interpolatecdo.py

# coding=utf-8
from earthdiagnostics.constants import Basins
from earthdiagnostics.diagnostic import Diagnostic, DiagnosticOption, DiagnosticDomainOption, DiagnosticBoolOption, \
    DiagnosticVariableOption
from earthdiagnostics.utils import Utils, TempFile
import numpy as np

from earthdiagnostics.modelingrealm import ModelingRealm, ModelingRealms


class InterpolateCDO(Diagnostic):
    """
    3-dimensional conservative interpolation to the regular atmospheric grid.
    It can also be used for 2D (i,j) variables

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

    :created: October 2016

    :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's name
    :type variable: str
    :param domain: variable's domain
    :type domain: ModelingRealm
    :param model_version: model version
    :type model_version: str
    """

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

    def __init__(self, data_manager, startdate, member, chunk, domain, variable, target_grid, model_version,
                 mask_oceans, original_grid):
        Diagnostic.__init__(self, data_manager)
        self.startdate = startdate
        self.member = member
        self.chunk = chunk
        self.variable = variable
        self.domain = domain
        self.model_version = model_version
        self.required_vars = [variable]
        self.generated_vars = [variable]
        self.tempTemplate = ''
        self.grid = target_grid
        self.mask_oceans = mask_oceans
        self.original_grid = original_grid

    def __eq__(self, other):
        return self.startdate == other.startdate and self.member == other.member and self.chunk == other.chunk and \
            self.model_version == other.model_version and self.domain == other.domain and \
            self.variable == other.variable and self.grid == other.grid and self.original_grid == other.original_grid

    def __str__(self):
        return 'Interpolate with CDO Startdate: {0} Member: {1} Chunk: {2} ' \
               'Variable: {3}:{4} Target grid: {5}  ' \
               'Model: {6}' .format(self.startdate, self.member, self.chunk, self.domain, self.variable, self.grid,
                                    self.model_version)

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

            :param diags: Diagnostics manager class
            :type diags: Diags
            :param options: target_grid, variable, domain=ocean
            :type options: list[str]
            :return:
            """
        options_available = (DiagnosticVariableOption('variable'),
                             DiagnosticOption('target_grid', diags.config.experiment.atmos_grid.lower()),
                             DiagnosticDomainOption('domain', ModelingRealms.ocean),
                             DiagnosticBoolOption('mask_oceans', True),
                             DiagnosticOption('original_grid'))
        options = cls.process_options(options, options_available)
        target_grid = cls._translate_ifs_grids_to_cdo_names(options['target_grid'])
        job_list = list()
        for startdate, member, chunk in diags.config.experiment.get_chunk_list():
            job_list.append(InterpolateCDO(diags.data_manager, startdate, member, chunk,
                                           options['domain'], options['variable'], target_grid,
                                           diags.config.experiment.model_version, options['mask_oceans'],
                                           options['original_grid']))
        return job_list

    @classmethod
    def _translate_ifs_grids_to_cdo_names(cls, target_grid):
        if target_grid.upper().startswith('T159L'):
            target_grid = 't106grid'
        if target_grid.upper().startswith('T255L'):
            target_grid = 't170grid'
        if target_grid.upper().startswith('T511L'):
            target_grid = 't340grid'
        return target_grid

    def compute(self):
        """
        Runs the diagnostic
        """
        variable_file = self.data_manager.get_file(self.domain, self.variable, self.startdate, self.member, self.chunk,
                                                   grid=self.original_grid)

        if self.mask_oceans:
            handler = Utils.openCdf(variable_file)
            var = handler.variables[self.variable]
            mask = Utils.get_mask(Basins.Global).astype(float)
            mask[mask == 0] = np.nan
            var[:] = mask * var[:]
            handler.close()

        cdo = Utils.cdo
        temp = TempFile.get()
        cdo.remapbil(self.grid, input=variable_file, output=temp)
        Utils.setminmax(temp, self.variable)
        self.send_file(temp, self.domain, self.variable, self.startdate, self.member, self.chunk, grid=self.grid)