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#!/usr/bin/env python3
#
# --- Analysis script for the hybrid-PIC example of magnetic reconnection.
import glob
import dill
from matplotlib import colors
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams.update({'font.size': 20})
# load simulation parameters
with open(f'sim_parameters.dpkl', 'rb') as f:
sim = dill.load(f)
x_idx = 2
z_idx = 4
Ey_idx = 6
Bx_idx = 8
plane_data = np.loadtxt(f'diags/plane.dat', skiprows=1)
steps = np.unique(plane_data[:,0])
num_steps = len(steps)
num_cells = plane_data.shape[0]//num_steps
plane_data = plane_data.reshape((num_steps, num_cells, plane_data.shape[1]))
times = plane_data[:, 0, 1]
dt = np.mean(np.diff(times))
plt.plot(
times / sim.t_ci,
np.mean(plane_data[:,:,Ey_idx], axis=1) / (sim.vA * sim.B0),
'o-'
)
plt.grid()
plt.xlabel(r'$t/\tau_{c,i}$')
plt.ylabel('$<E_y>/v_AB_0$')
plt.title("Reconnection rate")
plt.tight_layout()
plt.savefig("diags/reconnection_rate.png")
if not sim.test:
from matplotlib.animation import FFMpegWriter, FuncAnimation
from scipy import interpolate
# Animate the magnetic reconnection
fig, axes = plt.subplots(3, 1, sharex=True, figsize=(7, 9))
for ax in axes.flatten():
ax.set_aspect('equal')
ax.set_ylabel('$z/l_i$')
axes[2].set_xlabel('$x/l_i$')
datafiles = sorted(glob.glob("diags/fields/*.npz"))
num_steps = len(datafiles)
data0 = np.load(datafiles[0])
sX = axes[0].imshow(
data0['Jy'].T, origin='lower',
norm=colors.TwoSlopeNorm(vmin=-0.6, vcenter=0., vmax=1.6),
extent=[0, sim.LX, -sim.LZ/2, sim.LZ/2],
cmap=plt.cm.RdYlBu_r
)
# axes[0].set_ylim(-5, 5)
cb = plt.colorbar(sX, ax=axes[0], label='$J_y/J_0$')
cb.ax.set_yscale('linear')
cb.ax.set_yticks([-0.5, 0.0, 0.75, 1.5])
sY = axes[1].imshow(
data0['By'].T, origin='lower', extent=[0, sim.LX, -sim.LZ/2, sim.LZ/2],
cmap=plt.cm.plasma
)
# axes[1].set_ylim(-5, 5)
cb = plt.colorbar(sY, ax=axes[1], label='$B_y/B_0$')
cb.ax.set_yscale('linear')
sZ = axes[2].imshow(
data0['Bz'].T, origin='lower', extent=[0, sim.LX, -sim.LZ/2, sim.LZ/2],
# norm=colors.TwoSlopeNorm(vmin=-0.02, vcenter=0., vmax=0.02),
cmap=plt.cm.RdBu
)
cb = plt.colorbar(sZ, ax=axes[2], label='$B_z/B_0$')
cb.ax.set_yscale('linear')
# plot field lines
x_grid = np.linspace(0, sim.LX, data0['Bx'][:-1].shape[0])
z_grid = np.linspace(-sim.LZ/2.0, sim.LZ/2.0, data0['Bx'].shape[1])
n_lines = 10
start_x = np.zeros(n_lines)
start_x[:n_lines//2] = sim.LX
start_z = np.linspace(-sim.LZ/2.0*0.9, sim.LZ/2.0*0.9, n_lines)
step_size = 1.0 / 100.0
def get_field_lines(Bx, Bz):
field_line_coords = []
Bx_interp = interpolate.interp2d(x_grid, z_grid, Bx[:-1].T)
Bz_interp = interpolate.interp2d(x_grid, z_grid, Bz[:,:-1].T)
for kk, z in enumerate(start_z):
path_x = [start_x[kk]]
path_z = [z]
ii = 0
while ii < 10000:
ii+=1
Bx = Bx_interp(path_x[-1], path_z[-1])[0]
Bz = Bz_interp(path_x[-1], path_z[-1])[0]
# print(path_x[-1], path_z[-1], Bx, Bz)
# normalize and scale
B_mag = np.sqrt(Bx**2 + Bz**2)
if B_mag == 0:
break
dx = Bx / B_mag * step_size
dz = Bz / B_mag * step_size
x_new = path_x[-1] + dx
z_new = path_z[-1] + dz
if np.isnan(x_new) or x_new <= 0 or x_new > sim.LX or abs(z_new) > sim.LZ/2:
break
path_x.append(x_new)
path_z.append(z_new)
field_line_coords.append([path_x, path_z])
return field_line_coords
field_lines = []
for path in get_field_lines(data0['Bx'], data0['Bz']):
path_x = path[0]
path_z = path[1]
l, = axes[2].plot(path_x, path_z, '--', color='k')
# draws arrows on the field lines
# if path_x[10] > path_x[0]:
axes[2].arrow(
path_x[50], path_z[50],
path_x[250]-path_x[50], path_z[250]-path_z[50],
shape='full', length_includes_head=True, lw=0, head_width=1.0,
color='g'
)
field_lines.append(l)
def animate(i):
data = np.load(datafiles[i])
sX.set_array(data['Jy'].T)
sY.set_array(data['By'].T)
sZ.set_array(data['Bz'].T)
sZ.set_clim(-np.max(abs(data['Bz'])), np.max(abs(data['Bz'])))
for ii, path in enumerate(get_field_lines(data['Bx'], data['Bz'])):
path_x = path[0]
path_z = path[1]
field_lines[ii].set_data(path_x, path_z)
anim = FuncAnimation(
fig, animate, frames=num_steps-1, repeat=True
)
writervideo = FFMpegWriter(fps=14)
anim.save('diags/mag_reconnection.mp4', writer=writervideo)
if sim.test:
import os
import sys
sys.path.insert(1, '../../../../warpx/Regression/Checksum/')
import checksumAPI
# this will be the name of the plot file
fn = sys.argv[1]
test_name = os.path.split(os.getcwd())[1]
checksumAPI.evaluate_checksum(test_name, fn)
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