def plot_model(dirname):
model_name = dirname[dirname.index("_")+1:]
dirs = glob.glob("catalog*")
dirs.sort()
print "Directories to consider:"
for dir_ in dirs:
print "- ", dir_
rads = []
counts = []
for dir_ in dirs:
rads.append(np.loadtxt(os.path.join(dir_, "radial.dat")))
# print rads[-1]
counts.append(np.loadtxt(os.path.join(dir_, "count.dat")))
# print counts[-1]
# Isolates 1st and 2nd columns of all files
temp_nw = []
temp_like = []
temp_rgal = []
temp_nb = []
temp_nbl = []
temp_ntot = []
for rad, count in zip(rads, counts):
temp_nw.append(rad[:, 0:1])
# print temp_nw
# sys.exit()
temp_like.append(rad[:, 1:2])
temp_rgal.append(rad[:, 2:3])
temp_nb.append(count[0])
temp_nbl.append(count[1])
temp_ntot.append(count[5])
# Now stacks into matrices
nw = np.hstack(temp_nw)
like = np.hstack(temp_like)
rgal = np.hstack(temp_rgal)
#nb = np.hstack(temp_nb)
#nbl = np.hstack(temp_nbl)
#ntot = np.hstack(temp_ntot)
print nw
print like
print rgal
nlikebin = np.mean(like, 1)
serr = np.std(nw, 1)
errmed = np.mean(nw, 1)
err = errmed/nlikebin
serrmed = serr/nlikebin
radii = np.mean(rgal, 1)
nb_med=np.mean(temp_nb)
ntot_med=np.mean(temp_ntot)
nbl_med=np.mean(temp_nbl)
descr="%.0f, %.0f, %.0f" % (nb_med,nbl_med,ntot_med)
#stds_nw = np.std(nw, 1)
#print "means nw", means_nw
#print "stds nw", stds_nw
#read nw1 1 read nlike1 2 read rgal1 3 read ntot1 4
#read nb1 1 read nbl 2 read nd 3 read ndl 4 read nbclip1 7 read nbclipno1 8
plot_thing(radii, err, serrmed, title, descr)
plt.save_png("radial-%s.png" % model_name)
plt.show()
fit_sat = p1['xsat']
fd = fitres['fitdata']
print ('maximum count rate: %.1f cps, saturation power: %.1f microwatt'%(fit_A,fit_sat))
dat.add_value('fit')
plt.add_data(dat, coorddim=0, valdim=2)
dat.add_data_point(x_axis,y_NV-y_BG,fd)
plt.set_plottitle('power saturation '+name+', P_sat = '+str(int(fit_sat))+'uW, R_sat = '+str(int(A))+' counts/s')
else:
dat.add_data_point(x_axis,y_NV-y_BG)
print 'could not fit calibration curve!'
plt.set_legend(False)
plt.save_png(dat.get_filepath()+'png')
dat.close_file()
#result = open(path+'powerdependence_'+name+'.dat', 'w')
#result.write('# r_max (cps): %s\n'%fit_A)
#result.write('# p_sat (mW): %s\n'%fit_sat)
#result.write('\n')
#result.write('# P (mW)\tr (cps)\tbg (cps)\tr_NV (cps)\n')
#for a in x:
# result.write('%.4f\t%.1f\t%.1f\t%.1f\n'%(x_axis[a]/1000.0,y_NV[a],y_BG[a],(y_NV[a]-y_BG[a])))
#result.close()
#fig = plt.figure()
#dat = fig.add_subplot(111)
#x = x_axis/1000.0
