example/climate_index.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys,os\n",
    "sys.path.append(os.path.dirname(os.getcwd()))\n",
    "from maesters_of_clim import Climate_Maester\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Climate Index History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from maesters_of_clim import Climate_Maester\n",
    "c = Climate_Maester(['nina34a', 'pdo', 'soi'], 'psl')\n",
    "df = c.history()\n",
    "df.set_index('month').plot(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ncei': ['ninaa', 'nina', 'pdo', 'amo', 'iod'],\n",
       " 'psl': ['nina34a',\n",
       "  'nina4a',\n",
       "  'nina3a',\n",
       "  'nina1a',\n",
       "  'soi',\n",
       "  'oni',\n",
       "  'tni',\n",
       "  'censo',\n",
       "  'ao',\n",
       "  'pdo',\n",
       "  'wp',\n",
       "  'amo_us',\n",
       "  'amo_sm',\n",
       "  'dmi',\n",
       "  'dmiwest',\n",
       "  'dmieast',\n",
       "  'nao',\n",
       "  'np',\n",
       "  'tpi',\n",
       "  'glbts',\n",
       "  'glbtssst']}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Climate_Maester.list_history_indexes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Climate Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = Climate_Maester('enso', 'iri')\n",
    "d = datetime(2023, 10, 1)\n",
    "iridf = c.forecast(pred_at=d)\n",
    "cpcdf = c.forecast(pred_at=d, source='cpc')\n",
    "detaildf = c.forecast(pred_at=d, source='detail')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>LaNina</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>ElNino</th>\n",
       "      <th>Release_Month</th>\n",
       "      <th>Forecast_Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OND</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NDJ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2023-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DJF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JFM</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FMA</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>95</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MAM</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>82</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AMJ</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>55</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MJJ</td>\n",
       "      <td>8</td>\n",
       "      <td>58</td>\n",
       "      <td>34</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JJA</td>\n",
       "      <td>22</td>\n",
       "      <td>56</td>\n",
       "      <td>22</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Season LaNina Neutral ElNino Release_Month Forecast_Season\n",
       "0    OND      0       0    100       2023-10         2023-11\n",
       "1    NDJ      0       0    100       2023-10         2023-12\n",
       "2    DJF      0       0    100       2023-10         2024-01\n",
       "3    JFM      0       1     99       2023-10         2024-02\n",
       "4    FMA      0       5     95       2023-10         2024-03\n",
       "5    MAM      0      18     82       2023-10         2024-04\n",
       "6    AMJ      1      44     55       2023-10         2024-05\n",
       "7    MJJ      8      58     34       2023-10         2024-06\n",
       "8    JJA     22      56     22       2023-10         2024-07"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iridf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>LaNina</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>ElNino</th>\n",
       "      <th>Release_Month</th>\n",
       "      <th>Forecast_Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SON</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2023-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OND</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NDJ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2023-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DJF</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JFM</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>97</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FMA</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>91</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MAM</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AMJ</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>61</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MJJ</td>\n",
       "      <td>6</td>\n",
       "      <td>53</td>\n",
       "      <td>41</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Season LaNina Neutral ElNino Release_Month Forecast_Season\n",
       "0    SON      0       0    100       2023-10         2023-10\n",
       "1    OND      0       0    100       2023-10         2023-11\n",
       "2    NDJ      0       0    100       2023-10         2023-12\n",
       "3    DJF      0       1     99       2023-10         2024-01\n",
       "4    JFM      0       3     97       2023-10         2024-02\n",
       "5    FMA      0       9     91       2023-10         2024-03\n",
       "6    MAM      0      20     80       2023-10         2024-04\n",
       "7    AMJ      1      38     61       2023-10         2024-05\n",
       "8    MJJ      6      53     41       2023-10         2024-06"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpcdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>AUS-ACCESS</th>\n",
       "      <th>BCC_CSM11m</th>\n",
       "      <th>CMC CANSIP</th>\n",
       "      <th>COLA CCSM4</th>\n",
       "      <th>CS-IRI-MM</th>\n",
       "      <th>DWD</th>\n",
       "      <th>ECMWF</th>\n",
       "      <th>GFDL SPEAR</th>\n",
       "      <th>IOCAS ICM</th>\n",
       "      <th>...</th>\n",
       "      <th>CSU CLIPR</th>\n",
       "      <th>IAP-NN</th>\n",
       "      <th>NTU CODA</th>\n",
       "      <th>UCLA-TCD</th>\n",
       "      <th>UW PSL-CSLIM</th>\n",
       "      <th>UW PSL-LIM</th>\n",
       "      <th>Average, Statistical models</th>\n",
       "      <th>Average, All models</th>\n",
       "      <th>Release_Month</th>\n",
       "      <th>Forecast_Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OND</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.07</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.76</td>\n",
       "      <td>...</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.567</td>\n",
       "      <td>1.777</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NDJ</td>\n",
       "      <td>2.27</td>\n",
       "      <td>2.36</td>\n",
       "      <td>1.93</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.76</td>\n",
       "      <td>...</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.64</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.519</td>\n",
       "      <td>1.836</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2023-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DJF</td>\n",
       "      <td>2.43</td>\n",
       "      <td>2.38</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.91</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.02</td>\n",
       "      <td>1.74</td>\n",
       "      <td>...</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.371</td>\n",
       "      <td>1.769</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JFM</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.16</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.59</td>\n",
       "      <td>...</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.154</td>\n",
       "      <td>1.570</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FMA</td>\n",
       "      <td></td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.41</td>\n",
       "      <td></td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.899</td>\n",
       "      <td>1.203</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MAM</td>\n",
       "      <td></td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.13</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.867</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AMJ</td>\n",
       "      <td></td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.81</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.63</td>\n",
       "      <td>...</td>\n",
       "      <td>0.47</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.561</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MJJ</td>\n",
       "      <td></td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.61</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.31</td>\n",
       "      <td>...</td>\n",
       "      <td>0.26</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.277</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JJA</td>\n",
       "      <td></td>\n",
       "      <td>1.04</td>\n",
       "      <td>-0.50</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>0.003</td>\n",
       "      <td>2023-10</td>\n",
       "      <td>2024-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Season AUS-ACCESS BCC_CSM11m CMC CANSIP COLA CCSM4 CS-IRI-MM   DWD ECMWF  \\\n",
       "0    OND       2.03       2.07       1.89       1.51      1.90  1.76  1.98   \n",
       "1    NDJ       2.27       2.36       1.93       1.47      1.97  1.90  2.09   \n",
       "2    DJF       2.43       2.38       1.89       1.39      1.88  1.91  2.09   \n",
       "3    JFM       2.40       2.16       1.78       1.27      1.69  1.83  1.94   \n",
       "4    FMA                  1.82       1.52       1.15      1.41        1.70   \n",
       "5    MAM                  1.50       1.11       0.95      1.13               \n",
       "6    AMJ                  1.25       0.58       0.81                         \n",
       "7    MJJ                  1.10       0.04       0.61                         \n",
       "8    JJA                  1.04      -0.50                                    \n",
       "\n",
       "  GFDL SPEAR IOCAS ICM  ... CSU CLIPR IAP-NN NTU CODA UCLA-TCD UW PSL-CSLIM  \\\n",
       "0       1.85      1.76  ...      1.73   1.59     1.77     1.84         1.73   \n",
       "1       2.00      1.76  ...      1.56   1.47     1.85     1.79         1.64   \n",
       "2       2.02      1.74  ...      1.40   1.24     1.83     1.63         1.42   \n",
       "3       1.88      1.59  ...      1.23   0.95     1.52     1.38         1.13   \n",
       "4       1.63      1.34  ...      0.98   0.62     1.28     1.08         0.80   \n",
       "5       1.34      0.99  ...      0.72   0.26     1.22     0.78         0.50   \n",
       "6       0.98      0.63  ...      0.47  -0.08     1.05     0.50         0.24   \n",
       "7       0.61      0.31  ...      0.26  -0.40     0.80     0.27         0.03   \n",
       "8       0.33      0.05  ...      0.06  -0.67     0.46     0.07        -0.14   \n",
       "\n",
       "  UW PSL-LIM Average, Statistical models Average, All models Release_Month  \\\n",
       "0       1.75                       1.567               1.777       2023-10   \n",
       "1       1.75                       1.519               1.836       2023-10   \n",
       "2       1.65                       1.371               1.769       2023-10   \n",
       "3       1.47                       1.154               1.570       2023-10   \n",
       "4       1.20                       0.899               1.203       2023-10   \n",
       "5       0.91                       0.655               0.867       2023-10   \n",
       "6       0.62                       0.417               0.561       2023-10   \n",
       "7       0.37                       0.170               0.277       2023-10   \n",
       "8       0.14                      -0.057               0.003       2023-10   \n",
       "\n",
       "  Forecast_Season  \n",
       "0         2023-11  \n",
       "1         2023-12  \n",
       "2         2024-01  \n",
       "3         2024-02  \n",
       "4         2024-03  \n",
       "5         2024-04  \n",
       "6         2024-05  \n",
       "7         2024-06  \n",
       "8         2024-07  \n",
       "\n",
       "[9 rows x 32 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detaildf"
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  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "      <td>1.231974</td>\n",
       "      <td>0.658067</td>\n",
       "      <td>1.029337</td>\n",
       "      <td>1.086232</td>\n",
       "      <td>0.705422</td>\n",
       "      <td>1.024229</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.392148</td>\n",
       "      <td>1.360647</td>\n",
       "      <td>1.096233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.586874</td>\n",
       "      <td>1.042363</td>\n",
       "      <td>2023-12</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.693144</td>\n",
       "      <td>0.643113</td>\n",
       "      <td>0.799456</td>\n",
       "      <td>0.775818</td>\n",
       "      <td>0.914116</td>\n",
       "      <td>0.568564</td>\n",
       "      <td>0.656026</td>\n",
       "      <td>1.061512</td>\n",
       "      <td>0.606217</td>\n",
       "      <td>0.780071</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.578027</td>\n",
       "      <td>0.637246</td>\n",
       "      <td>0.903040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.879329</td>\n",
       "      <td>0.787127</td>\n",
       "      <td>2024-01</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.164003</td>\n",
       "      <td>-0.001132</td>\n",
       "      <td>0.402743</td>\n",
       "      <td>-0.233181</td>\n",
       "      <td>0.226465</td>\n",
       "      <td>-0.015045</td>\n",
       "      <td>0.596027</td>\n",
       "      <td>0.521986</td>\n",
       "      <td>0.104757</td>\n",
       "      <td>0.223248</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.422658</td>\n",
       "      <td>0.923663</td>\n",
       "      <td>0.324694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035070</td>\n",
       "      <td>-0.205428</td>\n",
       "      <td>2024-02</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.054737</td>\n",
       "      <td>0.201489</td>\n",
       "      <td>0.070049</td>\n",
       "      <td>-0.310218</td>\n",
       "      <td>0.164510</td>\n",
       "      <td>-0.203800</td>\n",
       "      <td>0.268819</td>\n",
       "      <td>-0.476760</td>\n",
       "      <td>-0.038085</td>\n",
       "      <td>-0.071388</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.042473</td>\n",
       "      <td>0.128160</td>\n",
       "      <td>0.346698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.196848</td>\n",
       "      <td>-0.349359</td>\n",
       "      <td>2024-03</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.025079</td>\n",
       "      <td>0.114816</td>\n",
       "      <td>0.295845</td>\n",
       "      <td>-0.566731</td>\n",
       "      <td>0.101067</td>\n",
       "      <td>0.300536</td>\n",
       "      <td>-0.140333</td>\n",
       "      <td>-0.175871</td>\n",
       "      <td>0.068239</td>\n",
       "      <td>-0.018081</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.200811</td>\n",
       "      <td>-0.106144</td>\n",
       "      <td>0.220650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.292427</td>\n",
       "      <td>-0.143925</td>\n",
       "      <td>2024-04</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.048468</td>\n",
       "      <td>-0.321668</td>\n",
       "      <td>-0.314554</td>\n",
       "      <td>-0.083253</td>\n",
       "      <td>0.355631</td>\n",
       "      <td>0.520656</td>\n",
       "      <td>-0.509384</td>\n",
       "      <td>-0.250982</td>\n",
       "      <td>0.064869</td>\n",
       "      <td>0.032068</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.729994</td>\n",
       "      <td>0.162401</td>\n",
       "      <td>-0.027692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.028820</td>\n",
       "      <td>0.452147</td>\n",
       "      <td>2024-05</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.029192</td>\n",
       "      <td>-0.665404</td>\n",
       "      <td>-0.083909</td>\n",
       "      <td>0.173352</td>\n",
       "      <td>0.481325</td>\n",
       "      <td>0.596224</td>\n",
       "      <td>-0.437844</td>\n",
       "      <td>-0.582283</td>\n",
       "      <td>-0.026888</td>\n",
       "      <td>-0.031495</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.719346</td>\n",
       "      <td>0.516146</td>\n",
       "      <td>-0.783964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.074662</td>\n",
       "      <td>0.136565</td>\n",
       "      <td>2024-06</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.102502</td>\n",
       "      <td>-0.609292</td>\n",
       "      <td>-0.190814</td>\n",
       "      <td>0.120524</td>\n",
       "      <td>0.525676</td>\n",
       "      <td>1.210806</td>\n",
       "      <td>-0.694265</td>\n",
       "      <td>-1.018683</td>\n",
       "      <td>-0.011541</td>\n",
       "      <td>-0.193464</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.829673</td>\n",
       "      <td>0.299437</td>\n",
       "      <td>-0.732081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.101568</td>\n",
       "      <td>0.316395</td>\n",
       "      <td>2024-07</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.330929</td>\n",
       "      <td>-0.469554</td>\n",
       "      <td>-0.297916</td>\n",
       "      <td>-0.495550</td>\n",
       "      <td>0.255466</td>\n",
       "      <td>1.575441</td>\n",
       "      <td>-1.222670</td>\n",
       "      <td>-1.803841</td>\n",
       "      <td>-0.058657</td>\n",
       "      <td>-0.603202</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.751265</td>\n",
       "      <td>-0.334907</td>\n",
       "      <td>-1.291626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009973</td>\n",
       "      <td>0.582475</td>\n",
       "      <td>2024-08</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.623000</td>\n",
       "      <td>-1.034516</td>\n",
       "      <td>0.005241</td>\n",
       "      <td>-0.592927</td>\n",
       "      <td>0.145991</td>\n",
       "      <td>1.622889</td>\n",
       "      <td>-1.574323</td>\n",
       "      <td>-2.292726</td>\n",
       "      <td>-0.263819</td>\n",
       "      <td>-0.982181</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.672980</td>\n",
       "      <td>-0.820570</td>\n",
       "      <td>-1.742812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.590384</td>\n",
       "      <td>0.298593</td>\n",
       "      <td>2024-09</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-10</td>\n",
       "      <td>2023-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Mean      N2K1     N3K2V   N2K2hiV    N1K1hi      N1K2   N3K2hiV  \\\n",
       "0   0.864826  0.852731  1.391649  1.027814  1.231974  0.658067  1.029337   \n",
       "1   0.693144  0.643113  0.799456  0.775818  0.914116  0.568564  0.656026   \n",
       "2   0.164003 -0.001132  0.402743 -0.233181  0.226465 -0.015045  0.596027   \n",
       "3  -0.054737  0.201489  0.070049 -0.310218  0.164510 -0.203800  0.268819   \n",
       "4   0.025079  0.114816  0.295845 -0.566731  0.101067  0.300536 -0.140333   \n",
       "5   0.048468 -0.321668 -0.314554 -0.083253  0.355631  0.520656 -0.509384   \n",
       "6  -0.029192 -0.665404 -0.083909  0.173352  0.481325  0.596224 -0.437844   \n",
       "7  -0.102502 -0.609292 -0.190814  0.120524  0.525676  1.210806 -0.694265   \n",
       "8  -0.330929 -0.469554 -0.297916 -0.495550  0.255466  1.575441 -1.222670   \n",
       "9  -0.623000 -1.034516  0.005241 -0.592927  0.145991  1.622889 -1.574323   \n",
       "10       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "       N3K1V   F2-Mean  F2-3DVAR-Mean  ...  sfe41d    N3K2hi     N2K2V  \\\n",
       "0   1.086232  0.705422       1.024229  ...     NaN  0.392148  1.360647   \n",
       "1   1.061512  0.606217       0.780071  ...     NaN  0.578027  0.637246   \n",
       "2   0.521986  0.104757       0.223248  ...     NaN  0.422658  0.923663   \n",
       "3  -0.476760 -0.038085      -0.071388  ...     NaN -0.042473  0.128160   \n",
       "4  -0.175871  0.068239      -0.018081  ...     NaN  0.200811 -0.106144   \n",
       "5  -0.250982  0.064869       0.032068  ...     NaN  0.729994  0.162401   \n",
       "6  -0.582283 -0.026888      -0.031495  ...     NaN  0.719346  0.516146   \n",
       "7  -1.018683 -0.011541      -0.193464  ...     NaN  0.829673  0.299437   \n",
       "8  -1.803841 -0.058657      -0.603202  ...     NaN  0.751265 -0.334907   \n",
       "9  -2.292726 -0.263819      -0.982181  ...     NaN  0.672980 -0.820570   \n",
       "10       NaN       NaN            NaN  ...     NaN       NaN       NaN   \n",
       "\n",
       "       N2K1V  sfe31d  sfe33d      N2K2     N1K2V  Forecast_Month  \\\n",
       "0   1.096233     NaN     NaN  0.586874  1.042363         2023-12   \n",
       "1   0.903040     NaN     NaN  0.879329  0.787127         2024-01   \n",
       "2   0.324694     NaN     NaN  0.035070 -0.205428         2024-02   \n",
       "3   0.346698     NaN     NaN  0.196848 -0.349359         2024-03   \n",
       "4   0.220650     NaN     NaN -0.292427 -0.143925         2024-04   \n",
       "5  -0.027692     NaN     NaN -0.028820  0.452147         2024-05   \n",
       "6  -0.783964     NaN     NaN  0.074662  0.136565         2024-06   \n",
       "7  -0.732081     NaN     NaN  0.101568  0.316395         2024-07   \n",
       "8  -1.291626     NaN     NaN  0.009973  0.582475         2024-08   \n",
       "9  -1.742812     NaN     NaN -0.590384  0.298593         2024-09   \n",
       "10       NaN     NaN     NaN       NaN       NaN         2024-10   \n",
       "\n",
       "    Release_Month  \n",
       "0         2023-11  \n",
       "1         2023-11  \n",
       "2         2023-11  \n",
       "3         2023-11  \n",
       "4         2023-11  \n",
       "5         2023-11  \n",
       "6         2023-11  \n",
       "7         2023-11  \n",
       "8         2023-11  \n",
       "9         2023-11  \n",
       "10        2023-11  \n",
       "\n",
       "[11 rows x 39 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = Climate_Maester('dmi', 'jamstec')\n",
    "dmidf = c.forecast()\n",
    "dmidf"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ENSO Event calc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>nina34a</th>\n",
       "      <th>pdo</th>\n",
       "      <th>soi</th>\n",
       "      <th>enso_event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1950-05-01</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1950-06-01</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1950-07-01</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1950-08-01</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1950-09-01</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.8</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>1.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>281 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         month  nina34a   pdo  soi  enso_event\n",
       "28  1950-05-01    -1.75 -2.23  NaN        -1.0\n",
       "29  1950-06-01    -1.27 -1.77  NaN        -1.0\n",
       "30  1950-07-01    -1.01 -2.93  NaN        -1.0\n",
       "31  1950-08-01    -0.97 -0.70  NaN        -1.0\n",
       "32  1950-09-01    -0.98 -2.14  NaN        -1.0\n",
       "..         ...      ...   ...  ...         ...\n",
       "897 2022-10-01    -0.99   NaN  2.8        -1.0\n",
       "898 2022-11-01    -0.90   NaN  0.5        -1.0\n",
       "899 2022-12-01    -0.85   NaN  3.5        -1.0\n",
       "900 2023-01-01    -0.71   NaN  2.3        -1.0\n",
       "909 2023-10-01     1.66   NaN -0.8         1.0\n",
       "\n",
       "[281 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from maesters_of_clim.analysis import enso_event\n",
    "df['enso_event'] = enso_event(df, column='nina34a', temp=0.5, months=5)\n",
    "df[~df['enso_event'].isna()]"
   ]
  },
  {
   "cell_type": "code",
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