def OrderStat(x,A,n,mu,s):
return (n/s)*fu.gauss1(x,A,mu,s)*((1./2)*((1 + sps.erf((x-mu)/s)))**(n-1))
PedFitComplete = True
GoodPedFit = False
FitComplete = True
GoodFit = False
continue
pl.PlotPedestal(pedxdata,pedydata,fu.gauss1,pedxdata,pedopt,"GaussPlusExpo")
above_ped = 0
past_ped = np.where(pedxdata > (pedopt[1] + 3*pedopt[2]))[0]
if FIT_TAIL:
plt.plot(pedxdata[past_ped],pedydata[past_ped])
plt.plot(pedxdata[past_ped],fu.expo(pedxdata[past_ped],pedopt[3],
pedopt[4],pedopt[5]))
above_ped = np.sum(pedydata[past_ped] - fu.expo(pedxdata[past_ped],pedopt[3],
pedopt[4],pedopt[5]))
else:
above_ped = np.sum(pedydata[past_ped] - fu.gauss1(pedxdata[past_ped],pedopt[0],
pedopt[1],pedopt[2]))
plt.show()
print("4SIGMA PAST PED, EXP. SUBTRACTED: " + str(above_ped))
if (above_ped < 300):
print("Low statistics beyond pedestal! May just be fitting on fluctuations.")
skip_fit = str(raw_input("Skip this fit?"))
if skip_fit in ["y","Y","yes","Yes","YES"]:
PedFitComplete = True
GoodPedFit = False
FitComplete = True
GoodFit = False
continue
ped_good = str(raw_input("Happy with pedestal fit? [y/N]:"))
if ped_good in ["y","Y","yes","Yes","YES"]:
PedFitComplete = True
import matplotlib.pyplot as plt
import numpy as np
import scipy as scp
import scipy.optimize as sco
import scipy.stats as sts
import scipy.special as sps
import lib.Functions as fu
def OrderStat(x,A,n,mu,s):
return (n/s)*fu.gauss1(x,A,mu,s)*((1./2)*((1 + sps.erf((x-mu)/s)))**(n-1))
maxes = []
for i in range(10000):
maxes.append(np.max(scp.randn(30)))
bin_height, bin_edges = np.histogram(maxes,bins=100)
bin_width = bin_edges[1]-bin_edges[0]
bin_edges_t = bin_edges[0:len(bin_edges) -1]
bin_centers = np.array(bin_edges_t) - (bin_width/2)
popt,pcov = sco.curve_fit(fu.gauss1,bin_centers,bin_height,p0=[1000,2,2])
yfit = fu.gauss1(bin_centers,popt[0],popt[1],popt[2])
plt.hist(maxes,bins=100)
plt.plot(bin_centers,yfit)
plt.show()
popt,pcov = sco.curve_fit(OrderStat,bin_centers,bin_height,p0=[300,30,1,2],maxfev=10000)
yfit = OrderStat(bin_centers,popt[0],popt[1],popt[2],popt[3])
plt.hist(maxes,bins=100)
plt.plot(bin_centers,yfit)
plt.show()
GoodPedFit = False
FitComplete = True
GoodFit = False
continue
pl.PlotPedestal(ped_xdata, ped_ydata, fn.gauss1, ped_xdata, ped_opt, "GaussPlusExpo")
above_ped = 0
past_ped = np.where(ped_xdata > (ped_opt[1] + 3.*ped_opt[2]))[0]
if FIT_TAIL:
plt.plot(ped_xdata[past_ped], ped_ydata[past_ped])
plt.plot(ped_xdata[past_ped], fn.expo(ped_xdata[past_ped], ped_opt[3], ped_opt[4], ped_opt[5]))
above_ped = np.sum(ped_ydata[past_ped] - fn.expo(ped_xdata[past_ped], ped_opt[3], ped_opt[4], ped_opt[5]))
else:
above_ped = np.sum(ped_ydata[past_ped] - fn.gauss1(ped_xdata[past_ped], ped_opt[0], ped_opt[1], ped_opt[2]))
plt.show()
print(" > 4SIGMA PAST PED, EXP. SUBTRACTED: " + str(above_ped))
if (above_ped < 300):
print("Low statistics beyond pedestal! May just be fitting on fluctuations.")
skip_fit = str(input("Skip this fit?"))
if (skip_fit in ["y", "Y", "yes", "Yes", "YES", "yEs"]):
PedFitComplete = True
GoodPedFit = False
FitComplete = True
GoodFit = False
continue
keep_ped = str(input("Keep pedestal fit? [y/N]: "))
if (keep_ped in ["y", "Y", "yes", "Yes", "YES", "yEs"]):
ped_good = str(input("Happy with pedestal fit? [y/N]: "))