Try remove get_updated_parameters

Author: Yngve S. Kristiansen

src/semeio/workflows/ahm_analysis/ahmanalysis.py

@@ -227,7 +227,11 @@ def _replace(s: str) -> str:
         # identify the set of actual parameters that was updated for now just go
         # through scalar parameters but in future if easier access to field parameter
         # updates should also include field parameters
-        dkeysf = get_updated_parameters(prior_data, gen_kw_groups)
+        dkeysf = [
+            p.name
+            for p in prior_ensemble.experiment.parameter_configuration.values()
+            if p.update and p.type == "gen_kw"
+        ]
         # setup dataframe for calculated data
         kolmogorov_smirnov_data, active_obs, misfitval = (
             pd.DataFrame(sorted(dkeysf), columns=["Parameters"]),
@@ -416,39 +420,6 @@ def calc_observationsgroup_misfit(obs_keys, df_update_log, misfit_df):
         )
     return mean.mean()
 
-
-def _filter_on_prefix(list_of_strings, prefixes):
-    """returns the set of strings that has a match for any of the given prefixes"""
-    return {
-        string
-        for string in list_of_strings
-        if any(string.startswith(prefix) for prefix in prefixes)
-    }
-
-
-def get_updated_parameters(prior_data, parameters):
-    """make list of updated parameters
-    (excluding duplicate transformed parameters)
-    """
-    parameter_keys = _filter_on_prefix(
-        list_of_strings=prior_data.keys(), prefixes=parameters
-    )
-    # remove parameters with constant prior distribution
-    p_keysf = []
-    for dkey in parameter_keys:
-        if prior_data[dkey].ndim > 1:
-            warnings.warn(
-                "WARNING: Parameter " + dkey + " defined several times.", stacklevel=1
-            )
-            flatten_arr = np.ravel(prior_data[dkey])
-            result = np.all(prior_data[dkey] == flatten_arr[0])
-            if not result:
-                p_keysf.append(dkey)
-        elif not all(x == prior_data[dkey][0] for x in prior_data[dkey]):
-            p_keysf.append(dkey)
-    return p_keysf
-
-
 def calc_kolmogorov_smirnov(columns, prior_data, target_data):
     """Calculate kolmogorov_smirnov matrix"""
     ks_param = {}

tests/workflows/ahm_analysis/test_ahm_analysis.py

@@ -236,53 +236,3 @@ def test_raise_if_empty(misfit_data, prior_data, expected_msg):
 def test_make_obs_groups(input_map, expected_keys):
     result = ahmanalysis.make_obs_groups(input_map)
     assert result == expected_keys
-
-
-@pytest.mark.parametrize(
-    "prior_data, expected_result",
-    [
-        [
-            {
-                "SNAKE_OIL_PARAM:OP1_PERSISTENCE": [0, 1, 2],
-                "SNAKE_OIL_PARAM:OP1_OCTAVES": [0, 1, 2],
-                "SNAKE_OIL_PARAM:OP1_DIVERGENCE_SCALE": [0, 1, 2],
-                "SNAKE_OIL_PRES:BPR_138_PERSISTENCE": [0, 1, 2],
-                "LOG10_SNAKE_OIL_PARAM:OP1_PERSISTENCE": [0, 1, 2],
-            },
-            [
-                "SNAKE_OIL_PARAM:OP1_DIVERGENCE_SCALE",
-                "SNAKE_OIL_PARAM:OP1_OCTAVES",
-                "SNAKE_OIL_PARAM:OP1_PERSISTENCE",
-                "SNAKE_OIL_PRES:BPR_138_PERSISTENCE",
-            ],
-        ],
-    ],
-)
-def test_get_updated_parameters(prior_data, expected_result):
-    """test function creates a dataframe with all scalar parameters"""
-    prior_data = pd.DataFrame(prior_data)
-    scalar_parameters = ["SNAKE_OIL_PARAM", "SNAKE_OIL_PRES"]
-    p_keysf = ahmanalysis.get_updated_parameters(prior_data, scalar_parameters)
-    assert sorted(p_keysf) == sorted(expected_result)
-
-
-@pytest.mark.parametrize(
-    "prior_data_w",
-    [
-        {
-            "SNAKE_OIL_PARAM:OP1_PERSISTENCE": np.array([0, 1, 2]),
-            "SNAKE_OIL_PARAM:OP1_OCTAVES": np.array([0, 1, 2]),
-            "SNAKE_OIL_PARAM:OP1_DIVERGENCE_SCALE": np.array([0, 1, 2]),
-            "SNAKE_OIL_PARAM:OP1_OFFSET": np.array([[0, 0, 0], [0, 0, 0]]),
-            "SNAKE_OIL_PRES:BPR_138_PERSISTENCE": np.array([0, 1, 2]),
-        }
-    ],
-)
-def test_warning_get_updated_parameters(prior_data_w):
-    """test function creates a dataframe with all scalar parameters"""
-    expected_msg = (
-        "WARNING: Parameter SNAKE_OIL_PARAM:OP1_OFFSET defined several times."
-    )
-    scalar_parameters = ["SNAKE_OIL_PARAM", "SNAKE_OIL_PRES"]
-    with pytest.warns(UserWarning, match=expected_msg):
-        ahmanalysis.get_updated_parameters(prior_data_w, scalar_parameters)