def time_particlescalars():
import postpic as pp
import timeit
pp.chooseCode('dummy')
dr1 = pp.readDump(0.01e6, dimensions=3)
dr2 = pp.readDump(1.0e6, dimensions=3)
ms1 = pp.MultiSpecies(dr1, 'electron')
ms2 = pp.MultiSpecies(dr2, 'electron')
testexprs = [
'x', 'x + y + z', 'gamma', 'beta', 'angle_xy', 'angle_xaxis',
'sqrt(x**2 + y**2 + z**2)', 'r_xyz',
'(gamma > 1.5) & (angle_xaxis < 0.2) & (r_xyz < 2)'
]
print('')
print(
'calculation times for n million particles, averaged over 3 calculations each...'
)
headformat = ' {:2s} | {:6s} | {:6s} | {:s}'
print(headformat.format('n', ' t', ' t/n', 'per particle quantity'))
print(headformat.format('', ' ms', 'ms/mio', ''))
def timeexpr(expr, ms):
t = timeit.Timer(lambda: ms(expr))
tc = t.timeit(number=3) / 3.0
npartmio = len(ms) / 1e6
print('{:4.2f} | {:6.2f} | {:6.2f} | "{}"'.format(
npartmio, tc * 1e3, (tc * 1e3) / npartmio, expr))
for expr in testexprs:
for ms in (ms1, ms2):
timeexpr(expr, ms)
def test_kspace_propagate(self):
pp.chooseCode('dummy')
dr = pp.readDump(10000, dimensions=2)
kspace = pp.helper.kspace("Ex",
fields=dict(Ex=dr.Ex(),
By=dr.By(),
Bz=dr.Bz()))
pp.helper.kspace_propagate(kspace, 0.1, moving_window_vect=(1, 0))
def test_kspace_epoch_like(self):
pp.chooseCode('dummy')
for d in (1,2,3):
dr = pp.readDump(10000, dimensions=d)
fields = dict(Ex=dr.Ex(), By=dr.By(), Bz=dr.Bz())
for k, v in fields.items():
print(k, v.extent)
kspace = pp.helper.kspace_epoch_like("Ex", fields, 0.1)
def test_kspace_epoch_like(self):
pp.chooseCode('dummy')
for d in (1, 2, 3):
dr = pp.readDump(10000, dimensions=d)
fields = dict(Ex=dr.Ex(), By=dr.By(), Bz=dr.Bz())
for k, v in fields.items():
print(k, v.extent)
kspace = pp.helper.kspace_epoch_like("Ex", fields, 0.1)
def test_kspace(self):
pp.chooseCode('dummy')
for d in (1,2,3):
dr = pp.readDump(10000, dimensions=d)
kspace = pp.helper.kspace("Ex", fields=dict(Ex=dr.Ex(), By=dr.By(), Bz=dr.Bz()))
omega = pp.helper.omega_yee_factory([ax.grid[1] - ax.grid[0] for ax in dr.Ey().axes], 1e-10)
kspace = pp.helper.kspace("Ex", fields=dict(Ex=dr.Ex(), By=dr.By(), Bz=dr.Bz()), omega_func=omega)
kspace = pp.helper.kspace("Ex", fields=dict(Ex=dr.Ex(), By=dr.By(), Bz=dr.Bz()), extent=[0, 0.5, 0, 0.5, 0, 0.5])
def time_particleidsort():
import postpic as pp
import numpy as np
from timeit import default_timer as timer
pp.chooseCode('dummy')
dr = pp.readDump(1e6, dimensions=3)
ms = pp.MultiSpecies(dr, 'electron')
def timeid(ms, ids):
t0 = timer()
ms2 = ms.compress(ids)
tf = timer()
print('npart = {:.1e}, fraction_taken ={:6.2f}%, time ={:6.2f}ms' \
.format(len(ms), len(ms2)/len(ms)*100, (tf-t0)*1e3))
print('')
print('time to take a fraction of the particles by their ids')
timeid(ms, np.arange(100))
timeid(ms, np.arange(len(ms) / 20))
timeid(ms, np.arange(len(ms)))
def time_particleidfilter():
import postpic as pp
import numpy as np
from timeit import default_timer as timer
pp.chooseCode('dummy')
dr = pp.readDump(1e6, dimensions=3)
ms = pp.MultiSpecies(dr, 'electron')
def timefilter(ms, expr):
t0 = timer()
ms2 = ms.filter(expr)
tf = timer()
print('npart = {:.1e}, fraction_taken = {:6.2f}%, time = {:6.2f}ms, expr: "{}"' \
.format(len(ms), len(ms2)/len(ms)*100, (tf-t0)*1e3, expr))
print('')
print('time to filter to a fraction of the particles by expression')
timefilter(ms, 'x > 0')
timefilter(ms, 'gamma > 1.5')
timefilter(ms, '(gamma > 1.5) & (angle_xaxis < 0.2) & (r_xyz < 2)')
def test_kspace(self):
pp.chooseCode('dummy')
for d in (1, 2, 3):
dr = pp.readDump(10000, dimensions=d)
kspace = pp.helper.kspace("Ex",
fields=dict(Ex=dr.Ex(),
By=dr.By(),
Bz=dr.Bz()))
omega = pp.helper.omega_yee_factory(
[ax.grid[1] - ax.grid[0] for ax in dr.Ey().axes], 1e-10)
kspace = pp.helper.kspace("Ex",
fields=dict(Ex=dr.Ex(),
By=dr.By(),
Bz=dr.Bz()),
omega_func=omega)
kspace = pp.helper.kspace("Ex",
fields=dict(Ex=dr.Ex(),
By=dr.By(),
Bz=dr.Bz()),
extent=[0, 0.5, 0, 0.5, 0, 0.5])
def main():
import numpy as np
import postpic as pp
# postpic will use matplotlib for plotting. Changing matplotlibs backend
# to "Agg" makes it possible to save plots without a display attached.
# This is necessary to run this example within the "run-tests" script
# on travis-ci.
import matplotlib
matplotlib.use('Agg')
# choose the dummy reader. This reader will create fake data for testing.
pp.chooseCode('dummy')
# Create a dummy reader with 300 particles, not initialized with a seed and use
# uniform distribution
dr = pp.readDump(300, seed=None, randfunc=np.random.random)
# set and create directory for pictures.
savedir = '_examplepictures/'
import os
if not os.path.exists(savedir):
os.mkdir(savedir)
# initialze the plotter object.
# project name will be prepended to all output names
plotter = pp.plotting.plottercls(dr,
outdir=savedir,
autosave=True,
project='particleshapedemo')
# we will need a refrence to the MultiSpecies quite often
from postpic import MultiSpecies as MS
# create MultiSpecies object for every particle species that exists.
pas = [MS(dr, s) for s in dr.listSpecies()]
# --- 1D visualization of particle contributions ---
def particleshapedemo(shape):
import postpic.cythonfunctions as cf
import matplotlib.pyplot as plt
ptclpos = np.array([4.5, 9.75, 15.0, 20.25])
y, edges = cf.histogram(ptclpos, bins=25, range=(0, 25), shape=shape)
x = np.convolve(edges, [0.5, 0.5], mode='valid')
fig = plt.figure()
fig.suptitle('ParticleShape: {:s}'.format(str(shape)))
ax = fig.add_subplot(111)
ax.plot(x, y)
ax.set_ylim((0, 1))
ax.set_xticks(x, minor=True)
ax.grid(which='minor')
for ix in ptclpos:
ax.axvline(x=ix, color='y')
fig.savefig(savedir + 'particleshapedemo{:s}.png'.format(str(shape)),
dpi=160)
plt.close(fig)
if True:
particleshapedemo(0)
particleshapedemo(1)
particleshapedemo(2)
# --- 1D ---
if True:
pa = pas[0]
plotargs = {'ylim': (0, 1600), 'log10plot': False}
# 1 particle per cell
plotter.plotField(
pa.createField(MS.X,
optargsh={
'bins': 300,
'shape': 0
},
title='1ppc_order0',
rangex=(0, 1)), **plotargs)
plotter.plotField(
pa.createField(MS.X,
optargsh={
'bins': 300,
'shape': 1
},
title='1ppc_order1',
rangex=(0, 1)), **plotargs)
plotter.plotField(
pa.createField(MS.X,
optargsh={
'bins': 300,
'shape': 2
},
title='1ppc_order2',
rangex=(0, 1)), **plotargs)
# 3 particles per cell
plotter.plotField(
pa.createField(MS.X,
optargsh={
'bins': 100,
'shape': 0
},
title='3ppc_order0',
rangex=(0, 1)), **plotargs)
plotter.plotField(
pa.createField(MS.X,
optargsh={
'bins': 100,
'shape': 1
},
title='3ppc_order1',
rangex=(0, 1)), **plotargs)
plotter.plotField(
pa.createField(MS.X,
optargsh={
'bins': 100,
'shape': 2
},
title='3ppc_order2',
rangex=(0, 1)), **plotargs)
# 10 particles per cell
plotter.plotField(
pa.createField(MS.X,
optargsh={
'bins': 30,
'shape': 0
},
title='10ppc_order0',
rangex=(0, 1)), **plotargs)
plotter.plotField(
pa.createField(MS.X,
optargsh={
'bins': 30,
'shape': 1
},
title='10ppc_order1',
rangex=(0, 1)), **plotargs)
plotter.plotField(
pa.createField(MS.X,
optargsh={
'bins': 30,
'shape': 2
},
title='10ppc_order2',
rangex=(0, 1)), **plotargs)
# --- 2D ---
if True:
dr = pp.readDump(300 * 30, seed=None, randfunc=np.random.random)
pa = MS(dr, dr.listSpecies()[0])
plotargs = {'clim': (0, 3e4), 'log10plot': False}
# 1 particle per cell
plotter.plotField(
pa.createField(MS.X,
MS.Y,
optargsh={
'bins': (300, 30),
'shape': 0
},
title='1ppc_order0',
rangex=(0, 1),
rangey=(0, 1)), **plotargs)
plotter.plotField(
pa.createField(MS.X,
MS.Y,
optargsh={
'bins': (300, 30),
'shape': 1
},
title='1ppc_order1',
rangex=(0, 1),
rangey=(0, 1)), **plotargs)
plotter.plotField(
pa.createField(MS.X,
MS.Y,
optargsh={
'bins': (300, 30),
'shape': 2
},
title='1ppc_order2',
rangex=(0, 1),
rangey=(0, 1)), **plotargs)
# 3 particles per cell
plotter.plotField(
pa.createField(MS.X,
MS.Y,
optargsh={
'bins': (100, 10),
'shape': 0
},
title='3ppc_order0',
rangex=(0, 1),
rangey=(0, 1)), **plotargs)
plotter.plotField(
pa.createField(MS.X,
MS.Y,
optargsh={
'bins': (100, 10),
'shape': 1
},
title='3ppc_order1',
rangex=(0, 1),
rangey=(0, 1)), **plotargs)
plotter.plotField(
pa.createField(MS.X,
MS.Y,
optargsh={
'bins': (100, 10),
'shape': 2
},
title='3ppc_order2',
rangex=(0, 1),
rangey=(0, 1)), **plotargs)
# --- 3D ---
if True:
dr = pp.readDump(300 * 30,
seed=None,
randfunc=np.random.random,
dimensions=3)
pa = MS(dr, dr.listSpecies()[0])
# just try to create the field. not plotting routines yet
f = pa.createField(MS.X,
MS.Y,
MS.Z,
optargsh={
'bins': (30, 30, 10),
'shape': 2
},
title='1ppc_order2',
rangex=(0, 1),
rangey=(0, 1),
rangez=(0, 1))
def test_kspace_propagate(self):
pp.chooseCode('dummy')
dr = pp.readDump(10000, dimensions=2)
kspace = pp.helper.kspace("Ex", fields=dict(Ex=dr.Ex(), By=dr.By(), Bz=dr.Bz()))
pp.helper.kspace_propagate(kspace, 0.1, moving_window_vect=(1,0))
def main():
import os
if not os.path.exists('examples/_openPMDdata'):
os.mkdir('examples/_openPMDdata')
download('https://github.com/openPMD/openPMD-example-datasets/'
+ 'raw/776ae3a96c02b20cfae56efafcbda6ca76d4c78d/example-2d.tar.gz',
'examples/_openPMDdata/example-2d.tar.gz')
import tarfile
tar = tarfile.open('examples/_openPMDdata/example-2d.tar.gz')
tar.extractall('examples/_openPMDdata')
# now that files are downloaded and extracted, start data evaluation
import numpy as np
import postpic as pp
# postpic will use matplotlib for plotting. Changing matplotlibs backend
# to "Agg" makes it possible to save plots without a display attached.
# This is necessary to run this example within the "run-tests" script
# on travis-ci.
import matplotlib; matplotlib.use('Agg')
pp.chooseCode('openpmd')
dr = pp.readDump('examples/_openPMDdata/example-2d/hdf5/data00000300.h5')
# set and create directory for pictures.
savedir = '_examplepictures/openPMD/'
import os
if not os.path.exists(savedir):
os.mkdir(savedir)
# initialze the plotter object.
# project name will be prepended to all output names
plotter = pp.plotting.plottercls(dr, outdir=savedir, autosave=True, project='OpenPMD')
# we will need a refrence to the MultiSpecies quite often
from postpic import MultiSpecies as MS
# create MultiSpecies Object for every particle species that exists.
pas = [MS(dr, s) for s in dr.listSpecies()]
if True:
# Plot Data from the FieldAnalyzer fa. This is very simple: every line creates one plot
plotter.plotField(dr.Ex()) # plot 0
plotter.plotField(dr.Ey()) # plot 1
plotter.plotField(dr.Ez()) # plot 2
plotter.plotField(dr.energydensityEM()) # plot 3
# Using the MultiSpecies requires an additional step:
# 1) The MultiSpecies.createField method will be used to create a Field object
# with choosen particle scalars on every axis
# 2) Plot the Field object
optargsh={'bins': [200,50]}
for pa in pas:
# Remark on 2D or 3D Simulation:
# the data fields for the plots in the following section are build by postpic
# only from the particle data. The following plots would just the same,
# if we would use the data of a 3D Simulation instead.
# create a Field object nd holding the number density
nd = pa.createField(MS.Z, MS.X, optargsh=optargsh,simextent=False)
# plot the Field object nd
plotter.plotField(nd, name='NumberDensity') # plot 4
# more advanced: create a field holding the total kinetic energy on grid
ekin = pa.createField(MS.Z, MS.X, weights=MS.Ekin_MeV, optargsh=optargsh, simextent=False)
# The Field objectes can be used for calculations. Here we use this to
# calculate the average kinetic energy on grid and plot
plotter.plotField(ekin / nd, name='Avg Kin Energy (MeV)') # plot 5
# use optargsh to force lower resolution
# plot number density
plotter.plotField(pa.createField(MS.Z, MS.X, optargsh=optargsh), lineoutx=True, lineouty=True) # plot 6
# plot phase space
plotter.plotField(pa.createField(MS.Z, MS.P, optargsh=optargsh)) # plot 7
plotter.plotField(pa.createField(MS.Z, MS.gamma, optargsh=optargsh)) # plot 8
plotter.plotField(pa.createField(MS.Z, MS.beta, optargsh=optargsh)) # plot 9
if True:
# instead of reading a single dump read a collection of dumps using the simulationreader
sr = pp.readSim('examples/_openPMDdata/example-2d/hdf5/*.h5')
print('There are {:} dumps in this simulationreader object:'.format(len(sr)))
# now you can iterate over the dumps written easily
for dr in sr:
print('Simulation time of current dump t = {:.2e} s'.format(dr.time()))
# there are the frames of a movie:
plotter.plotField(dr.Ez())
def main():
import numpy as np
import postpic as pp
# postpic will use matplotlib for plotting. Changing matplotlibs backend
# to "Agg" makes it possible to save plots without a display attached.
# This is necessary to run this example within the "run-tests" script
# on travis-ci.
import matplotlib; matplotlib.use('Agg')
# choose the dummy reader. This reader will create fake data for testing.
pp.chooseCode('dummy')
dr = pp.readDump(3e5) # Dummyreader takes a float as argument, not a string.
# set and create directory for pictures.
savedir = '_examplepictures/'
import os
if not os.path.exists(savedir):
os.mkdir(savedir)
# initialze the plotter object.
# project name will be prepended to all output names
plotter = pp.plotting.plottercls(dr, outdir=savedir, autosave=True, project='simpleexample')
# we will need a refrence to the MultiSpecies quite often
from postpic import MultiSpecies as MS
# create MultiSpecies Object for every particle species that exists.
pas = [MS(dr, s) for s in dr.listSpecies()]
if True:
# Plot Data from the FieldAnalyzer fa. This is very simple: every line creates one plot
plotter.plotField(dr.Ex()) # plot 0
plotter.plotField(dr.Ey()) # plot 1
plotter.plotField(dr.Ez()) # plot 2
plotter.plotField(dr.energydensityEM()) # plot 3
# Using the MultiSpecies requires an additional step:
# 1) The MultiSpecies.createField method will be used to create a Field object
# with choosen particle scalars on every axis
# 2) Plot the Field object
optargsh={'bins': [300,300]}
for pa in pas:
# create a Field object nd holding the number density
nd = pa.createField(MS.X, MS.Y, optargsh=optargsh,simextent=True)
# plot the Field object nd
plotter.plotField(nd, name='NumberDensity') # plot 4
# more advanced: create a field holding the total kinetic energy on grid
ekin = pa.createField(MS.X, MS.Y, weights=MS.Ekin_MeV, optargsh=optargsh, simextent=True)
# The Field objectes can be used for calculations. Here we use this to
# calculate the average kinetic energy on grid and plot
plotter.plotField(ekin / nd, name='Avg Kin Energy (MeV)') # plot 5
# use optargsh to force lower resolution
# plot number density
plotter.plotField(pa.createField(MS.X, MS.Y, optargsh=optargsh), lineoutx=True, lineouty=True) # plot 6
# plot phase space
plotter.plotField(pa.createField(MS.X, MS.P, optargsh=optargsh)) # plot 7
plotter.plotField(pa.createField(MS.X, MS.gamma, optargsh=optargsh)) # plot 8
plotter.plotField(pa.createField(MS.X, MS.beta, optargsh=optargsh)) # plot 9
# same with high resolution
plotter.plotField(pa.createField(MS.X, MS.Y, optargsh={'bins': [1000,1000]})) # plot 10
plotter.plotField(pa.createField(MS.X, MS.P, optargsh={'bins': [1000,1000]})) # plot 11
# advanced: postpic has already defined a lot of particle scalars as Px, Py, Pz, P, X, Y, Z, gamma, beta, Ekin, Ekin_MeV, Ekin_MeV_amu, ... but if needed you can also define your own particle scalar on the fly.
# In case its regularly used it should be added to postpic. If you dont know how, just let us know about your own useful particle scalar by email or adding an issue at
# https://github.com/skuschel/postpic/issues
# define your own particle scalar: p_r = sqrt(px**2 + py**2)/p
def p_r(ms):
return np.sqrt(ms.Px()**2 + ms.Py()**2) / ms.P()
# add unit and name for automatic labeling when plotted with plotField method
p_r.unit=''
p_r.name='$\sqrt{P_x^2 + P_y^2} / P$'
# define another own particle scalar: r = sqrt(x**2 + y**2)
def r(ms):
return np.sqrt(ms.X()**2 + ms.Y()**2)
r.unit='m'
r.name='r'
# use the plotter with the particle scalars defined above.
plotter.plotField(pa.createField(r, p_r, optargsh={'bins':[400,400]})) # plot 12
# choose particles by their properies
def cf(ms):
return ms.X() > 0.0 # only use particles with x > 0.0
cf.name = 'x>0'
pa.compressfn(cf)
# plot 13, compare with plot 10
plotter.plotField(pa.createField(MS.X, MS.Y, optargsh={'bins': [1000,1000]}))
# plot 14, compare with plot 12
plotter.plotField(pa.createField(r, p_r, optargsh={'bins':[400,400]}))
def setUp(self):
pp.chooseCode('dummy')
self.dr = pp.readDump(10000)
#################
## postpic define ###
import postpic as pp
print pp.__version__ # postpic version
pp.chooseCode('epoch')
dr = pp.readDump('Data/0020.sdf')
print(dr)
# the dumpreader knwos all the information of the simulation
print('The simulations was running on {} spatial dimensions.'.format(
dr.simdimensions()))
# if needed, postpic can be bypassed and the underlying datastructure (sdf in this case)
# can be accessed directly via keys
print(dr.keys())
print dr['Header']
print(dr.listSpecies()
) # the multispecies Object is used to access particle data
ms = pp.MultiSpecies(dr, 'electron')
# ms is representing the species "Electrons"
print(ms)
x = ms('x')
print(len(x))
# you can look at for a list of predefined values
#print pp.particle_scalars
def setUp(self):
self._tempfiles = []
pp.chooseCode('DUMMY')
self.dump = pp.datareader.readDump(100)
self.testfield = self.dump.Ey()
def setUp(self):
pp.chooseCode('dummy')
self.dr = pp.readDump(10000)
def setUp(self):
self._tempfiles = []
pp.chooseCode('DUMMY')
self.dump = pp.datareader.readDump(100)
self.testfield = self.dump.Ey()
def main():
import os
import os.path as osp
datadir = 'examples/kspace-test-2d'
tarball = osp.join(datadir, 'kspace-test-2d-resources.tar.gz')
tarball_url = 'https://blinne.net/files/postpic/kspace-test-2d-resources.tar.gz'
if not os.path.exists(datadir):
os.mkdir(datadir)
s = download(tarball_url, tarball)
if s:
import hashlib
chksum = hashlib.sha256(open(tarball, 'rb').read()).hexdigest()
if chksum != "54bbeae19e1f412ddd475f565c2193e92922ed8a22e7eb3ecb4d73b5cf193b24":
os.remove(tarball)
s = False
if not s:
print('Failed to Download example data. Skipping this example.')
return
import tarfile
tar = tarfile.open(tarball)
tar.extractall(datadir)
import matplotlib
matplotlib.use('Agg')
font = {'size': 12}
matplotlib.rc('font', **font)
import copy
import postpic as pp
import numpy as np
import pickle
try:
import sdf
sdfavail = True
except ImportError:
sdfavail = False
# basic constants
micro = 1e-6
femto = 1e-15
c = pp.PhysicalConstants.c
# known parameters from simulation
lam = 0.5 * micro
k0 = 2 * np.pi / lam
f0 = pp.PhysicalConstants.c / lam
pymajorver = str(sys.version_info[0])
if sdfavail:
pp.chooseCode("EPOCH")
dump = pp.readDump(osp.join(datadir, '0002.sdf'))
plotter = pp.plotting.plottercls(dump, autosave=False)
fields = dict()
for fc in ['Ey', 'Bz']:
fields[fc] = getattr(dump, fc)()
fields[fc].saveto(osp.join(datadir, '0002_' + fc + pymajorver),
compressed=False)
t = dump.time()
dt = t / dump.timestep()
pickle.dump(
dict(t=t, dt=dt),
open(osp.join(datadir, '0002_meta' + pymajorver + '.pickle'),
'wb'))
else:
fields = dict()
for fc in ['Ey', 'Bz']:
fields[fc] = pp.Field.loadfrom(
osp.join(datadir, '0002_{}.npz').format(fc + pymajorver))
meta = pickle.load(
open(osp.join(datadir, '0002_meta' + pymajorver + '.pickle'),
'rb'))
t = meta['t']
dt = meta['dt']
plotter = pp.plotting.plottercls(None, autosave=False)
Ey = fields['Ey']
# grid spacing from field
dx = [ax.grid[1] - ax.grid[0] for ax in Ey.axes]
#w0 = 2*np.pi*f0
#w0 = pp.PhysicalConstants.c * k0
wn = np.pi / dt
omega_yee = pp.helper.omega_yee_factory(dx=dx, dt=dt)
wyee = omega_yee([k0, 0])
w0 = pp.helper.omega_free([k0, 0])
print('dx', dx)
print('t', t)
print('dt', dt)
print('wn', wn)
print('w0', w0)
print('wyee', wyee)
print('wyee/w0', wyee / w0)
print('wyee/wn', wyee / wn)
print('lam/dx[0]', lam / dx[0])
print('cos(1/2 wyee dt)', np.cos(1 / 2 * wyee * dt))
vg_yee = c * np.cos(
k0 * dx[0] /
2.0) / np.sqrt(1 - (c * dt / dx[0] * np.sin(k0 * dx[0] / 2.0))**2)
print('vg/c', vg_yee / c)
r = np.sqrt(1.0 - (pp.PhysicalConstants.c * dt)**2 *
(1 / dx[0] * np.sin(1 / 2.0 * k0 * dx[0]))**2)
print('r', r)
# In[2]:
omega_yee = pp.helper.omega_yee_factory(dx=Ey.spacing, dt=dt)
lin_int_response_omega = pp.helper._linear_interpolation_frequency_response(
dt)
lin_int_response_k = pp.helper._linear_interpolation_frequency_response_on_k(
lin_int_response_omega,
Ey.fft().axes, omega_yee)
lin_int_response_k_vac = pp.helper._linear_interpolation_frequency_response_on_k(
lin_int_response_omega,
Ey.fft().axes, pp.helper.omega_free)
# In[3]:
_ = plotter.plotField(Ey[:, 0.0])
# In[4]:
kspace = dict()
component = "Ey"
# In[5]:
#key = 'default epoch map=yee, omega=vac'
key = 'linresponse map=yee, omega=vac'
if sdfavail:
kspace[key] = abs(dump.kspace_Ey(solver='yee'))
else:
kspace[key] = abs(
pp.helper.kspace(component,
fields=dict(Ey=fields['Ey'],
Bz=fields['Bz'].fft() /
lin_int_response_k),
interpolation='fourier'))
# using the helper function `kspace_epoch_like` would yield same result:
# kspace[key] = abs(pp.helper.kspace_epoch_like(component, fields=fields, dt=dt, omega_func=omega_yee))
normalisation = 1.0 / np.max(kspace[key].matrix)
kspace[key] *= normalisation
kspace[key].name = r'corrected $\vec{k}$-space, $\omega_0=c|\vec{k}|$'
kspace[key].unit = ''
# In[6]:
key = 'simple fft'
kspace[key] = abs(Ey.fft()) * normalisation
kspace[key].name = r'plain fft'
kspace[key].unit = ''
# In[7]:
key = 'fourier'
kspace[key] = abs(
pp.helper.kspace(component, fields=fields,
interpolation='fourier')) * normalisation
kspace[key].name = u'naïve $\\vec{k}$-space, $\\omega_0=c|\\vec{k}|$'
kspace[key].unit = ''
# In[8]:
key = 'fourier yee'
kspace[key] = abs(
pp.helper.kspace(component,
fields=fields,
interpolation='fourier',
omega_func=omega_yee)) * normalisation
kspace[
key].name = u'naïve $\\vec{k}$-space, $\\omega_0=\\omega_\\mathrm{grid}$'
kspace[key].unit = ''
# In[9]:
key = 'linresponse map=yee, omega=yee'
kspace[key] = abs(
pp.helper.kspace(component,
fields=dict(
Ey=fields['Ey'],
Bz=fields['Bz'].fft() / lin_int_response_k),
interpolation='fourier',
omega_func=omega_yee)) * normalisation
kspace[
key].name = r'corrected $\vec{k}$-space, $\omega_0=\omega_\mathrm{grid}$'
kspace[key].unit = ''
# In[10]:
slices = [slice(360 - 120, 360 + 120), slice(120, 121)]
# In[11]:
keys = [
'simple fft', 'fourier yee', 'fourier',
'linresponse map=yee, omega=yee', 'linresponse map=yee, omega=vac'
]
figure2 = plotter.plotFields1d(*[kspace[k][slices] for k in keys],
log10plot=True,
ylim=(5e-17, 5))
figure2.set_figwidth(8)
figure2.set_figheight(6)
while figure2.axes[0].texts:
figure2.axes[0].texts[-1].remove()
figure2.axes[0].set_title('')
figure2.axes[0].set_ylabel(r'$|E_y(k_x,0,0)|\, [a. u.]$')
figure2.axes[0].set_xlabel(r'$k_x\,[m^{-1}]$')
figure2.tight_layout()
figure2.savefig(osp.join(datadir, 'gaussian-kspace.pdf'))
print("Integrated ghost peaks")
for k in keys:
I = kspace[k][:0.0, :].integrate().matrix
print(k, I)
if k == 'linresponse map=yee, omega=vac':
if I < 30000000.:
print(
'linresponse map=yee, omega=vac value is low enough: YES')
else:
print('linresponse map=yee, omega=vac value is low enough: NO')
print('Something is WRONG')
sys.exit(1)
# In[13]:
if sdfavail:
kspace_ey = dump.kspace_Ey(solver='yee')
else:
kspace_ey = pp.helper.kspace_epoch_like(component,
fields=fields,
dt=dt,
omega_func=omega_yee)
complex_ey = kspace_ey.fft()
envelope_ey_2d = abs(complex_ey)[:, :].squeeze()
try:
from skimage.restoration import unwrap_phase
phase_ey = complex_ey.replace_data(unwrap_phase(np.angle(complex_ey)))
except ImportError:
phase_ey = complex_ey.replace_data(np.angle(complex_ey))
phase_ey_2d = phase_ey[:, :].squeeze()
# In[14]:
ey = complex_ey.real[-0.1e-5:0.2e-5, :]
#ey = Ey
ey.name = r'$E_y$'
ey.unit = r'$\frac{\mathrm{V}}{\mathrm{m}}$'
figure = plotter.plotField2d(ey)
figure.set_figwidth(6)
figure.axes[0].set_title(
r'') #$\Re E_y\ [\frac{\mathrm{V}}{\mathrm{m}}]$')
figure.axes[0].set_xlabel(u'$x\\,[µm]$')
figure.axes[0].set_ylabel(u'$y\\,[µm]$')
import matplotlib.ticker as ticker
ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x / 1e-6))
figure.axes[0].xaxis.set_major_formatter(ticks_x)
figure.axes[0].yaxis.set_major_formatter(ticks_x)
try:
figure.axes[0].images[0].colorbar.remove()
except AttributeError:
pass
figure.colorbar(figure.axes[0].images[0],
format='%6.0e',
pad=0.15,
label=r'$\Re E_y\ [\frac{\mathrm{V}}{\mathrm{m}}]$')
axes2 = figure.axes[0].twinx()
axes2.set_ylabel(r'$\operatorname{Arg}(E_y)\, [\pi],\;|E_y|\, [a. u.]$')
env = abs(complex_ey[-0.1e-5:0.2e-5, 0.0])
m = np.max(env)
env = (env / m * 40 / np.pi).squeeze()
p = phase_ey[-0.1e-5:0.2e-5, 0.0].squeeze() / np.pi
_ = axes2.plot(env.grid, env.matrix, label=r'$|E_y|\, [a. u.]$')
_ = axes2.plot(p.grid,
p.matrix,
label=r'$\operatorname{Arg}(E_y)\, [\pi]$')
handles, labels = axes2.get_legend_handles_labels()
axes2.legend(handles, labels)
#figure.axes[0].set_title(r'$E_y\,[V/m]$')
figure.set_figwidth(6)
figure.set_figheight(6)
figure.tight_layout()
figure.savefig(osp.join(datadir, 'gaussian-env-arg.pdf'))
def setUp(self):
pp.chooseCode('dummy')
self.dr = pp.readDump(10000)
self.p = pp.MultiSpecies(self.dr, 'electron')
def main():
import numpy as np
import postpic as pp
# postpic will use matplotlib for plotting. Changing matplotlibs backend
# to "Agg" makes it possible to save plots without a display attached.
# This is necessary to run this example within the "run-tests" script
# on travis-ci.
import matplotlib; matplotlib.use('Agg')
# choose the dummy reader. This reader will create fake data for testing.
pp.chooseCode('dummy')
dr = pp.readDump(3e5) # Dummyreader takes a float as argument, not a string.
# set and create directory for pictures.
savedir = '_examplepictures/'
import os
if not os.path.exists(savedir):
os.mkdir(savedir)
# initialze the plotter object.
# project name will be prepended to all output names
plotter = pp.plotting.plottercls(dr, outdir=savedir, autosave=True, project='simpleexample')
# we will need a refrence to the MultiSpecies quite often
from postpic.particles import MultiSpecies
# create MultiSpecies Object for every particle species that exists.
pas = [MultiSpecies(dr, s) for s in dr.listSpecies()]
if True:
# Plot Data from the FieldAnalyzer fa. This is very simple: every line creates one plot
plotter.plotField(dr.Ex()) # plot 0
plotter.plotField(dr.Ey()) # plot 1
plotter.plotField(dr.Ez()) # plot 2
plotter.plotField(dr.energydensityEM()) # plot 3
# Using the MultiSpecies requires an additional step:
# 1) The MultiSpecies.createField method will be used to create a Field object
# with choosen particle scalars on every axis
# 2) Plot the Field object
optargsh={'bins': [300,300]}
for pa in pas:
# create a Field object nd holding the number density
nd = pa.createField('x', 'y', simextent=True, **optargsh)
# plot the Field object nd
plotter.plotField(nd, name='NumberDensity') # plot 4
# if you like to keep working with the just created number density
# yourself, it will convert to an numpy array whenever needed:
arr = np.asarray(nd)
print('Shape of number density: {}'.format(arr.shape))
# more advanced: create a field holding the total kinetic energy on grid
ekin = pa.createField('x', 'y', weights='Ekin_MeV', simextent=True, **optargsh)
# The Field objectes can be used for calculations. Here we use this to
# calculate the average kinetic energy on grid and plot
plotter.plotField(ekin / nd, name='Avg Kin Energy (MeV)') # plot 5
# use optargsh to force lower resolution
# plot number density
plotter.plotField(pa.createField('x', 'y', **optargsh), lineoutx=True, lineouty=True) # plot 6
# plot phase space
plotter.plotField(pa.createField('x', 'p', **optargsh)) # plot 7
plotter.plotField(pa.createField('x', 'gamma', **optargsh)) # plot 8
plotter.plotField(pa.createField('x', 'beta', **optargsh)) # plot 9
# same with high resolution
plotter.plotField(pa.createField('x', 'y', bins=[1000,1000])) # plot 10
plotter.plotField(pa.createField('x', 'p', bins=[1000,1000])) # plot 11
# advanced: postpic has already defined a lot of particle scalars as Px, Py, Pz, P, X, Y, Z, gamma, beta, Ekin, Ekin_MeV, Ekin_MeV_amu, ... but if needed you can also define your own particle scalar on the fly.
# In case its regularly used it should be added to postpic. If you dont know how, just let us know about your own useful particle scalar by email or adding an issue at
# https://github.com/skuschel/postpic/issues
# define your own particle scalar: p_r = sqrt(px**2 + py**2)/p
plotter.plotField(pa.createField('sqrt(px**2 + py**2)/p', 'sqrt(x**2 + y**2)', bins=[400,400])) # plot 12
# however, since its unknown to the program, what quantities were calculated the axis of plot 12 will only say "unknown"
# this can be avoided in two ways:
# 1st: define your own ScalarProperty(name, expr, unit):
p_perp = pp.particles.ScalarProperty('sqrt(px**2 + py**2)/p', name='p_perp', unit='kg*m/s')
r_xy = pp.particles.ScalarProperty('sqrt(x**2 + y**2)', name='r_xy', unit='m')
# this will create an identical plot, but correcly labled
plotter.plotField(pa.createField(p_perp, r_xy, bins=[400,400])) # plot 13
# if those quantities are reused often, teach postip to recognize them within the string expression:
pp.particles.particle_scalars.add(p_perp)
#pp.particles.scalars.add(r_xy) # we cannot execute this line, because r_xy is already predefinded
plotter.plotField(pa.createField('p_perp', 'r_xy', bins=[400,400])) # plot 14
# choose particles by their properies
# this has been the old interface, which would still work
# def cf(ms):
# return ms('x') > 0.0 # only use particles with x > 0.0
# cf.name = 'x>0.0'
# pa.compress(cf)
# nicer is the new filter function, which does exactly the same:
pf = pa.filter('x>0')
# plot 15, compare with plot 10
plotter.plotField(pf.createField('x', 'y', bins=[1000,1000]))
# plot 16, compare with plot 12
plotter.plotField(pf.createField('p_perp', 'r_xy', bins=[400,400]))
plotter.plotField(dr.divE()) # plot 13
def main():
import numpy as np
import postpic as pp
# postpic will use matplotlib for plotting. Changing matplotlibs backend
# to "Agg" makes it possible to save plots without a display attached.
# This is necessary to run this example within the "run-tests" script
# on travis-ci.
import matplotlib; matplotlib.use('Agg')
# choose the dummy reader. This reader will create fake data for testing.
pp.chooseCode('dummy')
# Create a dummy reader with 300 particles, not initialized with a seed and use
# uniform distribution
dr = pp.readDump(300, seed=None, randfunc=np.random.random)
# set and create directory for pictures.
savedir = '_examplepictures/'
import os
if not os.path.exists(savedir):
os.mkdir(savedir)
# initialze the plotter object.
# project name will be prepended to all output names
plotter = pp.plotting.plottercls(dr, outdir=savedir, autosave=True, project='particleshapedemo')
# we will need a refrence to the MultiSpecies quite often
from postpic import MultiSpecies as MS
# create MultiSpecies object for every particle species that exists.
pas = [MS(dr, s) for s in dr.listSpecies()]
# --- 1D visualization of particle contributions ---
def particleshapedemo(shape):
from postpic.particles import histogramdd
import matplotlib.pyplot as plt
ptclpos = np.array([4.5, 9.75, 15.0, 20.25])
y, (edges, ) = histogramdd(ptclpos, bins=25, range=(0,25), shape=shape)
x = np.convolve(edges, [0.5, 0.5], mode='valid')
fig = plt.figure()
fig.suptitle('ParticleShape: {:s}'.format(str(shape)))
ax = fig.add_subplot(111)
ax.plot(x,y)
ax.set_ylim((0,1))
ax.set_xticks(x, minor=True)
ax.grid(which='minor')
for ix in ptclpos:
ax.axvline(x=ix, color='y')
fig.savefig(savedir + 'particleshapedemo{:s}.png'.format(str(shape)), dpi=160)
plt.close(fig)
if True:
particleshapedemo(0)
particleshapedemo(1)
particleshapedemo(2)
particleshapedemo(3)
# --- 1D ---
if True:
pa = pas[0]
plotargs = {'ylim': (0,1600), 'log10plot': False}
# 1 particle per cell
plotter.plotField(pa.createField('x', bins=300, shape=0, title='1ppc_order0', rangex=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', bins=300, shape=1, title='1ppc_order1', rangex=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', bins=300, shape=2, title='1ppc_order2', rangex=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', bins=300, shape=3, title='1ppc_order3', rangex=(0,1)), **plotargs)
# 3 particles per cell
plotter.plotField(pa.createField('x', bins=100, shape=0, title='3ppc_order0', rangex=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', bins=100, shape=1, title='3ppc_order1', rangex=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', bins=100, shape=2, title='3ppc_order2', rangex=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', bins=100, shape=3, title='3ppc_order3', rangex=(0,1)), **plotargs)
# 10 particles per cell
plotter.plotField(pa.createField('x', bins=30, shape=0, title='10ppc_order0', rangex=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', bins=30, shape=1, title='10ppc_order1', rangex=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', bins=30, shape=2, title='10ppc_order2', rangex=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', bins=30, shape=3, title='10ppc_order3', rangex=(0,1)), **plotargs)
# --- 2D ---
if True:
dr = pp.readDump(300*30, seed=None, randfunc=np.random.random)
pa = MS(dr, dr.listSpecies()[0])
plotargs = {'clim': (0,3e4), 'log10plot': False}
# 1 particle per cell
plotter.plotField(pa.createField('x', 'y', bins=(300,30), shape=0, title='1ppc_order0', rangex=(0,1), rangey=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', 'y', bins=(300,30), shape=1, title='1ppc_order1', rangex=(0,1), rangey=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', 'y', bins=(300,30), shape=2, title='1ppc_order2', rangex=(0,1), rangey=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', 'y', bins=(300,30), shape=3, title='1ppc_order3', rangex=(0,1), rangey=(0,1)), **plotargs)
# 3 particles per cell
plotter.plotField(pa.createField('x', 'y', bins=(100,10), shape=0, title='3ppc_order0', rangex=(0,1), rangey=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', 'y', bins=(100,10), shape=1, title='3ppc_order1', rangex=(0,1), rangey=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', 'y', bins=(100,10), shape=2, title='3ppc_order2', rangex=(0,1), rangey=(0,1)), **plotargs)
plotter.plotField(pa.createField('x', 'y', bins=(100,10), shape=3, title='3ppc_order3', rangex=(0,1), rangey=(0,1)), **plotargs)
# --- 3D ---
if True:
dr = pp.readDump(300*30, seed=None, randfunc=np.random.random, dimensions=3)
pa = MS(dr, dr.listSpecies()[0])
# just try to create the field. not plotting routines yet
f = pa.createField('x', 'y', 'z', bins=(30,30,10), shape=2, title='1ppc_order2', rangex=(0,1), rangey=(0,1), rangez=(0,1))
f = pa.createField('x', 'y', 'z', bins=(30,30,10), shape=3, title='1ppc_order3', rangex=(0,1), rangey=(0,1), rangez=(0,1))
tScale = 2. * np.pi / wL
tUnits = '$\\tau_L$'
EkScale = 1.e6 / (6.24e18) # Convert to Mev: M (1e6), ev (C = 6.24e18*qe)
EkUnits = '(MeV)'
Ec = pScale * wL / qe
nC = eps0 * me * wL * wL / (qe * qe)
nScale = nC
qScale = 1e-12
qUnits = '(pC)'
xWindow = 9. # plot region length
xForward = 6. # plot region to show preceding xAf
linear = 0 # Linear scale plot on/off
# Read all frame data
postpic.chooseCode('EPOCH')
fdata = postpic.readDump(str(frame_file))
# Calculate required plot variables
##xGrid = fdata['Grid/Grid_mid'].data[0] / xScale
##tStart = np.abs(fdata['Grid/Grid_mid'].data[0][0]) / c
##t = (fdata['Header']['time'] - tStart) / tScale
##output_file = Path.cwd().joinpath(
##'nrg_protons_' + '{:04}'.format(args.frame) + save_ext)
##if args.hd:
##monitor_dpi = 96 # NOTE: This is for Jnana, may be different for Brahman.
##plt.figure(figsize=(1920 / monitor_dpi, 1080 / monitor_dpi),
##dpi=monitor_dpi) # Gives us 1080p output
##mpl.rcParams.update({'font.size': 22})
def main():
import os
if not os.path.exists('examples/_openPMDdata'):
os.mkdir('examples/_openPMDdata')
download(
'https://github.com/openPMD/openPMD-example-datasets/' +
'raw/776ae3a96c02b20cfae56efafcbda6ca76d4c78d/example-2d.tar.gz',
'examples/_openPMDdata/example-2d.tar.gz')
import tarfile
tar = tarfile.open('examples/_openPMDdata/example-2d.tar.gz')
tar.extractall('examples/_openPMDdata')
# now that files are downloaded and extracted, start data evaluation
import numpy as np
import postpic as pp
# postpic will use matplotlib for plotting. Changing matplotlibs backend
# to "Agg" makes it possible to save plots without a display attached.
# This is necessary to run this example within the "run-tests" script
# on travis-ci.
import matplotlib
matplotlib.use('Agg')
pp.chooseCode('openpmd')
dr = pp.readDump('examples/_openPMDdata/example-2d/hdf5/data00000300.h5')
print('The simulations was running on {} spatial dimensions.'.format(
dr.simdimensions()))
# set and create directory for pictures.
savedir = '_examplepictures/openPMD/'
import os
if not os.path.exists(savedir):
os.mkdir(savedir)
# initialze the plotter object.
# project name will be prepended to all output names
plotter = pp.plotting.plottercls(dr,
outdir=savedir,
autosave=True,
project='openPMD')
# we will need a refrence to the MultiSpecies quite often
from postpic import MultiSpecies as MS
# create MultiSpecies Object for every particle species that exists.
pas = [MS(dr, s) for s in dr.listSpecies()]
if True:
# Plot Data from the FieldAnalyzer fa. This is very simple: every line creates one plot
plotter.plotField(dr.Ex()) # plot 0
plotter.plotField(dr.Ey()) # plot 1
plotter.plotField(dr.Ez()) # plot 2
plotter.plotField(dr.energydensityEM()) # plot 3
# plot additional, derived fields if available in `dr.getderived()`
#plotter.plotField(dr.createfieldfromkey("fields/e_chargeDensity"))
# Using the MultiSpecies requires an additional step:
# 1) The MultiSpecies.createField method will be used to create a Field object
# with choosen particle scalars on every axis
# 2) Plot the Field object
optargsh = {'bins': [200, 50]}
for pa in pas:
# Remark on 2D or 3D Simulation:
# the data fields for the plots in the following section are build by postpic
# only from the particle data. The following plots would just the same,
# if we would use the data of a 3D Simulation instead.
# create a Field object nd holding the number density
nd = pa.createField('z', 'x', simextent=False, **optargsh)
# plot the Field object nd
plotter.plotField(nd, name='NumberDensity') # plot 4
# create a Field object nd holding the charge density
qd = pa.createField('z',
'x',
weights='charge',
simextent=False,
**optargsh)
# plot the Field object qd
plotter.plotField(qd, name='ChargeDensity') # plot 5
# more advanced: create a field holding the total kinetic energy on grid
ekin = pa.createField('z',
'x',
weights='Ekin_MeV',
simextent=False,
**optargsh)
# The Field objectes can be used for calculations. Here we use this to
# calculate the average kinetic energy on grid and plot
plotter.plotField(ekin / nd, name='Avg Kin Energy (MeV)') # plot 6
# use optargsh to force lower resolution
# plot number density
plotter.plotField(pa.createField('z', 'x', **optargsh),
lineoutx=True,
lineouty=True) # plot 7
# plot phase space
plotter.plotField(pa.createField('z', 'p', **optargsh)) # plot 8
plotter.plotField(pa.createField('z', 'gamma',
**optargsh)) # plot 9
plotter.plotField(pa.createField('z', 'beta',
**optargsh)) # plot 10
if True:
# instead of reading a single dump read a collection of dumps using the simulationreader
sr = pp.readSim('examples/_openPMDdata/example-2d/hdf5/*.h5')
print('There are {:} dumps in this simulationreader object:'.format(
len(sr)))
# now you can iterate over the dumps written easily
for dr in sr:
print('Simulation time of current dump t = {:.2e} s'.format(
dr.time()))
# there are the frames of a movie:
plotter.plotField(dr.Ez())
def main():
import numpy as np
import postpic as pp
# postpic will use matplotlib for plotting. Changing matplotlibs backend
# to "Agg" makes it possible to save plots without a display attached.
# This is necessary to run this example within the "run-tests" script
# on travis-ci.
import matplotlib
matplotlib.use('Agg')
# choose the dummy reader. This reader will create fake data for testing.
pp.chooseCode('dummy')
dr = pp.readDump(
3e5) # Dummyreader takes a float as argument, not a string.
# set and create directory for pictures.
savedir = '_examplepictures/'
import os
if not os.path.exists(savedir):
os.mkdir(savedir)
# initialze the plotter object.
# project name will be prepended to all output names
plotter = pp.plotting.plottercls(dr,
outdir=savedir,
autosave=True,
project='simpleexample')
# we will need a refrence to the MultiSpecies quite often
from postpic.particles import MultiSpecies
# create MultiSpecies Object for every particle species that exists.
pas = [MultiSpecies(dr, s) for s in dr.listSpecies()]
if True:
# Plot Data from the FieldAnalyzer fa. This is very simple: every line creates one plot
plotter.plotField(dr.Ex()) # plot 0
plotter.plotField(dr.Ey()) # plot 1
plotter.plotField(dr.Ez()) # plot 2
plotter.plotField(dr.energydensityEM()) # plot 3
# Using the MultiSpecies requires an additional step:
# 1) The MultiSpecies.createField method will be used to create a Field object
# with choosen particle scalars on every axis
# 2) Plot the Field object
optargsh = {'bins': [300, 300]}
for pa in pas:
# create a Field object nd holding the number density
nd = pa.createField('x', 'y', simextent=True, **optargsh)
# plot the Field object nd
plotter.plotField(nd, name='NumberDensity') # plot 4
# if you like to keep working with the just created number density
# yourself, it will convert to an numpy array whenever needed:
arr = np.asarray(nd)
print('Shape of number density: {}'.format(arr.shape))
# more advanced: create a field holding the total kinetic energy on grid
ekin = pa.createField('x',
'y',
weights='Ekin_MeV',
simextent=True,
**optargsh)
# The Field objectes can be used for calculations. Here we use this to
# calculate the average kinetic energy on grid and plot
plotter.plotField(ekin / nd, name='Avg Kin Energy (MeV)') # plot 5
# use optargsh to force lower resolution
# plot number density
plotter.plotField(pa.createField('x', 'y', **optargsh),
lineoutx=True,
lineouty=True) # plot 6
# plot phase space
plotter.plotField(pa.createField('x', 'p', **optargsh)) # plot 7
plotter.plotField(pa.createField('x', 'gamma',
**optargsh)) # plot 8
plotter.plotField(pa.createField('x', 'beta',
**optargsh)) # plot 9
# same with high resolution
plotter.plotField(pa.createField('x', 'y', bins=[1000,
1000])) # plot 10
plotter.plotField(pa.createField('x', 'p', bins=[1000,
1000])) # plot 11
# advanced: postpic has already defined a lot of particle scalars as Px, Py, Pz, P, X, Y, Z, gamma, beta, Ekin, Ekin_MeV, Ekin_MeV_amu, ... but if needed you can also define your own particle scalar on the fly.
# In case its regularly used it should be added to postpic. If you dont know how, just let us know about your own useful particle scalar by email or adding an issue at
# https://github.com/skuschel/postpic/issues
# define your own particle scalar: p_r = sqrt(px**2 + py**2)/p
plotter.plotField(
pa.createField('sqrt(px**2 + py**2)/p',
'sqrt(x**2 + y**2)',
bins=[400, 400])) # plot 12
# however, since its unknown to the program, what quantities were calculated the axis of plot 12 will only say "unknown"
# this can be avoided in two ways:
# 1st: define your own ScalarProperty(name, expr, unit):
p_perp = pp.particles.ScalarProperty('sqrt(px**2 + py**2)/p',
name='p_perp',
unit='kg*m/s')
r_xy = pp.particles.ScalarProperty('sqrt(x**2 + y**2)',
name='r_xy',
unit='m')
# this will create an identical plot, but correcly labled
plotter.plotField(pa.createField(p_perp, r_xy,
bins=[400, 400])) # plot 13
# if those quantities are reused often, teach postip to recognize them within the string expression:
pp.particles.particle_scalars.add(p_perp)
#pp.particles.scalars.add(r_xy) # we cannot execute this line, because r_xy is already predefinded
plotter.plotField(pa.createField('p_perp', 'r_xy',
bins=[400, 400])) # plot 14
# choose particles by their properies
# this has been the old interface, which would still work
# def cf(ms):
# return ms('x') > 0.0 # only use particles with x > 0.0
# cf.name = 'x>0.0'
# pa.compress(cf)
# nicer is the new filter function, which does exactly the same:
pf = pa.filter('x>0')
# plot 15, compare with plot 10
plotter.plotField(pf.createField('x', 'y', bins=[1000, 1000]))
# plot 16, compare with plot 12
plotter.plotField(pf.createField('p_perp', 'r_xy', bins=[400,
400]))
plotter.plotField(dr.divE()) # plot 13
