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| -rw-r--r-- | example_exersize.py | 132 |
1 files changed, 132 insertions, 0 deletions
diff --git a/example_exersize.py b/example_exersize.py new file mode 100644 index 0000000..803ccac --- /dev/null +++ b/example_exersize.py @@ -0,0 +1,132 @@ + +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,:]) |