def benchmark_interp_trilin(precompile=True, profile=True):
which = "interp_trilin"
print(which)
print('-' * len(which))
f0, seeds = make_scalar_fld()
print("Timing", which)
ft_stats, cy_stats = dict(), dict()
retFT = viscid.timeit(fort_interp_trilin,
f0,
seeds,
timeit_repeat=10,
timeit_stats=ft_stats)
retCY = viscid.timeit(viscid.interp_trilin,
f0,
seeds,
timeit_repeat=10,
timeit_stats=cy_stats)
print_seedup("Cython",
cy_stats['min'],
"Fortran",
ft_stats['min'],
prefix="@ ")
assert np.allclose(retCY.data, retFT.data)
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--prof", action="store_true")
parser.add_argument("--show", "--plot", action="store_true")
args = vutil.common_argparse(parser)
dtype = 'float64'
# use 512 512 256 to inspect memory related things
x = np.array(np.linspace(-0.5, 0.5, 256), dtype=dtype)
y = np.array(np.linspace(-0.5, 0.5, 256), dtype=dtype)
z = np.array(np.linspace(-0.5, 0.5, 64), dtype=dtype)
v = viscid.empty([x, y, z], name="V", nr_comps=3, center="cell",
layout="interlaced")
exact_cc = viscid.empty([x, y, z], name="exact_cc", center='cell')
Xcc, Ycc, Zcc = exact_cc.get_crds_cc(shaped=True) # pylint: disable=W0612
if HAS_NUMEXPR:
v['x'] = ne.evaluate("(sin(Xcc))") # + Zcc
v['y'] = ne.evaluate("(cos(Ycc))") # + Xcc# + Zcc
v['z'] = ne.evaluate("-((sin(Zcc)))") # + Xcc# + Ycc
exact_cc[:, :, :] = ne.evaluate("cos(Xcc) - sin(Ycc) - cos(Zcc)")
else:
v['x'] = (np.sin(Xcc)) # + Zcc
v['y'] = (np.cos(Ycc)) # + Xcc# + Zcc
v['z'] = -((np.sin(Zcc))) # + Xcc# + Ycc
exact_cc[:, :, :] = np.cos(Xcc) - np.sin(Ycc) - np.cos(Zcc)
if args.prof:
print("Without boundaries")
viscid.timeit(viscid.div, v, bnd=False, timeit_repeat=10,
timeit_print_stats=True)
print("With boundaries")
viscid.timeit(viscid.div, v, bnd=True, timeit_repeat=10,
timeit_print_stats=True)
logger.info("node centered tests")
v_nc = v.as_centered('node')
exact_nc = viscid.empty_like(v_nc['x'])
X, Y, Z = exact_nc.get_crds_nc(shaped=True) # pylint: disable=W0612
if HAS_NUMEXPR:
exact_nc[:, :, :] = ne.evaluate("cos(X) - sin(Y) - cos(Z)")
else:
exact_nc[:, :, :] = np.cos(X) - np.sin(Y) - np.cos(Z)
# FIXME: why is the error so much larger here?
run_div_test(v_nc, exact_nc, title='Node Centered', show=args.show,
ignore_inexact=True)
logger.info("cell centered tests")
v_cc = v_nc.as_centered('cell')
run_div_test(v_cc, exact_cc, title="Cell Centered", show=args.show)
return 0
def benchmark_interp_trilin(precompile=True, profile=True):
which = "interp_trilin"
print(which)
print('-' * len(which))
f0, seeds = make_scalar_fld()
print("Timing", which)
ft_stats, cy_stats = dict(), dict()
retFT = viscid.timeit(fort_interp_trilin, f0, seeds,
timeit_repeat=10, timeit_stats=ft_stats)
retCY = viscid.timeit(viscid.interp_trilin, f0, seeds,
timeit_repeat=10, timeit_stats=cy_stats)
print_seedup("Cython", cy_stats['min'], "Fortran", ft_stats['min'], prefix="@ ")
assert np.allclose(retCY.data, retFT.data)
def lines_and_lsps(B, seeds, cotr=None, **kwargs):
"""Return a list of streamlines and the l-shell,phi for each point"""
tstats = dict()
lines, _ = viscid.timeit(viscid.calc_streamlines, B, seeds,
timeit_quiet=True, timeit_stats=tstats, **kwargs)
walltime = tstats['max']
# lsrlps = [viscid.xyz2lsrlp(line, cotr=cotr, crd_system=B) for line in lines]
lsrlps = [viscid.xyz2lsrlp(line, cotr=cotr, crd_system=B) for line in lines]
lsps = [np.array(lsrlp[(0, 3), :]) for lsrlp in lsrlps]
return lines, lsps, walltime
def lines_and_lsps(B, seeds, cotr=None, **kwargs):
"""Return a list of streamlines and the l-shell,phi for each point"""
tstats = dict()
lines, _ = viscid.timeit(viscid.calc_streamlines, B, seeds,
timeit_quiet=True, timeit_stats=tstats, **kwargs)
walltime = tstats['max']
# lsrlps = [viscid.xyz2lsrlp(line, cotr=cotr, crd_system=B) for line in lines]
lsrlps = [viscid.xyz2lsrlp(line, cotr=cotr, crd_system=B) for line in lines]
lsps = [np.array(lsrlp[(0, 3), :]) for lsrlp in lsrlps]
return lines, lsps, walltime
def benchmark_interp_nearest(precompile=True, profile=True):
which = "interp_nearest"
print(which)
print('-' * len(which))
f0, seeds = make_scalar_fld()
print("Timing", which)
cy_stats = dict()
retCY = viscid.timeit(viscid.interp_nearest, f0, seeds,
timeit_repeat=10, timeit_stats=cy_stats)
print("Cython Min:", cy_stats['min'])
assert np.all(f0.data == retCY.data)
def benchmark_interp_nearest(precompile=True, profile=True):
which = "interp_nearest"
print(which)
print('-' * len(which))
f0, seeds = make_scalar_fld()
print("Timing", which)
cy_stats = dict()
retCY = viscid.timeit(viscid.interp_nearest,
f0,
seeds,
timeit_repeat=10,
timeit_stats=cy_stats)
print("Cython Min:", cy_stats['min'])
assert np.all(f0.data == retCY.data)
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--prof", action="store_true")
parser.add_argument("--show", "--plot", action="store_true")
args = vutil.common_argparse(parser)
b = viscid.make_dipole(l=(-5, -5, -5), h=(5, 5, 5), n=(255, 255, 127),
m=(0, 0, -1))
b2 = np.sum(b * b, axis=b.nr_comp)
if args.prof:
print("Without boundaries")
viscid.timeit(viscid.grad, b2, bnd=False, timeit_repeat=10,
timeit_print_stats=True)
print("With boundaries")
viscid.timeit(viscid.grad, b2, bnd=True, timeit_repeat=10,
timeit_print_stats=True)
grad_b2 = viscid.grad(b2)
grad_b2.pretty_name = r"$\nabla$ B$^2$"
conv = viscid.convective_deriv(b)
conv.pretty_name = r"(B $\cdot \nabla$) B"
_ = plt.figure(figsize=(9, 4.2))
ax1 = vlt.subplot(231)
vlt.plot(b2['z=0f'], logscale=True)
vlt.plot(b2['z=0f'], logscale=True, style='contour', levels=10, colors='grey')
# vlt.plot2d_quiver(viscid.normalize(b['z=0f']), step=16, pivot='mid')
ax2 = vlt.subplot(234)
vlt.plot(b2['y=0f'], logscale=True)
vlt.plot(b2['y=0f'], logscale=True, style='contour', levels=10, colors='grey')
vlt.plot2d_quiver(viscid.normalize(b['y=0f'], preferred='numpy'),
step=16, pivot='mid')
vlt.subplot(232, sharex=ax1, sharey=ax1)
vlt.plot(1e-4 + viscid.magnitude(grad_b2['z=0f']), logscale=True)
vlt.plot(1e-4 + viscid.magnitude(grad_b2['z=0f']), logscale=True,
style='contour', levels=10, colors='grey')
vlt.plot2d_quiver(viscid.normalize(grad_b2['z=0f']), step=16, pivot='mid')
vlt.subplot(235, sharex=ax2, sharey=ax2)
vlt.plot(1e-4 + viscid.magnitude(grad_b2['y=0f']), logscale=True)
vlt.plot(1e-4 + viscid.magnitude(grad_b2['y=0f']), logscale=True,
style='contour', levels=10, colors='grey')
vlt.plot2d_quiver(viscid.normalize(grad_b2['y=0f']), step=16, pivot='mid')
vlt.subplot(233, sharex=ax1, sharey=ax1)
vlt.plot(viscid.magnitude(conv['z=0f']), logscale=True)
vlt.plot(viscid.magnitude(conv['z=0f']), logscale=True,
style='contour', levels=10, colors='grey')
vlt.plot2d_quiver(viscid.normalize(conv['z=0f']), step=16, pivot='mid')
vlt.subplot(236, sharex=ax2, sharey=ax2)
vlt.plot(viscid.magnitude(conv['y=0f']), logscale=True)
vlt.plot(viscid.magnitude(conv['y=0f']), logscale=True,
style='contour', levels=10, colors='grey')
vlt.plot2d_quiver(viscid.normalize(conv['y=0f']), step=16, pivot='mid')
vlt.auto_adjust_subplots()
plt.savefig(next_plot_fname(__file__))
if args.show:
vlt.show()
return 0
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--prof", action="store_true")
parser.add_argument("--show", "--plot", action="store_true")
args = vutil.common_argparse(parser)
b = viscid.make_dipole(l=(-5, -5, -5), h=(5, 5, 5), n=(255, 255, 127),
m=(0, 0, -1))
b2 = np.sum(b * b, axis=b.nr_comp)
if args.prof:
print("Without boundaries")
viscid.timeit(viscid.grad, b2, bnd=False, timeit_repeat=10,
timeit_print_stats=True)
print("With boundaries")
viscid.timeit(viscid.grad, b2, bnd=True, timeit_repeat=10,
timeit_print_stats=True)
grad_b2 = viscid.grad(b2)
grad_b2.pretty_name = r"$\nabla$ B$^2$"
conv = viscid.convective_deriv(b)
conv.pretty_name = r"(B $\cdot \nabla$) B"
_ = plt.figure(figsize=(9, 4.2))
ax1 = vlt.subplot(231)
vlt.plot(b2['z=0j'], logscale=True)
vlt.plot(b2['z=0j'], logscale=True, style='contour', levels=10, colors='grey')
# vlt.plot2d_quiver(viscid.normalize(b['z=0j']), step=16, pivot='mid')
ax2 = vlt.subplot(234)
vlt.plot(b2['y=0j'], logscale=True)
vlt.plot(b2['y=0j'], logscale=True, style='contour', levels=10, colors='grey')
vlt.plot2d_quiver(viscid.normalize(b['y=0j'], preferred='numpy'),
step=16, pivot='mid')
vlt.subplot(232, sharex=ax1, sharey=ax1)
vlt.plot(1e-4 + viscid.magnitude(grad_b2['z=0j']), logscale=True)
vlt.plot(1e-4 + viscid.magnitude(grad_b2['z=0j']), logscale=True,
style='contour', levels=10, colors='grey')
vlt.plot2d_quiver(viscid.normalize(grad_b2['z=0j']), step=16, pivot='mid')
vlt.subplot(235, sharex=ax2, sharey=ax2)
vlt.plot(1e-4 + viscid.magnitude(grad_b2['y=0j']), logscale=True)
vlt.plot(1e-4 + viscid.magnitude(grad_b2['y=0j']), logscale=True,
style='contour', levels=10, colors='grey')
vlt.plot2d_quiver(viscid.normalize(grad_b2['y=0j']), step=16, pivot='mid')
vlt.subplot(233, sharex=ax1, sharey=ax1)
vlt.plot(viscid.magnitude(conv['z=0j']), logscale=True)
vlt.plot(viscid.magnitude(conv['z=0j']), logscale=True,
style='contour', levels=10, colors='grey')
vlt.plot2d_quiver(viscid.normalize(conv['z=0j']), step=16, pivot='mid')
vlt.subplot(236, sharex=ax2, sharey=ax2)
vlt.plot(viscid.magnitude(conv['y=0j']), logscale=True)
vlt.plot(viscid.magnitude(conv['y=0j']), logscale=True,
style='contour', levels=10, colors='grey')
vlt.plot2d_quiver(viscid.normalize(conv['y=0j']), step=16, pivot='mid')
vlt.auto_adjust_subplots()
plt.savefig(next_plot_fname(__file__))
if args.show:
vlt.show()
return 0
def benchmark_streamline(precompile=True, profile=True, scale=True, plot=True):
which = "streamline"
print(which)
print('-' * len(which))
f0, seeds = make_vector_fld()
print("Timing", which)
sl_kwargs = dict(ibound=3.7, ds0=0.020)
lines, _ = viscid.streamlines(f0, seeds, **sl_kwargs)
nsegs_cython = np.sum([line.shape[1] for line in lines])
lines = None
ft_stats, cy_stats = dict(), dict()
bench_output_type = viscid.OUTPUT_TOPOLOGY
sl_kwargs.update(output=bench_output_type)
retFT = viscid.timeit(fort_topology,
f0,
seeds,
timeit_repeat=6,
timeit_stats=ft_stats)
_, retCY = viscid.timeit(viscid.streamlines,
f0,
seeds,
timeit_repeat=6,
timeit_stats=cy_stats,
**sl_kwargs)
fort_per_seg = ft_stats['min'] / retFT.get_info('nsegs')
cy_per_seg = cy_stats['min'] / nsegs_cython
print("Segs Fortran", retFT.get_info('nsegs'))
print("Segs Cython ", nsegs_cython)
print("Fortran took {0:.3g} sec/seg".format(fort_per_seg))
print("Cython took {0:.3g} sec/seg".format(cy_per_seg))
print_seedup("Cython", cy_per_seg, "Fortran", fort_per_seg, prefix="@ ")
if plot:
from viscid.plot import mpl
mpl.clf()
mpl.subplot(121, projection='polar')
mpl.plot(retCY, hemisphere='north')
mpl.subplot(122, projection='polar')
mpl.plot(retCY, hemisphere='south')
mpl.show()
if scale:
thetas = np.logspace(np.log10(3), np.log10(144), 8).astype('i')
cy_nsegs = [None] * len(thetas)
fort_nsegs = [None] * len(thetas)
cy_mintime = [None] * len(thetas)
fort_mintime = [None] * len(thetas)
for i, ntheta in enumerate(thetas):
seeds = viscid.Sphere(r=10.0, ntheta=ntheta, nphi=32)
_stats = dict()
topo = viscid.timeit(fort_topology,
f0,
seeds,
timeit_repeat=5,
timeit_stats=_stats,
timeit_quiet=True)
fort_nsegs[i] = topo.get_info('nsegs')
fort_mintime[i] = _stats['min']
_, topo = viscid.timeit(viscid.calc_streamlines,
f0,
seeds,
ibound=3.7,
ds0=0.020,
output=bench_output_type,
timeit_repeat=5,
timeit_stats=_stats,
timeit_quiet=True)
lines, _ = viscid.streamlines(f0, seeds, ibound=3.7, ds0=0.020)
cy_nsegs[i] = np.sum([line.shape[1] for line in lines])
cy_mintime[i] = _stats['min']
from viscid.plot import mpl
mpl.clf()
mpl.plt.plot(cy_nsegs, cy_mintime, label="Cython")
mpl.plt.plot(fort_nsegs, fort_mintime, label="Fortran")
mpl.plt.legend(loc=0)
mpl.plt.xlabel('Number of segments calculated')
mpl.plt.ylabel('time to calculate')
mpl.show()
mpl.clf()
cy_tperseg = np.array(cy_mintime) / np.array(cy_nsegs)
fort_tperseg = np.array(fort_mintime) / np.array(fort_nsegs)
mpl.plt.plot(thetas, cy_tperseg / fort_tperseg, label="over cython")
mpl.plt.xlabel('ntheta')
mpl.plt.ylabel('Fortran Speedup')
mpl.show()
def benchmark_streamline(precompile=True, profile=True, scale=True, plot=True):
which = "streamline"
print(which)
print('-' * len(which))
f0, seeds = make_vector_fld()
print("Timing", which)
sl_kwargs = dict(ibound=3.7, ds0=0.020)
lines, _ = viscid.streamlines(f0, seeds, **sl_kwargs)
nsegs_cython = np.sum([line.shape[1] for line in lines])
lines = None
ft_stats, cy_stats = dict(), dict()
bench_output_type = viscid.OUTPUT_TOPOLOGY
sl_kwargs.update(output=bench_output_type)
retFT = viscid.timeit(fort_topology, f0, seeds, timeit_repeat=6,
timeit_stats=ft_stats)
_, retCY = viscid.timeit(viscid.streamlines, f0, seeds, timeit_repeat=6,
timeit_stats=cy_stats, **sl_kwargs)
fort_per_seg = ft_stats['min'] / retFT.get_info('nsegs')
cy_per_seg = cy_stats['min'] / nsegs_cython
print("Segs Fortran", retFT.get_info('nsegs'))
print("Segs Cython ", nsegs_cython)
print("Fortran took {0:.3g} sec/seg".format(fort_per_seg))
print("Cython took {0:.3g} sec/seg".format(cy_per_seg))
print_seedup("Cython", cy_per_seg, "Fortran", fort_per_seg, prefix="@ ")
if plot:
from viscid.plot import mpl
mpl.clf()
mpl.subplot(121, projection='polar')
mpl.plot(retCY, hemisphere='north')
mpl.subplot(122, projection='polar')
mpl.plot(retCY, hemisphere='south')
mpl.show()
if scale:
thetas = np.logspace(np.log10(3), np.log10(144), 8).astype('i')
cy_nsegs = [None] * len(thetas)
fort_nsegs = [None] * len(thetas)
cy_mintime = [None] * len(thetas)
fort_mintime = [None] * len(thetas)
for i, ntheta in enumerate(thetas):
seeds = viscid.Sphere(r=10.0, ntheta=ntheta, nphi=32)
_stats = dict()
topo = viscid.timeit(fort_topology, f0, seeds, timeit_repeat=5,
timeit_stats=_stats, timeit_quiet=True)
fort_nsegs[i] = topo.get_info('nsegs')
fort_mintime[i] = _stats['min']
_, topo = viscid.timeit(viscid.calc_streamlines, f0, seeds,
ibound=3.7, ds0=0.020, output=bench_output_type,
timeit_repeat=5, timeit_stats=_stats,
timeit_quiet=True)
lines, _ = viscid.streamlines(f0, seeds, ibound=3.7, ds0=0.020)
cy_nsegs[i] = np.sum([line.shape[1] for line in lines])
cy_mintime[i] = _stats['min']
from viscid.plot import mpl
mpl.clf()
mpl.plt.plot(cy_nsegs, cy_mintime, label="Cython")
mpl.plt.plot(fort_nsegs, fort_mintime, label="Fortran")
mpl.plt.legend(loc=0)
mpl.plt.xlabel('Number of segments calculated')
mpl.plt.ylabel('time to calculate')
mpl.show()
mpl.clf()
cy_tperseg = np.array(cy_mintime) / np.array(cy_nsegs)
fort_tperseg = np.array(fort_mintime) / np.array(fort_nsegs)
mpl.plt.plot(thetas, cy_tperseg / fort_tperseg, label="over cython")
mpl.plt.xlabel('ntheta')
mpl.plt.ylabel('Fortran Speedup')
mpl.show()
