{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-19T14:37:53.875020Z",
     "start_time": "2024-10-19T14:37:53.872643Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Daten\n",
    "Wir haben 2 Datensätze:\n",
    "Einen für die Herzfrequenz pro Tag und einen für den Schlaf.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-19T14:37:53.901186Z",
     "start_time": "2024-10-19T14:37:53.889152Z"
    }
   },
   "outputs": [],
   "source": [
    "# Loading data\n",
    "hr_df = pd.read_csv(r'..\\..\\data\\Oliver\\raw\\raw_hr_hr.csv')\n",
    "\n",
    "sleep_df = pd.read_csv(r'..\\..\\data\\Oliver\\raw\\sleep.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "\n",
    "### Herzfrequenz\n",
    "\n",
    "### Schlaf\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-19T14:37:54.038370Z",
     "start_time": "2024-10-19T14:37:54.020132Z"
    }
   },
   "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>sleep_date</th>\n",
       "      <th>total_sleep_h</th>\n",
       "      <th>avg_sleep_hr</th>\n",
       "      <th>next_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-08-12</td>\n",
       "      <td>6.40</td>\n",
       "      <td>67</td>\n",
       "      <td>2024-08-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-08-13</td>\n",
       "      <td>8.17</td>\n",
       "      <td>69</td>\n",
       "      <td>2024-08-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>8.58</td>\n",
       "      <td>62</td>\n",
       "      <td>2024-08-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-08-15</td>\n",
       "      <td>7.53</td>\n",
       "      <td>60</td>\n",
       "      <td>2024-08-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-08-16</td>\n",
       "      <td>8.60</td>\n",
       "      <td>57</td>\n",
       "      <td>2024-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2024-10-14</td>\n",
       "      <td>8.65</td>\n",
       "      <td>65</td>\n",
       "      <td>2024-10-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2024-10-15</td>\n",
       "      <td>8.37</td>\n",
       "      <td>60</td>\n",
       "      <td>2024-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2024-10-16</td>\n",
       "      <td>7.73</td>\n",
       "      <td>61</td>\n",
       "      <td>2024-10-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2024-10-17</td>\n",
       "      <td>8.05</td>\n",
       "      <td>62</td>\n",
       "      <td>2024-10-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2024-10-18</td>\n",
       "      <td>9.93</td>\n",
       "      <td>63</td>\n",
       "      <td>2024-10-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sleep_date  total_sleep_h  avg_sleep_hr    next_day\n",
       "0   2024-08-12           6.40            67  2024-08-13\n",
       "1   2024-08-13           8.17            69  2024-08-14\n",
       "2   2024-08-14           8.58            62  2024-08-15\n",
       "3   2024-08-15           7.53            60  2024-08-16\n",
       "4   2024-08-16           8.60            57  2024-08-17\n",
       "..         ...            ...           ...         ...\n",
       "56  2024-10-14           8.65            65  2024-10-15\n",
       "57  2024-10-15           8.37            60  2024-10-16\n",
       "58  2024-10-16           7.73            61  2024-10-17\n",
       "59  2024-10-17           8.05            62  2024-10-18\n",
       "60  2024-10-18           9.93            63  2024-10-19\n",
       "\n",
       "[61 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Sleep data\n",
    "sleep_df['to'] = pd.to_datetime(sleep_df['to'])\n",
    "\n",
    "# Using 'to' column as date which the awake period corresponds to\n",
    "sleep_df['sleep_date'] = sleep_df['to'].dt.date\n",
    "\n",
    "# Duration from light, deep and rem sleep duration\n",
    "sleep_df['total_sleep_s'] = (sleep_df['light (s)'] + sleep_df['deep (s)'] + sleep_df['rem (s)'])\n",
    "# Duration in hours\n",
    "sleep_df['total_sleep_h'] = (sleep_df['total_sleep_s'] / 3600).round(2)\n",
    "\n",
    "sleep_df.rename(columns={'Average heart rate': 'avg_sleep_hr'}, inplace=True)\n",
    "\n",
    "# Clean Sleep Data\n",
    "sleep_data = sleep_df[['sleep_date', 'total_sleep_h', 'avg_sleep_hr']].copy()\n",
    "\n",
    "sleep_data['next_day'] = sleep_data['sleep_date'] + pd.Timedelta(days=1)\n",
    "\n",
    "sleep_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-19T14:37:54.148314Z",
     "start_time": "2024-10-19T14:37:54.090501Z"
    }
   },
   "outputs": [],
   "source": [
    "## Average heart rate\n",
    "\n",
    "# Parsing for 'value' and 'duration' columns\n",
    "def parse_hr_value(s: str):\n",
    "    s = s.strip('[]') # Remove brackets\n",
    "    values = s.split(',') # Split into multiple values\n",
    "\n",
    "    clean_values = []\n",
    "    # Clean every value\n",
    "    for v in values:\n",
    "        v_clean = v.strip().replace(' ', '').replace(',', '.')\n",
    "        try:\n",
    "            num = int(v_clean)\n",
    "            clean_values.append(num)\n",
    "        except ValueError:\n",
    "            print(f\"Found invalid value: '{v_clean}'\")\n",
    "\n",
    "    return clean_values\n",
    "\n",
    "\n",
    "\n",
    "# Parsing for 'value' and 'duration' columns\n",
    "hr_df['duration_list'] = hr_df['duration'].apply(parse_hr_value)\n",
    "hr_df['value_list'] = hr_df['value'].apply(parse_hr_value)\n",
    "\n",
    "# Explode multiple values into rows\n",
    "hr_expanded = hr_df.explode(['duration_list', 'value_list'])\n",
    "\n",
    "# Grouping by date\n",
    "hr_expanded['start'] = pd.to_datetime(hr_expanded['start'])\n",
    "hr_expanded['awake_date'] = hr_expanded['start'].dt.date\n",
    "\n",
    "# Calculate average bpm of the day\n",
    "hr_day_avg = hr_expanded.groupby('awake_date')['value_list'].mean().reset_index()\n",
    "hr_day_avg.rename(columns={'value_list': 'avg_hr_day'}, inplace=True)\n",
    "\n",
    "# Merging all relevant data\n",
    "merged_data = pd.merge(sleep_data, hr_day_avg, left_on='next_day', right_on='awake_date', how='inner')\n",
    "\n",
    "# Removing NaN and non numeric\n",
    "merged_data = merged_data.dropna(subset=['total_sleep_h', 'avg_hr_day', 'avg_sleep_hr'])\n",
    "merged_data['total_sleep_h'] = pd.to_numeric(merged_data['total_sleep_h'], errors='coerce')\n",
    "merged_data['avg_hr_day'] = pd.to_numeric(merged_data['avg_hr_day'], errors='coerce')\n",
    "merged_data['avg_sleep_hr'] = pd.to_numeric(merged_data['avg_sleep_hr'], errors='coerce')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-19T14:37:54.592246Z",
     "start_time": "2024-10-19T14:37:54.230829Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting with Matplotlib / Seaborn\n",
    "figure, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "# First Plot\n",
    "sns.regplot(x='total_sleep_h', y='avg_hr_day', data=merged_data, ax=ax1, dropna=True)\n",
    "ax1.set_title('Sleep Duration and Awake Heart Rate the next day')\n",
    "ax1.set_xlabel('Sleep Duration (hours)')\n",
    "ax1.set_ylabel('Average Heart Rate (bpm)')\n",
    "\n",
    "# Second plot\n",
    "sns.regplot(x='avg_sleep_hr', y='avg_hr_day', data=merged_data, ax=ax2, dropna=True)\n",
    "ax2.set_title('Sleeping hr and Awake hr')\n",
    "ax2.set_xlabel('Sleeping hr (bpm)')\n",
    "ax2.set_ylabel('Awake hr (bpm)')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-19T14:37:55.246335Z",
     "start_time": "2024-10-19T14:37:54.614729Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "marker": {
          "color": "blue"
         },
         "mode": "markers",
         "name": "Data",
         "type": "scatter",
         "x": [
          6.4,
          8.17,
          8.58,
          7.53,
          7.4,
          10.73,
          6.75,
          7.62,
          4.58,
          6.63,
          8.62,
          7.73,
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          9.03,
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          8.1,
          9.33,
          8.63,
          7.92,
          9.5,
          8.7,
          8.12,
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          7.45,
          12.63,
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          8.78,
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          8.67,
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          8.37,
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         ],
         "xaxis": "x",
         "y": [
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        },
        "title": {
         "text": "Sleep and Heart Rate Analysis"
        },
        "width": 1200,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          0.45
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         "title": {
          "text": "Sleep Duration (hours)"
         }
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
          0.55,
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         "title": {
          "text": "Sleeping Heart Rate (bpm)"
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        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
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         "title": {
          "text": "Average Heart Rate (bpm)"
         }
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         "anchor": "x2",
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          0,
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         "title": {
          "text": "Awake Heart Rate (bpm)"
         }
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       }
      }
     },
     "metadata": {},
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   ],
   "source": [
    "# Plotting with plot.ly\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "# Create a subplot with 1 row and 2 columns\n",
    "fig = make_subplots(\n",
    "    rows=1, cols=2,\n",
    "    subplot_titles=(\n",
    "        \"Sleep Duration and Awake Heart Rate the Next Day\",\n",
    "        \"Sleeping Heart Rate and Awake Heart Rate\"\n",
    "    )\n",
    ")\n",
    "\n",
    "# Function to add scatter and regression line to a subplot\n",
    "def add_regression_trace(fig, row, col, x, y, title, x_label, y_label, marker_color, line_color):\n",
    "    # Add scatter trace\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x=x,\n",
    "            y=y,\n",
    "            mode='markers',\n",
    "            name='Data',\n",
    "            marker=dict(color=marker_color)\n",
    "        ),\n",
    "        row=row, col=col\n",
    "    )\n",
    "\n",
    "    # Prepare data for regression (drop NaNs)\n",
    "    df = pd.DataFrame({'x': x, 'y': y}).dropna()\n",
    "    X = sm.add_constant(df['x'])  # Adds a constant term to the predictor\n",
    "    model = sm.OLS(df['y'], X).fit()\n",
    "    predictions = model.predict(X)\n",
    "\n",
    "    # Add regression line trace\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x=df['x'],\n",
    "            y=predictions,\n",
    "            mode='lines',\n",
    "            name='Fit',\n",
    "            line=dict(color=line_color)\n",
    "        ),\n",
    "        row=row, col=col\n",
    "    )\n",
    "\n",
    "    # Update axes titles\n",
    "    fig.update_xaxes(title_text=x_label, row=row, col=col)\n",
    "    fig.update_yaxes(title_text=y_label, row=row, col=col)\n",
    "\n",
    "# First Plot: Sleep Duration vs. Awake Heart Rate\n",
    "add_regression_trace(\n",
    "    fig=fig,\n",
    "    row=1,\n",
    "    col=1,\n",
    "    x=merged_data['total_sleep_h'],\n",
    "    y=merged_data['avg_hr_day'],\n",
    "    title=\"Sleep Duration and Awake Heart Rate the Next Day\",\n",
    "    x_label=\"Sleep Duration (hours)\",\n",
    "    y_label=\"Average Heart Rate (bpm)\",\n",
    "    marker_color='blue',\n",
    "    line_color='red'\n",
    ")\n",
    "\n",
    "# Second Plot: Sleeping HR vs. Awake HR\n",
    "add_regression_trace(\n",
    "    fig=fig,\n",
    "    row=1,\n",
    "    col=2,\n",
    "    x=merged_data['avg_sleep_hr'],\n",
    "    y=merged_data['avg_hr_day'],\n",
    "    title=\"Sleeping Heart Rate and Awake Heart Rate\",\n",
    "    x_label=\"Sleeping Heart Rate (bpm)\",\n",
    "    y_label=\"Awake Heart Rate (bpm)\",\n",
    "    marker_color='green',\n",
    "    line_color='orange'\n",
    ")\n",
    "\n",
    "# Update overall layout\n",
    "fig.update_layout(\n",
    "    height=600,\n",
    "    width=1200,\n",
    "    title_text=\"Sleep and Heart Rate Analysis\",\n",
    "    showlegend=False  # Hide legend\n",
    ")\n",
    "\n",
    "\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-19T14:37:55.293372Z",
     "start_time": "2024-10-19T14:37:55.289522Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation of Sleep duration and Avg Heart Rate the next day is:\n",
      " -0.22443039766661185\n",
      "Correlation of avg sleep Heart Rate and avg Awake Heart Rate is:\n",
      " 0.17074437211497479\n"
     ]
    }
   ],
   "source": [
    "# correlation of sleep duration and Avg Heart rate the next day\n",
    "corr_sleep_d__awake_hr = merged_data['total_sleep_h'].corr(merged_data['avg_hr_day'])\n",
    "\n",
    "# correlation of avg sleep hr and awake hr\n",
    "corr_sleep_hr__awake_hr = merged_data['avg_sleep_hr'].corr(merged_data['avg_hr_day'])\n",
    "\n",
    "print(f'Correlation of Sleep duration and Avg Heart Rate the next day is:\\n {corr_sleep_d__awake_hr}')\n",
    "\n",
    "print(f'Correlation of avg sleep Heart Rate and avg Awake Heart Rate is:\\n {corr_sleep_hr__awake_hr}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-19T14:37:55.406552Z",
     "start_time": "2024-10-19T14:37:55.404130Z"
    }
   },
   "outputs": [],
   "source": []
  }
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