diff --git a/src/ert/analysis/_es_update.py b/src/ert/analysis/_es_update.py
index 5c96b7c4051..ef8ab598907 100644
--- a/src/ert/analysis/_es_update.py
+++ b/src/ert/analysis/_es_update.py
@@ -5,6 +5,7 @@
 import time
 from collections.abc import Callable, Iterable, Sequence
 from fnmatch import fnmatch
+from itertools import groupby
 from typing import (
     TYPE_CHECKING,
     Generic,
@@ -168,6 +169,7 @@ def _load_observations_and_responses(
     npt.NDArray[np.float64],
     tuple[
         npt.NDArray[np.float64],
+        list[str],
         npt.NDArray[np.float64],
         list[ObservationAndResponseSnapshot],
     ],
@@ -315,6 +317,7 @@ def _load_observations_and_responses(
 
     return S[obs_mask], (
         observations[obs_mask],
+        obs_keys[obs_mask],
         scaled_errors[obs_mask],
         update_snapshot,
     )
@@ -458,6 +461,7 @@ def adaptive_localization_progress_callback(
         S,
         (
             observation_values,
+            observation_keys,
             observation_errors,
             update_snapshot,
         ),
@@ -474,6 +478,14 @@ def adaptive_localization_progress_callback(
     num_obs = len(observation_values)
 
     smoother_snapshot.update_step_snapshots = update_snapshot
+    # Used as labels for observations in cross-correlation matrix.
+    # Say we have two observation groups "FOPR" and "WOPR" where "FOPR" has
+    # 2 responses and "WOPR" has 3.
+    # In this case we create a list [FOPR_0, FOPR_1, WOPR_0, WOPR_1, WOPR_2]
+    # as labels for observations.
+    unique_obs_names = [
+        f"{k}_{i}" for k, g in groupby(observation_keys) for i, _ in enumerate(list(g))
+    ]
 
     if num_obs == 0:
         msg = "No active observations for update step"
@@ -577,6 +589,8 @@ def correlation_callback(
                         cross_correlations_,
                         param_group,
                         parameter_names[: cross_correlations_.shape[0]],
+                        unique_obs_names,
+                        list(observation_keys),
                     )
             logger.info(
                 f"Adaptive Localization of {param_group} completed in {(time.time() - start) / 60} minutes"
@@ -639,6 +653,7 @@ def analysis_IES(
         S,
         (
             observation_values,
+            _,
             observation_errors,
             update_snapshot,
         ),
diff --git a/src/ert/resources/forward_models/template_render.py b/src/ert/resources/forward_models/template_render.py
index 935589063c5..70cdd27c568 100755
--- a/src/ert/resources/forward_models/template_render.py
+++ b/src/ert/resources/forward_models/template_render.py
@@ -2,7 +2,8 @@
 import argparse
 import json
 import os
-from typing import Any, Sequence
+from collections.abc import Sequence
+from typing import Any
 
 import jinja2
 import yaml
diff --git a/src/ert/resources/workflows/jobs/internal-gui/scripts/csv_export.py b/src/ert/resources/workflows/jobs/internal-gui/scripts/csv_export.py
index 4d2d8b2309a..0cd80c8b67a 100644
--- a/src/ert/resources/workflows/jobs/internal-gui/scripts/csv_export.py
+++ b/src/ert/resources/workflows/jobs/internal-gui/scripts/csv_export.py
@@ -1,6 +1,6 @@
 import json
 import os
-from typing import Sequence
+from collections.abc import Sequence
 
 import pandas
 
diff --git a/src/ert/resources/workflows/jobs/internal-gui/scripts/gen_data_rft_export.py b/src/ert/resources/workflows/jobs/internal-gui/scripts/gen_data_rft_export.py
index 2928f9d9029..0b603d9b413 100644
--- a/src/ert/resources/workflows/jobs/internal-gui/scripts/gen_data_rft_export.py
+++ b/src/ert/resources/workflows/jobs/internal-gui/scripts/gen_data_rft_export.py
@@ -1,7 +1,8 @@
 import contextlib
 import json
 import os
-from typing import Any, Sequence
+from collections.abc import Sequence
+from typing import Any
 
 import numpy
 import pandas as pd
diff --git a/src/ert/storage/local_ensemble.py b/src/ert/storage/local_ensemble.py
index 5c10872b57f..5757e21cb03 100644
--- a/src/ert/storage/local_ensemble.py
+++ b/src/ert/storage/local_ensemble.py
@@ -12,6 +12,7 @@
 
 import numpy as np
 import pandas as pd
+import polars as pl
 import xarray as xr
 from pydantic import BaseModel
 from typing_extensions import deprecated
@@ -560,16 +561,15 @@ def load_parameters(
 
         return self._load_dataset(group, realizations)
 
-    def load_cross_correlations(self) -> xr.Dataset:
-        input_path = self.mount_point / "corr_XY.nc"
-
+    def load_cross_correlations(self) -> pl.DataFrame:
+        input_path = self.mount_point / "corr_XY.parquet"
         if not input_path.exists():
             raise FileNotFoundError(
                 f"No cross-correlation data available at '{input_path}'. Make sure to run the update with "
                 "Adaptive Localization enabled."
             )
         logger.info("Loading cross correlations")
-        return xr.open_dataset(input_path, engine="scipy")
+        return pl.read_parquet(input_path)
 
     @require_write
     def save_observation_scaling_factors(self, dataset: polars.DataFrame) -> None:
@@ -592,17 +592,28 @@ def save_cross_correlations(
         cross_correlations: npt.NDArray[np.float64],
         param_group: str,
         parameter_names: list[str],
+        unique_obs_names: list[str],
+        observation_keys: list[str],
     ) -> None:
-        data_vars = {
-            param_group: xr.DataArray(
-                data=cross_correlations,
-                dims=["parameter", "response"],
-                coords={"parameter": parameter_names},
-            )
-        }
-        dataset = xr.Dataset(data_vars)
-        file_path = os.path.join(self.mount_point, "corr_XY.nc")
-        self._storage._to_netcdf_transaction(file_path, dataset)
+        n_responses = cross_correlations.shape[1]
+        new_df = pl.DataFrame(
+            {
+                "param_group": [param_group]
+                * (len(parameter_names) * len(unique_obs_names)),
+                "param_name": np.repeat(parameter_names, n_responses),
+                "obs_group": observation_keys * len(parameter_names),
+                "obs_name": unique_obs_names * len(parameter_names),
+                "value": cross_correlations.ravel(),
+            }
+        )
+
+        file_path = os.path.join(self.mount_point, "corr_XY.parquet")
+        if os.path.exists(file_path):
+            existing_df = pl.read_parquet(file_path)
+            df = pl.concat([existing_df, new_df])
+        else:
+            df = new_df
+        self._storage._to_parquet_transaction(file_path, df)
 
     def load_responses(
         self, key: str, realizations: tuple[int, ...]
diff --git a/test-data/ert/heat_equation/Plot_correlations.ipynb b/test-data/ert/heat_equation/Plot_correlations.ipynb
new file mode 100644
index 00000000000..03b2e28ecf6
--- /dev/null
+++ b/test-data/ert/heat_equation/Plot_correlations.ipynb
@@ -0,0 +1,264 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e7c14853-686e-4646-aa13-af2dc0e11408",
+   "metadata": {},
+   "source": [
+    "# Get cross-correlations after running adaptive localization using the Heat Equation\n",
+    "\n",
+    "Note that you first need to run ert with ES-MDA and localization turned on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e4f4e841-efb7-4f5a-b984-f4c44663b9b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import polars as pl\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from ert.config import ErtConfig\n",
+    "from ert.storage import open_storage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "952539a1-4072-47e5-9b8c-8296ca086134",
+   "metadata": {},
+   "source": [
+    "## Load data frame by using API endpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "66e4e990-da5a-4a3b-9e4e-1bb17409365d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ert_config = ErtConfig.from_file(\"config.ert\")\n",
+    "# Load data for multiple iterations\n",
+    "iterations = [0, 1, 2]\n",
+    "dfs = []\n",
+    "for iter_num in iterations:\n",
+    "    with open_storage(ert_config.ens_path, mode=\"r\") as storage:\n",
+    "        experiment = storage.get_experiment_by_name(\"es_mda\")\n",
+    "        ensemble = experiment.get_ensemble_by_name(f\"default_{iter_num}\")\n",
+    "        df = ensemble.load_cross_correlations()\n",
+    "        df = df.with_columns(pl.lit(iter_num).alias(\"iteration\"))\n",
+    "        dfs.append(df)\n",
+    "df = pl.concat(dfs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f7a20a9-979a-4cb3-9994-ec5ec7064ce0",
+   "metadata": {},
+   "source": [
+    "## Find largest correlations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1aae4f96-4c5a-446a-91fc-acfbaf45eee0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (15, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>param_group</th><th>param_name</th><th>obs_group</th><th>obs_name</th><th>value</th><th>iteration</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>f64</td><td>i32</td></tr></thead><tbody><tr><td>&quot;INIT_TEMP_SCALE&quot;</td><td>&quot;x&quot;</td><td>&quot;MY_OBS_10&quot;</td><td>&quot;MY_OBS_10_3&quot;</td><td>0.823223</td><td>0</td></tr><tr><td>&quot;INIT_TEMP_SCALE&quot;</td><td>&quot;x&quot;</td><td>&quot;MY_OBS_10&quot;</td><td>&quot;MY_OBS_10_5&quot;</td><td>0.808215</td><td>0</td></tr><tr><td>&quot;INIT_TEMP_SCALE&quot;</td><td>&quot;x&quot;</td><td>&quot;MY_OBS_10&quot;</td><td>&quot;MY_OBS_10_1&quot;</td><td>0.654016</td><td>0</td></tr><tr><td>&quot;INIT_TEMP_SCALE&quot;</td><td>&quot;x&quot;</td><td>&quot;MY_OBS_10&quot;</td><td>&quot;MY_OBS_10_2&quot;</td><td>0.62915</td><td>0</td></tr><tr><td>&quot;INIT_TEMP_SCALE&quot;</td><td>&quot;x&quot;</td><td>&quot;MY_OBS_71&quot;</td><td>&quot;MY_OBS_71_0&quot;</td><td>0.536513</td><td>0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;INIT_TEMP_SCALE&quot;</td><td>&quot;x&quot;</td><td>&quot;MY_OBS_71&quot;</td><td>&quot;MY_OBS_71_5&quot;</td><td>0.366555</td><td>0</td></tr><tr><td>&quot;INIT_TEMP_SCALE&quot;</td><td>&quot;x&quot;</td><td>&quot;MY_OBS_132&quot;</td><td>&quot;MY_OBS_132_4&quot;</td><td>0.366474</td><td>0</td></tr><tr><td>&quot;INIT_TEMP_SCALE&quot;</td><td>&quot;x&quot;</td><td>&quot;MY_OBS_71&quot;</td><td>&quot;MY_OBS_71_3&quot;</td><td>0.347304</td><td>0</td></tr><tr><td>&quot;INIT_TEMP_SCALE&quot;</td><td>&quot;x&quot;</td><td>&quot;MY_OBS_132&quot;</td><td>&quot;MY_OBS_132_2&quot;</td><td>0.316461</td><td>0</td></tr><tr><td>&quot;INIT_TEMP_SCALE&quot;</td><td>&quot;x&quot;</td><td>&quot;MY_OBS_71&quot;</td><td>&quot;MY_OBS_71_5&quot;</td><td>-0.311819</td><td>2</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (15, 6)\n",
+       "┌─────────────────┬────────────┬────────────┬──────────────┬───────────┬───────────┐\n",
+       "│ param_group     ┆ param_name ┆ obs_group  ┆ obs_name     ┆ value     ┆ iteration │\n",
+       "│ ---             ┆ ---        ┆ ---        ┆ ---          ┆ ---       ┆ ---       │\n",
+       "│ str             ┆ str        ┆ str        ┆ str          ┆ f64       ┆ i32       │\n",
+       "╞═════════════════╪════════════╪════════════╪══════════════╪═══════════╪═══════════╡\n",
+       "│ INIT_TEMP_SCALE ┆ x          ┆ MY_OBS_10  ┆ MY_OBS_10_3  ┆ 0.823223  ┆ 0         │\n",
+       "│ INIT_TEMP_SCALE ┆ x          ┆ MY_OBS_10  ┆ MY_OBS_10_5  ┆ 0.808215  ┆ 0         │\n",
+       "│ INIT_TEMP_SCALE ┆ x          ┆ MY_OBS_10  ┆ MY_OBS_10_1  ┆ 0.654016  ┆ 0         │\n",
+       "│ INIT_TEMP_SCALE ┆ x          ┆ MY_OBS_10  ┆ MY_OBS_10_2  ┆ 0.62915   ┆ 0         │\n",
+       "│ INIT_TEMP_SCALE ┆ x          ┆ MY_OBS_71  ┆ MY_OBS_71_0  ┆ 0.536513  ┆ 0         │\n",
+       "│ …               ┆ …          ┆ …          ┆ …            ┆ …         ┆ …         │\n",
+       "│ INIT_TEMP_SCALE ┆ x          ┆ MY_OBS_71  ┆ MY_OBS_71_5  ┆ 0.366555  ┆ 0         │\n",
+       "│ INIT_TEMP_SCALE ┆ x          ┆ MY_OBS_132 ┆ MY_OBS_132_4 ┆ 0.366474  ┆ 0         │\n",
+       "│ INIT_TEMP_SCALE ┆ x          ┆ MY_OBS_71  ┆ MY_OBS_71_3  ┆ 0.347304  ┆ 0         │\n",
+       "│ INIT_TEMP_SCALE ┆ x          ┆ MY_OBS_132 ┆ MY_OBS_132_2 ┆ 0.316461  ┆ 0         │\n",
+       "│ INIT_TEMP_SCALE ┆ x          ┆ MY_OBS_71  ┆ MY_OBS_71_5  ┆ -0.311819 ┆ 2         │\n",
+       "└─────────────────┴────────────┴────────────┴──────────────┴───────────┴───────────┘"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.with_columns(pl.col(\"value\").abs().alias(\"abs_value\")).sort(\n",
+    "    \"abs_value\", descending=True\n",
+    ").limit(15).drop(\"abs_value\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5eaa959c-32c2-4331-820a-52625694f0a4",
+   "metadata": {},
+   "source": [
+    "## Plot heatmaps of correlation coefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "793cd249-a26b-48f2-a099-01567720ef57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get unique parameter groups\n",
+    "param_groups = df[\"param_group\"].unique().to_list()\n",
+    "\n",
+    "# Process each parameter group\n",
+    "for group in param_groups:\n",
+    "    # Process each iteration for this group\n",
+    "    for iter_num in iterations:\n",
+    "        iter_df = df.filter(pl.col(\"iteration\") == iter_num)\n",
+    "\n",
+    "        pivot_df = iter_df.pivot(\n",
+    "            index=[\"param_group\", \"param_name\"], on=\"obs_name\", values=\"value\"\n",
+    "        )\n",
+    "\n",
+    "        group_data = pivot_df.filter(pl.col(\"param_group\") == group)\n",
+    "        param_names = group_data[\"param_name\"].to_list()\n",
+    "        obs_columns = [\n",
+    "            col\n",
+    "            for col in group_data.columns\n",
+    "            if col not in [\"param_group\", \"param_name\"]\n",
+    "        ]\n",
+    "        data_array = group_data.select(obs_columns).to_numpy()\n",
+    "\n",
+    "        plt.figure(figsize=(20, 4))\n",
+    "        sns.heatmap(\n",
+    "            data_array,\n",
+    "            annot=True,\n",
+    "            fmt=\".2f\",\n",
+    "            cmap=\"coolwarm\",\n",
+    "            center=0,\n",
+    "            xticklabels=obs_columns,\n",
+    "            yticklabels=param_names,\n",
+    "            annot_kws={\"size\": 8},\n",
+    "        )\n",
+    "        plt.title(\n",
+    "            f\"Parameter-Observation Correlations for {group} (Iteration {iter_num})\"\n",
+    "        )\n",
+    "        plt.xlabel(\"Observations\")\n",
+    "        plt.ylabel(\"Parameters\")\n",
+    "        plt.xticks(rotation=45, ha=\"right\")\n",
+    "        plt.yticks(rotation=0)\n",
+    "        plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "862c9198-98a3-4e64-b80c-fcb10593128f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/test-data/ert/heat_equation/config.ert b/test-data/ert/heat_equation/config.ert
index 7d71daa6908..f23a785460b 100644
--- a/test-data/ert/heat_equation/config.ert
+++ b/test-data/ert/heat_equation/config.ert
@@ -13,6 +13,9 @@ QUEUE_OPTION LOCAL MAX_RUNNING 100
 
 RANDOM_SEED 11223344
 
+ANALYSIS_SET_VAR STD_ENKF LOCALIZATION True
+ANALYSIS_SET_VAR STD_ENKF LOCALIZATION_CORRELATION_THRESHOLD 0.1
+
 NUM_REALIZATIONS 100
 GRID CASE.EGRID
 
@@ -20,6 +23,9 @@ OBS_CONFIG observations
 
 FIELD COND PARAMETER cond.bgrdecl INIT_FILES:cond.bgrdecl FORWARD_INIT:True
 
+GEN_KW INIT_TEMP_SCALE init_temp_prior.txt
+GEN_KW CORR_LENGTH corr_length_prior.txt
+
 GEN_DATA MY_RESPONSE RESULT_FILE:gen_data_%d.out REPORT_STEPS:10,71,132,193,255,316,377,438 INPUT_FORMAT:ASCII
 
 INSTALL_JOB heat_equation HEAT_EQUATION
diff --git a/test-data/ert/heat_equation/corr_length_prior.txt b/test-data/ert/heat_equation/corr_length_prior.txt
new file mode 100644
index 00000000000..7b71a8cc6a0
--- /dev/null
+++ b/test-data/ert/heat_equation/corr_length_prior.txt
@@ -0,0 +1 @@
+x NORMAL 0.8 0.1
diff --git a/test-data/ert/heat_equation/heat_equation.py b/test-data/ert/heat_equation/heat_equation.py
index 6ec0231cacd..265528a713e 100755
--- a/test-data/ert/heat_equation/heat_equation.py
+++ b/test-data/ert/heat_equation/heat_equation.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 """Partial Differential Equations to use as forward models."""
 
+import json
 import sys
 
 import geostat
@@ -51,16 +52,28 @@ def heat_equation(
     return u_
 
 
-def sample_prior_conductivity(ensemble_size, nx, rng):
+def sample_prior_conductivity(ensemble_size, nx, rng, corr_length):
     mesh = np.meshgrid(np.linspace(0, 1, nx), np.linspace(0, 1, nx))
-    return np.exp(geostat.gaussian_fields(mesh, rng, ensemble_size, r=0.8))
+    return np.exp(geostat.gaussian_fields(mesh, rng, ensemble_size, r=corr_length))
+
+
+def load_parameters(filename):
+    with open(filename, encoding="utf-8") as f:
+        return json.load(f)
 
 
 if __name__ == "__main__":
     iens = int(sys.argv[1])
     iteration = int(sys.argv[2])
     rng = np.random.default_rng(iens)
-    cond = sample_prior_conductivity(ensemble_size=1, nx=nx, rng=rng).reshape(nx, nx)
+
+    parameters = load_parameters("parameters.json")
+    init_temp_scale = parameters["INIT_TEMP_SCALE"]
+    corr_length = parameters["CORR_LENGTH"]
+
+    cond = sample_prior_conductivity(
+        ensemble_size=1, nx=nx, rng=rng, corr_length=float(corr_length["x"])
+    ).reshape(nx, nx)
 
     if iteration == 0:
         resfo.write(
@@ -78,7 +91,9 @@ def sample_prior_conductivity(ensemble_size, nx, rng):
     # Note that this could be avoided if we used an implicit solver.
     dt = dx**2 / (4 * max(np.max(cond), np.max(cond)))
 
-    response = heat_equation(u_init, cond, dx, dt, k_start, k_end, rng)
+    scaled_u_init = u_init * float(init_temp_scale["x"])
+
+    response = heat_equation(scaled_u_init, cond, dx, dt, k_start, k_end, rng)
 
     index = sorted((obs.x, obs.y) for obs in obs_coordinates)
     for time_step in obs_times:
diff --git a/test-data/ert/heat_equation/init_temp_prior.txt b/test-data/ert/heat_equation/init_temp_prior.txt
new file mode 100644
index 00000000000..63d9d33ba9d
--- /dev/null
+++ b/test-data/ert/heat_equation/init_temp_prior.txt
@@ -0,0 +1 @@
+x UNIFORM 0 1
diff --git a/test-data/ert/poly_example/Plot_correlations.ipynb b/test-data/ert/poly_example/Plot_correlations.ipynb
index d85d8613dcb..37665559f36 100644
--- a/test-data/ert/poly_example/Plot_correlations.ipynb
+++ b/test-data/ert/poly_example/Plot_correlations.ipynb
@@ -1,471 +1,133 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e7c14853-686e-4646-aa13-af2dc0e11408",
+   "metadata": {},
+   "source": [
+    "# Get cross-correlations after running adaptive localization using the poly-case\n",
+    "\n",
+    "Note that you first need to run ert with ES-MDA and localization turned on."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "e4f4e841-efb7-4f5a-b984-f4c44663b9b9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ervOXE5JPAH+vXUmAyf9hR8kW6MH9djUleQh7QKBFAYHm9s+mknu9hemaDi4AAACAq2U1bLKa/HIGq2Ezt8FrBI8NAAAAAJiCzAUAAABcmk2GbDI3dWF2e9cKMhcAAAAATEHmAgAAAC7NJpvMniFhfovXBjIXAAAAAExB5gIAAAAuzWoYshrmzpEwu71rBZkLAAAAAKYgcwEAAACXxmpR5iG4AAAAgEuzyZCV4MIUDIsCAAAAYAoyFwAAAHBpDIsyD5kLAAAAAKYgcwEAAACXxlK05iFzAQAAAMAUZC4AAADg0mz/28xu0xWRuQAAAABgCjIXAAAAcGnWQnjPhdntXSsILgAAAODSrMb5zew2XRHDogAAAACYgswFAAAAXBoTus1D5gIAAACAKchcAAAAwKXZZJFVFtPbdEVkLgAAAACYgswFAAAAXJrNOL+Z3aYrInMBAAAAwBRkLgAAAODSrIUw58Ls9q4VBBcAAABwaQQX5mFYFAAAAABTkLkAAACAS7MZFtkMk5eiNbm9awWZCwAAAACmIHMBAAAAl8acC/OQuQAAAABgCjIXAAAAcGlWuclq8jN3q6mtXTvIXAAAAAAwBZkLAAAAuDSjEFaLMlx0tSiCCwAAALg0JnSbh2FRAAAAAExB5gIAAAAuzWq4yWqYPKHbMLW5awaZCwAAAACmIHMBAAAAl2aTRTaTn7nb5JqpCzIXAAAAAExB5gIAAAAujdWizEPmAgAAAIApyFwAAADApRXOalGuOeeC4AIAAAAu7fyEbnOHMZnd3rWCYVEAAAAATEHmAgAAAC7NJjdZWYrWFGQuAAAAAJiCzAUAAABcGhO6zUPmAgAAAIApyFwAAADApdnkJhtzLkxB5gIAAACAKchcAAAAwKVZDYushrnvpTC7vWsFwQUAAABcmrUQlqK1MiwKAAAAAJxH5gIAAAAuzWa4yWbyUrQ2lqIFAAAAAOeRuQAAAIBLY86FechcAAAAADAFmQsAAAC4NJvMXzrWZmpr1w4yFwAAAABMQeYCAAAALs0mN9lMfuZudnvXCoILAAAAuDSr4SaryUvRmt3etcI1rxoAAACA6chcAAAAwKXZZJFNZk/oNre9awWZCwAAAACmIHMBAAAAl8acC/MQXJQgvcfco9v63qKAEH/tWL9Hrz0xT8f3xxdY//qWtdTjyTtUvUEVhZcN1dh7pmr9ij/s+9093PXw+HvVtFN9lalcWunJ57T5u+169/kPlXgiqSguCQXo2aKe+rRppFKB/tpzIkEvL/9efx09mW/d7s3q6s5GdVQtKlyStPPYKc386meH+uEBfhp2e0u1qFFRgb7e2nQwTi9/9r1iT58tisvBv/jtg8P6ecFBpZ3OUlTNIN0+8jqVvz4k37rv9tmgw38k5imv0SpCD73RVJKUlZGrNdN3a9d3J5WRnK3Qcn668YFKanpvxcK8DFymjxelatFbqTqTYFX12l56elyI6tb3zrduTo6h+W+k6ItP05UQb1XFKp4a8mywWrTxtdfZ/FumFs1N1a7t2Tp9yqYpb4WrbUe/oroc/Iv5C9L1xpx0JSRYVae2pya8GKQGDbzyrZuTY2jW62n6eOk5xcdbVbWKh55/LlC3tPWx15kyNVVTp6c5HFe1qrt+/qF0oV4HYBbXDKlKoHtHdFG3QZ302uB3NaTlKGVmZGniF8/K09uzwGN8/L11cFusXh86L9/93n5eql6/st5/ebkeb/acxt07TdE1ymr8shGFdRm4DJ3q1dDTd96sN9f8qh4z3tee46f1Vr+7FRbgm2/9JlXLa9Wfu9V3zlI9OOtDxSenam7/u1U6yN9eZ+bDXVQ+PFhDFqxQj+nv63hSit4Z0F2+Xjw/KG7bVx/XV6/uUtvHqmvgxy0VVSNQCwf8prQzWfnW7zWjkZ7+vp19G7z8Zrm5W3RdhzL2Oqsn79S+XxJ0z6T6GvJ5azV/sLK+fHmHdn2ff4CKovPNygxNe+ms+g8N0vtfRqlGHU8N7p2gxNPWfOu/OSVZy5ak6+lxofrk2zLq/kCARgw4o91/ZdvrnMswVKO2l54ZH1pUl4HL9PmKcxo7PkVPDgvQ11+VUp06Hur1YKJOF3C/X5mcqvcWZ2jC+CD98F2Eej/kp0ceTdL2v3Ic6tWs6aGtm0vbt8+XlyqKy3FpVrkVyuaKXPOqS6C7nrhNSyYu14aVm3Roe6wm93lD4WVDdVPXxgUe8/vXW7VgzMf65fM/8t2fkXJOz3Z+WT8u/VXH9p7Q7o379frQ+arRqIoiosML61LwL3q3bqilv/2lz37fqYMnEzX+02+VmZOru5rUzbf+s0tW66P127TneIIOJSRpzMdr5Gax6MbqFSRJFUuFqH6lsnrx0+/019GTOpyQpBeXrZW3p4c6169VlJeGfKxfdEiNu0er4V3RKl01UF1GXy9PX3dtXn403/p+wV4KLOVj3w5sOC1PH3fV/VtwEbs1SfXvLK/KTcIVWs5PTXpUUFSNQMVtP1tEV4WCLH4nVXf1DNCd9waoSnVPPTchVD6+bvr84/R863+5PF19BwWqZVtfla/goR4PBeimtj5a/E6qvc5NbX31+Ihg3dKJbEVJ89bcdD3Qy0897/NTzRqemjwpWL4+Fn3w4bl86y9ddk5DnghQu3Y+qljRQzG9/XXLLT6a85ZjpsLDXSpd2t2+hYfxcw3XDv7fWgJEVS6t8DKh2vzdX/ayjJRz2r3xgGo3q27qufyD/WSz2ZR+NsPUdnF5PNzdVKdcpH7dG2svMwzp132xqlexzCWOvMjHy0Me7u5KzsiUJHl5uEuSsnNzHdrMybWqQeWyJvYeVyo3x6bjO5NV5caLTx3d3CyqemMpHd169rLa2LTsqK7vVEZefhezUBXqhWrPupNKOZkpwzB0cONpnT6SrmoteLpZnHKyDe3+K1tNb7o4BMrNzaKmN3lr++b8M1U52ZKXt+OKMt4+Fv35e/71UXJkZxvatj1HrVo53u9Wrby1aXN2/sdkGfL+x/328ZE2/u6YuTh4yKr6jU6qWYtTenxwko7F5Z8JgXlshqVQNmfMnj1blSpVko+Pj5o1a6aNGzdesv6MGTNUs2ZN+fr6Kjo6WsOGDVNmZqZT5zZDsQcXq1evVsuWLRUSEqLw8HDdcccdOnDgQHF3q0iFRQZLks6eTHYoTzqVrNCoENPO4+ntqUdf7qV1H61XRmr+T1VQuEL9feXh7qYzaY7B3ZnUDJUKurynksNvb6WE5DRt2Hc+QDl0KknHk1I0tHNLBfl6y8PdTX3bNlZUSKAi/jZ0CkUvIylbNquhgHDH8fYB4d4FDov6u2Pbz+rU/lQ16l7Bofz2565TRNUAvdp+rcY2/EqLHvtddzxfV5Uak5EsTmeTbLJapfBS7g7l4RHuOp1gy/eYG2/20fvvpCr2UI5sNkO//pSp71af0+kEfkyWdImJ5+93RITjT6mIUm46dSr/+92mtbfeejtdBw/mymYz9MOPWVr1VaZOnbp4vxs08NTM6cFa8l6YJr0crKNHrep29xmlpeXfJv5bPvroIw0fPlxjxozR5s2bVa9ePXXs2FGnTp3Kt/6SJUv07LPPasyYMdq1a5feffddffTRR3ruueeKuOcXFXtwkZ6eruHDh+uPP/7Q2rVr5ebmprvuuks2W96/RFlZWUpJSXHYrkW39LpJnyfOt2/unoU/Lt7dw10vfDBUslj02uD852ig5HukbRPdVr+mhi5cqezc8/8xyrXZ9H8LVqpSqRCtf/Fx/fHyE2paNVo/7jokm2EUc49xNTYtO6rI6oF5Jn//uuSwjm47qwdmNdbAD1uq04ja+mLCXzqw4XTxdBROe2pMiKIreap7u3jdWP2YJo9J0p09/OVmcc318f/rxo8PUuXK7mrVJkEVKsfr+ReS1fM+P7n97Xa3u8VHXe7wVZ06nmrbxluLF4UpJcWmFSuL70m0K7AVwnwLmxM/s6dNm6Z+/fqpT58+qlOnjubMmSM/Pz/Nm5f/b7f169frpptu0v33369KlSqpQ4cO6tWr179mOwpTsc/27N69u8PnefPmKSIiQjt37lTduo5j0CdOnKhx48YVZfcKxYaVm7R743775wuTtkMig5UYf9ZeHlo6WAe2Hr7q810ILEpXKKWnO7xE1qIYJaWfU67VpvAAxyxFeKCfTqdceqjaw60b6ZFbGqvfW8u094Tjj8idcad0z/T3FeDjJU93dyWln9OSIT21o4AVqFA0/EK95OZuyZOlSDuTlSeb8U/ZGbnavvq42g2q4VCek2nVtzP3qNfMRqp5c6QkKapmkOL3pOjnhQdVtTlDo4pLSKib3N2lM/+YzHsmwapSEfn/yAgNd9e0t0spK9NQ8lmrIiLdNWtSsspVcM+3PkqOsLDz9zvhH1mphNM2lS6d//0uFe6uBe+GKTPTUFKSTVFRbprwcqoqVCz451hwsJuqVPHQocO5BdbB1bMZbrKZvHTshfb++TDc29tb3t55/xuQnZ2tTZs2aeTIkfYyNzc3tW/fXhs2bMj3HC1atNDixYu1ceNGNW3aVAcPHtSqVav00EMPmXglV6bYMxf79u1Tr169VKVKFQUFBalSpUqSpNjY2Dx1R44cqeTkZPt29Gj+EyJLunNpmTp+4KR9O7LzmM6cSFKDtheDKb9AX9VqWlW7ftt3Vee6EFiUqxalZztNUGpi2r8fhEKTa7VpZ9xJNasebS+zWKRm1aK19ciJAo/r06axBrRvpsfeXq4dxwoOGNIys5WUfk4VSoXouvKR+n6Haw0xLGk8PN1Utk6wDv52MRi02Qwd/PWMouuFXPLYv745IWu2TfXuKOdQbs21yZpryPKPJ9sWN4sMG5mq4uTpZVGtul76ff3FYNJmM/T7+ixd3/DSwaS3j0WlozyUmyutXX1OrW/Nf/U4lBxeXhbdcL2nfv7Z8X7//HOWGjXMfynaC3x8LCpTxl25udKXqzLVsUPB//9IT7fpyOFcRZYm4LxWRUdHKzg42L5NnDgx33qnT5+W1WpVZGSkQ3lkZKTi4/N/NcH999+v8ePHq2XLlvL09FTVqlXVpk2bYh0WVeyZiy5duqhixYp6++23VbZsWdlsNtWtW1fZ2XknQxUU6f0XLJ/1le4f2U1x++MVf/iUHh7bQ2eOJzmsBPXK6uf1y+e/a8Wb30g6vxRt2WpR9v1RlSJUpV5FpSamKeHoGbl7uGvUR/+n6vUra9Rdk+Xm7qbQ/83vSE1MU24OY3qLw6IfNmtCz47aceyU/oqN14OtGsjXy1Of/b5DkvRyz446lZymGV/9Iknq27axBndsrqff/0pxSSkKDzyf9cjIytG57POTADvcUF1J6ed0IilV1cuE69mubfTdXwe0fm/eIB1Fq0Xvylr2/FaVuy5E5a4P1ob3Div7XK4adjsfYC597k8FlfZRh/9zXNlr8/KjqnVLpPxCHH+k+AR4qlLjMH09bZc8fdwVUsZXh/44oz9XHtNtT9UpsutC/h58NFBjnjyj2td7qW59Ly15N1XnMmy6s8f5+U+jh59RRKS7nngmRJK0fUuWEk5aVaOOlxLirXprRrIMm6GYAUH2NjPSbTr6t6fWx49atWdHtoJC3FSmXLH/Z9ylDejvr6HDzqpePU/Vr++pt9/JUMY5Qz3vOx8cPjH0rKKi3PT8yPP3c/PmbJ2It6rudZ46EW/T1GmpshnSoIEB9jbHvZiiW9t7K7q8u+JP2jRlaqrc3C3q1s0n3z7AHFZZZJW5wxEvtHf06FEFBV38O23mb9l169bp5Zdf1htvvKFmzZpp//79Gjp0qF588UWNGjXKtPNciWL9V+nMmTPas2eP3n77bbVq1UqS9PPPPxdnl4rNx1NWysffW//3xqMKCPHTX7/s0XNdJikn6+IKEmWqRCq4VKD9c41GVTTl29H2z49N6S1J+mbRD5ry6ByVKheqFl3OL2U7549XHM43ov14bftxV2FeEgqweutehQb4anDH5ioV6KfdxxP02DvL7ZO8y4QGOsyVuK/5DfLy8NCMmC4O7bzxzQa98c2vkqSIIH89fWdrhQf4KSE1XSv+2Kk53/5WdBeFAl3fqazSE7O1dvZepZ3OUplaQeo9p6kCSp3/j0vyiXN5xtcnHErTkc1Jinmrab5t3vtqA62ZsUefPLtF55JzFFLGV+2fqKkm91bItz6KTocufkpKtGrO9GSdSbCqRm0vzVoYofCI80+d4+Os+vvtzs4y9MaUZMXF5srX300t2/roxenhCgy+OLBg57ZsDeiVYP887aWzkqQ7uvtp3FQm8Renrnf66swZmyZPSVNCglXX1fHUkvfCFPG/+x0XZ5Xb38aIZGYZeuXVNMXG5srPz6J2t/ho1swQBf/tfp84YdXjg88qKcmm8DA3NW3qpS9XhKtUOJmLa1VQUJBDcFGQUqVKyd3dXSdPOo5QOHnypKKiovI9ZtSoUXrooYf06KOPSpKuv/56paenq3///nr++efl5lb0g5QshlF8Mz5tNptKly6t2267TWPGjFFsbKyeffZZ/f7771q+fLm6det2yeNTUlIUHBysth7d5WEp+GVz+O84PiT/H1v4b+r58Nri7gKKUOfAbcXdBRShsh7MIXAVqak21ah9UsnJyZf1I7soXfgtOe639vIJMPeZe2ZarsY0+/aKrrtZs2Zq2rSpZs2aJen8b+UKFSpo8ODBevbZZ/PUb9Sokdq3b69XXrn4EPmDDz7QI488otTUVLm7F31QWqyZCzc3N3344YcaMmSI6tatq5o1a+q1115TmzZtirNbAAAAQJEbPny4YmJi1LhxYzVt2lQzZsxQenq6+vTpI0nq3bu3ypUrZ5+30aVLF02bNk0NGjSwD4saNWqUunTpUiyBhVQC5ly0b99eO3fudCgrxmQKAAAAXIxVKoQ5F1fuvvvuU0JCgkaPHq34+HjVr19fq1evtk/yjo2NdRjq9MILL8hiseiFF15QXFycIiIi1KVLF02YMMGkq7hyxR5cAAAAADhv8ODBGjx4cL771q1b5/DZw8NDY8aM0ZgxY4qgZ5eH4AIAAAAurTDfc+FqCC4AAADg0qyGm6wmBwNmt3etcM2rBgAAAGA6MhcAAABwaYYsspk8odswub1rBZkLAAAAAKYgcwEAAACXxpwL87jmVQMAAAAwHZkLAAAAuDSbYZHNMHeOhNntXSvIXAAAAAAwBZkLAAAAuDSr3GQ1+Zm72e1dKwguAAAA4NIYFmUe1wypAAAAAJiOzAUAAABcmk1uspn8zN3s9q4VrnnVAAAAAExH5gIAAAAuzWpYZDV5joTZ7V0ryFwAAAAAMAWZCwAAALg0VosyD5kLAAAAAKYgcwEAAACXZhhushnmPnM3TG7vWkFwAQAAAJdmlUVWmTyh2+T2rhWuGVIBAAAAMB2ZCwAAALg0m2H+BGybYWpz1wwyFwAAAABMQeYCAAAALs1WCBO6zW7vWuGaVw0AAADAdGQuAAAA4NJssshm8upOZrd3rSBzAQAAAMAUZC4AAADg0qyGRVaTV4syu71rBcEFAAAAXBoTus3jmlcNAAAAwHRkLgAAAODSbLKY/xI9JnQDAAAAgPPIXAAAAMClGYWwFK1B5gIAAAAAnEfmAgAAAC7NZhTCnAsXXYqWzAUAAAAAU5C5AAAAgEvjPRfmIbgAAACAS2NYlHlcM6QCAAAAYDoyFwAAAHBptkJYipaX6AEAAADAVSBzAQAAAJfGnAvzkLkAAAAAYAoyFwAAAHBpZC7MQ+YCAAAAgCnIXAAAAMClkbkwD8EFAAAAXBrBhXkYFgUAAADAFGQuAAAA4NIMmf/SO8PU1q4dZC4AAAAAmILMBQAAAFwacy7MQ+YCAAAAgCnIXAAAAMClkbkwD5kLAAAAAKYgcwEAAACXRubCPAQXAAAAcGkEF+ZhWBQAAAAAU5C5AAAAgEszDIsMkzMNZrd3rSBzAQAAAMAUZC4AAADg0myyyCaT51yY3N61gswFAAAAAFOQuQAAAIBLY7Uo85C5AAAAAGAKMhcAAABwaawWZR4yFwAAAABMQeYCAAAALo05F+YhuAAAAIBLY1iUeRgWBQAAAMAU/4nMhZGbK8PimtGhq0mtnVPcXUAR+viddsXdBRShtTtaFncXUIS8jyUXdxdQRHKtWZKmF3c3LskohGFRZC4AAAAA4CoQXAAAAMClGZIMw+TNyb7Mnj1blSpVko+Pj5o1a6aNGzdesv7Zs2c1aNAglSlTRt7e3qpRo4ZWrVrl5Nmv3n9iWBQAAABwrfvoo480fPhwzZkzR82aNdOMGTPUsWNH7dmzR6VLl85TPzs7W7feeqtKly6tpUuXqly5cjpy5IhCQkKKvvP/Q3ABAAAAl2aTRRaZvBStE+1NmzZN/fr1U58+fSRJc+bM0Zdffql58+bp2WefzVN/3rx5SkxM1Pr16+Xp6SlJqlSp0lX1+2oxLAoAAAAoJCkpKQ5bVlZWvvWys7O1adMmtW/f3l7m5uam9u3ba8OGDfkes2LFCjVv3lyDBg1SZGSk6tatq5dffllWq7VQruVyEFwAAADApV14z4XZmyRFR0crODjYvk2cODHfPpw+fVpWq1WRkZEO5ZGRkYqPj8/3mIMHD2rp0qWyWq1atWqVRo0apalTp+qll14y9wu6AgyLAgAAgEuzGRZZCukN3UePHlVQUJC93Nvb27xz2GwqXbq05s6dK3d3dzVq1EhxcXF69dVXNWbMGNPOcyUILgAAAIBCEhQU5BBcFKRUqVJyd3fXyZMnHcpPnjypqKiofI8pU6aMPD095e7ubi+rXbu24uPjlZ2dLS8vr6vrvBMYFgUAAACXZvoytP/broSXl5caNWqktWvX2stsNpvWrl2r5s2b53vMTTfdpP3798tms9nL9u7dqzJlyhRLYCERXAAAAAAlwvDhw/X2229r4cKF2rVrlwYOHKj09HT76lG9e/fWyJEj7fUHDhyoxMREDR06VHv37tWXX36pl19+WYMGDSquS2BYFAAAAFzb3ydgm9nmlbrvvvuUkJCg0aNHKz4+XvXr19fq1avtk7xjY2Pl5nYxNxAdHa2vv/5aw4YN0w033KBy5cpp6NCheuaZZ0y7jitFcAEAAACUEIMHD9bgwYPz3bdu3bo8Zc2bN9evv/5ayL26fAQXAAAAcGklJXPxX8CcCwAAAACmIHMBAAAAl1aY77lwNQQXAAAAcGnOLB17OW26IoZFAQAAADAFmQsAAAC4tPOZC7MndJva3DWDzAUAAAAAU5C5AAAAgEtjKVrzkLkAAAAAXNDZs2f1zjvvaOTIkUpMTJQkbd68WXFxcU63SeYCAAAALs3432Z2myXZtm3b1L59ewUHB+vw4cPq16+fwsLCtGzZMsXGxmrRokVOtUvmAgAAAHAxw4cP18MPP6x9+/bJx8fHXt65c2f9+OOPTrdL5gIAAAAuzRXnXPz+++9666238pSXK1dO8fHxTrdLcAEAAADX5oLjory9vZWSkpKnfO/evYqIiHC6XYZFAQAAAC7mzjvv1Pjx45WTkyNJslgsio2N1TPPPKPu3bs73S7BBQAAAFzb/4ZFmbmphA+Lmjp1qtLS0lS6dGmdO3dOrVu3VrVq1RQYGKgJEyY43S7DogAAAAAXExwcrDVr1ujnn3/Wtm3blJaWpoYNG6p9+/ZX1S7BBQAAAFyaYZzfzG7zWtCyZUu1bNnStPYILgAAAAAXM378+EvuHz16tFPtElwAAADApbniUrTLly93+JyTk6NDhw7Jw8NDVatWJbgAAAAAcHm2bNmSpywlJUUPP/yw7rrrLqfbZbUoAAAAuLYLqzuZvV1jgoKCNG7cOI0aNcrpNshcAAAAwKW58oTuf0pOTlZycrLTxxNcAAAAAC7mtddec/hsGIZOnDih9957T7fddpvT7RJcAAAAwLUZ/9vMbrMEmz59usNnNzc3RUREKCYmRiNHjnS6XYILAAAAwMUcOnSoUNoluAAAAIBLc8WlaAsLwQUAAADgAu6+++7Lrrts2TKnzkFwAQAAAJTwORJmCA4OLvRzEFwAAAAALmD+/PmFfg6CCwAAALg05lyYh+ACAAAArs0Fl6KVpKVLl+rjjz9WbGyssrOzHfZt3rzZqTbdzOgYAAAAgGvHa6+9pj59+igyMlJbtmxR06ZNFR4eroMHD17VS/SuOLjIzc3V+PHjdezYMadPCgAAAJQclkLaSq433nhDc+fO1axZs+Tl5aWnn35aa9as0ZAhQ5ScnOx0u1ccXHh4eOjVV19Vbm6u0ycFAAAAUHxiY2PVokULSZKvr69SU1MlSQ899JA++OADp9t1aljULbfcoh9++MHpkwIAAAAlhlFIWwkWFRWlxMRESVKFChX066+/Sjr/5m7DcL7zTk3ovu222/Tss89q+/btatSokfz9/R3233nnnU53CAAAAEDhuuWWW7RixQo1aNBAffr00bBhw7R06VL98ccfV/SyvX9yKrh4/PHHJUnTpk3Ls89ischqtTrdIQAAAKBIueBqUXPnzpXNZpMkDRo0SOHh4Vq/fr3uvPNODRgwwOl2nQouLnQEAAAAwLXHzc1Nbm4XZ0j07NlTPXv2vPp2r7aBzMzMq+4EAAAAUGwMS+FsJVi1atU0duxY7d2719R2nQourFarXnzxRZUrV04BAQE6ePCgJGnUqFF69913Te0gAAAAUJgMo3C2kmzQoEH68ssvVbt2bTVp0kQzZ85UfHz8VbfrVHAxYcIELViwQJMnT5aXl5e9vG7dunrnnXeuulMAAAAACs+wYcP0+++/a9euXercubNmz56t6OhodejQQYsWLXK6XaeCi0WLFmnu3Ll64IEH5O7ubi+vV6+edu/e7XRnAAAAgCLngkvRXlCjRg2NGzdOe/fu1U8//aSEhAT16dPH6facmtAdFxenatWq5Sm32WzKyclxujMAAAAAitbGjRu1ZMkSffTRR0pJSVGPHj2cbsupzEWdOnX0008/5SlfunSpGjRo4HRnAAAAgCLnghO69+7dqzFjxqhGjRq66aabtGvXLr3yyis6efKkPvzwQ6fbdSpzMXr0aMXExCguLk42m03Lli3Tnj17tGjRIn3xxRdOdwYAAABA4atVq5aaNGmiQYMGqWfPnoqMjDSlXaeCi65du2rlypUaP368/P39NXr0aDVs2FArV67UrbfeakrHAAAAgKJgMc5vZrdZku3Zs0fVq1c3vV2nggtJatWqldasWWNmXwAAAAAUgcIILCQn51xUqVJFZ86cyVN+9uxZValS5ao7BQAAABQZF14tymxOZS4OHz4sq9WapzwrK0txcXFX3SkAAACgyBTGBOwSPqG7sFxRcLFixQr7n7/++msFBwfbP1utVq1du1aVKlUyrXMAAAAArh1XFFx069ZNkmSxWBQTE+Owz9PTU5UqVdLUqVNN6xwAAABQ6ApjGFMJHxY1fvx4jRgxQn5+fg7l586d06uvvqrRo0c71e4Vzbmw2Wyy2WyqUKGCTp06Zf9ss9mUlZWlPXv26I477nCqIwAAAACKxrhx45SWlpanPCMjQ+PGjXO6XafmXBw6dMj+58zMTPn4+DjdAQAAAKBYuWDmwjAMWSx554Vs3bpVYWFhTrfr1GpRNptNL774osqVK6eAgAAdPHhQkjRq1Ci9++67TncGAAAAQOEJDQ1VWFiYLBaLatSoobCwMPsWHBysW2+9Vffee6/T7TuVuXjppZe0cOFCTZ48Wf369bOX161bVzNmzNAjjzzidIcAAACAIuVCmYsZM2bIMAz17dtX48aNc1igycvLS5UqVVLz5s2dbt+p4GLRokWaO3eu2rVrp8cee8xeXq9ePe3evdvpzgAAAAAoPBcWZapcubJatGghT09PU9t3KriIi4tTtWrV8pTbbDbl5ORcdacAAACAIuOC77lo3bq1bDab9u7da1+o6e9uvvlmp9p1KrioU6eOfvrpJ1WsWNGhfOnSpWrQoIFTHQEAAABQNH799Vfdf//9OnLkiAzDcQyXxWLJ94XZl8Op4GL06NGKiYlRXFycbDabli1bpj179mjRokX64osvnOoIAAAAUBwsxvnN7DZLsscee0yNGzfWl19+qTJlyuS7cpQznAouunbtqpUrV2r8+PHy9/fX6NGj1bBhQ61cuVK33nqrKR1zRTHj7tNtj7ZTQIi/dvyyW689/rbi9scXWP/6VrXVY8SdqtGoisLLhmnMXZO1/vPfC6w/9M1+umNAB70xbL6Wz1xVGJeAy9S7dgP1v76pInz9tSvxlMZs+FZbTxd8ry/oUqWWXm97p74+sk/9v11uL/fz8NSzTVqrQ8XqCvX20dHUZM3fuVnv7/6zEK8Cl+u+lvX08C2NVCrQX3uPJ2jip9/rr9iT+dbtfmNddWlSR9XKhEuSdh49pde+/Nmh/rYZw/I9dtrnP2rB95vMvwBckW53NtR9PZopLMxfBw6c0muz12j3nhMF1m99c031jblZUVHBOhaXqLnvrNNvGw/a94eG+Kl/v7Zq3KiSAvx9tG37Ub02e43i4pKK4nJwGR56or1u69FE/oG+2rnliGaN+0zHj5y55DFd7r9R9/S9WaGlAnRwd7zemLBCe7cfs++/rUcTtb2jvqrWKSv/AB91bzpO6amZhX0prsuFJnRfsG/fPi1dujTfqQ5Xw6mlaCWpVatWWrNmjU6dOqWMjAz9/PPP6tChwxW10aZNG/3f//2fs134T7nv6a7q9sRtmjlwrp64caQy07M0cfUL8vQueJKNj7+3Dm47olmD/33535u6NVXtZjV0Oi7RzG7DCXdUrqUXmrXVzC2/6I7PF2pXYoLe63Svwn38Lnlc+YAgPd+0rX6LP5pn36hmt6h1+cr6v3VfqN2n7+rdHZs0vnl7ta9g7j8YuHIdG9TQU91u1pzVv+q+Ke9rT9xpzXnsboUF+OZbv3G18vpq8249MnupHpzxoeLPpmrOwLtVOtjfXqftqLcctlFLvpbNZmjNtv1FdVkoQNvWtTRwwC1auPhn9R84XwcOntLkifcpJCT/v9/X1SmnUc911arVW9Vv4Hz9/Ms+vTi2uypVKmWv8+K47ioTFaIXRn+q/gPn6+TJZE15pad8fMydhAnn9Hj0ZnV9sIVeG/uZ/u++N5SZka0Jb/eVp1fBz29vvu169Xvmdi2evVaDu7+ug3tOaMLbfRUcdvHvubevl/74aa8+emtdEVwFXFGzZs20f7/5/91wOri4IC0tTSkpKQ4brtxdQ2/X+xM+1YYVf+jQ9li9EvO6wsuG6qZuTQo85vfVf2rBqA/1y2cbL9l2eNkwDXqtryY+OFO5Oblmdx1X6NG6jfXhnm36ZN9f2nf2jJ775Wudy83RvTWuL/AYN4tFM9vcoembf1Zsytk8+xtFltWn+/7Sr/FHdSwtRR/s2apdiadUPyKqEK8El6N3m4b6dMNf+nzjTh08magXP/lW57Jz1a1Z3Xzrj1y8Wh/9sk174hJ0+FSSxn64Rm4Wi5rVqGCvcyY1w2Fre31V/b7/qOLOJBfVZaEAPbo31ZdfbdXqr7frSOwZTZu5WplZObqt4w351u9+V2Nt/P2gPvpko2Jjz2j+wp+0b3+87uraSJJUvlyorqtTTjNe+1p79sbr6LFETX/ta3l7eeiWtrWL8tJQgLt636QP5nyvX7/bpUN74/Xqsx8rvHSgWrSvU+Axd8e00upPftea5ZsUe+CUZo39TFmZ2ep4d2N7nc8W/aKP3/lBu7fGFsVlwAU98cQTevLJJ7VgwQJt2rRJ27Ztc9ic5VRwcejQId1+++3y9/dXcHCwQkNDFRoaqpCQEIWGhjrdGVcVVbm0wsuEasu32+1lGSkZ2v3bftVpXvOq2rZYLHpm0RP6ZMoKHdl57N8PQKHydHPT9aWi9PPxw/YyQ9LPx4+oYemyBR43tH4LnT6XoY/2bs93/6aTx9W+QjVF+gVIkpqXqaDKQWH6Me5wvvVRNDzc3VS7fKR+3Xvxx4FhSL/tjVW9SmUuqw0fLw95uLkrOT3/4RBhAX5qVaeylv/6lyl9hvM8PNxUo0aUNm0+bC8zDGnz5sO6rk65fI+pU6esQ31J+v2PQ7qu9vn6np7nn35nZ198MGQYUk6OVdfXjTb3AnDFosqHKiwiSFs2XHz6m5GWpd3bjqp2vQr5HuPh6a7q15V1OMYwDG3ZcEC16+d/DFAYunfvrl27dqlv375q0qSJ6tevrwYNGtj/11lOzbl48MEHZRiG5s2bp8jIyKuaAJKbm6vBgwfrvffek6enpwYOHKjx48ebNqnkWhAWFSJJSjp51qE86eRZhUaGXFXb9z3TVbZcq5a/xhyLkiDUx08ebm46fS7Dofz0uXRVDQ7L95jGkeV0X80bdNvyBQW2O2bDt5rYsqM29npcOTarbIahZ3/+WhvjCSiLU6i/rzzc3XQm1fF+n0nNUOXIy3sQM6xLKyWkpDkEKH/XtWkdZWTm6FuGRBW74GA/ubu7KSkp3aE8KSldFaLD8z0mLDRASWfz1g/93/CY2KNnFH8yWf0eaa2pM1YrMzNH93RvotKlgxT+tyE0KB6hpQIlSWfPpDmUnz2dptCIwHyPCQrxk7uHe95jzqQqunJE4XQU/8qiQpjQbW5zpjt06FChtOtUcLF161Zt2rRJNWte3VN1SVq4cKEeeeQRbdy4UX/88Yf69++vChUqOLz5+4KsrCxlZWXZP1+rQ7Buub+l/m/OAPvnF+6YWCjnqd6wiu4acrseb/R0obSPwufv6aUZrW/Xsz+vVlLWuQLrPVynoRpElFXfbz5VXFqKmkWV14vNb9XJjDT9cvxIEfYYZurbrok6Naipvq9/ouzc/JcE7NbsOn25aVeB+3Fts1ptGjNumZ56srNWLh8mq9WmTZsP69eNB0r8D5f/orZ31NeQsd3sn0cPXFh8nQGu0j9fKWEWp4KLJk2a6OjRo6YEF9HR0Zo+fbosFotq1qyp7du3a/r06fkGFxMnTtS4ceOu+pzFbcOKP7T7t4tPGT29z9+G0MgQJcaftZeHRobowNbDTp+nbqtaCikdpPePvGkvc/dw14ApMbp76O16qMogp9uGc5IyM5Rrs6mUr+PkzlK+/ko4l56nfsXAEEUHhujdW7vby9z+l9U70GeE2i59Rycz0vRU45s1YO1yfXf0/Aozu5MSVCc8Uv2vb0JwUYyS0s8p12pTeKDj/Q4P9NPplIwCjjovpm0j9W3fWP3fWKZ9J07nW6dhlXKqHBmmpxZ+aVqf4bzk5AxZrTaFhjpmFEJD/ZWYlPfvtyQlJqUpNCRv/aTEi/X37jupfo/Nl7+ftzw83ZScfE5vvNZbe/YVvAIVCsev3+3U7m0XF9Xw8nKXJIWEBygxIdVeHlIqQAd35X9/Us5myJprVUh4gEN5SHigkk6n5nsMioALvkRPkt577z3NmTNHhw4d0oYNG1SxYkXNmDFDlStXVteuXZ1q06k5F++8845eeeUVLVy48KongNx4440OQ6CaN2+uffv25fvijpEjRyo5Odm+HT2ad9Wca8G5tEwdPxBv347sPKYzJ5LUoN3FCZ5+gb6q1ayadm7Y4/R5vn3vRw2oN0KPNXjKvp2OS9QnU1ZoZKcJZlwKrlCOzabtp+N1U5mLTwsskm4qW1GbTx3PU/9A8hndumyebvtsgX1bE7tfG07E6rbPFuhEeoo83dzk5e4u2z9egGM1bPZABMUj12rTrmMn1az6xbHxFovUrEa0th4u+Idhn1saq3+HZnp8znLtPJr/krWSdNeN12lH7EntPZ5/8IGilZtr09698WrYoJK9zGKRGjaoqB074/I9ZufO4w71JalRw0rasStv/fSMLCUnn1O5cqGqUSNKv6zfZ2b3cRnOZWTrROwZ+3Zk/yklJqSo/o1V7XX8/L1V64Zo7SpgInZujlX7dhx3OMZisaj+jVW1608mb6PovPnmmxo+fLg6d+6ss2fP2n97h4SEaMaMGU6361TmIiEhQQcOHFCfPn3sZRaLRYZhXNUb/f6Nt7e3vL29C6Xt4rZ85pe6//nuitsXrxOHTunh8ffpzPEk/fLZxfdWTF4zWr98tlGfz14tSfLx91G5ahdXA4qqXFpV61VSSmKaEo6eVmpimlITHcd05ubkKjE+Scf25v0hi6Lxzl9/aOrNnbXtdLy2JpxQ37qN5efhqU/+N1l72s2dFZ+Rpsl//Kgsq1V7kxx/OKZknZ/Ye6E8x5atDSdi9VzTNsrMzT0/LKpMtLpXu04v/vZ90V4c8li0brNeur+jdh49pe2x8XqwdQP5ennqs992SJImPNBRJ5PT9NoXv0iS+rRrrEG3Ndezi75SXGKKPeuRkZWjc9k59nb9vb3UoV4NTfn8x6K/KBTok0836tmn79DevSe0a88J3XNXY/n4eGn11+cfvI18+g4lnE7VO/N+kCR9uvwPzZh6v3rc01S//rZft7Spo5o1ymjqjNX2NlvfXFNnz57TqVPJqlK5tAY/3l6/rN+nPzYdLo5LxD8sX/SLej12i44fOaP4Y4nqPeRWnTmVqvXf7rTXmTjvEa3/dqdWLtkgSVq28CeNmNhD+/6K057tR3VX75vk4+ulb5ZffE9NaKkAhZYKVNmK5+frVKoRpXPpWTp14qzSkgseJgsnueB7LmbNmqW3335b3bp106RJk+zljRs31ogRI5xu16ngom/fvmrQoIE++OCDq57Q/dtvvzl8/vXXX1W9enW5u7s73ea16KPJn8vH30f/99YABYT46a+fd2vkbROUk3Xxx0SZqpEKKnVxgliNxlU09fuLw8QGTntYkvTNgnV6te/sIus7rswXh3Yr3MdXwxu1VISvv3aeOaXeX3+i05nnh8mUDQjKk4X4N098v1JPN75ZM9vcoRBvHx1LS9Grm37SYl6iV+y+3rJXof6+evy25ioV5Kc9cQka+NZyJaadv99RoYEO9/vem26Ql4eHpvXt4tDOm6s36M3Vv9o/d2pYU7JIX23eXTQXgsvy/Q+7FRzip4djWiks9PxL9J557iMlnT1/v0uXdvz7vWNnnF6auEJ9H75Zj/a5WXFxSRo19lMdPnzxoUJ4WIAeH9BOoaH+OpOYpm/W/KX33v+lyK8N+fvknR/l4+ulIePuUkCQj3ZsPqIX+s9Xzt9W+CpbIVzBoReHR/741XYFhwbooSHtFVoqUAd3ndAL/ec7TPK+/b5menBwe/vnqYvPz9WcOvITrflscxFcmYtxweDi0KFD+a4K5e3trfT0/IdyXg6LYVzhrxhJ/v7+2rp161W/0a9NmzbatGmT+vXrpwEDBmjz5s3q16+fpk6dqgEDBvzr8SkpKQoODlYbdZWHhZcJuYK9bxf83g/89wRv5++1KwnfkfXvlfCf4X2M97K4ilxrltbum67k5GQFBQUVd3ccXPgtWfHlCXLz8TG1bVtmpo4893yJvG5JqlOnjiZOnKiuXbsqMDBQW7duVZUqVTRr1izNnz9fmzc7F8Q6lbm45ZZbTAkuJKl37946d+6cmjZtKnd3dw0dOlT9+/e/6nYBAACAy2ExCmEp2hKeuRg+fLgGDRqkzMxMGYahjRs36oMPPtDEiRP1zjvvON2uU8FFly5dNGzYMG3fvl3XX3+9PD0dny7eeeedl9XOunXr7H9+8803C64IAAAAuIDZs2fr1VdfVXx8vOrVq6dZs2apadOm/3rchx9+qF69eqlr16767LPP/rX+o48+Kl9fX73wwgvKyMjQ/fffr7Jly2rmzJnq2bOn0/13Krh47LHHJEnjx4/Ps68wJ3QDAAAApishcy4++ugjDR8+XHPmzFGzZs00Y8YMdezYUXv27FHp0qULPO7w4cMaMWKEWrVqdUXne+CBB/TAAw8oIyNDaWlplzzH5XJqKVqbzVbgRmABAAAAXLlp06apX79+6tOnj+rUqaM5c+bIz89P8+bNK/AYq9WqBx54QOPGjVOVKlWcOq+fn58pgYXkZOYCAAAA+M8oxMxFSkqKQ3FBr1bIzs7Wpk2bNHLkSHuZm5ub2rdvrw0bNhR4mvHjx6t06dJ65JFH9NNPP12ySw0bNtTatWsVGhqqBg0aXHLF1yKd0C1J6enp+uGHHxQbG6vs7GyHfUOGDHG2WQAAAOA/Izo62uHzmDFjNHbs2Dz1Tp8+LavVqsjISIfyyMhI7d6d/7LjP//8s9599139+eefl9WXrl272gObbt26XdYxV8qp4GLLli3q3LmzMjIylJ6errCwMJ0+fdqeUiG4AAAAwLWiMFeLOnr0qMNStGa9EDo1NVUPPfSQ3n77bZUqVeqyjhkzZky+fzaTU8HFsGHD1KVLF82ZM0fBwcH69ddf5enpqQcffFBDhw41u48AAABA4TEs5zez25QUFBR0We+5KFWqlNzd3XXy5EmH8pMnTyoqKipP/QMHDujw4cPq0uXiS1dtNpskycPDQ3v27FHVqlULPN/vv/8um82mZs2aOZT/9ttvcnd3V+PGjf+1z/lxakL3n3/+qSeffFJubm5yd3dXVlaWoqOjNXnyZD333HNOdQQAAABwVV5eXmrUqJHWrl1rL7PZbFq7dq2aN2+ep36tWrW0fft2/fnnn/btzjvvVNu2bfXnn3/mGY71T4MGDdLRo0fzlMfFxWnQoEFOX4dTmQtPT0+5uZ2PS0qXLq3Y2FjVrl1bwcHB+XYSAAAAKLFKyFK0w4cPV0xMjBo3bqymTZtqxowZSk9PV58+fSSdf/l0uXLlNHHiRPn4+Khu3boOx4eEhEhSnvL87Ny5Uw0bNsxT3qBBA+3cufPKO/8/TgUXDRo00O+//67q1aurdevWGj16tE6fPq333nvvsi4GAAAAgKP77rtPCQkJGj16tOLj41W/fn2tXr3aPsk7NjbW/oD/anl7e+vkyZN5lq89ceKEPDycX1DWqSNffvllpaamSpImTJig3r17a+DAgapevfol1+EFAAAASprCnNB9pQYPHqzBgwfnu2/dunWXPHbBggWXfZ4OHTpo5MiR+vzzzxUcHCxJOnv2rJ577jndeuutl93OP11xcGEYhkqXLm3PUJQuXVqrV692ugMAAAAAitaUKVN08803q2LFimrQoIGk8/OqIyMj9d577zndrlPBRbVq1bRjxw5Vr17d6RMDAAAAJUIJmXNRlMqVK6dt27bp/fff19atW+Xr66s+ffqoV69e8vT0dLrdKw4u3NzcVL16dZ05c4bgAgAAALhG+fv7q3///qa26dSci0mTJumpp57Sm2++yQRuAAAAXNsKYc5FScxcrFixQrfddps8PT21YsWKS9a98847nTqHU8FF7969lZGRoXr16snLy0u+vr4O+xMTE53qDAAAAFDkXGRYVLdu3RQfH6/SpUurW7duBdazWCyyWq1OncOp4GLGjBlOnQwAAABA8bjwBu9//tlMTgUXMTExZvcDAAAAKB4ukrkICwvT3r17VapUKfXt21czZ85UYGCgqee46rdwZGZmKiUlxWEDAAAAULJkZ2fbf6svXLhQmZmZpp/DqcxFenq6nnnmGX388cc6c+ZMnv3OjtECAAAAilpJeoleYWrevLm6deumRo0ayTAMDRkyJM/c6QucfTG2U5mLp59+Wt99953efPNNeXt765133tG4ceNUtmxZLVq0yKmOAAAAACg8ixcvVufOnZWWliZJSk5OVlJSUr6bs5zKXKxcuVKLFi1SmzZt1KdPH7Vq1UrVqlVTxYoV9f777+uBBx5wukMAAAAAzBcZGalJkyZJkipXrqz33ntP4eHhpp7DqcxFYmKiqlSpIkkKCgqyLz3bsmVL/fjjj+b1DgAAAIApwsLCdPr0aUlS27Zt5eXlZfo5nAouqlSpokOHDkmSatWqpY8//ljS+YxGSEiIaZ0DAAAACp1RSFsJU2IndPfp00dbt25V69at9eyzz6pLly56/fXXlZOTo2nTppndRwAAAKDQMKE7L2cndF9RcGGz2fTqq69qxYoVys7O1vHjxzVmzBjt3r1bmzZtUrVq1XTDDTc41REAAAAAhWfx4sWaPn26Dhw4IIvFouTkZNOzF1cUXEyYMEFjx45V+/bt5evrq5kzZ+rUqVOaN2+eKlasaGrHAAAAgCJTAjMNZitxE7oXLVqkN954Q19//bU+++wzrVy5Uu+//36hvT4cAAAAgPkOHTpkDyzMzF5cUXARGxurzp072z+3b99eFotFx48fN61DAAAAQJFykQndf2ez2fTiiy+qXLlyCggI0MGDByVJo0aN0rvvvut0u1cUXOTm5srHx8ehzNPTUzk5OU53AAAAAEDReumll7RgwQJNnjzZYUnaunXr6p133nG63Suac2EYhh5++GF5e3vbyzIzM/XYY4/J39/fXrZs2TKnOwQAAAAUJVdZLervFi1apLlz56pdu3Z67LHH7OX16tXT7t27nW73ioKLmJiYPGUPPvig0ycHAAAAUPTi4uJUrVq1POU2m+2qRiVdUXAxf/58p08EAAAAlEiFMUeihGcu6tSpo59++inPiq9Lly5VgwYNnG7XqZfoAQAAAP8VrjgsavTo0YqJiVFcXJxsNpuWLVumPXv2aNGiRfriiy+cbveKJnQDAAAAuPZ17dpVK1eu1Lfffit/f3+NHj1au3bt0sqVK3Xrrbc63S6ZCwAAALg2FxwWJUmtWrXSmjVrTG2T4AIAAABwUZs2bdKuXbskSdddd91VzbeQCC4AAADg6lwwc3Hq1Cn17NlT69atU0hIiCTp7Nmzatu2rT788ENFREQ41S5zLgAAAAAX88QTTyg1NVU7duxQYmKiEhMT9ddffyklJUVDhgxxul0yFwAAAHBprrha1OrVq/Xtt9+qdu3a9rI6depo9uzZ6tChg9PtkrkAAAAAXIzNZpOnp2eeck9PT9lsNqfbJbgAAACAazMKaSvBbrnlFg0dOlTHjx+3l8XFxWnYsGFq166d0+0SXAAAAMC1uWBw8frrryslJUWVKlVS1apVVbVqVVWuXFkpKSmaNWuW0+0y5wIAAABwMdHR0dq8ebO+/fZb7d69W5JUu3ZttW/f/qraJbgAAACAS3PFCd2SZLFYdOutt17VG7n/iWFRAAAAgIv47rvvVKdOHaWkpOTZl5ycrOuuu04//fST0+0TXAAAAMC1udCcixkzZqhfv34KCgrKsy84OFgDBgzQtGnTnG6f4AIAAABwEVu3blWnTp0K3N+hQwdt2rTJ6faZcwEAAACX5kpzLk6ePJnv+y0u8PDwUEJCgtPtk7kAAAAAXES5cuX0119/Fbh/27ZtKlOmjNPtE1wAAADAtbnQnIvOnTtr1KhRyszMzLPv3LlzGjNmjO644w6n22dYFAAAAFxbYQQDJTS4eOGFF7Rs2TLVqFFDgwcPVs2aNSVJu3fv1uzZs2W1WvX888873T7BBQAAAOAiIiMjtX79eg0cOFAjR46UYZyPgiwWizp27KjZs2crMjLS6fYJLgAAAODSLP/bzG6zpKpYsaJWrVqlpKQk7d+/X4ZhqHr16goNDb3qtgkuAAAAABcUGhqqJk2amNomwQUAAABcmwvNuShsrBYFAAAAwBRkLgAAAODSXOkleoWNzAUAAAAAU5C5AAAAgGtjzoVpCC4AAAAAFw0GzMawKAAAAACmIHMBAAAAl8aEbvOQuQAAAABgCjIXAAAAcG1M6DYNmQsAAAAApiBzAQAAAJfGnAvzkLkAAAAAYAoyFwAAAHBtzLkwDZkLAAAAAKb4T2Qulu/drqBA4iRXUGd93eLuAopQ+F+24u4CipDPrrji7gKKkC05pbi7gCJiGNnF3YV/xZwL8/wnggsAAADAaQyLMg2P+wEAAACYgswFAAAAXBuZC9OQuQAAAABgCjIXAAAAcGlM6DYPmQsAAAAApiBzAQAAANfGnAvTkLkAAAAAYAoyFwAAAHBpFsOQxTA31WB2e9cKggsAAAC4NoZFmYZhUQAAAABMQXABAAAAl3ZhKVqzN2fMnj1blSpVko+Pj5o1a6aNGzcWWPftt99Wq1atFBoaqtDQULVv3/6S9YsCwQUAAABQAnz00UcaPny4xowZo82bN6tevXrq2LGjTp06lW/9devWqVevXvr++++1YcMGRUdHq0OHDoqLiyvinl9EcAEAAADXZhTSdoWmTZumfv36qU+fPqpTp47mzJkjPz8/zZs3L9/677//vh5//HHVr19ftWrV0jvvvCObzaa1a9de+clNQnABAAAAFJKUlBSHLSsrK9962dnZ2rRpk9q3b28vc3NzU/v27bVhw4bLOldGRoZycnIUFhZmSt+dQXABAAAAl1aYcy6io6MVHBxs3yZOnJhvH06fPi2r1arIyEiH8sjISMXHx1/WdTzzzDMqW7asQ4BS1FiKFgAAACgkR48eVVBQkP2zt7d3oZxn0qRJ+vDDD7Vu3Tr5+PgUyjkuB8EFAAAAXFshvuciKCjIIbgoSKlSpeTu7q6TJ086lJ88eVJRUVGXPHbKlCmaNGmSvv32W91www1Od9kMDIsCAACASysJS9F6eXmpUaNGDpOxL0zObt68eYHHTZ48WS+++KJWr16txo0bO/sVmIbMBQAAAFACDB8+XDExMWrcuLGaNm2qGTNmKD09XX369JEk9e7dW+XKlbPP23jllVc0evRoLVmyRJUqVbLPzQgICFBAQECxXAPBBQAAAFxbIQ6LuhL33XefEhISNHr0aMXHx6t+/fpavXq1fZJ3bGys3NwuDjx68803lZ2drXvuucehnTFjxmjs2LFX03unEVwAAAAAJcTgwYM1ePDgfPetW7fO4fPhw4cLv0NXiOACAAAALu9K50ggf0zoBgAAAGAKMhcAAABwbYZxfjO7TRdE5gIAAACAKchcAAAAwKU5816Ky2nTFRFcAAAAwLWVkKVo/wsYFgUAAADAFGQuAAAA4NIstvOb2W26IjIXAAAAAExB5gIAAACujTkXpiFzAQAAAMAUZC4AAADg0liK1jxkLgAAAACYgswFAAAAXJthnN/MbtMFEVwAAADApTEsyjwMiwIAAABgCjIXAAAAcG0sRWsaMhcAAAAATEHmAgAAAC6NORfmIXMBAAAAwBRkLgAAAODaWIrWNGQuAAAAAJiCzAUAAABcGnMuzENwAQAAANfGUrSmYVgUAAAAAFOQuQAAAIBLY1iUechcAAAAADAFmQsAAAC4NptxfjO7TRdE5gIAAACAKchcAAAAwLWxWpRpyFwAAAAAMAWZCwAAALg0iwphtShzm7tmEFwAAADAtRnG+c3sNl0Qw6IAAAAAmILMBQAAAFwaL9EzD5kLAAAAAKYgcwEAAADXxlK0piFzAQAAAMAUZC4AAADg0iyGIYvJqzuZ3d61gswFAAAAAFOQuQAAAIBrs/1vM7tNF0RwAQAAAJfGsCjzMCwKAAAAgCnIXAAAAMC1sRStachcAAAAADAFmQsAAAC4NsM4v5ndpgsicwEAAADAFGQuAAAA4NIsxvnN7DZdEZkLAAAAAKYgc1FCvDH/rKa8cVbxCVbVq+OlmRMi1LSBT751c3IMTZqVpEUfpygu3qqaVT018flwdbrF314nNc2m0a+c0WdfpevUGasa1PXW9BdLqUn9/NtE0Ur6aqMSP/9F1rNp8q4UpdKP3Cbf6uXzrZv83RbFz/7coczi6a4aH45yKMs6lqCE99bo3M4jMqw2eZePUNmn7pVnREhhXQYuU7c7G+q+e5spLMxfBw6c0muvr9HuPScKrN/65prq+/DNiooK1rG4RM19e51+23jQvj80xE/9+7VV40aVFBDgo23bj+q119coLi6pKC4Hl+GhEZ3V6f4W8g/y1c4/Dun1kR/p+KGESx5zR0wr3TOwnUIjgnRwZ5zeHLVUe/884lCnVqNKinmmi2o1qCib1aYDO+L0wgNvKDszpzAvB/+i9wt3qdPDbRQQ7Kedv+7Ta/+3UMcPnCywft2baqrH0NtUvUElhZcJ1dieM7Xhi80OdW66s5Fuf+QWVa9fSUHhARrYfJQObo8t7EtxXcy5MA2ZixLgo89T9eTY0xr1ZJj++DpaN9Tx1m29juvU6dx864965YzmvpesmRMi9NcPFdS/d7C6PxKvLduz7HX6PXlK3/54TgtnRWrrd9G6tbWvOtx7XHEn8m8TRSfll7+UsOBrlbq3jSq+OkDeFSN17MXFyk1OK/AYNz9vVX3nSftWZc4wh/3Z8YmKfX6evMqVUvS4h1Vp2kCF97hZFi+eHxS3tm1qaeBjt2jhez+r/2PzdeDgKU2edJ9CQvzyrX9dnXIa9XxXrVq9Vf0em6+ff9mnF8d1V6VKpex1XhzfXWXKhOiFMZ+q/2PzdfJksqZM7ikfH8+iuixcQo/H2+vOvq0169mP9H9dpiozI0svvf+4PL0L/vt4850N1X/MXXp/2ld6otNkHdoZp5fef1zB4QH2OrUaVdJLix/X5h92a+jtUzTk9ilaueBHGTbX/AFTUtw7rLO6PnarZg1doKFtxiszPUsvfzZCnt4F/3308fPWwb+O6vXh712yzo4Ne/Xu6I8Lo9tAoSG4KAFmvHVWjz4QrD49g1SnppfenBwhP1+L5n+Qmm/9xUtTNXJIqDq381eVip4aGBOs227x07Q5559anjtn07Iv0zRpVLhubu6rapW9NGZEuKpV8tSchclFeWnIR9LKDQpu31DBtzSQd3RpRQ64Q27enkpeu+WSx3mEBl7cQgIc9p1eslYBDaurdO8O8qlSRl5RYQpoUksewQEFtIai0qN7U325aqtWf71dR2LPaNqM1crMytFtnW7It373uxtr4+8H9dHHGxUbe0bzF/ykffvjdVfXRpKk8uVCdV2dcpox82vt2ROvo8cSNX3m1/L28tAtbWsX5aWhAN0ebaMPZ36tX7/ZrsO7jmvK0PcUHhmsFh3zv+eSdFe/tvpqyQat+fg3xe6L16xnP1LWuWx16NncXmfA2Lv1+bwf9MnsNYrdG6+4A6f008otysnmoVFx6jaooz6YvFIbvtyiQzuOanL/uQovE6IWXRoWeMwfa7Zp4fhPtX7lpgLrrP1wvd6f9Lm2fL+jMLqNf7DYCmdzRQQXxSw729CmbVlq18rXXubmZlG7Vn7asCkz32Oysg15ezveOl8fi37ZeL5+rlWyWiUfb0s+dc6ZfAW4EkZOrjIPHJffDVXsZRY3N/ndUEWZe48VeJwtM1sHBkzXgf7TFDfpA2XFnrrYps2mtE375Fk2XEfHv6f9fSbryLNvK/W3XYV6Lfh3Hh5uqlEjSps2H7aXGYa0efNhXVenXL7H1KlT1qG+JP3++yF7fc//ZaOy//aD0jCknByrrq8bbe4F4IpFVQhXWGSwtvy8x16WkZqpPVsOq1ajyvke4+Hpruo3ROvPny4eYxiG/vx5j2o3qiRJCg4PUK2GlZV8OlVTPx+mJX9O0OSlQ3Rdkyr5tomiEVUpQuFRIdr8twAgI+Wcdv9xULWbVivGnuGKXRgWZfbmgkpEcGGz2TR58mRVq1ZN3t7eqlChgiZMmFDc3SoSpxOtslqlyAh3h/LICHedPJX/06gObfw0462z2ncwWzaboTU/ZGj5qnSd+F/9wAA3NW/sownTE3U8PldWq6HFS1O1YVOmTpyyFvo1oWDW1AzJZuTJPLgH+yv3bP7DorzKlVLUoK4q92xPlRl6twybodjn31XOmfNZKGtyuozMbCUu/1n+Daqp/OiHFNC0lo6/+pEydhwu7EvCJQQH+8nd3U1JSekO5UlJ6QoL9c/3mLDQgLz1z6YrNOx8/djYM4o/max+j7ZWQIC3PDzc1PO+ZipdOkjh4fm3iaITWjpIkpSU4Jh5Tjqdat/3T0Fh/nL3cFfS6RTHYxJSFRpx/pgyFc8Pi3vgyc5a/f56jXrgTe3/65gmfjRYZStHmH0ZuExhkcGSpLOnHEcFnD2VYt8HuJoSMSB75MiRevvttzV9+nS1bNlSJ06c0O7du/PUy8rKUlbWxXkFKSkpeeq4ghnjI9R/xCnVaRUri0WqWslTD/cM0vwPL34fC2dF6tFhJxXd4LDc3aWG13urZ7cAbd6WdYmWURL51oyWb81oh8+Hhr6u5G82qVSvW+xPRgKa1FRYl/NDKHwql9G5PUd19us/5HddpeLoNgqJ1WrTmLHL9NSTnbXys2GyWm3atPmwfv3tgCyWfz8e5mp7V2M98UpP++cxvecUynksbudv7qrFv2jNx79Jkg7sOKb6N9VQh/tu1IJJKwvlvHDU9t7mGvraw/bPo+6ZVnydgbmM/21mt+mCij24SE1N1cyZM/X6668rJiZGklS1alW1bNkyT92JEydq3LhxRd3FQlUqzF3u7tLJBMeMwskEqyJL5397Ikq5a/mCMsrMtOlMkk1lo9w1csIZValwcfJY1Uqe+n55eaVn2JSSalOZSA/1HBCvyhWZ8Fmc3AP9JDdLniyFNTk9TzajIBYPd3lXLqPsE4kX23R3k3e049NL7/IRytjFyiLFKTk5Q1arTaH/yFKEhvor8R/ZiQsSk9Ly1g/xV1Lixfp7951Uv8fmy9//fOYiOfmc3pjVW3v2FrwCFQrHr99s1+4th+2fLwxbC40IVNKpiw98QksF6sCOuHzbSElMlzXXqtBSjpmN0IhAJSWcbyPx5Pn/jf3HPY7df1Kly4Ve9XXg8vy6aov2/HHA/vnCpO2Q0sFKPHkxexFSOkgHtvHvL1xTsQ+L2rVrl7KystSuXbt/rTty5EglJyfbt6NHjxZBDwuXl5dFjW7w1nc/X5wLYbMZ+u7nDDVvdOllY3183FSujIdyc6VlX6brzo55h0T4+7mpTKSHks5a9c26jHzroOhYPD3kU7WsMrYfspcZNpsyth2UT438l6L9J8NqU/aRk/IIDbjYZrWyyo4741Av+/gZeUaQli9Oubk27d0br4YNK9nLLBapYYOK2rEz/x+aO3ceV8MGlRzKGjWqlG/99PQsJSefU7lyoapRI0q/rN9nZvdxGc6lZ+nE4dP2LXZvvBJPJqt+y5r2On4BPqrZoJJ2bzqUbxu5OVbt23ZU9VvWsJdZLBbVb1lDuzYdliSdPHpGp0+cVfmqkQ7Hlq8SoZMsQVxkzqVl6vjBU/btyK44nYk/qwZt6tjr+AX6qFbjKtq1cX8x9hRXymIYhbK5omLPXPj6+v57pf/x9vaWt7d3IfamePzfgBD1GXpKjep5q2l9H818+6zSMww93DNQkhTzxEmVi3LXy8+fH3P72+ZMxZ3IVf263oo7kavxUxNlsxl6alCIvc2vv0+XYUg1q3lp/6EcPfPiadWq5qk+PfMf84uiE9qlueJnLZdP1bLyqV5OSV/8KltWjoJvaSBJOvHaMnmEBSniwfaSpNMfr5NvjfLyjAqTLSNTiZ+tV87pZAW3v7gSSVjXm3R82ifyrVNRfnUrKX3LfqX9sUfR4x8ujkvE33zy6UY9+/Qd2rvnhHbtOaF77m4sHx8vrV69TZI08pk7lHA6Ve+8+4Mk6dNlf2jGtPvV456m+vW3/bqlbR3VrFFGU6evtrfZ+uaaOpt8TqdOJatK5dIa/Hh7/bJ+n/743w9RFK/P3lmnnkM6Ku7gKZ08ekYPPXWHzpxM1vqvt9nrTPxosNZ/tU0rF/woSVr+9vd6cvqD2rctVnu2HFG3fm3k7eutNR/9aj/m0zlr9eCTnXVoZ5wO7Dim9j2aqXzVSE3oP6/IrxEXfTb7a/V6+k7FHTip+CMJinnhbp05cVbrV158b8WkL57W+pWbteKtbyVJPv7eKlvlYqAYVTFCVa6voNSkNCUcO5+VDgz1V0T5cIWXCZEkRdeIkiQlnUxW0ilWfkTJVezBRfXq1eXr66u1a9fq0UcfLe7uFIv7ugbq9Bmrxk5OVHxCrupf561VS8oqMuL87TkalyO3v+WYMjMNjX7ljA7G5irAz6Lb2vlr4axIhQRfnBSenGrT8y+f0bETuQoLcdfdtwfopWfD5OnJoOziFnRTXVmT03X6w+/Pv0SvcpTKv/CgfVhUzulk/X3wvC09U/FvrpT1bJrcAnzkU6WsKkx4RN7Rpe11ApvVVlT/O3Rm2c86Ne8reZUNV9mn7pNf7YpFfn1w9P263QoO9tPDD7dSWOj5l+g9M/IjJZ3NkCSVLh0k29/eU7BjZ5xeenmF+va5WY/2vVlxcUkaNeZTHT582l4nPCxAjz/WTqGh/jqTmKZv1vyl9xb/UuTXhvx98sa38vHz0pDJvRQQ5Ksdvx/UqAffUE7WxUU6ylQspaCwi5nkH1dsVnBYgB4ccbvCIs4PoRr14Bs6e/rixPDP3lknT29P9R97twJD/HRwZ5ye7zVbJ46cForPx9NXycffW0NnPayAYD/t2LBPz981RTlZF19sWKZyaQX97Z0lNRpW1qtfjbR/fuyV+yVJ3yz+SVMfe0eSdGPnBhrxVj97necWDpIkvffyci1++bPCvCTXxEv0TGMxjOK/8nHjxmnmzJmaMWOGbrrpJiUkJGjHjh165JFHLnlcSkqKgoODlbS3ioICi32EF4pAnfUPFncXUITKvPHfy1SiYD678h8qhv8mW7JrLsriinKNbH2X8aGSk5MVFFSyRlBc+C3ZttFIeXhcejj6lcrNzdT3myaWyOsuTMWeuZCkUaNGycPDQ6NHj9bx48dVpkwZPfbYY8XdLQAAALgCQ5LZL70r9sf3xaNEBBdubm56/vnn9fzzzxd3VwAAAOBiCmMCtqtO6GYsEQAAAABTlIjMBQAAAFBsDBXChG5zm7tWkLkAAAAAYAoyFwAAAHBtLEVrGjIXAAAAAExB5gIAAACuzSbJ7PcMm7207TWCzAUAAAAAU5C5AAAAgEvjPRfmIbgAAACAa2NCt2kYFgUAAACUELNnz1alSpXk4+OjZs2aaePGjZes/8knn6hWrVry8fHR9ddfr1WrVhVRT/NHcAEAAADXdiFzYfZ2hT766CMNHz5cY8aM0ebNm1WvXj117NhRp06dyrf++vXr1atXLz3yyCPasmWLunXrpm7duumvv/662m/EaQQXAAAAQAkwbdo09evXT3369FGdOnU0Z84c+fn5ad68efnWnzlzpjp16qSnnnpKtWvX1osvvqiGDRvq9ddfL+KeX0RwAQAAANdWiJmLlJQUhy0rKyvfLmRnZ2vTpk1q3769vczNzU3t27fXhg0b8j1mw4YNDvUlqWPHjgXWLwoEFwAAAEAhiY6OVnBwsH2bOHFivvVOnz4tq9WqyMhIh/LIyEjFx8fne0x8fPwV1S8KrBYFAAAA11aIL9E7evSogoKC7MXe3t4mn6hkIbgAAAAACklQUJBDcFGQUqVKyd3dXSdPnnQoP3nypKKiovI9Jioq6orqFwWGRQEAAMClXXiJntnblfDy8lKjRo20du1ae5nNZtPatWvVvHnzfI9p3ry5Q31JWrNmTYH1iwKZCwAAALi2EvISveHDhysmJkaNGzdW06ZNNWPGDKWnp6tPnz6SpN69e6tcuXL2eRtDhw5V69atNXXqVN1+++368MMP9ccff2ju3LmmXsqVILgAAAAASoD77rtPCQkJGj16tOLj41W/fn2tXr3aPmk7NjZWbm4XBx61aNFCS5Ys0QsvvKDnnntO1atX12effaa6desW1yUQXAAAAMDF2QzJYnLmwuZce4MHD9bgwYPz3bdu3bo8ZT169FCPHj2cOldhYM4FAAAAAFOQuQAAAIBrKyFzLv4LyFwAAAAAMAWZCwAAALi4QshciMwFAAAAADiNzAUAAABcG3MuTENwAQAAANdmM2T6MCYnl6K91jEsCgAAAIApyFwAAADAtRm285vZbbogMhcAAAAATEHmAgAAAK6NCd2mIXMBAAAAwBRkLgAAAODaWC3KNGQuAAAAAJiCzAUAAABcG3MuTENwAQAAANdmqBCCC3Obu1YwLAoAAACAKchcAAAAwLUxLMo0ZC4AAAAAmILMBQAAAFybzSbJVghtuh4yFwAAAABMQeYCAAAAro05F6YhcwEAAADAFGQuAAAA4NrIXJiG4AIAAACuzWbI9Lfe2VwzuGBYFAAAAABTkLkAAACASzMMmwzD3KVjzW7vWkHmAgAAAIA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-      "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
+    "import polars as pl\n",
+    "import seaborn as sns\n",
     "\n",
     "from ert.config import ErtConfig\n",
-    "from ert.storage import open_storage\n",
-    "\n",
-    "ert_config = ErtConfig.from_file(\"poly.ert\")\n",
-    "\n",
-    "with open_storage(ert_config.ens_path, mode=\"r\") as storage:\n",
-    "    experiment = storage.get_experiment_by_name(\"ensemble-experiment\")\n",
-    "    ensemble = experiment.get_ensemble_by_name(\"default_1\")\n",
-    "    corr_XY = ensemble.load_cross_correlations()\n",
-    "\n",
-    "coeffs = corr_XY.COEFFS.values\n",
-    "parameter_names = list(corr_XY.parameter.values)\n",
-    "\n",
-    "# Plotting the heatmap\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "heatmap = plt.imshow(coeffs, cmap=\"viridis\", aspect=\"auto\")\n",
-    "plt.colorbar(heatmap, label=\"Coefficient value\")\n",
-    "plt.title(\"Heatmap of COEFFS\")\n",
-    "plt.xlabel(\"Response\")\n",
-    "plt.ylabel(\"Parameter\")\n",
-    "plt.xticks(\n",
-    "    range(coeffs.shape[1]), labels=[f\"Response {i+1}\" for i in range(coeffs.shape[1])]\n",
-    ")\n",
-    "plt.yticks(range(coeffs.shape[0]), labels=parameter_names)\n",
-    "\n",
-    "# Annotating each cell with the coefficient values\n",
-    "for i in range(coeffs.shape[0]):\n",
-    "    for j in range(coeffs.shape[1]):\n",
-    "        text_color = (\n",
-    "            \"white\" if coeffs[i, j] < 0.5 else \"black\"\n",
-    "        )  # Adjusting text color for readability\n",
-    "        plt.text(\n",
-    "            j, i, f\"{coeffs[i, j]:.2f}\", ha=\"center\", va=\"center\", color=text_color\n",
-    "        )\n",
-    "\n",
-    "plt.show()"
+    "from ert.storage import open_storage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "952539a1-4072-47e5-9b8c-8296ca086134",
+   "metadata": {},
+   "source": [
+    "## Load data frame by using API endpoint"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "2f939b96-8ad0-49ac-8e89-101718503b1b",
+   "execution_count": 3,
+   "id": "a1a040f7-2727-4740-a619-fab723225509",
    "metadata": {},
    "outputs": [
     {
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-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 144B\n",
-       "Dimensions:    (parameter: 3, response: 5)\n",
-       "Coordinates:\n",
-       "  * parameter  (parameter) object 24B &#x27;a&#x27; &#x27;b&#x27; &#x27;c&#x27;\n",
-       "Dimensions without coordinates: response\n",
-       "Data variables:\n",
-       "    COEFFS     (parameter, response) float64 120B -0.1236 0.2924 ... -0.1065</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f5ac1bb8-d31d-4137-8e6a-7c16eff81b16' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f5ac1bb8-d31d-4137-8e6a-7c16eff81b16' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>parameter</span>: 3</li><li><span>response</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8152304b-505d-41ff-b573-f35e3adaa95f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8152304b-505d-41ff-b573-f35e3adaa95f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>parameter</span></div><div class='xr-var-dims'>(parameter)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;a&#x27; &#x27;b&#x27; &#x27;c&#x27;</div><input id='attrs-44d0d85f-8a83-41eb-aca8-68413cd12bed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44d0d85f-8a83-41eb-aca8-68413cd12bed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2173d859-b781-4808-b7f3-097eb2492675' class='xr-var-data-in' type='checkbox'><label for='data-2173d859-b781-4808-b7f3-097eb2492675' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e1e1fd2e-a746-48a5-9b2b-f7703e7f05f1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e1e1fd2e-a746-48a5-9b2b-f7703e7f05f1' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>COEFFS</span></div><div class='xr-var-dims'>(parameter, response)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1236 0.2924 ... -0.05799 -0.1065</div><input id='attrs-6b296824-4074-4268-b73a-6060475038d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b296824-4074-4268-b73a-6060475038d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b5e55ca-af8e-4e28-945c-c0cc68844cf7' class='xr-var-data-in' type='checkbox'><label for='data-6b5e55ca-af8e-4e28-945c-c0cc68844cf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.123575,  0.292361,  0.781334,  0.906171,  0.947413],\n",
-       "       [-0.139679,  0.484481,  0.274759,  0.092571, -0.007974],\n",
-       "       [ 0.99046 ,  0.564414,  0.086698, -0.057991, -0.106489]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-307906b4-e671-47ca-8183-5bef811fc89e' class='xr-section-summary-in' type='checkbox'  ><label for='section-307906b4-e671-47ca-8183-5bef811fc89e' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>parameter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-37f7779a-5067-4ea4-a93c-c51a03c2b08b' class='xr-index-data-in' type='checkbox'/><label for='index-37f7779a-5067-4ea4-a93c-c51a03c2b08b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;], dtype=&#x27;object&#x27;, name=&#x27;parameter&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6d1d9d3-349b-425f-a8d0-2fcccfa09a5e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d6d1d9d3-349b-425f-a8d0-2fcccfa09a5e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
+      "image/png": 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AAAAAAAAAAMAPDTQAAAAAAAAAAADADw00AAAAAAAAAAAAwA8NNAAAAAAAAAAAAMAPDTQAAAAAAAAAAADADw00AAAAAAAAAAAAwA8NNAAAAAAAAAAAAMDPvwDKdY6kk9Dq6wAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<xarray.Dataset> Size: 144B\n",
-       "Dimensions:    (parameter: 3, response: 5)\n",
-       "Coordinates:\n",
-       "  * parameter  (parameter) object 24B 'a' 'b' 'c'\n",
-       "Dimensions without coordinates: response\n",
-       "Data variables:\n",
-       "    COEFFS     (parameter, response) float64 120B -0.1236 0.2924 ... -0.1065"
+       "<Figure size 2000x400 with 2 Axes>"
       ]
      },
-     "execution_count": 2,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "corr_XY"
+    "ert_config = ErtConfig.from_file(\"poly.ert\")\n",
+    "# Load data for multiple iterations\n",
+    "iterations = [0, 1, 2]\n",
+    "dfs = []\n",
+    "for iter_num in iterations:\n",
+    "    with open_storage(ert_config.ens_path, mode=\"r\") as storage:\n",
+    "        experiment = storage.get_experiment_by_name(\"es_mda\")\n",
+    "        ensemble = experiment.get_ensemble_by_name(f\"default_{iter_num}\")\n",
+    "        df = ensemble.load_cross_correlations()\n",
+    "        df = df.with_columns(pl.lit(iter_num).alias(\"iteration\"))\n",
+    "        dfs.append(df)\n",
+    "\n",
+    "# Combine all dataframes\n",
+    "df = pl.concat(dfs)\n",
+    "\n",
+    "# Get unique parameter groups\n",
+    "param_groups = df[\"param_group\"].unique().to_list()\n",
+    "\n",
+    "# Process each parameter group\n",
+    "for group in param_groups:\n",
+    "    # Process each iteration for this group\n",
+    "    for iter_num in iterations:\n",
+    "        iter_df = df.filter(pl.col(\"iteration\") == iter_num)\n",
+    "\n",
+    "        pivot_df = iter_df.pivot(\n",
+    "            index=[\"param_group\", \"param_name\"], on=\"obs_name\", values=\"value\"\n",
+    "        )\n",
+    "\n",
+    "        group_data = pivot_df.filter(pl.col(\"param_group\") == group)\n",
+    "        param_names = group_data[\"param_name\"].to_list()\n",
+    "        obs_columns = [\n",
+    "            col\n",
+    "            for col in group_data.columns\n",
+    "            if col not in [\"param_group\", \"param_name\"]\n",
+    "        ]\n",
+    "        data_array = group_data.select(obs_columns).to_numpy()\n",
+    "\n",
+    "        plt.figure(figsize=(20, 4))\n",
+    "        sns.heatmap(\n",
+    "            data_array,\n",
+    "            annot=True,\n",
+    "            fmt=\".2f\",\n",
+    "            cmap=\"coolwarm\",\n",
+    "            center=0,\n",
+    "            xticklabels=obs_columns,\n",
+    "            yticklabels=param_names,\n",
+    "            annot_kws={\"size\": 8},\n",
+    "        )\n",
+    "        plt.title(\n",
+    "            f\"Parameter-Observation Correlations for {group} (Iteration {iter_num})\"\n",
+    "        )\n",
+    "        plt.xlabel(\"Observations\")\n",
+    "        plt.ylabel(\"Parameters\")\n",
+    "        plt.xticks(rotation=45, ha=\"right\")\n",
+    "        plt.yticks(rotation=0)\n",
+    "        plt.tight_layout()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a1a040f7-2727-4740-a619-fab723225509",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -484,7 +146,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/tests/ert/ui_tests/cli/analysis/test_adaptive_localization.py b/tests/ert/ui_tests/cli/analysis/test_adaptive_localization.py
index e61ebc23d4d..f613ee42cbd 100644
--- a/tests/ert/ui_tests/cli/analysis/test_adaptive_localization.py
+++ b/tests/ert/ui_tests/cli/analysis/test_adaptive_localization.py
@@ -300,12 +300,15 @@ def test_that_posterior_generalized_variance_increases_in_cutoff():
     )
 
     cross_correlations = prior_ensemble_cutoff1.load_cross_correlations()
-    assert all(cross_correlations.parameter.to_numpy() == ["a", "b"])
-    assert cross_correlations["COEFFS"].values.shape == (2, 5)
-    assert (
-        (cross_correlations["COEFFS"].values >= -1)
-        & (cross_correlations["COEFFS"].values <= 1)
-    ).all()
+    assert cross_correlations["param_group"].unique().to_list() == ["COEFFS"]
+    assert sorted(cross_correlations["param_name"].unique().to_list()) == [
+        "a",
+        "b",
+        "c",
+    ]
+    # Make sure correlations are between -1 and 1.
+    is_valid = (cross_correlations["value"] >= -1) & (cross_correlations["value"] <= 1)
+    assert is_valid.all()
 
     prior_sample_cutoff1 = prior_ensemble_cutoff1.load_parameters("COEFFS")["values"]
     prior_cov = np.cov(prior_sample_cutoff1, rowvar=False)
diff --git a/tests/ert/ui_tests/cli/analysis/test_es_update.py b/tests/ert/ui_tests/cli/analysis/test_es_update.py
index 39897af2482..4438d529891 100644
--- a/tests/ert/ui_tests/cli/analysis/test_es_update.py
+++ b/tests/ert/ui_tests/cli/analysis/test_es_update.py
@@ -162,6 +162,10 @@ def sample_prior(nx, ny):
 @pytest.mark.integration_test
 @pytest.mark.usefixtures("copy_snake_oil_field")
 def test_update_multiple_param():
+    with open("snake_oil.ert", "a", encoding="utf-8") as f:
+        f.write("\nANALYSIS_SET_VAR STD_ENKF LOCALIZATION True\n")
+        f.write("ANALYSIS_SET_VAR STD_ENKF LOCALIZATION_CORRELATION_THRESHOLD 0.1\n")
+
     run_cli(
         ENSEMBLE_SMOOTHER_MODE,
         "--disable-monitor",
@@ -183,6 +187,22 @@ def test_update_multiple_param():
     # https://en.wikipedia.org/wiki/Variance#For_vector-valued_random_variables
     assert np.trace(np.cov(posterior_array)) < np.trace(np.cov(prior_array))
 
+    corr_XY = prior_ensemble.load_cross_correlations()
+    expected_obs_groups = [obs[0] for obs in ert_config.observation_config]
+    obs_groups = corr_XY["obs_group"].unique().to_list()
+    assert sorted(obs_groups) == sorted(expected_obs_groups)
+    # Check that obs names are created using obs groups
+    obs_name_starts_with_group = (
+        corr_XY.with_columns(
+            polars.col("obs_name")
+            .str.starts_with(polars.col("obs_group"))
+            .alias("starts_with_check")
+        )
+        .get_column("starts_with_check")
+        .all()
+    )
+    assert obs_name_starts_with_group
+
 
 @pytest.mark.usefixtures("copy_poly_case")
 def test_that_update_works_with_failed_realizations():
diff --git a/tests/ert/ui_tests/cli/test_field_parameter.py b/tests/ert/ui_tests/cli/test_field_parameter.py
index 6c1c7cb193a..19681cf982e 100644
--- a/tests/ert/ui_tests/cli/test_field_parameter.py
+++ b/tests/ert/ui_tests/cli/test_field_parameter.py
@@ -23,38 +23,63 @@
 from .run_cli import run_cli
 
 
-def test_field_param_update_using_heat_equation(heat_equation_storage):
-    config = ErtConfig.from_file("config.ert")
-    with open_storage(config.ens_path, mode="w") as storage:
+def test_shared_heat_equation_storage(heat_equation_storage):
+    """The fixture heat_equation_storage runs the heat equation test case.
+    This test verifies that results are as expected.
+    """
+    config = heat_equation_storage
+    with open_storage(config.ens_path) as storage:
         experiment = storage.get_experiment_by_name("es-mda")
-        prior = experiment.get_ensemble_by_name("default_0")
-        posterior = experiment.get_ensemble_by_name("default_1")
-
-        prior_result = prior.load_parameters("COND")["values"]
+        ensembles = [experiment.get_ensemble_by_name(f"default_{i}") for i in range(4)]
 
         param_config = config.ensemble_config.parameter_configs["COND"]
-        assert len(prior_result.x) == param_config.nx
-        assert len(prior_result.y) == param_config.ny
-        assert len(prior_result.z) == param_config.nz
 
-        posterior_result = posterior.load_parameters("COND")["values"]
-        prior_covariance = np.cov(
-            prior_result.values.reshape(
-                prior.ensemble_size, param_config.nx * param_config.ny * param_config.nz
-            ),
-            rowvar=False,
-        )
-        posterior_covariance = np.cov(
-            posterior_result.values.reshape(
-                posterior.ensemble_size,
+        # Check that generalized variance decreases across consecutive ensembles
+        covariances = []
+        for ensemble in ensembles:
+            results = ensemble.load_parameters("COND")["values"]
+            reshaped_values = results.values.reshape(
+                ensemble.ensemble_size,
                 param_config.nx * param_config.ny * param_config.nz,
-            ),
-            rowvar=False,
-        )
-        # Check that generalized variance is reduced by update step.
-        assert np.trace(prior_covariance) > np.trace(posterior_covariance)
+            )
+            covariances.append(np.cov(reshaped_values, rowvar=False))
+        for i in range(len(covariances) - 1):
+            assert np.trace(covariances[i]) > np.trace(
+                covariances[i + 1]
+            ), f"Generalized variance did not decrease from iteration {i} to {i + 1}"
+
+        # Check that the saved cross-correlations are as expected.
+        for i in range(3):
+            ensemble = ensembles[i]
+            corr_XY = ensemble.load_cross_correlations()
+
+            assert sorted(corr_XY["param_group"].unique().to_list()) == [
+                "CORR_LENGTH",
+                "INIT_TEMP_SCALE",
+            ]
+            assert corr_XY["param_name"].unique().to_list() == ["x"]
+
+            # Make sure correlations are between -1 and 1.
+            is_valid = (corr_XY["value"] >= -1) & (corr_XY["value"] <= 1)
+            assert is_valid.all()
+
+            # Check obs names and obs groups
+            expected_obs_groups = [obs[0] for obs in config.observation_config]
+            obs_groups = corr_XY["obs_group"].unique().to_list()
+            assert sorted(obs_groups) == sorted(expected_obs_groups)
+            # Check that obs names are created using obs groups
+            obs_name_starts_with_group = (
+                corr_XY.with_columns(
+                    pl.col("obs_name")
+                    .str.starts_with(pl.col("obs_group"))
+                    .alias("starts_with_check")
+                )
+                .get_column("starts_with_check")
+                .all()
+            )
+            assert obs_name_starts_with_group
 
-    # Check that fields in the runpath are different between iterations
+    # Check that fields in the runpath are different between ensembles
     cond_iter0 = resfo.read("simulations/realization-0/iter-0/cond.bgrdecl")[0][1]
     cond_iter1 = resfo.read("simulations/realization-0/iter-1/cond.bgrdecl")[0][1]
     assert (cond_iter0 != cond_iter1).all()
diff --git a/tests/ert/ui_tests/gui/baseline/test_that_all_snake_oil_visualisations_matches_snapshot_plot_figure3-0.png b/tests/ert/ui_tests/gui/baseline/test_that_all_snake_oil_visualisations_matches_snapshot_plot_figure3-0.png
index 4ad6e398bc9..380e2204f85 100644
Binary files a/tests/ert/ui_tests/gui/baseline/test_that_all_snake_oil_visualisations_matches_snapshot_plot_figure3-0.png and b/tests/ert/ui_tests/gui/baseline/test_that_all_snake_oil_visualisations_matches_snapshot_plot_figure3-0.png differ
diff --git a/tests/ert/unit_tests/analysis/test_es_update.py b/tests/ert/unit_tests/analysis/test_es_update.py
index 7667bed7b03..4f6d7b999f3 100644
--- a/tests/ert/unit_tests/analysis/test_es_update.py
+++ b/tests/ert/unit_tests/analysis/test_es_update.py
@@ -631,17 +631,19 @@ def test_that_autoscaling_applies_to_scaled_errors(storage):
 
         experiment = storage.create_experiment(name="dummyexp")
         ensemble = experiment.create_ensemble(name="dummy", ensemble_size=10)
-        _, (_, scaled_errors_with_autoscale, _) = _mock_load_observations_and_responses(
-            observations_and_responses,
-            alpha=alpha,
-            std_cutoff=std_cutoff,
-            global_std_scaling=global_std_scaling,
-            auto_scale_observations=[["obs1*"]],
-            progress_callback=progress_callback,
-            ensemble=ensemble,
+        _, (_, _, scaled_errors_with_autoscale, _) = (
+            _mock_load_observations_and_responses(
+                observations_and_responses,
+                alpha=alpha,
+                std_cutoff=std_cutoff,
+                global_std_scaling=global_std_scaling,
+                auto_scale_observations=[["obs1*"]],
+                progress_callback=progress_callback,
+                ensemble=ensemble,
+            )
         )
 
-        _, (_, scaled_errors_without_autoscale, _) = (
+        _, (_, _, scaled_errors_without_autoscale, _) = (
             _mock_load_observations_and_responses(
                 observations_and_responses,
                 alpha=alpha,