utils

extract_windows(seq, window_size, dilation=1, mode='memory')

Extract sliding windows from a sequence.

The step size is considered to be one, which results in len(seq) - window_size + 1 extracted windows. The resulting array has the shape [num_windows, window_size, num_channels].

If dilation is set to a value greater than one, the window will not contain consecutive time steps. Instead, the time steps are spaced by the dilation value. In this case, the number of extracted windows is len(seq) - (window_size - 1) * dilation.

Parameters:

Name	Type	Description	Default
seq	ndarray	sequence to extract windows from	required
window_size	int	length of the sliding window	required
dilation	int	dilation of the sliding window	1
mode	Literal['memory', 'memmap']	create windows either in memory or on disk	'memory'

Returns:

Type	Description
ndarray	array of sliding windows

feature_to_tensor(features, dtype=torch.float32, copy=False)

Convert a numpy array to a torch tensor of dtype and swap the last dimensions.

The function assumes that the last dimension of the numpy array is the channel dimension, and the second to last is the time dimension. All preceding dimensions are considered to be batch dimensions.

Parameters:

Name	Type	Description	Default
features	ndarray	numpy array to convert	required
dtype	dtype	dtype of the resulting tensor	float32
copy	bool	whether to copy the array before converting it	False

get_files_in_path(path, condition=None)

Return the paths of all files in a path that satisfy a condition in alphabetical
order.

If the condition is `None` all files are returned.

Args:
    path: the path to look into
    condition: the include-condition for files

Returns:

Type	Description
List[str]	all files that satisfy the condition in alphabetical order

get_targets_from_file_paths(file_paths, timestep_from_file_path)

Create the RUL targets based on the file paths of the feature files.

The function extracts the feature file path from each path. The supplied conversion function extracts the time step from it. Afterwards the RUL is calculated by subtracting each time step from the maximum time step plus 1.

Parameters:

Name	Type	Description	Default
file_paths	Dict[int, List[str]]	runs represented as dict of feature file paths	required
timestep_from_file_path	Callable	Function to convert a feature file path to a time step	required

Returns:

Type	Description
Dict[int, ndarray]	A list of RUL target arrays for each run