Datasets:

ctorre
/

self-calibrating-bci

#!/usr/bin/env python3
"""
Example script demonstrating how to load and explore the Self-Calibrating BCI Dataset (NeurIPS 2025).
This script shows:
1. How to open and read the HDF5 file
2. How to access the data arrays
3. How to read embedded metadata
4. Basic data exploration and statistics
Requirements:
    # Using uv (recommended)
    uv sync
    uv run python example_load_data.py
    # Or using pip
    pip install ... (check pyproject.toml)
"""

from enum import StrEnum, auto, unique
from pathlib import Path

import h5py
import numpy as np
from pydantic import BaseModel, ConfigDict, Field

_ROOT_PATH = Path(__file__).parent
_DATA_DIR_PATH = _ROOT_PATH / "data"
_DATA_FILE_PATH = _DATA_DIR_PATH / "eeg-net.h5"

_SEPARATOR = "=" * 60


class _Data(BaseModel):
    """Container for sample data arrays."""

    model_config = ConfigDict(frozen=True, arbitrary_types_allowed=True)

    target_faces: np.ndarray = Field(..., description="Target face latent vectors")
    observed_faces: np.ndarray = Field(..., description="Observed face latent vectors")
    eeg_features: np.ndarray = Field(..., description="EEG feature vectors")


# Rebuild model to handle forward references
_Data.model_rebuild()


@unique
class _RootMetadataKeys(StrEnum):
    """Root-level metadata keys in the HDF5 file."""

    TITLE = auto()
    PAPER_TITLE = auto()
    AUTHORS = auto()
    YEAR = auto()
    CONFERENCE = auto()
    LICENSE = auto()
    CONTACT_EMAIL = auto()


@unique
class _DatasetMetadataKeys(StrEnum):
    """Dataset-level metadata keys in the HDF5 file."""

    DESCRIPTION = auto()
    DIMENSIONS = auto()
    LATENT_DIM = auto()
    GAN_MODEL = auto()
    VALUE_RANGE = auto()


def _print_separator(title: str = "") -> None:
    """Print a formatted separator line.
    Args:
        title: Optional title to center in the separator
    """
    if title:
        print("\n{}".format(_SEPARATOR))
        print("{}".format(title).center(60))
        print("{}".format(_SEPARATOR))
    else:
        print("{}".format(_SEPARATOR))


def display_dataset_overview(file: h5py.File) -> None:
    """Display basic dataset information.
    Args:
        file: Open HDF5 file handle
    """
    _print_separator("Dataset Overview")

    print("\nAvailable datasets: {}".format(list(file.keys())))
    print("Number of samples: {}".format(file.attrs["n_samples"]))

    # Show dataset shapes
    print("\nDataset shapes:")
    for key in file.keys():
        shape = file[key].shape
        dtype = str(file[key].dtype)
        size_mb = file[key].nbytes / (1024**2)
        print(
            "  {:20s}: {:20s} {:8s} ({:5.1f} MB)".format(
                key,
                str(shape),
                dtype,
                size_mb,
            )
        )


def display_metadata(file: h5py.File, max_length: int = 80) -> None:
    """Display root-level metadata from the HDF5 file.
    Args:
        file: Open HDF5 file handle
        max_length: Maximum length for string values before truncation
    """
    _print_separator("Metadata")

    for attr in _RootMetadataKeys:
        attr_value = attr.value
        if attr_value in file.attrs:
            value = file.attrs[attr_value]
            # Truncate long values
            if isinstance(value, str) and len(value) > max_length:
                value = value[: max_length - 3] + "..."
            print("  {:20s}: {}".format(attr_value, value))


def _load_sample_data(
    file: h5py.File,
    *,
    n_samples: int = 100,
) -> _Data:
    """Load a sample of data for exploration.
    Args:
        file: Open HDF5 file handle
        n_samples: Number of samples to load (default: 100)
    Returns:
        Data container with target_faces, observed_faces, and eeg_features
    """
    _print_separator("Data Exploration")

    print("\nLoading first {} samples for exploration...".format(n_samples))
    target_faces = file["target_faces"][:n_samples]
    observed_faces = file["observed_faces"][:n_samples]
    eeg_features = file["eeg_net"][:n_samples]

    print("  Loaded target_faces: {}".format(target_faces.shape))
    print("  Loaded observed_faces: {}".format(observed_faces.shape))
    print("  Loaded eeg_features: {}".format(eeg_features.shape))

    return _Data(
        target_faces=target_faces,
        observed_faces=observed_faces,
        eeg_features=eeg_features,
    )


def compute_statistics(
    *,
    target_faces: np.ndarray,
    observed_faces: np.ndarray,
    eeg_features: np.ndarray,
) -> None:
    """Compute and display statistics on the sample data.
    Args:
        target_faces: Target face latent vectors
        observed_faces: Observed face latent vectors
        eeg_features: EEG feature vectors
    """
    _print_separator("Data Statistics (first 100 samples)")

    # Face distances (BCI performance metric)
    distances = np.linalg.norm(target_faces - observed_faces, axis=1)

    print("\nFace distances (target vs observed):")
    print("  Mean distance:   {:.4f}".format(distances.mean()))
    print("  Median distance: {:.4f}".format(np.median(distances)))
    print("  Std distance:    {:.4f}".format(distances.std()))
    print("  Min distance:    {:.4f}".format(distances.min()))
    print("  Max distance:    {:.4f}".format(distances.max()))

    # EEG feature statistics
    print("\nEEG features statistics:")
    print("  Mean: {:.6f}".format(eeg_features.mean()))
    print("  Std:  {:.6f}".format(eeg_features.std()))
    print("  Min:  {:.6f}".format(eeg_features.min()))
    print("  Max:  {:.6f}".format(eeg_features.max()))


def _display_dataset_metadata(
    file: h5py.File,
    *,
    dataset_name: str = "target_faces",
) -> None:
    """Display metadata for a specific dataset.
    Args:
        file: Open HDF5 file handle
        dataset_name: Name of the dataset to display metadata for
    """
    _print_separator("Dataset-Specific Metadata")

    formatted_name = dataset_name.capitalize().replace("_", " ")
    print("\n{} metadata:".format(formatted_name))
    ds = file[dataset_name]

    for key in _DatasetMetadataKeys:
        key_value = key.value
        if key_value in ds.attrs:
            value = ds.attrs[key_value]
            if isinstance(value, str) and len(value) > 60:
                value = value[:57] + "..."
            print("  {:15s}: {}".format(key_value, value))


def _load_and_explore_dataset(filepath: Path = _DATA_FILE_PATH) -> None:
    """Orchestrate loading and exploring the dataset.
    This function coordinates all the individual display functions to provide
    a complete overview of the dataset.
    Args:
        filepath: Path to the HDF5 data file
    """
    _print_separator("Self-Calibrating BCI Dataset (NeurIPS 2025)")
    print("Loading: {}".format(filepath))

    with h5py.File(str(filepath), "r") as f:
        # Display basic information
        display_dataset_overview(f)

        # Display metadata
        display_metadata(f)

        # Load sample data
        data = _load_sample_data(f, n_samples=100)

        # Compute and display statistics
        compute_statistics(
            target_faces=data.target_faces,
            observed_faces=data.observed_faces,
            eeg_features=data.eeg_features,
        )

        # Display dataset-specific metadata
        _display_dataset_metadata(f, dataset_name="target_faces")

        _print_separator()
        print("\n✅ Dataset loaded and explored successfully!")
        print()


def main() -> None:
    """Main entry point with error handling."""
    try:
        _load_and_explore_dataset(_DATA_FILE_PATH)
    except FileNotFoundError:
        print("\n❌ Error: {} not found!".format(_DATA_FILE_PATH))
        print("\nPlease ensure the data file is in the correct location.")
    except ImportError as e:
        print("\n❌ Error: Missing required package: {}".format(e))
        print("\nPlease install required packages:")
        print("  uv sync  (recommended)")
        print("  or: pip install ... (check pyproject.toml)")
    except Exception as e:
        print("\n❌ Error: {}".format(e))
        import traceback

        traceback.print_exc()


if __name__ == "__main__":
    main()

	#!/usr/bin/env python3
	"""
	Example script demonstrating how to load and explore the Self-Calibrating BCI Dataset (NeurIPS 2025).

	This script shows:
	1. How to open and read the HDF5 file
	2. How to access the data arrays
	3. How to read embedded metadata
	4. Basic data exploration and statistics

	Requirements:
	# Using uv (recommended)
	uv sync
	uv run python example_load_data.py

	# Or using pip
	pip install ... (check pyproject.toml)
	"""

	from enum import StrEnum, auto, unique
	from pathlib import Path

	import h5py
	import numpy as np
	from pydantic import BaseModel, ConfigDict, Field

	_ROOT_PATH = Path(__file__).parent
	_DATA_DIR_PATH = _ROOT_PATH / "data"
	_DATA_FILE_PATH = _DATA_DIR_PATH / "eeg-net.h5"

	_SEPARATOR = "=" * 60


	class _Data(BaseModel):
	"""Container for sample data arrays."""

	model_config = ConfigDict(frozen=True, arbitrary_types_allowed=True)

	target_faces: np.ndarray = Field(..., description="Target face latent vectors")
	observed_faces: np.ndarray = Field(..., description="Observed face latent vectors")
	eeg_features: np.ndarray = Field(..., description="EEG feature vectors")


	# Rebuild model to handle forward references
	_Data.model_rebuild()


	@unique
	class _RootMetadataKeys(StrEnum):
	"""Root-level metadata keys in the HDF5 file."""

	TITLE = auto()
	PAPER_TITLE = auto()
	AUTHORS = auto()
	YEAR = auto()
	CONFERENCE = auto()
	LICENSE = auto()
	CONTACT_EMAIL = auto()


	@unique
	class _DatasetMetadataKeys(StrEnum):
	"""Dataset-level metadata keys in the HDF5 file."""

	DESCRIPTION = auto()
	DIMENSIONS = auto()
	LATENT_DIM = auto()
	GAN_MODEL = auto()
	VALUE_RANGE = auto()


	def _print_separator(title: str = "") -> None:
	"""Print a formatted separator line.

	Args:
	title: Optional title to center in the separator
	"""
	if title:
	print("\n{}".format(_SEPARATOR))
	print("{}".format(title).center(60))
	print("{}".format(_SEPARATOR))
	else:
	print("{}".format(_SEPARATOR))


	def display_dataset_overview(file: h5py.File) -> None:
	"""Display basic dataset information.

	Args:
	file: Open HDF5 file handle
	"""
	_print_separator("Dataset Overview")

	print("\nAvailable datasets: {}".format(list(file.keys())))
	print("Number of samples: {}".format(file.attrs["n_samples"]))

	# Show dataset shapes
	print("\nDataset shapes:")
	for key in file.keys():
	shape = file[key].shape
	dtype = str(file[key].dtype)
	size_mb = file[key].nbytes / (1024**2)
	print(
	" {:20s}: {:20s} {:8s} ({:5.1f} MB)".format(
	key,
	str(shape),
	dtype,
	size_mb,
	)
	)


	def display_metadata(file: h5py.File, max_length: int = 80) -> None:
	"""Display root-level metadata from the HDF5 file.

	Args:
	file: Open HDF5 file handle
	max_length: Maximum length for string values before truncation
	"""
	_print_separator("Metadata")

	for attr in _RootMetadataKeys:
	attr_value = attr.value
	if attr_value in file.attrs:
	value = file.attrs[attr_value]
	# Truncate long values
	if isinstance(value, str) and len(value) > max_length:
	value = value[: max_length - 3] + "..."
	print(" {:20s}: {}".format(attr_value, value))


	def _load_sample_data(
	file: h5py.File,
	*,
	n_samples: int = 100,
	) -> _Data:
	"""Load a sample of data for exploration.

	Args:
	file: Open HDF5 file handle
	n_samples: Number of samples to load (default: 100)

	Returns:
	Data container with target_faces, observed_faces, and eeg_features
	"""
	_print_separator("Data Exploration")

	print("\nLoading first {} samples for exploration...".format(n_samples))
	target_faces = file["target_faces"][:n_samples]
	observed_faces = file["observed_faces"][:n_samples]
	eeg_features = file["eeg_net"][:n_samples]

	print(" Loaded target_faces: {}".format(target_faces.shape))
	print(" Loaded observed_faces: {}".format(observed_faces.shape))
	print(" Loaded eeg_features: {}".format(eeg_features.shape))

	return _Data(
	target_faces=target_faces,
	observed_faces=observed_faces,
	eeg_features=eeg_features,
	)


	def compute_statistics(
	*,
	target_faces: np.ndarray,
	observed_faces: np.ndarray,
	eeg_features: np.ndarray,
	) -> None:
	"""Compute and display statistics on the sample data.

	Args:
	target_faces: Target face latent vectors
	observed_faces: Observed face latent vectors
	eeg_features: EEG feature vectors
	"""
	_print_separator("Data Statistics (first 100 samples)")

	# Face distances (BCI performance metric)
	distances = np.linalg.norm(target_faces - observed_faces, axis=1)

	print("\nFace distances (target vs observed):")
	print(" Mean distance: {:.4f}".format(distances.mean()))
	print(" Median distance: {:.4f}".format(np.median(distances)))
	print(" Std distance: {:.4f}".format(distances.std()))
	print(" Min distance: {:.4f}".format(distances.min()))
	print(" Max distance: {:.4f}".format(distances.max()))

	# EEG feature statistics
	print("\nEEG features statistics:")
	print(" Mean: {:.6f}".format(eeg_features.mean()))
	print(" Std: {:.6f}".format(eeg_features.std()))
	print(" Min: {:.6f}".format(eeg_features.min()))
	print(" Max: {:.6f}".format(eeg_features.max()))


	def _display_dataset_metadata(
	file: h5py.File,
	*,
	dataset_name: str = "target_faces",
	) -> None:
	"""Display metadata for a specific dataset.

	Args:
	file: Open HDF5 file handle
	dataset_name: Name of the dataset to display metadata for
	"""
	_print_separator("Dataset-Specific Metadata")

	formatted_name = dataset_name.capitalize().replace("_", " ")
	print("\n{} metadata:".format(formatted_name))
	ds = file[dataset_name]

	for key in _DatasetMetadataKeys:
	key_value = key.value
	if key_value in ds.attrs:
	value = ds.attrs[key_value]
	if isinstance(value, str) and len(value) > 60:
	value = value[:57] + "..."
	print(" {:15s}: {}".format(key_value, value))


	def _load_and_explore_dataset(filepath: Path = _DATA_FILE_PATH) -> None:
	"""Orchestrate loading and exploring the dataset.

	This function coordinates all the individual display functions to provide
	a complete overview of the dataset.

	Args:
	filepath: Path to the HDF5 data file
	"""
	_print_separator("Self-Calibrating BCI Dataset (NeurIPS 2025)")
	print("Loading: {}".format(filepath))

	with h5py.File(str(filepath), "r") as f:
	# Display basic information
	display_dataset_overview(f)

	# Display metadata
	display_metadata(f)

	# Load sample data
	data = _load_sample_data(f, n_samples=100)

	# Compute and display statistics
	compute_statistics(
	target_faces=data.target_faces,
	observed_faces=data.observed_faces,
	eeg_features=data.eeg_features,
	)

	# Display dataset-specific metadata
	_display_dataset_metadata(f, dataset_name="target_faces")

	_print_separator()
	print("\n✅ Dataset loaded and explored successfully!")
	print()


	def main() -> None:
	"""Main entry point with error handling."""
	try:
	_load_and_explore_dataset(_DATA_FILE_PATH)
	except FileNotFoundError:
	print("\n❌ Error: {} not found!".format(_DATA_FILE_PATH))
	print("\nPlease ensure the data file is in the correct location.")
	except ImportError as e:
	print("\n❌ Error: Missing required package: {}".format(e))
	print("\nPlease install required packages:")
	print(" uv sync (recommended)")
	print(" or: pip install ... (check pyproject.toml)")
	except Exception as e:
	print("\n❌ Error: {}".format(e))
	import traceback

	traceback.print_exc()


	if __name__ == "__main__":
	main()