def _test_property(self, key):
for file in single_job_out_names:
self.assertEqual(QCOutput(os.path.join(test_dir, file)).data.get(key), single_job_dict[file].get(key))
for file in multi_job_out_names:
outputs = QCOutput.multiple_outputs_from_file(QCOutput, os.path.join(test_dir, file), keep_sub_files=False)
for i, sub_output in enumerate(outputs):
self.assertEqual(sub_output.data.get(key), multi_job_dict[file][i].get(key))
def process_qchem_multirun(dir_name, input_files, output_files):
"""
Process a QChem run which is known to include multiple calculations
in a single input/output pair.
"""
if len(input_files) != 1:
raise ValueError(
"ERROR: The drone can only process a directory containing a single input/output pair when each include multiple calculations."
)
else:
for key in input_files:
to_return = []
qchem_input_file = os.path.join(dir_name, input_files.get(key))
qchem_output_file = os.path.join(dir_name,
output_files.get(key))
multi_out = QCOutput.multiple_outputs_from_file(
QCOutput, qchem_output_file, keep_sub_files=False)
multi_in = QCInput.from_multi_jobs_file(qchem_input_file)
for ii, out in enumerate(multi_out):
d = out.data
d["input"] = {}
d["input"]["molecule"] = multi_in[ii].molecule
d["input"]["rem"] = multi_in[ii].rem
d["input"]["opt"] = multi_in[ii].opt
d["input"]["pcm"] = multi_in[ii].pcm
d["input"]["solvent"] = multi_in[ii].solvent
d["task"] = {"type": key, "name": "calc" + str(ii)}
to_return.append(d)
return to_return
def generate_multi_job_dict():
"""
Used to generate test dictionary for multiple jobs
"""
multi_job_dict = {}
for file in multi_job_out_names:
outputs = QCOutput.multiple_outputs_from_file(QCOutput, os.path.join(test_dir, file), keep_sub_files=False)
data = []
for sub_output in outputs:
data.append(sub_output.data)
multi_job_dict[file] = data
dumpfn(multi_job_dict, "multi_job.json")