Commit b338e000 authored by Andrew Quinn's avatar Andrew Quinn
Browse files

irasa and glm_irasa full refactor plus simulate data from fitted mvar

parent 1b7972cb

sails/_docstring_utils.py

+39 −1
Original line number Diff line number Diff line
@@ -136,6 +136,22 @@ docdict['fft_core'] = docdict['nfft'] + docdict['axis'] + docdict['fft_side'] + 
docdict['fft_user'] = docdict['nfft'] + docdict['axis'] + docdict['return_onesided'] + \

                      docdict['spec_mode'] + docdict['fft_scaling'] + docdict['fs'] + docdict['freq_range']



docdict['average'] = """

    average : { 'mean', 'median', 'median_bias' }, optional

        Method to use when averaging across sliding window segments in a periodograms. 

        Defaults to 'mean'."""



docdict['irasa'] = """

    method: {'original', 'modified'}

        whether to compute the original implementation of IRASA or the modified update

        (default is 'original')

    resample_factors : {None, array_like}

        array of resampling factors to average across or None, in which a set 

        of factors are automatically computed (default is None).

    aperiodic_average : {'mean', 'median', 'median_bias', 'min'}

        method for averaging across irregularly resampled spectra to estimate

        the aperiodic component (default is 'median')."""



docdict['nperseg'] = """

    nperseg : int

        Length of each segment. Defaults to None, but if window is str or
@@ -222,6 +238,26 @@ docdict['multitaper_core'] = """ 
        or to iterate through each in a loop. Broadcasting is probably faster but

        more memory intensive. (Default value = 'broadcast')"""



docdict['glmperiodogram'] = """

    reg_categorical : dict or None

        Dictionary of covariate time series to be added as binary regessors. (Default value = None)

    reg_ztrans : dict or None

        Dictionary of covariate time series to be added as z-standardised regessors. (Default value = None)

    reg_unitmax : dict or None

        Dictionary of confound time series to be added as positive-valued unitmax regessors. (Default value = None)

    contrasts : dict or None

        Dictionary of contrasts to be computed in the model.

        (Default value = None, will add a simple contrast for each regressor)

    fit_method : {'pinv', 'lstsq', 'glmtools', sklearn estimator instance}

        Specifies how the GLM parameters will be estimated.

        * `pinv` uses the design matrix psuedo-inverse method

        * `lstsq` uses np.linalg.lstsq.

        * `glmtools` uses the OLSModel from the glmtools package.

        * A parametrised instance of a sklearn estimator is used if specified here. (Default value = 'pinv')

    fit_intercept : bool

        Specifies whether a constant valued 'intercept' regressor is included in the model. (Default value = True)"""





stft_funcs = ['apply_sliding_window',

              'compute_fft',

              'compute_stft',
@@ -246,4 +282,6 @@ stft_funcs = ['apply_sliding_window', 
              'sw_multitaper',

              'multitaper',

              'glm_periodogram',

              'glm_multitaper']
 
              'glm_multitaper',

              'irasa',

              'glm_irasa']

sails/modelfit.py

+0 −1
Original line number Diff line number Diff line
@@ -195,7 +195,6 @@ class AbstractLinearModel(AbstractAnam): 
        

    def simulate_data(self, num_samples=1000, num_realisations=1, use_cov=True):

        num_sources = self.nsignals

        print('heyehy')

        # Preallocate output

        Y = np.zeros((num_sources, num_samples, num_realisations))

sails/stft.py

+408 −229

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