def test_incompatible_attributes(self):
invalid_vars = [
Variable(['t'], pd.date_range('2000-01-01', periods=3),
{'units': 'foobar'}),
Variable(['t'], [0, 1, 2], {'add_offset': 0}, {'add_offset': 2}),
Variable(['t'], [0, 1, 2], {'_FillValue': 0}, {'_FillValue': 2}),
]
for var in invalid_vars:
with self.assertRaises(ValueError):
conventions.encode_cf_variable(var)
def test_incompatible_attributes(self):
invalid_vars = [
Variable(['t'], pd.date_range('2000-01-01', periods=3),
{'units': 'foobar'}),
Variable(['t'], [0, 1, 2], {'add_offset': 0}, {'add_offset': 2}),
Variable(['t'], [0, 1, 2], {'_FillValue': 0}, {'_FillValue': 2}),
]
for var in invalid_vars:
with self.assertRaises(ValueError):
conventions.encode_cf_variable(var)
def set_variable(self, name, variable):
variable = encode_cf_variable(variable)
if self.format == 'NETCDF4':
values, datatype = _nc4_values_and_dtype(variable)
else:
variable = encode_nc3_variable(variable)
values = variable.values
datatype = variable.dtype
self.set_necessary_dimensions(variable)
fill_value = variable.attrs.pop('_FillValue', None)
encoding = variable.encoding
nc4_var = self.ds.createVariable(
varname=name,
datatype=datatype,
dimensions=variable.dimensions,
zlib=encoding.get('zlib', False),
complevel=encoding.get('complevel', 4),
shuffle=encoding.get('shuffle', True),
fletcher32=encoding.get('fletcher32', False),
contiguous=encoding.get('contiguous', False),
chunksizes=encoding.get('chunksizes'),
endian=encoding.get('endian', 'native'),
least_significant_digit=encoding.get('least_significant_digit'),
fill_value=fill_value)
nc4_var.set_auto_maskandscale(False)
nc4_var[:] = values
for k, v in iteritems(variable.attrs):
# set attributes one-by-one since netCDF4<1.0.10 can't handle
# OrderedDict as the input to setncatts
nc4_var.setncattr(k, v)
def set_variable(self, name, variable):
variable = encode_cf_variable(variable)
self.set_necessary_dimensions(variable)
fill_value = variable.attrs.pop('_FillValue', None)
encoding = variable.encoding
self.ds.createVariable(
varname=name,
datatype=variable.dtype,
dimensions=variable.dimensions,
zlib=encoding.get('zlib', False),
complevel=encoding.get('complevel', 4),
shuffle=encoding.get('shuffle', True),
fletcher32=encoding.get('fletcher32', False),
contiguous=encoding.get('contiguous', False),
chunksizes=encoding.get('chunksizes'),
endian=encoding.get('endian', 'native'),
least_significant_digit=encoding.get('least_significant_digit'),
fill_value=fill_value)
nc4_var = self.ds.variables[name]
nc4_var.set_auto_maskandscale(False)
if variable.values.ndim == 0:
nc4_var[:] = variable.values
else:
nc4_var[:] = variable.values[:]
nc4_var.setncatts(variable.attrs)
def set_variable(self, name, variable):
variable = encode_cf_variable(variable)
if self.format == 'NETCDF4':
values, datatype = _nc4_values_and_dtype(variable)
else:
variable = encode_nc3_variable(variable)
values = variable.values
datatype = variable.dtype
self.set_necessary_dimensions(variable)
fill_value = variable.attrs.pop('_FillValue', None)
encoding = variable.encoding
nc4_var = self.ds.createVariable(
varname=name,
datatype=datatype,
dimensions=variable.dimensions,
zlib=encoding.get('zlib', False),
complevel=encoding.get('complevel', 4),
shuffle=encoding.get('shuffle', True),
fletcher32=encoding.get('fletcher32', False),
contiguous=encoding.get('contiguous', False),
chunksizes=encoding.get('chunksizes'),
endian=encoding.get('endian', 'native'),
least_significant_digit=encoding.get('least_significant_digit'),
fill_value=fill_value)
nc4_var.set_auto_maskandscale(False)
nc4_var[:] = values
for k, v in iteritems(variable.attrs):
# set attributes one-by-one since netCDF4<1.0.10 can't handle
# OrderedDict as the input to setncatts
nc4_var.setncattr(k, v)
def set_variable(self, name, variable):
variable = encode_cf_variable(variable)
data = coerce_nc3_dtype(variable.values)
self.set_necessary_dimensions(variable)
self.ds.createVariable(name, data.dtype, variable.dimensions)
scipy_var = self.ds.variables[name]
if data.ndim == 0:
scipy_var.assignValue(data)
else:
scipy_var[:] = data[:]
for k, v in variable.attrs.iteritems():
self._validate_attr_key(k)
setattr(scipy_var, k, self._cast_attr_value(v))
def test_missing_fillvalue(self):
v = Variable(['x'], np.array([np.nan, 1, 2, 3]))
v.encoding = {'dtype': 'int16'}
with self.assertWarns('floating point data as an integer'):
conventions.encode_cf_variable(v)
def test_missing_fillvalue(self):
v = Variable(['x'], np.array([np.nan, 1, 2, 3]))
v.encoding = {'dtype': 'int16'}
with self.assertWarns('floating point data as an integer'):
conventions.encode_cf_variable(v)
