def main():
"""business logic for when running this module as the primary one!"""
args = get_cmd_args()
run_name = args.run_name
run_dir = args.run_dir
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
coords = get_coordinates(pic_info)
# runs_root_dir = "/net/scratch3/xiaocanli/reconnection/mime400/"
# run_names = ["mime400_beta002_bg00",
# "mime400_beta002_bg02",
# "mime400_beta002_bg04",
# "mime400_beta002_bg08"]
runs_root_dir = "/net/scratch3/xiaocanli/reconnection/frequent_dump/"
run_names = ["mime25_beta002_guide00_frequent_dump",
"mime25_beta002_guide02_frequent_dump",
"mime25_beta002_guide05_frequent_dump",
"mime25_beta002_guide10_frequent_dump"]
if args.multi_runs:
ncores = multiprocessing.cpu_count()
ncores = 4
Parallel(n_jobs=ncores)(delayed(process_input)(runs_root_dir,
run_name, coords)
for run_name in run_names)
else:
calc_exb(run_dir, run_name, 10, coords)
def main():
"""business logic for when running this module as the primary one!"""
args = get_cmd_args()
run_name = args.run_name
run_dir = args.run_dir
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
coords = get_coordinates(pic_info)
# runs_root_dir = "/net/scratch3/xiaocanli/reconnection/mime400/"
# run_names = ["mime400_beta002_bg00",
# "mime400_beta002_bg02",
# "mime400_beta002_bg04",
# "mime400_beta002_bg08"]
runs_root_dir = "/net/scratch3/xiaocanli/reconnection/frequent_dump/"
run_names = [
"mime25_beta002_guide00_frequent_dump",
"mime25_beta002_guide02_frequent_dump",
"mime25_beta002_guide05_frequent_dump",
"mime25_beta002_guide10_frequent_dump"
]
if args.multi_runs:
ncores = multiprocessing.cpu_count()
ncores = 4
Parallel(n_jobs=ncores)(
delayed(process_input)(runs_root_dir, run_name, coords)
for run_name in run_names)
else:
calc_exb(run_dir, run_name, 10, coords)
def process_input(runs_root_dir, run_name, coords):
"""process one PIC run"""
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
tframes = range(pic_info.ntf)
run_dir = runs_root_dir + run_name + '/'
for tframe in tframes:
calc_exb(run_dir, run_name, tframe, coords)
def process_input(runs_root_dir, run_name, coords):
"""process one PIC run"""
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
tframes = range(pic_info.ntf)
run_dir = runs_root_dir + run_name + '/'
for tframe in tframes:
calc_exb(run_dir, run_name, tframe, coords)
def processInput(run_efield):
run_name = run_efield["run_name"]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
coords = get_coordinates(pic_info)
tframes = range(pic_info.ntf)
efield_name = run_efield["efield_name"]
print("Run name and electric field name: %s %s" % (run_name, efield_name))
run_dir = runs_root_dir + run_name + '/'
for tframe in tframes:
# smooth_interp_emf(run_dir, pic_info, efield_name, tframe, coords)
smooth_emf(run_dir, pic_info, efield_name, tframe, coords)
def processInput(run_efield):
run_name = run_efield["run_name"]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
coords = get_coordinates(pic_info)
tframes = range(pic_info.ntf)
efield_name = run_efield["efield_name"]
print("Run name and electric field name: %s %s" %
(run_name, efield_name))
run_dir = runs_root_dir + run_name + '/'
for tframe in tframes:
# smooth_interp_emf(run_dir, pic_info, efield_name, tframe, coords)
smooth_emf(run_dir, pic_info, efield_name, tframe, coords)
def calc_exb(run_dir, run_name, tframe, coords):
"""Calculate ExB drift velocity
Args:
run_dir: PIC run directory
run_name: PIC run name
trame: time frame
coords: coordinates for different fields
"""
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
nx = pic_info.nx
nz = pic_info.nz
kwargs = {"current_time": tframe, "xl": 0, "xr": pic_info.lx_di,
"zb": -0.5 * pic_info.lz_di, "zt": 0.5 * pic_info.lz_di}
size_one_frame = pic_info.nx * pic_info.nz * 4
sigma = 3
fname = run_dir + "data/bx.gda"
_, _, bx = read_2d_fields(pic_info, fname, **kwargs)
f = MultilinearInterpolator(coords["smin_ez_bx"],
coords["smax_ez_bx"],
coords["orders"],
dtype=np.float32)
f.set_values(np.atleast_2d(np.transpose(bx).flatten()))
bx = np.transpose(f(coords["coord"]).reshape((nx, nz)))
fname = run_dir + "data/by.gda"
_, _, by = read_2d_fields(pic_info, fname, **kwargs)
f = MultilinearInterpolator(coords["smin_by"],
coords["smax_by"],
coords["orders"],
dtype=np.float32)
f.set_values(np.atleast_2d(np.transpose(by).flatten()))
by = np.transpose(f(coords["coord"]).reshape((nx, nz)))
fname = run_dir + "data/bz.gda"
_, _, bz = read_2d_fields(pic_info, fname, **kwargs)
f = MultilinearInterpolator(coords["smin_ex_bz"],
coords["smax_ex_bz"],
coords["orders"],
dtype=np.float32)
f.set_values(np.atleast_2d(np.transpose(bz).flatten()))
bz = np.transpose(f(coords["coord"]).reshape((nx, nz)))
fname = run_dir + "data/ex.gda"
_, _, ex = read_2d_fields(pic_info, fname, **kwargs)
ex = gaussian_filter(ex, sigma)
f = MultilinearInterpolator(coords["smin_ex_bz"],
coords["smax_ex_bz"],
coords["orders"],
dtype=np.float32)
f.set_values(np.atleast_2d(np.transpose(ex).flatten()))
ex = np.transpose(f(coords["coord"]).reshape((nx, nz)))
fname = run_dir + "data/ey.gda"
_, _, ey = read_2d_fields(pic_info, fname, **kwargs)
ey = gaussian_filter(ey, sigma)
f = MultilinearInterpolator(coords["smin_h"],
coords["smax_h"],
coords["orders"],
dtype=np.float32)
f.set_values(np.atleast_2d(np.transpose(ey).flatten()))
ey = np.transpose(f(coords["coord"]).reshape((nx, nz)))
fname = run_dir + "data/ez.gda"
_, _, ez = read_2d_fields(pic_info, fname, **kwargs)
ez = gaussian_filter(ez, sigma)
f = MultilinearInterpolator(coords["smin_ez_bx"],
coords["smax_ez_bx"],
coords["orders"],
dtype=np.float32)
f.set_values(np.atleast_2d(np.transpose(ez).flatten()))
ez = np.transpose(f(coords["coord"]).reshape((nx, nz)))
ib2 = 1.0 / (bx**2 + by**2 + bz**2)
exb_x = (ey * bz - ez * by) * ib2
exb_y = (ez * bx - ex * bz) * ib2
exb_z = (ex * by - ey * bx) * ib2
fname = run_dir + "data/exb_x.gda"
with open(fname, 'a+') as f:
offset = size_one_frame * tframe
f.seek(offset, os.SEEK_SET)
exb_x.tofile(f)
fname = run_dir + "data/exb_y.gda"
with open(fname, 'a+') as f:
offset = size_one_frame * tframe
f.seek(offset, os.SEEK_SET)
exb_y.tofile(f)
fname = run_dir + "data/exb_z.gda"
with open(fname, 'a+') as f:
offset = size_one_frame * tframe
f.seek(offset, os.SEEK_SET)
exb_z.tofile(f)
fig_dir = '../img/img_power_spectrum_du/' + run_name + '/'
mkdir_p(fig_dir)
fname = fig_dir + '/ps_comp_' + species + str(ct).zfill(3) + '.jpg'
fig.savefig(fname, dpi=200)
plt.close()
# plt.show()
if __name__ == "__main__":
# base_dir = '/net/scratch3/xiaocanli/2D-90-Mach4-sheet4-multi/'
# run_name = '2D-90-Mach4-sheet4-multi'
base_dir = '/net/scratch2/guofan/for_Senbei/2D-90-Mach4-sheet6-2/'
run_name = '2D-90-Mach4-sheet6-2'
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
ct = pic_info.ntf - 2
# ct = 200
cts = range(10, pic_info.ntf - 1)
xmin, xmax = 0, pic_info.lx_di
xmin, xmax = 0, 105
zmin, zmax = -0.5 * pic_info.lz_di, 0.5 * pic_info.lz_di
# kwargs = {"current_time":ct, "xl":xmin, "xr":xmax, "zb":zmin, "zt":zmax}
# fname = base_dir + 'data1/vex.gda'
# x, z, vel = read_2d_fields(pic_info, fname, **kwargs)
kwargs = {
"current_time": 0,
"xl": xmin,
"xr": xmax,
"zb": zmin,
def plot_energy_conversion_fraction():
"""Plot energy evolution for multiple runs.
"""
dir = '../data/pic_info/'
if not os.path.isdir('../img/'):
os.makedirs('../img/')
odir = '../img/ene_evolution/'
if not os.path.isdir(odir):
os.makedirs(odir)
fnames = list_pic_info_dir(dir)
print fnames
# run_id = [2, 5, 1, 6, 7, 8, 0, 4]
run_id = [2, 8, 1, 9, 10, 12, 0, 7]
nrun = len(run_id)
ene_fraction = np.zeros(nrun)
dke_dki = np.zeros(nrun)
irun = 0
i = 0
for fname in fnames:
print i, fname
i += 1
for i in run_id:
fname = fnames[i]
print fname
rname = fname.replace(".json", ".eps")
oname = rname.replace("pic_info", "enes")
oname = odir + oname
fname = dir + fname
pic_info = read_data_from_json(fname)
tenergy = pic_info.tenergy
ene_magnetic = pic_info.ene_magnetic
kene_e = pic_info.kene_e
kene_i = pic_info.kene_i
dke = kene_e[-1] - kene_e[0]
dki = kene_i[-1] - kene_i[0]
enorm = ene_magnetic[0]
ene_fraction[irun] = 1.0 - ene_magnetic[-1] / enorm
dke_dki[irun] = dke / dki
irun += 1
x = np.arange(8)
fig = plt.figure(figsize=[7, 5])
xs, ys = 0.16, 0.13
w1, h1 = 0.7, 0.8
ax = fig.add_axes([xs, ys, w1, h1])
ax.plot(
x,
ene_fraction,
color='r',
marker='o',
markersize=10,
linestyle='',
markeredgecolor='r')
labels = [r'R6', r'R1', r'R4', r'R2', r'R5', r'R3', r'R7', r'R8']
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.set_xlim([-0.5, 7.5])
ax.set_ylabel(
r'$|\Delta\varepsilon_b|/\varepsilon_{b0}$',
color='r',
fontdict=font,
fontsize=24)
ax.tick_params(labelsize=20)
ax.text(
0.4,
0.15,
r'$m_i/m_e=100$',
color='k',
fontsize=20,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
for tl in ax.get_yticklabels():
tl.set_color('r')
ax1 = ax.twinx()
ax1.plot(
x,
dke_dki,
color='b',
marker='v',
markersize=10,
linestyle='',
markeredgecolor='b')
ax1.set_xlim([-0.5, 7.5])
ax1.set_ylabel(
r'$\Delta K_e/\Delta K_i$', color='b', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=20)
for tl in ax1.get_yticklabels():
tl.set_color('b')
if not os.path.isdir('../img/'):
os.makedirs('../img/')
dir = '../img/img_dpp/'
if not os.path.isdir(dir):
os.makedirs(dir)
fname = dir + 'ene_fraction_.eps'
fig.savefig(fname)
plt.show()
def plot_compression_time_temp(species):
"""
Plot time evolution of compression and shear heating for runs with
different plasma temperature
Args:
species: particle species
"""
dir = '../data/compression/'
dir_jdote = '../data/jdote_data/'
if not os.path.isdir('../img/'):
os.makedirs('../img/')
odir = '../img/compression/'
if not os.path.isdir(odir):
os.makedirs(odir)
base_dirs, run_names = ApJ_long_paper_runs()
fig = plt.figure(figsize=[7, 5])
w1, h1 = 0.8, 0.27
xs, ys = 0.96 - w1, 0.97 - h1
gap = 0.02
nrun = len(run_names)
ylim1 = np.zeros((nrun, 2))
if species == 'e':
ylim1[0, :] = -0.05, 0.15
ylim1[1, :] = -0.3, 1.1
ylim1[2, :] = -1.0, 5
ylim1[3, :] = -10.0, 30.0
ylim1[4, :] = -2.0, 5.0
ylim1[5, :] = -0.1, 0.2
ylim1[6, :] = -0.5, 1.1
ylim1[7, :] = -3.0, 6.0
ylim1[8, :] = -1.0, 5.0
else:
ylim1[0, :] = -0.1, 0.25
ylim1[1, :] = -0.6, 2.2
ylim1[2, :] = -2.0, 10
ylim1[3, :] = -20.0, 60.0
ylim1[4, :] = -4.0, 13.0
ylim1[5, :] = -0.2, 0.4
ylim1[6, :] = -1.0, 2.2
ylim1[7, :] = -5.0, 15.0
ylim1[8, :] = -3.0, 7.0
runs = [5, 6, 2]
axs = []
for i in runs:
run_name = run_names[i]
base_dir = base_dirs[i]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
jdote_fname = dir_jdote + 'jdote_' + run_name + '_' + species + '.json'
pic_info = read_data_from_json(picinfo_fname)
jdote_data = read_data_from_json(jdote_fname)
fpath_comp = '../data/compression/' + run_name + '/'
b0 = pic_info.b0
ylim2 = ylim1 * b0**2
ax = fig.add_axes([xs, ys, w1, h1])
axs.append(ax)
compression_time(pic_info, species, jdote_data, ylim2[i, :], ax,
fpath_comp)
ys -= h1 + gap
for ax in axs:
ax.set_xlim([0, 1200])
if species == 'e':
axs[0].set_yticks(np.arange(0, 0.007, 0.002))
axs[1].set_yticks(np.arange(0, 0.025, 0.01))
axs[2].set_yticks(np.arange(0, 0.09, 0.02))
else:
axs[0].set_yticks(np.arange(0, 0.021, 0.005))
axs[1].set_yticks(np.arange(0, 0.07, 0.02))
axs[2].set_yticks(np.arange(0, 0.17, 0.04))
axs[-1].tick_params(axis='x', labelbottom='on')
axs[-1].set_xlabel(r'$t\Omega_{ci}$', fontdict=font, fontsize=20)
label1 = r'$-p\nabla\cdot\mathbf{u}$'
label2 = r'$-(p_\parallel - p_\perp)b_ib_j\sigma_{ij}$'
axs[0].text(
0.4,
0.2,
label1,
color='red',
fontsize=20,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=axs[0].transAxes)
axs[0].text(
0.6,
0.2,
label2,
color='green',
fontsize=20,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=axs[0].transAxes)
axs[0].text(
0.05,
0.8,
r'$v_\text{the}/c=0.045$',
color='black',
fontsize=20,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=axs[0].transAxes)
axs[1].text(
0.05,
0.8,
r'$v_\text{the}/c=0.08$',
color='black',
fontsize=20,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=axs[1].transAxes)
axs[2].text(
0.05,
0.8,
r'$v_\text{the}/c=0.14$',
color='black',
fontsize=20,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=axs[2].transAxes)
if not os.path.isdir('../img/'):
os.makedirs('../img/')
dir = '../img/img_dpp/'
if not os.path.isdir(dir):
os.makedirs(dir)
fname = dir + 'comp_temp_' + species + '.eps'
fig.savefig(fname)
plt.show()
def plot_jpara_dote_fraction():
"""Plot energy evolution from the parallel and perpendicular directions.
"""
dir = '../data/jdote_data/'
if not os.path.isdir('../img/'):
os.makedirs('../img/')
odir = '../img/jdote/'
if not os.path.isdir(odir):
os.makedirs(odir)
base_dirs, run_names = ApJ_long_paper_runs()
run_id = [3, 2, 6, 7, 4, 5, 1, 0]
nrun = len(run_id)
fraction_jpara_e = np.zeros(nrun)
fraction_jpara_i = np.zeros(nrun)
fraction_jperp_e = np.zeros(nrun)
fraction_jperp_i = np.zeros(nrun)
irun = 0
for i in run_id:
run_name = run_names[i]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
jdote_e_fname = '../data/jdote_data/jdote_' + run_name + '_e.json'
jdote_i_fname = '../data/jdote_data/jdote_' + run_name + '_i.json'
pic_info = read_data_from_json(picinfo_fname)
kene_e = pic_info.kene_e
kene_i = pic_info.kene_i
dkene_e = kene_e[-1] - kene_e[0]
dkene_i = kene_i[-1] - kene_i[0]
jdote_e = read_data_from_json(jdote_e_fname)
jdote_i = read_data_from_json(jdote_i_fname)
jpara_dote_e = jdote_e.jqnupara_dote_int
jperp_dote_e = jdote_e.jqnuperp_dote_int
jpara_dote_i = jdote_i.jqnupara_dote_int
jperp_dote_i = jdote_i.jqnuperp_dote_int
fraction_jpara_e[irun] = jpara_dote_e[-1] / dkene_e
fraction_jperp_e[irun] = jperp_dote_e[-1] / dkene_e
fraction_jpara_i[irun] = jpara_dote_i[-1] / dkene_i
fraction_jperp_i[irun] = jperp_dote_i[-1] / dkene_i
irun += 1
x = np.arange(8)
fig = plt.figure(figsize=[7, 5])
xs, ys = 0.18, 0.13
w1, h1 = 0.78, 0.8
ax = fig.add_axes([xs, ys, w1, h1])
ax.plot(x,
fraction_jpara_e[:8],
color='r',
marker='o',
markersize=10,
linestyle='')
ax.plot(x,
fraction_jpara_i[:8],
color='b',
marker='o',
markersize=10,
linestyle='')
labels = [r'R6', r'R1', r'R2', r'R4', r'R5', r'R3', r'R7', r'R8']
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.set_xlim([-0.5, 7.5])
ax.set_ylabel(r'Fraction of Parallel Acceleration',
fontdict=font,
fontsize=24)
ax.tick_params(labelsize=20)
ax.text(0.05,
0.15,
r'Electron',
color='r',
fontsize=24,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.text(0.05,
0.25,
r'Ion',
color='b',
fontsize=24,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
if not os.path.isdir('../img/'):
os.makedirs('../img/')
dir = '../img/img_dpp/'
if not os.path.isdir(dir):
os.makedirs(dir)
fname = dir + 'para_acc_fraction.eps'
fig.savefig(fname)
plt.show()
def calc_exb(run_dir, run_name, tframe, coords):
"""Calculate ExB drift velocity
Args:
run_dir: PIC run directory
run_name: PIC run name
trame: time frame
coords: coordinates for different fields
"""
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
nx = pic_info.nx
nz = pic_info.nz
kwargs = {
"current_time": tframe,
"xl": 0,
"xr": pic_info.lx_di,
"zb": -0.5 * pic_info.lz_di,
"zt": 0.5 * pic_info.lz_di
}
size_one_frame = pic_info.nx * pic_info.nz * 4
sigma = 3
fname = run_dir + "data/bx.gda"
_, _, bx = read_2d_fields(pic_info, fname, **kwargs)
f = MultilinearInterpolator(coords["smin_ez_bx"],
coords["smax_ez_bx"],
coords["orders"],
dtype=np.float32)
f.set_values(np.atleast_2d(np.transpose(bx).flatten()))
bx = np.transpose(f(coords["coord"]).reshape((nx, nz)))
fname = run_dir + "data/by.gda"
_, _, by = read_2d_fields(pic_info, fname, **kwargs)
f = MultilinearInterpolator(coords["smin_by"],
coords["smax_by"],
coords["orders"],
dtype=np.float32)
f.set_values(np.atleast_2d(np.transpose(by).flatten()))
by = np.transpose(f(coords["coord"]).reshape((nx, nz)))
fname = run_dir + "data/bz.gda"
_, _, bz = read_2d_fields(pic_info, fname, **kwargs)
f = MultilinearInterpolator(coords["smin_ex_bz"],
coords["smax_ex_bz"],
coords["orders"],
dtype=np.float32)
f.set_values(np.atleast_2d(np.transpose(bz).flatten()))
bz = np.transpose(f(coords["coord"]).reshape((nx, nz)))
fname = run_dir + "data/ex.gda"
_, _, ex = read_2d_fields(pic_info, fname, **kwargs)
ex = gaussian_filter(ex, sigma)
f = MultilinearInterpolator(coords["smin_ex_bz"],
coords["smax_ex_bz"],
coords["orders"],
dtype=np.float32)
f.set_values(np.atleast_2d(np.transpose(ex).flatten()))
ex = np.transpose(f(coords["coord"]).reshape((nx, nz)))
fname = run_dir + "data/ey.gda"
_, _, ey = read_2d_fields(pic_info, fname, **kwargs)
ey = gaussian_filter(ey, sigma)
f = MultilinearInterpolator(coords["smin_h"],
coords["smax_h"],
coords["orders"],
dtype=np.float32)
f.set_values(np.atleast_2d(np.transpose(ey).flatten()))
ey = np.transpose(f(coords["coord"]).reshape((nx, nz)))
fname = run_dir + "data/ez.gda"
_, _, ez = read_2d_fields(pic_info, fname, **kwargs)
ez = gaussian_filter(ez, sigma)
f = MultilinearInterpolator(coords["smin_ez_bx"],
coords["smax_ez_bx"],
coords["orders"],
dtype=np.float32)
f.set_values(np.atleast_2d(np.transpose(ez).flatten()))
ez = np.transpose(f(coords["coord"]).reshape((nx, nz)))
ib2 = 1.0 / (bx**2 + by**2 + bz**2)
exb_x = (ey * bz - ez * by) * ib2
exb_y = (ez * bx - ex * bz) * ib2
exb_z = (ex * by - ey * bx) * ib2
fname = run_dir + "data/exb_x.gda"
with open(fname, 'a+') as f:
offset = size_one_frame * tframe
f.seek(offset, os.SEEK_SET)
exb_x.tofile(f)
fname = run_dir + "data/exb_y.gda"
with open(fname, 'a+') as f:
offset = size_one_frame * tframe
f.seek(offset, os.SEEK_SET)
exb_y.tofile(f)
fname = run_dir + "data/exb_z.gda"
with open(fname, 'a+') as f:
offset = size_one_frame * tframe
f.seek(offset, os.SEEK_SET)
exb_z.tofile(f)
def plot_particle_number():
"""
"""
temp = 0.73
l0 = 0.13 # length in meter
potentials = [50, 100, 150, 200, 200]
run_names = ['v50', 'v100', 'v150', 'v200', 'v200_b0_wce']
nes = [1.4E6, 1.45E6, 1.48E6, 1.48E6, 1.48E6] # in cm^-3
lnames = ['50 V', '100 V', '150 V', '200 V', '200 V, B=0']
fig = plt.figure(figsize=[7, 5])
xs, ys = 0.13, 0.13
w1, h1 = 0.8, 0.8
ax = fig.add_axes([xs, ys, w1, h1])
tmax = 0
current_mean = []
for potential, ne, run_name, lname in zip(potentials, nes, run_names, lnames):
# run_name = 'v' + str(potential)
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = '../data/particle_number/nptl_' + run_name + '.dat'
data = np.genfromtxt(fname)
t = data[:, 0]
dt = t[1] - t[0]
ntot = data[:, 1:]
dne = np.gradient(ntot[:, 0], axis=0)
wpe = 1.78E9 * math.sqrt(ne / n0) # ne is normalized by n0
dt /= wpe
ntot = pic_info.nx * pic_info.nz * pic_info.nppc
smime = math.sqrt(pic_info.mime)
lx_de = pic_info.lx_di * smime
lz_de = pic_info.lz_di * smime
de = c0 * 1E3 / wpe
vol = lx_de * lz_de * de**2 # assume the third dimension is 1 meter
dne_real = dne * ne * 1E6 * vol / ntot
current = -dne_real * qe / dt
current *= 1E3 # A -> mA
lname = str(potential) + 'V'
ax.plot(t, current * l0, linewidth=2, label=lname)
tmax = max(t.max(), tmax)
current_mean.append(np.mean(current[-20:]))
ax.tick_params(labelsize=16)
ax.set_xlabel(r'$t\omega_{pe}$', fontdict=font, fontsize=20)
ax.set_ylabel(r'$I$/mA', fontdict=font, fontsize=20)
leg = ax.legend(
loc=1,
prop={'size': 20},
ncol=1,
shadow=False,
fancybox=False,
frameon=False)
ax.set_xlim([0, tmax])
ax.set_ylim([0, 1])
fig.savefig('../img/current_time.eps')
fig.savefig('../img/current_time.jpg', dpi=300)
r0 = 0.0925E-2 # in meter
area = 2 * math.pi * r0 # Assume 1m length
current_mean = np.asarray(current_mean)
current_mean /= area
current_thermal = np.asarray([29.9, 31.2, 31.8, 31.8, 31.8]) # mA/m^2
potential = np.asarray([50, 100, 150, 200, 200])
fig = plt.figure(figsize=[7, 5])
xs, ys = 0.13, 0.13
w1, h1 = 0.8, 0.8
ax = fig.add_axes([xs, ys, w1, h1])
current_norm = current_mean / current_thermal
ax.plot(
potential / temp, current_norm, linewidth=2, marker='o', markersize=10)
ax.tick_params(labelsize=16)
ax.set_xlabel(
'body bias / electron temperature', fontdict=font, fontsize=20)
ax.set_ylabel(
'collected current / thermal current', fontdict=font, fontsize=20)
fig.savefig('../img/current_potential.eps')
fig.savefig('../img/current_potential.jpg', dpi=300)
plt.show()
def plot_energy_conversion_fraction():
"""Plot energy evolution for multiple runs.
"""
dir = '../data/pic_info/'
if not os.path.isdir('../img/'):
os.makedirs('../img/')
odir = '../img/ene_evolution/'
if not os.path.isdir(odir):
os.makedirs(odir)
fnames = list_pic_info_dir(dir)
print fnames
# run_id = [2, 5, 1, 6, 7, 8, 0, 4]
run_id = [2, 8, 1, 9, 10, 12, 0, 7]
nrun = len(run_id)
ene_fraction = np.zeros(nrun)
dke_dki = np.zeros(nrun)
irun = 0
i = 0
for fname in fnames:
print i, fname
i += 1
for i in run_id:
fname = fnames[i]
print fname
rname = fname.replace(".json", ".eps")
oname = rname.replace("pic_info", "enes")
oname = odir + oname
fname = dir + fname
pic_info = read_data_from_json(fname)
tenergy = pic_info.tenergy
ene_magnetic = pic_info.ene_magnetic
kene_e = pic_info.kene_e
kene_i = pic_info.kene_i
dke = kene_e[-1] - kene_e[0]
dki = kene_i[-1] - kene_i[0]
enorm = ene_magnetic[0]
ene_fraction[irun] = 1.0 - ene_magnetic[-1] / enorm
dke_dki[irun] = dke / dki
irun += 1
x = np.arange(8)
fig = plt.figure(figsize=[7, 5])
xs, ys = 0.16, 0.13
w1, h1 = 0.7, 0.8
ax = fig.add_axes([xs, ys, w1, h1])
ax.plot(x,
ene_fraction,
color='r',
marker='o',
markersize=10,
linestyle='',
markeredgecolor='r')
labels = [r'R6', r'R1', r'R4', r'R2', r'R5', r'R3', r'R7', r'R8']
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.set_xlim([-0.5, 7.5])
ax.set_ylabel(r'$|\Delta\varepsilon_b|/\varepsilon_{b0}$',
color='r',
fontdict=font,
fontsize=24)
ax.tick_params(labelsize=20)
ax.text(0.4,
0.15,
r'$m_i/m_e=100$',
color='k',
fontsize=20,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
for tl in ax.get_yticklabels():
tl.set_color('r')
ax1 = ax.twinx()
ax1.plot(x,
dke_dki,
color='b',
marker='v',
markersize=10,
linestyle='',
markeredgecolor='b')
ax1.set_xlim([-0.5, 7.5])
ax1.set_ylabel(r'$\Delta K_e/\Delta K_i$',
color='b',
fontdict=font,
fontsize=24)
ax1.tick_params(labelsize=20)
for tl in ax1.get_yticklabels():
tl.set_color('b')
if not os.path.isdir('../img/'):
os.makedirs('../img/')
dir = '../img/img_dpp/'
if not os.path.isdir(dir):
os.makedirs(dir)
fname = dir + 'ene_fraction_.eps'
fig.savefig(fname)
plt.show()
def plot_compression_time_temp(species):
"""
Plot time evolution of compression and shear heating for runs with
different plasma temperature
Args:
species: particle species
"""
dir = '../data/compression/'
dir_jdote = '../data/jdote_data/'
if not os.path.isdir('../img/'):
os.makedirs('../img/')
odir = '../img/compression/'
if not os.path.isdir(odir):
os.makedirs(odir)
base_dirs, run_names = ApJ_long_paper_runs()
fig = plt.figure(figsize=[7, 5])
w1, h1 = 0.8, 0.27
xs, ys = 0.96 - w1, 0.97 - h1
gap = 0.02
nrun = len(run_names)
ylim1 = np.zeros((nrun, 2))
if species == 'e':
ylim1[0, :] = -0.05, 0.15
ylim1[1, :] = -0.3, 1.1
ylim1[2, :] = -1.0, 5
ylim1[3, :] = -10.0, 30.0
ylim1[4, :] = -2.0, 5.0
ylim1[5, :] = -0.1, 0.2
ylim1[6, :] = -0.5, 1.1
ylim1[7, :] = -3.0, 6.0
ylim1[8, :] = -1.0, 5.0
else:
ylim1[0, :] = -0.1, 0.25
ylim1[1, :] = -0.6, 2.2
ylim1[2, :] = -2.0, 10
ylim1[3, :] = -20.0, 60.0
ylim1[4, :] = -4.0, 13.0
ylim1[5, :] = -0.2, 0.4
ylim1[6, :] = -1.0, 2.2
ylim1[7, :] = -5.0, 15.0
ylim1[8, :] = -3.0, 7.0
runs = [5, 6, 2]
axs = []
for i in runs:
run_name = run_names[i]
base_dir = base_dirs[i]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
jdote_fname = dir_jdote + 'jdote_' + run_name + '_' + species + '.json'
pic_info = read_data_from_json(picinfo_fname)
jdote_data = read_data_from_json(jdote_fname)
fpath_comp = '../data/compression/' + run_name + '/'
b0 = pic_info.b0
ylim2 = ylim1 * b0**2
ax = fig.add_axes([xs, ys, w1, h1])
axs.append(ax)
compression_time(pic_info, species, jdote_data, ylim2[i, :], ax,
fpath_comp)
ys -= h1 + gap
for ax in axs:
ax.set_xlim([0, 1200])
if species == 'e':
axs[0].set_yticks(np.arange(0, 0.007, 0.002))
axs[1].set_yticks(np.arange(0, 0.025, 0.01))
axs[2].set_yticks(np.arange(0, 0.09, 0.02))
else:
axs[0].set_yticks(np.arange(0, 0.021, 0.005))
axs[1].set_yticks(np.arange(0, 0.07, 0.02))
axs[2].set_yticks(np.arange(0, 0.17, 0.04))
axs[-1].tick_params(axis='x', labelbottom='on')
axs[-1].set_xlabel(r'$t\Omega_{ci}$', fontdict=font, fontsize=20)
label1 = r'$-p\nabla\cdot\mathbf{u}$'
label2 = r'$-(p_\parallel - p_\perp)b_ib_j\sigma_{ij}$'
axs[0].text(0.4,
0.2,
label1,
color='red',
fontsize=20,
bbox=dict(facecolor='none',
alpha=1.0,
edgecolor='none',
pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=axs[0].transAxes)
axs[0].text(0.6,
0.2,
label2,
color='green',
fontsize=20,
bbox=dict(facecolor='none',
alpha=1.0,
edgecolor='none',
pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=axs[0].transAxes)
axs[0].text(0.05,
0.8,
r'$v_\text{the}/c=0.045$',
color='black',
fontsize=20,
bbox=dict(facecolor='none',
alpha=1.0,
edgecolor='none',
pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=axs[0].transAxes)
axs[1].text(0.05,
0.8,
r'$v_\text{the}/c=0.08$',
color='black',
fontsize=20,
bbox=dict(facecolor='none',
alpha=1.0,
edgecolor='none',
pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=axs[1].transAxes)
axs[2].text(0.05,
0.8,
r'$v_\text{the}/c=0.14$',
color='black',
fontsize=20,
bbox=dict(facecolor='none',
alpha=1.0,
edgecolor='none',
pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=axs[2].transAxes)
if not os.path.isdir('../img/'):
os.makedirs('../img/')
dir = '../img/img_dpp/'
if not os.path.isdir(dir):
os.makedirs(dir)
fname = dir + 'comp_temp_' + species + '.eps'
fig.savefig(fname)
plt.show()
def plot_energy_conversion_fraction_beta():
"""Plot energy evolution for multiple runs with different beta
"""
picinfo_dir = '../data/pic_info/'
mkdir_p(picinfo_dir)
odir = '../img/ene_evolution/'
mkdir_p(odir)
run_names = [
'sigma1-mime25-beta0002', 'mime25_beta0007', 'mime25_beta002',
'mime25_beta007', 'mime25_beta02'
]
labels = [
'R7\n 0.0002', 'R6\n 0.007', 'R1\n 0.02', 'R7\n 0.07', 'R8\n 0.2'
]
run_label = r'$\beta_e = $'
fname = 'ene_fraction_beta.eps'
# run_names = ['mime25_beta002', 'mime25_beta002_sigma033',
# 'mime25_beta002_sigma01']
# labels = ['R1\n $1.0$', 'R2\n $\sqrt{3}$', 'R3\n $\sqrt{10}$']
# run_label = r'$\omega_{pe} / \Omega_{ce} = $'
# fname = 'ene_fraction_wpe_wce.eps'
nrun = len(run_names)
ene_fraction = np.zeros(nrun)
dke_dki = np.zeros(nrun)
irun = 0
for run_name in run_names:
print run_name
rname = run_name.replace(".json", ".eps")
oname = rname.replace("pic_info", "enes")
oname = odir + oname
picinfo_fname = picinfo_dir + 'pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
tenergy = pic_info.tenergy
ene_magnetic = pic_info.ene_magnetic
kene_e = pic_info.kene_e
kene_i = pic_info.kene_i
dke = kene_e[-1] - kene_e[0]
dki = kene_i[-1] - kene_i[0]
enorm = ene_magnetic[0]
ene_fraction[irun] = 1.0 - ene_magnetic[-1] / enorm
dke_dki[irun] = dke / dki
irun += 1
x = np.arange(nrun)
fig = plt.figure(figsize=[7, 5])
xs, ys = 0.16, 0.13
w1, h1 = 0.7, 0.8
ax = fig.add_axes([xs, ys, w1, h1])
ax.plot(x,
ene_fraction,
color='r',
marker='o',
markersize=10,
linestyle='',
markeredgecolor='r')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.set_xlim([-0.5, nrun - 0.5])
ax.set_ylabel(r'$|\Delta\varepsilon_b|/\varepsilon_{b0}$',
color='r',
fontdict=font,
fontsize=24)
ax.tick_params(labelsize=20)
ax.text(-0.12,
-0.12,
run_label,
color='k',
fontsize=20,
bbox=dict(facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
for tl in ax.get_yticklabels():
tl.set_color('r')
ax1 = ax.twinx()
ax1.plot(x,
dke_dki,
color='b',
marker='D',
markersize=10,
linestyle='',
markeredgecolor='b')
ax1.set_xlim([-0.5, nrun - 0.5])
ax1.set_ylabel(r'$\Delta K_e/\Delta K_i$',
color='b',
fontdict=font,
fontsize=24)
ax1.tick_params(labelsize=20)
for tl in ax1.get_yticklabels():
tl.set_color('b')
img_dir = '../img/img_dpp/'
mkdir_p(img_dir)
fig.savefig(img_dir + fname)
plt.show()
def plot_jpara_dote_fraction():
"""Plot energy evolution from the parallel and perpendicular directions.
"""
dir = '../data/jdote_data/'
if not os.path.isdir('../img/'):
os.makedirs('../img/')
odir = '../img/jdote/'
if not os.path.isdir(odir):
os.makedirs(odir)
base_dirs, run_names = ApJ_long_paper_runs()
run_id = [3, 2, 6, 7, 4, 5, 1, 0]
nrun = len(run_id)
fraction_jpara_e = np.zeros(nrun)
fraction_jpara_i = np.zeros(nrun)
fraction_jperp_e = np.zeros(nrun)
fraction_jperp_i = np.zeros(nrun)
irun = 0
for i in run_id:
run_name = run_names[i]
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
jdote_e_fname = '../data/jdote_data/jdote_' + run_name + '_e.json'
jdote_i_fname = '../data/jdote_data/jdote_' + run_name + '_i.json'
pic_info = read_data_from_json(picinfo_fname)
kene_e = pic_info.kene_e
kene_i = pic_info.kene_i
dkene_e = kene_e[-1] - kene_e[0]
dkene_i = kene_i[-1] - kene_i[0]
jdote_e = read_data_from_json(jdote_e_fname)
jdote_i = read_data_from_json(jdote_i_fname)
jpara_dote_e = jdote_e.jqnupara_dote_int
jperp_dote_e = jdote_e.jqnuperp_dote_int
jpara_dote_i = jdote_i.jqnupara_dote_int
jperp_dote_i = jdote_i.jqnuperp_dote_int
fraction_jpara_e[irun] = jpara_dote_e[-1] / dkene_e
fraction_jperp_e[irun] = jperp_dote_e[-1] / dkene_e
fraction_jpara_i[irun] = jpara_dote_i[-1] / dkene_i
fraction_jperp_i[irun] = jperp_dote_i[-1] / dkene_i
irun += 1
x = np.arange(8)
fig = plt.figure(figsize=[7, 5])
xs, ys = 0.18, 0.13
w1, h1 = 0.78, 0.8
ax = fig.add_axes([xs, ys, w1, h1])
ax.plot(
x,
fraction_jpara_e[:8],
color='r',
marker='o',
markersize=10,
linestyle='')
ax.plot(
x,
fraction_jpara_i[:8],
color='b',
marker='o',
markersize=10,
linestyle='')
labels = [r'R6', r'R1', r'R2', r'R4', r'R5', r'R3', r'R7', r'R8']
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.set_xlim([-0.5, 7.5])
ax.set_ylabel(
r'Fraction of Parallel Acceleration', fontdict=font, fontsize=24)
ax.tick_params(labelsize=20)
ax.text(
0.05,
0.15,
r'Electron',
color='r',
fontsize=24,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.text(
0.05,
0.25,
r'Ion',
color='b',
fontsize=24,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
if not os.path.isdir('../img/'):
os.makedirs('../img/')
dir = '../img/img_dpp/'
if not os.path.isdir(dir):
os.makedirs(dir)
fname = dir + 'para_acc_fraction.eps'
fig.savefig(fname)
plt.show()
def plot_energy_conversion_fraction_beta():
"""Plot energy evolution for multiple runs with different beta
"""
picinfo_dir = '../data/pic_info/'
mkdir_p(picinfo_dir)
odir = '../img/ene_evolution/'
mkdir_p(odir)
run_names = [
'sigma1-mime25-beta0002', 'mime25_beta0007', 'mime25_beta002',
'mime25_beta007', 'mime25_beta02'
]
labels = [
'R7\n 0.0002', 'R6\n 0.007', 'R1\n 0.02', 'R7\n 0.07', 'R8\n 0.2'
]
run_label = r'$\beta_e = $'
fname = 'ene_fraction_beta.eps'
# run_names = ['mime25_beta002', 'mime25_beta002_sigma033',
# 'mime25_beta002_sigma01']
# labels = ['R1\n $1.0$', 'R2\n $\sqrt{3}$', 'R3\n $\sqrt{10}$']
# run_label = r'$\omega_{pe} / \Omega_{ce} = $'
# fname = 'ene_fraction_wpe_wce.eps'
nrun = len(run_names)
ene_fraction = np.zeros(nrun)
dke_dki = np.zeros(nrun)
irun = 0
for run_name in run_names:
print run_name
rname = run_name.replace(".json", ".eps")
oname = rname.replace("pic_info", "enes")
oname = odir + oname
picinfo_fname = picinfo_dir + 'pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
tenergy = pic_info.tenergy
ene_magnetic = pic_info.ene_magnetic
kene_e = pic_info.kene_e
kene_i = pic_info.kene_i
dke = kene_e[-1] - kene_e[0]
dki = kene_i[-1] - kene_i[0]
enorm = ene_magnetic[0]
ene_fraction[irun] = 1.0 - ene_magnetic[-1] / enorm
dke_dki[irun] = dke / dki
irun += 1
x = np.arange(nrun)
fig = plt.figure(figsize=[7, 5])
xs, ys = 0.16, 0.13
w1, h1 = 0.7, 0.8
ax = fig.add_axes([xs, ys, w1, h1])
ax.plot(
x,
ene_fraction,
color='r',
marker='o',
markersize=10,
linestyle='',
markeredgecolor='r')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.set_xlim([-0.5, nrun - 0.5])
ax.set_ylabel(
r'$|\Delta\varepsilon_b|/\varepsilon_{b0}$',
color='r',
fontdict=font,
fontsize=24)
ax.tick_params(labelsize=20)
ax.text(
-0.12,
-0.12,
run_label,
color='k',
fontsize=20,
bbox=dict(
facecolor='none', alpha=1.0, edgecolor='none', pad=10.0),
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
for tl in ax.get_yticklabels():
tl.set_color('r')
ax1 = ax.twinx()
ax1.plot(
x,
dke_dki,
color='b',
marker='D',
markersize=10,
linestyle='',
markeredgecolor='b')
ax1.set_xlim([-0.5, nrun - 0.5])
ax1.set_ylabel(
r'$\Delta K_e/\Delta K_i$', color='b', fontdict=font, fontsize=24)
ax1.tick_params(labelsize=20)
for tl in ax1.get_yticklabels():
tl.set_color('b')
img_dir = '../img/img_dpp/'
mkdir_p(img_dir)
fig.savefig(img_dir + fname)
plt.show()
vi = np.fromfile(fname, dtype=np.float32)
vs = (ve * ne + vi * ni * mime) * inrho
vs.tofile(fdir + 'vy.gda')
fname = run_dir + 'data/vez.gda'
ve = np.fromfile(fname, dtype=np.float32)
fname = run_dir + 'data/viz.gda'
vi = np.fromfile(fname, dtype=np.float32)
vs = (ve * ne + vi * ni * mime) * inrho
vs.tofile(fdir + 'vz.gda')
if __name__ == "__main__":
cmdargs = sys.argv
if (len(cmdargs) > 2):
run_dir = cmdargs[1]
run_name = cmdargs[2]
else:
run_dir = '/net/scratch3/xiaocanli/reconnection/mime25-sigma1-beta002-guide00-200-100/'
run_name = 'mime25_beta002_guide00'
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
mime = pic_info.mime
calc_vsingle(run_dir, mime)
def processInput(job_id):
print job_id
ncores = multiprocessing.cpu_count()
# Parallel(n_jobs=ncores)(delayed(processInput)(rank) for rank in ranks)
def plot_particle_number():
"""
"""
temp = 0.73
l0 = 0.13 # length in meter
potentials = [50, 100, 150, 200, 200]
run_names = ['v50', 'v100', 'v150', 'v200', 'v200_b0_wce']
nes = [1.4E6, 1.45E6, 1.48E6, 1.48E6, 1.48E6] # in cm^-3
lnames = ['50 V', '100 V', '150 V', '200 V', '200 V, B=0']
fig = plt.figure(figsize=[7, 5])
xs, ys = 0.13, 0.13
w1, h1 = 0.8, 0.8
ax = fig.add_axes([xs, ys, w1, h1])
tmax = 0
current_mean = []
for potential, ne, run_name, lname in zip(potentials, nes, run_names,
lnames):
# run_name = 'v' + str(potential)
picinfo_fname = '../data/pic_info/pic_info_' + run_name + '.json'
pic_info = read_data_from_json(picinfo_fname)
fname = '../data/particle_number/nptl_' + run_name + '.dat'
data = np.genfromtxt(fname)
t = data[:, 0]
dt = t[1] - t[0]
ntot = data[:, 1:]
dne = np.gradient(ntot[:, 0], axis=0)
wpe = 1.78E9 * math.sqrt(ne / n0) # ne is normalized by n0
dt /= wpe
ntot = pic_info.nx * pic_info.nz * pic_info.nppc
smime = math.sqrt(pic_info.mime)
lx_de = pic_info.lx_di * smime
lz_de = pic_info.lz_di * smime
de = c0 * 1E3 / wpe
vol = lx_de * lz_de * de**2 # assume the third dimension is 1 meter
dne_real = dne * ne * 1E6 * vol / ntot
current = -dne_real * qe / dt
current *= 1E3 # A -> mA
lname = str(potential) + 'V'
ax.plot(t, current * l0, linewidth=2, label=lname)
tmax = max(t.max(), tmax)
current_mean.append(np.mean(current[-20:]))
ax.tick_params(labelsize=16)
ax.set_xlabel(r'$t\omega_{pe}$', fontdict=font, fontsize=20)
ax.set_ylabel(r'$I$/mA', fontdict=font, fontsize=20)
leg = ax.legend(loc=1,
prop={'size': 20},
ncol=1,
shadow=False,
fancybox=False,
frameon=False)
ax.set_xlim([0, tmax])
ax.set_ylim([0, 1])
fig.savefig('../img/current_time.eps')
fig.savefig('../img/current_time.jpg', dpi=300)
r0 = 0.0925E-2 # in meter
area = 2 * math.pi * r0 # Assume 1m length
current_mean = np.asarray(current_mean)
current_mean /= area
current_thermal = np.asarray([29.9, 31.2, 31.8, 31.8, 31.8]) # mA/m^2
potential = np.asarray([50, 100, 150, 200, 200])
fig = plt.figure(figsize=[7, 5])
xs, ys = 0.13, 0.13
w1, h1 = 0.8, 0.8
ax = fig.add_axes([xs, ys, w1, h1])
current_norm = current_mean / current_thermal
ax.plot(potential / temp,
current_norm,
linewidth=2,
marker='o',
markersize=10)
ax.tick_params(labelsize=16)
ax.set_xlabel('body bias / electron temperature',
fontdict=font,
fontsize=20)
ax.set_ylabel('collected current / thermal current',
fontdict=font,
fontsize=20)
fig.savefig('../img/current_potential.eps')
fig.savefig('../img/current_potential.jpg', dpi=300)
plt.show()