From d06ba540e15c2dda4b4c6442245283d96358c517 Mon Sep 17 00:00:00 2001
From: Ian Bell <ian.bell@nist.gov>
Date: Thu, 8 Apr 2021 09:00:32 -0400
Subject: [PATCH] Commit rudimentary timing script
---
notebooks/time_teqp.py | 47 ++++++++++++++++++++++++++++++++++++++++++
1 file changed, 47 insertions(+)
create mode 100644 notebooks/time_teqp.py
diff --git a/notebooks/time_teqp.py b/notebooks/time_teqp.py
new file mode 100644
index 0000000..006ddba
--- /dev/null
+++ b/notebooks/time_teqp.py
@@ -0,0 +1,47 @@
+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)
\ No newline at end of file
--
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