def __init__(self, dataset, mask=None, features=None,
                 feature_threshold=None, min_voxels=None, min_studies=None):

        self.dataset = dataset
        self.masker = deepcopy(dataset.masker)

        # Condition study inclusion on specific features
        if features is not None:
            ids = dataset.get_studies(features=features,
                                      frequency_threshold=feature_threshold)
            data = dataset.get_image_data(ids, dense=False)
        else:
            data = dataset.image_table.data

        # Trim data based on minimum number of voxels or studies
        if min_studies is not None:
            av = self.masker.unmask(
                data.sum(1) >= min_studies, output='vector')
            self.masker.add({'voxels': av})

        if min_voxels is not None:
            data = data[:, np.array(min_voxels >= data.sum(0)).squeeze()]

        if mask is not None:
            self.masker.add({'roi': mask})

        self.data = data[self.masker.get_mask(['voxels', 'roi']), :].toarray()
dataset/ETHPy150Open neurosynth/neurosynth/neurosynth/analysis/cluster.py/Clusterable.__init__/swapped operands of `>=`