Rewrite trans_derrf to intended behaviour

src/ert/config/gen_kw_config.py

@@ -427,6 +427,13 @@ def trans_errf(x: float, arg: List[float]) -> float:
         """
         _min, _max, _skew, _width = arg[0], arg[1], arg[2], arg[3]
         y = 0.5 * (1 + math.erf((x + _skew) / (_width * math.sqrt(2.0))))
+        if np.isnan(y):
+            raise ValueError(
+                (
+                    "Output is nan, likely from triplet (x, skewness, width) "
+                    "leading to low/high-probability in normal CDF."
+                )
+            )
         return _min + y * (_max - _min)
 
     @staticmethod

tests/unit_tests/config/test_gen_kw_config.py

@@ -358,11 +358,6 @@ def test_gen_kw_params_parsing(tmpdir, params, error):
         ("MYNAME ERRF 1 2 0.1 0.1", 0.3, 1.99996832875816688002),
         ("MYNAME ERRF 1 2 0.1 0.1", 0.7, 1.99999999999999933387),
         ("MYNAME ERRF 1 2 0.1 0.1", 1.0, 2.00000000000000000000),
-        ("MYNAME DERRF 10 1 2 0.1 0.1", -1.0, 1.00000000000000000000),
-        ("MYNAME DERRF 10 1 2 0.1 0.1", 0.0, 1.00000000000000000000),
-        ("MYNAME DERRF 10 1 2 0.1 0.1", 0.3, 2.00000000000000000000),
-        ("MYNAME DERRF 10 1 2 0.1 0.1", 0.7, 2.00000000000000000000),
-        ("MYNAME DERRF 10 1 2 0.1 0.1", 1.0, 2.00000000000000000000),
     ],
 )
 def test_gen_kw_trans_func(tmpdir, params, xinput, expected):

tests/unit_tests/config/test_transfer_functions.py

@@ -1,4 +1,5 @@
 import numpy as np
+from scipy.stats import norm
 from hypothesis import given
 from hypothesis import strategies as st
 
@@ -114,14 +115,34 @@ def test_that_derrf_is_within_bounds(x, arg):
     st.lists(st.floats(allow_nan=False, allow_infinity=False), min_size=2),
     valid_derrf_parameters(),
 )
-def test_that_derrf_creates_at_least_steps_number_of_distinct_values(xlist, arg):
+def test_that_derrf_creates_at_least_steps_or_less_distinct_values(xlist, arg):
     """derrf cannot create more than steps distinct values"""
     res = []
     for x in xlist:
         res.append(TransferFunction.trans_derrf(x, arg))
     assert len(set(res)) <= arg[0]
 
 
+@given(st.floats(allow_nan=False, allow_infinity=False), valid_derrf_parameters())
+def test_that_derrf_corresponds_scaled_binned_normal_cdf(x, arg):
+    """Check correspondance to normal cdf with -mu=_skew and sd=_width"""
+    _steps, _min, _max, _skew, _width = arg
+    q_values = np.linspace(start=0, stop=1, num=_steps)
+    q_checks = np.linspace(start=0, stop=1, num=_steps + 1)[1:]
+    p = norm.cdf(x, loc=-_skew, scale=_width)
+    expected = q_values[-1]  # last
+    for i in range(_steps - 1):
+        if p < q_checks[i]:
+            expected = q_values[i]
+            break
+    # scale and ensure ok numerics
+    expected = _min + expected * (_max - _min)
+    if expected > _max or expected < _min:
+        np.clip(expected, _min, _max)
+    result = TransferFunction.trans_derrf(x, arg)
+    assert np.isclose(result, expected)
+
+
 @given(
     st.tuples(
         st.floats(allow_nan=False, allow_infinity=False),