def load_variable(var_name, path_to_file, squeeze=False,
fix_times=True, **extr_kwargs):
""" Interface for loading an extracted variable into memory, using
either iris or xarray. If `path_to_file` is instead a raw dataset,
then the entire contents of the file will be loaded!
Parameters
----------
var_name : string
The name of the variable to load
path_to_file : string
Location of file containing variable
squeeze : bool
Load only the requested field (ignore all others) and
associated dims
fix_times : bool
Correct the timestamps to the middle of the bounds
in the variable metadata (CESM puts them at the right
boundary which sucks!)
extr_kwargs : dict
Additional keyword arguments to pass to the extractor
"""
logger.info("Loading %s from %s" % (var_name, path_to_file))
ds = xr.open_dataset(path_to_file, decode_cf=False, **extr_kwargs)
# TODO: Revise this logic as part of generalizing time post-processing.
# Fix time unit, if necessary
# interval, timestamp = ds.time.units.split(" since ")
# timestamp = timestamp.split(" ")
# yr, mm, dy = timestamp[0].split("-")
#
# if int(yr) < 1650:
# yr = 2001
# yr = str(yr)
#
# # Re-construct at Jan 01, 2001 and re-set
# timestamp[0] = "-".join([yr, mm, dy])
# new_units = " ".join([interval, "since"] + timestamp)
# ds.time.attrs['units'] = new_units
# TODO: Generalize time post-processing.
# if fix_times:
# assert hasattr(ds, 'time_bnds')
# bnds = ds.time_bnds.values
# mean_times = np.mean(bnds, axis=1)
#
# ds.time.values = mean_times
# Be pedantic and check that we don't have a "missing_value" attr
for field in ds:
if hasattr(ds[field], 'missing_value'):
del ds[field].attrs['missing_value']
# Lazy decode CF
# TODO: There's potentially a bug where decode_cf eagerly loads dask arrays
# ds = xr.decode_cf(ds)
return ds
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