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 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 run_div_test(fld, exact, title='', show=False, ignore_inexact=False):
t0 = time()
result_numexpr = viscid.div(fld, preferred="numexpr", only=False)
t1 = time()
logger.info("numexpr magnitude runtime: %g", t1 - t0)
result_diff = viscid.diff(result_numexpr, exact)['x=1:-1, y=1:-1, z=1:-1']
if not ignore_inexact and not (result_diff.data < 5e-5).all():
logger.warning("numexpr result is far from the exact result")
logger.info("min/max(abs(numexpr - exact)): %g / %g",
np.min(result_diff.data), np.max(result_diff.data))
planes = ["y=0j", "z=0j"]
nrows = 2
ncols = len(planes)
_, axes = plt.subplots(nrows, ncols, squeeze=False)
for i, p in enumerate(planes):
vlt.plot(result_numexpr, p, ax=axes[0, i], show=False)
vlt.plot(result_diff, p, ax=axes[1, i], show=False)
plt.suptitle(title)
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9))
plt.savefig(next_plot_fname(__file__))
if show:
vlt.mplshow()
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 _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--show", "--plot", action="store_true")
args = vutil.common_argparse(parser)
f = viscid.load_file(os.path.join(sample_dir, 'vpic_sample', 'global.vpc'))
# some slices that are good to check
vlt.clf()
vlt.plot(f['bx']['x=:32.01j'])
plt.close()
vlt.clf()
vlt.plot(f['bx']['x=:33.0j'])
plt.close()
_, axes = vlt.subplots(2, 2, figsize=(8, 4))
for i, ti in enumerate([0, -1]):
f.activate_time(ti)
vlt.plot(f['n_e']['y=0j'], symmetric=False, ax=axes[0, i])
vlt.plot(f['bx']['y=0j'], symmetric=True, ax=axes[1, i])
axes[0, i].set_title(f.get_grid().time)
vlt.auto_adjust_subplots()
plt.savefig(next_plot_fname(__file__))
if args.show:
vlt.show()
plt.close()
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_mpl_testA(show=False):
logger.info("2D cell centered tests")
x = np.array(np.linspace(-10, 10, 100), dtype=dtype)
y = np.array(np.linspace(-10, 10, 120), dtype=dtype)
z = np.array(np.linspace(-1, 1, 2), dtype=dtype)
fld_s = viscid.empty([x, y, z], center='cell')
Xcc, Ycc, Zcc = fld_s.get_crds_cc(shaped=True) # pylint: disable=unused-variable
fld_s[:, :, :] = np.sin(Xcc) + np.cos(Ycc)
_, axes = plt.subplots(4, 1, squeeze=False)
vlt.plot(fld_s, "y=20j", ax=axes[0, 0], show=False, plot_opts="lin_0")
vlt.plot(fld_s, "x=0j:20j,y=0j:5j", ax=axes[1, 0], earth=True, show=False,
plot_opts="x_-10_0,y_0_7")
vlt.plot(fld_s, "y=0j", ax=axes[2, 0], show=False, plot_opts="lin_-1_1")
vlt.plot(fld_s, "z=0j,x=-20j:0j", ax=axes[3, 0], earth=True, show=False,
plot_opts="lin_-5_5")
plt.suptitle("2d cell centered")
vlt.auto_adjust_subplots()
plt.savefig(next_plot_fname(__file__))
if show:
vlt.mplshow()
def run_mpl_testB(show=False):
logger.info("3D node centered tests")
x = np.array(np.linspace(-10, 10, 100), dtype=dtype)
y = np.array(np.linspace(-10, 10, 120), dtype=dtype)
z = np.array(np.linspace(-10, 10, 140), dtype=dtype)
fld_s = viscid.empty([x, y, z], center='node')
X, Y, Z = fld_s.get_crds_nc(shaped=True) # pylint: disable=W0612
fld_s[:, :, :] = np.sin(X) + np.cos(Y) - np.cos(Z)
# print("shape: ", fld_s.data.shape)
_, axes = plt.subplots(4, 1, squeeze=False)
vlt.plot(fld_s, "z=0,x=:30", ax=axes[0, 0], earth=True, plot_opts="lin_0")
vlt.plot(fld_s, "z=0.75j,x=-4:-1,y=-3j:3j", ax=axes[1, 0], earth=True)
vlt.plot(fld_s, "x=-0.5j:,y=-3j:3j,z=0j", ax=axes[2, 0], earth=True)
vlt.plot(fld_s, "x=0.0j,y=-5.0j:5.0j", ax=axes[3, 0], earth=True,
plot_opts="log,g")
plt.suptitle("3d node centered")
vlt.auto_adjust_subplots()
plt.savefig(next_plot_fname(__file__))
if show:
vlt.mplshow()
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--show", "--plot", action="store_true")
args = vutil.common_argparse(parser)
####### test 5-moment uniform grids
gk_uniform = viscid.load_file(os.path.join(sample_dir,
'sample_gkeyll_uniform_q_*.h5'))
_, axes = plt.subplots(1, 2, figsize=(9, 3))
for i, grid in enumerate(gk_uniform.iter_times(":")):
vlt.plot(grid['rho_i'], logscale=True, style='contourf', levels=128,
ax=axes[i])
seeds = viscid.Line((-1.2, 0, 0), (1.4, 0, 0), 8)
b_lines, _ = viscid.calc_streamlines(grid['b'], seeds, method='euler1',
max_length=20.0)
vlt.plot2d_lines(b_lines, scalars='#000000', symdir='z', linewidth=1.0)
plt.title(grid.format_time('.02f'))
vlt.auto_adjust_subplots()
plt.suptitle("Uniform Gkeyll Dataset")
plt.savefig(next_plot_fname(__file__))
if args.show:
plt.show()
plt.clf()
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_mpl_testB(show=False):
logger.info("3D node centered tests")
x = np.array(np.linspace(-10, 10, 100), dtype=dtype)
y = np.array(np.linspace(-10, 10, 120), dtype=dtype)
z = np.array(np.linspace(-10, 10, 140), dtype=dtype)
fld_s = viscid.empty([x, y, z], center='node')
X, Y, Z = fld_s.get_crds_nc(shaped=True) # pylint: disable=W0612
fld_s[:, :, :] = np.sin(X) + np.cos(Y) - np.cos(Z)
# print("shape: ", fld_s.data.shape)
_, axes = plt.subplots(4, 1, squeeze=False)
vlt.plot(fld_s, "z=0,x=:30", ax=axes[0, 0], earth=True, plot_opts="lin_0")
vlt.plot(fld_s, "z=0.75j,x=-4:-1,y=-3j:3j", ax=axes[1, 0], earth=True)
vlt.plot(fld_s, "x=-0.5j:,y=-3j:3j,z=0j", ax=axes[2, 0], earth=True)
vlt.plot(fld_s,
"x=0.0j,y=-5.0j:5.0j",
ax=axes[3, 0],
earth=True,
plot_opts="log,g")
plt.suptitle("3d node centered")
vlt.auto_adjust_subplots()
plt.savefig(next_plot_fname(__file__))
if show:
vlt.mplshow()
def run_mpl_testA(show=False):
logger.info("2D cell centered tests")
x = np.array(np.linspace(-10, 10, 100), dtype=dtype)
y = np.array(np.linspace(-10, 10, 120), dtype=dtype)
z = np.array(np.linspace(-1, 1, 2), dtype=dtype)
fld_s = viscid.empty([x, y, z], center='cell')
Xcc, Ycc, Zcc = fld_s.get_crds_cc(shaped=True) # pylint: disable=unused-variable
fld_s[:, :, :] = np.sin(Xcc) + np.cos(Ycc)
_, axes = plt.subplots(4, 1, squeeze=False)
vlt.plot(fld_s, "y=20j", ax=axes[0, 0], show=False, plot_opts="lin_0")
vlt.plot(fld_s,
"x=0j:20j,y=0j:5j",
ax=axes[1, 0],
earth=True,
show=False,
plot_opts="x_-10_0,y_0_7")
vlt.plot(fld_s, "y=0j", ax=axes[2, 0], show=False, plot_opts="lin_-1_1")
vlt.plot(fld_s,
"z=0j,x=-20j:0j",
ax=axes[3, 0],
earth=True,
show=False,
plot_opts="lin_-5_5")
plt.suptitle("2d cell centered")
vlt.auto_adjust_subplots()
plt.savefig(next_plot_fname(__file__))
if show:
vlt.mplshow()
def run_mpl_testB(show=False):
logger.info("3D node centered tests")
x = np.array(np.linspace(-10, 10, 100), dtype=dtype)
y = np.array(np.linspace(-10, 10, 120), dtype=dtype)
z = np.array(np.linspace(-10, 10, 140), dtype=dtype)
fld_s = viscid.empty([x, y, z], center='node')
X, Y, Z = fld_s.get_crds_nc(shaped=True) # pylint: disable=W0612
fld_s[:, :, :] = np.sin(X) + np.cos(Y) - np.cos(Z)
# print("shape: ", fld_s.data.shape)
nrows = 4
ncols = 1
plt.subplot2grid((nrows, ncols), (0, 0))
vlt.plot(fld_s, "z=0,x=:30", earth=True, plot_opts="lin_0")
plt.subplot2grid((nrows, ncols), (1, 0))
vlt.plot(fld_s, "z=0.75f,x=-4:-1,y=-3f:3f", earth=True)
plt.subplot2grid((nrows, ncols), (2, 0))
vlt.plot(fld_s, "x=-0.5f:,y=-3f:3f,z=0f", earth=True)
plt.subplot2grid((nrows, ncols), (3, 0))
vlt.plot(fld_s, "x=0.0f,y=-5.0f:5.0f", earth=True, plot_opts="log,g")
plt.suptitle("3d node centered")
vlt.auto_adjust_subplots()
plt.savefig(next_plot_fname(__file__))
if show:
vlt.mplshow()
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--show", "--plot", action="store_true")
args = vutil.common_argparse(parser)
f = viscid.load_file(os.path.join(sample_dir, 'vpic_sample', 'global.vpc'))
# some slices that are good to check
vlt.clf()
vlt.plot(f['bx']['x=:32.01j'])
plt.close()
vlt.clf()
vlt.plot(f['bx']['x=:33.0j'])
plt.close()
_, axes = vlt.subplots(2, 2, figsize=(8, 4))
for i, ti in enumerate([0, -1]):
f.activate_time(ti)
vlt.plot(f['n_e']['y=0j'], symmetric=False, ax=axes[0, i])
vlt.plot(f['bx']['y=0j'], symmetric=True, ax=axes[1, i])
axes[0, i].set_title(f.get_grid().time)
vlt.auto_adjust_subplots()
plt.savefig(next_plot_fname(__file__))
if args.show:
vlt.show()
plt.close()
return 0
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--show", "--plot", action="store_true")
args = vutil.common_argparse(parser)
####### test 5-moment uniform grids
gk_uniform = viscid.load_file(os.path.join(sample_dir,
'sample_gkeyll_uniform_q_*.h5'))
plt.figure(figsize=(9, 3))
for i, grid in enumerate(gk_uniform.iter_times(":")):
plt.subplot2grid((1, 2), (0, i))
vlt.plot(grid['rho_i'], logscale=True, style='contourf', levels=128)
seeds = viscid.Line((-1.2, 0, 0), (1.4, 0, 0), 8)
b_lines, _ = viscid.calc_streamlines(grid['b'], seeds, method='euler1',
max_length=20.0)
vlt.plot2d_lines(b_lines, scalars='#000000', symdir='z', linewidth=1.0)
plt.title(grid.format_time('.02f'))
vlt.auto_adjust_subplots()
plt.suptitle("Uniform Gkeyll Dataset")
plt.savefig(next_plot_fname(__file__))
if args.show:
plt.show()
plt.clf()
return 0
def run_mag_test(fld, title="", show=False):
vx, vy, vz = fld.component_views() # pylint: disable=W0612
vx, vy, vz = fld.component_fields()
try:
t0 = time()
mag_ne = viscid.magnitude(fld, preferred="numexpr", only=False)
t1 = time()
logger.info("numexpr mag runtime: %g", t1 - t0)
except viscid.verror.BackendNotFound:
xfail("Numexpr is not installed")
planes = ["z=0", "y=0"]
nrows = 4
ncols = len(planes)
_, axes = plt.subplots(nrows, ncols, sharex=True, sharey=True, squeeze=False)
for ind, p in enumerate(planes):
vlt.plot(vx, p, ax=axes[0, ind], show=False)
vlt.plot(vy, p, ax=axes[1, ind], show=False)
vlt.plot(vz, p, ax=axes[2, ind], show=False)
vlt.plot(mag_ne, p, ax=axes[3, ind], show=False)
plt.suptitle(title)
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9, right=0.9))
plt.gcf().set_size_inches(6, 7)
plt.savefig(next_plot_fname(__file__))
if show:
vlt.mplshow()
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--show", "--plot", action="store_true")
args = vutil.common_argparse(parser)
####### test binary files
f_bin = viscid.load_file(os.path.join(sample_dir, 'ath_sample.*.bin'))
for i, grid in enumerate(f_bin.iter_times(":")):
plt.subplot2grid((2, 2), (0, i))
vlt.plot(grid['bx'])
plt.subplot2grid((2, 2), (1, i))
vlt.plot(grid['by'])
plt.suptitle("athena bin (binary) files")
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9))
plt.savefig(next_plot_fname(__file__))
if args.show:
vlt.show()
plt.clf()
####### test ascii files
f_tab = viscid.load_file(os.path.join(sample_dir, 'ath_sample.*.tab'))
for i, grid in enumerate(f_tab.iter_times(":")):
plt.subplot2grid((2, 2), (0, i))
vlt.plot(grid['bx'])
plt.subplot2grid((2, 2), (1, i))
vlt.plot(grid['by'])
plt.suptitle("athena tab (ascii) files")
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9))
plt.savefig(next_plot_fname(__file__))
if args.show:
vlt.show()
plt.clf()
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 run_mpl_testA(show=False):
logger.info("2D cell centered tests")
x = np.array(np.linspace(-10, 10, 100), dtype=dtype)
y = np.array(np.linspace(-10, 10, 120), dtype=dtype)
z = np.array(np.linspace(-1, 1, 2), dtype=dtype)
fld_s = viscid.empty([x, y, z], center='cell')
Xcc, Ycc, Zcc = fld_s.get_crds_cc(shaped=True) # pylint: disable=unused-variable
fld_s[:, :, :] = np.sin(Xcc) + np.cos(Ycc)
nrows = 4
ncols = 1
plt.subplot2grid((nrows, ncols), (0, 0))
vlt.plot(fld_s, "y=20f", show=False, plot_opts="lin_0")
plt.subplot2grid((nrows, ncols), (1, 0))
vlt.plot(fld_s,
"x=0f:20f,y=0f:5f",
earth=True,
show=False,
plot_opts="x_-10_0,y_0_7")
plt.subplot2grid((nrows, ncols), (2, 0))
vlt.plot(fld_s, "y=0f", show=False, plot_opts="lin_-1_1")
plt.subplot2grid((nrows, ncols), (3, 0))
vlt.plot(fld_s,
"z=0f,x=-20f:0f",
earth=True,
show=False,
plot_opts="lin_-5_5")
plt.suptitle("2d cell centered")
vlt.auto_adjust_subplots()
plt.savefig(next_plot_fname(__file__))
if show:
vlt.mplshow()
def _main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--show", "--plot", action="store_true")
args = vutil.common_argparse(parser)
####### test binary files
f_bin = viscid.load_file(os.path.join(sample_dir, 'ath_sample.*.bin'))
_, axes = plt.subplots(2, 2)
for i, grid in enumerate(f_bin.iter_times(":")):
vlt.plot(grid['bx'], ax=axes[0, i])
vlt.plot(grid['by'], ax=axes[1, i])
plt.suptitle("athena bin (binary) files")
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9))
plt.savefig(next_plot_fname(__file__))
if args.show:
vlt.show()
plt.close()
####### test ascii files
f_tab = viscid.load_file(os.path.join(sample_dir, 'ath_sample.*.tab'))
_, axes = plt.subplots(2, 2)
for i, grid in enumerate(f_tab.iter_times(":")):
vlt.plot(grid['bx'], ax=axes[0, i])
vlt.plot(grid['by'], ax=axes[1, i])
plt.suptitle("athena tab (ascii) files")
vlt.auto_adjust_subplots(subplot_params=dict(top=0.9))
plt.savefig(next_plot_fname(__file__))
if args.show:
vlt.show()
plt.close()
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("--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 _do_multiplot(tind, grid, plot_vars=None, global_popts=None, kwopts=None,
share_axes=False, show=False, subplot_params=None,
first_run_result=None, first_run=False, **kwargs):
import matplotlib.pyplot as plt
from viscid.plot import vpyplot as vlt
logger.info("Plotting timestep: %d, %g", tind, grid.time)
if plot_vars is None:
raise ValueError("No plot_vars given to `_do_multiplot` :(")
if kwargs:
logger.info("Unused kwargs: {0}".format(kwargs))
if kwopts is None:
kwopts = {}
transpose = kwopts.get("transpose", False)
plot_size = kwopts.get("plot_size", None)
dpi = kwopts.get("dpi", None)
out_prefix = kwopts.get("out_prefix", None)
out_format = kwopts.get("out_format", "png")
selection = kwopts.get("selection", None)
timeformat = kwopts.get("timeformat", ".02f")
tighten = kwopts.get("tighten", False)
# wicked hacky
# subplot_params = kwopts.get("subplot_params", _subplot_params)
# nrows = len(plot_vars)
nrows = len([pv[0] for pv in plot_vars if not pv[0].startswith('^')])
ncols = 1
if transpose:
nrows, ncols = ncols, nrows
if nrows == 0:
logger.warn("I have no variables to plot")
return
fig = plt.gcf()
if plot_size is not None:
fig.set_size_inches(*plot_size, forward=True)
if dpi is not None:
fig.set_dpi(dpi)
shareax = None
this_row = -1
for i, fld_meta in enumerate(plot_vars):
if not fld_meta[0].startswith('^'):
this_row += 1
same_axis = False
else:
same_axis = True
fld_name_meta = fld_meta[0].lstrip('^')
fld_name_split = fld_name_meta.split(',')
if '=' in fld_name_split[0]:
# if fld_name is actually an equation, assume
# there's no slice, and commas are part of the
# equation
fld_name = ",".join(fld_name_split)
fld_slc = ""
else:
fld_name = fld_name_split[0]
fld_slc = ",".join(fld_name_split[1:])
if selection is not None:
# fld_slc += ",{0}".format(selection)
if fld_slc != "":
fld_slc = ",".join([fld_slc, selection])
else:
fld_slc = selection
if fld_slc.strip() == "":
fld_slc = None
# print("fld_time:", fld.time)
if this_row < 0:
raise ValueError("first plot can't begin with a +")
row = this_row
col = 0
if transpose:
row, col = col, row
if not same_axis:
ax = plt.subplot2grid((nrows, ncols), (row, col),
sharex=shareax, sharey=shareax)
if i == 0 and share_axes:
shareax = ax
if "plot_opts" not in fld_meta[1]:
fld_meta[1]["plot_opts"] = global_popts
elif global_popts is not None:
fld_meta[1]["plot_opts"] = "{0},{1}".format(
fld_meta[1]["plot_opts"], global_popts)
with grid.get_field(fld_name, slc=fld_slc) as fld:
vlt.plot(fld, masknan=True, **fld_meta[1])
# print("fld cache", grid[fld_meta[0]]._cache)
if timeformat and timeformat.lower() != "none":
plt.suptitle(grid.format_time(timeformat))
# for adjusting subplots / tight_layout and applying the various
# hacks to keep plots from dancing around in movies
if not subplot_params and first_run_result:
subplot_params = first_run_result
if tighten:
tighten = dict(rect=[0, 0.03, 1, 0.90])
ret = vlt.auto_adjust_subplots(tight_layout=tighten,
subplot_params=subplot_params)
if not first_run:
ret = None
if out_prefix:
plt.savefig("{0}_{1:06d}.{2}".format(out_prefix, tind + 1, out_format))
if show:
plt.show()
plt.clf()
return ret
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 _do_multiplot(tind, grid, plot_vars=None, global_popts=None, kwopts=None,
share_axes=False, show=False, subplot_params=None,
first_run_result=None, first_run=False, **kwargs):
from viscid.plot import vpyplot as vlt
import matplotlib.pyplot as plt
logger.info("Plotting timestep: %d, %g", tind, grid.time)
if plot_vars is None:
raise ValueError("No plot_vars given to `_do_multiplot` :(")
if kwargs:
logger.info("Unused kwargs: {0}".format(kwargs))
if kwopts is None:
kwopts = {}
transpose = kwopts.get("transpose", False)
plot_size = kwopts.get("plot_size", None)
dpi = kwopts.get("dpi", None)
out_prefix = kwopts.get("out_prefix", None)
out_format = kwopts.get("out_format", "png")
selection = kwopts.get("selection", None)
timeformat = kwopts.get("timeformat", ".02f")
tighten = kwopts.get("tighten", False)
# wicked hacky
# subplot_params = kwopts.get("subplot_params", _subplot_params)
# nrows = len(plot_vars)
nrows = len([pv[0] for pv in plot_vars if not pv[0].startswith('^')])
ncols = 1
if transpose:
nrows, ncols = ncols, nrows
if nrows == 0:
logger.warning("I have no variables to plot")
return
fig = plt.gcf()
if plot_size is not None:
fig.set_size_inches(*plot_size, forward=True)
if dpi is not None:
fig.set_dpi(dpi)
shareax = None
this_row = -1
for i, fld_meta in enumerate(plot_vars):
if not fld_meta[0].startswith('^'):
this_row += 1
same_axis = False
else:
same_axis = True
fld_name_meta = fld_meta[0].lstrip('^')
fld_name_split = fld_name_meta.split(',')
if '=' in fld_name_split[0]:
# if fld_name is actually an equation, assume
# there's no slice, and commas are part of the
# equation
fld_name = ",".join(fld_name_split)
fld_slc = ""
else:
fld_name = fld_name_split[0]
fld_slc = ",".join(fld_name_split[1:])
if selection is not None:
# fld_slc += ",{0}".format(selection)
if fld_slc != "":
fld_slc = ",".join([fld_slc, selection])
else:
fld_slc = selection
if fld_slc.strip() == "":
fld_slc = Ellipsis
# print("fld_time:", fld.time)
if this_row < 0:
raise ValueError("first plot can't begin with a +")
row = this_row
col = 0
if transpose:
row, col = col, row
if not same_axis:
ax = plt.subplot2grid((nrows, ncols), (row, col),
sharex=shareax, sharey=shareax)
if i == 0 and share_axes:
shareax = ax
if "plot_opts" not in fld_meta[1]:
fld_meta[1]["plot_opts"] = global_popts
elif global_popts is not None:
fld_meta[1]["plot_opts"] = "{0},{1}".format(
fld_meta[1]["plot_opts"], global_popts)
with grid.get_field(fld_name, slc=fld_slc) as fld:
vlt.plot(fld, masknan=True, **fld_meta[1])
# print("fld cache", grid[fld_meta[0]]._cache)
if timeformat and timeformat.lower() != "none":
plt.suptitle(grid.format_time(timeformat))
# for adjusting subplots / tight_layout and applying the various
# hacks to keep plots from dancing around in movies
if not subplot_params and first_run_result:
subplot_params = first_run_result
if tighten:
tighten = dict(rect=[0, 0.03, 1, 0.90])
ret = vlt.auto_adjust_subplots(tight_layout=tighten,
subplot_params=subplot_params)
if not first_run:
ret = None
if out_prefix:
plt.savefig("{0}_{1:06d}.{2}".format(out_prefix, tind + 1, out_format))
if show:
plt.show()
plt.clf()
return ret
def main():
mhd_type = "C"
make_plots = 1
test_fc = 1
test_ec = 1
test_div = 1
test_interp = 1
test_streamline = 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 = 5e-6
elif mhd_type in ("F", "FORTRAN"):
f = viscid.load_file("$WORK/tmedium3/*.3df.[-1]")
catol = 1e-8
rtol = 7e-2
else:
raise ValueError()
ISLICE = slice(None)
# ISLICE = 'y=0f:0.15f'
# #################
# # test out fc2cc
if test_fc:
b = f['b'][ISLICE]
b1 = f['b1'][ISLICE]
compare_vectors(b,
b1,
viscid.fc2cc,
catol=catol,
rtol=rtol,
make_plots=make_plots)
#################
# test out ec2cc
if test_ec:
e_cc = f['e_cc'][ISLICE]
e_ec = f['e_ec'][ISLICE]
if mhd_type not in ("F", "FORTRAN"):
compare_vectors(e_cc,
e_ec,
viscid.ec2cc,
catol=catol,
rtol=rtol,
make_plots=make_plots)
#################
# test out divfc
# Note: Relative error on Div B is meaningless b/c the coordinates
# are not the same up to order (dx/4) I think. You can see this
# since (fcdiv - divb_trimmed) is both noisy and stripy
if test_div:
bnd = 0
if mhd_type not in ("F", "FORTRAN"):
b1 = f['b1'][ISLICE]
divb = f['divB'][ISLICE]
if bnd:
trimmed = divb
else:
trimmed = divb['x=1:-1, y=1:-1, z=1:-1']
b1mag = viscid.magnitude(viscid.fc2cc(b1, bnd=bnd))
divb1 = viscid.div_fc(b1, bnd=bnd)
viscid.set_in_region(trimmed,
trimmed,
alpha=0.0,
beta=0.0,
out=trimmed,
mask=viscid.make_spherical_mask(trimmed,
rmax=5.0))
viscid.set_in_region(divb1,
divb1,
alpha=0.0,
beta=0.0,
out=divb1,
mask=viscid.make_spherical_mask(divb1,
rmax=5.0))
reldiff = (divb1 - trimmed) / b1mag
reldiff = reldiff["x=1:-1, y=1:-1, z=1:-1"]
reldiff.name = divb1.name + " - " + trimmed.name
reldiff.pretty_name = divb1.pretty_name + " - " + trimmed.pretty_name
abs_max_rel_diff = np.nanmax(np.abs(reldiff))
max_crd_diff = [0.0] * 3
for i, d in enumerate('xyz'):
max_crd_diff[i] = np.max(trimmed.get_crd(d) - divb1.get_crd(d))
print("divB max absolute relative diff: {0:.3e} "
"(crds: X: {1[0]:.3e}, Y: {1[1]:.3e}, Z: {1[2]:.3e})"
"".format(abs_max_rel_diff, max_crd_diff))
# plot differences?
if make_plots:
ax1 = plt.subplot(311)
vlt.plot(divb['y=0f'], symmetric=True, earth=True)
plt.subplot(312, sharex=ax1, sharey=ax1)
vlt.plot(divb1['y=0f'], symmetric=True, earth=True)
plt.subplot(313, sharex=ax1, sharey=ax1)
vlt.plot(reldiff['y=0f'], symmetric=True, earth=True)
vlt.show()
# Since the coordinates will be different by order dx^2 (i think),
# there is no way to compare the divB from simulation with the
# one we get here. However, they should be the same up to a few %, and
# down to noise level with stripes of enhanced noise. These stripes
# are the errors in the coordinate values (since the output only
# gives us weird nc = averaged cc locations)
#
# if abs_max_rel_diff > rtol or np.any(np.abs(max_crd_diff) > catol):
# raise RuntimeError("Tolerance exceeded on divB calculation")
if test_streamline:
b_cc = f['b_cc']['x=-40f:12f, y=-15f:15f, z=-15f:15f']
b_fc = f['b_fc']['x=-40f:12f, y=-15f:15f, z=-15f:15f']
cotr = viscid.cotr.Cotr()
r_mask = 3.0
# set b_cc to dipole inside some sphere
isphere_mask = viscid.make_spherical_mask(b_cc, rmax=r_mask)
moment = cotr.get_dipole_moment(crd_system=b_cc)
viscid.fill_dipole(b_cc, m=moment, mask=isphere_mask)
# set b_fc to dipole inside some sphere
isphere_mask = viscid.make_spherical_mask(b_fc, rmax=r_mask)
moment = cotr.get_dipole_moment(crd_system=b_fc)
viscid.fill_dipole(b_fc, m=moment, mask=isphere_mask)
seeds = viscid.Volume([-10, 0, -5], [10, 0, 5], (16, 1, 3))
sl_kwargs = dict(ibound=1.0, method=viscid.EULER1A)
lines_cc, topo_cc = viscid.calc_streamlines(b_cc, seeds, **sl_kwargs)
lines_fc, topo_fc = viscid.calc_streamlines(b_fc, seeds, **sl_kwargs)
if make_plots:
plt.figure(figsize=(10, 6))
ax0 = plt.subplot(211)
topo_cc_colors = viscid.topology2color(topo_cc)
vlt.plot(f['pp']['y=0f'], logscale=True, earth=True, cmap='plasma')
vlt.plot2d_lines(lines_cc, topo_cc_colors, symdir='y')
ax0 = plt.subplot(212, sharex=ax0, sharey=ax0)
topo_fc_colors = viscid.topology2color(topo_fc)
vlt.plot(f['pp']['y=0f'], logscale=True, earth=True, cmap='plasma')
vlt.plot2d_lines(lines_fc, topo_fc_colors, symdir='y')
plt.xlim(-20, 10)
plt.ylim(-10, 10)
vlt.auto_adjust_subplots()
vlt.show()
if test_interp:
# test interpolation with E . B / B
b_cc = f['b_cc']
b_fc = f['b_fc']
e_cc = f['e_cc']
e_ec = f['e_ec']
cotr = viscid.cotr.Cotr()
r_mask = 3.0
# set b_cc to dipole inside some sphere
isphere_mask = viscid.make_spherical_mask(b_cc, rmax=r_mask)
moment = cotr.get_dipole_moment(crd_system=b_cc)
viscid.fill_dipole(b_cc, m=moment, mask=isphere_mask)
# set b_fc to dipole inside some sphere
isphere_mask = viscid.make_spherical_mask(b_fc, rmax=r_mask)
moment = cotr.get_dipole_moment(crd_system=b_fc)
viscid.fill_dipole(b_fc, m=moment, mask=isphere_mask)
# zero out e_cc inside some sphere
viscid.set_in_region(e_cc,
e_cc,
alpha=0.0,
beta=0.0,
out=e_cc,
mask=viscid.make_spherical_mask(e_cc,
rmax=r_mask))
# zero out e_ec inside some sphere
viscid.set_in_region(e_ec,
e_ec,
alpha=0.0,
beta=0.0,
out=e_ec,
mask=viscid.make_spherical_mask(e_ec,
rmax=r_mask))
tmp = viscid.empty([
np.linspace(-10, 10, 64),
np.linspace(-10, 10, 64),
np.linspace(-10, 10, 64)
],
center="Cell")
b_cc_interp = viscid.interp_linear(b_cc, tmp)
b_fc_interp = viscid.interp_linear(b_fc, tmp)
e_cc_interp = viscid.interp_linear(e_cc, tmp)
e_ec_interp = viscid.interp_linear(e_ec, tmp)
epar_cc = viscid.dot(e_cc_interp,
b_cc_interp) / viscid.magnitude(b_cc_interp)
epar_ecfc = viscid.dot(e_ec_interp,
b_fc_interp) / viscid.magnitude(b_fc_interp)
if make_plots:
# plt.figure()
# ax0 = plt.subplot(121)
# vlt.plot(b_cc['x']['y=0f'], clim=(-40, 40))
# plt.subplot(122, sharex=ax0, sharey=ax0)
# vlt.plot(b_fc['x']['y=0f'], clim=(-40, 40))
# vlt.show()
plt.figure(figsize=(14, 5))
ax0 = plt.subplot(131)
vlt.plot(epar_cc['y=0f'], symmetric=True, cbarlabel="Epar CC")
plt.subplot(132, sharex=ax0, sharey=ax0)
vlt.plot(epar_ecfc['y=0f'], symmetric=True, cbarlabel="Epar ECFC")
plt.subplot(133, sharex=ax0, sharey=ax0)
vlt.plot(((epar_cc - epar_ecfc) / epar_cc)['y=0f'],
clim=(-10, 10),
cbarlabel="Rel Diff")
vlt.auto_adjust_subplots()
vlt.show()
return 0
def main():
mhd_type = "C"
make_plots = 1
test_fc = 1
test_ec = 1
test_div = 1
test_interp = 1
test_streamline = 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("$WORK/tmedium2/*.3d.[-1].xdmf")
else:
raise ValueError()
catol = 1e-8
rtol = 5e-6
elif mhd_type in ("F", "FORTRAN"):
f = viscid.load_file("$WORK/tmedium3/*.3df.[-1]")
catol = 1e-8
rtol = 7e-2
else:
raise ValueError()
ISLICE = slice(None)
# ISLICE = 'y=0j:0.15j'
# #################
# # test out fc2cc
if test_fc:
b = f['b'][ISLICE]
b1 = f['b1'][ISLICE]
compare_vectors(b, b1, viscid.fc2cc, catol=catol, rtol=rtol,
make_plots=make_plots)
#################
# test out ec2cc
if test_ec:
e_cc = f['e_cc'][ISLICE]
e_ec = f['e_ec'][ISLICE]
if mhd_type not in ("F", "FORTRAN"):
compare_vectors(e_cc, e_ec, viscid.ec2cc, catol=catol, rtol=rtol,
make_plots=make_plots)
#################
# test out divfc
# Note: Relative error on Div B is meaningless b/c the coordinates
# are not the same up to order (dx/4) I think. You can see this
# since (fcdiv - divb_trimmed) is both noisy and stripy
if test_div:
bnd = 0
if mhd_type not in ("F", "FORTRAN"):
b1 = f['b1'][ISLICE]
divb = f['divB'][ISLICE]
if bnd:
trimmed = divb
else:
trimmed = divb['x=1:-1, y=1:-1, z=1:-1']
b1mag = viscid.magnitude(viscid.fc2cc(b1, bnd=bnd))
divb1 = viscid.div_fc(b1, bnd=bnd)
viscid.set_in_region(trimmed, trimmed, alpha=0.0, beta=0.0, out=trimmed,
mask=viscid.make_spherical_mask(trimmed, rmax=5.0))
viscid.set_in_region(divb1, divb1, alpha=0.0, beta=0.0, out=divb1,
mask=viscid.make_spherical_mask(divb1, rmax=5.0))
reldiff = (divb1 - trimmed) / b1mag
reldiff = reldiff["x=1:-1, y=1:-1, z=1:-1"]
reldiff.name = divb1.name + " - " + trimmed.name
reldiff.pretty_name = divb1.pretty_name + " - " + trimmed.pretty_name
abs_max_rel_diff = np.nanmax(np.abs(reldiff))
max_crd_diff = [0.0] * 3
for i, d in enumerate('xyz'):
max_crd_diff[i] = np.max(trimmed.get_crd(d) - divb1.get_crd(d))
print("divB max absolute relative diff: {0:.3e} "
"(crds: X: {1[0]:.3e}, Y: {1[1]:.3e}, Z: {1[2]:.3e})"
"".format(abs_max_rel_diff, max_crd_diff))
# plot differences?
if make_plots:
ax1 = plt.subplot(311)
vlt.plot(divb['y=0j'], symmetric=True, earth=True)
plt.subplot(312, sharex=ax1, sharey=ax1)
vlt.plot(divb1['y=0j'], symmetric=True, earth=True)
plt.subplot(313, sharex=ax1, sharey=ax1)
vlt.plot(reldiff['y=0j'], symmetric=True, earth=True)
vlt.show()
# Since the coordinates will be different by order dx^2 (i think),
# there is no way to compare the divB from simulation with the
# one we get here. However, they should be the same up to a few %, and
# down to noise level with stripes of enhanced noise. These stripes
# are the errors in the coordinate values (since the output only
# gives us weird nc = averaged cc locations)
#
# if abs_max_rel_diff > rtol or np.any(np.abs(max_crd_diff) > catol):
# raise RuntimeError("Tolerance exceeded on divB calculation")
if test_streamline:
b_cc = f['b_cc']['x=-40j:12j, y=-15j:15j, z=-15j:15j']
b_fc = f['b_fc']['x=-40j:12j, y=-15j:15j, z=-15j:15j']
cotr = viscid.cotr.Cotr()
r_mask = 3.0
# set b_cc to dipole inside some sphere
isphere_mask = viscid.make_spherical_mask(b_cc, rmax=r_mask)
moment = cotr.get_dipole_moment(crd_system=b_cc)
viscid.fill_dipole(b_cc, m=moment, mask=isphere_mask)
# set b_fc to dipole inside some sphere
isphere_mask = viscid.make_spherical_mask(b_fc, rmax=r_mask)
moment = cotr.get_dipole_moment(crd_system=b_fc)
viscid.fill_dipole(b_fc, m=moment, mask=isphere_mask)
seeds = viscid.Volume([-10, 0, -5], [10, 0, 5], (16, 1, 3))
sl_kwargs = dict(ibound=1.0, method=viscid.EULER1A)
lines_cc, topo_cc = viscid.calc_streamlines(b_cc, seeds, **sl_kwargs)
lines_fc, topo_fc = viscid.calc_streamlines(b_fc, seeds, **sl_kwargs)
if make_plots:
plt.figure(figsize=(10, 6))
ax0 = plt.subplot(211)
topo_cc_colors = viscid.topology2color(topo_cc)
vlt.plot(f['pp']['y=0j'], logscale=True, earth=True, cmap='plasma')
vlt.plot2d_lines(lines_cc, topo_cc_colors, symdir='y')
ax0 = plt.subplot(212, sharex=ax0, sharey=ax0)
topo_fc_colors = viscid.topology2color(topo_fc)
vlt.plot(f['pp']['y=0j'], logscale=True, earth=True, cmap='plasma')
vlt.plot2d_lines(lines_fc, topo_fc_colors, symdir='y')
plt.xlim(-20, 10)
plt.ylim(-10, 10)
vlt.auto_adjust_subplots()
vlt.show()
if test_interp:
# test interpolation with E . B / B
b_cc = f['b_cc']
b_fc = f['b_fc']
e_cc = f['e_cc']
e_ec = f['e_ec']
cotr = viscid.cotr.Cotr()
r_mask = 3.0
# set b_cc to dipole inside some sphere
isphere_mask = viscid.make_spherical_mask(b_cc, rmax=r_mask)
moment = cotr.get_dipole_moment(crd_system=b_cc)
viscid.fill_dipole(b_cc, m=moment, mask=isphere_mask)
# set b_fc to dipole inside some sphere
isphere_mask = viscid.make_spherical_mask(b_fc, rmax=r_mask)
moment = cotr.get_dipole_moment(crd_system=b_fc)
viscid.fill_dipole(b_fc, m=moment, mask=isphere_mask)
# zero out e_cc inside some sphere
viscid.set_in_region(e_cc, e_cc, alpha=0.0, beta=0.0, out=e_cc,
mask=viscid.make_spherical_mask(e_cc, rmax=r_mask))
# zero out e_ec inside some sphere
viscid.set_in_region(e_ec, e_ec, alpha=0.0, beta=0.0, out=e_ec,
mask=viscid.make_spherical_mask(e_ec, rmax=r_mask))
tmp = viscid.empty([np.linspace(-10, 10, 64), np.linspace(-10, 10, 64),
np.linspace(-10, 10, 64)], center="Cell")
b_cc_interp = viscid.interp_linear(b_cc, tmp)
b_fc_interp = viscid.interp_linear(b_fc, tmp)
e_cc_interp = viscid.interp_linear(e_cc, tmp)
e_ec_interp = viscid.interp_linear(e_ec, tmp)
epar_cc = viscid.dot(e_cc_interp, b_cc_interp) / viscid.magnitude(b_cc_interp)
epar_ecfc = viscid.dot(e_ec_interp, b_fc_interp) / viscid.magnitude(b_fc_interp)
if make_plots:
# plt.figure()
# ax0 = plt.subplot(121)
# vlt.plot(b_cc['x']['y=0j'], clim=(-40, 40))
# plt.subplot(122, sharex=ax0, sharey=ax0)
# vlt.plot(b_fc['x']['y=0j'], clim=(-40, 40))
# vlt.show()
plt.figure(figsize=(14, 5))
ax0 = plt.subplot(131)
vlt.plot(epar_cc['y=0j'], symmetric=True, cbarlabel="Epar CC")
plt.subplot(132, sharex=ax0, sharey=ax0)
vlt.plot(epar_ecfc['y=0j'], symmetric=True, cbarlabel="Epar ECFC")
plt.subplot(133, sharex=ax0, sharey=ax0)
vlt.plot(((epar_cc - epar_ecfc) / epar_cc)['y=0j'], clim=(-10, 10),
cbarlabel="Rel Diff")
vlt.auto_adjust_subplots()
vlt.show()
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
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
