cwitools.modeling.covar_curve

cwitools.modeling.covar_curve(params, ksizes)

Two-component model to describe increase in noise due to covariance

The model is divided into two regimes, based on the ‘threshold’ parameter:

for ksizes <= threshold: noise / ideal_noise = norm * (1 + alpha * ln(ksizes))

for ksizes > threshold: noise / ideal_noise = beta = norm * (1 + alpha * ln(threshold))

Parameters:

• params (float) – List containing parameters in following order: [alpha, norm, threshold]
• ksizes (np.array) – Array of 2D kernel or bin sizes (i.e. areas)

Returns:

The ratio of true noise to ‘ideal’ noise

Return type:

factor (np.array)