EPS_Meteograms_with_Herbie.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "3iYyzL_9EAm7"
      },
      "source": [
        "# **EPS Meteograms with Herbie**\n",
        "<a href=\"https://githubtocolab.com/csteele2/Wx4Colab/blob/master/EPS_Meteograms_with_Herbie.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/></a> <br/>\n",
        "This notebook uses [Herbie](https://blaylockbk.github.io/Herbie/_build/html/index.html) to download EPS [ECMWF OpenData](https://www.ecmwf.int/en/forecasts/datasets/open-data) (0.4 degree data), the [Synoptic Data metadata API](https://developers.synopticdata.com/mesonet/v2/stations/metadata/) to locate a station, and make ensemble meteorgrams- similar to that on WeatherBell or WeatherModels. Note this is really meant for archive data (there are better ways/places for real-time data), the data is pretty tempermental, and even one messed up timestep (which seems to be common with temperatures) will pretty much ruin your whole effort (although with temperature, this notebook will just skip the bad times, but doing so with precipitation is dangerous).\n",
        "\n",
        "There is pretty much nothing you can do to recover from that, other than try again later, or try a different run.\n",
        "\n",
        "- Caleb Steele, https://github.com/csteele2/Wx4Colab"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "k6DRBRAXCyNh"
      },
      "source": [
        "##**1 - Install CondaColab**\n",
        "This will manage the installation of the packages we need, like [Herbie](https://blaylockbk.github.io/Herbie/_build/html/index.html). <br />**NOTE**: this will restart the notebook, and you will get a crash popup in the lower-left corner that you can safely ignore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "brhZKdsjM7wZ",
        "outputId": "6f6a6a0c-db2e-4a53-f9f5-964d318f64f1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "⏬ Downloading https://github.com/jaimergp/miniforge/releases/latest/download/Mambaforge-colab-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:13\n",
            "🔁 Restarting kernel...\n"
          ]
        }
      ],
      "source": [
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pNRHFuNmC-2K"
      },
      "source": [
        "##**2 - Install and Import Packages**\n",
        "This will take about a minute to run."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJaogqYCNB-Q",
        "outputId": "6bddd16b-056b-4575-cedf-a642381da1a7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "                  __    __    __    __\n",
            "                 /  \\  /  \\  /  \\  /  \\\n",
            "                /    \\/    \\/    \\/    \\\n",
            "███████████████/  /██/  /██/  /██/  /████████████████████████\n",
            "              /  / \\   / \\   / \\   / \\  \\____\n",
            "             /  /   \\_/   \\_/   \\_/   \\    o \\__,\n",
            "            / _/                       \\_____/  `\n",
            "            |/\n",
            "        ███╗   ███╗ █████╗ ███╗   ███╗██████╗  █████╗\n",
            "        ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n",
            "        ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n",
            "        ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n",
            "        ██║ ╚═╝ ██║██║  ██║██║ ╚═╝ ██║██████╔╝██║  ██║\n",
            "        ╚═╝     ╚═╝╚═╝  ╚═╝╚═╝     ╚═╝╚═════╝ ╚═╝  ╚═╝\n",
            "\n",
            "        mamba (1.0.0) supported by @QuantStack\n",
            "\n",
            "        GitHub:  https://github.com/mamba-org/mamba\n",
            "        Twitter: https://twitter.com/QuantStack\n",
            "\n",
            "█████████████████████████████████████████████████████████████\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!mamba install -q -c conda-forge herbie-data cfgrib matplotlib seaborn xarray\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patheffects as PathEffects # to add outline to text, etc.\n",
        "import matplotlib.patches as mpatches\n",
        "from herbie import Herbie\n",
        "import seaborn as sns\n",
        "from datetime import datetime, timedelta\n",
        "import xarray as xr\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import requests\n",
        "import json"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBIIhXiDDQIa"
      },
      "source": [
        "##**3 - Download Data**\n",
        "This will download global ensemble data, trim it to north america, then store it in an xarray object. It will process a large amount of data, so **it will take 20-30+ minutes to run**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sceqr0nT-J34",
        "outputId": "5b6be77a-3cea-4c24-b573-5bb5b5e2d72c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "✅ Found ┊ model=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F03\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F06\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F09\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F12\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F15\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F18\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F21\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F24\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F27\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F30\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F33\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F36\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F39\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F42\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F45\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F48\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F51\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F54\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F57\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F60\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F63\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F66\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F69\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 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",
            "✅ Found ┊ model=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F75\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F78\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F81\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F84\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F87\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F90\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F93\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F99\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F102\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F105\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F108\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F111\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F114\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F117\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F120\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F123\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F126\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F129\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F132\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F135\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F138\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F141\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=ecmwf ┊ \u001b[3mproduct=enfo\u001b[0m ┊ \u001b[38;2;41;130;13m2022-Dec-07 00:00 UTC\u001b[92m F144\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"
          ]
        }
      ],
      "source": [
        "#@title Download Data | Options { display-mode: \"form\" }\n",
        "model_initialization = \"2022-12-07\" #@param {type:\"date\"}\n",
        "model_cycle = 0 #@param {type:\"integer\"}\n",
        "model_run = datetime.strptime(model_initialization,'%Y-%m-%d') + timedelta(hours = model_cycle)\n",
        "model_variable = \"precipitation\" #@param [\"precipitation\", \"2m temperature\"]\n",
        "if model_variable == \"precipitation\":\n",
        "  variable = \"tp\"\n",
        "elif model_variable == \"2m temperature\":\n",
        "  variable = \"2t\"\n",
        "\n",
        "#@markdown ----------------------------------------------------------\n",
        "\n",
        "#@markdown <b>Select forecast hour end and interval hours.</b><br />\n",
        "#@markdown &nbsp; &nbsp; &nbsp; &nbsp; --> For times 00z &12z: 0 to 144 by 3, 150 to 360 by 6.<br />\n",
        "#@markdown  &nbsp; &nbsp; &nbsp; &nbsp; --> For times 06z & 18z: 0 to 144 by 3.\n",
        "\n",
        "#forecast_hour_start = 0 #@param {type:\"integer\"}\n",
        "forecast_hour_end = 144 #@param {type:\"integer\"}\n",
        "forecast_hour_interval = 3 #@param {type:\"integer\"}\n",
        "if variable==\"tp\":\n",
        "  forecast_hour_start = forecast_hour_interval\n",
        "else:\n",
        "  forecast_hour_start = 0\n",
        "\n",
        "\n",
        "###########################################################################\n",
        "eps_all = []\n",
        "valid_times = []\n",
        "forecast_hours = list(range(forecast_hour_start, forecast_hour_end + forecast_hour_interval, forecast_hour_interval))\n",
        "model_search_string = \":\"+variable+\":\"\n",
        "\n",
        "for t in range(forecast_hour_start, forecast_hour_end + forecast_hour_interval, forecast_hour_interval):\n",
        "    H = Herbie(model_run.strftime('%Y-%m-%d %H:00'), model=\"ecmwf\", product=\"enfo\", fxx=t, priority=['azure'], save_dir=\"/content/data\")\n",
        "    with H.xarray(\":\"+variable+\":\")[0] as eps:\n",
        "      try:\n",
        "        if len(eps.number.values) > 0:\n",
        "          eps_all.append(eps.sel(longitude=slice(-180,-30), latitude=slice(85,5)))\n",
        "          valid_times.append(model_run + timedelta(hours=t))\n",
        "      except:\n",
        "        if variable==\"tp\":\n",
        "          print(\"Hypercube is improper, number of members = 0. Breaking the loop. Try another cycle or try again later. Sowwy 😢\")\n",
        "          break\n",
        "        else:\n",
        "          print(\"Hypercube is improper, number of members = 0. Skipping this forecast hour.\")\n",
        "          forecast_hours.remove(t)\n",
        "\n",
        "\n",
        "if len(valid_times) == len(forecast_hours):\n",
        "  ecmwfe = xr.concat([eps_all[i] for i in range(0,len(forecast_hours))], dim='step')\n",
        "else:\n",
        "  print(\"Missing forecast hours. Something must have gone wrong.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TARrDjw-DkLq"
      },
      "source": [
        "##**4 - Make Plots**\n",
        "This will extract values for you chosen station/point, and, if you selected a bucket precipitation, derive it from the total precipitation that is downloaded. The plot will display below and also save to a png if you click on the folder icon on the left bar."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yiGGBomL8TK2",
        "outputId": "2f2fe98d-af5e-4cd9-908e-611c76b45f7e"
      },
      "outputs": [],
      "source": [
        "#@title Make Plot | Options { display-mode: \"form\" }\n",
        "site_id = \"SWBC1\" #@param {type:\"string\"}\n",
        "synoptic_token = \"\"#@param {type:\"string\"}\n",
        "#@markdown If you downloaded precipitation, choose what precipitation you want\n",
        "precipitation_selection = \"24-hr\" #@param [\"Total\", \"24-hr\", \"48-hr\", \"72-hr\"]\n",
        "bucket = 24 # set a default value\n",
        "if precipitation_selection == \"24-hr\":\n",
        "  bucket = 24\n",
        "if precipitation_selection == \"48-hr\":\n",
        "  bucket = 48\n",
        "if precipitation_selection == \"72-hr\":\n",
        "  bucket = 72\n",
        "\n",
        "###########################################################\n",
        "def get_stn_meta(sid):\n",
        "  \"\"\" Use the synoptic data API to get metadata for a station by id \"\"\"\n",
        "  site_dict = {}\n",
        "  meta_api = \"https://api.synopticlabs.org/v2/stations/metadata\"\n",
        "  req_parms = {\"token\":synoptic_token,\n",
        "                 \"stid\":sid,\n",
        "                 \"fields\": \"latitude,longitude,name,state\"\n",
        "                 }\n",
        "  req_meta = requests.get(meta_api, req_parms)\n",
        "  meta_json = json.loads(req_meta.text)\n",
        "  for stn in meta_json[\"STATION\"]:\n",
        "      if float(stn[\"LATITUDE\"]) != 0. and float(stn[\"LONGITUDE\"]) != 0.:\n",
        "          site_dict['lat'] = float(stn[\"LATITUDE\"])\n",
        "          site_dict['lon'] = float(stn[\"LONGITUDE\"])\n",
        "          site_dict['name'] = stn[\"NAME\"]\n",
        "          site_dict['state'] = stn[\"STATE\"]\n",
        "  return site_dict\n",
        "\n",
        "site_dict = get_stn_meta(site_id)\n",
        "\n",
        "def get_nearest_loc(mlats, mlons):\n",
        "  point_lat = site_dict['lat']\n",
        "  point_lon = site_dict['lon']\n",
        "  latidx = np.abs(mlats - point_lat).argmin()\n",
        "  lonidx = np.abs(mlons - point_lon).argmin()\n",
        "  return(mlats[latidx], mlons[lonidx])\n",
        "\n",
        "def m_to_in(meters):\n",
        "  inches = meters * 39.3701\n",
        "  return inches\n",
        "\n",
        "def K_to_F(kelvin):\n",
        "  F = 1.8*(kelvin-273) + 32\n",
        "  return F\n",
        "\n",
        "QPF_COLS = [\"#f2f2f2\", \"#c7e9c0\",\"#a1d99b\", \"#74c476\", \"#31a353\", \"#006d2c\", \"#fff98a\", \"#ffcc4f\", \n",
        "            \"#fe8d3c\", \"#fc4e2a\", \"#d61a1d\", \"#ad0025\", \"#700025\", \"#3b0030\", \"#4d0073\", \"#ffdbff\"]\n",
        "QPF_LEVS = [0.0, 0.01,0.10,0.25,0.50,0.75,1.0,1.5,2.0,3.0,4.0,6.0,8.0,10.0,15.0,20.0]\n",
        "QPF_cmap = matplotlib.colors.LinearSegmentedColormap.from_list('NWS_Standard_QPF_Modified', QPF_COLS, N=len(QPF_LEVS))\n",
        "norm_qpf = matplotlib.colors.BoundaryNorm(QPF_LEVS, len(QPF_LEVS))\n",
        "\n",
        "T_COLS = [\n",
        "            \"#91003e\",\"#ce1257\",\"#e7298b\",\"#df65b0\",\"#ff73df\",\n",
        "            \"#ffbee8\",\"#ffffff\",\"#dadaeb\",\"#bcbddc\",\"#9e9ac8\",\n",
        "            \"#766bb1\",\"#54278f\",\"#0c007d\",\"#0d3d9c\",\"#0067c2\",\n",
        "            \"#299fff\",\"#4ac6ff\",\"#73d7ff\",\"#adffff\",\"#30cfc2\",\n",
        "            \"#009996\",\"#125757\",\"#066d2c\",\"#31a353\",\"#74c477\",\n",
        "            \"#a1d99b\",\"#d3ffbe\",\"#ffffb3\",\"#ffeea0\",\"#fed176\",\n",
        "            \"#fead2a\",\"#fd8c3c\",\"#fc4d2a\",\"#e31a1d\",\"#b10026\",\n",
        "            \"#800026\",\"#590041\",\"#280028\"\n",
        "        ]\n",
        "\n",
        "T_LEVS = np.arange(-60,130,5)\n",
        "T_cmap = matplotlib.colors.LinearSegmentedColormap.from_list('NWS_Standard_QPF_Modified', T_COLS, N=len(T_LEVS))\n",
        "norm_t = matplotlib.colors.BoundaryNorm(T_LEVS, len(T_LEVS))\n",
        "#################################################################\n",
        "\n",
        "\n",
        "def plot_pcp_bucket():\n",
        "    nearest_lat, nearest_lon = get_nearest_loc(ecmwfe.latitude.values, ecmwfe.longitude.values)\n",
        "    request_latlon = f\"({round(site_dict['lat'],4)}, {round(site_dict['lon'], 4)})\"\n",
        "    model_latlon = f\"({round(nearest_lat,4)}, {round(nearest_lon,4)})\"\n",
        "    apcp = pd.DataFrame(m_to_in(ecmwfe.sel(latitude=nearest_lat, longitude=nearest_lon).tp.values))\n",
        "    apcp = apcp.T\n",
        "    pbucket = apcp.copy()\n",
        "    bucket_step_width = int(bucket / forecast_hour_interval)\n",
        "    for col in range(bucket_step_width,len(pbucket.columns)):\n",
        "        pbucket.iloc[:,col] = apcp.iloc[:,col] - apcp.iloc[:,(col-bucket_step_width)]\n",
        "    pbucket[pbucket<=0.] = np.nan\n",
        "    fig, (ax1, ax2) = plt.subplots(2, 1,figsize=(10,8), gridspec_kw={'height_ratios': [3, 1]}, dpi=150)\n",
        "    plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0.2)\n",
        "\n",
        "    sitename = site_dict['name']\n",
        "    state = site_dict['state']\n",
        "\n",
        "    title = \"EPS (0.4°) %s-hr Precipitation (in) | Init %s \\n%s | %s, %s\" % (bucket, model_run.strftime('%HZ %m-%d-%Y'), site_id, sitename, state)\n",
        "    plt.text(1.00,1.01,\"Model grid box: %s\\nStation point: %s\" % (model_latlon, request_latlon),horizontalalignment='right',fontsize=6,color='k',transform = ax1.transAxes, weight='semibold')\n",
        "    ax1.set_title(title,fontsize=9, weight='bold', stretch='normal', family='sans-serif', loc='left',color='#272727')\n",
        "    \n",
        "    sns.heatmap(pbucket, yticklabels=[\"P\"+str(m) for m in np.arange(1,51)], xticklabels=[valid_times[v].strftime('%d-%Hz') for v in range(0,len(valid_times))], \n",
        "                cmap=QPF_cmap, annot=True, fmt=\".2f\", annot_kws={\"fontsize\":4}, norm=norm_qpf, cbar=False, ax=ax1)\n",
        "    ax1.set_yticklabels(ax1.get_ymajorticklabels(), fontsize = 6)\n",
        "    ax1.set_xticklabels(ax1.get_xmajorticklabels(), fontsize = 7)\n",
        "\n",
        "    proxy = [mpatches.Patch(color = QPF_COLS[b]) for b in range(len(QPF_LEVS))]\n",
        "    proxy = proxy[::-1]\n",
        "    LLABS = []\n",
        "    for i in range(0, len(QPF_LEVS)):\n",
        "      if QPF_LEVS[i] == 0.0:\n",
        "        label = \"T\"\n",
        "      else:\n",
        "        label = str(QPF_LEVS[i])\n",
        "      LLABS.append(label)\n",
        "    LLABS = LLABS[::-1]\n",
        "    l = ax1.legend(handles=proxy, labels=LLABS, fontsize=7, frameon=True, bbox_to_anchor=(1.001, 0.5), loc=6, title=\"INCHES\")\n",
        "    plt.setp(l.get_title(), fontsize=6, weight='bold')\n",
        "\n",
        "    labels = [\"FH\" + str(forecast_hours[v]) for v in range(0, len(forecast_hours))]\n",
        "    box_data = pd.melt(pbucket)\n",
        "    for row in range(0,box_data[box_data.columns[0]].count()):\n",
        "        box_data.iloc[row,0] = forecast_hours[box_data.iloc[row,0]]\n",
        "    sns.set_style(\"whitegrid\")\n",
        "    sns.boxplot(x=\"variable\", y=\"value\", data=box_data, ax=ax2, color='lightgrey', showmeans=True, meanprops={\"markerfacecolor\":\"tab:green\", \"markeredgecolor\":\"tab:green\"})\n",
        "    #sns.violinplot(data=box_data, x=\"variable\", y=\"value\", ax=ax2, cut=0, scale=\"count\", inner=\"box\", color='lightgrey', linewidth=0.75)\n",
        "    ax2.set(xlabel=None)\n",
        "    ax2.set_ylabel(f\"{bucket}-hr Precip (in)\", fontsize=9)\n",
        "    ax2.set_xticklabels(ax2.get_xmajorticklabels(), fontsize = 6)\n",
        "    ax2.set_xlabel(f\"Data Copyright © {model_run.strftime('%Y')} European Centre for Medium-Range Weather Forecasts (ECMWF)\", fontsize=7, style=\"italic\")\n",
        "    \n",
        "    plt.savefig(f\"EPS_{model_run.strftime('%Y%m%d-%H')}_pcp{bucket}_{site_id}\", bbox_inches='tight')\n",
        "\n",
        "\n",
        "def plot_pcp_total():\n",
        "    nearest_lat, nearest_lon = get_nearest_loc(ecmwfe.latitude.values, ecmwfe.longitude.values)\n",
        "    request_latlon = f\"({round(site_dict['lat'],4)}, {round(site_dict['lon'], 4)})\"\n",
        "    model_latlon = f\"({round(nearest_lat,4)}, {round(nearest_lon,4)})\"\n",
        "    apcp = pd.DataFrame(m_to_in(ecmwfe.sel(latitude=nearest_lat, longitude=nearest_lon).tp.values))\n",
        "    apcp = apcp.T\n",
        "    apcp[apcp<=0.] = np.nan\n",
        "    fig, (ax1, ax2) = plt.subplots(2, 1,figsize=(10,8), gridspec_kw={'height_ratios': [3, 1]}, dpi=150)\n",
        "    plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0.2)\n",
        "\n",
        "    sitename = site_dict['name']\n",
        "    state = site_dict['state']\n",
        "\n",
        "    title = \"EPS (0.4°) Total Precipitation (in) | Init %s \\n%s | %s, %s\" % (model_run.strftime('%HZ %m-%d-%Y'), site_id, sitename, state)\n",
        "    plt.text(1.00,1.01,\"Model grid box: %s\\nStation point: %s\" % (model_latlon, request_latlon),horizontalalignment='right',fontsize=6,color='k',transform = ax1.transAxes, weight='semibold')\n",
        "    ax1.set_title(title,fontsize=9, weight='bold', stretch='normal', family='sans-serif', loc='left',color='#272727')\n",
        "    sns.heatmap(apcp, yticklabels=[\"P\"+str(m) for m in np.arange(1,51)], xticklabels=[valid_times[v].strftime('%d-%Hz') for v in range(0,len(valid_times))], \n",
        "                cmap=QPF_cmap, annot=True, fmt=\".2f\", annot_kws={\"fontsize\":4}, norm=norm_qpf, cbar=False, ax=ax1)\n",
        "    ax1.set_yticklabels(ax1.get_ymajorticklabels(), fontsize = 6)\n",
        "    ax1.set_xticklabels(ax1.get_xmajorticklabels(), fontsize = 7)\n",
        "\n",
        "    proxy = [mpatches.Patch(color = QPF_COLS[b]) for b in range(len(QPF_LEVS))]\n",
        "    proxy = proxy[::-1]\n",
        "    LLABS = []\n",
        "    for i in range(0, len(QPF_LEVS)):\n",
        "      if QPF_LEVS[i] == 0.0:\n",
        "        label = \"T\"\n",
        "      else:\n",
        "        label = str(QPF_LEVS[i])\n",
        "      LLABS.append(label)\n",
        "    LLABS = LLABS[::-1]\n",
        "    l = ax1.legend(handles=proxy, labels=LLABS, fontsize=7, frameon=True, bbox_to_anchor=(1.001, 0.5), loc=6, title=\"INCHES\")\n",
        "    plt.setp(l.get_title(), fontsize=6, weight='bold')\n",
        "    \n",
        "    labels = [\"FH\" + str(forecast_hours[v]) for v in range(0, len(forecast_hours))]\n",
        "    box_data = pd.melt(apcp)\n",
        "    for row in range(0,box_data[box_data.columns[0]].count()):\n",
        "        box_data.iloc[row,0] = forecast_hours[box_data.iloc[row,0]]\n",
        "    sns.set_style(\"whitegrid\")\n",
        "    sns.boxplot(x=\"variable\", y=\"value\",data=box_data,ax=ax2,color='lightgrey', showmeans=True, meanprops={\"markerfacecolor\":\"tab:green\", \"markeredgecolor\":\"tab:green\"})\n",
        "    #sns.violinplot(data=box_data, x=\"variable\", y=\"value\", ax=ax2, cut=0, scale=\"count\", inner=\"box\", color='lightgrey', linewidth=0.75)\n",
        "    ax2.set(xlabel=None)\n",
        "    ax2.set_xticklabels(ax2.get_xmajorticklabels(), fontsize = 6)\n",
        "    ax2.set_ylabel(\"Total Precip (in)\", fontsize=9)\n",
        "    ax2.set_xlabel(f\"Data Copyright © {model_run.strftime('%Y')} European Centre for Medium-Range Weather Forecasts (ECMWF)\", fontsize=7, style=\"italic\")\n",
        "    \n",
        "    plt.savefig(f\"EPS_{model_run.strftime('%Y%m%d-%H')}_ptotal_{site_id}\", bbox_inches='tight')\n",
        "\n",
        "\n",
        "def plot_t2m():\n",
        "    nearest_lat, nearest_lon = get_nearest_loc(ecmwfe.latitude.values, ecmwfe.longitude.values)\n",
        "    request_latlon = f\"({round(site_dict['lat'],4)}, {round(site_dict['lon'], 4)})\"\n",
        "    model_latlon = f\"({round(nearest_lat,4)}, {round(nearest_lon,4)})\"\n",
        "    t2mdf = pd.DataFrame(K_to_F(ecmwfe.sel(latitude=nearest_lat, longitude=nearest_lon).t2m.values))\n",
        "    t2mdf = t2mdf.T\n",
        "    fig, (ax1, ax2) = plt.subplots(2, 1,figsize=(10,8), gridspec_kw={'height_ratios': [3, 1]}, dpi=150)\n",
        "    plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0.2)\n",
        "\n",
        "    sitename = site_dict['name']\n",
        "    state = site_dict['state']\n",
        "\n",
        "    title = \"EPS (0.4°) 2 Meter Temperature (F) | Init: %s\\n%s | %s, %s\" % (model_run.strftime('%HZ %m-%d-%Y'), site_id, sitename, state)\n",
        "    plt.text(1.00,1.01,\"Model grid box: %s\\nStation point: %s\" % (model_latlon, request_latlon),horizontalalignment='right',fontsize=6,color='k',transform = ax1.transAxes, weight='semibold')\n",
        "    ax1.set_title(title,fontsize=9, weight='bold', stretch='normal', family='sans-serif', loc='left',color='#272727')\n",
        "    sns.heatmap(t2mdf, yticklabels=[\"P\"+str(m) for m in np.arange(1,51)], xticklabels=[valid_times[v].strftime('%d-%Hz') for v in range(0,len(valid_times))], \n",
        "                cmap=T_cmap, annot=True, fmt=\".0f\", annot_kws={\"fontsize\":4}, norm=norm_t, cbar=False, ax=ax1)\n",
        "    ax1.set_yticklabels(ax1.get_ymajorticklabels(), fontsize = 6)\n",
        "    ax1.set_xticklabels(ax1.get_xmajorticklabels(), fontsize = 7)\n",
        "\n",
        "    proxy = [mpatches.Patch(color = T_COLS[b]) for b in range(len(T_LEVS))]\n",
        "    proxy = proxy[::-1]\n",
        "    LLABS = []\n",
        "    for i in range(0, len(T_LEVS)):\n",
        "      label = str(T_LEVS[i])\n",
        "      LLABS.append(label)\n",
        "    LLABS = LLABS[::-1]\n",
        "    l = ax1.legend(handles=proxy, labels=LLABS, fontsize=7, frameon=True, bbox_to_anchor=(1.001, 0.5), loc=6, title=\"DEG F\", labelspacing=0.08)\n",
        "    plt.setp(l.get_title(), fontsize=6, weight='bold')\n",
        "    \n",
        "    labels = [\"FH\" + str(forecast_hours[v]) for v in range(0, len(forecast_hours))]\n",
        "    box_data = pd.melt(t2mdf)\n",
        "    for row in range(0,box_data[box_data.columns[0]].count()):\n",
        "        box_data.iloc[row,0] = forecast_hours[box_data.iloc[row,0]]\n",
        "    sns.set_style(\"whitegrid\")\n",
        "    #sns.violinplot(data=box_data, x=\"variable\", y=\"value\", ax=ax2, cut=0, scale=\"count\", inner=\"box\", color='lightgrey', linewidth=0.75)\n",
        "    sns.boxplot(x=\"variable\", y=\"value\",data=box_data,ax=ax2,color='lightgrey', showmeans=True, meanprops={\"markerfacecolor\":\"maroon\", \"markeredgecolor\":\"maroon\"})\n",
        "    ax2.set(xlabel=None)\n",
        "    ax2.set_xticklabels(ax2.get_xmajorticklabels(), fontsize = 7)\n",
        "    ax2.set_ylabel(\"2m Temp (F)\", fontsize=9)\n",
        "    ax2.set_xlabel(f\"Data Copyright © {model_run.strftime('%Y')} European Centre for Medium-Range Weather Forecasts (ECMWF)\", fontsize=7, style=\"italic\")\n",
        "    \n",
        "    plt.savefig(f\"EPS_{model_run.strftime('%Y%m%d-%H')}_t2m_{site_id}\", bbox_inches='tight')\n",
        "\n",
        "#########################################################################\n",
        "\n",
        "if variable == \"tp\":\n",
        "  if precipitation_selection == \"Total\":\n",
        "    plot_pcp_total()\n",
        "  else:\n",
        "    plot_pcp_bucket()\n",
        "else:\n",
        "  plot_t2m()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "k6DRBRAXCyNh",
        "pNRHFuNmC-2K"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.13 | packaged by conda-forge | (main, May 27 2022, 17:00:33) \n[Clang 13.0.1 ]"
    },
    "vscode": {
      "interpreter": {
        "hash": "d9efab89797b4f7e4129f7fe7c375038c6a3f1b6c83da7efdea02c4da588d5be"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}