iterations.py

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import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
# import tikzplotlib
 
#global parameters
markersize=6
line_width=2
font_size=12
font_weight='normal'
plt.rcParams['figure.figsize'] = [16, 9]
plt.rcParams['figure.dpi'] = 100
plt.rcParams['font.family'] = "DejaVu Serif"
plt.rcParams['font.size'] = font_size
plt.rcParams['font.weight'] = font_weight
 
nb_columns=2
 
 
 
def main():
    # my_data1=get_data_from_string(my_multiplicative, nb_columns)
    # my_data2=get_data_from_string(my_additive, nb_columns)
    # my_data3=get_data_from_string(my_multifield, nb_columns)
    # my_data3[:,[0, 1]] = my_data3[:,[1, 0]]
    # print(my_data1[:,1])
        # 301 TS dt 0.0053405 time 1.24046
        # grep "SNES Function norm" log_constraint_bc_old > log_constraint_bc
        # grep "SNES Function norm" log_classical_bc_old > log_classical_bc
    newton_log_file1="/home/karol/mofem_install/users_modules_debug/multifield_plasticity/test_vertical_flow_form_3D/log_classical_bc"
    newton_log_file2="/home/karol/mofem_install/users_modules_debug/multifield_plasticity/test_vertical_flow_form_3D/log_constraint_bc"
 
    newton_data1=pd.read_fwf(newton_log_file1, header=None)
    newton_data2=pd.read_fwf(newton_log_file2, header=None)
 
    plt.plot(newton_data1[8].to_numpy(), ls='solid',lw=line_width, marker='o', markersize=markersize, c='blue', label="classical bc")
    plt.plot(newton_data2[8].to_numpy(), ls='solid', lw=line_width, marker='x', markersize=markersize, c='orange', label="constraint bc")
 
    plt.legend(loc='upper right')
 
    # plt.gca().set_xlim(left=0, right=None)
    # plt.gca().set_ylim(bottom=0, top=None)
 
    # plt.plot(newton_data[7].to_numpy(),'r^-')
    plt.title('Newton method convergence')
    plt.ylabel('absolute residual')
    plt.xlabel('accumulated iterations')
    plt.yscale('log')
    plt.grid(True)
    plt.show()
    plt.savefig("my_log.png")
 
    # tikzplotlib.save("my_graph.tex")
 
def get_data_from_string(data_string, columns, delimiter=' '):
    my_data=np.fromstring(data_string, dtype=float, sep=delimiter)
    my_data = my_data.reshape(int(my_data.size/nb_columns),nb_columns)
    return my_data
 
if __name__ == '__main__':
    main()