Getting startedΒΆ
The following script should just show an example how to get started using the postpic postprocessor. This script uses the dummy reader (thus auto generated random data). Thats why there is no input file needed to read the simulation data from.
#!/usr/bin/env python
#
# This file is part of postpic.
#
# postpic is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# postpic is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with postpic. If not, see <http://www.gnu.org/licenses/>.
#
# Copyright Stephan Kuschel 2015
#
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
if __name__=='__main__':
main()
