def _group_result(self, fields):
# Run the testee, but returning just the groups (not FieldCollations).
with mock.patch(
'iris.fileformats.um._fast_load_structured_fields.'
'FieldCollation',
new=lambda args: args):
result = list(group_structured_fields(fields))
return result
def _basic_load_function(filename, pp_filter=None, **kwargs):
# The low-level 'fields from filename' loader.
#
# This is the 'loader.generator' in the control structure passed to the
# generic rules code, :meth:`iris.fileformats.rules.load_cubes`.
#
# As called by generic rules code, this replaces pp.load (and the like).
# It yields a sequence of "fields", but in this case the 'fields' are
# :class:`iris.fileformats.um._fast_load_structured_fields.FieldCollation`
# objects.
#
# NOTE: so, a FieldCollation is passed as the 'field' in user callbacks.
#
# Also in our case, we need to support the basic single-field filtering
# operation that speeds up phenomenon selection.
# Therefore, the actual loader will pass this as the 'pp_filter' keyword,
# when it is present.
# Additional load keywords are 'passed on' to the lower-level function.
# Helper function to select the correct fields loader call.
def _select_raw_fields_loader(fname):
# Return the PPfield loading function for a file name.
#
# This decides whether the underlying file is an FF or PP file.
# Because it would be too awkward to modify the whole iris loading
# callchain to "pass down" the file format, this function instead
# 'recreates' that information by calling the format picker again.
# NOTE: this may be inefficient, especially for web resources.
from iris.fileformats import FORMAT_AGENT
from iris.fileformats.pp import load as pp_load
from iris.fileformats.um import um_to_pp
with open(fname, "rb") as fh:
spec = FORMAT_AGENT.get_spec(os.path.basename(fname), fh)
if spec.name.startswith(_FF_SPEC_NAME):
loader = um_to_pp
elif spec.name.startswith(_PP_SPEC_NAME):
loader = pp_load
else:
emsg = "Require {!r} to be a structured FieldsFile or a PP file."
raise ValueError(emsg.format(fname))
return loader
loader = _select_raw_fields_loader(filename)
def iter_fields_decorated_with_load_indices(fields_iter):
for i_field, field in enumerate(fields_iter):
field._index_in_structured_load_file = i_field
yield field
fields = iter_fields_decorated_with_load_indices(
field for field in loader(filename, **kwargs)
if pp_filter is None or pp_filter(field))
return group_structured_fields(fields,
collation_class=FieldCollation,
filepath=filename)
def _basic_load_function(filename, pp_filter=None, **kwargs):
# The low-level 'fields from filename' loader.
#
# This is the 'loader.generator' in the control structure passed to the
# generic rules code, :meth:`iris.fileformats.rules.load_cubes`.
#
# As called by generic rules code, this replaces pp.load (and the like).
# It yields a sequence of "fields", but in this case the 'fields' are
# :class:`iris.fileformats.um._fast_load_structured_fields.FieldCollation`
# objects.
#
# NOTE: so, a FieldCollation is passed as the 'field' in user callbacks.
#
# Also in our case, we need to support the basic single-field filtering
# operation that speeds up phenomenon selection.
# Therefore, the actual loader will pass this as the 'pp_filter' keyword,
# when it is present.
# Additional load keywords are 'passed on' to the lower-level function.
# Helper function to select the correct fields loader call.
def _select_raw_fields_loader(fname):
# Return the PPfield loading function for a file name.
#
# This decides whether the underlying file is an FF or PP file.
# Because it would be too awkward to modify the whole iris loading
# callchain to "pass down" the file format, this function instead
# 'recreates' that information by calling the format picker again.
# NOTE: this may be inefficient, especially for web resources.
from iris.fileformats import FORMAT_AGENT
from iris.fileformats.pp import load as pp_load
from iris.fileformats.um import um_to_pp
with open(fname, 'rb') as fh:
spec = FORMAT_AGENT.get_spec(os.path.basename(fname), fh)
if spec.name.startswith(_FF_SPEC_NAME):
loader = um_to_pp
elif spec.name.startswith(_PP_SPEC_NAME):
loader = pp_load
else:
emsg = 'Require {!r} to be a structured FieldsFile or a PP file.'
raise ValueError(emsg.format(fname))
return loader
loader = _select_raw_fields_loader(filename)
def iter_fields_decorated_with_load_indices(fields_iter):
for i_field, field in enumerate(fields_iter):
field._index_in_structured_load_file = i_field
yield field
fields = iter_fields_decorated_with_load_indices(
field
for field in loader(filename, **kwargs)
if pp_filter is None or pp_filter(field))
return group_structured_fields(fields,
collation_class=FieldCollation,
filepath=filename)
def _group_result(self, fields):
# Run the testee, but returning just the groups (not FieldCollations).
with mock.patch('iris.fileformats.um._fast_load_structured_fields.'
'FieldCollation', new=lambda args: args):
result = list(group_structured_fields(fields))
return result
def _group_result(self, fields):
# Run the testee, but returning just the groups (not FieldCollations).
result = list(group_structured_fields(fields, collation_class=tuple))
return result
def _collations_from_filename(filename):
fields = iter(um_to_pp(filename))
return group_structured_fields(fields)
def _collations_from_filename(filename):
loader = _structured_loader(filename)
fields = iter(loader(filename))
return group_structured_fields(fields)
def _collations_from_filename(filename):
loader = _structured_loader(filename)
fields = iter(loader(filename))
return group_structured_fields(fields)
def _group_result(self, fields):
# Run the testee, but returning just the groups (not FieldCollations).
result = list(group_structured_fields(fields, collation_class=tuple))
return result
def _collations_from_filename(filename):
fields = iter(um_to_pp(filename))
return group_structured_fields(fields)
