removing redundant tests

Author: skfegan

tests/unit/CodeEntropy/entropy/test_configurational_edges.py

@@ -1,90 +1,12 @@
-import logging
-from types import SimpleNamespace
-from unittest.mock import MagicMock
-
-import numpy as np
-import pytest
-
 from CodeEntropy.entropy.configurational import ConformationalEntropy
 
 
-def test_validate_assignment_config_step_must_be_positive():
-    ce = ConformationalEntropy()
-    with pytest.raises(ValueError):
-        ce.assign_conformation(
-            data_container=SimpleNamespace(trajectory=list(range(5))),
-            dihedral=MagicMock(value=lambda: 10.0),
-            number_frames=5,
-            bin_width=30,
-            start=0,
-            end=5,
-            step=0,
-        )
-
-
-def test_validate_assignment_config_bin_width_out_of_range():
-    ce = ConformationalEntropy()
-    with pytest.raises(ValueError):
-        ce.assign_conformation(
-            data_container=SimpleNamespace(trajectory=list(range(5))),
-            dihedral=MagicMock(value=lambda: 10.0),
-            number_frames=5,
-            bin_width=0,
-            start=0,
-            end=5,
-            step=1,
-        )
-
-
-def test_validate_assignment_config_warns_when_bin_width_not_dividing_360(caplog):
-    ce = ConformationalEntropy()
-    caplog.set_level(logging.WARNING)
-
-    data_container = SimpleNamespace(trajectory=list(range(5)))
-    dihedral = MagicMock()
-    dihedral.value.return_value = 10.0
-
-    ce.assign_conformation(
-        data_container=data_container,
-        dihedral=dihedral,
-        number_frames=5,
-        bin_width=7,
-        start=0,
-        end=5,
-        step=1,
-    )
-
-    assert any("does not evenly divide 360" in r.message for r in caplog.records)
-
-
-def test_collect_dihedral_angles_normalizes_negative_values():
-    ce = ConformationalEntropy()
-
-    traj_slice = list(range(3))
-    dihedral = MagicMock()
-    dihedral.value.side_effect = [-10.0, 0.0, 10.0]
-
-    phi = ce._collect_dihedral_angles(traj_slice, dihedral)
-
-    assert phi[0] == pytest.approx(350.0)
-
-
 def test_to_1d_array_returns_none_for_non_iterable_state_input():
     ce = ConformationalEntropy()
     # int is not iterable -> list(states) raises TypeError -> returns None
     assert ce._to_1d_array(123) is None
 
 
-def test_find_histogram_peaks_skips_zero_population_bins():
-    ce = ConformationalEntropy()
-
-    phi = np.zeros(50, dtype=float)
-
-    peaks = ce._find_histogram_peaks(phi, bin_width=30)
-
-    assert peaks.size >= 1
-
-
 def test_to_1d_array_returns_none_when_states_is_none():
     ce = ConformationalEntropy()
     assert ce._to_1d_array(None) is None

tests/unit/CodeEntropy/entropy/test_configurational_entropy_math.py

@@ -1,112 +1,17 @@
-from types import SimpleNamespace
-from unittest.mock import MagicMock
-
 import numpy as np
 import pytest
 
 from CodeEntropy.entropy.configurational import ConformationalEntropy
 
 
-def test_find_histogram_peaks_empty_histogram_returns_empty():
-    ce = ConformationalEntropy()
-    phi = np.zeros(100, dtype=float)
-
-    peaks = ce._find_histogram_peaks(phi, bin_width=30)
-
-    assert isinstance(peaks, np.ndarray)
-    assert peaks.dtype == float
-
-
-def test_find_histogram_peaks_returns_empty_for_empty_phi():
-    ce = ConformationalEntropy()
-    phi = np.array([], dtype=float)
-
-    peaks = ce._find_histogram_peaks(phi, bin_width=30)
-
-    assert isinstance(peaks, np.ndarray)
-    assert peaks.size == 0
-
-
-def test_assign_nearest_peaks_with_single_peak_assigns_all_zero():
-    ce = ConformationalEntropy()
-    phi = np.array([0.0, 10.0, 20.0], dtype=float)
-    peak_values = np.array([15.0], dtype=float)
-
-    states = ce._assign_nearest_peaks(phi, peak_values)
-
-    assert np.all(states == 0)
-
-
-def test_assign_conformation_no_peaks_returns_all_zero():
-    ce = ConformationalEntropy()
-
-    data_container = SimpleNamespace(trajectory=[])
-    dihedral = MagicMock()
-
-    states = ce.assign_conformation(
-        data_container=data_container,
-        dihedral=dihedral,
-        number_frames=0,
-        bin_width=30,
-        start=0,
-        end=0,
-        step=1,
-    )
-
-    assert states.size == 0
-
-
-def test_assign_conformation_fallback_when_peak_finder_returns_empty(monkeypatch):
-    ce = ConformationalEntropy()
-    data_container = SimpleNamespace(trajectory=list(range(5)))
-    dihedral = MagicMock()
-    dihedral.value.return_value = 10.0
-
-    monkeypatch.setattr(
-        ce, "_find_histogram_peaks", lambda phi, bw: np.array([], dtype=float)
-    )
-
-    states = ce.assign_conformation(
-        data_container=data_container,
-        dihedral=dihedral,
-        number_frames=5,
-        bin_width=30,
-        start=0,
-        end=5,
-        step=1,
-    )
-    assert np.all(states == 0)
-
-
-def test_assign_conformation_detects_multiple_states():
-    ce = ConformationalEntropy()
-
-    values = [0.0] * 50 + [180.0] * 50
-    data_container = SimpleNamespace(trajectory=list(range(len(values))))
-    dihedral = MagicMock()
-    dihedral.value.side_effect = values
-
-    states = ce.assign_conformation(
-        data_container=data_container,
-        dihedral=dihedral,
-        number_frames=len(values),
-        bin_width=30,
-        start=0,
-        end=len(values),
-        step=1,
-    )
-
-    assert len(np.unique(states)) >= 2
-
-
 def test_conformational_entropy_empty_returns_zero():
     ce = ConformationalEntropy()
-    assert ce.conformational_entropy_calculation([], number_frames=10) == 0.0
+    assert ce.conformational_entropy_calculation([]) == 0.0
 
 
 def test_conformational_entropy_single_state_returns_zero():
     ce = ConformationalEntropy()
-    assert ce.conformational_entropy_calculation([0, 0, 0], number_frames=3) == 0.0
+    assert ce.conformational_entropy_calculation([0, 0, 0]) == 0.0
 
 
 def test_conformational_entropy_known_distribution_matches_expected():
@@ -116,5 +21,5 @@ def test_conformational_entropy_known_distribution_matches_expected():
     probs = np.array([2 / 6, 3 / 6, 1 / 6], dtype=float)
     expected = -ce._GAS_CONST * float(np.sum(probs * np.log(probs)))
 
-    got = ce.conformational_entropy_calculation(states, number_frames=6)
+    got = ce.conformational_entropy_calculation(states)
     assert got == pytest.approx(expected)

tests/unit/CodeEntropy/entropy/test_orientational_entropy.py

@@ -8,12 +8,12 @@
 def test_orientational_negative_count_raises():
     oe = OrientationalEntropy(_GAS_CONST)
     with pytest.raises(ValueError):
-        oe.calculate_orientational(-1, 1, False)
+        oe.calculate(-1, 1, False)
 
 
 def test_orientational_zero_count_contributes_zero():
     oe = OrientationalEntropy(_GAS_CONST)
-    assert oe.calculate_orientational(0, 1, False) == 0.0
+    assert oe.calculate(0, 1, False) == 0.0
 
 
 def test_omega_linear():

tests/unit/CodeEntropy/results/test_reporter.py

@@ -329,8 +329,7 @@ def fake_resolve(self):
 
     assert ResultsReporter._try_get_git_sha() == "sha"
     _args, kwargs = mock_run.call_args
-    assert "stdout" in kwargs
-    assert "stderr" in kwargs
+    assert "capture_output" in kwargs
     assert kwargs.get("text") is True