1#!/usr/bin/env python3 2# Copyright (c) 2017-2018, Lawrence Livermore National Security, LLC. 3# Produced at the Lawrence Livermore National Laboratory. LLNL-CODE-734707. 4# All Rights reserved. See files LICENSE and NOTICE for details. 5# 6# This file is part of CEED, a collection of benchmarks, miniapps, software 7# libraries and APIs for efficient high-order finite element and spectral 8# element discretizations for exascale applications. For more information and 9# source code availability see http://github.com/ceed. 10# 11# The CEED research is supported by the Exascale Computing Project 17-SC-20-SC, 12# a collaborative effort of two U.S. Department of Energy organizations (Office 13# of Science and the National Nuclear Security Administration) responsible for 14# the planning and preparation of a capable exascale ecosystem, including 15# software, applications, hardware, advanced system engineering and early 16# testbed platforms, in support of the nation's exascale computing imperative. 17 18import numpy as np 19import pandas as pd 20import argparse 21from pylab import * 22from matplotlib import use 23 24 25def plot(): 26 # Define argparse for the input variables 27 parser = argparse.ArgumentParser(description='Get input arguments') 28 parser.add_argument('--conv_result_file', 29 dest='conv_result_file', 30 type=str, 31 required=True, 32 help='Path to the CSV file') 33 args = parser.parse_args() 34 conv_result_file = args.conv_result_file 35 36 # Load the data 37 runs = pd.read_csv(conv_result_file) 38 colors = ['orange', 'red', 'navy', 'green', 'magenta', 39 'gray', 'blue', 'purple', 'pink', 'black'] 40 res = 'mesh_res' 41 fig, ax = plt.subplots() 42 # Arbitrary coefficients 43 C = [2.2e-2, .24e0, .22e0, .7e0, 2.5e0, 44 3e0, 3.5e0, 4e0, 4.5e0, 5e0] 45 i = 0 46 for group in runs.groupby('degree'): 47 data = group[1] 48 data = data.sort_values('rel_error') 49 p = data['degree'].values[0] 50 h = 1/data[res] 51 H = C[i] * h**p # H = C h^p 52 E = data['rel_error'] 53 log_h = np.log10(h) 54 log_H = np.log10(H) 55 ax.loglog(h, E, 'o', color=colors[i]) 56 m, b = np.polyfit(log_h, log_H, 1) 57 ax.loglog(h, 10**b * h**m, '--', color=colors[i], label='O(h^' + str(p) + ')') 58 i = i + 1 59 60 ax.legend(loc='best') 61 ax.set_xlabel('h') 62 ax.set_ylabel('Relative Error') 63 ax.set_title('Convergence by h Refinement') 64 xlim(.03, .3) 65 fig.tight_layout() 66 plt.savefig('conv_plt_h.png', bbox_inches='tight') 67 68 69if __name__ == "__main__": 70 plot() 71