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Remove unused alpha from perturb_observations #178

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Nov 23, 2023
Merged

Remove unused alpha from perturb_observations #178

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12 changes: 3 additions & 9 deletions src/iterative_ensemble_smoother/esmda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -227,7 +227,7 @@ def assimilate(
# a zero cented normal means that y := L @ z, where z ~ norm(0, 1)
# Therefore, scaling C_D by alpha is equivalent to scaling L with sqrt(alpha)
size = (num_outputs, num_ensemble)
D = self.perturb_observations(size=size, alpha=self.alpha[self.iteration])
D = self.perturb_observations(size=size)
assert D.shape == (num_outputs, num_ensemble)

# Line 2 (c) in the description of ES-MDA in the 2013 Emerick paper
Expand Down Expand Up @@ -291,7 +291,7 @@ def compute_transition_matrix(
# or
# X += X @ K

D = self.perturb_observations(size=Y.shape, alpha=alpha)
D = self.perturb_observations(size=Y.shape)
inversion_func = self._inversion_methods[self.inversion]
return inversion_func(
alpha=alpha,
Expand All @@ -303,9 +303,7 @@ def compute_transition_matrix(
return_K=True, # Ensures that we don't need X
)

def perturb_observations(
self, *, size: Tuple[int, int], alpha: float
) -> npt.NDArray[np.double]:
def perturb_observations(self, *, size: Tuple[int, int]) -> npt.NDArray[np.double]:
"""Create a matrix D with perturbed observations.

In the Emerick (2013) paper, the matrix D is defined in section 6.
Expand All @@ -315,10 +313,6 @@ def perturb_observations(
----------
size : Tuple[int, int]
The size, a tuple with (num_observations, ensemble_size).
alpha : float
The covariance inflation factor. The sequence of alphas should
obey the equation sum_i (1/alpha_i) = 1. However, this is NOT enforced
in this method call. The user/caller is responsible for this.

Returns
-------
Expand Down