fouriax.PoissonNoise

class PoissonNoise(count_scale=1.0)

Bases: SensorNoiseModel

Shot noise model in normalized output units.

count_scale maps expected intensity to expected counts before sampling. The returned noisy sample is divided back by count_scale, so the output remains in the same units as the clean sensor measurement.

Parameters:

count_scale (float)

__init__(count_scale=1.0)

Parameters:

count_scale (float)

Return type:

None

Methods

__init__([count_scale])
covariance(expected)	Return the analytic measurement covariance matrix.
expected_variance(expected)	Return per-element variance for the noise model at the expected signal.
precision(expected, *[, regularize])	Return the analytic inverse covariance (precision) matrix.
sample(expected, *, key)	Draw a noisy sample from the measurement distribution.

Attributes

count_scale

count_scale: float = 1.0

sample(expected, *, key)

Draw a noisy sample from the measurement distribution.

Parameters:

• expected (Array)

• key (Array)

Return type:

Array

expected_variance(expected)

Return per-element variance for the noise model at the expected signal.

Parameters:

expected (Array)

Return type:

Array