# Copyright (c) 2018-2024 by xcube team and contributors
# Permissions are hereby granted under the terms of the MIT License:
# https://opensource.org/licenses/MIT.
import functools
import math
import numpy as np
import xarray as xr
from xcube.core.maskset import MaskSet
from xcube.util.config import NameDictPairList
from xcube.util.config import to_resolved_name_dict_pairs
from xcube.util.expression import compute_array_expr
[docs]
def evaluate_dataset(
dataset: xr.Dataset,
processed_variables: NameDictPairList = None,
errors: str = "raise",
) -> xr.Dataset:
"""Compute new variables or mask existing variables in *dataset*
by the evaluation of Python expressions, that may refer to other
existing or new variables.
Returns a new dataset that contains the old and new variables,
where both may bew now masked.
Expressions may be given by attributes of existing variables in
*dataset* or passed a via the *processed_variables* argument
which is a sequence of variable name / attributes tuples.
Two types of expression attributes are recognized in the attributes:
1. The attribute ``expression`` generates
a new variable computed from its attribute value.
2. The attribute ``valid_pixel_expression`` masks out
invalid variable values.
In both cases the attribuite value must be a string that forms
a valid Python expression that can reference any other preceding
variables by name.
The expression can also reference any flags defined by another
variable according the their CF attributes ``flag_meaning``
and ``flag_values``.
Invalid variable values may be masked out using the value the
``valid_pixel_expression`` attribute whose value should form
a Boolean Python expression. In case, the expression
returns zero or false, the value of the ``_FillValue`` attribute
or NaN will be used in the new variable.
Other attributes will be stored as variable metadata as-is.
Args:
dataset: A dataset.
processed_variables: Optional list of variable name-attributes
pairs that will processed in the given order.
errors: How to deal with errors while evaluating expressions.
May be be one of "raise", "warn", or "ignore".
Returns:
new dataset with computed variables
"""
if processed_variables:
processed_variables = to_resolved_name_dict_pairs(
processed_variables, dataset, keep=True
)
else:
var_names = list(dataset.data_vars)
var_names = sorted(var_names, key=functools.partial(_get_var_sort_key, dataset))
processed_variables = [(var_name, None) for var_name in var_names]
# Initialize namespace with some constants and modules
namespace = dict(NaN=np.nan, PI=math.pi, np=np, xr=xr)
# Now add all mask sets and variables
for var_name in dataset.data_vars:
var = dataset[var_name]
if MaskSet.is_flag_var(var):
namespace[var_name] = MaskSet(var)
else:
namespace[var_name] = var
for var_name, var_props in processed_variables:
if var_name in dataset.data_vars:
# Existing variable
var = dataset[var_name]
if var_props:
var_props_temp = var_props
var_props = dict(var.attrs)
var_props.update(var_props_temp)
else:
var_props = dict(var.attrs)
else:
# Computed variable
var = None
if var_props is None:
var_props = dict()
do_load = var_props.get("load", False)
expression = var_props.get("expression")
if expression:
# Compute new variable
computed_array = compute_array_expr(
expression,
namespace=namespace,
result_name=f"{var_name!r}",
errors=errors,
)
if computed_array is not None:
if hasattr(computed_array, "attrs"):
var = computed_array
var.attrs.update(var_props)
if do_load:
computed_array.load()
namespace[var_name] = computed_array
valid_pixel_expression = var_props.get("valid_pixel_expression")
if valid_pixel_expression:
# Compute new mask for existing variable
if var is None:
raise ValueError(f"undefined variable {var_name!r}")
valid_mask = compute_array_expr(
valid_pixel_expression,
namespace=namespace,
result_name=f"valid mask for {var_name!r}",
errors=errors,
)
if valid_mask is not None:
masked_var = var.where(valid_mask)
if hasattr(masked_var, "attrs"):
masked_var.attrs.update(var_props)
if do_load:
masked_var.load()
namespace[var_name] = masked_var
computed_dataset = dataset.copy()
for name, value in namespace.items():
if isinstance(value, xr.DataArray):
computed_dataset[name] = value
return computed_dataset
def _get_var_sort_key(dataset: xr.Dataset, var_name: str):
# noinspection SpellCheckingInspection
attrs = dataset[var_name].attrs
a1 = attrs.get("expression")
a2 = attrs.get("valid_pixel_expression")
v1 = 10 * len(a1) if a1 is not None else 0
v2 = 100 * len(a2) if a2 is not None else 0
return v1 + v2