def robust_mean(log_values):
if log_values.shape[<NUM_LIT:1>] <= <NUM_LIT:3>:<EOL><INDENT>return numpy.nanmean(log_values, axis=<NUM_LIT:1>)<EOL><DEDENT>without_nans = numpy.nan_to_num(log_values)  <EOL>mask = (<EOL>(~numpy.isnan(log_values)) &<EOL>(without_nans <= numpy.nanpercentile(log_values, <NUM_LIT>, axis=<NUM_LIT:1>).reshape((-<NUM_LIT:1>, <NUM_LIT:1>))) &<EOL>(without_nans >= numpy.nanpercentile(log_values, <NUM_LIT>, axis=<NUM_LIT:1>).reshape((-<NUM_LIT:1>, <NUM_LIT:1>))))<EOL>return (without_nans * mask.astype(float)).sum(<NUM_LIT:1>) / mask.sum(<NUM_LIT:1>)<EOL>
Mean of values falling within the 25-75 percentiles.

Parameters
----------
log_values : 2-d numpy.array
    Center is computed along the second axis (i.e. per row).

Returns
-------
center : numpy.array of length log_values.shape[1]