if __name__ == '__main__': # Make the traits data frame. traits_outrm = traits.drop(['Age', 'etiv'], axis=1).apply(qc.remove_outliers) traits_outrm['age'] = traits['Age'] traits_outrm['icv'] = traits['etiv'] traits1 = traits_outrm # Make jobs univariates = traits1.columns.tolist() univariates.remove('age') univariates.remove('icv') for trait in univariates: make_jobs.make_single_job( 'asymmetries_mpi', trait, [trait], cov='age sex n_icv', old_peds=False ) for chrom in xrange(1, 23): make_jobs.make_single_job( 'asymmetries_yale_1', '%s_%i' % (trait, chrom), [trait], chrom=chrom ) for structure in structures: make_jobs.make_single_job( 'asymmetries_mpi', 'rhog_' + structure, ['Left_%s' % structure, 'Right_%s' % structure], cov='age sex n_icv', tests='-testrhog', old_peds=False ) for chrom in xrange(1, 23): make_jobs.make_single_job( 'asymmetries_yale_1', 'rhog_%s_%i' %(structure, chrom), ['Left_%s' % struct, 'Right_%s' % struct], tests='-testrhog',
cols = [c for c in df.columns if '_tmp' in c and 'bl_' in c] y= df[cols].sum(axis=1) / df[[c for c in df.columns if '_area' in c and 'bl_' in c and '_p' not in c[-4:] and 'total' not in c]].sum(axis=1) print y.head() for region in regions: df['bl_%s_thickness_p' % region] = df['bl_%s_thickness_x' % region] - x cols = [c for c in df.columns if 'thickness_p' in c] df = df.drop([c for c in df.columns if '_tmp' in c], axis=1) df['avg_thick'] = x dont_screen_these = [ 'gcog_vm', 'gcog_wm', 'gcog_sm', 'gcog_ef', 'gcog_g', 'bifg_vm', 'bifg_wm', 'bifg_sm', 'bifg_ef', 'bifg_g', 'age' ] for trait in [c for c in df.columns if 'bl_' in c[:3]]: make_jobs.make_single_job( 'braincog', trait, [trait], cov='age^1,2#sex', old_peds=False, resid=True ) df.to_csv(paths.pj(paths.JOBS_DIR, 'braincog', '_traits.csv'), index_label='id')