def __cotr__(self):
if self.has_info("dipoletime"):
return viscid.Cotr(time=self.find_info('dipoletime'),
notilt1967=True)
elif self.has_info("basetime"):
return viscid.Cotr(time=self.find_info('basetime'),
notilt1967=True)
else:
return NotImplemented
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():
f = viscid.load_file('~/dev/work/xi_fte_001/*.3d.*.xdmf')
time_slice = ':'
times = np.array([grid.time for grid in f.iter_times(time_slice)])
# XYZ coordinates of virtual satelites in warped "plasma sheet coords"
x_sat_psc = np.linspace(-30, 0, 31) # X (GSE == PSC)
y_sat_psc = np.linspace(-10, 10, 21) # Y (GSE == PSC)
z_sat_psc = np.linspace(-2, 2, 5) # Z in PSC (z=0 is the plasma sheet)
# the GSE z location of the virtual satelites in the warped plasma sheet
# coordinates, so sat_z_gse_ts['x=5j, y=1j, z=0j'] would give the
# plasma sheet location at x=5.0, y=1.0
# These fields depend on time because the plasma sheet moves in time
sat_z_gse_ts = viscid.zeros([times, x_sat_psc, y_sat_psc, z_sat_psc],
crd_names='txyz', center='node',
name='PlasmaSheetZ_GSE')
vx_ts = viscid.zeros_like(sat_z_gse_ts)
bz_ts = viscid.zeros_like(sat_z_gse_ts)
for itime, grid in enumerate(f.iter_times(time_slice)):
print("Processing time slice", itime, grid.time)
gse_slice = 'x=-35j:0j, y=-15j:15j, z=-6j:6j'
bx = grid['bx'][gse_slice]
bx_argmin = np.argmin(bx**2, axis=2)
z_gse = bx.get_crd('z')
# ps_zloc_gse is the plasma sheet z location along the GGCM grid x/y
ps_z_gse = viscid.zeros_like(bx[:, :, 0:1])
ps_z_gse[...] = z_gse[bx_argmin]
# Note: Here you could apply a gaussian filter to
# ps_z_gse[:, :, 0].data in order to smooth the surface
# if desired. Scipy / Scikit-Image have some functions
# that do this
# ok, we found the plasma sheet z GSE location on the actual GGCM
# grid, but we just want a subset of that grid for our virtual
# satelites, so just interpolate the ps z location to our subset
ps_z_gse_subset = viscid.interp_trilin(ps_z_gse,
sat_z_gse_ts[itime, :, :, 0:1],
wrap=True)
# now we know the plasma sheet z location in GSE, and how far
# apart we want the satelites in z, so put those two things together
# to get a bunch of satelite locations
sat_z_gse_ts[itime] = ps_z_gse_subset.data + z_sat_psc.reshape(1, 1, -1)
# make a seed generator that we can use to fill the vx and bz
# time series for this instant in time
sat_loc_gse = sat_z_gse_ts[itime].get_points()
sat_loc_gse[2, :] = sat_z_gse_ts[itime].data.reshape(-1)
# slicing the field before doing the interpolation makes this
# faster for hdf5 data, but probably for other data too
vx_ts[itime] = viscid.interp_trilin(grid['vx'][gse_slice],
sat_loc_gse,
wrap=False
).reshape(vx_ts.shape[1:])
bz_ts[itime] = viscid.interp_trilin(grid['bz'][gse_slice],
sat_loc_gse,
wrap=False
).reshape(bz_ts.shape[1:])
# 2d plots of the plasma sheet z location to make sure we did the
# interpolation correctly
if False: # pylint: disable=using-constant-test
from viscid.plot import vpyplot as vlt
fig, (ax0, ax1) = vlt.subplots(2, 1) # pylint: disable=unused-variable
vlt.plot(ps_z_gse, ax=ax0, clim=(-5, 5))
vlt.plot(ps_z_gse_subset, ax=ax1, clim=(-5, 5))
vlt.auto_adjust_subplots()
vlt.show()
# make a 3d plot of the plasma sheet surface to verify that it
# makes sense
if True: # pylint: disable=using-constant-test
from viscid.plot import vlab
fig = vlab.figure(size=(1280, 800), bgcolor=(1, 1, 1),
fgcolor=(0, 0, 0))
vlab.clf()
# plot the plasma sheet coloured by vx
# Note: points closer to x = 0 are unsightly since the plasma
# sheet criteria starts to fall apart on the flanks, so
# just remove the first few rows
ps_z_gse_tail = ps_z_gse['x=:-2.25j']
ps_mesh_shape = [3, ps_z_gse_tail.shape[0], ps_z_gse_tail.shape[1]]
ps_pts = ps_z_gse_tail.get_points().reshape(ps_mesh_shape)
ps_pts[2, :, :] = ps_z_gse_tail[:, :, 0]
plasma_sheet = viscid.RectilinearMeshPoints(ps_pts)
ps_vx = viscid.interp_trilin(grid['vx'][gse_slice], plasma_sheet)
_ = vlab.mesh_from_seeds(plasma_sheet, scalars=ps_vx)
vx_clim = (-1400, 1400)
vx_cmap = 'viridis'
vlab.colorbar(title='Vx', clim=vx_clim, cmap=vx_cmap,
nb_labels=5)
# plot satelite locations as dots colored by Vx with the same
# limits and color as the plasma sheet mesh
sat3d = vlab.points3d(sat_loc_gse[0], sat_loc_gse[1], sat_loc_gse[2],
vx_ts[itime].data.reshape(-1),
scale_mode='none', scale_factor=0.2)
vlab.apply_cmap(sat3d, clim=vx_clim, cmap=vx_cmap)
# plot Earth for reference
cotr = viscid.Cotr(dip_tilt=0.0) # pylint: disable=not-callable
vlab.plot_blue_marble(r=1.0, lines=False, ntheta=64, nphi=128,
rotate=cotr, crd_system='mhd')
vlab.plot_earth_3d(radius=1.01, night_only=True, opacity=0.5,
crd_system='gse')
vlab.view(azimuth=45, elevation=70, distance=35.0,
focalpoint=[-9, 3, -1])
vlab.savefig('plasma_sheet_3d_{0:02d}.png'.format(itime))
vlab.show()
try:
vlab.mlab.close(fig)
except TypeError:
pass # this happens if the figure is already closed
# now do what we will with the time series... this is not a good
# presentation of this data, but you get the idea
from viscid.plot import vpyplot as vlt
fig, axes = vlt.subplots(4, 4, figsize=(12, 12))
for ax_row, yloc in zip(axes, np.linspace(-5, 5, len(axes))[::-1]):
for ax, xloc in zip(ax_row, np.linspace(4, 7, len(ax_row))):
vlt.plot(vx_ts['x={0}j, y={1}j, z=0j'.format(xloc, yloc)], ax=ax)
ax.set_ylabel('')
vlt.plt.title('x = {0:g}, y = {1:g}'.format(xloc, yloc))
vlt.plt.suptitle('Vx [km/s]')
vlt.auto_adjust_subplots()
vlt.show()
return 0
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--show", "--plot", action="store_true")
parser.add_argument("--interact", "-i", action="store_true")
args = vutil.common_argparse(parser)
f3d = viscid.load_file(os.path.join(sample_dir, 'sample_xdmf.3d.[0].xdmf'))
f_iono = viscid.load_file(
os.path.join(sample_dir, "sample_xdmf.iof.[0].xdmf"))
b = f3d["b"]
v = f3d["v"]
pp = f3d["pp"]
e = f3d["e_cc"]
vlab.mlab.options.offscreen = not args.show
vlab.figure(size=(1280, 800))
##########################################################
# make b a dipole inside 3.1Re and set e = 0 inside 4.0Re
cotr = viscid.Cotr(time='1990-03-21T14:48', dip_tilt=0.0) # pylint: disable=not-callable
moment = cotr.get_dipole_moment(crd_system=b)
isphere_mask = viscid.make_spherical_mask(b, rmax=3.1)
viscid.fill_dipole(b, m=moment, mask=isphere_mask)
e_mask = viscid.make_spherical_mask(b, rmax=4.0)
viscid.set_in_region(e, 0.0, alpha=0.0, mask=e_mask, out=e)
######################################
# plot a scalar cut plane of pressure
pp_src = vlab.field2source(pp, center='node')
scp = vlab.scalar_cut_plane(pp_src,
plane_orientation='z_axes',
opacity=0.5,
transparent=True,
view_controls=False,
cmap="inferno",
logscale=True)
scp.implicit_plane.normal = [0, 0, -1]
scp.implicit_plane.origin = [0, 0, 0]
scp.enable_contours = True
scp.contour.filled_contours = True
scp.contour.number_of_contours = 64
cbar = vlab.colorbar(scp, title=pp.name, orientation='vertical')
cbar.scalar_bar_representation.position = (0.01, 0.13)
cbar.scalar_bar_representation.position2 = (0.08, 0.76)
######################################
# plot a vector cut plane of the flow
vcp = vlab.vector_cut_plane(v,
scalars=pp_src,
plane_orientation='z_axes',
view_controls=False,
mode='arrow',
cmap='Greens_r')
vcp.implicit_plane.normal = [0, 0, -1]
vcp.implicit_plane.origin = [0, 0, 0]
##############################
# plot very faint isosurfaces
vx_src = vlab.field2source(v['x'], center='node')
iso = vlab.iso_surface(vx_src,
contours=[0.0],
opacity=0.008,
cmap='Pastel1')
##############################################################
# calculate B field lines && topology in Viscid and plot them
seedsA = viscid.SphericalPatch([0, 0, 0], [2, 0, 1],
30,
15,
r=5.0,
nalpha=5,
nbeta=5)
seedsB = viscid.SphericalPatch([0, 0, 0], [1.9, 0, -20],
30,
15,
r=5.0,
nalpha=1,
nbeta=5)
seeds = np.concatenate([seedsA, seedsB], axis=1)
b_lines, topo = viscid.calc_streamlines(b,
seeds,
ibound=3.5,
obound0=[-25, -20, -20],
obound1=[15, 20, 20],
wrap=True)
vlab.plot_lines(b_lines, scalars=viscid.topology2color(topo))
######################################################################
# plot a random circle at geosynchronus orbit with scalars colored
# by the Matplotlib viridis color map, just because we can; this is
# a useful toy for debugging
circle = viscid.Circle(p0=[0, 0, 0], r=6.618, n=128, endpoint=True)
scalar = np.sin(circle.as_local_coordinates().get_crd('phi'))
surf = vlab.plot_line(circle.get_points(),
scalars=scalar,
clim=0.8,
cmap="Spectral_r")
######################################################################
# Use Mayavi (VTK) to calculate field lines using an interactive seed
# These field lines are colored by E parallel
epar = viscid.project(e, b)
epar.name = "Epar"
bsl2 = vlab.streamline(b,
epar,
seedtype='plane',
seed_resolution=4,
integration_direction='both',
clim=(-0.05, 0.05))
# now tweak the VTK streamlines
bsl2.stream_tracer.maximum_propagation = 20.
bsl2.seed.widget.origin = [-11, -5.0, -2.0]
bsl2.seed.widget.point1 = [-11, 5.0, -2.0]
bsl2.seed.widget.point2 = [-11.0, -5.0, 2.0]
bsl2.streamline_type = 'tube'
bsl2.tube_filter.radius = 0.03
bsl2.stop() # this stop/start was a hack to get something to update
bsl2.start()
bsl2.seed.widget.enabled = False
cbar = vlab.colorbar(bsl2,
title=epar.name,
label_fmt='%.3f',
orientation='horizontal')
cbar.scalar_bar_representation.position = (0.15, 0.01)
cbar.scalar_bar_representation.position2 = (0.72, 0.10)
###############################################################
# Make a contour at the open-closed boundary in the ionosphere
seeds_iono = viscid.Sphere(r=1.063,
pole=-moment,
ntheta=256,
nphi=256,
thetalim=(0, 180),
philim=(0, 360),
crd_system=b)
_, topo_iono = viscid.calc_streamlines(b,
seeds_iono,
ibound=1.0,
nr_procs='all',
output=viscid.OUTPUT_TOPOLOGY)
topo_iono = np.log2(topo_iono)
m = vlab.mesh_from_seeds(seeds_iono,
scalars=topo_iono,
opacity=1.0,
clim=(0, 3),
color=(0.992, 0.445, 0.0))
m.enable_contours = True
m.actor.property.line_width = 4.0
m.contour.number_of_contours = 4
####################################################################
# Plot the ionosphere, note that the sample data has the ionosphere
# at a different time, so the open-closed boundary found above
# will not be consistant with the field aligned currents
fac_tot = 1e9 * f_iono['fac_tot']
m = vlab.plot_ionosphere(fac_tot,
bounding_lat=30.0,
vmin=-300,
vmax=300,
opacity=0.75,
rotate=cotr,
crd_system=b)
m.actor.property.backface_culling = True
########################################################################
# Add some markers for earth, i.e., real earth, and dayside / nightside
# representation
vlab.plot_blue_marble(r=1.0,
lines=False,
ntheta=64,
nphi=128,
rotate=cotr,
crd_system=b)
# now shade the night side with a transparent black hemisphere
vlab.plot_earth_3d(radius=1.01, night_only=True, opacity=0.5, crd_system=b)
####################
# Finishing Touches
# vlab.axes(pp_src, nb_labels=5)
oa = vlab.orientation_axes()
oa.marker.set_viewport(0.75, 0.75, 1.0, 1.0)
# note that resize won't work if the current figure has the
# off_screen_rendering flag set
# vlab.resize([1200, 800])
vlab.view(azimuth=45, elevation=70, distance=35.0, focalpoint=[-2, 0, 0])
##############
# Save Figure
# print("saving png")
# vlab.savefig('mayavi_msphere_sample.png')
# print("saving x3d")
# # x3d files can be turned into COLLADA files with meshlab, and
# # COLLADA (.dae) files can be opened in OS X's preview
# #
# # IMPORTANT: for some reason, using bounding_lat in vlab.plot_ionosphere
# # causes a segfault when saving x3d files
# #
# vlab.savefig('mayavi_msphere_sample.x3d')
# print("done")
vlab.savefig(next_plot_fname(__file__))
###########################
# Interact Programatically
if args.interact:
vlab.interact()
#######################
# Interact Graphically
if args.show:
vlab.show()
try:
vlab.mlab.close()
except AttributeError:
pass
return 0