def test_ensoindices_on_irregular_series(self):
"Try setting ensoindices on a non-regular series"
ensoi = self.ensoi
ensoi.set_monthly_indices(minimum_size=5, reference_season='NDJ')
#
bseries = climate_series(range(24),
start_date=ts.Date('M', '1982-01'),
ensoindicator=ensoi)
control_indices = [-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]
control_indices = ts.time_series(control_indices,
start_date=bseries.dates[0])
tseries = bseries[::2]
assert_equal(tseries.ensoindices, control_indices[::2])
mseries = climate_series(tseries, ensoindicator=bseries.ensoindicator)
assert_equal(mseries.ensoindices, bseries.ensoindices[::2])
mseries = climate_series(tseries, ensoindicator=bseries.ensoindicator)
def test_ensoindices_on_empty_series(self):
"Try on an empty series"
ensoi = self.ensoi
ensoi.set_monthly_indices(minimum_size=5, reference_season='NDJ')
bseries = climate_series(range(24),
start_date=hydro.Date('M', '1982-01'),
ensoindicator=ensoi)
bseries = bseries[[]]
assert (bseries.ensoindicator is None)
def test_ensoindices_on_empty_series(self):
"Try on an empty series"
ensoi = self.ensoi
ensoi.set_monthly_indices(minimum_size=5, reference_season='NDJ')
bseries = climate_series(range(24),
start_date=hydro.Date('M', '1982-01'),
ensoindicator=ensoi)
bseries = bseries[[]]
assert(bseries.ensoindicator is None)
def test_ensoindices_on_irregular_series(self):
"Try setting ensoindices on a non-regular series"
ensoi = self.ensoi
ensoi.set_monthly_indices(minimum_size=5, reference_season='NDJ')
#
bseries = climate_series(range(24),
start_date=ts.Date('M', '1982-01'),
ensoindicator=ensoi)
control_indices = [
-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1
]
control_indices = ts.time_series(control_indices,
start_date=bseries.dates[0])
tseries = bseries[::2]
assert_equal(tseries.ensoindices, control_indices[::2])
mseries = climate_series(tseries, ensoindicator=bseries.ensoindicator)
assert_equal(mseries.ensoindices, bseries.ensoindices[::2])
mseries = climate_series(tseries, ensoindicator=bseries.ensoindicator)
def test_ensoindices_from_daily_indicator(self):
"Try setting ensoindices from a daily indicator"
ensoi = self.ensoi
ensoi.set_monthly_indices(minimum_size=5, reference_season='NDJ')
#
bseries = climate_series(range(24),
start_date=ts.Date('M', '1982-01'),
ensoindicator=ensoi)
control_indices = [-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]
control_indices = ts.time_series(control_indices,
start_date=bseries.dates[0])
# Basic check
assert_equal(bseries.ensoindices, control_indices)
# Define daily series
dseries = climate_series(range(730),
start_date=ts.Date('D', '1982-01-01'),
ensoindicator=ensoi)
mseries = climate_series(range(24),
start_date=ts.Date('M', '1982-01'),
ensoindicator=dseries.ensoindicator)
assert_equal(mseries.ensoindices, bseries.ensoindices)
def wet_spells(rainfall, threshold=0, start=True, mode='both'):
"""
Compute the wet spells for a series of precipitations
Parameters
----------
rainfall : TimeSeries
TimeSeries of precipitations.
threshold : float, optional
Minimum amount of precipitation defining a wet day.
start : boolean, optional
Whether the spells are associated with the first or last day of the spell.
mode : {'durations', 'intensities', 'all'}
Whether to return only durations, intensities or both.
Returns
-------
wet_spells : TimeSeries
A :class:`TimeSeries` giving the duration and/or intensity of a spell
at either the first or last date.
"""
rdates = getattr(rainfall, 'dates', None)
rdata = ma.masked_array(rainfall, subok=False)
condition = ma.masked_less(rdata, threshold)
slices = ma.clump_unmasked(condition)
# Get the durations and starting dates of each spell
mode = (mode or 'both').lower()[0]
if mode == 'd':
# Durations
result = np.array([s.stop - s.start for s in slices], dtype=int)
elif mode == 'i':
# Intensities
result = np.array([rdata[s].sum() for s in slices])
else:
durations = [s.stop - s.start for s in slices]
intensities = [rdata[s].sum() for s in slices]
result = np.array(zip(durations, intensities),
dtype=[('durations', int), ('intensities', float)],)
if rdates is None:
return result
if start:
dates = rdates[[s.start for s in slices]]
else:
dates = rdates[[s.stop - 1 for s in slices]]
ensoi = getattr(rainfall, 'ensoindicator', None)
if mode in 'id':
spells = enso.climate_series(result, dates=dates, ensoindicator=ensoi)
else:
spells = enso.climate_records(result, dates=dates, ensoindicator=ensoi)
return spells
def test_ensoindices_from_daily_indicator(self):
"Try setting ensoindices from a daily indicator"
ensoi = self.ensoi
ensoi.set_monthly_indices(minimum_size=5, reference_season='NDJ')
#
bseries = climate_series(range(24),
start_date=ts.Date('M', '1982-01'),
ensoindicator=ensoi)
control_indices = [
-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1
]
control_indices = ts.time_series(control_indices,
start_date=bseries.dates[0])
# Basic check
assert_equal(bseries.ensoindices, control_indices)
# Define daily series
dseries = climate_series(range(730),
start_date=ts.Date('D', '1982-01-01'),
ensoindicator=ensoi)
mseries = climate_series(range(24),
start_date=ts.Date('M', '1982-01'),
ensoindicator=dseries.ensoindicator)
assert_equal(mseries.ensoindices, bseries.ensoindices)
def test_set_ensoindices(self):
"Test setting indices"
ONI = load_oni('standard')
mseries = climate_series(np.random.rand(120),
start_date=ts.Date('M', '1991-01'),
ensoindicator=ONI)
control = ONI['1991-01':'2001-01']
assert_equal(mseries.ensoindices, control.indices)
test = mseries.set_ensoindices(full_year=True).copy()
self.failUnless(mseries.ensoindicator.optinfo['full_year'])
assert_equal(test, control.set_indices(full_year=True))
self.failUnless(control.optinfo['full_year'])
assert_equal(test, mseries.ensoindices)
assert_equal(mseries.set_ensoindices(lag=6), test.tshift(-6))
#
mseries.set_ensoindices(full_year=True, lag=0)
series = ts.lib.backward_fill(mseries.convert('D'))
series_m = series.convert('M', ma.mean)
assert_equal(test, series_m.ensoindices)
def test_set_ensoindices(self):
"Test setting indices"
ONI = load_oni('standard')
mseries = climate_series(np.random.rand(120),
start_date=ts.Date('M','1991-01'),
ensoindicator=ONI)
control = ONI['1991-01':'2001-01']
assert_equal(mseries.ensoindices, control.indices)
test = mseries.set_ensoindices(full_year=True).copy()
self.failUnless(mseries.ensoindicator.optinfo['full_year'])
assert_equal(test,
control.set_indices(full_year=True))
self.failUnless(control.optinfo['full_year'])
assert_equal(test, mseries.ensoindices)
assert_equal(mseries.set_ensoindices(lag=6), test.tshift(-6))
#
mseries.set_ensoindices(full_year=True, lag=0)
series = ts.lib.backward_fill(mseries.convert('D'))
series_m = series.convert('M', ma.mean)
assert_equal(test, series_m.ensoindices)
def dry_spells(rainfall, threshold=0.254, start=True):
"""
Compute the dry spells for a series of precipitations
Parameters
----------
rainfall : TimeSeries
TimeSeries of precipitations.
threshold : float, optional
Minimum amount of precipitation defining a wet day.
start : boolean, optional
Whether the spells are associated with the first or last day of the spell.
Returns
-------
dry_spells : TimeSeries
A :class:`TimeSeries` giving the duration of the spell at either the first
or last date.
"""
rdates = getattr(rainfall, 'dates', None)
rdata = ma.masked_array(rainfall, subok=False)
condition = ma.masked_greater(rdata, threshold)
slices = ma.clump_unmasked(condition)
# Get the durations and starting dates of each spell
durations = [s.stop - s.start for s in slices]
# Give up if we have no dates
if rdates is None:
return np.array(durations)
# Get the dates, then...
if start:
dates = rdates[[s.start for s in slices]]
else:
dates = rdates[[s.stop - 1 for s in slices]]
ensoi = getattr(rainfall, 'ensoindicator', None)
spells = enso.climate_series(durations, dates=dates, ensoindicator=ensoi)
return spells
