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# This script tries to approximate the cost of the DTLS handshake

import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import math
import numpy as np
from scipy import optimize
import sys

figsize=(4.5, 3)


    

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# Read the data
ssl = pd.read_csv("ecdhe-openssl-fine.csv")
for _,r in ssl.iterrows():
    r['openssl'] /= r['numConns']


    

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#print(ssl['openssl'])

# This function will be fitted to the data
def fitfunc(x,t,o):
    # This is the actual mathematical function
    def runner(a):
        return (t/a) + o
    # Magic in case curve_fit hands us an array
    if isinstance(x, np.ndarray):
        ret = []
        for s in x:
            ret += [runner(s)]
        return ret
    else:
        return runner(x)

#params = [1.02,2**11,600]
# Try to fit the data
params, _ = optimize.curve_fit(fitfunc, ssl['numConns'], ssl['openssl'])

print(params)

fit = []
for s in ssl['numConns']:
    fit += [fitfunc(s,params[0],params[1])]
    
fig = plt.figure(figsize=figsize, dpi=300)

plt.ylabel('Cycles per Connection')
plt.xlabel('#Connection')

plt.ylim([0,8500000])

plt.plot(ssl['numConns'], ssl['openssl'], label="data")
plt.plot(ssl['numConns'], fit, label="fit")
plt.legend()
plt.grid(b=True)
plt.savefig('output/openssl_cost.pdf',bbox_inches='tight')
plt.show()


    

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