def run_solver(row, fname, atol=1e-8, rtol=1e-8, verbose=False):
ic = create_init_conds(row)
# create solver with nominal field
e_solver = trajectory_solver(ic, B_func=B_Mu2e_nom, bounds=bounds_Mu2e)
# calculate true trajectory
sol = e_solver.solve_trajectory(verbose=verbose, atol=atol, rtol=rtol)
# reco with distorted field
e_solver.analyze_trajectory(step=25,
stride=1,
query="z >= 8.41 & z <= 11.66",
B=B_Mu2e_dis) # analyze in tracker region
# save to pickle
e_solver.to_pickle(fname + '.dis.pkl')
df_dis = e_solver.df_reco
# reco again with distorted field
e_solver.analyze_trajectory(step=25,
stride=1,
query="z >= 8.41 & z <= 11.66",
B=B_Mu2e_nom) # analyze in tracker region
# save to pickle
e_solver.to_pickle(fname + '.nom.pkl')
df_nom = e_solver.df_reco
# trajectory_solver.to_pickle(e_solver, fname)
# return e_solver
# run 1 and 2, entrance to tracker
##return df_nom.p.iloc[0], df_nom.m.iloc[0], df_dis.p.iloc[0], df_dis.m.iloc[0]
# run 3, mean
return df_nom.p.mean(), df_nom.m.mean(), df_dis.p.mean(), df_dis.m.mean()
# run 4, mean, std
return df_nom.p.mean(), df_nom.p.std(), df_dis.p.mean(), df_dis.m.std()
def run_trajectory(ic_df_row, B_func, pid=-11, z_tand=10.175 - 1.6351):
ic_ = InitConds(t0=0,
tf=7e-7,
N_t=70001,
x0=ic_df_row.x,
y0=ic_df_row.y,
z0=ic_df_row.z,
p0=ic_df_row.p,
theta0=ic_df_row.theta,
phi0=ic_df_row.phi)
e = trajectory_solver(init_conds=ic_,
bounds=bounds_Mu2e,
particle_id=pid,
B_func=B_func)
e.solve_trajectory(atol=1e-8, rtol=1e-8)
df_ = e.dataframe[e.dataframe.z >= z_tand]
if len(df_) >= 1:
tand_tracker = (df_.pz / df_.pT).iloc[0]
df_.eval('r = (x**2 + y**2)**(1/2)', inplace=True)
Rmax = df_.r.max()
else:
tand_tracker = -1000
Rmax = -1000
tand_init = np.tan(np.pi / 2 - ic_df_row.theta)
return tand_init, tand_tracker, Rmax
def run_solver(row, fname, atol=1e-8, rtol=1e-8, verbose=False):
ic = create_init_conds(row)
# create solver with nominal field
e_solver = trajectory_solver(ic, B_func=B_Mu2e_nom, bounds=bounds_Mu2e)
# calculate true trajectory
sol = e_solver.solve_trajectory(verbose=verbose, atol=atol, rtol=rtol)
# reco with distorted field
# e_solver.analyze_trajectory(step=25, stride=1, query="z >= 8.41 & z <= 11.66", B=B_Mu2e_dis) # analyze in tracker region
# save to pickle
# e_solver.to_pickle(fname+'.dis.pkl')
# df_dis = e_solver.df_reco
# reco again with distorted field
# e_solver.analyze_trajectory(step=25, stride=1, query="z >= 8.41 & z <= 11.66", B=B_Mu2e_nom) # analyze in tracker region
e_solver.analyze_trajectory_LHelix()
# save to pickle
e_solver.to_pickle(fname + '.nom.pkl')
return e_solver.mom_LHelix
def run_solver(phi, N_calc, field, xnaught, ynaught, znaught, name):
ic_Mu2e = InitConds(t0=0.,
tf=4e-8,
N_t=N_calc,
x0=xnaught,
y0=ynaught,
z0=znaught,
p0=104.96,
theta0=np.pi / 3,
phi0=phi)
e_solver = trajectory_solver(ic_Mu2e, B_func=field, bounds=bounds_Mu2e)
sol = e_solver.solve_trajectory(
verbose=False, atol=1e-10,
rtol=1e-10) # high tolerance so it runs quickly for testing
e_solver.dataframe['r'] = ((e_solver.dataframe['x'])**2 +
(e_solver.dataframe['y'])**2)**(1 / 2)
e_solver.to_pickle(datadir + f'50_Gauss_z_3-14/{phi}_{name}.pkl')
return e_solver.dataframe