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LRMP_linear.py
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# -*- coding: utf-8 -*-
"""
Created March 2023
This script implements numerical evaluations for the LRMP linear setting.
@author: Jan Michael Breuer
"""
import convergence
import Paper_settings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# =============================================================================
# LRMP with linear binding and one-component and optional binding kinetics
# =============================================================================
def eval_LRMP_linear(run_sim, run_eval, _file_path_, _cadet_path_,
_mult_sim_rerun_=0):
tmp_path = _file_path_ + '\\LRMP_verification_linear\\'
_recalculate_results_ = run_eval
_plot_benchmark_ = run_eval
_plot_benchmark_collocation_DGSEM_ = run_eval
_export_results_ = run_eval
_save_path_ = _file_path_ + '\\results\\LRMP_Linear\\'
_models_ = ["LRMP_dyn_1comp", "LRMP_req_1comp"]
# same order as _models_[]
_image_names_ = ['LRMP with kinetic linear binding',
'LRMP with rapid-eq. linear binding']
_exact_names_ = ['LRMP_dyn_1comp_DGexInt_P6Z300.h5',
'LRMP_req_1comp_DGexInt_P6Z300.h5']
# =========================================================================
# Setup discretizations
# =========================================================================
ax_methods = [0, 1, 2, 3, 4, 5]
ax_cells = [
[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024],
[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024],
[1, 2, 4, 8, 16, 32, 64, 128, 256],
[1, 2, 4, 8, 16, 32, 64],
[1, 2, 4, 8, 16, 32],
[1, 2, 4, 8, 16]
]
if run_sim:
for isKinetic_ in [True, False]:
for m_idx in range(0, len(ax_methods)):
for ncells in ax_cells[m_idx]:
Paper_settings.LRMPlinear1Comp_VerificationSetting(
method_=ax_methods[m_idx], colCells_=ncells,
isKinetic_=isKinetic_, tolerance=1e-12,
plot_=False, run_=True,
save_path=tmp_path,
cadet_path=_cadet_path_)
# Exact integration DGSEM
if ax_methods[m_idx] != 0:
Paper_settings.LRMPlinear1Comp_VerificationSetting(
method_=-ax_methods[m_idx],
colCells_=ncells,
isKinetic_=isKinetic_,
tolerance=1e-12, plot_=False, run_=True,
save_path=tmp_path, cadet_path=_cadet_path_
)
# Compute high accuracy solution
Paper_settings.LRMPlinear1Comp_VerificationSetting(
method_=-6, colCells_=300,
isKinetic_=isKinetic_,
tolerance=1e-12, plot_=False, run_=True,
save_path=tmp_path, cadet_path=_cadet_path_
)
# Optionally rerun simulations multiple times for benchmarking
if _mult_sim_rerun_:
if _mult_sim_rerun_ > 0:
convergence.mult_sim_rerun(tmp_path, _cadet_path_,
n_wdh=_mult_sim_rerun_)
if _recalculate_results_:
convergence.recalculate_results(file_path=tmp_path,
models=_models_,
ax_methods=ax_methods, ax_cells=ax_cells,
exact_names=_exact_names_, unit='001',
transport_model='LRMP',
ncomp=1, nbound=1,
save_path_=_save_path_)
ax_methods = np.array(ax_methods) * -1 # exact integration DGSEM
convergence.recalculate_results(file_path=tmp_path,
models=_models_,
ax_methods=ax_methods, ax_cells=ax_cells,
exact_names=_exact_names_, unit='001',
transport_model='LRMP',
ncomp=1, nbound=1,
save_path_=_save_path_)
# back to collocation DGSEM (default)
ax_methods = np.array(ax_methods) * -1
# =========================================================================
# Generate output from results
# =========================================================================
# =========================================================================
# 1) Read data
# =========================================================================
tables_cDGSEM = {}
tables_DGSEM = {}
def read_tables():
for modelIdx in range(0, len(_models_)):
# read first method for current model
if ax_methods[0] != 0:
result = pd.read_csv(
_save_path_ + _models_[modelIdx] + '_DG_P' +
str(int(abs(ax_methods[0]))) + '.csv', delimiter=','
)
result2 = pd.read_csv(
_save_path_ + _models_[modelIdx] + '_DGexInt_P' +
str(int(abs(ax_methods[0]))) + '.csv', delimiter=','
)
else:
result = pd.read_csv(
_save_path_ + _models_[modelIdx] + '_FV' + '.csv',
delimiter=','
)
result2 = result
# read remaining methods for current model and concatenate to result
for m in range(1, len(ax_methods)):
if ax_methods[m] != 0:
result = pd.concat(
(result,
pd.read_csv(
_save_path_ + _models_[modelIdx] + '_DG_P' +
str(int(abs(ax_methods[m]))) + '.csv',
delimiter=',')
)
)
result2 = pd.concat(
(result2,
pd.read_csv(
_save_path_ + _models_[modelIdx] + '_DGexInt_P' +
str(int(abs(ax_methods[m]))) + '.csv',
delimiter=',')
)
)
else:
result = pd.concat(
(result,
pd.read_csv(
_save_path_ + _models_[modelIdx] + '_FV' + '.csv',
delimiter=',')
)
)
result2 = pd.concat(
(result2,
pd.read_csv(
_save_path_ + _models_[modelIdx] + '_FV' + '.csv',
delimiter=',')
)
)
result['$N_e^z$'] = result['$N_e^z$'].round().astype(int)
result['DoF'] = result['DoF'].round().astype(int)
result2['$N_e^z$'] = result2['$N_e^z$'].round().astype(int)
result2['DoF'] = result2['DoF'].round().astype(int)
tables_cDGSEM[_models_[modelIdx]] = result
tables_DGSEM[_models_[modelIdx]] = result2
if run_eval:
read_tables()
# =========================================================================
# 2) Create Latex convergence tables
# =========================================================================
# merge_columns = ['$N_d$', '$N_e^z$']
# latex_columns = merge_columns + ['max error', 'max EOC']#, 'Sim. time']
# latex_LRM_conv = pd.merge(tables_cDGSEM['LRMP_dyn_1comp'][latex_columns],
# tables_cDGSEM['LRMP_req_1comp'][latex_columns],
# on=merge_columns)
# print(convergence.std_latex_table(tables['LRM_dyn1comp'], latex_columns))
# =========================================================================
# 3) Benchmark images
# =========================================================================
def benchmark_LRMPlinear(tables=tables_cDGSEM):
plot_args = {'shape': [10, 10],
'y_label': 'Max. error in mol $/ m^3$'}
plt.rcParams["figure.figsize"] = (
plot_args['shape'][0], plot_args['shape'][1])
line_args = {'linestyle': 'dashed'}
for m in range(0, len(_models_)):
if not _export_results_:
plot_args['title'] = _image_names_[m]
# plot_args['y_lim'] = [1e-11, 2e0]
for method_idx in range(0, len(ax_methods)):
table = tables[_models_[m]].loc[tables[_models_[m]]['$N_d$']
== ax_methods[method_idx]]
if ax_methods[method_idx] == 0:
convergence.std_plot(table['Sim. time'], table['Max. error'],
label='FV',
color='purple', marker='s', **line_args)
else:
convergence.std_plot(
table['Sim. time'], table['Max. error'],
label='P'+str(ax_methods[method_idx]), **line_args)
plot_args['x_label'] = 'Compute time in seconds'
# plot_args['x_lim'] = [1e-3, 1e1]
convergence.std_plot_prep(**plot_args)
if _export_results_:
plt.savefig(_save_path_ +
'benchmark_'+_models_[m]+'_time.png',
bbox_inches='tight')
plt.show()
for method_idx in range(0, len(ax_methods)):
table = tables[_models_[m]].loc[tables[_models_[m]]['$N_d$']
== ax_methods[method_idx]]
if ax_methods[method_idx] == 0:
convergence.std_plot(table['DoF'], table['Max. error'],
label='FV',
color='purple', marker='s', **line_args)
else:
convergence.std_plot(
table['DoF'], table['Max. error'],
label='P'+str(ax_methods[method_idx]), **line_args)
plot_args['x_label'] = 'Degrees of freedom'
# plot_args['x_lim'] = [1e-3, 1e1]
convergence.std_plot_prep(**plot_args)
if _export_results_:
plt.savefig(_save_path_ +
'benchmark_'+_models_[m]+'_DOF.png',
bbox_inches='tight')
plt.show()
if _plot_benchmark_:
benchmark_LRMPlinear()
def benchmark_collocationDGSEM_LRMPlinear(
model=_models_[1],
methods=[1],
cells=[[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]],
title=None, save_path=None
):
colours = ['blue', 'red', 'green',
'orange', 'brown', 'grey', 'magenta']
plot_args = {'shape': [10, 10],
'y_label': '$L^\infty$ error in mol $/ m^3$'}
plt.rcParams["figure.figsize"] = (
plot_args['shape'][0], plot_args['shape'][1])
if title is not None:
plot_args['title'] = title
elif model == _models_[1]:
title = 'LRMP with rapid-eq. linear binding'
elif model == _models_[0]:
title = 'LRMP with kinetic linear binding'
line_args_DG = {'linestyle': 'dotted',
'markerfacecolor': 'white'}
line_args_cDG = {'linestyle': 'dashed',
'markerfacecolor': 'black'}
line_args_FV = {'linestyle': 'solid'}
if not _export_results_:
plot_args['title'] = title
# plot_args['y_lim'] = [1e-11, 2e0]
for method_idx in range(0, len(methods)):
table_DG = tables_DGSEM[model][
(tables_DGSEM[model]['$N_e^z$'].isin(cells[method_idx])) &
(tables_DGSEM[model]['$N_d$'] == methods[method_idx])
]
table_cDG = tables_cDGSEM[model][
(tables_cDGSEM[model]['$N_e^z$'].isin(cells[method_idx])) &
(tables_cDGSEM[model]['$N_d$'] == methods[method_idx])
]
convergence.std_plot(table_DG['Sim. time'], table_DG['Max. error'],
label='DG P'+str(int(methods[method_idx])),
color=colours[method_idx],
**line_args_DG)
convergence.std_plot(table_cDG['Sim. time'], table_cDG['Max. error'],
label='cDG P'+str(int(methods[method_idx])),
color=colours[method_idx],
**line_args_cDG)
plot_args['x_label'] = 'Compute time in seconds'
convergence.std_plot_prep(**plot_args)
if _export_results_:
save_m = 'DGexInt_P' if methods[method_idx] < 0 else 'DG_P'
save_m += str(abs(methods[method_idx]))
plt.savefig(_save_path_ +
'benchmark_cDG_'+model+"_"+save_m+'_time.png', bbox_inches='tight')
plt.show()
# convergence.std_plot(FV_DoF, FV_errors, label='FV',
# color='purple', marker='s', **line_args_FV)
for method_idx in range(0, len(methods)):
table_DG = tables_DGSEM[model][
(tables_DGSEM[model]['$N_e^z$'].isin(cells[method_idx])) &
(tables_DGSEM[model]['$N_d$'] == methods[method_idx])
]
table_cDG = tables_cDGSEM[model][
(tables_cDGSEM[model]['$N_e^z$'].isin(cells[method_idx])) &
(tables_cDGSEM[model]['$N_d$'] == methods[method_idx])
]
convergence.std_plot(table_DG['DoF'], table_DG['Max. error'],
label='DG P'+str(int(methods[method_idx])),
color=colours[method_idx],
**line_args_DG)
convergence.std_plot(table_cDG['DoF'], table_cDG['Max. error'],
label='cDG P'+str(int(methods[method_idx])),
color=colours[method_idx],
**line_args_cDG)
plot_args['x_label'] = 'DoF'
convergence.std_plot_prep(**plot_args)
if _export_results_:
save_m = 'DGexInt_P' if methods[method_idx] < 0 else 'DG_P'
save_m += str(abs(methods[method_idx]))
plt.savefig(_save_path_ +
'benchmark_cDG_'+model+"_"+save_m+'_DOF.png', bbox_inches='tight')
plt.show()
if _plot_benchmark_collocation_DGSEM_:
# ax_methods = np.array([1, 2, 3, 4, 5])
# benchmark_collocationDGSEM_LRMPlinear(model=_models_[1],
# methods=[1,3,5],
# cells=[ax_cells[0],ax_cells[2],ax_cells[4]],
# save_path=_save_path_
# )
# benchmark_collocationDGSEM_LRMPlinear(model=_models_[1],
# methods=[2,4],
# cells=[ax_cells[1],ax_cells[3]],
# save_path=_save_path_
# )
for method_idx in range(1, ax_methods.size):
# ax_cells = [
# [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024],
# [1, 2, 4, 8, 16, 32, 64, 128, 256],
# [1, 2, 4, 8, 16, 32, 64],
# [1, 2, 4, 8, 16, 32],
# [1, 2, 4, 8, 16]
# ]
benchmark_collocationDGSEM_LRMPlinear(model=_models_[0],
methods=[
ax_methods[method_idx]],
cells=[ax_cells[method_idx]],
save_path=_save_path_
)
for method_idx in range(1, ax_methods.size):
# ax_cells = [
# [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024],
# [1, 2, 4, 8, 16, 32, 64, 128, 256],
# [1, 2, 4, 8, 16, 32, 64, 128],
# [1, 2, 4, 8, 16, 32, 64],
# [1, 2, 4, 8, 16, 32]
# ]
benchmark_collocationDGSEM_LRMPlinear(model=_models_[1],
methods=[
ax_methods[method_idx]],
cells=[ax_cells[method_idx]],
save_path=_save_path_
)
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