nan
18799e8828
- Replaced the Mesh Detail parameter with a new Edge Length property to define cutout sampling in physical units. - Introduced Minimum Relative Edge Length preference to control effective spacing based on the bounding box of the cutout content. - Updated tests to validate the new edge length functionality and ensure consistent behavior across different resolutions and canvas sizes. - Removed deprecated parameters and adjusted related properties to align with the new edge length implementation. - Enhanced geometry sampling logic to utilize effective world spacing for both standard and fine outline modes.
188 lines
8.3 KiB
Python
188 lines
8.3 KiB
Python
"""Contracts for smooth depth volume, editable presets and normal masks."""
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import bpy
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import numpy as np
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import pytest
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from tools.nodes.groups.image_depth_cutout import build_volume_fields
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from anyimage.common.object import modifier_input_identifier, set_modifier_input
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def store_test_fields(group, geometry, fields):
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"""Expose evaluated fields only in the test output mesh."""
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for name, field in fields.items():
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store = group.nodes.new("GeometryNodeStoreNamedAttribute")
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store.domain = "POINT"
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store.data_type = "FLOAT_VECTOR" if field.type == "VECTOR" else "FLOAT"
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store.inputs["Name"].default_value = name
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group.links.new(geometry, store.inputs["Geometry"])
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group.links.new(field, store.inputs["Value"])
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geometry = store.outputs["Geometry"]
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return geometry
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def set_test_structure(monkeypatch, value):
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"""Control structural amplitude without depending on storage nodes."""
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from tools.nodes.groups import image_depth_cutout
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build = image_depth_cutout.build_front_fields
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def fields(group, volume, normal, normal_weight):
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result = build(group, volume, normal, normal_weight)
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structure = group.nodes.new("ShaderNodeValue")
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structure.outputs[0].default_value = value
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result["structure"] = structure.outputs[0]
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return result
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monkeypatch.setattr(image_depth_cutout, "build_front_fields", fields)
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def test_shell_thickness_extends_behind_the_tilted_front():
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from scipy.spatial import cKDTree
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from tests.support.depth_surface import surface, evaluated, assert_closed
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obj, set_value = surface(step=0, resolution=4)
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image = bpy.data.images["Camera"]
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pixels = np.array(image.pixels[:], dtype=np.float32).reshape(32, 64, 4)
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pixels[..., 2] = 1 + 0.4 * pixels[..., 0]
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image.pixels.foreach_set(pixels.ravel())
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image.update()
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obj.update_tag(refresh={"DATA"})
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bpy.context.view_layer.update()
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front, _ = evaluated(obj)
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set_value("Thickness", 0.2)
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set_value("Front Inflation", 0.5)
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points, faces = evaluated(obj)
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expected = front - 0.2 * np.array((0.4, 0, 1)) / np.sqrt(1.16)
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assert cKDTree(points).query(expected)[0].max() < 1e-5
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assert cKDTree(points).query(front)[0].max() < 1e-5
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assert_closed(faces)
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def test_balloon_shell_converges_to_front_with_thickness():
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from scipy.spatial import cKDTree
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from tests.support.depth_surface import surface, evaluated, assert_closed
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obj, set_value = surface(step=1, resolution=10)
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xy = np.array([v.co[:2] for v in obj.data.vertices])
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profile = 0.3 * np.sin(np.pi * xy[:, 0] / 2) * np.sin(np.pi * xy[:, 1])
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profile[abs(profile) < 1e-7] = 0
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obj.data.attributes.new("o_balloon", "FLOAT", "POINT").data.foreach_set("value", profile)
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set_value("Mode", 0)
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modifier = obj.modifiers[0]
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thickness = modifier_input_identifier(modifier.node_group, "Thickness", subtype="NONE")
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def amount(value):
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set_modifier_input(modifier, thickness, value)
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obj.update_tag(refresh={"DATA"})
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bpy.context.view_layer.update()
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return evaluated(obj)
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front, _ = amount(0)
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tree = cKDTree(front)
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gaps = []
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for value in (0.01, 0.001, 0.0001):
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points, faces = amount(value)
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assert_closed(faces)
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gaps.append(tree.query(points)[0].max())
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np.testing.assert_allclose(np.array(gaps[1:]) / gaps[:-1], 0.1, rtol=0.02)
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def test_smooth_back_rejects_front_noise_and_preserves_center_depth():
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bpy.ops.wm.read_factory_settings(use_empty=True)
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n = 41
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points = np.array([(x, y) for y in np.linspace(-1, 1, n) for x in np.linspace(-1, 1, n)])
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profile = np.sqrt(np.maximum(0, np.cos(points[:, 0]*np.pi/2)*np.cos(points[:, 1]*np.pi/2))) * 0.6
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boundary = np.max(abs(points), axis=1) > 0.999
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profile[boundary] = 0
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center = 0.3*points[:, 0] + 0.6*points[:, 1]
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noise = 0.08*np.sin(points[:, 0]*23)*np.sin(points[:, 1]*19)
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noise[boundary] = 0
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faces = [(y*n+x, y*n+x+1, (y+1)*n+x+1) for y in range(n-1) for x in range(n-1)]
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faces += [(y*n+x, (y+1)*n+x+1, (y+1)*n+x) for y in range(n-1) for x in range(n-1)]
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mesh = bpy.data.meshes.new("Noisy front")
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mesh.from_pydata(np.column_stack((points, center + profile + noise)), [], faces)
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mesh.attributes.new("o_balloon", "FLOAT", "POINT").data.foreach_set("value", profile.astype(np.float32))
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obj = bpy.data.objects.new("Noisy front", mesh)
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bpy.context.collection.objects.link(obj)
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group = bpy.data.node_groups.new("Volume field test", "GeometryNodeTree")
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for name, kind in (("Geometry", "NodeSocketGeometry"), ("Thickness", "NodeSocketFloat")):
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socket = group.interface.new_socket(name=name, in_out="INPUT", socket_type=kind)
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if name == "Thickness":
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socket.default_value = 1
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group.interface.new_socket(name="Geometry", in_out="OUTPUT", socket_type="NodeSocketGeometry")
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source = group.nodes.new("NodeGroupInput")
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fields = build_volume_fields(group)
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result = store_test_fields(group, source.outputs["Geometry"], {"thickness": fields["thickness"]})
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group.links.new(result, group.nodes.new("NodeGroupOutput").inputs["Geometry"])
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obj.modifiers.new("Volume", "NODES").node_group = group
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bpy.context.view_layer.update()
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evaluated = obj.evaluated_get(bpy.context.evaluated_depsgraph_get())
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result = evaluated.to_mesh()
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try:
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thickness = np.array([item.value for item in result.attributes["thickness"].data])
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rear = center + profile + noise - thickness
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error = rear - (center - profile)
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assert max(abs(error)) < 1e-5
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assert np.sqrt(np.mean(error**2)) < np.sqrt(np.mean(noise**2))*0.01
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assert max(abs(thickness[boundary])) == 0
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finally:
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evaluated.to_mesh_clear()
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def test_depth_cutout_normal_map_only_affects_front_faces():
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from tests.support.depth_surface import surface
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obj, set_value = surface()
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set_value("Thickness", 0.3)
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bpy.context.view_layer.update()
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evaluated = obj.evaluated_get(bpy.context.evaluated_depsgraph_get())
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mesh = evaluated.to_mesh()
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try:
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attribute = mesh.attributes["o_normal_scale"]
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assert attribute.domain == "FACE"
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weights = [item.value for item in attribute.data]
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assert weights.count(1) == len(obj.data.polygons)
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assert weights.count(0) == len(mesh.polygons) - len(obj.data.polygons)
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finally:
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evaluated.to_mesh_clear()
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@pytest.mark.parametrize("thickness,inflation", [
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(0.0, 1.0), (0.02, 0.0), (0.02, 0.0001),
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(0.02, 0.5), (0.1, 0.5), (0.1, 1.0),
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])
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def test_shell_ignores_front_inflation(thickness, inflation):
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from tests.support.depth_surface import surface, evaluated, assert_closed
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obj, set_value = surface(step=0, resolution=10)
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xy = np.array([v.co[:2] for v in obj.data.vertices])
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profile = 0.3 * np.sin(np.pi * xy[:, 0] / 2) * np.sin(np.pi * xy[:, 1])
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profile[abs(profile) < 1e-7] = 0
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obj.data.attributes.new("o_balloon", "FLOAT", "POINT").data.foreach_set("value", profile)
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set_value("Thickness", thickness)
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set_value("Front Inflation", 0.0)
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baseline, faces = evaluated(obj)
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set_value("Front Inflation", inflation)
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inflated, inflated_faces = evaluated(obj)
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assert faces == inflated_faces
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np.testing.assert_array_equal(inflated, baseline)
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if thickness:
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assert_closed(inflated_faces)
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def test_balloon_suppression_is_stronger_without_a_startup_threshold(monkeypatch):
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from tests.support.depth_surface import surface, evaluated
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set_test_structure(monkeypatch, 1)
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obj, set_value = surface(step=0, resolution=10)
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xy = np.array([v.co[:2] for v in obj.data.vertices])
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profile = 0.3 * np.sin(np.pi * xy[:, 0] / 2) * np.sin(np.pi * xy[:, 1])
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profile[abs(profile) < 1e-7] = 0
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obj.data.attributes.new("o_balloon", "FLOAT", "POINT").data.foreach_set("value", profile)
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group = obj.modifiers[0].node_group
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set_value("Mode", 0)
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thickness_id = modifier_input_identifier(group, "Thickness", subtype="NONE")
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previous = 0
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for thickness in (0.001, 0.01, 0.1, 0.5, 1):
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set_modifier_input(obj.modifiers[0], thickness_id, thickness)
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set_value("Front Inflation", 0)
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baseline, _ = evaluated(obj)
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set_value("Front Inflation", 1)
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inflated, _ = evaluated(obj)
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growth = np.linalg.norm(inflated - baseline, axis=1).max() / profile.max()
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assert growth > previous
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previous = growth
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assert growth == pytest.approx(0.323, abs=0.001)
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