ActivationMixin

ActivationMixin#

class snake.core.handlers.activations.activations.ActivationMixin[source]#

Bases: LogMixin

Add activation inside the region of interest. for a single type of event.

Parameters:

  • event_condition (pandas.core.frame.DataFrame | numpy.ndarray) – array-like of shape (3, n_events) yields description of events for this condition as a (onsets, durations, amplitudes) triplet

  • hrf_model (str) – Choice for the HRF, FIR is not

  • oversampling (int) – Oversampling factor to perform the convolution. Default=50.

  • min_onset (float) – Minimal onset relative to frame_times[0] (in seconds) events that start before frame_times[0] + min_onset are not considered. Default=-24.

  • roi_threshold (float, default 0.0) – If greater than 0, the roi becomes a binary mask, with roi_threshold as separation.

See also

nilearn.compute_regressors

Methods

__init__

apply_weights

Apply weights to the ROI.

get_dynamic

Get dynamic time series for adding Activations.

get_static

Get the static ROI.

Attributes

base_tissue_name

delta_r2s

hrf_model

log

Logger.

min_onset

offset

oversampling

roi_threshold

roi_tissue_name

event_condition

duration

event_name

get_static(phantom, sim_config)[source]#

Get the static ROI.

Parameters:
Return type:

Phantom

get_dynamic(phantom, sim_conf)[source]#

Get dynamic time series for adding Activations.

Parameters:
Return type:

DynamicData

static apply_weights(phantom, data, time_idx)[source]#

Apply weights to the ROI.

Parameters:
Return type:

Phantom

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