446 lines
15 KiB
Python
446 lines
15 KiB
Python
"""
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Thorough tests for the particles/particle analysis pipeline:
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ThresholdMask → GrainAnalysis
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Covers synthetic geometry (known answers), the demo nanoparticles image,
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edge cases, and physical-unit correctness.
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Run from project root:
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.venv/bin/python -m tests.test_particles
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"""
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import sys
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import numpy as np
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sys.path.insert(0, ".")
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from backend.data_types import DataField
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def make_field(data, xreal=1e-6, yreal=1e-6):
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return DataField(data=data.astype(np.float64), xreal=xreal, yreal=yreal,
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si_unit_xy="m", si_unit_z="m")
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# =========================================================================
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# ThresholdMask tests
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# =========================================================================
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def test_threshold_otsu_bimodal():
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"""Otsu on a clean bimodal image should separate the two populations."""
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print("=== Test: Otsu on bimodal image ===")
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from backend.nodes.particle import ThresholdMask
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node = ThresholdMask()
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data = np.zeros((128, 128))
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data[30:50, 30:50] = 10.0 # bright square
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data[70:100, 80:110] = 10.0 # another bright region
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field = make_field(data)
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mask, = node.process(field, method="otsu", threshold=0.0, direction="above")
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bright_pixels = (mask == 255)
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# Should capture both bright regions
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assert bright_pixels[40, 40], "Otsu missed bright region 1"
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assert bright_pixels[85, 95], "Otsu missed bright region 2"
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# Background should be dark
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assert not bright_pixels[0, 0], "Otsu false positive in background"
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assert not bright_pixels[60, 60], "Otsu false positive between regions"
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print(" PASS\n")
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def test_threshold_relative_range():
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"""Relative threshold at 0.5 should be the midpoint of [min, max]."""
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print("=== Test: Relative threshold at midpoint ===")
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from backend.nodes.particle import ThresholdMask
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node = ThresholdMask()
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data = np.full((64, 64), 2.0)
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data[10:20, 10:20] = 8.0 # bright patch, range = [2, 8], midpoint = 5
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field = make_field(data)
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mask, = node.process(field, method="relative", threshold=0.5, direction="above")
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# Only the bright patch (value 8 >= 5) should be masked
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assert np.all(mask[10:20, 10:20] == 255)
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assert np.all(mask[0:10, :] == 0)
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assert np.all(mask[20:, :] == 0)
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print(" PASS\n")
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def test_threshold_empty_mask():
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"""Very high absolute threshold on low data should produce an empty mask."""
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print("=== Test: Empty mask from high threshold ===")
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from backend.nodes.particle import ThresholdMask
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node = ThresholdMask()
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data = np.ones((64, 64))
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field = make_field(data)
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mask, = node.process(field, method="absolute", threshold=999.0, direction="above")
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assert mask.sum() == 0, "Mask should be completely empty"
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print(" PASS\n")
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def test_threshold_full_mask():
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"""Very low absolute threshold should produce an all-white mask."""
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print("=== Test: Full mask from low threshold ===")
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from backend.nodes.particle import ThresholdMask
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node = ThresholdMask()
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data = np.ones((64, 64)) * 5.0
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field = make_field(data)
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mask, = node.process(field, method="absolute", threshold=-1.0, direction="above")
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assert np.all(mask == 255), "Mask should be all white"
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print(" PASS\n")
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# =========================================================================
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# GrainAnalysis tests
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# =========================================================================
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def test_single_circle_area():
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"""A single filled circle — verify pixel count and physical area."""
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print("=== Test: Single circle area ===")
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from backend.nodes.particle import GrainAnalysis
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node = GrainAnalysis()
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N = 200
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XREAL = 2e-6 # 2 µm
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data = np.zeros((N, N))
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mask = np.zeros((N, N), dtype=np.uint8)
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# Draw a filled circle, radius 30 px, centred at (100, 100)
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yy, xx = np.mgrid[0:N, 0:N]
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r = 30
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circle = ((xx - 100) ** 2 + (yy - 100) ** 2) <= r ** 2
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data[circle] = 5.0
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mask[circle] = 255
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field = make_field(data, xreal=XREAL, yreal=XREAL)
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table, = node.process(field, mask=mask, min_size=1)
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assert len(table) == 1, f"Expected 1 particles, got {len(table)}"
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particles = table[0]
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# Pixel area of a discrete circle: should be close to π r²
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expected_px = np.pi * r ** 2
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assert abs(particles["area_px"] - expected_px) / expected_px < 0.02, \
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f"area_px={particles['area_px']}, expected≈{expected_px:.0f}"
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# Physical area
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pixel_area = (XREAL / N) ** 2
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expected_m2 = particles["area_px"] * pixel_area
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assert abs(particles["area_m2"] - expected_m2) < 1e-20, \
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f"area_m2 mismatch: {particles['area_m2']} vs {expected_m2}"
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# Equivalent diameter should be close to 2r in physical units
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expected_diam = 2 * r * (XREAL / N)
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assert abs(particles["equiv_diam_m"] - expected_diam) / expected_diam < 0.02, \
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f"equiv_diam={particles['equiv_diam_m']:.3e}, expected≈{expected_diam:.3e}"
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# Heights
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assert abs(particles["mean_height"] - 5.0) < 1e-10
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assert abs(particles["max_height"] - 5.0) < 1e-10
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print(" PASS\n")
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def test_multiple_particles_separation():
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"""Three well-separated particles of different sizes — check each is reported."""
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print("=== Test: Multiple particles separation ===")
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from backend.nodes.particle import GrainAnalysis
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node = GrainAnalysis()
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N = 128
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data = np.zeros((N, N))
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mask = np.zeros((N, N), dtype=np.uint8)
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# Grain A: 20×20 block, height 10
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data[10:30, 10:30] = 10.0
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mask[10:30, 10:30] = 255
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# Grain B: 10×10 block, height 7
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data[60:70, 60:70] = 7.0
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mask[60:70, 60:70] = 255
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# Grain C: 5×5 block, height 3
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data[100:105, 100:105] = 3.0
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mask[100:105, 100:105] = 255
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field = make_field(data)
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table, = node.process(field, mask=mask, min_size=1)
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assert len(table) == 3, f"Expected 3 particles, got {len(table)}"
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table.sort(key=lambda r: r["area_px"], reverse=True)
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assert table[0]["area_px"] == 400 # 20×20
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assert table[1]["area_px"] == 100 # 10×10
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assert table[2]["area_px"] == 25 # 5×5
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assert abs(table[0]["mean_height"] - 10.0) < 1e-10
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assert abs(table[1]["mean_height"] - 7.0) < 1e-10
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assert abs(table[2]["mean_height"] - 3.0) < 1e-10
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print(" PASS\n")
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def test_min_size_filtering():
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"""min_size should exclude particles smaller than the threshold."""
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print("=== Test: min_size filtering ===")
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from backend.nodes.particle import GrainAnalysis
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node = GrainAnalysis()
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N = 64
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data = np.zeros((N, N))
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mask = np.zeros((N, N), dtype=np.uint8)
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# Large particles: 15×15 = 225 px
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data[5:20, 5:20] = 1.0
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mask[5:20, 5:20] = 255
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# Medium particles: 8×8 = 64 px
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data[30:38, 30:38] = 1.0
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mask[30:38, 30:38] = 255
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# Tiny particles: 3×3 = 9 px
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data[50:53, 50:53] = 1.0
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mask[50:53, 50:53] = 255
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field = make_field(data)
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# min_size=1: all three
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table, = node.process(field, mask=mask, min_size=1)
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assert len(table) == 3
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# min_size=10: drops the 3×3
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table, = node.process(field, mask=mask, min_size=10)
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assert len(table) == 2
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# min_size=100: drops the 3×3 and 8×8
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table, = node.process(field, mask=mask, min_size=100)
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assert len(table) == 1
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assert table[0]["area_px"] == 225
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# min_size=300: drops everything
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table, = node.process(field, mask=mask, min_size=300)
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assert len(table) == 0
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print(" PASS\n")
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def test_particles_bounding_box():
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"""Bounding box should match the particles extents."""
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print("=== Test: Grain bounding box ===")
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from backend.nodes.particle import GrainAnalysis
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node = GrainAnalysis()
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N = 64
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data = np.zeros((N, N))
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mask = np.zeros((N, N), dtype=np.uint8)
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# Place a particles at rows 20:35, cols 10:45
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data[20:35, 10:45] = 2.0
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mask[20:35, 10:45] = 255
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field = make_field(data)
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table, = node.process(field, mask=mask, min_size=1)
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assert len(table) == 1
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bbox = table[0]["bbox"]
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# Format: "(xmin,ymin)-(xmax,ymax)" = "(10,20)-(44,34)"
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assert bbox == "(10,20)-(44,34)", f"bbox={bbox}, expected (10,20)-(44,34)"
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print(" PASS\n")
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def test_empty_mask_produces_no_particles():
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"""An all-zero mask should yield zero particles."""
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print("=== Test: Empty mask → no particles ===")
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from backend.nodes.particle import GrainAnalysis
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node = GrainAnalysis()
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field = make_field(np.ones((64, 64)))
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mask = np.zeros((64, 64), dtype=np.uint8)
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table, = node.process(field, mask=mask, min_size=1)
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assert len(table) == 0
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print(" PASS\n")
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def test_particles_at_image_edge():
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"""A particles touching the image border should still be detected."""
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print("=== Test: Grain at image edge ===")
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from backend.nodes.particle import GrainAnalysis
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node = GrainAnalysis()
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N = 64
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data = np.zeros((N, N))
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mask = np.zeros((N, N), dtype=np.uint8)
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# Grain touching top-left corner
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data[0:10, 0:10] = 4.0
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mask[0:10, 0:10] = 255
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field = make_field(data)
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table, = node.process(field, mask=mask, min_size=1)
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assert len(table) == 1
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assert table[0]["area_px"] == 100
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assert table[0]["bbox"] == "(0,0)-(9,9)"
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print(" PASS\n")
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def test_adjacent_particles_connectivity():
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"""Two diagonally-touching blocks should be separate particles
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(scipy.ndimage.label uses 4-connectivity by default)."""
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print("=== Test: Diagonal adjacency → separate particles ===")
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from backend.nodes.particle import GrainAnalysis
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node = GrainAnalysis()
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N = 32
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data = np.zeros((N, N))
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mask = np.zeros((N, N), dtype=np.uint8)
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# Block A
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data[5:10, 5:10] = 1.0
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mask[5:10, 5:10] = 255
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# Block B diagonally adjacent (touching only at corner 10,10)
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data[10:15, 10:15] = 1.0
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mask[10:15, 10:15] = 255
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field = make_field(data)
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table, = node.process(field, mask=mask, min_size=1)
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# Default label() uses structure that connects diagonals? Let's verify.
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# scipy.ndimage.label default is cross-shaped (no diagonals) for 2D
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assert len(table) == 2, f"Expected 2 separate particles, got {len(table)}"
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print(" PASS\n")
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# =========================================================================
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# End-to-end pipeline: ThresholdMask → GrainAnalysis
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# =========================================================================
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def test_pipeline_synthetic():
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"""Full pipeline on a synthetic image with known geometry."""
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print("=== Test: Full pipeline on synthetic particles ===")
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from backend.nodes.particle import ThresholdMask, GrainAnalysis
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N = 200
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XREAL = 10e-6 # 10 µm
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rng = np.random.default_rng(99)
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# Background at 0 with small noise, particles as raised bumps
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bg = rng.normal(0, 0.1, (N, N))
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particles = np.zeros((N, N))
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yy, xx = np.mgrid[0:N, 0:N]
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specs = [
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(50, 50, 15, 5.0), # (cx, cy, radius_px, height)
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(150, 50, 20, 8.0),
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(100, 100, 10, 3.0),
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(50, 160, 25, 6.0),
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(160, 160, 12, 4.0),
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]
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for cx, cy, r, h in specs:
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inside = ((xx - cx) ** 2 + (yy - cy) ** 2) <= r ** 2
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particles[inside] = h
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data = bg + particles
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field = make_field(data, xreal=XREAL, yreal=XREAL)
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# Step 1: threshold
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thresh = ThresholdMask()
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mask, = thresh.process(field, method="absolute", threshold=1.0, direction="above")
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# Particles are well above noise, so mask should capture all 5
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assert mask.max() == 255, "No particles detected"
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# Step 2: particles analysis
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ga = GrainAnalysis()
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table, = ga.process(field, mask=mask, min_size=5)
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assert len(table) == 5, f"Expected 5 particles, got {len(table)}"
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# Verify that detected areas are in the right ballpark
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table.sort(key=lambda r: r["area_px"], reverse=True)
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expected_areas = sorted([np.pi * r ** 2 for _, _, r, _ in specs], reverse=True)
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for particles, expected_px in zip(table, expected_areas):
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ratio = particles["area_px"] / expected_px
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assert 0.85 < ratio < 1.15, \
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f"particles area_px={particles['area_px']}, expected≈{expected_px:.0f}, ratio={ratio:.2f}"
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print(" PASS\n")
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def test_pipeline_demo_image():
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"""Run the full pipeline on the bundled demo nanoparticles image."""
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print("=== Test: Full pipeline on demo nanoparticles.npy ===")
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from pathlib import Path
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from backend.nodes.particle import ThresholdMask, GrainAnalysis
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from backend.runtime_paths import demo_dir
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npy_path = demo_dir() / "nanoparticles.npy"
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if not npy_path.exists():
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print(" SKIP (demo image not found)\n")
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return
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data = np.load(str(npy_path)).astype(np.float64)
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# The demo image is a 5 µm × 5 µm scan
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field = make_field(data, xreal=5e-6, yreal=5e-6)
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# Threshold to find particles (they are raised above background)
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thresh = ThresholdMask()
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mask, = thresh.process(field, method="otsu", threshold=0.0, direction="above")
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# Should detect particles
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assert mask.max() == 255, "No particles found in demo image"
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particle_fraction = (mask == 255).sum() / mask.size
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assert 0.01 < particle_fraction < 0.5, \
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f"Suspicious particle fraction: {particle_fraction:.3f}"
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print(f" Mask: {particle_fraction*100:.1f}% of pixels are particles")
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# Grain analysis
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ga = GrainAnalysis()
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table, = ga.process(field, mask=mask, min_size=20)
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assert len(table) > 0, "No particles detected"
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print(f" Found {len(table)} particles (min_size=20)")
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# Sanity checks on particles properties
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for particles in table:
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assert particles["area_px"] >= 20
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assert particles["area_m2"] > 0
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assert particles["equiv_diam_m"] > 0
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assert particles["max_height"] >= particles["mean_height"]
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assert particles["mean_height"] > 0
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# Physical size sanity: equivalent diameters should be in the nm–µm range
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diams_nm = [g["equiv_diam_m"] * 1e9 for g in table]
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print(f" Diameters: min={min(diams_nm):.0f} nm, max={max(diams_nm):.0f} nm")
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assert all(1 < d < 2000 for d in diams_nm), \
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f"Grain diameters out of expected range: {diams_nm}"
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print(" PASS\n")
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# =========================================================================
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# Run all tests
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# =========================================================================
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if __name__ == "__main__":
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# ThresholdMask
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test_threshold_otsu_bimodal()
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test_threshold_relative_range()
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test_threshold_empty_mask()
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test_threshold_full_mask()
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# GrainAnalysis
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test_single_circle_area()
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test_multiple_particles_separation()
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test_min_size_filtering()
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test_particles_bounding_box()
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test_empty_mask_produces_no_particles()
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test_particles_at_image_edge()
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test_adjacent_particles_connectivity()
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# End-to-end pipeline
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test_pipeline_synthetic()
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test_pipeline_demo_image()
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print("All particles tests passed!")
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