Utilities

Natural_Neighbor

Bases: object

Natural Neighbor algorithm implementation for finding natural neighbors in a dataset.

This class implements the Natural Neighbor algorithm which finds mutual neighbors in a dataset by iteratively expanding the neighborhood radius until convergence.

Source code in pythoneeg/core/utils.py

class Natural_Neighbor(object):
    """
    Natural Neighbor algorithm implementation for finding natural neighbors in a dataset.

    This class implements the Natural Neighbor algorithm which finds mutual neighbors
    in a dataset by iteratively expanding the neighborhood radius until convergence.
    """

    def __init__(self):
        """
        Initialize the Natural Neighbor algorithm.

        Attributes:
            nan_edges (dict): Graph of mutual neighbors
            nan_num (dict): Number of natural neighbors for each instance
            repeat (dict): Data structure that counts repetitions of the count method
            target (list): Set of classes
            data (list): Set of instances
            knn (dict): Structure that stores neighbors of each instance
        """
        self.nan_edges = {}  # Graph of mutual neighbors
        self.nan_num = {}  # Number of natural neighbors for each instance
        self.repeat = {}  # Data structure that counts repetitions of the count method
        self.target = []  # Set of classes
        self.data = []  # Set of instances
        self.knn = {}  # Structure that stores neighbors of each instance

    def load(self, filename):
        """
        Load dataset from a CSV file, separating attributes and classes.

        Args:
            filename (str): Path to the CSV file containing the dataset
        """
        aux = []
        with open(filename, "r") as dataset:
            data = list(csv.reader(dataset))
            for inst in data:
                inst_class = inst.pop(-1)
                self.target.append(inst_class)
                row = [float(x) for x in inst]
                aux.append(row)
        self.data = np.array(aux)

    def read(self, data: np.ndarray):
        """
        Load data directly from a numpy array.

        Args:
            data (np.ndarray): Input data array
        """
        self.data = data

    def asserts(self):
        """
        Initialize data structures for the algorithm.

        Sets up the necessary data structures including:
        - nan_edges as an empty set
        - knn, nan_num, and repeat dictionaries for each instance
        """
        self.nan_edges = set()
        for j in range(len(self.data)):
            self.knn[j] = set()
            self.nan_num[j] = 0
            self.repeat[j] = 0

    def count(self):
        """
        Count the number of instances that have no natural neighbors.

        Returns:
            int: Number of instances with zero natural neighbors
        """
        nan_zeros = 0
        for x in self.nan_num:
            if self.nan_num[x] == 0:
                nan_zeros += 1
        return nan_zeros

    def findKNN(self, inst, r, tree):
        """
        Find the indices of the k nearest neighbors.

        Args:
            inst: Instance to find neighbors for
            r (int): Radius/parameter for neighbor search
            tree: KDTree object for efficient neighbor search

        Returns:
            np.ndarray: Array of neighbor indices (excluding the instance itself)
        """
        _, ind = tree.query([inst], r + 1)
        return np.delete(ind[0], 0)

    def algorithm(self):
        """
        Execute the Natural Neighbor algorithm.

        The algorithm iteratively expands the neighborhood radius until convergence,
        finding mutual neighbors between instances.

        Returns:
            int: The final radius value when convergence is reached
        """
        # Initialize KDTree for efficient neighbor search
        tree = KDTree(self.data)
        self.asserts()
        flag = 0
        r = 1

        while flag == 0:
            for i in range(len(self.data)):
                knn = self.findKNN(self.data[i], r, tree)
                n = knn[-1]
                self.knn[i].add(n)
                if i in self.knn[n] and (i, n) not in self.nan_edges:
                    self.nan_edges.add((i, n))
                    self.nan_edges.add((n, i))
                    self.nan_num[i] += 1
                    self.nan_num[n] += 1

            cnt = self.count()
            rep = self.repeat[cnt]
            self.repeat[cnt] += 1
            if cnt == 0 or rep >= math.sqrt(r - rep):
                flag = 1
            else:
                r += 1
        return r

__init__()

Initialize the Natural Neighbor algorithm.

Attributes:

Name	Type	Description
nan_edges	dict	Graph of mutual neighbors
nan_num	dict	Number of natural neighbors for each instance
repeat	dict	Data structure that counts repetitions of the count method
target	list	Set of classes
data	list	Set of instances
knn	dict	Structure that stores neighbors of each instance

Source code in pythoneeg/core/utils.py

def __init__(self):
    """
    Initialize the Natural Neighbor algorithm.

    Attributes:
        nan_edges (dict): Graph of mutual neighbors
        nan_num (dict): Number of natural neighbors for each instance
        repeat (dict): Data structure that counts repetitions of the count method
        target (list): Set of classes
        data (list): Set of instances
        knn (dict): Structure that stores neighbors of each instance
    """
    self.nan_edges = {}  # Graph of mutual neighbors
    self.nan_num = {}  # Number of natural neighbors for each instance
    self.repeat = {}  # Data structure that counts repetitions of the count method
    self.target = []  # Set of classes
    self.data = []  # Set of instances
    self.knn = {}  # Structure that stores neighbors of each instance

algorithm()

Execute the Natural Neighbor algorithm.

The algorithm iteratively expands the neighborhood radius until convergence, finding mutual neighbors between instances.

Returns:

Name	Type	Description
int		The final radius value when convergence is reached

Source code in pythoneeg/core/utils.py

def algorithm(self):
    """
    Execute the Natural Neighbor algorithm.

    The algorithm iteratively expands the neighborhood radius until convergence,
    finding mutual neighbors between instances.

    Returns:
        int: The final radius value when convergence is reached
    """
    # Initialize KDTree for efficient neighbor search
    tree = KDTree(self.data)
    self.asserts()
    flag = 0
    r = 1

    while flag == 0:
        for i in range(len(self.data)):
            knn = self.findKNN(self.data[i], r, tree)
            n = knn[-1]
            self.knn[i].add(n)
            if i in self.knn[n] and (i, n) not in self.nan_edges:
                self.nan_edges.add((i, n))
                self.nan_edges.add((n, i))
                self.nan_num[i] += 1
                self.nan_num[n] += 1

        cnt = self.count()
        rep = self.repeat[cnt]
        self.repeat[cnt] += 1
        if cnt == 0 or rep >= math.sqrt(r - rep):
            flag = 1
        else:
            r += 1
    return r

asserts()

Initialize data structures for the algorithm.

Sets up the necessary data structures including: - nan_edges as an empty set - knn, nan_num, and repeat dictionaries for each instance

Source code in pythoneeg/core/utils.py

def asserts(self):
    """
    Initialize data structures for the algorithm.

    Sets up the necessary data structures including:
    - nan_edges as an empty set
    - knn, nan_num, and repeat dictionaries for each instance
    """
    self.nan_edges = set()
    for j in range(len(self.data)):
        self.knn[j] = set()
        self.nan_num[j] = 0
        self.repeat[j] = 0

count()

Count the number of instances that have no natural neighbors.

Returns:

Name	Type	Description
int		Number of instances with zero natural neighbors

Source code in pythoneeg/core/utils.py

def count(self):
    """
    Count the number of instances that have no natural neighbors.

    Returns:
        int: Number of instances with zero natural neighbors
    """
    nan_zeros = 0
    for x in self.nan_num:
        if self.nan_num[x] == 0:
            nan_zeros += 1
    return nan_zeros

findKNN(inst, r, tree)

Find the indices of the k nearest neighbors.

Parameters:

Name	Type	Description	Default
inst		Instance to find neighbors for	required
r	int	Radius/parameter for neighbor search	required
tree		KDTree object for efficient neighbor search	required

Returns:

Type	Description
	np.ndarray: Array of neighbor indices (excluding the instance itself)

Source code in pythoneeg/core/utils.py

def findKNN(self, inst, r, tree):
    """
    Find the indices of the k nearest neighbors.

    Args:
        inst: Instance to find neighbors for
        r (int): Radius/parameter for neighbor search
        tree: KDTree object for efficient neighbor search

    Returns:
        np.ndarray: Array of neighbor indices (excluding the instance itself)
    """
    _, ind = tree.query([inst], r + 1)
    return np.delete(ind[0], 0)

load(filename)

Load dataset from a CSV file, separating attributes and classes.

Parameters:

Name	Type	Description	Default
filename	str	Path to the CSV file containing the dataset	required

Source code in pythoneeg/core/utils.py

def load(self, filename):
    """
    Load dataset from a CSV file, separating attributes and classes.

    Args:
        filename (str): Path to the CSV file containing the dataset
    """
    aux = []
    with open(filename, "r") as dataset:
        data = list(csv.reader(dataset))
        for inst in data:
            inst_class = inst.pop(-1)
            self.target.append(inst_class)
            row = [float(x) for x in inst]
            aux.append(row)
    self.data = np.array(aux)

read(data)

Load data directly from a numpy array.

Parameters:

Name	Type	Description	Default
data	ndarray	Input data array	required

Source code in pythoneeg/core/utils.py

def read(self, data: np.ndarray):
    """
    Load data directly from a numpy array.

    Args:
        data (np.ndarray): Input data array
    """
    self.data = data

TimestampMapper

Map each fragment to its source file's timestamp.

This class provides functionality to map data fragments back to their original file timestamps when data has been concatenated from multiple files with different recording times.

Attributes:

Name	Type	Description
file_end_datetimes	list[datetime]	The end datetimes of each source file.
file_durations	list[float]	The durations of each source file in seconds.
file_start_datetimes	list[datetime]	Computed start datetimes of each file.
cumulative_durations	ndarray	Cumulative sum of file durations.

Examples:

>>> from datetime import datetime, timedelta
>>> # Set up files with known end times and durations
>>> end_times = [datetime(2023, 1, 1, 12, 0), datetime(2023, 1, 1, 13, 0)]
>>> durations = [3600.0, 1800.0]  # 1 hour, 30 minutes
>>> mapper = TimestampMapper(end_times, durations)
>>>
>>> # Get timestamp for fragment at index 2 with 60s fragments
>>> timestamp = mapper.get_fragment_timestamp(2, 60.0)
>>> print(timestamp)
2023-01-01 11:02:00

Source code in pythoneeg/core/utils.py

class TimestampMapper:
    """
    Map each fragment to its source file's timestamp.

    This class provides functionality to map data fragments back to their original
    file timestamps when data has been concatenated from multiple files with
    different recording times.

    Attributes:
        file_end_datetimes (list[datetime]): The end datetimes of each source file.
        file_durations (list[float]): The durations of each source file in seconds.
        file_start_datetimes (list[datetime]): Computed start datetimes of each file.
        cumulative_durations (np.ndarray): Cumulative sum of file durations.

    Examples:
        >>> from datetime import datetime, timedelta
        >>> # Set up files with known end times and durations
        >>> end_times = [datetime(2023, 1, 1, 12, 0), datetime(2023, 1, 1, 13, 0)]
        >>> durations = [3600.0, 1800.0]  # 1 hour, 30 minutes
        >>> mapper = TimestampMapper(end_times, durations)
        >>>
        >>> # Get timestamp for fragment at index 2 with 60s fragments
        >>> timestamp = mapper.get_fragment_timestamp(2, 60.0)
        >>> print(timestamp)
        2023-01-01 11:02:00
    """

    def __init__(self, file_end_datetimes: list[datetime], file_durations: list[float]):
        """
        Initialize the TimestampMapper.

        Args:
            file_end_datetimes (list[datetime]): The end datetimes of each file.
            file_durations (list[float]): The durations of each file in seconds.

        Raises:
            ValueError: If the lengths of file_end_datetimes and file_durations don't match.
        """
        if len(file_end_datetimes) != len(file_durations):
            raise ValueError("file_end_datetimes and file_durations must have the same length")

        self.file_end_datetimes = file_end_datetimes
        self.file_durations = file_durations

        self.file_start_datetimes = [
            file_end_datetime - timedelta(seconds=file_duration)
            for file_end_datetime, file_duration in zip(self.file_end_datetimes, self.file_durations)
        ]
        self.cumulative_durations = np.cumsum(self.file_durations)

    def get_fragment_timestamp(self, fragment_idx: int, fragment_len_s: float) -> datetime:
        """
        Get the timestamp for a specific fragment based on its index and length.

        Args:
            fragment_idx (int): The index of the fragment (0-based).
            fragment_len_s (float): The length of each fragment in seconds.

        Returns:
            datetime: The timestamp corresponding to the start of the specified fragment.

        Examples:
            >>> # Get timestamp for the 5th fragment (index 4) with 30-second fragments
            >>> timestamp = mapper.get_fragment_timestamp(4, 30.0)
            >>> # This returns the timestamp 2 minutes into the first file
        """
        # Find which file this fragment belongs to
        fragment_start_time = fragment_idx * fragment_len_s
        file_idx = np.searchsorted(self.cumulative_durations, fragment_start_time)
        file_idx = min(file_idx, len(self.cumulative_durations) - 1)

        offset_in_file = fragment_start_time - self.cumulative_durations[file_idx]  # Negative

        # Return actual timestamp + offset
        return self.file_end_datetimes[file_idx] + timedelta(seconds=offset_in_file)

__init__(file_end_datetimes, file_durations)

Initialize the TimestampMapper.

Parameters:

Name	Type	Description	Default
file_end_datetimes	list[datetime]	The end datetimes of each file.	required
file_durations	list[float]	The durations of each file in seconds.	required

Raises:

Type	Description
ValueError	If the lengths of file_end_datetimes and file_durations don't match.

Source code in pythoneeg/core/utils.py

def __init__(self, file_end_datetimes: list[datetime], file_durations: list[float]):
    """
    Initialize the TimestampMapper.

    Args:
        file_end_datetimes (list[datetime]): The end datetimes of each file.
        file_durations (list[float]): The durations of each file in seconds.

    Raises:
        ValueError: If the lengths of file_end_datetimes and file_durations don't match.
    """
    if len(file_end_datetimes) != len(file_durations):
        raise ValueError("file_end_datetimes and file_durations must have the same length")

    self.file_end_datetimes = file_end_datetimes
    self.file_durations = file_durations

    self.file_start_datetimes = [
        file_end_datetime - timedelta(seconds=file_duration)
        for file_end_datetime, file_duration in zip(self.file_end_datetimes, self.file_durations)
    ]
    self.cumulative_durations = np.cumsum(self.file_durations)

get_fragment_timestamp(fragment_idx, fragment_len_s)

Get the timestamp for a specific fragment based on its index and length.

Parameters:

Name	Type	Description	Default
fragment_idx	int	The index of the fragment (0-based).	required
fragment_len_s	float	The length of each fragment in seconds.	required

Returns:

Name	Type	Description
datetime	datetime	The timestamp corresponding to the start of the specified fragment.

Examples:

>>> # Get timestamp for the 5th fragment (index 4) with 30-second fragments
>>> timestamp = mapper.get_fragment_timestamp(4, 30.0)
>>> # This returns the timestamp 2 minutes into the first file

Source code in pythoneeg/core/utils.py

def get_fragment_timestamp(self, fragment_idx: int, fragment_len_s: float) -> datetime:
    """
    Get the timestamp for a specific fragment based on its index and length.

    Args:
        fragment_idx (int): The index of the fragment (0-based).
        fragment_len_s (float): The length of each fragment in seconds.

    Returns:
        datetime: The timestamp corresponding to the start of the specified fragment.

    Examples:
        >>> # Get timestamp for the 5th fragment (index 4) with 30-second fragments
        >>> timestamp = mapper.get_fragment_timestamp(4, 30.0)
        >>> # This returns the timestamp 2 minutes into the first file
    """
    # Find which file this fragment belongs to
    fragment_start_time = fragment_idx * fragment_len_s
    file_idx = np.searchsorted(self.cumulative_durations, fragment_start_time)
    file_idx = min(file_idx, len(self.cumulative_durations) - 1)

    offset_in_file = fragment_start_time - self.cumulative_durations[file_idx]  # Negative

    # Return actual timestamp + offset
    return self.file_end_datetimes[file_idx] + timedelta(seconds=offset_in_file)

cache_fragments_to_zarr(np_fragments, n_fragments, tmpdir=None)

Cache numpy fragments array to zarr format for efficient memory management.

This function converts a numpy array of recording fragments to a zarr array stored in a temporary location. This allows better memory management and garbage collection by avoiding keeping large numpy arrays in memory for extended periods.

Parameters:

Name	Type	Description	Default
np_fragments	ndarray	Numpy array of shape (n_fragments, n_samples, n_channels) containing the recording fragments to cache.	required
n_fragments	int	Number of fragments to cache (allows for subset caching).	required
tmpdir	str	Directory path for temporary zarr storage. If None, uses get_temp_directory(). Defaults to None.	None

Returns:

Type	Description
tuple[str, Array]	tuple[str, zarr.Array]: A tuple containing: - str: Path to the temporary zarr file - zarr.Array: The zarr array object for accessing cached data

Raises:

Type	Description
ImportError	If zarr is not available

Source code in pythoneeg/core/utils.py

def cache_fragments_to_zarr(
    np_fragments: np.ndarray, n_fragments: int, tmpdir: Optional[str] = None
) -> tuple[str, "zarr.Array"]:
    """
    Cache numpy fragments array to zarr format for efficient memory management.

    This function converts a numpy array of recording fragments to a zarr array stored
    in a temporary location. This allows better memory management and garbage collection
    by avoiding keeping large numpy arrays in memory for extended periods.

    Args:
        np_fragments (np.ndarray): Numpy array of shape (n_fragments, n_samples, n_channels)
            containing the recording fragments to cache.
        n_fragments (int): Number of fragments to cache (allows for subset caching).
        tmpdir (str, optional): Directory path for temporary zarr storage. If None,
            uses get_temp_directory(). Defaults to None.

    Returns:
        tuple[str, zarr.Array]: A tuple containing:
            - str: Path to the temporary zarr file
            - zarr.Array: The zarr array object for accessing cached data

    Raises:
        ImportError: If zarr is not available
    """
    try:
        import zarr
    except ImportError:
        raise ImportError("zarr package is required for fragment caching")

    if tmpdir is None:
        tmpdir = get_temp_directory()

    # Generate unique temporary path
    tmppath = os.path.join(tmpdir, f"temp_{os.urandom(24).hex()}.zarr")

    logging.debug(f"Caching numpy array with zarr in {tmppath}")

    # Create Zarr array with optimal settings for fragment-wise access
    chunk_size = min(100, n_fragments)  # Cap at 100 fragments per chunk
    zarr_array = zarr.open(
        tmppath,
        mode="w",
        shape=np_fragments.shape,
        chunks=(
            chunk_size,
            -1,  # No chunking along timestamp dimension
            -1,  # No chunking along channel dimension
        ),
        dtype=np_fragments.dtype,
        compressor=zarr.Blosc(cname="lz4", clevel=3, shuffle=zarr.Blosc.SHUFFLE),  # Fast compression
    )
    zarr_array[:n_fragments] = np_fragments[:n_fragments]

    # Log debug properties of the zarr array
    total_memory_bytes = zarr_array.nbytes
    total_memory_mb = total_memory_bytes / (1024 * 1024)
    total_memory_gb = total_memory_mb / 1024

    logging.debug(f"  - Total memory footprint: {total_memory_mb:.2f} MB, {total_memory_gb:.3f} GB")
    logging.debug(f"  - Zarr array shape: {zarr_array.shape}")
    logging.debug(f"  - Zarr array chunks: {zarr_array.chunks}")

    return tmppath, zarr_array

convert_colpath_to_rowpath(rowdir_path, col_path, gzip=True, aspath=True)

Convert a ColMajor file path to its corresponding RowMajor file path.

This function transforms file paths from column-major format to row-major format, which is used when converting between different data storage layouts in PyEEG.

Parameters:

Name	Type	Description	Default
rowdir_path	str | Path	Directory path where the RowMajor file should be located.	required
col_path	str | Path	Path to the ColMajor file to be converted. Must contain 'ColMajor' in the path.	required
gzip	bool	If True, append '.npy.gz' extension. If False, append '.bin'. Defaults to True.	True
aspath	bool	If True, return as Path object. If False, return as string. Defaults to True.	True

Returns:

Type	Description
str | Path	str | Path: The converted RowMajor file path, either as string or Path object based on aspath parameter.

Raises:

Type	Description
ValueError	If 'ColMajor' is not found in col_path.

Examples:

>>> convert_colpath_to_rowpath("/data/row/", "/data/col/file_ColMajor_001.bin")
PosixPath('/data/row/file_RowMajor_001.npy.gz')
>>> convert_colpath_to_rowpath("/data/row/", "/data/col/file_ColMajor_001.bin", gzip=False)
PosixPath('/data/row/file_RowMajor_001.bin')
>>> convert_colpath_to_rowpath("/data/row/", "/data/col/file_ColMajor_001.bin", aspath=False)
'/data/row/file_RowMajor_001.npy.gz'

Source code in pythoneeg/core/utils.py

def convert_colpath_to_rowpath(
    rowdir_path: str | Path, col_path: str | Path, gzip: bool = True, aspath: bool = True
) -> str | Path:
    """
    Convert a ColMajor file path to its corresponding RowMajor file path.

    This function transforms file paths from column-major format to row-major format,
    which is used when converting between different data storage layouts in PyEEG.

    Args:
        rowdir_path (str | Path): Directory path where the RowMajor file should be located.
        col_path (str | Path): Path to the ColMajor file to be converted. Must contain 'ColMajor' in the path.
        gzip (bool, optional): If True, append '.npy.gz' extension. If False, append '.bin'. Defaults to True.
        aspath (bool, optional): If True, return as Path object. If False, return as string. Defaults to True.

    Returns:
        str | Path: The converted RowMajor file path, either as string or Path object based on aspath parameter.

    Raises:
        ValueError: If 'ColMajor' is not found in col_path.

    Examples:
        >>> convert_colpath_to_rowpath("/data/row/", "/data/col/file_ColMajor_001.bin")
        PosixPath('/data/row/file_RowMajor_001.npy.gz')
        >>> convert_colpath_to_rowpath("/data/row/", "/data/col/file_ColMajor_001.bin", gzip=False)
        PosixPath('/data/row/file_RowMajor_001.bin')
        >>> convert_colpath_to_rowpath("/data/row/", "/data/col/file_ColMajor_001.bin", aspath=False)
        '/data/row/file_RowMajor_001.npy.gz'
    """
    # TODO it would make more sense to not have a rowdir_path aparameter, since this is outside the scope of the function
    if "ColMajor" not in col_path:
        raise ValueError(f"Expected 'ColMajor' in col_path: {col_path}")

    out = Path(rowdir_path) / f"{get_file_stem(Path(col_path).name).replace('ColMajor', 'RowMajor')}"
    if gzip:
        out = str(out) + ".npy.gz"
    else:
        out = str(out) + ".bin"
    return Path(out) if aspath else out

convert_units_to_multiplier(current_units, target_units='µV')

Convert between different voltage units and return the multiplication factor.

This function calculates the conversion factor needed to transform values from one voltage unit to another (e.g., from mV to µV).

Parameters:

Name	Type	Description	Default
current_units	str	The current unit of the values. Must be one of: 'µV', 'mV', 'V', 'nV'.	required
target_units	str	The target unit to convert to. Defaults to 'µV'. Must be one of: 'µV', 'mV', 'V', 'nV'.	'µV'

Returns:

Name	Type	Description
float	float	The multiplication factor to convert from current_units to target_units. To convert values, multiply your data by this factor.

Raises:

Type	Description
AssertionError	If current_units or target_units are not supported.

Examples:

>>> convert_units_to_multiplier("mV", "µV")
1000.0
>>> convert_units_to_multiplier("V", "mV")
1000.0
>>> convert_units_to_multiplier("µV", "V")
1e-06

Source code in pythoneeg/core/utils.py

def convert_units_to_multiplier(current_units: str, target_units: str = "µV") -> float:
    """
    Convert between different voltage units and return the multiplication factor.

    This function calculates the conversion factor needed to transform values
    from one voltage unit to another (e.g., from mV to µV).

    Args:
        current_units (str): The current unit of the values. Must be one of: 'µV', 'mV', 'V', 'nV'.
        target_units (str, optional): The target unit to convert to. Defaults to 'µV'.
            Must be one of: 'µV', 'mV', 'V', 'nV'.

    Returns:
        float: The multiplication factor to convert from current_units to target_units.
            To convert values, multiply your data by this factor.

    Raises:
        AssertionError: If current_units or target_units are not supported.

    Examples:
        >>> convert_units_to_multiplier("mV", "µV")
        1000.0
        >>> convert_units_to_multiplier("V", "mV")
        1000.0
        >>> convert_units_to_multiplier("µV", "V")
        1e-06
    """
    units_to_mult = {"µV": 1e-6, "mV": 1e-3, "V": 1, "nV": 1e-9}

    assert current_units in units_to_mult.keys(), f"No valid current unit called '{current_units}' found"
    assert target_units in units_to_mult.keys(), f"No valid target unit called '{target_units}' found"

    return units_to_mult[current_units] / units_to_mult[target_units]

filepath_to_index(filepath)

Extract the index number from a filepath.

This function extracts the last number found in a filepath after removing common suffixes and file extensions. For example, from "/path/to/data_ColMajor_001.bin" it returns 1.

Parameters:

Name	Type	Description	Default
filepath	str | Path	Path to the file to extract index from.	required

Returns:

Name	Type	Description
int	int	The extracted index number.

Examples:

>>> filepath_to_index("/path/to/data_ColMajor_001.bin")
1
>>> filepath_to_index("/path/to/data_2023_015_ColMajor.bin")
15
>>> filepath_to_index("/path/to/data_Meta_010.json")
10

Source code in pythoneeg/core/utils.py

def filepath_to_index(filepath) -> int:
    """
    Extract the index number from a filepath.

    This function extracts the last number found in a filepath after removing common suffixes
    and file extensions. For example, from "/path/to/data_ColMajor_001.bin" it returns 1.

    Args:
        filepath (str | Path): Path to the file to extract index from.

    Returns:
        int: The extracted index number.

    Examples:
        >>> filepath_to_index("/path/to/data_ColMajor_001.bin")
        1
        >>> filepath_to_index("/path/to/data_2023_015_ColMajor.bin")
        15
        >>> filepath_to_index("/path/to/data_Meta_010.json")
        10
    """
    fpath = str(filepath)
    for suffix in ["_RowMajor", "_ColMajor", "_Meta"]:
        fpath = fpath.replace(suffix, "")

    # Remove only the actual file extension, not dots within the filename
    path_obj = Path(fpath)
    if path_obj.suffix:
        fpath = str(path_obj.with_suffix(""))

    fname = Path(fpath).name
    fname = re.split(r"\D+", fname)
    fname = list(filter(None, fname))
    return int(fname[-1])

get_file_stem(filepath)

Get the true stem for files, handling double extensions like .npy.gz.

Source code in pythoneeg/core/utils.py

def get_file_stem(filepath: Union[str, Path]) -> str:
    """Get the true stem for files, handling double extensions like .npy.gz."""
    filepath = Path(filepath)
    name = filepath.name

    return name.split(".")[0]

get_temp_directory()

Get the current temporary directory used by PyEEG.

Returns:

Name	Type	Description
Path	Path	Path object representing the current temporary directory.

Examples:

>>> temp_dir = get_temp_directory()
>>> print(f"Current temp directory: {temp_dir}")
Current temp directory: /tmp/pyeeg_temp

Raises:

Type	Description
KeyError	If TMPDIR environment variable is not set.

Source code in pythoneeg/core/utils.py

def get_temp_directory() -> Path:
    """
    Get the current temporary directory used by PyEEG.

    Returns:
        Path: Path object representing the current temporary directory.

    Examples:
        >>> temp_dir = get_temp_directory()
        >>> print(f"Current temp directory: {temp_dir}")
        Current temp directory: /tmp/pyeeg_temp

    Raises:
        KeyError: If TMPDIR environment variable is not set.
    """
    return Path(os.environ["TMPDIR"])

is_day(dt, sunrise=6, sunset=18)

Check if a datetime object is during the day.

Parameters:

Name	Type	Description	Default
dt	datetime	Datetime object to check	required
sunrise	int	Sunrise hour (0-23). Defaults to 6.	6
sunset	int	Sunset hour (0-23). Defaults to 18.	18

Returns:

Name	Type	Description
bool		True if the datetime is during the day, False otherwise

Raises:

Type	Description
TypeError	If dt is not a datetime object

Source code in pythoneeg/core/utils.py

def is_day(dt: datetime, sunrise=6, sunset=18):
    """
    Check if a datetime object is during the day.

    Args:
        dt (datetime): Datetime object to check
        sunrise (int, optional): Sunrise hour (0-23). Defaults to 6.
        sunset (int, optional): Sunset hour (0-23). Defaults to 18.

    Returns:
        bool: True if the datetime is during the day, False otherwise

    Raises:
        TypeError: If dt is not a datetime object
    """
    if not isinstance(dt, datetime):
        raise TypeError(f"Expected datetime object, got {type(dt).__name__}")
    return sunrise <= dt.hour < sunset

log_transform(rec, **kwargs)

Log transform the signal

Parameters:

Name	Type	Description	Default
rec	ndarray	The signal to log transform.	required

Returns:

Type	Description
ndarray	np.ndarray: ln(rec + 1)

Source code in pythoneeg/core/utils.py

def log_transform(rec: np.ndarray, **kwargs) -> np.ndarray:
    """Log transform the signal

    Args:
        rec (np.ndarray): The signal to log transform.

    Returns:
        np.ndarray: ln(rec + 1)
    """
    if rec is not None:
        return np.log(rec + 1)
    else:
        return None

nanaverage(A, weights, axis=-1)

Compute weighted average of an array, ignoring NaN values.

This function computes a weighted average along the specified axis while properly handling NaN values by masking them out of the calculation.

Parameters:

Name	Type	Description	Default
A	ndarray	Input array containing the values to average.	required
weights	ndarray	Array of weights corresponding to the values in A. Must be broadcastable with A along the specified axis.	required
axis	int	Axis along which to compute the average. Defaults to -1 (last axis).	-1

Returns:

Type	Description
ndarray	np.ndarray: Weighted average with NaN values properly handled. If all values along an axis are NaN, the result will be NaN for that position.

Examples:

>>> import numpy as np
>>> A = np.array([[1.0, 2.0, np.nan], [4.0, np.nan, 6.0]])
>>> weights = np.array([1, 2, 1])
>>> nanaverage(A, weights, axis=1)
array([1.66666667, 5.        ])

Note

Be careful with zero or negative weights as they may produce unexpected results. The function uses numpy's masked array functionality for robust NaN handling.

Source code in pythoneeg/core/utils.py

def nanaverage(A: np.ndarray, weights: np.ndarray, axis: int = -1) -> np.ndarray:
    """
    Compute weighted average of an array, ignoring NaN values.

    This function computes a weighted average along the specified axis while
    properly handling NaN values by masking them out of the calculation.

    Args:
        A (np.ndarray): Input array containing the values to average.
        weights (np.ndarray): Array of weights corresponding to the values in A.
            Must be broadcastable with A along the specified axis.
        axis (int, optional): Axis along which to compute the average. Defaults to -1 (last axis).

    Returns:
        np.ndarray: Weighted average with NaN values properly handled. If all values
            along an axis are NaN, the result will be NaN for that position.

    Examples:
        >>> import numpy as np
        >>> A = np.array([[1.0, 2.0, np.nan], [4.0, np.nan, 6.0]])
        >>> weights = np.array([1, 2, 1])
        >>> nanaverage(A, weights, axis=1)
        array([1.66666667, 5.        ])

    Note:
        Be careful with zero or negative weights as they may produce unexpected results.
        The function uses numpy's masked array functionality for robust NaN handling.
    """
    masked = np.ma.masked_array(A, np.isnan(A))
    avg = np.ma.average(masked, axis=axis, weights=weights)

    # Handle case where np.ma.average returns a scalar instead of masked array
    if np.ma.is_masked(avg):
        return avg.filled(np.nan)
    else:
        # avg is a scalar or regular array, convert to array and handle NaN
        result = np.asarray(avg)
        return np.where(np.isfinite(result), result, np.nan)

nanmean_series_of_np(x, axis=0)

Efficiently compute NaN-aware mean of a pandas Series containing numpy arrays.

This function is optimized for computing the mean across a Series where each element is a numpy array. It uses different strategies based on the size of the Series for optimal performance.

Parameters:

Name	Type	Description	Default
x	Series	Series containing numpy arrays as elements.	required
axis	int	Axis along which to compute the mean. Defaults to 0. - axis=0: Mean across the Series elements (most common) - axis=1: Mean within each array element	0

Returns:

Type	Description
ndarray	np.ndarray: Array containing the computed means with NaN values properly handled.

Examples:

>>> import pandas as pd
>>> import numpy as np
>>> # Create a Series of numpy arrays
>>> arrays = [np.array([1.0, 2.0, np.nan]),
...           np.array([4.0, np.nan, 6.0]),
...           np.array([7.0, 8.0, 9.0])]
>>> series = pd.Series(arrays)
>>> nanmean_series_of_np(series)
array([4. , 5. , 7.5])

Performance Notes

• For Series with more than 1000 elements containing numpy arrays, uses np.stack() for better performance
• Falls back to list conversion for smaller Series or mixed types
• Handles shape mismatches gracefully by falling back to the slower method

Source code in pythoneeg/core/utils.py

def nanmean_series_of_np(x: pd.Series, axis: int = 0) -> np.ndarray:
    """
    Efficiently compute NaN-aware mean of a pandas Series containing numpy arrays.

    This function is optimized for computing the mean across a Series where each element
    is a numpy array. It uses different strategies based on the size of the Series
    for optimal performance.

    Args:
        x (pd.Series): Series containing numpy arrays as elements.
        axis (int, optional): Axis along which to compute the mean. Defaults to 0.
            - axis=0: Mean across the Series elements (most common)
            - axis=1: Mean within each array element

    Returns:
        np.ndarray: Array containing the computed means with NaN values properly handled.

    Examples:
        >>> import pandas as pd
        >>> import numpy as np
        >>> # Create a Series of numpy arrays
        >>> arrays = [np.array([1.0, 2.0, np.nan]),
        ...           np.array([4.0, np.nan, 6.0]),
        ...           np.array([7.0, 8.0, 9.0])]
        >>> series = pd.Series(arrays)
        >>> nanmean_series_of_np(series)
        array([4. , 5. , 7.5])

    Performance Notes:
        - For Series with more than 1000 elements containing numpy arrays,
          uses `np.stack()` for better performance
        - Falls back to list conversion for smaller Series or mixed types
        - Handles shape mismatches gracefully by falling back to the slower method
    """
    # logging.debug(f"Unique shapes in x: {set(np.shape(item) for item in x)}")

    if len(x) > 1000:
        try:
            if isinstance(x.iloc[0], np.ndarray):
                xmean: np.ndarray = np.nanmean(np.stack(x.values, axis=0), axis=axis)
                return xmean
        except (ValueError, TypeError):
            pass

    xmean: np.ndarray = np.nanmean(np.array(list(x)), axis=axis)
    return xmean

parse_chname_to_abbrev(channel_name, assume_from_number=False, strict_matching=True)

Parses the channel name to get the abbreviation.

Parameters:

Name	Type	Description	Default
channel_name	str	Name of the channel.	required
assume_from_number	bool	If True, assume the abbreviation based on the last number in the channel name when normal parsing fails. Defaults to False.	False
strict_matching	bool	If True, ensures the input matches exactly one L/R alias and one channel alias. If False, allows multiple matches and uses longest. Defaults to True.	True

Returns:

Name	Type	Description
str	str	Abbreviation of the channel name.

Raises:

Type	Description
ValueError	When channel_name cannot be parsed or contains ambiguous matches in strict mode.
KeyError	When assume_from_number=True but the detected number is not a valid channel ID.

Examples:

>>> parse_chname_to_abbrev("left Aud")
'LAud'
>>> parse_chname_to_abbrev("Right VIS")
'RVis'
>>> parse_chname_to_abbrev("channel_9", assume_from_number=True)
'LAud'
>>> parse_chname_to_abbrev("LRAud", strict_matching=False)  # Would work in non-strict mode
'LAud'  # Uses longest L/R match

Source code in pythoneeg/core/utils.py

def parse_chname_to_abbrev(channel_name: str, assume_from_number=False, strict_matching=True) -> str:
    """
    Parses the channel name to get the abbreviation.

    Args:
        channel_name (str): Name of the channel.
        assume_from_number (bool, optional): If True, assume the abbreviation based on the last number
            in the channel name when normal parsing fails. Defaults to False.
        strict_matching (bool, optional): If True, ensures the input matches exactly one L/R alias and
            one channel alias. If False, allows multiple matches and uses longest. Defaults to True.

    Returns:
        str: Abbreviation of the channel name.

    Raises:
        ValueError: When channel_name cannot be parsed or contains ambiguous matches in strict mode.
        KeyError: When assume_from_number=True but the detected number is not a valid channel ID.

    Examples:
        >>> parse_chname_to_abbrev("left Aud")
        'LAud'
        >>> parse_chname_to_abbrev("Right VIS")
        'RVis'
        >>> parse_chname_to_abbrev("channel_9", assume_from_number=True)
        'LAud'
        >>> parse_chname_to_abbrev("LRAud", strict_matching=False)  # Would work in non-strict mode
        'LAud'  # Uses longest L/R match
    """
    if channel_name in constants.DEFAULT_ID_TO_NAME.values():
        logging.debug(f"{channel_name} is already an abbreviation")
        return channel_name

    try:
        lr = _get_key_from_match_values(channel_name, constants.LR_ALIASES, strict_matching)
        chname = _get_key_from_match_values(channel_name, constants.CHNAME_ALIASES, strict_matching)
    except ValueError as e:
        if assume_from_number:
            logging.warning(f"{channel_name} does not match name aliases. Assuming alias from number in channel name.")
            nums = re.findall(r"\d+", channel_name)

            if not nums:
                raise ValueError(
                    f"Expected to find a number in channel name '{channel_name}' when assume_from_number=True, but no numbers were found."
                )

            num = int(nums[-1])
            if num not in constants.DEFAULT_ID_TO_NAME:
                available_ids = sorted(constants.DEFAULT_ID_TO_NAME.keys())
                raise KeyError(
                    f"Channel number {num} found in '{channel_name}' is not a valid channel ID. Available channel IDs: {available_ids}"
                )

            return constants.DEFAULT_ID_TO_NAME[num]
        else:
            raise e

    return lr + chname

parse_path_to_animalday(filepath, animal_param=(0, None), day_sep=None, mode='concat', **day_parse_kwargs)

Parses the filename of a binfolder to get the animalday identifier (animal id, genotype, and day).

Parameters:

Name	Type	Description	Default
filepath	str | Path	Filepath of the binfolder.	required
animal_param	tuple[int, str] | str | list[str]	Parameter specifying how to parse the animal ID: tuple[int, str]: (index, separator) for simple split and index str: regex pattern to extract ID list[str]: list of possible animal IDs to match against	(0, None)
day_sep	str	Separator for day in filename. Defaults to None.	None
mode	Literal['nest', 'concat', 'base', 'noday']	Mode to parse the filename. Defaults to 'concat'. 'nest': Extracts genotype/animal from parent directory name and date from filename e.g. "/WT_A10/recording_2023-04-01." 'concat': Extracts all info from filename, expects genotype_animal_date format e.g. "/WT_A10_2023-04-01." 'base': Same as concat 'noday': Extracts only genotype and animal ID, uses default date e.g. "/WT_A10_recording.*"	'concat'
**day_parse_kwargs		Additional keyword arguments to pass to parse_str_to_day function. Common options include parse_params dict for dateutil.parser.parse.	{}

Returns:

Type	Description
	dict[str, str]: Dictionary with keys "animal", "genotype", "day", and "animalday" (concatenated). Example: {"animal": "A10", "genotype": "WT", "day": "Apr-01-2023", "animalday": "A10 WT Apr-01-2023"}

Raises:

Type	Description
ValueError	If mode is invalid or required components cannot be extracted
TypeError	If filepath is not str or Path

Source code in pythoneeg/core/utils.py

def parse_path_to_animalday(
    filepath: str | Path,
    animal_param: tuple[int, str] | str | list[str] = (0, None),
    day_sep: str | None = None,
    mode: Literal["nest", "concat", "base", "noday"] = "concat",
    **day_parse_kwargs,
):
    """
    Parses the filename of a binfolder to get the animalday identifier (animal id, genotype, and day).

    Args:
        filepath (str | Path): Filepath of the binfolder.
        animal_param (tuple[int, str] | str | list[str], optional): Parameter specifying how to parse the animal ID:
            tuple[int, str]: (index, separator) for simple split and index
            str: regex pattern to extract ID
            list[str]: list of possible animal IDs to match against
        day_sep (str, optional): Separator for day in filename. Defaults to None.
        mode (Literal['nest', 'concat', 'base', 'noday'], optional): Mode to parse the filename. Defaults to 'concat'.
            'nest': Extracts genotype/animal from parent directory name and date from filename
                   e.g. "/WT_A10/recording_2023-04-01.*"
            'concat': Extracts all info from filename, expects genotype_animal_date format
                     e.g. "/WT_A10_2023-04-01.*"
            'base': Same as concat
            'noday': Extracts only genotype and animal ID, uses default date
                    e.g. "/WT_A10_recording.*"
        **day_parse_kwargs: Additional keyword arguments to pass to parse_str_to_day function.
                           Common options include parse_params dict for dateutil.parser.parse.

    Returns:
        dict[str, str]: Dictionary with keys "animal", "genotype", "day", and "animalday" (concatenated).
            Example: {"animal": "A10", "genotype": "WT", "day": "Apr-01-2023", "animalday": "A10 WT Apr-01-2023"}

    Raises:
        ValueError: If mode is invalid or required components cannot be extracted
        TypeError: If filepath is not str or Path
    """
    filepath = Path(filepath)
    match mode:
        case "nest":
            geno = parse_str_to_genotype(filepath.parent.name)
            animid = parse_str_to_animal(filepath.parent.name, animal_param=animal_param)
            day = parse_str_to_day(filepath.name, sep=day_sep, **day_parse_kwargs).strftime("%b-%d-%Y")
        case "concat" | "base":
            geno = parse_str_to_genotype(filepath.name)
            animid = parse_str_to_animal(filepath.name, animal_param=animal_param)
            day = parse_str_to_day(filepath.name, sep=day_sep, **day_parse_kwargs).strftime("%b-%d-%Y")
        case "noday":
            geno = parse_str_to_genotype(filepath.name)
            animid = parse_str_to_animal(filepath.name, animal_param=animal_param)
            day = constants.DEFAULT_DAY.strftime("%b-%d-%Y")
        case _:
            raise ValueError(f"Invalid mode: {mode}")
    return {
        "animal": animid,
        "genotype": geno,
        "day": day,
        "animalday": f"{animid} {geno} {day}",
    }

parse_str_to_animal(string, animal_param=(0, None))

Parses the filename of a binfolder to get the animal id.

Parameters:

Name	Type	Description	Default
string	str	String to parse.	required
animal_param	tuple[int, str] | str | list[str]	Parameter specifying how to parse the animal ID: tuple[int, str]: (index, separator) for simple split and index. Not recommended for inconsistent naming conventions. str: regex pattern to extract ID. Most general use case. If multiple matches are found, returns the first match. list[str]: list of possible animal IDs to match against. Returns first match in list order, case-sensitive, ignoring empty strings.	(0, None)

Returns:

Name	Type	Description
str	str	Animal id.

Examples:

Tuple format: (index, separator)

>>> parse_str_to_animal("WT_A10_2023-01-01_data.bin", (1, "_"))
'A10'
>>> parse_str_to_animal("A10_WT_recording.bin", (0, "_"))
'A10'

Regex pattern format

>>> parse_str_to_animal("WT_A10_2023-01-01_data.bin", r"A\d+")
'A10'
>>> parse_str_to_animal("subject_123_data.bin", r"\d+")
'123'

List format: possible IDs to match

>>> parse_str_to_animal("WT_A10_2023-01-01_data.bin", ["A10", "A11", "A12"])
'A10'
>>> parse_str_to_animal("WT_A10_data.bin", ["B15", "C20"])  # No match
ValueError: No matching ID found in WT_A10_data.bin from possible IDs: ['B15', 'C20']

Source code in pythoneeg/core/utils.py

def parse_str_to_animal(string: str, animal_param: tuple[int, str] | str | list[str] = (0, None)) -> str:
    """
    Parses the filename of a binfolder to get the animal id.

    Args:
        string (str): String to parse.
        animal_param: Parameter specifying how to parse the animal ID:
            tuple[int, str]: (index, separator) for simple split and index. Not recommended for inconsistent naming conventions.
            str: regex pattern to extract ID. Most general use case. If multiple matches are found, returns the first match.
            list[str]: list of possible animal IDs to match against. Returns first match in list order, case-sensitive, ignoring empty strings.

    Returns:
        str: Animal id.

    Examples:
        # Tuple format: (index, separator)
        >>> parse_str_to_animal("WT_A10_2023-01-01_data.bin", (1, "_"))
        'A10'
        >>> parse_str_to_animal("A10_WT_recording.bin", (0, "_"))
        'A10'

        # Regex pattern format
        >>> parse_str_to_animal("WT_A10_2023-01-01_data.bin", r"A\\d+")
        'A10'
        >>> parse_str_to_animal("subject_123_data.bin", r"\\d+")
        '123'

        # List format: possible IDs to match
        >>> parse_str_to_animal("WT_A10_2023-01-01_data.bin", ["A10", "A11", "A12"])
        'A10'
        >>> parse_str_to_animal("WT_A10_data.bin", ["B15", "C20"])  # No match
        ValueError: No matching ID found in WT_A10_data.bin from possible IDs: ['B15', 'C20']
    """
    if isinstance(animal_param, tuple):
        index, sep = animal_param
        animid = string.split(sep)
        return animid[index]
    elif isinstance(animal_param, str):
        pattern = animal_param
        match = re.search(pattern, string)
        if match:
            return match.group()
        raise ValueError(f"No match found for pattern {pattern} in string {string}")
    elif isinstance(animal_param, list):
        possible_ids = animal_param
        for id in possible_ids:
            # Skip empty or whitespace-only strings
            if id and id.strip() and id in string:
                return id
        raise ValueError(f"No matching ID found in {string} from possible IDs: {possible_ids}")
    else:
        raise ValueError(f"Invalid animal_param type: {type(animal_param)}")

parse_str_to_day(string, sep=None, parse_params=None, parse_mode='split')

Parses the filename of a binfolder to get the day.

Parameters:

Name	Type	Description	Default
string	str	String to parse.	required
sep	str	Separator to split string by. If None, split by whitespace. Defaults to None.	None
parse_params	dict	Parameters to pass to dateutil.parser.parse. Defaults to {'fuzzy':True}.	None
parse_mode	Literal['full', 'split', 'window', 'all']	Mode for parsing the string. Defaults to "split". "full": Try parsing the entire cleaned string only "split": Try parsing individual tokens only "window": Try parsing sliding windows of tokens (2-4 tokens) only "all": Use all three approaches in sequence	'split'

Returns: datetime: Datetime object corresponding to the day of the binfolder.

Raises:

Type	Description
ValueError	If no valid date token is found in the string.

Note

The function is designed to be conservative to avoid false positives. Some complex date formats may parse with the default year (2000) instead of the actual year in the string, which is acceptable behavior for maintaining safety against false positives.

Source code in pythoneeg/core/utils.py

def parse_str_to_day(
    string: str,
    sep: str = None,
    parse_params: dict = None,
    parse_mode: Literal["full", "split", "window", "all"] = "split",
) -> datetime:
    """
    Parses the filename of a binfolder to get the day.

    Args:
        string (str): String to parse.
        sep (str, optional): Separator to split string by. If None, split by whitespace. Defaults to None.
        parse_params (dict, optional): Parameters to pass to dateutil.parser.parse. Defaults to {'fuzzy':True}.
        parse_mode (Literal["full", "split", "window", "all"], optional): Mode for parsing the string. Defaults to "split".
            "full": Try parsing the entire cleaned string only
            "split": Try parsing individual tokens only
            "window": Try parsing sliding windows of tokens (2-4 tokens) only
            "all": Use all three approaches in sequence
    Returns:
        datetime: Datetime object corresponding to the day of the binfolder.

    Raises:
        ValueError: If no valid date token is found in the string.

    Note:
        The function is designed to be conservative to avoid false positives.
        Some complex date formats may parse with the default year (2000) instead
        of the actual year in the string, which is acceptable behavior for
        maintaining safety against false positives.
    """
    if parse_params is None:
        parse_params = {"fuzzy": True}

    # Validate parse_mode
    valid_modes = ["full", "split", "window", "all"]
    if parse_mode not in valid_modes:
        raise ValueError(f"Invalid parse_mode: {parse_mode}. Must be one of {valid_modes}")

    clean_str = _clean_str_for_date(string)
    # logging.debug(f'raw str: {string}, clean_str: {clean_str}')

    # Try parsing based on the specified mode
    if parse_mode in ["full", "all"]:
        # Pass 1: Try parsing the entire cleaned string
        try:
            date = dateutil.parser.parse(clean_str, default=constants.DEFAULT_DAY, **parse_params)
            if date.year > 1980:
                return date
        except ParserError:
            pass

    if parse_mode in ["split", "all"]:
        # Pass 2: Try individual tokens
        tokens = clean_str.split(sep)
        for token in tokens:
            try:
                # logging.debug(f'token: {token}')
                date = dateutil.parser.parse(token, default=constants.DEFAULT_DAY, **parse_params)
                if date.year <= 1980:
                    continue
                return date
            except ParserError:
                continue

    if parse_mode in ["window", "all"]:
        # Pass 3: Try sliding window of tokens
        tokens = clean_str.split(sep)
        for window_size in range(2, min(5, len(tokens) + 1)):
            for i in range(len(tokens) - window_size + 1):
                grouped = " ".join(tokens[i : i + window_size])
                try:
                    date = dateutil.parser.parse(grouped, default=constants.DEFAULT_DAY, **parse_params)
                    if date.year <= 1980:
                        continue
                    return date
                except ParserError:
                    continue

    raise ValueError(f"No valid date token found in string: {string}")

parse_str_to_genotype(string, strict_matching=False)

Parses the filename of a binfolder to get the genotype.

Parameters:

Name	Type	Description	Default
string	str	String to parse.	required
strict_matching	bool	If True, ensures the input matches exactly one genotype. If False, allows overlapping matches and uses longest. Defaults to False for backward compatibility.	False

Returns:

Name	Type	Description
str	str	Genotype.

Raises:

Type	Description
ValueError	When string cannot be parsed or contains ambiguous matches in strict mode.

Examples:

>>> parse_str_to_genotype("WT_A10_data")
'WT'
>>> parse_str_to_genotype("WT_KO_comparison", strict_matching=True)  # Would raise error
ValueError: Ambiguous match...
>>> parse_str_to_genotype("WT_KO_comparison", strict_matching=False)  # Uses longest match
'WT'  # or 'KO' depending on which alias is longer

Source code in pythoneeg/core/utils.py

def parse_str_to_genotype(string: str, strict_matching: bool = False) -> str:
    """
    Parses the filename of a binfolder to get the genotype.

    Args:
        string (str): String to parse.
        strict_matching (bool, optional): If True, ensures the input matches exactly one genotype.
            If False, allows overlapping matches and uses longest. Defaults to False for
            backward compatibility.

    Returns:
        str: Genotype.

    Raises:
        ValueError: When string cannot be parsed or contains ambiguous matches in strict mode.

    Examples:
        >>> parse_str_to_genotype("WT_A10_data")
        'WT'
        >>> parse_str_to_genotype("WT_KO_comparison", strict_matching=True)  # Would raise error
        ValueError: Ambiguous match...
        >>> parse_str_to_genotype("WT_KO_comparison", strict_matching=False)  # Uses longest match
        'WT'  # or 'KO' depending on which alias is longer
    """
    return _get_key_from_match_values(string, constants.GENOTYPE_ALIASES, strict_matching)

parse_truncate(truncate)

Parse the truncate parameter to determine how many characters to truncate.

If truncate is a boolean, returns 10 if True and 0 if False. If truncate is an integer, returns that integer value directly.

Parameters:

Name	Type	Description	Default
truncate	int | bool	If bool, True=10 chars and False=0 chars. If int, specifies exact number of chars.	required

Returns:

Name	Type	Description
int	int	Number of characters to truncate (0 means no truncation)

Raises:

Type	Description
ValueError	If truncate is not a boolean or integer

Source code in pythoneeg/core/utils.py

def parse_truncate(truncate: int | bool) -> int:
    """
    Parse the truncate parameter to determine how many characters to truncate.

    If truncate is a boolean, returns 10 if True and 0 if False.
    If truncate is an integer, returns that integer value directly.

    Args:
        truncate (int | bool): If bool, True=10 chars and False=0 chars.
                              If int, specifies exact number of chars.

    Returns:
        int: Number of characters to truncate (0 means no truncation)

    Raises:
        ValueError: If truncate is not a boolean or integer
    """
    if isinstance(truncate, bool):
        return 10 if truncate else 0
    elif isinstance(truncate, int):
        return truncate
    else:
        raise ValueError(f"Invalid truncate value: {truncate}")

set_temp_directory(path)

Set the temporary directory for PyEEG operations.

This function configures the temporary directory used by PyEEG for intermediate files and operations. The directory will be created if it doesn't exist.

Parameters:

Name	Type	Description	Default
path	str | Path	Path to the temporary directory. Will be created if it doesn't exist.	required

Examples:

>>> set_temp_directory("/tmp/pyeeg_temp")
>>> set_temp_directory(Path.home() / "pyeeg_workspace" / "temp")

Note

This function modifies the TMPDIR environment variable, which affects the behavior of other temporary file operations in the process.

Source code in pythoneeg/core/utils.py

def set_temp_directory(path: str | Path) -> None:
    """
    Set the temporary directory for PyEEG operations.

    This function configures the temporary directory used by PyEEG for intermediate
    files and operations. The directory will be created if it doesn't exist.

    Args:
        path (str | Path): Path to the temporary directory. Will be created if it doesn't exist.

    Examples:
        >>> set_temp_directory("/tmp/pyeeg_temp")
        >>> set_temp_directory(Path.home() / "pyeeg_workspace" / "temp")

    Note:
        This function modifies the TMPDIR environment variable, which affects
        the behavior of other temporary file operations in the process.
    """
    path = Path(path)
    if not path.exists():
        path.mkdir(parents=True, exist_ok=True)
    os.environ["TMPDIR"] = str(path)
    logging.info(f"Temporary directory set to {path}")

sort_dataframe_by_plot_order(df, df_sort_order=None)

Sort DataFrame columns according to predefined orders.

Parameters

df : pd.DataFrame DataFrame to sort df_sort_order : dict Dictionary mapping column names to the order of the values in the column.

Returns

pd.DataFrame Sorted DataFrame

Raises

ValueError If df_sort_order is not a valid dictionary or contains invalid categories

Source code in pythoneeg/core/utils.py

def sort_dataframe_by_plot_order(df: pd.DataFrame, df_sort_order: Optional[dict] = None) -> pd.DataFrame:
    """
    Sort DataFrame columns according to predefined orders.

    Parameters
    ----------
    df : pd.DataFrame
        DataFrame to sort
    df_sort_order : dict
        Dictionary mapping column names to the order of the values in the column.

    Returns
    -------
    pd.DataFrame
        Sorted DataFrame

    Raises
    ------
    ValueError
        If df_sort_order is not a valid dictionary or contains invalid categories
    """
    if df_sort_order is None:
        df_sort_order = constants.DF_SORT_ORDER.copy()
    elif not isinstance(df_sort_order, dict):
        raise ValueError("df_sort_order must be a dictionary")

    if df.empty:
        return df.copy()

    for col, categories in df_sort_order.items():
        if not isinstance(categories, (list, tuple)):
            raise ValueError(f"Categories for column '{col}' must be a list or tuple")

    columns_to_sort = [col for col in df.columns if col in df_sort_order]
    df_sorted = df.copy()

    if not columns_to_sort:
        return df_sorted

    for col in columns_to_sort:
        categories = df_sort_order[col]

        # Check for values not in predefined categories
        unique_values = set(df_sorted[col].dropna().unique())
        missing_values = unique_values - set(categories)

        if missing_values:
            raise ValueError(
                f"Column '{col}' contains values not in sort order dictionary: {missing_values}. Add them to plot_order in ExperimentPlotter init."
            )

        # Filter categories to only include those that exist in the DataFrame
        existing_categories = [cat for cat in categories if cat in unique_values]

        df_sorted[col] = pd.Categorical(df_sorted[col], categories=existing_categories, ordered=True)

    df_sorted = df_sorted.sort_values(columns_to_sort)
    # REVIEW since "sex" is not inherently part of the pipeline (add ad-hoc), this could be a feature worth sorting
    # But this might mean rewriting the data loading pipeline, file-reading, etc.
    # Maybe a dictionary corresponding to animal/id -> sex would be good enough, instead of reading it in from filenames
    # which would be difficult since name conventions are not standardized

    return df_sorted

validate_timestamps(timestamps, gap_threshold_seconds=60)

Validate that timestamps are in chronological order and check for large gaps.

Parameters:

Name	Type	Description	Default
timestamps	list[datetime]	List of timestamps to validate	required
gap_threshold_seconds	float	Threshold in seconds for warning about large gaps. Defaults to 60.	60

Returns:

Type	Description
list[datetime]	list[datetime]: The validated timestamps in chronological order

Raises:

Type	Description
ValueError	If no valid timestamps are provided

Source code in pythoneeg/core/utils.py

def validate_timestamps(timestamps: list[datetime], gap_threshold_seconds: float = 60) -> list[datetime]:
    """
    Validate that timestamps are in chronological order and check for large gaps.

    Args:
        timestamps (list[datetime]): List of timestamps to validate
        gap_threshold_seconds (float, optional): Threshold in seconds for warning about large gaps. Defaults to 60.

    Returns:
        list[datetime]: The validated timestamps in chronological order

    Raises:
        ValueError: If no valid timestamps are provided
    """
    if not timestamps:
        raise ValueError("No timestamps provided for validation")

    valid_timestamps = [ts for ts in timestamps if ts is not None]
    if len(valid_timestamps) < len(timestamps):
        warnings.warn(f"Found {len(timestamps) - len(valid_timestamps)} None timestamps that were filtered out")

    if not valid_timestamps:
        raise ValueError("No valid timestamps found (all were None)")

    # Check chronological order
    sorted_timestamps = sorted(valid_timestamps)
    if valid_timestamps != sorted_timestamps:
        warnings.warn("Timestamps are not in chronological order. This may cause issues with the data.")

    # Check for large gaps between consecutive timestamps
    for i in range(1, len(valid_timestamps)):
        gap = valid_timestamps[i] - valid_timestamps[i - 1]
        gap_seconds = gap.total_seconds()

        if gap_seconds > gap_threshold_seconds:
            warnings.warn(
                f"Large gap detected between timestamps: {gap} exceeds threshold of {gap_threshold_seconds} seconds"
            )

    return valid_timestamps