def search_all_vs_one(key, plot=False, **kwargs): """For each pair of variables, look for a significant regression slope.""" sess = PINGDataSession() # username/password stored in an env variable for me. sess.login() all_data = compute_all_asymmetries(prefix=['MRI_cort_area', 'MRI_cort_thick', 'MRI_subcort_vol', 'DTI_fiber_vol']) results = [] keys = list(set(all_data.keys()) - set(('SubjID',))) for loop_key in keys: results.append(sess.regress(key, loop_key, plot=plot, **kwargs)) if plot: plt.show()
def search_all_vs_itself(covariates, plot=False, **kwargs): """For each pair of variables, look for a significant regression slope.""" def add_subplot(fh, *args): return fh.add_subplot(*args) if plot else None sess = PINGDataSession() # username/password stored in an env variable for me. sess.login() all_data = compute_all_asymmetries(prefix=['MRI_cort_area', 'MRI_cort_thick', 'MRI_subcort_vol', 'DTI_fiber_vol']) results = [] keys = list(set(all_data.keys()) - set(('SubjID',))) for X in keys: # Get relevant covariates try: added_covariates = sess.AI2flds(X) # Then regress on each side fh2 = plt.figure(figsize=(18, 6)) if plot else None sess.regress(X.replace('_AI', '_LH_PLUS_RH'), X, covariates=covariates, plot=add_subplot(fh2, 1, 3, 1), **kwargs) sess.regress(added_covariates[1], X, covariates=covariates + ['MRI_cort_area_ctx_total_LH_PLUS_RH'], plot=add_subplot(fh2, 1, 3, 2), **kwargs) sess.regress('MRI_cort_area_ctx_total_LH_PLUS_RH', X, covariates=covariates, plot=add_subplot(fh2, 1, 3, 3), **kwargs) except Exception as e: print("Failed for %s (%s); moving on..." % (X, e)) if plot: plt.show()