NoiseGenerator

class baseband_tasks.generators.NoiseGenerator(shape, start_time, sample_rate, samples_per_frame, dtype=<class 'numpy.complex64'>, seed=None, **kwargs)

Bases: baseband_tasks.generators.StreamGenerator

Genertator of a stream of normally distributed noise.

To mimic proper streams, data is guaranteed to be identical if read multiple times from a given instance. This is done by storing the state of the random number generator for each “data frame”. Given this, it is important to choose samples_per_frame wisely, such that frame sizes are at least of order millions of samples.

Parameters

shapetuple

First element is the total number of samples of the fake file, the others are the sample shape.

start_timeTime

Start time of the fake file.

sample_rateQuantity

Sample rate, in units of frequency.

samples_per_frameint, optional

Blocking factor, setting the size of the fake data frames. No default, since should typically be large (see above).

dtypedtype or anything that initializes one, optional

Type of data produced. Default: complex64

seedint, optional

Possible seed to initialize the random number generator.

— **kwargsmeta data for the stream, which usually include

frequencyQuantity, optional

Frequencies for each channel. Should be broadcastable to the sample shape. Default: unknown.

sidebandarray, optional

Whether frequencies are upper (+1) or lower (-1) sideband. Should be broadcastable to the sample shape. Default: unknown.

polarizationarray or (nested) list of char, optional

Polarization labels. Should broadcast to the sample shape, i.e., the labels are in the correct axis. For instance, ['X', 'Y'], or [['L'], ['R']]. Default: unknown.

Notes

Between instances, data is identical only if seeded identically and if first access of frames is done in the same order, with the same number of samples per frame.