tau_damp_x = 50.
tau_damp_y = 100.
flag_check_Qs = True
Q_s = np.nan
#ob = mfm.monitorh5_to_obj('bunch_evolution_00.h5')
ob = mfm.monitorh5list_to_obj(
['bunch_evolution_%02d.h5' % ii for ii in range(1)])
plt.close('all')
# Plot transverse positions
fig1 = plt.figure(1, figsize=(8, 6 * 1.2))
fig1.set_facecolor('w')
ms.mystyle_arial(fontsz=16)
axx = fig1.add_subplot(2, 1, 1)
axy = fig1.add_subplot(2, 1, 2, sharex=axx)
axx.plot(ob.mean_x)
axy.plot(ob.mean_y)
if flag_check_damp_time:
turn_num = np.arange(0, len(ob.mean_x), dtype=np.float)
ix_max = np.argmax(ob.mean_x)
iy_max = np.argmax(ob.mean_y)
axx.plot(ob.mean_x[ix_max] * np.exp(-(turn_num - ix_max) / tau_damp_x),
linewidth=2,
color='red',
for ss in these_slices:
thisbuffer = ch.beam_2_buffer(ss,verbose=True, mode='pickle')
list_buffers.append(thisbuffer)
list_bunch_buffers[-1].append(thisbuffer)
big_buffer = ch.combine_float_buffers(list_buffers)
# Build profile of the full beam
thin_slicer = UniformBinSlicer(n_slices=10000, z_cuts=(-len(filling_pattern)*bucket_length_m*b_spac_buckets, bucket_length_m))
thin_slice_set = beam.get_slices(thin_slicer, statistics=True)
import matplotlib.pyplot as plt
plt.close('all')
ms.mystyle_arial(fontsz=14, dist_tick_lab=5)
# re-split buffer
list_buffers_rec = ch.split_float_buffers(big_buffer)
# Plot including sub-slicing
fig1 = plt.figure(1, figsize=(8, 6*1.3))
fig1.set_facecolor('w')
sp1 = plt.subplot(3,1,1)
sp2 = plt.subplot(3,1,2, sharex=sp1)
sp3 = plt.subplot(3,1,3, sharex=sp1)
for ibuf, buf in enumerate(list_buffers_rec):
# 'mpsim_fname': None,
# 'label': r'$\Delta$Q$_\Phi\neq$0, $\Delta$Q$_R\neq$0'},
# }
dict_plot = {
'wquad': {
'fname': '../008c_eigen_with_detuning_fine/eigenvalues.mat',
'mpsim_fname': None,
'label': r'$\Delta$Q$_\Phi\neq$0, $\Delta$Q$_R\neq$0'
},
}
import matplotlib
import matplotlib.pyplot as plt
plt.close('all')
ms.mystyle_arial(fontsz=14, dist_tick_lab=5, traditional_look=False)
fig1 = plt.figure(1, figsize=(6.4 * 1.2, 4.8))
ax1 = fig1.add_subplot(111)
fig_re_list = []
fig_im_list = []
for ill, ll in enumerate(dict_plot.keys()):
fname = dict_plot[ll]['fname']
ob = mfm.myloadmat_to_obj(fname)
Omega_mat = ob.Omega_mat
strength_scan = ob.strength_scan
strength_scan_0 = strength_scan
# Omega_mat = np.zeros((ob.Omega_mat.shape[0]*2, ob.Omega_mat.shape[1]),
# dtype=np.complex)
# strength_scan = np.zeros(Omega_mat.shape[0])
# generate example figures with main PyECLOUD outputs
# Thanks for E. Belli, L. Mether and A. Romano for preparing this script
import sys, os
BIN = os.path.expanduser("../../../")
sys.path.append(BIN)
import pylab as pl
import numpy as np
import PyECLOUD.myloadmat_to_obj as mlo
import PyECLOUD.mystyle as ms
pl.close('all')
ms.mystyle_arial(fontsz=16)
dpiset = 200
ob=mlo.myloadmat_to_obj('../../testing/tests_buildup/LHC_ArcDipReal_450GeV_sey1.60_2.5e11ppb_bl_1.00ns/Pyecltest_ref.mat')
ifig = 0
#################################
# Variables saved per time step #
#################################
#8.
ifig+=1; pl.figure(ifig)
pl.plot(ob.t, ob.lam_t_array, linewidth=2)
pl.xlabel('Time [s]')
pl.ylabel('Beam profile [p/m]')
ms.scix(); pl.grid('on')
pl.close('all')
sim_folder = './'
dict_ref = mlm.myloadmat(
'../tests_buildup/LHC_ArcDipReal_450GeV_sey1.70_2.5e11ppb_bl_1.00ns_multigrid/'
+ '/Pyecltest_angle3D_ref.mat'
) # load dictionary of the reference simulation
dict_curr0 = mlm.myloadmat(
'./test_saving__iter0.mat') # load dictionary of the current simulation
dict_curr1 = mlm.myloadmat(
'./test_saving__iter1.mat') # load dictionary of the current simulation
pl.close('all')
myfontsz = 16
ms.mystyle_arial(fontsz=myfontsz)
fig1 = pl.figure(1)
fig1.set_facecolor('w')
ax1 = fig1.add_subplot(1, 1, 1)
ax1.plot(dict_ref['t'],
dict_ref['Nel_timep'],
'-',
linewidth=2,
label="Buildup simulation")
ax1.plot(dict_curr0['t'] - 10e-9 - dict_curr0['t'][0],
dict_curr0['Nel_timep'],
'r-',
linewidth=2,
label='PyHT beam, turn 1')
ax1.plot(dict_curr1['t'] - 10e-9 - dict_curr1['t'][0],
ob.xg = ob.xg[N_discard:-N_discard]
ob.yg = ob.yg[N_discard:-N_discard]
ob.rho = ob.rho[:, N_discard:-N_discard, N_discard:-N_discard]
ob.phi = ob.phi[:, N_discard:-N_discard, N_discard:-N_discard]
ob.Ex = ob.Ex[:, N_discard:-N_discard, N_discard:-N_discard]
ob.Ey = ob.Ey[:, N_discard:-N_discard, N_discard:-N_discard]
ob.rho /= -qe
x_obs = 0.
ix_obs = np.argmin(np.abs(ob.xg - x_obs))
y_obs = 0.
iy_obs = np.argmin(np.abs(ob.yg - y_obs))
plt.close('all')
ms.mystyle_arial()
fig1 = plt.figure(1, figsize=(8, 6 * 1.5))
fig1.set_facecolor('w')
ax1 = fig1.add_subplot(3, 1, 1)
ax2 = fig1.add_subplot(3, 1, 2, sharex=ax1)
ax3 = fig1.add_subplot(3, 1, 3, sharex=ax1)
mbl = ax1.pcolormesh(ob.zg, ob.yg, ob.rho[:, ix_obs, :].T)
plt.colorbar(mappable=mbl, ax=ax1, aspect=5)
mbl = ax2.pcolormesh(ob.zg, ob.yg, ob.Ey[:, ix_obs, :].T)
plt.colorbar(mappable=mbl, ax=ax2, aspect=5, format='%.1e')
mbl = ax3.pcolormesh(ob.zg, ob.yg, ob.phi[:, ix_obs, :].T)
plt.colorbar(mappable=mbl, ax=ax3, aspect=5)
fig1.subplots_adjust(hspace=.4)
N_dec = 1
lim_dens = [10, 14]
flag_log = True
outp_filname = 'pass%d.avi'%passlist[0]
########
########
qe = 1.60217657e-19
c = 299792458.
i_photog = 0
ms.mystyle_arial(fontsz=fontsz, dist_tick_lab=10)
chamber = mlo.myloadmat_to_obj(folder_sim + '/LHC_chm_ver.mat')
Vx = chamber.Vx
Vy = chamber.Vy
##load output file
obout = mlo.myloadmat_to_obj(folder_sim + '/' + main_outp_filename)
t = obout.t
lam_b1 = obout.lam_t_array
cendens = np.squeeze(obout.cen_density)
#every bunch passage
for pass_ind in passlist:
filename_rho = 'rho_video/rho_pass%d.mat'%pass_ind
ref_folder = sim_folder
curr_folder = sim_folder
folder_plot = sim_folder+'/comparison_plots'
try:
os.mkdir(folder_plot)
except OSError as err:
print 'Folder not created due to exception:'
print err
myfontsz = 14
ms.mystyle_arial(fontsz=myfontsz)
dict_ref = mlm.myloadmat(ref_folder+'/Pyecltest_ref.mat') # load dictionary of the reference simulation
dict_curr = mlm.myloadmat(curr_folder+'/Pyecltest.mat') # load dictionary of the current simulation
out_var_ref = dict_ref.keys() # returns the list of keys
out_var_curr = dict_curr.keys()
out_var_ref.sort() # sort the keys in alphabetical order
out_var_curr.sort()
n_pass = 35 # reference passage
