"""aospy DataLoader objects"""
import logging
import os
import pprint
import warnings
import numpy as np
import xarray as xr
from .internal_names import (
ETA_STR,
GRID_ATTRS,
TIME_STR,
TIME_BOUNDS_STR,
)
from .utils import times, io
def _preprocess_and_rename_grid_attrs(func, grid_attrs=None, **kwargs):
"""Call a custom preprocessing method first then rename grid attrs.
This wrapper is needed to generate a single function to pass to the
``preprocesss`` of xr.open_mfdataset. It makes sure that the
user-specified preprocess function is called on the loaded Dataset before
aospy's is applied. An example for why this might be needed is output from
the WRF model; one needs to add a CF-compliant units attribute to the time
coordinate of all input files, because it is not present by default.
Parameters
----------
func : function
An arbitrary function to call before calling
``grid_attrs_to_aospy_names`` in ``_load_data_from_disk``. Must take
an xr.Dataset as an argument as well as ``**kwargs``.
grid_attrs : dict (optional)
Overriding dictionary of grid attributes mapping aospy internal
names to names of grid attributes used in a particular model.
Returns
-------
function
A function that calls the provided function ``func`` on the Dataset
before calling ``grid_attrs_to_aospy_names``; this is meant to be
passed as a ``preprocess`` argument to ``xr.open_mfdataset``.
"""
def func_wrapper(ds):
return grid_attrs_to_aospy_names(func(ds, **kwargs), grid_attrs)
return func_wrapper
def grid_attrs_to_aospy_names(data, grid_attrs=None):
"""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
grid_attrs : dict (default None)
Overriding dictionary of grid attributes mapping aospy internal
names to names of grid attributes used in a particular model.
Returns
-------
xr.Dataset
Data returned with coordinates consistent with aospy
conventions
"""
if grid_attrs is None:
grid_attrs = {}
# Override GRID_ATTRS with entries in grid_attrs
attrs = GRID_ATTRS.copy()
for k, v in grid_attrs.items():
if k not in attrs:
raise ValueError(
'Unrecognized internal name, {!r}, specified for a custom '
'grid attribute name. See the full list of valid internal '
'names below:\n\n{}'.format(k, list(GRID_ATTRS.keys())))
attrs[k] = (v, )
dims_and_vars = set(data.variables).union(set(data.dims))
for name_int, names_ext in attrs.items():
data_coord_name = set(names_ext).intersection(dims_and_vars)
if data_coord_name:
data = data.rename({data_coord_name.pop(): name_int})
return set_grid_attrs_as_coords(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
"""
grid_attrs_in_ds = set(GRID_ATTRS.keys()).intersection(
set(ds.coords) | set(ds.data_vars))
ds = ds.set_coords(grid_attrs_in_ds)
return ds
def _maybe_cast_to_float64(da):
"""Cast DataArrays to np.float64 if they are of type np.float32.
Parameters
----------
da : xr.DataArray
Input DataArray
Returns
-------
DataArray
"""
if da.dtype == np.float32:
logging.warning('Datapoints were stored using the np.float32 datatype.'
'For accurate reduction operations using bottleneck, '
'datapoints are being cast to the np.float64 datatype.'
' For more information see: https://github.com/pydata/'
'xarray/issues/1346')
return da.astype(np.float64)
else:
return da
def _sel_var(ds, var, upcast_float32=True):
"""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
upcast_float32 : bool (default True)
Whether to cast a float32 DataArray up to float64
Returns
-------
DataArray
Raises
------
KeyError
If the variable is not in the Dataset
"""
for name in var.names:
try:
da = ds[name].rename(var.name)
if upcast_float32:
return _maybe_cast_to_float64(da)
else:
return da
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 coordinate information in Dataset for use in aospy.
1. 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
2. If the Dataset contains a time bounds coordinate, overwrite the time
coordinate values with the averages of the time bounds at each timestep
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
The processed Dataset
"""
ds = times.ensure_time_as_index(ds)
if TIME_BOUNDS_STR in ds:
ds = times.ensure_time_avg_has_cf_metadata(ds)
ds[TIME_STR] = times.average_time_bounds(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)
# Suppress enable_cftimeindex is a no-op warning; we'll keep setting it for
# now to maintain backwards compatibility for older xarray versions.
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
with xr.set_options(enable_cftimeindex=True):
ds = xr.decode_cf(ds, decode_times=True, decode_coords=False,
mask_and_scale=True)
return ds
def _load_data_from_disk(file_set, preprocess_func=lambda ds: ds,
data_vars='minimal', coords='minimal',
grid_attrs=None, **kwargs):
"""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
preprocess_func : function (optional)
Custom function to call before applying any aospy logic
to the loaded dataset
data_vars : str (default 'minimal')
Mode for concatenating data variables in call to ``xr.open_mfdataset``
coords : str (default 'minimal')
Mode for concatenating coordinate variables in call to
``xr.open_mfdataset``.
grid_attrs : dict
Overriding dictionary of grid attributes mapping aospy internal
names to names of grid attributes used in a particular model.
Returns
-------
Dataset
"""
apply_preload_user_commands(file_set)
func = _preprocess_and_rename_grid_attrs(preprocess_func, grid_attrs,
**kwargs)
return xr.open_mfdataset(file_set, preprocess=func, concat_dim=TIME_STR,
decode_times=False, decode_coords=False,
mask_and_scale=True, data_vars=data_vars,
coords=coords)
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)
def _setattr_default(obj, attr, value, default):
"""Set an attribute of an object to a value or default value."""
if value is None:
setattr(obj, attr, default)
else:
setattr(obj, attr, value)
[docs]class DataLoader(object):
"""A fundamental DataLoader object."""
[docs] def load_variable(self, var=None, start_date=None, end_date=None,
time_offset=None, grid_attrs=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.
grid_attrs : dict (optional)
Overriding dictionary of grid attributes mapping aospy internal
names to names of grid attributes used in a particular model.
**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, self.preprocess_func, data_vars=self.data_vars,
coords=self.coords, start_date=start_date, end_date=end_date,
time_offset=time_offset, grid_attrs=grid_attrs, **DataAttrs
)
if var.def_time:
ds = _prep_time_data(ds)
start_date = times.maybe_convert_to_index_date_type(
ds.indexes[TIME_STR], start_date)
end_date = times.maybe_convert_to_index_date_type(
ds.indexes[TIME_STR], end_date)
ds = set_grid_attrs_as_coords(ds)
da = _sel_var(ds, var, self.upcast_float32)
if var.def_time:
da = self._maybe_apply_time_shift(da, time_offset, **DataAttrs)
return times.sel_time(da, start_date, end_date).load()
else:
return da.load()
def _load_or_get_from_model(self, var, start_date=None, end_date=None,
time_offset=None, model=None, **DataAttrs):
"""Load a DataArray for the requested variable and time range
Supports both access of grid attributes either through the DataLoader
or through an optionally-provided Model object. Defaults to using
the version found in the DataLoader first.
"""
grid_attrs = None if model is None else model.grid_attrs
try:
return self.load_variable(
var, start_date=start_date, end_date=end_date,
time_offset=time_offset, grid_attrs=grid_attrs, **DataAttrs)
except (KeyError, IOError) as e:
if var.name not in GRID_ATTRS or model is None:
raise e
else:
try:
return getattr(model, var.name)
except AttributeError:
raise AttributeError(
'Grid attribute {} could not be located either '
'through this DataLoader or in the provided Model '
'object: {}.'.format(var, model))
[docs] def recursively_compute_variable(self, var, start_date=None, end_date=None,
time_offset=None, model=None,
**DataAttrs):
"""Compute a variable recursively, loading data where needed.
An obvious requirement here is that the variable must eventually be
able to be expressed in terms of model-native quantities; otherwise the
recursion will never stop.
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.
model : Model
aospy Model object (optional)
**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
"""
if var.variables is None:
return self._load_or_get_from_model(
var, start_date, end_date, time_offset, model, **DataAttrs)
else:
data = [self.recursively_compute_variable(
v, start_date, end_date, time_offset, model, **DataAttrs)
for v in var.variables]
return var.func(*data).rename(var.name)
@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[TIME_STR], **time_offset)
da[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
upcast_float32 : bool (default True)
Whether to cast loaded DataArrays with the float32 datatype to float64
before doing calculations
data_vars : str (default 'minimal')
Mode for concatenating data variables in call to ``xr.open_mfdataset``
coords : str (default 'minimal')
Mode for concatenating coordinate variables in call to
``xr.open_mfdataset``.
preprocess_func : function (optional)
A function to apply to every Dataset before processing in aospy. Must
take a Dataset and ``**kwargs`` as its two arguments.
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)
If one wanted to correct a CF-incompliant units attribute on each Dataset
read in, which depended on the ``intvl_in`` of the fileset one could
define a ``preprocess_func`` which took into account the ``intvl_in``
keyword argument.
>>> def preprocess(ds, **kwargs):
... if kwargs['intvl_in'] == 'monthly':
... ds['time'].attrs['units'] = 'days since 0001-01-0000'
... if kwargs['intvl_in'] == '3hr':
... ds['time'].attrs['units'] = 'hours since 0001-01-0000'
... return ds
>>> data_loader = DictDataLoader(file_map, preprocess)
"""
[docs] def __init__(self, file_map=None, upcast_float32=True, data_vars='minimal',
coords='minimal', preprocess_func=lambda ds, **kwargs: ds):
"""Create a new DictDataLoader."""
self.file_map = file_map
self.upcast_float32 = upcast_float32
self.data_vars = data_vars
self.coords = coords
self.preprocess_func = preprocess_func
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
upcast_float32 : bool (default True)
Whether to cast loaded DataArrays with the float32 datatype to float64
before doing calculations
data_vars : str (default 'minimal')
Mode for concatenating data variables in call to ``xr.open_mfdataset``
coords : str (default 'minimal')
Mode for concatenating coordinate variables in call to
``xr.open_mfdataset``.
preprocess_func : function (optional)
A function to apply to every Dataset before processing in aospy. Must
take a Dataset and ``**kwargs`` as its two arguments.
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)
See :py:class:`aospy.data_loader.DictDataLoader` for an example of a
possible function to pass as a ``preprocess_func``.
"""
[docs] def __init__(self, file_map=None, upcast_float32=True, data_vars='minimal',
coords='minimal', preprocess_func=lambda ds, **kwargs: ds):
"""Create a new NestedDictDataLoader"""
self.file_map = file_map
self.upcast_float32 = upcast_float32
self.data_vars = data_vars
self.coords = coords
self.preprocess_func = preprocess_func
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
upcast_float32 : bool (default True)
Whether to cast loaded DataArrays with the float32 datatype to float64
before doing calculations
data_vars : str (default 'minimal')
Mode for concatenating data variables in call to ``xr.open_mfdataset``
coords : str (default 'minimal')
Mode for concatenating coordinate variables in call to
``xr.open_mfdataset``.
preprocess_func : function (optional)
A function to apply to every Dataset before processing in aospy. Must
take a Dataset and ``**kwargs`` as its two arguments.
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')
See :py:class:`aospy.data_loader.DictDataLoader` for an example of a
possible function to pass as a ``preprocess_func``.
"""
[docs] def __init__(self, template=None, data_direc=None, data_dur=None,
data_start_date=None, data_end_date=None,
upcast_float32=None, data_vars=None, coords=None,
preprocess_func=None):
"""Create a new GFDLDataLoader"""
if template:
_setattr_default(self, 'data_direc', data_direc,
getattr(template, 'data_direc'))
_setattr_default(self, 'data_dur', data_dur,
getattr(template, 'data_dur'))
_setattr_default(self, 'data_start_date', data_start_date,
getattr(template, 'data_start_date'))
_setattr_default(self, 'data_end_date', data_end_date,
getattr(template, 'data_end_date'))
_setattr_default(self, 'upcast_float32', upcast_float32,
getattr(template, 'upcast_float32'))
_setattr_default(self, 'data_vars', data_vars,
getattr(template, 'data_vars'))
_setattr_default(self, 'coords', coords,
getattr(template, 'coords'))
_setattr_default(self, 'preprocess_func', preprocess_func,
getattr(template, 'preprocess_func'))
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
_setattr_default(self, 'upcast_float32', upcast_float32, True)
_setattr_default(self, 'data_vars', data_vars, 'minimal')
_setattr_default(self, 'coords', coords, 'minimal')
_setattr_default(self, 'preprocess_func', preprocess_func,
lambda ds, **kwargs: ds)
@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[TIME_STR], **time_offset)
da[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[TIME_STR], hours=offset)
da[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):
attempted_file_sets = []
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)
attempted_file_sets.append(file_set)
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. '
'Attempted using the following sets of paths:\n\n'
'{1}'.format(var, pprint.pformat(attempted_file_sets)))
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 == 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)
data_start_year = times.infer_year(self.data_start_date)
start_year = times.infer_year(start_date)
end_year = times.infer_year(end_date)
files = [os.path.join(direc, io.data_name_gfdl(
name, domain, dtype, intvl_in, year, intvl_out,
data_start_year, self.data_dur))
for year in range(start_year, end_year + 1)]
files = list(set(files))
files.sort()
return files