Source code for mpas_analysis.shared.time_series.anomaly

# -*- coding: utf-8 -*-
# This software is open source software available under the BSD-3 license.
#
# Copyright (c) 2020 Triad National Security, LLC. All rights reserved.
# Copyright (c) 2020 Lawrence Livermore National Security, LLC. All rights
# reserved.
# Copyright (c) 2020 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/master/LICENSE
#

from __future__ import absolute_import, division, print_function, \
    unicode_literals

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) return ds
# }}} # vim: foldmethod=marker ai ts=4 sts=4 et sw=4 ft=python