adding more nodes

tests/node_tests/relate_fields.py

@@ -0,0 +1,54 @@
+import numpy as np
+import pytest
+from tests.node_tests._shared import make_field
+
+
+def test_linear_fit():
+    from backend.nodes.relate_fields import RelateFields
+
+    node = RelateFields()
+    rng = np.random.default_rng(42)
+    a_data = rng.uniform(0.5, 5.0, (32, 32))
+    b_data = 2.0 * a_data + 1.0
+    field_a = make_field(data=a_data)
+    field_b = make_field(data=b_data)
+
+    predicted, records = node.process(field_a, field_b, function="linear")
+
+    params = {r["quantity"]: r["value"] for r in records}
+    assert float(params["slope"]) == pytest.approx(2.0, abs=1e-6)
+    assert float(params["intercept"]) == pytest.approx(1.0, abs=1e-6)
+    assert float(params["R\u00b2"]) == pytest.approx(1.0, abs=1e-6)
+
+
+def test_r_squared_reported():
+    from backend.nodes.relate_fields import RelateFields
+
+    node = RelateFields()
+    rng = np.random.default_rng(0)
+    field_a = make_field(data=rng.standard_normal((32, 32)))
+    field_b = make_field(data=rng.standard_normal((32, 32)))
+
+    _, records = node.process(field_a, field_b, function="linear")
+    quantities = [r["quantity"] for r in records]
+    assert "R\u00b2" in quantities, f"Expected 'R\u00b2' in {quantities}"
+
+
+def test_power_fit():
+    from backend.nodes.relate_fields import RelateFields
+
+    node = RelateFields()
+    rng = np.random.default_rng(99)
+    a_data = rng.uniform(1.0, 10.0, (32, 32))
+    # b = 3.0 * a^2.0
+    b_data = 3.0 * np.power(a_data, 2.0)
+    field_a = make_field(data=a_data)
+    field_b = make_field(data=b_data)
+
+    predicted, records = node.process(field_a, field_b, function="power")
+
+    params = {r["quantity"]: r["value"] for r in records}
+    assert "exponent" in params, f"Expected 'exponent' in {params}"
+    assert "coefficient" in params, f"Expected 'coefficient' in {params}"
+    assert float(params["exponent"]) == pytest.approx(2.0, abs=0.05)
+    assert float(params["coefficient"]) == pytest.approx(3.0, abs=0.1)