def check_cf_grib2(standard_name, long_name, discipline, category, number, units, expect_none=False):
a_cf_unit = cf_units.Unit(units)
gribdata = gptx.cf_phenom_to_grib2_info(standard_name, long_name)
if expect_none:
self.assertIsNone(gribdata)
else:
self.assertEqual(gribdata.discipline, discipline)
self.assertEqual(gribdata.category, category)
self.assertEqual(gribdata.number, number)
self.assertEqual(gribdata.units, a_cf_unit)
def check_cf_grib2(standard_name, long_name,
discipline, category, number, units,
expect_none=False):
a_cf_unit = cf_units.Unit(units)
gribdata = gptx.cf_phenom_to_grib2_info(standard_name, long_name)
if expect_none:
self.assertIsNone(gribdata)
else:
self.assertEqual(gribdata.discipline, discipline)
self.assertEqual(gribdata.category, category)
self.assertEqual(gribdata.number, number)
self.assertEqual(gribdata.units, a_cf_unit)
def set_discipline_and_parameter(cube, grib):
# NOTE: for now, can match by *either* standard_name or long_name.
# This allows workarounds for data with no identified standard_name.
grib2_info = gptx.cf_phenom_to_grib2_info(cube.standard_name,
cube.long_name)
if grib2_info is not None:
gribapi.grib_set(grib, "discipline", grib2_info.discipline)
gribapi.grib_set(grib, "parameterCategory", grib2_info.category)
gribapi.grib_set(grib, "parameterNumber", grib2_info.number)
else:
gribapi.grib_set(grib, "discipline", 255)
gribapi.grib_set(grib, "parameterCategory", 255)
gribapi.grib_set(grib, "parameterNumber", 255)
warnings.warn('Unable to determine Grib2 parameter code for cube.\n'
'discipline, parameterCategory and parameterNumber '
'have been set to "missing".')
def set_discipline_and_parameter(cube, grib):
# NOTE: for now, can match by *either* standard_name or long_name.
# This allows workarounds for data with no identified standard_name.
grib2_info = gptx.cf_phenom_to_grib2_info(cube.standard_name,
cube.long_name)
if grib2_info is not None:
gribapi.grib_set(grib, "discipline", grib2_info.discipline)
gribapi.grib_set(grib, "parameterCategory", grib2_info.category)
gribapi.grib_set(grib, "parameterNumber", grib2_info.number)
else:
gribapi.grib_set(grib, "discipline", 255)
gribapi.grib_set(grib, "parameterCategory", 255)
gribapi.grib_set(grib, "parameterNumber", 255)
warnings.warn('Unable to determine Grib2 parameter code for cube.\n'
'discipline, parameterCategory and parameterNumber '
'have been set to "missing".')
def data_section(cube, grib):
# Masked data?
if isinstance(cube.data, ma.core.MaskedArray):
# What missing value shall we use?
if not np.isnan(cube.data.fill_value):
# Use the data's fill value.
fill_value = float(cube.data.fill_value)
else:
# We can't use the data's fill value if it's NaN,
# the GRIB API doesn't like it.
# Calculate an MDI outside the data range.
min, max = cube.data.min(), cube.data.max()
fill_value = min - (max - min) * 0.1
# Prepare the unmaksed data array, using fill_value as the MDI.
data = cube.data.filled(fill_value)
else:
fill_value = None
data = cube.data
# units scaling
grib2_info = gptx.cf_phenom_to_grib2_info(cube.standard_name,
cube.long_name)
if grib2_info is None:
# for now, just allow this
warnings.warn('Unable to determine Grib2 parameter code for cube.\n'
'Message data may not be correctly scaled.')
else:
if cube.units != grib2_info.units:
data = cube.units.convert(data, grib2_info.units)
if fill_value is not None:
fill_value = cube.units.convert(fill_value, grib2_info.units)
if fill_value is None:
# Disable missing values in the grib message.
gribapi.grib_set(grib, "bitmapPresent", 0)
else:
# Enable missing values in the grib message.
gribapi.grib_set(grib, "bitmapPresent", 1)
gribapi.grib_set_double(grib, "missingValue", fill_value)
gribapi.grib_set_double_array(grib, "values", data.flatten())
def data_section(cube, grib):
# Masked data?
if isinstance(cube.data, ma.core.MaskedArray):
# What missing value shall we use?
if not np.isnan(cube.data.fill_value):
# Use the data's fill value.
fill_value = float(cube.data.fill_value)
else:
# We can't use the data's fill value if it's NaN,
# the GRIB API doesn't like it.
# Calculate an MDI outside the data range.
min, max = cube.data.min(), cube.data.max()
fill_value = min - (max - min) * 0.1
# Prepare the unmaksed data array, using fill_value as the MDI.
data = cube.data.filled(fill_value)
else:
fill_value = None
data = cube.data
# units scaling
grib2_info = gptx.cf_phenom_to_grib2_info(cube.standard_name,
cube.long_name)
if grib2_info is None:
# for now, just allow this
warnings.warn('Unable to determine Grib2 parameter code for cube.\n'
'Message data may not be correctly scaled.')
else:
if cube.units != grib2_info.units:
data = cube.units.convert(data, grib2_info.units)
if fill_value is not None:
fill_value = cube.units.convert(fill_value, grib2_info.units)
if fill_value is None:
# Disable missing values in the grib message.
gribapi.grib_set(grib, "bitmapPresent", 0)
else:
# Enable missing values in the grib message.
gribapi.grib_set(grib, "bitmapPresent", 1)
gribapi.grib_set_double(grib, "missingValue", fill_value)
gribapi.grib_set_double_array(grib, "values", data.flatten())
def data(cube, grib):
# mdi
if isinstance(cube.data, ma.core.MaskedArray):
gribapi.grib_set(grib, "bitmapPresent", 1)
gribapi.grib_set_double(grib, "missingValue",
float(cube.data.fill_value))
data = cube.data.filled()
else:
gribapi.grib_set_double(grib, "missingValue", float(-1e9))
data = cube.data
# units scaling
grib2_info = gptx.cf_phenom_to_grib2_info(cube.standard_name,
cube.long_name)
if grib2_info is None:
# for now, just allow this
warnings.warn('Unable to determine Grib2 parameter code for cube.\n'
'Message data may not be correctly scaled.')
else:
if cube.units != grib2_info.units:
data = cube.units.convert(data, grib2_info.units)
# values
gribapi.grib_set_double_array(grib, "values", data.flatten())
def data(cube, grib):
# mdi
if isinstance(cube.data, ma.core.MaskedArray):
gribapi.grib_set(grib, "bitmapPresent", 1)
gribapi.grib_set_double(grib, "missingValue",
float(cube.data.fill_value))
data = cube.data.filled()
else:
gribapi.grib_set_double(grib, "missingValue", float(-1e9))
data = cube.data
# units scaling
grib2_info = gptx.cf_phenom_to_grib2_info(cube.standard_name,
cube.long_name)
if grib2_info is None:
# for now, just allow this
warnings.warn('Unable to determine Grib2 parameter code for cube.\n'
'Message data may not be correctly scaled.')
else:
if cube.units != grib2_info.units:
data = cube.units.convert(data, grib2_info.units)
# values
gribapi.grib_set_double_array(grib, "values", data.flatten())
