def launch_GNPS_librarysearchworkflow(files_list, job_description, username,
password, email):
invokeParameters = {}
invokeParameters["workflow"] = "MOLECULAR-LIBRARYSEARCH-V2"
invokeParameters["protocol"] = "None"
invokeParameters["desc"] = job_description
invokeParameters["library_on_server"] = "d.speclibs;"
invokeParameters["spec_on_server"] = ";".join(files_list)
invokeParameters["tolerance.PM_tolerance"] = "2.0"
invokeParameters["tolerance.Ion_tolerance"] = "0.5"
invokeParameters["MIN_MATCHED_PEAKS"] = "6"
invokeParameters["TOP_K_RESULTS"] = "1"
invokeParameters["FILTER_STDDEV_PEAK_datasetsINT"] = "0.0"
invokeParameters["MIN_PEAK_INT"] = "0.0"
invokeParameters["FILTER_PRECURSOR_WINDOW"] = "1"
invokeParameters["FILTER_LIBRARY"] = "1"
invokeParameters["WINDOW_FILTER"] = "1"
invokeParameters["SEARCH_LIBQUALITY"] = "3"
invokeParameters["MAX_SHIFT_MASS"] = "100.0"
invokeParameters["ANALOG_SEARCH"] = "0"
invokeParameters["SCORE_THRESHOLD"] = "0.7"
invokeParameters["email"] = email
invokeParameters["uuid"] = "1DCE40F7-1211-0001-979D-15DAB2D0B500"
task_id = ming_proteosafe_library.invoke_workflow("gnps.ucsd.edu",
invokeParameters,
username, password)
return task_id
def main():
parser = argparse.ArgumentParser(description='Invoking new workflow with parameters of given workflow')
parser.add_argument('workflowparamters', help='workflowparamters')
parser.add_argument('credentials', help='credentials.json')
parser.add_argument('outputhtml', default='output.html', help='output html with a url')
parser.add_argument('--serverurl', default='proteomics2.ucsd.edu', help='Server URL, default is proteomics2.ucsd.edu, other options are massive.ucsd.edu and gnps.ucsd.edu')
parser.add_argument('--parametermapping', action='append', help='mapping of current workflow parameters to new parameters in the format: <old parameter>:<new parameter>')
parser.add_argument('--newparameters', action='append', help='parameter key: <param name>:<parameter value>')
parser.add_argument('--runparameter', default='NONE', help='Workflow xml parameter to check if this parameter is equal to "1" to actually invoke the workflow')
args = parser.parse_args()
credentials = json.loads(open(args.credentials).read())
workflow_parameters_map = ming_proteosafe_library.parse_xml_file(open(args.workflowparamters))
if args.runparameter != "NONE":
if workflow_parameters_map[args.runparameter][0] == "0":
output_html_file = open(args.outputhtml, "w")
output_html_file.write("User chose not to run tool\n")
output_html_file.close()
exit(0)
new_parameters = {}
new_parameters["desc"] = "Analysis subroutine from ProteoSAFe job %s" % (workflow_parameters_map["task"][0])
if args.newparameters != None:
for parameter_string in args.newparameters:
parameter_key = parameter_string.split(":")[0]
parameter_value = parameter_string.split(":")[1]
new_parameters[parameter_key] = parameter_value
if args.parametermapping != None:
for parameter_string in args.parametermapping:
parameter_old_key = parameter_string.split(":")[0]
parameter_new_key = parameter_string.split(":")[1]
new_parameters[parameter_new_key] = workflow_parameters_map[parameter_old_key][0]
task_id = ming_proteosafe_library.invoke_workflow(args.serverurl, new_parameters, credentials["username"], credentials["password"])
if task_id == None:
exit(1)
ming_proteosafe_library.wait_for_workflow_finish(args.serverurl, task_id)
"""Writing HTML output"""
output_html_file = open(args.outputhtml, "w")
output_html_file.write("<script>\n")
output_html_file.write('window.open("https://%s/ProteoSAFe/status.jsp?task=%s", "_blank")\n' % (args.serverurl, task_id))
output_html_file.write("</script>\n")
output_html_file.close()
def main():
parser = argparse.ArgumentParser(description='Invoking new workflow with parameters of given workflow')
parser.add_argument('workflowparamters', help='workflowparamters')
parser.add_argument('credentials', help='credentials.json')
parser.add_argument('outputresults', help='output params')
args = parser.parse_args()
workflow_parameters_map = ming_proteosafe_library.parse_xml_file(open(args.workflowparamters))
usi_list = workflow_parameters_map["usi_string"][0].split("\n")
usi_list = [usi for usi in usi_list if len(usi) > 5]
all_tasks = []
for usi in usi_list:
invokeParameters = {}
invokeParameters["desc"] = "Analysis subroutine from ProteoSAFe job %s" % (workflow_parameters_map["task"][0])
invokeParameters["workflow"] = "SEARCH_SINGLE_SPECTRUM"
invokeParameters["workflow_version"] = "release_25"
invokeParameters["protocol"] = "None"
invokeParameters["library_on_server"] = workflow_parameters_map["library_on_server"][0]
#Search Parameters
invokeParameters["tolerance.PM_tolerance"] = workflow_parameters_map["tolerance.PM_tolerance"][0]
invokeParameters["tolerance.Ion_tolerance"] = workflow_parameters_map["tolerance.Ion_tolerance"][0]
invokeParameters["ANALOG_SEARCH"] = workflow_parameters_map["ANALOG_SEARCH"][0]
invokeParameters["FIND_MATCHES_IN_PUBLIC_DATA"] = "1"
invokeParameters["MAX_SHIFT_MASS"] = "100"
invokeParameters["MIN_MATCHED_PEAKS"] = workflow_parameters_map["MIN_MATCHED_PEAKS"][0]
invokeParameters["SCORE_THRESHOLD"] = workflow_parameters_map["SCORE_THRESHOLD"][0]
invokeParameters["SEARCH_LIBQUALITY"] = "3"
invokeParameters["SEARCH_RAW"] = "0"
invokeParameters["TOP_K_RESULTS"] = "1"
invokeParameters["DATABASES"] = workflow_parameters_map["DATABASES"][0]
#Filter Parameters
invokeParameters["FILTER_LIBRARY"] = workflow_parameters_map["FILTER_LIBRARY"][0]
invokeParameters["FILTER_PRECURSOR_WINDOW"] = workflow_parameters_map["FILTER_PRECURSOR_WINDOW"][0]
invokeParameters["FILTER_SNR_PEAK_INT"] = workflow_parameters_map["FILTER_SNR_PEAK_INT"][0]
invokeParameters["FILTER_STDDEV_PEAK_INT"] = workflow_parameters_map["FILTER_STDDEV_PEAK_INT"][0]
invokeParameters["MIN_PEAK_INT"] = workflow_parameters_map["MIN_PEAK_INT"][0]
invokeParameters["WINDOW_FILTER"] = workflow_parameters_map["WINDOW_FILTER"][0]
#Spectrum
precursor_mz, peaks = _get_spectrum(usi)
if precursor_mz == None:
continue
invokeParameters["precursor_mz"] = precursor_mz
invokeParameters["spectrum_string"] = "\n".join(["{}\t{}".format(peak[0], peak[1]) for peak in peaks])
invokeParameters["email"] = "*****@*****.**"
invokeParameters["uuid"] = "1DCE40F7-1211-0001-979D-15DAB2D0B500"
credentials = json.loads(open(args.credentials).read())
task_id = ming_proteosafe_library.invoke_workflow("gnps.ucsd.edu", invokeParameters, credentials["username"], credentials["password"])
if task_id == None:
continue
all_tasks.append({"usi" : usi, "task_id" : task_id})
time.sleep(60)
for task in all_tasks:
ming_proteosafe_library.wait_for_workflow_finish("gnps.ucsd.edu", task["task_id"])
tasks_df = pd.DataFrame(all_tasks)
tasks_df.to_csv(args.outputresults, sep="\t", index=False)