Model_Ensemble_Percentile.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fortworthjack/NotebooksFromColab/blob/main/Model_Ensemble_Percentile.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ensemble Model Data Image Creater\n",
        "V1.0 - Steven Fleegel (steven.fleegel@noaa.gov)\n",
        "\n",
        "This script works with the GEFS/EC-Ensemble Data and can plot 30/50-panels or single panel images of percentile or prob of exceedance calculations.\n",
        "\n",
        "**Data Availability:** <br>\n",
        "GEFS: 1/1/2017 to Present <br>\n",
        "EC-EPS: 1/18/2023 to Present"
      ],
      "metadata": {
        "id": "1fDlJEzIneVq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Run this to download, install, and import the appropriate packages.**  \n",
        "This first cell will take around a minute to run and get everything ready for us to download the data and make the image(s)."
      ],
      "metadata": {
        "id": "n3aMI2TiwCyv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Install the packages needed for this notebook.\n",
        "!pip install -q cartopy contextily pyproj pyepsg xesmf netCDF4 herbie-data metpy eccodes --progress-bar off\n",
        "\n",
        "## Imports\n",
        "from herbie import Herbie\n",
        "from herbie import FastHerbie\n",
        "#from toolbox import EasyMap, pc\n",
        "#from paint.standard2 import cm_tmp\n",
        "import pandas as pd\n",
        "from datetime import datetime, timedelta\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patheffects as pe\n",
        "import matplotlib.colors\n",
        "from matplotlib import ticker\n",
        "import cartopy.crs as ccrs\n",
        "import cartopy.feature as cfeature\n",
        "\n",
        "import numpy as np\n",
        "import scipy.ndimage as ndimage\n",
        "import xarray as xr\n",
        "from copy import deepcopy\n",
        "#import xoak\n",
        "\n",
        "from metpy.cbook import get_test_data\n",
        "#from metpy.plots import add_metpy_logo\n",
        "import metpy.plots\n",
        "from metpy.plots import ctables\n",
        "import metpy.calc as mpcalc\n",
        "#from metpy.units import units\n",
        "# Import for plotting High/Low\n",
        "from scipy.ndimage import maximum_filter, minimum_filter\n",
        "\n",
        "print (\"Install & Import Done!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WD4t0_uuMA58",
        "outputId": "b608b0f2-4155-4b8c-8d93-51ed8873ff70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for findlibs (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            " ╭─\u001b[48;2;255;255;255m\u001b[38;2;136;33;27m▌\u001b[0m\u001b[38;2;12;53;118m\u001b[48;2;240;234;210m▌\u001b[38;2;0;0;0m\u001b[1mHerbie\u001b[0m─────────────────────────────────────────────╮\n",
            " │ INFO: Created a default config file.                 │\n",
            " │ You may view/edit Herbie's configuration here:       │\n",
            " │ \u001b[38;2;255;153;0m         /root/.config/herbie/config.toml         \u001b[0m   │\n",
            " ╰──────────────────────────────────────────────────────╯\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
            "  setattr(self, word, getattr(machar, word).flat[0])\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
            "  return self._float_to_str(self.smallest_subnormal)\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
            "  setattr(self, word, getattr(machar, word).flat[0])\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
            "  return self._float_to_str(self.smallest_subnormal)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Install & Import Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Download Data and Prep for image creation**\n",
        "\n",
        "Because this is downloading all of the ensemble members, this may take some time to complete (GEFS takes 5-10 min for 72hr Accumulations, but individual hours are much faster).  As long as you are not changing the valid times or Accumumlation Period, you will be able to create different images with selections in the next section below."
      ],
      "metadata": {
        "id": "yYpg4w_e2xte"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#### GEFS Info #####\n",
        "#The GEFS model product could be any of the following:\n",
        "#- 'atmos.5' - Half degree atmos PRIMARY fields (pgrb2ap5); ~83 most common variables.\n",
        "#- 'atmos.5b' - Half degree atmos SECONDARY fields (pgrb2bp5); ~500 least common variables\n",
        "#- 'atmos.25' - Quarter degree atmos PRIMARY fields (pgrb2sp25); ~35 most common variables\n",
        "#- 'wave' - Global wave products. - 'chem.5' - Chemistry fields on 0.5 degree grid\n",
        "#- 'chem.25' - Chemistry fields on 0.25 degree grid\n",
        "#\n",
        "#ou also must specify a member.\n",
        "#For the atmos output, this should be something like:\n",
        "# 0 or \"c00\" for control member,\n",
        "# 1-30 or \"p01\"-\"p30\" for members 1-30,\n",
        "# or 'avg' or 'mean' for the ensemble mean,\n",
        "# or 'spr' for ensemble spread.\n",
        "\n",
        "# Base Variables\n",
        "Map_Type_Single = False\n",
        "IncludeCounties = False\n",
        "tick_bins = None\n",
        "Add_Sfc_Contours = False\n",
        "\n",
        "# Google Colab Selections\n",
        "#@markdown # **Initial Forecast Info**\n",
        "#@markdown <b>Select Model - ifs = EC-EPS</b>\n",
        "Model = \"ifs\" #@param ['gefs','ifs']\n",
        "#@markdown <b>Select Model Run</b>\n",
        "Model_Date = \"2024-03-22\" #@param {type:\"date\"}\n",
        "Model_Run = 12 #@param {type:\"slider\", min:0, max:18, step:6}\n",
        "Model_Run=str(Model_Run).zfill(2)+\":00\"\n",
        "dtg = Model_Date+\" \"+Model_Run\n",
        "\n",
        "#@markdown <b>Select Forecast Valid Time</b><br />\n",
        "Forecast_Date = \"2024-03-26\" #@param {type:\"date\"}\n",
        "Forecast_Time = 12 #@param {type:\"slider\", min:0, max:23, step:1}\n",
        "Forecast_Time=str(Forecast_Time)+\":00\"\n",
        "Forecast_Valid_Time = Forecast_Date+\" \"+Forecast_Time\n",
        "\n",
        "# Parse strings into datetime objects\n",
        "time1 = datetime.strptime(dtg, \"%Y-%m-%d %H:%M\")\n",
        "time2 = datetime.strptime(Forecast_Valid_Time, \"%Y-%m-%d %H:%M\")\n",
        "# Calculate time difference\n",
        "time_difference = time2 - time1\n",
        "# Convert time difference to hours\n",
        "fhr = int(time_difference.total_seconds() / 3600)\n",
        "##@markdown <b>Select forecast hour</b><br />\n",
        "#fhr = 96 #@param {type:\"slider\", min:0, max:360, step:6}\n",
        "step = np.arange(0, 73, 1, dtype=int)\n",
        "dt = str.split(Model_Date,'-')\n",
        "\n",
        "#@markdown # **Model Data**\n",
        "#@markdown <b>Select Model Data</b>\n",
        "Map_Data = \"QPF\" #@param [\"QPF\",\"Temp\", \"SnowDepth\"]\n",
        "#@markdown Note: Snow Depth is only available for the GEFS.\n",
        "#@markdown If you selected \"Temp\", what level do you want?<br />\n",
        "Temp_Level = \"850\" #@param [\"925\",\"850\", \"700\"]\n",
        "#@markdown If you selected \"QPF\", what accumulation period do you want?<br />\n",
        "Accum_Period = \"24\" #@param [\"6\",\"12\",\"24\", \"36\", \"48\", \"72\", \"RunTotal\",\"None\"]\n",
        "\n",
        "#@markdown # **Map: Area and Type**\n",
        "#@markdown <b>Select Map Info - Zoom</b>\n",
        "Map_Zoom = \"SD\" #@param [\"ABR\", \"SD\", \"MN\", \"Dakotas\", \"Hawaii\", \"AR\", \"CONUS\", \"NA\", \"SPC_13\", \"SPC_15\", \"SPC_18\", \"GulfMex\", \"Custom\"]\n",
        "#@markdown ### If a Custom domain is selected, adjust the items below<br>\n",
        "#@markdown Enter a custom name if you want (mainly used for filename)\n",
        "custom_name = \"FSD\" #@param {type:\"string\"}\n",
        "#@markdown Enter custom lat/lon bounding box if custom was selected\n",
        "custom_bottom_lat =  42.2 #@param {type:\"number\"}\n",
        "custom_left_lon = -101.32 #@param {type:\"number\"}\n",
        "custom_top_lat = 45.32 #@param {type:\"number\"}\n",
        "custom_right_lon = -94.97 #@param {type:\"number\"}\n",
        "\n",
        "# Model Info.\n",
        "if Model == \"gefs\":\n",
        "  if Map_Data == \"Wind Gust\":\n",
        "    Model_Product = 'atmos.5b'\n",
        "  else:\n",
        "    Model_Product = 'atmos.5' #'atmos.25'\n",
        "  Model_Members = 30\n",
        "if Model == \"ifs\":\n",
        "  Model_Product = 'enfo'\n",
        "  Model_Members = 50\n",
        "\n",
        "## Define Map Extents\n",
        "if Map_Zoom == \"ABR\":\n",
        "    #ABR\n",
        "    Map_Extent = [-102.32, -95.97, 43.2, 46.32]\n",
        "elif Map_Zoom == \"SD\":\n",
        "    #SD\n",
        "    Map_Extent = [-104.5, -95.85, 42.3, 46.4]\n",
        "elif Map_Zoom == \"Dakotas\":\n",
        "    #Dakotas\n",
        "    Map_Extent = [-104.5, -95.85, 42.3, 49.27]\n",
        "elif Map_Zoom == \"MN\":\n",
        "    #CONUS\n",
        "    Map_Extent = [-97.8, -89.1, 43.2, 49.7]\n",
        "elif Map_Zoom == \"CONUS\":\n",
        "    #CONUS\n",
        "    Map_Extent = [-125, -65, 18, 58]\n",
        "elif Map_Zoom == \"NA\":\n",
        "    #North America\n",
        "    Map_Extent = [-156, -46, 10, 67]\n",
        "elif Map_Zoom == \"Hawaii\":\n",
        "    #Hawaii\n",
        "    Map_Extent = [-160.5, -154, 18, 23]\n",
        "elif Map_Zoom == \"SPC_13\":\n",
        "    #SPC MesoA Zoom 13\n",
        "    Map_Extent = [-109.28846, -86.61031, 38.20657, 50]\n",
        "elif Map_Zoom == \"SPC_15\":\n",
        "    #SPC MesoA Zoom 15\n",
        "    Map_Extent = [-108.85, -86.395, 25.52, 38.494]\n",
        "elif Map_Zoom == \"SPC_18\":\n",
        "    #SPC MesoA Zoom 18\n",
        "    Map_Extent = [-96.58446, -76.61933, 23.15515, 37.45531]\n",
        "elif Map_Zoom == \"AR\":\n",
        "    #CONUS\n",
        "    Map_Extent = [-95.5, -89.395, 32.75, 37.23]\n",
        "elif Map_Zoom == \"GulfMex\":\n",
        "    #Gulf of Mexico\n",
        "    Map_Extent = [-101, -77, 16.5, 32]\n",
        "elif Map_Zoom == \"Custom\":\n",
        "    # Custom\n",
        "    Map_Extent = [custom_left_lon, custom_right_lon, custom_bottom_lat, custom_top_lat]\n",
        "\n",
        "# Set Projection\n",
        "# Make the Central Lat/Lon in the middle of the map\n",
        "cent_lon = ((Map_Extent[0] - Map_Extent[1])/2) + Map_Extent[1]\n",
        "cent_lat = ((Map_Extent[3] - Map_Extent[2])/2) + Map_Extent[2]\n",
        "# Assign projection\n",
        "#crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)\n",
        "crs = ccrs.LambertConformal(central_longitude=cent_lon, central_latitude=cent_lat)\n",
        "\n",
        "# Download data via Herbie\n",
        "### GRIB Info: https://www.nco.ncep.noaa.gov/pmb/products/gens/gep01.t00z.pgrb2a.0p50.f003.shtml\n",
        "#data = H.xarray(\":APCP:\")\n",
        "#data['tp'] = data.tp * 0.0393701\n",
        "#H = Herbie(dtg, model=Model, product=Model_Product, member=Model_Members, fxx=fhr)\n",
        "#data = H.xarray(\":APCP:\")\n",
        "#data['tp'] = data.tp * 0.0393701\n",
        "#data = data.rename({'tp': 'p01'})\n",
        "if Model == \"gefs\":\n",
        "  if Map_Data == \"Wind Gust\":\n",
        "    search_param = \":GUST:\"\n",
        "  elif Map_Data == \"Temp\":\n",
        "    search_param = \"TMP:\"+ Temp_Level + \" mb\"\n",
        "  elif Map_Data == \"SnowDepth\":\n",
        "    search_param = \":SNOD:\"\n",
        "  else:\n",
        "    search_param = \":APCP:\"\n",
        "if Model == \"ifs\":\n",
        "  if Map_Data == \"Temp\":\n",
        "    search_param = \":t:\"+ Temp_Level\n",
        "  else:\n",
        "    search_param = \":tp:\"\n",
        "\n",
        "if Model == \"gefs\":\n",
        "  for x in range(1, Model_Members+1):\n",
        "    data_variable = \"p\"+str(x).zfill(2)\n",
        "    print(\"Downloading GEFS member: \", data_variable)\n",
        "    if Map_Data == \"QPF\" and Accum_Period != \"RunTotal\" and Accum_Period != \"None\":\n",
        "      fcstdl = range((fhr-int(Accum_Period)+6), fhr+1, 6)\n",
        "    else:\n",
        "      fcstdl = [fhr]\n",
        "    DATES = pd.date_range(\n",
        "        start=dtg,\n",
        "        periods=1,\n",
        "        freq=\"1h\",\n",
        "    )\n",
        "    FH = FastHerbie(DATES, model=Model, product=Model_Product, member=data_variable, fxx=fcstdl, remove_grib=False) #, remove_grib=False\n",
        "    if x == 1:\n",
        "      data = FH.xarray(search_param)\n",
        "      if Map_Data == \"QPF\":\n",
        "        data[data_variable] = data.tp * 0.0393701\n",
        "        data = data.drop_vars(['tp'])\n",
        "      elif Map_Data == \"SnowDepth\":\n",
        "        data[data_variable] = data.sde * 39.3701#0.0393701\n",
        "        data = data.drop_vars(['sde'])\n",
        "      else:\n",
        "        data[data_variable] = data.t - 273.15 #Convert to C\n",
        "        data = data.drop_vars(['t'])\n",
        "    else:\n",
        "      datatemp = FH.xarray(search_param)\n",
        "      if Map_Data == \"QPF\":\n",
        "        data[data_variable] = datatemp.tp * 0.0393701\n",
        "      elif Map_Data == \"SnowDepth\":\n",
        "        data[data_variable] = datatemp.sde * 39.3701 #0.0393701\n",
        "      else:\n",
        "        data[data_variable] = datatemp.t - 273.15 #Convert to C\n",
        "      del datatemp\n",
        "    if Map_Data == \"QPF\":\n",
        "      if Accum_Period != \"6\":\n",
        "        data = data.sum(dim='step')\n",
        "#    H = Herbie(dtg, model=Model, product=Model_Product, member=data_variable, fxx=fhr)\n",
        "#    if x == 1:\n",
        "#      data = H.xarray(search_param)\n",
        "#      data[data_variable] = data.tp * 0.0393701\n",
        "#      data = data.drop_vars(['tp'])\n",
        "#    else:\n",
        "#      datatemp = H.xarray(search_param)\n",
        "#      data[data_variable] = datatemp.tp * 0.0393701\n",
        "#      del datatemp\n",
        "else:\n",
        "  H = Herbie(dtg, model=Model, product=Model_Product, fxx=fhr, remove_grib=False)\n",
        "  data_per1 = H.xarray(search_param)\n",
        "  if Map_Data == \"QPF\" and Accum_Period != \"RunTotal\" and Accum_Period != \"None\":\n",
        "    # Since ECMWF provides total precip, need to grab previous to get 6hr data\n",
        "    # Grab 3hrs before, if within 144hrs\n",
        "    #if fhr < 145:\n",
        "    #  H1 = Herbie(dtg, model=Model, product=Model_Product, fxx=fhr-3)\n",
        "    #  data_per2 = H1.xarray(search_param)\n",
        "    #else:\n",
        "    H1 = Herbie(dtg, model=Model, product=Model_Product, fxx=fhr-int(Accum_Period), remove_grib=False)\n",
        "    data_per2 = H1.xarray(search_param)\n",
        "    #testdata = data[0] - data1[0]\n",
        "    data = data_per1[0].tp - data_per2[0].tp\n",
        "    data = data * 0.0393701 * 1000\n",
        "  else:\n",
        "    #data = data_per1[0].tp - data_per2[0].tp\n",
        "    if Map_Data == \"QPF\":\n",
        "      data = data_per1[0].tp * 0.0393701 * 1000\n",
        "    elif Map_Data == \"SnowDepth\":\n",
        "      data = data_per1[0].tp * 39.3701 #0.0393701\n",
        "    else:\n",
        "      data = data_per1[0].t - 273.15 # Convert to C\n",
        "\n",
        "#if Map_Type == \"Sfc Wind Gust\":\n",
        "#  data = H.xarray(\"GUST\")\n",
        "#  data['gust'] = data.gust.metpy.convert_units('mph')\n",
        "#  max_type = 'gust'\n",
        "\n",
        "\n",
        "padding = 1.5 # padding around domain\n",
        "\n",
        "# Clip Model Data to save processing/mapping time\n",
        "# Clip 1D Lat/Lon\n",
        "if Model == \"gefs\":\n",
        "  final_data = data.sel(latitude=slice((Map_Extent[3]+padding),(Map_Extent[2]-padding)), longitude=slice((Map_Extent[0]+360.-padding),(Map_Extent[1]+360.+padding)))\n",
        "else:\n",
        "  final_data = data.sel(latitude=slice((Map_Extent[3]+padding),(Map_Extent[2]-padding)), longitude=slice((Map_Extent[0]-padding),(Map_Extent[1]+padding)))\n",
        "  # Needed for Original - Single file data??? below\n",
        "  #final_data = data[0].sel(latitude=slice((Map_Extent[3]+padding),(Map_Extent[2]-padding)), longitude=slice((Map_Extent[0]-padding),(Map_Extent[1]+padding)))\n",
        "\n",
        "# Get Max Values\n",
        "#if max_type != None:\n",
        "#  max_value = final_data.get(max_type).max()#.max_type.values\n",
        "\n",
        "#Remove extra variable\n",
        "if Model == \"gefs\":\n",
        "  final_data = final_data.drop_vars(\"gribfile_projection\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8lBLy6LELhOj",
        "outputId": "2bca0da1-5f27-487f-904e-b0de4b151fdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Found ┊ model=ifs ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Mar-22 12:00 UTC\u001b[92m F96\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ azure\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ azure\u001b[0m\n",
            "Note: Returning a list of [2] xarray.Datasets because cfgrib opened with multiple hypercubes.\n",
            "✅ Found ┊ model=ifs ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Mar-22 12:00 UTC\u001b[92m F72\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ azure\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ azure\u001b[0m\n",
            "Note: Returning a list of [2] xarray.Datasets because cfgrib opened with multiple hypercubes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Create Image**\n",
        "\n",
        "This section will create the image based on the data selections.  Remember, if you want to change the Map_Type (i.e. QPF to Snow Depth), you'll need to download the data above. Otherwise, you can change selections and rerun this section of code as much as you want."
      ],
      "metadata": {
        "id": "ZSDCoTb_27b2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set Variables\n",
        "Map_Type_Single = False\n",
        "\n",
        "#Default Map Size\n",
        "Map_Fig_Size = (13, 8)\n",
        "\n",
        "#@markdown # **Individual Map Type Selections**\n",
        "#@markdown ## What type of graphic do you want to create?\n",
        "Map_Type = \"Percentile\" #@param [\"All_Members\", \"ProbExceed\", \"Percentile\", \"Percentile-6Panel\"]\n",
        "\n",
        "#@markdown ## <br>ProbExceed Map - Select Probability of Exceedance\n",
        "#@markdown ### Select QPF Probability of Exceedance\n",
        "qpf_poe = 0.5 #@param {type:\"slider\", min:0, max:3, step:0.01}\n",
        "#@markdown ### Select Temp Probability of Exceedance\n",
        "Temp_poe = -15 #@param {type:\"slider\", min:-30, max:30, step:1}\n",
        "#@markdown ### Select Snow Depth Probability of Exceedance\n",
        "SD_poe = 6 #@param {type:\"slider\", min:0, max:36, step:1}\n",
        "#@markdown ## <br>Single Percentile Map - Select Percentile\n",
        "Pctl_Single = 50 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "#@markdown ## <br> Percenitle 6-Panel Map - Select Percentiles\n",
        "Pctl_UL = 10 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "Pctl_UM = 25 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "Pctl_UR = 50 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "Pctl_LL = 75 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "Pctl_LM = 90 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "Pctl_LR = 95 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "\n",
        "#@markdown # **Custom Map Adjustments**\n",
        "\n",
        "# @markdown ###If needed, adjust for map panel spacing\n",
        "# @markdown ##### Adjust the height and width dimensions of the figure. - May require some testing\n",
        "# @markdown For a traditional landscape rectangle, these values should get you close:\n",
        "# @markdown <ol><li>All_Members: <ol><li>GEFS: Height = 13, Width = 20</li><li>ECMWF-Ens: Height = 11, Width = 28</li></ol>\n",
        "# @markdown <li>ProbExceed: Height = 9, Width = 13</li>\n",
        "# @markdown <li>Percentile: Height = 9, Width = 13</li>\n",
        "# @markdown <li>Percentile-6Panel: Height = 8, Width = 13</li>\n",
        "fig_height = 11 # @param {type:\"raw\"}\n",
        "fig_height = int(fig_height)\n",
        "fig_width = 28 # @param {type:\"raw\"}\n",
        "fig_width = int(fig_width)\n",
        "\n",
        "## Redefine Map Info for Changing QPF items\n",
        "## Define Map Extents\n",
        "if Map_Zoom == \"ABR\":\n",
        "    #ABR\n",
        "    IncludeCounties = True\n",
        "elif Map_Zoom == \"SD\":\n",
        "    #SD\n",
        "    if Map_Type == \"All_Members\":\n",
        "      if Model == \"ifs\":\n",
        "        Map_Fig_Size = (28, 11)\n",
        "      else:\n",
        "        Map_Fig_Size = (20, 13)\n",
        "    IncludeCounties = True\n",
        "elif Map_Zoom == \"Dakotas\":\n",
        "    #Dakotas\n",
        "    if Map_Type == \"All_Members\":\n",
        "      if Model == \"ifs\":\n",
        "        Map_Fig_Size = (16, 10)\n",
        "      else:\n",
        "        Map_Fig_Size = (14, 14.5)\n",
        "    elif Map_Type == \"Percentile-6Panel\":\n",
        "      Map_Fig_Size = (14,12)\n",
        "    else:\n",
        "      Map_Fig_Size = (14,16)\n",
        "    IncludeCounties = True\n",
        "elif Map_Zoom == \"MN\":\n",
        "    #CONUS\n",
        "    Map_Fig_Size = (14,16)\n",
        "    IncludeCounties = True\n",
        "elif Map_Zoom == \"CONUS\":\n",
        "    #CONUS\n",
        "    Map_Fig_Size = (14, 14)\n",
        "#elif Map_Zoom == \"NA\":\n",
        "#    #North America\n",
        "elif Map_Zoom == \"Hawaii\":\n",
        "    #Hawaii\n",
        "    IncludeCounties = True\n",
        "elif Map_Zoom == \"SPC_13\":\n",
        "    #SPC MesoA Zoom 13\n",
        "    if Map_Type == \"All_Members\":\n",
        "      if Model == \"ifs\":\n",
        "        Map_Fig_Size = (28, 11)\n",
        "      else:\n",
        "        Map_Fig_Size = (20, 11)\n",
        "    else:\n",
        "      Map_Fig_Size = (14, 11)\n",
        "    IncludeCounties = True\n",
        "elif Map_Zoom == \"SPC_15\":\n",
        "    #SPC MesoA Zoom 15\n",
        "    IncludeCounties = True\n",
        "elif Map_Zoom == \"SPC_18\":\n",
        "    #SPC MesoA Zoom 18\n",
        "    IncludeCounties = True\n",
        "    Map_Fig_Size = (16,14)\n",
        "elif Map_Zoom == \"AR\":\n",
        "    #CONUS\n",
        "    #Map_Fig_Size = (17,16)\n",
        "    IncludeCounties = True\n",
        "#elif Map_Zoom == \"GulfMex\":\n",
        "    #Gulf of Mexico\n",
        "    #Map_Fig_Size = (16,14)\n",
        "elif Map_Zoom == \"Custom\":\n",
        "    # Custom\n",
        "    Map_Fig_Size = (fig_width, fig_height)\n",
        "    IncludeCounties = True\n",
        "\n",
        "# Function used to create the map subplots\n",
        "def plot_background(ax):\n",
        "    ax.set_extent(Map_Extent)\n",
        "    if IncludeCounties:\n",
        "      ## Get data to plot state and county boundaries, roads\n",
        "      counties = cfeature.NaturalEarthFeature(\n",
        "            category='cultural',\n",
        "            name='admin_2_counties',\n",
        "            scale='10m',\n",
        "            facecolor='none')\n",
        "      ax.add_feature(counties,edgecolor='grey',linewidth=0.25) #, zorder=3.4)\n",
        "    ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=0.5)\n",
        "    ax.add_feature(cfeature.STATES, linewidth=0.5)\n",
        "    ax.add_feature(cfeature.BORDERS, linewidth=0.5)\n",
        "    return ax\n",
        "\n",
        "# Data Projection\n",
        "map_crs = ccrs.PlateCarree()\n",
        "\n",
        "# Set projection of map display\n",
        "# This significantly increases run time. - Would need to change this line: ax = plt.subplot(projection=mapproj)\n",
        "mapproj = ccrs.LambertConformal(central_longitude=cent_lon, central_latitude=cent_lat)\n",
        "\n",
        "# Valid Time\n",
        "# Create a clean datetime object for plotting based on time of Geopotential heights\n",
        "if Model == \"gefs\":\n",
        "  ### Previous version before moving to FastHerbie\n",
        "  #vtime = datetime.strptime(str(final_data.valid_time.data.astype('datetime64[ms]')),\n",
        "  #                        '%Y-%m-%dT%H:%M:%S.%f')\n",
        "  # Convert the string to a datetime object\n",
        "  vtime = datetime.strptime(dtg, '%Y-%m-%d %H:%M')\n",
        "  # Add 120 hours to the datetime\n",
        "  vtime += timedelta(hours=fhr)\n",
        "  #vtime_txt = \"TEST\"\n",
        "else:\n",
        "  vtime = datetime.strptime(str(data_per1[0].valid_time.data.astype('datetime64[ms]')),\n",
        "                          '%Y-%m-%dT%H:%M:%S.%f')\n",
        "#COMMENTED THIS OUT BELOW FOR TESTING\n",
        "vtime_txt = vtime.strftime(\"%m/%d/%Y %HZ\")\n",
        "# Model Time\n",
        "# Create a clean datetime object for plotting based on time of Geopotential heights\n",
        "model_time = datetime.strptime(str(final_data.time.data.astype('datetime64[ms]')),\n",
        "                          '%Y-%m-%dT%H:%M:%S.%f')\n",
        "model_time_txt = model_time.strftime(\"%m/%d/%Y %HZ\")\n",
        "\n",
        "#### Figure Creation ####\n",
        "# Create the figure and plot background on different axes\n",
        "if Map_Type == \"All_Members\":\n",
        "  if Model == \"gefs\":\n",
        "    fig, axarr = plt.subplots(nrows=5, ncols=6, figsize=Map_Fig_Size, constrained_layout=True,\n",
        "                            subplot_kw={'projection': mapproj})\n",
        "  else:\n",
        "    fig, axarr = plt.subplots(nrows=5, ncols=10, figsize=Map_Fig_Size, constrained_layout=True,\n",
        "                            subplot_kw={'projection': mapproj})\n",
        "elif Map_Type == \"Percentile-6Panel\":\n",
        "  fig, axarr = plt.subplots(nrows=2, ncols=3, figsize=Map_Fig_Size, constrained_layout=True,\n",
        "                            subplot_kw={'projection': mapproj})\n",
        "else:\n",
        "  fig = plt.figure(figsize=Map_Fig_Size)\n",
        "  #ax = plt.axes(projection=mapproj)\n",
        "  ax = plt.subplot(projection=mapproj)\n",
        "  ax.set_extent(Map_Extent)\n",
        "\n",
        "#Accum_Period\n",
        "# Map info for Single Panel Plots\n",
        "if Map_Type == \"ProbExceed\" or Map_Type == \"Percentile\":\n",
        "  if IncludeCounties:\n",
        "    ## Get data to plot state and county boundaries, roads\n",
        "    counties = cfeature.NaturalEarthFeature(\n",
        "        category='cultural',\n",
        "        name='admin_2_counties',\n",
        "        scale='10m',\n",
        "        facecolor='none')\n",
        "    ax.add_feature(counties,edgecolor='black',linewidth=0.25) #, zorder=3.4)\n",
        "  ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=0.5)\n",
        "  ax.add_feature(cfeature.STATES, linewidth=1)\n",
        "  ax.add_feature(cfeature.BORDERS, linewidth=2)\n",
        "\n",
        "#Run Total Accum\n",
        "if Map_Data == \"QPF\" and Map_Type != \"ProbExceed\":\n",
        "  if Map_Type == \"Percentile\":\n",
        "    Map_Type_Single = True\n",
        "  #Data_Values = final_data.p01\n",
        "  if Accum_Period == \"RunTotal\":\n",
        "    Panel_1_range = [0.00, 0.01, 0.10, 0.25, 0.5, 1.0, 1.5, 2., 3., 4.,\n",
        "         6., 8., 10., 15., 20., 30., 50.]\n",
        "    if Map_Type == \"Percentile-6Panel\":\n",
        "      Tick_range = [0.00, 0.01, 0.25, 1.0, 2., 4.,\n",
        "          8., 15., 30., 50.]\n",
        "    else:\n",
        "      Tick_range = [0.00, 0.01, 0.10, 0.25, 0.5, 1.0, 1.5, 2., 3., 4.,\n",
        "          6., 8., 10., 15., 20., 30., 50.]\n",
        "    if Model == \"ifs\":\n",
        "      panel_title_text = ' EPS - Run Total Precipitation (inches) '\n",
        "    else:\n",
        "      panel_title_text = ' Run Total Precipitation (inches) '\n",
        "    #NWS QPF Colortable\n",
        "    colors = [\"#FFFFFF\", \"#c7e9c0\", \"#a1d99b\", \"#74c477\", \"#31a353\", \"#006d2c\", \"#fff98a\", \"#ffcd4f\",\n",
        "            \"#fe8d3c\", \"#fc4d2a\", \"#d61a1d\", \"#ad0025\",\n",
        "            \"#700025\", \"#3b0030\", \"#3b0030\", \"#4d0073\", \"#ffdbff\"]\n",
        "  else:\n",
        "    Panel_1_range = [0.00, 0.01, 0.03, 0.05, 0.075, 0.10, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5,\n",
        "                   0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.4, 1.6, 1.8, 2, 2.5, 3, 3.5, 4,\n",
        "                   6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
        "    if Map_Type == \"Percentile-6Panel\":\n",
        "      Tick_range = [0.01, 0.10, 0.5, 1.0, 2, 5,\n",
        "                   10, 20]\n",
        "    else:\n",
        "      Tick_range = [0.01, 0.05, 0.10, 0.2, 0.3, 0.5,\n",
        "                   0.7, 0.9, 1.2, 1.6, 2, 3, 4,\n",
        "                   8, 12, 16, 20]\n",
        "    if Model == \"ifs\":\n",
        "      panel_title_text = ' EPS - '+str(Accum_Period)+'hr Precipitation (inches)'\n",
        "    else:\n",
        "      panel_title_text = ' '+str(Accum_Period)+'hr Precipitation (inches)'\n",
        "    #WxBell Colortable\n",
        "    colors = [\"#FFFFFF\", \"#BFBFBF\", \"#ABA5A5\", \"#838383\", \"#6E6E6E\", \"#B5FBAB\", \"#97F58D\",\n",
        "            \"#78F572\", \"#50F150\", \"#1EB51E\", \"#0EA10E\", \"#1464D3\", \"#2883F1\", \"#50A5F5\",\n",
        "            \"#97D3FB\", \"#FFFBAB\", \"#FFE978\", \"#FFC13C\", \"#FFA100\", \"#FF6000\", \"#FF3200\",\n",
        "            \"#E11400\", \"#C10000\", \"#A50000\", \"#870000\", \"#643C32\", \"#8D645A\", \"#B58D83\",\n",
        "            \"#C9A197\", \"#F1DDD3\", \"#CFC9DF\", \"#ADA1C9\", \"#9B89BD\", \"#725CA3\", \"#685293\",\n",
        "            \"#760076\", \"#8C008C\", \"#B100B1\", \"#C100C1\", \"#DB00DB\", ]\n",
        "  if Map_Type == \"Percentile\" or Map_Type == \"Percentile-6Panel\":\n",
        "    panel_title_text = panel_title_text + str(Pctl_Single) + 'th Percentile'\n",
        "  # Needed to help make sure the colortable works with low level ramp up.\n",
        "  colornorm = matplotlib.colors.BoundaryNorm(boundaries=Panel_1_range, ncolors=256)\n",
        "  map_label = 'inches'\n",
        "  max_label = 'Max Accum QPF: \\n'\n",
        "  cmap_1D = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", colors, N=256)\n",
        "\n",
        "if Map_Data == \"QPF\" and Map_Type == \"ProbExceed\":\n",
        "  Map_Type_Single = True\n",
        "  if Accum_Period == \"RunTotal\":\n",
        "    Map_Duration = 'Run Total'\n",
        "  else:\n",
        "    Map_Duration = str(Accum_Period)+'hr'\n",
        "  panel_title_text = ' '+Map_Duration+' Probability of Exceeding '+str(qpf_poe)+'\\\" QPF'\n",
        "  map_label = '%'\n",
        "  # Set range of contourf mapping.\n",
        "  Panel_1_range = np.arange(5, 101, 5)\n",
        "  norm = matplotlib.colors.Normalize(0,1)\n",
        "  ProbRaincolors = [[norm( 0.0), \"#FFFFFF\"],\n",
        "                [norm( 0.04), \"#FFFFFF\"],\n",
        "                [norm( 0.05), \"#F5FBF2\"],\n",
        "                [norm( 0.25), \"#C7E5BD\"],\n",
        "                [norm( 0.5), \"#7FBD79\"],\n",
        "                [norm( 0.75), \"#408347\"],\n",
        "                [norm( 1.0), \"#1E4B23\"]]\n",
        "  cmap_1D = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", ProbRaincolors, N=100)\n",
        "  #Calculate the Prob of Exceedance\n",
        "  if Model == \"gefs\":\n",
        "    Data_Values = (final_data > qpf_poe).to_array().sum(axis=0)\n",
        "    Data_Values = (Data_Values / 30)*100\n",
        "  else:\n",
        "    Data_Values = (final_data > qpf_poe).sum(dim='number')\n",
        "    Data_Values = (Data_Values / 50)*100\n",
        "    #### Orig for Total Accum\n",
        "    #Data_Values = (final_data > ((qpf_poe/1000)/0.0393701)).to_array().sum(dim='number')\n",
        "    #Data_Values = (Data_Values[0] / 50)*100\n",
        "\n",
        "if Map_Data == \"Temp\" and Map_Type != \"ProbExceed\":\n",
        "  if Map_Type == \"Percentile\":\n",
        "    Map_Type_Single = True\n",
        "  if Model == \"ifs\":\n",
        "    panel_title_text = ' EPS - ' + Temp_Level + 'mb Temp '\n",
        "  else:\n",
        "    panel_title_text = ' - ' + Temp_Level + 'mb Temp '\n",
        "  if Map_Type == \"Percentile\" or Map_Type == \"Percentile-6Panel\":\n",
        "    panel_title_text = panel_title_text + str(Pctl_Single) + 'th Percentile'\n",
        "  #Sfc Temp Colortable - WeatherBell\n",
        "  Panel_1_range = np.arange(-50, 41, 1)\n",
        "  Tick_range = [-50, -40, -30, -20, -10, 0,\n",
        "                   10, 20, 30, 40]\n",
        "  norm = matplotlib.colors.Normalize(0,1)\n",
        "  colors = [[norm( 0.0), \"#3f0390\"],\n",
        "                [norm( 0.074), \"#bc0482\"],\n",
        "                [norm( 0.139), \"#fa4da4\"],\n",
        "                [norm( 0.204), \"#dca9d3\"],\n",
        "                [norm( 0.296), \"#92f2f0\"],\n",
        "                [norm( 0.365), \"#11504e\"],\n",
        "                [norm( 0.43), \"#d2d2d2\"],\n",
        "                [norm( 0.435), \"#341e9d\"],\n",
        "                [norm( 0.504), \"#aa2c2c\"],\n",
        "                [norm( 0.574), \"#f7e7e7\"],\n",
        "                [norm( 0.596), \"#a1dfeb\"],\n",
        "                [norm( 0.648), \"#555590\"],\n",
        "                [norm( 0.696), \"#ffff78\"],\n",
        "                [norm( 0.8), \"#780000\"],\n",
        "                [norm( 0.817), \"#632720\"],\n",
        "                [norm( 0.861), \"#bca79e\"],\n",
        "                [norm( 0.943), \"#722944\"],\n",
        "                [norm( 1.0), \"#008c40\"]]\n",
        "  #N_Number = 90\n",
        "  # Frequency of labels on colorbar - Tick marks\n",
        "  #tick_bins = 23\n",
        "  # Needed to help make sure the colortable works with low level ramp up.\n",
        "  colornorm = matplotlib.colors.BoundaryNorm(boundaries=Panel_1_range, ncolors=256)\n",
        "  map_label = 'deg C'\n",
        "  max_label = Temp_Level + 'mb Temp: \\n'\n",
        "  cmap_1D = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", colors, N=256)\n",
        "\n",
        "if Map_Data == \"Temp\" and Map_Type == \"ProbExceed\":\n",
        "  Map_Type_Single = True\n",
        "  if Model == \"ifs\":\n",
        "    panel_title_text = ' EPS - Probability of ' + Temp_Level + 'mb Temperature >= '+str(Temp_poe)+' deg C'\n",
        "  else:\n",
        "    panel_title_text = ' Probability of ' + Temp_Level + 'mb Temperature >= '+str(Temp_poe)+' deg C'\n",
        "  map_label = '%'\n",
        "  # Set range of contourf mapping.\n",
        "  Panel_1_range = np.arange(5, 101, 5)\n",
        "  norm = matplotlib.colors.Normalize(0,1)\n",
        "  ProbRaincolors = [[norm( 0.0), \"#FFFFFF\"],\n",
        "                [norm( 0.04), \"#FFFFFF\"],\n",
        "                [norm( 0.05), \"#F5FBF2\"],\n",
        "                [norm( 0.25), \"#C7E5BD\"],\n",
        "                [norm( 0.5), \"#7FBD79\"],\n",
        "                [norm( 0.75), \"#408347\"],\n",
        "                [norm( 1.0), \"#1E4B23\"]]\n",
        "  cmap_1D = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", ProbRaincolors, N=100)\n",
        "  #Calculate the Prob of Exceedance\n",
        "  if Model == \"gefs\":\n",
        "    Data_Values = (final_data >= Temp_poe).to_array().sum(axis=0)\n",
        "    Data_Values = (Data_Values / 30)*100\n",
        "  else:\n",
        "    Data_Values = (final_data >= Temp_poe).sum(dim='number')\n",
        "    Data_Values = (Data_Values / 50)*100\n",
        "    #### Orig for Total Accum\n",
        "    #Data_Values = (final_data > ((qpf_poe/1000)/0.0393701)).to_array().sum(dim='number')\n",
        "    #Data_Values = (Data_Values[0] / 50)*100\n",
        "\n",
        "if Map_Data == \"SnowDepth\" and Map_Type != \"ProbExceed\":\n",
        "  if Map_Type == \"Percentile\":\n",
        "    Map_Type_Single = True\n",
        "  if Model == \"ifs\":\n",
        "    panel_title_text = ' EPS - Snow Depth (inches)'\n",
        "  else:\n",
        "    panel_title_text = ' - Snow Depth (inches)'\n",
        "  if Map_Type == \"Percentile\" or Map_Type == \"Percentile-6Panel\":\n",
        "    panel_title_text = panel_title_text + str(Pctl_Single) + 'th Percentile'\n",
        "  #Sfc Temp Colortable - WeatherBell\n",
        "  #Panel_1_range = np.arange(-50, 41, 1)\n",
        "  Panel_1_range = [0, 0.1, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 15, 18,\n",
        "         24, 36]\n",
        "  if Map_Type == \"Percentile-6Panel\":\n",
        "    Tick_range = [0, 0.1, 0.5, 1, 2, 4, 6, 8, 12, 18, 36]\n",
        "  else:\n",
        "    Tick_range = [0, 0.1, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 15, 18,\n",
        "         24, 36]\n",
        "  #norm = matplotlib.colors.Normalize(0,1)\n",
        "  #N_Number = 90\n",
        "  #NWS QPF Colortable\n",
        "  colors = [\"#FFFFFF\", \"#BDD7E6\", \"#94C3DE\", \"#6BAFD6\", \"#3282BD\", \"#08519D\", \"#083C99\", \"#082694\", \"#FFFF9B\",\n",
        "            \"#FFE14E\", \"#FFC300\", \"#FFA500\",\n",
        "            \"#FE8700\", \"#EC4E00\", \"#DA1400\", \"#9D0000\", \"#690000\",]\n",
        "  #cmap_1D = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", colors, N=17)\n",
        "  extend_val = 'neither'\n",
        "  # Frequency of labels on colorbar - Tick marks\n",
        "  #tick_bins = 23\n",
        "  # Needed to help make sure the colortable works with low level ramp up.\n",
        "  colornorm = matplotlib.colors.BoundaryNorm(boundaries=Panel_1_range, ncolors=17)#256\n",
        "  map_label = 'inches'\n",
        "  max_label = 'Snow Depth: \\n'\n",
        "  cmap_1D = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", colors, N=17)#256\n",
        "\n",
        "if Map_Data == \"SnowDepth\" and Map_Type == \"ProbExceed\":\n",
        "  Map_Type_Single = True\n",
        "  panel_title_text = ' Probability of Snow Depth >= '+str(SD_poe)+' in'\n",
        "  map_label = '%'\n",
        "  # Set range of contourf mapping.\n",
        "  Panel_1_range = np.arange(5, 101, 5)\n",
        "  norm = matplotlib.colors.Normalize(0,1)\n",
        "  ProbRaincolors = [[norm( 0.0), \"#FFFFFF\"],\n",
        "                [norm( 0.04), \"#FFFFFF\"],\n",
        "                [norm( 0.05), \"#F5FBF2\"],\n",
        "                [norm( 0.25), \"#C7E5BD\"],\n",
        "                [norm( 0.5), \"#7FBD79\"],\n",
        "                [norm( 0.75), \"#408347\"],\n",
        "                [norm( 1.0), \"#1E4B23\"]]\n",
        "  cmap_1D = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", ProbRaincolors, N=100)\n",
        "  #Calculate the Prob of Exceedance\n",
        "  if Model == \"gefs\":\n",
        "    Data_Values = (final_data >= SD_poe).to_array().sum(axis=0)\n",
        "    Data_Values = (Data_Values / 30)*100\n",
        "  else:\n",
        "    Data_Values = (final_data >= SD_poe).sum(dim='number')\n",
        "    Data_Values = (Data_Values / 50)\n",
        "    #### Orig for Total Accum\n",
        "    #Data_Values = (final_data > ((qpf_poe/1000)/0.0393701)).to_array().sum(dim='number')\n",
        "    #Data_Values = (Data_Values[0] / 50)*100\n",
        "\n",
        "### Create Contours for Pctl-6P maps\n",
        "# Get the Pctnl Data\n",
        "if Map_Type == \"Percentile-6Panel\":\n",
        "  Add_Sfc_Contours = True\n",
        "  if Model == 'gefs':\n",
        "    Data_Values_UL = np.percentile(final_data.to_array(), Pctl_UL, axis=0)\n",
        "    Data_Values_UM = np.percentile(final_data.to_array(), Pctl_UM, axis=0)\n",
        "    Data_Values_UR = np.percentile(final_data.to_array(), Pctl_UR, axis=0)\n",
        "    Data_Values_LL = np.percentile(final_data.to_array(), Pctl_LL, axis=0)\n",
        "    Data_Values_LM = np.percentile(final_data.to_array(), Pctl_LM, axis=0)\n",
        "    Data_Values_LR = np.percentile(final_data.to_array(), Pctl_LR, axis=0)\n",
        "  else:\n",
        "    Data_Values_UL = np.percentile(final_data, Pctl_UL, axis=0)\n",
        "    Data_Values_UM = np.percentile(final_data, Pctl_UM, axis=0)\n",
        "    Data_Values_UR = np.percentile(final_data, Pctl_UR, axis=0)\n",
        "    Data_Values_LL = np.percentile(final_data, Pctl_LL, axis=0)\n",
        "    Data_Values_LM = np.percentile(final_data, Pctl_LM, axis=0)\n",
        "    Data_Values_LR = np.percentile(final_data, Pctl_LR, axis=0)\n",
        "elif Map_Type == \"Percentile\":\n",
        "  Add_Sfc_Contours = True\n",
        "  if Model == 'gefs':\n",
        "    Data_Values = np.percentile(final_data.to_array(), Pctl_Single, axis=0)\n",
        "  else:\n",
        "    Data_Values = np.percentile(final_data, Pctl_Single, axis=0)\n",
        "\n",
        "# 30-50 Panel Plot\n",
        "if Map_Type == \"All_Members\":\n",
        "  axlist = axarr.flatten()\n",
        "  for ax in axlist:\n",
        "    plot_background(ax)\n",
        "  if Model == \"gefs\":\n",
        "    for p in range(1,31):\n",
        "      Model_P = \"p\"+str(p).zfill(2)\n",
        "      #print (Model_P)\n",
        "      cf1 = axlist[p-1].contourf(final_data.longitude, final_data.latitude, final_data.get(str(Model_P)), Panel_1_range, norm=colornorm,\n",
        "                                 cmap=cmap_1D, transform=map_crs)\n",
        "      # Preset Ticks due to low level ramp up.\n",
        "      #cb1 = fig.colorbar(cf1, ax=ax, ticks=Panel_1_range, orientation='horizontal', shrink=0.74, aspect= 35, pad=0)\n",
        "      # Add Pert # in map\n",
        "      #axlist[p-1].text(.01, .99, Model_P, ha='left', va='top', transform=ax.transAxes)\n",
        "      axlist[p-1].annotate(Model_P, (0,0), (2, 8), xycoords='axes points', textcoords='offset points', va='top', ha=\"left\",\n",
        "             fontweight='bold', fontsize=9, color='black',)\n",
        "  else:\n",
        "    for p in range(1,51):\n",
        "      #Model_P = \"p\"+str(p).zfill(2)\n",
        "      #print (Model_P)\n",
        "      cf1 = axlist[p-1].contourf(final_data.longitude, final_data.latitude, final_data[p-1], Panel_1_range, norm=colornorm, cmap=cmap_1D,\n",
        "                                 transform=map_crs)\n",
        "      #cf1 = axlist[p-1].contourf(final_data.longitude, final_data.latitude, final_data[p], cmap=plt.cm.hsv, transform=map_crs)\n",
        "      # Preset Ticks due to low level ramp up.\n",
        "      #cb1 = fig.colorbar(cf1, ax=ax, ticks=Panel_1_range, orientation='horizontal', shrink=0.74, aspect= 35, pad=0)\n",
        "      # Add Pert # in map\n",
        "      #axlist[p-1].text(.01, .99, Model_P, ha='left', va='top', transform=ax.transAxes)\n",
        "      #axlist[p-1].set_label(p-1)\n",
        "      axlist[p-1].annotate(p, (0,0), (2, 8), xycoords='axes points', textcoords='offset points', va='top', ha=\"left\",\n",
        "             fontweight='bold', fontsize=9, color='black',)\n",
        "elif Map_Type == \"Percentile-6Panel\":\n",
        "  axlist = axarr.flatten()\n",
        "  for ax in axlist:\n",
        "    plot_background(ax)\n",
        "  # Upper left plot - ProbSnow UL\n",
        "  cf1 = axlist[0].contourf(final_data.longitude, final_data.latitude, Data_Values_UL, Panel_1_range, norm=colornorm, cmap=cmap_1D,\n",
        "                          transform=map_crs)\n",
        "  #axlist[0].clabel(cf1, fontsize=16, inline=1, inline_spacing=1, fmt='%i', rightside_up=True)\n",
        "  cb1 = fig.colorbar(cf1, ax=axlist[0], ticks=Tick_range, orientation='horizontal', shrink=0.9, pad=0)\n",
        "  cb1.set_label(map_label, size='large')\n",
        "\n",
        "  # Upper middle plot - ProbSnow UM\n",
        "\n",
        "  cf2 = axlist[1].contourf(final_data.longitude, final_data.latitude, Data_Values_UM, Panel_1_range, norm=colornorm, cmap=cmap_1D,\n",
        "                          transform=map_crs)\n",
        "  cb2 = fig.colorbar(cf2, ax=axlist[1], ticks=Tick_range, orientation='horizontal', shrink=0.9, pad=0)\n",
        "  cb2.set_label(map_label, size='large')\n",
        "\n",
        "  # Upper right plot - ProbSnow UR\n",
        "\n",
        "  cf3 = axlist[2].contourf(final_data.longitude, final_data.latitude, Data_Values_UR, Panel_1_range, norm=colornorm, cmap=cmap_1D,\n",
        "                          transform=map_crs)\n",
        "  cb3 = fig.colorbar(cf3, ax=axlist[2], ticks=Tick_range, orientation='horizontal', shrink=0.9, pad=0)\n",
        "  cb3.set_label(map_label, size='large')\n",
        "\n",
        "  # Lower left plot - ProbSnow LL\n",
        "  cf4 = axlist[3].contourf(final_data.longitude, final_data.latitude, Data_Values_LL, Panel_1_range, norm=colornorm, cmap=cmap_1D,\n",
        "                           transform=map_crs)\n",
        "  cb4 = fig.colorbar(cf4, ax=axlist[3], ticks=Tick_range, orientation='horizontal', shrink=0.9, pad=0)\n",
        "  cb4.set_label(map_label, size='large')\n",
        "\n",
        "  # Lower middle plot - ProbSnow LM\n",
        "  cf5 = axlist[4].contourf(final_data.longitude, final_data.latitude, Data_Values_LM, Panel_1_range, norm=colornorm, cmap=cmap_1D,\n",
        "                           transform=map_crs)\n",
        "  cb5 = fig.colorbar(cf5, ax=axlist[4], ticks=Tick_range, orientation='horizontal', shrink=0.9, pad=0)\n",
        "  cb5.set_label(map_label, size='large')\n",
        "\n",
        "  #### Lower left plot - ProbSnow\n",
        "\n",
        "  cf6 = axlist[5].contourf(final_data.longitude, final_data.latitude, Data_Values_LR, Panel_1_range, norm=colornorm, cmap=cmap_1D,\n",
        "                           transform=map_crs)\n",
        "  cb6 = fig.colorbar(cf6, ax=axlist[5], ticks=Tick_range, orientation='horizontal', shrink=0.9, pad=0)\n",
        "  cb6.set_label(map_label, size='large')\n",
        "\n",
        "  if Add_Sfc_Contours:\n",
        "    # Contour Range and Formatting\n",
        "    if Map_Data == \"QPF\":\n",
        "      contourrange = [0.1, 0.25, 0.5, 1, 2, 3, 4]\n",
        "      contour_fmt = '%.2f'\n",
        "    elif Map_Data == \"Temp\":\n",
        "      contourrange = np.arange(-50, 40, 5)\n",
        "      contour_fmt = '%.0f'\n",
        "    elif Map_Data == \"SnowDepth\":\n",
        "      contourrange = np.arange(0, 60, 2)\n",
        "      contour_fmt = '%.0f'\n",
        "    ## UL\n",
        "    cf1c = axlist[0].contour(final_data.longitude, final_data.latitude, Data_Values_UL, levels=contourrange, colors='black',\n",
        "                          linewidths=0.5, linestyles='solid', transform=map_crs)\n",
        "    cf1cl = axlist[0].clabel(cf1c, fontsize=14, inline=1, inline_spacing=1, fmt=contour_fmt, rightside_up=True)\n",
        "    ## UM\n",
        "    cf2c = axlist[1].contour(final_data.longitude, final_data.latitude, Data_Values_UM, levels=contourrange, colors='black',\n",
        "                          linewidths=0.5, linestyles='solid', transform=map_crs)\n",
        "    cf2cl = axlist[1].clabel(cf2c, fontsize=14, inline=1, inline_spacing=1, fmt=contour_fmt, rightside_up=True)\n",
        "    ## UR\n",
        "    cf3c = axlist[2].contour(final_data.longitude, final_data.latitude, Data_Values_UR, levels=contourrange, colors='black',\n",
        "                          linewidths=0.5, linestyles='solid', transform=map_crs)\n",
        "    cf3cl = axlist[2].clabel(cf3c, fontsize=14, inline=1, inline_spacing=1, fmt=contour_fmt, rightside_up=True)\n",
        "    ## LL\n",
        "    cf4c = axlist[3].contour(final_data.longitude, final_data.latitude, Data_Values_LL, levels=contourrange, colors='black',\n",
        "                          linewidths=0.5, linestyles='solid', transform=map_crs)\n",
        "    cf4cl = axlist[3].clabel(cf4c, fontsize=14, inline=1, inline_spacing=1, fmt=contour_fmt, rightside_up=True)\n",
        "    ## LM\n",
        "    cf5c = axlist[4].contour(final_data.longitude, final_data.latitude, Data_Values_LM, levels=contourrange, colors='black',\n",
        "                          linewidths=0.5, linestyles='solid', transform=map_crs)\n",
        "    cf5cl = axlist[4].clabel(cf5c, fontsize=14, inline=1, inline_spacing=1, fmt=contour_fmt, rightside_up=True)\n",
        "    ## LR\n",
        "    cf6c = axlist[5].contour(final_data.longitude, final_data.latitude, Data_Values_LR, levels=contourrange, colors='black',\n",
        "                          linewidths=0.5, linestyles='solid', transform=map_crs)\n",
        "    cf6cl = axlist[5].clabel(cf6c, fontsize=14, inline=1, inline_spacing=1, fmt=contour_fmt, rightside_up=True)\n",
        "else:\n",
        "  # Single Panel Plot\n",
        "  if Map_Type == \"Percentile\":\n",
        "    cf1 = ax.contourf(final_data.longitude, final_data.latitude, Data_Values, Panel_1_range, norm=colornorm, cmap=cmap_1D, transform=map_crs, zorder=1) #cmap_1D\n",
        "    # Preset Ticks due to low level ramp up.\n",
        "    cb1 = fig.colorbar(cf1, ax=ax, ticks=Tick_range, orientation='horizontal', shrink=0.74, aspect= 35, pad=0)\n",
        "    if Add_Sfc_Contours:\n",
        "      # Contour Range and Formatting\n",
        "      if Map_Data == \"QPF\":\n",
        "        contourrange = [0.1, 0.25, 0.5, 1, 2, 3, 4]\n",
        "        contour_fmt = '%.2f'\n",
        "      elif Map_Data == \"Temp\":\n",
        "        contourrange = np.arange(-50, 40, 5)\n",
        "        contour_fmt = '%.0f'\n",
        "      elif Map_Data == \"SnowDepth\":\n",
        "        contourrange = np.arange(0, 60, 2)\n",
        "        contour_fmt = '%.0f'\n",
        "      ## UL\n",
        "      cf1c = ax.contour(final_data.longitude, final_data.latitude, Data_Values, levels=contourrange, colors='black',\n",
        "                          linewidths=0.5, linestyles='solid', transform=map_crs)\n",
        "      cf1cl = ax.clabel(cf1c, fontsize=14, inline=1, inline_spacing=1, fmt=contour_fmt, rightside_up=True)\n",
        "  else:\n",
        "    # Contour Range and Formatting\n",
        "    contourrange = np.arange(0, 101, 10)\n",
        "    contour_fmt = '%.0f'\n",
        "    ## Add Contours and Text Label\n",
        "    cf1c = ax.contour(final_data.longitude, final_data.latitude, Data_Values, levels=contourrange, colors='black',\n",
        "                        linewidths=0.5, linestyles='solid', transform=map_crs)\n",
        "    cf1cl = ax.clabel(cf1c, fontsize=14, inline=1, inline_spacing=1, fmt=contour_fmt, rightside_up=True)\n",
        "    ## Add ProbExceedance data\n",
        "    cf1 = ax.contourf(final_data.longitude, final_data.latitude, Data_Values, Panel_1_range, cmap='jet', transform=map_crs, zorder=1) #cmap_1D\n",
        "    # Preset Ticks due to low level ramp up.\n",
        "    cb1 = fig.colorbar(cf1, ax=ax, ticks=Panel_1_range, orientation='horizontal', shrink=0.74, aspect= 35, pad=0)\n",
        "\n",
        "# Add Colorbar for Multiple Subplots\n",
        "if Map_Type == \"All_Members\":\n",
        "  #cb1 = fig.colorbar(cf1, ax=axarr.ravel().tolist(), ticks=Panel_1_range, orientation='horizontal', shrink=0.74, aspect= 35, pad=0)\n",
        "  cb1 = fig.colorbar(cf1, ax=axarr, ticks=Tick_range, orientation='horizontal', shrink=0.54, aspect= 45, pad=0)\n",
        "\n",
        "# Add Colorbar Tick Bins\n",
        "if tick_bins != None:\n",
        "  # Adjust Tick Marks on Colorbar\n",
        "  tick_locator = ticker.MaxNLocator(nbins=tick_bins)\n",
        "  cb1.locator = tick_locator\n",
        "  cb1.update_ticks()\n",
        "cb1.set_label(map_label, size='large')\n",
        "\n",
        "if Model == 'ifs':\n",
        "  Model_Name = 'ec-eps'\n",
        "else:\n",
        "  Model_Name = Model\n",
        "# Add panel title\n",
        "if Map_Type == \"All_Members\":\n",
        "  #fig.suptitle(model_time_txt + ' ' + Model.upper() + panel_title_text)\n",
        "  Model_Title_Left = model_time_txt + ' ' + Model_Name.upper() + panel_title_text\n",
        "  fig.text(0.01, 0.99, Model_Title_Left, transform=fig.transFigure, horizontalalignment='left', fontweight='bold', fontsize='14')\n",
        "  #fig.suptitle('Fcst Hr: ' + str(fhr) + ' - Valid Time: {}'.format(vtime_txt), loc='right')\n",
        "  Model_Title_Right = 'Fcst Hr: ' + str(fhr) + ' - Valid Time: {}'.format(vtime_txt)\n",
        "  fig.text(0.99, 0.99, Model_Title_Right, transform=fig.transFigure, horizontalalignment='right', fontweight='bold', fontsize='14')\n",
        "elif Map_Type == \"Percentile-6Panel\":\n",
        "  if Map_Data == \"QPF\":\n",
        "    Base_Panel_Text = ' - ' + Map_Duration + ' QPF '\n",
        "  elif Map_Data == \"Temp\":\n",
        "    Base_Panel_Text = ' - ' + Temp_Level + 'mb ' + Map_Data + ' '\n",
        "  elif Map_Data == \"SnowDepth\":\n",
        "    Base_Panel_Text = ' - Snow Depth '\n",
        "  # Loop to add each panel title\n",
        "  Pctl_Text_UL = Base_Panel_Text + str(Pctl_UL) + 'th Pctl'\n",
        "  Pctl_Text_UM = Base_Panel_Text + str(Pctl_UM) + 'th Pctl'\n",
        "  Pctl_Text_UR = Base_Panel_Text + str(Pctl_UR) + 'th Pctl'\n",
        "  Pctl_Text_LL = Base_Panel_Text + str(Pctl_LL) + 'th Pctl'\n",
        "  Pctl_Text_LM = Base_Panel_Text + str(Pctl_LM) + 'th Pctl'\n",
        "  Pctl_Text_LR = Base_Panel_Text + str(Pctl_LR) + 'th Pctl'\n",
        "  panel_title_text = [Pctl_Text_UL, Pctl_Text_UM, Pctl_Text_UR, Pctl_Text_LL, Pctl_Text_LM, Pctl_Text_LR]\n",
        "  #Set title second row location\n",
        "  title_y_adjust = 1.055\n",
        "  for i in range(0,6):\n",
        "    axlist[i].set_title(model_time_txt + ' ' + Model_Name.upper() + panel_title_text[i], loc='left', fontweight='bold', fontsize=12, y=title_y_adjust)\n",
        "    axlist[i].set_title('\\nFcst Hr: ' + str(fhr) + ' - Valid: {}'.format(vtime_txt), loc='right')\n",
        "else:\n",
        "  ax.set_title(model_time_txt + ' ' + Model_Name.upper() + panel_title_text, loc='left', fontweight='bold', fontsize='12', y=1.045)\n",
        "  ax.set_title('\\nFcst Hr: ' + str(fhr) + ' - Valid Time: {}'.format(vtime_txt), loc='right', fontsize='12')\n",
        "\n",
        "# Set height padding for plots\n",
        "#fig.set_constrained_layout_pads(w_pad=0., h_pad=0.1, hspace=0., wspace=0.)\n",
        "\n",
        "# Set figure title\n",
        "#fig.suptitle(str(model_time) + \" \" + mdl.upper() + \" Analysis - \" + final_data['time'].dt.strftime('%d %B %Y %H:%MZ').values + ' Forecast Hour: ' + str(fhr), fontsize=24)\n",
        "\n",
        "# Add Credits\n",
        "if Model == \"ifs\":\n",
        "  if Map_Type_Single:\n",
        "    plt.figtext(0.99, -0.15, 'Copyright European Centre for Medium-Range Weather Forecasts (ECMWF)', horizontalalignment='right',\n",
        "                fontweight='bold', fontsize=12, color='white', transform=ax.transAxes,\n",
        "                path_effects=[pe.withStroke(linewidth=3, foreground=\"black\")])\n",
        "  elif Map_Type == \"Percentile-6Panel\":\n",
        "    plt.figtext(0.99, -0.03, 'Copyright European Centre for Medium-Range Weather Forecasts (ECMWF)', horizontalalignment='right',\n",
        "                fontweight='bold', fontsize=10, color='white',\n",
        "                path_effects=[pe.withStroke(linewidth=3, foreground=\"black\")])\n",
        "  else:\n",
        "    plt.figtext(0.99, -0.03, 'Copyright European Centre for Medium-Range Weather Forecasts (ECMWF)', horizontalalignment='right',\n",
        "                fontweight='bold', fontsize=12, color='white',\n",
        "                path_effects=[pe.withStroke(linewidth=3, foreground=\"black\")])\n",
        "\n",
        "# Tighten up the plot\n",
        "if Map_Type != \"All_Members\" and Map_Type != \"Percentile-6Panel\":\n",
        "  #plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
        "  plt.tight_layout()\n",
        "\n",
        "## Save Image\n",
        "\n",
        "#Set domain name for the image name.\n",
        "if Map_Zoom == \"Custom\" and custom_name != \"\":\n",
        "  domain_name = custom_name\n",
        "else:\n",
        "  domain_name = Map_Zoom\n",
        "# Preset Image Name\n",
        "Img_Name_Pre = Model_Name + \"_\" + domain_name + \"_\" + Map_Data + \"_\" + Map_Type\n",
        "if Map_Type == \"All_Members\":\n",
        "  Img_Name_Type = \"_\" + str(Accum_Period) + \"hrAccum_Run-\"\n",
        "elif Map_Type == \"ProbExceed\":\n",
        "  if Map_Data == \"QPF\":\n",
        "    Img_POE = qpf_poe\n",
        "  elif Map_Data == \"Temp\":\n",
        "    Img_POE = Temp_poe\n",
        "  elif Map_Data == \"SnowDepth\":\n",
        "    Img_POE = SD_poe\n",
        "  Img_Name_Type = \"Prob\" + str(Img_POE).replace(\".\", \"_\") + \"_\" + str(Accum_Period) + \"hrAccum_Run-\"\n",
        "elif Map_Type == \"Percentile\":\n",
        "  Img_Name_Type = \"_\" + str(Pctl_Single) + \"Pctl_\" + str(Accum_Period) + \"hrAccum_Run-\"\n",
        "elif Map_Type == \"Percentile-6Panel\":\n",
        "  Img_Name_Type = \"Pctl_\" + str(Accum_Period) + \"hrAccum_Run-\"\n",
        "Img_Name = Img_Name_Pre + Img_Name_Type + Model_Date + \"_\" + str(Model_Run)[:-3] + \"Z_F\" + str(fhr) + \".png\"\n",
        "plt.savefig(f\"{Img_Name}\", dpi = 150, facecolor='w')#, bbox_inches = \"tight\")\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "ZNQ8IYsw2stT",
        "outputId": "9317ae3f-38a6-473c-c2c3-377db70e5063",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}