def test_e2c(PM_da_ds1d):
"""Test ensemble_mean-to-control (which can be any other one member) (e2c) comparison basic functionality.
Clean comparison: Remove one control member from ensemble to use as reference. Take the remaining member mean as forecasts."""
ds = PM_da_ds1d
aforecast, areference = _e2c(ds)
control_member = [0]
supervector_dim = 'svd'
reference = ds.isel(member=control_member).squeeze()
if 'member' in reference.coords:
del reference['member']
reference = reference.rename({'init': supervector_dim})
# drop the member being reference
ds = _drop_members(ds, rmd_member=[ds.member.values[control_member]])
forecast = ds.mean('member')
forecast = forecast.rename({'init': supervector_dim})
eforecast, ereference = forecast, reference
# very weak testing on shape
assert eforecast.size == aforecast.size
assert ereference.size == areference.size
assert_equal(eforecast, aforecast)
assert_equal(ereference, areference)
def test_m2e(PM_da_ds1d):
"""Test many-to-ensemble-mean (m2e) comparison basic functionality.
Clean comparison: Remove one member from ensemble to use as reference. Take the remaining members as forecasts."""
ds = PM_da_ds1d
aforecast, areference = _m2e(ds)
supervector_dim = 'svd'
reference_list = []
forecast_list = []
for m in ds.member.values:
forecast = _drop_members(ds, rmd_member=[m]).mean('member')
reference = ds.sel(member=m).squeeze()
forecast, reference = xr.broadcast(forecast, reference)
forecast_list.append(forecast)
reference_list.append(reference)
reference = xr.concat(reference_list,
'init').rename({'init': supervector_dim})
forecast = xr.concat(forecast_list,
'init').rename({'init': supervector_dim})
eforecast, ereference = forecast, reference
# very weak testing on shape
assert eforecast.size == aforecast.size
assert ereference.size == areference.size
assert_equal(eforecast, aforecast)
assert_equal(ereference, areference)
def test_m2m(PM_da_ds1d):
"""Test many-to-many (m2m) comparison basic functionality.
Clean comparison: Remove one member from ensemble to use as reference. Take the remaining members as forecasts."""
ds = PM_da_ds1d
aforecast, areference = _m2m(ds)
supervector_dim = 'svd'
reference_list = []
forecast_list = []
for m in ds.member.values:
# drop the member being reference
ds_reduced = _drop_members(ds, rmd_member=[m])
reference = ds.sel(member=m)
for m2 in ds_reduced.member:
for i in ds.init:
reference_list.append(reference.sel(init=i))
forecast_list.append(ds_reduced.sel(member=m2, init=i))
reference = xr.concat(reference_list, supervector_dim)
reference[supervector_dim] = np.arange(1, 1 + reference.svd.size)
forecast = xr.concat(forecast_list, supervector_dim)
forecast[supervector_dim] = np.arange(1, 1 + forecast.svd.size)
eforecast, ereference = forecast, reference
# very weak testing here
assert eforecast.size == aforecast.size
assert ereference.size == areference.size
def test_m2e(PM_da_initialized_1d):
"""Test many-to-ensemble-mean (m2e) comparison basic functionality.
Clean comparison: Remove one member from ensemble to use as reference.
Take the remaining members as forecasts."""
ds = PM_da_initialized_1d
aforecast, areference = __m2e.function(ds, metric=metric)
reference_list = []
forecast_list = []
for m in ds.member.values:
forecast = _drop_members(ds, removed_member=[m]).mean('member')
reference = ds.sel(member=m).squeeze()
forecast, reference = xr.broadcast(forecast, reference)
forecast_list.append(forecast)
reference_list.append(reference)
reference = xr.concat(reference_list, 'member')
forecast = xr.concat(forecast_list, 'member')
forecast['member'] = np.arange(forecast.member.size)
reference['member'] = np.arange(reference.member.size)
eforecast, ereference = forecast, reference
# very weak testing on shape
assert eforecast.size == aforecast.size
assert ereference.size == areference.size
assert_equal(eforecast, aforecast)
assert_equal(ereference, areference)
def test_m2m(PM_da_initialized_1d):
"""Test many-to-many (m2m) comparison basic functionality.
Clean comparison: Remove one member from ensemble to use as reference. Take the
remaining members as forecasts."""
ds = PM_da_initialized_1d
aforecast, areference = __m2m.function(ds, metric=metric)
reference_list = []
forecast_list = []
for m in ds.member.values:
forecast = _drop_members(ds, removed_member=[m])
forecast['member'] = np.arange(1, 1 + forecast.member.size)
reference = ds.sel(member=m).squeeze()
forecast, reference = xr.broadcast(forecast, reference)
reference_list.append(reference)
forecast_list.append(forecast)
supervector_dim = 'forecast_member'
reference = xr.concat(reference_list, supervector_dim)
forecast = xr.concat(forecast_list, supervector_dim)
reference[supervector_dim] = np.arange(reference[supervector_dim].size)
forecast[supervector_dim] = np.arange(forecast[supervector_dim].size)
eforecast, ereference = forecast, reference
# very weak testing here
assert eforecast.size == aforecast.size
assert ereference.size == areference.size
def test_m2c(PM_da_ds1d):
"""Test many-to-control (which can be any other one member) (m2c) comparison basic
functionality.
Clean comparison: Remove one control member from ensemble to use as reference.
Take the remaining members as forecasts."""
ds = PM_da_ds1d
aforecast, areference = _m2c(ds)
supervector_dim = 'svd'
control_member = [0]
reference = ds.isel(member=control_member).squeeze()
# drop the member being reference
ds_dropped = _drop_members(ds, rmd_member=ds.member.values[control_member])
forecast, reference = xr.broadcast(ds_dropped, reference)
forecast = _stack_to_supervector(forecast, new_dim=supervector_dim)
reference = _stack_to_supervector(reference, new_dim=supervector_dim)
eforecast, ereference = forecast, reference
# very weak testing on shape
assert eforecast.size == aforecast.size
assert ereference.size == areference.size
assert_equal(eforecast, aforecast)
assert_equal(ereference, areference)
def m2e(ds, supervector_dim='svd'):
reference_list = []
forecast_list = []
for m in ds.member.values:
forecast = _drop_members(ds, rmd_member=[m]).mean('member')
reference = ds.sel(member=m).squeeze()
forecast, reference = xr.broadcast(forecast, reference)
forecast_list.append(forecast)
reference_list.append(reference)
reference = xr.concat(reference_list,
'init').rename({'init': supervector_dim})
forecast = xr.concat(forecast_list,
'init').rename({'init': supervector_dim})
return forecast, reference
def my_m2me_comparison(ds, metric=None):
"""Identical to m2e but median."""
reference_list = []
forecast_list = []
supervector_dim = 'member'
for m in ds.member.values:
forecast = _drop_members(ds, removed_member=[m]).median('member')
reference = ds.sel(member=m).squeeze()
forecast_list.append(forecast)
reference_list.append(reference)
reference = xr.concat(reference_list, supervector_dim)
forecast = xr.concat(forecast_list, supervector_dim)
forecast[supervector_dim] = np.arange(forecast[supervector_dim].size)
reference[supervector_dim] = np.arange(reference[supervector_dim].size)
return forecast, reference
def test_m2c(PM_da_initialized_1d):
"""Test many-to-control (which can be any other one member) (m2c) comparison basic
functionality.
Clean comparison: Remove one control member from ensemble to use as reference.
Take the remaining members as forecasts."""
ds = PM_da_initialized_1d
aforecast, areference = __m2c.function(ds, metric=metric)
control_member = [0]
reference = ds.isel(member=control_member).squeeze()
# drop the member being reference
ds_dropped = _drop_members(ds,
removed_member=ds.member.values[control_member])
forecast, reference = xr.broadcast(ds_dropped, reference)
eforecast, ereference = forecast, reference
# very weak testing on shape
assert eforecast.size == aforecast.size
assert ereference.size == areference.size
assert_equal(eforecast, aforecast)
assert_equal(ereference, areference)