def run(self, variable_name: str) -> Optional[np.ndarray]:
# If this is a time variable, we check for 'NaT'
if self.ds[variable_name].data.dtype.type == np.datetime64:
results_array = np.isnat(self.ds[variable_name].data)
else:
fill_value = DSUtil.get_fill_value(self.ds, variable_name)
# If the variable has no _FillValue attribute, then
# we select a default value to use
if fill_value is None:
fill_value = -9999
# Make sure fill value has same data type as the variable
fill_value = np.array(fill_value,
dtype=self.ds[variable_name].data.dtype.type)
# First check if any values are assigned to _FillValue
results_array = np.equal(self.ds[variable_name].data, fill_value)
# Then, if the value is numeric, we should also check if any values are assigned to NaN
if self.ds[variable_name].data.dtype.type in (
type(0.0),
np.float16,
np.float32,
np.float64,
):
results_array |= np.isnan(self.ds[variable_name].data)
return results_array
def run(self, variable_name: str) -> Optional[np.ndarray]:
# If this is a time variable, we check for 'NaT'
if self.ds[variable_name].values.dtype.type == np.datetime64:
results_array = np.isnat(self.ds[variable_name].values)
else:
fill_value = DSUtil.get_fill_value(self.ds, variable_name)
# If the variable has no _FillValue attribute, then
# we select a default value to use
if fill_value is None:
fill_value = -9999
# Make sure fill value has same data type as the variable
fill_value = np.array(fill_value, dtype=self.ds[variable_name].values.dtype.type)
# First replace any values that are outside valid_range to be fill_value so
# it will get flagged as missing
self._replace_invalid_values(fill_value, variable_name)
# First check if any values are assigned to _FillValue
results_array = np.equal(self.ds[variable_name].values, fill_value)
# Then, if the value is numeric, we should also check if any values are assigned to
# NaN
if self.ds[variable_name].values.dtype.type in (type(0.0), np.float16, np.float32, np.float64):
results_array |= np.isnan(self.ds[variable_name].values)
return results_array
def run(self, variable_name: str, results_array: np.ndarray):
if results_array.any():
fill_value = DSUtil.get_fill_value(self.ds, variable_name)
keep_array = np.logical_not(results_array)
var_values = self.ds[variable_name].data
replaced_values = np.where(keep_array, var_values, fill_value)
self.ds[variable_name].data = replaced_values
self.record_correction(variable_name)