neurosynth/analysis/decode.py
def _load_features_from_dataset(self, features=None, image_type=None,
                                    threshold=0.001):
        """ Load feature image data from the current Dataset instance. See
        load_features() for documentation.
        """
        self.feature_names = self.dataset.feature_table.feature_names
        if features is not None:
            self.feature_names = [f for f in features
                                  if f in self.feature_names]
        from neurosynth.analysis import meta
        self.feature_images = meta.analyze_features(
            self.dataset, self.feature_names, image_type=image_type,
            threshold=threshold)
        # Apply a mask if one was originally passed
        if self.masker.layers:
            in_mask = self.masker.get_mask(in_global_mask=True)
            self.feature_images = self.feature_images[in_mask, :]
def _load_features_from_dataset(self, features=None, image_type=None,
                                    threshold=0.001):
        """ Load feature image data from the current Dataset instance. See
        load_features() for documentation.
        """
        self.feature_names = self.dataset.feature_table.feature_names
        if features is not None:
            self.feature_names = [f for f in features
                                  if f in self.feature_names]
        from neurosynth.analysis import meta
        self.feature_images = meta.analyze_features(
            self.dataset, self.feature_names, image_type=image_type,
            threshold=threshold)
        # Apply a mask if one was originally passed
        if self.masker.layers:
            in_mask = self.masker.get_mask(in_global_mask=True)
            self.feature_images = self.feature_images[in_mask, :]
def _load_features_from_dataset ( self , features = None , image_type = None , threshold = 0.001 ) : self . feature_names = self . dataset . feature_table . feature_names if features is not None : self . feature_names = [ f for f in features if f in self . feature_names ] from neurosynth . analysis import meta self . feature_images = meta . analyze_features ( self . dataset , self . feature_names , image_type = image_type , threshold = threshold ) # Apply a mask if one was originally passed if self . masker . layers : in_mask = self . masker . get_mask ( in_global_mask = True ) self . feature_images = self . feature_images [ in_mask , : ]
Load feature image data from the current Dataset instance. See
        load_features() for documentation.
Load feature image data from the current Dataset instance . See load_features () for documentation .