gca.py 文件源码

def process(self, obj_data):
        ''' 
        Perform component analysis on data:

        Results are added to the data wrapper as a dictionary with
        results['CA'] = Eigenvenctors
        results['Projection'] = Projection on to the eigenvectors

        @param obj_data: Data wrapper containing the data
        '''

        num_components = self.ap_paramList[0]()
        component_type = self.ap_paramList[1]()
        start_time = self.ap_paramList[2]()
        end_time = self.ap_paramList[3]()

        results = dict()
        results['start_date'] = start_time
        results['end_date'] = end_time

        if len(self.ap_paramList) >= 5:
            label_names = self.ap_paramList[4]()
        else:
            label_names = None

        cut_data = []
        for label, data, err in obj_data.getIterator():
            if label_names == None or label in label_names:
                cut_data.append(data[start_time:end_time])

        cut_data = np.array(cut_data)

        if len(cut_data) > 0:
            if component_type == 'ICA' :
                ca = FastICA(n_components = num_components)
            else:
                ca = PCA(n_components = num_components)

            time_projection = ca.fit_transform(cut_data.T)
            results['CA'] = ca
            results['Projection'] = time_projection

        else:
            results['CA'] = None
            results['Projection'] = None

        obj_data.addResult(self.str_description, results)