From b1bfd15dc799d7c1ede6ac8100fdc760a00e3e0f Mon Sep 17 00:00:00 2001
From: Feda Curic <feda.curic@gmail.com>
Date: Thu, 1 Dec 2022 09:46:20 +0100
Subject: [PATCH] Use better variable names

---
 src/ert/analysis/_es_update.py | 40 +++++++++++++++++++---------------
 1 file changed, 22 insertions(+), 18 deletions(-)

diff --git a/src/ert/analysis/_es_update.py b/src/ert/analysis/_es_update.py
index 9ab3bf83fe2..bd34432d6a4 100644
--- a/src/ert/analysis/_es_update.py
+++ b/src/ert/analysis/_es_update.py
@@ -150,8 +150,8 @@ def _correlated_parameter_response_pairs(
     # State-measurement correlation matrix
     c_AY = np.linalg.inv(Sigma_A) @ C_AY @ np.linalg.inv(Sigma_Y)
 
-    _, corr_idx_Y = np.where(np.abs(c_AY) > correlation_threshold)
-    return corr_idx_Y
+    _, responses_to_keep = np.where(np.abs(c_AY) > correlation_threshold)
+    return responses_to_keep
 
 
 def analysis_ES(
@@ -192,7 +192,9 @@ def analysis_ES(
         ] = observation_handle.update_snapshot
         observation_values = observation_handle.observation_values
         observation_errors = observation_handle.observation_errors
-        if len(observation_values) == 0:
+
+        num_obs = len(observation_values)
+        if num_obs == 0:
             raise ErtAnalysisError(
                 f"No active observations for update step: {update_step.name}."
             )
@@ -203,28 +205,28 @@ def analysis_ES(
         )
 
         if A is not None:
-            noise = rng.standard_normal(size=(len(observation_values), Y.shape[1]))
+            ensemble_size = A.shape[1]
+            num_params = A.shape[0]
+            noise = rng.standard_normal(size=(num_obs, ensemble_size))
             if module.localization():
                 A_ES_loc = []
-                N = A.shape[1]
-                p = A.shape[0]
-                for i in range(A.shape[0]):
-                    A_chunk = A[i, :].reshape(-1, N)
-                    corr_idx_Y = _correlated_parameter_response_pairs(
+                for i in range(num_params):
+                    A_chunk = A[i, :].reshape(1, ensemble_size)
+                    responses_to_keep = _correlated_parameter_response_pairs(
                         A_chunk,
                         Y,
                         module.localization_correlation_threshold(),
                     )
-                    Y_loc = Y[corr_idx_Y, :]
-                    observation_errors_loc = observation_errors[corr_idx_Y]
-                    observation_values_loc = observation_values[corr_idx_Y]
+                    Y_loc = Y[responses_to_keep, :]
+                    observation_errors_loc = observation_errors[responses_to_keep]
+                    observation_values_loc = observation_values[responses_to_keep]
 
                     A_loc = ies.ensemble_smoother_update_step(
                         Y_loc,
                         A_chunk,
                         observation_errors_loc,
                         observation_values_loc,
-                        noise[corr_idx_Y],
+                        noise[responses_to_keep],
                         module.get_truncation(),
                         ies.InversionType(module.inversion),
                         projection=False,
@@ -291,7 +293,7 @@ def analysis_IES(
     # Looping over local analysis update_step
     for update_step in updatestep:
 
-        S, observation_handle = update.load_observations_and_responses(
+        Y, observation_handle = update.load_observations_and_responses(
             source_fs,
             obs,
             alpha,
@@ -307,16 +309,18 @@ def analysis_IES(
         observation_values = observation_handle.observation_values
         observation_errors = observation_handle.observation_errors
         observation_mask = observation_handle.obs_mask
-        if len(observation_values) == 0:
+
+        num_obs = len(observation_values)
+        if num_obs == 0:
             raise ErtAnalysisError(
                 f"No active observations for update step: {update_step.name}."
             )
 
         A = _get_A_matrix(temp_storage, update_step.parameters)
-
-        noise = rng.standard_normal(size=(len(observation_values), S.shape[1]))
+        ensemble_size = A.shape[1]
+        noise = rng.standard_normal(size=(num_obs, ensemble_size))
         A = iterative_ensemble_smoother.update_step(
-            S,
+            Y,
             A,
             observation_errors,
             observation_values,