In [1]:

    

from scipy import stats
import pandas as pd
import numpy as np
import nibabel as nib
import os
from os import listdir
from os.path import isfile, join


    

In [2]:

    

# first import the input list from the csv file
import pandas as pd
# read in csv
df_phen = pd.read_csv('./data/SelectedSubjects.csv')
selected = list(df_phen.filename_npy)


    

In [5]:

    

grdnt_slope = []
for i in selected:
    # load gradients
    # print i
    filename = i
    grdnt = np.load("./data/Outputs/Regs/" + filename)
    # do we need a specific ordering of the nodes??
    x = list(xrange(392))
    temp = []
    for ii in range(10):
        y = sorted(grdnt[:,ii]) # just sort in ascending order?
        slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
        temp.append(slope)
        
    grdnt_slope.append(temp)
grdnt_slope = np.array(grdnt_slope)
# make it into a dataframe
data_grdnt = pd.DataFrame(grdnt_slope)
data_grdnt['file'] = selected


    

In [12]:

    

output = df_phen.merge(data_grdnt, left_on='filename_npy',right_on='file',how='outer')
output.to_csv('./data/Gradients.csv')


    

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