macro_eeg_model.simulation.delay_calculator

macro_eeg_model.simulation.delay_calculator#

Classes#

DelayCalculator

A class to calculate delay distributions based on distance and various statistical parameters.

Module Contents#

class macro_eeg_model.simulation.delay_calculator.DelayCalculator(shape_param, scale_param, location_param, truncation_percentile)[source]#

A class to calculate delay distributions based on distance and various statistical parameters. The delay is modeled using inverse generalized extreme value (GEV) distributions.

_shape_param#

The shape parameter (xi) for the GEV distribution.

Type:

float

_scale_param#

The scale parameter (sigma) for the GEV distribution.

Type:

float

_location_param#

The location parameter (mu) for the GEV distribution.

Type:

float

_truncation_percentile#

The percentile at which to truncate the resulting delay distribution.

Type:

float

_velocity_factor#

A constant velocity factor (= 6) used to calculate the scale coefficient from the distance. Expressed in meters per second per micron diameter.

Type:

float

__init__(shape_param, scale_param, location_param, truncation_percentile)[source]#

Initializes the DelayCalculator with specified parameters for the GEV distribution and the truncation percentile.

Parameters:

  • shape_param (float) – The shape parameter (xi) for the GEV distribution.

  • scale_param (float) – The scale parameter (sigma) for the GEV distribution.

  • location_param (float) – The location parameter (mu) for the GEV distribution.

  • truncation_percentile (float) – The percentile at which to truncate the resulting delay distribution. Must be in the range [0, 1).

get_delays_distribution(tempx, distance)[source]#

Generates a probability density function (PDF) for delays using inverse GEV distributions. The distributions are also scaled with parameter computed by _calculate_scale_coefficient().

Depending on whether the distance is a single value or a tuple (in case of a relay station), it either sums inverse GEV distributions or uses a single inverse GEV distribution (see src.simulation.distributions.LagDistributions and src.simulation.distributions.DistributionFactory). It then truncates the resulting PDF using _truncate_result().

Parameters:

  • tempx (numpy.ndarray) – The array of time points (x-axis) over which to calculate the delay distribution.

  • distance (float or tuple) – The distance(s) over which to calculate the delay. If a tuple, the method will use the sum of two inverse GEV distributions.

Returns:

The truncated probability density function (PDF) representing the delay distribution.

Return type:

numpy.ndarray

Raises:

AssertionError – If the distribution cannot be created.

_calculate_scale_coefficient(distance)[source]#

Calculates the scale coefficient for the GEV distribution based on the given distance.

Parameters:

distance (float) – The distance for which to calculate the scale coefficient.

Returns:

The scale coefficient used in the GEV distribution.

Return type:

float

_truncate_result(tempx, result)[source]#

Truncates the PDF by setting values beyond a certain index to zero, based on the cumulative distribution function (CDF) and the truncation percentile.

Parameters:

  • tempx (numpy.ndarray) – The array of time points (x-axis) corresponding to the PDF.

  • result (numpy.ndarray) – The PDF to be truncated.

Returns:

The truncated PDF.

Return type:

numpy.ndarray

Raises:

AssertionError – If the truncation percentile is outside the valid range [0, 1).

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