01_GenerateMicrostructures.py

import numpy as np
import argparse
import splinepy as sp
import pandas as pd
import os
import shutil

from gismo.gismo_export import AdditionalBlocks, export
from gismo.GenerateMicrostructures_fun import generate

EPS = 1e-8
BOX_LENGTH = 0.14
BOX_HEIGHT = 0.041
SHOW_MICROSTRUCTURE = False
FILENAME = "microstructure.xml"
N_THREADS = 1
sp.settings.NTHREADS = N_THREADS

# Simulation parameters
TILING = [6, 3]#[5, 3]
N_REFINEMENTS = 0
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=False, type=int)
args = parser.parse_args()
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
print('DEGREE_ELEVATIONS:', DEGREE_ELEVATIONS)
print('H_REFINEMENTS:', H_REFINEMENTS)
if not os.path.exists(f'Data/H_REFINEMENTS_{H_REFINEMENTS}'):
        os.mkdir(f'Data/H_REFINEMENTS_{H_REFINEMENTS}')
INLET_BOUNDARY_ID = 2
OUTLET_BOUNDARY_ID = 3
INLET_PEAK_VELOCITY = 1.4336534897721067
CLOSING_FACE = "x"
# OBJECTIVE_FUNCTION = [1, 3]
# OBJECTIVE_FUNCTION_WEIGHTS = [49927, 10.027]
OBJECTIVE_FUNCTION = [2, 3]
# OBJECTIVE_FUNCTION_WEIGHTS = [6.673e13, 5.700225]
OBJECTIVE_FUNCTION_WEIGHTS = [7e5, 2e-3]

microtile_string = 'HollowOctagon'
MICROTILE = sp.microstructure.tiles.HollowOctagon()
# ! Use random sizes
SIZES_lower_bound = 0.04
SIZES_upper_bound = 0.36
SIZES_TRAIN = [0.04, 0.12, 0.20, 0.28, 0.36]
SIZES_TEST = [0.08, 0.16, 0.24, 0.32]


# Dictionary to store the setup
data_setup = {
    'EPS': EPS,
    'BOX_LENGTH': BOX_LENGTH,
    'BOX_HEIGHT': BOX_HEIGHT,
    'SHOW_MICROSTRUCTURE': SHOW_MICROSTRUCTURE,
    'FILENAME': FILENAME,
    'N_THREADS': N_THREADS,

    # Simulation parameters
    'TILING': TILING,
    'N_REFINEMENTS': N_REFINEMENTS,
    'DEGREE_ELEVATIONS': DEGREE_ELEVATIONS,
    'H_REFINEMENTS': H_REFINEMENTS,
    'INLET_BOUNDARY_ID': INLET_BOUNDARY_ID,
    'OUTLET_BOUNDARY_ID': OUTLET_BOUNDARY_ID,
    'INLET_PEAK_VELOCITY': INLET_PEAK_VELOCITY,
    'CLOSING_FACE': CLOSING_FACE,
    'OBJECTIVE_FUNCTION': OBJECTIVE_FUNCTION,
    'OBJECTIVE_FUNCTION_WEIGHTS': OBJECTIVE_FUNCTION_WEIGHTS,

    'MICROTILE': microtile_string,
    'N_SIZES_TRAIN': len(SIZES_TRAIN)**TILING[1],
    'N_SIZES_TEST': len(SIZES_TEST)**TILING[1],
}

data_sizes = {
    'size_1': [],
    'size_2': [],
    'size_3': [],
}



if __name__ == "__main__":
    # Define microstructure deformation function
    initial_macro_spline = sp.helpme.create.box(BOX_LENGTH, BOX_HEIGHT)
    # x0_max, x1_max = initial_macro_spline.cps.max(axis=0)
    # print(f'x0_max: {x0_max}, x1_max: {x1_max}')
    
    # Define identifier functions for microstructure boundaries
    def identifier_inlet(points):
        return points[:, 0] < EPS

    def identifier_outlet(points):
        return points[:, 0] > BOX_LENGTH - EPS

    boundary_identifier_dict = {
        identifier_inlet: INLET_BOUNDARY_ID,
        identifier_outlet: OUTLET_BOUNDARY_ID,
    }

    knots_y = np.linspace(0, 1, TILING[1] * 2 + 1)[1:-1:2]
    # print(f'knots_y: {knots_y}')
    train_index = 0
    # Train models
    if not os.path.exists(f'Data/H_REFINEMENTS_{H_REFINEMENTS}'): os.mkdir(f'Data/H_REFINEMENTS_{H_REFINEMENTS}')
    if os.path.exists(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/train'): shutil.rmtree(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/train')
    os.mkdir(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/train')
    if os.path.exists(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/train/microstructure'): shutil.rmtree(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/train/microstructure/')
    os.mkdir(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/train/microstructure')
    if os.path.exists(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/test'): shutil.rmtree(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/test')
    os.mkdir(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/test')
    if os.path.exists(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/test/microstructure'): shutil.rmtree(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/test/microstructure/')
    os.mkdir(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/test/microstructure')
    for size_1 in SIZES_TRAIN:
        for size_2 in SIZES_TRAIN:
            for size_3 in SIZES_TRAIN:
                data_sizes['size_1'].append(size_1)
                data_sizes['size_2'].append(size_2)
                data_sizes['size_3'].append(size_3)
                
                microstructure, gismo_export_options = generate(
                    size_1, size_2, size_3, BOX_LENGTH, BOX_HEIGHT, EPS, INLET_BOUNDARY_ID, OUTLET_BOUNDARY_ID, knots_y, TILING, CLOSING_FACE, MICROTILE
                )
                export(
                    fname=f'Data/H_REFINEMENTS_{H_REFINEMENTS}/train/microstructure/index_{train_index}.xml', multipatch=microstructure,
                    indent=True,
                    additional_blocks=gismo_export_options,
                    as_base64=False
                )


                train_index+=1
    data_setup['N_SIZES_TRAIN'] = train_index
    df_sizes = pd.DataFrame(data_sizes)
    df_sizes.to_excel(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/train/parameter_input.xlsx', index=True)

    # Test models
    test_index = 0
    for size_1 in SIZES_TEST:
        for size_2 in SIZES_TEST:
            for size_3 in SIZES_TEST:
                data_sizes['size_1'].append(size_1)
                data_sizes['size_2'].append(size_2)
                data_sizes['size_3'].append(size_3)
                
                microstructure, gismo_export_options = generate(
                    size_1, size_2, size_3, BOX_LENGTH, BOX_HEIGHT, EPS, INLET_BOUNDARY_ID, OUTLET_BOUNDARY_ID, knots_y, TILING, CLOSING_FACE, MICROTILE
                )
                export(
                    fname=f'Data/H_REFINEMENTS_{H_REFINEMENTS}/test/microstructure/index_{test_index}.xml', multipatch=microstructure,
                    indent=True,
                    additional_blocks=gismo_export_options,
                    as_base64=False
                )

                test_index+=1

    data_setup['N_SIZES_TEST'] = test_index
    df_sizes = pd.DataFrame(data_sizes).iloc[train_index:]
    df_sizes.to_excel(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/test/parameter_input.xlsx', index=True)


# df = pd.DataFrame(data)
df_setup = pd.DataFrame.from_dict(data_setup, orient='index')
df_setup = df_setup.transpose()
df_setup.to_excel(f'Data/H_REFINEMENTS_{H_REFINEMENTS}/HollowOctagon_Setup.xlsx', index=False)