def from_hdf5(cls, filename, dataset_name=None):
r"""Attempts read in and convert a dataset in an hdf5 file into a YTArray.
Parameters
----------
filename: string
The filename to of the hdf5 file.
dataset_name: string
The name of the dataset to read from. If the dataset has a units
attribute, attempt to infer units as well.
"""
import h5py
from yt.extern.six.moves import cPickle as pickle
if dataset_name is None:
dataset_name = 'array_data'
f = h5py.File(filename)
dataset = f[dataset_name]
data = dataset[:]
units = dataset.attrs.get('units', '')
if 'unit_registry' in dataset.attrs.keys():
unit_lut = pickle.loads(dataset.attrs['unit_registry'].tostring())
else:
unit_lut = None
f.close()
registry = UnitRegistry(lut=unit_lut, add_default_symbols=False)
return cls(data, units, registry=registry)
def from_hdf5(cls, filename, dataset_name=None):
r"""Attempts read in and convert a dataset in an hdf5 file into a YTArray.
Parameters
----------
filename: string
The filename to of the hdf5 file.
dataset_name: string
The name of the dataset to read from. If the dataset has a units
attribute, attempt to infer units as well.
"""
import h5py
from yt.extern.six.moves import cPickle as pickle
if dataset_name is None:
dataset_name = "array_data"
f = h5py.File(filename)
dataset = f[dataset_name]
data = dataset[:]
units = dataset.attrs.get("units", "")
if "unit_registry" in dataset.attrs.keys():
unit_lut = pickle.loads(dataset.attrs["unit_registry"].tostring())
else:
unit_lut = None
f.close()
registry = UnitRegistry(lut=unit_lut, add_default_symbols=False)
return cls(data, units, registry=registry)
def load_object(self, name):
"""
Load and return and object from the data_file using the Pickle protocol,
under the name *name* on the node /Objects.
"""
obj = self.get_data("/Objects", name)
if obj is None:
return
obj = cPickle.loads(obj.value)
if iterable(obj) and len(obj) == 2:
obj = obj[1] # Just the object, not the ds
if hasattr(obj, '_fix_pickle'): obj._fix_pickle()
return obj
def load_object(self, name):
"""
Load and return and object from the data_file using the Pickle protocol,
under the name *name* on the node /Objects.
"""
obj = self.get_data("/Objects", name)
if obj is None:
return
obj = cPickle.loads(obj.value)
if iterable(obj) and len(obj) == 2:
obj = obj[1] # Just the object, not the ds
if hasattr(obj, '_fix_pickle'): obj._fix_pickle()
return obj
def get(self, ds_name, default = None):
if self.reference_name is None: return default
if ds_name in self.cache: return self.cache[ds_name]
url = _url_path.format(self.reference_name, ds_name)
try:
resp = urllib.request.urlopen(url)
except urllib.error.HTTPError:
raise YTNoOldAnswer(url)
else:
for this_try in range(3):
try:
data = resp.read()
except Exception:
time.sleep(0.01)
else:
# We were succesful
break
else:
# Raise error if all tries were unsuccessful
raise YTCloudError(url)
# This is dangerous, but we have a controlled S3 environment
rv = cPickle.loads(data)
self.cache[ds_name] = rv
return rv
def get(self, ds_name, default = None):
if self.reference_name is None: return default
if ds_name in self.cache: return self.cache[ds_name]
url = _url_path.format(self.reference_name, ds_name)
try:
resp = urllib.request.urlopen(url)
except urllib.error.HTTPError as ex:
raise YTNoOldAnswer(url)
else:
for this_try in range(3):
try:
data = resp.read()
except:
time.sleep(0.01)
else:
# We were succesful
break
else:
# Raise error if all tries were unsuccessful
raise YTCloudError(url)
# This is dangerous, but we have a controlled S3 environment
rv = cPickle.loads(data)
self.cache[ds_name] = rv
return rv