from __future__ import annotations
from pathlib import Path
from typing import Any
import logging
from openghg.store.base import BaseStore
from openghg.util import load_standardise_parser, split_function_inputs
logger = logging.getLogger("openghg.store")
logger.setLevel(logging.DEBUG) # Have to set level for logger as well as handler
__all__ = ["EulerianModel"]
# TODO: Currently built around these keys but will probably need more unique distiguishers for different setups
# model name
# species
# date (start_date)
# ...
# setup (included as option for now)
[docs]
class EulerianModel(BaseStore):
"""This class is used to process Eulerian model data"""
_data_type = "eulerian_model"
_root = "EulerianModel"
_uuid = "63ff2365-3ba2-452a-a53d-110140805d06"
_metakey = f"{_root}/uuid/{_uuid}/metastore"
[docs]
def read_file(
self,
filepath: str | Path,
model: str,
species: str,
source_format: str = "openghg",
start_date: str | None = None,
end_date: str | None = None,
setup: str | None = None,
if_exists: str = "auto",
save_current: str = "auto",
overwrite: bool = False,
force: bool = False,
compressor: Any | None = None,
filters: Any | None = None,
chunks: dict | None = None,
optional_metadata: dict | None = None,
) -> list[dict]:
"""Read Eulerian model output
Args:
filepath: Path of Eulerian model species output
model: Eulerian model name
species: Species name
source_format: Data format, for example openghg (internal format)
start_date: Start date (inclusive) associated with model run
end_date: End date (exclusive) associated with model run
setup: Additional setup details for run
if_exists: What to do if existing data is present.
- "auto" - checks new and current data for timeseries overlap
- adds data if no overlap
- raises DataOverlapError if there is an overlap
- "new" - just include new data and ignore previous
- "combine" - replace and insert new data into current timeseries
save_current: Whether to save data in current form and create a new version.
- "auto" - this will depend on if_exists input ("auto" -> False), (other -> True)
- "y" / "yes" - Save current data exactly as it exists as a separate (previous) version
- "n" / "no" - Allow current data to updated / deleted
overwrite: Deprecated. This will use options for if_exists="new".
force: Force adding of data even if this is identical to data stored.
compressor: A custom compressor to use. If None, this will default to
`Blosc(cname="zstd", clevel=5, shuffle=Blosc.SHUFFLE)`.
See https://zarr.readthedocs.io/en/stable/api/codecs.html for more information on compressors.
filters: Filters to apply to the data on storage, this defaults to no filtering. See
https://zarr.readthedocs.io/en/stable/tutorial.html#filters for more information on picking filters.
chunks: Chunking schema to use when storing data. It expects a dictionary of dimension name and chunk size,
for example {"time": 100}. If None then a chunking schema will be set automatically by OpenGHG.
See documentation for guidance on chunking: https://docs.openghg.org/tutorials/local/Adding_data/Adding_ancillary_data.html#chunking.
To disable chunking pass in an empty dictionary.
optional_metadata: Allows to pass in additional tags to distinguish added data. e.g {"project":"paris", "baseline":"Intem"}
"""
# TODO: As written, this currently includes some light assumptions that we're dealing with GEOSChem SpeciesConc format.
# May need to split out into multiple modules (like with ObsSurface) or into separate retrieve functions as needed.
# Get initial values which exist within this function scope using locals
# MUST be at the top of the function
fn_input_parameters = locals().copy()
from openghg.store.spec import define_standardise_parsers
from openghg.util import (
clean_string,
check_if_need_new_version,
)
model = clean_string(model)
species = clean_string(species)
start_date = clean_string(start_date)
end_date = clean_string(end_date)
setup = clean_string(setup)
# Specify any additional metadata to be added
additional_metadata = {}
if overwrite and if_exists == "auto":
logger.warning(
"Overwrite flag is deprecated in preference to `if_exists` (and `save_current`) inputs."
"See documentation for details of these inputs and options."
)
if_exists = "new"
# Making sure new version will be created by default if force keyword is included.
if force and if_exists == "auto":
if_exists = "new"
new_version = check_if_need_new_version(if_exists, save_current)
filepath = Path(filepath)
standardise_parsers = define_standardise_parsers()[self._data_type]
try:
source_format = standardise_parsers[source_format.upper()].value
except KeyError:
raise ValueError(f"Unknown data type {source_format} selected.")
# Loading parse
parser_fn = load_standardise_parser(data_type=self._data_type, source_format=source_format)
_, unseen_hashes = self.check_hashes(filepaths=filepath, force=force)
if not unseen_hashes:
return [{}]
filepath = next(iter(unseen_hashes.values()))
if chunks is None:
chunks = {}
# Get current parameter values and filter to only include function inputs
fn_current_parameters = locals().copy() # Make a copy of parameters passed to function
fn_input_parameters = {key: fn_current_parameters[key] for key in fn_input_parameters}
fn_input_parameters["filepath"] = filepath
# Define parameters to pass to the parser function and remaining keys
parser_input_parameters, additional_input_parameters = split_function_inputs(
fn_input_parameters, parser_fn
)
# Call appropriate standardisation function with input parameters
eulerian_model_data = parser_fn(**parser_input_parameters)
# # TODO: Add schema and validate methods so this can be checked against expected format
# for split_data in eulerian_model_data.values():
# em_data = split_data["data"]
# EulerianModel.validate_data(em_data)
# Check to ensure no required keys are being passed through optional_metadata dict
self.check_info_keys(optional_metadata)
if optional_metadata is not None:
additional_metadata.update(optional_metadata)
# Mop up and add additional keys to metadata which weren't passed to the parser
model_data = self.update_metadata(
eulerian_model_data, additional_input_parameters, additional_metadata
)
data_type = "eulerian_model"
datasource_uuids = self.assign_data(
data=model_data,
if_exists=if_exists,
new_version=new_version,
data_type=data_type,
compressor=compressor,
filters=filters,
)
# TODO: MAY NEED TO ADD BACK IN OR CAN DELETE
# update_keys = ["start_date", "end_date", "latest_version"]
# model_data = update_metadata(
# data_dict=model_data, uuid_dict=datasource_uuids, update_keys=update_keys
# )
# em_store.add_datasources(
# uuids=datasource_uuids, data=model_data, metastore=metastore, update_keys=update_keys
# )
logger.info(f"Completed processing: {filepath.name}.")
# Record the file hash in case we see this file again
self.store_hashes(unseen_hashes)
return datasource_uuids
