def init():
if len(sys.argv) != 7:
print(''.join(["Usage: ", os.path.basename(__file__), ' params.ctd jobname.txt registername.txt user.txt filestostage.ctd workflow.ctd' ]))
sys.exit(-1)
params = {}
ctd = args_from_file(sys.argv[1])
params["duration"] = ctd['duration']
params['filesystem'] = ctd['filesystem']
params['wfname'] = ctd['wfname']
params['wfrepo'] = ctd['wf_repo']
params['wf']= os.path.join(params['wfrepo'],params['wfname'])
params['jobname'] = read_firstline(sys.argv[2])
params['registername'] = read_firstline(sys.argv[3])
params['user'] = read_firstline(sys.argv[4])
params['group'] = None
ctdmodel = CTDModel(from_file=sys.argv[5])
params['inputfiles'] = get_filestostage(ctdmodel,"input")
params['db'] = get_filestostage(ctdmodel, "db")
#wfdir = ws_allocate("cfc", "init_works_qbic_dw", 1)
print("allocating work space " + params['registername'])
params['wfspace'] = ws_allocate(params['filesystem'], params['registername'], params['duration'])
params['params'] = sys.argv[6]
return params
def test_args_from_file():
ctd = args_from_file("inputs/INIT-CTD")
duration = ctd['duration']
filesystem = ctd['filesystem']
snakemake_wf_name = ctd['wfname']
wf_repo = ctd['wf_repo']
assert (duration == 5 or duration == '5')
assert snakemake_wf_name == "qcprot"
assert wf_repo == "/lustre_cfc/qbic/workflow_repos"
assert filesystem == "cfc"
from CTDopts.CTDopts import _InFile, CTDModel, args_from_file
import sys
import os
import subprocess
wf_dir = sys.argv[1]
ctd_files = args_from_file(wf_dir + '/IN-FILESTOSTAGE')
command = './annotateVariants.sh '
data_path = '%s/data/' % wf_dir
result_path = '%s/result/' % wf_dir
files = []
for f in ctd_files["input"]:
fileName = f.split('/')[-1]
command += ('%s' % (data_path, fileName))
command += '%s ' % wf_dir
command += '%s ' fileName
subprocess.call(command.split())
from CTDopts.CTDopts import _InFile, CTDModel, args_from_file
import sys
import os
import subprocess
wf_dir = sys.argv[1]
ctd_params = args_from_file(wf_dir + '/WORKFLOW-CTD')
ctd_files = args_from_file(wf_dir + '/IN-FILESTOSTAGE')
#TODO: define parameters using flags (input files, pheno.txt and var of interest)
command = 'python prepare_r.py '
data_path = '%s/data/' % wf_dir
result_path = '%s/result/' % wf_dir
log_path = '%s/logs/' % wf_dir
command += " -d " + wf_dir
files = []
for f in ctd_files["input"]:
fileName = f.split('/')[-1]
files.append('%s%s' % (data_path, fileName))
#command += '%s' % ' '.join(files)
for param in ctd_params.keys():
command += ' -%s %s' % (param, ctd_params[param])
log = open("qclogs.txt", 'w')
subprocess.call(command.split(), stderr=log, stdout=log)
pout2mzidparams.add(
'validate',
type=bool,
description='This is a flag: Type 1 to set flag. Sets that validation of XML schema should not be performed. Faster parsing.'
)
pout2mzidparams.add(
'warning',
type=bool,
description='This is a flag: Type 1 to set flag. Sets that upon warning the software should terminate'
)
args = msgf_percolator_model.parse_cl_args()
try:
args = args_from_file(args['ctd'])
print "try to load ctd"
### MZID2PIN Parameter ###
try:
args['mzid2pin']['mzid2pin_path']
args['mzid2pin']['target_input']
args['mzid2pin']['decoy_input']
except:
print "Path or decoy/target-files missing!"
str = ''
try:
if args['mzid2pin']['outputXML'] != '':
str += ' --outputXML \'%s\'' % args['mzid2pin']['outputXML']
except:
pass
print ("We can validate these arguments against the model, and get a dictionary with parameter types correctly casted "
"and defaults set. Note that subparams:param_1 was casted from string to a floating point number because that's how it "
"was defined in the model.")
validated = model.validate_args(new_values)
pretty_print(validated)
print()
print ('We can write a CTD file containing these validated argument values. Just call CTDModel.write_ctd() with an extra '
'parameter: the nested argument dictionary containing the actual values.')
model.write_ctd('exampleTool_preset_params.ctd', validated)
print()
print ('As mentioned earlier, CTDopts can load argument values from CTD files. Feel free to change some values in '
"exampleTool_preset_params.ctd you've just written, and load it back.")
args_from_ctd = args_from_file('exampleTool_preset_params.ctd')
pretty_print(args_from_ctd)
print()
print ("Notice that all the argument values are strings now. This is because we didn't validate them against the model, "
"just loaded some stuff from a file into a dictionary. If you want to cast them, call CTDModel.validate_args():")
validated_2 = model.validate_args(args_from_ctd)
pretty_print(validated_2)
print()
print ("Now certain parameters may have restrictions that we might want to validate for as well. Let's set the parameter "
"positive_int to a negative value, and try to validate it with a strictness level enforce_restrictions=1. This "
"will register a warning, but still accept the value.")
validated_2['positive_int'] = -5
_ = model.validate_args(validated_2, enforce_restrictions=1)
print()
"and defaults set. Note that subparams:param_1 was casted from string to a floating point number because that's how it "
"was defined in the model.")
validated = model.validate_args(new_values)
pretty_print(validated)
print()
print(
'We can write a CTD file containing these validated argument values. Just call CTDModel.write_ctd() with an extra '
'parameter: the nested argument dictionary containing the actual values.')
model.write_ctd('exampleTool_preset_params.ctd', validated)
print()
print(
'As mentioned earlier, CTDopts can load argument values from CTD files. Feel free to change some values in '
"exampleTool_preset_params.ctd you've just written, and load it back.")
args_from_ctd = args_from_file('exampleTool_preset_params.ctd')
pretty_print(args_from_ctd)
print()
print(
"Notice that all the argument values are strings now. This is because we didn't validate them against the model, "
"just loaded some stuff from a file into a dictionary. If you want to cast them, call CTDModel.validate_args():"
)
validated_2 = model.validate_args(args_from_ctd)
pretty_print(validated_2)
print()
print(
"Now certain parameters may have restrictions that we might want to validate for as well. Let's set the parameter "
"positive_int to a negative value, and try to validate it with a strictness level enforce_restrictions=1. This "
"will register a warning, but still accept the value.")
validated_2['positive_int'] = -5
