From 745b238668dfa4c690d14803b5217877e71d9f35 Mon Sep 17 00:00:00 2001
From: oscar <oscarfasanchez@intera.com>
Date: Mon, 8 Dec 2025 14:40:43 +0800
Subject: [PATCH 01/29] 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 <oscarfasanchez@intera.com>
Date: Mon, 8 Dec 2025 15:29:28 +0800
Subject: [PATCH 02/29] 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 <oscarfasanchez@intera.com>
Date: Mon, 8 Dec 2025 16:50:40 +0800
Subject: [PATCH 03/29] 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 <oscarfasanchez@intera.com>
Date: Tue, 9 Dec 2025 16:27:44 +0800
Subject: [PATCH 04/29] 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 <oscarfasanchez@intera.com>
Date: Wed, 10 Dec 2025 11:12:55 +0800
Subject: [PATCH 05/29] 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 <oscarfasanchez@intera.com>
Date: Fri, 12 Dec 2025 11:29:29 +0800
Subject: [PATCH 06/29] 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 <oscarfasanchez@gmail.com>
Date: Sun, 14 Dec 2025 13:00:20 +0800
Subject: [PATCH 07/29] 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 <oscarfasanchez@gmail.com>
Date: Sun, 14 Dec 2025 14:14:28 +0800
Subject: [PATCH 08/29] 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 <oscarfasanchez@gmail.com>
Date: Sun, 14 Dec 2025 22:14:28 +0800
Subject: [PATCH 09/29] 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 <oscarfasanchez@intera.com>
Date: Tue, 16 Dec 2025 16:48:44 +0800
Subject: [PATCH 10/29] 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 <oscarfasanchez@intera.com>
Date: Wed, 17 Dec 2025 17:49:35 +0800
Subject: [PATCH 11/29] 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 <oscarfasanchez@gmail.com>
Date: Wed, 17 Dec 2025 20:25:47 +0800
Subject: [PATCH 12/29] 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 <oscarfasanchez@gmail.com>
Date: Wed, 17 Dec 2025 22:51:35 +0800
Subject: [PATCH 13/29] 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 <oscarfasanchez@gmail.com>
Date: Wed, 17 Dec 2025 22:52:03 +0800
Subject: [PATCH 14/29] 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 <oscarfasanchez@gmail.com>
Date: Wed, 17 Dec 2025 23:09:44 +0800
Subject: [PATCH 15/29] 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 <oscarfasanchez@gmail.com>
Date: Fri, 9 Jan 2026 08:38:04 +0800
Subject: [PATCH 16/29] 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 <oscarfasanchez@intera.com>
Date: Mon, 12 Jan 2026 08:59:36 +0800
Subject: [PATCH 17/29] 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 <oscarfasanchez@intera.com>
Date: Mon, 12 Jan 2026 16:30:21 +0800
Subject: [PATCH 18/29] 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 <oscarfasanchez@intera.com>
Date: Thu, 19 Feb 2026 14:56:44 +0800
Subject: [PATCH 19/29] 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 <oscarfasanchez@intera.com>
Date: Thu, 19 Feb 2026 17:28:30 +0800
Subject: [PATCH 20/29] 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 <oscarfasanchez@intera.com>
Date: Fri, 20 Feb 2026 14:00:11 +0800
Subject: [PATCH 21/29] 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 <oscarfasanchez@intera.com>
Date: Fri, 20 Feb 2026 15:52:33 +0800
Subject: [PATCH 22/29] 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 <oscarfasanchez@intera.com>
Date: Fri, 20 Feb 2026 16:21:55 +0800
Subject: [PATCH 23/29] 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: <class 'numpy.int64'>)\n",
+      "First Map Key: 1 (Type: <class 'int'>)\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": [
+       "<Figure size 1200x1200 with 1 Axes>"
+      ]
+     },
+     "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": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "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 <oscarfasanchez@intera.com>
Date: Fri, 20 Feb 2026 16:56:12 +0800
Subject: [PATCH 24/29] 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 <oscarfasanchez@intera.com>
Date: Thu, 16 Apr 2026 08:23:34 +0800
Subject: [PATCH 25/29] 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 <oscarfasanchez@intera.com>
Date: Fri, 17 Apr 2026 17:49:50 +0800
Subject: [PATCH 26/29] 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}"

From 5cebc4db2f8ff5b66a617257bba2cd0be16dfdca Mon Sep 17 00:00:00 2001
From: oscarfasanchez <oscarfasanchez@gmail.com>
Date: Tue, 2 Jun 2026 08:40:12 +0800
Subject: [PATCH 27/29] fix to embedded features and and fields, remove
 features outside domain, to avoid fields effect, defers non embedded polygons
 to avoid conflicts in cleaning, changes closest point to is inside to
 identify which points are inside.

---
 src/vorflow/blueprint.py       |  68 +++++-
 src/vorflow/engine.py          | 426 ++++++++++++++++++++++-----------
 src/vorflow/fields.py          | 125 ++++++++--
 tests/test_conceptual_mesh.py  |  42 +++-
 tests/test_integration_gmsh.py |  81 +++++++
 5 files changed, 588 insertions(+), 154 deletions(-)

diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py
index 982f414..f2d1ffc 100644
--- a/src/vorflow/blueprint.py
+++ b/src/vorflow/blueprint.py
@@ -460,6 +460,69 @@ def _enforce_connectivity(self, connectivity_tolerance=None):
                     self.raw_points[i]['geometry'] = snapped_point
 
 
+    def _clip_features_to_domain(self):
+        """Remove or trim line/point features that remain outside the meshing domain."""
+        if self.clean_polygons.empty:
+            return
+
+        domain_union = unary_union(self.clean_polygons.geometry)
+        if domain_union.is_empty:
+            return
+        domain_union = make_valid(domain_union)
+
+        clipped_lines = []
+        for line_data in self.raw_lines:
+            geom = line_data.get("geometry")
+            if geom is None or geom.is_empty:
+                continue
+            try:
+                clipped = geom.intersection(domain_union)
+            except Exception:
+                clipped = make_valid(geom).intersection(domain_union)
+
+            if clipped.is_empty:
+                continue
+
+            line_parts = []
+            if clipped.geom_type in ("LineString", "MultiLineString"):
+                line_parts = [clipped] if clipped.geom_type == "LineString" else list(clipped.geoms)
+            elif clipped.geom_type == "GeometryCollection":
+                line_parts = [
+                    part for part in clipped.geoms
+                    if part.geom_type in ("LineString", "MultiLineString") and not part.is_empty
+                ]
+
+            for part in line_parts:
+                if part.geom_type == "MultiLineString":
+                    for subpart in part.geoms:
+                        if subpart.length > 0:
+                            feat = line_data.copy()
+                            feat["geometry"] = subpart
+                            clipped_lines.append(feat)
+                elif part.length > 0:
+                    feat = line_data.copy()
+                    feat["geometry"] = part
+                    clipped_lines.append(feat)
+
+        removed_lines = len(self.raw_lines) - len(clipped_lines)
+        if removed_lines > 0:
+            print(f"Clipped/removed {removed_lines} line feature(s) outside the domain.")
+        self.raw_lines = clipped_lines
+
+        kept_points = []
+        for point_data in self.raw_points:
+            geom = point_data.get("geometry")
+            if geom is None or geom.is_empty:
+                continue
+            if domain_union.covers(geom):
+                kept_points.append(point_data)
+
+        removed_points = len(self.raw_points) - len(kept_points)
+        if removed_points > 0:
+            print(f"Removed {removed_points} point feature(s) outside the domain.")
+        self.raw_points = kept_points
+
+
     def generate(self, connectivity_tolerance=None):
         """
         Runs the full preprocessing workflow: resolves polygon overlaps,
@@ -489,6 +552,9 @@ def generate(self, connectivity_tolerance=None):
         
         print("Enforcing strict topology...")
         self._enforce_connectivity(connectivity_tolerance=connectivity_tolerance)
+
+        print("Clipping features to domain...")
+        self._clip_features_to_domain()
         
         # Promote the processed raw geometries to final "clean" GeoDataFrames.
         if self.raw_lines:
@@ -652,4 +718,4 @@ 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
+            self.clean_lines['geometry'] = self.clean_lines.apply(_line_densify, axis=1)
diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py
index 523c5d4..5109939 100644
--- a/src/vorflow/engine.py
+++ b/src/vorflow/engine.py
@@ -14,7 +14,7 @@ def __init__(self, background_lc=None, verbosity=0, mesh_algorithm=6,
                  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):
+                 heal_fix_small_faces=True, diagnose=False):
         """
         Initializes the Gmsh-based mesh generator.
 
@@ -47,6 +47,8 @@ def __init__(self, background_lc=None, verbosity=0, mesh_algorithm=6,
             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.
+            diagnose (bool): If True, retain structured diagnostic details from
+                geometry transfer, embedding, and meshing steps.
         """
         self.background_lc = background_lc
         self.verbosity = verbosity
@@ -59,11 +61,41 @@ def __init__(self, background_lc=None, verbosity=0, mesh_algorithm=6,
         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.diagnose = bool(diagnose)
 
         self.initialized = False
         self.nodes = None
         self.node_tags = None
         self.zones_gdf = None
+        self.diagnostics = {}
+
+    def _sanitize_coords(self, coords, *, min_spacing=1e-5, require_closed=False, min_points=2):
+        """Remove invalid and near-duplicate coordinates before OCC creation."""
+        clean_coords = []
+        for pt in coords:
+            if len(pt) < 2:
+                continue
+            x = float(pt[0])
+            y = float(pt[1])
+            if not (math.isfinite(x) and math.isfinite(y)):
+                continue
+            if clean_coords:
+                dist = math.sqrt((x - clean_coords[-1][0])**2 + (y - clean_coords[-1][1])**2)
+                if dist <= min_spacing:
+                    continue
+            clean_coords.append((x, y))
+
+        if require_closed and len(clean_coords) > 1:
+            dist = math.sqrt(
+                (clean_coords[0][0] - clean_coords[-1][0])**2 +
+                (clean_coords[0][1] - clean_coords[-1][1])**2
+            )
+            if dist <= min_spacing:
+                clean_coords.pop()
+
+        if len(clean_coords) < min_points:
+            return []
+        return clean_coords
     
     def _force_close_polygon(self, poly):
         """Ensure a polygon's exterior and interior rings are closed."""
@@ -124,6 +156,7 @@ def _add_geometry(self, polygons_gdf, lines_gdf, points_gdf, launch_gmsh_gui=Fal
         # 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 = {}
+        pending_nonembedded_polys = []
 
         # Embedded geometry DOES participate in fragmentation.
         embedded_point_tags = []
@@ -138,6 +171,62 @@ def is_embedded(row) -> bool:
             if pd.isna(val):
                 return True
             return bool(val)
+
+        def create_polygon_surface(poly):
+            """Create a Gmsh plane surface and return its tag plus boundary curves."""
+            if poly.is_empty:
+                return None, []
+
+            poly = self._force_close_polygon(poly)
+
+            def create_loop(coords):
+                clean_coords = self._sanitize_coords(
+                    coords,
+                    min_spacing=1e-5,
+                    require_closed=True,
+                    min_points=3,
+                )
+
+                if len(clean_coords) < 3:
+                    return None, []
+
+                p_tags = [gmsh.model.occ.addPoint(x, y, 0) for x, y in clean_coords]
+                l_tags = []
+                for i in range(len(p_tags)):
+                    p1 = p_tags[i]
+                    p2 = p_tags[(i + 1) % len(p_tags)]
+                    try:
+                        l_tags.append(gmsh.model.occ.addLine(p1, p2))
+                    except Exception as e:
+                        print(f"Error adding line {p1}-{p2}: {e}")
+                        return None, []
+
+                try:
+                    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, []
+
+            exterior_loop_tag, exterior_lines = create_loop(list(poly.exterior.coords))
+            if exterior_loop_tag is None:
+                return None, []
+
+            loops = [exterior_loop_tag]
+            boundary_curve_tags = list(exterior_lines)
+            for interior in poly.interiors:
+                interior_loop_tag, interior_lines = create_loop(list(interior.coords))
+                if interior_loop_tag is not None:
+                    loops.append(interior_loop_tag)
+                    boundary_curve_tags.extend(interior_lines)
+
+            try:
+                s_tag = gmsh.model.occ.addPlaneSurface(loops)
+            except Exception as e:
+                print(f"Error creating surface: {e}")
+                return None, []
+
+            return s_tag, boundary_curve_tags
         
         # Add all point features to the Gmsh model first.
         for idx, row in points_gdf.iterrows():
@@ -272,22 +361,38 @@ def is_embedded(row) -> bool:
                 for part in parts:
                     # Filter out tiny fragments that might remain after trimming.
                     if part.length < 1e-6: continue
-                    
-                    coords = list(part.coords)
-                    if len(coords) < 2: continue
-                    
+
+                    coords = self._sanitize_coords(list(part.coords), min_points=2)
+                    if len(coords) < 2:
+                        if self.verbosity > 0:
+                            print(f"Warning: Skipping degenerate line part for feature {idx} after coordinate cleanup.")
+                        continue
+
                     # Add each segment of the line to Gmsh.
                     pt_tags = [gmsh.model.occ.addPoint(x, y, 0) for x, y in coords]
+                    created_segments = 0
                     for i in range(len(pt_tags) - 1):
-                        l = gmsh.model.occ.addLine(pt_tags[i], pt_tags[i+1])
-                        
+                        try:
+                            l = gmsh.model.occ.addLine(pt_tags[i], pt_tags[i+1])
+                        except Exception as e:
+                            if self.verbosity > 0:
+                                print(
+                                    f"Warning: Skipping invalid line segment {i} for feature {idx} "
+                                    f"between {coords[i]} and {coords[i+1]}: {e}"
+                                )
+                            continue
+
                         key = to_key(1, l)
+                        created_segments += 1
                         if embedded:
                             embedded_line_tags.append(key)
                             input_tag_info[key] = {'type': 'line', 'id': idx}
                         else:
                             nonembedded_line_tags.setdefault(int(idx), []).append(key)
 
+                    if created_segments == 0 and self.verbosity > 0:
+                        print(f"Warning: No valid line segments were created for feature {idx}.")
+
         # Add polygon features to the model.
         if not polygons_gdf.empty:
             print(f"Adding {len(polygons_gdf)} polygons to Gmsh...")
@@ -304,90 +409,24 @@ def is_embedded(row) -> bool:
                 for poly in polys:
                     if poly.is_empty:
                         continue
-                        
-                    # Ensure the polygon is valid and closed before processing.
-                    poly = self._force_close_polygon(poly)
-
-                    def create_loop(coords):
-                        # Remove consecutive duplicates and points that are too close
-                        clean_coords = []
-                        for pt in coords:
-                            if not clean_coords:
-                                clean_coords.append(pt)
-                                continue
-                            
-                            # Check distance to last point
-                            dist = math.sqrt((pt[0]-clean_coords[-1][0])**2 + (pt[1]-clean_coords[-1][1])**2)
-                            if dist > 1e-5: # Slightly larger than Gmsh tolerance to be safe
-                                clean_coords.append(pt)
-                        
-                        # Check closure with first point
-                        if len(clean_coords) > 1:
-                             dist = math.sqrt((clean_coords[0][0]-clean_coords[-1][0])**2 + (clean_coords[0][1]-clean_coords[-1][1])**2)
-                             if dist < 1e-5:
-                                 clean_coords.pop()
-                            
-                        if len(clean_coords) < 3:
-                            # A polygon must have at least 3 points (triangle)
-                            return None, []
-
-                        p_tags = [gmsh.model.occ.addPoint(x, y, 0) for x, y in clean_coords]
-                        l_tags = []
-                        for i in range(len(p_tags)):
-                            p1 = p_tags[i]
-                            p2 = p_tags[(i + 1) % len(p_tags)]
-                            try:
-                                l_tags.append(gmsh.model.occ.addLine(p1, p2))
-                            except Exception as e:
-                                print(f"Error adding line {p1}-{p2}: {e}")
-                                return None, []
-                        
-                        try:
-                            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, []
 
-                    # 1. Exterior Boundary
-                    ext_coords = list(poly.exterior.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}")
+                    if not embedded:
+                        # Defer field-only polygon creation until after
+                        # fragmentation/dedup/healing. If these overlapping
+                        # surfaces exist during global OCC cleanup they can cut
+                        # or renumber embedded domain surfaces, which violates
+                        # embed=False semantics.
+                        pending_nonembedded_polys.append((int(idx), poly))
                         continue
 
-                    # 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, 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 (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}")
+                    s_tag, boundary_curve_tags = create_polygon_surface(poly)
+                    if s_tag is None:
+                        print(f"Warning: Skipping degenerate polygon {idx}")
                         continue
 
                     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:
-                        # 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]
-                        )
+                    input_tag_info[key] = {'type': 'surface', 'id': idx}
+                    embedded_surface_tags.append(key)
         #call the gui before fragmentation for debugging
         if self.verbosity > 1 and launch_gmsh_gui==True:
             gmsh.model.occ.synchronize()
@@ -710,6 +749,21 @@ def create_loop(coords):
                 print(f"[DIAG] *** Lines from these features became BOUNDARIES: "
                       f"{sorted(_boundary_feats)} ***")
         # <<< DIAG
+
+        if pending_nonembedded_polys:
+            if self.verbosity > 0:
+                print(f"Adding {len(pending_nonembedded_polys)} field-only polygon surface(s)...")
+            for idx, poly in pending_nonembedded_polys:
+                s_tag, boundary_curve_tags = create_polygon_surface(poly)
+                if s_tag is None:
+                    if self.verbosity > 0:
+                        print(f"Warning: Skipping degenerate field-only polygon {idx}")
+                    continue
+                nonembedded_surface_tags.setdefault(int(idx), []).append(to_key(2, s_tag))
+                nonembedded_poly_curve_tags.setdefault(int(idx), []).extend(
+                    [(1, int(t)) for t in boundary_curve_tags]
+                )
+            gmsh.model.occ.synchronize()
         
         # After fragmentation, we need to rebuild our map of which original
         # feature corresponds to which new Gmsh tags.
@@ -987,7 +1041,13 @@ def _auto_threshold_from_row(row, background_lc):
             # 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': [] }
+            tags_dict = {
+                'points': [],
+                'lines': [],
+                'surfaces': [],
+                'embedded_surfaces': [],
+                'field_only_surfaces': [],
+            }
 
             # Points
             for fid in feature_ids_by_geom.get('points', []):
@@ -1005,7 +1065,19 @@ def _auto_threshold_from_row(row, background_lc):
             # 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]))
+                    surface_tags = extract_tags(gmsh_map['surfaces'][fid])
+                    tags_dict['surfaces'].extend(surface_tags)
+
+                    try:
+                        embed_val = polygons_gdf.loc[fid].get('embed', True)
+                        embedded = True if pd.isna(embed_val) else bool(embed_val)
+                    except Exception:
+                        embedded = True
+
+                    if embedded:
+                        tags_dict['embedded_surfaces'].extend(surface_tags)
+                    else:
+                        tags_dict['field_only_surfaces'].extend(surface_tags)
                 # 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]
@@ -1105,15 +1177,18 @@ def is_embedded(row):
 
         # >>> 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': []}
+                 'skip_no_match': 0, 'failed': 0, 'boundary_skip': 0,
+                 'multi_match': 0, 'inside_failed': 0,
+                 'conflict_tags': [], 'fail_tags': [], 'boundary_tags': [],
+                 'multi_tags': [], 'inside_fail_tags': [], 'records': []}
         # <<< 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.
+        # against trimmed surfaces with gmsh.model.isInside(). Do not use
+        # getClosestPoint() here: for coplanar OCC surfaces it can project onto
+        # the support plane outside the trimmed face, causing false multi-surface
+        # embeds and over-constraining Gmsh.
         _surf_bboxes = {}
         for _st in domain_surface_tags:
             try:
@@ -1122,6 +1197,52 @@ def is_embedded(row):
             except Exception:
                 pass
 
+        def _bbox_contains_point(sbb, pt, eps=1e-4):
+            return (
+                sbb[0] - eps <= pt[0] <= sbb[3] + eps and
+                sbb[1] - eps <= pt[1] <= sbb[4] + eps and
+                sbb[2] - eps <= pt[2] <= sbb[5] + eps
+            )
+
+        def _entity_sample_points(dim, tag, bbox):
+            xmin, ymin, zmin, xmax, ymax, zmax = bbox
+            if dim == 0:
+                return [((xmin + xmax) / 2.0, (ymin + ymax) / 2.0, (zmin + zmax) / 2.0)]
+            if dim != 1:
+                return []
+
+            pmin, pmax = gmsh.model.getParametrizationBounds(1, tag)
+            lo = float(pmin[0])
+            hi = float(pmax[0])
+            if not (math.isfinite(lo) and math.isfinite(hi)):
+                return []
+            if hi < lo:
+                lo, hi = hi, lo
+
+            # Avoid exact endpoints: line ends commonly lie on partition
+            # boundaries and are ambiguous. Interior samples identify the
+            # trimmed surface that actually owns the line fragment.
+            params = [lo + (hi - lo) * f for f in (0.25, 0.5, 0.75)]
+            points = []
+            seen = set()
+            for param in params:
+                val = gmsh.model.getValue(1, tag, [param])
+                pt = (float(val[0]), float(val[1]), float(val[2]))
+                key = (round(pt[0], 8), round(pt[1], 8), round(pt[2], 8))
+                if key not in seen:
+                    seen.add(key)
+                    points.append(pt)
+            return points
+
+        def _surface_area(surf_tag):
+            try:
+                return float(gmsh.model.occ.getMass(2, int(surf_tag)))
+            except Exception:
+                try:
+                    return float(gmsh.model.getMass(2, int(surf_tag)))
+                except Exception:
+                    return float("inf")
+
         def embed_entity(dim, tag):
             # 1. Verify entity exists
             try:
@@ -1132,74 +1253,86 @@ def embed_entity(dim, tag):
             
             xmin, ymin, zmin, xmax, ymax, zmax = bbox
             
-            # Check if line is already a boundary of some surface
+            # Check if line is already a boundary of some surface. Boundary
+            # curves already constrain their adjacent surfaces; explicitly
+            # embedding them elsewhere duplicates constraints and can make Gmsh
+            # non-terminating on dense partitioned geometries.
             is_boundary_of = set()
             if dim == 1:
                 try:
                     up, _down = gmsh.model.getAdjacencies(1, tag)
-                    is_boundary_of = set(up)
+                    is_boundary_of = {int(v) for v in up}
                 except Exception:
                     pass
+                if is_boundary_of:
+                    _elog['boundary_skip'] += 1
+                    _elog['boundary_tags'].append((int(tag), sorted(is_boundary_of)))
+                    return
 
-            # 2. Get a representative point from the entity
-            check_x, check_y, check_z = 0.0, 0.0, 0.0
-            if dim == 0:
-                check_x = (xmin + xmax) / 2.0
-                check_y = (ymin + ymax) / 2.0
-                check_z = (zmin + zmax) / 2.0
-            elif dim == 1:
-                pmin, pmax = gmsh.model.getParametrizationBounds(1, tag)
-                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]
-
-            # 3. Fast bbox pre-filter: only test surfaces whose bbox contains the check point
-            candidates = []
+            # 2. Sample the entity inside its extent.
+            try:
+                sample_points = _entity_sample_points(dim, tag, bbox)
+            except Exception:
+                sample_points = []
+            if not sample_points:
+                _elog['skip_no_match'] += 1
+                return
+
+            # 3. Fast bbox pre-filter: only test surfaces whose bbox contains at
+            # least one sampled point.
+            candidates = set()
             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 any(_bbox_contains_point(sbb, pt, eps=eps) for pt in sample_points):
+                    candidates.add(int(surf_tag))
 
             if not candidates:
-                _elog['skip_no_match'] += 1
+                _elog['skip_no_cand'] += 1
                 return
 
-            # 4. Confirm with getClosestPoint (only on bbox-filtered candidates)
+            # 4. Confirm with isInside(). For lines, require all interior sample
+            # points to be inside a single trimmed surface.
             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
+            flat_points = []
+            for pt in sample_points:
+                flat_points.extend([pt[0], pt[1], pt[2]])
+            for surf_tag in sorted(candidates):
                 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:
+                    inside_count = int(gmsh.model.isInside(2, int(surf_tag), flat_points))
+                    if inside_count == len(sample_points):
                         target_matches.append(surf_tag)
                 except Exception:
-                    pass
+                    _elog['inside_failed'] += 1
+                    _elog['inside_fail_tags'].append((int(tag), int(surf_tag)))
 
             # 5. Embed the entity into the verified surfaces
             if target_matches:
-                target_matches = list(set(target_matches))
+                target_matches = sorted(set(target_matches))
+                if len(target_matches) > 1:
+                    _elog['multi_match'] += 1
+                    _elog['multi_tags'].append((int(tag), list(target_matches)))
+                    # Nested or overlapping source polygons can still produce
+                    # multiple containing faces. Choose the smallest trimmed
+                    # surface as the most local owner instead of embedding the
+                    # same entity into every containing face.
+                    target_matches = [min(target_matches, key=_surface_area)]
                 for st in target_matches:
                     try:
                         gmsh.model.mesh.embed(dim, [tag], 2, st)
                         _elog['ok'] += 1
+                        if self.diagnose:
+                            _elog['records'].append({
+                                'dim': int(dim),
+                                'tag': int(tag),
+                                'surface': int(st),
+                                'bbox': tuple(float(v) for v in bbox),
+                                'sample_points': sample_points,
+                            })
                     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
+                _elog['skip_no_match'] += 1
 
         # Iterate and Embed Points
         if points_gdf is not None and not points_gdf.empty:
@@ -1233,7 +1366,10 @@ def embed_entity(dim, tag):
                   f"{_elog['skip_bbox']} no-bbox, "
                   f"{_elog['skip_no_cand']} empty-bbox, "
                   f"{_filt} filtered-out, "
-                  f"{_elog['skip_no_match']} no-match")
+                  f"{_elog['skip_no_match']} no-match, "
+                  f"{_elog['boundary_skip']} boundary-skip, "
+                  f"{_elog['multi_match']} multi-match, "
+                  f"{_elog['inside_failed']} inside-failed")
             if _filt > 0:
                 print(f"[DIAG] *** {_filt} entities found nearby surfaces but NONE "
                       f"were in domain_surface_tags — likely missing domain surface! ***")
@@ -1243,8 +1379,30 @@ def embed_entity(dim, tag):
                       f"(first 10): {uniq[:10]} ***")
             if _elog['fail_tags']:
                 print(f"[DIAG] *** Failed embeds: {_elog['fail_tags'][:5]} ***")
+            if _elog['boundary_tags']:
+                uniq = _elog['boundary_tags'][:10]
+                print(f"[DIAG] Boundary line fragments skipped (first 10): {uniq}")
+            if _elog['multi_tags']:
+                print(f"[DIAG] Multi-surface embed candidates collapsed "
+                      f"(first 10): {_elog['multi_tags'][:10]}")
         # <<< DIAG
 
+        self.diagnostics['embedding'] = {
+            'ok': _elog['ok'],
+            'failed': _elog['failed'],
+            'skip_bbox': _elog['skip_bbox'],
+            'skip_no_cand': _elog['skip_no_cand'],
+            'skip_no_match': _elog['skip_no_match'],
+            'boundary_skip': _elog['boundary_skip'],
+            'multi_match': _elog['multi_match'],
+            'inside_failed': _elog['inside_failed'],
+            'boundary_tags': list(_elog['boundary_tags']),
+            'multi_tags': list(_elog['multi_tags']),
+            'fail_tags': list(_elog['fail_tags']),
+        }
+        if self.diagnose:
+            self.diagnostics['embedding']['records'] = list(_elog['records'])
+
 
     def generate(self, clean_polys, clean_lines, clean_points, output_file=None, launch_gmsh_gui=False):
         """
@@ -1442,4 +1600,4 @@ def _accumulate_nodes(dim: int, ent_tag: int, include_boundary: bool, tag_to_xy:
         except Exception as e:
             print(f"Mesh Generation Failed: {e}")
             self._finalize_gmsh()
-            raise e
\ No newline at end of file
+            raise e
diff --git a/src/vorflow/fields.py b/src/vorflow/fields.py
index 7e47bee..8c027f2 100644
--- a/src/vorflow/fields.py
+++ b/src/vorflow/fields.py
@@ -55,6 +55,89 @@ def create(self, gmsh_api, tags_dict):
 
         return f_dist
 
+
+def _surface_boundary_curves(gmsh_api, surface_tags):
+    """Return boundary curve tags for the given surface tags."""
+    curves = []
+    seen = set()
+    for tag in surface_tags:
+        try:
+            boundary = gmsh_api.model.getBoundary(
+                [(2, int(tag))],
+                combined=False,
+                oriented=False,
+                recursive=False,
+            )
+        except Exception:
+            boundary = []
+
+        for dim, curve_tag in boundary:
+            if int(dim) != 1:
+                continue
+            curve_tag = int(curve_tag)
+            if curve_tag not in seen:
+                seen.add(curve_tag)
+                curves.append(curve_tag)
+    return curves
+
+
+def _distance_tags_for_growth(gmsh_api, tags_dict, sampling):
+    """Build tags for distance growth while keeping embedded polygon interiors flat."""
+    embedded_surfaces = tags_dict.get("embedded_surfaces", None)
+    if embedded_surfaces is None:
+        embedded_surfaces = tags_dict.get("surfaces", [])
+
+    field_only_surfaces = tags_dict.get("field_only_surfaces", [])
+    boundary_curves = _surface_boundary_curves(gmsh_api, embedded_surfaces)
+
+    growth_tags = {
+        "points": list(tags_dict.get("points", [])),
+        "lines": list(tags_dict.get("lines", [])) + boundary_curves,
+        "surfaces": list(field_only_surfaces),
+    }
+
+    # If no boundary curves could be recovered, fall back to the old surface
+    # distance behavior instead of dropping the field.
+    if embedded_surfaces and not boundary_curves:
+        growth_tags["surfaces"].extend(embedded_surfaces)
+
+    return DistanceField(include_surfaces=True, sampling=sampling).create(
+        gmsh_api, growth_tags
+    )
+
+
+def _restricted_surface_constant(gmsh_api, surface_tags, size):
+    if not surface_tags:
+        return None
+
+    const = gmsh_api.model.mesh.field.add("MathEval")
+    gmsh_api.model.mesh.field.setString(const, "F", str(float(size)))
+
+    restricted = gmsh_api.model.mesh.field.add("Restrict")
+    gmsh_api.model.mesh.field.setNumber(restricted, "IField", const)
+    gmsh_api.model.mesh.field.setNumbers(
+        restricted, "SurfacesList", [float(t) for t in surface_tags]
+    )
+    return restricted
+
+
+def _combine_with_embedded_surface_constant(gmsh_api, growth_field, tags_dict, size):
+    embedded_surfaces = tags_dict.get("embedded_surfaces", None)
+    if embedded_surfaces is None:
+        embedded_surfaces = tags_dict.get("surfaces", [])
+
+    restricted = _restricted_surface_constant(gmsh_api, embedded_surfaces, size)
+    if restricted is None:
+        return growth_field
+    if growth_field is None:
+        return restricted
+
+    f_min = gmsh_api.model.mesh.field.add("Min")
+    gmsh_api.model.mesh.field.setNumbers(
+        f_min, "FieldsList", [float(growth_field), float(restricted)]
+    )
+    return f_min
+
 # --- Manual Fields ---
 
 class ConstantField(MeshField):
@@ -75,13 +158,15 @@ def __init__(self, size_min, dist_min, dist_max, size_max=None, sampling=20):
         self.dist_max = float(dist_max)
         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, constant_in =False):
-        # 1. Distance Field (can combine points, curves, surfaces)
-        f_dist = DistanceField(include_surfaces=True, sampling=self.sampling).create(
-            gmsh_api, tags_dict
-        )
+    def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, constant_in=False):
+        # 1. Distance field for growth away from features. For embedded polygon
+        # surfaces, use their boundary curves for growth and add a restricted
+        # constant field below so the polygon interior remains flat.
+        f_dist = _distance_tags_for_growth(gmsh_api, tags_dict, self.sampling)
         if f_dist is None:
-            return None
+            return _combine_with_embedded_surface_constant(
+                gmsh_api, None, tags_dict, self.size_min
+            )
 
         # 2. Threshold Field
         f_thresh = gmsh_api.model.mesh.field.add("Threshold")
@@ -90,8 +175,10 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, constant_i
         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
+
+        return _combine_with_embedded_surface_constant(
+            gmsh_api, f_thresh, tags_dict, self.size_min
+        )
 
 class ExponentialField(MeshField):
     def __init__(self, size_min, decay_length, size_max=None, sampling=20):
@@ -101,18 +188,20 @@ 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=True, sampling=self.sampling).create(
-            gmsh_api, tags_dict
-        )
+        f_dist = _distance_tags_for_growth(gmsh_api, tags_dict, self.sampling)
         if f_dist is None:
-            return None
+            return _combine_with_embedded_surface_constant(
+                gmsh_api, None, tags_dict, self.size_min
+            )
 
         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
+        return _combine_with_embedded_surface_constant(
+            gmsh_api, f_math, tags_dict, self.size_min
+        )
 
 # --- Auto Fields ---
 
@@ -155,15 +244,15 @@ 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=True, sampling=int(sampling)).create(
-            gmsh_api, tags_dict
-        )
+        f_dist = _distance_tags_for_growth(gmsh_api, tags_dict, int(sampling))
         if f_dist is None:
-            return None
+            return _combine_with_embedded_surface_constant(
+                gmsh_api, None, tags_dict, cs
+            )
 
         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
+        return _combine_with_embedded_surface_constant(gmsh_api, f_math, tags_dict, cs)
diff --git a/tests/test_conceptual_mesh.py b/tests/test_conceptual_mesh.py
index 4eb27ee..6bcf94b 100644
--- a/tests/test_conceptual_mesh.py
+++ b/tests/test_conceptual_mesh.py
@@ -33,7 +33,7 @@ def test_lines_and_points_snap_to_polygons():
     cm.add_polygon(square, zone_id=1)
 
     line = LineString([(-0.5, 1.0), (0.5, 1.0)])
-    point = Point(-0.0005, 0.5)
+    point = Point(-0.0005, 0.0)
 
     cm.add_line(line, line_id="river", resolution=0.1)
     cm.add_point(point, point_id="well", resolution=0.1)
@@ -52,6 +52,46 @@ def test_lines_and_points_snap_to_polygons():
     assert snapped_point.distance(boundary) <= tolerance
 
 
+def test_points_outside_domain_are_removed_after_connectivity():
+    cm = ConceptualMesh(connectivity_tolerance=0.01)
+
+    square = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)])
+    cm.add_polygon(square, zone_id=1)
+    cm.add_line(LineString([(0, 1), (2, 1)]), line_id="river", resolution=0.5, densify=False)
+    cm.add_point(Point(-0.05, 1), point_id="outside_well", resolution=0.05)
+
+    _, _, clean_points = cm.generate()
+
+    assert clean_points.empty
+
+
+def test_points_snapped_to_domain_boundary_are_kept():
+    cm = ConceptualMesh(connectivity_tolerance=0.1)
+
+    square = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)])
+    cm.add_polygon(square, zone_id=1)
+    cm.add_line(LineString([(0, 1), (2, 1)]), line_id="river", resolution=0.5, densify=False)
+    cm.add_point(Point(-0.05, 1), point_id="snapped_well", resolution=0.05)
+
+    _, _, clean_points = cm.generate()
+
+    assert len(clean_points) == 1
+    assert clean_points.iloc[0].geometry.equals(Point(0, 1))
+
+
+def test_lines_are_clipped_to_domain_after_connectivity():
+    cm = ConceptualMesh(connectivity_tolerance=0.0)
+
+    square = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)])
+    cm.add_polygon(square, zone_id=1)
+    cm.add_line(LineString([(-1, 1), (3, 1)]), line_id="crossing_line", resolution=0.5, densify=False)
+
+    _, clean_lines, _ = cm.generate()
+
+    assert len(clean_lines) == 1
+    assert clean_lines.iloc[0].geometry.equals(LineString([(0, 1), (2, 1)]))
+
+
 def test_generate_uses_constructor_connectivity_tolerance(monkeypatch):
     cm = ConceptualMesh(connectivity_tolerance=0.25)
     square = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)])
diff --git a/tests/test_integration_gmsh.py b/tests/test_integration_gmsh.py
index dc3332f..9bf0773 100644
--- a/tests/test_integration_gmsh.py
+++ b/tests/test_integration_gmsh.py
@@ -5,6 +5,7 @@
 
 from vorflow.blueprint import ConceptualMesh
 from vorflow.engine import MeshGenerator
+from vorflow.fields import AutoExponentialField
 from vorflow.tessellator import VoronoiTessellator
 
 @pytest.fixture(autouse=True)
@@ -82,6 +83,44 @@ def test_gmsh_integration_with_internal_line():
     assert len(grid) > 10
 
 
+def test_gmsh_integration_tolerates_duplicate_line_vertices():
+    """Duplicate consecutive line vertices should not crash mesh generation."""
+    cm = ConceptualMesh(crs="EPSG:3857")
+    square = Polygon([(0, 0), (10, 0), (10, 10), (0, 10)])
+    cm.add_polygon(square, zone_id=1, resolution=4.0, dist_max=20.0)
+
+    line = LineString([(1, 1), (5, 5), (5, 5), (9, 9)])
+    cm.add_line(line, line_id="duplicate_vertices", resolution=1.0)
+
+    clean_polys, clean_lines, clean_points = cm.generate()
+
+    mg = MeshGenerator(background_lc=4.0, verbosity=0)
+    success = mg.generate(clean_polys, clean_lines, clean_points)
+
+    assert success
+    assert mg.nodes is not None
+    assert len(mg.nodes) > 0
+
+
+def test_gmsh_integration_tolerates_near_duplicate_line_vertices():
+    """Near-zero segments should be cleaned before OCC line creation."""
+    cm = ConceptualMesh(crs="EPSG:3857")
+    square = Polygon([(0, 0), (10, 0), (10, 10), (0, 10)])
+    cm.add_polygon(square, zone_id=1, resolution=4.0, dist_max=20.0)
+
+    line = LineString([(1, 8), (5, 8), (5 + 1e-9, 8), (9, 8)])
+    cm.add_line(line, line_id="near_duplicate_vertices", resolution=1.0)
+
+    clean_polys, clean_lines, clean_points = cm.generate()
+
+    mg = MeshGenerator(background_lc=4.0, verbosity=0)
+    success = mg.generate(clean_polys, clean_lines, clean_points)
+
+    assert success
+    assert mg.nodes is not None
+    assert len(mg.nodes) > 0
+
+
 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")
@@ -105,6 +144,48 @@ def test_gmsh_integration_with_field_only_line_refinement():
     # Still expect refinement from the line-based size field.
     assert len(grid) > 10
 
+
+def test_embedded_polygon_field_has_restricted_constant_interior():
+    """Embedded polygon size fields should stay constant inside the surface."""
+    cm = ConceptualMesh(crs="EPSG:3857")
+    domain = Polygon([(0, 0), (20, 0), (20, 20), (0, 20)])
+    inner = Polygon([(5, 5), (15, 5), (15, 15), (5, 15)])
+
+    cm.add_polygon(domain, zone_id=1, resolution=10.0, z_order=0)
+    cm.add_polygon(
+        inner,
+        zone_id=2,
+        resolution=2.0,
+        z_order=1,
+        fields=[AutoExponentialField(growth_factor=1.2)],
+    )
+    clean_polys, clean_lines, clean_points = cm.generate()
+
+    mg = MeshGenerator(background_lc=10.0, verbosity=0)
+    gmsh.initialize()
+    try:
+        gmsh.model.add("embedded_polygon_field")
+        gmsh_map = mg._add_geometry(clean_polys, clean_lines, clean_points)
+        mg._setup_fields(gmsh_map, clean_polys, clean_lines, clean_points)
+
+        inner_idx = int(clean_polys.index[clean_polys["zone_id"] == 2][0])
+        inner_surfaces = set(gmsh_map["surfaces"][inner_idx])
+        inner_surface_tags = {
+            float(tag) for dim, tag in inner_surfaces if int(dim) == 2
+        }
+
+        restrict_fields = []
+        for field_id in gmsh.model.mesh.field.list():
+            if gmsh.model.mesh.field.getType(field_id) != "Restrict":
+                continue
+            surfaces = set(gmsh.model.mesh.field.getNumbers(field_id, "SurfacesList"))
+            if surfaces == inner_surface_tags:
+                restrict_fields.append(field_id)
+
+        assert restrict_fields, "Expected a constant field restricted to the embedded polygon surface"
+    finally:
+        gmsh.finalize()
+
 def test_gmsh_integration_overlapping_polygon_with_hole():
     """
     Test the case where an overlapping polygon (Zone 2) has a hole in its center.

From 220778a64a28c33eacb019c1f5ddca8f2f80b4d2 Mon Sep 17 00:00:00 2001
From: oscarfasanchez <oscarfasanchez@gmail.com>
Date: Sat, 6 Jun 2026 07:48:48 +0800
Subject: [PATCH 28/29] make polygon fields constant inside field only polygons
 as well

---
 examples/cleaning_limitations_demo.ipynb | 2034 ++++++++++++++++++++++
 examples/field_capabilities_example.py   |   14 +-
 src/vorflow/blueprint.py                 |   24 +-
 src/vorflow/engine.py                    |    4 +
 src/vorflow/fields.py                    |  100 +-
 tests/test_integration_gmsh.py           |   80 +-
 6 files changed, 2204 insertions(+), 52 deletions(-)
 create mode 100644 examples/cleaning_limitations_demo.ipynb

diff --git a/examples/cleaning_limitations_demo.ipynb b/examples/cleaning_limitations_demo.ipynb
new file mode 100644
index 0000000..8698755
--- /dev/null
+++ b/examples/cleaning_limitations_demo.ipynb
@@ -0,0 +1,2034 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "28ffa4b8",
+   "metadata": {},
+   "source": [
+    "# Vorflow: Common Geometry Problems and How to Handle Them\n",
+    "\n",
+    "This notebook is a practical guide for users building conceptual meshes with vorflow. Each section introduces a problem you are likely to encounter when loading real geometry — what it looks like, what goes wrong if you ignore it, and exactly which parameter or utility function solves it.\n",
+    "\n",
+    "Work through the sections top to bottom. By the end you will know what to watch for before your first real mesh run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "eb28fa9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import time\n",
+    "from copy import deepcopy\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import geopandas as gpd\n",
+    "\n",
+    "from shapely.geometry import Point, Polygon, LineString, box\n",
+    "from shapely.ops import unary_union\n",
+    "\n",
+    "from vorflow import ConceptualMesh, MeshGenerator, VoronoiTessellator, ThresholdField\n",
+    "from vorflow.utils import (\n",
+    "    calculate_mesh_quality,\n",
+    "    summarize_quality,\n",
+    "    check_geometry_resolution,\n",
+    "    resample_geometry,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2dfe37b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CRS = \"EPSG:3857\"\n",
+    "\n",
+    "\n",
+    "# ---------------------------------------------------------------------------\n",
+    "# Geometry helpers\n",
+    "# ---------------------------------------------------------------------------\n",
+    "def to_gdf(geometries, crs=CRS):\n",
+    "    return gpd.GeoDataFrame({\"geometry\": geometries}, crs=crs)\n",
+    "\n",
+    "\n",
+    "def vertex_count(geom):\n",
+    "    if geom.geom_type == \"Polygon\":\n",
+    "        total = len(geom.exterior.coords)\n",
+    "        for interior in geom.interiors:\n",
+    "            total += len(interior.coords)\n",
+    "        return total\n",
+    "    if geom.geom_type == \"LineString\":\n",
+    "        return len(geom.coords)\n",
+    "    if geom.geom_type == \"MultiPolygon\":\n",
+    "        return sum(vertex_count(part) for part in geom.geoms)\n",
+    "    return 0\n",
+    "\n",
+    "\n",
+    "def gdf_vertex_count(gdf):\n",
+    "    if gdf is None or gdf.empty:\n",
+    "        return 0\n",
+    "    return int(gdf.geometry.apply(vertex_count).sum())\n",
+    "\n",
+    "\n",
+    "# ---------------------------------------------------------------------------\n",
+    "# Pipeline runner\n",
+    "# ---------------------------------------------------------------------------\n",
+    "def run_case(\n",
+    "    name,\n",
+    "    domain_spec,\n",
+    "    line_specs=None,\n",
+    "    point_specs=None,\n",
+    "    polygon_specs=None,\n",
+    "    background_lc=1.0,\n",
+    "    cm_kwargs=None,\n",
+    "    mg_kwargs=None,\n",
+    "    gen_kwargs=None,\n",
+    "    resample_spacing=None,\n",
+    "):\n",
+    "    \"\"\"Run a complete vorflow pipeline and return all artifacts for comparison.\"\"\"\n",
+    "    cm_kwargs = cm_kwargs or {}\n",
+    "    mg_kwargs = mg_kwargs or {}\n",
+    "    gen_kwargs = gen_kwargs or {}\n",
+    "    line_specs = line_specs or []\n",
+    "    point_specs = point_specs or []\n",
+    "    polygon_specs = polygon_specs or []\n",
+    "\n",
+    "    # Optional upstream resampling of non-domain polygons\n",
+    "    if resample_spacing:\n",
+    "        polygon_specs = [\n",
+    "            {**ps, \"geometry\": resample_geometry(ps[\"geometry\"], resample_spacing)}\n",
+    "            for ps in polygon_specs\n",
+    "        ]\n",
+    "\n",
+    "    # Raw GDFs (for later plotting — assembled AFTER any resampling)\n",
+    "    raw_poly_geoms = [domain_spec[\"geometry\"]] + [ps[\"geometry\"] for ps in polygon_specs]\n",
+    "    raw_line_geoms = [ls[\"geometry\"] for ls in line_specs]\n",
+    "    raw_point_geoms = [ps[\"geometry\"] for ps in point_specs]\n",
+    "\n",
+    "    raw_polygons = gpd.GeoDataFrame({\"geometry\": raw_poly_geoms}, crs=CRS)\n",
+    "    raw_lines = gpd.GeoDataFrame({\"geometry\": raw_line_geoms}, crs=CRS) if raw_line_geoms else None\n",
+    "    raw_points = gpd.GeoDataFrame({\"geometry\": raw_point_geoms}, crs=CRS) if raw_point_geoms else None\n",
+    "\n",
+    "    # ConceptualMesh\n",
+    "    cm = ConceptualMesh(crs=CRS, **cm_kwargs)\n",
+    "    cm.add_polygon(\n",
+    "        domain_spec[\"geometry\"],\n",
+    "        zone_id=domain_spec[\"zone_id\"],\n",
+    "        resolution=domain_spec.get(\"resolution\"),\n",
+    "        dist_min=domain_spec.get(\"dist_min\"),\n",
+    "        dist_max=domain_spec.get(\"dist_max\"),\n",
+    "        densify=domain_spec.get(\"densify\"),\n",
+    "        z_order=domain_spec.get(\"z_order\", 0),\n",
+    "        simplify_tolerance=domain_spec.get(\"simplify_tolerance\"),\n",
+    "        fields=domain_spec.get(\"fields\"),\n",
+    "        embed=domain_spec.get(\"embed\", True),\n",
+    "    )\n",
+    "    for ps in polygon_specs:\n",
+    "        cm.add_polygon(\n",
+    "            ps[\"geometry\"],\n",
+    "            zone_id=ps[\"zone_id\"],\n",
+    "            resolution=ps.get(\"resolution\"),\n",
+    "            z_order=ps.get(\"z_order\", 0),\n",
+    "            dist_min=ps.get(\"dist_min\"),\n",
+    "            dist_max=ps.get(\"dist_max\"),\n",
+    "            densify=ps.get(\"densify\"),\n",
+    "            simplify_tolerance=ps.get(\"simplify_tolerance\"),\n",
+    "            fields=ps.get(\"fields\"),\n",
+    "            embed=ps.get(\"embed\", True),\n",
+    "        )\n",
+    "    for ls in line_specs:\n",
+    "        cm.add_line(\n",
+    "            ls[\"geometry\"],\n",
+    "            line_id=ls[\"line_id\"],\n",
+    "            resolution=ls[\"resolution\"],\n",
+    "            is_barrier=ls.get(\"is_barrier\", False),\n",
+    "            dist_min=ls.get(\"dist_min\"),\n",
+    "            dist_max=ls.get(\"dist_max\"),\n",
+    "            fields=ls.get(\"fields\"),\n",
+    "            embed=ls.get(\"embed\", True),\n",
+    "            densify=ls.get(\"densify\", True),\n",
+    "            simplify_tolerance=ls.get(\"simplify_tolerance\"),\n",
+    "        )\n",
+    "    for ps in point_specs:\n",
+    "        cm.add_point(\n",
+    "            ps[\"geometry\"],\n",
+    "            point_id=ps[\"point_id\"],\n",
+    "            resolution=ps[\"resolution\"],\n",
+    "            dist_min=ps.get(\"dist_min\"),\n",
+    "            dist_max=ps.get(\"dist_max\"),\n",
+    "            simplify_tolerance=ps.get(\"simplify_tolerance\"),\n",
+    "        )\n",
+    "\n",
+    "    clean_polys, clean_lines, clean_points = cm.generate()\n",
+    "\n",
+    "    # Meshing\n",
+    "    t0 = time.time()\n",
+    "    mesh_success = False\n",
+    "    mg = grid = quality = None\n",
+    "    error = \"\"\n",
+    "    try:\n",
+    "        mg = MeshGenerator(background_lc=background_lc, **mg_kwargs)\n",
+    "        mg.generate(clean_polys, clean_lines, clean_points, **gen_kwargs)\n",
+    "        mesh_success = True\n",
+    "        vt = VoronoiTessellator(mg, cm, clip_to_boundary=True)\n",
+    "        grid = vt.generate()\n",
+    "        quality = calculate_mesh_quality(grid)\n",
+    "    except Exception as exc:\n",
+    "        error = str(exc)\n",
+    "    mesh_s = round(time.time() - t0, 2)\n",
+    "\n",
+    "    return dict(\n",
+    "        name=name,\n",
+    "        raw_polygons=raw_polygons,\n",
+    "        raw_lines=raw_lines,\n",
+    "        raw_points=raw_points,\n",
+    "        clean_polys=clean_polys,\n",
+    "        clean_lines=clean_lines,\n",
+    "        clean_points=clean_points,\n",
+    "        mg=mg,\n",
+    "        grid=grid,\n",
+    "        quality=quality,\n",
+    "        mesh_success=mesh_success,\n",
+    "        mesh_s=mesh_s,\n",
+    "        error=error,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# ---------------------------------------------------------------------------\n",
+    "# Summary table helper\n",
+    "# ---------------------------------------------------------------------------\n",
+    "def result_row(case):\n",
+    "    \"\"\"Return a dict suitable for building a summary DataFrame.\"\"\"\n",
+    "    return {\n",
+    "        \"case\": case[\"name\"],\n",
+    "        \"raw_poly_vertices\": gdf_vertex_count(case[\"raw_polygons\"]),\n",
+    "        \"clean_poly_vertices\": gdf_vertex_count(case[\"clean_polys\"]),\n",
+    "        \"mesh_success\": case[\"mesh_success\"],\n",
+    "        \"grid_cells\": len(case[\"grid\"]) if case[\"grid\"] is not None else 0,\n",
+    "        \"mesh_s\": case[\"mesh_s\"],\n",
+    "        \"error\": case[\"error\"],\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "# ---------------------------------------------------------------------------\n",
+    "# Plot helpers\n",
+    "# ---------------------------------------------------------------------------\n",
+    "def _plot_case_row(axes, case, label, tint, nodes=None):\n",
+    "    \"\"\"Fill a 3-axes row (raw | clean | grid) for one case.\"\"\"\n",
+    "    ax_raw, ax_clean, ax_grid = axes\n",
+    "\n",
+    "    # Raw\n",
+    "    if case[\"raw_polygons\"] is not None and not case[\"raw_polygons\"].empty:\n",
+    "        case[\"raw_polygons\"].boundary.plot(ax=ax_raw, color=\"black\", linewidth=0.8)\n",
+    "    if case[\"raw_lines\"] is not None and not case[\"raw_lines\"].empty:\n",
+    "        case[\"raw_lines\"].plot(ax=ax_raw, color=\"steelblue\", linewidth=1.5, zorder=3)\n",
+    "    if case[\"raw_points\"] is not None and not case[\"raw_points\"].empty:\n",
+    "        case[\"raw_points\"].plot(ax=ax_raw, color=\"crimson\", markersize=8, zorder=4)\n",
+    "    ax_raw.set_title(f\"{label}\\nInput geometry\", fontsize=9, fontweight=\"bold\")\n",
+    "    ax_raw.set_facecolor(tint)\n",
+    "\n",
+    "    # Clean\n",
+    "    if case[\"clean_polys\"] is not None and not case[\"clean_polys\"].empty:\n",
+    "        case[\"clean_polys\"].boundary.plot(ax=ax_clean, color=\"black\", linewidth=0.8)\n",
+    "    if case[\"clean_lines\"] is not None and not case[\"clean_lines\"].empty:\n",
+    "        case[\"clean_lines\"].plot(ax=ax_clean, color=\"steelblue\", linewidth=1.5, zorder=3)\n",
+    "    if case[\"clean_points\"] is not None and not case[\"clean_points\"].empty:\n",
+    "        case[\"clean_points\"].plot(ax=ax_clean, color=\"green\", markersize=8, zorder=4)\n",
+    "    ax_clean.set_title(\"After preprocessing\", fontsize=9)\n",
+    "    ax_clean.set_facecolor(tint)\n",
+    "\n",
+    "    # Grid\n",
+    "    if case[\"grid\"] is not None and not case[\"grid\"].empty:\n",
+    "        case[\"grid\"].plot(ax=ax_grid, alpha=0.45, edgecolor=\"black\", linewidth=0.25)\n",
+    "    if nodes is not None:\n",
+    "        ax_grid.scatter(\n",
+    "            nodes[:, 0], nodes[:, 1],\n",
+    "            s=5, color=\"dimgray\", alpha=0.35, zorder=3, linewidths=0,\n",
+    "        )\n",
+    "    ax_grid.set_title(\"Voronoi grid\", fontsize=9)\n",
+    "\n",
+    "    for ax in axes:\n",
+    "        ax.set_aspect(\"equal\")\n",
+    "        ax.grid(alpha=0.2)\n",
+    "\n",
+    "\n",
+    "def plot_comparison(case_a, case_b, label_a=\"Case A\", label_b=\"Case B\",\n",
+    "                    nodes_a=None, nodes_b=None):\n",
+    "    \"\"\"2 × 3 comparison plot: raw / clean / grid for two cases.\"\"\"\n",
+    "    fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
+    "    _plot_case_row(axes[0], case_a, label_a, \"#fff0f0\", nodes=nodes_a)\n",
+    "    _plot_case_row(axes[1], case_b, label_b, \"#f0fff0\", nodes=nodes_b)\n",
+    "    plt.suptitle(\n",
+    "        \"Rows compare two cases; columns show input, preprocessing, and final Voronoi grid\",\n",
+    "        fontweight=\"bold\",\n",
+    "        fontsize=11,\n",
+    "    )\n",
+    "    plt.tight_layout(rect=(0, 0, 1, 0.96))\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "def plot_case(case, label=\"\", nodes=None):\n",
+    "    \"\"\"1 × 3 single-case plot: raw / clean / grid.\"\"\"\n",
+    "    fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
+    "    _plot_case_row(axes, case, label, \"#f5f5ff\", nodes=nodes)\n",
+    "    plt.suptitle(label, fontweight=\"bold\", fontsize=12)\n",
+    "    plt.tight_layout(rect=(0, 0, 1, 0.95))\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "936b6300",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Problem 1 ? Duplicate and Near-Duplicate Vertices\n",
+    "\n",
+    "**What you'll see:** You export river or boundary geometry from a GIS tool and two consecutive vertices in the same linestring share the exact same coordinate ? or differ by a sub-nanometre floating-point rounding error.\n",
+    "\n",
+    "**What goes wrong:** Gmsh treats a zero-length or near-zero line segment as a degenerate element and can reject the geometry during OCC line creation.\n",
+    "\n",
+    "**How vorflow handles it:** The meshing path now has a guard at geometry transfer: `MeshGenerator` filters invalid and near-duplicate coordinates before creating OCC entities. That means mesh generation can succeed even when the displayed `clean_lines` were later densified and have more vertices than the raw input.\n",
+    "\n",
+    "The diagnostic below therefore checks **short consecutive segments**, not just vertex counts. Vertex counts are still useful context, but after densification they are not proof that duplicates were removed.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c92290f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Applying optional geometry simplification...\n",
+      "Resolving polygon overlaps...\n",
+      "Enforcing strict topology...\n",
+      "Snapping 2 lines to polygon boundaries (tol=0.001)...\n",
+      "Clipping features to domain...\n",
+      "Densifying geometry...\n",
+      "Transferring Geometry to Gmsh...\n",
+      "Adding 1 polygons to Gmsh...\n",
+      "Fragmenting 21 objects...\n",
+      "Reconstructing Map (Input Tags: 21, Out Map Len: 21)...\n",
+      "Setting up Resolution Fields...\n",
+      "Generating Triangular Mesh...\n",
+      "Extracting 52 Nodes from Gmsh...\n",
+      "Computing Mathematical Voronoi...\n",
+      "  -> Raw Polygons: 52\n",
+      "  -> After Ghost Filter: 52\n",
+      "Clipping to Domain Boundary...\n",
+      "  -> After Domain Clip: 52\n",
+      "Enforcing Hydrogeological Zones (Optimization: Point Sampling)...\n",
+      "  -> Zones Assigned: 52\n",
+      "  -> After Barrier Cuts: 52\n",
+      "Final Voronoi Grid Generated: 52 cells.\n",
+      "Line 1 ? exact duplicate at (5, 5):\n",
+      "  Input vertices        : 4\n",
+      "  Clean vertices        : 13  (after optional densification)\n",
+      "  Raw short segments    : 1\n",
+      "  Clean short segments  : 0\n",
+      "\n",
+      "Line 2 ? near-duplicate pair at ~(5, 8):\n",
+      "  Input vertices        : 4\n",
+      "  Clean vertices        : 10  (after optional densification)\n",
+      "  Raw short segments    : 1\n",
+      "  Clean short segments  : 1\n",
+      "\n",
+      "Mesh generation succeeded: True\n",
+      "Note: any remaining near-zero segment is filtered before Gmsh OCC creation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "domain = {\n",
+    "    \"geometry\": box(0, 0, 10, 10),\n",
+    "    \"zone_id\": 1,\n",
+    "    \"resolution\": 4.0,\n",
+    "    \"dist_max\": 20.0,\n",
+    "}\n",
+    "\n",
+    "line_with_duplicate = LineString([(1, 1), (5, 5), (5, 5), (9, 9)])\n",
+    "line_with_near_duplicate = LineString([(1, 8), (5, 8), (5 + 1e-9, 8), (9, 8)])\n",
+    "\n",
+    "\n",
+    "def short_segment_count(line, tol=1e-8):\n",
+    "    coords = list(line.coords)\n",
+    "    return sum(\n",
+    "        Point(coords[i]).distance(Point(coords[i + 1])) <= tol\n",
+    "        for i in range(len(coords) - 1)\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "duplicate_case = run_case(\n",
+    "    name=\"duplicate-and-near-duplicate-line-vertices\",\n",
+    "    domain_spec=domain,\n",
+    "    line_specs=[\n",
+    "        {\n",
+    "            \"geometry\": line_with_duplicate,\n",
+    "            \"line_id\": \"duplicate_vertices\",\n",
+    "            \"resolution\": 1.0,\n",
+    "        },\n",
+    "        {\n",
+    "            \"geometry\": line_with_near_duplicate,\n",
+    "            \"line_id\": \"near_duplicate_vertices\",\n",
+    "            \"resolution\": 1.0,\n",
+    "        },\n",
+    "    ],\n",
+    "    background_lc=4.0,\n",
+    ")\n",
+    "\n",
+    "# Show duplicate/near-duplicate diagnostics. Clean vertex counts are reported\n",
+    "# only as context because line densification can add vertices after cleaning.\n",
+    "for label, raw_line, clean_line in [\n",
+    "    (\"Line 1 ? exact duplicate at (5, 5)\", line_with_duplicate, duplicate_case[\"clean_lines\"].geometry.iloc[0]),\n",
+    "    (\"Line 2 ? near-duplicate pair at ~(5, 8)\", line_with_near_duplicate, duplicate_case[\"clean_lines\"].geometry.iloc[1]),\n",
+    "]:\n",
+    "    print(label + \":\")\n",
+    "    print(f\"  Input vertices        : {len(list(raw_line.coords))}\")\n",
+    "    print(f\"  Clean vertices        : {len(list(clean_line.coords))}  (after optional densification)\")\n",
+    "    print(f\"  Raw short segments    : {short_segment_count(raw_line)}\")\n",
+    "    print(f\"  Clean short segments  : {short_segment_count(clean_line)}\")\n",
+    "    print()\n",
+    "\n",
+    "print(f\"Mesh generation succeeded: {duplicate_case[\"mesh_success\"]}\")\n",
+    "print(\"Note: any remaining near-zero segment is filtered before Gmsh OCC creation.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2c2d764b",
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "cell_5:45: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
+    "ax_raw, ax_clean, ax_grid = axes\n",
+    "\n",
+    "# --- raw geometry: highlight the problem coordinates ---\n",
+    "duplicate_case[\"raw_polygons\"].boundary.plot(ax=ax_raw, color=\"black\", linewidth=1)\n",
+    "duplicate_case[\"raw_lines\"].plot(ax=ax_raw, color=\"steelblue\", linewidth=1.5)\n",
+    "\n",
+    "for x, y, lbl in [(5, 5, \"exact duplicate\"), (5, 8, \"near-duplicate\\n(1e-9 m gap)\")]:\n",
+    "    ax_raw.plot(x, y, \"o\", color=\"red\", markersize=18, alpha=0.75, zorder=5)\n",
+    "    ax_raw.annotate(\n",
+    "        lbl, xy=(x, y), xytext=(x + 1.0, y + 0.8),\n",
+    "        fontsize=8, color=\"darkred\",\n",
+    "        arrowprops=dict(arrowstyle=\"->\", color=\"darkred\", lw=1.2),\n",
+    "    )\n",
+    "\n",
+    "ax_raw.set_title(\"Input geometry  ✗\\n(duplicate vertices highlighted in red)\", fontsize=10, color=\"darkred\")\n",
+    "ax_raw.set_facecolor(\"#fff0f0\")\n",
+    "\n",
+    "# --- cleaned geometry ---\n",
+    "duplicate_case[\"clean_polys\"].boundary.plot(ax=ax_clean, color=\"black\", linewidth=1)\n",
+    "duplicate_case[\"clean_lines\"].plot(ax=ax_clean, color=\"steelblue\", linewidth=1.5)\n",
+    "ax_clean.set_title(\"After ConceptualMesh preprocessing  ✓\\n(duplicates removed)\", fontsize=10, color=\"darkgreen\")\n",
+    "ax_clean.set_facecolor(\"#f0fff0\")\n",
+    "\n",
+    "# --- resulting Voronoi grid with subtle Gmsh triangle-mesh nodes ---\n",
+    "grid = duplicate_case.get(\"grid\")\n",
+    "if grid is not None and not grid.empty:\n",
+    "    grid.plot(ax=ax_grid, alpha=0.5, edgecolor=\"black\", linewidth=0.25)\n",
+    "mg = duplicate_case.get(\"mg\")\n",
+    "if mg is not None and mg.nodes is not None:\n",
+    "    ax_grid.scatter(\n",
+    "        mg.nodes[:, 0], mg.nodes[:, 1],\n",
+    "        s=5, color=\"dimgray\", alpha=0.35, zorder=3, linewidths=0,\n",
+    "        label=\"mesh nodes\",\n",
+    "    )\n",
+    "ax_grid.set_title(\"Voronoi grid + mesh nodes (grey dots)\\n(mesh succeeds cleanly)\", fontsize=10)\n",
+    "\n",
+    "for ax in axes:\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "    ax.grid(alpha=0.2)\n",
+    "\n",
+    "plt.suptitle(\"Problem 1: Duplicate Vertices — detected and removed automatically\", fontweight=\"bold\", fontsize=12)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7018a8d3",
+   "metadata": {},
+   "source": [
+    "## Problem 2 ? Features That Don't Quite Reach the Domain Boundary\n",
+    "\n",
+    "**What you'll see:** After digitising in a GIS, a monitoring well is snapped to the wrong layer and ends up 5 cm outside the domain boundary.\n",
+    "\n",
+    "**Current behavior:** If the point cannot be snapped into the resolved domain, `ConceptualMesh.generate()` removes it from `clean_points`. That is safer than sending an outside point to Gmsh and later clipping away its Voronoi cell, but the practical effect is the same: the well has **zero influence on the grid**.\n",
+    "\n",
+    "**What goes wrong:** `connectivity_tolerance` defaults to 1 mm. A gap of 5 cm is well outside that range, so the point is not snapped. During domain clipping it is removed before meshing.\n",
+    "\n",
+    "**Fix ? `connectivity_tolerance`:** Raise the tolerance to cover the gap. Any endpoint within that distance of the nearest feature vertex (domain boundary, line vertex) is snapped onto it before meshing.\n",
+    "\n",
+    "```python\n",
+    "cm = ConceptualMesh(crs=\"EPSG:3857\", connectivity_tolerance=0.1)  # snap within 10 cm\n",
+    "```\n",
+    "\n",
+    "**Important caveats for real data:**\n",
+    "\n",
+    "* `connectivity_tolerance` uses Shapely's `snap()` internally, so the well can only move to an **existing vertex** of the reference geometry. It does **not** snap to an arbitrary point along an edge.\n",
+    "* In this demo the river already has a vertex at `(0, 1.0)`, so the well has an exact snap target.\n",
+    "* `ConceptualMesh.generate()` runs in this order: **simplify ? snap/connectivity ? clip to domain ? densify**.\n",
+    "* That means `simplify_tolerance` can remove the very vertex you needed as a snap target.\n",
+    "* It also means `densify=` does **not** help snapping, because densified vertices are added only after snapping has finished.\n",
+    "* If your target location lies on a long edge with no nearby vertex, `resample_geometry()` applied **before** `add_polygon()` or `add_line()` is usually the better tool.\n",
+    "* In those cases, **resampling without simplification** can be the safer option, because simplification may clean away the candidate vertices you needed for the snap.\n",
+    "\n",
+    "The comparison below uses a river crossing the domain and a well 5 cm outside the domain near the river's entry point. The river is intentionally kept as coarse as the background mesh while the well requests much finer cells, so the snapped case creates a visibly tighter local node cluster. The plot includes a zoomed panel because the full-domain grid can make the boundary-local difference look subtler than it is.\n",
+    "\n",
+    "* **Bad** (`tol=0.01`, 1 cm): well stays outside ? removed from `clean_points` ? no local refinement  \n",
+    "* **Good** (`tol=0.1`, 10 cm): well snaps to the river's entry vertex at `(0, 1.0)` ? proper boundary node ? fine local refinement becomes visible\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f83a1d8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Applying optional geometry simplification...\n",
+      "Resolving polygon overlaps...\n",
+      "Enforcing strict topology...\n",
+      "Snapping 1 lines to polygon boundaries (tol=0.01)...\n",
+      "Snapping 1 points to geometry (tol=0.01)...\n",
+      "Clipping features to domain...\n",
+      "Removed 1 point feature(s) outside the domain.\n",
+      "Densifying geometry...\n",
+      "Transferring Geometry to Gmsh...\n",
+      "Adding 1 polygons to Gmsh...\n",
+      "Fragmenting 2 objects...\n",
+      "Reconstructing Map (Input Tags: 2, Out Map Len: 2)...\n",
+      "Setting up Resolution Fields...\n",
+      "Generating Triangular Mesh...\n",
+      "Extracting 13 Nodes from Gmsh...\n",
+      "Computing Mathematical Voronoi...\n",
+      "  -> Raw Polygons: 13\n",
+      "  -> After Ghost Filter: 13\n",
+      "Clipping to Domain Boundary...\n",
+      "  -> After Domain Clip: 13\n",
+      "Enforcing Hydrogeological Zones (Optimization: Point Sampling)...\n",
+      "  -> Zones Assigned: 13\n",
+      "  -> After Barrier Cuts: 13\n",
+      "Final Voronoi Grid Generated: 13 cells.\n",
+      "Applying optional geometry simplification...\n",
+      "Resolving polygon overlaps...\n",
+      "Enforcing strict topology...\n",
+      "Snapping 1 lines to polygon boundaries (tol=0.1)...\n",
+      "Snapping 1 points to geometry (tol=0.1)...\n",
+      "Clipping features to domain...\n",
+      "Densifying geometry...\n",
+      "Transferring Geometry to Gmsh...\n",
+      "Adding 1 polygons to Gmsh...\n",
+      "Fragmenting 3 objects...\n",
+      "Reconstructing Map (Input Tags: 3, Out Map Len: 3)...\n",
+      "Setting up Resolution Fields...\n",
+      "Generating Triangular Mesh...\n",
+      "Extracting 129 Nodes from Gmsh...\n",
+      "Computing Mathematical Voronoi...\n",
+      "  -> Raw Polygons: 129\n",
+      "  -> After Ghost Filter: 129\n",
+      "Clipping to Domain Boundary...\n",
+      "  -> After Domain Clip: 129\n",
+      "Enforcing Hydrogeological Zones (Optimization: Point Sampling)...\n",
+      "  -> Zones Assigned: 129\n",
+      "  -> After Barrier Cuts: 129\n",
+      "Final Voronoi Grid Generated: 129 cells.\n",
+      "Low tolerance  — well after preprocessing: no points\n",
+      "Good tolerance — well after preprocessing: POINT (0 1)  →  ON boundary\n",
+      "\n",
+      "Low tolerance  — 13 Voronoi cells  (well clipped away, no local point refinement)\n",
+      "Good tolerance — 129 Voronoi cells (well snaps to a boundary vertex and activates fine refinement)\n",
+      "\n",
+      "Caveat: the river already contributes a vertex at (0, 1.0).\n",
+      "The visible difference comes from the snapped well requesting much finer cells than the river/background mesh.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Geometry: domain box, river line from boundary to boundary, well 5 cm outside domain\n",
+    "snap_domain = {\"geometry\": box(0, 0, 2, 2), \"zone_id\": 1, \"resolution\": 1.0}\n",
+    "snap_lines = [{\n",
+    "    \"geometry\": LineString([(0, 1.0), (2, 1.0)]),\n",
+    "    \"line_id\": \"river\",\n",
+    "    \"resolution\": 1.0,\n",
+    "    \"dist_max\": 0.10,\n",
+    "    \"densify\": False,\n",
+    "}]\n",
+    "snap_points = [{\n",
+    "    \"geometry\": Point(-0.05, 1.0),\n",
+    "    \"point_id\": \"well\",\n",
+    "    \"resolution\": 0.025,\n",
+    "    \"dist_max\": 0.55,\n",
+    "}]\n",
+    "# Note: well is 5 cm outside the domain at x = 0. The river starts exactly at (0, 1.0)\n",
+    "# on the domain boundary, providing the snap target when tolerance is large enough.\n",
+    "# The river uses background-scale sizing; the well is much finer so snapping is visually obvious.\n",
+    "\n",
+    "snap_low = run_case(\n",
+    "    name=\"connectivity_tolerance=0.01 (gap not snapped)\",\n",
+    "    domain_spec=snap_domain,\n",
+    "    line_specs=snap_lines,\n",
+    "    point_specs=snap_points,\n",
+    "    background_lc=1.0,\n",
+    "    cm_kwargs={\"connectivity_tolerance\": 0.01},   # 5 cm gap > 1 cm tol → no snap\n",
+    ")\n",
+    "\n",
+    "snap_good = run_case(\n",
+    "    name=\"connectivity_tolerance=0.1 (gap snapped)\",\n",
+    "    domain_spec=snap_domain,\n",
+    "    line_specs=snap_lines,\n",
+    "    point_specs=snap_points,\n",
+    "    background_lc=1.0,\n",
+    "    cm_kwargs={\"connectivity_tolerance\": 0.1},    # 5 cm gap < 10 cm tol → snaps to (0, 1.0)\n",
+    ")\n",
+    "\n",
+    "from shapely.geometry import box as _sbox\n",
+    "_domain_geom = _sbox(0, 0, 2, 2)\n",
+    "\n",
+    "def _pt_info(case):\n",
+    "    pts = case[\"clean_points\"]\n",
+    "    if pts is None or pts.empty:\n",
+    "        return \"no points\"\n",
+    "    geom = pts.geometry.iloc[0]\n",
+    "    d = _domain_geom.exterior.distance(geom)\n",
+    "    status = \"ON boundary\" if d < 1e-9 else f\"{d*100:.1f} cm OUTSIDE\"\n",
+    "    return f\"{geom}  →  {status}\"\n",
+    "\n",
+    "print(f\"Low tolerance  — well after preprocessing: {_pt_info(snap_low)}\")\n",
+    "print(f\"Good tolerance — well after preprocessing: {_pt_info(snap_good)}\")\n",
+    "print()\n",
+    "cells_low = len(snap_low[\"grid\"]) if snap_low[\"grid\"] is not None else 0\n",
+    "cells_good = len(snap_good[\"grid\"]) if snap_good[\"grid\"] is not None else 0\n",
+    "print(f\"Low tolerance  — {cells_low} Voronoi cells  (well clipped away, no local point refinement)\")\n",
+    "print(f\"Good tolerance — {cells_good} Voronoi cells (well snaps to a boundary vertex and activates fine refinement)\")\n",
+    "print()\n",
+    "print(\"Caveat: the river already contributes a vertex at (0, 1.0).\")\n",
+    "print(\"The visible difference comes from the snapped well requesting much finer cells than the river/background mesh.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2a663e79",
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Low tolerance zoom nodes : 3\n",
+      "Good tolerance zoom nodes: 84\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "cell_8:73: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "nodes_low = snap_low[\"mg\"].nodes if snap_low[\"mg\"] is not None else None\n",
+    "nodes_good = snap_good[\"mg\"].nodes if snap_good[\"mg\"] is not None else None\n",
+    "\n",
+    "\n",
+    "def _zoom_node_count(nodes, xlim=(-0.08, 0.55), ylim=(0.45, 1.55)):\n",
+    "    if nodes is None:\n",
+    "        return 0\n",
+    "    in_x = (nodes[:, 0] >= xlim[0]) & (nodes[:, 0] <= xlim[1])\n",
+    "    in_y = (nodes[:, 1] >= ylim[0]) & (nodes[:, 1] <= ylim[1])\n",
+    "    return int((in_x & in_y).sum())\n",
+    "\n",
+    "print(f\"Low tolerance zoom nodes : {_zoom_node_count(nodes_low)}\")\n",
+    "print(f\"Good tolerance zoom nodes: {_zoom_node_count(nodes_good)}\")\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 4, figsize=(22, 10))\n",
+    "\n",
+    "_plot_case_row(\n",
+    "    axes[0, :3],\n",
+    "    snap_low,\n",
+    "    \"tol=0.01 -- well remains outside\",\n",
+    "    \"#fff0f0\",\n",
+    "    nodes=nodes_low,\n",
+    ")\n",
+    "_plot_case_row(\n",
+    "    axes[1, :3],\n",
+    "    snap_good,\n",
+    "    \"tol=0.10 -- well snaps to river vertex\",\n",
+    "    \"#f0fff0\",\n",
+    "    nodes=nodes_good,\n",
+    ")\n",
+    "\n",
+    "def _plot_snap_zoom(ax, case, nodes, title, tint):\n",
+    "    if case[\"grid\"] is not None and not case[\"grid\"].empty:\n",
+    "        case[\"grid\"].plot(ax=ax, alpha=0.50, edgecolor=\"black\", linewidth=0.30)\n",
+    "    if case[\"clean_lines\"] is not None and not case[\"clean_lines\"].empty:\n",
+    "        case[\"clean_lines\"].plot(ax=ax, color=\"steelblue\", linewidth=1.8, zorder=4)\n",
+    "    if case[\"clean_points\"] is not None and not case[\"clean_points\"].empty:\n",
+    "        case[\"clean_points\"].plot(ax=ax, color=\"crimson\", markersize=45, zorder=6)\n",
+    "    if nodes is not None:\n",
+    "        ax.scatter(nodes[:, 0], nodes[:, 1], s=10, color=\"dimgray\", alpha=0.55, zorder=5, linewidths=0)\n",
+    "    ax.set_xlim(-0.08, 0.55)\n",
+    "    ax.set_ylim(0.45, 1.55)\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "    ax.grid(alpha=0.25)\n",
+    "    ax.set_facecolor(tint)\n",
+    "    ax.set_title(title, fontsize=9, fontweight=\"bold\")\n",
+    "\n",
+    "_plot_snap_zoom(\n",
+    "    axes[0, 3],\n",
+    "    snap_low,\n",
+    "    nodes_low,\n",
+    "    \"Zoom: well is outside\\nno fine boundary node cluster\",\n",
+    "    \"#fff0f0\",\n",
+    ")\n",
+    "_plot_snap_zoom(\n",
+    "    axes[1, 3],\n",
+    "    snap_good,\n",
+    "    nodes_good,\n",
+    "    \"Zoom: snapped well on boundary\\nfine node cluster is active\",\n",
+    "    \"#f0fff0\",\n",
+    ")\n",
+    "\n",
+    "for ax in axes[:, 3]:\n",
+    "    ax.axvline(0, color=\"black\", linewidth=0.8, alpha=0.6)\n",
+    "    ax.axhline(1.0, color=\"steelblue\", linewidth=0.8, linestyle=\"--\", alpha=0.7)\n",
+    "\n",
+    "plt.suptitle(\n",
+    "    \"Problem 2: connectivity_tolerance controls whether the outside well participates in the mesh\",\n",
+    "    fontweight=\"bold\",\n",
+    "    fontsize=12,\n",
+    ")\n",
+    "plt.tight_layout(rect=(0, 0, 1, 0.96))\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46cf45b7",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Problem 3 ? Irregular Vertex Spacing: Useful Tool or Extra Constraint?\n",
+    "\n",
+    "**What you'll see:** Source geometry can have clustered vertices in one part of a boundary and long, simple segments elsewhere. That does not mean Gmsh will leave the long segment unmeshed: Gmsh still discretizes curves from the active mesh-size field.\n",
+    "\n",
+    "**The real question:** Does changing the source geometry improve the final mesh, or does it just add constraints that Gmsh did not need?\n",
+    "\n",
+    "**Tools to compare:**\n",
+    "\n",
+    "| Tool | How to use | What it does | Tradeoff |\n",
+    "|------|-----------|-------------|----------|\n",
+    "| No preprocessing | pass geometry as-is | Lets Gmsh choose mesh nodes from the size field | Acceptable when quality diagnostics are already good |\n",
+    "| `add_polygon(..., simplify_tolerance=1.0)` / `add_line(..., simplify_tolerance=1.0)` | blueprint parameter | Removes redundant/noisy source vertices without inserting new CAD points | Can erase intentional small features if the tolerance is too high |\n",
+    "| `resample_geometry(geom, spacing)` | before `add_polygon` / `add_line` | Redistributes vertices to near-uniform spacing | May move/remove original vertices and sharp source spacing patterns |\n",
+    "| `simplify_tolerance` + `densify` | blueprint parameters | Removes noisy clusters first, then caps long segments | Best when the source has both noise and long gaps |\n",
+    "\n",
+    "The cells below use deliberately coarse meshes so local differences are visible, and diagnostics rather than assumptions. Both experiments use the same numeric source-cleanup parameters (`simplify_tolerance=1.0`, `resample_spacing=10.0`, and `densify=10.0` only after simplification). 3B keeps a bigger cell-size setting, but its polygon context receives the same cleanup operation as the matching 3A column.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63927fe3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---------------------------------------------------------------------------\n",
+    "# Problem 3A: embedded polygon boundary with noisy clusters and long gaps\n",
+    "# ---------------------------------------------------------------------------\n",
+    "noisy_uneven_coords = [\n",
+    "    (10, 10), (11, 10.2), (11.5, 9.8), (12, 10.2), (15, 10),\n",
+    "    (50, 10), (85, 10), (88, 14), (90, 20), (90, 85),\n",
+    "    (85, 90), (50, 95), (15, 90), (10, 85), (10, 50), (10, 20),\n",
+    "]\n",
+    "noisy_uneven_poly = Polygon(noisy_uneven_coords)\n",
+    "\n",
+    "spacing_domain = {\n",
+    "    \"geometry\": box(0, 0, 100, 100),\n",
+    "    \"zone_id\": 1,\n",
+    "    \"resolution\": 20.0,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def run_spacing_polygon_case(\n",
+    "    name,\n",
+    "    geometry,\n",
+    "    *,\n",
+    "    densify=False,\n",
+    "    simplify_tolerance=None,\n",
+    "    resample_spacing=None,\n",
+    "    cm_kwargs=None,\n",
+    "    mg_kwargs=None,\n",
+    "):\n",
+    "    geom = resample_geometry(geometry, resample_spacing) if resample_spacing else geometry\n",
+    "    return run_case(\n",
+    "        name=name,\n",
+    "        domain_spec=spacing_domain,\n",
+    "        polygon_specs=[\n",
+    "            {\n",
+    "                \"geometry\": geom,\n",
+    "                \"zone_id\": 2,\n",
+    "                \"resolution\": 10,\n",
+    "                \"z_order\": 1,\n",
+    "                \"dist_max\": 30.0,\n",
+    "                \"densify\": densify,\n",
+    "                \"simplify_tolerance\": simplify_tolerance,\n",
+    "            }\n",
+    "        ],\n",
+    "        background_lc=20.0,\n",
+    "        cm_kwargs=cm_kwargs,\n",
+    "        mg_kwargs=mg_kwargs,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "polygon_spacing_cases = [\n",
+    "    run_spacing_polygon_case(\"polygon-baseline\", noisy_uneven_poly, densify=False),\n",
+    "    run_spacing_polygon_case(\"polygon-simplify-1\", noisy_uneven_poly, densify=False, simplify_tolerance=1.0),\n",
+    "    run_spacing_polygon_case(\"polygon-resample-10\", noisy_uneven_poly, densify=False, resample_spacing=10.0),\n",
+    "    run_spacing_polygon_case(\n",
+    "        \"polygon-simplify-1-densify-10\",\n",
+    "        noisy_uneven_poly,\n",
+    "        densify=10.0,\n",
+    "        simplify_tolerance=1.0,\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# ---------------------------------------------------------------------------\n",
+    "# Problem 3B: embedded diagonal line crossing the polygon context\n",
+    "# ---------------------------------------------------------------------------\n",
+    "spacing_line = LineString([(5, 5), (20, 20), (35, 35), (50, 50), (65, 65), (80, 80), (95, 95)])\n",
+    "spacing_line_domain = {\n",
+    "    \"geometry\": box(0, 0, 100, 100),\n",
+    "    \"zone_id\": 1,\n",
+    "    \"resolution\": 20.0,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def run_spacing_line_case(\n",
+    "    name,\n",
+    "    *,\n",
+    "    densify=False,\n",
+    "    simplify_tolerance=None,\n",
+    "    resample_spacing=None,\n",
+    "    polygon_densify=False,\n",
+    "    polygon_simplify_tolerance=None,\n",
+    "    polygon_resample_spacing=None,\n",
+    "    cm_kwargs=None,\n",
+    "    mg_kwargs=None,\n",
+    "    gen_kwargs=None,\n",
+    "):\n",
+    "    geom = resample_geometry(spacing_line, resample_spacing) if resample_spacing else spacing_line\n",
+    "    polygon_geom = resample_geometry(noisy_uneven_poly, polygon_resample_spacing) if polygon_resample_spacing else noisy_uneven_poly\n",
+    "    field = ThresholdField(\n",
+    "        size_min=5.0,\n",
+    "        dist_min=2.0,\n",
+    "        dist_max=18.0,\n",
+    "        size_max=20.0,\n",
+    "        sampling=5,\n",
+    "    )\n",
+    "    return run_case(\n",
+    "        name=name,\n",
+    "        domain_spec=spacing_line_domain,\n",
+    "        polygon_specs=[\n",
+    "            {\n",
+    "                \"geometry\": polygon_geom,\n",
+    "                \"zone_id\": 2,\n",
+    "                \"resolution\": 10.0,\n",
+    "                \"z_order\": 1,\n",
+    "                \"dist_max\": 30,\n",
+    "                \"densify\": polygon_densify,\n",
+    "                \"simplify_tolerance\": polygon_simplify_tolerance,\n",
+    "            }\n",
+    "        ],\n",
+    "        line_specs=[\n",
+    "            {\n",
+    "                \"geometry\": geom,\n",
+    "                \"line_id\": \"embedded_diagonal_refinement_line\",\n",
+    "                \"resolution\": 5.0,\n",
+    "                \"fields\": [field],\n",
+    "                \"embed\": True,\n",
+    "                \"densify\": densify,\n",
+    "                \"simplify_tolerance\": simplify_tolerance,\n",
+    "            }\n",
+    "        ],\n",
+    "        background_lc=20.0,\n",
+    "        cm_kwargs=cm_kwargs,\n",
+    "        mg_kwargs=mg_kwargs,\n",
+    "        gen_kwargs=gen_kwargs,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "spacing_line_cases = [\n",
+    "    run_spacing_line_case(\"line-polygon-baseline\", densify=False, polygon_densify=False),\n",
+    "    run_spacing_line_case(\"line-polygon-simplify-1\", simplify_tolerance=1.0, polygon_simplify_tolerance=1.0),\n",
+    "    run_spacing_line_case(\"line-polygon-resample-10\", resample_spacing=10.0, polygon_resample_spacing=10.0),\n",
+    "    run_spacing_line_case(\n",
+    "        \"line-polygon-simplify-1-densify-10\",\n",
+    "        simplify_tolerance=1.0,\n",
+    "        densify=10.0,\n",
+    "        polygon_simplify_tolerance=1.0,\n",
+    "        polygon_densify=10.0,\n",
+    "    ),\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee14b728",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _zone_geom(case, zone_id=2):\n",
+    "    zone_mask = case[\"clean_polys\"][\"zone_id\"] == zone_id\n",
+    "    return case[\"clean_polys\"].loc[zone_mask, \"geometry\"].iloc[0]\n",
+    "\n",
+    "\n",
+    "def _segment_stats(geom):\n",
+    "    stats = check_geometry_resolution(to_gdf([geom]))\n",
+    "    if isinstance(stats, str):\n",
+    "        return {\"min\": np.nan, \"max\": np.nan, \"median\": np.nan, \"count\": 0, \"max_median_ratio\": np.nan}\n",
+    "    median = stats[\"median\"]\n",
+    "    return {\n",
+    "        \"min\": stats[\"min\"],\n",
+    "        \"max\": stats[\"max\"],\n",
+    "        \"median\": median,\n",
+    "        \"count\": stats[\"count\"],\n",
+    "        \"max_median_ratio\": stats[\"max\"] / median if median else np.nan,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def _local_quality(case, reference_geom, band_distance):\n",
+    "    quality = case[\"quality\"]\n",
+    "    if quality is None or quality.empty:\n",
+    "        return {\"local_cells\": 0, \"compact_p05\": np.nan, \"compact_median\": np.nan, \"drift_p95\": np.nan, \"drift_max\": np.nan, \"area_cv\": np.nan}\n",
+    "\n",
+    "    generators = gpd.GeoSeries(gpd.points_from_xy(quality.x, quality.y), crs=CRS)\n",
+    "    target = reference_geom.boundary if reference_geom.geom_type in (\"Polygon\", \"MultiPolygon\") else reference_geom\n",
+    "    local = quality.loc[generators.distance(target) <= band_distance]\n",
+    "    if local.empty:\n",
+    "        return {\"local_cells\": 0, \"compact_p05\": np.nan, \"compact_median\": np.nan, \"drift_p95\": np.nan, \"drift_max\": np.nan, \"area_cv\": np.nan}\n",
+    "\n",
+    "    return {\n",
+    "        \"local_cells\": int(len(local)),\n",
+    "        \"compact_p05\": local[\"compactness\"].quantile(0.05),\n",
+    "        \"compact_median\": local[\"compactness\"].median(),\n",
+    "        \"drift_p95\": local[\"drift_ratio\"].quantile(0.95),\n",
+    "        \"drift_max\": local[\"drift_ratio\"].max(),\n",
+    "        \"area_cv\": local[\"area\"].std() / local[\"area\"].mean(),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def _vertex_context_row(label, geom, note):\n",
+    "    spacing = _segment_stats(geom)\n",
+    "    coords = list(geom.exterior.coords) if geom.geom_type == \"Polygon\" else list(geom.coords)\n",
+    "    return {\n",
+    "        \"case\": label,\n",
+    "        \"vertices\": len(coords),\n",
+    "        \"min_seg\": spacing[\"min\"],\n",
+    "        \"max_seg\": spacing[\"max\"],\n",
+    "        \"median_seg\": spacing[\"median\"],\n",
+    "        \"max_median_ratio\": spacing[\"max_median_ratio\"],\n",
+    "        \"note\": note,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def _polygon_diagnostic_row(case):\n",
+    "    clean_geom = _zone_geom(case)\n",
+    "    spacing = _segment_stats(clean_geom)\n",
+    "    local = _local_quality(case, clean_geom, band_distance=15.0)\n",
+    "    return {\n",
+    "        \"case\": case[\"name\"],\n",
+    "        \"clean_vertices\": len(clean_geom.exterior.coords),\n",
+    "        \"clean_max_seg\": spacing[\"max\"],\n",
+    "        \"max_median_ratio\": spacing[\"max_median_ratio\"],\n",
+    "        \"cells\": len(case[\"grid\"]) if case[\"grid\"] is not None else 0,\n",
+    "        **local,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def _line_diagnostic_row(case):\n",
+    "    line_geom = case[\"clean_lines\"].geometry.iloc[0]\n",
+    "    spacing = _segment_stats(line_geom)\n",
+    "    local = _local_quality(case, line_geom, band_distance=6.0)\n",
+    "    return {\n",
+    "        \"case\": case[\"name\"],\n",
+    "        \"clean_vertices\": len(line_geom.coords),\n",
+    "        \"clean_max_seg\": spacing[\"max\"],\n",
+    "        \"cells\": len(case[\"grid\"]) if case[\"grid\"] is not None else 0,\n",
+    "        **local,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "polygon_vertex_variants = [\n",
+    "    (\"Baseline input\", noisy_uneven_poly, \"clustered vertices plus long gaps\"),\n",
+    "    (\"simplify=1.0\", _zone_geom(polygon_spacing_cases[1]), \"removes small wiggles without adding vertices\"),\n",
+    "    (\"resample_geometry(..., 10.0)\", _zone_geom(polygon_spacing_cases[2]), \"redistributes the ring to near-uniform spacing\"),\n",
+    "    (\"simplify=1.0 + densify=10.0\", _zone_geom(polygon_spacing_cases[3]), \"removes small wiggles, then caps long segments\"),\n",
+    "]\n",
+    "\n",
+    "vertex_context_df = pd.DataFrame([_vertex_context_row(label, geom, note) for label, geom, note in polygon_vertex_variants])\n",
+    "print(\"Source/clean vertex spacing context:\")\n",
+    "display(vertex_context_df.round({\"min_seg\": 2, \"max_seg\": 2, \"median_seg\": 2, \"max_median_ratio\": 2}))\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 11))\n",
+    "for ax, (label, geom, note) in zip(axes.ravel(), polygon_vertex_variants):\n",
+    "    coords = list(geom.exterior.coords)\n",
+    "    xs, ys = zip(*coords)\n",
+    "    ax.plot(xs, ys, color=\"black\", linewidth=1.2)\n",
+    "    ax.plot(xs, ys, \"o\", color=\"red\", markersize=2, zorder=5)\n",
+    "    ax.set_title(f\"{label}\\n{len(coords)} vertices - {note}\", fontsize=10)\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "    ax.grid(alpha=0.2)\n",
+    "    ax.set_xlim(5, 95)\n",
+    "    ax.set_ylim(5, 100)\n",
+    "plt.suptitle(\"Problem 3A: source vertices before judging mesh quality\", fontweight=\"bold\", fontsize=12)\n",
+    "plt.tight_layout(rect=(0, 0, 1, 0.96))\n",
+    "plt.show()\n",
+    "\n",
+    "polygon_diagnostics = pd.DataFrame([_polygon_diagnostic_row(case) for case in polygon_spacing_cases])\n",
+    "line_diagnostics = pd.DataFrame([_line_diagnostic_row(case) for case in spacing_line_cases])\n",
+    "\n",
+    "print(\"Metric guide: compact_p05/compact_median higher is better; drift_p95/drift_max and area_cv lower are better; clean_max_seg is source spacing, not a quality score.\")\n",
+    "print(\"Experiment 3A - embedded polygon boundary\")\n",
+    "display(polygon_diagnostics.round({\"clean_max_seg\": 2, \"max_median_ratio\": 2, \"compact_p05\": 4, \"compact_median\": 4, \"drift_p95\": 4, \"drift_max\": 4, \"area_cv\": 4}))\n",
+    "\n",
+    "print(\"Experiment 3B - embedded diagonal line crossing the polygon\")\n",
+    "display(line_diagnostics.round({\"clean_max_seg\": 2, \"compact_p05\": 4, \"compact_median\": 4, \"drift_p95\": 4, \"drift_max\": 4, \"area_cv\": 4}))\n",
+    "\n",
+    "print(\"Interpretation:\")\n",
+    "print(\"  * Gmsh meshes long curves from the active size field; source densification is not mandatory.\")\n",
+    "print(\"  * Simplify-only removes redundant/noisy source detail without adding new CAD points.\")\n",
+    "print(\"  * Resampling redistributes source vertices; simplify + densify removes noise first, then caps long remaining segments.\")\n",
+    "print(\"  * In 3B, the same cleanup is applied to the polygon context and the embedded line, while keeping the coarser cell-size settings.\")\n",
+    "print(\"  * Use the least intrusive tool that improves the local diagnostics you care about; baseline is acceptable when metrics are already good.\")\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 4, figsize=(22, 11), constrained_layout=True)\n",
+    "quality_metric = \"compactness\"\n",
+    "quality_vmin = 0.55\n",
+    "quality_vmax = 0.95\n",
+    "quality_cmap = \"RdYlGn\"\n",
+    "\n",
+    "\n",
+    "def _plot_quality_mesh(ax, case, title, overlay_geoms, xlim, ylim):\n",
+    "    quality = case[\"quality\"]\n",
+    "    if quality is not None and not quality.empty:\n",
+    "        quality.plot(\n",
+    "            ax=ax,\n",
+    "            column=quality_metric,\n",
+    "            cmap=quality_cmap,\n",
+    "            vmin=quality_vmin,\n",
+    "            vmax=quality_vmax,\n",
+    "            edgecolor=\"black\",\n",
+    "            linewidth=0.25,\n",
+    "            legend=False,\n",
+    "        )\n",
+    "    if not isinstance(overlay_geoms, (list, tuple)):\n",
+    "        overlay_geoms = [overlay_geoms]\n",
+    "    for geom, color, linewidth, linestyle in overlay_geoms:\n",
+    "        gpd.GeoSeries([geom], crs=CRS).plot(\n",
+    "            ax=ax,\n",
+    "            color=color,\n",
+    "            linewidth=linewidth,\n",
+    "            linestyle=linestyle,\n",
+    "            zorder=4,\n",
+    "        )\n",
+    "    ax.set_xlim(*xlim)\n",
+    "    ax.set_ylim(*ylim)\n",
+    "    ax.set_title(title, fontsize=10, fontweight=\"bold\")\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "    ax.grid(alpha=0.18)\n",
+    "\n",
+    "\n",
+    "polygon_plot_cases = [\n",
+    "    (polygon_spacing_cases[0], \"Polygon: baseline\"),\n",
+    "    (polygon_spacing_cases[1], \"Polygon: simplify=1\"),\n",
+    "    (polygon_spacing_cases[2], \"Polygon: resample=10\"),\n",
+    "    (polygon_spacing_cases[3], \"Polygon: simplify=1 + densify=10\"),\n",
+    "]\n",
+    "for ax, (case, title) in zip(axes[0], polygon_plot_cases):\n",
+    "    _plot_quality_mesh(ax, case, title, [(_zone_geom(case).boundary, \"black\", 1.3, \"-\")], xlim=(0, 100), ylim=(0, 100))\n",
+    "\n",
+    "line_plot_cases = [\n",
+    "    (spacing_line_cases[0], \"Line + polygon: baseline\"),\n",
+    "    (spacing_line_cases[1], \"Line + polygon: simplify=1\"),\n",
+    "    (spacing_line_cases[2], \"Line + polygon: resample=10\"),\n",
+    "    (spacing_line_cases[3], \"Line + polygon: simplify=1 + densify=10\"),\n",
+    "]\n",
+    "for ax, (case, title) in zip(axes[1], line_plot_cases):\n",
+    "    line_overlays = [\n",
+    "        (_zone_geom(case).boundary, \"black\", 1.1, \"-\"),\n",
+    "        (case[\"clean_lines\"].geometry.iloc[0], \"tab:blue\", 1.8, \"--\"),\n",
+    "    ]\n",
+    "    _plot_quality_mesh(ax, case, title, line_overlays, xlim=(0, 100), ylim=(0, 100))\n",
+    "\n",
+    "norm = mpl.colors.Normalize(vmin=quality_vmin, vmax=quality_vmax)\n",
+    "sm = mpl.cm.ScalarMappable(norm=norm, cmap=quality_cmap)\n",
+    "sm.set_array([])\n",
+    "fig.colorbar(\n",
+    "    sm,\n",
+    "    ax=axes.ravel().tolist(),\n",
+    "    orientation=\"horizontal\",\n",
+    "    shrink=0.72,\n",
+    "    pad=0.08,\n",
+    "    aspect=40,\n",
+    "    label=\"Cell compactness (same scale for all panels; higher is better)\",\n",
+    ")\n",
+    "fig.suptitle(\"Problem 3: Voronoi cell quality comparison with a shared color scale\", fontweight=\"bold\", fontsize=12)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5137334d",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Problem 4 - Snapping, Healing, and Sliver Side Effects\n",
+    "\n",
+    "**What you'll see:** A clean set of embedded lines is hard but meshable without healing: a horizontal line, a 5 degree line pinned at its midpoint, and a nearby vertical line. Then we add a tiny unintended polygon sliver near that same intersection cluster. The sliver creates extra local topology that `connectivity_tolerance` does not remove.\n",
+    "\n",
+    "**Why this matters:** `connectivity_tolerance` is a preprocessing snap. In the current workflow it snaps line endpoints to polygon boundaries and points to nearby geometry; it does not solve interior line-line intersections or remove tiny embedded polygon slivers. Those are left for Gmsh/OCC fragmentation and, optionally, OCC healing.\n",
+    "\n",
+    "**Knobs compared:**\n",
+    "\n",
+    "```python\n",
+    "ConceptualMesh(connectivity_tolerance=...)\n",
+    "MeshGenerator(heal_shapes=True, heal_tolerance=...)\n",
+    "```\n",
+    "\n",
+    "> **Rule of thumb:** First check whether the clean geometry meshes without healing. Use healing for real tiny artifacts such as slivers, and judge it by cost, mesh quality, and constraint preservation. A lower cell count is only useful if the embedded features are still represented and the quality metrics remain acceptable.\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "07f5c711",
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Pinned embedded-line geometry: angle=5 deg, vertical offset=0.0001, local intersection gap=8.75e-06\n",
+      "Sliver artifact: width=0.001, length=0.05, origin=(1.00018, 1.00002)\n",
+      "Constraint preservation check: at least 10 nodes per line within 2.19e-06 units\n",
+      "Topology note: connectivity_tolerance is held at 1e-12; it does not remove this interior sliver or line-line cluster.\n",
+      "Running Clean lines, no healing...\n",
+      "Applying optional geometry simplification...\n",
+      "Resolving polygon overlaps...\n",
+      "Enforcing strict topology...\n",
+      "Snapping 3 lines to polygon boundaries (tol=1e-12)...\n",
+      "Clipping features to domain...\n",
+      "Densifying geometry...\n",
+      "Transferring Geometry to Gmsh...\n",
+      "Adding 1 polygons to Gmsh...\n",
+      "Fragmenting 110 objects...\n",
+      "Reconstructing Map (Input Tags: 110, Out Map Len: 110)...\n",
+      "Setting up Resolution Fields...\n",
+      "Generating Triangular Mesh...\n",
+      "Extracting 862 Nodes from Gmsh...\n",
+      "Computing Mathematical Voronoi...\n",
+      "  -> Raw Polygons: 862\n",
+      "  -> After Ghost Filter: 862\n",
+      "Clipping to Domain Boundary...\n",
+      "  -> After Domain Clip: 862\n",
+      "Enforcing Hydrogeological Zones (Optimization: Point Sampling)...\n",
+      "  -> Zones Assigned: 862\n",
+      "  -> After Barrier Cuts: 862\n",
+      "Final Voronoi Grid Generated: 862 cells.\n",
+      "  mesh_success=True  constraints_preserved=True  cells=862  node_counts=[40, 40, 36]\n",
+      "Running Sliver, no healing...\n",
+      "Applying optional geometry simplification...\n",
+      "Resolving polygon overlaps...\n",
+      "Enforcing strict topology...\n",
+      "Snapping 3 lines to polygon boundaries (tol=1e-12)...\n",
+      "Clipping features to domain...\n",
+      "Densifying geometry...\n",
+      "Transferring Geometry to Gmsh...\n",
+      "Adding 2 polygons to Gmsh...\n",
+      "Fragmenting 111 objects...\n",
+      "Reconstructing Map (Input Tags: 111, Out Map Len: 111)...\n",
+      "Setting up Resolution Fields...\n",
+      "Generating Triangular Mesh...\n",
+      "Extracting 862 Nodes from Gmsh...\n",
+      "Computing Mathematical Voronoi...\n",
+      "  -> Raw Polygons: 862\n",
+      "  -> After Ghost Filter: 862\n",
+      "Clipping to Domain Boundary...\n",
+      "  -> After Domain Clip: 862\n",
+      "Enforcing Hydrogeological Zones (Optimization: Point Sampling)...\n",
+      "  -> Zones Assigned: 862\n",
+      "  -> After Barrier Cuts: 862\n",
+      "Final Voronoi Grid Generated: 862 cells.\n",
+      "  mesh_success=True  constraints_preserved=True  cells=862  node_counts=[42, 42, 36]\n",
+      "Running Sliver, healing tol=5e-5...\n",
+      "Applying optional geometry simplification...\n",
+      "Resolving polygon overlaps...\n",
+      "Enforcing strict topology...\n",
+      "Snapping 3 lines to polygon boundaries (tol=1e-12)...\n",
+      "Clipping features to domain...\n",
+      "Densifying geometry...\n",
+      "Transferring Geometry to Gmsh...\n",
+      "Adding 2 polygons to Gmsh...\n",
+      "Fragmenting 111 objects...\n",
+      "Healing OCC shapes (tolerance=5e-05, degenerated=True, small_edges=True, small_faces=True)...\n",
+      "Heal post-processing: remapped 116, pruned 1 tag(s) from fragment map.\n",
+      "Reconstructing Map (Input Tags: 111, Out Map Len: 111)...\n",
+      "Setting up Resolution Fields...\n",
+      "Generating Triangular Mesh...\n",
+      "Extracting 911 Nodes from Gmsh...\n",
+      "Computing Mathematical Voronoi...\n",
+      "  -> Raw Polygons: 911\n",
+      "  -> After Ghost Filter: 911\n",
+      "Clipping to Domain Boundary...\n",
+      "  -> After Domain Clip: 911\n",
+      "Enforcing Hydrogeological Zones (Optimization: Point Sampling)...\n",
+      "  -> Zones Assigned: 911\n",
+      "  -> After Barrier Cuts: 911\n",
+      "Final Voronoi Grid Generated: 911 cells.\n",
+      "  mesh_success=True  constraints_preserved=True  cells=911  node_counts=[42, 67, 36]\n",
+      "Running Sliver, healing tol=1e-3...\n",
+      "Applying optional geometry simplification...\n",
+      "Resolving polygon overlaps...\n",
+      "Enforcing strict topology...\n",
+      "Snapping 3 lines to polygon boundaries (tol=1e-12)...\n",
+      "Clipping features to domain...\n",
+      "Densifying geometry...\n",
+      "Transferring Geometry to Gmsh...\n",
+      "Adding 2 polygons to Gmsh...\n",
+      "Fragmenting 111 objects...\n",
+      "Healing OCC shapes (tolerance=0.001, degenerated=True, small_edges=True, small_faces=True)...\n",
+      "Heal post-processing: remapped 112, pruned 12 tag(s) from fragment map.\n",
+      "Reconstructing Map (Input Tags: 111, Out Map Len: 111)...\n",
+      "Setting up Resolution Fields...\n",
+      "Generating Triangular Mesh...\n",
+      "Extracting 811 Nodes from Gmsh...\n",
+      "Computing Mathematical Voronoi...\n",
+      "  -> Raw Polygons: 811\n",
+      "  -> After Ghost Filter: 811\n",
+      "Clipping to Domain Boundary...\n",
+      "  -> After Domain Clip: 811\n",
+      "Enforcing Hydrogeological Zones (Optimization: Point Sampling)...\n",
+      "  -> Zones Assigned: 811\n",
+      "  -> After Barrier Cuts: 811\n",
+      "Final Voronoi Grid Generated: 811 cells.\n",
+      "  mesh_success=True  constraints_preserved=False  cells=811  node_counts=[2, 2, 0]\n",
+      "Metric guide: mesh_success should be True and constraints_preserved should also be True. Fewer cells is only better when constraints are preserved. compact_median higher is better; area_cv lower is better.\n",
+      "                  case  has_sliver  ...  compact_median  area_cv\n",
+      "0          clean-lines       False  ...          0.8570   1.3996\n",
+      "1    sliver-no-healing        True  ...          0.8523   1.4182\n",
+      "2  sliver-mild-healing        True  ...          0.8486   1.4773\n",
+      "3  sliver-over-healing        True  ...          0.8800   1.2867\n",
+      "\n",
+      "[4 rows x 13 columns]\n",
+      "Mild healing comparison: sliver case changes from 862 to 911 cells while constraints_preserved=True. Inspect compactness/area_cv before accepting that tradeoff.\n",
+      "Result: every setting meshed, but over-healing lost at least one embedded-line constraint.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pinned_domain = {\n",
+    "    \"geometry\": box(0, 0, 2, 2),\n",
+    "    \"zone_id\": 1,\n",
+    "    \"resolution\": 0.45,\n",
+    "}\n",
+    "\n",
+    "pinned_angle_deg = 5.0\n",
+    "vertical_offset = 1e-4\n",
+    "pinned_line_resolution = 0.04\n",
+    "pinned_background_lc = 0.45\n",
+    "pinned_center = (1.0, 1.0)\n",
+    "pinned_half_length = 0.75\n",
+    "\n",
+    "sliver_width = 1e-3\n",
+    "sliver_length = 0.05\n",
+    "sliver_origin = (1.00018, 1.00002)\n",
+    "\n",
+    "theta = np.deg2rad(pinned_angle_deg)\n",
+    "dx = np.cos(theta) * pinned_half_length\n",
+    "dy = np.sin(theta) * pinned_half_length\n",
+    "cx, cy = pinned_center\n",
+    "\n",
+    "pinned_line_geoms = [\n",
+    "    (\"horizontal\", LineString([(0.25, cy), (1.75, cy)])),\n",
+    "    (f\"angled_{pinned_angle_deg:g}deg\", LineString([(cx - dx, cy - dy), (cx + dx, cy + dy)])),\n",
+    "    (\"near_vertical\", LineString([(cx + vertical_offset, 0.35), (cx + vertical_offset, 1.65)])),\n",
+    "]\n",
+    "\n",
+    "pinned_line_specs = [\n",
+    "    {\n",
+    "        \"geometry\": geom,\n",
+    "        \"line_id\": name,\n",
+    "        \"resolution\": pinned_line_resolution,\n",
+    "        \"is_barrier\": False,\n",
+    "        \"dist_max\": 0.12,\n",
+    "        \"embed\": True,\n",
+    "        \"densify\": True,\n",
+    "    }\n",
+    "    for name, geom in pinned_line_geoms\n",
+    "]\n",
+    "\n",
+    "sliver_x, sliver_y = sliver_origin\n",
+    "sliver_polygon = Polygon(\n",
+    "    [\n",
+    "        (sliver_x, sliver_y - sliver_width / 2),\n",
+    "        (sliver_x + sliver_length, sliver_y - sliver_width / 2),\n",
+    "        (sliver_x + sliver_length, sliver_y + sliver_width / 2),\n",
+    "        (sliver_x, sliver_y + sliver_width / 2),\n",
+    "    ]\n",
+    ")\n",
+    "sliver_specs = [\n",
+    "    {\n",
+    "        \"geometry\": sliver_polygon,\n",
+    "        \"zone_id\": 2,\n",
+    "        \"resolution\": pinned_line_resolution,\n",
+    "        \"z_order\": 2,\n",
+    "        \"embed\": True,\n",
+    "        \"densify\": False,\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# At the vertical line, the horizontal and angled-line intersections are this far apart.\n",
+    "local_intersection_gap = abs(np.tan(theta) * vertical_offset)\n",
+    "constraint_node_tolerance = max(local_intersection_gap / 4, 1e-7)\n",
+    "minimum_nodes_per_line = 10\n",
+    "\n",
+    "robustness_settings = [\n",
+    "    {\n",
+    "        \"name\": \"clean-lines\",\n",
+    "        \"label\": \"Clean lines, no healing\",\n",
+    "        \"has_sliver\": False,\n",
+    "        \"polygon_specs\": [],\n",
+    "        \"mg_kwargs\": {\"tolerance_initial_delaunay\": 1e-8, \"heal_shapes\": False, \"optimization_cycles\": 0, \"smoothing_steps\": 0},\n",
+    "    },\n",
+    "    {\n",
+    "        \"name\": \"sliver-no-healing\",\n",
+    "        \"label\": \"Sliver, no healing\",\n",
+    "        \"has_sliver\": True,\n",
+    "        \"polygon_specs\": sliver_specs,\n",
+    "        \"mg_kwargs\": {\"tolerance_initial_delaunay\": 1e-8, \"heal_shapes\": False, \"optimization_cycles\": 0, \"smoothing_steps\": 0},\n",
+    "    },\n",
+    "    {\n",
+    "        \"name\": \"sliver-mild-healing\",\n",
+    "        \"label\": \"Sliver, healing tol=5e-5\",\n",
+    "        \"has_sliver\": True,\n",
+    "        \"polygon_specs\": sliver_specs,\n",
+    "        \"mg_kwargs\": {\"tolerance_initial_delaunay\": 1e-8, \"heal_shapes\": True, \"heal_tolerance\": 5e-5, \"optimization_cycles\": 0, \"smoothing_steps\": 0},\n",
+    "    },\n",
+    "    {\n",
+    "        \"name\": \"sliver-over-healing\",\n",
+    "        \"label\": \"Sliver, healing tol=1e-3\",\n",
+    "        \"has_sliver\": True,\n",
+    "        \"polygon_specs\": sliver_specs,\n",
+    "        \"mg_kwargs\": {\"tolerance_initial_delaunay\": 1e-8, \"heal_shapes\": True, \"heal_tolerance\": 1e-3, \"optimization_cycles\": 0, \"smoothing_steps\": 0},\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def _constraint_node_counts(case, source_lines, distance_tolerance):\n",
+    "    if not case[\"mesh_success\"] or case[\"mg\"] is None or case[\"mg\"].nodes is None:\n",
+    "        return [0 for _ in source_lines]\n",
+    "    nodes = case[\"mg\"].nodes\n",
+    "    counts = []\n",
+    "    for _, line in source_lines:\n",
+    "        counts.append(int(sum(line.distance(Point(x, y)) <= distance_tolerance for x, y in nodes)))\n",
+    "    return counts\n",
+    "\n",
+    "\n",
+    "print(\n",
+    "    \"Pinned embedded-line geometry: \"\n",
+    "    f\"angle={pinned_angle_deg:g} deg, vertical offset={vertical_offset:g}, \"\n",
+    "    f\"local intersection gap={local_intersection_gap:.2e}\"\n",
+    ")\n",
+    "print(\n",
+    "    \"Sliver artifact: \"\n",
+    "    f\"width={sliver_width:g}, length={sliver_length:g}, origin=({sliver_x:g}, {sliver_y:g})\"\n",
+    ")\n",
+    "print(\n",
+    "    \"Constraint preservation check: \"\n",
+    "    f\"at least {minimum_nodes_per_line} nodes per line within {constraint_node_tolerance:.2e} units\"\n",
+    ")\n",
+    "print(\"Topology note: connectivity_tolerance is held at 1e-12; it does not remove this interior sliver or line-line cluster.\")\n",
+    "\n",
+    "robustness_cases = []\n",
+    "for setting in robustness_settings:\n",
+    "    print(f\"Running {setting['label']}...\")\n",
+    "    case = run_case(\n",
+    "        name=setting[\"name\"],\n",
+    "        domain_spec=pinned_domain,\n",
+    "        polygon_specs=setting[\"polygon_specs\"],\n",
+    "        line_specs=pinned_line_specs,\n",
+    "        background_lc=pinned_background_lc,\n",
+    "        cm_kwargs={\"connectivity_tolerance\": 1e-12},\n",
+    "        mg_kwargs=setting[\"mg_kwargs\"],\n",
+    "    )\n",
+    "    node_counts = _constraint_node_counts(case, pinned_line_geoms, constraint_node_tolerance)\n",
+    "    case[\"label\"] = setting[\"label\"]\n",
+    "    case[\"has_sliver\"] = setting[\"has_sliver\"]\n",
+    "    case[\"settings\"] = setting[\"mg_kwargs\"]\n",
+    "    case[\"constraint_node_counts\"] = node_counts\n",
+    "    case[\"constraint_preserved\"] = case[\"mesh_success\"] and min(node_counts) >= minimum_nodes_per_line\n",
+    "    robustness_cases.append(case)\n",
+    "    cells = len(case[\"grid\"]) if case[\"grid\"] is not None else 0\n",
+    "    print(\n",
+    "        f\"  mesh_success={case['mesh_success']}  constraints_preserved={case['constraint_preserved']}  \"\n",
+    "        f\"cells={cells}  node_counts={node_counts}\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def _robustness_row(case):\n",
+    "    quality = case[\"quality\"]\n",
+    "    node_counts = case[\"constraint_node_counts\"]\n",
+    "    lost_lines = sum(count < minimum_nodes_per_line for count in node_counts)\n",
+    "    settings = case[\"settings\"]\n",
+    "    base = {\n",
+    "        \"case\": case[\"name\"],\n",
+    "        \"has_sliver\": case[\"has_sliver\"],\n",
+    "        \"heal_tolerance\": settings.get(\"heal_tolerance\", np.nan),\n",
+    "        \"mesh_success\": case[\"mesh_success\"],\n",
+    "        \"constraints_preserved\": case[\"constraint_preserved\"],\n",
+    "        \"lost_lines\": lost_lines,\n",
+    "        \"node_counts\": str(node_counts),\n",
+    "        \"min_nodes_per_line\": min(node_counts),\n",
+    "        \"cells\": len(case[\"grid\"]) if case[\"grid\"] is not None else 0,\n",
+    "        \"mesh_s\": case[\"mesh_s\"],\n",
+    "        \"error\": case[\"error\"][:80],\n",
+    "    }\n",
+    "    if quality is None or quality.empty:\n",
+    "        return {**base, \"compact_median\": np.nan, \"area_cv\": np.nan}\n",
+    "    return {\n",
+    "        **base,\n",
+    "        \"compact_median\": quality[\"compactness\"].median(),\n",
+    "        \"area_cv\": quality[\"area\"].std() / quality[\"area\"].mean(),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "robustness_diagnostics = pd.DataFrame([_robustness_row(case) for case in robustness_cases])\n",
+    "print(\n",
+    "    \"Metric guide: mesh_success should be True and constraints_preserved should also be True. \"\n",
+    "    \"Fewer cells is only better when constraints are preserved. compact_median higher is better; area_cv lower is better.\"\n",
+    ")\n",
+    "display(robustness_diagnostics.round({\"heal_tolerance\": 8, \"mesh_s\": 2, \"compact_median\": 4, \"area_cv\": 4}))\n",
+    "\n",
+    "sliver_no_healing_cells = int(robustness_diagnostics.loc[robustness_diagnostics[\"case\"] == \"sliver-no-healing\", \"cells\"].iloc[0])\n",
+    "sliver_mild_healing_cells = int(robustness_diagnostics.loc[robustness_diagnostics[\"case\"] == \"sliver-mild-healing\", \"cells\"].iloc[0])\n",
+    "sliver_mild_ok = bool(robustness_diagnostics.loc[robustness_diagnostics[\"case\"] == \"sliver-mild-healing\", \"constraints_preserved\"].iloc[0])\n",
+    "print(\n",
+    "    \"Mild healing comparison: \"\n",
+    "    f\"sliver case changes from {sliver_no_healing_cells} to {sliver_mild_healing_cells} cells \"\n",
+    "    f\"while constraints_preserved={sliver_mild_ok}. Inspect compactness/area_cv before accepting that tradeoff.\"\n",
+    ")\n",
+    "\n",
+    "if robustness_diagnostics[\"constraints_preserved\"].all():\n",
+    "    print(\"Result: every setting preserved the embedded lines; compare cell cost and quality.\")\n",
+    "elif robustness_diagnostics[\"mesh_success\"].all():\n",
+    "    print(\"Result: every setting meshed, but over-healing lost at least one embedded-line constraint.\")\n",
+    "else:\n",
+    "    print(\"Result: at least one setting failed before producing a mesh.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "407e870e",
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "cell_14:136: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(\n",
+    "    2,\n",
+    "    4,\n",
+    "    figsize=(20, 9),\n",
+    "    gridspec_kw={\"height_ratios\": [3.0, 2.1]},\n",
+    "    constrained_layout=True,\n",
+    ")\n",
+    "map_axes = axes[0]\n",
+    "zoom_axes = axes[1]\n",
+    "quality_metric = \"compactness\"\n",
+    "quality_vmin = 0.55\n",
+    "quality_vmax = 0.95\n",
+    "quality_cmap = \"RdYlGn\"\n",
+    "line_colors = {\n",
+    "    \"horizontal\": \"#d73027\",\n",
+    "    f\"angled_{pinned_angle_deg:g}deg\": \"#4575b4\",\n",
+    "    \"near_vertical\": \"#111111\",\n",
+    "}\n",
+    "sliver_face = \"#fdae61\"\n",
+    "sliver_edge = \"#7f2704\"\n",
+    "\n",
+    "\n",
+    "def _plot_problem4_lines(ax, linewidth=1.8):\n",
+    "    for name, geom in pinned_line_geoms:\n",
+    "        gpd.GeoSeries([geom], crs=CRS).plot(\n",
+    "            ax=ax,\n",
+    "            color=line_colors[name],\n",
+    "            linewidth=linewidth,\n",
+    "            zorder=6,\n",
+    "        )\n",
+    "    ax.scatter([cx], [cy], s=20, color=\"white\", edgecolor=\"black\", linewidth=0.7, zorder=7)\n",
+    "\n",
+    "\n",
+    "def _plot_problem4_sliver(ax, case):\n",
+    "    if not case[\"has_sliver\"]:\n",
+    "        return\n",
+    "    gpd.GeoSeries([sliver_polygon], crs=CRS).plot(\n",
+    "        ax=ax,\n",
+    "        facecolor=sliver_face,\n",
+    "        edgecolor=sliver_edge,\n",
+    "        alpha=0.45,\n",
+    "        linewidth=1.0,\n",
+    "        zorder=5,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def _plot_case_quality(ax, case, linewidth):\n",
+    "    quality = case[\"quality\"]\n",
+    "    if quality is not None and not quality.empty:\n",
+    "        quality.plot(\n",
+    "            ax=ax,\n",
+    "            column=quality_metric,\n",
+    "            cmap=quality_cmap,\n",
+    "            vmin=quality_vmin,\n",
+    "            vmax=quality_vmax,\n",
+    "            edgecolor=\"black\",\n",
+    "            linewidth=linewidth,\n",
+    "            legend=False,\n",
+    "        )\n",
+    "    else:\n",
+    "        gpd.GeoSeries([pinned_domain[\"geometry\"]], crs=CRS).boundary.plot(\n",
+    "            ax=ax,\n",
+    "            color=\"black\",\n",
+    "            linewidth=1.0,\n",
+    "        )\n",
+    "        ax.text(\n",
+    "            0.04,\n",
+    "            0.94,\n",
+    "            \"mesh failed\",\n",
+    "            transform=ax.transAxes,\n",
+    "            ha=\"left\",\n",
+    "            va=\"top\",\n",
+    "            fontsize=10,\n",
+    "            fontweight=\"bold\",\n",
+    "            color=\"#b2182b\",\n",
+    "            bbox={\"facecolor\": \"white\", \"edgecolor\": \"#b2182b\", \"alpha\": 0.85, \"pad\": 2},\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "for ax_map, ax_zoom, case in zip(map_axes, zoom_axes, robustness_cases):\n",
+    "    _plot_case_quality(ax_map, case, linewidth=0.22)\n",
+    "    _plot_problem4_sliver(ax_map, case)\n",
+    "    _plot_problem4_lines(ax_map, linewidth=1.5)\n",
+    "    status = \"OK\" if case[\"mesh_success\"] and case[\"constraint_preserved\"] else \"FAIL\"\n",
+    "    cells = len(case[\"grid\"]) if case[\"grid\"] is not None else 0\n",
+    "    min_nodes = min(case[\"constraint_node_counts\"])\n",
+    "    heal_label = \"no healing\" if not case[\"settings\"].get(\"heal_shapes\") else f\"heal={case['settings']['heal_tolerance']:.0e}\"\n",
+    "    ax_map.set_title(\n",
+    "        f\"{case['name']}\\n{status}, {cells} cells, min nodes/line={min_nodes}\\n{heal_label}\",\n",
+    "        fontsize=8.5,\n",
+    "        fontweight=\"bold\",\n",
+    "    )\n",
+    "    ax_map.set_xlim(0, 2)\n",
+    "    ax_map.set_ylim(0, 2)\n",
+    "    ax_map.set_aspect(\"equal\")\n",
+    "    ax_map.grid(alpha=0.18)\n",
+    "    ax_map.set_xlabel(\"\")\n",
+    "    ax_map.set_ylabel(\"\")\n",
+    "\n",
+    "    _plot_case_quality(ax_zoom, case, linewidth=0.45)\n",
+    "    _plot_problem4_sliver(ax_zoom, case)\n",
+    "    _plot_problem4_lines(ax_zoom, linewidth=2.1)\n",
+    "    if case[\"mesh_success\"] and case[\"mg\"] is not None and case[\"mg\"].nodes is not None:\n",
+    "        nodes = case[\"mg\"].nodes\n",
+    "        window = (\n",
+    "            (nodes[:, 0] >= 0.94)\n",
+    "            & (nodes[:, 0] <= 1.08)\n",
+    "            & (nodes[:, 1] >= 0.965)\n",
+    "            & (nodes[:, 1] <= 1.035)\n",
+    "        )\n",
+    "        ax_zoom.scatter(nodes[window, 0], nodes[window, 1], s=8, color=\"black\", alpha=0.65, zorder=8)\n",
+    "    ax_zoom.set_xlim(0.94, 1.08)\n",
+    "    ax_zoom.set_ylim(0.965, 1.035)\n",
+    "    ax_zoom.set_aspect(\"equal\")\n",
+    "    ax_zoom.grid(alpha=0.25)\n",
+    "    ax_zoom.set_xlabel(\"intersection/sliver zoom\")\n",
+    "    ax_zoom.set_ylabel(\"\")\n",
+    "\n",
+    "norm = mpl.colors.Normalize(vmin=quality_vmin, vmax=quality_vmax)\n",
+    "sm = mpl.cm.ScalarMappable(norm=norm, cmap=quality_cmap)\n",
+    "sm.set_array([])\n",
+    "fig.colorbar(\n",
+    "    sm,\n",
+    "    ax=map_axes.ravel().tolist(),\n",
+    "    orientation=\"horizontal\",\n",
+    "    shrink=0.78,\n",
+    "    pad=0.03,\n",
+    "    aspect=36,\n",
+    "    label=\"Cell compactness (same range for all map panels; higher is better)\",\n",
+    ")\n",
+    "fig.suptitle(\n",
+    "    \"Problem 4: topology snapping is not sliver healing, and over-healing can lose constraints\",\n",
+    "    fontweight=\"bold\",\n",
+    "    fontsize=12,\n",
+    ")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "088b9722",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Known Limitations\n",
+    "\n",
+    "The following cases show where the vorflow preprocessing pipeline **cannot** fully infer user intent ? either because the problem is fundamentally topological, or because any automatic fix could destroy intended geometry.\n",
+    "\n",
+    "---\n",
+    "### Limitation 1 ? Polygon Holes Mean \"Not This Zone,\" Not Automatically \"Empty Void\"\n",
+    "\n",
+    "**What you'll see:** You create a polygon with a hole (donut shape) in zone 2 and place it inside zone 1.\n",
+    "\n",
+    "**What vorflow can know:** The hole is definitely not zone 2. Vorflow cannot infer whether you intended that hole to be empty space, lower-priority zone 1, or a separate material.\n",
+    "\n",
+    "**Current effect:** If a lower-priority polygon exists underneath, cells inside the hole are assigned to that lower-priority zone. In this demo the hole belongs to zone 1. That is valid behavior, and some examples/tests rely on it.\n",
+    "\n",
+    "**Choose the geometry that matches your intent:**\n",
+    "\n",
+    "```python\n",
+    "# 1) Hole should belong to the lower-priority/background zone:\n",
+    "#    use the donut polygon as-is. This is the current demo behavior.\n",
+    "cm.add_polygon(domain, zone_id=1, z_order=0)\n",
+    "cm.add_polygon(zone2_with_hole, zone_id=2, z_order=1)\n",
+    "\n",
+    "# 2) Hole should be empty / outside the mesh:\n",
+    "#    remove the hole from the underlying domain too.\n",
+    "domain_with_void = domain.difference(hole_polygon)\n",
+    "cm.add_polygon(domain_with_void, zone_id=1, z_order=0)\n",
+    "cm.add_polygon(zone2_with_hole, zone_id=2, z_order=1)\n",
+    "\n",
+    "# 3) Hole should be a third material/zone:\n",
+    "#    add it explicitly as a higher-priority polygon.\n",
+    "cm.add_polygon(domain, zone_id=1, z_order=0)\n",
+    "cm.add_polygon(zone2_with_hole, zone_id=2, z_order=1)\n",
+    "cm.add_polygon(hole_polygon, zone_id=3, z_order=2)\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0de9589e",
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Applying optional geometry simplification...\n",
+      "Resolving polygon overlaps...\n",
+      "Enforcing strict topology...\n",
+      "Clipping features to domain...\n",
+      "Densifying geometry...\n",
+      "Transferring Geometry to Gmsh...\n",
+      "Adding 3 polygons to Gmsh...\n",
+      "Fragmenting 3 objects...\n",
+      "Reconstructing Map (Input Tags: 3, Out Map Len: 3)...\n",
+      "Setting up Resolution Fields...\n",
+      "Generating Triangular Mesh...\n",
+      "Extracting 136 Nodes from Gmsh...\n",
+      "Computing Mathematical Voronoi...\n",
+      "  -> Raw Polygons: 136\n",
+      "  -> After Ghost Filter: 136\n",
+      "Clipping to Domain Boundary...\n",
+      "  -> After Domain Clip: 136\n",
+      "Enforcing Hydrogeological Zones (Optimization: Point Sampling)...\n",
+      "  -> Zones Assigned: 136\n",
+      "  -> After Barrier Cuts: 136\n",
+      "Final Voronoi Grid Generated: 136 cells.\n",
+      "Zone assignment in grid:\n",
+      "zone_id\n",
+      "1    88\n",
+      "2    48\n",
+      "\n",
+      "? Cells inside the hole (the 4?4 m interior square) are assigned to zone 1.\n",
+      "  A hole means 'not zone 2'; it is not automatically an empty mesh void.\n"
+     ]
+    }
+   ],
+   "source": [
+    "donut_case = run_case(\n",
+    "    name=\"donut-with-overlapping-hole\",\n",
+    "    domain_spec={\n",
+    "        \"geometry\": Polygon([(0, 0), (20, 0), (20, 20), (0, 20)]),\n",
+    "        \"zone_id\": 1,\n",
+    "        \"resolution\": 5.0,\n",
+    "        \"z_order\": 0,\n",
+    "        \"dist_max\": 25.0,\n",
+    "    },\n",
+    "    polygon_specs=[\n",
+    "        {\n",
+    "            \"geometry\": Polygon(\n",
+    "                [(5, 5), (15, 5), (15, 15), (5, 15)],\n",
+    "                [[(8, 8), (12, 8), (12, 12), (8, 12)]],\n",
+    "            ),\n",
+    "            \"zone_id\": 2,\n",
+    "            \"resolution\": 2.0,\n",
+    "            \"z_order\": 1,\n",
+    "            \"dist_max\": 10.0,\n",
+    "        }\n",
+    "    ],\n",
+    "    background_lc=5.0,\n",
+    ")\n",
+    "\n",
+    "if donut_case[\"grid\"] is not None and not donut_case[\"grid\"].empty:\n",
+    "    print(\"Zone assignment in grid:\")\n",
+    "    print(donut_case[\"grid\"][\"zone_id\"].value_counts().to_string())\n",
+    "    print()\n",
+    "    print(\"? Cells inside the hole (the 4?4 m interior square) are assigned to zone 1.\")\n",
+    "    print(\"  A hole means 'not zone 2'; it is not automatically an empty mesh void.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "53ce2ac8",
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "cell_17:25: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 6))\n",
+    "\n",
+    "if donut_case[\"grid\"] is not None and not donut_case[\"grid\"].empty:\n",
+    "    donut_case[\"grid\"].plot(\n",
+    "        ax=ax, column=\"zone_id\", cmap=\"Set1\", alpha=0.6,\n",
+    "        edgecolor=\"black\", linewidth=0.3,\n",
+    "        categorical=True, legend=True,\n",
+    "    )\n",
+    "\n",
+    "# Draw the intended hole boundary for reference\n",
+    "hole_ring = Polygon([(8, 8), (12, 8), (12, 12), (8, 12)])\n",
+    "gpd.GeoSeries([hole_ring], crs=CRS).boundary.plot(\n",
+    "    ax=ax, color=\"red\", linewidth=2, linestyle=\"--\", label=\"Intended hole boundary\"\n",
+    ")\n",
+    "\n",
+    "ax.set_title(\n",
+    "    \"Limitation 1: hole inside zone 2 polygon\\n\"\n",
+    "    \"Red dashed line = intended hole — interior cells belong to zone 1 (not removed)\",\n",
+    "    fontsize=10,\n",
+    ")\n",
+    "ax.set_aspect(\"equal\")\n",
+    "ax.grid(alpha=0.2)\n",
+    "ax.legend(fontsize=9)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d249edcf",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "### Limitation 2 — Connectivity Tolerance Is a Blanket Setting\n",
+    "\n",
+    "**What you need to know:** `connectivity_tolerance` is applied globally to every feature — it is not selective. When you raise it to 1.0 m to heal a 0.0005 m gap at one boundary, that same 1.0 m radius is applied to every endpoint everywhere. Any feature endpoint within 1.0 m of the domain boundary (or another nearby feature) will also be snapped, whether you intended it to move or not.\n",
+    "\n",
+    "**How to stay safe:** After calling `cm.generate()`, always inspect `clean_points` and `clean_lines` to confirm features are where you expect:\n",
+    "\n",
+    "```python\n",
+    "clean_polys, clean_lines, clean_points = cm.generate()\n",
+    "print(clean_points[[\"point_id\", \"geometry\"]])   # verify positions\n",
+    "```\n",
+    "\n",
+    "The cell below shows a 100 m × 2 m domain (very thin) with a centreline and two monitoring points 0.1 m above and below the line. With `connectivity_tolerance=1.0`, the snapping routine processes both points (they are within 1.0 m of the line), but since they are interior to the domain they stay put. In a real workflow with features near the domain boundary, the same tolerance can pull them to unexpected positions — always verify."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6d4d5163",
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Applying optional geometry simplification...\n",
+      "Resolving polygon overlaps...\n",
+      "Enforcing strict topology...\n",
+      "Snapping 1 lines to polygon boundaries (tol=1.0)...\n",
+      "Snapping 2 points to geometry (tol=1.0)...\n",
+      "Clipping features to domain...\n",
+      "Densifying geometry...\n",
+      "Transferring Geometry to Gmsh...\n",
+      "Adding 1 polygons to Gmsh...\n",
+      "Fragmenting 4 objects...\n",
+      "Reconstructing Map (Input Tags: 4, Out Map Len: 4)...\n",
+      "Setting up Resolution Fields...\n",
+      "Generating Triangular Mesh...\n",
+      "Extracting 255 Nodes from Gmsh...\n",
+      "Computing Mathematical Voronoi...\n",
+      "  -> Raw Polygons: 255\n",
+      "  -> After Ghost Filter: 255\n",
+      "Clipping to Domain Boundary...\n",
+      "  -> After Domain Clip: 255\n",
+      "Enforcing Hydrogeological Zones (Optimization: Point Sampling)...\n",
+      "  -> Zones Assigned: 255\n",
+      "  -> After Barrier Cuts: 255\n",
+      "Final Voronoi Grid Generated: 255 cells.\n",
+      "Input:              2 distinct points  (50, +0.1) and (50, -0.1)\n",
+      "  Clean point coords: (50.0000, 0.1000)\n",
+      "  Clean point coords: (50.0000, -0.1000)\n",
+      "\n",
+      "→ Both points STAYED at their original positions — the centreline has no vertex at x=50\n",
+      "  so there is nothing to snap to at that location.\n",
+      "\n",
+      "  In a real workflow, features near the domain boundary or near another feature's\n",
+      "  vertex CAN be pulled unexpectedly. Always inspect clean_points/clean_lines\n",
+      "  after cm.generate() to confirm feature positions.\n",
+      "\n",
+      "Verification pattern — inspect clean geometry:\n",
+      "  point_id         geometry\n",
+      "0    above   POINT (50 0.1)\n",
+      "1    below  POINT (50 -0.1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "aggressive_connectivity = run_case(\n",
+    "    name=\"aggressive-connectivity-collapse\",\n",
+    "    domain_spec={\"geometry\": box(0, -1, 100, 1), \"zone_id\": 1, \"resolution\": 2.0},\n",
+    "    line_specs=[\n",
+    "        {\"geometry\": LineString([(0, 0), (100, 0)]), \"line_id\": \"centerline\", \"resolution\": 0.5, \"densify\": False}\n",
+    "    ],\n",
+    "    point_specs=[\n",
+    "        {\"geometry\": Point(50,  0.1), \"point_id\": \"above\", \"resolution\": 0.2},\n",
+    "        {\"geometry\": Point(50, -0.1), \"point_id\": \"below\", \"resolution\": 0.2},\n",
+    "    ],\n",
+    "    background_lc=2.0,\n",
+    "    cm_kwargs={\"connectivity_tolerance\": 1.0},\n",
+    ")\n",
+    "\n",
+    "clean_pts = aggressive_connectivity[\"clean_points\"]\n",
+    "print(f\"Input:              2 distinct points  (50, +0.1) and (50, -0.1)\")\n",
+    "if clean_pts is not None and not clean_pts.empty:\n",
+    "    for _, row in clean_pts.iterrows():\n",
+    "        print(f\"  Clean point coords: ({row.geometry.x:.4f}, {row.geometry.y:.4f})\")\n",
+    "print()\n",
+    "print(\"→ Both points STAYED at their original positions — the centreline has no vertex at x=50\")\n",
+    "print(\"  so there is nothing to snap to at that location.\")\n",
+    "print()\n",
+    "print(\"  In a real workflow, features near the domain boundary or near another feature's\")\n",
+    "print(\"  vertex CAN be pulled unexpectedly. Always inspect clean_points/clean_lines\")\n",
+    "print(\"  after cm.generate() to confirm feature positions.\")\n",
+    "\n",
+    "print(\"\\nVerification pattern — inspect clean geometry:\")\n",
+    "if clean_pts is not None:\n",
+    "    print(clean_pts[[\"point_id\", \"geometry\"]].to_string(index=True))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2a899baa",
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Verification pattern — inspect clean geometry:\n",
+      "  point_id         geometry\n",
+      "0    above   POINT (50 0.1)\n",
+      "1    below  POINT (50 -0.1)\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "cell_20:46: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "clean_pts = aggressive_connectivity[\"clean_points\"]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "ax_raw, ax_clean = axes\n",
+    "\n",
+    "# Zoom tight around the points so the y=±0.1 offset is visible\n",
+    "xlim, ylim = (44, 58), (-0.5, 0.5)\n",
+    "\n",
+    "for ax, pts_gdf, pt_color, title in [\n",
+    "    (ax_raw,   aggressive_connectivity[\"raw_points\"],  \"red\",   \"Input: 2 monitoring points\\n(0.1 m above and 0.1 m below the centreline)\"),\n",
+    "    (ax_clean, clean_pts,                              \"green\", \"After preprocessing (connectivity_tolerance=1.0)\\nBoth points stayed at original positions ✓\"),\n",
+    "]:\n",
+    "    aggressive_connectivity[\"raw_polygons\"].boundary.plot(ax=ax, color=\"black\", linewidth=0.6)\n",
+    "    aggressive_connectivity[\"raw_lines\"].plot(ax=ax, color=\"steelblue\", linewidth=2, zorder=3)\n",
+    "\n",
+    "    if pts_gdf is not None and not pts_gdf.empty:\n",
+    "        pts_gdf.plot(ax=ax, color=pt_color, markersize=60, zorder=5)\n",
+    "        # stagger annotations: first point up, second down\n",
+    "        offsets = [(2, 0.15), (2, -0.20)]\n",
+    "        for i, (_, row) in enumerate(pts_gdf.iterrows()):\n",
+    "            dx, dy = offsets[i]\n",
+    "            ax.annotate(\n",
+    "                f\"{row.geometry.y:+.1f} m\",\n",
+    "                xy=(row.geometry.x, row.geometry.y),\n",
+    "                xytext=(row.geometry.x + dx, row.geometry.y + dy),\n",
+    "                fontsize=9, color=pt_color,\n",
+    "                arrowprops=dict(arrowstyle=\"->\", color=pt_color, lw=1.2),\n",
+    "            )\n",
+    "\n",
+    "    ax.set_title(title, fontsize=10)\n",
+    "    ax.set_xlim(*xlim)\n",
+    "    ax.set_ylim(*ylim)\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "    ax.grid(alpha=0.2)\n",
+    "    ax.axhline(0, color=\"steelblue\", linewidth=0.5, linestyle=\"--\", alpha=0.5)\n",
+    "\n",
+    "ax_raw.set_facecolor(\"#fff8f8\")\n",
+    "ax_clean.set_facecolor(\"#f8fff8\")\n",
+    "\n",
+    "plt.suptitle(\n",
+    "    \"Limitation 2: connectivity_tolerance is global\\n\"\n",
+    "    \"Always inspect clean_points / clean_lines after cm.generate() to verify positions\",\n",
+    "    fontweight=\"bold\", fontsize=11,\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Practical verification pattern\n",
+    "print(\"Verification pattern — inspect clean geometry:\")\n",
+    "if clean_pts is not None and not clean_pts.empty:\n",
+    "    print(clean_pts[[\"point_id\", \"geometry\"]].to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e820d914",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Quick Reference\n",
+    "\n",
+    "| Problem | Symptom | Fix | API |\n",
+    "|---------|---------|-----|-----|\n",
+    "| Duplicate / near-duplicate vertices | Gmsh would reject degenerate zero-length segments | Handled during robust geometry transfer before OCC creation; inspect short-segment diagnostics, not post-densification vertex counts | `MeshGenerator` geometry transfer |\n",
+    "| Feature doesn't reach domain boundary | Feature is removed from `clean_points` or clipped from `clean_lines`, so it has no local influence | Snap within `N` units, but only to an existing vertex | `ConceptualMesh(connectivity_tolerance=N)` |\n",
+    "| No snap vertex exists where you need one | Large tolerance still does not connect the feature | Resample the reference geometry before adding it | `resample_geometry(geom, spacing)` |\n",
+    "| Irregular source spacing | Local quality diagnostics improve after adding/redistributing vertices | Use the least intrusive spacing tool that improves metrics | `densify=spacing`, `resample_geometry()`, or `simplify_tolerance` + `densify` |\n",
+    "| Field-only refinement line under-refines | Refinement coverage is sparse even though the line is present | Increase DistanceField sampling when the field is under-sampled; fix topology first if the feature is misplaced or clipped | `ThresholdField(..., sampling=N)` |\n",
+    "| Over-digitised / noisy boundaries | Too many redundant vertices drive unnecessary detail | Remove redundant vertices while preserving overall shape | `add_polygon(..., simplify_tolerance=X)` or `add_line(..., simplify_tolerance=X)` |\n",
+    "| Narrow zone + barrier / sliver topology causes Gmsh issues | `generate()` returns `False`, raises, or constraints disappear after healing | Inspect clean geometry and embedding diagnostics first; use healing/tolerance changes only as measured tradeoffs | `MeshGenerator(diagnose=True, heal_shapes=True, tolerance_initial_delaunay=...)` |\n",
+    "| Polygon hole overlapping another zone | Hole is assigned to the lower-priority zone, but user intent may differ | Keep as-is for lower-zone holes; subtract the hole from the domain for an empty void; add a new polygon for a separate zone | `domain.difference(hole)` or `add_polygon(hole, z_order=...)` |\n",
+    "| `connectivity_tolerance` too large | Closely-spaced features can move unexpectedly | Lower tolerance to match the gap size only | Inspect `clean_points` / `clean_lines` after `cm.generate()` |\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "vorflow",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/field_capabilities_example.py b/examples/field_capabilities_example.py
index 16363f8..2d9f01b 100644
--- a/examples/field_capabilities_example.py
+++ b/examples/field_capabilities_example.py
@@ -125,7 +125,7 @@
     dist_min=feature_lc/2,
     dist_max=background_lc * 1.5,
     fields=[auto_linear],
-    embed=False,  # field-only polygon
+    embed=False,  # field-only boundary line
 )
 
 blueprint.add_polygon(
@@ -215,14 +215,18 @@
     "lower-center": "tab:brown",
     "lower-right": "tab:pink",
 }
-field_only_polys = {"upper-center", "lower-left"}
+field_only = {
+    "upper-center": "polygon",
+    "lower-left": "boundary line",
+    "lower-right": "polygon",
+}
 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
+    field_only_kind = field_only.get(name)
+    label = f"{name} (field-only {field_only_kind})" if field_only_kind else name
     ax.plot(
         *poly.exterior.xy,
         lw=1,
-        ls=":" if is_field_only else "--",
+        ls=":" if field_only_kind else "--",
         color=poly_colors.get(name),
         label=label,
     )
diff --git a/src/vorflow/blueprint.py b/src/vorflow/blueprint.py
index f2d1ffc..376b0b7 100644
--- a/src/vorflow/blueprint.py
+++ b/src/vorflow/blueprint.py
@@ -613,8 +613,30 @@ def generate(self, connectivity_tolerance=None):
 
             if clipped_features:
                 field_only_gdf = gpd.GeoDataFrame(clipped_features, crs=self.crs)
+                # Align schemas before concat to avoid pandas dtype inference warnings
+                # while preserving the canonical polygon columns.
+                polygon_columns = [
+                    'geometry',
+                    'zone_id',
+                    'lc',
+                    'z_order',
+                    'dist_min',
+                    'dist_max',
+                    'fields',
+                    'embed',
+                    'densify',
+                    'simplify_tolerance',
+                ]
+                self.clean_polygons = self.clean_polygons.reindex(columns=polygon_columns)
+                field_only_gdf = field_only_gdf.reindex(columns=polygon_columns)
+                # Drop all-null non-geometry columns only during concat; they are
+                # restored immediately after so the public GeoDataFrame shape is unchanged.
+                concat_frames = [
+                    frame.dropna(axis=1, how='all')
+                    for frame in (self.clean_polygons, field_only_gdf)
+                ]
                 self.clean_polygons = gpd.GeoDataFrame(
-                    pd.concat([self.clean_polygons, field_only_gdf], ignore_index=True),
+                    pd.concat(concat_frames, ignore_index=True).reindex(columns=polygon_columns),
                     crs=self.crs,
                 )
 
diff --git a/src/vorflow/engine.py b/src/vorflow/engine.py
index 5109939..87f3c06 100644
--- a/src/vorflow/engine.py
+++ b/src/vorflow/engine.py
@@ -1085,6 +1085,10 @@ def _auto_threshold_from_row(row, background_lc):
 
             if not any(tags_dict.values()):
                 continue
+
+            # Private metadata for built-in field helpers; custom MeshField
+            # implementations can ignore it because tag lists remain unchanged.
+            tags_dict['_verbosity'] = self.verbosity
             
             # Create the Gmsh field using the provided MeshField object.
             f_id = field.create(
diff --git a/src/vorflow/fields.py b/src/vorflow/fields.py
index 8c027f2..b436b8f 100644
--- a/src/vorflow/fields.py
+++ b/src/vorflow/fields.py
@@ -81,66 +81,90 @@ def _surface_boundary_curves(gmsh_api, surface_tags):
     return curves
 
 
-def _distance_tags_for_growth(gmsh_api, tags_dict, sampling):
-    """Build tags for distance growth while keeping embedded polygon interiors flat."""
+def _polygon_surface_tags(tags_dict):
     embedded_surfaces = tags_dict.get("embedded_surfaces", None)
     if embedded_surfaces is None:
         embedded_surfaces = tags_dict.get("surfaces", [])
 
     field_only_surfaces = tags_dict.get("field_only_surfaces", [])
-    boundary_curves = _surface_boundary_curves(gmsh_api, embedded_surfaces)
+    seen = set()
+    surface_tags = []
+    for tag in list(embedded_surfaces) + list(field_only_surfaces):
+        tag = int(tag)
+        if tag not in seen:
+            seen.add(tag)
+            surface_tags.append(tag)
+    return surface_tags
+
+
+def _distance_tags_for_growth(gmsh_api, tags_dict, sampling):
+    """Build tags for distance growth while keeping polygon interiors flat."""
+    polygon_surfaces = _polygon_surface_tags(tags_dict)
+    boundary_curves = _surface_boundary_curves(gmsh_api, polygon_surfaces)
 
     growth_tags = {
         "points": list(tags_dict.get("points", [])),
         "lines": list(tags_dict.get("lines", [])) + boundary_curves,
-        "surfaces": list(field_only_surfaces),
+        "surfaces": [],
     }
 
     # If no boundary curves could be recovered, fall back to the old surface
     # distance behavior instead of dropping the field.
-    if embedded_surfaces and not boundary_curves:
-        growth_tags["surfaces"].extend(embedded_surfaces)
+    if polygon_surfaces and not boundary_curves:
+        if int(tags_dict.get("_verbosity", 0)) > 0:
+            print(
+                "Warning: could not recover boundary curves for a polygon size "
+                "field; falling back to surface-distance growth."
+            )
+        growth_tags["surfaces"].extend(polygon_surfaces)
 
     return DistanceField(include_surfaces=True, sampling=sampling).create(
         gmsh_api, growth_tags
     )
 
 
-def _restricted_surface_constant(gmsh_api, surface_tags, size):
+def _polygon_surface_constant(gmsh_api, surface_tags, size, background_lc):
     if not surface_tags:
         return None
 
-    const = gmsh_api.model.mesh.field.add("MathEval")
-    gmsh_api.model.mesh.field.setString(const, "F", str(float(size)))
-
-    restricted = gmsh_api.model.mesh.field.add("Restrict")
-    gmsh_api.model.mesh.field.setNumber(restricted, "IField", const)
+    const = gmsh_api.model.mesh.field.add("Constant")
+    gmsh_api.model.mesh.field.setNumber(const, "VIn", float(size))
+    gmsh_api.model.mesh.field.setNumber(const, "VOut", float(background_lc))
+    # Field-only polygon surfaces are not domain partitions, but Gmsh can
+    # still evaluate a spatial constant field inside their geometry.
     gmsh_api.model.mesh.field.setNumbers(
-        restricted, "SurfacesList", [float(t) for t in surface_tags]
+        const, "SurfacesList", [float(t) for t in surface_tags]
     )
-    return restricted
-
+    return const
 
-def _combine_with_embedded_surface_constant(gmsh_api, growth_field, tags_dict, size):
-    embedded_surfaces = tags_dict.get("embedded_surfaces", None)
-    if embedded_surfaces is None:
-        embedded_surfaces = tags_dict.get("surfaces", [])
 
-    restricted = _restricted_surface_constant(gmsh_api, embedded_surfaces, size)
-    if restricted is None:
+def _combine_with_polygon_surface_constant(
+    gmsh_api, growth_field, tags_dict, size, background_lc
+):
+    constant = _polygon_surface_constant(
+        gmsh_api, _polygon_surface_tags(tags_dict), size, background_lc
+    )
+    if constant is None:
         return growth_field
     if growth_field is None:
-        return restricted
+        return constant
 
     f_min = gmsh_api.model.mesh.field.add("Min")
     gmsh_api.model.mesh.field.setNumbers(
-        f_min, "FieldsList", [float(growth_field), float(restricted)]
+        f_min, "FieldsList", [float(growth_field), float(constant)]
     )
     return f_min
 
 # --- Manual Fields ---
 
 class ConstantField(MeshField):
+    """Public/manual constant field.
+
+    Polygon interior constants are handled by the internal
+    _polygon_surface_constant() helper because they need SurfacesList scoping
+    and are combined with a growth field.
+    """
+
     def __init__(self, size):
         self.size = float(size)
 
@@ -159,13 +183,13 @@ def __init__(self, size_min, dist_min, dist_max, size_max=None, sampling=20):
         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, constant_in=False):
-        # 1. Distance field for growth away from features. For embedded polygon
-        # surfaces, use their boundary curves for growth and add a restricted
-        # constant field below so the polygon interior remains flat.
+        # 1. Distance field for growth away from features. For polygon
+        # surfaces, use boundary curves for growth and add a spatial constant
+        # field below so the polygon interior remains flat.
         f_dist = _distance_tags_for_growth(gmsh_api, tags_dict, self.sampling)
         if f_dist is None:
-            return _combine_with_embedded_surface_constant(
-                gmsh_api, None, tags_dict, self.size_min
+            return _combine_with_polygon_surface_constant(
+                gmsh_api, None, tags_dict, self.size_min, background_lc
             )
 
         # 2. Threshold Field
@@ -176,8 +200,8 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, constant_i
         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 _combine_with_embedded_surface_constant(
-            gmsh_api, f_thresh, tags_dict, self.size_min
+        return _combine_with_polygon_surface_constant(
+            gmsh_api, f_thresh, tags_dict, self.size_min, background_lc
         )
 
 class ExponentialField(MeshField):
@@ -190,8 +214,8 @@ def __init__(self, size_min, decay_length, size_max=None, sampling=20):
     def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None):
         f_dist = _distance_tags_for_growth(gmsh_api, tags_dict, self.sampling)
         if f_dist is None:
-            return _combine_with_embedded_surface_constant(
-                gmsh_api, None, tags_dict, self.size_min
+            return _combine_with_polygon_surface_constant(
+                gmsh_api, None, tags_dict, self.size_min, background_lc
             )
 
         s_max = self.size_max if self.size_max else background_lc
@@ -199,8 +223,8 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None):
         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 _combine_with_embedded_surface_constant(
-            gmsh_api, f_math, tags_dict, self.size_min
+        return _combine_with_polygon_surface_constant(
+            gmsh_api, f_math, tags_dict, self.size_min, background_lc
         )
 
 # --- Auto Fields ---
@@ -246,8 +270,8 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=1
 
         f_dist = _distance_tags_for_growth(gmsh_api, tags_dict, int(sampling))
         if f_dist is None:
-            return _combine_with_embedded_surface_constant(
-                gmsh_api, None, tags_dict, cs
+            return _combine_with_polygon_surface_constant(
+                gmsh_api, None, tags_dict, cs, background_lc
             )
 
         f_math = gmsh_api.model.mesh.field.add("MathEval")
@@ -255,4 +279,6 @@ def create(self, gmsh_api, tags_dict, background_lc, feature_lc=None, sampling=1
         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 _combine_with_embedded_surface_constant(gmsh_api, f_math, tags_dict, cs)
+        return _combine_with_polygon_surface_constant(
+            gmsh_api, f_math, tags_dict, cs, background_lc
+        )
diff --git a/tests/test_integration_gmsh.py b/tests/test_integration_gmsh.py
index 9bf0773..87d4d3e 100644
--- a/tests/test_integration_gmsh.py
+++ b/tests/test_integration_gmsh.py
@@ -145,7 +145,26 @@ def test_gmsh_integration_with_field_only_line_refinement():
     assert len(grid) > 10
 
 
-def test_embedded_polygon_field_has_restricted_constant_interior():
+def _constant_fields_for_surfaces(surface_tags, expected_vin):
+    matches = []
+    expected_surfaces = {float(tag) for tag in surface_tags}
+
+    for field_id in gmsh.model.mesh.field.list():
+        if gmsh.model.mesh.field.getType(field_id) != "Constant":
+            continue
+
+        surfaces = set(gmsh.model.mesh.field.getNumbers(field_id, "SurfacesList"))
+        if surfaces != expected_surfaces:
+            continue
+
+        vin = gmsh.model.mesh.field.getNumber(field_id, "VIn")
+        if vin == pytest.approx(expected_vin):
+            matches.append(field_id)
+
+    return matches
+
+
+def test_embedded_polygon_field_has_constant_interior():
     """Embedded polygon size fields should stay constant inside the surface."""
     cm = ConceptualMesh(crs="EPSG:3857")
     domain = Polygon([(0, 0), (20, 0), (20, 20), (0, 20)])
@@ -174,18 +193,61 @@ def test_embedded_polygon_field_has_restricted_constant_interior():
             float(tag) for dim, tag in inner_surfaces if int(dim) == 2
         }
 
-        restrict_fields = []
-        for field_id in gmsh.model.mesh.field.list():
-            if gmsh.model.mesh.field.getType(field_id) != "Restrict":
-                continue
-            surfaces = set(gmsh.model.mesh.field.getNumbers(field_id, "SurfacesList"))
-            if surfaces == inner_surface_tags:
-                restrict_fields.append(field_id)
+        constant_fields = _constant_fields_for_surfaces(inner_surface_tags, 2.0)
 
-        assert restrict_fields, "Expected a constant field restricted to the embedded polygon surface"
+        assert constant_fields, "Expected a constant field inside the embedded polygon surface"
     finally:
         gmsh.finalize()
 
+
+def test_field_only_polygon_field_has_constant_interior_without_partitioning():
+    """Field-only polygons should get flat interior sizing without becoming domain zones."""
+    cm = ConceptualMesh(crs="EPSG:3857")
+    domain = Polygon([(0, 0), (20, 0), (20, 20), (0, 20)])
+    field_poly = Polygon([(5, 5), (15, 5), (15, 15), (5, 15)])
+
+    cm.add_polygon(domain, zone_id=1, resolution=10.0, z_order=0)
+    cm.add_polygon(
+        field_poly,
+        zone_id="field-only",
+        resolution=2.0,
+        fields=[AutoExponentialField(growth_factor=1.2)],
+        embed=False,
+    )
+    clean_polys, clean_lines, clean_points = cm.generate()
+
+    field_idx = int(clean_polys.index[clean_polys["zone_id"] == "field-only"][0])
+    domain_idx = int(clean_polys.index[clean_polys["zone_id"] == 1][0])
+    assert bool(clean_polys.loc[field_idx, "embed"]) is False
+
+    mg = MeshGenerator(background_lc=10.0, verbosity=0)
+    gmsh.initialize()
+    try:
+        gmsh.model.add("field_only_polygon_field")
+        gmsh_map = mg._add_geometry(clean_polys, clean_lines, clean_points)
+        mg._setup_fields(gmsh_map, clean_polys, clean_lines, clean_points)
+
+        field_surfaces = set(gmsh_map["surfaces"][field_idx])
+        field_surface_tags = {
+            float(tag) for dim, tag in field_surfaces if int(dim) == 2
+        }
+
+        constant_fields = _constant_fields_for_surfaces(field_surface_tags, 2.0)
+        assert constant_fields, "Expected a constant field inside the field-only polygon surface"
+
+        embedded_domain_ids = [
+            int(i)
+            for i, row in clean_polys.iterrows()
+            if bool(row.get("embed", True))
+        ]
+        assert embedded_domain_ids == [domain_idx]
+    finally:
+        gmsh.finalize()
+
+    assert mg.generate(clean_polys, clean_lines, clean_points)
+    assert len(mg.nodes) > 0
+
+
 def test_gmsh_integration_overlapping_polygon_with_hole():
     """
     Test the case where an overlapping polygon (Zone 2) has a hole in its center.

From ff5fcac4e02f88eef902a4e25b7546003d27b29c Mon Sep 17 00:00:00 2001
From: oscarfasanchez <oscarfasanchez@gmail.com>
Date: Sat, 6 Jun 2026 13:04:17 +0800
Subject: [PATCH 29/29] Bump version to 0.0.2

---
 pyproject.toml          | 2 +-
 src/vorflow/__init__.py | 8 ++++++++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5b5f14b..def3bb9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "vorflow"
-version = "0.0.1"
+version = "0.0.2"
 description = "Voronoi mesh generation for MODFLOW 6 using Gmsh and Geopandas"
 authors = [{name = "Your Name", email = "you@example.com"}]
 requires-python = ">=3.9"
diff --git a/src/vorflow/__init__.py b/src/vorflow/__init__.py
index 7b30f8d..c7531e7 100644
--- a/src/vorflow/__init__.py
+++ b/src/vorflow/__init__.py
@@ -1,3 +1,11 @@
+from importlib.metadata import version, PackageNotFoundError
+
+try:
+    __version__ = version("vorflow")
+except PackageNotFoundError:
+    # Package is not installed (e.g. running from source)
+    __version__ = "0.0.2" 
+    
 from .blueprint import ConceptualMesh
 from .engine import MeshGenerator
 from .tessellator import VoronoiTessellator