def calc_reconnection_rate(base_dir):
"""Calculate reconnection rate.
Args:
base_dir: the directory base.
"""
pic_info = pic_information.get_pic_info(base_dir)
ntf = pic_info.ntf
phi = np.zeros(ntf)
fname = base_dir + 'data/Ay.gda'
for ct in range(ntf):
kwargs = {"current_time": ct, "xl": 0, "xr": 200, "zb": -1, "zt": 1}
x, z, Ay = read_2d_fields(pic_info, fname, **kwargs)
nz, = z.shape
# max_ay = np.max(np.sum(Ay[nz/2-1:nz/2+1, :], axis=0)/2)
# min_ay = np.min(np.sum(Ay[nz/2-1:nz/2+1, :], axis=0)/2)
max_ay = np.max(Ay[nz / 2 - 1:nz / 2 + 1, :])
min_ay = np.min(Ay[nz / 2 - 1:nz / 2 + 1, :])
phi[ct] = max_ay - min_ay
nk = 3
phi = signal.medfilt(phi, kernel_size=nk)
dtwpe = pic_info.dtwpe
dtwce = pic_info.dtwce
dtwci = pic_info.dtwci
mime = pic_info.mime
dtf_wpe = pic_info.dt_fields * dtwpe / dtwci
reconnection_rate = np.gradient(phi) / dtf_wpe
b0 = pic_info.b0
va = dtwce * math.sqrt(1.0 / mime) / dtwpe
reconnection_rate /= b0 * va
reconnection_rate[-1] = reconnection_rate[-2]
tfields = pic_info.tfields
return (tfields, reconnection_rate)
def __init__(self, **kwargs):
self.var_field = kwargs['var_field']
self.var_name = kwargs['var_name']
self.ct = kwargs['ct']
self.field_range = kwargs['field_range']
self.pic_info = pic_information.get_pic_info('../../')
self.read_field_data()
self.nptl = kwargs['nptl']
self.iptl = kwargs['iptl']
self.species = kwargs['species']
self.traj_names = kwargs['traj_names']
self.emin = kwargs['emin']
self.emax = kwargs['emax']
self.ymin = kwargs['ymin']
self.ymax = kwargs['ymax']
self.indicator_color = kwargs['indicator_color']
self.ptl = read_traj_data(self.traj_names[self.iptl])
self.smime = math.sqrt(self.pic_info.mime)
self.ct_ptl = np.zeros(3, dtype='int')
for i in range(3):
self.ct_ptl[i] = self.ct[i] * \
self.pic_info.fields_interval / self.pic_info.trace_interval
self.lx_di = self.pic_info.lx_di
self.ly_di = self.pic_info.ly_di
self.xmax1 = np.max(self.x1)
self.zmax1 = np.max(self.z1)
self.xmin1 = np.min(self.x1)
self.zmin1 = np.min(self.z1)
self.xmax2 = np.max(self.x2)
self.zmax2 = np.max(self.z2)
self.xmin2 = np.min(self.x2)
self.zmin2 = np.min(self.z2)
self.xmax3 = np.max(self.x3)
self.zmax3 = np.max(self.z3)
self.xmin3 = np.min(self.x3)
self.zmin3 = np.min(self.z3)
self.calc_derived_particle_info()
self.get_particle_current_time()
if self.species == 'e':
self.threshold_ene = 0.5
else:
self.threshold_ene = 0.05
# For saving figures
if not os.path.isdir('../img/'):
os.makedirs('../img/')
self.fig_dir = '../img/img_traj_apj/'
if not os.path.isdir(self.fig_dir):
os.makedirs(self.fig_dir)
self.fig_width = 7
self.energy_plot()
self.energy_plot_indicator()
self.save_figures()
def save_particle_number():
"""Save PIC information and particle number information
"""
root_dir = '/net/scratch3/xiaocanli/herts/tether_potential_tests/'
# run_names = ['v50', 'v100', 'v150', 'v200']
run_names = ['v200_b0_wce']
mkdir_p('../data/pic_info/')
mkdir_p('../data/particle_number/')
for run_name in run_names:
fdir = root_dir + run_name + '/'
pic_info = pic_information.get_pic_info(fdir)
pic_info_json = data_to_json(pic_info)
fname = '../data/pic_info/pic_info_' + run_name + '.json'
with open(fname, 'w') as f:
json.dump(pic_info_json, f)
t, ntot = read_particle_number(fdir, pic_info)
fname = '../data/particle_number/nptl_' + run_name + '.dat'
data = np.column_stack((t, ntot))
np.savetxt(fname, data)
def traj_sigma1():
"""
"""
# base_directory = '../../'
base_directory = '/net/scratch2/guofan/sigma1-mime25-beta001/'
pic_info = pic_information.get_pic_info(base_directory)
ntp = pic_info.ntp
vthe = pic_info.vthe
particle_interval = pic_info.particle_interval
pos = [pic_info.lx_di / 10, 0.0, 2.0]
corners, mpi_ranks = set_mpi_ranks(pic_info, pos)
ct = 5 * particle_interval
get_particle_distribution(base_directory, pic_info, ct, corners, mpi_ranks)
smime = math.sqrt(pic_info.mime)
lx_de = pic_info.lx_di * smime
center = [0.5 * lx_de, 0, 0]
sizes = [256, 1, 256]
kwargs = {
'center': center,
'sizes': sizes,
'nbins': 64,
'vmax': 2.0,
'vmin': 0,
'tframe': 10,
'species': 'e'
}
# get_spectrum_vdist(pic_info, **kwargs)
species = 'e'
ct = 10
fpath = base_directory + 'pic_analysis/' + 'vdistributions/'
fname_1d = 'vdist_1d-' + species + '.' + str(ct)
fname_2d = 'vdist_2d-' + species + '.' + str(ct)
fvel = read_velocity_distribution('e', ct, pic_info, fname_1d, fname_2d,
fpath)
fpath = base_directory + 'pic_analysis/' + 'spectrum/'
fname_ene = 'spectrum-' + species + '.' + str(ct)
fene = read_energy_distribution('e', ct, pic_info, fname_ene, fpath)
plot_ptl_vdist('e', pic_info, base_directory)
ax.text(0.05,
0.8,
tags[i],
color='k',
fontsize=20,
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ys -= h1 + gap
ax.set_xlabel(r'$t\Omega_{ci}$', fontdict=font, fontsize=20)
if not os.path.isdir('../img/'):
os.makedirs('../img/')
fig_dir = '../img/img_traj_apj/'
if not os.path.isdir(fig_dir):
os.makedirs(fig_dir)
fname = fig_dir + 'tene_i.eps'
fig.savefig(fname)
plt.show()
if __name__ == "__main__":
pic_info = pic_information.get_pic_info('../../')
ntp = pic_info.ntp
vthe = pic_info.vthe
print("Number of field frames: %d" % pic_info.ntf)
fnames_e, fnames_i = get_file_names()
# plot_electron_trajectory(fnames_e, 'e', pic_info)
# plot_ion_trajectory(fnames_i, 'i', pic_info)
plot_particle_energies(fnames_e, 'e', pic_info)
# plot_ions_energies(fnames_i, 'e', pic_info)
import os.path
import struct
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import rc
from matplotlib.colors import LogNorm
from matplotlib.ticker import MaxNLocator
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.mplot3d import Axes3D
import pic_information
rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
mpl.rc('text', usetex=True)
mpl.rcParams['text.latex.preamble'] = [r"\usepackage{amsmath}"]
font = {
'family': 'serif',
#'color' : 'darkred',
'color': 'black',
'weight': 'normal',
'size': 24,
}
if __name__ == "__main__":
pic_info = pic_information.get_pic_info()
ntp = pic_info.ntp
vthe = pic_info.ntp
def __init__(self, nptl, iptl, x, y, fdata, Ay, init_ft, var_field,
var_name, species, traj_names):
"""
Args:
nptl: number of particles.
x, y: the x, y coordinates for the 2D fields.
fdata: the field data array.
Ay: y component of the vector potential.
init_ft: the initial field time frame.
var_field: the name of the field data for reading.
var_name: the name of the field for labeling.
species: particle species.
traj_names: the names of the trajectory files.
iptl: particle index for current plot.
"""
self.x = x
self.y = y
self.fdata = fdata
self.Ay = Ay
self.nptl = nptl
self.ct = init_ft
self.iptl = iptl
self.var_field = var_field
self.var_name = var_name
self.species = species
self.traj_names = traj_names
self.ptl = read_traj_data(self.traj_names[self.iptl])
self.pic_info = pic_information.get_pic_info('../../')
self.smime = math.sqrt(self.pic_info.mime)
self.ct_ptl = self.ct * \
self.pic_info.fields_interval / self.pic_info.trace_interval
self.lx_di = self.pic_info.lx_di
self.ly_di = self.pic_info.ly_di
self.xmax = np.max(self.x)
self.ymax = np.max(self.y)
self.xmin = np.min(self.x)
self.ymin = np.min(self.y)
self.calc_derived_particle_info()
self.get_particle_current_time()
if self.species == 'e':
self.threshold_ene = 0.5
else:
self.threshold_ene = 0.05
# For saving figures
if not os.path.isdir('../img/'):
os.makedirs('../img/')
self.fig_dir = '../img/img_traj_' + species + '/'
if not os.path.isdir(self.fig_dir):
os.makedirs(self.fig_dir)
self.fig_dir_energetic = '../img/img_traj_energetic_' + species + '/'
if not os.path.isdir(self.fig_dir_energetic):
os.makedirs(self.fig_dir_energetic)
self.fig_width = 10
self.energy_plot()
self.energy_plot_indicator()
# self.fields_plot()
# self.jdote_plot()
self.save_figures()
interpolation='bicubic')
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = fig.colorbar(p1, cax=cax)
cbar.ax.tick_params(labelsize=16)
ax.set_ylabel(r'$y/d_i$', fontdict=font, fontsize=20)
ax.set_xlabel(r'$x/d_i$', fontdict=font, fontsize=20)
ax.tick_params(labelsize=16)
fname = r'$t\omega_{pe} = ' + str(tindex) + '$'
cbar.ax.set_ylabel(
r'$\boldsymbol{j}\cdot\boldsymbol{E}$', fontdict=font, fontsize=20)
if __name__ == "__main__":
root_dir = '../../'
pic_info = pic_information.get_pic_info(root_dir)
fint = pic_info.fields_interval
ny = pic_info.ny
ct = fint * 10
ntf = pic_info.ntf
print ntf
itf = 25
for ct in range(itf, itf + 1):
# for ct in range(ntf):
print ct
tindex = ct * fint
# fname = root_dir + 'data/jz_' + str(tindex) + '.gda'
# # plot_3d_fields(pic_info, fname, ny/2, tindex)
plot_vsingle(pic_info, root_dir, ny / 2, tindex, fint)
plot_j_jdote(pic_info, root_dir, ny / 2, tindex, fint)
# if not os.path.isdir('../img/'):
dmin = -100.0
dmax = 100.0
nb = 1000
print np.sum(field_data)
bins = np.linspace(dmin, dmax, nb)
hist, bin_edges = np.histogram(field_data, bins)
print np.sum(hist)
print 'Maximum and Minimum of the field: ', dmax, dmin
fig = plt.figure(figsize=[7, 5])
width = 0.8
height = 0.8
ax = fig.add_axes([0.12, 0.15, width, height])
hist_f = np.array(hist, dtype=np.float64)
hist_f /= np.max(hist_f)
p1 = ax.semilogy(bins[:nb - 1], hist_f, linewidth=2)
ax.tick_params(labelsize=16)
ax.set_xlabel(var_name, fontdict=font, fontsize=20)
qname = r'$f($' + var_name + '$)$'
ax.set_ylabel(qname, fontdict=font, fontsize=20)
plt.show()
if __name__ == "__main__":
pic_info = pic_information.get_pic_info("../../")
kwargs = {"current_time": 40, "xl": 0, "xr": 400, "zb": -100, "zt": 100}
#histogram_field(pic_info, r'$n_e$', "../data/ne.gda", **kwargs)
#histogram_field(pic_info, r'$|\mathbf{B}|$', "../data/absB.gda", **kwargs)
# histogram_field(pic_info, r'$A_e$', "../data1/agyrotropy00_e.gda", **kwargs)
histogram_field(pic_info, r'$A_e$', "../../data1/jpolar_dote00_e.gda",
**kwargs)
