From 70886d9c9dd0295851b9859402fada4a3a1aeff4 Mon Sep 17 00:00:00 2001 From: Paloma Martinez <104762252+paloma-martinez@users.noreply.github.com> Date: Wed, 28 Jan 2026 09:06:31 +0100 Subject: [PATCH 01/11] make filter class from existing script --- .../pre_processing/TetQualityAnalysis.py | 1209 +++++++++++++++++ 1 file changed, 1209 insertions(+) create mode 100644 geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py diff --git a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py new file mode 100644 index 00000000..05fbbbbe --- /dev/null +++ b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py @@ -0,0 +1,1209 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright 2023-2024 TotalEnergies. +# SPDX-FileContributor: Bertrand Denel, Paloma Martinez +import logging +import numpy as np +import numpy.typing as npt +from typing_extensions import Self +from geos.utils.Logger import ( Logger, getLogger ) +import vtk +import numpy as np +import sys +import matplotlib + +from vtkmodules.vtkCommonDataModel import vtkDataSet +from vtkmodules.util.numpy_support import vtk_to_numpy +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec +from matplotlib.patches import Rectangle +import seaborn as sns + +__doc__ = """ +TetQualityAnalysis module is a filter that performs an an analysis of tetrahedras of one or several meshes and plot a summary. +""" + +loggerName: str = "Tetrahedra Quality Analysis" + +class TetQualityAnalysis: + + def __init__( + self: Self, + meshes: dict[str, vtkDataSet], + speHandler: bool = False + ) -> None: + + self.meshes: dict[ str, vtkDataSet ] = meshes + # self.meshes = Set( ) + self.analyzedMesh: dict[ Any ] = {} + self.issues: dict[ Any ] = {} + self.overallQualityScore: dict[ Any] = {} + self.validMetrics: dict[Any] = {} + self.sample = {} + + # Logger. + self.logger: Logger + if not speHandler: + self.logger = getLogger( loggerName, True ) + else: + self.logger = logging.getLogger( loggerName ) + self.logger.setLevel( logging.INFO ) + self.logger.propagate = False + + + def setLoggerHandler( self: Self, handler: logging.Handler ) -> None: + """Set a specific handler for the filter logger. + + In this filter 4 log levels are use, .info, .error, .warning and .critical, + be sure to have at least the same 4 levels. + + Args: + handler (logging.Handler): The handler to add. + """ + self.handler = handler + if len( self.logger.handlers ) == 0: + self.logger.addHandler( handler ) + else: + self.logger.warning( "The logger already has an handler, to use yours set the argument 'speHandler'" + " to True during the filter initialization." ) + + + def applyFilter( self: Self) -> None: + self.logger.info( f"Apply filter { self.logger.name }." ) + + self.__loggerSection( "MESH COMPARISON DASHBOARD" ) + + for n, (filename1, mesh1) in enumerate( self.meshes.items() ): + # print( n, filename1 ) + coords1 = self.get_coordinates_double_precision(mesh1) + tet_ids1, tet_conn1 = self.extract_tet_connectivity(mesh1) + n_tets1 = len( tet_ids1 ) + + self.logger.info( f" Mesh {n} info: " ) + self.logger.info( f" Name: {filename1}" ) + self.logger.info( f" Total cells: {mesh1.GetNumberOfCells()}" ) + self.logger.info( f" Tetrahedra: {n_tets1}" ) + self.logger.info( f" Points: {mesh1.GetNumberOfPoints()}" ) + + # Analyze both meshes + print(f"\nAnalyzing Mesh {n} (double precision)...") + # metrics1 = self.analyze_all_tets_vectorized(n, coords1, tet_conn1) + self.analyze_all_tets_vectorized(n, coords1, tet_conn1) + metrics1 = self.analyzedMesh[ n ] + self.analyzedMesh[n]["tet"] = n_tets1 + + # Extract data with consistent filtering + ar_valid1 = np.isfinite(metrics1['aspect_ratio']) + rr_valid1 = np.isfinite(metrics1['radius_ratio']) + + # Combined valid mask + valid1 = ar_valid1 & rr_valid1 + + ar1 = metrics1['aspect_ratio'][valid1] + rr1 = metrics1['radius_ratio'][valid1] + fr1 = metrics1['flatness_ratio'][valid1] + vol1 = metrics1['volumes'][valid1] + sq1 = metrics1['shape_quality'][valid1] + + # Edge length data + min_edge1 = metrics1['min_edge'][valid1] + max_edge1 = metrics1['max_edge'][valid1] + + # Edge length ratio + edge_ratio1 = max_edge1 / np.maximum(min_edge1, 1e-15) + + # Dihedral angles + min_dih1 = metrics1['min_dihedral'][valid1] + max_dih1 = metrics1['max_dihedral'][valid1] + dih_range1 = metrics1['dihedral_range'][valid1] + + score1 = compute_quality_score(ar1, sq1, edge_ratio1, min_dih1) + + # Store all values + self.validMetrics[n] = { 'aspect_ratio' : ar1, 'radius_ratio': rr1, 'flatness_ratio': fr1, 'volume': vol1, 'shape_quality': sq1, 'min_edge': min_edge1, 'max_edge': max_edge1, 'edge_ratio': edge_ratio1, 'min_dihedral': min_dih1, 'max_dihedral': max_dih1, 'dihedral_range': dih_range1, 'quality_score': score1} + + # # ==================== Print Distribution Statistics ==================== + + # Problem element counts + + high_ar1 = np.sum(ar1 > 100) + low_sq1 = np.sum(sq1 < 0.3) + low_flat1 = np.sum(fr1 < 0.01) + high_edge_ratio1 = np.sum(edge_ratio1 > 10) + critical_dih1 = np.sum(min_dih1 < 5) + critical_max_dih1 = np.sum(max_dih1 > 175) + problem1 = np.sum((ar1 > 100) & (sq1 < 0.3)) + critical_combo1 = np.sum((ar1 > 100) & (sq1 < 0.3) & (min_dih1 < 5)) + + self.issues[n] = { "high_aspect_ratio": high_ar1, "low_shape_quality": low_sq1, "low_flatness": low_flat1, "high_edge_ratio": high_edge_ratio1, "critical_dihedral": critical_dih1, "critical_max_dihedral": critical_max_dih1, "combined": problem1, "critical_combo": critical_combo1 } + + # Overall quality scores + + excellent1 = np.sum(score1 > 80) / len(score1) * 100 + good1 = np.sum((score1 > 60) & (score1 <= 80)) / len(score1) * 100 + fair1 = np.sum((score1 > 30) & (score1 <= 60)) / len(score1) * 100 + poor1 = np.sum(score1 <= 30) / len(score1) * 100 + + self.overallQualityScore[n] = { 'excellent': excellent1, 'good': good1, 'fair': fair1, 'poor': poor1 } + + extreme_ar1 = ar1 > 1e4 + self.issues[ n ][ "extreme_aspect_ratio" ] = extreme_ar1 + + # Nearly degenerate elements + degenerate1 = (min_dih1 < 0.1) | (max_dih1 > 179.9) + self.issues[ n ][ "degenerate" ] = degenerate1 + + print("\n" + "="*80 + "\n") + + # ==================== Create Dashboard ==================== + + # ==================== Print Distribution Statistics ==================== + self.__orderMeshes() + + self.printDistributionStatistics() + + # Percentile information + self.printPercentileAnalysis() + + self.printQualityIssueSummary() + + self.printExtremeOutlierAnalysis() + + self.printComparisonSummary() + self.computeDeltasFromBest() + + self.createComparisonDashboard() + + + def get_coordinates_double_precision( self, mesh ): + points = mesh.GetPoints() + n_points = points.GetNumberOfPoints() + + coords = np.zeros((n_points, 3), dtype=np.float64) + for i in range(n_points): + point = points.GetPoint(i) + coords[i] = [point[0], point[1], point[2]] + + return coords + + def extract_tet_connectivity( self, mesh ): + """Extract connectivity for all tetrahedra.""" + n_cells = mesh.GetNumberOfCells() + tet_ids = [] + tet_connectivity = [] + + for cell_id in range(n_cells): + if mesh.GetCellType(cell_id) == vtk.VTK_TETRA: + cell = mesh.GetCell(cell_id) + point_ids = cell.GetPointIds() + conn = [point_ids.GetId(i) for i in range(4)] + tet_ids.append(cell_id) + tet_connectivity.append(conn) + + return np.array(tet_ids), np.array(tet_connectivity) + + + + + + def analyze_all_tets_vectorized(self, n, coords, connectivity) -> dict: + """Vectorized analysis of all tetrahedra.""" + n_tets = connectivity.shape[0] + + # Get coordinates for all vertices + v0 = coords[connectivity[:, 0]] + v1 = coords[connectivity[:, 1]] + v2 = coords[connectivity[:, 2]] + v3 = coords[connectivity[:, 3]] + + # Compute edges + e01 = v1 - v0 + e02 = v2 - v0 + e03 = v3 - v0 + e12 = v2 - v1 + e13 = v3 - v1 + e23 = v3 - v2 + + # Edge lengths + l01 = np.linalg.norm(e01, axis=1) + l02 = np.linalg.norm(e02, axis=1) + l03 = np.linalg.norm(e03, axis=1) + l12 = np.linalg.norm(e12, axis=1) + l13 = np.linalg.norm(e13, axis=1) + l23 = np.linalg.norm(e23, axis=1) + + all_edges = np.stack([l01, l02, l03, l12, l13, l23], axis=1) + min_edge = np.min(all_edges, axis=1) + max_edge = np.max(all_edges, axis=1) + + # Volumes + cross_product = np.cross(e01, e02) + volumes = np.abs(np.sum(cross_product * e03, axis=1) / 6.0) + + # Face areas + face_012 = 0.5 * np.linalg.norm(np.cross(e01, e02), axis=1) + face_013 = 0.5 * np.linalg.norm(np.cross(e01, e03), axis=1) + face_023 = 0.5 * np.linalg.norm(np.cross(e02, e03), axis=1) + face_123 = 0.5 * np.linalg.norm(np.cross(e12, e13), axis=1) + + all_faces = np.stack([face_012, face_013, face_023, face_123], axis=1) + max_face_area = np.max(all_faces, axis=1) + total_surface_area = np.sum(all_faces, axis=1) + + # Aspect ratio + min_altitude = 3.0 * volumes / np.maximum(max_face_area, 1e-15) + aspect_ratio = max_edge / np.maximum(min_altitude, 1e-15) + aspect_ratio[volumes < 1e-15] = np.inf + + # Radius ratio + inradius = 3.0 * volumes / np.maximum(total_surface_area, 1e-15) + circumradius = (max_edge ** 3) / np.maximum(24.0 * volumes, 1e-15) + radius_ratio = circumradius / np.maximum(inradius, 1e-15) + + # Flatness ratio + flatness_ratio = volumes / np.maximum(max_face_area * min_edge, 1e-15) + + # Shape quality + sum_edge_sq = np.sum(all_edges ** 2, axis=1) + shape_quality = 12.0 * (3.0 * volumes) ** (2.0/3.0) / np.maximum(sum_edge_sq, 1e-15) + shape_quality[volumes < 1e-15] = 0.0 + + # Dihedral angles + def compute_dihedral_angle(normal1, normal2): + """Compute dihedral angle between two face normals.""" + n1_norm = normal1 / np.maximum(np.linalg.norm(normal1, axis=1, keepdims=True), 1e-15) + n2_norm = normal2 / np.maximum(np.linalg.norm(normal2, axis=1, keepdims=True), 1e-15) + cos_angle = np.sum(n1_norm * n2_norm, axis=1) + cos_angle = np.clip(cos_angle, -1.0, 1.0) + angle = np.arccos(cos_angle) * 180.0 / np.pi + return angle + + # Face normals + normal_012 = np.cross(e01, e02) + normal_013 = np.cross(e01, e03) + normal_023 = np.cross(e02, e03) + normal_123 = np.cross(e12, e13) + + # Dihedral angles for each edge + dihedral_01 = compute_dihedral_angle(normal_012, normal_013) + dihedral_02 = compute_dihedral_angle(normal_012, normal_023) + dihedral_03 = compute_dihedral_angle(normal_013, normal_023) + dihedral_12 = compute_dihedral_angle(normal_012, normal_123) + dihedral_13 = compute_dihedral_angle(normal_013, normal_123) + dihedral_23 = compute_dihedral_angle(normal_023, normal_123) + + all_dihedrals = np.stack([dihedral_01, dihedral_02, dihedral_03, + dihedral_12, dihedral_13, dihedral_23], axis=1) + + min_dihedral = np.min(all_dihedrals, axis=1) + max_dihedral = np.max(all_dihedrals, axis=1) + dihedral_range = max_dihedral - min_dihedral + + self.analyzedMesh[ n ] = { + 'volumes': volumes, + 'aspect_ratio': aspect_ratio, + 'radius_ratio': radius_ratio, + 'flatness_ratio': flatness_ratio, + 'shape_quality': shape_quality, + 'min_edge': min_edge, + 'max_edge': max_edge, + 'min_dihedral': min_dihedral, + 'max_dihedral': max_dihedral, + 'dihedral_range': dihedral_range + } + + return { + 'volumes': volumes, + 'aspect_ratio': aspect_ratio, + 'radius_ratio': radius_ratio, + 'flatness_ratio': flatness_ratio, + 'shape_quality': shape_quality, + 'min_edge': min_edge, + 'max_edge': max_edge, + 'min_dihedral': min_dihedral, + 'max_dihedral': max_dihedral, + 'dihedral_range': dihedral_range + } + + def printDistributionStatistics( self: Self, fmt='.2e'): + self.__loggerSection( "DISTRIBUTION STATISTICS (MIN / MEDIAN / MAX)" ) + + def print_metric_stats( metricName, dataName, fmt='.2e'): + """Helper function to print min/median/max for a metric.""" + # self.logger.info(f"\n{metricName}:") + self.logger.info(f"{metricName}:") + for n, _ in enumerate( self.meshes.items() ): + data = self.validMetrics[ n ][ dataName ] + self.logger.info(f" Mesh {n}: Min={data.min():{fmt}} Median={np.median(data):{fmt}} Max={data.max():{fmt}}") + + + print_metric_stats( "Aspect Ratio", 'aspect_ratio' ) + print_metric_stats( "Radius Ratio", 'radius_ratio' ) + print_metric_stats( "Flatness Ratio", 'flatness_ratio' ) + print_metric_stats( "Shape Quality", 'shape_quality', fmt='.4f' ) + print_metric_stats( "Volume", 'volume' ) + print_metric_stats( "Min Edge Length", 'min_edge' ) + print_metric_stats( "Max Edge Length", 'max_edge' ) + print_metric_stats( "Edge Length Ratio", 'edge_ratio', fmt='.2f' ) + print_metric_stats( "Min Dihedral Angle (degrees)", 'min_dihedral', fmt='.2f' ) + print_metric_stats( "Max Dihedral Angle (degrees)", 'max_dihedral', fmt='.2f' ) + print_metric_stats( "Dihedral Range (degrees)", 'dihedral_range',fmt='.2f' ) + print_metric_stats( "Overall Quality Score (0-100)", 'quality_score', fmt='.2f' ) + + + def printPercentileAnalysis( self: Self, fmt='.2f' ): + self.__loggerSection( "PERCENTILE ANALYSIS (25th / 75th / 90th / 99th)" ) + + def print_percentiles(metricName, dataName, fmt='.2f'): + """Helper function to print percentiles.""" + self.logger.info(f"{metricName}:") + # self.logger.info(f"\n{metricName}:") + for n, _ in enumerate( self.meshes.items() ): + data = self.validMetrics[ n ][ dataName ] + p1 = np.percentile(data, [25, 75, 90, 99]) + self.logger.info(f" Mesh {n}: 25th={p1[0]:{fmt}} 75th={p1[1]:{fmt}} 90th={p1[2]:{fmt}} 99th={p1[3]:{fmt}}") + + print_percentiles( "Aspect Ratio", 'aspect_ratio' ) + print_percentiles( "Shape Quality", 'shape_quality' ) + print_percentiles( "Edge Length Ratio", 'edge_ratio' ) + print_percentiles( "Min Dihedral Angle (degrees)", 'min_dihedral' ) + print_percentiles( "Overall Quality Score", 'quality_score' ) + + + def printQualityIssueSummary( self: Self ): + self.__loggerSection( "QUALITY ISSUE SUMMARY" ) + + def print_issue(meshID, metricName, issueDataName, dataName ): + pb = self.issues[ meshID ][ issueDataName ] + m = len( self.validMetrics[ meshID ][ dataName ] ) + fmt = '.2f' + self.logger.info(f" {metricName:20}: {pb:,} ({(pb/m*100):{fmt}}%)") + + for n, _ in enumerate( self.meshes.items() ): + # self.logger.info( f"\nMesh {n} Issues:" ) + self.logger.info( f"Mesh {n} Issues:" ) + print_issue( n, "Aspect Ratio > 100", "high_aspect_ratio", 'aspect_ratio' ) + print_issue( n, "Shape Quality < 0.3", "low_shape_quality", 'shape_quality' ) + print_issue( n, "Flatness < 0.01", "low_flatness", "flatness_ratio" ) + print_issue( n, "Edge Ratio > 10", "high_edge_ratio", "edge_ratio" ) + print_issue( n, "Min Dihedral < 5°", "critical_dihedral", "min_dihedral" ) + print_issue( n, "Max Dihedral > 175°", "critical_max_dihedral",'max_dihedral' ) + print_issue( n, "Combined (AR>100 & Q<0.3)", "combined" ,'aspect_ratio' ) + print_issue( n, "CRITICAL (AR>100 & Q<0.3 & MinDih<5°", "critical_combo", 'aspect_ratio' ) + + self.compareIssuesFromBest() + + self.printOverallQualityScore() + + + def printOverallQualityScore( self: Self ): + # self.logger.info( f"\nOverall Quality Score Distribution:" ) + self.logger.info( f"Overall Quality Score Distribution:" ) + for n, _ in enumerate( self.meshes.items() ): + qualityScore = self.overallQualityScore[ n ] + self.logger.info(f" Mesh {n}: Excellent (>80): {qualityScore[ 'excellent' ]:.1f}% Good (60-80): {qualityScore[ 'good' ]:.1f}% Fair (30-60): {qualityScore[ 'fair' ]:.1f}% Poor (≤30): {qualityScore[ 'poor' ]:.1f}%") + + def printExtremeOutlierAnalysis( self: Self ): + #TODO : info in warning if bad ? + self.__loggerSection( "EXTREME OUTLIER ANALYSIS" ) + + # self.logger.warning( f"\n🚨 Elements with Aspect Ratio > 10,000:" ) + self.logger.warning( f"🚨 Elements with Aspect Ratio > 10,000:" ) + for n, _ in enumerate( self.meshes ): + extreme_ar = self.issues[n][ 'extreme_aspect_ratio' ] + data = self.analyzedMesh[n] + ar1 = data[ 'aspect_ratio' ] + self.logger.info(f" Mesh {n}: {np.sum(extreme_ar):,} elements ({np.sum(extreme_ar)/len(ar1)*100:.3f}%)") + + if np.sum(extreme_ar) > 0: + vol1 = data[ "volume" ] + min_dih1 = data[ "min_dihedral" ] + sq1 = data[ "shape_quality" ] + self.logger.info(f" Worst AR: {ar1[extreme_ar].max():.2e}") + self.logger.info(f" Avg volume: {vol1[extreme_ar].mean():.2e}") + self.logger.info(f" Min dihedral: {min_dih1[extreme_ar].min():.2f}° - {min_dih1[extreme_ar].mean():.2f}° (avg)") + self.logger.info(f" Shape quality: {sq1[extreme_ar].min():.4f} - {sq1[extreme_ar].mean():.4f} (avg)") + + if np.sum(extreme_ar) > 10: + # self.logger.warning(f"\n💡 Recommendation: Investigate/remove {np.sum(extreme_ar):,} extreme elements in Mesh {n}") + self.logger.warning(f"💡 Recommendation: Investigate/remove {np.sum(extreme_ar):,} extreme elements in Mesh {n}") + self.logger.warning(f" These are likely artifacts from mesh generation or geometry issues.") + + # Nearly degenerate elements + # self.logger.warning( f"\n🚨 Nearly Degenerate Elements (dihedral < 0.1° or > 179.9°):") + self.logger.warning( f"🚨 Nearly Degenerate Elements (dihedral < 0.1° or > 179.9°):") + for n, _ in enumerate( self.meshes ): + degenerate = self.issues[ n ][ "degenerate" ] + data = self.validMetrics[ n ][ "min_dihedral" ] + self.logger.warning( f" Mesh {n}: {np.sum(degenerate):,} elements ({np.sum(degenerate)/len(data)*100:.3f}%)") + + + def printComparisonSummary( self: Self): + self.__loggerSection( "COMPARISON SUMMARY" ) + + for n, _ in enumerate( self.meshes ): + name = f"Mesh {n}" + if n == self.best: + name += " [BEST]" + elif n == self.worst: + name += " [LEAST GOOD]" + + ar1_med = np.median( self.validMetrics[n]["aspect_ratio"] ) + sq1_med = np.median( self.validMetrics[n]["shape_quality"] ) + vol1_med = np.median(self.validMetrics[n]["volume"] ) + min_edge1_med = np.median(self.validMetrics[n]["min_edge"] ) + max_edge1_med = np.median(self.validMetrics[n]["max_edge"] ) + edge_ratio1_med = np.median(self.validMetrics[n]["edge_ratio"] ) + min_dih1_med = np.median( self.validMetrics[n]["min_dihedral"] ) + max_dih1_med = np.median( self.validMetrics[n]["max_dihedral"] ) + score1_med = np.median( self.validMetrics[n]["quality_score"] ) + + self.logger.info(f"{name}") + self.logger.info(f" Tetrahedra: {self.analyzedMesh[n]['tet']:,}") + self.logger.info(f" Median Aspect Ratio: {ar1_med:.2f}") + self.logger.info(f" Median Shape Quality: {sq1_med:.4f}") + self.logger.info(f" Median Volume: {vol1_med:.2e}") + self.logger.info(f" Median Min Edge: {min_edge1_med:.2e}") + self.logger.info(f" Median Max Edge: {max_edge1_med:.2e}") + self.logger.info(f" Median Edge Ratio: {edge_ratio1_med:.2f}") + self.logger.info(f" Median Min Dihedral: {min_dih1_med:.1f}°") + self.logger.info(f" Median Max Dihedral: {max_dih1_med:.1f}°") + self.logger.info(f" Median Quality Score: {score1_med:.1f}/100") + + self.logger.info("\n" + "="*80) + + + + def computeDeltasFromBest( self: Self ): + self.logger.info( f"Best mesh : Mesh {self.best}") + self.deltas = {} + + n_tets_best = self.analyzedMesh[self.best][ 'tet'] + ar_med_best = np.median( self.validMetrics[self.best]["aspect_ratio"] ) + sq_med_best = np.median(self.validMetrics[self.best]["shape_quality"]) + vol_med_best = np.median(self.validMetrics[self.best]["volume"]) + min_edge_med_best = np.median(self.validMetrics[self.best]["min_edge"]) + max_edge_med_best = np.median(self.validMetrics[self.best]["max_edge"]) + edge_ratio_med_best = np.median(self.validMetrics[self.best]["edge_ratio"]) + + for n, _ in enumerate( self.meshes ): + n_tets = self.analyzedMesh[n][ 'tet'] + ar_med = np.median( self.validMetrics[n]["aspect_ratio"] ) + sq_med = np.median(self.validMetrics[n]["shape_quality"]) + vol_med = np.median(self.validMetrics[n]["volume"]) + min_edge_med = np.median(self.validMetrics[n]["min_edge"]) + max_edge_med = np.median(self.validMetrics[n]["max_edge"]) + edge_ratio_med = np.median(self.validMetrics[n]["edge_ratio"]) + + + delta_tets = ((n_tets - n_tets_best) / n_tets_best * 100) if n_tets_best > 0 else 0 + delta_ar = ((ar_med - ar_med_best) / ar_med_best * 100) if ar_med_best > 0 else 0 + delta_sq = ((sq_med - sq_med_best) / sq_med_best * 100) if sq_med_best > 0 else 0 + delta_vol = ((vol_med - vol_med_best) / vol_med_best * 100) if vol_med_best > 0 else 0 + delta_min_edge = ((min_edge_med - min_edge_med_best) / min_edge_med_best * 100) if min_edge_med_best > 0 else 0 + delta_max_edge = ((max_edge_med - max_edge_med_best) / max_edge_med_best * 100) if max_edge_med_best > 0 else 0 + delta_edge_ratio = ((edge_ratio_med - edge_ratio_med_best) / edge_ratio_med_best * 100) if edge_ratio_med_best > 0 else 0 + self.deltas[ n ] = { "tetrahedra": delta_tets, "aspect_ratio": delta_ar, "shape_quality": delta_sq, "volume": delta_vol, "min_edge": delta_min_edge, "max_edge": delta_max_edge, "edge_ratio": delta_edge_ratio } + + + dtets = [ f"{self.deltas[ n ][ 'tetrahedra' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] + dar = [ f"{self.deltas[ n ][ 'aspect_ratio' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] + dsq = [ f"{self.deltas[ n ][ 'shape_quality' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] + dvol = [ f"{self.deltas[ n ][ 'volume' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] + d_min_edge = [ f"{self.deltas[ n ][ 'min_edge' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] + d_max_edge = [ f"{self.deltas[ n ][ 'max_edge' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] + d_edge_ratio = [ f"{self.deltas[ n ][ 'edge_ratio' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] + names = [ f"{f'Mesh {n}':16}" for n, _ in self.sorted[1:]] + + self.logger.info(f"Changes vs Mesh {self.best} [BEST]:") + self.logger.info(f"{' Mesh:':25}{('').join( names )}") + self.logger.info(f"{' Tetrahedra:':25} {('').join(dtets)}%") + self.logger.info(f"{' Aspect Ratio:':25}{('').join( dar)}%") + self.logger.info(f"{' Shape Quality:':25}{('').join( dsq)}%") + self.logger.info(f"{' Volume:':25}{('').join( dvol)}%") + self.logger.info(f"{' Min Edge Length:':25}{('').join( d_min_edge)}%") + self.logger.info(f"{' Max Edge Length:':25}{('').join( d_max_edge)}%") + self.logger.info(f"{' Edge Length Ratio:':25}{('').join( d_edge_ratio)}%") + + + + def createComparisonDashboard( self: Self ): + lbl = [ f'Mesh {n}' for n, _ in enumerate( self.meshes ) ] + # Determine smart plot limits + + ar_99 = [] + for n, _ in enumerate( self.meshes ): + ar_99.append( np.percentile( self.validMetrics[n]["aspect_ratio"], 99 ) ) + + ar_99_max = np.max( np.array( ar_99 ) ) + + # ar_99_1 = np.percentile(ar1, 99) + # # ar_99_2 = np.percentile(ar2, 99) + # # ar_99_max = max(ar_99_1, ar_99_2) + # ar_99_max = ar_99_1 + + if ar_99_max < 10: + ar_plot_limit = 100 + elif ar_99_max < 100: + ar_plot_limit = 1000 + else: + ar_plot_limit = 10000 + + # print(f"Using AR plot limit: {ar_plot_limit} (99th percentiles: M1={ar_99_1:.1f}, M2={ar_99_2:.1f})") + # print(f"Using AR plot limit: {ar_plot_limit} (99th percentiles: M1={ar_99_1:.1f}") + + # Set style + sns.set_style("whitegrid") + plt.rcParams['figure.facecolor'] = 'white' + plt.rcParams.update({ + 'font.size': 9, + 'axes.titlesize': 10, + 'axes.labelsize': 9, + 'xtick.labelsize': 8, + 'ytick.labelsize': 8, + 'legend.fontsize': 8 + }) + + # Create figure with flexible layout + fig = plt.figure(figsize=(25, 20)) + + # Row 1: Executive Summary (3 columns - wider) + gs_row1 = gridspec.GridSpec(1, 3, figure=fig, left=0.05, right=0.95, + top=0.96, bottom=0.84, wspace=0.20) + + # Rows 2-5: Main dashboard (5 columns each) + gs_main = gridspec.GridSpec(4, 5, figure=fig, left=0.05, right=0.95, + top=0.82, bottom=0.05, hspace=0.35, wspace=0.30) + + # Title + # mesh1_name = filename1.split('/')[-1] + # mesh2_name = filename2.split('/')[-1] + suptitle = 'Mesh Quality Comparison Dashboard (Progressive Detail Layout)\n' + for n, _ in enumerate( self.meshes ): + # suptitle += f'Mesh {n}: {n_tets1:,} tets\t' + suptitle += f'Mesh {n}: tets\t' + fig.suptitle( suptitle, + fontsize=16, fontweight='bold', y=0.99) + # fig.suptitle(f'Mesh Quality Comparison Dashboard (Progressive Detail Layout)\n' + + # f'Mesh 1: {mesh1_name} ({n_tets1:,} tets)', + # # f'Mesh 1: {mesh1_name} ({n_tets1:,} tets) vs Mesh 2: {mesh2_name} ({n_tets2:,} tets)', + # fontsize=16, fontweight='bold', y=0.99) + + # Color scheme + # color = matplotlib.colormaps[ 'tab20' ] + # def color[n]: + # nn = np.linspace(0,20) + # return matplotlib.colormaps[ 'tab20' ][nn] + color = matplotlib.pyplot.cm.tab10( np.arange(20)) + + # ==================== ROW 1: EXECUTIVE SUMMARY ==================== + + # 1. Overall Quality Score Distribution + ax1 = fig.add_subplot(gs_row1[0, 0]) + bins = np.linspace(0, 100, 40) + for n, _ in enumerate( self.meshes ): + score1 = self.validMetrics[n][ 'quality_score' ] + ax1.hist(score1, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n], edgecolor='black', linewidth=0.5) + ax1.axvline(np.median(score1), color=color[n], linestyle='--', linewidth=2.5, alpha=0.9) + + # Add quality zones + ax1.axvspan(0, 30, alpha=0.15, color='red', zorder=0) + ax1.axvspan(30, 60, alpha=0.15, color='yellow', zorder=0) + ax1.axvspan(60, 80, alpha=0.15, color='lightgreen', zorder=0) + ax1.axvspan(80, 100, alpha=0.15, color='darkgreen', zorder=0) + + # Add median lines + # ax1.axvline(np.median(score1), color=color1, linestyle='--', linewidth=2.5, alpha=0.9) + # ax1.axvline(np.median(score2), color=color2, linestyle='--', linewidth=2.5, alpha=0.9) + + # Add summary text #### ONLY BEST AND WORST MESH? + # TODO + # ax1.text(0.98, 0.98, + # f'Median Score:\nM1: {np.median(score1):.1f}\n\n' + + # f'Excellent (>80):\nM1: {excellent1:.1f}%', + # # f'Median Score:\nM1: {np.median(score1):.1f}\nM2: {np.median(score2):.1f}\n\n' + + # # f'Excellent (>80):\nM1: {excellent1:.1f}%\nM2: {excellent2:.1f}%', + # transform=ax1.transAxes, va='top', ha='right', + # bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.9), + # fontsize=9, fontweight='bold') + + ax1.set_xlabel('Combined Quality Score', fontweight='bold') + ax1.set_ylabel('Count', fontweight='bold') + ax1.set_title('OVERALL MESH QUALITY VERDICT', fontsize=12, fontweight='bold', + color='darkblue', pad=10) + ax1.legend(loc='upper left', fontsize=9) + ax1.grid(True, alpha=0.3) + + # Add zone labels + ax1.text(15, ax1.get_ylim()[1]*0.95, 'Poor', ha='center', fontsize=8, color='darkred') + ax1.text(45, ax1.get_ylim()[1]*0.95, 'Fair', ha='center', fontsize=8, color='orange') + ax1.text(70, ax1.get_ylim()[1]*0.95, 'Good', ha='center', fontsize=8, color='green') + ax1.text(90, ax1.get_ylim()[1]*0.95, 'Excellent', ha='center', fontsize=8, color='darkgreen') + + # 2. Shape Quality vs Aspect Ratio + ax2 = fig.add_subplot(gs_row1[0, 1]) + + # Create sample for plotting + for n, _ in enumerate( self.meshes ): + ar1 = self.validMetrics[n]["aspect_ratio"] + sq1 = self.validMetrics[n]["shape_quality"] + self.setSampleForPlot( ar1, n ) + + idx = self.sample[n] + + mask1_plot = ar1[idx] < ar_plot_limit + + ax2.scatter(ar1[idx][mask1_plot], sq1[idx][mask1_plot], alpha=0.4, s=5, + color=color[n], label=f'Mesh {n}', edgecolors='none') + + # Add quality threshold lines + ax2.axhline(y=0.3, color='red', linestyle='--', linewidth=2, + alpha=0.8, label='Poor (Q < 0.3)', zorder=5) + ax2.axhline(y=0.7, color='green', linestyle='--', linewidth=2, + alpha=0.8, label='Good (Q > 0.7)', zorder=5) + ax2.axvline(x=100, color='orange', linestyle='--', linewidth=2, + alpha=0.8, label='High AR (> 100)', zorder=5) + + # Highlight problem zone + problem_zone = Rectangle((100, 0), ar_plot_limit-100, 0.3, + alpha=0.2, facecolor='red', edgecolor='none', zorder=0) + ax2.add_patch(problem_zone) + + # Count ALL elements + problem1_all = np.sum((ar1 > 100) & (sq1 < 0.3)) + extreme1 = np.sum(ar1 > ar_plot_limit) + + # Problem annotation + #TODO + # ax2.text(0.98, 0.02, + # f'PROBLEM ELEMENTS\n(AR>100 & Q<0.3):\n\n' + + # f'M1: {problem1_all:,}\n\n', + # # f'Change: {((problem2_all-problem1_all)/max(problem1_all,1)*100):+.1f}%', + # # f'M1: {problem1_all:,}\nM2: {problem2_all:,}\n\n' + + # transform=ax2.transAxes, va='bottom', ha='right', + # bbox=dict(boxstyle='round', facecolor='#ffcccc', alpha=0.95, + # edgecolor='darkred', linewidth=2), + # fontsize=9, fontweight='bold') + + # # Outlier warning + # if extreme1 + extreme2 > 100: + # ax2.text(0.02, 0.98, + # f'⚠️ AR > {ar_plot_limit}\n(not shown):\n' + + # f'M1: {extreme1:,}\nM2: {extreme2:,}', + # transform=ax2.transAxes, va='top', ha='left', + # bbox=dict(boxstyle='round', facecolor='#ffe6cc', alpha=0.9), + # fontsize=7) + + ax2.set_xscale('log') + ax2.set_xlabel('Aspect Ratio', fontweight='bold') + ax2.set_ylabel('Shape Quality', fontweight='bold') + ax2.set_title('KEY QUALITY INDICATOR: Shape Quality vs Aspect Ratio', + fontsize=12, fontweight='bold', color='darkred', pad=10) + ax2.set_xlim([1, ar_plot_limit]) + ax2.set_ylim([0, 1.05]) + ax2.legend(loc='upper right', fontsize=7, framealpha=0.95) + ax2.grid(True, alpha=0.3) + + # 3. Critical Issues Summary Table + ax3 = fig.add_subplot(gs_row1[0, 2]) + ax3.axis('off') + + summary_stats = [] + summary_stats.append(['CRITICAL ISSUE', 'WORST', 'BEST', 'Change']) + summary_stats.append(['─' * 18, '─' * 10, '─' * 10, '─' * 10]) + + critical_combo1 = self.issues[ self.best ][ "critical_combo" ] + critical_combo2 = self.issues[ self.worst ][ "critical_combo" ] + ar1 = self.validMetrics[self.best][ "aspect_ratio" ] + ar2 = self.validMetrics[self.worst][ "aspect_ratio" ] + + high_ar1 = self.issues[ self.best ][ "high_aspect_ratio" ] + high_ar2 = self.issues[ self.worst ][ "high_aspect_ratio" ] + + low_sq1 = self.issues[self.best ][ "low_shape_quality" ] + low_sq2 = self.issues[self.worst][ "low_shape_quality" ] + + critical_dih1 = self.issues[self.best]["critical_dihedral"] + critical_dih2 = self.issues[self.worst]["critical_dihedral"] + + critical_max_dih1 = self.issues[self.best][ "critical_max_dihedral" ] + critical_max_dih2 = self.issues[self.worst][ "critical_max_dihedral" ] + + high_edge_ratio1 = self.issues[self.best][ "high_edge_ratio" ] + high_edge_ratio2 = self.issues[self.worst][ "high_edge_ratio" ] + + + summary_stats.append(['CRITICAL Combo', f'{critical_combo1:,}', f'{critical_combo2:,}', + f'{((critical_combo2-critical_combo1)/max(critical_combo1,1)*100):+.1f}%' if critical_combo1 > 0 else 'N/A']) + summary_stats.append(['(AR>100 & Q<0.3', f'({critical_combo1/len(ar1)*100:.2f}%)', + f'({critical_combo2/len(ar2)*100:.2f}%)', '']) + summary_stats.append([' & MinDih<5°)', '', '', '']) + + summary_stats.append(['', '', '', '']) + summary_stats.append(['AR > 100', f'{high_ar1:,}', f'{high_ar2:,}', + f'{((high_ar2-high_ar1)/max(high_ar1,1)*100):+.1f}%']) + + summary_stats.append(['Quality < 0.3', f'{low_sq1:,}', f'{low_sq2:,}', + f'{((low_sq2-low_sq1)/max(low_sq1,1)*100):+.1f}%']) + + summary_stats.append(['MinDih < 5°', f'{critical_dih1:,}', f'{critical_dih2:,}', + f'{((critical_dih2-critical_dih1)/max(critical_dih1,1)*100):+.1f}%' if critical_dih1 > 0 else 'N/A']) + + summary_stats.append(['MaxDih > 175°', f'{critical_max_dih1:,}', f'{critical_max_dih2:,}', + f'{((critical_max_dih2-critical_max_dih1)/max(critical_max_dih1,1)*100):+.1f}%' if critical_max_dih1 > 0 else 'N/A']) + + summary_stats.append(['Edge Ratio > 10', f'{high_edge_ratio1:,}', f'{high_edge_ratio2:,}', + f'{((high_edge_ratio2-high_edge_ratio1)/max(high_edge_ratio1,1)*100):+.1f}%']) + + summary_stats.append(['─' * 18, '─' * 10, '─' * 10, '─' * 10]) + summary_stats.append(['Quality Grade', '', '', '']) + excellent1 = self.overallQualityScore[self.best]["excellent"] + excellent2 = self.overallQualityScore[self.worst]["excellent"] + good1 = self.overallQualityScore[self.best]["good"] + good2 = self.overallQualityScore[self.worst]["good"] + poor1 = self.overallQualityScore[self.best]["poor"] + poor2 = self.overallQualityScore[self.worst]["poor"] + + + summary_stats.append([' Excellent (>80)', f'{excellent1:.1f}%', f'{excellent2:.1f}%', + f'{excellent2-excellent1:+.1f}%']) + summary_stats.append([' Good (60-80)', f'{good1:.1f}%', f'{good2:.1f}%', + f'{good2-good1:+.1f}%']) + summary_stats.append([' Poor (≤30)', f'{poor1:.1f}%', f'{poor2:.1f}%', + f'{poor2-poor1:+.1f}%']) + + table = ax3.table(cellText=summary_stats, cellLoc='left', + bbox=[0, 0, 1, 1], edges='open') + table.auto_set_font_size(False) + table.set_fontsize(8) + + # Style header + for i in range(4): + table[(0, i)].set_facecolor('#34495e') + table[(0, i)].set_text_props(weight='bold', color='white', fontsize=9) + + # Highlight CRITICAL row + for col in range(4): + table[(2, col)].set_facecolor('#fadbd8') + table[(2, col)].set_text_props(weight='bold', fontsize=9) + + # Color code changes + for row in [2, 6, 7, 8, 9, 10, 13, 14, 15]: + if row < len(summary_stats): + change_text = summary_stats[row][3] + if '%' in change_text and change_text != 'N/A': + try: + val = float(change_text.replace('%', '').replace('+', '')) + if row in [2, 6, 7, 8, 9, 10, 15]: # Lower is better + if val < -10: + table[(row, 3)].set_facecolor('#d5f4e6') # Green + elif val > 10: + table[(row, 3)].set_facecolor('#fadbd8') # Red + else: # Higher is better (excellent, good) + if val > 10: + table[(row, 3)].set_facecolor('#d5f4e6') + elif val < -10: + table[(row, 3)].set_facecolor('#fadbd8') + except: + pass + + ax3.set_title('CRITICAL ISSUES SUMMARY', fontsize=12, fontweight='bold', + color='darkgreen', pad=10) + + # ==================== ROW 2: QUALITY DISTRIBUTIONS ==================== + + # 4. Shape Quality Histogram + ax4 = fig.add_subplot(gs_main[0, 0]) + bins = np.linspace(0, 1, 40) + for n, _ in enumerate( self.meshes ): + sq1 = self.validMetrics[n][ "shape_quality" ] + ax4.hist(sq1, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n], edgecolor='black', linewidth=0.5) + ax4.set_xlabel('Shape Quality', fontweight='bold') + ax4.set_ylabel('Count', fontweight='bold') + ax4.set_title('Shape Quality Distribution', fontweight='bold') + ax4.legend() + ax4.grid(True, alpha=0.3) + + # 5. Aspect Ratio Histogram + ax5 = fig.add_subplot(gs_main[0, 1]) + # bins = np.logspace(0, np.log10(min(ar_plot_limit, ar1.max(), ar2.max())), 40) + ar_max = np.array( [ self.validMetrics[n]["aspect_ratio"].max() for n, _ in enumerate( self.meshes )] ) + + bins = np.logspace(0, np.log10(min(ar_plot_limit, ar_max.max())), 40) + for n, _ in enumerate( self.meshes ): + ar1 = self.validMetrics[n]['aspect_ratio'] + ax5.hist(ar1[ar1 < ar_plot_limit], bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n], edgecolor='black', linewidth=0.5) + ax5.set_xscale('log') + ax5.set_xlabel('Aspect Ratio', fontweight='bold') + ax5.set_ylabel('Count', fontweight='bold') + ax5.set_title('Aspect Ratio Distribution', fontweight='bold') + ax5.legend() + ax5.grid(True, alpha=0.3) + + # 6. Min Dihedral Histogram + ax6 = fig.add_subplot(gs_main[0, 2]) + bins = np.linspace(0, 90, 40) + for n, _ in enumerate( self.meshes ): + min_dih1 = self.validMetrics[ n ]["min_dihedral"] + ax6.hist(min_dih1, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n], edgecolor='black', linewidth=0.5) + ax6.axvline(5, color='red', linestyle='--', linewidth=1.5, alpha=0.7) + ax6.set_xlabel('Min Dihedral Angle (degrees)', fontweight='bold') + ax6.set_ylabel('Count', fontweight='bold') + ax6.set_title('Min Dihedral Angle Distribution', fontweight='bold') + ax6.legend() + ax6.grid(True, alpha=0.3) + + # 7. Edge Ratio Histogram + ax7 = fig.add_subplot(gs_main[0, 3]) + bins = np.logspace(0, 3, 40) + for n, _ in enumerate( self.meshes ): + edge_ratio1 = self.validMetrics[ n ][ "edge_ratio" ] + ax7.hist(edge_ratio1[edge_ratio1 < 1000], bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n], edgecolor='black', linewidth=0.5) + ax7.set_xscale('log') + ax7.axvline(1, color='green', linestyle='--', linewidth=1.5, alpha=0.7) + ax7.set_xlabel('Edge Length Ratio', fontweight='bold') + ax7.set_ylabel('Count', fontweight='bold') + ax7.set_title('Edge Length Ratio Distribution', fontweight='bold') + ax7.legend() + ax7.grid(True, alpha=0.3) + + # 8. Volume Histogram + ax8 = fig.add_subplot(gs_main[0, 4]) + vol_min = np.array( [ self.validMetrics[n]["volume"].min() for n, _ in enumerate( self.meshes )] ).min() + vol_max = np.array( [ self.validMetrics[n]["volume"].max() for n, _ in enumerate( self.meshes )] ).max() + + + bins = np.logspace(np.log10(vol_min), np.log10(vol_max), 40) + for n, _ in enumerate( self.meshes ): + vol1 = self.validMetrics[n][ "volume" ] + ax8.hist(vol1, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n], edgecolor='black', linewidth=0.5) + ax8.set_xscale('log') + ax8.set_xlabel('Volume', fontweight='bold') + ax8.set_ylabel('Count', fontweight='bold') + ax8.set_title('Volume Distribution', fontweight='bold') + ax8.legend() + ax8.grid(True, alpha=0.3) + + # ==================== ROW 3: STATISTICAL COMPARISON (BOX PLOTS) ==================== + + # 9. Shape Quality Box Plot + ax9 = fig.add_subplot(gs_main[1, 0]) + sq = [ self.validMetrics[n][ "shape_quality" ] for n, _ in enumerate( self.meshes ) ] + bp1 = ax9.boxplot( sq, labels=lbl, + patch_artist=True, showfliers=False) + ax9.set_ylabel('Shape Quality', fontweight='bold') + ax9.set_title('Shape Quality Comparison', fontweight='bold') + ax9.grid(True, alpha=0.3, axis='y') + + # 10. Aspect Ratio Box Plot + ax10 = fig.add_subplot(gs_main[1, 1]) + ar = [ self.validMetrics[n][ "aspect_ratio" ] for n, _ in enumerate( self.meshes ) ] + bp2 = ax10.boxplot( ar, labels=lbl, + patch_artist=True, showfliers=False) + ax10.set_yscale('log') + ax10.set_ylabel('Aspect Ratio (log)', fontweight='bold') + ax10.set_title('Aspect Ratio Comparison', fontweight='bold') + ax10.grid(True, alpha=0.3, axis='y') + + # 11. Min Dihedral Box Plot + ax11 = fig.add_subplot(gs_main[1, 2]) + min_dih1 = [ self.validMetrics[n][ "min_dihedral" ] for n, _ in enumerate( self.meshes ) ] + bp3 = ax11.boxplot( min_dih1, labels=lbl, + patch_artist=True, showfliers=False) + ax11.set_ylabel('Min Dihedral Angle (degrees)', fontweight='bold') + ax11.set_title('Min Dihedral Comparison', fontweight='bold') + ax11.grid(True, alpha=0.3, axis='y') + + # 12. Edge Ratio Box Plot + ax12 = fig.add_subplot(gs_main[1, 3]) + edge_ratio = [ self.validMetrics[n][ "edge_ratio" ] for n, _ in enumerate( self.meshes ) ] + bp4 = ax12.boxplot(edge_ratio, labels=lbl, + patch_artist=True, showfliers=False) + ax12.set_yscale('log') + ax12.set_ylabel('Edge Length Ratio (log)', fontweight='bold') + ax12.set_title('Edge Ratio Comparison', fontweight='bold') + ax12.grid(True, alpha=0.3, axis='y') + + # 13. Volume Box Plot + ax13 = fig.add_subplot(gs_main[1, 4]) + vol = [ self.validMetrics[n][ "volume" ] for n, _ in enumerate( self.meshes ) ] + bp5 = ax13.boxplot( vol, labels=lbl, + patch_artist=True, showfliers=False) + ax13.set_yscale('log') + ax13.set_ylabel('Volume (log)', fontweight='bold') + ax13.set_title('Volume Comparison', fontweight='bold') + ax13.grid(True, alpha=0.3, axis='y') + + for n, _ in enumerate( self.meshes ): + bp1['boxes'][n].set_facecolor(color[n]) + bp1['medians'][n].set_color("black") + bp2['boxes'][n].set_facecolor(color[n]) + bp2['medians'][n].set_color("black") + bp3['boxes'][n].set_facecolor(color[n]) + bp3['medians'][n].set_color("black") + bp4['boxes'][n].set_facecolor(color[n]) + bp4['medians'][n].set_color("black") + bp5['boxes'][n].set_facecolor(color[n]) + bp5['medians'][n].set_color("black") + + # ==================== ROW 4: CORRELATION ANALYSIS (SCATTER PLOTS) ==================== + + # Use existing idx1, idx2 from executive summary + + # 14. Shape Quality vs Aspect Ratio (duplicate for detail) + ax14 = fig.add_subplot(gs_main[2, 0]) + for n, _ in enumerate( self.meshes ): + idx = self.sample[n] + ar1 = self.validMetrics[n][ 'aspect_ratio'] + sq1 = self.validMetrics[n][ 'shape_quality'] + mask1 = ar1[idx] < ar_plot_limit + ax14.scatter(ar1[idx][mask1], sq1[idx][mask1], alpha=0.4, s=5, + color=color[n], label=f'Mesh {n}', edgecolors='none') + ax14.set_xscale('log') + ax14.set_xlabel('Aspect Ratio', fontweight='bold') + ax14.set_ylabel('Shape Quality', fontweight='bold') + ax14.set_title('Shape Quality vs Aspect Ratio', fontweight='bold') + ax14.set_xlim([1, ar_plot_limit]) + ax14.set_ylim([0, 1.05]) + ax14.legend(loc='upper right', fontsize=7) + ax14.grid(True, alpha=0.3) + + # 15. Aspect Ratio vs Flatness + ax15 = fig.add_subplot(gs_main[2, 1]) + for n, _ in enumerate( self.meshes ): + idx = self.sample[n] + ar1 = self.validMetrics[n]["aspect_ratio"] + fr1 = self.validMetrics[n][ 'flatness_ratio' ] + mask1 = ar1[idx] < ar_plot_limit + ax15.scatter(ar1[idx][mask1], fr1[idx][mask1], alpha=0.4, s=5, + color=color[n], label=f'Mesh {n}', edgecolors='none') + ax15.set_xscale('log') + ax15.set_yscale('log') + ax15.set_xlabel('Aspect Ratio', fontweight='bold') + ax15.set_ylabel('Flatness Ratio', fontweight='bold') + ax15.set_title('Aspect Ratio vs Flatness', fontweight='bold') + ax15.set_xlim([1, ar_plot_limit]) + ax15.legend(loc='upper right', fontsize=7) + ax15.grid(True, alpha=0.3) + + # 16. Volume vs Aspect Ratio + ax16 = fig.add_subplot(gs_main[2, 2]) + for n, _ in enumerate( self.meshes ): + idx = self.sample[n] + ar1 = self.validMetrics[n]["aspect_ratio"] + vol1 = self.validMetrics[n][ 'volume' ] + mask1 = ar1[idx] < ar_plot_limit + ax16.scatter(vol1[idx][mask1], ar1[idx][mask1], alpha=0.4, s=5, + color=color[n], label=f'Mesh {n}', edgecolors='none') + ax16.set_xscale('log') + ax16.set_yscale('log') + ax16.set_xlabel('Volume', fontweight='bold') + ax16.set_ylabel('Aspect Ratio', fontweight='bold') + ax16.set_title('Volume vs Aspect Ratio', fontweight='bold') + ax16.set_ylim([1, ar_plot_limit]) + ax16.legend(loc='upper right', fontsize=7) + ax16.grid(True, alpha=0.3) + + # 17. Volume vs Shape Quality + ax17 = fig.add_subplot(gs_main[2, 3]) + for n, _ in enumerate( self.meshes ): + idx = self.sample[n] + vol1 = self.validMetrics[n][ 'volume' ] + sq1 = self.validMetrics[n][ 'shape_quality'] + ax17.scatter(vol1[idx], sq1[idx], alpha=0.4, s=5, + color=color[n], label=f'Mesh {n}', edgecolors='none') + ax17.set_xscale('log') + ax17.set_xlabel('Volume', fontweight='bold') + ax17.set_ylabel('Shape Quality', fontweight='bold') + ax17.set_title('Volume vs Shape Quality', fontweight='bold') + ax17.legend(loc='upper right', fontsize=7) + ax17.grid(True, alpha=0.3) + + # 18. Edge Ratio vs Volume + ax18 = fig.add_subplot(gs_main[2, 4]) + for n, _ in enumerate( self.meshes ): + idx = self.sample[n] + vol1 = self.validMetrics[n][ 'volume' ] + edge_ratio = self.validMetrics[n][ 'edge_ratio' ] + ax18.scatter(vol1[idx], edge_ratio[idx], alpha=0.4, s=5, + color=color[n], label=f'Mesh {n}', edgecolors='none') + ax18.axhline(y=1, color='green', linestyle='--', linewidth=1.5, alpha=0.7) + ax18.set_xscale('log') + ax18.set_yscale('log') + ax18.set_xlabel('Volume', fontweight='bold') + ax18.set_ylabel('Edge Length Ratio', fontweight='bold') + ax18.set_title('Edge Ratio vs Volume', fontweight='bold') + ax18.legend(loc='upper right', fontsize=7) + ax18.grid(True, alpha=0.3) + + # ==================== ROW 5: DETAILED DIAGNOSTICS ==================== + + # 19. Min Edge Length Histogram + ax19 = fig.add_subplot(gs_main[3, 0]) + edge_min = np.array( [ self.validMetrics[n]["min_edge"].min() for n,_ in enumerate( self.meshes ) ] ).min() + edge_max_min = np.array( [ self.validMetrics[n]["min_edge"].max() for n,_ in enumerate( self.meshes ) ] ).min() + + bins = np.logspace(np.log10(edge_min), np.log10(edge_max_min), 40) + + for n, _ in enumerate( self.meshes ): + # edge_min = min(min_edge1.min(), min_edge2.min()) + # edge_max_min = max(min_edge1.max(), min_edge2.max()) + # bins = np.logspace(np.log10(edge_min), np.log10(edge_max_min), 40) + min_edge = self.validMetrics[n]['min_edge'] + ax19.hist(min_edge, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n], edgecolor='black', linewidth=0.5) + ax19.axvline(np.median(min_edge), color=color[n], linestyle=':', linewidth=2, alpha=0.8) + ax19.set_xscale('log') + + ax19.set_xlabel('Minimum Edge Length', fontweight='bold') + ax19.set_ylabel('Count', fontweight='bold') + ax19.set_title('Min Edge Length Distribution', fontweight='bold') + ax19.legend() + ax19.grid(True, alpha=0.3) + + # 20. Max Edge Length Histogram + ax20 = fig.add_subplot(gs_main[3, 1]) + edge_max = np.array( [ self.validMetrics[n]["max_edge"].max() for n,_ in enumerate( self.meshes ) ] ).max() + edge_min_max = np.array( [ self.validMetrics[n]["max_edge"].min() for n,_ in enumerate( self.meshes ) ] ).min() + + # edge_max = max(max_edge1.max(), max_edge2.max()) + # edge_min_max = min(max_edge1.min(), max_edge2.min()) + bins = np.logspace(np.log10(edge_min_max), np.log10(edge_max), 40) + for n, _ in enumerate( self.meshes ): + max_edge1 = self.validMetrics[n]["max_edge"] + ax20.hist(max_edge1, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n], edgecolor='black', linewidth=0.5) + ax20.axvline(np.median(max_edge1), color=color[n], linestyle=':', linewidth=2, alpha=0.8) + ax20.set_xscale('log') + ax20.set_xlabel('Maximum Edge Length', fontweight='bold') + ax20.set_ylabel('Count', fontweight='bold') + ax20.set_title('Max Edge Length Distribution', fontweight='bold') + ax20.legend() + ax20.grid(True, alpha=0.3) + + # 21. Max Dihedral Histogram + ax21 = fig.add_subplot(gs_main[3, 2]) + bins = np.linspace(90, 180, 40) + for n, _ in enumerate( self.meshes ): + max_dih1 = self.validMetrics[n][ "max_dihedral" ] + ax21.hist(max_dih1, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n], edgecolor='black', linewidth=0.5) + ax21.axvline(175, color='red', linestyle='--', linewidth=1.5, alpha=0.7) + ax21.set_xlabel('Max Dihedral Angle (degrees)', fontweight='bold') + ax21.set_ylabel('Count', fontweight='bold') + ax21.set_title('Max Dihedral Angle Distribution', fontweight='bold') + ax21.legend() + ax21.grid(True, alpha=0.3) + + # 22. Dihedral Range Box Plot + ax22 = fig.add_subplot(gs_main[3, 3]) + nmesh = len( self.meshes ) + positions = np.delete( np.arange( 1, nmesh*2+2 ), nmesh ) + dih = [ self.validMetrics[n]["min_dihedral"] for n, _ in enumerate( self.meshes )] + [ self.validMetrics[n]["max_dihedral"] for n, _ in enumerate( self.meshes )] + lbl_boxplot = [ f'M{n}Min' for n, _ in enumerate( self.meshes )] + [ f'M{n}Max' for n, _ in enumerate( self.meshes )] + boxplot_color = [ n for n, _ in enumerate( self.meshes ) ] + [ n for n, _ in enumerate( self.meshes ) ] + bp_dih = ax22.boxplot(dih, + positions=positions, + labels=lbl_boxplot, + patch_artist=True, showfliers=False, widths=0.6) + for m in range( len(self.meshes)*2 ): + bp_dih['boxes'][m].set_facecolor( color[ boxplot_color[m] ]) + + ax22.axhline(5, color='red', linestyle='--', linewidth=1, alpha=0.5, zorder=0) + ax22.axhline(175, color='red', linestyle='--', linewidth=1, alpha=0.5, zorder=0) + ax22.axhline(70.5, color='green', linestyle=':', linewidth=1, alpha=0.5, zorder=0) + ax22.set_ylabel('Dihedral Angle (degrees)', fontweight='bold') + ax22.set_title('Dihedral Angle Comparison', fontweight='bold') + ax22.grid(True, alpha=0.3, axis='y') + + # 23. Shape Quality CDF + ax23 = fig.add_subplot(gs_main[3, 4]) + for n, _ in enumerate( self.meshes ): + sq1 = self.validMetrics[n][ "shape_quality"] + sorted_sq1 = np.sort(sq1) + cdf_sq1 = np.arange(1, len(sorted_sq1) + 1) / len(sorted_sq1) * 100 + ax23.plot(sorted_sq1, cdf_sq1, color=color[n], linewidth=2, label=f'Mesh {n}') + + ax23.axvline(0.3, color='red', linestyle='--', linewidth=1, alpha=0.5) + ax23.axvline(0.7, color='green', linestyle='--', linewidth=1, alpha=0.5) + ax23.axhline(50, color='gray', linestyle='--', linewidth=1, alpha=0.5) + ax23.set_xlabel('Shape Quality', fontweight='bold') + ax23.set_ylabel('Cumulative %', fontweight='bold') + ax23.set_title('Cumulative Distribution - Shape Quality', fontweight='bold') + ax23.legend(loc='lower right') + ax23.grid(True, alpha=0.3) + + # Save figure + output_png = 'mesh_comparison.png' + print(f"\nSaving dashboard to: {output_png}") + plt.savefig(output_png, dpi=300, bbox_inches='tight', facecolor='white') + print(f"Dashboard saved successfully!") + + + + + def __loggerSection( self: Self, sectionName: str ): + # self.logger.info("\n" + "="*80) + self.logger.info("="*80) + self.logger.info( sectionName ) + self.logger.info("="*80) + + + def __orderMeshes( self: Self ): + """Proposition of ordering as fonction of median quality score""" + self.logger.info( "Ordering the meshes" ) + median_score = { n : np.median( self.validMetrics[n]["quality_score"] ) for n, _ in enumerate (self.meshes) } + + sorted_meshes = sorted( median_score.items(), key=lambda x:x[1], reverse = True ) + self.sorted = sorted_meshes + self.best = sorted_meshes[ 0 ][0] + self.worst = sorted_meshes[ -1 ][0] + + self.logger.info( f"Best Mesh: Mesh {self.best} vs worst Mesh [{self.worst}]") + + self.logger.info( f"Mesh order from median quality score:" ) + top = [ f"Mesh {m[0]} ({m[1]:.2f})" for m in sorted_meshes ] + toprint = (" > ").join( top ) + self.logger.info( " [+] " + toprint + " [-]" ) + + def compareIssuesFromBest( self: Self ): + high_ar1 = self.issues[ self.best ][ "high_aspect_ratio" ] + critical_dih1 = self.issues[ self.best ]["critical_dihedral"] + low_sq1 = self.issues[ self.best ]["low_shape_quality"] + critical_combo1 = self.issues[ self.best ]["critical_combo"] + + def getPercentChangeFromBest( data, ref ): + return (data - ref) / max( ref, 1)*100 + + self.logger.info (f"Change from BEST [Mesh {self.best}]" ) + high_ar = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'high_aspect_ratio' ], high_ar1 ):+15,.1f}%" for n, _ in self.sorted ] + low_sq = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'low_shape_quality' ], low_sq1 ):+15,.1f}%" for n, _ in self.sorted ] + critical_dih = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'critical_dihedral' ], critical_dih1 ):+15,.1f}%" if critical_dih1 > 0 else f"{'N/A':16}" for n, _ in self.sorted] + critical_combo = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'critical_combo' ], critical_combo1 ):+15,.1f}%" if critical_combo1 > 0 else f"{'N/A':16}" for n, _ in self.sorted] + + self.logger.info( f"{' AR > 100:':25}{('').join( high_ar )}" ) + self.logger.info( f"{' Quality < 0.3:':25}{('').join( low_sq )}" ) + self.logger.info( f"{' MinDih < 5°:':25}{('').join( critical_dih )}" ) + self.logger.info( f"{' CRITICAL combo:':25}{('').join( critical_combo )}" ) + + + def setSampleForPlot( self: Self, data, n ): + n_sample = min(10000, len( data )) + self.sample[n] = np.random.choice(len(data), n_sample, replace=False) + +# Combined quality score +def compute_quality_score(aspectRatio, shapeQuality, edgeRatio, minDihedralAngle): + """Compute combined quality score (0-100).""" + ar_norm = np.clip(1.0 / (aspectRatio / 1.73), 0, 1) + sq_norm = shapeQuality + er_norm = np.clip(1.0 / edgeRatio, 0, 1) + dih_norm = np.clip(minDihedralAngle / 60.0, 0, 1) + score = (0.3 * ar_norm + 0.4 * sq_norm + 0.2 * er_norm + 0.1 * dih_norm) * 100 + return score + + From e1bb281f99571d4c8f0d291b4f06aa7362fefcc8 Mon Sep 17 00:00:00 2001 From: Paloma Martinez <104762252+paloma-martinez@users.noreply.github.com> Date: Fri, 30 Jan 2026 15:14:19 +0100 Subject: [PATCH 02/11] log display --- .../pre_processing/TetQualityAnalysis.py | 963 +++++++++--------- 1 file changed, 475 insertions(+), 488 deletions(-) diff --git a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py index 05fbbbbe..2681f1da 100644 --- a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py +++ b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py @@ -16,7 +16,6 @@ import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from matplotlib.patches import Rectangle -import seaborn as sns __doc__ = """ TetQualityAnalysis module is a filter that performs an an analysis of tetrahedras of one or several meshes and plot a summary. @@ -33,11 +32,11 @@ def __init__( ) -> None: self.meshes: dict[ str, vtkDataSet ] = meshes - # self.meshes = Set( ) self.analyzedMesh: dict[ Any ] = {} self.issues: dict[ Any ] = {} - self.overallQualityScore: dict[ Any] = {} - self.validMetrics: dict[Any] = {} + self.qualityScore: dict[ Any] = {} + self.validMetrics: dict[int, dict[str, np.float64]] = {} + self.medians: dict[ int, dict[str, np.float64 ]] = {} self.sample = {} # Logger. @@ -72,95 +71,103 @@ def applyFilter( self: Self) -> None: self.__loggerSection( "MESH COMPARISON DASHBOARD" ) - for n, (filename1, mesh1) in enumerate( self.meshes.items() ): - # print( n, filename1 ) - coords1 = self.get_coordinates_double_precision(mesh1) - tet_ids1, tet_conn1 = self.extract_tet_connectivity(mesh1) - n_tets1 = len( tet_ids1 ) + for n, (nfilename, mesh) in enumerate( self.meshes.items(), 1 ): + # print( n, nfilename ) + coords = self.getCoordinatesDoublePrecision(mesh) + tetrahedraIds, tetrahedraConnectivity = self.extractTetConnectivity(mesh) + ntets = len( tetrahedraIds ) - self.logger.info( f" Mesh {n} info: " ) - self.logger.info( f" Name: {filename1}" ) - self.logger.info( f" Total cells: {mesh1.GetNumberOfCells()}" ) - self.logger.info( f" Tetrahedra: {n_tets1}" ) - self.logger.info( f" Points: {mesh1.GetNumberOfPoints()}" ) + self.logger.info( f" Mesh {n} info: \n" + + f" Name: {nfilename}\n" + + f" Total cells: {mesh.GetNumberOfCells()}\n" + + f" Tetrahedra: {ntets}\n" + + f" Points: {mesh.GetNumberOfPoints()}" + + f"\n" + "-"*80 + "\n" ) # Analyze both meshes - print(f"\nAnalyzing Mesh {n} (double precision)...") - # metrics1 = self.analyze_all_tets_vectorized(n, coords1, tet_conn1) - self.analyze_all_tets_vectorized(n, coords1, tet_conn1) - metrics1 = self.analyzedMesh[ n ] - self.analyzedMesh[n]["tet"] = n_tets1 + self.analyzeAllTets(n, coords, tetrahedraConnectivity) + metrics = self.analyzedMesh[ n ] + self.analyzedMesh[n]["tet"] = ntets # Extract data with consistent filtering - ar_valid1 = np.isfinite(metrics1['aspect_ratio']) - rr_valid1 = np.isfinite(metrics1['radius_ratio']) + validAspectRatio = np.isfinite(metrics['aspectRatio']) + validRadiusRatio = np.isfinite(metrics['radiusRatio']) # Combined valid mask - valid1 = ar_valid1 & rr_valid1 + validMask = validAspectRatio & validRadiusRatio - ar1 = metrics1['aspect_ratio'][valid1] - rr1 = metrics1['radius_ratio'][valid1] - fr1 = metrics1['flatness_ratio'][valid1] - vol1 = metrics1['volumes'][valid1] - sq1 = metrics1['shape_quality'][valid1] + aspectRatio = metrics['aspectRatio'][validMask] + radiusRatio = metrics['radiusRatio'][validMask] + flatnessRatio = metrics['flatnessRatio'][validMask] + volume = metrics['volumes'][validMask] + shapeQuality = metrics['shapeQuality'][validMask] # Edge length data - min_edge1 = metrics1['min_edge'][valid1] - max_edge1 = metrics1['max_edge'][valid1] + minEdge = metrics['minEdge'][validMask] + maxEdge = metrics['maxEdge'][validMask] # Edge length ratio - edge_ratio1 = max_edge1 / np.maximum(min_edge1, 1e-15) + edgeRatio = maxEdge / np.maximum(minEdge, 1e-15) # Dihedral angles - min_dih1 = metrics1['min_dihedral'][valid1] - max_dih1 = metrics1['max_dihedral'][valid1] - dih_range1 = metrics1['dihedral_range'][valid1] + minDihedral = metrics['minDihedral'][validMask] + maxDihedral = metrics['maxDihedral'][validMask] + dihedralRange = metrics['dihedral_range'][validMask] - score1 = compute_quality_score(ar1, sq1, edge_ratio1, min_dih1) + qualityScore = compute_quality_score(aspectRatio, shapeQuality, edgeRatio, minDihedral) # Store all values - self.validMetrics[n] = { 'aspect_ratio' : ar1, 'radius_ratio': rr1, 'flatness_ratio': fr1, 'volume': vol1, 'shape_quality': sq1, 'min_edge': min_edge1, 'max_edge': max_edge1, 'edge_ratio': edge_ratio1, 'min_dihedral': min_dih1, 'max_dihedral': max_dih1, 'dihedral_range': dih_range1, 'quality_score': score1} + self.validMetrics[n] = { 'aspectRatio' : aspectRatio, 'radiusRatio': radiusRatio, 'flatnessRatio': flatnessRatio, 'volume': volume, 'shapeQuality': shapeQuality, 'minEdge': minEdge, 'maxEdge': maxEdge, 'edgeRatio': edgeRatio, 'minDihedral': minDihedral, 'maxDihedral': maxDihedral, 'dihedral_range': dihedralRange, 'qualityScore': qualityScore} # # ==================== Print Distribution Statistics ==================== # Problem element counts - high_ar1 = np.sum(ar1 > 100) - low_sq1 = np.sum(sq1 < 0.3) - low_flat1 = np.sum(fr1 < 0.01) - high_edge_ratio1 = np.sum(edge_ratio1 > 10) - critical_dih1 = np.sum(min_dih1 < 5) - critical_max_dih1 = np.sum(max_dih1 > 175) - problem1 = np.sum((ar1 > 100) & (sq1 < 0.3)) - critical_combo1 = np.sum((ar1 > 100) & (sq1 < 0.3) & (min_dih1 < 5)) + highAspectRatio = np.sum(aspectRatio > 100) + lowShapeQuality = np.sum(shapeQuality < 0.3) + lowFlatness = np.sum(flatnessRatio < 0.01) + highEdgeRatio = np.sum(edgeRatio > 10) + criticalMinDihedral = np.sum(minDihedral < 5) + criticalMaxDihedral = np.sum(maxDihedral > 175) + combined = np.sum((aspectRatio > 100) & (shapeQuality < 0.3)) + criticalCombo = np.sum((aspectRatio > 100) & (shapeQuality < 0.3) & (minDihedral < 5)) - self.issues[n] = { "high_aspect_ratio": high_ar1, "low_shape_quality": low_sq1, "low_flatness": low_flat1, "high_edge_ratio": high_edge_ratio1, "critical_dihedral": critical_dih1, "critical_max_dihedral": critical_max_dih1, "combined": problem1, "critical_combo": critical_combo1 } + self.issues[n] = { "highAspectRatio": highAspectRatio, "lowShapeQuality": lowShapeQuality, "lowFlatness": lowFlatness, "highEdgeRatio": highEdgeRatio, "criticalMinDihedral": criticalMinDihedral, "criticalMaxDihedral": criticalMaxDihedral, "combined": combined, "criticalCombo": criticalCombo } # Overall quality scores - excellent1 = np.sum(score1 > 80) / len(score1) * 100 - good1 = np.sum((score1 > 60) & (score1 <= 80)) / len(score1) * 100 - fair1 = np.sum((score1 > 30) & (score1 <= 60)) / len(score1) * 100 - poor1 = np.sum(score1 <= 30) / len(score1) * 100 + excellent = np.sum(qualityScore > 80) / len(qualityScore) * 100 + good = np.sum((qualityScore > 60) & (qualityScore <= 80)) / len(qualityScore) * 100 + fair = np.sum((qualityScore > 30) & (qualityScore <= 60)) / len(qualityScore) * 100 + poor = np.sum(qualityScore <= 30) / len(qualityScore) * 100 - self.overallQualityScore[n] = { 'excellent': excellent1, 'good': good1, 'fair': fair1, 'poor': poor1 } + self.qualityScore[n] = { 'excellent': excellent, 'good': good, 'fair': fair, 'poor': poor } - extreme_ar1 = ar1 > 1e4 - self.issues[ n ][ "extreme_aspect_ratio" ] = extreme_ar1 + extremeAspectRatio = aspectRatio > 1e4 + self.issues[ n ][ "extremeAspectRatio" ] = extremeAspectRatio # Nearly degenerate elements - degenerate1 = (min_dih1 < 0.1) | (max_dih1 > 179.9) - self.issues[ n ][ "degenerate" ] = degenerate1 + degenerateElements = (minDihedral < 0.1) | (maxDihedral > 179.9) + self.issues[ n ][ "degenerate" ] = degenerateElements + + self.medians[n] = { + "aspectRatio": np.median( self.validMetrics[n]["aspectRatio"] ), + "shapeQuality": np.median(self.validMetrics[n]["shapeQuality"] ), + "volume": np.median(self.validMetrics[n]["volume"]), + "minEdge": np.median(self.validMetrics[n]["minEdge"]), + "maxEdge": np.median(self.validMetrics[n]["maxEdge"]), + "edgeRatio": np.median(self.validMetrics[n]["edgeRatio"]), + "minDihedral": np.median( self.validMetrics[n]["minDihedral"]), + "maxDihedral": np.median( self.validMetrics[n]["maxDihedral"]), + "qualityScore": np.median( self.validMetrics[n]["qualityScore"]), + } + + # ==================== Report ==================== - print("\n" + "="*80 + "\n") - - # ==================== Create Dashboard ==================== + self.printDistributionStatistics() - # ==================== Print Distribution Statistics ==================== self.__orderMeshes() - self.printDistributionStatistics() - # Percentile information self.printPercentileAnalysis() @@ -169,45 +176,42 @@ def applyFilter( self: Self) -> None: self.printExtremeOutlierAnalysis() self.printComparisonSummary() - self.computeDeltasFromBest() + self.computeDeltasFromBest() self.createComparisonDashboard() - def get_coordinates_double_precision( self, mesh ): + def getCoordinatesDoublePrecision( self, mesh ): points = mesh.GetPoints() - n_points = points.GetNumberOfPoints() + npoints = points.GetNumberOfPoints() - coords = np.zeros((n_points, 3), dtype=np.float64) - for i in range(n_points): + coords = np.zeros((npoints, 3), dtype=np.float64) + for i in range(npoints): point = points.GetPoint(i) coords[i] = [point[0], point[1], point[2]] return coords - def extract_tet_connectivity( self, mesh ): + def extractTetConnectivity( self, mesh ): """Extract connectivity for all tetrahedra.""" - n_cells = mesh.GetNumberOfCells() - tet_ids = [] - tet_connectivity = [] + ncells = mesh.GetNumberOfCells() + tetrahedraIds = [] + tetrahedraConnectivity = [] - for cell_id in range(n_cells): - if mesh.GetCellType(cell_id) == vtk.VTK_TETRA: - cell = mesh.GetCell(cell_id) + for cellID in range(ncells): + if mesh.GetCellType(cellID) == vtk.VTK_TETRA: + cell = mesh.GetCell(cellID) point_ids = cell.GetPointIds() conn = [point_ids.GetId(i) for i in range(4)] - tet_ids.append(cell_id) - tet_connectivity.append(conn) - - return np.array(tet_ids), np.array(tet_connectivity) + tetrahedraIds.append(cellID) + tetrahedraConnectivity.append(conn) + return np.array(tetrahedraIds), np.array(tetrahedraConnectivity) - - - def analyze_all_tets_vectorized(self, n, coords, connectivity) -> dict: + def analyzeAllTets(self, n, coords, connectivity) -> dict: """Vectorized analysis of all tetrahedra.""" - n_tets = connectivity.shape[0] + ntets = connectivity.shape[0] # Get coordinates for all vertices v0 = coords[connectivity[:, 0]] @@ -231,39 +235,39 @@ def analyze_all_tets_vectorized(self, n, coords, connectivity) -> dict: l13 = np.linalg.norm(e13, axis=1) l23 = np.linalg.norm(e23, axis=1) - all_edges = np.stack([l01, l02, l03, l12, l13, l23], axis=1) - min_edge = np.min(all_edges, axis=1) - max_edge = np.max(all_edges, axis=1) + allEdges = np.stack([l01, l02, l03, l12, l13, l23], axis=1) + minEdge = np.min(allEdges, axis=1) + maxEdge = np.max(allEdges, axis=1) # Volumes cross_product = np.cross(e01, e02) volumes = np.abs(np.sum(cross_product * e03, axis=1) / 6.0) # Face areas - face_012 = 0.5 * np.linalg.norm(np.cross(e01, e02), axis=1) - face_013 = 0.5 * np.linalg.norm(np.cross(e01, e03), axis=1) - face_023 = 0.5 * np.linalg.norm(np.cross(e02, e03), axis=1) - face_123 = 0.5 * np.linalg.norm(np.cross(e12, e13), axis=1) + face012 = 0.5 * np.linalg.norm(np.cross(e01, e02), axis=1) + face013 = 0.5 * np.linalg.norm(np.cross(e01, e03), axis=1) + face023 = 0.5 * np.linalg.norm(np.cross(e02, e03), axis=1) + face123 = 0.5 * np.linalg.norm(np.cross(e12, e13), axis=1) - all_faces = np.stack([face_012, face_013, face_023, face_123], axis=1) - max_face_area = np.max(all_faces, axis=1) - total_surface_area = np.sum(all_faces, axis=1) + allFaces = np.stack([face012, face013, face023, face123], axis=1) + maxFaceArea = np.max(allFaces, axis=1) + totalSurfaceArea = np.sum(allFaces, axis=1) # Aspect ratio - min_altitude = 3.0 * volumes / np.maximum(max_face_area, 1e-15) - aspect_ratio = max_edge / np.maximum(min_altitude, 1e-15) - aspect_ratio[volumes < 1e-15] = np.inf + minAltitude = 3.0 * volumes / np.maximum(maxFaceArea, 1e-15) + aspectRatio = maxEdge / np.maximum(minAltitude, 1e-15) + aspectRatio[volumes < 1e-15] = np.inf # Radius ratio - inradius = 3.0 * volumes / np.maximum(total_surface_area, 1e-15) - circumradius = (max_edge ** 3) / np.maximum(24.0 * volumes, 1e-15) + inradius = 3.0 * volumes / np.maximum(totalSurfaceArea, 1e-15) + circumradius = (maxEdge ** 3) / np.maximum(24.0 * volumes, 1e-15) radius_ratio = circumradius / np.maximum(inradius, 1e-15) # Flatness ratio - flatness_ratio = volumes / np.maximum(max_face_area * min_edge, 1e-15) + flatness_ratio = volumes / np.maximum(maxFaceArea * minEdge, 1e-15) # Shape quality - sum_edge_sq = np.sum(all_edges ** 2, axis=1) + sum_edge_sq = np.sum(allEdges ** 2, axis=1) shape_quality = 12.0 * (3.0 * volumes) ** (2.0/3.0) / np.maximum(sum_edge_sq, 1e-15) shape_quality[volumes < 1e-15] = 0.0 @@ -300,95 +304,91 @@ def compute_dihedral_angle(normal1, normal2): self.analyzedMesh[ n ] = { 'volumes': volumes, - 'aspect_ratio': aspect_ratio, - 'radius_ratio': radius_ratio, - 'flatness_ratio': flatness_ratio, - 'shape_quality': shape_quality, - 'min_edge': min_edge, - 'max_edge': max_edge, - 'min_dihedral': min_dihedral, - 'max_dihedral': max_dihedral, + 'aspectRatio': aspectRatio, + 'radiusRatio': radius_ratio, + 'flatnessRatio': flatness_ratio, + 'shapeQuality': shape_quality, + 'minEdge': minEdge, + 'maxEdge': maxEdge, + 'minDihedral': min_dihedral, + 'maxDihedral': max_dihedral, 'dihedral_range': dihedral_range } return { 'volumes': volumes, - 'aspect_ratio': aspect_ratio, - 'radius_ratio': radius_ratio, - 'flatness_ratio': flatness_ratio, - 'shape_quality': shape_quality, - 'min_edge': min_edge, - 'max_edge': max_edge, - 'min_dihedral': min_dihedral, - 'max_dihedral': max_dihedral, + 'aspectRatio': aspectRatio, + 'radiusRatio': radius_ratio, + 'flatnessRatio': flatness_ratio, + 'shapeQuality': shape_quality, + 'minEdge': minEdge, + 'maxEdge': maxEdge, + 'minDihedral': min_dihedral, + 'maxDihedral': max_dihedral, 'dihedral_range': dihedral_range } def printDistributionStatistics( self: Self, fmt='.2e'): self.__loggerSection( "DISTRIBUTION STATISTICS (MIN / MEDIAN / MAX)" ) - def print_metric_stats( metricName, dataName, fmt='.2e'): + def printMetricStats( metricName, dataName, fmt='.2e'): """Helper function to print min/median/max for a metric.""" - # self.logger.info(f"\n{metricName}:") - self.logger.info(f"{metricName}:") - for n, _ in enumerate( self.meshes.items() ): + msg = f"{metricName}:\n" + for n, _ in enumerate( self.meshes.items(), 1 ): data = self.validMetrics[ n ][ dataName ] - self.logger.info(f" Mesh {n}: Min={data.min():{fmt}} Median={np.median(data):{fmt}} Max={data.max():{fmt}}") + msg += f" Mesh{n:2}: Min={data.min():<10{fmt}}Median={np.median(data):<10{fmt}}Max={data.max():<10{fmt}}\n" + self.logger.info( msg ) - print_metric_stats( "Aspect Ratio", 'aspect_ratio' ) - print_metric_stats( "Radius Ratio", 'radius_ratio' ) - print_metric_stats( "Flatness Ratio", 'flatness_ratio' ) - print_metric_stats( "Shape Quality", 'shape_quality', fmt='.4f' ) - print_metric_stats( "Volume", 'volume' ) - print_metric_stats( "Min Edge Length", 'min_edge' ) - print_metric_stats( "Max Edge Length", 'max_edge' ) - print_metric_stats( "Edge Length Ratio", 'edge_ratio', fmt='.2f' ) - print_metric_stats( "Min Dihedral Angle (degrees)", 'min_dihedral', fmt='.2f' ) - print_metric_stats( "Max Dihedral Angle (degrees)", 'max_dihedral', fmt='.2f' ) - print_metric_stats( "Dihedral Range (degrees)", 'dihedral_range',fmt='.2f' ) - print_metric_stats( "Overall Quality Score (0-100)", 'quality_score', fmt='.2f' ) + printMetricStats( "Aspect Ratio", 'aspectRatio' ) + printMetricStats( "Radius Ratio", 'radiusRatio' ) + printMetricStats( "Flatness Ratio", 'flatnessRatio' ) + printMetricStats( "Shape Quality", 'shapeQuality', fmt='.4f' ) + printMetricStats( "Volume", 'volume' ) + printMetricStats( "Min Edge Length", 'minEdge' ) + printMetricStats( "Max Edge Length", 'maxEdge' ) + printMetricStats( "Edge Length Ratio", 'edgeRatio', fmt='.2f' ) + printMetricStats( "Min Dihedral Angle (degrees)", 'minDihedral', fmt='.2f' ) + printMetricStats( "Max Dihedral Angle (degrees)", 'maxDihedral', fmt='.2f' ) + printMetricStats( "Dihedral Range (degrees)", 'dihedral_range',fmt='.2f' ) + printMetricStats( "Overall Quality Score (0-100)", 'qualityScore', fmt='.2f' ) def printPercentileAnalysis( self: Self, fmt='.2f' ): self.__loggerSection( "PERCENTILE ANALYSIS (25th / 75th / 90th / 99th)" ) - def print_percentiles(metricName, dataName, fmt='.2f'): - """Helper function to print percentiles.""" - self.logger.info(f"{metricName}:") - # self.logger.info(f"\n{metricName}:") - for n, _ in enumerate( self.meshes.items() ): - data = self.validMetrics[ n ][ dataName ] + for metricName, name in zip(*[ + ( "Aspect Ratio", "Shape Quality", "Edge Length Ratio", "Min Dihedral Angle (degrees)", "Overall Quality Score" ), + ( 'aspectRatio', 'shapeQuality', 'edgeRatio', 'minDihedral', 'qualityScore' ) ]): + msg = f"{metricName}:\n" + for n, _ in enumerate( self.meshes.items(), 1 ): + data = self.validMetrics[ n ][ name ] p1 = np.percentile(data, [25, 75, 90, 99]) - self.logger.info(f" Mesh {n}: 25th={p1[0]:{fmt}} 75th={p1[1]:{fmt}} 90th={p1[2]:{fmt}} 99th={p1[3]:{fmt}}") - - print_percentiles( "Aspect Ratio", 'aspect_ratio' ) - print_percentiles( "Shape Quality", 'shape_quality' ) - print_percentiles( "Edge Length Ratio", 'edge_ratio' ) - print_percentiles( "Min Dihedral Angle (degrees)", 'min_dihedral' ) - print_percentiles( "Overall Quality Score", 'quality_score' ) + msg += f" Mesh {n}: 25th = {p1[0]:<7,{fmt}}75th = {p1[1]:<7,{fmt}}90th = {p1[2]:<7,{fmt}}99th = {p1[3]:<7,{fmt}}\n" + self.logger.info( msg ) - def printQualityIssueSummary( self: Self ): + def printQualityIssueSummary( self: Self ) -> None: self.__loggerSection( "QUALITY ISSUE SUMMARY" ) - def print_issue(meshID, metricName, issueDataName, dataName ): - pb = self.issues[ meshID ][ issueDataName ] - m = len( self.validMetrics[ meshID ][ dataName ] ) - fmt = '.2f' - self.logger.info(f" {metricName:20}: {pb:,} ({(pb/m*100):{fmt}}%)") - - for n, _ in enumerate( self.meshes.items() ): - # self.logger.info( f"\nMesh {n} Issues:" ) - self.logger.info( f"Mesh {n} Issues:" ) - print_issue( n, "Aspect Ratio > 100", "high_aspect_ratio", 'aspect_ratio' ) - print_issue( n, "Shape Quality < 0.3", "low_shape_quality", 'shape_quality' ) - print_issue( n, "Flatness < 0.01", "low_flatness", "flatness_ratio" ) - print_issue( n, "Edge Ratio > 10", "high_edge_ratio", "edge_ratio" ) - print_issue( n, "Min Dihedral < 5°", "critical_dihedral", "min_dihedral" ) - print_issue( n, "Max Dihedral > 175°", "critical_max_dihedral",'max_dihedral' ) - print_issue( n, "Combined (AR>100 & Q<0.3)", "combined" ,'aspect_ratio' ) - print_issue( n, "CRITICAL (AR>100 & Q<0.3 & MinDih<5°", "critical_combo", 'aspect_ratio' ) + fmt = '.2f' + for n, _ in enumerate( self.meshes.items(), 1 ): + msg = f"Mesh {n} Issues:\n" + + w = False + for issueType, name, reference in zip(*[ + ( "Aspect Ratio > 100", "Shape Quality < 0.3", "Flatness < 0.01", "Edge Ratio > 10", "Min Dihedral < 5°", "Max Dihedral > 175°", "Combined (AR>100 & Q<0.3)", "CRITICAL (AR>100 & Q<0.3 & MinDih<5°"), + ( "highAspectRatio", "lowShapeQuality", "lowFlatness", "highEdgeRatio", "criticalMinDihedral", "criticalMaxDihedral", "combined", "criticalCombo" ), + ( 'aspectRatio', 'shapeQuality', "flatnessRatio", "edgeRatio", "minDihedral", 'maxDihedral', 'aspectRatio', 'aspectRatio' ) + ]): + pb = self.issues[ n ][ name ] + m = len( self.validMetrics[ n ][ reference ] ) + percent = pb/m*100 + msg += f" {f'{issueType}:':37}{pb:>8,} ({(pb/m*100):{fmt}}%)\n" + if pb != 0: + w = True + self.logger.warning( msg ) if w else self.logger.info( msg ) + self.compareIssuesFromBest() @@ -396,80 +396,81 @@ def print_issue(meshID, metricName, issueDataName, dataName ): def printOverallQualityScore( self: Self ): - # self.logger.info( f"\nOverall Quality Score Distribution:" ) - self.logger.info( f"Overall Quality Score Distribution:" ) - for n, _ in enumerate( self.meshes.items() ): - qualityScore = self.overallQualityScore[ n ] - self.logger.info(f" Mesh {n}: Excellent (>80): {qualityScore[ 'excellent' ]:.1f}% Good (60-80): {qualityScore[ 'good' ]:.1f}% Fair (30-60): {qualityScore[ 'fair' ]:.1f}% Poor (≤30): {qualityScore[ 'poor' ]:.1f}%") + msg = f"Overall Quality Score Distribution:\n" + msg += f" {f'Mesh n':10}{f'Excellent (>80)':20}{f'Good (60-80)':17}{f'Fair (30-60)':15}{f'Poor (≤30)':15}\n" + + for n, _ in enumerate( self.meshes.items(), 1 ): + qualityScore = self.qualityScore[ n ] + msg += f" {f'Mesh {n}':10}{qualityScore[ 'excellent' ]:10,.1f}%{qualityScore[ 'good' ]:15,.1f}% {qualityScore[ 'fair' ]:15,.1f}%{qualityScore[ 'poor' ]:15,.1f}%\n" + self.logger.info( msg ) def printExtremeOutlierAnalysis( self: Self ): - #TODO : info in warning if bad ? self.__loggerSection( "EXTREME OUTLIER ANALYSIS" ) # self.logger.warning( f"\n🚨 Elements with Aspect Ratio > 10,000:" ) - self.logger.warning( f"🚨 Elements with Aspect Ratio > 10,000:" ) - for n, _ in enumerate( self.meshes ): - extreme_ar = self.issues[n][ 'extreme_aspect_ratio' ] + msg = f"Elements with Aspect Ratio > 10,000:\n" + msg2 = "" + w = False + w2 = False + for n, _ in enumerate( self.meshes, 1 ): + extremeAspectRatio = self.issues[n][ 'extremeAspectRatio' ] data = self.analyzedMesh[n] - ar1 = data[ 'aspect_ratio' ] - self.logger.info(f" Mesh {n}: {np.sum(extreme_ar):,} elements ({np.sum(extreme_ar)/len(ar1)*100:.3f}%)") - - if np.sum(extreme_ar) > 0: - vol1 = data[ "volume" ] - min_dih1 = data[ "min_dihedral" ] - sq1 = data[ "shape_quality" ] - self.logger.info(f" Worst AR: {ar1[extreme_ar].max():.2e}") - self.logger.info(f" Avg volume: {vol1[extreme_ar].mean():.2e}") - self.logger.info(f" Min dihedral: {min_dih1[extreme_ar].min():.2f}° - {min_dih1[extreme_ar].mean():.2f}° (avg)") - self.logger.info(f" Shape quality: {sq1[extreme_ar].min():.4f} - {sq1[extreme_ar].mean():.4f} (avg)") - - if np.sum(extreme_ar) > 10: - # self.logger.warning(f"\n💡 Recommendation: Investigate/remove {np.sum(extreme_ar):,} extreme elements in Mesh {n}") - self.logger.warning(f"💡 Recommendation: Investigate/remove {np.sum(extreme_ar):,} extreme elements in Mesh {n}") - self.logger.warning(f" These are likely artifacts from mesh generation or geometry issues.") + aspectRatio = data[ 'aspectRatio' ] + + if np.sum(extremeAspectRatio) > 0: + msg += f" Mesh {n}: {np.sum(extremeAspectRatio):,} elements ({np.sum(extremeAspectRatio)/len(aspectRatio)*100:.3f}%)" + w = True + volume = data[ "volume" ] + minDihedral = data[ "minDihedral" ] + shapeQuality = data[ "shapeQuality" ] + + msg += f" Worst AR: {aspectRatio[extremeAspectRatio].max():.2e}\n" + msg += f" Avg volume: {volume[extremeAspectRatio].mean():.2e}\n" + msg += f" Min dihedral: {minDihedral[extremeAspectRatio].min():.2f}° - {minDihedral[extremeAspectRatio].mean():.2f}° (avg)\n" + msg += f" Shape quality: {shapeQuality[extremeAspectRatio].min():.4f} - {shapeQuality[extremeAspectRatio].mean():.4f} (avg)\n" + + if np.sum(extremeAspectRatio) > 10: + w2 = True + msg2 += f" Recommendation: Investigate/remove {np.sum(extremeAspectRatio):,} extreme elements in Mesh {n}\n These are likely artifacts from mesh generation or geometry issues.\n" + + self.logger.warning( msg ) if w else self.logger.info( msg + " N/A\n" ) + if w2: self.logger.warning( msg2 ) # Nearly degenerate elements - # self.logger.warning( f"\n🚨 Nearly Degenerate Elements (dihedral < 0.1° or > 179.9°):") - self.logger.warning( f"🚨 Nearly Degenerate Elements (dihedral < 0.1° or > 179.9°):") - for n, _ in enumerate( self.meshes ): + degMsg = f"Nearly Degenerate Elements (dihedral < 0.1° or > 179.9°):\n" + ww = False + for n, _ in enumerate( self.meshes, 1 ): degenerate = self.issues[ n ][ "degenerate" ] - data = self.validMetrics[ n ][ "min_dihedral" ] - self.logger.warning( f" Mesh {n}: {np.sum(degenerate):,} elements ({np.sum(degenerate)/len(data)*100:.3f}%)") + data = self.validMetrics[ n ][ "minDihedral" ] + if np.sum(degenerate) > 0: + ww = True + degMsg += f" Mesh {n}: {np.sum(degenerate):,} elements ({np.sum(degenerate)/len(data)*100:.3f}%)\n" + + self.logger.warning( degMsg ) if ww else self.logger.info( degMsg + " N/A\n" ) def printComparisonSummary( self: Self): self.__loggerSection( "COMPARISON SUMMARY" ) - for n, _ in enumerate( self.meshes ): + for n, _ in enumerate( self.meshes, 1 ): name = f"Mesh {n}" if n == self.best: name += " [BEST]" elif n == self.worst: name += " [LEAST GOOD]" - ar1_med = np.median( self.validMetrics[n]["aspect_ratio"] ) - sq1_med = np.median( self.validMetrics[n]["shape_quality"] ) - vol1_med = np.median(self.validMetrics[n]["volume"] ) - min_edge1_med = np.median(self.validMetrics[n]["min_edge"] ) - max_edge1_med = np.median(self.validMetrics[n]["max_edge"] ) - edge_ratio1_med = np.median(self.validMetrics[n]["edge_ratio"] ) - min_dih1_med = np.median( self.validMetrics[n]["min_dihedral"] ) - max_dih1_med = np.median( self.validMetrics[n]["max_dihedral"] ) - score1_med = np.median( self.validMetrics[n]["quality_score"] ) + msg = f"{name}\n" - self.logger.info(f"{name}") - self.logger.info(f" Tetrahedra: {self.analyzedMesh[n]['tet']:,}") - self.logger.info(f" Median Aspect Ratio: {ar1_med:.2f}") - self.logger.info(f" Median Shape Quality: {sq1_med:.4f}") - self.logger.info(f" Median Volume: {vol1_med:.2e}") - self.logger.info(f" Median Min Edge: {min_edge1_med:.2e}") - self.logger.info(f" Median Max Edge: {max_edge1_med:.2e}") - self.logger.info(f" Median Edge Ratio: {edge_ratio1_med:.2f}") - self.logger.info(f" Median Min Dihedral: {min_dih1_med:.1f}°") - self.logger.info(f" Median Max Dihedral: {max_dih1_med:.1f}°") - self.logger.info(f" Median Quality Score: {score1_med:.1f}/100") - - self.logger.info("\n" + "="*80) + msg += f" Tetrahedra: {self.analyzedMesh[n]['tet']:,}\n" + msg += f" Median Aspect Ratio: {self.medians[n]['aspectRatio']:.2f}\n" + msg += f" Median Shape Quality: {self.medians[n]['shapeQuality']:.4f}\n" + msg += f" Median Volume: {self.medians[n]['volume']:.2e}\n" + msg += f" Median Min Edge: {self.medians[n]['minEdge']:.2e}\n" + msg += f" Median Max Edge: {self.medians[n]['maxEdge']:.2e}\n" + msg += f" Median Edge Ratio: {self.medians[n]['edgeRatio']:.2f}\n" + msg += f" Median Min Dihedral: {self.medians[n]['minDihedral']:.1f}°\n" + msg += f" Median Max Dihedral: {self.medians[n]['maxDihedral']:.1f}°\n" + msg += f" Median Quality Score: {self.medians[n]['qualityScore']:.1f}/100\n" @@ -477,66 +478,64 @@ def computeDeltasFromBest( self: Self ): self.logger.info( f"Best mesh : Mesh {self.best}") self.deltas = {} - n_tets_best = self.analyzedMesh[self.best][ 'tet'] - ar_med_best = np.median( self.validMetrics[self.best]["aspect_ratio"] ) - sq_med_best = np.median(self.validMetrics[self.best]["shape_quality"]) - vol_med_best = np.median(self.validMetrics[self.best]["volume"]) - min_edge_med_best = np.median(self.validMetrics[self.best]["min_edge"]) - max_edge_med_best = np.median(self.validMetrics[self.best]["max_edge"]) - edge_ratio_med_best = np.median(self.validMetrics[self.best]["edge_ratio"]) - - for n, _ in enumerate( self.meshes ): - n_tets = self.analyzedMesh[n][ 'tet'] - ar_med = np.median( self.validMetrics[n]["aspect_ratio"] ) - sq_med = np.median(self.validMetrics[n]["shape_quality"]) - vol_med = np.median(self.validMetrics[n]["volume"]) - min_edge_med = np.median(self.validMetrics[n]["min_edge"]) - max_edge_med = np.median(self.validMetrics[n]["max_edge"]) - edge_ratio_med = np.median(self.validMetrics[n]["edge_ratio"]) - - - delta_tets = ((n_tets - n_tets_best) / n_tets_best * 100) if n_tets_best > 0 else 0 - delta_ar = ((ar_med - ar_med_best) / ar_med_best * 100) if ar_med_best > 0 else 0 - delta_sq = ((sq_med - sq_med_best) / sq_med_best * 100) if sq_med_best > 0 else 0 - delta_vol = ((vol_med - vol_med_best) / vol_med_best * 100) if vol_med_best > 0 else 0 - delta_min_edge = ((min_edge_med - min_edge_med_best) / min_edge_med_best * 100) if min_edge_med_best > 0 else 0 - delta_max_edge = ((max_edge_med - max_edge_med_best) / max_edge_med_best * 100) if max_edge_med_best > 0 else 0 - delta_edge_ratio = ((edge_ratio_med - edge_ratio_med_best) / edge_ratio_med_best * 100) if edge_ratio_med_best > 0 else 0 - self.deltas[ n ] = { "tetrahedra": delta_tets, "aspect_ratio": delta_ar, "shape_quality": delta_sq, "volume": delta_vol, "min_edge": delta_min_edge, "max_edge": delta_max_edge, "edge_ratio": delta_edge_ratio } - - - dtets = [ f"{self.deltas[ n ][ 'tetrahedra' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] - dar = [ f"{self.deltas[ n ][ 'aspect_ratio' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] - dsq = [ f"{self.deltas[ n ][ 'shape_quality' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] - dvol = [ f"{self.deltas[ n ][ 'volume' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] - d_min_edge = [ f"{self.deltas[ n ][ 'min_edge' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] - d_max_edge = [ f"{self.deltas[ n ][ 'max_edge' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] - d_edge_ratio = [ f"{self.deltas[ n ][ 'edge_ratio' ]:+15,.1f}%" for n, _ in self.sorted[1:] ] - names = [ f"{f'Mesh {n}':16}" for n, _ in self.sorted[1:]] - - self.logger.info(f"Changes vs Mesh {self.best} [BEST]:") - self.logger.info(f"{' Mesh:':25}{('').join( names )}") - self.logger.info(f"{' Tetrahedra:':25} {('').join(dtets)}%") - self.logger.info(f"{' Aspect Ratio:':25}{('').join( dar)}%") - self.logger.info(f"{' Shape Quality:':25}{('').join( dsq)}%") - self.logger.info(f"{' Volume:':25}{('').join( dvol)}%") - self.logger.info(f"{' Min Edge Length:':25}{('').join( d_min_edge)}%") - self.logger.info(f"{' Max Edge Length:':25}{('').join( d_max_edge)}%") - self.logger.info(f"{' Edge Length Ratio:':25}{('').join( d_edge_ratio)}%") - + ntetsBest = self.analyzedMesh[self.best][ 'tet'] + aspectRatioBest = self.medians[self.best]["aspectRatio"] + shapeQualityBest = self.medians[self.best]["shapeQuality"] + volumeBest = self.medians[self.best]["volume"] + minEdgeBest = self.medians[self.best]["minEdge"] + maxEdgeBest = self.medians[self.best]["maxEdge"] + edgeRatioBest = self.medians[self.best]["edgeRatio"] + + for n, _ in enumerate( self.meshes, 1 ): + self.deltas[n] = {} + ntets = self.analyzedMesh[n][ 'tet'] + + aspectRatio = np.median( self.validMetrics[n]["aspectRatio"] ) + shapeQuality = np.median(self.validMetrics[n]["shapeQuality"]) + volume = np.median(self.validMetrics[n]["volume"]) + minEdge = np.median(self.validMetrics[n]["minEdge"]) + maxEdge = np.median(self.validMetrics[n]["maxEdge"]) + edgeRatio = np.median(self.validMetrics[n]["edgeRatio"]) + + self.deltas[n]["tetrahedra"] = ( ( self.analyzedMesh[n]["tet"] - self.analyzedMesh[self.best]["tet"]) / self.analyzedMesh[self.best]["tet"] * 100 ) if self.analyzedMesh[self.best]["tet"] > 0 else 0 + for metric in ( "aspectRatio", "shapeQuality", "volume", "minEdge", "maxEdge", "edgeRatio" ): + value = self.medians[n][metric] + valueBest = self.medians[self.best][metric] + self.deltas[n][metric] = ( ( value - valueBest) / valueBest * 100 ) if valueBest > 0 else 0 + + + dtets = [ f"{self.deltas[ n ][ 'tetrahedra' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] + dar = [ f"{self.deltas[ n ][ 'aspectRatio' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] + dsq = [ f"{self.deltas[ n ][ 'shapeQuality' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] + dvol = [ f"{self.deltas[ n ][ 'volume' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] + d_min_edge = [ f"{self.deltas[ n ][ 'minEdge' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] + d_maxEdge = [ f"{self.deltas[ n ][ 'maxEdge' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] + d_edge_ratio = [ f"{self.deltas[ n ][ 'edgeRatio' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] + names = [ f"{f'Mesh {n}':>13}" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] + + self.logger.info(f"Changes vs BEST [Mesh {self.best}]:\n" + f"{' Mesh:':<20}{('').join( names )}\n" + + f"{' Tetrahedra:':<20}{('').join(dtets)}\n" + + f"{' Aspect Ratio:':<20}{('').join( dar)}\n" + + f"{' Shape Quality:':<20}{('').join( dsq)}\n" + + f"{' Volume:':<20}{('').join( dvol)}\n" + + f"{' Min Edge Length:':<20}{('').join( d_min_edge)}\n" + + f"{' Max Edge Length:':<20}{('').join( d_maxEdge)}\n" + + f"{' Edge Length Ratio:':<20}{('').join( d_edge_ratio)}\n" + ) def createComparisonDashboard( self: Self ): - lbl = [ f'Mesh {n}' for n, _ in enumerate( self.meshes ) ] + lbl = [ f'Mesh {n}' for n, _ in enumerate( self.meshes, 1 ) ] # Determine smart plot limits ar_99 = [] - for n, _ in enumerate( self.meshes ): - ar_99.append( np.percentile( self.validMetrics[n]["aspect_ratio"], 99 ) ) + for n, _ in enumerate( self.meshes, 1 ): + ar_99.append( np.percentile( self.validMetrics[n]["aspectRatio"], 99 ) ) ar_99_max = np.max( np.array( ar_99 ) ) - # ar_99_1 = np.percentile(ar1, 99) + # ar_99_1 = np.percentile(aspectRatio, 99) # # ar_99_2 = np.percentile(ar2, 99) # # ar_99_max = max(ar_99_1, ar_99_2) # ar_99_max = ar_99_1 @@ -552,7 +551,6 @@ def createComparisonDashboard( self: Self ): # print(f"Using AR plot limit: {ar_plot_limit} (99th percentiles: M1={ar_99_1:.1f}") # Set style - sns.set_style("whitegrid") plt.rcParams['figure.facecolor'] = 'white' plt.rcParams.update({ 'font.size': 9, @@ -575,24 +573,14 @@ def createComparisonDashboard( self: Self ): top=0.82, bottom=0.05, hspace=0.35, wspace=0.30) # Title - # mesh1_name = filename1.split('/')[-1] - # mesh2_name = filename2.split('/')[-1] suptitle = 'Mesh Quality Comparison Dashboard (Progressive Detail Layout)\n' - for n, _ in enumerate( self.meshes ): - # suptitle += f'Mesh {n}: {n_tets1:,} tets\t' - suptitle += f'Mesh {n}: tets\t' + for n, _ in enumerate( self.meshes, 1 ): + # suptitle += f'Mesh {n}: {ntets:,} tets\t' + suptitle += f'Mesh {n}: {self.analyzedMesh[n]["tet"]} tets - ' fig.suptitle( suptitle, fontsize=16, fontweight='bold', y=0.99) - # fig.suptitle(f'Mesh Quality Comparison Dashboard (Progressive Detail Layout)\n' + - # f'Mesh 1: {mesh1_name} ({n_tets1:,} tets)', - # # f'Mesh 1: {mesh1_name} ({n_tets1:,} tets) vs Mesh 2: {mesh2_name} ({n_tets2:,} tets)', - # fontsize=16, fontweight='bold', y=0.99) # Color scheme - # color = matplotlib.colormaps[ 'tab20' ] - # def color[n]: - # nn = np.linspace(0,20) - # return matplotlib.colormaps[ 'tab20' ][nn] color = matplotlib.pyplot.cm.tab10( np.arange(20)) # ==================== ROW 1: EXECUTIVE SUMMARY ==================== @@ -600,11 +588,11 @@ def createComparisonDashboard( self: Self ): # 1. Overall Quality Score Distribution ax1 = fig.add_subplot(gs_row1[0, 0]) bins = np.linspace(0, 100, 40) - for n, _ in enumerate( self.meshes ): - score1 = self.validMetrics[n][ 'quality_score' ] - ax1.hist(score1, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n], edgecolor='black', linewidth=0.5) - ax1.axvline(np.median(score1), color=color[n], linestyle='--', linewidth=2.5, alpha=0.9) + for n, _ in enumerate( self.meshes, 1 ): + qualityScore = self.validMetrics[n][ 'qualityScore' ] + ax1.hist(qualityScore, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n-1], edgecolor='black', linewidth=0.5) + ax1.axvline(np.median(qualityScore), color=color[n-1], linestyle='--', linewidth=2.5, alpha=0.9) # Add quality zones ax1.axvspan(0, 30, alpha=0.15, color='red', zorder=0) @@ -612,20 +600,15 @@ def createComparisonDashboard( self: Self ): ax1.axvspan(60, 80, alpha=0.15, color='lightgreen', zorder=0) ax1.axvspan(80, 100, alpha=0.15, color='darkgreen', zorder=0) - # Add median lines - # ax1.axvline(np.median(score1), color=color1, linestyle='--', linewidth=2.5, alpha=0.9) - # ax1.axvline(np.median(score2), color=color2, linestyle='--', linewidth=2.5, alpha=0.9) - # Add summary text #### ONLY BEST AND WORST MESH? - # TODO - # ax1.text(0.98, 0.98, - # f'Median Score:\nM1: {np.median(score1):.1f}\n\n' + - # f'Excellent (>80):\nM1: {excellent1:.1f}%', - # # f'Median Score:\nM1: {np.median(score1):.1f}\nM2: {np.median(score2):.1f}\n\n' + - # # f'Excellent (>80):\nM1: {excellent1:.1f}%\nM2: {excellent2:.1f}%', - # transform=ax1.transAxes, va='top', ha='right', - # bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.9), - # fontsize=9, fontweight='bold') + ax1.text(0.98, 0.98, + f'Median Score:\n{f"M{self.best}[+]:":<5}{np.median(self.validMetrics[self.best][ "qualityScore" ]):.1f}\n'+ + f'{f"M{self.worst}[-]:":<5}{np.median(self.validMetrics[self.worst][ "qualityScore" ]):.1f}\n\n' + + f'Excellent (>80):\n{f"M{self.best}[+]:":<5}{self.qualityScore[self.best]["excellent"]:.1f}%\n'+ + f'{f"M{self.worst}[-]:":<5}{self.qualityScore[self.worst]["excellent"]:.1f}%', + transform=ax1.transAxes, va='top', ha='right', + bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.9), + fontsize=9, fontweight='bold') ax1.set_xlabel('Combined Quality Score', fontweight='bold') ax1.set_ylabel('Count', fontweight='bold') @@ -644,17 +627,17 @@ def createComparisonDashboard( self: Self ): ax2 = fig.add_subplot(gs_row1[0, 1]) # Create sample for plotting - for n, _ in enumerate( self.meshes ): - ar1 = self.validMetrics[n]["aspect_ratio"] - sq1 = self.validMetrics[n]["shape_quality"] - self.setSampleForPlot( ar1, n ) + for n, _ in enumerate( self.meshes, 1 ): + aspectRatio = self.validMetrics[n]["aspectRatio"] + shapeQuality = self.validMetrics[n]["shapeQuality"] + self.setSampleForPlot( aspectRatio, n ) idx = self.sample[n] - mask1_plot = ar1[idx] < ar_plot_limit + mask1_plot = aspectRatio[idx] < ar_plot_limit - ax2.scatter(ar1[idx][mask1_plot], sq1[idx][mask1_plot], alpha=0.4, s=5, - color=color[n], label=f'Mesh {n}', edgecolors='none') + ax2.scatter(aspectRatio[idx][mask1_plot], shapeQuality[idx][mask1_plot], alpha=0.4, s=5, + color=color[n-1], label=f'Mesh {n}', edgecolors='none') # Add quality threshold lines ax2.axhline(y=0.3, color='red', linestyle='--', linewidth=2, @@ -670,22 +653,22 @@ def createComparisonDashboard( self: Self ): ax2.add_patch(problem_zone) # Count ALL elements - problem1_all = np.sum((ar1 > 100) & (sq1 < 0.3)) - extreme1 = np.sum(ar1 > ar_plot_limit) + problem1_all = np.sum((aspectRatio > 100) & (shapeQuality < 0.3)) + extreme1 = np.sum(aspectRatio > ar_plot_limit) # Problem annotation #TODO - # ax2.text(0.98, 0.02, - # f'PROBLEM ELEMENTS\n(AR>100 & Q<0.3):\n\n' + - # f'M1: {problem1_all:,}\n\n', - # # f'Change: {((problem2_all-problem1_all)/max(problem1_all,1)*100):+.1f}%', - # # f'M1: {problem1_all:,}\nM2: {problem2_all:,}\n\n' + - # transform=ax2.transAxes, va='bottom', ha='right', - # bbox=dict(boxstyle='round', facecolor='#ffcccc', alpha=0.95, - # edgecolor='darkred', linewidth=2), - # fontsize=9, fontweight='bold') - - # # Outlier warning + annotateIssues = ('\n').join([ f"{f'M{n}':4}{np.sum(self.issues[n]['combined']):,}" for n, _ in enumerate( self.meshes, 1 )]) + + ax2.text(0.98, 0.02, + f'PROBLEM ELEMENTS\n(AR>100 & Q<0.3):\n\n' + + annotateIssues, + transform=ax2.transAxes, va='bottom', ha='right', + bbox=dict(boxstyle='round', facecolor='#ffcccc', alpha=0.95, + edgecolor='darkred', linewidth=2), + fontsize=8, fontweight='bold') + + # Outlier warning # if extreme1 + extreme2 > 100: # ax2.text(0.02, 0.98, # f'⚠️ AR > {ar_plot_limit}\n(not shown):\n' + @@ -708,71 +691,72 @@ def createComparisonDashboard( self: Self ): ax3 = fig.add_subplot(gs_row1[0, 2]) ax3.axis('off') - summary_stats = [] - summary_stats.append(['CRITICAL ISSUE', 'WORST', 'BEST', 'Change']) - summary_stats.append(['─' * 18, '─' * 10, '─' * 10, '─' * 10]) + summaryStats = [] + summaryStats.append(['CRITICAL ISSUE', 'WORST', 'BEST', 'Change']) + summaryStats.append(['─' * 18, '─' * 10, '─' * 10, '─' * 10]) - critical_combo1 = self.issues[ self.best ][ "critical_combo" ] - critical_combo2 = self.issues[ self.worst ][ "critical_combo" ] - ar1 = self.validMetrics[self.best][ "aspect_ratio" ] - ar2 = self.validMetrics[self.worst][ "aspect_ratio" ] + criticalCombo = self.issues[ self.best ][ "criticalCombo" ] + critical_combo2 = self.issues[ self.worst ][ "criticalCombo" ] - high_ar1 = self.issues[ self.best ][ "high_aspect_ratio" ] - high_ar2 = self.issues[ self.worst ][ "high_aspect_ratio" ] + aspectRatio = self.validMetrics[self.best][ "aspectRatio" ] + ar2 = self.validMetrics[self.worst][ "aspectRatio" ] - low_sq1 = self.issues[self.best ][ "low_shape_quality" ] - low_sq2 = self.issues[self.worst][ "low_shape_quality" ] + highAspectRatio = self.issues[ self.best ][ "highAspectRatio" ] + high_ar2 = self.issues[ self.worst ][ "highAspectRatio" ] - critical_dih1 = self.issues[self.best]["critical_dihedral"] - critical_dih2 = self.issues[self.worst]["critical_dihedral"] + lowShapeQuality = self.issues[self.best ][ "lowShapeQuality" ] + low_sq2 = self.issues[self.worst][ "lowShapeQuality" ] - critical_max_dih1 = self.issues[self.best][ "critical_max_dihedral" ] - critical_max_dih2 = self.issues[self.worst][ "critical_max_dihedral" ] + criticalMinDihedral = self.issues[self.best]["criticalMinDihedral"] + critical_dih2 = self.issues[self.worst]["criticalMinDihedral"] - high_edge_ratio1 = self.issues[self.best][ "high_edge_ratio" ] - high_edge_ratio2 = self.issues[self.worst][ "high_edge_ratio" ] + criticalMaxDihedral = self.issues[self.best][ "criticalMaxDihedral" ] + critical_max_dih2 = self.issues[self.worst][ "criticalMaxDihedral" ] + highEdgeRatio = self.issues[self.best][ "highEdgeRatio" ] + highEdgeRatio2 = self.issues[self.worst][ "highEdgeRatio" ] - summary_stats.append(['CRITICAL Combo', f'{critical_combo1:,}', f'{critical_combo2:,}', - f'{((critical_combo2-critical_combo1)/max(critical_combo1,1)*100):+.1f}%' if critical_combo1 > 0 else 'N/A']) - summary_stats.append(['(AR>100 & Q<0.3', f'({critical_combo1/len(ar1)*100:.2f}%)', + + summaryStats.append(['CRITICAL Combo', f'{criticalCombo:,}', f'{critical_combo2:,}', + f'{((critical_combo2-criticalCombo)/max(criticalCombo,1)*100):+.1f}%' if criticalCombo > 0 else 'N/A']) + summaryStats.append(['(AR>100 & Q<0.3', f'({criticalCombo/len(aspectRatio)*100:.2f}%)', f'({critical_combo2/len(ar2)*100:.2f}%)', '']) - summary_stats.append([' & MinDih<5°)', '', '', '']) + summaryStats.append([' & MinDih<5°)', '', '', '']) - summary_stats.append(['', '', '', '']) - summary_stats.append(['AR > 100', f'{high_ar1:,}', f'{high_ar2:,}', - f'{((high_ar2-high_ar1)/max(high_ar1,1)*100):+.1f}%']) + summaryStats.append(['', '', '', '']) + summaryStats.append(['AR > 100', f'{highAspectRatio:,}', f'{high_ar2:,}', + f'{((high_ar2-highAspectRatio)/max(highAspectRatio,1)*100):+.1f}%']) - summary_stats.append(['Quality < 0.3', f'{low_sq1:,}', f'{low_sq2:,}', - f'{((low_sq2-low_sq1)/max(low_sq1,1)*100):+.1f}%']) + summaryStats.append(['Quality < 0.3', f'{lowShapeQuality:,}', f'{low_sq2:,}', + f'{((low_sq2-lowShapeQuality)/max(lowShapeQuality,1)*100):+.1f}%']) - summary_stats.append(['MinDih < 5°', f'{critical_dih1:,}', f'{critical_dih2:,}', - f'{((critical_dih2-critical_dih1)/max(critical_dih1,1)*100):+.1f}%' if critical_dih1 > 0 else 'N/A']) + summaryStats.append(['MinDih < 5°', f'{criticalMinDihedral:,}', f'{critical_dih2:,}', + f'{((critical_dih2-criticalMinDihedral)/max(criticalMinDihedral,1)*100):+.1f}%' if criticalMinDihedral > 0 else 'N/A']) - summary_stats.append(['MaxDih > 175°', f'{critical_max_dih1:,}', f'{critical_max_dih2:,}', - f'{((critical_max_dih2-critical_max_dih1)/max(critical_max_dih1,1)*100):+.1f}%' if critical_max_dih1 > 0 else 'N/A']) + summaryStats.append(['MaxDih > 175°', f'{criticalMaxDihedral:,}', f'{critical_max_dih2:,}', + f'{((critical_max_dih2-criticalMaxDihedral)/max(criticalMaxDihedral,1)*100):+.1f}%' if criticalMaxDihedral > 0 else 'N/A']) - summary_stats.append(['Edge Ratio > 10', f'{high_edge_ratio1:,}', f'{high_edge_ratio2:,}', - f'{((high_edge_ratio2-high_edge_ratio1)/max(high_edge_ratio1,1)*100):+.1f}%']) + summaryStats.append(['Edge Ratio > 10', f'{highEdgeRatio:,}', f'{highEdgeRatio2:,}', + f'{((highEdgeRatio2-highEdgeRatio)/max(highEdgeRatio,1)*100):+.1f}%']) - summary_stats.append(['─' * 18, '─' * 10, '─' * 10, '─' * 10]) - summary_stats.append(['Quality Grade', '', '', '']) - excellent1 = self.overallQualityScore[self.best]["excellent"] - excellent2 = self.overallQualityScore[self.worst]["excellent"] - good1 = self.overallQualityScore[self.best]["good"] - good2 = self.overallQualityScore[self.worst]["good"] - poor1 = self.overallQualityScore[self.best]["poor"] - poor2 = self.overallQualityScore[self.worst]["poor"] + summaryStats.append(['─' * 18, '─' * 10, '─' * 10, '─' * 10]) + summaryStats.append(['Quality Grade', '', '', '']) + excellent = self.qualityScore[self.best]["excellent"] + excellent2 = self.qualityScore[self.worst]["excellent"] + good = self.qualityScore[self.best]["good"] + good2 = self.qualityScore[self.worst]["good"] + poor = self.qualityScore[self.best]["poor"] + poor2 = self.qualityScore[self.worst]["poor"] - summary_stats.append([' Excellent (>80)', f'{excellent1:.1f}%', f'{excellent2:.1f}%', - f'{excellent2-excellent1:+.1f}%']) - summary_stats.append([' Good (60-80)', f'{good1:.1f}%', f'{good2:.1f}%', - f'{good2-good1:+.1f}%']) - summary_stats.append([' Poor (≤30)', f'{poor1:.1f}%', f'{poor2:.1f}%', - f'{poor2-poor1:+.1f}%']) + summaryStats.append([' Excellent (>80)', f'{excellent:.1f}%', f'{excellent2:.1f}%', + f'{excellent2-excellent:+.1f}%']) + summaryStats.append([' Good (60-80)', f'{good:.1f}%', f'{good2:.1f}%', + f'{good2-good:+.1f}%']) + summaryStats.append([' Poor (≤30)', f'{poor:.1f}%', f'{poor2:.1f}%', + f'{poor2-poor:+.1f}%']) - table = ax3.table(cellText=summary_stats, cellLoc='left', + table = ax3.table(cellText=summaryStats, cellLoc='left', bbox=[0, 0, 1, 1], edges='open') table.auto_set_font_size(False) table.set_fontsize(8) @@ -789,8 +773,8 @@ def createComparisonDashboard( self: Self ): # Color code changes for row in [2, 6, 7, 8, 9, 10, 13, 14, 15]: - if row < len(summary_stats): - change_text = summary_stats[row][3] + if row < len(summaryStats): + change_text = summaryStats[row][3] if '%' in change_text and change_text != 'N/A': try: val = float(change_text.replace('%', '').replace('+', '')) @@ -815,10 +799,10 @@ def createComparisonDashboard( self: Self ): # 4. Shape Quality Histogram ax4 = fig.add_subplot(gs_main[0, 0]) bins = np.linspace(0, 1, 40) - for n, _ in enumerate( self.meshes ): - sq1 = self.validMetrics[n][ "shape_quality" ] - ax4.hist(sq1, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n], edgecolor='black', linewidth=0.5) + for n, _ in enumerate( self.meshes, 1 ): + shapeQuality = self.validMetrics[n][ "shapeQuality" ] + ax4.hist(shapeQuality, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n-1], edgecolor='black', linewidth=0.5) ax4.set_xlabel('Shape Quality', fontweight='bold') ax4.set_ylabel('Count', fontweight='bold') ax4.set_title('Shape Quality Distribution', fontweight='bold') @@ -827,14 +811,14 @@ def createComparisonDashboard( self: Self ): # 5. Aspect Ratio Histogram ax5 = fig.add_subplot(gs_main[0, 1]) - # bins = np.logspace(0, np.log10(min(ar_plot_limit, ar1.max(), ar2.max())), 40) - ar_max = np.array( [ self.validMetrics[n]["aspect_ratio"].max() for n, _ in enumerate( self.meshes )] ) + # bins = np.logspace(0, np.log10(min(ar_plot_limit, aspectRatio.max(), ar2.max())), 40) + ar_max = np.array( [ self.validMetrics[n]["aspectRatio"].max() for n, _ in enumerate( self.meshes, 1 )] ) bins = np.logspace(0, np.log10(min(ar_plot_limit, ar_max.max())), 40) - for n, _ in enumerate( self.meshes ): - ar1 = self.validMetrics[n]['aspect_ratio'] - ax5.hist(ar1[ar1 < ar_plot_limit], bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n], edgecolor='black', linewidth=0.5) + for n, _ in enumerate( self.meshes, 1 ): + aspectRatio = self.validMetrics[n]['aspectRatio'] + ax5.hist(aspectRatio[aspectRatio < ar_plot_limit], bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n-1], edgecolor='black', linewidth=0.5) ax5.set_xscale('log') ax5.set_xlabel('Aspect Ratio', fontweight='bold') ax5.set_ylabel('Count', fontweight='bold') @@ -845,10 +829,10 @@ def createComparisonDashboard( self: Self ): # 6. Min Dihedral Histogram ax6 = fig.add_subplot(gs_main[0, 2]) bins = np.linspace(0, 90, 40) - for n, _ in enumerate( self.meshes ): - min_dih1 = self.validMetrics[ n ]["min_dihedral"] - ax6.hist(min_dih1, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n], edgecolor='black', linewidth=0.5) + for n, _ in enumerate( self.meshes, 1 ): + minDihedral = self.validMetrics[ n ]["minDihedral"] + ax6.hist(minDihedral, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n-1], edgecolor='black', linewidth=0.5) ax6.axvline(5, color='red', linestyle='--', linewidth=1.5, alpha=0.7) ax6.set_xlabel('Min Dihedral Angle (degrees)', fontweight='bold') ax6.set_ylabel('Count', fontweight='bold') @@ -859,10 +843,10 @@ def createComparisonDashboard( self: Self ): # 7. Edge Ratio Histogram ax7 = fig.add_subplot(gs_main[0, 3]) bins = np.logspace(0, 3, 40) - for n, _ in enumerate( self.meshes ): - edge_ratio1 = self.validMetrics[ n ][ "edge_ratio" ] - ax7.hist(edge_ratio1[edge_ratio1 < 1000], bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n], edgecolor='black', linewidth=0.5) + for n, _ in enumerate( self.meshes, 1 ): + edgeRatio = self.validMetrics[ n ][ "edgeRatio" ] + ax7.hist(edgeRatio[edgeRatio < 1000], bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n-1], edgecolor='black', linewidth=0.5) ax7.set_xscale('log') ax7.axvline(1, color='green', linestyle='--', linewidth=1.5, alpha=0.7) ax7.set_xlabel('Edge Length Ratio', fontweight='bold') @@ -873,15 +857,15 @@ def createComparisonDashboard( self: Self ): # 8. Volume Histogram ax8 = fig.add_subplot(gs_main[0, 4]) - vol_min = np.array( [ self.validMetrics[n]["volume"].min() for n, _ in enumerate( self.meshes )] ).min() - vol_max = np.array( [ self.validMetrics[n]["volume"].max() for n, _ in enumerate( self.meshes )] ).max() + vol_min = np.array( [ self.validMetrics[n]["volume"].min() for n, _ in enumerate( self.meshes, 1 )] ).min() + vol_max = np.array( [ self.validMetrics[n]["volume"].max() for n, _ in enumerate( self.meshes, 1 )] ).max() bins = np.logspace(np.log10(vol_min), np.log10(vol_max), 40) - for n, _ in enumerate( self.meshes ): - vol1 = self.validMetrics[n][ "volume" ] - ax8.hist(vol1, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n], edgecolor='black', linewidth=0.5) + for n, _ in enumerate( self.meshes, 1 ): + volume = self.validMetrics[n][ "volume" ] + ax8.hist(volume, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n-1], edgecolor='black', linewidth=0.5) ax8.set_xscale('log') ax8.set_xlabel('Volume', fontweight='bold') ax8.set_ylabel('Count', fontweight='bold') @@ -893,7 +877,7 @@ def createComparisonDashboard( self: Self ): # 9. Shape Quality Box Plot ax9 = fig.add_subplot(gs_main[1, 0]) - sq = [ self.validMetrics[n][ "shape_quality" ] for n, _ in enumerate( self.meshes ) ] + sq = [ self.validMetrics[n][ "shapeQuality" ] for n, _ in enumerate( self.meshes, 1 ) ] bp1 = ax9.boxplot( sq, labels=lbl, patch_artist=True, showfliers=False) ax9.set_ylabel('Shape Quality', fontweight='bold') @@ -902,7 +886,7 @@ def createComparisonDashboard( self: Self ): # 10. Aspect Ratio Box Plot ax10 = fig.add_subplot(gs_main[1, 1]) - ar = [ self.validMetrics[n][ "aspect_ratio" ] for n, _ in enumerate( self.meshes ) ] + ar = [ self.validMetrics[n][ "aspectRatio" ] for n, _ in enumerate( self.meshes, 1 ) ] bp2 = ax10.boxplot( ar, labels=lbl, patch_artist=True, showfliers=False) ax10.set_yscale('log') @@ -912,8 +896,8 @@ def createComparisonDashboard( self: Self ): # 11. Min Dihedral Box Plot ax11 = fig.add_subplot(gs_main[1, 2]) - min_dih1 = [ self.validMetrics[n][ "min_dihedral" ] for n, _ in enumerate( self.meshes ) ] - bp3 = ax11.boxplot( min_dih1, labels=lbl, + minDihedral = [ self.validMetrics[n][ "minDihedral" ] for n, _ in enumerate( self.meshes, 1 ) ] + bp3 = ax11.boxplot( minDihedral, labels=lbl, patch_artist=True, showfliers=False) ax11.set_ylabel('Min Dihedral Angle (degrees)', fontweight='bold') ax11.set_title('Min Dihedral Comparison', fontweight='bold') @@ -921,7 +905,7 @@ def createComparisonDashboard( self: Self ): # 12. Edge Ratio Box Plot ax12 = fig.add_subplot(gs_main[1, 3]) - edge_ratio = [ self.validMetrics[n][ "edge_ratio" ] for n, _ in enumerate( self.meshes ) ] + edge_ratio = [ self.validMetrics[n][ "edgeRatio" ] for n, _ in enumerate( self.meshes, 1 ) ] bp4 = ax12.boxplot(edge_ratio, labels=lbl, patch_artist=True, showfliers=False) ax12.set_yscale('log') @@ -931,7 +915,7 @@ def createComparisonDashboard( self: Self ): # 13. Volume Box Plot ax13 = fig.add_subplot(gs_main[1, 4]) - vol = [ self.validMetrics[n][ "volume" ] for n, _ in enumerate( self.meshes ) ] + vol = [ self.validMetrics[n][ "volume" ] for n, _ in enumerate( self.meshes, 1 ) ] bp5 = ax13.boxplot( vol, labels=lbl, patch_artist=True, showfliers=False) ax13.set_yscale('log') @@ -939,17 +923,17 @@ def createComparisonDashboard( self: Self ): ax13.set_title('Volume Comparison', fontweight='bold') ax13.grid(True, alpha=0.3, axis='y') - for n, _ in enumerate( self.meshes ): - bp1['boxes'][n].set_facecolor(color[n]) - bp1['medians'][n].set_color("black") - bp2['boxes'][n].set_facecolor(color[n]) - bp2['medians'][n].set_color("black") - bp3['boxes'][n].set_facecolor(color[n]) - bp3['medians'][n].set_color("black") - bp4['boxes'][n].set_facecolor(color[n]) - bp4['medians'][n].set_color("black") - bp5['boxes'][n].set_facecolor(color[n]) - bp5['medians'][n].set_color("black") + for n, _ in enumerate( self.meshes, 1 ): + bp1['boxes'][n-1].set_facecolor(color[n-1]) + bp1['medians'][n-1].set_color("black") + bp2['boxes'][n-1].set_facecolor(color[n-1]) + bp2['medians'][n-1].set_color("black") + bp3['boxes'][n-1].set_facecolor(color[n-1]) + bp3['medians'][n-1].set_color("black") + bp4['boxes'][n-1].set_facecolor(color[n-1]) + bp4['medians'][n-1].set_color("black") + bp5['boxes'][n-1].set_facecolor(color[n-1]) + bp5['medians'][n-1].set_color("black") # ==================== ROW 4: CORRELATION ANALYSIS (SCATTER PLOTS) ==================== @@ -957,13 +941,13 @@ def createComparisonDashboard( self: Self ): # 14. Shape Quality vs Aspect Ratio (duplicate for detail) ax14 = fig.add_subplot(gs_main[2, 0]) - for n, _ in enumerate( self.meshes ): + for n, _ in enumerate( self.meshes, 1 ): idx = self.sample[n] - ar1 = self.validMetrics[n][ 'aspect_ratio'] - sq1 = self.validMetrics[n][ 'shape_quality'] - mask1 = ar1[idx] < ar_plot_limit - ax14.scatter(ar1[idx][mask1], sq1[idx][mask1], alpha=0.4, s=5, - color=color[n], label=f'Mesh {n}', edgecolors='none') + aspectRatio = self.validMetrics[n][ 'aspectRatio'] + shapeQuality = self.validMetrics[n][ 'shapeQuality'] + mask1 = aspectRatio[idx] < ar_plot_limit + ax14.scatter(aspectRatio[idx][mask1], shapeQuality[idx][mask1], alpha=0.4, s=5, + color=color[n-1], label=f'Mesh {n}', edgecolors='none') ax14.set_xscale('log') ax14.set_xlabel('Aspect Ratio', fontweight='bold') ax14.set_ylabel('Shape Quality', fontweight='bold') @@ -975,13 +959,13 @@ def createComparisonDashboard( self: Self ): # 15. Aspect Ratio vs Flatness ax15 = fig.add_subplot(gs_main[2, 1]) - for n, _ in enumerate( self.meshes ): + for n, _ in enumerate( self.meshes, 1 ): idx = self.sample[n] - ar1 = self.validMetrics[n]["aspect_ratio"] - fr1 = self.validMetrics[n][ 'flatness_ratio' ] - mask1 = ar1[idx] < ar_plot_limit - ax15.scatter(ar1[idx][mask1], fr1[idx][mask1], alpha=0.4, s=5, - color=color[n], label=f'Mesh {n}', edgecolors='none') + aspectRatio = self.validMetrics[n]["aspectRatio"] + flatnessRatio = self.validMetrics[n][ 'flatnessRatio' ] + mask1 = aspectRatio[idx] < ar_plot_limit + ax15.scatter(aspectRatio[idx][mask1], flatnessRatio[idx][mask1], alpha=0.4, s=5, + color=color[n-1], label=f'Mesh {n}', edgecolors='none') ax15.set_xscale('log') ax15.set_yscale('log') ax15.set_xlabel('Aspect Ratio', fontweight='bold') @@ -993,13 +977,13 @@ def createComparisonDashboard( self: Self ): # 16. Volume vs Aspect Ratio ax16 = fig.add_subplot(gs_main[2, 2]) - for n, _ in enumerate( self.meshes ): + for n, _ in enumerate( self.meshes, 1 ): idx = self.sample[n] - ar1 = self.validMetrics[n]["aspect_ratio"] - vol1 = self.validMetrics[n][ 'volume' ] - mask1 = ar1[idx] < ar_plot_limit - ax16.scatter(vol1[idx][mask1], ar1[idx][mask1], alpha=0.4, s=5, - color=color[n], label=f'Mesh {n}', edgecolors='none') + aspectRatio = self.validMetrics[n]["aspectRatio"] + volume = self.validMetrics[n][ 'volume' ] + mask1 = aspectRatio[idx] < ar_plot_limit + ax16.scatter(volume[idx][mask1], aspectRatio[idx][mask1], alpha=0.4, s=5, + color=color[n-1], label=f'Mesh {n}', edgecolors='none') ax16.set_xscale('log') ax16.set_yscale('log') ax16.set_xlabel('Volume', fontweight='bold') @@ -1011,12 +995,12 @@ def createComparisonDashboard( self: Self ): # 17. Volume vs Shape Quality ax17 = fig.add_subplot(gs_main[2, 3]) - for n, _ in enumerate( self.meshes ): + for n, _ in enumerate( self.meshes, 1 ): idx = self.sample[n] - vol1 = self.validMetrics[n][ 'volume' ] - sq1 = self.validMetrics[n][ 'shape_quality'] - ax17.scatter(vol1[idx], sq1[idx], alpha=0.4, s=5, - color=color[n], label=f'Mesh {n}', edgecolors='none') + volume = self.validMetrics[n][ 'volume' ] + shapeQuality = self.validMetrics[n][ 'shapeQuality'] + ax17.scatter(volume[idx], shapeQuality[idx], alpha=0.4, s=5, + color=color[n-1], label=f'Mesh {n}', edgecolors='none') ax17.set_xscale('log') ax17.set_xlabel('Volume', fontweight='bold') ax17.set_ylabel('Shape Quality', fontweight='bold') @@ -1026,12 +1010,12 @@ def createComparisonDashboard( self: Self ): # 18. Edge Ratio vs Volume ax18 = fig.add_subplot(gs_main[2, 4]) - for n, _ in enumerate( self.meshes ): + for n, _ in enumerate( self.meshes, 1 ): idx = self.sample[n] - vol1 = self.validMetrics[n][ 'volume' ] - edge_ratio = self.validMetrics[n][ 'edge_ratio' ] - ax18.scatter(vol1[idx], edge_ratio[idx], alpha=0.4, s=5, - color=color[n], label=f'Mesh {n}', edgecolors='none') + volume = self.validMetrics[n][ 'volume' ] + edge_ratio = self.validMetrics[n][ 'edgeRatio' ] + ax18.scatter(volume[idx], edge_ratio[idx], alpha=0.4, s=5, + color=color[n-1], label=f'Mesh {n}', edgecolors='none') ax18.axhline(y=1, color='green', linestyle='--', linewidth=1.5, alpha=0.7) ax18.set_xscale('log') ax18.set_yscale('log') @@ -1045,19 +1029,19 @@ def createComparisonDashboard( self: Self ): # 19. Min Edge Length Histogram ax19 = fig.add_subplot(gs_main[3, 0]) - edge_min = np.array( [ self.validMetrics[n]["min_edge"].min() for n,_ in enumerate( self.meshes ) ] ).min() - edge_max_min = np.array( [ self.validMetrics[n]["min_edge"].max() for n,_ in enumerate( self.meshes ) ] ).min() + edge_min = np.array( [ self.validMetrics[n]["minEdge"].min() for n,_ in enumerate( self.meshes, 1 ) ] ).min() + edge_max_min = np.array( [ self.validMetrics[n]["minEdge"].max() for n,_ in enumerate( self.meshes, 1 ) ] ).min() bins = np.logspace(np.log10(edge_min), np.log10(edge_max_min), 40) - for n, _ in enumerate( self.meshes ): - # edge_min = min(min_edge1.min(), min_edge2.min()) - # edge_max_min = max(min_edge1.max(), min_edge2.max()) + for n, _ in enumerate( self.meshes, 1 ): + # edge_min = min(minEdge.min(), min_edge2.min()) + # edge_max_min = max(minEdge.max(), min_edge2.max()) # bins = np.logspace(np.log10(edge_min), np.log10(edge_max_min), 40) - min_edge = self.validMetrics[n]['min_edge'] - ax19.hist(min_edge, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n], edgecolor='black', linewidth=0.5) - ax19.axvline(np.median(min_edge), color=color[n], linestyle=':', linewidth=2, alpha=0.8) + minEdge = self.validMetrics[n]['minEdge'] + ax19.hist(minEdge, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n-1], edgecolor='black', linewidth=0.5) + ax19.axvline(np.median(minEdge), color=color[n-1], linestyle=':', linewidth=2, alpha=0.8) ax19.set_xscale('log') ax19.set_xlabel('Minimum Edge Length', fontweight='bold') @@ -1068,17 +1052,17 @@ def createComparisonDashboard( self: Self ): # 20. Max Edge Length Histogram ax20 = fig.add_subplot(gs_main[3, 1]) - edge_max = np.array( [ self.validMetrics[n]["max_edge"].max() for n,_ in enumerate( self.meshes ) ] ).max() - edge_min_max = np.array( [ self.validMetrics[n]["max_edge"].min() for n,_ in enumerate( self.meshes ) ] ).min() + edge_max = np.array( [ self.validMetrics[n]["maxEdge"].max() for n,_ in enumerate( self.meshes, 1 ) ] ).max() + edge_min_max = np.array( [ self.validMetrics[n]["maxEdge"].min() for n,_ in enumerate( self.meshes, 1 ) ] ).min() - # edge_max = max(max_edge1.max(), max_edge2.max()) - # edge_min_max = min(max_edge1.min(), max_edge2.min()) + # edge_max = max(maxEdge.max(), max_edge2.max()) + # edge_min_max = min(maxEdge.min(), max_edge2.min()) bins = np.logspace(np.log10(edge_min_max), np.log10(edge_max), 40) - for n, _ in enumerate( self.meshes ): - max_edge1 = self.validMetrics[n]["max_edge"] - ax20.hist(max_edge1, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n], edgecolor='black', linewidth=0.5) - ax20.axvline(np.median(max_edge1), color=color[n], linestyle=':', linewidth=2, alpha=0.8) + for n, _ in enumerate( self.meshes, 1 ): + maxEdge = self.validMetrics[n]["maxEdge"] + ax20.hist(maxEdge, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n-1], edgecolor='black', linewidth=0.5) + ax20.axvline(np.median(maxEdge), color=color[n-1], linestyle=':', linewidth=2, alpha=0.8) ax20.set_xscale('log') ax20.set_xlabel('Maximum Edge Length', fontweight='bold') ax20.set_ylabel('Count', fontweight='bold') @@ -1089,10 +1073,10 @@ def createComparisonDashboard( self: Self ): # 21. Max Dihedral Histogram ax21 = fig.add_subplot(gs_main[3, 2]) bins = np.linspace(90, 180, 40) - for n, _ in enumerate( self.meshes ): - max_dih1 = self.validMetrics[n][ "max_dihedral" ] - ax21.hist(max_dih1, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n], edgecolor='black', linewidth=0.5) + for n, _ in enumerate( self.meshes, 1 ): + maxDihedral = self.validMetrics[n][ "maxDihedral" ] + ax21.hist(maxDihedral, bins=bins, alpha=0.6, label=f'Mesh {n}', + color=color[n-1], edgecolor='black', linewidth=0.5) ax21.axvline(175, color='red', linestyle='--', linewidth=1.5, alpha=0.7) ax21.set_xlabel('Max Dihedral Angle (degrees)', fontweight='bold') ax21.set_ylabel('Count', fontweight='bold') @@ -1104,15 +1088,16 @@ def createComparisonDashboard( self: Self ): ax22 = fig.add_subplot(gs_main[3, 3]) nmesh = len( self.meshes ) positions = np.delete( np.arange( 1, nmesh*2+2 ), nmesh ) - dih = [ self.validMetrics[n]["min_dihedral"] for n, _ in enumerate( self.meshes )] + [ self.validMetrics[n]["max_dihedral"] for n, _ in enumerate( self.meshes )] - lbl_boxplot = [ f'M{n}Min' for n, _ in enumerate( self.meshes )] + [ f'M{n}Max' for n, _ in enumerate( self.meshes )] - boxplot_color = [ n for n, _ in enumerate( self.meshes ) ] + [ n for n, _ in enumerate( self.meshes ) ] + dih = [ self.validMetrics[n]["minDihedral"] for n, _ in enumerate( self.meshes, 1 )] + [ self.validMetrics[n]["maxDihedral"] for n, _ in enumerate( self.meshes, 1 )] + lbl_boxplot = [ f'M{n}Min' for n, _ in enumerate( self.meshes, 1 )] + [ f'M{n}Max' for n, _ in enumerate( self.meshes, 1 )] + boxplot_color = [ n for n, _ in enumerate( self.meshes, 1 ) ] + [ n for n, _ in enumerate( self.meshes, ) ] bp_dih = ax22.boxplot(dih, positions=positions, labels=lbl_boxplot, patch_artist=True, showfliers=False, widths=0.6) for m in range( len(self.meshes)*2 ): bp_dih['boxes'][m].set_facecolor( color[ boxplot_color[m] ]) + bp_dih['medians'][m].set_color("black") ax22.axhline(5, color='red', linestyle='--', linewidth=1, alpha=0.5, zorder=0) ax22.axhline(175, color='red', linestyle='--', linewidth=1, alpha=0.5, zorder=0) @@ -1123,11 +1108,11 @@ def createComparisonDashboard( self: Self ): # 23. Shape Quality CDF ax23 = fig.add_subplot(gs_main[3, 4]) - for n, _ in enumerate( self.meshes ): - sq1 = self.validMetrics[n][ "shape_quality"] - sorted_sq1 = np.sort(sq1) + for n, _ in enumerate( self.meshes, 1 ): + shapeQuality = self.validMetrics[n][ "shapeQuality"] + sorted_sq1 = np.sort(shapeQuality) cdf_sq1 = np.arange(1, len(sorted_sq1) + 1) / len(sorted_sq1) * 100 - ax23.plot(sorted_sq1, cdf_sq1, color=color[n], linewidth=2, label=f'Mesh {n}') + ax23.plot(sorted_sq1, cdf_sq1, color=color[n-1], linewidth=2, label=f'Mesh {n}') ax23.axvline(0.3, color='red', linestyle='--', linewidth=1, alpha=0.5) ax23.axvline(0.7, color='green', linestyle='--', linewidth=1, alpha=0.5) @@ -1139,7 +1124,7 @@ def createComparisonDashboard( self: Self ): ax23.grid(True, alpha=0.3) # Save figure - output_png = 'mesh_comparison.png' + output_png = '/data/pau901/SIM_CS/04_WORKSPACE/USERS/PalomaMartinez/geosPythonPackages/TESST/mesh_comparison.png' print(f"\nSaving dashboard to: {output_png}") plt.savefig(output_png, dpi=300, bbox_inches='tight', facecolor='white') print(f"Dashboard saved successfully!") @@ -1148,7 +1133,6 @@ def createComparisonDashboard( self: Self ): def __loggerSection( self: Self, sectionName: str ): - # self.logger.info("\n" + "="*80) self.logger.info("="*80) self.logger.info( sectionName ) self.logger.info("="*80) @@ -1156,54 +1140,57 @@ def __loggerSection( self: Self, sectionName: str ): def __orderMeshes( self: Self ): """Proposition of ordering as fonction of median quality score""" - self.logger.info( "Ordering the meshes" ) - median_score = { n : np.median( self.validMetrics[n]["quality_score"] ) for n, _ in enumerate (self.meshes) } + self.__loggerSection( "ORDERING MESHES (from median quality score)") + median_score = { n : np.median( self.validMetrics[n]["qualityScore"] ) for n, _ in enumerate (self.meshes, 1) } sorted_meshes = sorted( median_score.items(), key=lambda x:x[1], reverse = True ) self.sorted = sorted_meshes self.best = sorted_meshes[ 0 ][0] self.worst = sorted_meshes[ -1 ][0] - self.logger.info( f"Best Mesh: Mesh {self.best} vs worst Mesh [{self.worst}]") - self.logger.info( f"Mesh order from median quality score:" ) top = [ f"Mesh {m[0]} ({m[1]:.2f})" for m in sorted_meshes ] toprint = (" > ").join( top ) - self.logger.info( " [+] " + toprint + " [-]" ) + self.logger.info( " [+] " + toprint + " [-]\n" ) + def compareIssuesFromBest( self: Self ): - high_ar1 = self.issues[ self.best ][ "high_aspect_ratio" ] - critical_dih1 = self.issues[ self.best ]["critical_dihedral"] - low_sq1 = self.issues[ self.best ]["low_shape_quality"] - critical_combo1 = self.issues[ self.best ]["critical_combo"] + highAspectRatio = self.issues[ self.best ][ "highAspectRatio" ] + criticalMinDihedral = self.issues[ self.best ]["criticalMinDihedral"] + lowShapeQuality = self.issues[ self.best ]["lowShapeQuality"] + criticalCombo = self.issues[ self.best ]["criticalCombo"] def getPercentChangeFromBest( data, ref ): return (data - ref) / max( ref, 1)*100 - self.logger.info (f"Change from BEST [Mesh {self.best}]" ) - high_ar = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'high_aspect_ratio' ], high_ar1 ):+15,.1f}%" for n, _ in self.sorted ] - low_sq = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'low_shape_quality' ], low_sq1 ):+15,.1f}%" for n, _ in self.sorted ] - critical_dih = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'critical_dihedral' ], critical_dih1 ):+15,.1f}%" if critical_dih1 > 0 else f"{'N/A':16}" for n, _ in self.sorted] - critical_combo = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'critical_combo' ], critical_combo1 ):+15,.1f}%" if critical_combo1 > 0 else f"{'N/A':16}" for n, _ in self.sorted] + msg = f"Change from BEST [Mesh {self.best}]\n" + msg += f" {f'Mesh':20}"+ ("").join([f"{f'Mesh {n}':>16}" for n, _ in enumerate( self.meshes, 1 )]) + "\n" + highAspectRatio = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'highAspectRatio' ], highAspectRatio ):>+15,.1f}%" if n!= self.best else f"{'N/A':>16}" for n, _ in enumerate( self.meshes, 1 ) ] + lowShapeQuality = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'lowShapeQuality' ], lowShapeQuality ):>+15,.1f}%" if n!= self.best else f"{'N/A':>16}" for n, _ in enumerate( self.meshes, 1 ) ] + criticalMinDihedral = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'criticalMinDihedral' ], criticalMinDihedral ):>+15,.1f}%" if criticalMinDihedral > 0 and n!= self.best else f"{'N/A':>16}" for n, _ in enumerate( self.meshes, 1 )] + criticalCombo = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'criticalCombo' ], criticalCombo ):>+15,.1f}%" if criticalCombo > 0 and n!= self.best else f"{'N/A':>16}" for n, _ in enumerate( self.meshes, 1 )] + + msg += f"{' AR > 100:':20}{('').join( highAspectRatio )}\n" + msg += f"{' Quality < 0.3:':20}{('').join( lowShapeQuality )}\n" + msg += f"{' MinDih < 5°:':20}{('').join( criticalMinDihedral )}\n" + msg += f"{' CRITICAL combo:':20}{('').join( criticalCombo )}\n" - self.logger.info( f"{' AR > 100:':25}{('').join( high_ar )}" ) - self.logger.info( f"{' Quality < 0.3:':25}{('').join( low_sq )}" ) - self.logger.info( f"{' MinDih < 5°:':25}{('').join( critical_dih )}" ) - self.logger.info( f"{' CRITICAL combo:':25}{('').join( critical_combo )}" ) + self.logger.info( msg ) def setSampleForPlot( self: Self, data, n ): n_sample = min(10000, len( data )) self.sample[n] = np.random.choice(len(data), n_sample, replace=False) + # Combined quality score def compute_quality_score(aspectRatio, shapeQuality, edgeRatio, minDihedralAngle): """Compute combined quality score (0-100).""" - ar_norm = np.clip(1.0 / (aspectRatio / 1.73), 0, 1) - sq_norm = shapeQuality - er_norm = np.clip(1.0 / edgeRatio, 0, 1) - dih_norm = np.clip(minDihedralAngle / 60.0, 0, 1) - score = (0.3 * ar_norm + 0.4 * sq_norm + 0.2 * er_norm + 0.1 * dih_norm) * 100 + aspectRatioNorm = np.clip(1.0 / (aspectRatio / 1.73), 0, 1) + shapeQualityNorm = shapeQuality + edgeRatioNorm = np.clip(1.0 / edgeRatio, 0, 1) + dihedralMinNorm = np.clip(minDihedralAngle / 60.0, 0, 1) + score = (0.3 * aspectRatioNorm + 0.4 * shapeQualityNorm + 0.2 * edgeRatioNorm + 0.1 * dihedralMinNorm) * 100 return score From 845eb21c310308e87f6cc2d840526ef8f6ad6471 Mon Sep 17 00:00:00 2001 From: Paloma Martinez <104762252+paloma-martinez@users.noreply.github.com> Date: Fri, 30 Jan 2026 15:32:14 +0100 Subject: [PATCH 03/11] Adding docstring --- .../pre_processing/TetQualityAnalysis.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py index 2681f1da..a07f1378 100644 --- a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py +++ b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py @@ -114,7 +114,7 @@ def applyFilter( self: Self) -> None: maxDihedral = metrics['maxDihedral'][validMask] dihedralRange = metrics['dihedral_range'][validMask] - qualityScore = compute_quality_score(aspectRatio, shapeQuality, edgeRatio, minDihedral) + qualityScore = computeQualityScore(aspectRatio, shapeQuality, edgeRatio, minDihedral) # Store all values self.validMetrics[n] = { 'aspectRatio' : aspectRatio, 'radiusRatio': radiusRatio, 'flatnessRatio': flatnessRatio, 'volume': volume, 'shapeQuality': shapeQuality, 'minEdge': minEdge, 'maxEdge': maxEdge, 'edgeRatio': edgeRatio, 'minDihedral': minDihedral, 'maxDihedral': maxDihedral, 'dihedral_range': dihedralRange, 'qualityScore': qualityScore} @@ -1184,8 +1184,18 @@ def setSampleForPlot( self: Self, data, n ): # Combined quality score -def compute_quality_score(aspectRatio, shapeQuality, edgeRatio, minDihedralAngle): - """Compute combined quality score (0-100).""" +def computeQualityScore(aspectRatio, shapeQuality, edgeRatio, minDihedralAngle): + """Compute combined quality score (0-100). + + Args: + aspectRatio(npt.NDArray[np.float64]): Aspect ratio + shapeQuality(npt.NDArray[np.float64]): Shape quality + edgeRatio(npt.NDArray[np.float64]): Edge ratio + minDihedralAngle(npt.NDArray[np.float64]): Minimal edge ratio + + Returns: + np.float64: Quality score + """ aspectRatioNorm = np.clip(1.0 / (aspectRatio / 1.73), 0, 1) shapeQualityNorm = shapeQuality edgeRatioNorm = np.clip(1.0 / edgeRatio, 0, 1) From 4af6efd0d72d533df700364c97155ff11084c197 Mon Sep 17 00:00:00 2001 From: Paloma Martinez <104762252+paloma-martinez@users.noreply.github.com> Date: Fri, 30 Jan 2026 17:51:43 +0100 Subject: [PATCH 04/11] Typing/linting --- .../pre_processing/TetQualityAnalysis.py | 1631 +++++++++-------- 1 file changed, 905 insertions(+), 726 deletions(-) diff --git a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py index a07f1378..2a87a11a 100644 --- a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py +++ b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py @@ -4,15 +4,11 @@ import logging import numpy as np import numpy.typing as npt -from typing_extensions import Self +from typing_extensions import Self, Any #, Union from geos.utils.Logger import ( Logger, getLogger ) import vtk -import numpy as np -import sys -import matplotlib from vtkmodules.vtkCommonDataModel import vtkDataSet -from vtkmodules.util.numpy_support import vtk_to_numpy import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from matplotlib.patches import Rectangle @@ -23,21 +19,26 @@ loggerName: str = "Tetrahedra Quality Analysis" + class TetQualityAnalysis: - def __init__( - self: Self, - meshes: dict[str, vtkDataSet], - speHandler: bool = False - ) -> None: + def __init__( self: Self, meshes: dict[ str, vtkDataSet ], speHandler: bool = False ) -> None: + """Tetrahedra Quality Analysis. + Args: + meshes (dict[str,vtkUnstructuredGrid]): Meshes to analyze. + speHandler (bool, optional): True to use a specific handler, False to use the internal handler. + Defaults to False. + """ self.meshes: dict[ str, vtkDataSet ] = meshes - self.analyzedMesh: dict[ Any ] = {} - self.issues: dict[ Any ] = {} - self.qualityScore: dict[ Any] = {} - self.validMetrics: dict[int, dict[str, np.float64]] = {} - self.medians: dict[ int, dict[str, np.float64 ]] = {} - self.sample = {} + self.analyzedMesh: dict[ int, dict[ str, Any ] ] = {} + self.issues: dict[ int, Any ] = {} + self.qualityScore: dict[ int, Any ] = {} + self.validMetrics: dict[ int, dict[ str, Any ] ] = {} + self.medians: dict[ int, dict[ str, Any ] ] = {} + self.sample: dict[ int, npt.NDArray[ Any ] ] = {} + self.tets: dict[ int, int ] = {} + self.filename = 'mesh_comparison.png' # Logger. self.logger: Logger @@ -48,7 +49,6 @@ def __init__( self.logger.setLevel( logging.INFO ) self.logger.propagate = False - def setLoggerHandler( self: Self, handler: logging.Handler ) -> None: """Set a specific handler for the filter logger. @@ -65,101 +65,120 @@ def setLoggerHandler( self: Self, handler: logging.Handler ) -> None: self.logger.warning( "The logger already has an handler, to use yours set the argument 'speHandler'" " to True during the filter initialization." ) - - def applyFilter( self: Self) -> None: + def applyFilter( self: Self ) -> None: + """Apply Tetrahedra Analysis.""" self.logger.info( f"Apply filter { self.logger.name }." ) self.__loggerSection( "MESH COMPARISON DASHBOARD" ) - for n, (nfilename, mesh) in enumerate( self.meshes.items(), 1 ): + for n, ( nfilename, mesh ) in enumerate( self.meshes.items(), 1 ): # print( n, nfilename ) - coords = self.getCoordinatesDoublePrecision(mesh) - tetrahedraIds, tetrahedraConnectivity = self.extractTetConnectivity(mesh) + coords = self.getCoordinatesDoublePrecision( mesh ) + tetrahedraIds, tetrahedraConnectivity = self.extractTetConnectivity( mesh ) ntets = len( tetrahedraIds ) - self.logger.info( f" Mesh {n} info: \n" + - f" Name: {nfilename}\n" + - f" Total cells: {mesh.GetNumberOfCells()}\n" + - f" Tetrahedra: {ntets}\n" + - f" Points: {mesh.GetNumberOfPoints()}" + - f"\n" + "-"*80 + "\n" ) + self.logger.info( f" Mesh {n} info: \n" + f" Name: {nfilename}\n" + + f" Total cells: {mesh.GetNumberOfCells()}\n" + f" Tetrahedra: {ntets}\n" + + f" Points: {mesh.GetNumberOfPoints()}" + "\n" + "-" * 80 + "\n" ) # Analyze both meshes - self.analyzeAllTets(n, coords, tetrahedraConnectivity) + self.analyzeAllTets( n, coords, tetrahedraConnectivity ) metrics = self.analyzedMesh[ n ] - self.analyzedMesh[n]["tet"] = ntets + self.tets[ n ] = ntets # Extract data with consistent filtering - validAspectRatio = np.isfinite(metrics['aspectRatio']) - validRadiusRatio = np.isfinite(metrics['radiusRatio']) + validAspectRatio = np.isfinite( metrics[ 'aspectRatio' ] ) + validRadiusRatio = np.isfinite( metrics[ 'radiusRatio' ] ) # Combined valid mask validMask = validAspectRatio & validRadiusRatio - aspectRatio = metrics['aspectRatio'][validMask] - radiusRatio = metrics['radiusRatio'][validMask] - flatnessRatio = metrics['flatnessRatio'][validMask] - volume = metrics['volumes'][validMask] - shapeQuality = metrics['shapeQuality'][validMask] + aspectRatio = metrics[ 'aspectRatio' ][ validMask ] + radiusRatio = metrics[ 'radiusRatio' ][ validMask ] + flatnessRatio = metrics[ 'flatnessRatio' ][ validMask ] + volume = metrics[ 'volumes' ][ validMask ] + shapeQuality = metrics[ 'shapeQuality' ][ validMask ] # Edge length data - minEdge = metrics['minEdge'][validMask] - maxEdge = metrics['maxEdge'][validMask] + minEdge = metrics[ 'minEdge' ][ validMask ] + maxEdge = metrics[ 'maxEdge' ][ validMask ] # Edge length ratio - edgeRatio = maxEdge / np.maximum(minEdge, 1e-15) + edgeRatio = maxEdge / np.maximum( minEdge, 1e-15 ) # Dihedral angles - minDihedral = metrics['minDihedral'][validMask] - maxDihedral = metrics['maxDihedral'][validMask] - dihedralRange = metrics['dihedral_range'][validMask] + minDihedral = metrics[ 'minDihedral' ][ validMask ] + maxDihedral = metrics[ 'maxDihedral' ][ validMask ] + dihedralRange = metrics[ 'dihedralRange' ][ validMask ] - qualityScore = computeQualityScore(aspectRatio, shapeQuality, edgeRatio, minDihedral) + qualityScore = computeQualityScore( aspectRatio, shapeQuality, edgeRatio, minDihedral ) # Store all values - self.validMetrics[n] = { 'aspectRatio' : aspectRatio, 'radiusRatio': radiusRatio, 'flatnessRatio': flatnessRatio, 'volume': volume, 'shapeQuality': shapeQuality, 'minEdge': minEdge, 'maxEdge': maxEdge, 'edgeRatio': edgeRatio, 'minDihedral': minDihedral, 'maxDihedral': maxDihedral, 'dihedral_range': dihedralRange, 'qualityScore': qualityScore} + self.validMetrics[ n ] = { + 'aspectRatio': aspectRatio, + 'radiusRatio': radiusRatio, + 'flatnessRatio': flatnessRatio, + 'volume': volume, + 'shapeQuality': shapeQuality, + 'minEdge': minEdge, + 'maxEdge': maxEdge, + 'edgeRatio': edgeRatio, + 'minDihedral': minDihedral, + 'maxDihedral': maxDihedral, + 'dihedralRange': dihedralRange, + 'qualityScore': qualityScore + } # # ==================== Print Distribution Statistics ==================== # Problem element counts - highAspectRatio = np.sum(aspectRatio > 100) - lowShapeQuality = np.sum(shapeQuality < 0.3) - lowFlatness = np.sum(flatnessRatio < 0.01) - highEdgeRatio = np.sum(edgeRatio > 10) - criticalMinDihedral = np.sum(minDihedral < 5) - criticalMaxDihedral = np.sum(maxDihedral > 175) - combined = np.sum((aspectRatio > 100) & (shapeQuality < 0.3)) - criticalCombo = np.sum((aspectRatio > 100) & (shapeQuality < 0.3) & (minDihedral < 5)) - - self.issues[n] = { "highAspectRatio": highAspectRatio, "lowShapeQuality": lowShapeQuality, "lowFlatness": lowFlatness, "highEdgeRatio": highEdgeRatio, "criticalMinDihedral": criticalMinDihedral, "criticalMaxDihedral": criticalMaxDihedral, "combined": combined, "criticalCombo": criticalCombo } + highAspectRatio = np.sum( aspectRatio > 100 ) + lowShapeQuality = np.sum( shapeQuality < 0.3 ) + lowFlatness = np.sum( flatnessRatio < 0.01 ) + highEdgeRatio = np.sum( edgeRatio > 10 ) + criticalMinDihedral = np.sum( minDihedral < 5 ) + criticalMaxDihedral = np.sum( maxDihedral > 175 ) + combined = np.sum( ( aspectRatio > 100 ) & ( shapeQuality < 0.3 ) ) + criticalCombo = np.sum( ( aspectRatio > 100 ) & ( shapeQuality < 0.3 ) & ( minDihedral < 5 ) ) + + self.issues[ n ] = { + "highAspectRatio": highAspectRatio, + "lowShapeQuality": lowShapeQuality, + "lowFlatness": lowFlatness, + "highEdgeRatio": highEdgeRatio, + "criticalMinDihedral": criticalMinDihedral, + "criticalMaxDihedral": criticalMaxDihedral, + "combined": combined, + "criticalCombo": criticalCombo + } # Overall quality scores - excellent = np.sum(qualityScore > 80) / len(qualityScore) * 100 - good = np.sum((qualityScore > 60) & (qualityScore <= 80)) / len(qualityScore) * 100 - fair = np.sum((qualityScore > 30) & (qualityScore <= 60)) / len(qualityScore) * 100 - poor = np.sum(qualityScore <= 30) / len(qualityScore) * 100 + excellent = np.sum( qualityScore > 80 ) / len( qualityScore ) * 100 + good = np.sum( ( qualityScore > 60 ) & ( qualityScore <= 80 ) ) / len( qualityScore ) * 100 + fair = np.sum( ( qualityScore > 30 ) & ( qualityScore <= 60 ) ) / len( qualityScore ) * 100 + poor = np.sum( qualityScore <= 30 ) / len( qualityScore ) * 100 - self.qualityScore[n] = { 'excellent': excellent, 'good': good, 'fair': fair, 'poor': poor } + self.qualityScore[ n ] = { 'excellent': excellent, 'good': good, 'fair': fair, 'poor': poor } extremeAspectRatio = aspectRatio > 1e4 self.issues[ n ][ "extremeAspectRatio" ] = extremeAspectRatio # Nearly degenerate elements - degenerateElements = (minDihedral < 0.1) | (maxDihedral > 179.9) + degenerateElements = ( minDihedral < 0.1 ) | ( maxDihedral > 179.9 ) self.issues[ n ][ "degenerate" ] = degenerateElements - self.medians[n] = { - "aspectRatio": np.median( self.validMetrics[n]["aspectRatio"] ), - "shapeQuality": np.median(self.validMetrics[n]["shapeQuality"] ), - "volume": np.median(self.validMetrics[n]["volume"]), - "minEdge": np.median(self.validMetrics[n]["minEdge"]), - "maxEdge": np.median(self.validMetrics[n]["maxEdge"]), - "edgeRatio": np.median(self.validMetrics[n]["edgeRatio"]), - "minDihedral": np.median( self.validMetrics[n]["minDihedral"]), - "maxDihedral": np.median( self.validMetrics[n]["maxDihedral"]), - "qualityScore": np.median( self.validMetrics[n]["qualityScore"]), + self.medians[ n ] = { + "aspectRatio": np.median( self.validMetrics[ n ][ "aspectRatio" ] ), + "shapeQuality": np.median( self.validMetrics[ n ][ "shapeQuality" ] ), + "volume": np.median( self.validMetrics[ n ][ "volume" ] ), + "minEdge": np.median( self.validMetrics[ n ][ "minEdge" ] ), + "maxEdge": np.median( self.validMetrics[ n ][ "maxEdge" ] ), + "edgeRatio": np.median( self.validMetrics[ n ][ "edgeRatio" ] ), + "minDihedral": np.median( self.validMetrics[ n ][ "minDihedral" ] ), + "maxDihedral": np.median( self.validMetrics[ n ][ "maxDihedral" ] ), + "qualityScore": np.median( self.validMetrics[ n ][ "qualityScore" ] ), } # ==================== Report ==================== @@ -175,49 +194,71 @@ def applyFilter( self: Self) -> None: self.printExtremeOutlierAnalysis() - self.printComparisonSummary() + self.printSummary() self.computeDeltasFromBest() self.createComparisonDashboard() + def getCoordinatesDoublePrecision( self, mesh: vtkDataSet ) -> npt.NDArray[ np.float64 ]: + """Get coordinates with double precision. + + Args: + mesh (vtkDataSet): Input mesh. + + Returns: + npt.NDArray[np.float64]: Coordinates - def getCoordinatesDoublePrecision( self, mesh ): + """ points = mesh.GetPoints() npoints = points.GetNumberOfPoints() - coords = np.zeros((npoints, 3), dtype=np.float64) - for i in range(npoints): - point = points.GetPoint(i) - coords[i] = [point[0], point[1], point[2]] + coords = np.zeros( ( npoints, 3 ), dtype=np.float64 ) + for i in range( npoints ): + point = points.GetPoint( i ) + coords[ i ] = [ point[ 0 ], point[ 1 ], point[ 2 ] ] return coords - def extractTetConnectivity( self, mesh ): - """Extract connectivity for all tetrahedra.""" + def extractTetConnectivity( self: Self, + mesh: vtkDataSet ) -> tuple[ npt.NDArray[ np.float64 ], npt.NDArray[ np.float64 ] ]: + """Extract connectivity for all tetrahedra. + + Args: + mesh (vtkDataSet): Mesh to analyze. + + Returns: + tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]: + Cell IDS corresponding to tetrahedra, + Connectivity of these cells + """ ncells = mesh.GetNumberOfCells() tetrahedraIds = [] tetrahedraConnectivity = [] - for cellID in range(ncells): - if mesh.GetCellType(cellID) == vtk.VTK_TETRA: - cell = mesh.GetCell(cellID) + for cellID in range( ncells ): + if mesh.GetCellType( cellID ) == vtk.VTK_TETRA: + cell = mesh.GetCell( cellID ) point_ids = cell.GetPointIds() - conn = [point_ids.GetId(i) for i in range(4)] - tetrahedraIds.append(cellID) - tetrahedraConnectivity.append(conn) - - return np.array(tetrahedraIds), np.array(tetrahedraConnectivity) + conn = [ point_ids.GetId( i ) for i in range( 4 ) ] + tetrahedraIds.append( cellID ) + tetrahedraConnectivity.append( conn ) + return np.array( tetrahedraIds ), np.array( tetrahedraConnectivity ) - def analyzeAllTets(self, n, coords, connectivity) -> dict: - """Vectorized analysis of all tetrahedra.""" - ntets = connectivity.shape[0] + def analyzeAllTets( self, n: int, coords: npt.NDArray[ np.float64 ], + connectivity: npt.NDArray[ np.float64 ] ) -> None: + """Vectorized analysis of all tetrahedra. + Args: + n (int): Mesh id. + coords (npt.NDArray[np.float64]): Tetrahedra coordinates. + connectivity (npt.NDArray[np.float64]): Connectivity. + """ # Get coordinates for all vertices - v0 = coords[connectivity[:, 0]] - v1 = coords[connectivity[:, 1]] - v2 = coords[connectivity[:, 2]] - v3 = coords[connectivity[:, 3]] + v0 = coords[ connectivity[ :, 0 ] ] + v1 = coords[ connectivity[ :, 1 ] ] + v2 = coords[ connectivity[ :, 2 ] ] + v3 = coords[ connectivity[ :, 3 ] ] # Compute edges e01 = v1 - v0 @@ -228,117 +269,121 @@ def analyzeAllTets(self, n, coords, connectivity) -> dict: e23 = v3 - v2 # Edge lengths - l01 = np.linalg.norm(e01, axis=1) - l02 = np.linalg.norm(e02, axis=1) - l03 = np.linalg.norm(e03, axis=1) - l12 = np.linalg.norm(e12, axis=1) - l13 = np.linalg.norm(e13, axis=1) - l23 = np.linalg.norm(e23, axis=1) + l01 = np.linalg.norm( e01, axis=1 ) + l02 = np.linalg.norm( e02, axis=1 ) + l03 = np.linalg.norm( e03, axis=1 ) + l12 = np.linalg.norm( e12, axis=1 ) + l13 = np.linalg.norm( e13, axis=1 ) + l23 = np.linalg.norm( e23, axis=1 ) - allEdges = np.stack([l01, l02, l03, l12, l13, l23], axis=1) - minEdge = np.min(allEdges, axis=1) - maxEdge = np.max(allEdges, axis=1) + allEdges = np.stack( [ l01, l02, l03, l12, l13, l23 ], axis=1 ) + minEdge = np.min( allEdges, axis=1 ) + maxEdge = np.max( allEdges, axis=1 ) # Volumes - cross_product = np.cross(e01, e02) - volumes = np.abs(np.sum(cross_product * e03, axis=1) / 6.0) + cross_product = np.cross( e01, e02 ) + volumes = np.abs( np.sum( cross_product * e03, axis=1 ) / 6.0 ) # Face areas - face012 = 0.5 * np.linalg.norm(np.cross(e01, e02), axis=1) - face013 = 0.5 * np.linalg.norm(np.cross(e01, e03), axis=1) - face023 = 0.5 * np.linalg.norm(np.cross(e02, e03), axis=1) - face123 = 0.5 * np.linalg.norm(np.cross(e12, e13), axis=1) + face012 = 0.5 * np.linalg.norm( np.cross( e01, e02 ), axis=1 ) + face013 = 0.5 * np.linalg.norm( np.cross( e01, e03 ), axis=1 ) + face023 = 0.5 * np.linalg.norm( np.cross( e02, e03 ), axis=1 ) + face123 = 0.5 * np.linalg.norm( np.cross( e12, e13 ), axis=1 ) - allFaces = np.stack([face012, face013, face023, face123], axis=1) - maxFaceArea = np.max(allFaces, axis=1) - totalSurfaceArea = np.sum(allFaces, axis=1) + allFaces = np.stack( [ face012, face013, face023, face123 ], axis=1 ) + maxFaceArea = np.max( allFaces, axis=1 ) + totalSurfaceArea = np.sum( allFaces, axis=1 ) # Aspect ratio - minAltitude = 3.0 * volumes / np.maximum(maxFaceArea, 1e-15) - aspectRatio = maxEdge / np.maximum(minAltitude, 1e-15) - aspectRatio[volumes < 1e-15] = np.inf + minAltitude = 3.0 * volumes / np.maximum( maxFaceArea, 1e-15 ) + aspectRatio = maxEdge / np.maximum( minAltitude, 1e-15 ) + aspectRatio[ volumes < 1e-15 ] = np.inf # Radius ratio - inradius = 3.0 * volumes / np.maximum(totalSurfaceArea, 1e-15) - circumradius = (maxEdge ** 3) / np.maximum(24.0 * volumes, 1e-15) - radius_ratio = circumradius / np.maximum(inradius, 1e-15) + inradius = 3.0 * volumes / np.maximum( totalSurfaceArea, 1e-15 ) + circumradius = ( maxEdge**3 ) / np.maximum( 24.0 * volumes, 1e-15 ) + radiusRatio = circumradius / np.maximum( inradius, 1e-15 ) # Flatness ratio - flatness_ratio = volumes / np.maximum(maxFaceArea * minEdge, 1e-15) + flatnessRatio = volumes / np.maximum( maxFaceArea * minEdge, 1e-15 ) # Shape quality - sum_edge_sq = np.sum(allEdges ** 2, axis=1) - shape_quality = 12.0 * (3.0 * volumes) ** (2.0/3.0) / np.maximum(sum_edge_sq, 1e-15) - shape_quality[volumes < 1e-15] = 0.0 + sumEdgeShapeQuality = np.sum( allEdges**2, axis=1 ) + shapeQuality = 12.0 * ( 3.0 * volumes )**( 2.0 / 3.0 ) / np.maximum( sumEdgeShapeQuality, 1e-15 ) + shapeQuality[ volumes < 1e-15 ] = 0.0 # Dihedral angles - def compute_dihedral_angle(normal1, normal2): - """Compute dihedral angle between two face normals.""" - n1_norm = normal1 / np.maximum(np.linalg.norm(normal1, axis=1, keepdims=True), 1e-15) - n2_norm = normal2 / np.maximum(np.linalg.norm(normal2, axis=1, keepdims=True), 1e-15) - cos_angle = np.sum(n1_norm * n2_norm, axis=1) - cos_angle = np.clip(cos_angle, -1.0, 1.0) - angle = np.arccos(cos_angle) * 180.0 / np.pi + def computeDihedralAngle( normal1: npt.NDArray[ np.float64 ], + normal2: npt.NDArray[ np.float64 ] ) -> npt.NDArray[ np.float64 ]: + """Compute dihedral angle between two face normals. + + Args: + normal1 (npt.NDArray[np.float64]): Normal vector 1 + normal2 (npt.NDArray[np.float64]): Normal vector 2 + + Returns: + npt.NDArray[ np.float64 ]: Dihedral angle + + """ + n1_norm = normal1 / np.maximum( np.linalg.norm( normal1, axis=1, keepdims=True ), 1e-15 ) + n2_norm = normal2 / np.maximum( np.linalg.norm( normal2, axis=1, keepdims=True ), 1e-15 ) + cos_angle = np.sum( n1_norm * n2_norm, axis=1 ) + cos_angle = np.clip( cos_angle, -1.0, 1.0 ) + angle = np.arccos( cos_angle ) * 180.0 / np.pi return angle # Face normals - normal_012 = np.cross(e01, e02) - normal_013 = np.cross(e01, e03) - normal_023 = np.cross(e02, e03) - normal_123 = np.cross(e12, e13) + normal012: npt.NDArray[ np.float64 ] = np.cross( e01, e02 ) + normal013: npt.NDArray[ np.float64 ] = np.cross( e01, e03 ) + normal023: npt.NDArray[ np.float64 ] = np.cross( e02, e03 ) + normal123: npt.NDArray[ np.float64 ] = np.cross( e12, e13 ) # Dihedral angles for each edge - dihedral_01 = compute_dihedral_angle(normal_012, normal_013) - dihedral_02 = compute_dihedral_angle(normal_012, normal_023) - dihedral_03 = compute_dihedral_angle(normal_013, normal_023) - dihedral_12 = compute_dihedral_angle(normal_012, normal_123) - dihedral_13 = compute_dihedral_angle(normal_013, normal_123) - dihedral_23 = compute_dihedral_angle(normal_023, normal_123) + dihedral01 = computeDihedralAngle( normal012, normal013 ) + dihedral02 = computeDihedralAngle( normal012, normal023 ) + dihedral03 = computeDihedralAngle( normal013, normal023 ) + dihedral12 = computeDihedralAngle( normal012, normal123 ) + dihedral13 = computeDihedralAngle( normal013, normal123 ) + dihedral23 = computeDihedralAngle( normal023, normal123 ) - all_dihedrals = np.stack([dihedral_01, dihedral_02, dihedral_03, - dihedral_12, dihedral_13, dihedral_23], axis=1) + allDihedrals = np.stack( [ dihedral01, dihedral02, dihedral03, dihedral12, dihedral13, dihedral23 ], axis=1 ) - min_dihedral = np.min(all_dihedrals, axis=1) - max_dihedral = np.max(all_dihedrals, axis=1) - dihedral_range = max_dihedral - min_dihedral + minDihedral = np.min( allDihedrals, axis=1 ) + maxDihedral = np.max( allDihedrals, axis=1 ) + dihedralRange = maxDihedral - minDihedral self.analyzedMesh[ n ] = { 'volumes': volumes, 'aspectRatio': aspectRatio, - 'radiusRatio': radius_ratio, - 'flatnessRatio': flatness_ratio, - 'shapeQuality': shape_quality, + 'radiusRatio': radiusRatio, + 'flatnessRatio': flatnessRatio, + 'shapeQuality': shapeQuality, 'minEdge': minEdge, 'maxEdge': maxEdge, - 'minDihedral': min_dihedral, - 'maxDihedral': max_dihedral, - 'dihedral_range': dihedral_range + 'minDihedral': minDihedral, + 'maxDihedral': maxDihedral, + 'dihedralRange': dihedralRange } - return { - 'volumes': volumes, - 'aspectRatio': aspectRatio, - 'radiusRatio': radius_ratio, - 'flatnessRatio': flatness_ratio, - 'shapeQuality': shape_quality, - 'minEdge': minEdge, - 'maxEdge': maxEdge, - 'minDihedral': min_dihedral, - 'maxDihedral': max_dihedral, - 'dihedral_range': dihedral_range - } - - def printDistributionStatistics( self: Self, fmt='.2e'): + def printDistributionStatistics( self: Self ) -> None: + """Print the distribution statistics for various metrics.""" self.__loggerSection( "DISTRIBUTION STATISTICS (MIN / MEDIAN / MAX)" ) - def printMetricStats( metricName, dataName, fmt='.2e'): - """Helper function to print min/median/max for a metric.""" + def printMetricStats( metricName: str, name: str, fmt: str = '.2e' ) -> None: + """Helper function to print min/median/max for a metric. + + Args: + metricName (str): The metric name to display. + name (str): Metric name in dict of values. + fmt (str): Display format. + """ msg = f"{metricName}:\n" + for n, _ in enumerate( self.meshes.items(), 1 ): - data = self.validMetrics[ n ][ dataName ] + data = self.validMetrics[ n ][ name ] msg += f" Mesh{n:2}: Min={data.min():<10{fmt}}Median={np.median(data):<10{fmt}}Max={data.max():<10{fmt}}\n" - self.logger.info( msg ) + self.logger.info( msg ) printMetricStats( "Aspect Ratio", 'aspectRatio' ) printMetricStats( "Radius Ratio", 'radiusRatio' ) @@ -350,25 +395,32 @@ def printMetricStats( metricName, dataName, fmt='.2e'): printMetricStats( "Edge Length Ratio", 'edgeRatio', fmt='.2f' ) printMetricStats( "Min Dihedral Angle (degrees)", 'minDihedral', fmt='.2f' ) printMetricStats( "Max Dihedral Angle (degrees)", 'maxDihedral', fmt='.2f' ) - printMetricStats( "Dihedral Range (degrees)", 'dihedral_range',fmt='.2f' ) + printMetricStats( "Dihedral Range (degrees)", 'dihedralRange', fmt='.2f' ) printMetricStats( "Overall Quality Score (0-100)", 'qualityScore', fmt='.2f' ) + def printPercentileAnalysis( self: Self, fmt: str = '.2f' ) -> None: + """Print percentile analysis. - def printPercentileAnalysis( self: Self, fmt='.2f' ): + Args: + fmt (str): Display formatting. + """ self.__loggerSection( "PERCENTILE ANALYSIS (25th / 75th / 90th / 99th)" ) - for metricName, name in zip(*[ - ( "Aspect Ratio", "Shape Quality", "Edge Length Ratio", "Min Dihedral Angle (degrees)", "Overall Quality Score" ), - ( 'aspectRatio', 'shapeQuality', 'edgeRatio', 'minDihedral', 'qualityScore' ) ]): + for metricName, name in zip( *[ ( + "Aspect Ratio", "Shape Quality", "Edge Length Ratio", "Min Dihedral Angle (degrees)", + "Overall Quality Score" ), ( 'aspectRatio', 'shapeQuality', 'edgeRatio', 'minDihedral', + 'qualityScore' ) ] ): msg = f"{metricName}:\n" + for n, _ in enumerate( self.meshes.items(), 1 ): data = self.validMetrics[ n ][ name ] - p1 = np.percentile(data, [25, 75, 90, 99]) + p1 = np.percentile( data, [ 25, 75, 90, 99 ] ) msg += f" Mesh {n}: 25th = {p1[0]:<7,{fmt}}75th = {p1[1]:<7,{fmt}}90th = {p1[2]:<7,{fmt}}99th = {p1[3]:<7,{fmt}}\n" - self.logger.info( msg ) + self.logger.info( msg ) def printQualityIssueSummary( self: Self ) -> None: + """Print the quality issues.""" self.__loggerSection( "QUALITY ISSUE SUMMARY" ) fmt = '.2f' @@ -376,48 +428,54 @@ def printQualityIssueSummary( self: Self ) -> None: msg = f"Mesh {n} Issues:\n" w = False - for issueType, name, reference in zip(*[ - ( "Aspect Ratio > 100", "Shape Quality < 0.3", "Flatness < 0.01", "Edge Ratio > 10", "Min Dihedral < 5°", "Max Dihedral > 175°", "Combined (AR>100 & Q<0.3)", "CRITICAL (AR>100 & Q<0.3 & MinDih<5°"), - ( "highAspectRatio", "lowShapeQuality", "lowFlatness", "highEdgeRatio", "criticalMinDihedral", "criticalMaxDihedral", "combined", "criticalCombo" ), - ( 'aspectRatio', 'shapeQuality', "flatnessRatio", "edgeRatio", "minDihedral", 'maxDihedral', 'aspectRatio', 'aspectRatio' ) - ]): + for issueType, name, reference in zip( *[ ( "Aspect Ratio > 100", "Shape Quality < 0.3", "Flatness < 0.01", + "Edge Ratio > 10", "Min Dihedral < 5°", "Max Dihedral > 175°", + "Combined (AR>100 & Q<0.3)", + "CRITICAL (AR>100 & Q<0.3 & MinDih<5°" ), + ( "highAspectRatio", "lowShapeQuality", "lowFlatness", + "highEdgeRatio", "criticalMinDihedral", "criticalMaxDihedral", + "combined", "criticalCombo" ), + ( 'aspectRatio', 'shapeQuality', "flatnessRatio", "edgeRatio", + "minDihedral", 'maxDihedral', 'aspectRatio', + 'aspectRatio' ) ] ): pb = self.issues[ n ][ name ] m = len( self.validMetrics[ n ][ reference ] ) - percent = pb/m*100 + pb / m * 100 msg += f" {f'{issueType}:':37}{pb:>8,} ({(pb/m*100):{fmt}}%)\n" if pb != 0: w = True self.logger.warning( msg ) if w else self.logger.info( msg ) - self.compareIssuesFromBest() self.printOverallQualityScore() - - def printOverallQualityScore( self: Self ): - msg = f"Overall Quality Score Distribution:\n" - msg += f" {f'Mesh n':10}{f'Excellent (>80)':20}{f'Good (60-80)':17}{f'Fair (30-60)':15}{f'Poor (≤30)':15}\n" + def printOverallQualityScore( self: Self ) -> None: + """Print the quality score distribution from excellent to poor.""" + msg = "Overall Quality Score Distribution:\n" + msg += f" {'Mesh n':10}{'Excellent (>80)':20}{'Good (60-80)':17}{'Fair (30-60)':15}{'Poor (≤30)':15}\n" for n, _ in enumerate( self.meshes.items(), 1 ): qualityScore = self.qualityScore[ n ] msg += f" {f'Mesh {n}':10}{qualityScore[ 'excellent' ]:10,.1f}%{qualityScore[ 'good' ]:15,.1f}% {qualityScore[ 'fair' ]:15,.1f}%{qualityScore[ 'poor' ]:15,.1f}%\n" self.logger.info( msg ) - def printExtremeOutlierAnalysis( self: Self ): + def printExtremeOutlierAnalysis( self: Self ) -> None: + """Print the extreme outlier analysis results.""" self.__loggerSection( "EXTREME OUTLIER ANALYSIS" ) - # self.logger.warning( f"\n🚨 Elements with Aspect Ratio > 10,000:" ) - msg = f"Elements with Aspect Ratio > 10,000:\n" + msg = "Elements with Aspect Ratio > 10,000:\n" msg2 = "" + + # Change log type to warning if problematic elements w = False w2 = False for n, _ in enumerate( self.meshes, 1 ): - extremeAspectRatio = self.issues[n][ 'extremeAspectRatio' ] - data = self.analyzedMesh[n] + extremeAspectRatio = self.issues[ n ][ 'extremeAspectRatio' ] + data = self.analyzedMesh[ n ] aspectRatio = data[ 'aspectRatio' ] - if np.sum(extremeAspectRatio) > 0: + if np.sum( extremeAspectRatio ) > 0: msg += f" Mesh {n}: {np.sum(extremeAspectRatio):,} elements ({np.sum(extremeAspectRatio)/len(aspectRatio)*100:.3f}%)" w = True volume = data[ "volume" ] @@ -429,27 +487,28 @@ def printExtremeOutlierAnalysis( self: Self ): msg += f" Min dihedral: {minDihedral[extremeAspectRatio].min():.2f}° - {minDihedral[extremeAspectRatio].mean():.2f}° (avg)\n" msg += f" Shape quality: {shapeQuality[extremeAspectRatio].min():.4f} - {shapeQuality[extremeAspectRatio].mean():.4f} (avg)\n" - if np.sum(extremeAspectRatio) > 10: + if np.sum( extremeAspectRatio ) > 10: w2 = True msg2 += f" Recommendation: Investigate/remove {np.sum(extremeAspectRatio):,} extreme elements in Mesh {n}\n These are likely artifacts from mesh generation or geometry issues.\n" self.logger.warning( msg ) if w else self.logger.info( msg + " N/A\n" ) - if w2: self.logger.warning( msg2 ) + if w2: + self.logger.warning( msg2 ) # Nearly degenerate elements - degMsg = f"Nearly Degenerate Elements (dihedral < 0.1° or > 179.9°):\n" + degMsg = "Nearly Degenerate Elements (dihedral < 0.1° or > 179.9°):\n" ww = False for n, _ in enumerate( self.meshes, 1 ): degenerate = self.issues[ n ][ "degenerate" ] data = self.validMetrics[ n ][ "minDihedral" ] - if np.sum(degenerate) > 0: + if np.sum( degenerate ) > 0: ww = True degMsg += f" Mesh {n}: {np.sum(degenerate):,} elements ({np.sum(degenerate)/len(data)*100:.3f}%)\n" self.logger.warning( degMsg ) if ww else self.logger.info( degMsg + " N/A\n" ) - - def printComparisonSummary( self: Self): + def printSummary( self: Self ) -> None: + """Print the summary.""" self.__loggerSection( "COMPARISON SUMMARY" ) for n, _ in enumerate( self.meshes, 1 ): @@ -460,8 +519,7 @@ def printComparisonSummary( self: Self): name += " [LEAST GOOD]" msg = f"{name}\n" - - msg += f" Tetrahedra: {self.analyzedMesh[n]['tet']:,}\n" + msg += f" Tetrahedra: {self.tets[n]:,}\n" msg += f" Median Aspect Ratio: {self.medians[n]['aspectRatio']:.2f}\n" msg += f" Median Shape Quality: {self.medians[n]['shapeQuality']:.4f}\n" msg += f" Median Volume: {self.medians[n]['volume']:.2e}\n" @@ -472,74 +530,72 @@ def printComparisonSummary( self: Self): msg += f" Median Max Dihedral: {self.medians[n]['maxDihedral']:.1f}°\n" msg += f" Median Quality Score: {self.medians[n]['qualityScore']:.1f}/100\n" + self.logger.info( msg ) - - def computeDeltasFromBest( self: Self ): - self.logger.info( f"Best mesh : Mesh {self.best}") - self.deltas = {} - - ntetsBest = self.analyzedMesh[self.best][ 'tet'] - aspectRatioBest = self.medians[self.best]["aspectRatio"] - shapeQualityBest = self.medians[self.best]["shapeQuality"] - volumeBest = self.medians[self.best]["volume"] - minEdgeBest = self.medians[self.best]["minEdge"] - maxEdgeBest = self.medians[self.best]["maxEdge"] - edgeRatioBest = self.medians[self.best]["edgeRatio"] + def computeDeltasFromBest( self: Self ) -> None: + """Compute and print the.""" + self.logger.info( f"Best mesh: Mesh {self.best}" ) + self.deltas: dict[ int, Any ] = {} for n, _ in enumerate( self.meshes, 1 ): - self.deltas[n] = {} - ntets = self.analyzedMesh[n][ 'tet'] - - aspectRatio = np.median( self.validMetrics[n]["aspectRatio"] ) - shapeQuality = np.median(self.validMetrics[n]["shapeQuality"]) - volume = np.median(self.validMetrics[n]["volume"]) - minEdge = np.median(self.validMetrics[n]["minEdge"]) - maxEdge = np.median(self.validMetrics[n]["maxEdge"]) - edgeRatio = np.median(self.validMetrics[n]["edgeRatio"]) + self.deltas[ n ] = {} - self.deltas[n]["tetrahedra"] = ( ( self.analyzedMesh[n]["tet"] - self.analyzedMesh[self.best]["tet"]) / self.analyzedMesh[self.best]["tet"] * 100 ) if self.analyzedMesh[self.best]["tet"] > 0 else 0 + self.deltas[ n ][ "tetrahedra" ] = ( ( self.tets[ n ] - self.tets[ self.best ] ) / self.tets[ self.best ] * + 100 ) if self.tets[ self.best ] > 0 else 0 for metric in ( "aspectRatio", "shapeQuality", "volume", "minEdge", "maxEdge", "edgeRatio" ): - value = self.medians[n][metric] - valueBest = self.medians[self.best][metric] - self.deltas[n][metric] = ( ( value - valueBest) / valueBest * 100 ) if valueBest > 0 else 0 - - - dtets = [ f"{self.deltas[ n ][ 'tetrahedra' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - dar = [ f"{self.deltas[ n ][ 'aspectRatio' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - dsq = [ f"{self.deltas[ n ][ 'shapeQuality' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - dvol = [ f"{self.deltas[ n ][ 'volume' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - d_min_edge = [ f"{self.deltas[ n ][ 'minEdge' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - d_maxEdge = [ f"{self.deltas[ n ][ 'maxEdge' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - d_edge_ratio = [ f"{self.deltas[ n ][ 'edgeRatio' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] + value = self.medians[ n ][ metric ] + valueBest = self.medians[ self.best ][ metric ] + self.deltas[ n ][ metric ] = ( ( value - valueBest ) / valueBest * 100 ) if valueBest > 0 else 0 + + dtets = [ + f"{self.deltas[ n ][ 'tetrahedra' ]:>+12,.1f}%" if n != self.best else "" + for n, _ in enumerate( self.meshes, 1 ) + ] + dar = [ + f"{self.deltas[ n ][ 'aspectRatio' ]:>+12,.1f}%" if n != self.best else "" + for n, _ in enumerate( self.meshes, 1 ) + ] + dsq = [ + f"{self.deltas[ n ][ 'shapeQuality' ]:>+12,.1f}%" if n != self.best else "" + for n, _ in enumerate( self.meshes, 1 ) + ] + dvol = [ + f"{self.deltas[ n ][ 'volume' ]:>+12,.1f}%" if n != self.best else "" + for n, _ in enumerate( self.meshes, 1 ) + ] + d_min_edge = [ + f"{self.deltas[ n ][ 'minEdge' ]:>+12,.1f}%" if n != self.best else "" + for n, _ in enumerate( self.meshes, 1 ) + ] + d_maxEdge = [ + f"{self.deltas[ n ][ 'maxEdge' ]:>+12,.1f}%" if n != self.best else "" + for n, _ in enumerate( self.meshes, 1 ) + ] + d_edge_ratio = [ + f"{self.deltas[ n ][ 'edgeRatio' ]:>+12,.1f}%" if n != self.best else "" + for n, _ in enumerate( self.meshes, 1 ) + ] names = [ f"{f'Mesh {n}':>13}" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - self.logger.info(f"Changes vs BEST [Mesh {self.best}]:\n" - f"{' Mesh:':<20}{('').join( names )}\n" + - f"{' Tetrahedra:':<20}{('').join(dtets)}\n" + - f"{' Aspect Ratio:':<20}{('').join( dar)}\n" + - f"{' Shape Quality:':<20}{('').join( dsq)}\n" + - f"{' Volume:':<20}{('').join( dvol)}\n" + - f"{' Min Edge Length:':<20}{('').join( d_min_edge)}\n" + - f"{' Max Edge Length:':<20}{('').join( d_maxEdge)}\n" + - f"{' Edge Length Ratio:':<20}{('').join( d_edge_ratio)}\n" - ) + self.logger.info( f"Changes vs BEST [Mesh {self.best}]:\n" + f"{' Mesh:':<20}{('').join( names )}\n" + + f"{' Tetrahedra:':<20}{('').join(dtets)}\n" + + f"{' Aspect Ratio:':<20}{('').join( dar)}\n" + + f"{' Shape Quality:':<20}{('').join( dsq)}\n" + f"{' Volume:':<20}{('').join( dvol)}\n" + + f"{' Min Edge Length:':<20}{('').join( d_min_edge)}\n" + + f"{' Max Edge Length:':<20}{('').join( d_maxEdge)}\n" + + f"{' Edge Length Ratio:':<20}{('').join( d_edge_ratio)}\n" ) - - def createComparisonDashboard( self: Self ): + def createComparisonDashboard( self: Self ) -> None: + """Create the comparison dashboard.""" lbl = [ f'Mesh {n}' for n, _ in enumerate( self.meshes, 1 ) ] # Determine smart plot limits ar_99 = [] for n, _ in enumerate( self.meshes, 1 ): - ar_99.append( np.percentile( self.validMetrics[n]["aspectRatio"], 99 ) ) + ar_99.append( np.percentile( self.validMetrics[ n ][ "aspectRatio" ], 99 ) ) ar_99_max = np.max( np.array( ar_99 ) ) - # ar_99_1 = np.percentile(aspectRatio, 99) - # # ar_99_2 = np.percentile(ar2, 99) - # # ar_99_max = max(ar_99_1, ar_99_2) - # ar_99_max = ar_99_1 - if ar_99_max < 10: ar_plot_limit = 100 elif ar_99_max < 100: @@ -547,628 +603,745 @@ def createComparisonDashboard( self: Self ): else: ar_plot_limit = 10000 - # print(f"Using AR plot limit: {ar_plot_limit} (99th percentiles: M1={ar_99_1:.1f}, M2={ar_99_2:.1f})") - # print(f"Using AR plot limit: {ar_plot_limit} (99th percentiles: M1={ar_99_1:.1f}") - # Set style - plt.rcParams['figure.facecolor'] = 'white' - plt.rcParams.update({ + plt.rcParams[ 'figure.facecolor' ] = 'white' + plt.rcParams.update( { 'font.size': 9, 'axes.titlesize': 10, 'axes.labelsize': 9, 'xtick.labelsize': 8, 'ytick.labelsize': 8, 'legend.fontsize': 8 - }) + } ) # Create figure with flexible layout - fig = plt.figure(figsize=(25, 20)) + fig = plt.figure( figsize=( 25, 20 ) ) # Row 1: Executive Summary (3 columns - wider) - gs_row1 = gridspec.GridSpec(1, 3, figure=fig, left=0.05, right=0.95, - top=0.96, bottom=0.84, wspace=0.20) + gs_row1 = gridspec.GridSpec( 1, 3, figure=fig, left=0.05, right=0.95, top=0.96, bottom=0.84, wspace=0.20 ) # Rows 2-5: Main dashboard (5 columns each) - gs_main = gridspec.GridSpec(4, 5, figure=fig, left=0.05, right=0.95, - top=0.82, bottom=0.05, hspace=0.35, wspace=0.30) + gs_main = gridspec.GridSpec( 4, + 5, + figure=fig, + left=0.05, + right=0.95, + top=0.82, + bottom=0.05, + hspace=0.35, + wspace=0.30 ) # Title suptitle = 'Mesh Quality Comparison Dashboard (Progressive Detail Layout)\n' for n, _ in enumerate( self.meshes, 1 ): # suptitle += f'Mesh {n}: {ntets:,} tets\t' - suptitle += f'Mesh {n}: {self.analyzedMesh[n]["tet"]} tets - ' - fig.suptitle( suptitle, - fontsize=16, fontweight='bold', y=0.99) + suptitle += f'Mesh {n}: {self.tets[n]:<15} tets' + fig.suptitle( suptitle, fontsize=16, fontweight='bold', y=0.99 ) # Color scheme - color = matplotlib.pyplot.cm.tab10( np.arange(20)) + color = plt.cm.tab10( np.arange( 20 ) ) # type: ignore[attr-defined] # ==================== ROW 1: EXECUTIVE SUMMARY ==================== # 1. Overall Quality Score Distribution - ax1 = fig.add_subplot(gs_row1[0, 0]) - bins = np.linspace(0, 100, 40) + ax1 = fig.add_subplot( gs_row1[ 0, 0 ] ) + bins = np.linspace( 0, 100, 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): - qualityScore = self.validMetrics[n][ 'qualityScore' ] - ax1.hist(qualityScore, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n-1], edgecolor='black', linewidth=0.5) - ax1.axvline(np.median(qualityScore), color=color[n-1], linestyle='--', linewidth=2.5, alpha=0.9) + qualityScore = self.validMetrics[ n ][ 'qualityScore' ] + ax1.hist( qualityScore, + bins=bins, + alpha=0.6, + label=f'Mesh {n}', + color=color[ n - 1 ], + edgecolor='black', + linewidth=0.5 ) + ax1.axvline( np.median( qualityScore ), color=color[ n - 1 ], linestyle='--', linewidth=2.5, alpha=0.9 ) # Add quality zones - ax1.axvspan(0, 30, alpha=0.15, color='red', zorder=0) - ax1.axvspan(30, 60, alpha=0.15, color='yellow', zorder=0) - ax1.axvspan(60, 80, alpha=0.15, color='lightgreen', zorder=0) - ax1.axvspan(80, 100, alpha=0.15, color='darkgreen', zorder=0) + ax1.axvspan( 0, 30, alpha=0.15, color='red', zorder=0 ) + ax1.axvspan( 30, 60, alpha=0.15, color='yellow', zorder=0 ) + ax1.axvspan( 60, 80, alpha=0.15, color='lightgreen', zorder=0 ) + ax1.axvspan( 80, 100, alpha=0.15, color='darkgreen', zorder=0 ) # Add summary text #### ONLY BEST AND WORST MESH? - ax1.text(0.98, 0.98, - f'Median Score:\n{f"M{self.best}[+]:":<5}{np.median(self.validMetrics[self.best][ "qualityScore" ]):.1f}\n'+ - f'{f"M{self.worst}[-]:":<5}{np.median(self.validMetrics[self.worst][ "qualityScore" ]):.1f}\n\n' + - f'Excellent (>80):\n{f"M{self.best}[+]:":<5}{self.qualityScore[self.best]["excellent"]:.1f}%\n'+ - f'{f"M{self.worst}[-]:":<5}{self.qualityScore[self.worst]["excellent"]:.1f}%', - transform=ax1.transAxes, va='top', ha='right', - bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.9), - fontsize=9, fontweight='bold') - - ax1.set_xlabel('Combined Quality Score', fontweight='bold') - ax1.set_ylabel('Count', fontweight='bold') - ax1.set_title('OVERALL MESH QUALITY VERDICT', fontsize=12, fontweight='bold', - color='darkblue', pad=10) - ax1.legend(loc='upper left', fontsize=9) - ax1.grid(True, alpha=0.3) + ax1.text( + 0.98, + 0.98, + f'Median Score:\n{f"M{self.best}[+]:":<5}{np.median(self.validMetrics[self.best][ "qualityScore" ]):.1f}\n' + + f'{f"M{self.worst}[-]:":<5}{np.median(self.validMetrics[self.worst][ "qualityScore" ]):.1f}\n\n' + + f'Excellent (>80):\n{f"M{self.best}[+]:":<5}{self.qualityScore[self.best]["excellent"]:.1f}%\n' + + f'{f"M{self.worst}[-]:":<5}{self.qualityScore[self.worst]["excellent"]:.1f}%', + transform=ax1.transAxes, + va='top', + ha='right', + bbox={ + "boxstyle": 'round', + "facecolor": 'wheat', + "alpha": 0.9 + }, + fontsize=9, + fontweight='bold' ) + + ax1.set_xlabel( 'Combined Quality Score', fontweight='bold' ) + ax1.set_ylabel( 'Count', fontweight='bold' ) + ax1.set_title( 'OVERALL MESH QUALITY VERDICT', fontsize=12, fontweight='bold', color='darkblue', pad=10 ) + ax1.legend( loc='upper left', fontsize=9 ) + ax1.grid( True, alpha=0.3 ) # Add zone labels - ax1.text(15, ax1.get_ylim()[1]*0.95, 'Poor', ha='center', fontsize=8, color='darkred') - ax1.text(45, ax1.get_ylim()[1]*0.95, 'Fair', ha='center', fontsize=8, color='orange') - ax1.text(70, ax1.get_ylim()[1]*0.95, 'Good', ha='center', fontsize=8, color='green') - ax1.text(90, ax1.get_ylim()[1]*0.95, 'Excellent', ha='center', fontsize=8, color='darkgreen') + ax1.text( 15, ax1.get_ylim()[ 1 ] * 0.95, 'Poor', ha='center', fontsize=8, color='darkred' ) + ax1.text( 45, ax1.get_ylim()[ 1 ] * 0.95, 'Fair', ha='center', fontsize=8, color='orange' ) + ax1.text( 70, ax1.get_ylim()[ 1 ] * 0.95, 'Good', ha='center', fontsize=8, color='green' ) + ax1.text( 90, ax1.get_ylim()[ 1 ] * 0.95, 'Excellent', ha='center', fontsize=8, color='darkgreen' ) # 2. Shape Quality vs Aspect Ratio - ax2 = fig.add_subplot(gs_row1[0, 1]) + ax2 = fig.add_subplot( gs_row1[ 0, 1 ] ) # Create sample for plotting for n, _ in enumerate( self.meshes, 1 ): - aspectRatio = self.validMetrics[n]["aspectRatio"] - shapeQuality = self.validMetrics[n]["shapeQuality"] + aspectRatio = self.validMetrics[ n ][ "aspectRatio" ] + shapeQuality = self.validMetrics[ n ][ "shapeQuality" ] self.setSampleForPlot( aspectRatio, n ) - idx = self.sample[n] + idx = self.sample[ n ] - mask1_plot = aspectRatio[idx] < ar_plot_limit + mask1_plot = aspectRatio[ idx ] < ar_plot_limit - ax2.scatter(aspectRatio[idx][mask1_plot], shapeQuality[idx][mask1_plot], alpha=0.4, s=5, - color=color[n-1], label=f'Mesh {n}', edgecolors='none') + ax2.scatter( aspectRatio[ idx ][ mask1_plot ], + shapeQuality[ idx ][ mask1_plot ], + alpha=0.4, + s=5, + color=color[ n - 1 ], + label=f'Mesh {n}', + edgecolors='none' ) # Add quality threshold lines - ax2.axhline(y=0.3, color='red', linestyle='--', linewidth=2, - alpha=0.8, label='Poor (Q < 0.3)', zorder=5) - ax2.axhline(y=0.7, color='green', linestyle='--', linewidth=2, - alpha=0.8, label='Good (Q > 0.7)', zorder=5) - ax2.axvline(x=100, color='orange', linestyle='--', linewidth=2, - alpha=0.8, label='High AR (> 100)', zorder=5) + ax2.axhline( y=0.3, color='red', linestyle='--', linewidth=2, alpha=0.8, label='Poor (Q < 0.3)', zorder=5 ) + ax2.axhline( y=0.7, color='green', linestyle='--', linewidth=2, alpha=0.8, label='Good (Q > 0.7)', zorder=5 ) + ax2.axvline( x=100, color='orange', linestyle='--', linewidth=2, alpha=0.8, label='High AR (> 100)', zorder=5 ) # Highlight problem zone - problem_zone = Rectangle((100, 0), ar_plot_limit-100, 0.3, - alpha=0.2, facecolor='red', edgecolor='none', zorder=0) - ax2.add_patch(problem_zone) + problem_zone = Rectangle( ( 100, 0 ), + ar_plot_limit - 100, + 0.3, + alpha=0.2, + facecolor='red', + edgecolor='none', + zorder=0 ) + ax2.add_patch( problem_zone ) # Count ALL elements - problem1_all = np.sum((aspectRatio > 100) & (shapeQuality < 0.3)) - extreme1 = np.sum(aspectRatio > ar_plot_limit) + np.sum( ( aspectRatio > 100 ) & ( shapeQuality < 0.3 ) ) + np.sum( aspectRatio > ar_plot_limit ) # Problem annotation - #TODO - annotateIssues = ('\n').join([ f"{f'M{n}':4}{np.sum(self.issues[n]['combined']):,}" for n, _ in enumerate( self.meshes, 1 )]) - - ax2.text(0.98, 0.02, - f'PROBLEM ELEMENTS\n(AR>100 & Q<0.3):\n\n' + - annotateIssues, - transform=ax2.transAxes, va='bottom', ha='right', - bbox=dict(boxstyle='round', facecolor='#ffcccc', alpha=0.95, - edgecolor='darkred', linewidth=2), - fontsize=8, fontweight='bold') - - # Outlier warning - # if extreme1 + extreme2 > 100: - # ax2.text(0.02, 0.98, - # f'⚠️ AR > {ar_plot_limit}\n(not shown):\n' + - # f'M1: {extreme1:,}\nM2: {extreme2:,}', - # transform=ax2.transAxes, va='top', ha='left', - # bbox=dict(boxstyle='round', facecolor='#ffe6cc', alpha=0.9), - # fontsize=7) - - ax2.set_xscale('log') - ax2.set_xlabel('Aspect Ratio', fontweight='bold') - ax2.set_ylabel('Shape Quality', fontweight='bold') - ax2.set_title('KEY QUALITY INDICATOR: Shape Quality vs Aspect Ratio', - fontsize=12, fontweight='bold', color='darkred', pad=10) - ax2.set_xlim([1, ar_plot_limit]) - ax2.set_ylim([0, 1.05]) - ax2.legend(loc='upper right', fontsize=7, framealpha=0.95) - ax2.grid(True, alpha=0.3) + annotateIssues = ( '\n' ).join( + [ f"{f'M{n}':4}{np.sum(self.issues[n]['combined']):,}" for n, _ in enumerate( self.meshes, 1 ) ] ) + + ax2.text( 0.98, + 0.02, + 'PROBLEM ELEMENTS\n(AR>100 & Q<0.3):\n\n' + annotateIssues, + transform=ax2.transAxes, + va='bottom', + ha='right', + bbox={ + "boxstyle": 'round', + "facecolor": '#ffcccc', + "alpha": 0.95, + "edgecolor": 'darkred', + "linewidth": 2 + }, + fontsize=8, + fontweight='bold' ) + + ax2.set_xscale( 'log' ) + ax2.set_xlabel( 'Aspect Ratio', fontweight='bold' ) + ax2.set_ylabel( 'Shape Quality', fontweight='bold' ) + ax2.set_title( 'KEY QUALITY INDICATOR: Shape Quality vs Aspect Ratio', + fontsize=12, + fontweight='bold', + color='darkred', + pad=10 ) + ax2.set_xlim( ( 1, ar_plot_limit ) ) + ax2.set_ylim( ( 0, 1.05 ) ) + ax2.legend( loc='upper right', fontsize=7, framealpha=0.95 ) + ax2.grid( True, alpha=0.3 ) # 3. Critical Issues Summary Table - ax3 = fig.add_subplot(gs_row1[0, 2]) - ax3.axis('off') + ax3 = fig.add_subplot( gs_row1[ 0, 2 ] ) + ax3.axis( 'off' ) summaryStats = [] - summaryStats.append(['CRITICAL ISSUE', 'WORST', 'BEST', 'Change']) - summaryStats.append(['─' * 18, '─' * 10, '─' * 10, '─' * 10]) + summaryStats.append( [ 'CRITICAL ISSUE', 'WORST', 'BEST', 'Change' ] ) + summaryStats.append( [ '─' * 18, '─' * 10, '─' * 10, '─' * 10 ] ) criticalCombo = self.issues[ self.best ][ "criticalCombo" ] critical_combo2 = self.issues[ self.worst ][ "criticalCombo" ] - aspectRatio = self.validMetrics[self.best][ "aspectRatio" ] - ar2 = self.validMetrics[self.worst][ "aspectRatio" ] + aspectRatio = self.validMetrics[ self.best ][ "aspectRatio" ] + ar2 = self.validMetrics[ self.worst ][ "aspectRatio" ] highAspectRatio = self.issues[ self.best ][ "highAspectRatio" ] high_ar2 = self.issues[ self.worst ][ "highAspectRatio" ] - lowShapeQuality = self.issues[self.best ][ "lowShapeQuality" ] - low_sq2 = self.issues[self.worst][ "lowShapeQuality" ] - - criticalMinDihedral = self.issues[self.best]["criticalMinDihedral"] - critical_dih2 = self.issues[self.worst]["criticalMinDihedral"] - - criticalMaxDihedral = self.issues[self.best][ "criticalMaxDihedral" ] - critical_max_dih2 = self.issues[self.worst][ "criticalMaxDihedral" ] - - highEdgeRatio = self.issues[self.best][ "highEdgeRatio" ] - highEdgeRatio2 = self.issues[self.worst][ "highEdgeRatio" ] - - - summaryStats.append(['CRITICAL Combo', f'{criticalCombo:,}', f'{critical_combo2:,}', - f'{((critical_combo2-criticalCombo)/max(criticalCombo,1)*100):+.1f}%' if criticalCombo > 0 else 'N/A']) - summaryStats.append(['(AR>100 & Q<0.3', f'({criticalCombo/len(aspectRatio)*100:.2f}%)', - f'({critical_combo2/len(ar2)*100:.2f}%)', '']) - summaryStats.append([' & MinDih<5°)', '', '', '']) - - summaryStats.append(['', '', '', '']) - summaryStats.append(['AR > 100', f'{highAspectRatio:,}', f'{high_ar2:,}', - f'{((high_ar2-highAspectRatio)/max(highAspectRatio,1)*100):+.1f}%']) - - summaryStats.append(['Quality < 0.3', f'{lowShapeQuality:,}', f'{low_sq2:,}', - f'{((low_sq2-lowShapeQuality)/max(lowShapeQuality,1)*100):+.1f}%']) - - summaryStats.append(['MinDih < 5°', f'{criticalMinDihedral:,}', f'{critical_dih2:,}', - f'{((critical_dih2-criticalMinDihedral)/max(criticalMinDihedral,1)*100):+.1f}%' if criticalMinDihedral > 0 else 'N/A']) - - summaryStats.append(['MaxDih > 175°', f'{criticalMaxDihedral:,}', f'{critical_max_dih2:,}', - f'{((critical_max_dih2-criticalMaxDihedral)/max(criticalMaxDihedral,1)*100):+.1f}%' if criticalMaxDihedral > 0 else 'N/A']) - - summaryStats.append(['Edge Ratio > 10', f'{highEdgeRatio:,}', f'{highEdgeRatio2:,}', - f'{((highEdgeRatio2-highEdgeRatio)/max(highEdgeRatio,1)*100):+.1f}%']) - - summaryStats.append(['─' * 18, '─' * 10, '─' * 10, '─' * 10]) - summaryStats.append(['Quality Grade', '', '', '']) - excellent = self.qualityScore[self.best]["excellent"] - excellent2 = self.qualityScore[self.worst]["excellent"] - good = self.qualityScore[self.best]["good"] - good2 = self.qualityScore[self.worst]["good"] - poor = self.qualityScore[self.best]["poor"] - poor2 = self.qualityScore[self.worst]["poor"] - - - summaryStats.append([' Excellent (>80)', f'{excellent:.1f}%', f'{excellent2:.1f}%', - f'{excellent2-excellent:+.1f}%']) - summaryStats.append([' Good (60-80)', f'{good:.1f}%', f'{good2:.1f}%', - f'{good2-good:+.1f}%']) - summaryStats.append([' Poor (≤30)', f'{poor:.1f}%', f'{poor2:.1f}%', - f'{poor2-poor:+.1f}%']) - - table = ax3.table(cellText=summaryStats, cellLoc='left', - bbox=[0, 0, 1, 1], edges='open') - table.auto_set_font_size(False) - table.set_fontsize(8) + lowShapeQuality = self.issues[ self.best ][ "lowShapeQuality" ] + low_sq2 = self.issues[ self.worst ][ "lowShapeQuality" ] + + criticalMinDihedral = self.issues[ self.best ][ "criticalMinDihedral" ] + critical_dih2 = self.issues[ self.worst ][ "criticalMinDihedral" ] + + criticalMaxDihedral = self.issues[ self.best ][ "criticalMaxDihedral" ] + critical_max_dih2 = self.issues[ self.worst ][ "criticalMaxDihedral" ] + + highEdgeRatio = self.issues[ self.best ][ "highEdgeRatio" ] + highEdgeRatio2 = self.issues[ self.worst ][ "highEdgeRatio" ] + + summaryStats.append( [ + 'CRITICAL Combo', f'{criticalCombo:,}', f'{critical_combo2:,}', + f'{((critical_combo2-criticalCombo)/max(criticalCombo,1)*100):+.1f}%' if criticalCombo > 0 else 'N/A' + ] ) + summaryStats.append( [ + '(AR>100 & Q<0.3', f'({criticalCombo/len(aspectRatio)*100:.2f}%)', f'({critical_combo2/len(ar2)*100:.2f}%)', + '' + ] ) + summaryStats.append( [ ' & MinDih<5°)', '', '', '' ] ) + + summaryStats.append( [ '', '', '', '' ] ) + summaryStats.append( [ + 'AR > 100', f'{highAspectRatio:,}', f'{high_ar2:,}', + f'{((high_ar2-highAspectRatio)/max(highAspectRatio,1)*100):+.1f}%' + ] ) + + summaryStats.append( [ + 'Quality < 0.3', f'{lowShapeQuality:,}', f'{low_sq2:,}', + f'{((low_sq2-lowShapeQuality)/max(lowShapeQuality,1)*100):+.1f}%' + ] ) + + summaryStats.append( [ + 'MinDih < 5°', f'{criticalMinDihedral:,}', f'{critical_dih2:,}', + f'{((critical_dih2-criticalMinDihedral)/max(criticalMinDihedral,1)*100):+.1f}%' + if criticalMinDihedral > 0 else 'N/A' + ] ) + + summaryStats.append( [ + 'MaxDih > 175°', f'{criticalMaxDihedral:,}', f'{critical_max_dih2:,}', + f'{((critical_max_dih2-criticalMaxDihedral)/max(criticalMaxDihedral,1)*100):+.1f}%' + if criticalMaxDihedral > 0 else 'N/A' + ] ) + + summaryStats.append( [ + 'Edge Ratio > 10', f'{highEdgeRatio:,}', f'{highEdgeRatio2:,}', + f'{((highEdgeRatio2-highEdgeRatio)/max(highEdgeRatio,1)*100):+.1f}%' + ] ) + + summaryStats.append( [ '─' * 18, '─' * 10, '─' * 10, '─' * 10 ] ) + summaryStats.append( [ 'Quality Grade', '', '', '' ] ) + excellent = self.qualityScore[ self.best ][ "excellent" ] + excellent2 = self.qualityScore[ self.worst ][ "excellent" ] + good = self.qualityScore[ self.best ][ "good" ] + good2 = self.qualityScore[ self.worst ][ "good" ] + poor = self.qualityScore[ self.best ][ "poor" ] + poor2 = self.qualityScore[ self.worst ][ "poor" ] + + summaryStats.append( + [ ' Excellent (>80)', f'{excellent:.1f}%', f'{excellent2:.1f}%', f'{excellent2-excellent:+.1f}%' ] ) + summaryStats.append( [ ' Good (60-80)', f'{good:.1f}%', f'{good2:.1f}%', f'{good2-good:+.1f}%' ] ) + summaryStats.append( [ ' Poor (≤30)', f'{poor:.1f}%', f'{poor2:.1f}%', f'{poor2-poor:+.1f}%' ] ) + + table = ax3.table( + cellText=summaryStats, + cellLoc='left', + bbox=[ 0, 0, 1, 1 ], # type: ignore[arg-type] + edges='open' ) + table.auto_set_font_size( False ) + table.set_fontsize( 8 ) # Style header - for i in range(4): - table[(0, i)].set_facecolor('#34495e') - table[(0, i)].set_text_props(weight='bold', color='white', fontsize=9) + for i in range( 4 ): + table[ ( 0, i ) ].set_facecolor( '#34495e' ) + table[ ( 0, i ) ].set_text_props( weight='bold', color='white', fontsize=9 ) # Highlight CRITICAL row - for col in range(4): - table[(2, col)].set_facecolor('#fadbd8') - table[(2, col)].set_text_props(weight='bold', fontsize=9) + for col in range( 4 ): + table[ ( 2, col ) ].set_facecolor( '#fadbd8' ) + table[ ( 2, col ) ].set_text_props( weight='bold', fontsize=9 ) # Color code changes - for row in [2, 6, 7, 8, 9, 10, 13, 14, 15]: - if row < len(summaryStats): - change_text = summaryStats[row][3] + for row in [ 2, 6, 7, 8, 9, 10, 13, 14, 15 ]: + if row < len( summaryStats ): + change_text = summaryStats[ row ][ 3 ] if '%' in change_text and change_text != 'N/A': - try: - val = float(change_text.replace('%', '').replace('+', '')) - if row in [2, 6, 7, 8, 9, 10, 15]: # Lower is better - if val < -10: - table[(row, 3)].set_facecolor('#d5f4e6') # Green - elif val > 10: - table[(row, 3)].set_facecolor('#fadbd8') # Red - else: # Higher is better (excellent, good) - if val > 10: - table[(row, 3)].set_facecolor('#d5f4e6') - elif val < -10: - table[(row, 3)].set_facecolor('#fadbd8') - except: - pass - - ax3.set_title('CRITICAL ISSUES SUMMARY', fontsize=12, fontweight='bold', - color='darkgreen', pad=10) + val = float( change_text.replace( '%', '' ).replace( '+', '' ) ) + if row in [ 2, 6, 7, 8, 9, 10, 15 ]: # Lower is better + if val < -10: + table[ ( row, 3 ) ].set_facecolor( '#d5f4e6' ) # Green + elif val > 10: + table[ ( row, 3 ) ].set_facecolor( '#fadbd8' ) # Red + else: # Higher is better (excellent, good) + if val > 10: + table[ ( row, 3 ) ].set_facecolor( '#d5f4e6' ) + elif val < -10: + table[ ( row, 3 ) ].set_facecolor( '#fadbd8' ) + + ax3.set_title( 'CRITICAL ISSUES SUMMARY', fontsize=12, fontweight='bold', color='darkgreen', pad=10 ) # ==================== ROW 2: QUALITY DISTRIBUTIONS ==================== # 4. Shape Quality Histogram - ax4 = fig.add_subplot(gs_main[0, 0]) - bins = np.linspace(0, 1, 40) + ax4 = fig.add_subplot( gs_main[ 0, 0 ] ) + bins = np.linspace( 0, 1, 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): - shapeQuality = self.validMetrics[n][ "shapeQuality" ] - ax4.hist(shapeQuality, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n-1], edgecolor='black', linewidth=0.5) - ax4.set_xlabel('Shape Quality', fontweight='bold') - ax4.set_ylabel('Count', fontweight='bold') - ax4.set_title('Shape Quality Distribution', fontweight='bold') + shapeQuality = self.validMetrics[ n ][ "shapeQuality" ] + ax4.hist( shapeQuality, + bins=bins, + alpha=0.6, + label=f'Mesh {n}', + color=color[ n - 1 ], + edgecolor='black', + linewidth=0.5 ) + ax4.set_xlabel( 'Shape Quality', fontweight='bold' ) + ax4.set_ylabel( 'Count', fontweight='bold' ) + ax4.set_title( 'Shape Quality Distribution', fontweight='bold' ) ax4.legend() - ax4.grid(True, alpha=0.3) + ax4.grid( True, alpha=0.3 ) # 5. Aspect Ratio Histogram - ax5 = fig.add_subplot(gs_main[0, 1]) - # bins = np.logspace(0, np.log10(min(ar_plot_limit, aspectRatio.max(), ar2.max())), 40) - ar_max = np.array( [ self.validMetrics[n]["aspectRatio"].max() for n, _ in enumerate( self.meshes, 1 )] ) + ax5 = fig.add_subplot( gs_main[ 0, 1 ] ) + ar_max = np.array( [ self.validMetrics[ n ][ "aspectRatio" ].max() for n, _ in enumerate( self.meshes, 1 ) ] ) - bins = np.logspace(0, np.log10(min(ar_plot_limit, ar_max.max())), 40) + bins = np.logspace( 0, np.log10( min( ar_plot_limit, ar_max.max() ) ), 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): - aspectRatio = self.validMetrics[n]['aspectRatio'] - ax5.hist(aspectRatio[aspectRatio < ar_plot_limit], bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n-1], edgecolor='black', linewidth=0.5) - ax5.set_xscale('log') - ax5.set_xlabel('Aspect Ratio', fontweight='bold') - ax5.set_ylabel('Count', fontweight='bold') - ax5.set_title('Aspect Ratio Distribution', fontweight='bold') + aspectRatio = self.validMetrics[ n ][ 'aspectRatio' ] + ax5.hist( aspectRatio[ aspectRatio < ar_plot_limit ], + bins=bins, + alpha=0.6, + label=f'Mesh {n}', + color=color[ n - 1 ], + edgecolor='black', + linewidth=0.5 ) + ax5.set_xscale( 'log' ) + ax5.set_xlabel( 'Aspect Ratio', fontweight='bold' ) + ax5.set_ylabel( 'Count', fontweight='bold' ) + ax5.set_title( 'Aspect Ratio Distribution', fontweight='bold' ) ax5.legend() - ax5.grid(True, alpha=0.3) + ax5.grid( True, alpha=0.3 ) # 6. Min Dihedral Histogram - ax6 = fig.add_subplot(gs_main[0, 2]) - bins = np.linspace(0, 90, 40) + ax6 = fig.add_subplot( gs_main[ 0, 2 ] ) + bins = np.linspace( 0, 90, 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): - minDihedral = self.validMetrics[ n ]["minDihedral"] - ax6.hist(minDihedral, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n-1], edgecolor='black', linewidth=0.5) - ax6.axvline(5, color='red', linestyle='--', linewidth=1.5, alpha=0.7) - ax6.set_xlabel('Min Dihedral Angle (degrees)', fontweight='bold') - ax6.set_ylabel('Count', fontweight='bold') - ax6.set_title('Min Dihedral Angle Distribution', fontweight='bold') + minDihedral = self.validMetrics[ n ][ "minDihedral" ] + ax6.hist( minDihedral, + bins=bins, + alpha=0.6, + label=f'Mesh {n}', + color=color[ n - 1 ], + edgecolor='black', + linewidth=0.5 ) + ax6.axvline( 5, color='red', linestyle='--', linewidth=1.5, alpha=0.7 ) + ax6.set_xlabel( 'Min Dihedral Angle (degrees)', fontweight='bold' ) + ax6.set_ylabel( 'Count', fontweight='bold' ) + ax6.set_title( 'Min Dihedral Angle Distribution', fontweight='bold' ) ax6.legend() - ax6.grid(True, alpha=0.3) + ax6.grid( True, alpha=0.3 ) # 7. Edge Ratio Histogram - ax7 = fig.add_subplot(gs_main[0, 3]) - bins = np.logspace(0, 3, 40) + ax7 = fig.add_subplot( gs_main[ 0, 3 ] ) + bins = np.logspace( 0, 3, 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): edgeRatio = self.validMetrics[ n ][ "edgeRatio" ] - ax7.hist(edgeRatio[edgeRatio < 1000], bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n-1], edgecolor='black', linewidth=0.5) - ax7.set_xscale('log') - ax7.axvline(1, color='green', linestyle='--', linewidth=1.5, alpha=0.7) - ax7.set_xlabel('Edge Length Ratio', fontweight='bold') - ax7.set_ylabel('Count', fontweight='bold') - ax7.set_title('Edge Length Ratio Distribution', fontweight='bold') + ax7.hist( edgeRatio[ edgeRatio < 1000 ], + bins=bins, + alpha=0.6, + label=f'Mesh {n}', + color=color[ n - 1 ], + edgecolor='black', + linewidth=0.5 ) + ax7.set_xscale( 'log' ) + ax7.axvline( 1, color='green', linestyle='--', linewidth=1.5, alpha=0.7 ) + ax7.set_xlabel( 'Edge Length Ratio', fontweight='bold' ) + ax7.set_ylabel( 'Count', fontweight='bold' ) + ax7.set_title( 'Edge Length Ratio Distribution', fontweight='bold' ) ax7.legend() - ax7.grid(True, alpha=0.3) + ax7.grid( True, alpha=0.3 ) # 8. Volume Histogram - ax8 = fig.add_subplot(gs_main[0, 4]) - vol_min = np.array( [ self.validMetrics[n]["volume"].min() for n, _ in enumerate( self.meshes, 1 )] ).min() - vol_max = np.array( [ self.validMetrics[n]["volume"].max() for n, _ in enumerate( self.meshes, 1 )] ).max() - + ax8 = fig.add_subplot( gs_main[ 0, 4 ] ) + vol_min = np.array( [ self.validMetrics[ n ][ "volume" ].min() for n, _ in enumerate( self.meshes, 1 ) ] ).min() + vol_max = np.array( [ self.validMetrics[ n ][ "volume" ].max() for n, _ in enumerate( self.meshes, 1 ) ] ).max() - bins = np.logspace(np.log10(vol_min), np.log10(vol_max), 40) + bins = np.logspace( np.log10( vol_min ), np.log10( vol_max ), 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): - volume = self.validMetrics[n][ "volume" ] - ax8.hist(volume, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n-1], edgecolor='black', linewidth=0.5) - ax8.set_xscale('log') - ax8.set_xlabel('Volume', fontweight='bold') - ax8.set_ylabel('Count', fontweight='bold') - ax8.set_title('Volume Distribution', fontweight='bold') + volume = self.validMetrics[ n ][ "volume" ] + ax8.hist( volume, + bins=bins, + alpha=0.6, + label=f'Mesh {n}', + color=color[ n - 1 ], + edgecolor='black', + linewidth=0.5 ) + ax8.set_xscale( 'log' ) + ax8.set_xlabel( 'Volume', fontweight='bold' ) + ax8.set_ylabel( 'Count', fontweight='bold' ) + ax8.set_title( 'Volume Distribution', fontweight='bold' ) ax8.legend() - ax8.grid(True, alpha=0.3) + ax8.grid( True, alpha=0.3 ) # ==================== ROW 3: STATISTICAL COMPARISON (BOX PLOTS) ==================== # 9. Shape Quality Box Plot - ax9 = fig.add_subplot(gs_main[1, 0]) - sq = [ self.validMetrics[n][ "shapeQuality" ] for n, _ in enumerate( self.meshes, 1 ) ] - bp1 = ax9.boxplot( sq, labels=lbl, - patch_artist=True, showfliers=False) - ax9.set_ylabel('Shape Quality', fontweight='bold') - ax9.set_title('Shape Quality Comparison', fontweight='bold') - ax9.grid(True, alpha=0.3, axis='y') + ax9 = fig.add_subplot( gs_main[ 1, 0 ] ) + sq = [ self.validMetrics[ n ][ "shapeQuality" ] for n, _ in enumerate( self.meshes, 1 ) ] + bp1 = ax9.boxplot( sq, label=lbl, patch_artist=True, showfliers=False ) + ax9.set_ylabel( 'Shape Quality', fontweight='bold' ) + ax9.set_title( 'Shape Quality Comparison', fontweight='bold' ) + ax9.grid( True, alpha=0.3, axis='y' ) # 10. Aspect Ratio Box Plot - ax10 = fig.add_subplot(gs_main[1, 1]) - ar = [ self.validMetrics[n][ "aspectRatio" ] for n, _ in enumerate( self.meshes, 1 ) ] - bp2 = ax10.boxplot( ar, labels=lbl, - patch_artist=True, showfliers=False) - ax10.set_yscale('log') - ax10.set_ylabel('Aspect Ratio (log)', fontweight='bold') - ax10.set_title('Aspect Ratio Comparison', fontweight='bold') - ax10.grid(True, alpha=0.3, axis='y') + ax10 = fig.add_subplot( gs_main[ 1, 1 ] ) + ar = [ self.validMetrics[ n ][ "aspectRatio" ] for n, _ in enumerate( self.meshes, 1 ) ] + bp2 = ax10.boxplot( ar, label=lbl, patch_artist=True, showfliers=False ) + ax10.set_yscale( 'log' ) + ax10.set_ylabel( 'Aspect Ratio (log)', fontweight='bold' ) + ax10.set_title( 'Aspect Ratio Comparison', fontweight='bold' ) + ax10.grid( True, alpha=0.3, axis='y' ) # 11. Min Dihedral Box Plot - ax11 = fig.add_subplot(gs_main[1, 2]) - minDihedral = [ self.validMetrics[n][ "minDihedral" ] for n, _ in enumerate( self.meshes, 1 ) ] - bp3 = ax11.boxplot( minDihedral, labels=lbl, - patch_artist=True, showfliers=False) - ax11.set_ylabel('Min Dihedral Angle (degrees)', fontweight='bold') - ax11.set_title('Min Dihedral Comparison', fontweight='bold') - ax11.grid(True, alpha=0.3, axis='y') + ax11 = fig.add_subplot( gs_main[ 1, 2 ] ) + minDihedral = [ self.validMetrics[ n ][ "minDihedral" ] for n, _ in enumerate( self.meshes, 1 ) ] + bp3 = ax11.boxplot( minDihedral, label=lbl, patch_artist=True, showfliers=False ) + ax11.set_ylabel( 'Min Dihedral Angle (degrees)', fontweight='bold' ) + ax11.set_title( 'Min Dihedral Comparison', fontweight='bold' ) + ax11.grid( True, alpha=0.3, axis='y' ) # 12. Edge Ratio Box Plot - ax12 = fig.add_subplot(gs_main[1, 3]) - edge_ratio = [ self.validMetrics[n][ "edgeRatio" ] for n, _ in enumerate( self.meshes, 1 ) ] - bp4 = ax12.boxplot(edge_ratio, labels=lbl, - patch_artist=True, showfliers=False) - ax12.set_yscale('log') - ax12.set_ylabel('Edge Length Ratio (log)', fontweight='bold') - ax12.set_title('Edge Ratio Comparison', fontweight='bold') - ax12.grid(True, alpha=0.3, axis='y') + ax12 = fig.add_subplot( gs_main[ 1, 3 ] ) + edge_ratio = [ self.validMetrics[ n ][ "edgeRatio" ] for n, _ in enumerate( self.meshes, 1 ) ] + bp4 = ax12.boxplot( edge_ratio, label=lbl, patch_artist=True, showfliers=False ) + ax12.set_yscale( 'log' ) + ax12.set_ylabel( 'Edge Length Ratio (log)', fontweight='bold' ) + ax12.set_title( 'Edge Ratio Comparison', fontweight='bold' ) + ax12.grid( True, alpha=0.3, axis='y' ) # 13. Volume Box Plot - ax13 = fig.add_subplot(gs_main[1, 4]) - vol = [ self.validMetrics[n][ "volume" ] for n, _ in enumerate( self.meshes, 1 ) ] - bp5 = ax13.boxplot( vol, labels=lbl, - patch_artist=True, showfliers=False) - ax13.set_yscale('log') - ax13.set_ylabel('Volume (log)', fontweight='bold') - ax13.set_title('Volume Comparison', fontweight='bold') - ax13.grid(True, alpha=0.3, axis='y') + ax13 = fig.add_subplot( gs_main[ 1, 4 ] ) + vol = [ self.validMetrics[ n ][ "volume" ] for n, _ in enumerate( self.meshes, 1 ) ] + bp5 = ax13.boxplot( vol, label=lbl, patch_artist=True, showfliers=False ) + ax13.set_yscale( 'log' ) + ax13.set_ylabel( 'Volume (log)', fontweight='bold' ) + ax13.set_title( 'Volume Comparison', fontweight='bold' ) + ax13.grid( True, alpha=0.3, axis='y' ) for n, _ in enumerate( self.meshes, 1 ): - bp1['boxes'][n-1].set_facecolor(color[n-1]) - bp1['medians'][n-1].set_color("black") - bp2['boxes'][n-1].set_facecolor(color[n-1]) - bp2['medians'][n-1].set_color("black") - bp3['boxes'][n-1].set_facecolor(color[n-1]) - bp3['medians'][n-1].set_color("black") - bp4['boxes'][n-1].set_facecolor(color[n-1]) - bp4['medians'][n-1].set_color("black") - bp5['boxes'][n-1].set_facecolor(color[n-1]) - bp5['medians'][n-1].set_color("black") + bp1[ 'boxes' ][ n - 1 ].set_facecolor( color[ n - 1 ] ) + bp1[ 'medians' ][ n - 1 ].set_color( "black" ) + bp2[ 'boxes' ][ n - 1 ].set_facecolor( color[ n - 1 ] ) + bp2[ 'medians' ][ n - 1 ].set_color( "black" ) + bp3[ 'boxes' ][ n - 1 ].set_facecolor( color[ n - 1 ] ) + bp3[ 'medians' ][ n - 1 ].set_color( "black" ) + bp4[ 'boxes' ][ n - 1 ].set_facecolor( color[ n - 1 ] ) + bp4[ 'medians' ][ n - 1 ].set_color( "black" ) + bp5[ 'boxes' ][ n - 1 ].set_facecolor( color[ n - 1 ] ) + bp5[ 'medians' ][ n - 1 ].set_color( "black" ) # ==================== ROW 4: CORRELATION ANALYSIS (SCATTER PLOTS) ==================== - - # Use existing idx1, idx2 from executive summary - # 14. Shape Quality vs Aspect Ratio (duplicate for detail) - ax14 = fig.add_subplot(gs_main[2, 0]) + ax14 = fig.add_subplot( gs_main[ 2, 0 ] ) for n, _ in enumerate( self.meshes, 1 ): - idx = self.sample[n] - aspectRatio = self.validMetrics[n][ 'aspectRatio'] - shapeQuality = self.validMetrics[n][ 'shapeQuality'] - mask1 = aspectRatio[idx] < ar_plot_limit - ax14.scatter(aspectRatio[idx][mask1], shapeQuality[idx][mask1], alpha=0.4, s=5, - color=color[n-1], label=f'Mesh {n}', edgecolors='none') - ax14.set_xscale('log') - ax14.set_xlabel('Aspect Ratio', fontweight='bold') - ax14.set_ylabel('Shape Quality', fontweight='bold') - ax14.set_title('Shape Quality vs Aspect Ratio', fontweight='bold') - ax14.set_xlim([1, ar_plot_limit]) - ax14.set_ylim([0, 1.05]) - ax14.legend(loc='upper right', fontsize=7) - ax14.grid(True, alpha=0.3) + idx = self.sample[ n ] + aspectRatio = self.validMetrics[ n ][ 'aspectRatio' ] + shapeQuality = self.validMetrics[ n ][ 'shapeQuality' ] + mask1 = aspectRatio[ idx ] < ar_plot_limit + ax14.scatter( aspectRatio[ idx ][ mask1 ], + shapeQuality[ idx ][ mask1 ], + alpha=0.4, + s=5, + color=color[ n - 1 ], + label=f'Mesh {n}', + edgecolors='none' ) + ax14.set_xscale( 'log' ) + ax14.set_xlabel( 'Aspect Ratio', fontweight='bold' ) + ax14.set_ylabel( 'Shape Quality', fontweight='bold' ) + ax14.set_title( 'Shape Quality vs Aspect Ratio', fontweight='bold' ) + ax14.set_xlim( ( 1, ar_plot_limit ) ) + ax14.set_ylim( ( 0, 1.05 ) ) + ax14.legend( loc='upper right', fontsize=7 ) + ax14.grid( True, alpha=0.3 ) # 15. Aspect Ratio vs Flatness - ax15 = fig.add_subplot(gs_main[2, 1]) + ax15 = fig.add_subplot( gs_main[ 2, 1 ] ) for n, _ in enumerate( self.meshes, 1 ): - idx = self.sample[n] - aspectRatio = self.validMetrics[n]["aspectRatio"] - flatnessRatio = self.validMetrics[n][ 'flatnessRatio' ] - mask1 = aspectRatio[idx] < ar_plot_limit - ax15.scatter(aspectRatio[idx][mask1], flatnessRatio[idx][mask1], alpha=0.4, s=5, - color=color[n-1], label=f'Mesh {n}', edgecolors='none') - ax15.set_xscale('log') - ax15.set_yscale('log') - ax15.set_xlabel('Aspect Ratio', fontweight='bold') - ax15.set_ylabel('Flatness Ratio', fontweight='bold') - ax15.set_title('Aspect Ratio vs Flatness', fontweight='bold') - ax15.set_xlim([1, ar_plot_limit]) - ax15.legend(loc='upper right', fontsize=7) - ax15.grid(True, alpha=0.3) + idx = self.sample[ n ] + aspectRatio = self.validMetrics[ n ][ "aspectRatio" ] + flatnessRatio = self.validMetrics[ n ][ 'flatnessRatio' ] + mask1 = aspectRatio[ idx ] < ar_plot_limit + ax15.scatter( aspectRatio[ idx ][ mask1 ], + flatnessRatio[ idx ][ mask1 ], + alpha=0.4, + s=5, + color=color[ n - 1 ], + label=f'Mesh {n}', + edgecolors='none' ) + ax15.set_xscale( 'log' ) + ax15.set_yscale( 'log' ) + ax15.set_xlabel( 'Aspect Ratio', fontweight='bold' ) + ax15.set_ylabel( 'Flatness Ratio', fontweight='bold' ) + ax15.set_title( 'Aspect Ratio vs Flatness', fontweight='bold' ) + ax15.set_xlim( ( 1, ar_plot_limit ) ) + ax15.legend( loc='upper right', fontsize=7 ) + ax15.grid( True, alpha=0.3 ) # 16. Volume vs Aspect Ratio - ax16 = fig.add_subplot(gs_main[2, 2]) + ax16 = fig.add_subplot( gs_main[ 2, 2 ] ) for n, _ in enumerate( self.meshes, 1 ): - idx = self.sample[n] - aspectRatio = self.validMetrics[n]["aspectRatio"] - volume = self.validMetrics[n][ 'volume' ] - mask1 = aspectRatio[idx] < ar_plot_limit - ax16.scatter(volume[idx][mask1], aspectRatio[idx][mask1], alpha=0.4, s=5, - color=color[n-1], label=f'Mesh {n}', edgecolors='none') - ax16.set_xscale('log') - ax16.set_yscale('log') - ax16.set_xlabel('Volume', fontweight='bold') - ax16.set_ylabel('Aspect Ratio', fontweight='bold') - ax16.set_title('Volume vs Aspect Ratio', fontweight='bold') - ax16.set_ylim([1, ar_plot_limit]) - ax16.legend(loc='upper right', fontsize=7) - ax16.grid(True, alpha=0.3) + idx = self.sample[ n ] + aspectRatio = self.validMetrics[ n ][ "aspectRatio" ] + volume = self.validMetrics[ n ][ 'volume' ] + mask1 = aspectRatio[ idx ] < ar_plot_limit + ax16.scatter( volume[ idx ][ mask1 ], + aspectRatio[ idx ][ mask1 ], + alpha=0.4, + s=5, + color=color[ n - 1 ], + label=f'Mesh {n}', + edgecolors='none' ) + ax16.set_xscale( 'log' ) + ax16.set_yscale( 'log' ) + ax16.set_xlabel( 'Volume', fontweight='bold' ) + ax16.set_ylabel( 'Aspect Ratio', fontweight='bold' ) + ax16.set_title( 'Volume vs Aspect Ratio', fontweight='bold' ) + ax16.set_ylim( ( 1, ar_plot_limit ) ) + ax16.legend( loc='upper right', fontsize=7 ) + ax16.grid( True, alpha=0.3 ) # 17. Volume vs Shape Quality - ax17 = fig.add_subplot(gs_main[2, 3]) + ax17 = fig.add_subplot( gs_main[ 2, 3 ] ) for n, _ in enumerate( self.meshes, 1 ): - idx = self.sample[n] - volume = self.validMetrics[n][ 'volume' ] - shapeQuality = self.validMetrics[n][ 'shapeQuality'] - ax17.scatter(volume[idx], shapeQuality[idx], alpha=0.4, s=5, - color=color[n-1], label=f'Mesh {n}', edgecolors='none') - ax17.set_xscale('log') - ax17.set_xlabel('Volume', fontweight='bold') - ax17.set_ylabel('Shape Quality', fontweight='bold') - ax17.set_title('Volume vs Shape Quality', fontweight='bold') - ax17.legend(loc='upper right', fontsize=7) - ax17.grid(True, alpha=0.3) + idx = self.sample[ n ] + volume = self.validMetrics[ n ][ 'volume' ] + shapeQuality = self.validMetrics[ n ][ 'shapeQuality' ] + ax17.scatter( volume[ idx ], + shapeQuality[ idx ], + alpha=0.4, + s=5, + color=color[ n - 1 ], + label=f'Mesh {n}', + edgecolors='none' ) + ax17.set_xscale( 'log' ) + ax17.set_xlabel( 'Volume', fontweight='bold' ) + ax17.set_ylabel( 'Shape Quality', fontweight='bold' ) + ax17.set_title( 'Volume vs Shape Quality', fontweight='bold' ) + ax17.legend( loc='upper right', fontsize=7 ) + ax17.grid( True, alpha=0.3 ) # 18. Edge Ratio vs Volume - ax18 = fig.add_subplot(gs_main[2, 4]) + ax18 = fig.add_subplot( gs_main[ 2, 4 ] ) for n, _ in enumerate( self.meshes, 1 ): - idx = self.sample[n] - volume = self.validMetrics[n][ 'volume' ] - edge_ratio = self.validMetrics[n][ 'edgeRatio' ] - ax18.scatter(volume[idx], edge_ratio[idx], alpha=0.4, s=5, - color=color[n-1], label=f'Mesh {n}', edgecolors='none') - ax18.axhline(y=1, color='green', linestyle='--', linewidth=1.5, alpha=0.7) - ax18.set_xscale('log') - ax18.set_yscale('log') - ax18.set_xlabel('Volume', fontweight='bold') - ax18.set_ylabel('Edge Length Ratio', fontweight='bold') - ax18.set_title('Edge Ratio vs Volume', fontweight='bold') - ax18.legend(loc='upper right', fontsize=7) - ax18.grid(True, alpha=0.3) + idx = self.sample[ n ] + volume = self.validMetrics[ n ][ 'volume' ] + edge_ratio = self.validMetrics[ n ][ 'edgeRatio' ] + ax18.scatter( volume[ idx ], + edge_ratio[ idx ], + alpha=0.4, + s=5, + color=color[ n - 1 ], + label=f'Mesh {n}', + edgecolors='none' ) + ax18.axhline( y=1, color='green', linestyle='--', linewidth=1.5, alpha=0.7 ) + ax18.set_xscale( 'log' ) + ax18.set_yscale( 'log' ) + ax18.set_xlabel( 'Volume', fontweight='bold' ) + ax18.set_ylabel( 'Edge Length Ratio', fontweight='bold' ) + ax18.set_title( 'Edge Ratio vs Volume', fontweight='bold' ) + ax18.legend( loc='upper right', fontsize=7 ) + ax18.grid( True, alpha=0.3 ) # ==================== ROW 5: DETAILED DIAGNOSTICS ==================== # 19. Min Edge Length Histogram - ax19 = fig.add_subplot(gs_main[3, 0]) - edge_min = np.array( [ self.validMetrics[n]["minEdge"].min() for n,_ in enumerate( self.meshes, 1 ) ] ).min() - edge_max_min = np.array( [ self.validMetrics[n]["minEdge"].max() for n,_ in enumerate( self.meshes, 1 ) ] ).min() + ax19 = fig.add_subplot( gs_main[ 3, 0 ] ) + edge_min = np.array( [ self.validMetrics[ n ][ "minEdge" ].min() + for n, _ in enumerate( self.meshes, 1 ) ] ).min() + edge_max_min = np.array( [ self.validMetrics[ n ][ "minEdge" ].max() + for n, _ in enumerate( self.meshes, 1 ) ] ).min() - bins = np.logspace(np.log10(edge_min), np.log10(edge_max_min), 40) + bins = np.logspace( np.log10( edge_min ), np.log10( edge_max_min ), 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): - # edge_min = min(minEdge.min(), min_edge2.min()) - # edge_max_min = max(minEdge.max(), min_edge2.max()) - # bins = np.logspace(np.log10(edge_min), np.log10(edge_max_min), 40) - minEdge = self.validMetrics[n]['minEdge'] - ax19.hist(minEdge, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n-1], edgecolor='black', linewidth=0.5) - ax19.axvline(np.median(minEdge), color=color[n-1], linestyle=':', linewidth=2, alpha=0.8) - ax19.set_xscale('log') - - ax19.set_xlabel('Minimum Edge Length', fontweight='bold') - ax19.set_ylabel('Count', fontweight='bold') - ax19.set_title('Min Edge Length Distribution', fontweight='bold') + minEdge = self.validMetrics[ n ][ 'minEdge' ] + ax19.hist( minEdge, + bins=bins, + alpha=0.6, + label=f'Mesh {n}', + color=color[ n - 1 ], + edgecolor='black', + linewidth=0.5 ) + ax19.axvline( np.median( minEdge ), color=color[ n - 1 ], linestyle=':', linewidth=2, alpha=0.8 ) + ax19.set_xscale( 'log' ) + + ax19.set_xlabel( 'Minimum Edge Length', fontweight='bold' ) + ax19.set_ylabel( 'Count', fontweight='bold' ) + ax19.set_title( 'Min Edge Length Distribution', fontweight='bold' ) ax19.legend() - ax19.grid(True, alpha=0.3) + ax19.grid( True, alpha=0.3 ) # 20. Max Edge Length Histogram - ax20 = fig.add_subplot(gs_main[3, 1]) - edge_max = np.array( [ self.validMetrics[n]["maxEdge"].max() for n,_ in enumerate( self.meshes, 1 ) ] ).max() - edge_min_max = np.array( [ self.validMetrics[n]["maxEdge"].min() for n,_ in enumerate( self.meshes, 1 ) ] ).min() + ax20 = fig.add_subplot( gs_main[ 3, 1 ] ) + edge_max = np.array( [ self.validMetrics[ n ][ "maxEdge" ].max() + for n, _ in enumerate( self.meshes, 1 ) ] ).max() + edge_min_max = np.array( [ self.validMetrics[ n ][ "maxEdge" ].min() + for n, _ in enumerate( self.meshes, 1 ) ] ).min() - # edge_max = max(maxEdge.max(), max_edge2.max()) - # edge_min_max = min(maxEdge.min(), max_edge2.min()) - bins = np.logspace(np.log10(edge_min_max), np.log10(edge_max), 40) + bins = np.logspace( np.log10( edge_min_max ), np.log10( edge_max ), 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): - maxEdge = self.validMetrics[n]["maxEdge"] - ax20.hist(maxEdge, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n-1], edgecolor='black', linewidth=0.5) - ax20.axvline(np.median(maxEdge), color=color[n-1], linestyle=':', linewidth=2, alpha=0.8) - ax20.set_xscale('log') - ax20.set_xlabel('Maximum Edge Length', fontweight='bold') - ax20.set_ylabel('Count', fontweight='bold') - ax20.set_title('Max Edge Length Distribution', fontweight='bold') + maxEdge = self.validMetrics[ n ][ "maxEdge" ] + ax20.hist( maxEdge, + bins=bins, + alpha=0.6, + label=f'Mesh {n}', + color=color[ n - 1 ], + edgecolor='black', + linewidth=0.5 ) + ax20.axvline( np.median( maxEdge ), color=color[ n - 1 ], linestyle=':', linewidth=2, alpha=0.8 ) + ax20.set_xscale( 'log' ) + ax20.set_xlabel( 'Maximum Edge Length', fontweight='bold' ) + ax20.set_ylabel( 'Count', fontweight='bold' ) + ax20.set_title( 'Max Edge Length Distribution', fontweight='bold' ) ax20.legend() - ax20.grid(True, alpha=0.3) + ax20.grid( True, alpha=0.3 ) # 21. Max Dihedral Histogram - ax21 = fig.add_subplot(gs_main[3, 2]) - bins = np.linspace(90, 180, 40) + ax21 = fig.add_subplot( gs_main[ 3, 2 ] ) + bins = np.linspace( 90, 180, 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): - maxDihedral = self.validMetrics[n][ "maxDihedral" ] - ax21.hist(maxDihedral, bins=bins, alpha=0.6, label=f'Mesh {n}', - color=color[n-1], edgecolor='black', linewidth=0.5) - ax21.axvline(175, color='red', linestyle='--', linewidth=1.5, alpha=0.7) - ax21.set_xlabel('Max Dihedral Angle (degrees)', fontweight='bold') - ax21.set_ylabel('Count', fontweight='bold') - ax21.set_title('Max Dihedral Angle Distribution', fontweight='bold') + maxDihedral = self.validMetrics[ n ][ "maxDihedral" ] + ax21.hist( maxDihedral, + bins=bins, + alpha=0.6, + label=f'Mesh {n}', + color=color[ n - 1 ], + edgecolor='black', + linewidth=0.5 ) + ax21.axvline( 175, color='red', linestyle='--', linewidth=1.5, alpha=0.7 ) + ax21.set_xlabel( 'Max Dihedral Angle (degrees)', fontweight='bold' ) + ax21.set_ylabel( 'Count', fontweight='bold' ) + ax21.set_title( 'Max Dihedral Angle Distribution', fontweight='bold' ) ax21.legend() - ax21.grid(True, alpha=0.3) + ax21.grid( True, alpha=0.3 ) # 22. Dihedral Range Box Plot - ax22 = fig.add_subplot(gs_main[3, 3]) + ax22 = fig.add_subplot( gs_main[ 3, 3 ] ) nmesh = len( self.meshes ) - positions = np.delete( np.arange( 1, nmesh*2+2 ), nmesh ) - dih = [ self.validMetrics[n]["minDihedral"] for n, _ in enumerate( self.meshes, 1 )] + [ self.validMetrics[n]["maxDihedral"] for n, _ in enumerate( self.meshes, 1 )] - lbl_boxplot = [ f'M{n}Min' for n, _ in enumerate( self.meshes, 1 )] + [ f'M{n}Max' for n, _ in enumerate( self.meshes, 1 )] + positions = np.delete( np.arange( 1, nmesh * 2 + 2 ), nmesh ) + dih = [ self.validMetrics[ n ][ "minDihedral" ] for n, _ in enumerate( self.meshes, 1 ) + ] + [ self.validMetrics[ n ][ "maxDihedral" ] for n, _ in enumerate( self.meshes, 1 ) ] + lbl_boxplot = [ f'M{n}Min' for n, _ in enumerate( self.meshes, 1 ) + ] + [ f'M{n}Max' for n, _ in enumerate( self.meshes, 1 ) ] boxplot_color = [ n for n, _ in enumerate( self.meshes, 1 ) ] + [ n for n, _ in enumerate( self.meshes, ) ] - bp_dih = ax22.boxplot(dih, - positions=positions, - labels=lbl_boxplot, - patch_artist=True, showfliers=False, widths=0.6) - for m in range( len(self.meshes)*2 ): - bp_dih['boxes'][m].set_facecolor( color[ boxplot_color[m] ]) - bp_dih['medians'][m].set_color("black") - - ax22.axhline(5, color='red', linestyle='--', linewidth=1, alpha=0.5, zorder=0) - ax22.axhline(175, color='red', linestyle='--', linewidth=1, alpha=0.5, zorder=0) - ax22.axhline(70.5, color='green', linestyle=':', linewidth=1, alpha=0.5, zorder=0) - ax22.set_ylabel('Dihedral Angle (degrees)', fontweight='bold') - ax22.set_title('Dihedral Angle Comparison', fontweight='bold') - ax22.grid(True, alpha=0.3, axis='y') + bp_dih = ax22.boxplot( dih, + positions=positions, + label=lbl_boxplot, + patch_artist=True, + showfliers=False, + widths=0.6 ) + for m in range( len( self.meshes ) * 2 ): + bp_dih[ 'boxes' ][ m ].set_facecolor( color[ boxplot_color[ m ] ] ) + bp_dih[ 'medians' ][ m ].set_color( "black" ) + + ax22.axhline( 5, color='red', linestyle='--', linewidth=1, alpha=0.5, zorder=0 ) + ax22.axhline( 175, color='red', linestyle='--', linewidth=1, alpha=0.5, zorder=0 ) + ax22.axhline( 70.5, color='green', linestyle=':', linewidth=1, alpha=0.5, zorder=0 ) + ax22.set_ylabel( 'Dihedral Angle (degrees)', fontweight='bold' ) + ax22.set_title( 'Dihedral Angle Comparison', fontweight='bold' ) + ax22.grid( True, alpha=0.3, axis='y' ) # 23. Shape Quality CDF - ax23 = fig.add_subplot(gs_main[3, 4]) + ax23 = fig.add_subplot( gs_main[ 3, 4 ] ) for n, _ in enumerate( self.meshes, 1 ): - shapeQuality = self.validMetrics[n][ "shapeQuality"] - sorted_sq1 = np.sort(shapeQuality) - cdf_sq1 = np.arange(1, len(sorted_sq1) + 1) / len(sorted_sq1) * 100 - ax23.plot(sorted_sq1, cdf_sq1, color=color[n-1], linewidth=2, label=f'Mesh {n}') - - ax23.axvline(0.3, color='red', linestyle='--', linewidth=1, alpha=0.5) - ax23.axvline(0.7, color='green', linestyle='--', linewidth=1, alpha=0.5) - ax23.axhline(50, color='gray', linestyle='--', linewidth=1, alpha=0.5) - ax23.set_xlabel('Shape Quality', fontweight='bold') - ax23.set_ylabel('Cumulative %', fontweight='bold') - ax23.set_title('Cumulative Distribution - Shape Quality', fontweight='bold') - ax23.legend(loc='lower right') - ax23.grid(True, alpha=0.3) + shapeQuality = self.validMetrics[ n ][ "shapeQuality" ] + sorted_sq1 = np.sort( shapeQuality ) + cdf_sq1 = np.arange( 1, len( sorted_sq1 ) + 1 ) / len( sorted_sq1 ) * 100 + ax23.plot( sorted_sq1, cdf_sq1, color=color[ n - 1 ], linewidth=2, label=f'Mesh {n}' ) + + ax23.axvline( 0.3, color='red', linestyle='--', linewidth=1, alpha=0.5 ) + ax23.axvline( 0.7, color='green', linestyle='--', linewidth=1, alpha=0.5 ) + ax23.axhline( 50, color='gray', linestyle='--', linewidth=1, alpha=0.5 ) + ax23.set_xlabel( 'Shape Quality', fontweight='bold' ) + ax23.set_ylabel( 'Cumulative %', fontweight='bold' ) + ax23.set_title( 'Cumulative Distribution - Shape Quality', fontweight='bold' ) + ax23.legend( loc='lower right' ) + ax23.grid( True, alpha=0.3 ) # Save figure output_png = '/data/pau901/SIM_CS/04_WORKSPACE/USERS/PalomaMartinez/geosPythonPackages/TESST/mesh_comparison.png' - print(f"\nSaving dashboard to: {output_png}") - plt.savefig(output_png, dpi=300, bbox_inches='tight', facecolor='white') - print(f"Dashboard saved successfully!") + print( f"\nSaving dashboard to: {output_png}" ) + plt.savefig( output_png, dpi=300, bbox_inches='tight', facecolor='white' ) + print( "Dashboard saved successfully!" ) + def setDashboardFilename( self: Self, filename: str ) + self.filename = filename - - - def __loggerSection( self: Self, sectionName: str ): - self.logger.info("="*80) + def __loggerSection( self: Self, sectionName: str ) -> None: + self.logger.info( "=" * 80 ) self.logger.info( sectionName ) - self.logger.info("="*80) - - - def __orderMeshes( self: Self ): - """Proposition of ordering as fonction of median quality score""" - self.__loggerSection( "ORDERING MESHES (from median quality score)") - median_score = { n : np.median( self.validMetrics[n]["qualityScore"] ) for n, _ in enumerate (self.meshes, 1) } + self.logger.info( "=" * 80 ) + + def __orderMeshes( self: Self ) -> None: + """Proposition of ordering as fonction of median quality score.""" + self.__loggerSection( "ORDERING MESHES (from median quality score)" ) + median_score = { + n: np.median( self.validMetrics[ n ][ "qualityScore" ] ) + for n, _ in enumerate( self.meshes, 1 ) + } - sorted_meshes = sorted( median_score.items(), key=lambda x:x[1], reverse = True ) + sorted_meshes = sorted( median_score.items(), key=lambda x: x[ 1 ], reverse=True ) self.sorted = sorted_meshes - self.best = sorted_meshes[ 0 ][0] - self.worst = sorted_meshes[ -1 ][0] + self.best = sorted_meshes[ 0 ][ 0 ] + self.worst = sorted_meshes[ -1 ][ 0 ] - self.logger.info( f"Mesh order from median quality score:" ) + self.logger.info( "Mesh order from median quality score:" ) top = [ f"Mesh {m[0]} ({m[1]:.2f})" for m in sorted_meshes ] - toprint = (" > ").join( top ) + toprint = ( " > " ).join( top ) self.logger.info( " [+] " + toprint + " [-]\n" ) - - def compareIssuesFromBest( self: Self ): - highAspectRatio = self.issues[ self.best ][ "highAspectRatio" ] - criticalMinDihedral = self.issues[ self.best ]["criticalMinDihedral"] - lowShapeQuality = self.issues[ self.best ]["lowShapeQuality"] - criticalCombo = self.issues[ self.best ]["criticalCombo"] - - def getPercentChangeFromBest( data, ref ): - return (data - ref) / max( ref, 1)*100 - - msg = f"Change from BEST [Mesh {self.best}]\n" - msg += f" {f'Mesh':20}"+ ("").join([f"{f'Mesh {n}':>16}" for n, _ in enumerate( self.meshes, 1 )]) + "\n" - highAspectRatio = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'highAspectRatio' ], highAspectRatio ):>+15,.1f}%" if n!= self.best else f"{'N/A':>16}" for n, _ in enumerate( self.meshes, 1 ) ] - lowShapeQuality = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'lowShapeQuality' ], lowShapeQuality ):>+15,.1f}%" if n!= self.best else f"{'N/A':>16}" for n, _ in enumerate( self.meshes, 1 ) ] - criticalMinDihedral = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'criticalMinDihedral' ], criticalMinDihedral ):>+15,.1f}%" if criticalMinDihedral > 0 and n!= self.best else f"{'N/A':>16}" for n, _ in enumerate( self.meshes, 1 )] - criticalCombo = [ f"{getPercentChangeFromBest( self.issues[ n ][ 'criticalCombo' ], criticalCombo ):>+15,.1f}%" if criticalCombo > 0 and n!= self.best else f"{'N/A':>16}" for n, _ in enumerate( self.meshes, 1 )] + def compareIssuesFromBest( self: Self ) -> None: + """Compare issues values vs [BEST] mesh.""" + highAspectRatioBest = self.issues[ self.best ][ "highAspectRatio" ] + criticalMinDihedralBest = self.issues[ self.best ][ "criticalMinDihedral" ] + lowShapeQualityBest = self.issues[ self.best ][ "lowShapeQuality" ] + criticalComboBest = self.issues[ self.best ][ "criticalCombo" ] + + def getPercentChange( data: np.float64, ref: np.float64 ) -> np.float64: + """Compute and return the percent change. + + Args: + data (np.float64): Data to compare. + ref (np.float64): Reference. + + Returns: + np.float64: The percent change from reference. + """ + return ( data - ref ) / max( ref, 1 ) * 100 + + msg: str = f"Change from BEST [Mesh {self.best}]\n" + msg += f" {'Mesh':20}" + ( "" ).join( [ f"{f'Mesh {n}':>16}" for n, _ in enumerate( self.meshes, 1 ) ] ) + "\n" + highAspectRatio: list[ str ] = [ + f"{getPercentChange( self.issues[ n ][ 'highAspectRatio' ], highAspectRatioBest ):>+15,.1f}%" + if n != self.best else f"{'N/A':>16}" for n, _ in enumerate( self.meshes, 1 ) + ] + lowShapeQuality: list[ str ] = [ + f"{getPercentChange( self.issues[ n ][ 'lowShapeQuality' ], lowShapeQualityBest ):>+15,.1f}%" + if n != self.best else f"{'N/A':>16}" for n, _ in enumerate( self.meshes, 1 ) + ] + criticalMinDihedral: list[ str ] = [ + f"{getPercentChange( self.issues[ n ][ 'criticalMinDihedral' ], criticalMinDihedralBest ):>+15,.1f}%" + if criticalMinDihedralBest > 0 and n != self.best else f"{'N/A':>16}" + for n, _ in enumerate( self.meshes, 1 ) + ] + criticalCombo: list[ str ] = [ + f"{getPercentChange( self.issues[ n ][ 'criticalCombo' ], criticalComboBest ):>+15,.1f}%" + if criticalComboBest > 0 and n != self.best else f"{'N/A':>16}" for n, _ in enumerate( self.meshes, 1 ) + ] msg += f"{' AR > 100:':20}{('').join( highAspectRatio )}\n" msg += f"{' Quality < 0.3:':20}{('').join( lowShapeQuality )}\n" @@ -1177,14 +1350,21 @@ def getPercentChangeFromBest( data, ref ): self.logger.info( msg ) + def setSampleForPlot( self: Self, data: npt.NDArray[ Any ], n: int ) -> None: + """Set sampling for a given metric of mesh n. - def setSampleForPlot( self: Self, data, n ): - n_sample = min(10000, len( data )) - self.sample[n] = np.random.choice(len(data), n_sample, replace=False) + Args: + data (npt.NDArray[Any]): Metric array to sample. + n (int): Mesh id. + """ + sampleSize = min( 10000, len( data ) ) + self.sample[ n ] = np.random.choice( len( data ), sampleSize, replace=False ) # Combined quality score -def computeQualityScore(aspectRatio, shapeQuality, edgeRatio, minDihedralAngle): +def computeQualityScore( aspectRatio: npt.NDArray[ np.float64 ], shapeQuality: npt.NDArray[ np.float64 ], + edgeRatio: npt.NDArray[ np.float64 ], + minDihedralAngle: npt.NDArray[ np.float64 ] ) -> npt.NDArray[ np.float64 ]: """Compute combined quality score (0-100). Args: @@ -1196,11 +1376,10 @@ def computeQualityScore(aspectRatio, shapeQuality, edgeRatio, minDihedralAngle): Returns: np.float64: Quality score """ - aspectRatioNorm = np.clip(1.0 / (aspectRatio / 1.73), 0, 1) + aspectRatioNorm = np.clip( 1.0 / ( aspectRatio / 1.73 ), 0, 1 ) shapeQualityNorm = shapeQuality - edgeRatioNorm = np.clip(1.0 / edgeRatio, 0, 1) - dihedralMinNorm = np.clip(minDihedralAngle / 60.0, 0, 1) - score = (0.3 * aspectRatioNorm + 0.4 * shapeQualityNorm + 0.2 * edgeRatioNorm + 0.1 * dihedralMinNorm) * 100 - return score - + edgeRatioNorm = np.clip( 1.0 / edgeRatio, 0, 1 ) + dihedralMinNorm = np.clip( minDihedralAngle / 60.0, 0, 1 ) + score = ( 0.3 * aspectRatioNorm + 0.4 * shapeQualityNorm + 0.2 * edgeRatioNorm + 0.1 * dihedralMinNorm ) * 100 + return score From a98cfe227fcd755a1e91c9a1754ec4c8b0c6d6eb Mon Sep 17 00:00:00 2001 From: Paloma Martinez <104762252+paloma-martinez@users.noreply.github.com> Date: Mon, 2 Feb 2026 11:19:20 +0100 Subject: [PATCH 05/11] Doc, typing and small fixes --- .../pre_processing/TetQualityAnalysis.py | 298 ++++++++++-------- 1 file changed, 171 insertions(+), 127 deletions(-) diff --git a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py index 2a87a11a..058ac62b 100644 --- a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py +++ b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py @@ -14,7 +14,39 @@ from matplotlib.patches import Rectangle __doc__ = """ -TetQualityAnalysis module is a filter that performs an an analysis of tetrahedras of one or several meshes and plot a summary. +TetQualityAnalysis module is a filter that performs an analysis of tetrahedras quality of one or several meshes and generates a plot as summary. + +Filter input should be vtkUnstructuredGrid. + + +To use the filter: + +.. code-block:: python + from geos.processing.pre_processing.TetQualityAnalysis import TetQualityAnalysis + + # Filter inputs + inputMesh: dict[str, vtkUnstructuredGrid] + speHandler: bool # optional + + # Instantiate the filter + tetQualityAnalysisFilter: TetQualityAnalysis = TetQualityAnalysis( inputMesh, speHandler ) + + # Use your own handler (if speHandler is True) + yourHandler: logging.Handler + tetQualityAnalysisFilter.setLoggerHandler( yourHandler ) + + # Change output filename [optional] + tetQualityAnalysisFilter.SetFilename( filename ) + + + # Do calculations + try: + tetQualityAnalysisFilter.applyFilter() + except ( ValueError, IndexError, TypeError, AttributeError ) as e: + tetQualityAnalysisFilter.logger.error( f"The filter { tetQualityAnalysisFilter.logger.name } failed due to: { e }" ) + except Exception as e: + mess: str = f"The filter { meshQualityEnhancedFilter.logger.name } failed due to: { e }" + tetQualityAnalysisFilter.logger.critical( mess, exc_info=True ) """ loggerName: str = "Tetrahedra Quality Analysis" @@ -72,7 +104,6 @@ def applyFilter( self: Self ) -> None: self.__loggerSection( "MESH COMPARISON DASHBOARD" ) for n, ( nfilename, mesh ) in enumerate( self.meshes.items(), 1 ): - # print( n, nfilename ) coords = self.getCoordinatesDoublePrecision( mesh ) tetrahedraIds, tetrahedraConnectivity = self.extractTetConnectivity( mesh ) ntets = len( tetrahedraIds ) @@ -132,7 +163,6 @@ def applyFilter( self: Self ) -> None: # # ==================== Print Distribution Statistics ==================== # Problem element counts - highAspectRatio = np.sum( aspectRatio > 100 ) lowShapeQuality = np.sum( shapeQuality < 0.3 ) lowFlatness = np.sum( flatnessRatio < 0.01 ) @@ -154,7 +184,6 @@ def applyFilter( self: Self ) -> None: } # Overall quality scores - excellent = np.sum( qualityScore > 80 ) / len( qualityScore ) * 100 good = np.sum( ( qualityScore > 60 ) & ( qualityScore <= 80 ) ) / len( qualityScore ) * 100 fair = np.sum( ( qualityScore > 30 ) & ( qualityScore <= 60 ) ) / len( qualityScore ) * 100 @@ -187,7 +216,6 @@ def applyFilter( self: Self ) -> None: self.__orderMeshes() - # Percentile information self.printPercentileAnalysis() self.printQualityIssueSummary() @@ -199,6 +227,8 @@ def applyFilter( self: Self ) -> None: self.computeDeltasFromBest() self.createComparisonDashboard() + self.logger.info( f"The filter { self.logger.name } succeeded." ) + def getCoordinatesDoublePrecision( self, mesh: vtkDataSet ) -> npt.NDArray[ np.float64 ]: """Get coordinates with double precision. @@ -238,8 +268,8 @@ def extractTetConnectivity( self: Self, for cellID in range( ncells ): if mesh.GetCellType( cellID ) == vtk.VTK_TETRA: cell = mesh.GetCell( cellID ) - point_ids = cell.GetPointIds() - conn = [ point_ids.GetId( i ) for i in range( 4 ) ] + pointIds = cell.GetPointIds() + conn = [ pointIds.GetId( i ) for i in range( 4 ) ] tetrahedraIds.append( cellID ) tetrahedraConnectivity.append( conn ) @@ -325,11 +355,11 @@ def computeDihedralAngle( normal1: npt.NDArray[ np.float64 ], npt.NDArray[ np.float64 ]: Dihedral angle """ - n1_norm = normal1 / np.maximum( np.linalg.norm( normal1, axis=1, keepdims=True ), 1e-15 ) - n2_norm = normal2 / np.maximum( np.linalg.norm( normal2, axis=1, keepdims=True ), 1e-15 ) - cos_angle = np.sum( n1_norm * n2_norm, axis=1 ) - cos_angle = np.clip( cos_angle, -1.0, 1.0 ) - angle = np.arccos( cos_angle ) * 180.0 / np.pi + n1Norm = normal1 / np.maximum( np.linalg.norm( normal1, axis=1, keepdims=True ), 1e-15 ) + n2Norm = normal2 / np.maximum( np.linalg.norm( normal2, axis=1, keepdims=True ), 1e-15 ) + cosAngle = np.sum( n1Norm * n2Norm, axis=1 ) + cosAngle = np.clip( cosAngle, -1.0, 1.0 ) + angle = np.arccos( cosAngle ) * 180.0 / np.pi return angle # Face normals @@ -547,61 +577,62 @@ def computeDeltasFromBest( self: Self ) -> None: valueBest = self.medians[ self.best ][ metric ] self.deltas[ n ][ metric ] = ( ( value - valueBest ) / valueBest * 100 ) if valueBest > 0 else 0 - dtets = [ + deltaTets = [ f"{self.deltas[ n ][ 'tetrahedra' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - dar = [ + deltaAspectRatio = [ f"{self.deltas[ n ][ 'aspectRatio' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - dsq = [ + deltaShapeQuality = [ f"{self.deltas[ n ][ 'shapeQuality' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - dvol = [ + deltaVolume = [ f"{self.deltas[ n ][ 'volume' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - d_min_edge = [ + deltaMinEdge = [ f"{self.deltas[ n ][ 'minEdge' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - d_maxEdge = [ + deltaMaxEdge = [ f"{self.deltas[ n ][ 'maxEdge' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - d_edge_ratio = [ + deltaEdgeRatio = [ f"{self.deltas[ n ][ 'edgeRatio' ]:>+12,.1f}%" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] names = [ f"{f'Mesh {n}':>13}" if n != self.best else "" for n, _ in enumerate( self.meshes, 1 ) ] - self.logger.info( f"Changes vs BEST [Mesh {self.best}]:\n" + f"{' Mesh:':<20}{('').join( names )}\n" + - f"{' Tetrahedra:':<20}{('').join(dtets)}\n" + - f"{' Aspect Ratio:':<20}{('').join( dar)}\n" + - f"{' Shape Quality:':<20}{('').join( dsq)}\n" + f"{' Volume:':<20}{('').join( dvol)}\n" + - f"{' Min Edge Length:':<20}{('').join( d_min_edge)}\n" + - f"{' Max Edge Length:':<20}{('').join( d_maxEdge)}\n" + - f"{' Edge Length Ratio:':<20}{('').join( d_edge_ratio)}\n" ) + self.logger.info( f"Changes vs BEST [Mesh {self.best}]:\n" + f"{' Mesh:':<20}{('').join(names)}\n" + + f"{' Tetrahedra:':<20}{('').join(deltaTets)}\n" + + f"{' Aspect Ratio:':<20}{('').join(deltaAspectRatio)}\n" + + f"{' Shape Quality:':<20}{('').join(deltaShapeQuality)}\n" + + f"{' Volume:':<20}{('').join(deltaVolume)}\n" + + f"{' Min Edge Length:':<20}{('').join(deltaMinEdge)}\n" + + f"{' Max Edge Length:':<20}{('').join(deltaMaxEdge)}\n" + + f"{' Edge Length Ratio:':<20}{('').join(deltaEdgeRatio)}\n" ) def createComparisonDashboard( self: Self ) -> None: """Create the comparison dashboard.""" lbl = [ f'Mesh {n}' for n, _ in enumerate( self.meshes, 1 ) ] # Determine smart plot limits - ar_99 = [] + ar99 = [] for n, _ in enumerate( self.meshes, 1 ): - ar_99.append( np.percentile( self.validMetrics[ n ][ "aspectRatio" ], 99 ) ) + ar99.append( np.percentile( self.validMetrics[ n ][ "aspectRatio" ], 99 ) ) - ar_99_max = np.max( np.array( ar_99 ) ) + ar99Max = np.max( np.array( ar99 ) ) - if ar_99_max < 10: - ar_plot_limit = 100 - elif ar_99_max < 100: - ar_plot_limit = 1000 + if ar99Max < 10: + arPlotLimit = 100 + elif ar99Max < 100: + arPlotLimit = 1000 else: - ar_plot_limit = 10000 + arPlotLimit = 10000 # Set style plt.rcParams[ 'figure.facecolor' ] = 'white' @@ -618,7 +649,7 @@ def createComparisonDashboard( self: Self ) -> None: fig = plt.figure( figsize=( 25, 20 ) ) # Row 1: Executive Summary (3 columns - wider) - gs_row1 = gridspec.GridSpec( 1, 3, figure=fig, left=0.05, right=0.95, top=0.96, bottom=0.84, wspace=0.20 ) + gs_row1 = gridspec.GridSpec( 1, 3, figure=fig, left=0.05, right=0.95, top=0.94, bottom=0.84, wspace=0.20 ) # Rows 2-5: Main dashboard (5 columns each) gs_main = gridspec.GridSpec( 4, @@ -626,16 +657,16 @@ def createComparisonDashboard( self: Self ) -> None: figure=fig, left=0.05, right=0.95, - top=0.82, + top=0.80, bottom=0.05, hspace=0.35, wspace=0.30 ) # Title suptitle = 'Mesh Quality Comparison Dashboard (Progressive Detail Layout)\n' - for n, _ in enumerate( self.meshes, 1 ): - # suptitle += f'Mesh {n}: {ntets:,} tets\t' - suptitle += f'Mesh {n}: {self.tets[n]:<15} tets' + suptitle += (' - ').join( [ f'Mesh {n}: {self.tets[n]} tets ' for n,_ in enumerate( self.meshes, 1)] ) + # for n, _ in enumerate( self.meshes, 1 ): + # suptitle += f'Mesh {n}: {self.tets[n]:<15} tets ' fig.suptitle( suptitle, fontsize=16, fontweight='bold', y=0.99 ) # Color scheme @@ -666,7 +697,7 @@ def createComparisonDashboard( self: Self ) -> None: # Add summary text #### ONLY BEST AND WORST MESH? ax1.text( 0.98, - 0.98, + 0.92, f'Median Score:\n{f"M{self.best}[+]:":<5}{np.median(self.validMetrics[self.best][ "qualityScore" ]):.1f}\n' + f'{f"M{self.worst}[-]:":<5}{np.median(self.validMetrics[self.worst][ "qualityScore" ]):.1f}\n\n' + f'Excellent (>80):\n{f"M{self.best}[+]:":<5}{self.qualityScore[self.best]["excellent"]:.1f}%\n' + @@ -684,7 +715,11 @@ def createComparisonDashboard( self: Self ) -> None: ax1.set_xlabel( 'Combined Quality Score', fontweight='bold' ) ax1.set_ylabel( 'Count', fontweight='bold' ) - ax1.set_title( 'OVERALL MESH QUALITY VERDICT', fontsize=12, fontweight='bold', color='darkblue', pad=10 ) + ax1.set_title( 'OVERALL MESH QUALITY VERDICT', + fontsize=12, + fontweight='bold', + color='darkblue', + pad=10 ) ax1.legend( loc='upper left', fontsize=9 ) ax1.grid( True, alpha=0.3 ) @@ -705,10 +740,10 @@ def createComparisonDashboard( self: Self ) -> None: idx = self.sample[ n ] - mask1_plot = aspectRatio[ idx ] < ar_plot_limit + mask1Plot = aspectRatio[ idx ] < arPlotLimit - ax2.scatter( aspectRatio[ idx ][ mask1_plot ], - shapeQuality[ idx ][ mask1_plot ], + ax2.scatter( aspectRatio[ idx ][ mask1Plot ], + shapeQuality[ idx ][ mask1Plot ], alpha=0.4, s=5, color=color[ n - 1 ], @@ -721,18 +756,18 @@ def createComparisonDashboard( self: Self ) -> None: ax2.axvline( x=100, color='orange', linestyle='--', linewidth=2, alpha=0.8, label='High AR (> 100)', zorder=5 ) # Highlight problem zone - problem_zone = Rectangle( ( 100, 0 ), - ar_plot_limit - 100, - 0.3, - alpha=0.2, - facecolor='red', - edgecolor='none', - zorder=0 ) - ax2.add_patch( problem_zone ) + problemZone = Rectangle( ( 100, 0 ), + arPlotLimit - 100, + 0.3, + alpha=0.2, + facecolor='red', + edgecolor='none', + zorder=0 ) + ax2.add_patch( problemZone ) # Count ALL elements np.sum( ( aspectRatio > 100 ) & ( shapeQuality < 0.3 ) ) - np.sum( aspectRatio > ar_plot_limit ) + np.sum( aspectRatio > arPlotLimit ) # Problem annotation annotateIssues = ( '\n' ).join( @@ -762,7 +797,7 @@ def createComparisonDashboard( self: Self ) -> None: fontweight='bold', color='darkred', pad=10 ) - ax2.set_xlim( ( 1, ar_plot_limit ) ) + ax2.set_xlim( ( 1, arPlotLimit ) ) ax2.set_ylim( ( 0, 1.05 ) ) ax2.legend( loc='upper right', fontsize=7, framealpha=0.95 ) ax2.grid( True, alpha=0.3 ) @@ -772,60 +807,60 @@ def createComparisonDashboard( self: Self ) -> None: ax3.axis( 'off' ) summaryStats = [] - summaryStats.append( [ 'CRITICAL ISSUE', 'WORST', 'BEST', 'Change' ] ) + summaryStats.append( [ 'CRITICAL ISSUE', f'BEST [M{self.best}]', f'WORST [M{self.worst}]', 'CHANGE' ] ) summaryStats.append( [ '─' * 18, '─' * 10, '─' * 10, '─' * 10 ] ) criticalCombo = self.issues[ self.best ][ "criticalCombo" ] - critical_combo2 = self.issues[ self.worst ][ "criticalCombo" ] + criticalCombo2 = self.issues[ self.worst ][ "criticalCombo" ] aspectRatio = self.validMetrics[ self.best ][ "aspectRatio" ] - ar2 = self.validMetrics[ self.worst ][ "aspectRatio" ] + aspectRatio2 = self.validMetrics[ self.worst ][ "aspectRatio" ] highAspectRatio = self.issues[ self.best ][ "highAspectRatio" ] - high_ar2 = self.issues[ self.worst ][ "highAspectRatio" ] + highAspectRatio2 = self.issues[ self.worst ][ "highAspectRatio" ] lowShapeQuality = self.issues[ self.best ][ "lowShapeQuality" ] - low_sq2 = self.issues[ self.worst ][ "lowShapeQuality" ] + lowShapeQuality2 = self.issues[ self.worst ][ "lowShapeQuality" ] criticalMinDihedral = self.issues[ self.best ][ "criticalMinDihedral" ] - critical_dih2 = self.issues[ self.worst ][ "criticalMinDihedral" ] + criticalMinDihedral2 = self.issues[ self.worst ][ "criticalMinDihedral" ] criticalMaxDihedral = self.issues[ self.best ][ "criticalMaxDihedral" ] - critical_max_dih2 = self.issues[ self.worst ][ "criticalMaxDihedral" ] + criticalMaxDihedral2 = self.issues[ self.worst ][ "criticalMaxDihedral" ] highEdgeRatio = self.issues[ self.best ][ "highEdgeRatio" ] highEdgeRatio2 = self.issues[ self.worst ][ "highEdgeRatio" ] summaryStats.append( [ - 'CRITICAL Combo', f'{criticalCombo:,}', f'{critical_combo2:,}', - f'{((critical_combo2-criticalCombo)/max(criticalCombo,1)*100):+.1f}%' if criticalCombo > 0 else 'N/A' + 'CRITICAL Combo', f'{criticalCombo:,}', f'{criticalCombo2:,}', + f'{((criticalCombo2-criticalCombo)/max(criticalCombo,1)*100):+.1f}%' if criticalCombo > 0 else 'N/A' ] ) summaryStats.append( [ - '(AR>100 & Q<0.3', f'({criticalCombo/len(aspectRatio)*100:.2f}%)', f'({critical_combo2/len(ar2)*100:.2f}%)', - '' + '(AR>100 & Q<0.3', f'({criticalCombo/len(aspectRatio)*100:.2f}%)', + f'({criticalCombo2/len(aspectRatio2)*100:.2f}%)', '' ] ) summaryStats.append( [ ' & MinDih<5°)', '', '', '' ] ) summaryStats.append( [ '', '', '', '' ] ) summaryStats.append( [ - 'AR > 100', f'{highAspectRatio:,}', f'{high_ar2:,}', - f'{((high_ar2-highAspectRatio)/max(highAspectRatio,1)*100):+.1f}%' + 'AR > 100', f'{highAspectRatio:,}', f'{highAspectRatio2:,}', + f'{((highAspectRatio2-highAspectRatio)/max(highAspectRatio,1)*100):+.1f}%' ] ) summaryStats.append( [ - 'Quality < 0.3', f'{lowShapeQuality:,}', f'{low_sq2:,}', - f'{((low_sq2-lowShapeQuality)/max(lowShapeQuality,1)*100):+.1f}%' + 'Quality < 0.3', f'{lowShapeQuality:,}', f'{lowShapeQuality2:,}', + f'{((lowShapeQuality2-lowShapeQuality)/max(lowShapeQuality,1)*100):+.1f}%' ] ) summaryStats.append( [ - 'MinDih < 5°', f'{criticalMinDihedral:,}', f'{critical_dih2:,}', - f'{((critical_dih2-criticalMinDihedral)/max(criticalMinDihedral,1)*100):+.1f}%' + 'MinDih < 5°', f'{criticalMinDihedral:,}', f'{criticalMinDihedral2:,}', + f'{((criticalMinDihedral2-criticalMinDihedral)/max(criticalMinDihedral,1)*100):+.1f}%' if criticalMinDihedral > 0 else 'N/A' ] ) summaryStats.append( [ - 'MaxDih > 175°', f'{criticalMaxDihedral:,}', f'{critical_max_dih2:,}', - f'{((critical_max_dih2-criticalMaxDihedral)/max(criticalMaxDihedral,1)*100):+.1f}%' + 'MaxDih > 175°', f'{criticalMaxDihedral:,}', f'{criticalMaxDihedral2:,}', + f'{((criticalMaxDihedral2-criticalMaxDihedral)/max(criticalMaxDihedral,1)*100):+.1f}%' if criticalMaxDihedral > 0 else 'N/A' ] ) @@ -859,7 +894,7 @@ def createComparisonDashboard( self: Self ) -> None: # Style header for i in range( 4 ): table[ ( 0, i ) ].set_facecolor( '#34495e' ) - table[ ( 0, i ) ].set_text_props( weight='bold', color='white', fontsize=9 ) + table[ ( 0, i ) ].set_text_props( weight='bold', color='black', fontsize=9 ) # Highlight CRITICAL row for col in range( 4 ): @@ -869,9 +904,9 @@ def createComparisonDashboard( self: Self ) -> None: # Color code changes for row in [ 2, 6, 7, 8, 9, 10, 13, 14, 15 ]: if row < len( summaryStats ): - change_text = summaryStats[ row ][ 3 ] - if '%' in change_text and change_text != 'N/A': - val = float( change_text.replace( '%', '' ).replace( '+', '' ) ) + changeText = summaryStats[ row ][ 3 ] + if '%' in changeText and changeText != 'N/A': + val = float( changeText.replace( '%', '' ).replace( '+', '' ) ) if row in [ 2, 6, 7, 8, 9, 10, 15 ]: # Lower is better if val < -10: table[ ( row, 3 ) ].set_facecolor( '#d5f4e6' ) # Green @@ -883,7 +918,11 @@ def createComparisonDashboard( self: Self ) -> None: elif val < -10: table[ ( row, 3 ) ].set_facecolor( '#fadbd8' ) - ax3.set_title( 'CRITICAL ISSUES SUMMARY', fontsize=12, fontweight='bold', color='darkgreen', pad=10 ) + ax3.set_title( 'CRITICAL ISSUES SUMMARY', + fontsize=12, + fontweight='bold', + color='darkgreen', + pad=10 ) # ==================== ROW 2: QUALITY DISTRIBUTIONS ==================== @@ -907,12 +946,12 @@ def createComparisonDashboard( self: Self ) -> None: # 5. Aspect Ratio Histogram ax5 = fig.add_subplot( gs_main[ 0, 1 ] ) - ar_max = np.array( [ self.validMetrics[ n ][ "aspectRatio" ].max() for n, _ in enumerate( self.meshes, 1 ) ] ) + arMax = np.array( [ self.validMetrics[ n ][ "aspectRatio" ].max() for n, _ in enumerate( self.meshes, 1 ) ] ) - bins = np.logspace( 0, np.log10( min( ar_plot_limit, ar_max.max() ) ), 40 ).tolist() + bins = np.logspace( 0, np.log10( min( arPlotLimit, arMax.max() ) ), 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): aspectRatio = self.validMetrics[ n ][ 'aspectRatio' ] - ax5.hist( aspectRatio[ aspectRatio < ar_plot_limit ], + ax5.hist( aspectRatio[ aspectRatio < arPlotLimit ], bins=bins, alpha=0.6, label=f'Mesh {n}', @@ -967,10 +1006,10 @@ def createComparisonDashboard( self: Self ) -> None: # 8. Volume Histogram ax8 = fig.add_subplot( gs_main[ 0, 4 ] ) - vol_min = np.array( [ self.validMetrics[ n ][ "volume" ].min() for n, _ in enumerate( self.meshes, 1 ) ] ).min() - vol_max = np.array( [ self.validMetrics[ n ][ "volume" ].max() for n, _ in enumerate( self.meshes, 1 ) ] ).max() + volMin = np.array( [ self.validMetrics[ n ][ "volume" ].min() for n, _ in enumerate( self.meshes, 1 ) ] ).min() + volMax = np.array( [ self.validMetrics[ n ][ "volume" ].max() for n, _ in enumerate( self.meshes, 1 ) ] ).max() - bins = np.logspace( np.log10( vol_min ), np.log10( vol_max ), 40 ).tolist() + bins = np.logspace( np.log10( volMin ), np.log10( volMax ), 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): volume = self.validMetrics[ n ][ "volume" ] ax8.hist( volume, @@ -992,7 +1031,7 @@ def createComparisonDashboard( self: Self ) -> None: # 9. Shape Quality Box Plot ax9 = fig.add_subplot( gs_main[ 1, 0 ] ) sq = [ self.validMetrics[ n ][ "shapeQuality" ] for n, _ in enumerate( self.meshes, 1 ) ] - bp1 = ax9.boxplot( sq, label=lbl, patch_artist=True, showfliers=False ) + bp1 = ax9.boxplot( sq, labels=lbl, patch_artist=True, showfliers=False ) # type: ignore[call-arg] ax9.set_ylabel( 'Shape Quality', fontweight='bold' ) ax9.set_title( 'Shape Quality Comparison', fontweight='bold' ) ax9.grid( True, alpha=0.3, axis='y' ) @@ -1000,7 +1039,7 @@ def createComparisonDashboard( self: Self ) -> None: # 10. Aspect Ratio Box Plot ax10 = fig.add_subplot( gs_main[ 1, 1 ] ) ar = [ self.validMetrics[ n ][ "aspectRatio" ] for n, _ in enumerate( self.meshes, 1 ) ] - bp2 = ax10.boxplot( ar, label=lbl, patch_artist=True, showfliers=False ) + bp2 = ax10.boxplot( ar, labels=lbl, patch_artist=True, showfliers=False ) # type: ignore[call-arg] ax10.set_yscale( 'log' ) ax10.set_ylabel( 'Aspect Ratio (log)', fontweight='bold' ) ax10.set_title( 'Aspect Ratio Comparison', fontweight='bold' ) @@ -1009,15 +1048,15 @@ def createComparisonDashboard( self: Self ) -> None: # 11. Min Dihedral Box Plot ax11 = fig.add_subplot( gs_main[ 1, 2 ] ) minDihedral = [ self.validMetrics[ n ][ "minDihedral" ] for n, _ in enumerate( self.meshes, 1 ) ] - bp3 = ax11.boxplot( minDihedral, label=lbl, patch_artist=True, showfliers=False ) + bp3 = ax11.boxplot( minDihedral, labels=lbl, patch_artist=True, showfliers=False ) # type: ignore[call-arg] ax11.set_ylabel( 'Min Dihedral Angle (degrees)', fontweight='bold' ) ax11.set_title( 'Min Dihedral Comparison', fontweight='bold' ) ax11.grid( True, alpha=0.3, axis='y' ) # 12. Edge Ratio Box Plot ax12 = fig.add_subplot( gs_main[ 1, 3 ] ) - edge_ratio = [ self.validMetrics[ n ][ "edgeRatio" ] for n, _ in enumerate( self.meshes, 1 ) ] - bp4 = ax12.boxplot( edge_ratio, label=lbl, patch_artist=True, showfliers=False ) + edgeRatio = [ self.validMetrics[ n ][ "edgeRatio" ] for n, _ in enumerate( self.meshes, 1 ) ] + bp4 = ax12.boxplot( edgeRatio, labels=lbl, patch_artist=True, showfliers=False ) # type: ignore[call-arg] ax12.set_yscale( 'log' ) ax12.set_ylabel( 'Edge Length Ratio (log)', fontweight='bold' ) ax12.set_title( 'Edge Ratio Comparison', fontweight='bold' ) @@ -1026,7 +1065,7 @@ def createComparisonDashboard( self: Self ) -> None: # 13. Volume Box Plot ax13 = fig.add_subplot( gs_main[ 1, 4 ] ) vol = [ self.validMetrics[ n ][ "volume" ] for n, _ in enumerate( self.meshes, 1 ) ] - bp5 = ax13.boxplot( vol, label=lbl, patch_artist=True, showfliers=False ) + bp5 = ax13.boxplot( vol, labels=lbl, patch_artist=True, showfliers=False ) # type: ignore[call-arg] ax13.set_yscale( 'log' ) ax13.set_ylabel( 'Volume (log)', fontweight='bold' ) ax13.set_title( 'Volume Comparison', fontweight='bold' ) @@ -1051,7 +1090,7 @@ def createComparisonDashboard( self: Self ) -> None: idx = self.sample[ n ] aspectRatio = self.validMetrics[ n ][ 'aspectRatio' ] shapeQuality = self.validMetrics[ n ][ 'shapeQuality' ] - mask1 = aspectRatio[ idx ] < ar_plot_limit + mask1 = aspectRatio[ idx ] < arPlotLimit ax14.scatter( aspectRatio[ idx ][ mask1 ], shapeQuality[ idx ][ mask1 ], alpha=0.4, @@ -1063,7 +1102,7 @@ def createComparisonDashboard( self: Self ) -> None: ax14.set_xlabel( 'Aspect Ratio', fontweight='bold' ) ax14.set_ylabel( 'Shape Quality', fontweight='bold' ) ax14.set_title( 'Shape Quality vs Aspect Ratio', fontweight='bold' ) - ax14.set_xlim( ( 1, ar_plot_limit ) ) + ax14.set_xlim( ( 1, arPlotLimit ) ) ax14.set_ylim( ( 0, 1.05 ) ) ax14.legend( loc='upper right', fontsize=7 ) ax14.grid( True, alpha=0.3 ) @@ -1074,7 +1113,7 @@ def createComparisonDashboard( self: Self ) -> None: idx = self.sample[ n ] aspectRatio = self.validMetrics[ n ][ "aspectRatio" ] flatnessRatio = self.validMetrics[ n ][ 'flatnessRatio' ] - mask1 = aspectRatio[ idx ] < ar_plot_limit + mask1 = aspectRatio[ idx ] < arPlotLimit ax15.scatter( aspectRatio[ idx ][ mask1 ], flatnessRatio[ idx ][ mask1 ], alpha=0.4, @@ -1087,7 +1126,7 @@ def createComparisonDashboard( self: Self ) -> None: ax15.set_xlabel( 'Aspect Ratio', fontweight='bold' ) ax15.set_ylabel( 'Flatness Ratio', fontweight='bold' ) ax15.set_title( 'Aspect Ratio vs Flatness', fontweight='bold' ) - ax15.set_xlim( ( 1, ar_plot_limit ) ) + ax15.set_xlim( ( 1, arPlotLimit ) ) ax15.legend( loc='upper right', fontsize=7 ) ax15.grid( True, alpha=0.3 ) @@ -1097,7 +1136,7 @@ def createComparisonDashboard( self: Self ) -> None: idx = self.sample[ n ] aspectRatio = self.validMetrics[ n ][ "aspectRatio" ] volume = self.validMetrics[ n ][ 'volume' ] - mask1 = aspectRatio[ idx ] < ar_plot_limit + mask1 = aspectRatio[ idx ] < arPlotLimit ax16.scatter( volume[ idx ][ mask1 ], aspectRatio[ idx ][ mask1 ], alpha=0.4, @@ -1110,7 +1149,7 @@ def createComparisonDashboard( self: Self ) -> None: ax16.set_xlabel( 'Volume', fontweight='bold' ) ax16.set_ylabel( 'Aspect Ratio', fontweight='bold' ) ax16.set_title( 'Volume vs Aspect Ratio', fontweight='bold' ) - ax16.set_ylim( ( 1, ar_plot_limit ) ) + ax16.set_ylim( ( 1, arPlotLimit ) ) ax16.legend( loc='upper right', fontsize=7 ) ax16.grid( True, alpha=0.3 ) @@ -1139,9 +1178,9 @@ def createComparisonDashboard( self: Self ) -> None: for n, _ in enumerate( self.meshes, 1 ): idx = self.sample[ n ] volume = self.validMetrics[ n ][ 'volume' ] - edge_ratio = self.validMetrics[ n ][ 'edgeRatio' ] + edgeRatio = self.validMetrics[ n ][ 'edgeRatio' ] ax18.scatter( volume[ idx ], - edge_ratio[ idx ], + edgeRatio[ idx ], alpha=0.4, s=5, color=color[ n - 1 ], @@ -1160,12 +1199,12 @@ def createComparisonDashboard( self: Self ) -> None: # 19. Min Edge Length Histogram ax19 = fig.add_subplot( gs_main[ 3, 0 ] ) - edge_min = np.array( [ self.validMetrics[ n ][ "minEdge" ].min() - for n, _ in enumerate( self.meshes, 1 ) ] ).min() - edge_max_min = np.array( [ self.validMetrics[ n ][ "minEdge" ].max() - for n, _ in enumerate( self.meshes, 1 ) ] ).min() + edgeMinMin = np.array( [ self.validMetrics[ n ][ "minEdge" ].min() + for n, _ in enumerate( self.meshes, 1 ) ] ).min() + edgeMaxMin = np.array( [ self.validMetrics[ n ][ "minEdge" ].max() + for n, _ in enumerate( self.meshes, 1 ) ] ).min() - bins = np.logspace( np.log10( edge_min ), np.log10( edge_max_min ), 40 ).tolist() + bins = np.logspace( np.log10( edgeMinMin ), np.log10( edgeMaxMin ), 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): minEdge = self.validMetrics[ n ][ 'minEdge' ] @@ -1187,12 +1226,12 @@ def createComparisonDashboard( self: Self ) -> None: # 20. Max Edge Length Histogram ax20 = fig.add_subplot( gs_main[ 3, 1 ] ) - edge_max = np.array( [ self.validMetrics[ n ][ "maxEdge" ].max() - for n, _ in enumerate( self.meshes, 1 ) ] ).max() - edge_min_max = np.array( [ self.validMetrics[ n ][ "maxEdge" ].min() - for n, _ in enumerate( self.meshes, 1 ) ] ).min() + edgeMaxMax = np.array( [ self.validMetrics[ n ][ "maxEdge" ].max() + for n, _ in enumerate( self.meshes, 1 ) ] ).max() + edgeMinMax = np.array( [ self.validMetrics[ n ][ "maxEdge" ].min() + for n, _ in enumerate( self.meshes, 1 ) ] ).min() - bins = np.logspace( np.log10( edge_min_max ), np.log10( edge_max ), 40 ).tolist() + bins = np.logspace( np.log10( edgeMinMax ), np.log10( edgeMaxMax ), 40 ).tolist() for n, _ in enumerate( self.meshes, 1 ): maxEdge = self.validMetrics[ n ][ "maxEdge" ] ax20.hist( maxEdge, @@ -1237,13 +1276,14 @@ def createComparisonDashboard( self: Self ) -> None: ] + [ self.validMetrics[ n ][ "maxDihedral" ] for n, _ in enumerate( self.meshes, 1 ) ] lbl_boxplot = [ f'M{n}Min' for n, _ in enumerate( self.meshes, 1 ) ] + [ f'M{n}Max' for n, _ in enumerate( self.meshes, 1 ) ] - boxplot_color = [ n for n, _ in enumerate( self.meshes, 1 ) ] + [ n for n, _ in enumerate( self.meshes, ) ] - bp_dih = ax22.boxplot( dih, - positions=positions, - label=lbl_boxplot, - patch_artist=True, - showfliers=False, - widths=0.6 ) + boxplot_color = [ n for n, _ in enumerate( self.meshes, ) ] * 2 + bp_dih = ax22.boxplot( + dih, + positions=positions, + labels=lbl_boxplot, # type: ignore[call-arg] + patch_artist=True, + showfliers=False, + widths=0.6 ) for m in range( len( self.meshes ) * 2 ): bp_dih[ 'boxes' ][ m ].set_facecolor( color[ boxplot_color[ m ] ] ) bp_dih[ 'medians' ][ m ].set_color( "black" ) @@ -1273,12 +1313,16 @@ def createComparisonDashboard( self: Self ) -> None: ax23.grid( True, alpha=0.3 ) # Save figure - output_png = '/data/pau901/SIM_CS/04_WORKSPACE/USERS/PalomaMartinez/geosPythonPackages/TESST/mesh_comparison.png' - print( f"\nSaving dashboard to: {output_png}" ) - plt.savefig( output_png, dpi=300, bbox_inches='tight', facecolor='white' ) - print( "Dashboard saved successfully!" ) - def setDashboardFilename( self: Self, filename: str ) + plt.savefig( self.filename, dpi=300, bbox_inches='tight', facecolor='white' ) + self.logger.info( f"Dashboard saved successfully: {self.filename}" ) + + def setDashboardFilename( self: Self, filename: str ) -> None: + """Set comparison dashboard output filename. + + Args: + filename (str): Output filename. + """ self.filename = filename def __loggerSection( self: Self, sectionName: str ) -> None: @@ -1289,19 +1333,19 @@ def __loggerSection( self: Self, sectionName: str ) -> None: def __orderMeshes( self: Self ) -> None: """Proposition of ordering as fonction of median quality score.""" self.__loggerSection( "ORDERING MESHES (from median quality score)" ) - median_score = { + medianScore = { n: np.median( self.validMetrics[ n ][ "qualityScore" ] ) for n, _ in enumerate( self.meshes, 1 ) } - sorted_meshes = sorted( median_score.items(), key=lambda x: x[ 1 ], reverse=True ) - self.sorted = sorted_meshes - self.best = sorted_meshes[ 0 ][ 0 ] - self.worst = sorted_meshes[ -1 ][ 0 ] + sortedMeshes = sorted( medianScore.items(), key=lambda x: x[ 1 ], reverse=True ) + self.sorted = sortedMeshes + self.best = sortedMeshes[ 0 ][ 0 ] + self.worst = sortedMeshes[ -1 ][ 0 ] self.logger.info( "Mesh order from median quality score:" ) - top = [ f"Mesh {m[0]} ({m[1]:.2f})" for m in sorted_meshes ] - toprint = ( " > " ).join( top ) + top = [ f"Mesh {m[0]} ({m[1]:.2f})" for m in sortedMeshes ] + toprint: str = ( " > " ).join( top ) self.logger.info( " [+] " + toprint + " [-]\n" ) def compareIssuesFromBest( self: Self ) -> None: From 07af2676970afddea3fb2fb6f9eda779844db4ab Mon Sep 17 00:00:00 2001 From: Paloma Martinez <104762252+paloma-martinez@users.noreply.github.com> Date: Mon, 2 Feb 2026 11:47:38 +0100 Subject: [PATCH 06/11] Add corresponding plugin --- .../pv/plugins/qc/PVTetQualityAnalysis.py | 114 ++++++++++++++++++ 1 file changed, 114 insertions(+) create mode 100644 geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py diff --git a/geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py b/geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py new file mode 100644 index 00000000..c3ea5660 --- /dev/null +++ b/geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py @@ -0,0 +1,114 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright 2023-2024 TotalEnergies. +# SPDX-FileContributor: Paloma Martinez +# ruff: noqa: E402 # disable Module level import not at top of file +import sys +from pathlib import Path +from typing_extensions import Self, Optional + +from vtkmodules.vtkCommonCore import vtkInformation, vtkInformationVector +from vtkmodules.vtkCommonDataModel import vtkUnstructuredGrid + +# update sys.path to load all GEOS Python Package dependencies +geos_pv_path: Path = Path( __file__ ).parent.parent.parent.parent.parent.parent +sys.path.insert( 0, str( geos_pv_path / "src" ) ) +from geos.pv.utils.config import update_paths + +update_paths() + +from paraview.util.vtkAlgorithm import ( # type: ignore[import-not-found] + VTKPythonAlgorithmBase, smdomain, smhint, smproperty, smproxy ) +# source: https://github.com/Kitware/ParaView/blob/master/Wrapping/Python/paraview/util/vtkAlgorithm.py +from paraview.detail.loghandler import VTKHandler # type: ignore[import-not-found] +# source: https://github.com/Kitware/ParaView/blob/master/Wrapping/Python/paraview/detail/loghandler.py + +from geos.processing.pre_processing.TetQualityAnalysis import TetQualityAnalysis +from geos.pv.utils.details import FilterCategory + +__doc__ = f""" +Tetrahedra QC is a ParaView plugin filter that analyze and compare the tetrahedras of two given vtkUnstructured grid datasets. + +A figure with relevant quality metrics is saved at the end of the process for a visual comparison. + +To use it: + +* Load the plugin in ParaView: Tools > Manage Plugins ... > Load New ... > .../geosPythonPackages/geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis +* Select the first dataset +* Select the filter: Filters > { FilterCategory.QC.value } > Tetrahedras QC +* In the dialog box, select the `mesh 1` and the `mesh 2` +* Change the output filename if needed +* Apply +""" + + +@smproxy.filter( name="PVTetQualityAnalysis", label="Tetrahedra QC" ) +@smhint.xml( f'' ) +@smproperty.input( name="inputMesh", port_index=0, label="mesh 1" ) +@smdomain.datatype( + dataTypes=[ "vtkUnstructuredGrid" ], + composite_data_supported=True, +) +@smproperty.input( name="inputMesh2", port_index=1, label="mesh 2" ) +@smdomain.datatype( + dataTypes=[ "vtkUnstructuredGrid" ], + composite_data_supported=True, +) +class PVTetQualityAnalysis( VTKPythonAlgorithmBase ): + + def __init__( self: Self ) -> None: + """QC analysis of the tetrahedras from 2 meshes.""" + super().__init__( nInputPorts=2, inputType="vtkObject" ) + + self._filename: Optional[ str ] = None + self._meshes: dict[ str, vtkUnstructuredGrid ] = {} + self._outputFilename: bool = True + + @smproperty.stringvector( name="FilePath", label="File Path" ) + @smdomain.xml( """ + + Output file path. + + + + + """ ) + def SetFileName( self: Self, fname: str ) -> None: + """Specify filename for the output figure. + + Args: + fname (str): File path + """ + if self._filename != fname: + self._filename = fname + self.Modified() + + def RequestData( + self: Self, + request: vtkInformation, #noqa: F841 + inInfoVec: list[ vtkInformationVector ], + outInfoVec: vtkInformationVector, + ) -> int: + """Inherited from VTKPythonAlgorithmBase::RequestData. + + Args: + request (vtkInformation): Request. + inInfoVec (list[vtkInformationVector]): Input objects. + outInfoVec (vtkInformationVector): Output objects. + + Returns: + int: 1 if calculation successfully ended, 0 otherwise. + """ + for n in range( 2 ): + self._meshes[ str( n ) ] = self.GetInputData( inInfoVec, n, 0 ) + + tetQualityAnalysisFilter: TetQualityAnalysis = TetQualityAnalysis( self._meshes, True ) + + if len( tetQualityAnalysisFilter.logger.handlers ) == 0: + tetQualityAnalysisFilter.setLoggerHandler( VTKHandler() ) + + try: + tetQualityAnalysisFilter.applyFilter() + except Exception as e: + tetQualityAnalysisFilter.logger.error( f' FATAL ERROR due to:\n{e}' ) + + return 1 From 37b4611b6318f869912140bc5a018716a5d55de6 Mon Sep 17 00:00:00 2001 From: Paloma Martinez <104762252+paloma-martinez@users.noreply.github.com> Date: Mon, 2 Feb 2026 14:08:45 +0100 Subject: [PATCH 07/11] Adding a test --- .../mesh/stats/tetrahedraAnalysisHelpers.py | 203 + .../pre_processing/TetQualityAnalysis.py | 216 +- geos-processing/tests/conftest.py | 4 + geos-processing/tests/data/mesh1.vtu | 3534 +++++++++++++++++ geos-processing/tests/data/mesh1b.vtu | 2876 ++++++++++++++ .../tests/test_TetQualityAnalysis.py | 17 + 6 files changed, 6644 insertions(+), 206 deletions(-) create mode 100644 geos-mesh/src/geos/mesh/stats/tetrahedraAnalysisHelpers.py create mode 100755 geos-processing/tests/data/mesh1.vtu create mode 100755 geos-processing/tests/data/mesh1b.vtu create mode 100644 geos-processing/tests/test_TetQualityAnalysis.py diff --git a/geos-mesh/src/geos/mesh/stats/tetrahedraAnalysisHelpers.py b/geos-mesh/src/geos/mesh/stats/tetrahedraAnalysisHelpers.py new file mode 100644 index 00000000..d040e6ad --- /dev/null +++ b/geos-mesh/src/geos/mesh/stats/tetrahedraAnalysisHelpers.py @@ -0,0 +1,203 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright 2023-2024 TotalEnergies. +# SPDX-FileContributor: Bertrand Denel, Paloma Martinez +import numpy as np +import numpy.typing as npt +from typing_extensions import Any + +from vtkmodules.vtkCommonDataModel import vtkDataSet, VTK_TETRA + + +def getCoordinatesDoublePrecision( mesh: vtkDataSet ) -> npt.NDArray[ np.float64 ]: + """Get coordinates with double precision. + + Args: + mesh (vtkDataSet): Input mesh. + + Returns: + npt.NDArray[np.float64]: Coordinates + + """ + points = mesh.GetPoints() + npoints = points.GetNumberOfPoints() + + coords = np.zeros( ( npoints, 3 ), dtype=np.float64 ) + for i in range( npoints ): + point = points.GetPoint( i ) + coords[ i ] = [ point[ 0 ], point[ 1 ], point[ 2 ] ] + + return coords + + +def extractTetConnectivity( mesh: vtkDataSet ) -> tuple[ npt.NDArray[ np.float64 ], npt.NDArray[ np.float64 ] ]: + """Extract connectivity for all tetrahedra. + + Args: + mesh (vtkDataSet): Mesh to analyze. + + Returns: + tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]: + Cell IDS corresponding to tetrahedra, + Connectivity of these cells + """ + ncells = mesh.GetNumberOfCells() + tetrahedraIds = [] + tetrahedraConnectivity = [] + + for cellID in range( ncells ): + if mesh.GetCellType( cellID ) == VTK_TETRA: + cell = mesh.GetCell( cellID ) + pointIds = cell.GetPointIds() + conn = [ pointIds.GetId( i ) for i in range( 4 ) ] + tetrahedraIds.append( cellID ) + tetrahedraConnectivity.append( conn ) + + return np.array( tetrahedraIds ), np.array( tetrahedraConnectivity ) + + +def analyzeAllTets( n: int, coords: npt.NDArray[ np.float64 ], + connectivity: npt.NDArray[ np.float64 ] ) -> dict[ str, dict[ str, Any ] ]: + """Vectorized analysis of all tetrahedra. + + Args: + n (int): Mesh id. + coords (npt.NDArray[np.float64]): Tetrahedra coordinates. + connectivity (npt.NDArray[np.float64]): Connectivity. + + Returns: + dict[str, dict[str, Any]]: Dictionary with keys 'volumes', 'aspectRatio', 'radiusRatio', 'flatnessRatio', 'shapeQuality', 'minEdge', 'maxEdge', 'minDihedral', 'maxDihedral', 'dihedralRange' + """ + # Get coordinates for all vertices + v0 = coords[ connectivity[ :, 0 ] ] + v1 = coords[ connectivity[ :, 1 ] ] + v2 = coords[ connectivity[ :, 2 ] ] + v3 = coords[ connectivity[ :, 3 ] ] + + # Compute edges + e01 = v1 - v0 + e02 = v2 - v0 + e03 = v3 - v0 + e12 = v2 - v1 + e13 = v3 - v1 + e23 = v3 - v2 + + # Edge lengths + l01 = np.linalg.norm( e01, axis=1 ) + l02 = np.linalg.norm( e02, axis=1 ) + l03 = np.linalg.norm( e03, axis=1 ) + l12 = np.linalg.norm( e12, axis=1 ) + l13 = np.linalg.norm( e13, axis=1 ) + l23 = np.linalg.norm( e23, axis=1 ) + + allEdges = np.stack( [ l01, l02, l03, l12, l13, l23 ], axis=1 ) + minEdge = np.min( allEdges, axis=1 ) + maxEdge = np.max( allEdges, axis=1 ) + + # Volumes + cross_product = np.cross( e01, e02 ) + volumes = np.abs( np.sum( cross_product * e03, axis=1 ) / 6.0 ) + + # Face areas + face012 = 0.5 * np.linalg.norm( np.cross( e01, e02 ), axis=1 ) + face013 = 0.5 * np.linalg.norm( np.cross( e01, e03 ), axis=1 ) + face023 = 0.5 * np.linalg.norm( np.cross( e02, e03 ), axis=1 ) + face123 = 0.5 * np.linalg.norm( np.cross( e12, e13 ), axis=1 ) + + allFaces = np.stack( [ face012, face013, face023, face123 ], axis=1 ) + maxFaceArea = np.max( allFaces, axis=1 ) + totalSurfaceArea = np.sum( allFaces, axis=1 ) + + # Aspect ratio + minAltitude = 3.0 * volumes / np.maximum( maxFaceArea, 1e-15 ) + aspectRatio = maxEdge / np.maximum( minAltitude, 1e-15 ) + aspectRatio[ volumes < 1e-15 ] = np.inf + + # Radius ratio + inradius = 3.0 * volumes / np.maximum( totalSurfaceArea, 1e-15 ) + circumradius = ( maxEdge**3 ) / np.maximum( 24.0 * volumes, 1e-15 ) + radiusRatio = circumradius / np.maximum( inradius, 1e-15 ) + + # Flatness ratio + flatnessRatio = volumes / np.maximum( maxFaceArea * minEdge, 1e-15 ) + + # Shape quality + sumEdgeShapeQuality = np.sum( allEdges**2, axis=1 ) + shapeQuality = 12.0 * ( 3.0 * volumes )**( 2.0 / 3.0 ) / np.maximum( sumEdgeShapeQuality, 1e-15 ) + shapeQuality[ volumes < 1e-15 ] = 0.0 + + # Dihedral angles + def computeDihedralAngle( normal1: npt.NDArray[ np.float64 ], + normal2: npt.NDArray[ np.float64 ] ) -> npt.NDArray[ np.float64 ]: + """Compute dihedral angle between two face normals. + + Args: + normal1 (npt.NDArray[np.float64]): Normal vector 1 + normal2 (npt.NDArray[np.float64]): Normal vector 2 + + Returns: + npt.NDArray[ np.float64 ]: Dihedral angle + + """ + n1Norm = normal1 / np.maximum( np.linalg.norm( normal1, axis=1, keepdims=True ), 1e-15 ) + n2Norm = normal2 / np.maximum( np.linalg.norm( normal2, axis=1, keepdims=True ), 1e-15 ) + cosAngle = np.sum( n1Norm * n2Norm, axis=1 ) + cosAngle = np.clip( cosAngle, -1.0, 1.0 ) + angle = np.arccos( cosAngle ) * 180.0 / np.pi + return angle + + # Face normals + normal012: npt.NDArray[ np.float64 ] = np.cross( e01, e02 ) + normal013: npt.NDArray[ np.float64 ] = np.cross( e01, e03 ) + normal023: npt.NDArray[ np.float64 ] = np.cross( e02, e03 ) + normal123: npt.NDArray[ np.float64 ] = np.cross( e12, e13 ) + + # Dihedral angles for each edge + dihedral01 = computeDihedralAngle( normal012, normal013 ) + dihedral02 = computeDihedralAngle( normal012, normal023 ) + dihedral03 = computeDihedralAngle( normal013, normal023 ) + dihedral12 = computeDihedralAngle( normal012, normal123 ) + dihedral13 = computeDihedralAngle( normal013, normal123 ) + dihedral23 = computeDihedralAngle( normal023, normal123 ) + + allDihedrals = np.stack( [ dihedral01, dihedral02, dihedral03, dihedral12, dihedral13, dihedral23 ], axis=1 ) + + minDihedral = np.min( allDihedrals, axis=1 ) + maxDihedral = np.max( allDihedrals, axis=1 ) + dihedralRange = maxDihedral - minDihedral + + return { + 'volumes': volumes, + 'aspectRatio': aspectRatio, + 'radiusRatio': radiusRatio, + 'flatnessRatio': flatnessRatio, + 'shapeQuality': shapeQuality, + 'minEdge': minEdge, + 'maxEdge': maxEdge, + 'minDihedral': minDihedral, + 'maxDihedral': maxDihedral, + 'dihedralRange': dihedralRange + } + + +# Combined quality score +def computeQualityScore( aspectRatio: npt.NDArray[ np.float64 ], shapeQuality: npt.NDArray[ np.float64 ], + edgeRatio: npt.NDArray[ np.float64 ], + minDihedralAngle: npt.NDArray[ np.float64 ] ) -> npt.NDArray[ np.float64 ]: + """Compute combined quality score (0-100). + + Args: + aspectRatio(npt.NDArray[np.float64]): Aspect ratio + shapeQuality(npt.NDArray[np.float64]): Shape quality + edgeRatio(npt.NDArray[np.float64]): Edge ratio + minDihedralAngle(npt.NDArray[np.float64]): Minimal edge ratio + + Returns: + np.float64: Quality score + """ + aspectRatioNorm = np.clip( 1.0 / ( aspectRatio / 1.73 ), 0, 1 ) + shapeQualityNorm = shapeQuality + edgeRatioNorm = np.clip( 1.0 / edgeRatio, 0, 1 ) + dihedralMinNorm = np.clip( minDihedralAngle / 60.0, 0, 1 ) + score = ( 0.3 * aspectRatioNorm + 0.4 * shapeQualityNorm + 0.2 * edgeRatioNorm + 0.1 * dihedralMinNorm ) * 100 + + return score diff --git a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py index 058ac62b..7203ff2c 100644 --- a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py +++ b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py @@ -4,15 +4,17 @@ import logging import numpy as np import numpy.typing as npt -from typing_extensions import Self, Any #, Union +from typing_extensions import Self, Any from geos.utils.Logger import ( Logger, getLogger ) -import vtk from vtkmodules.vtkCommonDataModel import vtkDataSet import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from matplotlib.patches import Rectangle +from geos.mesh.stats.tetrahedraAnalysisHelpers import ( getCoordinatesDoublePrecision, extractTetConnectivity, + analyzeAllTets, computeQualityScore ) + __doc__ = """ TetQualityAnalysis module is a filter that performs an analysis of tetrahedras quality of one or several meshes and generates a plot as summary. @@ -104,8 +106,8 @@ def applyFilter( self: Self ) -> None: self.__loggerSection( "MESH COMPARISON DASHBOARD" ) for n, ( nfilename, mesh ) in enumerate( self.meshes.items(), 1 ): - coords = self.getCoordinatesDoublePrecision( mesh ) - tetrahedraIds, tetrahedraConnectivity = self.extractTetConnectivity( mesh ) + coords = getCoordinatesDoublePrecision( mesh ) + tetrahedraIds, tetrahedraConnectivity = extractTetConnectivity( mesh ) ntets = len( tetrahedraIds ) self.logger.info( f" Mesh {n} info: \n" + f" Name: {nfilename}\n" + @@ -113,7 +115,7 @@ def applyFilter( self: Self ) -> None: f" Points: {mesh.GetNumberOfPoints()}" + "\n" + "-" * 80 + "\n" ) # Analyze both meshes - self.analyzeAllTets( n, coords, tetrahedraConnectivity ) + self.analyzedMesh[ n ] = analyzeAllTets( n, coords, tetrahedraConnectivity ) metrics = self.analyzedMesh[ n ] self.tets[ n ] = ntets @@ -229,172 +231,6 @@ def applyFilter( self: Self ) -> None: self.logger.info( f"The filter { self.logger.name } succeeded." ) - def getCoordinatesDoublePrecision( self, mesh: vtkDataSet ) -> npt.NDArray[ np.float64 ]: - """Get coordinates with double precision. - - Args: - mesh (vtkDataSet): Input mesh. - - Returns: - npt.NDArray[np.float64]: Coordinates - - """ - points = mesh.GetPoints() - npoints = points.GetNumberOfPoints() - - coords = np.zeros( ( npoints, 3 ), dtype=np.float64 ) - for i in range( npoints ): - point = points.GetPoint( i ) - coords[ i ] = [ point[ 0 ], point[ 1 ], point[ 2 ] ] - - return coords - - def extractTetConnectivity( self: Self, - mesh: vtkDataSet ) -> tuple[ npt.NDArray[ np.float64 ], npt.NDArray[ np.float64 ] ]: - """Extract connectivity for all tetrahedra. - - Args: - mesh (vtkDataSet): Mesh to analyze. - - Returns: - tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]: - Cell IDS corresponding to tetrahedra, - Connectivity of these cells - """ - ncells = mesh.GetNumberOfCells() - tetrahedraIds = [] - tetrahedraConnectivity = [] - - for cellID in range( ncells ): - if mesh.GetCellType( cellID ) == vtk.VTK_TETRA: - cell = mesh.GetCell( cellID ) - pointIds = cell.GetPointIds() - conn = [ pointIds.GetId( i ) for i in range( 4 ) ] - tetrahedraIds.append( cellID ) - tetrahedraConnectivity.append( conn ) - - return np.array( tetrahedraIds ), np.array( tetrahedraConnectivity ) - - def analyzeAllTets( self, n: int, coords: npt.NDArray[ np.float64 ], - connectivity: npt.NDArray[ np.float64 ] ) -> None: - """Vectorized analysis of all tetrahedra. - - Args: - n (int): Mesh id. - coords (npt.NDArray[np.float64]): Tetrahedra coordinates. - connectivity (npt.NDArray[np.float64]): Connectivity. - """ - # Get coordinates for all vertices - v0 = coords[ connectivity[ :, 0 ] ] - v1 = coords[ connectivity[ :, 1 ] ] - v2 = coords[ connectivity[ :, 2 ] ] - v3 = coords[ connectivity[ :, 3 ] ] - - # Compute edges - e01 = v1 - v0 - e02 = v2 - v0 - e03 = v3 - v0 - e12 = v2 - v1 - e13 = v3 - v1 - e23 = v3 - v2 - - # Edge lengths - l01 = np.linalg.norm( e01, axis=1 ) - l02 = np.linalg.norm( e02, axis=1 ) - l03 = np.linalg.norm( e03, axis=1 ) - l12 = np.linalg.norm( e12, axis=1 ) - l13 = np.linalg.norm( e13, axis=1 ) - l23 = np.linalg.norm( e23, axis=1 ) - - allEdges = np.stack( [ l01, l02, l03, l12, l13, l23 ], axis=1 ) - minEdge = np.min( allEdges, axis=1 ) - maxEdge = np.max( allEdges, axis=1 ) - - # Volumes - cross_product = np.cross( e01, e02 ) - volumes = np.abs( np.sum( cross_product * e03, axis=1 ) / 6.0 ) - - # Face areas - face012 = 0.5 * np.linalg.norm( np.cross( e01, e02 ), axis=1 ) - face013 = 0.5 * np.linalg.norm( np.cross( e01, e03 ), axis=1 ) - face023 = 0.5 * np.linalg.norm( np.cross( e02, e03 ), axis=1 ) - face123 = 0.5 * np.linalg.norm( np.cross( e12, e13 ), axis=1 ) - - allFaces = np.stack( [ face012, face013, face023, face123 ], axis=1 ) - maxFaceArea = np.max( allFaces, axis=1 ) - totalSurfaceArea = np.sum( allFaces, axis=1 ) - - # Aspect ratio - minAltitude = 3.0 * volumes / np.maximum( maxFaceArea, 1e-15 ) - aspectRatio = maxEdge / np.maximum( minAltitude, 1e-15 ) - aspectRatio[ volumes < 1e-15 ] = np.inf - - # Radius ratio - inradius = 3.0 * volumes / np.maximum( totalSurfaceArea, 1e-15 ) - circumradius = ( maxEdge**3 ) / np.maximum( 24.0 * volumes, 1e-15 ) - radiusRatio = circumradius / np.maximum( inradius, 1e-15 ) - - # Flatness ratio - flatnessRatio = volumes / np.maximum( maxFaceArea * minEdge, 1e-15 ) - - # Shape quality - sumEdgeShapeQuality = np.sum( allEdges**2, axis=1 ) - shapeQuality = 12.0 * ( 3.0 * volumes )**( 2.0 / 3.0 ) / np.maximum( sumEdgeShapeQuality, 1e-15 ) - shapeQuality[ volumes < 1e-15 ] = 0.0 - - # Dihedral angles - def computeDihedralAngle( normal1: npt.NDArray[ np.float64 ], - normal2: npt.NDArray[ np.float64 ] ) -> npt.NDArray[ np.float64 ]: - """Compute dihedral angle between two face normals. - - Args: - normal1 (npt.NDArray[np.float64]): Normal vector 1 - normal2 (npt.NDArray[np.float64]): Normal vector 2 - - Returns: - npt.NDArray[ np.float64 ]: Dihedral angle - - """ - n1Norm = normal1 / np.maximum( np.linalg.norm( normal1, axis=1, keepdims=True ), 1e-15 ) - n2Norm = normal2 / np.maximum( np.linalg.norm( normal2, axis=1, keepdims=True ), 1e-15 ) - cosAngle = np.sum( n1Norm * n2Norm, axis=1 ) - cosAngle = np.clip( cosAngle, -1.0, 1.0 ) - angle = np.arccos( cosAngle ) * 180.0 / np.pi - return angle - - # Face normals - normal012: npt.NDArray[ np.float64 ] = np.cross( e01, e02 ) - normal013: npt.NDArray[ np.float64 ] = np.cross( e01, e03 ) - normal023: npt.NDArray[ np.float64 ] = np.cross( e02, e03 ) - normal123: npt.NDArray[ np.float64 ] = np.cross( e12, e13 ) - - # Dihedral angles for each edge - dihedral01 = computeDihedralAngle( normal012, normal013 ) - dihedral02 = computeDihedralAngle( normal012, normal023 ) - dihedral03 = computeDihedralAngle( normal013, normal023 ) - dihedral12 = computeDihedralAngle( normal012, normal123 ) - dihedral13 = computeDihedralAngle( normal013, normal123 ) - dihedral23 = computeDihedralAngle( normal023, normal123 ) - - allDihedrals = np.stack( [ dihedral01, dihedral02, dihedral03, dihedral12, dihedral13, dihedral23 ], axis=1 ) - - minDihedral = np.min( allDihedrals, axis=1 ) - maxDihedral = np.max( allDihedrals, axis=1 ) - dihedralRange = maxDihedral - minDihedral - - self.analyzedMesh[ n ] = { - 'volumes': volumes, - 'aspectRatio': aspectRatio, - 'radiusRatio': radiusRatio, - 'flatnessRatio': flatnessRatio, - 'shapeQuality': shapeQuality, - 'minEdge': minEdge, - 'maxEdge': maxEdge, - 'minDihedral': minDihedral, - 'maxDihedral': maxDihedral, - 'dihedralRange': dihedralRange - } - def printDistributionStatistics( self: Self ) -> None: """Print the distribution statistics for various metrics.""" self.__loggerSection( "DISTRIBUTION STATISTICS (MIN / MEDIAN / MAX)" ) @@ -664,7 +500,7 @@ def createComparisonDashboard( self: Self ) -> None: # Title suptitle = 'Mesh Quality Comparison Dashboard (Progressive Detail Layout)\n' - suptitle += (' - ').join( [ f'Mesh {n}: {self.tets[n]} tets ' for n,_ in enumerate( self.meshes, 1)] ) + suptitle += ( ' - ' ).join( [ f'Mesh {n}: {self.tets[n]} tets ' for n, _ in enumerate( self.meshes, 1 ) ] ) # for n, _ in enumerate( self.meshes, 1 ): # suptitle += f'Mesh {n}: {self.tets[n]:<15} tets ' fig.suptitle( suptitle, fontsize=16, fontweight='bold', y=0.99 ) @@ -715,11 +551,7 @@ def createComparisonDashboard( self: Self ) -> None: ax1.set_xlabel( 'Combined Quality Score', fontweight='bold' ) ax1.set_ylabel( 'Count', fontweight='bold' ) - ax1.set_title( 'OVERALL MESH QUALITY VERDICT', - fontsize=12, - fontweight='bold', - color='darkblue', - pad=10 ) + ax1.set_title( 'OVERALL MESH QUALITY VERDICT', fontsize=12, fontweight='bold', color='darkblue', pad=10 ) ax1.legend( loc='upper left', fontsize=9 ) ax1.grid( True, alpha=0.3 ) @@ -918,11 +750,7 @@ def createComparisonDashboard( self: Self ) -> None: elif val < -10: table[ ( row, 3 ) ].set_facecolor( '#fadbd8' ) - ax3.set_title( 'CRITICAL ISSUES SUMMARY', - fontsize=12, - fontweight='bold', - color='darkgreen', - pad=10 ) + ax3.set_title( 'CRITICAL ISSUES SUMMARY', fontsize=12, fontweight='bold', color='darkgreen', pad=10 ) # ==================== ROW 2: QUALITY DISTRIBUTIONS ==================== @@ -1403,27 +1231,3 @@ def setSampleForPlot( self: Self, data: npt.NDArray[ Any ], n: int ) -> None: """ sampleSize = min( 10000, len( data ) ) self.sample[ n ] = np.random.choice( len( data ), sampleSize, replace=False ) - - -# Combined quality score -def computeQualityScore( aspectRatio: npt.NDArray[ np.float64 ], shapeQuality: npt.NDArray[ np.float64 ], - edgeRatio: npt.NDArray[ np.float64 ], - minDihedralAngle: npt.NDArray[ np.float64 ] ) -> npt.NDArray[ np.float64 ]: - """Compute combined quality score (0-100). - - Args: - aspectRatio(npt.NDArray[np.float64]): Aspect ratio - shapeQuality(npt.NDArray[np.float64]): Shape quality - edgeRatio(npt.NDArray[np.float64]): Edge ratio - minDihedralAngle(npt.NDArray[np.float64]): Minimal edge ratio - - Returns: - np.float64: Quality score - """ - aspectRatioNorm = np.clip( 1.0 / ( aspectRatio / 1.73 ), 0, 1 ) - shapeQualityNorm = shapeQuality - edgeRatioNorm = np.clip( 1.0 / edgeRatio, 0, 1 ) - dihedralMinNorm = np.clip( minDihedralAngle / 60.0, 0, 1 ) - score = ( 0.3 * aspectRatioNorm + 0.4 * shapeQualityNorm + 0.2 * edgeRatioNorm + 0.1 * dihedralMinNorm ) * 100 - - return score diff --git a/geos-processing/tests/conftest.py b/geos-processing/tests/conftest.py index 8a1767df..5da07469 100644 --- a/geos-processing/tests/conftest.py +++ b/geos-processing/tests/conftest.py @@ -46,6 +46,10 @@ def _get_dataset( datasetType: str ) -> Union[ vtkMultiBlockDataSet, vtkPolyData vtkFilename = "data/domain_res5_id_empty.vtu" elif datasetType == "meshGeosExtractBlockTmp": vtkFilename = "data/meshGeosExtractBlockTmp.vtm" + elif datasetType == "meshtet1": + vtkFilename = "data/mesh1.vtu" + elif datasetType == "meshtet1b": + vtkFilename = "data/mesh1b.vtu" datapath: str = os.path.join( os.path.dirname( os.path.realpath( __file__ ) ), vtkFilename ) reader.SetFileName( datapath ) diff --git a/geos-processing/tests/data/mesh1.vtu b/geos-processing/tests/data/mesh1.vtu new file mode 100755 index 00000000..275fd492 --- /dev/null +++ b/geos-processing/tests/data/mesh1.vtu @@ -0,0 +1,3534 @@ + + + + + + 0 +0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 +0 0 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10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 10 10 +10 10 10 10 10 10 + + + + diff --git a/geos-processing/tests/test_TetQualityAnalysis.py b/geos-processing/tests/test_TetQualityAnalysis.py new file mode 100644 index 00000000..6d79f64d --- /dev/null +++ b/geos-processing/tests/test_TetQualityAnalysis.py @@ -0,0 +1,17 @@ +# SPDX-FileContributor: Paloma Martinez +# SPDX-License-Identifier: Apache 2.0 +# ruff: noqa: E402 # disable Module level import not at top of file +# mypy: disable-error-code="operator" +from vtkmodules.vtkCommonDataModel import vtkUnstructuredGrid +from geos.processing.pre_processing.TetQualityAnalysis import TetQualityAnalysis + + +def test_TetQualityAnalysis( dataSetTest: vtkUnstructuredGrid ) -> None: + """Test applying TetQualityAnalysis filter.""" + meshes: dict[ str, vtkUnstructuredGrid ] = { + 'mesh1': dataSetTest( "meshtet1" ), + 'mesh1b': dataSetTest( "meshtet1b" ) + } + tetQualityFilter: TetQualityAnalysis = TetQualityAnalysis( meshes ) + + tetQualityFilter.applyFilter() From 7a33895309534eca3ac435c0b9142d0e6a04cd91 Mon Sep 17 00:00:00 2001 From: Paloma Martinez <104762252+paloma-martinez@users.noreply.github.com> Date: Mon, 2 Feb 2026 15:13:22 +0100 Subject: [PATCH 08/11] little commit to launch ci again --- geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py b/geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py index c3ea5660..0f0ab97c 100644 --- a/geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py +++ b/geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py @@ -56,7 +56,7 @@ class PVTetQualityAnalysis( VTKPythonAlgorithmBase ): def __init__( self: Self ) -> None: - """QC analysis of the tetrahedras from 2 meshes.""" + """QC analysis of the tetrahedras from two meshes.""" super().__init__( nInputPorts=2, inputType="vtkObject" ) self._filename: Optional[ str ] = None From 069a5970b5383208b6a1bec97d830bf3b4959bf0 Mon Sep 17 00:00:00 2001 From: Paloma Martinez <104762252+paloma-martinez@users.noreply.github.com> Date: Wed, 4 Feb 2026 14:31:19 +0100 Subject: [PATCH 09/11] Modifs from review comments --- .../mesh/stats/tetrahedraAnalysisHelpers.py | 22 ++++++++++++------- .../pre_processing/TetQualityAnalysis.py | 18 ++++++--------- .../tests/test_TetQualityAnalysis.py | 14 ++++++++++++ .../pv/plugins/qc/PVTetQualityAnalysis.py | 2 +- 4 files changed, 36 insertions(+), 20 deletions(-) diff --git a/geos-mesh/src/geos/mesh/stats/tetrahedraAnalysisHelpers.py b/geos-mesh/src/geos/mesh/stats/tetrahedraAnalysisHelpers.py index d040e6ad..400e06fe 100644 --- a/geos-mesh/src/geos/mesh/stats/tetrahedraAnalysisHelpers.py +++ b/geos-mesh/src/geos/mesh/stats/tetrahedraAnalysisHelpers.py @@ -16,13 +16,12 @@ def getCoordinatesDoublePrecision( mesh: vtkDataSet ) -> npt.NDArray[ np.float64 Returns: npt.NDArray[np.float64]: Coordinates - """ points = mesh.GetPoints() - npoints = points.GetNumberOfPoints() + nPoints = points.GetNumberOfPoints() - coords = np.zeros( ( npoints, 3 ), dtype=np.float64 ) - for i in range( npoints ): + coords = np.zeros( ( nPoints, 3 ), dtype=np.float64 ) + for i in range( nPoints ): point = points.GetPoint( i ) coords[ i ] = [ point[ 0 ], point[ 1 ], point[ 2 ] ] @@ -55,12 +54,14 @@ def extractTetConnectivity( mesh: vtkDataSet ) -> tuple[ npt.NDArray[ np.float64 return np.array( tetrahedraIds ), np.array( tetrahedraConnectivity ) -def analyzeAllTets( n: int, coords: npt.NDArray[ np.float64 ], +def analyzeAllTets( coords: npt.NDArray[ np.float64 ], connectivity: npt.NDArray[ np.float64 ] ) -> dict[ str, dict[ str, Any ] ]: """Vectorized analysis of all tetrahedra. + This analysis computes the following metrics: volumes, aspect ratio, radius ratio, flatness ratio,shape quality, min and max edge, min and max dihedral angles, dihedral range. + + Args: - n (int): Mesh id. coords (npt.NDArray[np.float64]): Tetrahedra coordinates. connectivity (npt.NDArray[np.float64]): Connectivity. @@ -136,7 +137,6 @@ def computeDihedralAngle( normal1: npt.NDArray[ np.float64 ], Returns: npt.NDArray[ np.float64 ]: Dihedral angle - """ n1Norm = normal1 / np.maximum( np.linalg.norm( normal1, axis=1, keepdims=True ), 1e-15 ) n2Norm = normal2 / np.maximum( np.linalg.norm( normal2, axis=1, keepdims=True ), 1e-15 ) @@ -183,7 +183,13 @@ def computeDihedralAngle( normal1: npt.NDArray[ np.float64 ], def computeQualityScore( aspectRatio: npt.NDArray[ np.float64 ], shapeQuality: npt.NDArray[ np.float64 ], edgeRatio: npt.NDArray[ np.float64 ], minDihedralAngle: npt.NDArray[ np.float64 ] ) -> npt.NDArray[ np.float64 ]: - """Compute combined quality score (0-100). + """Compute combined quality score (0-100) from aspect ratio, shape quality, minimal dihedral angle and edge ratio. + + The quality score can be interpreted with the following scale: + Excellent (>80) + Good (60-80) + Fair (30-60) + Poor (≤30) Args: aspectRatio(npt.NDArray[np.float64]): Aspect ratio diff --git a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py index 7203ff2c..baf33973 100644 --- a/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py +++ b/geos-processing/src/geos/processing/pre_processing/TetQualityAnalysis.py @@ -17,6 +17,9 @@ __doc__ = """ TetQualityAnalysis module is a filter that performs an analysis of tetrahedras quality of one or several meshes and generates a plot as summary. +Metrics computed include aspect ratio, shape quality, volume, min and max edges, edge ratio, min and max dihedral angles, quality score. + +The meshes are compared based on their median quality score and the change from the best one is evaluated for these metrics. Filter input should be vtkUnstructuredGrid. @@ -38,17 +41,13 @@ tetQualityAnalysisFilter.setLoggerHandler( yourHandler ) # Change output filename [optional] - tetQualityAnalysisFilter.SetFilename( filename ) - + tetQualityAnalysisFilter.setFilename( filename ) # Do calculations try: tetQualityAnalysisFilter.applyFilter() - except ( ValueError, IndexError, TypeError, AttributeError ) as e: - tetQualityAnalysisFilter.logger.error( f"The filter { tetQualityAnalysisFilter.logger.name } failed due to: { e }" ) except Exception as e: - mess: str = f"The filter { meshQualityEnhancedFilter.logger.name } failed due to: { e }" - tetQualityAnalysisFilter.logger.critical( mess, exc_info=True ) + tetQualityAnalysisFilter.logger.error( f"The filter { tetQualityAnalysisFilter.logger.name } failed due to: { e }" ) """ loggerName: str = "Tetrahedra Quality Analysis" @@ -115,7 +114,7 @@ def applyFilter( self: Self ) -> None: f" Points: {mesh.GetNumberOfPoints()}" + "\n" + "-" * 80 + "\n" ) # Analyze both meshes - self.analyzedMesh[ n ] = analyzeAllTets( n, coords, tetrahedraConnectivity ) + self.analyzedMesh[ n ] = analyzeAllTets( coords, tetrahedraConnectivity ) metrics = self.analyzedMesh[ n ] self.tets[ n ] = ntets @@ -501,8 +500,6 @@ def createComparisonDashboard( self: Self ) -> None: # Title suptitle = 'Mesh Quality Comparison Dashboard (Progressive Detail Layout)\n' suptitle += ( ' - ' ).join( [ f'Mesh {n}: {self.tets[n]} tets ' for n, _ in enumerate( self.meshes, 1 ) ] ) - # for n, _ in enumerate( self.meshes, 1 ): - # suptitle += f'Mesh {n}: {self.tets[n]:<15} tets ' fig.suptitle( suptitle, fontsize=16, fontweight='bold', y=0.99 ) # Color scheme @@ -1141,11 +1138,10 @@ def createComparisonDashboard( self: Self ) -> None: ax23.grid( True, alpha=0.3 ) # Save figure - plt.savefig( self.filename, dpi=300, bbox_inches='tight', facecolor='white' ) self.logger.info( f"Dashboard saved successfully: {self.filename}" ) - def setDashboardFilename( self: Self, filename: str ) -> None: + def setFilename( self: Self, filename: str ) -> None: """Set comparison dashboard output filename. Args: diff --git a/geos-processing/tests/test_TetQualityAnalysis.py b/geos-processing/tests/test_TetQualityAnalysis.py index 6d79f64d..f4346974 100644 --- a/geos-processing/tests/test_TetQualityAnalysis.py +++ b/geos-processing/tests/test_TetQualityAnalysis.py @@ -15,3 +15,17 @@ def test_TetQualityAnalysis( dataSetTest: vtkUnstructuredGrid ) -> None: tetQualityFilter: TetQualityAnalysis = TetQualityAnalysis( meshes ) tetQualityFilter.applyFilter() + + +def test_TetQualityAnalysisRaisePathError( dataSetTest: vtkUnstructuredGrid ) -> None: + """Test applying TetQualityAnalysis filter.""" + meshes: dict[ str, vtkUnstructuredGrid ] = { + 'mesh1': dataSetTest( "meshtet1" ), + 'mesh1b': dataSetTest( "meshtet1b" ) + } + tetQualityFilter: TetQualityAnalysis = TetQualityAnalysis( meshes ) + + tetQualityFilter.setFilename( "/qliuf/moidh/meshComparison.png" ) + + with pytest.raises( FileNotFoundError ): + tetQualityFilter.applyFilter() \ No newline at end of file diff --git a/geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py b/geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py index 0f0ab97c..7955f4a2 100644 --- a/geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py +++ b/geos-pv/src/geos/pv/plugins/qc/PVTetQualityAnalysis.py @@ -80,7 +80,7 @@ def SetFileName( self: Self, fname: str ) -> None: """ if self._filename != fname: self._filename = fname - self.Modified() + self.Modified() def RequestData( self: Self, From 3e4a3c6c20caeaee7d8fffb19e66343b9f6cfacf Mon Sep 17 00:00:00 2001 From: Paloma Martinez <104762252+paloma-martinez@users.noreply.github.com> Date: Wed, 4 Feb 2026 14:35:25 +0100 Subject: [PATCH 10/11] Fix mypy issue --- geos-processing/tests/test_TetQualityAnalysis.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/geos-processing/tests/test_TetQualityAnalysis.py b/geos-processing/tests/test_TetQualityAnalysis.py index f4346974..810b78b1 100644 --- a/geos-processing/tests/test_TetQualityAnalysis.py +++ b/geos-processing/tests/test_TetQualityAnalysis.py @@ -2,6 +2,8 @@ # SPDX-License-Identifier: Apache 2.0 # ruff: noqa: E402 # disable Module level import not at top of file # mypy: disable-error-code="operator" +import pytest + from vtkmodules.vtkCommonDataModel import vtkUnstructuredGrid from geos.processing.pre_processing.TetQualityAnalysis import TetQualityAnalysis From 3ea9b21b034c2fef27470e87fe1e399c16c81b7a Mon Sep 17 00:00:00 2001 From: Paloma Martinez <104762252+paloma-martinez@users.noreply.github.com> Date: Wed, 4 Feb 2026 14:39:50 +0100 Subject: [PATCH 11/11] ruff --- geos-processing/tests/test_TetQualityAnalysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/geos-processing/tests/test_TetQualityAnalysis.py b/geos-processing/tests/test_TetQualityAnalysis.py index 810b78b1..59ceeac7 100644 --- a/geos-processing/tests/test_TetQualityAnalysis.py +++ b/geos-processing/tests/test_TetQualityAnalysis.py @@ -30,4 +30,4 @@ def test_TetQualityAnalysisRaisePathError( dataSetTest: vtkUnstructuredGrid ) -> tetQualityFilter.setFilename( "/qliuf/moidh/meshComparison.png" ) with pytest.raises( FileNotFoundError ): - tetQualityFilter.applyFilter() \ No newline at end of file + tetQualityFilter.applyFilter()