add snapshot tool, masks, and build for mac

tests/test_nodes.py

@@ -85,6 +85,88 @@ def test_edge_detect():
     print("  PASS\n")
 
 
+def test_fft_filter_1d():
+    print("=== Test: FFTFilter1D ===")
+    from backend.nodes.filters import FFTFilter1D
+    node = FFTFilter1D()
+
+    # Signal: low-frequency sine + high-frequency sine
+    n = 256
+    t = np.arange(n, dtype=np.float64) / n
+    low = np.sin(2 * np.pi * 3 * t)   # 3 cycles — low freq
+    high = np.sin(2 * np.pi * 80 * t)  # 80 cycles — high freq
+    line = low + high
+
+    # Lowpass should keep low, suppress high
+    filtered_lp, = node.process(line, filter_type="lowpass", cutoff=0.15, cutoff_high=0.4, order=4)
+    assert len(filtered_lp) == n
+    corr_low = np.corrcoef(filtered_lp, low)[0, 1]
+    corr_high = np.corrcoef(filtered_lp, high)[0, 1]
+    assert corr_low > 0.95, f"Lowpass: correlation with low={corr_low}"
+    assert abs(corr_high) < 0.3, f"Lowpass: correlation with high={corr_high}"
+
+    # Highpass should keep high, suppress low
+    filtered_hp, = node.process(line, filter_type="highpass", cutoff=0.4, cutoff_high=0.4, order=4)
+    corr_low_hp = np.corrcoef(filtered_hp, low)[0, 1]
+    corr_high_hp = np.corrcoef(filtered_hp, high)[0, 1]
+    assert abs(corr_low_hp) < 0.3, f"Highpass: correlation with low={corr_low_hp}"
+    assert corr_high_hp > 0.95, f"Highpass: correlation with high={corr_high_hp}"
+
+    # Bandpass centred on the high frequency
+    filtered_bp, = node.process(line, filter_type="bandpass", cutoff=0.4, cutoff_high=0.8, order=4)
+    corr_low_bp = np.corrcoef(filtered_bp, low)[0, 1]
+    corr_high_bp = np.corrcoef(filtered_bp, high)[0, 1]
+    assert abs(corr_low_bp) < 0.3, f"Bandpass: correlation with low={corr_low_bp}"
+    assert corr_high_bp > 0.9, f"Bandpass: correlation with high={corr_high_bp}"
+
+    # Notch (band-reject) centred on the high frequency — should remove it
+    filtered_notch, = node.process(line, filter_type="notch", cutoff=0.4, cutoff_high=0.8, order=4)
+    corr_low_notch = np.corrcoef(filtered_notch, low)[0, 1]
+    corr_high_notch = np.corrcoef(filtered_notch, high)[0, 1]
+    assert corr_low_notch > 0.95, f"Notch: correlation with low={corr_low_notch}"
+    assert abs(corr_high_notch) < 0.3, f"Notch: correlation with high={corr_high_notch}"
+
+    print("  PASS\n")
+
+
+def test_fft_filter_2d():
+    print("=== Test: FFTFilter2D ===")
+    from backend.nodes.filters import FFTFilter2D
+    node = FFTFilter2D()
+
+    N = 128
+    y, x = np.mgrid[0:N, 0:N] / N
+    # Low-frequency 2D pattern + high-frequency pattern
+    low_2d = np.sin(2 * np.pi * 3 * x) + np.sin(2 * np.pi * 3 * y)
+    high_2d = np.sin(2 * np.pi * 40 * x) + np.sin(2 * np.pi * 40 * y)
+    data = low_2d + high_2d
+    field = make_field(data=data, shape=None, xreal=1e-6, yreal=1e-6)
+
+    # Lowpass — should preserve low, remove high
+    result_lp, = node.process(field, filter_type="lowpass", cutoff=0.15, cutoff_high=0.4, order=4)
+    assert result_lp.data.shape == (N, N)
+    assert result_lp.xreal == field.xreal
+    assert result_lp.si_unit_z == field.si_unit_z
+    corr_low = np.corrcoef(result_lp.data.ravel(), low_2d.ravel())[0, 1]
+    corr_high = np.corrcoef(result_lp.data.ravel(), high_2d.ravel())[0, 1]
+    assert corr_low > 0.9, f"2D lowpass: correlation with low={corr_low}"
+    assert abs(corr_high) < 0.3, f"2D lowpass: correlation with high={corr_high}"
+
+    # Highpass — should preserve high, remove low
+    result_hp, = node.process(field, filter_type="highpass", cutoff=0.4, cutoff_high=0.4, order=4)
+    corr_low_hp = np.corrcoef(result_hp.data.ravel(), low_2d.ravel())[0, 1]
+    corr_high_hp = np.corrcoef(result_hp.data.ravel(), high_2d.ravel())[0, 1]
+    assert abs(corr_low_hp) < 0.3, f"2D highpass: correlation with low={corr_low_hp}"
+    assert corr_high_hp > 0.9, f"2D highpass: correlation with high={corr_high_hp}"
+
+    # Constant field should be unchanged by lowpass (DC preservation)
+    const = make_field(data=np.ones((32, 32)) * 7.0)
+    result_const, = node.process(const, filter_type="lowpass", cutoff=0.5, cutoff_high=0.5, order=2)
+    assert np.allclose(result_const.data, 7.0, atol=1e-10), "Lowpass should preserve constant field"
+
+    print("  PASS\n")
+
+
 # =========================================================================
 # Level
 # =========================================================================
@@ -199,7 +281,7 @@ def test_height_histogram():
     data = np.linspace(0, 1, 1000).reshape(25, 40)
     field = make_field(data=data)
 
-    counts, bin_centers = node.process(field, n_bins=10)
+    counts, bin_centers = node.process(field, n_bins=10, y_scale="linear")
     assert len(counts) == 10
     assert len(bin_centers) == 10
     assert counts.dtype == np.float64
@@ -265,7 +347,7 @@ def test_cross_section():
 
 def test_threshold_mask():
     print("=== Test: ThresholdMask ===")
-    from backend.nodes.grains import ThresholdMask
+    from backend.nodes.mask import ThresholdMask
     node = ThresholdMask()
 
     # Clear bimodal data: left half = 0, right half = 1
@@ -273,6 +355,11 @@ def test_threshold_mask():
     data[:, 32:] = 1.0
     field = make_field(data=data)
 
+    # Capture overlay preview
+    previews = []
+    ThresholdMask._broadcast_fn = lambda nid, uri: previews.append(uri)
+    ThresholdMask._current_node_id = "test"
+
     # Absolute threshold at 0.5
     mask, = node.process(field, method="absolute", threshold=0.5, direction="above")
     assert mask.dtype == np.uint8
@@ -280,6 +367,10 @@ def test_threshold_mask():
     assert np.all(mask[:, :32] == 0)
     assert np.all(mask[:, 32:] == 255)
 
+    # Verify overlay preview was broadcast
+    assert len(previews) == 1
+    assert previews[0].startswith("data:image/png;base64,")
+
     # Direction "below"
     mask_below, = node.process(field, method="absolute", threshold=0.5, direction="below")
     assert np.all(mask_below[:, :32] == 255)
@@ -292,20 +383,117 @@ def test_threshold_mask():
     # Otsu should find the bimodal threshold
     mask_otsu, = node.process(field, method="otsu", threshold=0.0, direction="above")
     assert mask_otsu[:, 32:].sum() > mask_otsu[:, :32].sum()
+
+    ThresholdMask._broadcast_fn = None
     print("  PASS\n")
 
 
-def test_grain_analysis():
-    print("=== Test: GrainAnalysis ===")
-    from backend.nodes.grains import GrainAnalysis
-    node = GrainAnalysis()
+def test_mask_morphology():
+    print("=== Test: MaskMorphology ===")
+    from backend.nodes.mask import MaskMorphology
+    node = MaskMorphology()
 
-    # Create a field with two distinct "grains"
+    # Small square blob in the centre
+    mask = np.zeros((64, 64), dtype=np.uint8)
+    mask[28:36, 28:36] = 255  # 8x8 block
+    orig_count = np.count_nonzero(mask)
+
+    # Dilate should grow the region
+    dilated, = node.process(mask, operation="dilate", radius=1, shape="square")
+    assert dilated.dtype == np.uint8
+    assert np.count_nonzero(dilated) > orig_count
+
+    # Erode should shrink it
+    eroded, = node.process(mask, operation="erode", radius=1, shape="square")
+    assert np.count_nonzero(eroded) < orig_count
+
+    # Open on a clean block should give back roughly the same block
+    opened, = node.process(mask, operation="open", radius=1, shape="square")
+    assert np.count_nonzero(opened) <= orig_count
+
+    # Close on a mask with a 1-pixel hole should fill the hole
+    mask_hole = mask.copy()
+    mask_hole[32, 32] = 0  # poke a hole
+    assert np.count_nonzero(mask_hole) == orig_count - 1
+    closed, = node.process(mask_hole, operation="close", radius=1, shape="square")
+    assert closed[32, 32] == 255, "Close should fill the 1-pixel hole"
+
+    # Disk structuring element should also work
+    dilated_disk, = node.process(mask, operation="dilate", radius=2, shape="disk")
+    assert np.count_nonzero(dilated_disk) > orig_count
+
+    print("  PASS\n")
+
+
+def test_mask_invert():
+    print("=== Test: MaskInvert ===")
+    from backend.nodes.mask import MaskInvert
+    node = MaskInvert()
+
+    mask = np.zeros((64, 64), dtype=np.uint8)
+    mask[10:20, 10:20] = 255
+
+    inverted, = node.process(mask)
+    assert inverted.dtype == np.uint8
+    assert np.all(inverted[10:20, 10:20] == 0)
+    assert np.all(inverted[0:10, 0:10] == 255)
+    # Double-invert should return to original
+    double, = node.process(inverted)
+    assert np.array_equal(double, mask)
+
+    print("  PASS\n")
+
+
+def test_mask_combine():
+    print("=== Test: MaskCombine ===")
+    from backend.nodes.mask import MaskCombine
+    node = MaskCombine()
+
+    # Two overlapping squares
+    a = np.zeros((64, 64), dtype=np.uint8)
+    a[10:30, 10:30] = 255  # 20x20
+    b = np.zeros((64, 64), dtype=np.uint8)
+    b[20:40, 20:40] = 255  # 20x20, overlaps 10x10
+
+    # AND — only the overlap
+    result_and, = node.process(a, b, operation="and")
+    assert np.all(result_and[20:30, 20:30] == 255)
+    assert result_and[15, 15] == 0  # a-only region
+    assert result_and[35, 35] == 0  # b-only region
+
+    # OR — union
+    result_or, = node.process(a, b, operation="or")
+    assert result_or[15, 15] == 255
+    assert result_or[35, 35] == 255
+    assert result_or[25, 25] == 255
+    assert result_or[5, 5] == 0
+
+    # XOR — symmetric difference
+    result_xor, = node.process(a, b, operation="xor")
+    assert result_xor[15, 15] == 255  # a-only
+    assert result_xor[35, 35] == 255  # b-only
+    assert result_xor[25, 25] == 0    # overlap excluded
+
+    # Subtract — a minus b
+    result_sub, = node.process(a, b, operation="subtract")
+    assert result_sub[15, 15] == 255  # a-only kept
+    assert result_sub[25, 25] == 0    # overlap removed
+    assert result_sub[35, 35] == 0    # b-only not included
+
+    print("  PASS\n")
+
+
+def test_particle_analysis():
+    print("=== Test: ParticleAnalysis ===")
+    from backend.nodes.grains import ParticleAnalysis
+    node = ParticleAnalysis()
+
+    # Create a field with two distinct particles
     N = 64
     data = np.zeros((N, N))
-    # Grain 1: 10x10 block at top-left with height 5
+    # Particle 1: 10x10 block at top-left with height 5
     data[5:15, 5:15] = 5.0
-    # Grain 2: 8x8 block at bottom-right with height 3
+    # Particle 2: 8x8 block at bottom-right with height 3
     data[45:53, 45:53] = 3.0
     field = make_field(data=data, xreal=1e-6, yreal=1e-6)
 
@@ -315,7 +503,7 @@ def test_grain_analysis():
     mask[45:53, 45:53] = 255
 
     table, = node.process(field, mask=mask, min_size=10)
-    assert len(table) == 2, f"Expected 2 grains, got {len(table)}"
+    assert len(table) == 2, f"Expected 2 particles, got {len(table)}"
 
     # Sort by area descending
     table.sort(key=lambda r: r["area_px"], reverse=True)
@@ -324,7 +512,7 @@ def test_grain_analysis():
     assert abs(table[0]["mean_height"] - 5.0) < 1e-10
     assert abs(table[1]["mean_height"] - 3.0) < 1e-10
 
-    # min_size filtering: only keep grains >= 80 px
+    # min_size filtering: only keep particles >= 80 px
     table_filtered, = node.process(field, mask=mask, min_size=80)
     assert len(table_filtered) == 1
     assert table_filtered[0]["area_px"] == 100
@@ -462,6 +650,8 @@ if __name__ == "__main__":
     test_gaussian_filter()
     test_median_filter()
     test_edge_detect()
+    test_fft_filter_1d()
+    test_fft_filter_2d()
 
     # Level
     test_plane_level()
@@ -473,9 +663,14 @@ if __name__ == "__main__":
     test_height_histogram()
     test_cross_section()
 
-    # Grains
+    # Mask
     test_threshold_mask()
-    test_grain_analysis()
+    test_mask_morphology()
+    test_mask_invert()
+    test_mask_combine()
+
+    # Grains
+    test_particle_analysis()
 
     # I/O
     test_load_image()