def np_corr(ts1, ts2):
# Ensure the ts are aligned and missing values ignored
ts1, ts2 = broadcast(ts1, ts2)
valid_values = ts1.notnull() & ts2.notnull()
ts1 = ts1.where(valid_values)
ts2 = ts2.where(valid_values)
return np.corrcoef(ts1.data.flatten(), ts2.data.flatten())[0, 1]
def np_corr_ind(ts1, ts2, a, x):
# Ensure the ts are aligned and missing values ignored
ts1, ts2 = broadcast(ts1, ts2)
valid_values = ts1.notnull() & ts2.notnull()
ts1 = ts1.where(valid_values)
ts2 = ts2.where(valid_values)
return np.ma.corrcoef(
np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()),
np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()),
)[0, 1]
def np_cov_ind(ts1, ts2, a, x):
# Ensure the ts are aligned and missing values ignored
ts1, ts2 = broadcast(ts1, ts2)
valid_values = ts1.notnull() & ts2.notnull()
ts1 = ts1.where(valid_values)
ts2 = ts2.where(valid_values)
return np.cov(
ts1.sel(a=a, x=x).data.flatten(),
ts2.sel(a=a, x=x).data.flatten(),
ddof=ddof,
)[0, 1]
def test_covcorr_consistency(da_a, da_b, dim):
# Testing that xr.corr and xr.cov are consistent with each other
# 1. Broadcast the two arrays
da_a, da_b = broadcast(da_a, da_b)
# 2. Ignore the nans
valid_values = da_a.notnull() & da_b.notnull()
da_a = da_a.where(valid_values)
da_b = da_b.where(valid_values)
expected = xr.cov(da_a, da_b, dim=dim,
ddof=0) / (da_a.std(dim=dim) * da_b.std(dim=dim))
actual = xr.corr(da_a, da_b, dim=dim)
assert_allclose(actual, expected)
def np_cov(ts1, ts2):
# Ensure the ts are aligned and missing values ignored
ts1, ts2 = broadcast(ts1, ts2)
valid_values = ts1.notnull() & ts2.notnull()
ts1 = ts1.where(valid_values)
ts2 = ts2.where(valid_values)
return np.ma.cov(
np.ma.masked_invalid(ts1.data.flatten()),
np.ma.masked_invalid(ts2.data.flatten()),
ddof=ddof,
)[0, 1]
def np_cov_ind(ts1, ts2, a, x):
# Ensure the ts are aligned and missing values ignored
ts1, ts2 = broadcast(ts1, ts2)
valid_values = ts1.notnull() & ts2.notnull()
# While dropping isn't ideal here, numpy will return nan
# if any segment contains a NaN.
ts1 = ts1.where(valid_values)
ts2 = ts2.where(valid_values)
return np.ma.cov(
np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()),
np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()),
ddof=ddof,
)[0, 1]