def drift_velocity(dst, plots_dir, opt_dict):
length_demo = 310
run = opt_dict["run"]
fit_z_min = float(opt_dict["fit_z_min"])
fit_z_max = float(opt_dict["fit_z_max"])
fit_z_range = (fit_z_min, fit_z_max)
print(f'fit z range = {fit_z_range}')
Z = dst[in_range(dst.Z, fit_z_min, fit_z_max)].Z
sigmoid = lambda x, A, B, C, D: A / (1 + np.exp((x - B) / C)) + D
mypdf = Extended(sigmoid)
#describe(mypdf)
bx2 = BinnedChi2(mypdf, Z, bins=50,
bound=fit_z_range) #create cost function
#bx2.show(args={'A':1400, 'B':340, 'C':1.1, 'D':43 , 'N':0.1}) #another way to draw it
m = Minuit(bx2, A=1400, B=340, C=1.1, D=43, N=0.1)
m.migrad()
my_parmloc = (0.60, 0.90)
bx2.draw(m, parmloc=my_parmloc)
v = length_demo / m.args[1]
print(v)
v_err = (length_demo - 2) / (m.args[1] - m.errors[1])
v_u = v - v_err
print(f"Drift velocity = {v:.4f} +/- {v_u:.4f} ")
plt.savefig(f'{plots_dir}/fit_drift_vel_{run}.pdf')
print(f'plots saved in {plots_dir}/fit_drift_vel_{run}.pdf')
def energy_resolution(dst, fout, plots_dir, opt_dict):
run = opt_dict["run"]
fit_e_min = float(opt_dict["fit_e_min"])
fit_e_max = float(opt_dict["fit_e_max"])
fit_seed = float(opt_dict["fit_seed"])
fit_erange = (fit_e_min, fit_e_max)
chi2_func = BinnedChi2(gaussC, dst.S2e, bins=50, bound=fit_erange)
m = Minuit(chi2_func, mu=fit_seed, sigma=90, N=10, Ny=0)
m.migrad()
chi2_val = m.fval / chi2_func.ndof
mean = m.values[0]
mean_u = m.errors[0]
reso, fig = plot_fits(dst, 1, fit_erange, m, chi2_val)
fout.write(f'e_reso {reso:.3f}\n')
fout.write(f's2_fit {mean:.3f}\n')
fout.write(f's2u_fit {mean_u:.5f}\n')
fout.write(f'chi2_fit {chi2_val:.3f}\n')
print(f'Resolution = {reso}')
plt.savefig(f'{plots_dir}/fit_energy_reso_{run}.pdf')
plt.savefig(f'/Users/neus/current-work/ana-reso/fit_energy_reso_{run}.pdf')
print(f'plots saved in {plots_dir}fit_energy_reso_{run}.pdf')
print(
f'plots saved in /Users/neus/current-work/ana-reso/fit_energy_reso_{run}.pdf'
)
return fig, reso, chi2_val, mean, mean_u
def drift_velocity(plots_dir, dst, label=''):
"""
Input Z distribution
Return value for the drift velocity
"""
# To do: make plotting of drift velocities as for energy resolution
Z = dst[in_range(dst.Z, 305, 350)].Z
fit_z_range = (305, 350)
fig = plt.figure(figsize=(12, 4))
ax = fig.add_subplot(1, 2, 1)
(_) = hist(dst.DT,
bins=100,
range=(0, 400),
histtype='stepfilled',
color='crimson')
labels('Drit time ($\mu$s)', 'Entries')
plt.legend(loc='upper right')
ax = fig.add_subplot(1, 2, 2)
(_) = hist(Z,
bins=100,
range=fit_z_range,
histtype='stepfilled',
color='crimson')
labels('Drit time ($\mu$s)', 'Entries')
plt.legend(loc='upper right')
plt.show()
sigmoid = lambda x, A, B, C, D: A / (1 + np.exp((x - B) / C)) + D
mypdf = Extended(sigmoid)
#describe(mypdf)
chi2 = BinnedChi2(mypdf, Z, bins=100,
bound=fit_z_range) #create cost function
plt.figure(figsize=(7, 5))
chi2.show(args={
'A': 1400,
'B': 330,
'C': 1.1,
'D': 43,
'N': 1
}) #another way to draw it
m = Minuit(chi2, A=800, B=330, C=1.3, D=100, N=1)
m.migrad()
my_parmloc = (0.60, 0.90)
#chi2.draw(m, parmloc=my_parmloc)
plt.figure(figsize=(7, 5))
chi2.show(m, parmloc=my_parmloc)
plt.savefig(f'{plots_dir}/fit_energy_reso_{label}.png')
print('plots saved in ' + plots_dir)
def energy_resolution(plots_dir, dst, label=''):
"""
Input s2 energy signal
Return value for the energy resolution
"""
fit_erange = (4500, 5300)
# To do: work in the plotting
plt.figure(figsize=(7, 5))
#fig = plt.figure(figsize=(8,6))
#ax = fig.add_subplot(5, 2, 1)
chi2 = BinnedChi2(gaussC, dst.S2e, bins=50,
bound=fit_erange) #create cost function
chi2.show(args={
'mu': 4900,
'sigma': 70,
'N': 400,
'Ny': 25
}) #another way to draw it
plt.figure(figsize=(7, 5))
#ax = fig.add_subplot(5, 2, 2)
m = Minuit(chi2, mu=4900, sigma=70, N=400, Ny=25)
m.migrad()
chi2.show(m)
#plt.show()
mean = minuit.values[0]
mean_u = minuit.errors[0]
sigma = minuit.values[1]
sigma_u = minuit.errors[1]
N = minuit.values[2]
N_u = minuit.errors[2]
N2 = minuit.values[3]
N2_u = minuit.errors[3]
print(f'Mean: {mean:.2f} +/- {mean_u:.2f} ')
print(f'Sigma: {sigma:.2f} +/- {sigma_u:.2f} ')
print(f'N: {N:.1f} +/- {N_u:.1f} ')
print(f'N2: {N2:.1f} +/- {N2_u:.1f} ')
# Pass an Tuple instead of a long list of variables
plt.style.use('classic')
plot_residuals_E_reso_gaussC(plots_dir, label, dst.S2e, 50, fit_erange,
mean, mean_u, sigma, sigma_u, N, N_u, N2,
N2_u)
def energy_reso_vs_z_r(dst, corr, file_fits, file_plot, ring='yes'):
"""
"""
pp = PdfPages(file_fits)
fit_erange = (4700, 5250)
bins_r = (0, 30, 40, 50, 60, 70)
bins_z = (0, 50, 100, 150, 200, 250, 300)
#bins_r = (0,30)
#bins_z = (0,50,100)
dst_list = []
reso_list = []
num = 0
print(f'-----> Start fits to dst selected by r and z ranges\n')
for i in range(len(bins_r) - 1):
for j in range(len(bins_z) - 1):
if ring == 'yes':
dst_inrange = select_dst_ring_r_z(dst, bins_r, bins_z, i, j)
elif ring == 'no':
dst_inrange = select_dst_disk_r_z(dst, bins_r, bins_z, i, j)
print(f'Region id = {num}, i index = {i}, j index = {j}')
dst_list.append(dst_inrange)
chi2 = BinnedChi2(gaussC,
dst_inrange.S2e * corr,
bins=50,
bound=fit_erange)
m = Minuit(chi2, mu=4900, sigma=70, N=400, Ny=25)
m.migrad()
reso, fig = plot_fits(dst_inrange, corr, fit_erange, m)
reso_list.append(reso)
pp.savefig(fig)
num += 1
print(f'-----> Fits saved in {file_fits}---->\n')
pp.close()
plot_e_resolution_vs_z_r(reso_list, file_plot)
# But, binned likelihood support both extended and unextended fit.
# <codecell>
from probfit import Extended, BinnedChi2
seed(0)
gdata = randn(10000)
# <codecell>
mypdf = Extended(gaussian)
describe(mypdf) # just basically N*gaussian(x,mean,sigma)
# <codecell>
bx2 = BinnedChi2(mypdf, gdata, bound=(-3,3))#create cost function
bx2.show(args={'mean':1.0, 'sigma':1.0, 'N':10000}) #another way to draw it
# <codecell>
m = Minuit(bx2, mean=1.0, sigma=1.0, N=1000.)
m.migrad()
bx2.show(m)
# <markdowncell>
# ####Binned Likelihood
# Poisson binned log likelihood with minimum subtractacted(aka likelihood ratio).
# <codecell>
from iminuit import Minuit
from probfit import BinnedChi2, Extended, gaussian
from matplotlib import pyplot as plt
from numpy.random import randn, seed
seed(0)
data = randn(1000) * 2 + 1
ext_gauss = Extended(gaussian)
ulh = BinnedChi2(ext_gauss, data)
m = Minuit(ulh, mean=0., sigma=0.5, N=800)
plt.figure(figsize=(8, 3))
plt.subplot(121)
ulh.draw(m)
plt.title('Before')
m.migrad() # fit
plt.subplot(122)
ulh.draw(m)
plt.title('After')