def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--timeit", '-t', action="store_true")
args = viscid.vutil.common_argparse(parser, default_verb=0)
for nu in (False, True):
viscid.logger.info("Test set, nonuniform = {0}".format(nu))
b = viscid.make_dipole(l=(-20, -6.4, -6.4), h=(20, 6.4, 6.4),
n=(256, 128, 128), dtype='f4', nonuniform=nu)
seed = viscid.Circle(p0=(0, 0, 0), pole=(0, 0, 1), r=5.5, n=int(1e4))
kwargs = dict(method='euler1', ibound=0.1)
viscid.logger.info("Serial test...")
l0, t0 = do(args.timeit, viscid.calc_streamlines, b, seed,
nr_procs=1, threads=False, **kwargs)
viscid.logger.info("Parallel test (processes)...")
l1, t1 = do(args.timeit, viscid.calc_streamlines, b, seed,
nr_procs=2, threads=False, **kwargs)
viscid.logger.info("Parallel test (threads)...")
l2, t2 = do(args.timeit, viscid.calc_streamlines, b, seed,
nr_procs=2, threads=True, **kwargs)
np.testing.assert_almost_equal(t0.data, t1.data)
np.testing.assert_almost_equal(t0.data, t2.data)
assert len(l0) > 0
assert len(l0) == len(l1)
assert len(l0) == len(l2)
for i in range(len(l0)):
np.testing.assert_almost_equal(l0[i], l1[i])
np.testing.assert_almost_equal(l0[i], l2[i])
return 0
def check():
"""Runtime check compiled modules"""
import os
import sys
import numpy as np
import viscid
ret = 0
check_version()
print()
#####################################################
# run streamline calculation (checks cython modules)
try:
cotr = viscid.Cotr(dip_tilt=15.0, dip_gsm=21.0) # pylint: disable=not-callable
m = cotr.get_dipole_moment(crd_system='gse')
seeds = viscid.seed.Sphere((0.0, 0.0, 0.0),
2.0,
pole=-m,
ntheta=25,
nphi=25,
thetalim=(5, 90),
philim=(5, 360),
phi_endpoint=False)
B = viscid.make_dipole(m=m,
crd_system='gse',
n=(32, 32, 32),
l=(-25, -25, -25),
h=(25, 25, 25),
dtype='f8')
lines, _ = viscid.calc_streamlines(B, seeds, ibound=1.0)
for line in lines:
if np.any(np.isnan(line)):
raise ValueError("NaN in line")
print("Cython module ran successfully")
except Exception as e:
print("Cython module has runtime errors.")
print(str(e))
ret |= (1 << 0)
print()
####################################
# load a jrrle file (checks fortran)
try:
f3d = viscid.load_file(
os.path.join(viscid.sample_dir, 'sample_jrrle.3df.*'))
_ = np.array(f3d['pp'].data)
print("Fortran module ran successfully")
except Exception as e:
print("Fortran module has runtime errors.")
print(str(e))
ret |= (1 << 1)
print()
return ret
def _main():
parser = argparse.ArgumentParser()
parser.add_argument("--notwo", dest="notwo", action="store_true")
parser.add_argument("--nothree", dest="nothree", action="store_true")
parser.add_argument("--show", "--plot", action="store_true")
args = viscid.vutil.common_argparse(parser, default_verb=0)
plot2d = not args.notwo
plot3d = not args.nothree
viscid.logger.info("Testing field lines on 2d field...")
B = viscid.make_dipole(twod=True)
line = viscid.seed.Line((0.2, 0.0, 0.0), (1.0, 0.0, 0.0), 10)
obound0 = np.array([-4, -4, -4], dtype=B.data.dtype)
obound1 = np.array([4, 4, 4], dtype=B.data.dtype)
run_test(
B, line, plot2d=plot2d, plot3d=plot3d, title="2D", show=args.show, ibound=0.07, obound0=obound0, obound1=obound1
)
viscid.logger.info("Testing field lines on 3d field...")
B = viscid.make_dipole(m=[0.2, 0.3, -0.9])
sphere = viscid.seed.Sphere((0.0, 0.0, 0.0), 2.0, ntheta=20, nphi=10)
obound0 = np.array([-4, -4, -4], dtype=B.data.dtype)
obound1 = np.array([4, 4, 4], dtype=B.data.dtype)
run_test(
B,
sphere,
plot2d=plot2d,
plot3d=plot3d,
title="3D",
show=args.show,
ibound=0.07,
obound0=obound0,
obound1=obound1,
method=viscid.RK12,
)
def check():
"""Runtime check compiled modules"""
import os
import sys
import numpy as np
import viscid
ret = 0
check_version()
print()
#####################################################
# run streamline calculation (checks cython modules)
try:
cotr = viscid.Cotr(dip_tilt=15.0, dip_gsm=21.0) # pylint: disable=not-callable
m = cotr.get_dipole_moment(crd_system='gse')
seeds = viscid.seed.Sphere((0.0, 0.0, 0.0), 2.0, pole=-m, ntheta=25,
nphi=25, thetalim=(5, 90), philim=(5, 360),
phi_endpoint=False)
B = viscid.make_dipole(m=m, crd_system='gse', n=(32, 32, 32),
l=(-25, -25, -25), h=(25, 25, 25), dtype='f8')
lines, _ = viscid.calc_streamlines(B, seeds, ibound=1.0)
for line in lines:
if np.any(np.isnan(line)):
raise ValueError("NaN in line")
print("Cython module ran successfully")
except Exception as e:
print("Cython module has runtime errors.")
print(str(e))
ret |= (1 << 0)
print()
####################################
# load a jrrle file (checks fortran)
try:
f3d = viscid.load_file(os.path.join(viscid.sample_dir,
'sample_jrrle.3df.*'))
_ = np.array(f3d['pp'].data)
print("Fortran module ran successfully")
except Exception as e:
print("Fortran module has runtime errors.")
print(str(e))
ret |= (1 << 1)
print()
return ret
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--notwo", dest='notwo', action="store_true")
parser.add_argument("--nothree", dest='nothree', action="store_true")
parser.add_argument("--show", "--plot", action="store_true")
args = viscid.vutil.common_argparse(parser, default_verb=0)
plot2d = not args.notwo
plot3d = not args.nothree
# #################################################
# viscid.logger.info("Testing field lines on 2d field...")
B = viscid.make_dipole(twod=True)
line = viscid.seed.Line((0.2, 0.0, 0.0), (1.0, 0.0, 0.0), 10)
obound0 = np.array([-4, -4, -4], dtype=B.data.dtype)
obound1 = np.array([4, 4, 4], dtype=B.data.dtype)
run_test(B,
line,
plot2d=plot2d,
plot3d=plot3d,
title='2D',
show=args.show,
ibound=0.07,
obound0=obound0,
obound1=obound1)
#################################################
viscid.logger.info("Testing field lines on 3d field...")
B = viscid.make_dipole(m=[0.2, 0.3, -0.9])
sphere = viscid.seed.Sphere((0.0, 0.0, 0.0), 2.0, ntheta=20, nphi=10)
obound0 = np.array([-4, -4, -4], dtype=B.data.dtype)
obound1 = np.array([4, 4, 4], dtype=B.data.dtype)
run_test(B,
sphere,
plot2d=plot2d,
plot3d=plot3d,
title='3D',
show=args.show,
ibound=0.12,
obound0=obound0,
obound1=obound1,
method=viscid.RK12)
# The Remainder of this test makes sure higher order methods are indeed
# more accurate than lower order methods... this could find a bug in
# the integrators
##################################################
# test accuracy of streamlines in an ideal dipole
cotr = viscid.Cotr(dip_tilt=15.0, dip_gsm=21.0) # pylint: disable=not-callable
m = cotr.get_dipole_moment(crd_system='gse')
seeds = viscid.seed.Sphere((0.0, 0.0, 0.0),
2.0,
pole=-m,
ntheta=25,
nphi=25,
thetalim=(5, 90),
philim=(5, 360),
phi_endpoint=False)
B = viscid.make_dipole(m=m,
crd_system='gse',
n=(256, 256, 256),
l=(-25, -25, -25),
h=(25, 25, 25),
dtype='f8')
seeds_xyz = seeds.get_points()
# seeds_lsp = viscid.xyz2lsrlp(seeds_xyz, cotr=cotr, crd_system=B)[(0, 3), :]
seeds_lsp = viscid.xyz2lsrlp(seeds_xyz, cotr=cotr, crd_system=B)[(0, 3), :]
e1_lines, e1_lsps, t_e1 = lines_and_lsps(B,
seeds,
method='euler1',
ibound=1.0,
cotr=cotr)
rk2_lines, rk2_lsps, t_rk2 = lines_and_lsps(B,
seeds,
method='rk2',
ibound=1.0,
cotr=cotr)
rk4_lines, rk4_lsps, t_rk4 = lines_and_lsps(B,
seeds,
method='rk4',
ibound=1.0,
cotr=cotr)
e1a_lines, e1a_lsps, t_e1a = lines_and_lsps(B,
seeds,
method='euler1a',
ibound=1.0,
cotr=cotr)
rk12_lines, rk12_lsps, t_rk12 = lines_and_lsps(B,
seeds,
method='rk12',
ibound=1.0,
cotr=cotr)
rk45_lines, rk45_lsps, t_rk45 = lines_and_lsps(B,
seeds,
method='rk45',
ibound=1.0,
cotr=cotr)
def _calc_rel_diff(_lsp, _ideal_lsp, _d):
_diffs = []
for _ilsp, _iideal in zip(_lsp, _ideal_lsp.T):
_a = _ilsp[_d, :]
_b = _iideal[_d]
_diffs.append((_a - _b) / _b)
return _diffs
lshell_diff_e1 = _calc_rel_diff(e1_lsps, seeds_lsp, 0)
phi_diff_e1 = _calc_rel_diff(e1_lsps, seeds_lsp, 1)
lshell_diff_rk2 = _calc_rel_diff(rk2_lsps, seeds_lsp, 0)
phi_diff_rk2 = _calc_rel_diff(rk2_lsps, seeds_lsp, 1)
lshell_diff_rk4 = _calc_rel_diff(rk4_lsps, seeds_lsp, 0)
phi_diff_rk4 = _calc_rel_diff(rk4_lsps, seeds_lsp, 1)
lshell_diff_e1a = _calc_rel_diff(e1a_lsps, seeds_lsp, 0)
phi_diff_e1a = _calc_rel_diff(e1a_lsps, seeds_lsp, 1)
lshell_diff_rk12 = _calc_rel_diff(rk12_lsps, seeds_lsp, 0)
phi_diff_rk12 = _calc_rel_diff(rk12_lsps, seeds_lsp, 1)
lshell_diff_rk45 = _calc_rel_diff(rk45_lsps, seeds_lsp, 0)
phi_diff_rk45 = _calc_rel_diff(rk45_lsps, seeds_lsp, 1)
methods = [
'Euler 1', 'Runge Kutta 2', 'Runge Kutta 4', 'Euler 1 Adaptive Step',
'Runge Kutta 12 Adaptive Step', 'Runge Kutta 45 Adaptive Step'
]
wall_ts = [t_e1, t_rk2, t_rk4, t_e1a, t_rk12, t_rk45]
all_lines = [
e1_lines, rk2_lines, rk4_lines, e1a_lines, rk12_lines, rk45_lines
]
all_lshell_diffs = [
lshell_diff_e1, lshell_diff_rk2, lshell_diff_rk4, lshell_diff_e1a,
lshell_diff_rk12, lshell_diff_rk45
]
lshell_diffs = [
np.abs(np.concatenate(lshell_diff_e1, axis=0)),
np.abs(np.concatenate(lshell_diff_rk2, axis=0)),
np.abs(np.concatenate(lshell_diff_rk4, axis=0)),
np.abs(np.concatenate(lshell_diff_e1a, axis=0)),
np.abs(np.concatenate(lshell_diff_rk12, axis=0)),
np.abs(np.concatenate(lshell_diff_rk45, axis=0))
]
phi_diffs = [
np.abs(np.concatenate(phi_diff_e1, axis=0)),
np.abs(np.concatenate(phi_diff_rk2, axis=0)),
np.abs(np.concatenate(phi_diff_rk4, axis=0)),
np.abs(np.concatenate(phi_diff_e1a, axis=0)),
np.abs(np.concatenate(phi_diff_rk12, axis=0)),
np.abs(np.concatenate(phi_diff_rk45, axis=0))
]
npts = [len(lsd) for lsd in lshell_diffs]
lshell_75 = [np.percentile(lsdiff, 75) for lsdiff in lshell_diffs]
# # 3D DEBUG PLOT:: for really getting under the covers
# vlab.clf()
# earth1 = viscid.seed.Sphere((0.0, 0.0, 0.0), 1.0, pole=-m, ntheta=60, nphi=120,
# thetalim=(15, 165), philim=(0, 360))
# ls1 = viscid.xyz2lsrlp(earth1.get_points(), cotr=cotr, crd_system='gse')[0, :]
# earth2 = viscid.seed.Sphere((0.0, 0.0, 0.0), 2.0, pole=-m, ntheta=60, nphi=120,
# thetalim=(15, 165), philim=(0, 360))
# ls2 = viscid.xyz2lsrlp(earth2.get_points(), cotr=cotr, crd_system='gse')[0, :]
# earth4 = viscid.seed.Sphere((0.0, 0.0, 0.0), 4.0, pole=-m, ntheta=60, nphi=120,
# thetalim=(15, 165), philim=(0, 360))
# ls4 = viscid.xyz2lsrlp(earth4.get_points(), cotr=cotr, crd_system='gse')[0, :]
# clim = [2.0, 6.0]
# vlab.mesh_from_seeds(earth1, scalars=ls1, clim=clim, logscale=True)
# vlab.mesh_from_seeds(earth2, scalars=ls2, clim=clim, logscale=True, opacity=0.5)
# vlab.mesh_from_seeds(earth4, scalars=ls2, clim=clim, logscale=True, opacity=0.25)
# vlab.plot3d_lines(e1_lines, scalars=[_e1_lsp[0, :] for _e1_lsp in e1_lsps],
# clim=clim, logscale=True)
# vlab.colorbar(title="L-Shell")
# vlab.show()
assert lshell_75[1] < lshell_75[0], "RK2 should have less error than Euler"
assert lshell_75[2] < lshell_75[1], "RK4 should have less error than RK2"
assert lshell_75[3] < lshell_75[
0], "Euler 1a should have less error than Euler 1"
assert lshell_75[4] < lshell_75[
0], "RK 12 should have less error than Euler 1"
assert lshell_75[5] < lshell_75[1], "RK 45 should have less error than RK2"
try:
if not plot2d:
raise ImportError
from matplotlib import pyplot as plt
from viscid.plot import vpyplot as vlt
# stats on error for all points on all lines
_ = plt.figure(figsize=(15, 8))
ax1 = vlt.subplot(121)
v = plt.violinplot(lshell_diffs,
showextrema=False,
showmedians=False,
vert=False)
colors = set_violin_colors(v)
xl, xh = plt.gca().get_xlim()
for i, txt, c in zip(count(), methods, colors):
t_txt = ", took {0:.2e} seconds".format(wall_ts[i])
stat_txt = format_data_range(lshell_diffs[i])
plt.text(xl + 0.35 * (xh - xl), i + 1.15, txt + t_txt, color=c)
plt.text(xl + 0.35 * (xh - xl), i + 0.85, stat_txt, color=c)
ax1.get_yaxis().set_visible(False)
plt.title('L-Shell')
plt.xlabel('Relative Difference from Ideal (as fraction)')
ax2 = vlt.subplot(122)
v = plt.violinplot(phi_diffs,
showextrema=False,
showmedians=False,
vert=False)
colors = set_violin_colors(v)
xl, xh = plt.gca().get_xlim()
for i, txt, c in zip(count(), methods, colors):
t_txt = ", took {0:.2e} seconds".format(wall_ts[i])
stat_txt = format_data_range(phi_diffs[i])
plt.text(xl + 0.35 * (xh - xl), i + 1.15, txt + t_txt, color=c)
plt.text(xl + 0.35 * (xh - xl), i + 0.85, stat_txt, color=c)
ax2.get_yaxis().set_visible(False)
plt.title('Longitude')
plt.xlabel('Relative Difference from Ideal (as fraction)')
vlt.auto_adjust_subplots()
vlt.savefig(next_plot_fname(__file__, series='q2'))
if args.show:
vlt.show()
# stats for ds for all points on all lines
_ = plt.figure(figsize=(10, 8))
ax1 = vlt.subplot(111)
ds = [
np.concatenate([
np.linalg.norm(_l[:, 1:] - _l[:, :-1], axis=0) for _l in lines
]) for lines in all_lines
]
v = plt.violinplot(ds,
showextrema=False,
showmedians=False,
vert=False)
colors = set_violin_colors(v)
xl, xh = plt.gca().get_xlim()
for i, txt, c in zip(count(), methods, colors):
stat_txt = format_data_range(ds[i])
plt.text(xl + 0.01 * (xh - xl), i + 1.15, txt, color=c)
plt.text(xl + 0.01 * (xh - xl), i + 0.85, stat_txt, color=c)
ax1.get_yaxis().set_visible(False)
plt.xscale('log')
plt.title('Step Size')
plt.xlabel('Absolute Step Size')
vlt.savefig(next_plot_fname(__file__, series='q2'))
if args.show:
vlt.show()
# random other information
_ = plt.figure(figsize=(13, 10))
## wall time for each method
vlt.subplot(221)
plt.scatter(range(len(methods)),
wall_ts,
color=colors,
s=150,
marker='s',
edgecolors='none')
for i, meth in enumerate(methods):
meth = meth.replace(" Adaptive Step", "\nAdaptive Step")
plt.annotate(meth, (i, wall_ts[i]),
xytext=(0, 15.0),
color=colors[i],
horizontalalignment='center',
verticalalignment='bottom',
textcoords='offset points')
plt.ylabel("Wall Time (s)")
x_padding = 0.5
plt.xlim(-x_padding, len(methods) - x_padding)
yl, yh = np.min(wall_ts), np.max(wall_ts)
y_padding = 0.4 * (yh - yl)
plt.ylim(yl - y_padding, yh + y_padding)
plt.gca().get_xaxis().set_visible(False)
for _which in ('right', 'top'):
plt.gca().spines[_which].set_color('none')
## number of points calculated for each method
vlt.subplot(222)
plt.scatter(range(len(methods)),
npts,
color=colors,
s=150,
marker='s',
edgecolors='none')
for i, meth in enumerate(methods):
meth = meth.replace(" Adaptive Step", "\nAdaptive Step")
plt.annotate(meth, (i, npts[i]),
xytext=(0, 15.0),
color=colors[i],
horizontalalignment='center',
verticalalignment='bottom',
textcoords='offset points')
plt.ylabel("Number of Streamline Points Calculated")
x_padding = 0.5
plt.xlim(-x_padding, len(methods) - x_padding)
yl, yh = np.min(npts), np.max(npts)
y_padding = 0.4 * (yh - yl)
plt.ylim(yl - y_padding, yh + y_padding)
plt.gca().get_xaxis().set_visible(False)
for _which in ('right', 'top'):
plt.gca().spines[_which].set_color('none')
## Wall time per segment, this should show the overhead of the method
vlt.subplot(223)
wall_t_per_seg = np.asarray(wall_ts) / np.asarray(npts)
plt.scatter(range(len(methods)),
wall_t_per_seg,
color=colors,
s=150,
marker='s',
edgecolors='none')
for i, meth in enumerate(methods):
meth = meth.replace(" Adaptive Step", "\nAdaptive Step")
plt.annotate(meth, (i, wall_t_per_seg[i]),
xytext=(0, 15.0),
color=colors[i],
horizontalalignment='center',
verticalalignment='bottom',
textcoords='offset points')
plt.ylabel("Wall Time Per Line Segment")
x_padding = 0.5
plt.xlim(-x_padding, len(methods) - x_padding)
yl, yh = np.min(wall_t_per_seg), np.max(wall_t_per_seg)
y_padding = 0.4 * (yh - yl)
plt.ylim(yl - y_padding, yh + y_padding)
plt.gca().get_xaxis().set_visible(False)
plt.gca().xaxis.set_major_formatter(viscid.plot.mpl_extra.steve_axfmt)
for _which in ('right', 'top'):
plt.gca().spines[_which].set_color('none')
## 75th percentile of l-shell error for each method
vlt.subplot(224)
plt.scatter(range(len(methods)),
lshell_75,
color=colors,
s=150,
marker='s',
edgecolors='none')
plt.yscale('log')
for i, meth in enumerate(methods):
meth = meth.replace(" Adaptive Step", "\nAdaptive Step")
plt.annotate(meth, (i, lshell_75[i]),
xytext=(0, 15.0),
color=colors[i],
horizontalalignment='center',
verticalalignment='bottom',
textcoords='offset points')
plt.ylabel("75th Percentile of Relative L-Shell Error")
x_padding = 0.5
plt.xlim(-x_padding, len(methods) - x_padding)
ymin, ymax = np.min(lshell_75), np.max(lshell_75)
plt.ylim(0.75 * ymin, 2.5 * ymax)
plt.gca().get_xaxis().set_visible(False)
for _which in ('right', 'top'):
plt.gca().spines[_which].set_color('none')
vlt.auto_adjust_subplots(subplot_params=dict(wspace=0.25, hspace=0.15))
vlt.savefig(next_plot_fname(__file__, series='q2'))
if args.show:
vlt.show()
except ImportError:
pass
try:
if not plot3d:
raise ImportError
from viscid.plot import vlab
try:
fig = _global_ns['figure']
vlab.clf()
except KeyError:
fig = vlab.figure(size=[1200, 800],
offscreen=not args.show,
bgcolor=(1, 1, 1),
fgcolor=(0, 0, 0))
_global_ns['figure'] = fig
for i, method in zip(count(), methods):
# if i in (3, 4):
# next_plot_fname(__file__, series='q3')
# print(i, "::", [line.shape[1] for line in all_lines[i]])
# # continue
vlab.clf()
_lshell_diff = [np.abs(s) for s in all_lshell_diffs[i]]
vlab.plot3d_lines(all_lines[i], scalars=_lshell_diff)
vlab.colorbar(title="Relative L-Shell Error (as fraction)")
vlab.title(method, size=0.5)
vlab.orientation_axes()
vlab.view(azimuth=40,
elevation=140,
distance=80.0,
focalpoint=[0, 0, 0])
vlab.savefig(next_plot_fname(__file__, series='q3'))
if args.show:
vlab.show()
except ImportError:
pass
# prevent weird xorg bad-instructions on tear down
if 'figure' in _global_ns and _global_ns['figure'] is not None:
from viscid.plot import vlab
vlab.mlab.close(_global_ns['figure'])
return 0
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--show", "--plot", action="store_true")
args = viscid.vutil.common_argparse(parser, default_verb=0)
viscid.logger.setLevel(viscid.logging.DEBUG)
args.show = False
cotr = viscid.Cotr(dip_tilt=20.0, dip_gsm=15.0) # pylint: disable=not-callable
b = viscid.make_dipole(m=cotr.get_dipole_moment(), n=(32, 32, 32))
seeds = viscid.Circle(n=5, r=1.5, pole=[0, 0, 1])
lines, topo = viscid.calc_streamlines(b, seeds, ibound=1.4, method='rk45')
for i in range(2):
# make sure this works for lines with 0, 1, 2, 3 vertices
if i == 1:
lines[1] = lines[2][:, :0]
lines[2] = lines[2][:, :1]
lines[3] = lines[3][:, :2]
lines[4] = lines[4][:, :3]
viscid.logger.debug('---')
viscid.logger.debug('{0}'.format(len(lines)))
for line in lines:
viscid.logger.debug('line shape: {0}'.format(line.shape))
viscid.logger.debug('---')
do_test(lines,
scalars=None,
txt='given None',
show=args.show)
do_test(lines,
scalars='#ff0000',
txt='given a single 24bit rgb hex color',
show=args.show)
do_test(lines,
scalars='#ff000066',
txt='given a single 32bit rgba hex color',
show=args.show)
do_test(lines,
scalars='#f00',
txt='given a single 12bit rgb hex color',
show=args.show)
do_test(lines,
scalars='#f006',
txt='given a single 16bit rgba hex color',
show=args.show)
do_test(lines,
scalars=['#ff0000', '#cc0000', '#aa0000', '#880000', '#660000'],
txt='given a list of Nlines 24bit rgb hex colors',
show=args.show)
do_test(lines,
scalars=['#ff000066', '#cc000066', '#aa000066', '#88000066',
'#66000066'],
txt='given a list of Nlines 32bit rgba hex colors',
show=args.show)
do_test(lines,
scalars=['#f00', '#c00', '#a00', '#800', '#600'],
txt='given a list of Nlines 12bit rgb hex colors',
show=args.show)
do_test(lines,
scalars=['#f00a', '#c009', '#a008', '#8007', '#6006'],
txt='given a list of Nlines 16bit rgba hex colors',
show=args.show)
do_test(lines,
scalars=[0.8, 0.0, 0.2],
txt='given a single rgb [0..1] color',
show=args.show)
do_test(lines,
scalars=[0.8, 0.0, 0.2, 0.8],
txt='given a single rgba [0..1] color',
show=args.show)
do_test(lines,
scalars=[(0.8, 0.0, 0.2), (0.7, 0.0, 0.3), (0.6, 0.0, 0.4),
(0.5, 0.0, 0.5), (0.4, 0.0, 0.6)],
txt='given a list of Nlines rgb [0..1] tuples',
show=args.show)
do_test(lines,
scalars=[(0.8, 0.0, 0.2, 1.0), (0.7, 0.0, 0.3, 0.9),
(0.6, 0.0, 0.4, 0.8), (0.5, 0.0, 0.5, 0.7),
(0.4, 0.0, 0.6, 0.6)],
txt='given a list of Nlines rgba [0..1] tuples',
show=args.show)
do_test(lines,
scalars=[250, 0, 250],
txt='given a single rgb [0..255] color',
show=args.show)
do_test(lines,
scalars=[250, 0, 250, 190],
txt='given a single rgba [0..255] color',
show=args.show)
do_test(lines,
scalars=[(204, 0, 51), (179, 0, 77), (153, 0, 102),
(127, 0, 127), (0.4, 0, 102)],
txt='given a list of Nlines rgb [0..255] tuples',
show=args.show)
do_test(lines,
scalars=[(204, 0, 51, 255), (179, 0, 77, 230),
(153, 0, 102, 204), (127, 0, 127, 179),
(102, 0, 102, 153)],
txt='given a list of Nlines rgba [0..255] tuples',
show=args.show)
do_test(lines,
scalars=['#ff000088', 'blue', 'lavenderblush', 'c', '#4f4'],
txt='given a mix of color hex/html color names',
show=args.show)
do_test(lines,
scalars=topo,
txt='scalars == topo value',
show=args.show)
do_test(lines,
scalars=viscid.topology2color(topo),
txt='scalars == topo2color value',
show=args.show)
do_test(lines,
scalars=np.log(viscid.magnitude(b)),
txt='given bmag',
show=args.show)
# prevent weird xorg bad-instructions on tear down
if 'figure' in _global_ns and _global_ns['figure'] is not None:
from viscid.plot import vlab
vlab.mlab.close(_global_ns['figure'])
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=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 _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--show", "--plot", action="store_true")
args = viscid.vutil.common_argparse(parser, default_verb=0)
viscid.logger.setLevel(viscid.logging.DEBUG)
args.show = False
cotr = viscid.Cotr(dip_tilt=20.0, dip_gsm=15.0) # pylint: disable=not-callable
b = viscid.make_dipole(m=cotr.get_dipole_moment(), n=(32, 32, 32))
seeds = viscid.Circle(n=5, r=1.5, pole=[0, 0, 1])
lines, topo = viscid.calc_streamlines(b, seeds, ibound=1.4, method='rk45')
for i in range(2):
# make sure this works for lines with 0, 1, 2, 3 vertices
if i == 1:
lines[1] = lines[2][:, :0]
lines[2] = lines[2][:, :1]
lines[3] = lines[3][:, :2]
lines[4] = lines[4][:, :3]
viscid.logger.debug('---')
viscid.logger.debug('{0}'.format(len(lines)))
for line in lines:
viscid.logger.debug('line shape: {0}'.format(line.shape))
viscid.logger.debug('---')
do_test(lines,
scalars=None,
txt='given None',
show=args.show)
do_test(lines,
scalars='#ff0000',
txt='given a single 24bit rgb hex color',
show=args.show)
do_test(lines,
scalars='#ff000066',
txt='given a single 32bit rgba hex color',
show=args.show)
do_test(lines,
scalars='#f00',
txt='given a single 12bit rgb hex color',
show=args.show)
do_test(lines,
scalars='#f006',
txt='given a single 16bit rgba hex color',
show=args.show)
do_test(lines,
scalars=['#ff0000', '#cc0000', '#aa0000', '#880000', '#660000'],
txt='given a list of Nlines 24bit rgb hex colors',
show=args.show)
do_test(lines,
scalars=['#ff000066', '#cc000066', '#aa000066', '#88000066',
'#66000066'],
txt='given a list of Nlines 32bit rgba hex colors',
show=args.show)
do_test(lines,
scalars=['#f00', '#c00', '#a00', '#800', '#600'],
txt='given a list of Nlines 12bit rgb hex colors',
show=args.show)
do_test(lines,
scalars=['#f00a', '#c009', '#a008', '#8007', '#6006'],
txt='given a list of Nlines 16bit rgba hex colors',
show=args.show)
do_test(lines,
scalars=[0.8, 0.0, 0.2],
txt='given a single rgb [0..1] color',
show=args.show)
do_test(lines,
scalars=[0.8, 0.0, 0.2, 0.8],
txt='given a single rgba [0..1] color',
show=args.show)
do_test(lines,
scalars=[(0.8, 0.0, 0.2), (0.7, 0.0, 0.3), (0.6, 0.0, 0.4),
(0.5, 0.0, 0.5), (0.4, 0.0, 0.6)],
txt='given a list of Nlines rgb [0..1] tuples',
show=args.show)
do_test(lines,
scalars=[(0.8, 0.0, 0.2, 1.0), (0.7, 0.0, 0.3, 0.9),
(0.6, 0.0, 0.4, 0.8), (0.5, 0.0, 0.5, 0.7),
(0.4, 0.0, 0.6, 0.6)],
txt='given a list of Nlines rgba [0..1] tuples',
show=args.show)
do_test(lines,
scalars=[250, 0, 250],
txt='given a single rgb [0..255] color',
show=args.show)
do_test(lines,
scalars=[250, 0, 250, 190],
txt='given a single rgba [0..255] color',
show=args.show)
do_test(lines,
scalars=[(204, 0, 51), (179, 0, 77), (153, 0, 102),
(127, 0, 127), (0.4, 0, 102)],
txt='given a list of Nlines rgb [0..255] tuples',
show=args.show)
do_test(lines,
scalars=[(204, 0, 51, 255), (179, 0, 77, 230),
(153, 0, 102, 204), (127, 0, 127, 179),
(102, 0, 102, 153)],
txt='given a list of Nlines rgba [0..255] tuples',
show=args.show)
do_test(lines,
scalars=['#ff000088', 'blue', 'lavenderblush', 'c', '#4f4'],
txt='given a mix of color hex/html color names',
show=args.show)
do_test(lines,
scalars=topo,
txt='scalars == topo value',
show=args.show)
do_test(lines,
scalars=viscid.topology2color(topo),
txt='scalars == topo2color value',
show=args.show)
do_test(lines,
scalars=np.log(viscid.magnitude(b)),
txt='given bmag',
show=args.show)
# prevent weird xorg bad-instructions on tear down
if 'figure' in _global_ns and _global_ns['figure'] is not None:
from viscid.plot import vlab
vlab.mlab.close(_global_ns['figure'])
return 0
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--notwo", dest='notwo', action="store_true")
parser.add_argument("--nothree", dest='nothree', action="store_true")
parser.add_argument("--show", "--plot", action="store_true")
args = viscid.vutil.common_argparse(parser, default_verb=0)
plot2d = not args.notwo
plot3d = not args.nothree
# #################################################
# viscid.logger.info("Testing field lines on 2d field...")
B = viscid.make_dipole(twod=True)
line = viscid.seed.Line((0.2, 0.0, 0.0), (1.0, 0.0, 0.0), 10)
obound0 = np.array([-4, -4, -4], dtype=B.data.dtype)
obound1 = np.array([4, 4, 4], dtype=B.data.dtype)
run_test(B, line, plot2d=plot2d, plot3d=plot3d, title='2D', show=args.show,
ibound=0.07, obound0=obound0, obound1=obound1)
#################################################
viscid.logger.info("Testing field lines on 3d field...")
B = viscid.make_dipole(m=[0.2, 0.3, -0.9])
sphere = viscid.seed.Sphere((0.0, 0.0, 0.0), 2.0, ntheta=20, nphi=10)
obound0 = np.array([-4, -4, -4], dtype=B.data.dtype)
obound1 = np.array([4, 4, 4], dtype=B.data.dtype)
run_test(B, sphere, plot2d=plot2d, plot3d=plot3d, title='3D', show=args.show,
ibound=0.12, obound0=obound0, obound1=obound1, method=viscid.RK12)
# The Remainder of this test makes sure higher order methods are indeed
# more accurate than lower order methods... this could find a bug in
# the integrators
##################################################
# test accuracy of streamlines in an ideal dipole
cotr = viscid.Cotr(dip_tilt=15.0, dip_gsm=21.0) # pylint: disable=not-callable
m = cotr.get_dipole_moment(crd_system='gse')
seeds = viscid.seed.Sphere((0.0, 0.0, 0.0), 2.0, pole=-m, ntheta=25, nphi=25,
thetalim=(5, 90), philim=(5, 360), phi_endpoint=False)
B = viscid.make_dipole(m=m, crd_system='gse', n=(256, 256, 256),
l=(-25, -25, -25), h=(25, 25, 25), dtype='f8')
seeds_xyz = seeds.get_points()
# seeds_lsp = viscid.xyz2lsrlp(seeds_xyz, cotr=cotr, crd_system=B)[(0, 3), :]
seeds_lsp = viscid.xyz2lsrlp(seeds_xyz, cotr=cotr, crd_system=B)[(0, 3), :]
e1_lines, e1_lsps, t_e1 = lines_and_lsps(B, seeds, method='euler1',
ibound=1.0, cotr=cotr)
rk2_lines, rk2_lsps, t_rk2 = lines_and_lsps(B, seeds, method='rk2',
ibound=1.0, cotr=cotr)
rk4_lines, rk4_lsps, t_rk4 = lines_and_lsps(B, seeds, method='rk4',
ibound=1.0, cotr=cotr)
e1a_lines, e1a_lsps, t_e1a = lines_and_lsps(B, seeds, method='euler1a',
ibound=1.0, cotr=cotr)
rk12_lines, rk12_lsps, t_rk12 = lines_and_lsps(B, seeds, method='rk12',
ibound=1.0, cotr=cotr)
rk45_lines, rk45_lsps, t_rk45 = lines_and_lsps(B, seeds, method='rk45',
ibound=1.0, cotr=cotr)
def _calc_rel_diff(_lsp, _ideal_lsp, _d):
_diffs = []
for _ilsp, _iideal in zip(_lsp, _ideal_lsp.T):
_a = _ilsp[_d, :]
_b = _iideal[_d]
_diffs.append((_a - _b) / _b)
return _diffs
lshell_diff_e1 = _calc_rel_diff(e1_lsps, seeds_lsp, 0)
phi_diff_e1 = _calc_rel_diff(e1_lsps, seeds_lsp, 1)
lshell_diff_rk2 = _calc_rel_diff(rk2_lsps, seeds_lsp, 0)
phi_diff_rk2 = _calc_rel_diff(rk2_lsps, seeds_lsp, 1)
lshell_diff_rk4 = _calc_rel_diff(rk4_lsps, seeds_lsp, 0)
phi_diff_rk4 = _calc_rel_diff(rk4_lsps, seeds_lsp, 1)
lshell_diff_e1a = _calc_rel_diff(e1a_lsps, seeds_lsp, 0)
phi_diff_e1a = _calc_rel_diff(e1a_lsps, seeds_lsp, 1)
lshell_diff_rk12 = _calc_rel_diff(rk12_lsps, seeds_lsp, 0)
phi_diff_rk12 = _calc_rel_diff(rk12_lsps, seeds_lsp, 1)
lshell_diff_rk45 = _calc_rel_diff(rk45_lsps, seeds_lsp, 0)
phi_diff_rk45 = _calc_rel_diff(rk45_lsps, seeds_lsp, 1)
methods = ['Euler 1', 'Runge Kutta 2', 'Runge Kutta 4',
'Euler 1 Adaptive Step', 'Runge Kutta 12 Adaptive Step',
'Runge Kutta 45 Adaptive Step']
wall_ts = [t_e1, t_rk2, t_rk4, t_e1a, t_rk12, t_rk45]
all_lines = [e1_lines, rk2_lines, rk4_lines, e1a_lines, rk12_lines,
rk45_lines]
all_lshell_diffs = [lshell_diff_e1, lshell_diff_rk2, lshell_diff_rk4,
lshell_diff_e1a, lshell_diff_rk12, lshell_diff_rk45]
lshell_diffs = [np.abs(np.concatenate(lshell_diff_e1, axis=0)),
np.abs(np.concatenate(lshell_diff_rk2, axis=0)),
np.abs(np.concatenate(lshell_diff_rk4, axis=0)),
np.abs(np.concatenate(lshell_diff_e1a, axis=0)),
np.abs(np.concatenate(lshell_diff_rk12, axis=0)),
np.abs(np.concatenate(lshell_diff_rk45, axis=0))]
phi_diffs = [np.abs(np.concatenate(phi_diff_e1, axis=0)),
np.abs(np.concatenate(phi_diff_rk2, axis=0)),
np.abs(np.concatenate(phi_diff_rk4, axis=0)),
np.abs(np.concatenate(phi_diff_e1a, axis=0)),
np.abs(np.concatenate(phi_diff_rk12, axis=0)),
np.abs(np.concatenate(phi_diff_rk45, axis=0))]
npts = [len(lsd) for lsd in lshell_diffs]
lshell_75 = [np.percentile(lsdiff, 75) for lsdiff in lshell_diffs]
# # 3D DEBUG PLOT:: for really getting under the covers
# vlab.clf()
# earth1 = viscid.seed.Sphere((0.0, 0.0, 0.0), 1.0, pole=-m, ntheta=60, nphi=120,
# thetalim=(15, 165), philim=(0, 360))
# ls1 = viscid.xyz2lsrlp(earth1.get_points(), cotr=cotr, crd_system='gse')[0, :]
# earth2 = viscid.seed.Sphere((0.0, 0.0, 0.0), 2.0, pole=-m, ntheta=60, nphi=120,
# thetalim=(15, 165), philim=(0, 360))
# ls2 = viscid.xyz2lsrlp(earth2.get_points(), cotr=cotr, crd_system='gse')[0, :]
# earth4 = viscid.seed.Sphere((0.0, 0.0, 0.0), 4.0, pole=-m, ntheta=60, nphi=120,
# thetalim=(15, 165), philim=(0, 360))
# ls4 = viscid.xyz2lsrlp(earth4.get_points(), cotr=cotr, crd_system='gse')[0, :]
# clim = [2.0, 6.0]
# vlab.mesh_from_seeds(earth1, scalars=ls1, clim=clim, logscale=True)
# vlab.mesh_from_seeds(earth2, scalars=ls2, clim=clim, logscale=True, opacity=0.5)
# vlab.mesh_from_seeds(earth4, scalars=ls2, clim=clim, logscale=True, opacity=0.25)
# vlab.plot3d_lines(e1_lines, scalars=[_e1_lsp[0, :] for _e1_lsp in e1_lsps],
# clim=clim, logscale=True)
# vlab.colorbar(title="L-Shell")
# vlab.show()
assert lshell_75[1] < lshell_75[0], "RK2 should have less error than Euler"
assert lshell_75[2] < lshell_75[1], "RK4 should have less error than RK2"
assert lshell_75[3] < lshell_75[0], "Euler 1a should have less error than Euler 1"
assert lshell_75[4] < lshell_75[0], "RK 12 should have less error than Euler 1"
assert lshell_75[5] < lshell_75[1], "RK 45 should have less error than RK2"
try:
if not plot2d:
raise ImportError
from viscid.plot import vpyplot as vlt
from matplotlib import pyplot as plt
# stats on error for all points on all lines
_ = plt.figure(figsize=(15, 8))
ax1 = vlt.subplot(121)
v = plt.violinplot(lshell_diffs, showextrema=False, showmedians=False,
vert=False)
colors = set_violin_colors(v)
xl, xh = plt.gca().get_xlim()
for i, txt, c in zip(count(), methods, colors):
t_txt = ", took {0:.2e} seconds".format(wall_ts[i])
stat_txt = format_data_range(lshell_diffs[i])
plt.text(xl + 0.35 * (xh - xl), i + 1.15, txt + t_txt, color=c)
plt.text(xl + 0.35 * (xh - xl), i + 0.85, stat_txt, color=c)
ax1.get_yaxis().set_visible(False)
plt.title('L-Shell')
plt.xlabel('Relative Difference from Ideal (as fraction)')
ax2 = vlt.subplot(122)
v = plt.violinplot(phi_diffs, showextrema=False, showmedians=False,
vert=False)
colors = set_violin_colors(v)
xl, xh = plt.gca().get_xlim()
for i, txt, c in zip(count(), methods, colors):
t_txt = ", took {0:.2e} seconds".format(wall_ts[i])
stat_txt = format_data_range(phi_diffs[i])
plt.text(xl + 0.35 * (xh - xl), i + 1.15, txt + t_txt, color=c)
plt.text(xl + 0.35 * (xh - xl), i + 0.85, stat_txt, color=c)
ax2.get_yaxis().set_visible(False)
plt.title('Longitude')
plt.xlabel('Relative Difference from Ideal (as fraction)')
vlt.auto_adjust_subplots()
vlt.savefig(next_plot_fname(__file__, series='q2'))
if args.show:
vlt.show()
# stats for ds for all points on all lines
_ = plt.figure(figsize=(10, 8))
ax1 = vlt.subplot(111)
ds = [np.concatenate([np.linalg.norm(_l[:, 1:] - _l[:, :-1], axis=0)
for _l in lines]) for lines in all_lines]
v = plt.violinplot(ds, showextrema=False, showmedians=False,
vert=False)
colors = set_violin_colors(v)
xl, xh = plt.gca().get_xlim()
for i, txt, c in zip(count(), methods, colors):
stat_txt = format_data_range(ds[i])
plt.annotate(txt, xy=(0.55, i / len(methods) + 0.1), color=c,
xycoords='axes fraction')
plt.annotate(stat_txt, xy=(0.55, i / len(methods) + 0.04), color=c,
xycoords='axes fraction')
ax1.get_yaxis().set_visible(False)
plt.xscale('log')
plt.title('Step Size')
plt.xlabel('Absolute Step Size')
vlt.savefig(next_plot_fname(__file__, series='q2'))
if args.show:
vlt.show()
# random other information
_ = plt.figure(figsize=(13, 10))
## wall time for each method
vlt.subplot(221)
plt.scatter(range(len(methods)), wall_ts, color=colors,
s=150, marker='s', edgecolors='none')
for i, meth in enumerate(methods):
meth = meth.replace(" Adaptive Step", "\nAdaptive Step")
plt.annotate(meth, (i, wall_ts[i]), xytext=(0, 15.0),
color=colors[i], horizontalalignment='center',
verticalalignment='bottom',
textcoords='offset points')
plt.ylabel("Wall Time (s)")
x_padding = 0.5
plt.xlim(-x_padding, len(methods) - x_padding)
yl, yh = np.min(wall_ts), np.max(wall_ts)
y_padding = 0.4 * (yh - yl)
plt.ylim(yl - y_padding, yh + y_padding)
plt.gca().get_xaxis().set_visible(False)
for _which in ('right', 'top'):
plt.gca().spines[_which].set_color('none')
## number of points calculated for each method
vlt.subplot(222)
plt.scatter(range(len(methods)), npts, color=colors,
s=150, marker='s', edgecolors='none')
for i, meth in enumerate(methods):
meth = meth.replace(" Adaptive Step", "\nAdaptive Step")
plt.annotate(meth, (i, npts[i]), xytext=(0, 15.0),
color=colors[i], horizontalalignment='center',
verticalalignment='bottom',
textcoords='offset points')
plt.ylabel("Number of Streamline Points Calculated")
x_padding = 0.5
plt.xlim(-x_padding, len(methods) - x_padding)
yl, yh = np.min(npts), np.max(npts)
y_padding = 0.4 * (yh - yl)
plt.ylim(yl - y_padding, yh + y_padding)
plt.gca().get_xaxis().set_visible(False)
for _which in ('right', 'top'):
plt.gca().spines[_which].set_color('none')
## Wall time per segment, this should show the overhead of the method
vlt.subplot(223)
wall_t_per_seg = np.asarray(wall_ts) / np.asarray(npts)
plt.scatter(range(len(methods)), wall_t_per_seg, color=colors,
s=150, marker='s', edgecolors='none')
for i, meth in enumerate(methods):
meth = meth.replace(" Adaptive Step", "\nAdaptive Step")
plt.annotate(meth, (i, wall_t_per_seg[i]), xytext=(0, 15.0),
color=colors[i], horizontalalignment='center',
verticalalignment='bottom',
textcoords='offset points')
plt.ylabel("Wall Time Per Line Segment")
x_padding = 0.5
plt.xlim(-x_padding, len(methods) - x_padding)
yl, yh = np.min(wall_t_per_seg), np.max(wall_t_per_seg)
y_padding = 0.4 * (yh - yl)
plt.ylim(yl - y_padding, yh + y_padding)
plt.gca().get_xaxis().set_visible(False)
plt.gca().xaxis.set_major_formatter(viscid.plot.mpl_extra.steve_axfmt)
for _which in ('right', 'top'):
plt.gca().spines[_which].set_color('none')
## 75th percentile of l-shell error for each method
vlt.subplot(224)
plt.scatter(range(len(methods)), lshell_75, color=colors,
s=150, marker='s', edgecolors='none')
plt.yscale('log')
for i, meth in enumerate(methods):
meth = meth.replace(" Adaptive Step", "\nAdaptive Step")
plt.annotate(meth, (i, lshell_75[i]), xytext=(0, 15.0),
color=colors[i], horizontalalignment='center',
verticalalignment='bottom',
textcoords='offset points')
plt.ylabel("75th Percentile of Relative L-Shell Error")
x_padding = 0.5
plt.xlim(-x_padding, len(methods) - x_padding)
ymin, ymax = np.min(lshell_75), np.max(lshell_75)
plt.ylim(0.75 * ymin, 2.5 * ymax)
plt.gca().get_xaxis().set_visible(False)
for _which in ('right', 'top'):
plt.gca().spines[_which].set_color('none')
vlt.auto_adjust_subplots(subplot_params=dict(wspace=0.25, hspace=0.15))
vlt.savefig(next_plot_fname(__file__, series='q2'))
if args.show:
vlt.show()
except ImportError:
pass
try:
if not plot3d:
raise ImportError
from viscid.plot import vlab
try:
fig = _global_ns['figure']
vlab.clf()
except KeyError:
fig = vlab.figure(size=[1200, 800], offscreen=not args.show,
bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
_global_ns['figure'] = fig
for i, method in zip(count(), methods):
# if i in (3, 4):
# next_plot_fname(__file__, series='q3')
# print(i, "::", [line.shape[1] for line in all_lines[i]])
# # continue
vlab.clf()
_lshell_diff = [np.abs(s) for s in all_lshell_diffs[i]]
vlab.plot3d_lines(all_lines[i], scalars=_lshell_diff)
vlab.colorbar(title="Relative L-Shell Error (as fraction)")
vlab.title(method, size=0.5)
vlab.orientation_axes()
vlab.view(azimuth=40, elevation=140, distance=80.0,
focalpoint=[0, 0, 0])
vlab.savefig(next_plot_fname(__file__, series='q3'))
if args.show:
vlab.show()
except ImportError:
pass
# prevent weird xorg bad-instructions on tear down
if 'figure' in _global_ns and _global_ns['figure'] is not None:
from viscid.plot import vlab
vlab.mlab.close(_global_ns['figure'])
return 0
