diff --git a/DAL_ABCD_merged_pcqcinfo_importer.m b/DAL_ABCD_merged_pcqcinfo_importer.m
deleted file mode 100644
index e53ab63..0000000
--- a/DAL_ABCD_merged_pcqcinfo_importer.m
+++ /dev/null
@@ -1,122 +0,0 @@
-function data = DAL_ABCD_merged_pcqcinfo_importer(filename)
-%% Import data from text file.
-% Script for importing data from the following text file:
-%
-%    spreadsheets/DAL_ABCD_merged_pcqcinfo.csv
-%
-% To extend the code to different selected data or a different text file,
-% generate a function instead of a script.
-
-% Auto-generated by MATLAB on 2018/08/14 13:31:43
-
-%% Initialize variables.
-delimiter = ',';
-startRow = 2;
-
-%% Read columns of data as text:
-% For more information, see the TEXTSCAN documentation.
-formatSpec = '%s%s%s%s%s%s%s%s%s%s%s%*s%*s%*s%*s%*s%*s%*s%*s%*s%s%s%s%*s%*s%*s%s%s%s%s%s%*s%s%*s%*s%*s%*s%*s%*s%*s%*s%*s%s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%s%s%[^\n\r]';
-
-%% Open the text file.
-fileID = fopen(filename,'r');
-
-%% Read columns of data according to the format.
-% This call is based on the structure of the file used to generate this
-% code. If an error occurs for a different file, try regenerating the code
-% from the Import Tool.
-dataArray = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'TextType', 'string', 'HeaderLines' ,startRow-1, 'ReturnOnError', false, 'EndOfLine', '\r\n');
-
-%% Close the text file.
-fclose(fileID);
-
-%% Convert the contents of columns containing numeric text to numbers.
-% Replace non-numeric text with NaN.
-raw = repmat({''},length(dataArray{1}),length(dataArray)-1);
-for col=1:length(dataArray)-1
-    raw(1:length(dataArray{col}),col) = mat2cell(dataArray{col}, ones(length(dataArray{col}), 1));
-end
-numericData = NaN(size(dataArray{1},1),size(dataArray,2));
-
-for col=[9,11,12,17,18,19,20,21]
-    % Converts text in the input cell array to numbers. Replaced non-numeric
-    % text with NaN.
-    rawData = dataArray{col};
-    for row=1:size(rawData, 1)
-        % Create a regular expression to detect and remove non-numeric prefixes and
-        % suffixes.
-        regexstr = '(?<prefix>.*?)(?<numbers>([-]*(\d+[\,]*)+[\.]{0,1}\d*[eEdD]{0,1}[-+]*\d*[i]{0,1})|([-]*(\d+[\,]*)*[\.]{1,1}\d+[eEdD]{0,1}[-+]*\d*[i]{0,1}))(?<suffix>.*)';
-        try
-            result = regexp(rawData(row), regexstr, 'names');
-            numbers = result.numbers;
-            
-            % Detected commas in non-thousand locations.
-            invalidThousandsSeparator = false;
-            if numbers.contains(',')
-                thousandsRegExp = '^\d+?(\,\d{3})*\.{0,1}\d*$';
-                if isempty(regexp(numbers, thousandsRegExp, 'once'))
-                    numbers = NaN;
-                    invalidThousandsSeparator = true;
-                end
-            end
-            % Convert numeric text to numbers.
-            if ~invalidThousandsSeparator
-                numbers = textscan(char(strrep(numbers, ',', '')), '%f');
-                numericData(row, col) = numbers{1};
-                raw{row, col} = numbers{1};
-            end
-        catch
-            raw{row, col} = rawData{row};
-        end
-    end
-end
-
-
-%% Split data into numeric and string columns.
-rawNumericColumns = raw(:, [9,11,12,17,18,19,20,21]);
-rawStringColumns = string(raw(:, [1,2,3,4,5,6,7,8,10,13,14,15,16,22,23]));
-
-
-%% Replace non-numeric cells with NaN
-R = cellfun(@(x) ~isnumeric(x) && ~islogical(x),rawNumericColumns); % Find non-numeric cells
-rawNumericColumns(R) = {NaN}; % Replace non-numeric cells
-
-%% Make sure any text containing <undefined> is properly converted to an <undefined> categorical
-for catIdx = [1,3,4,5,6,7,8,9,10,11,12,14,15]
-    idx = (rawStringColumns(:, catIdx) == "<undefined>");
-    rawStringColumns(idx, catIdx) = "";
-end
-
-%% Create output variable
-DALABCDmergedpcqcinfo = table;
-DALABCDmergedpcqcinfo.pGUID = categorical(rawStringColumns(:, 1));
-DALABCDmergedpcqcinfo.VisitID = rawStringColumns(:, 2);
-DALABCDmergedpcqcinfo.EventName = categorical(rawStringColumns(:, 3));
-DALABCDmergedpcqcinfo.SessionType = categorical(rawStringColumns(:, 4));
-DALABCDmergedpcqcinfo.SiteName = categorical(rawStringColumns(:, 5));
-DALABCDmergedpcqcinfo.SeriesType = categorical(rawStringColumns(:, 6));
-DALABCDmergedpcqcinfo.ABCD_Compliant = categorical(rawStringColumns(:, 7));
-DALABCDmergedpcqcinfo.SeriesDescription = categorical(rawStringColumns(:, 8));
-DALABCDmergedpcqcinfo.Completed = cell2mat(rawNumericColumns(:, 1));
-DALABCDmergedpcqcinfo.AdditionalInfo = categorical(rawStringColumns(:, 9));
-DALABCDmergedpcqcinfo.NumberOfFiles = cell2mat(rawNumericColumns(:, 2));
-DALABCDmergedpcqcinfo.SeriesNumber = cell2mat(rawNumericColumns(:, 3));
-DALABCDmergedpcqcinfo.Manufacturer = categorical(rawStringColumns(:, 10));
-DALABCDmergedpcqcinfo.SequenceName = categorical(rawStringColumns(:, 11));
-DALABCDmergedpcqcinfo.StudyInstanceUID = categorical(rawStringColumns(:, 12));
-DALABCDmergedpcqcinfo.SeriesInstanceUID = rawStringColumns(:, 13);
-DALABCDmergedpcqcinfo.StudyDate = cell2mat(rawNumericColumns(:, 4));
-DALABCDmergedpcqcinfo.StudyTime = cell2mat(rawNumericColumns(:, 5));
-DALABCDmergedpcqcinfo.SeriesTime = cell2mat(rawNumericColumns(:, 6));
-DALABCDmergedpcqcinfo.SiteID = cell2mat(rawNumericColumns(:, 7));
-DALABCDmergedpcqcinfo.QC = cell2mat(rawNumericColumns(:, 8));
-DALABCDmergedpcqcinfo.stype = categorical(rawStringColumns(:, 14));
-DALABCDmergedpcqcinfo.ManufacturersModelName = categorical(rawStringColumns(:, 15));
-
-%% Clear temporary variables
-clearvars filename delimiter startRow formatSpec fileID dataArray ans raw col numericData rawData row regexstr result numbers invalidThousandsSeparator thousandsRegExp rawNumericColumns rawStringColumns R catIdx idx;
-
-%%
-data = DALABCDmergedpcqcinfo;
-QC_flag = data.QC == 1;
-cleandata_idx = QC_flag;
-
diff --git a/FSL_identity_transformation_matrix.mat b/FSL_identity_transformation_matrix.mat
deleted file mode 100644
index 00451f0..0000000
--- a/FSL_identity_transformation_matrix.mat
+++ /dev/null
@@ -1,4 +0,0 @@
-1.000000 0.000000 0.000000 0.000000 
-0.000000 1.000000 0.000000 0.000000 
-0.000000 0.000000 1.000000 0.000000 
-0.000000 0.000000 0.000000 1.000000 
diff --git a/correct_jsons.py b/correct_jsons.py
deleted file mode 100755
index 958a414..0000000
--- a/correct_jsons.py
+++ /dev/null
@@ -1,115 +0,0 @@
-#! /usr/bin/env python3
-
-import json,os,sys,argparse,re
-
-__doc__ = \
-"""
-This scripts is meant to correct ABCD BIDS input data to
-conform to the Official BIDS Validator.
-"""
-__version__ = "1.0.0"
-
-def read_json_field(json_path, json_field):
-
-    with open(json_path, 'r') as f:
-        data = json.load(f)
-
-    if json_field in data:
-        return data[json_field]
-    else:
-        return None
-
-def remove_json_field(json_path, json_field):
-
-    with open(json_path, 'r+') as f:
-        data = json.load(f)
-        
-        if json_field in data:
-            del data[json_field]
-            f.seek(0)
-            json.dump(data, f, indent=4)
-            f.truncate()
-            flag = True
-        else:
-            flag = False
-
-    return flag
-
-def update_json_field(json_path, json_field, value):
-
-    with open(json_path, 'r+') as f:
-        data = json.load(f)
-
-        if json_field in data:
-            flag = True
-        else:
-            flag = False
-
-        data[json_field] = value
-        f.seek(0)
-        json.dump(data, f, indent=4)
-        f.truncate()
-
-    return flag
-
-def main(argv=sys.argv):
-    parser = argparse.ArgumentParser(
-        prog='correct_jsons.py',
-        description=__doc__,
-        usage='%(prog)s BIDS_DIR'
-    )
-    parser.add_argument(
-        'BIDS_DIR',
-        help='Path to the input BIDS dataset root directory.  Read more '
-             'about the BIDS standard in the link in the description.  It is '
-             'recommended to use Dcm2Bids to convert from participant dicoms '
-             'into BIDS format.'
-    )
-    parser.add_argument(
-        '--version', '-v', action='version', version='%(prog)s ' + __version__
-    )
-
-    args = parser.parse_args()
-
-    for root, dirs, files in os.walk(args.BIDS_DIR):
-        for filename in files:
-            fn, ext = os.path.splitext(filename)
-
-            if ext == '.json':
-                json_path = os.path.join(root, filename)
-
-                with open(json_path, 'r') as f:
-                    data = json.load(f)
-
-                # If TotalReadoutTime is missing from fmap JSON
-                if ('fmap' in root or 'func' in root) and 'TotalReadoutTime' not in data:
-                    # Then check for EffectiveEchoSpacing and ReconMatrixPE
-                    if 'EffectiveEchoSpacing' in data and 'ReconMatrixPE' in data:
-                        # If both are present then update the JSON with a calculated TotalReadoutTime
-                        EffectiveEchoSpacing = data['EffectiveEchoSpacing']
-                        ReconMatrixPE = data['ReconMatrixPE']
-                        # Calculated TotalReadoutTime = EffectiveEchoSpacing * (ReconMatrixPE - 1)
-                        TotalReadoutTime = EffectiveEchoSpacing * (ReconMatrixPE - 1)
-                        update_json_field(json_path, 'TotalReadoutTime', TotalReadoutTime)
-
-                    # If EffectiveEchoSpacing is missing print error
-                    if 'EffectiveEchoSpacing' not in data:
-                        print(json_path + ': No EffectiveEchoSpacing')
-
-                    # If ReconMatrixPE is missing print error
-                    if 'ReconMatrixPE' not in data:
-                        print(json_path + ': No ReconMatrixPE')
-
-                # Find the IntendedFor field that is a non-empty list
-                if 'fmap' in root and 'IntendedFor' in data and len(data['IntendedFor']) > 0:
-                    # Regular expression replace all paths in that list with a relative path to ses-SESSION
-                    intended_list = data['IntendedFor']
-                    corrected_intended_list = [re.sub(r'.*(ses-.*_ses-.+)','\g<1>',entry) for entry in intended_list]
-                    update_json_field(json_path, 'IntendedFor', corrected_intended_list)
-
-                # Remove SliceTiming field from func JSONs
-                if 'func' in root and 'SliceTiming' in data:
-                    remove_json_field(json_path, 'SliceTiming')
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/data_gatherer.m b/data_gatherer.m
deleted file mode 100755
index 84df85f..0000000
--- a/data_gatherer.m
+++ /dev/null
@@ -1,57 +0,0 @@
-%% variable initialization
-
-clear variables
-load mapping.mat
-
-QC_file = 'spreadsheets/DAL_ABCD_QC_merged_pcqcinfo.csv';
-image03_file = 'spreadsheets/image03.txt';
-output_csv = 'spreadsheets/ABCD_good_and_bad_series_table.csv';
-
-%% QC parsing
-
-data = DAL_ABCD_merged_pcqcinfo_importer(QC_file);
-
-for i = 1:height(data) 
-    if data.SeriesTime(i) < 100000 
-        data.timestamp{i} = ['0' num2str(floor(data.SeriesTime(i)))];
-    else
-        data.timestamp{i} = num2str(floor(data.SeriesTime(i)));
-    end
-end
-data.CleanFlag = cleandata_idx;
-
-%% image03 parsing
-
-image03 = image03_importer(image03_file);
-
-for i = 1:height(image03)
-    image03.timestamp{i} = image03.image_file{i}(end-10:end-5);
-end
-
-image03_1 = innerjoin(image03,map_image03_qc);
-image03_2 = innerjoin(image03_1,map_image03_descriptor);
-
-image03_2 = sortrows(image03_2,'image_file','ascend');
-image03_2.Properties.VariableNames{1} = 'pGUID';
-image03_2.Properties.VariableNames{9} = 'EventName';
-
-%% table joins
-
-data_1 = innerjoin(data,map_qc_descriptor);
-data_1 = sortrows(data_1,'pGUID','ascend');
-
-% Hack to deal with quotations around strings in table
-foo = image03_2.SeriesType;
-[l,w] = size(foo);
-for i=1:l
-    foo(i) = strjoin(['"' string(foo(i)) '"'],'');
-end
-image03_2.SeriesType = foo;
-
-data_2 = innerjoin(data_1,image03_2);
-
-
-%% final output table (path hardcoded)
-
-writetable(data_2,output_csv);
-
diff --git a/eta_squared b/eta_squared
deleted file mode 100755
index a3a8f1a..0000000
Binary files a/eta_squared and /dev/null differ
diff --git a/good_bad_series_parser.py b/good_bad_series_parser.py
deleted file mode 100755
index 11f355d..0000000
--- a/good_bad_series_parser.py
+++ /dev/null
@@ -1,214 +0,0 @@
-#! /usr/bin/env python
-
-
-import pandas as pd
-import csv
-import subprocess
-import os
-
-#######################################
-# Read in ABCD_good_and_bad_series_table.csv that is continually updated
-#   Create a log of all subjects that have been checked
-#   If they are not able to be processed report what is wrong with them
-#
-#######################################
-
-
-# Logging variables
-num_sub_visits = 0
-num_siemens = 0
-num_ge = 0
-num_philips = 0
-num_rsfmri = 0
-num_sst = 0
-num_mid = 0
-num_nback = 0
-num_t2 = 0
-num_invalid = 0
-num_valid = 0
-num_subjects_after_checks = 0
-with open('abcd_download_log.csv','w') as f:
-    writer = csv.writer(f)
-
-
-    # Read csv as pandas dataframe, drop duplicate entries, sort, and group by subject/visit
-    series_csv = "./spreadsheets/ABCD_good_and_bad_series_table.csv"
-    series_df = pd.read_csv(series_csv)
-    subject_dfs = series_df.drop_duplicates().sort_values(by='SeriesTime', ascending=True).groupby(["pGUID", "EventName"])
-
-    for name, group in subject_dfs:
-
-        ### Logging information
-        # initialize logging variables
-        has_t1 = 0
-        has_t2 = 0
-        has_sefm = 0
-        has_rsfmri = 0
-        has_mid = 0
-        has_sst = 0
-        has_nback = 0
-
-        # TODO: Add pGUID and EventName (Subject ID and Visit) to csv for logging information
-        num_sub_visits += 1
-
-        scanner = group.iloc[0]['Manufacturer']
-        if scanner == 'Philips Medical Systems':
-            num_philips += 1
-        elif scanner == 'GE MEDICAL SYSTEMS':
-            num_ge += 1
-        elif scanner == 'SIEMENS':
-            num_siemens += 1
-        else:
-            print("Unexpected scanner type: %s" % scanner)
-
-        # TODO: Create tgz directory if it doesn't already exist
-        sub_id = name[0]
-        visit = name[1]
-        sub = "sub-" + sub_id.replace("_","")
-        #print(sub_id, visit)
-        tgz_dir = './download' + sub + '/' + visit
-        new_tgz_dir = './new_download/' + sub + '/' + visit
-        if os.path.exists(tgz_dir):
-            print("{0} already exists from old download. Updating now.".format(name))
-            #continue
-        elif os.path.exists(new_tgz_dir):
-            print("{0} already exists from the most recent download. Updating now.".format(name))
-            tgz_dir = new_tgz_dir
-        else:
-            print("{0} downloading now.".format(name))
-            tgz_dir = new_tgz_dir
-            os.makedirs(tgz_dir)
-
-        ### Get ready to download only good QC'd data that passes all of our criteria !
-
-        passed_QC_group = group.loc[group['QC'] == 1.0]
-
-        file_paths = []
-
-        ### Identify valid scans
-        # Download only T1, T2, fMRI_FM_PA, fMRI_FM_AP, fMRI_FM, rsfMRI, fMRI_MID_task, fMRI_SST_task, fMRI_nBack_task
-
-        ## Check if T1_NORM exists and download that instead of just T1
-        #   If there is a T1_NORM in the df of good T1s then use it. Else just use good T1
-        T1_df = passed_QC_group.loc[passed_QC_group['image_description'] == 'ABCD-T1-NORM']
-        if T1_df.empty:
-            T1_df = passed_QC_group.loc[passed_QC_group['image_description'] == 'ABCD-T1']
-            if T1_df.empty:
-                has_t1 = 0 # No T1s. Invalid subject
-            else:
-                for file_path in T1_df['image_file']:
-                    file_paths += [file_path]
-                has_t1 = T1_df.shape[0]
-        else:
-            for file_path in T1_df['image_file']:
-                file_paths += [file_path]
-            has_t1 = T1_df.shape[0]
-
-        T2_df = passed_QC_group.loc[passed_QC_group['image_description'] == 'ABCD-T2-NORM']
-        if T2_df.empty:
-            T2_df = passed_QC_group.loc[passed_QC_group['image_description'] == 'ABCD-T2']
-            if T2_df.empty:
-                has_t2 = 0 # No T2s
-            else:
-                for file_path in T2_df['image_file']:
-                    file_paths += [file_path]
-                has_t2 = T2_df.shape[0]
-        else:
-            for file_path in T2_df['image_file']:
-                file_paths += [file_path]
-            has_t2 = T2_df.shape[0]
-
-        ## Pair SEFMs and only download if both pass QC
-        #   Check first if just the FM exists
-        FM_df = passed_QC_group.loc[passed_QC_group['image_description'] == 'ABCD-fMRI-FM']
-        if FM_df.empty:
-            FM_AP_df = group.loc[group['image_description'] == 'ABCD-fMRI-FM-AP']
-            FM_PA_df = group.loc[group['image_description'] == 'ABCD-fMRI-FM-PA']
-            if FM_AP_df.shape[0] != FM_PA_df.shape[0] or FM_AP_df.empty:
-                has_sefm = 0 # No SEFMs. Invalid subject
-            else:
-                for i in range(0, FM_AP_df.shape[0]):
-                    if FM_AP_df.iloc[i]['QC'] == 1.0 and FM_PA_df.iloc[i]['QC'] == 1.0:
-                        FM_df = FM_df.append(FM_AP_df.iloc[i])
-                        FM_df = FM_df.append(FM_PA_df.iloc[i])
-        if FM_df.empty:
-            has_sefm = 0 # No SEFMs. Invalid subject
-        else:
-            for file_path in FM_df['image_file']:
-                file_paths += [file_path]
-            has_sefm = FM_df.shape[0]
-
-
-        ## List all rsfMRI scans that pass QC
-        RS_df = passed_QC_group.loc[passed_QC_group['image_description'] == 'ABCD-rsfMRI']
-        if RS_df.empty:
-            has_rsfmri = 0
-        else:
-            for file_path in RS_df['image_file']:
-                file_paths += [file_path]
-            has_rsfmri = RS_df.shape[0]
-
-        ## List only download task iff their is a pair of scans for the task that passed QC
-        MID_df = passed_QC_group.loc[passed_QC_group['image_description'] == 'ABCD-MID-fMRI']
-        if MID_df.shape[0] != 2:
-            has_mid = MID_df.shape[0]
-        else:
-            for file_path in MID_df['image_file']:
-                file_paths += [file_path]
-            has_mid = MID_df.shape[0]
-        SST_df = passed_QC_group.loc[passed_QC_group['image_description'] == 'ABCD-SST-fMRI']
-        if SST_df.shape[0] != 2:
-            has_sst = SST_df.shape[0]
-        else:
-            for file_path in SST_df['image_file']:
-                file_paths += [file_path]
-            has_sst = SST_df.shape[0]
-        nBack_df = passed_QC_group.loc[passed_QC_group['image_description'] == 'ABCD-nBack-fMRI']
-        if nBack_df.shape[0] != 2:
-            has_nback = nBack_df.shape[0]
-        else:
-            for file_path in nBack_df['image_file']:
-                file_paths += [file_path]
-            has_nback = nBack_df.shape[0]
-
-        # TODO: log subject level information
-        if has_t1 == 0:
-            num_invalid += 1
-            print('%s: t1=%s, t2=%s, sefm=%s, rsfmri=%s, mid=%s, sst=%s, nback=%s INVALID' % (sub, has_t1, has_t2, has_sefm, has_rsfmri, has_mid, has_sst, has_nback))
-            writer.writerow([sub, has_t1, has_t2, has_sefm, has_rsfmri, has_mid, has_sst, has_nback])
-        else:
-            num_valid += 1
-            print('%s: t1=%s, t2=%s, sefm=%s, rsfmri=%s, mid=%s, sst=%s, nback=%s' % (sub, has_t1, has_t2, has_sefm, has_rsfmri, has_mid, has_sst, has_nback))
-            writer.writerow([sub, has_t1, has_t2, has_sefm, has_rsfmri, has_mid, has_sst, has_nback])
-            if has_t2 != 0:
-                num_t2 += 1
-            if has_rsfmri != 0:
-                num_rsfmri += 1
-            if has_mid != 0:
-                num_mid += 1
-            if has_sst != 0:
-                num_sst += 1
-            if has_nback != 0:
-                num_nback += 1
-            subprocess.call(["./nda_aws_token_maker.py", ">", "/dev/null"])
-            for i in file_paths:
-                tgz_name = os.path.basename(i)
-                tgz_path = tgz_dir + '/' + tgz_name
-                if os.path.exists(tgz_path):
-                    continue
-                else:
-                    aws_cmd = ["aws", "s3", "cp", i, tgz_dir + "/", "--profile", "NDA"]
-                    #print(aws_cmd)
-                    subprocess.call(aws_cmd)
-
-
-print("There are %s subject visits" % num_sub_visits)
-print("%s are valid. %s are invalid" % (num_valid, num_invalid))
-print("%s Siemens" % num_siemens)
-print("%s Philips" % num_philips)
-print("%s GE" % num_ge)
-print("number of valid subjects with a T2 : %s" % num_t2)
-print("number of valid subjects with rest : %s" % num_rsfmri)
-print("number of valid subjects with mid  : %s" % num_mid)
-print("number of valid subjects with sst  : %s" % num_sst)
-print("number of valid subjects with nBack: %s" % num_nback)
diff --git a/image03_importer.m b/image03_importer.m
deleted file mode 100644
index b4739bd..0000000
--- a/image03_importer.m
+++ /dev/null
@@ -1,100 +0,0 @@
-function image03 = image03_importer(filename)
-%% Import data from text file.
-% Script for importing data from the following text file:
-%
-%    /mnt/max/shared/projects/ABCD/daic_spreadsheet/image03.txt
-%
-% To extend the code to different selected data or a different text file,
-% generate a function instead of a script.
-
-% Auto-generated by MATLAB on 2018/08/14 10:58:42
-
-%% Initialize variables.
-delimiter = '\t';
-startRow = 3;
-
-%% Read columns of data as text:
-% For more information, see the TEXTSCAN documentation.
-formatSpec = '%*s%*s%*s%s%*s%*s%*s%*s%s%s%*s%s%*s%s%*s%*s%*s%*s%*s%s%s%s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%*s%s%[^\n\r]';
-
-%% Open the text file.
-fileID = fopen(filename,'r');
-
-%% Read columns of data according to the format.
-% This call is based on the structure of the file used to generate this
-% code. If an error occurs for a different file, try regenerating the code
-% from the Import Tool.
-dataArray = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'TextType', 'string', 'HeaderLines' ,startRow-1, 'ReturnOnError', false, 'EndOfLine', '\r\n');
-
-%% Close the text file.
-fclose(fileID);
-
-%% Convert the contents of columns containing numeric text to numbers.
-% Replace non-numeric text with NaN.
-raw = repmat({''},length(dataArray{1}),length(dataArray)-1);
-for col=1:length(dataArray)-1
-    raw(1:length(dataArray{col}),col) = mat2cell(dataArray{col}, ones(length(dataArray{col}), 1));
-end
-numericData = NaN(size(dataArray{1},1),size(dataArray,2));
-
-% Converts text in the input cell array to numbers. Replaced non-numeric
-% text with NaN.
-rawData = dataArray{8};
-for row=1:size(rawData, 1)
-    % Create a regular expression to detect and remove non-numeric prefixes and
-    % suffixes.
-    regexstr = '(?<prefix>.*?)(?<numbers>([-]*(\d+[\,]*)+[\.]{0,1}\d*[eEdD]{0,1}[-+]*\d*[i]{0,1})|([-]*(\d+[\,]*)*[\.]{1,1}\d+[eEdD]{0,1}[-+]*\d*[i]{0,1}))(?<suffix>.*)';
-    try
-        result = regexp(rawData(row), regexstr, 'names');
-        numbers = result.numbers;
-        
-        % Detected commas in non-thousand locations.
-        invalidThousandsSeparator = false;
-        if numbers.contains(',')
-            thousandsRegExp = '^\d+?(\,\d{3})*\.{0,1}\d*$';
-            if isempty(regexp(numbers, thousandsRegExp, 'once'))
-                numbers = NaN;
-                invalidThousandsSeparator = true;
-            end
-        end
-        % Convert numeric text to numbers.
-        if ~invalidThousandsSeparator
-            numbers = textscan(char(strrep(numbers, ',', '')), '%f');
-            numericData(row, 8) = numbers{1};
-            raw{row, 8} = numbers{1};
-        end
-    catch
-        raw{row, 8} = rawData{row};
-    end
-end
-
-
-%% Split data into numeric and string columns.
-rawNumericColumns = raw(:, 8);
-rawStringColumns = string(raw(:, [1,2,3,4,5,6,7,9]));
-
-
-%% Replace non-numeric cells with NaN
-R = cellfun(@(x) ~isnumeric(x) && ~islogical(x),rawNumericColumns); % Find non-numeric cells
-rawNumericColumns(R) = {NaN}; % Replace non-numeric cells
-
-%% Make sure any text containing <undefined> is properly converted to an <undefined> categorical
-for catIdx = [1,2,4,5,6,7,8]
-    idx = (rawStringColumns(:, catIdx) == "<undefined>");
-    rawStringColumns(idx, catIdx) = "";
-end
-
-%% Create output variable
-image03 = table;
-image03.subjectkey = categorical(rawStringColumns(:, 1));
-image03.comments_misc = categorical(rawStringColumns(:, 2));
-image03.image_file = rawStringColumns(:, 3);
-image03.image_description = categorical(rawStringColumns(:, 4));
-image03.scan_type = categorical(rawStringColumns(:, 5));
-image03.scanner_manufacturer_pd = categorical(rawStringColumns(:, 6));
-image03.scanner_type_pd = categorical(rawStringColumns(:, 7));
-image03.scanner_software_versions_pd = cell2mat(rawNumericColumns(:, 1));
-image03.visit = categorical(rawStringColumns(:, 8));
-
-%% Clear temporary variables
-clearvars filename delimiter startRow formatSpec fileID dataArray ans raw col numericData rawData row regexstr result numbers invalidThousandsSeparator thousandsRegExp rawNumericColumns rawStringColumns R catIdx idx;
diff --git a/mapping.mat b/mapping.mat
deleted file mode 100644
index 0adaa77..0000000
Binary files a/mapping.mat and /dev/null differ
diff --git a/nda_aws_token_maker.py b/nda_aws_token_maker.py
deleted file mode 100755
index 7ec9635..0000000
--- a/nda_aws_token_maker.py
+++ /dev/null
@@ -1,39 +0,0 @@
-#! /usr/bin/env python
-
-from nda_aws_token_generator import *
-import getpass
-
-web_service_url = 'https://ndar.nih.gov/DataManager/dataManager'
-#username  = raw_input('Enter your NIMH Data Archives username:')
-#password  = getpass.getpass('Enter your NIMH Data Archives password:')
-username = "FILL_IN_YOUR_NDA_USERNAME"
-password = "FILL_IN_YOUR_NDA_PASSWORD"
-
-generator = NDATokenGenerator(web_service_url)
-
-token = generator.generate_token(username,password)
-
-print('aws_access_key_id=%s\n'
-      'aws_secret_access_key=%s\n'
-      'security_token=%s\n'
-      'expiration=%s\n'
-      %(token.access_key,
-        token.secret_key,
-        token.session,
-        token.expiration)
-      )
-
-import os
-from configparser import ConfigParser
-parser = ConfigParser()
-parser.read(os.path.expanduser('~/.aws/credentials'))
-
-if not parser.has_section('NDA'):
-    parser.add_section('NDA')
-
-parser.set('NDA', 'aws_access_key_id', token.access_key)
-parser.set('NDA', 'aws_secret_access_key', token.secret_key)
-parser.set('NDA', 'aws_session_token', token.session)
-
-with open (os.path.expanduser('~/.aws/credentials'), 'w') as configfile:
-    parser.write(configfile)
diff --git a/run_eta_squared.sh b/run_eta_squared.sh
deleted file mode 100755
index 88c1920..0000000
--- a/run_eta_squared.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/bin/sh
-# script for execution of deployed applications
-#
-# Sets up the MATLAB Runtime environment for the current $ARCH and executes 
-# the specified command.
-#
-exe_name=$0
-exe_dir=`dirname "$0"`
-if [ ! -d $TMPDIR/$USER ]; then
-    mkdir $TMPDIR/$USER
-fi
-export MCR_CACHE_ROOT=$TMPDIR/$USER
-echo "------------------------------------------"
-if [ "x$1" = "x" ]; then
-  echo Usage:
-  echo    $0 \<deployedMCRroot\> args
-else
-  echo Setting up environment variables
-  MCRROOT="$1"
-  echo ---
-  LD_LIBRARY_PATH=.:${MCRROOT}/runtime/glnxa64 ;
-  LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/bin/glnxa64 ;
-  LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/sys/os/glnxa64;
-  LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/sys/opengl/lib/glnxa64;
-  export LD_LIBRARY_PATH;
-  echo LD_LIBRARY_PATH is ${LD_LIBRARY_PATH};
-  shift 1
-  args=
-  while [ $# -gt 0 ]; do
-      token=$1
-      args="${args} \"${token}\"" 
-      shift
-  done
-  eval "\"${exe_dir}/eta_squared\"" $args
-fi
-exit
-
diff --git a/run_order_fix.py b/run_order_fix.py
deleted file mode 100755
index 37fc2ff..0000000
--- a/run_order_fix.py
+++ /dev/null
@@ -1,266 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-import datetime
-import json
-import os
-import re
-import shutil
-import tempfile
-from collections import OrderedDict
-
-taskmatch = re.compile('^.*task-([A-z0-9]+)_run-(\d+).*.nii.gz$')
-
-
-def _cli():
-    parser = generate_parser()
-    args = parser.parse_args()
-    if args.all:
-        args.get_bids_errors = True
-        args.generate_map = True
-        args.execute_swap = True
-
-    assert args.get_bids_errors or args.generate_map or args.execute_swap, \
-        'INPUT: no mode selected!'
-
-    # run stages
-    if args.get_bids_errors:
-        bids_input = args.bids_input
-        output_json = args.error_json
-        subject_list = args.subject
-
-        get_bids_errors(bids_input, output_json, subject_list)
-
-    if args.generate_map:
-        bids_input = args.bids_input
-        input_json = args.error_json
-        output_map = args.file_map
-
-        get_bids_errors_correction_map(input_json, output_map, bids_input)
-
-    if args.execute_swap:
-        input_map = args.file_map
-
-        swap_files(input_map)
-
-
-def generate_parser():
-
-    parser = argparse.ArgumentParser(
-        description='can be chained together.  For full pipe: '
-                    'run_order_fix.py --get-bids-errors --generate-map '
-                    '--execute-swap ... inputs',
-        epilog='Example call:   ./run_order_fix.py '
-               '/mnt/max/shared/projects/ABCD/example_BIDS error.json map.json '
-               '--all --subject [NDARINVXXXXXX|sub-NDARINVXXXXXX]'
-    )
-    modes = parser.add_argument_group(
-        title='modes',
-        description='flags to execute each of three stages.')
-    modes.add_argument(
-        '--get-bids-errors', action='store_true',
-        help='records all fmri mismatches in the json file.'
-    )
-    modes.add_argument(
-        '--generate-map', action='store_true',
-        help='creates a map between errors and true files to swap in the swap '
-             'folder.'
-    )
-    modes.add_argument(
-        '--execute-swap', action='store_true',
-        help='uses output map file'
-    )
-    modes.add_argument(
-        '--all', action='store_true',
-        help='runs start to finish.'
-    )
-    parser.add_argument(
-        '--subject', nargs='+',
-        help='optional subject list to narrow down bids inputs. ONLY USED '
-             'DURING GET BIDS ERRORS'
-    )
-    parser.add_argument(
-        'bids_input', default=None,
-        help='path to bids input folder to detect errors.  Will also fix if '
-             '--fix-bids-errors flag is turned on.'
-    )
-    parser.add_argument(
-        'error_json', default='bids_errors.json',
-        help='path to bids error json file (output/input depending on mode). '
-             'Generated by the --get-bids-errors flag'
-    )
-    parser.add_argument(
-        'file_map', default='file_map.json',
-        help='map of files to be swapped around, in format {before: after}, '
-             'generated by the --generate-map flag'
-    )
-    # parser.add_argument(
-    #     '--bids-output',
-    #     help='path to bids output folder to fix.  Should not be input if the '
-    #          'intent is to fix the bids-input folder.'
-    # )
-    # parser.add_argument(
-    #     '--short-json',
-    #     help='optional path to an output file which will have no filenames.'
-    # )
-
-    return parser
-
-
-def get_bids_errors(bids_input, output_json, subject_list=None, detailed=False):
-
-    if subject_list:
-        subject_list = ['sub-%s' % x if not x.startswith('sub-') else x for x
-                        in subject_list]
-
-    func_folders = get_func_folders(bids_input)
-
-    submatch = re.compile('^.*(sub-[A-z0-9]*).*$')
-    structured_output = {}
-
-    for folder in func_folders:
-        subject = submatch.match(folder).group(1)
-        if subject_list and subject not in subject_list:
-            continue
-        print(subject)
-        contents = os.listdir(folder)
-        # sort contents
-        tasks = task_splitter(contents)
-        # filenames
-        new = True
-        for name, task_set in tasks.items():
-            task_set = sorted(task_set)
-            run_nums = [int(taskmatch.match(t).group(2)) for t in task_set]
-            files = [os.path.join(folder, t) for t in task_set]
-            acq_times = [acquisition_time(f) for f in files]
-            order = sorted(range(0, len(acq_times)),
-                           key=acq_times.__getitem__)
-            order = [1 + n for n in order]
-            if run_nums == order:
-                if detailed:
-                    structured_output[subject][name] = 'correct'
-                continue
-            if new:
-                structured_output[subject] = {}
-                new = False
-            structured_output[subject][name] = {}
-            structured_output[subject][name]['current_order'] = run_nums
-            structured_output[subject][name]['actual_order'] = order
-
-    if output_json:
-        if os.path.exists(output_json):
-            os.remove(output_json)
-        with open(output_json, 'w') as fd:
-            json.dump(structured_output, fd)
-
-
-def get_bids_errors_correction_map(input_json,  output_map, bids_input=None):
-    with open(input_json) as fd:
-        jso = json.load(fd)
-
-    mapping = OrderedDict()
-
-    for subject, tasks in jso.items():
-        subject_folder = bids_input + '/{subject}/{session}'.format(
-            subject=subject, session='ses-baselineYear1Arm1')
-        for name, map_data in tasks.items():
-            if map_data == 'correct':
-                continue
-            else:
-                if name != 'rest':
-                    print('subject %s on %s has bad task data!' % (subject,
-                                                                   name))
-
-                current_order = map_data['current_order']
-                actual_order = map_data['actual_order']
-                if bids_input:
-                    current_names = ['task-%s_run-0%s' % (name, i) for i in
-                                     current_order]
-                    actual_names = ['task-%s_run-0%s' % (name, i) for i in
-                                    actual_order]
-                else:
-                    current_names = ['task-%s0%s' % (name, i) for i in
-                                     current_order]
-                    actual_names = ['task-%s0%s' % (name, i) for i in
-                                    actual_order]
-                for (current, end) in zip(current_names, actual_names):
-                    mapping.update(
-                        generate_file_map(subject_folder, current, end))
-
-    if os.path.exists(output_map):
-        os.remove(output_map)
-    with open(output_map, 'w') as fd:
-        json.dump(mapping, fd, indent=4)
-
-
-def swap_files(json_file):
-
-    with open(json_file) as fd:
-        file_mapper = json.load(fd)
-        file_mapper = OrderedDict(sorted(file_mapper.items()))
-    swapped = []
-    _, tmp = tempfile.mkstemp()
-    for before, after in file_mapper.items():
-        if before in swapped:
-            continue
-        try:
-            shutil.move(before, tmp)
-        except:
-            print('failed to move %s' % before)
-            raise
-        try:
-            shutil.move(after, before)
-        except:
-            print('failed to move %s' % after)
-            shutil.move(tmp, before)
-            raise
-        shutil.move(tmp, after)
-        print('swapped %s with %s' % (before, after))
-        swapped.append(after)
-
-
-def generate_file_map(subject_directory, current_task, end_task,
-                      tempspace=None):
-    file_map = {}
-    task_directory = subject_directory
-
-    for pathspec in os.walk(task_directory):
-        for filepath in (os.path.join(pathspec[0], f) for f in pathspec[2]):
-            end_filepath = filepath.replace(current_task, end_task)
-            if filepath != end_filepath:
-                file_map[filepath] = end_filepath
-
-    return file_map
-
-
-def get_func_folders(bids_input):
-    full_paths = os.walk(bids_input)
-    func_paths = filter(lambda x: os.path.basename(x[0]) == 'func', full_paths)
-    func_paths = (i[0] for i in func_paths)
-    # func_paths = itertools.islice(func_paths, 10)
-    return func_paths
-
-
-def task_splitter(filenames):
-    tasks = filter(lambda x: x, (taskmatch.match(x) for x in filenames))
-    task_dict = {}
-    for t in tasks:
-        name = t.group(1)
-        if name in task_dict.keys():
-            task_dict[name] += [t.string]
-        else:
-            task_dict[name] = [t.string]
-
-    return task_dict
-
-
-def acquisition_time(filename):
-    sidecar = filename[:-7] + '.json'
-    with open(sidecar) as fd:
-        jso = json.load(fd)
-        time = datetime.datetime.strptime(jso['AcquisitionTime'],
-                                          '%H:%M:%S.%f').time()
-    return time
-
-
-if __name__ == '__main__':
-    _cli()
diff --git a/sefm_eval_and_json_editor.py b/sefm_eval_and_json_editor.py
deleted file mode 100755
index 8d611be..0000000
--- a/sefm_eval_and_json_editor.py
+++ /dev/null
@@ -1,329 +0,0 @@
-#! /home/exacloud/lustre1/fnl_lab/code/external/utilities/exahead1-anaconda3/bin/python3
-
-import os, sys, glob, argparse, subprocess, socket, operator, shutil, json
-from bids.grabbids import BIDSLayout
-from itertools import product
-
-os.environ['FSLOUTPUTTYPE'] = 'NIFTI_GZ'
-
-# Last modified
-last_modified = "Created by Anders Perrone 3/21/2017. Last modified by Eric Earl 8/29/2018"
-
-# Program description
-prog_descrip =  """%(prog)s: sefm_eval pairs each of the pos/neg sefm and returns the pair that is most representative
-                   of the average by calculating the eta squared value for each sefm pair to the average sefm.""" + last_modified
-
-# Get path to the FSL directory, compiled Matlab eta squared function, and Matlab Runtime Environment
-try:
-    if socket.gethostname() == 'rushmore':
-        FSL_DIR = '/usr/share/fsl/5.0'
-        ETA_DIR = '/mnt/max/shared/utilities/compiled_Matlab_code'
-        MRE = '/mnt/max/shared/code/external/utilities/Matlab2016bRuntime/v91'
-    elif 'exa' in socket.gethostname():
-        FSL_DIR = '/opt/installed/fsl-5.0.10'
-        ETA_DIR = '/home/exacloud/tempwork/fnl_lab/code/internal/utilities/ABCD'
-        MRE = '/home/exacloud/tempwork/fnl_lab/code/external/utilities/matlab_runtime/v91'
-    else:
-        print('This script has not yet been configured to run on ' + socket.gethostname() + ', sorry.')
-except:
-        print("Error: Host not recognized")
-finally:
-    os.environ['FSL_DIR'] = FSL_DIR
-    os.environ['FSLDIR'] = FSL_DIR
-    # for this script's usage of FSL_DIR...
-    FSL_DIR = FSL_DIR + '/bin/'
-
-
-def read_bids_layout(layout, subject_list=None, collect_on_subject=False):
-    """
-    :param bids_input: path to input bids folder
-    :param subject_list: a list of subject ids to filter on
-    :param collect_on_subject: collapses all sessions, for cases with
-    non-longitudinal data spread across scan sessions.
-    """
-
-    subjects = layout.get_subjects()
-
-    # filter subject list
-    if isinstance(subject_list, list):
-        subjects = [s for s in subjects if s in subject_list]
-    elif isinstance(subject_list, dict):
-        subjects = [s for s in subjects if s in subject_list.keys()]
-
-    subsess = []
-    # filter session list
-    for s in subjects:
-        sessions = layout.get_sessions(subject=s)
-        if not sessions:
-            subsess += [(s, 'session')]
-        elif collect_on_subject:
-            subsess += [(s, sessions)]
-        else:
-            subsess += list(product([s], sessions))
-
-    assert len(subsess), 'bids data not found for participants. If labels ' \
-            'were provided, check the participant labels for errors.  ' \
-            'Otherwise check that the bids folder provided is correct.'
-
-    return subsess
-
-
-def sefm_select(layout, subject, sessions, base_temp_dir, debug=False):
-    pos = 'PA'
-    neg = 'AP'
-
-    # Make a temporary working directory
-    temp_dir = os.path.join(base_temp_dir, subject + '_eta_temp')
-    try:
-        os.mkdir(temp_dir)
-    except:
-        print(temp_dir + " already exists")
-        pass
-
-    print("Pairing for subject " + subject + ":")
-    print(subject, sessions)
-    fmap = layout.get(subject=subject, session=sessions, modality='fmap', extensions='.nii.gz')
-    print(fmap)    
-    if len(fmap):
-        list_pos = [x.filename for i, x in enumerate(fmap) if 'dir-PA' in x.filename]
-        list_neg = [x.filename for i, x in enumerate(fmap) if 'dir-AP' in x.filename]
-    
-    try:
-        len(list_pos) == len(list_neg)
-    except:
-        print("Error: There are a mismatched number of SEFMs. This should never happen!")
-    
-    pairs = []
-    for pair in zip(list_pos, list_neg):
-        pairs.append(pair)
-    
-    pos_ref = pairs[0][0]
-    neg_ref = pairs[0][1]
-    
-    print("Aligning SEFMs and creating template")
-    for i, pair in enumerate(pairs):
-        pos_input = pair[0]
-        neg_input = pair[1]
-        for pedir,ref,flirt_in in [(pos,pos_ref,pos_input),(neg,neg_ref,neg_input)]:
-            out = os.path.join(temp_dir,'init_' + pedir + '_reg_' + str(i) + '.nii.gz')
-            cmd = [FSL_DIR + 'flirt', '-in', flirt_in, '-ref', ref, '-dof', str(6), '-out', out]
-            subprocess.run(cmd, stdout=subprocess.DEVNULL, env=os.environ)
-
-    # Average the pos/neg SEFMs after alignment
-    
-    # First sum all of the images together
-    for pedir in [pos,neg]:
-        sum_cmd = [os.path.join(FSL_DIR,'fslmaths'), os.path.join(temp_dir,'init_' + pedir + '_reg_0.nii.gz')]
-        for i in range(1,len(pairs)):
-            sum_cmd += ['-add', os.path.join(temp_dir,'init_' + pedir + '_reg_' + str(i) + '.nii.gz')]
-        sum_cmd += [os.path.join(temp_dir, pedir + '_sum.nii.gz')]
-        subprocess.run(sum_cmd, env=os.environ)
-
-    # Divide the sum by the number of pos/neg SEFMs to get the average
-    num_sefm = len(pairs)
-    for pedir in [pos,neg]:
-        avg_cmd = [os.path.join(FSL_DIR, 'fslmaths'), os.path.join(temp_dir, pedir + '_sum.nii.gz'), '-div', str(num_sefm), os.path.join(temp_dir,pedir + '_mean.nii.gz')]
-        subprocess.run(avg_cmd, env=os.environ)
-    
-    print("Computing ETA squared value for each image to the template")
-    
-    # Calculate the eta squared value of each aligned image to the average and return the pair with the highest average
-    #avg_eta_dict = {}
-    min_eta_dict = {}
-    for i, pair in enumerate(pairs):
-        eta_list = []
-        for pedir,image in [(pos,pair[0]),(neg,pair[1])]:
-            mat_cmd = [os.path.join(ETA_DIR,'run_eta_squared.sh'), MRE, os.path.join(temp_dir,'init_' + pedir + '_reg_' + str(i) + '.nii.gz'), os.path.join(temp_dir,pedir + '_mean.nii.gz')]
-            mat_stdout = subprocess.check_output(mat_cmd)
-            eta = float(mat_stdout.split()[-1])
-            print(image + " eta value = " + str(eta))
-            eta_list.append(eta)
-        # instead of finding the average between eta values between pairs. Take the pair with the highest lowest eta value.
-        min_eta = min(eta_list)
-        min_eta_dict[pair] = min_eta
-    best_pos, best_neg = max(min_eta_dict, key=min_eta_dict.get)
-    print(best_pos)
-    print(best_neg)
-
-    # Add metadata
-    func_list = [x.filename for x in layout.get(subject=subject, session=sessions, modality='func', extensions='.nii.gz')]
-    anat_list = [x.filename for x in layout.get(subject=subject, session=sessions, modality='anat', extensions='.nii.gz')]
-    for pair in pairs:
-        pos_nifti = pair[0]
-        neg_nifti = pair[1]
-        pos_json = pos_nifti.replace(".nii.gz", ".json")
-        neg_json = neg_nifti.replace(".nii.gz", ".json")
-        insert_edit_json(pos_json, "PhaseEncodingDirection", "j")
-        insert_edit_json(neg_json, "PhaseEncodingDirection", "j-")
-        
-        if pair == (best_pos, best_neg):
-            insert_edit_json(pos_json, "IntendedFor", anat_list + func_list)
-            insert_edit_json(neg_json, "IntendedFor", anat_list + func_list)
-        else:
-            insert_edit_json(pos_json, "IntendedFor", [])
-            insert_edit_json(neg_json, "IntendedFor", [])
-        
-            
-    # Delete the temp directory containing all the intermediate images
-    if not debug:
-        rm_cmd = ['rm', '-rf', temp_dir]
-        subprocess.run(rm_cmd, env=os.environ)
-    
-    print("Success! Best SEFM pair has been chosen and linked in " + subject + "'s nifti directory.")
-    
-    return best_pos, best_neg
-
-def seperate_concatenated_fm(bids_layout, subject, session):
-    print("actually running")
-    fmap = bids_layout.get(subject=subject, session=session, modality='fmap', extensions='.nii.gz')
-    # use the first functional image as the reference for the nifti header after fslswapdim
-    func_ref = bids_layout.get(subject=subject, session=session, modality='func', extensions='.nii.gz')[0].filename
-    print("functional reference: {}".format(func_ref))
-    for FM in [x.filename for x in fmap]:
-        subject_dir = os.path.dirname(FM)
-        if "-both_" in FM:
-            print("Splitting up {}".format(FM))
-            AP_filename = FM.replace("-both_", "-AP_")
-            PA_filename = FM.replace("-both_", "-PA_")
-            split = [FSL_DIR + "/fslsplit", FM, subject_dir + "/vol" ,"-t"]
-            subprocess.run(split, env=os.environ)
-            swap_dim = [FSL_DIR + "/fslswapdim", subject_dir + "/vol0000.nii.gz" ,"x", "-y", "z", subject_dir + "/vol0000.nii.gz"]
-            subprocess.run(swap_dim, env=os.environ)
-            os.rename(subject_dir + "/vol0000.nii.gz",AP_filename)
-            os.rename(subject_dir + "/vol0001.nii.gz",PA_filename)
-            AP_flirt = [FSL_DIR + "/flirt", "-out", AP_filename, "-in", AP_filename, "-ref", func_ref, "-applyxfm", "-init", "/home/exacloud/tempwork/fnl_lab/code/internal/utilities/ABCD/FSL_identity_transformation_matrix.mat", "-interp", "spline"]
-            PA_flirt = [FSL_DIR + "/flirt", "-out", PA_filename, "-in", PA_filename, "-ref", func_ref, "-applyxfm", "-init", "/home/exacloud/tempwork/fnl_lab/code/internal/utilities/ABCD/FSL_identity_transformation_matrix.mat", "-interp", "spline"]
-            subprocess.run(AP_flirt, env=os.environ)
-            subprocess.run(PA_flirt, env=os.environ)
-            
-            # create the side car jsons for the new pair
-            orig_json = FM.replace(".nii.gz", ".json")
-            AP_json = AP_filename.replace(".nii.gz", ".json")
-            PA_json = PA_filename.replace(".nii.gz", ".json")
-            shutil.copyfile(orig_json, AP_json)
-            shutil.copyfile(orig_json, PA_json)
-            insert_edit_json(orig_json, 'PhaseEncodingDirection', 'NA')
-            insert_edit_json(AP_json, 'PhaseEncodingDirection', 'j-')
-            insert_edit_json(PA_json, 'PhaseEncodingDirection', 'j')
-            # add required fields to the orig json as well
-            insert_edit_json(orig_json, 'IntendedFor', [])
-    return
-
-def insert_edit_json(json_path, json_field, value):
-    with open(json_path, 'r+') as f:
-        data = json.load(f)
-        data[json_field] = value
-        f.seek(0)
-        json.dump(data, f, indent=4)
-        f.truncate
-    return
-        
-
-def generate_parser(parser=None):
-    """
-    Generates the command line parser for this program.
-    :param parser: optional subparser for wrapping this program as a submodule.
-    :return: ArgumentParser for this script/module
-    """
-    if not parser:
-        parser = argparse.ArgumentParser(
-            description=prog_descrip
-        )
-    parser.add_argument(
-        'bids_dir',
-        help='path to the input bids dataset root directory.  It is recommended to use '
-             'the dcan bids gui or Dcm2Bids to convert from participant dicoms.'
-    )
-    parser.add_argument(
-        '--participant-label', dest='subject_list', metavar='ID', nargs='+',
-        help='optional list of participant ids to run. Default is all ids '
-             'found under the bids input directory.  A participant label '
-             'does not include "sub-"'
-    )
-    parser.add_argument(
-        '-a','--all-sessions', dest='collect', action='store_true',
-        help='collapses all sessions into one when running a subject.'
-    )
-    parser.add_argument(
-        '-d', '--debug', dest='debug', action='store_true', default=False,
-        help='debug mode, leaves behind the "eta_temp" directory.'
-    )
-    parser.add_argument(
-        '-v', '--version', action='version', version=last_modified,
-        help="Return script's last modified date."
-    )
-    
-    return parser
-
-
-def main(argv=sys.argv):
-    parser = generate_parser()
-    args = parser.parse_args()
-
-    # Load the bids layout
-    layout = BIDSLayout(args.bids_dir)
-    subsess = read_bids_layout(layout, subject_list=args.subject_list, collect_on_subject=args.collect)
-    print(subsess)
-
-    for subject,sessions in subsess:
-        # fmap directory = base dir
-        fmap = layout.get(subject=subject, session=sessions, modality='fmap', extensions='.nii.gz')
-        base_temp_dir = os.path.dirname(fmap[0].filename)
- 
-        # Check if fieldmaps are concatenated
-        print(fmap[0].filename)
-        print("-both_" in fmap[0].filename)
-        if "-both_" in fmap[0].filename:
-            print("Running seperate_concatenate_fm")
-            seperate_concatenated_fm(layout, subject, sessions)
-            # recreate layout with the additional SEFMS
-            layout = BIDSLayout(args.bids_dir)
-                
-        # Return a list of each SEFM pos/neg pair
-        bes_pos, best_neg = sefm_select(layout, subject, sessions, base_temp_dir, args.debug)
-
-        # Additional edits to the anat json sidecar
-        anat = layout.get(subject=subject, session=sessions, modality='anat', extensions='.nii.gz')
-        for TX in [x.filename for x in anat]:
-            TX_json = TX.replace('.nii.gz', '.json') 
-            TX_metadata = layout.get_metadata(TX)
-                #if 'T1' in TX_metadata['SeriesDescription']:
-            if 'Philips' in TX_metadata['Manufacturer']:
-                insert_edit_json(TX_json, 'DwellTime', 0.00062771)
-            if 'GE' in TX_metadata['Manufacturer']:
-                insert_edit_json(TX_json, 'DwellTime', 0.000536)
-            if 'Siemens' in TX_metadata['Manufacturer']:
-                insert_edit_json(TX_json, 'DwellTime', 0.00051001152626)
-        
-        # add EffectiveEchoSpacing if it doesn't already exist
-        fmap = layout.get(subject=subject, session=sessions, modality='fmap', extensions='.nii.gz')
-        for sefm in [x.filename for x in fmap]:
-            sefm_json = sefm.replace('.nii.gz', '.json')
-            sefm_metadata = layout.get_metadata(sefm)
-            if 'Philips' in sefm_metadata['Manufacturer']:
-                insert_edit_json(sefm_json, 'EffectiveEchoSpacing', 0.00062771)
-            if 'GE' in sefm_metadata['Manufacturer']:
-                insert_edit_json(sefm_json, 'EffectiveEchoSpacing', 0.000536)
-            if 'Siemens' in sefm_metadata['Manufacturer']:
-                insert_edit_json(sefm_json, 'EffectiveEchoSpacing', 0.00051001152626)
-
-        # PE direction vs axis
-        func = layout.get(subject=subject, session=sessions, modality='func', extensions='.nii.gz')
-        for task in [x.filename for x in func]:
-            task_json = task.replace('.nii.gz', '.json')
-            task_metadata = layout.get_metadata(task)
-            print('Inserting PE into func')
-            if "PhaseEncodingAxis" in task_metadata:
-                print('Adding PEDirection')
-                print(task_json)
-                print('PhaseEncodingDirection')
-                print(task_metadata['PhaseEncodingAxis'])
-                insert_edit_json(task_json, 'PhaseEncodingDirection', task_metadata['PhaseEncodingAxis'])
-            elif "PhaseEncodingDirection" in task_metadata:
-                insert_edit_json(task_json, 'PhaseEncodingAxis', task_metadata['PhaseEncodingDirection'])
- 
-
-if __name__ == "__main__":
-    sys.exit(main())
-
-
diff --git a/unpack_and_setup.sh b/unpack_and_setup.sh
deleted file mode 100755
index d19fe44..0000000
--- a/unpack_and_setup.sh
+++ /dev/null
@@ -1,142 +0,0 @@
-#! /bin/bash
-
-set -e
-
-# Given a subject ID, session, and tgz directory:
-#   1) Copy all tgzs to compute node's disk
-#   2) Unpack tgzs
-#   3) Convert dcms to niftis in BIDS
-#   4) Select the best SEFM
-#   5) Rename and move Eprime files
-#   6) Copy back to Lustre
-
-## Necessary dependencies
-# dcm2bids (https://github.com/DCAN-Labs/Dcm2Bids)
-# microgl_lx (https://github.com/rordenlab/dcm2niix)
-# pigz-2.4 (https://zlib.net/pigz)
-# run_order_fix.py (in this repo)
-# sefm_eval_and_json_editor.py (in this repo)
-
-ScratchSpaceDir=/tmp/abcd-dicom2bids
-ROOT_BIDSINPUT=./ABCD-HCP
-SUB=$1 # Full BIDS formatted subject ID (sub-SUBJECTID)
-VISIT=$2 # Full BIDS formatted session ID (ses-SESSIONID)
-TGZDIR=$3 # Path to directory containing all .tgz for this subject's session
-
-participant=`echo ${SUB} | sed 's|sub-||'`
-session=`echo ${VISIT} | sed 's|ses-||'`
-
-
-date
-hostname
-echo ${SLURM_JOB_ID}
-
-# Setup scratch space directory
-if [ ! -d ${ScratchSpaceDir} ]; then
-    mkdir -p ${ScratchSpaceDir}
-    # chown :fnl_lab ${ScratchSpaceDir} || true 
-    chmod 770 ${ScratchSpaceDir} || true
-fi
-RandomHash=`cat /dev/urandom | tr -cd 'a-f0-9' | head -c 16`
-TempSubjectDir=${ScratchSpaceDir}/${RandomHash}
-mkdir -p ${TempSubjectDir}
-# chown :fnl_lab ${TempSubjectDir} || true
-echo "TempSubjectDir = ${TempSubjectDir}"
-
-# copy all tgz to the scratch space dir
-echo `date`" :COPYING TGZs TO SCRATCH: ${TempSubjectDir}"
-cp ${TGZDIR}/* ${TempSubjectDir}
-
-# unpack tgz to ABCD_DCMs directory
-mkdir ${TempSubjectDir}/DCMs
-echo `date`" :UNPACKING DCMs: ${TempSubjectDir}/DCMs"
-for tgz in ${TempSubjectDir}/*.tgz; do
-    echo $tgz
-    tar -xzf ${tgz} -C ${TempSubjectDir}/DCMs
-done
-
-
-# # IMPORTANT PATH DEPENDENCY VARIABLES AT OHSU IN SLURM CLUSTER
-# export PATH=.../anaconda2/bin:${PATH} # relevant Python path with dcm2bids
-# export PATH=.../mricrogl_lx/:${PATH} # relevant dcm2niix path
-# export PATH=.../pigz-2.4/:${PATH} # relevant pigz path for improved (de)compression
-
-
-# convert DCM to BIDS and move to ABCD directory
-mkdir ${TempSubjectDir}/BIDS_unprocessed
-echo ${participant}
-echo `date`" :RUNNING dcm2bids"
-dcm2bids -d ${TempSubjectDir}/DCMs/${SUB} -p ${participant} -s ${session} -c ./abcd_dcm2bids.conf -o ${TempSubjectDir}/BIDS_unprocessed --forceDcm2niix --clobber
-
-echo `date`" :CHECKING BIDS ORDERING OF EPIs"
-if [ -e ${TempSubjectDir}/BIDS_unprocessed/${SUB}/${VISIT}/func ]; then
-    if [ `./run_order_fix.py ${TempSubjectDir}/BIDS_unprocessed ${TempSubjectDir}/bids_order_error.json ${TempSubjectDir}/bids_order_map.json --all --subject ${SUB}` == ${SUB} ]; then
-        echo BIDS correctly ordered
-    else
-        echo ERROR: BIDS incorrectly ordered even after running run_order_fix.py
-        exit
-    fi
-else
-    echo ERROR: No functional images found T1 only processing not yet enabeled
-    exit
-fi
-
-# select best fieldmap and update sidecar jsons
-echo `date`" :RUNNING SEFM SELECTION AND EDITING SIDECAR JSONS"
-./sefm_eval_and_json_editor.py ${TempSubjectDir}/BIDS_unprocessed/${SUB} --participant-label=${participant}
-
-rm ${TempSubjectDir}/BIDS_unprocessed/${SUB}/ses-baselineYear1Arm1/fmap/*dir-both* 2> /dev/null || true
-
-# rename EventRelatedInformation
-echo `date`" :COPY AND RENAME SOURCE DATA"
-srcdata_dir=${TempSubjectDir}/BIDS_unprocessed/sourcedata/${SUB}/ses-baselineYear1Arm1/func
-if ls ${TempSubjectDir}/DCMs/${SUB}/ses-baselineYear1Arm1/func/*EventRelatedInformation.txt > /dev/null 2>&1; then
-    mkdir -p ${srcdata_dir}
-fi
-MID_evs=`ls ${TempSubjectDir}/DCMs/${SUB}/ses-baselineYear1Arm1/func/*MID*EventRelatedInformation.txt 2>/dev/null`
-SST_evs=`ls ${TempSubjectDir}/DCMs/${SUB}/ses-baselineYear1Arm1/func/*SST*EventRelatedInformation.txt 2>/dev/null`
-nBack_evs=`ls ${TempSubjectDir}/DCMs/${SUB}/ses-baselineYear1Arm1/func/*nBack*EventRelatedInformation.txt 2>/dev/null`
-if [ `echo ${MID_evs} | wc -w` -eq 2 ]; then
-    i=1
-    for ev in ${MID_evs}; do
-        cp ${ev} ${srcdata_dir}/${SUB}_ses-baselineYear1Arm1_task-MID_run-0${i}_bold_EventRelatedInformation.txt
-        ((i++))
-    done
-fi
-if [ `echo ${SST_evs} | wc -w` -eq 2 ]; then
-    i=1
-    for ev in ${SST_evs}; do
-        cp ${ev} ${srcdata_dir}/${SUB}_ses-baselineYear1Arm1_task-SST_run-0${i}_bold_EventRelatedInformation.txt
-        ((i++))
-    done
-fi
-if [ `echo ${nBack_evs} | wc -w` -eq 2 ]; then
-    i=1
-    for ev in ${nBack_evs}; do
-        cp ${ev} ${srcdata_dir}/${SUB}_ses-baselineYear1Arm1_task-nback_run-0${i}_bold_EventRelatedInformation.txt
-        ((i++))
-    done
-fi
-
-echo `date`" :COPYING SOURCE AND SORTED DATA BACK: ${ROOT_BIDSINPUT}"
-
-TEMPBIDSINPUT=${TempSubjectDir}/BIDS_unprocessed/${SUB}
-if [ -d ${TEMPBIDSINPUT} ] ; then
-    echo `date`" :CHMOD BIDS INPUT"
-    chmod g+rw -R ${TEMPBIDSINPUT} || true
-    echo `date`" :COPY BIDS INPUT"
-    mkdir -p ${ROOT_BIDSINPUT}
-    cp -r ${TEMPBIDSINPUT} ${ROOT_BIDSINPUT}/
-fi
-
-ROOT_SRCDATA=${ROOT_BIDSINPUT}/sourcedata
-TEMPSRCDATA=${TempSubjectDir}/BIDS_unprocessed/sourcedata/${SUB}
-if [ -d ${TEMPSRCDATA} ] ; then
-    echo `date`" :CHMOD SOURCEDATA"
-    chmod g+rw -R ${TEMPSRCDATA} || true
-    echo `date`" :COPY SOURCEDATA"
-    mkdir -p ${ROOT_SRCDATA}
-    cp -r ${TEMPSRCDATA} ${ROOT_SRCDATA}/
-fi
-
-echo `date`" :UNPACKING AND SETUP COMPLETE: ${SUB}/${VISIT}"