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histograma_bines_gral.py
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histograma_bines_gral.py
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#! /usr/bin/env python
import numpy
def histograma_bins_norm(lista,Nbins, name_h):
#if i want to compare several distrib. i MUST give same Nbins and (max,min) range too!!!
file = open(name_h,'wt')
minimum=float(min(lista))
maximum=float(max(lista)) # fixed range so i can compare histograms from diff. models
print "min:",minimum, " max:",maximum, " N:", len(lista), " Nbins:", Nbins
counts, bin_edges= numpy.histogram(lista,bins=Nbins, range=(minimum,maximum), normed= False)
counts_n= counts/ sum(counts* numpy.diff(bin_edges))
list_diff_bins=numpy.diff(bin_edges)
bin_size=float(bin_edges[1]-bin_edges[0])
Cumul_prob=[0]*500000
norm=0.
for item in lista:
for b in range (len(bin_edges)):
value_bin=bin_edges[b]
if value_bin<= item:
Cumul_prob[b]+=1.
norm+=1.
for i in range(len(counts_n)):
print >> file, bin_edges[i]-list_diff_bins[i]/2., counts_n[i], counts[i],float(Cumul_prob[i])/(float(len(lista))*bin_size), Cumul_prob[i]
print "written histogram:",name_h
################################
################################
def histograma_bins(lista,Nbins, name_h):
minimum=float(min(lista))
maximum=float(max(lista)) # fixed range so i can compare histograms from diff. models
print "min:",minimum, " max:",maximum, " N:", len(lista)
hist, bin_edges= numpy.histogram(lista, bins=Nbins, range=(minimum,maximum)) # optional: range=( , ) The lower and upper range of the bins.
#if i wanna compare several distrib. i MUST give same Nbins and (max,min) range too!!!
#print "\n",name_h,"max:",max(lista),"min:",min(lista)
Cumul_prob=[0]*500000
norm=0.
for item in lista:
for b in range (len(bin_edges)):
value_bin=bin_edges[b]
if value_bin<= item:
Cumul_prob[b]+=1.
norm+=1.
# print "bin size:", bin_edges[1]-bin_edges[0], bin_edges[2]-bin_edges[1], bin_edges[3]-bin_edges[2]
bin_size=float(bin_edges[1]-bin_edges[0])
# ojoooooo! normalizar tb por bin size, ademas de N puntos
lista_tuplas=[]
origin=float(bin_edges[0])
file = open(name_h,'wt')
for b in range (len(bin_edges)-1):
#if hist[b] !=0:
print >> file,origin+(bin_edges[b+1]-bin_edges[b])/2.0, float(hist[b])/(float(len(lista))*bin_size), float(hist[b]),float(Cumul_prob[b])/(float(len(lista))*bin_size),float(Cumul_prob[b]), float(hist[b])/float(len(lista))
tupla=(origin+(bin_edges[b+1]-bin_edges[b])/2.0, float(hist[b])/(float(len(lista))*bin_size))
lista_tuplas.append(tupla)
origin=origin+(bin_edges[b+1]-bin_edges[b])
file.close()
print "written histogram:",name_h
print " Data Columns: x, norm_count, count, norm_cumulat, cumulat, float(hist[b])/float(len(lista))"
# print "written:", name_h, " colum names: norm_prob, raw_count, cumul_prob, raw_cumul_count (normalization by N events times bin_size), cumul_prob (normalization by N events only)"
return lista_tuplas
################################
################################
def histograma_bins_zero(lista,Nbins, name_h):# bins centered on the left corner of the bin (not the center)
print min(lista),max(lista)
minimum=0#float(min(lista))
maximum=20000#float(max(lista)) # fixed range so i can compare histograms from diff. models
hist, bin_edges= numpy.histogram(lista, bins=Nbins, range=(minimum,maximum)) # optional: range=( , ) The lower and upper range of the bins.
#if i wanna compare several distrib. i MUST give same Nbins and (max,min) range too!!!
#print "\n",name_h,"max:",max(lista),"min:",min(lista)
Cumul_prob=[0]*500000
norm=0.
for item in lista:
for b in range (len(bin_edges)):
value_bin=bin_edges[b]
if value_bin<= item:
Cumul_prob[b]+=1.
norm+=1.
# print "bin size:", bin_edges[1]-bin_edges[0], bin_edges[2]-bin_edges[1], bin_edges[3]-bin_edges[2]
bin_size=float(bin_edges[1]-bin_edges[0])
# ojoooooo! normalizar tb por bin size, ademas de N puntos
lista_tuplas=[]
origin=float(bin_edges[0])
file = open(name_h,'wt')
for b in range (len(bin_edges)-1):
#if hist[b] !=0:
print >> file,bin_edges[b], float(hist[b])/(float(len(lista))*bin_size), float(hist[b]),float(Cumul_prob[b])/(float(len(lista))*bin_size),float(Cumul_prob[b]), float(hist[b])/float(len(lista))
tupla=(bin_edges[b], float(hist[b])/(float(len(lista))*bin_size))
lista_tuplas.append(tupla)
origin=origin+(bin_edges[b+1]-bin_edges[b])
file.close()
print "written histogram:",name_h
# print "written:", name_h, " colum names: norm_prob, raw_count, cumul_prob, raw_cumul_count (normalization by N events times bin_size), cumul_prob (normalization by N events only)"
return lista_tuplas
########################
########################
def histograma_bins_zero_small_bins_at_start(lista,Nbins, name_h):# bins centered on the left corner of the bin (not the center)
minimum=0#float(min(lista))
maximum=60000#float(max(lista)) # fixed range so i can compare histograms from diff. models
num_small_bins=10 # the rest of the bins, will be collapsed to a big one
hist, bin_edges= numpy.histogram(lista, bins=Nbins, range=(minimum,maximum)) # optional: range=( , ) The lower and upper range of the bins.
#if i wanna compare several distrib. i MUST give same Nbins and (max,min) range too!!!
#print "\n",name_h,"max:",max(lista),"min:",min(lista)
Cumul_prob=[0]*500000
norm=0.
for item in lista:
for b in range (len(bin_edges)):
value_bin=bin_edges[b]
if value_bin<= item:
Cumul_prob[b]+=1.
norm+=1.
# print "bin size:", bin_edges[1]-bin_edges[0], bin_edges[2]-bin_edges[1], bin_edges[3]-bin_edges[2]
bin_size=float(bin_edges[1]-bin_edges[0])
# ojoooooo! normalizar tb por bin size, ademas de N puntos
lista_tuplas=[]
origin=float(bin_edges[0])
file = open(name_h,'wt')
for b in range (num_small_bins -1):
#if hist[b] !=0:
print >> file,bin_edges[b], float(hist[b])/(float(len(lista))*bin_size), float(hist[b]),float(Cumul_prob[b])/(float(len(lista))*bin_size),float(Cumul_prob[b]), float(hist[b])/float(len(lista))
tupla=(bin_edges[b], float(hist[b])/(float(len(lista))*bin_size))
lista_tuplas.append(tupla)
origin=origin+(bin_edges[b+1]-bin_edges[b])
rest_prob=0.
for element in hist[num_small_bins:]:
rest_prob+=element
bin_size=float(bin_edges[-1]-bin_edges[num_small_bins])
print >> file,bin_edges[num_small_bins],rest_prob/(float(len(lista))*bin_size)
file.close()
print "written histogram:",name_h
# print "written:", name_h, " colum names: norm_prob, raw_count, cumul_prob, raw_cumul_count (normalization by N events times bin_size), cumul_prob (normalization by N events only)"
return lista_tuplas
def histograma_bins_return_only_freq(lista,Nbins, name_h):
hist, bin_edges= numpy.histogram(lista, bins=Nbins, range=(float(min(lista)),float(max(lista)))) # optional: range=( , ) The lower and upper range of the bins.
#if i wanna compare several distrib. i MUST give same Nbins and (max,min) range too!!!
# print "\n",name_h,"max:",max(lista),"min:",min(lista)
Cumul_prob=[0]*50000
norm=0.
for item in lista:
for b in range (len(bin_edges)):
value_bin=bin_edges[b]
if value_bin<= item:
Cumul_prob[b]+=1.
norm+=1.
# print "bin size:", bin_edges[1]-bin_edges[0], bin_edges[2]-bin_edges[1], bin_edges[3]-bin_edges[2]
bin_size=float(bin_edges[1]-bin_edges[0])
# ojoooooo! normalizar tb por bin size, ademas de N puntos
origin=float(bin_edges[0])
file = open(name_h,'wt')
for b in range (len(bin_edges)-1):
if hist[b] !=0:
print >> file,origin+(bin_edges[b+1]-bin_edges[b])/2.0, float(hist[b])/(float(len(lista))*bin_size), float(hist[b]),float(Cumul_prob[b])/(float(len(lista))*bin_size),float(Cumul_prob[b])/float(len(lista)),float(Cumul_prob[b])
origin=origin+(bin_edges[b+1]-bin_edges[b])
file.close()
print "written:", name_h, " colum names: norm_prob, raw_count, cumul_prob, raw_cumul_count (normalization by N events times bin_size)"
return hist
def histograma_bins_return_prob_and_cumul(lista,Nbins, name_h):
hist, bin_edges= numpy.histogram(lista, bins=Nbins, range=(float(min(lista)),float(max(lista)))) # optional: range=( , ) The lower and upper range of the bins.
#if i wanna compare several distrib. i MUST give same Nbins and (max,min) range too!!!
# print "\n",name_h,"max:",max(lista),"min:",min(lista)
Cumul_prob=[0]*50000
norm=0.
for item in lista:
for b in range (len(bin_edges)):
value_bin=bin_edges[b]
if value_bin<= item:
Cumul_prob[b]+=1.
norm+=1.
# print "bin size:", bin_edges[1]-bin_edges[0], bin_edges[2]-bin_edges[1], bin_edges[3]-bin_edges[2]
bin_size=float(bin_edges[1]-bin_edges[0])
# ojoooooo! normalizar tb por bin size, ademas de N puntos
lista_tuplas_prob=[]
lista_tuplas_cumulat_prob=[]
origin=float(bin_edges[0])
file = open(name_h,'wt')
for b in range (len(bin_edges)-1):
if hist[b] !=0:
print >> file,origin+(bin_edges[b+1]-bin_edges[b])/2.0, float(hist[b])/(float(len(lista))*bin_size), float(hist[b]),float(Cumul_prob[b])/(float(len(lista))*bin_size),float(Cumul_prob[b])/float(len(lista)),float(Cumul_prob[b])
tupla_prob=(origin+(bin_edges[b+1]-bin_edges[b])/2.0, float(hist[b])/(float(len(lista))*bin_size))
tupla_cumulat=(origin+(bin_edges[b+1]-bin_edges[b])/2.0, float(Cumul_prob[b])/(float(len(lista))*bin_size))
lista_tuplas_prob.append(tupla_prob)
lista_tuplas_cumulat_prob.append(tupla_cumulat)
origin=origin+(bin_edges[b+1]-bin_edges[b])
file.close()
print "written:", name_h, " colum names: norm_prob, raw_count, cumul_prob, raw_cumul_count (normalization by N events times bin_size)"
return lista_tuplas_prob,lista_tuplas_cumulat_prob