def run_test_iof(f, main__file__, show=False):
vlt.clf()
fac_tot = 1e9 * f["fac_tot"]
plot_args = dict(projection="polar",
lin=[-300, 300],
bounding_lat=35.0,
drawcoastlines=True, # for basemap only
title="Total FAC\n",
gridec='gray',
label_lat=True,
label_mlt=True,
colorbar=True,
cbar_kwargs=dict(pad=0.15) # pad the colorbar away from the plot
)
ax1 = vlt.subplot(121, projection='polar')
vlt.plot(fac_tot, ax=ax1, hemisphere='north', **plot_args)
ax1.annotate('(a)', xy=(0, 0), textcoords="axes fraction",
xytext=(-0.1, 1.0), fontsize=18)
ax2 = vlt.subplot(122, projection='polar')
plot_args['gridec'] = False
vlt.plot(fac_tot, ax=ax2, hemisphere="south", style="contourf",
levels=50, extend="both", **plot_args)
ax2.annotate('(b)', xy=(0, 0), textcoords="axes fraction",
xytext=(-0.1, 1.0), fontsize=18)
vlt.auto_adjust_subplots(subplot_params=dict())
plt.gcf().set_size_inches(8, 4)
plt.savefig(next_plot_fname(main__file__))
if show:
vlt.mplshow()
def _main():
x = np.linspace(-1, 1, 128)
y = z = np.linspace(-0.25, 0.25, 8)
B = viscid.zeros((x, y, z), nr_comps=3, layout='interlaced', name="B")
X, Y, Z = B.get_crds("xyz", shaped=True) # pylint: disable=unused-variable
xl, yl, zl = B.xl # pylint: disable=unused-variable
xh, yh, zh = B.xh # pylint: disable=unused-variable
xm, ym, zm = 0.5 * (B.xl + B.xh) # pylint: disable=unused-variable
B['x'] = 0.0 # np.sin(1.0 * np.pi * X / (xh - xl) + 0.5 * np.pi)
B['y'] = np.sin(1.0 * np.pi * X / (xh - xl) + 0.5 * np.pi)
B['z'] = np.sin(1.0 * np.pi * X / (xh - xl) - 1.0 * np.pi)
B += 0.33 * np.random.random_sample(B.shape)
# R = viscid.a2b_rotm((1, 0, 0), (1, 0, 1))
# B[...] = np.einsum("ij,lmnj->lmni", R, B)
lmn = find_minvar_lmn(B, (xl, ym, zm), (xh, ym, zm), l_basis=None)
# lmn = find_minvar_lmn(B, (xl, ym, zm), (xh, ym, zm), l_basis=(0, 0, 1))
print("LMN matrix:\n", lmn, sep='')
##########
from viscid.plot import vpyplot as vlt
from matplotlib import pyplot as plt
p0 = np.array((xm, ym, zm)).reshape((3, ))
pl = p0 + 0.25 * lmn[:, 0]
pm = p0 + 0.25 * lmn[:, 1]
pn = p0 + 0.25 * lmn[:, 2]
print("p0", p0)
print("pl", pl)
print("pm", pm)
print("pn", pn)
vlt.subplot(211)
vlt.plot2d_quiver(B['z=0j'])
plt.plot([p0[0], pl[0]], [p0[1], pl[1]], color='r', ls='-')
plt.plot([p0[0], pm[0]], [p0[1], pm[1]], color='c', ls='-')
plt.plot([p0[0], pn[0]], [p0[1], pn[1]], color='b', ls='-')
plt.ylabel("Y")
vlt.subplot(212)
vlt.plot2d_quiver(B['y=0j'])
plt.plot([p0[0], pl[0]], [p0[2], pl[2]], color='r', ls='-')
plt.plot([p0[0], pm[0]], [p0[2], pm[2]], color='c', ls='-')
plt.plot([p0[0], pn[0]], [p0[2], pn[2]], color='b', ls='-')
plt.xlabel("X")
plt.ylabel("Z")
vlt.show()
##########
return 0
def _main():
x = np.linspace(-1, 1, 128)
y = z = np.linspace(-0.25, 0.25, 8)
B = viscid.zeros((x, y, z), nr_comps=3, layout='interlaced', name="B")
X, Y, Z = B.get_crds("xyz", shaped=True) # pylint: disable=unused-variable
xl, yl, zl = B.xl # pylint: disable=unused-variable
xh, yh, zh = B.xh # pylint: disable=unused-variable
xm, ym, zm = 0.5 * (B.xl + B.xh) # pylint: disable=unused-variable
B['x'] = 0.0 # np.sin(1.0 * np.pi * X / (xh - xl) + 0.5 * np.pi)
B['y'] = np.sin(1.0 * np.pi * X / (xh - xl) + 0.5 * np.pi)
B['z'] = np.sin(1.0 * np.pi * X / (xh - xl) - 1.0 * np.pi)
B += 0.33 * np.random.random_sample(B.shape)
# R = viscid.a2b_rotm((1, 0, 0), (1, 0, 1))
# B[...] = np.einsum("ij,lmnj->lmni", R, B)
lmn = find_minvar_lmn(B, (xl, ym, zm), (xh, ym, zm), l_basis=None)
# lmn = find_minvar_lmn(B, (xl, ym, zm), (xh, ym, zm), l_basis=(0, 0, 1))
print("LMN matrix:\n", lmn, sep='')
##########
from viscid.plot import vpyplot as vlt
from matplotlib import pyplot as plt
p0 = np.array((xm, ym, zm)).reshape((3,))
pl = p0 + 0.25 * lmn[:, 0]
pm = p0 + 0.25 * lmn[:, 1]
pn = p0 + 0.25 * lmn[:, 2]
print("p0", p0)
print("pl", pl)
print("pm", pm)
print("pn", pn)
vlt.subplot(211)
vlt.plot2d_quiver(B['z=0j'])
plt.plot([p0[0], pl[0]], [p0[1], pl[1]], color='r', ls='-')
plt.plot([p0[0], pm[0]], [p0[1], pm[1]], color='c', ls='-')
plt.plot([p0[0], pn[0]], [p0[1], pn[1]], color='b', ls='-')
plt.ylabel("Y")
vlt.subplot(212)
vlt.plot2d_quiver(B['y=0j'])
plt.plot([p0[0], pl[0]], [p0[2], pl[2]], color='r', ls='-')
plt.plot([p0[0], pm[0]], [p0[2], pm[2]], color='c', ls='-')
plt.plot([p0[0], pn[0]], [p0[2], pn[2]], color='b', ls='-')
plt.xlabel("X")
plt.ylabel("Z")
vlt.show()
##########
return 0
def main():
f = viscid.load_file("~/dev/work/tmedium/*.3d.[-1].xdmf")
grid = f.get_grid()
gslc = "x=-26j:12.5j, y=-15j:15j, z=-15j:15j"
# gslc = "x=-12.5j:26j, y=-15j:15j, z=-15j:15j"
b_cc = f['b_cc'][gslc]
b_cc.name = "b_cc"
b_fc = f['b_fc'][gslc]
b_fc.name = "b_fc"
e_cc = f['e_cc'][gslc]
e_cc.name = "e_cc"
e_ec = f['e_ec'][gslc]
e_ec.name = "e_ec"
pp = f['pp'][gslc]
pp.name = 'pp'
pargs = dict(logscale=True, earth=True)
# vlt.clf()
# ax1 = vlt.subplot(211)
# vlt.plot(f['pp']['y=0j'], **pargs)
# # vlt.plot(viscid.magnitude(f['b_cc']['y=0j']), **pargs)
# # vlt.show()
# vlt.subplot(212, sharex=ax1, sharey=ax1)
# vlt.plot(viscid.magnitude(viscid.fc2cc(f['b_fc'])['y=0j']), **pargs)
# vlt.show()
basename = './tmediumR.3d.{0:06d}'.format(int(grid.time))
viscid.save_fields(basename + '.h5', [b_cc, b_fc, e_cc, e_ec, pp])
f2 = viscid.load_file(basename + ".xdmf")
pargs = dict(logscale=True, earth=True)
vlt.clf()
ax1 = vlt.subplot(211)
vlt.plot(f2['pp']['y=0j'], style='contour', levels=5, colorbar=None,
colors='k', **pargs)
vlt.plot(viscid.magnitude(f2['b_cc']['y=0j']), **pargs)
vlt.subplot(212, sharex=ax1, sharey=ax1)
vlt.plot(viscid.magnitude(viscid.fc2cc(f2['b_fc'])['y=0j']), **pargs)
vlt.show()
os.remove(basename + '.h5')
os.remove(basename + '.xdmf')
return 0
def main():
f = viscid.load_file("~/dev/work/tmedium/*.3d.[-1].xdmf")
grid = f.get_grid()
gslc = "x=-26f:12.5f, y=-15f:15f, z=-15f:15f"
# gslc = "x=-12.5f:26f, y=-15f:15f, z=-15f:15f"
b_cc = f['b_cc'][gslc]
b_cc.name = "b_cc"
b_fc = f['b_fc'][gslc]
b_fc.name = "b_fc"
e_cc = f['e_cc'][gslc]
e_cc.name = "e_cc"
e_ec = f['e_ec'][gslc]
e_ec.name = "e_ec"
pp = f['pp'][gslc]
pp.name = 'pp'
pargs = dict(logscale=True, earth=True)
# vlt.clf()
# ax1 = vlt.subplot(211)
# vlt.plot(f['pp']['y=0f'], **pargs)
# # vlt.plot(viscid.magnitude(f['b_cc']['y=0f']), **pargs)
# # vlt.show()
# vlt.subplot(212, sharex=ax1, sharey=ax1)
# vlt.plot(viscid.magnitude(viscid.fc2cc(f['b_fc'])['y=0f']), **pargs)
# vlt.show()
basename = './tmediumR.3d.{0:06d}'.format(int(grid.time))
viscid.save_fields(basename + '.h5', [b_cc, b_fc, e_cc, e_ec, pp])
f2 = viscid.load_file(basename + ".xdmf")
pargs = dict(logscale=True, earth=True)
vlt.clf()
ax1 = vlt.subplot(211)
vlt.plot(f2['pp']['y=0f'], style='contour', levels=5, colorbar=None,
colors='k', **pargs)
vlt.plot(viscid.magnitude(f2['b_cc']['y=0f']), **pargs)
vlt.subplot(212, sharex=ax1, sharey=ax1)
vlt.plot(viscid.magnitude(viscid.fc2cc(f2['b_fc'])['y=0f']), **pargs)
vlt.show()
os.remove(basename + '.h5')
os.remove(basename + '.xdmf')
return 0
def run_test_2d(f, main__file__, show=False):
vlt.clf()
slc = "x=-20j:12j, y=0j"
plot_kwargs = dict(title=True, earth=True)
vlt.subplot(141)
vlt.plot(f['pp'], slc, logscale=True, **plot_kwargs)
vlt.plot(np.abs(f['psi']), style='contour', logscale=True, levels=30,
linewidths=0.8, colors='grey', linestyles='solid', colorbar=None,
x=(-20, 12))
vlt.subplot(142)
vlt.plot(viscid.magnitude(f['bcc']), slc, logscale=True, **plot_kwargs)
vlt.plot2d_quiver(f['v'][slc], step=5, color='y', pivot='mid', width=0.03,
scale=600)
vlt.subplot(143)
vlt.plot(f['jy'], slc, clim=[-0.005, 0.005], **plot_kwargs)
vlt.streamplot(f['v'][slc], linewidth=0.3)
vlt.subplot(144)
vlt.plot(f['jy'], "x=7j:12j, y=0j, z=0j")
plt.suptitle("2D File")
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9, wspace=1.3))
plt.gcf().set_size_inches(10, 4)
vlt.savefig(next_plot_fname(main__file__))
if show:
vlt.show()
def run_test_3d(f, main__file__, show=False):
vlt.clf()
slc = "x=-20f:12f, y=0f"
plot_kwargs = dict(title=True, earth=True)
vlt.subplot(141)
vlt.plot(f['pp'], slc, logscale=True, **plot_kwargs)
vlt.subplot(142)
vlt.plot(viscid.magnitude(f['bcc']), slc, logscale=True, **plot_kwargs)
vlt.plot2d_quiver(f['v'][slc],
step=5,
color='y',
pivot='mid',
width=0.03,
scale=600)
vlt.subplot(143)
vlt.plot(f['jy'], slc, clim=(-0.005, 0.005), **plot_kwargs)
vlt.streamplot(f['v'][slc], linewidth=0.3)
vlt.subplot(144)
vlt.plot(f['jy'], "x=7f:12f, y=0f, z=0f")
plt.suptitle("3D File")
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9, wspace=1.3))
plt.gcf().set_size_inches(10, 4)
vlt.savefig(next_plot_fname(main__file__))
if show:
vlt.show()
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--show", "--plot", action="store_true")
args = viscid.vutil.common_argparse(parser)
# args.show = True
t = viscid.linspace_datetime64('2006-06-10 12:30:00.0',
'2006-06-10 12:33:00.0', 16)
tL = viscid.as_datetime64('2006-06-10 12:31:00.0')
tR = viscid.as_datetime64('2006-06-10 12:32:00.0')
y = np.linspace(2 * np.pi, 4 * np.pi, 12)
### plots with a datetime64 axis
f0 = viscid.ones([t, y], crd_names='ty', center='node')
T, Y = f0.get_crds(shaped=True)
f0.data += np.arange(T.size).reshape(T.shape)
f0.data += np.cos(Y)
fig = plt.figure(figsize=(10, 5))
# 1D plot
vlt.subplot(121)
vlt.plot(f0[tL:tR]['y=0'], marker='^')
plt.xlim(*viscid.as_datetime(t[[0, -1]]).tolist())
# 2D plot
vlt.subplot(122)
vlt.plot(f0, x=(t[0], t[-1]))
plt.suptitle("datetime64")
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9))
plt.savefig(next_plot_fname(__file__))
if args.show:
vlt.show()
plt.close(fig)
### plots with a timedelta64 axis
tL = tL - t[0]
tR = tR - t[0]
t = t - t[0]
f0 = viscid.ones([t, y], crd_names='ty', center='node')
T, Y = f0.get_crds(shaped=True)
f0.data += np.arange(T.size).reshape(T.shape)
f0.data += np.cos(Y)
fig = plt.figure(figsize=(10, 5))
# 1D plot
vlt.subplot(121)
vlt.plot(f0[tL:tR]['y=0'], marker='^')
plt.xlim(*viscid.as_datetime(t[[0, -1]]).tolist())
# 2D plot
vlt.subplot(122)
vlt.plot(f0, x=(t[0], t[-1]))
plt.suptitle("timedelta64")
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9))
plt.savefig(next_plot_fname(__file__))
if args.show:
vlt.show()
plt.close(fig)
return 0
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--show", "--plot", action="store_true")
args = viscid.vutil.common_argparse(parser)
# args.show = True
t = viscid.linspace_datetime64('2006-06-10 12:30:00.0',
'2006-06-10 12:33:00.0', 16)
tL = viscid.as_datetime64('2006-06-10 12:31:00.0')
tR = viscid.as_datetime64('2006-06-10 12:32:00.0')
y = np.linspace(2 * np.pi, 4 * np.pi, 12)
### plots with a datetime64 axis
f0 = viscid.ones([t, y], crd_names='ty', center='node')
T, Y = f0.get_crds(shaped=True)
f0.data += np.arange(T.size).reshape(T.shape)
f0.data += np.cos(Y)
fig = plt.figure(figsize=(10, 5))
# 1D plot
vlt.subplot(121)
vlt.plot(f0[tL:tR]['y=0'], marker='^')
plt.xlim(*viscid.as_datetime(t[[0, -1]]).tolist())
# 2D plot
vlt.subplot(122)
vlt.plot(f0, x=(t[0], t[-1]))
plt.suptitle("datetime64")
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9))
plt.savefig(next_plot_fname(__file__))
if args.show:
vlt.show()
plt.close(fig)
### plots with a timedelta64 axis
tL = tL - t[0]
tR = tR - t[0]
t = t - t[0]
f0 = viscid.ones([t, y], crd_names='ty', center='node')
T, Y = f0.get_crds(shaped=True)
f0.data += np.arange(T.size).reshape(T.shape)
f0.data += np.cos(Y)
fig = plt.figure(figsize=(10, 5))
# 1D plot
vlt.subplot(121)
vlt.plot(f0[tL:tR]['y=0'], marker='^')
plt.xlim(*viscid.as_datetime(t[[0, -1]]).tolist())
# 2D plot
vlt.subplot(122)
vlt.plot(f0, x=(t[0], t[-1]))
plt.suptitle("timedelta64")
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9))
plt.savefig(next_plot_fname(__file__))
if args.show:
vlt.show()
plt.close(fig)
return 0
def run_test_3d(f, main__file__, show=False):
vlt.clf()
slc = "x=-20j:12j, y=0j"
plot_kwargs = dict(title=True, earth=True)
vlt.subplot(141)
vlt.plot(f['pp'], slc, logscale=True, **plot_kwargs)
vlt.subplot(142)
vlt.plot(viscid.magnitude(f['bcc']), slc, logscale=True, **plot_kwargs)
vlt.plot2d_quiver(f['v'][slc], step=5, color='y', pivot='mid', width=0.03,
scale=600)
vlt.subplot(143)
vlt.plot(f['jy'], slc, clim=(-0.005, 0.005), **plot_kwargs)
vlt.streamplot(f['v'][slc], linewidth=0.3)
vlt.subplot(144)
vlt.plot(f['jy'], "x=7j:12j, y=0j, z=0j")
plt.suptitle("3D File")
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9, wspace=1.3))
plt.gcf().set_size_inches(10, 4)
vlt.savefig(next_plot_fname(main__file__))
if show:
vlt.show()
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
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("--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 topology_bitor_clusters(fld,
min_depth=1,
max_depth=10,
multiple=True,
plot=False,
sep_val=streamline.TOPOLOGY_MS_SEPARATOR,
mask_limit=0b1111,
periodic="00",
pt_bnds=()):
"""Find separator as intersection of all global topologies
Neighbors are bitwise ORed until at least one value matches
`sep_val` which is presumably (Close | Open N | Open S | SW).
This happens between min_depth and max_depth times,
where the resolution of each iteration is reduced by a factor
of two, ie, worst case 2**(max_depth).
Args:
fld (Field): Topology (bitmask) as a field
min_depth (int): Iterate at least this many times
max_depth (int): Iterate at most this many times
multiple (bool): passed to :py:func:`viscid.cluster`
sep_val (int): Value of bitmask that indicates a separator
plot (bool): Make a 2D plot of Fld and the sep candidates
mask_limit (int): if > 0, then bitmask fld with mask_limit,
i.e., fld = fld & mask_limit (bitwise and)
periodic (sequence): indicate whether that direction is
periodic, and if so, whether the coordinate arrays are
overlapped or not. Values can be True, False, or '+'. '+'
indicates that x[0] and x[-1] are not colocated, so assume
they're dx apart where dx = x[-1] - x[-2].
pt_bnd (sequence): Boundaries that come to a point, i.e., all
values along that boundary are neighbors such as the poles
of a sphere. Specified like "0-" for lower boundary of
dimension 0 or "1+" for the upper boundary of dimension 1.
Returns:
ndarray: 2xN for N clusters of separator points in the same
coordinates as `fld`
"""
pd = [False if pi == "0" else bool(pi) for pi in periodic]
fld = fld.slice_reduce(":")
if mask_limit:
fld = np.bitwise_and(fld, mask_limit)
a = fld.data
x, y = fld.get_crds()
for i in range(max_depth):
if pd[0]:
a[(0, -1), :] |= a[(-1, 0), :]
if pd[1]:
a[:, (0, -1)] |= a[:, (-1, 0)]
a = (
a[:-1, :-1] | a[:-1, 1:] | # pylint: disable=bad-whitespace
a[1:, :-1] | a[1:, 1:]) # pylint: disable=bad-whitespace
x = 0.5 * (x[1:] + x[:-1])
y = 0.5 * (y[1:] + y[:-1])
# bitwise_or an entire bounary if all points are neighbors, like
# at the poles of a sphere
for bnd in pt_bnds:
slc = [slice(None), slice(None)]
slc[int(bnd[0])] = -1 if bnd[1] == "+" else 0
a[slc] = np.bitwise_or.reduce(a[slc])
indx, indy = np.where(a == sep_val)
if i + 1 >= min_depth and len(indx):
break
pts = viscid.cluster(indx,
indy,
x,
y,
multiple=multiple,
periodic=periodic)
if plot:
from matplotlib import pyplot as plt
from viscid.plot import vpyplot as vlt
vlt.clf()
ax0 = vlt.subplot(121)
vlt.plot(fld, title=True)
vlt.subplot(122, sharex=ax0, sharey=ax0)
or_fld = viscid.arrays2field((x, y), a, name="OR")
vlt.plot(or_fld, title=True)
_x, _y = or_fld.get_crds()
plt.plot(_x[indx], _y[indy], 'ko')
plt.plot(pts[0], pts[1], 'y^')
plt.show()
return pts
def main():
mhd_type = "C3"
make_plots = 1
mhd_type = mhd_type.upper()
if mhd_type.startswith("C"):
if mhd_type in ("C",):
f = viscid.load_file("$WORK/tmedium/*.3d.[-1].xdmf")
elif mhd_type in ("C2", "C3"):
f = viscid.load_file("$WORK/tmedium2/*.3d.[-1].xdmf")
else:
raise ValueError()
catol = 1e-8
rtol = 2e-6
elif mhd_type in ("F", "FORTRAN"):
f = viscid.load_file("$WORK/tmedium3/*.3df.[-1]")
catol = 1e-8
rtol = 7e-2
else:
raise ValueError()
b = f['b_cc']
b1 = f['b_fc']
e_cc = f['e_cc']
e_ec = f['e_ec']
# divb = f['divB']
# viscid.interact()
if True:
bD = viscid.empty_like(b)
bD.data = np.array(b.data)
b1D = viscid.empty_like(b1)
b1D.data = np.array(b1.data)
mask5 = viscid.make_spherical_mask(bD, rmax=3.5)
mask1_5 = viscid.make_spherical_mask(bD, rmax=1.5)
viscid.fill_dipole(bD, mask=mask5)
viscid.set_in_region(bD, bD, 0.0, 0.0, mask=mask1_5, out=bD)
# compare_vectors(_b, bD, make_plots=True)
mask5 = viscid.make_spherical_mask(b1D, rmax=3.5)
mask1_5 = viscid.make_spherical_mask(b1D, rmax=1.5)
viscid.fill_dipole(b1D, mask=mask5)
viscid.set_in_region(b1D, b1D, 0.0, 0.0, mask=mask1_5, out=b1D)
compare_vectors(bD["x=1:-1, y=1:-1, z=1:-1"], b1D.as_cell_centered(),
make_plots=True)
# plt.clf()
# dkwargs = dict(symmetric=True, earth=True, clim=(-1e2, 1e2))
# ax1 = plt.subplot(311)
# vlt.plot(viscid.div(b1)['y=0j'], **dkwargs)
# plt.subplot(312, sharex=ax1, sharey=ax1)
# vlt.plot(viscid.div(b)['y=0j'], **dkwargs)
# plt.subplot(313, sharex=ax1, sharey=ax1)
# vlt.plot(viscid.div(b1D)['y=0j'], **dkwargs)
# vlt.show()
bD = b1D = mask5 = mask1_5 = None
# straight up interpolate b1 to cc crds and compare with b
if True:
b1_cc = viscid.interp_trilin(b1, b).as_flat()
viscid.set_in_region(b, b, alpha=0.0, beta=0.0, out=b,
mask=viscid.make_spherical_mask(b, rmax=5.0))
viscid.set_in_region(b1_cc, b1_cc, alpha=0.0, beta=0.0, out=b1_cc,
mask=viscid.make_spherical_mask(b1_cc, rmax=5.0))
compare_vectors(b, b1_cc, make_plots=True)
# make div?
if True:
# make seeds for 1.5x supersampling b1
n = 128
seeds = viscid.Volume((5.1, -0.02, -5.0), (12.0, 0.02, 5.0), (n, 3, n))
# do interpolation onto new seeds
b2 = viscid.interp_trilin(b1, seeds)
div_b = viscid.div(b)
div_b1 = viscid.div(b1)
div_b2 = viscid.div(b2)
viscid.set_in_region(div_b, div_b, alpha=0.0, beta=0.0, out=div_b,
mask=viscid.make_spherical_mask(div_b, rmax=5.0))
viscid.set_in_region(div_b1, div_b1, alpha=0.0, beta=0.0, out=div_b1,
mask=viscid.make_spherical_mask(div_b1, rmax=5.0))
viscid.set_in_region(div_b2, div_b2, alpha=0.0, beta=0.0, out=div_b2,
mask=viscid.make_spherical_mask(div_b2, rmax=5.0))
viscid.set_in_region(divb, divb, alpha=0.0, beta=0.0, out=divb,
mask=viscid.make_spherical_mask(divb, rmax=5.0))
plt.clf()
ax1 = vlt.subplot(311)
vlt.plot(div_b['y=0j'], symmetric=True, earth=True)
vlt.subplot(312, sharex=ax1, sharey=ax1)
# vlt.plot(div_b1['y=0j'], symmetric=True, earth=True)
vlt.plot(div_b2['y=0j'], symmetric=True, earth=True)
vlt.subplot(313, sharex=ax1, sharey=ax1)
vlt.plot(divb['y=0j'], symmetric=True, earth=True)
vlt.show()
return 0
def main():
mhd_type = "C3"
make_plots = 1
mhd_type = mhd_type.upper()
if mhd_type.startswith("C"):
if mhd_type in ("C", ):
f = viscid.load_file("$WORK/tmedium/*.3d.[-1].xdmf")
elif mhd_type in ("C2", "C3"):
f = viscid.load_file("$WORK/tmedium2/*.3d.[-1].xdmf")
else:
raise ValueError()
catol = 1e-8
rtol = 2e-6
elif mhd_type in ("F", "FORTRAN"):
f = viscid.load_file("$WORK/tmedium3/*.3df.[-1]")
catol = 1e-8
rtol = 7e-2
else:
raise ValueError()
b = f['b_cc']
b1 = f['b_fc']
e_cc = f['e_cc']
e_ec = f['e_ec']
# divb = f['divB']
# viscid.interact()
if True:
bD = viscid.empty_like(b)
bD.data = np.array(b.data)
b1D = viscid.empty_like(b1)
b1D.data = np.array(b1.data)
mask5 = viscid.make_spherical_mask(bD, rmax=3.5)
mask1_5 = viscid.make_spherical_mask(bD, rmax=1.5)
viscid.fill_dipole(bD, mask=mask5)
viscid.set_in_region(bD, bD, 0.0, 0.0, mask=mask1_5, out=bD)
# compare_vectors(_b, bD, make_plots=True)
mask5 = viscid.make_spherical_mask(b1D, rmax=3.5)
mask1_5 = viscid.make_spherical_mask(b1D, rmax=1.5)
viscid.fill_dipole(b1D, mask=mask5)
viscid.set_in_region(b1D, b1D, 0.0, 0.0, mask=mask1_5, out=b1D)
compare_vectors(bD["x=1:-1, y=1:-1, z=1:-1"],
b1D.as_cell_centered(),
make_plots=True)
# plt.clf()
# dkwargs = dict(symmetric=True, earth=True, clim=(-1e2, 1e2))
# ax1 = plt.subplot(311)
# vlt.plot(viscid.div(b1)['y=0j'], **dkwargs)
# plt.subplot(312, sharex=ax1, sharey=ax1)
# vlt.plot(viscid.div(b)['y=0j'], **dkwargs)
# plt.subplot(313, sharex=ax1, sharey=ax1)
# vlt.plot(viscid.div(b1D)['y=0j'], **dkwargs)
# vlt.show()
bD = b1D = mask5 = mask1_5 = None
# straight up interpolate b1 to cc crds and compare with b
if True:
b1_cc = viscid.interp_trilin(b1, b).as_flat()
viscid.set_in_region(b,
b,
alpha=0.0,
beta=0.0,
out=b,
mask=viscid.make_spherical_mask(b, rmax=5.0))
viscid.set_in_region(b1_cc,
b1_cc,
alpha=0.0,
beta=0.0,
out=b1_cc,
mask=viscid.make_spherical_mask(b1_cc, rmax=5.0))
compare_vectors(b, b1_cc, make_plots=True)
# make div?
if True:
# make seeds for 1.5x supersampling b1
n = 128
seeds = viscid.Volume((5.1, -0.02, -5.0), (12.0, 0.02, 5.0), (n, 3, n))
# do interpolation onto new seeds
b2 = viscid.interp_trilin(b1, seeds)
div_b = viscid.div(b)
div_b1 = viscid.div(b1)
div_b2 = viscid.div(b2)
viscid.set_in_region(div_b,
div_b,
alpha=0.0,
beta=0.0,
out=div_b,
mask=viscid.make_spherical_mask(div_b, rmax=5.0))
viscid.set_in_region(div_b1,
div_b1,
alpha=0.0,
beta=0.0,
out=div_b1,
mask=viscid.make_spherical_mask(div_b1, rmax=5.0))
viscid.set_in_region(div_b2,
div_b2,
alpha=0.0,
beta=0.0,
out=div_b2,
mask=viscid.make_spherical_mask(div_b2, rmax=5.0))
viscid.set_in_region(divb,
divb,
alpha=0.0,
beta=0.0,
out=divb,
mask=viscid.make_spherical_mask(divb, rmax=5.0))
plt.clf()
ax1 = vlt.subplot(311)
vlt.plot(div_b['y=0j'], symmetric=True, earth=True)
vlt.subplot(312, sharex=ax1, sharey=ax1)
# vlt.plot(div_b1['y=0j'], symmetric=True, earth=True)
vlt.plot(div_b2['y=0j'], symmetric=True, earth=True)
vlt.subplot(313, sharex=ax1, sharey=ax1)
vlt.plot(divb['y=0j'], symmetric=True, earth=True)
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():
mhd_type = "C"
make_plots = 1
mhd_type = mhd_type.upper()
if mhd_type.startswith("C"):
if mhd_type in ("C",):
f = viscid.load_file("$WORK/tmedium/*.3d.[-1].xdmf")
elif mhd_type in ("C2", "C3"):
f = viscid.load_file("$WORK/tmedium2/*.3d.[-1].xdmf")
else:
raise ValueError()
catol = 1e-8
rtol = 2e-6
elif mhd_type in ("F", "FORTRAN"):
f = viscid.load_file("$WORK/tmedium3/*.3df.[-1]")
catol = 1e-8
rtol = 7e-2
else:
raise ValueError()
do_fill_dipole = True
gslc = "x=-21.2j:12j, y=-11j:11j, z=-11j:11j"
b = f['b_cc'][gslc]
b1 = f['b_fc'][gslc]
e_cc = f['e_cc'][gslc]
e_ec = f['e_ec'][gslc]
if do_fill_dipole:
mask = viscid.make_spherical_mask(b, rmax=3.5)
viscid.fill_dipole(b, mask=mask)
mask = viscid.make_spherical_mask(b1, rmax=3.5)
viscid.fill_dipole(b1, mask=mask)
mask = None
# seeds = viscid.SphericalCap(r=1.02, ntheta=64, nphi=32, angle0=17, angle=20,
# philim=(100, 260), roll=-180.0)
# seeds = viscid.SphericalCap(r=1.02, ntheta=64, nphi=32, angle0=17, angle=20,
# philim=(0, 10), roll=0.0)
seedsN = viscid.Sphere(r=1.02, ntheta=16, nphi=16, thetalim=(15, 25),
philim=(0, 300), crd_system=b)
seedsS = viscid.Sphere(r=1.02, ntheta=16, nphi=16, thetalim=(155, 165),
philim=(0, 300), crd_system=b)
bl_kwargs = dict(ibound=0.9, obound0=(-20, -10, -10), obound1=(11, 10, 10))
# blines_cc, topo_cc = viscid.streamlines(b, seeds, **bl_kwargs)
blinesN_fc, topoN_fc = viscid.streamlines(b1, seedsN, **bl_kwargs)
_, topoS_fc = viscid.streamlines(b1, seedsS, output=viscid.OUTPUT_TOPOLOGY,
**bl_kwargs)
if True:
from viscid.plot import vlab
mesh = vlab.mesh_from_seeds(seedsN, scalars=topoN_fc)
mesh.actor.property.backface_culling = True
# vlab.plot_lines(blines_cc, scalars="#000000", tube_radius=0.03)
vlab.plot_lines(blinesN_fc, scalars=viscid.topology2color(topoN_fc),
opacity=0.7)
vlab.plot_blue_marble(r=1.0)
vlab.plot_earth_3d(radius=1.01, crd_system=b, night_only=True,
opacity=0.5)
vlab.show()
if True:
vlt.subplot(121, projection='polar')
vlt.plot(topoN_fc)
vlt.subplot(122, projection='polar')
vlt.plot(topoS_fc)
vlt.show()
return 0
def topology_bitor_clusters(fld, min_depth=1, max_depth=10, multiple=True,
plot=False, sep_val=streamline.TOPOLOGY_MS_SEPARATOR,
mask_limit=0b1111, periodic="00", pt_bnds=()):
"""Find separator as intersection of all global topologies
Neighbors are bitwise ORed until at least one value matches
`sep_val` which is presumably (Close | Open N | Open S | SW).
This happens between min_depth and max_depth times,
where the resolution of each iteration is reduced by a factor
of two, ie, worst case 2**(max_depth).
Args:
fld (Field): Topology (bitmask) as a field
min_depth (int): Iterate at least this many times
max_depth (int): Iterate at most this many times
multiple (bool): passed to :py:func:`viscid.cluster`
sep_val (int): Value of bitmask that indicates a separator
plot (bool): Make a 2D plot of Fld and the sep candidates
mask_limit (int): if > 0, then bitmask fld with mask_limit,
i.e., fld = fld & mask_limit (bitwise and)
periodic (sequence): indicate whether that direction is
periodic, and if so, whether the coordinate arrays are
overlapped or not. Values can be True, False, or '+'. '+'
indicates that x[0] and x[-1] are not colocated, so assume
they're dx apart where dx = x[-1] - x[-2].
pt_bnd (sequence): Boundaries that come to a point, i.e., all
values along that boundary are neighbors such as the poles
of a sphere. Specified like "0-" for lower boundary of
dimension 0 or "1+" for the upper boundary of dimension 1.
Returns:
ndarray: 2xN for N clusters of separator points in the same
coordinates as `fld`
"""
pd = [False if pi == "0" else bool(pi) for pi in periodic]
fld = fld.slice_reduce(":")
if mask_limit:
fld = np.bitwise_and(fld, mask_limit)
a = fld.data
x, y = fld.get_crds()
for i in range(max_depth):
if pd[0]:
a[(0, -1), :] |= a[(-1, 0), :]
if pd[1]:
a[:, (0, -1)] |= a[:, (-1, 0)]
a = (a[ :-1, :-1] | a[ :-1, 1: ] | # pylint: disable=bad-whitespace
a[1: , :-1] | a[1: , 1: ]) # pylint: disable=bad-whitespace
x = 0.5 * (x[1:] + x[:-1])
y = 0.5 * (y[1:] + y[:-1])
# bitwise_or an entire bounary if all points are neighbors, like
# at the poles of a sphere
for bnd in pt_bnds:
slc = (slice(None), slice(None))
slc[int(bnd[0])] = -1 if bnd[1] == "+" else 0
a[slc] = np.bitwise_or.reduce(a[slc])
indx, indy = np.where(a == sep_val)
if i + 1 >= min_depth and len(indx):
break
pts = viscid.find_clusters(indx, indy, x, y, multiple=multiple,
periodic=periodic)
if plot:
from viscid.plot import vpyplot as vlt
from matplotlib import pyplot as plt
vlt.clf()
ax0 = vlt.subplot(121)
vlt.plot(fld, title=True)
vlt.subplot(122, sharex=ax0, sharey=ax0)
or_fld = viscid.arrays2field((x, y), a, name="OR")
vlt.plot(or_fld, title=True)
_x, _y = or_fld.get_crds()
plt.plot(_x[indx], _y[indy], 'ko')
plt.plot(pts[0], pts[1], 'y^')
plt.show()
return pts
def main():
mhd_type = "C"
make_plots = 1
mhd_type = mhd_type.upper()
if mhd_type.startswith("C"):
if mhd_type in ("C", ):
f = viscid.load_file("$WORK/tmedium/*.3d.[-1].xdmf")
elif mhd_type in ("C2", "C3"):
f = viscid.load_file("$WORK/tmedium2/*.3d.[-1].xdmf")
else:
raise ValueError()
catol = 1e-8
rtol = 2e-6
elif mhd_type in ("F", "FORTRAN"):
f = viscid.load_file("$WORK/tmedium3/*.3df.[-1]")
catol = 1e-8
rtol = 7e-2
else:
raise ValueError()
do_fill_dipole = True
gslc = "x=-21.2j:12j, y=-11j:11j, z=-11j:11j"
b = f['b_cc'][gslc]
b1 = f['b_fc'][gslc]
e_cc = f['e_cc'][gslc]
e_ec = f['e_ec'][gslc]
if do_fill_dipole:
mask = viscid.make_spherical_mask(b, rmax=3.5)
viscid.fill_dipole(b, mask=mask)
mask = viscid.make_spherical_mask(b1, rmax=3.5)
viscid.fill_dipole(b1, mask=mask)
mask = None
# seeds = viscid.SphericalCap(r=1.02, ntheta=64, nphi=32, angle0=17, angle=20,
# philim=(100, 260), roll=-180.0)
# seeds = viscid.SphericalCap(r=1.02, ntheta=64, nphi=32, angle0=17, angle=20,
# philim=(0, 10), roll=0.0)
seedsN = viscid.Sphere(r=1.02,
ntheta=16,
nphi=16,
thetalim=(15, 25),
philim=(0, 300),
crd_system=b)
seedsS = viscid.Sphere(r=1.02,
ntheta=16,
nphi=16,
thetalim=(155, 165),
philim=(0, 300),
crd_system=b)
bl_kwargs = dict(ibound=0.9, obound0=(-20, -10, -10), obound1=(11, 10, 10))
# blines_cc, topo_cc = viscid.streamlines(b, seeds, **bl_kwargs)
blinesN_fc, topoN_fc = viscid.streamlines(b1, seedsN, **bl_kwargs)
_, topoS_fc = viscid.streamlines(b1,
seedsS,
output=viscid.OUTPUT_TOPOLOGY,
**bl_kwargs)
if True:
from viscid.plot import vlab
mesh = vlab.mesh_from_seeds(seedsN, scalars=topoN_fc)
mesh.actor.property.backface_culling = True
# vlab.plot_lines(blines_cc, scalars="#000000", tube_radius=0.03)
vlab.plot_lines(blinesN_fc,
scalars=viscid.topology2color(topoN_fc),
opacity=0.7)
vlab.plot_blue_marble(r=1.0)
vlab.plot_earth_3d(radius=1.01,
crd_system=b,
night_only=True,
opacity=0.5)
vlab.show()
if True:
vlt.subplot(121, projection='polar')
vlt.plot(topoN_fc)
vlt.subplot(122, projection='polar')
vlt.plot(topoS_fc)
vlt.show()
return 0
