def main():
# print(random.random())
# time.sleep(1.)
'''
INITIAL STATE
'''
LookForTarget = True
# slow_base_filename = './data/1_lone_wolf.npy'
# slow_base_filename = './data/1_1_short_ellipse.npy'
# slow_base_filename = './data/1_1_long_ellipse.npy'
# slow_base_filename = './data/1_1_cercle.npy'
slow_base_filename = './data/2_cercle.npy'
# slow_base_filename = './data/3_cercle.npy'
# slow_base_filename = './data/3_huit.npy'
# slow_base_filename = './data/3_heart.npy'
# slow_base_filename = './data/4_trefoil.npy'
# slow_base_filename = './data/1x4_trefoil.npy'
# fast_base_filename_list = ['./data/1_lone_wolf.npy' ]
fast_base_filename_list = ['./data/2_cercle.npy']
# fast_base_filename_list = ['./data/3_cercle.npy' ]
# fast_base_filename_list = ['./data/3_huit.npy' ]
# fast_base_filename_list = ['./data/3_heart.npy' ]
# fast_base_filename_list = ['./data/3_dbl_heart.npy' ]
# fast_base_filename_list = ['./data/4_13_2_2_cercle.npy']
# fast_base_filename_list = ['./data/4_trefoil.npy']
# fast_base_filename_list = ['./data/2_cercle.npy','./data/3_huit.npy' ]
# fast_base_filename_list = ['./data/1_lone_wolf.npy','./data/2_cercle.npy' ]
# fast_base_filename_list = ['./data/1_lone_wolf.npy','./data/1_lone_wolf.npy' ]
# fast_base_filename_list = ['./data/1_lone_wolf.npy','./data/1_lone_wolf.npy' ,'./data/1_lone_wolf.npy','./data/1_lone_wolf.npy' ]
# fast_base_filename_list = ['./data/1_lone_wolf.npy','./data/2_cercle.npy' ,'./data/1_lone_wolf.npy','./data/2_cercle.npy' ]
# fast_base_filename_list = ['./data/1_lone_wolf.npy','./data/1_lone_wolf.npy' ,'./data/1_lone_wolf.npy','./data/3_huit.npy' ]
nfl = len(fast_base_filename_list)
mass_mul = [1]
nTf = [13]
nbs = [2]
nbf = [2]
epsmul = 0.
# mass_mul = [1,1,1,1]
# mass_mul = [1.,1.+epsmul,1.+2*epsmul,1.+3*epsmul]
# nTf = [1,1,1,1]
# nbs = [1,1,1,1]
# nbf = [1,1,1,1]
# mass_mul = [1,1]
# mass_mul = [3,2]
# nTf = [37,37]
# nbs = [1,1]
# nbf = [2,3]
mul_loops_ini = True
# mul_loops_ini = False
# mul_loops_ini = (np.random.random() > 1./2.)
mul_loops = [mul_loops_ini for _ in range(nfl)]
# Remove_Choreo_Sym = mul_loops
Remove_Choreo_Sym = [True, False]
# Remove_Choreo_Sym = [False,False]
# Rotate_fast_with_slow = True
Rotate_fast_with_slow = False
# Rotate_fast_with_slow = (np.random.random() > 1./2.)
# Optimize_Init = True
Optimize_Init = False
Randomize_Fast_Init = True
# Randomize_Fast_Init = False
all_coeffs_slow_load = np.load(slow_base_filename)
all_coeffs_fast_load_list = []
nbpl = []
mass = []
for i in range(nfl):
fast_base_filename = fast_base_filename_list[i]
all_coeffs_fast_load = np.load(fast_base_filename)
if Remove_Choreo_Sym[i]:
all_coeffs_fast_load_list_temp = []
for ib in range(nbf[i]):
tshift = (ib * 1.0) / nbf[i]
all_coeffs_fast_load_list_temp.append(
choreo.Transform_Coeffs(np.identity(choreo.ndim), 1,
tshift, 1, all_coeffs_fast_load))
nbpl.append(nbs[i])
mass.extend([mass_mul[i] for j in range(nbs[i])])
all_coeffs_fast_load_list.append(
np.concatenate(all_coeffs_fast_load_list_temp, axis=0))
else:
all_coeffs_fast_load_list.append(all_coeffs_fast_load)
nbpl.append(nbs[i] * nbf[i])
mass.extend([mass_mul[i] for j in range(nbs[i] * nbf[i])])
mass = np.array(mass, dtype=np.float64)
coeff_ampl_o = 1e-16
k_infl = 1
k_max = 200
coeff_ampl_min = 1e-16
'''
SYMMETRIES
'''
Sym_list = []
the_lcm = m.lcm(*nbpl)
SymName = None
Sym_list, nbody = choreo.Make2DChoreoSymManyLoops(nbpl=nbpl,
SymName=SymName)
# mass = np.ones((nbody))*mass_mul
# ibody = 0
# rot_angle = 0.
# s = -1
# st = -1
# Sym_list.append(ChoreoSym(
# LoopTarget=ibody,
# LoopSource=ibody,
# SpaceRot = np.array([[s*np.cos(rot_angle),-s*np.sin(rot_angle)],[np.sin(rot_angle),np.cos(rot_angle)]],dtype=np.float64),
# TimeRev=st,
# TimeShift=fractions.Fraction(numerator=0,denominator=1)
# ))
# MomConsImposed = True
MomConsImposed = False
'''
I/O
'''
file_basename = ''
store_folder = os.path.join(__PROJECT_ROOT__, 'Target_res/')
store_folder = store_folder + str(nbody)
if not (os.path.isdir(store_folder)):
os.makedirs(store_folder)
save_init = False
# save_init = True
Save_img = True
# Save_img = False
# Save_thumb = True
Save_thumb = False
# img_size = (12,12) # Image size in inches
img_size = (8, 8) # Image size in inches
thumb_size = (2, 2) # Image size in inches
nint_plot_img = 10000
color = "body"
# color = "loop"
# color = "velocity"
# color = "all"
Save_anim = True
# Save_anim = False
vid_size = (8, 8) # Image size in inches
nint_plot_anim = 2 * 2 * 2 * 3 * 3 * 5
# nperiod_anim = 1./nbody
dnint = 30
nint_plot_img = nint_plot_anim * dnint
try:
the_lcm
except NameError:
period_div = 1.
else:
period_div = the_lcm
nperiod_anim = 1. / period_div
Plot_trace_anim = True
# Plot_trace_anim = False
# Save_Newton_Error = True
Save_Newton_Error = False
'''
SOLVER & SOLVER LOOP
'''
Use_exact_Jacobian = True
# Use_exact_Jacobian = False
Look_for_duplicates = True
# Look_for_duplicates = False
Check_Escape = True
# Check_Escape = False
n_reconverge_it_max = 4
# n_reconverge_it_max = 1
# ncoeff_init = 300
# ncoeff_init = 600
ncoeff_init = 900
# ncoeff_init = 1800
disp_scipy_opt = False
# disp_scipy_opt = True
max_norm_on_entry = 1e20
Newt_err_norm_max = 1e-10
# Newt_err_norm_max_save = Newt_err_norm_max*1000
Newt_err_norm_max_save = 1e-1
duplicate_eps = 1e-8
krylov_method = 'lgmres'
# krylov_method = 'gmres'
# krylov_method = 'bicgstab'
# krylov_method = 'cgs'
# krylov_method = 'minres'
# line_search = 'armijo'
line_search = 'wolfe'
gradtol_list = [1e-3, 1e-5, 1e-7, 1e-9, 1e-11, 1e-13, 1e-15]
maxiter_list = [1000, 1000, 1000, 500, 500, 300, 100]
inner_maxiter_list = [30, 50, 60, 70, 80, 100, 100]
outer_k_list = [5, 5, 5, 5, 7, 7, 7]
store_outer_Av_list = [False, False, False, False, True, True, True]
n_optim_param = len(gradtol_list)
gradtol_max = 100 * gradtol_list[n_optim_param - 1]
# foundsol_tol = 1000*gradtol_list[0]
foundsol_tol = 1e10
n_grad_change = 1.
# n_grad_change = 1.5
freq_erase_dict = 1000
hash_dict = {}
# n_opt_max = 1
# n_opt_max = 5
n_opt_max = 1e10
all_kwargs = choreo.Pick_Named_Args_From_Dict(choreo.Find_Choreo,
dict(globals(), **locals()))
choreo.Find_Choreo(**all_kwargs)
def main():
input_folder = os.path.join(__PROJECT_ROOT__,'Sniff_all_sym/3/')
# input_folder = os.path.join(__PROJECT_ROOT__,'Sniff_all_sym/copy/')
# input_folder = os.path.join(__PROJECT_ROOT__,'Sniff_all_sym/keep/13')
# ''' Include all files in tree '''
# input_names_list = []
# for root, dirnames, filenames in os.walk(input_folder):
#
# for filename in filenames:
# file_path = os.path.join(root, filename)
# file_root, file_ext = os.path.splitext(os.path.basename(file_path))
#
# if (file_ext == '.txt' ):
#
# file_path = os.path.join(root, file_root)
# the_name = file_path[len(input_folder):]
# input_names_list.append(the_name)
''' Include all files in folder '''
input_names_list = []
for file_path in os.listdir(input_folder):
file_path = os.path.join(input_folder, file_path)
file_root, file_ext = os.path.splitext(os.path.basename(file_path))
if (file_ext == '.txt' ):
input_names_list.append(file_root)
# input_names_list = ['00006']
store_folder = os.path.join(__PROJECT_ROOT__,'Sniff_all_sym/mod')
# store_folder = input_folder
Save_All_Coeffs = True
# Save_All_Coeffs = False
Save_All_Coeffs_No_Sym = True
# Save_All_Coeffs_No_Sym = False
# Save_Newton_Error = True
Save_Newton_Error = False
Save_img = True
# Save_img = False
#
# Save_thumb = True
Save_thumb = False
# img_size = (12,12) # Image size in inches
img_size = (8,8) # Image size in inches
thumb_size = (2,2) # Image size in inches
color = "body"
# color = "loop"
# color = "velocity"
# color = "all"
Save_anim = True
# Save_anim = False
Save_ODE_anim = True
# Save_ODE_anim = False
# ODE_method = 'RK23'
# ODE_method = 'RK45'
ODE_method = 'DOP853'
# ODE_method = 'Radau'
# ODE_method = 'BDF'
atol_ode = 1e-10
rtol_ode = 1e-12
vid_size = (8,8) # Image size in inches
# nint_plot_anim = 2*2*2*3*3
nint_plot_anim = 2*2*2*3*3*5
dnint = 30
nint_plot_img = nint_plot_anim * dnint
min_n_steps_ode = 1*nint_plot_anim
try:
the_lcm
except NameError:
period_div = 1.
else:
period_div = the_lcm
#
nperiod_anim = 1.
# nperiod_anim = 1./period_div
Plot_trace_anim = True
# Plot_trace_anim = False
GradActionThresh = 1e-8
for the_name in input_names_list:
print('')
print(the_name)
input_filename = os.path.join(input_folder,the_name)
input_filename = input_filename + '.npy'
bare_name = the_name.split('/')[-1]
all_coeffs = np.load(input_filename)
theta = 2*np.pi * 0.5
SpaceRevscal = 1.
SpaceRot = np.array( [[SpaceRevscal*np.cos(theta) , SpaceRevscal*np.sin(theta)] , [-np.sin(theta),np.cos(theta)]])
TimeRev = 1.
TimeShiftNum = 0
TimeShiftDen = 2
all_coeffs = choreo.Transform_Coeffs(SpaceRot, TimeRev, TimeShiftNum, TimeShiftDen, all_coeffs)
ncoeff_init = all_coeffs.shape[2]
the_i = -1
the_i_max = 20
Gradaction_OK = False
while (not(Gradaction_OK) and (the_i < the_i_max)):
the_i += 1
# p = 1
p_list = range(the_i_max)
# p_list = [3]
p = p_list[the_i%len(p_list)]
nc = 3
mm = 1
# mm_list = [1]
# mm = mm_list[the_i%len(mm_list)]
nbpl=[nc]
SymType = {
'name' : 'D',
'n' : nc,
'm' : mm,
'l' : 0,
'k' : 1,
'p' : p,
'q' : nc,
}
Sym_list = choreo.Make2DChoreoSym(SymType,range(nc))
nbody = nc
mass = np.ones((nbody),dtype=np.float64)
# MomConsImposed = True
MomConsImposed = False
n_reconverge_it_max = 0
n_grad_change = 1.
callfun = choreo.setup_changevar(nbody,ncoeff_init,mass,n_reconverge_it_max,Sym_list=Sym_list,MomCons=MomConsImposed,n_grad_change=n_grad_change,CrashOnIdentity=False)
x = choreo.Package_all_coeffs(all_coeffs,callfun)
Action,Gradaction = choreo.Compute_action(x,callfun)
Gradaction_OK = (np.linalg.norm(Gradaction) < GradActionThresh)
if not(Gradaction_OK):
raise(ValueError('Correct Symmetries not found'))
filename_output = os.path.join(store_folder,bare_name)
print('Saving solution as '+filename_output+'.*')
choreo.Write_Descriptor(x,callfun,filename_output+'.txt')
if Save_img :
choreo.plot_all_2D(x,nint_plot_img,callfun,filename_output+'.png',fig_size=img_size,color=color)
if Save_thumb :
choreo.plot_all_2D(x,nint_plot_img,callfun,filename_output+'_thumb.png',fig_size=thumb_size,color=color)
if Save_anim :
choreo.plot_all_2D_anim(x,nint_plot_anim,callfun,filename_output+'.mp4',nperiod_anim,Plot_trace=Plot_trace_anim,fig_size=vid_size,dnint=dnint)
if Save_Newton_Error :
choreo.plot_Newton_Error(x,callfun,filename_output+'_newton.png')
if Save_All_Coeffs:
np.save(filename_output+'.npy',all_coeffs)
if Save_All_Coeffs_No_Sym:
all_coeffs_nosym = choreo.RemoveSym(x,callfun)
np.save(filename_output+'_nosym.npy',all_coeffs_nosym)
if Save_ODE_anim:
y0 = choreo.Compute_init_pos_vel(x,callfun).reshape(-1)
t_eval = np.array([i/nint_plot_img for i in range(nint_plot_img)])
ode_res = scipy.integrate.solve_ivp(fun=choreo.Compute_ODE_RHS, t_span=(0.,1.), y0=y0, method=ODE_method, t_eval=t_eval, dense_output=False, events=None, vectorized=False, args=callfun,max_step=1./min_n_steps_ode,atol=atol_ode,rtol=rtol_ode)
all_pos_vel = ode_res['y'].reshape(2,nbody,choreo.ndim,nint_plot_img)
all_pos_ode = all_pos_vel[0,:,:,:]
choreo.plot_all_2D_anim(x,nint_plot_anim,callfun,filename_output+'_ode.mp4',nperiod_anim,Plot_trace=Plot_trace_anim,fig_size=vid_size,dnint=dnint,all_pos_trace=all_pos_ode,all_pos_points=all_pos_ode)
y0 = choreo.Compute_init_pos_vel(x,callfun).reshape(-1)
t_eval = np.array([0.,1.])
ode_res = scipy.integrate.solve_ivp(fun=choreo.Compute_ODE_RHS, t_span=(0.,1.), y0=y0, method=ODE_method, t_eval=t_eval, dense_output=False, events=None, vectorized=False, args=callfun,max_step=1./min_n_steps_ode,atol=atol_ode,rtol=rtol_ode)
all_pos_vel = ode_res['y'].reshape(2,nbody,choreo.ndim,2)
all_pos_ode = all_pos_vel[0,:,:,:]
print(np.linalg.norm(all_pos_ode[:,:,0]-all_pos_ode[:,:,1]))
def main():
input_folder = os.path.join(__PROJECT_ROOT__,'Sniff_all_sym/mod')
# input_folder = os.path.join(__PROJECT_ROOT__,'Sniff_all_sym/10/')
# input_folder = os.path.join(__PROJECT_ROOT__,'Sniff_all_sym/copy/')
# input_folder = os.path.join(__PROJECT_ROOT__,'Sniff_all_sym/keep/13')
# ''' Include all files in tree '''
# input_names_list = []
# for root, dirnames, filenames in os.walk(input_folder):
#
# for filename in filenames:
# file_path = os.path.join(root, filename)
# file_root, file_ext = os.path.splitext(os.path.basename(file_path))
#
# if (file_ext == '.txt' ):
#
# file_path = os.path.join(root, file_root)
# the_name = file_path[len(input_folder):]
# input_names_list.append(the_name)
''' Include all files in folder '''
input_names_list = []
for file_path in os.listdir(input_folder):
file_path = os.path.join(input_folder, file_path)
file_root, file_ext = os.path.splitext(os.path.basename(file_path))
if (file_ext == '.txt' ):
# input_names_list.append(file_root)
input_names_list.append(file_root+'_nosym')
# input_names_list = ['00006']
# input_names_list = ['00006_nosym']
GradActionThresh = 1e-8
for the_name in input_names_list:
print('')
print(the_name)
input_filename = os.path.join(input_folder,the_name)
input_filename = input_filename + '.npy'
bare_name = the_name.split('/')[-1]
all_coeffs = np.load(input_filename)
theta = 2*np.pi * 0.5
SpaceRevscal = 1.
SpaceRot = np.array( [[SpaceRevscal*np.cos(theta) , SpaceRevscal*np.sin(theta)] , [-np.sin(theta),np.cos(theta)]])
TimeRev = 1.
TimeShiftNum = 0
TimeShiftDen = 2
all_coeffs = choreo.Transform_Coeffs(SpaceRot, TimeRev, TimeShiftNum, TimeShiftDen, all_coeffs)
ncoeff_init = all_coeffs.shape[2]
the_i = -1
the_i_max = 0
Gradaction_OK = False
while (not(Gradaction_OK) and (the_i < the_i_max)):
the_i += 1
p = 1
# p_list = range(the_i_max)
# p_list = [3]
# p = p_list[the_i%len(p_list)]
nc = 3
mm = 1
# mm_list = [1]
# mm = mm_list[the_i%len(mm_list)]
# nbpl=[nc]
nbpl=[1 for i in range(nc)]
#
# SymType = {
# 'name' : 'D',
# 'n' : nc,
# 'm' : mm,
# 'l' : 0,
# 'k' : 1,
# 'p' : p,
# 'q' : nc,
# }
# Sym_list = choreo.Make2DChoreoSym(SymType,range(nc))
# nbody = nc
Sym_list,nbody = choreo.Make2DChoreoSymManyLoops(nbpl=nbpl,SymName='C')
mass = np.ones((nbody),dtype=np.float64)
# MomConsImposed = True
MomConsImposed = False
n_reconverge_it_max = 0
n_grad_change = 1.
callfun = choreo.setup_changevar(nbody,ncoeff_init,mass,n_reconverge_it_max,Sym_list=Sym_list,MomCons=MomConsImposed,n_grad_change=n_grad_change,CrashOnIdentity=False)
x = choreo.Package_all_coeffs(all_coeffs,callfun)
Action,Gradaction = choreo.Compute_action(x,callfun)
Gradaction_OK = (np.linalg.norm(Gradaction) < GradActionThresh)
if not(Gradaction_OK):
raise(ValueError('Correct Symmetries not found'))
n_eig = 10
HessMat = choreo.Compute_action_hess_LinOpt(x,callfun)
w ,v = scipy.sparse.linalg.eigsh(HessMat,k=n_eig,which='SA')
print(w)
def main(the_i=0):
# if (the_i != 0):
#
# preprint_msg = str(the_i).zfill(2)+' : '
#
# def print(*args, **kwargs):
# """My custom print() function."""
# builtins.print(preprint_msg,end='')
# return builtins.print(*args, **kwargs)
#
file_basename = ''
np.random.seed(int(time.time() * 10000) % 5000)
LookForTarget = True
# slow_base_filename = './data/1_lone_wolf.npy'
# slow_base_filename = './data/1_1_short_ellipse.npy'
# slow_base_filename = './data/1_1_long_ellipse.npy'
slow_base_filename = './data/1_1_cercle.npy'
# slow_base_filename = './data/2_cercle.npy'
# slow_base_filename = './data/3_cercle.npy'
# slow_base_filename = './data/3_huit.npy'
# slow_base_filename = './data/3_heart.npy'
# slow_base_filename = './data/4_trefoil.npy'
# slow_base_filename = './data/1x4_trefoil.npy'
# fast_base_filename_list = ['./data/1_lone_wolf.npy' ]
# fast_base_filename_list = ['./data/2_cercle.npy' ]
# fast_base_filename_list = ['./data/3_cercle.npy' ]
# fast_base_filename_list = ['./data/3_huit.npy' ]
# fast_base_filename_list = ['./data/3_heart.npy' ]
# fast_base_filename_list = ['./data/3_dbl_heart.npy' ]
# fast_base_filename_list = ['./data/4_13_2_2_cercle.npy']
# fast_base_filename_list = ['./data/4_trefoil.npy']
# fast_base_filename_list = ['./data/2_cercle.npy','./data/2_cercle.npy' ]
# fast_base_filename_list = ['./data/3_cercle.npy' ,'./data/3_cercle.npy' ]
fast_base_filename_list = ['./data/2_cercle.npy', './data/3_huit.npy']
# fast_base_filename_list = ['./data/1_lone_wolf.npy','./data/1_lone_wolf.npy' ]
# fast_base_filename_list = ['./data/1_lone_wolf.npy','./data/1_lone_wolf.npy' ,'./data/1_lone_wolf.npy','./data/1_lone_wolf.npy' ]
# fast_base_filename_list = ['./data/1_lone_wolf.npy','./data/2_cercle.npy' ,'./data/1_lone_wolf.npy','./data/2_cercle.npy' ]
# fast_base_filename_list = ['./data/1_lone_wolf.npy','./data/1_lone_wolf.npy' ,'./data/1_lone_wolf.npy','./data/3_huit.npy' ]
nfl = len(fast_base_filename_list)
mass_mul = [3, 2]
nTf = [13, 13]
nbs = [1, 1]
nbf = [2, 3]
epsmul = 0.
# mass_mul = [1,1,1,1]
# mass_mul = [1.,1.+epsmul,1.+2*epsmul,1.+3*epsmul]
# nTf = [1,1,1,1]
# nbs = [1,1,1,1]
# nbf = [1,1,1,1]
# mass_mul = [1,1]
# mass_mul = [3,2]
# nTf = [37,37]
# nbs = [1,1]
# nbf = [2,3]
mul_loops_ini = True
# mul_loops_ini = False
# mul_loops_ini = (np.random.random() > 1./2.)
mul_loops = [mul_loops_ini for _ in range(nfl)]
Remove_Choreo_Sym = mul_loops
# Remove_Choreo_Sym = [False,False]
# Remove_Choreo_Sym = [False,False]
Rotate_fast_with_slow = True
# Rotate_fast_with_slow = False
# Rotate_fast_with_slow = (np.random.random() > 1./2.)
# Optimize_Init = True
Optimize_Init = False
# Optimize_Init = (np.random.random() > 1./2.)
Randomize_Fast_Init = True
# Randomize_Fast_Init = False
all_coeffs_slow_load = np.load(slow_base_filename)
all_coeffs_fast_load_list = []
nbpl = []
mass = []
for i in range(nfl):
fast_base_filename = fast_base_filename_list[i]
all_coeffs_fast_load = np.load(fast_base_filename)
if Remove_Choreo_Sym[i]:
all_coeffs_fast_load_list_temp = []
for ib in range(nbf[i]):
tshift = (ib * 1.0) / nbf[i]
all_coeffs_fast_load_list_temp.append(
choreo.Transform_Coeffs(np.identity(2), 1, tshift, 1,
all_coeffs_fast_load))
nbpl.append(nbs[i])
mass.extend([mass_mul[i] for j in range(nbs[i])])
all_coeffs_fast_load_list.append(
np.concatenate(all_coeffs_fast_load_list_temp, axis=0))
else:
all_coeffs_fast_load_list.append(all_coeffs_fast_load)
nbpl.append(nbs[i] * nbf[i])
mass.extend([mass_mul[i] for j in range(nbs[i] * nbf[i])])
mass = np.array(mass, dtype=np.float64)
Sym_list = []
the_lcm = m.lcm(*nbpl)
SymName = None
Sym_list, nbody = choreo.Make2DChoreoSymManyLoops(nbpl=nbpl,
SymName=SymName)
# mass = np.ones((nbody))*mass_mul
# for ibody in range(6):
# # for ibody in [0,2]:
# # for ibody in [0]:
#
# l_rot = 11
# k_rot = 13
# rot_angle = 2* np.pi * l_rot / k_rot
# s = 1
# st = 1
#
# Sym_list.append(choreo.ChoreoSym(
# LoopTarget=ibody,
# LoopSource=ibody,
# SpaceRot = np.array([[s*np.cos(rot_angle),-s*np.sin(rot_angle)],[np.sin(rot_angle),np.cos(rot_angle)]],dtype=np.float64),
# TimeRev=st,
# TimeShift=fractions.Fraction(numerator=1,denominator=k_rot)
# ))
#
# MomConsImposed = True
MomConsImposed = False
store_folder = './Target_res/'
store_folder = store_folder + str(nbody)
if not (os.path.isdir(store_folder)):
os.makedirs(store_folder)
Use_exact_Jacobian = True
# Use_exact_Jacobian = False
Look_for_duplicates = True
# Look_for_duplicates = False
Check_Escape = True
# Check_Escape = False
# Penalize_Escape = True
Penalize_Escape = False
#
# save_first_init = False
save_first_init = True
#
save_all_inits = False
# save_all_inits = True
Save_img = True
# Save_img = False
Save_thumb = True
# Save_thumb = False
# img_size = (12,12) # Image size in inches
img_size = (8, 8) # Image size in inches
thumb_size = (2, 2) # Image size in inches
nint_plot_img = 10000
color = "body"
# color = "loop"
# color = "velocity"
# color = "all"
Save_anim = True
# Save_anim = False
vid_size = (8, 8) # Image size in inches
nint_plot_anim = 2 * 2 * 2 * 3 * 3 * 5
# nperiod_anim = 1./nbody
dnint = 30
nint_plot_img = nint_plot_anim * dnint
try:
the_lcm
except NameError:
period_div = 1.
else:
period_div = the_lcm
nperiod_anim = 1. / period_div
Plot_trace_anim = True
# Plot_trace_anim = False
# Save_Newton_Error = True
Save_Newton_Error = False
n_reconverge_it_max = 3
# n_reconverge_it_max = 1
# ncoeff_init = 102
# ncoeff_init = 800
# ncoeff_init = 201
# ncoeff_init = 300
# ncoeff_init = 600
ncoeff_init = 900
# ncoeff_init = 1800
# ncoeff_init = 3600
# ncoeff_init = 1206
# ncoeff_init = 90
disp_scipy_opt = False
# disp_scipy_opt = True
max_norm_on_entry = 1e20
Newt_err_norm_max = 1e-10
# Newt_err_norm_max_save = Newt_err_norm_max*1000
Newt_err_norm_max_save = 1e-1
duplicate_eps = 1e-8
krylov_method = 'lgmres'
# krylov_method = 'gmres'
# krylov_method = 'bicgstab'
# krylov_method = 'cgs'
# krylov_method = 'minres'
# line_search = 'armijo'
line_search = 'wolfe'
gradtol_list = [1e-3, 1e-5, 1e-7, 1e-9, 1e-11, 1e-13, 1e-15]
inner_maxiter_list = [30, 50, 60, 70, 80, 100, 100]
maxiter_list = [1000, 1000, 1000, 500, 500, 300, 100]
outer_k_list = [5, 5, 5, 5, 7, 7, 7]
store_outer_Av_list = [False, False, False, False, True, True, True]
n_optim_param = len(gradtol_list)
gradtol_max = 100 * gradtol_list[n_optim_param - 1]
# foundsol_tol = 1000*gradtol_list[0]
foundsol_tol = 1e10
escape_fac = 1e0
# escape_fac = 1e-1
# escape_fac = 1e-2
# escape_fac = 1e-3
# escape_fac = 1e-4
# escape_fac = 1e-5
# escape_fac = 0
escape_min_dist = 1
escape_pow = 2.0
# escape_pow = 2.5
# escape_pow = 1.5
# escape_pow = 0.5
n_grad_change = 1.
# n_grad_change = 1.5
coeff_ampl_o = 1e-16
k_infl = 200
k_max = 800
coeff_ampl_min = 1e-16
freq_erase_dict = 1000
hash_dict = {}
n_opt = 0
n_opt_max = 1
# n_opt_max = 5
# n_opt_max = 0
all_kwargs = choreo.Pick_Named_Args_From_Dict(choreo.Find_Choreo,
dict(globals(), **locals()))
choreo.Find_Choreo(**all_kwargs)