from os import listdir, path

from os.path import join

import numpy as np

from nipype.interfaces.utility import IdentityInterface

from nipype.interfaces.io import SelectFiles, DataSink

from os.path import join as opj

from nipype import Workflow, Node

import interfaces.ComputeCorrelationMatrix as c

preprocessing_path = '/home/jrudascas/Desktop/Tesis/data/datasets/mcs/output/datasink/preprocessing'

hypergraph_path = '/home/jrudascas/PycharmProjects/hypergraph_analysis/_hypergraph_median.txt'

correlation_matrix_out_file = 'hypergraph_correlation_matrix.txt'

correlation_matrix_plot_out_file = 'hypergraph_correlation_matrix_plot.png'

experiment_dir = join(preprocessing_path, 'output/')

output_dir = 'datasink'

working_dir = 'workingdir'

subject_list = ['sub-MCS_AI',

'sub-MCS_NL']

#'sub-MCS_PA',

#'sub-MCS_PJ',

#'sub-MCS_RB',

#'sub-MCS_RP',

#'sub-MCS_SC',

#'sub-MCS_ST',

#'sub-MCS_TP',

#'sub-MCS_VW']

# Infosource-a function free node to iterate over the list of subject names

infosource = Node(IdentityInterface(fields=['subject_id']), name="infosource")

infosource.iterables = [('subject_id', subject_list)]

# SelectFiles-to grab the data (alternativ to DataGrabber)

time_serie_file = opj('{subject_id}', 'parcellation_from_lasso/time_series.txt')

templates = {'time_series': time_serie_file}

selectfiles = Node(SelectFiles(templates, base_directory=preprocessing_path), name="selectfiles")

datasink = Node(DataSink(base_directory=experiment_dir, container=output_dir), name="datasink")

experiment_dir = opj(preprocessing_path, 'output/')

preproc = Workflow(name='preproc')

preproc.base_dir = opj(experiment_dir, working_dir)

compute_correlation_matrix = Node(

c.ComputeCorrelationMatrix(hypergraph_path=hypergraph_path, correlation_matrix_out_file=correlation_matrix_out_file,

correlation_matrix_plot_out_file=correlation_matrix_plot_out_file),

name='compute_correlation_matrix')

preproc.connect([(infosource, selectfiles, [('subject_id', 'subject_id')]),

(selectfiles, compute_correlation_matrix, [('time_series', 'time_series_path')]),

(compute_correlation_matrix, datasink,

[('correlation_matrix_out_file', 'preprocessing.@correlation_matrix_out_file')]),

(compute_correlation_matrix, datasink,

[('correlation_matrix_plot_out_file', 'preprocessing.@correlation_matrix_plot_out_file')])])

plugin_args = {'n_procs':1}

preproc.run(plugin='MultiProc', plugin_args=plugin_args)

#14:33