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import timeit
import sys
sys.path.append('bld/Release')
import teqp
import numpy as np
import matplotlib.pyplot as plt
import pandas
import scipy.optimize
def build_models():
return [
teqp.PCSAFTEOS(['Methane']),
teqp.vdWEOS([150.687], [4863000.0])
]
def time(*, model, n, Nrep):
molefrac = [1.0]
rho = 3.0
T = 300
f = getattr(teqp, f"get_Ar0{n}n") if n > 2 else getattr(teqp, f"get_Ar0{n}")
tic = timeit.default_timer()
for i in range(Nrep):
f(model, T, rho, molefrac)
toc = timeit.default_timer()
elap = (toc-tic)/Nrep
return elap
def timeall(*, models, Nrep):
o = []
for model in models:
for n in [1,2,3,4,5,6]:
t = time(model=model, n=n, Nrep=Nrep)
o.append({'model': str(model), 'n': n, 't / s': t})
df = pandas.DataFrame(o)
for model,gp in df.groupby('model'):
plt.plot(gp['n'], gp['t / s']*1e6, label=model)
plt.gca().set(xlabel='n', ylabel=r't / $\mu$s')
plt.legend(loc='best')
# plt.xscale('log')
plt.yscale('log')
plt.title(r'Timing of $A^{\rm r}_{0n}$')
plt.show()
if __name__ == '__main__':
timeall(models=build_models(), Nrep= 10000)