climate_ref_pmp.diagnostics.annual_cycle #

class AnnualCycle(CommandLineDiagnostic):
    """
    Calculate the annual cycle for a dataset
    """

    name = "Annual Cycle"
    slug = "annual-cycle"
    facets = (
        "source_id",
        "member_id",
        "experiment_id",
        "variable_id",
        "reference_source_id",
        "region",
        "statistic",
        "season",
    )

    data_requirements = (
        # ERA-5 as reference dataset, spatial 2-D variables
        make_data_requirement("ts", "ERA-5"),
        make_data_requirement("uas", "ERA-5"),
        make_data_requirement("vas", "ERA-5"),
        make_data_requirement("psl", "ERA-5"),
        # ERA-5 as reference dataset, spatial 3-D variables
        make_data_requirement("ta", "ERA-5"),
        make_data_requirement("ua", "ERA-5"),
        make_data_requirement("va", "ERA-5"),
        make_data_requirement("zg", "ERA-5"),
        # Other reference datasets, spatial 2-D variables
        make_data_requirement("pr", "GPCP-Monthly-3-2"),
        make_data_requirement("rlds", "CERES-EBAF-4-2"),
        make_data_requirement("rlus", "CERES-EBAF-4-2"),
        make_data_requirement("rlut", "CERES-EBAF-4-2"),
        make_data_requirement("rsds", "CERES-EBAF-4-2"),
        make_data_requirement("rsdt", "CERES-EBAF-4-2"),
        make_data_requirement("rsus", "CERES-EBAF-4-2"),
        make_data_requirement("rsut", "CERES-EBAF-4-2"),
    )

    def __init__(self) -> None:
        self.parameter_file_1 = "pmp_param_annualcycle_1-clims.py"
        self.parameter_file_2 = "pmp_param_annualcycle_2-metrics.py"

    def build_cmds(self, definition: ExecutionDefinition) -> list[list[str]]:
        """
        Build the command to run the diagnostic

        Parameters
        ----------
        definition
            Definition of the diagnostic execution

        Returns
        -------
            Command arguments to execute in the PMP environment
        """
        input_datasets = definition.datasets[SourceDatasetType.CMIP6]
        reference_datasets = definition.datasets[SourceDatasetType.PMPClimatology]

        source_id = input_datasets["source_id"].unique()[0]
        experiment_id = input_datasets["experiment_id"].unique()[0]
        member_id = input_datasets["member_id"].unique()[0]
        variable_id = input_datasets["variable_id"].unique()[0]

        model_files_raw = input_datasets.path.to_list()
        if len(model_files_raw) == 1:
            model_files = model_files_raw[0]  # If only one file, use it directly
        elif len(model_files_raw) > 1:
            model_files = build_glob_pattern(model_files_raw)  # If multiple files, build a glob pattern
        else:
            raise ValueError("No model files found")

        logger.debug("build_cmd start")

        logger.debug(f"input_datasets: {input_datasets}")
        logger.debug(f"input_datasets.keys(): {input_datasets.keys()}")

        reference_dataset_name = reference_datasets["source_id"].unique()[0]
        reference_dataset_path = reference_datasets.datasets.iloc[0]["path"]

        logger.debug(f"reference_dataset.datasets: {reference_datasets.datasets}")
        logger.debug(f"reference_dataset_name: {reference_dataset_name}")
        logger.debug(f"reference_dataset_path: {reference_dataset_path}")

        output_directory_path = str(definition.output_directory)

        cmds = []

        # ----------------------------------------------
        # PART 1: Build the command to get climatologies
        # ----------------------------------------------
        # Model
        data_name = f"{source_id}_{experiment_id}_{member_id}"
        data_path = model_files
        params = {
            "vars": variable_id,
            "infile": data_path,
            "outfile": f"{output_directory_path}/{variable_id}_{data_name}_clims.nc",
        }

        cmds.append(
            build_pmp_command(
                driver_file="pcmdi_compute_climatologies.py",
                parameter_file=self.parameter_file_1,
                **params,
            )
        )

        # --------------------------------------------------
        # PART 2: Build the command to calculate diagnostics
        # --------------------------------------------------
        # Reference
        obs_dict = {
            variable_id: {
                reference_dataset_name: {
                    "template": reference_dataset_path,
                },
                "default": reference_dataset_name,
            }
        }

        # Generate a JSON file based on the obs_dict
        with open(f"{output_directory_path}/obs_dict.json", "w") as f:
            json.dump(obs_dict, f)

        date = datetime.datetime.now().strftime("%Y%m%d")

        if variable_id in ["ua", "va", "ta"]:
            levels = ["200", "850"]
        elif variable_id in ["zg"]:
            levels = ["500"]
        else:
            levels = None

        variables = []
        if levels is not None:
            for level in levels:
                variable_id_with_level = f"{variable_id}-{level}"
                variables.append(variable_id_with_level)
        else:
            variables = [variable_id]

        logger.debug(f"variables: {variables}")
        logger.debug(f"levels: {levels}")

        # Build the command for each level
        params = {
            "vars": variables,
            "custom_observations": f"{output_directory_path}/obs_dict.json",
            "test_data_path": output_directory_path,
            "test_data_set": source_id,
            "realization": member_id,
            "filename_template": f"%(variable)_{data_name}_clims.198101-200512.AC.v{date}.nc",
            "metrics_output_path": output_directory_path,
            "cmec": "",
        }

        cmds.append(
            build_pmp_command(
                driver_file="mean_climate_driver.py",
                parameter_file=self.parameter_file_2,
                **params,
            )
        )

        logger.debug("build_cmd end")
        logger.debug(f"cmds: {cmds}")

        return cmds

    def build_execution_result(self, definition: ExecutionDefinition) -> ExecutionResult:
        """
        Build a diagnostic result from the output of the PMP driver

        Parameters
        ----------
        definition
            Definition of the diagnostic execution

        Returns
        -------
            Result of the diagnostic execution
        """
        input_datasets = definition.datasets[SourceDatasetType.CMIP6]
        variable_id = input_datasets["variable_id"].unique()[0]

        if variable_id in ["ua", "va", "ta"]:
            variable_dir_pattern = f"{variable_id}-???"
        else:
            variable_dir_pattern = variable_id

        results_directory = definition.output_directory
        png_directory = results_directory / variable_dir_pattern
        data_directory = results_directory / variable_dir_pattern

        logger.debug(f"results_directory: {results_directory}")
        logger.debug(f"png_directory: {png_directory}")
        logger.debug(f"data_directory: {data_directory}")

        # Find the CMEC JSON file(s)
        results_files = transform_results_files(list(results_directory.glob("*_cmec.json")))

        if len(results_files) == 1:
            # If only one file, use it directly
            results_file = results_files[0]
            logger.debug(f"results_file: {results_file}")
        elif len(results_files) > 1:
            logger.info(f"More than one cmec file found: {results_files}")
            results_file = combine_results_files(results_files, definition.output_directory)
        else:
            logger.error("Unexpected case: no cmec file found")
            return ExecutionResult.build_from_failure(definition)

        # Find the other outputs: PNG and NetCDF files
        png_files = list(png_directory.glob("*.png"))
        data_files = list(data_directory.glob("*.nc"))

        # Prepare the output bundles
        cmec_output_bundle, cmec_metric_bundle = process_json_result(results_file, png_files, data_files)

        # Add missing dimensions to the output
        input_selectors = input_datasets.selector_dict()
        reference_selectors = definition.datasets[SourceDatasetType.PMPClimatology].selector_dict()
        cmec_metric_bundle = cmec_metric_bundle.prepend_dimensions(
            {
                "source_id": input_selectors["source_id"],
                "member_id": input_selectors["member_id"],
                "experiment_id": input_selectors["experiment_id"],
                "variable_id": input_selectors["variable_id"],
                "reference_source_id": reference_selectors["source_id"],
            }
        )

        return ExecutionResult.build_from_output_bundle(
            definition,
            cmec_output_bundle=cmec_output_bundle,
            cmec_metric_bundle=cmec_metric_bundle,
        )

    def execute(self, definition: ExecutionDefinition) -> None:
        """
        Run the diagnostic on the given configuration.

        Parameters
        ----------
        definition : ExecutionDefinition
            The configuration to run the diagnostic on.

        Returns
        -------
        :
            The result of running the diagnostic.
        """
        cmds = self.build_cmds(definition)

        runs = [self.provider.run(cmd) for cmd in cmds]
        logger.debug(f"runs: {runs}")