def dih_timehist2(dirname,histname):
indata = tlm.dih_timelistmaker(dirname)
y, binedges = np.histogram(indata,bins=10)#create hist data
bincenters = .5*(binedges[1:]+binedges[:-1])#create bin centers
width = 3
list1 = [i for i, j in enumerate(y) if j != 0.0]#find indices of non zero bin values
list2 = [0]*len(y)
for num in list1:#populate error list
list2[num] = 2.0
plt.bar(bincenters,y,width=width, color ='r',yerr=list2)#create bar plot with errors
plt.xlabel("Time of Peak")
plt.ylabel("# of peaks")
plt.title("Time Distribution of Peaks")
(mu,sigma) = norm.fit(indata)#gaussian fitting raw data
plotline = mlab.normpdf(bincenters,mu,sigma)#creates the gaussian
#alternative method for fitting
#p0 = [1.,0.,1.]
#coeff,var_matrix = curve_fit(gauss,bincenters,y,p0=p0)
#hist_fit = gauss(bincenters, *coeff)
#plt.plot(bincenters,hist_fit)
plt.plot(bincenters,plotline)
plt.savefig(histname)
#create chi squared value
residuals = plotline - y
weighted = sp.sqrt(residuals**2/2.0**2)
return sum(weighted)
def dih_basictimehist(dirname,histname):
indata = tlm.dih_timelistmaker(dirname)
P.figure
n, bins, patches = P.hist(indata,12,normed = 1, histtype = "stepfilled")
plt.xlabel("Time of Peak")
plt.ylabel("Fraction of total peaks")
plt.title("Time Distribution of Peaks")
plt.savefig(histname)
return indata