filter_list = ['TR > 2.0', 'FD < .3']
modality = 'bold'
T1apicsv = os.path.join(here, 'demo_api', 'T1w_demo.csv')
T2apicsv = os.path.join(here, 'demo_api', 'T2w_demo.csv')
boldapicsv = os.path.join(here, 'demo_api', 'bold_demo.csv')
if modality == 'T1w':
api_file = T1apicsv
elif modality == 'T2w':
api_file = T1apicsv
elif modality == 'bold':
api_file = boldapicsv
# load user csv as df #
userdf = load_groupfile(group_file)
# load and filter api csv as df #
apidf = pd.read_csv(api_file)
filtered_apidf = filterIQM(apidf, filter_list)
# merge dataframes together #
vis_ready_df = merge_dfs(userdf, filtered_apidf)
IQM_to_plot = ['fwhm_avg', 'fber']
v = make_vio_plot(vis_ready_df, IQM_to_plot, "")
# print(vis_ready_df.head)
# for col in userdf.columns:
# print(col)
def main(argv=sys.argv):
arg_parser = argparse.ArgumentParser(
description=prog_desc,
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Check for arguments. #
if len(sys.argv[1:]) == 0:
print('\nArguments required. Use -h option to print FULL usage.\n')
arg_parser.add_argument('-g',
metavar='GROUP_FILE',
action='store',
type=os.path.abspath,
required=True,
help=('FULL path to your group csv/tsv file - '
'the output from MRIQC.'),
dest='group_file')
arg_parser.add_argument('-s',
metavar='SEARCH_PHRASE',
action='store',
type=str,
required=True,
help=('Search phrase to filter API query.'
'Format: xxxx xxxxx xxxxxx xxxxxx'),
dest='search_phrase')
arg_parser.add_argument('-t',
metavar='SCAN_TYPE',
action='store',
type=str,
choices=['bold', 'T1w', 'T2w'],
required=False,
default='T1w',
help=('Scan type to query. Can choose from bold,'
' T1w, or T2w.'),
dest='scan_type')
args = arg_parser.parse_args()
#################################################
## Script Argument Verification and Assignment ##
#################################################
if os.path.isfile(args.group_file):
pass
else:
print('The groupfile you are trying to use was not found. Exiting...')
sys.exit()
#################################################
## Global Variable Assignment ##
#################################################
start_time = time.time()
time.sleep(1)
today_date = datetime.datetime.now().strftime('%m%d%Y')
print('Querying API for ' + args.scan_type + ' scans.')
## GROUP FILE COULD TURN INTO A LIST OF FILES!!! Just need a way to keep track and name them...perhaps turn it into a tuple
## of (name,path) or maybe even a key: val?
## Check that this dataframe is the same format as the result_df output from the query_api function!!
loaded_df = load_groupfile(args.group_file)
# result_df = query_api(args.scan_type,'MultibandAccelerationFactor>3','RepetitionTime>1')
# result_df = query_api(args.scan_type, ['MultibandAccelerationFactor>3', 'EchoTime>1'])
result_df = query_api(args.scan_type,
'MultibandAccelerationFactor>3&EchoTime>1')
## Scater plot/visualization functions would go below here and pass result_df as well as loaded_df pandas dataframes
# something like this:
# scatter(loaded_df, result_df)
full_runtime = time.time() - start_time
print('\nFull Script Runtime: ', datetime.timedelta(seconds=full_runtime),
'\n')
def main(argv=sys.argv):
arg_parser = argparse.ArgumentParser(description=prog_desc,
formatter_class=
argparse.ArgumentDefaultsHelpFormatter)
# Check for arguments. #
if len(sys.argv[1:]) == 0:
print('\nArguments required. Use -h option to print FULL usage.\n')
arg_parser.add_argument('-g', metavar='GROUP_FILE', action='store',
type=os.path.abspath, required=True,
help=('FULL path to your group csv/tsv file - '
'the output from MRIQC.'),
dest='group_file'
)
# arg_parser.add_argument('-s', metavar='SEARCH_PHRASE', action='store', type=str,
# required=True, help=('Search phrase to filter API query.'
# 'Format: xxxx xxxxx xxxxxx xxxxxx'),
# dest='search_phrase'
# )
# arg_parser.add_argument('-t', metavar='SCAN_TYPE', action='store', type=str,
# choices=['bold', 'T1w', 'T2w'], required=False,
# default='T1w',
# help=('Scan type to query. Can choose from bold,'
# ' T1w, or T2w.'),
# dest='scan_type'
# )
args = arg_parser.parse_args()
#################################################
## Script Argument Verification and Assignment ##
#################################################
if os.path.isfile(args.group_file):
pass
else:
print('The groupfile you are trying to use was not found. Exiting...')
sys.exit()
#################################################
## Global Variable Assignment ##
#################################################
start_time = time.time()
time.sleep(1)
today_date = datetime.datetime.now().strftime('%m%d%Y')
# print('Querying API for ' + args.scan_type + ' scans.')
filter_list = ['TR > 1.0','FD < .3']
modality = 'bold'
here = os.path.dirname(os.path.abspath(os.path.realpath(__file__)))
T1apicsv = os.path.join(here, 'demo_api', 'T1w_demo.csv')
T2apicsv = os.path.join(here, 'demo_api', 'T2w_demo.csv')
boldapicsv = os.path.join(here, 'demo_api', 'bold_demo.csv')
if modality == 'T1w':
group_file = T1apicsv
elif modality == 'T2w':
group_file = T1apicsv
elif modality == 'bold':
group_file = boldapicsv
# load user csv as df #
userdf = load_groupfile(group_file)
# load and filter api csv as df #
apidf = pd.read_csv(group_file)
filtered_apidf = filterIQM(,apidf,filter_list)
# merge dataframes together #
vis_ready_df = merge_dfs(userdf, filtered_apidf)
# result_df = query_api(args.scan_type,'MultibandAccelerationFactor>3','RepetitionTime>1')
# # result_df = query_api(args.scan_type, ['MultibandAccelerationFactor>3', 'EchoTime>1'])
# result_df = query_api(args.scan_type, 'MultibandAccelerationFactor>3&EchoTime>1')
## Scater plot/visualization functions would go below here and pass result_df as well as loaded_df pandas dataframes
# something like this:
# scatter(loaded_df, result_df)
full_runtime = time.time() - start_time
print('\nFull Script Runtime: ', datetime.timedelta(seconds=full_runtime), '\n')
def main(argv=sys.argv):
arg_parser = argparse.ArgumentParser(
description=prog_desc, formatter_class=argparse.RawTextHelpFormatter)
arg_parser.add_argument('-i',
'--input',
metavar='GROUP_FILE',
action='store',
type=os.path.abspath,
required=True,
help=("""
FULL path to your group csv/tsv file - the output from MRIQC.
"""),
dest='group_file')
arg_parser.add_argument('-t',
'--type',
metavar='SCAN_TYPE',
action='store',
type=str,
choices=['bold', 'T1w', 'T2w'],
required=True,
help=("""
Scan type to query. Can choose from bold, T1w, or T2w.
"""),
dest='scan_type')
arg_parser.add_argument('-f',
'--filter',
metavar='FILTER_LIST',
action='append',
type=str,
required=True,
help=("""
Strings to filter the queried database.
Several filters can be given at the same time.
The string formats should be:
-f "(VAR) (Operator) (Value)"; Example:
"-f 'TR == 3.0' " or "-f 'TR > 1.0' -f 'TR < 3.0' -f 'FD < .3' "
Note: Each element in each string is separated by SPACES
The IQMs depend on the --type argument given:
BOLD:
{SNR; TSNR; DVAR; FD; FWHM; GSR_X; GSR_Y;
TESLA; TE; TR}
T1W | T2W:
{SNR; SNR_GM; SNR_WM; SNR_CSF; CNR; EFC; FWHM;
TESLA; TE; TR}
"""),
dest='filter_list')
args = arg_parser.parse_args()
#################################################
## Script Argument Verification and Assignment ##
#################################################
# Check for arguments. #
if len(sys.argv) == 1:
arg_parser.print_help()
sys.exit(0)
elif os.path.isfile(args.group_file):
pass
else:
print('The groupfile you are trying to use was not found. Exiting...')
sys.exit()
#################################################
## Global Variable Assignment ##
#################################################
start_time = time.time()
time.sleep(1)
today_date = datetime.datetime.now().strftime('%m%d%Y')
# print('Querying API for ' + args.scan_type + ' scans.')
# filter_list = ['TR > 1.0','FD < .3']
# modality = 'bold'
here = os.path.dirname(os.path.abspath(os.path.realpath(__file__)))
T1apicsv = os.path.join(here, 'demo_api', 'T1w_demo.csv')
T2apicsv = os.path.join(here, 'demo_api', 'T2w_demo.csv')
boldapicsv = os.path.join(here, 'demo_api', 'bold_demo.csv')
if args.scan_type.lower() == 't1w':
group_file = T1apicsv
elif args.scan_type.lower() == 't2w':
group_file = T1apicsv
elif args.scan_type.lower() == 'bold':
group_file = boldapicsv
# load user csv as df #
userdf = load_groupfile(group_file)
# load and filter api csv as df #
apidf = pd.read_csv(group_file)
# Make the filter_list
filter_list = []
filtered_apidf = filterIQM(apidf, args.scan_type, args.filter_list)
# merge dataframes together #
# vis_ready_df = merge_dfs(userdf, filtered_apidf)
return filtered_apidf