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batch_download.py
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batch_download.py
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#useful resource for aws:
# AWS S3 high level api:
# https://docs.aws.amazon.com/cli/latest/userguide/cli-services-s3-commands.html
# search for CHANGE for locations of where to customize for new files
import os
import time
import subprocess
from pathlib import Path
import datetime
from scipy.io import savemat
encoding = 'utf-8'
r_num = '2'
############
#relative file path for each patient:
lr_msmall = f"rfMRI_REST{r_num}_LR_Atlas_MSMAll_hp2000_clean.dtseries.nii"
rl_msmall = f"rfMRI_REST{r_num}_RL_Atlas_MSMAll_hp2000_clean.dtseries.nii"
lr_no_msmall = f"rfMRI_REST{r_num}_LR_Atlas_hp2000_clean.dtseries.nii"
rl_no_msmall = f"rfMRI_REST{r_num}_RL_Atlas_hp2000_clean.dtseries.nii"
lr_msmall_hcp_rel_path = f"/MNINonLinear/Results/rfMRI_REST{r_num}_LR/" + lr_msmall
rl_msmall_hcp_rel_path = f"/MNINonLinear/Results/rfMRI_REST{r_num}_RL/" + rl_msmall
lr_no_msmall_hcp_rel_path = f"/MNINonLinear/Results/rfMRI_REST{r_num}_LR/" + lr_no_msmall
rl_no_msmall_hcp_rel_path = f"/MNINonLinear/Results/rfMRI_REST{r_num}_RL/" + rl_no_msmall
#"HCP_1200/996782/MNINonLinear/Results/rfMRI_REST1_RL/rfMRI_REST1_RL_Atlas_hp2000_clean.dtseries.nii"
#LR stands for Left and Right hemisphere not Left to Right scanning (as in the dtseries.nii)
atlas_file_pre = "/MNINonLinear/fsaverage_LR32k/"
destrieux_atlas_file_post = ".aparc.a2009s.32k_fs_LR.dlabel.nii"
desikan_atlas_file_post = ".aparc.32k_fs_LR.dlabel.nii"
#paths on aws machine
path2HCP_1200 = "/hcp-openaccess/HCP_1200/"
#paths on local machine
external_hd_path = "/run/media/mwasser6/Elements" #"/Volumes/Elements"
log_file = external_hd_path + "/download_scripts/log.txt"
local_dir = external_hd_path + "/brain_data"
#raw file location:
# /hcp-openaccess/HCP_1200/100206/MNINonLinear/Results/rfMRI_REST1_RL/rfMRI_REST1_RL_Atlas_MSMAll_hp2000_clean.dtseries.nii
############
#which files to download
# input: patient_id is a string of the patient id w/o any extra characters (e.g. '/')
def list_files(patient_id):
patient_id = str(patient_id)
#list of dictionaries representing each file
files = []
#path to directory for this suject
subject_dir = local_dir+"/"+patient_id
############
# for each file:
# hcp_path = path to file on the aws server
# readable_name = name to be used for printing
# local_path = path to file on local machine
############
#CHANGE: include new block (see above) for a new file.
#msall
hcp_path = path2HCP_1200 + patient_id + lr_msmall_hcp_rel_path
readable_name = "LR_msmall"
local_path = subject_dir + "/" + lr_msmall
files.append({"hcp_path": hcp_path, "readable_name": readable_name, "local_path": local_path})
hcp_path = path2HCP_1200 + patient_id + rl_msmall_hcp_rel_path
readable_name = "RL_msmall"
local_path = subject_dir + "/" + rl_msmall
files.append({"hcp_path": hcp_path, "readable_name": readable_name, "local_path": local_path})
#no msall
hcp_path = path2HCP_1200 + patient_id + lr_no_msmall_hcp_rel_path
readable_name = "LR_no_msmall"
local_path = subject_dir + "/" + lr_no_msmall
files.append({"hcp_path": hcp_path, "readable_name": readable_name, "local_path": local_path})
hcp_path = path2HCP_1200 + patient_id + rl_no_msmall_hcp_rel_path
readable_name = "RL_no_msmall"
local_path = subject_dir + "/" + rl_no_msmall
files.append({"hcp_path": hcp_path, "readable_name": readable_name, "local_path": local_path})
#Destrieux atlas
hcp_path = path2HCP_1200 + patient_id + atlas_file_pre + patient_id + destrieux_atlas_file_post
readable_name = "Destrieux_aparc32k"
local_path = subject_dir + "/" + patient_id + destrieux_atlas_file_post
files.append({"hcp_path": hcp_path, "readable_name": readable_name, "local_path": local_path})
#Desikan atlas
hcp_path = path2HCP_1200 + patient_id + atlas_file_pre + patient_id + desikan_atlas_file_post
readable_name = "Desikan_aparc32k"
local_path = subject_dir + "/" + patient_id + desikan_atlas_file_post
files.append({"hcp_path": hcp_path, "readable_name": readable_name, "local_path": local_path})
return files
#create list of string subject id's in HCP_1200. There are 1114 directories (each representing a patient)
def subject_list_HCP_1200():
args = ["aws s3 ls s3://hcp-openaccess/HCP_1200/"]
completed_process = subprocess.run(args, capture_output=True, shell=True)
all_dirs = completed_process.stdout
dir_list = str.splitlines(all_dirs.decode(encoding))
subject_list = []
for idx, subject in enumerate(dir_list):
#bug in decoding? 1000th aws call has date/time returned instead of patient_id
if idx == 999:
#print(f'aws returns junk on 1000th call')
continue
subject_id = subject.split()[1] #remove extranous stuff
subject_id = subject_id[:-1] #remove trailing '/'
subject_list.append(subject_id)
return subject_list
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.savemat.html
def save_subject_list_to_mat():
subject_list = subject_list_HCP_1200()
print(f'Saving subject list of {len(subject_list)} hcp subject')
savemat("hcp_1200_subject_list.mat", {'hcp1200_subject_list': subject_list})
#Check to see if this file exists on hcp server
def check_exist_hcp(rel_subj_path, patient_id):
hcp_path = path2HCP_1200 + patient_id + rel_subj_path
print(f'contructed path: {hcp_path}')
print(f'\tExist? -> {rel_subj_path} || ',end='')
args = ["aws s3 ls s3:/" + hcp_path + " " + "--human-readable"]
completed_process = subprocess.run(args, capture_output=True, shell=True)
out = completed_process.stdout.decode(encoding)
#if download did not complete properly, print this and save this info
if completed_process.returncode != 0:
print(f'NO...\n\t{out} ')#'ERROR (likely not on server or faulty path given')
else:
print(f'YES...\n\t{out}')
"""
patient_id = "100307"
#desikan_atlas_file_post_RL = ".aparc.32k_fs_LR.dlabel.nii"
#rel_hcp_path = atlas_file_pre + patient_id + desikan_atlas_file_post_RL
check_exist_hcp(rl_no_msmall_hcp_rel_path, patient_id)
check_exist_hcp(lr_no_msmall_hcp_rel_path, patient_id)
"""
#for each patient, create a local diretory in local_dir and download all files. If there is an issue downloading, print to terminal and record which patient could not download each file
def download_files():
#record who is missing which files
files = list_files('000')
patients_missing_file = {f["readable_name"]:set() for f in files}
subject_list = subject_list_HCP_1200()
for idx, subject in enumerate(subject_list):
print(f"\n\n{idx}th subject: {subject}")
#create directory with this subject if it does not exist:
subject_dir = local_dir+"/"+subject
Path(subject_dir).mkdir(parents=True, exist_ok=True) #will ignore if exists
#download all files into this dir
start = time.time()
for f in list_files(subject):
if os.path.isfile(f['local_path']):
print(f'\t{f["readable_name"]} already exists, skipping...')
continue
#if file does not already exist in local directory, attempt to download it
print(f'\tdownloading {f["readable_name"]} into {subject_dir}...',end='')
args = ["aws s3 cp s3:/" + f["hcp_path"] + " " + subject_dir]
completed_process = subprocess.run(args, capture_output=True, shell=True)
#if download did not complete properly, print this and save this info
if completed_process.returncode != 0:
print(f'ERROR (likely not on server or faulty path given')
patients_missing_file[f["readable_name"]].add(subject)
else:
print(f'success')
end = time.time()
print(f"Time for download: {(end-start):.1f}")
#Summarize whats missing: CHANGE - include new files here
lr_msmall_miss = patients_missing_file["LR_msmall"]
rl_msmall_miss = patients_missing_file["RL_msmall"]
both_msmall_miss = lr_msmall_miss.intersection(rl_msmall_miss)
lr_len, rl_len, both_len= len(lr_msmall_miss), len(rl_msmall_miss), len(both_msmall_miss)
print(f"MSMALL missing: lr {lr_len} | rl {rl_len} | both {both_len}")
lr_no_msmall_miss = patients_missing_file["LR_no_msmall"]
rl_no_msmall_miss = patients_missing_file["RL_no_msmall"]
both_no_msmall_miss = lr_no_msmall_miss.intersection(rl_no_msmall_miss)
lr_len,rl_len,both_len=len(lr_no_msmall_miss),len(rl_no_msmall_miss), len(both_no_msmall_miss)
print(f"NO_MSMALL missing: lr {lr_len} | rl {rl_len} | both {both_len}")
#End of Download Summary: CHANGE - include new files here
print(f"======END OF DOWNLOAD=======")
print(f"Total Patients: {len(subject_list)}")
lr_msmall_miss, rl_msmall_miss = patients_missing_file["LR_msmall"],patients_missing_file["RL_msmall"]
both_msmall_miss = lr_msmall_miss.intersection(rl_msmall_miss)
lr_len, rl_len, both_len= len(lr_msmall_miss), len(rl_msmall_miss), len(both_msmall_miss)
print(f"MSMALL missing: lr {lr_len} | rl {rl_len} | both {both_len}")
lr_no_msmall_miss = patients_missing_file["LR_no_msmall"]
rl_no_msmall_miss = patients_missing_file["RL_no_msmall"]
both_no_msmall_miss = lr_no_msmall_miss.intersection(rl_no_msmall_miss)
lr_len,rl_len,both_len=len(lr_no_msmall_miss),len(rl_no_msmall_miss), len(both_no_msmall_miss)
print(f"NO_MSMALL missing: lr {lr_len} | rl {rl_len} | both {both_len}")
print(f"DESIKAN missing: {len(patients_missing_file['Desikan_aparc32k'])}")
print(f"DESTRIE missing: {len(patients_missing_file['Destrieux_aparc32k'])}")
print(f"======END OF DOWNLOAD=======")
print(f"List of patients with missing files")
print(f"MSMALL")
print(f"LR: \n{lr_msmall_miss}")
print(f"RL: \n{rl_msmall_miss}")
print(f"BOTH: \n{both_msmall_miss}")
print(f"\nNO MSMALL")
print(f"LR: same as msmall lr? {lr_msmall_miss==lr_no_msmall_miss} \n{lr_no_msmall_miss}")
print(f"RL: same as msmall rl? {rl_msmall_miss==rl_no_msmall_miss} \n{rl_no_msmall_miss}")
print(f"BOTH: same as msmall both? {both_msmall_miss==both_no_msmall_miss} \n{both_no_msmall_miss}")
print(f"\nDESIKAN missing: {patients_missing_file['Desikan_aparc32k']}")
print(f"DESTRIE missing: {patients_missing_file['Destrieux_aparc32k']}")
lr_no_msmall_miss = [int(a) for a in lr_no_msmall_miss]
rl_no_msmall_miss = [int(a) for a in rl_no_msmall_miss]
both_no_msmall_miss = [int(a) for a in both_no_msmall_miss]
#savemat("subjects_missing_data.mat", {'missing_LR': lr_no_msmall_miss, 'missing_RL': rl_no_msmall_miss, 'missing_LR_and_RL': both_no_msmall_miss, 'type': 'no_msmall'})
#Begin download
download_files()
#save_subject_list_to_mat()
#sl = subject_list_HCP_1200()
#print(f"999th subject_id: {sl[997:1001]}")