# Copyright Iris contributors
#
# This file is part of Iris and is released under the LGPL license.
# See COPYING and COPYING.LESSER in the root of the repository for full
# licensing details.
"""
Provides the capability to load netCDF files and interpret them
according to the 'NetCDF Climate and Forecast (CF) Metadata Conventions'.
References:
[CF] NetCDF Climate and Forecast (CF) Metadata conventions.
[NUG] NetCDF User's Guide, https://www.unidata.ucar.edu/software/netcdf/documentation/NUG/
"""
from abc import ABCMeta, abstractmethod
from collections.abc import Iterable, MutableMapping
import os
import re
import warnings
import numpy as np
import numpy.ma as ma
from iris.fileformats.netcdf import _thread_safe_nc
import iris.util
#
# CF parse pattern common to both formula terms and measure CF variables.
#
_CF_PARSE = re.compile(
r"""
\s*
(?P<lhs>[\w_]+)
\s*:\s*
(?P<rhs>[\w_]+)
\s*
""",
re.VERBOSE,
)
# NetCDF variable attributes handled by the netCDF4 module and
# therefore automatically classed as "used" attributes.
_CF_ATTRS_IGNORE = set(
["_FillValue", "add_offset", "missing_value", "scale_factor"]
)
#: Supported dimensionless vertical coordinate reference surface/phemomenon
#: formula terms. Ref: [CF] Appendix D.
reference_terms = dict(
atmosphere_sigma_coordinate=["ps"],
atmosphere_hybrid_sigma_pressure_coordinate=["ps"],
atmosphere_hybrid_height_coordinate=["orog"],
atmosphere_sleve_coordinate=["zsurf1", "zsurf2"],
ocean_sigma_coordinate=["eta", "depth"],
ocean_s_coordinate=["eta", "depth"],
ocean_sigma_z_coordinate=["eta", "depth"],
ocean_s_coordinate_g1=["eta", "depth"],
ocean_s_coordinate_g2=["eta", "depth"],
)
# NetCDF returns a different type for strings depending on Python version.
def _is_str_dtype(var):
return np.issubdtype(var.dtype, np.bytes_)
################################################################################
[docs]class CFVariable(metaclass=ABCMeta):
"""Abstract base class wrapper for a CF-netCDF variable."""
#: Name of the netCDF variable attribute that identifies this
#: CF-netCDF variable.
cf_identity = None
def __init__(self, name, data):
# Accessing the list of netCDF attributes is surprisingly slow.
# Since it's used repeatedly, caching the list makes things
# quite a bit faster.
self._nc_attrs = data.ncattrs()
#: NetCDF variable name.
self.cf_name = name
#: NetCDF4 Variable data instance.
self.cf_data = data
#: Collection of CF-netCDF variables associated with this variable.
self.cf_group = None
#: CF-netCDF formula terms that his variable participates in.
self.cf_terms_by_root = {}
self.cf_attrs_reset()
@staticmethod
def _identify_common(variables, ignore, target):
if ignore is None:
ignore = []
if target is None:
target = variables
elif isinstance(target, str):
if target not in variables:
raise ValueError(
"Cannot identify unknown target CF-netCDF variable %r"
% target
)
target = {target: variables[target]}
else:
raise TypeError("Expect a target CF-netCDF variable name")
return (ignore, target)
[docs] @abstractmethod
def identify(self, variables, ignore=None, target=None, warn=True):
"""
Identify all variables that match the criterion for this CF-netCDF variable class.
Args:
* variables:
Dictionary of netCDF4.Variable instance by variable name.
Kwargs:
* ignore:
List of variable names to ignore.
* target:
Name of a single variable to check.
* warn:
Issue a warning if a missing variable is referenced.
Returns:
Dictionary of CFVariable instance by variable name.
"""
pass
[docs] def spans(self, cf_variable):
"""
Determine whether the dimensionality of this variable
is a subset of the specified target variable.
Note that, by default scalar variables always span the
dimensionality of the target variable.
Args:
* cf_variable:
Compare dimensionality with the :class:`CFVariable`.
Returns:
Boolean.
"""
result = set(self.dimensions).issubset(cf_variable.dimensions)
return result
def __eq__(self, other):
# CF variable names are unique.
return self.cf_name == other.cf_name
def __ne__(self, other):
# CF variable names are unique.
return self.cf_name != other.cf_name
def __hash__(self):
# CF variable names are unique.
return hash(self.cf_name)
def __getattr__(self, name):
# Accessing netCDF attributes is surprisingly slow. Since
# they're often read repeatedly, caching the values makes things
# quite a bit faster.
if name in self._nc_attrs:
self._cf_attrs.add(name)
value = getattr(self.cf_data, name)
setattr(self, name, value)
return value
def __getitem__(self, key):
return self.cf_data.__getitem__(key)
def __len__(self):
return self.cf_data.__len__()
def __repr__(self):
return "%s(%r, %r)" % (
self.__class__.__name__,
self.cf_name,
self.cf_data,
)
[docs] def cf_attrs(self):
"""Return a list of all attribute name and value pairs of the CF-netCDF variable."""
return tuple(
(attr, self.getncattr(attr)) for attr in sorted(self._nc_attrs)
)
[docs] def cf_attrs_ignored(self):
"""Return a list of all ignored attribute name and value pairs of the CF-netCDF variable."""
return tuple(
(attr, self.getncattr(attr))
for attr in sorted(set(self._nc_attrs) & _CF_ATTRS_IGNORE)
)
[docs] def cf_attrs_used(self):
"""Return a list of all accessed attribute name and value pairs of the CF-netCDF variable."""
return tuple(
(attr, self.getncattr(attr)) for attr in sorted(self._cf_attrs)
)
[docs] def cf_attrs_unused(self):
"""Return a list of all non-accessed attribute name and value pairs of the CF-netCDF variable."""
return tuple(
(attr, self.getncattr(attr))
for attr in sorted(set(self._nc_attrs) - self._cf_attrs)
)
[docs] def cf_attrs_reset(self):
"""Reset the history of accessed attribute names of the CF-netCDF variable."""
self._cf_attrs = set([item[0] for item in self.cf_attrs_ignored()])
[docs]class CFAncillaryDataVariable(CFVariable):
"""
A CF-netCDF ancillary data variable is a variable that provides metadata
about the individual values of another data variable.
Identified by the CF-netCDF variable attribute 'ancillary_variables'.
Ref: [CF] Section 3.4. Ancillary Data.
"""
cf_identity = "ancillary_variables"
[docs] @classmethod
def identify(cls, variables, ignore=None, target=None, warn=True):
result = {}
ignore, target = cls._identify_common(variables, ignore, target)
# Identify all CF ancillary data variables.
for nc_var_name, nc_var in target.items():
# Check for ancillary data variable references.
nc_var_att = getattr(nc_var, cls.cf_identity, None)
if nc_var_att is not None:
for name in nc_var_att.split():
if name not in ignore:
if name not in variables:
if warn:
message = "Missing CF-netCDF ancillary data variable %r, referenced by netCDF variable %r"
warnings.warn(message % (name, nc_var_name))
else:
result[name] = CFAncillaryDataVariable(
name, variables[name]
)
return result
[docs]class CFAuxiliaryCoordinateVariable(CFVariable):
"""
A CF-netCDF auxiliary coordinate variable is any netCDF variable that contains
coordinate data, but is not a CF-netCDF coordinate variable by definition.
There is no relationship between the name of a CF-netCDF auxiliary coordinate
variable and the name(s) of its dimension(s).
Identified by the CF-netCDF variable attribute 'coordinates'.
Also see :class:`iris.fileformats.cf.CFLabelVariable`.
Ref: [CF] Chapter 5. Coordinate Systems.
[CF] Section 6.2. Alternative Coordinates.
"""
cf_identity = "coordinates"
[docs] @classmethod
def identify(cls, variables, ignore=None, target=None, warn=True):
result = {}
ignore, target = cls._identify_common(variables, ignore, target)
# Identify all CF auxiliary coordinate variables.
for nc_var_name, nc_var in target.items():
# Check for auxiliary coordinate variable references.
nc_var_att = getattr(nc_var, cls.cf_identity, None)
if nc_var_att is not None:
for name in nc_var_att.split():
if name not in ignore:
if name not in variables:
if warn:
message = "Missing CF-netCDF auxiliary coordinate variable %r, referenced by netCDF variable %r"
warnings.warn(message % (name, nc_var_name))
else:
# Restrict to non-string type i.e. not a CFLabelVariable.
if not _is_str_dtype(variables[name]):
result[name] = CFAuxiliaryCoordinateVariable(
name, variables[name]
)
return result
[docs]class CFBoundaryVariable(CFVariable):
"""
A CF-netCDF boundary variable is associated with a CF-netCDF variable that contains
coordinate data. When a data value provides information about conditions in a cell
occupying a region of space/time or some other dimension, the boundary variable
provides a description of cell extent.
A CF-netCDF boundary variable will have one more dimension than its associated
CF-netCDF coordinate variable or CF-netCDF auxiliary coordinate variable.
Identified by the CF-netCDF variable attribute 'bounds'.
Ref: [CF] Section 7.1. Cell Boundaries.
"""
cf_identity = "bounds"
[docs] @classmethod
def identify(cls, variables, ignore=None, target=None, warn=True):
result = {}
ignore, target = cls._identify_common(variables, ignore, target)
# Identify all CF boundary variables.
for nc_var_name, nc_var in target.items():
# Check for a boundary variable reference.
nc_var_att = getattr(nc_var, cls.cf_identity, None)
if nc_var_att is not None:
name = nc_var_att.strip()
if name not in ignore:
if name not in variables:
if warn:
message = "Missing CF-netCDF boundary variable %r, referenced by netCDF variable %r"
warnings.warn(message % (name, nc_var_name))
else:
result[name] = CFBoundaryVariable(
name, variables[name]
)
return result
[docs] def spans(self, cf_variable):
"""
Determine whether the dimensionality of this variable
is a subset of the specified target variable.
Note that, by default scalar variables always span the
dimensionality of the target variable.
Args:
* cf_variable:
Compare dimensionality with the :class:`CFVariable`.
Returns:
Boolean.
"""
# Scalar variables always span the target variable.
result = True
if self.dimensions:
source = self.dimensions
target = cf_variable.dimensions
# Ignore the bounds extent dimension.
result = set(source[:-1]).issubset(target) or set(
source[1:]
).issubset(target)
return result
[docs]class CFClimatologyVariable(CFVariable):
"""
A CF-netCDF climatology variable is associated with a CF-netCDF variable that contains
coordinate data. When a data value provides information about conditions in a cell
occupying a region of space/time or some other dimension, the climatology variable
provides a climatological description of cell extent.
A CF-netCDF climatology variable will have one more dimension than its associated
CF-netCDF coordinate variable.
Identified by the CF-netCDF variable attribute 'climatology'.
Ref: [CF] Section 7.4. Climatological Statistics
"""
cf_identity = "climatology"
[docs] @classmethod
def identify(cls, variables, ignore=None, target=None, warn=True):
result = {}
ignore, target = cls._identify_common(variables, ignore, target)
# Identify all CF climatology variables.
for nc_var_name, nc_var in target.items():
# Check for a climatology variable reference.
nc_var_att = getattr(nc_var, cls.cf_identity, None)
if nc_var_att is not None:
name = nc_var_att.strip()
if name not in ignore:
if name not in variables:
if warn:
message = "Missing CF-netCDF climatology variable %r, referenced by netCDF variable %r"
warnings.warn(message % (name, nc_var_name))
else:
result[name] = CFClimatologyVariable(
name, variables[name]
)
return result
[docs] def spans(self, cf_variable):
"""
Determine whether the dimensionality of this variable
is a subset of the specified target variable.
Note that, by default scalar variables always span the
dimensionality of the target variable.
Args:
* cf_variable:
Compare dimensionality with the :class:`CFVariable`.
Returns:
Boolean.
"""
# Scalar variables always span the target variable.
result = True
if self.dimensions:
source = self.dimensions
target = cf_variable.dimensions
# Ignore the climatology extent dimension.
result = set(source[:-1]).issubset(target) or set(
source[1:]
).issubset(target)
return result
[docs]class CFCoordinateVariable(CFVariable):
"""
A CF-netCDF coordinate variable is a one-dimensional variable with the same name
as its dimension, and it is defined as a numeric data type with values that are
ordered monotonically. Missing values are not allowed in CF-netCDF coordinate
variables. Also see [NUG] Section 2.3.1.
Identified by the above criterion, there is no associated CF-netCDF variable
attribute.
Ref: [CF] 1.2. Terminology.
"""
[docs] @classmethod
def identify(
cls, variables, ignore=None, target=None, warn=True, monotonic=False
):
result = {}
ignore, target = cls._identify_common(variables, ignore, target)
# Identify all CF coordinate variables.
for nc_var_name, nc_var in target.items():
if nc_var_name in ignore:
continue
# String variables can't be coordinates
if _is_str_dtype(nc_var):
continue
# Restrict to one-dimensional with name as dimension
if not (nc_var.ndim == 1 and nc_var_name in nc_var.dimensions):
continue
# Restrict to monotonic?
if monotonic:
data = nc_var[:]
# Gracefully fill a masked coordinate.
if ma.isMaskedArray(data):
data = ma.filled(data)
if (
nc_var.shape == ()
or nc_var.shape == (1,)
or iris.util.monotonic(data)
):
result[nc_var_name] = CFCoordinateVariable(
nc_var_name, nc_var
)
else:
result[nc_var_name] = CFCoordinateVariable(nc_var_name, nc_var)
return result
[docs]class CFDataVariable(CFVariable):
"""
A CF-netCDF variable containing data pay-load that maps to an Iris :class:`iris.cube.Cube`.
"""
[docs] @classmethod
def identify(cls, variables, ignore=None, target=None, warn=True):
raise NotImplementedError
class _CFFormulaTermsVariable(CFVariable):
"""
A CF-netCDF formula terms variable corresponds to a term in a formula that
allows dimensional vertical coordinate values to be computed from dimensionless
vertical coordinate values and associated variables at specific grid points.
Identified by the CF-netCDF variable attribute 'formula_terms'.
Ref: [CF] Section 4.3.2. Dimensional Vertical Coordinate.
[CF] Appendix D. Dimensionless Vertical Coordinates.
"""
cf_identity = "formula_terms"
def __init__(self, name, data, formula_root, formula_term):
CFVariable.__init__(self, name, data)
# Register the formula root and term relationship.
self.add_formula_term(formula_root, formula_term)
@classmethod
def identify(cls, variables, ignore=None, target=None, warn=True):
result = {}
ignore, target = cls._identify_common(variables, ignore, target)
# Identify all CF formula terms variables.
for nc_var_name, nc_var in target.items():
# Check for formula terms variable references.
nc_var_att = getattr(nc_var, cls.cf_identity, None)
if nc_var_att is not None:
for match_item in _CF_PARSE.finditer(nc_var_att):
match_group = match_item.groupdict()
# Ensure that term name is lower case, as expected.
term_name = match_group["lhs"].lower()
variable_name = match_group["rhs"]
if variable_name not in ignore:
if variable_name not in variables:
if warn:
message = "Missing CF-netCDF formula term variable %r, referenced by netCDF variable %r"
warnings.warn(
message % (variable_name, nc_var_name)
)
else:
if variable_name not in result:
result[
variable_name
] = _CFFormulaTermsVariable(
variable_name,
variables[variable_name],
nc_var_name,
term_name,
)
else:
result[variable_name].add_formula_term(
nc_var_name, term_name
)
return result
def __repr__(self):
return "%s(%r, %r, %r)" % (
self.__class__.__name__,
self.cf_name,
self.cf_data,
self.cf_terms_by_root,
)
[docs]class CFGridMappingVariable(CFVariable):
"""
A CF-netCDF grid mapping variable contains a list of specific attributes that
define a particular grid mapping. A CF-netCDF grid mapping variable must contain
the attribute 'grid_mapping_name'.
Based on the value of the 'grid_mapping_name' attribute, there are associated
standard names of CF-netCDF coordinate variables that contain the mapping's
independent variables.
Identified by the CF-netCDF variable attribute 'grid_mapping'.
Ref: [CF] Section 5.6. Horizontal Coordinate Reference Systems, Grid Mappings, and Projections.
[CF] Appendix F. Grid Mappings.
"""
cf_identity = "grid_mapping"
[docs] @classmethod
def identify(cls, variables, ignore=None, target=None, warn=True):
result = {}
ignore, target = cls._identify_common(variables, ignore, target)
# Identify all grid mapping variables.
for nc_var_name, nc_var in target.items():
# Check for a grid mapping variable reference.
nc_var_att = getattr(nc_var, cls.cf_identity, None)
if nc_var_att is not None:
name = nc_var_att.strip()
if name not in ignore:
if name not in variables:
if warn:
message = "Missing CF-netCDF grid mapping variable %r, referenced by netCDF variable %r"
warnings.warn(message % (name, nc_var_name))
else:
result[name] = CFGridMappingVariable(
name, variables[name]
)
return result
[docs]class CFLabelVariable(CFVariable):
"""
A CF-netCDF CF label variable is any netCDF variable that contain string
textual information, or labels.
Identified by the CF-netCDF variable attribute 'coordinates'.
Also see :class:`iris.fileformats.cf.CFAuxiliaryCoordinateVariable`.
Ref: [CF] Section 6.1. Labels.
"""
cf_identity = "coordinates"
[docs] @classmethod
def identify(cls, variables, ignore=None, target=None, warn=True):
result = {}
ignore, target = cls._identify_common(variables, ignore, target)
# Identify all CF label variables.
for nc_var_name, nc_var in target.items():
# Check for label variable references.
nc_var_att = getattr(nc_var, cls.cf_identity, None)
if nc_var_att is not None:
for name in nc_var_att.split():
if name not in ignore:
if name not in variables:
if warn:
message = "Missing CF-netCDF label variable %r, referenced by netCDF variable %r"
warnings.warn(message % (name, nc_var_name))
else:
# Register variable, but only allow string type.
var = variables[name]
if _is_str_dtype(var):
result[name] = CFLabelVariable(name, var)
return result
[docs] def cf_label_data(self, cf_data_var):
"""
Return the associated CF-netCDF label variable strings.
Args:
* cf_data_var (:class:`iris.fileformats.cf.CFDataVariable`):
The CF-netCDF data variable which the CF-netCDF label variable describes.
Returns:
String labels.
"""
if not isinstance(cf_data_var, CFDataVariable):
raise TypeError(
"cf_data_var argument should be of type CFDataVariable. Got %r."
% type(cf_data_var)
)
# Determine the name of the label string (or length) dimension by
# finding the dimension name that doesn't exist within the data dimensions.
str_dim_name = list(set(self.dimensions) - set(cf_data_var.dimensions))
if len(str_dim_name) != 1:
raise ValueError(
"Invalid string dimensions for CF-netCDF label variable %r"
% self.cf_name
)
str_dim_name = str_dim_name[0]
label_data = self[:]
if ma.isMaskedArray(label_data):
label_data = label_data.filled()
# Determine whether we have a string-valued scalar label
# i.e. a character variable that only has one dimension (the length of the string).
if self.ndim == 1:
label_string = b"".join(label_data).strip()
label_string = label_string.decode("utf8")
data = np.array([label_string])
else:
# Determine the index of the string dimension.
str_dim = self.dimensions.index(str_dim_name)
# Calculate new label data shape (without string dimension) and create payload array.
new_shape = tuple(
dim_len for i, dim_len in enumerate(self.shape) if i != str_dim
)
string_basetype = "|U%d"
string_dtype = string_basetype % self.shape[str_dim]
data = np.empty(new_shape, dtype=string_dtype)
for index in np.ndindex(new_shape):
# Create the slice for the label data.
if str_dim == 0:
label_index = (slice(None, None),) + index
else:
label_index = index + (slice(None, None),)
label_string = b"".join(label_data[label_index]).strip()
label_string = label_string.decode("utf8")
data[index] = label_string
return data
[docs] def cf_label_dimensions(self, cf_data_var):
"""
Return the name of the associated CF-netCDF label variable data dimensions.
Args:
* cf_data_var (:class:`iris.fileformats.cf.CFDataVariable`):
The CF-netCDF data variable which the CF-netCDF label variable describes.
Returns:
Tuple of label data dimension names.
"""
if not isinstance(cf_data_var, CFDataVariable):
raise TypeError(
"cf_data_var argument should be of type CFDataVariable. Got %r."
% type(cf_data_var)
)
return tuple(
[
dim_name
for dim_name in self.dimensions
if dim_name in cf_data_var.dimensions
]
)
[docs] def spans(self, cf_variable):
"""
Determine whether the dimensionality of this variable
is a subset of the specified target variable.
Note that, by default scalar variables always span the
dimensionality of the target variable.
Args:
* cf_variable:
Compare dimensionality with the :class:`CFVariable`.
Returns:
Boolean.
"""
# Scalar variables always span the target variable.
result = True
if self.dimensions:
source = self.dimensions
target = cf_variable.dimensions
# Ignore label string length dimension.
result = set(source[:-1]).issubset(target) or set(
source[1:]
).issubset(target)
return result
[docs]class CFMeasureVariable(CFVariable):
"""
A CF-netCDF measure variable is a variable that contains cell areas or volumes.
Identified by the CF-netCDF variable attribute 'cell_measures'.
Ref: [CF] Section 7.2. Cell Measures.
"""
cf_identity = "cell_measures"
def __init__(self, name, data, measure):
CFVariable.__init__(self, name, data)
#: Associated cell measure of the cell variable
self.cf_measure = measure
[docs] @classmethod
def identify(cls, variables, ignore=None, target=None, warn=True):
result = {}
ignore, target = cls._identify_common(variables, ignore, target)
# Identify all CF measure variables.
for nc_var_name, nc_var in target.items():
# Check for measure variable references.
nc_var_att = getattr(nc_var, cls.cf_identity, None)
if nc_var_att is not None:
for match_item in _CF_PARSE.finditer(nc_var_att):
match_group = match_item.groupdict()
measure = match_group["lhs"]
variable_name = match_group["rhs"]
var_matches_nc = variable_name != nc_var_name
if variable_name not in ignore and var_matches_nc:
if variable_name not in variables:
if warn:
message = "Missing CF-netCDF measure variable %r, referenced by netCDF variable %r"
warnings.warn(
message % (variable_name, nc_var_name)
)
else:
result[variable_name] = CFMeasureVariable(
variable_name,
variables[variable_name],
measure,
)
return result
################################################################################
[docs]class CFGroup(MutableMapping):
"""
Represents a collection of 'NetCDF Climate and Forecast (CF) Metadata
Conventions' variables and netCDF global attributes.
"""
def __init__(self):
#: Collection of CF-netCDF variables
self._cf_variables = {}
#: Collection of netCDF global attributes
self.global_attributes = {}
#: Collection of CF-netCDF variables promoted to a CFDataVariable.
self.promoted = {}
def _cf_getter(self, cls):
# Generate dictionary with dictionary comprehension.
return {
cf_name: cf_var
for cf_name, cf_var in self._cf_variables.items()
if isinstance(cf_var, cls)
}
@property
def ancillary_variables(self):
"""Collection of CF-netCDF ancillary variables."""
return self._cf_getter(CFAncillaryDataVariable)
@property
def auxiliary_coordinates(self):
"""Collection of CF-netCDF auxiliary coordinate variables."""
return self._cf_getter(CFAuxiliaryCoordinateVariable)
@property
def bounds(self):
"""Collection of CF-netCDF boundary variables."""
return self._cf_getter(CFBoundaryVariable)
@property
def climatology(self):
"""Collection of CF-netCDF climatology variables."""
return self._cf_getter(CFClimatologyVariable)
@property
def coordinates(self):
"""Collection of CF-netCDF coordinate variables."""
return self._cf_getter(CFCoordinateVariable)
@property
def data_variables(self):
"""Collection of CF-netCDF data pay-load variables."""
return self._cf_getter(CFDataVariable)
@property
def formula_terms(self):
"""Collection of CF-netCDF variables that participate in a CF-netCDF formula term."""
return {
cf_name: cf_var
for cf_name, cf_var in self._cf_variables.items()
if cf_var.has_formula_terms()
}
@property
def grid_mappings(self):
"""Collection of CF-netCDF grid mapping variables."""
return self._cf_getter(CFGridMappingVariable)
@property
def labels(self):
"""Collection of CF-netCDF label variables."""
return self._cf_getter(CFLabelVariable)
@property
def cell_measures(self):
"""Collection of CF-netCDF measure variables."""
return self._cf_getter(CFMeasureVariable)
@property
def non_data_variable_names(self):
"""
:class:`set` of the names of the CF-netCDF variables that are not
the data pay-load.
"""
non_data_variables = (
self.ancillary_variables,
self.auxiliary_coordinates,
self.bounds,
self.climatology,
self.coordinates,
self.grid_mappings,
self.labels,
self.cell_measures,
)
result = set()
for variable in non_data_variables:
result |= set(variable)
return result
[docs] def keys(self):
"""Return the names of all the CF-netCDF variables in the group."""
return self._cf_variables.keys()
def __len__(self):
return len(self._cf_variables)
def __iter__(self):
for item in self._cf_variables:
yield item
def __setitem__(self, name, variable):
if not isinstance(variable, CFVariable):
raise TypeError(
"Attempted to add an invalid CF-netCDF variable to the %s"
% self.__class__.__name__
)
if name != variable.cf_name:
raise ValueError(
"Mismatch between key name %r and CF-netCDF variable name %r"
% (str(name), variable.cf_name)
)
self._cf_variables[name] = variable
def __getitem__(self, name):
if name not in self._cf_variables:
raise KeyError(
"Cannot get unknown CF-netCDF variable name %r" % str(name)
)
return self._cf_variables[name]
def __delitem__(self, name):
if name not in self._cf_variables:
raise KeyError(
"Cannot delete unknown CF-netcdf variable name %r" % str(name)
)
del self._cf_variables[name]
def __repr__(self):
result = []
result.append("variables:%d" % len(self._cf_variables))
result.append("global_attributes:%d" % len(self.global_attributes))
result.append("promoted:%d" % len(self.promoted))
return "<%s of %s>" % (self.__class__.__name__, ", ".join(result))
################################################################################
[docs]class CFReader:
"""
This class allows the contents of a netCDF file to be interpreted according
to the 'NetCDF Climate and Forecast (CF) Metadata Conventions'.
"""
# All CF variable types EXCEPT for the "special cases" of
# CFDataVariable, CFCoordinateVariable and _CFFormulaTermsVariable.
_variable_types = (
CFAncillaryDataVariable,
CFAuxiliaryCoordinateVariable,
CFBoundaryVariable,
CFClimatologyVariable,
CFGridMappingVariable,
CFLabelVariable,
CFMeasureVariable,
)
# TODO: remove once iris.experimental.ugrid.CFUGridReader is folded in.
CFGroup = CFGroup
def __init__(self, file_source, warn=False, monotonic=False):
# Ensure safe operation for destructor, should init fail.
self._own_file = False
if isinstance(file_source, str):
# Create from filepath : open it + own it (=close when we die).
self._filename = os.path.expanduser(file_source)
self._dataset = _thread_safe_nc.DatasetWrapper(
self._filename, mode="r"
)
self._own_file = True
else:
# We have been passed an open dataset.
# We use it but don't own it (don't close it).
self._dataset = file_source
self._filename = self._dataset.filepath()
#: Collection of CF-netCDF variables associated with this netCDF file
self.cf_group = self.CFGroup()
# Issue load optimisation warning.
if warn and self._dataset.file_format in [
"NETCDF3_CLASSIC",
"NETCDF3_64BIT",
]:
warnings.warn(
"Optimise CF-netCDF loading by converting data from NetCDF3 "
'to NetCDF4 file format using the "nccopy" command.'
)
self._check_monotonic = monotonic
self._translate()
self._build_cf_groups()
self._reset()
def __enter__(self):
# Enable use as a context manager
# N.B. this **guarantees* closure of the file, when the context is exited.
# Note: ideally, the class would not do so much work in the __init__ call, and
# would do all that here, after acquiring necessary permissions/locks.
# But for legacy reasons, we can't do that. So **effectively**, the context
# (in terms of access control) already started, when we created the object.
return self
def __exit__(self, exc_type, exc_value, traceback):
# When used as a context-manager, **always** close the file on exit.
self._close()
@property
def filename(self):
"""The file that the CFReader is reading."""
return self._filename
def __repr__(self):
return "%s(%r)" % (self.__class__.__name__, self._filename)
def _translate(self):
"""Classify the netCDF variables into CF-netCDF variables."""
netcdf_variable_names = list(self._dataset.variables.keys())
# Identify all CF coordinate variables first. This must be done
# first as, by CF convention, the definition of a CF auxiliary
# coordinate variable may include a scalar CF coordinate variable,
# whereas we want these two types of variables to be mutually exclusive.
coords = CFCoordinateVariable.identify(
self._dataset.variables, monotonic=self._check_monotonic
)
self.cf_group.update(coords)
coordinate_names = list(self.cf_group.coordinates.keys())
# Identify all CF variables EXCEPT for the "special cases".
for variable_type in self._variable_types:
# Prevent grid mapping variables being mis-identified as CF coordinate variables.
ignore = (
None
if issubclass(variable_type, CFGridMappingVariable)
else coordinate_names
)
self.cf_group.update(
variable_type.identify(self._dataset.variables, ignore=ignore)
)
# Identify global netCDF attributes.
attr_dict = {
attr_name: _getncattr(self._dataset, attr_name, "")
for attr_name in self._dataset.ncattrs()
}
self.cf_group.global_attributes.update(attr_dict)
# Identify and register all CF formula terms.
formula_terms = _CFFormulaTermsVariable.identify(
self._dataset.variables
)
for cf_var in formula_terms.values():
for cf_root, cf_term in cf_var.cf_terms_by_root.items():
# Ignore formula terms owned by a bounds variable.
if cf_root not in self.cf_group.bounds:
cf_name = cf_var.cf_name
if cf_var.cf_name not in self.cf_group:
self.cf_group[cf_name] = CFAuxiliaryCoordinateVariable(
cf_name, cf_var.cf_data
)
self.cf_group[cf_name].add_formula_term(cf_root, cf_term)
# Determine the CF data variables.
data_variable_names = (
set(netcdf_variable_names) - self.cf_group.non_data_variable_names
)
for name in data_variable_names:
self.cf_group[name] = CFDataVariable(
name, self._dataset.variables[name]
)
def _build_cf_groups(self):
"""Build the first order relationships between CF-netCDF variables."""
def _build(cf_variable):
# TODO: isinstance(cf_variable, UGridMeshVariable)
# UGridMeshVariable currently in experimental.ugrid - circular import.
is_mesh_var = cf_variable.cf_identity == "mesh"
ugrid_coord_names = []
ugrid_coords = getattr(self.cf_group, "ugrid_coords", None)
if ugrid_coords is not None:
ugrid_coord_names = list(ugrid_coords.keys())
coordinate_names = list(self.cf_group.coordinates.keys())
cf_group = self.CFGroup()
# Build CF variable relationships.
for variable_type in self._variable_types:
ignore = []
# Avoid UGridAuxiliaryCoordinateVariables also being
# processed as CFAuxiliaryCoordinateVariables.
if not is_mesh_var:
ignore += ugrid_coord_names
# Prevent grid mapping variables being mis-identified as CF coordinate variables.
if not issubclass(variable_type, CFGridMappingVariable):
ignore += coordinate_names
match = variable_type.identify(
self._dataset.variables,
ignore=ignore,
target=cf_variable.cf_name,
warn=False,
)
# Sanity check dimensionality coverage.
for cf_name, cf_var in match.items():
# No span check is necessary if variable is attached to a mesh.
if is_mesh_var or cf_var.spans(cf_variable):
cf_group[cf_name] = self.cf_group[cf_name]
else:
# Register the ignored variable.
# N.B. 'ignored' variable from enclosing scope.
ignored.add(cf_name)
msg = (
"Ignoring variable {!r} referenced "
"by variable {!r}: Dimensions {!r} do not "
"span {!r}".format(
cf_name,
cf_variable.cf_name,
cf_var.dimensions,
cf_variable.dimensions,
)
)
warnings.warn(msg)
# Build CF data variable relationships.
if isinstance(cf_variable, CFDataVariable):
# Add global netCDF attributes.
cf_group.global_attributes.update(
self.cf_group.global_attributes
)
# Add appropriate "dimensioned" CF coordinate variables.
cf_group.update(
{
cf_name: self.cf_group[cf_name]
for cf_name in cf_variable.dimensions
if cf_name in self.cf_group.coordinates
}
)
# Add appropriate "dimensionless" CF coordinate variables.
coordinates_attr = getattr(cf_variable, "coordinates", "")
cf_group.update(
{
cf_name: self.cf_group[cf_name]
for cf_name in coordinates_attr.split()
if cf_name in self.cf_group.coordinates
}
)
# Add appropriate formula terms.
for cf_var in self.cf_group.formula_terms.values():
for cf_root in cf_var.cf_terms_by_root:
if (
cf_root in cf_group
and cf_var.cf_name not in cf_group
):
# Sanity check dimensionality.
if cf_var.spans(cf_variable):
cf_group[cf_var.cf_name] = cf_var
else:
# Register the ignored variable.
# N.B. 'ignored' variable from enclosing scope.
ignored.add(cf_var.cf_name)
msg = (
"Ignoring formula terms variable {!r} "
"referenced by data variable {!r} via "
"variable {!r}: Dimensions {!r} do not "
"span {!r}".format(
cf_var.cf_name,
cf_variable.cf_name,
cf_root,
cf_var.dimensions,
cf_variable.dimensions,
)
)
warnings.warn(msg)
# Add the CF group to the variable.
cf_variable.cf_group = cf_group
# Ignored variables are those that cannot be attached to a
# data variable as the dimensionality of that variable is not
# a subset of the dimensionality of the data variable.
ignored = set()
for cf_variable in self.cf_group.values():
_build(cf_variable)
# Determine whether there are any formula terms that
# may be promoted to a CFDataVariable and restrict promotion to only
# those formula terms that are reference surface/phenomenon.
for cf_var in self.cf_group.formula_terms.values():
for cf_root, cf_term in cf_var.cf_terms_by_root.items():
cf_root_var = self.cf_group[cf_root]
name = cf_root_var.standard_name or cf_root_var.long_name
terms = reference_terms.get(name, [])
if isinstance(terms, str) or not isinstance(terms, Iterable):
terms = [terms]
cf_var_name = cf_var.cf_name
if (
cf_term in terms
and cf_var_name not in self.cf_group.promoted
):
data_var = CFDataVariable(cf_var_name, cf_var.cf_data)
self.cf_group.promoted[cf_var_name] = data_var
_build(data_var)
break
# Promote any ignored variables.
promoted = set()
not_promoted = ignored.difference(promoted)
while not_promoted:
cf_name = not_promoted.pop()
if (
cf_name not in self.cf_group.data_variables
and cf_name not in self.cf_group.promoted
):
data_var = CFDataVariable(
cf_name, self.cf_group[cf_name].cf_data
)
self.cf_group.promoted[cf_name] = data_var
_build(data_var)
# Determine whether there are still any ignored variables
# yet to be promoted.
promoted.add(cf_name)
not_promoted = ignored.difference(promoted)
def _reset(self):
"""Reset the attribute touch history of each variable."""
for nc_var_name in self._dataset.variables.keys():
self.cf_group[nc_var_name].cf_attrs_reset()
def _close(self):
# Explicitly close dataset to prevent file remaining open.
if self._own_file and self._dataset is not None:
self._dataset.close()
self._dataset = None
def __del__(self):
# Be sure to close dataset when CFReader is destroyed / garbage-collected.
self._close()
def _getncattr(dataset, attr, default=None):
"""
Simple wrapper round `netCDF4.Dataset.getncattr` to make it behave
more like `getattr`.
"""
try:
value = dataset.getncattr(attr)
except AttributeError:
value = default
return value
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