# coding=utf-8
import iris
import iris.analysis
import iris.exceptions

from earthdiagnostics.box import Box
from earthdiagnostics.diagnostic import Diagnostic, DiagnosticFloatOption, DiagnosticDomainOption, \
    DiagnosticVariableOption
from earthdiagnostics.utils import TempFile
from earthdiagnostics.modelingrealm import ModelingRealms


class VerticalMeanMetersIris(Diagnostic):
    """
    Averages vertically any given variable

    :original author: Virginie Guemas <virginie.guemas@bsc.es>
    :contributor: Javier Vegas-Regidor<javier.vegas@bsc.es>

    :created: February 2012
    :last modified: June 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 to average
    :type variable: str
    :param box: box used to restrict the vertical mean
    :type box: Box

    """

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

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

    def __eq__(self, other):
        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 'Vertical mean meters Startdate: {0} Member: {1} Chunk: {2} Variable: {3}:{4} ' \
               'Box: {5}'.format(self.startdate, self.member, self.chunk, self.domain, self.variable, self.box)

    @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: variable, minimum depth (meters), maximum depth (meters)
        :type options: list[str]
        :return:
        """
        options_available = (DiagnosticVariableOption(diags.data_manager.config.var_manager),
                             DiagnosticFloatOption('min_depth', -1),
                             DiagnosticFloatOption('max_depth', -1),
                             DiagnosticDomainOption(default_value=ModelingRealms.ocean))
        options = cls.process_options(options, options_available)

        box = Box(True)
        if options['min_depth'] >= 0:
            box.min_depth = options['min_depth']
        if options['max_depth'] >= 0:
            box.max_depth = options['max_depth']

        job_list = list()
        for startdate, member, chunk in diags.config.experiment.get_chunk_list():
            job_list.append(VerticalMeanMetersIris(diags.data_manager, startdate, member, chunk,
                                                   options['domain'], options['variable'], box))
        return job_list

    def request_data(self):
        self.variable_file = self.request_chunk(ModelingRealms.ocean, self.variable, self.startdate, self.member,
                                                self.chunk)

    def declare_data_generated(self):
        self.results = self.declare_chunk(self.domain, self.variable + 'vmean', self.startdate, self.member,
                                          self.chunk, box=self.box)

    def compute(self):
        """
        Runs the diagnostic
        """
        iris.FUTURE.netcdf_no_unlimited = True
        iris.FUTURE.netcdf_promote = True

        var_cube = iris.load_cube(self.variable_file.local_file)

        lev_names = ('lev', 'depth')
        coord = None
        for coord_name in lev_names:
            try:
                coord = var_cube.coord(coord_name)
            except iris.exceptions.CoordinateNotFoundError:
                pass

        if self.box.min_depth is None:
            lev_min = coord.points[0]
        else:
            lev_min = self.box.min_depth

        if self.box.max_depth is None:
            lev_max = coord.points[-1]
        else:
            lev_max = self.box.max_depth
        var_cube = var_cube.extract(iris.Constraint(coord_values=
                                                    {coord.var_name: lambda cell: lev_min <= cell <= lev_max}))
        var_cube = var_cube.collapsed(coord, iris.analysis.MEAN)
        temp = TempFile.get()
        iris.save(var_cube, temp, zlib=True)
        self.results.set_local_file(temp, rename_var=var_cube.var_name)