regionsum.py

# coding=utf-8
"""Diagnostic to calculate a region total"""
import os

import iris
import iris.util
import iris.coords
import iris.analysis
import iris.exceptions

import numpy as np

import netCDF4

from earthdiagnostics import cdftools
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.regsum as regsum
from diagonals.mesh_helpers.nemo import Nemo


class RegionSum(Diagnostic):
    """
    Computes the sum 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 = 'regsum'
    "Diagnostic alias for the configuration file"

    def __init__(self, data_manager, startdate, member, chunk, domain,
                 variable, grid_point, box, save3d, basins,
                 grid):
        Diagnostic.__init__(self, data_manager)
        self.startdate = startdate
        self.member = member
        self.chunk = chunk
        self.domain = domain
        self.variable = variable
        self.grid_point = grid_point
        self.box = box
        self.save3d = save3d
        self.basins = basins
        self.grid = grid
        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 and \
            self.grid_point == other.grid_point and self.grid == other.grid \
            and self.basin == other.basin

    def __str__(self):
        return 'Region sum Startdate: {0.startdate} Member: {0.member}' \
               'Chunk: {0.chunk} Variable: {0.variable} ' \
               'Grid point: {0.grid_point} Box: {0.box} Save 3D: {0.save3d}' \
               'Original grid: {0.grid} Basin: {0.basins}'.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', -1),
                             DiagnosticIntOption('max_lat', -1),
                             DiagnosticIntOption('min_lon', -1),
                             DiagnosticIntOption('max_lon', -1),
                             DiagnosticBoolOption('save3D', True),
                             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_list.append(RegionSum(diags.data_manager, startdate, member, chunk,
                                      options['domain'], options['variable'],
                                      options['grid_point'].lower(), box,
                                      options['save3D'], options['basins'],
                                      options['grid']))
        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,
                                                grid=self.grid)

    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('sum', False, box_save)
        self._declare_var('sum', 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 -1 and self.box.max_lat is not -1 and self.box.min_lon is not -1 and \
           self.box.max_lat is not -1:
            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._sum_3d_variable(data, mesh, masks)
        else:
            self._sum_2d_var(data, mesh, masks)

    def _sum_2d_var(self, data, mesh, masks):
        areacello = mesh.get_areacello(cell_point=self.grid_point)
        varsum = regsum.compute_regsum_2D(data.data, masks, areacello)
        self._save_result_2D('sum', varsum, data)

    def _sum_3d_variable(self, data, mesh, masks):
        areacello = mesh.get_areacello(cell_point=self.grid_point)
        tmask = iris.load_cube('mesh_hgr.nc', '{0}mask'.format(self.grid_point))
        e3 = self._try_load_cube(3)
        e3 = self._rename_depth(e3)
        e3.coord('depth').bounds = data.coord('depth').bounds
        if self.box.min_depth is not -1 and self.box.max_depth is not -1:
            depth_constraint = iris.Constraint(depth=lambda c: self.box.min_depth <= c <= self.box.max_depth)
            e3 = e3.extract(depth_constraint)
            data = data.extract(depth_constraint)
            tmask = tmask.extract(depth_constraint)
        volcello = areacello*e3.data.astype(np.float32)
        varsum = regsum.compute_regsum_3D(data.data, masks, volcello,
                                          tmask.data)
        self._save_result_2D('sum', varsum, data)
        if self.save3d:
            varsum3d = regsum.compute_regsum_levels(data.data, masks,
                                                    volcello, tmask)
            self._save_result_3D('sum', varsum3d, data)

    def _try_load_cube(self, number):
        try:
            cube = iris.load_cube('mesh_hgr.nc', 'e{0}{1}'.format(number, self.grid_point))
        except iris.exceptions.ConstraintMismatchError:
            cube = iris.load_cube('mesh_hgr.nc', 'e{0}{1}_0'.format(number, self.grid_point))
        cube = iris.util.squeeze(cube)
        dims = len(cube.shape)
        try:
            cube.coord('i')
        except iris.exceptions.CoordinateNotFoundError:
            cube.add_dim_coord(iris.coords.DimCoord(np.arange(cube.shape[dims - 1]), var_name='i'), dims - 1)
        try:
            cube.coord('j')
        except iris.exceptions.CoordinateNotFoundError:
            cube.add_dim_coord(iris.coords.DimCoord(np.arange(cube.shape[dims - 2]), var_name='j'), dims - 2)
        return cube

    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, threed, mean_file):
        var = 'sum'
        if threed:
            if not self.save3d:
                return False
            original_name = '{0}_{1}'.format(var, self.variable)
            final_name = '{1}3d{0}'.format(var, self.variable)
            levels = ',lev'
        else:
            original_name = '{0}_3D{1}'.format(var, self.variable)
            final_name = '{1}{0}'.format(var, self.variable)
            levels = ''

        temp2 = TempFile.get()
        Utils.nco().ncks(
            input=mean_file,
            output=temp2,
            options=('-v {0},{2.lat_name},{2.lon_name}{1}'.format(original_name, levels, self),)
        )
        handler = Utils.open_cdf(temp2)
        var_handler = handler.variables[original_name]
        if hasattr(var_handler, 'valid_min'):
            del var_handler.valid_min
        if hasattr(var_handler, 'valid_max'):
            del var_handler.valid_max
        handler.close()
        self.declared[final_name].set_local_file(
            temp2, diagnostic=self, rename_var=original_name, region=self.basins
        )

    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, grid=self.grid
        )

    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}3d{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)
        handler_temp.createDimension('lev', data.shape[1])
        var_level = handler_temp.createVariable('lev', float, 'lev')
        var_level[...] = data.coord('depth').points
        var_level.units = 'm'
        var_level.axis = 'Z'
        var_level.positive = 'down'
        var_level.long_name = 'ocean depth coordinate'
        var_level.standard_name = 'depth'
        var_region = handler_temp.createVariable('region', 'S1',
                                                 ('region', 'region_length'))
        var = handler_temp.createVariable('{1}3d{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)