def writeGroup()

in analysis/webservice/algorithms/doms/BaseDomsHandler.py [0:0]

• 39 lines of code
• 18 McCabe index (conditional complexity)

    def writeGroup(self):
        #
        # Create variables, enrich with attributes, and add data
        #
        lonVar = self.group.createVariable('lon', 'f4', ('dim',), fill_value=-32767.0)
        latVar = self.group.createVariable('lat', 'f4', ('dim',), fill_value=-32767.0)
        timeVar = self.group.createVariable('time', 'f4', ('dim',), fill_value=-32767.0)

        self.__enrichLon(lonVar, min(self.lon), max(self.lon))
        self.__enrichLat(latVar, min(self.lat), max(self.lat))
        self.__enrichTime(timeVar)

        latVar[:] = self.lat
        lonVar[:] = self.lon
        timeVar[:] = self.time

        # Add depth variable, if present
        if self.depth and any(self.depth):
            depthVar = self.group.createVariable('depth', 'f4', ('dim',), fill_value=-32767.0)
            self.__enrichDepth(depthVar, self.__calcMin(self.depth), max(self.depth))
            depthVar[:] = self.depth

        for variable_name, data in self.data_map.items():
            units = {}

            variables = dict.fromkeys(
                ((variable['variable_name'], variable['cf_variable_name']) for match in data for variable in match),
                None
            )

            for variable in variables:
                variables[variable] = np.repeat(np.nan, len(data))

            for i, match in enumerate(data):
                for variable in match:
                    key = (variable['variable_name'], variable['cf_variable_name'])
                    unit = variable['variable_unit']
                    units[key] = str(unit) if unit is not None else 'UNKNOWN'
                    variables[key][i] = variable['variable_value']

            for variable in variables:
                # Create a variable for each data point
                name = variable[0]
                cf_name = variable[1]

                data_variable = self.group.createVariable(
                    cf_name if cf_name is not None and cf_name != '' else name, 'f4', ('dim',), fill_value=-32767.0)
                # Find min/max for data variables. It is possible for 'None' to
                # be in this list, so filter those out when doing the calculation.
                min_data = np.nanmin(variables[variable])
                max_data = np.nanmax(variables[variable])
                self.__enrichVariable(data_variable, min_data, max_data, has_depth=None, unit=units[variable])
                data_variable[:] = np.ma.masked_invalid(variables[variable])
                data_variable.long_name = name
                data_variable.standard_name = cf_name