test_graynet.py 文件源码

def test_run_roi_stats_via_API():
    "Tests whether roi stats can be computed (not their accuracy) and the return values match in size."

    summary_methods = ['median', 'mean', 'std', 'variation', 'entropy', 'skew', 'kurtosis']
    # 'mode' returns more than one value; 'gmean' requires only positive values,
    # 'hmean' can not always be computed
    from scipy.stats import  trim_mean, kstat
    from functools import partial
    trimmed_mean = partial(trim_mean, proportiontocut=0.05)
    third_kstat = partial(kstat, n=3)

    summary_methods.extend([trimmed_mean, third_kstat])
    # checking support for nan-handling callables
    summary_methods.extend([np.nanmedian, np.nanmean])

    for summary_method in summary_methods:
        roi_medians = graynet.roiwise_stats_indiv(subject_id_list, fs_dir, base_feature=base_feature,
                                                  chosen_roi_stats=summary_method, atlas=atlas,
                                                  smoothing_param=fwhm, out_dir=out_dir, return_results=True)
        for sub in subject_id_list:
            if roi_medians[sub].size != num_roi_wholebrain:
                raise ValueError('invalid summary stats - #nodes do not match.')