def test_time_slice(directory, sourcefiles, sd, ed):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
ens = cd.time_slice(ens, start_date=sd, end_date=ed)
data = ctt.sha(ens)
return data
def test(directory, sourcefiles, cwd):
""" Makes a file in /testdata/ name testclasses_file.txt whcih has the ensemble info
after using match ensembles and match_realizations
"""
# first change to a scratch dir and link in test data. The paths are CCCma specific.
ctt.loadtestfiles(directory, sourcefiles)
f = cwd + '/testdata/testclasses_file.txt'
# =========================================================================
# mkensemble
# =========================================================================
fp = '*historical*'
ens = cd.mkensemble(fp, prefix='')
ens.fulldetails_tofile(f)
# Create a second cmipdata ensemble
fp2 = '*ts*'
ens2 = cd.mkensemble(fp2, prefix='')
ens.fulldetails_tofile(f)
# =========================================================================
# match_ensembles
# =========================================================================
print 'Matching ensembles...'
enso1, enso2 = cd.match_ensembles(ens, ens2)
enso1.sinfo()
enso2.sinfo()
ens.fulldetails_tofile(f)
# =========================================================================
# match_realizations
# =========================================================================
print 'Matching realizations...'
ensem1, ensem2 = cd.match_realizations(ens, ens2)
ensem1.sinfo()
ensem2.sinfo()
ens.fulldetails_tofile(f)
def test_zonal_mean(directory, sourcefiles):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = "*.nc"
ens = cd.mkensemble(filepattern)
ens = cd.zonmean(ens, delete=True)
data = ctt.sha(ens)
return data
def test_cat_experiments(directory, sourcefiles, var, e1name, e2name):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = "*.nc"
ens = cd.mkensemble(filepattern)
ens = cd.cat_experiments(ens, var, exp1_name=e1name, exp2_name=e2name)
data = ctt.sha(ens)
return data
def test_remap(directory, sourcefiles):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
ens = cd.remap(ens, remap='r1x180')
data = ctt.sha(ens)
return data
def test_1dtimeseries(directory, sourcefiles, var, cwd):
""" Tests plot_realizations_1d for a timeseries plot
Parameters
----------
directory : string
the directory to link the files to
sourcefiles : list
a list of the locations of the files to link
var : string
the variable plotted
cwd : string
directory of the test modules
"""
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
data = cd.loadfiles(ens, var, cdostr='-fldmean -yearmean', toDatetime=True)
fig, axt = plt.subplots(1, 1, figsize=(8, 8))
cd.plot_realizations_1d(data,
var,
'time',
ax=axt,
pdf='tsplot',
kwargs={
'color': 'r',
'alpha': 0.3
})
plotcompare(directory, cwd, 'tsplot')
os.system('rm tsplot.pdf')
def test_cat_experiments(directory, sourcefiles, var, e1name, e2name):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
ens = cd.cat_experiments(ens, var, exp1_name=e1name, exp2_name=e2name)
data = ctt.sha(ens)
return data
def test_zonal_mean(directory, sourcefiles):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
ens = cd.zonmean(ens, delete=True)
data = ctt.sha(ens)
return data
def test_climatology(directory, sourcefiles):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
ens = cd.climatology(ens)
data = ctt.sha(ens)
return data
def test_areamean(directory, sourcefiles):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = "*.nc"
ens = cd.mkensemble(filepattern)
ens = cd.areamean(ens)
data = ctt.sha(ens)
return data
def test_remap(directory, sourcefiles):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = "*.nc"
ens = cd.mkensemble(filepattern)
ens = cd.remap(ens, remap="r1x180")
data = ctt.sha(ens)
return data
def test_areamean(directory, sourcefiles):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
ens = cd.areamean(ens)
data = ctt.sha(ens)
return data
def test_time_slice(directory, sourcefiles, sd, ed):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = "*.nc"
ens = cd.mkensemble(filepattern)
ens = cd.time_slice(ens, start_date=sd, end_date=ed)
data = ctt.sha(ens)
return data
def test(directory, sourcefiles, cwd):
""" Makes a file in /testdata/ name testclasses_file.txt whcih has the ensemble info
after using match ensembles and match_realizations
"""
# first change to a scratch dir and link in test data. The paths are CCCma specific.
ctt.loadtestfiles(directory,sourcefiles)
f = cwd + '/testdata/testclasses_file.txt'
# =========================================================================
# mkensemble
# =========================================================================
fp = '*historical*'
ens = cd.mkensemble(fp, prefix='')
ens.fulldetails_tofile(f)
# Create a second cmipdata ensemble
fp2 = '*ts*'
ens2 = cd.mkensemble(fp2, prefix='')
ens.fulldetails_tofile(f)
# =========================================================================
# match_ensembles
# =========================================================================
print 'Matching ensembles...'
enso1, enso2 = cd.match_ensembles(ens, ens2)
enso1.sinfo()
enso2.sinfo()
ens.fulldetails_tofile(f)
# =========================================================================
# match_realizations
# =========================================================================
print 'Matching realizations...'
ensem1, ensem2 = cd.match_realizations(ens, ens2)
ensem1.sinfo()
ensem2.sinfo()
ens.fulldetails_tofile(f)
def test_ensembleenvelope(directory, sourcefiles, var, sd, ed, cwd):
""" Tests ensemble_envelope_timeseries
Parameters
----------
directory : string
the directory to link the files to
sourcefiles : list
a list of the locations of the files to link
var : string
the variable plotted
sd : int
start date for time slice
ed : int
end date for time slice
cwd : string
directory of the test modules
"""
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
ens = cd.time_slice(ens, sd, ed)
ens = cd.remap(ens, 'r360x180', delete=True)
ens = cd.my_operator(ens, my_cdo_str='cdo -fldmean -yearmean {infile} {outfile}', output_prefix='processed_', delete=True)
cd.ens_stats(ens, var)
cd.ensemble_envelope_timeseries(ens, 'ENS-MEAN_processed_remap_ts_Amon_CanESM2_historical_R-MEAN_195001-200012.nc', 'ENS-STD_processed_remap_ts_Amon_CanESM2_historical_STD_195001-200012.nc', var, pdf='ensenv')
plotcompare(directory, cwd, 'ensenv')
os.system('rm ensenv.pdf')
def test_cat_exp_slices(directory, sourcefiles):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = "*.nc"
ens = cd.mkensemble(filepattern)
ens = cd.cat_exp_slices(ens)
data = ctt.sha(ens)
return data
def test_cat_exp_slices(directory, sourcefiles):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
ens = cd.cat_exp_slices(ens)
data = ctt.sha(ens)
return data
def test_climatology(directory, sourcefiles):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = "*.nc"
ens = cd.mkensemble(filepattern)
ens = cd.climatology(ens)
data = ctt.sha(ens)
return data
def test_time_anomaly(directory, sourcefiles, sd, ed):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
ens = cd.time_anomaly(ens, start_date=sd, end_date=ed, delete=True)
ens.fulldetails()
data = ctt.sha(ens)
return data
def test_loadingtools(directory, sourcefiles, var):
""" Loads the data from a set of files
"""
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
data = hashlib.sha1(cd.loadfiles(ens,var)['data']).hexdigest()
return {var: data}
def test_time_anomaly(directory, sourcefiles, sd, ed):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = "*.nc"
ens = cd.mkensemble(filepattern)
ens = cd.time_anomaly(ens, start_date=sd, end_date=ed, delete=True)
ens.fulldetails()
data = ctt.sha(ens)
return data
def test_ens_stats(directory, sourcefiles, var):
ctt.loadtestfiles(directory, [sourcefiles[0]])
filepattern = "*.nc"
ens = cd.mkensemble(filepattern)
cd.ens_stats(ens, var)
filepattern = "ENS*"
ens = cd.mkensemble(filepattern)
data = ctt.sha(ens)
return data
def test_ens_stats(directory, sourcefiles, var):
ctt.loadtestfiles(directory, [sourcefiles[0]])
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
cd.ens_stats(ens, var)
filepattern = 'ENS*'
ens = cd.mkensemble(filepattern)
data = ctt.sha(ens)
return data
def test_my_operator(directory, sourcefiles, var, e1name, e2name):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = "*.nc"
ens = cd.mkensemble(filepattern)
ens = cd.cat_experiments(ens, var, exp1_name=e1name, exp2_name=e2name)
filepattern = "*.nc"
ens = cd.mkensemble(filepattern)
my_cdo_str = "cdo remapdis,r1x180 -zonmean -seldate,1950-01-01,2000-12-31\
-sub {infile} -timmean -seldate,1950-01-01,2000-12-31 {infile}\
{outfile}"
ens = cd.my_operator(ens, my_cdo_str, output_prefix="test_", delete=True)
data = ctt.sha(ens)
return data
def test_my_operator(directory, sourcefiles, var, e1name, e2name):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
ens = cd.cat_experiments(ens, var, exp1_name=e1name, exp2_name=e2name)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
my_cdo_str = 'cdo remapdis,r1x180 -zonmean -seldate,1950-01-01,2000-12-31\
-sub {infile} -timmean -seldate,1950-01-01,2000-12-31 {infile}\
{outfile}'
ens = cd.my_operator(ens, my_cdo_str, output_prefix='test_', delete=True)
data = ctt.sha(ens)
return data
def test_1dtimeseries(directory, sourcefiles, var, cwd):
""" Tests plot_realizations_1d for a timeseries plot
Parameters
----------
directory : string
the directory to link the files to
sourcefiles : list
a list of the locations of the files to link
var : string
the variable plotted
cwd : string
directory of the test modules
"""
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
data = cd.loadfiles(ens, var, cdostr='-fldmean -yearmean', toDatetime=True)
fig, axt = plt.subplots(1, 1, figsize=(8, 8))
cd.plot_realizations_1d(data, var, 'time', ax=axt, pdf='tsplot',
kwargs={'color': 'r', 'alpha': 0.3})
plotcompare(directory, cwd, 'tsplot')
os.system('rm tsplot.pdf')
def test_ensembleenvelope(directory, sourcefiles, var, sd, ed, cwd):
""" Tests ensemble_envelope_timeseries
Parameters
----------
directory : string
the directory to link the files to
sourcefiles : list
a list of the locations of the files to link
var : string
the variable plotted
sd : int
start date for time slice
ed : int
end date for time slice
cwd : string
directory of the test modules
"""
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
ens = cd.time_slice(ens, sd, ed)
ens = cd.remap(ens, 'r360x180', delete=True)
ens = cd.my_operator(
ens,
my_cdo_str='cdo -fldmean -yearmean {infile} {outfile}',
output_prefix='processed_',
delete=True)
cd.ens_stats(ens, var)
cd.ensemble_envelope_timeseries(
ens,
'ENS-MEAN_processed_remap_ts_Amon_CanESM2_historical_R-MEAN_195001-200012.nc',
'ENS-STD_processed_remap_ts_Amon_CanESM2_historical_STD_195001-200012.nc',
var,
pdf='ensenv')
plotcompare(directory, cwd, 'ensenv')
os.system('rm ensenv.pdf')
def test_starting_files(directory, sourcefiles):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = '*.nc'
ens = cd.mkensemble(filepattern)
data = ctt.sha(ens)
return data
def test_starting_files(directory, sourcefiles):
ctt.loadtestfiles(directory, sourcefiles)
filepattern = "*.nc"
ens = cd.mkensemble(filepattern)
data = ctt.sha(ens)
return data