# load matplotlib to show figures in inline mode
%pylab inline

import numpy as np
import nibabel as nb
import dicom as dcm
from scipy import stats

table1 = np.genfromtxt('phenotype.csv', usemask=True, names=True, delimiter=',')

table1['Weight']

table2 = np.recfromtxt('phenotype.csv', usemask=True, names=True, delimiter=',')

np.mean(table2.Weight)

np.std(table2.Weight)

_ = hist(table2.Weight[table2.Weight.mask==False], 128)
_ = xlabel('Weight')
_ = ylabel('Frequency')

savefig('task1.png')

np.unique1d(table2.Gender)

import dicom as dcm

from glob import glob

fl = glob('/opt/data/NIPYPE_DATA/2475376/TfMRI_breathHold_1400/*.dcm')

TR = []
for filename in fl:
    dcmobj = dcm.read_file(filename)
    TR.append(float(dcmobj.RepetitionTime))

np.unique1d(TR)

img = nb.load('fmri.nii.gz')

a = img.get_data()

TSNR = np.mean(a, axis=3)/np.var(a, axis=3)

TSNR.shape

imshow(TSNR[:, :, 32], cmap=cm.hot)