How to use the perfplot.plot function in perfplot
To help you get started, we’ve selected a few perfplot examples, based on popular ways it is used in public projects.
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nschloe / accupy / test / test_sums.py View on Github 
def test_speed_comparison1(n_range=None):
if n_range is None:
n_range = [2 ** k for k in range(2)]
numpy.random.seed(0)
perfplot.plot(
setup=lambda n: numpy.random.rand(n, 100),
kernels=[
sum,
lambda p: numpy.sum(p, axis=0),
accupy.kahan_sum,
lambda p: accupy.ksum(p, K=2),
lambda p: accupy.ksum(p, K=3),
accupy.fsum,
],
labels=[
"sum",
"numpy.sum",
"accupy.kahan_sum",
"accupy.ksum[2]",
"accupy.ksum[3]",
"accupy.fsum",def test_speed_comparison1(n_range=None):
if n_range is None:
n_range = [2 ** k for k in range(2)]
numpy.random.seed(0)
perfplot.plot(
setup=lambda n: (numpy.random.rand(n, 100), numpy.random.rand(100, n)),
kernels=[
lambda xy: numpy.dot(*xy),
lambda xy: accupy.kdot(*xy, K=2),
lambda xy: accupy.kdot(*xy, K=3),
lambda xy: accupy.fdot(*xy),
],
labels=["numpy.dot", "accupy.kdot[2]", "accupy.kdot[3]", "accupy.fdot"],
colors=plt.rcParams["axes.prop_cycle"].by_key()["color"][:4],
n_range=n_range,
title="dot(random(n, 100), random(100, n))",
xlabel="n",
logx=True,
logy=True,
)
plt.gca().set_aspect(0.2)Y_b = 20
L_A = 64 / numpy.pi / 5
c = 0.69 # average
cam16 = colorio.CAM16(c, Y_b, L_A)
def cio(x):
return cam16.to_xyz100(x, "JCh")
cam16_legacy = CAM16Legacy(c, Y_b, L_A)
def cio_legacy(x):
return cam16_legacy.to_xyz100(x, "JCh")
perfplot.plot(
setup=lambda n: numpy.random.rand(3, n),
kernels=[cio, cio_legacy],
n_range=100000 * numpy.arange(11),
xlabel="Number of input samples",
)
# import matplotlib2tikz
# matplotlib2tikz.save('fig.tikz')
returndef test_speed_comparison2(n_range=None):
if n_range is None:
n_range = [2 ** k for k in range(2)]
numpy.random.seed(0)
perfplot.plot(
setup=lambda n: (numpy.random.rand(100, n), numpy.random.rand(n, 100)),
kernels=[
lambda xy: numpy.dot(*xy),
lambda xy: accupy.kdot(*xy, K=2),
lambda xy: accupy.kdot(*xy, K=3),
lambda xy: accupy.fdot(*xy),
],
labels=["numpy.dot", "accupy.kdot[2]", "accupy.kdot[3]", "accupy.fdot"],
colors=plt.rcParams["axes.prop_cycle"].by_key()["color"][:4],
n_range=n_range,
title="dot(random(100, n), random(n, 100))",
xlabel="n",
logx=True,
logy=True,
)
plt.gca().set_aspect(0.2)def test_speed_comparison2(n_range=None):
if n_range is None:
n_range = [2 ** k for k in range(2)]
numpy.random.seed(0)
perfplot.plot(
setup=lambda n: numpy.random.rand(100, n),
kernels=[
sum,
lambda p: numpy.sum(p, axis=0),
accupy.kahan_sum,
lambda p: accupy.ksum(p, K=2),
lambda p: accupy.ksum(p, K=3),
accupy.fsum,
],
labels=[
"sum",
"numpy.sum",
"accupy.kahan_sum",
"accupy.ksum[2]",
"accupy.ksum[3]",
"accupy.fsum",perfplot
Performance plots for Python code snippets
