"""aospy DataLoader objects"""
import logging
import os
import dask
import numpy as np
import xarray as xr
from . import internal_names
from .utils import times, io
# Dask must use its serial scheduler if computations are to be performed
# in parallel using multiprocess
dask.set_options(get=dask.async.get_sync)
def grid_attrs_to_aospy_names(data):
"""Rename grid attributes to be consistent with aospy conventions.
Search all of the dataset's coords and dims looking for matches to known
grid attribute names; any that are found subsequently get renamed to the
aospy name as specified in ``aospy.internal_names.GRID_ATTRS``.
Also forces any renamed grid attribute that is saved as a dim without a
coord to have a coord, which facilitates subsequent slicing/subsetting.
This function does not compare to Model coordinates or add missing
coordinates from Model objects.
Parameters
----------
data : xr.Dataset
Returns
-------
xr.Dataset
Data returned with coordinates consistent with aospy
conventions
"""
for name_int, names_ext in internal_names.GRID_ATTRS.items():
dims_and_vars = set(data.variables).union(set(data.dims))
data_coord_name = set(names_ext).intersection(dims_and_vars)
if data_coord_name:
data = data.rename({data_coord_name.pop(): name_int})
# Unless a dim is scalar, force it to have a coord.
# Prevents headaches when subsequently sub-setting.
if name_int in data.dims and not data[name_int].coords:
data = data.assign_coords(**{name_int: data[name_int]})
return data
def set_grid_attrs_as_coords(ds):
"""Set available grid attributes as coordinates in a given Dataset.
Grid attributes are assumed to have their internal aospy names. Grid
attributes are set as coordinates, such that they are carried by all
selected DataArrays with overlapping index dimensions.
Parameters
----------
ds : Dataset
Input data
Returns
-------
Dataset
Dataset with grid attributes set as coordinates
"""
int_names = internal_names.GRID_ATTRS.keys()
grid_attrs_in_ds = set(int_names).intersection(set(ds.coords) |
set(ds.data_vars))
ds.set_coords(grid_attrs_in_ds, inplace=True)
return ds
def _sel_var(ds, var):
"""Select the specified variable by trying all possible alternative names.
Parameters
----------
ds : Dataset
Dataset possibly containing var
var : aospy.Var
Variable to find data for
Returns
-------
DataArray
Raises
------
KeyError
If the variable is not in the Dataset
"""
for name in var.names:
try:
da = ds[name]
return da.rename(var.name)
except KeyError:
pass
msg = '{0} not found among names: {1} in\n{2}'.format(var, var.names, ds)
raise LookupError(msg)
def _prep_time_data(ds):
"""Prepare time coord. information in Dataset for use in aospy.
1. Edit units attribute of time variable if it contains
a Timestamp invalid date
2. If the Dataset contains a time bounds coordinate, add
attributes representing the true beginning and end dates of
the time interval used to construct the Dataset
3. Decode the times into np.datetime64 objects for time
indexing
Parameters
----------
ds : Dataset
Pre-processed Dataset with time coordinate renamed to
internal_names.TIME_STR
Returns
-------
Dataset, int
The processed Dataset and minimum year in the loaded data
"""
ds = times.ensure_time_as_dim(ds)
ds, min_year = times.numpy_datetime_workaround_encode_cf(ds)
if internal_names.TIME_BOUNDS_STR in ds:
ds = times.ensure_time_avg_has_cf_metadata(ds)
else:
logging.warning("dt array not found. Assuming equally spaced "
"values in time, even though this may not be "
"the case")
ds = times.add_uniform_time_weights(ds)
ds = xr.decode_cf(
ds, decode_times=True, decode_coords=False, mask_and_scale=False
)
return ds, min_year
def _load_data_from_disk(file_set):
"""Load a Dataset from a list or glob-string of files.
Datasets from files are concatenated along time,
and all grid attributes are renamed to their aospy internal names.
Parameters
----------
file_set : list or str
List of paths to files or glob-string
Returns
-------
Dataset
"""
apply_preload_user_commands(file_set)
return xr.open_mfdataset(file_set, preprocess=grid_attrs_to_aospy_names,
concat_dim=internal_names.TIME_STR,
decode_cf=False)
def apply_preload_user_commands(file_set, cmd=io.dmget):
"""Call desired functions on file list before loading.
For example, on the NOAA Geophysical Fluid Dynamics Laboratory
computational cluster, data that is saved on their tape archive
must be accessed via a `dmget` (or `hsmget`) command before being used.
"""
if cmd is not None:
cmd(file_set)
[docs]class DataLoader(object):
"""A fundamental DataLoader object"""
[docs] def load_variable(self, var=None, start_date=None, end_date=None,
time_offset=None, **DataAttrs):
"""Load a DataArray for requested variable and time range.
Automatically renames all grid attributes to match aospy conventions.
Parameters
----------
var : Var
aospy Var object
start_date : datetime.datetime
start date for interval
end_date : datetime.datetime
end date for interval
time_offset : dict
Option to add a time offset to the time coordinate to correct for
incorrect metadata.
**DataAttrs
Attributes needed to identify a unique set of files to load from
Returns
-------
da : DataArray
DataArray for the specified variable, date range, and interval in
"""
file_set = self._generate_file_set(var=var, start_date=start_date,
end_date=end_date, **DataAttrs)
ds = _load_data_from_disk(file_set)
ds, min_year = _prep_time_data(ds)
ds = set_grid_attrs_as_coords(ds)
da = _sel_var(ds, var)
da = self._maybe_apply_time_shift(da, time_offset, **DataAttrs)
start_date_xarray = times.numpy_datetime_range_workaround(
start_date, min_year)
end_date_xarray = start_date_xarray + (end_date - start_date)
return times.sel_time(da, np.datetime64(start_date_xarray),
np.datetime64(end_date_xarray)).load()
@staticmethod
def _maybe_apply_time_shift(da, time_offset=None, **DataAttrs):
"""Apply specified time shift to DataArray"""
if time_offset is not None:
time = times.apply_time_offset(da[internal_names.TIME_STR],
**time_offset)
da[internal_names.TIME_STR] = time
return da
def _generate_file_set(self, var=None, start_date=None, end_date=None,
domain=None, intvl_in=None, dtype_in_vert=None,
dtype_in_time=None, intvl_out=None):
raise NotImplementedError(
'All DataLoaders require a _generate_file_set method')
[docs]class DictDataLoader(DataLoader):
"""A DataLoader that uses a dict mapping lists of files to string tags
This is the simplest DataLoader; it is useful for instance if one is
dealing with raw model history files, which tend to group all variables
of a single output interval into single filesets. The
intvl_in parameter is a string description of the time frequency of the
data one is referencing (e.g. 'monthly', 'daily', '3-hourly'). In
principle, one can give it any string value.
Parameters
----------
file_map : dict
A dict mapping an input interval to a list of files
Examples
--------
Case of two sets of files, one with monthly average output, and one with
3-hourly output.
>>> file_map = {'monthly': '000[4-6]0101.atmos_month.nc',
... '3hr': '000[4-6]0101.atmos_8xday.nc'}
>>> data_loader = DictDataLoader(file_map)
"""
[docs] def __init__(self, file_map=None):
"""Create a new DictDataLoader"""
self.file_map = file_map
def _generate_file_set(self, var=None, start_date=None, end_date=None,
domain=None, intvl_in=None, dtype_in_vert=None,
dtype_in_time=None, intvl_out=None):
"""Returns the file_set for the given interval in."""
try:
return self.file_map[intvl_in]
except KeyError:
raise KeyError('File set does not exist for the specified'
' intvl_in {0}'.format(intvl_in))
[docs]class NestedDictDataLoader(DataLoader):
"""DataLoader that uses a nested dictionary mapping to load files
This is the most flexible existing type of DataLoader; it allows for the
specification of different sets of files for different variables. The
intvl_in parameter is a string description of the time frequency of the
data one is referencing (e.g. 'monthly', 'daily', '3-hourly'). In
principle, one can give it any string value. The variable name
can be any variable name in your aospy object library (including
alternative names).
Parameters
----------
file_map : dict
A dict mapping intvl_in to dictionaries mapping Var
objects to lists of files
Examples
--------
Case of a set of monthly average files for large scale precipitation,
and another monthly average set of files for convective precipitation.
>>> file_map = {'monthly': {'precl': '000[4-6]0101.precl.nc',
... 'precc': '000[4-6]0101.precc.nc'}}
>>> data_loader = NestedDictDataLoader(file_map)
"""
[docs] def __init__(self, file_map=None):
"""Create a new NestedDictDataLoader"""
self.file_map = file_map
def _generate_file_set(self, var=None, start_date=None, end_date=None,
domain=None, intvl_in=None, dtype_in_vert=None,
dtype_in_time=None, intvl_out=None):
for name in var.names:
try:
return self.file_map[intvl_in][name]
except KeyError:
pass
raise KeyError('Files for the var {0} cannot be found in for the '
'intvl_in {1} in this'
' OneDirDataLoader'.format(var, intvl_in))
[docs]class GFDLDataLoader(DataLoader):
"""DataLoader for NOAA GFDL model output
This is an example of a domain-specific custom DataLoader, designed
specifically for finding files output by the Geophysical Fluid Dynamics
Laboratory's model history file post-processing tools.
Parameters
----------
template : GFDLDataLoader
Optional argument to specify a base GFDLDataLoader to inherit
parameters from
data_direc : str
Root directory of data files
data_dur : int
Number of years included per post-processed file
data_start_date : datetime.datetime
Start date of data files
data_end_date : datetime.datetime
End date of data files
Examples
--------
Case without a template to start from.
>>> base = GFDLDataLoader(data_direc='/archive/control/pp', data_dur=5,
... data_start_date=datetime(2000, 1, 1),
... data_end_date=datetime(2010, 12, 31))
Case with a starting template.
>>> data_loader = GFDLDataLoader(base, data_direc='/archive/2xCO2/pp')
"""
[docs] def __init__(self, template=None, data_direc=None, data_dur=None,
data_start_date=None, data_end_date=None):
"""Create a new GFDLDataLoader"""
attrs = ['data_direc', 'data_dur', 'data_start_date', 'data_end_date']
if template:
for attr in attrs:
setattr(self, attr, getattr(template, attr))
if data_direc is not None:
self.data_direc = data_direc
else:
self.data_direc = template.data_direc
if data_dur is not None:
self.data_dur = data_dur
else:
self.data_dur = template.data_dur
if data_start_date is not None:
self.data_start_date = data_start_date
else:
self.data_start_date = template.data_start_date
if data_end_date is not None:
self.data_end_date = data_end_date
else:
self.data_end_date = template.data_end_date
else:
self.data_direc = data_direc
self.data_dur = data_dur
self.data_start_date = data_start_date
self.data_end_date = data_end_date
@staticmethod
def _maybe_apply_time_shift(da, time_offset=None, **DataAttrs):
"""Correct off-by-one error in GFDL instantaneous model data.
Instantaneous data that is outputted by GFDL models is generally off by
one timestep. For example, a netCDF file that is supposed to
correspond to 6 hourly data for the month of January, will have its
last time value be in February.
"""
if time_offset is not None:
time = times.apply_time_offset(da[internal_names.TIME_STR],
**time_offset)
da[internal_names.TIME_STR] = time
else:
if DataAttrs['dtype_in_time'] == 'inst':
if DataAttrs['intvl_in'].endswith('hr'):
offset = -1 * int(DataAttrs['intvl_in'][0])
else:
offset = 0
time = times.apply_time_offset(da[internal_names.TIME_STR],
hours=offset)
da[internal_names.TIME_STR] = time
return da
def _generate_file_set(self, var=None, start_date=None, end_date=None,
domain=None, intvl_in=None, dtype_in_vert=None,
dtype_in_time=None, intvl_out=None):
for name in var.names:
file_set = self._input_data_paths_gfdl(
name, start_date, end_date, domain, intvl_in, dtype_in_vert,
dtype_in_time, intvl_out)
if all([os.path.isfile(filename) for filename in file_set]):
return file_set
raise IOError('Files for the var {0} cannot be located'
'using GFDL post-processing conventions'.format(var))
def _input_data_paths_gfdl(self, name, start_date, end_date, domain,
intvl_in, dtype_in_vert, dtype_in_time,
intvl_out):
dtype_lbl = dtype_in_time
if intvl_in == 'daily':
domain += '_daily'
if dtype_in_vert == internal_names.ETA_STR and name != 'ps':
domain += '_level'
if dtype_in_time == 'inst':
domain += '_inst'
dtype_lbl = 'ts'
if 'monthly_from_' in dtype_in_time:
dtype = dtype_in_time.replace('monthly_from_', '')
dtype_lbl = dtype
else:
dtype = dtype_in_time
dur_str = str(self.data_dur) + 'yr'
if dtype_in_time == 'av':
subdir = intvl_in + '_' + dur_str
else:
subdir = os.path.join(intvl_in, dur_str)
direc = os.path.join(self.data_direc, domain, dtype_lbl, subdir)
files = [os.path.join(direc, io.data_name_gfdl(
name, domain, dtype, intvl_in, year, intvl_out,
self.data_start_date.year, self.data_dur))
for year in range(start_date.year, end_date.year + 1)]
files = list(set(files))
files.sort()
return files