-
Notifications
You must be signed in to change notification settings - Fork 0
/
linearization_horizontal.py
415 lines (348 loc) · 20.2 KB
/
linearization_horizontal.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from runAstra_3boosters import getGunFunction, getFitParams, runAstraFunction, runAstraFunction2, runAstraCombination, runAstraFunction_energy
from scipy.optimize import minimize
from ToF_Jitter_callable import ToF_Jitter
def polinomial(x,a0,a1,a2,a3):
return a0+a1*x+a2*x**2+a3*x**3
def linearizer2order(amplitude_gun,phase_gun,phase_b2):
amplitude_booster1 = 10e6
phase_booster1 = 218.0*np.pi/180 #(remember that in Astra the phase is this-90deg)
amplitude_booster2 = 10.0e6
phase_booster2 = 130.5*np.pi/180 #(remember that in Astra the phase is this-90deg)
booster3_phase_range=[-25.0,25.0]
booster3_amplitude_range=[5.0e6,7.0e6]
m_e = 0.511e6 #eV
#-----------------------------ACTIVATE THIS BLOCK FOR LINEARIZATION USING GUN PARAMETERS------------------------------------
# G0,G1,G2,G3 = getFitParams(phase_gun,7e-12,amplitude_gun)
# z_start = 0.31
#---------------------------------------------------------------------------------------------------------------------------
#------------------------ACTIVATE THIS BLOCK FOR LINEARIZATION USING THE LONGITUDINAL PROFILE OF AN EXISTING BUNCH----------
input_file = "Gun_transverse.0175.001"
bunch_dataframe = pd.read_csv(input_file,header=None, delim_whitespace = True)
bunch_dataframe.columns=['x','y','z','px','py','pz','clock','macro_charge','particle_index','status']
delta_z = np.array(bunch_dataframe['z'].astype(float).tolist())
delta_pz = np.array(bunch_dataframe['pz'].astype(float).tolist())
px = np.array(bunch_dataframe['px'].astype(float).tolist())
py = np.array(bunch_dataframe['py'].astype(float).tolist())
z_ref = delta_z[0]
pz_ref = delta_pz[0]
delta_z[0] = 0.0 #reference particle in the center
pz = delta_pz + pz_ref
pz[0] = pz_ref
p = np.sqrt(pz**2 + px**2 + py**2)
energy = np.sqrt(p**2 + m_e**2)
gamma = energy/m_e
max_delta_z = np.amax(delta_z)
min_delta_z = np.amin(delta_z)
fitting_delta_z = np.linspace(min_delta_z,max_delta_z,2000)
popt,pcov = curve_fit(polinomial,delta_z,gamma) #3rd order poli.
G0,G1,G2,G3 = popt[0],popt[1],popt[2],popt[3]
z_start = 1.75
#---------------------------------------------------------------------------------------------------------------------------
nu = 1.3e9 #s^-1
k = 2.0*np.pi*13.0/3.0
wavelength = 2*np.pi/k #m
L = wavelength
z_booster1 = 3.2079
z_booster2 = 4.0267
z_booster3 = 4.8809
z_focus = 7.64
gamma_central = G0
beta_central = np.sqrt(gamma_central**2-1)/gamma_central
n11 = 1/(beta_central**2*gamma_central**3)
n12 = (2-3*gamma_central**2)/(2*gamma_central**6*beta_central**4)
n13 = (2-5*gamma_central**2+4*gamma_central**4)/(2*gamma_central**9*beta_central**6)
x11 = 1+(z_booster1-z_start)*n11*G1
x12 = (z_booster1-z_start)*(n11*G2+n12*G1**2)
x13 = (z_booster1-z_start)*(n11*G3+2*n12*G1*G2+n13*G1**3)
g0 = G0
g1 = G1/x11
g2 = (G2*x11-G1*x12)/x11**3
g3 = (G3-g1*x13-2*g2*x11*x12)/x11**3
#-----------------BOOSTER1----------------
e = 1.6e-19
me = 9.1e-31
c=3e8
k = 2*np.pi*13/3 #L-band
wavelength = 2*np.pi/k #m
L = wavelength
alpha1 = e*amplitude_booster1/(2*me*c**2*k)
B10 = alpha1*k*L*np.sin(phase_booster1) #+ alpha*np.cos(phase_booster)
B11 = -alpha1*L*k**2*np.cos(phase_booster1) #+ alpha*k*np.sin(phase_booster)
B12 = -alpha1*L*k**3*np.sin(phase_booster1)/2 #- k**2*alpha*np.cos(phase_booster)/2
B13 = alpha1*L*k**4*np.cos(phase_booster1)/6 #-k**3*alpha*np.sin(phase_booster)/6
gamma_booster1 = g0+B10
beta_booster1 = np.sqrt(gamma_booster1**2-1)/gamma_booster1
n21 = 1/(beta_booster1**2*gamma_booster1**3)
n22 = (2-3*gamma_booster1**2)/(2*gamma_booster1**6*beta_booster1**4)
n23 = (2-5*gamma_booster1**2+4*gamma_booster1**4)/(2*gamma_booster1**9*beta_booster1**6)
x21 = 1+(z_booster2-z_booster1)*(n21*(g1+B11))
x22 = (z_booster2-z_booster1)*(n21*(g2+B12)+n22*(g1+B11)**2)
x23 = (z_booster2-z_booster1)*(n21*(g3+B13)+2*n22*(g1+B11)*(g2+B12)+n23*(g1+B11)**3)
b10 = g0+B10
b11 = (g1+B11)/x21
b12 = ((g2+B12)*x21 -(g1+B11)*x22)/x21**3
b13 = ((g3+B13)-b11*x23-2*b12*x21*x22)/x21**3
#-----------------BOOSTER2----------------
e = 1.6e-19
me = 9.1e-31
c=3e8
k = 2*np.pi*13/3 #L-band
wavelength = 2*np.pi/k #m
L = wavelength
alpha2 = e*amplitude_booster2/(2*me*c**2*k)
B20 = alpha2*k*L*np.sin(phase_booster2) #+ alpha*np.cos(phase_booster)
B21 = -alpha2*L*k**2*np.cos(phase_booster2) #+ alpha*k*np.sin(phase_booster)
B22 = -alpha2*L*k**3*np.sin(phase_booster2)/2 #- k**2*alpha*np.cos(phase_booster)/2
B23 = alpha2*L*k**4*np.cos(phase_booster2)/6 #-k**3*alpha*np.sin(phase_booster)/6
gamma_booster2 = b10+B20
beta_booster2 = np.sqrt(gamma_booster2**2-1)/gamma_booster2
n31 = 1/(beta_booster2**2*gamma_booster2**3)
n32 = (2-3*gamma_booster2**2)/(2*gamma_booster2**6*beta_booster2**4)
n33 = (2-5*gamma_booster2**2+4*gamma_booster2**4)/(2*gamma_booster2**9*beta_booster2**6)
x31 = 1+(z_booster3-z_booster2)*(n31*(b11+B21))
x32 = (z_booster3-z_booster2)*(n31*(b12+B22)+n32*(b11+B21)**2)
x33 = (z_booster3-z_booster2)*(n31*(b13+B23)+2*n32*(b11+B21)*(b12+B22)+n33*(b11+B21)**3)
b20 = b10+B20
b21 = (b11+B21)/x31
b22 = ((b12+B22)*x31 -(b11+B21)*x32)/x31**3
b23 = ((b13+B23)-b21*x33-2*b22*x31*x32)/x31**3
#----------------BOOSTER3---------------------------------------------------------
results_B31_phase = np.array([])
results_B31_amplitude = np.array([])
results_B32_phase = np.array([])
results_B32_amplitude = np.array([])
results_B33_phase = np.array([])
results_B33_amplitude = np.array([])
optimization_results_phase = np.array([])
optimization_results_amplitude = np.array([])
# n = 5000
# for i in range(n):
# phase_booster3 = booster3_phase_range[0]*np.pi/180 + i*(booster3_phase_range[1]-booster3_phase_range[0])/float(n)*np.pi/180
# for j in range(n):
# amplitude_booster3 = booster3_amplitude_range[0] + j*(booster3_amplitude_range[1]-booster3_amplitude_range[0])/float(n)
# #Relation between E0 and alpha
# e = 1.6e-19
# me = 9.1e-31
# c=3e8
# k = 2*np.pi*13/3 #L-band
# wavelength = 2*np.pi/k #m
# L = wavelength
# alpha3 = e*amplitude_booster3/(2*me*c**2*k)
# B30 = alpha3*k*L*np.sin(phase_booster3) #+ alpha*np.cos(phase_booster)
# B31 = -alpha3*L*k**2*np.cos(phase_booster3) #+ alpha*k*np.sin(phase_booster)
# B32 = -alpha3*L*k**3*np.sin(phase_booster3)/2 #- k**2*alpha*np.cos(phase_booster)/2
# B33 = alpha3*L*k**4*np.cos(phase_booster3)/6 #-k**3*alpha*np.sin(phase_booster)/6
# gamma_booster3 = b20+B30
# #if (gamma_booster3<1.0):
# # continue
# B31_total = b21+B31
# B32_total = b22+B32
# B33_total = b23+B33
#
# beta_booster3 = np.sqrt(gamma_booster3**2-1)/gamma_booster3
# n41 = 1/(beta_booster3**2*gamma_booster3**3)
# n42 = (2-3*gamma_booster3**2)/(2*gamma_booster3**6*beta_booster3**4)
# n43 = (2-5*gamma_booster3**2+4*gamma_booster3**4)/(2*gamma_booster3**9*beta_booster3**6)
# x41 = 1+(z_focus-z_booster3)*(n41*(b21+B31))
# x42 = (z_focus-z_booster3)*(n41*(b22+B32)+n42*(b21+B31)**2)
# x43 = (z_focus-z_booster3)*(n41*(b23+B33)+2*n42*(b21+B31)*(b22+B32)+n43*(b21+B31)**3)
# if (abs(B31_total)<=0.002):
# results_B31_phase = np.append(results_B31_phase,phase_booster3)
# results_B31_amplitude = np.append(results_B31_amplitude,amplitude_booster3)
# if (abs(B32_total)<=0.002):
# results_B32_phase = np.append(results_B32_phase,phase_booster3)
# results_B32_amplitude = np.append(results_B32_amplitude,amplitude_booster3)
# if (abs(B33_total)<=0.02):
# results_B33_phase = np.append(results_B33_phase,phase_booster3)
# results_B33_amplitude = np.append(results_B33_amplitude,amplitude_booster3)
# if (abs(B31_total)<0.003 and abs(B32_total)<0.003):
# optimization_results_phase = np.append(optimization_results_phase,phase_booster3)
# optimization_results_amplitude = np.append(optimization_results_amplitude,amplitude_booster3)
#
#
# print(optimization_results_phase*180/np.pi)
# print(optimization_results_amplitude)
#
# fig2,ax21 = plt.subplots()
# ax21.set_xlabel(r'$\phi_{booster3}$ [deg]')
# ax21.set_ylabel(r'$E_{booster3}$ [MV/m]')
# #ax21.set_title(r'$E_G$='+str(amplitude_booster1)+'MV, $\phi_G$='+str(phase_booster1*180/np.pi)+'deg')
# ax21.scatter(results_B31_phase*180/np.pi, results_B31_amplitude, label = r'X1$\approx$0',s=5)
# ax21.scatter(results_B32_phase*180/np.pi, results_B32_amplitude, label = r'X2$\approx$0',s=5)
# ax21.scatter(results_B33_phase*180/np.pi, results_B33_amplitude, label = r'X3$\approx$0',s=5)
# #ax21.scatter(optimization_results_phase*180/np.pi, optimization_results_amplitude, label = r'X1$\approx$0 and X2$\approx$0',color='red',s=5)
# plt.grid()
# fig2.tight_layout()
# plt.legend()
# fig2.savefig('Energy_spread_3boosters.png')
# plt.show()
#
# #-----------ASTRA OPTIMIZATION---------------------
# Booster1_amplitude = amplitude_booster1*1e-6
# Booster1_phase = phase_booster1*180/np.pi - 90 #We apply the necessary changes to the phase for Astra
# Booster2_amplitude = amplitude_booster2*1e-6
# Booster2_phase = phase_booster2*180/np.pi - 90 #We apply the necessary changes to the phase for Astra
# phase_astra_seed = (np.amax(optimization_results_phase)+np.amin(optimization_results_phase))*180/(np.pi*2.0) - 90
# amplitude_astra_seed = (np.amax(optimization_results_amplitude)+np.amin(optimization_results_amplitude))*1e-6/2.0
n = 2000
booster3_phases = np.linspace(booster3_phase_range[0],booster3_phase_range[1],n)*np.pi/180.0
booster3_amplitudes = np.linspace(booster3_amplitude_range[0],booster3_amplitude_range[1],n)
phases_mesh, amplitudes_mesh = np.meshgrid(booster3_phases,booster3_amplitudes)
phases_mesh = phases_mesh.ravel()
amplitudes_mesh = amplitudes_mesh.ravel()
#print(len(phases_mesh))
#print(len(amplitudes_mesh))
e = 1.6e-19
me = 9.1e-31
c=3e8
k = 2*np.pi*13/3 #L-band
wavelength = 2*np.pi/k #m
L = wavelength
alpha3 = e*amplitudes_mesh/(2*me*c**2*k)
#B0 = operate_on_Narray(phases_mesh,amplitudes_mesh, lambda a,b: e*b/(2*me*c**2*k)*k*L*np.sin(a))
#B1 = operate_on_Narray(phases_mesh,amplitudes_mesh, lambda a,b: -e*b/(2*me*c**2*k)*k**2*L*np.cos(a))
#B2 = operate_on_Narray(phases_mesh,amplitudes_mesh, lambda a,b: -e*b/(2*me*c**2*k)*k**3*L*np.sin(a))
#B3 = operate_on_Narray(phases_mesh,amplitudes_mesh, lambda a,b: e*b/(2*me*c**2*k)*k**4*L*np.cos(a))
B30 = e*amplitudes_mesh/(2*me*c**2*k)*k*L*np.sin(phases_mesh) #+ alpha*np.cos(phase_booster)
B31 = -e*amplitudes_mesh/(2*me*c**2*k)*L*k**2*np.cos(phases_mesh) #+ alpha*k*np.sin(phase_booster)
B32 = -e*amplitudes_mesh/(2*me*c**2*k)*L*k**3*np.sin(phases_mesh)/2 #- k**2*alpha*np.cos(phase_booster)/2
B33 = e*amplitudes_mesh/(2*me*c**2*k)*L*k**4*np.cos(phases_mesh)/6 #-k**3*alpha*np.sin(phase_booster)/6
gamma_booster3 = b20+B30
#Check and delete any entry that may have gamma smaller than one at the end
phases_mesh = phases_mesh[gamma_booster3>=1.0]
amplitudes_mesh = amplitudes_mesh[gamma_booster3>=1.0]
B30 = B30[gamma_booster3>=1.0]
B31 = B31[gamma_booster3>=1.0]
B32 = B32[gamma_booster3>=1.0]
B33 = B33[gamma_booster3>=1.0]
gamma_booster3 = gamma_booster3[gamma_booster3>=1.0]
#print(len(B30))
B31_total = b21+B31
B32_total = b22+B32
B33_total = b23+B33
beta_booster3 = np.sqrt(gamma_booster3**2-1)/gamma_booster3
n41 = 1/(beta_booster3**2*gamma_booster3**3)
n42 = (2-3*gamma_booster3**2)/(2*gamma_booster3**6*beta_booster3**4)
n43 = (2-5*gamma_booster3**2+4*gamma_booster3**4)/(2*gamma_booster3**9*beta_booster3**6)
x41 = 1+(z_focus-z_booster3)*(n41*(b21+B31))
x42 = (z_focus-z_booster3)*(n41*(b22+B32)+n42*(b21+B31)**2)
x43 = (z_focus-z_booster3)*(n41*(b23+B33)+2*n42*(b21+B31)*(b22+B32)+n43*(b21+B31)**3)
#Check when X1,X2 and X3 cross 0:
idx_X1 = []
idx_X2 = []
idx_X3 = []
idx_X1_tmp = np.argwhere(np.diff(np.sign(B31_total))).flatten()
idx_X2_tmp = np.argwhere(np.diff(np.sign(B32_total))).flatten()
idx_X3_tmp = np.argwhere(np.diff(np.sign(B33_total))).flatten()
#Remove border cases (sign changes when going from the end of a line to the next one)
for i in range(len(idx_X1_tmp)):
if (idx_X1_tmp[i]%n != 0) and ((idx_X1_tmp[i]+1)%n != 0):
idx_X1.append(idx_X1_tmp[i])
for i in range(len(idx_X2_tmp)):
if (idx_X2_tmp[i]%n != 0) and ((idx_X2_tmp[i]+1)%n != 0):
idx_X2.append(idx_X2_tmp[i])
for i in range(len(idx_X3_tmp)):
if (idx_X3_tmp[i]%n != 0) and ((idx_X3_tmp[i]+1)%n != 0):
idx_X3.append(idx_X3_tmp[i])
idx_X1 = np.asarray(idx_X1)
idx_X2 = np.asarray(idx_X2)
idx_X3 = np.asarray(idx_X3)
#Check when X1=X2, for that we first slightly expand the lines in which X1=0 and X2=0:
for i in idx_X1:
idx_X1 = np.append(idx_X1,i-1)
idx_X1 = np.append(idx_X1,i-2)
idx_X1 = np.append(idx_X1,i-3)
idx_X1 = np.append(idx_X1,i-4)
idx_X1 = np.append(idx_X1,i-5)
idx_X1 = np.append(idx_X1,i+1)
idx_X1 = np.append(idx_X1,i+2)
idx_X1 = np.append(idx_X1,i+3)
idx_X1 = np.append(idx_X1,i+4)
idx_X1 = np.append(idx_X1,i+5)
for i in idx_X2:
idx_X2 = np.append(idx_X2,i-1)
idx_X2 = np.append(idx_X2,i-2)
idx_X2 = np.append(idx_X2,i-3)
idx_X2 = np.append(idx_X2,i-4)
idx_X2 = np.append(idx_X2,i-5)
idx_X2 = np.append(idx_X2,i+1)
idx_X2 = np.append(idx_X2,i+2)
idx_X2 = np.append(idx_X2,i+3)
idx_X2 = np.append(idx_X2,i+4)
idx_X2 = np.append(idx_X2,i+5)
idx_crossing = np.intersect1d(idx_X1, idx_X2, return_indices=False)
print(phases_mesh[idx_crossing]*180/np.pi)
print(amplitudes_mesh[idx_crossing])
fig2,ax21 = plt.subplots()
ax21.set_xlabel(r'$\phi_{booster}$ [deg]')
ax21.set_ylabel(r'$E_{booster}$ [MV/m]')
ax21.scatter(phases_mesh[idx_X1]*180/np.pi, amplitudes_mesh[idx_X1], label = r'X1$\approx$0',s=5)
ax21.scatter(phases_mesh[idx_X2]*180/np.pi, amplitudes_mesh[idx_X2], label = r'X2$\approx$0',s=5)
ax21.scatter(phases_mesh[idx_X3]*180/np.pi, amplitudes_mesh[idx_X3], label = r'X3$\approx$0',s=5)
ax21.scatter(phases_mesh[idx_crossing]*180/np.pi, amplitudes_mesh[idx_crossing], color='red')# 'ro')
#ax21.scatter(optimization_results_phase*180/np.pi, optimization_results_amplitude, label = r'X1$\approx$0 and X2$\approx$0',color='red',s=5)
plt.grid()
fig2.tight_layout()
plt.legend()
#fig2.savefig('Solutions_'+str(Gun_phase)+'deg_'+str(Gun_amplitude)+'MV.pdf')
plt.show()
#Now we will use the parametes obtained by the linearization as a seed for Astra and optimize for minimum bunch size and minimum emittance at the focus point to get the 'real' values of booster phase and amplitude, we will look in an area bounded by 10% of the values given by the analytical solution.
Booster1_amplitude = amplitude_booster1*1e-6
Booster1_phase = phase_booster1*180/np.pi - 90 #We apply the necessary changes to the phase for Astra
Booster2_amplitude = amplitude_booster2*1e-6
Booster2_phase = phase_booster2*180/np.pi - 90 #We apply the necessary changes to the phase for Astra
phase_astra_seed = (np.amax(phases_mesh[idx_crossing])+np.amin(phases_mesh[idx_crossing]))*180/(np.pi*2.0) - 90
#phase_astra_seed = (np.amax(optimization_results_phase[1])+np.amin(optimization_results_phase[0]))*180/(np.pi*2.0) - 90
amplitude_astra_seed = (np.amax(amplitudes_mesh[idx_crossing])+np.amin(amplitudes_mesh[idx_crossing]))*1e-6/2.0
#We will optimize for the position of the bunch minimum by varying the booster amplitude for each booster phase inside a function, this function will return the emittance and will be also optimized by looking for an emittance minimum.
#Arrays to keep results
phase_values = np.array([])
amplitude_values = np.array([])
bunch_size_evolution = np.array([])
bunch_emittance_evolution = np.array([])
focus_point_amplitudes = []
print(phase_astra_seed,amplitude_astra_seed)
phase_bounds = (phase_astra_seed - 0.1*abs(phase_astra_seed), phase_astra_seed + 0.1*abs(phase_astra_seed))
amplitude_bounds = (amplitude_astra_seed - 0.1*amplitude_astra_seed, amplitude_astra_seed + 0.1*amplitude_astra_seed)
result_for_min_delta_e = minimize(runAstraFunction_energy, (phase_astra_seed,amplitude_astra_seed), args=(z_focus,Booster1_phase,Booster1_amplitude,Booster2_phase,Booster2_amplitude),method='SLSQP',bounds=(phase_bounds,amplitude_bounds),options={'eps':0.1})
for_minimal = result_for_min_delta_e.x
print(for_minimal)
print('-------------B1 AMPLITUDE = '+ str(Booster1_amplitude) +'MV/m ---------------B1 PHASE = '+ str(phase_booster1*180/np.pi) +'deg--------------------')
print('-------------B2 AMPLITUDE = '+ str(Booster2_amplitude) +'MV/m ---------------B2 PHASE = '+ str(phase_booster2*180/np.pi) +'deg--------------------')
print('Phase and amplitude for minimums are: ' + str(for_minimal[0]+90) +', ' +str(for_minimal[1]))
#print('Minimum ENERGY SPREAD/Ekin is: ' + str(minimal_size) + ', ' +str(emittance))
#
# m = 10
# phase_values = np.array([])
# amplitude_values = np.array([])
# bunch_size_evolution = np.array([])
# bunch_emittance_evolution = np.array([])
# for i in range(m):
# phase = (phase_astra_seed-0.1*phase_astra_seed) + float(i)*0.2*phase_astra_seed/float(m)
# phase_values = np.append(phase_values,phase)
# bounds = [(amplitude_astra_seed-0.1*amplitude_astra_seed, amplitude_astra_seed+0.1*amplitude_astra_seed)]
# res = minimize(runAstraFunction, amplitude_astra_seed, args=(phase,z_focus,Gun_phase,Gun_amplitude), method ='SLSQP', bounds=bounds, options={'eps':0.1}) #This one converges quite ok and it is bounded.
# amplitude_for_minimal_size = res.x #Returns the amplitude value for which the bunch size is minimum at focus point
# amplitude_values = np.append(amplitude_values, amplitude_for_minimal_size)
# minimal_size, emittance = runAstraFunction2(amplitude_for_minimal_size,phase,z_focus,Gun_phase,Gun_amplitude)
# bunch_size_evolution = np.append(bunch_size_evolution,minimal_size)
# bunch_emittance_evolution = np.append(bunch_emittance_evolution, emittance)
#
# #Out of all the conbinations of phase and amplitude that minimize bunch size at focus, we take the one which also minimizes emittance:
# minimal_emittance_index = np.argmin(bunch_emittance_evolution)
# print('-------------GUN AMPLITUDE = '+ str(Gun_amplitude) +'MV/m ---------------GUN PHASE = '+ str(Gun_phase) +'deg--------------------')
# print('Phase and amplitude for minimums are: ' + str(phase_values[minimal_emittance_index]) +', ' +str(amplitude_values[minimal_emittance_index]))
# print('Minimum bunch size and emittance obtained values are: ' + str(bunch_size_evolution[minimal_emittance_index]) + ', ' +str(bunch_emittance_evolution[minimal_emittance_index]))
return 0#minimal_size
#def emittanceForBunchMinimum(phase,amplitude,focus,Gun_pha,Gun_ampl,bounds_ampl,array_for_values):
# amplitude_for_focus = minimize(runAstraFunction,amplitude,args(phase,focus,Gun_pha,Gun_ampl),method='SLSQP',bounds=bounds_ampl,options={'eps':0.1})
# array_for_values=np.append(array_for_values,amplitude_for_focus.x)
# size_focus, emittance = runAstraFunction2(amplitude_for_focus.x,phase,focus,Gun_pha,Gun_ampl)
# return emittance
phases_b2 = [30.0]#np.linspace(270.0,276.0,13)
for phases in phases_b2:
get_minimal_bunch = linearizer2order(20.0,0.0,phases)