REFACTOR:

Fitter class refactored. Include getter and setter
ADD:
test/ dir with code tests

etc/tests/test_fitter.py

@@ -0,0 +1,267 @@
+import numpy as np
+import pytest
+from scipy.stats import norm, gamma, expon, weibull_min
+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+from fitting.fitter import DistributionSummary, Fitter
+
+
+# ── Data ──────────────────────────────────────────────────────────────────
+
+RNG = np.random.default_rng(42)
+GAMMA_DATA = RNG.gamma(shape=2.0, scale=1.5, size=200)
+NORM_DATA = RNG.normal(loc=5.0, scale=1.0, size=200)
+
+
+# ── DistributionSummary ───────────────────────────────────────────────────────
+
+
+class TestDistributionSummary:
+    def _make_summary(self, dist=gamma, fit_params=(2.0, 0.0, 1.5)):
+        return DistributionSummary(
+            distribution_object=dist,
+            distribution_name=dist.name,
+            fit_result_params=fit_params,
+        )
+
+    def test_mean_std_var_are_floats(self):
+        s = self._make_summary()
+        assert isinstance(s.mean, float)
+        assert isinstance(s.std, float)
+        assert isinstance(s.var, float)
+
+    def test_var_equals_std_squared(self):
+        s = self._make_summary()
+        assert pytest.approx(s.var, rel=1e-6) == s.std**2
+
+    def test_pvalue_none_before_validate(self):
+        s = self._make_summary()
+        assert s.pvalue is None
+
+    def test_gof_statistic_none_before_validate(self):
+        s = self._make_summary()
+        assert s.gof_statistic is None
+
+    def test_repr_contains_name(self):
+        s = self._make_summary()
+        assert "gamma" in repr(s)
+
+    def test_repr_shows_na_when_no_test(self):
+        s = self._make_summary()
+        assert "N/A" in repr(s)
+
+    def test_default_statistic_method(self):
+        s = self._make_summary()
+        assert s.statistic_method == "ad"
+
+
+# ── Fitter construction ───────────────────────────────────────────────────────
+
+
+class TestFitterConstruction:
+    def test_distributions_registered(self):
+        f = Fitter([gamma, expon])
+        assert "gamma" in f
+        assert "expon" in f
+
+    def test_getitem_by_name(self):
+        f = Fitter([gamma])
+        s = f["gamma"]
+        assert isinstance(s, DistributionSummary)
+
+    def test_getitem_by_object(self):
+        f = Fitter([gamma])
+        s = f[gamma]
+        assert isinstance(s, DistributionSummary)
+
+    def test_getitem_missing_raises(self):
+        f = Fitter([gamma])
+        with pytest.raises(KeyError):
+            _ = f["norm"]
+
+    def test_contains_true(self):
+        f = Fitter([gamma])
+        assert gamma in f
+        assert "gamma" in f
+
+    def test_contains_false(self):
+        f = Fitter([gamma])
+        assert norm not in f
+
+    def test_kwargs_args_stored(self):
+        f = Fitter([gamma], gamma_args=(2.0,), gamma_params={"floc": 0})
+        assert f["gamma"].args_fit_params == (2.0,)
+        assert f["gamma"].kwds_fit_params == {"floc": 0}
+
+    def test_setitem_valid(self):
+        f = Fitter([gamma])
+        original = f["gamma"]
+        f["gamma"] = original
+        assert f["gamma"] is original
+
+    def test_setitem_invalid_type(self):
+        f = Fitter([gamma])
+        with pytest.raises(TypeError):
+            f["gamma"] = "not_a_summary"
+
+    def test_setitem_missing_key_raises(self):
+        f = Fitter([gamma])
+        dummy = DistributionSummary(distribution_object=norm, distribution_name="norm")
+        with pytest.raises(KeyError):
+            f["norm"] = dummy
+
+
+# ── Fitter iteration ──────────────────────────────────────────────────────────
+
+
+class TestFitterIteration:
+    def test_iterates_all_distributions(self):
+        f = Fitter([gamma, expon, weibull_min])
+        names = [name for name, _ in f]
+        assert set(names) == {"gamma", "expon", "weibull_min"}
+
+    def test_iterate_twice(self):
+        f = Fitter([gamma, expon])
+        first = [name for name, _ in f]
+        second = [name for name, _ in f]
+        assert first == second
+
+    def test_iterator_yields_summary_instances(self):
+        f = Fitter([gamma])
+        for _, summary in f:
+            assert isinstance(summary, DistributionSummary)
+
+
+# ── Fitter.fit ────────────────────────────────────────────────────────────────
+
+
+class TestFitterFit:
+    def test_fit_populates_params(self):
+        f = Fitter([gamma], gamma_params={"floc": 0})
+        f.fit(GAMMA_DATA)
+        assert len(f["gamma"].fit_result_params) > 0
+
+    def test_fit_uses_abs_value(self):
+        f = Fitter([gamma], gamma_params={"floc": 0})
+        neg_data = -np.abs(GAMMA_DATA)
+        f.fit(neg_data)
+        assert len(f["gamma"].fit_result_params) > 0
+
+    def test_fit_flattens_2d(self):
+        f = Fitter([gamma], gamma_params={"floc": 0})
+        data_2d = GAMMA_DATA.reshape(10, 20)
+        f.fit(data_2d)
+        assert hasattr(f, "_last_data_flat")
+        assert f._last_data_flat.ndim == 1
+
+    def test_fit_multiple_dists(self):
+        f = Fitter([gamma, expon], gamma_params={"floc": 0}, expon_params={"floc": 0})
+        f.fit(GAMMA_DATA)
+        assert len(f["gamma"].fit_result_params) > 0
+        assert len(f["expon"].fit_result_params) > 0
+
+
+# ── Fitter.validate ───────────────────────────────────────────────────────────
+
+
+class TestFitterValidate:
+    def test_validate_without_fit_raises(self):
+        f = Fitter([gamma])
+        with pytest.raises(RuntimeError, match="fit\\(\\)"):
+            f.validate()
+
+    def test_validate_populates_test_result(self):
+        f = Fitter([gamma], gamma_params={"floc": 0})
+        f.fit(GAMMA_DATA)
+        f.validate(n_mc_samples=99)
+        assert f["gamma"].test_result is not None
+
+    def test_validate_pvalue_in_range(self):
+        f = Fitter([gamma], gamma_params={"floc": 0})
+        f.fit(GAMMA_DATA)
+        f.validate(n_mc_samples=99)
+        pval = f["gamma"].pvalue
+        assert 0.0 <= pval <= 1.0
+
+    def test_validate_gof_statistic_positive(self):
+        f = Fitter([gamma], gamma_params={"floc": 0})
+        f.fit(GAMMA_DATA)
+        f.validate(n_mc_samples=99)
+        assert f["gamma"].gof_statistic >= 0.0
+
+    def test_validate_custom_statistic(self):
+        f = Fitter([gamma], statistic_method="ks", gamma_params={"floc": 0})
+        f.fit(GAMMA_DATA)
+        f.validate(n_mc_samples=99)
+        assert f["gamma"].test_result is not None
+
+
+# ── Fitter.summary ────────────────────────────────────────────────────────────
+
+
+class TestFitterSummary:
+    def test_summary_runs_without_error(self, capsys):
+        f = Fitter([gamma], gamma_params={"floc": 0})
+        f.fit(GAMMA_DATA)
+        f.validate(n_mc_samples=99)
+        f.summary()
+        captured = capsys.readouterr()
+        assert "gamma" in captured.out
+
+
+# ── Fitter.plot_qq_plots ──────────────────────────────────────────────────────
+
+
+class TestFitterPlotQQ:
+    def setup_method(self):
+        self.f = Fitter([gamma], gamma_params={"floc": 0})
+        self.f.fit(GAMMA_DATA)
+        self.f.validate(n_mc_samples=99)
+
+    def test_qq_without_fit_raises(self):
+        f = Fitter([gamma])
+        with pytest.raises(RuntimeError, match="fit\\(\\)"):
+            f.plot_qq_plots()
+
+    def test_qq_invalid_method_raises(self):
+        with pytest.raises(ValueError, match="method"):
+            self.f.plot_qq_plots(method="invalid")
+
+    def test_qq_hazen_returns_figure(self):
+        import plotly.graph_objects as go
+        fig = self.f.plot_qq_plots(method="hazen")
+        assert isinstance(fig, go.Figure)
+
+    def test_qq_filliben_returns_figure(self):
+        import plotly.graph_objects as go
+        fig = self.f.plot_qq_plots(method="filliben")
+        assert isinstance(fig, go.Figure)
+
+
+# ── Fitter.histogram_with_fits ────────────────────────────────────────────────
+
+
+class TestFitterHistogram:
+    def setup_method(self):
+        self.f = Fitter([gamma], gamma_params={"floc": 0})
+        self.f.fit(GAMMA_DATA)
+        self.f.validate(n_mc_samples=99)
+
+    def test_histogram_without_fit_raises(self):
+        f = Fitter([gamma])
+        with pytest.raises(RuntimeError, match="fit\\(\\)"):
+            f.histogram_with_fits()
+
+    def test_histogram_returns_figure(self):
+        import plotly.graph_objects as go
+        fig = self.f.histogram_with_fits()
+        assert isinstance(fig, go.Figure)
+
+    def test_histogram_seaborn_returns_figure(self):
+        import matplotlib.pyplot as plt
+        fig = self.f.histogram_with_fits_seaborn()
+        assert isinstance(fig, plt.Figure)
+        plt.close("all")