def complex_cos():
def real_cos(x):
vml.py_vdCos(x[0], x[5])
def complex_cos(x):
vml.py_vzCos(x[1], x[4])
def complex_noimag_cos(x):
vml.py_vzCos(x[2], x[4])
def complex_noreal_cos(x):
vml.py_vzCos(x[3], x[4])
perfplot.show(
setup=lambda n: [
np.zeros(n, dtype=np.float64) + 0.25,
np.zeros(n, dtype=np.complex128) + (0.25 + .25j),
np.zeros(n, dtype=np.complex128) + 0.25,
np.zeros(n, dtype=np.complex128) + 0.25j,
np.zeros(n, dtype=np.complex128) + 0.25j,
np.zeros(n, dtype=np.float64) + 0.25
],
n_range=list(range(1000, 10000, 1000)),
kernels=[
real_cos, complex_cos, complex_noimag_cos, complex_noreal_cos
],
logx=True,
logy=True,
title="real vs. complex cos",
xlabel='len(vec)',
equality_check=None,
)
def test_speed(N=2):
numpy.random.seed(1)
osa = colorio.OsaUcs()
cielab = colorio.CIELAB()
# cam16 = colorio.CAM16(0.69, 20, L_A=64 / numpy.pi / 5)
ciecam02 = colorio.CIECAM02(0.69, 20, L_A=64 / numpy.pi / 5)
# This close probably means that another figure hasn't been properly closed.
import matplotlib.pyplot as plt
plt.close()
perfplot.show(
# Don't use numpy.random.rand(3, n) to avoid the CIECAM breakdown
setup=lambda n: numpy.outer(numpy.random.rand(3), numpy.ones(n)) * 10,
equality_check=None,
kernels=[
osa.to_xyz100,
cielab.to_xyz100,
# cam16.to_xyz100,
lambda Jsh: ciecam02.to_xyz100(Jsh, "Jsh"),
numpy.cbrt,
],
labels=["OSA-UCS", "CIELAB", "CIECAM02", "cbrt"],
n_range=[2 ** n for n in range(N)],
logx=True,
logy=True,
# relative_to=3
)
def main():
argp = argparse.ArgumentParser()
argp.add_argument('-t',
'--test',
action='store_true',
help='run unit test')
argp.add_argument('-v',
'--verify',
action='store_true',
help='verify equality of methods')
args = argp.parse_args()
kernels = [
vec, list_comp, apply, iterrows, itertuples, cython_iter, iloc_iter
]
if args.test:
print(doctest.testmod())
elif args.verify:
df = make_dataframe(100)
v = kernels[0](df)
for k in kernels[1:]:
assert (v == k(df)).all(), f"{k.__name__} doesn't match"
else:
perfplot.show(setup=lambda n: make_dataframe(n),
kernels=kernels,
labels=[str(k.__name__) for k in kernels],
n_range=[2**k for k in range(16)],
xlabel='N',
logx=True,
logy=True,
equality_check=False)
def performance_comparison_from():
import perfplot
def setup(n):
out = numpy.empty((3, n))
rgb = numpy.random.rand(3)
for k in range(3):
out[k] = rgb[k]
return out
Y_b = 20
L_A = 64 / numpy.pi / 5
c = 0.69 # average
cam16 = colorio.CAM16(c, Y_b, L_A)
cam16_legacy = CAM16Legacy(c, Y_b, L_A)
perfplot.show(
setup=setup,
kernels=[cam16.from_xyz100, cam16_legacy.from_xyz100],
labels=["new", "legacy"],
n_range=1000 * numpy.arange(6),
equality_check=False,
)
return
def noerror_impact():
def acos_with_errorhandling(x):
vml.py_vmlSetMode(vml.PY_VML_ERRMODE_DEFAULT)
vml.py_vdAcos(x[1], x[1])
def acos_without_error_errorhandling(x):
vml.py_vmlSetMode(vml.PY_VML_ERRMODE_ERRNO)
vml.py_vdAcos(x[1], x[1])
def acos_without_error(x):
vml.py_vmlSetMode(vml.PY_VML_ERRMODE_DEFAULT)
vml.py_vdAcos(x[0], x[0])
perfplot.show(
setup=lambda n: [
np.zeros(int(n), dtype=np.float64) + 0.25,
np.zeros(int(n), dtype=np.float64) + 2.0
],
n_range=[1e4, 1e5, 1e6, 1e7, 1e8],
kernels=[
acos_without_error, acos_with_errorhandling,
acos_without_error_errorhandling
],
logx=True,
logy=True,
title="acos with errors",
xlabel='len(vec)',
equality_check=None,
)
def test0():
perfplot.show(
setup=np.random.rand,
kernels=kernels,
labels=["c_"],
n_range=r,
xlabel="len(a)",
)
def test_flops():
perfplot.show(
setup=np.random.rand,
kernels=kernels,
labels=["c_"],
n_range=r,
xlabel="len(a)",
flops=lambda n: n,
)
def test_no_labels():
def mytest(a):
return numpy.c_[a, a]
kernels = [mytest]
r = [2**k for k in range(4)]
perfplot.show(
setup=numpy.random.rand,
kernels=kernels, n_range=r, xlabel='len(a)'
)
return
def test1():
perfplot.show(
setup=np.random.rand,
kernels=kernels,
labels=["c_"],
n_range=r,
xlabel="len(a)",
logx=True,
logy=False,
)
def test_return_tuples():
def setup(n):
return np.ones(n), np.full(n + 2, 3.1)
def times(a, b):
return 2 * a, 3 * b
def times_reversed(a, b):
return a * 2, b * 3
perfplot.show(setup=setup,
kernels=[times, times_reversed],
n_range=[2**k for k in range(3)])
def test():
kernels = [lambda a: numpy.c_[a, a]]
r = [2**k for k in range(4)]
perfplot.show(setup=numpy.random.rand,
kernels=kernels,
labels=['c_'],
n_range=r,
xlabel='len(a)')
perfplot.show(setup=numpy.random.rand,
kernels=kernels,
labels=['c_'],
n_range=r,
xlabel='len(a)',
logx=True,
logy=False)
perfplot.show(setup=numpy.random.rand,
kernels=kernels,
labels=['c_'],
n_range=r,
xlabel='len(a)',
logx=False,
logy=True)
perfplot.show(setup=numpy.random.rand,
kernels=kernels,
labels=['c_'],
n_range=r,
xlabel='len(a)',
logx=True,
logy=True)
return
def test3():
def setup(n):
assert isinstance(n, int)
return np.random.rand(n)
perfplot.show(
setup=setup,
kernels=kernels,
labels=["c_"],
n_range=r,
xlabel="len(a)",
logx=True,
logy=True,
)
def plot(title, kernels, labels, n_vals):
"""Plot timings as a function of the parameter n.
Args:
title (str): plot title
kernels (Sequence[Callable[[Any], Any]]): parametrized benchmarks to time
labels (Sequence[str]): names of the kernels
n_vals (Sequence[Any]): values the benchmark parameter n takes
"""
import perfplot
perfplot.show(
setup=lambda n: n,
kernels=kernels,
labels=labels,
n_range=n_vals,
xlabel="n",
title=title,
)
def service_function_costs():
perfplot.show(
setup=lambda n: n,
n_range=[1e3, 1e4, 1e5],
kernels=[
vml.onoff_multithreading,
vml.onoff_multithreading_local,
vml.onoff_mode,
vml.onoff_mkl_dynamic,
],
labels=[
"(2*set+1*get) thread number", "2*set local thread number",
"2*set_mode", "(2*set+1*get) mkl_dynamic"
],
logx=True,
logy=True,
title="service functions",
xlabel='number of times',
equality_check=None,
)
def perfplot():
"""Convenience entry point to perf-plot different function samples"""
import perfplot
functions = []
labels = []
for name, func in globals().items():
if name.startswith("_bench"):
name = name[1:]
functions.append(func)
labels.append(name)
perfplot.show(
setup=lambda n: n, # or simply setup=numpy.random.rand
kernels=functions,
labels=labels,
n_range=[2 ** k for k in range(15)],
xlabel="len(a)",
equality_check=None,
target_time_per_measurement=5.0,
time_unit="us", # set to one of ("auto", "s", "ms", "us", or "ns") to force plot units
)
def sincos():
def sincos(x):
vml.py_vdSinCos(x[0], x[1], x[2])
def sin_and_cos(x):
vml.py_vdCos(x[0], x[1])
vml.py_vdSin(x[0], x[2])
perfplot.show(
setup=lambda n: [
np.zeros(n, dtype=np.float64) + 0.25,
np.zeros(n, dtype=np.float64) + 0.25,
np.zeros(n, dtype=np.float64) + 0.25
],
n_range=list(range(1000, 10000, 1000)),
kernels=[sincos, sin_and_cos],
logx=False,
logy=False,
title="sincos vs sin+cos",
xlabel='len(vec)',
equality_check=None,
)
def expimag_sincos():
def expimag(x):
vml.py_vzExp(x[0], x[1])
def sincos(x):
vml.py_vdSinCos(x[2], x[3], x[4])
perfplot.show(
setup=lambda n: [
np.zeros(n, dtype=np.complex128) + 0.25j,
np.zeros(n, dtype=np.complex128) + 0.25,
np.zeros(n, dtype=np.float64) + 0.25,
np.zeros(n, dtype=np.float64) + 0.25,
np.zeros(n, dtype=np.float64) + 0.25
],
n_range=list(range(1000, 10000, 1000)),
kernels=[expimag, sincos],
logx=False,
logy=False,
title="sincos vs expimag",
xlabel='len(vec)',
equality_check=None,
)
def test_speed(n=3):
path_pts = [[0, 0], [0, 1], [1, 1], [1, 0]]
path0 = path.Path(path_pts)
path1 = pypathlib.ClosedPath(path_pts)
def _mpl_path(pts):
return path0.contains_points(pts)
def _pypathlib_contains_points(pts):
return path1.contains_points(pts)
numpy.random.seed(0)
perfplot.show(
setup=lambda n: numpy.random.rand(n, 2),
kernels=[_mpl_path, _pypathlib_contains_points],
n_range=[2 ** k for k in range(n)],
labels=["matplotlib.path.contains_points", "pypathlib.contains_points"],
logx=True,
logy=True,
xlabel="num points",
)
return
def test():
kernels = [lambda a: numpy.c_[a, a]]
r = [2**k for k in range(4)]
perfplot.show(
setup=numpy.random.rand,
kernels=kernels,
labels=["c_"],
n_range=r,
xlabel="len(a)",
)
perfplot.show(
setup=numpy.random.rand,
kernels=kernels,
labels=["c_"],
n_range=r,
xlabel="len(a)",
logx=True,
logy=False,
)
out = perfplot.bench(
setup=numpy.random.rand,
kernels=kernels,
labels=["c_"],
n_range=r,
xlabel="len(a)",
logx=False,
logy=True,
)
print(out)
perfplot.show(
setup=numpy.random.rand,
kernels=kernels,
labels=["c_"],
n_range=r,
xlabel="len(a)",
logx=True,
logy=True,
)
return
def test():
perfplot.show(
setup=numpy.random.rand,
kernels=kernels,
labels=["c_"],
n_range=r,
xlabel="len(a)",
)
perfplot.show(
setup=numpy.random.rand,
kernels=kernels,
labels=["c_"],
n_range=r,
xlabel="len(a)",
logx=True,
logy=False,
)
out = perfplot.bench(
setup=numpy.random.rand,
kernels=kernels,
labels=["c_"],
n_range=r,
xlabel="len(a)",
logx=False,
logy=True,
)
print(out)
perfplot.show(
setup=numpy.random.rand,
kernels=kernels,
labels=["c_"],
n_range=r,
xlabel="len(a)",
logx=True,
logy=True,
)
return
def solve_3(in_val):
mat, V = in_val
return solve(mat, V, is_flat=False, solver=3)
def solve_4(in_val):
mat, V = in_val
return solve(mat, V, is_flat=False, solver=4)
def solve_5(in_val):
mat, V = in_val
return np.linalg.solve(mat, V)
perfplot.show(
setup=get_les,
kernels=[solve_1, solve_2, solve_3, solve_4, solve_5],
labels=[
"PTRANS-I",
"PTRANS-II",
"scipy.linalg.solve_banded",
"scipy.sparse.linalg.spsolve",
"numpy.linalg.solve",
],
n_range=[2 ** k for k in range(2, 13)],
xlabel="Size [n]",
logy=True,
)
import numpy
import perfplot
perfplot.show(
setup=numpy.random.rand,
kernels=[
lambda a: numpy.c_[a, a],
lambda a: numpy.stack([a, a]).T,
lambda a: numpy.vstack([a, a]).T,
lambda a: numpy.column_stack([a, a]),
lambda a: numpy.concatenate([a[:, None], a[:, None]], axis=1),
],
labels=["c_", "stack", "vstack", "column_stack", "concat"],
n_range=[2**k for k in range(15)],
xlabel="len(a)",
)
"""Benchmark czt.iczt function."""
import numpy as np
import czt
import perfplot
def model(t):
output = (1.0 * np.sin(2 * np.pi * 1e3 * t) +
0.3 * np.sin(2 * np.pi * 2e3 * t) +
0.1 * np.sin(2 * np.pi * 3e3 * t)) * np.exp(-1e3 * t)
return output
perfplot.show(
setup=lambda n: czt.czt(model(np.linspace(0, 20e-3, n))),
kernels=[
lambda a: czt.iczt(a, simple=True),
lambda a: czt.iczt(a, simple=False),
],
labels=["simple=True", "simple=False"],
n_range=[10**k for k in range(1, 8)],
xlabel="Input length",
# equality_check=np.allclose,
equality_check=False,
target_time_per_measurement=0.1,
)
x, s, t = ds
c, cn = get_pair(x)
assert c in s
assert cn not in s
return True
def setup_ds(n):
x = numpy.random.rand(n)
s = set()
t = pyavl.AVL()
for xi in x:
t.add(xi)
s.add(xi)
return (x, s, t)
perfplot.show(
setup=setup_ds,
kernels=[
avl_query,
set_query,
],
n_range=[2**k for k in range(20)],
labels=[
"AVL tree",
"set",
],
title='Querying array performance',
xlabel='len(x)',
)
import numpy as np
import perfplot
import colorio
np.random.seed(1)
osa = colorio.cs.OsaUcs()
cielab = colorio.cs.CIELAB()
# cam16 = colorio.CAM16(0.69, 20, L_A=64 / np.pi / 5)
ciecam02 = colorio.cs.CIECAM02(0.69, 20, L_A=64 / np.pi / 5)
perfplot.show(
# Don't use np.random.rand(3, n) to avoid the CIECAM breakdown
setup=lambda n: np.outer(np.random.rand(3) * 10, np.ones(n)),
equality_check=None,
kernels=[
osa.to_xyz100,
cielab.to_xyz100,
# cam16.to_xyz100,
lambda Jsh: ciecam02.to_xyz100(Jsh, "Jsh"),
# np.cbrt,
],
labels=["OSA-UCS", "CIELAB", "CIECAM02", "cbrt"],
n_range=[2**n for n in range(23)],
# relative_to=3
)
# import tikzplotlib as tpl
# tpl.save("out.tex")
num_interior_edges = np.sum(mesh0.is_interior_edge)
idx = random.sample(range(num_interior_edges), n // 10)
print(num_interior_edges, len(idx), len(idx) / num_interior_edges)
# # move interior points a little bit such that we have edges to flip
# max_step = np.min(mesh.cell_inradius) / 2
# mesh.points = mesh.points + max_step * np.random.rand(*mesh.points.shape)
# print(mesh.num_delaunay_violations)
return mesh0, mesh1, idx
def flip_old(data):
mesh0, mesh1, idx = data
mesh0.flip_interior_edges_old(idx)
def flip_new(data):
mesh0, mesh1, idx = data
mesh1.flip_interior_edges(idx)
perfplot.show(
setup=setup,
kernels=[flip_old, flip_new],
n_range=[2**k for k in range(5, 13)],
equality_check=None,
# set target time to 0 to avoid more than one repetition
target_time_per_measurement=0.0,
)
def parallelization_impact_complex():
def acos(x):
vml.py_vzAcos(x[0], x[1])
def cos(x):
vml.py_vzCos(x[0], x[1])
def sin(x):
vml.py_vzSin(x[0], x[1])
def asin(x):
vml.py_vzAsin(x[0], x[1])
def exp(x):
vml.py_vzExp(x[0], x[1])
def ln(x):
vml.py_vzLn(x[0], x[1])
def mul(x):
vml.py_vzMul(x[0], x[0], x[1])
def tan(x):
vml.py_vzTan(x[0], x[1])
def atan(x):
vml.py_vzAtan(x[0], x[1])
perfplot.show(
setup=lambda n: [
np.zeros(n, dtype=np.complex128) + 0.25 + 0.25j,
np.zeros(n, dtype=np.complex128) + 0.25 + 0.25j
],
n_range=list(range(10, 200, 10)),
kernels=[tan, atan],
logx=False,
logy=False,
title="tan, atan",
xlabel='len(vec)',
equality_check=None,
)
perfplot.show(
setup=lambda n: [
np.zeros(n, dtype=np.complex128) + 0.25 + 0.25j,
np.zeros(n, dtype=np.complex128) + 0.25 + 0.25j
],
n_range=list(range(100, 3000, 300)),
kernels=[exp, ln, mul],
logx=False,
logy=False,
title="exp, ln, mul",
xlabel='len(vec)',
equality_check=None,
)
perfplot.show(
setup=lambda n: [
np.zeros(n, dtype=np.complex128) + 0.25 + 0.25j,
np.zeros(n, dtype=np.complex128) + 0.25 + 0.25j
],
n_range=list(range(10, 200, 10)),
kernels=[sin, cos, asin, acos],
logx=False,
logy=False,
title="sin, cos, asin, acos",
xlabel='len(vec)',
equality_check=None,
)
f = h5py.File(tmp+"with_keys_fixed_copy.hdf",'r')
for i in indices_to_read:
path = f"event/t{i}"
table = f[path+'/block0_values'][:]
columns = f[path+'/axis0'][:]
indices = f[path +'/axis1'][:]
df = pd.DataFrame(table,indices,columns)
f.close()
# %%
def random_samples(lenght):
return np.random.randint(0,sample_size,lenght)
perfplot.show(setup=random_samples,
kernels=[read_with_keys_fixed,read_with_single, read_manual_retrival_with_keys],#[read_with_keys_table,read_with_keys_fixed,read_with_single,read_files],
n_range=[ 10000, 20000, 80000],
equality_check=None,
)
# %%
read_with_single([0])
# %%
f = pd.HDFStore(tmp+"single.hdf",complib='blosc:zstd')
f.keys()
# %%
f.select('table',"index=='10'")
# %%
sto = f.get_storer('table')
# %%
def set_insert(ds):
x, s, t = ds
r = random.random()
s.add(r)
return True
def setup_ds(n):
x = numpy.random.rand(n)
s = set()
t = pyavl.AVL()
for xi in x:
t.add(xi)
s.add(xi)
return (x, s, t)
perfplot.show(
setup=setup_ds,
kernels=[
avl_insert,
set_insert,
],
n_range=[2**k for k in range(20)],
labels=[
"AVL tree",
"set",
],
title='Inserting in array performance',
xlabel='len(x)',
)
def parallelization_impact():
def acos(x):
vml.py_vdAcos(x[0], x[1])
def cos(x):
vml.py_vdCos(x[0], x[1])
def sin(x):
vml.py_vdSin(x[0], x[1])
def asin(x):
vml.py_vdAsin(x[0], x[1])
def exp(x):
vml.py_vdExp(x[0], x[1])
def ln(x):
vml.py_vdLn(x[0], x[1])
def mul(x):
vml.py_vdMul(x[0], x[1], x)
def tan(x):
vml.py_vdTan(x[0], x[1])
def atan(x):
vml.py_vdAtan(x[0], x[1])
perfplot.show(
setup=lambda n: np.zeros(n, dtype=np.float64) + 0.25,
n_range=list(range(1000, 10000, 1000)),
kernels=[tan, atan],
logx=False,
logy=False,
title="tan vs atan",
xlabel='len(vec)',
equality_check=None,
)
perfplot.show(
setup=lambda n: np.zeros(n, dtype=np.float64) + 0.25,
n_range=list(range(1000, 10000, 1000)),
kernels=[exp, ln, mul],
logx=False,
logy=False,
title="exp, ln, mul",
xlabel='len(vec)',
equality_check=None,
)
perfplot.show(
setup=lambda n: np.zeros(n, dtype=np.float64) + 0.25,
n_range=list(range(1000, 10000, 1000)),
kernels=[
sin,
cos,
],
logx=False,
logy=False,
title="sin, cos",
xlabel='len(vec)',
equality_check=None,
)
perfplot.show(
setup=lambda n: np.zeros(n, dtype=np.float64) + 0.25,
n_range=list(range(1000, 10000, 1000)),
kernels=[
asin,
acos,
],
logx=False,
logy=False,
title="asin, acos",
xlabel='len(vec)',
equality_check=None,
)
