add a linear ParamFit test

1 files changed, 52 insertions, 0 deletions

diff --git a/test/test_parameters.py b/test/test_parameters.py
new file mode 100644
index 0000000..a466d91
--- /dev/null
+++ b/test/test_parameters.py
@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+
+from dfatool import dfatool as dt
+from dfatool import parameters
+from dfatool.utils import by_name_to_by_param
+import unittest
+
+import numpy as np
+
+
+class TestModels(unittest.TestCase):
+    def test_distinct_param_values(self):
+        X = np.arange(35)
+        by_name = {
+            "TX": {
+                "param": [(x % 5, x % 7) for x in X],
+                "power": X,
+                "attributes": ["power"],
+            }
+        }
+        self.assertEqual(
+            parameters.distinct_param_values(by_name, "TX"),
+            [list(range(5)), list(range(7))],
+        )
+
+    def test_parameter_detection_linear(self):
+        rng = np.random.default_rng()
+        X = np.arange(200) % 50
+        Y = X + rng.normal(size=X.size)
+        by_name = {
+            "TX": {
+                "param": [(x % 5, x) for x in X],
+                "power": Y,
+                "attributes": ["power"],
+            }
+        }
+        by_param = by_name_to_by_param(by_name)
+        stats = parameters.ParamStats(by_name, by_param, ["p_mod5", "p_linear"], dict())
+
+        self.assertEqual(stats.depends_on_param("TX", "power", "p_mod5"), False)
+        self.assertEqual(stats.depends_on_param("TX", "power", "p_linear"), True)
+
+        paramfit = dt.ParallelParamFit(by_param)
+        paramfit.enqueue("TX", "power", 1, "p_linear")
+        paramfit.fit()
+
+        fit_result = dt.get_fit_result(paramfit.results, "TX", "power")
+        self.assertEqual(fit_result["p_linear"]["best"], "linear")
+
+
+if __name__ == "__main__":
+    unittest.main()