def validate_opchars(scenario_id, subscenarios, subproblem, stage, conn, op_type):
"""
:param subscenarios:
:param subproblem:
:param stage:
:param conn:
:param op_type:
:return:
"""
# TODO: deal with fuel and variable O&M column, which are nor optional nor
# required for any?
# Get the opchar inputs for this operational type
df = get_optype_inputs_from_db(scenario_id, subscenarios, conn, op_type)
# Get the required, optional, and other columns with their types
(
required_columns_types,
optional_columns_types,
other_columns_types,
) = get_optype_param_requirements(op_type=op_type)
req_cols = required_columns_types.keys()
na_cols = other_columns_types.keys()
# Check that required inputs are present
write_validation_to_database(
conn=conn,
scenario_id=scenario_id,
subproblem_id=subproblem,
stage_id=stage,
gridpath_module=__name__,
db_table="inputs_project_operational_chars",
severity="High",
errors=validate_req_cols(df, req_cols, True, op_type),
)
# Check that other (not required or optional) inputs are not present
write_validation_to_database(
conn=conn,
scenario_id=scenario_id,
subproblem_id=subproblem,
stage_id=stage,
gridpath_module=__name__,
db_table="inputs_project_operational_chars",
severity="Low",
errors=validate_req_cols(df, na_cols, False, op_type),
)
# TODO: do data-type and numeric non-negativity checking here rather than
# in project.init?
# Return the opchar df (sometimes used for further validations)
return df
def test_validate_req_cols(self):
"""
:return:
"""
df_columns = ["project", "min_stable_level_fraction", "unit_size_mw",
"startup_cost_per_mw", "shutdown_cost_per_mw"]
test_cases = {
# Make sure correct inputs don't throw error
1: {"df": pd.DataFrame(
columns=df_columns,
data=[["nuclear", 0.5, 100, None, None]]),
"columns": ["min_stable_level_fraction", "unit_size_mw"],
"required": True,
"category": "Always_on",
"result": []
},
# Make sure missing required inputs are flagged
2: {"df": pd.DataFrame(
columns=df_columns,
data=[["nuclear", None, 100, None, None]]),
"columns": ["min_stable_level_fraction", "unit_size_mw"],
"required": True,
"category": "Always_on",
"result": ["project(s) 'nuclear'; Always_on should have "
"inputs for 'min_stable_level_fraction'"]
},
# Make sure incompatible inputs are flagged
3: {"df": pd.DataFrame(
columns=df_columns,
data=[["nuclear", 0.5, 100, 1000, None]]),
"columns": ["startup_cost_per_mw", "shutdown_cost_per_mw"],
"required": False,
"category": "Always_on",
"result": ["project(s) 'nuclear'; Always_on should not have inputs for 'startup_cost_per_mw'"]
}
}
for test_case in test_cases.keys():
expected_list = test_cases[test_case]["result"]
actual_list = module_to_test.validate_req_cols(
df=test_cases[test_case]["df"],
columns=test_cases[test_case]["columns"],
required=test_cases[test_case]["required"],
category=test_cases[test_case]["category"]
)
self.assertListEqual(expected_list, actual_list)