Update

05_CreatePatches.py

@@ -62,11 +62,11 @@ models = {
     # 'GP_DotWhite': GaussianProcessRegressionModel(),
     # 'GP_RBF': GaussianProcessRegressionModel(),
     # 'GP_Matern': GaussianProcessRegressionModel(),
-    # 'GP_RationalQuadratic': GaussianProcessRegressionModel(),
+    'GP_RationalQuadratic': GaussianProcessRegressionModel(),
     # 'GP_ExpSineSquared': GaussianProcessRegressionModel(),
-    # 'RBF_Linear': RadialBasisRegressionModel(),
-    # 'RBF_thinplatespline': RadialBasisRegressionModel(),
-    # 'RBF_cubic': RadialBasisRegressionModel(),
+    'RBF_Linear': RadialBasisRegressionModel(),
+    'RBF_thinplatespline': RadialBasisRegressionModel(),
+    'RBF_cubic': RadialBasisRegressionModel(),
     # 'RBF_quintic': RadialBasisRegressionModel(),
     # 'RBF_multiquadric': RadialBasisRegressionModel(),
     # 'RBF_inversemultiquadric': RadialBasisRegressionModel(),

06_EvaluationError.py

@@ -38,8 +38,8 @@ models = {
     'GP_RationalQuadratic',
     # 'GP_ExpSineSquared',
     'RBF_Linear',
-    # 'RBF_thinplatespline',
-    # 'RBF_cubic',
+    'RBF_thinplatespline',
+    'RBF_cubic',
     # 'RBF_quintic',
     # 'RBF_multiquadric',
     # 'RBF_inversemultiquadric',

09_VisualizeReconstructed.py

@@ -34,7 +34,7 @@ models = {
     # 'gaussianProcessMod_RBF': GaussianProcessRegressionModel(),
     # 'gaussianProcessMod_Matern': GaussianProcessRegressionModel(),
     'gaussianProcessMod_RationalQuadratic': GaussianProcessRegressionModel(),
-    'gaussianProcessMod_ExpSineSquared': GaussianProcessRegressionModel(),
+    # 'gaussianProcessMod_ExpSineSquared': GaussianProcessRegressionModel(),
     # 'radialBasisMod_Linear': RadialBasisRegressionModel(),
     'radialBasisMod_thinplatespline': RadialBasisRegressionModel(),
     'radialBasisMod_cubic': RadialBasisRegressionModel(),

10_Timings.py

 import numpy as np
+import pandas as pd
 import time
 import os
+import shutil
+import subprocess
 import argparse
 
 from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel, RBF, Matern, RationalQuadratic, ExpSineSquared
@@ -9,46 +12,75 @@ from models.LinearRegression import LinearRegressionModel
 from models.RBFInterpolator import RadialBasisRegressionModel
 from models.GaussianProcessRegressor import GaussianProcessRegressionModel
 
+from miscellaneous.DataPreparation import save_numpy_to_xml, get_matrix_size
+
 
 # Degree Elevation
 parser = argparse.ArgumentParser(
         description="Script that reads the Elevation Degrees from CMD"
     )
+parser.add_argument("--e", required=False, type=int)
 parser.add_argument("--r", required=True, type=int)
 parser.add_argument("--n", required=False, type=int)
 parser.add_argument("--R_lower", required=False, type=int)
 parser.add_argument("--R_upper", required=False, type=int)
 parser.add_argument("--R_step", required=False, type=int)
+parser.add_argument("--Architecture", required=False, type=str)
 args = parser.parse_args()
-DEGREE_ELEVATIONS = args.r
+if args.e is not None:
+    DEGREE_ELEVATIONS = args.e
+else:
+    DEGREE_ELEVATIONS = 1
+if args.r is not None:
+    H_REFINEMENTS = args.r
+else:
+    H_REFINEMENTS = 0
 if args.n is not None:
     N_RUNS = args.n
 else:
     N_RUNS = 1
 print('DEGREE_ELEVATIONS:', DEGREE_ELEVATIONS)
-DataFolder = f'Data/DegreeElevations_{DEGREE_ELEVATIONS}'
+print(f'H_REFINEMENTS: \t\t{H_REFINEMENTS}')
 
 # Create the folder to store the trained models
-DataFolder = f'Data/DegreeElevations_{DEGREE_ELEVATIONS}'
+DataFolder = f'Data/H_REFINEMENTS_{H_REFINEMENTS}'
 TimingsFolder = f'{DataFolder}/Timings'
 if not os.path.exists(TimingsFolder):
     os.mkdir(TimingsFolder)
 
+df = pd.read_excel(f'{DataFolder}/test/parameter_input.xlsx').transpose()
+SAMPLES_TEST = df.shape[1]
+
+# Sizes of the microstructures
+VELOCITY_SIZE = get_matrix_size(f'{DataFolder}/train/velocities/velocity_field_0.xml')
+PRESSURE_SIZE = get_matrix_size(f'{DataFolder}/train/pressure/pressure_field_0.xml')
+
+path_to_executable = 'gismo/stokes_recreation'
+if args.Architecture is not None:
+    if args.Architecture == 'MacOS':
+        path_to_executable = 'gismo/stokes_recreation'
+    elif args.Architecture == 'Linux':
+        path_to_executable =  'gismo/stokes_recreation_linux'
+    elif args.Architecture == 'Windows':
+        path_to_executable =  'gismo/stokes_recreation_linux'
+    else:
+        raise ValueError('Architecture not recognized')
+
 models = {
-    'LinearRegression': LinearRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
-    'RBF_Linear': RadialBasisRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
-    'RBF_thinplatespline': RadialBasisRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
-    'RBF_cubic': RadialBasisRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
-    # 'RBF_quintic': RadialBasisRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
-    # 'RBF_multiquadric': RadialBasisRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
-    # 'RBF_inversemultiquadric': RadialBasisRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
-    # 'RBF_inversequadratic': RadialBasisRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
-    # 'RBF_gaussian': RadialBasisRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
-    # 'GP_DotWhite': GaussianProcessRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
-    # 'GP_RBF': GaussianProcessRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
-    # 'GP_Matern': GaussianProcessRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
-    'GP_RationalQuadratic': GaussianProcessRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS), # Sehr langsam, besser raus nehmen
-    # 'GP_ExpSineSquared': GaussianProcessRegressionModel(DEGREE_ELEVATIONS=DEGREE_ELEVATIONS),
+    'LinearRegression': LinearRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
+    'RBF_Linear': RadialBasisRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
+    'RBF_thinplatespline': RadialBasisRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
+    'RBF_cubic': RadialBasisRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
+    # 'RBF_quintic': RadialBasisRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
+    # 'RBF_multiquadric': RadialBasisRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
+    # 'RBF_inversemultiquadric': RadialBasisRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
+    # 'RBF_inversequadratic': RadialBasisRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
+    # 'RBF_gaussian': RadialBasisRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
+    # 'GP_DotWhite': GaussianProcessRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
+    # 'GP_RBF': GaussianProcessRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
+    # 'GP_Matern': GaussianProcessRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
+    'GP_RationalQuadratic': GaussianProcessRegressionModel(H_REFINEMENTS=H_REFINEMENTS), # Sehr langsam, besser raus nehmen
+    # 'GP_ExpSineSquared': GaussianProcessRegressionModel(H_REFINEMENTS=H_REFINEMENTS),
 }
 
 Kernel_RBF = [
@@ -92,6 +124,7 @@ for ModelIndex, model_name in enumerate(models):
     timings_train_w_POD = np.zeros((N_RUNS, len(R)))
     timings_train_wo_POD = np.zeros((N_RUNS, len(R)))
     timings_evaluate = np.zeros((N_RUNS, len(R)))
+    timings_reconstruct = np.zeros((N_RUNS, len(R)))
     for RunIndex in range(N_RUNS):
         print(f'Run: \t\t\t{RunIndex}')
         for PODIndex, R_value in enumerate(R):
@@ -126,6 +159,36 @@ for ModelIndex, model_name in enumerate(models):
             end_evaluate = time.time()
             timings_evaluate[RunIndex, PODIndex] = end_evaluate - start_evaluate
 
+            # Create Patches
+            start_reconstruct = time.time()
+            for sample in range(SAMPLES_TEST):
+                save_numpy_to_xml(test_velocity_predict[:,sample], f'temp_{model_name}/predict_velocity_field_{sample}.xml', rows=VELOCITY_SIZE, cols='1')
+
+                save_numpy_to_xml(test_pressure_predict[:,sample], f'temp_{model_name}/predict_pressure_field_{sample}.xml', rows=PRESSURE_SIZE, cols='1')
+
+                # Get all the input files for the evaluation
+                geometry_file = f'{DataFolder}/test/microstructure/index_{sample}.xml'
+                velocity_solution = f'{DataFolder}/test/velocities/velocity_field_{sample}.xml'
+                pressure_solution = f'{DataFolder}/test/pressure/pressure_field_{sample}.xml'
+                velocity_predicted = f'temp_{model_name}/predict_velocity_field_{sample}.xml'
+                pressure_predicted = f'temp_{model_name}/predict_pressure_field_{sample}.xml'
+
+                # Create patches for the geometry with gismo script
+                program_output = subprocess.Popen(
+                    [f'./{path_to_executable}', f'-f {geometry_file}', f'-v {velocity_solution}', f'-p {pressure_solution}', f'-w {velocity_predicted}', f'-q {pressure_predicted}', f'-e {DEGREE_ELEVATIONS}', f'-r {H_REFINEMENTS}', '--no-plot'],
+                    stdout=subprocess.PIPE,
+                    text=True
+                ).communicate()[0]
+            end_reconstruct = time.time()
+            timings_reconstruct[RunIndex, PODIndex] = end_reconstruct - start_reconstruct
+            
+            os.remove('pressure_field_patches.xml')
+            os.remove('velocity_field_patches.xml')
+            os.remove('pressure_field_rec_patches.xml')
+            os.remove('velocity_field_rec_patches.xml')
+
+    shutil.rmtree(f'temp_{model_name}')
+
     # Save the timings
     np.savez(f'{TimingsFolder}/{model_name}.npz', timings_train_w_POD=timings_train_w_POD, timings_train_wo_POD=timings_train_wo_POD, timings_evaluate=timings_evaluate)