def _load_activations(self, filename):
logger.info("<STR_LIT>" % filename)<EOL>activations = pd.read_csv(filename, sep='<STR_LIT:\t>')<EOL>activations.columns = [col.lower()<EOL>for col in list(activations.columns)]<EOL>mc = ['<STR_LIT:x>', '<STR_LIT:y>', '<STR_LIT:z>', '<STR_LIT:id>', '<STR_LIT>']<EOL>if (set(mc) - set(list(activations.columns))):<EOL><INDENT>logger.error(<EOL>"<STR_LIT>"<EOL>"<STR_LIT>")<EOL>return<EOL><DEDENT>spaces = activations['<STR_LIT>'].unique()<EOL>xyz = activations[['<STR_LIT:x>', '<STR_LIT:y>', '<STR_LIT:z>']].values<EOL>for s in spaces:<EOL><INDENT>if s != self.transformer.target:<EOL><INDENT>inds = activations['<STR_LIT>'] == s<EOL>xyz[inds] = self.transformer.apply(s, xyz[inds])<EOL><DEDENT><DEDENT>activations[['<STR_LIT:x>', '<STR_LIT:y>', '<STR_LIT:z>']] = xyz<EOL>ijk = pd.DataFrame(<EOL>transformations.xyz_to_mat(xyz), columns=['<STR_LIT:i>', '<STR_LIT>', '<STR_LIT:k>'])<EOL>activations = pd.concat([activations, ijk], axis=<NUM_LIT:1>)<EOL>return activations<EOL>
Load activation data from a text file.

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