import warnings from collections import defaultdict import numpy as np import pandas as pd from .coding import strings, times, variables from .coding.variables import SerializationWarning, pop_to from .core import duck_array_ops, indexing from .core.common import contains_cftime_datetimes from .core.pycompat import is_duck_dask_array from .core.variable import IndexVariable, Variable, as_variable CF_RELATED_DATA = ( "bounds", "grid_mapping", "climatology", "geometry", "node_coordinates", "node_count", "part_node_count", "interior_ring", "cell_measures", "formula_terms", ) CF_RELATED_DATA_NEEDS_PARSING = ( "cell_measures", "formula_terms", ) class NativeEndiannessArray(indexing.ExplicitlyIndexedNDArrayMixin): """Decode arrays on the fly from non-native to native endianness This is useful for decoding arrays from netCDF3 files (which are all big endian) into native endianness, so they can be used with Cython functions, such as those found in bottleneck and pandas. >>> x = np.arange(5, dtype=">i2") >>> x.dtype dtype('>i2') >>> NativeEndiannessArray(x).dtype dtype('int16') >>> indexer = indexing.BasicIndexer((slice(None),)) >>> NativeEndiannessArray(x)[indexer].dtype dtype('int16') """ __slots__ = ("array",) def __init__(self, array): self.array = indexing.as_indexable(array) @property def dtype(self): return np.dtype(self.array.dtype.kind + str(self.array.dtype.itemsize)) def __getitem__(self, key): return np.asarray(self.array[key], dtype=self.dtype) class BoolTypeArray(indexing.ExplicitlyIndexedNDArrayMixin): """Decode arrays on the fly from integer to boolean datatype This is useful for decoding boolean arrays from integer typed netCDF variables. >>> x = np.array([1, 0, 1, 1, 0], dtype="i1") >>> x.dtype dtype('int8') >>> BoolTypeArray(x).dtype dtype('bool') >>> indexer = indexing.BasicIndexer((slice(None),)) >>> BoolTypeArray(x)[indexer].dtype dtype('bool') """ __slots__ = ("array",) def __init__(self, array): self.array = indexing.as_indexable(array) @property def dtype(self): return np.dtype("bool") def __getitem__(self, key): return np.asarray(self.array[key], dtype=self.dtype) def _var_as_tuple(var): return var.dims, var.data, var.attrs.copy(), var.encoding.copy() def maybe_encode_nonstring_dtype(var, name=None): if "dtype" in var.encoding and var.encoding["dtype"] not in ("S1", str): dims, data, attrs, encoding = _var_as_tuple(var) dtype = np.dtype(encoding.pop("dtype")) if dtype != var.dtype: if np.issubdtype(dtype, np.integer): if ( np.issubdtype(var.dtype, np.floating) and "_FillValue" not in var.attrs and "missing_value" not in var.attrs ): warnings.warn( f"saving variable {name} with floating " "point data as an integer dtype without " "any _FillValue to use for NaNs", SerializationWarning, stacklevel=10, ) data = duck_array_ops.around(data)[...] data = data.astype(dtype=dtype) var = Variable(dims, data, attrs, encoding) return var def maybe_default_fill_value(var): # make NaN the fill value for float types: if ( "_FillValue" not in var.attrs and "_FillValue" not in var.encoding and np.issubdtype(var.dtype, np.floating) ): var.attrs["_FillValue"] = var.dtype.type(np.nan) return var def maybe_encode_bools(var): if ( (var.dtype == bool) and ("dtype" not in var.encoding) and ("dtype" not in var.attrs) ): dims, data, attrs, encoding = _var_as_tuple(var) attrs["dtype"] = "bool" data = data.astype(dtype="i1", copy=True) var = Variable(dims, data, attrs, encoding) return var def _infer_dtype(array, name=None): """Given an object array with no missing values, infer its dtype from its first element """ if array.dtype.kind != "O": raise TypeError("infer_type must be called on a dtype=object array") if array.size == 0: return np.dtype(float) element = array[(0,) * array.ndim] if isinstance(element, (bytes, str)): return strings.create_vlen_dtype(type(element)) dtype = np.array(element).dtype if dtype.kind != "O": return dtype raise ValueError( "unable to infer dtype on variable {!r}; xarray " "cannot serialize arbitrary Python objects".format(name) ) def ensure_not_multiindex(var, name=None): if isinstance(var, IndexVariable) and isinstance(var.to_index(), pd.MultiIndex): raise NotImplementedError( "variable {!r} is a MultiIndex, which cannot yet be " "serialized to netCDF files " "(https://github.com/pydata/xarray/issues/1077). Use " "reset_index() to convert MultiIndex levels into coordinate " "variables instead.".format(name) ) def _copy_with_dtype(data, dtype): """Create a copy of an array with the given dtype. We use this instead of np.array() to ensure that custom object dtypes end up on the resulting array. """ result = np.empty(data.shape, dtype) result[...] = data return result def ensure_dtype_not_object(var, name=None): # TODO: move this from conventions to backends? (it's not CF related) if var.dtype.kind == "O": dims, data, attrs, encoding = _var_as_tuple(var) if is_duck_dask_array(data): warnings.warn( "variable {} has data in the form of a dask array with " "dtype=object, which means it is being loaded into memory " "to determine a data type that can be safely stored on disk. " "To avoid this, coerce this variable to a fixed-size dtype " "with astype() before saving it.".format(name), SerializationWarning, ) data = data.compute() missing = pd.isnull(data) if missing.any(): # nb. this will fail for dask.array data non_missing_values = data[~missing] inferred_dtype = _infer_dtype(non_missing_values, name) # There is no safe bit-pattern for NA in typical binary string # formats, we so can't set a fill_value. Unfortunately, this means # we can't distinguish between missing values and empty strings. if strings.is_bytes_dtype(inferred_dtype): fill_value = b"" elif strings.is_unicode_dtype(inferred_dtype): fill_value = "" else: # insist on using float for numeric values if not np.issubdtype(inferred_dtype, np.floating): inferred_dtype = np.dtype(float) fill_value = inferred_dtype.type(np.nan) data = _copy_with_dtype(data, dtype=inferred_dtype) data[missing] = fill_value else: data = _copy_with_dtype(data, dtype=_infer_dtype(data, name)) assert data.dtype.kind != "O" or data.dtype.metadata var = Variable(dims, data, attrs, encoding) return var def encode_cf_variable(var, needs_copy=True, name=None): """ Converts an Variable into an Variable which follows some of the CF conventions: - Nans are masked using _FillValue (or the deprecated missing_value) - Rescaling via: scale_factor and add_offset - datetimes are converted to the CF 'units since time' format - dtype encodings are enforced. Parameters ---------- var : Variable A variable holding un-encoded data. Returns ------- out : Variable A variable which has been encoded as described above. """ ensure_not_multiindex(var, name=name) for coder in [ times.CFDatetimeCoder(), times.CFTimedeltaCoder(), variables.CFScaleOffsetCoder(), variables.CFMaskCoder(), variables.UnsignedIntegerCoder(), ]: var = coder.encode(var, name=name) # TODO(shoyer): convert all of these to use coders, too: var = maybe_encode_nonstring_dtype(var, name=name) var = maybe_default_fill_value(var) var = maybe_encode_bools(var) var = ensure_dtype_not_object(var, name=name) for attr_name in CF_RELATED_DATA: pop_to(var.encoding, var.attrs, attr_name) return var def decode_cf_variable( name, var, concat_characters=True, mask_and_scale=True, decode_times=True, decode_endianness=True, stack_char_dim=True, use_cftime=None, decode_timedelta=None, ): """ Decodes a variable which may hold CF encoded information. This includes variables that have been masked and scaled, which hold CF style time variables (this is almost always the case if the dataset has been serialized) and which have strings encoded as character arrays. Parameters ---------- name : str Name of the variable. Used for better error messages. var : Variable A variable holding potentially CF encoded information. concat_characters : bool Should character arrays be concatenated to strings, for example: ["h", "e", "l", "l", "o"] -> "hello" mask_and_scale : bool Lazily scale (using scale_factor and add_offset) and mask (using _FillValue). If the _Unsigned attribute is present treat integer arrays as unsigned. decode_times : bool Decode cf times ("hours since 2000-01-01") to np.datetime64. decode_endianness : bool Decode arrays from non-native to native endianness. stack_char_dim : bool Whether to stack characters into bytes along the last dimension of this array. Passed as an argument because we need to look at the full dataset to figure out if this is appropriate. use_cftime : bool, optional Only relevant if encoded dates come from a standard calendar (e.g. "gregorian", "proleptic_gregorian", "standard", or not specified). If None (default), attempt to decode times to ``np.datetime64[ns]`` objects; if this is not possible, decode times to ``cftime.datetime`` objects. If True, always decode times to ``cftime.datetime`` objects, regardless of whether or not they can be represented using ``np.datetime64[ns]`` objects. If False, always decode times to ``np.datetime64[ns]`` objects; if this is not possible raise an error. Returns ------- out : Variable A variable holding the decoded equivalent of var. """ var = as_variable(var) original_dtype = var.dtype if decode_timedelta is None: decode_timedelta = decode_times if concat_characters: if stack_char_dim: var = strings.CharacterArrayCoder().decode(var, name=name) var = strings.EncodedStringCoder().decode(var) if mask_and_scale: for coder in [ variables.UnsignedIntegerCoder(), variables.CFMaskCoder(), variables.CFScaleOffsetCoder(), ]: var = coder.decode(var, name=name) if decode_timedelta: var = times.CFTimedeltaCoder().decode(var, name=name) if decode_times: var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name) dimensions, data, attributes, encoding = variables.unpack_for_decoding(var) # TODO(shoyer): convert everything below to use coders if decode_endianness and not data.dtype.isnative: # do this last, so it's only done if we didn't already unmask/scale data = NativeEndiannessArray(data) original_dtype = data.dtype encoding.setdefault("dtype", original_dtype) if "dtype" in attributes and attributes["dtype"] == "bool": del attributes["dtype"] data = BoolTypeArray(data) if not is_duck_dask_array(data): data = indexing.LazilyIndexedArray(data) return Variable(dimensions, data, attributes, encoding=encoding) def _update_bounds_attributes(variables): """Adds time attributes to time bounds variables. Variables handling time bounds ("Cell boundaries" in the CF conventions) do not necessarily carry the necessary attributes to be decoded. This copies the attributes from the time variable to the associated boundaries. See Also: http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/ cf-conventions.html#cell-boundaries https://github.com/pydata/xarray/issues/2565 """ # For all time variables with bounds for v in variables.values(): attrs = v.attrs has_date_units = "units" in attrs and "since" in attrs["units"] if has_date_units and "bounds" in attrs: if attrs["bounds"] in variables: bounds_attrs = variables[attrs["bounds"]].attrs bounds_attrs.setdefault("units", attrs["units"]) if "calendar" in attrs: bounds_attrs.setdefault("calendar", attrs["calendar"]) def _update_bounds_encoding(variables): """Adds time encoding to time bounds variables. Variables handling time bounds ("Cell boundaries" in the CF conventions) do not necessarily carry the necessary attributes to be decoded. This copies the encoding from the time variable to the associated bounds variable so that we write CF-compliant files. See Also: http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/ cf-conventions.html#cell-boundaries https://github.com/pydata/xarray/issues/2565 """ # For all time variables with bounds for v in variables.values(): attrs = v.attrs encoding = v.encoding has_date_units = "units" in encoding and "since" in encoding["units"] is_datetime_type = np.issubdtype( v.dtype, np.datetime64 ) or contains_cftime_datetimes(v) if ( is_datetime_type and not has_date_units and "bounds" in attrs and attrs["bounds"] in variables ): warnings.warn( "Variable '{0}' has datetime type and a " "bounds variable but {0}.encoding does not have " "units specified. The units encodings for '{0}' " "and '{1}' will be determined independently " "and may not be equal, counter to CF-conventions. " "If this is a concern, specify a units encoding for " "'{0}' before writing to a file.".format(v.name, attrs["bounds"]), UserWarning, ) if has_date_units and "bounds" in attrs: if attrs["bounds"] in variables: bounds_encoding = variables[attrs["bounds"]].encoding bounds_encoding.setdefault("units", encoding["units"]) if "calendar" in encoding: bounds_encoding.setdefault("calendar", encoding["calendar"]) def decode_cf_variables( variables, attributes, concat_characters=True, mask_and_scale=True, decode_times=True, decode_coords=True, drop_variables=None, use_cftime=None, decode_timedelta=None, ): """ Decode several CF encoded variables. See: decode_cf_variable """ dimensions_used_by = defaultdict(list) for v in variables.values(): for d in v.dims: dimensions_used_by[d].append(v) def stackable(dim): # figure out if a dimension can be concatenated over if dim in variables: return False for v in dimensions_used_by[dim]: if v.dtype.kind != "S" or dim != v.dims[-1]: return False return True coord_names = set() if isinstance(drop_variables, str): drop_variables = [drop_variables] elif drop_variables is None: drop_variables = [] drop_variables = set(drop_variables) # Time bounds coordinates might miss the decoding attributes if decode_times: _update_bounds_attributes(variables) new_vars = {} for k, v in variables.items(): if k in drop_variables: continue stack_char_dim = ( concat_characters and v.dtype == "S1" and v.ndim > 0 and stackable(v.dims[-1]) ) new_vars[k] = decode_cf_variable( k, v, concat_characters=concat_characters, mask_and_scale=mask_and_scale, decode_times=decode_times, stack_char_dim=stack_char_dim, use_cftime=use_cftime, decode_timedelta=decode_timedelta, ) if decode_coords in [True, "coordinates", "all"]: var_attrs = new_vars[k].attrs if "coordinates" in var_attrs: coord_str = var_attrs["coordinates"] var_coord_names = coord_str.split() if all(k in variables for k in var_coord_names): new_vars[k].encoding["coordinates"] = coord_str del var_attrs["coordinates"] coord_names.update(var_coord_names) if decode_coords == "all": for attr_name in CF_RELATED_DATA: if attr_name in var_attrs: attr_val = var_attrs[attr_name] if attr_name not in CF_RELATED_DATA_NEEDS_PARSING: var_names = attr_val.split() else: roles_and_names = [ role_or_name for part in attr_val.split(":") for role_or_name in part.split() ] if len(roles_and_names) % 2 == 1: warnings.warn( f"Attribute {attr_name:s} malformed", stacklevel=5 ) var_names = roles_and_names[1::2] if all(var_name in variables for var_name in var_names): new_vars[k].encoding[attr_name] = attr_val coord_names.update(var_names) else: referenced_vars_not_in_variables = [ proj_name for proj_name in var_names if proj_name not in variables ] warnings.warn( f"Variable(s) referenced in {attr_name:s} not in variables: {referenced_vars_not_in_variables!s}", stacklevel=5, ) del var_attrs[attr_name] if decode_coords and "coordinates" in attributes: attributes = dict(attributes) coord_names.update(attributes.pop("coordinates").split()) return new_vars, attributes, coord_names def decode_cf( obj, concat_characters=True, mask_and_scale=True, decode_times=True, decode_coords=True, drop_variables=None, use_cftime=None, decode_timedelta=None, ): """Decode the given Dataset or Datastore according to CF conventions into a new Dataset. Parameters ---------- obj : Dataset or DataStore Object to decode. concat_characters : bool, optional Should character arrays be concatenated to strings, for example: ["h", "e", "l", "l", "o"] -> "hello" mask_and_scale : bool, optional Lazily scale (using scale_factor and add_offset) and mask (using _FillValue). decode_times : bool, optional Decode cf times (e.g., integers since "hours since 2000-01-01") to np.datetime64. decode_coords : bool or {"coordinates", "all"}, optional Controls which variables are set as coordinate variables: - "coordinates" or True: Set variables referred to in the ``'coordinates'`` attribute of the datasets or individual variables as coordinate variables. - "all": Set variables referred to in ``'grid_mapping'``, ``'bounds'`` and other attributes as coordinate variables. drop_variables : str or iterable, optional A variable or list of variables to exclude from being parsed from the dataset. This may be useful to drop variables with problems or inconsistent values. use_cftime : bool, optional Only relevant if encoded dates come from a standard calendar (e.g. "gregorian", "proleptic_gregorian", "standard", or not specified). If None (default), attempt to decode times to ``np.datetime64[ns]`` objects; if this is not possible, decode times to ``cftime.datetime`` objects. If True, always decode times to ``cftime.datetime`` objects, regardless of whether or not they can be represented using ``np.datetime64[ns]`` objects. If False, always decode times to ``np.datetime64[ns]`` objects; if this is not possible raise an error. decode_timedelta : bool, optional If True, decode variables and coordinates with time units in {"days", "hours", "minutes", "seconds", "milliseconds", "microseconds"} into timedelta objects. If False, leave them encoded as numbers. If None (default), assume the same value of decode_time. Returns ------- decoded : Dataset """ from .backends.common import AbstractDataStore from .core.dataset import Dataset if isinstance(obj, Dataset): vars = obj._variables attrs = obj.attrs extra_coords = set(obj.coords) close = obj._close encoding = obj.encoding elif isinstance(obj, AbstractDataStore): vars, attrs = obj.load() extra_coords = set() close = obj.close encoding = obj.get_encoding() else: raise TypeError("can only decode Dataset or DataStore objects") vars, attrs, coord_names = decode_cf_variables( vars, attrs, concat_characters, mask_and_scale, decode_times, decode_coords, drop_variables=drop_variables, use_cftime=use_cftime, decode_timedelta=decode_timedelta, ) ds = Dataset(vars, attrs=attrs) ds = ds.set_coords(coord_names.union(extra_coords).intersection(vars)) ds.set_close(close) ds.encoding = encoding return ds def cf_decoder( variables, attributes, concat_characters=True, mask_and_scale=True, decode_times=True, ): """ Decode a set of CF encoded variables and attributes. Parameters ---------- variables : dict A dictionary mapping from variable name to xarray.Variable attributes : dict A dictionary mapping from attribute name to value concat_characters : bool Should character arrays be concatenated to strings, for example: ["h", "e", "l", "l", "o"] -> "hello" mask_and_scale : bool Lazily scale (using scale_factor and add_offset) and mask (using _FillValue). decode_times : bool Decode cf times ("hours since 2000-01-01") to np.datetime64. Returns ------- decoded_variables : dict A dictionary mapping from variable name to xarray.Variable objects. decoded_attributes : dict A dictionary mapping from attribute name to values. See Also -------- decode_cf_variable """ variables, attributes, _ = decode_cf_variables( variables, attributes, concat_characters, mask_and_scale, decode_times ) return variables, attributes def _encode_coordinates(variables, attributes, non_dim_coord_names): # calculate global and variable specific coordinates non_dim_coord_names = set(non_dim_coord_names) for name in list(non_dim_coord_names): if isinstance(name, str) and " " in name: warnings.warn( "coordinate {!r} has a space in its name, which means it " "cannot be marked as a coordinate on disk and will be " "saved as a data variable instead".format(name), SerializationWarning, stacklevel=6, ) non_dim_coord_names.discard(name) global_coordinates = non_dim_coord_names.copy() variable_coordinates = defaultdict(set) not_technically_coordinates = set() for coord_name in non_dim_coord_names: target_dims = variables[coord_name].dims for k, v in variables.items(): if ( k not in non_dim_coord_names and k not in v.dims and set(target_dims) <= set(v.dims) ): variable_coordinates[k].add(coord_name) if any( attr_name in v.encoding and coord_name in v.encoding.get(attr_name) for attr_name in CF_RELATED_DATA ): not_technically_coordinates.add(coord_name) global_coordinates.discard(coord_name) variables = {k: v.copy(deep=False) for k, v in variables.items()} # keep track of variable names written to file under the "coordinates" attributes written_coords = set() for name, var in variables.items(): encoding = var.encoding attrs = var.attrs if "coordinates" in attrs and "coordinates" in encoding: raise ValueError( f"'coordinates' found in both attrs and encoding for variable {name!r}." ) # if coordinates set to None, don't write coordinates attribute if ( "coordinates" in attrs and attrs.get("coordinates") is None or "coordinates" in encoding and encoding.get("coordinates") is None ): # make sure "coordinates" is removed from attrs/encoding attrs.pop("coordinates", None) encoding.pop("coordinates", None) continue # this will copy coordinates from encoding to attrs if "coordinates" in attrs # after the next line, "coordinates" is never in encoding # we get support for attrs["coordinates"] for free. coords_str = pop_to(encoding, attrs, "coordinates") if not coords_str and variable_coordinates[name]: coordinates_text = " ".join( str(coord_name) for coord_name in variable_coordinates[name] if coord_name not in not_technically_coordinates ) if coordinates_text: attrs["coordinates"] = coordinates_text if "coordinates" in attrs: written_coords.update(attrs["coordinates"].split()) # These coordinates are not associated with any particular variables, so we # save them under a global 'coordinates' attribute so xarray can roundtrip # the dataset faithfully. Because this serialization goes beyond CF # conventions, only do it if necessary. # Reference discussion: # http://mailman.cgd.ucar.edu/pipermail/cf-metadata/2014/007571.html global_coordinates.difference_update(written_coords) if global_coordinates: attributes = dict(attributes) if "coordinates" in attributes: warnings.warn( f"cannot serialize global coordinates {global_coordinates!r} because the global " f"attribute 'coordinates' already exists. This may prevent faithful roundtripping" f"of xarray datasets", SerializationWarning, ) else: attributes["coordinates"] = " ".join(map(str, global_coordinates)) return variables, attributes def encode_dataset_coordinates(dataset): """Encode coordinates on the given dataset object into variable specific and global attributes. When possible, this is done according to CF conventions. Parameters ---------- dataset : Dataset Object to encode. Returns ------- variables : dict attrs : dict """ non_dim_coord_names = set(dataset.coords) - set(dataset.dims) return _encode_coordinates( dataset._variables, dataset.attrs, non_dim_coord_names=non_dim_coord_names ) def cf_encoder(variables, attributes): """ Encode a set of CF encoded variables and attributes. Takes a dicts of variables and attributes and encodes them to conform to CF conventions as much as possible. This includes masking, scaling, character array handling, and CF-time encoding. Parameters ---------- variables : dict A dictionary mapping from variable name to xarray.Variable attributes : dict A dictionary mapping from attribute name to value Returns ------- encoded_variables : dict A dictionary mapping from variable name to xarray.Variable, encoded_attributes : dict A dictionary mapping from attribute name to value See Also -------- decode_cf_variable, encode_cf_variable """ # add encoding for time bounds variables if present. _update_bounds_encoding(variables) new_vars = {k: encode_cf_variable(v, name=k) for k, v in variables.items()} # Remove attrs from bounds variables (issue #2921) for var in new_vars.values(): bounds = var.attrs["bounds"] if "bounds" in var.attrs else None if bounds and bounds in new_vars: # see http://cfconventions.org/cf-conventions/cf-conventions.html#cell-boundaries for attr in [ "units", "standard_name", "axis", "positive", "calendar", "long_name", "leap_month", "leap_year", "month_lengths", ]: if attr in new_vars[bounds].attrs and attr in var.attrs: if new_vars[bounds].attrs[attr] == var.attrs[attr]: new_vars[bounds].attrs.pop(attr) return new_vars, attributes