def test_datetime_to_days(self):
referenceDate = '0001-01-01'
for calendar in ['gregorian', 'gregorian_noleap']:
for dateString, expected_days in [('0001-01-01', 0.),
('0001-01-02', 1.),
('0001-02-01', 31.),
('0002-01-01', 365.)]:
days = datetime_to_days(dates=string_to_datetime(dateString),
calendar=calendar,
referenceDate=referenceDate)
self.assertEqual(days, expected_days)
referenceDate = '2016-01-01'
for calendar, expected_days in [('gregorian', 366.),
('gregorian_noleap', 365.)]:
days = datetime_to_days(dates=string_to_datetime('2017-01-01'),
calendar=calendar,
referenceDate=referenceDate)
self.assertEqual(days, expected_days)
def run_task(self): # {{{
'''
Computes NINO34 index and plots the time series and power spectrum with
95 and 99% confidence bounds
'''
# Authors
# -------
# Luke Van Roekel, Xylar Asay-Davis
config = self.config
calendar = self.calendar
regionToPlot = config.get('indexNino34', 'region')
ninoIndexNumber = regionToPlot[4:]
self.logger.info("\nPlotting El Nino {} Index time series and power "
"spectrum....".format(ninoIndexNumber))
self.logger.info(' Load SST data...')
fieldName = 'nino'
startDate = self.config.get('index', 'startDate')
endDate = self.config.get('index', 'endDate')
startYear = self.config.getint('index', 'startYear')
endYear = self.config.getint('index', 'endYear')
dataSource = config.get('indexNino34', 'observationData')
observationsDirectory = build_obs_path(
config, 'ocean', '{}Subdirectory'.format(fieldName))
# specify obsTitle based on data path
# These are the only data sets supported
if dataSource == 'HADIsst':
dataPath = "{}/HADIsst_nino34_20180710.nc".format(
observationsDirectory)
obsTitle = 'HADSST'
refDate = '1870-01-01'
elif dataSource == 'ERS_SSTv4':
dataPath = "{}/ERS_SSTv4_nino34_20180710.nc".format(
observationsDirectory)
obsTitle = 'ERS SSTv4'
refDate = '1800-01-01'
else:
raise ValueError('Bad value for config option observationData {} '
'in [indexNino34] section.'.format(dataSource))
mainRunName = config.get('runs', 'mainRunName')
# regionIndex should correspond to NINO34 in surface weighted Average
# AM
regions = config.getExpression('regions', 'regions')
regionToPlot = config.get('indexNino34', 'region')
regionIndex = regions.index(regionToPlot)
# Load data:
ds = open_mpas_dataset(fileName=self.inputFile,
calendar=calendar,
variableList=self.variableList,
startDate=startDate,
endDate=endDate)
# Observations have been processed to the nino34Index prior to reading
dsObs = xr.open_dataset(dataPath, decode_cf=False, decode_times=False)
# add the days between 0001-01-01 and the refDate so we have a new
# reference date of 0001-01-01 (like for the model Time)
dsObs["Time"] = dsObs.Time + \
string_to_days_since_date(dateString=refDate, calendar=calendar)
nino34Obs = dsObs.sst
self.logger.info(
' Compute El Nino {} Index...'.format(ninoIndexNumber))
varName = self.variableList[0]
regionSST = ds[varName].isel(nOceanRegions=regionIndex)
nino34Main = self._compute_nino34_index(regionSST, calendar)
# Compute the observational index over the entire time range
# nino34Obs = compute_nino34_index(dsObs.sst, calendar)
self.logger.info(
' Computing El Nino {} power spectra...'.format(ninoIndexNumber))
spectraMain = self._compute_nino34_spectra(nino34Main)
# Compute the observational spectra over the whole record
spectraObs = self._compute_nino34_spectra(nino34Obs)
# Compute the observational spectra over the last 30 years for
# comparison. Only saving the spectra
subsetEndYear = 2016
if self.controlConfig is None:
subsetStartYear = 1976
else:
# make the subset the same length as the input data set
subsetStartYear = subsetEndYear - (endYear - startYear)
time_start = datetime_to_days(datetime.datetime(subsetStartYear, 1, 1),
calendar=calendar)
time_end = datetime_to_days(datetime.datetime(subsetEndYear, 12, 31),
calendar=calendar)
nino34Subset = nino34Obs.sel(Time=slice(time_start, time_end))
spectraSubset = self._compute_nino34_spectra(nino34Subset)
if self.controlConfig is None:
nino34s = [nino34Obs[2:-3], nino34Subset, nino34Main[2:-3]]
titles = [
'{} (Full Record)'.format(obsTitle),
'{} ({} - {})'.format(obsTitle, subsetStartYear,
subsetEndYear), mainRunName
]
spectra = [spectraObs, spectraSubset, spectraMain]
else:
baseDirectory = build_config_full_path(self.controlConfig,
'output',
'timeSeriesSubdirectory')
refFileName = '{}/{}.nc'.format(
baseDirectory, self.mpasTimeSeriesTask.fullTaskName)
dsRef = open_mpas_dataset(fileName=refFileName,
calendar=calendar,
variableList=self.variableList)
regionSSTRef = dsRef[varName].isel(nOceanRegions=regionIndex)
nino34Ref = self._compute_nino34_index(regionSSTRef, calendar)
nino34s = [nino34Subset, nino34Main[2:-3], nino34Ref[2:-3]]
controlRunName = self.controlConfig.get('runs', 'mainRunName')
spectraRef = self._compute_nino34_spectra(nino34Ref)
titles = [
'{} ({} - {})'.format(obsTitle, subsetStartYear,
subsetEndYear), mainRunName,
'Control: {}'.format(controlRunName)
]
spectra = [spectraSubset, spectraMain, spectraRef]
# Convert frequencies to period in years
for s in spectra:
s['period'] = \
1.0 / (constants.eps + s['f'] * constants.sec_per_year)
self.logger.info(
' Plot El Nino {} index and spectra...'.format(ninoIndexNumber))
outFileName = '{}/nino{}_{}.png'.format(self.plotsDirectory,
ninoIndexNumber, mainRunName)
self._nino34_timeseries_plot(
nino34s=nino34s,
title=u'El Niño {} Index'.format(ninoIndexNumber),
panelTitles=titles,
outFileName=outFileName)
self._write_xml(filePrefix='nino{}_{}'.format(ninoIndexNumber,
mainRunName),
plotType='Time Series',
ninoIndexNumber=ninoIndexNumber)
outFileName = '{}/nino{}_spectra_{}.png'.format(
self.plotsDirectory, ninoIndexNumber, mainRunName)
self._nino34_spectra_plot(
spectra=spectra,
title=u'El Niño {} power spectrum'.format(ninoIndexNumber),
panelTitles=titles,
outFileName=outFileName)
self._write_xml(filePrefix='nino{}_spectra_{}'.format(
ninoIndexNumber, mainRunName),
plotType='Spectra',
ninoIndexNumber=ninoIndexNumber)
def _parse_dataset_time(ds, inTimeVariableName, calendar, simulationStartTime,
outTimeVariableName, referenceDate): # {{{
"""
A helper function for computing a time coordinate from an MPAS time
variable. Given a data set and a time variable name (or tuple of 2
time names), returns a new data set with time coordinate
`outTimeVariableName` filled with days since `referenceDate`
Parameters
----------
ds : xarray.DataSet object
The data set containing an MPAS time variable to be used to build
an xarray time coordinate.
inTimeVariableName : string or tuple or list of strings
The name of the time variable in the MPAS data set that will be
used to build the 'Time' coordinate. The array(s) named by
inTimeVariableName should contain date strings or the number of
days since the start of the simulation. Typically,
inTimeVariableName is one of {'daysSinceStartOfSim','xtime'}.
If a list of two variable
names is provided, times from the two are averaged together to
determine the value of the time coordinate. In such cases,
inTimeVariableName is typically {['xtime_start', 'xtime_end']}.
calendar : {'gregorian', 'gregorian_noleap'}
The name of one of the calendars supported by MPAS cores
simulationStartTime : string
The start date of the simulation, used to convert from time variables
expressed as days since the start of the simulation to days since the
reference date. `simulationStartTime` takes one of the following
forms::
0001-01-01
0001-01-01 00:00:00
simulationStartTime is only required if the MPAS time variable
(identified by timeVariableName) is a number of days since the
start of the simulation.
outTimeVariableName : string
The name of the coordinate to assign times to, typically 'Time'.
referenceDate : string
The reference date for the time variable, typically '0001-01-01',
taking one of the following forms::
0001-01-01
0001-01-01 00:00:00
Returns
-------
dataset : xarray.dataset object
A copy of the input data set with the `outTimeVariableName`
coordinate containing the time coordinate parsed from
`inTimeVariableName`.
Raises
------
TypeError
If the time variable has an unsupported type (not a date string
or a floating-pont number of days since the start of the simulatio).
ValueError
If the time variable is a number of days since the start of the
simulation but simulationStartTime is None.
"""
# Authors
# -------
# Xylar Asay-Davis
if isinstance(inTimeVariableName, (tuple, list)):
# we want to average the two
assert (len(inTimeVariableName) == 2)
dsStart = _parse_dataset_time(ds=ds,
inTimeVariableName=inTimeVariableName[0],
calendar=calendar,
simulationStartTime=simulationStartTime,
outTimeVariableName=outTimeVariableName,
referenceDate=referenceDate)
dsEnd = _parse_dataset_time(ds=ds,
inTimeVariableName=inTimeVariableName[1],
calendar=calendar,
simulationStartTime=simulationStartTime,
outTimeVariableName=outTimeVariableName,
referenceDate=referenceDate)
starts = dsStart[outTimeVariableName].values
ends = dsEnd[outTimeVariableName].values
# replace the time in starts with the mean of starts and ends
dsOut = dsStart.copy()
dsOut.coords['startTime'] = (outTimeVariableName, starts)
dsOut.coords['endTime'] = (outTimeVariableName, ends)
dsOut.coords[outTimeVariableName] = (outTimeVariableName, [
starts[i] + (ends[i] - starts[i]) / 2 for i in range(len(starts))
])
else:
# there is just one time variable (either because we're recursively
# calling the function or because we're not averaging).
# The contents of the time variable is expected to be either a string
# (|S64) or a float (meaning days since start of the simulation).
timeVar = ds[inTimeVariableName]
if timeVar.dtype == '|S64':
# this is an array of date strings like 'xtime'
# convert to string
timeStrings = [
''.join(str(xtime.astype('U'))).strip()
for xtime in timeVar.values
]
days = string_to_days_since_date(dateString=timeStrings,
referenceDate=referenceDate,
calendar=calendar)
elif timeVar.dtype == 'float64':
# this array contains floating-point days like
# 'daysSinceStartOfSim'
if simulationStartTime is None:
raise ValueError('MPAS time variable {} appears to be a '
'number of days since start \n'
'of sim but simulationStartTime was not'
' supplied.'.format(inTimeVariableName))
if (string_to_datetime(referenceDate) == string_to_datetime(
simulationStartTime)):
days = timeVar.values
else:
# a conversion may be required
dates = days_to_datetime(days=timeVar.values,
referenceDate=simulationStartTime,
calendar=calendar)
days = datetime_to_days(dates=dates,
referenceDate=referenceDate,
calendar=calendar)
elif timeVar.dtype == 'timedelta64[ns]':
raise TypeError('timeVar of unsupported type {}. This is likely '
'because xarray.open_dataset \n'
'was called with decode_times=True, which can '
'mangle MPAS times.'.format(timeVar.dtype))
else:
raise TypeError("timeVar of unsupported type {}".format(
timeVar.dtype))
dsOut = ds.copy()
dsOut.coords[outTimeVariableName] = (outTimeVariableName, days)
return dsOut # }}}
def _replicate_cycle(self, ds, dsToReplicate, calendar): # {{{
"""
Replicates a periodic time series `dsToReplicate` to cover the
timeframe of the dataset `ds`.
Parameters
----------
ds : dataset used to find the start and end time of the replicated
cycle
dsToReplicate : dataset to replicate. The period of the cycle is the
length of dsToReplicate plus the time between the first two time
values (typically one year total).
calendar : {'gregorian', 'gregorian_noleap'}
The name of one of the calendars supported by MPAS cores
Returns:
--------
dsShift : a cyclicly repeated version of `dsToReplicte` covering the
range of time of `ds`.
"""
# Authors
# -------
# Xylar Asay-Davis, Milena Veneziani
dsStartTime = days_to_datetime(ds.Time.min(), calendar=calendar)
dsEndTime = days_to_datetime(ds.Time.max(), calendar=calendar)
repStartTime = days_to_datetime(dsToReplicate.Time.min(),
calendar=calendar)
repEndTime = days_to_datetime(dsToReplicate.Time.max(),
calendar=calendar)
repSecondTime = days_to_datetime(dsToReplicate.Time.isel(Time=1),
calendar=calendar)
period = (MpasRelativeDelta(repEndTime, repStartTime) +
MpasRelativeDelta(repSecondTime, repStartTime))
startIndex = 0
while(dsStartTime > repStartTime + (startIndex + 1) * period):
startIndex += 1
endIndex = 0
while(dsEndTime > repEndTime + endIndex * period):
endIndex += 1
dsShift = dsToReplicate.copy()
times = days_to_datetime(dsShift.Time, calendar=calendar)
dsShift.coords['Time'] = ('Time',
datetime_to_days(times + startIndex * period,
calendar=calendar))
# replicate cycle:
for cycleIndex in range(startIndex, endIndex):
dsNew = dsToReplicate.copy()
dsNew.coords['Time'] = \
('Time', datetime_to_days(times + (cycleIndex + 1) * period,
calendar=calendar))
dsShift = xr.concat([dsShift, dsNew], dim='Time')
# clip dsShift to the range of ds
dsStartTime = dsShift.Time.sel(Time=ds.Time.min(),
method=str('nearest')).values
dsEndTime = dsShift.Time.sel(Time=ds.Time.max(),
method=str('nearest')).values
dsShift = dsShift.sel(Time=slice(dsStartTime, dsEndTime))
return dsShift # }}}