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TADPOLE_D3.py
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TADPOLE_D3.py
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#!/usr/bin/env python
# encoding: utf-8
"""
TADPOLE_D3.py
A script to generate the D3 dataset for TADPOLE Challenge:
The Alzheimer's Disease Prediction Of Longitudinal Evolution Challenge
http://tadpole.grand-challenge.org
Called by TADPOLE_D1.py
Pre-requisite: must have already run TADPOLE_D2.py
Created by Neil P. Oxtoby in June 2017.
Copyright (c) 2017 Neil P. Oxtoby. All rights reserved.
http://neiloxtoby.com
"""
import os
import numpy as np
from datetime import datetime
import pandas as pd
import argparse
from argparse import RawTextHelpFormatter
parser = argparse.ArgumentParser(
description=r'''A script to generate the D3 dataset for TADPOLE Challenge:
The Alzheimer's Disease Prediction Of Longitudinal Evolution Challenge
http://tadpole.grand-challenge.org
Called by TADPOLE_D1.py
Pre-requisite: must have already run TADPOLE_D2.py
''', formatter_class=RawTextHelpFormatter
)
parser.add_argument('--spreadsheetFolder', dest='spreadsheetFolder', default='.',
help='folder of output spreadsheets')
np.random.seed(1)
args = parser.parse_args()
def representsInt(s):
try:
int(s)
return True
except ValueError:
return False
#********************************************************************#
if __name__ == '__main__':
dataSaveLocation = os.getcwd()
#*** TADPOLE D2: historical data for D2_RID
D1_file = 'TADPOLE_D1_D2.csv'
D1_table = pd.read_csv(D1_file)
D2_indicator = D1_table.D2
D2_indicator_numeric = 1*D2_indicator
D2_ = D1_table[['RID','VISCODE']]
D2 = D2_.assign(D2=D2_indicator_numeric)
#*** TADPOLE D3: D2 final visit per individual; selected columns relevant to clinical trials selection
#* Identify UCSFFSL columns (STxxx) for both versions of FreeSurfer: 4.3 and 5.1
UCSFFSX4p3_csv = '%s/UCSFFSX_11_02_15.csv' % args.spreadsheetFolder
UCSFFSX5p1_csv = '%s/UCSFFSX51_08_01_16.csv' % args.spreadsheetFolder
UCSFFSX1 = pd.read_csv(UCSFFSX4p3_csv) # ADNI1: 1.5T
UCSFFSX51 = pd.read_csv(UCSFFSX5p1_csv) # ADNIGO/2: 3T
UCSFFSX4p3_columns = list(UCSFFSX1.columns.values)
UCSFFSX5p1_columns = list(UCSFFSX51.columns.values)
# Remove key columns as we're interested in the other columns
UCSFFSX4p3_columns.remove('RID')
UCSFFSX4p3_columns.remove('VISCODE')
UCSFFSX4p3_columns.remove('update_stamp')
UCSFFSX5p1_columns.remove('COLPROT')
UCSFFSX5p1_columns.remove('RID')
UCSFFSX5p1_columns.remove('VISCODE')
UCSFFSX5p1_columns.remove('VISCODE2')
UCSFFSX5p1_columns.remove('update_stamp')
# Add 5.1 columns missing from 4.3
UCSFFSX_columns = UCSFFSX4p3_columns + [c for c in UCSFFSX5p1_columns if c not in UCSFFSX4p3_columns]
# Append table names to column names
UCSFFSX_columns = [c + '_UCSFFSX_11_02_15_UCSFFSX51_08_01_16' for c in UCSFFSX_columns]
# Selected columns
D3_columns = ['RID','VISCODE','EXAMDATE','DX','AGE','PTGENDER','PTEDUCAT','PTETHCAT','PTRACCAT','PTMARRY','COLPROT','ADAS13','MMSE','Ventricles','Hippocampus','WholeBrain','Entorhinal','Fusiform','MidTemp','ICV']
D3_columns = D3_columns + UCSFFSX_columns
#* Extract selected individuals and columns from D1 & D2, then select most recent visit
D3_table = D1_table.loc[D2_indicator==1]
################################
columnsThatAreNotInD1D2 = ['IMAGETYPE_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','LHIPQC_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','RHIPQC_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST28SA_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST87SA_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST131HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST132HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST133HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST134HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST135HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST136HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST137HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST138HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST139HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST140HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST141HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST142HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST143HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST144HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST145HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST146HS_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST147SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST148SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST149SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST150SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST151SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST152SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST153SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST154SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16','ST155SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16']
D3_columns_subset = D3_columns
for k in range(0,len(columnsThatAreNotInD1D2)):
s = columnsThatAreNotInD1D2[k]
D3_columns_subset.remove(s)
################################
M = D3_table.M.values
D3_table = D3_table[D3_columns_subset]
#* Identify most recent visit
RID = D3_table.RID.values
mostRecentVisit = np.zeros((len(RID),1))
RID_u = np.unique(RID)
for k in range(0,len(RID_u)):
rowz = np.where(RID_u[k]==RID)[0]
mrv = rowz[M[rowz]==max(M[rowz])][-1]
mostRecentVisit[mrv] = True
D3_table = D3_table.iloc[mostRecentVisit.flatten()==True]
D3_table = D3_table.fillna('-4') # ADNI's default missing value code
D3_file = 'TADPOLE_D3.csv' # D3_file = 'TADPOLE_D3_{0}.csv'.format(runDate)
D3_table.to_csv(os.path.join(dataSaveLocation,D3_file),index=False,na_rep='-4')