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from collections.abc import MutableMapping
import numpy as np
_HIDDEN_ATTRS = frozenset(
[
"REFERENCE_LIST",
"CLASS",
"DIMENSION_LIST",
"NAME",
"_Netcdf4Dimid",
"_Netcdf4Coordinates",
"_nc3_strict",
"_NCProperties",
]
)
class Attributes(MutableMapping):
def __init__(self, h5attrs, check_dtype, h5py_pckg):
self._h5attrs = h5attrs
self._check_dtype = check_dtype
self._h5py = h5py_pckg
def __getitem__(self, key):
if key in _HIDDEN_ATTRS:
raise KeyError(key)
# get original attribute via h5py low level api
# see https://github.com/h5py/h5py/issues/2045
if self._h5py.__name__ == "h5py":
attr = self._h5attrs.get_id(key)
else:
attr = self._h5attrs[key]
# handle Empty types
if isinstance(self._h5attrs[key], self._h5py.Empty):
# see https://github.com/h5netcdf/h5netcdf/issues/94 for details
string_info = self._h5py.check_string_dtype(self._h5attrs[key].dtype)
if string_info and string_info.length == 1:
return b""
# see https://github.com/h5netcdf/h5netcdf/issues/154 for details
else:
return np.array([], dtype=attr.dtype)
output = self._h5attrs[key]
# string decoding subtleties
# vlen strings are already decoded -> only decode fixed length strings
# see https://github.com/h5netcdf/h5netcdf/issues/116
# netcdf4-python returns string arrays as lists, we do as well
if self._h5py.__name__ == "h5py":
string_info = self._h5py.check_string_dtype(attr.dtype)
if string_info is not None:
# do not decode "S1"-type char arrays, as they are actually wanted as bytes
# see https://github.com/Unidata/netcdf4-python/issues/271
if string_info.length is not None and string_info.length > 1:
encoding = string_info.encoding
if np.isscalar(output):
output = output.decode(encoding, "surrogateescape")
else:
output = [
b.decode(encoding, "surrogateescape") for b in output.flat
]
else:
# transform string array to list
if not np.isscalar(output):
output = output.tolist()
# return item if single element list/array
# see https://github.com/h5netcdf/h5netcdf/issues/116
if not np.isscalar(output) and len(output) == 1:
return output[0]
return output
def __setitem__(self, key, value):
if key in _HIDDEN_ATTRS:
raise AttributeError(f"cannot write attribute with reserved name {key!r}")
if hasattr(value, "dtype"):
dtype = value.dtype
else:
dtype = np.asarray(value).dtype
self._check_dtype(dtype)
self._h5attrs[key] = value
def __delitem__(self, key):
del self._h5attrs[key]
def __iter__(self):
for key in self._h5attrs:
if key not in _HIDDEN_ATTRS:
yield key
def __len__(self):
hidden_count = sum(1 if attr in self._h5attrs else 0 for attr in _HIDDEN_ATTRS)
return len(self._h5attrs) - hidden_count
def __repr__(self):
return "\n".join([f"{type(self)!r}"] + [f"{k}: {v!r}" for k, v in self.items()])
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