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pdeSipm.py
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import sys
import numpy as np
import tables as tb
import matplotlib.pyplot as plt
from scipy.optimize import leastsq
from invisible_cities.database import load_db as DB
from invisible_cities.core.core_functions import weighted_mean_and_std
det_db = 'new'
def compare_runs(run_no, infiles):
#run_no = int(sys.argv[1]) # Only for sensor positions and mapping
#infiles = sys.argv[2:]
sensor_x = DB.DataSiPM(det_db, run_no).X.values
sensor_y = DB.DataSiPM(det_db, run_no).Y.values
chi_vals = []
mu_vals = []
for ifile in infiles:
chis, mus = pde(run_no, ifile)
chi_vals.append(chis)
mu_vals.append(mus)
fig, axes = plt.subplots(nrows=2, ncols=len(infiles), figsize=(20,6))
for chis, mus, ax, fl in zip(chi_vals, mu_vals, axes[0], infiles):
plt_info = ax.scatter(sensor_x[chis<5], sensor_y[chis<5], c=mus[chis<5])
ax.set_title("Poisson mu map file "+fl)
ax.set_xlabel("X (mm)")
ax.set_ylabel("Y (mm)")
plt.colorbar(plt_info, ax=ax)
plt.tight_layout()
conditions = (chi_vals[0] < 5) & (chi_vals[1] < 5) & (mu_vals[0] > 0.9) & (mu_vals[1] > 0.9)
dif_plt = axes[1][0].scatter(sensor_x[conditions], sensor_y[conditions],
c=mu_vals[0][conditions] - mu_vals[1][conditions])
plt.colorbar(dif_plt, ax=axes[1][0])
rat_plt = axes[1][1].scatter(sensor_x[conditions], sensor_y[conditions],
c=(mu_vals[0][conditions] / mu_vals[1][conditions])/(mu_vals[0][1024]/mu_vals[1][1024]))
plt.colorbar(rat_plt, ax=axes[1][1])
fig.show()
plt.show()
def pde(run_no, file_name):
#file_name = sys.argv[1]
#run_no = int(sys.argv[2])
m_channels = DB.DataSiPM(det_db, run_no).Active.values
sensor_id = DB.DataSiPM(det_db, run_no).SensorID.values
## For some checks
chans = [19040, 19041, 19042, 19048, 19049, 19050, 19056, 19057, 19058,
17043, 17044, 17045, 17051, 17052, 17053, 17059, 17060, 17061]
mu_vals = []
chi_vals = []
with tb.open_file(file_name, 'r') as data_file:
bins = np.array(data_file.root.HIST.sipm_dark_bins)
specsL = np.array(data_file.root.HIST.sipm_spe) .sum(axis=0)
specsD = np.array(data_file.root.HIST.sipm_dark).sum(axis=0)
for ich, (led, dar, act) in enumerate(zip(specsL, specsD, m_channels)):
if not act:
print('Channel ', sensor_id[ich], ' not active')
mu_vals.append(0)
chi_vals.append(0)
continue
valid_bins = np.argwhere(led >= 10)
b1 = valid_bins[0][0]
b2 = np.argwhere(bins <= 1)[-1][0]
dscale = led[b1:b2].sum() / dar[b1:b2].sum()
pfit = leastsq(scale_chi, dscale, args=(dar[b1:b2], led[b1:b2]))
chi2 = np.sum(scale_chi(pfit[0], dar[b1:b2], led[b1:b2])**2) / (b2 - b1 - 1)
mu_vals.append(pfit[0][0])
chi_vals.append(chi2)
if sensor_id[ich] in chans:
print('Interesting channel ', sensor_id[ich])
print('Poisson mu = ', pfit[0][0], ' chi2 = ', chi2)
## if not ich%64 or chi2 > 10:
## print('Check: ', pfit, chi2)
## plt.errorbar(bins, led,
## xerr=0.5*np.diff(bins)[0], yerr=np.sqrt(led), fmt='b.')
## plt.plot(bins, np.exp(-pfit[0]) * dar, 'r')
## plt.title('Scale fit to channel '+str(sensor_id[ich]))
## plt.xlabel('ADC')
## plt.ylabel('AU')
## plt.show()
## print(sensor_x.shape, sensor_y.shape, len(chi_vals), len(mu_vals))
chi_vals = np.array(chi_vals)
mu_vals = np.array(mu_vals)
## plt.scatter(sensor_x, sensor_y, c=chi_vals)
## plt.title("Fit chi^2 map")
## plt.xlabel("X (mm)")
## plt.ylabel("Y (mm)")
## plt.colorbar()
## plt.show()
## plt.scatter(sensor_x[(chi_vals>0) & (chi_vals<10)],
## sensor_y[(chi_vals>0) & (chi_vals<10)],
## c=np.array(mu_vals)[(chi_vals>0) & (chi_vals<10)])
## plt.title("Fit Poisson mu map")
## plt.xlabel("X (mm)")
## plt.ylabel("Y (mm)")
## plt.colorbar()
## plt.show()
return chi_vals, mu_vals
def elec_dark_comp(run_no, infiles):
elec_file = infiles[0]
dark_file = infiles[1]
sensor_id = DB.DataSiPM(det_db, run_no).SensorID.values
sensor_x = DB.DataSiPM(det_db, run_no).X.values
sensor_y = DB.DataSiPM(det_db, run_no).Y.values
chans = [19040, 19041, 19042, 19048, 19049, 19050, 19056, 19057, 19058,
17043, 17044, 17045, 17051, 17052, 17053, 17059, 17060, 17061]
mu_vals = []
chi_vals = []
dark_mean = []
elec_mean = []
with tb.open_file(elec_file) as efile, tb.open_file(dark_file) as dfile:
bins = np.array(dfile.root.HIST.sipm_dark_bins)
specsD = np.array(dfile.root.HIST.sipm_spe ).sum(axis=0)
specsE = np.array(efile.root.HIST.sipm_dark ).sum(axis=0)
for ich, (dar, ele) in enumerate(zip(specsD, specsE)):
valid_bins = np.argwhere(dar >= 10)
b1 = valid_bins[0][0]
b2 = np.argwhere(bins <= 0)[-1][0]
dscale = dar[b1:b2].sum() / ele[b1:b2].sum()
pfit = leastsq(scale_chi, dscale, args=(ele[b1:b2], dar[b1:b2]))
chi2 = np.sum(scale_chi(pfit[0], ele[b1:b2], dar[b1:b2])**2) / (b2 - b1 - 1)
#if sensor_id[ich] in chans:
## if pfit[0] < 0:
## print('Interesting channel ', sensor_id[ich])
## print('Poisson mu = ', pfit[0][0], ' chi2 = ', chi2)
## plt.errorbar(bins, dar,
## xerr=0.5*np.diff(bins)[0],
## yerr=np.sqrt(dar), fmt='b.')
## plt.errorbar(bins, ele,
## xerr=0.5*np.diff(bins)[0],
## yerr=np.sqrt(ele), fmt='g.')
## plt.plot(bins, np.exp(-pfit[0]) * ele, 'r')
## plt.title('Scale fit to channel '+str(sensor_id[ich]))
## plt.xlabel('ADC')
## plt.ylabel('AU')
## plt.show()
mu_vals .append(pfit[0][0])
chi_vals.append(chi2)
dmean, _ = weighted_mean_and_std(bins, dar)
if dmean > 70.:
dark_mean.append(2)
elec_mean.append(0)
continue
dark_mean.append(dmean)
emean, _ = weighted_mean_and_std(bins, ele)
elec_mean.append(emean)
a_dark_mean = np.array(dark_mean)
a_elec_mean = np.array(elec_mean)
plt.scatter(sensor_x, sensor_y, c=dark_mean)
plt.title("Mean dark spectrum")
plt.xlabel("X (mm)")
plt.ylabel("Y (mm)")
plt.colorbar()
plt.show()
plt.scatter(sensor_x, sensor_y, c=elec_mean)
plt.title("Mean elec spectrum")
plt.xlabel("X (mm)")
plt.ylabel("Y (mm)")
plt.colorbar()
plt.show()
plt.scatter(sensor_x, sensor_y, c=a_dark_mean-a_elec_mean)
plt.title("Mean dark - elec spectrum")
plt.xlabel("X (mm)")
plt.ylabel("Y (mm)")
plt.colorbar()
plt.show()
plt.scatter(sensor_x, sensor_y, c=chi_vals)
plt.title("Chi^2 map for scale")
plt.xlabel("X (mm)")
plt.ylabel("Y (mm)")
plt.colorbar()
plt.show()
plt.scatter(sensor_x, sensor_y, c=mu_vals)
plt.title("Poisson mu map, average dark counts")
plt.xlabel("X (mm)")
plt.ylabel("Y (mm)")
plt.colorbar()
plt.show()
def scale_chi(p, dark, led):
scaled_dark = np.exp(-p[0]) * dark
return (led - scaled_dark) / np.sqrt(led + scaled_dark)
if __name__ == '__main__':
run_no = int(sys.argv[1])
run_type = sys.argv[2]
file_names = sys.argv[3:]
if 'comp' in run_type:
compare_runs(run_no, file_names)
elif 'elec' in run_type:
elec_dark_comp(run_no, file_names)
else:
pde(run_no, file_names[0])