neurosynth/base/dataset.py
def trim(self, ids):
        """ Trim ImageTable to keep only the passed studies. This is a
        convenience method, and should generally be avoided in favor of
        non-destructive alternatives that don't require slicing (e.g.,
            matrix multiplication). """
        self.data = self.get_image_data(ids, dense=False)  # .tocoo()
        idxs = np.where(np.in1d(np.array(self.ids), np.array(ids)))[0]
        self.ids = [self.ids[i] for i in idxs]
def trim(self, ids):
        """ Trim ImageTable to keep only the passed studies. This is a
        convenience method, and should generally be avoided in favor of
        non-destructive alternatives that don't require slicing (e.g.,
            matrix multiplication). """
        self.data = self.get_image_data(ids, dense=False)  # .tocoo()
        idxs = np.where(np.in1d(np.array(self.ids), np.array(ids)))[0]
        self.ids = [self.ids[i] for i in idxs]
def trim ( self , ids ) : self . data = self . get_image_data ( ids , dense = False ) # .tocoo() idxs = np . where ( np . in1d ( np . array ( self . ids ) , np . array ( ids ) ) ) [ 0 ] self . ids = [ self . ids [ i ] for i in idxs ]
Trim ImageTable to keep only the passed studies. This is a
        convenience method, and should generally be avoided in favor of
        non-destructive alternatives that don't require slicing (e.g.,
            matrix multiplication).
Trim ImageTable to keep only the passed studies . This is a convenience method and should generally be avoided in favor of non - destructive alternatives that don t require slicing ( e . g . matrix multiplication ) .