def epoch_mean(query, name='epoch_mean', center_time=True, time_range=None):
"""Instantiate a `funnel.Collection` object for computing epoch means."""
if center_time:
postproccess = [ops.center_time]
postproccess_kwargs = [{}]
else:
postproccess = []
postproccess_kwargs = []
if time_range is None:
postproccess += [_mean_time_for_experiment]
postproccess_kwargs += [{}]
else:
sel_dict = dict(time=slice(time_range[0], time_range[1]))
postproccess += [ops.mean_time]
postproccess_kwargs += [dict(sel_dict=sel_dict)]
return fn.Collection(
name=name,
esm_collection_json=esm_collection_json,
postproccess=postproccess,
postproccess_kwargs=postproccess_kwargs,
query=query,
cache_dir=config_dict['cache_dir'],
persist=True,
cdf_kwargs=dict(chunks={'time': 4}),
)
def global_mean_timeseries_ann(query,
name='global_mean_timeseries_ann',
center_time=True):
"""
Instantiate a `funnel.Collection` object for computing
global mean, annual mean timeseries.
"""
postproccess = [ops.global_mean, ops.resample_ann]
postproccess_kwargs = [dict(normalize=True, include_ms=False), {}]
if center_time:
postproccess = [ops.center_time] + postproccess
postproccess_kwargs = [{}] + postproccess_kwargs
return fn.Collection(
name=name,
esm_collection_json=esm_collection_json,
postproccess=postproccess,
postproccess_kwargs=postproccess_kwargs,
query=query,
cache_dir=config_dict['cache_dir'],
persist=True,
cdf_kwargs=dict(chunks={'time': 4}, decode_coords=False),
)
def epoch_mean(query, name='epoch_mean', center_time=True):
"""Instantiate a `funnel.Collection` object for computing epoch means."""
postproccess = [_mean_time_for_experiment]
postproccess_kwargs = [{}]
if center_time:
postproccess = [ops.center_time] + postproccess
postproccess_kwargs = [{}] + postproccess_kwargs
return fn.Collection(
name=name,
esm_collection_json=config_dict['esm_collection'],
postproccess=postproccess,
postproccess_kwargs=postproccess_kwargs,
query=query,
cache_dir=config_dict['cache_dir'],
persist=True,
cdf_kwargs=dict(chunks={'time': 4}),
)