Source code for miranda.convert._aggregation

"""Aggregation module."""
from __future__ import annotations

import logging.config

import xarray as xr
from xclim.indices import tas

from miranda.scripting import LOGGING_CONFIG
from miranda.units import get_time_frequency


__all__ = ["aggregations_possible", "aggregate"]

# There needs to be a better way (is there something in xclim?)
_resampling_keys = dict()
_resampling_keys["hour"] = "H"
_resampling_keys["day"] = "D"
_resampling_keys["month"] = "M"
_resampling_keys["year"] = "A"

[docs] def aggregations_possible(ds: xr.Dataset, freq: str = "day") -> dict[str, set[str]]: """Determine which aggregations are possible based on variables within a dataset. Parameters ---------- ds : xarray.Dataset freq : str Returns ------- dict[str, set[str]] """"Determining potential upscaled climate variables.") offset, meaning = get_time_frequency(ds, minimum_continuous_period="1h") aggregation_legend = dict() for v in ["tas", "tdps"]: if freq == meaning: if not hasattr(ds, v) and ( hasattr(ds, f"{v}max") and hasattr(ds, f"{v}min") ): aggregation_legend[f"_{v}"] = {"mean"} for variable in ds.data_vars: if variable in ["tas", "tdps"]: aggregation_legend[variable] = {"max", "mean", "min"} elif variable in [ "evspsblpot", "hfls", "hfss", "hur", "hus", "pr", "prsn", "ps", "psl", "rsds", "rss", "rlds", "rls", "snd", "snr", "snw", "swe", ]: aggregation_legend[variable] = {"mean"} return aggregation_legend
[docs] def aggregate(ds: xr.Dataset, freq: str = "day") -> dict[str, xr.Dataset]: """ Parameters ---------- ds : xarray.Dataset freq : str Returns ------- dict[str, xarray.Dataset] """ mappings = aggregations_possible(ds) try: xarray_agg = _resampling_keys[freq] except KeyError: xarray_agg = freq aggregated = dict() for variable, transformations in mappings.items(): for op in transformations: ds_out = xr.Dataset() ds_out.attrs = ds.attrs.copy() ds_out.attrs["frequency"] = freq with xr.set_options(keep_attrs=True): if variable.startswith("_"): if op == "mean": var = variable.strip("_") min_var = "".join([var, "min"]) max_var = "".join([var, "max"]) mean_variable = tas( tasmin=ds[min_var], tasmax=ds[max_var] ).resample(time=xarray_agg) ds_out[var] = mean_variable.mean(dim="time", keep_attrs=True) method = f"time: mean (interval: 1 {freq})" ds_out[var].attrs["cell_methods"] = method aggregated[var] = ds_out continue else: if op in {"max", "min"}: transformed = f"{variable}{op}" else: transformed = variable r = ds[variable].resample(time=xarray_agg) ds_out[transformed] = getattr(r, op)(dim="time", keep_attrs=True) method = f"time: {op}{'imum' if op != 'mean' else ''} (interval: 1 {freq})" ds_out[transformed].attrs["cell_methods"] = method aggregated[transformed] = ds_out return aggregated