From 745b238668dfa4c690d14803b5217877e71d9f35 Mon Sep 17 00:00:00 2001 From: oscar Date: Mon, 8 Dec 2025 14:40:43 +0800 Subject: [PATCH 01/26] added optional simplification and densification argument for lines, but still not implemented the last one --- src/vorflow/blueprint.py | 86 +++++++++++++++++++++++++++++++++++++--- 1 file changed, 80 insertions(+), 6 deletions(-) diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index f8d7e3e..fca094b 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -29,7 +29,8 @@ def __init__(self, crs="EPSG:4326"): self.clean_lines = gpd.GeoDataFrame() self.clean_points = gpd.GeoDataFrame() - def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refinement=True, dist_min=None, dist_max=None, dist_max_in=None, dist_max_out=None, border_density=None): + def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refinement=True, dist_min=None, + dist_max=None, dist_max_in=None, dist_max_out=None, border_density=None, simplify_tolerance=None): """ Adds a polygon feature, such as a model boundary or a refinement zone. @@ -51,6 +52,8 @@ def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refine mesh transitions to the background resolution. border_density (float, optional): If set, densifies the polygon's boundary by adding vertices, ensuring no segment is longer than this value. + simplify_tolerance (float, optional): Tolerance for simplifying the polygon geometry, no simplification is applied + if None. """ if not geometry.is_valid: geometry = make_valid(geometry) @@ -68,10 +71,12 @@ def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refine 'dist_min': dist_min, 'dist_max_in': dist_max_in, 'dist_max_out': dist_max_out, - 'border_density': border_density + 'border_density': border_density, + 'simplify_tolerance': simplify_tolerance }) - def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barrier=False, dist_min=None, dist_max=None, straddle_width=None): + def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barrier=False, + dist_min=None, dist_max=None, straddle_width=None, densify=None, simplify_tolerance=None): """ Adds a line feature, such as a river, fault, or other linear boundary. @@ -89,6 +94,9 @@ def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barr transitions to the background resolution. straddle_width (float, optional): If set, forces Voronoi cell edges to align perfectly with the line by creating a "virtual straddle" of mesh nodes. + densify (float, optional): If set, densifies the line by adding vertices, + ensuring no segment is longer than this value. + simplify_tolerance (float, optional): Tolerance for simplifying the line geometry. """ if not geometry.is_valid: geometry = make_valid(geometry) @@ -100,10 +108,11 @@ def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barr 'is_barrier': is_barrier, 'dist_min': dist_min, 'dist_max': dist_max, - 'straddle_width': straddle_width + 'straddle_width': straddle_width, + 'densify': densify }) - def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None): + def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None, simplify_tolerance=None): """ Adds a point feature, such as a well or an observation point. @@ -121,8 +130,70 @@ def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None 'point_id': point_id, 'lc': resolution, 'dist_min': dist_min, - 'dist_max': dist_max + 'dist_max': dist_max, + 'simplify_tolerance': simplify_tolerance }) + def _apply_simplification(self): + """ + Applies geometry simplification to raw polygons, lines, and points + based on their specified tolerances. + """ + # Simplify Polygons + for i, poly_data in enumerate(self.raw_polygons): + tol = poly_data.get('simplify_tolerance') + if tol is True: + lc = poly_data.get('lc') + tol = lc * 0.5 if lc is not None else None + + if tol is not None and tol > 0: + self.raw_polygons[i]['geometry'] = poly_data['geometry'].simplify(tol, preserve_topology=True) + + # Simplify Lines + for i, line_data in enumerate(self.raw_lines): + tol = line_data.get('simplify_tolerance') + if tol is True: + lc = line_data.get('lc') + tol = lc * 0.5 if lc is not None else None + if tol is not None and tol > 0: + simplified_geom = line_data['geometry'].simplify(tol, preserve_topology=True) + self.raw_lines[i]['geometry'] = simplified_geom + + # lets merge points that are very close to each other + if self.raw_points: + #lets sort by resolution first + sorted_points = sorted( + self.raw_points, + key=lambda x: x['lc'] if x['lc'] is not None else float('inf')) + final_points = [] + + for point_data in sorted_points: + current_geom = point_data['geometry'] + # Use specific tolerance if provided, otherwise default to a small value or skip + tol = point_data.get('simplify_tolerance') + if tol is None or tol <= 0: + final_points.append(point_data) + continue + + + if tol is True: + lc = point_data.get('lc') + tol = lc * 0.5 if lc is not None else 1e-6 + is_merged = False + + for kept in final_points: + dist = kept['geometry'].distance(current_geom) + if dist < tol: + is_merged = True + break + if not is_merged: + final_points.append(point_data) + + if len(self.raw_points) != len(final_points): + print(f"Simplification merged {len(self.raw_points) - len(final_points)} points.") + + self.raw_points = final_points + + def _resolve_overlaps(self): """ @@ -228,6 +299,9 @@ def generate(self): ensures topological connectivity, and prepares clean GeoDataFrames for the mesher. """ + print("Applying optional geometry simplification...") + self._apply_simplification() + print("Resolving polygon overlaps...") self._resolve_overlaps() From decd562e2b6f1eb66439c199c56a73e30aa4609e Mon Sep 17 00:00:00 2001 From: oscar Date: Mon, 8 Dec 2025 15:29:28 +0800 Subject: [PATCH 02/26] added option for no densification of lines or custom densification if desired --- src/vorflow/blueprint.py | 24 +++++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index fca094b..3a44802 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -76,7 +76,7 @@ def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refine }) def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barrier=False, - dist_min=None, dist_max=None, straddle_width=None, densify=None, simplify_tolerance=None): + dist_min=None, dist_max=None, straddle_width=None, densify=True, simplify_tolerance=None): """ Adds a line feature, such as a river, fault, or other linear boundary. @@ -94,8 +94,8 @@ def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barr transitions to the background resolution. straddle_width (float, optional): If set, forces Voronoi cell edges to align perfectly with the line by creating a "virtual straddle" of mesh nodes. - densify (float, optional): If set, densifies the line by adding vertices, - ensuring no segment is longer than this value. + densify (float or bool, optional): If set, densifies the line by adding vertices, + ensuring no segment is longer than this value. If False, disables densification. simplify_tolerance (float, optional): Tolerance for simplifying the line geometry. """ if not geometry.is_valid: @@ -390,6 +390,20 @@ def _apply_densification(self): # Densify lines based on their target resolution ('lc'). if not self.clean_lines.empty: + # Helper to determine the target resolution for a line row + def get_line_resolution(row): + d = row.get('densify') + + # 1. Explicitly disabled (densify=False) + if d is False: + return None + + # 2. Explicit custom resolution (e.g., densify=5.0) + if isinstance(d, (int, float)) and d > 0: + return d + + # 3. Default behavior (True or None): use the mesh resolution (lc) + return row.get('lc') self.clean_lines['geometry'] = self.clean_lines.apply( - lambda row: self._densify_geometry(row['geometry'], row['lc']), axis=1 - ) \ No newline at end of file + lambda row: self._densify_geometry(row['geometry'], get_line_resolution(row)) + if get_line_resolution(row) is not None else row['geometry'], axis=1) \ No newline at end of file From 28d83240b12699813b4e58893c28ca351700c1b8 Mon Sep 17 00:00:00 2001 From: oscar Date: Mon, 8 Dec 2025 16:50:40 +0800 Subject: [PATCH 03/26] added test and debugged the tests, fixed bug on line implementation --- src/vorflow/blueprint.py | 11 +++- tests/test_conceptual_mesh.py | 97 +++++++++++++++++++++++++++++++++++ 2 files changed, 106 insertions(+), 2 deletions(-) diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index 3a44802..add3535 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -109,7 +109,8 @@ def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barr 'dist_min': dist_min, 'dist_max': dist_max, 'straddle_width': straddle_width, - 'densify': densify + 'densify': densify, + 'simplify_tolerance': simplify_tolerance }) def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None, simplify_tolerance=None): @@ -146,7 +147,7 @@ def _apply_simplification(self): tol = lc * 0.5 if lc is not None else None if tol is not None and tol > 0: - self.raw_polygons[i]['geometry'] = poly_data['geometry'].simplify(tol, preserve_topology=True) + self.raw_polygons[i]['geometry'] = poly_data['geometry'].simplify(tol, preserve_topology=False) # Simplify Lines for i, line_data in enumerate(self.raw_lines): @@ -202,6 +203,12 @@ def _resolve_overlaps(self): lower ones. """ # Sort polygons by priority, with the highest z_order processed first. + if self.raw_polygons == []: + #if this is empty, just create an empty GeoDataFrame + self.clean_polygons = gpd.GeoDataFrame(columns=['geometry', 'zone_id', 'lc', 'z_order', 'refine', + 'dist_min', 'dist_max_in', 'dist_max_out', + 'border_density', 'simplify_tolerance'], crs=self.crs) + return df = pd.DataFrame(self.raw_polygons) df = df.sort_values(by='z_order', ascending=False) diff --git a/tests/test_conceptual_mesh.py b/tests/test_conceptual_mesh.py index 6c8388b..c611d69 100644 --- a/tests/test_conceptual_mesh.py +++ b/tests/test_conceptual_mesh.py @@ -49,3 +49,100 @@ def test_lines_and_points_snap_to_polygons(): tolerance = 1e-3 assert snapped_line.distance(boundary) <= tolerance assert snapped_point.distance(boundary) <= tolerance + +def test_polygon_simplification(): + """Test that polygons are simplified when tolerance is provided.""" + cm = ConceptualMesh() + + # Create a "noisy" square with a tiny bump on the top edge + # (0,1) -> (0.5, 1.001) -> (1,1) + poly = Polygon([ + (0, 0), (1, 0), + (1, 1), (0.5, 1.001), (0, 1) + ]) + + # Add with a tolerance larger than the noise (0.001) + cm.add_polygon(poly, zone_id=1, simplify_tolerance=0.01) + + clean_polys, _, _ = cm.generate() + + simplified_geom = clean_polys.iloc[0].geometry + + # The original polygon has 5 vertices + closing = 6 points in exterior ring + # The simplified one should remove the bump, leaving 4 corners + closing = 5 points + assert len(simplified_geom.exterior.coords) == 5 + assert len(simplified_geom.exterior.coords) < len(poly.exterior.coords) + +def test_line_simplification(): + """Test that lines are simplified when tolerance is provided.""" + cm = ConceptualMesh() + # Noisy line: straight but with a midpoint slightly off + line = LineString([(0, 0), (0.5, 0.001), (1, 0)]) + + cm.add_line(line, line_id="noisy_line", resolution=0.1, simplify_tolerance=0.01, densify=False) + + _, clean_lines, _ = cm.generate() + + simplified_line = clean_lines.iloc[0].geometry + # Should be simplified to just start and end points + assert len(simplified_line.coords) == 2 + +def test_point_deduplication(): + """Test that close points are merged and the finest resolution is kept.""" + cm = ConceptualMesh() + p1 = Point(0, 0) + p2 = Point(0.0001, 0) # Very close to p1 + + # Case 1: No simplification (default) -> Should keep both + cm.add_point(p1, "p1", resolution=1.0) + cm.add_point(p2, "p2", resolution=0.5) + + _, _, clean_points = cm.generate() + assert len(clean_points) == 2 + + # Case 2: With simplification -> Should merge + cm2 = ConceptualMesh() + # p2 has finer resolution (0.5), so it should be the one kept + cm2.add_point(p1, "p1", resolution=1.0, simplify_tolerance=0.01) + cm2.add_point(p2, "p2", resolution=0.5, simplify_tolerance=0.01) + + _, _, clean_points_merged = cm2.generate() + + assert len(clean_points_merged) == 1 + + # Verify we kept the point with the finer resolution (0.5) + kept_point = clean_points_merged.iloc[0] + assert kept_point['lc'] == 0.5 + assert kept_point['point_id'] == "p2" + +def test_line_densification_options(): + """Test the three modes of line densification: False, True, and float.""" + cm = ConceptualMesh() + # A line of length 10 + line = LineString([(0, 0), (10, 0)]) + + # 1. densify=False: Should NOT add vertices + cm.add_line(line, "no_densify", resolution=1.0, densify=False) + + # 2. densify=True (default): Should use resolution (1.0) -> ~10 segments + cm.add_line(line, "default_densify", resolution=1.0, densify=True) + + # 3. densify=5.0: Should use custom spacing (5.0) -> ~2 segments + cm.add_line(line, "custom_densify", resolution=1.0, densify=5.0) + + _, clean_lines, _ = cm.generate() + + # Check 1: No densification + l1 = clean_lines[clean_lines['line_id'] == "no_densify"].iloc[0].geometry + assert len(l1.coords) == 2 # Just start and end + + # Check 2: Default densification (lc=1.0) + l2 = clean_lines[clean_lines['line_id'] == "default_densify"].iloc[0].geometry + # Should have roughly 11 points (10 segments) + assert len(l2.coords) >= 11 + + # Check 3: Custom densification (val=5.0) + l3 = clean_lines[clean_lines['line_id'] == "custom_densify"].iloc[0].geometry + # Should have roughly 3 points (2 segments) + assert len(l3.coords) == 3 + \ No newline at end of file From 145bd2137d78891adc21c90c9886f7df0dbe806d Mon Sep 17 00:00:00 2001 From: oscar Date: Tue, 9 Dec 2025 16:27:44 +0800 Subject: [PATCH 04/26] added fixes and recommendations to pull request of optional densify and simplify --- src/vorflow/blueprint.py | 110 ++++++++++++++++++++++++++-------- tests/test_conceptual_mesh.py | 9 +-- 2 files changed, 87 insertions(+), 32 deletions(-) diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index 7acdda8..999a16e 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -4,6 +4,7 @@ from shapely.geometry import Polygon, LineString, Point, box, MultiPolygon from shapely.ops import unary_union, snap, linemerge from shapely.validation import make_valid +from shapely.strtree import STRtree class ConceptualMesh: def __init__(self, crs="EPSG:4326"): @@ -52,11 +53,16 @@ def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refine mesh transitions to the background resolution. border_density (float, optional): If set, densifies the polygon's boundary by adding vertices, ensuring no segment is longer than this value. - simplify_tolerance (float, optional): Tolerance for simplifying the polygon geometry, no simplification is applied - if None. + simplify_tolerance (float or bool, optional): If a float > 0, applies Douglas-Peucker + simplification with this tolerance. If True, computes an automatic tolerance from + the polygon's resolution ('lc') using a heuristic (currently lc * 0.5). + If None or 0, no simplification is applied. Raises ValueError if negative. """ if not geometry.is_valid: geometry = make_valid(geometry) + + if isinstance(simplify_tolerance, (int, float)) and simplify_tolerance < 0: + raise ValueError(f"simplify_tolerance must be non-negative. Got {simplify_tolerance}.") # For backward compatibility, allow 'dist_max' to function as 'dist_max_out'. if dist_max is not None and dist_max_out is None: @@ -94,13 +100,25 @@ def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barr transitions to the background resolution. straddle_width (float, optional): If set, forces Voronoi cell edges to align perfectly with the line by creating a "virtual straddle" of mesh nodes. - densify (float or bool, optional): If set, densifies the line by adding vertices, - ensuring no segment is longer than this value. If False, disables densification. - simplify_tolerance (float, optional): Tolerance for simplifying the line geometry. + densify (float or bool, optional): Controls line densification: + - If False, disables densification. + - If True, densifies the line using the `resolution` value. + - If a float, densifies the line so that no segment is longer than this value. + Raises ValueError if negative or zero. + simplify_tolerance (float or bool, optional): If a float > 0, simplifies the line with + this tolerance using Douglas-Peucker algorithm. + If True, computes an automatic tolerance from 'lc' (currently lc * 0.5). + If None or 0, no simplification is applied. Raises ValueError if negative. """ if not geometry.is_valid: geometry = make_valid(geometry) + if isinstance(simplify_tolerance, (int, float)) and simplify_tolerance < 0: + raise ValueError(f"simplify_tolerance must be non-negative. Got {simplify_tolerance}.") + + if isinstance(densify, (int, float)) and not isinstance(densify, bool) and densify <= 0: + raise ValueError(f"densify must be positive when specified as a float. Got {densify}.") + self.raw_lines.append({ 'geometry': geometry, 'line_id': line_id, @@ -125,7 +143,13 @@ def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None held constant at the point's resolution. dist_max (float, optional): Distance from the point over which the mesh transitions to the background resolution. + simplify_tolerance (float or bool, optional): If a float > 0, merges points + that are closer than this tolerance. If True, computes an automatic tolerance + from 'lc' (currently lc * 0.5). If None or 0, no simplification is applied. + Raises ValueError if negative. """ + if isinstance(simplify_tolerance, (int, float)) and simplify_tolerance < 0: + raise ValueError(f"simplify_tolerance must be non-negative. Got {simplify_tolerance}.") self.raw_points.append({ 'geometry': geometry, 'point_id': point_id, @@ -137,7 +161,16 @@ def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None def _apply_simplification(self): """ Applies geometry simplification to raw polygons, lines, and points - based on their specified tolerances. + based on their specified tolerances to reduce + geometric complexity.For polygon and lines + the Douglas-Peucker algorithm is used. + For points, this method performs deduplication: points that are within + a specified tolerance of each other are merged, and only the point with the + finest (smallest) resolution is kept. The tolerance for merging is taken from + the 'simplify_tolerance' attribute of each point, or derived from the point's + resolution if set to True. This ensures that closely spaced points do not + result in redundant mesh nodes, and that the most restrictive mesh size is + preserved at each location. """ # Simplify Polygons for i, poly_data in enumerate(self.raw_polygons): @@ -147,8 +180,15 @@ def _apply_simplification(self): tol = lc * 0.5 if lc is not None else None if tol is not None and tol > 0: - self.raw_polygons[i]['geometry'] = poly_data['geometry'].simplify(tol, preserve_topology=False) - + org_area = poly_data['geometry'].area + simplified_geom = poly_data['geometry'].simplify(tol, preserve_topology=True) + self.raw_polygons[i]['geometry'] = simplified_geom + new_area = simplified_geom.area + if new_area < org_area and org_area > 0: + reduction_pct = 100 * (org_area - new_area) / org_area + if reduction_pct > 1.0: + print(f"Simplified polygon (zone_id={poly_data['zone_id']}) " + f"reduced area by {reduction_pct:.2f}% using tolerance {tol}.") # Simplify Lines for i, line_data in enumerate(self.raw_lines): tol = line_data.get('simplify_tolerance') @@ -156,23 +196,33 @@ def _apply_simplification(self): lc = line_data.get('lc') tol = lc * 0.5 if lc is not None else None if tol is not None and tol > 0: + org_length = line_data['geometry'].length simplified_geom = line_data['geometry'].simplify(tol, preserve_topology=True) self.raw_lines[i]['geometry'] = simplified_geom - - # lets merge points that are very close to each other + new_length = simplified_geom.length + if new_length < org_length and org_length > 0: + reduction_pct = 100 * (org_length - new_length) / org_length + if reduction_pct > 1.0: + print(f"Simplified line (line_id={line_data['line_id']}) " + f"reduced length by {reduction_pct:.2f}% using tolerance {tol}.") + + # Let's merge points that are very close to each other if self.raw_points: - #lets sort by resolution first + # Let's sort by resolution first sorted_points = sorted( self.raw_points, - key=lambda x: x['lc'] if x['lc'] is not None else float('inf')) + key=lambda x: x['lc'] if x['lc'] is not None else float('inf')) final_points = [] - - for point_data in sorted_points: + geoms = [p['geometry'] for p in sorted_points] + tree = STRtree(geoms) + kept_indices = set() + for i, point_data in enumerate(sorted_points): current_geom = point_data['geometry'] # Use specific tolerance if provided, otherwise default to a small value or skip tol = point_data.get('simplify_tolerance') if tol is None or tol <= 0: final_points.append(point_data) + kept_indices.add(i) continue @@ -181,17 +231,23 @@ def _apply_simplification(self): tol = lc * 0.5 if lc is not None else 1e-6 is_merged = False - for kept in final_points: - dist = kept['geometry'].distance(current_geom) - if dist < tol: - is_merged = True - break + #query tree for potential neighbors + # tree.query returns indices of geometries that intersect the buffer + search_area = current_geom.buffer(tol) + candidate_indices = tree.query(search_area) + for candidate_idx in candidate_indices: + if candidate_idx in kept_indices: + dist = geoms[candidate_idx].distance(current_geom) + if dist < tol: + is_merged = True + break if not is_merged: final_points.append(point_data) + kept_indices.add(i) if len(self.raw_points) != len(final_points): - print(f"Simplification merged {len(self.raw_points) - len(final_points)} points.") - + print(f"Simplification merged {len(self.raw_points) - len(final_points)} " + f"points out of {len(self.raw_points)}") self.raw_points = final_points @@ -203,8 +259,8 @@ def _resolve_overlaps(self): lower ones. """ # Sort polygons by priority, with the highest z_order processed first. - if self.raw_polygons == []: - #if this is empty, just create an empty GeoDataFrame + if not self.raw_polygons: + # If this is empty, just create an empty GeoDataFrame. self.clean_polygons = gpd.GeoDataFrame(columns=['geometry', 'zone_id', 'lc', 'z_order', 'refine', 'dist_min', 'dist_max_in', 'dist_max_out', 'border_density', 'simplify_tolerance'], crs=self.crs) @@ -414,6 +470,8 @@ def get_line_resolution(row): # 3. Default behavior (True or None): use the mesh resolution (lc) return row.get('lc') - self.clean_lines['geometry'] = self.clean_lines.apply( - lambda row: self._densify_geometry(row['geometry'], get_line_resolution(row)) - if get_line_resolution(row) is not None else row['geometry'], axis=1) \ No newline at end of file + def _line_densify(row): + res = get_line_resolution(row) + return self._densify_geometry(row['geometry'], res) if res is not None else row['geometry'] + + self.clean_lines['geometry'] = self.clean_lines.apply(_line_densify, axis=1) \ No newline at end of file diff --git a/tests/test_conceptual_mesh.py b/tests/test_conceptual_mesh.py index c611d69..68a074d 100644 --- a/tests/test_conceptual_mesh.py +++ b/tests/test_conceptual_mesh.py @@ -56,10 +56,7 @@ def test_polygon_simplification(): # Create a "noisy" square with a tiny bump on the top edge # (0,1) -> (0.5, 1.001) -> (1,1) - poly = Polygon([ - (0, 0), (1, 0), - (1, 1), (0.5, 1.001), (0, 1) - ]) + poly = Polygon([(0, 0), (1, 0), (1, 1), (0.5, 1.001), (0, 1)]) # Add with a tolerance larger than the noise (0.001) cm.add_polygon(poly, zone_id=1, simplify_tolerance=0.01) @@ -138,8 +135,8 @@ def test_line_densification_options(): # Check 2: Default densification (lc=1.0) l2 = clean_lines[clean_lines['line_id'] == "default_densify"].iloc[0].geometry - # Should have roughly 11 points (10 segments) - assert len(l2.coords) >= 11 + # Should have 11 points (10 segments) + assert len(l2.coords) == 11 # Check 3: Custom densification (val=5.0) l3 = clean_lines[clean_lines['line_id'] == "custom_densify"].iloc[0].geometry From a848493187e54bdc401bc89ea249c0b91aa8ce84 Mon Sep 17 00:00:00 2001 From: oscar Date: Wed, 10 Dec 2025 11:12:55 +0800 Subject: [PATCH 05/26] adding second round of refinements from copilot, mainly grammar and set of variables for magic numbers --- src/vorflow/blueprint.py | 23 ++++++++++++++--------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index 999a16e..8f67f66 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -6,6 +6,11 @@ from shapely.validation import make_valid from shapely.strtree import STRtree +# Constants for geometry simplification and reporting +SIGNIFICANT_REDUCTION_PCT = 1.0 +AUTO_SIMPLIFY_FACTOR = 0.5 +DEFAULT_TOLERANCE = 1e-3 + class ConceptualMesh: def __init__(self, crs="EPSG:4326"): """ @@ -162,7 +167,7 @@ def _apply_simplification(self): """ Applies geometry simplification to raw polygons, lines, and points based on their specified tolerances to reduce - geometric complexity.For polygon and lines + geometric complexity.For polygons and lines the Douglas-Peucker algorithm is used. For points, this method performs deduplication: points that are within a specified tolerance of each other are merged, and only the point with the @@ -177,7 +182,7 @@ def _apply_simplification(self): tol = poly_data.get('simplify_tolerance') if tol is True: lc = poly_data.get('lc') - tol = lc * 0.5 if lc is not None else None + tol = lc * AUTO_SIMPLIFY_FACTOR if lc is not None else None if tol is not None and tol > 0: org_area = poly_data['geometry'].area @@ -186,7 +191,7 @@ def _apply_simplification(self): new_area = simplified_geom.area if new_area < org_area and org_area > 0: reduction_pct = 100 * (org_area - new_area) / org_area - if reduction_pct > 1.0: + if reduction_pct > SIGNIFICANT_REDUCTION_PCT: print(f"Simplified polygon (zone_id={poly_data['zone_id']}) " f"reduced area by {reduction_pct:.2f}% using tolerance {tol}.") # Simplify Lines @@ -194,7 +199,7 @@ def _apply_simplification(self): tol = line_data.get('simplify_tolerance') if tol is True: lc = line_data.get('lc') - tol = lc * 0.5 if lc is not None else None + tol = lc * AUTO_SIMPLIFY_FACTOR if lc is not None else None if tol is not None and tol > 0: org_length = line_data['geometry'].length simplified_geom = line_data['geometry'].simplify(tol, preserve_topology=True) @@ -202,7 +207,7 @@ def _apply_simplification(self): new_length = simplified_geom.length if new_length < org_length and org_length > 0: reduction_pct = 100 * (org_length - new_length) / org_length - if reduction_pct > 1.0: + if reduction_pct > SIGNIFICANT_REDUCTION_PCT: print(f"Simplified line (line_id={line_data['line_id']}) " f"reduced length by {reduction_pct:.2f}% using tolerance {tol}.") @@ -228,10 +233,10 @@ def _apply_simplification(self): if tol is True: lc = point_data.get('lc') - tol = lc * 0.5 if lc is not None else 1e-6 + tol = lc * AUTO_SIMPLIFY_FACTOR if lc is not None else DEFAULT_TOLERANCE is_merged = False - #query tree for potential neighbors + # Query tree for potential neighbors # tree.query returns indices of geometries that intersect the buffer search_area = current_geom.buffer(tol) candidate_indices = tree.query(search_area) @@ -314,7 +319,7 @@ def _resolve_overlaps(self): self.clean_polygons = gpd.GeoDataFrame(final_features, crs=self.crs) - def _enforce_connectivity(self, tolerance=1e-3): + def _enforce_connectivity(self, tolerance=DEFAULT_TOLERANCE): """ Snaps features together to ensure they are topologically connected before being passed to the mesher. This is crucial for Gmsh to correctly @@ -465,7 +470,7 @@ def get_line_resolution(row): return None # 2. Explicit custom resolution (e.g., densify=5.0) - if isinstance(d, (int, float)) and d > 0: + if isinstance(d, (int, float)) and not isinstance(d, bool) and d > 0: return d # 3. Default behavior (True or None): use the mesh resolution (lc) From bb499e47a20aa6c60c9e0ec4e203e6d9d0c55662 Mon Sep 17 00:00:00 2001 From: oscar Date: Fri, 12 Dec 2025 11:29:29 +0800 Subject: [PATCH 06/26] added some functions to use flexible fields --- src/vorflow/__init__.py | 7 +- src/vorflow/blueprint.py | 11 ++- src/vorflow/fields.py | 152 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 168 insertions(+), 2 deletions(-) create mode 100644 src/vorflow/fields.py diff --git a/src/vorflow/__init__.py b/src/vorflow/__init__.py index 2ad235b..7b30f8d 100644 --- a/src/vorflow/__init__.py +++ b/src/vorflow/__init__.py @@ -1,5 +1,10 @@ from .blueprint import ConceptualMesh from .engine import MeshGenerator from .tessellator import VoronoiTessellator +from .fields import (MeshField, ThresholdField, + ExponentialField, AutoLinearField, + AutoExponentialField) -__all__ = ["ConceptualMesh", "MeshGenerator", "VoronoiTessellator"] \ No newline at end of file +__all__ = ["ConceptualMesh", "MeshGenerator", "VoronoiTessellator", + "MeshField", "ThresholdField", "ExponentialField", + "AutoLinearField", "AutoExponentialField"] \ No newline at end of file diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index bb869a9..406e4ec 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -4,6 +4,7 @@ from shapely.geometry import Polygon, LineString, Point, box, MultiPolygon from shapely.ops import unary_union, snap, linemerge from shapely.validation import make_valid +from .fields import ThresholdField, ExponentialField, AutoLinearField, AutoExponentialField class ConceptualMesh: def __init__(self, crs="EPSG:4326"): @@ -29,7 +30,9 @@ def __init__(self, crs="EPSG:4326"): self.clean_lines = gpd.GeoDataFrame() self.clean_points = gpd.GeoDataFrame() - def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refinement=True, dist_min=None, dist_max=None, dist_max_in=None, dist_max_out=None, border_density=None): + def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refinement=True, + dist_min=None, dist_max=None, dist_max_in=None, dist_max_out=None, border_density=None, + fields=None, embed=True): """ Adds a polygon feature, such as a model boundary or a refinement zone. @@ -51,6 +54,8 @@ def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refine mesh transitions to the background resolution. border_density (float, optional): If set, densifies the polygon's boundary by adding vertices, ensuring no segment is longer than this value. + fields (list, optional): List of MeshField objects. + embed (bool): If True, the polygon is embedded in the mesh. If False, it is used only for fields. """ if not geometry.is_valid: geometry = make_valid(geometry) @@ -59,6 +64,10 @@ def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refine if dist_max is not None and dist_max_out is None: dist_max_out = dist_max + if dist_max_out is not None and dist_max_out > 0: + final_fields.append(ThresholdField(resolution, d_min, dist_max_out)) + + self.raw_polygons.append({ 'geometry': geometry, 'zone_id': zone_id, diff --git a/src/vorflow/fields.py b/src/vorflow/fields.py new file mode 100644 index 0000000..d031dd3 --- /dev/null +++ b/src/vorflow/fields.py @@ -0,0 +1,152 @@ +import math + +class MeshField: + """ + Base class for all mesh size fields. + """ + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): + """ + Creates the Gmsh field(s) and returns the field ID. + + Args: + gmsh_api: The gmsh module. + tags_dict (dict): Dictionary of tags {'points': [], 'lines': [], 'surfaces': []}. + background_lc (float): Global background mesh size. + feature_lc (float, optional): The target resolution of the specific feature group. + """ + raise NotImplementedError("Subclasses must implement create()") + + def __eq__(self, other): + """Equality check for grouping.""" + return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ + + def __hash__(self): + """Hash for dictionary keys.""" + # Create a tuple of sorted item pairs to ensure consistent hashing + return hash((self.__class__.__name__, tuple(sorted(self.__dict__.items())))) + +# --- Manual Fields --- + +class ThresholdField(MeshField): + def __init__(self, size_min, dist_min, dist_max, size_max=None): + self.size_min = float(size_min) + self.dist_min = float(dist_min) + self.dist_max = float(dist_max) + self.size_max = float(size_max) if size_max is not None else None + + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): + # 1. Distance Field (can combine points, curves, surfaces) + f_dist = gmsh_api.model.mesh.field.add("Distance") + + has_entities = False + if tags_dict.get('points'): + gmsh_api.model.mesh.field.setNumbers(f_dist, "PointsList", tags_dict['points']) + has_entities = True + if tags_dict.get('lines'): + gmsh_api.model.mesh.field.setNumbers(f_dist, "CurvesList", tags_dict['lines']) + has_entities = True + if tags_dict.get('surfaces'): + gmsh_api.model.mesh.field.setNumbers(f_dist, "SurfacesList", tags_dict['surfaces']) + has_entities = True + + if not has_entities: + gmsh_api.model.mesh.field.remove(f_dist) + return None + + # 2. Threshold Field + f_thresh = gmsh_api.model.mesh.field.add("Threshold") + gmsh_api.model.mesh.field.setNumber(f_thresh, "InField", f_dist) + gmsh_api.model.mesh.field.setNumber(f_thresh, "SizeMin", self.size_min) + gmsh_api.model.mesh.field.setNumber(f_thresh, "SizeMax", self.size_max if self.size_max else background_lc) + gmsh_api.model.mesh.field.setNumber(f_thresh, "DistMin", self.dist_min) + gmsh_api.model.mesh.field.setNumber(f_thresh, "DistMax", self.dist_max) + + return f_thresh + +class ExponentialField(MeshField): + def __init__(self, size_min, decay_length, size_max=None): + self.size_min = float(size_min) + self.decay_length = float(decay_length) + self.size_max = float(size_max) if size_max is not None else None + + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): + f_dist = gmsh_api.model.mesh.field.add("Distance") + + has_entities = False + if tags_dict.get('points'): + gmsh_api.model.mesh.field.setNumbers(f_dist, "PointsList", tags_dict['points']) + has_entities = True + if tags_dict.get('lines'): + gmsh_api.model.mesh.field.setNumbers(f_dist, "CurvesList", tags_dict['lines']) + has_entities = True + + if not has_entities: + gmsh_api.model.mesh.field.remove(f_dist) + return None + + s_max = self.size_max if self.size_max else background_lc + + f_math = gmsh_api.model.mesh.field.add("MathEval") + expr = f"{s_max} - ({s_max} - {self.size_min}) * Exp(-F{f_dist} / {self.decay_length})" + gmsh_api.model.mesh.field.setString(f_math, "F", expr) + return f_math + +# --- Auto Fields --- + +class AutoLinearField(MeshField): + def __init__(self, growth_factor=1.2): + self.fac = float(growth_factor) + + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): + if feature_lc is None: return None + + cs = float(feature_lc) + cs_dom = float(background_lc) + fac = self.fac + + if fac <= 1.0: raise ValueError("Growth factor must be > 1.0") + if cs >= cs_dom: return None + + # Calculate transition + min_trans_cells = math.log(cs_dom / cs) / math.log(fac) + min_trans_dist = cs * ((fac ** min_trans_cells) - 1) / math.log(fac) + + dist_min = cs / 2.0 + dist_max = min_trans_dist / 2.0 + + # Delegate to ThresholdField logic + # We create a temporary ThresholdField to reuse its create logic + temp_field = ThresholdField(cs, dist_min, dist_max, cs_dom) + return temp_field.create(gmsh_api, tags_dict, background_lc) + +class AutoExponentialField(MeshField): + def __init__(self, growth_factor=1.1): + self.fac = float(growth_factor) + + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): + if feature_lc is None: return None + + cs = float(feature_lc) + fac = self.fac + + if fac <= 1.0: raise ValueError("Growth factor must be > 1.0") + + f_dist = gmsh_api.model.mesh.field.add("Distance") + has_entities = False + if tags_dict.get('points'): + gmsh_api.model.mesh.field.setNumbers(f_dist, "PointsList", tags_dict['points']) + has_entities = True + if tags_dict.get('lines'): + gmsh_api.model.mesh.field.setNumbers(f_dist, "CurvesList", tags_dict['lines']) + has_entities = True + + if not has_entities: + gmsh_api.model.mesh.field.remove(f_dist) + return None + + f_math = gmsh_api.model.mesh.field.add("MathEval") + log_fac = math.log(fac) + expr = f"{cs} * {fac}^(Log(1 + F{f_dist} * 2 * {log_fac} / {cs}) / {log_fac})" + + gmsh_api.model.mesh.field.setString(f_math, "F", expr) + return f_math \ No newline at end of file From 728765dd7e5e5bf1001e79c562ccd38fcaa343f6 Mon Sep 17 00:00:00 2001 From: oscarfasanchez Date: Sun, 14 Dec 2025 13:00:20 +0800 Subject: [PATCH 07/26] deleted the simplify default and standardized the densify keyword --- src/vorflow/blueprint.py | 209 ++++++++++++++++++++++------------ src/vorflow/engine.py | 10 +- tests/test_conceptual_mesh.py | 17 ++- tests/test_pipeline.py | 2 +- 4 files changed, 163 insertions(+), 75 deletions(-) diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index 8f67f66..bb524b6 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -8,7 +8,6 @@ # Constants for geometry simplification and reporting SIGNIFICANT_REDUCTION_PCT = 1.0 -AUTO_SIMPLIFY_FACTOR = 0.5 DEFAULT_TOLERANCE = 1e-3 class ConceptualMesh: @@ -35,8 +34,20 @@ def __init__(self, crs="EPSG:4326"): self.clean_lines = gpd.GeoDataFrame() self.clean_points = gpd.GeoDataFrame() - def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refinement=True, dist_min=None, - dist_max=None, dist_max_in=None, dist_max_out=None, border_density=None, simplify_tolerance=None): + def add_polygon( + self, + geometry, + zone_id, + resolution=None, + z_order=0, + mesh_refinement=True, + dist_min=None, + dist_max=None, + dist_max_in=None, + dist_max_out=None, + densify=None, + simplify_tolerance=None, + ): """ Adds a polygon feature, such as a model boundary or a refinement zone. @@ -56,35 +67,49 @@ def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refine transitions from the boundary resolution to the internal resolution. dist_max_out (float, optional): Distance outside the polygon over which the mesh transitions to the background resolution. - border_density (float, optional): If set, densifies the polygon's boundary - by adding vertices, ensuring no segment is longer than this value. - simplify_tolerance (float or bool, optional): If a float > 0, applies Douglas-Peucker - simplification with this tolerance. If True, computes an automatic tolerance from - the polygon's resolution ('lc') using a heuristic (currently lc * 0.5). - If None or 0, no simplification is applied. Raises ValueError if negative. + densify (float|bool|None, optional): Controls polygon boundary densification: + - If False, disables densification. + - If True, densifies using `resolution` (lc). Requires `resolution` to be set. + - If a float, densifies so no boundary segment is longer than this value. + Raises ValueError if non-positive when specified as a float. + simplify_tolerance (float|int|None, optional): If a number > 0, applies Douglas-Peucker + simplification with this tolerance. If None or 0, no simplification is applied. + Raises ValueError if negative. Boolean values are not supported. """ if not geometry.is_valid: geometry = make_valid(geometry) + if isinstance(simplify_tolerance, bool): + raise ValueError( + "simplify_tolerance must be a non-negative number (or None/0 to disable). " + "Boolean values are not supported." + ) if isinstance(simplify_tolerance, (int, float)) and simplify_tolerance < 0: raise ValueError(f"simplify_tolerance must be non-negative. Got {simplify_tolerance}.") - + + if isinstance(densify, (int, float)) and not isinstance(densify, bool) and densify <= 0: + raise ValueError(f"densify must be positive when specified as a float. Got {densify}.") + if densify is True and (resolution is None or resolution <= 0): + raise ValueError("densify=True for polygons requires a positive `resolution` (lc).") + # For backward compatibility, allow 'dist_max' to function as 'dist_max_out'. if dist_max is not None and dist_max_out is None: dist_max_out = dist_max - self.raw_polygons.append({ - 'geometry': geometry, - 'zone_id': zone_id, - 'lc': resolution, - 'z_order': z_order, - 'refine': mesh_refinement, - 'dist_min': dist_min, - 'dist_max_in': dist_max_in, - 'dist_max_out': dist_max_out, - 'border_density': border_density, - 'simplify_tolerance': simplify_tolerance - }) + self.raw_polygons.append( + { + "geometry": geometry, + "zone_id": zone_id, + "lc": resolution, + "z_order": z_order, + "refine": mesh_refinement, + "dist_min": dist_min, + "dist_max_in": dist_max_in, + "dist_max_out": dist_max_out, + "densify": densify, + "simplify_tolerance": simplify_tolerance, + } + ) def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barrier=False, dist_min=None, dist_max=None, straddle_width=None, densify=True, simplify_tolerance=None): @@ -110,14 +135,18 @@ def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barr - If True, densifies the line using the `resolution` value. - If a float, densifies the line so that no segment is longer than this value. Raises ValueError if negative or zero. - simplify_tolerance (float or bool, optional): If a float > 0, simplifies the line with - this tolerance using Douglas-Peucker algorithm. - If True, computes an automatic tolerance from 'lc' (currently lc * 0.5). - If None or 0, no simplification is applied. Raises ValueError if negative. + simplify_tolerance (float|int|None, optional): If a number > 0, simplifies the line with + this tolerance using Douglas-Peucker algorithm. If None or 0, no simplification is applied. + Raises ValueError if negative. Boolean values are not supported. """ if not geometry.is_valid: geometry = make_valid(geometry) - + + if isinstance(simplify_tolerance, bool): + raise ValueError( + "simplify_tolerance must be a non-negative number (or None/0 to disable). " + "Boolean values are not supported." + ) if isinstance(simplify_tolerance, (int, float)) and simplify_tolerance < 0: raise ValueError(f"simplify_tolerance must be non-negative. Got {simplify_tolerance}.") @@ -148,11 +177,15 @@ def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None held constant at the point's resolution. dist_max (float, optional): Distance from the point over which the mesh transitions to the background resolution. - simplify_tolerance (float or bool, optional): If a float > 0, merges points - that are closer than this tolerance. If True, computes an automatic tolerance - from 'lc' (currently lc * 0.5). If None or 0, no simplification is applied. - Raises ValueError if negative. + simplify_tolerance (float|int|None, optional): If a number > 0, merges points that are closer + than this tolerance. If None or 0, no merging is applied. Raises ValueError if negative. + Boolean values are not supported. """ + if isinstance(simplify_tolerance, bool): + raise ValueError( + "simplify_tolerance must be a non-negative number (or None/0 to disable). " + "Boolean values are not supported." + ) if isinstance(simplify_tolerance, (int, float)) and simplify_tolerance < 0: raise ValueError(f"simplify_tolerance must be non-negative. Got {simplify_tolerance}.") self.raw_points.append({ @@ -166,23 +199,21 @@ def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None def _apply_simplification(self): """ Applies geometry simplification to raw polygons, lines, and points - based on their specified tolerances to reduce - geometric complexity.For polygons and lines - the Douglas-Peucker algorithm is used. + based on their specified tolerances to reduce geometric complexity. + + For polygons and lines the Douglas-Peucker algorithm is used. For points, this method performs deduplication: points that are within a specified tolerance of each other are merged, and only the point with the - finest (smallest) resolution is kept. The tolerance for merging is taken from - the 'simplify_tolerance' attribute of each point, or derived from the point's - resolution if set to True. This ensures that closely spaced points do not - result in redundant mesh nodes, and that the most restrictive mesh size is - preserved at each location. + finest (smallest) resolution is kept. """ # Simplify Polygons for i, poly_data in enumerate(self.raw_polygons): tol = poly_data.get('simplify_tolerance') - if tol is True: - lc = poly_data.get('lc') - tol = lc * AUTO_SIMPLIFY_FACTOR if lc is not None else None + if isinstance(tol, bool): + raise ValueError( + "simplify_tolerance must be a non-negative number (or None/0 to disable). " + "Boolean values are not supported." + ) if tol is not None and tol > 0: org_area = poly_data['geometry'].area @@ -192,14 +223,20 @@ def _apply_simplification(self): if new_area < org_area and org_area > 0: reduction_pct = 100 * (org_area - new_area) / org_area if reduction_pct > SIGNIFICANT_REDUCTION_PCT: - print(f"Simplified polygon (zone_id={poly_data['zone_id']}) " - f"reduced area by {reduction_pct:.2f}% using tolerance {tol}.") + print( + f"Simplified polygon (zone_id={poly_data['zone_id']}) " + f"reduced area by {reduction_pct:.2f}% using tolerance {tol}." + ) + # Simplify Lines for i, line_data in enumerate(self.raw_lines): tol = line_data.get('simplify_tolerance') - if tol is True: - lc = line_data.get('lc') - tol = lc * AUTO_SIMPLIFY_FACTOR if lc is not None else None + if isinstance(tol, bool): + raise ValueError( + "simplify_tolerance must be a non-negative number (or None/0 to disable). " + "Boolean values are not supported." + ) + if tol is not None and tol > 0: org_length = line_data['geometry'].length simplified_geom = line_data['geometry'].simplify(tol, preserve_topology=True) @@ -208,54 +245,62 @@ def _apply_simplification(self): if new_length < org_length and org_length > 0: reduction_pct = 100 * (org_length - new_length) / org_length if reduction_pct > SIGNIFICANT_REDUCTION_PCT: - print(f"Simplified line (line_id={line_data['line_id']}) " - f"reduced length by {reduction_pct:.2f}% using tolerance {tol}.") + print( + f"Simplified line (line_id={line_data['line_id']}) " + f"reduced length by {reduction_pct:.2f}% using tolerance {tol}." + ) - # Let's merge points that are very close to each other + # Merge points that are very close to each other (deduplication) if self.raw_points: # Let's sort by resolution first sorted_points = sorted( self.raw_points, - key=lambda x: x['lc'] if x['lc'] is not None else float('inf')) + key=lambda x: x['lc'] if x['lc'] is not None else float('inf') + ) final_points = [] geoms = [p['geometry'] for p in sorted_points] tree = STRtree(geoms) kept_indices = set() + for i, point_data in enumerate(sorted_points): current_geom = point_data['geometry'] - # Use specific tolerance if provided, otherwise default to a small value or skip tol = point_data.get('simplify_tolerance') + + if isinstance(tol, bool): + raise ValueError( + "simplify_tolerance must be a non-negative number (or None/0 to disable). " + "Boolean values are not supported." + ) + + # None or <=0 => no merging for this point (keep as-is) if tol is None or tol <= 0: final_points.append(point_data) kept_indices.add(i) continue - - if tol is True: - lc = point_data.get('lc') - tol = lc * AUTO_SIMPLIFY_FACTOR if lc is not None else DEFAULT_TOLERANCE is_merged = False # Query tree for potential neighbors # tree.query returns indices of geometries that intersect the buffer search_area = current_geom.buffer(tol) candidate_indices = tree.query(search_area) + for candidate_idx in candidate_indices: if candidate_idx in kept_indices: - dist = geoms[candidate_idx].distance(current_geom) - if dist < tol: + if geoms[candidate_idx].distance(current_geom) < tol: is_merged = True break + if not is_merged: final_points.append(point_data) kept_indices.add(i) if len(self.raw_points) != len(final_points): - print(f"Simplification merged {len(self.raw_points) - len(final_points)} " - f"points out of {len(self.raw_points)}") + print( + f"Simplification merged {len(self.raw_points) - len(final_points)} " + f"points out of {len(self.raw_points)}" + ) self.raw_points = final_points - - def _resolve_overlaps(self): """ @@ -266,9 +311,21 @@ def _resolve_overlaps(self): # Sort polygons by priority, with the highest z_order processed first. if not self.raw_polygons: # If this is empty, just create an empty GeoDataFrame. - self.clean_polygons = gpd.GeoDataFrame(columns=['geometry', 'zone_id', 'lc', 'z_order', 'refine', - 'dist_min', 'dist_max_in', 'dist_max_out', - 'border_density', 'simplify_tolerance'], crs=self.crs) + self.clean_polygons = gpd.GeoDataFrame( + columns=[ + "geometry", + "zone_id", + "lc", + "z_order", + "refine", + "dist_min", + "dist_max_in", + "dist_max_out", + "densify", + "simplify_tolerance", + ], + crs=self.crs, + ) return df = pd.DataFrame(self.raw_polygons) df = df.sort_values(by='z_order', ascending=False) @@ -452,12 +509,24 @@ def densify_line(line, max_segment_length): def _apply_densification(self): """Applies densification to the clean polygon and line features.""" - # Densify polygon boundaries where a 'border_density' is specified. + # Densify polygon boundaries based on `densify`. if not self.clean_polygons.empty: - self.clean_polygons['geometry'] = self.clean_polygons.apply( - lambda row: self._densify_geometry(row['geometry'], row['border_density']) - if pd.notna(row.get('border_density')) else row['geometry'], axis=1 - ) + + def get_poly_resolution(row): + d = row.get("densify") + if d is False or pd.isna(d): + return None + if d is True: + return row.get("lc") + if isinstance(d, (int, float)) and not isinstance(d, bool) and d > 0: + return d + return None + + def _poly_densify(row): + res = get_poly_resolution(row) + return self._densify_geometry(row["geometry"], res) if res is not None else row["geometry"] + + self.clean_polygons["geometry"] = self.clean_polygons.apply(_poly_densify, axis=1) # Densify lines based on their target resolution ('lc'). if not self.clean_lines.empty: diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py index 942cde2..ed66d4d 100644 --- a/src/vorflow/engine.py +++ b/src/vorflow/engine.py @@ -494,10 +494,14 @@ def add_refinement(entity_dim, entity_tags, size_target, dist_min, dist_max, siz for idx, row in polygons_gdf.iterrows(): if idx in gmsh_map['surfaces']: tags = extract_tags(gmsh_map['surfaces'][idx]) - + target_lc = get_row_param(row, 'lc', global_max_lc) - border_dens = get_row_param(row, 'border_density', target_lc) - boundary_lc = min(target_lc, border_dens) + + densify_val = row.get("densify", None) + if isinstance(densify_val, (int, float)) and not isinstance(densify_val, bool) and densify_val > 0: + boundary_lc = min(target_lc, float(densify_val)) + else: + boundary_lc = target_lc if self.verbosity > 1: print(f"Poly {idx}: Target={target_lc}, Border={boundary_lc}, Global={global_max_lc}") diff --git a/tests/test_conceptual_mesh.py b/tests/test_conceptual_mesh.py index 68a074d..2ced0bb 100644 --- a/tests/test_conceptual_mesh.py +++ b/tests/test_conceptual_mesh.py @@ -142,4 +142,19 @@ def test_line_densification_options(): l3 = clean_lines[clean_lines['line_id'] == "custom_densify"].iloc[0].geometry # Should have roughly 3 points (2 segments) assert len(l3.coords) == 3 - \ No newline at end of file + +@pytest.mark.parametrize("bool_tol", [True, False]) +def test_simplify_tolerance_bool_is_rejected(bool_tol): + cm = ConceptualMesh() + + poly = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) + with pytest.raises(ValueError): + cm.add_polygon(poly, zone_id=1, simplify_tolerance=bool_tol) + + line = LineString([(0, 0), (1, 0)]) + with pytest.raises(ValueError): + cm.add_line(line, line_id="l1", resolution=0.1, simplify_tolerance=bool_tol, densify=False) + + pt = Point(0, 0) + with pytest.raises(ValueError): + cm.add_point(pt, point_id="p1", resolution=0.1, simplify_tolerance=bool_tol) diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index a814166..e477a26 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -55,7 +55,7 @@ def __init__(self): def _build_simple_conceptual_mesh(): cm = ConceptualMesh(crs="EPSG:3857") square = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]) - cm.add_polygon(square, zone_id=99, border_density=0.5) + cm.add_polygon(square, zone_id=99, densify=0.5) return cm From c47ad119af5769148075bd57521416307eb62ef3 Mon Sep 17 00:00:00 2001 From: oscarfasanchez Date: Sun, 14 Dec 2025 14:14:28 +0800 Subject: [PATCH 08/26] added the frame in fields and blueprint mainly to set later the fields in engine which is a big rework to do --- src/vorflow/blueprint.py | 21 +++++++++++++++------ src/vorflow/engine.py | 1 + src/vorflow/fields.py | 10 ++++++++++ 3 files changed, 26 insertions(+), 6 deletions(-) diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index 406e4ec..3c1bf1d 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -4,7 +4,7 @@ from shapely.geometry import Polygon, LineString, Point, box, MultiPolygon from shapely.ops import unary_union, snap, linemerge from shapely.validation import make_valid -from .fields import ThresholdField, ExponentialField, AutoLinearField, AutoExponentialField +from .fields import ThresholdField, ExponentialField, AutoLinearField, AutoExponentialField, ConstantField class ConceptualMesh: def __init__(self, crs="EPSG:4326"): @@ -77,10 +77,13 @@ def add_polygon(self, geometry, zone_id, resolution=None, z_order=0, mesh_refine 'dist_min': dist_min, 'dist_max_in': dist_max_in, 'dist_max_out': dist_max_out, - 'border_density': border_density + 'border_density': border_density, + 'fields': fields, + 'embed': embed }) - def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barrier=False, dist_min=None, dist_max=None, straddle_width=None): + def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barrier=False, + dist_min=None, dist_max=None, straddle_width=None, fields=None, embed=True): """ Adds a line feature, such as a river, fault, or other linear boundary. @@ -98,6 +101,8 @@ def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barr transitions to the background resolution. straddle_width (float, optional): If set, forces Voronoi cell edges to align perfectly with the line by creating a "virtual straddle" of mesh nodes. + fields (list, optional): List of MeshField objects. + embed (bool): If True, the line is embedded in the mesh. If False, it is used only for fields. """ if not geometry.is_valid: geometry = make_valid(geometry) @@ -109,10 +114,12 @@ def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barr 'is_barrier': is_barrier, 'dist_min': dist_min, 'dist_max': dist_max, - 'straddle_width': straddle_width + 'straddle_width': straddle_width, + 'fields': fields, + 'embed': embed, }) - def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None): + def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None, fields=None, embed=True): """ Adds a point feature, such as a well or an observation point. @@ -130,7 +137,9 @@ def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None 'point_id': point_id, 'lc': resolution, 'dist_min': dist_min, - 'dist_max': dist_max + 'dist_max': dist_max, + 'fields': fields, + 'embed': embed, }) def _resolve_overlaps(self): diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py index 942cde2..fb71d29 100644 --- a/src/vorflow/engine.py +++ b/src/vorflow/engine.py @@ -6,6 +6,7 @@ from shapely.geometry import Point, LineString, Polygon from shapely.ops import unary_union from shapely.validation import make_valid +from .fields import ThresholdField, ExponentialField, AutoLinearField, AutoExponentialField, ConstantField class MeshGenerator: def __init__(self, background_lc=None,verbosity=0, mesh_algorithm=6, smoothing_steps=10, optimization_cycles=2): diff --git a/src/vorflow/fields.py b/src/vorflow/fields.py index d031dd3..b20efb9 100644 --- a/src/vorflow/fields.py +++ b/src/vorflow/fields.py @@ -27,6 +27,16 @@ def __hash__(self): # --- Manual Fields --- +class ConstantField(MeshField): + def __init__(self, size): + self.size = float(size) + + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): + const = gmsh_api.model.mesh.field.add("Constant") + gmsh_api.model.mesh.field.setNumber(const, "VIn", background_lc) + gmsh_api.model.mesh.field.setNumber(const, "VOut", background_lc) + + class ThresholdField(MeshField): def __init__(self, size_min, dist_min, dist_max, size_max=None): self.size_min = float(size_min) From 16ef1dbdb2b61f23dbfeb5d3dc18ea29e3606569 Mon Sep 17 00:00:00 2001 From: oscarfasanchez Date: Sun, 14 Dec 2025 22:14:28 +0800 Subject: [PATCH 09/26] added the not embedded features --- src/vorflow/blueprint.py | 48 ++++++++++--- src/vorflow/engine.py | 127 +++++++++++++++++++++++++-------- tests/test_integration_gmsh.py | 32 +++++++-- 3 files changed, 163 insertions(+), 44 deletions(-) diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index d19609e..b25159c 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -41,7 +41,6 @@ def add_polygon( zone_id, resolution=None, z_order=0, - mesh_refinement=True, dist_min=None, dist_max=None, dist_max_in=None, @@ -61,8 +60,6 @@ def add_polygon( the background mesh size will be used. z_order (int): Stacking order for resolving overlaps. Higher values are processed first and will "cut" into lower-order polygons. - mesh_refinement (bool): If True, this polygon will be used to control mesh - refinement. If False, it is used only for tagging the final cells. dist_min (float, optional): Distance from the polygon boundary where the mesh size is held constant at the boundary's resolution. dist_max (float, optional): Legacy alias for `dist_max_out`. @@ -97,12 +94,23 @@ def add_polygon( if densify is True and (resolution is None or resolution <= 0): raise ValueError("densify=True for polygons requires a positive `resolution` (lc).") + if resolution is not None and resolution <= 0: + raise ValueError(f"resolution must be positive (or None). Got {resolution}.") + # For backward compatibility, allow 'dist_max' to function as 'dist_max_out'. if dist_max is not None and dist_max_out is None: dist_max_out = dist_max - if dist_max_out is not None and dist_max_out > 0: - final_fields.append(ThresholdField(resolution, d_min, dist_max_out)) + if dist_max_out is not None and dist_max_out < 0: + raise ValueError(f"dist_max_out must be non-negative (or None). Got {dist_max_out}.") + + # No defaults: when a polygon provides an explicit resolution, require an + # explicit exterior transition length to avoid sharp size jumps. + if resolution is not None and (dist_max_out is None or dist_max_out <= 0): + raise ValueError( + "Polygons with an explicit `resolution` must provide `dist_max_out > 0` " + "to ensure a smooth size transition across the boundary." + ) self.raw_polygons.append( @@ -111,7 +119,6 @@ def add_polygon( "zone_id": zone_id, "lc": resolution, "z_order": z_order, - "refine": mesh_refinement, "dist_min": dist_min, "dist_max_in": dist_max_in, "dist_max_out": dist_max_out, @@ -175,7 +182,7 @@ def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barr 'dist_max': dist_max, 'straddle_width': straddle_width, 'fields': fields, - 'embed': embed,, + 'embed': embed, 'densify': densify, 'simplify_tolerance': simplify_tolerance }) @@ -210,7 +217,7 @@ def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None 'dist_min': dist_min, 'dist_max': dist_max, 'fields': fields, - 'embed': embed,, + 'embed': embed, 'simplify_tolerance': simplify_tolerance }) def _apply_simplification(self): @@ -334,12 +341,13 @@ def _resolve_overlaps(self): "zone_id", "lc", "z_order", - "refine", "dist_min", "dist_max_in", "dist_max_out", "densify", "simplify_tolerance", + "fields", + "embed", ], crs=self.crs, ) @@ -457,13 +465,31 @@ def generate(self): if self.raw_lines: self.clean_lines = gpd.GeoDataFrame(self.raw_lines, crs=self.crs) else: - self.clean_lines = gpd.GeoDataFrame(columns=['geometry', 'line_id', 'lc', 'is_barrier', 'dist_min', 'dist_max', 'straddle_width'], crs=self.crs) + self.clean_lines = gpd.GeoDataFrame( + columns=[ + 'geometry', + 'line_id', + 'lc', + 'is_barrier', + 'dist_min', + 'dist_max', + 'straddle_width', + 'fields', + 'embed', + 'densify', + 'simplify_tolerance', + ], + crs=self.crs, + ) # Clean Points if self.raw_points: self.clean_points = gpd.GeoDataFrame(self.raw_points, crs=self.crs) else: - self.clean_points = gpd.GeoDataFrame(columns=['geometry', 'point_id', 'lc', 'dist_min', 'dist_max'], crs=self.crs) + self.clean_points = gpd.GeoDataFrame( + columns=['geometry', 'point_id', 'lc', 'dist_min', 'dist_max', 'fields', 'embed', 'simplify_tolerance'], + crs=self.crs, + ) print("Densifying geometry...") self._apply_densification() diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py index b1d5cdc..75e3a19 100644 --- a/src/vorflow/engine.py +++ b/src/vorflow/engine.py @@ -84,18 +84,31 @@ def _add_geometry(self, polygons_gdf, lines_gdf, points_gdf): pre-processes barrier features before fragmenting all geometries to create a consistent topological model. """ - input_tag_info = {} + input_tag_info = {} + + field_only_points = {} + field_only_lines = {} + field_only_poly_curves = {} def to_key(dim, tag): return (int(dim), int(tag)) + + def is_embedded(row) -> bool: + val = row.get('embed', True) + if pd.isna(val): + return True + return bool(val) # Add all point features to the Gmsh model first. - all_point_tags = [] + embedded_point_tags = [] for idx, row in points_gdf.iterrows(): tag = gmsh.model.occ.addPoint(row.geometry.x, row.geometry.y, 0) key = to_key(0, tag) - input_tag_info[key] = {'type': 'point', 'id': idx} - all_point_tags.append(key) + if is_embedded(row): + input_tag_info[key] = {'type': 'point', 'id': idx} + embedded_point_tags.append(key) + else: + field_only_points.setdefault(int(idx), []).append(key) # Create a buffer zone around barrier lines. This is used to trim back # other lines, preventing their endpoints from interfering with the @@ -129,8 +142,8 @@ def to_key(dim, tag): print(f"Constructed Barrier Zone from {len(barrier_buffers)} barriers.") # Add line features to the model, handling barriers and standard lines differently. - all_line_tags = [] - all_surface_tags = [] + embedded_line_tags = [] + embedded_surface_tags = [] for idx, row in lines_gdf.iterrows(): val = row.get('is_barrier', False) @@ -139,6 +152,8 @@ def to_key(dim, tag): lc = max(row.get('lc', 10.0), 0.001) use_virtual_straddle = is_barrier or (straddle is not None and straddle > 0) + + embedded = is_embedded(row) if use_virtual_straddle: # For barriers or "straddle" lines, we don't add the line itself. @@ -174,14 +189,20 @@ def to_key(dim, tag): lx, ly = p.x + nx*epsilon, p.y + ny*epsilon lt = gmsh.model.occ.addPoint(lx, ly, 0) k_l = to_key(0, lt) - input_tag_info[k_l] = {'type': 'point', 'id': idx} - all_point_tags.append(k_l) + if embedded: + input_tag_info[k_l] = {'type': 'point', 'id': idx} + embedded_point_tags.append(k_l) + else: + field_only_points.setdefault(int(idx), []).append(k_l) rx, ry = p.x - nx*epsilon, p.y - ny*epsilon rt = gmsh.model.occ.addPoint(rx, ry, 0) k_r = to_key(0, rt) - input_tag_info[k_r] = {'type': 'point', 'id': idx} - all_point_tags.append(k_r) + if embedded: + input_tag_info[k_r] = {'type': 'point', 'id': idx} + embedded_point_tags.append(k_r) + else: + field_only_points.setdefault(int(idx), []).append(k_r) else: # This is a standard line feature that will act as a constraint @@ -225,13 +246,17 @@ def to_key(dim, tag): l = gmsh.model.occ.addLine(pt_tags[i], pt_tags[i+1]) key = to_key(1, l) - all_line_tags.append(key) - input_tag_info[key] = {'type': 'line', 'id': idx} + if embedded: + embedded_line_tags.append(key) + input_tag_info[key] = {'type': 'line', 'id': idx} + else: + field_only_lines.setdefault(int(idx), []).append(key) # Add polygon features to the model. if not polygons_gdf.empty: print(f"Adding {len(polygons_gdf)} polygons to Gmsh...") for idx, row in polygons_gdf.iterrows(): + embedded = is_embedded(row) geom = row['geometry'] if geom.geom_type == 'Polygon': polys = [geom] @@ -282,14 +307,15 @@ def create_loop(coords): return None try: - return gmsh.model.occ.addCurveLoop(l_tags) + loop_tag = gmsh.model.occ.addCurveLoop(l_tags) + return loop_tag, l_tags except Exception as e: print(f"Error adding curve loop: {e}") - return None + return None, [] # 1. Exterior Boundary ext_coords = list(poly.exterior.coords) - exterior_loop_tag = create_loop(ext_coords) + exterior_loop_tag, exterior_lines = create_loop(ext_coords) if exterior_loop_tag is None: print(f"Warning: Skipping degenerate polygon {idx}") @@ -297,31 +323,48 @@ def create_loop(coords): # 2. Interior Boundaries (Holes) loops = [exterior_loop_tag] + boundary_curve_tags = list(exterior_lines) for interior in poly.interiors: int_coords = list(interior.coords) - interior_loop_tag = create_loop(int_coords) + interior_loop_tag, interior_lines = create_loop(int_coords) if interior_loop_tag is not None: loops.append(interior_loop_tag) + boundary_curve_tags.extend(interior_lines) - # Create plane surface with holes - try: - s_tag = gmsh.model.occ.addPlaneSurface(loops) - except Exception as e: - print(f"Error creating surface for polygon {idx}: {e}") - continue - - key = to_key(2, s_tag) - input_tag_info[key] = {'type': 'surface', 'id': idx} - all_surface_tags.append(key) + if embedded: + # Create plane surface with holes (embedded polygons participate in fragment) + try: + s_tag = gmsh.model.occ.addPlaneSurface(loops) + except Exception as e: + print(f"Error creating surface for polygon {idx}: {e}") + continue + + key = to_key(2, s_tag) + input_tag_info[key] = {'type': 'surface', 'id': idx} + embedded_surface_tags.append(key) + else: + # Field-only polygons are represented by their boundary curves only. + # These curves are NOT included in fragment, so they won't cut the domain. + if boundary_curve_tags: + field_only_poly_curves.setdefault(int(idx), []).extend( + [to_key(1, t) for t in boundary_curve_tags] + ) # "Fragment" combines all the individual geometries into a single, # topologically consistent model. This is where intersections are # calculated and new, smaller entities are created at overlaps. - object_tags = all_surface_tags + all_line_tags + all_point_tags + # Only embedded geometry participates in fragmentation. + object_tags = embedded_surface_tags + embedded_line_tags + embedded_point_tags if not object_tags: print("Warning: No geometry to mesh.") - return {'points': {}, 'lines': {}, 'surfaces': {}, 'straddle_surfs': {}} + return { + 'points': field_only_points, + 'lines': field_only_lines, + 'surfaces': {}, + 'straddle_surfs': {}, + 'poly_curves': field_only_poly_curves, + } print(f"Fragmenting {len(object_tags)} objects...") out_dt, out_map = gmsh.model.occ.fragment(object_tags, []) @@ -329,7 +372,13 @@ def create_loop(coords): # After fragmentation, we need to rebuild our map of which original # feature corresponds to which new Gmsh tags. - final_map = {'points': {}, 'lines': {}, 'surfaces': {}, 'straddle_surfs': {}} + final_map = { + 'points': dict(field_only_points), + 'lines': dict(field_only_lines), + 'surfaces': {}, + 'straddle_surfs': {}, + 'poly_curves': dict(field_only_poly_curves), + } print(f"Reconstructing Map (Input Tags: {len(object_tags)}, Out Map Len: {len(out_map)})...") @@ -572,6 +621,26 @@ def add_refinement(entity_dim, entity_tags, size_target, dist_min, dist_max, siz fid_grad = add_refinement(1, curve_tags, boundary_lc, d_min, d_max_out, size_max_limit=global_max_lc) if fid_grad: field_list.append(fid_grad) + # Field-only polygons: treat as boundary curves only (line-like distance field). + elif idx in gmsh_map.get('poly_curves', {}): + curve_dimtags = gmsh_map['poly_curves'][idx] + curve_tags = extract_tags(curve_dimtags) + + target_lc = get_row_param(row, 'lc', global_max_lc) + + densify_val = row.get("densify", None) + if isinstance(densify_val, (int, float)) and not isinstance(densify_val, bool) and densify_val > 0: + boundary_lc = min(target_lc, float(densify_val)) + else: + boundary_lc = target_lc + + d_max_out = get_row_param(row, 'dist_max_out', 0.0) + if curve_tags and d_max_out > 0 and boundary_lc < global_max_lc: + d_min = get_row_param(row, 'dist_min', 0.0) + fid_grad = add_refinement(1, curve_tags, boundary_lc, d_min, d_max_out, size_max_limit=global_max_lc) + if fid_grad: + field_list.append(fid_grad) + # 4. Straddle surfaces (placeholder for future transfinite enforcement). for idx, tags in gmsh_map.get('straddle_surfs', {}).items(): clean_tags = extract_tags(tags) diff --git a/tests/test_integration_gmsh.py b/tests/test_integration_gmsh.py index d0253ab..d5f33e5 100644 --- a/tests/test_integration_gmsh.py +++ b/tests/test_integration_gmsh.py @@ -25,7 +25,7 @@ def test_gmsh_integration_simple_square(): # 10x10 square square = Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]) # Zone ID 1, resolution 2.0 (coarse mesh for speed) - cm.add_polygon(square, zone_id=1, resolution=2.0) + cm.add_polygon(square, zone_id=1, resolution=2.0, dist_max_out=10.0) clean_polys, clean_lines, clean_points = cm.generate() @@ -59,7 +59,7 @@ def test_gmsh_integration_with_internal_line(): """ cm = ConceptualMesh(crs="EPSG:3857") square = Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]) - cm.add_polygon(square, zone_id=1, resolution=5.0) + cm.add_polygon(square, zone_id=1, resolution=5.0, dist_max_out=25.0) # Diagonal line with finer resolution line = LineString([(1, 1), (9, 9)]) @@ -81,6 +81,30 @@ def test_gmsh_integration_with_internal_line(): # because of the 1.0 resolution line. assert len(grid) > 10 + +def test_gmsh_integration_with_field_only_line_refinement(): + """A non-embedded (field-only) line should refine the mesh without partitioning it.""" + cm = ConceptualMesh(crs="EPSG:3857") + square = Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]) + cm.add_polygon(square, zone_id=1, resolution=5.0, dist_max_out=25.0) + + # Field-only diagonal line with finer resolution. + line = LineString([(1, 1), (9, 9)]) + cm.add_line(line, line_id="fault", resolution=1.0, embed=False) + + clean_polys, clean_lines, clean_points = cm.generate() + + mg = MeshGenerator(background_lc=5.0, verbosity=1) + success = mg.generate(clean_polys, clean_lines, clean_points) + assert success + + vt = VoronoiTessellator(mg, cm, clip_to_boundary=True) + grid = vt.generate() + assert not grid.empty + + # Still expect refinement from the line-based size field. + assert len(grid) > 10 + def test_gmsh_integration_overlapping_polygon_with_hole(): """ Test the case where an overlapping polygon (Zone 2) has a hole in its center. @@ -91,7 +115,7 @@ def test_gmsh_integration_overlapping_polygon_with_hole(): # 1. Base Domain (Large Square) - Zone 1 # 20x20 square domain = Polygon([(0, 0), (20, 0), (20, 20), (0, 20)]) - cm.add_polygon(domain, zone_id=1, resolution=5.0, z_order=0) + cm.add_polygon(domain, zone_id=1, resolution=5.0, dist_max_out=25.0, z_order=0) # 2. Overlapping Polygon with Hole (Donut) - Zone 2 # Outer: 5,5 to 15,15 @@ -100,7 +124,7 @@ def test_gmsh_integration_overlapping_polygon_with_hole(): donut_hole = [(8, 8), (12, 8), (12, 12), (8, 12)] donut = Polygon(donut_shell, [donut_hole]) - cm.add_polygon(donut, zone_id=2, resolution=2.0, z_order=1) + cm.add_polygon(donut, zone_id=2, resolution=2.0, dist_max_out=10.0, z_order=1) # 3. Generate Conceptual Mesh clean_polys, clean_lines, clean_points = cm.generate() From 750a65ea54de55b2a4ff93f7f9d8b447db2de50f Mon Sep 17 00:00:00 2001 From: oscar Date: Tue, 16 Dec 2025 16:48:44 +0800 Subject: [PATCH 10/26] adding some framing for fields, added independent field distance, and some TODOs --- src/vorflow/blueprint.py | 17 +- src/vorflow/engine.py | 375 +++++++++++++++++++----------------- src/vorflow/field_engine.py | 0 src/vorflow/fields.py | 91 ++++----- 4 files changed, 255 insertions(+), 228 deletions(-) create mode 100644 src/vorflow/field_engine.py diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index b25159c..33967ef 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -43,8 +43,6 @@ def add_polygon( z_order=0, dist_min=None, dist_max=None, - dist_max_in=None, - dist_max_out=None, densify=None, fields=None, embed=True, @@ -62,11 +60,9 @@ def add_polygon( processed first and will "cut" into lower-order polygons. dist_min (float, optional): Distance from the polygon boundary where the mesh size is held constant at the boundary's resolution. - dist_max (float, optional): Legacy alias for `dist_max_out`. - dist_max_in (float, optional): Distance inside the polygon over which the mesh - transitions from the boundary resolution to the internal resolution. - dist_max_out (float, optional): Distance outside the polygon over which the - mesh transitions to the background resolution. + dist_max (float, optional): Distance from the polygon boundary over which the mesh + transitions to the background resolution. + densify (float|bool|None, optional): Controls polygon boundary densification: - If False, disables densification. - If True, densifies using `resolution` (lc). Requires `resolution` to be set. @@ -103,12 +99,15 @@ def add_polygon( if dist_max_out is not None and dist_max_out < 0: raise ValueError(f"dist_max_out must be non-negative (or None). Got {dist_max_out}.") + + if dist_max_in or dist_max_out is not None or dist_max is not None or dist_min is not None: + # No defaults: when a polygon provides an explicit resolution, require an # explicit exterior transition length to avoid sharp size jumps. - if resolution is not None and (dist_max_out is None or dist_max_out <= 0): + if resolution is None and (dist_max_out is None or dist_max_out <= 0): raise ValueError( - "Polygons with an explicit `resolution` must provide `dist_max_out > 0` " + "Polygons without an explicit `resolution` must provide `dist_max_out > 0` " "to ensure a smooth size transition across the boundary." ) diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py index 75e3a19..87b9f30 100644 --- a/src/vorflow/engine.py +++ b/src/vorflow/engine.py @@ -6,7 +6,7 @@ from shapely.geometry import Point, LineString, Polygon from shapely.ops import unary_union from shapely.validation import make_valid -from .fields import ThresholdField, ExponentialField, AutoLinearField, AutoExponentialField, ConstantField +from .fields import MeshField, ThresholdField, ExponentialField, AutoLinearField, AutoExponentialField, ConstantField class MeshGenerator: def __init__(self, background_lc=None,verbosity=0, mesh_algorithm=6, smoothing_steps=10, optimization_cycles=2): @@ -331,7 +331,7 @@ def create_loop(coords): loops.append(interior_loop_tag) boundary_curve_tags.extend(interior_lines) - if embedded: + if embedded:#TODO check if I should add not embedded for fields # Create plane surface with holes (embedded polygons participate in fragment) try: s_tag = gmsh.model.occ.addPlaneSurface(loops) @@ -462,212 +462,233 @@ def extract_tags(entry_list): def get_row_param(row, key, default): if key in row and not pd.isna(row[key]): return float(row[key]) return float(default) + + #we are gonna set the fields using the field objects defined in fields.py and provided by the user + #we need to grop by field/object type(class and parameters) and by geometry type(points, lines, surfaces) + #lets get all the unique fields first + field_objects = {}#TODO check if loops are required for non embedded surfaces + for gdf, geom_type in [(points_gdf, 'points'), (lines_gdf, 'lines'), (polygons_gdf, 'surfaces')]: + for idx, row in gdf.iterrows(): + field = row.get('field', None) + if field is not None and isinstance(field, MeshField): + key = (field.__class__.__name__, tuple(sorted(field.__dict__.items()))) + if key not in field_objects: + field_objects[key] = { + 'field': field, + 'geom_type': geom_type, + 'feature_ids': [] + } + field_objects[key]['feature_ids'].append(int(idx)) + - def add_refinement(entity_dim, entity_tags, size_target, dist_min, dist_max, size_max_limit=None): - if not entity_tags: return None - valid_tags = [float(t) for t in entity_tags] + + + + # def add_refinement(entity_dim, entity_tags, size_target, dist_min, dist_max, size_max_limit=None): + # if not entity_tags: return None + # valid_tags = [float(t) for t in entity_tags] - if size_max_limit is None: - size_max_limit = global_max_lc - - # To ensure the meshing algorithm converges efficiently, the rate of - # change in element size must be controlled. This heuristic enforces - # a minimum transition distance to prevent the mesh size gradient - # from becoming too steep, which can stall the mesher. - size_diff = size_max_limit - size_target - if size_diff > 0: - min_span_required = size_diff / (0.5 * size_target) + # if size_max_limit is None: + # size_max_limit = global_max_lc + + # # To ensure the meshing algorithm converges efficiently, the rate of + # # change in element size must be controlled. This heuristic enforces + # # a minimum transition distance to prevent the mesh size gradient + # # from becoming too steep, which can stall the mesher. + # size_diff = size_max_limit - size_target + # if size_diff > 0: + # min_span_required = size_diff / (0.5 * size_target) - current_span = dist_max - dist_min - if current_span < min_span_required: - dist_max = dist_min + min_span_required + # current_span = dist_max - dist_min + # if current_span < min_span_required: + # dist_max = dist_min + min_span_required - if dist_max <= dist_min: - dist_max = dist_min + max(size_target, 1e-3) + # if dist_max <= dist_min: + # dist_max = dist_min + max(size_target, 1e-3) - # Create a `Distance` field, which calculates the distance from the specified entities. - f_dist = gmsh.model.mesh.field.add("Distance") - if entity_dim == 0: gmsh.model.mesh.field.setNumbers(f_dist, "PointsList", valid_tags) - elif entity_dim == 1: gmsh.model.mesh.field.setNumbers(f_dist, "CurvesList", valid_tags) + # # Create a `Distance` field, which calculates the distance from the specified entities. + # f_dist = gmsh.model.mesh.field.add("Distance") + # if entity_dim == 0: gmsh.model.mesh.field.setNumbers(f_dist, "PointsList", valid_tags) + # elif entity_dim == 1: gmsh.model.mesh.field.setNumbers(f_dist, "CurvesList", valid_tags) - # Create a `Threshold` field, which uses the `Distance` field to - # define a mesh size that varies linearly from `SizeMin` to `SizeMax` - # over the range `DistMin` to `DistMax`. This is more efficient than `MathEval`. - f_thresh = gmsh.model.mesh.field.add("Threshold") - gmsh.model.mesh.field.setNumber(f_thresh, "InField", f_dist) - gmsh.model.mesh.field.setNumber(f_thresh, "SizeMin", float(size_target)) - gmsh.model.mesh.field.setNumber(f_thresh, "SizeMax", float(size_max_limit)) - gmsh.model.mesh.field.setNumber(f_thresh, "DistMin", float(dist_min)) - gmsh.model.mesh.field.setNumber(f_thresh, "DistMax", float(dist_max)) + # # Create a `Threshold` field, which uses the `Distance` field to + # # define a mesh size that varies linearly from `SizeMin` to `SizeMax` + # # over the range `DistMin` to `DistMax`. This is more efficient than `MathEval`. + # f_thresh = gmsh.model.mesh.field.add("Threshold") + # gmsh.model.mesh.field.setNumber(f_thresh, "InField", f_dist) + # gmsh.model.mesh.field.setNumber(f_thresh, "SizeMin", float(size_target)) + # gmsh.model.mesh.field.setNumber(f_thresh, "SizeMax", float(size_max_limit)) + # gmsh.model.mesh.field.setNumber(f_thresh, "DistMin", float(dist_min)) + # gmsh.model.mesh.field.setNumber(f_thresh, "DistMax", float(dist_max)) - return f_thresh - - # 1. Point-based refinement fields. - for idx, row in points_gdf.iterrows(): - if idx in gmsh_map['points']: - tags = extract_tags(gmsh_map['points'][idx]) - lc = max(get_row_param(row, 'lc', 5.0), 0.001) - d_min = get_row_param(row, 'dist_min', lc * 2.0) - d_max = get_row_param(row, 'dist_max', global_max_lc * 1.5) + # return f_thresh + + # # 1. Point-based refinement fields. + # for idx, row in points_gdf.iterrows(): + # if idx in gmsh_map['points']: + # tags = extract_tags(gmsh_map['points'][idx]) + # lc = max(get_row_param(row, 'lc', 5.0), 0.001) + # d_min = get_row_param(row, 'dist_min', lc * 2.0) + # d_max = get_row_param(row, 'dist_max', global_max_lc * 1.5) - fid = add_refinement(0, tags, lc, d_min, d_max) - if fid: field_list.append(fid) - - # 2. Line-based refinement fields (including straddle barriers). - for idx, row in lines_gdf.iterrows(): - # Standard lines that exist as curves in Gmsh. - if idx in gmsh_map['lines']: - tags = extract_tags(gmsh_map['lines'][idx]) - lc = max(get_row_param(row, 'lc', 10.0), 0.001) - d_min = get_row_param(row, 'dist_min', lc * 1.0) - d_max = get_row_param(row, 'dist_max', global_max_lc * 1.5) + # fid = add_refinement(0, tags, lc, d_min, d_max) + # if fid: field_list.append(fid) + + # # 2. Line-based refinement fields (including straddle barriers). + # for idx, row in lines_gdf.iterrows(): + # # Standard lines that exist as curves in Gmsh. + # if idx in gmsh_map['lines']: + # tags = extract_tags(gmsh_map['lines'][idx]) + # lc = max(get_row_param(row, 'lc', 10.0), 0.001) + # d_min = get_row_param(row, 'dist_min', lc * 1.0) + # d_max = get_row_param(row, 'dist_max', global_max_lc * 1.5) - fid = add_refinement(1, tags, lc, d_min, d_max) - if fid: field_list.append(fid) + # fid = add_refinement(1, tags, lc, d_min, d_max) + # if fid: field_list.append(fid) - # "Straddle" lines, which were converted into pairs of points. - # Refinement must be applied to these points to resolve the gap. - elif idx in gmsh_map['points']: - is_barrier = row.get('is_barrier', False) - straddle = row.get('straddle_width', 0) - if is_barrier or (straddle and straddle > 0): - tags = extract_tags(gmsh_map['points'][idx]) - lc = max(get_row_param(row, 'lc', 10.0), 0.001) + # # "Straddle" lines, which were converted into pairs of points. + # # Refinement must be applied to these points to resolve the gap. + # elif idx in gmsh_map['points']: + # is_barrier = row.get('is_barrier', False) + # straddle = row.get('straddle_width', 0) + # if is_barrier or (straddle and straddle > 0): + # tags = extract_tags(gmsh_map['points'][idx]) + # lc = max(get_row_param(row, 'lc', 10.0), 0.001) - d_min = get_row_param(row, 'dist_min', lc * 2.0) - d_max = get_row_param(row, 'dist_max', global_max_lc * 1.5) + # d_min = get_row_param(row, 'dist_min', lc * 2.0) + # d_max = get_row_param(row, 'dist_max', global_max_lc * 1.5) - fid = add_refinement(0, tags, lc, d_min, d_max) - if fid: field_list.append(fid) + # fid = add_refinement(0, tags, lc, d_min, d_max) + # if fid: field_list.append(fid) - # 3. Polygon-based refinement fields. - for idx, row in polygons_gdf.iterrows(): - if idx in gmsh_map['surfaces']: - tags = extract_tags(gmsh_map['surfaces'][idx]) + # # 3. Polygon-based refinement fields. + # for idx, row in polygons_gdf.iterrows(): + # if idx in gmsh_map['surfaces']: + # tags = extract_tags(gmsh_map['surfaces'][idx]) - target_lc = get_row_param(row, 'lc', global_max_lc) + # target_lc = get_row_param(row, 'lc', global_max_lc) - densify_val = row.get("densify", None) - if isinstance(densify_val, (int, float)) and not isinstance(densify_val, bool) and densify_val > 0: - boundary_lc = min(target_lc, float(densify_val)) - else: - boundary_lc = target_lc + # densify_val = row.get("densify", None) + # if isinstance(densify_val, (int, float)) and not isinstance(densify_val, bool) and densify_val > 0: + # boundary_lc = min(target_lc, float(densify_val)) + # else: + # boundary_lc = target_lc - if self.verbosity > 1: - print(f"Poly {idx}: Target={target_lc}, Border={boundary_lc}, Global={global_max_lc}") + # if self.verbosity > 1: + # print(f"Poly {idx}: Target={target_lc}, Border={boundary_lc}, Global={global_max_lc}") - # Set up a field for the polygon's interior. - if boundary_lc < global_max_lc or target_lc < global_max_lc: + # # Set up a field for the polygon's interior. + # if boundary_lc < global_max_lc or target_lc < global_max_lc: - dim_tags = [(2, int(t)) for t in tags] - boundaries = gmsh.model.getBoundary(dim_tags, combined=True, oriented=False, recursive=False) - curve_tags = [b[1] for b in boundaries if b[0] == 1] + # dim_tags = [(2, int(t)) for t in tags] + # boundaries = gmsh.model.getBoundary(dim_tags, combined=True, oriented=False, recursive=False) + # curve_tags = [b[1] for b in boundaries if b[0] == 1] - f_inner = None + # f_inner = None - if curve_tags and boundary_lc < target_lc: - # Create a gradient from the finer boundary to the coarser interior. - d_min = get_row_param(row, 'dist_min', 0.0) - d_max_in = get_row_param(row, 'dist_max_in', -1.0) + # if curve_tags and boundary_lc < target_lc: + # # Create a gradient from the finer boundary to the coarser interior. + # d_min = get_row_param(row, 'dist_min', 0.0) + # d_max_in = get_row_param(row, 'dist_max_in', -1.0) - if d_max_in > d_min: - d_max_inner = d_max_in - else: - d_max_inner = d_min + (boundary_lc * 5.0) - d_max_inner = max(d_max_inner, d_min + (target_lc - boundary_lc) * 0.2) + # if d_max_in > d_min: + # d_max_inner = d_max_in + # else: + # d_max_inner = d_min + (boundary_lc * 5.0) + # d_max_inner = max(d_max_inner, d_min + (target_lc - boundary_lc) * 0.2) - if self.verbosity > 1: - print(f" -> Grading Interior: DistMin={d_min}, DistMax={d_max_inner}, SizeMax={target_lc}") + # if self.verbosity > 1: + # print(f" -> Grading Interior: DistMin={d_min}, DistMax={d_max_inner}, SizeMax={target_lc}") - f_inner = add_refinement(1, curve_tags, boundary_lc, d_min, d_max_inner, size_max_limit=target_lc) + # f_inner = add_refinement(1, curve_tags, boundary_lc, d_min, d_max_inner, size_max_limit=target_lc) - else: - # Apply a constant mesh size throughout the interior. - if self.verbosity > 1: - print(f" -> Constant Interior: Size={target_lc}") + # else: + # # Apply a constant mesh size throughout the interior. + # if self.verbosity > 1: + # print(f" -> Constant Interior: Size={target_lc}") - f_dist_inner = gmsh.model.mesh.field.add("Distance") - gmsh.model.mesh.field.setNumbers(f_dist_inner, "CurvesList", curve_tags) + # f_dist_inner = gmsh.model.mesh.field.add("Distance") + # gmsh.model.mesh.field.setNumbers(f_dist_inner, "CurvesList", curve_tags) - f_const = gmsh.model.mesh.field.add("Threshold") - gmsh.model.mesh.field.setNumber(f_const, "InField", f_dist_inner) - gmsh.model.mesh.field.setNumber(f_const, "SizeMin", target_lc) - gmsh.model.mesh.field.setNumber(f_const, "SizeMax", target_lc) - gmsh.model.mesh.field.setNumber(f_const, "DistMin", 1e22) - gmsh.model.mesh.field.setNumber(f_const, "DistMax", 1e22) + # f_const = gmsh.model.mesh.field.add("Threshold") + # gmsh.model.mesh.field.setNumber(f_const, "InField", f_dist_inner) + # gmsh.model.mesh.field.setNumber(f_const, "SizeMin", target_lc) + # gmsh.model.mesh.field.setNumber(f_const, "SizeMax", target_lc) + # gmsh.model.mesh.field.setNumber(f_const, "DistMin", 1e22) + # gmsh.model.mesh.field.setNumber(f_const, "DistMax", 1e22) - f_inner = f_const + # f_inner = f_const - # Restrict this field to apply only inside the polygon surface. - if f_inner: - f_rest = gmsh.model.mesh.field.add("Restrict") - gmsh.model.mesh.field.setNumber(f_rest, "IField", f_inner) - gmsh.model.mesh.field.setNumbers(f_rest, "SurfacesList", [float(t) for t in tags]) - field_list.append(f_rest) + # # Restrict this field to apply only inside the polygon surface. + # if f_inner: + # f_rest = gmsh.model.mesh.field.add("Restrict") + # gmsh.model.mesh.field.setNumber(f_rest, "IField", f_inner) + # gmsh.model.mesh.field.setNumbers(f_rest, "SurfacesList", [float(t) for t in tags]) + # field_list.append(f_rest) - # Set up a field for the polygon's exterior, grading to the global size. - d_max_out = get_row_param(row, 'dist_max_out', 0.0) + # # Set up a field for the polygon's exterior, grading to the global size. + # d_max_out = get_row_param(row, 'dist_max_out', 0.0) - if d_max_out > 0: - dim_tags = [(2, int(t)) for t in tags] - boundaries = gmsh.model.getBoundary(dim_tags, combined=True, oriented=False, recursive=False) - curve_tags = [b[1] for b in boundaries if b[0] == 1] + # if d_max_out > 0: + # dim_tags = [(2, int(t)) for t in tags] + # boundaries = gmsh.model.getBoundary(dim_tags, combined=True, oriented=False, recursive=False) + # curve_tags = [b[1] for b in boundaries if b[0] == 1] - if curve_tags: - d_min = get_row_param(row, 'dist_min', 0.0) - if self.verbosity > 1: - print(f" -> Grading Exterior: DistMax={d_max_out}") + # if curve_tags: + # d_min = get_row_param(row, 'dist_min', 0.0) + # if self.verbosity > 1: + # print(f" -> Grading Exterior: DistMax={d_max_out}") - fid_grad = add_refinement(1, curve_tags, boundary_lc, d_min, d_max_out, size_max_limit=global_max_lc) - if fid_grad: field_list.append(fid_grad) - - # Field-only polygons: treat as boundary curves only (line-like distance field). - elif idx in gmsh_map.get('poly_curves', {}): - curve_dimtags = gmsh_map['poly_curves'][idx] - curve_tags = extract_tags(curve_dimtags) - - target_lc = get_row_param(row, 'lc', global_max_lc) - - densify_val = row.get("densify", None) - if isinstance(densify_val, (int, float)) and not isinstance(densify_val, bool) and densify_val > 0: - boundary_lc = min(target_lc, float(densify_val)) - else: - boundary_lc = target_lc - - d_max_out = get_row_param(row, 'dist_max_out', 0.0) - if curve_tags and d_max_out > 0 and boundary_lc < global_max_lc: - d_min = get_row_param(row, 'dist_min', 0.0) - fid_grad = add_refinement(1, curve_tags, boundary_lc, d_min, d_max_out, size_max_limit=global_max_lc) - if fid_grad: - field_list.append(fid_grad) - - # 4. Straddle surfaces (placeholder for future transfinite enforcement). - for idx, tags in gmsh_map.get('straddle_surfs', {}).items(): - clean_tags = extract_tags(tags) - for s_tag in clean_tags: - pass - - # 5. Set the final background field. This is a constant field that - # provides the mesh size for any area not covered by other fields. - f_bg = gmsh.model.mesh.field.add("MathEval") - gmsh.model.mesh.field.setString(f_bg, "F", str(global_max_lc)) - field_list.append(f_bg) - - # 6. Combine all fields using a `Min` field. At any point in the - # domain, the mesh size will be the minimum of all active fields. - if field_list: - min_field = gmsh.model.mesh.field.add("Min") - field_list = [float(f) for f in field_list] - gmsh.model.mesh.field.setNumbers(min_field, "FieldsList", field_list) - gmsh.model.mesh.field.setAsBackgroundMesh(min_field) + # fid_grad = add_refinement(1, curve_tags, boundary_lc, d_min, d_max_out, size_max_limit=global_max_lc) + # if fid_grad: field_list.append(fid_grad) + + # # Field-only polygons: treat as boundary curves only (line-like distance field). + # elif idx in gmsh_map.get('poly_curves', {}): + # curve_dimtags = gmsh_map['poly_curves'][idx] + # curve_tags = extract_tags(curve_dimtags) + + # target_lc = get_row_param(row, 'lc', global_max_lc) + + # densify_val = row.get("densify", None) + # if isinstance(densify_val, (int, float)) and not isinstance(densify_val, bool) and densify_val > 0: + # boundary_lc = min(target_lc, float(densify_val)) + # else: + # boundary_lc = target_lc + + # d_max_out = get_row_param(row, 'dist_max_out', 0.0) + # if curve_tags and d_max_out > 0 and boundary_lc < global_max_lc: + # d_min = get_row_param(row, 'dist_min', 0.0) + # fid_grad = add_refinement(1, curve_tags, boundary_lc, d_min, d_max_out, size_max_limit=global_max_lc) + # if fid_grad: + # field_list.append(fid_grad) + + # # 4. Straddle surfaces (placeholder for future transfinite enforcement). + # for idx, tags in gmsh_map.get('straddle_surfs', {}).items(): + # clean_tags = extract_tags(tags) + # for s_tag in clean_tags: + # pass + + # # 5. Set the final background field. This is a constant field that + # # provides the mesh size for any area not covered by other fields. + # f_bg = gmsh.model.mesh.field.add("MathEval") + # gmsh.model.mesh.field.setString(f_bg, "F", str(global_max_lc)) + # field_list.append(f_bg) + + # # 6. Combine all fields using a `Min` field. At any point in the + # # domain, the mesh size will be the minimum of all active fields. + # if field_list: + # min_field = gmsh.model.mesh.field.add("Min") + # field_list = [float(f) for f in field_list] + # gmsh.model.mesh.field.setNumbers(min_field, "FieldsList", field_list) + # gmsh.model.mesh.field.setAsBackgroundMesh(min_field) - # Disable Gmsh's default size-setting mechanisms. We want our fields - # to have complete control over the mesh size. - gmsh.option.setNumber("Mesh.MeshSizeExtendFromBoundary", 0) - gmsh.option.setNumber("Mesh.MeshSizeFromPoints", 0) - gmsh.option.setNumber("Mesh.MeshSizeFromCurvature", 0) + # # Disable Gmsh's default size-setting mechanisms. We want our fields + # # to have complete control over the mesh size. + # gmsh.option.setNumber("Mesh.MeshSizeExtendFromBoundary", 0) + # gmsh.option.setNumber("Mesh.MeshSizeFromPoints", 0) + # gmsh.option.setNumber("Mesh.MeshSizeFromCurvature", 0) - def generate(self, clean_polys, clean_lines, clean_points, output_file=None): + def generate(self, clean_polys, clean_lines, clean_points, output_file=None, launch_gmsh_gui=False): """ Executes the full mesh generation workflow. @@ -684,6 +705,9 @@ def generate(self, clean_polys, clean_lines, clean_points, output_file=None): clean_lines (GeoDataFrame): Snapped and cleaned lines. clean_points (GeoDataFrame): Snapped and cleaned points. output_file (str, optional): If provided, saves the mesh to this path. + launch_gmsh_gui (Boolean, optional): This allow to see triangular mesh results + using the GMSH GUI, and allow to review visually the fields and the triangular + mesh quality Returns: bool: True if generation was successful. @@ -730,7 +754,8 @@ def generate(self, clean_polys, clean_lines, clean_points, output_file=None): self.nodes = nodes_3d[:, :2] self.node_tags = node_tags self.zones_gdf = clean_polys - + if launch_gmsh_gui: + gmsh.fltk.run() self._finalize_gmsh() return True diff --git a/src/vorflow/field_engine.py b/src/vorflow/field_engine.py new file mode 100644 index 0000000..e69de29 diff --git a/src/vorflow/fields.py b/src/vorflow/fields.py index b20efb9..b717c27 100644 --- a/src/vorflow/fields.py +++ b/src/vorflow/fields.py @@ -25,6 +25,35 @@ def __hash__(self): # Create a tuple of sorted item pairs to ensure consistent hashing return hash((self.__class__.__name__, tuple(sorted(self.__dict__.items())))) + +class DistanceField(MeshField): + """Creates a Gmsh Distance field from points/lines/surfaces tags.""" + + def __init__(self, include_surfaces=True): + self.include_surfaces = bool(include_surfaces) + + def create(self, gmsh_api, tags_dict, sampling=10): + f_dist = gmsh_api.model.mesh.field.add("Distance") + + has_entities = False + if tags_dict.get('points'): + gmsh_api.model.mesh.field.setNumbers(f_dist, "PointsList", tags_dict['points']) + has_entities = True + if tags_dict.get('lines'): + gmsh_api.model.mesh.field.setNumbers(f_dist, "CurvesList", tags_dict['lines']) + gmsh_api.model.mesh.field.setNumbers(f_dist, 'Sampling', sampling) + has_entities = True + if self.include_surfaces and tags_dict.get('surfaces'):#TODO check if loops needed + gmsh_api.model.mesh.field.setNumbers(f_dist, "SurfacesList", tags_dict['surfaces']) + gmsh_api.model.mesh.field.setNumbers(f_dist, 'Sampling', sampling) + has_entities = True + + if not has_entities: + gmsh_api.model.mesh.field.remove(f_dist) + return None + + return f_dist + # --- Manual Fields --- class ConstantField(MeshField): @@ -33,7 +62,7 @@ def __init__(self, size): def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): const = gmsh_api.model.mesh.field.add("Constant") - gmsh_api.model.mesh.field.setNumber(const, "VIn", background_lc) + gmsh_api.model.mesh.field.setNumber(const, "VIn", self.size) gmsh_api.model.mesh.field.setNumber(const, "VOut", background_lc) @@ -44,23 +73,12 @@ def __init__(self, size_min, dist_min, dist_max, size_max=None): self.dist_max = float(dist_max) self.size_max = float(size_max) if size_max is not None else None - def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=10): # 1. Distance Field (can combine points, curves, surfaces) - f_dist = gmsh_api.model.mesh.field.add("Distance") - - has_entities = False - if tags_dict.get('points'): - gmsh_api.model.mesh.field.setNumbers(f_dist, "PointsList", tags_dict['points']) - has_entities = True - if tags_dict.get('lines'): - gmsh_api.model.mesh.field.setNumbers(f_dist, "CurvesList", tags_dict['lines']) - has_entities = True - if tags_dict.get('surfaces'): - gmsh_api.model.mesh.field.setNumbers(f_dist, "SurfacesList", tags_dict['surfaces']) - has_entities = True - - if not has_entities: - gmsh_api.model.mesh.field.remove(f_dist) + f_dist = DistanceField(include_surfaces=True).create( + gmsh_api, tags_dict, background_lc, feature_lc=feature_lc, sampling=sampling + ) + if f_dist is None: return None # 2. Threshold Field @@ -79,19 +97,11 @@ def __init__(self, size_min, decay_length, size_max=None): self.decay_length = float(decay_length) self.size_max = float(size_max) if size_max is not None else None - def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): - f_dist = gmsh_api.model.mesh.field.add("Distance") - - has_entities = False - if tags_dict.get('points'): - gmsh_api.model.mesh.field.setNumbers(f_dist, "PointsList", tags_dict['points']) - has_entities = True - if tags_dict.get('lines'): - gmsh_api.model.mesh.field.setNumbers(f_dist, "CurvesList", tags_dict['lines']) - has_entities = True - - if not has_entities: - gmsh_api.model.mesh.field.remove(f_dist) + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=10): + f_dist = DistanceField(include_surfaces=False).create( + gmsh_api, tags_dict, background_lc, feature_lc=feature_lc, sampling=sampling + ) + if f_dist is None: return None s_max = self.size_max if self.size_max else background_lc @@ -107,7 +117,7 @@ class AutoLinearField(MeshField): def __init__(self, growth_factor=1.2): self.fac = float(growth_factor) - def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=10): if feature_lc is None: return None cs = float(feature_lc) @@ -127,13 +137,13 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): # Delegate to ThresholdField logic # We create a temporary ThresholdField to reuse its create logic temp_field = ThresholdField(cs, dist_min, dist_max, cs_dom) - return temp_field.create(gmsh_api, tags_dict, background_lc) + return temp_field.create(gmsh_api, tags_dict, background_lc, sampling=sampling) class AutoExponentialField(MeshField): def __init__(self, growth_factor=1.1): self.fac = float(growth_factor) - def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=10): if feature_lc is None: return None cs = float(feature_lc) @@ -141,17 +151,10 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): if fac <= 1.0: raise ValueError("Growth factor must be > 1.0") - f_dist = gmsh_api.model.mesh.field.add("Distance") - has_entities = False - if tags_dict.get('points'): - gmsh_api.model.mesh.field.setNumbers(f_dist, "PointsList", tags_dict['points']) - has_entities = True - if tags_dict.get('lines'): - gmsh_api.model.mesh.field.setNumbers(f_dist, "CurvesList", tags_dict['lines']) - has_entities = True - - if not has_entities: - gmsh_api.model.mesh.field.remove(f_dist) + f_dist = DistanceField(include_surfaces=False).create( + gmsh_api, tags_dict, background_lc, feature_lc=feature_lc, sampling=sampling + ) + if f_dist is None: return None f_math = gmsh_api.model.mesh.field.add("MathEval") From 26300074f7e1f9fbf04817f609d9071b01250799 Mon Sep 17 00:00:00 2001 From: oscar Date: Wed, 17 Dec 2025 17:49:35 +0800 Subject: [PATCH 11/26] set new engine to setup fields --- src/vorflow/blueprint.py | 34 ++- src/vorflow/engine.py | 506 ++++++++++++++++++++------------------- src/vorflow/fields.py | 39 +-- 3 files changed, 291 insertions(+), 288 deletions(-) diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index 33967ef..a8c3d14 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -62,7 +62,6 @@ def add_polygon( size is held constant at the boundary's resolution. dist_max (float, optional): Distance from the polygon boundary over which the mesh transitions to the background resolution. - densify (float|bool|None, optional): Controls polygon boundary densification: - If False, disables densification. - If True, densifies using `resolution` (lc). Requires `resolution` to be set. @@ -93,24 +92,14 @@ def add_polygon( if resolution is not None and resolution <= 0: raise ValueError(f"resolution must be positive (or None). Got {resolution}.") - # For backward compatibility, allow 'dist_max' to function as 'dist_max_out'. - if dist_max is not None and dist_max_out is None: - dist_max_out = dist_max - - if dist_max_out is not None and dist_max_out < 0: - raise ValueError(f"dist_max_out must be non-negative (or None). Got {dist_max_out}.") - - if dist_max_in or dist_max_out is not None or dist_max is not None or dist_min is not None: - - - # No defaults: when a polygon provides an explicit resolution, require an - # explicit exterior transition length to avoid sharp size jumps. - if resolution is None and (dist_max_out is None or dist_max_out <= 0): - raise ValueError( - "Polygons without an explicit `resolution` must provide `dist_max_out > 0` " - "to ensure a smooth size transition across the boundary." - ) + if dist_max is not None and dist_max < 0: + raise ValueError(f"dist_max must be non-negative (or None). Got {dist_max}.") + + if dist_min is not None and dist_min < 0: + raise ValueError(f"dist_min must be non-negative (or None). Got {dist_min}.") + if fields is None: + fields = [] self.raw_polygons.append( { @@ -119,8 +108,7 @@ def add_polygon( "lc": resolution, "z_order": z_order, "dist_min": dist_min, - "dist_max_in": dist_max_in, - "dist_max_out": dist_max_out, + "dist_max": dist_max, "densify": densify, "simplify_tolerance": simplify_tolerance, 'fields': fields, @@ -171,6 +159,9 @@ def add_line(self, geometry, line_id, resolution, snap_to_polygons=True, is_barr if isinstance(densify, (int, float)) and not isinstance(densify, bool) and densify <= 0: raise ValueError(f"densify must be positive when specified as a float. Got {densify}.") + + if fields is None: + fields = [] self.raw_lines.append({ 'geometry': geometry, @@ -209,6 +200,9 @@ def add_point(self, geometry, point_id, resolution, dist_min=None, dist_max=None ) if isinstance(simplify_tolerance, (int, float)) and simplify_tolerance < 0: raise ValueError(f"simplify_tolerance must be non-negative. Got {simplify_tolerance}.") + + if fields is None: + fields = [] self.raw_points.append({ 'geometry': geometry, 'point_id': point_id, diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py index 87b9f30..baf534e 100644 --- a/src/vorflow/engine.py +++ b/src/vorflow/engine.py @@ -86,9 +86,19 @@ def _add_geometry(self, polygons_gdf, lines_gdf, points_gdf): """ input_tag_info = {} - field_only_points = {} - field_only_lines = {} - field_only_poly_curves = {} + # Non-embedded geometry does not participate in fragmentation. + # We still track it so mesh-size fields can be applied later. + nonembedded_point_tags = {} + nonembedded_line_tags = {} + nonembedded_surface_tags = {} + # For non-embedded polygons, we track their boundary curves so size + # fields can be applied without forcing the polygon to cut/fragment the domain. + nonembedded_poly_curve_tags = {} + + # Embedded geometry DOES participate in fragmentation. + embedded_point_tags = [] + embedded_line_tags = [] + embedded_surface_tags = [] def to_key(dim, tag): return (int(dim), int(tag)) @@ -100,7 +110,6 @@ def is_embedded(row) -> bool: return bool(val) # Add all point features to the Gmsh model first. - embedded_point_tags = [] for idx, row in points_gdf.iterrows(): tag = gmsh.model.occ.addPoint(row.geometry.x, row.geometry.y, 0) key = to_key(0, tag) @@ -108,7 +117,7 @@ def is_embedded(row) -> bool: input_tag_info[key] = {'type': 'point', 'id': idx} embedded_point_tags.append(key) else: - field_only_points.setdefault(int(idx), []).append(key) + nonembedded_point_tags.setdefault(int(idx), []).append(key) # Create a buffer zone around barrier lines. This is used to trim back # other lines, preventing their endpoints from interfering with the @@ -142,9 +151,6 @@ def is_embedded(row) -> bool: print(f"Constructed Barrier Zone from {len(barrier_buffers)} barriers.") # Add line features to the model, handling barriers and standard lines differently. - embedded_line_tags = [] - embedded_surface_tags = [] - for idx, row in lines_gdf.iterrows(): val = row.get('is_barrier', False) is_barrier = (val is True) or (str(val).lower() in ['true', '1', 'yes']) @@ -189,11 +195,11 @@ def is_embedded(row) -> bool: lx, ly = p.x + nx*epsilon, p.y + ny*epsilon lt = gmsh.model.occ.addPoint(lx, ly, 0) k_l = to_key(0, lt) - if embedded: + if embedded:#TODO probably this always true for barriers input_tag_info[k_l] = {'type': 'point', 'id': idx} embedded_point_tags.append(k_l) else: - field_only_points.setdefault(int(idx), []).append(k_l) + nonembedded_point_tags.setdefault(int(idx), []).append(k_l) rx, ry = p.x - nx*epsilon, p.y - ny*epsilon rt = gmsh.model.occ.addPoint(rx, ry, 0) @@ -202,7 +208,7 @@ def is_embedded(row) -> bool: input_tag_info[k_r] = {'type': 'point', 'id': idx} embedded_point_tags.append(k_r) else: - field_only_points.setdefault(int(idx), []).append(k_r) + nonembedded_point_tags.setdefault(int(idx), []).append(k_r) else: # This is a standard line feature that will act as a constraint @@ -250,7 +256,7 @@ def is_embedded(row) -> bool: embedded_line_tags.append(key) input_tag_info[key] = {'type': 'line', 'id': idx} else: - field_only_lines.setdefault(int(idx), []).append(key) + nonembedded_line_tags.setdefault(int(idx), []).append(key) # Add polygon features to the model. if not polygons_gdf.empty: @@ -331,24 +337,24 @@ def create_loop(coords): loops.append(interior_loop_tag) boundary_curve_tags.extend(interior_lines) - if embedded:#TODO check if I should add not embedded for fields - # Create plane surface with holes (embedded polygons participate in fragment) - try: - s_tag = gmsh.model.occ.addPlaneSurface(loops) - except Exception as e: - print(f"Error creating surface for polygon {idx}: {e}") - continue + # Create plane surface with holes (embedded polygons participate in fragment) + try: + s_tag = gmsh.model.occ.addPlaneSurface(loops) + except Exception as e: + print(f"Error creating surface for polygon {idx}: {e}") + continue - key = to_key(2, s_tag) + key = to_key(2, s_tag) + if embedded: # TODO check if I should add not embedded for fields input_tag_info[key] = {'type': 'surface', 'id': idx} embedded_surface_tags.append(key) else: - # Field-only polygons are represented by their boundary curves only. - # These curves are NOT included in fragment, so they won't cut the domain. - if boundary_curve_tags: - field_only_poly_curves.setdefault(int(idx), []).extend( - [to_key(1, t) for t in boundary_curve_tags] - ) + # These surfaces/curves are NOT included in fragment, so they won't cut the domain. + nonembedded_surface_tags.setdefault(int(idx), []).append(key) + nonembedded_poly_curve_tags.setdefault(int(idx), []).extend( + [(1, int(t)) for t in boundary_curve_tags] + ) + # "Fragment" combines all the individual geometries into a single, # topologically consistent model. This is where intersections are @@ -359,11 +365,11 @@ def create_loop(coords): if not object_tags: print("Warning: No geometry to mesh.") return { - 'points': field_only_points, - 'lines': field_only_lines, - 'surfaces': {}, + 'points': nonembedded_point_tags, + 'lines': nonembedded_line_tags, + 'surfaces': nonembedded_surface_tags, 'straddle_surfs': {}, - 'poly_curves': field_only_poly_curves, + 'poly_curves': nonembedded_poly_curve_tags, } print(f"Fragmenting {len(object_tags)} objects...") @@ -373,11 +379,11 @@ def create_loop(coords): # After fragmentation, we need to rebuild our map of which original # feature corresponds to which new Gmsh tags. final_map = { - 'points': dict(field_only_points), - 'lines': dict(field_only_lines), - 'surfaces': {}, + 'points': dict(nonembedded_point_tags), + 'lines': dict(nonembedded_line_tags), + 'surfaces': dict(nonembedded_surface_tags), 'straddle_surfs': {}, - 'poly_curves': dict(field_only_poly_curves), + 'poly_curves': dict(nonembedded_poly_curve_tags), } print(f"Reconstructing Map (Input Tags: {len(object_tags)}, Out Map Len: {len(out_map)})...") @@ -444,13 +450,24 @@ def _setup_fields(self, gmsh_map, polygons_gdf, lines_gdf, points_gdf): else: print("Gmsh Surface Map is EMPTY.") + # Collect all created Gmsh field ids so we can combine them at the end. field_list = [] - # The global background mesh size is the fallback resolution. - global_max_lc = self.background_lc + # The global background mesh size is always required. + # We create a Constant field for it and always set a background mesh. + if self.background_lc is None: + raise ValueError( + "MeshGenerator.background_lc must be provided. " + "If you don't want to constrain the mesh, pass a very large value." + ) + global_max_lc = float(self.background_lc) def extract_tags(entry_list): - """Helper to get a clean list of integer tags from Gmsh's output.""" + """Return a clean list of integer tags from Gmsh's dimtag-ish output. + + Gmsh commonly returns lists of (dim, tag) tuples; some maps in this + code also store raw tag ints. We normalize both to an int tag list. + """ clean_tags = [] for item in entry_list: if isinstance(item, (tuple, list)) and len(item) >= 2: @@ -460,233 +477,220 @@ def extract_tags(entry_list): return clean_tags def get_row_param(row, key, default): - if key in row and not pd.isna(row[key]): return float(row[key]) + if key in row and not pd.isna(row[key]): + return float(row[key]) return float(default) + + def _normalize_fields(value): + """Normalize feature field specifications to a list[MeshField]. + + Supported inputs: + - None / NaN -> [] + - MeshField -> [field] + - list/tuple/set of mixed values -> only MeshField entries are kept + """ + if value is None: + return [] + if isinstance(value, float) and pd.isna(value): + return [] + if isinstance(value, MeshField): + return [value] + if isinstance(value, (list, tuple, set)): + return [v for v in value if isinstance(v, MeshField)] + return [] + + def _auto_threshold_from_row(row, background_lc): + """Create a default ThresholdField from dist_min/dist_max + lc. + + This centralizes the legacy behavior that used to live in + ConceptualMesh.add_*: if a feature specifies dist_min/dist_max it + implies a distance-based size transition around that feature. + + Design notes: + - We only auto-create this field when at least one of dist_min/dist_max + is provided AND the feature has a valid lc. + - This is a *distance-based* transition around the feature: + - SizeMin = lc (feature resolution) + - SizeMax = background_lc (global resolution) + - DistMin >= 0.5 * lc (avoid near-zero gradients) + - DistMax defaults to ~5 * background_lc (transition length scale) + - We enforce (DistMax - DistMin) >= 3 * SizeMax for a gentle gradient. + """ + dist_min = row.get('dist_min', None) + dist_max = row.get('dist_max', None) + + if (dist_min is None or (isinstance(dist_min, float) and pd.isna(dist_min))) and ( + dist_max is None or (isinstance(dist_max, float) and pd.isna(dist_max)) + ): + return None + + # We need both the feature resolution and a global background resolution + # to define an implicit ThresholdField. + if background_lc is None or (isinstance(background_lc, float) and pd.isna(background_lc)): + return None + + feature_lc = row.get('lc', None) + if feature_lc is None or (isinstance(feature_lc, float) and pd.isna(feature_lc)): + return None + + feature_lc = float(feature_lc) + dist_min = None if (dist_min is None or (isinstance(dist_min, float) and pd.isna(dist_min))) else float(dist_min) + dist_max = None if (dist_max is None or (isinstance(dist_max, float) and pd.isna(dist_max))) else float(dist_max) + + # Default distances if one of the values is omitted. + # - DistMin: at least one local element size. + # - DistMax: a broader transition scale based on global mesh size. + if dist_min is None: + dist_min = feature_lc + if dist_max is None: + dist_max = float(background_lc) * 5.0 + + # Force a reasonable relationship with the target resolution. + # DistMin should not be smaller than the local size. + dist_min = max(dist_min, feature_lc*0.5) + + # Enforce a minimum transition span relative to SizeMax. + # This avoids very steep growth that can make the mesher struggle. + min_span = 3.0 * float(background_lc) + if (dist_max - dist_min) < min_span: + dist_max = dist_min + min_span + + # Final safety. + if dist_max <= dist_min: + dist_max = dist_min + max(float(background_lc), feature_lc, 1e-3) + + return ThresholdField(size_min=feature_lc, dist_min=dist_min, dist_max=dist_max, size_max=background_lc) - #we are gonna set the fields using the field objects defined in fields.py and provided by the user - #we need to grop by field/object type(class and parameters) and by geometry type(points, lines, surfaces) - #lets get all the unique fields first - field_objects = {}#TODO check if loops are required for non embedded surfaces + # Configure mesh size fields using MeshField objects attached to features. + # + # Data model expectations: + # - ConceptualMesh stores the user's desired behavior in GeoDataFrame rows. + # - Fields are created here (engine side) because the engine has the Gmsh + # tags and is responsible for mapping features -> CAD entities. + # + # How fields can be specified per feature: + # - `fields`: list[MeshField] (the only supported explicit mechanism) + # - `dist_min/dist_max` (+ lc): shorthand for an automatic ThresholdField + # + # Grouping: + # - We build ONE gmsh field per unique (field parameters + lc). + # - We intentionally do NOT split by geometry type because a single Gmsh + # Distance/Threshold field can target points/curves/surfaces at once. + # - `feature_lc` is part of grouping because Auto* fields compute their + # transition based on the local target size. + # + # Each group accumulates feature ids per geometry type so we can later + # gather all relevant gmsh tags into a single tags_dict. + field_objects = {} for gdf, geom_type in [(points_gdf, 'points'), (lines_gdf, 'lines'), (polygons_gdf, 'surfaces')]: for idx, row in gdf.iterrows(): - field = row.get('field', None) - if field is not None and isinstance(field, MeshField): - key = (field.__class__.__name__, tuple(sorted(field.__dict__.items()))) + # 1) Collect explicitly specified fields. + row_fields = [] + row_fields.extend(_normalize_fields(row.get('fields', None))) + + # 2) Optionally add an implicit ThresholdField based on dist_min/dist_max. + auto_field = _auto_threshold_from_row(row, global_max_lc) + if auto_field is not None: + row_fields.append(auto_field) + + if not row_fields: + continue + + # Cache the feature lc for Auto* fields. + feature_lc = row.get('lc', None) + if feature_lc is None or (isinstance(feature_lc, float) and pd.isna(feature_lc)): + feature_lc = None + else: + feature_lc = float(feature_lc) + + for field in row_fields: + if field is None or not isinstance(field, MeshField): + continue + key = (hash(field), feature_lc) if key not in field_objects: field_objects[key] = { 'field': field, - 'geom_type': geom_type, - 'feature_ids': [] + 'feature_lc': feature_lc, + 'feature_ids_by_geom': {'points': [], 'lines': [], 'surfaces': []}, } - field_objects[key]['feature_ids'].append(int(idx)) - - - + field_objects[key]['feature_ids_by_geom'][geom_type].append(int(idx)) - - # def add_refinement(entity_dim, entity_tags, size_target, dist_min, dist_max, size_max_limit=None): - # if not entity_tags: return None - # valid_tags = [float(t) for t in entity_tags] + # Now create and apply each unique field to the corresponding features. + # We gather all Gmsh entity tags for the features and let the MeshField + # implementation create the appropriate Distance/Threshold/etc field. + for key, info in field_objects.items(): + field = info['field'] + feature_lc = info.get('feature_lc', None) + feature_ids_by_geom = info.get('feature_ids_by_geom', {'points': [], 'lines': [], 'surfaces': []}) - # if size_max_limit is None: - # size_max_limit = global_max_lc - - # # To ensure the meshing algorithm converges efficiently, the rate of - # # change in element size must be controlled. This heuristic enforces - # # a minimum transition distance to prevent the mesh size gradient - # # from becoming too steep, which can stall the mesher. - # size_diff = size_max_limit - size_target - # if size_diff > 0: - # min_span_required = size_diff / (0.5 * size_target) - - # current_span = dist_max - dist_min - # if current_span < min_span_required: - # dist_max = dist_min + min_span_required - - # if dist_max <= dist_min: - # dist_max = dist_min + max(size_target, 1e-3) + # Gather all Gmsh tags for the features using this field. + # Note: embedded geometry is mapped under gmsh_map[geom_type]. + # For embed=False polygons, we keep their boundary curves under + # gmsh_map['poly_curves'] so fields can still be applied without + # cutting/fragmenting the domain. + tags_dict = { 'points': [], 'lines': [], 'surfaces': [] } + + # Points + for fid in feature_ids_by_geom.get('points', []): + if fid in gmsh_map.get('points', {}): + tags_dict['points'].extend(extract_tags(gmsh_map['points'][fid])) + + # Lines + for fid in feature_ids_by_geom.get('lines', []): + if fid in gmsh_map.get('lines', {}): + tags_dict['lines'].extend(extract_tags(gmsh_map['lines'][fid])) + # Straddle/barrier lines may have been converted into points. + elif fid in gmsh_map.get('points', {}): + tags_dict['points'].extend(extract_tags(gmsh_map['points'][fid])) + + # Surfaces + for fid in feature_ids_by_geom.get('surfaces', []): + if fid in gmsh_map.get('surfaces', {}): + tags_dict['surfaces'].extend(extract_tags(gmsh_map['surfaces'][fid])) + # Field-only polygons (embed=False): apply distance-based fields to boundary curves. + elif fid in gmsh_map.get('poly_curves', {}): + curve_dimtags = gmsh_map['poly_curves'][fid] + tags_dict['lines'].extend(extract_tags(curve_dimtags)) + + if not any(tags_dict.values()): + continue - # # Create a `Distance` field, which calculates the distance from the specified entities. - # f_dist = gmsh.model.mesh.field.add("Distance") - # if entity_dim == 0: gmsh.model.mesh.field.setNumbers(f_dist, "PointsList", valid_tags) - # elif entity_dim == 1: gmsh.model.mesh.field.setNumbers(f_dist, "CurvesList", valid_tags) + # Create the Gmsh field using the provided MeshField object. + f_id = field.create( + gmsh_api=gmsh, + tags_dict=tags_dict, + background_lc=global_max_lc, + feature_lc=feature_lc + ) - # # Create a `Threshold` field, which uses the `Distance` field to - # # define a mesh size that varies linearly from `SizeMin` to `SizeMax` - # # over the range `DistMin` to `DistMax`. This is more efficient than `MathEval`. - # f_thresh = gmsh.model.mesh.field.add("Threshold") - # gmsh.model.mesh.field.setNumber(f_thresh, "InField", f_dist) - # gmsh.model.mesh.field.setNumber(f_thresh, "SizeMin", float(size_target)) - # gmsh.model.mesh.field.setNumber(f_thresh, "SizeMax", float(size_max_limit)) - # gmsh.model.mesh.field.setNumber(f_thresh, "DistMin", float(dist_min)) - # gmsh.model.mesh.field.setNumber(f_thresh, "DistMax", float(dist_max)) - - # return f_thresh - - # # 1. Point-based refinement fields. - # for idx, row in points_gdf.iterrows(): - # if idx in gmsh_map['points']: - # tags = extract_tags(gmsh_map['points'][idx]) - # lc = max(get_row_param(row, 'lc', 5.0), 0.001) - # d_min = get_row_param(row, 'dist_min', lc * 2.0) - # d_max = get_row_param(row, 'dist_max', global_max_lc * 1.5) - - # fid = add_refinement(0, tags, lc, d_min, d_max) - # if fid: field_list.append(fid) - - # # 2. Line-based refinement fields (including straddle barriers). - # for idx, row in lines_gdf.iterrows(): - # # Standard lines that exist as curves in Gmsh. - # if idx in gmsh_map['lines']: - # tags = extract_tags(gmsh_map['lines'][idx]) - # lc = max(get_row_param(row, 'lc', 10.0), 0.001) - # d_min = get_row_param(row, 'dist_min', lc * 1.0) - # d_max = get_row_param(row, 'dist_max', global_max_lc * 1.5) - - # fid = add_refinement(1, tags, lc, d_min, d_max) - # if fid: field_list.append(fid) - - # # "Straddle" lines, which were converted into pairs of points. - # # Refinement must be applied to these points to resolve the gap. - # elif idx in gmsh_map['points']: - # is_barrier = row.get('is_barrier', False) - # straddle = row.get('straddle_width', 0) - # if is_barrier or (straddle and straddle > 0): - # tags = extract_tags(gmsh_map['points'][idx]) - # lc = max(get_row_param(row, 'lc', 10.0), 0.001) - - # d_min = get_row_param(row, 'dist_min', lc * 2.0) - # d_max = get_row_param(row, 'dist_max', global_max_lc * 1.5) - - # fid = add_refinement(0, tags, lc, d_min, d_max) - # if fid: field_list.append(fid) - - # # 3. Polygon-based refinement fields. - # for idx, row in polygons_gdf.iterrows(): - # if idx in gmsh_map['surfaces']: - # tags = extract_tags(gmsh_map['surfaces'][idx]) - - # target_lc = get_row_param(row, 'lc', global_max_lc) - - # densify_val = row.get("densify", None) - # if isinstance(densify_val, (int, float)) and not isinstance(densify_val, bool) and densify_val > 0: - # boundary_lc = min(target_lc, float(densify_val)) - # else: - # boundary_lc = target_lc - - # if self.verbosity > 1: - # print(f"Poly {idx}: Target={target_lc}, Border={boundary_lc}, Global={global_max_lc}") - - # # Set up a field for the polygon's interior. - # if boundary_lc < global_max_lc or target_lc < global_max_lc: - - # dim_tags = [(2, int(t)) for t in tags] - # boundaries = gmsh.model.getBoundary(dim_tags, combined=True, oriented=False, recursive=False) - # curve_tags = [b[1] for b in boundaries if b[0] == 1] - - # f_inner = None - - # if curve_tags and boundary_lc < target_lc: - # # Create a gradient from the finer boundary to the coarser interior. - # d_min = get_row_param(row, 'dist_min', 0.0) - # d_max_in = get_row_param(row, 'dist_max_in', -1.0) - - # if d_max_in > d_min: - # d_max_inner = d_max_in - # else: - # d_max_inner = d_min + (boundary_lc * 5.0) - # d_max_inner = max(d_max_inner, d_min + (target_lc - boundary_lc) * 0.2) - - # if self.verbosity > 1: - # print(f" -> Grading Interior: DistMin={d_min}, DistMax={d_max_inner}, SizeMax={target_lc}") + if f_id is not None: + field_list.append(f_id) + + #now lets add the background constant field if specified + if self.background_lc is not None: + const_field = ConstantField(size=self.background_lc) + f_id = const_field.create( + gmsh_api=gmsh, + tags_dict={}, + background_lc=self.background_lc, + feature_lc=None + ) + if f_id is not None: + field_list.append(f_id) + + # Combine all active fields using a Min field and set it as the background mesh. + # At any (x,y), Gmsh will take the smallest requested element size. + if field_list: + min_field = gmsh.model.mesh.field.add("Min") + gmsh.model.mesh.field.setNumbers(min_field, "FieldsList", [float(f) for f in field_list]) + gmsh.model.mesh.field.setAsBackgroundMesh(min_field) + + # Disable Gmsh's default sizing mechanisms so fields fully control mesh size. + # Otherwise, mesh sizing from points/curvature/boundary can compete with fields. + gmsh.option.setNumber("Mesh.MeshSizeExtendFromBoundary", 0) + gmsh.option.setNumber("Mesh.MeshSizeFromPoints", 0) + gmsh.option.setNumber("Mesh.MeshSizeFromCurvature", 0) - # f_inner = add_refinement(1, curve_tags, boundary_lc, d_min, d_max_inner, size_max_limit=target_lc) - - # else: - # # Apply a constant mesh size throughout the interior. - # if self.verbosity > 1: - # print(f" -> Constant Interior: Size={target_lc}") - - # f_dist_inner = gmsh.model.mesh.field.add("Distance") - # gmsh.model.mesh.field.setNumbers(f_dist_inner, "CurvesList", curve_tags) - - # f_const = gmsh.model.mesh.field.add("Threshold") - # gmsh.model.mesh.field.setNumber(f_const, "InField", f_dist_inner) - # gmsh.model.mesh.field.setNumber(f_const, "SizeMin", target_lc) - # gmsh.model.mesh.field.setNumber(f_const, "SizeMax", target_lc) - # gmsh.model.mesh.field.setNumber(f_const, "DistMin", 1e22) - # gmsh.model.mesh.field.setNumber(f_const, "DistMax", 1e22) - - # f_inner = f_const - - # # Restrict this field to apply only inside the polygon surface. - # if f_inner: - # f_rest = gmsh.model.mesh.field.add("Restrict") - # gmsh.model.mesh.field.setNumber(f_rest, "IField", f_inner) - # gmsh.model.mesh.field.setNumbers(f_rest, "SurfacesList", [float(t) for t in tags]) - # field_list.append(f_rest) - - # # Set up a field for the polygon's exterior, grading to the global size. - # d_max_out = get_row_param(row, 'dist_max_out', 0.0) - - # if d_max_out > 0: - # dim_tags = [(2, int(t)) for t in tags] - # boundaries = gmsh.model.getBoundary(dim_tags, combined=True, oriented=False, recursive=False) - # curve_tags = [b[1] for b in boundaries if b[0] == 1] - - # if curve_tags: - # d_min = get_row_param(row, 'dist_min', 0.0) - # if self.verbosity > 1: - # print(f" -> Grading Exterior: DistMax={d_max_out}") - - # fid_grad = add_refinement(1, curve_tags, boundary_lc, d_min, d_max_out, size_max_limit=global_max_lc) - # if fid_grad: field_list.append(fid_grad) - - # # Field-only polygons: treat as boundary curves only (line-like distance field). - # elif idx in gmsh_map.get('poly_curves', {}): - # curve_dimtags = gmsh_map['poly_curves'][idx] - # curve_tags = extract_tags(curve_dimtags) - - # target_lc = get_row_param(row, 'lc', global_max_lc) - - # densify_val = row.get("densify", None) - # if isinstance(densify_val, (int, float)) and not isinstance(densify_val, bool) and densify_val > 0: - # boundary_lc = min(target_lc, float(densify_val)) - # else: - # boundary_lc = target_lc - - # d_max_out = get_row_param(row, 'dist_max_out', 0.0) - # if curve_tags and d_max_out > 0 and boundary_lc < global_max_lc: - # d_min = get_row_param(row, 'dist_min', 0.0) - # fid_grad = add_refinement(1, curve_tags, boundary_lc, d_min, d_max_out, size_max_limit=global_max_lc) - # if fid_grad: - # field_list.append(fid_grad) - - # # 4. Straddle surfaces (placeholder for future transfinite enforcement). - # for idx, tags in gmsh_map.get('straddle_surfs', {}).items(): - # clean_tags = extract_tags(tags) - # for s_tag in clean_tags: - # pass - - # # 5. Set the final background field. This is a constant field that - # # provides the mesh size for any area not covered by other fields. - # f_bg = gmsh.model.mesh.field.add("MathEval") - # gmsh.model.mesh.field.setString(f_bg, "F", str(global_max_lc)) - # field_list.append(f_bg) - - # # 6. Combine all fields using a `Min` field. At any point in the - # # domain, the mesh size will be the minimum of all active fields. - # if field_list: - # min_field = gmsh.model.mesh.field.add("Min") - # field_list = [float(f) for f in field_list] - # gmsh.model.mesh.field.setNumbers(min_field, "FieldsList", field_list) - # gmsh.model.mesh.field.setAsBackgroundMesh(min_field) - - # # Disable Gmsh's default size-setting mechanisms. We want our fields - # # to have complete control over the mesh size. - # gmsh.option.setNumber("Mesh.MeshSizeExtendFromBoundary", 0) - # gmsh.option.setNumber("Mesh.MeshSizeFromPoints", 0) - # gmsh.option.setNumber("Mesh.MeshSizeFromCurvature", 0) def generate(self, clean_polys, clean_lines, clean_points, output_file=None, launch_gmsh_gui=False): """ diff --git a/src/vorflow/fields.py b/src/vorflow/fields.py index b717c27..edbe314 100644 --- a/src/vorflow/fields.py +++ b/src/vorflow/fields.py @@ -29,10 +29,11 @@ def __hash__(self): class DistanceField(MeshField): """Creates a Gmsh Distance field from points/lines/surfaces tags.""" - def __init__(self, include_surfaces=True): + def __init__(self, include_surfaces=True, sampling=20): self.include_surfaces = bool(include_surfaces) + self.sampling = int(sampling) - def create(self, gmsh_api, tags_dict, sampling=10): + def create(self, gmsh_api, tags_dict): f_dist = gmsh_api.model.mesh.field.add("Distance") has_entities = False @@ -41,11 +42,11 @@ def create(self, gmsh_api, tags_dict, sampling=10): has_entities = True if tags_dict.get('lines'): gmsh_api.model.mesh.field.setNumbers(f_dist, "CurvesList", tags_dict['lines']) - gmsh_api.model.mesh.field.setNumbers(f_dist, 'Sampling', sampling) + gmsh_api.model.mesh.field.setNumber(f_dist, 'Sampling', self.sampling) has_entities = True if self.include_surfaces and tags_dict.get('surfaces'):#TODO check if loops needed gmsh_api.model.mesh.field.setNumbers(f_dist, "SurfacesList", tags_dict['surfaces']) - gmsh_api.model.mesh.field.setNumbers(f_dist, 'Sampling', sampling) + gmsh_api.model.mesh.field.setNumber(f_dist, 'Sampling', self.sampling) has_entities = True if not has_entities: @@ -64,19 +65,20 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): const = gmsh_api.model.mesh.field.add("Constant") gmsh_api.model.mesh.field.setNumber(const, "VIn", self.size) gmsh_api.model.mesh.field.setNumber(const, "VOut", background_lc) + return const class ThresholdField(MeshField): - def __init__(self, size_min, dist_min, dist_max, size_max=None): + def __init__(self, size_min, dist_min, dist_max, size_max=None, sampling=20): self.size_min = float(size_min) self.dist_min = float(dist_min) self.dist_max = float(dist_max) self.size_max = float(size_max) if size_max is not None else None - - def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=10): + self.sampling = int(sampling) + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, constant_in =False): # 1. Distance Field (can combine points, curves, surfaces) - f_dist = DistanceField(include_surfaces=True).create( - gmsh_api, tags_dict, background_lc, feature_lc=feature_lc, sampling=sampling + f_dist = DistanceField(include_surfaces=True, sampling=self.sampling).create( + gmsh_api, tags_dict ) if f_dist is None: return None @@ -92,14 +94,15 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=1 return f_thresh class ExponentialField(MeshField): - def __init__(self, size_min, decay_length, size_max=None): + def __init__(self, size_min, decay_length, size_max=None, sampling=20): self.size_min = float(size_min) self.decay_length = float(decay_length) self.size_max = float(size_max) if size_max is not None else None + self.sampling = int(sampling) - def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=10): - f_dist = DistanceField(include_surfaces=False).create( - gmsh_api, tags_dict, background_lc, feature_lc=feature_lc, sampling=sampling + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): + f_dist = DistanceField(include_surfaces=False, sampling=self.sampling).create( + gmsh_api, tags_dict ) if f_dist is None: return None @@ -114,10 +117,11 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=1 # --- Auto Fields --- class AutoLinearField(MeshField): - def __init__(self, growth_factor=1.2): + def __init__(self, growth_factor=1.2, sampling=20): self.fac = float(growth_factor) + self.sampling = int(sampling) - def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=10): + def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): if feature_lc is None: return None cs = float(feature_lc) @@ -151,8 +155,9 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=1 if fac <= 1.0: raise ValueError("Growth factor must be > 1.0") + # DistanceField.create() signature is (gmsh_api, tags_dict, sampling=10) f_dist = DistanceField(include_surfaces=False).create( - gmsh_api, tags_dict, background_lc, feature_lc=feature_lc, sampling=sampling + gmsh_api, tags_dict, sampling=sampling ) if f_dist is None: return None @@ -160,6 +165,6 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=1 f_math = gmsh_api.model.mesh.field.add("MathEval") log_fac = math.log(fac) expr = f"{cs} * {fac}^(Log(1 + F{f_dist} * 2 * {log_fac} / {cs}) / {log_fac})" - + gmsh_api.model.mesh.field.setString(f_math, "F", expr) return f_math \ No newline at end of file From 8f4037845f5d3331d2df8459c9b871cfc17f65fb Mon Sep 17 00:00:00 2001 From: oscarfasanchez Date: Wed, 17 Dec 2025 20:25:47 +0800 Subject: [PATCH 12/26] fixed bug sampling --- src/vorflow/fields.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/src/vorflow/fields.py b/src/vorflow/fields.py index edbe314..fd358dc 100644 --- a/src/vorflow/fields.py +++ b/src/vorflow/fields.py @@ -140,8 +140,8 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): # Delegate to ThresholdField logic # We create a temporary ThresholdField to reuse its create logic - temp_field = ThresholdField(cs, dist_min, dist_max, cs_dom) - return temp_field.create(gmsh_api, tags_dict, background_lc, sampling=sampling) + temp_field = ThresholdField(cs, dist_min, dist_max, cs_dom, sampling=self.sampling) + return temp_field.create(gmsh_api, tags_dict, background_lc) class AutoExponentialField(MeshField): def __init__(self, growth_factor=1.1): @@ -155,9 +155,8 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=1 if fac <= 1.0: raise ValueError("Growth factor must be > 1.0") - # DistanceField.create() signature is (gmsh_api, tags_dict, sampling=10) - f_dist = DistanceField(include_surfaces=False).create( - gmsh_api, tags_dict, sampling=sampling + f_dist = DistanceField(include_surfaces=False, sampling=int(sampling)).create( + gmsh_api, tags_dict ) if f_dist is None: return None From a8e283b0ad989701cec7115eec03d60c34c83d88 Mon Sep 17 00:00:00 2001 From: oscarfasanchez Date: Wed, 17 Dec 2025 22:51:35 +0800 Subject: [PATCH 13/26] got only field polygon out of the cleaning process --- src/vorflow/blueprint.py | 46 ++++++++++++++++++++++++++++++++++++++-- 1 file changed, 44 insertions(+), 2 deletions(-) diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index a8c3d14..80c1b9a 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -335,8 +335,7 @@ def _resolve_overlaps(self): "lc", "z_order", "dist_min", - "dist_max_in", - "dist_max_out", + "dist_max", "densify", "simplify_tolerance", "fields", @@ -448,6 +447,17 @@ def generate(self): print("Applying optional geometry simplification...") self._apply_simplification() + # --- Embed semantics for polygons --- + # Polygons with embed=True define the actual meshing domain and therefore + # participate in the cookie-cutter (overlap resolution) process. + # Polygons with embed=False are refinement-only (field-only) regions and + # must NOT affect domain topology. + embedded_polys = [p for p in self.raw_polygons if bool(p.get("embed", True))] + field_only_polys = [p for p in self.raw_polygons if not bool(p.get("embed", True))] + + # Only embedded polygons are used to build the domain partition. + self.raw_polygons = embedded_polys + print("Resolving polygon overlaps...") self._resolve_overlaps() @@ -484,6 +494,38 @@ def generate(self): crs=self.crs, ) + # Clip field-only polygons to the final embedded domain. + # Field-only polygons should not extend outside the domain, but they also + # should not cut/modify domain topology. + if field_only_polys and not self.clean_polygons.empty: + domain_union = unary_union(self.clean_polygons.geometry) + domain_union = make_valid(domain_union) + + clipped_features = [] + for poly_data in field_only_polys: + geom = poly_data.get("geometry") + if geom is None or geom.is_empty: + continue + geom = make_valid(geom) + try: + clipped = geom.intersection(domain_union) + except Exception: + clipped = make_valid(geom).intersection(make_valid(domain_union)) + + if clipped.is_empty: + continue + + feat = poly_data.copy() + feat["geometry"] = make_valid(clipped) + clipped_features.append(feat) + + if clipped_features: + field_only_gdf = gpd.GeoDataFrame(clipped_features, crs=self.crs) + self.clean_polygons = gpd.GeoDataFrame( + pd.concat([self.clean_polygons, field_only_gdf], ignore_index=True), + crs=self.crs, + ) + print("Densifying geometry...") self._apply_densification() From c293371334544a4816af80db61dcb3ac5ec8c07b Mon Sep 17 00:00:00 2001 From: oscarfasanchez Date: Wed, 17 Dec 2025 22:52:03 +0800 Subject: [PATCH 14/26] updated test with the new API --- tests/test_integration_gmsh.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/test_integration_gmsh.py b/tests/test_integration_gmsh.py index d5f33e5..dc3332f 100644 --- a/tests/test_integration_gmsh.py +++ b/tests/test_integration_gmsh.py @@ -25,7 +25,7 @@ def test_gmsh_integration_simple_square(): # 10x10 square square = Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]) # Zone ID 1, resolution 2.0 (coarse mesh for speed) - cm.add_polygon(square, zone_id=1, resolution=2.0, dist_max_out=10.0) + cm.add_polygon(square, zone_id=1, resolution=2.0, dist_max=10.0) clean_polys, clean_lines, clean_points = cm.generate() @@ -59,7 +59,7 @@ def test_gmsh_integration_with_internal_line(): """ cm = ConceptualMesh(crs="EPSG:3857") square = Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]) - cm.add_polygon(square, zone_id=1, resolution=5.0, dist_max_out=25.0) + cm.add_polygon(square, zone_id=1, resolution=5.0, dist_max=25.0) # Diagonal line with finer resolution line = LineString([(1, 1), (9, 9)]) @@ -86,7 +86,7 @@ def test_gmsh_integration_with_field_only_line_refinement(): """A non-embedded (field-only) line should refine the mesh without partitioning it.""" cm = ConceptualMesh(crs="EPSG:3857") square = Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]) - cm.add_polygon(square, zone_id=1, resolution=5.0, dist_max_out=25.0) + cm.add_polygon(square, zone_id=1, resolution=5.0, dist_max=25.0) # Field-only diagonal line with finer resolution. line = LineString([(1, 1), (9, 9)]) @@ -115,7 +115,7 @@ def test_gmsh_integration_overlapping_polygon_with_hole(): # 1. Base Domain (Large Square) - Zone 1 # 20x20 square domain = Polygon([(0, 0), (20, 0), (20, 20), (0, 20)]) - cm.add_polygon(domain, zone_id=1, resolution=5.0, dist_max_out=25.0, z_order=0) + cm.add_polygon(domain, zone_id=1, resolution=5.0, dist_max=25.0, z_order=0) # 2. Overlapping Polygon with Hole (Donut) - Zone 2 # Outer: 5,5 to 15,15 @@ -124,7 +124,7 @@ def test_gmsh_integration_overlapping_polygon_with_hole(): donut_hole = [(8, 8), (12, 8), (12, 12), (8, 12)] donut = Polygon(donut_shell, [donut_hole]) - cm.add_polygon(donut, zone_id=2, resolution=2.0, dist_max_out=10.0, z_order=1) + cm.add_polygon(donut, zone_id=2, resolution=2.0, dist_max=10.0, z_order=1) # 3. Generate Conceptual Mesh clean_polys, clean_lines, clean_points = cm.generate() From 6b8bd75ff486ab163abc71e193db9b62409cf35f Mon Sep 17 00:00:00 2001 From: oscarfasanchez Date: Wed, 17 Dec 2025 23:09:44 +0800 Subject: [PATCH 15/26] added solution to handle many non embededd when retrieving the final nodes, but still need refinement --- examples/field_capabilities_example.py | 321 +++++++++++++++++++++++++ src/vorflow/engine.py | 89 ++++++- 2 files changed, 405 insertions(+), 5 deletions(-) create mode 100644 examples/field_capabilities_example.py diff --git a/examples/field_capabilities_example.py b/examples/field_capabilities_example.py new file mode 100644 index 0000000..4555a97 --- /dev/null +++ b/examples/field_capabilities_example.py @@ -0,0 +1,321 @@ +#%% +"""Field capabilities example (pseudo-notebook). + +Run this file in VS Code with the Python extension. The `#%%` markers create +cell-like execution, similar to a notebook. + +This example duplicates polygon and line geometries to exercise multiple mesh +field types: +- Implicit threshold via `dist_min`/`dist_max` +- Explicit `ThresholdField` +- `ExponentialField` +- `AutoLinearField` +- `AutoExponentialField` +- Field-only polygon via `embed=False` +- Barrier/straddle line to validate point-pair representation +""" + +from __future__ import annotations + +import matplotlib.pyplot as plt +import numpy as np + +from shapely.affinity import translate +from shapely.geometry import LineString, Point, box + +from vorflow import ConceptualMesh, MeshGenerator, VoronoiTessellator +from vorflow.fields import ( + AutoExponentialField, + AutoLinearField, + ThresholdField, +) + + +#%% +# Geometry +domain = box(0, 0, 400, 200) +# Base refinement polygon (simplified set) +poly_base = box(40, 40, 70, 70) + +# Create a 2x3 layout (upper/lower × left/center/right) +ul = translate(poly_base, xoff=0, yoff=80) +uc = translate(poly_base, xoff=120, yoff=80) +ur = translate(poly_base, xoff=240, yoff=80) +ll = translate(poly_base, xoff=0, yoff=0) +lc = translate(poly_base, xoff=120, yoff=0) +lr = translate(poly_base, xoff=240, yoff=0) + +polys = { + "upper-left": ul, + "upper-center": uc, + "upper-right": ur, + "lower-left": ll, + "lower-center": lc, + "lower-right": lr, +} + +# Base line geometry (simplified) +fault_base = LineString([(100, 0), (100, 200)]) +faults = { + "fault-left": translate(fault_base, xoff=-40, yoff=0), + "fault-center": translate(fault_base, xoff=80, yoff=0), + "fault-right": translate(fault_base, xoff=200, yoff=0), +} +# A gentle sine-wave river +xs = np.linspace(-10, 430, 45) +river_y = 140 + 18 * np.sin(0.06 * xs) +river_base = LineString(list(zip(xs, river_y))) +rivers = { + "river-center-up": translate(river_base, xoff=0, yoff=0), + "river-center-down": translate(river_base, xoff=0, yoff=-60), +} + +# Points (simplified) +points = { + "pt-lower-left": Point(25, 25), + "pt-lower-center": Point(185, 25), + "pt-lower-right": Point(325, 25), +} +#%% +# Fields used below + +auto_linear = AutoLinearField(growth_factor=1.1) +auto_exp = AutoExponentialField(growth_factor=1.1) +threshold = ThresholdField(size_min=5.0, dist_min=5.0, dist_max=50.0, size_max=20.0) + + + + + +#%% +# 1) Setup Blueprint + 2) Mesh Generation + 3) Voronoi Conversion + +background_lc = 100 +feature_lc = 10 # representative base feature length for features + +blueprint = ConceptualMesh(crs="EPSG:3857") +blueprint.add_polygon(domain, zone_id="domain") # color: black (domain boundary) + +# Polygons (IDs are location-based) +blueprint.add_polygon( + polys["upper-left"], + zone_id="upper-left", + resolution=feature_lc/5, + z_order=10, + dist_min=feature_lc, + dist_max=background_lc * 3.0, +) + +blueprint.add_polygon( + polys["upper-center"], + zone_id="upper-center", + resolution=feature_lc/5, + z_order=10, + fields=[auto_exp], + embed=False, # field-only polygon +) + +blueprint.add_polygon( + polys["upper-right"], + zone_id="upper-right", + resolution=feature_lc/5, + z_order=10, + fields=[auto_linear], +) + + +blueprint.add_polygon( + polys["lower-left"].boundary(),#TODO change to boundary only + zone_id="lower-left", + resolution=feature_lc/5, + z_order=5, + dist_min=feature_lc, + dist_max=background_lc * 1.5, + fields=[auto_linear], + embed=False, # field-only polygon +) + +blueprint.add_polygon( + polys["lower-center"], + zone_id="lower-center", + resolution=feature_lc/5, + z_order=5, + fields=[auto_exp], +) + +blueprint.add_polygon( + polys["lower-right"], + zone_id="lower-right", + resolution=feature_lc/5, + z_order=5, + fields=[threshold], +) + +# Lines (IDs are location-based) +# Colors: fault-left -> 'tab:red', fault-center -> 'tab:purple', fault-right -> 'tab:blue', river-center -> 'tab:cyan' +blueprint.add_line( + faults["fault-left"], + line_id="fault-left", + resolution=feature_lc, + is_barrier=False, + dist_min=feature_lc, + dist_max=background_lc * 5.0, +) + +blueprint.add_line( + faults["fault-center"], + line_id="fault-center", + resolution=feature_lc, + is_barrier=True, + straddle_width=4.0, + fields=[auto_exp], +) + +blueprint.add_line( + faults["fault-right"], + line_id="fault-right", + resolution=feature_lc, + is_barrier=False, + fields=[auto_linear], +) + +blueprint.add_line( + rivers["river-center-up"], + line_id="river-center-up", + resolution=feature_lc/2, + is_barrier=False, + fields=[threshold], +) + +blueprint.add_line( + rivers["river-center-down"], + line_id="river-center-down", + resolution=feature_lc/2, + is_barrier=False, + fields=[auto_exp], + embed=False, # field-only line +) + +# Points (IDs are location-based) +# Colors: pt-lower-left -> 'tab:red', pt-lower-center -> 'tab:purple' +blueprint.add_point( + points["pt-lower-left"], + point_id="pt-lower-left", + resolution=feature_lc, + dist_min=feature_lc/5, + dist_max=background_lc * 1.5, +) +blueprint.add_point( + points["pt-lower-center"], + point_id="pt-lower-center", + resolution=feature_lc/5, + fields=[auto_exp], + embed=False, # field-only point +) + +blueprint.add_point( + points["pt-lower-right"], + point_id="pt-lower-right", + resolution=feature_lc/5, + fields=[auto_linear], + embed=False, # field-only point +) + +clean_polys, clean_lines, clean_pts = blueprint.generate() + +mesher = MeshGenerator(background_lc=background_lc, verbosity=5) +mesher.generate(clean_polys, clean_lines, clean_pts, launch_gmsh_gui=True) + +tessellator = VoronoiTessellator(mesher, blueprint, clip_to_boundary=True) +grid_gdf = tessellator.generate() + + +#%% +# 4) Visual sanity-check + +fig, ax = plt.subplots(1, 1, figsize=(14, 7)) +ax.set_aspect("equal") + +# Domain +ax.plot(*domain.exterior.xy, color="black", lw=1) + +# Polygons +plot_polys = polys +poly_colors = { + "upper-left": "tab:orange", + "upper-center": "tab:green", + "upper-right": "tab:blue", + "lower-left": "tab:purple", + "lower-center": "tab:brown", + "lower-right": "tab:pink", +} +field_only_polys = {"upper-center", "lower-left"} +for name, poly in plot_polys.items(): + is_field_only = name in field_only_polys + label = f"{name} (field-only)" if is_field_only else name + ax.plot( + *poly.exterior.xy, + lw=1, + ls=":" if is_field_only else "--", + color=poly_colors.get(name), + label=label, + ) + +# Lines +plot_faults = { + "fault-left": faults["fault-left"], + "fault-center": faults["fault-center"], + "fault-right": faults["fault-right"], +} +line_colors = { + "fault-left": "tab:red", + "fault-center": "tab:purple", + "fault-right": "tab:blue", + "river-center-up": "tab:cyan", + "river-center-down": "tab:cyan", +} +for name, ln in plot_faults.items(): + ax.plot(*ln.xy, lw=1, color=line_colors.get(name), label=name) +ax.plot( + *rivers["river-center-up"].xy, + lw=1, + color=line_colors["river-center-up"], + label="river-center-up", +) +ax.plot( + *rivers["river-center-down"].xy, + lw=1, + color=line_colors["river-center-down"], + label="river-center-down (field-only)", +) + +# Points +point_colors = { + "pt-lower-left": "tab:red", + "pt-lower-center": "tab:purple", + "pt-lower-right": "tab:blue", +} +for name, pt in points.items(): + ax.scatter(pt.x, pt.y, s=25, marker="x", color=point_colors.get(name), label=name) + +grid_gdf.plot(ax=ax, alpha=0.35, edgecolor="k", linewidth=0.15) +ax.legend(loc="upper right", fontsize=7, ncol=2) +fig.tight_layout() +plt.show() + + +#%% +# Quick check: smaller cells => smaller polygon areas (proxy for refinement) + +grid_gdf2 = grid_gdf.copy() +grid_gdf2["area"] = grid_gdf2.geometry.area + +fig, ax = plt.subplots(1, 1, figsize=(14, 6)) +ax.set_aspect("equal") +ax.plot(*domain.exterior.xy, color="black", lw=1) +grid_gdf2.plot(ax=ax, column="area", cmap="viridis", legend=True, linewidth=0.0) +ax.set_title("Voronoi cell area (proxy for refinement)") +fig.tight_layout() +plt.show() + +# %% diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py index baf534e..8d7acc6 100644 --- a/src/vorflow/engine.py +++ b/src/vorflow/engine.py @@ -752,11 +752,90 @@ def generate(self, clean_polys, clean_lines, clean_points, output_file=None, lau if output_file: gmsh.write(output_file) - - node_tags, coords, _ = gmsh.model.mesh.getNodes() - nodes_3d = np.array(coords).reshape(-1, 3) - self.nodes = nodes_3d[:, :2] - self.node_tags = node_tags + + # --- Node extraction (domain-only) --- + # Do NOT use gmsh.model.mesh.getNodes() without args here. + # That returns nodes from all entities, including standalone 1D meshes + # on curves (e.g. field-only rivers) and any non-fragmented 2D surfaces. + # Those extra nodes can unintentionally constrain downstream Voronoi + # tessellation. + + def _is_embedded_row(row) -> bool: + val = row.get('embed', True) + if pd.isna(val): + return True + return bool(val) + + def _accumulate_nodes(dim: int, ent_tag: int, include_boundary: bool, tag_to_xy: dict[int, tuple[float, float]]): + nt, nc, _ = gmsh.model.mesh.getNodes(dim, int(ent_tag), includeBoundary=bool(include_boundary)) + if len(nt) == 0: + return + pts = np.array(nc, dtype=float).reshape(-1, 3) + for t, p in zip(nt, pts): + tt = int(t) + if tt not in tag_to_xy: + tag_to_xy[tt] = (float(p[0]), float(p[1])) + + tag_to_xy: dict[int, tuple[float, float]] = {} + + # 1) Domain surfaces: embedded polygons only + domain_surface_tags: list[int] = [] + if clean_polys is not None and not clean_polys.empty and 'embed' in clean_polys.columns: + embedded_poly_ids = [int(i) for i, r in clean_polys.iterrows() if _is_embedded_row(r)] + elif clean_polys is not None and not clean_polys.empty: + # Historical behavior: polygons were all embedded. + embedded_poly_ids = [int(i) for i in clean_polys.index] + else: + embedded_poly_ids = [] + + for fid in embedded_poly_ids: + if fid in gmsh_map.get('surfaces', {}): + for dimtag in gmsh_map['surfaces'][fid]: + if isinstance(dimtag, (tuple, list)) and len(dimtag) >= 2 and int(dimtag[0]) == 2: + domain_surface_tags.append(int(dimtag[1])) + + # If we cannot determine domain surfaces from the map, fall back to + # all 2D nodes (still avoids 1D-only nodes). + if not domain_surface_tags: + node_tags, coords, _ = gmsh.model.mesh.getNodes(2, -1, includeBoundary=True) + nodes_3d = np.array(coords, dtype=float).reshape(-1, 3) + self.nodes = nodes_3d[:, :2] + self.node_tags = node_tags + else: + for s in domain_surface_tags: + _accumulate_nodes(2, s, True, tag_to_xy) + + # 2) Embedded constraints (optional safety) + if clean_points is not None and not clean_points.empty and 'embed' in clean_points.columns: + for fid, row in clean_points.iterrows(): + if not _is_embedded_row(row): + continue + if int(fid) in gmsh_map.get('points', {}): + for dimtag in gmsh_map['points'][int(fid)]: + if isinstance(dimtag, (tuple, list)) and len(dimtag) >= 2 and int(dimtag[0]) == 0: + _accumulate_nodes(0, int(dimtag[1]), True, tag_to_xy) + + if clean_lines is not None and not clean_lines.empty and 'embed' in clean_lines.columns: + for fid, row in clean_lines.iterrows(): + if not _is_embedded_row(row): + continue + + if int(fid) in gmsh_map.get('lines', {}): + for dimtag in gmsh_map['lines'][int(fid)]: + if isinstance(dimtag, (tuple, list)) and len(dimtag) >= 2 and int(dimtag[0]) == 1: + _accumulate_nodes(1, int(dimtag[1]), True, tag_to_xy) + # Straddle/barrier lines may have been converted into points. + elif int(fid) in gmsh_map.get('points', {}): + for dimtag in gmsh_map['points'][int(fid)]: + if isinstance(dimtag, (tuple, list)) and len(dimtag) >= 2 and int(dimtag[0]) == 0: + _accumulate_nodes(0, int(dimtag[1]), True, tag_to_xy) + + # Finalize de-duplicated node arrays + node_tags = np.array(list(tag_to_xy.keys()), dtype=np.uint64) + nodes_xy = np.array([tag_to_xy[int(t)] for t in node_tags], dtype=float) + self.nodes = nodes_xy + self.node_tags = node_tags + self.zones_gdf = clean_polys if launch_gmsh_gui: gmsh.fltk.run() From 87ca5b1766ae58e4f8faf5346fd2bdc2cbf62fbe Mon Sep 17 00:00:00 2001 From: oscarfasanchez Date: Fri, 9 Jan 2026 08:38:04 +0800 Subject: [PATCH 16/26] typo fix --- examples/field_capabilities_example.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/field_capabilities_example.py b/examples/field_capabilities_example.py index 4555a97..4f0ff0d 100644 --- a/examples/field_capabilities_example.py +++ b/examples/field_capabilities_example.py @@ -125,7 +125,7 @@ blueprint.add_polygon( - polys["lower-left"].boundary(),#TODO change to boundary only + polys["lower-left"].boundary, #TODO change to boundary only zone_id="lower-left", resolution=feature_lc/5, z_order=5, From 137706e4d38111597601e1d024444bac803c2c1b Mon Sep 17 00:00:00 2001 From: oscar Date: Mon, 12 Jan 2026 08:59:36 +0800 Subject: [PATCH 17/26] simplifying examples to identify problems --- examples/field_capabilities_example.py | 59 ++++---------------------- 1 file changed, 9 insertions(+), 50 deletions(-) diff --git a/examples/field_capabilities_example.py b/examples/field_capabilities_example.py index 4555a97..808520e 100644 --- a/examples/field_capabilities_example.py +++ b/examples/field_capabilities_example.py @@ -54,13 +54,6 @@ "lower-right": lr, } -# Base line geometry (simplified) -fault_base = LineString([(100, 0), (100, 200)]) -faults = { - "fault-left": translate(fault_base, xoff=-40, yoff=0), - "fault-center": translate(fault_base, xoff=80, yoff=0), - "fault-right": translate(fault_base, xoff=200, yoff=0), -} # A gentle sine-wave river xs = np.linspace(-10, 430, 45) river_y = 140 + 18 * np.sin(0.06 * xs) @@ -102,8 +95,8 @@ zone_id="upper-left", resolution=feature_lc/5, z_order=10, - dist_min=feature_lc, - dist_max=background_lc * 3.0, + dist_min=feature_lc/2, + dist_max=background_lc * 5.0, ) blueprint.add_polygon( @@ -124,12 +117,12 @@ ) -blueprint.add_polygon( - polys["lower-left"].boundary(),#TODO change to boundary only - zone_id="lower-left", +blueprint.add_line( + polys["lower-left"].boundary,#TODO change to boundary only + line_id='lower-left',# zone_id="lower-left", resolution=feature_lc/5, - z_order=5, - dist_min=feature_lc, + # z_order=5, + dist_min=feature_lc/2, dist_max=background_lc * 1.5, fields=[auto_linear], embed=False, # field-only polygon @@ -149,34 +142,7 @@ resolution=feature_lc/5, z_order=5, fields=[threshold], -) - -# Lines (IDs are location-based) -# Colors: fault-left -> 'tab:red', fault-center -> 'tab:purple', fault-right -> 'tab:blue', river-center -> 'tab:cyan' -blueprint.add_line( - faults["fault-left"], - line_id="fault-left", - resolution=feature_lc, - is_barrier=False, - dist_min=feature_lc, - dist_max=background_lc * 5.0, -) - -blueprint.add_line( - faults["fault-center"], - line_id="fault-center", - resolution=feature_lc, - is_barrier=True, - straddle_width=4.0, - fields=[auto_exp], -) - -blueprint.add_line( - faults["fault-right"], - line_id="fault-right", - resolution=feature_lc, - is_barrier=False, - fields=[auto_linear], + embed=False, # field-only polygon ) blueprint.add_line( @@ -261,12 +227,6 @@ label=label, ) -# Lines -plot_faults = { - "fault-left": faults["fault-left"], - "fault-center": faults["fault-center"], - "fault-right": faults["fault-right"], -} line_colors = { "fault-left": "tab:red", "fault-center": "tab:purple", @@ -274,8 +234,7 @@ "river-center-up": "tab:cyan", "river-center-down": "tab:cyan", } -for name, ln in plot_faults.items(): - ax.plot(*ln.xy, lw=1, color=line_colors.get(name), label=name) + ax.plot( *rivers["river-center-up"].xy, lw=1, From b78533002b5b175de9f86d416983bbec4dbb38dc Mon Sep 17 00:00:00 2001 From: oscar Date: Mon, 12 Jan 2026 16:30:21 +0800 Subject: [PATCH 18/26] added explicit embedding, which doesnt work yet --- src/vorflow/engine.py | 127 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 126 insertions(+), 1 deletion(-) diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py index 8d7acc6..670aae9 100644 --- a/src/vorflow/engine.py +++ b/src/vorflow/engine.py @@ -354,7 +354,10 @@ def create_loop(coords): nonembedded_poly_curve_tags.setdefault(int(idx), []).extend( [(1, int(t)) for t in boundary_curve_tags] ) - + #call the gui before fragmentation for debugging + if self.verbosity > 1: + gmsh.model.occ.synchronize() + gmsh.fltk.run() # "Fragment" combines all the individual geometries into a single, # topologically consistent model. This is where intersections are @@ -692,6 +695,125 @@ def _auto_threshold_from_row(row, background_lc): gmsh.option.setNumber("Mesh.MeshSizeFromCurvature", 0) + def _embed_features(self, gmsh_map, polygons_gdf, lines_gdf, points_gdf): + """ + Explicitly embeds features into domain surfaces to ensure mesh conformity. + + This handles cases where fragmentation splits surfaces, requiring + geometric discovery to find the correct surface for points/lines. + """ + if self.verbosity > 0: + print("Explicitly embedding features into domain surfaces...") + + def is_embedded(row): + val = row.get('embed', True) + if pd.isna(val): return True + return bool(val) + + # 1. Collect Domain Surfaces (Candidate Pool) + domain_surface_tags = set() + if not polygons_gdf.empty: + for idx, row in polygons_gdf.iterrows(): + if is_embedded(row) and idx in gmsh_map.get('surfaces', {}): + for dt in gmsh_map['surfaces'][idx]: + # Ensure we are tracking actual surfaces (dim=2) + if isinstance(dt, (tuple, list)) and len(dt) >= 2 and dt[0] == 2: + domain_surface_tags.add(dt[1]) + + if not domain_surface_tags: + return + + # Helper for geometric embedding search and application + def embed_entity(dim, tag): + # 1. Get Bounding Box + try: + bbox = gmsh.model.getBoundingBox(dim, tag) + except Exception: + return # Entity might not exist or be invalid + + xmin, ymin, zmin, xmax, ymax, zmax = bbox + + # Expand slightly to find touching surfaces + eps = 1e-4 + + # 2. Find Candidate Surfaces + candidates = gmsh.model.getEntitiesInBoundingBox( + xmin - eps, ymin - eps, zmin - eps, + xmax + eps, ymax + eps, zmax + eps, + dim=2 + ) + + # 3. Filter candidates to only include our domain surfaces + valid_candidates = [ + c[1] for c in candidates + if c[0] == 2 and c[1] in domain_surface_tags + ] + + if not valid_candidates: + return + + # 4. Correctness Check: Verify entity is actually on the surface + target_matches = [] + + check_x, check_y, check_z = 0.0, 0.0, 0.0 + + if dim == 0: # Point + # For a point, the bbox center is the point + check_x = (xmin + xmax) / 2.0 + check_y = (ymin + ymax) / 2.0 + check_z = (zmin + zmax) / 2.0 + elif dim == 1: # Line + # Use Midpoint for valid check (avoid endpoints that might touch multiple surfaces) + pmin, pmax = gmsh.model.getParametrizationBounds(1, tag) + pmid = (pmin + pmax) / 2.0 + val = gmsh.model.getValue(1, tag, [pmid]) + check_x, check_y, check_z = val[0], val[1], val[2] + + for surf_tag in valid_candidates: + # Project testing point to surface + cp_coords, _ = gmsh.model.getClosestPoint(2, surf_tag, [check_x, check_y, check_z]) + + # Calculate Euclidean distance + dist = math.sqrt( + (check_x - cp_coords[0])**2 + + (check_y - cp_coords[1])**2 + + (check_z - cp_coords[2])**2 + ) + + if dist < 1e-6: + target_matches.append(surf_tag) + + # 5. Embed the entity into the verified surfaces + if target_matches: + # Deduplicate tags + target_matches = list(set(target_matches)) + for st in target_matches: + gmsh.model.mesh.embed(dim, [tag], 2, st) + + # Iterate and Embed Points + if points_gdf is not None and not points_gdf.empty: + for idx, row in points_gdf.iterrows(): + if is_embedded(row) and idx in gmsh_map.get('points', {}): + for dt in gmsh_map['points'][idx]: + if dt[0] == 0: + embed_entity(0, dt[1]) + + # Iterate and Embed Lines + if lines_gdf is not None and not lines_gdf.empty: + for idx, row in lines_gdf.iterrows(): + if is_embedded(row): + # Standard Lines + if idx in gmsh_map.get('lines', {}): + for dt in gmsh_map['lines'][idx]: + if dt[0] == 1: + embed_entity(1, dt[1]) + # Barrier/Straddle Points (these are points derived from lines) + if idx in gmsh_map.get('points', {}): + for dt in gmsh_map['points'][idx]: + if dt[0] == 0: + embed_entity(0, dt[1]) + + def generate(self, clean_polys, clean_lines, clean_points, output_file=None, launch_gmsh_gui=False): """ Executes the full mesh generation workflow. @@ -724,6 +846,9 @@ def generate(self, clean_polys, clean_lines, clean_points, output_file=None, lau print("Transferring Geometry to Gmsh...") gmsh_map = self._add_geometry(clean_polys, clean_lines, clean_points) + # Ensure features are correctly embedded in surfaces before meshing + self._embed_features(gmsh_map, clean_polys, clean_lines, clean_points) + print("Setting up Resolution Fields...") self._setup_fields(gmsh_map, clean_polys, clean_lines, clean_points) From 678edccb641f9b33982c2be2039a24b3a1aee43f Mon Sep 17 00:00:00 2001 From: oscar Date: Thu, 19 Feb 2026 14:56:44 +0800 Subject: [PATCH 19/26] solver silly bug with the conflict in field example --- examples/field_capabilities_example.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/examples/field_capabilities_example.py b/examples/field_capabilities_example.py index 5f5ca10..808520e 100644 --- a/examples/field_capabilities_example.py +++ b/examples/field_capabilities_example.py @@ -117,15 +117,9 @@ ) -<<<<<<< HEAD blueprint.add_line( polys["lower-left"].boundary,#TODO change to boundary only line_id='lower-left',# zone_id="lower-left", -======= -blueprint.add_polygon( - polys["lower-left"].boundary, #TODO change to boundary only - zone_id="lower-left", ->>>>>>> 87ca5b1766ae58e4f8faf5346fd2bdc2cbf62fbe resolution=feature_lc/5, # z_order=5, dist_min=feature_lc/2, From 310c3e709730be69cbaf129bbc765103c63f9e96 Mon Sep 17 00:00:00 2001 From: oscar Date: Thu, 19 Feb 2026 17:28:30 +0800 Subject: [PATCH 20/26] solved bug that caused after fragmentation embedding not to work --- src/vorflow/engine.py | 269 ++++++++++++++++++++++++++++++++++-------- 1 file changed, 223 insertions(+), 46 deletions(-) diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py index 670aae9..faad401 100644 --- a/src/vorflow/engine.py +++ b/src/vorflow/engine.py @@ -359,6 +359,17 @@ def create_loop(coords): gmsh.model.occ.synchronize() gmsh.fltk.run() + # >>> DIAG: Pre-fragment inventory (summary) + if self.verbosity >= 2: + _line_feats = sorted(set( + input_tag_info.get(to_key(dt[0], dt[1]), {}).get('id', '?') + for dt in embedded_line_tags + )) if embedded_line_tags else [] + print(f"\n[DIAG] Pre-fragment: {len(embedded_surface_tags)} surfs, " + f"{len(embedded_line_tags)} lines, {len(embedded_point_tags)} pts " + f"| line features: {_line_feats}") + # <<< DIAG + # "Fragment" combines all the individual geometries into a single, # topologically consistent model. This is where intersections are # calculated and new, smaller entities are created at overlaps. @@ -378,6 +389,60 @@ def create_loop(coords): print(f"Fragmenting {len(object_tags)} objects...") out_dt, out_map = gmsh.model.occ.fragment(object_tags, []) gmsh.model.occ.synchronize() + + # >>> DIAG: Post-fragment summary + if self.verbosity >= 2: + all_surfs_post = gmsh.model.getEntities(2) + all_lines_post = gmsh.model.getEntities(1) + all_pts_post = gmsh.model.getEntities(0) + + # Classify line fragments: boundary vs interior vs orphan + _n_boundary, _n_interior, _n_orphan, _n_dim0 = 0, 0, 0, 0 + _boundary_feats = set() # feature names whose lines became boundaries + for i, input_dimtag in enumerate(object_tags): + key = to_key(input_dimtag[0], input_dimtag[1]) + info = input_tag_info.get(key, {}) + if info.get('type') != 'line': + continue + res = out_map[i] if i < len(out_map) else [input_dimtag] + for dt in res: + dim_r, tag_r = int(dt[0]), int(dt[1]) + if dim_r == 0: + _n_dim0 += 1 + continue + try: + gmsh.model.getBoundingBox(dim_r, tag_r) + up, _ = gmsh.model.getAdjacencies(1, tag_r) + if len(up) > 0: + _n_boundary += 1 + _boundary_feats.add(info.get('id', '?')) + else: + _n_interior += 1 + except Exception: + _n_orphan += 1 + + # Count auto-embeddings + _n_auto = sum( + 1 for s in all_surfs_post + if (lambda: (gmsh.model.mesh.getEmbedded(2, s[1]) or None) is not None)() + ) if False else 0 # placeholder + _n_auto = 0 + for s in all_surfs_post: + try: + if gmsh.model.mesh.getEmbedded(2, s[1]): + _n_auto += 1 + except Exception: + pass + + print(f"[DIAG] Post-fragment: {len(all_surfs_post)} surfs, " + f"{len(all_lines_post)} lines, {len(all_pts_post)} pts") + print(f"[DIAG] Line fragments: {_n_interior} interior, " + f"{_n_boundary} BOUNDARY, {_n_orphan} orphan, " + f"{_n_dim0} became-points | auto-embed surfs: {_n_auto}") + if _boundary_feats: + print(f"[DIAG] *** Lines from these features became BOUNDARIES: " + f"{sorted(_boundary_feats)} ***") + # <<< DIAG # After fragmentation, we need to rebuild our map of which original # feature corresponds to which new Gmsh tags. @@ -720,75 +785,130 @@ def is_embedded(row): if isinstance(dt, (tuple, list)) and len(dt) >= 2 and dt[0] == 2: domain_surface_tags.add(dt[1]) + # >>> DIAG: domain surface collection summary + if self.verbosity >= 2: + _gdf_idxs = list(polygons_gdf.index) if not polygons_gdf.empty else [] + _map_keys = list(gmsh_map.get('surfaces', {}).keys()) + _matching = [i for i in _gdf_idxs if i in gmsh_map.get('surfaces', {})] + print(f"[DIAG] Embed pool: GDF indices={_gdf_idxs}, map keys={_map_keys}, " + f"matched={len(_matching)}, domain_surface_tags={sorted(domain_surface_tags)}") + # Dump bbox of ALL surfaces - shows which surfaces cover which area + _all_surfs = gmsh.model.getEntities(2) + for _s in _all_surfs: + _in_pool = "POOL" if _s[1] in domain_surface_tags else "----" + try: + _sbb = gmsh.model.getBoundingBox(2, _s[1]) + print(f"[DIAG] surf {_s[1]:3d} [{_in_pool}] " + f"x=[{_sbb[0]:7.1f},{_sbb[3]:7.1f}] " + f"y=[{_sbb[1]:7.1f},{_sbb[4]:7.1f}]") + except Exception: + print(f"[DIAG] surf {_s[1]:3d} [{_in_pool}] bbox FAILED") + # Which feature id maps to which surface tags? + for _feat_id, _dts in gmsh_map.get('surfaces', {}).items(): + _stags = [int(dt[1]) for dt in _dts if isinstance(dt, (tuple,list)) and dt[0]==2] + print(f"[DIAG] map[surfaces][{_feat_id}] -> tags {_stags}") + # <<< DIAG + if not domain_surface_tags: return - # Helper for geometric embedding search and application + # >>> DIAG: Accumulator for embed summary + _elog = {'ok': 0, 'conflict': 0, 'skip_bbox': 0, 'skip_no_cand': 0, + 'skip_no_match': 0, 'failed': 0, + 'conflict_tags': [], 'fail_tags': []} + # <<< DIAG + + # Helper for geometric embedding search and application. + # We pre-compute surface bboxes for a fast spatial filter, then confirm + # with getClosestPoint only on candidates whose bbox contains the entity. + # This replaces getEntitiesInBoundingBox which requires containment + # (not intersection) and fails for small entities inside large surfaces. + _surf_bboxes = {} + for _st in domain_surface_tags: + try: + _bb = gmsh.model.getBoundingBox(2, _st) + _surf_bboxes[_st] = _bb # (xmin, ymin, zmin, xmax, ymax, zmax) + except Exception: + pass + def embed_entity(dim, tag): - # 1. Get Bounding Box + # 1. Verify entity exists try: bbox = gmsh.model.getBoundingBox(dim, tag) except Exception: - return # Entity might not exist or be invalid + _elog['skip_bbox'] += 1 + return xmin, ymin, zmin, xmax, ymax, zmax = bbox - # Expand slightly to find touching surfaces - eps = 1e-4 - - # 2. Find Candidate Surfaces - candidates = gmsh.model.getEntitiesInBoundingBox( - xmin - eps, ymin - eps, zmin - eps, - xmax + eps, ymax + eps, zmax + eps, - dim=2 - ) - - # 3. Filter candidates to only include our domain surfaces - valid_candidates = [ - c[1] for c in candidates - if c[0] == 2 and c[1] in domain_surface_tags - ] - - if not valid_candidates: - return - - # 4. Correctness Check: Verify entity is actually on the surface - target_matches = [] - + # Check if line is already a boundary of some surface + is_boundary_of = set() + if dim == 1: + try: + up, _down = gmsh.model.getAdjacencies(1, tag) + is_boundary_of = set(up) + except Exception: + pass + + # 2. Get a representative point from the entity check_x, check_y, check_z = 0.0, 0.0, 0.0 - - if dim == 0: # Point - # For a point, the bbox center is the point + if dim == 0: check_x = (xmin + xmax) / 2.0 check_y = (ymin + ymax) / 2.0 check_z = (zmin + zmax) / 2.0 - elif dim == 1: # Line - # Use Midpoint for valid check (avoid endpoints that might touch multiple surfaces) + elif dim == 1: pmin, pmax = gmsh.model.getParametrizationBounds(1, tag) - pmid = (pmin + pmax) / 2.0 + pmid = (float(pmin[0]) + float(pmax[0])) / 2.0 val = gmsh.model.getValue(1, tag, [pmid]) check_x, check_y, check_z = val[0], val[1], val[2] - for surf_tag in valid_candidates: - # Project testing point to surface - cp_coords, _ = gmsh.model.getClosestPoint(2, surf_tag, [check_x, check_y, check_z]) - - # Calculate Euclidean distance - dist = math.sqrt( - (check_x - cp_coords[0])**2 + - (check_y - cp_coords[1])**2 + - (check_z - cp_coords[2])**2 - ) - - if dist < 1e-6: - target_matches.append(surf_tag) + # 3. Fast bbox pre-filter: only test surfaces whose bbox contains the check point + candidates = [] + eps = 1e-4 + for surf_tag, sbb in _surf_bboxes.items(): + if (sbb[0] - eps <= check_x <= sbb[3] + eps and + sbb[1] - eps <= check_y <= sbb[4] + eps): + candidates.append(surf_tag) + + if not candidates: + _elog['skip_no_match'] += 1 + return + + # 4. Confirm with getClosestPoint (only on bbox-filtered candidates) + target_matches = [] + for surf_tag in candidates: + # Skip if entity is already a boundary of this surface + if surf_tag in is_boundary_of: + continue + try: + cp_coords, _ = gmsh.model.getClosestPoint(2, surf_tag, [check_x, check_y, check_z]) + dist = math.sqrt( + (check_x - cp_coords[0])**2 + + (check_y - cp_coords[1])**2 + + (check_z - cp_coords[2])**2 + ) + if dist < 1e-6: + target_matches.append(surf_tag) + except Exception: + pass # 5. Embed the entity into the verified surfaces if target_matches: - # Deduplicate tags target_matches = list(set(target_matches)) for st in target_matches: - gmsh.model.mesh.embed(dim, [tag], 2, st) + try: + gmsh.model.mesh.embed(dim, [tag], 2, st) + _elog['ok'] += 1 + except Exception as e: + _elog['failed'] += 1 + _elog['fail_tags'].append((tag, str(e)[:60])) + else: + # All candidates were boundaries or didn't match + if is_boundary_of & set(candidates): + _elog['conflict'] += 1 + _elog['conflict_tags'].append(tag) + else: + _elog['skip_no_match'] += 1 # Iterate and Embed Points if points_gdf is not None and not points_gdf.empty: @@ -813,6 +933,27 @@ def embed_entity(dim, tag): if dt[0] == 0: embed_entity(0, dt[1]) + # >>> DIAG: Embed summary + if self.verbosity >= 2: + _filt = _elog.get('skip_filtered', 0) + print(f"[DIAG] Embed results: {_elog['ok']} OK, " + f"{_elog['conflict']} boundary-conflicts, " + f"{_elog['failed']} failed, " + f"{_elog['skip_bbox']} no-bbox, " + f"{_elog['skip_no_cand']} empty-bbox, " + f"{_filt} filtered-out, " + f"{_elog['skip_no_match']} no-match") + if _filt > 0: + print(f"[DIAG] *** {_filt} entities found nearby surfaces but NONE " + f"were in domain_surface_tags — likely missing domain surface! ***") + if _elog['conflict_tags']: + uniq = sorted(set(_elog['conflict_tags'])) + print(f"[DIAG] *** {len(uniq)} unique line tags had BOUNDARY CONFLICTS " + f"(first 10): {uniq[:10]} ***") + if _elog['fail_tags']: + print(f"[DIAG] *** Failed embeds: {_elog['fail_tags'][:5]} ***") + # <<< DIAG + def generate(self, clean_polys, clean_lines, clean_points, output_file=None, launch_gmsh_gui=False): """ @@ -848,6 +989,42 @@ def generate(self, clean_polys, clean_lines, clean_points, output_file=None, lau # Ensure features are correctly embedded in surfaces before meshing self._embed_features(gmsh_map, clean_polys, clean_lines, clean_points) + + # >>> DIAG: Post-embed summary + if self.verbosity >= 2: + all_surfs = gmsh.model.getEntities(2) + _with_emb, _without_emb, _total_emb = 0, 0, 0 + for surf_dt in all_surfs: + try: + emb = gmsh.model.mesh.getEmbedded(2, surf_dt[1]) + if emb: + _with_emb += 1 + _total_emb += len(emb) + else: + _without_emb += 1 + except Exception: + _without_emb += 1 + # Count line boundary vs interior in gmsh_map + _map_bnd, _map_int, _map_miss = 0, 0, 0 + for feat_id, dimtags in gmsh_map.get('lines', {}).items(): + for dt in dimtags: + if not (isinstance(dt, (tuple, list)) and len(dt) >= 2): + continue + if int(dt[0]) != 1: + continue + try: + up, _ = gmsh.model.getAdjacencies(1, int(dt[1])) + if len(up) > 0: + _map_bnd += 1 + else: + _map_int += 1 + except Exception: + _map_miss += 1 + print(f"[DIAG] Post-embed: {_with_emb}/{len(all_surfs)} surfaces have embeddings " + f"({_total_emb} total entities) | " + f"{_without_emb} surfaces empty") + print(f"[DIAG] Line map: {_map_int} interior, {_map_bnd} boundary, {_map_miss} missing") + # <<< DIAG print("Setting up Resolution Fields...") self._setup_fields(gmsh_map, clean_polys, clean_lines, clean_points) From 0de27971bc87fa5c823743ed5920e966ccc380f3 Mon Sep 17 00:00:00 2001 From: oscar Date: Fri, 20 Feb 2026 14:00:11 +0800 Subject: [PATCH 21/26] asume surfaces are used for the field generation to solve bug about exponential field not being applied --- src/vorflow/fields.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/vorflow/fields.py b/src/vorflow/fields.py index fd358dc..7e47bee 100644 --- a/src/vorflow/fields.py +++ b/src/vorflow/fields.py @@ -101,7 +101,7 @@ def __init__(self, size_min, decay_length, size_max=None, sampling=20): self.sampling = int(sampling) def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None): - f_dist = DistanceField(include_surfaces=False, sampling=self.sampling).create( + f_dist = DistanceField(include_surfaces=True, sampling=self.sampling).create( gmsh_api, tags_dict ) if f_dist is None: @@ -155,7 +155,7 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=1 if fac <= 1.0: raise ValueError("Growth factor must be > 1.0") - f_dist = DistanceField(include_surfaces=False, sampling=int(sampling)).create( + f_dist = DistanceField(include_surfaces=True, sampling=int(sampling)).create( gmsh_api, tags_dict ) if f_dist is None: From 6be208ac5fcb8bf396cd432413da27e7b89e6e8a Mon Sep 17 00:00:00 2001 From: oscar Date: Fri, 20 Feb 2026 15:52:33 +0800 Subject: [PATCH 22/26] fixed how the gmsh gui is displayed for debugging --- src/vorflow/engine.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py index faad401..bea9ee5 100644 --- a/src/vorflow/engine.py +++ b/src/vorflow/engine.py @@ -75,7 +75,7 @@ def _finalize_gmsh(self): gmsh.finalize() self.initialized = False - def _add_geometry(self, polygons_gdf, lines_gdf, points_gdf): + def _add_geometry(self, polygons_gdf, lines_gdf, points_gdf, launch_gmsh_gui=False): """ Transfers Shapely geometries from GeoDataFrames into the Gmsh model. @@ -355,7 +355,7 @@ def create_loop(coords): [(1, int(t)) for t in boundary_curve_tags] ) #call the gui before fragmentation for debugging - if self.verbosity > 1: + if self.verbosity > 1 and launch_gmsh_gui==True: gmsh.model.occ.synchronize() gmsh.fltk.run() @@ -985,7 +985,7 @@ def generate(self, clean_polys, clean_lines, clean_points, output_file=None, lau self._initialize_gmsh() try: print("Transferring Geometry to Gmsh...") - gmsh_map = self._add_geometry(clean_polys, clean_lines, clean_points) + gmsh_map = self._add_geometry(clean_polys, clean_lines, clean_points, launch_gmsh_gui=launch_gmsh_gui) # Ensure features are correctly embedded in surfaces before meshing self._embed_features(gmsh_map, clean_polys, clean_lines, clean_points) From 7711696ed1d12fd2cde9c0574be86aa5aa59e06c Mon Sep 17 00:00:00 2001 From: oscar Date: Fri, 20 Feb 2026 16:21:55 +0800 Subject: [PATCH 23/26] updating examples with the new field capabilities --- examples/basic_example.ipynb | 107 ++++++++++++++++++++++--- examples/comprehensive_demo.ipynb | 6 +- examples/field_capabilities_example.py | 23 ++++-- 3 files changed, 113 insertions(+), 23 deletions(-) diff --git a/examples/basic_example.ipynb b/examples/basic_example.ipynb index 2385cd7..2726e6c 100644 --- a/examples/basic_example.ipynb +++ b/examples/basic_example.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "f2412c60", "metadata": {}, "outputs": [], @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "96e8fbed", "metadata": {}, "outputs": [], @@ -38,11 +38,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "c970674e", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "river_coords = [(x, 165 + 20 * 1) for x in range(50, 150, 10)]\n", + "river_coords = LineString(river_coords)" + ] }, { "cell_type": "code", @@ -57,7 +60,63 @@ "execution_count": null, "id": "71f205cc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applying optional geometry simplification...\n", + "Resolving polygon overlaps...\n", + "Enforcing strict topology...\n", + "Snapping 2 lines to polygon boundaries (tol=0.001)...\n", + "Snapping 1 points to geometry (tol=0.001)...\n", + "Densifying geometry...\n", + "Transferring Geometry to Gmsh...\n", + "Constructed Barrier Zone from 1 barriers.\n", + "Adding 2 polygons to Gmsh...\n", + "Fragmenting 2 surfaces with 393 tools...\n", + "Reconstructing Map (Input Tags: 395, Out Map Len: 395)...\n", + "Setting up Resolution Fields...\n", + "--- Setup Fields Debug ---\n", + "Polygons GDF: 2 rows\n", + "Gmsh Surface Map: 2 entries\n", + "First Poly Index: 1 (Type: )\n", + "First Map Key: 1 (Type: )\n", + "Match? True\n", + "Gmsh Map Keys: points=[0], lines=[1], surfaces=[1, 0]\n", + " line key=1 -> raw tags=[(1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (1, 11), (1, 12), (1, 13), (1, 14), (1, 15), (1, 16), (1, 17), (1, 18), (1, 19), (1, 20), (1, 21), (1, 22), (1, 23), (1, 24), (1, 25), (1, 26), (1, 27), (1, 28), (1, 29), (1, 30), (1, 31), (1, 32), (1, 33), (1, 34), (1, 35), (1, 36), (1, 37), (1, 38), (1, 39), (1, 40), (1, 41), (1, 42), (1, 43), (1, 44), (1, 45), (1, 46), (1, 47), (1, 48), (1, 49), (1, 50), (1, 51), (1, 52), (1, 53), (1, 54), (1, 55), (1, 56), (1, 57), (1, 58), (1, 59), (1, 60), (1, 61), (1, 62), (1, 63), (1, 64), (1, 65), (1, 66), (1, 67), (1, 68), (1, 69), (1, 70), (1, 71), (1, 72), (1, 73), (1, 74), (1, 75), (1, 76), (1, 77), (1, 78), (1, 79), (1, 80), (1, 81), (1, 82), (1, 83), (1, 84), (1, 85), (1, 86), (1, 87), (1, 88), (1, 89), (1, 90)]\n", + "FieldGraph: line 1 sizing uses curves=90 points=91 lc=1.0 dist_min=80.0 dist_max=15.0\n", + "FieldGraph: 5 groups -> 6 fields (plus Min).\n", + "Generating Triangular Mesh...\n", + "TriStats Line 0: 8312 tris inside buffer (mean area=0.5836319070268174 , min=0.09999591689348933) ; outside mean=0.7999829662651772\n", + "TriStats Line 1: 45 tris inside buffer (mean area=79.21515223136574 , min=14.484696083358267) ; outside mean=0.6979411184887853\n", + "Running 2 Optimization Cycles (Relocate2D & Laplace2D)...\n", + " -> Cycle 1/2\n", + " -> Cycle 2/2\n", + "Extracting 26760 Nodes from Gmsh...\n", + "Computing Mathematical Voronoi...\n", + " -> Raw Polygons: 26760\n", + " -> After Ghost Filter: 26760\n", + "Clipping to Domain Boundary...\n", + " -> After Domain Clip: 26760\n", + "Enforcing Hydrogeological Zones (Optimization: Point Sampling)...\n", + " -> Zones Assigned: 26760\n", + "Enforcing Barrier Cuts on 1 lines (Straddle lines skipped)...\n", + " -> After Barrier Cuts: 26898\n", + "Final Voronoi Grid Generated: 26898 cells.\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# 1. Setup Blueprint\n", "background_lc=100\n", @@ -66,8 +125,8 @@ "blueprint.add_polygon(domain, zone_id=1)#,border_density=100,dist_max_in=background_lc)\n", "blueprint.add_polygon(poly1, zone_id=2, \n", " resolution=1, z_order=2,\n", - " dist_max_out=3* background_lc,\n", - " #dist_max_in=10\n", + " dist_max=3* background_lc,\n", + " \n", " ) \n", "blueprint.add_point(well_point, point_id=\"Well-A\", \n", " resolution=2,\n", @@ -80,12 +139,13 @@ "blueprint.add_line(river_coords, line_id=\"riv-1\", \n", " resolution=1, \n", " is_barrier=False,\n", - " dist_max=3* 5,)\n", + " dist_max=3* 5,\n", + " dist_min=1)\n", "\n", "clean_polys, clean_lines, clean_pts = blueprint.generate()\n", "\n", "# 2. Mesh Generation\n", - "mesher = MeshGenerator(background_lc=background_lc, verbosity=10)\n", + "mesher = MeshGenerator(background_lc=background_lc, verbosity=20)\n", "mesher.generate(clean_polys, clean_lines, clean_pts)\n", "\n", "# 3. Voronoi Conversion\n", @@ -136,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "0ba0153a", "metadata": {}, "outputs": [], @@ -146,10 +206,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "cfd21be8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "gdf = calculate_mesh_quality(grid_gdf,calc_ortho=True)\n", "gdf.plot(column='drift_ratio', cmap='viridis', legend=True, figsize=(10,8),vmax=.25)" @@ -190,7 +271,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.0" + "version": "3.14.2" } }, "nbformat": 4, diff --git a/examples/comprehensive_demo.ipynb b/examples/comprehensive_demo.ipynb index 8df3770..5a18b4c 100644 --- a/examples/comprehensive_demo.ipynb +++ b/examples/comprehensive_demo.ipynb @@ -99,8 +99,8 @@ "cm.add_polygon(domain, zone_id=1, resolution=20.0)\n", "\n", "# 2. Add Refined Zone\n", - "# dist_max_out controls how fast the mesh grows outside this zone\n", - "cm.add_polygon(zone_poly,z_order=2, zone_id=2, resolution=3.0, dist_max_out=20.0)\n", + "# dist_max controls how fast the mesh grows outside this zone\n", + "cm.add_polygon(zone_poly,z_order=2, zone_id=2, resolution=3.0, dist_max=20.0)\n", "\n", "\n", "# 3. Add River (Standard Line)\n", @@ -258,7 +258,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.0" + "version": "3.14.2" } }, "nbformat": 4, diff --git a/examples/field_capabilities_example.py b/examples/field_capabilities_example.py index 808520e..16363f8 100644 --- a/examples/field_capabilities_example.py +++ b/examples/field_capabilities_example.py @@ -29,7 +29,7 @@ AutoLinearField, ThresholdField, ) - +from vorflow.utils import calculate_mesh_quality, summarize_quality #%% # Geometry @@ -95,7 +95,7 @@ zone_id="upper-left", resolution=feature_lc/5, z_order=10, - dist_min=feature_lc/2, + dist_min=feature_lc/2,#using the implicit threshold approach here (instead of an explicit ThresholdField) to validate both code paths dist_max=background_lc * 5.0, ) @@ -118,7 +118,7 @@ blueprint.add_line( - polys["lower-left"].boundary,#TODO change to boundary only + polys["lower-left"].boundary, line_id='lower-left',# zone_id="lower-left", resolution=feature_lc/5, # z_order=5, @@ -156,7 +156,7 @@ blueprint.add_line( rivers["river-center-down"], line_id="river-center-down", - resolution=feature_lc/2, + resolution=feature_lc/4, is_barrier=False, fields=[auto_exp], embed=False, # field-only line @@ -167,7 +167,7 @@ blueprint.add_point( points["pt-lower-left"], point_id="pt-lower-left", - resolution=feature_lc, + resolution=feature_lc/5, dist_min=feature_lc/5, dist_max=background_lc * 1.5, ) @@ -189,8 +189,8 @@ clean_polys, clean_lines, clean_pts = blueprint.generate() -mesher = MeshGenerator(background_lc=background_lc, verbosity=5) -mesher.generate(clean_polys, clean_lines, clean_pts, launch_gmsh_gui=True) +mesher = MeshGenerator(background_lc=background_lc, verbosity=0) +mesher.generate(clean_polys, clean_lines, clean_pts, launch_gmsh_gui=False) tessellator = VoronoiTessellator(mesher, blueprint, clip_to_boundary=True) grid_gdf = tessellator.generate() @@ -278,3 +278,12 @@ plt.show() # %% +quality_gdf = calculate_mesh_quality(grid_gdf, calc_ortho=True) +summarize_quality(quality_gdf) + +# Plot Orthogonality Error +fig, ax = plt.subplots(figsize=(10, 8)) +quality_gdf.plot(column='ortho_error', ax=ax, legend=True, cmap='Reds', vmin=0, vmax=1) +plt.title("Orthogonality Error (Degrees)") +plt.show() +# %% From c7d1b9d9312e0cc6520cf0a0377b64d5df54edc4 Mon Sep 17 00:00:00 2001 From: oscar Date: Fri, 20 Feb 2026 16:56:12 +0800 Subject: [PATCH 24/26] hotfix for collapsed geometries --- src/vorflow/engine.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py index bea9ee5..5862e68 100644 --- a/src/vorflow/engine.py +++ b/src/vorflow/engine.py @@ -299,7 +299,7 @@ def create_loop(coords): if len(clean_coords) < 3: # A polygon must have at least 3 points (triangle) - return None + return None, [] p_tags = [gmsh.model.occ.addPoint(x, y, 0) for x, y in clean_coords] l_tags = [] @@ -310,7 +310,7 @@ def create_loop(coords): l_tags.append(gmsh.model.occ.addLine(p1, p2)) except Exception as e: print(f"Error adding line {p1}-{p2}: {e}") - return None + return None, [] try: loop_tag = gmsh.model.occ.addCurveLoop(l_tags) @@ -321,7 +321,11 @@ def create_loop(coords): # 1. Exterior Boundary ext_coords = list(poly.exterior.coords) - exterior_loop_tag, exterior_lines = create_loop(ext_coords) + exterior_result = create_loop(ext_coords) + if exterior_result is None: + print(f"Warning: Skipping degenerate polygon {idx}") + continue + exterior_loop_tag, exterior_lines = exterior_result if exterior_loop_tag is None: print(f"Warning: Skipping degenerate polygon {idx}") From b66832ffa56da47293689bd5036684a0d620fcdf Mon Sep 17 00:00:00 2001 From: oscar Date: Thu, 16 Apr 2026 08:23:34 +0800 Subject: [PATCH 25/26] Default snapping tolerance now can be changed to help topology problems in the original data --- src/vorflow/blueprint.py | 36 +++++++++++++++++++++++++++++++----- 1 file changed, 31 insertions(+), 5 deletions(-) diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py index 80c1b9a..982f414 100644 --- a/src/vorflow/blueprint.py +++ b/src/vorflow/blueprint.py @@ -9,10 +9,23 @@ # Constants for geometry simplification and reporting SIGNIFICANT_REDUCTION_PCT = 1.0 -DEFAULT_TOLERANCE = 1e-3 +DEFAULT_CONNECTIVITY_TOLERANCE = 1e-3 + +def _coerce_connectivity_tolerance(value, parameter_name="connectivity_tolerance"): + if isinstance(value, bool): + raise ValueError( + f"{parameter_name} must be a non-negative number. Boolean values are not supported." + ) + if not isinstance(value, (int, float)): + raise TypeError( + f"{parameter_name} must be a non-negative number. Got {type(value).__name__}." + ) + if value < 0: + raise ValueError(f"{parameter_name} must be non-negative. Got {value}.") + return float(value) class ConceptualMesh: - def __init__(self, crs="EPSG:4326"): + def __init__(self, crs="EPSG:4326", connectivity_tolerance=DEFAULT_CONNECTIVITY_TOLERANCE): """ Initializes the conceptual model, which holds raw geometric inputs. @@ -22,8 +35,12 @@ def __init__(self, crs="EPSG:4326"): Args: crs: The coordinate reference system for the project (e.g., "EPSG:4326"). + connectivity_tolerance (float, optional): Default snapping tolerance used + during topology cleanup in generate(). Larger values make lines and + points connect more aggressively to nearby geometry. """ self.crs = crs + self.connectivity_tolerance = _coerce_connectivity_tolerance(connectivity_tolerance) # Store raw geometric inputs before processing. self.raw_polygons = [] self.raw_lines = [] @@ -393,13 +410,18 @@ def _resolve_overlaps(self): self.clean_polygons = gpd.GeoDataFrame(final_features, crs=self.crs) - def _enforce_connectivity(self, tolerance=DEFAULT_TOLERANCE): + def _enforce_connectivity(self, connectivity_tolerance=None): """ Snaps features together to ensure they are topologically connected before being passed to the mesher. This is crucial for Gmsh to correctly interpret shared boundaries. """ + if connectivity_tolerance is None: + tolerance = self.connectivity_tolerance + else: + tolerance = _coerce_connectivity_tolerance(connectivity_tolerance) + # 1. Collect all polygon boundaries into a single geometry. # We snap to the linear boundaries, not the polygon areas. if not self.clean_polygons.empty: @@ -438,11 +460,15 @@ def _enforce_connectivity(self, tolerance=DEFAULT_TOLERANCE): self.raw_points[i]['geometry'] = snapped_point - def generate(self): + def generate(self, connectivity_tolerance=None): """ Runs the full preprocessing workflow: resolves polygon overlaps, ensures topological connectivity, and prepares clean GeoDataFrames for the mesher. + + Args: + connectivity_tolerance (float, optional): Override for the instance's + default topology snapping tolerance during this preprocessing run. """ print("Applying optional geometry simplification...") self._apply_simplification() @@ -462,7 +488,7 @@ def generate(self): self._resolve_overlaps() print("Enforcing strict topology...") - self._enforce_connectivity() + self._enforce_connectivity(connectivity_tolerance=connectivity_tolerance) # Promote the processed raw geometries to final "clean" GeoDataFrames. if self.raw_lines: From 045217967ef7e0407d0c943ebafb8584a5cab707 Mon Sep 17 00:00:00 2001 From: oscar Date: Fri, 17 Apr 2026 17:49:50 +0800 Subject: [PATCH 26/26] added some cleaning helpers and some tests to be sure of the bookkeeping and other issues --- src/vorflow/engine.py | 307 +++++++++++++- tests/test_conceptual_mesh.py | 51 ++- tests/test_line_embedding.py | 208 +++++++++ tests/test_point_tracking.py | 777 ++++++++++++++++++++++++++++++++++ 4 files changed, 1334 insertions(+), 9 deletions(-) create mode 100644 tests/test_line_embedding.py create mode 100644 tests/test_point_tracking.py diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py index 5862e68..523c5d4 100644 --- a/src/vorflow/engine.py +++ b/src/vorflow/engine.py @@ -9,7 +9,12 @@ from .fields import MeshField, ThresholdField, ExponentialField, AutoLinearField, AutoExponentialField, ConstantField class MeshGenerator: - def __init__(self, background_lc=None,verbosity=0, mesh_algorithm=6, smoothing_steps=10, optimization_cycles=2): + def __init__(self, background_lc=None, verbosity=0, mesh_algorithm=6, + smoothing_steps=10, optimization_cycles=2, + tolerance_initial_delaunay=1e-8, + heal_shapes=False, heal_tolerance=1e-8, + heal_fix_degenerated=True, heal_fix_small_edges=True, + heal_fix_small_faces=True): """ Initializes the Gmsh-based mesh generator. @@ -26,12 +31,34 @@ def __init__(self, background_lc=None,verbosity=0, mesh_algorithm=6, smoothing_s performed by Gmsh during mesh generation. optimization_cycles (int): Number of explicit optimization passes (e.g., Relocate2D, Laplace2D) to run after the initial mesh is generated. + tolerance_initial_delaunay (float): Tolerance for the initial Delaunay + point insertion. Increase this (e.g. 1e-4, 1e-2) to handle + "Could not insert point" errors caused by near-degenerate geometry + after fragmentation. This is a meshing-phase tolerance — it does NOT + alter the CAD topology, so no surfaces or lines are lost. + Default is 1e-8 (Gmsh default). + heal_shapes (bool): If True, run OCC topology healing after + fragmentation. This can fix degenerate geometry that causes + meshing failures, but may also merge or delete small entities. + Use with caution on complex models — keep heal_tolerance small. + Default is False. + heal_tolerance (float): Size threshold for healShapes. Entities + smaller than this may be removed or merged. Default 1e-8. only works if heal_shapes=True. + heal_fix_degenerated (bool): Fix degenerated edges/faces. Default True. Only works if heal_shapes=True. + heal_fix_small_edges (bool): Remove edges smaller than tolerance. Default True. Only works if heal_shapes=True. + heal_fix_small_faces (bool): Remove faces smaller than tolerance. Default True. Only works if heal_shapes=True. """ self.background_lc = background_lc self.verbosity = verbosity self.mesh_algorithm = mesh_algorithm self.smoothing_steps = smoothing_steps self.optimization_cycles = optimization_cycles + self.tolerance_initial_delaunay = tolerance_initial_delaunay + self.heal_shapes = heal_shapes + self.heal_tolerance = heal_tolerance + self.heal_fix_degenerated = heal_fix_degenerated + self.heal_fix_small_edges = heal_fix_small_edges + self.heal_fix_small_faces = heal_fix_small_faces self.initialized = False self.nodes = None @@ -62,13 +89,16 @@ def _force_close_polygon(self, poly): return Polygon(poly.exterior, new_interiors) def _initialize_gmsh(self): - if not gmsh.is_initialized(): - gmsh.initialize() - gmsh.option.setNumber("General.Verbosity", self.verbosity) - gmsh.option.setNumber("Geometry.Tolerance", 1e-6) - gmsh.option.setNumber("Geometry.OCCBooleanPreserveNumbering", 1) - gmsh.model.add("mesh_model") - self.initialized = True + # If Gmsh is already initialized (e.g. leftover from a previous failed + # run in the same Jupyter kernel), tear it down first so we start clean. + if gmsh.is_initialized(): + gmsh.finalize() + gmsh.initialize() + gmsh.option.setNumber("General.Verbosity", self.verbosity) + gmsh.option.setNumber("Geometry.Tolerance", 1e-6) + gmsh.option.setNumber("Geometry.OCCBooleanPreserveNumbering", 1) + gmsh.model.add("mesh_model") + self.initialized = True def _finalize_gmsh(self): if gmsh.is_initialized(): @@ -392,8 +422,241 @@ def create_loop(coords): print(f"Fragmenting {len(object_tags)} objects...") out_dt, out_map = gmsh.model.occ.fragment(object_tags, []) + + # Remove geometrically coincident (duplicate) entities left by + # fragmentation. Unlike healShapes this does NOT delete or merge + # entities based on a size tolerance, so it cannot destroy surfaces + # or convert interior lines into boundaries. + # + # Because removeAllDuplicates() can merge entities (changing tags) + # without returning a mapping, we snapshot the coordinates of all + # out_map entries beforehand and remap any that disappear. + _pre_dedup_coords = {} # (dim, tag) -> (x, y, z) for dim-0 entries + for i in range(len(out_map)): + for dt in out_map[i]: + d, t = int(dt[0]), int(dt[1]) + if d == 0 and (d, t) not in _pre_dedup_coords: + try: + bb = gmsh.model.occ.getBoundingBox(0, t) + _pre_dedup_coords[(d, t)] = (bb[0], bb[1], bb[2]) + except Exception: + pass + + gmsh.model.occ.removeAllDuplicates() + + # Refresh out_map: replace tags killed by removeAllDuplicates with + # the surviving entity at the same location. + if len(out_map) > 0: + occ_alive = set() + _alive_pts_by_coord = {} # (round_x, round_y, round_z) -> tag + for dim in range(3): + for dt in gmsh.model.occ.getEntities(dim): + d, t = int(dt[0]), int(dt[1]) + occ_alive.add((d, t)) + if d == 0: + try: + bb = gmsh.model.occ.getBoundingBox(0, t) + # Round to ~nm precision to match coordinates + coord_key = (round(bb[0], 6), round(bb[1], 6), round(bb[2], 6)) + _alive_pts_by_coord[coord_key] = t + except Exception: + pass + + _dup_pruned = 0 + _dup_remapped = 0 + for i in range(len(out_map)): + new_entries = [] + for dt in out_map[i]: + d, t = int(dt[0]), int(dt[1]) + if (d, t) in occ_alive: + new_entries.append(dt) + elif d == 0 and (d, t) in _pre_dedup_coords: + # Tag was killed by dedup — find the surviving point + x, y, z = _pre_dedup_coords[(d, t)] + coord_key = (round(x, 6), round(y, 6), round(z, 6)) + new_tag = _alive_pts_by_coord.get(coord_key) + if new_tag is not None: + new_entries.append((0, new_tag)) + _dup_remapped += 1 + else: + _dup_pruned += 1 + else: + _dup_pruned += 1 + out_map[i] = new_entries + if _dup_pruned > 0 or _dup_remapped > 0: + print(f"removeAllDuplicates: remapped {_dup_remapped}, pruned {_dup_pruned} tag(s) from fragment map.") + + # DIAG: Per-feature point tracking after dedup + if self.verbosity >= 2: + _pt_feat_status = [] + for i, input_dimtag in enumerate(object_tags): + key = to_key(input_dimtag[0], input_dimtag[1]) + info = input_tag_info.get(key, {}) + if info.get('type') == 'point': + feat_id = info['id'] + dim0 = [dt for dt in (out_map[i] if i < len(out_map) else []) + if int(dt[0]) == 0] + alive = [(d, t) for d, t in dim0 if (int(d), int(t)) in occ_alive] + _pt_feat_status.append((feat_id, len(dim0), len(alive))) + _n_empty = sum(1 for _, n, a in _pt_feat_status if a == 0) + print(f"[DIAG] Post-dedup point features: {len(_pt_feat_status)} total, " + f"{_n_empty} with 0 alive tags") + if _n_empty > 0: + for fid, nd, na in _pt_feat_status: + if na == 0: + print(f" [DIAG] Point feat_id={fid}: {nd} map entries, 0 alive") + + if self.heal_shapes: + # Snapshot coordinates of ALL out_map entities before heal so we + # can remap tags that healShapes renumbers. + _pre_heal_coords = {} # (dim, tag) -> (x, y, z) for dim-0 entries + for i in range(len(out_map)): + for dt in out_map[i]: + d, t = int(dt[0]), int(dt[1]) + if d == 0 and (d, t) not in _pre_heal_coords: + try: + bb = gmsh.model.occ.getBoundingBox(0, t) + _pre_heal_coords[(d, t)] = (bb[0], bb[1], bb[2]) + except Exception: + pass + elif d == 1 and (d, t) not in _pre_heal_coords: + try: + bb = gmsh.model.occ.getBoundingBox(1, t) + _pre_heal_coords[(d, t)] = (bb[0], bb[1], bb[2], bb[3], bb[4], bb[5]) + except Exception: + pass + elif d == 2 and (d, t) not in _pre_heal_coords: + try: + bb = gmsh.model.occ.getBoundingBox(2, t) + _pre_heal_coords[(d, t)] = (bb[0], bb[1], bb[2], bb[3], bb[4], bb[5]) + except Exception: + pass + + pre_heal = set() + for dim in range(3): + for dt in gmsh.model.occ.getEntities(dim): + pre_heal.add((int(dt[0]), int(dt[1]))) + + if self.heal_tolerance > 1e-2: + print(f"WARNING: heal_tolerance={self.heal_tolerance} is large. " + f"This may destroy fragment boundaries and lose surfaces/lines. " + f"Consider values <= 1e-3.") + print(f"Healing OCC shapes (tolerance={self.heal_tolerance}, " + f"degenerated={self.heal_fix_degenerated}, " + f"small_edges={self.heal_fix_small_edges}, " + f"small_faces={self.heal_fix_small_faces})...") + gmsh.model.occ.healShapes( + [], tolerance=self.heal_tolerance, + fixDegenerated=self.heal_fix_degenerated, + fixSmallEdges=self.heal_fix_small_edges, + fixSmallFaces=self.heal_fix_small_faces, + sewFaces=False, + makeSolids=False, + ) + gmsh.model.occ.synchronize() + # After healing, entity tags may have been renumbered (healShapes + # rebuilds OCC topology even when all fix flags are off). + # Remap out_map entries using coordinate matching, similar to dedup. + if self.heal_shapes and len(out_map) > 0: + surviving = set() + # Build coordinate lookup for surviving entities per dimension. + # healShapes introduces ~1e-6 coordinate drift, so we round to + # 4 decimal places (0.1 mm) — enough to distinguish any two + # intentionally distinct points while absorbing the drift. + _HEAL_ROUND = 4 + _heal_alive_by_dim = {0: {}, 1: {}, 2: {}} # dim -> coord_key -> tag + for dim in range(3): + for dt in gmsh.model.getEntities(dim): + d, t = int(dt[0]), int(dt[1]) + surviving.add((d, t)) + try: + bb = gmsh.model.getBoundingBox(d, t) + if d == 0: + coord_key = (round(bb[0], _HEAL_ROUND), round(bb[1], _HEAL_ROUND), round(bb[2], _HEAL_ROUND)) + else: + coord_key = (round(bb[0], _HEAL_ROUND), round(bb[1], _HEAL_ROUND), round(bb[2], _HEAL_ROUND), + round(bb[3], _HEAL_ROUND), round(bb[4], _HEAL_ROUND), round(bb[5], _HEAL_ROUND)) + _heal_alive_by_dim[d][coord_key] = t + except Exception: + pass + + # healShapes can reuse the same tag number for a DIFFERENT entity, + # so we must ALWAYS remap by coordinates — never trust tag identity. + _heal_remapped = 0 + _heal_pruned = 0 + _heal_kept = 0 + for i in range(len(out_map)): + new_entries = [] + for dt in out_map[i]: + d, t = int(dt[0]), int(dt[1]) + if (d, t) not in _pre_heal_coords: + # Entity wasn't snapshotted (shouldn't happen); keep if alive + if (d, t) in surviving: + new_entries.append(dt) + _heal_kept += 1 + else: + _heal_pruned += 1 + continue + + # Look up the old coordinates and find the matching new tag + old_coords = _pre_heal_coords[(d, t)] + if d == 0: + coord_key = (round(old_coords[0], _HEAL_ROUND), + round(old_coords[1], _HEAL_ROUND), + round(old_coords[2], _HEAL_ROUND)) + else: + coord_key = (round(old_coords[0], _HEAL_ROUND), round(old_coords[1], _HEAL_ROUND), + round(old_coords[2], _HEAL_ROUND), round(old_coords[3], _HEAL_ROUND), + round(old_coords[4], _HEAL_ROUND), round(old_coords[5], _HEAL_ROUND)) + new_tag = _heal_alive_by_dim.get(d, {}).get(coord_key) + if new_tag is not None: + if new_tag == t: + new_entries.append(dt) + _heal_kept += 1 + else: + new_entries.append((d, new_tag)) + _heal_remapped += 1 + else: + _heal_pruned += 1 + out_map[i] = new_entries + + if _heal_remapped > 0 or _heal_pruned > 0: + print(f"Heal post-processing: remapped {_heal_remapped}, pruned {_heal_pruned} tag(s) from fragment map.") + + # DIAG: Per-feature point tracking after heal + if self.verbosity >= 2: + _pt_feat_heal = [] + for i, input_dimtag in enumerate(object_tags): + key = to_key(input_dimtag[0], input_dimtag[1]) + info = input_tag_info.get(key, {}) + if info.get('type') == 'point': + feat_id = info['id'] + dim0 = [dt for dt in (out_map[i] if i < len(out_map) else []) + if int(dt[0]) == 0] + alive = [(d, t) for d, t in dim0 + if (int(d), int(t)) in surviving] + _pt_feat_heal.append((feat_id, len(dim0), len(alive))) + _n_empty_h = sum(1 for _, n, a in _pt_feat_heal if a == 0) + print(f"[DIAG] Post-heal point features: {len(_pt_feat_heal)} total, " + f"{_n_empty_h} with 0 alive tags (remapped {_heal_remapped}, pruned {_heal_pruned})") + if _n_empty_h > 0: + for fid, nd, na in _pt_feat_heal: + if na == 0: + print(f" [DIAG] Point feat_id={fid}: {nd} map entries, 0 alive after heal") + # Report what heal removed/added + heal_removed = pre_heal - surviving + heal_added = surviving - pre_heal + dim0_removed = [(d, t) for d, t in heal_removed if d == 0] + dim0_added = [(d, t) for d, t in heal_added if d == 0] + if dim0_removed or dim0_added: + print(f"[DIAG] Heal dim-0 changes: removed {len(dim0_removed)}, added {len(dim0_added)}") + if dim0_removed: + print(f" [DIAG] Removed point tags: {sorted(t for _, t in dim0_removed)}") + if dim0_added: + print(f" [DIAG] Added point tags: {sorted(t for _, t in dim0_added)}") + # >>> DIAG: Post-fragment summary if self.verbosity >= 2: all_surfs_post = gmsh.model.getEntities(2) @@ -496,6 +759,30 @@ def create_loop(coords): else: print(f"Warning: Tag {key} lost during fragmentation mapping.") + # DIAG: Final map point summary + if self.verbosity >= 2: + _n_pt_feats = len(final_map.get('points', {})) + _empty_feats = [] + _stale_feats = [] + model_ents = set() + for dim in range(3): + for dt in gmsh.model.getEntities(dim): + model_ents.add((int(dt[0]), int(dt[1]))) + for fid, dimtags in final_map.get('points', {}).items(): + dim0 = [dt for dt in dimtags if isinstance(dt, (tuple, list)) and int(dt[0]) == 0] + if not dim0: + _empty_feats.append(fid) + else: + for dt in dim0: + if (int(dt[0]), int(dt[1])) not in model_ents: + _stale_feats.append((fid, int(dt[1]))) + print(f"[DIAG] Final map: {_n_pt_feats} point features, " + f"{len(_empty_feats)} empty, {len(_stale_feats)} with stale tags") + if _empty_feats: + print(f" [DIAG] Empty point feat_ids: {sorted(_empty_feats)}") + if _stale_feats: + print(f" [DIAG] Stale point (feat_id, tag): {_stale_feats}") + return final_map def _setup_fields(self, gmsh_map, polygons_gdf, lines_gdf, points_gdf): @@ -1039,6 +1326,10 @@ def generate(self, clean_polys, clean_lines, clean_points, output_file=None, lau # Set the number of internal smoothing steps. gmsh.option.setNumber("Mesh.Smoothing", self.smoothing_steps) + # Tolerance for the initial Delaunay insertion — helps with + # "Could not insert point" from near-degenerate geometry. + gmsh.option.setNumber("Mesh.ToleranceInitialDelaunay", self.tolerance_initial_delaunay) + print("Generating Triangular Mesh...") gmsh.model.mesh.generate(2) diff --git a/tests/test_conceptual_mesh.py b/tests/test_conceptual_mesh.py index 2ced0bb..4eb27ee 100644 --- a/tests/test_conceptual_mesh.py +++ b/tests/test_conceptual_mesh.py @@ -3,6 +3,7 @@ from shapely.geometry import LineString, Point, Polygon from shapely.ops import unary_union +import vorflow.blueprint as blueprint_module from vorflow.blueprint import ConceptualMesh @@ -26,7 +27,7 @@ def test_resolve_overlaps_respects_z_order(): def test_lines_and_points_snap_to_polygons(): - cm = ConceptualMesh() + cm = ConceptualMesh(connectivity_tolerance=1.0) square = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]) cm.add_polygon(square, zone_id=1) @@ -50,6 +51,48 @@ def test_lines_and_points_snap_to_polygons(): assert snapped_line.distance(boundary) <= tolerance assert snapped_point.distance(boundary) <= tolerance + +def test_generate_uses_constructor_connectivity_tolerance(monkeypatch): + cm = ConceptualMesh(connectivity_tolerance=0.25) + square = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]) + + cm.add_polygon(square, zone_id=1) + cm.add_line(LineString([(0, 0), (1, 0)]), line_id="river", resolution=0.1, densify=False) + cm.add_point(Point(0.1, 0.1), point_id="well", resolution=0.1) + + recorded_tolerances = [] + + def fake_snap(geometry, reference_geometry, tolerance): + recorded_tolerances.append(tolerance) + return geometry + + monkeypatch.setattr(blueprint_module, "snap", fake_snap) + + cm.generate() + + assert recorded_tolerances == [pytest.approx(0.25), pytest.approx(0.25)] + + +def test_generate_can_override_connectivity_tolerance(monkeypatch): + cm = ConceptualMesh(connectivity_tolerance=1.0) + square = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]) + + cm.add_polygon(square, zone_id=1) + cm.add_line(LineString([(0, 0), (1, 0)]), line_id="river", resolution=0.1, densify=False) + cm.add_point(Point(0.1, 0.1), point_id="well", resolution=0.1) + + recorded_tolerances = [] + + def fake_snap(geometry, reference_geometry, tolerance): + recorded_tolerances.append(tolerance) + return geometry + + monkeypatch.setattr(blueprint_module, "snap", fake_snap) + + cm.generate(connectivity_tolerance=0.05) + + assert recorded_tolerances == [pytest.approx(0.05), pytest.approx(0.05)] + def test_polygon_simplification(): """Test that polygons are simplified when tolerance is provided.""" cm = ConceptualMesh() @@ -158,3 +201,9 @@ def test_simplify_tolerance_bool_is_rejected(bool_tol): pt = Point(0, 0) with pytest.raises(ValueError): cm.add_point(pt, point_id="p1", resolution=0.1, simplify_tolerance=bool_tol) + + +@pytest.mark.parametrize("bad_tolerance", [True, False, -1]) +def test_connectivity_tolerance_validation(bad_tolerance): + with pytest.raises(ValueError): + ConceptualMesh(connectivity_tolerance=bad_tolerance) diff --git a/tests/test_line_embedding.py b/tests/test_line_embedding.py new file mode 100644 index 0000000..616e5c0 --- /dev/null +++ b/tests/test_line_embedding.py @@ -0,0 +1,208 @@ +""" +Tests for line embedding in the presence of polygons. + +Regression tests for the bug where lines were not properly embedded into +the triangular mesh when polygons were also present. Root causes: + 1. getEntitiesInBoundingBox requires containment, not intersection — tiny + entity bboxes could never contain large domain surfaces. + 2. Boundary lines (created by fragment when a line crosses a polygon edge) + were re-embedded into their own surface, corrupting the mesh. +""" + +import pytest +import numpy as np +from shapely.geometry import Polygon, LineString, Point +import gmsh + +from vorflow.blueprint import ConceptualMesh +from vorflow.engine import MeshGenerator +from vorflow.tessellator import VoronoiTessellator + + +@pytest.fixture(autouse=True) +def ensure_gmsh_finalized(): + """Ensure gmsh is finalized before and after each test.""" + if gmsh.is_initialized(): + gmsh.finalize() + yield + if gmsh.is_initialized(): + gmsh.finalize() + + +def _nodes_near_line(nodes, line, tolerance): + """Count mesh nodes that lie within `tolerance` of a LineString.""" + count = 0 + for x, y in nodes: + pt = Point(x, y) + if line.distance(pt) < tolerance: + count += 1 + return count + + +class TestLineEmbeddingWithPolygons: + """ + Core regression: a line crossing through a domain that also contains + interior polygons must produce mesh nodes along the full line path, + not just at polygon boundaries. + """ + + def test_line_embedded_with_polygon_present(self): + """ + A line crossing through a domain with an interior polygon must + produce nodes along the line — the defining symptom of the bug + was that zero nodes appeared along interior line segments. + """ + cm = ConceptualMesh(crs="EPSG:3857") + + # Large domain + domain = Polygon([(0, 0), (100, 0), (100, 50), (0, 50)]) + cm.add_polygon(domain, zone_id=1, resolution=10.0, z_order=0) + + # Small interior square that the line crosses through + square = Polygon([(40, 15), (60, 15), (60, 35), (40, 35)]) + cm.add_polygon(square, zone_id=2, resolution=5.0, z_order=1) + + # Horizontal line crossing through the square + line = LineString([(10, 25), (90, 25)]) + cm.add_line(line, line_id="crossing_line", resolution=3.0) + + clean_polys, clean_lines, clean_points = cm.generate() + + mg = MeshGenerator(background_lc=10.0, verbosity=0) + success = mg.generate(clean_polys, clean_lines, clean_points) + assert success, "Mesh generation failed" + + # Count nodes near the line — before the fix this was ~0 + nodes_on_line = _nodes_near_line(mg.nodes, line, tolerance=1.0) + + # With resolution=3.0 on an 80-unit line, we expect ~25+ nodes + assert nodes_on_line >= 10, ( + f"Only {nodes_on_line} nodes found near line — " + f"line embedding likely broken (expected >= 10)" + ) + + def test_line_embedded_without_polygon(self): + """ + Baseline: line embedding works correctly without interior polygons. + This should always pass — it's the control case. + """ + cm = ConceptualMesh(crs="EPSG:3857") + + domain = Polygon([(0, 0), (100, 0), (100, 50), (0, 50)]) + cm.add_polygon(domain, zone_id=1, resolution=10.0, z_order=0) + + line = LineString([(10, 25), (90, 25)]) + cm.add_line(line, line_id="simple_line", resolution=3.0) + + clean_polys, clean_lines, clean_points = cm.generate() + + mg = MeshGenerator(background_lc=10.0, verbosity=0) + success = mg.generate(clean_polys, clean_lines, clean_points) + assert success + + nodes_on_line = _nodes_near_line(mg.nodes, line, tolerance=1.0) + assert nodes_on_line >= 10, ( + f"Only {nodes_on_line} nodes near line in no-polygon case" + ) + + def test_line_node_count_comparable_with_and_without_polygons(self): + """ + The number of nodes near a line should be roughly similar whether + or not an interior polygon exists. The original bug caused a near- + total loss of line nodes when polygons were present. + """ + line = LineString([(10, 25), (90, 25)]) + + # Case A: without polygon + cm_a = ConceptualMesh(crs="EPSG:3857") + domain = Polygon([(0, 0), (100, 0), (100, 50), (0, 50)]) + cm_a.add_polygon(domain, zone_id=1, resolution=10.0, z_order=0) + cm_a.add_line(line, line_id="line_a", resolution=3.0) + polys_a, lines_a, pts_a = cm_a.generate() + mg_a = MeshGenerator(background_lc=10.0, verbosity=0) + mg_a.generate(polys_a, lines_a, pts_a) + nodes_a = _nodes_near_line(mg_a.nodes, line, tolerance=1.0) + + # Case B: with polygon + cm_b = ConceptualMesh(crs="EPSG:3857") + cm_b.add_polygon(domain, zone_id=1, resolution=10.0, z_order=0) + square = Polygon([(40, 15), (60, 15), (60, 35), (40, 35)]) + cm_b.add_polygon(square, zone_id=2, resolution=5.0, z_order=1) + cm_b.add_line(line, line_id="line_b", resolution=3.0) + polys_b, lines_b, pts_b = cm_b.generate() + mg_b = MeshGenerator(background_lc=10.0, verbosity=0) + mg_b.generate(polys_b, lines_b, pts_b) + nodes_b = _nodes_near_line(mg_b.nodes, line, tolerance=1.0) + + # Case B should have at least 50% of Case A's nodes + # (it may have more due to the finer polygon resolution) + ratio = nodes_b / max(nodes_a, 1) + assert ratio >= 0.5, ( + f"With-polygon case has {nodes_b} nodes vs {nodes_a} without — " + f"ratio {ratio:.2f} < 0.5, embedding likely broken" + ) + + def test_line_crossing_multiple_polygons(self): + """ + A line that crosses through multiple interior polygons must still + produce nodes along its full length. + """ + cm = ConceptualMesh(crs="EPSG:3857") + + domain = Polygon([(0, 0), (200, 0), (200, 50), (0, 50)]) + cm.add_polygon(domain, zone_id=1, resolution=15.0, z_order=0) + + # Three squares along the line path + for i, x_start in enumerate([30, 80, 140]): + sq = Polygon([ + (x_start, 15), (x_start + 20, 15), + (x_start + 20, 35), (x_start, 35) + ]) + cm.add_polygon(sq, zone_id=10 + i, resolution=5.0, z_order=1) + + line = LineString([(10, 25), (190, 25)]) + cm.add_line(line, line_id="multi_cross", resolution=5.0) + + clean_polys, clean_lines, clean_points = cm.generate() + + mg = MeshGenerator(background_lc=15.0, verbosity=0) + success = mg.generate(clean_polys, clean_lines, clean_points) + assert success + + nodes_on_line = _nodes_near_line(mg.nodes, line, tolerance=1.5) + # 180-unit line at resolution 5 → ~36 segments → ~30+ nodes expected + assert nodes_on_line >= 15, ( + f"Only {nodes_on_line} nodes on line crossing 3 polygons" + ) + + def test_mesh_area_conservation_with_embedded_line(self): + """ + Total mesh area must match the domain area, ensuring no + garbage triangles extend outside the domain. + """ + cm = ConceptualMesh(crs="EPSG:3857") + + domain = Polygon([(0, 0), (100, 0), (100, 50), (0, 50)]) + cm.add_polygon(domain, zone_id=1, resolution=10.0, z_order=0) + + square = Polygon([(40, 15), (60, 15), (60, 35), (40, 35)]) + cm.add_polygon(square, zone_id=2, resolution=5.0, z_order=1) + + line = LineString([(10, 25), (90, 25)]) + cm.add_line(line, line_id="area_test", resolution=3.0) + + clean_polys, clean_lines, clean_points = cm.generate() + + mg = MeshGenerator(background_lc=10.0, verbosity=0) + success = mg.generate(clean_polys, clean_lines, clean_points) + assert success + + vt = VoronoiTessellator(mg, cm, clip_to_boundary=True) + grid = vt.generate() + + total_area = grid.geometry.area.sum() + expected_area = domain.area # 5000 + assert pytest.approx(total_area, rel=0.02) == expected_area, ( + f"Area mismatch: {total_area:.1f} vs {expected_area:.1f} — " + f"possible out-of-domain triangles" + ) diff --git a/tests/test_point_tracking.py b/tests/test_point_tracking.py new file mode 100644 index 0000000..5843e34 --- /dev/null +++ b/tests/test_point_tracking.py @@ -0,0 +1,777 @@ +""" +Tests for point entity tracking through the fragment → removeAllDuplicates → healShapes pipeline. + +These tests verify that dim-0 (point) entities added to the Gmsh model survive +each stage of the _add_geometry pipeline and are correctly reflected in the +returned gmsh_map, so that _embed_features and _setup_fields can find them. +""" +import pytest +import gmsh +import geopandas as gpd +from shapely.geometry import Point, Polygon, LineString + +from vorflow.blueprint import ConceptualMesh +from vorflow.engine import MeshGenerator + + +@pytest.fixture(autouse=True) +def ensure_gmsh_finalized(): + """Ensure gmsh is finalized before and after each test.""" + if gmsh.is_initialized(): + gmsh.finalize() + yield + if gmsh.is_initialized(): + gmsh.finalize() + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _build_model(polygon_coords, points, lines=None, + background_lc=5.0, polygon_res=5.0, point_res=1.0, + heal_shapes=False, heal_tolerance=1e-8, + heal_fix_degenerated=True, heal_fix_small_edges=True, + heal_fix_small_faces=True): + """ + Build a ConceptualMesh with a single polygon + N points, run + _add_geometry, and return (mg, gmsh_map, clean_points). + """ + cm = ConceptualMesh(crs="EPSG:3857") + poly = Polygon(polygon_coords) + cm.add_polygon(poly, zone_id=1, resolution=polygon_res, dist_max=25.0) + + for i, pt in enumerate(points): + cm.add_point(pt, point_id=f"pt_{i}", resolution=point_res, dist_min=0, dist_max=5.0) + + if lines: + for i, ln in enumerate(lines): + cm.add_line(ln, line_id=f"ln_{i}", resolution=point_res) + + clean_polys, clean_lines, clean_points = cm.generate() + + mg = MeshGenerator( + background_lc=background_lc, + verbosity=2, + heal_shapes=heal_shapes, + heal_tolerance=heal_tolerance, + heal_fix_degenerated=heal_fix_degenerated, + heal_fix_small_edges=heal_fix_small_edges, + heal_fix_small_faces=heal_fix_small_faces, + ) + mg._initialize_gmsh() + gmsh_map = mg._add_geometry(clean_polys, clean_lines, clean_points) + return mg, gmsh_map, clean_points + + +def _count_mapped_points(gmsh_map): + """Count how many point feature IDs have at least one valid dim-0 tag.""" + n_features_with_tags = 0 + n_total_dim0 = 0 + for feat_id, dimtags in gmsh_map.get('points', {}).items(): + dim0_tags = [dt for dt in dimtags if isinstance(dt, (tuple, list)) and int(dt[0]) == 0] + if dim0_tags: + n_features_with_tags += 1 + n_total_dim0 += len(dim0_tags) + return n_features_with_tags, n_total_dim0 + + +def _verify_tags_exist_in_model(gmsh_map): + """Verify every tag in gmsh_map['points'] actually exists in the synchronized model.""" + model_entities = set() + for dim in range(3): + for dt in gmsh.model.getEntities(dim): + model_entities.add((int(dt[0]), int(dt[1]))) + + missing = [] + for feat_id, dimtags in gmsh_map.get('points', {}).items(): + for dt in dimtags: + if isinstance(dt, (tuple, list)) and len(dt) >= 2: + key = (int(dt[0]), int(dt[1])) + if key not in model_entities: + missing.append((feat_id, key)) + return missing + + +# --------------------------------------------------------------------------- +# Test: Basic point survival (no heal) +# --------------------------------------------------------------------------- + +class TestPointTrackingNoHeal: + """Points should survive fragment + removeAllDuplicates without heal.""" + + def test_single_point_inside_polygon(self): + """One point in the center of a square.""" + mg, gmap, pts = _build_model( + polygon_coords=[(0, 0), (10, 0), (10, 10), (0, 10)], + points=[Point(5, 5)], + heal_shapes=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == 1, f"Expected 1 point feature mapped, got {n_feat}" + assert n_tags >= 1, f"Expected >= 1 dim-0 tag, got {n_tags}" + assert _verify_tags_exist_in_model(gmap) == [], "Stale tags in map" + + def test_multiple_points_spread(self): + """Several points spread across a polygon.""" + pts = [Point(2, 2), Point(5, 5), Point(8, 8), Point(3, 7)] + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (10, 0), (10, 10), (0, 10)], + points=pts, + heal_shapes=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == len(pts), f"Expected {len(pts)} point features, got {n_feat}" + assert n_tags >= len(pts), f"Expected >= {len(pts)} dim-0 tags, got {n_tags}" + assert _verify_tags_exist_in_model(gmap) == [] + + def test_point_on_polygon_vertex(self): + """Point exactly on a polygon vertex — fragment may merge them.""" + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (10, 0), (10, 10), (0, 10)], + points=[Point(0, 0)], + heal_shapes=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == 1, f"Expected 1 point feature, got {n_feat}" + # The tag might have been merged with the polygon vertex, but the + # map entry must still reference a valid dim-0 entity. + assert n_tags >= 1, f"Expected >= 1 dim-0 tag, got {n_tags}" + assert _verify_tags_exist_in_model(gmap) == [] + + def test_point_on_polygon_edge(self): + """Point on a polygon edge midpoint.""" + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (10, 0), (10, 10), (0, 10)], + points=[Point(5, 0)], + heal_shapes=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == 1 + assert n_tags >= 1 + assert _verify_tags_exist_in_model(gmap) == [] + + def test_many_points(self): + """29 points (matching user's real case) inside a large polygon.""" + import random + random.seed(42) + pts = [Point(random.uniform(1, 99), random.uniform(1, 99)) for _ in range(29)] + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (100, 0), (100, 100), (0, 100)], + points=pts, + heal_shapes=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == 29, f"Expected 29 point features, got {n_feat}" + assert n_tags >= 29 + assert _verify_tags_exist_in_model(gmap) == [] + + +# --------------------------------------------------------------------------- +# Test: Points with heal_shapes ON (all fix options OFF) +# --------------------------------------------------------------------------- + +class TestPointTrackingHealAllOff: + """Points should survive when heal_shapes=True but all fix flags are False.""" + + def test_single_point_heal_noop(self): + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (10, 0), (10, 10), (0, 10)], + points=[Point(5, 5)], + heal_shapes=True, + heal_tolerance=1e-8, + heal_fix_degenerated=False, + heal_fix_small_edges=False, + heal_fix_small_faces=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == 1 + assert n_tags >= 1 + assert _verify_tags_exist_in_model(gmap) == [] + + def test_multiple_points_heal_noop(self): + pts = [Point(2, 2), Point(5, 5), Point(8, 8)] + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (10, 0), (10, 10), (0, 10)], + points=pts, + heal_shapes=True, + heal_tolerance=1e-8, + heal_fix_degenerated=False, + heal_fix_small_edges=False, + heal_fix_small_faces=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == len(pts), f"Expected {len(pts)}, got {n_feat}" + assert n_tags >= len(pts) + assert _verify_tags_exist_in_model(gmap) == [] + + def test_many_points_heal_noop(self): + """29 points, heal on but all fixes off.""" + import random + random.seed(42) + pts = [Point(random.uniform(1, 99), random.uniform(1, 99)) for _ in range(29)] + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (100, 0), (100, 100), (0, 100)], + points=pts, + heal_shapes=True, + heal_tolerance=1e-8, + heal_fix_degenerated=False, + heal_fix_small_edges=False, + heal_fix_small_faces=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == 29, f"Expected 29, got {n_feat}" + assert n_tags >= 29 + assert _verify_tags_exist_in_model(gmap) == [] + + def test_large_tolerance_heal_all_off(self): + """heal_tolerance=1 (large) but all fix flags off — should be no-op.""" + pts = [Point(2, 2), Point(5, 5), Point(8, 8)] + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (10, 0), (10, 10), (0, 10)], + points=pts, + heal_shapes=True, + heal_tolerance=1.0, + heal_fix_degenerated=False, + heal_fix_small_edges=False, + heal_fix_small_faces=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == len(pts), f"Expected {len(pts)}, got {n_feat}" + assert n_tags >= len(pts) + assert _verify_tags_exist_in_model(gmap) == [] + + +# --------------------------------------------------------------------------- +# Test: Points with heal_shapes ON (default fix options) +# --------------------------------------------------------------------------- + +class TestPointTrackingHealDefaults: + """Points should survive healShapes with default fix options at small tolerance.""" + + def test_single_point_heal_defaults(self): + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (10, 0), (10, 10), (0, 10)], + points=[Point(5, 5)], + heal_shapes=True, + heal_tolerance=1e-8, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == 1 + assert n_tags >= 1 + assert _verify_tags_exist_in_model(gmap) == [] + + def test_many_points_heal_defaults(self): + import random + random.seed(42) + pts = [Point(random.uniform(1, 99), random.uniform(1, 99)) for _ in range(29)] + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (100, 0), (100, 100), (0, 100)], + points=pts, + heal_shapes=True, + heal_tolerance=1e-8, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == 29, f"Expected 29, got {n_feat}" + assert n_tags >= 29 + assert _verify_tags_exist_in_model(gmap) == [] + + +# --------------------------------------------------------------------------- +# Test: Points with large coordinates (projected CRS like user's ~585000) +# --------------------------------------------------------------------------- + +class TestPointTrackingLargeCoords: + """Test with coordinate magnitudes matching real projected CRS data.""" + + ORIGIN_X = 584000.0 + ORIGIN_Y = 2366000.0 + + def _large_poly(self): + ox, oy = self.ORIGIN_X, self.ORIGIN_Y + return [(ox, oy), (ox + 13000, oy), (ox + 13000, oy + 9000), (ox, oy + 9000)] + + def test_large_coords_no_heal(self): + ox, oy = self.ORIGIN_X, self.ORIGIN_Y + pts = [ + Point(ox + 1000, oy + 1000), + Point(ox + 6000, oy + 4500), + Point(ox + 12000, oy + 8000), + ] + mg, gmap, _ = _build_model( + polygon_coords=self._large_poly(), + points=pts, + background_lc=500.0, + polygon_res=500.0, + point_res=100.0, + heal_shapes=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == len(pts), f"Expected {len(pts)}, got {n_feat}" + assert n_tags >= len(pts) + assert _verify_tags_exist_in_model(gmap) == [] + + def test_large_coords_heal_all_off(self): + ox, oy = self.ORIGIN_X, self.ORIGIN_Y + pts = [ + Point(ox + 1000, oy + 1000), + Point(ox + 6000, oy + 4500), + Point(ox + 12000, oy + 8000), + ] + mg, gmap, _ = _build_model( + polygon_coords=self._large_poly(), + points=pts, + background_lc=500.0, + polygon_res=500.0, + point_res=100.0, + heal_shapes=True, + heal_tolerance=1.0, + heal_fix_degenerated=False, + heal_fix_small_edges=False, + heal_fix_small_faces=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == len(pts), f"Expected {len(pts)}, got {n_feat}" + assert n_tags >= len(pts) + assert _verify_tags_exist_in_model(gmap) == [] + + def test_large_coords_heal_defaults(self): + ox, oy = self.ORIGIN_X, self.ORIGIN_Y + pts = [ + Point(ox + 1000, oy + 1000), + Point(ox + 6000, oy + 4500), + Point(ox + 12000, oy + 8000), + ] + mg, gmap, _ = _build_model( + polygon_coords=self._large_poly(), + points=pts, + background_lc=500.0, + polygon_res=500.0, + point_res=100.0, + heal_shapes=True, + heal_tolerance=1e-8, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == len(pts), f"Expected {len(pts)}, got {n_feat}" + assert n_tags >= len(pts) + assert _verify_tags_exist_in_model(gmap) == [] + + def test_29_points_large_coords_heal_all_off_tol1(self): + """Closest to user's actual scenario: 29 pts, large coords, heal on, all off, tol=1.""" + import random + random.seed(99) + ox, oy = self.ORIGIN_X, self.ORIGIN_Y + pts = [ + Point(ox + random.uniform(500, 12500), oy + random.uniform(500, 8500)) + for _ in range(29) + ] + mg, gmap, _ = _build_model( + polygon_coords=self._large_poly(), + points=pts, + background_lc=500.0, + polygon_res=500.0, + point_res=100.0, + heal_shapes=True, + heal_tolerance=1.0, + heal_fix_degenerated=False, + heal_fix_small_edges=False, + heal_fix_small_faces=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == 29, f"Expected 29, got {n_feat}" + assert n_tags >= 29 + missing = _verify_tags_exist_in_model(gmap) + assert missing == [], f"Stale tags: {missing}" + + +# --------------------------------------------------------------------------- +# Test: Points with multiple overlapping polygons +# --------------------------------------------------------------------------- + +class TestPointTrackingMultiPolygon: + """Points inside overlapping polygons (forces non-trivial fragmentation).""" + + def test_points_in_overlapping_polygons_no_heal(self): + cm = ConceptualMesh(crs="EPSG:3857") + cm.add_polygon(Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]), + zone_id=1, resolution=5.0, dist_max=25.0, z_order=0) + cm.add_polygon(Polygon([(3, 3), (7, 3), (7, 7), (3, 7)]), + zone_id=2, resolution=2.0, dist_max=10.0, z_order=1) + cm.add_point(Point(5, 5), point_id="center", resolution=0.5, dist_min=0, dist_max=3) + cm.add_point(Point(1, 1), point_id="outer", resolution=0.5, dist_min=0, dist_max=3) + + clean_polys, clean_lines, clean_points = cm.generate() + mg = MeshGenerator(background_lc=5.0, verbosity=2, heal_shapes=False) + mg._initialize_gmsh() + gmap = mg._add_geometry(clean_polys, clean_lines, clean_points) + + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == 2, f"Expected 2, got {n_feat}" + assert n_tags >= 2 + assert _verify_tags_exist_in_model(gmap) == [] + + def test_points_in_overlapping_polygons_heal_all_off(self): + cm = ConceptualMesh(crs="EPSG:3857") + cm.add_polygon(Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]), + zone_id=1, resolution=5.0, dist_max=25.0, z_order=0) + cm.add_polygon(Polygon([(3, 3), (7, 3), (7, 7), (3, 7)]), + zone_id=2, resolution=2.0, dist_max=10.0, z_order=1) + cm.add_point(Point(5, 5), point_id="center", resolution=0.5, dist_min=0, dist_max=3) + cm.add_point(Point(1, 1), point_id="outer", resolution=0.5, dist_min=0, dist_max=3) + + clean_polys, clean_lines, clean_points = cm.generate() + mg = MeshGenerator( + background_lc=5.0, verbosity=2, + heal_shapes=True, heal_tolerance=1.0, + heal_fix_degenerated=False, heal_fix_small_edges=False, heal_fix_small_faces=False, + ) + mg._initialize_gmsh() + gmap = mg._add_geometry(clean_polys, clean_lines, clean_points) + + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat == 2, f"Expected 2, got {n_feat}" + assert n_tags >= 2 + assert _verify_tags_exist_in_model(gmap) == [] + + +# --------------------------------------------------------------------------- +# Test: Points with lines (combined features) +# --------------------------------------------------------------------------- + +class TestPointTrackingWithLines: + """Points + lines together — fragments create more complex topology.""" + + def test_points_and_line_no_heal(self): + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (10, 0), (10, 10), (0, 10)], + points=[Point(5, 5), Point(2, 8)], + lines=[LineString([(1, 1), (9, 9)])], + heal_shapes=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat >= 2, f"Expected >= 2, got {n_feat}" + assert n_tags >= 2 + assert _verify_tags_exist_in_model(gmap) == [] + + def test_points_and_line_heal_all_off(self): + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (10, 0), (10, 10), (0, 10)], + points=[Point(5, 5), Point(2, 8)], + lines=[LineString([(1, 1), (9, 9)])], + heal_shapes=True, + heal_tolerance=1.0, + heal_fix_degenerated=False, + heal_fix_small_edges=False, + heal_fix_small_faces=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat >= 2, f"Expected >= 2, got {n_feat}" + assert n_tags >= 2 + assert _verify_tags_exist_in_model(gmap) == [] + + def test_point_on_line_endpoint(self): + """Point coincident with a line endpoint — high merge probability.""" + mg, gmap, _ = _build_model( + polygon_coords=[(0, 0), (10, 0), (10, 10), (0, 10)], + points=[Point(1, 1)], + lines=[LineString([(1, 1), (9, 9)])], + heal_shapes=True, + heal_tolerance=1e-8, + heal_fix_degenerated=False, + heal_fix_small_edges=False, + heal_fix_small_faces=False, + ) + n_feat, n_tags = _count_mapped_points(gmap) + assert n_feat >= 1 + assert n_tags >= 1 + assert _verify_tags_exist_in_model(gmap) == [] + + +# --------------------------------------------------------------------------- +# Test: Full pipeline (generate mesh, check for mesh nodes near points) +# --------------------------------------------------------------------------- + +class TestPointEmbeddingEndToEnd: + """Full pipeline: points should produce mesh vertices at their locations.""" + + def _get_mesh_node_near(self, mg, x, y, tol): + """Check if any mesh node is within tol of (x, y).""" + if mg.nodes is None: + return False + import numpy as np + dists = np.sqrt((mg.nodes[:, 0] - x) ** 2 + (mg.nodes[:, 1] - y) ** 2) + return bool(np.any(dists < tol)) + + def test_embedded_point_creates_mesh_vertex_no_heal(self): + cm = ConceptualMesh(crs="EPSG:3857") + cm.add_polygon(Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]), + zone_id=1, resolution=2.0, dist_max=10.0) + cm.add_point(Point(5, 5), point_id="well", resolution=0.5, dist_min=0, dist_max=3) + clean_polys, clean_lines, clean_points = cm.generate() + + mg = MeshGenerator(background_lc=2.0, verbosity=2, heal_shapes=False) + success = mg.generate(clean_polys, clean_lines, clean_points) + assert success + assert self._get_mesh_node_near(mg, 5.0, 5.0, 0.01), \ + "No mesh node found near embedded point (5,5) with heal OFF" + + def test_embedded_point_creates_mesh_vertex_heal_all_off(self): + cm = ConceptualMesh(crs="EPSG:3857") + cm.add_polygon(Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]), + zone_id=1, resolution=2.0, dist_max=10.0) + cm.add_point(Point(5, 5), point_id="well", resolution=0.5, dist_min=0, dist_max=3) + clean_polys, clean_lines, clean_points = cm.generate() + + mg = MeshGenerator( + background_lc=2.0, verbosity=2, + heal_shapes=True, heal_tolerance=1.0, + heal_fix_degenerated=False, heal_fix_small_edges=False, heal_fix_small_faces=False, + ) + success = mg.generate(clean_polys, clean_lines, clean_points) + assert success + assert self._get_mesh_node_near(mg, 5.0, 5.0, 0.01), \ + "No mesh node found near embedded point (5,5) with heal ON (all off, tol=1)" + + def test_multiple_points_create_mesh_vertices_heal_all_off(self): + cm = ConceptualMesh(crs="EPSG:3857") + cm.add_polygon(Polygon([(0, 0), (20, 0), (20, 20), (0, 20)]), + zone_id=1, resolution=5.0, dist_max=25.0) + test_pts = [(5, 5), (15, 5), (10, 15)] + for i, (x, y) in enumerate(test_pts): + cm.add_point(Point(x, y), point_id=f"pt{i}", resolution=1.0, dist_min=0, dist_max=5) + clean_polys, clean_lines, clean_points = cm.generate() + + mg = MeshGenerator( + background_lc=5.0, verbosity=2, + heal_shapes=True, heal_tolerance=1.0, + heal_fix_degenerated=False, heal_fix_small_edges=False, heal_fix_small_faces=False, + ) + success = mg.generate(clean_polys, clean_lines, clean_points) + assert success + + missing = [] + for x, y in test_pts: + if not self._get_mesh_node_near(mg, x, y, 0.1): + missing.append((x, y)) + assert missing == [], f"No mesh node near these points: {missing}" + + def test_large_coords_embedded_point_heal_all_off(self): + """User scenario: large projected coords, heal on, all off, tol=1.""" + ox, oy = 584000.0, 2366000.0 + cm = ConceptualMesh(crs="EPSG:3857") + cm.add_polygon( + Polygon([(ox, oy), (ox + 13000, oy), (ox + 13000, oy + 9000), (ox, oy + 9000)]), + zone_id=1, resolution=500.0, dist_max=2000.0, + ) + test_pts = [ + (ox + 2000, oy + 2000), + (ox + 6500, oy + 4500), + (ox + 11000, oy + 7000), + ] + for i, (x, y) in enumerate(test_pts): + cm.add_point(Point(x, y), point_id=f"well_{i}", resolution=100.0, dist_min=0, dist_max=500) + clean_polys, clean_lines, clean_points = cm.generate() + + mg = MeshGenerator( + background_lc=500.0, verbosity=2, + heal_shapes=True, heal_tolerance=1.0, + heal_fix_degenerated=False, heal_fix_small_edges=False, heal_fix_small_faces=False, + ) + success = mg.generate(clean_polys, clean_lines, clean_points) + assert success + + missing = [] + for x, y in test_pts: + if not self._get_mesh_node_near(mg, x, y, 1.0): + missing.append((x, y)) + assert missing == [], f"No mesh node near these points: {missing}" + + +# --------------------------------------------------------------------------- +# Test: Isolation — raw Gmsh API to prove what removeAllDuplicates/healShapes do +# --------------------------------------------------------------------------- + +class TestRawGmshPointBehavior: + """ + Directly test Gmsh API behavior to isolate whether removeAllDuplicates + or healShapes destroy/renumber dim-0 entities. + """ + + def test_removeAllDuplicates_preserves_interior_point(self): + """A point inside a surface should survive removeAllDuplicates.""" + gmsh.initialize() + gmsh.model.add("test_dup") + occ = gmsh.model.occ + + # Square surface + p1 = occ.addPoint(0, 0, 0) + p2 = occ.addPoint(10, 0, 0) + p3 = occ.addPoint(10, 10, 0) + p4 = occ.addPoint(0, 10, 0) + l1 = occ.addLine(p1, p2) + l2 = occ.addLine(p2, p3) + l3 = occ.addLine(p3, p4) + l4 = occ.addLine(p4, p1) + cl = occ.addCurveLoop([l1, l2, l3, l4]) + s = occ.addPlaneSurface([cl]) + + # Interior point + pt_interior = occ.addPoint(5, 5, 0) + + # Fragment + all_tags = [(2, s), (0, pt_interior)] + out_dt, out_map = occ.fragment(all_tags, []) + + pts_before = set(t for d, t in occ.getEntities(0)) + occ.removeAllDuplicates() + pts_after = set(t for d, t in occ.getEntities(0)) + + # The interior point should not have been removed + # (it's not a duplicate of any vertex) + assert len(pts_after) >= len(pts_before), \ + f"removeAllDuplicates removed points: before={pts_before}, after={pts_after}" + + def test_removeAllDuplicates_merges_coincident_points(self): + """Two coincident points should be merged by removeAllDuplicates.""" + gmsh.initialize() + gmsh.model.add("test_dup_merge") + occ = gmsh.model.occ + + pt1 = occ.addPoint(5, 5, 0) + pt2 = occ.addPoint(5, 5, 0) + + pts_before = set(t for d, t in occ.getEntities(0)) + assert len(pts_before) == 2 + + occ.removeAllDuplicates() + pts_after = set(t for d, t in occ.getEntities(0)) + + # After dedup, only one should remain + assert len(pts_after) == 1, f"Expected 1 point after dedup, got {pts_after}" + + def test_healShapes_all_off_preserves_points(self): + """healShapes with all fix options off should not remove points (may renumber).""" + gmsh.initialize() + gmsh.model.add("test_heal_noop") + occ = gmsh.model.occ + + p1 = occ.addPoint(0, 0, 0) + p2 = occ.addPoint(10, 0, 0) + p3 = occ.addPoint(10, 10, 0) + p4 = occ.addPoint(0, 10, 0) + l1 = occ.addLine(p1, p2) + l2 = occ.addLine(p2, p3) + l3 = occ.addLine(p3, p4) + l4 = occ.addLine(p4, p1) + cl = occ.addCurveLoop([l1, l2, l3, l4]) + s = occ.addPlaneSurface([cl]) + pt = occ.addPoint(5, 5, 0) + + all_tags = [(2, s), (0, pt)] + occ.fragment(all_tags, []) + + pts_before_heal = set(t for d, t in occ.getEntities(0)) + n_before = len(pts_before_heal) + + # Save coordinates of interior point for verification + bb = occ.getBoundingBox(0, pt) + pt_coord = (round(bb[0], 6), round(bb[1], 6), round(bb[2], 6)) + + occ.healShapes( + [], tolerance=1.0, + fixDegenerated=False, fixSmallEdges=False, fixSmallFaces=False, + sewFaces=False, makeSolids=False, + ) + pts_after_heal = occ.getEntities(0) + n_after = len(pts_after_heal) + + # NOTE: healShapes renumbers tags even with all options off. + # What matters is that the same NUMBER of points survive and + # coordinates are preserved. + assert n_after == n_before, \ + f"healShapes(all off) lost points: {n_before} -> {n_after}" + + # Verify the interior point's coordinates still exist + found = False + for d, t in pts_after_heal: + bb2 = occ.getBoundingBox(0, t) + c2 = (round(bb2[0], 6), round(bb2[1], 6), round(bb2[2], 6)) + if c2 == pt_coord: + found = True + break + assert found, f"Interior point at {pt_coord} not found after healShapes" + + def test_healShapes_all_off_preserves_points_large_coords(self): + """Same as above but with large coordinates matching user scenario.""" + gmsh.initialize() + gmsh.model.add("test_heal_large") + occ = gmsh.model.occ + + ox, oy = 584000.0, 2366000.0 + p1 = occ.addPoint(ox, oy, 0) + p2 = occ.addPoint(ox + 13000, oy, 0) + p3 = occ.addPoint(ox + 13000, oy + 9000, 0) + p4 = occ.addPoint(ox, oy + 9000, 0) + l1 = occ.addLine(p1, p2) + l2 = occ.addLine(p2, p3) + l3 = occ.addLine(p3, p4) + l4 = occ.addLine(p4, p1) + cl = occ.addCurveLoop([l1, l2, l3, l4]) + s = occ.addPlaneSurface([cl]) + pt = occ.addPoint(ox + 6000, oy + 4500, 0) + + all_tags = [(2, s), (0, pt)] + occ.fragment(all_tags, []) + + n_before = len(occ.getEntities(0)) + bb = occ.getBoundingBox(0, pt) + pt_coord = (round(bb[0], 6), round(bb[1], 6), round(bb[2], 6)) + + occ.healShapes( + [], tolerance=1.0, + fixDegenerated=False, fixSmallEdges=False, fixSmallFaces=False, + sewFaces=False, makeSolids=False, + ) + pts_after = occ.getEntities(0) + n_after = len(pts_after) + + assert n_after == n_before, \ + f"healShapes(all off, tol=1) lost points with large coords: {n_before} -> {n_after}" + + found = False + for d, t in pts_after: + bb2 = occ.getBoundingBox(0, t) + c2 = (round(bb2[0], 6), round(bb2[1], 6), round(bb2[2], 6)) + if c2 == pt_coord: + found = True + break + assert found, f"Interior point at {pt_coord} not found after healShapes (large coords)" + + def test_synchronize_preserves_occ_points(self): + """Verify that occ.synchronize() doesn't lose dim-0 entities.""" + gmsh.initialize() + gmsh.model.add("test_sync") + occ = gmsh.model.occ + + p1 = occ.addPoint(0, 0, 0) + p2 = occ.addPoint(10, 0, 0) + p3 = occ.addPoint(10, 10, 0) + p4 = occ.addPoint(0, 10, 0) + l1 = occ.addLine(p1, p2) + l2 = occ.addLine(p2, p3) + l3 = occ.addLine(p3, p4) + l4 = occ.addLine(p4, p1) + cl = occ.addCurveLoop([l1, l2, l3, l4]) + s = occ.addPlaneSurface([cl]) + pt = occ.addPoint(5, 5, 0) + + all_tags = [(2, s), (0, pt)] + occ.fragment(all_tags, []) + occ.removeAllDuplicates() + + occ_pts_before_sync = set(t for d, t in occ.getEntities(0)) + occ.synchronize() + model_pts_after_sync = set(t for d, t in gmsh.model.getEntities(0)) + + assert occ_pts_before_sync == model_pts_after_sync, \ + f"synchronize lost points: occ={occ_pts_before_sync}, model={model_pts_after_sync}"