def test_failed_operation(water, result_input, opti_failure):
failed = FailedOperation(garbage=water, **result_input, **opti_failure)
assert isinstance(failed.error, ComputeError)
assert isinstance(failed.dict(), dict)
failed_json = failed.json()
assert isinstance(failed_json, str)
assert 'its all good' in failed_json
def test_failed_operation(water, result_input):
failed = FailedOperation(extras={"garbage": water},
input_data=result_input,
error={
"error_type": "expected_testing_error",
"error_message": "If you see this, its all good"
})
assert isinstance(failed.error, ComputeError)
assert isinstance(failed.dict(), dict)
failed_json = failed.json()
assert isinstance(failed_json, str)
assert 'its all good' in failed_json
def handle_output_metadata(
output_data: Union[Dict[str, Any], 'BaseModel'],
metadata: Dict[str, Any],
raise_error: bool = False,
return_dict: bool = True) -> Union[Dict[str, Any], 'BaseModel']:
"""
Fuses general metadata and output together.
Returns
-------
result : dict or pydantic.models.Result
Output type depends on return_dict or a dict if an error was generated in model construction
"""
if isinstance(output_data, dict):
output_fusion = output_data # Error handling
else:
output_fusion = output_data.dict()
# Do not override if computer generates
output_fusion["stdout"] = output_fusion.get("stdout",
None) or metadata["stdout"]
output_fusion["stderr"] = output_fusion.get("stderr",
None) or metadata["stderr"]
if metadata["success"] is not True:
output_fusion["success"] = False
output_fusion["error"] = {
"error_type": metadata["error_type"],
"error_message": metadata["error_message"]
}
# Raise an error if one exists and a user requested a raise
if raise_error and (output_fusion["success"] is not True):
msg = "stdout:\n{}".format(output_fusion["stdout"])
msg += "\nstderr:\n{}".format(output_fusion["stderr"])
LOGGER.info(msg)
raise ValueError(output_fusion["error"]["error_message"])
# Fill out provenance datadata
provenance_augments = get_provenance_augments()
provenance_augments["wall_time"] = metadata["wall_time"]
if "provenance" in output_fusion:
output_fusion["provenance"].update(provenance_augments)
else:
# Add onto the augments with some missing info
provenance_augments["creator"] = "QCEngine"
provenance_augments["version"] = provenance_augments[
"qcengine_version"]
output_fusion["provenance"] = provenance_augments
if metadata["retries"] != 0:
output_fusion["provenance"]["retries"] = metadata["retries"]
# Make sure pydantic sparsity is upheld
for val in ["stdout", "stderr"]:
if output_fusion[val] is None:
output_fusion.pop(val)
# We need to return the correct objects; e.g. Results, Procedures
if output_fusion["success"]:
# This will only execute if everything went well
ret = output_data.__class__(**output_fusion)
else:
# Should only be reachable on failures
ret = FailedOperation(success=output_fusion.pop("success", False),
error=output_fusion.pop("error"),
input_data=output_fusion)
if return_dict:
return json.loads(
ret.json()
) # Use Pydantic to serialize, then reconstruct as Python dict of Python Primals
else:
return ret
def handle_output_metadata(output_data,
metadata,
raise_error=False,
return_dict=True):
"""
Fuses general metadata and output together.
Returns
-------
result : dict or pydantic.models.Result
Output type depends on return_dict or a dict if an error was generated in model construction
"""
if isinstance(output_data, dict):
output_fusion = output_data # Error handling
else:
output_fusion = output_data.dict()
output_fusion["stdout"] = metadata["stdout"]
output_fusion["stderr"] = metadata["stderr"]
if metadata["success"] is not True:
output_fusion["success"] = False
output_fusion["error"] = {
"error_type": "meta_error",
"error_message": metadata["error_message"]
}
# Raise an error if one exists and a user requested a raise
if raise_error and (output_fusion["success"] is not True):
msg = "stdout:\n" + output_fusion["stdout"]
msg += "\nstderr:\n" + output_fusion["stderr"]
print(msg)
raise ValueError(output_fusion["error"]["error_message"])
# Fill out provenance datadata
wall_time = metadata["wall_time"]
provenance_augments = config.get_provenance_augments()
provenance_augments["wall_time"] = wall_time
if "provenance" in output_fusion:
output_fusion["provenance"].update(provenance_augments)
else:
# Add onto the augments with some missing info
provenance_augments["creator"] = "QCEngine"
provenance_augments["version"] = provenance_augments[
"qcengine_version"]
output_fusion["provenance"] = provenance_augments
# We need to return the correct objects; e.g. Results, Procedures
if output_fusion["success"]:
# This will only execute if everything went well
ret = output_data.__class__(**output_fusion)
else:
# Should only be reachable on failures
ret = FailedOperation(success=output_fusion.pop("success", False),
error=output_fusion.pop("error"),
input_data=output_fusion)
if return_dict:
return json.loads(
ret.json()
) # Use Pydantic to serialize, then reconstruct as Python dict of Python Primals
else:
return ret