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from pylab import *
import pylab
from scipy import *
def plotfit_gaus(indict,newfig=True,txtp=True):
if not indict.has_key('x'):
print "forgot to use ret_all on the fittodata?";
return;
p1 = indict['params'];
x = indict['x']
y = indict['y']
err = indict['err']
ffunc = indict['ffunc']
pylab.errorbar(x,y,fmt='b.',yerr=err)
d=pylab.plot(x,y,'k.')
fx = x
if pylab.size(fx) < 100:
fx = r_[min(x):max(x):(max(x)-min(x))/150.]
f=pylab.plot(fx,ffunc(p1,fx),'r-')
#fy = indict['ffunc'](indict['params'],indict['x'])
pylab.title("Gaussian Fit of Events")
pylab.xlabel("Number of Charged Particles")
pylab.ylabel("Events")
p1 = indict['params']
p1_err = indict['param_error']
ax = pylab.gca()
# ax.set_xlim([-1,14])
extra_txt = 'reduced chi squared: %g'\
% (indict['rchisqr'])
for i in range(scipy.size(p1)):
extra_txt += '\np[%i]: %g +/- %g' \
% (i,p1[i],p1_err[i]);
# if(pylab.size(p1) == 2):
# extra_txt = 'reduced chi squared: %g\np[0]: %g\np[1]: %g' \
# % (indict['rchisqr'],p1[0],p1[1])
# else:
# extra_txt= 'reduced chi squared:%g\np[0]:%g, +/- %g\n p[1]:%g\np[2]:%g' \
# % (indict['rchisqr'],p1[0],p1[1],p1[2])
if txtp:
t = pylab.text(.77, .75, extra_txt,
horizontalalignment='center',
verticalalignment='center',
transform = ax.transAxes)
pylab.legend((d,f),('Data', 'Fit'))
def plotfit_poiss(indict,newfig=True,txtp=True):
if not indict.has_key('x'):
print "forgot to use ret_all on the fittodata?";
return;
p1 = indict['params'];
x = indict['x']
y = indict['y']
err = indict['err']
ffunc = indict['ffunc']
# if newfig: pylab.figure();
pylab.errorbar(x,y,fmt='b.',yerr=err)
d=pylab.plot(x,y,'k.')
fx = x
if pylab.size(fx) < 100:
fx = r_[min(x):max(x):(max(x)-min(x))/150.]
p1= p1[0]
if txtp:
f2=pylab.plot(fx,ffunc(p1,fx),'r-')
else:
f2=pylab.plot(fx,ffunc(p1,fx),'g-')
#fy = indict['ffunc'](indict['params'],indict['x'])
pylab.title("Poisson Fit of Events")
pylab.xlabel("Number of Charged Particles")
pylab.ylabel("Events")
p1 = indict['params']
p1_err = indict['param_error']
p1= p1[0]
ax = pylab.gca()
#ax.set_xlim([-1,20])
extra_txt = 'reduced chi squared: %g'\
% (indict['rchisqr'])
for i in range(scipy.size(p1)):
if type(p1_err) is not type(9.2):
extra_txt += '\np[%i]: %g +/- %g' \
% (i,p1[i],p1_err[i]);
else:
extra_txt += '\np[%i]: %g' \
% (i,p1[i]);
# if(pylab.size(p1) == 2):
# extra_txt = 'reduced chi squared: %g\np[0]: %g\np[1]: %g' \
# % (indict['rchisqr'],p1[0],p1[1])
# else:
# extra_txt= 'reduced chi squared:%g\np[0]:%g, +/- %g\n p[1]:%g\np[2]:%g' \
# % (indict['rchisqr'],p1[0],p1[1],p1[2])
if txtp:
t = pylab.text(.77, .75, extra_txt,
horizontalalignment='center',
verticalalignment='center',
transform = ax.transAxes)
if txtp:
pylab.legend((d,f2),('Data', 'Fit'))
lsd = pylab.load("lineshapedata.txt")
ls1 = pylab.load("lineshape1.txt")
rawlsd = []
for i in range(size(lsd[0,:])):
for j in range(lsd[1,i]):
rawlsd.append(lsd[0,i])
rawls1 = []
for i in range(size(ls1[0,:])):
for j in range(ls1[1,i]):
rawls1.append(ls1[0,i])
plotfit(fittodata_lorentz(lsd[0,:],lsd[1,:],[50.0,20.0],err=sqrt(lsd[1,:]),ret_all=True))
hist(rawlsd, bins=lsd[0,:],align='center',facecolor="lightblue")
pylab.title("Lorentzian fit to lineshapedata.txt")
plotfit(fittodata_gaussian(lsd[0,:],lsd[1,:],[50.0,20.0],err=sqrt(lsd[1,:]),ret_all=True))
hist(rawlsd, bins=lsd[0,:],align='center',facecolor="lightblue")
pylab.title("Gaussian fit to lineshapedata.txt")
plotfit(fittodata_lorentz(ls1[0,:],ls1[1,:],[50.0,20.0],err=sqrt(ls1[1,:]),ret_all=True))
hist(rawls1, bins=ls1[0,:],align='center',facecolor="lightblue")
pylab.title("Lorentzian fit to lineshape1.txt")
plotfit(fittodata_gaussian(ls1[0,:],ls1[1,:],[50.0,20.0],err=sqrt(ls1[1,:]),ret_all=True))
hist(rawls1, bins=ls1[0,:],align='center',facecolor="lightblue")
pylab.title("Gaussian fit to lineshape1.txt")
#pylab.figure()
#plot(ls1[0,:],ls1[1,:])
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