{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from IPython.core.display import display, HTML\n",
    "#display(HTML(\"<style>.container {width:80% !important; } </style?\"))\n",
    "import icepython as ice\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from datetime import datetime, timedelta\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "import matplotlib.dates as mdates\n",
    "from enum import Enum\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################\n",
    "####### Varables that can be changed ###########\n",
    "model_name           = 'GEFS'  #  GEFS or ECE\n",
    "location             = 'GWUS'  # There are 100 stations and over 80+ regions\n",
    "num_of_days_back     = 90\n",
    "################################################\n",
    "################################################\n",
    "\n",
    "model_run            = '00Z'\n",
    "model_run_and_name   = model_run + '-' + model_name\n",
    "now                  = datetime.now()\n",
    "model_run_start_date = now\n",
    "model_run_end_date   = now \n",
    "start_date_history   = (now - timedelta(days=num_of_days_back)).strftime('%Y-%m-%d')\n",
    "end_date_history     = (now - timedelta(days=1)).strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MODEL_NAME(Enum):\n",
    "    ECE    = 'ECE'\n",
    "    GEFS   = 'GEFS'\n",
    " \n",
    "MODEL_SPECS  = {\n",
    "    MODEL_NAME.ECE.value:{\n",
    "        \"num_of_fcst_days\" : 15\n",
    "     },\n",
    "    MODEL_NAME.GEFS.value:{\n",
    "        \"num_of_fcst_days\" : 16\n",
    "     }\n",
    "}\n",
    "\n",
    "num_of_fcst_days = MODEL_SPECS[model_name]['num_of_fcst_days']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(symbol, field, start_date, end_date):\n",
    "    data    = ice.get_timeseries(symbol, field, granularity = 'D', start_date= start_date, end_date  = end_date)   \n",
    "   \n",
    "    return data\n",
    "\n",
    "def get_climo():\n",
    "    field               = 'GWDD official 30yr Climo'\n",
    "    start_date_climo    = (datetime(2022, 1, 1)).strftime('%Y-%m-%d')\n",
    "    end_date_climo      = (datetime(2022, 12, 31)).strftime('%Y-%m-%d')\n",
    "    symbol              = location + '-CLMO'\n",
    "    climo               = get_data([symbol], field, start_date_climo, end_date_climo)\n",
    "\n",
    "    return climo\n",
    "\n",
    "def get_history(climo_type, start_date_history, end_date_history):\n",
    "    field         = 'GWDD'\n",
    "    symbol        = [location + '-' + climo_type]\n",
    "    history       = get_data(symbol, field, start_date_history, end_date_history)\n",
    "    \n",
    "    return history\n",
    "\n",
    "def get_fcst(model_run_start_date, model_run_end_date):\n",
    "    fcst_series1=[]; fcst_series2=[];fcst_series3=[];fcst_series4=[];fcst_series5=[]\n",
    "    for model_run_date in np.arange(model_run_start_date, model_run_end_date, timedelta(days=1)).astype(datetime):\n",
    "        model_run_date_str        = model_run_date.strftime('%y%m%d')\n",
    "        symbol                    = location + ' MR' + model_run_date_str + '_' + model_run_and_name\n",
    "        start_date                = model_run_date.strftime('%Y-%m-%d')\n",
    "        end_date                  = (model_run_date + timedelta(days=num_of_fcst_days)).strftime('%Y-%m-%d')\n",
    "      \n",
    "        field       = ['GWDD',  'GWDD CHNG 1 Forecast Prior', 'GWDD CHNG 2 Forecast Prior', 'GWDD CHNG 4 Forecast Prior','GWDD official 30yr Anomaly']\n",
    "        fcst                      = get_data(symbol, field, start_date, end_date)\n",
    "        df_data                   = pd.DataFrame(list(fcst))\n",
    "        df_data                   = df_data.iloc[1:] \n",
    "        df_data                   = df_data.iloc[:,1:]\n",
    "    \n",
    "        fcst_total                = round(df_data,1)\n",
    "    \n",
    "        fcst_series1               = np.append(fcst_series1, fcst_total[1])\n",
    "        fcst_series2               = np.append(fcst_series2, fcst_total[2])\n",
    "        fcst_series3               = np.append(fcst_series3, fcst_total[3])\n",
    "        fcst_series4               = np.append(fcst_series4, fcst_total[4])\n",
    "        fcst_series5               = np.append(fcst_series5, fcst_total[5])\n",
    "        \n",
    "    return fcst_series1, fcst_series2, fcst_series3, fcst_series4, fcst_series5\n",
    "\n",
    "def get_df(data, data_name):\n",
    "    df                 = pd.DataFrame(list(data))\n",
    "    df_history         = df.iloc[1:]\n",
    "    df_history         = df_history.rename(columns={0:'Date', 1:'GWDD'})\n",
    "    df_history['Date'] = pd.DatetimeIndex(df_history['Date'])\n",
    "    df_history['GWDD'] = pd.to_numeric(df_history['GWDD'])\n",
    "    df_history         = df_history.set_index(['Date'])\n",
    "    df_history         = df_history.drop(df_history[(df_history.index.month == 2) & (df_history.index.day == 29)].index)\n",
    "    \n",
    "    return df_history\n",
    "\n",
    "def get_df_fcst(fcst1, fcst2, fcst3, fcst4, fcst5, index):\n",
    "    df_data             = pd.DataFrame({'GWDD':fcst1,'Change_1':fcst2,'Change_2':fcst3,'Change_4':fcst4,'Anomaly':fcst5, 'Date':index})\n",
    "    date_rng            = pd.DatetimeIndex(df_data['Date']) \n",
    "    df_data['Month']    = date_rng.month.values\n",
    "    df_data['Day']      = date_rng.day.values\n",
    "    \n",
    "    return df_data\n",
    "\n",
    "\n",
    "def get_df_anomaly(df_history, df_climo):\n",
    "    df_merge            = pd.merge(df_history, df_climo, on=['Month', 'Day'], how='inner') # or outer for the entire year\n",
    "    df_merge            = df_merge[['Date_x', 'History', 'Climo']]\n",
    "    df_merge            = df_merge.rename(columns={'Date_x':'Date'})\n",
    "\n",
    "    df_merge['Anomaly'] = df_merge['History'] - df_merge['Climo']\n",
    "    df_merge            = df_merge.sort_values(by='Date')\n",
    "\n",
    "    df_anomaly          = df_merge[['Date', 'Anomaly']]\n",
    "    df_anomaly          = df_anomaly.set_index('Date')\n",
    "    df_anomaly.index    = pd.to_datetime(df_anomaly.index).date\n",
    "\n",
    "    return df_anomaly\n",
    "    \n",
    "def merge_CFSR_and_METAR(df_history_METAR, df_history_CFSR):\n",
    "    df_merge                            = pd.merge(df_history_METAR, df_history_CFSR,  how='outer', left_index=True, right_index=True)\n",
    "    df_merge['GWDD_y']                  = df_merge['GWDD_y'].fillna(df_merge['GWDD_x'])\n",
    "    df_merged_CFSR_and_METAR            = df_merge.drop('GWDD_x', axis=1)\n",
    "    df_merged_CFSR_and_METAR            = df_merged_CFSR_and_METAR.rename(columns={'GWDD_y':'GWDD'})\n",
    "    date_rng                            = df_merged_CFSR_and_METAR.index\n",
    "    df_merged_CFSR_and_METAR['Date']    = date_rng\n",
    "    df_merged_CFSR_and_METAR['Month']   = date_rng.month.values\n",
    "    df_merged_CFSR_and_METAR['Day']     = date_rng.day.values\n",
    "    df_merged_CFSR_and_METAR            = df_merged_CFSR_and_METAR.rename(columns={'GWDD':'History'})\n",
    "    \n",
    "    return df_merged_CFSR_and_METAR\n",
    "\n",
    "def plot_time_series(df_history_and_fcst_anomaly):\n",
    "    fig, ax1    = plt.subplots(1,1, figsize=(16,9), dpi=100)\n",
    "    vertical_line = df_history_and_fcst_anomaly.index[df_history_and_fcst_anomaly.index==model_run_start_date.date()]\n",
    "\n",
    "    tseries     = df_history_and_fcst_anomaly['Anomaly']\n",
    "    color       = (tseries > 0).apply(lambda x: 'green' if x else 'brown')\n",
    "    ax1.bar(df_history_and_fcst_anomaly.index, df_history_and_fcst_anomaly['Anomaly'], color=color, label = 'GWDD Anomaly')\n",
    "\n",
    "    ax1.set_ylabel('GWDD Anomaly',fontsize=18)\n",
    "    ax1.set_ylim(-20, 20)\n",
    "    ax1.set_yticks(np.linspace(ax1.get_yticks()[0], ax1.get_yticks()[-1], len(ax1.get_yticks())))\n",
    "    ax1.axhline(0, color='grey', linewidth=0.8)\n",
    "\n",
    "    ax1.xaxis.set_major_locator(mdates.DayLocator(interval=2))\n",
    "    ax1.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d-%Y'))\n",
    "    plt.gcf().autofmt_xdate(rotation=45)\n",
    "\n",
    "    ax1.legend()\n",
    "    ax1.set_title('Daily ' + location + ' GWDD Anomalies (Relative to the 30-yr Normal)\\n'\n",
    "            + 'Constructed from: Observations and ' + model_run_and_name + ' Forecast\\n'\n",
    "            + 'Model Run Date: ' + now.strftime('%a, %b %d'), fontsize=18)\n",
    "    ax1.grid()\n",
    "\n",
    "    ax1.axvline(vertical_line[0]-timedelta(hours=12),color='black', linewidth=2)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "climo                              = get_climo()\n",
    "history_CFSR                       = get_history('CFSR', start_date_history, end_date_history)\n",
    "history_METAR                      = get_history('METR', start_date_history, end_date_history)\n",
    "\n",
    "df_history_CFSR                    = get_df(history_CFSR, 'History')\n",
    "df_history_METAR                   = get_df(history_METAR, 'History')\n",
    "df_merged_CFSR_and_METAR           = merge_CFSR_and_METAR(df_history_METAR, df_history_CFSR)\n",
    "\n",
    "df_climo                           = get_df(climo,   'Climo')\n",
    "date_rng                           = df_climo.index\n",
    "df_climo['Date']                   = date_rng\n",
    "df_climo['Month']                  = date_rng.month.values\n",
    "df_climo['Day']                    = date_rng.day.values\n",
    "df_climo                           = df_climo.rename(columns={'GWDD':'Climo'})\n",
    "\n",
    "df_history_anomaly                 = get_df_anomaly(df_merged_CFSR_and_METAR, df_climo)\n",
    "index_fcst_days                    = np.arange(model_run_start_date, model_run_end_date +  timedelta(days=num_of_fcst_days), timedelta(days=1)).astype(datetime)\n",
    "fcst1, fcst2, fcst3, fcst4, fcst5  = get_fcst(model_run_start_date, model_run_end_date + timedelta(days=1))\n",
    "df_fcst                            = get_df_fcst(fcst1, fcst2, fcst3, fcst4, fcst5, index_fcst_days)\n",
    "df_fcst                            = df_fcst.set_index('Date')\n",
    "df_fcst.index                      = pd.to_datetime(df_fcst.index).date\n",
    "df_fcst                            = df_fcst[['GWDD', 'Anomaly', 'Change_1', 'Change_2', 'Change_4']]\n",
    "\n",
    "df_fcst_anomaly                    = df_fcst[['Anomaly']]\n",
    "\n",
    "df_history_and_fcst_anomaly        =  df_history_anomaly.append(df_fcst_anomaly, ignore_index=False)\n",
    "df_history_and_fcst_anomaly.tail()\n",
    "\n",
    "plot_time_series(df_history_and_fcst_anomaly)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}