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FL_test.py
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FL_test.py
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import numpy as np
import matplotlib.pyplot as plt
from MANGA_bench import Noodle
import ConfigParser
import pyfits
import ADEUtils as ADE
from glob import glob
import pickle
import os
from matplotlib.backends.backend_pdf import PdfPages as PDF
def fratio(searchstr,EEfigs,apfigs,bigfig,EE=0.50):
'''pronounced frat-eeo, not f ratio'''
print int(EE*100)
pp = PDF(EEfigs)
pp2 = PDF(apfigs)
pp3 = PDF(bigfig)
inifiles = glob(searchstr)
datadict = get_sizes(inifiles,pp,EE=EE)
pp.close()
ratiodict = {}
aps = np.array([])
ratios = np.array([])
for apsize in datadict.keys():
x = np.array(datadict[apsize].keys(),dtype=float)
r = np.array(datadict[apsize].values())
fit_coef = ADE.polyclip(x,r,1,niter=100).c
slope = fit_coef[0]
N = (2.*slope)**-1
fit = np.poly1d(fit_coef)
ratiodict[apsize] = {'f/#':N,
'fl':float(apsize)/N}
aps = np.append(aps,float(apsize))
ratios = np.append(ratios,N)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,r,marker='s',linestyle='')
fitx = np.linspace(x.min(),x.max(),50)
ax.plot(fitx,fit(fitx),'k:',label='linear fit')
ax.set_xlabel('back distance - C [mm]')
ax.set_ylabel('beam radius [mm]')
ax.legend(loc=0)
ax.set_title('Aperture: {:n} mm\nN: {:3.2f}'.format(float(apsize),N))
pp2.savefig(fig)
pp2.close()
ratiofit = ADE.polyclip(aps**-1,ratios,1,niter=50,clip_high=1,clip_low=1)
# ratiofit = np.poly1d(np.polyfit(aps**-1,ratios,1))
ratioslope = ratiofit.c[0]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(aps**-1,ratios,marker='s',linestyle='')
fitaps = np.linspace(aps.min(),aps.max(),50)
ax.plot(fitaps**-1,ratiofit(fitaps**-1),':',label='linear fit')
ax.set_xlabel('1/D [$mm^{-1}$]')
ax.set_ylabel('f-ratio')
# ax.set_ylim(0, ratiofit(fitaps.max())*1.1)
ax.legend(loc=0)
ax.set_title('slope = {:n} mm'.format(ratioslope))
pp3.savefig(fig)
pp3.close()
return ratiodict
def EE_test(searchstr,EElist):
inifiles = glob(searchstr)
FLlist = np.array([])
for EE in EElist:
print '************ EE'+str(int(EE*100))+' ***************'
datadict = get_sizes(inifiles,EErad=EE)
ratiodict = {}
aps = np.array([])
ratios = np.array([])
for apsize in datadict.keys():
x = np.array(datadict[apsize].keys(),dtype=float)
r = np.array(datadict[apsize].values())
fit_coef = np.polyfit(x,r,1)
slope = fit_coef[0]
fit = np.poly1d(fit_coef)
ratiodict[apsize] = {'f/#':(2.*slope)**-1,
'fl':float(apsize)/(2.*slope)}
aps = np.append(aps,float(apsize))
ratios = np.append(ratios,(2.*slope)**-1)
ratiocoef = np.polyfit(aps**-1,ratios,1)
ratioslope = ratiocoef[0]
ratiofit = np.poly1d(ratiocoef)
FLlist = np.append(FLlist,ratioslope)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(np.array(EElist),FLlist)
fig.show()
return np.array(EElist),FLlist
def ratio_boot(ratiodict,numtrys):
D = np.array([])
N = np.array([])
for ap in ratiodict.keys():
D = np.append(D,float(ap))
N = np.append(N,ratiodict[ap]['f/#'])
slopes = np.array([])
for i in range(numtrys):
sampleidx = np.random.randint(D.size, size = D.size)
tempD = D[sampleidx]
tempN = N[sampleidx]
slopes = np.append(slopes,np.polyfit(D**-1,N,1)[0])
print 'After {:n} trys:\n\tmean: {:3.4f} mm\n\tstd: {:3.4f} mm'.\
format(numtrys,slopes.mean(),slopes.std())
return slopes
def get_sizes(inifiles,pp,EE=0.5):
datadict = {}
for ini in inifiles:
#for L2
# apsize = ini.split('m')[0]
# backd = ini.split('m')[2][1:5]
#for L3
apsize = ini.split('m')[0].split('_')[1]
backd = ini.split('m')[2][1:5]
print (apsize,backd)
datafile = yaki(ini)
print 'Got {}, reducing...'.format(datafile)
radius = omnom(datafile,pp,EEcut=EE)
try: datadict[apsize][backd] = radius
except KeyError: datadict[apsize] = {backd: radius}
return datadict
def yaki(ini):
options = ConfigParser.ConfigParser()
options.read(ini)
picklename = '.'.join(ini.split('.')[:-1])+'.pkl'
try:
N = pickle.load(open(picklename,'rb'))
print "loaded pickel from {}".format(picklename)
except IOError:
N = Noodle(options)
N.build_run()
pickle.dump(N,open(picklename,'wb'))
key = N.ratios.keys()[0]
# for L3
return N.ratios[key]['data']['V']['L3']['final']
#for L2
# return N.ratios[key]['data']['V']['direct']['final']
def omnom(fitsfile,pp,EEcut=0.5,fitsexten=0):
data = pyfits.open(fitsfile)[fitsexten].data
r, sb, err = ADE.annulize(data,300)
# r *= 0.024
flux = np.cumsum(sb)
EE = flux/flux.max()
cutr = np.where(EE >= EEcut)[0][0]
EEfit = np.poly1d(np.polyfit(r[:cutr],EE[:cutr],2))
fitr = np.linspace(r.min(),r.max(),500)
fitEE = EEfit(fitr)
r1 = np.interp(1.0,fitEE,fitr)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(r,EE,marker='.',linestyle='',markersize=0.5)
ax.plot(fitr,fitEE,':')
ax.set_xlabel('r [mm]')
ax.set_ylabel('EE')
ax.axvline(r1,linestyle='-',alpha=0.4)
ax.axhline(1.0,linestyle=':',color='k',alpha=0.2)
ax.set_ylim(0,1.1)
ax.set_xlim(0,1.3*r1)
ax.set_title('{}\nr: {:3.2f} mm'.format(fitsfile,r1))
pp.savefig(fig)
return r1
######################################
def nomlom(fitsfile,x,fitsexten=0):
data = pyfits.open(fitsfile)[0].data
r, sb, err = ADE.annulize(data,300)
# r *= 0.024
flux = np.cumsum(sb)
EE = flux/flux.max()
radii = np.array([])
print ' Extracting radii for EE = 1/x:'
for i in x:
print '\tx = {:n}'.format(i)
targetEE = 1./i
radii = np.append(radii,np.interp(targetEE,EE,r))
return radii
def size_get(inifiles):
datadict = {}
x = np.arange(1.05,5,0.5)
for ini in inifiles:
#for L2
apsize = ini.split('m')[0]
backd = ini.split('m')[2][1:5]
#for L3
# apsize = ini.split('m')[0].split('_')[1]
# backd = ini.split('m')[2][1:5]
print (apsize,backd)
datafile = yaki(ini)
print 'Got {}, reducing...'.format(datafile)
radii = nomlom(datafile,x)
for i,r in zip(x,radii):
try:
datadict[apsize][i] = np.vstack((datadict[apsize][i],np.array([backd,r],dtype=np.float32)))
except KeyError:
try: datadict[apsize][i] = np.array([backd,r],dtype=np.float32)
except KeyError:
datadict[apsize] = {i: np.array([backd,r],dtype=np.float32)}
return datadict
def sortio(searchstr,Nfigs,apfigs,bigfig):
inifiles = glob(searchstr)
datadict = size_get(inifiles)
ratiodict = {}
aps = np.array([])
ratios = np.array([])
pp = PDF(Nfigs)
pp2 = PDF(apfigs)
pp3 = PDF(bigfig)
for apsize in datadict.keys():
print '\nApsize = {:n} mm:\n\t{:7}{:7}\n\t'.format(int(apsize),'x','N_x')+'-'*10
xvec = np.array([])
Nprime = np.array([])
sortedkeys = datadict[apsize].keys()[:]
sortedkeys.sort()
for x in sortedkeys[::-1]:
d = datadict[apsize][x].T[0]
r = datadict[apsize][x].T[1]
Npcoef = np.polyfit(d,r,1)
xvec = np.append(xvec,x)
Nprime = np.append(Nprime,(2.*Npcoef[0])**-1)
print '\t{:4.3f} {:4.3f}'.format(x,(2.*Npcoef[0])**-1)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(d,r,marker='s',linestyle='')
fitvec = np.linspace(d.min(),d.max(),50)
ax.plot(fitvec,np.poly1d(Npcoef)(fitvec),':')
ax.set_title('Aperture: {:3n} mm\n$x={:3.2f}\Rightarrow EE={:3.2f}$\n$N_x$: ${:3.2f}$'.\
format(int(apsize),x,1./x,(2.*Npcoef[0])**-1),
fontsize=10)
ax.set_xlabel('d')
ax.set_ylabel('r')
pp.savefig()
bigNcoef = np.polyfit(xvec,Nprime,1)
print bigNcoef
N = bigNcoef[0]
print (2.*N)**-1
Nfit = np.poly1d(bigNcoef)
ratiodict[apsize] = {'f/#':N,
'fl':float(apsize)*N}
aps = np.append(aps,float(apsize))
ratios = np.append(ratios,N)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(xvec,Nprime,marker='s',linestyle='')
fitx = np.linspace(xvec.min(),xvec.max(),50)
ax.plot(fitx,Nfit(fitx),'k:',label='linear fit')
ax.set_xlabel('$x$',fontsize=14)
ax.set_ylabel('$N_x$',fontsize=14)
ax.legend(loc=0)
ax.set_title('Aperture: {:n} mm\nN: {:3.4f}'.format(float(apsize),N))
pp2.savefig(fig)
pp.close()
pp2.close()
ratiofit = ADE.polyclip(aps**-1,ratios,1,niter=50,clip_high=1,clip_low=1)
# ratiofit = np.poly1d(np.polyfit(aps**-1,ratios,1))
ratioslope = ratiofit.c[0]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(aps**-1,ratios,marker='s',linestyle='')
fitaps = np.linspace(aps.min(),aps.max(),50)
ax.plot(fitaps**-1,ratiofit(fitaps**-1),':',label='linear fit')
ax.set_xlabel('1/D [$mm^{-1}$]')
ax.set_ylabel('f-ratio')
# ax.set_ylim(0, ratiofit(fitaps.max())*1.1)
ax.legend(loc=0)
ax.set_title('slope = {:n} mm'.format(ratioslope))
pp3.savefig(fig)
pp3.close()
return ratiodict