Replace Jupyter notebooks with Python scripts in documentation

docs/docs/source/examples/anomaly_detection.rst

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+Anomaly Detection
+================
+
+This guide demonstrates the anomaly detection capabilities of Time Series RAG.
+
+Synthetic Data Generation
+-----------------------
+
+.. literalinclude:: scripts/anomaly_detection.py
+   :language: python
+   :lines: 1-7
+   :caption: Anomaly Detection Examples
+
+Create synthetic sensor data with anomalies:
+
+.. literalinclude:: scripts/anomaly_detection.py
+   :language: python
+   :pyobject: create_synthetic_sensor_data
+
+Anomaly Detection
+---------------
+
+Detect anomalies in sensor data:
+
+.. literalinclude:: scripts/anomaly_detection.py
+   :language: python
+   :pyobject: detect_anomalies_example
+
+Anomaly Pattern Analysis
+----------------------
+
+Analyze patterns in detected anomalies:
+
+.. literalinclude:: scripts/anomaly_detection.py
+   :language: python
+   :pyobject: analyze_anomaly_patterns
+
+Complete analysis example:
+
+.. literalinclude:: scripts/anomaly_detection.py
+   :language: python
+   :pyobject: anomaly_pattern_analysis_example
+
+Running the Examples
+------------------
+
+Execute all examples with:
+
+.. code-block:: bash
+
+   python anomaly_detection.py

docs/docs/source/examples/pattern_recognition.rst

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+Pattern Recognition
+==================
+
+This guide demonstrates the pattern recognition capabilities of Time Series RAG.
+
+Pattern Extraction
+----------------
+
+.. literalinclude:: scripts/pattern_recognition.py
+   :language: python
+   :lines: 1-7
+   :caption: Pattern Recognition Examples
+
+Extract and analyze patterns in time series data:
+
+.. literalinclude:: scripts/pattern_recognition.py
+   :language: python
+   :pyobject: extract_patterns_example
+
+Pattern Search
+-------------
+
+Search for similar patterns using the RAG system:
+
+.. literalinclude:: scripts/pattern_recognition.py
+   :language: python
+   :pyobject: pattern_search_example
+
+Seasonality Analysis
+------------------
+
+Analyze seasonal patterns in time series:
+
+.. literalinclude:: scripts/pattern_recognition.py
+   :language: python
+   :pyobject: seasonality_analysis_example
+
+Running the Examples
+------------------
+
+Execute all examples with:
+
+.. code-block:: bash
+
+   python pattern_recognition.py

docs/docs/source/examples/quickstart.rst

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+Quick Start Guide
+===============
+
+This guide demonstrates basic usage of the Time Series RAG system.
+
+Basic Example
+------------
+
+.. literalinclude:: scripts/quickstart.py
+   :language: python
+   :lines: 1-7
+   :caption: Quick Start Example
+
+The following example shows how to create and search time series data:
+
+.. literalinclude:: scripts/quickstart.py
+   :language: python
+   :pyobject: basic_example
+
+Visualization Example
+-------------------
+
+For a more visual example that includes plotting:
+
+.. literalinclude:: scripts/quickstart.py
+   :language: python
+   :pyobject: visualization_example
+
+Running the Examples
+------------------
+
+You can run these examples by executing the script:
+
+.. code-block:: bash
+
+   python quickstart.py
+
+This will run both examples and display the results.

docs/docs/source/examples/scripts/anomaly_detection.py

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+"""Anomaly Detection Examples for Time Series RAG.
+
+This script demonstrates anomaly detection capabilities using Time Series RAG
+and the analytics module.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from timeseries_rag.models import TimeSeriesEmbedder
+from timeseries_rag.rag import TimeSeriesRAG, TimeSeriesDocument
+from timeseries_rag.analytics import TimeSeriesAnalytics
+
+def create_synthetic_sensor_data():
+    """Create synthetic sensor data with anomalies.
+    
+    Returns:
+        tuple: Three time series (temperature, vibration, pressure) with anomalies
+    """
+    # Time points
+    t = np.linspace(0, 10, 1000)
+
+    # Temperature with daily cycle and anomalies
+    temp_base = 20 + 5 * np.sin(2 * np.pi * t / 1)  # Daily pattern
+    temp_anomalies = np.zeros_like(t)
+    temp_anomalies[300:320] = 10  # Sudden spike
+    temp_anomalies[600:700] = 5   # Sustained deviation
+    temperature = temp_base + temp_anomalies + np.random.normal(0, 0.5, size=len(t))
+
+    # Vibration with intermittent spikes
+    vibration = np.random.normal(0, 0.1, size=len(t))
+    spike_locations = np.random.choice(len(t), size=10, replace=False)
+    vibration[spike_locations] += np.random.uniform(1, 2, size=len(spike_locations))
+
+    # Pressure with gradual drift and sudden drop
+    pressure = 100 + 0.1 * t + np.random.normal(0, 0.1, size=len(t))
+    pressure[800:] -= 5  # Sudden drop
+
+    return temperature, vibration, pressure, t
+
+def detect_anomalies_example():
+    """Example of detecting anomalies in sensor data.
+    
+    This example demonstrates:
+    1. Creating synthetic sensor data with known anomalies
+    2. Using TimeSeriesAnalytics to detect anomalies
+    3. Visualizing the results
+    """
+    # Get synthetic data
+    temperature, vibration, pressure, t = create_synthetic_sensor_data()
+
+    # Initialize analytics for each sensor
+    temp_analytics = TimeSeriesAnalytics(temperature)
+    vib_analytics = TimeSeriesAnalytics(vibration)
+    press_analytics = TimeSeriesAnalytics(pressure)
+
+    # Detect anomalies
+    temp_anomalies = temp_analytics.detect_anomalies(window_size=50)
+    vib_anomalies = vib_analytics.detect_anomalies(window_size=20)
+    press_anomalies = press_analytics.detect_anomalies(window_size=100)
+
+    # Visualize results
+    plt.figure(figsize=(15, 10))
+
+    # Temperature
+    plt.subplot(3, 1, 1)
+    plt.plot(t, temperature, label='Temperature')
+    if temp_anomalies:
+        anomaly_idx = [x[0] for x in temp_anomalies]
+        plt.scatter(t[anomaly_idx], temperature[anomaly_idx],
+                   color='red', label='Anomalies')
+    plt.title('Temperature Sensor')
+    plt.legend()
+
+    # Vibration
+    plt.subplot(3, 1, 2)
+    plt.plot(t, vibration, label='Vibration')
+    if vib_anomalies:
+        anomaly_idx = [x[0] for x in vib_anomalies]
+        plt.scatter(t[anomaly_idx], vibration[anomaly_idx],
+                   color='red', label='Anomalies')
+    plt.title('Vibration Sensor')
+    plt.legend()
+
+    # Pressure
+    plt.subplot(3, 1, 3)
+    plt.plot(t, pressure, label='Pressure')
+    if press_anomalies:
+        anomaly_idx = [x[0] for x in press_anomalies]
+        plt.scatter(t[anomaly_idx], pressure[anomaly_idx],
+                   color='red', label='Anomalies')
+    plt.title('Pressure Sensor')
+    plt.legend()
+
+    plt.tight_layout()
+    plt.show()
+
+    return temp_anomalies, vib_anomalies, press_anomalies
+
+def analyze_anomaly_patterns(anomalies, data, window_size=50):
+    """Extract and analyze patterns around anomalies.
+    
+    Args:
+        anomalies: List of (index, value) tuples indicating anomalies
+        data: Original time series data
+        window_size: Size of window around anomalies to analyze
+    
+    Returns:
+        List of windows around anomalies
+    """
+    windows = []
+    for idx, _ in anomalies:
+        start = max(0, idx - window_size//2)
+        end = min(len(data), idx + window_size//2)
+        windows.append(data[start:end])
+    return windows
+
+def anomaly_pattern_analysis_example():
+    """Example of analyzing patterns in anomalies.
+    
+    This example demonstrates:
+    1. Detecting anomalies in multiple sensors
+    2. Extracting patterns around anomalies
+    3. Using RAG to find similar anomaly patterns
+    """
+    # Get data and detect anomalies
+    temperature, vibration, pressure, t = create_synthetic_sensor_data()
+    temp_anomalies, vib_anomalies, press_anomalies = detect_anomalies_example()
+
+    # Initialize RAG system
+    embedder = TimeSeriesEmbedder()
+    rag = TimeSeriesRAG()
+
+    # Extract and store anomaly patterns
+    for sensor_name, anomalies, data in [
+        ('temperature', temp_anomalies, temperature),
+        ('vibration', vib_anomalies, vibration),
+        ('pressure', press_anomalies, pressure)
+    ]:
+        windows = analyze_anomaly_patterns(anomalies, data)
+        for i, window in enumerate(windows):
+            embedding = embedder.embed(window)
+            doc = TimeSeriesDocument(
+                id=f'{sensor_name}_anomaly_{i}',
+                data=window,
+                metadata={'sensor': sensor_name, 'type': 'anomaly'},
+                embedding=embedding
+            )
+            rag.add_document(doc)
+
+    # Find similar anomaly patterns
+    if temp_anomalies:  # Use first temperature anomaly as query
+        query_window = analyze_anomaly_patterns(
+            [temp_anomalies[0]], temperature
+        )[0]
+        query_embedding = embedder.embed(query_window)
+        results = rag.search(query_embedding, k=3)
+
+        # Visualize similar anomalies
+        plt.figure(figsize=(12, 4))
+
+        plt.subplot(1, 2, 1)
+        plt.plot(query_window)
+        plt.title('Query Anomaly Pattern')
+        plt.xlabel('Time')
+        plt.ylabel('Value')
+
+        plt.subplot(1, 2, 2)
+        for result in results:
+            plt.plot(result['data'],
+                    label=f"{result['metadata']['sensor']}\n"
+                          f"Distance: {result['distance']:.2f}")
+        plt.title('Similar Anomaly Patterns')
+        plt.xlabel('Time')
+        plt.ylabel('Value')
+        plt.legend()
+        plt.tight_layout()
+        plt.show()
+
+if __name__ == "__main__":
+    print("Running anomaly detection example...")
+    detect_anomalies_example()
+
+    print("\nRunning anomaly pattern analysis example...")
+    anomaly_pattern_analysis_example()