Utils module

This module contains all kinds of utility methods, and can be imported as follows:

>>> from dtaianomaly import utils

dtaianomaly.utils.is_valid_list(value, target_type) → bool[source]

Check if the given list is a valid, with each instance being a member of the given type.

Parameters:

Returns:

is_valid – True if and only if the given value is a list and all elements in the list are of type Type, otherwise False.

Return type:

bool

dtaianomaly.utils.is_valid_array_like(array) → bool[source]

Check if input is “array-like”. Within dtaianomaly, this is either a numpy array of numerical values or a python sequence of numerical values.

Parameters:: array (object) – The array to check if it is a valid array-like
Returns:: is_valid – True if and only if the given array is either a numpy array or a python sequence, in which the type entirely consists of numerical values, otherwise False.
Return type:: bool

dtaianomaly.utils.is_univariate(X: ndarray) → bool[source]

Checks if the given array equals a univariate time series.

Parameters:: X (array-like of shape (n_samples, n_attributes)) – The time series data to check if it is univariate.
Returns:: is_univariate – True if and only if the given time series has only one dimension, or if the second dimension of the time series is of size 1.
Return type:: bool

dtaianomaly.utils.get_dimension(X: ndarray) → int[source]

Get the dimension of the given array.

Parameters:: X (array-like of shape (n_samples, n_attributes)) – The time series data to get the dimension from
Returns:: n_attributes – The number of attributes in the given time series.
Return type:: int