCZgmt2584do9VZkStF9Oh1aH/tBptHUxQS35yKF7dNxZZvNmPq1Klqk1WHDRuG6OhoeHl56aPKOqfmltNchnfNOIRIJEL//v31XY1mkZvwAKkzvoFH0ZMlG1UiM2Qsn4Xn3wzE3LlzsX37drVrpk6dij179sDCwqK1q9tqUBQFV1dXk7h3tV6m1kvUEqbQvS0pKYGDg4NRzay+czgeFe//AKcaPR2plTVK189FyDPtMHbsWLXV8wDw/vvvY8WKFTA3N2/t6rY6crncJO5dbWOwrlnNtUdchGEYpKWlGZXO2A2nQM/fCusaRuiRowuoPcvQJcAaQUFBtUJ4bN26FV9++SUyMjKMSmtzUI37cV2nLvQZbI/ImGfYNgWhUIi+ffvquxpNgpEpEDV3LzxOqQ9KJ3t3gd8v8/AoKwmDBr2gFtLV1tYWBw4cwLPPPgsARqO1JVAUxYYB4TJGGQakuZjCr0pBQYHB6yzNKsb5sLW1jFDcoEEYdnwxThz/FaGhoWpGyNPTEzExMawRMhat2qCqqorzOnWhz2ANkSn42VlZWQat817kXdwa9Qna3k9j82iKguSliRi36xW8t+BtzJs3Ty1iX2BgICQSCfz8/Ng8Y9CqDbg4W74ujGJmtbYwhe6tv7+/vqtRLzHfnIX46/1wrPGottzCEg9XzMKwkW54JnQYrl27pnbN9OnTsWPHjlohPAxdq7bgg+c3H4PtEZlC9zYnJ8fgdJaXVOL4tB/gsuFXmNUwQg9d24D67UNU2txDv3591YyQUCjEN998g19++aXOOEKGqlUXVFZWcl6nSQ1Wm0L3trCwEK6urvquCkvc2XsoX7QDXQrz1fKT/Pzgs34iPvxoHo4cOaJ2zM3NDQcPHqwzHrkKQ9SqCwghqKqqMol7V9sYrCEyhe5tr1699F0NAECFTIkjHx2H74F/YFdj5TwDCjenjkVZ78cYOqwfiouL1a4bMGAA/vrrr0YD3xmSVl1CURQcHR1N4t7VNrxrpicYhkFWVpbedcYl5ONk2Ab03X8Y5jWMUKGdGOmrZ2JX8s+YNfM1NSMkEonwySef4MKFC02KvmkoWluDiooKzuvkXTMOQQhBWVkZ3N3d9fL5VUqCvd9K4Pfdr/CtVN9zLi3ADw9G2WPJwnG1tnfq27cvdu3apfZUrDH0rbW1IITwM6ubicH2iEyhe+vj46NfnZGJcKxhhGQiM5S8Pw2jjy5EXJJEzQiZm5tjzZo1uHz5skZGCDAQra0ARVFwcHDgvE7eNeMQDMPgwYMHetNpKaLQ/+tpSHdyAwAUtvdAu39WYeB7o0BRFL766it2j7kBAwbg2rVr+OCDD5oV8EvfWluT8vJyzuvkXTMOQQiBTCbTq86ATrb4ZeUbcL54BYPWToXA8omRsbe3x86dO5GQkIBFixa1aMW1IWhtDVT7jZmCTm2j033NmoNqXzNt7pnEw6NrVPuaAUB4eDinQ4Ho4jvKu2Z6whhX3zcXU9JaVlbGeZ1Gtdbsu+++Q8eOHWFpaYmBAwfiypUruvooHh4eI0cnhujAgQNYtGgRPv74Y1y7dg3+/v4YPXo08vLyml4xgcF21rSCQCCAt7c353UCpqXVzs6O8zp1oU8njuzXX3+Nt956CzNnzgQAbN++HcePH8fPP/+M5cuXq50rk8nU4hpLpVIAQGFhIYAn3UCBQACapkFRlEZpgUAAiqKgVCrZfebrSgPqW0DTNA2RSMTuUV9XmmEYCIVCdoBSkzRN03jw4AE8PT0hEok4oam+dlI9NWtIq7FpejrNMAzKy8tRXl6O4uJiVqcxa6qvnVSbIWh1eJloGZlMRoRCITl06JBa/quvvkomTJhQ6/yPP/6YAOBf/It/Gdnr/v37WrMbWu8RFRQUgKZptGnTRi2/TZs2SE5OrnX+ihUrsGjRIvZ9SUkJOnTogAcPHkAsFmu7egZDaWkpPD098fDhQ84/HTQVraaiUyqVwsvLC05OTlorU+/PGC0sLOrc2UEsFnO6MVXY29ubhE7AdLSaik5tjhVpfdRJFbO3ZuhQAMjNzeX8WiMeHp7moXVDZG5ujn79+uHs2bNsHsMwOHv2LAYPHqztj+Ph4eEAOnHNFi1ahNdeew2BgYEYMGAANm/ejMePH7NP0RrCwsICH3/8Mac34gNMRydgOlp5nc1HZ0s8vv32W2zYsAE5OTno06cPtmzZgoEDB+rio3h4eIwcg1trxsPDY3pwewooDw+PUcAbIh4eHr3DGyIeHh69wxsiHh4evWMQhqioqAjTp0+Hvb09HBwc8MYbb6C8vLzBa95++2107twZVlZWcHV1xXPPPVfnEhJDQlOdRUVFmD9/Prp37w4rKyt4eXlhwYIF7MJgQ6U57bljxw6EhobC3t4eFEWxCysNDU3D2xw8eBA+Pj6wtLRE7969ceLEiVaqacvQROfNmzfxwgsvoGPHjqAoCps3b9b48wzCEE2fPh03b95EREQEjh07hqioKMyePbvBa/r164ddu3bh9u3bOH36NAghCAsLA11jd1JDQ1Odjx49wqNHj/DVV18hKSkJu3fvxqlTp/DGG2+0Yq01pzntWVFRgTFjxuCDDz5opVpqjqbhbS5evIiXX34Zb7zxBuLj4zFx4kRMnDgRSUlJrVxzzdBUZ0VFBTp16oR169Y1f/WE1pbPNpNbt24RACQ2NpbNO3nyJKEoimRlZTW5nISEBAKA3Lt3TxfVbDHa0vnHH38Qc3NzolAodFHNFtNSnefOnSMASHFxsQ5r2TwGDBhA5s6dy76naZq0bduWrF27ts7zX3zxRTJ27Fi1vIEDB5K3335bp/VsKZrqrEmHDh3Ipk2bNP5MvfeILl26BAcHBwQGBrJ5I0eOhEAggEQiaVIZjx8/xq5du+Dt7c3uPGFoaEMnADZOsKHGRNaWTkNDLpfj6tWrGDlyJJsnEAgwcuRIXLp0qc5rLl26pHY+AIwePbre8w2B5ujUBno3RDk5OXBzc1PLE4lEcHJyQk5OToPXbtu2Dba2trC1tcXJkycREREBc3NzXVa32bREp4qCggKsXr26UTdHn2hDpyHSUHib+nTl5ORodL4h0Byd2kBnhmj58uWgKKrBV0sHl6dPn474+HicP38e3bp1w4svvoiqqiotKWgaraETqI51M3bsWPTs2ROffPJJyyuuIa2lk8c00Vn//v3338frr7/e4DmdOnWCu7t7rUEwpVKJoqKiRge+xGIxxGIxunbtikGDBsHR0RGHDh3Cyy+/3NLqN5nW0FlWVoYxY8bAzs4Ohw4datYmhy2lNXQaMs0Jb+Pu7m504XD0FcZHZ4bI1dUVrq6ujZ43ePBglJSU4OrVq+jXrx8A4L///gPDMBotkiWEsBv5tSa61llaWorRo0fDwsICR48ehaWlpdbqrgmt3Z6GRs3wNhMnTgTwJLzNvHnz6rxm8ODBOHv2LBYuXMjmRUREGHQ4nObo1AoaD2/rgDFjxpCAgAAikUhIdHQ06dq1K3n55ZfZ45mZmaR79+5EIpEQQgi5f/8++eKLL0hcXBzJyMggMTExZPz48cTJyYnk5ubqS0ajaKpTKpWSgQMHkt69e5N79+6R7Oxs9qVUKvUlo1E01UkIIdnZ2SQ+Pp7s3LmTACBRUVEkPj6eFBYW6kNCnfz+++/EwsKC7N69m9y6dYvMnj2bODg4kJycHEIIITNmzCDLly9nz4+JiSEikYh89dVX5Pbt2+Tjjz8mZmZm5MaNG/qS0CQ01SmTyUh8fDyJj48nHh4eZPHixSQ+Pp6kpKQ0+TMNwhAVFhaSl19+mdja2hJ7e3syc+ZMUlZWxh5PS0sjAMi5c+cIIYRkZWWR8PBw4ubmRszMzEj79u3JtGnTSHJysp4UNA1NdaoeZdf1SktL04+IJqCpTkLq30Rh165drS+gAbZu3Uq8vLyIubk5GTBgALl8+TJ7LCQkhLz22mtq5//xxx+kW7duxNzcnPTq1YscP368lWvcPDTRqWrPp18hISFN/jw+DAgPD4/e0fvjex4eHh7eEPHw8Ogd3hDx8PDoHd4Q8fDw6B3eEPHw8Ogd3hDx8PDoHd4Q8fDw6B3eEPHw8Ogd3hDx8PDoHd4Q8fAYCQ8fPkRoaCh69uwJPz8/HDx4UN9V0hr8Eg8eHiMhOzsbubm56NOnD3JyctCvXz/cvXsXNjY2+q5ai+F7RA0QGhqqFsLBGDDGOhsihYWFcHNzQ3p6ur6rwuLh4YE+ffoAqI515OLigqKiIgDASy+9hI0bN+qxdi3DZA1RY9EG9REFsTV4/fXX1XQ6OztjzJgxSExM1HfVDIo1a9bgueeeQ8eOHfVdlTq5evUqaJpmY7R/9NFHWLNmjcFvNVUfJmuIsrOz2dfmzZthb2+vlrd48WKdfK5cLtdJuZowZswYVufZs2chEokwbtw4fVfLYKioqMBPP/2kl22b+vTpA19f31qvR48esecUFRXh1VdfxY4dO9g8X19fdO7cGb/++mur11kbmKwhcnd3Z19isRgURanl2draAqiOTrd06VI4OTnB3d29Vk+JYRisXbsW3t7esLKygr+/P/7880/2eGhoKObNm4eFCxfCxcUFo0ePZvPnz5+PhQsXwtHREW3atMHOnTvx+PFjzJw5E3Z2dujSpQtOnjyp9nmnTp1CcHAwHBwc4OzsjHHjxuH+/fsaabewsGB19unTB8uXL8fDhw+Rn5/PntOxY8daG+X16dNHTb9MJsOCBQvg5uYGS0tLBAcHIzY2FgCwd+9eODs714qYOXHiRMyYMaPJWkJDQ7FgwYJG22D9+vXo0qULLCws4OXlhTVr1jSpDnVx4sQJWFhYYNCgQWr1aE57Pc2ff/6J3r17w8rKCs7Ozhg5ciQeP37MHr9+/TqSkpJqvdq2bcv+zydOnIjly5djyJAhamWPHz8ev//+e4Ofb6iYrCFqKnv27IGNjQ0kEgnWr1+Pzz77DBEREezxtWvXYu/evdi+fTtu3ryJ9957D6+88grOnz+vVoa5uTliYmKwfft2tXwXFxdcuXIF8+fPx5w5czBlyhQMGTIE165dQ1hYGGbMmIGKigr2msePH2PRokWIi4vD2bNnIRAI8Pzzz4NhmGbpKy8vx6+//oouXbrA2dlZo2uXLl2Kv/76C3v27MG1a9fQpUsXjB49GkVFRZgyZQpomsbRo0fZ8/Py8nD8+HHMmjVLIy2NtcGKFSuwbt06rFy5Erdu3cJvv/2GNm3aNKkOdXHhwgU2zO3T9dC0vWqSnZ2Nl19+GbNmzcLt27cRGRmJSZMmoanPiwgheP311/HMM8/UaUgHDBiAK1eutHq4ZK2glXBuRs6uXbuIWCyulR8SEkKCg4PV8vr370+WLVtGCCGkqqqKWFtbk4sXL6qd88Ybb7ChUUNCQkhAQECjZSuVSmJjY0NmzJjB5mVnZxMA5NKlS/XWPT8/nwBgw4+GhISQd999t97zX3vtNSIUComNjQ2xsbEhAIiHhwe5evWq2nl1bZTn7+9PPv74Y0IIIeXl5cTMzIzs27ePPS6Xy0nbtm3J+vXrCSGEzJkzh4SHh7PHN27cSDp16kQYhmmSFpWehtqgtLSUWFhYkJ07d9ZZpqZ1IISQ5557jsyaNUstTxvtdfXqVQKApKen1/vZDXHhwgVCURTx9/dnX4mJiexx1SajzS1fnxjmLn0GhJ+fn9p7Dw8PdpeKe/fuoaKiAqNGjVI7Ry6XIyAggH1f16/r02ULhUI4Ozujd+/ebJ5qb6mau2KkpKRg1apVkEgkKCgoYHsPDx48gK+vb5M0DR8+HN9//z0AoLi4GNu2bUN4eDiuXLmCDh06NKmM+/fvQ6FQICgoiM0zMzPDgAEDcPv2bQDAW2+9hf79+yMrKwvt2rXD7t272cFyTbQ01Aa3b9+GTCbDiBEj6qxnY3Woi8rKyjo3KWhOe9XE398fI0aMQO/evTF69GiEhYVh8uTJcHR0rLcuNQkODm6w52tlZQUA9fbIDBneEDXC01v3UBTF3gzl5eUAgOPHj6Ndu3Zq51lYWLDp+uZ51FV2zTzVl6XmzTd+/Hh06NABO3fuRNu2bcEwDHx9fTUaBLexsUGXLl3Y9z/++CPEYjF27tyJzz//HED17p7kKZdBoVA0+TMAICAgAP7+/ti7dy/CwsJw8+ZNHD9+XGMtDbWB6svX3DrUhYuLC4qLi2vlN6e9aiIUChEREYGLFy/izJkz2Lp1Kz788ENIJBJ4e3s3WKemoHqU35TdVgwNfoyoBfTs2RMWFhZ48OABunTpovbSxdbXhYWFuHPnDj766COMGDECPXr0qPMLoykURUEgEKCyspLNc3V1RXZ2Nvu+tLQUaWlp7PvOnTuz414qFAoFYmNj0bNnTzbvzTffxO7du7Fr1y6MHDmS/b9oS0vXrl1hZWWFs2fP1ntOfXWoj4CAANy6dUvjujQFiqIQFBSETz/9FPHx8TA3N8ehQ4e0UnZSUhLat28PFxcXrZTXmvA9ohZgZ2eHxYsX47333gPDMAgODoZUKkVMTAzs7e3x2muvafXzHB0d4ezsjB07dsDDwwMPHjzA8uXLNS5HJpOx2wcXFxfj22+/RXl5OcaPH8+e88wzz2D37t0YP348HBwcsGrVKgiFQva4jY0N5syZgyVLlsDJyQleXl5Yv349Kioq1B57T5s2DYsXL8bOnTuxd+9erWuxtLTEsmXLsHTpUpibmyMoKAj5+fm4efMmW4/66lAfo0ePxooVK1BcXNxkt6kpSCQSnD17FmFhYXBzc4NEIkF+fj569OihlfIvXLiAsLAwrZTV2vCGqIWsXr0arq6uWLt2LVJTU+Hg4IC+ffvigw8+0PpnCQQC/P7771iwYAF8fX3RvXt3bNmyBaGhoRqVc+rUKXh4eACoNqY+Pj44ePCgWjkrVqxAWloaxo0bB7FYjNWrV6v1iABg3bp1YBgGM2bMQFlZGQIDA3H69Gm1L69YLMYLL7yA48ePsxv2aVMLAKxcuRIikQirVq3Co0eP4OHhgXfeeafROtRH79690bdvX/zxxx94++23Na5Pfdjb2yMqKgqbN29GaWkpOnTogI0bNyI8PLzFZVdVVeHw4cM4deqUFmra+vBrzXh0zogRI9CrVy9s2bLFaOpw/PhxLFmyBElJSRAIDH8E4/vvv8ehQ4dw5swZfVelWfA9Ih6dUVxcjMjISERGRmLbtm1GVYexY8ciJSUFWVlZOhnv0zZmZmbYunWrvqvRbPgeEY/O6NixI4qLi7Fy5UqdLZkxhjrwNA5viHh4ePSO4Tu/PDw8nIc3RDw8PHqHN0Q8PDx6hzdEPDw8eoc3RDw8PHqHN0Q8PDx6hzdEPDw8eoc3RDw8PHqHN0Q8PDx6hzdEPDw8eoc3RDw8PHqHN0Q8PDx65/8Ager5wEekKPoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b_var='B_thermal'\n",
    "\n",
    "c_B_mean = c_buoyancy[b_var].mean(dim=['Time'])\n",
    "f_B_mean = f_buoyancy[b_var].mean(dim=['Time'])\n",
    "fu_B_mean = fu_buoyancy[b_var].mean(dim=['Time'])\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(3, 3))\n",
    "\n",
    "#ax2 = ax1.twiny()\n",
    "\n",
    "# w>0\n",
    "ax1.plot(c_B_mean, c_B_mean.level, label=f\"Current\", color=\"black\", linewidth=2.5)\n",
    "ax1.plot(f_B_mean, f_B_mean.level, label=f\"Future\", color=\"#1E88E5\", linewidth=2.5)\n",
    "ax1.plot(fu_B_mean, fu_B_mean.level, label=f\"Future+Urban\", color=\"#D81B60\", linewidth=2.5)\n",
    "\n",
    "\n",
    "ax1.plot(np.zeros(17),np.arange(0,17,1), color=\"gray\", alpha=0.6)\n",
    "\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",
    "if w_direction == 'updraft':\n",
    "    title='Updraft'\n",
    "else:\n",
    "    title='Downdraft'\n",
    "\n",
    "# Labels and legend\n",
    "#ax1.set_title(f\"Mean Buoyancy in Convective Cores by Simulation ({month})\")\n",
    "ax1.set_xlabel(\"Thermal Buoyancy (m s$^{-2}$)\")\n",
    "#ax1.set_ylabel(\"Height (km)\")\n",
    "#ax1.legend(loc=\"center\", bbox_to_anchor=(0.14, 0.6), fontsize=10, title='Simulations')\n",
    "#ax1.legend(loc=\"lower left\", fontsize=10, title='Simulations')\n",
    "#ax1.legend(loc=\"center\", bbox_to_anchor=(0.86, 0.22), fontsize=10, title='Simulations')\n",
    "ax1.set_ylim(0,15)\n",
    "ax1.set_xlim(-0.3,0.1)\n",
    "#ax1.invert_xaxis()\n",
    "ax1.grid(alpha=0.75,axis='both', linestyle=':')\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def plot_buoyancy_profiles(c_buoyancy, f_buoyancy, fu_buoyancy, monlist=monlist):\n",
    "    variables = ['B_total', 'B_thermal', 'B_vapor', 'B_cl']\n",
    "    titles = [\"Total Buoyancy\", \"Thermal Contribution\", \"Vapor Contribution\", \"Condenstate Loading Contribution\"]\n",
    "    colors = {\"Current\": \"black\", \"Future\": \"#1E88E5\", \"Future-Urban\": \"#D81B60\"}\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",
    "    fig, axes = plt.subplots(1, 4, figsize=(20, 5), sharey=True)\n",
    "    \n",
    "    for ax, var, var_title in zip(axes, variables, titles):\n",
    "        c_B_mean = c_buoyancy[var].mean(dim=['Time'])\n",
    "        f_B_mean = f_buoyancy[var].mean(dim=['Time'])\n",
    "        fu_B_mean = fu_buoyancy[var].mean(dim=['Time'])\n",
    "\n",
    "        ax.axvline(0, color=\"gray\", alpha=1)\n",
    "        \n",
    "        ax.plot(c_B_mean, c_B_mean.level, label=\"Current\", color=colors[\"Current\"], linewidth=3)\n",
    "        ax.plot(f_B_mean, f_B_mean.level, label=\"Future\", color=colors[\"Future\"], linewidth=3)\n",
    "        ax.plot(fu_B_mean, fu_B_mean.level, label=\"Future-Urban\", color=colors[\"Future-Urban\"], linewidth=3)\n",
    "        \n",
    "        ax.set_title(var_title, loc='left')\n",
    "        ax.set_title(f'{month}', loc='right')\n",
    "        ax.set_xlabel(\"Buoyancy Acceleration (m $s^{-2}$)\")\n",
    "        ax.grid(alpha=0.75, axis='both', linestyle=':')\n",
    "\n",
    "        if var == 'B_vapor' or var == 'B_cl':\n",
    "            ax.set_xlim(-0.05, 0.05)\n",
    "        else:\n",
    "            ax.set_xlim(-0.3, 0.1)\n",
    "\n",
    "        if var_title == 'Total Buoyancy':\n",
    "            ax.legend(loc=\"lower left\", fontsize=10, title='Simulations')\n",
    "        \n",
    "    axes[0].set_ylabel(\"Height (km)\")\n",
    "    axes[0].set_ylim(0, 15)\n",
    "    \n",
    "    #handles, labels = axes[0].get_legend_handles_labels()\n",
    "    #fig.legend(handles, labels, loc='lower center', fontsize=10, title='Simulations', ncol=3)\n",
    "    #fig.suptitle(f\"Convective Core Mean Buoyancy Contributions by Simulation\", fontsize=14)\n",
    "    \n",
    "    plt.tight_layout(rect=[0, 0.05, 1, 1])\n",
    "    plt.show()\n",
    "\n",
    "plot_buoyancy_profiles(c_buoyancy, f_buoyancy, fu_buoyancy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#################### Updraft Width ######################\n",
    "c_width = xr.open_dataset(f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/current/updraft_width_w>5_month{monlist[0]}.nc') \n",
    "f_width = xr.open_dataset(f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future/updraft_width_w>5_month{monlist[0]}.nc')\n",
    "fu_width = xr.open_dataset(f'/pscratch/sd/d/dbrooks/acc2017_analysis/vertical_motion/future_urban/updraft_width_w>5_month{monlist[0]}.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.20039449 2.42123025 3.15230522 3.75768606 3.99436969 3.87235234\n",
      " 3.46713175 3.0971059  2.73136589 2.41630871 2.1966021  2.02027295\n",
      " 1.91941149 1.89797411 1.86613743 1.87692097 1.89037334 1.91761157\n",
      " 1.898031   1.91653252 1.9436362  1.947742   1.9309022  1.93919642\n",
      " 1.95899967 1.99762577 2.01834268 2.02592976 2.05858653 2.08165156\n",
      " 2.09680449 2.12061935 2.13610695 2.13584579 2.1506196  2.1560779\n",
      " 2.15095723 2.14439657 2.16722497 2.18476312 2.18618972 2.20366619\n",
      " 2.20793251 2.19298633 2.1806362  2.17648594 2.16165869 2.18835291\n",
      " 2.21901961 2.25352741 2.27338432 2.33221306 2.41450616 2.65340674\n",
      " 3.03384716 3.49377341 3.5544266  3.36555996 2.88967188]\n"
     ]
    }
   ],
   "source": [
    "print(c_width['std_updraft_width'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c_width_mean = c_width['mean_updraft_width'].where(c_width['mean_updraft_width']).mean(dim='Time')\n",
    "f_width_mean = f_width['mean_updraft_width'].where(f_width['mean_updraft_width']).mean(dim='Time')\n",
    "fu_width_mean = fu_width['mean_updraft_width'].where(fu_width['mean_updraft_width']).mean(dim='Time')\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(3, 3))\n",
    "\n",
    "ax2 = ax1.twiny()\n",
    "\n",
    "# w>0\n",
    "ax1.plot(c_width_mean, c_width_mean.level, label=f\"Current\", color=\"black\", linewidth=2.5)\n",
    "ax1.plot(f_width_mean, f_width_mean.level, label=f\"Future\", color=\"#1E88E5\", linewidth=2.5)\n",
    "ax1.plot(fu_width_mean, fu_width_mean.level, label=f\"Future-Urban\", color=\"#D81B60\", linewidth=2.5)\n",
    "\n",
    "rel_change_acc = calculate_relative_change(f_width_mean, c_width_mean)\n",
    "rel_change_urban = calculate_relative_change_urbanization(f_width_mean, c_width_mean, fu_width_mean)\n",
    "\n",
    "\n",
    "ax2.plot(rel_change_acc, c_width_mean.level, label=f\"ACC Impact\", color=\"black\", linestyle='--', linewidth=2.5)\n",
    "ax2.plot(rel_change_urban, c_width_mean.level, label=f\"Urbanization Impact\", color=\"black\", linestyle=':', linewidth=2.5)\n",
    "ax2.axvline(0, color=\"gray\", alpha=1)\n",
    "\n",
    "# std\n",
    "c_std_var = c_width[f'std_updraft_width']\n",
    "f_std_var = f_width[f'std_updraft_width']\n",
    "fu_std_var = fu_width[f'std_updraft_width']\n",
    "\n",
    "ax1.fill_betweenx(c_width_mean.level, c_width_mean - c_std_var, c_width_mean + c_std_var, color='black', alpha=0.1)\n",
    "ax1.fill_betweenx(f_width_mean.level, f_width_mean - f_std_var, f_width_mean + f_std_var, color=\"#1E88E5\", alpha=0.1)\n",
    "ax1.fill_betweenx(fu_width_mean.level, fu_width_mean - fu_std_var, fu_width_mean + fu_std_var, color=\"#D81B60\", alpha=0.1)\n",
    "\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",
    "w_direction = 'updraft'\n",
    "\n",
    "if w_direction == 'updraft':\n",
    "    title='Updraft'\n",
    "else:\n",
    "    title='Downdraft'\n",
    "\n",
    "# Labels and legend\n",
    "#ax1.set_title(f\"Mean Updraft Volume in Convective Cores by Simulation ({month})\")\n",
    "ax1.set_xlabel(\"# of Grid Cells\")\n",
    "ax1.set_ylabel(\"Height (km)\")\n",
    "#ax1.legend(loc=\"center\", bbox_to_anchor=(0.14, 0.6), fontsize=10, title='Simulations')\n",
    "#ax1.legend(loc=\"lower left\", fontsize=10, title='Simulations')\n",
    "#ax1.legend(loc=\"center\", bbox_to_anchor=(0.86, 0.22), fontsize=10, title='Simulations')\n",
    "ax1.set_ylim(0,15)\n",
    "ax1.set_xlim(0,7)\n",
    "#ax1.invert_xaxis()\n",
    "ax1.grid(alpha=0.75,axis='y', linestyle=':')\n",
    "\n",
    "#ax2.legend(loc=\"center\", bbox_to_anchor=(0.185, 0.45),  fontsize=10, title='Relative Change')\n",
    "#ax2.legend(loc=\"center\", bbox_to_anchor=(0.815, 0.07),  fontsize=10, title='Relative Change')\n",
    "ax2.set_xlim(-15,15)\n",
    "ax2.set_xlabel('Relative Change (%)')\n",
    "ax2.grid(alpha=0.75, linestyle=':')\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "# Create dummy figure\n",
    "fig, ax = plt.subplots(figsize=(10,2))\n",
    "\n",
    "# Row 1: Simulations (solid lines)\n",
    "current_line = mlines.Line2D([], [], color='black', \n",
    "                              label='Current', linewidth=3)\n",
    "warming_line = mlines.Line2D([], [], color='#1E88E5', \n",
    "                              label='Warming', linewidth=3)\n",
    "warming_urban_line = mlines.Line2D([], [], color='#D81B60', \n",
    "                                   label='Warming+Urban', linewidth=3)\n",
    "\n",
    "# Row 2: Relative changes (different line styles)\n",
    "combined_change = mlines.Line2D([], [], color='#FFB507', linestyle='-', \n",
    "                                label='Combined Effect', linewidth=3)\n",
    "warming_effect = mlines.Line2D([], [], color='black', linestyle='--',\n",
    "                               label='Warming Effect', linewidth=3)\n",
    "urban_effect = mlines.Line2D([], [], color='black', linestyle=':',\n",
    "                             label='Urbanization Effect', linewidth=3)\n",
    "\n",
    "# Plot nothing, just showing legends\n",
    "ax.axis('off')  # Hide axes\n",
    "\n",
    "# Combined legend: 3 rows x 2 columns\n",
    "# All simulation lines in left column, all relative change lines in right column\n",
    "legend = ax.legend(handles=[current_line, warming_line, warming_urban_line,\n",
    "                            combined_change, warming_effect, urban_effect],\n",
    "                   loc='center', fontsize=12, ncol=2)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "# Create dummy figure\n",
    "fig, ax = plt.subplots(figsize=(10,2))\n",
    "\n",
    "# Row 1: Simulations (bars)\n",
    "current_bar = mpatches.Patch(color='black', label='Current')\n",
    "warming_bar = mpatches.Patch(color='#1E88E5', label='Warming')\n",
    "warming_urban_bar = mpatches.Patch(color='#D81B60', label='Warming+Urban')\n",
    "\n",
    "# Row 2: Relative changes (different line styles)\n",
    "combined_change = mlines.Line2D([], [], color='#FFB507', linestyle='-', \n",
    "                                label='Combined Effect', linewidth=3)\n",
    "warming_effect = mlines.Line2D([], [], color='black', linestyle='--',\n",
    "                               label='Warming Effect', linewidth=3)\n",
    "urban_effect = mlines.Line2D([], [], color='black', linestyle=':',\n",
    "                             label='Urbanization Effect', linewidth=3)\n",
    "\n",
    "# Plot nothing, just showing legends\n",
    "ax.axis('off')  # Hide axes\n",
    "\n",
    "# Combined legend: 3 rows x 2 columns\n",
    "# All bars in left column, all lines in right column\n",
    "legend = ax.legend(handles=[current_bar, warming_bar, warming_urban_bar,\n",
    "                            combined_change, warming_effect, urban_effect],\n",
    "                   loc='center', fontsize=12, ncol=2)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x90 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create dummy figure\n",
    "fig, ax = plt.subplots(figsize=(2,0.9))\n",
    "\n",
    "\n",
    "# First legend handles line styles\n",
    "accurban_impact = mlines.Line2D([], [], \n",
    "                                color='black', \n",
    "                                #linestyle='-', \n",
    "                                #marker='o',\n",
    "                                #markersize=8, \n",
    "                                label='Current (Mean: 2325.13)', linewidth=3)\n",
    "acc_impact = mlines.Line2D([], [], \n",
    "                           color='#1E88E5', \n",
    "                           #color='blue',\n",
    "                           #linestyle='--',\n",
    "                           #marker='o',\n",
    "                           #markersize=8, \n",
    "                           label='Warming (Mean: 2319.56)', linewidth=3)\n",
    "urban_impact = mlines.Line2D([], [], \n",
    "                             color='#D81B60', \n",
    "                             #color='red',\n",
    "                             #linestyle=':',\n",
    "                             #marker='o', \n",
    "                             #markersize=7, \n",
    "                             label='Warming+Urban (Mean: 2338.16)', linewidth=3)\n",
    "\n",
    "cc_lin = mlines.Line2D([], [], color=\"green\", linestyle='--',\n",
    "                               label='Landfall: 2017-06-22 09:00z', linewidth=3)\n",
    "'''\n",
    "warming_bar = mpatches.Patch(color='#1E88E5', label='Warming vs. Current')\n",
    "warming_urban_bar = mpatches.Patch(color='#D81B60', label='Warming+Urban vs. Current')\n",
    "'''\n",
    "# Plot nothing, just showing legends\n",
    "ax.axis('off')  # Hide axes\n",
    "\n",
    "# First legend: style\n",
    "legend1 = ax.legend(handles=[accurban_impact, acc_impact, urban_impact],\n",
    "                    loc='center left', \n",
    "                    #bbox_to_anchor=(0, 0.5), \n",
    "                    fontsize=12)\n",
    "\n",
    "# Add both legends to axes\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cloud frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "monlist=['06']\n",
    "\n",
    "filename1 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/cloud_stuff/current/vertical_cloud_frequency_month{monlist[0]}.nc'\n",
    "filename2 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/cloud_stuff/future/vertical_cloud_frequency_month{monlist[0]}.nc'\n",
    "filename3 = f'/pscratch/sd/d/dbrooks/acc2017_analysis/cloud_stuff/future_urban/vertical_cloud_frequency_month{monlist[0]}.nc'\n",
    "\n",
    "c_freq_ds = xr.open_dataset(filename1)\n",
    "f_freq_ds = xr.open_dataset(filename2)\n",
    "fu_freq_ds = xr.open_dataset(filename3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- 6.0 km ---\n",
      "Current:        11296855 occurrences\n",
      "Future:         11016993 occurrences\n",
      "Future+Urban:   10969763 occurrences\n",
      "\n",
      "Relative Changes:\n",
      "ACC Impact:                -2.48%\n",
      "ACC+Urban:                 -2.90%\n",
      "Urbanization Impact:       -0.43%\n",
      "\n",
      "--- 12.0 km ---\n",
      "Current:        6023675 occurrences\n",
      "Future:         6867535 occurrences\n",
      "Future+Urban:   6878596 occurrences\n",
      "\n",
      "Relative Changes:\n",
      "ACC Impact:                14.01%\n",
      "ACC+Urban:                 14.19%\n",
      "Urbanization Impact:       0.16%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############### Frequency profile ################\n",
    "fig, ax1 = plt.subplots(figsize=(3, 3))\n",
    "\n",
    "ax2 = ax1.twiny()\n",
    "\n",
    "# w>0\n",
    "ax1.plot(c_freq_ds['cloudy_point_frequency'], c_freq_ds.level, label=f\"Current\", color=\"black\", linewidth=2.5)\n",
    "ax1.plot(f_freq_ds['cloudy_point_frequency'], f_freq_ds.level, label=f\"Future\", color=\"#1E88E5\", linewidth=2.5)\n",
    "ax1.plot(fu_freq_ds['cloudy_point_frequency'], fu_freq_ds.level, label=f\"Future+Urban\", color=\"#D81B60\", linewidth=2.5)\n",
    "\n",
    "rel_change_acc = calculate_relative_change(f_freq_ds['cloudy_point_frequency'], c_freq_ds['cloudy_point_frequency'])\n",
    "rel_change_acc_urban = calculate_relative_change(fu_freq_ds['cloudy_point_frequency'], c_freq_ds['cloudy_point_frequency'])\n",
    "re_change_urban = calculate_relative_change_urbanization(f_freq_ds['cloudy_point_frequency'], c_freq_ds['cloudy_point_frequency'], fu_freq_ds['cloudy_point_frequency'])\n",
    "\n",
    "ax2.plot(rel_change_acc_urban, c_freq_ds.level, color=\"#FFB507\", linestyle='-', linewidth=2.5)\n",
    "ax2.plot(rel_change_acc, f_freq_ds.level, label=f\"ACC Impact\", color=\"black\", linestyle='--', linewidth=2.5)\n",
    "ax2.plot(re_change_urban, fu_freq_ds.level, label=f\"Urbanization Impact\", color=\"black\", linestyle=':', linewidth=2.5)\n",
    "ax2.plot(np.zeros(17),np.arange(0,17,1), color=\"gray\", alpha=0.6)\n",
    "\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",
    "\n",
    "# Labels and legend\n",
    "#fig.suptitle(f\"Frequency of {title} Speeds >99th Percentile ({month})\")\n",
    "ax1.set_xlabel(\"# of Occurrences\")\n",
    "ax1.set_ylabel(\"Height (km)\")\n",
    "#ax1.legend(loc=\"center\", bbox_to_anchor=(0.14, 0.6), fontsize=10, title='Simulations')\n",
    "#ax1.legend(loc=\"center\", bbox_to_anchor=(0.86, 0.22), fontsize=10, title='Simulations')\n",
    "ax1.set_ylim(0,15)\n",
    "#ax1.set_xlim(0,3000)\n",
    "ax1.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))\n",
    "#ax1.invert_xaxis()\n",
    "ax1.grid(alpha=0.75,axis='y', linestyle=':')\n",
    "\n",
    "#ax2.legend(loc=\"center\", bbox_to_anchor=(0.185, 0.45),  fontsize=10, title='Relative Change')\n",
    "#ax2.legend(loc=\"center\", bbox_to_anchor=(0.815, 0.07),  fontsize=10, title='Relative Change')\n",
    "ax2.set_xlim(-20,80)\n",
    "ax2.set_xlabel('Relative Change (%)')\n",
    "ax2.grid(alpha=0.75, linestyle=':')\n",
    "\n",
    "for height in [6.0, 12.0]:\n",
    "    print(f\"\\n--- {height} km ---\")\n",
    "    print(f\"Current:        {c_freq_ds['cloudy_point_frequency'].sel(level=height, method='nearest').values:.0f} occurrences\")\n",
    "    print(f\"Future:         {f_freq_ds['cloudy_point_frequency'].sel(level=height, method='nearest').values:.0f} occurrences\")\n",
    "    print(f\"Future+Urban:   {fu_freq_ds['cloudy_point_frequency'].sel(level=height, method='nearest').values:.0f} occurrences\")\n",
    "    print(f\"\\nRelative Changes:\")\n",
    "    print(f\"ACC Impact:                {rel_change_acc.sel(level=height, method='nearest').values:.2f}%\")\n",
    "    print(f\"ACC+Urban:                 {rel_change_acc_urban.sel(level=height, method='nearest').values:.2f}%\")\n",
    "    print(f\"Urbanization Impact:       {re_change_urban.sel(level=height, method='nearest').values:.2f}%\")\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "atms-shap",
   "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": "3.11.14"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}
