neurosynth/base/dataset.py
def get_ids(self, features, threshold=0.0, func=np.sum, get_weights=False):
        """ Returns a list of all studies in the table that meet the desired
        feature-based criteria.

        Will most commonly be used to retrieve studies that use one or more
        features with some minimum frequency; e.g.,:

            get_ids(['fear', 'anxiety'], threshold=0.001)

        Args:
            features (lists): a list of feature names to search on.
            threshold (float): optional float indicating threshold features
                must pass to be included.
            func (Callable): any numpy function to use for thresholding
                (default: sum). The function will be applied to the list of
                features and the result compared to the threshold. This can be
                used to change the meaning of the query in powerful ways. E.g,:
                    max: any of the features have to pass threshold
                        (i.e., max > thresh)
                    min: all features must each individually pass threshold
                        (i.e., min > thresh)
                    sum: the summed weight of all features must pass threshold
                        (i.e., sum > thresh)
            get_weights (bool): if True, returns a dict with ids => weights.

        Returns:
            When get_weights is false (default), returns a list of study
                names. When true, returns a dict, with study names as keys
                and feature weights as values.
        """
        if isinstance(features, str):
            features = [features]
        features = self.search_features(features)  # Expand wild cards
        feature_weights = self.data.ix[:, features]
        weights = feature_weights.apply(func, 1)
        above_thresh = weights[weights >= threshold]
        # ids_to_keep = self.ids[above_thresh]
        return above_thresh if get_weights else list(above_thresh.index)
def get_ids(self, features, threshold=0.0, func=np.sum, get_weights=False):
        if isinstance(features, str):
            features = [features]
        features = self.search_features(features)  # Expand wild cards
        feature_weights = self.data.ix[:, features]
        weights = feature_weights.apply(func, 1)
        above_thresh = weights[weights >= threshold]
        # ids_to_keep = self.ids[above_thresh]
        return above_thresh if get_weights else list(above_thresh.index)
def get_ids ( self , features , threshold = 0.0 , func = np . sum , get_weights = False ) : if isinstance ( features , str ) : features = [ features ] features = self . search_features ( features ) # Expand wild cards feature_weights = self . data . ix [ : , features ] weights = feature_weights . apply ( func , 1 ) above_thresh = weights [ weights >= threshold ] # ids_to_keep = self.ids[above_thresh] return above_thresh if get_weights else list ( above_thresh . index )
Returns a list of all studies in the table that meet the desired
        feature-based criteria.

        Will most commonly be used to retrieve studies that use one or more
        features with some minimum frequency; e.g.,:

            get_ids(['fear', 'anxiety'], threshold=0.001)

        Args:
            features (lists): a list of feature names to search on.
            threshold (float): optional float indicating threshold features
                must pass to be included.
            func (Callable): any numpy function to use for thresholding
                (default: sum). The function will be applied to the list of
                features and the result compared to the threshold. This can be
                used to change the meaning of the query in powerful ways. E.g,:
                    max: any of the features have to pass threshold
                        (i.e., max > thresh)
                    min: all features must each individually pass threshold
                        (i.e., min > thresh)
                    sum: the summed weight of all features must pass threshold
                        (i.e., sum > thresh)
            get_weights (bool): if True, returns a dict with ids => weights.

        Returns:
            When get_weights is false (default), returns a list of study
                names. When true, returns a dict, with study names as keys
                and feature weights as values.
Returns a list of all studies in the table that meet the desired feature - based criteria .