i_turn = 300
obbea = mfm.myloadmat_to_obj(tag+'_matrices.mat')
x_mat = obbea.mean_x
y_mat = obbea.mean_y
n_mat = obbea.macroparticlenumber
n_turns = x_mat.shape[0]
mask_bunch = n_mat[1, :]>0
n_bunches = np.sum(mask_bunch)
bslots = np.where(mask_bunch)[0]/N_slots_bsp
plt.close('all')
ms.mystyle_arial(fontsz=16, dist_tick_lab=5)
figrt = plt.figure(2000)
axx = plt.subplot(3,1,1)
axx.plot(x_mat[:,mask_bunch])
axy = plt.subplot(3,1,2, sharex=axx)
axy.plot(y_mat[:,mask_bunch])
axn = plt.subplot(3,1,3, sharex=axx)
axn.plot(n_mat[:,mask_bunch])
figm = plt.figure(10, figsize=(8,6*1.3))
axm1 = figm.add_subplot(3,1,1)
axm2 = figm.add_subplot(3,1,2, sharex=axm1)
axm3 = figm.add_subplot(3,1,3, sharex=axm1)
mask_bunch = n_mat[1, :]>0
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(3)])
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', linestyle='--',
label=r'Damping time = %.0f turns'%tau_damp_x)
deltas_r = extract_sey_curves(n_rep,
E_impact_eV_test_r,
cos_theta_test,
charge=qe,
mass=me)
del_true_mat = deltas['true']
del_elast_mat = deltas['elast']
del_rediff_mat = deltas['rediff']
del_absorb_mat = deltas['absorb']
del_elast_mat_2 = deltas_e['elast']
del_rediff_mat_2 = deltas_r['rediff']
plt.close('all')
ms.mystyle_arial()
fig1 = plt.figure(1, figsize=(3 * 8, 2 * 8))
fig1.set_facecolor('w')
sp1 = fig1.add_subplot(2, 3, 1)
sp2 = fig1.add_subplot(2, 3, 2, sharex=sp1)
sp3 = fig1.add_subplot(2, 3, 3, sharex=sp1)
sp4 = fig1.add_subplot(2, 3, 4, sharex=sp1)
sp5 = fig1.add_subplot(2, 3, 5, sharex=sp1)
sp6 = fig1.add_subplot(2, 3, 6, sharex=sp1)
fig_ts = plt.figure(2, figsize=(12, 9), facecolor='white')
plt.xticks(fontsize=sz - 5)
plt.yticks(fontsize=sz - 5)
plt.subplots_adjust(bottom=.11)
sp_ts = fig_ts.add_subplot(1, 1, 1)
Qp_tag_vect = []
for index_tag,Qp_x in enumerate(Qp_x_vect):
if Qp_x < 0:
Qp_tag_vect.append(('minus' + '%s')%(np.abs(Qp_x)))
else:
Qp_tag_vect.append(Qp_x)
folder_plot = 'footprint'
if not os.path.exists(folder_plot):
os.makedirs(folder_plot)
ms.mystyle_arial(18)
i_fig = 0
#~ for betax, betay in zip(betax_vect, betay_vect):
#~ for fraction_device_quad in fraction_device_quad_vec:
# 1. Intensity
for intensity in intensity_vect:
# 2. Chromaticity
for Qp_x,Qp_y,Qp_tag in zip(Qp_x_vect,Qp_y_vect,Qp_tag_vect):
sim_ident = '../simulations_PyPARIS/Inj_ArcQuad_T0_x_slices_750_seg_8_MPslice_5e3_eMPs_250e3_length_7_VRF_4MV_intensity_%.1fe11ppb_Qp_xy_%s'%(intensity/1e11,Qp_tag)
filename_footprint = 'footprint.h5'
print sim_ident
t_ref_str = '13-04-2015 07:00'
t_ref = time.mktime(time.strptime(t_ref_str, '%d-%m-%Y %H:%M'))
filename='bct_overview.pkl'
with open(filename) as fid:
beams_bct = pickle.load(fid)
filename='ws_overview.pkl'
with open(filename) as fid:
ws_dict = pickle.load(fid)
list_SPSusers = [ 'MD3' ]
pl.close('all')
ms.mystyle_arial(fontsz=16, dist_tick_lab=10)
fig = pl.figure(figsize=(14,12))
for SPSuser in list_SPSusers:
n_ws_meas = len(ws_dict[SPSuser]['timestamp_ws'])
for ii in range(n_ws_meas):
try:
pl.clf()
sp1 = pl.subplot2grid((3,2), (0,0), colspan=2, rowspan=1)
for SPSuser_ in beams_bct.keys():
pl.plot((np.array(beams_bct[SPSuser_]['timestamp_float'])-t_ref)/3600/24., np.array(beams_bct[SPSuser_]['bct_max_vect']),'.', label=SPSuser_)
timestamp_bct = ws_dict[SPSuser]['timestamp_bct'][ii]
timestamp_ws = ws_dict[SPSuser]['timestamp_ws'][ii]
sp1.axvline(x=(timestamp_bct- t_ref)/24./3600, color = 'k', linestyle='--', linewidth = 3)
smooth = partial(gaussian_filter1d, sigma=2, mode='nearest')
n_turns_window = 20
n_sigmaz_sim = 10. #we are simulating 10 long sigmas
i_want_to_count_over = 4.
flag_weighted = True
#Figure parameters
ii_fig = 0
tick_size = 20
axis_font = {'fontname':'Arial', 'size':'24'}
fig_size = (15, 5)
line_width = 3.5
ms.mystyle_arial(16)
# calculate intra-bunch modes
for fraction_device_quad in fraction_device_quad_vect:
kk = np.argmin(np.abs(dic['fraction_device_quad_vect']-fraction_device_quad))
for betax, betay in zip(betax_vect, betay_vect):
jj = np.argmin(np.abs(dic['betax_vect']-betax))
subfolder_plot = folder_plot + 'betaxy_%d_length_%.2f/'%(betax,fraction_device_quad)
if not os.path.exists(subfolder_plot) and savefigure:
os.makedirs(subfolder_plot)
for n_slices in n_slices_vect:
ii = np.argmin(np.abs(dic['n_slices_vect']-n_slices))
if not math.isnan(tt[ii,jj,kk]):
print('Done extracting emission angles.')
return cos_theta_emit, divider, flag_divide_by_zero
# Test parameters
cos_theta_test = 0.7
E_impact_eV_test = 300.
n_rep = int(5e5)
# Extraction
cos_theta_emit, divider, flag_divide_by_zero = extract_emission_angles(
n_rep, E_impact_eV_test, cos_theta_test, charge=qe, mass=me)
plt.close('all')
ms.mystyle_arial(25)
# Plotting emission angles
plt.figure(1, facecolor='white', figsize=(12, 9))
plt.hist(np.arccos(cos_theta_emit), density=True, bins=60)
plt.xlabel(r'$\theta_{emit}$')
plt.ylabel('Normalized emission angle spectrum')
plt.title('cos_theta_imp=%.2f' % cos_theta_test)
plt.grid(alpha=.5)
plt.figure(2, facecolor='white', figsize=(12, 9))
plt.hist(cos_theta_emit, density=True, bins=60)
plt.xlabel(r'cos($\theta_{emit}$)')
plt.title('cos_theta_imp=%.2f' % cos_theta_test)
plt.ylabel('Normalized emission angle spectrum')
plt.grid(alpha=.5)
sp3.set_ylabel('Charge weighted\nhorizontal centroid rms [a.u]')
sp4.set_ylabel('Charge weighted\nvertical centroid rms [a.u]')
sp5.set_ylabel('Normalized emittance x [um]')
sp6.set_ylabel('Normalized emittance y [um]')
sp7.set_ylabel('Number of macroparticles')
sp8.set_ylabel('Bunch length (4$\sigma$) [ns]')
#sp1.set_xlim(0, 1.0)
for sp in [sp1, sp2, sp3, sp4, sp5, sp6, sp7, sp8]:
sp.set_xlabel('Turns')
sp.ticklabel_format(useOffset=False)
sp.grid('on')
ms.sciy()
#~ sp.set_xlim(0, 20000)
ms.mystyle_arial(14)
gs1.tight_layout(fig, rect=[0.02, 0, 0.86, .95], w_pad=.2, h_pad=1.5)
#~ title = fig.suptitle(os.getcwd().split('/')[-2])
title = fig.suptitle(
'ArcQuad: Segments = %d MPs/Slice = %d Length: %.2f Beta_xy: %.0f m'
% (n_segments, macroparticles_per_slice, fraction_device_quad,
betax))
#~ pl.title('ArcQuad Drift Length = %.2f'%(fraction_device_quad))
if savefigures:
fig.savefig(folder_plot + 'scanSlice_length%.2f_betaxy%.0f.png' %
(fraction_device_quad, betax),
dpi=300,
bbox_inches='tight',
bbox_extra_artists=[legend, title])
pl.show()
t_ref_str = '13-04-2015 07:00'
t_ref = time.mktime(time.strptime(t_ref_str, '%d-%m-%Y %H:%M'))
filename = 'bct_overview.pkl'
with open(filename) as fid:
beams_bct = pickle.load(fid)
filename='ecm_overview.pkl'
with open(filename) as fid:
beams_ecm = pickle.load(fid)
# ylim_ec_max = 3.5e5
# thresh_max_good_shot = 1.1e11
pl.close('all')
ms.mystyle_arial(fontsz=13, dist_tick_lab=10)
#ms.mystyle(fontsz=13)
for ecm_id in '1a 1b 2a 2b'.split():
fig = pl.figure()
for SPS_user in beams_ecm[ecm_id].keys():
N_cycles = len(np.array(beams_bct[SPS_user]['bct_1st_inj']))
# mask_good_shots = np.array(beams_bct[SPS_user]['bct_max_vect']) > thresh_max_good_shot
sp1 = pl.subplot(4,1,1)
pl.plot((array(beams_bct[SPS_user]['timestamp_float'])-t_ref)/3600./24., array(beams_bct[SPS_user]['bct_max_vect']),'.', label = SPS_user)
ms.sciy()
pl.grid('on')
flag_check_damp_time = True
tau_damp_x = 200.
tau_damp_y = 100.
flag_check_Qs = True
Q_s = 5.664e-03
ob = mfm.myloadmat_to_obj(tag+'_matrices.mat')
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[:, i_bunch])
axy.plot(ob.mean_y[:, i_bunch])
if flag_check_damp_time:
turn_num = np.arange(0, len(ob.mean_x[:, i_bunch]), dtype=np.float)
axx.plot(ob.mean_x[0, i_bunch]*np.exp(-turn_num/tau_damp_x),
linewidth=2, color='red', linestyle='--',
label=r'Damping time = %.0f turns'%tau_damp_x)
axy.plot(ob.mean_y[0, i_bunch]*np.exp(-turn_num/tau_damp_y),
linewidth=2, color='red', linestyle='--',
label=r'Damping time = %.0f turns'%tau_damp_y)