Removing deprecated use of lmfit (#344)

Removed the lmfit based fitting and the old API for calling functions

closes #296

src/Fit.test.cpp

@@ -0,0 +1,81 @@
+// SPDX-License-Identifier: MPL-2.0
+#include "aare/Fit.hpp"
+#include "aare/FitModel.hpp"
+#include "aare/Models.hpp"
+
+#include <catch2/catch_approx.hpp>
+#include <catch2/catch_test_macros.hpp>
+#include <cmath>
+
+namespace {
+
+constexpr ssize_t n_points = 61;
+
+void fill_gaussian(aare::NDArray<double, 1> &x, aare::NDArray<double, 1> &y,
+                   double amplitude, double mean, double sigma) {
+    for (ssize_t i = 0; i < n_points; ++i) {
+        x(i) = -6.0 + 0.2 * static_cast<double>(i);
+        const double z = (x(i) - mean) / sigma;
+        y(i) = amplitude * std::exp(-0.5 * z * z);
+    }
+}
+
+} // namespace
+
+TEST_CASE("Minuit2 fits weighted and unweighted Gaussian data", "[fit]") {
+    aare::NDArray<double, 1> x({n_points});
+    aare::NDArray<double, 1> y({n_points});
+    aare::NDArray<double, 1> y_err({n_points}, 1.0);
+    fill_gaussian(x, y, 120.0, 0.8, 1.3);
+
+    const aare::FitModel<aare::model::Gaussian> unweighted_model;
+    const auto unweighted =
+        aare::fit_pixel(unweighted_model, x.view(), y.view());
+
+    REQUIRE(unweighted.size() == 4);
+    CHECK(unweighted(0) == Catch::Approx(120.0).epsilon(1e-5));
+    CHECK(unweighted(1) == Catch::Approx(0.8).epsilon(1e-5));
+    CHECK(unweighted(2) == Catch::Approx(1.3).epsilon(1e-5));
+    CHECK(unweighted(3) == Catch::Approx(0.0).margin(1e-4));
+
+    const aare::FitModel<aare::model::Gaussian> weighted_model(0, 100, 0.5,
+                                                               true);
+    const auto weighted =
+        aare::fit_pixel(weighted_model, x.view(), y.view(), y_err.view());
+
+    REQUIRE(weighted.size() == 7);
+    CHECK(weighted(0) == Catch::Approx(120.0).epsilon(1e-5));
+    CHECK(weighted(1) == Catch::Approx(0.8).epsilon(1e-5));
+    CHECK(weighted(2) == Catch::Approx(1.3).epsilon(1e-5));
+    CHECK(weighted(6) == Catch::Approx(0.0).margin(1e-4));
+}
+
+TEST_CASE("Minuit2 fits a Gaussian data cube in parallel", "[fit]") {
+    aare::NDArray<double, 1> x({n_points});
+    aare::NDArray<double, 1> values({n_points});
+    fill_gaussian(x, values, 80.0, -0.6, 0.9);
+
+    aare::NDArray<double, 3> y({2, 2, n_points});
+    for (ssize_t row = 0; row < 2; ++row) {
+        for (ssize_t col = 0; col < 2; ++col) {
+            for (ssize_t i = 0; i < n_points; ++i)
+                y(row, col, i) = values(i);
+        }
+    }
+
+    aare::NDArray<double, 3> par({2, 2, 3});
+    aare::NDArray<double, 2> chi2({2, 2});
+    const aare::FitModel<aare::model::Gaussian> model;
+
+    aare::fit_3d(model, x.view(), y.view(), aare::NDView<double, 3>{},
+                 par.view(), aare::NDView<double, 3>{}, chi2.view(), 2);
+
+    for (ssize_t row = 0; row < 2; ++row) {
+        for (ssize_t col = 0; col < 2; ++col) {
+            CHECK(par(row, col, 0) == Catch::Approx(80.0).epsilon(1e-5));
+            CHECK(par(row, col, 1) == Catch::Approx(-0.6).epsilon(1e-5));
+            CHECK(par(row, col, 2) == Catch::Approx(0.9).epsilon(1e-5));
+            CHECK(chi2(row, col) == Catch::Approx(0.0).margin(1e-8));
+        }
+    }
+}