regionmean.py

# coding=utf-8
"""Diagnostic to compute regional averages"""
import iris
import iris.util
import iris.coords
import iris.analysis
import iris.exceptions

import numpy as np

import netCDF4

from earthdiagnostics.box import Box
from earthdiagnostics.constants import Basins
from earthdiagnostics.diagnostic import Diagnostic, DiagnosticOption, DiagnosticIntOption, DiagnosticDomainOption, \
    DiagnosticBoolOption, DiagnosticBasinListOption, DiagnosticVariableOption
from earthdiagnostics.modelingrealm import ModelingRealms
from earthdiagnostics.utils import Utils, TempFile

import diagonals.regmean as regmean
from diagonals.mesh_helpers.nemo import Nemo



class RegionMean(Diagnostic):
    """
    Computes the mean value of the field (3D, weighted).

    For 3D fields, a horizontal mean for each level is also given. If a spatial window
    is specified, the mean value is computed only in this window.

    :original author: Javier Vegas-Regidor <javier.vegas@bsc.es>

    :created: March 2017

    :param data_manager: data management object
    :type data_manager: DataManager
    :param startdate: startdate
    :type startdate: str
    :param member: member number
    :type member: int
    :param chunk: chunk's number
    :type chunk: int
    :param variable: variable to average
    :type variable: str
    :param box: box used to restrict the vertical mean
    :type box: Box
    """

    alias = 'regmean'
    "Diagnostic alias for the configuration file"

    def __init__(self, data_manager, startdate, member, chunk, domain, variable, box, save3d,
                 variance, basins, grid_point):
        Diagnostic.__init__(self, data_manager)
        self.startdate = startdate
        self.member = member
        self.chunk = chunk
        self.domain = domain
        self.variable = variable
        self.box = box
        self.save3d = save3d
        self.variance = variance
        self.basins = basins
        self.grid_point = grid_point

        self.declared = {}

        self.lat_name = 'lat'
        self.lon_name = 'lon'

    def __eq__(self, other):
        if self._different_type(other):
            return False
        return self.startdate == other.startdate and self.member == other.member and self.chunk == other.chunk and \
            self.box == other.box and self.variable == other.variable

    def __str__(self):
        return 'Region mean Startdate: {0.startdate} Member: {0.member} Chunk: {0.chunk} Variable: {0.variable} ' \
               'Box: {0.box} Save 3D: {0.save3d} Save variance: {0.variance} Grid point: {0.grid_point}'.format(self)

    def __hash__(self):
        return hash(str(self))

    @classmethod
    def generate_jobs(cls, diags, options):
        """
        Create a job for each chunk to compute the diagnostic

        :param diags: Diagnostics manager class
        :type diags: Diags
        :param options: variable, minimum depth (level), maximum depth (level)
        :type options: list[str]
        :return:
        """
        options_available = (DiagnosticDomainOption(),
                             DiagnosticVariableOption(diags.data_manager.config.var_manager),
                             DiagnosticOption('grid_point', 'T'),
                             DiagnosticBasinListOption('basins', Basins().Global),
                             DiagnosticIntOption('min_depth', -1),
                             DiagnosticIntOption('max_depth', -1),
                             DiagnosticIntOption('min_lat', None),
                             DiagnosticIntOption('max_lat', None),
                             DiagnosticIntOption('min_lon', None),
                             DiagnosticIntOption('max_lon', None),
                             DiagnosticBoolOption('save3D', True),
                             DiagnosticBoolOption('variance', False),
                             DiagnosticOption('grid', ''))
        options = cls.process_options(options, options_available)

        box = Box()
        box.min_depth = options['min_depth']
        box.max_depth = options['max_depth']
        box.min_lat = options['min_lat']
        box.max_lat = options['max_lat']
        box.min_lon = options['min_lon']
        box.max_lon = options['max_lon']

        basins = options['basins']
        if not basins:
            Log.error('Basins not recognized')
            return()


        job_list = list()
        for startdate, member, chunk in diags.config.experiment.get_chunk_list():
            job = RegionMean(diags.data_manager, startdate, member, chunk,
                             options['domain'], options['variable'], box,
                             options['save3D'], options['variance'], options['basins'],
                             options['grid_point'].lower())
            job_list.append(job)

        return job_list

    def request_data(self):
        """Request data required by the diagnostic"""
        self.variable_file = self.request_chunk(self.domain, self.variable, self.startdate, self.member, self.chunk)

    def declare_data_generated(self):
        """Declare data to be generated by the diagnostic"""
        if self.box.min_depth == -1 and self.box.max_depth == -1:
            box_save = None
        else:
            box_save = self.box

        self._declare_var('mean', False, box_save)
        self._declare_var('mean', True, box_save)

    def compute(self):
        """Run the diagnostic"""
        has_levels = self._fix_file_metadata()
        data = self._load_data()
        masks = {}
        self.basins.sort()
        for basin in self.basins:
            masks[basin] = Utils.get_mask(basin)

        mesh = Nemo('mesh_hgr.nc', 'mask_regions.nc')
        if self.box.min_lat is not None and self.box.max_lat is not None and self.box.min_lon is not None and self.box.max_lat is not None:
            name = '{0}_{1}'.format(Box.get_lat_str(self.box), Box.get_lon_str(self.box))

            masks[name] = mesh.get_region_mask(self.box.min_lat,
                                               self.box.max_lat,
                                               self.box.min_lon,
                                               self.box.max_lon)
        if has_levels:
            self._meand_3d_variable(data, mesh, masks)
        else:
            self._mean_2d_var(data, mesh, masks)

    def _mean_2d_var(self, data, mesh, masks):
        areacello = mesh.get_areacello()
        mean = regmean.compute_regmean_2D(data.data, masks, areacello)
        self._save_result_2D('mean', mean, data)


    def _meand_3d_variable(self, data, mesh, masks):
        areacello = mesh.get_areacello()
        e3 = self._try_load_cube(3)
        e3 = self._rename_depth(e3)
        depth_constraint = iris.Constraint(depth=lambda c: self.box.min_depth <= c <= self.box.max_depth)
        volcello = areacello*e3.extract(depth_constraint).data
        data = data.extract(depth_constraint)
        mean = regmean.compute_regmean_3D(data.data, masks, volcello)
        self._save_result_2D('mean', mean, data)
        if self.save3d:
            mean3d = regmean.compute_regmean_levels(data.data, masks, volcello)
            self._save_result_3D('mean3d', mean3d, data)



    def _load_data(self):
        coords = []
        handler = Utils.open_cdf(self.variable_file.local_file)
        for variable in handler.variables:
            if variable in ('time', 'lev', 'lat', 'lon', 'latitude', 'longitude', 'leadtime', 'time_centered'):
                coords.append(variable)
            if variable == 'time_centered':
                handler.variables[variable].standard_name = ''

        handler.variables[self.variable].coordinates = ' '.join(coords)
        handler.close()

        data = iris.load_cube(self.variable_file.local_file)
        return self._rename_depth(data)

    def _rename_depth(self, data):
        for coord_name in ('model_level_number', 'Vertical T levels', 'lev'):
            if data.coords(coord_name):
                coord = data.coord(coord_name)
                coord.standard_name = 'depth'
                coord.long_name = 'depth'
                break
        return data

    def _fix_file_metadata(self):
        handler = Utils.open_cdf(self.variable_file.local_file)
        var = handler.variables[self.variable]
        coordinates = ''
        has_levels = False
        for dimension in handler.variables.keys():
            if dimension in ['time', 'lev', 'lat', 'latitude', 'lon', 'longitude', 'i', 'j']:
                coordinates += ' {0}'.format(dimension)
            if dimension == 'lev':
                has_levels = True
        var.coordinates = coordinates
        handler.close()
        return has_levels

    def _declare_var(self, var, threed, box_save):
        if threed:
            if not self.save3d:
                return False
            final_name = '{1}3d{0}'.format(var, self.variable)
        else:
            final_name = '{1}{0}'.format(var, self.variable)

        self.declared[final_name] = self.declare_chunk(ModelingRealms.ocean, final_name, self.startdate, self.member,
                                                       self.chunk, box=box_save, region=self.basins)

    def _send_var(self, var, threed, cube_list):
        if threed:
            if not self.save3d and threed:
                return False
            final_name = '{1}3d{0}'.format(var, self.variable)
        else:
            final_name = '{1}{0}'.format(var, self.variable)
        cube = cube_list.merge_cube()
        cube.var_name = 'result'
        cube.remove_coord('latitude')
        cube.remove_coord('longitude')
        try:
            cube.remove_coord('depth')
        except iris.exceptions.CoordinateNotFoundError:
            pass
        try:
            cube.remove_coord('lev')
        except iris.exceptions.CoordinateNotFoundError:
            pass
        temp = TempFile.get()
        iris.save(cube, temp)
        self.declared[final_name].set_local_file(temp, diagnostic=self, rename_var='result', region=self.basins)


    def _save_result_2D(self, var, result, data):
        final_name = '{1}{0}'.format(var, self.variable)
        temp = TempFile.get()
        handler_source = Utils.open_cdf(self.variable_file.local_file)
        handler_temp = Utils.open_cdf(temp, 'w')
        Utils.copy_variable(handler_source, handler_temp, 'time', True, True)
        handler_temp.createDimension('region', len(result))
        handler_temp.createDimension('region_length', 50)
        var_region = handler_temp.createVariable('region', 'S1',
                                                 ('region', 'region_length'))
        var = handler_temp.createVariable('{1}{0}'.format(var, self.variable),
                                          float, ('time', 'region',),)
        var.units = '{0}'.format(data.units)
        for i, basin in enumerate(result):
            var_region[i, ...] = netCDF4.stringtoarr(str(basin), 50)
            var[..., i] = result[basin]
        handler_temp.close()
        self.declared[final_name].set_local_file(temp, diagnostic=self)



    def _save_result_3D(self, var, result, data):
        final_name = '{1}{0}'.format(var, self.variable)
        temp = TempFile.get()
        handler_source = Utils.open_cdf(self.variable_file.local_file)
        handler_temp = Utils.open_cdf(temp, 'w')
        Utils.copy_variable(handler_source, handler_temp, 'time', True, True)
        Utils.copy_variable(handler_source, handler_temp, 'lev', True, True)
        handler_temp.createDimension('region', len(result))
        handler_temp.createDimension('region_length', 50)
        var_region = handler_temp.createVariable('region', 'S1',
                                                 ('region', 'region_length'))
        var = handler_temp.createVariable('{1}{0}'.format(var, self.variable),
                                          float, ('time', 'lev', 'region',),)
        var.units = '{0}'.format(data.units)
        for i, basin in enumerate(result):
            var_region[i, ...] = netCDF4.stringtoarr(str(basin), 50)
            var[..., i] = result[basin]
        handler_temp.close()
        self.declared[final_name].set_local_file(temp, diagnostic=self)