def calculateDifference(self, fsim, fanl, fout):
import csv
xsim, ysim, zsim = [None, None, None]
xanl, yanl, zanl = [None, None, None]
tlist = None
with open(fsim) as f:
xsim, ysim, zsim = putil.extractData(f, [1, 2, 3])
xsim = np.array(xsim)
ysim = np.array(ysim)
zsim = np.array(zsim)
with open(fsim) as f:
tlist = putil.extractData(f)[0]
with open(fanl) as f:
xanl, yanl, zanl = putil.extractData(f, [1, 2, 3])
xanl = np.array(xanl)
yanl = np.array(yanl)
zanl = np.array(zanl)
rsim = np.hypot(xsim, ysim)
ranl = np.hypot(xanl, yanl)
rerr = np.abs(ranl - rsim) # / len(ranl)
zerr = np.abs(zanl - zsim) # / len(zanl)
#rerr = np.cumsum(rerr)
#zerr = np.cumsum(zerr)
with open(fout, 'w') as f:
writer = csv.writer(f, delimiter=' ')
for i in xrange(len(tlist)):
writer.writerow([tlist[i], rerr[i], zerr[i]])
writer.writerow(['# Mean r err: ', np.mean(rerr)])
writer.writerow(['# Mean r stdev: ', np.var(rerr)])
writer.writerow(['# Mean z err: ', np.mean(zerr)])
writer.writerow(['# Mean z stdev: ', np.var(zerr)])
def plotLinearRegression(self, alpha):
import matplotlib.pyplot as plt
from itertools import cycle
from plot_utility import extractData
s = settings.Settings()
cols = cycle(['r', 'g', 'b'])
for particle in self.particles:
s.outfile = self.filename(particle, alpha)
c = cols.next()
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
R = np.hypot(np.array(x), np.array(y))
plt.plot(z,
R,
'.' + c,
markersize=1,
label=self.label(particle))
s.outfile = self.filename(particle, alpha, 'drift_linreg_')
with open(s.outpath()) as f:
t, R, z = extractData(f, [0, 1, 2])
plt.plot(z, R, '-' + c, linewidth=2)
plt.legend()
plt.tight_layout()
plt.show()
def plotDifferences(self, particle, alphaStart, alphaEnd, alphaStep):
from os import path
import matplotlib.pyplot as plt
import itertools
from plot_utility import extractData
s = settings.Settings()
symbols = itertools.cycle(['o', '^', 's'])
alpha = alphaStart
while alpha < alphaEnd:
with open(path.join(s.outdir, self.filename(particle, alpha, 'drift_difference_')) + s.outext) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
t = np.array(t)
x = np.array(x)
y = np.array(y)
z = np.array(z)
mag = self.magnitude(x, y, z)
plt.plot(t, mag, '-', label='$\\alpha = {alpha:>02}'.format(alpha=alpha))
alpha += alphaStep
plt.xlabel('t')
plt.ylabel('$\\nabla B$ drift')
plt.legend(framealpha=0.5)
plt.tight_layout()
plt.show()
def plotSuperimposed(self, alpha, beta):
s = settings.Settings()
app = mag.Application()
field = SmoothField()
field.alpha = alpha
field.beta = beta
field.Bz0 = app.fieldBaseStrength[0]
for particle in self.particles:
outfile = self.filename(particle, alpha, beta)
s.outfile = outfile
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
start = len(z) / 4 - 100
end = len(z) / 4 + 260
# start = 0
# end = len(z)
plt.plot(z[start:end], y[start:end], self.style[particle],
linewidth=1, label=self.label(particle))
# self.plotField(field, -1.6, 1.6, 0.35, 0.6)
xmin, xmax = plt.xlim()
ymin, ymax = plt.ylim()
self.plotField(field, xmin, xmax, ymin, ymax)
plt.xlabel('z (m)')
plt.ylabel('y (m)')
plt.tight_layout()
plt.show()
def plotDifferences(self, particle, alphaStart, alphaEnd, alphaStep):
from os import path
import matplotlib.pyplot as plt
import itertools
from plot_utility import extractData
s = settings.Settings()
symbols = itertools.cycle(['o', '^', 's'])
alpha = alphaStart
while alpha < alphaEnd:
with open(
path.join(
s.outdir,
self.filename(particle, alpha, 'drift_difference_')) +
s.outext) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
t = np.array(t)
x = np.array(x)
y = np.array(y)
z = np.array(z)
mag = self.magnitude(x, y, z)
plt.plot(t,
mag,
'-',
label='$\\alpha = {alpha:>02}'.format(alpha=alpha))
alpha += alphaStep
plt.xlabel('t')
plt.ylabel('$\\nabla B$ drift')
plt.legend(framealpha=0.5)
plt.tight_layout()
plt.show()
def plotSuperimposed(self, alpha, beta, gamma, L, n):
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from plot_utility import extractData
s = settings.Settings()
import itertools
cmap = itertools.cycle(self.cmaps)
lmap = itertools.cycle(self.lmaps)
handles = []
labels = []
for particle in self.particles:
s.outfile = self.filename(particle, alpha, beta, gamma, L, n)
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
r = np.hypot(np.array(x), np.array(y))
# plt.plot(z, r, '-', label=self.label(particle))
plt.scatter(z, r, c=t, cmap=cmap.next())
handles.append(mpatches.Patch(color=lmap.next()))
labels.append(self.label(particle))
plt.xlabel('z (m)')
plt.ylabel('R (m)')
# plt.legend(handles, labels, ncol=1, loc=4, framealpha=0.5)
# plt.legend(ncol=1, loc=4, framealpha=0.5)
plt.tight_layout()
plt.show()
def animate(self, alpha, beta, L, n, div=32, particle='de+'):
s = settings.Settings()
field = SineField()
field.alpha = alpha
field.beta = beta
field.L = L
field.n = n
outfile = self.filename(particle, alpha, beta, L, n)
s.outfile = outfile
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
length = len(z) / div
for i in xrange(div):
start = i * length
if i > 0:
start = (i-1) * length
end = (i+1) * length
plt.plot(z[start:end], y[start:end], 'b-', linewidth=1, label=self.label(particle))
xmin, xmax = [ -1.15, 1.15 ]
ymin, ymax = [ -1.0, 1.0 ]
self.plotField(field, xmin, xmax, ymin, ymax)
plt.xlabel('$z$ (m)')
plt.ylabel('$y$ (m)')
plt.tight_layout()
s.outext = '_{index:03}.png'.format(index=i)
plt.text(0.25, 0.8, r'$B_z = B_{z0} \left( 1 + \alpha r + \beta z\ \sin (\frac{n \pi z}{L}) \right)$',
fontsize=15)
plt.text(0.25, 0.7, '$\\alpha = {alp},\\ \\beta = {beta}$'.format(alp=alpha, beta=beta),
fontsize=15)
plt.savefig(s.outpath())
plt.clf()
def plotDeviations(self):
from os import path
import matplotlib.pyplot as plt
import itertools
from plot_utility import extractData
s = settings.Settings()
symbols = itertools.cycle(['o', '^', 's'])
for particle in self.particles:
with open(
path.join(s.outdir,
self.filename(particle, 0, 'drift_mean_')) +
s.outext, 'r') as f:
alphas, means = extractData(f, [0, 1])
plt.plot(alphas,
means,
symbols.next(),
markersize=8,
label=self.label(particle))
plt.xlabel('$\\alpha$')
plt.ylabel('$\\nabla B$ drift')
# plt.legend(ncol=1, framealpha=0.5, loc=1)
plt.tight_layout()
plt.show()
def calculateDifference(self, alpha):
from os import path
import csv
from plot_utility import extractData
app = mag.Application()
Bz0 = app.fieldBaseStrength[2]
Ek = app.kineticEnergy
s = settings.Settings()
for particle in self.particles:
xdiff = []
ydiff = []
zdiff = []
tlist = []
with open(path.join(s.outdir, self.filename(particle, alpha)) + s.outext) as f:
tlist, x, y, z = extractData(f, [0, 1, 2, 3])
self.prepareAnalytic(Ek, self.mass(particle))
for i in xrange(len(tlist)):
xa, ya, za = self.analytic(tlist[i], Bz0, self.charge(particle), self.mass(particle))
xdiff.append(xa - x[i])
ydiff.append(ya - y[i])
zdiff.append(za - z[i])
with open(path.join(s.outdir, self.filename(particle, alpha, 'drift_difference_')) + s.outext, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter=' ')
self.prepareAnalytic(Ek, self.mass(particle))
for i in range(len(tlist)):
writer.writerow([ tlist[i], xdiff[i], ydiff[i], zdiff[i] ])
def plotSuperimposed(self, alpha, beta, gamma, L, n):
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from plot_utility import extractData
s = settings.Settings()
import itertools
cmap = itertools.cycle(self.cmaps)
lmap = itertools.cycle(self.lmaps)
handles = []
labels = []
for particle in self.particles:
s.outfile = self.filename(particle, alpha, beta, gamma, L, n)
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
r = np.hypot(np.array(x), np.array(y))
# plt.plot(z, r, '-', label=self.label(particle))
plt.scatter(z, r, c=t, cmap=cmap.next())
handles.append(mpatches.Patch(color=lmap.next()))
labels.append(self.label(particle))
plt.xlabel('z (m)')
plt.ylabel('R (m)')
# plt.legend(handles, labels, ncol=1, loc=4, framealpha=0.5)
# plt.legend(ncol=1, loc=4, framealpha=0.5)
plt.tight_layout()
plt.show()
def calculateDeviations(self, alphaStart, alphaEnd, alphaStep):
from os import path
import csv
from plot_utility import extractData
s = settings.Settings()
for particle in self.particles:
gradient = []
alphas = []
alpha = alphaStart
while alpha < alphaEnd:
s.outfile = self.filename(particle, alpha, 'drift_linreg_')
with open(s.outpath()) as f:
t, R, z = extractData(f, [0, 1, 2])
g = (R[-1] - R[0]) / (t[-1] - t[0])
gradient.append(g)
alphas.append(alpha)
alpha += alphaStep
with open(
path.join(s.outdir,
self.filename(particle, 0, 'drift_mean_')) +
s.outext, 'w') as f:
writer = csv.writer(f, delimiter=' ')
for i in xrange(len(alphas)):
writer.writerow([alphas[i], gradient[i]])
def plotSuperimposed(self, alpha, beta, gamma, eps, rho, L, n, particle):
s = settings.Settings()
field = Fields.TokamakField()
field.setupField(alpha=alpha, beta=beta, gamma=gamma, eps=eps, rho=rho, L=L, n=n)
s.outfile = self.filename(particle, alpha, beta, gamma, eps, rho, L, n)
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
plt.plot(x, y, self.style[particle], label=self.label(particle))
plt.xlabel('$x$ m')
plt.ylabel('$y$ m')
xmin, xmax = [ -0.5, 0.5 ]
ymin, ymax = [ -0.5, 0.5 ]
# xmin, xmax = plt.xlim()
# ymin, ymax = plt.ylim()
self.plotField(field, xmin, xmax, ymin, ymax, orientation='front')
s.outext = '_front.pdf'
plt.savefig(s.outpath())
plt.show()
plt.plot(z, y, self.style[particle], label=self.label(particle))
plt.xlabel('$z$ m')
plt.ylabel('$y$ m')
xmin, xmax = [ -0.5, 0.5 ]
ymin, ymax = [ -0.5, 0.5 ]
# xmin, xmax = plt.xlim()
# ymin, ymax = plt.ylim()
self.plotField(field, xmin, xmax, ymin, ymax, orientation='right')
s.outext = '_right.pdf'
plt.savefig(s.outpath())
plt.show()
def plotSuperimposed(self, alpha, beta, L, n):
s = settings.Settings()
field = SineField()
field.alpha = alpha
field.beta = beta
field.L = L
field.n = n
for particle in self.particles:
outfile = self.filename(particle, alpha, beta, L, n)
s.outfile = outfile
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
#start = len(z) / 4 + 250
#end = len(z) / 4 + 180
start = 0
end = len(z)
plt.plot(z[start:end], y[start:end], self.style[particle],
linewidth=1, label=self.label(particle))
xmin, xmax = [ -0.4, 0.4 ]
ymin, ymax = [ 0.1, 0.3 ]
# xmin, xmax = plt.xlim()
# ymin, ymax = plt.ylim()
self.plotField(field, xmin, xmax, ymin, ymax)
plt.xlabel('$z$ (m)')
plt.ylabel('$y$ (m)')
# plt.text(0.25, 0.8, r'$B_z = B_{z0} \left( 1 + \alpha r + \beta z\ \sin (\frac{n \pi z}{L}) \right)$',
# fontsize=15)
# plt.text(0.25, 0.7, '$\\alpha = {alp},\\ \\beta = {beta}$'.format(alp=alpha, beta=beta),
# fontsize=15)
plt.tight_layout()
s.outext = '.pdf'
plt.savefig(s.outpath())
plt.show()
def plotTop(fn, out, comment='#', skipline=1, skiprow=2):
"""Plot of z = f(x)
"""
t, x, z = extractData(fn, [0, 1, 3])
plt.scatter(x[skipline::skiprow], z[skipline::skiprow], c=t[skipline::skiprow], cmap='gray_r')
plt.xlabel('x (m)')
plt.ylabel('z (m)')
plt.savefig(out)
def _extract(filename):
with open(filename) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
t = np.array(t)
x = np.array(x)
y = np.array(y)
z = np.array(z)
return [t, x, y, z]
def plotRight(fn, out, comment='#', skipline=1, skiprow=2):
"""Plot of y = f(z)
"""
t, y, z = extractData(fn, [0, 2, 3])
plt.scatter(z[skipline::skiprow], y[skipline::skiprow], c=t[skipline::skiprow], cmap='gray_r')
plt.xlabel('z (m)')
plt.ylabel('y (m)')
plt.savefig(out)
def plotFront(fn, out, comment='#', skipline=1, skiprow=2):
"""Plot of y = f(x)
"""
t, x, y = extractData(fn, [0, 1, 2])
plt.scatter(x[skipline::skiprow], y[skipline::skiprow], c=t[skipline::skiprow],
cmap='gray')
plt.xlabel('x (m)')
plt.ylabel('y (m)')
plt.savefig(out)
def plotPoloid(fn, out, comment='#', skipline=1, skiprow=2):
"""Plot of R = x**2 + y**2 vs z
"""
t, x, y, z = extractData(fn, [0, 1, 2, 3])
x = np.array(x)
y = np.array(y)
R = np.hypot(x, y)
plt.scatter(R[skipline::skiprow], z[skipline::skiprow], c=t[skipline::skiprow], cmap='gray_r')
plt.xlabel('R (m)')
plt.ylabel('z (m)')
plt.savefig(out)
def plotFront(fn, out, comment='#', skipline=1, skiprow=2):
"""Plot of y = f(x)
"""
t, x, y = extractData(fn, [0, 1, 2])
plt.scatter(x[skipline::skiprow],
y[skipline::skiprow],
c=t[skipline::skiprow],
cmap='gray')
plt.xlabel('x (m)')
plt.ylabel('y (m)')
plt.savefig(out)
def plotRight(fn, out, comment='#', skipline=1, skiprow=2):
"""Plot of y = f(z)
"""
t, y, z = extractData(fn, [0, 2, 3])
plt.scatter(z[skipline::skiprow],
y[skipline::skiprow],
c=t[skipline::skiprow],
cmap='gray_r')
plt.xlabel('z (m)')
plt.ylabel('y (m)')
plt.savefig(out)
def plotTop(fn, out, comment='#', skipline=1, skiprow=2):
"""Plot of z = f(x)
"""
t, x, z = extractData(fn, [0, 1, 3])
plt.scatter(x[skipline::skiprow],
z[skipline::skiprow],
c=t[skipline::skiprow],
cmap='gray_r')
plt.xlabel('x (m)')
plt.ylabel('z (m)')
plt.savefig(out)
def calculateAnalytic(self, fin, fout):
"""Calculate analytic solution for given simulation result file fin
"""
import csv
tlist = None
with open(fin) as f:
tlist = putil.extractData(f)[0]
with open(fout, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter=' ')
for t in tlist:
x, y, z = self.analytic(t)
writer.writerow([t, x, y, z])
def plotSuperimposed(self, fsims, fanl):
for i in range(len(fsims)):
with open(fsims[i]) as f:
t, x, y, z = putil.extractData(f, [0, 1, 2, 3])
x = np.array(x)
y = np.array(y)
# r = np.hypot(x, y)
plt.plot(z, y, 'o--', label='{step} $\\tau_c$'.format(step=(i+1)*self.step))
with open(fanl) as f:
t, x, y, z = putil.extractData(f, [0, 1, 2, 3])
x = np.array(x)
y = np.array(y)
# r = np.hypot(x, y)
plt.plot(z, y, 'k-', linewidth=1.5, label='Analytic')
plt.xlabel('$z$ (m)')
plt.ylabel('$y$ (m)')
plt.legend(ncol=2, loc=4, framealpha=0.5)
plt.tight_layout()
plt.show()
def plot3d(self, alpha, beta, gamma, eps, rho, L, n):
from mpl_toolkits.mplot3d import Axes3D
s = settings.Settings()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for particle in self.particles:
s.outfile = self.filename(particle, alpha, beta, gamma, eps, rho, L, n)
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
ax.plot(x, y, z)
plt.show()
def plotGuidingCenter(self, alpha):
from os import path
import matplotlib.pyplot as plt
from plot_utility import extractData
s = settings.Settings()
for particle in self.particles:
with open(path.join(s.outdir, self.filename(particle, alpha, 'drift_center')) + s.outext) as f:
t, R = extractData(f, [0, 1])
plt.plot(t, R, '--', label=self.label(particle))
plt.show()
def plotLinearRegression(self, alpha):
import matplotlib.pyplot as plt
from itertools import cycle
from plot_utility import extractData
s = settings.Settings()
cols = cycle([ 'r', 'g', 'b' ])
for particle in self.particles:
s.outfile = self.filename(particle, alpha)
c = cols.next()
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
R = np.hypot(np.array(x), np.array(y))
plt.plot(z, R, '.' + c, markersize=1, label=self.label(particle))
s.outfile = self.filename(particle, alpha, 'drift_linreg_')
with open(s.outpath()) as f:
t, R, z = extractData(f, [0, 1, 2])
plt.plot(z, R, '-' + c, linewidth=2)
plt.legend()
plt.tight_layout()
plt.show()
def plotPoloid(fn, out, comment='#', skipline=1, skiprow=2):
"""Plot of R = x**2 + y**2 vs z
"""
t, x, y, z = extractData(fn, [0, 1, 2, 3])
x = np.array(x)
y = np.array(y)
R = np.hypot(x, y)
plt.scatter(R[skipline::skiprow],
z[skipline::skiprow],
c=t[skipline::skiprow],
cmap='gray_r')
plt.xlabel('R (m)')
plt.ylabel('z (m)')
plt.savefig(out)
def plotGuidingCenter(self, alpha):
from os import path
import matplotlib.pyplot as plt
from plot_utility import extractData
s = settings.Settings()
for particle in self.particles:
with open(
path.join(s.outdir,
self.filename(particle, alpha, 'drift_center')) +
s.outext) as f:
t, R = extractData(f, [0, 1])
plt.plot(t, R, '--', label=self.label(particle))
plt.show()
def animate(self, alpha, div=4, particle='tr+'):
s = settings.Settings()
app = mag.Application()
field = RadialField()
field.alpha = alpha
field.Bz0 = app.fieldBaseStrength[2]
outfile = self.filename(particle, alpha)
s.outfile = outfile
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
length = len(z) / div
for i in xrange(div):
# start = i * length
# if i > 0:
# start = (i) * length
start=0
end = (i+1) * length
# Plot front view, no real way to refactor these *sigh*
plt.plot(x[start:end], y[start:end], self.style[particle],
linewidth=1, label=self.label(particle))
xmin, xmax = [ -0.03, 0.03 ]
ymin, ymax = [ -0.03, 0.03 ]
self.plotField(field, xmin, xmax, ymin, ymax, orientation='front')
plt.xlabel('$x$ (m)')
plt.ylabel('$y$ (m)')
plt.tight_layout()
s.outext = '_long_front_{index:03}.pdf'.format(index=i)
plt.savefig(s.outpath())
plt.show()
plt.clf()
# Plot right view, no real way to refactor these *sigh*
plt.plot(z[start:end], y[start:end], self.style[particle],
linewidth=1, label=self.label(particle))
xmin, xmax = [ 0.0, 25 ]
ymin, ymax = [ -0.03, 0.03 ]
self.plotField(field, xmin, xmax, ymin, ymax, orientation='right')
plt.xlabel('$z$ (m)')
plt.ylabel('$y$ (m)')
plt.tight_layout()
s.outext = '_long_right_{index:03}.pdf'.format(index=i)
plt.savefig(s.outpath())
plt.show()
plt.clf()
def errorPlots(self):
"""Plot errors to answer hypothesis
"""
s = settings.Settings()
for i in range(1, self.count):
errfile = os.path.join(s.outdir,
'Error1_{ct:0>2}'.format(ct=i)) + s.outext
with open(errfile) as f:
t, r, z = putil.extractData(f, [0, 1, 2])
r = np.array(r)
z = np.array(z)
t = np.array(t) * 10**6
plt.plot(t, r, '--', label='{step} $\\tau_c$'.format(step=i*self.step))
plt.xlabel('Time ($\\mu$s)')
plt.ylabel('Error Accumulation')
plt.legend(loc=2, ncol=2, framealpha=0.5)
plt.tight_layout()
plt.show()
def savePlot(self, alpha, beta, gamma, L, n):
import numpy as np
import matplotlib.pyplot as plt
from plot_utility import extractData
s = settings.Settings()
for particle in self.particles:
s.outfile = self.filename(particle, alpha, beta, gamma, L, n)
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
R = np.hypot(np.array(x), np.array(y))
plt.plot(z, R, '-', label=self.label(particle))
plt.xlabel('z (m)')
plt.ylabel('R (m)')
plt.legend(loc=0)
s.outext = '.png'
plt.savefig(s.outpath())
plt.clf()
def calculateGuidingCenter(self, particle, alpha):
from os import path
import csv
from plot_utility import extractData
app = mag.Application()
s = settings.Settings()
gyro_period = self.gyro_period(app.fieldBaseStrength[2],
self.charge(particle),
self.mass(particle))
center = []
tp = []
print gyro_period
with open(
path.join(s.outdir, self.filename(particle, alpha)) +
s.outext) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
R = np.hypot(x, y)
period = 0
Rs = []
for i in xrange(len(t)):
if period > gyro_period:
c = np.mean(Rs)
center.append(c)
Rs = []
if len(tp) == 0:
tp.append(period)
else:
tp.append(tp[-1] + period)
period = 0
Rs.append(R[i])
if i > 0:
period += t[i] - t[i - 1]
with open(
path.join(s.outdir,
self.filename(particle, alpha, 'drift_center')) +
s.outext, 'w') as f:
writer = csv.writer(f, delimiter=' ')
for i in xrange(len(center)):
writer.writerow([tp[i], center[i]])
def animate(self, alpha, beta, gamma, eps, rho, L, n, div=32, particle='de+'):
field = Fields.TokamakField()
field.setupField(alpha=alpha, beta=beta, gamma=gamma, eps=eps, rho=rho, L=L, n=n)
s = settings.Settings()
s.outfile = self.filename(particle, alpha, beta, gamma, eps, rho, L, n)
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
length = len(z) / div
for i in xrange(div):
start = i * length
if i > 0:
start = (i-1) * length
end = (i+1) * length
plt.subplot(121)
plt.plot(x[start:end], y[start:end], '-b', label=self.label(particle))
plt.xlabel('x')
plt.ylabel('y')
min, max = [-0.5, 0.5]
levels = np.arange(4.2, 6.3, 0.4)
XX = np.arange(min, max, (max-min)/1000)
YY = np.arange(min, max, (max-min)/1000)
X, Y = np.meshgrid(XX, YY)
Z = 0.1
F = field.zField(X, Y, Z)
CS = plt.contour(X, Y, F, levels)
plt.clabel(CS, fontsize=9, colors='k', inline=1)
plt.subplot(122)
plt.plot(z[start:end], y[start:end], '-b', label=self.label(particle))
plt.xlabel('z')
plt.ylabel('y')
min, max = [-2.2, 2.0]
ZZ = np.arange(min, max, (max-min)/1000)
min, max = [-0.5, 0.5]
YY = np.arange(min, max, (max-min)/1000)
Z, Y = np.meshgrid(ZZ, YY)
X = 0.1
F = field.zField(X, Y, Z)
CS = plt.contour(Z, Y, F, levels)
plt.clabel(CS, fontsize=9, colors='k', inline=1)
s.outext = '_{index:03}.png'.format(index=i)
plt.savefig(s.outpath())
plt.clf()
def plotDeviations(self):
from os import path
import matplotlib.pyplot as plt
import itertools
from plot_utility import extractData
s = settings.Settings()
symbols = itertools.cycle(['o', '^', 's'])
for particle in self.particles:
with open(path.join(s.outdir, self.filename(particle, 0, 'drift_mean_')) + s.outext, 'r') as f:
alphas, means = extractData(f, [0, 1])
plt.plot(alphas, means, symbols.next(), markersize=8, label=self.label(particle))
plt.xlabel('$\\alpha$')
plt.ylabel('$\\nabla B$ drift')
# plt.legend(ncol=1, framealpha=0.5, loc=1)
plt.tight_layout()
plt.show()
def animate(self, alpha, beta, L, n, div=32, particle='de+'):
s = settings.Settings()
field = SineField()
field.alpha = alpha
field.beta = beta
field.L = L
field.n = n
outfile = self.filename(particle, alpha, beta, L, n)
s.outfile = outfile
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
length = len(z) / div
for i in xrange(div):
start = i * length
if i > 0:
start = (i - 1) * length
end = (i + 1) * length
plt.plot(z[start:end],
y[start:end],
'b-',
linewidth=1,
label=self.label(particle))
xmin, xmax = [-1.15, 1.15]
ymin, ymax = [-1.0, 1.0]
self.plotField(field, xmin, xmax, ymin, ymax)
plt.xlabel('$z$ (m)')
plt.ylabel('$y$ (m)')
plt.tight_layout()
s.outext = '_{index:03}.png'.format(index=i)
plt.text(
0.25,
0.8,
r'$B_z = B_{z0} \left( 1 + \alpha r + \beta z\ \sin (\frac{n \pi z}{L}) \right)$',
fontsize=15)
plt.text(0.25,
0.7,
'$\\alpha = {alp},\\ \\beta = {beta}$'.format(
alp=alpha, beta=beta),
fontsize=15)
plt.savefig(s.outpath())
plt.clf()
def plotMirrorPoints(self):
import matplotlib.pyplot as plt
import itertools
from plot_utility import extractData
symbols = itertools.cycle(['--bo', '--g^', '--rs'])
s = settings.Settings()
for particle in self.particles:
s.outfile = self.filename(particle, 0, 0, 0, 0, 'sine_mirrors_')
with open(s.outpath()) as f:
beta, mx, mn = extractData(f, [0, 1, 2])
sym = symbols.next()
plt.plot(beta, mx, sym, markersize=8, label=self.label(particle))
plt.plot(beta, mn, sym, markersize=8)
plt.xlabel('$\\beta$')
plt.ylabel('Mirror points (m)')
# plt.legend(loc=0)
plt.show()
def plotSuperimposed(self, alpha):
s = settings.Settings()
app = mag.Application()
field = RadialField()
field.alpha = alpha
field.Bz0 = app.fieldBaseStrength[2]
for particle in self.particles:
outfile = self.filename(particle, alpha)
s.outfile = outfile
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
plt.plot(x, y, self.style[particle], label=self.label(particle))
xmin, xmax = [ -0.03, 0.03 ]
ymin, ymax = [ -0.03, 0.03 ]
self.plotField(field, xmin, xmax, ymin, ymax)
plt.xlabel('$z$ (m)')
plt.ylabel('$y$ (m)')
plt.tight_layout()
plt.show()
def calculateLinearRegression(self, particle, alpha):
from numpy import linalg
import csv
from plot_utility import extractData
s = settings.Settings()
s.outfile = self.filename(particle, alpha)
z = []
t = []
m = 0.0
c = 0.0
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
R = np.hypot(np.array(x), np.array(y))
A = np.vstack([t, np.ones(len(t))]).T
m, c = linalg.lstsq(A, R)[0]
s.outfile = self.filename(particle, alpha, 'drift_linreg_')
with open(s.outpath(), 'w') as f:
writer = csv.writer(f, delimiter=' ')
for i in xrange(len(t)):
writer.writerow([t[i], m * t[i] + c, z[i]])
def calculateLinearRegression(self, particle, alpha):
from numpy import linalg
import csv
from plot_utility import extractData
s = settings.Settings()
s.outfile = self.filename(particle, alpha)
z = []
t = []
m = 0.0
c = 0.0
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
R = np.hypot(np.array(x), np.array(y))
A = np.vstack([t, np.ones(len(t))]).T
m, c = linalg.lstsq(A, R)[0]
s.outfile = self.filename(particle, alpha, 'drift_linreg_')
with open(s.outpath(), 'w') as f:
writer = csv.writer(f, delimiter=' ')
for i in xrange(len(t)):
writer.writerow([ t[i], m*t[i] + c, z[i] ])
def plotSuperimposed(self, alpha, beta, L, n):
s = settings.Settings()
field = SineField()
field.alpha = alpha
field.beta = beta
field.L = L
field.n = n
for particle in self.particles:
outfile = self.filename(particle, alpha, beta, L, n)
s.outfile = outfile
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
#start = len(z) / 4 + 250
#end = len(z) / 4 + 180
start = 0
end = len(z)
plt.plot(z[start:end],
y[start:end],
self.style[particle],
linewidth=1,
label=self.label(particle))
xmin, xmax = [-0.4, 0.4]
ymin, ymax = [0.1, 0.3]
# xmin, xmax = plt.xlim()
# ymin, ymax = plt.ylim()
self.plotField(field, xmin, xmax, ymin, ymax)
plt.xlabel('$z$ (m)')
plt.ylabel('$y$ (m)')
# plt.text(0.25, 0.8, r'$B_z = B_{z0} \left( 1 + \alpha r + \beta z\ \sin (\frac{n \pi z}{L}) \right)$',
# fontsize=15)
# plt.text(0.25, 0.7, '$\\alpha = {alp},\\ \\beta = {beta}$'.format(alp=alpha, beta=beta),
# fontsize=15)
plt.tight_layout()
s.outext = '.pdf'
plt.savefig(s.outpath())
plt.show()
def calculateDifference(self, alpha):
from os import path
import csv
from plot_utility import extractData
app = mag.Application()
Bz0 = app.fieldBaseStrength[2]
Ek = app.kineticEnergy
s = settings.Settings()
for particle in self.particles:
xdiff = []
ydiff = []
zdiff = []
tlist = []
with open(
path.join(s.outdir, self.filename(particle, alpha)) +
s.outext) as f:
tlist, x, y, z = extractData(f, [0, 1, 2, 3])
self.prepareAnalytic(Ek, self.mass(particle))
for i in xrange(len(tlist)):
xa, ya, za = self.analytic(tlist[i], Bz0,
self.charge(particle),
self.mass(particle))
xdiff.append(xa - x[i])
ydiff.append(ya - y[i])
zdiff.append(za - z[i])
with open(
path.join(
s.outdir,
self.filename(particle, alpha, 'drift_difference_')) +
s.outext, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter=' ')
self.prepareAnalytic(Ek, self.mass(particle))
for i in range(len(tlist)):
writer.writerow([tlist[i], xdiff[i], ydiff[i], zdiff[i]])
def animate(self, alpha, beta, div=4, particle='tr+'):
s = settings.Settings()
app = mag.Application()
field = SmoothField()
field.alpha = alpha
field.beta = beta
field.Bz0 = app.fieldBaseStrength[0]
outfile = self.filename(particle, alpha, beta)
s.outfile = outfile
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
length = len(z) / div
for i in xrange(div):
start = i * length
# if i > 0:
# start = (i-1) * length
# start=0
end = (i+1) * length
if i > 0:
plt.plot(z[0:start], y[0:start], 'm-', alpha=0.5,
linewidth=0.5, label=self.label(particle))
plt.plot(z[start:end], y[start:end], self.style[particle],
linewidth=1, label=self.label(particle))
self.plotField(field, -1.6, 1.6, 0.35, 0.6)
# xmin, xmax = plt.xlim()
# ymin, ymax = plt.ylim()
# self.plotField(field, xmin, xmax, ymin, ymax)
plt.xlabel('$z$ (m)')
plt.ylabel('$y$ (m)')
plt.tight_layout()
s.outext = '_{index:03}.pdf'.format(index=i)
plt.savefig(s.outpath())
plt.show()
plt.clf()
def calculateGuidingCenter(self, particle, alpha):
from os import path
import csv
from plot_utility import extractData
app = mag.Application()
s = settings.Settings()
gyro_period = self.gyro_period(app.fieldBaseStrength[2], self.charge(particle), self.mass(particle))
center = []
tp = []
print gyro_period
with open(path.join(s.outdir, self.filename(particle, alpha)) + s.outext) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
R = np.hypot(x, y)
period = 0
Rs = []
for i in xrange(len(t)):
if period > gyro_period:
c = np.mean(Rs)
center.append(c)
Rs = []
if len(tp) == 0:
tp.append(period)
else:
tp.append(tp[-1] + period)
period = 0
Rs.append(R[i])
if i > 0:
period += t[i] - t[i-1]
with open(path.join(s.outdir, self.filename(particle, alpha, 'drift_center')) + s.outext, 'w') as f:
writer = csv.writer(f, delimiter=' ')
for i in xrange(len(center)):
writer.writerow([ tp[i], center[i] ])
def plotMirrorPoints(self):
import matplotlib.pyplot as plt
import itertools
from plot_utility import extractData
symbols = itertools.cycle(['--bo', '--g^', '--rs'])
s = settings.Settings()
for particle in self.particles:
s.outfile = self.filename(particle, 0, 0, 0, 0, 'sine_mirrors_')
with open(s.outpath()) as f:
beta, mx, mn = extractData(f, [0, 1, 2])
sym = symbols.next()
plt.plot(beta,
mx,
sym,
markersize=8,
label=self.label(particle))
plt.plot(beta, mn, sym, markersize=8)
plt.xlabel('$\\beta$')
plt.ylabel('Mirror points (m)')
# plt.legend(loc=0)
plt.show()
def plotSuperimposed(self, alpha):
s = settings.Settings()
app = mag.Application()
field = RadialField()
field.alpha = alpha
field.Bz0 = app.fieldBaseStrength[2]
for particle in self.particles:
outfile = self.filename(particle, alpha)
s.outfile = outfile
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
plt.plot(x,
y,
self.style[particle],
label=self.label(particle))
xmin, xmax = [-0.03, 0.03]
ymin, ymax = [-0.03, 0.03]
self.plotField(field, xmin, xmax, ymin, ymax)
plt.xlabel('$z$ (m)')
plt.ylabel('$y$ (m)')
plt.tight_layout()
plt.show()
def calculateDeviations(self, alphaStart, alphaEnd, alphaStep):
from os import path
import csv
from plot_utility import extractData
s = settings.Settings()
for particle in self.particles:
gradient = []
alphas = []
alpha = alphaStart
while alpha < alphaEnd:
s.outfile = self.filename(particle, alpha, 'drift_linreg_')
with open(s.outpath()) as f:
t, R, z = extractData(f, [0, 1, 2])
g = (R[-1] - R[0]) / (t[-1] - t[0])
gradient.append(g)
alphas.append(alpha)
alpha += alphaStep
with open(path.join(s.outdir, self.filename(particle, 0, 'drift_mean_')) + s.outext, 'w') as f:
writer = csv.writer(f, delimiter=' ')
for i in xrange(len(alphas)):
writer.writerow([ alphas[i], gradient[i] ])
def animate(self, alpha, div=4, particle='tr+'):
s = settings.Settings()
app = mag.Application()
field = RadialField()
field.alpha = alpha
field.Bz0 = app.fieldBaseStrength[2]
outfile = self.filename(particle, alpha)
s.outfile = outfile
with open(s.outpath()) as f:
t, x, y, z = extractData(f, [0, 1, 2, 3])
length = len(z) / div
for i in xrange(div):
# start = i * length
# if i > 0:
# start = (i) * length
start = 0
end = (i + 1) * length
# Plot front view, no real way to refactor these *sigh*
plt.plot(x[start:end],
y[start:end],
self.style[particle],
linewidth=1,
label=self.label(particle))
xmin, xmax = [-0.03, 0.03]
ymin, ymax = [-0.03, 0.03]
self.plotField(field,
xmin,
xmax,
ymin,
ymax,
orientation='front')
plt.xlabel('$x$ (m)')
plt.ylabel('$y$ (m)')
plt.tight_layout()
s.outext = '_long_front_{index:03}.pdf'.format(index=i)
plt.savefig(s.outpath())
plt.show()
plt.clf()
# Plot right view, no real way to refactor these *sigh*
plt.plot(z[start:end],
y[start:end],
self.style[particle],
linewidth=1,
label=self.label(particle))
xmin, xmax = [0.0, 25]
ymin, ymax = [-0.03, 0.03]
self.plotField(field,
xmin,
xmax,
ymin,
ymax,
orientation='right')
plt.xlabel('$z$ (m)')
plt.ylabel('$y$ (m)')
plt.tight_layout()
s.outext = '_long_right_{index:03}.pdf'.format(index=i)
plt.savefig(s.outpath())
plt.show()
plt.clf()
