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# coding=utf-8
from bscearth.utils.date import parse_date, add_months
from earthdiagnostics.diagnostic import *
from earthdiagnostics.frequency import Frequencies
import iris
import iris.coord_categorisation
from iris.time import PartialDateTime
import iris.analysis
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class DaysOverPercentile(Diagnostic):
"""
Calculates the montlhy percentiles
: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
"""
alias = 'daysover'
"Diagnostic alias for the configuration file"
def __init__(self, data_manager, startdate, member, chunk, domain, variable, leadtime, percentile):
Diagnostic.__init__(self, data_manager)
self.startdate = startdate
self.member = member
self.chunk = chunk
self.variable = variable
self.domain = domain
self.leadtime = leadtime
self.percentile = percentile
def __eq__(self, other):
return self.startdate == other.startdate and self.member == other.member and self.chunk == other.chunk and \
self.domain == other.domain and self.variable == other.variable and self.leadtime == other.leadtime and \
self.percentile == other.percentile
def __str__(self):
return 'Days over percentile Startdate: {0} Member: {1} Chunk: {2} ' \
'Variable: {3}:{4} Leadtime: {5}'.format(self.startdate, self.member, self.chunk, self.domain,
self.variable, self.leadtime)
@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: domain, variable, percentil number, maximum depth (level)
:type options: list[str]
:return:
"""
options_available = (DiagnosticDomainOption(),
DiagnosticOption('variable'),
DiagnosticListIntOption('leadtimes'),
DiagnosticFloatOption('percentile'))
options = cls.process_options(options, options_available)
job_list = list()
for startdate, member, chunk in diags.config.experiment.get_chunk_list():
for leadtime in options['leadtimes']:
job_list.append(DaysOverPercentile(diags.data_manager, startdate, member, chunk,
options['domain'], options['variable'], leadtime,
options['percentile']))
return job_list
def request_data(self):
var_name = self.variable + 'prct'
self.percentiles_file = self.request_chunk(self.domain, var_name, None, None, None,
frequency=Frequencies.climatology)
self.variable_file = self.request_chunk(self.domain, self.variable, self.startdate, self.member, self.chunk)
def declare_data_generated(self):
var_name = self.variable + '_daysover'.format(self.leadtime)
self.percentiles_file = self.declare_chunk(self.domain, var_name, None, None, None,
frequency=Frequencies.climatology, vartype=VariableType.STATISTIC)
def compute(self):
"""
Runs the diagnostic
"""
percentiles = iris.load_cube(self.percentiles_file.local_file)
var = iris.load_cube(self.variable_file.local_file)
date = parse_date(self.startdate)
leadtimes = {0: PartialDateTime(date.year, date.month)}
def assign_leadtime(coord, x):
try:
leadtime_month = 0
partial_date = leadtimes[leadtime_month]
while coord.units.num2date(x) != partial_date:
leadtime_month += 1
try:
partial_date = leadtimes[leadtime_month]
except KeyError:
new_date = add_months(date, leadtime_month, self.experiment_config.calendar)
partial_date = PartialDateTime(new_date.year, new_date.month)
leadtimes[leadtime_month] = partial_date
return leadtime_month
except Exception as ex:
pass
for leadtime_slice in var.slices_over('leadtime'):
percentiles_leadtime = percentiles.extract(iris.Constraint(leadtime=leadtime_slice.coord('leadtime').points[0]))
for percentile_slice in percentiles_leadtime.slices_over('percentile'):
over = leadtime_slice > percentile_slice
print(over)