gaitsetpy.eda API documentation

# gaitsetpy.eda

eda: Provides exploratory data analysis tools for gait datasets.

This module provides both the new class-based EDA analyzers and legacy function-based API. All EDA analyzers inherit from BaseEDAAnalyzer and are registered with the EDAManager.

Features:

- Comprehensive visualization of sensor data (thigh, shank, trunk)
- Statistical analysis and summaries
- Feature visualization and overlay
- Dataset comparison and analysis

Maintainer: @aharshit123456

View Source

      1"""
      2eda: Provides exploratory data analysis tools for gait datasets.
      3
      4This module provides both the new class-based EDA analyzers and legacy function-based API.
      5All EDA analyzers inherit from BaseEDAAnalyzer and are registered with the EDAManager.
      6
      7Features:
      8- Comprehensive visualization of sensor data (thigh, shank, trunk)
      9- Statistical analysis and summaries
     10- Feature visualization and overlay
     11- Dataset comparison and analysis
     12
     13Maintainer: @aharshit123456
     14"""
     15
     16# Import the new class-based EDA analyzers
     17from .analyzers import (
     18    DaphnetVisualizationAnalyzer,
     19    SensorStatisticsAnalyzer
     20)
     21
     22# Import legacy functions for backward compatibility
     23from .visualization import (
     24    plot_thigh_data,
     25    plot_shank_data,
     26    plot_trunk_data,
     27    plot_all_data,
     28    plot_all_thigh_data,
     29    plot_all_shank_data,
     30    plot_all_trunk_data,
     31    plot_all_datasets
     32)
     33
     34from .statistics import plot_sensor_with_features
     35
     36# Import managers
     37from ..core.managers import EDAManager
     38
     39# Register all EDA analyzers with the manager
     40def _register_analyzers():
     41    """Register all available EDA analyzers with the EDAManager."""
     42    manager = EDAManager()
     43    manager.register_analyzer("daphnet_visualization", DaphnetVisualizationAnalyzer)
     44    manager.register_analyzer("sensor_statistics", SensorStatisticsAnalyzer)
     45
     46# Auto-register analyzers when module is imported
     47_register_analyzers()
     48
     49# Convenient access to the EDA manager
     50def get_eda_manager():
     51    """Get the singleton EDAManager instance."""
     52    return EDAManager()
     53
     54# Helper function to get available analyzers
     55def get_available_analyzers():
     56    """Get list of available EDA analyzer names."""
     57    return EDAManager().get_available_components()
     58
     59# Helper function to analyze data using manager
     60def analyze_data(analyzer_name: str, data, **kwargs):
     61    """
     62    Analyze data using the EDAManager.
     63
     64    Args:
     65        analyzer_name: Name of the EDA analyzer
     66        data: Input data to analyze
     67        **kwargs: Additional arguments for analysis
     68
     69    Returns:
     70        Analysis results dictionary
     71    """
     72    return EDAManager().analyze_data(analyzer_name, data, **kwargs)
     73
     74# Helper function to visualize data using manager
     75def visualize_data(analyzer_name: str, data, **kwargs):
     76    """
     77    Create visualizations using the EDAManager.
     78
     79    Args:
     80        analyzer_name: Name of the EDA analyzer
     81        data: Input data to visualize
     82        **kwargs: Additional arguments for visualization
     83    """
     84    return EDAManager().visualize_data(analyzer_name, data, **kwargs)
     85
     86# Convenient wrapper functions for common operations
     87def plot_daphnet_data(data, names=None, sensor_type='all', dataset_index=0):
     88    """
     89    Plot Daphnet dataset using the DaphnetVisualizationAnalyzer.
     90
     91    Args:
     92        data: Input data (DataFrame or list of DataFrames)
     93        names: List of dataset names
     94        sensor_type: Type of sensor to plot ('all', 'thigh', 'shank', 'trunk')
     95        dataset_index: Index of dataset to plot (if data is a list)
     96    """
     97    analyzer = DaphnetVisualizationAnalyzer()
     98    analyzer.visualize(data, sensor_type=sensor_type, dataset_index=dataset_index, names=names or [])
     99
    100def analyze_sensor_statistics(data):
    101    """
    102    Analyze sensor statistics using the SensorStatisticsAnalyzer.
    103
    104    Args:
    105        data: Input data (DataFrame or list of DataFrames)
    106
    107    Returns:
    108        Dictionary containing statistical analysis results
    109    """
    110    analyzer = SensorStatisticsAnalyzer()
    111    return analyzer.analyze(data)
    112
    113def plot_sensor_features(sliding_windows, features, sensor_name='shank', start_idx=0, end_idx=1000, num_windows=10):
    114    """
    115    Plot sensor data with overlaid features using the SensorStatisticsAnalyzer.
    116
    117    Args:
    118        sliding_windows: List of sliding window dictionaries
    119        features: List of feature dictionaries
    120        sensor_name: Name of the sensor to plot
    121        start_idx: Start index of the time window
    122        end_idx: End index of the time window
    123        num_windows: Number of sliding windows to plot
    124    """
    125    analyzer = SensorStatisticsAnalyzer()
    126    analyzer.visualize(sliding_windows, features, sensor_name=sensor_name,
    127                      start_idx=start_idx, end_idx=end_idx, num_windows=num_windows)
    128
    129__all__ = [
    130    # New class-based analyzers
    131    'DaphnetVisualizationAnalyzer',
    132    'SensorStatisticsAnalyzer',
    133    # Legacy functions for backward compatibility
    134    'plot_thigh_data',
    135    'plot_shank_data',
    136    'plot_trunk_data',
    137    'plot_all_data',
    138    'plot_all_thigh_data',
    139    'plot_all_shank_data',
    140    'plot_all_trunk_data',
    141    'plot_all_datasets',
    142    'plot_sensor_with_features',
    143    # Manager functions
    144    'get_eda_manager',
    145    'get_available_analyzers',
    146    'analyze_data',
    147    'visualize_data',
    148    # Convenient wrapper functions
    149    'plot_daphnet_data',
    150    'analyze_sensor_statistics',
    151    'plot_sensor_features'
    152]

class DaphnetVisualizationAnalyzer(gaitsetpy.core.base_classes.BaseEDAAnalyzer): View Source

     18class DaphnetVisualizationAnalyzer(BaseEDAAnalyzer):
     19    """
     20    EDA analyzer for Daphnet dataset visualization.
     21
     22    This analyzer provides comprehensive visualization capabilities for Daphnet dataset
     23    including thigh, shank, and trunk sensor data.
     24    """
     25
     26    def __init__(self):
     27        super().__init__(
     28            name="daphnet_visualization",
     29            description="Comprehensive visualization analyzer for Daphnet dataset sensor data"
     30        )
     31        self.config = {
     32            'figsize': (20, 16),
     33            'colors': {
     34                'no_freeze': 'orange',
     35                'freeze': 'purple'
     36            },
     37            'alpha': 0.6
     38        }
     39
     40    def analyze(self, data: Union[pd.DataFrame, List[pd.DataFrame]], **kwargs) -> Dict[str, Any]:
     41        """
     42        Analyze the data and return statistical summaries.
     43
     44        Args:
     45            data: Input data to analyze
     46            **kwargs: Additional arguments
     47
     48        Returns:
     49            Dictionary containing analysis results
     50        """
     51        if isinstance(data, list):
     52            # Multiple datasets
     53            results = {}
     54            for i, df in enumerate(data):
     55                results[f'dataset_{i}'] = self._analyze_single_dataset(df)
     56            return results
     57        else:
     58            # Single dataset
     59            return self._analyze_single_dataset(data)
     60
     61    def _analyze_single_dataset(self, df: pd.DataFrame) -> Dict[str, Any]:
     62        """Analyze a single dataset."""
     63        # Basic statistics
     64        stats = {
     65            'shape': df.shape,
     66            'columns': df.columns.tolist(),
     67            'annotation_distribution': df['annotations'].value_counts().to_dict() if 'annotations' in df.columns else {},
     68            'missing_values': df.isnull().sum().to_dict(),
     69            'data_range': {
     70                'min': df.select_dtypes(include=[np.number]).min().to_dict(),
     71                'max': df.select_dtypes(include=[np.number]).max().to_dict()
     72            }
     73        }
     74
     75        # Sensor-specific statistics
     76        sensor_stats = {}
     77        for sensor in ['thigh', 'shank', 'trunk']:
     78            if sensor in df.columns:
     79                sensor_stats[sensor] = {
     80                    'mean': df[sensor].mean(),
     81                    'std': df[sensor].std(),
     82                    'min': df[sensor].min(),
     83                    'max': df[sensor].max()
     84                }
     85
     86        stats['sensor_statistics'] = sensor_stats
     87        return stats
     88
     89    def visualize(self, data: Union[pd.DataFrame, List[pd.DataFrame]], **kwargs):
     90        """
     91        Create visualizations of the data.
     92
     93        Args:
     94            data: Input data to visualize
     95            **kwargs: Additional arguments including sensor_type, dataset_index, names
     96        """
     97        sensor_type = kwargs.get('sensor_type', 'all')
     98        dataset_index = kwargs.get('dataset_index', 0)
     99        names = kwargs.get('names', [])
    100
    101        if isinstance(data, list):
    102            if dataset_index < len(data):
    103                df = data[dataset_index]
    104                dataset_name = names[dataset_index] if dataset_index < len(names) else f"Dataset {dataset_index}"
    105            else:
    106                print(f"Dataset index {dataset_index} out of range")
    107                return
    108        else:
    109            df = data
    110            dataset_name = names[0] if names else "Dataset"
    111
    112        if sensor_type == 'all':
    113            self._plot_all_sensors(df, dataset_name)
    114        elif sensor_type == 'thigh':
    115            self._plot_thigh_data(df, dataset_name)
    116        elif sensor_type == 'shank':
    117            self._plot_shank_data(df, dataset_name)
    118        elif sensor_type == 'trunk':
    119            self._plot_trunk_data(df, dataset_name)
    120        else:
    121            print(f"Unknown sensor type: {sensor_type}")
    122
    123    def _plot_thigh_data(self, df: pd.DataFrame, dataset_name: str):
    124        """Plot thigh sensor data."""
    125        print(f"Plotting thigh data for {dataset_name}")
    126
    127        # Filter data
    128        df_filtered = df[df.annotations > 0] if 'annotations' in df.columns else df
    129
    130        if df_filtered.empty:
    131            print("No valid data to plot")
    132            return
    133
    134        # Create figure
    135        fig, axes = plt.subplots(4, 1, sharex=True, figsize=self.config['figsize'])
    136        fig.suptitle(f"Thigh Data from {dataset_name}")
    137
    138        # Separate freeze and no-freeze data
    139        if 'annotations' in df.columns:
    140            neg = df_filtered[df_filtered.annotations == 1]  # No freeze
    141            pos = df_filtered[df_filtered.annotations == 2]  # Freeze
    142        else:
    143            neg = df_filtered
    144            pos = pd.DataFrame()
    145
    146        # Plot each component
    147        components = ['thigh_h_fd', 'thigh_v', 'thigh_h_l', 'thigh']
    148        labels = ['Horizontal Forward', 'Vertical', 'Horizontal Lateral', 'Overall']
    149
    150        for i, (component, label) in enumerate(zip(components, labels)):
    151            if component in df_filtered.columns:
    152                # Plot main signal
    153                axes[i].plot(df_filtered.index, df_filtered[component])
    154                axes[i].set_ylabel(f"{label} Thigh Acceleration")
    155
    156                # Plot annotations if available
    157                if not neg.empty:
    158                    axes[i].scatter(neg.index, neg[component],
    159                                  c=self.config['colors']['no_freeze'],
    160                                  label="no freeze", alpha=self.config['alpha'])
    161                if not pos.empty:
    162                    axes[i].scatter(pos.index, pos[component],
    163                                  c=self.config['colors']['freeze'],
    164                                  label="freeze", alpha=self.config['alpha'])
    165
    166                axes[i].legend()
    167
    168        plt.xlabel("Time")
    169        plt.tight_layout()
    170        plt.show()
    171
    172    def _plot_shank_data(self, df: pd.DataFrame, dataset_name: str):
    173        """Plot shank sensor data."""
    174        print(f"Plotting shank data for {dataset_name}")
    175
    176        # Filter data
    177        df_filtered = df[df.annotations > 0] if 'annotations' in df.columns else df
    178
    179        if df_filtered.empty:
    180            print("No valid data to plot")
    181            return
    182
    183        # Create figure
    184        fig, axes = plt.subplots(4, 1, sharex=True, figsize=self.config['figsize'])
    185        fig.suptitle(f"Shank Data from {dataset_name}")
    186
    187        # Separate freeze and no-freeze data
    188        if 'annotations' in df.columns:
    189            neg = df_filtered[df_filtered.annotations == 1]  # No freeze
    190            pos = df_filtered[df_filtered.annotations == 2]  # Freeze
    191        else:
    192            neg = df_filtered
    193            pos = pd.DataFrame()
    194
    195        # Plot each component
    196        components = ['shank_h_fd', 'shank_v', 'shank_h_l', 'shank']
    197        labels = ['Horizontal Forward', 'Vertical', 'Horizontal Lateral', 'Overall']
    198
    199        for i, (component, label) in enumerate(zip(components, labels)):
    200            if component in df_filtered.columns:
    201                # Plot main signal
    202                axes[i].plot(df_filtered.index, df_filtered[component])
    203                axes[i].set_ylabel(f"{label} Shank Acceleration")
    204
    205                # Plot annotations if available
    206                if not neg.empty:
    207                    axes[i].scatter(neg.index, neg[component],
    208                                  c=self.config['colors']['no_freeze'],
    209                                  label="no freeze", alpha=self.config['alpha'])
    210                if not pos.empty:
    211                    axes[i].scatter(pos.index, pos[component],
    212                                  c=self.config['colors']['freeze'],
    213                                  label="freeze", alpha=self.config['alpha'])
    214
    215                axes[i].legend()
    216
    217        plt.xlabel("Time")
    218        plt.tight_layout()
    219        plt.show()
    220
    221    def _plot_trunk_data(self, df: pd.DataFrame, dataset_name: str):
    222        """Plot trunk sensor data."""
    223        print(f"Plotting trunk data for {dataset_name}")
    224
    225        # Filter data
    226        df_filtered = df[df.annotations > 0] if 'annotations' in df.columns else df
    227
    228        if df_filtered.empty:
    229            print("No valid data to plot")
    230            return
    231
    232        # Create figure
    233        fig, axes = plt.subplots(4, 1, sharex=True, figsize=self.config['figsize'])
    234        fig.suptitle(f"Trunk Data from {dataset_name}")
    235
    236        # Separate freeze and no-freeze data
    237        if 'annotations' in df.columns:
    238            neg = df_filtered[df_filtered.annotations == 1]  # No freeze
    239            pos = df_filtered[df_filtered.annotations == 2]  # Freeze
    240        else:
    241            neg = df_filtered
    242            pos = pd.DataFrame()
    243
    244        # Plot each component
    245        components = ['trunk_h_fd', 'trunk_v', 'trunk_h_l', 'trunk']
    246        labels = ['Horizontal Forward', 'Vertical', 'Horizontal Lateral', 'Overall']
    247
    248        for i, (component, label) in enumerate(zip(components, labels)):
    249            if component in df_filtered.columns:
    250                # Plot main signal
    251                axes[i].plot(df_filtered.index, df_filtered[component])
    252                axes[i].set_ylabel(f"{label} Trunk Acceleration")
    253
    254                # Plot annotations if available
    255                if not neg.empty:
    256                    axes[i].scatter(neg.index, neg[component],
    257                                  c=self.config['colors']['no_freeze'],
    258                                  label="no freeze", alpha=self.config['alpha'])
    259                if not pos.empty:
    260                    axes[i].scatter(pos.index, pos[component],
    261                                  c=self.config['colors']['freeze'],
    262                                  label="freeze", alpha=self.config['alpha'])
    263
    264                axes[i].legend()
    265
    266        plt.xlabel("Time")
    267        plt.tight_layout()
    268        plt.show()
    269
    270    def _plot_all_sensors(self, df: pd.DataFrame, dataset_name: str):
    271        """Plot all sensor data in a combined view."""
    272        print(f"Plotting all sensor data for {dataset_name}")
    273
    274        # Create figure with subplots for each sensor
    275        fig, axes = plt.subplots(3, 1, sharex=True, figsize=self.config['figsize'])
    276        fig.suptitle(f"All Sensor Data from {dataset_name}")
    277
    278        # Filter data
    279        df_filtered = df[df.annotations > 0] if 'annotations' in df.columns else df
    280
    281        if df_filtered.empty:
    282            print("No valid data to plot")
    283            return
    284
    285        sensors = ['thigh', 'shank', 'trunk']
    286        for i, sensor in enumerate(sensors):
    287            if sensor in df_filtered.columns:
    288                axes[i].plot(df_filtered.index, df_filtered[sensor])
    289                axes[i].set_ylabel(f"{sensor.capitalize()} Acceleration")
    290
    291                # Add annotations if available
    292                if 'annotations' in df_filtered.columns:
    293                    neg = df_filtered[df_filtered.annotations == 1]
    294                    pos = df_filtered[df_filtered.annotations == 2]
    295
    296                    if not neg.empty:
    297                        axes[i].scatter(neg.index, neg[sensor],
    298                                      c=self.config['colors']['no_freeze'],
    299                                      label="no freeze", alpha=self.config['alpha'])
    300                    if not pos.empty:
    301                        axes[i].scatter(pos.index, pos[sensor],
    302                                      c=self.config['colors']['freeze'],
    303                                      label="freeze", alpha=self.config['alpha'])
    304
    305                    axes[i].legend()
    306
    307        plt.xlabel("Time")
    308        plt.tight_layout()
    309        plt.show()

EDA analyzer for Daphnet dataset visualization.

This analyzer provides comprehensive visualization capabilities for Daphnet dataset including thigh, shank, and trunk sensor data.

DaphnetVisualizationAnalyzer() View Source

    26    def __init__(self):
    27        super().__init__(
    28            name="daphnet_visualization",
    29            description="Comprehensive visualization analyzer for Daphnet dataset sensor data"
    30        )
    31        self.config = {
    32            'figsize': (20, 16),
    33            'colors': {
    34                'no_freeze': 'orange',
    35                'freeze': 'purple'
    36            },
    37            'alpha': 0.6
    38        }

Initialize the EDA analyzer.

Args: name: Name of the EDA analyzer description: Description of the EDA analyzer

config
def analyze( self, data: Union[pandas.core.frame.DataFrame, List[pandas.core.frame.DataFrame]], **kwargs) -> Dict[str, Any]: View Source

    40    def analyze(self, data: Union[pd.DataFrame, List[pd.DataFrame]], **kwargs) -> Dict[str, Any]:
    41        """
    42        Analyze the data and return statistical summaries.
    43
    44        Args:
    45            data: Input data to analyze
    46            **kwargs: Additional arguments
    47
    48        Returns:
    49            Dictionary containing analysis results
    50        """
    51        if isinstance(data, list):
    52            # Multiple datasets
    53            results = {}
    54            for i, df in enumerate(data):
    55                results[f'dataset_{i}'] = self._analyze_single_dataset(df)
    56            return results
    57        else:
    58            # Single dataset
    59            return self._analyze_single_dataset(data)

Analyze the data and return statistical summaries.

Args: data: Input data to analyze **kwargs: Additional arguments

Returns: Dictionary containing analysis results

def visualize( self, data: Union[pandas.core.frame.DataFrame, List[pandas.core.frame.DataFrame]], **kwargs): View Source

     89    def visualize(self, data: Union[pd.DataFrame, List[pd.DataFrame]], **kwargs):
     90        """
     91        Create visualizations of the data.
     92
     93        Args:
     94            data: Input data to visualize
     95            **kwargs: Additional arguments including sensor_type, dataset_index, names
     96        """
     97        sensor_type = kwargs.get('sensor_type', 'all')
     98        dataset_index = kwargs.get('dataset_index', 0)
     99        names = kwargs.get('names', [])
    100
    101        if isinstance(data, list):
    102            if dataset_index < len(data):
    103                df = data[dataset_index]
    104                dataset_name = names[dataset_index] if dataset_index < len(names) else f"Dataset {dataset_index}"
    105            else:
    106                print(f"Dataset index {dataset_index} out of range")
    107                return
    108        else:
    109            df = data
    110            dataset_name = names[0] if names else "Dataset"
    111
    112        if sensor_type == 'all':
    113            self._plot_all_sensors(df, dataset_name)
    114        elif sensor_type == 'thigh':
    115            self._plot_thigh_data(df, dataset_name)
    116        elif sensor_type == 'shank':
    117            self._plot_shank_data(df, dataset_name)
    118        elif sensor_type == 'trunk':
    119            self._plot_trunk_data(df, dataset_name)
    120        else:
    121            print(f"Unknown sensor type: {sensor_type}")

Create visualizations of the data.

Args: data: Input data to visualize **kwargs: Additional arguments including sensor_type, dataset_index, names

##### Inherited Members

gaitsetpy.core.base_classes.BaseEDAAnalyzer
  name
  description
  configure
  get_info

class SensorStatisticsAnalyzer(gaitsetpy.core.base_classes.BaseEDAAnalyzer): View Source

    312class SensorStatisticsAnalyzer(BaseEDAAnalyzer):
    313    """
    314    EDA analyzer for sensor data statistics and feature visualization.
    315
    316    This analyzer provides statistical analysis and feature visualization capabilities
    317    for sensor data including sliding windows and extracted features.
    318    """
    319
    320    def __init__(self):
    321        super().__init__(
    322            name="sensor_statistics",
    323            description="Statistical analysis and feature visualization for sensor data"
    324        )
    325        self.config = {
    326            'figsize': (20, 10),
    327            'feature_markers': {
    328                'mean': 'x',
    329                'rms': 'o',
    330                'peak_height': 'v',
    331                'mode': '<',
    332                'median': '^'
    333            }
    334        }
    335
    336    def analyze(self, data: Union[pd.DataFrame, List[pd.DataFrame]], **kwargs) -> Dict[str, Any]:
    337        """
    338        Analyze sensor data and return statistical summaries.
    339
    340        Args:
    341            data: Input data to analyze
    342            **kwargs: Additional arguments
    343
    344        Returns:
    345            Dictionary containing analysis results
    346        """
    347        if isinstance(data, list):
    348            # Multiple datasets
    349            results = {}
    350            for i, df in enumerate(data):
    351                results[f'dataset_{i}'] = self._compute_statistics(df)
    352            return results
    353        else:
    354            # Single dataset
    355            return self._compute_statistics(data)
    356
    357    def _compute_statistics(self, df: pd.DataFrame) -> Dict[str, Any]:
    358        """Compute comprehensive statistics for a dataset."""
    359        stats = {
    360            'basic_stats': df.describe().to_dict(),
    361            'correlation_matrix': df.corr().to_dict() if len(df.select_dtypes(include=[np.number]).columns) > 1 else {},
    362            'skewness': df.skew().to_dict(),
    363            'kurtosis': df.kurtosis().to_dict()
    364        }
    365
    366        # Add sensor-specific statistics
    367        sensor_stats = {}
    368        for sensor in ['thigh', 'shank', 'trunk']:
    369            if sensor in df.columns:
    370                sensor_data = df[sensor].dropna()
    371                sensor_stats[sensor] = {
    372                    'mean': sensor_data.mean(),
    373                    'std': sensor_data.std(),
    374                    'variance': sensor_data.var(),
    375                    'min': sensor_data.min(),
    376                    'max': sensor_data.max(),
    377                    'range': sensor_data.max() - sensor_data.min(),
    378                    'median': sensor_data.median(),
    379                    'q25': sensor_data.quantile(0.25),
    380                    'q75': sensor_data.quantile(0.75),
    381                    'iqr': sensor_data.quantile(0.75) - sensor_data.quantile(0.25)
    382                }
    383
    384        stats['sensor_statistics'] = sensor_stats
    385        return stats
    386
    387    def visualize(self, sliding_windows: List[Dict], features: List[Dict], **kwargs):
    388        """
    389        Create visualizations of sensor data with overlaid features.
    390
    391        Args:
    392            sliding_windows: List of sliding window dictionaries
    393            features: List of feature dictionaries
    394            **kwargs: Additional arguments including sensor_name, start_idx, end_idx, num_windows
    395        """
    396        sensor_name = kwargs.get('sensor_name', 'shank')
    397        start_idx = kwargs.get('start_idx', 0)
    398        end_idx = kwargs.get('end_idx', 1000)
    399        num_windows = kwargs.get('num_windows', 10)
    400        save = kwargs.get('save', False)
    401
    402        self._plot_sensor_with_features(sliding_windows, features, start_idx, end_idx,
    403                                      sensor_name, num_windows, save)
    404
    405    def _plot_sensor_with_features(self, sliding_windows: List[Dict], features: List[Dict],
    406                                 start_idx: int, end_idx: int, sensor_name: str = "shank",
    407                                 num_windows: int = 10, save: bool = False):
    408        """
    409        Plot sliding windows of sensor data with overlaid statistical features.
    410
    411        Args:
    412            sliding_windows: List of sliding window dictionaries
    413            features: List of feature dictionaries
    414            start_idx: Start index of the time window
    415            end_idx: End index of the time window
    416            sensor_name: Name of the sensor to plot
    417            num_windows: Number of sliding windows to plot
    418            save: Whether to save the plot
    419        """
    420        fig, axes = plt.subplots(2, 1, figsize=self.config['figsize'],
    421                                gridspec_kw={'height_ratios': [3, 1]})
    422
    423        # Extract sensor windows
    424        sensor_windows = next((sw['data'] for sw in sliding_windows if sw['name'] == sensor_name), None)
    425        if sensor_windows is None:
    426            print(f"Sensor '{sensor_name}' not found in sliding_windows.")
    427            return
    428
    429        # Extract corresponding features
    430        sensor_features = next((feat['features'] for feat in features if feat['name'] == sensor_name), None)
    431        if sensor_features is None:
    432            print(f"Sensor '{sensor_name}' not found in features.")
    433            return
    434
    435        # Filter windows based on start_idx and end_idx
    436        filtered_windows = [series for series in sensor_windows
    437                           if start_idx <= series.index[0] and series.index[-1] <= end_idx]
    438
    439        if not filtered_windows:
    440            print(f"No windows found in the specified index range ({start_idx} - {end_idx}).")
    441            return
    442
    443        # Store entropy & frequency features for separate plotting
    444        entropy_values = []
    445        dominant_frequencies = []
    446
    447        # Plot first num_windows windows
    448        for i in range(min(num_windows, len(filtered_windows))):
    449            series = filtered_windows[i]
    450
    451            # Extract time and signal values
    452            time_values = series.index.to_numpy()
    453            signal_values = series.values
    454
    455            # Determine actual start and end indices for this window
    456            window_start, window_end = time_values[0], time_values[-1]
    457
    458            # Plot time series data
    459            axes[0].plot(time_values, signal_values, alpha=0.6)
    460
    461            # Mark start and end of each window with vertical dotted lines
    462            axes[0].axvline(x=window_start, color='black', linestyle='dotted', alpha=0.7)
    463            axes[0].axvline(x=window_end, color='black', linestyle='dotted', alpha=0.7)
    464
    465            # Overlay statistical features
    466            for feature_name, marker in self.config['feature_markers'].items():
    467                if feature_name in sensor_features and len(sensor_features[feature_name]) > i:
    468                    feature_value = sensor_features[feature_name][i]
    469                    if feature_value != 0:  # Skip zero values
    470                        closest_index = np.argmin(np.abs(signal_values - feature_value))
    471                        closest_time = time_values[closest_index]
    472                        axes[0].scatter(closest_time, feature_value, color='red',
    473                                      marker=marker, s=100, label=feature_name if i == 0 else "")
    474
    475            # Store entropy & frequency features for separate plotting
    476            if 'entropy' in sensor_features and len(sensor_features['entropy']) > i:
    477                entropy_values.append(sensor_features['entropy'][i])
    478            if 'dominant_frequency' in sensor_features and len(sensor_features['dominant_frequency']) > i:
    479                dominant_frequencies.append(sensor_features['dominant_frequency'][i])
    480
    481        # Labels and title for time-series plot
    482        axes[0].set_xlabel('Time')
    483        axes[0].set_ylabel(f'{sensor_name} Signal')
    484        axes[0].set_title(f'First {num_windows} windows of {sensor_name} in range {start_idx}-{end_idx} with Features')
    485        axes[0].legend()
    486
    487        # Frequency-domain & entropy plot
    488        if dominant_frequencies:
    489            window_indices = list(range(len(dominant_frequencies)))
    490            axes[1].plot(window_indices, dominant_frequencies,
    491                        label="Dominant Frequency", marker="o", linestyle="dashed", color="blue")
    492
    493        if entropy_values:
    494            axes[1].bar(window_indices, entropy_values, alpha=0.6, label="Entropy", color="green")
    495
    496        axes[1].set_xlabel("Window Index")
    497        axes[1].set_ylabel("Feature Value")
    498        axes[1].set_title("Frequency & Entropy Features")
    499        axes[1].legend()
    500
    501        plt.tight_layout()
    502
    503        # Save or show plot
    504        if save:
    505            file_path = input("Enter the file path to save the plot (e.g., 'plot.png'): ")
    506            plt.savefig(file_path, dpi=300)
    507            print(f"Plot saved at {file_path}")
    508        else:
    509            plt.show()

EDA analyzer for sensor data statistics and feature visualization.

This analyzer provides statistical analysis and feature visualization capabilities for sensor data including sliding windows and extracted features.

SensorStatisticsAnalyzer() View Source

    320    def __init__(self):
    321        super().__init__(
    322            name="sensor_statistics",
    323            description="Statistical analysis and feature visualization for sensor data"
    324        )
    325        self.config = {
    326            'figsize': (20, 10),
    327            'feature_markers': {
    328                'mean': 'x',
    329                'rms': 'o',
    330                'peak_height': 'v',
    331                'mode': '<',
    332                'median': '^'
    333            }
    334        }

Initialize the EDA analyzer.

Args: name: Name of the EDA analyzer description: Description of the EDA analyzer

config
def analyze( self, data: Union[pandas.core.frame.DataFrame, List[pandas.core.frame.DataFrame]], **kwargs) -> Dict[str, Any]: View Source

    336    def analyze(self, data: Union[pd.DataFrame, List[pd.DataFrame]], **kwargs) -> Dict[str, Any]:
    337        """
    338        Analyze sensor data and return statistical summaries.
    339
    340        Args:
    341            data: Input data to analyze
    342            **kwargs: Additional arguments
    343
    344        Returns:
    345            Dictionary containing analysis results
    346        """
    347        if isinstance(data, list):
    348            # Multiple datasets
    349            results = {}
    350            for i, df in enumerate(data):
    351                results[f'dataset_{i}'] = self._compute_statistics(df)
    352            return results
    353        else:
    354            # Single dataset
    355            return self._compute_statistics(data)

Analyze sensor data and return statistical summaries.

Args: data: Input data to analyze **kwargs: Additional arguments

Returns: Dictionary containing analysis results

def visualize(self, sliding_windows: List[Dict], features: List[Dict], **kwargs): View Source

    387    def visualize(self, sliding_windows: List[Dict], features: List[Dict], **kwargs):
    388        """
    389        Create visualizations of sensor data with overlaid features.
    390
    391        Args:
    392            sliding_windows: List of sliding window dictionaries
    393            features: List of feature dictionaries
    394            **kwargs: Additional arguments including sensor_name, start_idx, end_idx, num_windows
    395        """
    396        sensor_name = kwargs.get('sensor_name', 'shank')
    397        start_idx = kwargs.get('start_idx', 0)
    398        end_idx = kwargs.get('end_idx', 1000)
    399        num_windows = kwargs.get('num_windows', 10)
    400        save = kwargs.get('save', False)
    401
    402        self._plot_sensor_with_features(sliding_windows, features, start_idx, end_idx,
    403                                      sensor_name, num_windows, save)

Create visualizations of sensor data with overlaid features.

Args: sliding_windows: List of sliding window dictionaries features: List of feature dictionaries **kwargs: Additional arguments including sensor_name, start_idx, end_idx, num_windows

##### Inherited Members

gaitsetpy.core.base_classes.BaseEDAAnalyzer
  name
  description
  configure
  get_info

def plot_thigh_data(daphnetThigh, daphnetNames, i): View Source

    19def plot_thigh_data(daphnetThigh, daphnetNames, i):
    20    """
    21    Plot thigh acceleration data for a specific dataset.
    22    Args:
    23        daphnetThigh (list): List of DataFrames containing thigh acceleration data.
    24        daphnetNames (list): List of dataset names.
    25        i (int): Index of the dataset to plot.
    26    """
    27    print(daphnetNames[i])
    28    fig, axes = plt.subplots(4, 1, sharex=True, sharey=True, figsize=(20, 16))
    29    fig.suptitle("Thigh Data from " + daphnetNames[i])
    30    plt.xlabel("Time")
    31
    32    df = daphnetThigh[i]
    33    df = df[df.annotations > 0]  # Filter out rows with no annotations
    34    neg = df[df.annotations == 1]  # No freeze
    35    pos = df[df.annotations == 2]  # Freeze
    36
    37    # Plot horizontal forward thigh acceleration
    38    ax1 = axes[0]
    39    ax1.plot(df.thigh_h_fd)
    40    ax1.set_ylabel("Horizontal Forward Thigh Acceleration")
    41    ax1.scatter(neg.index, neg.thigh_h_fd, c='orange', label="no freeze")
    42    ax1.scatter(pos.index, pos.thigh_h_fd, c='purple', label="freeze")
    43    ax1.legend()
    44
    45    # Plot vertical thigh acceleration
    46    ax2 = axes[1]
    47    ax2.plot(df.thigh_v)
    48    ax2.set_ylabel("Vertical Thigh Acceleration")
    49    ax2.scatter(neg.index, neg.thigh_v, c='orange', label="no freeze")
    50    ax2.scatter(pos.index, pos.thigh_v, c='purple', label="freeze")
    51    ax2.legend()
    52
    53    # Plot horizontal lateral thigh acceleration
    54    ax3 = axes[2]
    55    ax3.plot(df.thigh_h_l)
    56    ax3.set_ylabel("Horizontal Lateral Thigh Acceleration")
    57    ax3.scatter(neg.index, neg.thigh_h_l, c='orange', label="no freeze")
    58    ax3.scatter(pos.index, pos.thigh_h_l, c='purple', label="freeze")
    59    ax3.legend()
    60
    61    # Plot overall thigh acceleration
    62    ax4 = axes[3]
    63    ax4.plot(df.thigh)
    64    ax4.set_ylabel("Overall Thigh Acceleration")
    65    ax4.scatter(neg.index, neg.thigh, c='orange', label="no freeze")
    66    ax4.scatter(pos.index, pos.thigh, c='purple', label="freeze")
    67    ax4.legend()
    68
    69    plt.tight_layout()
    70    plt.show()

Plot thigh acceleration data for a specific dataset. Args: daphnetThigh (list): List of DataFrames containing thigh acceleration data. daphnetNames (list): List of dataset names. i (int): Index of the dataset to plot.

def plot_shank_data(daphnetShank, daphnetNames, i): View Source

     73def plot_shank_data(daphnetShank, daphnetNames, i):
     74    """
     75    Plot shank acceleration data for a specific dataset.
     76    Args:
     77        daphnetShank (list): List of DataFrames containing shank acceleration data.
     78        daphnetNames (list): List of dataset names.
     79        i (int): Index of the dataset to plot.
     80    """
     81    print(daphnetNames[i])
     82    fig, axes = plt.subplots(4, 1, sharex=True, sharey=True, figsize=(20, 16))
     83    fig.suptitle("Shank Data from " + daphnetNames[i])
     84    plt.xlabel("Time")
     85
     86    df = daphnetShank[i]
     87    df["shank"] = np.sqrt(df["shank_h_l"]**2 + df["shank_v"]**2 + df["shank_h_fd"]**2)
     88    df = df[df.annotations > 0]
     89    neg = df[df.annotations == 1]
     90    pos = df[df.annotations == 2]
     91
     92    ax1 = axes[0]
     93    ax1.plot(df.shank_h_fd)
     94    ax1.set_ylabel("Horizontal Forward Shank Acceleration")
     95    ax1.scatter(neg.index, neg.shank_h_fd, c='orange', label="no freeze")
     96    ax1.scatter(pos.index, pos.shank_h_fd, c='purple', label="freeze")
     97    ax1.legend()
     98
     99    ax2 = axes[1]
    100    ax2.plot(df.shank_v)
    101    ax2.set_ylabel("Vertical Shank Acceleration")
    102    ax2.scatter(neg.index, neg.shank_v, c='orange', label="no freeze")
    103    ax2.scatter(pos.index, pos.shank_v, c='purple', label="freeze")
    104    ax2.legend()
    105
    106    ax3 = axes[2]
    107    ax3.plot(df.shank_h_l)
    108    ax3.set_ylabel("Horizontal Lateral Shank Acceleration")
    109    ax3.scatter(neg.index, neg.shank_h_l, c='orange', label="no freeze")
    110    ax3.scatter(pos.index, pos.shank_h_l, c='purple', label="freeze")
    111    ax3.legend()
    112
    113    ax4 = axes[3]
    114    ax4.plot(df.shank)
    115    ax4.set_ylabel("Overall Shank Acceleration")
    116    ax4.scatter(neg.index, neg.shank, c='orange', label="no freeze")
    117    ax4.scatter(pos.index, pos.shank, c='purple', label="freeze")
    118    ax4.legend()
    119
    120    plt.tight_layout()
    121    plt.show()

Plot shank acceleration data for a specific dataset. Args: daphnetShank (list): List of DataFrames containing shank acceleration data. daphnetNames (list): List of dataset names. i (int): Index of the dataset to plot.

def plot_trunk_data(daphnetTrunk, daphnetNames, i): View Source

    124def plot_trunk_data(daphnetTrunk, daphnetNames, i):
    125    """
    126    Plot trunk acceleration data for a specific dataset.
    127    Args:
    128        daphnetTrunk (list): List of DataFrames containing trunk acceleration data.
    129        daphnetNames (list): List of dataset names.
    130        i (int): Index of the dataset to plot.
    131    """
    132    print(daphnetNames[i])
    133    fig, axes = plt.subplots(4, 1, sharex=True, sharey=True, figsize=(20, 16))
    134    fig.suptitle("Trunk Data from " + daphnetNames[i])
    135    plt.xlabel("Time")
    136
    137    df = daphnetTrunk[i]
    138    df["trunk"] = np.sqrt(df["trunk_h_l"]**2 + df["trunk_v"]**2 + df["trunk_h_fd"]**2)
    139    df = df[df.annotations > 0]
    140    neg = df[df.annotations == 1]
    141    pos = df[df.annotations == 2]
    142
    143    ax1 = axes[0]
    144    ax1.plot(df.trunk_h_fd)
    145    ax1.set_ylabel("Horizontal Forward Trunk Acceleration")
    146    ax1.scatter(neg.index, neg.trunk_h_fd, c='orange', label="no freeze")
    147    ax1.scatter(pos.index, pos.trunk_h_fd, c='purple', label="freeze")
    148    ax1.legend()
    149
    150    ax2 = axes[1]
    151    ax2.plot(df.trunk_v)
    152    ax2.set_ylabel("Vertical Trunk Acceleration")
    153    ax2.scatter(neg.index, neg.trunk_v, c='orange', label="no freeze")
    154    ax2.scatter(pos.index, pos.trunk_v, c='purple', label="freeze")
    155    ax2.legend()
    156
    157    ax3 = axes[2]
    158    ax3.plot(df.trunk_h_l)
    159    ax3.set_ylabel("Horizontal Lateral Trunk Acceleration")
    160    ax3.scatter(neg.index, neg.trunk_h_l, c='orange', label="no freeze")
    161    ax3.scatter(pos.index, pos.trunk_h_l, c='purple', label="freeze")
    162    ax3.legend()
    163
    164    ax4 = axes[3]
    165    ax4.plot(df.trunk)
    166    ax4.set_ylabel("Overall Trunk Acceleration")
    167    ax4.scatter(neg.index, neg.trunk, c='orange', label="no freeze")
    168    ax4.scatter(pos.index, pos.trunk, c='purple', label="freeze")
    169    ax4.legend()
    170
    171    plt.tight_layout()
    172    plt.show()

Plot trunk acceleration data for a specific dataset. Args: daphnetTrunk (list): List of DataFrames containing trunk acceleration data. daphnetNames (list): List of dataset names. i (int): Index of the dataset to plot.

def plot_all_data(daphnetThigh, daphnetShank, daphnetTrunk, daphnetNames, i): View Source

    191def plot_all_data(daphnetThigh, daphnetShank, daphnetTrunk, daphnetNames, i):
    192    """
    193    Plot thigh, shank, and trunk acceleration data for a specific dataset.
    194    Args:
    195        daphnetThigh (list): List of DataFrames containing thigh acceleration data.
    196        daphnetShank (list): List of DataFrames containing shank acceleration data.
    197        daphnetTrunk (list): List of DataFrames containing trunk acceleration data.
    198        daphnetNames (list): List of dataset names.
    199        i (int): Index of the dataset to plot.
    200    """
    201    plot_thigh_data(daphnetThigh, daphnetNames, i)
    202    plot_shank_data(daphnetShank, daphnetNames, i)
    203    plot_trunk_data(daphnetTrunk, daphnetNames, i)

Plot thigh, shank, and trunk acceleration data for a specific dataset. Args: daphnetThigh (list): List of DataFrames containing thigh acceleration data. daphnetShank (list): List of DataFrames containing shank acceleration data. daphnetTrunk (list): List of DataFrames containing trunk acceleration data. daphnetNames (list): List of dataset names. i (int): Index of the dataset to plot.

def plot_all_thigh_data(daphnetThigh, daphnetNames): View Source

    175def plot_all_thigh_data(daphnetThigh, daphnetNames):
    176    """Plot thigh acceleration data for all datasets."""
    177    for i in range(len(daphnetThigh)):
    178        plot_thigh_data(daphnetThigh, daphnetNames, i)

Plot thigh acceleration data for all datasets.

def plot_all_shank_data(daphnetShank, daphnetNames): View Source

    180def plot_all_shank_data(daphnetShank, daphnetNames):
    181    """Plot shank acceleration data for all datasets."""
    182    for i in range(len(daphnetShank)):
    183        plot_shank_data(daphnetShank, daphnetNames, i)

Plot shank acceleration data for all datasets.

def plot_all_trunk_data(daphnetTrunk, daphnetNames): View Source

    185def plot_all_trunk_data(daphnetTrunk, daphnetNames):
    186    """Plot trunk acceleration data for all datasets."""
    187    for i in range(len(daphnetTrunk)):
    188        plot_trunk_data(daphnetTrunk, daphnetNames, i)

Plot trunk acceleration data for all datasets.

def plot_all_datasets(daphnetThigh, daphnetShank, daphnetTrunk, daphnetNames): View Source

    205def plot_all_datasets(daphnetThigh, daphnetShank, daphnetTrunk, daphnetNames):
    206    """Plot thigh, shank, and trunk acceleration data for all datasets."""
    207    for i in range(len(daphnetThigh)):
    208        plot_all_data(daphnetThigh, daphnetShank, daphnetTrunk, daphnetNames, i)

Plot thigh, shank, and trunk acceleration data for all datasets.

def plot_sensor_with_features( sliding_windows, features, start_idx, end_idx, sensor_name='shank', num_windows=10, save=False): View Source

     11def plot_sensor_with_features(sliding_windows, features, start_idx, end_idx, sensor_name="shank", num_windows=10, save=False):
     12    """
     13    @brief Plots sliding windows of a sensor's time series data with overlaid statistical features.
     14
     15    This function plots the first `num_windows` sliding windows within the given `start_idx` and `end_idx`
     16    for a specified sensor and overlays feature values at their corresponding time indices.
     17    It also displays entropy and dominant frequency in a separate plot.
     18
     19    @param[in] sliding_windows List of dictionaries, where each dictionary contains:
     20                   - 'name': sensor name (str)
     21                   - 'data': List of time-series windows (each as a Pandas Series)
     22    @param[in] features List of dictionaries, where each dictionary contains:
     23                   - 'name': sensor name (str)
     24                   - 'features': Dictionary of extracted feature lists
     25    @param[in] start_idx Start index of the time window to be plotted.
     26    @param[in] end_idx End index of the time window to be plotted.
     27    @param[in] sensor_name Name of the sensor to be plotted (default: "shank").
     28    @param[in] num_windows Number of sliding windows to plot (default: 10).
     29    @param[in] save If True, saves the plot to a file instead of displaying it.
     30
     31    @return None
     32    """
     33
     34    fig, axes = plt.subplots(2, 1, figsize=(20, 10), gridspec_kw={'height_ratios': [3, 1]})
     35
     36    # Extract sensor windows
     37    sensor_windows = next((sw['data'] for sw in sliding_windows if sw['name'] == sensor_name), None)
     38    if sensor_windows is None:
     39        print(f"Sensor '{sensor_name}' not found in sliding_windows.")
     40        return
     41
     42    # Extract corresponding features
     43    sensor_features = next((feat['features'] for feat in features if feat['name'] == sensor_name), None)
     44    if sensor_features is None:
     45        print(f"Sensor '{sensor_name}' not found in features.")
     46        return
     47
     48    # Filter windows based on start_idx and end_idx
     49    filtered_windows = [series for series in sensor_windows if start_idx <= series.index[0] and series.index[-1] <= end_idx]
     50
     51    if not filtered_windows:
     52        print(f"No windows found in the specified index range ({start_idx} - {end_idx}).")
     53        return
     54
     55    # Store entropy & frequency features for separate plotting
     56    entropy_values = []
     57    dominant_frequencies = []
     58
     59    # Plot first `num_windows` windows
     60    for i in range(min(num_windows, len(filtered_windows))):
     61        series = filtered_windows[i]  # Each window is a Pandas Series
     62
     63        # Extract time and signal values
     64        time_values = series.index.to_numpy()  # Time is the index
     65        signal_values = series.values  # Sensor readings
     66
     67        # Determine actual start and end indices for this window
     68        window_start, window_end = time_values[0], time_values[-1]
     69
     70        # Plot time series data
     71        axes[0].plot(time_values, signal_values, alpha=0.6)
     72
     73        # Mark start and end of each window with vertical dotted lines
     74        axes[0].axvline(x=window_start, color='black', linestyle='dotted', alpha=0.7)
     75        axes[0].axvline(x=window_end, color='black', linestyle='dotted', alpha=0.7)
     76
     77        # Overlay statistical features
     78        for feature, marker in zip(['mean', 'rms', 'peak_height', 'mode', 'median'], ['x', 'o', 'v', '<', '^']):
     79            if feature in sensor_features and len(sensor_features[feature]) > i:
     80                feature_value = sensor_features[feature][i]
     81                if feature_value != 0:  # Skip zero values
     82                    closest_index = np.argmin(np.abs(signal_values - feature_value))
     83                    closest_time = time_values[closest_index]
     84                    axes[0].scatter(closest_time, feature_value, color='red', marker=marker, s=100)
     85
     86        # Store entropy & frequency features for separate plotting
     87        if 'entropy' in sensor_features and len(sensor_features['entropy']) > i:
     88            entropy_values.append(sensor_features['entropy'][i])
     89        if 'dominant_frequency' in sensor_features and len(sensor_features['dominant_frequency']) > i:
     90            dominant_frequencies.append(sensor_features['dominant_frequency'][i])
     91
     92    # Labels and title for time-series plot
     93    axes[0].set_xlabel('Time')
     94    axes[0].set_ylabel(f'{sensor_name} Signal')
     95    axes[0].set_title(f'First {num_windows} windows of {sensor_name} in range {start_idx}-{end_idx} with Features')
     96
     97    # Frequency-domain & entropy plot (axes[1])
     98    if dominant_frequencies:
     99        window_indices = list(range(len(dominant_frequencies)))
    100        axes[1].plot(window_indices, dominant_frequencies, label="Dominant Frequency", marker="o", linestyle="dashed", color="blue")
    101
    102    if entropy_values:
    103        axes[1].bar(window_indices, entropy_values, alpha=0.6, label="Entropy", color="green")
    104
    105    axes[1].set_xlabel("Window Index")
    106    axes[1].set_ylabel("Feature Value")
    107    axes[1].set_title("Frequency & Entropy Features")
    108    axes[1].legend()
    109
    110    plt.tight_layout()
    111
    112    # Save or show plot
    113    if save:
    114        file_path = input("Enter the file path to save the plot (e.g., 'plot.png'): ")
    115        plt.savefig(file_path, dpi=300)
    116        print(f"Plot saved at {file_path}")
    117    else:
    118        plt.show()

@brief Plots sliding windows of a sensor's time series data with overlaid statistical features.

This function plots the first `num_windows` sliding windows within the given `start_idx` and `end_idx` for a specified sensor and overlays feature values at their corresponding time indices. It also displays entropy and dominant frequency in a separate plot.

@param[in] sliding_windows List of dictionaries, where each dictionary contains: - 'name': sensor name (str) - 'data': List of time-series windows (each as a Pandas Series) @param[in] features List of dictionaries, where each dictionary contains: - 'name': sensor name (str) - 'features': Dictionary of extracted feature lists @param[in] start_idx Start index of the time window to be plotted. @param[in] end_idx End index of the time window to be plotted. @param[in] sensor_name Name of the sensor to be plotted (default: "shank"). @param[in] num_windows Number of sliding windows to plot (default: 10). @param[in] save If True, saves the plot to a file instead of displaying it.

@return None

def get_eda_manager(): View Source

    51def get_eda_manager():
    52    """Get the singleton EDAManager instance."""
    53    return EDAManager()

Get the singleton EDAManager instance.

def get_available_analyzers(): View Source

    56def get_available_analyzers():
    57    """Get list of available EDA analyzer names."""
    58    return EDAManager().get_available_components()

Get list of available EDA analyzer names.

def analyze_data(analyzer_name: str, data, **kwargs): View Source

    61def analyze_data(analyzer_name: str, data, **kwargs):
    62    """
    63    Analyze data using the EDAManager.
    64
    65    Args:
    66        analyzer_name: Name of the EDA analyzer
    67        data: Input data to analyze
    68        **kwargs: Additional arguments for analysis
    69
    70    Returns:
    71        Analysis results dictionary
    72    """
    73    return EDAManager().analyze_data(analyzer_name, data, **kwargs)

Analyze data using the EDAManager.

Args: analyzer_name: Name of the EDA analyzer data: Input data to analyze **kwargs: Additional arguments for analysis

Returns: Analysis results dictionary

def visualize_data(analyzer_name: str, data, **kwargs): View Source

    76def visualize_data(analyzer_name: str, data, **kwargs):
    77    """
    78    Create visualizations using the EDAManager.
    79
    80    Args:
    81        analyzer_name: Name of the EDA analyzer
    82        data: Input data to visualize
    83        **kwargs: Additional arguments for visualization
    84    """
    85    return EDAManager().visualize_data(analyzer_name, data, **kwargs)

Create visualizations using the EDAManager.

Args: analyzer_name: Name of the EDA analyzer data: Input data to visualize **kwargs: Additional arguments for visualization

def plot_daphnet_data(data, names=None, sensor_type='all', dataset_index=0): View Source

    88def plot_daphnet_data(data, names=None, sensor_type='all', dataset_index=0):
    89    """
    90    Plot Daphnet dataset using the DaphnetVisualizationAnalyzer.
    91
    92    Args:
    93        data: Input data (DataFrame or list of DataFrames)
    94        names: List of dataset names
    95        sensor_type: Type of sensor to plot ('all', 'thigh', 'shank', 'trunk')
    96        dataset_index: Index of dataset to plot (if data is a list)
    97    """
    98    analyzer = DaphnetVisualizationAnalyzer()
    99    analyzer.visualize(data, sensor_type=sensor_type, dataset_index=dataset_index, names=names or [])

Plot Daphnet dataset using the DaphnetVisualizationAnalyzer.

Args: data: Input data (DataFrame or list of DataFrames) names: List of dataset names sensor_type: Type of sensor to plot ('all', 'thigh', 'shank', 'trunk') dataset_index: Index of dataset to plot (if data is a list)

def analyze_sensor_statistics(data): View Source

    101def analyze_sensor_statistics(data):
    102    """
    103    Analyze sensor statistics using the SensorStatisticsAnalyzer.
    104
    105    Args:
    106        data: Input data (DataFrame or list of DataFrames)
    107
    108    Returns:
    109        Dictionary containing statistical analysis results
    110    """
    111    analyzer = SensorStatisticsAnalyzer()
    112    return analyzer.analyze(data)

Analyze sensor statistics using the SensorStatisticsAnalyzer.

Args: data: Input data (DataFrame or list of DataFrames)

Returns: Dictionary containing statistical analysis results

def plot_sensor_features( sliding_windows, features, sensor_name='shank', start_idx=0, end_idx=1000, num_windows=10): View Source

    114def plot_sensor_features(sliding_windows, features, sensor_name='shank', start_idx=0, end_idx=1000, num_windows=10):
    115    """
    116    Plot sensor data with overlaid features using the SensorStatisticsAnalyzer.
    117
    118    Args:
    119        sliding_windows: List of sliding window dictionaries
    120        features: List of feature dictionaries
    121        sensor_name: Name of the sensor to plot
    122        start_idx: Start index of the time window
    123        end_idx: End index of the time window
    124        num_windows: Number of sliding windows to plot
    125    """
    126    analyzer = SensorStatisticsAnalyzer()
    127    analyzer.visualize(sliding_windows, features, sensor_name=sensor_name,
    128                      start_idx=start_idx, end_idx=end_idx, num_windows=num_windows)

Plot sensor data with overlaid features using the SensorStatisticsAnalyzer.

Args: sliding_windows: List of sliding window dictionaries features: List of feature dictionaries sensor_name: Name of the sensor to plot start_idx: Start index of the time window end_idx: End index of the time window num_windows: Number of sliding windows to plot