First Level Analysis

First Level Analysis (within subject - within run/set)

Unlike the create_ev.ipynb script, the lvl1.py script is a simple python file that is executed using a second python "submit" script.

Go to /home/data/madlab/scripts/tools/sample_scripts to find the sample scripts wmaze_lvl1.py and wmaze_lvl1_submit.py.

• Open wmaze_lvl1.py
• Make sure you see #!/usr/bin/env python as the first line; if it's missing, add it!
• The next part is important -- it provides information about the current analysis. This is especially important for future you and other researchers in the lab: a description of what you're doing. It is also the perfect place to write special notes so you don't have to reinvent the wheel 37 times.

"""
=============================================================================
wmaze_fMRI: Mandy Thesis -- Fixed before Conditional -- Model 3 Version 1.0.0
=============================================================================
First level workflow for UM GE 750 wmaze task data
- WMAZE Model 3 Version 1.0.0
 1 - Normal general linear model (not LSS)
 0 - Does not account for the last three volumes (scanner artifacts)
 0 - No method to control or account for the last three volumes/trials
     - EV Directory (Model3)--- /home/data/madlab/data/mri/wmaze/scanner_behav/WMAZE_001/MRthesis/model3
- python wmaze_lvl1.py -s WMAZE_001
                       -o /home/data/madlab/data/mri/wmaze/frstlvl/wmaze/model3_1-0-0/lvl1
                       -w /home/data/madlab/scripts/wmaze/model3/model3_1-0-0/status/lvl1
**Note: DOF file is writing out in numpy hexadecimal format
    Example: 0x1.64p+7 
    print((1 + 6./0x10 + 4./0x100) * 2**7) = 178
"""

• Import all your libraries and tools (most of which will be Nipype)

import os
from nipype.pipeline.engine import Workflow, Node, MapNode
from nipype.interfaces.utility import IdentityInterface
from nipype.interfaces.utility import Function
from nipype.utils.misc import getsource
from nipype.interfaces.io import DataGrabber
from nipype.algorithms.modelgen import SpecifyModel
from nipype.interfaces.fsl.model import Level1Design
from nipype.interfaces.fsl.model import FEATModel
from nipype.interfaces.fsl.model import FILMGLS
from nipype.interfaces.fsl.model import ContrastMgr
from nipype.interfaces.fsl.utils import ImageMaths
from nipype.interfaces.io import DataSink
from nipype.interfaces.utility import Merge

###################
#### Functions ####
###################

# Grab the first dimension of an array/matrix
pop_lambda = lambda x : x[0]

def subjectinfo(subject_id):
    import os
    from nipype.interfaces.base import Bunch
    from copy import deepcopy
    import numpy as np
    base_proj_dir = '/home/data/madlab/data/mri/wmaze/scanner_behav'
    # Empty array to contain info from each run (index 1-6)
    output = []

    # For the current run, of which there are 6
    for curr_run in range(1,7):
        names = []
        onsets = []
        durations = []
        amplitudes = []


        # All EVs for *all before correct B* responses in the current run
        data_all_before_B_corr = np.genfromtxt(base_proj_dir + 
                                            '/{0}/MRthesis/model3/EVs/' +
                                          run{1}_all_before_B_corr.txt'.format(subject_id,
                                                                                                               curr_run), 
                                                                                                               dtype = str)

        # All EVs for *all before incorrect B* responses in the current run
        data_before_B_incorr = np.genfromtxt(base_proj_dir + 
                                             '/{0}/MRthesis/model3/EVs/' +                                             
                                             'r{1}_before_B_incorr.txt'.format(subject_id,
                                                                               curr_run), 
                                                                               dtype = str)
        
        # All remaining responses in the current run
        data_all_remain = np.genfromtxt(base_proj_dir + 
                                        '/{0}/MRthesis/model3/EVs/' +
                                        r{1}_all_remain.txt'.format(subject_id,
                                                                    curr_run), 
                                                                    dtype = str)
        # All remaining responses in the current run
        data_nonresponse = np.genfromtxt(base_proj_dir + 
                                         '/{0}/MRthesis/model3/EVs/' +
                                         'r{1}_nonresponse.txt'.format(subject_id,
                                                                       curr_run), 
                                                                       dtype = str)

        
        
        sequence = ['all_before_B']
        for curr_type in sequence:
            corr_array_name = eval('data_{0}_corr'.format(curr_type))
            incorr_array_name = eval('data_{0}_incorr'.format(curr_type))
            if incorr_array_name.size > 0: #MORE THAN ONE MISTAKE MADE
                # Array to contain name of trial:  _corr 
                curr_names = ['{0}_corr'.format(curr_type), '{0}_incorr'.format(curr_type)]
                curr_corr_onsets = map(float, corr_array_name[:,0])
                curr_corr_durations = map(float, corr_array_name[:,1])
                curr_corr_amplitudes = map(float, corr_array_name[:,2])

                # If there is only one incorrect response in the current run
                if incorr_array_name.size == 3: #ONLY ONE ERROR 
                    curr_incorr_onsets = [float(incorr_array_name[0])]
                    curr_incorr_durations = [float(incorr_array_name[1])]
                    curr_incorr_amplitudes = [float(incorr_array_name[2])]
                else: #MORE THAN ONE ERROR
                    curr_incorr_onsets = map(float, incorr_array_name[:,0])
                    curr_incorr_durations = map(float, incorr_array_name[:,1])
                    curr_incorr_amplitudes = map(float,incorr_array_name[:,2])

                # Variables to contain all onset times, durations, 
                # and amplitudes for the current run  
                curr_onsets = [curr_corr_onsets, curr_incorr_onsets]
                curr_durations = [curr_corr_durations, curr_incorr_durations]
                curr_amplitudes = [curr_corr_amplitudes, curr_incorr_amplitudes]

            else: #NO MISTAKES WERE MADE
                curr_names = ['{0}_corr'.format(curr_type)]
                curr_corr_onsets = map(float, corr_array_name[:,0])
                curr_corr_durations = map(float, corr_array_name[:,1])
                curr_corr_amplitudes = map(float, corr_array_name[:,2])
                # Variables containing all onsets, durations, 
                # and amplitudes for current stim type in the current run
                curr_onsets = [curr_corr_onsets]
                curr_durations = [curr_corr_durations]
                curr_amplitudes = [curr_corr_amplitudes]
            
            # Append the current sequence/run name to names 
            names.append(curr_names) 
            onsets.append(curr_onsets)
            durations.append(curr_durations)
            amplitudes.append(curr_amplitudes)

        ## ALL REMAINING TRIALS ## 
        curr_names = ['all_remaining']
        curr_corr_onsets = map(float, data_all_remaining[:,0])
        curr_corr_durations = map(float, data_all_remaining[:,1])
        curr_corr_amplitudes = map(float, data_all_remaining[:,2])

        curr_onsets = [curr_corr_onsets]
        curr_durations = [curr_corr_durations]
        curr_amplitudes = [curr_corr_amplitudes] 
        
        # Append the current sequence/run name to names 
        names.append(curr_names)  
        onsets.append(curr_onsets)
        durations.append(curr_durations)
        amplitudes.append(curr_amplitudes) 

        # If any element in names is a list instead of a single value, for those elements
        if any(isinstance(el, list) for el in names):
            # Unpacks subarrays into one mega array!
            names = [el for sublist in names for el in sublist]  
        if any(isinstance(el, list) for el in onsets):
            onsets = [el_o for sublist_o in onsets for el_o in sublist_o]
        if any(isinstance(el, list) for el in durations):
            durations = [el_d for sublist_d in durations for el_d in sublist_d]
        if any(isinstance(el, list) for el in amplitudes):
            amplitudes = [el_a for sublist_a in amplitudes for el_a in sublist_a]

        # Insert the contents of each run at the index of curr_run (1-6) 
        output.insert(curr_run,
                      Bunch(conditions = names,
                            onsets = deepcopy(onsets),
                            durations = deepcopy(durations),
                            amplitudes = deepcopy(amplitudes),
                            tmod = None,
                            pmod = None,
                            regressor_names = None,
                            regressors = None))
    return output