from __future__ import annotations
from pathlib import Path
from typing import Any, DefaultDict, Dict, Optional, Union
import logging
from openghg.store.base import BaseStore
from xarray import Dataset
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: Union[str, Path],
model: str,
species: str,
start_date: Optional[str] = None,
end_date: Optional[str] = None,
setup: Optional[str] = None,
if_exists: str = "auto",
save_current: str = "auto",
overwrite: bool = False,
force: bool = False,
compressor: Optional[Any] = None,
filters: Optional[Any] = None,
chunks: Optional[Dict] = None,
) -> Dict:
"""Read Eulerian model output
Args:
filepath: Path of Eulerian model species output
model: Eulerian model name
species: Species name
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.
"""
# 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.
from collections import defaultdict
from openghg.util import (
clean_string,
timestamp_now,
timestamp_tzaware,
check_if_need_new_version,
)
from pandas import Timestamp as pd_Timestamp
from xarray import open_dataset
model = clean_string(model)
species = clean_string(species)
start_date = clean_string(start_date)
end_date = clean_string(end_date)
setup = clean_string(setup)
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)
_, 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 = {}
with open_dataset(filepath).chunk(chunks) as em_data:
# Check necessary 4D coordinates are present and rename if necessary (for consistency)
check_coords = {
"time": ["time"],
"lat": ["lat", "latitude"],
"lon": ["lon", "longitude"],
"lev": ["lev", "level", "layer", "sigma_level"],
}
for name, coord_options in check_coords.items():
for coord in coord_options:
if coord in em_data.coords:
break
else:
raise ValueError(f"Input data must contain one of '{coord_options}' co-ordinate")
if name != coord:
logger.info(f"Renaming co-ordinate '{coord}' to '{name}'")
em_data = em_data.rename({coord: name})
attrs = em_data.attrs
# author_name = "OpenGHG Cloud"
# em_data.attrs["author"] = author_name
metadata = {}
metadata.update(attrs)
metadata["model"] = model
metadata["species"] = species
metadata["processed"] = str(timestamp_now())
metadata["data_type"] = "eulerian_model"
if start_date is None:
if len(em_data["time"]) > 1:
start_date = str(timestamp_tzaware(em_data.time[0].values))
else:
try:
start_date = attrs["simulation_start_date_and_time"]
except KeyError:
raise Exception("Unable to derive start_date from data, please provide as an input.")
else:
start_date = timestamp_tzaware(start_date)
start_date = str(start_date)
if end_date is None:
if len(em_data["time"]) > 1:
end_date = str(timestamp_tzaware(em_data.time[-1].values))
else:
try:
end_date = attrs["simulation_end_date_and_time"]
except KeyError:
raise Exception("Unable to derive `end_date` from data, please provide as an input.")
else:
end_date = timestamp_tzaware(end_date)
end_date = str(end_date)
date = str(pd_Timestamp(start_date).date())
metadata["date"] = date
metadata["start_date"] = start_date
metadata["end_date"] = end_date
metadata["max_longitude"] = round(float(em_data["lon"].max()), 5)
metadata["min_longitude"] = round(float(em_data["lon"].min()), 5)
metadata["max_latitude"] = round(float(em_data["lat"].max()), 5)
metadata["min_latitude"] = round(float(em_data["lat"].min()), 5)
history = metadata.get("history")
if history is None:
history = ""
metadata["history"] = history + f" {str(timestamp_now())} Processed onto OpenGHG cloud"
key = "_".join((model, species, date))
model_data: DefaultDict[str, Dict[str, Union[Dict, Dataset]]] = defaultdict(dict)
model_data[key]["data"] = em_data
model_data[key]["metadata"] = metadata
required = ("model", "species", "date")
data_type = "eulerian_model"
datasource_uuids = self.assign_data(
data=model_data,
if_exists=if_exists,
new_version=new_version,
data_type=data_type,
required_keys=required,
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
# )
# Record the file hash in case we see this file again
self.store_hashes(unseen_hashes)
return datasource_uuids
