# 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
"""
Functions for creating climatologies from monthly time series data
"""
# Authors
# -------
# Xylar Asay-Davis
from __future__ import absolute_import, division, print_function, \
unicode_literals
import xarray as xr
import os
import numpy
from tempfile import TemporaryDirectory
from pyremap import Remapper, LatLonGridDescriptor, ProjectionGridDescriptor
from mpas_analysis.shared.constants import constants
from mpas_analysis.shared.timekeeping.utility import days_to_datetime
from mpas_analysis.shared.io.utility import build_config_full_path, \
make_directories, fingerprint_generator
from mpas_analysis.shared.io import write_netcdf
from mpas_analysis.shared.climatology.comparison_descriptors import \
get_comparison_descriptor
[docs]def get_remapper(config, sourceDescriptor, comparisonDescriptor,
mappingFilePrefix, method, logger=None): # {{{
"""
Given config options and descriptions of the source and comparison grids,
returns a ``pyremap.Remapper`` object that can be used to remap from source
files or data sets to corresponding data sets on the comparison grid.
If necessary, creates the mapping file containing weights and indices
needed to perform remapping.
Parameters
----------
config : instance of ``MpasAnalysisConfigParser``
Contains configuration options
sourceDescriptor : ``MeshDescriptor`` subclass object
A description of the source mesh or grid
comparisonDescriptor : ``MeshDescriptor`` subclass object
A description of the comparison grid
mappingFilePrefix : str
A prefix to be prepended to the mapping file name
method : {'bilinear', 'neareststod', 'conserve'}
The method of interpolation used.
logger : ``logging.Logger``, optional
A logger to which ncclimo output should be redirected
Returns
-------
remapper : ``pyremap.Remapper`` object
A remapper that can be used to remap files or data sets from the source
grid or mesh to the comparison grid.
"""
# Authors
# -------
# Xylar Asay-Davis
mappingFileName = None
if not _matches_comparison(sourceDescriptor, comparisonDescriptor):
# we need to remap because the grids don't match
mappingBaseName = '{}_{}_to_{}_{}.nc'.format(
mappingFilePrefix,
sourceDescriptor.meshName,
comparisonDescriptor.meshName,
method)
tryCustom = config.get('diagnostics', 'customDirectory') != 'none'
if tryCustom:
# first see if mapping files are in the custom directory
mappingSubdirectory = build_config_full_path(
config, 'diagnostics', 'mappingSubdirectory',
baseDirectoryOption='customDirectory')
mappingFileName = '{}/{}'.format(mappingSubdirectory,
mappingBaseName)
if not tryCustom or not os.path.exists(mappingFileName):
# second see if mapping files are in the base directory
mappingSubdirectory = build_config_full_path(
config, 'diagnostics', 'mappingSubdirectory',
baseDirectoryOption='baseDirectory')
mappingFileName = '{}/{}'.format(mappingSubdirectory,
mappingBaseName)
if not os.path.exists(mappingFileName):
# we don't have a mapping file yet, so get ready to create one
# in the output subfolder if needed
mappingSubdirectory = \
build_config_full_path(config, 'output',
'mappingSubdirectory')
make_directories(mappingSubdirectory)
mappingFileName = '{}/{}'.format(mappingSubdirectory,
mappingBaseName)
remapper = Remapper(sourceDescriptor, comparisonDescriptor,
mappingFileName)
mpiTasks = config.getWithDefault('execute', 'mapMpiTasks', 1)
mappingSubdirectory = \
build_config_full_path(config, 'output',
'mappingSubdirectory')
make_directories(mappingSubdirectory)
with TemporaryDirectory(dir=mappingSubdirectory) as tempdir:
remapper.build_mapping_file(method=method, logger=logger,
mpiTasks=mpiTasks, tempdir=tempdir)
return remapper # }}}
[docs]def compute_monthly_climatology(ds, calendar=None, maskVaries=True): # {{{
"""
Compute monthly climatologies from a data set. The mean is weighted but
the number of days in each month of the data set, ignoring values masked
out with NaNs. If the month coordinate is not present, a data array
``month`` will be added based on ``Time`` and the provided calendar.
Parameters
----------
ds : xarray.Dataset or xarray.DataArray
A data set with a ``Time`` coordinate expressed as days since
0001-01-01 or ``month`` coordinate
calendar : {'gregorian', 'gregorian_noleap'}, optional
The name of one of the calendars supported by MPAS cores, used to
determine ``month`` from ``Time`` coordinate, so must be supplied if
``ds`` does not already have a ``month`` coordinate or data array
maskVaries : bool, optional
If the mask (where variables in ``ds`` are ``NaN``) varies with time.
If not, the weighted average does not need make extra effort to account
for the mask. Most MPAS fields will have masks that don't vary in
time, whereas observations may sometimes be present only at some
times and not at others, requiring ``maskVaries = True``.
Returns
-------
climatology : object of same type as ``ds``
A data set without the ``'Time'`` coordinate containing the mean
of ds over all months in monthValues, weighted by the number of days
in each month.
"""
# Authors
# -------
# Xylar Asay-Davis
def compute_one_month_climatology(ds):
monthValues = list(ds.month.values)
return compute_climatology(ds, monthValues, calendar, maskVaries)
ds = add_years_months_days_in_month(ds, calendar)
monthlyClimatology = \
ds.groupby('month').map(compute_one_month_climatology)
return monthlyClimatology # }}}
[docs]def compute_climatology(ds, monthValues, calendar=None,
maskVaries=True): # {{{
"""
Compute a monthly, seasonal or annual climatology data set from a data
set. The mean is weighted but the number of days in each month of
the data set, ignoring values masked out with NaNs. If the month
coordinate is not present, a data array ``month`` will be added based
on ``Time`` and the provided calendar.
Parameters
----------
ds : xarray.Dataset or xarray.DataArray
A data set with a ``Time`` coordinate expressed as days since
0001-01-01 or ``month`` coordinate
monthValues : int or array-like of ints
A single month or an array of months to be averaged together
calendar : {'gregorian', 'gregorian_noleap'}, optional
The name of one of the calendars supported by MPAS cores, used to
determine ``month`` from ``Time`` coordinate, so must be supplied if
``ds`` does not already have a ``month`` coordinate or data array
maskVaries : bool, optional
If the mask (where variables in ``ds`` are ``NaN``) varies with time.
If not, the weighted average does not need make extra effort to account
for the mask. Most MPAS fields will have masks that don't vary in
time, whereas observations may sometimes be present only at some
times and not at others, requiring ``maskVaries = True``.
Returns
-------
climatology : object of same type as ``ds``
A data set without the ``'Time'`` coordinate containing the mean
of ds over all months in monthValues, weighted by the number of days
in each month.
"""
# Authors
# -------
# Xylar Asay-Davis
ds = add_years_months_days_in_month(ds, calendar)
mask = xr.zeros_like(ds.month, bool)
for month in monthValues:
mask = numpy.logical_or(mask, ds.month == month)
climatologyMonths = ds.where(mask, drop=True)
climatology = _compute_masked_mean(climatologyMonths, maskVaries)
return climatology # }}}
[docs]def add_years_months_days_in_month(ds, calendar=None): # {{{
'''
Add ``year``, ``month`` and ``daysInMonth`` as data arrays in ``ds``.
The number of days in each month of ``ds`` is computed either using the
``startTime`` and ``endTime`` if available or assuming ``gregorian_noleap``
calendar and ignoring leap years. ``year`` and ``month`` are computed
accounting correctly for the the calendar.
Parameters
----------
ds : ``xarray.Dataset`` or ``xarray.DataArray`` object
A data set with a ``Time`` coordinate expressed as days since
0001-01-01
calendar : {'gregorian', 'gregorian_noleap'}, optional
The name of one of the calendars supported by MPAS cores, used to
determine ``year`` and ``month`` from ``Time`` coordinate
Returns
-------
ds : object of same type as ``ds``
The data set with ``year``, ``month`` and ``daysInMonth`` data arrays
added (if not already present)
'''
# Authors
# -------
# Xylar Asay-Davis
if ('year' in ds.coords and 'month' in ds.coords and
'daysInMonth' in ds.coords):
return ds
ds = ds.copy()
if 'year' not in ds.coords or 'month' not in ds.coords:
if calendar is None:
raise ValueError('calendar must be provided if month and year '
'coordinate is not in ds.')
datetimes = days_to_datetime(ds.Time, calendar=calendar)
if 'year' not in ds.coords:
ds.coords['year'] = ('Time', [date.year for date in datetimes])
if 'month' not in ds.coords:
ds.coords['month'] = ('Time', [date.month for date in datetimes])
if 'daysInMonth' not in ds.coords:
if 'startTime' in ds.coords and 'endTime' in ds.coords:
ds.coords['daysInMonth'] = ds.endTime - ds.startTime
else:
if calendar == 'gregorian':
print('Warning: The MPAS run used the Gregorian calendar '
'but does not appear to have\n'
'supplied start and end times. Climatologies '
'will be computed with\n'
'month durations ignoring leap years.')
daysInMonth = numpy.array(
[constants.daysInMonth[int(month) - 1] for
month in ds.month.values], float)
ds.coords['daysInMonth'] = ('Time', daysInMonth)
return ds # }}}
def remap_and_write_climatology(config, climatologyDataSet,
climatologyFileName, remappedFileName,
remapper, logger=None): # {{{
"""
Given a field in a climatology data set, use the ``remapper`` to remap
horizontal dimensions of all fields, write the results to an output file,
and return the remapped data set.
Note that ``climatologyFileName`` and ``remappedFileName`` will be
overwritten if they exist, so if this behavior is not desired, the calling
code should skip this call if the files exist and simply load the contents
of ``remappedFileName``.
Parameters
----------
config : instance of ``MpasAnalysisConfigParser``
Contains configuration options
climatologyDataSet : ``xarray.DataSet`` or ``xarray.DataArray`` object
A data set containing a climatology
fieldName : str
A field within the climatology to be remapped
climatologyFileName : str
The name of the output file to which the data set should be written
before remapping (if using ncremap).
remappedFileName : str
The name of the output file to which the remapped data set should
be written.
remapper : ``pyremap.Remapper`` object
A remapper that can be used to remap files or data sets to a
comparison grid.
logger : ``logging.Logger``, optional
A logger to which ncclimo output should be redirected
Returns
-------
remappedClimatology : ``xarray.DataSet`` or ``xarray.DataArray`` object
A data set containing the remapped climatology
"""
# Authors
# -------
# Xylar Asay-Davis
useNcremap = config.getboolean('climatology', 'useNcremap')
if remapper.mappingFileName is None:
# no remapping is needed
remappedClimatology = climatologyDataSet
else:
renormalizationThreshold = config.getfloat(
'climatology', 'renormalizationThreshold')
if useNcremap:
if not os.path.exists(climatologyFileName):
write_netcdf(climatologyDataSet, climatologyFileName)
remapper.remap_file(inFileName=climatologyFileName,
outFileName=remappedFileName,
overwrite=True,
renormalize=renormalizationThreshold,
logger=logger)
remappedClimatology = xr.open_dataset(remappedFileName)
else:
remappedClimatology = remapper.remap(climatologyDataSet,
renormalizationThreshold)
write_netcdf(remappedClimatology, remappedFileName)
return remappedClimatology # }}}
[docs]def get_unmasked_mpas_climatology_directory(config, op='avg'): # {{{
"""
Get the directory for an unmasked MPAS climatology produced by ncclimo,
making the directory if it doesn't already exist
Parameters
----------
config : ``MpasAnalysisConfigParser``
configuration options
op : {'avg', 'min', 'max'}
operator for monthly stats
"""
# Authors
# -------
# Xylar Asay-Davis
climatologyOpDirectory = get_climatology_op_directory(config, op)
mpasMeshName = config.get('input', 'mpasMeshName')
directory = '{}/unmasked_{}'.format(climatologyOpDirectory,
mpasMeshName)
make_directories(directory)
return directory # }}}
[docs]def get_unmasked_mpas_climatology_file_name(config, season, componentName,
op='avg'):
# {{{
"""
Get the file name for an unmasked MPAS climatology produced by ncclimo
Parameters
----------
config : ``MpasAnalysisConfigParser``
configuration options
season : str
One of the seasons in ``constants.monthDictionary``
componentName : {'ocean', 'seaIce'}
The MPAS component for which the climatology is being computed
op : {'avg', 'min', 'max'}
operator for monthly stats
"""
# Authors
# -------
# Xylar Asay-Davis
startYear = config.getint('climatology', 'startYear')
endYear = config.getint('climatology', 'endYear')
if componentName == 'ocean':
ncclimoModel = 'mpaso'
elif componentName == 'seaIce':
ncclimoModel = 'mpascice'
else:
raise ValueError('component {} is not supported by ncclimo.\n'
'Check with Charlie Zender and Xylar Asay-Davis\n'
'about getting it added'.format(componentName))
directory = get_unmasked_mpas_climatology_directory(config, op)
make_directories(directory)
monthValues = sorted(constants.monthDictionary[season])
startMonth = monthValues[0]
endMonth = monthValues[-1]
suffix = '{:04d}{:02d}_{:04d}{:02d}_climo'.format(
startYear, startMonth, endYear, endMonth)
if season in constants.abrevMonthNames:
season = '{:02d}'.format(monthValues[0])
fileName = '{}/{}_{}_{}.nc'.format(directory, ncclimoModel,
season, suffix)
return fileName # }}}
[docs]def get_masked_mpas_climatology_file_name(config, season, componentName,
climatologyName, op='avg'): # {{{
"""
Get the file name for a masked MPAS climatology
Parameters
----------
config : ``MpasAnalysisConfigParser``
Configuration options
season : str
One of the seasons in ``constants.monthDictionary``
componentName : {'ocean', 'seaIce'}
The MPAS component for which the climatology is being computed
climatologyName : str
The name of the climatology (typically the name of a field to mask
and later remap)
op : {'avg', 'min', 'max'}
operator for monthly stats
"""
# Authors
# -------
# Xylar Asay-Davis
startYear = config.getint('climatology', 'startYear')
endYear = config.getint('climatology', 'endYear')
mpasMeshName = config.get('input', 'mpasMeshName')
if componentName == 'ocean':
ncclimoModel = 'mpaso'
elif componentName == 'seaIce':
ncclimoModel = 'mpascice'
else:
raise ValueError('component {} is not supported by ncclimo.\n'
'Check with Charlie Zender and Xylar Asay-Davis\n'
'about getting it added'.format(componentName))
climatologyOpDirectory = get_climatology_op_directory(config, op)
stageDirectory = '{}/masked'.format(climatologyOpDirectory)
directory = '{}/{}_{}'.format(
stageDirectory, climatologyName,
mpasMeshName)
make_directories(directory)
monthValues = sorted(constants.monthDictionary[season])
startMonth = monthValues[0]
endMonth = monthValues[-1]
suffix = '{:04d}{:02d}_{:04d}{:02d}_climo'.format(
startYear, startMonth, endYear, endMonth)
if season in constants.abrevMonthNames:
season = '{:02d}'.format(monthValues[0])
fileName = '{}/{}_{}_{}.nc'.format(
directory, ncclimoModel, season, suffix)
return fileName # }}}
[docs]def get_remapped_mpas_climatology_file_name(config, season, componentName,
climatologyName,
comparisonGridName,
op='avg'): # {{{
"""
Get the file name for a masked MPAS climatology
Parameters
----------
config : ``MpasAnalysisConfigParser``
Configuration options
season : str
One of the seasons in ``constants.monthDictionary``
componentName : {'ocean', 'seaIce'}
The MPAS component for which the climatology is being computed
climatologyName : str
The name of the climatology (typically the name of a field to mask
and later remap)
comparisonGridName : str
The name of the comparison grid to use for remapping. If it is one
of the default comparison grid names ``{'latlon', 'antarctic',
'arctic'}``, the full grid name is looked up via
get_comparison_descriptor
op : {'avg', 'min', 'max'}
operator for monthly stats
"""
# Authors
# -------
# Xylar Asay-Davis
startYear = config.getint('climatology', 'startYear')
endYear = config.getint('climatology', 'endYear')
mpasMeshName = config.get('input', 'mpasMeshName')
if componentName == 'ocean':
ncclimoModel = 'mpaso'
elif componentName == 'seaIce':
ncclimoModel = 'mpascice'
else:
raise ValueError('component {} is not supported by ncclimo.\n'
'Check with Charlie Zender and Xylar Asay-Davis\n'
'about getting it added'.format(componentName))
climatologyOpDirectory = get_climatology_op_directory(config, op)
if comparisonGridName in ['latlon', 'antarctic', 'arctic']:
comparisonDescriptor = get_comparison_descriptor(config,
comparisonGridName)
comparisonFullMeshName = comparisonDescriptor.meshName
else:
comparisonFullMeshName = comparisonGridName
stageDirectory = '{}/remapped'.format(climatologyOpDirectory)
directory = '{}/{}_{}_to_{}'.format(stageDirectory, climatologyName,
mpasMeshName, comparisonFullMeshName)
make_directories(directory)
monthValues = sorted(constants.monthDictionary[season])
startMonth = monthValues[0]
endMonth = monthValues[-1]
suffix = '{:04d}{:02d}_{:04d}{:02d}_climo'.format(
startYear, startMonth, endYear, endMonth)
if season in constants.abrevMonthNames:
season = '{:02d}'.format(monthValues[0])
fileName = '{}/{}_{}_{}.nc'.format(
directory, ncclimoModel, season, suffix)
return fileName # }}}
def get_climatology_op_directory(config, op='avg'):
'''
Get the output directory for MPAS climatologies from output with the given
monthly operator: avg, min or max
'''
climatologyBaseDirectory = build_config_full_path(
config, 'output', 'mpasClimatologySubdirectory')
return '{}/{}'.format(climatologyBaseDirectory, op)
def _compute_masked_mean(ds, maskVaries): # {{{
'''
Compute the time average of data set, masked out where the variables in ds
are NaN and, if ``maskVaries == True``, weighting by the number of days
used to compute each monthly mean time in ds.
'''
# Authors
# -------
# Xylar Asay-Davis
def ds_to_weights(ds):
# make an identical data set to ds but replacing all data arrays with
# nonnull applied to that data array
weights = ds.copy(deep=True)
if isinstance(ds, xr.core.dataarray.DataArray):
weights = ds.notnull()
elif isinstance(ds, xr.core.dataset.Dataset):
for var in ds.data_vars:
weights[var] = ds[var].notnull()
else:
raise TypeError('ds must be an instance of either xarray.Dataset '
'or xarray.DataArray.')
return weights
if maskVaries:
dsWeightedSum = (ds * ds.daysInMonth).sum(dim='Time', keep_attrs=True)
weights = ds_to_weights(ds)
weightSum = (weights * ds.daysInMonth).sum(dim='Time')
timeMean = dsWeightedSum / weightSum.where(weightSum > 0.)
else:
days = ds.daysInMonth.sum(dim='Time')
dsWeightedSum = (ds * ds.daysInMonth).sum(dim='Time', keep_attrs=True)
timeMean = dsWeightedSum / days.where(days > 0.)
return timeMean # }}}
def _matches_comparison(obsDescriptor, comparisonDescriptor): # {{{
'''
Determine if the two meshes are the same
'''
# Authors
# -------
# Xylar Asay-Davis
if isinstance(obsDescriptor, ProjectionGridDescriptor) and \
isinstance(comparisonDescriptor, ProjectionGridDescriptor):
# pretty hard to determine if projections are the same, so we'll rely
# on the grid names
match = obsDescriptor.meshName == comparisonDescriptor.meshName and \
len(obsDescriptor.x) == len(comparisonDescriptor.x) and \
len(obsDescriptor.y) == len(comparisonDescriptor.y) and \
numpy.all(numpy.isclose(obsDescriptor.x,
comparisonDescriptor.x)) and \
numpy.all(numpy.isclose(obsDescriptor.y,
comparisonDescriptor.y))
elif isinstance(obsDescriptor, LatLonGridDescriptor) and \
isinstance(comparisonDescriptor, LatLonGridDescriptor):
match = ((('degree' in obsDescriptor.units and
'degree' in comparisonDescriptor.units) or
('radian' in obsDescriptor.units and
'radian' in comparisonDescriptor.units)) and
len(obsDescriptor.lat) == len(comparisonDescriptor.lat) and
len(obsDescriptor.lon) == len(comparisonDescriptor.lon) and
numpy.all(numpy.isclose(obsDescriptor.lat,
comparisonDescriptor.lat)) and
numpy.all(numpy.isclose(obsDescriptor.lon,
comparisonDescriptor.lon)))
else:
match = False
return match # }}}
def _setup_climatology_caching(ds, startYearClimo, endYearClimo,
yearsPerCacheFile, cachePrefix,
monthValues): # {{{
'''
Determine which cache files already exist, which are incomplete and which
years are present in each cache file (whether existing or to be created).
'''
# Authors
# -------
# Xylar Asay-Davis
cacheInfo = []
cacheIndices = -1 * numpy.ones(ds.dims['Time'], int)
monthsInDs = ds.month.values
yearsInDs = ds.year.values
# figure out which files to load and which years go in each file
for firstYear in range(startYearClimo, endYearClimo + 1,
yearsPerCacheFile):
years = range(firstYear, firstYear + yearsPerCacheFile)
yearString, fileSuffix = _get_year_string(years[0], years[-1])
outputFileClimo = '{}_{}.nc'.format(cachePrefix, fileSuffix)
done = False
if os.path.exists(outputFileClimo):
# already cached
dsCached = None
try:
dsCached = xr.open_dataset(outputFileClimo)
except IOError:
# assuming the cache file is corrupt, so deleting it.
print('Warning: Deleting cache file {}, which appears to '
'have been corrupted.'.format(outputFileClimo))
os.remove(outputFileClimo)
monthsIfDone = len(monthValues) * len(years)
if ((dsCached is not None) and
(dsCached.attrs['totalMonths'] == monthsIfDone)):
# also complete, so we can move on
done = True
if dsCached is not None:
dsCached.close()
cacheIndex = len(cacheInfo)
for year in years:
for month in monthValues:
mask = numpy.logical_and(yearsInDs == year,
monthsInDs == month)
cacheIndices[mask] = cacheIndex
if numpy.count_nonzero(cacheIndices == cacheIndex) == 0:
continue
cacheInfo.append((outputFileClimo, done, yearString))
ds = ds.copy()
ds.coords['cacheIndices'] = ('Time', cacheIndices)
return cacheInfo, cacheIndices # }}}
def _cache_individual_climatologies(ds, cacheInfo, printProgress,
yearsPerCacheFile, monthValues,
calendar): # {{{
'''
Cache individual climatologies for later aggregation.
'''
# Authors
# -------
# Xylar Asay-Davis
for cacheIndex, info in enumerate(cacheInfo):
outputFileClimo, done, yearString = info
if done:
continue
dsYear = ds.where(ds.cacheIndices == cacheIndex, drop=True)
if printProgress:
print(' {}'.format(yearString))
totalDays = dsYear.daysInMonth.sum(dim='Time').values
monthCount = dsYear.dims['Time']
climatology = compute_climatology(dsYear, monthValues, calendar,
maskVaries=False)
climatology.attrs['totalDays'] = totalDays
climatology.attrs['totalMonths'] = monthCount
climatology.attrs['fingerprintClimo'] = fingerprint_generator()
write_netcdf(climatology, outputFileClimo)
climatology.close()
# }}}
def _cache_aggregated_climatology(startYearClimo, endYearClimo, cachePrefix,
printProgress, monthValues,
cacheInfo): # {{{
'''
Cache aggregated climatology from individual climatologies.
'''
# Authors
# -------
# Xylar Asay-Davis
yearString, fileSuffix = _get_year_string(startYearClimo, endYearClimo)
outputFileClimo = '{}_{}.nc'.format(cachePrefix, fileSuffix)
done = False
if len(cacheInfo) == 0:
climatology = None
done = True
if os.path.exists(outputFileClimo):
# already cached
climatology = None
try:
climatology = xr.open_dataset(outputFileClimo)
except IOError:
# assuming the cache file is corrupt, so deleting it.
print('Warning: Deleting cache file {}, which appears to have '
'been corrupted.'.format(outputFileClimo))
os.remove(outputFileClimo)
if len(cacheInfo) == 1 and outputFileClimo == cacheInfo[0][0]:
# theres only one cache file and it already has the same name
# as the aggregated file so no need to aggregate
done = True
elif climatology is not None:
monthsIfDone = (
endYearClimo - startYearClimo + 1) * len(monthValues)
if climatology.attrs['totalMonths'] == monthsIfDone:
# also complete, so we can move on
done = True
else:
climatology.close()
if not done:
if printProgress:
print(' Computing aggregated climatology '
'{}...'.format(yearString))
first = True
for cacheIndex, info in enumerate(cacheInfo):
inFileClimo = info[0]
ds = xr.open_dataset(inFileClimo)
days = ds.attrs['totalDays']
months = ds.attrs['totalMonths']
if first:
totalDays = days
totalMonths = months
climatology = ds * days
first = False
else:
totalDays += days
totalMonths += months
climatology = climatology + ds * days
ds.close()
climatology = climatology / totalDays
climatology.attrs['totalDays'] = totalDays
climatology.attrs['totalMonths'] = totalMonths
climatology.attrs['fingerprintClimo'] = fingerprint_generator()
write_netcdf(climatology, outputFileClimo)
return climatology # }}}
def _get_year_string(startYear, endYear):
if startYear == endYear:
yearString = '{:04d}'.format(startYear)
fileSuffix = 'year{}'.format(yearString)
else:
yearString = '{:04d}-{:04d}'.format(startYear, endYear)
fileSuffix = 'years{}'.format(yearString)
return yearString, fileSuffix
# vim: foldmethod=marker ai ts=4 sts=4 et sw=4 ft=python