def test_varweighted_mean_period_dim(PM_da_control_3d):
"""Test varweighted_mean_period for different dims."""
for d in PM_da_control_3d.dims:
# single dim
varweighted_mean_period(PM_da_control_3d, dim=d)
# all but one dim
di = [di for di in PM_da_control_3d.dims if di != d]
varweighted_mean_period(PM_da_control_3d, dim=di)
def test_bootstrap_vwmp_sig50_similar_vwmp(PM_da_control_3d):
sig = 50
actual = varweighted_mean_period_threshold(PM_da_control_3d,
iterations=ITERATIONS,
sig=sig).drop_vars("quantile")
expected = varweighted_mean_period(PM_da_control_3d)
xr.testing.assert_allclose(actual, expected, atol=2, rtol=0.5)
def test_bootstrap_vwmp_sig50_similar_vwmp(control_3d_NA):
ds = control_3d_NA
bootstrap = 5
sig = 50
actual = varweighted_mean_period_threshold(ds, bootstrap=bootstrap, sig=sig).drop(
'quantile'
)
expected = varweighted_mean_period(ds)
xr.testing.assert_allclose(actual, expected, atol=2, rtol=0.5)
def vwmp_bootstrap(control, l=100, bootstrap=10, sig=99):
"""Bootstrap variance weighted mean period from control. Masked areas are non-significant. Rejected at 1-sig% level."""
from climpred.bootstrap import varweighted_mean_period_threshold
d = []
for _ in range(int(bootstrap / 5)):
r = np.random.randint(0, control.time.size - l)
d.append(varweighted_mean_period(control.isel(time=slice(r, r + l))))
data = xr.concat(d, 'bootstrap')
data['lon'] = control.lon
data['lat'] = control.lat
vwmp = data.mean('bootstrap')
threshold = varweighted_mean_period_threshold(
control, sig=sig, bootstrap=bootstrap)
threshold['lon'] = control.lon
threshold['lat'] = control.lat
vwmp = vwmp.where(vwmp > threshold)
vwmp.name = 'Period [years]'
return vwmp
