Add a fitting benchmark

Author: MarJMue

tests/benchmark_fitting.py

@@ -0,0 +1,114 @@
+"""Benchmark for using the formula dispersion"""
+
+import elli
+import numpy as np
+from elli.fitting import ParamsHist, fit
+from fixtures import datadir
+from pytest import fixture
+
+
+def test_fitting_structure_creation(benchmark, datadir):
+    ANGLE = 70
+    rii_db = elli.db.RII()
+    Si = rii_db.get_mat("Si", "Aspnes")
+
+    psi_delta = (
+        elli.read_nexus_psi_delta(datadir / "SiO2onSi.ellips.nxs")
+        .loc[ANGLE]
+        .loc[210:800]
+    )
+
+    params = ParamsHist()
+    params.add("SiO2_n0", value=1.6, min=-100, max=100, vary=True)
+    params.add("SiO2_n1", value=36, min=-40000, max=40000, vary=False)
+    params.add("SiO2_n2", value=0, min=-40000, max=40000, vary=False)
+    params.add("SiO2_k0", value=0, min=-100, max=100, vary=False)
+    params.add("SiO2_k1", value=0, min=-40000, max=40000, vary=False)
+    params.add("SiO2_k2", value=0, min=-40000, max=40000, vary=False)
+    params.add("SiO2_d", value=20, min=0, max=40000, vary=True)
+
+    @fit(psi_delta, params)
+    def model(lbda, params):
+        SiO2 = elli.Cauchy(
+            params["SiO2_n0"],
+            params["SiO2_n1"],
+            params["SiO2_n2"],
+            params["SiO2_k0"],
+            params["SiO2_k1"],
+            params["SiO2_k2"],
+        ).get_mat()
+
+        return elli.Structure(
+            elli.AIR,
+            [elli.Layer(SiO2, params["SiO2_d"])],
+            Si,
+        ).evaluate(lbda, ANGLE, solver=elli.Solver4x4, save_experiment=True)
+
+    result = benchmark.pedantic(
+        model.fit,
+        args=(),
+        iterations=1,
+        rounds=10,
+    )
+    assert result.chisqr < 0.02
+
+
+def test_fitting_structure_updates(benchmark, datadir):
+    ANGLE = 70
+    rii_db = elli.db.RII()
+    Si = rii_db.get_mat("Si", "Aspnes")
+
+    psi_delta = (
+        elli.read_nexus_psi_delta(datadir / "SiO2onSi.ellips.nxs")
+        .loc[ANGLE]
+        .loc[210:800]
+    )
+
+    params = ParamsHist()
+    params.add("SiO2_n0", value=1.6, min=-100, max=100, vary=True)
+    params.add("SiO2_n1", value=36, min=-40000, max=40000, vary=False)
+    params.add("SiO2_n2", value=0, min=-40000, max=40000, vary=False)
+    params.add("SiO2_k0", value=0, min=-100, max=100, vary=False)
+    params.add("SiO2_k1", value=0, min=-40000, max=40000, vary=False)
+    params.add("SiO2_k2", value=0, min=-40000, max=40000, vary=False)
+    params.add("SiO2_d", value=20, min=0, max=40000, vary=True)
+
+    SiO2 = elli.Cauchy(
+        params["SiO2_n0"],
+        params["SiO2_n1"],
+        params["SiO2_n2"],
+        params["SiO2_k0"],
+        params["SiO2_k1"],
+        params["SiO2_k2"],
+    )
+
+    SiO2_mat = SiO2.get_mat()
+
+    layer = elli.Layer(SiO2_mat, params["SiO2_d"])
+
+    structure = elli.Structure(
+        elli.AIR,
+        [layer],
+        Si,
+    )
+
+    @fit(psi_delta, params)
+    def model(lbda, params):
+        SiO2.single_params["n0"] = params["SiO2_n0"]
+        SiO2.single_params["n1"] = params["SiO2_n1"]
+        SiO2.single_params["n2"] = params["SiO2_n2"]
+        SiO2.single_params["k0"] = params["SiO2_k0"]
+        SiO2.single_params["k1"] = params["SiO2_k1"]
+        SiO2.single_params["k2"] = params["SiO2_k2"]
+
+        layer.set_thickness(params["SiO2_d"])
+
+        return structure.evaluate(lbda, ANGLE, solver=elli.Solver2x2)
+
+    result = benchmark.pedantic(
+        model.fit,
+        args=(),
+        iterations=1,
+        rounds=10,
+    )
+    assert result.chisqr < 0.02