import os
import copy
from roger.diagnostics.base import RogerDiagnostic
from roger.variables import TIMESTEPS, Variable
from roger.core.operators import numpy as npx
[docs]class Average(RogerDiagnostic):
"""Average output diagnostic.
All registered variables are summed up when :meth:`diagnose` is called,
and averaged and output upon calling :meth:`output`.
"""
name = "average" #:
output_path = "{identifier}.average.nc" #: File to write to. May contain format strings that are replaced with Roger attributes.
output_variables = None #: Iterable containing all variables to be averaged. Changes have no effect after ``initialize`` has been called.
output_frequency = None #: Frequency (in seconds) in which output is written.
sampling_frequency = None #: Frequency (in seconds) in which variables are accumulated.
def __init__(self, state):
self.var_meta = {
"average_nitts": Variable("average_nitts", None, write_to_restart=True),
}
self.output_variables = []
def initialize(self, state):
"""Register all variables to be averaged"""
for var in self.output_variables:
var_meta = copy.copy(state.var_meta[var])
var_meta.time_dependent = True
var_meta.write_to_restart = True
if self._has_timestep_dim(state, var):
var_meta.dims = var_meta.dims[:-1]
if self._has_fourth_dim(state, var):
var_meta.dims = tuple((var_meta.dims[0], var_meta.dims[1], var_meta.dims[-1]))
self.var_meta[var] = var_meta
self.initialize_variables(state)
self.initialize_output(state)
@staticmethod
def _has_timestep_dim(state, var):
if state.var_meta[var].dims is None:
return False
return state.var_meta[var].dims[-1] == TIMESTEPS[0]
@staticmethod
def _has_fourth_dim(state, var):
if state.var_meta[var].dims is None:
return False
return state.var_meta[var].dims[-2] == TIMESTEPS[0]
def reset(self):
"""Reset values to zero"""
avg_vs = self.variables
for key in self.output_variables:
val = getattr(avg_vs, key)
setattr(avg_vs, key, 0 * val)
avg_vs.average_nitts = 0
def diagnose(self, state):
vs = state.variables
avg_vs = self.variables
avg_vs.average_nitts = avg_vs.average_nitts + 1
for key in self.output_variables:
var_data = npx.where(npx.isnan(getattr(avg_vs, key)), 0, getattr(avg_vs, key))
if self._has_timestep_dim(state, key):
setattr(avg_vs, key, var_data + getattr(vs, key)[..., vs.tau])
elif self._has_fourth_dim(state, key):
setattr(avg_vs, key, var_data + getattr(vs, key)[:, :, vs.tau, :])
else:
setattr(avg_vs, key, var_data + getattr(vs, key))
def output(self, state):
"""Write average to netcdf file and zero array"""
avg_vs = self.variables
if not os.path.isfile(self.get_output_file_name(state)):
self.initialize_output(state)
if avg_vs.average_nitts > 0:
for key in self.output_variables:
val = getattr(avg_vs, key)
setattr(avg_vs, key, val / avg_vs.average_nitts)
self.write_output(state)