get_dpp = lambda b, bta: np.sqrt(bta.getCorrelation(5,5)) / (b.getSyncParticle().gamma()*b.mass()*b.getSyncParticle().beta()**2)
get_bunch_length = lambda b, bta: 4 * np.sqrt(bta.getCorrelation(4,4)) / (speed_of_light*b.getSyncParticle().beta())
get_eps_z = lambda b, bta: 1e9 * 4 * pi * bta.getEmittance(2) / (speed_of_light*b.getSyncParticle().beta())
output = Output_dictionary()
output.addParameter('turn', lambda: turn)
output.addParameter('intensity', lambda: bunchtwissanalysis.getGlobalMacrosize())
output.addParameter('n_mp', lambda: bunchtwissanalysis.getGlobalCount())
output.addParameter('gamma', lambda: bunch.getSyncParticle().gamma())
output.addParameter('mean_x', lambda: bunchtwissanalysis.getAverage(0))
output.addParameter('mean_xp', lambda: bunchtwissanalysis.getAverage(1))
output.addParameter('mean_y', lambda: bunchtwissanalysis.getAverage(2))
output.addParameter('mean_yp', lambda: bunchtwissanalysis.getAverage(3))
output.addParameter('mean_z', lambda: bunchtwissanalysis.getAverage(4))
output.addParameter('mean_dE', lambda: bunchtwissanalysis.getAverage(5))
output.addParameter('epsn_x', lambda: bunchtwissanalysis.getEmittanceNormalized(0))
output.addParameter('epsn_y', lambda: bunchtwissanalysis.getEmittanceNormalized(1))
output.addParameter('eps_z', lambda: get_eps_z(bunch, bunchtwissanalysis))
output.addParameter('bunchlength', lambda: get_bunch_length(bunch, bunchtwissanalysis))
output.addParameter('dpp_rms', lambda: get_dpp(bunch, bunchtwissanalysis))
if frozen or slicebyslice:
output.addParameter('BE_intensity1', lambda: sc_params1['intensity'])
output.addParameter('BE_epsn_x1', lambda: sc_params1['epsn_x'])
output.addParameter('BE_epsn_y1', lambda: sc_params1['epsn_y'])
output.addParameter('BE_dpp_rms1', lambda: sc_params1['dpp_rms'])
#-----------------------------------------------------------------------
# Define particle output dictionary - used to plot trapping
#-----------------------------------------------------------------------
particle_output = Particle_output_dictionary()
file_writer.close()
b.dumpBunch(pre_list + "/initial_bunch_" + str(test_name) + ".dat")
ana = BunchTwissAnalysis()
f = open(pre_list + "/tracking_" + str(test_name) + ".txt", 'w')
for i in range(1000):
ana.analyzeBunch(b)
if rank == 0:
f.write(
str(i) + " " + str(ana.getEmittanceNormalized(0)) + " " +
str(ana.getEmittanceNormalized(1)) + " " + str(ana.getBeta(0)) +
" " + str(ana.getBeta(1)) + " " + str(lostbunch.getSize()) + "\n")
lattice.trackBunch(b, paramsDict)
if rank == 0:
print("==========================")
print("turn: " + str(i))
print("lost: " + str(lostbunch.getSize()))
print("remain: " + str(b.getSize()))
print("charge: " + str(b.macroSize()))
print("intensity: " + str(b.macroSize() * b.getSize() * size))
print(
str(ana.getEmittanceNormalized(0)) + " " +
str(ana.getEmittanceNormalized(1)))
output_dictionary.addParameter('gamma',
lambda: bunch.getSyncParticle().gamma())
output_dictionary.addParameter('mean_x',
lambda: bunchtwissanalysis.getAverage(0))
output_dictionary.addParameter('mean_xp',
lambda: bunchtwissanalysis.getAverage(1))
output_dictionary.addParameter('mean_y',
lambda: bunchtwissanalysis.getAverage(2))
output_dictionary.addParameter('mean_yp',
lambda: bunchtwissanalysis.getAverage(3))
output_dictionary.addParameter('mean_z',
lambda: bunchtwissanalysis.getAverage(4))
output_dictionary.addParameter('mean_dE',
lambda: bunchtwissanalysis.getAverage(5))
output_dictionary.addParameter(
'epsn_x', lambda: bunchtwissanalysis.getEmittanceNormalized(0))
output_dictionary.addParameter(
'epsn_y', lambda: bunchtwissanalysis.getEmittanceNormalized(1))
output_dictionary.addParameter(
'eps_z', lambda: bunchtwissanalysis.getEmittance(2) /
(speed_of_light * bunch.getSyncParticle().beta()) * 1e9 * 4 * pi)
output_dictionary.addParameter(
'bunchlength', lambda: np.sqrt(bunchtwissanalysis.getCorrelation(4, 4)) /
(speed_of_light * bunch.getSyncParticle().beta()) * 4)
output_dictionary.addParameter(
'dpp_rms', lambda: np.sqrt(bunchtwissanalysis.getCorrelation(5, 5)) /
(bunch.getSyncParticle().gamma() * bunch.mass() * bunch.getSyncParticle().
beta()**2))
# save initial distribution
mainbunch_file = 'output/mainbunch_start.dat'
output_file = 'Output/output.dat'
output = Output_dictionary()
output.addParameter('turn', lambda: turn)
output.addParameter('intensity',
lambda: bunchtwissanalysis.getGlobalMacrosize())
output.addParameter('n_mp', lambda: bunchtwissanalysis.getGlobalCount())
output.addParameter('gamma', lambda: bunch.getSyncParticle().gamma())
output.addParameter('mean_x', lambda: bunchtwissanalysis.getAverage(0))
output.addParameter('mean_xp', lambda: bunchtwissanalysis.getAverage(1))
output.addParameter('mean_y', lambda: bunchtwissanalysis.getAverage(2))
output.addParameter('mean_yp', lambda: bunchtwissanalysis.getAverage(3))
output.addParameter('mean_z', lambda: bunchtwissanalysis.getAverage(4))
output.addParameter('mean_dE', lambda: bunchtwissanalysis.getAverage(5))
output.addParameter('epsn_x',
lambda: bunchtwissanalysis.getEmittanceNormalized(0))
output.addParameter('epsn_y',
lambda: bunchtwissanalysis.getEmittanceNormalized(1))
output.addParameter('eps_z', lambda: get_eps_z(bunch, bunchtwissanalysis))
output.addParameter('bunchlength',
lambda: get_bunch_length(bunch, bunchtwissanalysis))
output.addParameter('dpp_rms', lambda: get_dpp(bunch, bunchtwissanalysis))
if os.path.exists(output_file):
output.import_from_matfile(output_file)
#----------------------------------------------------
# Injecting turn by turn
#----------------------------------------------------
print '\n\n now start injecting...'
for index_files in range(index_files, index_files_max + 1):
print("==========================")
lattice.trackBunch(b, paramsDict)
for i in range(5000):
ana.analyzeBunch(b)
#if rank == 0:
# f.write(str(i) + " " + str(ana.getEmittanceNormalized(0)) + " " + str(ana.getEmittanceNormalized(1)) + " " + str(ana.getBeta(0)) + " " + str(ana.getBeta(1)) + " " + str(lostbunch.getSize()) + "\n")
lattice.trackBunch(b, paramsDict)
if rank == 0:
print("==========================")
print("turn: " + str(i))
print("lost: " + str(lostbunch.getSize()))
print("remain: " + str(b.getSize()))
print("charge: " + str(b.macroSize()))
print("intensity: " + str(b.macroSize() * b.getSize() * size))
print(
str(ana.getEmittanceNormalized(0)) + " " +
str(ana.getEmittanceNormalized(1)))
print("==========================")
for j in range(b.getSize()):
id = int(b.partAttrValue("ParticleIdNumber", j, 0))
f = f_list[id]
f.write(str(i) + " " + str(b.x(j)) + " " + str(b.y(j)) + "\n")
