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Fix moving average of preprocessed OHC data #325

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Apr 12, 2018
Merged

Fix moving average of preprocessed OHC data #325

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29 changes: 24 additions & 5 deletions mpas_analysis/ocean/plot_depth_integrated_time_series_subtask.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,15 +11,15 @@
from __future__ import absolute_import, division, print_function, \
unicode_literals

import xarray as xr
import os
import xarray

from mpas_analysis.shared import AnalysisTask

from mpas_analysis.shared.plot.plotting import timeseries_analysis_plot

from mpas_analysis.shared.generalized_reader import open_multifile_dataset
from mpas_analysis.shared.io import open_mpas_dataset
from mpas_analysis.shared.io import open_mpas_dataset, write_netcdf

from mpas_analysis.shared.timekeeping.utility import date_to_days, \
days_to_datetime
Expand Down Expand Up @@ -216,6 +216,18 @@ def setup_and_check(self): # {{{
self.refFileName = '{}/{}'.format(baseDirectory,
self.inFileName)

preprocessedReferenceRunName = config.get(
'runs', 'preprocessedReferenceRunName')
if preprocessedReferenceRunName != 'None':

assert(not os.path.isabs(self.inFileName))

baseDirectory = build_config_full_path(
config, 'output', 'timeSeriesSubdirectory')

self.preprocessedFileName = '{}/preprocessed_{}'.format(
baseDirectory, self.inFileName)

if not os.path.isabs(self.inFileName):
baseDirectory = build_config_full_path(
config, 'output', 'timeSeriesSubdirectory')
Expand Down Expand Up @@ -354,6 +366,13 @@ def run_task(self): # {{{
'not be plotted.')
preprocessedReferenceRunName = 'None'

# rolling mean seems to have trouble with dask data sets so we
# write out the data set and read it back as a single-file data set
# (without dask)
dsPreprocessed = dsPreprocessed.drop('xtime')
write_netcdf(dsPreprocessed, self.preprocessedFileName)
dsPreprocessed = xarray.open_dataset(self.preprocessedFileName)
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@pwolfram, if you have time to take a look at this, it's mainly a question of seeing if you're good with this solution for converting a multi-file data set to a single file data set or if you'd suggest some other way of handling the issue.

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My understanding from S Hoyer is that this is the preferred way to handle these types of problems. The only issue I foresee with this is that as data becomes larger, this doesn't scale too well. However, recent work has been to develop parallel writing functionality into xarray so I wouldn't worry about this in the short term.

I think this is a reasonable way to convert a multi-file dataset into a single file data set 👍


if preprocessedReferenceRunName != 'None':
color = 'r'
title = '{} \n {} (red)'.format(title,
Expand All @@ -369,13 +388,13 @@ def run_task(self): # {{{
divisionDepths] + ['btm']

# these preprocessed data are OHC *anomalies*
dsPreprocessed = compute_moving_avg(dsPreprocessed,
movingAveragePoints)
for rangeIndex in range(len(suffixes)):
variableName = '{}_{}'.format(preprocessedFieldPrefix,
suffixes[rangeIndex])
if variableName in list(dsPreprocessed.data_vars.keys()):
field = dsPreprocessed[variableName]
field = compute_moving_avg(field, movingAveragePoints)
timeSeries.append(field)
timeSeries.append(dsPreprocessed[variableName])
else:
self.logger.warning('Warning: Preprocessed variable {} '
'not found. Skipping.'.format(
Expand Down