def plot_charge_neutrality(pic_info, current_time):
"""Plot compression related terms.
Args:
pic_info: namedtuple for the PIC simulation information.
species: 'e' for electrons, 'i' for ions.
current_time: current time frame.
"""
kwargs = {
"current_time": current_time,
"xl": 0,
"xr": 400,
"zb": -100,
"zt": 100
}
x, z, ne = read_2d_fields(pic_info, "../../data/ne.gda", **kwargs)
x, z, ni = read_2d_fields(pic_info, "../../data/ni.gda", **kwargs)
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
nx, = x.shape
nz, = z.shape
width = 0.75
height = 0.76
xs = 0.12
ys = 0.95 - height
gap = 0.05
fig = plt.figure(figsize=[10, 4])
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": -1, "vmax": 1}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x, z,
(ni - ne) / (ne + ni), ax1, fig, **kwargs_plot)
# p1.set_cmap(cmaps.inferno)
p1.set_cmap(plt.cm.seismic)
ax1.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=20)
cbar1.ax.set_ylabel(r'$(n_i-n_e)/(n_i+n_e)$', fontdict=font, fontsize=24)
# cbar1.set_ticks(np.arange(-0.1, 0.15, 0.1))
cbar1.ax.tick_params(labelsize=20)
if not os.path.isdir('../img/'):
os.makedirs('../img/')
fname = '../img/q_' + str(current_time).zfill(3) + '.jpg'
fig.savefig(fname, dpi=200)
plt.show()
def plot_charge_neutrality(pic_info, current_time):
"""Plot compression related terms.
Args:
pic_info: namedtuple for the PIC simulation information.
species: 'e' for electrons, 'i' for ions.
current_time: current time frame.
"""
kwargs = {
"current_time": current_time,
"xl": 0,
"xr": 400,
"zb": -100,
"zt": 100
}
x, z, ne = read_2d_fields(pic_info, "../../data/ne.gda", **kwargs)
x, z, ni = read_2d_fields(pic_info, "../../data/ni.gda", **kwargs)
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
nx, = x.shape
nz, = z.shape
width = 0.75
height = 0.76
xs = 0.12
ys = 0.95 - height
gap = 0.05
fig = plt.figure(figsize=[10, 4])
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": -1, "vmax": 1}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x, z, (ni - ne) / (ne + ni), ax1, fig,
**kwargs_plot)
# p1.set_cmap(cmaps.inferno)
p1.set_cmap(plt.cm.seismic)
ax1.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=20)
cbar1.ax.set_ylabel(r'$(n_i-n_e)/(n_i+n_e)$', fontdict=font, fontsize=24)
# cbar1.set_ticks(np.arange(-0.1, 0.15, 0.1))
cbar1.ax.tick_params(labelsize=20)
if not os.path.isdir('../img/'):
os.makedirs('../img/')
fname = '../img/q_' + str(current_time).zfill(3) + '.jpg'
fig.savefig(fname, dpi=200)
plt.show()
def plot_number_density(run_name, root_dir, pic_info, species, ct):
"""Plot particle number density
"""
kwargs = {"current_time": ct, "xl": 0, "xr": 1000, "zb": -250, "zt": 250}
fname = root_dir + 'data/n' + species + '.gda'
# fname = root_dir + 'data/jy.gda'
x, z, nrho = read_2d_fields(pic_info, fname, **kwargs)
fname = root_dir + 'data/Ay.gda'
# x, z, Ay = read_2d_fields(pic_info, fname, **kwargs)
nx, = x.shape
nz, = z.shape
xs, ys = 0.1, 0.15
w1, h1 = 0.85, 0.8
fig = plt.figure(figsize=[10, 5])
ax1 = fig.add_axes([xs, ys, w1, h1])
kwargs_plot = {"xstep": 2, "zstep": 2, "vmin": 0.0, "vmax": 10}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1 = plot_2d_contour(x, z, nrho, ax1, fig, is_cbar=0, **kwargs_plot)
p1.set_cmap(plt.cm.get_cmap('jet'))
# ax1.contour(x[0:nx:xstep], z[0:nz:zstep], Ay[0:nz:zstep, 0:nx:xstep],
# colors='black', linewidths=0.5)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=20)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax1.tick_params(labelsize=16)
t_wci = ct * pic_info.dt_fields
title = r'$t = ' + "{:10.1f}".format(t_wci) + '/\Omega_{ci}$'
ax1.text(0.02,
0.9,
title,
color='white',
fontsize=20,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none',
pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax1.transAxes)
nrho = 'n' + species
fdir = '../img/' + nrho + '/'
mkdir_p(fdir)
fname = fdir + nrho + '_' + str(ct) + '.jpg'
fig.savefig(fname, dpi=200)
plt.show()
def plot_number_density(run_name, root_dir, pic_info, species, ct):
"""Plot particle number density
"""
kwargs = {"current_time": ct, "xl": 0, "xr": 1000, "zb": -250, "zt": 250}
fname = root_dir + 'data/n' + species + '.gda'
# fname = root_dir + 'data/jy.gda'
x, z, nrho = read_2d_fields(pic_info, fname, **kwargs)
fname = root_dir + 'data/Ay.gda'
# x, z, Ay = read_2d_fields(pic_info, fname, **kwargs)
nx, = x.shape
nz, = z.shape
xs, ys = 0.1, 0.15
w1, h1 = 0.85, 0.8
fig = plt.figure(figsize=[10, 5])
ax1 = fig.add_axes([xs, ys, w1, h1])
kwargs_plot = {"xstep": 2, "zstep": 2, "vmin": 0.0, "vmax": 10}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1 = plot_2d_contour(x, z, nrho, ax1, fig, is_cbar=0, **kwargs_plot)
p1.set_cmap(plt.cm.get_cmap('jet'))
# ax1.contour(x[0:nx:xstep], z[0:nz:zstep], Ay[0:nz:zstep, 0:nx:xstep],
# colors='black', linewidths=0.5)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=20)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax1.tick_params(labelsize=16)
t_wci = ct * pic_info.dt_fields
title = r'$t = ' + "{:10.1f}".format(t_wci) + '/\Omega_{ci}$'
ax1.text(0.02, 0.9, title, color='white', fontsize=20,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left', verticalalignment='center',
transform=ax1.transAxes)
nrho = 'n' + species
fdir = '../img/' + nrho + '/'
mkdir_p(fdir)
fname = fdir + nrho + '_' + str(ct) + '.jpg'
fig.savefig(fname, dpi=200)
plt.show()
def plot_ptl_traj(filename, pic_info, species, iptl, mint, maxt):
"""Plot particle trajectory information.
Args:
filename: the filename to read the data.
pic_info: namedtuple for the PIC simulation information.
species: particle species. 'e' for electron. 'i' for ion.
iptl: particle ID.
mint, maxt: minimum and maximum time for plotting.
"""
ptl_traj = read_traj_data(filename)
gama = np.sqrt(ptl_traj.ux**2 + ptl_traj.uy**2 + ptl_traj.uz**2 + 1.0)
mime = pic_info.mime
# de scale to di scale
ptl_x = ptl_traj.x / math.sqrt(mime)
ptl_y = ptl_traj.y / math.sqrt(mime)
ptl_z = ptl_traj.z / math.sqrt(mime)
# 1/wpe to 1/wci
t = ptl_traj.t * pic_info.dtwci / pic_info.dtwpe
xl, xr = 0, 50
zb, zt = -20, 20
kwargs = {"current_time": 65, "xl": xl, "xr": xr, "zb": zb, "zt": zt}
fname = "../../data/uex.gda"
x, z, uex = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/ne.gda"
x, z, ne = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/uix.gda"
x, z, uix = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/ni.gda"
x, z, ni = read_2d_fields(pic_info, fname, **kwargs)
ux = (uex * ne + uix * ni * pic_info.mime) / (ne + ni * pic_info.mime)
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
nx, = x.shape
nz, = z.shape
width = 8.0
height = 8.0
fig = plt.figure(figsize=[width, height])
w1, h1 = 0.74, 0.42
xs, ys = 0.13, 0.98 - h1
ax1 = fig.add_axes([xs, ys, w1, h1])
kwargs_plot = {
"xstep": 2,
"zstep": 2,
"is_log": False,
"vmin": -1.0,
"vmax": 1.0
}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
va = 0.2 # Alfven speed
im1, cbar1 = plot_2d_contour(x, z, ux / va, ax1, fig, **kwargs_plot)
im1.set_cmap(plt.cm.seismic)
ax1.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.tick_params(labelsize=20)
ax1.tick_params(axis='x', labelbottom='off')
ax1.set_xlim([xl, xr])
ax1.set_ylim([zb, zt])
ax1.set_ylabel(r'$z/d_i$', fontdict=font)
cbar1.ax.tick_params(labelsize=20)
cbar1.ax.set_ylabel(r'$u_x/V_A$', fontdict=font, fontsize=24)
tstop = 1990
p1 = ax1.plot(ptl_x[0:tstop], ptl_z[0:tstop], linewidth=2, color='k')
gap = 0.04
ys -= h1 + gap
ax2 = fig.add_axes([xs, ys, w1 * 0.98 - 0.05 / width, h1])
eth = pic_info.vthi**2 * 3
p2 = ax2.plot(ptl_x[0:tstop], (gama[0:tstop] - 1.0) / eth,
color='k',
linewidth=2)
ax2.set_xlabel(r'$x/d_i$', fontdict=font)
ax2.set_ylabel(r'$E/E_{\text{thi}}$', fontdict=font)
ax2.tick_params(labelsize=20)
# ax2.tick_params(axis='x', labelbottom='off')
xmin, xmax = ax1.get_xlim()
ax2.set_xlim([xmin, xmax])
if not os.path.isdir('../img/'):
os.makedirs('../img/')
fname = '../img/' + 'traj_' + species + '_' + str(iptl).zfill(4) + '_1.jpg'
fig.savefig(fname, dpi=300)
plt.show()
def plot_jy_guide():
"""Plot jy contour for the runs with guide field
Args:
run_name: the name of this run.
root_dir: the root directory of this run.
pic_info: PIC simulation information in a namedtuple.
"""
cts = [40, 20, 20, 20, 30]
var = 'ez'
vmin, vmax = -0.2, 0.2
ticks = np.arange(-0.2, 0.25, 0.1)
cmap = plt.cm.get_cmap('seismic')
label = r'$E_z$'
# # cmap = cmaps.inferno
# var = 'jy'
# vmin, vmax = -0.2, 0.5
# ticks = np.arange(-0.2, 0.6, 0.2)
# cmap = plt.cm.get_cmap('jet')
# label = r'$j_y$'
# var = 'by'
# vmin, vmax = -0.2, 0.5
# ticks = np.arange(-0.2, 0.6, 0.2)
# cmap = plt.cm.get_cmap('jet')
# label = r'$B_y$'
base_dirs, run_names = guide_field_runs()
bdir = base_dirs[0]
run_name = run_names[0]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
xl, xr = 50, 150
kwargs = {"current_time": cts[0], "xl": xl, "xr": xr, "zb": -10, "zt": 10}
fname = bdir + 'data/' + var + '.gda'
x, z, jy1 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay1 = read_2d_fields(pic_info, fname, **kwargs)
kwargs = {"current_time": cts[1], "xl": xl, "xr": xr, "zb": -10, "zt": 10}
bdir = base_dirs[1]
run_name = run_names[1]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = bdir + 'data/' + var + '.gda'
x, z, jy2 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay2 = read_2d_fields(pic_info, fname, **kwargs)
kwargs = {"current_time": cts[2], "xl": xl, "xr": xr, "zb": -10, "zt": 10}
bdir = base_dirs[2]
run_name = run_names[2]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = bdir + 'data/' + var + '.gda'
x, z, jy3 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay3 = read_2d_fields(pic_info, fname, **kwargs)
kwargs = {"current_time": cts[3], "xl": xl, "xr": xr, "zb": -10, "zt": 10}
bdir = base_dirs[3]
run_name = run_names[3]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = bdir + 'data/' + var + '.gda'
x, z, jy4 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay4 = read_2d_fields(pic_info, fname, **kwargs)
kwargs = {"current_time": cts[4], "xl": xl, "xr": xr, "zb": -10, "zt": 10}
bdir = base_dirs[4]
run_name = run_names[4]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = bdir + 'data/' + var + '.gda'
x, z, jy5 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay5 = read_2d_fields(pic_info, fname, **kwargs)
nx, = x.shape
nz, = z.shape
width = 0.8
height = 0.16
xs = 0.1
ys = 0.98 - height
gap = 0.025
fig = plt.figure(figsize=[10, 8])
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": vmin, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x, z, jy1, ax1, fig, **kwargs_plot)
p1.set_cmap(cmap)
ax1.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay1[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax1.tick_params(labelsize=16)
cbar1.ax.set_ylabel(label, fontdict=font, fontsize=20)
cbar1.set_ticks(ticks)
cbar1.ax.tick_params(labelsize=16)
ax1.tick_params(axis='x', labelbottom='off')
ys -= height + gap
ax2 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": vmin, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p2, cbar2 = plot_2d_contour(x, z, jy2, ax2, fig, **kwargs_plot)
p2.set_cmap(cmap)
ax2.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay2[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax2.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax2.tick_params(labelsize=16)
cbar2.ax.set_ylabel(label, fontdict=font, fontsize=20)
cbar2.set_ticks(ticks)
cbar2.ax.tick_params(labelsize=16)
ax2.tick_params(axis='x', labelbottom='off')
ys -= height + gap
ax3 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": vmin, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p3, cbar3 = plot_2d_contour(x, z, jy3, ax3, fig, **kwargs_plot)
p3.set_cmap(cmap)
ax3.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay3[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
# ax3.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=20)
ax3.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax3.tick_params(labelsize=16)
cbar3.ax.set_ylabel(label, fontdict=font, fontsize=20)
cbar3.set_ticks(ticks)
cbar3.ax.tick_params(labelsize=16)
ax3.tick_params(axis='x', labelbottom='off')
ys -= height + gap
ax4 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": vmin, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p4, cbar4 = plot_2d_contour(x, z, jy4, ax4, fig, **kwargs_plot)
p4.set_cmap(cmap)
ax4.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay4[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
# ax4.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=20)
ax4.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax4.tick_params(labelsize=16)
cbar4.ax.set_ylabel(label, fontdict=font, fontsize=20)
cbar4.set_ticks(ticks)
cbar4.ax.tick_params(labelsize=16)
ax4.tick_params(axis='x', labelbottom='off')
ys -= height + gap
ax5 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": vmin, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p5, cbar5 = plot_2d_contour(x, z, jy5, ax5, fig, **kwargs_plot)
p5.set_cmap(cmap)
ax5.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay5[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax5.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=20)
ax5.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax5.tick_params(labelsize=16)
cbar5.ax.set_ylabel(label, fontdict=font, fontsize=20)
cbar5.set_ticks(ticks)
cbar5.ax.tick_params(labelsize=16)
axs = [ax1, ax2, ax3, ax4, ax5]
bgs = [0.0, 0.2, 0.5, 1.0, 4.0]
for ax, bg in zip(axs, bgs):
tname = r'$B_g = ' + str(bg) + '$'
ax.text(
0.02,
0.8,
tname,
color='k',
fontsize=24,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
fname = '../img/thesis/' + var + '.jpg'
fig.savefig(fname, dpi=200)
plt.show()
def plot_jy_multi():
"""Plot the jy for multiple runs
"""
base_dirs, run_names = ApJ_long_paper_runs()
bdir = base_dirs[0]
run_name = run_names[0]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
kwargs = {"current_time": 11, "xl": 0, "xr": 200, "zb": -10, "zt": 10}
fname = bdir + 'data/jy.gda'
x, z, jy1 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay1 = read_2d_fields(pic_info, fname, **kwargs)
bdir = base_dirs[1]
run_name = run_names[1]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = bdir + 'data/jy.gda'
x, z, jy2 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay2 = read_2d_fields(pic_info, fname, **kwargs)
bdir = base_dirs[3]
run_name = run_names[3]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = bdir + 'data/jy.gda'
x, z, jy3 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay3 = read_2d_fields(pic_info, fname, **kwargs)
nx, = x.shape
nz, = z.shape
width = 0.74
height = 0.25
xs = 0.13
ys = 0.97 - height
gap = 0.05
fig = plt.figure(figsize=[7, 5])
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": -0.5, "vmax": 0.5}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x, z, jy1, ax1, fig, **kwargs_plot)
p1.set_cmap(plt.cm.get_cmap('seismic'))
ax1.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay1[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax1.tick_params(labelsize=16)
cbar1.ax.set_ylabel(r'$j_y$', fontdict=font, fontsize=20)
cbar1.set_ticks(np.arange(-0.4, 0.5, 0.2))
cbar1.ax.tick_params(labelsize=16)
ax1.tick_params(axis='x', labelbottom='off')
ys -= height + gap
ax2 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": -0.5, "vmax": 0.5}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p2, cbar2 = plot_2d_contour(x, z, jy2, ax2, fig, **kwargs_plot)
p2.set_cmap(plt.cm.get_cmap('seismic'))
ax2.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay2[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax2.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax2.tick_params(labelsize=16)
cbar2.ax.set_ylabel(r'$j_y$', fontdict=font, fontsize=20)
cbar2.set_ticks(np.arange(-0.4, 0.5, 0.2))
cbar2.ax.tick_params(labelsize=16)
ax2.tick_params(axis='x', labelbottom='off')
ys -= height + gap
ax3 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": -1.0, "vmax": 1.0}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p3, cbar3 = plot_2d_contour(x, z, jy3, ax3, fig, **kwargs_plot)
p3.set_cmap(plt.cm.get_cmap('seismic'))
ax3.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay3[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax3.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=20)
ax3.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax3.tick_params(labelsize=16)
cbar3.ax.set_ylabel(r'$j_y$', fontdict=font, fontsize=20)
cbar3.set_ticks(np.arange(-1.0, 1.1, 0.5))
cbar3.ax.tick_params(labelsize=16)
ax1.text(
0.02,
0.78,
r'R8 $\beta_e=0.2$',
color='k',
fontsize=20,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax1.transAxes)
ax2.text(
0.02,
0.78,
r'R7 $\beta_e=0.07$',
color='k',
fontsize=20,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax2.transAxes)
ax3.text(
0.02,
0.78,
r'R6 $\beta_e=0.007$',
color='k',
fontsize=20,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax3.transAxes)
fig.savefig('../img/jy_multi.jpg', dpi=300)
plt.show()
def cal_phi_parallel(pic_info):
"""Calculate parallel potential defined by Jan Egedal.
Args:
pic_info: namedtuple for the PIC simulation information.
"""
kwargs = {"current_time": 160, "xl": 0, "xr": 200, "zb": -50, "zt": 50}
x, z, Ay = contour_plots.read_2d_fields(pic_info, "../data/Ay.gda",
**kwargs)
x, z, Bx = contour_plots.read_2d_fields(pic_info, "../data/bx.gda",
**kwargs)
x, z, By = contour_plots.read_2d_fields(pic_info, "../data/by.gda",
**kwargs)
xarr, zarr, Bz = contour_plots.read_2d_fields(pic_info, "../data/bz.gda",
**kwargs)
x, z, Ex = contour_plots.read_2d_fields(pic_info, "../data/ex.gda",
**kwargs)
x, z, Ey = contour_plots.read_2d_fields(pic_info, "../data/ey.gda",
**kwargs)
x, z, Ez = contour_plots.read_2d_fields(pic_info, "../data/ez.gda",
**kwargs)
absB = np.sqrt(Bx * Bx + By * By + Bz * Bz)
Epara = (Ex * Bx + Ey * By + Ez * Bz) / absB
etot = np.sqrt(Ex * Ex + Ey * Ey + Ez * Ez)
nx, = x.shape
nz, = z.shape
print nx, nz
width = 0.78
height = 0.75
xs = 0.14
xe = 0.94 - xs
ys = 0.9 - height
#fig = plt.figure(figsize=(7,5))
fig = plt.figure(figsize=(7, 2))
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 2, "zstep": 2}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
#p1, cbar1 = contour_plots.plot_2d_contour(xarr, zarr, etot,
# ax1, fig, **kwargs_plot)
#p1.set_cmap(plt.cm.seismic)
cs = ax1.contour(xarr[0:nx:xstep],
zarr[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='white',
linewidths=0.5,
levels=np.arange(150, 252, 1))
xlist = []
zlist = []
dx_di = pic_info.dx_di
dz_di = pic_info.dz_di
fig, ax = plt.subplots()
#fig, ax2 = plt.subplots()
phi_parallel = np.zeros((nz, nx))
#for csp in cs.collections[65:70]:
for csp in cs.collections[0:10]:
for p in csp.get_paths():
v = p.vertices
x = v[:, 0]
z = v[:, 1]
if (math.fabs(x[0] - x[-1]) > dx_di
or math.fabs(z[0] - z[-1]) > dz_di):
xlist.extend(x)
zlist.extend(z)
lenx = len(x)
phip = np.zeros(lenx)
epara = np.zeros(lenx)
if (x[-1] < x[0]):
x = x[::-1]
z = z[::-1]
for i in range(1, lenx):
dx = x[i] - x[i - 1]
dz = z[i] - z[i - 1]
indices_bl, indices_tr, delta = grid_indices(
x[i] - xarr[0], 0, z[i] - zarr[0], nx, 1, nz, dx_di, 1,
dz_di)
ix1 = indices_bl[0]
iz1 = indices_bl[2]
ix2 = indices_tr[0]
iz2 = indices_tr[2]
offsetx = delta[0]
offsetz = delta[2]
v1 = (1.0 - offsetx) * (1.0 - offsetz)
v2 = offsetx * (1.0 - offsetz)
v3 = offsetx * offsetz
v4 = (1.0 - offsetx) * offsetz
bx = Bx[iz1, ix1] * v1 + Bx[iz1, ix2] * v2 + Bx[
iz2, ix2] * v3 + Bx[iz2, ix1] * v4
by = By[iz1, ix1] * v1 + By[iz1, ix2] * v2 + By[
iz2, ix2] * v3 + By[iz2, ix1] * v4
bz = Bz[iz1, ix1] * v1 + Bz[iz1, ix2] * v2 + Bz[
iz2, ix2] * v3 + Bz[iz2, ix1] * v4
ex = Ex[iz1, ix1] * v1 + Ex[iz1, ix2] * v2 + Ex[
iz2, ix2] * v3 + Ex[iz2, ix1] * v4
ey = Ey[iz1, ix1] * v1 + Ey[iz1, ix2] * v2 + Ey[
iz2, ix2] * v3 + Ey[iz2, ix1] * v4
ez = Ez[iz1, ix1] * v1 + Ez[iz1, ix2] * v2 + Ez[
iz2, ix2] * v3 + Ez[iz2, ix1] * v4
epara[i] = Epara[iz1,ix1]*v1 + Epara[iz1,ix2]*v2 + \
Epara[iz2,ix2]*v3 + Epara[iz2,ix1]*v4
dy = by * dx / bx
#btot = math.sqrt(bx*bx + by*by + bz*bz)
#ds = math.sqrt(dx*dx + dy*dy + dz*dz)
#print math.acos((bx*dx + by*dy + bz*dz) / (btot*ds))
phip[i] = phip[i - 1] + ex * dx + ey * dy + ez * dz
ix = int(math.floor(x[i] / dx_di))
iz = int(math.floor((z[i] - zarr[0]) / dz_di))
phi_parallel[iz, ix] = phip[i]
ax.plot(x, phip)
#ax.plot(x, epara)
print np.max(phi_parallel), np.min(phi_parallel)
fig = plt.figure(figsize=(7, 2))
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 2, "zstep": 2}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = contour_plots.plot_2d_contour(xarr, zarr, phi_parallel, ax1,
fig, **kwargs_plot)
p1.set_cmap(plt.cm.seismic)
plt.show()
def parallel_potential(pic_info):
"""Calculate parallel potential defined by Jan Egedal.
Args:
pic_info: namedtuple for the PIC simulation information.
"""
kwargs = {"current_time": 40, "xl": 0, "xr": 200, "zb": -50, "zt": 50}
x, z, Ay = contour_plots.read_2d_fields(pic_info, "../data/Ay.gda",
**kwargs)
x, z, Ex = contour_plots.read_2d_fields(pic_info, "../data/ex.gda",
**kwargs)
x, z, Ey = contour_plots.read_2d_fields(pic_info, "../data/ey.gda",
**kwargs)
x, z, Ez = contour_plots.read_2d_fields(pic_info, "../data/ez.gda",
**kwargs)
x, z, Bx = contour_plots.read_2d_fields(pic_info, "../data/bx.gda",
**kwargs)
x, z, By = contour_plots.read_2d_fields(pic_info, "../data/by.gda",
**kwargs)
xarr, zarr, Bz = contour_plots.read_2d_fields(pic_info, "../data/bz.gda",
**kwargs)
absB = np.sqrt(Bx**2 + By**2 + Bz**2)
Epara = (Ex * Bx + Ey * By + Ez * Bz) / absB
nx, = x.shape
nz, = z.shape
phi_parallel = np.zeros((nz, nx))
dx_di = pic_info.dx_di
dz_di = pic_info.dz_di
#deltas = math.sqrt(dx_di**2 + dz_di**2)
#hds = deltas * 0.5
hmax = dx_di * 100
h = hmax / 4.0
# Cash-Karp parameters
a = [0.0, 0.2, 0.3, 0.6, 1.0, 0.875]
b = [[], [0.2], [3.0 / 40.0, 9.0 / 40.0], [0.3, -0.9, 1.2],
[-11.0 / 54.0, 2.5, -70.0 / 27.0, 35.0 / 27.0],
[
1631.0 / 55296.0, 175.0 / 512.0, 575.0 / 13824.0,
44275.0 / 110592.0, 253.0 / 4096.0
]]
c = [37.0 / 378.0, 0.0, 250.0 / 621.0, 125.0 / 594.0, 0.0, 512.0 / 1771.0]
dc = [
c[0] - 2825.0 / 27648.0, c[1] - 0.0, c[2] - 18575.0 / 48384.0,
c[3] - 13525.0 / 55296.0, c[4] - 277.00 / 14336.0, c[5] - 0.25
]
def F(t, x, z):
indices_bl, indices_tr, delta = grid_indices(x, 0, z, nx, 1, nz, dx_di,
1, dz_di)
ix1 = indices_bl[0]
iz1 = indices_bl[2]
ix2 = indices_tr[0]
iz2 = indices_tr[2]
offsetx = delta[0]
offsetz = delta[2]
v1 = (1.0 - offsetx) * (1.0 - offsetz)
v2 = offsetx * (1.0 - offsetz)
v3 = offsetx * offsetz
v4 = (1.0 - offsetx) * offsetz
if (ix1 < nx and ix2 < nx and iz1 < nz and iz2 < nz):
bx = Bx[iz1, ix1] * v1 + Bx[iz1, ix2] * v2 + Bx[
iz2, ix2] * v3 + Bx[iz2, ix1] * v4
by = By[iz1, ix1] * v1 + By[iz1, ix2] * v2 + By[
iz2, ix2] * v3 + By[iz2, ix1] * v4
bz = Bz[iz1, ix1] * v1 + Bz[iz1, ix2] * v2 + Bz[
iz2, ix2] * v3 + Bz[iz2, ix1] * v4
ex = Ex[iz1, ix1] * v1 + Ex[iz1, ix2] * v2 + Ex[
iz2, ix2] * v3 + Ex[iz2, ix1] * v4
ey = Ey[iz1, ix1] * v1 + Ey[iz1, ix2] * v2 + Ey[
iz2, ix2] * v3 + Ey[iz2, ix1] * v4
ez = Ez[iz1, ix1] * v1 + Ez[iz1, ix2] * v2 + Ez[
iz2, ix2] * v3 + Ez[iz2, ix1] * v4
absB = math.sqrt(bx**2 + bz**2)
deltax1 = bx / absB
deltay1 = by * deltax1 / bx
deltaz1 = bz / absB
else:
ex = 0
ey = 0
ez = 0
deltax1 = 0
deltay1 = 0
deltaz1 = 0
return (deltax1, deltay1, deltaz1, ex, ey, ez)
tol = 1e-5
for i in range(2900, 3300):
print i
x0 = xarr[i] - xarr[0]
#for k in range(0,nz,8):
for k in range(950, 1098):
z0 = zarr[k] - zarr[0]
y = [x0, z0]
nstep = 0
xlist = [x0]
zlist = [z0]
t = 0
x = x0
z = z0
while (x >= 0 and x <= (xarr[-1] - xarr[0]) and z >= 0 and z <=
(zarr[-1] - zarr[0]) and t < 4E2):
# Compute k[i] function values.
kx = [None] * 6
ky = [None] * 6
kz = [None] * 6
kx[0], ky[0], kz[0], ex0, ey0, ez0 = F(t, x, z)
kx[1], ky[1], kz[1], ex1, ey1, ez1 = F(
t + a[1] * h, x + h * (kx[0] * b[1][0]),
z + h * (kz[0] * b[1][0]))
kx[2], ky[2], kz[2], ex2, ey2, ez2 = F(
t + a[2] * h, x + h * (kx[0] * b[2][0] + kx[1] * b[2][1]),
z + h * (kz[0] * b[2][0] + kz[1] * b[2][1]))
kx[3], ky[3], kz[3], ex3, ey3, ez3 = F(
t + a[3] * h, x + h *
(kx[0] * b[3][0] + kx[1] * b[3][1] + kx[2] * b[3][2]), z +
h * (kz[0] * b[3][0] + kz[1] * b[3][1] + kz[2] * b[3][2]))
kx[4], ky[4], kz[4], ex4, ey4, ez4 = F(
t + a[4] * h, x + h * (kx[0] * b[4][0] + kx[1] * b[4][1] +
kx[2] * b[4][2] + kx[3] * b[4][3]),
z + h * (kz[0] * b[4][0] + kz[1] * b[4][1] +
kz[2] * b[4][2] + kz[3] * b[4][3]))
kx[5], ky[5], kz[5], ex5, ey5, ez5 = F(
t + a[5] * h, x + h *
(kx[0] * b[5][0] + kx[1] * b[5][1] + kx[2] * b[5][2] +
kx[3] * b[5][3] + kx[4] * b[5][4]), z + h *
(kz[0] * b[5][0] + kz[1] * b[5][1] + kz[2] * b[5][2] +
kz[3] * b[5][3] + kz[4] * b[5][4]))
# Estimate current error and current maximum error.
E = norm([
h * (kx[0] * dc[0] + kx[1] * dc[1] + kx[2] * dc[2] +
kx[3] * dc[3] + kx[4] * dc[4] + kx[5] * dc[5]),
h * (kz[0] * dc[0] + kz[1] * dc[1] + kz[2] * dc[2] +
kz[3] * dc[3] + kz[4] * dc[4] + kz[5] * dc[5])
])
Emax = tol * max(norm(y), 1.0)
# Update solution if error is OK.
if E < Emax:
t += h
dx = h * (kx[0] * c[0] + kx[1] * c[1] + kx[2] * c[2] +
kx[3] * c[3] + kx[4] * c[4] + kx[5] * c[5])
dy = h * (ky[0] * c[0] + ky[1] * c[1] + ky[2] * c[2] +
ky[3] * c[3] + ky[4] * c[4] + ky[5] * c[5])
dz = h * (kz[0] * c[0] + kz[1] * c[1] + kz[2] * c[2] +
kz[3] * c[3] + kz[4] * c[4] + kz[5] * c[5])
y[0] += dx
y[1] += dz
x = y[0]
z = y[1]
xlist.append(x)
zlist.append(z)
phi_parallel[k, i] += \
h*(kx[0]*c[0]*ex0+kx[1]*c[1]*ex1+kx[2]*c[2]*ex2+ \
kx[3]*c[3]*ex3+kx[4]*c[4]*ex4+kx[5]*c[5]*ex5) + \
h*(ky[0]*c[0]*ey0+ky[1]*c[1]*ey1+ky[2]*c[2]*ey2+ \
ky[3]*c[3]*ey3+ky[4]*c[4]*ey4+ky[5]*c[5]*ey5) + \
h*(kz[0]*c[0]*ez0+kz[1]*c[1]*ez1+kz[2]*c[2]*ez2+ \
kz[3]*c[3]*ez3+kz[4]*c[4]*ez4+kz[5]*c[5]*ez5)
#out += [(t, list(y))]
# Update step size
if E > 0.0:
h = min(hmax, 0.85 * h * (Emax / E)**0.2)
if (t > 395):
phi_parallel[k, i] = 0
#
# deltax1, deltaz1, x1, z1, ex1, ez1 = middle_step_rk4(x, z, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, hds)
# deltax2, deltaz2, x2, z2, ex2, ez2 = middle_step_rk4(x1, z1, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, hds)
# deltax3, deltaz3, x3, z3, ex3, ez3 = middle_step_rk4(x, z, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, deltas)
# deltax4, deltaz4, x4, z4, ex4, ez4 = middle_step_rk4(x, z, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, hds)
#
# deltax = deltas/6 * (deltax1 + 2*deltax2 + 2*deltax3 + deltax4)
# deltaz = deltas/6 * (deltaz1 + 2*deltaz2 + 2*deltaz3 + deltaz4)
# x += deltax
# z += deltaz
# total_lengh += deltas
# xlist.append(x)
# zlist.append(z)
# nstep += 1
# phi_parallel[k, i] += deltas/6 * (ex1*deltax1 + ez1*deltaz1 +
# 2.0*ex2*deltax2 + 2.0*ez2*deltaz2 +
# 2.0*ex3*deltax3 + 2.0*ez3*deltaz3 +
# ex4*deltax4 + ez4*deltaz4)
# length = math.sqrt((x-x0)**2 + (z-z0)**2)
# if (length < dx_di and nstep > 20):
# phi_parallel[k, i] = 0
# break
# #print k, nstep, total_lengh
# phi_parallel = np.fromfile('phi_parallel.dat')
# phi_parallel.tofile('phi_parallel.dat')
# print np.max(phi_parallel), np.min(phi_parallel)
width = 0.78
height = 0.75
xs = 0.14
xe = 0.94 - xs
ys = 0.9 - height
#fig = plt.figure(figsize=(7,5))
fig = plt.figure(figsize=(7, 2))
ax1 = fig.add_axes([xs, ys, width, height])
#kwargs_plot = {"xstep":2, "zstep":2, "vmin":-0.05, "vmax":0.05}
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": -0.2, "vmax": 0.2}
#kwargs_plot = {"xstep":1, "zstep":1, "vmin":-0.05, "vmax":0.05}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
phi_parallel = np.reshape(phi_parallel, (nz, nx))
p1, cbar1 = contour_plots.plot_2d_contour(xarr, zarr, phi_parallel, ax1,
fig, **kwargs_plot)
# xmin = np.min(xarr)
# zmin = np.min(zarr)
# xmax = np.max(xarr)
# zmax = np.max(zarr)
# p1 = ax1.imshow(phi_parallel[0:nz:8, 0:nx:8], cmap=plt.cm.jet,
# extent=[xmin, xmax, zmin, zmax],
# aspect='auto', origin='lower',
# vmin=kwargs_plot["vmin"], vmax=kwargs_plot["vmax"],
# interpolation='quadric')
p1.set_cmap(plt.cm.seismic)
cs = ax1.contour(xarr[0:nx:xstep],
zarr[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5,
levels=np.arange(1, 250, 10))
#cbar1.set_ticks(np.arange(-0.8, 1.0, 0.4))
#ax1.tick_params(axis='x', labelbottom='off')
plt.show()
def bulk_energy(pic_info, species, current_time):
"""Bulk energy and internal energy.
Args:
pic_info: namedtuple for the PIC simulation information.
species: 'e' for electrons, 'i' for ions.
current_time: current time frame.
"""
kwargs = {
"current_time": current_time,
"xl": 0,
"xr": 200,
"zb": -20,
"zt": 20
}
fname = "../../data/u" + species + "x.gda"
x, z, ux = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "y.gda"
x, z, uy = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "z.gda"
x, z, uz = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/n" + species + ".gda"
x, z, nrho = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/p" + species + "-xx.gda"
x, z, pxx = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/p" + species + "-yy.gda"
x, z, pyy = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/p" + species + "-zz.gda"
x, z, pzz = read_2d_fields(pic_info, fname, **kwargs)
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
if species == 'e':
ptl_mass = 1.0
else:
ptl_mass = pic_info.mime
internal_ene = (pxx + pyy + pzz) * 0.5
bulk_ene = 0.5 * ptl_mass * nrho * (ux**2 + uy**2 + uz**2)
# gama = 1.0 / np.sqrt(1.0 - (ux**2 + uy**2 + uz**2))
# gama = np.sqrt(1.0 + ux**2 + uy**2 + uz**2)
# bulk_ene2 = (gama - 1) * ptl_mass * nrho
# print np.sum(bulk_ene), np.sum(bulk_ene2)
nx, = x.shape
nz, = z.shape
width = 0.75
height = 0.23
xs = 0.12
ys = 0.93 - height
fig = plt.figure(figsize=[10, 6])
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {
"xstep": 1,
"zstep": 1,
"is_log": True,
"vmin": 0.1,
"vmax": 10.0
}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x, z, bulk_ene / internal_ene, ax1, fig,
**kwargs_plot)
p1.set_cmap(plt.cm.seismic)
ax1.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='white',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
# ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(axis='x', labelbottom='off')
ax1.tick_params(labelsize=20)
cbar1.ax.set_ylabel(r'$K/u$', fontdict=font, fontsize=24)
cbar1.ax.tick_params(labelsize=20)
t_wci = current_time * pic_info.dt_fields
title = r'$t = ' + "{:10.1f}".format(t_wci) + '/\Omega_{ci}$'
ax1.set_title(title, fontdict=font, fontsize=24)
ys -= height + 0.05
ax2 = fig.add_axes([xs, ys, width, height])
if species == 'e':
vmax = 0.2
else:
vmax = 0.8
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": 0, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p2, cbar2 = plot_2d_contour(x, z, bulk_ene, ax2, fig, **kwargs_plot)
p2.set_cmap(plt.cm.nipy_spectral)
ax2.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='white',
linewidths=0.5)
ax2.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax2.tick_params(axis='x', labelbottom='off')
ax2.tick_params(labelsize=20)
cbar2.ax.set_ylabel(r'$K$', fontdict=font, fontsize=24)
if species == 'e':
cbar2.set_ticks(np.arange(0, 0.2, 0.04))
else:
cbar2.set_ticks(np.arange(0, 0.9, 0.2))
cbar2.ax.tick_params(labelsize=20)
ys -= height + 0.05
ax3 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": 0, "vmax": 0.8}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p3, cbar3 = plot_2d_contour(x, z, internal_ene, ax3, fig, **kwargs_plot)
p3.set_cmap(plt.cm.nipy_spectral)
ax3.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='white',
linewidths=0.5)
ax3.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax3.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax3.tick_params(labelsize=20)
cbar3.ax.set_ylabel(r'$u$', fontdict=font, fontsize=24)
cbar3.set_ticks(np.arange(0, 0.9, 0.2))
cbar3.ax.tick_params(labelsize=20)
# plt.show()
if not os.path.isdir('../img/'):
os.makedirs('../img/')
if not os.path.isdir('../img/img_bulk_internal/'):
os.makedirs('../img/img_bulk_internal/')
dir = '../img/img_bulk_internal/'
fname = 'bulk_internal' + str(current_time).zfill(
3) + '_' + species + '.jpg'
fname = dir + fname
fig.savefig(fname, dpi=400)
plt.close()
def cal_phi_parallel(pic_info):
"""Calculate parallel potential defined by Jan Egedal.
Args:
pic_info: namedtuple for the PIC simulation information.
"""
kwargs = {"current_time": 160, "xl": 0, "xr": 200, "zb": -50, "zt": 50}
x, z, Ay = contour_plots.read_2d_fields(pic_info, "../data/Ay.gda",
**kwargs)
x, z, Bx = contour_plots.read_2d_fields(pic_info, "../data/bx.gda",
**kwargs)
x, z, By = contour_plots.read_2d_fields(pic_info, "../data/by.gda",
**kwargs)
xarr, zarr, Bz = contour_plots.read_2d_fields(pic_info, "../data/bz.gda",
**kwargs)
x, z, Ex = contour_plots.read_2d_fields(pic_info, "../data/ex.gda",
**kwargs)
x, z, Ey = contour_plots.read_2d_fields(pic_info, "../data/ey.gda",
**kwargs)
x, z, Ez = contour_plots.read_2d_fields(pic_info, "../data/ez.gda",
**kwargs)
absB = np.sqrt(Bx * Bx + By * By + Bz * Bz)
Epara = (Ex * Bx + Ey * By + Ez * Bz) / absB
etot = np.sqrt(Ex * Ex + Ey * Ey + Ez * Ez)
nx, = x.shape
nz, = z.shape
print nx, nz
width = 0.78
height = 0.75
xs = 0.14
xe = 0.94 - xs
ys = 0.9 - height
#fig = plt.figure(figsize=(7,5))
fig = plt.figure(figsize=(7, 2))
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 2, "zstep": 2}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
#p1, cbar1 = contour_plots.plot_2d_contour(xarr, zarr, etot,
# ax1, fig, **kwargs_plot)
#p1.set_cmap(plt.cm.seismic)
cs = ax1.contour(
xarr[0:nx:xstep],
zarr[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='white',
linewidths=0.5,
levels=np.arange(150, 252, 1))
xlist = []
zlist = []
dx_di = pic_info.dx_di
dz_di = pic_info.dz_di
fig, ax = plt.subplots()
#fig, ax2 = plt.subplots()
phi_parallel = np.zeros((nz, nx))
#for csp in cs.collections[65:70]:
for csp in cs.collections[0:10]:
for p in csp.get_paths():
v = p.vertices
x = v[:, 0]
z = v[:, 1]
if (math.fabs(x[0] - x[-1]) > dx_di or
math.fabs(z[0] - z[-1]) > dz_di):
xlist.extend(x)
zlist.extend(z)
lenx = len(x)
phip = np.zeros(lenx)
epara = np.zeros(lenx)
if (x[-1] < x[0]):
x = x[::-1]
z = z[::-1]
for i in range(1, lenx):
dx = x[i] - x[i - 1]
dz = z[i] - z[i - 1]
indices_bl, indices_tr, delta = grid_indices(
x[i] - xarr[0], 0, z[i] - zarr[0], nx, 1, nz, dx_di, 1,
dz_di)
ix1 = indices_bl[0]
iz1 = indices_bl[2]
ix2 = indices_tr[0]
iz2 = indices_tr[2]
offsetx = delta[0]
offsetz = delta[2]
v1 = (1.0 - offsetx) * (1.0 - offsetz)
v2 = offsetx * (1.0 - offsetz)
v3 = offsetx * offsetz
v4 = (1.0 - offsetx) * offsetz
bx = Bx[iz1, ix1] * v1 + Bx[iz1, ix2] * v2 + Bx[
iz2, ix2] * v3 + Bx[iz2, ix1] * v4
by = By[iz1, ix1] * v1 + By[iz1, ix2] * v2 + By[
iz2, ix2] * v3 + By[iz2, ix1] * v4
bz = Bz[iz1, ix1] * v1 + Bz[iz1, ix2] * v2 + Bz[
iz2, ix2] * v3 + Bz[iz2, ix1] * v4
ex = Ex[iz1, ix1] * v1 + Ex[iz1, ix2] * v2 + Ex[
iz2, ix2] * v3 + Ex[iz2, ix1] * v4
ey = Ey[iz1, ix1] * v1 + Ey[iz1, ix2] * v2 + Ey[
iz2, ix2] * v3 + Ey[iz2, ix1] * v4
ez = Ez[iz1, ix1] * v1 + Ez[iz1, ix2] * v2 + Ez[
iz2, ix2] * v3 + Ez[iz2, ix1] * v4
epara[i] = Epara[iz1,ix1]*v1 + Epara[iz1,ix2]*v2 + \
Epara[iz2,ix2]*v3 + Epara[iz2,ix1]*v4
dy = by * dx / bx
#btot = math.sqrt(bx*bx + by*by + bz*bz)
#ds = math.sqrt(dx*dx + dy*dy + dz*dz)
#print math.acos((bx*dx + by*dy + bz*dz) / (btot*ds))
phip[i] = phip[i - 1] + ex * dx + ey * dy + ez * dz
ix = int(math.floor(x[i] / dx_di))
iz = int(math.floor((z[i] - zarr[0]) / dz_di))
phi_parallel[iz, ix] = phip[i]
ax.plot(x, phip)
#ax.plot(x, epara)
print np.max(phi_parallel), np.min(phi_parallel)
fig = plt.figure(figsize=(7, 2))
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 2, "zstep": 2}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = contour_plots.plot_2d_contour(xarr, zarr, phi_parallel, ax1,
fig, **kwargs_plot)
p1.set_cmap(plt.cm.seismic)
plt.show()
def plot_traj(filename, pic_info, species, iptl, mint, maxt):
"""Plot particle trajectory information.
Args:
filename: the filename to read the data.
pic_info: namedtuple for the PIC simulation information.
species: particle species. 'e' for electron. 'i' for ion.
iptl: particle ID.
mint, maxt: minimum and maximum time for plotting.
"""
ptl_traj = read_traj_data(filename)
gama = np.sqrt(ptl_traj.ux**2 + ptl_traj.uy**2 + ptl_traj.uz**2 + 1.0)
mime = pic_info.mime
# de scale to di scale
ptl_x = ptl_traj.x / math.sqrt(mime)
ptl_y = ptl_traj.y / math.sqrt(mime)
ptl_z = ptl_traj.z / math.sqrt(mime)
# 1/wpe to 1/wci
t = ptl_traj.t * pic_info.dtwci / pic_info.dtwpe
xl, xr = 0, 200
zb, zt = -20, 20
kwargs = {"current_time": 50, "xl": xl, "xr": xr, "zb": zb, "zt": zt}
fname = "../../data/ey.gda"
x, z, data_2d = read_2d_fields(pic_info, fname, **kwargs)
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
nx, = x.shape
nz, = z.shape
# p1 = ax1.plot(ptl_x, gama-1.0, color='k')
# ax1.set_xlabel(r'$t\Omega_{ci}$', fontdict=font)
# ax1.set_ylabel(r'$E/m_ic^2$', fontdict=font)
# ax1.tick_params(labelsize=20)
width = 14.0
height = 8.0
fig = plt.figure(figsize=[width, height])
w1, h1 = 0.82, 0.27
xs, ys = 0.10, 0.98 - h1
ax1 = fig.add_axes([xs, ys, w1, h1])
kwargs_plot = {
"xstep": 2,
"zstep": 2,
"is_log": False,
"vmin": -1.0,
"vmax": 1.0
}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
im1, cbar1 = plot_2d_contour(x, z, data_2d, ax1, fig, **kwargs_plot)
im1.set_cmap(plt.cm.seismic)
ax1.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.tick_params(labelsize=20)
ax1.tick_params(axis='x', labelbottom='off')
ax1.set_xlim([xl, xr])
ax1.set_ylim([zb, zt])
ax1.set_ylabel(r'$z/d_i$', fontdict=font)
cbar1.ax.tick_params(labelsize=20)
cbar1.ax.set_ylabel(r'$B_y$', fontdict=font, fontsize=24)
p1 = ax1.scatter(ptl_x, ptl_z, s=0.5)
# ax2 = fig.add_axes([xs, ys, w1, h1])
# p2 = ax2.plot(t, gama-1.0, color='k')
# ax2.set_xlabel(r'$t\Omega_{ci}$', fontdict=font)
# ax2.set_ylabel(r'$E/m_ic^2$', fontdict=font)
# ax2.tick_params(labelsize=20)
gap = 0.04
ys -= h1 + gap
ax2 = fig.add_axes([xs, ys, w1 * 0.98 - 0.05 / width, h1])
p2 = ax2.plot(ptl_x, gama - 1.0, color='k')
ax2.set_ylabel(r'$E/m_ic^2$', fontdict=font)
ax2.tick_params(labelsize=20)
ax2.tick_params(axis='x', labelbottom='off')
xmin, xmax = ax2.get_xlim()
ys -= h1 + gap
ax3 = fig.add_axes([xs, ys, w1 * 0.98 - 0.05 / width, h1])
p3 = ax3.plot(ptl_x, ptl_y, color='k')
ax3.set_xlabel(r'$x/d_i$', fontdict=font)
ax3.set_ylabel(r'$y/d_i$', fontdict=font)
ax3.tick_params(labelsize=20)
ax1.set_xlim([xmin, xmax])
ax3.set_xlim([xmin, xmax])
if not os.path.isdir('../img/'):
os.makedirs('../img/')
dir = '../img/img_traj_' + species + '/'
if not os.path.isdir(dir):
os.makedirs(dir)
fname = dir + 'traj_' + species + '_' + str(iptl).zfill(4) + '_1.jpg'
fig.savefig(fname, dpi=300)
height = 15.0
fig = plt.figure(figsize=[width, height])
w1, h1 = 0.88, 0.135
xs, ys = 0.10, 0.98 - h1
gap = 0.025
dt = t[1] - t[0]
ct1 = int(mint / dt)
ct2 = int(maxt / dt)
ax1 = fig.add_axes([xs, ys, w1, h1])
p1 = ax1.plot(t[ct1:ct2], gama[ct1:ct2] - 1.0, color='k')
ax1.set_ylabel(r'$E/m_ic^2$', fontdict=font)
ax1.tick_params(labelsize=20)
ax1.tick_params(axis='x', labelbottom='off')
ax1.plot([mint, maxt], [0, 0], '--', color='k')
ax1.text(
0.4,
-0.07,
r'$x$',
color='red',
fontsize=32,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
ax1.text(
0.5,
-0.07,
r'$y$',
color='green',
fontsize=32,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
ax1.text(
0.6,
-0.07,
r'$z$',
color='blue',
fontsize=32,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
ys -= h1 + gap
ax2 = fig.add_axes([xs, ys, w1, h1])
p21 = ax2.plot(t[ct1:ct2], ptl_traj.ux[ct1:ct2], color='r', label=r'u_x')
p22 = ax2.plot(t[ct1:ct2], ptl_traj.uy[ct1:ct2], color='g', label=r'u_y')
p23 = ax2.plot(t[ct1:ct2], ptl_traj.uz[ct1:ct2], color='b', label=r'u_z')
ax2.plot([mint, maxt], [0, 0], '--', color='k')
ax2.set_ylabel(r'$u_x, u_y, u_z$', fontdict=font)
ax2.tick_params(labelsize=20)
ax2.tick_params(axis='x', labelbottom='off')
kernel = 9
tmax = np.max(t)
ys -= h1 + gap
ax31 = fig.add_axes([xs, ys, w1, h1])
ex = signal.medfilt(ptl_traj.ex, kernel_size=(kernel))
p31 = ax31.plot(t[ct1:ct2], ex[ct1:ct2], color='r', label=r'E_x')
ax31.set_ylabel(r'$E_x$', fontdict=font)
ax31.tick_params(labelsize=20)
ax31.tick_params(axis='x', labelbottom='off')
ax31.plot([mint, maxt], [0, 0], '--', color='k')
ys -= h1 + gap
ax32 = fig.add_axes([xs, ys, w1, h1])
ey = signal.medfilt(ptl_traj.ey, kernel_size=(kernel))
p32 = ax32.plot(t[ct1:ct2], ey[ct1:ct2], color='g', label=r'E_y')
ax32.set_ylabel(r'$E_y$', fontdict=font)
ax32.tick_params(labelsize=20)
ax32.tick_params(axis='x', labelbottom='off')
ax32.plot([mint, maxt], [0, 0], '--', color='k')
ys -= h1 + gap
ax33 = fig.add_axes([xs, ys, w1, h1])
ez = signal.medfilt(ptl_traj.ez, kernel_size=(kernel))
p33 = ax33.plot(t[ct1:ct2], ez[ct1:ct2], color='b', label=r'E_z')
ax33.set_ylabel(r'$E_z$', fontdict=font)
ax33.tick_params(labelsize=20)
ax33.tick_params(axis='x', labelbottom='off')
ax33.plot([mint, maxt], [0, 0], '--', color='k')
ys -= h1 + gap
ax4 = fig.add_axes([xs, ys, w1, h1])
p41 = ax4.plot(t[ct1:ct2], ptl_traj.bx[ct1:ct2], color='r', label=r'B_x')
p42 = ax4.plot(t[ct1:ct2], ptl_traj.by[ct1:ct2], color='g', label=r'B_y')
p43 = ax4.plot(t[ct1:ct2], ptl_traj.bz[ct1:ct2], color='b', label=r'B_z')
ax4.set_xlabel(r'$t\Omega_{ci}$', fontdict=font)
ax4.set_ylabel(r'$B_x, B_y, B_z$', fontdict=font)
ax4.tick_params(labelsize=20)
ax4.plot([mint, maxt], [0, 0], '--', color='k')
ax1.set_xlim([mint, maxt])
ax2.set_xlim([mint, maxt])
ax31.set_xlim([mint, maxt])
ax32.set_xlim([mint, maxt])
ax33.set_xlim([mint, maxt])
ax4.set_xlim([mint, maxt])
fname = dir + 'traj_' + species + '_' + str(iptl).zfill(4) + '_2.jpg'
fig.savefig(fname, dpi=300)
height = 6.0
fig = plt.figure(figsize=[width, height])
w1, h1 = 0.88, 0.4
xs, ys = 0.10, 0.97 - h1
gap = 0.05
dt = t[1] - t[0]
ct1 = int(mint / dt)
ct2 = int(maxt / dt)
if species == 'e':
charge = -1.0
else:
charge = 1.0
nt, = t.shape
jdote_x = ptl_traj.ux * ptl_traj.ex * charge / gama
jdote_y = ptl_traj.uy * ptl_traj.ey * charge / gama
jdote_z = ptl_traj.uz * ptl_traj.ez * charge / gama
# jdote_x = (ptl_traj.uy * ptl_traj.bz / gama -
# ptl_traj.uz * ptl_traj.by / gama + ptl_traj.ex) * charge
# jdote_y = (ptl_traj.uz * ptl_traj.bx / gama -
# ptl_traj.ux * ptl_traj.bz / gama + ptl_traj.ey) * charge
# jdote_z = (ptl_traj.ux * ptl_traj.by / gama -
# ptl_traj.uy * ptl_traj.bx / gama + ptl_traj.ez) * charge
dt = np.zeros(nt)
dt[0:nt - 1] = np.diff(t)
jdote_x_cum = np.cumsum(jdote_x) * dt
jdote_y_cum = np.cumsum(jdote_y) * dt
jdote_z_cum = np.cumsum(jdote_z) * dt
jdote_tot_cum = jdote_x_cum + jdote_y_cum + jdote_z_cum
ax1 = fig.add_axes([xs, ys, w1, h1])
p1 = ax1.plot(t[ct1:ct2], jdote_x[ct1:ct2], color='r')
p2 = ax1.plot(t[ct1:ct2], jdote_y[ct1:ct2], color='g')
p3 = ax1.plot(t[ct1:ct2], jdote_z[ct1:ct2], color='b')
ax1.tick_params(labelsize=20)
ax1.tick_params(axis='x', labelbottom='off')
ax1.plot([mint, maxt], [0, 0], '--', color='k')
if species == 'e':
charge = '-e'
else:
charge = 'e'
text1 = r'$' + charge + 'u_x' + 'E_x' + '$'
ax1.text(
0.1,
0.1,
text1,
color='red',
fontsize=32,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
text2 = r'$' + charge + 'u_y' + 'E_y' + '$'
ax1.text(
0.2,
0.1,
text2,
color='green',
fontsize=32,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
text3 = r'$' + charge + 'u_z' + 'E_z' + '$'
ax1.text(
0.3,
0.1,
text3,
color='blue',
fontsize=32,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
ys -= h1 + gap
ax2 = fig.add_axes([xs, ys, w1, h1])
p1 = ax2.plot(t[ct1:ct2], jdote_x_cum[ct1:ct2], color='r')
p2 = ax2.plot(t[ct1:ct2], jdote_y_cum[ct1:ct2], color='g')
p3 = ax2.plot(t[ct1:ct2], jdote_z_cum[ct1:ct2], color='b')
p4 = ax2.plot(t[ct1:ct2], jdote_tot_cum[ct1:ct2], color='k')
ax2.tick_params(labelsize=20)
ax2.set_xlabel(r'$t\Omega_{ci}$', fontdict=font)
ax2.plot([mint, maxt], [0, 0], '--', color='k')
plt.show()
def trace_field_line(pic_info):
"""Calculate parallel potential defined by Jan Egedal.
Args:
pic_info: namedtuple for the PIC simulation information.
"""
kwargs = {"current_time": 40, "xl": 0, "xr": 200, "zb": -50, "zt": 50}
x, z, Ay = contour_plots.read_2d_fields(pic_info, "../data/Ay.gda",
**kwargs)
x, z, Bx = contour_plots.read_2d_fields(pic_info, "../data/bx.gda",
**kwargs)
x, z, Bz = contour_plots.read_2d_fields(pic_info, "../data/bz.gda",
**kwargs)
xarr, zarr, Bz = contour_plots.read_2d_fields(pic_info, "../data/bz.gda",
**kwargs)
x, z, Ex = contour_plots.read_2d_fields(pic_info, "../data/ex.gda",
**kwargs)
x, z, Ez = contour_plots.read_2d_fields(pic_info, "../data/ez.gda",
**kwargs)
nx, = x.shape
nz, = z.shape
print nx, nz
x0 = 199.0
z0 = 45.0
i = int(x0 / pic_info.dx_di)
k = int(z0 / pic_info.dz_di)
# x0 = xarr[i]
# z0 = zarr[k] - zarr[0]
# nstep = 0
# xlist = [x0]
# zlist = [z0]
# dx_di = pic_info.dx_di
# dz_di = pic_info.dz_di
# deltas = math.sqrt(dx_di**2 + dz_di**2)*0.1
# hds = deltas * 0.5
# total_lengh = 0
# x = x0
# z = z0
# while (x > xarr[0] and x < xarr[-1] and z > 0
# and z < (zarr[-1]-zarr[0]) and total_lengh < 1E2):
# deltax1, deltaz1, x1, z1, ex, ez = middle_step_rk4(x, z, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, hds)
# deltax2, deltaz2, x2, z2, ex, ez = middle_step_rk4(x1, z1, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, hds)
# deltax3, deltaz3, x3, z3, ex, ez = middle_step_rk4(x, z, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, deltas)
# deltax4, deltaz4, x4, z4, ex, ez = middle_step_rk4(x, z, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, hds)
#
# x += deltas/6 * (deltax1 + 2*deltax2 + 2*deltax3 + deltax4)
# z += deltas/6 * (deltaz1 + 2*deltaz2 + 2*deltaz3 + deltaz4)
# total_lengh += deltas
# xlist.append(x)
# zlist.append(z)
# nstep += 1
# length = math.sqrt((x-x0)**2 + (z-z0)**2)
# if (length < dx_di and nstep > 20):
# print length
# break
dx_di = pic_info.dx_di
dz_di = pic_info.dz_di
#deltas = math.sqrt(dx_di**2 + dz_di**2)
#hds = deltas * 0.5
hmax = dx_di * 100
h = hmax / 4.0
# Cash-Karp parameters
a = [0.0, 0.2, 0.3, 0.6, 1.0, 0.875]
b = [[], [0.2], [3.0 / 40.0, 9.0 / 40.0], [0.3, -0.9, 1.2],
[-11.0 / 54.0, 2.5, -70.0 / 27.0, 35.0 / 27.0], [
1631.0 / 55296.0, 175.0 / 512.0, 575.0 / 13824.0, 44275.0 /
110592.0, 253.0 / 4096.0
]]
c = [37.0 / 378.0, 0.0, 250.0 / 621.0, 125.0 / 594.0, 0.0, 512.0 / 1771.0]
dc = [
c[0] - 2825.0 / 27648.0, c[1] - 0.0, c[2] - 18575.0 / 48384.0,
c[3] - 13525.0 / 55296.0, c[4] - 277.00 / 14336.0, c[5] - 0.25
]
def F(t, x, z):
indices_bl, indices_tr, delta = grid_indices(x, 0, z, nx, 1, nz, dx_di,
1, dz_di)
ix1 = indices_bl[0]
iz1 = indices_bl[2]
ix2 = indices_tr[0]
iz2 = indices_tr[2]
offsetx = delta[0]
offsetz = delta[2]
v1 = (1.0 - offsetx) * (1.0 - offsetz)
v2 = offsetx * (1.0 - offsetz)
v3 = offsetx * offsetz
v4 = (1.0 - offsetx) * offsetz
if (ix1 < nx and ix2 < nx and iz1 < nz and iz2 < nz):
bx = Bx[iz1, ix1] * v1 + Bx[iz1, ix2] * v2 + Bx[
iz2, ix2] * v3 + Bx[iz2, ix1] * v4
bz = Bz[iz1, ix1] * v1 + Bz[iz1, ix2] * v2 + Bz[
iz2, ix2] * v3 + Bz[iz2, ix1] * v4
ex = Ex[iz1, ix1] * v1 + Ex[iz1, ix2] * v2 + Ex[
iz2, ix2] * v3 + Ex[iz2, ix1] * v4
ez = Ez[iz1, ix1] * v1 + Ez[iz1, ix2] * v2 + Ez[
iz2, ix2] * v3 + Ez[iz2, ix1] * v4
absB = math.sqrt(bx**2 + bz**2)
deltax1 = bx / absB
deltaz1 = bz / absB
else:
ex = 0
ez = 0
deltax1 = 0
deltaz1 = 0
return (deltax1, deltaz1, ex, ez)
tol = 1e-5
x0 = xarr[i] - xarr[0]
z0 = zarr[k] - zarr[0]
y = [x0, z0]
nstep = 0
xlist = [x0]
zlist = [z0]
t = 0
x = x0
z = z0
while (x > 0 and x < (xarr[-1] - xarr[0]) and z > 0 and
z < (zarr[-1] - zarr[0]) and t < 4E2):
# Compute k[i] function values.
kx = [None] * 6
kz = [None] * 6
kx[0], kz[0], ex0, ez0 = F(t, x, z)
kx[1], kz[1], ex1, ez1 = F(t + a[1] * h, x + h * (kx[0] * b[1][0]),
z + h * (kz[0] * b[1][0]))
kx[2], kz[2], ex2, ez2 = F(t + a[2] * h,
x + h * (kx[0] * b[2][0] + kx[1] * b[2][1]),
z + h * (kz[0] * b[2][0] + kz[1] * b[2][1]))
kx[3], kz[3], ex3, ez3 = F(
t + a[3] * h,
x + h * (kx[0] * b[3][0] + kx[1] * b[3][1] + kx[2] * b[3][2]),
z + h * (kz[0] * b[3][0] + kz[1] * b[3][1] + kz[2] * b[3][2]))
kx[4], kz[4], ex4, ez4 = F(t + a[4] * h,
x + h * (kx[0] * b[4][0] + kx[1] * b[4][1] +
kx[2] * b[4][2] + kx[3] * b[4][3]),
z + h * (kz[0] * b[4][0] + kz[1] * b[4][1] +
kz[2] * b[4][2] + kz[3] * b[4][3]))
kx[5], kz[5], ex5, ez5 = F(
t + a[5] * h,
x + h * (kx[0] * b[5][0] + kx[1] * b[5][1] + kx[2] * b[5][2] +
kx[3] * b[5][3] + kx[4] * b[5][4]),
z + h * (kz[0] * b[5][0] + kz[1] * b[5][1] + kz[2] * b[5][2] +
kz[3] * b[5][3] + kz[4] * b[5][4]))
# Estimate current error and current maximum error.
E = norm([
h * (kx[0] * dc[0] + kx[1] * dc[1] + kx[2] * dc[2] + kx[3] * dc[3]
+ kx[4] * dc[4] + kx[5] * dc[5]),
h * (kz[0] * dc[0] + kz[1] * dc[1] + kz[2] * dc[2] + kz[3] * dc[3]
+ kz[4] * dc[4] + kz[5] * dc[5])
])
Emax = tol * max(norm(y), 1.0)
# Update solution if error is OK.
if E < Emax:
t += h
y[0] += h * (kx[0] * c[0] + kx[1] * c[1] + kx[2] * c[2] + kx[3] *
c[3] + kx[4] * c[4] + kx[5] * c[5])
y[1] += h * (kz[0] * c[0] + kz[1] * c[1] + kz[2] * c[2] + kz[3] *
c[3] + kz[4] * c[4] + kz[5] * c[5])
x = y[0]
z = y[1]
xlist.append(x)
zlist.append(z)
#out += [(t, list(y))]
# Update step size
if E > 0.0:
h = min(hmax, 0.85 * h * (Emax / E)**0.2)
width = 0.78
height = 0.75
xs = 0.14
xe = 0.94 - xs
ys = 0.9 - height
#fig = plt.figure(figsize=(7,5))
fig = plt.figure(figsize=(7, 2))
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 2, "zstep": 2}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = contour_plots.plot_2d_contour(xarr, zarr, Ay, ax1, fig,
**kwargs_plot)
p1.set_cmap(plt.cm.seismic)
# cs = ax1.contour(xarr[0:nx:xstep], zarr[0:nz:zstep], Ay[0:nz:zstep, 0:nx:xstep],
# colors='white', linewidths=0.5, levels=np.arange(0, 252, 1))
p2 = ax1.plot(xlist, zlist + zarr[0], color='black')
#cbar1.set_ticks(np.arange(-0.8, 1.0, 0.4))
#ax1.tick_params(axis='x', labelbottom='off')
plt.show()
def plot_average_energy(pic_info, species, current_time):
"""Plot plasma beta and number density.
Args:
pic_info: namedtuple for the PIC simulation information.
species: 'e' for electrons, 'i' for ions.
current_time: current time frame.
"""
kwargs = {
"current_time": current_time,
"xl": 0,
"xr": 80,
"zb": -20,
"zt": 20
}
fname = "../../data/n" + species + ".gda"
x, z, num_rho = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/p" + species + "-xx.gda"
x, z, pxx = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/p" + species + "-yy.gda"
x, z, pyy = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/p" + species + "-zz.gda"
x, z, pzz = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "x.gda"
x, z, ux = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "y.gda"
x, z, uy = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "z.gda"
x, z, uz = read_2d_fields(pic_info, fname, **kwargs)
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
if species == 'e':
ptl_mass = 1
else:
ptl_mass = pic_info.mime
ene_avg = (pxx + pyy + pzz) / (2.0*num_rho) + \
0.5*(ux*ux + uy*uy + uz*uz)*ptl_mass
nx, = x.shape
nz, = z.shape
width = 0.78
height = 0.78
xs = 0.12
ys = 0.92 - height
fig = plt.figure(figsize=[10, 5])
ax1 = fig.add_axes([xs, ys, width, height])
if species == 'e':
vmax = 0.2
else:
vmax = 1.4
kwargs_plot = {
"xstep": 2,
"zstep": 2,
"is_log": False,
"vmin": 0.0,
"vmax": vmax
}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x, z, ene_avg, ax1, fig, **kwargs_plot)
# p1.set_cmap(plt.cm.seismic)
ax1.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=20)
fname = r'$E_{avg}$'
cbar1.ax.set_ylabel(fname, fontdict=font, fontsize=24)
# cbar1.set_ticks(np.arange(0.0, 0.22, 0.05))
# cbar1.ax.set_yticklabels(['$0.2$', '$1.0$', '$5.0$'])
cbar1.ax.tick_params(labelsize=20)
t_wci = current_time * pic_info.dt_fields
title = r'$t = ' + "{:10.1f}".format(t_wci) + '/\Omega_{ci}$'
ax1.set_title(title, fontdict=font, fontsize=24)
if not os.path.isdir('../img_num_rho/'):
os.makedirs('../img_num_rho/')
fname = '../img_num_rho/ene_avg_' + species + '_' + \
str(current_time).zfill(3) + '.jpg'
fig.savefig(fname, dpi=300)
plt.show()
def number_density_along_cut(current_time, coords, lcorner, weights):
"""Particle number density along a cut.
Args:
current_time: current time frame.
coords: the coordinates of the points along the cut.
lcorner: the indices of the lower left of the cells in which
the line points are.
weights: the weight for 2D linear interpolation.
"""
xl, xr = 45, 80
zb, zt = -10, 10
kwargs = {
"current_time": current_time,
"xl": xl,
"xr": xr,
"zb": zb,
"zt": zt
}
fname = '../../data/ne.gda'
x, z, ne = read_2d_fields(pic_info, fname, **kwargs)
fname = '../../data/ni.gda'
x, z, ni = read_2d_fields(pic_info, fname, **kwargs)
tmp, npoints = lcorner.shape
dists = np.zeros(npoints)
for i in range(npoints):
dists[i] = math.sqrt((coords[1, i] - coords[1, 0])**2 + (
coords[0, i] - coords[0, 0])**2)
ne_total = np.zeros(npoints)
ni_total = np.zeros(npoints)
xshift = math.floor(xl / pic_info.dx_di)
zshift = math.floor((zb + pic_info.lz_di * 0.5) / pic_info.dz_di)
lcorner[0, :] -= xshift
lcorner[1, :] -= zshift
nbands = 10
# for iband in range(1, nbands+1):
for iband in range(1, 2):
fname = '../../data/eEB' + str(iband).zfill(2) + '.gda'
x, z, eEB = read_2d_fields(pic_info, fname, **kwargs)
fname = '../../data/iEB' + str(iband).zfill(2) + '.gda'
x, z, iEB = read_2d_fields(pic_info, fname, **kwargs)
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
nrho_band_e = eEB * ne
nrho_band_i = iEB * ni
ne_line = np.zeros(npoints)
ni_line = np.zeros(npoints)
for i in range(npoints):
ne_line[i] += nrho_band_e[lcorner[1, i], lcorner[0, i]] * weights[
0, i]
ne_line[i] += nrho_band_e[lcorner[1, i], lcorner[0, i] +
1] * weights[1, i]
ne_line[i] += nrho_band_e[lcorner[1, i] + 1, lcorner[0, i] +
1] * weights[2, i]
ne_line[i] += nrho_band_e[lcorner[1, i] + 1, lcorner[
0, i]] * weights[3, i]
ni_line[i] += nrho_band_i[lcorner[1, i], lcorner[0, i]] * weights[
0, i]
ni_line[i] += nrho_band_i[lcorner[1, i], lcorner[0, i] +
1] * weights[1, i]
ni_line[i] += nrho_band_i[lcorner[1, i] + 1, lcorner[0, i] +
1] * weights[2, i]
ni_line[i] += nrho_band_i[lcorner[1, i] + 1, lcorner[
0, i]] * weights[3, i]
ne_total += ne_line
ni_total += ni_line
nx, = x.shape
nz, = z.shape
width = 0.78
height = 0.78
xs = 0.12
ys = 0.92 - height
fig = plt.figure(figsize=[10, 5])
ax1 = fig.add_axes([xs, ys, width, height])
# vmax = math.floor(np.max(nrho_band_i) / 0.1 - 1.0) * 0.1
kwargs_plot = {"xstep": 1, "zstep": 1, "is_log": False}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
ixs = 0
p1, cbar1 = plot_2d_contour(x[ixs:nx], z, nrho_band_e[:, ixs:nx], ax1,
fig, **kwargs_plot)
# p1.set_cmap(plt.cm.seismic)
ax1.contour(
x[ixs:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, ixs:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=20)
cbar1.ax.set_ylabel(r'$n_e$', fontdict=font, fontsize=24)
# cbar1.set_ticks(np.arange(0.0, 0.2, 0.04))
# cbar1.ax.set_yticklabels(['$0.2$', '$1.0$', '$5.0$'])
cbar1.ax.tick_params(labelsize=20)
p2 = ax1.plot(coords[0, :], coords[1, :], color='white', linewidth=2)
ax1.text(
coords[0, -1] + 1.0,
coords[1, -1] - 1.0,
'B',
color='white',
fontsize=24,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0))
ax1.text(
coords[0, 0] - 2.5,
coords[1, 0] - 1.0,
'A',
color='white',
fontsize=24,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0))
fname = '../img_nrho_band/nrho_e_' + str(iband).zfill(2) + '.jpg'
fig.savefig(fname, dpi=300)
fig = plt.figure(figsize=[10, 5])
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "is_log": False}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x[ixs:nx], z, nrho_band_i[:, ixs:nx], ax1,
fig, **kwargs_plot)
# p1.set_cmap(plt.cm.seismic)
ax1.contour(
x[ixs:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, ixs:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=20)
cbar1.ax.set_ylabel(r'$n_i$', fontdict=font, fontsize=24)
# cbar1.set_ticks(np.arange(0.0, 0.4, 0.1))
# cbar1.ax.set_yticklabels(['$0.2$', '$1.0$', '$5.0$'])
cbar1.ax.tick_params(labelsize=20)
p2 = ax1.plot(coords[0, :], coords[1, :], color='white', linewidth=2)
ax1.text(
coords[0, -1] + 1.0,
coords[1, -1] - 1.0,
'B',
color='white',
fontsize=24,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0))
ax1.text(
coords[0, 0] - 2.5,
coords[1, 0] - 1.0,
'A',
color='white',
fontsize=24,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0))
fname = '../img_nrho_band/nrho_i_' + str(iband).zfill(2) + '.jpg'
fig.savefig(fname, dpi=300)
t_wci = current_time * pic_info.dt_fields
title = r'$t = ' + "{:10.1f}".format(t_wci) + '/\Omega_{ci}$'
ax1.set_title(title, fontdict=font, fontsize=24)
fig = plt.figure(figsize=[7, 5])
width = 0.69
height = 0.8
xs = 0.16
ys = 0.95 - height
ax = fig.add_axes([xs, ys, width, height])
ax.plot(dists, ne_line, color='r', linewidth=2, label='Electron')
ax.set_xlim([np.min(dists), np.max(dists)])
ax.set_xlabel(r'Length/$d_i$', fontdict=font, fontsize=24)
ax.set_ylabel(r'$n_e$', fontdict=font, fontsize=24, color='r')
ax.text(
0.0,
-0.13,
'A',
color='black',
fontsize=24,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
ax.text(
1.0,
-0.13,
'B',
color='black',
fontsize=24,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
ax.set_xlim([np.min(dists), np.max(dists)])
ax.tick_params(labelsize=20)
for tl in ax.get_yticklabels():
tl.set_color('r')
ax1 = ax.twinx()
ax1.plot(dists, ni_line, color='b', linewidth=2, label='Ion')
ax1.set_ylabel(r'$n_i$', fontdict=font, fontsize=24, color='b')
ax1.tick_params(labelsize=20)
ax1.set_xlim([np.min(dists), np.max(dists)])
for tl in ax1.get_yticklabels():
tl.set_color('b')
# ax.legend(loc=2, prop={'size':24}, ncol=1,
# shadow=False, fancybox=False, frameon=False)
fname = '../img_nrho_band/nei_' + str(current_time).zfill(3) + \
'_' + str(iband).zfill(2) + '.eps'
fig.savefig(fname)
# plt.show()
plt.close('all')
# p1 = plt.plot(ne_total/ni_total, linewidth=2)
plt.show()
def plot_jy_guide():
"""Plot jy contour for the runs with guide field
Args:
run_name: the name of this run.
root_dir: the root directory of this run.
pic_info: PIC simulation information in a namedtuple.
"""
cts = [40, 20, 20, 20, 30]
var = 'ez'
vmin, vmax = -0.2, 0.2
ticks = np.arange(-0.2, 0.25, 0.1)
cmap = plt.cm.get_cmap('seismic')
label = r'$E_z$'
# # cmap = cmaps.inferno
# var = 'jy'
# vmin, vmax = -0.2, 0.5
# ticks = np.arange(-0.2, 0.6, 0.2)
# cmap = plt.cm.get_cmap('jet')
# label = r'$j_y$'
# var = 'by'
# vmin, vmax = -0.2, 0.5
# ticks = np.arange(-0.2, 0.6, 0.2)
# cmap = plt.cm.get_cmap('jet')
# label = r'$B_y$'
base_dirs, run_names = guide_field_runs()
bdir = base_dirs[0]
run_name = run_names[0]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
xl, xr = 50, 150
kwargs = {"current_time": cts[0], "xl": xl, "xr": xr, "zb": -10, "zt": 10}
fname = bdir + 'data/' + var + '.gda'
x, z, jy1 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay1 = read_2d_fields(pic_info, fname, **kwargs)
kwargs = {"current_time": cts[1], "xl": xl, "xr": xr, "zb": -10, "zt": 10}
bdir = base_dirs[1]
run_name = run_names[1]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = bdir + 'data/' + var + '.gda'
x, z, jy2 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay2 = read_2d_fields(pic_info, fname, **kwargs)
kwargs = {"current_time": cts[2], "xl": xl, "xr": xr, "zb": -10, "zt": 10}
bdir = base_dirs[2]
run_name = run_names[2]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = bdir + 'data/' + var + '.gda'
x, z, jy3 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay3 = read_2d_fields(pic_info, fname, **kwargs)
kwargs = {"current_time": cts[3], "xl": xl, "xr": xr, "zb": -10, "zt": 10}
bdir = base_dirs[3]
run_name = run_names[3]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = bdir + 'data/' + var + '.gda'
x, z, jy4 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay4 = read_2d_fields(pic_info, fname, **kwargs)
kwargs = {"current_time": cts[4], "xl": xl, "xr": xr, "zb": -10, "zt": 10}
bdir = base_dirs[4]
run_name = run_names[4]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = bdir + 'data/' + var + '.gda'
x, z, jy5 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay5 = read_2d_fields(pic_info, fname, **kwargs)
nx, = x.shape
nz, = z.shape
width = 0.8
height = 0.16
xs = 0.1
ys = 0.98 - height
gap = 0.025
fig = plt.figure(figsize=[10, 8])
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": vmin, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x, z, jy1, ax1, fig, **kwargs_plot)
p1.set_cmap(cmap)
ax1.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay1[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax1.tick_params(labelsize=16)
cbar1.ax.set_ylabel(label, fontdict=font, fontsize=20)
cbar1.set_ticks(ticks)
cbar1.ax.tick_params(labelsize=16)
ax1.tick_params(axis='x', labelbottom='off')
ys -= height + gap
ax2 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": vmin, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p2, cbar2 = plot_2d_contour(x, z, jy2, ax2, fig, **kwargs_plot)
p2.set_cmap(cmap)
ax2.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay2[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax2.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax2.tick_params(labelsize=16)
cbar2.ax.set_ylabel(label, fontdict=font, fontsize=20)
cbar2.set_ticks(ticks)
cbar2.ax.tick_params(labelsize=16)
ax2.tick_params(axis='x', labelbottom='off')
ys -= height + gap
ax3 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": vmin, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p3, cbar3 = plot_2d_contour(x, z, jy3, ax3, fig, **kwargs_plot)
p3.set_cmap(cmap)
ax3.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay3[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
# ax3.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=20)
ax3.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax3.tick_params(labelsize=16)
cbar3.ax.set_ylabel(label, fontdict=font, fontsize=20)
cbar3.set_ticks(ticks)
cbar3.ax.tick_params(labelsize=16)
ax3.tick_params(axis='x', labelbottom='off')
ys -= height + gap
ax4 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": vmin, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p4, cbar4 = plot_2d_contour(x, z, jy4, ax4, fig, **kwargs_plot)
p4.set_cmap(cmap)
ax4.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay4[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
# ax4.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=20)
ax4.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax4.tick_params(labelsize=16)
cbar4.ax.set_ylabel(label, fontdict=font, fontsize=20)
cbar4.set_ticks(ticks)
cbar4.ax.tick_params(labelsize=16)
ax4.tick_params(axis='x', labelbottom='off')
ys -= height + gap
ax5 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": vmin, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p5, cbar5 = plot_2d_contour(x, z, jy5, ax5, fig, **kwargs_plot)
p5.set_cmap(cmap)
ax5.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay5[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax5.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=20)
ax5.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax5.tick_params(labelsize=16)
cbar5.ax.set_ylabel(label, fontdict=font, fontsize=20)
cbar5.set_ticks(ticks)
cbar5.ax.tick_params(labelsize=16)
axs = [ax1, ax2, ax3, ax4, ax5]
bgs = [0.0, 0.2, 0.5, 1.0, 4.0]
for ax, bg in zip(axs, bgs):
tname = r'$B_g = ' + str(bg) + '$'
ax.text(0.02,
0.8,
tname,
color='k',
fontsize=24,
bbox=dict(facecolor='none',
alpha=1.0,
edgecolor='none',
pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
fname = '../img/thesis/' + var + '.jpg'
fig.savefig(fname, dpi=200)
plt.show()
def plot_jy_multi():
"""Plot the jy for multiple runs
"""
base_dirs, run_names = ApJ_long_paper_runs()
bdir = base_dirs[0]
run_name = run_names[0]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
kwargs = {"current_time": 11, "xl": 0, "xr": 200, "zb": -10, "zt": 10}
fname = bdir + 'data/jy.gda'
x, z, jy1 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay1 = read_2d_fields(pic_info, fname, **kwargs)
bdir = base_dirs[1]
run_name = run_names[1]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = bdir + 'data/jy.gda'
x, z, jy2 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay2 = read_2d_fields(pic_info, fname, **kwargs)
bdir = base_dirs[3]
run_name = run_names[3]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = bdir + 'data/jy.gda'
x, z, jy3 = read_2d_fields(pic_info, fname, **kwargs)
fname = bdir + 'data/Ay.gda'
x, z, Ay3 = read_2d_fields(pic_info, fname, **kwargs)
nx, = x.shape
nz, = z.shape
width = 0.74
height = 0.25
xs = 0.13
ys = 0.97 - height
gap = 0.05
fig = plt.figure(figsize=[7, 5])
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": -0.5, "vmax": 0.5}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x, z, jy1, ax1, fig, **kwargs_plot)
p1.set_cmap(plt.cm.get_cmap('seismic'))
ax1.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay1[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax1.tick_params(labelsize=16)
cbar1.ax.set_ylabel(r'$j_y$', fontdict=font, fontsize=20)
cbar1.set_ticks(np.arange(-0.4, 0.5, 0.2))
cbar1.ax.tick_params(labelsize=16)
ax1.tick_params(axis='x', labelbottom='off')
ys -= height + gap
ax2 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": -0.5, "vmax": 0.5}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p2, cbar2 = plot_2d_contour(x, z, jy2, ax2, fig, **kwargs_plot)
p2.set_cmap(plt.cm.get_cmap('seismic'))
ax2.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay2[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax2.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax2.tick_params(labelsize=16)
cbar2.ax.set_ylabel(r'$j_y$', fontdict=font, fontsize=20)
cbar2.set_ticks(np.arange(-0.4, 0.5, 0.2))
cbar2.ax.tick_params(labelsize=16)
ax2.tick_params(axis='x', labelbottom='off')
ys -= height + gap
ax3 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": -1.0, "vmax": 1.0}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p3, cbar3 = plot_2d_contour(x, z, jy3, ax3, fig, **kwargs_plot)
p3.set_cmap(plt.cm.get_cmap('seismic'))
ax3.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay3[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax3.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=20)
ax3.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=20)
ax3.tick_params(labelsize=16)
cbar3.ax.set_ylabel(r'$j_y$', fontdict=font, fontsize=20)
cbar3.set_ticks(np.arange(-1.0, 1.1, 0.5))
cbar3.ax.tick_params(labelsize=16)
ax1.text(0.02,
0.78,
r'R8 $\beta_e=0.2$',
color='k',
fontsize=20,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none',
pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax1.transAxes)
ax2.text(0.02,
0.78,
r'R7 $\beta_e=0.07$',
color='k',
fontsize=20,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none',
pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax2.transAxes)
ax3.text(0.02,
0.78,
r'R6 $\beta_e=0.007$',
color='k',
fontsize=20,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none',
pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax3.transAxes)
fig.savefig('../img/jy_multi.jpg', dpi=300)
plt.show()
def plot_traj(filename, pic_info, species, iptl, mint, maxt):
"""Plot particle trajectory information.
Args:
filename: the filename to read the data.
pic_info: namedtuple for the PIC simulation information.
species: particle species. 'e' for electron. 'i' for ion.
iptl: particle ID.
mint, maxt: minimum and maximum time for plotting.
"""
ptl_traj = read_traj_data(filename)
gama = np.sqrt(ptl_traj.ux**2 + ptl_traj.uy**2 + ptl_traj.uz**2 + 1.0)
mime = pic_info.mime
# de scale to di scale
ptl_x = ptl_traj.x / math.sqrt(mime)
ptl_y = ptl_traj.y / math.sqrt(mime)
ptl_z = ptl_traj.z / math.sqrt(mime)
# 1/wpe to 1/wci
t = ptl_traj.t * pic_info.dtwci / pic_info.dtwpe
xl, xr = 0, 200
zb, zt = -20, 20
kwargs = {"current_time": 50, "xl": xl, "xr": xr, "zb": zb, "zt": zt}
fname = "../../data/ey.gda"
x, z, data_2d = read_2d_fields(pic_info, fname, **kwargs)
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
nx, = x.shape
nz, = z.shape
# p1 = ax1.plot(ptl_x, gama-1.0, color='k')
# ax1.set_xlabel(r'$t\Omega_{ci}$', fontdict=font)
# ax1.set_ylabel(r'$E/m_ic^2$', fontdict=font)
# ax1.tick_params(labelsize=20)
width = 14.0
height = 8.0
fig = plt.figure(figsize=[width, height])
w1, h1 = 0.82, 0.27
xs, ys = 0.10, 0.98 - h1
ax1 = fig.add_axes([xs, ys, w1, h1])
kwargs_plot = {
"xstep": 2,
"zstep": 2,
"is_log": False,
"vmin": -1.0,
"vmax": 1.0
}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
im1, cbar1 = plot_2d_contour(x, z, data_2d, ax1, fig, **kwargs_plot)
im1.set_cmap(plt.cm.seismic)
ax1.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.tick_params(labelsize=20)
ax1.tick_params(axis='x', labelbottom='off')
ax1.set_xlim([xl, xr])
ax1.set_ylim([zb, zt])
ax1.set_ylabel(r'$z/d_i$', fontdict=font)
cbar1.ax.tick_params(labelsize=20)
cbar1.ax.set_ylabel(r'$B_y$', fontdict=font, fontsize=24)
p1 = ax1.scatter(ptl_x, ptl_z, s=0.5)
# ax2 = fig.add_axes([xs, ys, w1, h1])
# p2 = ax2.plot(t, gama-1.0, color='k')
# ax2.set_xlabel(r'$t\Omega_{ci}$', fontdict=font)
# ax2.set_ylabel(r'$E/m_ic^2$', fontdict=font)
# ax2.tick_params(labelsize=20)
gap = 0.04
ys -= h1 + gap
ax2 = fig.add_axes([xs, ys, w1 * 0.98 - 0.05 / width, h1])
p2 = ax2.plot(ptl_x, gama - 1.0, color='k')
ax2.set_ylabel(r'$E/m_ic^2$', fontdict=font)
ax2.tick_params(labelsize=20)
ax2.tick_params(axis='x', labelbottom='off')
xmin, xmax = ax2.get_xlim()
ys -= h1 + gap
ax3 = fig.add_axes([xs, ys, w1 * 0.98 - 0.05 / width, h1])
p3 = ax3.plot(ptl_x, ptl_y, color='k')
ax3.set_xlabel(r'$x/d_i$', fontdict=font)
ax3.set_ylabel(r'$y/d_i$', fontdict=font)
ax3.tick_params(labelsize=20)
ax1.set_xlim([xmin, xmax])
ax3.set_xlim([xmin, xmax])
if not os.path.isdir('../img/'):
os.makedirs('../img/')
dir = '../img/img_traj_' + species + '/'
if not os.path.isdir(dir):
os.makedirs(dir)
fname = dir + 'traj_' + species + '_' + str(iptl).zfill(4) + '_1.jpg'
fig.savefig(fname, dpi=300)
height = 15.0
fig = plt.figure(figsize=[width, height])
w1, h1 = 0.88, 0.135
xs, ys = 0.10, 0.98 - h1
gap = 0.025
dt = t[1] - t[0]
ct1 = int(mint / dt)
ct2 = int(maxt / dt)
ax1 = fig.add_axes([xs, ys, w1, h1])
p1 = ax1.plot(t[ct1:ct2], gama[ct1:ct2] - 1.0, color='k')
ax1.set_ylabel(r'$E/m_ic^2$', fontdict=font)
ax1.tick_params(labelsize=20)
ax1.tick_params(axis='x', labelbottom='off')
ax1.plot([mint, maxt], [0, 0], '--', color='k')
ax1.text(0.4,
-0.07,
r'$x$',
color='red',
fontsize=32,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none',
pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
ax1.text(0.5,
-0.07,
r'$y$',
color='green',
fontsize=32,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none',
pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
ax1.text(0.6,
-0.07,
r'$z$',
color='blue',
fontsize=32,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none',
pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
ys -= h1 + gap
ax2 = fig.add_axes([xs, ys, w1, h1])
p21 = ax2.plot(t[ct1:ct2], ptl_traj.ux[ct1:ct2], color='r', label=r'u_x')
p22 = ax2.plot(t[ct1:ct2], ptl_traj.uy[ct1:ct2], color='g', label=r'u_y')
p23 = ax2.plot(t[ct1:ct2], ptl_traj.uz[ct1:ct2], color='b', label=r'u_z')
ax2.plot([mint, maxt], [0, 0], '--', color='k')
ax2.set_ylabel(r'$u_x, u_y, u_z$', fontdict=font)
ax2.tick_params(labelsize=20)
ax2.tick_params(axis='x', labelbottom='off')
kernel = 9
tmax = np.max(t)
ys -= h1 + gap
ax31 = fig.add_axes([xs, ys, w1, h1])
ex = signal.medfilt(ptl_traj.ex, kernel_size=(kernel))
p31 = ax31.plot(t[ct1:ct2], ex[ct1:ct2], color='r', label=r'E_x')
ax31.set_ylabel(r'$E_x$', fontdict=font)
ax31.tick_params(labelsize=20)
ax31.tick_params(axis='x', labelbottom='off')
ax31.plot([mint, maxt], [0, 0], '--', color='k')
ys -= h1 + gap
ax32 = fig.add_axes([xs, ys, w1, h1])
ey = signal.medfilt(ptl_traj.ey, kernel_size=(kernel))
p32 = ax32.plot(t[ct1:ct2], ey[ct1:ct2], color='g', label=r'E_y')
ax32.set_ylabel(r'$E_y$', fontdict=font)
ax32.tick_params(labelsize=20)
ax32.tick_params(axis='x', labelbottom='off')
ax32.plot([mint, maxt], [0, 0], '--', color='k')
ys -= h1 + gap
ax33 = fig.add_axes([xs, ys, w1, h1])
ez = signal.medfilt(ptl_traj.ez, kernel_size=(kernel))
p33 = ax33.plot(t[ct1:ct2], ez[ct1:ct2], color='b', label=r'E_z')
ax33.set_ylabel(r'$E_z$', fontdict=font)
ax33.tick_params(labelsize=20)
ax33.tick_params(axis='x', labelbottom='off')
ax33.plot([mint, maxt], [0, 0], '--', color='k')
ys -= h1 + gap
ax4 = fig.add_axes([xs, ys, w1, h1])
p41 = ax4.plot(t[ct1:ct2], ptl_traj.bx[ct1:ct2], color='r', label=r'B_x')
p42 = ax4.plot(t[ct1:ct2], ptl_traj.by[ct1:ct2], color='g', label=r'B_y')
p43 = ax4.plot(t[ct1:ct2], ptl_traj.bz[ct1:ct2], color='b', label=r'B_z')
ax4.set_xlabel(r'$t\Omega_{ci}$', fontdict=font)
ax4.set_ylabel(r'$B_x, B_y, B_z$', fontdict=font)
ax4.tick_params(labelsize=20)
ax4.plot([mint, maxt], [0, 0], '--', color='k')
ax1.set_xlim([mint, maxt])
ax2.set_xlim([mint, maxt])
ax31.set_xlim([mint, maxt])
ax32.set_xlim([mint, maxt])
ax33.set_xlim([mint, maxt])
ax4.set_xlim([mint, maxt])
fname = dir + 'traj_' + species + '_' + str(iptl).zfill(4) + '_2.jpg'
fig.savefig(fname, dpi=300)
height = 6.0
fig = plt.figure(figsize=[width, height])
w1, h1 = 0.88, 0.4
xs, ys = 0.10, 0.97 - h1
gap = 0.05
dt = t[1] - t[0]
ct1 = int(mint / dt)
ct2 = int(maxt / dt)
if species == 'e':
charge = -1.0
else:
charge = 1.0
nt, = t.shape
jdote_x = ptl_traj.ux * ptl_traj.ex * charge / gama
jdote_y = ptl_traj.uy * ptl_traj.ey * charge / gama
jdote_z = ptl_traj.uz * ptl_traj.ez * charge / gama
# jdote_x = (ptl_traj.uy * ptl_traj.bz / gama -
# ptl_traj.uz * ptl_traj.by / gama + ptl_traj.ex) * charge
# jdote_y = (ptl_traj.uz * ptl_traj.bx / gama -
# ptl_traj.ux * ptl_traj.bz / gama + ptl_traj.ey) * charge
# jdote_z = (ptl_traj.ux * ptl_traj.by / gama -
# ptl_traj.uy * ptl_traj.bx / gama + ptl_traj.ez) * charge
dt = np.zeros(nt)
dt[0:nt - 1] = np.diff(t)
jdote_x_cum = np.cumsum(jdote_x) * dt
jdote_y_cum = np.cumsum(jdote_y) * dt
jdote_z_cum = np.cumsum(jdote_z) * dt
jdote_tot_cum = jdote_x_cum + jdote_y_cum + jdote_z_cum
ax1 = fig.add_axes([xs, ys, w1, h1])
p1 = ax1.plot(t[ct1:ct2], jdote_x[ct1:ct2], color='r')
p2 = ax1.plot(t[ct1:ct2], jdote_y[ct1:ct2], color='g')
p3 = ax1.plot(t[ct1:ct2], jdote_z[ct1:ct2], color='b')
ax1.tick_params(labelsize=20)
ax1.tick_params(axis='x', labelbottom='off')
ax1.plot([mint, maxt], [0, 0], '--', color='k')
if species == 'e':
charge = '-e'
else:
charge = 'e'
text1 = r'$' + charge + 'u_x' + 'E_x' + '$'
ax1.text(0.1,
0.1,
text1,
color='red',
fontsize=32,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none',
pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
text2 = r'$' + charge + 'u_y' + 'E_y' + '$'
ax1.text(0.2,
0.1,
text2,
color='green',
fontsize=32,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none',
pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
text3 = r'$' + charge + 'u_z' + 'E_z' + '$'
ax1.text(0.3,
0.1,
text3,
color='blue',
fontsize=32,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none',
pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
ys -= h1 + gap
ax2 = fig.add_axes([xs, ys, w1, h1])
p1 = ax2.plot(t[ct1:ct2], jdote_x_cum[ct1:ct2], color='r')
p2 = ax2.plot(t[ct1:ct2], jdote_y_cum[ct1:ct2], color='g')
p3 = ax2.plot(t[ct1:ct2], jdote_z_cum[ct1:ct2], color='b')
p4 = ax2.plot(t[ct1:ct2], jdote_tot_cum[ct1:ct2], color='k')
ax2.tick_params(labelsize=20)
ax2.set_xlabel(r'$t\Omega_{ci}$', fontdict=font)
ax2.plot([mint, maxt], [0, 0], '--', color='k')
plt.show()
def bulk_energy(pic_info, species, current_time):
"""Bulk energy and internal energy.
Args:
pic_info: namedtuple for the PIC simulation information.
species: 'e' for electrons, 'i' for ions.
current_time: current time frame.
"""
kwargs = {
"current_time": current_time,
"xl": 0,
"xr": 200,
"zb": -20,
"zt": 20
}
fname = "../../data/u" + species + "x.gda"
x, z, ux = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "y.gda"
x, z, uy = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "z.gda"
x, z, uz = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/n" + species + ".gda"
x, z, nrho = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/p" + species + "-xx.gda"
x, z, pxx = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/p" + species + "-yy.gda"
x, z, pyy = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/p" + species + "-zz.gda"
x, z, pzz = read_2d_fields(pic_info, fname, **kwargs)
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
if species == 'e':
ptl_mass = 1.0
else:
ptl_mass = pic_info.mime
internal_ene = (pxx + pyy + pzz) * 0.5
bulk_ene = 0.5 * ptl_mass * nrho * (ux**2 + uy**2 + uz**2)
# gama = 1.0 / np.sqrt(1.0 - (ux**2 + uy**2 + uz**2))
# gama = np.sqrt(1.0 + ux**2 + uy**2 + uz**2)
# bulk_ene2 = (gama - 1) * ptl_mass * nrho
# print np.sum(bulk_ene), np.sum(bulk_ene2)
nx, = x.shape
nz, = z.shape
width = 0.75
height = 0.23
xs = 0.12
ys = 0.93 - height
fig = plt.figure(figsize=[10, 6])
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {
"xstep": 1,
"zstep": 1,
"is_log": True,
"vmin": 0.1,
"vmax": 10.0
}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x, z, bulk_ene / internal_ene, ax1, fig,
**kwargs_plot)
p1.set_cmap(plt.cm.seismic)
ax1.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='white',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
# ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(axis='x', labelbottom='off')
ax1.tick_params(labelsize=20)
cbar1.ax.set_ylabel(r'$K/u$', fontdict=font, fontsize=24)
cbar1.ax.tick_params(labelsize=20)
t_wci = current_time * pic_info.dt_fields
title = r'$t = ' + "{:10.1f}".format(t_wci) + '/\Omega_{ci}$'
ax1.set_title(title, fontdict=font, fontsize=24)
ys -= height + 0.05
ax2 = fig.add_axes([xs, ys, width, height])
if species == 'e':
vmax = 0.2
else:
vmax = 0.8
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": 0, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p2, cbar2 = plot_2d_contour(x, z, bulk_ene, ax2, fig, **kwargs_plot)
p2.set_cmap(plt.cm.nipy_spectral)
ax2.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='white',
linewidths=0.5)
ax2.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax2.tick_params(axis='x', labelbottom='off')
ax2.tick_params(labelsize=20)
cbar2.ax.set_ylabel(r'$K$', fontdict=font, fontsize=24)
if species == 'e':
cbar2.set_ticks(np.arange(0, 0.2, 0.04))
else:
cbar2.set_ticks(np.arange(0, 0.9, 0.2))
cbar2.ax.tick_params(labelsize=20)
ys -= height + 0.05
ax3 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": 0, "vmax": 0.8}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p3, cbar3 = plot_2d_contour(x, z, internal_ene, ax3, fig, **kwargs_plot)
p3.set_cmap(plt.cm.nipy_spectral)
ax3.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='white',
linewidths=0.5)
ax3.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax3.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax3.tick_params(labelsize=20)
cbar3.ax.set_ylabel(r'$u$', fontdict=font, fontsize=24)
cbar3.set_ticks(np.arange(0, 0.9, 0.2))
cbar3.ax.tick_params(labelsize=20)
# plt.show()
if not os.path.isdir('../img/'):
os.makedirs('../img/')
if not os.path.isdir('../img/img_bulk_internal/'):
os.makedirs('../img/img_bulk_internal/')
dir = '../img/img_bulk_internal/'
fname = 'bulk_internal' + str(current_time).zfill(
3) + '_' + species + '.jpg'
fname = dir + fname
fig.savefig(fname, dpi=400)
plt.close()
def plot_particle_drift(pic_info, species, current_time):
"""Plot compression related terms.
Args:
pic_info: namedtuple for the PIC simulation information.
species: 'e' for electrons, 'i' for ions.
current_time: current time frame.
"""
print(current_time)
width = 0.75
height = 0.11
xs = 0.12
ys = 0.98 - height
gap = 0.025
if species == 'i':
vmin = -0.5
vmax = 0.5
else:
vmin = -0.8
vmax = 0.8
fig = plt.figure(figsize=[10, 14])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": vmin, "vmax": vmax}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
ratio = pic_info.particle_interval / pic_info.fields_interval
ct = (current_time + 1) * ratio
kwargs = {"current_time": ct, "xl": 0, "xr": 200, "zb": -50, "zt": 50}
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
nx, = x.shape
nz, = z.shape
zl = nz / 4
zt = nz - zl
nbands = 5
data_sum = np.zeros((nbands, nx))
data_acc = np.zeros((nbands, nx))
nb = 0
kwargs = {
"current_time": current_time,
"xl": 0,
"xr": 200,
"zb": -50,
"zt": 50
}
for iband in range(1, nbands + 1):
fname = "../../data1/jpara_dote_" + species + "_" + str(iband).zfill(
2) + ".gda"
x, z, jpara_dote = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data1/jperp_dote_" + species + "_" + str(iband).zfill(
2) + ".gda"
x, z, jperp_dote = read_2d_fields(pic_info, fname, **kwargs)
data = jpara_dote + jperp_dote
nk = 5
kernel = np.ones((nk, nk)) / float(nk * nk)
data = signal.convolve2d(data, kernel, mode='same')
ax1 = fig.add_axes([xs, ys, width, height])
p1, cbar1 = plot_2d_contour(x, z[zl:zt], data[zl:zt:zstep, 0:nx:xstep],
ax1, fig, **kwargs_plot)
p1.set_cmap(plt.cm.seismic)
ax1.contour(
x[0:nx:xstep],
z[zl:zt:zstep],
Ay[zl:zt:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=20)
ax1.tick_params(axis='x', labelbottom='off')
cbar1.ax.tick_params(labelsize=20)
if species == 'i':
cbar1.set_ticks(np.arange(-0.4, 0.5, 0.2))
else:
cbar1.set_ticks(np.arange(-0.8, 0.9, 0.4))
ys -= height + gap
data_sum[nb, :] = np.sum(data, axis=0)
data_acc[nb, :] = np.cumsum(data_sum[nb, :])
nb += 1
dv = pic_info.dx_di * pic_info.dz_di * pic_info.mime
data_sum *= dv
data_acc *= dv
ys0 = 0.1
height0 = ys + height - ys0
w1, h1 = fig.get_size_inches()
width0 = width * 0.98 - 0.05 / w1
ax1 = fig.add_axes([xs, ys0, width0, height0])
for i in range(nb):
fname = 'Band' + str(i + 1).zfill(2)
# ax1.plot(x, data_sum[i, :], linewidth=2, label=fname)
ax1.plot(x, data_acc[i, :], linewidth=2, label=fname)
ax1.tick_params(labelsize=20)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.set_ylabel(r'Accumulation', fontdict=font, fontsize=24)
ax1.legend(
loc=2,
prop={'size': 20},
ncol=1,
shadow=False,
fancybox=False,
frameon=False)
if not os.path.isdir('../img/'):
os.makedirs('../img/')
if not os.path.isdir('../img/img_particle_drift/'):
os.makedirs('../img/img_particle_drift/')
fname = 'ene_gain_' + str(current_time).zfill(3) + '_' + species + '.jpg'
fname = '../img/img_particle_drift/' + fname
fig.savefig(fname, dpi=200)
# plt.close()
plt.show()
def parallel_potential(pic_info):
"""Calculate parallel potential defined by Jan Egedal.
Args:
pic_info: namedtuple for the PIC simulation information.
"""
kwargs = {"current_time": 40, "xl": 0, "xr": 200, "zb": -50, "zt": 50}
x, z, Ay = contour_plots.read_2d_fields(pic_info, "../data/Ay.gda",
**kwargs)
x, z, Ex = contour_plots.read_2d_fields(pic_info, "../data/ex.gda",
**kwargs)
x, z, Ey = contour_plots.read_2d_fields(pic_info, "../data/ey.gda",
**kwargs)
x, z, Ez = contour_plots.read_2d_fields(pic_info, "../data/ez.gda",
**kwargs)
x, z, Bx = contour_plots.read_2d_fields(pic_info, "../data/bx.gda",
**kwargs)
x, z, By = contour_plots.read_2d_fields(pic_info, "../data/by.gda",
**kwargs)
xarr, zarr, Bz = contour_plots.read_2d_fields(pic_info, "../data/bz.gda",
**kwargs)
absB = np.sqrt(Bx**2 + By**2 + Bz**2)
Epara = (Ex * Bx + Ey * By + Ez * Bz) / absB
nx, = x.shape
nz, = z.shape
phi_parallel = np.zeros((nz, nx))
dx_di = pic_info.dx_di
dz_di = pic_info.dz_di
#deltas = math.sqrt(dx_di**2 + dz_di**2)
#hds = deltas * 0.5
hmax = dx_di * 100
h = hmax / 4.0
# Cash-Karp parameters
a = [0.0, 0.2, 0.3, 0.6, 1.0, 0.875]
b = [[], [0.2], [3.0 / 40.0, 9.0 / 40.0], [0.3, -0.9, 1.2],
[-11.0 / 54.0, 2.5, -70.0 / 27.0, 35.0 / 27.0], [
1631.0 / 55296.0, 175.0 / 512.0, 575.0 / 13824.0, 44275.0 /
110592.0, 253.0 / 4096.0
]]
c = [37.0 / 378.0, 0.0, 250.0 / 621.0, 125.0 / 594.0, 0.0, 512.0 / 1771.0]
dc = [
c[0] - 2825.0 / 27648.0, c[1] - 0.0, c[2] - 18575.0 / 48384.0,
c[3] - 13525.0 / 55296.0, c[4] - 277.00 / 14336.0, c[5] - 0.25
]
def F(t, x, z):
indices_bl, indices_tr, delta = grid_indices(x, 0, z, nx, 1, nz, dx_di,
1, dz_di)
ix1 = indices_bl[0]
iz1 = indices_bl[2]
ix2 = indices_tr[0]
iz2 = indices_tr[2]
offsetx = delta[0]
offsetz = delta[2]
v1 = (1.0 - offsetx) * (1.0 - offsetz)
v2 = offsetx * (1.0 - offsetz)
v3 = offsetx * offsetz
v4 = (1.0 - offsetx) * offsetz
if (ix1 < nx and ix2 < nx and iz1 < nz and iz2 < nz):
bx = Bx[iz1, ix1] * v1 + Bx[iz1, ix2] * v2 + Bx[
iz2, ix2] * v3 + Bx[iz2, ix1] * v4
by = By[iz1, ix1] * v1 + By[iz1, ix2] * v2 + By[
iz2, ix2] * v3 + By[iz2, ix1] * v4
bz = Bz[iz1, ix1] * v1 + Bz[iz1, ix2] * v2 + Bz[
iz2, ix2] * v3 + Bz[iz2, ix1] * v4
ex = Ex[iz1, ix1] * v1 + Ex[iz1, ix2] * v2 + Ex[
iz2, ix2] * v3 + Ex[iz2, ix1] * v4
ey = Ey[iz1, ix1] * v1 + Ey[iz1, ix2] * v2 + Ey[
iz2, ix2] * v3 + Ey[iz2, ix1] * v4
ez = Ez[iz1, ix1] * v1 + Ez[iz1, ix2] * v2 + Ez[
iz2, ix2] * v3 + Ez[iz2, ix1] * v4
absB = math.sqrt(bx**2 + bz**2)
deltax1 = bx / absB
deltay1 = by * deltax1 / bx
deltaz1 = bz / absB
else:
ex = 0
ey = 0
ez = 0
deltax1 = 0
deltay1 = 0
deltaz1 = 0
return (deltax1, deltay1, deltaz1, ex, ey, ez)
tol = 1e-5
for i in range(2900, 3300):
print i
x0 = xarr[i] - xarr[0]
#for k in range(0,nz,8):
for k in range(950, 1098):
z0 = zarr[k] - zarr[0]
y = [x0, z0]
nstep = 0
xlist = [x0]
zlist = [z0]
t = 0
x = x0
z = z0
while (x >= 0 and x <= (xarr[-1] - xarr[0]) and z >= 0 and
z <= (zarr[-1] - zarr[0]) and t < 4E2):
# Compute k[i] function values.
kx = [None] * 6
ky = [None] * 6
kz = [None] * 6
kx[0], ky[0], kz[0], ex0, ey0, ez0 = F(t, x, z)
kx[1], ky[1], kz[1], ex1, ey1, ez1 = F(
t + a[1] * h, x + h * (kx[0] * b[1][0]),
z + h * (kz[0] * b[1][0]))
kx[2], ky[2], kz[2], ex2, ey2, ez2 = F(
t + a[2] * h, x + h * (kx[0] * b[2][0] + kx[1] * b[2][1]),
z + h * (kz[0] * b[2][0] + kz[1] * b[2][1]))
kx[3], ky[3], kz[3], ex3, ey3, ez3 = F(
t + a[3] * h, x + h *
(kx[0] * b[3][0] + kx[1] * b[3][1] + kx[2] * b[3][2]),
z + h *
(kz[0] * b[3][0] + kz[1] * b[3][1] + kz[2] * b[3][2]))
kx[4], ky[4], kz[4], ex4, ey4, ez4 = F(
t + a[4] * h, x + h * (kx[0] * b[4][0] + kx[1] * b[4][1] +
kx[2] * b[4][2] + kx[3] * b[4][3]),
z + h * (kz[0] * b[4][0] + kz[1] * b[4][1] + kz[2] *
b[4][2] + kz[3] * b[4][3]))
kx[5], ky[5], kz[5], ex5, ey5, ez5 = F(
t + a[5] * h,
x + h * (kx[0] * b[5][0] + kx[1] * b[5][1] + kx[2] *
b[5][2] + kx[3] * b[5][3] + kx[4] * b[5][4]),
z + h * (kz[0] * b[5][0] + kz[1] * b[5][1] + kz[2] *
b[5][2] + kz[3] * b[5][3] + kz[4] * b[5][4]))
# Estimate current error and current maximum error.
E = norm([
h * (kx[0] * dc[0] + kx[1] * dc[1] + kx[2] * dc[2] + kx[3]
* dc[3] + kx[4] * dc[4] + kx[5] * dc[5]),
h * (kz[0] * dc[0] + kz[1] * dc[1] + kz[2] * dc[2] + kz[3]
* dc[3] + kz[4] * dc[4] + kz[5] * dc[5])
])
Emax = tol * max(norm(y), 1.0)
# Update solution if error is OK.
if E < Emax:
t += h
dx = h * (kx[0] * c[0] + kx[1] * c[1] + kx[2] * c[2] +
kx[3] * c[3] + kx[4] * c[4] + kx[5] * c[5])
dy = h * (ky[0] * c[0] + ky[1] * c[1] + ky[2] * c[2] +
ky[3] * c[3] + ky[4] * c[4] + ky[5] * c[5])
dz = h * (kz[0] * c[0] + kz[1] * c[1] + kz[2] * c[2] +
kz[3] * c[3] + kz[4] * c[4] + kz[5] * c[5])
y[0] += dx
y[1] += dz
x = y[0]
z = y[1]
xlist.append(x)
zlist.append(z)
phi_parallel[k, i] += \
h*(kx[0]*c[0]*ex0+kx[1]*c[1]*ex1+kx[2]*c[2]*ex2+ \
kx[3]*c[3]*ex3+kx[4]*c[4]*ex4+kx[5]*c[5]*ex5) + \
h*(ky[0]*c[0]*ey0+ky[1]*c[1]*ey1+ky[2]*c[2]*ey2+ \
ky[3]*c[3]*ey3+ky[4]*c[4]*ey4+ky[5]*c[5]*ey5) + \
h*(kz[0]*c[0]*ez0+kz[1]*c[1]*ez1+kz[2]*c[2]*ez2+ \
kz[3]*c[3]*ez3+kz[4]*c[4]*ez4+kz[5]*c[5]*ez5)
#out += [(t, list(y))]
# Update step size
if E > 0.0:
h = min(hmax, 0.85 * h * (Emax / E)**0.2)
if (t > 395):
phi_parallel[k, i] = 0
#
# deltax1, deltaz1, x1, z1, ex1, ez1 = middle_step_rk4(x, z, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, hds)
# deltax2, deltaz2, x2, z2, ex2, ez2 = middle_step_rk4(x1, z1, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, hds)
# deltax3, deltaz3, x3, z3, ex3, ez3 = middle_step_rk4(x, z, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, deltas)
# deltax4, deltaz4, x4, z4, ex4, ez4 = middle_step_rk4(x, z, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, hds)
#
# deltax = deltas/6 * (deltax1 + 2*deltax2 + 2*deltax3 + deltax4)
# deltaz = deltas/6 * (deltaz1 + 2*deltaz2 + 2*deltaz3 + deltaz4)
# x += deltax
# z += deltaz
# total_lengh += deltas
# xlist.append(x)
# zlist.append(z)
# nstep += 1
# phi_parallel[k, i] += deltas/6 * (ex1*deltax1 + ez1*deltaz1 +
# 2.0*ex2*deltax2 + 2.0*ez2*deltaz2 +
# 2.0*ex3*deltax3 + 2.0*ez3*deltaz3 +
# ex4*deltax4 + ez4*deltaz4)
# length = math.sqrt((x-x0)**2 + (z-z0)**2)
# if (length < dx_di and nstep > 20):
# phi_parallel[k, i] = 0
# break
# #print k, nstep, total_lengh
# phi_parallel = np.fromfile('phi_parallel.dat')
# phi_parallel.tofile('phi_parallel.dat')
# print np.max(phi_parallel), np.min(phi_parallel)
width = 0.78
height = 0.75
xs = 0.14
xe = 0.94 - xs
ys = 0.9 - height
#fig = plt.figure(figsize=(7,5))
fig = plt.figure(figsize=(7, 2))
ax1 = fig.add_axes([xs, ys, width, height])
#kwargs_plot = {"xstep":2, "zstep":2, "vmin":-0.05, "vmax":0.05}
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": -0.2, "vmax": 0.2}
#kwargs_plot = {"xstep":1, "zstep":1, "vmin":-0.05, "vmax":0.05}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
phi_parallel = np.reshape(phi_parallel, (nz, nx))
p1, cbar1 = contour_plots.plot_2d_contour(xarr, zarr, phi_parallel, ax1,
fig, **kwargs_plot)
# xmin = np.min(xarr)
# zmin = np.min(zarr)
# xmax = np.max(xarr)
# zmax = np.max(zarr)
# p1 = ax1.imshow(phi_parallel[0:nz:8, 0:nx:8], cmap=plt.cm.jet,
# extent=[xmin, xmax, zmin, zmax],
# aspect='auto', origin='lower',
# vmin=kwargs_plot["vmin"], vmax=kwargs_plot["vmax"],
# interpolation='quadric')
p1.set_cmap(plt.cm.seismic)
cs = ax1.contour(
xarr[0:nx:xstep],
zarr[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5,
levels=np.arange(1, 250, 10))
#cbar1.set_ticks(np.arange(-0.8, 1.0, 0.4))
#ax1.tick_params(axis='x', labelbottom='off')
plt.show()
def plot_ptl_traj(filename, pic_info, species, iptl, mint, maxt):
"""Plot particle trajectory information.
Args:
filename: the filename to read the data.
pic_info: namedtuple for the PIC simulation information.
species: particle species. 'e' for electron. 'i' for ion.
iptl: particle ID.
mint, maxt: minimum and maximum time for plotting.
"""
ptl_traj = read_traj_data(filename)
gama = np.sqrt(ptl_traj.ux**2 + ptl_traj.uy**2 + ptl_traj.uz**2 + 1.0)
mime = pic_info.mime
# de scale to di scale
ptl_x = ptl_traj.x / math.sqrt(mime)
ptl_y = ptl_traj.y / math.sqrt(mime)
ptl_z = ptl_traj.z / math.sqrt(mime)
# 1/wpe to 1/wci
t = ptl_traj.t * pic_info.dtwci / pic_info.dtwpe
xl, xr = 0, 50
zb, zt = -20, 20
kwargs = {"current_time": 65, "xl": xl, "xr": xr, "zb": zb, "zt": zt}
fname = "../../data/uex.gda"
x, z, uex = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/ne.gda"
x, z, ne = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/uix.gda"
x, z, uix = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/ni.gda"
x, z, ni = read_2d_fields(pic_info, fname, **kwargs)
ux = (uex * ne + uix * ni * pic_info.mime) / (ne + ni * pic_info.mime)
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
nx, = x.shape
nz, = z.shape
width = 8.0
height = 8.0
fig = plt.figure(figsize=[width, height])
w1, h1 = 0.74, 0.42
xs, ys = 0.13, 0.98 - h1
ax1 = fig.add_axes([xs, ys, w1, h1])
kwargs_plot = {
"xstep": 2,
"zstep": 2,
"is_log": False,
"vmin": -1.0,
"vmax": 1.0
}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
va = 0.2 # Alfven speed
im1, cbar1 = plot_2d_contour(x, z, ux / va, ax1, fig, **kwargs_plot)
im1.set_cmap(plt.cm.seismic)
ax1.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.tick_params(labelsize=20)
ax1.tick_params(axis='x', labelbottom='off')
ax1.set_xlim([xl, xr])
ax1.set_ylim([zb, zt])
ax1.set_ylabel(r'$z/d_i$', fontdict=font)
cbar1.ax.tick_params(labelsize=20)
cbar1.ax.set_ylabel(r'$u_x/V_A$', fontdict=font, fontsize=24)
tstop = 1990
p1 = ax1.plot(ptl_x[0:tstop], ptl_z[0:tstop], linewidth=2, color='k')
gap = 0.04
ys -= h1 + gap
ax2 = fig.add_axes([xs, ys, w1 * 0.98 - 0.05 / width, h1])
eth = pic_info.vthi**2 * 3
p2 = ax2.plot(
ptl_x[0:tstop], (gama[0:tstop] - 1.0) / eth, color='k', linewidth=2)
ax2.set_xlabel(r'$x/d_i$', fontdict=font)
ax2.set_ylabel(r'$E/E_{\text{thi}}$', fontdict=font)
ax2.tick_params(labelsize=20)
# ax2.tick_params(axis='x', labelbottom='off')
xmin, xmax = ax1.get_xlim()
ax2.set_xlim([xmin, xmax])
if not os.path.isdir('../img/'):
os.makedirs('../img/')
fname = '../img/' + 'traj_' + species + '_' + str(iptl).zfill(4) + '_1.jpg'
fig.savefig(fname, dpi=300)
plt.show()
def number_density_along_cut(current_time, coords, lcorner, weights):
"""Particle number density along a cut.
Args:
current_time: current time frame.
coords: the coordinates of the points along the cut.
lcorner: the indices of the lower left of the cells in which
the line points are.
weights: the weight for 2D linear interpolation.
"""
xl, xr = 45, 80
zb, zt = -10, 10
kwargs = {
"current_time": current_time,
"xl": xl,
"xr": xr,
"zb": zb,
"zt": zt
}
fname = '../../data/ne.gda'
x, z, ne = read_2d_fields(pic_info, fname, **kwargs)
fname = '../../data/ni.gda'
x, z, ni = read_2d_fields(pic_info, fname, **kwargs)
tmp, npoints = lcorner.shape
dists = np.zeros(npoints)
for i in range(npoints):
dists[i] = math.sqrt((coords[1, i] - coords[1, 0])**2 +
(coords[0, i] - coords[0, 0])**2)
ne_total = np.zeros(npoints)
ni_total = np.zeros(npoints)
xshift = math.floor(xl / pic_info.dx_di)
zshift = math.floor((zb + pic_info.lz_di * 0.5) / pic_info.dz_di)
lcorner[0, :] -= xshift
lcorner[1, :] -= zshift
nbands = 10
# for iband in range(1, nbands+1):
for iband in range(1, 2):
fname = '../../data/eEB' + str(iband).zfill(2) + '.gda'
x, z, eEB = read_2d_fields(pic_info, fname, **kwargs)
fname = '../../data/iEB' + str(iband).zfill(2) + '.gda'
x, z, iEB = read_2d_fields(pic_info, fname, **kwargs)
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
nrho_band_e = eEB * ne
nrho_band_i = iEB * ni
ne_line = np.zeros(npoints)
ni_line = np.zeros(npoints)
for i in range(npoints):
ne_line[i] += nrho_band_e[lcorner[1, i],
lcorner[0, i]] * weights[0, i]
ne_line[i] += nrho_band_e[lcorner[1, i],
lcorner[0, i] + 1] * weights[1, i]
ne_line[i] += nrho_band_e[lcorner[1, i] + 1,
lcorner[0, i] + 1] * weights[2, i]
ne_line[i] += nrho_band_e[lcorner[1, i] + 1,
lcorner[0, i]] * weights[3, i]
ni_line[i] += nrho_band_i[lcorner[1, i],
lcorner[0, i]] * weights[0, i]
ni_line[i] += nrho_band_i[lcorner[1, i],
lcorner[0, i] + 1] * weights[1, i]
ni_line[i] += nrho_band_i[lcorner[1, i] + 1,
lcorner[0, i] + 1] * weights[2, i]
ni_line[i] += nrho_band_i[lcorner[1, i] + 1,
lcorner[0, i]] * weights[3, i]
ne_total += ne_line
ni_total += ni_line
nx, = x.shape
nz, = z.shape
width = 0.78
height = 0.78
xs = 0.12
ys = 0.92 - height
fig = plt.figure(figsize=[10, 5])
ax1 = fig.add_axes([xs, ys, width, height])
# vmax = math.floor(np.max(nrho_band_i) / 0.1 - 1.0) * 0.1
kwargs_plot = {"xstep": 1, "zstep": 1, "is_log": False}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
ixs = 0
p1, cbar1 = plot_2d_contour(x[ixs:nx], z, nrho_band_e[:, ixs:nx], ax1,
fig, **kwargs_plot)
# p1.set_cmap(plt.cm.seismic)
ax1.contour(x[ixs:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, ixs:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=20)
cbar1.ax.set_ylabel(r'$n_e$', fontdict=font, fontsize=24)
# cbar1.set_ticks(np.arange(0.0, 0.2, 0.04))
# cbar1.ax.set_yticklabels(['$0.2$', '$1.0$', '$5.0$'])
cbar1.ax.tick_params(labelsize=20)
p2 = ax1.plot(coords[0, :], coords[1, :], color='white', linewidth=2)
ax1.text(coords[0, -1] + 1.0,
coords[1, -1] - 1.0,
'B',
color='white',
fontsize=24,
bbox=dict(facecolor='none',
alpha=1.0,
edgecolor='none',
pad=10.0))
ax1.text(coords[0, 0] - 2.5,
coords[1, 0] - 1.0,
'A',
color='white',
fontsize=24,
bbox=dict(facecolor='none',
alpha=1.0,
edgecolor='none',
pad=10.0))
fname = '../img_nrho_band/nrho_e_' + str(iband).zfill(2) + '.jpg'
fig.savefig(fname, dpi=300)
fig = plt.figure(figsize=[10, 5])
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "is_log": False}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x[ixs:nx], z, nrho_band_i[:, ixs:nx], ax1,
fig, **kwargs_plot)
# p1.set_cmap(plt.cm.seismic)
ax1.contour(x[ixs:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, ixs:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=20)
cbar1.ax.set_ylabel(r'$n_i$', fontdict=font, fontsize=24)
# cbar1.set_ticks(np.arange(0.0, 0.4, 0.1))
# cbar1.ax.set_yticklabels(['$0.2$', '$1.0$', '$5.0$'])
cbar1.ax.tick_params(labelsize=20)
p2 = ax1.plot(coords[0, :], coords[1, :], color='white', linewidth=2)
ax1.text(coords[0, -1] + 1.0,
coords[1, -1] - 1.0,
'B',
color='white',
fontsize=24,
bbox=dict(facecolor='none',
alpha=1.0,
edgecolor='none',
pad=10.0))
ax1.text(coords[0, 0] - 2.5,
coords[1, 0] - 1.0,
'A',
color='white',
fontsize=24,
bbox=dict(facecolor='none',
alpha=1.0,
edgecolor='none',
pad=10.0))
fname = '../img_nrho_band/nrho_i_' + str(iband).zfill(2) + '.jpg'
fig.savefig(fname, dpi=300)
t_wci = current_time * pic_info.dt_fields
title = r'$t = ' + "{:10.1f}".format(t_wci) + '/\Omega_{ci}$'
ax1.set_title(title, fontdict=font, fontsize=24)
fig = plt.figure(figsize=[7, 5])
width = 0.69
height = 0.8
xs = 0.16
ys = 0.95 - height
ax = fig.add_axes([xs, ys, width, height])
ax.plot(dists, ne_line, color='r', linewidth=2, label='Electron')
ax.set_xlim([np.min(dists), np.max(dists)])
ax.set_xlabel(r'Length/$d_i$', fontdict=font, fontsize=24)
ax.set_ylabel(r'$n_e$', fontdict=font, fontsize=24, color='r')
ax.text(0.0,
-0.13,
'A',
color='black',
fontsize=24,
bbox=dict(facecolor='none',
alpha=1.0,
edgecolor='none',
pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
ax.text(1.0,
-0.13,
'B',
color='black',
fontsize=24,
bbox=dict(facecolor='none',
alpha=1.0,
edgecolor='none',
pad=10.0),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
ax.set_xlim([np.min(dists), np.max(dists)])
ax.tick_params(labelsize=20)
for tl in ax.get_yticklabels():
tl.set_color('r')
ax1 = ax.twinx()
ax1.plot(dists, ni_line, color='b', linewidth=2, label='Ion')
ax1.set_ylabel(r'$n_i$', fontdict=font, fontsize=24, color='b')
ax1.tick_params(labelsize=20)
ax1.set_xlim([np.min(dists), np.max(dists)])
for tl in ax1.get_yticklabels():
tl.set_color('b')
# ax.legend(loc=2, prop={'size':24}, ncol=1,
# shadow=False, fancybox=False, frameon=False)
fname = '../img_nrho_band/nei_' + str(current_time).zfill(3) + \
'_' + str(iband).zfill(2) + '.eps'
fig.savefig(fname)
# plt.show()
plt.close('all')
# p1 = plt.plot(ne_total/ni_total, linewidth=2)
plt.show()
def bulk_energy_change_rate(pic_info, species, current_time):
"""Bulk energy change rate.
Args:
pic_info: namedtuple for the PIC simulation information.
species: 'e' for electrons, 'i' for ions.
current_time: current time frame.
"""
kwargs = {
"current_time": current_time - 1,
"xl": 0,
"xr": 200,
"zb": -15,
"zt": 15
}
fname = "../../data/u" + species + "x.gda"
x, z, ux = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "y.gda"
x, z, uy = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "z.gda"
x, z, uz = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/n" + species + ".gda"
x, z, nrho = read_2d_fields(pic_info, fname, **kwargs)
if species == 'e':
ptl_mass = 1.0
else:
ptl_mass = pic_info.mime
bulk_ene1 = 0.5 * ptl_mass * nrho * (ux**2 + uy**2 + uz**2)
kwargs = {
"current_time": current_time + 1,
"xl": 0,
"xr": 200,
"zb": -15,
"zt": 15
}
fname = "../../data/u" + species + "x.gda"
x, z, ux = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "y.gda"
x, z, uy = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "z.gda"
x, z, uz = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/n" + species + ".gda"
x, z, nrho = read_2d_fields(pic_info, fname, **kwargs)
bulk_ene2 = 0.5 * ptl_mass * nrho * (ux**2 + uy**2 + uz**2)
kwargs = {
"current_time": current_time,
"xl": 0,
"xr": 200,
"zb": -15,
"zt": 15
}
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
bulk_ene_rate = bulk_ene2 - bulk_ene1
nx, = x.shape
nz, = z.shape
width = 0.75
height = 0.7
xs = 0.12
ys = 0.9 - height
fig = plt.figure(figsize=[10, 4])
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": 0.1, "vmax": -0.1}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x, z, bulk_ene_rate, ax1, fig, **kwargs_plot)
p1.set_cmap(plt.cm.seismic)
ax1.contour(
x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=24)
cbar1.ax.set_ylabel(r'$K/u$', fontdict=font, fontsize=24)
cbar1.ax.tick_params(labelsize=24)
t_wci = current_time * pic_info.dt_fields
title = r'$t = ' + "{:10.1f}".format(t_wci) + '/\Omega_{ci}$'
ax1.set_title(title, fontdict=font, fontsize=24)
plt.show()
def plot_average_energy(pic_info, species, current_time):
"""Plot plasma beta and number density.
Args:
pic_info: namedtuple for the PIC simulation information.
species: 'e' for electrons, 'i' for ions.
current_time: current time frame.
"""
kwargs = {
"current_time": current_time,
"xl": 0,
"xr": 80,
"zb": -20,
"zt": 20
}
fname = "../../data/n" + species + ".gda"
x, z, num_rho = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/p" + species + "-xx.gda"
x, z, pxx = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/p" + species + "-yy.gda"
x, z, pyy = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/p" + species + "-zz.gda"
x, z, pzz = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "x.gda"
x, z, ux = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "y.gda"
x, z, uy = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "z.gda"
x, z, uz = read_2d_fields(pic_info, fname, **kwargs)
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
if species == 'e':
ptl_mass = 1
else:
ptl_mass = pic_info.mime
ene_avg = (pxx + pyy + pzz) / (2.0*num_rho) + \
0.5*(ux*ux + uy*uy + uz*uz)*ptl_mass
nx, = x.shape
nz, = z.shape
width = 0.78
height = 0.78
xs = 0.12
ys = 0.92 - height
fig = plt.figure(figsize=[10, 5])
ax1 = fig.add_axes([xs, ys, width, height])
if species == 'e':
vmax = 0.2
else:
vmax = 1.4
kwargs_plot = {
"xstep": 2,
"zstep": 2,
"is_log": False,
"vmin": 0.0,
"vmax": vmax
}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x, z, ene_avg, ax1, fig, **kwargs_plot)
# p1.set_cmap(plt.cm.seismic)
ax1.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=20)
fname = r'$E_{avg}$'
cbar1.ax.set_ylabel(fname, fontdict=font, fontsize=24)
# cbar1.set_ticks(np.arange(0.0, 0.22, 0.05))
# cbar1.ax.set_yticklabels(['$0.2$', '$1.0$', '$5.0$'])
cbar1.ax.tick_params(labelsize=20)
t_wci = current_time * pic_info.dt_fields
title = r'$t = ' + "{:10.1f}".format(t_wci) + '/\Omega_{ci}$'
ax1.set_title(title, fontdict=font, fontsize=24)
if not os.path.isdir('../img_num_rho/'):
os.makedirs('../img_num_rho/')
fname = '../img_num_rho/ene_avg_' + species + '_' + \
str(current_time).zfill(3) + '.jpg'
fig.savefig(fname, dpi=300)
plt.show()
def trace_field_line(pic_info):
"""Calculate parallel potential defined by Jan Egedal.
Args:
pic_info: namedtuple for the PIC simulation information.
"""
kwargs = {"current_time": 40, "xl": 0, "xr": 200, "zb": -50, "zt": 50}
x, z, Ay = contour_plots.read_2d_fields(pic_info, "../data/Ay.gda",
**kwargs)
x, z, Bx = contour_plots.read_2d_fields(pic_info, "../data/bx.gda",
**kwargs)
x, z, Bz = contour_plots.read_2d_fields(pic_info, "../data/bz.gda",
**kwargs)
xarr, zarr, Bz = contour_plots.read_2d_fields(pic_info, "../data/bz.gda",
**kwargs)
x, z, Ex = contour_plots.read_2d_fields(pic_info, "../data/ex.gda",
**kwargs)
x, z, Ez = contour_plots.read_2d_fields(pic_info, "../data/ez.gda",
**kwargs)
nx, = x.shape
nz, = z.shape
print nx, nz
x0 = 199.0
z0 = 45.0
i = int(x0 / pic_info.dx_di)
k = int(z0 / pic_info.dz_di)
# x0 = xarr[i]
# z0 = zarr[k] - zarr[0]
# nstep = 0
# xlist = [x0]
# zlist = [z0]
# dx_di = pic_info.dx_di
# dz_di = pic_info.dz_di
# deltas = math.sqrt(dx_di**2 + dz_di**2)*0.1
# hds = deltas * 0.5
# total_lengh = 0
# x = x0
# z = z0
# while (x > xarr[0] and x < xarr[-1] and z > 0
# and z < (zarr[-1]-zarr[0]) and total_lengh < 1E2):
# deltax1, deltaz1, x1, z1, ex, ez = middle_step_rk4(x, z, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, hds)
# deltax2, deltaz2, x2, z2, ex, ez = middle_step_rk4(x1, z1, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, hds)
# deltax3, deltaz3, x3, z3, ex, ez = middle_step_rk4(x, z, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, deltas)
# deltax4, deltaz4, x4, z4, ex, ez = middle_step_rk4(x, z, nx, nz,
# dx_di, dz_di, Bx, Bz, Ex, Ez, hds)
#
# x += deltas/6 * (deltax1 + 2*deltax2 + 2*deltax3 + deltax4)
# z += deltas/6 * (deltaz1 + 2*deltaz2 + 2*deltaz3 + deltaz4)
# total_lengh += deltas
# xlist.append(x)
# zlist.append(z)
# nstep += 1
# length = math.sqrt((x-x0)**2 + (z-z0)**2)
# if (length < dx_di and nstep > 20):
# print length
# break
dx_di = pic_info.dx_di
dz_di = pic_info.dz_di
#deltas = math.sqrt(dx_di**2 + dz_di**2)
#hds = deltas * 0.5
hmax = dx_di * 100
h = hmax / 4.0
# Cash-Karp parameters
a = [0.0, 0.2, 0.3, 0.6, 1.0, 0.875]
b = [[], [0.2], [3.0 / 40.0, 9.0 / 40.0], [0.3, -0.9, 1.2],
[-11.0 / 54.0, 2.5, -70.0 / 27.0, 35.0 / 27.0],
[
1631.0 / 55296.0, 175.0 / 512.0, 575.0 / 13824.0,
44275.0 / 110592.0, 253.0 / 4096.0
]]
c = [37.0 / 378.0, 0.0, 250.0 / 621.0, 125.0 / 594.0, 0.0, 512.0 / 1771.0]
dc = [
c[0] - 2825.0 / 27648.0, c[1] - 0.0, c[2] - 18575.0 / 48384.0,
c[3] - 13525.0 / 55296.0, c[4] - 277.00 / 14336.0, c[5] - 0.25
]
def F(t, x, z):
indices_bl, indices_tr, delta = grid_indices(x, 0, z, nx, 1, nz, dx_di,
1, dz_di)
ix1 = indices_bl[0]
iz1 = indices_bl[2]
ix2 = indices_tr[0]
iz2 = indices_tr[2]
offsetx = delta[0]
offsetz = delta[2]
v1 = (1.0 - offsetx) * (1.0 - offsetz)
v2 = offsetx * (1.0 - offsetz)
v3 = offsetx * offsetz
v4 = (1.0 - offsetx) * offsetz
if (ix1 < nx and ix2 < nx and iz1 < nz and iz2 < nz):
bx = Bx[iz1, ix1] * v1 + Bx[iz1, ix2] * v2 + Bx[
iz2, ix2] * v3 + Bx[iz2, ix1] * v4
bz = Bz[iz1, ix1] * v1 + Bz[iz1, ix2] * v2 + Bz[
iz2, ix2] * v3 + Bz[iz2, ix1] * v4
ex = Ex[iz1, ix1] * v1 + Ex[iz1, ix2] * v2 + Ex[
iz2, ix2] * v3 + Ex[iz2, ix1] * v4
ez = Ez[iz1, ix1] * v1 + Ez[iz1, ix2] * v2 + Ez[
iz2, ix2] * v3 + Ez[iz2, ix1] * v4
absB = math.sqrt(bx**2 + bz**2)
deltax1 = bx / absB
deltaz1 = bz / absB
else:
ex = 0
ez = 0
deltax1 = 0
deltaz1 = 0
return (deltax1, deltaz1, ex, ez)
tol = 1e-5
x0 = xarr[i] - xarr[0]
z0 = zarr[k] - zarr[0]
y = [x0, z0]
nstep = 0
xlist = [x0]
zlist = [z0]
t = 0
x = x0
z = z0
while (x > 0 and x < (xarr[-1] - xarr[0]) and z > 0 and z <
(zarr[-1] - zarr[0]) and t < 4E2):
# Compute k[i] function values.
kx = [None] * 6
kz = [None] * 6
kx[0], kz[0], ex0, ez0 = F(t, x, z)
kx[1], kz[1], ex1, ez1 = F(t + a[1] * h, x + h * (kx[0] * b[1][0]),
z + h * (kz[0] * b[1][0]))
kx[2], kz[2], ex2, ez2 = F(t + a[2] * h,
x + h * (kx[0] * b[2][0] + kx[1] * b[2][1]),
z + h * (kz[0] * b[2][0] + kz[1] * b[2][1]))
kx[3], kz[3], ex3, ez3 = F(
t + a[3] * h,
x + h * (kx[0] * b[3][0] + kx[1] * b[3][1] + kx[2] * b[3][2]),
z + h * (kz[0] * b[3][0] + kz[1] * b[3][1] + kz[2] * b[3][2]))
kx[4], kz[4], ex4, ez4 = F(
t + a[4] * h, x + h * (kx[0] * b[4][0] + kx[1] * b[4][1] +
kx[2] * b[4][2] + kx[3] * b[4][3]),
z + h * (kz[0] * b[4][0] + kz[1] * b[4][1] + kz[2] * b[4][2] +
kz[3] * b[4][3]))
kx[5], kz[5], ex5, ez5 = F(
t + a[5] * h,
x + h * (kx[0] * b[5][0] + kx[1] * b[5][1] + kx[2] * b[5][2] +
kx[3] * b[5][3] + kx[4] * b[5][4]),
z + h * (kz[0] * b[5][0] + kz[1] * b[5][1] + kz[2] * b[5][2] +
kz[3] * b[5][3] + kz[4] * b[5][4]))
# Estimate current error and current maximum error.
E = norm([
h * (kx[0] * dc[0] + kx[1] * dc[1] + kx[2] * dc[2] +
kx[3] * dc[3] + kx[4] * dc[4] + kx[5] * dc[5]),
h * (kz[0] * dc[0] + kz[1] * dc[1] + kz[2] * dc[2] +
kz[3] * dc[3] + kz[4] * dc[4] + kz[5] * dc[5])
])
Emax = tol * max(norm(y), 1.0)
# Update solution if error is OK.
if E < Emax:
t += h
y[0] += h * (kx[0] * c[0] + kx[1] * c[1] + kx[2] * c[2] +
kx[3] * c[3] + kx[4] * c[4] + kx[5] * c[5])
y[1] += h * (kz[0] * c[0] + kz[1] * c[1] + kz[2] * c[2] +
kz[3] * c[3] + kz[4] * c[4] + kz[5] * c[5])
x = y[0]
z = y[1]
xlist.append(x)
zlist.append(z)
#out += [(t, list(y))]
# Update step size
if E > 0.0:
h = min(hmax, 0.85 * h * (Emax / E)**0.2)
width = 0.78
height = 0.75
xs = 0.14
xe = 0.94 - xs
ys = 0.9 - height
#fig = plt.figure(figsize=(7,5))
fig = plt.figure(figsize=(7, 2))
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 2, "zstep": 2}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = contour_plots.plot_2d_contour(xarr, zarr, Ay, ax1, fig,
**kwargs_plot)
p1.set_cmap(plt.cm.seismic)
# cs = ax1.contour(xarr[0:nx:xstep], zarr[0:nz:zstep], Ay[0:nz:zstep, 0:nx:xstep],
# colors='white', linewidths=0.5, levels=np.arange(0, 252, 1))
p2 = ax1.plot(xlist, zlist + zarr[0], color='black')
#cbar1.set_ticks(np.arange(-0.8, 1.0, 0.4))
#ax1.tick_params(axis='x', labelbottom='off')
plt.show()
def bulk_energy_change_rate(pic_info, species, current_time):
"""Bulk energy change rate.
Args:
pic_info: namedtuple for the PIC simulation information.
species: 'e' for electrons, 'i' for ions.
current_time: current time frame.
"""
kwargs = {
"current_time": current_time - 1,
"xl": 0,
"xr": 200,
"zb": -15,
"zt": 15
}
fname = "../../data/u" + species + "x.gda"
x, z, ux = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "y.gda"
x, z, uy = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "z.gda"
x, z, uz = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/n" + species + ".gda"
x, z, nrho = read_2d_fields(pic_info, fname, **kwargs)
if species == 'e':
ptl_mass = 1.0
else:
ptl_mass = pic_info.mime
bulk_ene1 = 0.5 * ptl_mass * nrho * (ux**2 + uy**2 + uz**2)
kwargs = {
"current_time": current_time + 1,
"xl": 0,
"xr": 200,
"zb": -15,
"zt": 15
}
fname = "../../data/u" + species + "x.gda"
x, z, ux = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "y.gda"
x, z, uy = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/u" + species + "z.gda"
x, z, uz = read_2d_fields(pic_info, fname, **kwargs)
fname = "../../data/n" + species + ".gda"
x, z, nrho = read_2d_fields(pic_info, fname, **kwargs)
bulk_ene2 = 0.5 * ptl_mass * nrho * (ux**2 + uy**2 + uz**2)
kwargs = {
"current_time": current_time,
"xl": 0,
"xr": 200,
"zb": -15,
"zt": 15
}
x, z, Ay = read_2d_fields(pic_info, "../../data/Ay.gda", **kwargs)
bulk_ene_rate = bulk_ene2 - bulk_ene1
nx, = x.shape
nz, = z.shape
width = 0.75
height = 0.7
xs = 0.12
ys = 0.9 - height
fig = plt.figure(figsize=[10, 4])
ax1 = fig.add_axes([xs, ys, width, height])
kwargs_plot = {"xstep": 1, "zstep": 1, "vmin": 0.1, "vmax": -0.1}
xstep = kwargs_plot["xstep"]
zstep = kwargs_plot["zstep"]
p1, cbar1 = plot_2d_contour(x, z, bulk_ene_rate, ax1, fig, **kwargs_plot)
p1.set_cmap(plt.cm.seismic)
ax1.contour(x[0:nx:xstep],
z[0:nz:zstep],
Ay[0:nz:zstep, 0:nx:xstep],
colors='black',
linewidths=0.5)
ax1.set_ylabel(r'$z/d_i$', fontdict=font, fontsize=24)
ax1.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=24)
cbar1.ax.set_ylabel(r'$K/u$', fontdict=font, fontsize=24)
cbar1.ax.tick_params(labelsize=24)
t_wci = current_time * pic_info.dt_fields
title = r'$t = ' + "{:10.1f}".format(t_wci) + '/\Omega_{ci}$'
ax1.set_title(title, fontdict=font, fontsize=24)
plt.show()