01_understand_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sys, os\n",
    "data_dir = '../noaa-runtime/data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dst_labs = pd.read_csv(data_dir+'dst_labels.csv')\n",
    "sat_pos  = pd.read_csv(data_dir+'satellite_positions.csv')\n",
    "sol_wind = pd.read_csv(data_dir+'solar_wind.csv')\n",
    "sunspots = pd.read_csv(data_dir+'sunspots.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data shape, Columns and Nans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_df_info(df):\n",
    "    print (\"data shape:\", df.shape, '\\n')\n",
    "    print (\"data columns:\", df.columns, '\\n')\n",
    "    print (\"The Null values in the data:\\n\", df.isnull().sum(), '\\n' )\n",
    "    display ( df.head(5) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape: (139872, 3) \n",
      "\n",
      "data columns: Index(['period', 'timedelta', 'dst'], dtype='object') \n",
      "\n",
      "The Null values in the data:\n",
      " period       0\n",
      "timedelta    0\n",
      "dst          0\n",
      "dtype: int64 \n",
      "\n",
      "['train_a' 'train_b' 'train_c'] \n",
      "\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</th>\n",
       "      <th>dst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 01:00:00</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 02:00:00</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 03:00:00</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 04:00:00</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    period        timedelta  dst\n",
       "0  train_a  0 days 00:00:00   -7\n",
       "1  train_a  0 days 01:00:00  -10\n",
       "2  train_a  0 days 02:00:00  -10\n",
       "3  train_a  0 days 03:00:00   -6\n",
       "4  train_a  0 days 04:00:00   -2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_df_info(dst_labs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape: (5828, 8) \n",
      "\n",
      "data columns: Index(['period', 'timedelta', 'gse_x_ace', 'gse_y_ace', 'gse_z_ace',\n",
      "       'gse_x_dscovr', 'gse_y_dscovr', 'gse_z_dscovr'],\n",
      "      dtype='object') \n",
      "\n",
      "The Null values in the data:\n",
      " period             0\n",
      "timedelta          0\n",
      "gse_x_ace          0\n",
      "gse_y_ace          0\n",
      "gse_z_ace          0\n",
      "gse_x_dscovr    4794\n",
      "gse_y_dscovr    4794\n",
      "gse_z_dscovr    4794\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</th>\n",
       "      <th>gse_x_ace</th>\n",
       "      <th>gse_y_ace</th>\n",
       "      <th>gse_z_ace</th>\n",
       "      <th>gse_x_dscovr</th>\n",
       "      <th>gse_y_dscovr</th>\n",
       "      <th>gse_z_dscovr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days</td>\n",
       "      <td>1522376.9</td>\n",
       "      <td>143704.6</td>\n",
       "      <td>149496.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_a</td>\n",
       "      <td>1 days</td>\n",
       "      <td>1525410.9</td>\n",
       "      <td>136108.8</td>\n",
       "      <td>151034.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_a</td>\n",
       "      <td>2 days</td>\n",
       "      <td>1528484.9</td>\n",
       "      <td>128470.5</td>\n",
       "      <td>152387.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_a</td>\n",
       "      <td>3 days</td>\n",
       "      <td>1531570.3</td>\n",
       "      <td>120818.4</td>\n",
       "      <td>153561.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_a</td>\n",
       "      <td>4 days</td>\n",
       "      <td>1534633.1</td>\n",
       "      <td>113178.5</td>\n",
       "      <td>154534.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    period timedelta  gse_x_ace  gse_y_ace  gse_z_ace  gse_x_dscovr  \\\n",
       "0  train_a    0 days  1522376.9   143704.6   149496.7           NaN   \n",
       "1  train_a    1 days  1525410.9   136108.8   151034.1           NaN   \n",
       "2  train_a    2 days  1528484.9   128470.5   152387.7           NaN   \n",
       "3  train_a    3 days  1531570.3   120818.4   153561.4           NaN   \n",
       "4  train_a    4 days  1534633.1   113178.5   154534.6           NaN   \n",
       "\n",
       "   gse_y_dscovr  gse_z_dscovr  \n",
       "0           NaN           NaN  \n",
       "1           NaN           NaN  \n",
       "2           NaN           NaN  \n",
       "3           NaN           NaN  \n",
       "4           NaN           NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_df_info(sat_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape: (8392320, 17) \n",
      "\n",
      "data columns: Index(['period', 'timedelta', 'bx_gse', 'by_gse', 'bz_gse', 'theta_gse',\n",
      "       'phi_gse', 'bx_gsm', 'by_gsm', 'bz_gsm', 'theta_gsm', 'phi_gsm', 'bt',\n",
      "       'density', 'speed', 'temperature', 'source'],\n",
      "      dtype='object') \n",
      "\n",
      "The Null values in the data:\n",
      " period              0\n",
      "timedelta           0\n",
      "bx_gse         325888\n",
      "by_gse         325888\n",
      "bz_gse         325888\n",
      "theta_gse      325888\n",
      "phi_gse        326388\n",
      "bx_gsm         325888\n",
      "by_gsm         325888\n",
      "bz_gsm         325888\n",
      "theta_gsm      325888\n",
      "phi_gsm        326388\n",
      "bt             325888\n",
      "density        684890\n",
      "speed          689555\n",
      "temperature    811768\n",
      "source         316816\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</th>\n",
       "      <th>bx_gse</th>\n",
       "      <th>by_gse</th>\n",
       "      <th>bz_gse</th>\n",
       "      <th>theta_gse</th>\n",
       "      <th>phi_gse</th>\n",
       "      <th>bx_gsm</th>\n",
       "      <th>by_gsm</th>\n",
       "      <th>bz_gsm</th>\n",
       "      <th>theta_gsm</th>\n",
       "      <th>phi_gsm</th>\n",
       "      <th>bt</th>\n",
       "      <th>density</th>\n",
       "      <th>speed</th>\n",
       "      <th>temperature</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>-5.55</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>11.09</td>\n",
       "      <td>153.37</td>\n",
       "      <td>-5.55</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>11.09</td>\n",
       "      <td>153.37</td>\n",
       "      <td>6.80</td>\n",
       "      <td>1.53</td>\n",
       "      <td>383.92</td>\n",
       "      <td>110237.0</td>\n",
       "      <td>ac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 00:01:00</td>\n",
       "      <td>-5.58</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>10.10</td>\n",
       "      <td>151.91</td>\n",
       "      <td>-5.58</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>10.10</td>\n",
       "      <td>151.91</td>\n",
       "      <td>6.83</td>\n",
       "      <td>1.69</td>\n",
       "      <td>381.79</td>\n",
       "      <td>123825.0</td>\n",
       "      <td>ac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 00:02:00</td>\n",
       "      <td>-5.15</td>\n",
       "      <td>3.66</td>\n",
       "      <td>0.85</td>\n",
       "      <td>7.87</td>\n",
       "      <td>146.04</td>\n",
       "      <td>-5.15</td>\n",
       "      <td>3.66</td>\n",
       "      <td>0.85</td>\n",
       "      <td>7.87</td>\n",
       "      <td>146.04</td>\n",
       "      <td>6.77</td>\n",
       "      <td>1.97</td>\n",
       "      <td>389.11</td>\n",
       "      <td>82548.0</td>\n",
       "      <td>ac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 00:03:00</td>\n",
       "      <td>-5.20</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0.68</td>\n",
       "      <td>6.17</td>\n",
       "      <td>146.17</td>\n",
       "      <td>-5.20</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0.68</td>\n",
       "      <td>6.17</td>\n",
       "      <td>146.17</td>\n",
       "      <td>6.74</td>\n",
       "      <td>1.97</td>\n",
       "      <td>389.11</td>\n",
       "      <td>82548.0</td>\n",
       "      <td>ac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 00:04:00</td>\n",
       "      <td>-5.12</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0.49</td>\n",
       "      <td>4.62</td>\n",
       "      <td>145.72</td>\n",
       "      <td>-5.12</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0.49</td>\n",
       "      <td>4.62</td>\n",
       "      <td>145.72</td>\n",
       "      <td>6.65</td>\n",
       "      <td>1.77</td>\n",
       "      <td>384.26</td>\n",
       "      <td>94269.0</td>\n",
       "      <td>ac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    period        timedelta  bx_gse  by_gse  bz_gse  theta_gse  phi_gse  \\\n",
       "0  train_a  0 days 00:00:00   -5.55    3.00    1.25      11.09   153.37   \n",
       "1  train_a  0 days 00:01:00   -5.58    3.16    1.17      10.10   151.91   \n",
       "2  train_a  0 days 00:02:00   -5.15    3.66    0.85       7.87   146.04   \n",
       "3  train_a  0 days 00:03:00   -5.20    3.68    0.68       6.17   146.17   \n",
       "4  train_a  0 days 00:04:00   -5.12    3.68    0.49       4.62   145.72   \n",
       "\n",
       "   bx_gsm  by_gsm  bz_gsm  theta_gsm  phi_gsm    bt  density   speed  \\\n",
       "0   -5.55    3.00    1.25      11.09   153.37  6.80     1.53  383.92   \n",
       "1   -5.58    3.16    1.17      10.10   151.91  6.83     1.69  381.79   \n",
       "2   -5.15    3.66    0.85       7.87   146.04  6.77     1.97  389.11   \n",
       "3   -5.20    3.68    0.68       6.17   146.17  6.74     1.97  389.11   \n",
       "4   -5.12    3.68    0.49       4.62   145.72  6.65     1.77  384.26   \n",
       "\n",
       "   temperature source  \n",
       "0     110237.0     ac  \n",
       "1     123825.0     ac  \n",
       "2      82548.0     ac  \n",
       "3      82548.0     ac  \n",
       "4      94269.0     ac  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_df_info(sol_wind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape: (192, 3) \n",
      "\n",
      "data columns: Index(['period', 'timedelta', 'smoothed_ssn'], dtype='object') \n",
      "\n",
      "The Null values in the data:\n",
      " period          0\n",
      "timedelta       0\n",
      "smoothed_ssn    0\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</th>\n",
       "      <th>smoothed_ssn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>65.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_a</td>\n",
       "      <td>13 days 00:00:00</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_a</td>\n",
       "      <td>44 days 00:00:00</td>\n",
       "      <td>76.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_a</td>\n",
       "      <td>74 days 00:00:00</td>\n",
       "      <td>80.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_a</td>\n",
       "      <td>105 days 00:00:00</td>\n",
       "      <td>85.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    period          timedelta  smoothed_ssn\n",
       "0  train_a    0 days 00:00:00          65.4\n",
       "1  train_a   13 days 00:00:00          72.0\n",
       "2  train_a   44 days 00:00:00          76.9\n",
       "3  train_a   74 days 00:00:00          80.8\n",
       "4  train_a  105 days 00:00:00          85.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_df_info(sunspots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Information so far\n",
    "- `dst_labs` file has no Null values. \n",
    "- `sat_pos` file has some Null values in `gse_x_dscovr`, `gse_y_dscovr` and `gse_z_dscovr`.\n",
    "- `sol_wind` file has a lot of Null values.\n",
    "- `sunspots` file has no Null values. \n",
    "\n",
    "also `sol_wind` is the main file which has 8392320 rows, too big to make plots and so on so for the data vizualization purpose we'll use only the first 10000 rows.\n",
    "\n",
    "With the discussion in the group we also decided to discard the columns `'bx_gsm','by_gsm', 'bz_gsm', 'theta_gsm', 'phi_gsm', 'source'` from the `sol_wind` data. As `bx_gse` and `bx_gsm` and similarly for `y` and `z` they look the same. And we are ignoring the theta dn phi coordinate of measurement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sw = sol_wind[:10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>period</th>\n",
       "      <th>timedelta</th>\n",
       "      <th>bx_gse</th>\n",
       "      <th>by_gse</th>\n",
       "      <th>bz_gse</th>\n",
       "      <th>theta_gse</th>\n",
       "      <th>phi_gse</th>\n",
       "      <th>bx_gsm</th>\n",
       "      <th>by_gsm</th>\n",
       "      <th>bz_gsm</th>\n",
       "      <th>theta_gsm</th>\n",
       "      <th>phi_gsm</th>\n",
       "      <th>bt</th>\n",
       "      <th>density</th>\n",
       "      <th>speed</th>\n",
       "      <th>temperature</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>-5.55</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>11.09</td>\n",
       "      <td>153.37</td>\n",
       "      <td>-5.55</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>11.09</td>\n",
       "      <td>153.37</td>\n",
       "      <td>6.80</td>\n",
       "      <td>1.53</td>\n",
       "      <td>383.92</td>\n",
       "      <td>110237.0</td>\n",
       "      <td>ac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_a</td>\n",
       "      <td>0 days 00:01:00</td>\n",
       "      <td>-5.58</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>10.10</td>\n",
       "      <td>151.91</td>\n",
       "      <td>-5.58</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>10.10</td>\n",
       "      <td>151.91</td>\n",
       "      <td>6.83</td>\n",
       "      <td>1.69</td>\n",
       "      <td>381.79</td>\n",
       "      <td>123825.0</td>\n",
       "      <td>ac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    period        timedelta  bx_gse  by_gse  bz_gse  theta_gse  phi_gse  \\\n",
       "0  train_a  0 days 00:00:00   -5.55    3.00    1.25      11.09   153.37   \n",
       "1  train_a  0 days 00:01:00   -5.58    3.16    1.17      10.10   151.91   \n",
       "\n",
       "   bx_gsm  by_gsm  bz_gsm  theta_gsm  phi_gsm    bt  density   speed  \\\n",
       "0   -5.55    3.00    1.25      11.09   153.37  6.80     1.53  383.92   \n",
       "1   -5.58    3.16    1.17      10.10   151.91  6.83     1.69  381.79   \n",
       "\n",
       "   temperature source  \n",
       "0     110237.0     ac  \n",
       "1     123825.0     ac  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data.head()\n",
    "#\n",
    "plt.plot(sw.timedelta, sw.bx_gse )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11f939430>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(20,10))\n",
    "plt.subplot(231)\n",
    "plt.scatter(sw['bx_gse'], sw['theta_gse'])\n",
    "plt.subplot(232)\n",
    "plt.scatter(sw['by_gse'], sw['theta_gse'])\n",
    "plt.subplot(233)\n",
    "plt.scatter(sw['bz_gse'], sw['theta_gse'])\n",
    "\n",
    "plt.subplot(234)\n",
    "plt.scatter(sw['bx_gse'], sw['phi_gse'])\n",
    "plt.subplot(235)\n",
    "plt.scatter(sw['by_gse'], sw['phi_gse'])\n",
    "plt.subplot(236)\n",
    "plt.scatter(sw['bz_gse'], sw['phi_gse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data = sol_wind[:10000]\n",
    "# convert the timedelta to datetime object\n",
    "data['timedelta'] = pd.to_timedelta( data['timedelta'] )\n",
    "display ( data.head(2) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dst_labs.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat_pos.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol_wind.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol_wind.timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sunspots.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print ( sol_wind.shape )\n",
    "data = sol_wind[:5000]\n",
    "print (data.shape )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.head()\n",
    "#\n",
    "plt.plot(data.timedelta, data.bx_gse )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = data['bx_gse']**2+data['by_gse']**2+data['bz_gse']**2\n",
    "plt.scatter(data['bt'], data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(1, figsize=(20,4))\n",
    "plt.subplot(121)\n",
    "plt.scatter(data['bt'], data['theta_gse'])\n",
    "plt.subplot(122)\n",
    "plt.scatter(data['bt'], data['phi_gse'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(1, figsize=(20,10))\n",
    "plt.subplot(231)\n",
    "plt.scatter(data['bx_gse'], data['theta_gse'])\n",
    "plt.subplot(232)\n",
    "plt.scatter(data['by_gse'], data['theta_gse'])\n",
    "plt.subplot(233)\n",
    "plt.scatter(data['bz_gse'], data['theta_gse'])\n",
    "\n",
    "plt.subplot(234)\n",
    "plt.scatter(data['bx_gse'], data['phi_gse'])\n",
    "plt.subplot(235)\n",
    "plt.scatter(data['by_gse'], data['phi_gse'])\n",
    "plt.subplot(236)\n",
    "plt.scatter(data['bz_gse'], data['phi_gse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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