Commit rudimentary timing script

notebooks/time_teqp.py

+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)
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