dufour_peaks_manual.ipynb

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   "source": [
    "# Importing necessary libraries for data manipulation and visualization\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Setting the Seaborn theme for better visualization clarity\n",
    "sns.set_theme(style=\"whitegrid\")"
   ]
  },
  {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peaks_in_sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.398730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.623492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.863492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.080635</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.340952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.553016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.782857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16.017778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.245079</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.490159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22.709841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>24.954921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27.182222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>29.424762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31.641905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>33.881905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>36.129524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>38.389841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>40.604444</td>\n",
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       "      <th>19</th>\n",
       "      <td>42.852063</td>\n",
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       "      <th>20</th>\n",
       "      <td>45.074286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>47.311746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>49.536508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>51.781587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>53.998730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56.236190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>58.466032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>60.693333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>62.938413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65.158095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>67.408254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>69.627937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    peaks_in_sec\n",
       "0       0.398730\n",
       "1       2.623492\n",
       "2       4.863492\n",
       "3       7.080635\n",
       "4       9.340952\n",
       "5      11.553016\n",
       "6      13.782857\n",
       "7      16.017778\n",
       "8      18.245079\n",
       "9      20.490159\n",
       "10     22.709841\n",
       "11     24.954921\n",
       "12     27.182222\n",
       "13     29.424762\n",
       "14     31.641905\n",
       "15     33.881905\n",
       "16     36.129524\n",
       "17     38.389841\n",
       "18     40.604444\n",
       "19     42.852063\n",
       "20     45.074286\n",
       "21     47.311746\n",
       "22     49.536508\n",
       "23     51.781587\n",
       "24     53.998730\n",
       "25     56.236190\n",
       "26     58.466032\n",
       "27     60.693333\n",
       "28     62.938413\n",
       "29     65.158095\n",
       "30     67.408254\n",
       "31     69.627937"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 1: Import pandas\n",
    "import pandas as pd\n",
    "\n",
    "# Step 2: Load CSV from URL\n",
    "url = 'https://raw.githubusercontent.com/egorpol/beat_it/main/csv/dufour_manual.csv'\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "# Step 3: Display the first few rows of the DataFrame\n",
    "display(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
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    "executionInfo": {
     "elapsed": 361,
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     "timestamp": 1726593008682,
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    },
    "id": "jeRH40dK71wT",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.2247619  2.24       2.21714286 2.26031746 2.21206349 2.22984127\n",
      " 2.23492063 2.22730159 2.24507937 2.21968254 2.24507937 2.22730159\n",
      " 2.24253968 2.21714286 2.24       2.24761905 2.26031746 2.21460317\n",
      " 2.24761905 2.22222222 2.23746032 2.22476191 2.24507936 2.21714286\n",
      " 2.23746032 2.22984127 2.22730159 2.24507937 2.21968254 2.25015873\n",
      " 2.21968254]\n"
     ]
    }
   ],
   "source": [
    "# Extract time points (replace 'peaks_in_sec' with the actual column name if different)\n",
    "peaks_in_sec = df['peaks_in_sec'].values\n",
    "\n",
    "# Calculate IOIs (differences between consecutive peaks)\n",
    "iois = np.diff(peaks_in_sec)\n",
    "\n",
    "print(iois)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 277,
     "status": "ok",
     "timestamp": 1726594542061,
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      "displayName": "Egor P",
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     },
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    },
    "id": "iAJsIDDs9q6-",
    "outputId": "ce49ed64-1ec2-408a-bf09-ab14cf407b65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range of IOIs: 0.04825396799999915\n",
      "Minimum IOI: 2.2120634920000004\n",
      "Maximum IOI: 2.2603174599999996\n",
      "Mean of IOIs: 2.2332002048064514\n",
      "Median of IOIs: 2.2298412700000014\n",
      "Average BPM: 26.867273194254487\n"
     ]
    }
   ],
   "source": [
    "# Calculate the range of IOIs (max - min), which gives the spread between the longest and shortest intervals\n",
    "range = np.ptp(iois)\n",
    "\n",
    "# Calculate the mean of the IOIs, representing the average interval between peaks\n",
    "mean = np.mean(iois)\n",
    "\n",
    "# Calculate the median of the IOIs, representing the midpoint value of the intervals\n",
    "median = np.median(iois)\n",
    "\n",
    "# Calculate the average BPM (beats per minute), where BPM is inversely related to the average IOI (60 seconds / mean IOI)\n",
    "bpm = 60 / mean\n",
    "\n",
    "# Calculate the minimum IOI, representing the shortest interval between peaks\n",
    "min_ioi = np.min(iois)\n",
    "\n",
    "# Calculate the maximum IOI, representing the longest interval between peaks\n",
    "max_ioi = np.max(iois)\n",
    "\n",
    "# Print the results\n",
    "print(\"Range of IOIs:\", range)\n",
    "print(\"Minimum IOI:\", min_ioi)\n",
    "print(\"Maximum IOI:\", max_ioi)\n",
    "print(\"Mean of IOIs:\", mean)\n",
    "print(\"Median of IOIs:\", median)\n",
    "print(\"Average BPM:\", bpm)\n",
    "\n"
   ]
  },
  {
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    {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an array of onset numbers to match the IOIs\n",
    "numbers = np.arange(1, len(iois) + 1)\n",
    "\n",
    "# Creating a DataFrame to hold onset numbers and IOIs (manual times)\n",
    "data_plot = pd.DataFrame({\"onset_nr\": numbers, \"time_manual\": iois})\n",
    "\n",
    "# Plotting the manual IOIs\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Use scatterplot to visualize IOIs (manual cues)\n",
    "sns.scatterplot(x=\"onset_nr\", y=\"time_manual\", data=data_plot, label=\"Manual Cues\", marker=\"o\", color=\"blue\")\n",
    "\n",
    "# Add legend, labels, and title to the plot\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.xlabel(\"Onset Number\")  # X-axis label\n",
    "plt.ylabel(\"Time Difference (s)\")  # Y-axis label\n",
    "plt.title(\"Visualization of IOIs\")  # Plot title\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 662
    },
    "executionInfo": {
     "elapsed": 668,
     "status": "ok",
     "timestamp": 1726595495277,
     "user": {
      "displayName": "Egor P",
      "userId": "11648005462717752555"
     },
     "user_tz": -120
    },
    "id": "EblU36qjEczs",
    "outputId": "ca7927f8-b279-4929-ddaf-f8608c3631c6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'Slope': 1.1264722983904362e-05,\n",
       " 'Intercept': 2.233019969238709,\n",
       " 'R-squared': 5.7619068638825524e-05,\n",
       " 'P-value': 0.9676729255262038,\n",
       " 'Standard Error': 0.00027556616235645183}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "# Regression Analysis for Manual Data\n",
    "# Using scipy to get the slope, intercept, r_value, p_value, std_err\n",
    "slope_manual, intercept_manual, r_value_manual, p_value_manual, std_err_manual = stats.linregress(data_plot['onset_nr'], data_plot['time_manual'])\n",
    "\n",
    "# Compute the R-squared value for manual data\n",
    "r_squared_manual = r_value_manual ** 2\n",
    "\n",
    "# Organize the regression results into a structured format\n",
    "regression_results_manual = {\n",
    "    'Slope': slope_manual,\n",
    "    'Intercept': intercept_manual,\n",
    "    'R-squared': r_squared_manual,\n",
    "    'P-value': p_value_manual,\n",
    "    'Standard Error': std_err_manual\n",
    "}\n",
    "\n",
    "# Plotting the regression line with seaborn, including the confidence interval\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# `ci=95` adds the 95% confidence interval to the regression line\n",
    "sns.regplot(x=\"onset_nr\", y=\"time_manual\", data=data_plot, line_kws={\"color\":\"red\"}, ci=95)\n",
    "\n",
    "# Plot title and labels\n",
    "plt.title(\"Linear Regression with 95% Confidence Interval for Manual Data\")\n",
    "plt.xlabel(\"Onset Number\")\n",
    "plt.ylabel(\"Time Difference (s)\")\n",
    "\n",
    "# Displaying the equation of the regression line and R-squared on the plot\n",
    "plt.text(0.05, 0.95, f'y = {slope_manual:.6f}x + {intercept_manual:.3f}\\nR² = {r_squared_manual:.3f}',\n",
    "         transform=plt.gca().transAxes, va='top', bbox=dict(facecolor='white', alpha=0.5))\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# Display the regression results\n",
    "regression_results_manual\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mS72FKzUHD33"
   },
   "source": [
    "**Key Findings from the Linear Regression:**\n",
    "\n",
    "- A slope value of approximately **1.13e-05** is extremely small, indicating that the `time_manual` values (the IOIs) change very little as `onset_nr` increases. In practical terms, there’s almost no linear relationship between onset number and the time differences.\n",
    "\n",
    "- An R-squared value of approximately **0.0000576** is very close to zero, meaning the linear regression model explains almost none of the variability in the `time_manual` values. In other words, there's almost no linear correlation between `onset_nr` and `time_manual`.\n",
    "\n",
    "- A p-value of **0.9677** is far greater than the typical significance level (e.g., 0.05), meaning that you cannot reject the null hypothesis. There’s no statistically significant linear relationship between the onset number and time intervals (IOIs).\n",
    "\n",
    "**Conclusion:**  \n",
    "The regression analysis suggests there’s **no meaningful linear relationship** between the onset number and the IOIs, as shown by the near-zero slope, high p-value, and very low R-squared.\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPPc943IJ+rRmYds0yYOBkh",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
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 },
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}