adding more nodes

tests/node_tests/grain_cross.py

@@ -0,0 +1,75 @@
+import numpy as np
+import pytest
+from tests.node_tests._shared import make_field
+from backend.nodes.helpers import bool_to_mask
+
+
+def test_basic_correlation():
+    from backend.nodes.grain_cross import GrainCross
+
+    node = GrainCross()
+
+    rng = np.random.default_rng(42)
+    data_a = rng.standard_normal((64, 64))
+    data_b = rng.standard_normal((64, 64))
+    field_a = make_field(data=data_a)
+    field_b = make_field(data=data_b)
+
+    # Create mask with two distinct grains
+    mask_bool = np.zeros((64, 64), dtype=bool)
+    mask_bool[5:20, 5:20] = True
+    mask_bool[40:55, 40:55] = True
+    mask = bool_to_mask(mask_bool)
+
+    (table,) = node.process(field_a, field_b, mask=mask,
+                            property_a="mean_height", property_b="max_height",
+                            min_size=10)
+    assert len(table) > 0, "Should return entries for detected grains"
+
+
+def test_pearson_reported():
+    from backend.nodes.grain_cross import GrainCross
+
+    node = GrainCross()
+
+    rng = np.random.default_rng(7)
+    data_a = rng.standard_normal((64, 64))
+    data_b = rng.standard_normal((64, 64))
+    field_a = make_field(data=data_a)
+    field_b = make_field(data=data_b)
+
+    # Two grains so Pearson can be computed
+    mask_bool = np.zeros((64, 64), dtype=bool)
+    mask_bool[5:20, 5:20] = True
+    mask_bool[40:55, 40:55] = True
+    mask = bool_to_mask(mask_bool)
+
+    (table,) = node.process(field_a, field_b, mask=mask,
+                            property_a="mean_height", property_b="mean_height",
+                            min_size=10)
+    quantities = [row["quantity"] for row in table]
+    assert "Pearson r" in quantities, f"Expected 'Pearson r' in {quantities}"
+
+
+def test_min_size_filters():
+    from backend.nodes.grain_cross import GrainCross
+
+    node = GrainCross()
+
+    data_a = np.zeros((64, 64))
+    data_b = np.zeros((64, 64))
+    field_a = make_field(data=data_a)
+    field_b = make_field(data=data_b)
+
+    # Small grain (10x10 = 100 pixels)
+    mask_bool = np.zeros((64, 64), dtype=bool)
+    mask_bool[5:15, 5:15] = True
+    mask = bool_to_mask(mask_bool)
+
+    # min_size larger than any grain
+    (table,) = node.process(field_a, field_b, mask=mask,
+                            property_a="area", property_b="area",
+                            min_size=200)
+    # No grain entries (only maybe no Pearson either since < 2 grains)
+    grain_entries = [r for r in table if r["quantity"].startswith("Grain")]
+    assert len(grain_entries) == 0, "No grains should pass with large min_size"