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')
