# -*- coding: utf-8 -*-
# This software is open source software available under the BSD-3 license.
#
# Copyright (c) 2022 Triad National Security, LLC. All rights reserved.
# Copyright (c) 2022 Lawrence Livermore National Security, LLC. All rights
# reserved.
# Copyright (c) 2022 UT-Battelle, LLC. All rights reserved.
#
# Additional copyright and license information can be found in the LICENSE file
# distributed with this code, or at
# https://raw.githubusercontent.com/MPAS-Dev/MPAS-Analysis/main/LICENSE
#
from mpas_analysis.shared.io import open_mpas_dataset
from mpas_analysis.shared.time_series.moving_average import compute_moving_avg
[docs]
def compute_moving_avg_anomaly_from_start(timeSeriesFileName, variableList,
anomalyStartTime, anomalyEndTime,
startDate, endDate, calendar,
movingAveragePoints=12,
alter_dataset=None):
"""
Compute the rolling mean of the anomaly of a quantity from the beginning
of the simulation (such that the rolling mean starts at zero by definition)
Parameters
----------
timeSeriesFileName : str
a file produced by ``MpasTimeSeriesTask`` containing variables, the
anomaly and rolling mean of which is to be computed
variableList : list of str
variable names to include in the resulting data set
anomalyStartTime, anomalyEndTime : str
the start and end times of the reference point for the anomaly
startDate, endDate : str
the start and end dates of the time series
calendar : {'gregorian', 'gregoraian_noleap'}
The calendar used in the MPAS run
movingAveragePoints : int, optional
The number of points (months) over which to perform the rolling average
of the data set
alter_dataset : function
A function for manipulating the data set (e.g. computing new
variables), taking an ``xarray.Dataset`` as input argument and
returning an ``xarray.Dataset``
Returns
-------
ds : ``xarray.Dataset``
The anomaly of the rolling time mean from the start of the simulation
"""
# Authors
# -------
# Xylar Asay-Davis
ds = open_mpas_dataset(fileName=timeSeriesFileName,
calendar=calendar,
variableList=variableList,
startDate=startDate,
endDate=endDate)
if alter_dataset is not None:
ds = alter_dataset(ds)
dsStart = open_mpas_dataset(
fileName=timeSeriesFileName,
calendar=calendar,
variableList=variableList,
startDate=anomalyStartTime,
endDate=anomalyEndTime)
if alter_dataset is not None:
dsStart = alter_dataset(dsStart)
dsStart = dsStart.isel(Time=slice(0, movingAveragePoints)).mean('Time')
for variable in ds.data_vars:
ds[variable] = ds[variable] - dsStart[variable]
ds = compute_moving_avg(ds, movingAveragePoints=movingAveragePoints)
return ds