sidpy.hdf.dtype_utils.stack_real_to_complex

sidpy.hdf.dtype_utils.stack_real_to_complex(ds_real, lazy=False)

Puts the real and imaginary sections of the provided matrix (in the last axis) together to make complex matrix

Parameters:

• ds_real (numpy.ndarray, dask.array.core.Array, or h5py.Dataset) – n dimensional real-valued numpy array or HDF5 dataset where data arranged as [instance, 2 x features], where the first half of the features are the real component and the second half contains the imaginary components

• lazy (bool, optional. Default = False) – If set to True, will use lazy Dask arrays instead of in-memory numpy arrays

Returns:

ds_compound – 2D complex array arranged as [sample, features]

Return type:

numpy.ndarray or dask.array.core.Array

Examples

>>> import numpy as np
>>> import sidpy
>>> real_val = np.hstack([5 * np.random.rand(6),
>>>                       7 * np.random.rand(6)])
>>> print('Real valued dataset of shape {}:'.format(real_val.shape))
>>> print(real_val)
Real valued dataset of shape (12,):
[3.59249723 1.05674621 4.41035214 1.84720102 1.79672691 4.7636207
 3.09574246 0.76396171 3.38140637 4.97629028 0.83303717 0.32816285]

>>> comp_val = sidpy.dtype_utils.stack_real_to_complex(real_val)
>>> print('Complex-valued array of shape: {}'.format(comp_val.shape))
>>> print(comp_val)
Complex-valued array of shape: (6,)
[3.59249723+3.09574246j 1.05674621+0.76396171j 4.41035214+3.38140637j
 1.84720102+4.97629028j 1.79672691+0.83303717j 4.7636207 +0.32816285j]