eda_and_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step #1: Loading all the spectral data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Init a data list:\n",
    "data_list = []\n",
    "\n",
    "#Loop over all the files in the data folder:\n",
    "for file in os.listdir('data/'):\n",
    "    #Check if the file is a csv file:\n",
    "    if 'dsc_fc' in file and file.endswith('.csv'):\n",
    "        #Read the csv file:\n",
    "        spec_data = pd.read_csv(\"data/\"+file, \\\n",
    "                                delimiter = ',', parse_dates=[0], \\\n",
    "                                    infer_datetime_format=True, na_values='0', \\\n",
    "                                        header = None)\n",
    "        data_list.append(spec_data)\n",
    "\n",
    "#Make a dataframe from the data list:\n",
    "spectral_data = pd.concat(data_list, axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "                   datetime        0         1         2   3   4   5   6   7  \\\n",
       "0       2020-02-25 00:00:00  4.04158 -0.337277  0.278823 NaN NaN NaN NaN NaN   \n",
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       "...                     ...      ...       ...       ...  ..  ..  ..  ..  ..   \n",
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       "3277439 2019-06-27 23:59:00      NaN       NaN       NaN NaN NaN NaN NaN NaN   \n",
       "\n",
       "          8  ...  43  44  45  46  47  48  49  50  51  52  \n",
       "0       NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
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       "...      ..  ...  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  \n",
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     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "#Adding a datetime column:\n",
    "spectral_data.columns = ['datetime'] + [i for i in range(spectral_data.shape[1] - 1)]\n",
    "spectral_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "                   datetime        0         1         2\n",
       "0       2020-02-25 00:00:00  4.04158 -0.337277  0.278823\n",
       "1       2020-02-25 00:01:00  4.02179 -0.469653  0.141173\n",
       "2       2020-02-25 00:02:00  3.98937 -0.655307  0.151470\n",
       "3       2020-02-25 00:03:00  3.89345 -0.966260 -0.149979\n",
       "4       2020-02-25 00:04:00  3.83742 -0.969013 -0.514656\n",
       "...                     ...      ...       ...       ...\n",
       "3277435 2019-06-27 23:55:00      NaN       NaN       NaN\n",
       "3277436 2019-06-27 23:56:00      NaN       NaN       NaN\n",
       "3277437 2019-06-27 23:57:00      NaN       NaN       NaN\n",
       "3277438 2019-06-27 23:58:00      NaN       NaN       NaN\n",
       "3277439 2019-06-27 23:59:00      NaN       NaN       NaN\n",
       "\n",
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking into the subset of data:\n",
    "spectral_data.loc[:,:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Aggregating & pre-processing the data to 1 hour time scale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Group by 1 hour and taking the mean:\n",
    "spectral_data['datetime'] = pd.to_datetime(spectral_data['datetime'], errors='coerce')  \n",
    "agg_data = spectral_data.loc[:,:2].groupby(pd.Grouper(key='datetime', freq='1H')).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-25 00:00:00</td>\n",
       "      <td>4.04158</td>\n",
       "      <td>-0.337277</td>\n",
       "      <td>0.278823</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-25 00:01:00</td>\n",
       "      <td>4.02179</td>\n",
       "      <td>-0.469653</td>\n",
       "      <td>0.141173</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-25 00:02:00</td>\n",
       "      <td>3.98937</td>\n",
       "      <td>-0.655307</td>\n",
       "      <td>0.151470</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-25 00:03:00</td>\n",
       "      <td>3.89345</td>\n",
       "      <td>-0.966260</td>\n",
       "      <td>-0.149979</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-25 00:04:00</td>\n",
       "      <td>3.83742</td>\n",
       "      <td>-0.969013</td>\n",
       "      <td>-0.514656</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3277435</th>\n",
       "      <td>2019-06-27 23:55:00</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3277436</th>\n",
       "      <td>2019-06-27 23:56:00</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3277437</th>\n",
       "      <td>2019-06-27 23:57:00</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3277438</th>\n",
       "      <td>2019-06-27 23:58:00</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3277439</th>\n",
       "      <td>2019-06-27 23:59:00</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3277440 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   datetime        0         1         2   3   4   5   6   7  \\\n",
       "0       2020-02-25 00:00:00  4.04158 -0.337277  0.278823 NaN NaN NaN NaN NaN   \n",
       "1       2020-02-25 00:01:00  4.02179 -0.469653  0.141173 NaN NaN NaN NaN NaN   \n",
       "2       2020-02-25 00:02:00  3.98937 -0.655307  0.151470 NaN NaN NaN NaN NaN   \n",
       "3       2020-02-25 00:03:00  3.89345 -0.966260 -0.149979 NaN NaN NaN NaN NaN   \n",
       "4       2020-02-25 00:04:00  3.83742 -0.969013 -0.514656 NaN NaN NaN NaN NaN   \n",
       "...                     ...      ...       ...       ...  ..  ..  ..  ..  ..   \n",
       "3277435 2019-06-27 23:55:00      NaN       NaN       NaN NaN NaN NaN NaN NaN   \n",
       "3277436 2019-06-27 23:56:00      NaN       NaN       NaN NaN NaN NaN NaN NaN   \n",
       "3277437 2019-06-27 23:57:00      NaN       NaN       NaN NaN NaN NaN NaN NaN   \n",
       "3277438 2019-06-27 23:58:00      NaN       NaN       NaN NaN NaN NaN NaN NaN   \n",
       "3277439 2019-06-27 23:59:00      NaN       NaN       NaN NaN NaN NaN NaN NaN   \n",
       "\n",
       "          8  ...  43  44  45  46  47  48  49  50  51  52  \n",
       "0       NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "1       NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "2       NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "3       NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "4       NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "...      ..  ...  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  \n",
       "3277435 NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "3277436 NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "3277437 NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "3277438 NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "3277439 NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "\n",
       "[3277440 rows x 54 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spectral_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Keep only the vector components and drop the na values:\n",
    "agg_data = agg_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>5.733193</td>\n",
       "      <td>-2.296756</td>\n",
       "      <td>-13.282797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 01:00:00</td>\n",
       "      <td>4.668200</td>\n",
       "      <td>-1.917471</td>\n",
       "      <td>-12.976817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 02:00:00</td>\n",
       "      <td>2.898088</td>\n",
       "      <td>0.470586</td>\n",
       "      <td>-13.237948</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "      <td>3.107440</td>\n",
       "      <td>0.077172</td>\n",
       "      <td>-12.819543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 04:00:00</td>\n",
       "      <td>3.387239</td>\n",
       "      <td>-2.777962</td>\n",
       "      <td>-11.102431</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>64291</th>\n",
       "      <td>2023-05-02 19:00:00</td>\n",
       "      <td>3.625422</td>\n",
       "      <td>3.330697</td>\n",
       "      <td>-1.425394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64292</th>\n",
       "      <td>2023-05-02 20:00:00</td>\n",
       "      <td>3.129382</td>\n",
       "      <td>3.551040</td>\n",
       "      <td>-1.273540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64293</th>\n",
       "      <td>2023-05-02 21:00:00</td>\n",
       "      <td>4.297767</td>\n",
       "      <td>3.180053</td>\n",
       "      <td>-1.004981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64294</th>\n",
       "      <td>2023-05-02 22:00:00</td>\n",
       "      <td>4.677120</td>\n",
       "      <td>3.084730</td>\n",
       "      <td>0.446979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64295</th>\n",
       "      <td>2023-05-02 23:00:00</td>\n",
       "      <td>3.723668</td>\n",
       "      <td>3.607139</td>\n",
       "      <td>0.398778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54235 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime         0         1          2\n",
       "0     2016-01-01 00:00:00  5.733193 -2.296756 -13.282797\n",
       "1     2016-01-01 01:00:00  4.668200 -1.917471 -12.976817\n",
       "2     2016-01-01 02:00:00  2.898088  0.470586 -13.237948\n",
       "3     2016-01-01 03:00:00  3.107440  0.077172 -12.819543\n",
       "4     2016-01-01 04:00:00  3.387239 -2.777962 -11.102431\n",
       "...                   ...       ...       ...        ...\n",
       "64291 2023-05-02 19:00:00  3.625422  3.330697  -1.425394\n",
       "64292 2023-05-02 20:00:00  3.129382  3.551040  -1.273540\n",
       "64293 2023-05-02 21:00:00  4.297767  3.180053  -1.004981\n",
       "64294 2023-05-02 22:00:00  4.677120  3.084730   0.446979\n",
       "64295 2023-05-02 23:00:00  3.723668  3.607139   0.398778\n",
       "\n",
       "[54235 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Loading & pre-processing the indices data (containing ground truth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's load the indices data:\n",
    "indices_data = pd.read_csv('data/dataset_2013_2023_normalised.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Epoch</th>\n",
       "      <th>YR</th>\n",
       "      <th>Day</th>\n",
       "      <th>HR</th>\n",
       "      <th>Rot#</th>\n",
       "      <th>IMF</th>\n",
       "      <th>PLS</th>\n",
       "      <th>IMF_PTS</th>\n",
       "      <th>PLS_PTS</th>\n",
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       "      <th>...</th>\n",
       "      <th>F10_INDEX</th>\n",
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       "      <th>AL_INDEX</th>\n",
       "      <th>AU_INDEX</th>\n",
       "      <th>PC_N_INDEX</th>\n",
       "      <th>Solar_Lyman_alpha</th>\n",
       "      <th>Proton_QI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>6.374506e+13</td>\n",
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       "      <td>36</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>69.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-6</td>\n",
       "      <td>9999</td>\n",
       "      <td>2</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.005875</td>\n",
       "      <td>0.0034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.374506e+13</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2542</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>57</td>\n",
       "      <td>36</td>\n",
       "      <td>2.9</td>\n",
       "      <td>...</td>\n",
       "      <td>69.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-1</td>\n",
       "      <td>9999</td>\n",
       "      <td>2</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.005875</td>\n",
       "      <td>0.0057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.374506e+13</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2542</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>62</td>\n",
       "      <td>36</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>69.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3</td>\n",
       "      <td>9999</td>\n",
       "      <td>2</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.005875</td>\n",
       "      <td>0.0102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.374507e+13</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2542</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>57</td>\n",
       "      <td>35</td>\n",
       "      <td>5.3</td>\n",
       "      <td>...</td>\n",
       "      <td>69.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>9999</td>\n",
       "      <td>0</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005875</td>\n",
       "      <td>0.0274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.374507e+13</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2542</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>64</td>\n",
       "      <td>38</td>\n",
       "      <td>4.4</td>\n",
       "      <td>...</td>\n",
       "      <td>69.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>9999</td>\n",
       "      <td>0</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.005875</td>\n",
       "      <td>0.0133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94135</th>\n",
       "      <td>6.374504e+13</td>\n",
       "      <td>2019</td>\n",
       "      <td>365</td>\n",
       "      <td>19</td>\n",
       "      <td>2542</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>60</td>\n",
       "      <td>36</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>68.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-2</td>\n",
       "      <td>9999</td>\n",
       "      <td>5</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.006103</td>\n",
       "      <td>0.0071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94136</th>\n",
       "      <td>6.374504e+13</td>\n",
       "      <td>2019</td>\n",
       "      <td>365</td>\n",
       "      <td>20</td>\n",
       "      <td>2542</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>33</td>\n",
       "      <td>4.2</td>\n",
       "      <td>...</td>\n",
       "      <td>68.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "      <td>9999</td>\n",
       "      <td>5</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006103</td>\n",
       "      <td>0.0086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94137</th>\n",
       "      <td>6.374505e+13</td>\n",
       "      <td>2019</td>\n",
       "      <td>365</td>\n",
       "      <td>21</td>\n",
       "      <td>2542</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>59</td>\n",
       "      <td>36</td>\n",
       "      <td>3.2</td>\n",
       "      <td>...</td>\n",
       "      <td>68.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-1</td>\n",
       "      <td>9999</td>\n",
       "      <td>3</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006103</td>\n",
       "      <td>0.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94138</th>\n",
       "      <td>6.374505e+13</td>\n",
       "      <td>2019</td>\n",
       "      <td>365</td>\n",
       "      <td>22</td>\n",
       "      <td>2542</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>35</td>\n",
       "      <td>2.7</td>\n",
       "      <td>...</td>\n",
       "      <td>68.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-3</td>\n",
       "      <td>9999</td>\n",
       "      <td>3</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.006103</td>\n",
       "      <td>0.0040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94139</th>\n",
       "      <td>6.374505e+13</td>\n",
       "      <td>2019</td>\n",
       "      <td>365</td>\n",
       "      <td>23</td>\n",
       "      <td>2542</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>60</td>\n",
       "      <td>36</td>\n",
       "      <td>4.6</td>\n",
       "      <td>...</td>\n",
       "      <td>68.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-5</td>\n",
       "      <td>9999</td>\n",
       "      <td>3</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.006103</td>\n",
       "      <td>0.0209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94140 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Epoch    YR  Day  HR  Rot#  IMF  PLS  IMF_PTS  PLS_PTS  ABS_B  \\\n",
       "0      6.374506e+13  2020    1   0  2542   51   52       60       36    2.5   \n",
       "1      6.374506e+13  2020    1   1  2542   51   52       57       36    2.9   \n",
       "2      6.374506e+13  2020    1   2  2542   51   52       62       36    4.0   \n",
       "3      6.374507e+13  2020    1   3  2542   51   52       57       35    5.3   \n",
       "4      6.374507e+13  2020    1   4  2542   51   52       64       38    4.4   \n",
       "...             ...   ...  ...  ..   ...  ...  ...      ...      ...    ...   \n",
       "94135  6.374504e+13  2019  365  19  2542   51   52       60       36    4.0   \n",
       "94136  6.374504e+13  2019  365  20  2542   51   52       58       33    4.2   \n",
       "94137  6.374505e+13  2019  365  21  2542   51   52       59       36    3.2   \n",
       "94138  6.374505e+13  2019  365  22  2542   51   52       58       35    2.7   \n",
       "94139  6.374505e+13  2019  365  23  2542   51   52       60       36    4.6   \n",
       "\n",
       "       ...  F10_INDEX   KP  DST    AE  AP_INDEX  AL_INDEX  AU_INDEX  \\\n",
       "0      ...       69.4  0.3   -6  9999         2     99999     99999   \n",
       "1      ...       69.4  0.3   -1  9999         2     99999     99999   \n",
       "2      ...       69.4  0.3    3  9999         2     99999     99999   \n",
       "3      ...       69.4  0.0    5  9999         0     99999     99999   \n",
       "4      ...       69.4  0.0    7  9999         0     99999     99999   \n",
       "...    ...        ...  ...  ...   ...       ...       ...       ...   \n",
       "94135  ...       68.2  1.3   -2  9999         5     99999     99999   \n",
       "94136  ...       68.2  1.3    0  9999         5     99999     99999   \n",
       "94137  ...       68.2  0.7   -1  9999         3     99999     99999   \n",
       "94138  ...       68.2  0.7   -3  9999         3     99999     99999   \n",
       "94139  ...       68.2  0.7   -5  9999         3     99999     99999   \n",
       "\n",
       "       PC_N_INDEX  Solar_Lyman_alpha  Proton_QI  \n",
       "0             0.2           0.005875     0.0034  \n",
       "1             0.1           0.005875     0.0057  \n",
       "2             0.2           0.005875     0.0102  \n",
       "3             0.0           0.005875     0.0274  \n",
       "4            -0.2           0.005875     0.0133  \n",
       "...           ...                ...        ...  \n",
       "94135         0.4           0.006103     0.0071  \n",
       "94136         0.5           0.006103     0.0086  \n",
       "94137         0.5           0.006103     0.0052  \n",
       "94138         0.4           0.006103     0.0040  \n",
       "94139         0.2           0.006103     0.0209  \n",
       "\n",
       "[94140 rows x 58 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's make a column with all the date features combined:\n",
    "indices_data['dkey'] = indices_data['YR'].astype(str) + '-' + \\\n",
    "                                          indices_data['Day'].astype(str) + '-' + \\\n",
    "                                              indices_data['HR'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           2020-1-0\n",
       "1           2020-1-1\n",
       "2           2020-1-2\n",
       "3           2020-1-3\n",
       "4           2020-1-4\n",
       "            ...     \n",
       "94135    2019-365-19\n",
       "94136    2019-365-20\n",
       "94137    2019-365-21\n",
       "94138    2019-365-22\n",
       "94139    2019-365-23\n",
       "Name: dkey, Length: 94140, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices_data.dkey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's do the same for the spectral data:\n",
    "agg_data['dkey'] = agg_data['datetime'].dt.year.astype(str) + '-' + agg_data['datetime'].dt.dayofyear.astype(str) + '-'\\\n",
    "    + agg_data['datetime'].dt.hour.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           2016-1-0\n",
       "1           2016-1-1\n",
       "2           2016-1-2\n",
       "3           2016-1-3\n",
       "4           2016-1-4\n",
       "            ...     \n",
       "64291    2023-122-19\n",
       "64292    2023-122-20\n",
       "64293    2023-122-21\n",
       "64294    2023-122-22\n",
       "64295    2023-122-23\n",
       "Name: dkey, Length: 54235, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_data.dkey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           2020-1-0\n",
       "1           2020-1-1\n",
       "2           2020-1-2\n",
       "3           2020-1-3\n",
       "4           2020-1-4\n",
       "            ...     \n",
       "94135    2019-365-19\n",
       "94136    2019-365-20\n",
       "94137    2019-365-21\n",
       "94138    2019-365-22\n",
       "94139    2019-365-23\n",
       "Name: dkey, Length: 94140, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices_data.dkey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Time to merge the two datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>datetime</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>dkey</th>\n",
       "      <th>Epoch</th>\n",
       "      <th>YR</th>\n",
       "      <th>Day</th>\n",
       "      <th>HR</th>\n",
       "      <th>Rot#</th>\n",
       "      <th>...</th>\n",
       "      <th>F10_INDEX</th>\n",
       "      <th>KP</th>\n",
       "      <th>DST</th>\n",
       "      <th>AE</th>\n",
       "      <th>AP_INDEX</th>\n",
       "      <th>AL_INDEX</th>\n",
       "      <th>AU_INDEX</th>\n",
       "      <th>PC_N_INDEX</th>\n",
       "      <th>Solar_Lyman_alpha</th>\n",
       "      <th>Proton_QI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>5.733193</td>\n",
       "      <td>-2.296756</td>\n",
       "      <td>-13.282797</td>\n",
       "      <td>2016-1-0</td>\n",
       "      <td>6.361883e+13</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2488</td>\n",
       "      <td>...</td>\n",
       "      <td>95.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-116</td>\n",
       "      <td>686</td>\n",
       "      <td>80</td>\n",
       "      <td>-679</td>\n",
       "      <td>8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.2497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 01:00:00</td>\n",
       "      <td>4.668200</td>\n",
       "      <td>-1.917471</td>\n",
       "      <td>-12.976817</td>\n",
       "      <td>2016-1-1</td>\n",
       "      <td>6.361883e+13</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2488</td>\n",
       "      <td>...</td>\n",
       "      <td>95.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-114</td>\n",
       "      <td>574</td>\n",
       "      <td>80</td>\n",
       "      <td>-512</td>\n",
       "      <td>62</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.6232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 02:00:00</td>\n",
       "      <td>2.898088</td>\n",
       "      <td>0.470586</td>\n",
       "      <td>-13.237948</td>\n",
       "      <td>2016-1-2</td>\n",
       "      <td>6.361883e+13</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2488</td>\n",
       "      <td>...</td>\n",
       "      <td>95.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-102</td>\n",
       "      <td>788</td>\n",
       "      <td>80</td>\n",
       "      <td>-564</td>\n",
       "      <td>224</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.4127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "      <td>3.107440</td>\n",
       "      <td>0.077172</td>\n",
       "      <td>-12.819543</td>\n",
       "      <td>2016-1-3</td>\n",
       "      <td>6.361884e+13</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2488</td>\n",
       "      <td>...</td>\n",
       "      <td>95.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-98</td>\n",
       "      <td>710</td>\n",
       "      <td>56</td>\n",
       "      <td>-555</td>\n",
       "      <td>156</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.2708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 04:00:00</td>\n",
       "      <td>3.387239</td>\n",
       "      <td>-2.777962</td>\n",
       "      <td>-11.102431</td>\n",
       "      <td>2016-1-4</td>\n",
       "      <td>6.361884e+13</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2488</td>\n",
       "      <td>...</td>\n",
       "      <td>95.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-96</td>\n",
       "      <td>909</td>\n",
       "      <td>56</td>\n",
       "      <td>-672</td>\n",
       "      <td>237</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.4906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54230</th>\n",
       "      <td>2023-05-02 19:00:00</td>\n",
       "      <td>3.625422</td>\n",
       "      <td>3.330697</td>\n",
       "      <td>-1.425394</td>\n",
       "      <td>2023-122-19</td>\n",
       "      <td>6.385027e+13</td>\n",
       "      <td>2023</td>\n",
       "      <td>122</td>\n",
       "      <td>19</td>\n",
       "      <td>2587</td>\n",
       "      <td>...</td>\n",
       "      <td>159.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-23</td>\n",
       "      <td>9999</td>\n",
       "      <td>6</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>0.0317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54231</th>\n",
       "      <td>2023-05-02 20:00:00</td>\n",
       "      <td>3.129382</td>\n",
       "      <td>3.551040</td>\n",
       "      <td>-1.273540</td>\n",
       "      <td>2023-122-20</td>\n",
       "      <td>6.385028e+13</td>\n",
       "      <td>2023</td>\n",
       "      <td>122</td>\n",
       "      <td>20</td>\n",
       "      <td>2587</td>\n",
       "      <td>...</td>\n",
       "      <td>159.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-21</td>\n",
       "      <td>9999</td>\n",
       "      <td>6</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54232</th>\n",
       "      <td>2023-05-02 21:00:00</td>\n",
       "      <td>4.297767</td>\n",
       "      <td>3.180053</td>\n",
       "      <td>-1.004981</td>\n",
       "      <td>2023-122-21</td>\n",
       "      <td>6.385028e+13</td>\n",
       "      <td>2023</td>\n",
       "      <td>122</td>\n",
       "      <td>21</td>\n",
       "      <td>2587</td>\n",
       "      <td>...</td>\n",
       "      <td>159.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-19</td>\n",
       "      <td>9999</td>\n",
       "      <td>3</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>0.0265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54233</th>\n",
       "      <td>2023-05-02 22:00:00</td>\n",
       "      <td>4.677120</td>\n",
       "      <td>3.084730</td>\n",
       "      <td>0.446979</td>\n",
       "      <td>2023-122-22</td>\n",
       "      <td>6.385028e+13</td>\n",
       "      <td>2023</td>\n",
       "      <td>122</td>\n",
       "      <td>22</td>\n",
       "      <td>2587</td>\n",
       "      <td>...</td>\n",
       "      <td>159.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-17</td>\n",
       "      <td>9999</td>\n",
       "      <td>3</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>0.0352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54234</th>\n",
       "      <td>2023-05-02 23:00:00</td>\n",
       "      <td>3.723668</td>\n",
       "      <td>3.607139</td>\n",
       "      <td>0.398778</td>\n",
       "      <td>2023-122-23</td>\n",
       "      <td>6.385029e+13</td>\n",
       "      <td>2023</td>\n",
       "      <td>122</td>\n",
       "      <td>23</td>\n",
       "      <td>2587</td>\n",
       "      <td>...</td>\n",
       "      <td>159.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-13</td>\n",
       "      <td>9999</td>\n",
       "      <td>3</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>0.0524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54235 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime         0         1          2         dkey  \\\n",
       "0     2016-01-01 00:00:00  5.733193 -2.296756 -13.282797     2016-1-0   \n",
       "1     2016-01-01 01:00:00  4.668200 -1.917471 -12.976817     2016-1-1   \n",
       "2     2016-01-01 02:00:00  2.898088  0.470586 -13.237948     2016-1-2   \n",
       "3     2016-01-01 03:00:00  3.107440  0.077172 -12.819543     2016-1-3   \n",
       "4     2016-01-01 04:00:00  3.387239 -2.777962 -11.102431     2016-1-4   \n",
       "...                   ...       ...       ...        ...          ...   \n",
       "54230 2023-05-02 19:00:00  3.625422  3.330697  -1.425394  2023-122-19   \n",
       "54231 2023-05-02 20:00:00  3.129382  3.551040  -1.273540  2023-122-20   \n",
       "54232 2023-05-02 21:00:00  4.297767  3.180053  -1.004981  2023-122-21   \n",
       "54233 2023-05-02 22:00:00  4.677120  3.084730   0.446979  2023-122-22   \n",
       "54234 2023-05-02 23:00:00  3.723668  3.607139   0.398778  2023-122-23   \n",
       "\n",
       "              Epoch    YR  Day  HR  Rot#  ...  F10_INDEX   KP  DST    AE  \\\n",
       "0      6.361883e+13  2016    1   0  2488  ...       95.1  6.0 -116   686   \n",
       "1      6.361883e+13  2016    1   1  2488  ...       95.1  6.0 -114   574   \n",
       "2      6.361883e+13  2016    1   2  2488  ...       95.1  6.0 -102   788   \n",
       "3      6.361884e+13  2016    1   3  2488  ...       95.1  5.3  -98   710   \n",
       "4      6.361884e+13  2016    1   4  2488  ...       95.1  5.3  -96   909   \n",
       "...             ...   ...  ...  ..   ...  ...        ...  ...  ...   ...   \n",
       "54230  6.385027e+13  2023  122  19  2587  ...      159.2  1.7  -23  9999   \n",
       "54231  6.385028e+13  2023  122  20  2587  ...      159.2  1.7  -21  9999   \n",
       "54232  6.385028e+13  2023  122  21  2587  ...      159.2  0.7  -19  9999   \n",
       "54233  6.385028e+13  2023  122  22  2587  ...      159.2  0.7  -17  9999   \n",
       "54234  6.385029e+13  2023  122  23  2587  ...      159.2  0.7  -13  9999   \n",
       "\n",
       "       AP_INDEX  AL_INDEX  AU_INDEX  PC_N_INDEX  Solar_Lyman_alpha  Proton_QI  \n",
       "0            80      -679         8         4.4           0.007000     0.2497  \n",
       "1            80      -512        62         4.6           0.007000     0.6232  \n",
       "2            80      -564       224         6.2           0.007000     0.4127  \n",
       "3            56      -555       156         5.6           0.007000     0.2708  \n",
       "4            56      -672       237         4.5           0.007000     0.4906  \n",
       "...         ...       ...       ...         ...                ...        ...  \n",
       "54230         6     99999     99999       999.9           0.008897     0.0317  \n",
       "54231         6     99999     99999       999.9           0.008897     0.0300  \n",
       "54232         3     99999     99999       999.9           0.008897     0.0265  \n",
       "54233         3     99999     99999       999.9           0.008897     0.0352  \n",
       "54234         3     99999     99999       999.9           0.008897     0.0524  \n",
       "\n",
       "[54235 rows x 63 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data = agg_data.merge(indices_data, on='dkey')\n",
    "merged_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>B_x</th>\n",
       "      <th>B_y</th>\n",
       "      <th>B_z</th>\n",
       "      <th>dkey</th>\n",
       "      <th>Epoch</th>\n",
       "      <th>YR</th>\n",
       "      <th>Day</th>\n",
       "      <th>HR</th>\n",
       "      <th>Rot#</th>\n",
       "      <th>...</th>\n",
       "      <th>F10_INDEX</th>\n",
       "      <th>KP</th>\n",
       "      <th>DST</th>\n",
       "      <th>AE</th>\n",
       "      <th>AP_INDEX</th>\n",
       "      <th>AL_INDEX</th>\n",
       "      <th>AU_INDEX</th>\n",
       "      <th>PC_N_INDEX</th>\n",
       "      <th>Solar_Lyman_alpha</th>\n",
       "      <th>Proton_QI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>5.733193</td>\n",
       "      <td>-2.296756</td>\n",
       "      <td>-13.282797</td>\n",
       "      <td>2016-1-0</td>\n",
       "      <td>6.361883e+13</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2488</td>\n",
       "      <td>...</td>\n",
       "      <td>95.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-116</td>\n",
       "      <td>686</td>\n",
       "      <td>80</td>\n",
       "      <td>-679</td>\n",
       "      <td>8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.2497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 01:00:00</td>\n",
       "      <td>4.668200</td>\n",
       "      <td>-1.917471</td>\n",
       "      <td>-12.976817</td>\n",
       "      <td>2016-1-1</td>\n",
       "      <td>6.361883e+13</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2488</td>\n",
       "      <td>...</td>\n",
       "      <td>95.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-114</td>\n",
       "      <td>574</td>\n",
       "      <td>80</td>\n",
       "      <td>-512</td>\n",
       "      <td>62</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.6232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 02:00:00</td>\n",
       "      <td>2.898088</td>\n",
       "      <td>0.470586</td>\n",
       "      <td>-13.237948</td>\n",
       "      <td>2016-1-2</td>\n",
       "      <td>6.361883e+13</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2488</td>\n",
       "      <td>...</td>\n",
       "      <td>95.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-102</td>\n",
       "      <td>788</td>\n",
       "      <td>80</td>\n",
       "      <td>-564</td>\n",
       "      <td>224</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.4127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "      <td>3.107440</td>\n",
       "      <td>0.077172</td>\n",
       "      <td>-12.819543</td>\n",
       "      <td>2016-1-3</td>\n",
       "      <td>6.361884e+13</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2488</td>\n",
       "      <td>...</td>\n",
       "      <td>95.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-98</td>\n",
       "      <td>710</td>\n",
       "      <td>56</td>\n",
       "      <td>-555</td>\n",
       "      <td>156</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.2708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 04:00:00</td>\n",
       "      <td>3.387239</td>\n",
       "      <td>-2.777962</td>\n",
       "      <td>-11.102431</td>\n",
       "      <td>2016-1-4</td>\n",
       "      <td>6.361884e+13</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2488</td>\n",
       "      <td>...</td>\n",
       "      <td>95.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-96</td>\n",
       "      <td>909</td>\n",
       "      <td>56</td>\n",
       "      <td>-672</td>\n",
       "      <td>237</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.4906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54230</th>\n",
       "      <td>2023-05-02 19:00:00</td>\n",
       "      <td>3.625422</td>\n",
       "      <td>3.330697</td>\n",
       "      <td>-1.425394</td>\n",
       "      <td>2023-122-19</td>\n",
       "      <td>6.385027e+13</td>\n",
       "      <td>2023</td>\n",
       "      <td>122</td>\n",
       "      <td>19</td>\n",
       "      <td>2587</td>\n",
       "      <td>...</td>\n",
       "      <td>159.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-23</td>\n",
       "      <td>9999</td>\n",
       "      <td>6</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>0.0317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54231</th>\n",
       "      <td>2023-05-02 20:00:00</td>\n",
       "      <td>3.129382</td>\n",
       "      <td>3.551040</td>\n",
       "      <td>-1.273540</td>\n",
       "      <td>2023-122-20</td>\n",
       "      <td>6.385028e+13</td>\n",
       "      <td>2023</td>\n",
       "      <td>122</td>\n",
       "      <td>20</td>\n",
       "      <td>2587</td>\n",
       "      <td>...</td>\n",
       "      <td>159.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-21</td>\n",
       "      <td>9999</td>\n",
       "      <td>6</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54232</th>\n",
       "      <td>2023-05-02 21:00:00</td>\n",
       "      <td>4.297767</td>\n",
       "      <td>3.180053</td>\n",
       "      <td>-1.004981</td>\n",
       "      <td>2023-122-21</td>\n",
       "      <td>6.385028e+13</td>\n",
       "      <td>2023</td>\n",
       "      <td>122</td>\n",
       "      <td>21</td>\n",
       "      <td>2587</td>\n",
       "      <td>...</td>\n",
       "      <td>159.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-19</td>\n",
       "      <td>9999</td>\n",
       "      <td>3</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>0.0265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54233</th>\n",
       "      <td>2023-05-02 22:00:00</td>\n",
       "      <td>4.677120</td>\n",
       "      <td>3.084730</td>\n",
       "      <td>0.446979</td>\n",
       "      <td>2023-122-22</td>\n",
       "      <td>6.385028e+13</td>\n",
       "      <td>2023</td>\n",
       "      <td>122</td>\n",
       "      <td>22</td>\n",
       "      <td>2587</td>\n",
       "      <td>...</td>\n",
       "      <td>159.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-17</td>\n",
       "      <td>9999</td>\n",
       "      <td>3</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>0.0352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54234</th>\n",
       "      <td>2023-05-02 23:00:00</td>\n",
       "      <td>3.723668</td>\n",
       "      <td>3.607139</td>\n",
       "      <td>0.398778</td>\n",
       "      <td>2023-122-23</td>\n",
       "      <td>6.385029e+13</td>\n",
       "      <td>2023</td>\n",
       "      <td>122</td>\n",
       "      <td>23</td>\n",
       "      <td>2587</td>\n",
       "      <td>...</td>\n",
       "      <td>159.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-13</td>\n",
       "      <td>9999</td>\n",
       "      <td>3</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>0.0524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54235 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime       B_x       B_y        B_z         dkey  \\\n",
       "0     2016-01-01 00:00:00  5.733193 -2.296756 -13.282797     2016-1-0   \n",
       "1     2016-01-01 01:00:00  4.668200 -1.917471 -12.976817     2016-1-1   \n",
       "2     2016-01-01 02:00:00  2.898088  0.470586 -13.237948     2016-1-2   \n",
       "3     2016-01-01 03:00:00  3.107440  0.077172 -12.819543     2016-1-3   \n",
       "4     2016-01-01 04:00:00  3.387239 -2.777962 -11.102431     2016-1-4   \n",
       "...                   ...       ...       ...        ...          ...   \n",
       "54230 2023-05-02 19:00:00  3.625422  3.330697  -1.425394  2023-122-19   \n",
       "54231 2023-05-02 20:00:00  3.129382  3.551040  -1.273540  2023-122-20   \n",
       "54232 2023-05-02 21:00:00  4.297767  3.180053  -1.004981  2023-122-21   \n",
       "54233 2023-05-02 22:00:00  4.677120  3.084730   0.446979  2023-122-22   \n",
       "54234 2023-05-02 23:00:00  3.723668  3.607139   0.398778  2023-122-23   \n",
       "\n",
       "              Epoch    YR  Day  HR  Rot#  ...  F10_INDEX   KP  DST    AE  \\\n",
       "0      6.361883e+13  2016    1   0  2488  ...       95.1  6.0 -116   686   \n",
       "1      6.361883e+13  2016    1   1  2488  ...       95.1  6.0 -114   574   \n",
       "2      6.361883e+13  2016    1   2  2488  ...       95.1  6.0 -102   788   \n",
       "3      6.361884e+13  2016    1   3  2488  ...       95.1  5.3  -98   710   \n",
       "4      6.361884e+13  2016    1   4  2488  ...       95.1  5.3  -96   909   \n",
       "...             ...   ...  ...  ..   ...  ...        ...  ...  ...   ...   \n",
       "54230  6.385027e+13  2023  122  19  2587  ...      159.2  1.7  -23  9999   \n",
       "54231  6.385028e+13  2023  122  20  2587  ...      159.2  1.7  -21  9999   \n",
       "54232  6.385028e+13  2023  122  21  2587  ...      159.2  0.7  -19  9999   \n",
       "54233  6.385028e+13  2023  122  22  2587  ...      159.2  0.7  -17  9999   \n",
       "54234  6.385029e+13  2023  122  23  2587  ...      159.2  0.7  -13  9999   \n",
       "\n",
       "       AP_INDEX  AL_INDEX  AU_INDEX  PC_N_INDEX  Solar_Lyman_alpha  Proton_QI  \n",
       "0            80      -679         8         4.4           0.007000     0.2497  \n",
       "1            80      -512        62         4.6           0.007000     0.6232  \n",
       "2            80      -564       224         6.2           0.007000     0.4127  \n",
       "3            56      -555       156         5.6           0.007000     0.2708  \n",
       "4            56      -672       237         4.5           0.007000     0.4906  \n",
       "...         ...       ...       ...         ...                ...        ...  \n",
       "54230         6     99999     99999       999.9           0.008897     0.0317  \n",
       "54231         6     99999     99999       999.9           0.008897     0.0300  \n",
       "54232         3     99999     99999       999.9           0.008897     0.0265  \n",
       "54233         3     99999     99999       999.9           0.008897     0.0352  \n",
       "54234         3     99999     99999       999.9           0.008897     0.0524  \n",
       "\n",
       "[54235 rows x 63 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Renaming the columns for better readability:\n",
    "merged_data.rename(columns={0:'B_x', 1:'B_y', 2:'B_z'}, inplace=True)\n",
    "merged_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Creating a bit more features for the model:\n",
    "We are going to create the following features:\n",
    "- Day of the week\n",
    "- Month of the year\n",
    "- Hour of the day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_data['day_of_week'] = merged_data['datetime'].dt.dayofweek\n",
    "merged_data['month'] = merged_data['datetime'].dt.month\n",
    "merged_data['hour'] = merged_data['datetime'].dt.hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Split the data into training and testing:\n",
    "\n",
    "We are going to use all of the data except 2023 to train and predict/test on 2023 data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the data into training and testing:\n",
    "X_train = merged_data[merged_data.YR < 2023][['B_x', 'B_y', 'B_z', 'day_of_week', 'month', 'hour']]\n",
    "y_train = merged_data[merged_data.YR < 2023][['KP']].values\n",
    "\n",
    "X_test = merged_data[merged_data.YR == 2023][['B_x', 'B_y', 'B_z', 'day_of_week', 'month', 'hour']]\n",
    "y_test = merged_data[merged_data.YR == 2023][['KP']].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: ML modeling\n",
    "\n",
    "Testing a bunch of ML models for the regression task of predicting the Kp index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in /Users/sahirsahir/opt/anaconda3/lib/python3.9/site-packages (2.0.0)\n",
      "Requirement already satisfied: numpy in /Users/sahirsahir/opt/anaconda3/lib/python3.9/site-packages (from xgboost) (1.25.2)\n",
      "Requirement already satisfied: scipy in /Users/sahirsahir/opt/anaconda3/lib/python3.9/site-packages (from xgboost) (1.11.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   1,   1, ..., 122, 122, 122])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data[merged_data.YR == 2023]['Day'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predictions(days, y_test, y_pred):\n",
    "    #Plot actual vs predicted values\n",
    "    plt.plot(days, y_test, color='blue', label='Actual Kp values')\n",
    "    plt.plot(days, y_pred, color='purple', label='Predicted Kp values')\n",
    "    plt.xlabel('Days of 2023')\n",
    "    plt.ylabel('Kp index')\n",
    "    plt.title('Actual vs Predicted Kp values')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for LinearRegression() is 1.404431834717085\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_k/35kllvrj02q2ys061y573jt00000gn/T/ipykernel_47653/610461179.py:26: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  model.fit(X_train, y_train)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:  [0.15243816 0.26618143 0.36934892 0.02164854 0.1467155  0.04366745]\n",
      "RMSE for RandomForestRegressor(max_depth=10, random_state=0) is 1.186657787982034\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:  [0.12663338 0.16519104 0.34975713 0.01201952 0.21295182 0.13344707]\n",
      "RMSE for XGBRegressor(alpha=10, base_score=None, booster=None, callbacks=None,\n",
      "             colsample_bylevel=None, colsample_bynode=None,\n",
      "             colsample_bytree=0.3, device=None, early_stopping_rounds=None,\n",
      "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "             gamma=None, grow_policy=None, importance_type=None,\n",
      "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
      "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "             max_delta_step=None, max_depth=10, max_leaves=None,\n",
      "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
      "             num_parallel_tree=None, ...) is 1.2043412075450044\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:  [0.14170157 0.26172433 0.37832353 0.01757671 0.16292361 0.03775025]\n",
      "RMSE for DecisionTreeRegressor(max_depth=10, random_state=0) is 1.2636595472748604\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for Ensemble Average Model  is 1.199314579017207\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "\n",
    "#Initialize a list of regressors:\n",
    "lr = LinearRegression()\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=100, max_depth=10, min_samples_split=2, min_samples_leaf=1, random_state=0)\n",
    "\n",
    "xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,\n",
    "                max_depth = 10, alpha = 10, n_estimators = 100)\n",
    "\n",
    "clf = DecisionTreeRegressor(max_depth=10, min_samples_split=2, min_samples_leaf=1, random_state=0)\n",
    "\n",
    "models = [lr, rf, xg_reg, clf]\n",
    "\n",
    "#To store the predictions - for averaging them later as another model:\n",
    "predictions = []\n",
    "\n",
    "idx = 0\n",
    "for model in models:\n",
    "    #Fit the model:\n",
    "    model.fit(X_train, y_train)\n",
    "    #Predict on the test set:\n",
    "    y_pred = model.predict(X_test)\n",
    "    #Append the predictions to the list:\n",
    "    predictions.append(y_pred.reshape(-1,1))\n",
    "    #Print out the feature importances and RMSE:\n",
    "    if idx > 0:\n",
    "        print('Feature importances: ', model.feature_importances_)\n",
    "\n",
    "    print('RMSE for {} is {}'.format(model, np.sqrt(mean_squared_error(y_test, y_pred))))\n",
    "    #Plot the predictions:\n",
    "    plot_predictions(merged_data[merged_data.YR == 2023]['Day'], y_test, y_pred)\n",
    "    idx+=1\n",
    "\n",
    "#Let's average the predictions:\n",
    "y_pred = np.mean(predictions, axis=0)\n",
    "print('RMSE for {} is {}'.format('Ensemble Average Model ', np.sqrt(mean_squared_error(y_test, y_pred))))\n",
    "plot_predictions(merged_data[merged_data.YR == 2023]['Day'], y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7: Formulating the problem as a classification problem and re-doing most of the modelling\n",
    "Predict if Kp is going to be over 5 (indicating heavy solar storm) or not. Recreate training, and test sets and run through the same models with classification instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a target column:\n",
    "merged_data['target'] = np.where(merged_data['KP'] > 5, 1, 0)\n",
    "\n",
    "#Split the data into training and testing:\n",
    "X_train = merged_data[merged_data.YR < 2023][['B_x', 'B_y', 'B_z', 'day_of_week', 'month', 'hour']]\n",
    "y_train = merged_data[merged_data.YR < 2023][['target']].values\n",
    "\n",
    "X_test = merged_data[merged_data.YR == 2023][['B_x', 'B_y', 'B_z', 'day_of_week', 'month', 'hour']]\n",
    "y_test = merged_data[merged_data.YR == 2023][['target']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    53746\n",
       "1      489\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sahirsahir/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/var/folders/_k/35kllvrj02q2ys061y573jt00000gn/T/ipykernel_47653/2661145880.py:28: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  model.fit(X_train, y_train)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score for LogisticRegression() is 0.0\n",
      "Confusion Matrix:  [[2851    1]\n",
      " [  75    0]]\n",
      "Feature importances:  [0.1868508  0.25947806 0.30988374 0.07244718 0.08241819 0.08892202]\n",
      "F1-score for RandomForestClassifier(max_depth=10, random_state=0) is 0.18823529411764706\n",
      "Confusion Matrix:  [[2850    2]\n",
      " [  67    8]]\n",
      "Feature importances:  [0.12443167 0.28660786 0.52831507 0.01119743 0.0377478  0.0117001 ]\n",
      "F1-score for XGBClassifier(alpha=10, base_score=None, booster=None, callbacks=None,\n",
      "              colsample_bylevel=None, colsample_bynode=None,\n",
      "              colsample_bytree=0.3, device=None, early_stopping_rounds=None,\n",
      "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "              gamma=None, grow_policy=None, importance_type=None,\n",
      "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
      "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "              max_delta_step=None, max_depth=10, max_leaves=None,\n",
      "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
      "              num_parallel_tree=None, ...) is 0.0\n",
      "Confusion Matrix:  [[2852    0]\n",
      " [  75    0]]\n",
      "Feature importances:  [0.14540192 0.24345638 0.30019829 0.08387591 0.11094123 0.11612626]\n",
      "F1-score for DecisionTreeClassifier(max_depth=10, random_state=0) is 0.3076923076923077\n",
      "Confusion Matrix:  [[2839   13]\n",
      " [  59   16]]\n",
      "F1-score for Majority Voting Classifer is 0.0\n",
      "Confusion Matrix:  [[2852    0]\n",
      " [  75    0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import xgboost as xgb\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from scipy.stats import mode\n",
    "\n",
    "\n",
    "#Initialize a list of regressors:\n",
    "lr = LogisticRegression()\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100, max_depth=10, min_samples_split=2, min_samples_leaf=1, random_state=0)\n",
    "\n",
    "xg_reg = xgb.XGBClassifier(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,\n",
    "                max_depth = 10, alpha = 10, n_estimators = 100)\n",
    "\n",
    "clf = DecisionTreeClassifier(max_depth=10, min_samples_split=2, min_samples_leaf=1, random_state=0)\n",
    "\n",
    "models = [lr, rf, xg_reg, clf]\n",
    "\n",
    "#To store the predictions - for averaging them later as another model:\n",
    "predictions = []\n",
    "\n",
    "idx = 0\n",
    "for model in models:\n",
    "    #Fit the model:\n",
    "    model.fit(X_train, y_train)\n",
    "    #Predict on the test set:\n",
    "    y_pred = model.predict(X_test)\n",
    "    #Append the predictions to the list:\n",
    "    predictions.append(y_pred.reshape(-1,1))\n",
    "    #Print out the feature importances and RMSE:\n",
    "    if idx > 0:\n",
    "        print('Feature importances: ', model.feature_importances_)\n",
    "\n",
    "    print('F1-score for {} is {}'.format(model, f1_score(y_test, y_pred)))\n",
    "    print('Confusion Matrix: ', confusion_matrix(y_test, y_pred))\n",
    "    idx+=1\n",
    "\n",
    "#Let's average the predictions:\n",
    "y_pred, _ = mode(predictions, axis=0)\n",
    "print('F1-score for {} is {}'.format('Majority Voting Classifer', f1_score(y_test, y_pred)))\n",
    "print('Confusion Matrix: ', confusion_matrix(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formulating it as a classification task poses more challenges due to class imbalance. We think it would be easier and safer to focus on predicting the real-time kp-values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Misc Analysis: Plotting the relationship of B_z and Kp:\n",
    "\n",
    "The data does suggest some correlation between the B_z and Kp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc548a1af70>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "\n",
    "# Plot the feature against the target variable\n",
    "plt.scatter(merged_data.loc[:, 'B_z'], merged_data.loc[:,'KP'])\n",
    "plt.xlabel('B_z')\n",
    "plt.ylabel('KP')\n",
    "\n",
    "# Fit linear regression model\n",
    "X = merged_data.loc[:, 'B_z'].values.reshape(-1, 1)\n",
    "y = merged_data.loc[:, 'KP'].values.reshape(-1, 1)\n",
    "reg = LinearRegression().fit(X, y)\n",
    "\n",
    "# Calculate regression line\n",
    "x_line = np.linspace(np.min(X), np.max(X), 100)\n",
    "y_line = reg.predict(x_line.reshape(-1, 1))\n",
    "\n",
    "# plot regression line\n",
    "plt.plot(x_line, y_line, c='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the correlation between the vector components of the magnetic field to kp:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson correlation for B_z: -0.20971575473275636\n",
      "P-value: 0.0\n",
      "Pearson correlation for B_x: -0.04256205189847266\n",
      "P-value: 3.536593378473079e-23\n",
      "Pearson correlation for B_y: 0.042533218975899734\n",
      "P-value: 3.7828612814761654e-23\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n",
    "# Compute Pearson correlation between two variables\n",
    "corr, p_value = pearsonr(merged_data['B_z'], merged_data['KP'])\n",
    "\n",
    "print(f\"Pearson correlation for B_z: {corr}\")\n",
    "print(f\"P-value: {p_value}\")\n",
    "\n",
    "# Compute Pearson correlation between two variables\n",
    "corr, p_value = pearsonr(merged_data['B_x'], merged_data['KP'])\n",
    "\n",
    "print(f\"Pearson correlation for B_x: {corr}\")\n",
    "print(f\"P-value: {p_value}\")\n",
    "\n",
    "# Compute Pearson correlation between two variables\n",
    "corr, p_value = pearsonr(merged_data['B_y'], merged_data['KP'])\n",
    "\n",
    "print(f\"Pearson correlation for B_y: {corr}\")\n",
    "print(f\"P-value: {p_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There exists a small negative correlation (of -0.21) between B_z and Kp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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