mydatapreprocessing.datasets package

Test data definition.

Data can be used for example for validating machine learning time series prediction results.

Only ‘real’ data are ECG heart signal returned with function get_ecg().

mydatapreprocessing.datasets.get_ecg(n: int = 1000) → numpy.ndarray

Download real ECG data.

Parameters:n (int, optional) – Length of data. Defaults to 1000. Returns:Slope test data. Return type:np.ndarray

Example

>>> data = get_ecg(50)
>>> data.shape
(50, 1)

mydatapreprocessing.datasets.ramp(n: int = 1000) → numpy.ndarray

Generate ramp data (linear slope) of defined length.

Parameters:n (int, optional) – Length of data. Defaults to 1000. Returns:Ramp test data. Return type:np.ndarray

Example

>>> ramp(50)
array([ 0,  1,  2,  3,  4,  5,  6,  7, ...

mydatapreprocessing.datasets.random(n: int = 1000) → numpy.ndarray

Generate random test data of defined length.

Parameters:n (int, optional) – Length of data. Defaults to 1000. Returns:Random test data. Return type:np.ndarray

Example

>>> data = random(50)
>>> data.shape
(50,)

mydatapreprocessing.datasets.sin(n: int = 1000) → numpy.ndarray

Generate test data of length n in sinus shape.

Parameters:n (int, optional) – Length of data. Defaults to 1000. Returns:Sinus shaped data. Return type:np.ndarray

Example

>>> sin(50)
array([0.        , 0.03925982, 0.0784591 , 0.1175374 , 0.15643447,...

mydatapreprocessing.datasets.sign(n: int = 1000) → numpy.ndarray

Generate test data of length n in signum shape.

Parameters:n (int, optional) – Length of data. Defaults to 1000. Returns:Signum shaped data. Return type:np.ndarray

Example

>>> sign(50)
array([0., 1., 1., 1., 1., 1., 1., 1., 1., ...