def test_trimmedmeanci(self):
"Tests the confidence intervals of the trimmed mean."
data = ma.array([545,555,558,572,575,576,578,580,
594,605,635,651,653,661,666])
assert_almost_equal(ms.trimmed_mean(data,0.2), 596.2, 1)
assert_equal(np.round(ms.trimmed_mean_ci(data,(0.2,0.2)),1),
[561.8, 630.6])
def test_trimmed_mean_ci():
# Tests the confidence intervals of the trimmed mean.
data = ma.array([545,555,558,572,575,576,578,580,
594,605,635,651,653,661,666])
assert_almost_equal(ms.trimmed_mean(data,0.2), 596.2, 1)
assert_equal(np.round(ms.trimmed_mean_ci(data,(0.2,0.2)),1),
[561.8, 630.6])
import numpy as np
import featurespace_fun as fsf
import matplotlib.pyplot as plt
from nilearn.masking import apply_mask
from nilearn.image import smooth_img
from scipy.stats import norm
from statsmodels.sandbox.stats.multicomp import fdrcorrection0
from scipy.stats import ttest_1samp
import sys
from scipy.stats.mstats import trimmed_mean_ci
from scipy.stats import ttest_1samp, trim_mean
models = sys.argv[1:]
#models = ['logBSC_H200_ds_conv', 'logMFS_ds']
mask = 'brainmask_group_template.nii.gz'
scores_bsc = np.arctanh(apply_mask(smooth_img(glob.glob('MaThe/avg_maps/model_{}_*whole*'.format(models[0])), fwhm=3.0), mask_img=mask))
scores_mfs = np.arctanh(apply_mask(smooth_img(glob.glob('MaThe/avg_maps/model_{}_*whole*'.format(models[1])), fwhm=3.0), mask_img=mask))
diff_scores = scores_bsc - scores_mfs
mean_diff = trim_mean(diff_scores, 0.08, axis=0)
trim_mean_ci = trimmed_mean_ci(diff_scores, (0.08, 0.08), axis=0)
which_ones = np.logical_not(np.logical_or(trim_mean_ci[0,:] > 0, trim_mean_ci[1,:] < 0))
mean_diff[which_ones] = 0
display = fsf.plot_diff_avg_whole(mean_diff, 0.001)
display.savefig('mean_diff_smoothed_trim_model_{}.svg'.format('_'.join(models)))
display.savefig('mean_diff_smoothed_trim_model_{}.png'.format('_'.join(models)))
fsf.save_map_avg_whole(mean_diff, threshold=None, model='diff_smooth_trim_'+'_'.join(models))