import os
from nilearn import image as nimg
#from nilearn import plotting as nplot
#import matplotlib.pyplot as plt
from nilearn import datasets
import numpy as np
import nibabel as nib
import pandas as pd
from os.path import join
from bids import BIDSLayout
from nilearn.maskers import NiftiMasker
from nilearn.maskers import NiftiLabelsMasker
from nilearn.connectome import ConnectivityMeasure
from nilearn.interfaces.fmriprep import load_confounds

#Use PyBIDS to parse BIDS data structure
sub = 'MTL0002'
ses = 'FU84'

# Define the local path to the atlas
atlas_path = "/project/rrg-villens/javan/nilearn_prep_all_part2/nilearn_data/schaefer_2018"

# Specify the paths to the atlas components
schaefer_img = f"{atlas_path}/Schaefer2018_200Parcels_7Networks_order_FSLMNI152_2mm.nii.gz"
schaefer_labels = f"{atlas_path}/Schaefer2018_200Parcels_7Networks_order.txt"

# Read labels from the text file
with open(schaefer_labels, "r") as f:
    labels = [line.strip() for line in f.readlines()]

# Creating and applying the masker
masker = NiftiLabelsMasker(labels_img=schaefer_img, labels=labels, 
                           standardize="zscore_sample", t_r=1)

connectome_measure = ConnectivityMeasure(
    kind="correlation",
    standardize="zscore_sample",
)

timeseries_schaefer200 = masker.fit_transform(f"/project/rrg-villens/dataset/PreventAD/mri/derivatives/fmriprep-20.2.7/fmriprep_w2n3/derivatives_APBv24_1_0/tedana_fmriprep_denoised/sub-{sub}_ses-{ses}_task-rest_run-01_space-MNI152NLin2009cAsym_res-2_trimmed_desc-aggrDenoised_bold.nii.gz")

correlation_matrices = connectome_measure.fit_transform([timeseries_schaefer200])
#save the timeseries csv files in a folder required

address = f"connectivity_schaefer200_{sub}_{ses}.csv"
add = f"/project/rrg-villens/javan/schaefer200_connectivity_wave2_3/{address}"
np.savetxt(add, correlation_matrices[0], delimiter=",")
#np.savetxt(add, timeseries_schaefer400, delimiter=",")
#np.savetxt(add2, timeseries_schaefer400, delimiter=",")

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