Merge branch 'master' into develop

Author: salman2135

examples/sleep_activity_analysis/1_activity_over_time.png

@@ -0,0 +1,6 @@
+activity level,sum,mean,median,std,Time elapsed (min.),ID
+Sedentary,0.0,,,,0.0,processed_data/processed_acc_data.csv
+Light,12118.935017161717,12.118935017161718,11.387,1.9789273764997568,500.0,processed_data/processed_acc_data.csv
+Moderate,1885.5722970297031,25.829757493557576,23.093876237623764,8.075341358743886,36.5,processed_data/processed_acc_data.csv
+Vigorous,728.2869999999999,80.92077777777777,78.04899999999999,12.164052632426598,4.5,processed_data/processed_acc_data.csv
+Very Vigorous,0.0,,,,0.0,processed_data/processed_acc_data.csv

examples/sleep_activity_analysis/2_sleep_stages_over_time.png

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+import pandas as pd
+import json
+from pyActigraphy.io.base import BaseRaw
+from datetime import datetime
+import uuid as uuid_lib
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+df = pd.read_csv("accelerometer_results/processed_acc_data.csv.gz", compression='gzip')
+
+df['time'] = df['time'].str.split(r' \[').str[0]
+df['time'] = df['time'].str.replace(r"\+0000", "+00:00", regex=True)
+df['time'] = pd.to_datetime(df['time'])
+df.set_index('time', inplace=True)
+df.index.freq = "30S"
+
+with open("accelerometer_results/processed_acc_data-summary.json", "r") as f:
+    meta = json.load(f)
+
+name = meta["file-name"]
+uuid_val = uuid_lib.uuid4()
+fmt = "BBA"
+axial_mode = "uni"
+raw_time_str = meta["file-startTime"]
+cleaned_time_str = raw_time_str.split(' [')[0]
+
+period = df.index.max() - df.index.min() + pd.Timedelta(seconds=30)
+frequency = 1 / period.total_seconds()
+
+df = df.reset_index()
+
+df['time'] = pd.to_datetime(df['time']).dt.floor('30S')
+
+full_range = pd.date_range(start=df['time'].min().floor('30S'), 
+                           end=df['time'].max().ceil('30S'), 
+                           freq='30S', 
+                           tz='UTC')
+
+df = df.set_index('time').reindex(full_range).interpolate(method='time')
+
+start_time = df.index.min()
+data = df["acc"]
+light = None
+
+act = BaseRaw(
+    name=name,
+    uuid=uuid_val,
+    format=fmt,
+    axial_mode=axial_mode,
+    start_time=start_time,
+    data=data,
+    light=light,
+    fpath="accelerometer_results/acc_data.csv.gz",
+    period=period,
+    frequency=frequency
+)
+
+cut_points = [8, 20, 60, 120]
+labels = ["Sedentary", "Light", "Moderate", "Vigorous", "Very Vigorous"]
+
+act.create_activity_report(
+    cut_points=cut_points,
+    labels=labels,
+    threshold=10,
+    start_time="06:00:00",
+    stop_time="22:00:00",
+    oformat="minute",
+    verbose=True
+)
+
+report = act.activity_report
+print("Activity Report:")
+print(report)
+
+report.to_csv("activity_report.csv", index=False)
+print("\nActivity report saved to 'activity_report.csv'")
+
+processed_data = pd.DataFrame({
+    'timestamp': act.data.index,
+    'acceleration': act.data.values
+})
+processed_data.to_csv("processed_accelerometer_data.csv", index=False)
+print("Processed data saved to 'processed_accelerometer_data.csv'")
+
+def categorize_activity(value, cut_points, labels):
+    """Categorize activity level based on cut points"""
+    if pd.isna(value):
+        return "Unknown"
+    for i, cp in enumerate(cut_points):
+        if value <= cp:
+            return labels[i]
+    return labels[-1]
+
+activity_data = processed_data.copy()
+activity_data['activity_level'] = activity_data['acceleration'].apply(
+    lambda x: categorize_activity(x, cut_points, labels)
+)
+activity_data['hour'] = activity_data['timestamp'].dt.hour
+activity_data['date'] = activity_data['timestamp'].dt.date
+
+activity_data.to_csv("categorized_activity_data.csv", index=False)
+print("Categorized data saved to 'categorized_activity_data.csv'")
+
+plt.figure(figsize=(14, 8))
+colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#ff99cc']
+
+for level, color in zip(labels, colors):
+    level_data = activity_data[activity_data['activity_level'] == level]
+    if not level_data.empty:
+        plt.scatter(level_data['timestamp'], level_data['acceleration'], 
+                   c=color, label=level, alpha=0.6, s=1)
+
+plt.title('Activity Pattern Over Time', fontsize=14, fontweight='bold')
+plt.xlabel('Time')
+plt.ylabel('Acceleration')
+plt.legend(title='Activity Level')
+plt.xticks(rotation=45)
+plt.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.savefig('activity_pattern_over_time.png', dpi=300, bbox_inches='tight')
+plt.show()
+
+print("\n" + "="*50)
+print("SUMMARY STATISTICS")
+print("="*50)
+print(f"Total data points: {len(activity_data):,}")
+print(f"Time range: {activity_data['timestamp'].min()} to {activity_data['timestamp'].max()}")
+print(f"Duration: {activity_data['timestamp'].max() - activity_data['timestamp'].min()}")
+print(f"\nAcceleration statistics:")
+print(f"  Mean: {activity_data['acceleration'].mean():.2f}")
+print(f"  Median: {activity_data['acceleration'].median():.2f}")
+print(f"  Std: {activity_data['acceleration'].std():.2f}")
+print(f"  Min: {activity_data['acceleration'].min():.2f}")
+print(f"  Max: {activity_data['acceleration'].max():.2f}")
+
+print(f"\nActivity level breakdown:")
+for level in labels:
+    count = (activity_data['activity_level'] == level).sum()
+    percentage = (count / len(activity_data)) * 100
+    print(f"  {level}: {count:,} ({percentage:.1f}%)")
+
+print(f"\nFiles saved:")
+print(f"  - activity_report.csv")
+print(f"  - processed_accelerometer_data.csv") 
+print(f"  - categorized_activity_data.csv")
+print(f"  - activity_pattern_over_time.png")