CircularRR_AOPS_Fig_06bc.py

"""
The code for the research presented in the paper titled "A deep learning method for empirical spectral prediction and inverse design of all-optical nonlinear plasmonic ring resonator switches

@authors: Ehsan Adibnia, Majid Ghadrdan and Mohammad Ali Mansouri-Birjandi
Corresponding author: mansouri@ece.usb.ac.ir

This code is corresponding to the Inverse Deep Neural Network (DNN) section of the article.
This code regenerates the Fig 6b and 6c of the paper.
Please cite the paper in any publication using this code.
"""
import matplotlib.pyplot as plt 
import pandas as pd 
from matplotlib.font_manager import FontProperties

lum = pd.read_csv("CircularRR_AOPS_Fig_06bc_furthest_predicted.csv", header=None)
g = "\u00D7"

lum=lum.to_numpy()

# plot of transmission of furthest data and predicted data from inverse model using FDTD solver
plt.plot(lum[1:800,0],lum[1:800,6], linewidth=2, color='#ff7f0e')
plt.plot(lum[1:800,0],lum[1:800,13], linewidth=2, linestyle='--', color='#1f77b4')
plt.title('Results of inverse design for through port', fontname='Times New Roman', fontsize=18, loc='center')
plt.xlabel('Wavelength (nm)', fontname='Times New Roman', fontsize=18)
plt.ylabel('Transmission', fontname='Times New Roman', fontsize=18)
plt.xticks(fontfamily='Times New Roman', fontsize=14)
plt.yticks(fontfamily='Times New Roman', fontsize=14)
font_prop = FontProperties(family="Times New Roman", size=14)
plt.legend(['Desierd (49,37,43,18,22)', 'DL (57,38,43,15,21)'], prop=font_prop)
plt.show()

plt.plot(lum[1:800,0],lum[1:800,7], linewidth=2, color='#ff7f0e')
plt.plot(lum[1:800,0],lum[1:800,14], linewidth=2, linestyle='--', color='#1f77b4')
plt.title('Results of inverse design for drop port', fontname='Times New Roman', fontsize=18, loc='center')
plt.xlabel('Wavelength (nm)', fontname='Times New Roman', fontsize=18)
plt.ylabel('Transmission', fontname='Times New Roman', fontsize=18)
plt.xticks(fontfamily='Times New Roman', fontsize=14)
plt.yticks(fontfamily='Times New Roman', fontsize=14)
font_prop = FontProperties(family="Times New Roman", size=14)
plt.legend(['Desierd (49,37,43,18,22)', 'DL (57,38,43,15,21)'], prop=font_prop)
plt.show()