neurosynth/base/dataset.py
def _load_activations(self, filename):
        """ Load activation data from a text file.

        Args:
            filename (str): a string pointing to the location of the txt file
                to read from.
        """
        logger.info("Loading activation data from %s..." % filename)

        activations = pd.read_csv(filename, sep='\t')
        activations.columns = [col.lower()
                               for col in list(activations.columns)]

        # Make sure all mandatory columns exist
        mc = ['x', 'y', 'z', 'id', 'space']
        if (set(mc) - set(list(activations.columns))):
            logger.error(
                "At least one of mandatory columns (x, y, z, id, and space) "
                "is missing from input file.")
            return

        # Transform to target space where needed
        spaces = activations['space'].unique()
        xyz = activations[['x', 'y', 'z']].values
        for s in spaces:
            if s != self.transformer.target:
                inds = activations['space'] == s
                xyz[inds] = self.transformer.apply(s, xyz[inds])
        activations[['x', 'y', 'z']] = xyz

        # xyz --> ijk
        ijk = pd.DataFrame(
            transformations.xyz_to_mat(xyz), columns=['i', 'j', 'k'])
        activations = pd.concat([activations, ijk], axis=1)
        return activations
def _load_activations(self, filename):
        logger.info("Loading activation data from %s..." % filename)

        activations = pd.read_csv(filename, sep='\t')
        activations.columns = [col.lower()
                               for col in list(activations.columns)]

        # Make sure all mandatory columns exist
        mc = ['x', 'y', 'z', 'id', 'space']
        if (set(mc) - set(list(activations.columns))):
            logger.error(
                "At least one of mandatory columns (x, y, z, id, and space) "
                "is missing from input file.")
            return

        # Transform to target space where needed
        spaces = activations['space'].unique()
        xyz = activations[['x', 'y', 'z']].values
        for s in spaces:
            if s != self.transformer.target:
                inds = activations['space'] == s
                xyz[inds] = self.transformer.apply(s, xyz[inds])
        activations[['x', 'y', 'z']] = xyz

        # xyz --> ijk
        ijk = pd.DataFrame(
            transformations.xyz_to_mat(xyz), columns=['i', 'j', 'k'])
        activations = pd.concat([activations, ijk], axis=1)
        return activations
def _load_activations ( self , filename ) : logger . info ( "Loading activation data from %s..." % filename ) activations = pd . read_csv ( filename , sep = '\t' ) activations . columns = [ col . lower ( ) for col in list ( activations . columns ) ] # Make sure all mandatory columns exist mc = [ 'x' , 'y' , 'z' , 'id' , 'space' ] if ( set ( mc ) - set ( list ( activations . columns ) ) ) : logger . error ( "At least one of mandatory columns (x, y, z, id, and space) " "is missing from input file." ) return # Transform to target space where needed spaces = activations [ 'space' ] . unique ( ) xyz = activations [ [ 'x' , 'y' , 'z' ] ] . values for s in spaces : if s != self . transformer . target : inds = activations [ 'space' ] == s xyz [ inds ] = self . transformer . apply ( s , xyz [ inds ] ) activations [ [ 'x' , 'y' , 'z' ] ] = xyz # xyz --> ijk ijk = pd . DataFrame ( transformations . xyz_to_mat ( xyz ) , columns = [ 'i' , 'j' , 'k' ] ) activations = pd . concat ( [ activations , ijk ] , axis = 1 ) return activations
Load activation data from a text file.

        Args:
            filename (str): a string pointing to the location of the txt file
                to read from.
Load activation data from a text file .