def fat_angel(findstr, num_ap, EXTEN=0, OUTPUT=0,
PIXELPITCH=1, FIBERSIZE=500):
'''
Description:
Fat_angel is deisgned to process a large number of data from
the laser bench. It takes an input string, findstr, and computes
the width and radius of the ring in each data image. Surface brightness
profiles are found using Annulize in the ADEUtils package with
the number of annuli specfied in the num_ap input. Output can be
directed to a file for plotting bliss.
Inputs:
findstr- Str
A string containing the names of the data files to use.
Wildcards are allowed so you can chose a single file or
as many as want. Files are assumed to be FITS files.
num_ap - Int
The number of annuli to use when constructing the surface
brightness profile.
EXTEN - Int
The FITS extension where the primary data is stored. As of
right now there is no way to specify different extensions
for different files.
OUTPUT - Str
Name of output file. Output contains angle, ring radius, and
ring width in column form
PIXELPITCH - Float
Size of the camera pixels in micrometers. This is only used to
get the correct scale on the debug plots.
Output:
Output is a tuple of Numpy vectors containing angle, ring radius, and
ring width.
Example:
Assume you have a bunch of data called X_red.fits where X is some
data iterator.
>>lb.fat_angel('*_red.fits',150,EXTEN=1,OUTPUT='my_data.dat')
'''
file_list = glob.glob(findstr)
numfiles = len(file_list)
'initialize some data arrays'
widths = np.zeros(numfiles)
radii = np.zeros(numfiles)
angles = np.zeros(numfiles)
r1_vec = np.zeros(numfiles)
r2_vec = np.zeros(numfiles)
frd_widths = np.zeros(numfiles)
xcent = np.zeros(numfiles)
ycent = np.zeros(numfiles)
t1 = time.time()
for i in range(numfiles):
print(file_list[i])
ADE.mediclean(file_list[i],'clean'+file_list[i],exten=EXTEN)
huds = pyfits.open('clean'+file_list[i])
data = huds[EXTEN].data
'get the angle from the FITS header. MUCH easier in python than IDL!'
angles[i] = huds[EXTEN].header['ANGLE']
'''poor man's background subtraction'''
mode = ADE.mode(data)[0][0]
if debug: print 'Mode = '+str( mode)
'Annulize!'
if debug:
fig0 = plt.figure(0)
plt.clf()
t0 = time.time()
(r_vec,fluxes,center) = ADE.annulize(data,num_ap,NOREAD=1)#,MODE=mode)
print "Annulize took "+str(time.time() - t0)+" seconds"
plt.plot(r_vec*PIXELPITCH,fluxes)
fig0.show()
else:
(r_vec,fluxes,center) = ADE.annulize(data,num_ap,NOREAD=1)#,MODE=mode)
'''Compute the cdf from the pdf (fluxes). Working with the CDF
allows us to assume that the annulus is gaussian-ish without
having to worry about the particulars'''
(rm_idx,r1,r2) = find_peak(r_vec,fluxes)
rm = r_vec[rm_idx]
# r1 = r_vec[r1_idx]
# r2 = r_vec[r2_idx]
'Now deconvolve and find the width of the FRD smearing kernel'
# (frd_width,frd) = decon(r_vec,fluxes,rm_idx,r2,r1,FIBERSIZE,PIXELPITCH)
# global frd_sav
# frd_sav = frd
if debug:
fig1 = plt.figure(fignum)
plt.clf()
sp0 = fig1.add_subplot(222)
sp0.plot(r_vec*PIXELPITCH,
np.cumsum(fluxes)/np.max(np.cumsum(fluxes)))
sp0.axvline(x=rm*PIXELPITCH,ls='--',lw=0.3)
sp0.axvline(x=r1*PIXELPITCH,ls='--',lw=0.3)
sp0.axvline(x=r2*PIXELPITCH,ls='--',lw=0.3)
sp0.set_xlabel("Radius (um)")
sp0.set_ylabel("% of total counts")
sp0.set_title("Normalized CDF")
# plt.figure(0)
sp1 = fig1.add_subplot(221)
sp1.plot(r_vec*PIXELPITCH,fluxes)
sp1.axvline(x=rm*PIXELPITCH,ls='--',lw=0.3)
sp1.axvline(x=r1*PIXELPITCH,ls='--',lw=0.3)
sp1.axvline(x=r2*PIXELPITCH,ls='--',lw=0.3)
sp1.set_xlabel("Radius (um)")
sp1.set_ylabel("Counts")
sp1.set_title("Ring Profile")
# sp2 = fig1.add_subplot(224)
# sp2.plot(r_vec*PIXELPITCH, frd)
# sp2.set_xlabel("Radius (um)")
# sp2.set_ylabel("??")
# sp2.set_title("FRD kernel")
plt.suptitle("Angle = "+str(angles[i])+" degrees\n"+file_list[i])
fig1.show()
print "Center: "+str(center)
if numfiles > 1: raw_input("press enter to continue...\n")
widths[i] = r2 - r1
radii[i] = rm
r1_vec[i] = r1
r2_vec[i] = r2
# frd_widths[i] = frd_width
xcent[i] = center[0]
ycent[i] = center[1]
print "Total annulize time was "+str(time.time()-t1)+" seconds"
'We sort the data just make the output a little more readable'
sort_idx = np.argsort(angles)
widths *= PIXELPITCH
radii *= PIXELPITCH
r1_vec *= PIXELPITCH
r2_vec *= PIXELPITCH
frd_widths *= PIXELPITCH
if OUTPUT:
f = open(OUTPUT, 'w')
f.write('#angle radius width r1 r2 frd width center\n')
for i in range(angles.shape[0]):
np.array([angles[sort_idx][i],
radii[sort_idx][i],
widths[sort_idx][i],
r1_vec[sort_idx][i],
r2_vec[sort_idx][i],
frd_widths[sort_idx][i],
xcent[sort_idx][i],
ycent[sort_idx][i]]).\
tofile(f, sep=' ',format='%3.4f')
f.write('\n')
return (angles[sort_idx],
radii[sort_idx],
widths[sort_idx],
r1_vec[sort_idx],
r2_vec[sort_idx],
frd_widths[sort_idx],
xcent[sort_idx],
ycent[sort_idx])
